From 52dd4b2b277eb48bc89db9b21d25f5e836c1d348 Mon Sep 17 00:00:00 2001 From: x1- Date: Sat, 21 Mar 2015 13:22:34 -0700 Subject: [PATCH 001/591] [SPARK-5320][SQL]Add statistics method at NoRelation (override super). Because of no statistics override, in spute of super class say 'LeafNode must override'. fix issue [SPARK-5320: Joins on simple table created using select gives error](https://issues.apache.org/jira/browse/SPARK-5320) Author: x1- Closes #5105 from x1-/SPARK-5320 and squashes the following commits: e561aac [x1-] Add statistics method at NoRelation (override super). --- .../sql/catalyst/plans/logical/basicOperators.scala | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala index 1e7b449d75b80..384fe53a68362 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala @@ -289,6 +289,15 @@ case class Distinct(child: LogicalPlan) extends UnaryNode { case object NoRelation extends LeafNode { override def output = Nil + + /** + * Computes [[Statistics]] for this plan. The default implementation assumes the output + * cardinality is the product of of all child plan's cardinality, i.e. applies in the case + * of cartesian joins. + * + * [[LeafNode]]s must override this. + */ + override def statistics: Statistics = Statistics(sizeInBytes = 1) } case class Intersect(left: LogicalPlan, right: LogicalPlan) extends BinaryNode { From ee569a0c7171d149eee52877def902378eaf695e Mon Sep 17 00:00:00 2001 From: Venkata Ramana Gollamudi Date: Sat, 21 Mar 2015 13:24:24 -0700 Subject: [PATCH 002/591] [SPARK-5680][SQL] Sum function on all null values, should return zero SELECT sum('a'), avg('a'), variance('a'), std('a') FROM src; Should give output as 0.0 NULL NULL NULL This fixes hive udaf_number_format.q Author: Venkata Ramana G Author: Venkata Ramana Gollamudi Closes #4466 from gvramana/sum_fix and squashes the following commits: 42e14d1 [Venkata Ramana Gollamudi] Added comments 39415c0 [Venkata Ramana Gollamudi] Handled the partitioned Sum expression scenario df66515 [Venkata Ramana Gollamudi] code style fix 4be2606 [Venkata Ramana Gollamudi] Add udaf_number_format to whitelist and golden answer 330fd64 [Venkata Ramana Gollamudi] fix sum function for all null data --- .../sql/catalyst/expressions/aggregates.scala | 68 ++++++++++++++++++- .../execution/HiveCompatibilitySuite.scala | 1 + ..._format-0-eff4ef3c207d14d5121368f294697964 | 0 ..._format-1-4a03c4328565c60ca99689239f07fb16 | 1 + 4 files changed, 67 insertions(+), 3 deletions(-) create mode 100644 sql/hive/src/test/resources/golden/udaf_number_format-0-eff4ef3c207d14d5121368f294697964 create mode 100644 sql/hive/src/test/resources/golden/udaf_number_format-1-4a03c4328565c60ca99689239f07fb16 diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala index 735b7488fdcbd..5297d1e31246c 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala @@ -346,13 +346,13 @@ case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[ case DecimalType.Fixed(_, _) => val partialSum = Alias(Sum(Cast(child, DecimalType.Unlimited)), "PartialSum")() SplitEvaluation( - Cast(Sum(partialSum.toAttribute), dataType), + Cast(CombineSum(partialSum.toAttribute), dataType), partialSum :: Nil) case _ => val partialSum = Alias(Sum(child), "PartialSum")() SplitEvaluation( - Sum(partialSum.toAttribute), + CombineSum(partialSum.toAttribute), partialSum :: Nil) } } @@ -360,6 +360,30 @@ case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[ override def newInstance() = new SumFunction(child, this) } +/** + * Sum should satisfy 3 cases: + * 1) sum of all null values = zero + * 2) sum for table column with no data = null + * 3) sum of column with null and not null values = sum of not null values + * Require separate CombineSum Expression and function as it has to distinguish "No data" case + * versus "data equals null" case, while aggregating results and at each partial expression.i.e., + * Combining PartitionLevel InputData + * <-- null + * Zero <-- Zero <-- null + * + * <-- null <-- no data + * null <-- null <-- no data + */ +case class CombineSum(child: Expression) extends AggregateExpression { + def this() = this(null) + + override def children = child :: Nil + override def nullable = true + override def dataType = child.dataType + override def toString = s"CombineSum($child)" + override def newInstance() = new CombineSumFunction(child, this) +} + case class SumDistinct(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { @@ -565,7 +589,8 @@ case class SumFunction(expr: Expression, base: AggregateExpression) extends Aggr private val sum = MutableLiteral(null, calcType) - private val addFunction = Coalesce(Seq(Add(Coalesce(Seq(sum, zero)), Cast(expr, calcType)), sum)) + private val addFunction = + Coalesce(Seq(Add(Coalesce(Seq(sum, zero)), Cast(expr, calcType)), sum, zero)) override def update(input: Row): Unit = { sum.update(addFunction, input) @@ -580,6 +605,43 @@ case class SumFunction(expr: Expression, base: AggregateExpression) extends Aggr } } +case class CombineSumFunction(expr: Expression, base: AggregateExpression) + extends AggregateFunction { + + def this() = this(null, null) // Required for serialization. + + private val calcType = + expr.dataType match { + case DecimalType.Fixed(_, _) => + DecimalType.Unlimited + case _ => + expr.dataType + } + + private val zero = Cast(Literal(0), calcType) + + private val sum = MutableLiteral(null, calcType) + + private val addFunction = + Coalesce(Seq(Add(Coalesce(Seq(sum, zero)), Cast(expr, calcType)), sum, zero)) + + override def update(input: Row): Unit = { + val result = expr.eval(input) + // partial sum result can be null only when no input rows present + if(result != null) { + sum.update(addFunction, input) + } + } + + override def eval(input: Row): Any = { + expr.dataType match { + case DecimalType.Fixed(_, _) => + Cast(sum, dataType).eval(null) + case _ => sum.eval(null) + } + } +} + case class SumDistinctFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction { diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala index 5180a7f09d80f..2ae9d018e1b1b 100644 --- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala +++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala @@ -800,6 +800,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { "udaf_covar_pop", "udaf_covar_samp", "udaf_histogram_numeric", + "udaf_number_format", "udf2", "udf5", "udf6", diff --git a/sql/hive/src/test/resources/golden/udaf_number_format-0-eff4ef3c207d14d5121368f294697964 b/sql/hive/src/test/resources/golden/udaf_number_format-0-eff4ef3c207d14d5121368f294697964 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/udaf_number_format-1-4a03c4328565c60ca99689239f07fb16 b/sql/hive/src/test/resources/golden/udaf_number_format-1-4a03c4328565c60ca99689239f07fb16 new file mode 100644 index 0000000000000..c6f275a0db131 --- /dev/null +++ b/sql/hive/src/test/resources/golden/udaf_number_format-1-4a03c4328565c60ca99689239f07fb16 @@ -0,0 +1 @@ +0.0 NULL NULL NULL From 94a102acb80a7c77f57409ece1f8dbbba791b774 Mon Sep 17 00:00:00 2001 From: Yin Huai Date: Sat, 21 Mar 2015 13:27:53 -0700 Subject: [PATCH 003/591] [SPARK-6250][SPARK-6146][SPARK-5911][SQL] Types are now reserved words in DDL parser. This PR creates a trait `DataTypeParser` used to parse data types. This trait aims to be single place to provide the functionality of parsing data types' string representation. It is currently mixed in with `DDLParser` and `SqlParser`. It is also used to parse the data type for `DataFrame.cast` and to convert Hive metastore's data type string back to a `DataType`. JIRA: https://issues.apache.org/jira/browse/SPARK-6250 Author: Yin Huai Closes #5078 from yhuai/ddlKeywords and squashes the following commits: 0e66097 [Yin Huai] Special handle struct<>. fea6012 [Yin Huai] Style. c9733fb [Yin Huai] Create a trait to parse data types. --- .../apache/spark/sql/catalyst/SqlParser.scala | 27 +--- .../spark/sql/types/DataTypeParser.scala | 115 +++++++++++++++++ .../spark/sql/types/DataTypeParserSuite.scala | 116 ++++++++++++++++++ .../scala/org/apache/spark/sql/Column.scala | 15 +-- .../org/apache/spark/sql/sources/ddl.scala | 80 +----------- .../spark/sql/hive/HiveMetastoreCatalog.scala | 8 +- 6 files changed, 241 insertions(+), 120 deletions(-) create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala index 54ab13ca352d2..ea7d44a3723d1 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala @@ -35,7 +35,7 @@ import org.apache.spark.sql.types._ * This is currently included mostly for illustrative purposes. Users wanting more complete support * for a SQL like language should checkout the HiveQL support in the sql/hive sub-project. */ -class SqlParser extends AbstractSparkSQLParser { +class SqlParser extends AbstractSparkSQLParser with DataTypeParser { def parseExpression(input: String): Expression = { // Initialize the Keywords. @@ -61,11 +61,8 @@ class SqlParser extends AbstractSparkSQLParser { protected val CAST = Keyword("CAST") protected val COALESCE = Keyword("COALESCE") protected val COUNT = Keyword("COUNT") - protected val DATE = Keyword("DATE") - protected val DECIMAL = Keyword("DECIMAL") protected val DESC = Keyword("DESC") protected val DISTINCT = Keyword("DISTINCT") - protected val DOUBLE = Keyword("DOUBLE") protected val ELSE = Keyword("ELSE") protected val END = Keyword("END") protected val EXCEPT = Keyword("EXCEPT") @@ -78,7 +75,6 @@ class SqlParser extends AbstractSparkSQLParser { protected val IF = Keyword("IF") protected val IN = Keyword("IN") protected val INNER = Keyword("INNER") - protected val INT = Keyword("INT") protected val INSERT = Keyword("INSERT") protected val INTERSECT = Keyword("INTERSECT") protected val INTO = Keyword("INTO") @@ -105,13 +101,11 @@ class SqlParser extends AbstractSparkSQLParser { protected val SELECT = Keyword("SELECT") protected val SEMI = Keyword("SEMI") protected val SQRT = Keyword("SQRT") - protected val STRING = Keyword("STRING") protected val SUBSTR = Keyword("SUBSTR") protected val SUBSTRING = Keyword("SUBSTRING") protected val SUM = Keyword("SUM") protected val TABLE = Keyword("TABLE") protected val THEN = Keyword("THEN") - protected val TIMESTAMP = Keyword("TIMESTAMP") protected val TRUE = Keyword("TRUE") protected val UNION = Keyword("UNION") protected val UPPER = Keyword("UPPER") @@ -315,7 +309,9 @@ class SqlParser extends AbstractSparkSQLParser { ) protected lazy val cast: Parser[Expression] = - CAST ~ "(" ~> expression ~ (AS ~> dataType) <~ ")" ^^ { case exp ~ t => Cast(exp, t) } + CAST ~ "(" ~> expression ~ (AS ~> dataType) <~ ")" ^^ { + case exp ~ t => Cast(exp, t) + } protected lazy val literal: Parser[Literal] = ( numericLiteral @@ -387,19 +383,4 @@ class SqlParser extends AbstractSparkSQLParser { (ident <~ ".") ~ ident ~ rep("." ~> ident) ^^ { case i1 ~ i2 ~ rest => UnresolvedAttribute((Seq(i1, i2) ++ rest).mkString(".")) } - - protected lazy val dataType: Parser[DataType] = - ( STRING ^^^ StringType - | TIMESTAMP ^^^ TimestampType - | DOUBLE ^^^ DoubleType - | fixedDecimalType - | DECIMAL ^^^ DecimalType.Unlimited - | DATE ^^^ DateType - | INT ^^^ IntegerType - ) - - protected lazy val fixedDecimalType: Parser[DataType] = - (DECIMAL ~ "(" ~> numericLit) ~ ("," ~> numericLit <~ ")") ^^ { - case precision ~ scale => DecimalType(precision.toInt, scale.toInt) - } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala new file mode 100644 index 0000000000000..89278f7dbc806 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala @@ -0,0 +1,115 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import scala.language.implicitConversions +import scala.util.matching.Regex +import scala.util.parsing.combinator.syntactical.StandardTokenParsers + +import org.apache.spark.sql.catalyst.SqlLexical + +/** + * This is a data type parser that can be used to parse string representations of data types + * provided in SQL queries. This parser is mixed in with DDLParser and SqlParser. + */ +private[sql] trait DataTypeParser extends StandardTokenParsers { + + // This is used to create a parser from a regex. We are using regexes for data type strings + // since these strings can be also used as column names or field names. + import lexical.Identifier + implicit def regexToParser(regex: Regex): Parser[String] = acceptMatch( + s"identifier matching regex ${regex}", + { case Identifier(str) if regex.unapplySeq(str).isDefined => str } + ) + + protected lazy val primitiveType: Parser[DataType] = + "(?i)string".r ^^^ StringType | + "(?i)float".r ^^^ FloatType | + "(?i)int".r ^^^ IntegerType | + "(?i)tinyint".r ^^^ ByteType | + "(?i)smallint".r ^^^ ShortType | + "(?i)double".r ^^^ DoubleType | + "(?i)bigint".r ^^^ LongType | + "(?i)binary".r ^^^ BinaryType | + "(?i)boolean".r ^^^ BooleanType | + fixedDecimalType | + "(?i)decimal".r ^^^ DecimalType.Unlimited | + "(?i)date".r ^^^ DateType | + "(?i)timestamp".r ^^^ TimestampType | + varchar + + protected lazy val fixedDecimalType: Parser[DataType] = + ("(?i)decimal".r ~> "(" ~> numericLit) ~ ("," ~> numericLit <~ ")") ^^ { + case precision ~ scale => + DecimalType(precision.toInt, scale.toInt) + } + + protected lazy val varchar: Parser[DataType] = + "(?i)varchar".r ~> "(" ~> (numericLit <~ ")") ^^^ StringType + + protected lazy val arrayType: Parser[DataType] = + "(?i)array".r ~> "<" ~> dataType <~ ">" ^^ { + case tpe => ArrayType(tpe) + } + + protected lazy val mapType: Parser[DataType] = + "(?i)map".r ~> "<" ~> dataType ~ "," ~ dataType <~ ">" ^^ { + case t1 ~ _ ~ t2 => MapType(t1, t2) + } + + protected lazy val structField: Parser[StructField] = + ident ~ ":" ~ dataType ^^ { + case name ~ _ ~ tpe => StructField(name, tpe, nullable = true) + } + + protected lazy val structType: Parser[DataType] = + ("(?i)struct".r ~> "<" ~> repsep(structField, ",") <~ ">" ^^ { + case fields => new StructType(fields.toArray) + }) | + ("(?i)struct".r ~ "<>" ^^^ StructType(Nil)) + + protected lazy val dataType: Parser[DataType] = + arrayType | + mapType | + structType | + primitiveType + + def toDataType(dataTypeString: String): DataType = synchronized { + phrase(dataType)(new lexical.Scanner(dataTypeString)) match { + case Success(result, _) => result + case failure: NoSuccess => throw new DataTypeException(failMessage(dataTypeString)) + } + } + + private def failMessage(dataTypeString: String): String = { + s"Unsupported dataType: $dataTypeString. If you have a struct and a field name of it has " + + "any special characters, please use backticks (`) to quote that field name, e.g. `x+y`. " + + "Please note that backtick itself is not supported in a field name." + } +} + +private[sql] object DataTypeParser { + lazy val dataTypeParser = new DataTypeParser { + override val lexical = new SqlLexical + } + + def apply(dataTypeString: String): DataType = dataTypeParser.toDataType(dataTypeString) +} + +/** The exception thrown from the [[DataTypeParser]]. */ +protected[sql] class DataTypeException(message: String) extends Exception(message) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala new file mode 100644 index 0000000000000..1ba21b64603ac --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala @@ -0,0 +1,116 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql.types + +import org.scalatest.FunSuite + +class DataTypeParserSuite extends FunSuite { + + def checkDataType(dataTypeString: String, expectedDataType: DataType): Unit = { + test(s"parse ${dataTypeString.replace("\n", "")}") { + assert(DataTypeParser(dataTypeString) === expectedDataType) + } + } + + def unsupported(dataTypeString: String): Unit = { + test(s"$dataTypeString is not supported") { + intercept[DataTypeException](DataTypeParser(dataTypeString)) + } + } + + checkDataType("int", IntegerType) + checkDataType("BooLean", BooleanType) + checkDataType("tinYint", ByteType) + checkDataType("smallINT", ShortType) + checkDataType("INT", IntegerType) + checkDataType("bigint", LongType) + checkDataType("float", FloatType) + checkDataType("dOUBle", DoubleType) + checkDataType("decimal(10, 5)", DecimalType(10, 5)) + checkDataType("decimal", DecimalType.Unlimited) + checkDataType("DATE", DateType) + checkDataType("timestamp", TimestampType) + checkDataType("string", StringType) + checkDataType("varchAr(20)", StringType) + checkDataType("BINARY", BinaryType) + + checkDataType("array", ArrayType(DoubleType, true)) + checkDataType("Array>", ArrayType(MapType(IntegerType, ByteType, true), true)) + checkDataType( + "array>", + ArrayType(StructType(StructField("tinYint", ByteType, true) :: Nil), true) + ) + checkDataType("MAP", MapType(IntegerType, StringType, true)) + checkDataType("MAp>", MapType(IntegerType, ArrayType(DoubleType), true)) + checkDataType( + "MAP>", + MapType(IntegerType, StructType(StructField("varchar", StringType, true) :: Nil), true) + ) + + checkDataType( + "struct", + StructType( + StructField("intType", IntegerType, true) :: + StructField("ts", TimestampType, true) :: Nil) + ) + // It is fine to use the data type string as the column name. + checkDataType( + "Struct", + StructType( + StructField("int", IntegerType, true) :: + StructField("timestamp", TimestampType, true) :: Nil) + ) + checkDataType( + """ + |struct< + | struct:struct, + | MAP:Map, + | arrAy:Array> + """.stripMargin, + StructType( + StructField("struct", + StructType( + StructField("deciMal", DecimalType.Unlimited, true) :: + StructField("anotherDecimal", DecimalType(5, 2), true) :: Nil), true) :: + StructField("MAP", MapType(TimestampType, StringType), true) :: + StructField("arrAy", ArrayType(DoubleType, true), true) :: Nil) + ) + // A column name can be a reserved word in our DDL parser and SqlParser. + checkDataType( + "Struct", + StructType( + StructField("TABLE", StringType, true) :: + StructField("CASE", BooleanType, true) :: Nil) + ) + // Use backticks to quote column names having special characters. + checkDataType( + "struct<`x+y`:int, `!@#$%^&*()`:string, `1_2.345<>:\"`:varchar(20)>", + StructType( + StructField("x+y", IntegerType, true) :: + StructField("!@#$%^&*()", StringType, true) :: + StructField("1_2.345<>:\"", StringType, true) :: Nil) + ) + // Empty struct. + checkDataType("strUCt<>", StructType(Nil)) + + unsupported("it is not a data type") + unsupported("struct") + unsupported("struct") + unsupported("struct<`x``y` int>") +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala index b7a13a1b26802..ec7d15f5bc4e7 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala @@ -624,20 +624,7 @@ class Column(protected[sql] val expr: Expression) { * * @group expr_ops */ - def cast(to: String): Column = cast(to.toLowerCase match { - case "string" | "str" => StringType - case "boolean" => BooleanType - case "byte" => ByteType - case "short" => ShortType - case "int" => IntegerType - case "long" => LongType - case "float" => FloatType - case "double" => DoubleType - case "decimal" => DecimalType.Unlimited - case "date" => DateType - case "timestamp" => TimestampType - case _ => throw new RuntimeException(s"""Unsupported cast type: "$to"""") - }) + def cast(to: String): Column = cast(DataTypeParser(to)) /** * Returns an ordering used in sorting. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala index d57406645eefa..d2e807d3a69b6 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala @@ -34,7 +34,8 @@ import org.apache.spark.util.Utils * A parser for foreign DDL commands. */ private[sql] class DDLParser( - parseQuery: String => LogicalPlan) extends AbstractSparkSQLParser with Logging { + parseQuery: String => LogicalPlan) + extends AbstractSparkSQLParser with DataTypeParser with Logging { def apply(input: String, exceptionOnError: Boolean): Option[LogicalPlan] = { try { @@ -46,14 +47,6 @@ private[sql] class DDLParser( } } - def parseType(input: String): DataType = { - lexical.initialize(reservedWords) - phrase(dataType)(new lexical.Scanner(input)) match { - case Success(r, x) => r - case x => throw new DDLException(s"Unsupported dataType: $x") - } - } - // Keyword is a convention with AbstractSparkSQLParser, which will scan all of the `Keyword` // properties via reflection the class in runtime for constructing the SqlLexical object protected val CREATE = Keyword("CREATE") @@ -70,24 +63,6 @@ private[sql] class DDLParser( protected val COMMENT = Keyword("COMMENT") protected val REFRESH = Keyword("REFRESH") - // Data types. - protected val STRING = Keyword("STRING") - protected val BINARY = Keyword("BINARY") - protected val BOOLEAN = Keyword("BOOLEAN") - protected val TINYINT = Keyword("TINYINT") - protected val SMALLINT = Keyword("SMALLINT") - protected val INT = Keyword("INT") - protected val BIGINT = Keyword("BIGINT") - protected val FLOAT = Keyword("FLOAT") - protected val DOUBLE = Keyword("DOUBLE") - protected val DECIMAL = Keyword("DECIMAL") - protected val DATE = Keyword("DATE") - protected val TIMESTAMP = Keyword("TIMESTAMP") - protected val VARCHAR = Keyword("VARCHAR") - protected val ARRAY = Keyword("ARRAY") - protected val MAP = Keyword("MAP") - protected val STRUCT = Keyword("STRUCT") - protected lazy val ddl: Parser[LogicalPlan] = createTable | describeTable | refreshTable protected def start: Parser[LogicalPlan] = ddl @@ -189,58 +164,9 @@ private[sql] class DDLParser( new MetadataBuilder().putString(COMMENT.str.toLowerCase, comment).build() case None => Metadata.empty } - StructField(columnName, typ, nullable = true, meta) - } - - protected lazy val primitiveType: Parser[DataType] = - STRING ^^^ StringType | - BINARY ^^^ BinaryType | - BOOLEAN ^^^ BooleanType | - TINYINT ^^^ ByteType | - SMALLINT ^^^ ShortType | - INT ^^^ IntegerType | - BIGINT ^^^ LongType | - FLOAT ^^^ FloatType | - DOUBLE ^^^ DoubleType | - fixedDecimalType | // decimal with precision/scale - DECIMAL ^^^ DecimalType.Unlimited | // decimal with no precision/scale - DATE ^^^ DateType | - TIMESTAMP ^^^ TimestampType | - VARCHAR ~ "(" ~ numericLit ~ ")" ^^^ StringType - - protected lazy val fixedDecimalType: Parser[DataType] = - (DECIMAL ~ "(" ~> numericLit) ~ ("," ~> numericLit <~ ")") ^^ { - case precision ~ scale => DecimalType(precision.toInt, scale.toInt) - } - - protected lazy val arrayType: Parser[DataType] = - ARRAY ~> "<" ~> dataType <~ ">" ^^ { - case tpe => ArrayType(tpe) - } - protected lazy val mapType: Parser[DataType] = - MAP ~> "<" ~> dataType ~ "," ~ dataType <~ ">" ^^ { - case t1 ~ _ ~ t2 => MapType(t1, t2) - } - - protected lazy val structField: Parser[StructField] = - ident ~ ":" ~ dataType ^^ { - case fieldName ~ _ ~ tpe => StructField(fieldName, tpe, nullable = true) + StructField(columnName, typ, nullable = true, meta) } - - protected lazy val structType: Parser[DataType] = - (STRUCT ~> "<" ~> repsep(structField, ",") <~ ">" ^^ { - case fields => StructType(fields) - }) | - (STRUCT ~> "<>" ^^ { - case fields => StructType(Nil) - }) - - private[sql] lazy val dataType: Parser[DataType] = - arrayType | - mapType | - structType | - primitiveType } private[sql] object ResolvedDataSource { diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index fe86bd206a71c..949a4e54e6c30 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -756,7 +756,7 @@ private[hive] case class MetastoreRelation implicit class SchemaAttribute(f: FieldSchema) { def toAttribute = AttributeReference( f.getName, - sqlContext.ddlParser.parseType(f.getType), + HiveMetastoreTypes.toDataType(f.getType), // Since data can be dumped in randomly with no validation, everything is nullable. nullable = true )(qualifiers = Seq(alias.getOrElse(tableName))) @@ -779,11 +779,7 @@ private[hive] case class MetastoreRelation private[hive] object HiveMetastoreTypes { - protected val ddlParser = new DDLParser(HiveQl.parseSql(_)) - - def toDataType(metastoreType: String): DataType = synchronized { - ddlParser.parseType(metastoreType) - } + def toDataType(metastoreType: String): DataType = DataTypeParser(metastoreType) def toMetastoreType(dt: DataType): String = dt match { case ArrayType(elementType, _) => s"array<${toMetastoreType(elementType)}>" From b6090f902e6ec24923b4dde4aabc9076956521c1 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Sat, 21 Mar 2015 14:30:04 -0700 Subject: [PATCH 004/591] [SPARK-6428][SQL] Added explicit type for all public methods for Hive module Author: Reynold Xin Closes #5108 from rxin/hive-public-type and squashes the following commits: a320328 [Reynold Xin] [SPARK-6428][SQL] Added explicit type for all public methods for Hive module. --- .../hive/thriftserver/SparkSQLCLIDriver.scala | 4 +- .../apache/spark/sql/hive/HiveContext.scala | 6 +-- .../spark/sql/hive/HiveMetastoreCatalog.scala | 21 ++++----- .../org/apache/spark/sql/hive/HiveQl.scala | 10 ++--- .../spark/sql/hive/HiveStrategies.scala | 6 +-- .../apache/spark/sql/hive/TableReader.scala | 2 +- .../hive/execution/CreateTableAsSelect.scala | 2 +- .../execution/DescribeHiveTableCommand.scala | 2 +- .../hive/execution/HiveNativeCommand.scala | 4 +- .../sql/hive/execution/HiveTableScan.scala | 5 ++- .../hive/execution/InsertIntoHiveTable.scala | 4 +- .../hive/execution/ScriptTransformation.scala | 5 ++- .../spark/sql/hive/execution/commands.scala | 8 ++-- .../org/apache/spark/sql/hive/hiveUdfs.scala | 45 +++++++++++-------- .../spark/sql/hive/hiveWriterContainers.scala | 4 +- .../apache/spark/sql/hive/test/TestHive.scala | 13 ++++-- 16 files changed, 79 insertions(+), 62 deletions(-) diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala index 895688ab2ec2e..6272cdedb3e48 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala @@ -194,8 +194,8 @@ private[hive] object SparkSQLCLIDriver { val currentDB = ReflectionUtils.invokeStatic(classOf[CliDriver], "getFormattedDb", classOf[HiveConf] -> conf, classOf[CliSessionState] -> sessionState) - def promptWithCurrentDB = s"$prompt$currentDB" - def continuedPromptWithDBSpaces = continuedPrompt + ReflectionUtils.invokeStatic( + def promptWithCurrentDB: String = s"$prompt$currentDB" + def continuedPromptWithDBSpaces: String = continuedPrompt + ReflectionUtils.invokeStatic( classOf[CliDriver], "spacesForString", classOf[String] -> currentDB) var currentPrompt = promptWithCurrentDB diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala index a5c435fdfa778..c06c2e396bbc1 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala @@ -183,7 +183,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { // Circular buffer to hold what hive prints to STDOUT and ERR. Only printed when failures occur. @transient - protected lazy val outputBuffer = new java.io.OutputStream { + protected lazy val outputBuffer = new java.io.OutputStream { var pos: Int = 0 var buffer = new Array[Int](10240) def write(i: Int): Unit = { @@ -191,7 +191,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { pos = (pos + 1) % buffer.size } - override def toString = { + override def toString: String = { val (end, start) = buffer.splitAt(pos) val input = new java.io.InputStream { val iterator = (start ++ end).iterator @@ -227,7 +227,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { @transient override protected[sql] lazy val functionRegistry = new HiveFunctionRegistry with OverrideFunctionRegistry { - def caseSensitive = false + def caseSensitive: Boolean = false } /* An analyzer that uses the Hive metastore. */ diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index 949a4e54e6c30..4c5eb48661f7d 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -58,7 +58,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with // TODO: Use this everywhere instead of tuples or databaseName, tableName,. /** A fully qualified identifier for a table (i.e., database.tableName) */ case class QualifiedTableName(database: String, name: String) { - def toLowerCase = QualifiedTableName(database.toLowerCase, name.toLowerCase) + def toLowerCase: QualifiedTableName = QualifiedTableName(database.toLowerCase, name.toLowerCase) } /** A cache of Spark SQL data source tables that have been accessed. */ @@ -629,7 +629,8 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with castChildOutput(p, table, child) } - def castChildOutput(p: InsertIntoTable, table: MetastoreRelation, child: LogicalPlan) = { + def castChildOutput(p: InsertIntoTable, table: MetastoreRelation, child: LogicalPlan) + : LogicalPlan = { val childOutputDataTypes = child.output.map(_.dataType) val tableOutputDataTypes = (table.attributes ++ table.partitionKeys).take(child.output.length).map(_.dataType) @@ -667,7 +668,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with */ override def unregisterTable(tableIdentifier: Seq[String]): Unit = ??? - override def unregisterAllTables() = {} + override def unregisterAllTables(): Unit = {} } /** @@ -682,10 +683,10 @@ private[hive] case class InsertIntoHiveTable( overwrite: Boolean) extends LogicalPlan { - override def children = child :: Nil - override def output = child.output + override def children: Seq[LogicalPlan] = child :: Nil + override def output: Seq[Attribute] = child.output - override lazy val resolved = childrenResolved && child.output.zip(table.output).forall { + override lazy val resolved: Boolean = childrenResolved && child.output.zip(table.output).forall { case (childAttr, tableAttr) => childAttr.dataType.sameType(tableAttr.dataType) } } @@ -704,13 +705,13 @@ private[hive] case class MetastoreRelation // org.apache.hadoop.hive.ql.metadata.Partition will cause a NotSerializableException // which indicates the SerDe we used is not Serializable. - @transient val hiveQlTable = new Table(table) + @transient val hiveQlTable: Table = new Table(table) - @transient val hiveQlPartitions = partitions.map { p => + @transient val hiveQlPartitions: Seq[Partition] = partitions.map { p => new Partition(hiveQlTable, p) } - @transient override lazy val statistics = Statistics( + @transient override lazy val statistics: Statistics = Statistics( sizeInBytes = { val totalSize = hiveQlTable.getParameters.get(HiveShim.getStatsSetupConstTotalSize) val rawDataSize = hiveQlTable.getParameters.get(HiveShim.getStatsSetupConstRawDataSize) @@ -754,7 +755,7 @@ private[hive] case class MetastoreRelation ) implicit class SchemaAttribute(f: FieldSchema) { - def toAttribute = AttributeReference( + def toAttribute: AttributeReference = AttributeReference( f.getName, HiveMetastoreTypes.toDataType(f.getType), // Since data can be dumped in randomly with no validation, everything is nullable. diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala index ced99cd082614..51775eb4cd6a0 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala @@ -196,8 +196,8 @@ private[hive] object HiveQl { * Right now this function only checks the name, type, text and children of the node * for equality. */ - def checkEquals(other: ASTNode) { - def check(field: String, f: ASTNode => Any) = if (f(n) != f(other)) { + def checkEquals(other: ASTNode): Unit = { + def check(field: String, f: ASTNode => Any): Unit = if (f(n) != f(other)) { sys.error(s"$field does not match for trees. " + s"'${f(n)}' != '${f(other)}' left: ${dumpTree(n)}, right: ${dumpTree(other)}") } @@ -209,7 +209,7 @@ private[hive] object HiveQl { val leftChildren = nilIfEmpty(n.getChildren).asInstanceOf[Seq[ASTNode]] val rightChildren = nilIfEmpty(other.getChildren).asInstanceOf[Seq[ASTNode]] leftChildren zip rightChildren foreach { - case (l,r) => l checkEquals r + case (l, r) => l checkEquals r } } } @@ -269,7 +269,7 @@ private[hive] object HiveQl { } /** Creates LogicalPlan for a given VIEW */ - def createPlanForView(view: Table, alias: Option[String]) = alias match { + def createPlanForView(view: Table, alias: Option[String]): Subquery = alias match { // because hive use things like `_c0` to build the expanded text // currently we cannot support view from "create view v1(c1) as ..." case None => Subquery(view.getTableName, createPlan(view.getViewExpandedText)) @@ -323,7 +323,7 @@ private[hive] object HiveQl { clauses } - def getClause(clauseName: String, nodeList: Seq[Node]) = + def getClause(clauseName: String, nodeList: Seq[Node]): Node = getClauseOption(clauseName, nodeList).getOrElse(sys.error( s"Expected clause $clauseName missing from ${nodeList.map(dumpTree(_)).mkString("\n")}")) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala index e63cea60457d9..5f7e897295117 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala @@ -58,9 +58,9 @@ private[hive] trait HiveStrategies { @Experimental object ParquetConversion extends Strategy { implicit class LogicalPlanHacks(s: DataFrame) { - def lowerCase = DataFrame(s.sqlContext, s.logicalPlan) + def lowerCase: DataFrame = DataFrame(s.sqlContext, s.logicalPlan) - def addPartitioningAttributes(attrs: Seq[Attribute]) = { + def addPartitioningAttributes(attrs: Seq[Attribute]): DataFrame = { // Don't add the partitioning key if its already present in the data. if (attrs.map(_.name).toSet.subsetOf(s.logicalPlan.output.map(_.name).toSet)) { s @@ -75,7 +75,7 @@ private[hive] trait HiveStrategies { } implicit class PhysicalPlanHacks(originalPlan: SparkPlan) { - def fakeOutput(newOutput: Seq[Attribute]) = + def fakeOutput(newOutput: Seq[Attribute]): OutputFaker = OutputFaker( originalPlan.output.map(a => newOutput.find(a.name.toLowerCase == _.name.toLowerCase) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala index f22c9eaeedc7d..af309c0c6ce2c 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala @@ -175,7 +175,7 @@ class HadoopTableReader( relation.partitionKeys.contains(attr) } - def fillPartitionKeys(rawPartValues: Array[String], row: MutableRow) = { + def fillPartitionKeys(rawPartValues: Array[String], row: MutableRow): Unit = { partitionKeyAttrs.foreach { case (attr, ordinal) => val partOrdinal = relation.partitionKeys.indexOf(attr) row(ordinal) = Cast(Literal(rawPartValues(partOrdinal)), attr.dataType).eval(null) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala index a0c91cbc4e86f..fade9e5852eaa 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala @@ -45,7 +45,7 @@ case class CreateTableAsSelect( allowExisting: Boolean, desc: Option[CreateTableDesc]) extends RunnableCommand { - override def run(sqlContext: SQLContext) = { + override def run(sqlContext: SQLContext): Seq[Row] = { val hiveContext = sqlContext.asInstanceOf[HiveContext] lazy val metastoreRelation: MetastoreRelation = { // Create Hive Table diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala index d0510aa342796..6fce69b58b85e 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala @@ -37,7 +37,7 @@ case class DescribeHiveTableCommand( override val output: Seq[Attribute], isExtended: Boolean) extends RunnableCommand { - override def run(sqlContext: SQLContext) = { + override def run(sqlContext: SQLContext): Seq[Row] = { // Trying to mimic the format of Hive's output. But not exactly the same. var results: Seq[(String, String, String)] = Nil diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveNativeCommand.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveNativeCommand.scala index 9636da206087f..60a9bb630d0d9 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveNativeCommand.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveNativeCommand.scala @@ -26,9 +26,9 @@ import org.apache.spark.sql.types.StringType private[hive] case class HiveNativeCommand(sql: String) extends RunnableCommand { - override def output = + override def output: Seq[AttributeReference] = Seq(AttributeReference("result", StringType, nullable = false)()) - override def run(sqlContext: SQLContext) = + override def run(sqlContext: SQLContext): Seq[Row] = sqlContext.asInstanceOf[HiveContext].runSqlHive(sql).map(Row(_)) } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala index 5b3cf2861e8ef..0a5f19eee7105 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala @@ -26,6 +26,7 @@ import org.apache.hadoop.hive.serde2.objectinspector._ import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils.ObjectInspectorCopyOption import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils +import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.execution._ import org.apache.spark.sql.hive._ @@ -128,11 +129,11 @@ case class HiveTableScan( } } - override def execute() = if (!relation.hiveQlTable.isPartitioned) { + override def execute(): RDD[Row] = if (!relation.hiveQlTable.isPartitioned) { hadoopReader.makeRDDForTable(relation.hiveQlTable) } else { hadoopReader.makeRDDForPartitionedTable(prunePartitions(relation.hiveQlPartitions)) } - override def output = attributes + override def output: Seq[Attribute] = attributes } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala index ba5c8e028a151..da53d30354551 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala @@ -33,7 +33,7 @@ import org.apache.hadoop.hive.serde2.objectinspector._ import org.apache.hadoop.mapred.{FileOutputCommitter, FileOutputFormat, JobConf} import org.apache.spark.rdd.RDD -import org.apache.spark.sql.catalyst.expressions.Row +import org.apache.spark.sql.catalyst.expressions.{Attribute, Row} import org.apache.spark.sql.execution.{UnaryNode, SparkPlan} import org.apache.spark.sql.hive._ import org.apache.spark.sql.hive.{ ShimFileSinkDesc => FileSinkDesc} @@ -58,7 +58,7 @@ case class InsertIntoHiveTable( serializer } - def output = child.output + def output: Seq[Attribute] = child.output def saveAsHiveFile( rdd: RDD[Row], diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala index 0c9aee33985bc..8efed7f0299bf 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala @@ -27,6 +27,7 @@ import org.apache.hadoop.hive.serde.serdeConstants import org.apache.hadoop.hive.serde2.AbstractSerDe import org.apache.hadoop.hive.serde2.objectinspector._ +import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical.ScriptInputOutputSchema import org.apache.spark.sql.execution._ @@ -51,9 +52,9 @@ case class ScriptTransformation( ioschema: HiveScriptIOSchema)(@transient sc: HiveContext) extends UnaryNode { - override def otherCopyArgs = sc :: Nil + override def otherCopyArgs: Seq[HiveContext] = sc :: Nil - def execute() = { + def execute(): RDD[Row] = { child.execute().mapPartitions { iter => val cmd = List("/bin/bash", "-c", script) val builder = new ProcessBuilder(cmd) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala index 63ad145a6a980..4345ffbf30f77 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala @@ -38,7 +38,7 @@ import org.apache.spark.sql.types.StructType private[hive] case class AnalyzeTable(tableName: String) extends RunnableCommand { - override def run(sqlContext: SQLContext) = { + override def run(sqlContext: SQLContext): Seq[Row] = { sqlContext.asInstanceOf[HiveContext].analyze(tableName) Seq.empty[Row] } @@ -52,7 +52,7 @@ case class DropTable( tableName: String, ifExists: Boolean) extends RunnableCommand { - override def run(sqlContext: SQLContext) = { + override def run(sqlContext: SQLContext): Seq[Row] = { val hiveContext = sqlContext.asInstanceOf[HiveContext] val ifExistsClause = if (ifExists) "IF EXISTS " else "" try { @@ -75,7 +75,7 @@ case class DropTable( private[hive] case class AddJar(path: String) extends RunnableCommand { - override def run(sqlContext: SQLContext) = { + override def run(sqlContext: SQLContext): Seq[Row] = { val hiveContext = sqlContext.asInstanceOf[HiveContext] hiveContext.runSqlHive(s"ADD JAR $path") hiveContext.sparkContext.addJar(path) @@ -86,7 +86,7 @@ case class AddJar(path: String) extends RunnableCommand { private[hive] case class AddFile(path: String) extends RunnableCommand { - override def run(sqlContext: SQLContext) = { + override def run(sqlContext: SQLContext): Seq[Row] = { val hiveContext = sqlContext.asInstanceOf[HiveContext] hiveContext.runSqlHive(s"ADD FILE $path") hiveContext.sparkContext.addFile(path) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala index 4a702d96563d5..bfe43373d9534 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala @@ -45,7 +45,7 @@ import scala.collection.JavaConversions._ private[hive] abstract class HiveFunctionRegistry extends analysis.FunctionRegistry with HiveInspectors { - def getFunctionInfo(name: String) = FunctionRegistry.getFunctionInfo(name) + def getFunctionInfo(name: String): FunctionInfo = FunctionRegistry.getFunctionInfo(name) def lookupFunction(name: String, children: Seq[Expression]): Expression = { // We only look it up to see if it exists, but do not include it in the HiveUDF since it is @@ -78,7 +78,7 @@ private[hive] case class HiveSimpleUdf(funcWrapper: HiveFunctionWrapper, childre type EvaluatedType = Any type UDFType = UDF - def nullable = true + override def nullable: Boolean = true @transient lazy val function = funcWrapper.createFunction[UDFType]() @@ -96,7 +96,7 @@ private[hive] case class HiveSimpleUdf(funcWrapper: HiveFunctionWrapper, childre udfType != null && udfType.deterministic() } - override def foldable = isUDFDeterministic && children.forall(_.foldable) + override def foldable: Boolean = isUDFDeterministic && children.forall(_.foldable) // Create parameter converters @transient @@ -110,7 +110,7 @@ private[hive] case class HiveSimpleUdf(funcWrapper: HiveFunctionWrapper, childre method.getGenericReturnType(), ObjectInspectorOptions.JAVA) @transient - protected lazy val cached = new Array[AnyRef](children.length) + protected lazy val cached: Array[AnyRef] = new Array[AnyRef](children.length) // TODO: Finish input output types. override def eval(input: Row): Any = { @@ -120,17 +120,19 @@ private[hive] case class HiveSimpleUdf(funcWrapper: HiveFunctionWrapper, childre returnInspector) } - override def toString = s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + override def toString: String = { + s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + } } // Adapter from Catalyst ExpressionResult to Hive DeferredObject private[hive] class DeferredObjectAdapter(oi: ObjectInspector) extends DeferredObject with HiveInspectors { private var func: () => Any = _ - def set(func: () => Any) { + def set(func: () => Any): Unit = { this.func = func } - override def prepare(i: Int) = {} + override def prepare(i: Int): Unit = {} override def get(): AnyRef = wrap(func(), oi) } @@ -139,7 +141,7 @@ private[hive] case class HiveGenericUdf(funcWrapper: HiveFunctionWrapper, childr type UDFType = GenericUDF type EvaluatedType = Any - def nullable = true + override def nullable: Boolean = true @transient lazy val function = funcWrapper.createFunction[UDFType]() @@ -158,7 +160,7 @@ private[hive] case class HiveGenericUdf(funcWrapper: HiveFunctionWrapper, childr (udfType != null && udfType.deterministic()) } - override def foldable = + override def foldable: Boolean = isUDFDeterministic && returnInspector.isInstanceOf[ConstantObjectInspector] @transient @@ -182,7 +184,9 @@ private[hive] case class HiveGenericUdf(funcWrapper: HiveFunctionWrapper, childr unwrap(function.evaluate(deferedObjects), returnInspector) } - override def toString = s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + override def toString: String = { + s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + } } private[hive] case class HiveGenericUdaf( @@ -209,9 +213,11 @@ private[hive] case class HiveGenericUdaf( def nullable: Boolean = true - override def toString = s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + override def toString: String = { + s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + } - def newInstance() = new HiveUdafFunction(funcWrapper, children, this) + def newInstance(): HiveUdafFunction = new HiveUdafFunction(funcWrapper, children, this) } /** It is used as a wrapper for the hive functions which uses UDAF interface */ @@ -240,10 +246,11 @@ private[hive] case class HiveUdaf( def nullable: Boolean = true - override def toString = s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + override def toString: String = { + s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + } - def newInstance() = - new HiveUdafFunction(funcWrapper, children, this, true) + def newInstance(): HiveUdafFunction = new HiveUdafFunction(funcWrapper, children, this, true) } /** @@ -314,21 +321,23 @@ private[hive] case class HiveGenericUdtf( collected += unwrap(input, outputInspector).asInstanceOf[Row] } - def collectRows() = { + def collectRows(): Seq[Row] = { val toCollect = collected collected = new ArrayBuffer[Row] toCollect } } - override def toString = s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + override def toString: String = { + s"$nodeName#${funcWrapper.functionClassName}(${children.mkString(",")})" + } } /** * Resolve Udtfs Alias. */ private[spark] object ResolveUdtfsAlias extends Rule[LogicalPlan] { - def apply(plan: LogicalPlan) = plan transform { + def apply(plan: LogicalPlan): LogicalPlan = plan transform { case p @ Project(projectList, _) if projectList.exists(_.isInstanceOf[MultiAlias]) && projectList.size != 1 => throw new TreeNodeException(p, "only single Generator supported for SELECT clause") diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala index f136e43acc8f2..ba2bf67aed684 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala @@ -222,7 +222,7 @@ private[spark] class SparkHiveDynamicPartitionWriterContainer( s"/$col=$colString" }.mkString - def newWriter = { + def newWriter(): FileSinkOperator.RecordWriter = { val newFileSinkDesc = new FileSinkDesc( fileSinkConf.getDirName + dynamicPartPath, fileSinkConf.getTableInfo, @@ -246,6 +246,6 @@ private[spark] class SparkHiveDynamicPartitionWriterContainer( Reporter.NULL) } - writers.getOrElseUpdate(dynamicPartPath, newWriter) + writers.getOrElseUpdate(dynamicPartPath, newWriter()) } } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala index b4aee78046383..dc61e9d2e3522 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala @@ -155,8 +155,8 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { protected[hive] class HiveQLQueryExecution(hql: String) extends this.QueryExecution(HiveQl.parseSql(hql)) { - def hiveExec() = runSqlHive(hql) - override def toString = hql + "\n" + super.toString + def hiveExec(): Seq[String] = runSqlHive(hql) + override def toString: String = hql + "\n" + super.toString } /** @@ -186,7 +186,9 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { case class TestTable(name: String, commands: (()=>Unit)*) protected[hive] implicit class SqlCmd(sql: String) { - def cmd = () => new HiveQLQueryExecution(sql).stringResult(): Unit + def cmd: () => Unit = { + () => new HiveQLQueryExecution(sql).stringResult(): Unit + } } /** @@ -194,7 +196,10 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { * demand when a query are run against it. */ lazy val testTables = new mutable.HashMap[String, TestTable]() - def registerTestTable(testTable: TestTable) = testTables += (testTable.name -> testTable) + + def registerTestTable(testTable: TestTable): Unit = { + testTables += (testTable.name -> testTable) + } // The test tables that are defined in the Hive QTestUtil. // /itests/util/src/main/java/org/apache/hadoop/hive/ql/QTestUtil.java From 9b1e1f20d4498bda72dd53a832110883a7ca41b5 Mon Sep 17 00:00:00 2001 From: ypcat Date: Sun, 22 Mar 2015 15:49:13 +0800 Subject: [PATCH 005/591] [SPARK-6408] [SQL] Fix JDBCRDD filtering string literals Author: ypcat Author: Pei-Lun Lee Closes #5087 from ypcat/spark-6408 and squashes the following commits: 1becc16 [ypcat] [SPARK-6408] [SQL] styling 1bc4455 [ypcat] [SPARK-6408] [SQL] move nested function outside e57fa4a [ypcat] [SPARK-6408] [SQL] fix test case 245ab6f [ypcat] [SPARK-6408] [SQL] add test cases for filtering quoted strings 8962534 [Pei-Lun Lee] [SPARK-6408] [SQL] Fix filtering string literals --- .../org/apache/spark/sql/jdbc/JDBCRDD.scala | 19 ++++++++++++++----- .../org/apache/spark/sql/jdbc/JDBCSuite.scala | 12 ++++++++++-- 2 files changed, 24 insertions(+), 7 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala index 3266b972128ea..76f8593180e85 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala @@ -19,6 +19,7 @@ package org.apache.spark.sql.jdbc import java.sql.{Connection, DriverManager, ResultSet, ResultSetMetaData, SQLException} +import org.apache.commons.lang.StringEscapeUtils.escapeSql import org.apache.spark.{Logging, Partition, SparkContext, TaskContext} import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.expressions.{Row, SpecificMutableRow} @@ -226,16 +227,24 @@ private[sql] class JDBCRDD( if (sb.length == 0) "1" else sb.substring(1) } + /** + * Converts value to SQL expression. + */ + private def compileValue(value: Any): Any = value match { + case stringValue: String => s"'${escapeSql(stringValue)}'" + case _ => value + } + /** * Turns a single Filter into a String representing a SQL expression. * Returns null for an unhandled filter. */ private def compileFilter(f: Filter): String = f match { - case EqualTo(attr, value) => s"$attr = $value" - case LessThan(attr, value) => s"$attr < $value" - case GreaterThan(attr, value) => s"$attr > $value" - case LessThanOrEqual(attr, value) => s"$attr <= $value" - case GreaterThanOrEqual(attr, value) => s"$attr >= $value" + case EqualTo(attr, value) => s"$attr = ${compileValue(value)}" + case LessThan(attr, value) => s"$attr < ${compileValue(value)}" + case GreaterThan(attr, value) => s"$attr > ${compileValue(value)}" + case LessThanOrEqual(attr, value) => s"$attr <= ${compileValue(value)}" + case GreaterThanOrEqual(attr, value) => s"$attr >= ${compileValue(value)}" case _ => null } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala index cd737c0b62767..5eb6ab2e92e8b 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala @@ -24,6 +24,7 @@ import java.util.{Calendar, GregorianCalendar} import org.apache.spark.sql.test._ import org.scalatest.{FunSuite, BeforeAndAfter} import TestSQLContext._ +import TestSQLContext.implicits._ class JDBCSuite extends FunSuite with BeforeAndAfter { val url = "jdbc:h2:mem:testdb0" @@ -38,7 +39,7 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { conn.prepareStatement("create table test.people (name TEXT(32) NOT NULL, theid INTEGER NOT NULL)").executeUpdate() conn.prepareStatement("insert into test.people values ('fred', 1)").executeUpdate() conn.prepareStatement("insert into test.people values ('mary', 2)").executeUpdate() - conn.prepareStatement("insert into test.people values ('joe', 3)").executeUpdate() + conn.prepareStatement("insert into test.people values ('joe ''foo'' \"bar\"', 3)").executeUpdate() conn.commit() sql( @@ -129,13 +130,20 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { assert(sql("SELECT * FROM foobar WHERE THEID < 1").collect().size == 0) assert(sql("SELECT * FROM foobar WHERE THEID != 2").collect().size == 2) assert(sql("SELECT * FROM foobar WHERE THEID = 1").collect().size == 1) + assert(sql("SELECT * FROM foobar WHERE NAME = 'fred'").collect().size == 1) + assert(sql("SELECT * FROM foobar WHERE NAME > 'fred'").collect().size == 2) + assert(sql("SELECT * FROM foobar WHERE NAME != 'fred'").collect().size == 2) + } + + test("SELECT * WHERE (quoted strings)") { + assert(sql("select * from foobar").where('NAME === "joe 'foo' \"bar\"").collect().size == 1) } test("SELECT first field") { val names = sql("SELECT NAME FROM foobar").collect().map(x => x.getString(0)).sortWith(_ < _) assert(names.size == 3) assert(names(0).equals("fred")) - assert(names(1).equals("joe")) + assert(names(1).equals("joe 'foo' \"bar\"")) assert(names(2).equals("mary")) } From b9fe504b497cfa509310b4045de4873739c76667 Mon Sep 17 00:00:00 2001 From: Ryan Williams Date: Sun, 22 Mar 2015 11:54:23 +0000 Subject: [PATCH 006/591] [SPARK-6448] Make history server log parse exceptions This helped me to debug a parse error that was due to the event log format changing recently. Author: Ryan Williams Closes #5122 from ryan-williams/histerror and squashes the following commits: 5831656 [Ryan Williams] line length c3742ae [Ryan Williams] Make history server log parse exceptions --- .../org/apache/spark/deploy/history/FsHistoryProvider.scala | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala index 7fde02040927d..db7c499661319 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala @@ -233,7 +233,8 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis } catch { case e: Exception => logError( - s"Exception encountered when attempting to load application log ${fileStatus.getPath}") + s"Exception encountered when attempting to load application log ${fileStatus.getPath}", + e) None } }.toSeq.sortWith(compareAppInfo) From ab4f516fbe63e24e076c68f4933a171a72b6f1fd Mon Sep 17 00:00:00 2001 From: Hangchen Yu Date: Sun, 22 Mar 2015 15:51:10 +0000 Subject: [PATCH 007/591] [SPARK-6455] [docs] Correct some mistakes and typos Correct some typos. Correct a mistake in lib/PageRank.scala. The first PageRank implementation uses standalone Graph interface, but the second uses Pregel interface. It may mislead the code viewers. Author: Hangchen Yu Closes #5128 from yuhc/master and squashes the following commits: 53e5432 [Hangchen Yu] Merge branch 'master' of https://github.com/yuhc/spark 67b77b5 [Hangchen Yu] [SPARK-6455] [docs] Correct some mistakes and typos 206f2dc [Hangchen Yu] Correct some mistakes and typos. --- graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala | 4 ++-- graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala | 4 ++-- .../src/main/scala/org/apache/spark/graphx/lib/PageRank.scala | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala b/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala index dc8b4789c4b61..86f611d55aa8a 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala @@ -113,7 +113,7 @@ class GraphOps[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]) extends Seriali * Collect the neighbor vertex attributes for each vertex. * * @note This function could be highly inefficient on power-law - * graphs where high degree vertices may force a large ammount of + * graphs where high degree vertices may force a large amount of * information to be collected to a single location. * * @param edgeDirection the direction along which to collect @@ -187,7 +187,7 @@ class GraphOps[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]) extends Seriali /** * Join the vertices with an RDD and then apply a function from the - * the vertex and RDD entry to a new vertex value. The input table + * vertex and RDD entry to a new vertex value. The input table * should contain at most one entry for each vertex. If no entry is * provided the map function is skipped and the old value is used. * diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala b/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala index 5e55620147df8..01b013ff716fc 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala @@ -78,8 +78,8 @@ object Pregel extends Logging { * * @param graph the input graph. * - * @param initialMsg the message each vertex will receive at the on - * the first iteration + * @param initialMsg the message each vertex will receive at the first + * iteration * * @param maxIterations the maximum number of iterations to run for * diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala index e139959c3f5c1..ca3b513821e13 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala @@ -25,7 +25,7 @@ import org.apache.spark.graphx._ /** * PageRank algorithm implementation. There are two implementations of PageRank implemented. * - * The first implementation uses the [[Pregel]] interface and runs PageRank for a fixed number + * The first implementation uses the standalone [[Graph]] interface and runs PageRank for a fixed number * of iterations: * {{{ * var PR = Array.fill(n)( 1.0 ) @@ -38,7 +38,7 @@ import org.apache.spark.graphx._ * } * }}} * - * The second implementation uses the standalone [[Graph]] interface and runs PageRank until + * The second implementation uses the [[Pregel]] interface and runs PageRank until * convergence: * * {{{ From adb2ff752fa8bda54c969b60a3168d87cd70237d Mon Sep 17 00:00:00 2001 From: Jongyoul Lee Date: Sun, 22 Mar 2015 15:53:18 +0000 Subject: [PATCH 008/591] [SPARK-6453][Mesos] Some Mesos*Suite have a different package with their classes - Moved Suites from o.a.s.s.mesos to o.a.s.s.cluster.mesos Author: Jongyoul Lee Closes #5126 from jongyoul/SPARK-6453 and squashes the following commits: 4f24a3e [Jongyoul Lee] [SPARK-6453][Mesos] Some Mesos*Suite have a different package with their classes - Fixed imports orders 8ab149d [Jongyoul Lee] [SPARK-6453][Mesos] Some Mesos*Suite have a different package with their classes - Moved Suites from o.a.s.s.mesos to o.a.s.s.cluster.mesos --- .../mesos/MesosSchedulerBackendSuite.scala | 13 ++++++------- .../mesos/MesosTaskLaunchDataSuite.scala | 4 +--- 2 files changed, 7 insertions(+), 10 deletions(-) rename core/src/test/scala/org/apache/spark/scheduler/{ => cluster}/mesos/MesosSchedulerBackendSuite.scala (98%) rename core/src/test/scala/org/apache/spark/scheduler/{ => cluster}/mesos/MesosTaskLaunchDataSuite.scala (92%) diff --git a/core/src/test/scala/org/apache/spark/scheduler/mesos/MesosSchedulerBackendSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala similarity index 98% rename from core/src/test/scala/org/apache/spark/scheduler/mesos/MesosSchedulerBackendSuite.scala rename to core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala index afbaa9ade811f..f1a4380d349b3 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/mesos/MesosSchedulerBackendSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala @@ -15,7 +15,7 @@ * limitations under the License. */ -package org.apache.spark.scheduler.mesos +package org.apache.spark.scheduler.cluster.mesos import java.nio.ByteBuffer import java.util @@ -24,21 +24,20 @@ import java.util.Collections import scala.collection.mutable import scala.collection.mutable.ArrayBuffer -import org.apache.mesos.SchedulerDriver -import org.apache.mesos.Protos._ import org.apache.mesos.Protos.Value.Scalar -import org.mockito.Mockito._ +import org.apache.mesos.Protos._ +import org.apache.mesos.SchedulerDriver import org.mockito.Matchers._ +import org.mockito.Mockito._ import org.mockito.{ArgumentCaptor, Matchers} import org.scalatest.FunSuite import org.scalatest.mock.MockitoSugar -import org.apache.spark.{LocalSparkContext, SparkConf, SparkContext} import org.apache.spark.executor.MesosExecutorBackend +import org.apache.spark.scheduler.cluster.ExecutorInfo import org.apache.spark.scheduler.{LiveListenerBus, SparkListenerExecutorAdded, TaskDescription, TaskSchedulerImpl, WorkerOffer} -import org.apache.spark.scheduler.cluster.ExecutorInfo -import org.apache.spark.scheduler.cluster.mesos.{MesosSchedulerBackend, MemoryUtils} +import org.apache.spark.{LocalSparkContext, SparkConf, SparkContext} class MesosSchedulerBackendSuite extends FunSuite with LocalSparkContext with MockitoSugar { diff --git a/core/src/test/scala/org/apache/spark/scheduler/mesos/MesosTaskLaunchDataSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosTaskLaunchDataSuite.scala similarity index 92% rename from core/src/test/scala/org/apache/spark/scheduler/mesos/MesosTaskLaunchDataSuite.scala rename to core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosTaskLaunchDataSuite.scala index 86a42a7398e4d..eebcba40f8a1c 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/mesos/MesosTaskLaunchDataSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosTaskLaunchDataSuite.scala @@ -15,14 +15,12 @@ * limitations under the License. */ -package org.apache.spark.scheduler.mesos +package org.apache.spark.scheduler.cluster.mesos import java.nio.ByteBuffer import org.scalatest.FunSuite -import org.apache.spark.scheduler.cluster.mesos.MesosTaskLaunchData - class MesosTaskLaunchDataSuite extends FunSuite { test("serialize and deserialize data must be same") { val serializedTask = ByteBuffer.allocate(40) From 6ef48632fbf3e6659ceacaab1dbb8be8238d4d33 Mon Sep 17 00:00:00 2001 From: Kamil Smuga Date: Sun, 22 Mar 2015 15:56:25 +0000 Subject: [PATCH 009/591] SPARK-6454 [DOCS] Fix links to pyspark api Author: Kamil Smuga Author: stderr Closes #5120 from kamilsmuga/master and squashes the following commits: fee3281 [Kamil Smuga] more python api links fixed for docs 13240cb [Kamil Smuga] resolved merge conflicts with upstream/master 6649b3b [Kamil Smuga] fix broken docs links to Python API 92f03d7 [stderr] Fix links to pyspark api --- docs/mllib-data-types.md | 8 ++++---- docs/mllib-naive-bayes.md | 6 +++--- docs/mllib-statistics.md | 10 +++++----- docs/programming-guide.md | 12 ++++++------ docs/sql-programming-guide.md | 2 +- 5 files changed, 19 insertions(+), 19 deletions(-) diff --git a/docs/mllib-data-types.md b/docs/mllib-data-types.md index fe6c1bf7bfd99..4f2a2f71048f7 100644 --- a/docs/mllib-data-types.md +++ b/docs/mllib-data-types.md @@ -78,13 +78,13 @@ MLlib recognizes the following types as dense vectors: and the following as sparse vectors: -* MLlib's [`SparseVector`](api/python/pyspark.mllib.linalg.SparseVector-class.html). +* MLlib's [`SparseVector`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.SparseVector). * SciPy's [`csc_matrix`](http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csc_matrix.html#scipy.sparse.csc_matrix) with a single column We recommend using NumPy arrays over lists for efficiency, and using the factory methods implemented -in [`Vectors`](api/python/pyspark.mllib.linalg.Vectors-class.html) to create sparse vectors. +in [`Vectors`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.Vector) to create sparse vectors. {% highlight python %} import numpy as np @@ -151,7 +151,7 @@ LabeledPoint neg = new LabeledPoint(1.0, Vectors.sparse(3, new int[] {0, 2}, new
A labeled point is represented by -[`LabeledPoint`](api/python/pyspark.mllib.regression.LabeledPoint-class.html). +[`LabeledPoint`](api/python/pyspark.mllib.html#pyspark.mllib.regression.LabeledPoint). {% highlight python %} from pyspark.mllib.linalg import SparseVector @@ -211,7 +211,7 @@ JavaRDD examples =
-[`MLUtils.loadLibSVMFile`](api/python/pyspark.mllib.util.MLUtils-class.html) reads training +[`MLUtils.loadLibSVMFile`](api/python/pyspark.mllib.html#pyspark.mllib.util.MLUtils) reads training examples stored in LIBSVM format. {% highlight python %} diff --git a/docs/mllib-naive-bayes.md b/docs/mllib-naive-bayes.md index 55b8f2ce6c364..a83472f5be52e 100644 --- a/docs/mllib-naive-bayes.md +++ b/docs/mllib-naive-bayes.md @@ -106,11 +106,11 @@ NaiveBayesModel sameModel = NaiveBayesModel.load(sc.sc(), "myModelPath");
-[NaiveBayes](api/python/pyspark.mllib.classification.NaiveBayes-class.html) implements multinomial +[NaiveBayes](api/python/pyspark.mllib.html#pyspark.mllib.classification.NaiveBayes) implements multinomial naive Bayes. It takes an RDD of -[LabeledPoint](api/python/pyspark.mllib.regression.LabeledPoint-class.html) and an optionally +[LabeledPoint](api/python/pyspark.mllib.html#pyspark.mllib.regression.LabeledPoint) and an optionally smoothing parameter `lambda` as input, and output a -[NaiveBayesModel](api/python/pyspark.mllib.classification.NaiveBayesModel-class.html), which can be +[NaiveBayesModel](api/python/pyspark.mllib.html#pyspark.mllib.classification.NaiveBayesModel), which can be used for evaluation and prediction. Note that the Python API does not yet support model save/load but will in the future. diff --git a/docs/mllib-statistics.md b/docs/mllib-statistics.md index ca8c29218f52d..887eae7f4f07b 100644 --- a/docs/mllib-statistics.md +++ b/docs/mllib-statistics.md @@ -81,8 +81,8 @@ System.out.println(summary.numNonzeros()); // number of nonzeros in each column
-[`colStats()`](api/python/pyspark.mllib.stat.Statistics-class.html#colStats) returns an instance of -[`MultivariateStatisticalSummary`](api/python/pyspark.mllib.stat.MultivariateStatisticalSummary-class.html), +[`colStats()`](api/python/pyspark.mllib.html#pyspark.mllib.stat.Statistics.colStats) returns an instance of +[`MultivariateStatisticalSummary`](api/python/pyspark.mllib.html#pyspark.mllib.stat.MultivariateStatisticalSummary), which contains the column-wise max, min, mean, variance, and number of nonzeros, as well as the total count. @@ -169,7 +169,7 @@ Matrix correlMatrix = Statistics.corr(data.rdd(), "pearson");
-[`Statistics`](api/python/pyspark.mllib.stat.Statistics-class.html) provides methods to +[`Statistics`](api/python/pyspark.mllib.html#pyspark.mllib.stat.Statistics) provides methods to calculate correlations between series. Depending on the type of input, two `RDD[Double]`s or an `RDD[Vector]`, the output will be a `Double` or the correlation `Matrix` respectively. @@ -258,7 +258,7 @@ JavaPairRDD exactSample = data.sampleByKeyExact(false, fractions); {% endhighlight %}
-[`sampleByKey()`](api/python/pyspark.rdd.RDD-class.html#sampleByKey) allows users to +[`sampleByKey()`](api/python/pyspark.html#pyspark.RDD.sampleByKey) allows users to sample approximately $\lceil f_k \cdot n_k \rceil \, \forall k \in K$ items, where $f_k$ is the desired fraction for key $k$, $n_k$ is the number of key-value pairs for key $k$, and $K$ is the set of keys. @@ -476,7 +476,7 @@ JavaDoubleRDD v = u.map(
-[`RandomRDDs`](api/python/pyspark.mllib.random.RandomRDDs-class.html) provides factory +[`RandomRDDs`](api/python/pyspark.mllib.html#pyspark.mllib.random.RandomRDDs) provides factory methods to generate random double RDDs or vector RDDs. The following example generates a random double RDD, whose values follows the standard normal distribution `N(0, 1)`, and then map it to `N(1, 4)`. diff --git a/docs/programming-guide.md b/docs/programming-guide.md index eda3a95426182..5fe832b6fa100 100644 --- a/docs/programming-guide.md +++ b/docs/programming-guide.md @@ -142,8 +142,8 @@ JavaSparkContext sc = new JavaSparkContext(conf);
-The first thing a Spark program must do is to create a [SparkContext](api/python/pyspark.context.SparkContext-class.html) object, which tells Spark -how to access a cluster. To create a `SparkContext` you first need to build a [SparkConf](api/python/pyspark.conf.SparkConf-class.html) object +The first thing a Spark program must do is to create a [SparkContext](api/python/pyspark.html#pyspark.SparkContext) object, which tells Spark +how to access a cluster. To create a `SparkContext` you first need to build a [SparkConf](api/python/pyspark.html#pyspark.SparkConf) object that contains information about your application. {% highlight python %} @@ -912,7 +912,7 @@ The following table lists some of the common transformations supported by Spark. RDD API doc ([Scala](api/scala/index.html#org.apache.spark.rdd.RDD), [Java](api/java/index.html?org/apache/spark/api/java/JavaRDD.html), - [Python](api/python/pyspark.rdd.RDD-class.html)) + [Python](api/python/pyspark.html#pyspark.RDD)) and pair RDD functions doc ([Scala](api/scala/index.html#org.apache.spark.rdd.PairRDDFunctions), [Java](api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html)) @@ -1025,7 +1025,7 @@ The following table lists some of the common actions supported by Spark. Refer t RDD API doc ([Scala](api/scala/index.html#org.apache.spark.rdd.RDD), [Java](api/java/index.html?org/apache/spark/api/java/JavaRDD.html), - [Python](api/python/pyspark.rdd.RDD-class.html)) + [Python](api/python/pyspark.html#pyspark.RDD)) and pair RDD functions doc ([Scala](api/scala/index.html#org.apache.spark.rdd.PairRDDFunctions), [Java](api/java/index.html?org/apache/spark/api/java/JavaPairRDD.html)) @@ -1105,7 +1105,7 @@ replicate it across nodes, or store it off-heap in [Tachyon](http://tachyon-proj These levels are set by passing a `StorageLevel` object ([Scala](api/scala/index.html#org.apache.spark.storage.StorageLevel), [Java](api/java/index.html?org/apache/spark/storage/StorageLevel.html), -[Python](api/python/pyspark.storagelevel.StorageLevel-class.html)) +[Python](api/python/pyspark.html#pyspark.StorageLevel)) to `persist()`. The `cache()` method is a shorthand for using the default storage level, which is `StorageLevel.MEMORY_ONLY` (store deserialized objects in memory). The full set of storage levels is: @@ -1374,7 +1374,7 @@ scala> accum.value {% endhighlight %} While this code used the built-in support for accumulators of type Int, programmers can also -create their own types by subclassing [AccumulatorParam](api/python/pyspark.accumulators.AccumulatorParam-class.html). +create their own types by subclassing [AccumulatorParam](api/python/pyspark.html#pyspark.AccumulatorParam). The AccumulatorParam interface has two methods: `zero` for providing a "zero value" for your data type, and `addInPlace` for adding two values together. For example, supposing we had a `Vector` class representing mathematical vectors, we could write: diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index 2cbb4c967eb81..a7d35741a48c3 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -56,7 +56,7 @@ SQLContext sqlContext = new org.apache.spark.sql.SQLContext(sc);
The entry point into all relational functionality in Spark is the -[`SQLContext`](api/python/pyspark.sql.SQLContext-class.html) class, or one +[`SQLContext`](api/python/pyspark.sql.html#pyspark.sql.SQLContext) class, or one of its decedents. To create a basic `SQLContext`, all you need is a SparkContext. {% highlight python %} From a41b9c6004cfee84bd56dfa1faf5a0cf084551ae Mon Sep 17 00:00:00 2001 From: Calvin Jia Date: Sun, 22 Mar 2015 11:11:29 -0700 Subject: [PATCH 010/591] [SPARK-6122][Core] Upgrade Tachyon client version to 0.6.1. Changes the Tachyon client version from 0.5 to 0.6 in spark core and distribution script. New dependencies in Tachyon 0.6.0 include commons-codec:commons-codec:jar:1.5:compile io.netty:netty-all:jar:4.0.23.Final:compile These are already in spark core. Author: Calvin Jia Closes #4867 from calvinjia/upgrade_tachyon_0.6.0 and squashes the following commits: eed9230 [Calvin Jia] Update tachyon version to 0.6.1. 11907b3 [Calvin Jia] Use TachyonURI for tachyon paths instead of strings. 71bf441 [Calvin Jia] Upgrade Tachyon client version to 0.6.0. --- core/pom.xml | 2 +- .../spark/storage/TachyonBlockManager.scala | 27 +++++++++---------- .../scala/org/apache/spark/util/Utils.scala | 4 ++- make-distribution.sh | 2 +- 4 files changed, 18 insertions(+), 17 deletions(-) diff --git a/core/pom.xml b/core/pom.xml index 6cd1965ec37c2..868834dd505ef 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -275,7 +275,7 @@ org.tachyonproject tachyon-client - 0.5.0 + 0.6.1 org.apache.hadoop diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala index af873034215a9..2ab6a8f3ec1d4 100644 --- a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala @@ -20,8 +20,8 @@ package org.apache.spark.storage import java.text.SimpleDateFormat import java.util.{Date, Random} -import tachyon.client.TachyonFS -import tachyon.client.TachyonFile +import tachyon.TachyonURI +import tachyon.client.{TachyonFile, TachyonFS} import org.apache.spark.Logging import org.apache.spark.executor.ExecutorExitCode @@ -40,7 +40,7 @@ private[spark] class TachyonBlockManager( val master: String) extends Logging { - val client = if (master != null && master != "") TachyonFS.get(master) else null + val client = if (master != null && master != "") TachyonFS.get(new TachyonURI(master)) else null if (client == null) { logError("Failed to connect to the Tachyon as the master address is not configured") @@ -60,11 +60,11 @@ private[spark] class TachyonBlockManager( addShutdownHook() def removeFile(file: TachyonFile): Boolean = { - client.delete(file.getPath(), false) + client.delete(new TachyonURI(file.getPath()), false) } def fileExists(file: TachyonFile): Boolean = { - client.exist(file.getPath()) + client.exist(new TachyonURI(file.getPath())) } def getFile(filename: String): TachyonFile = { @@ -81,7 +81,7 @@ private[spark] class TachyonBlockManager( if (old != null) { old } else { - val path = tachyonDirs(dirId) + "/" + "%02x".format(subDirId) + val path = new TachyonURI(s"${tachyonDirs(dirId)}/${"%02x".format(subDirId)}") client.mkdir(path) val newDir = client.getFile(path) subDirs(dirId)(subDirId) = newDir @@ -89,7 +89,7 @@ private[spark] class TachyonBlockManager( } } } - val filePath = subDir + "/" + filename + val filePath = new TachyonURI(s"$subDir/$filename") if(!client.exist(filePath)) { client.createFile(filePath) } @@ -101,7 +101,7 @@ private[spark] class TachyonBlockManager( // TODO: Some of the logic here could be consolidated/de-duplicated with that in the DiskStore. private def createTachyonDirs(): Array[TachyonFile] = { - logDebug("Creating tachyon directories at root dirs '" + rootDirs + "'") + logDebug(s"Creating tachyon directories at root dirs '$rootDirs'") val dateFormat = new SimpleDateFormat("yyyyMMddHHmmss") rootDirs.split(",").map { rootDir => var foundLocalDir = false @@ -113,22 +113,21 @@ private[spark] class TachyonBlockManager( tries += 1 try { tachyonDirId = "%s-%04x".format(dateFormat.format(new Date), rand.nextInt(65536)) - val path = rootDir + "/" + "spark-tachyon-" + tachyonDirId + val path = new TachyonURI(s"$rootDir/spark-tachyon-$tachyonDirId") if (!client.exist(path)) { foundLocalDir = client.mkdir(path) tachyonDir = client.getFile(path) } } catch { case e: Exception => - logWarning("Attempt " + tries + " to create tachyon dir " + tachyonDir + " failed", e) + logWarning(s"Attempt $tries to create tachyon dir $tachyonDir failed", e) } } if (!foundLocalDir) { - logError("Failed " + MAX_DIR_CREATION_ATTEMPTS + " attempts to create tachyon dir in " + - rootDir) + logError(s"Failed $MAX_DIR_CREATION_ATTEMPTS attempts to create tachyon dir in $rootDir") System.exit(ExecutorExitCode.TACHYON_STORE_FAILED_TO_CREATE_DIR) } - logInfo("Created tachyon directory at " + tachyonDir) + logInfo(s"Created tachyon directory at $tachyonDir") tachyonDir } } @@ -145,7 +144,7 @@ private[spark] class TachyonBlockManager( } } catch { case e: Exception => - logError("Exception while deleting tachyon spark dir: " + tachyonDir, e) + logError(s"Exception while deleting tachyon spark dir: $tachyonDir", e) } } client.close() diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index fa56bb09e4e5c..91d833295e376 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -42,6 +42,8 @@ import org.apache.hadoop.security.UserGroupInformation import org.apache.log4j.PropertyConfigurator import org.eclipse.jetty.util.MultiException import org.json4s._ + +import tachyon.TachyonURI import tachyon.client.{TachyonFS, TachyonFile} import org.apache.spark._ @@ -970,7 +972,7 @@ private[spark] object Utils extends Logging { * Delete a file or directory and its contents recursively. */ def deleteRecursively(dir: TachyonFile, client: TachyonFS) { - if (!client.delete(dir.getPath(), true)) { + if (!client.delete(new TachyonURI(dir.getPath()), true)) { throw new IOException("Failed to delete the tachyon dir: " + dir) } } diff --git a/make-distribution.sh b/make-distribution.sh index 9ed1abfe8c598..8162fe94c1af0 100755 --- a/make-distribution.sh +++ b/make-distribution.sh @@ -32,7 +32,7 @@ SPARK_HOME="$(cd "`dirname "$0"`"; pwd)" DISTDIR="$SPARK_HOME/dist" SPARK_TACHYON=false -TACHYON_VERSION="0.5.0" +TACHYON_VERSION="0.6.1" TACHYON_TGZ="tachyon-${TACHYON_VERSION}-bin.tar.gz" TACHYON_URL="https://github.com/amplab/tachyon/releases/download/v${TACHYON_VERSION}/${TACHYON_TGZ}" From 7a0da47708b0e6b117b5c1a35aa3e93b8a914d5f Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Sun, 22 Mar 2015 12:08:15 -0700 Subject: [PATCH 011/591] [HOTFIX] Build break due to https://github.com/apache/spark/pull/5128 --- .../src/main/scala/org/apache/spark/graphx/lib/PageRank.scala | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala index ca3b513821e13..570440ba4441f 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala @@ -25,8 +25,8 @@ import org.apache.spark.graphx._ /** * PageRank algorithm implementation. There are two implementations of PageRank implemented. * - * The first implementation uses the standalone [[Graph]] interface and runs PageRank for a fixed number - * of iterations: + * The first implementation uses the standalone [[Graph]] interface and runs PageRank + * for a fixed number of iterations: * {{{ * var PR = Array.fill(n)( 1.0 ) * val oldPR = Array.fill(n)( 1.0 ) From 2bf40c58e6e89e061783c999204107069df17f73 Mon Sep 17 00:00:00 2001 From: vinodkc Date: Sun, 22 Mar 2015 20:00:08 +0000 Subject: [PATCH 012/591] [SPARK-6337][Documentation, SQL]Spark 1.3 doc fixes Author: vinodkc Closes #5112 from vinodkc/spark_1.3_doc_fixes and squashes the following commits: 2c6aee6 [vinodkc] Spark 1.3 doc fixes --- docs/sql-programming-guide.md | 7 +++++-- mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala | 2 +- .../src/main/scala/org/apache/spark/sql/DataFrame.scala | 2 +- 3 files changed, 7 insertions(+), 4 deletions(-) diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index a7d35741a48c3..6a333fdb562a7 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -509,8 +509,11 @@ val people = sc.textFile("examples/src/main/resources/people.txt") // The schema is encoded in a string val schemaString = "name age" -// Import Spark SQL data types and Row. -import org.apache.spark.sql._ +// Import Row. +import org.apache.spark.sql.Row; + +// Import Spark SQL data types +import org.apache.spark.sql.types.{StructType,StructField,StringType}; // Generate the schema based on the string of schema val schema = diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala index 5bbcd2e080e07..c4a36103303a2 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala @@ -33,7 +33,7 @@ import org.apache.spark.sql.types.StructType abstract class PipelineStage extends Serializable with Logging { /** - * :: DeveloperAPI :: + * :: DeveloperApi :: * * Derives the output schema from the input schema and parameters. * The schema describes the columns and types of the data. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 8b8f86c4127e0..5aece166aad22 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -89,7 +89,7 @@ private[sql] object DataFrame { * val people = sqlContext.parquetFile("...") * val department = sqlContext.parquetFile("...") * - * people.filter("age" > 30) + * people.filter("age > 30") * .join(department, people("deptId") === department("id")) * .groupBy(department("name"), "gender") * .agg(avg(people("salary")), max(people("age"))) From 4659468f369d69e7f777130e5e3b4c5d47a624f1 Mon Sep 17 00:00:00 2001 From: Daoyuan Wang Date: Mon, 23 Mar 2015 11:46:16 +0800 Subject: [PATCH 013/591] [SPARK-4985] [SQL] parquet support for date type This PR might have some issues with #3732 , and this would have merge conflicts with #3820 so the review can be delayed till that 2 were merged. Author: Daoyuan Wang Closes #3822 from adrian-wang/parquetdate and squashes the following commits: 2c5d54d [Daoyuan Wang] add a test case faef887 [Daoyuan Wang] parquet support for primitive date 97e9080 [Daoyuan Wang] parquet support for date type --- .../spark/sql/parquet/ParquetConverter.scala | 12 ++++++++++++ .../spark/sql/parquet/ParquetTableSupport.scala | 2 ++ .../apache/spark/sql/parquet/ParquetTypes.scala | 4 ++++ .../apache/spark/sql/parquet/ParquetIOSuite.scala | 15 +++++++++++++++ .../spark/sql/parquet/ParquetSchemaSuite.scala | 3 ++- 5 files changed, 35 insertions(+), 1 deletion(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala index f898e4b37a56b..43ca359b51735 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala @@ -127,6 +127,12 @@ private[sql] object CatalystConverter { parent.updateByte(fieldIndex, value.asInstanceOf[ByteType.JvmType]) } } + case DateType => { + new CatalystPrimitiveConverter(parent, fieldIndex) { + override def addInt(value: Int): Unit = + parent.updateDate(fieldIndex, value.asInstanceOf[DateType.JvmType]) + } + } case d: DecimalType => { new CatalystPrimitiveConverter(parent, fieldIndex) { override def addBinary(value: Binary): Unit = @@ -192,6 +198,9 @@ private[parquet] abstract class CatalystConverter extends GroupConverter { protected[parquet] def updateInt(fieldIndex: Int, value: Int): Unit = updateField(fieldIndex, value) + protected[parquet] def updateDate(fieldIndex: Int, value: Int): Unit = + updateField(fieldIndex, value) + protected[parquet] def updateLong(fieldIndex: Int, value: Long): Unit = updateField(fieldIndex, value) @@ -388,6 +397,9 @@ private[parquet] class CatalystPrimitiveRowConverter( override protected[parquet] def updateInt(fieldIndex: Int, value: Int): Unit = current.setInt(fieldIndex, value) + override protected[parquet] def updateDate(fieldIndex: Int, value: Int): Unit = + current.update(fieldIndex, value) + override protected[parquet] def updateLong(fieldIndex: Int, value: Long): Unit = current.setLong(fieldIndex, value) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala index 19bfba34b8f4a..5a1b15490d273 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala @@ -212,6 +212,7 @@ private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging { case DoubleType => writer.addDouble(value.asInstanceOf[Double]) case FloatType => writer.addFloat(value.asInstanceOf[Float]) case BooleanType => writer.addBoolean(value.asInstanceOf[Boolean]) + case DateType => writer.addInteger(value.asInstanceOf[Int]) case d: DecimalType => if (d.precisionInfo == None || d.precisionInfo.get.precision > 18) { sys.error(s"Unsupported datatype $d, cannot write to consumer") @@ -358,6 +359,7 @@ private[parquet] class MutableRowWriteSupport extends RowWriteSupport { case DoubleType => writer.addDouble(record.getDouble(index)) case FloatType => writer.addFloat(record.getFloat(index)) case BooleanType => writer.addBoolean(record.getBoolean(index)) + case DateType => writer.addInteger(record.getInt(index)) case TimestampType => writeTimestamp(record(index).asInstanceOf[java.sql.Timestamp]) case d: DecimalType => if (d.precisionInfo == None || d.precisionInfo.get.precision > 18) { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala index 5209581fa8357..da668f068613b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala @@ -64,6 +64,8 @@ private[parquet] object ParquetTypesConverter extends Logging { case ParquetPrimitiveTypeName.BOOLEAN => BooleanType case ParquetPrimitiveTypeName.DOUBLE => DoubleType case ParquetPrimitiveTypeName.FLOAT => FloatType + case ParquetPrimitiveTypeName.INT32 + if originalType == ParquetOriginalType.DATE => DateType case ParquetPrimitiveTypeName.INT32 => IntegerType case ParquetPrimitiveTypeName.INT64 => LongType case ParquetPrimitiveTypeName.INT96 if int96AsTimestamp => TimestampType @@ -222,6 +224,8 @@ private[parquet] object ParquetTypesConverter extends Logging { // There is no type for Byte or Short so we promote them to INT32. case ShortType => Some(ParquetTypeInfo(ParquetPrimitiveTypeName.INT32)) case ByteType => Some(ParquetTypeInfo(ParquetPrimitiveTypeName.INT32)) + case DateType => Some(ParquetTypeInfo( + ParquetPrimitiveTypeName.INT32, Some(ParquetOriginalType.DATE))) case LongType => Some(ParquetTypeInfo(ParquetPrimitiveTypeName.INT64)) case TimestampType => Some(ParquetTypeInfo(ParquetPrimitiveTypeName.INT96)) case DecimalType.Fixed(precision, scale) if precision <= 18 => diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala index 5438095addeaf..203bc79f153dd 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala @@ -135,6 +135,21 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest { } } + test("date type") { + def makeDateRDD(): DataFrame = + sparkContext + .parallelize(0 to 1000) + .map(i => Tuple1(DateUtils.toJavaDate(i))) + .toDF() + .select($"_1") + + withTempPath { dir => + val data = makeDateRDD() + data.saveAsParquetFile(dir.getCanonicalPath) + checkAnswer(parquetFile(dir.getCanonicalPath), data.collect().toSeq) + } + } + test("map") { val data = (1 to 4).map(i => Tuple1(Map(i -> s"val_$i"))) checkParquetFile(data) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala index ad880e2bc3679..321832cd43211 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala @@ -57,7 +57,7 @@ class ParquetSchemaSuite extends FunSuite with ParquetTest { |} """.stripMargin) - testSchema[(Byte, Short, Int, Long)]( + testSchema[(Byte, Short, Int, Long, java.sql.Date)]( "logical integral types", """ |message root { @@ -65,6 +65,7 @@ class ParquetSchemaSuite extends FunSuite with ParquetTest { | required int32 _2 (INT_16); | required int32 _3 (INT_32); | required int64 _4 (INT_64); + | optional int32 _5 (DATE); |} """.stripMargin) From e566fe5982bac5d24e6be76e5d7d6270544a85e6 Mon Sep 17 00:00:00 2001 From: q00251598 Date: Mon, 23 Mar 2015 12:06:13 +0800 Subject: [PATCH 014/591] [SPARK-6397][SQL] Check the missingInput simply Author: q00251598 Closes #5082 from watermen/sql-missingInput and squashes the following commits: 25766b9 [q00251598] Check the missingInput simply --- .../apache/spark/sql/catalyst/analysis/CheckAnalysis.scala | 5 ++--- .../spark/sql/catalyst/plans/logical/basicOperators.scala | 2 ++ 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala index 4e8fc892f3eea..fb975ee5e7296 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala @@ -85,9 +85,8 @@ class CheckAnalysis { cleaned.foreach(checkValidAggregateExpression) - case o if o.children.nonEmpty && - !o.references.filter(_.name != "grouping__id").subsetOf(o.inputSet) => - val missingAttributes = (o.references -- o.inputSet).map(_.prettyString).mkString(",") + case o if o.children.nonEmpty && o.missingInput.nonEmpty => + val missingAttributes = o.missingInput.map(_.prettyString).mkString(",") val input = o.inputSet.map(_.prettyString).mkString(",") failAnalysis(s"resolved attributes $missingAttributes missing from $input") diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala index 384fe53a68362..a94b2d2095d12 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala @@ -191,6 +191,8 @@ case class Expand( val sizeInBytes = child.statistics.sizeInBytes * projections.length Statistics(sizeInBytes = sizeInBytes) } + + override def missingInput = super.missingInput.filter(_.name != VirtualColumn.groupingIdName) } trait GroupingAnalytics extends UnaryNode { From bf044def4c3a37a0fd4d5e70c2d57685cfd9fd71 Mon Sep 17 00:00:00 2001 From: Cheng Lian Date: Mon, 23 Mar 2015 12:15:19 +0800 Subject: [PATCH 015/591] Revert "[SPARK-6397][SQL] Check the missingInput simply" This reverts commit e566fe5982bac5d24e6be76e5d7d6270544a85e6. --- .../apache/spark/sql/catalyst/analysis/CheckAnalysis.scala | 5 +++-- .../spark/sql/catalyst/plans/logical/basicOperators.scala | 2 -- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala index fb975ee5e7296..4e8fc892f3eea 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala @@ -85,8 +85,9 @@ class CheckAnalysis { cleaned.foreach(checkValidAggregateExpression) - case o if o.children.nonEmpty && o.missingInput.nonEmpty => - val missingAttributes = o.missingInput.map(_.prettyString).mkString(",") + case o if o.children.nonEmpty && + !o.references.filter(_.name != "grouping__id").subsetOf(o.inputSet) => + val missingAttributes = (o.references -- o.inputSet).map(_.prettyString).mkString(",") val input = o.inputSet.map(_.prettyString).mkString(",") failAnalysis(s"resolved attributes $missingAttributes missing from $input") diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala index a94b2d2095d12..384fe53a68362 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala @@ -191,8 +191,6 @@ case class Expand( val sizeInBytes = child.statistics.sizeInBytes * projections.length Statistics(sizeInBytes = sizeInBytes) } - - override def missingInput = super.missingInput.filter(_.name != VirtualColumn.groupingIdName) } trait GroupingAnalytics extends UnaryNode { From 9f3273bd9c919f6c48a95383b3d5be357c89998c Mon Sep 17 00:00:00 2001 From: Yadong Qi Date: Mon, 23 Mar 2015 18:16:49 +0800 Subject: [PATCH 016/591] [SPARK-6397][SQL] Check the missingInput simply https://github.com/apache/spark/pull/5082 /cc liancheng Author: Yadong Qi Closes #5132 from watermen/sql-missingInput-new and squashes the following commits: 1e5bdc5 [Yadong Qi] Check the missingInput simply --- .../apache/spark/sql/catalyst/analysis/CheckAnalysis.scala | 5 ++--- .../org/apache/spark/sql/catalyst/plans/QueryPlan.scala | 5 +++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala index 4e8fc892f3eea..fb975ee5e7296 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala @@ -85,9 +85,8 @@ class CheckAnalysis { cleaned.foreach(checkValidAggregateExpression) - case o if o.children.nonEmpty && - !o.references.filter(_.name != "grouping__id").subsetOf(o.inputSet) => - val missingAttributes = (o.references -- o.inputSet).map(_.prettyString).mkString(",") + case o if o.children.nonEmpty && o.missingInput.nonEmpty => + val missingAttributes = o.missingInput.map(_.prettyString).mkString(",") val input = o.inputSet.map(_.prettyString).mkString(",") failAnalysis(s"resolved attributes $missingAttributes missing from $input") diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala index 17a88e07de15f..400a6b2825c10 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql.catalyst.plans -import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeSet, Expression} +import org.apache.spark.sql.catalyst.expressions.{VirtualColumn, Attribute, AttributeSet, Expression} import org.apache.spark.sql.catalyst.trees.TreeNode import org.apache.spark.sql.types.{ArrayType, DataType, StructField, StructType} @@ -48,7 +48,8 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy * Subclasses should override this method if they produce attributes internally as it is used by * assertions designed to prevent the construction of invalid plans. */ - def missingInput: AttributeSet = references -- inputSet + def missingInput: AttributeSet = (references -- inputSet) + .filter(_.name != VirtualColumn.groupingIdName) /** * Runs [[transform]] with `rule` on all expressions present in this query operator. From 474d1320c9b93c501710ad1cfa836b8284562a2c Mon Sep 17 00:00:00 2001 From: MechCoder Date: Mon, 23 Mar 2015 13:30:21 -0700 Subject: [PATCH 017/591] [SPARK-6308] [MLlib] [Sql] Override TypeName in VectorUDT and MatrixUDT Author: MechCoder Closes #5118 from MechCoder/spark-6308 and squashes the following commits: 6c8ffab [MechCoder] Add test for simpleString b966242 [MechCoder] [SPARK-6308] [MLlib][Sql] VectorUDT is displayed as vecto in dtypes --- .../src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala | 2 ++ .../src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala | 2 ++ .../scala/org/apache/spark/mllib/linalg/MatricesSuite.scala | 2 ++ .../test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala | 2 ++ 4 files changed, 8 insertions(+) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala index 849f44295f089..d1a174063caba 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala @@ -187,6 +187,8 @@ private[spark] class MatrixUDT extends UserDefinedType[Matrix] { override def hashCode(): Int = 1994 + override def typeName: String = "matrix" + private[spark] override def asNullable: MatrixUDT = this } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala index 2cda9b252ee06..328dbe2ce11fa 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala @@ -185,6 +185,8 @@ private[spark] class VectorUDT extends UserDefinedType[Vector] { override def hashCode: Int = 7919 + override def typeName: String = "vector" + private[spark] override def asNullable: VectorUDT = this } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala index 96f677db3f377..0d2cec58e2c03 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala @@ -436,5 +436,7 @@ class MatricesSuite extends FunSuite { Seq(dm1, dm2, dm3, sm1, sm2, sm3).foreach { mat => assert(mat.toArray === mUDT.deserialize(mUDT.serialize(mat)).toArray) } + assert(mUDT.typeName == "matrix") + assert(mUDT.simpleString == "matrix") } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala index 5def899cea117..2839c4c289b2d 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala @@ -187,6 +187,8 @@ class VectorsSuite extends FunSuite { for (v <- Seq(dv0, dv1, sv0, sv1)) { assert(v === udt.deserialize(udt.serialize(v))) } + assert(udt.typeName == "vector") + assert(udt.simpleString == "vector") } test("fromBreeze") { From 6cd7058b369ec8d01938961f148734ee9eaf76de Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Mon, 23 Mar 2015 15:08:39 -0700 Subject: [PATCH 018/591] Revert "[SPARK-6122][Core] Upgrade Tachyon client version to 0.6.1." This reverts commit a41b9c6004cfee84bd56dfa1faf5a0cf084551ae. --- core/pom.xml | 2 +- .../spark/storage/TachyonBlockManager.scala | 27 ++++++++++--------- .../scala/org/apache/spark/util/Utils.scala | 4 +-- make-distribution.sh | 2 +- 4 files changed, 17 insertions(+), 18 deletions(-) diff --git a/core/pom.xml b/core/pom.xml index 868834dd505ef..6cd1965ec37c2 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -275,7 +275,7 @@ org.tachyonproject tachyon-client - 0.6.1 + 0.5.0 org.apache.hadoop diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala index 2ab6a8f3ec1d4..af873034215a9 100644 --- a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala @@ -20,8 +20,8 @@ package org.apache.spark.storage import java.text.SimpleDateFormat import java.util.{Date, Random} -import tachyon.TachyonURI -import tachyon.client.{TachyonFile, TachyonFS} +import tachyon.client.TachyonFS +import tachyon.client.TachyonFile import org.apache.spark.Logging import org.apache.spark.executor.ExecutorExitCode @@ -40,7 +40,7 @@ private[spark] class TachyonBlockManager( val master: String) extends Logging { - val client = if (master != null && master != "") TachyonFS.get(new TachyonURI(master)) else null + val client = if (master != null && master != "") TachyonFS.get(master) else null if (client == null) { logError("Failed to connect to the Tachyon as the master address is not configured") @@ -60,11 +60,11 @@ private[spark] class TachyonBlockManager( addShutdownHook() def removeFile(file: TachyonFile): Boolean = { - client.delete(new TachyonURI(file.getPath()), false) + client.delete(file.getPath(), false) } def fileExists(file: TachyonFile): Boolean = { - client.exist(new TachyonURI(file.getPath())) + client.exist(file.getPath()) } def getFile(filename: String): TachyonFile = { @@ -81,7 +81,7 @@ private[spark] class TachyonBlockManager( if (old != null) { old } else { - val path = new TachyonURI(s"${tachyonDirs(dirId)}/${"%02x".format(subDirId)}") + val path = tachyonDirs(dirId) + "/" + "%02x".format(subDirId) client.mkdir(path) val newDir = client.getFile(path) subDirs(dirId)(subDirId) = newDir @@ -89,7 +89,7 @@ private[spark] class TachyonBlockManager( } } } - val filePath = new TachyonURI(s"$subDir/$filename") + val filePath = subDir + "/" + filename if(!client.exist(filePath)) { client.createFile(filePath) } @@ -101,7 +101,7 @@ private[spark] class TachyonBlockManager( // TODO: Some of the logic here could be consolidated/de-duplicated with that in the DiskStore. private def createTachyonDirs(): Array[TachyonFile] = { - logDebug(s"Creating tachyon directories at root dirs '$rootDirs'") + logDebug("Creating tachyon directories at root dirs '" + rootDirs + "'") val dateFormat = new SimpleDateFormat("yyyyMMddHHmmss") rootDirs.split(",").map { rootDir => var foundLocalDir = false @@ -113,21 +113,22 @@ private[spark] class TachyonBlockManager( tries += 1 try { tachyonDirId = "%s-%04x".format(dateFormat.format(new Date), rand.nextInt(65536)) - val path = new TachyonURI(s"$rootDir/spark-tachyon-$tachyonDirId") + val path = rootDir + "/" + "spark-tachyon-" + tachyonDirId if (!client.exist(path)) { foundLocalDir = client.mkdir(path) tachyonDir = client.getFile(path) } } catch { case e: Exception => - logWarning(s"Attempt $tries to create tachyon dir $tachyonDir failed", e) + logWarning("Attempt " + tries + " to create tachyon dir " + tachyonDir + " failed", e) } } if (!foundLocalDir) { - logError(s"Failed $MAX_DIR_CREATION_ATTEMPTS attempts to create tachyon dir in $rootDir") + logError("Failed " + MAX_DIR_CREATION_ATTEMPTS + " attempts to create tachyon dir in " + + rootDir) System.exit(ExecutorExitCode.TACHYON_STORE_FAILED_TO_CREATE_DIR) } - logInfo(s"Created tachyon directory at $tachyonDir") + logInfo("Created tachyon directory at " + tachyonDir) tachyonDir } } @@ -144,7 +145,7 @@ private[spark] class TachyonBlockManager( } } catch { case e: Exception => - logError(s"Exception while deleting tachyon spark dir: $tachyonDir", e) + logError("Exception while deleting tachyon spark dir: " + tachyonDir, e) } } client.close() diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index 91d833295e376..fa56bb09e4e5c 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -42,8 +42,6 @@ import org.apache.hadoop.security.UserGroupInformation import org.apache.log4j.PropertyConfigurator import org.eclipse.jetty.util.MultiException import org.json4s._ - -import tachyon.TachyonURI import tachyon.client.{TachyonFS, TachyonFile} import org.apache.spark._ @@ -972,7 +970,7 @@ private[spark] object Utils extends Logging { * Delete a file or directory and its contents recursively. */ def deleteRecursively(dir: TachyonFile, client: TachyonFS) { - if (!client.delete(new TachyonURI(dir.getPath()), true)) { + if (!client.delete(dir.getPath(), true)) { throw new IOException("Failed to delete the tachyon dir: " + dir) } } diff --git a/make-distribution.sh b/make-distribution.sh index 8162fe94c1af0..9ed1abfe8c598 100755 --- a/make-distribution.sh +++ b/make-distribution.sh @@ -32,7 +32,7 @@ SPARK_HOME="$(cd "`dirname "$0"`"; pwd)" DISTDIR="$SPARK_HOME/dist" SPARK_TACHYON=false -TACHYON_VERSION="0.6.1" +TACHYON_VERSION="0.5.0" TACHYON_TGZ="tachyon-${TACHYON_VERSION}-bin.tar.gz" TACHYON_URL="https://github.com/amplab/tachyon/releases/download/v${TACHYON_VERSION}/${TACHYON_TGZ}" From bfd3ee9f76aaab3dcde71d92e2b8ca60a0e42262 Mon Sep 17 00:00:00 2001 From: Volodymyr Lyubinets Date: Mon, 23 Mar 2015 17:00:27 -0700 Subject: [PATCH 019/591] [SPARK-6124] Support jdbc connection properties in OPTIONS part of the query One more thing if this PR is considered to be OK - it might make sense to add extra .jdbc() API's that take Properties to SQLContext. Author: Volodymyr Lyubinets Closes #4859 from vlyubin/jdbcProperties and squashes the following commits: 7a8cfda [Volodymyr Lyubinets] Support jdbc connection properties in OPTIONS part of the query --- .../org/apache/spark/sql/jdbc/JDBCRDD.scala | 14 +++-- .../apache/spark/sql/jdbc/JDBCRelation.scala | 19 ++++--- .../org/apache/spark/sql/jdbc/JDBCSuite.scala | 55 +++++++++++++------ 3 files changed, 59 insertions(+), 29 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala index 76f8593180e85..463e1dcc268bc 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.jdbc import java.sql.{Connection, DriverManager, ResultSet, ResultSetMetaData, SQLException} +import java.util.Properties import org.apache.commons.lang.StringEscapeUtils.escapeSql import org.apache.spark.{Logging, Partition, SparkContext, TaskContext} @@ -90,9 +91,9 @@ private[sql] object JDBCRDD extends Logging { * @throws SQLException if the table specification is garbage. * @throws SQLException if the table contains an unsupported type. */ - def resolveTable(url: String, table: String): StructType = { + def resolveTable(url: String, table: String, properties: Properties): StructType = { val quirks = DriverQuirks.get(url) - val conn: Connection = DriverManager.getConnection(url) + val conn: Connection = DriverManager.getConnection(url, properties) try { val rs = conn.prepareStatement(s"SELECT * FROM $table WHERE 1=0").executeQuery() try { @@ -147,7 +148,7 @@ private[sql] object JDBCRDD extends Logging { * * @return A function that loads the driver and connects to the url. */ - def getConnector(driver: String, url: String): () => Connection = { + def getConnector(driver: String, url: String, properties: Properties): () => Connection = { () => { try { if (driver != null) Class.forName(driver) @@ -156,7 +157,7 @@ private[sql] object JDBCRDD extends Logging { logWarning(s"Couldn't find class $driver", e); } } - DriverManager.getConnection(url) + DriverManager.getConnection(url, properties) } } /** @@ -179,6 +180,7 @@ private[sql] object JDBCRDD extends Logging { schema: StructType, driver: String, url: String, + properties: Properties, fqTable: String, requiredColumns: Array[String], filters: Array[Filter], @@ -189,7 +191,7 @@ private[sql] object JDBCRDD extends Logging { return new JDBCRDD( sc, - getConnector(driver, url), + getConnector(driver, url, properties), prunedSchema, fqTable, requiredColumns, @@ -361,7 +363,7 @@ private[sql] class JDBCRDD( var ans = 0L var j = 0 while (j < bytes.size) { - ans = 256*ans + (255 & bytes(j)) + ans = 256 * ans + (255 & bytes(j)) j = j + 1; } mutableRow.setLong(i, ans) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala index df687e6da9bea..4fa84dc076f7e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala @@ -17,16 +17,17 @@ package org.apache.spark.sql.jdbc -import org.apache.spark.rdd.RDD -import org.apache.spark.sql.catalyst.expressions.Row -import org.apache.spark.sql.types.StructType +import java.sql.DriverManager +import java.util.Properties import scala.collection.mutable.ArrayBuffer -import java.sql.DriverManager import org.apache.spark.Partition +import org.apache.spark.rdd.RDD import org.apache.spark.sql.SQLContext +import org.apache.spark.sql.catalyst.expressions.Row import org.apache.spark.sql.sources._ +import org.apache.spark.sql.types.StructType /** * Data corresponding to one partition of a JDBCRDD. @@ -115,18 +116,21 @@ private[sql] class DefaultSource extends RelationProvider { numPartitions.toInt) } val parts = JDBCRelation.columnPartition(partitionInfo) - JDBCRelation(url, table, parts)(sqlContext) + val properties = new Properties() // Additional properties that we will pass to getConnection + parameters.foreach(kv => properties.setProperty(kv._1, kv._2)) + JDBCRelation(url, table, parts, properties)(sqlContext) } } private[sql] case class JDBCRelation( url: String, table: String, - parts: Array[Partition])(@transient val sqlContext: SQLContext) + parts: Array[Partition], + properties: Properties = new Properties())(@transient val sqlContext: SQLContext) extends BaseRelation with PrunedFilteredScan { - override val schema: StructType = JDBCRDD.resolveTable(url, table) + override val schema: StructType = JDBCRDD.resolveTable(url, table, properties) override def buildScan(requiredColumns: Array[String], filters: Array[Filter]): RDD[Row] = { val driver: String = DriverManager.getDriver(url).getClass.getCanonicalName @@ -135,6 +139,7 @@ private[sql] case class JDBCRelation( schema, driver, url, + properties, table, requiredColumns, filters, diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala index 5eb6ab2e92e8b..592ed4b23b7d3 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala @@ -19,22 +19,31 @@ package org.apache.spark.sql.jdbc import java.math.BigDecimal import java.sql.DriverManager -import java.util.{Calendar, GregorianCalendar} +import java.util.{Calendar, GregorianCalendar, Properties} import org.apache.spark.sql.test._ +import org.h2.jdbc.JdbcSQLException import org.scalatest.{FunSuite, BeforeAndAfter} import TestSQLContext._ import TestSQLContext.implicits._ class JDBCSuite extends FunSuite with BeforeAndAfter { val url = "jdbc:h2:mem:testdb0" + val urlWithUserAndPass = "jdbc:h2:mem:testdb0;user=testUser;password=testPass" var conn: java.sql.Connection = null val testBytes = Array[Byte](99.toByte, 134.toByte, 135.toByte, 200.toByte, 205.toByte) before { Class.forName("org.h2.Driver") - conn = DriverManager.getConnection(url) + // Extra properties that will be specified for our database. We need these to test + // usage of parameters from OPTIONS clause in queries. + val properties = new Properties() + properties.setProperty("user", "testUser") + properties.setProperty("password", "testPass") + properties.setProperty("rowId", "false") + + conn = DriverManager.getConnection(url, properties) conn.prepareStatement("create schema test").executeUpdate() conn.prepareStatement("create table test.people (name TEXT(32) NOT NULL, theid INTEGER NOT NULL)").executeUpdate() conn.prepareStatement("insert into test.people values ('fred', 1)").executeUpdate() @@ -46,15 +55,15 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { s""" |CREATE TEMPORARY TABLE foobar |USING org.apache.spark.sql.jdbc - |OPTIONS (url '$url', dbtable 'TEST.PEOPLE') + |OPTIONS (url '$url', dbtable 'TEST.PEOPLE', user 'testUser', password 'testPass') """.stripMargin.replaceAll("\n", " ")) sql( s""" |CREATE TEMPORARY TABLE parts |USING org.apache.spark.sql.jdbc - |OPTIONS (url '$url', dbtable 'TEST.PEOPLE', - |partitionColumn 'THEID', lowerBound '1', upperBound '4', numPartitions '3') + |OPTIONS (url '$url', dbtable 'TEST.PEOPLE', user 'testUser', password 'testPass', + | partitionColumn 'THEID', lowerBound '1', upperBound '4', numPartitions '3') """.stripMargin.replaceAll("\n", " ")) conn.prepareStatement("create table test.inttypes (a INT, b BOOLEAN, c TINYINT, " @@ -68,12 +77,12 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { s""" |CREATE TEMPORARY TABLE inttypes |USING org.apache.spark.sql.jdbc - |OPTIONS (url '$url', dbtable 'TEST.INTTYPES') + |OPTIONS (url '$url', dbtable 'TEST.INTTYPES', user 'testUser', password 'testPass') """.stripMargin.replaceAll("\n", " ")) conn.prepareStatement("create table test.strtypes (a BINARY(20), b VARCHAR(20), " + "c VARCHAR_IGNORECASE(20), d CHAR(20), e BLOB, f CLOB)").executeUpdate() - var stmt = conn.prepareStatement("insert into test.strtypes values (?, ?, ?, ?, ?, ?)") + val stmt = conn.prepareStatement("insert into test.strtypes values (?, ?, ?, ?, ?, ?)") stmt.setBytes(1, testBytes) stmt.setString(2, "Sensitive") stmt.setString(3, "Insensitive") @@ -85,7 +94,7 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { s""" |CREATE TEMPORARY TABLE strtypes |USING org.apache.spark.sql.jdbc - |OPTIONS (url '$url', dbtable 'TEST.STRTYPES') + |OPTIONS (url '$url', dbtable 'TEST.STRTYPES', user 'testUser', password 'testPass') """.stripMargin.replaceAll("\n", " ")) conn.prepareStatement("create table test.timetypes (a TIME, b DATE, c TIMESTAMP)" @@ -97,7 +106,7 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { s""" |CREATE TEMPORARY TABLE timetypes |USING org.apache.spark.sql.jdbc - |OPTIONS (url '$url', dbtable 'TEST.TIMETYPES') + |OPTIONS (url '$url', dbtable 'TEST.TIMETYPES', user 'testUser', password 'testPass') """.stripMargin.replaceAll("\n", " ")) @@ -112,7 +121,7 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { s""" |CREATE TEMPORARY TABLE flttypes |USING org.apache.spark.sql.jdbc - |OPTIONS (url '$url', dbtable 'TEST.FLTTYPES') + |OPTIONS (url '$url', dbtable 'TEST.FLTTYPES', user 'testUser', password 'testPass') """.stripMargin.replaceAll("\n", " ")) // Untested: IDENTITY, OTHER, UUID, ARRAY, and GEOMETRY types. @@ -174,16 +183,17 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { } test("Basic API") { - assert(TestSQLContext.jdbc(url, "TEST.PEOPLE").collect.size == 3) + assert(TestSQLContext.jdbc(urlWithUserAndPass, "TEST.PEOPLE").collect.size == 3) } test("Partitioning via JDBCPartitioningInfo API") { - assert(TestSQLContext.jdbc(url, "TEST.PEOPLE", "THEID", 0, 4, 3).collect.size == 3) + assert(TestSQLContext.jdbc(urlWithUserAndPass, "TEST.PEOPLE", "THEID", 0, 4, 3) + .collect.size == 3) } test("Partitioning via list-of-where-clauses API") { val parts = Array[String]("THEID < 2", "THEID >= 2") - assert(TestSQLContext.jdbc(url, "TEST.PEOPLE", parts).collect.size == 3) + assert(TestSQLContext.jdbc(urlWithUserAndPass, "TEST.PEOPLE", parts).collect.size == 3) } test("H2 integral types") { @@ -216,7 +226,6 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { assert(rows(0).getString(5).equals("I am a clob!")) } - test("H2 time types") { val rows = sql("SELECT * FROM timetypes").collect() val cal = new GregorianCalendar(java.util.Locale.ROOT) @@ -246,17 +255,31 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { .equals(new BigDecimal("123456789012345.54321543215432100000"))) } - test("SQL query as table name") { sql( s""" |CREATE TEMPORARY TABLE hack |USING org.apache.spark.sql.jdbc - |OPTIONS (url '$url', dbtable '(SELECT B, B*B FROM TEST.FLTTYPES)') + |OPTIONS (url '$url', dbtable '(SELECT B, B*B FROM TEST.FLTTYPES)', + | user 'testUser', password 'testPass') """.stripMargin.replaceAll("\n", " ")) val rows = sql("SELECT * FROM hack").collect() assert(rows(0).getDouble(0) == 1.00000011920928955) // Yes, I meant ==. // For some reason, H2 computes this square incorrectly... assert(math.abs(rows(0).getDouble(1) - 1.00000023841859331) < 1e-12) } + + test("Pass extra properties via OPTIONS") { + // We set rowId to false during setup, which means that _ROWID_ column should be absent from + // all tables. If rowId is true (default), the query below doesn't throw an exception. + intercept[JdbcSQLException] { + sql( + s""" + |CREATE TEMPORARY TABLE abc + |USING org.apache.spark.sql.jdbc + |OPTIONS (url '$url', dbtable '(SELECT _ROWID_ FROM test.people)', + | user 'testUser', password 'testPass') + """.stripMargin.replaceAll("\n", " ")) + } + } } From 4ce2782a61e23ed0326faac2ee97a9bd36ec8963 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Mon, 23 Mar 2015 23:41:06 -0700 Subject: [PATCH 020/591] [SPARK-6428] Added explicit types for all public methods in core. Author: Reynold Xin Closes #5125 from rxin/core-explicit-type and squashes the following commits: f471415 [Reynold Xin] Revert style checker changes. 81b66e4 [Reynold Xin] Code review feedback. a7533e3 [Reynold Xin] Mima excludes. 1d795f5 [Reynold Xin] [SPARK-6428] Added explicit types for all public methods in core. --- .../scala/org/apache/spark/Accumulators.scala | 23 +++-- .../scala/org/apache/spark/Dependency.scala | 6 +- .../scala/org/apache/spark/FutureAction.scala | 4 +- .../org/apache/spark/HeartbeatReceiver.scala | 2 +- .../org/apache/spark/MapOutputTracker.scala | 2 +- .../scala/org/apache/spark/Partitioner.scala | 4 +- .../apache/spark/SerializableWritable.scala | 6 +- .../scala/org/apache/spark/SparkConf.scala | 2 +- .../scala/org/apache/spark/SparkContext.scala | 48 +++++----- .../scala/org/apache/spark/TaskState.scala | 4 +- .../scala/org/apache/spark/TestUtils.scala | 2 +- .../org/apache/spark/api/java/JavaRDD.scala | 2 +- .../spark/api/java/JavaSparkContext.scala | 2 +- .../org/apache/spark/api/java/JavaUtils.scala | 35 ++++---- .../apache/spark/api/python/PythonRDD.scala | 39 ++++---- .../apache/spark/api/python/SerDeUtil.scala | 2 +- .../WriteInputFormatTestDataGenerator.scala | 27 +++--- .../apache/spark/broadcast/Broadcast.scala | 2 +- .../spark/broadcast/BroadcastManager.scala | 2 +- .../spark/broadcast/HttpBroadcast.scala | 4 +- .../broadcast/HttpBroadcastFactory.scala | 2 +- .../spark/broadcast/TorrentBroadcast.scala | 2 +- .../broadcast/TorrentBroadcastFactory.scala | 2 +- .../org/apache/spark/deploy/Client.scala | 4 +- .../apache/spark/deploy/ClientArguments.scala | 2 +- .../apache/spark/deploy/DeployMessage.scala | 2 +- .../spark/deploy/FaultToleranceTest.scala | 2 +- .../apache/spark/deploy/JsonProtocol.scala | 15 ++-- .../apache/spark/deploy/SparkHadoopUtil.scala | 2 +- .../org/apache/spark/deploy/SparkSubmit.scala | 2 +- .../spark/deploy/SparkSubmitArguments.scala | 2 +- .../spark/deploy/client/AppClient.scala | 2 +- .../deploy/history/FsHistoryProvider.scala | 4 +- .../spark/deploy/history/HistoryServer.scala | 10 +-- .../master/FileSystemPersistenceEngine.scala | 2 +- .../apache/spark/deploy/master/Master.scala | 2 +- .../deploy/master/RecoveryModeFactory.scala | 15 +++- .../spark/deploy/master/WorkerInfo.scala | 2 +- .../master/ZooKeeperPersistenceEngine.scala | 2 +- .../spark/deploy/master/ui/MasterPage.scala | 2 +- .../spark/deploy/worker/DriverRunner.scala | 22 +++-- .../apache/spark/deploy/worker/Worker.scala | 2 +- .../spark/deploy/worker/WorkerWatcher.scala | 2 +- .../CoarseGrainedExecutorBackend.scala | 2 +- .../apache/spark/executor/ExecutorActor.scala | 2 +- .../apache/spark/executor/TaskMetrics.scala | 90 +++++++++---------- .../spark/input/PortableDataStream.scala | 17 ++-- .../spark/mapred/SparkHadoopMapRedUtil.scala | 2 +- .../mapreduce/SparkHadoopMapReduceUtil.scala | 2 +- .../apache/spark/metrics/MetricsSystem.scala | 3 +- .../spark/metrics/sink/MetricsServlet.scala | 18 ++-- .../org/apache/spark/metrics/sink/Sink.scala | 4 +- .../spark/network/nio/BlockMessageArray.scala | 6 +- .../spark/network/nio/BufferMessage.scala | 10 +-- .../apache/spark/network/nio/Connection.scala | 8 +- .../spark/network/nio/ConnectionId.scala | 4 +- .../spark/network/nio/ConnectionManager.scala | 2 +- .../network/nio/ConnectionManagerId.scala | 2 +- .../apache/spark/network/nio/Message.scala | 6 +- .../spark/network/nio/MessageChunk.scala | 6 +- .../network/nio/MessageChunkHeader.scala | 4 +- .../apache/spark/partial/PartialResult.scala | 2 +- .../org/apache/spark/rdd/CartesianRDD.scala | 2 +- .../org/apache/spark/rdd/CoalescedRDD.scala | 10 +-- .../org/apache/spark/rdd/HadoopRDD.scala | 13 ++- .../scala/org/apache/spark/rdd/JdbcRDD.scala | 10 ++- .../apache/spark/rdd/MapPartitionsRDD.scala | 2 +- .../org/apache/spark/rdd/NewHadoopRDD.scala | 2 +- .../spark/rdd/ParallelCollectionRDD.scala | 2 +- .../spark/rdd/PartitionPruningRDD.scala | 10 ++- .../scala/org/apache/spark/rdd/PipedRDD.scala | 8 +- .../main/scala/org/apache/spark/rdd/RDD.scala | 12 +-- .../org/apache/spark/rdd/ShuffledRDD.scala | 4 +- .../org/apache/spark/rdd/SubtractedRDD.scala | 2 +- .../scala/org/apache/spark/rdd/UnionRDD.scala | 2 +- .../spark/rdd/ZippedPartitionsRDD.scala | 2 +- .../spark/scheduler/AccumulableInfo.scala | 7 +- .../apache/spark/scheduler/DAGScheduler.scala | 2 +- .../scheduler/EventLoggingListener.scala | 61 ++++++++----- .../apache/spark/scheduler/JobLogger.scala | 2 +- .../apache/spark/scheduler/JobWaiter.scala | 2 +- .../scheduler/OutputCommitCoordinator.scala | 2 +- .../apache/spark/scheduler/ResultTask.scala | 2 +- .../spark/scheduler/ShuffleMapTask.scala | 4 +- .../spark/scheduler/SparkListener.scala | 4 +- .../org/apache/spark/scheduler/Stage.scala | 2 +- .../apache/spark/scheduler/TaskLocation.scala | 19 ++-- .../spark/scheduler/TaskSchedulerImpl.scala | 16 ++-- .../spark/scheduler/TaskSetManager.scala | 13 +-- .../CoarseGrainedSchedulerBackend.scala | 2 +- .../cluster/YarnSchedulerBackend.scala | 2 +- .../scheduler/cluster/mesos/MemoryUtils.scala | 2 +- .../cluster/mesos/MesosSchedulerBackend.scala | 2 +- .../spark/scheduler/local/LocalBackend.scala | 4 +- .../spark/serializer/JavaSerializer.scala | 5 +- .../spark/serializer/KryoSerializer.scala | 2 +- .../shuffle/FileShuffleBlockManager.scala | 4 +- .../shuffle/IndexShuffleBlockManager.scala | 4 +- .../org/apache/spark/storage/BlockId.scala | 34 +++---- .../apache/spark/storage/BlockManagerId.scala | 8 +- .../spark/storage/BlockManagerMaster.scala | 2 +- .../storage/BlockManagerMasterActor.scala | 4 +- .../storage/BlockManagerSlaveActor.scala | 2 +- .../spark/storage/BlockObjectWriter.scala | 17 ++-- .../apache/spark/storage/FileSegment.scala | 4 +- .../org/apache/spark/storage/RDDInfo.scala | 4 +- .../apache/spark/storage/StorageLevel.scala | 16 ++-- .../spark/storage/StorageStatusListener.scala | 7 +- .../spark/storage/TachyonFileSegment.scala | 4 +- .../scala/org/apache/spark/ui/SparkUI.scala | 2 +- .../scala/org/apache/spark/ui/UIUtils.scala | 6 +- .../apache/spark/ui/UIWorkloadGenerator.scala | 4 +- .../apache/spark/ui/exec/ExecutorsTab.scala | 10 +-- .../spark/ui/jobs/JobProgressListener.scala | 14 +-- .../org/apache/spark/ui/jobs/JobsTab.scala | 2 +- .../org/apache/spark/ui/jobs/StagePage.scala | 10 ++- .../org/apache/spark/ui/jobs/StagesTab.scala | 6 +- .../org/apache/spark/ui/jobs/UIData.scala | 10 +-- .../apache/spark/ui/storage/StorageTab.scala | 12 +-- .../spark/util/CompletionIterator.scala | 10 +-- .../org/apache/spark/util/Distribution.scala | 2 +- .../org/apache/spark/util/ManualClock.scala | 30 +++---- .../apache/spark/util/MetadataCleaner.scala | 7 +- .../org/apache/spark/util/MutablePair.scala | 2 +- .../apache/spark/util/ParentClassLoader.scala | 2 +- .../spark/util/SerializableBuffer.scala | 2 +- .../org/apache/spark/util/StatCounter.scala | 4 +- .../util/TimeStampedWeakValueHashMap.scala | 6 +- .../scala/org/apache/spark/util/Utils.scala | 30 +++---- .../apache/spark/util/collection/BitSet.scala | 4 +- .../collection/ExternalAppendOnlyMap.scala | 4 +- .../util/collection/ExternalSorter.scala | 2 +- .../spark/util/collection/OpenHashMap.scala | 6 +- .../spark/util/collection/OpenHashSet.scala | 4 +- .../collection/PrimitiveKeyOpenHashMap.scala | 8 +- .../apache/spark/util/collection/Utils.scala | 2 +- .../spark/util/logging/FileAppender.scala | 4 +- .../spark/util/random/RandomSampler.scala | 44 +++++---- .../util/random/StratifiedSamplingUtils.scala | 2 +- .../spark/util/random/XORShiftRandom.scala | 2 +- project/MimaExcludes.scala | 8 +- scalastyle-config.xml | 4 +- 142 files changed, 597 insertions(+), 526 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala index bcf832467f00b..330df1d59a9b1 100644 --- a/core/src/main/scala/org/apache/spark/Accumulators.scala +++ b/core/src/main/scala/org/apache/spark/Accumulators.scala @@ -18,8 +18,6 @@ package org.apache.spark import java.io.{ObjectInputStream, Serializable} -import java.util.concurrent.atomic.AtomicLong -import java.lang.ThreadLocal import scala.collection.generic.Growable import scala.collection.mutable.Map @@ -109,7 +107,7 @@ class Accumulable[R, T] ( * The typical use of this method is to directly mutate the local value, eg., to add * an element to a Set. */ - def localValue = value_ + def localValue: R = value_ /** * Set the accumulator's value; only allowed on master. @@ -137,7 +135,7 @@ class Accumulable[R, T] ( Accumulators.register(this, false) } - override def toString = if (value_ == null) "null" else value_.toString + override def toString: String = if (value_ == null) "null" else value_.toString } /** @@ -257,22 +255,22 @@ object AccumulatorParam { implicit object DoubleAccumulatorParam extends AccumulatorParam[Double] { def addInPlace(t1: Double, t2: Double): Double = t1 + t2 - def zero(initialValue: Double) = 0.0 + def zero(initialValue: Double): Double = 0.0 } implicit object IntAccumulatorParam extends AccumulatorParam[Int] { def addInPlace(t1: Int, t2: Int): Int = t1 + t2 - def zero(initialValue: Int) = 0 + def zero(initialValue: Int): Int = 0 } implicit object LongAccumulatorParam extends AccumulatorParam[Long] { - def addInPlace(t1: Long, t2: Long) = t1 + t2 - def zero(initialValue: Long) = 0L + def addInPlace(t1: Long, t2: Long): Long = t1 + t2 + def zero(initialValue: Long): Long = 0L } implicit object FloatAccumulatorParam extends AccumulatorParam[Float] { - def addInPlace(t1: Float, t2: Float) = t1 + t2 - def zero(initialValue: Float) = 0f + def addInPlace(t1: Float, t2: Float): Float = t1 + t2 + def zero(initialValue: Float): Float = 0f } // TODO: Add AccumulatorParams for other types, e.g. lists and strings @@ -351,6 +349,7 @@ private[spark] object Accumulators extends Logging { } } - def stringifyPartialValue(partialValue: Any) = "%s".format(partialValue) - def stringifyValue(value: Any) = "%s".format(value) + def stringifyPartialValue(partialValue: Any): String = "%s".format(partialValue) + + def stringifyValue(value: Any): String = "%s".format(value) } diff --git a/core/src/main/scala/org/apache/spark/Dependency.scala b/core/src/main/scala/org/apache/spark/Dependency.scala index 9a7cd4523e5ab..fc8cdde9348ee 100644 --- a/core/src/main/scala/org/apache/spark/Dependency.scala +++ b/core/src/main/scala/org/apache/spark/Dependency.scala @@ -74,7 +74,7 @@ class ShuffleDependency[K, V, C]( val mapSideCombine: Boolean = false) extends Dependency[Product2[K, V]] { - override def rdd = _rdd.asInstanceOf[RDD[Product2[K, V]]] + override def rdd: RDD[Product2[K, V]] = _rdd.asInstanceOf[RDD[Product2[K, V]]] val shuffleId: Int = _rdd.context.newShuffleId() @@ -91,7 +91,7 @@ class ShuffleDependency[K, V, C]( */ @DeveloperApi class OneToOneDependency[T](rdd: RDD[T]) extends NarrowDependency[T](rdd) { - override def getParents(partitionId: Int) = List(partitionId) + override def getParents(partitionId: Int): List[Int] = List(partitionId) } @@ -107,7 +107,7 @@ class OneToOneDependency[T](rdd: RDD[T]) extends NarrowDependency[T](rdd) { class RangeDependency[T](rdd: RDD[T], inStart: Int, outStart: Int, length: Int) extends NarrowDependency[T](rdd) { - override def getParents(partitionId: Int) = { + override def getParents(partitionId: Int): List[Int] = { if (partitionId >= outStart && partitionId < outStart + length) { List(partitionId - outStart + inStart) } else { diff --git a/core/src/main/scala/org/apache/spark/FutureAction.scala b/core/src/main/scala/org/apache/spark/FutureAction.scala index e97a7375a267b..91f9ef8ce7185 100644 --- a/core/src/main/scala/org/apache/spark/FutureAction.scala +++ b/core/src/main/scala/org/apache/spark/FutureAction.scala @@ -168,7 +168,7 @@ class SimpleFutureAction[T] private[spark](jobWaiter: JobWaiter[_], resultFunc: } } - def jobIds = Seq(jobWaiter.jobId) + def jobIds: Seq[Int] = Seq(jobWaiter.jobId) } @@ -276,7 +276,7 @@ class ComplexFutureAction[T] extends FutureAction[T] { override def value: Option[Try[T]] = p.future.value - def jobIds = jobs + def jobIds: Seq[Int] = jobs } diff --git a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala index 69178da1a7773..715f292f03469 100644 --- a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala +++ b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala @@ -65,7 +65,7 @@ private[spark] class HeartbeatReceiver(sc: SparkContext, scheduler: TaskSchedule super.preStart() } - override def receiveWithLogging = { + override def receiveWithLogging: PartialFunction[Any, Unit] = { case Heartbeat(executorId, taskMetrics, blockManagerId) => val unknownExecutor = !scheduler.executorHeartbeatReceived( executorId, taskMetrics, blockManagerId) diff --git a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala index 6e4edc7c80d7a..c9426c5de23a2 100644 --- a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala +++ b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala @@ -43,7 +43,7 @@ private[spark] class MapOutputTrackerMasterActor(tracker: MapOutputTrackerMaster extends Actor with ActorLogReceive with Logging { val maxAkkaFrameSize = AkkaUtils.maxFrameSizeBytes(conf) - override def receiveWithLogging = { + override def receiveWithLogging: PartialFunction[Any, Unit] = { case GetMapOutputStatuses(shuffleId: Int) => val hostPort = sender.path.address.hostPort logInfo("Asked to send map output locations for shuffle " + shuffleId + " to " + hostPort) diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala index e53a78ead2c0e..b8d244408bc5b 100644 --- a/core/src/main/scala/org/apache/spark/Partitioner.scala +++ b/core/src/main/scala/org/apache/spark/Partitioner.scala @@ -76,7 +76,7 @@ object Partitioner { * produce an unexpected or incorrect result. */ class HashPartitioner(partitions: Int) extends Partitioner { - def numPartitions = partitions + def numPartitions: Int = partitions def getPartition(key: Any): Int = key match { case null => 0 @@ -154,7 +154,7 @@ class RangePartitioner[K : Ordering : ClassTag, V]( } } - def numPartitions = rangeBounds.length + 1 + def numPartitions: Int = rangeBounds.length + 1 private var binarySearch: ((Array[K], K) => Int) = CollectionsUtils.makeBinarySearch[K] diff --git a/core/src/main/scala/org/apache/spark/SerializableWritable.scala b/core/src/main/scala/org/apache/spark/SerializableWritable.scala index 55cb25946c2ad..cb2cae185256a 100644 --- a/core/src/main/scala/org/apache/spark/SerializableWritable.scala +++ b/core/src/main/scala/org/apache/spark/SerializableWritable.scala @@ -28,8 +28,10 @@ import org.apache.spark.util.Utils @DeveloperApi class SerializableWritable[T <: Writable](@transient var t: T) extends Serializable { - def value = t - override def toString = t.toString + + def value: T = t + + override def toString: String = t.toString private def writeObject(out: ObjectOutputStream): Unit = Utils.tryOrIOException { out.defaultWriteObject() diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala index 2ca19f53d2f07..0c123c96b8d7b 100644 --- a/core/src/main/scala/org/apache/spark/SparkConf.scala +++ b/core/src/main/scala/org/apache/spark/SparkConf.scala @@ -133,7 +133,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { } /** Set multiple parameters together */ - def setAll(settings: Traversable[(String, String)]) = { + def setAll(settings: Traversable[(String, String)]): SparkConf = { this.settings.putAll(settings.toMap.asJava) this } diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 228ff715fe7cb..a70be16f77eeb 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -986,7 +986,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli union(Seq(first) ++ rest) /** Get an RDD that has no partitions or elements. */ - def emptyRDD[T: ClassTag] = new EmptyRDD[T](this) + def emptyRDD[T: ClassTag]: EmptyRDD[T] = new EmptyRDD[T](this) // Methods for creating shared variables @@ -994,7 +994,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * Create an [[org.apache.spark.Accumulator]] variable of a given type, which tasks can "add" * values to using the `+=` method. Only the driver can access the accumulator's `value`. */ - def accumulator[T](initialValue: T)(implicit param: AccumulatorParam[T]) = + def accumulator[T](initialValue: T)(implicit param: AccumulatorParam[T]): Accumulator[T] = { val acc = new Accumulator(initialValue, param) cleaner.foreach(_.registerAccumulatorForCleanup(acc)) @@ -1006,7 +1006,8 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * in the Spark UI. Tasks can "add" values to the accumulator using the `+=` method. Only the * driver can access the accumulator's `value`. */ - def accumulator[T](initialValue: T, name: String)(implicit param: AccumulatorParam[T]) = { + def accumulator[T](initialValue: T, name: String)(implicit param: AccumulatorParam[T]) + : Accumulator[T] = { val acc = new Accumulator(initialValue, param, Some(name)) cleaner.foreach(_.registerAccumulatorForCleanup(acc)) acc @@ -1018,7 +1019,8 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * @tparam R accumulator result type * @tparam T type that can be added to the accumulator */ - def accumulable[R, T](initialValue: R)(implicit param: AccumulableParam[R, T]) = { + def accumulable[R, T](initialValue: R)(implicit param: AccumulableParam[R, T]) + : Accumulable[R, T] = { val acc = new Accumulable(initialValue, param) cleaner.foreach(_.registerAccumulatorForCleanup(acc)) acc @@ -1031,7 +1033,8 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * @tparam R accumulator result type * @tparam T type that can be added to the accumulator */ - def accumulable[R, T](initialValue: R, name: String)(implicit param: AccumulableParam[R, T]) = { + def accumulable[R, T](initialValue: R, name: String)(implicit param: AccumulableParam[R, T]) + : Accumulable[R, T] = { val acc = new Accumulable(initialValue, param, Some(name)) cleaner.foreach(_.registerAccumulatorForCleanup(acc)) acc @@ -1209,7 +1212,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli override def killExecutor(executorId: String): Boolean = super.killExecutor(executorId) /** The version of Spark on which this application is running. */ - def version = SPARK_VERSION + def version: String = SPARK_VERSION /** * Return a map from the slave to the max memory available for caching and the remaining @@ -1659,7 +1662,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli } } - def getCheckpointDir = checkpointDir + def getCheckpointDir: Option[String] = checkpointDir /** Default level of parallelism to use when not given by user (e.g. parallelize and makeRDD). */ def defaultParallelism: Int = { @@ -1900,28 +1903,28 @@ object SparkContext extends Logging { "backward compatibility.", "1.3.0") object DoubleAccumulatorParam extends AccumulatorParam[Double] { def addInPlace(t1: Double, t2: Double): Double = t1 + t2 - def zero(initialValue: Double) = 0.0 + def zero(initialValue: Double): Double = 0.0 } @deprecated("Replaced by implicit objects in AccumulatorParam. This is kept here only for " + "backward compatibility.", "1.3.0") object IntAccumulatorParam extends AccumulatorParam[Int] { def addInPlace(t1: Int, t2: Int): Int = t1 + t2 - def zero(initialValue: Int) = 0 + def zero(initialValue: Int): Int = 0 } @deprecated("Replaced by implicit objects in AccumulatorParam. This is kept here only for " + "backward compatibility.", "1.3.0") object LongAccumulatorParam extends AccumulatorParam[Long] { - def addInPlace(t1: Long, t2: Long) = t1 + t2 - def zero(initialValue: Long) = 0L + def addInPlace(t1: Long, t2: Long): Long = t1 + t2 + def zero(initialValue: Long): Long = 0L } @deprecated("Replaced by implicit objects in AccumulatorParam. This is kept here only for " + "backward compatibility.", "1.3.0") object FloatAccumulatorParam extends AccumulatorParam[Float] { - def addInPlace(t1: Float, t2: Float) = t1 + t2 - def zero(initialValue: Float) = 0f + def addInPlace(t1: Float, t2: Float): Float = t1 + t2 + def zero(initialValue: Float): Float = 0f } // The following deprecated functions have already been moved to `object RDD` to @@ -1931,18 +1934,18 @@ object SparkContext extends Logging { @deprecated("Replaced by implicit functions in the RDD companion object. This is " + "kept here only for backward compatibility.", "1.3.0") def rddToPairRDDFunctions[K, V](rdd: RDD[(K, V)]) - (implicit kt: ClassTag[K], vt: ClassTag[V], ord: Ordering[K] = null) = { + (implicit kt: ClassTag[K], vt: ClassTag[V], ord: Ordering[K] = null): PairRDDFunctions[K, V] = RDD.rddToPairRDDFunctions(rdd) - } @deprecated("Replaced by implicit functions in the RDD companion object. This is " + "kept here only for backward compatibility.", "1.3.0") - def rddToAsyncRDDActions[T: ClassTag](rdd: RDD[T]) = RDD.rddToAsyncRDDActions(rdd) + def rddToAsyncRDDActions[T: ClassTag](rdd: RDD[T]): AsyncRDDActions[T] = + RDD.rddToAsyncRDDActions(rdd) @deprecated("Replaced by implicit functions in the RDD companion object. This is " + "kept here only for backward compatibility.", "1.3.0") def rddToSequenceFileRDDFunctions[K <% Writable: ClassTag, V <% Writable: ClassTag]( - rdd: RDD[(K, V)]) = { + rdd: RDD[(K, V)]): SequenceFileRDDFunctions[K, V] = { val kf = implicitly[K => Writable] val vf = implicitly[V => Writable] // Set the Writable class to null and `SequenceFileRDDFunctions` will use Reflection to get it @@ -1954,16 +1957,17 @@ object SparkContext extends Logging { @deprecated("Replaced by implicit functions in the RDD companion object. This is " + "kept here only for backward compatibility.", "1.3.0") def rddToOrderedRDDFunctions[K : Ordering : ClassTag, V: ClassTag]( - rdd: RDD[(K, V)]) = + rdd: RDD[(K, V)]): OrderedRDDFunctions[K, V, (K, V)] = RDD.rddToOrderedRDDFunctions(rdd) @deprecated("Replaced by implicit functions in the RDD companion object. This is " + "kept here only for backward compatibility.", "1.3.0") - def doubleRDDToDoubleRDDFunctions(rdd: RDD[Double]) = RDD.doubleRDDToDoubleRDDFunctions(rdd) + def doubleRDDToDoubleRDDFunctions(rdd: RDD[Double]): DoubleRDDFunctions = + RDD.doubleRDDToDoubleRDDFunctions(rdd) @deprecated("Replaced by implicit functions in the RDD companion object. This is " + "kept here only for backward compatibility.", "1.3.0") - def numericRDDToDoubleRDDFunctions[T](rdd: RDD[T])(implicit num: Numeric[T]) = + def numericRDDToDoubleRDDFunctions[T](rdd: RDD[T])(implicit num: Numeric[T]): DoubleRDDFunctions = RDD.numericRDDToDoubleRDDFunctions(rdd) // The following deprecated functions have already been moved to `object WritableFactory` to @@ -2134,7 +2138,7 @@ object SparkContext extends Logging { (backend, scheduler) case LOCAL_N_REGEX(threads) => - def localCpuCount = Runtime.getRuntime.availableProcessors() + def localCpuCount: Int = Runtime.getRuntime.availableProcessors() // local[*] estimates the number of cores on the machine; local[N] uses exactly N threads. val threadCount = if (threads == "*") localCpuCount else threads.toInt if (threadCount <= 0) { @@ -2146,7 +2150,7 @@ object SparkContext extends Logging { (backend, scheduler) case LOCAL_N_FAILURES_REGEX(threads, maxFailures) => - def localCpuCount = Runtime.getRuntime.availableProcessors() + def localCpuCount: Int = Runtime.getRuntime.availableProcessors() // local[*, M] means the number of cores on the computer with M failures // local[N, M] means exactly N threads with M failures val threadCount = if (threads == "*") localCpuCount else threads.toInt diff --git a/core/src/main/scala/org/apache/spark/TaskState.scala b/core/src/main/scala/org/apache/spark/TaskState.scala index c415fe99b105e..fe19f07e32d1b 100644 --- a/core/src/main/scala/org/apache/spark/TaskState.scala +++ b/core/src/main/scala/org/apache/spark/TaskState.scala @@ -27,9 +27,9 @@ private[spark] object TaskState extends Enumeration { type TaskState = Value - def isFailed(state: TaskState) = (LOST == state) || (FAILED == state) + def isFailed(state: TaskState): Boolean = (LOST == state) || (FAILED == state) - def isFinished(state: TaskState) = FINISHED_STATES.contains(state) + def isFinished(state: TaskState): Boolean = FINISHED_STATES.contains(state) def toMesos(state: TaskState): MesosTaskState = state match { case LAUNCHING => MesosTaskState.TASK_STARTING diff --git a/core/src/main/scala/org/apache/spark/TestUtils.scala b/core/src/main/scala/org/apache/spark/TestUtils.scala index 35b324ba6f573..398ca41e16151 100644 --- a/core/src/main/scala/org/apache/spark/TestUtils.scala +++ b/core/src/main/scala/org/apache/spark/TestUtils.scala @@ -107,7 +107,7 @@ private[spark] object TestUtils { private class JavaSourceFromString(val name: String, val code: String) extends SimpleJavaFileObject(createURI(name), SOURCE) { - override def getCharContent(ignoreEncodingErrors: Boolean) = code + override def getCharContent(ignoreEncodingErrors: Boolean): String = code } /** Creates a compiled class with the given name. Class file will be placed in destDir. */ diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala index 3e9beb670f7ad..18ccd625fc8d1 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala @@ -179,7 +179,7 @@ class JavaRDD[T](val rdd: RDD[T])(implicit val classTag: ClassTag[T]) def subtract(other: JavaRDD[T], p: Partitioner): JavaRDD[T] = wrapRDD(rdd.subtract(other, p)) - override def toString = rdd.toString + override def toString: String = rdd.toString /** Assign a name to this RDD */ def setName(name: String): JavaRDD[T] = { diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala index 6d6ed693be752..3be6783bba49d 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala @@ -108,7 +108,7 @@ class JavaSparkContext(val sc: SparkContext) private[spark] val env = sc.env - def statusTracker = new JavaSparkStatusTracker(sc) + def statusTracker: JavaSparkStatusTracker = new JavaSparkStatusTracker(sc) def isLocal: java.lang.Boolean = sc.isLocal diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaUtils.scala b/core/src/main/scala/org/apache/spark/api/java/JavaUtils.scala index 71b26737b8c02..8f9647eea9e25 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaUtils.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaUtils.scala @@ -17,6 +17,8 @@ package org.apache.spark.api.java +import java.util.Map.Entry + import com.google.common.base.Optional import java.{util => ju} @@ -30,8 +32,8 @@ private[spark] object JavaUtils { } // Workaround for SPARK-3926 / SI-8911 - def mapAsSerializableJavaMap[A, B](underlying: collection.Map[A, B]) = - new SerializableMapWrapper(underlying) + def mapAsSerializableJavaMap[A, B](underlying: collection.Map[A, B]): SerializableMapWrapper[A, B] + = new SerializableMapWrapper(underlying) // Implementation is copied from scala.collection.convert.Wrappers.MapWrapper, // but implements java.io.Serializable. It can't just be subclassed to make it @@ -40,36 +42,33 @@ private[spark] object JavaUtils { class SerializableMapWrapper[A, B](underlying: collection.Map[A, B]) extends ju.AbstractMap[A, B] with java.io.Serializable { self => - override def size = underlying.size + override def size: Int = underlying.size override def get(key: AnyRef): B = try { - underlying get key.asInstanceOf[A] match { - case None => null.asInstanceOf[B] - case Some(v) => v - } + underlying.getOrElse(key.asInstanceOf[A], null.asInstanceOf[B]) } catch { case ex: ClassCastException => null.asInstanceOf[B] } override def entrySet: ju.Set[ju.Map.Entry[A, B]] = new ju.AbstractSet[ju.Map.Entry[A, B]] { - def size = self.size + override def size: Int = self.size - def iterator = new ju.Iterator[ju.Map.Entry[A, B]] { + override def iterator: ju.Iterator[ju.Map.Entry[A, B]] = new ju.Iterator[ju.Map.Entry[A, B]] { val ui = underlying.iterator var prev : Option[A] = None - def hasNext = ui.hasNext + def hasNext: Boolean = ui.hasNext - def next() = { - val (k, v) = ui.next + def next(): Entry[A, B] = { + val (k, v) = ui.next() prev = Some(k) new ju.Map.Entry[A, B] { import scala.util.hashing.byteswap32 - def getKey = k - def getValue = v - def setValue(v1 : B) = self.put(k, v1) - override def hashCode = byteswap32(k.hashCode) + (byteswap32(v.hashCode) << 16) - override def equals(other: Any) = other match { + override def getKey: A = k + override def getValue: B = v + override def setValue(v1 : B): B = self.put(k, v1) + override def hashCode: Int = byteswap32(k.hashCode) + (byteswap32(v.hashCode) << 16) + override def equals(other: Any): Boolean = other match { case e: ju.Map.Entry[_, _] => k == e.getKey && v == e.getValue case _ => false } @@ -81,7 +80,7 @@ private[spark] object JavaUtils { case Some(k) => underlying match { case mm: mutable.Map[A, _] => - mm remove k + mm.remove(k) prev = None case _ => throw new UnsupportedOperationException("remove") diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala index 4c71b69069eb3..19f4c95fcad74 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala @@ -54,9 +54,11 @@ private[spark] class PythonRDD( val bufferSize = conf.getInt("spark.buffer.size", 65536) val reuse_worker = conf.getBoolean("spark.python.worker.reuse", true) - override def getPartitions = firstParent.partitions + override def getPartitions: Array[Partition] = firstParent.partitions - override val partitioner = if (preservePartitoning) firstParent.partitioner else None + override val partitioner: Option[Partitioner] = { + if (preservePartitoning) firstParent.partitioner else None + } override def compute(split: Partition, context: TaskContext): Iterator[Array[Byte]] = { val startTime = System.currentTimeMillis @@ -92,7 +94,7 @@ private[spark] class PythonRDD( // Return an iterator that read lines from the process's stdout val stream = new DataInputStream(new BufferedInputStream(worker.getInputStream, bufferSize)) val stdoutIterator = new Iterator[Array[Byte]] { - def next(): Array[Byte] = { + override def next(): Array[Byte] = { val obj = _nextObj if (hasNext) { _nextObj = read() @@ -175,7 +177,7 @@ private[spark] class PythonRDD( var _nextObj = read() - def hasNext = _nextObj != null + override def hasNext: Boolean = _nextObj != null } new InterruptibleIterator(context, stdoutIterator) } @@ -303,11 +305,10 @@ private class PythonException(msg: String, cause: Exception) extends RuntimeExce * Form an RDD[(Array[Byte], Array[Byte])] from key-value pairs returned from Python. * This is used by PySpark's shuffle operations. */ -private class PairwiseRDD(prev: RDD[Array[Byte]]) extends - RDD[(Long, Array[Byte])](prev) { - override def getPartitions = prev.partitions - override val partitioner = prev.partitioner - override def compute(split: Partition, context: TaskContext) = +private class PairwiseRDD(prev: RDD[Array[Byte]]) extends RDD[(Long, Array[Byte])](prev) { + override def getPartitions: Array[Partition] = prev.partitions + override val partitioner: Option[Partitioner] = prev.partitioner + override def compute(split: Partition, context: TaskContext): Iterator[(Long, Array[Byte])] = prev.iterator(split, context).grouped(2).map { case Seq(a, b) => (Utils.deserializeLongValue(a), b) case x => throw new SparkException("PairwiseRDD: unexpected value: " + x) @@ -435,7 +436,7 @@ private[spark] object PythonRDD extends Logging { keyConverterClass: String, valueConverterClass: String, minSplits: Int, - batchSize: Int) = { + batchSize: Int): JavaRDD[Array[Byte]] = { val keyClass = Option(keyClassMaybeNull).getOrElse("org.apache.hadoop.io.Text") val valueClass = Option(valueClassMaybeNull).getOrElse("org.apache.hadoop.io.Text") val kc = Utils.classForName(keyClass).asInstanceOf[Class[K]] @@ -462,7 +463,7 @@ private[spark] object PythonRDD extends Logging { keyConverterClass: String, valueConverterClass: String, confAsMap: java.util.HashMap[String, String], - batchSize: Int) = { + batchSize: Int): JavaRDD[Array[Byte]] = { val mergedConf = getMergedConf(confAsMap, sc.hadoopConfiguration()) val rdd = newAPIHadoopRDDFromClassNames[K, V, F](sc, @@ -488,7 +489,7 @@ private[spark] object PythonRDD extends Logging { keyConverterClass: String, valueConverterClass: String, confAsMap: java.util.HashMap[String, String], - batchSize: Int) = { + batchSize: Int): JavaRDD[Array[Byte]] = { val conf = PythonHadoopUtil.mapToConf(confAsMap) val rdd = newAPIHadoopRDDFromClassNames[K, V, F](sc, @@ -505,7 +506,7 @@ private[spark] object PythonRDD extends Logging { inputFormatClass: String, keyClass: String, valueClass: String, - conf: Configuration) = { + conf: Configuration): RDD[(K, V)] = { val kc = Utils.classForName(keyClass).asInstanceOf[Class[K]] val vc = Utils.classForName(valueClass).asInstanceOf[Class[V]] val fc = Utils.classForName(inputFormatClass).asInstanceOf[Class[F]] @@ -531,7 +532,7 @@ private[spark] object PythonRDD extends Logging { keyConverterClass: String, valueConverterClass: String, confAsMap: java.util.HashMap[String, String], - batchSize: Int) = { + batchSize: Int): JavaRDD[Array[Byte]] = { val mergedConf = getMergedConf(confAsMap, sc.hadoopConfiguration()) val rdd = hadoopRDDFromClassNames[K, V, F](sc, @@ -557,7 +558,7 @@ private[spark] object PythonRDD extends Logging { keyConverterClass: String, valueConverterClass: String, confAsMap: java.util.HashMap[String, String], - batchSize: Int) = { + batchSize: Int): JavaRDD[Array[Byte]] = { val conf = PythonHadoopUtil.mapToConf(confAsMap) val rdd = hadoopRDDFromClassNames[K, V, F](sc, @@ -686,7 +687,7 @@ private[spark] object PythonRDD extends Logging { pyRDD: JavaRDD[Array[Byte]], batchSerialized: Boolean, path: String, - compressionCodecClass: String) = { + compressionCodecClass: String): Unit = { saveAsHadoopFile( pyRDD, batchSerialized, path, "org.apache.hadoop.mapred.SequenceFileOutputFormat", null, null, null, null, new java.util.HashMap(), compressionCodecClass) @@ -711,7 +712,7 @@ private[spark] object PythonRDD extends Logging { keyConverterClass: String, valueConverterClass: String, confAsMap: java.util.HashMap[String, String], - compressionCodecClass: String) = { + compressionCodecClass: String): Unit = { val rdd = SerDeUtil.pythonToPairRDD(pyRDD, batchSerialized) val (kc, vc) = getKeyValueTypes(keyClass, valueClass).getOrElse( inferKeyValueTypes(rdd, keyConverterClass, valueConverterClass)) @@ -741,7 +742,7 @@ private[spark] object PythonRDD extends Logging { valueClass: String, keyConverterClass: String, valueConverterClass: String, - confAsMap: java.util.HashMap[String, String]) = { + confAsMap: java.util.HashMap[String, String]): Unit = { val rdd = SerDeUtil.pythonToPairRDD(pyRDD, batchSerialized) val (kc, vc) = getKeyValueTypes(keyClass, valueClass).getOrElse( inferKeyValueTypes(rdd, keyConverterClass, valueConverterClass)) @@ -766,7 +767,7 @@ private[spark] object PythonRDD extends Logging { confAsMap: java.util.HashMap[String, String], keyConverterClass: String, valueConverterClass: String, - useNewAPI: Boolean) = { + useNewAPI: Boolean): Unit = { val conf = PythonHadoopUtil.mapToConf(confAsMap) val converted = convertRDD(SerDeUtil.pythonToPairRDD(pyRDD, batchSerialized), keyConverterClass, valueConverterClass, new JavaToWritableConverter) diff --git a/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala b/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala index fb52a960e0765..257491e90dd66 100644 --- a/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala +++ b/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala @@ -84,7 +84,7 @@ private[spark] object SerDeUtil extends Logging { private var initialized = false // This should be called before trying to unpickle array.array from Python // In cluster mode, this should be put in closure - def initialize() = { + def initialize(): Unit = { synchronized{ if (!initialized) { Unpickler.registerConstructor("array", "array", new ArrayConstructor()) diff --git a/core/src/main/scala/org/apache/spark/api/python/WriteInputFormatTestDataGenerator.scala b/core/src/main/scala/org/apache/spark/api/python/WriteInputFormatTestDataGenerator.scala index cf289fb3ae39f..8f30ff9202c83 100644 --- a/core/src/main/scala/org/apache/spark/api/python/WriteInputFormatTestDataGenerator.scala +++ b/core/src/main/scala/org/apache/spark/api/python/WriteInputFormatTestDataGenerator.scala @@ -18,38 +18,37 @@ package org.apache.spark.api.python import java.io.{DataOutput, DataInput} +import java.{util => ju} import com.google.common.base.Charsets.UTF_8 import org.apache.hadoop.io._ import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat + +import org.apache.spark.SparkException import org.apache.spark.api.java.JavaSparkContext -import org.apache.spark.{SparkContext, SparkException} /** * A class to test Pyrolite serialization on the Scala side, that will be deserialized * in Python - * @param str - * @param int - * @param double */ case class TestWritable(var str: String, var int: Int, var double: Double) extends Writable { def this() = this("", 0, 0.0) - def getStr = str + def getStr: String = str def setStr(str: String) { this.str = str } - def getInt = int + def getInt: Int = int def setInt(int: Int) { this.int = int } - def getDouble = double + def getDouble: Double = double def setDouble(double: Double) { this.double = double } - def write(out: DataOutput) = { + def write(out: DataOutput): Unit = { out.writeUTF(str) out.writeInt(int) out.writeDouble(double) } - def readFields(in: DataInput) = { + def readFields(in: DataInput): Unit = { str = in.readUTF() int = in.readInt() double = in.readDouble() @@ -57,28 +56,28 @@ case class TestWritable(var str: String, var int: Int, var double: Double) exten } private[python] class TestInputKeyConverter extends Converter[Any, Any] { - override def convert(obj: Any) = { + override def convert(obj: Any): Char = { obj.asInstanceOf[IntWritable].get().toChar } } private[python] class TestInputValueConverter extends Converter[Any, Any] { import collection.JavaConversions._ - override def convert(obj: Any) = { + override def convert(obj: Any): ju.List[Double] = { val m = obj.asInstanceOf[MapWritable] seqAsJavaList(m.keySet.map(w => w.asInstanceOf[DoubleWritable].get()).toSeq) } } private[python] class TestOutputKeyConverter extends Converter[Any, Any] { - override def convert(obj: Any) = { + override def convert(obj: Any): Text = { new Text(obj.asInstanceOf[Int].toString) } } private[python] class TestOutputValueConverter extends Converter[Any, Any] { import collection.JavaConversions._ - override def convert(obj: Any) = { + override def convert(obj: Any): DoubleWritable = { new DoubleWritable(obj.asInstanceOf[java.util.Map[Double, _]].keySet().head) } } @@ -86,7 +85,7 @@ private[python] class TestOutputValueConverter extends Converter[Any, Any] { private[python] class DoubleArrayWritable extends ArrayWritable(classOf[DoubleWritable]) private[python] class DoubleArrayToWritableConverter extends Converter[Any, Writable] { - override def convert(obj: Any) = obj match { + override def convert(obj: Any): DoubleArrayWritable = obj match { case arr if arr.getClass.isArray && arr.getClass.getComponentType == classOf[Double] => val daw = new DoubleArrayWritable daw.set(arr.asInstanceOf[Array[Double]].map(new DoubleWritable(_))) diff --git a/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala index a5ea478f231d7..12d79f6ed311b 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala @@ -146,5 +146,5 @@ abstract class Broadcast[T: ClassTag](val id: Long) extends Serializable with Lo } } - override def toString = "Broadcast(" + id + ")" + override def toString: String = "Broadcast(" + id + ")" } diff --git a/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala b/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala index 8f8a0b11f9f2e..685313ac009ba 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/BroadcastManager.scala @@ -58,7 +58,7 @@ private[spark] class BroadcastManager( private val nextBroadcastId = new AtomicLong(0) - def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean) = { + def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean): Broadcast[T] = { broadcastFactory.newBroadcast[T](value_, isLocal, nextBroadcastId.getAndIncrement()) } diff --git a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala index 1444c0dd3d2d6..74ccfa6d3c9a3 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala @@ -160,7 +160,7 @@ private[broadcast] object HttpBroadcast extends Logging { logInfo("Broadcast server started at " + serverUri) } - def getFile(id: Long) = new File(broadcastDir, BroadcastBlockId(id).name) + def getFile(id: Long): File = new File(broadcastDir, BroadcastBlockId(id).name) private def write(id: Long, value: Any) { val file = getFile(id) @@ -222,7 +222,7 @@ private[broadcast] object HttpBroadcast extends Logging { * If removeFromDriver is true, also remove these persisted blocks on the driver * and delete the associated broadcast file. */ - def unpersist(id: Long, removeFromDriver: Boolean, blocking: Boolean) = synchronized { + def unpersist(id: Long, removeFromDriver: Boolean, blocking: Boolean): Unit = synchronized { SparkEnv.get.blockManager.master.removeBroadcast(id, removeFromDriver, blocking) if (removeFromDriver) { val file = getFile(id) diff --git a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcastFactory.scala b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcastFactory.scala index c7ef02d572a19..cf3ae36f27949 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcastFactory.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcastFactory.scala @@ -31,7 +31,7 @@ class HttpBroadcastFactory extends BroadcastFactory { HttpBroadcast.initialize(isDriver, conf, securityMgr) } - override def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean, id: Long) = + override def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean, id: Long): Broadcast[T] = new HttpBroadcast[T](value_, isLocal, id) override def stop() { HttpBroadcast.stop() } diff --git a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala index 94142d33369c7..23b02e60338fb 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala @@ -222,7 +222,7 @@ private object TorrentBroadcast extends Logging { * Remove all persisted blocks associated with this torrent broadcast on the executors. * If removeFromDriver is true, also remove these persisted blocks on the driver. */ - def unpersist(id: Long, removeFromDriver: Boolean, blocking: Boolean) = { + def unpersist(id: Long, removeFromDriver: Boolean, blocking: Boolean): Unit = { logDebug(s"Unpersisting TorrentBroadcast $id") SparkEnv.get.blockManager.master.removeBroadcast(id, removeFromDriver, blocking) } diff --git a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcastFactory.scala b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcastFactory.scala index fb024c12094f2..96d8dd79908c8 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcastFactory.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcastFactory.scala @@ -30,7 +30,7 @@ class TorrentBroadcastFactory extends BroadcastFactory { override def initialize(isDriver: Boolean, conf: SparkConf, securityMgr: SecurityManager) { } - override def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean, id: Long) = { + override def newBroadcast[T: ClassTag](value_ : T, isLocal: Boolean, id: Long): Broadcast[T] = { new TorrentBroadcast[T](value_, id) } diff --git a/core/src/main/scala/org/apache/spark/deploy/Client.scala b/core/src/main/scala/org/apache/spark/deploy/Client.scala index 237d26fc6bd0e..65238af2caa24 100644 --- a/core/src/main/scala/org/apache/spark/deploy/Client.scala +++ b/core/src/main/scala/org/apache/spark/deploy/Client.scala @@ -38,7 +38,7 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) var masterActor: ActorSelection = _ val timeout = AkkaUtils.askTimeout(conf) - override def preStart() = { + override def preStart(): Unit = { masterActor = context.actorSelection( Master.toAkkaUrl(driverArgs.master, AkkaUtils.protocol(context.system))) @@ -118,7 +118,7 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) } } - override def receiveWithLogging = { + override def receiveWithLogging: PartialFunction[Any, Unit] = { case SubmitDriverResponse(success, driverId, message) => println(message) diff --git a/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala b/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala index 53bc62aff7395..5cbac787dceeb 100644 --- a/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala @@ -42,7 +42,7 @@ private[deploy] class ClientArguments(args: Array[String]) { var memory: Int = DEFAULT_MEMORY var cores: Int = DEFAULT_CORES private var _driverOptions = ListBuffer[String]() - def driverOptions = _driverOptions.toSeq + def driverOptions: Seq[String] = _driverOptions.toSeq // kill parameters var driverId: String = "" diff --git a/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala b/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala index 7f600d89604a2..0997507d016f5 100644 --- a/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala @@ -162,7 +162,7 @@ private[deploy] object DeployMessages { Utils.checkHost(host, "Required hostname") assert (port > 0) - def uri = "spark://" + host + ":" + port + def uri: String = "spark://" + host + ":" + port def restUri: Option[String] = restPort.map { p => "spark://" + host + ":" + p } } diff --git a/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala b/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala index 5668b53fc6f4f..a7c89276a045e 100644 --- a/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala +++ b/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala @@ -426,7 +426,7 @@ private object SparkDocker { } private class DockerId(val id: String) { - override def toString = id + override def toString: String = id } private object Docker extends Logging { diff --git a/core/src/main/scala/org/apache/spark/deploy/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/deploy/JsonProtocol.scala index 458a7c3a455de..dfc5b97e6a6c8 100644 --- a/core/src/main/scala/org/apache/spark/deploy/JsonProtocol.scala +++ b/core/src/main/scala/org/apache/spark/deploy/JsonProtocol.scala @@ -17,6 +17,7 @@ package org.apache.spark.deploy +import org.json4s.JsonAST.JObject import org.json4s.JsonDSL._ import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, WorkerStateResponse} @@ -24,7 +25,7 @@ import org.apache.spark.deploy.master.{ApplicationInfo, DriverInfo, WorkerInfo} import org.apache.spark.deploy.worker.ExecutorRunner private[deploy] object JsonProtocol { - def writeWorkerInfo(obj: WorkerInfo) = { + def writeWorkerInfo(obj: WorkerInfo): JObject = { ("id" -> obj.id) ~ ("host" -> obj.host) ~ ("port" -> obj.port) ~ @@ -39,7 +40,7 @@ private[deploy] object JsonProtocol { ("lastheartbeat" -> obj.lastHeartbeat) } - def writeApplicationInfo(obj: ApplicationInfo) = { + def writeApplicationInfo(obj: ApplicationInfo): JObject = { ("starttime" -> obj.startTime) ~ ("id" -> obj.id) ~ ("name" -> obj.desc.name) ~ @@ -51,7 +52,7 @@ private[deploy] object JsonProtocol { ("duration" -> obj.duration) } - def writeApplicationDescription(obj: ApplicationDescription) = { + def writeApplicationDescription(obj: ApplicationDescription): JObject = { ("name" -> obj.name) ~ ("cores" -> obj.maxCores) ~ ("memoryperslave" -> obj.memoryPerSlave) ~ @@ -59,14 +60,14 @@ private[deploy] object JsonProtocol { ("command" -> obj.command.toString) } - def writeExecutorRunner(obj: ExecutorRunner) = { + def writeExecutorRunner(obj: ExecutorRunner): JObject = { ("id" -> obj.execId) ~ ("memory" -> obj.memory) ~ ("appid" -> obj.appId) ~ ("appdesc" -> writeApplicationDescription(obj.appDesc)) } - def writeDriverInfo(obj: DriverInfo) = { + def writeDriverInfo(obj: DriverInfo): JObject = { ("id" -> obj.id) ~ ("starttime" -> obj.startTime.toString) ~ ("state" -> obj.state.toString) ~ @@ -74,7 +75,7 @@ private[deploy] object JsonProtocol { ("memory" -> obj.desc.mem) } - def writeMasterState(obj: MasterStateResponse) = { + def writeMasterState(obj: MasterStateResponse): JObject = { ("url" -> obj.uri) ~ ("workers" -> obj.workers.toList.map(writeWorkerInfo)) ~ ("cores" -> obj.workers.map(_.cores).sum) ~ @@ -87,7 +88,7 @@ private[deploy] object JsonProtocol { ("status" -> obj.status.toString) } - def writeWorkerState(obj: WorkerStateResponse) = { + def writeWorkerState(obj: WorkerStateResponse): JObject = { ("id" -> obj.workerId) ~ ("masterurl" -> obj.masterUrl) ~ ("masterwebuiurl" -> obj.masterWebUiUrl) ~ diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala index e0a32fb65cd51..c2568eb4b60ac 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala @@ -193,7 +193,7 @@ class SparkHadoopUtil extends Logging { * that file. */ def listLeafStatuses(fs: FileSystem, basePath: Path): Seq[FileStatus] = { - def recurse(path: Path) = { + def recurse(path: Path): Array[FileStatus] = { val (directories, leaves) = fs.listStatus(path).partition(_.isDir) leaves ++ directories.flatMap(f => listLeafStatuses(fs, f.getPath)) } diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala index 4f506be63fe59..660307d19eab4 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -777,7 +777,7 @@ private[deploy] object SparkSubmitUtils { } /** A nice function to use in tests as well. Values are dummy strings. */ - def getModuleDescriptor = DefaultModuleDescriptor.newDefaultInstance( + def getModuleDescriptor: DefaultModuleDescriptor = DefaultModuleDescriptor.newDefaultInstance( ModuleRevisionId.newInstance("org.apache.spark", "spark-submit-parent", "1.0")) /** diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala index 2250d5a28e4ef..6eb73c43470a5 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala @@ -252,7 +252,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S master.startsWith("spark://") && deployMode == "cluster" } - override def toString = { + override def toString: String = { s"""Parsed arguments: | master $master | deployMode $deployMode diff --git a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala index 2d24083a77b73..3b729725257ef 100644 --- a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala +++ b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala @@ -116,7 +116,7 @@ private[spark] class AppClient( masterAkkaUrls.map(AddressFromURIString(_).hostPort).contains(remoteUrl.hostPort) } - override def receiveWithLogging = { + override def receiveWithLogging: PartialFunction[Any, Unit] = { case RegisteredApplication(appId_, masterUrl) => appId = appId_ registered = true diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala index db7c499661319..80c9c13ddec1e 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala @@ -93,7 +93,7 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis */ private def getRunner(operateFun: () => Unit): Runnable = { new Runnable() { - override def run() = Utils.tryOrExit { + override def run(): Unit = Utils.tryOrExit { operateFun() } } @@ -141,7 +141,7 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis } } - override def getListing() = applications.values + override def getListing(): Iterable[FsApplicationHistoryInfo] = applications.values override def getAppUI(appId: String): Option[SparkUI] = { try { diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala index af483d560b33e..72f6048239297 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala @@ -61,7 +61,7 @@ class HistoryServer( private val appCache = CacheBuilder.newBuilder() .maximumSize(retainedApplications) .removalListener(new RemovalListener[String, SparkUI] { - override def onRemoval(rm: RemovalNotification[String, SparkUI]) = { + override def onRemoval(rm: RemovalNotification[String, SparkUI]): Unit = { detachSparkUI(rm.getValue()) } }) @@ -149,14 +149,14 @@ class HistoryServer( * * @return List of all known applications. */ - def getApplicationList() = provider.getListing() + def getApplicationList(): Iterable[ApplicationHistoryInfo] = provider.getListing() /** * Returns the provider configuration to show in the listing page. * * @return A map with the provider's configuration. */ - def getProviderConfig() = provider.getConfig() + def getProviderConfig(): Map[String, String] = provider.getConfig() } @@ -195,9 +195,7 @@ object HistoryServer extends Logging { server.bind() Runtime.getRuntime().addShutdownHook(new Thread("HistoryServerStopper") { - override def run() = { - server.stop() - } + override def run(): Unit = server.stop() }) // Wait until the end of the world... or if the HistoryServer process is manually stopped diff --git a/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala index d2d30bfd7fcba..32499b3a784a1 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala @@ -48,7 +48,7 @@ private[master] class FileSystemPersistenceEngine( new File(dir + File.separator + name).delete() } - override def read[T: ClassTag](prefix: String) = { + override def read[T: ClassTag](prefix: String): Seq[T] = { val files = new File(dir).listFiles().filter(_.getName.startsWith(prefix)) files.map(deserializeFromFile[T]) } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala index 1b42121c8db05..80506621f4d24 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala @@ -204,7 +204,7 @@ private[master] class Master( self ! RevokedLeadership } - override def receiveWithLogging = { + override def receiveWithLogging: PartialFunction[Any, Unit] = { case ElectedLeader => { val (storedApps, storedDrivers, storedWorkers) = persistenceEngine.readPersistedData() state = if (storedApps.isEmpty && storedDrivers.isEmpty && storedWorkers.isEmpty) { diff --git a/core/src/main/scala/org/apache/spark/deploy/master/RecoveryModeFactory.scala b/core/src/main/scala/org/apache/spark/deploy/master/RecoveryModeFactory.scala index 1583bf1f60032..351db8fab2041 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/RecoveryModeFactory.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/RecoveryModeFactory.scala @@ -51,20 +51,27 @@ abstract class StandaloneRecoveryModeFactory(conf: SparkConf, serializer: Serial */ private[master] class FileSystemRecoveryModeFactory(conf: SparkConf, serializer: Serialization) extends StandaloneRecoveryModeFactory(conf, serializer) with Logging { + val RECOVERY_DIR = conf.get("spark.deploy.recoveryDirectory", "") - def createPersistenceEngine() = { + def createPersistenceEngine(): PersistenceEngine = { logInfo("Persisting recovery state to directory: " + RECOVERY_DIR) new FileSystemPersistenceEngine(RECOVERY_DIR, serializer) } - def createLeaderElectionAgent(master: LeaderElectable) = new MonarchyLeaderAgent(master) + def createLeaderElectionAgent(master: LeaderElectable): LeaderElectionAgent = { + new MonarchyLeaderAgent(master) + } } private[master] class ZooKeeperRecoveryModeFactory(conf: SparkConf, serializer: Serialization) extends StandaloneRecoveryModeFactory(conf, serializer) { - def createPersistenceEngine() = new ZooKeeperPersistenceEngine(conf, serializer) - def createLeaderElectionAgent(master: LeaderElectable) = + def createPersistenceEngine(): PersistenceEngine = { + new ZooKeeperPersistenceEngine(conf, serializer) + } + + def createLeaderElectionAgent(master: LeaderElectable): LeaderElectionAgent = { new ZooKeeperLeaderElectionAgent(master, conf) + } } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala index e94aae93e4495..9b3d48c6edc84 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala @@ -104,7 +104,7 @@ private[spark] class WorkerInfo( "http://" + this.publicAddress + ":" + this.webUiPort } - def setState(state: WorkerState.Value) = { + def setState(state: WorkerState.Value): Unit = { this.state = state } } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala index 1ac6677ad2b6d..a285783f72000 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala @@ -46,7 +46,7 @@ private[master] class ZooKeeperPersistenceEngine(conf: SparkConf, val serializat zk.delete().forPath(WORKING_DIR + "/" + name) } - override def read[T: ClassTag](prefix: String) = { + override def read[T: ClassTag](prefix: String): Seq[T] = { val file = zk.getChildren.forPath(WORKING_DIR).filter(_.startsWith(prefix)) file.map(deserializeFromFile[T]).flatten } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala index dee2e4a447c6e..46509e39c0f23 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala @@ -95,7 +95,7 @@ private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") { // For now we only show driver information if the user has submitted drivers to the cluster. // This is until we integrate the notion of drivers and applications in the UI. - def hasDrivers = activeDrivers.length > 0 || completedDrivers.length > 0 + def hasDrivers: Boolean = activeDrivers.length > 0 || completedDrivers.length > 0 val content =
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala index 27a9eabb1ede7..e0948e16ef354 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala @@ -56,8 +56,14 @@ private[deploy] class DriverRunner( private var finalExitCode: Option[Int] = None // Decoupled for testing - def setClock(_clock: Clock) = clock = _clock - def setSleeper(_sleeper: Sleeper) = sleeper = _sleeper + def setClock(_clock: Clock): Unit = { + clock = _clock + } + + def setSleeper(_sleeper: Sleeper): Unit = { + sleeper = _sleeper + } + private var clock: Clock = new SystemClock() private var sleeper = new Sleeper { def sleep(seconds: Int): Unit = (0 until seconds).takeWhile(f => {Thread.sleep(1000); !killed}) @@ -155,7 +161,7 @@ private[deploy] class DriverRunner( private def launchDriver(builder: ProcessBuilder, baseDir: File, supervise: Boolean) { builder.directory(baseDir) - def initialize(process: Process) = { + def initialize(process: Process): Unit = { // Redirect stdout and stderr to files val stdout = new File(baseDir, "stdout") CommandUtils.redirectStream(process.getInputStream, stdout) @@ -169,8 +175,8 @@ private[deploy] class DriverRunner( runCommandWithRetry(ProcessBuilderLike(builder), initialize, supervise) } - def runCommandWithRetry(command: ProcessBuilderLike, initialize: Process => Unit, - supervise: Boolean) { + def runCommandWithRetry( + command: ProcessBuilderLike, initialize: Process => Unit, supervise: Boolean): Unit = { // Time to wait between submission retries. var waitSeconds = 1 // A run of this many seconds resets the exponential back-off. @@ -216,8 +222,8 @@ private[deploy] trait ProcessBuilderLike { } private[deploy] object ProcessBuilderLike { - def apply(processBuilder: ProcessBuilder) = new ProcessBuilderLike { - def start() = processBuilder.start() - def command = processBuilder.command() + def apply(processBuilder: ProcessBuilder): ProcessBuilderLike = new ProcessBuilderLike { + override def start(): Process = processBuilder.start() + override def command: Seq[String] = processBuilder.command() } } diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala index c1b0a295f9f74..c4c24a7866aa3 100755 --- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala @@ -275,7 +275,7 @@ private[worker] class Worker( } } - override def receiveWithLogging = { + override def receiveWithLogging: PartialFunction[Any, Unit] = { case RegisteredWorker(masterUrl, masterWebUiUrl) => logInfo("Successfully registered with master " + masterUrl) registered = true diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala index 09d866fb0cd90..e0790274d7d3e 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala @@ -50,7 +50,7 @@ private[spark] class WorkerWatcher(workerUrl: String) private def exitNonZero() = if (isTesting) isShutDown = true else System.exit(-1) - override def receiveWithLogging = { + override def receiveWithLogging: PartialFunction[Any, Unit] = { case AssociatedEvent(localAddress, remoteAddress, inbound) if isWorker(remoteAddress) => logInfo(s"Successfully connected to $workerUrl") diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala index dd19e4947db1e..b5205d4e997ae 100644 --- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala +++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala @@ -62,7 +62,7 @@ private[spark] class CoarseGrainedExecutorBackend( .map(e => (e._1.substring(prefix.length).toLowerCase, e._2)) } - override def receiveWithLogging = { + override def receiveWithLogging: PartialFunction[Any, Unit] = { case RegisteredExecutor => logInfo("Successfully registered with driver") val (hostname, _) = Utils.parseHostPort(hostPort) diff --git a/core/src/main/scala/org/apache/spark/executor/ExecutorActor.scala b/core/src/main/scala/org/apache/spark/executor/ExecutorActor.scala index 41925f7e97e84..3e47d13f7545d 100644 --- a/core/src/main/scala/org/apache/spark/executor/ExecutorActor.scala +++ b/core/src/main/scala/org/apache/spark/executor/ExecutorActor.scala @@ -33,7 +33,7 @@ private[spark] case object TriggerThreadDump private[spark] class ExecutorActor(executorId: String) extends Actor with ActorLogReceive with Logging { - override def receiveWithLogging = { + override def receiveWithLogging: PartialFunction[Any, Unit] = { case TriggerThreadDump => sender ! Utils.getThreadDump() } diff --git a/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala b/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala index 07b152651dedf..06152f16ae618 100644 --- a/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala +++ b/core/src/main/scala/org/apache/spark/executor/TaskMetrics.scala @@ -17,13 +17,10 @@ package org.apache.spark.executor -import java.util.concurrent.atomic.AtomicLong - -import org.apache.spark.executor.DataReadMethod.DataReadMethod - import scala.collection.mutable.ArrayBuffer import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.executor.DataReadMethod.DataReadMethod import org.apache.spark.storage.{BlockId, BlockStatus} /** @@ -44,14 +41,14 @@ class TaskMetrics extends Serializable { * Host's name the task runs on */ private var _hostname: String = _ - def hostname = _hostname + def hostname: String = _hostname private[spark] def setHostname(value: String) = _hostname = value /** * Time taken on the executor to deserialize this task */ private var _executorDeserializeTime: Long = _ - def executorDeserializeTime = _executorDeserializeTime + def executorDeserializeTime: Long = _executorDeserializeTime private[spark] def setExecutorDeserializeTime(value: Long) = _executorDeserializeTime = value @@ -59,14 +56,14 @@ class TaskMetrics extends Serializable { * Time the executor spends actually running the task (including fetching shuffle data) */ private var _executorRunTime: Long = _ - def executorRunTime = _executorRunTime + def executorRunTime: Long = _executorRunTime private[spark] def setExecutorRunTime(value: Long) = _executorRunTime = value /** * The number of bytes this task transmitted back to the driver as the TaskResult */ private var _resultSize: Long = _ - def resultSize = _resultSize + def resultSize: Long = _resultSize private[spark] def setResultSize(value: Long) = _resultSize = value @@ -74,31 +71,31 @@ class TaskMetrics extends Serializable { * Amount of time the JVM spent in garbage collection while executing this task */ private var _jvmGCTime: Long = _ - def jvmGCTime = _jvmGCTime + def jvmGCTime: Long = _jvmGCTime private[spark] def setJvmGCTime(value: Long) = _jvmGCTime = value /** * Amount of time spent serializing the task result */ private var _resultSerializationTime: Long = _ - def resultSerializationTime = _resultSerializationTime + def resultSerializationTime: Long = _resultSerializationTime private[spark] def setResultSerializationTime(value: Long) = _resultSerializationTime = value /** * The number of in-memory bytes spilled by this task */ private var _memoryBytesSpilled: Long = _ - def memoryBytesSpilled = _memoryBytesSpilled - private[spark] def incMemoryBytesSpilled(value: Long) = _memoryBytesSpilled += value - private[spark] def decMemoryBytesSpilled(value: Long) = _memoryBytesSpilled -= value + def memoryBytesSpilled: Long = _memoryBytesSpilled + private[spark] def incMemoryBytesSpilled(value: Long): Unit = _memoryBytesSpilled += value + private[spark] def decMemoryBytesSpilled(value: Long): Unit = _memoryBytesSpilled -= value /** * The number of on-disk bytes spilled by this task */ private var _diskBytesSpilled: Long = _ - def diskBytesSpilled = _diskBytesSpilled - def incDiskBytesSpilled(value: Long) = _diskBytesSpilled += value - def decDiskBytesSpilled(value: Long) = _diskBytesSpilled -= value + def diskBytesSpilled: Long = _diskBytesSpilled + def incDiskBytesSpilled(value: Long): Unit = _diskBytesSpilled += value + def decDiskBytesSpilled(value: Long): Unit = _diskBytesSpilled -= value /** * If this task reads from a HadoopRDD or from persisted data, metrics on how much data was read @@ -106,7 +103,7 @@ class TaskMetrics extends Serializable { */ private var _inputMetrics: Option[InputMetrics] = None - def inputMetrics = _inputMetrics + def inputMetrics: Option[InputMetrics] = _inputMetrics /** * This should only be used when recreating TaskMetrics, not when updating input metrics in @@ -128,7 +125,7 @@ class TaskMetrics extends Serializable { */ private var _shuffleReadMetrics: Option[ShuffleReadMetrics] = None - def shuffleReadMetrics = _shuffleReadMetrics + def shuffleReadMetrics: Option[ShuffleReadMetrics] = _shuffleReadMetrics /** * This should only be used when recreating TaskMetrics, not when updating read metrics in @@ -177,17 +174,18 @@ class TaskMetrics extends Serializable { * Once https://issues.apache.org/jira/browse/SPARK-5225 is addressed, * we can store all the different inputMetrics (one per readMethod). */ - private[spark] def getInputMetricsForReadMethod( - readMethod: DataReadMethod): InputMetrics = synchronized { - _inputMetrics match { - case None => - val metrics = new InputMetrics(readMethod) - _inputMetrics = Some(metrics) - metrics - case Some(metrics @ InputMetrics(method)) if method == readMethod => - metrics - case Some(InputMetrics(method)) => - new InputMetrics(readMethod) + private[spark] def getInputMetricsForReadMethod(readMethod: DataReadMethod): InputMetrics = { + synchronized { + _inputMetrics match { + case None => + val metrics = new InputMetrics(readMethod) + _inputMetrics = Some(metrics) + metrics + case Some(metrics @ InputMetrics(method)) if method == readMethod => + metrics + case Some(InputMetrics(method)) => + new InputMetrics(readMethod) + } } } @@ -256,14 +254,14 @@ case class InputMetrics(readMethod: DataReadMethod.Value) { */ private var _bytesRead: Long = _ def bytesRead: Long = _bytesRead - def incBytesRead(bytes: Long) = _bytesRead += bytes + def incBytesRead(bytes: Long): Unit = _bytesRead += bytes /** * Total records read. */ private var _recordsRead: Long = _ def recordsRead: Long = _recordsRead - def incRecordsRead(records: Long) = _recordsRead += records + def incRecordsRead(records: Long): Unit = _recordsRead += records /** * Invoke the bytesReadCallback and mutate bytesRead. @@ -293,15 +291,15 @@ case class OutputMetrics(writeMethod: DataWriteMethod.Value) { * Total bytes written */ private var _bytesWritten: Long = _ - def bytesWritten = _bytesWritten - private[spark] def setBytesWritten(value : Long) = _bytesWritten = value + def bytesWritten: Long = _bytesWritten + private[spark] def setBytesWritten(value : Long): Unit = _bytesWritten = value /** * Total records written */ private var _recordsWritten: Long = 0L - def recordsWritten = _recordsWritten - private[spark] def setRecordsWritten(value: Long) = _recordsWritten = value + def recordsWritten: Long = _recordsWritten + private[spark] def setRecordsWritten(value: Long): Unit = _recordsWritten = value } /** @@ -314,7 +312,7 @@ class ShuffleReadMetrics extends Serializable { * Number of remote blocks fetched in this shuffle by this task */ private var _remoteBlocksFetched: Int = _ - def remoteBlocksFetched = _remoteBlocksFetched + def remoteBlocksFetched: Int = _remoteBlocksFetched private[spark] def incRemoteBlocksFetched(value: Int) = _remoteBlocksFetched += value private[spark] def decRemoteBlocksFetched(value: Int) = _remoteBlocksFetched -= value @@ -322,7 +320,7 @@ class ShuffleReadMetrics extends Serializable { * Number of local blocks fetched in this shuffle by this task */ private var _localBlocksFetched: Int = _ - def localBlocksFetched = _localBlocksFetched + def localBlocksFetched: Int = _localBlocksFetched private[spark] def incLocalBlocksFetched(value: Int) = _localBlocksFetched += value private[spark] def decLocalBlocksFetched(value: Int) = _localBlocksFetched -= value @@ -332,7 +330,7 @@ class ShuffleReadMetrics extends Serializable { * still not finished processing block A, it is not considered to be blocking on block B. */ private var _fetchWaitTime: Long = _ - def fetchWaitTime = _fetchWaitTime + def fetchWaitTime: Long = _fetchWaitTime private[spark] def incFetchWaitTime(value: Long) = _fetchWaitTime += value private[spark] def decFetchWaitTime(value: Long) = _fetchWaitTime -= value @@ -340,7 +338,7 @@ class ShuffleReadMetrics extends Serializable { * Total number of remote bytes read from the shuffle by this task */ private var _remoteBytesRead: Long = _ - def remoteBytesRead = _remoteBytesRead + def remoteBytesRead: Long = _remoteBytesRead private[spark] def incRemoteBytesRead(value: Long) = _remoteBytesRead += value private[spark] def decRemoteBytesRead(value: Long) = _remoteBytesRead -= value @@ -348,24 +346,24 @@ class ShuffleReadMetrics extends Serializable { * Shuffle data that was read from the local disk (as opposed to from a remote executor). */ private var _localBytesRead: Long = _ - def localBytesRead = _localBytesRead + def localBytesRead: Long = _localBytesRead private[spark] def incLocalBytesRead(value: Long) = _localBytesRead += value /** * Total bytes fetched in the shuffle by this task (both remote and local). */ - def totalBytesRead = _remoteBytesRead + _localBytesRead + def totalBytesRead: Long = _remoteBytesRead + _localBytesRead /** * Number of blocks fetched in this shuffle by this task (remote or local) */ - def totalBlocksFetched = _remoteBlocksFetched + _localBlocksFetched + def totalBlocksFetched: Int = _remoteBlocksFetched + _localBlocksFetched /** * Total number of records read from the shuffle by this task */ private var _recordsRead: Long = _ - def recordsRead = _recordsRead + def recordsRead: Long = _recordsRead private[spark] def incRecordsRead(value: Long) = _recordsRead += value private[spark] def decRecordsRead(value: Long) = _recordsRead -= value } @@ -380,7 +378,7 @@ class ShuffleWriteMetrics extends Serializable { * Number of bytes written for the shuffle by this task */ @volatile private var _shuffleBytesWritten: Long = _ - def shuffleBytesWritten = _shuffleBytesWritten + def shuffleBytesWritten: Long = _shuffleBytesWritten private[spark] def incShuffleBytesWritten(value: Long) = _shuffleBytesWritten += value private[spark] def decShuffleBytesWritten(value: Long) = _shuffleBytesWritten -= value @@ -388,7 +386,7 @@ class ShuffleWriteMetrics extends Serializable { * Time the task spent blocking on writes to disk or buffer cache, in nanoseconds */ @volatile private var _shuffleWriteTime: Long = _ - def shuffleWriteTime= _shuffleWriteTime + def shuffleWriteTime: Long = _shuffleWriteTime private[spark] def incShuffleWriteTime(value: Long) = _shuffleWriteTime += value private[spark] def decShuffleWriteTime(value: Long) = _shuffleWriteTime -= value @@ -396,7 +394,7 @@ class ShuffleWriteMetrics extends Serializable { * Total number of records written to the shuffle by this task */ @volatile private var _shuffleRecordsWritten: Long = _ - def shuffleRecordsWritten = _shuffleRecordsWritten + def shuffleRecordsWritten: Long = _shuffleRecordsWritten private[spark] def incShuffleRecordsWritten(value: Long) = _shuffleRecordsWritten += value private[spark] def decShuffleRecordsWritten(value: Long) = _shuffleRecordsWritten -= value private[spark] def setShuffleRecordsWritten(value: Long) = _shuffleRecordsWritten = value diff --git a/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala b/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala index 593a62b3e3b32..6cda7772f77bc 100644 --- a/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala +++ b/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala @@ -73,16 +73,16 @@ private[spark] abstract class StreamBasedRecordReader[T]( private var key = "" private var value: T = null.asInstanceOf[T] - override def initialize(split: InputSplit, context: TaskAttemptContext) = {} - override def close() = {} + override def initialize(split: InputSplit, context: TaskAttemptContext): Unit = {} + override def close(): Unit = {} - override def getProgress = if (processed) 1.0f else 0.0f + override def getProgress: Float = if (processed) 1.0f else 0.0f - override def getCurrentKey = key + override def getCurrentKey: String = key - override def getCurrentValue = value + override def getCurrentValue: T = value - override def nextKeyValue = { + override def nextKeyValue: Boolean = { if (!processed) { val fileIn = new PortableDataStream(split, context, index) value = parseStream(fileIn) @@ -119,7 +119,8 @@ private[spark] class StreamRecordReader( * The format for the PortableDataStream files */ private[spark] class StreamInputFormat extends StreamFileInputFormat[PortableDataStream] { - override def createRecordReader(split: InputSplit, taContext: TaskAttemptContext) = { + override def createRecordReader(split: InputSplit, taContext: TaskAttemptContext) + : CombineFileRecordReader[String, PortableDataStream] = { new CombineFileRecordReader[String, PortableDataStream]( split.asInstanceOf[CombineFileSplit], taContext, classOf[StreamRecordReader]) } @@ -204,7 +205,7 @@ class PortableDataStream( /** * Close the file (if it is currently open) */ - def close() = { + def close(): Unit = { if (isOpen) { try { fileIn.close() diff --git a/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala b/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala index 21b782edd2a9e..87c2aa481095d 100644 --- a/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala +++ b/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala @@ -52,7 +52,7 @@ trait SparkHadoopMapRedUtil { jobId: Int, isMap: Boolean, taskId: Int, - attemptId: Int) = { + attemptId: Int): TaskAttemptID = { new TaskAttemptID(jtIdentifier, jobId, isMap, taskId, attemptId) } diff --git a/core/src/main/scala/org/apache/spark/mapreduce/SparkHadoopMapReduceUtil.scala b/core/src/main/scala/org/apache/spark/mapreduce/SparkHadoopMapReduceUtil.scala index 3340673f91156..cfd20392d12f1 100644 --- a/core/src/main/scala/org/apache/spark/mapreduce/SparkHadoopMapReduceUtil.scala +++ b/core/src/main/scala/org/apache/spark/mapreduce/SparkHadoopMapReduceUtil.scala @@ -45,7 +45,7 @@ trait SparkHadoopMapReduceUtil { jobId: Int, isMap: Boolean, taskId: Int, - attemptId: Int) = { + attemptId: Int): TaskAttemptID = { val klass = Class.forName("org.apache.hadoop.mapreduce.TaskAttemptID") try { // First, attempt to use the old-style constructor that takes a boolean isMap diff --git a/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala b/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala index 345db36630fd5..9150ad35712a1 100644 --- a/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala +++ b/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala @@ -23,6 +23,7 @@ import java.util.concurrent.TimeUnit import scala.collection.mutable import com.codahale.metrics.{Metric, MetricFilter, MetricRegistry} +import org.eclipse.jetty.servlet.ServletContextHandler import org.apache.spark.{Logging, SecurityManager, SparkConf} import org.apache.spark.metrics.sink.{MetricsServlet, Sink} @@ -84,7 +85,7 @@ private[spark] class MetricsSystem private ( /** * Get any UI handlers used by this metrics system; can only be called after start(). */ - def getServletHandlers = { + def getServletHandlers: Array[ServletContextHandler] = { require(running, "Can only call getServletHandlers on a running MetricsSystem") metricsServlet.map(_.getHandlers).getOrElse(Array()) } diff --git a/core/src/main/scala/org/apache/spark/metrics/sink/MetricsServlet.scala b/core/src/main/scala/org/apache/spark/metrics/sink/MetricsServlet.scala index 2f65bc8b46609..0c2e212a33074 100644 --- a/core/src/main/scala/org/apache/spark/metrics/sink/MetricsServlet.scala +++ b/core/src/main/scala/org/apache/spark/metrics/sink/MetricsServlet.scala @@ -30,8 +30,12 @@ import org.eclipse.jetty.servlet.ServletContextHandler import org.apache.spark.SecurityManager import org.apache.spark.ui.JettyUtils._ -private[spark] class MetricsServlet(val property: Properties, val registry: MetricRegistry, - securityMgr: SecurityManager) extends Sink { +private[spark] class MetricsServlet( + val property: Properties, + val registry: MetricRegistry, + securityMgr: SecurityManager) + extends Sink { + val SERVLET_KEY_PATH = "path" val SERVLET_KEY_SAMPLE = "sample" @@ -45,10 +49,12 @@ private[spark] class MetricsServlet(val property: Properties, val registry: Metr val mapper = new ObjectMapper().registerModule( new MetricsModule(TimeUnit.SECONDS, TimeUnit.MILLISECONDS, servletShowSample)) - def getHandlers = Array[ServletContextHandler]( - createServletHandler(servletPath, - new ServletParams(request => getMetricsSnapshot(request), "text/json"), securityMgr) - ) + def getHandlers: Array[ServletContextHandler] = { + Array[ServletContextHandler]( + createServletHandler(servletPath, + new ServletParams(request => getMetricsSnapshot(request), "text/json"), securityMgr) + ) + } def getMetricsSnapshot(request: HttpServletRequest): String = { mapper.writeValueAsString(registry) diff --git a/core/src/main/scala/org/apache/spark/metrics/sink/Sink.scala b/core/src/main/scala/org/apache/spark/metrics/sink/Sink.scala index 0d83d8c425ca4..9fad4e7deacb6 100644 --- a/core/src/main/scala/org/apache/spark/metrics/sink/Sink.scala +++ b/core/src/main/scala/org/apache/spark/metrics/sink/Sink.scala @@ -18,7 +18,7 @@ package org.apache.spark.metrics.sink private[spark] trait Sink { - def start: Unit - def stop: Unit + def start(): Unit + def stop(): Unit def report(): Unit } diff --git a/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala b/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala index a1a2c00ed1542..1ba25aa74aa02 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala @@ -32,11 +32,11 @@ class BlockMessageArray(var blockMessages: Seq[BlockMessage]) def this() = this(null.asInstanceOf[Seq[BlockMessage]]) - def apply(i: Int) = blockMessages(i) + def apply(i: Int): BlockMessage = blockMessages(i) - def iterator = blockMessages.iterator + def iterator: Iterator[BlockMessage] = blockMessages.iterator - def length = blockMessages.length + def length: Int = blockMessages.length def set(bufferMessage: BufferMessage) { val startTime = System.currentTimeMillis diff --git a/core/src/main/scala/org/apache/spark/network/nio/BufferMessage.scala b/core/src/main/scala/org/apache/spark/network/nio/BufferMessage.scala index 3b245c5c7a4f3..9a9e22b0c2366 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/BufferMessage.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/BufferMessage.scala @@ -31,9 +31,9 @@ class BufferMessage(id_ : Int, val buffers: ArrayBuffer[ByteBuffer], var ackId: val initialSize = currentSize() var gotChunkForSendingOnce = false - def size = initialSize + def size: Int = initialSize - def currentSize() = { + def currentSize(): Int = { if (buffers == null || buffers.isEmpty) { 0 } else { @@ -100,11 +100,11 @@ class BufferMessage(id_ : Int, val buffers: ArrayBuffer[ByteBuffer], var ackId: buffers.foreach(_.flip) } - def hasAckId() = (ackId != 0) + def hasAckId(): Boolean = ackId != 0 - def isCompletelyReceived() = !buffers(0).hasRemaining + def isCompletelyReceived: Boolean = !buffers(0).hasRemaining - override def toString = { + override def toString: String = { if (hasAckId) { "BufferAckMessage(aid = " + ackId + ", id = " + id + ", size = " + size + ")" } else { diff --git a/core/src/main/scala/org/apache/spark/network/nio/Connection.scala b/core/src/main/scala/org/apache/spark/network/nio/Connection.scala index c2d9578be7ebb..04eb2bf9ba4ab 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/Connection.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/Connection.scala @@ -101,9 +101,11 @@ abstract class Connection(val channel: SocketChannel, val selector: Selector, socketRemoteConnectionManagerId } - def key() = channel.keyFor(selector) + def key(): SelectionKey = channel.keyFor(selector) - def getRemoteAddress() = channel.socket.getRemoteSocketAddress().asInstanceOf[InetSocketAddress] + def getRemoteAddress(): InetSocketAddress = { + channel.socket.getRemoteSocketAddress().asInstanceOf[InetSocketAddress] + } // Returns whether we have to register for further reads or not. def read(): Boolean = { @@ -280,7 +282,7 @@ class SendingConnection(val address: InetSocketAddress, selector_ : Selector, /* channel.socket.setSendBufferSize(256 * 1024) */ - override def getRemoteAddress() = address + override def getRemoteAddress(): InetSocketAddress = address val DEFAULT_INTEREST = SelectionKey.OP_READ diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionId.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionId.scala index 764dc5e5503ed..b3b281ff465f1 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionId.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/ConnectionId.scala @@ -18,7 +18,9 @@ package org.apache.spark.network.nio private[nio] case class ConnectionId(connectionManagerId: ConnectionManagerId, uniqId: Int) { - override def toString = connectionManagerId.host + "_" + connectionManagerId.port + "_" + uniqId + override def toString: String = { + connectionManagerId.host + "_" + connectionManagerId.port + "_" + uniqId + } } private[nio] object ConnectionId { diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala index ee22c6656e69e..741fe3e1ea750 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala @@ -188,7 +188,7 @@ private[nio] class ConnectionManager( private val readRunnableStarted: HashSet[SelectionKey] = new HashSet[SelectionKey]() private val selectorThread = new Thread("connection-manager-thread") { - override def run() = ConnectionManager.this.run() + override def run(): Unit = ConnectionManager.this.run() } selectorThread.setDaemon(true) // start this thread last, since it invokes run(), which accesses members above diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManagerId.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManagerId.scala index cbb37ec5ced1f..1cd13d887c6f6 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManagerId.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManagerId.scala @@ -26,7 +26,7 @@ private[nio] case class ConnectionManagerId(host: String, port: Int) { Utils.checkHost(host) assert (port > 0) - def toSocketAddress() = new InetSocketAddress(host, port) + def toSocketAddress(): InetSocketAddress = new InetSocketAddress(host, port) } diff --git a/core/src/main/scala/org/apache/spark/network/nio/Message.scala b/core/src/main/scala/org/apache/spark/network/nio/Message.scala index fb4a979b824c3..85d2fe2bf9c20 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/Message.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/Message.scala @@ -42,7 +42,9 @@ private[nio] abstract class Message(val typ: Long, val id: Int) { def timeTaken(): String = (finishTime - startTime).toString + " ms" - override def toString = this.getClass.getSimpleName + "(id = " + id + ", size = " + size + ")" + override def toString: String = { + this.getClass.getSimpleName + "(id = " + id + ", size = " + size + ")" + } } @@ -51,7 +53,7 @@ private[nio] object Message { var lastId = 1 - def getNewId() = synchronized { + def getNewId(): Int = synchronized { lastId += 1 if (lastId == 0) { lastId += 1 diff --git a/core/src/main/scala/org/apache/spark/network/nio/MessageChunk.scala b/core/src/main/scala/org/apache/spark/network/nio/MessageChunk.scala index 278c5ac356ef2..a4568e849fa13 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/MessageChunk.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/MessageChunk.scala @@ -24,9 +24,9 @@ import scala.collection.mutable.ArrayBuffer private[nio] class MessageChunk(val header: MessageChunkHeader, val buffer: ByteBuffer) { - val size = if (buffer == null) 0 else buffer.remaining + val size: Int = if (buffer == null) 0 else buffer.remaining - lazy val buffers = { + lazy val buffers: ArrayBuffer[ByteBuffer] = { val ab = new ArrayBuffer[ByteBuffer]() ab += header.buffer if (buffer != null) { @@ -35,7 +35,7 @@ class MessageChunk(val header: MessageChunkHeader, val buffer: ByteBuffer) { ab } - override def toString = { + override def toString: String = { "" + this.getClass.getSimpleName + " (id = " + header.id + ", size = " + size + ")" } } diff --git a/core/src/main/scala/org/apache/spark/network/nio/MessageChunkHeader.scala b/core/src/main/scala/org/apache/spark/network/nio/MessageChunkHeader.scala index 6e20f291c5cec..7b3da4bb9d5ee 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/MessageChunkHeader.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/MessageChunkHeader.scala @@ -50,8 +50,10 @@ private[nio] class MessageChunkHeader( flip.asInstanceOf[ByteBuffer] } - override def toString = "" + this.getClass.getSimpleName + ":" + id + " of type " + typ + + override def toString: String = { + "" + this.getClass.getSimpleName + ":" + id + " of type " + typ + " and sizes " + totalSize + " / " + chunkSize + " bytes, securityNeg: " + securityNeg + } } diff --git a/core/src/main/scala/org/apache/spark/partial/PartialResult.scala b/core/src/main/scala/org/apache/spark/partial/PartialResult.scala index cadd0c7ed19ba..53c4b32c95ab3 100644 --- a/core/src/main/scala/org/apache/spark/partial/PartialResult.scala +++ b/core/src/main/scala/org/apache/spark/partial/PartialResult.scala @@ -99,7 +99,7 @@ class PartialResult[R](initialVal: R, isFinal: Boolean) { case None => "(partial: " + initialValue + ")" } } - def getFinalValueInternal() = PartialResult.this.getFinalValueInternal().map(f) + def getFinalValueInternal(): Option[T] = PartialResult.this.getFinalValueInternal().map(f) } } diff --git a/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala index 1cbd684224b7c..9059eb13bb5d8 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala @@ -70,7 +70,7 @@ class CartesianRDD[T: ClassTag, U: ClassTag]( (rdd1.preferredLocations(currSplit.s1) ++ rdd2.preferredLocations(currSplit.s2)).distinct } - override def compute(split: Partition, context: TaskContext) = { + override def compute(split: Partition, context: TaskContext): Iterator[(T, U)] = { val currSplit = split.asInstanceOf[CartesianPartition] for (x <- rdd1.iterator(currSplit.s1, context); y <- rdd2.iterator(currSplit.s2, context)) yield (x, y) diff --git a/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala index b073eba8a1574..5117ccfabfcc2 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala @@ -186,7 +186,7 @@ private class PartitionCoalescer(maxPartitions: Int, prev: RDD[_], balanceSlack: override val isEmpty = !it.hasNext // initializes/resets to start iterating from the beginning - def resetIterator() = { + def resetIterator(): Iterator[(String, Partition)] = { val iterators = (0 to 2).map( x => prev.partitions.iterator.flatMap(p => { if (currPrefLocs(p).size > x) Some((currPrefLocs(p)(x), p)) else None @@ -196,10 +196,10 @@ private class PartitionCoalescer(maxPartitions: Int, prev: RDD[_], balanceSlack: } // hasNext() is false iff there are no preferredLocations for any of the partitions of the RDD - def hasNext(): Boolean = { !isEmpty } + override def hasNext: Boolean = { !isEmpty } // return the next preferredLocation of some partition of the RDD - def next(): (String, Partition) = { + override def next(): (String, Partition) = { if (it.hasNext) { it.next() } else { @@ -237,7 +237,7 @@ private class PartitionCoalescer(maxPartitions: Int, prev: RDD[_], balanceSlack: val rotIt = new LocationIterator(prev) // deal with empty case, just create targetLen partition groups with no preferred location - if (!rotIt.hasNext()) { + if (!rotIt.hasNext) { (1 to targetLen).foreach(x => groupArr += PartitionGroup()) return } @@ -343,7 +343,7 @@ private class PartitionCoalescer(maxPartitions: Int, prev: RDD[_], balanceSlack: private case class PartitionGroup(prefLoc: Option[String] = None) { var arr = mutable.ArrayBuffer[Partition]() - def size = arr.size + def size: Int = arr.size } private object PartitionGroup { diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala index 486e86ce1bb19..f77abac42b623 100644 --- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala @@ -215,8 +215,7 @@ class HadoopRDD[K, V]( logInfo("Input split: " + split.inputSplit) val jobConf = getJobConf() - val inputMetrics = context.taskMetrics - .getInputMetricsForReadMethod(DataReadMethod.Hadoop) + val inputMetrics = context.taskMetrics.getInputMetricsForReadMethod(DataReadMethod.Hadoop) // Find a function that will return the FileSystem bytes read by this thread. Do this before // creating RecordReader, because RecordReader's constructor might read some bytes @@ -240,7 +239,7 @@ class HadoopRDD[K, V]( val key: K = reader.createKey() val value: V = reader.createValue() - override def getNext() = { + override def getNext(): (K, V) = { try { finished = !reader.next(key, value) } catch { @@ -337,11 +336,11 @@ private[spark] object HadoopRDD extends Logging { * The three methods below are helpers for accessing the local map, a property of the SparkEnv of * the local process. */ - def getCachedMetadata(key: String) = SparkEnv.get.hadoopJobMetadata.get(key) + def getCachedMetadata(key: String): Any = SparkEnv.get.hadoopJobMetadata.get(key) - def containsCachedMetadata(key: String) = SparkEnv.get.hadoopJobMetadata.containsKey(key) + def containsCachedMetadata(key: String): Boolean = SparkEnv.get.hadoopJobMetadata.containsKey(key) - def putCachedMetadata(key: String, value: Any) = + private def putCachedMetadata(key: String, value: Any): Unit = SparkEnv.get.hadoopJobMetadata.put(key, value) /** Add Hadoop configuration specific to a single partition and attempt. */ @@ -371,7 +370,7 @@ private[spark] object HadoopRDD extends Logging { override def getPartitions: Array[Partition] = firstParent[T].partitions - override def compute(split: Partition, context: TaskContext) = { + override def compute(split: Partition, context: TaskContext): Iterator[U] = { val partition = split.asInstanceOf[HadoopPartition] val inputSplit = partition.inputSplit.value f(inputSplit, firstParent[T].iterator(split, context)) diff --git a/core/src/main/scala/org/apache/spark/rdd/JdbcRDD.scala b/core/src/main/scala/org/apache/spark/rdd/JdbcRDD.scala index e2267861e79df..0c28f045e46e9 100644 --- a/core/src/main/scala/org/apache/spark/rdd/JdbcRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/JdbcRDD.scala @@ -17,7 +17,7 @@ package org.apache.spark.rdd -import java.sql.{Connection, ResultSet} +import java.sql.{PreparedStatement, Connection, ResultSet} import scala.reflect.ClassTag @@ -28,8 +28,9 @@ import org.apache.spark.util.NextIterator import org.apache.spark.{Logging, Partition, SparkContext, TaskContext} private[spark] class JdbcPartition(idx: Int, val lower: Long, val upper: Long) extends Partition { - override def index = idx + override def index: Int = idx } + // TODO: Expose a jdbcRDD function in SparkContext and mark this as semi-private /** * An RDD that executes an SQL query on a JDBC connection and reads results. @@ -70,7 +71,8 @@ class JdbcRDD[T: ClassTag]( }).toArray } - override def compute(thePart: Partition, context: TaskContext) = new NextIterator[T] { + override def compute(thePart: Partition, context: TaskContext): Iterator[T] = new NextIterator[T] + { context.addTaskCompletionListener{ context => closeIfNeeded() } val part = thePart.asInstanceOf[JdbcPartition] val conn = getConnection() @@ -88,7 +90,7 @@ class JdbcRDD[T: ClassTag]( stmt.setLong(2, part.upper) val rs = stmt.executeQuery() - override def getNext: T = { + override def getNext(): T = { if (rs.next()) { mapRow(rs) } else { diff --git a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala index 4883fb828814c..a838aac6e8d1a 100644 --- a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala @@ -31,6 +31,6 @@ private[spark] class MapPartitionsRDD[U: ClassTag, T: ClassTag]( override def getPartitions: Array[Partition] = firstParent[T].partitions - override def compute(split: Partition, context: TaskContext) = + override def compute(split: Partition, context: TaskContext): Iterator[U] = f(context, split.index, firstParent[T].iterator(split, context)) } diff --git a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala index 7fb94840df99c..2ab967f4bb313 100644 --- a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala @@ -238,7 +238,7 @@ private[spark] object NewHadoopRDD { override def getPartitions: Array[Partition] = firstParent[T].partitions - override def compute(split: Partition, context: TaskContext) = { + override def compute(split: Partition, context: TaskContext): Iterator[U] = { val partition = split.asInstanceOf[NewHadoopPartition] val inputSplit = partition.serializableHadoopSplit.value f(inputSplit, firstParent[T].iterator(split, context)) diff --git a/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala index f12d0cffaba34..e2394e28f8d26 100644 --- a/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala @@ -98,7 +98,7 @@ private[spark] class ParallelCollectionRDD[T: ClassTag]( slices.indices.map(i => new ParallelCollectionPartition(id, i, slices(i))).toArray } - override def compute(s: Partition, context: TaskContext) = { + override def compute(s: Partition, context: TaskContext): Iterator[T] = { new InterruptibleIterator(context, s.asInstanceOf[ParallelCollectionPartition[T]].iterator) } diff --git a/core/src/main/scala/org/apache/spark/rdd/PartitionPruningRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PartitionPruningRDD.scala index f781a8d776f2a..a00f4c1cdff91 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PartitionPruningRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PartitionPruningRDD.scala @@ -40,7 +40,7 @@ private[spark] class PruneDependency[T](rdd: RDD[T], @transient partitionFilterF .filter(s => partitionFilterFunc(s.index)).zipWithIndex .map { case(split, idx) => new PartitionPruningRDDPartition(idx, split) : Partition } - override def getParents(partitionId: Int) = { + override def getParents(partitionId: Int): List[Int] = { List(partitions(partitionId).asInstanceOf[PartitionPruningRDDPartition].parentSplit.index) } } @@ -59,8 +59,10 @@ class PartitionPruningRDD[T: ClassTag]( @transient partitionFilterFunc: Int => Boolean) extends RDD[T](prev.context, List(new PruneDependency(prev, partitionFilterFunc))) { - override def compute(split: Partition, context: TaskContext) = firstParent[T].iterator( - split.asInstanceOf[PartitionPruningRDDPartition].parentSplit, context) + override def compute(split: Partition, context: TaskContext): Iterator[T] = { + firstParent[T].iterator( + split.asInstanceOf[PartitionPruningRDDPartition].parentSplit, context) + } override protected def getPartitions: Array[Partition] = getDependencies.head.asInstanceOf[PruneDependency[T]].partitions @@ -74,7 +76,7 @@ object PartitionPruningRDD { * Create a PartitionPruningRDD. This function can be used to create the PartitionPruningRDD * when its type T is not known at compile time. */ - def create[T](rdd: RDD[T], partitionFilterFunc: Int => Boolean) = { + def create[T](rdd: RDD[T], partitionFilterFunc: Int => Boolean): PartitionPruningRDD[T] = { new PartitionPruningRDD[T](rdd, partitionFilterFunc)(rdd.elementClassTag) } } diff --git a/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala index ed79032893d33..dc60d48927624 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala @@ -149,10 +149,10 @@ private[spark] class PipedRDD[T: ClassTag]( }.start() // Return an iterator that read lines from the process's stdout - val lines = Source.fromInputStream(proc.getInputStream).getLines + val lines = Source.fromInputStream(proc.getInputStream).getLines() new Iterator[String] { - def next() = lines.next() - def hasNext = { + def next(): String = lines.next() + def hasNext: Boolean = { if (lines.hasNext) { true } else { @@ -162,7 +162,7 @@ private[spark] class PipedRDD[T: ClassTag]( } // cleanup task working directory if used - if (workInTaskDirectory == true) { + if (workInTaskDirectory) { scala.util.control.Exception.ignoring(classOf[IOException]) { Utils.deleteRecursively(new File(taskDirectory)) } diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala index a4c74ed03e330..ddbfd5624e741 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -186,7 +186,7 @@ abstract class RDD[T: ClassTag]( } /** Get the RDD's current storage level, or StorageLevel.NONE if none is set. */ - def getStorageLevel = storageLevel + def getStorageLevel: StorageLevel = storageLevel // Our dependencies and partitions will be gotten by calling subclass's methods below, and will // be overwritten when we're checkpointed @@ -746,13 +746,13 @@ abstract class RDD[T: ClassTag]( def zip[U: ClassTag](other: RDD[U]): RDD[(T, U)] = { zipPartitions(other, preservesPartitioning = false) { (thisIter, otherIter) => new Iterator[(T, U)] { - def hasNext = (thisIter.hasNext, otherIter.hasNext) match { + def hasNext: Boolean = (thisIter.hasNext, otherIter.hasNext) match { case (true, true) => true case (false, false) => false case _ => throw new SparkException("Can only zip RDDs with " + "same number of elements in each partition") } - def next = (thisIter.next, otherIter.next) + def next(): (T, U) = (thisIter.next(), otherIter.next()) } } } @@ -868,8 +868,8 @@ abstract class RDD[T: ClassTag]( // Our partitioner knows how to handle T (which, since we have a partitioner, is // really (K, V)) so make a new Partitioner that will de-tuple our fake tuples val p2 = new Partitioner() { - override def numPartitions = p.numPartitions - override def getPartition(k: Any) = p.getPartition(k.asInstanceOf[(Any, _)]._1) + override def numPartitions: Int = p.numPartitions + override def getPartition(k: Any): Int = p.getPartition(k.asInstanceOf[(Any, _)]._1) } // Unfortunately, since we're making a new p2, we'll get ShuffleDependencies // anyway, and when calling .keys, will not have a partitioner set, even though @@ -1394,7 +1394,7 @@ abstract class RDD[T: ClassTag]( } /** The [[org.apache.spark.SparkContext]] that this RDD was created on. */ - def context = sc + def context: SparkContext = sc /** * Private API for changing an RDD's ClassTag. diff --git a/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala index d9fe6847254fa..2dc47f95937cb 100644 --- a/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala @@ -17,14 +17,12 @@ package org.apache.spark.rdd -import scala.reflect.ClassTag - import org.apache.spark._ import org.apache.spark.annotation.DeveloperApi import org.apache.spark.serializer.Serializer private[spark] class ShuffledRDDPartition(val idx: Int) extends Partition { - override val index = idx + override val index: Int = idx override def hashCode(): Int = idx } diff --git a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala index ed24ea22a661c..c27f435eb9c5a 100644 --- a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala @@ -105,7 +105,7 @@ private[spark] class SubtractedRDD[K: ClassTag, V: ClassTag, W: ClassTag]( seq } } - def integrate(dep: CoGroupSplitDep, op: Product2[K, V] => Unit) = dep match { + def integrate(dep: CoGroupSplitDep, op: Product2[K, V] => Unit): Unit = dep match { case NarrowCoGroupSplitDep(rdd, _, itsSplit) => rdd.iterator(itsSplit, context).asInstanceOf[Iterator[Product2[K, V]]].foreach(op) diff --git a/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala b/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala index aece683ff3199..4239e7e22af89 100644 --- a/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala @@ -44,7 +44,7 @@ private[spark] class UnionPartition[T: ClassTag]( var parentPartition: Partition = rdd.partitions(parentRddPartitionIndex) - def preferredLocations() = rdd.preferredLocations(parentPartition) + def preferredLocations(): Seq[String] = rdd.preferredLocations(parentPartition) override val index: Int = idx diff --git a/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala index 95b2dd954e9f4..d0be304762e1f 100644 --- a/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala @@ -32,7 +32,7 @@ private[spark] class ZippedPartitionsPartition( override val index: Int = idx var partitionValues = rdds.map(rdd => rdd.partitions(idx)) - def partitions = partitionValues + def partitions: Seq[Partition] = partitionValues @throws(classOf[IOException]) private def writeObject(oos: ObjectOutputStream): Unit = Utils.tryOrIOException { diff --git a/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala index fa83372bb4d11..e0edd7d4ae968 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala @@ -39,8 +39,11 @@ class AccumulableInfo ( } object AccumulableInfo { - def apply(id: Long, name: String, update: Option[String], value: String) = + def apply(id: Long, name: String, update: Option[String], value: String): AccumulableInfo = { new AccumulableInfo(id, name, update, value) + } - def apply(id: Long, name: String, value: String) = new AccumulableInfo(id, name, None, value) + def apply(id: Long, name: String, value: String): AccumulableInfo = { + new AccumulableInfo(id, name, None, value) + } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala index 8feac6cb6b7a1..b405bd3338e7c 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -946,7 +946,7 @@ class DAGScheduler( val stage = stageIdToStage(task.stageId) - def markStageAsFinished(stage: Stage, errorMessage: Option[String] = None) = { + def markStageAsFinished(stage: Stage, errorMessage: Option[String] = None): Unit = { val serviceTime = stage.latestInfo.submissionTime match { case Some(t) => "%.03f".format((clock.getTimeMillis() - t) / 1000.0) case _ => "Unknown" diff --git a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala index 34fa6d27c3a45..c0d889360ae99 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala @@ -149,47 +149,60 @@ private[spark] class EventLoggingListener( } // Events that do not trigger a flush - override def onStageSubmitted(event: SparkListenerStageSubmitted) = - logEvent(event) - override def onTaskStart(event: SparkListenerTaskStart) = - logEvent(event) - override def onTaskGettingResult(event: SparkListenerTaskGettingResult) = - logEvent(event) - override def onTaskEnd(event: SparkListenerTaskEnd) = - logEvent(event) - override def onEnvironmentUpdate(event: SparkListenerEnvironmentUpdate) = - logEvent(event) + override def onStageSubmitted(event: SparkListenerStageSubmitted): Unit = logEvent(event) + + override def onTaskStart(event: SparkListenerTaskStart): Unit = logEvent(event) + + override def onTaskGettingResult(event: SparkListenerTaskGettingResult): Unit = logEvent(event) + + override def onTaskEnd(event: SparkListenerTaskEnd): Unit = logEvent(event) + + override def onEnvironmentUpdate(event: SparkListenerEnvironmentUpdate): Unit = logEvent(event) // Events that trigger a flush - override def onStageCompleted(event: SparkListenerStageCompleted) = - logEvent(event, flushLogger = true) - override def onJobStart(event: SparkListenerJobStart) = - logEvent(event, flushLogger = true) - override def onJobEnd(event: SparkListenerJobEnd) = + override def onStageCompleted(event: SparkListenerStageCompleted): Unit = { logEvent(event, flushLogger = true) - override def onBlockManagerAdded(event: SparkListenerBlockManagerAdded) = + } + + override def onJobStart(event: SparkListenerJobStart): Unit = logEvent(event, flushLogger = true) + + override def onJobEnd(event: SparkListenerJobEnd): Unit = logEvent(event, flushLogger = true) + + override def onBlockManagerAdded(event: SparkListenerBlockManagerAdded): Unit = { logEvent(event, flushLogger = true) - override def onBlockManagerRemoved(event: SparkListenerBlockManagerRemoved) = + } + + override def onBlockManagerRemoved(event: SparkListenerBlockManagerRemoved): Unit = { logEvent(event, flushLogger = true) - override def onUnpersistRDD(event: SparkListenerUnpersistRDD) = + } + + override def onUnpersistRDD(event: SparkListenerUnpersistRDD): Unit = { logEvent(event, flushLogger = true) - override def onApplicationStart(event: SparkListenerApplicationStart) = + } + + override def onApplicationStart(event: SparkListenerApplicationStart): Unit = { logEvent(event, flushLogger = true) - override def onApplicationEnd(event: SparkListenerApplicationEnd) = + } + + override def onApplicationEnd(event: SparkListenerApplicationEnd): Unit = { logEvent(event, flushLogger = true) - override def onExecutorAdded(event: SparkListenerExecutorAdded) = + } + override def onExecutorAdded(event: SparkListenerExecutorAdded): Unit = { logEvent(event, flushLogger = true) - override def onExecutorRemoved(event: SparkListenerExecutorRemoved) = + } + + override def onExecutorRemoved(event: SparkListenerExecutorRemoved): Unit = { logEvent(event, flushLogger = true) + } // No-op because logging every update would be overkill - override def onExecutorMetricsUpdate(event: SparkListenerExecutorMetricsUpdate) { } + override def onExecutorMetricsUpdate(event: SparkListenerExecutorMetricsUpdate): Unit = { } /** * Stop logging events. The event log file will be renamed so that it loses the * ".inprogress" suffix. */ - def stop() = { + def stop(): Unit = { writer.foreach(_.close()) val target = new Path(logPath) diff --git a/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala b/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala index 8aa528ac573d0..e55b76c36cc5f 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/JobLogger.scala @@ -57,7 +57,7 @@ class JobLogger(val user: String, val logDirName: String) extends SparkListener private val stageIdToJobId = new HashMap[Int, Int] private val jobIdToStageIds = new HashMap[Int, Seq[Int]] private val dateFormat = new ThreadLocal[SimpleDateFormat]() { - override def initialValue() = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss") + override def initialValue(): SimpleDateFormat = new SimpleDateFormat("yyyy/MM/dd HH:mm:ss") } createLogDir() diff --git a/core/src/main/scala/org/apache/spark/scheduler/JobWaiter.scala b/core/src/main/scala/org/apache/spark/scheduler/JobWaiter.scala index 29879b374b801..382b09422a4a0 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/JobWaiter.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/JobWaiter.scala @@ -34,7 +34,7 @@ private[spark] class JobWaiter[T]( @volatile private var _jobFinished = totalTasks == 0 - def jobFinished = _jobFinished + def jobFinished: Boolean = _jobFinished // If the job is finished, this will be its result. In the case of 0 task jobs (e.g. zero // partition RDDs), we set the jobResult directly to JobSucceeded. diff --git a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala index 759df023a6dcf..a3caa9f000c89 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala @@ -160,7 +160,7 @@ private[spark] object OutputCommitCoordinator { class OutputCommitCoordinatorActor(outputCommitCoordinator: OutputCommitCoordinator) extends Actor with ActorLogReceive with Logging { - override def receiveWithLogging = { + override def receiveWithLogging: PartialFunction[Any, Unit] = { case AskPermissionToCommitOutput(stage, partition, taskAttempt) => sender ! outputCommitCoordinator.handleAskPermissionToCommit(stage, partition, taskAttempt) case StopCoordinator => diff --git a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala index 4a9ff918afe25..e074ce6ebff0b 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala @@ -64,5 +64,5 @@ private[spark] class ResultTask[T, U]( // This is only callable on the driver side. override def preferredLocations: Seq[TaskLocation] = preferredLocs - override def toString = "ResultTask(" + stageId + ", " + partitionId + ")" + override def toString: String = "ResultTask(" + stageId + ", " + partitionId + ")" } diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala index 79709089c0da4..fd0d484b45460 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala @@ -47,7 +47,7 @@ private[spark] class ShuffleMapTask( /** A constructor used only in test suites. This does not require passing in an RDD. */ def this(partitionId: Int) { - this(0, null, new Partition { override def index = 0 }, null) + this(0, null, new Partition { override def index: Int = 0 }, null) } @transient private val preferredLocs: Seq[TaskLocation] = { @@ -83,5 +83,5 @@ private[spark] class ShuffleMapTask( override def preferredLocations: Seq[TaskLocation] = preferredLocs - override def toString = "ShuffleMapTask(%d, %d)".format(stageId, partitionId) + override def toString: String = "ShuffleMapTask(%d, %d)".format(stageId, partitionId) } diff --git a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala index 52720d48ca67f..b711ff209af94 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala @@ -300,7 +300,7 @@ private[spark] object StatsReportListener extends Logging { } def showDistribution(heading: String, dOpt: Option[Distribution], format:String) { - def f(d: Double) = format.format(d) + def f(d: Double): String = format.format(d) showDistribution(heading, dOpt, f _) } @@ -346,7 +346,7 @@ private[spark] object StatsReportListener extends Logging { /** * Reformat a time interval in milliseconds to a prettier format for output */ - def millisToString(ms: Long) = { + def millisToString(ms: Long): String = { val (size, units) = if (ms > hours) { (ms.toDouble / hours, "hours") diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala index cc13f57a49b89..4cbc6e84a6bdd 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala @@ -133,7 +133,7 @@ private[spark] class Stage( def attemptId: Int = nextAttemptId - override def toString = "Stage " + id + override def toString: String = "Stage " + id override def hashCode(): Int = id diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskLocation.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskLocation.scala index 10c685f29d3ac..da07ce2c6ea49 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskLocation.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskLocation.scala @@ -29,23 +29,22 @@ private[spark] sealed trait TaskLocation { /** * A location that includes both a host and an executor id on that host. */ -private [spark] case class ExecutorCacheTaskLocation(override val host: String, - val executorId: String) extends TaskLocation { -} +private [spark] +case class ExecutorCacheTaskLocation(override val host: String, executorId: String) + extends TaskLocation /** * A location on a host. */ private [spark] case class HostTaskLocation(override val host: String) extends TaskLocation { - override def toString = host + override def toString: String = host } /** * A location on a host that is cached by HDFS. */ -private [spark] case class HDFSCacheTaskLocation(override val host: String) - extends TaskLocation { - override def toString = TaskLocation.inMemoryLocationTag + host +private [spark] case class HDFSCacheTaskLocation(override val host: String) extends TaskLocation { + override def toString: String = TaskLocation.inMemoryLocationTag + host } private[spark] object TaskLocation { @@ -54,14 +53,16 @@ private[spark] object TaskLocation { // confusion. See RFC 952 and RFC 1123 for information about the format of hostnames. val inMemoryLocationTag = "hdfs_cache_" - def apply(host: String, executorId: String) = new ExecutorCacheTaskLocation(host, executorId) + def apply(host: String, executorId: String): TaskLocation = { + new ExecutorCacheTaskLocation(host, executorId) + } /** * Create a TaskLocation from a string returned by getPreferredLocations. * These strings have the form [hostname] or hdfs_cache_[hostname], depending on whether the * location is cached. */ - def apply(str: String) = { + def apply(str: String): TaskLocation = { val hstr = str.stripPrefix(inMemoryLocationTag) if (hstr.equals(str)) { new HostTaskLocation(str) diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala index f33fd4450b2a6..076b36e86c0ce 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala @@ -373,17 +373,17 @@ private[spark] class TaskSchedulerImpl( } def handleSuccessfulTask( - taskSetManager: TaskSetManager, - tid: Long, - taskResult: DirectTaskResult[_]) = synchronized { + taskSetManager: TaskSetManager, + tid: Long, + taskResult: DirectTaskResult[_]): Unit = synchronized { taskSetManager.handleSuccessfulTask(tid, taskResult) } def handleFailedTask( - taskSetManager: TaskSetManager, - tid: Long, - taskState: TaskState, - reason: TaskEndReason) = synchronized { + taskSetManager: TaskSetManager, + tid: Long, + taskState: TaskState, + reason: TaskEndReason): Unit = synchronized { taskSetManager.handleFailedTask(tid, taskState, reason) if (!taskSetManager.isZombie && taskState != TaskState.KILLED) { // Need to revive offers again now that the task set manager state has been updated to @@ -423,7 +423,7 @@ private[spark] class TaskSchedulerImpl( starvationTimer.cancel() } - override def defaultParallelism() = backend.defaultParallelism() + override def defaultParallelism(): Int = backend.defaultParallelism() // Check for speculatable tasks in all our active jobs. def checkSpeculatableTasks() { diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala index 529237f0d35dc..d509881c74fef 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala @@ -20,6 +20,7 @@ package org.apache.spark.scheduler import java.io.NotSerializableException import java.nio.ByteBuffer import java.util.Arrays +import java.util.concurrent.ConcurrentLinkedQueue import scala.collection.mutable.ArrayBuffer import scala.collection.mutable.HashMap @@ -29,6 +30,7 @@ import scala.util.control.NonFatal import org.apache.spark._ import org.apache.spark.executor.TaskMetrics +import org.apache.spark.scheduler.SchedulingMode._ import org.apache.spark.TaskState.TaskState import org.apache.spark.util.{Clock, SystemClock, Utils} @@ -97,7 +99,8 @@ private[spark] class TaskSetManager( var calculatedTasks = 0 val runningTasksSet = new HashSet[Long] - override def runningTasks = runningTasksSet.size + + override def runningTasks: Int = runningTasksSet.size // True once no more tasks should be launched for this task set manager. TaskSetManagers enter // the zombie state once at least one attempt of each task has completed successfully, or if the @@ -168,9 +171,9 @@ private[spark] class TaskSetManager( var currentLocalityIndex = 0 // Index of our current locality level in validLocalityLevels var lastLaunchTime = clock.getTimeMillis() // Time we last launched a task at this level - override def schedulableQueue = null + override def schedulableQueue: ConcurrentLinkedQueue[Schedulable] = null - override def schedulingMode = SchedulingMode.NONE + override def schedulingMode: SchedulingMode = SchedulingMode.NONE var emittedTaskSizeWarning = false @@ -585,7 +588,7 @@ private[spark] class TaskSetManager( /** * Marks the task as getting result and notifies the DAG Scheduler */ - def handleTaskGettingResult(tid: Long) = { + def handleTaskGettingResult(tid: Long): Unit = { val info = taskInfos(tid) info.markGettingResult() sched.dagScheduler.taskGettingResult(info) @@ -612,7 +615,7 @@ private[spark] class TaskSetManager( /** * Marks the task as successful and notifies the DAGScheduler that a task has ended. */ - def handleSuccessfulTask(tid: Long, result: DirectTaskResult[_]) = { + def handleSuccessfulTask(tid: Long, result: DirectTaskResult[_]): Unit = { val info = taskInfos(tid) val index = info.index info.markSuccessful() diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala index 87ebf31139ce9..5d258d9da4d1a 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala @@ -85,7 +85,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste context.system.scheduler.schedule(0.millis, reviveInterval.millis, self, ReviveOffers) } - def receiveWithLogging = { + def receiveWithLogging: PartialFunction[Any, Unit] = { case RegisterExecutor(executorId, hostPort, cores, logUrls) => Utils.checkHostPort(hostPort, "Host port expected " + hostPort) if (executorDataMap.contains(executorId)) { diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala index f14aaeea0a25c..5a38ad9f2b12c 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala @@ -109,7 +109,7 @@ private[spark] abstract class YarnSchedulerBackend( context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) } - override def receive = { + override def receive: PartialFunction[Any, Unit] = { case RegisterClusterManager => logInfo(s"ApplicationMaster registered as $sender") amActor = Some(sender) diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MemoryUtils.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MemoryUtils.scala index aa3ec0f8cfb9c..8df4f3b554c41 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MemoryUtils.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MemoryUtils.scala @@ -24,7 +24,7 @@ private[spark] object MemoryUtils { val OVERHEAD_FRACTION = 0.10 val OVERHEAD_MINIMUM = 384 - def calculateTotalMemory(sc: SparkContext) = { + def calculateTotalMemory(sc: SparkContext): Int = { sc.conf.getInt("spark.mesos.executor.memoryOverhead", math.max(OVERHEAD_FRACTION * sc.executorMemory, OVERHEAD_MINIMUM).toInt) + sc.executorMemory } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala index 06bb527522141..b381436839227 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala @@ -387,7 +387,7 @@ private[spark] class MesosSchedulerBackend( } // TODO: query Mesos for number of cores - override def defaultParallelism() = sc.conf.getInt("spark.default.parallelism", 8) + override def defaultParallelism(): Int = sc.conf.getInt("spark.default.parallelism", 8) override def applicationId(): String = Option(appId).getOrElse { diff --git a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala index d95426d918e19..eb3f999b5b375 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala @@ -59,7 +59,7 @@ private[spark] class LocalActor( private val executor = new Executor( localExecutorId, localExecutorHostname, SparkEnv.get, isLocal = true) - override def receiveWithLogging = { + override def receiveWithLogging: PartialFunction[Any, Unit] = { case ReviveOffers => reviveOffers() @@ -117,7 +117,7 @@ private[spark] class LocalBackend(scheduler: TaskSchedulerImpl, val totalCores: localActor ! ReviveOffers } - override def defaultParallelism() = + override def defaultParallelism(): Int = scheduler.conf.getInt("spark.default.parallelism", totalCores) override def killTask(taskId: Long, executorId: String, interruptThread: Boolean) { diff --git a/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala index 1baa0e009f3ae..dfbde7c8a1b0d 100644 --- a/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala +++ b/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala @@ -59,9 +59,10 @@ private[spark] class JavaSerializationStream( } private[spark] class JavaDeserializationStream(in: InputStream, loader: ClassLoader) -extends DeserializationStream { + extends DeserializationStream { + private val objIn = new ObjectInputStream(in) { - override def resolveClass(desc: ObjectStreamClass) = + override def resolveClass(desc: ObjectStreamClass): Class[_] = Class.forName(desc.getName, false, loader) } diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala index dc7aa99738c17..f83bcaa5cc09e 100644 --- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala +++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala @@ -60,7 +60,7 @@ class KryoSerializer(conf: SparkConf) .split(',') .filter(!_.isEmpty) - def newKryoOutput() = new KryoOutput(bufferSize, math.max(bufferSize, maxBufferSize)) + def newKryoOutput(): KryoOutput = new KryoOutput(bufferSize, math.max(bufferSize, maxBufferSize)) def newKryo(): Kryo = { val instantiator = new EmptyScalaKryoInstantiator diff --git a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala index 7de2f9cbb2866..660df00bc32f5 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala @@ -106,7 +106,7 @@ class FileShuffleBlockManager(conf: SparkConf) * when the writers are closed successfully */ def forMapTask(shuffleId: Int, mapId: Int, numBuckets: Int, serializer: Serializer, - writeMetrics: ShuffleWriteMetrics) = { + writeMetrics: ShuffleWriteMetrics): ShuffleWriterGroup = { new ShuffleWriterGroup { shuffleStates.putIfAbsent(shuffleId, new ShuffleState(numBuckets)) private val shuffleState = shuffleStates(shuffleId) @@ -268,7 +268,7 @@ object FileShuffleBlockManager { new PrimitiveVector[Long]() } - def apply(bucketId: Int) = files(bucketId) + def apply(bucketId: Int): File = files(bucketId) def recordMapOutput(mapId: Int, offsets: Array[Long], lengths: Array[Long]) { assert(offsets.length == lengths.length) diff --git a/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala index b292587d37028..87fd161e06c85 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala @@ -80,7 +80,7 @@ class IndexShuffleBlockManager(conf: SparkConf) extends ShuffleBlockManager { * end of the output file. This will be used by getBlockLocation to figure out where each block * begins and ends. * */ - def writeIndexFile(shuffleId: Int, mapId: Int, lengths: Array[Long]) = { + def writeIndexFile(shuffleId: Int, mapId: Int, lengths: Array[Long]): Unit = { val indexFile = getIndexFile(shuffleId, mapId) val out = new DataOutputStream(new BufferedOutputStream(new FileOutputStream(indexFile))) try { @@ -121,5 +121,5 @@ class IndexShuffleBlockManager(conf: SparkConf) extends ShuffleBlockManager { } } - override def stop() = {} + override def stop(): Unit = {} } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockId.scala b/core/src/main/scala/org/apache/spark/storage/BlockId.scala index 1f012941c85ab..c186fd360fef6 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockId.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockId.scala @@ -35,13 +35,13 @@ sealed abstract class BlockId { def name: String // convenience methods - def asRDDId = if (isRDD) Some(asInstanceOf[RDDBlockId]) else None - def isRDD = isInstanceOf[RDDBlockId] - def isShuffle = isInstanceOf[ShuffleBlockId] - def isBroadcast = isInstanceOf[BroadcastBlockId] + def asRDDId: Option[RDDBlockId] = if (isRDD) Some(asInstanceOf[RDDBlockId]) else None + def isRDD: Boolean = isInstanceOf[RDDBlockId] + def isShuffle: Boolean = isInstanceOf[ShuffleBlockId] + def isBroadcast: Boolean = isInstanceOf[BroadcastBlockId] - override def toString = name - override def hashCode = name.hashCode + override def toString: String = name + override def hashCode: Int = name.hashCode override def equals(other: Any): Boolean = other match { case o: BlockId => getClass == o.getClass && name.equals(o.name) case _ => false @@ -50,54 +50,54 @@ sealed abstract class BlockId { @DeveloperApi case class RDDBlockId(rddId: Int, splitIndex: Int) extends BlockId { - def name = "rdd_" + rddId + "_" + splitIndex + override def name: String = "rdd_" + rddId + "_" + splitIndex } // Format of the shuffle block ids (including data and index) should be kept in sync with // org.apache.spark.network.shuffle.StandaloneShuffleBlockManager#getBlockData(). @DeveloperApi case class ShuffleBlockId(shuffleId: Int, mapId: Int, reduceId: Int) extends BlockId { - def name = "shuffle_" + shuffleId + "_" + mapId + "_" + reduceId + override def name: String = "shuffle_" + shuffleId + "_" + mapId + "_" + reduceId } @DeveloperApi case class ShuffleDataBlockId(shuffleId: Int, mapId: Int, reduceId: Int) extends BlockId { - def name = "shuffle_" + shuffleId + "_" + mapId + "_" + reduceId + ".data" + override def name: String = "shuffle_" + shuffleId + "_" + mapId + "_" + reduceId + ".data" } @DeveloperApi case class ShuffleIndexBlockId(shuffleId: Int, mapId: Int, reduceId: Int) extends BlockId { - def name = "shuffle_" + shuffleId + "_" + mapId + "_" + reduceId + ".index" + override def name: String = "shuffle_" + shuffleId + "_" + mapId + "_" + reduceId + ".index" } @DeveloperApi case class BroadcastBlockId(broadcastId: Long, field: String = "") extends BlockId { - def name = "broadcast_" + broadcastId + (if (field == "") "" else "_" + field) + override def name: String = "broadcast_" + broadcastId + (if (field == "") "" else "_" + field) } @DeveloperApi case class TaskResultBlockId(taskId: Long) extends BlockId { - def name = "taskresult_" + taskId + override def name: String = "taskresult_" + taskId } @DeveloperApi case class StreamBlockId(streamId: Int, uniqueId: Long) extends BlockId { - def name = "input-" + streamId + "-" + uniqueId + override def name: String = "input-" + streamId + "-" + uniqueId } /** Id associated with temporary local data managed as blocks. Not serializable. */ private[spark] case class TempLocalBlockId(id: UUID) extends BlockId { - def name = "temp_local_" + id + override def name: String = "temp_local_" + id } /** Id associated with temporary shuffle data managed as blocks. Not serializable. */ private[spark] case class TempShuffleBlockId(id: UUID) extends BlockId { - def name = "temp_shuffle_" + id + override def name: String = "temp_shuffle_" + id } // Intended only for testing purposes private[spark] case class TestBlockId(id: String) extends BlockId { - def name = "test_" + id + override def name: String = "test_" + id } @DeveloperApi @@ -112,7 +112,7 @@ object BlockId { val TEST = "test_(.*)".r /** Converts a BlockId "name" String back into a BlockId. */ - def apply(id: String) = id match { + def apply(id: String): BlockId = id match { case RDD(rddId, splitIndex) => RDDBlockId(rddId.toInt, splitIndex.toInt) case SHUFFLE(shuffleId, mapId, reduceId) => diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala index b177a59c721df..a6f1ebf325a7c 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala @@ -77,11 +77,11 @@ class BlockManagerId private ( @throws(classOf[IOException]) private def readResolve(): Object = BlockManagerId.getCachedBlockManagerId(this) - override def toString = s"BlockManagerId($executorId, $host, $port)" + override def toString: String = s"BlockManagerId($executorId, $host, $port)" override def hashCode: Int = (executorId.hashCode * 41 + host.hashCode) * 41 + port - override def equals(that: Any) = that match { + override def equals(that: Any): Boolean = that match { case id: BlockManagerId => executorId == id.executorId && port == id.port && host == id.host case _ => @@ -100,10 +100,10 @@ private[spark] object BlockManagerId { * @param port Port of the block manager. * @return A new [[org.apache.spark.storage.BlockManagerId]]. */ - def apply(execId: String, host: String, port: Int) = + def apply(execId: String, host: String, port: Int): BlockManagerId = getCachedBlockManagerId(new BlockManagerId(execId, host, port)) - def apply(in: ObjectInput) = { + def apply(in: ObjectInput): BlockManagerId = { val obj = new BlockManagerId() obj.readExternal(in) getCachedBlockManagerId(obj) diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala index 654796f23c96e..061964826f08b 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala @@ -79,7 +79,7 @@ class BlockManagerMaster( * Check if block manager master has a block. Note that this can be used to check for only * those blocks that are reported to block manager master. */ - def contains(blockId: BlockId) = { + def contains(blockId: BlockId): Boolean = { !getLocations(blockId).isEmpty } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala index 787b0f96bec32..5b5328016124e 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala @@ -52,7 +52,7 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus private val akkaTimeout = AkkaUtils.askTimeout(conf) - override def receiveWithLogging = { + override def receiveWithLogging: PartialFunction[Any, Unit] = { case RegisterBlockManager(blockManagerId, maxMemSize, slaveActor) => register(blockManagerId, maxMemSize, slaveActor) sender ! true @@ -421,7 +421,7 @@ private[spark] class BlockManagerInfo( // Mapping from block id to its status. private val _blocks = new JHashMap[BlockId, BlockStatus] - def getStatus(blockId: BlockId) = Option(_blocks.get(blockId)) + def getStatus(blockId: BlockId): Option[BlockStatus] = Option(_blocks.get(blockId)) def updateLastSeenMs() { _lastSeenMs = System.currentTimeMillis() diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala index 8462871e798a5..52fb896c4e21f 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala @@ -38,7 +38,7 @@ class BlockManagerSlaveActor( import context.dispatcher // Operations that involve removing blocks may be slow and should be done asynchronously - override def receiveWithLogging = { + override def receiveWithLogging: PartialFunction[Any, Unit] = { case RemoveBlock(blockId) => doAsync[Boolean]("removing block " + blockId, sender) { blockManager.removeBlock(blockId) diff --git a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala index 81164178b9e8e..f703e50b6b0ac 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala @@ -82,11 +82,13 @@ private[spark] class DiskBlockObjectWriter( { /** Intercepts write calls and tracks total time spent writing. Not thread safe. */ private class TimeTrackingOutputStream(out: OutputStream) extends OutputStream { - def write(i: Int): Unit = callWithTiming(out.write(i)) - override def write(b: Array[Byte]) = callWithTiming(out.write(b)) - override def write(b: Array[Byte], off: Int, len: Int) = callWithTiming(out.write(b, off, len)) - override def close() = out.close() - override def flush() = out.flush() + override def write(i: Int): Unit = callWithTiming(out.write(i)) + override def write(b: Array[Byte]): Unit = callWithTiming(out.write(b)) + override def write(b: Array[Byte], off: Int, len: Int): Unit = { + callWithTiming(out.write(b, off, len)) + } + override def close(): Unit = out.close() + override def flush(): Unit = out.flush() } /** The file channel, used for repositioning / truncating the file. */ @@ -141,8 +143,9 @@ private[spark] class DiskBlockObjectWriter( if (syncWrites) { // Force outstanding writes to disk and track how long it takes objOut.flush() - def sync = fos.getFD.sync() - callWithTiming(sync) + callWithTiming { + fos.getFD.sync() + } } objOut.close() diff --git a/core/src/main/scala/org/apache/spark/storage/FileSegment.scala b/core/src/main/scala/org/apache/spark/storage/FileSegment.scala index 132502b75f8cd..95e2d688d9b17 100644 --- a/core/src/main/scala/org/apache/spark/storage/FileSegment.scala +++ b/core/src/main/scala/org/apache/spark/storage/FileSegment.scala @@ -24,5 +24,7 @@ import java.io.File * based off an offset and a length. */ private[spark] class FileSegment(val file: File, val offset: Long, val length: Long) { - override def toString = "(name=%s, offset=%d, length=%d)".format(file.getName, offset, length) + override def toString: String = { + "(name=%s, offset=%d, length=%d)".format(file.getName, offset, length) + } } diff --git a/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala b/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala index 120c327a7e580..0186eb30a1905 100644 --- a/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala +++ b/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala @@ -36,7 +36,7 @@ class RDDInfo( def isCached: Boolean = (memSize + diskSize + tachyonSize > 0) && numCachedPartitions > 0 - override def toString = { + override def toString: String = { import Utils.bytesToString ("RDD \"%s\" (%d) StorageLevel: %s; CachedPartitions: %d; TotalPartitions: %d; " + "MemorySize: %s; TachyonSize: %s; DiskSize: %s").format( @@ -44,7 +44,7 @@ class RDDInfo( bytesToString(memSize), bytesToString(tachyonSize), bytesToString(diskSize)) } - override def compare(that: RDDInfo) = { + override def compare(that: RDDInfo): Int = { this.id - that.id } } diff --git a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala index e5e1cf5a69a19..134abea866218 100644 --- a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala +++ b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala @@ -50,11 +50,11 @@ class StorageLevel private( def this() = this(false, true, false, false) // For deserialization - def useDisk = _useDisk - def useMemory = _useMemory - def useOffHeap = _useOffHeap - def deserialized = _deserialized - def replication = _replication + def useDisk: Boolean = _useDisk + def useMemory: Boolean = _useMemory + def useOffHeap: Boolean = _useOffHeap + def deserialized: Boolean = _deserialized + def replication: Int = _replication assert(replication < 40, "Replication restricted to be less than 40 for calculating hash codes") @@ -80,7 +80,7 @@ class StorageLevel private( false } - def isValid = (useMemory || useDisk || useOffHeap) && (replication > 0) + def isValid: Boolean = (useMemory || useDisk || useOffHeap) && (replication > 0) def toInt: Int = { var ret = 0 @@ -183,7 +183,7 @@ object StorageLevel { useMemory: Boolean, useOffHeap: Boolean, deserialized: Boolean, - replication: Int) = { + replication: Int): StorageLevel = { getCachedStorageLevel( new StorageLevel(useDisk, useMemory, useOffHeap, deserialized, replication)) } @@ -197,7 +197,7 @@ object StorageLevel { useDisk: Boolean, useMemory: Boolean, deserialized: Boolean, - replication: Int = 1) = { + replication: Int = 1): StorageLevel = { getCachedStorageLevel(new StorageLevel(useDisk, useMemory, false, deserialized, replication)) } diff --git a/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala b/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala index def49e80a3605..7d75929b96f75 100644 --- a/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala +++ b/core/src/main/scala/org/apache/spark/storage/StorageStatusListener.scala @@ -19,7 +19,6 @@ package org.apache.spark.storage import scala.collection.mutable -import org.apache.spark.SparkContext import org.apache.spark.annotation.DeveloperApi import org.apache.spark.scheduler._ @@ -32,7 +31,7 @@ class StorageStatusListener extends SparkListener { // This maintains only blocks that are cached (i.e. storage level is not StorageLevel.NONE) private[storage] val executorIdToStorageStatus = mutable.Map[String, StorageStatus]() - def storageStatusList = executorIdToStorageStatus.values.toSeq + def storageStatusList: Seq[StorageStatus] = executorIdToStorageStatus.values.toSeq /** Update storage status list to reflect updated block statuses */ private def updateStorageStatus(execId: String, updatedBlocks: Seq[(BlockId, BlockStatus)]) { @@ -56,7 +55,7 @@ class StorageStatusListener extends SparkListener { } } - override def onTaskEnd(taskEnd: SparkListenerTaskEnd) = synchronized { + override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = synchronized { val info = taskEnd.taskInfo val metrics = taskEnd.taskMetrics if (info != null && metrics != null) { @@ -67,7 +66,7 @@ class StorageStatusListener extends SparkListener { } } - override def onUnpersistRDD(unpersistRDD: SparkListenerUnpersistRDD) = synchronized { + override def onUnpersistRDD(unpersistRDD: SparkListenerUnpersistRDD): Unit = synchronized { updateStorageStatus(unpersistRDD.rddId) } diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonFileSegment.scala b/core/src/main/scala/org/apache/spark/storage/TachyonFileSegment.scala index b86abbda1d3e7..65fa81704c365 100644 --- a/core/src/main/scala/org/apache/spark/storage/TachyonFileSegment.scala +++ b/core/src/main/scala/org/apache/spark/storage/TachyonFileSegment.scala @@ -24,5 +24,7 @@ import tachyon.client.TachyonFile * a length. */ private[spark] class TachyonFileSegment(val file: TachyonFile, val offset: Long, val length: Long) { - override def toString = "(name=%s, offset=%d, length=%d)".format(file.getPath(), offset, length) + override def toString: String = { + "(name=%s, offset=%d, length=%d)".format(file.getPath(), offset, length) + } } diff --git a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala index 0c24ad2760e08..adfa6bbada256 100644 --- a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala +++ b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala @@ -60,7 +60,7 @@ private[spark] class SparkUI private ( } initialize() - def getAppName = appName + def getAppName: String = appName /** Set the app name for this UI. */ def setAppName(name: String) { diff --git a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala index b5022fe853c49..f07864141a21c 100644 --- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala +++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala @@ -149,9 +149,11 @@ private[spark] object UIUtils extends Logging { } } - def prependBaseUri(basePath: String = "", resource: String = "") = uiRoot + basePath + resource + def prependBaseUri(basePath: String = "", resource: String = ""): String = { + uiRoot + basePath + resource + } - def commonHeaderNodes = { + def commonHeaderNodes: Seq[Node] = { diff --git a/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala b/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala index fc1844600f1cb..19ac7a826e306 100644 --- a/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala +++ b/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala @@ -51,7 +51,7 @@ private[spark] object UIWorkloadGenerator { val nJobSet = args(2).toInt val sc = new SparkContext(conf) - def setProperties(s: String) = { + def setProperties(s: String): Unit = { if(schedulingMode == SchedulingMode.FAIR) { sc.setLocalProperty("spark.scheduler.pool", s) } @@ -59,7 +59,7 @@ private[spark] object UIWorkloadGenerator { } val baseData = sc.makeRDD(1 to NUM_PARTITIONS * 10, NUM_PARTITIONS) - def nextFloat() = new Random().nextFloat() + def nextFloat(): Float = new Random().nextFloat() val jobs = Seq[(String, () => Long)]( ("Count", baseData.count), diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala index 3afd7ef07d7c9..69053fe44d7e4 100644 --- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala +++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala @@ -22,7 +22,7 @@ import scala.collection.mutable.HashMap import org.apache.spark.ExceptionFailure import org.apache.spark.annotation.DeveloperApi import org.apache.spark.scheduler._ -import org.apache.spark.storage.StorageStatusListener +import org.apache.spark.storage.{StorageStatus, StorageStatusListener} import org.apache.spark.ui.{SparkUI, SparkUITab} private[ui] class ExecutorsTab(parent: SparkUI) extends SparkUITab(parent, "executors") { @@ -55,19 +55,19 @@ class ExecutorsListener(storageStatusListener: StorageStatusListener) extends Sp val executorToShuffleWrite = HashMap[String, Long]() val executorToLogUrls = HashMap[String, Map[String, String]]() - def storageStatusList = storageStatusListener.storageStatusList + def storageStatusList: Seq[StorageStatus] = storageStatusListener.storageStatusList - override def onExecutorAdded(executorAdded: SparkListenerExecutorAdded) = synchronized { + override def onExecutorAdded(executorAdded: SparkListenerExecutorAdded): Unit = synchronized { val eid = executorAdded.executorId executorToLogUrls(eid) = executorAdded.executorInfo.logUrlMap } - override def onTaskStart(taskStart: SparkListenerTaskStart) = synchronized { + override def onTaskStart(taskStart: SparkListenerTaskStart): Unit = synchronized { val eid = taskStart.taskInfo.executorId executorToTasksActive(eid) = executorToTasksActive.getOrElse(eid, 0) + 1 } - override def onTaskEnd(taskEnd: SparkListenerTaskEnd) = synchronized { + override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = synchronized { val info = taskEnd.taskInfo if (info != null) { val eid = info.executorId diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala index 937d95a934b59..949e80d30f5eb 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala @@ -73,7 +73,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { // Misc: val executorIdToBlockManagerId = HashMap[ExecutorId, BlockManagerId]() - def blockManagerIds = executorIdToBlockManagerId.values.toSeq + def blockManagerIds: Seq[BlockManagerId] = executorIdToBlockManagerId.values.toSeq var schedulingMode: Option[SchedulingMode] = None @@ -146,7 +146,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { } } - override def onJobStart(jobStart: SparkListenerJobStart) = synchronized { + override def onJobStart(jobStart: SparkListenerJobStart): Unit = synchronized { val jobGroup = for ( props <- Option(jobStart.properties); group <- Option(props.getProperty(SparkContext.SPARK_JOB_GROUP_ID)) @@ -182,7 +182,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { } } - override def onJobEnd(jobEnd: SparkListenerJobEnd) = synchronized { + override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = synchronized { val jobData = activeJobs.remove(jobEnd.jobId).getOrElse { logWarning(s"Job completed for unknown job ${jobEnd.jobId}") new JobUIData(jobId = jobEnd.jobId) @@ -219,7 +219,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { } } - override def onStageCompleted(stageCompleted: SparkListenerStageCompleted) = synchronized { + override def onStageCompleted(stageCompleted: SparkListenerStageCompleted): Unit = synchronized { val stage = stageCompleted.stageInfo stageIdToInfo(stage.stageId) = stage val stageData = stageIdToData.getOrElseUpdate((stage.stageId, stage.attemptId), { @@ -260,7 +260,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { } /** For FIFO, all stages are contained by "default" pool but "default" pool here is meaningless */ - override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) = synchronized { + override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted): Unit = synchronized { val stage = stageSubmitted.stageInfo activeStages(stage.stageId) = stage pendingStages.remove(stage.stageId) @@ -288,7 +288,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { } } - override def onTaskStart(taskStart: SparkListenerTaskStart) = synchronized { + override def onTaskStart(taskStart: SparkListenerTaskStart): Unit = synchronized { val taskInfo = taskStart.taskInfo if (taskInfo != null) { val stageData = stageIdToData.getOrElseUpdate((taskStart.stageId, taskStart.stageAttemptId), { @@ -312,7 +312,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { // stageToTaskInfos already has the updated status. } - override def onTaskEnd(taskEnd: SparkListenerTaskEnd) = synchronized { + override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = synchronized { val info = taskEnd.taskInfo // If stage attempt id is -1, it means the DAGScheduler had no idea which attempt this task // completion event is for. Let's just drop it here. This means we might have some speculation diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala index b2bbfdee56946..7ffcf291b5cc6 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala @@ -24,7 +24,7 @@ import org.apache.spark.ui.{SparkUI, SparkUITab} private[ui] class JobsTab(parent: SparkUI) extends SparkUITab(parent, "jobs") { val sc = parent.sc val killEnabled = parent.killEnabled - def isFairScheduler = listener.schedulingMode.exists(_ == SchedulingMode.FAIR) + def isFairScheduler: Boolean = listener.schedulingMode.exists(_ == SchedulingMode.FAIR) val listener = parent.jobProgressListener attachPage(new AllJobsPage(this)) diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala index 110f8780a9a12..e03442894c5cc 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala @@ -20,7 +20,7 @@ package org.apache.spark.ui.jobs import java.util.Date import javax.servlet.http.HttpServletRequest -import scala.xml.{Node, Unparsed} +import scala.xml.{Elem, Node, Unparsed} import org.apache.commons.lang3.StringEscapeUtils @@ -170,7 +170,8 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
val accumulableHeaders: Seq[String] = Seq("Accumulable", "Value") - def accumulableRow(acc: AccumulableInfo) = {acc.name}{acc.value} + def accumulableRow(acc: AccumulableInfo): Elem = + {acc.name}{acc.value} val accumulableTable = UIUtils.listingTable(accumulableHeaders, accumulableRow, accumulables.values.toSeq) @@ -293,10 +294,11 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { val schedulerDelayQuantiles = schedulerDelayTitle +: getFormattedTimeQuantiles(schedulerDelays) - def getFormattedSizeQuantiles(data: Seq[Double]) = + def getFormattedSizeQuantiles(data: Seq[Double]): Seq[Elem] = getDistributionQuantiles(data).map(d => {Utils.bytesToString(d.toLong)}) - def getFormattedSizeQuantilesWithRecords(data: Seq[Double], records: Seq[Double]) = { + def getFormattedSizeQuantilesWithRecords(data: Seq[Double], records: Seq[Double]) + : Seq[Elem] = { val recordDist = getDistributionQuantiles(records).iterator getDistributionQuantiles(data).map(d => {s"${Utils.bytesToString(d.toLong)} / ${recordDist.next().toLong}"} diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagesTab.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagesTab.scala index 937261de00e3a..1bd2d87e00796 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/StagesTab.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagesTab.scala @@ -32,10 +32,10 @@ private[ui] class StagesTab(parent: SparkUI) extends SparkUITab(parent, "stages" attachPage(new StagePage(this)) attachPage(new PoolPage(this)) - def isFairScheduler = listener.schedulingMode.exists(_ == SchedulingMode.FAIR) + def isFairScheduler: Boolean = listener.schedulingMode.exists(_ == SchedulingMode.FAIR) - def handleKillRequest(request: HttpServletRequest) = { - if ((killEnabled) && (parent.securityManager.checkModifyPermissions(request.getRemoteUser))) { + def handleKillRequest(request: HttpServletRequest): Unit = { + if (killEnabled && parent.securityManager.checkModifyPermissions(request.getRemoteUser)) { val killFlag = Option(request.getParameter("terminate")).getOrElse("false").toBoolean val stageId = Option(request.getParameter("id")).getOrElse("-1").toInt if (stageId >= 0 && killFlag && listener.activeStages.contains(stageId)) { diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala b/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala index dbf1ceeda1878..711a3697bda15 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala @@ -94,11 +94,11 @@ private[jobs] object UIData { var taskData = new HashMap[Long, TaskUIData] var executorSummary = new HashMap[String, ExecutorSummary] - def hasInput = inputBytes > 0 - def hasOutput = outputBytes > 0 - def hasShuffleRead = shuffleReadTotalBytes > 0 - def hasShuffleWrite = shuffleWriteBytes > 0 - def hasBytesSpilled = memoryBytesSpilled > 0 && diskBytesSpilled > 0 + def hasInput: Boolean = inputBytes > 0 + def hasOutput: Boolean = outputBytes > 0 + def hasShuffleRead: Boolean = shuffleReadTotalBytes > 0 + def hasShuffleWrite: Boolean = shuffleWriteBytes > 0 + def hasBytesSpilled: Boolean = memoryBytesSpilled > 0 && diskBytesSpilled > 0 } /** diff --git a/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala b/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala index a81291d505583..045bd784990d1 100644 --- a/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala +++ b/core/src/main/scala/org/apache/spark/ui/storage/StorageTab.scala @@ -40,10 +40,10 @@ private[ui] class StorageTab(parent: SparkUI) extends SparkUITab(parent, "storag class StorageListener(storageStatusListener: StorageStatusListener) extends SparkListener { private[ui] val _rddInfoMap = mutable.Map[Int, RDDInfo]() // exposed for testing - def storageStatusList = storageStatusListener.storageStatusList + def storageStatusList: Seq[StorageStatus] = storageStatusListener.storageStatusList /** Filter RDD info to include only those with cached partitions */ - def rddInfoList = _rddInfoMap.values.filter(_.numCachedPartitions > 0).toSeq + def rddInfoList: Seq[RDDInfo] = _rddInfoMap.values.filter(_.numCachedPartitions > 0).toSeq /** Update the storage info of the RDDs whose blocks are among the given updated blocks */ private def updateRDDInfo(updatedBlocks: Seq[(BlockId, BlockStatus)]): Unit = { @@ -56,19 +56,19 @@ class StorageListener(storageStatusListener: StorageStatusListener) extends Spar * Assumes the storage status list is fully up-to-date. This implies the corresponding * StorageStatusSparkListener must process the SparkListenerTaskEnd event before this listener. */ - override def onTaskEnd(taskEnd: SparkListenerTaskEnd) = synchronized { + override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = synchronized { val metrics = taskEnd.taskMetrics if (metrics != null && metrics.updatedBlocks.isDefined) { updateRDDInfo(metrics.updatedBlocks.get) } } - override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) = synchronized { + override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted): Unit = synchronized { val rddInfos = stageSubmitted.stageInfo.rddInfos rddInfos.foreach { info => _rddInfoMap.getOrElseUpdate(info.id, info) } } - override def onStageCompleted(stageCompleted: SparkListenerStageCompleted) = synchronized { + override def onStageCompleted(stageCompleted: SparkListenerStageCompleted): Unit = synchronized { // Remove all partitions that are no longer cached in current completed stage val completedRddIds = stageCompleted.stageInfo.rddInfos.map(r => r.id).toSet _rddInfoMap.retain { case (id, info) => @@ -76,7 +76,7 @@ class StorageListener(storageStatusListener: StorageStatusListener) extends Spar } } - override def onUnpersistRDD(unpersistRDD: SparkListenerUnpersistRDD) = synchronized { + override def onUnpersistRDD(unpersistRDD: SparkListenerUnpersistRDD): Unit = synchronized { _rddInfoMap.remove(unpersistRDD.rddId) } } diff --git a/core/src/main/scala/org/apache/spark/util/CompletionIterator.scala b/core/src/main/scala/org/apache/spark/util/CompletionIterator.scala index 390310243ee0a..9044aaeef2d48 100644 --- a/core/src/main/scala/org/apache/spark/util/CompletionIterator.scala +++ b/core/src/main/scala/org/apache/spark/util/CompletionIterator.scala @@ -27,8 +27,8 @@ abstract class CompletionIterator[ +A, +I <: Iterator[A]](sub: I) extends Iterat // scalastyle:on private[this] var completed = false - def next() = sub.next() - def hasNext = { + def next(): A = sub.next() + def hasNext: Boolean = { val r = sub.hasNext if (!r && !completed) { completed = true @@ -37,13 +37,13 @@ abstract class CompletionIterator[ +A, +I <: Iterator[A]](sub: I) extends Iterat r } - def completion() + def completion(): Unit } private[spark] object CompletionIterator { - def apply[A, I <: Iterator[A]](sub: I, completionFunction: => Unit) : CompletionIterator[A,I] = { + def apply[A, I <: Iterator[A]](sub: I, completionFunction: => Unit) : CompletionIterator[A, I] = { new CompletionIterator[A,I](sub) { - def completion() = completionFunction + def completion(): Unit = completionFunction } } } diff --git a/core/src/main/scala/org/apache/spark/util/Distribution.scala b/core/src/main/scala/org/apache/spark/util/Distribution.scala index a465298c8c5ab..9aea8efa38c7a 100644 --- a/core/src/main/scala/org/apache/spark/util/Distribution.scala +++ b/core/src/main/scala/org/apache/spark/util/Distribution.scala @@ -57,7 +57,7 @@ private[spark] class Distribution(val data: Array[Double], val startIdx: Int, va out.println } - def statCounter = StatCounter(data.slice(startIdx, endIdx)) + def statCounter: StatCounter = StatCounter(data.slice(startIdx, endIdx)) /** * print a summary of this distribution to the given PrintStream. diff --git a/core/src/main/scala/org/apache/spark/util/ManualClock.scala b/core/src/main/scala/org/apache/spark/util/ManualClock.scala index cf89c1782fd67..1718554061985 100644 --- a/core/src/main/scala/org/apache/spark/util/ManualClock.scala +++ b/core/src/main/scala/org/apache/spark/util/ManualClock.scala @@ -39,31 +39,27 @@ private[spark] class ManualClock(private var time: Long) extends Clock { /** * @param timeToSet new time (in milliseconds) that the clock should represent */ - def setTime(timeToSet: Long) = - synchronized { - time = timeToSet - notifyAll() - } + def setTime(timeToSet: Long): Unit = synchronized { + time = timeToSet + notifyAll() + } /** * @param timeToAdd time (in milliseconds) to add to the clock's time */ - def advance(timeToAdd: Long) = - synchronized { - time += timeToAdd - notifyAll() - } + def advance(timeToAdd: Long): Unit = synchronized { + time += timeToAdd + notifyAll() + } /** * @param targetTime block until the clock time is set or advanced to at least this time * @return current time reported by the clock when waiting finishes */ - def waitTillTime(targetTime: Long): Long = - synchronized { - while (time < targetTime) { - wait(100) - } - getTimeMillis() + def waitTillTime(targetTime: Long): Long = synchronized { + while (time < targetTime) { + wait(100) } - + getTimeMillis() + } } diff --git a/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala b/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala index ac40f19ed6799..375ed430bde45 100644 --- a/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala +++ b/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala @@ -67,14 +67,15 @@ private[spark] object MetadataCleanerType extends Enumeration { type MetadataCleanerType = Value - def systemProperty(which: MetadataCleanerType.MetadataCleanerType) = - "spark.cleaner.ttl." + which.toString + def systemProperty(which: MetadataCleanerType.MetadataCleanerType): String = { + "spark.cleaner.ttl." + which.toString + } } // TODO: This mutates a Conf to set properties right now, which is kind of ugly when used in the // initialization of StreamingContext. It's okay for users trying to configure stuff themselves. private[spark] object MetadataCleaner { - def getDelaySeconds(conf: SparkConf) = { + def getDelaySeconds(conf: SparkConf): Int = { conf.getInt("spark.cleaner.ttl", -1) } diff --git a/core/src/main/scala/org/apache/spark/util/MutablePair.scala b/core/src/main/scala/org/apache/spark/util/MutablePair.scala index 74fa77b68de0b..dad888548ed10 100644 --- a/core/src/main/scala/org/apache/spark/util/MutablePair.scala +++ b/core/src/main/scala/org/apache/spark/util/MutablePair.scala @@ -43,7 +43,7 @@ case class MutablePair[@specialized(Int, Long, Double, Char, Boolean/* , AnyRef this } - override def toString = "(" + _1 + "," + _2 + ")" + override def toString: String = "(" + _1 + "," + _2 + ")" override def canEqual(that: Any): Boolean = that.isInstanceOf[MutablePair[_,_]] } diff --git a/core/src/main/scala/org/apache/spark/util/ParentClassLoader.scala b/core/src/main/scala/org/apache/spark/util/ParentClassLoader.scala index 6d8d9e8da3678..73d126ff6254e 100644 --- a/core/src/main/scala/org/apache/spark/util/ParentClassLoader.scala +++ b/core/src/main/scala/org/apache/spark/util/ParentClassLoader.scala @@ -22,7 +22,7 @@ package org.apache.spark.util */ private[spark] class ParentClassLoader(parent: ClassLoader) extends ClassLoader(parent) { - override def findClass(name: String) = { + override def findClass(name: String): Class[_] = { super.findClass(name) } diff --git a/core/src/main/scala/org/apache/spark/util/SerializableBuffer.scala b/core/src/main/scala/org/apache/spark/util/SerializableBuffer.scala index 770ff9d5ad6ae..a06b6f84ef11b 100644 --- a/core/src/main/scala/org/apache/spark/util/SerializableBuffer.scala +++ b/core/src/main/scala/org/apache/spark/util/SerializableBuffer.scala @@ -27,7 +27,7 @@ import java.nio.channels.Channels */ private[spark] class SerializableBuffer(@transient var buffer: ByteBuffer) extends Serializable { - def value = buffer + def value: ByteBuffer = buffer private def readObject(in: ObjectInputStream): Unit = Utils.tryOrIOException { val length = in.readInt() diff --git a/core/src/main/scala/org/apache/spark/util/StatCounter.scala b/core/src/main/scala/org/apache/spark/util/StatCounter.scala index d80eed455c427..8586da1996cf3 100644 --- a/core/src/main/scala/org/apache/spark/util/StatCounter.scala +++ b/core/src/main/scala/org/apache/spark/util/StatCounter.scala @@ -141,8 +141,8 @@ class StatCounter(values: TraversableOnce[Double]) extends Serializable { object StatCounter { /** Build a StatCounter from a list of values. */ - def apply(values: TraversableOnce[Double]) = new StatCounter(values) + def apply(values: TraversableOnce[Double]): StatCounter = new StatCounter(values) /** Build a StatCounter from a list of values passed as variable-length arguments. */ - def apply(values: Double*) = new StatCounter(values) + def apply(values: Double*): StatCounter = new StatCounter(values) } diff --git a/core/src/main/scala/org/apache/spark/util/TimeStampedWeakValueHashMap.scala b/core/src/main/scala/org/apache/spark/util/TimeStampedWeakValueHashMap.scala index f5be5856c2109..310c0c109416c 100644 --- a/core/src/main/scala/org/apache/spark/util/TimeStampedWeakValueHashMap.scala +++ b/core/src/main/scala/org/apache/spark/util/TimeStampedWeakValueHashMap.scala @@ -82,7 +82,7 @@ private[spark] class TimeStampedWeakValueHashMap[A, B](updateTimeStampOnGet: Boo this } - override def update(key: A, value: B) = this += ((key, value)) + override def update(key: A, value: B): Unit = this += ((key, value)) override def apply(key: A): B = internalMap.apply(key) @@ -92,14 +92,14 @@ private[spark] class TimeStampedWeakValueHashMap[A, B](updateTimeStampOnGet: Boo override def size: Int = internalMap.size - override def foreach[U](f: ((A, B)) => U) = nonNullReferenceMap.foreach(f) + override def foreach[U](f: ((A, B)) => U): Unit = nonNullReferenceMap.foreach(f) def putIfAbsent(key: A, value: B): Option[B] = internalMap.putIfAbsent(key, value) def toMap: Map[A, B] = iterator.toMap /** Remove old key-value pairs with timestamps earlier than `threshTime`. */ - def clearOldValues(threshTime: Long) = internalMap.clearOldValues(threshTime) + def clearOldValues(threshTime: Long): Unit = internalMap.clearOldValues(threshTime) /** Remove entries with values that are no longer strongly reachable. */ def clearNullValues() { diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index fa56bb09e4e5c..d9a671687aad0 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -85,7 +85,7 @@ private[spark] object Utils extends Logging { def deserialize[T](bytes: Array[Byte], loader: ClassLoader): T = { val bis = new ByteArrayInputStream(bytes) val ois = new ObjectInputStream(bis) { - override def resolveClass(desc: ObjectStreamClass) = + override def resolveClass(desc: ObjectStreamClass): Class[_] = Class.forName(desc.getName, false, loader) } ois.readObject.asInstanceOf[T] @@ -106,11 +106,10 @@ private[spark] object Utils extends Logging { /** Serialize via nested stream using specific serializer */ def serializeViaNestedStream(os: OutputStream, ser: SerializerInstance)( - f: SerializationStream => Unit) = { + f: SerializationStream => Unit): Unit = { val osWrapper = ser.serializeStream(new OutputStream { - def write(b: Int) = os.write(b) - - override def write(b: Array[Byte], off: Int, len: Int) = os.write(b, off, len) + override def write(b: Int): Unit = os.write(b) + override def write(b: Array[Byte], off: Int, len: Int): Unit = os.write(b, off, len) }) try { f(osWrapper) @@ -121,10 +120,9 @@ private[spark] object Utils extends Logging { /** Deserialize via nested stream using specific serializer */ def deserializeViaNestedStream(is: InputStream, ser: SerializerInstance)( - f: DeserializationStream => Unit) = { + f: DeserializationStream => Unit): Unit = { val isWrapper = ser.deserializeStream(new InputStream { - def read(): Int = is.read() - + override def read(): Int = is.read() override def read(b: Array[Byte], off: Int, len: Int): Int = is.read(b, off, len) }) try { @@ -137,7 +135,7 @@ private[spark] object Utils extends Logging { /** * Get the ClassLoader which loaded Spark. */ - def getSparkClassLoader = getClass.getClassLoader + def getSparkClassLoader: ClassLoader = getClass.getClassLoader /** * Get the Context ClassLoader on this thread or, if not present, the ClassLoader that @@ -146,7 +144,7 @@ private[spark] object Utils extends Logging { * This should be used whenever passing a ClassLoader to Class.ForName or finding the currently * active loader when setting up ClassLoader delegation chains. */ - def getContextOrSparkClassLoader = + def getContextOrSparkClassLoader: ClassLoader = Option(Thread.currentThread().getContextClassLoader).getOrElse(getSparkClassLoader) /** Determines whether the provided class is loadable in the current thread. */ @@ -155,12 +153,14 @@ private[spark] object Utils extends Logging { } /** Preferred alternative to Class.forName(className) */ - def classForName(className: String) = Class.forName(className, true, getContextOrSparkClassLoader) + def classForName(className: String): Class[_] = { + Class.forName(className, true, getContextOrSparkClassLoader) + } /** * Primitive often used when writing [[java.nio.ByteBuffer]] to [[java.io.DataOutput]] */ - def writeByteBuffer(bb: ByteBuffer, out: ObjectOutput) = { + def writeByteBuffer(bb: ByteBuffer, out: ObjectOutput): Unit = { if (bb.hasArray) { out.write(bb.array(), bb.arrayOffset() + bb.position(), bb.remaining()) } else { @@ -1557,7 +1557,7 @@ private[spark] object Utils extends Logging { /** Return the class name of the given object, removing all dollar signs */ - def getFormattedClassName(obj: AnyRef) = { + def getFormattedClassName(obj: AnyRef): String = { obj.getClass.getSimpleName.replace("$", "") } @@ -1570,7 +1570,7 @@ private[spark] object Utils extends Logging { } /** Return an empty JSON object */ - def emptyJson = JObject(List[JField]()) + def emptyJson: JsonAST.JObject = JObject(List[JField]()) /** * Return a Hadoop FileSystem with the scheme encoded in the given path. @@ -1618,7 +1618,7 @@ private[spark] object Utils extends Logging { /** * Indicates whether Spark is currently running unit tests. */ - def isTesting = { + def isTesting: Boolean = { sys.env.contains("SPARK_TESTING") || sys.props.contains("spark.testing") } diff --git a/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala b/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala index af1f64649f354..f79e8e0491ea1 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala @@ -156,10 +156,10 @@ class BitSet(numBits: Int) extends Serializable { /** * Get an iterator over the set bits. */ - def iterator = new Iterator[Int] { + def iterator: Iterator[Int] = new Iterator[Int] { var ind = nextSetBit(0) override def hasNext: Boolean = ind >= 0 - override def next() = { + override def next(): Int = { val tmp = ind ind = nextSetBit(ind + 1) tmp diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala index 8a0f5a602de12..9ff4744593d4d 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala @@ -159,7 +159,7 @@ class ExternalAppendOnlyMap[K, V, C]( val batchSizes = new ArrayBuffer[Long] // Flush the disk writer's contents to disk, and update relevant variables - def flush() = { + def flush(): Unit = { val w = writer writer = null w.commitAndClose() @@ -355,7 +355,7 @@ class ExternalAppendOnlyMap[K, V, C]( val pairs: ArrayBuffer[(K, C)]) extends Comparable[StreamBuffer] { - def isEmpty = pairs.length == 0 + def isEmpty: Boolean = pairs.length == 0 // Invalid if there are no more pairs in this stream def minKeyHash: Int = { diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala index d69f2d9048055..3262e670c2030 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala @@ -283,7 +283,7 @@ private[spark] class ExternalSorter[K, V, C]( // Flush the disk writer's contents to disk, and update relevant variables. // The writer is closed at the end of this process, and cannot be reused. - def flush() = { + def flush(): Unit = { val w = writer writer = null w.commitAndClose() diff --git a/core/src/main/scala/org/apache/spark/util/collection/OpenHashMap.scala b/core/src/main/scala/org/apache/spark/util/collection/OpenHashMap.scala index b8de4ff9aa494..c52591b352340 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/OpenHashMap.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/OpenHashMap.scala @@ -109,7 +109,7 @@ class OpenHashMap[K : ClassTag, @specialized(Long, Int, Double) V: ClassTag]( } } - override def iterator = new Iterator[(K, V)] { + override def iterator: Iterator[(K, V)] = new Iterator[(K, V)] { var pos = -1 var nextPair: (K, V) = computeNextPair() @@ -132,9 +132,9 @@ class OpenHashMap[K : ClassTag, @specialized(Long, Int, Double) V: ClassTag]( } } - def hasNext = nextPair != null + def hasNext: Boolean = nextPair != null - def next() = { + def next(): (K, V) = { val pair = nextPair nextPair = computeNextPair() pair diff --git a/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala b/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala index 4e363b74f4bef..c80057f95e0b2 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala @@ -85,7 +85,7 @@ class OpenHashSet[@specialized(Long, Int) T: ClassTag]( protected var _bitset = new BitSet(_capacity) - def getBitSet = _bitset + def getBitSet: BitSet = _bitset // Init of the array in constructor (instead of in declaration) to work around a Scala compiler // specialization bug that would generate two arrays (one for Object and one for specialized T). @@ -183,7 +183,7 @@ class OpenHashSet[@specialized(Long, Int) T: ClassTag]( /** Return the value at the specified position. */ def getValue(pos: Int): T = _data(pos) - def iterator = new Iterator[T] { + def iterator: Iterator[T] = new Iterator[T] { var pos = nextPos(0) override def hasNext: Boolean = pos != INVALID_POS override def next(): T = { diff --git a/core/src/main/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMap.scala b/core/src/main/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMap.scala index 2e1ef06cbc4e1..61e22642761f0 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMap.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMap.scala @@ -46,7 +46,7 @@ class PrimitiveKeyOpenHashMap[@specialized(Long, Int) K: ClassTag, private var _oldValues: Array[V] = null - override def size = _keySet.size + override def size: Int = _keySet.size /** Get the value for a given key */ def apply(k: K): V = { @@ -87,7 +87,7 @@ class PrimitiveKeyOpenHashMap[@specialized(Long, Int) K: ClassTag, } } - override def iterator = new Iterator[(K, V)] { + override def iterator: Iterator[(K, V)] = new Iterator[(K, V)] { var pos = 0 var nextPair: (K, V) = computeNextPair() @@ -103,9 +103,9 @@ class PrimitiveKeyOpenHashMap[@specialized(Long, Int) K: ClassTag, } } - def hasNext = nextPair != null + def hasNext: Boolean = nextPair != null - def next() = { + def next(): (K, V) = { val pair = nextPair nextPair = computeNextPair() pair diff --git a/core/src/main/scala/org/apache/spark/util/collection/Utils.scala b/core/src/main/scala/org/apache/spark/util/collection/Utils.scala index c5268c0fae0ef..bdbca00a00622 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/Utils.scala @@ -32,7 +32,7 @@ private[spark] object Utils { */ def takeOrdered[T](input: Iterator[T], num: Int)(implicit ord: Ordering[T]): Iterator[T] = { val ordering = new GuavaOrdering[T] { - override def compare(l: T, r: T) = ord.compare(l, r) + override def compare(l: T, r: T): Int = ord.compare(l, r) } collectionAsScalaIterable(ordering.leastOf(asJavaIterator(input), num)).iterator } diff --git a/core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala b/core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala index 1d5467060623c..14b6ba4af489a 100644 --- a/core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala +++ b/core/src/main/scala/org/apache/spark/util/logging/FileAppender.scala @@ -121,7 +121,7 @@ private[spark] object FileAppender extends Logging { val rollingSizeBytes = conf.get(SIZE_PROPERTY, STRATEGY_DEFAULT) val rollingInterval = conf.get(INTERVAL_PROPERTY, INTERVAL_DEFAULT) - def createTimeBasedAppender() = { + def createTimeBasedAppender(): FileAppender = { val validatedParams: Option[(Long, String)] = rollingInterval match { case "daily" => logInfo(s"Rolling executor logs enabled for $file with daily rolling") @@ -149,7 +149,7 @@ private[spark] object FileAppender extends Logging { } } - def createSizeBasedAppender() = { + def createSizeBasedAppender(): FileAppender = { rollingSizeBytes match { case IntParam(bytes) => logInfo(s"Rolling executor logs enabled for $file with rolling every $bytes bytes") diff --git a/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala b/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala index 76e7a2760bcd1..786b97ad7b9ec 100644 --- a/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala +++ b/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala @@ -105,7 +105,7 @@ class BernoulliCellSampler[T](lb: Double, ub: Double, complement: Boolean = fals private val rng: Random = new XORShiftRandom - override def setSeed(seed: Long) = rng.setSeed(seed) + override def setSeed(seed: Long): Unit = rng.setSeed(seed) override def sample(items: Iterator[T]): Iterator[T] = { if (ub - lb <= 0.0) { @@ -131,7 +131,7 @@ class BernoulliCellSampler[T](lb: Double, ub: Double, complement: Boolean = fals def cloneComplement(): BernoulliCellSampler[T] = new BernoulliCellSampler[T](lb, ub, !complement) - override def clone = new BernoulliCellSampler[T](lb, ub, complement) + override def clone: BernoulliCellSampler[T] = new BernoulliCellSampler[T](lb, ub, complement) } @@ -153,7 +153,7 @@ class BernoulliSampler[T: ClassTag](fraction: Double) extends RandomSampler[T, T private val rng: Random = RandomSampler.newDefaultRNG - override def setSeed(seed: Long) = rng.setSeed(seed) + override def setSeed(seed: Long): Unit = rng.setSeed(seed) override def sample(items: Iterator[T]): Iterator[T] = { if (fraction <= 0.0) { @@ -167,7 +167,7 @@ class BernoulliSampler[T: ClassTag](fraction: Double) extends RandomSampler[T, T } } - override def clone = new BernoulliSampler[T](fraction) + override def clone: BernoulliSampler[T] = new BernoulliSampler[T](fraction) } @@ -209,7 +209,7 @@ class PoissonSampler[T: ClassTag](fraction: Double) extends RandomSampler[T, T] } } - override def clone = new PoissonSampler[T](fraction) + override def clone: PoissonSampler[T] = new PoissonSampler[T](fraction) } @@ -228,15 +228,18 @@ class GapSamplingIterator[T: ClassTag]( val arrayClass = Array.empty[T].iterator.getClass val arrayBufferClass = ArrayBuffer.empty[T].iterator.getClass data.getClass match { - case `arrayClass` => ((n: Int) => { data = data.drop(n) }) - case `arrayBufferClass` => ((n: Int) => { data = data.drop(n) }) - case _ => ((n: Int) => { + case `arrayClass` => + (n: Int) => { data = data.drop(n) } + case `arrayBufferClass` => + (n: Int) => { data = data.drop(n) } + case _ => + (n: Int) => { var j = 0 while (j < n && data.hasNext) { data.next() j += 1 } - }) + } } } @@ -244,21 +247,21 @@ class GapSamplingIterator[T: ClassTag]( override def next(): T = { val r = data.next() - advance + advance() r } private val lnq = math.log1p(-f) /** skip elements that won't be sampled, according to geometric dist P(k) = (f)(1-f)^k. */ - private def advance: Unit = { + private def advance(): Unit = { val u = math.max(rng.nextDouble(), epsilon) val k = (math.log(u) / lnq).toInt iterDrop(k) } /** advance to first sample as part of object construction. */ - advance + advance() // Attempting to invoke this closer to the top with other object initialization // was causing it to break in strange ways, so I'm invoking it last, which seems to // work reliably. @@ -279,15 +282,18 @@ class GapSamplingReplacementIterator[T: ClassTag]( val arrayClass = Array.empty[T].iterator.getClass val arrayBufferClass = ArrayBuffer.empty[T].iterator.getClass data.getClass match { - case `arrayClass` => ((n: Int) => { data = data.drop(n) }) - case `arrayBufferClass` => ((n: Int) => { data = data.drop(n) }) - case _ => ((n: Int) => { + case `arrayClass` => + (n: Int) => { data = data.drop(n) } + case `arrayBufferClass` => + (n: Int) => { data = data.drop(n) } + case _ => + (n: Int) => { var j = 0 while (j < n && data.hasNext) { data.next() j += 1 } - }) + } } } @@ -300,7 +306,7 @@ class GapSamplingReplacementIterator[T: ClassTag]( override def next(): T = { val r = v rep -= 1 - if (rep <= 0) advance + if (rep <= 0) advance() r } @@ -309,7 +315,7 @@ class GapSamplingReplacementIterator[T: ClassTag]( * Samples 'k' from geometric distribution P(k) = (1-q)(q)^k, where q = e^(-f), that is * q is the probabililty of Poisson(0; f) */ - private def advance: Unit = { + private def advance(): Unit = { val u = math.max(rng.nextDouble(), epsilon) val k = (math.log(u) / (-f)).toInt iterDrop(k) @@ -343,7 +349,7 @@ class GapSamplingReplacementIterator[T: ClassTag]( } /** advance to first sample as part of object construction. */ - advance + advance() // Attempting to invoke this closer to the top with other object initialization // was causing it to break in strange ways, so I'm invoking it last, which seems to // work reliably. diff --git a/core/src/main/scala/org/apache/spark/util/random/StratifiedSamplingUtils.scala b/core/src/main/scala/org/apache/spark/util/random/StratifiedSamplingUtils.scala index 2ae308dacf1ae..9e29bf9d61f17 100644 --- a/core/src/main/scala/org/apache/spark/util/random/StratifiedSamplingUtils.scala +++ b/core/src/main/scala/org/apache/spark/util/random/StratifiedSamplingUtils.scala @@ -311,7 +311,7 @@ private[random] class AcceptanceResult(var numItems: Long = 0L, var numAccepted: var acceptBound: Double = Double.NaN // upper bound for accepting item instantly var waitListBound: Double = Double.NaN // upper bound for adding item to waitlist - def areBoundsEmpty = acceptBound.isNaN || waitListBound.isNaN + def areBoundsEmpty: Boolean = acceptBound.isNaN || waitListBound.isNaN def merge(other: Option[AcceptanceResult]): Unit = { if (other.isDefined) { diff --git a/core/src/main/scala/org/apache/spark/util/random/XORShiftRandom.scala b/core/src/main/scala/org/apache/spark/util/random/XORShiftRandom.scala index 467b890fb4bb9..c4a7b4441c85c 100644 --- a/core/src/main/scala/org/apache/spark/util/random/XORShiftRandom.scala +++ b/core/src/main/scala/org/apache/spark/util/random/XORShiftRandom.scala @@ -83,7 +83,7 @@ private[spark] object XORShiftRandom { * @return Map of execution times for {@link java.util.Random java.util.Random} * and XORShift */ - def benchmark(numIters: Int) = { + def benchmark(numIters: Int): Map[String, Long] = { val seed = 1L val million = 1e6.toInt diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala index 328d59485a731..56f5dbe53fad4 100644 --- a/project/MimaExcludes.scala +++ b/project/MimaExcludes.scala @@ -44,7 +44,13 @@ object MimaExcludes { // the maven-generated artifacts in 1.3. excludePackage("org.spark-project.jetty"), MimaBuild.excludeSparkPackage("unused"), - ProblemFilters.exclude[MissingClassProblem]("com.google.common.base.Optional") + ProblemFilters.exclude[MissingClassProblem]("com.google.common.base.Optional"), + ProblemFilters.exclude[IncompatibleResultTypeProblem]( + "org.apache.spark.rdd.JdbcRDD.compute"), + ProblemFilters.exclude[IncompatibleResultTypeProblem]( + "org.apache.spark.broadcast.HttpBroadcastFactory.newBroadcast"), + ProblemFilters.exclude[IncompatibleResultTypeProblem]( + "org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast") ) case v if v.startsWith("1.3") => diff --git a/scalastyle-config.xml b/scalastyle-config.xml index 0ff521706c71a..459a5035d4984 100644 --- a/scalastyle-config.xml +++ b/scalastyle-config.xml @@ -137,9 +137,9 @@ - + - + From 1afcf773d0cafdfd9bf106fdc5c429ed2ba3dd36 Mon Sep 17 00:00:00 2001 From: Cheng Lian Date: Tue, 24 Mar 2015 01:12:11 -0700 Subject: [PATCH 021/591] [SPARK-6452] [SQL] Checks for missing attributes and unresolved operator for all types of operator In `CheckAnalysis`, `Filter` and `Aggregate` are checked in separate case clauses, thus never hit those clauses for unresolved operators and missing input attributes. This PR also removes the `prettyString` call when generating error message for missing input attributes. Because result of `prettyString` doesn't contain expression ID, and may give confusing messages like > resolved attributes a missing from a cc rxin [Review on Reviewable](https://reviewable.io/reviews/apache/spark/5129) Author: Cheng Lian Closes #5129 from liancheng/spark-6452 and squashes the following commits: 52cdc69 [Cheng Lian] Addresses comments 029f9bd [Cheng Lian] Checks for missing attributes and unresolved operator for all types of operator --- .../sql/catalyst/analysis/CheckAnalysis.scala | 15 ++++++++++----- .../spark/sql/catalyst/plans/QueryPlan.scala | 7 +++++-- .../sql/catalyst/analysis/AnalysisSuite.scala | 18 ++++++++++++++++++ 3 files changed, 33 insertions(+), 7 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala index fb975ee5e7296..425e1e41cbf21 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala @@ -63,7 +63,7 @@ class CheckAnalysis { s"filter expression '${f.condition.prettyString}' " + s"of type ${f.condition.dataType.simpleString} is not a boolean.") - case aggregatePlan@Aggregate(groupingExprs, aggregateExprs, child) => + case Aggregate(groupingExprs, aggregateExprs, child) => def checkValidAggregateExpression(expr: Expression): Unit = expr match { case _: AggregateExpression => // OK case e: Attribute if !groupingExprs.contains(e) => @@ -85,13 +85,18 @@ class CheckAnalysis { cleaned.foreach(checkValidAggregateExpression) + case _ => // Fallbacks to the following checks + } + + operator match { case o if o.children.nonEmpty && o.missingInput.nonEmpty => - val missingAttributes = o.missingInput.map(_.prettyString).mkString(",") - val input = o.inputSet.map(_.prettyString).mkString(",") + val missingAttributes = o.missingInput.mkString(",") + val input = o.inputSet.mkString(",") - failAnalysis(s"resolved attributes $missingAttributes missing from $input") + failAnalysis( + s"resolved attribute(s) $missingAttributes missing from $input " + + s"in operator ${operator.simpleString}") - // Catch all case o if !o.resolved => failAnalysis( s"unresolved operator ${operator.simpleString}") diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala index 400a6b2825c10..48191f31198f3 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala @@ -47,9 +47,12 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy * Attributes that are referenced by expressions but not provided by this nodes children. * Subclasses should override this method if they produce attributes internally as it is used by * assertions designed to prevent the construction of invalid plans. + * + * Note that virtual columns should be excluded. Currently, we only support the grouping ID + * virtual column. */ - def missingInput: AttributeSet = (references -- inputSet) - .filter(_.name != VirtualColumn.groupingIdName) + def missingInput: AttributeSet = + (references -- inputSet).filter(_.name != VirtualColumn.groupingIdName) /** * Runs [[transform]] with `rule` on all expressions present in this query operator. diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala index c1dd5aa913ddc..359aec4a7b5ab 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala @@ -199,4 +199,22 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { assert(pl(3).dataType == DecimalType.Unlimited) assert(pl(4).dataType == DoubleType) } + + test("SPARK-6452 regression test") { + // CheckAnalysis should throw AnalysisException when Aggregate contains missing attribute(s) + val plan = + Aggregate( + Nil, + Alias(Sum(AttributeReference("a", StringType)(exprId = ExprId(1))), "b")() :: Nil, + LocalRelation( + AttributeReference("a", StringType)(exprId = ExprId(2)))) + + assert(plan.resolved) + + val message = intercept[AnalysisException] { + caseSensitiveAnalyze(plan) + }.getMessage + + assert(message.contains("resolved attribute(s) a#1 missing from a#2")) + } } From 37fac1dcd2b0f2845d2952a417fcf85d40351f57 Mon Sep 17 00:00:00 2001 From: Brennon York Date: Tue, 24 Mar 2015 10:33:04 +0000 Subject: [PATCH 022/591] [SPARK-6477][Build]: Run MIMA tests before the Spark test suite This moves the MIMA checks to before the full Spark test suite such that, if new PR's fail the MIMA check, they will return much faster having not run the entire test suite. This is preferable to the current scenario where a user would have to wait until the entire test suite completes before realizing it failed on a MIMA check in which case, once the MIMA issues are fixed, the user would have to resubmit and rerun the full test suite again. Author: Brennon York Closes #5145 from brennonyork/SPARK-6477 and squashes the following commits: 12b0aee [Brennon York] updated to put the mima checks before the spark test suite --- dev/run-tests | 18 +++++++++--------- dev/run-tests-codes.sh | 6 +++--- dev/run-tests-jenkins | 4 ++-- 3 files changed, 14 insertions(+), 14 deletions(-) diff --git a/dev/run-tests b/dev/run-tests index d6935a61c6d29..561d7fc9e7b1f 100755 --- a/dev/run-tests +++ b/dev/run-tests @@ -178,6 +178,15 @@ CURRENT_BLOCK=$BLOCK_BUILD fi } +echo "" +echo "=========================================================================" +echo "Detecting binary incompatibilities with MiMa" +echo "=========================================================================" + +CURRENT_BLOCK=$BLOCK_MIMA + +./dev/mima + echo "" echo "=========================================================================" echo "Running Spark unit tests" @@ -227,12 +236,3 @@ echo "=========================================================================" CURRENT_BLOCK=$BLOCK_PYSPARK_UNIT_TESTS ./python/run-tests - -echo "" -echo "=========================================================================" -echo "Detecting binary incompatibilities with MiMa" -echo "=========================================================================" - -CURRENT_BLOCK=$BLOCK_MIMA - -./dev/mima diff --git a/dev/run-tests-codes.sh b/dev/run-tests-codes.sh index 1348e0609dda4..8ab6db6925d6e 100644 --- a/dev/run-tests-codes.sh +++ b/dev/run-tests-codes.sh @@ -22,6 +22,6 @@ readonly BLOCK_RAT=11 readonly BLOCK_SCALA_STYLE=12 readonly BLOCK_PYTHON_STYLE=13 readonly BLOCK_BUILD=14 -readonly BLOCK_SPARK_UNIT_TESTS=15 -readonly BLOCK_PYSPARK_UNIT_TESTS=16 -readonly BLOCK_MIMA=17 +readonly BLOCK_MIMA=15 +readonly BLOCK_SPARK_UNIT_TESTS=16 +readonly BLOCK_PYSPARK_UNIT_TESTS=17 diff --git a/dev/run-tests-jenkins b/dev/run-tests-jenkins index 5f4000e83925c..3a937b637e003 100755 --- a/dev/run-tests-jenkins +++ b/dev/run-tests-jenkins @@ -199,12 +199,12 @@ done failing_test="Python style tests" elif [ "$test_result" -eq "$BLOCK_BUILD" ]; then failing_test="to build" + elif [ "$test_result" -eq "$BLOCK_MIMA" ]; then + failing_test="MiMa tests" elif [ "$test_result" -eq "$BLOCK_SPARK_UNIT_TESTS" ]; then failing_test="Spark unit tests" elif [ "$test_result" -eq "$BLOCK_PYSPARK_UNIT_TESTS" ]; then failing_test="PySpark unit tests" - elif [ "$test_result" -eq "$BLOCK_MIMA" ]; then - failing_test="MiMa tests" else failing_test="some tests" fi From c12312f8b16bb8f9355d5f9e786c5a608863eb01 Mon Sep 17 00:00:00 2001 From: Cong Yue Date: Tue, 24 Mar 2015 12:56:13 +0000 Subject: [PATCH 023/591] Update the command to use IPython notebook As for "notebook --pylab inline" is not supported any more, update the related documentation for this. Author: Cong Yue Closes #5111 from yuecong/patch-1 and squashes the following commits: 872df76 [Cong Yue] Update the command to use IPython notebook --- docs/programming-guide.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/docs/programming-guide.md b/docs/programming-guide.md index 5fe832b6fa100..f5b775da7930a 100644 --- a/docs/programming-guide.md +++ b/docs/programming-guide.md @@ -237,9 +237,13 @@ You can customize the `ipython` command by setting `PYSPARK_DRIVER_PYTHON_OPTS`. the [IPython Notebook](http://ipython.org/notebook.html) with PyLab plot support: {% highlight bash %} -$ PYSPARK_DRIVER_PYTHON=ipython PYSPARK_DRIVER_PYTHON_OPTS="notebook --pylab inline" ./bin/pyspark +$ PYSPARK_DRIVER_PYTHON=ipython PYSPARK_DRIVER_PYTHON_OPTS="notebook" ./bin/pyspark {% endhighlight %} +After the IPython Notebook server is launched, you can create a new "Python 2" notebook from +the "Files" tab. Inside the notebook, you can input the command `%pylab inline` as part of +your notebook before you start to try Spark from the IPython notebook. +
From b293afc42c370da0ff308c16ba4af81289ea6f89 Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Tue, 24 Mar 2015 13:48:33 +0000 Subject: [PATCH 024/591] [SPARK-6473] [core] Do not try to figure out Scala version if not needed... .... Author: Marcelo Vanzin Closes #5143 from vanzin/SPARK-6473 and squashes the following commits: a2e5e2d [Marcelo Vanzin] [SPARK-6473] [core] Do not try to figure out Scala version if not needed. --- .../org/apache/spark/launcher/AbstractCommandBuilder.java | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java index dc90e9e987234..2da5f7278729e 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java +++ b/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java @@ -147,7 +147,6 @@ void addOptionString(List cmd, String options) { */ List buildClassPath(String appClassPath) throws IOException { String sparkHome = getSparkHome(); - String scala = getScalaVersion(); List cp = new ArrayList(); addToClassPath(cp, getenv("SPARK_CLASSPATH")); @@ -158,6 +157,7 @@ List buildClassPath(String appClassPath) throws IOException { boolean prependClasses = !isEmpty(getenv("SPARK_PREPEND_CLASSES")); boolean isTesting = "1".equals(getenv("SPARK_TESTING")); if (prependClasses || isTesting) { + String scala = getScalaVersion(); List projects = Arrays.asList("core", "repl", "mllib", "bagel", "graphx", "streaming", "tools", "sql/catalyst", "sql/core", "sql/hive", "sql/hive-thriftserver", "yarn", "launcher"); @@ -182,7 +182,7 @@ List buildClassPath(String appClassPath) throws IOException { addToClassPath(cp, String.format("%s/core/target/jars/*", sparkHome)); } - String assembly = findAssembly(scala); + String assembly = findAssembly(); addToClassPath(cp, assembly); // When Hive support is needed, Datanucleus jars must be included on the classpath. Datanucleus @@ -330,7 +330,7 @@ String getenv(String key) { return firstNonEmpty(childEnv.get(key), System.getenv(key)); } - private String findAssembly(String scalaVersion) { + private String findAssembly() { String sparkHome = getSparkHome(); File libdir; if (new File(sparkHome, "RELEASE").isFile()) { @@ -338,7 +338,7 @@ private String findAssembly(String scalaVersion) { checkState(libdir.isDirectory(), "Library directory '%s' does not exist.", libdir.getAbsolutePath()); } else { - libdir = new File(sparkHome, String.format("assembly/target/scala-%s", scalaVersion)); + libdir = new File(sparkHome, String.format("assembly/target/scala-%s", getScalaVersion())); } final Pattern re = Pattern.compile("spark-assembly.*hadoop.*\\.jar"); From 85cf0636825d1997d64d0bdc04618f29b7222da1 Mon Sep 17 00:00:00 2001 From: Kousuke Saruta Date: Tue, 24 Mar 2015 16:13:25 +0000 Subject: [PATCH 025/591] [SPARK-5559] [Streaming] [Test] Remove oppotunity we met flakiness when running FlumeStreamSuite When we run FlumeStreamSuite on Jenkins, sometimes we get error like as follows. sbt.ForkMain$ForkError: The code passed to eventually never returned normally. Attempted 52 times over 10.094849836 seconds. Last failure message: Error connecting to localhost/127.0.0.1:23456. at org.scalatest.concurrent.Eventually$class.tryTryAgain$1(Eventually.scala:420) at org.scalatest.concurrent.Eventually$class.eventually(Eventually.scala:438) at org.scalatest.concurrent.Eventually$.eventually(Eventually.scala:478) at org.scalatest.concurrent.Eventually$class.eventually(Eventually.scala:307) at org.scalatest.concurrent.Eventually$.eventually(Eventually.scala:478) at org.apache.spark.streaming.flume.FlumeStreamSuite.writeAndVerify(FlumeStreamSuite.scala:116) at org.apache.spark.streaming.flume.FlumeStreamSuite.org$apache$spark$streaming$flume$FlumeStreamSuite$$testFlumeStream(FlumeStreamSuite.scala:74) at org.apache.spark.streaming.flume.FlumeStreamSuite$$anonfun$3.apply$mcV$sp(FlumeStreamSuite.scala:66) at org.apache.spark.streaming.flume.FlumeStreamSuite$$anonfun$3.apply(FlumeStreamSuite.scala:66) at org.apache.spark.streaming.flume.FlumeStreamSuite$$anonfun$3.apply(FlumeStreamSuite.scala:66) at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22) at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85) at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104) at org.scalatest.Transformer.apply(Transformer.scala:22) at org.scalatest.Transformer.apply(Transformer.scala:20) at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:166) at org.scalatest.Suite$class.withFixture(Suite.scala:1122) at org.scalatest.FunSuite.withFixture(FunSuite.scala:1555) at org.scalatest.FunSuiteLike$class.invokeWithFixture$1(FunSuiteLike.scala:163) at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175) at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175) at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306) at org.scalatest.FunSuiteLike$class.runTest(FunSuiteLike.scala:175) This error is caused by check-then-act logic when it find free-port . /** Find a free port */ private def findFreePort(): Int = { Utils.startServiceOnPort(23456, (trialPort: Int) => { val socket = new ServerSocket(trialPort) socket.close() (null, trialPort) }, conf)._2 } Removing the check-then-act is not easy but we can reduce the chance of having the error by choosing random value for initial port instead of 23456. Author: Kousuke Saruta Closes #4337 from sarutak/SPARK-5559 and squashes the following commits: 16f109f [Kousuke Saruta] Added `require` to Utils#startServiceOnPort c39d8b6 [Kousuke Saruta] Merge branch 'SPARK-5559' of github.com:sarutak/spark into SPARK-5559 1610ba2 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-5559 33357e3 [Kousuke Saruta] Changed "findFreePort" method in MQTTStreamSuite and FlumeStreamSuite so that it can choose valid random port a9029fe [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-5559 9489ef9 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-5559 8212e42 [Kousuke Saruta] Modified default port used in FlumeStreamSuite from 23456 to random value --- core/src/main/scala/org/apache/spark/util/Utils.scala | 4 ++++ .../org/apache/spark/streaming/flume/FlumeStreamSuite.scala | 5 +++-- .../org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala | 4 +++- 3 files changed, 10 insertions(+), 3 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index d9a671687aad0..0b5a914e7dbbf 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -1876,6 +1876,10 @@ private[spark] object Utils extends Logging { startService: Int => (T, Int), conf: SparkConf, serviceName: String = ""): (T, Int) = { + + require(startPort == 0 || (1024 <= startPort && startPort < 65536), + "startPort should be between 1024 and 65535 (inclusive), or 0 for a random free port.") + val serviceString = if (serviceName.isEmpty) "" else s" '$serviceName'" val maxRetries = portMaxRetries(conf) for (offset <- 0 to maxRetries) { diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala index 322de7bf2fed8..51d273af8da84 100644 --- a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala +++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala @@ -28,6 +28,7 @@ import scala.language.postfixOps import com.google.common.base.Charsets import org.apache.avro.ipc.NettyTransceiver import org.apache.avro.ipc.specific.SpecificRequestor +import org.apache.commons.lang3.RandomUtils import org.apache.flume.source.avro import org.apache.flume.source.avro.{AvroFlumeEvent, AvroSourceProtocol} import org.jboss.netty.channel.ChannelPipeline @@ -40,7 +41,6 @@ import org.scalatest.concurrent.Eventually._ import org.apache.spark.{Logging, SparkConf} import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.{Milliseconds, StreamingContext, TestOutputStream} -import org.apache.spark.streaming.scheduler.{StreamingListener, StreamingListenerReceiverStarted} import org.apache.spark.util.Utils class FlumeStreamSuite extends FunSuite with BeforeAndAfter with Matchers with Logging { @@ -76,7 +76,8 @@ class FlumeStreamSuite extends FunSuite with BeforeAndAfter with Matchers with L /** Find a free port */ private def findFreePort(): Int = { - Utils.startServiceOnPort(23456, (trialPort: Int) => { + val candidatePort = RandomUtils.nextInt(1024, 65536) + Utils.startServiceOnPort(candidatePort, (trialPort: Int) => { val socket = new ServerSocket(trialPort) socket.close() (null, trialPort) diff --git a/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala index 0f3298af6234a..24d78ecb3a97d 100644 --- a/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala +++ b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala @@ -25,6 +25,7 @@ import scala.concurrent.duration._ import scala.language.postfixOps import org.apache.activemq.broker.{TransportConnector, BrokerService} +import org.apache.commons.lang3.RandomUtils import org.eclipse.paho.client.mqttv3._ import org.eclipse.paho.client.mqttv3.persist.MqttDefaultFilePersistence @@ -113,7 +114,8 @@ class MQTTStreamSuite extends FunSuite with Eventually with BeforeAndAfter { } private def findFreePort(): Int = { - Utils.startServiceOnPort(23456, (trialPort: Int) => { + val candidatePort = RandomUtils.nextInt(1024, 65536) + Utils.startServiceOnPort(candidatePort, (trialPort: Int) => { val socket = new ServerSocket(trialPort) socket.close() (null, trialPort) From 08d452801195cc6cf0697a594e98cd4778f358ee Mon Sep 17 00:00:00 2001 From: Peter Rudenko Date: Tue, 24 Mar 2015 16:33:38 +0000 Subject: [PATCH 026/591] [ML][docs][minor] Define LabeledDocument/Document classes in CV example To easier copy/paste Cross-Validation example code snippet need to define LabeledDocument/Document in it, since they difined in a previous example. Author: Peter Rudenko Closes #5135 from petro-rudenko/patch-3 and squashes the following commits: 5190c75 [Peter Rudenko] Fix primitive types for java examples. 1d35383 [Peter Rudenko] [SQL][docs][minor] Define LabeledDocument/Document classes in CV example --- docs/ml-guide.md | 51 ++++++++++++++++++++++++++++++++++++++++-------- 1 file changed, 43 insertions(+), 8 deletions(-) diff --git a/docs/ml-guide.md b/docs/ml-guide.md index da6aef7f14c4c..c08c76d226713 100644 --- a/docs/ml-guide.md +++ b/docs/ml-guide.md @@ -408,31 +408,31 @@ import org.apache.spark.sql.SQLContext; // Labeled and unlabeled instance types. // Spark SQL can infer schema from Java Beans. public class Document implements Serializable { - private Long id; + private long id; private String text; - public Document(Long id, String text) { + public Document(long id, String text) { this.id = id; this.text = text; } - public Long getId() { return this.id; } - public void setId(Long id) { this.id = id; } + public long getId() { return this.id; } + public void setId(long id) { this.id = id; } public String getText() { return this.text; } public void setText(String text) { this.text = text; } } public class LabeledDocument extends Document implements Serializable { - private Double label; + private double label; - public LabeledDocument(Long id, String text, Double label) { + public LabeledDocument(long id, String text, double label) { super(id, text); this.label = label; } - public Double getLabel() { return this.label; } - public void setLabel(Double label) { this.label = label; } + public double getLabel() { return this.label; } + public void setLabel(double label) { this.label = label; } } // Set up contexts. @@ -565,6 +565,11 @@ import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator} import org.apache.spark.mllib.linalg.Vector import org.apache.spark.sql.{Row, SQLContext} +// Labeled and unlabeled instance types. +// Spark SQL can infer schema from case classes. +case class LabeledDocument(id: Long, text: String, label: Double) +case class Document(id: Long, text: String) + val conf = new SparkConf().setAppName("CrossValidatorExample") val sc = new SparkContext(conf) val sqlContext = new SQLContext(sc) @@ -655,6 +660,36 @@ import org.apache.spark.sql.DataFrame; import org.apache.spark.sql.Row; import org.apache.spark.sql.SQLContext; +// Labeled and unlabeled instance types. +// Spark SQL can infer schema from Java Beans. +public class Document implements Serializable { + private long id; + private String text; + + public Document(long id, String text) { + this.id = id; + this.text = text; + } + + public long getId() { return this.id; } + public void setId(long id) { this.id = id; } + + public String getText() { return this.text; } + public void setText(String text) { this.text = text; } +} + +public class LabeledDocument extends Document implements Serializable { + private double label; + + public LabeledDocument(long id, String text, double label) { + super(id, text); + this.label = label; + } + + public double getLabel() { return this.label; } + public void setLabel(double label) { this.label = label; } +} + SparkConf conf = new SparkConf().setAppName("JavaCrossValidatorExample"); JavaSparkContext jsc = new JavaSparkContext(conf); SQLContext jsql = new SQLContext(jsc); From a1d1529daebee30b76b954d16a30849407f795d1 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Tue, 24 Mar 2015 10:11:27 -0700 Subject: [PATCH 027/591] [SPARK-6475][SQL] recognize array types when infer data types from JavaBeans Right now if there is a array field in a JavaBean, the user wold see an exception in `createDataFrame`. liancheng Author: Xiangrui Meng Closes #5146 from mengxr/SPARK-6475 and squashes the following commits: 51e87e5 [Xiangrui Meng] validate schemas 4f2df5e [Xiangrui Meng] recognize array types when infer data types from JavaBeans --- .../org/apache/spark/sql/SQLContext.scala | 80 ++++++++++++------- .../apache/spark/sql/JavaDataFrameSuite.java | 41 +++++++++- 2 files changed, 89 insertions(+), 32 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala index dc9912b52dcab..e59cf9b9e037b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala @@ -1210,38 +1210,56 @@ class SQLContext(@transient val sparkContext: SparkContext) * Returns a Catalyst Schema for the given java bean class. */ protected def getSchema(beanClass: Class[_]): Seq[AttributeReference] = { + val (dataType, _) = inferDataType(beanClass) + dataType.asInstanceOf[StructType].fields.map { f => + AttributeReference(f.name, f.dataType, f.nullable)() + } + } + + /** + * Infers the corresponding SQL data type of a Java class. + * @param clazz Java class + * @return (SQL data type, nullable) + */ + private def inferDataType(clazz: Class[_]): (DataType, Boolean) = { // TODO: All of this could probably be moved to Catalyst as it is mostly not Spark specific. - val beanInfo = Introspector.getBeanInfo(beanClass) - - // Note: The ordering of elements may differ from when the schema is inferred in Scala. - // This is because beanInfo.getPropertyDescriptors gives no guarantees about - // element ordering. - val fields = beanInfo.getPropertyDescriptors.filterNot(_.getName == "class") - fields.map { property => - val (dataType, nullable) = property.getPropertyType match { - case c: Class[_] if c.isAnnotationPresent(classOf[SQLUserDefinedType]) => - (c.getAnnotation(classOf[SQLUserDefinedType]).udt().newInstance(), true) - case c: Class[_] if c == classOf[java.lang.String] => (StringType, true) - case c: Class[_] if c == java.lang.Short.TYPE => (ShortType, false) - case c: Class[_] if c == java.lang.Integer.TYPE => (IntegerType, false) - case c: Class[_] if c == java.lang.Long.TYPE => (LongType, false) - case c: Class[_] if c == java.lang.Double.TYPE => (DoubleType, false) - case c: Class[_] if c == java.lang.Byte.TYPE => (ByteType, false) - case c: Class[_] if c == java.lang.Float.TYPE => (FloatType, false) - case c: Class[_] if c == java.lang.Boolean.TYPE => (BooleanType, false) - - case c: Class[_] if c == classOf[java.lang.Short] => (ShortType, true) - case c: Class[_] if c == classOf[java.lang.Integer] => (IntegerType, true) - case c: Class[_] if c == classOf[java.lang.Long] => (LongType, true) - case c: Class[_] if c == classOf[java.lang.Double] => (DoubleType, true) - case c: Class[_] if c == classOf[java.lang.Byte] => (ByteType, true) - case c: Class[_] if c == classOf[java.lang.Float] => (FloatType, true) - case c: Class[_] if c == classOf[java.lang.Boolean] => (BooleanType, true) - case c: Class[_] if c == classOf[java.math.BigDecimal] => (DecimalType(), true) - case c: Class[_] if c == classOf[java.sql.Date] => (DateType, true) - case c: Class[_] if c == classOf[java.sql.Timestamp] => (TimestampType, true) - } - AttributeReference(property.getName, dataType, nullable)() + clazz match { + case c: Class[_] if c.isAnnotationPresent(classOf[SQLUserDefinedType]) => + (c.getAnnotation(classOf[SQLUserDefinedType]).udt().newInstance(), true) + + case c: Class[_] if c == classOf[java.lang.String] => (StringType, true) + case c: Class[_] if c == java.lang.Short.TYPE => (ShortType, false) + case c: Class[_] if c == java.lang.Integer.TYPE => (IntegerType, false) + case c: Class[_] if c == java.lang.Long.TYPE => (LongType, false) + case c: Class[_] if c == java.lang.Double.TYPE => (DoubleType, false) + case c: Class[_] if c == java.lang.Byte.TYPE => (ByteType, false) + case c: Class[_] if c == java.lang.Float.TYPE => (FloatType, false) + case c: Class[_] if c == java.lang.Boolean.TYPE => (BooleanType, false) + + case c: Class[_] if c == classOf[java.lang.Short] => (ShortType, true) + case c: Class[_] if c == classOf[java.lang.Integer] => (IntegerType, true) + case c: Class[_] if c == classOf[java.lang.Long] => (LongType, true) + case c: Class[_] if c == classOf[java.lang.Double] => (DoubleType, true) + case c: Class[_] if c == classOf[java.lang.Byte] => (ByteType, true) + case c: Class[_] if c == classOf[java.lang.Float] => (FloatType, true) + case c: Class[_] if c == classOf[java.lang.Boolean] => (BooleanType, true) + + case c: Class[_] if c == classOf[java.math.BigDecimal] => (DecimalType(), true) + case c: Class[_] if c == classOf[java.sql.Date] => (DateType, true) + case c: Class[_] if c == classOf[java.sql.Timestamp] => (TimestampType, true) + + case c: Class[_] if c.isArray => + val (dataType, nullable) = inferDataType(c.getComponentType) + (ArrayType(dataType, nullable), true) + + case _ => + val beanInfo = Introspector.getBeanInfo(clazz) + val properties = beanInfo.getPropertyDescriptors.filterNot(_.getName == "class") + val fields = properties.map { property => + val (dataType, nullable) = inferDataType(property.getPropertyType) + new StructField(property.getName, dataType, nullable) + } + (new StructType(fields), true) } } } diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java index 2d586f784ac5a..1ff2d5a190521 100644 --- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java @@ -17,29 +17,39 @@ package test.org.apache.spark.sql; +import java.io.Serializable; +import java.util.Arrays; + import org.junit.After; import org.junit.Assert; import org.junit.Before; import org.junit.Ignore; import org.junit.Test; +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.sql.*; +import org.apache.spark.sql.test.TestSQLContext; import org.apache.spark.sql.test.TestSQLContext$; -import static org.apache.spark.sql.functions.*; +import org.apache.spark.sql.types.*; +import static org.apache.spark.sql.functions.*; public class JavaDataFrameSuite { + private transient JavaSparkContext jsc; private transient SQLContext context; @Before public void setUp() { // Trigger static initializer of TestData TestData$.MODULE$.testData(); + jsc = new JavaSparkContext(TestSQLContext.sparkContext()); context = TestSQLContext$.MODULE$; } @After public void tearDown() { + jsc = null; context = null; } @@ -90,4 +100,33 @@ public void testShow() { df.show(); df.show(1000); } + + public static class Bean implements Serializable { + private double a = 0.0; + private Integer[] b = new Integer[]{0, 1}; + + public double getA() { + return a; + } + + public Integer[] getB() { + return b; + } + } + + @Test + public void testCreateDataFrameFromJavaBeans() { + Bean bean = new Bean(); + JavaRDD rdd = jsc.parallelize(Arrays.asList(bean)); + DataFrame df = context.createDataFrame(rdd, Bean.class); + StructType schema = df.schema(); + Assert.assertEquals(new StructField("a", DoubleType$.MODULE$, false, Metadata.empty()), + schema.apply("a")); + Assert.assertEquals( + new StructField("b", new ArrayType(IntegerType$.MODULE$, true), true, Metadata.empty()), + schema.apply("b")); + Row first = df.select("a", "b").first(); + Assert.assertEquals(bean.getA(), first.getDouble(0), 0.0); + Assert.assertArrayEquals(bean.getB(), first.getAs(1)); + } } From 6bdddb6f6ffbd1bee4c45880904e9561b18764a7 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Tue, 24 Mar 2015 12:08:19 -0700 Subject: [PATCH 028/591] [SPARK-6361][SQL] support adding a column with metadata in DF This is used by ML pipelines to embed ML attributes in columns created by ML transformers/estimators. marmbrus Author: Xiangrui Meng Closes #5151 from mengxr/SPARK-6361 and squashes the following commits: bb30de3 [Xiangrui Meng] support adding a column with metadata in DF --- .../expressions/namedExpressions.scala | 20 ++++++++++++------- .../scala/org/apache/spark/sql/Column.scala | 13 ++++++++++++ .../spark/sql/ColumnExpressionSuite.scala | 15 +++++++++++--- 3 files changed, 38 insertions(+), 10 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala index 17f7f9fe51376..3dd7d38847b44 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala @@ -95,9 +95,12 @@ abstract class Attribute extends NamedExpression { * @param name the name to be associated with the result of computing [[child]]. * @param exprId A globally unique id used to check if an [[AttributeReference]] refers to this * alias. Auto-assigned if left blank. + * @param explicitMetadata Explicit metadata associated with this alias that overwrites child's. */ -case class Alias(child: Expression, name: String) - (val exprId: ExprId = NamedExpression.newExprId, val qualifiers: Seq[String] = Nil) +case class Alias(child: Expression, name: String)( + val exprId: ExprId = NamedExpression.newExprId, + val qualifiers: Seq[String] = Nil, + val explicitMetadata: Option[Metadata] = None) extends NamedExpression with trees.UnaryNode[Expression] { override type EvaluatedType = Any @@ -107,9 +110,11 @@ case class Alias(child: Expression, name: String) override def dataType = child.dataType override def nullable = child.nullable override def metadata: Metadata = { - child match { - case named: NamedExpression => named.metadata - case _ => Metadata.empty + explicitMetadata.getOrElse { + child match { + case named: NamedExpression => named.metadata + case _ => Metadata.empty + } } } @@ -123,11 +128,12 @@ case class Alias(child: Expression, name: String) override def toString: String = s"$child AS $name#${exprId.id}$typeSuffix" - override protected final def otherCopyArgs = exprId :: qualifiers :: Nil + override protected final def otherCopyArgs = exprId :: qualifiers :: explicitMetadata :: Nil override def equals(other: Any): Boolean = other match { case a: Alias => - name == a.name && exprId == a.exprId && child == a.child && qualifiers == a.qualifiers + name == a.name && exprId == a.exprId && child == a.child && qualifiers == a.qualifiers && + explicitMetadata == a.explicitMetadata case _ => false } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala index ec7d15f5bc4e7..2ae47e07d45ec 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala @@ -594,6 +594,19 @@ class Column(protected[sql] val expr: Expression) { */ def as(alias: Symbol): Column = Alias(expr, alias.name)() + /** + * Gives the column an alias with metadata. + * {{{ + * val metadata: Metadata = ... + * df.select($"colA".as("colB", metadata)) + * }}} + * + * @group expr_ops + */ + def as(alias: String, metadata: Metadata): Column = { + Alias(expr, alias)(explicitMetadata = Some(metadata)) + } + /** * Casts the column to a different data type. * {{{ diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala index a53ae97d6243a..bc8fae100db6a 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala @@ -17,12 +17,10 @@ package org.apache.spark.sql -import org.apache.spark.sql.catalyst.expressions.NamedExpression -import org.apache.spark.sql.catalyst.plans.logical.{Project, NoRelation} import org.apache.spark.sql.functions._ import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.test.TestSQLContext.implicits._ -import org.apache.spark.sql.types.{BooleanType, IntegerType, StructField, StructType} +import org.apache.spark.sql.types._ class ColumnExpressionSuite extends QueryTest { @@ -322,4 +320,15 @@ class ColumnExpressionSuite extends QueryTest { assert('key.desc == 'key.desc) assert('key.desc != 'key.asc) } + + test("alias with metadata") { + val metadata = new MetadataBuilder() + .putString("originName", "value") + .build() + val schema = testData + .select($"*", col("value").as("abc", metadata)) + .schema + assert(schema("value").metadata === Metadata.empty) + assert(schema("abc").metadata === metadata) + } } From 32efadd0500f10bddf2ae8456c9e719ec52940f1 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Tue, 24 Mar 2015 12:09:02 -0700 Subject: [PATCH 029/591] [SPARK-6459][SQL] Warn when constructing trivially true equals predicate For example, one might expect the following code to work, but it does not. Now you will at least get a warning with a suggestion to use aliases. ```scala val df = sqlContext.load(path, "parquet") val txns = df.groupBy("cust_id").agg($"cust_id", countDistinct($"day_num").as("txns")) val spend = df.groupBy("cust_id").agg($"cust_id", sum($"extended_price").as("spend")) val rmJoin = txns.join(spend, txns("cust_id") === spend("cust_id"), "inner") ``` Author: Michael Armbrust Closes #5163 from marmbrus/selfJoinError and squashes the following commits: 16c1f0b [Michael Armbrust] fix visibility 1b57e8d [Michael Armbrust] Warn when constructing trivially true equals predicate --- .../main/scala/org/apache/spark/sql/Column.scala | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala index 2ae47e07d45ec..3cd7adf8cab5e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala @@ -20,6 +20,7 @@ package org.apache.spark.sql import scala.language.implicitConversions import org.apache.spark.annotation.Experimental +import org.apache.spark.Logging import org.apache.spark.sql.functions.lit import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.analysis.{UnresolvedAttribute, UnresolvedStar, UnresolvedGetField} @@ -46,7 +47,7 @@ private[sql] object Column { * @groupname Ungrouped Support functions for DataFrames. */ @Experimental -class Column(protected[sql] val expr: Expression) { +class Column(protected[sql] val expr: Expression) extends Logging { def this(name: String) = this(name match { case "*" => UnresolvedStar(None) @@ -109,7 +110,15 @@ class Column(protected[sql] val expr: Expression) { * * @group expr_ops */ - def === (other: Any): Column = EqualTo(expr, lit(other).expr) + def === (other: Any): Column = { + val right = lit(other).expr + if (this.expr == right) { + logWarning( + s"Constructing trivially true equals predicate, '${this.expr} = $right'. " + + "Perhaps you need to use aliases.") + } + EqualTo(expr, right) + } /** * Equality test. From 26c6ce3d2947df5a294b1ad4a22fae5d31d06c19 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Tue, 24 Mar 2015 12:10:30 -0700 Subject: [PATCH 030/591] [SPARK-6437][SQL] Use completion iterator to close external sorter Otherwise we will leak files when spilling occurs. Author: Michael Armbrust Closes #5161 from marmbrus/cleanupAfterSort and squashes the following commits: cb13d3c [Michael Armbrust] hint to inferencer cdebdf5 [Michael Armbrust] Use completion iterator to close external sorter --- .../org/apache/spark/sql/execution/basicOperators.scala | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala index 20c9bc3e75542..1f5251a20376f 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala @@ -25,7 +25,7 @@ import org.apache.spark.sql.catalyst.ScalaReflection import org.apache.spark.sql.catalyst.errors._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.physical._ -import org.apache.spark.util.MutablePair +import org.apache.spark.util.{CompletionIterator, MutablePair} import org.apache.spark.util.collection.ExternalSorter /** @@ -194,7 +194,9 @@ case class ExternalSort( val ordering = newOrdering(sortOrder, child.output) val sorter = new ExternalSorter[Row, Null, Row](ordering = Some(ordering)) sorter.insertAll(iterator.map(r => (r, null))) - sorter.iterator.map(_._1) + val baseIterator = sorter.iterator.map(_._1) + // TODO(marmbrus): The complex type signature below thwarts inference for no reason. + CompletionIterator[Row, Iterator[Row]](baseIterator, sorter.stop()) }, preservesPartitioning = true) } From 3fa3d121dfec60f9768d3859e8450ee482b2d4e8 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Tue, 24 Mar 2015 12:28:01 -0700 Subject: [PATCH 031/591] [SPARK-6054][SQL] Fix transformations of TreeNodes that hold StructTypes Due to a recent change that made `StructType` a `Seq` we started inadvertently turning `StructType`s into generic `Traversable` when attempting nested tree transformations. In this PR we explicitly avoid descending into `DataType`s to avoid this bug. Author: Michael Armbrust Closes #5157 from marmbrus/udfFix and squashes the following commits: 26f7087 [Michael Armbrust] Fix transformations of TreeNodes that hold StructTypes --- .../spark/sql/catalyst/plans/QueryPlan.scala | 2 ++ .../spark/sql/catalyst/trees/TreeNode.scala | 20 ++++++++++++++++--- .../scala/org/apache/spark/sql/UDFSuite.scala | 6 ++++++ 3 files changed, 25 insertions(+), 3 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala index 48191f31198f3..bd9291e9ba5d7 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala @@ -85,6 +85,7 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy case e: Expression => transformExpressionDown(e) case Some(e: Expression) => Some(transformExpressionDown(e)) case m: Map[_,_] => m + case d: DataType => d // Avoid unpacking Structs case seq: Traversable[_] => seq.map { case e: Expression => transformExpressionDown(e) case other => other @@ -117,6 +118,7 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy case e: Expression => transformExpressionUp(e) case Some(e: Expression) => Some(transformExpressionUp(e)) case m: Map[_,_] => m + case d: DataType => d // Avoid unpacking Structs case seq: Traversable[_] => seq.map { case e: Expression => transformExpressionUp(e) case other => other diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala index f84ffe4e176cc..0ae9f6b2965d4 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.catalyst.trees import org.apache.spark.sql.catalyst.errors._ +import org.apache.spark.sql.types.DataType /** Used by [[TreeNode.getNodeNumbered]] when traversing the tree for a given number */ private class MutableInt(var i: Int) @@ -220,6 +221,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { Some(arg) } case m: Map[_,_] => m + case d: DataType => d // Avoid unpacking Structs case args: Traversable[_] => args.map { case arg: TreeNode[_] if children contains arg => val newChild = arg.asInstanceOf[BaseType].transformDown(rule) @@ -276,6 +278,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { Some(arg) } case m: Map[_,_] => m + case d: DataType => d // Avoid unpacking Structs case args: Traversable[_] => args.map { case arg: TreeNode[_] if children contains arg => val newChild = arg.asInstanceOf[BaseType].transformUp(rule) @@ -307,10 +310,15 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { * @param newArgs the new product arguments. */ def makeCopy(newArgs: Array[AnyRef]): this.type = attachTree(this, "makeCopy") { + val defaultCtor = + getClass.getConstructors + .find(_.getParameterTypes.size != 0) + .headOption + .getOrElse(sys.error(s"No valid constructor for $nodeName")) + try { CurrentOrigin.withOrigin(origin) { // Skip no-arg constructors that are just there for kryo. - val defaultCtor = getClass.getConstructors.find(_.getParameterTypes.size != 0).head if (otherCopyArgs.isEmpty) { defaultCtor.newInstance(newArgs: _*).asInstanceOf[this.type] } else { @@ -320,8 +328,14 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { } catch { case e: java.lang.IllegalArgumentException => throw new TreeNodeException( - this, s"Failed to copy node. Is otherCopyArgs specified correctly for $nodeName? " - + s"Exception message: ${e.getMessage}.") + this, + s""" + |Failed to copy node. + |Is otherCopyArgs specified correctly for $nodeName. + |Exception message: ${e.getMessage} + |ctor: $defaultCtor? + |args: ${newArgs.mkString(", ")} + """.stripMargin) } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala index be105c6e83594..d615542ab50a7 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala @@ -50,4 +50,10 @@ class UDFSuite extends QueryTest { .select($"ret.f1").head().getString(0) assert(result === "test") } + + test("udf that is transformed") { + udf.register("makeStruct", (x: Int, y: Int) => (x, y)) + // 1 + 1 is constant folded causing a transformation. + assert(sql("SELECT makeStruct(1 + 1, 2)").first().getAs[Row](0) === Row(2, 2)) + } } From 046c1e2aa459147bf592371bb9fb7a65edb182e7 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Tue, 24 Mar 2015 13:22:46 -0700 Subject: [PATCH 032/591] [SPARK-6375][SQL] Fix formatting of error messages. Author: Michael Armbrust Closes #5155 from marmbrus/errorMessages and squashes the following commits: b898188 [Michael Armbrust] Fix formatting of error messages. --- .../apache/spark/sql/AnalysisException.scala | 6 +++++ .../sql/catalyst/analysis/Analyzer.scala | 5 ++-- .../spark/sql/catalyst/analysis/package.scala | 8 ++++++ .../sql/catalyst/analysis/unresolved.scala | 4 +++ .../expressions/namedExpressions.scala | 7 ++++++ .../catalyst/plans/logical/LogicalPlan.scala | 3 ++- .../spark/sql/hive/ErrorPositionSuite.scala | 25 +++++++++++++++++-- 7 files changed, 53 insertions(+), 5 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/AnalysisException.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/AnalysisException.scala index 15add84878ecf..34fedead44db3 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/AnalysisException.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/AnalysisException.scala @@ -30,6 +30,12 @@ class AnalysisException protected[sql] ( val startPosition: Option[Int] = None) extends Exception with Serializable { + def withPosition(line: Option[Int], startPosition: Option[Int]) = { + val newException = new AnalysisException(message, line, startPosition) + newException.setStackTrace(getStackTrace) + newException + } + override def getMessage: String = { val lineAnnotation = line.map(l => s" line $l").getOrElse("") val positionAnnotation = startPosition.map(p => s" pos $p").getOrElse("") diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala index 92d3db077c5e1..c93af79795bc7 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala @@ -175,7 +175,7 @@ class Analyzer(catalog: Catalog, catalog.lookupRelation(u.tableIdentifier, u.alias) } catch { case _: NoSuchTableException => - u.failAnalysis(s"no such table ${u.tableIdentifier}") + u.failAnalysis(s"no such table ${u.tableName}") } } @@ -275,7 +275,8 @@ class Analyzer(catalog: Catalog, q.asInstanceOf[GroupingAnalytics].gid case u @ UnresolvedAttribute(name) => // Leave unchanged if resolution fails. Hopefully will be resolved next round. - val result = q.resolveChildren(name, resolver).getOrElse(u) + val result = + withPosition(u) { q.resolveChildren(name, resolver).getOrElse(u) } logDebug(s"Resolving $u to $result") result case UnresolvedGetField(child, fieldName) if child.resolved => diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/package.scala index e95f19e69ed43..a7d3a8ee7deb3 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/package.scala @@ -42,4 +42,12 @@ package object analysis { throw new AnalysisException(msg, t.origin.line, t.origin.startPosition) } } + + /** Catches any AnalysisExceptions thrown by `f` and attaches `t`'s position if any. */ + def withPosition[A](t: TreeNode[_])(f: => A) = { + try f catch { + case a: AnalysisException => + throw a.withPosition(t.origin.line, t.origin.startPosition) + } + } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala index a7cd4124e56f3..ad5172c0349eb 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala @@ -36,6 +36,10 @@ class UnresolvedException[TreeType <: TreeNode[_]](tree: TreeType, function: Str case class UnresolvedRelation( tableIdentifier: Seq[String], alias: Option[String] = None) extends LeafNode { + + /** Returns a `.` separated name for this relation. */ + def tableName = tableIdentifier.mkString(".") + override def output = Nil override lazy val resolved = false } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala index 3dd7d38847b44..08361d043b6ed 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala @@ -41,6 +41,13 @@ abstract class NamedExpression extends Expression { def name: String def exprId: ExprId + /** + * Returns a dot separated fully qualified name for this attribute. Given that there can be + * multiple qualifiers, it is possible that there are other possible way to refer to this + * attribute. + */ + def qualifiedName: String = (qualifiers.headOption.toSeq :+ name).mkString(".") + /** * All possible qualifiers for the expression. * diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala index 8c4f09b58a4f2..0f8b144ccc113 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala @@ -208,8 +208,9 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { // More than one match. case ambiguousReferences => + val referenceNames = ambiguousReferences.map(_._1.qualifiedName).mkString(", ") throw new AnalysisException( - s"Ambiguous references to $name: ${ambiguousReferences.mkString(",")}") + s"Reference '$name' is ambiguous, could be: $referenceNames.") } } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala index f04437c595bf6..968557c9c4686 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala @@ -19,12 +19,29 @@ package org.apache.spark.sql.hive import java.io.{OutputStream, PrintStream} +import scala.util.Try + +import org.scalatest.BeforeAndAfter + import org.apache.spark.sql.hive.test.TestHive._ +import org.apache.spark.sql.hive.test.TestHive.implicits._ import org.apache.spark.sql.{AnalysisException, QueryTest} -import scala.util.Try -class ErrorPositionSuite extends QueryTest { +class ErrorPositionSuite extends QueryTest with BeforeAndAfter { + + before { + Seq((1, 1, 1)).toDF("a", "a", "b").registerTempTable("dupAttributes") + } + + positionTest("ambiguous attribute reference 1", + "SELECT a from dupAttributes", "a") + + positionTest("ambiguous attribute reference 2", + "SELECT a, b from dupAttributes", "a") + + positionTest("ambiguous attribute reference 3", + "SELECT b, a from dupAttributes", "a") positionTest("unresolved attribute 1", "SELECT x FROM src", "x") @@ -127,6 +144,10 @@ class ErrorPositionSuite extends QueryTest { val error = intercept[AnalysisException] { quietly(sql(query)) } + + assert(!error.getMessage.contains("Seq(")) + assert(!error.getMessage.contains("List(")) + val (line, expectedLineNum) = query.split("\n").zipWithIndex.collect { case (l, i) if l.contains(token) => (l, i + 1) }.headOption.getOrElse(sys.error(s"Invalid test. Token $token not in $query")) From cbeaf9ebab31a0bcbca884d4db7a791fd9edbff3 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Tue, 24 Mar 2015 14:08:20 -0700 Subject: [PATCH 033/591] [SPARK-6376][SQL] Avoid eliminating subqueries until optimization Previously it was okay to throw away subqueries after analysis, as we would never try to use that tree for resolution again. However, with eager analysis in `DataFrame`s this can cause errors for queries such as: ```scala val df = Seq(1,2,3).map(i => (i, i.toString)).toDF("int", "str") df.as('x).join(df.as('y), $"x.str" === $"y.str").groupBy("x.str").count() ``` As a result, in this PR we defer the elimination of subqueries until the optimization phase. Author: Michael Armbrust Closes #5160 from marmbrus/subqueriesInDfs and squashes the following commits: a9bb262 [Michael Armbrust] Update Optimizer.scala 27d25bf [Michael Armbrust] fix hive tests 9137e03 [Michael Armbrust] add type 81cd597 [Michael Armbrust] Avoid eliminating subqueries until optimization --- .../spark/sql/catalyst/analysis/Analyzer.scala | 4 +--- .../apache/spark/sql/catalyst/dsl/package.scala | 4 ++-- .../spark/sql/catalyst/optimizer/Optimizer.scala | 4 ++++ .../sql/catalyst/plans/logical/LogicalPlan.scala | 16 ++++++++++------ .../sql/catalyst/analysis/AnalysisSuite.scala | 8 ++++++-- .../org/apache/spark/sql/DataFrameSuite.scala | 7 +++++++ .../scala/org/apache/spark/sql/JoinSuite.scala | 4 ++-- .../sql/hive/MetastoreDataSourcesSuite.scala | 2 +- .../apache/spark/sql/hive/parquetSuites.scala | 2 +- 9 files changed, 34 insertions(+), 17 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala index c93af79795bc7..13d2ae4c6fcb6 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala @@ -64,9 +64,7 @@ class Analyzer(catalog: Catalog, UnresolvedHavingClauseAttributes :: TrimGroupingAliases :: typeCoercionRules ++ - extendedResolutionRules : _*), - Batch("Remove SubQueries", fixedPoint, - EliminateSubQueries) + extendedResolutionRules : _*) ) /** diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala index 51a09ac0e1249..7f5f617812919 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala @@ -22,7 +22,7 @@ import java.sql.{Date, Timestamp} import scala.language.implicitConversions import scala.reflect.runtime.universe.{TypeTag, typeTag} -import org.apache.spark.sql.catalyst.analysis.{UnresolvedGetField, UnresolvedAttribute} +import org.apache.spark.sql.catalyst.analysis.{EliminateSubQueries, UnresolvedGetField, UnresolvedAttribute} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.plans.{Inner, JoinType} @@ -289,7 +289,7 @@ package object dsl { InsertIntoTable( analysis.UnresolvedRelation(Seq(tableName)), Map.empty, logicalPlan, overwrite) - def analyze = analysis.SimpleAnalyzer(logicalPlan) + def analyze: LogicalPlan = EliminateSubQueries(analysis.SimpleAnalyzer(logicalPlan)) } object plans { // scalastyle:ignore diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala index 1a75fcf3545bd..74edaacc4f609 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.catalyst.optimizer import scala.collection.immutable.HashSet +import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.Inner import org.apache.spark.sql.catalyst.plans.FullOuter @@ -32,6 +33,9 @@ abstract class Optimizer extends RuleExecutor[LogicalPlan] object DefaultOptimizer extends Optimizer { val batches = + // SubQueries are only needed for analysis and can be removed before execution. + Batch("Remove SubQueries", FixedPoint(100), + EliminateSubQueries) :: Batch("Combine Limits", FixedPoint(100), CombineLimits) :: Batch("ConstantFolding", FixedPoint(100), diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala index 0f8b144ccc113..b01a61d7bf8d6 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.plans.logical import org.apache.spark.Logging import org.apache.spark.sql.AnalysisException -import org.apache.spark.sql.catalyst.analysis.{UnresolvedGetField, Resolver} +import org.apache.spark.sql.catalyst.analysis.{EliminateSubQueries, UnresolvedGetField, Resolver} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.QueryPlan import org.apache.spark.sql.catalyst.trees.TreeNode @@ -73,12 +73,16 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { * can do better should override this function. */ def sameResult(plan: LogicalPlan): Boolean = { - plan.getClass == this.getClass && - plan.children.size == children.size && { - logDebug(s"[${cleanArgs.mkString(", ")}] == [${plan.cleanArgs.mkString(", ")}]") - cleanArgs == plan.cleanArgs + val cleanLeft = EliminateSubQueries(this) + val cleanRight = EliminateSubQueries(plan) + + cleanLeft.getClass == cleanRight.getClass && + cleanLeft.children.size == cleanRight.children.size && { + logDebug( + s"[${cleanRight.cleanArgs.mkString(", ")}] == [${cleanLeft.cleanArgs.mkString(", ")}]") + cleanRight.cleanArgs == cleanLeft.cleanArgs } && - (plan.children, children).zipped.forall(_ sameResult _) + (cleanLeft.children, cleanRight.children).zipped.forall(_ sameResult _) } /** Args that have cleaned such that differences in expression id should not affect equality */ diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala index 359aec4a7b5ab..756cd36f05c8c 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala @@ -32,9 +32,13 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { val caseInsensitiveCatalog = new SimpleCatalog(false) val caseSensitiveAnalyzer = - new Analyzer(caseSensitiveCatalog, EmptyFunctionRegistry, caseSensitive = true) + new Analyzer(caseSensitiveCatalog, EmptyFunctionRegistry, caseSensitive = true) { + override val extendedResolutionRules = EliminateSubQueries :: Nil + } val caseInsensitiveAnalyzer = - new Analyzer(caseInsensitiveCatalog, EmptyFunctionRegistry, caseSensitive = false) + new Analyzer(caseInsensitiveCatalog, EmptyFunctionRegistry, caseSensitive = false) { + override val extendedResolutionRules = EliminateSubQueries :: Nil + } val checkAnalysis = new CheckAnalysis diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index ff441ef26f9c0..c30ed694a62f0 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -108,6 +108,13 @@ class DataFrameSuite extends QueryTest { ) } + test("self join with aliases") { + val df = Seq(1,2,3).map(i => (i, i.toString)).toDF("int", "str") + checkAnswer( + df.as('x).join(df.as('y), $"x.str" === $"y.str").groupBy("x.str").count(), + Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil) + } + test("explode") { val df = Seq((1, "a b c"), (2, "a b"), (3, "a")).toDF("number", "letters") val df2 = diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala index dd0948ad824be..e4dee87849fd4 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala @@ -34,7 +34,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { test("equi-join is hash-join") { val x = testData2.as("x") val y = testData2.as("y") - val join = x.join(y, $"x.a" === $"y.a", "inner").queryExecution.analyzed + val join = x.join(y, $"x.a" === $"y.a", "inner").queryExecution.optimizedPlan val planned = planner.HashJoin(join) assert(planned.size === 1) } @@ -109,7 +109,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { test("multiple-key equi-join is hash-join") { val x = testData2.as("x") val y = testData2.as("y") - val join = x.join(y, ($"x.a" === $"y.a") && ($"x.b" === $"y.b")).queryExecution.analyzed + val join = x.join(y, ($"x.a" === $"y.a") && ($"x.b" === $"y.b")).queryExecution.optimizedPlan val planned = planner.HashJoin(join) assert(planned.size === 1) } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala index ff2e6ea9ea51d..e5ad0bf552073 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala @@ -579,7 +579,7 @@ class MetastoreDataSourcesSuite extends QueryTest with BeforeAndAfterEach { Row(3) :: Row(4) :: Nil ) - table("test_parquet_ctas").queryExecution.analyzed match { + table("test_parquet_ctas").queryExecution.optimizedPlan match { case LogicalRelation(p: ParquetRelation2) => // OK case _ => fail( diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala index d891c4e8903d9..8a31bd03092d1 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala @@ -292,7 +292,7 @@ class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase { Seq(Row(1, "str1")) ) - table("test_parquet_ctas").queryExecution.analyzed match { + table("test_parquet_ctas").queryExecution.optimizedPlan match { case LogicalRelation(p: ParquetRelation2) => // OK case _ => fail( From a8f51b82968147abebbe61b8b68b066d21a0c6e6 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Tue, 24 Mar 2015 14:10:56 -0700 Subject: [PATCH 034/591] [SPARK-6458][SQL] Better error messages for invalid data sources Avoid unclear match errors and use `AnalysisException`. Author: Michael Armbrust Closes #5158 from marmbrus/dataSourceError and squashes the following commits: af9f82a [Michael Armbrust] Yins comment 90c6ba4 [Michael Armbrust] Better error messages for invalid data sources --- .../scala/org/apache/spark/sql/sources/ddl.scala | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala index d2e807d3a69b6..eb46b46ca5bf4 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala @@ -21,7 +21,7 @@ import scala.language.existentials import scala.language.implicitConversions import org.apache.spark.Logging -import org.apache.spark.sql.{SaveMode, DataFrame, SQLContext} +import org.apache.spark.sql.{AnalysisException, SaveMode, DataFrame, SQLContext} import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.AbstractSparkSQLParser import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation @@ -204,19 +204,25 @@ private[sql] object ResolvedDataSource { provider: String, options: Map[String, String]): ResolvedDataSource = { val clazz: Class[_] = lookupDataSource(provider) + def className = clazz.getCanonicalName val relation = userSpecifiedSchema match { case Some(schema: StructType) => clazz.newInstance() match { case dataSource: SchemaRelationProvider => dataSource.createRelation(sqlContext, new CaseInsensitiveMap(options), schema) case dataSource: org.apache.spark.sql.sources.RelationProvider => - sys.error(s"${clazz.getCanonicalName} does not allow user-specified schemas.") + throw new AnalysisException(s"$className does not allow user-specified schemas.") + case _ => + throw new AnalysisException(s"$className is not a RelationProvider.") } case None => clazz.newInstance() match { case dataSource: RelationProvider => dataSource.createRelation(sqlContext, new CaseInsensitiveMap(options)) case dataSource: org.apache.spark.sql.sources.SchemaRelationProvider => - sys.error(s"A schema needs to be specified when using ${clazz.getCanonicalName}.") + throw new AnalysisException( + s"A schema needs to be specified when using $className.") + case _ => + throw new AnalysisException(s"$className is not a RelationProvider.") } } new ResolvedDataSource(clazz, relation) From 7215aa745590a3eec9c1ff35d28194235a550db7 Mon Sep 17 00:00:00 2001 From: Josh Rosen Date: Tue, 24 Mar 2015 14:38:20 -0700 Subject: [PATCH 035/591] [SPARK-6209] Clean up connections in ExecutorClassLoader after failing to load classes (master branch PR) ExecutorClassLoader does not ensure proper cleanup of network connections that it opens. If it fails to load a class, it may leak partially-consumed InputStreams that are connected to the REPL's HTTP class server, causing that server to exhaust its thread pool, which can cause the entire job to hang. See [SPARK-6209](https://issues.apache.org/jira/browse/SPARK-6209) for more details, including a bug reproduction. This patch fixes this issue by ensuring proper cleanup of these resources. It also adds logging for unexpected error cases. This PR is an extended version of #4935 and adds a regression test. Author: Josh Rosen Closes #4944 from JoshRosen/executorclassloader-leak-master-branch and squashes the following commits: e0e3c25 [Josh Rosen] Wrap try block around getReponseCode; re-enable keep-alive by closing error stream 961c284 [Josh Rosen] Roll back changes that were added to get the regression test to fail 7ee2261 [Josh Rosen] Add a failing regression test e2d70a3 [Josh Rosen] Properly clean up after errors in ExecutorClassLoader --- repl/pom.xml | 5 ++ .../spark/repl/ExecutorClassLoader.scala | 85 +++++++++++++++---- .../spark/repl/ExecutorClassLoaderSuite.scala | 70 ++++++++++++++- 3 files changed, 140 insertions(+), 20 deletions(-) diff --git a/repl/pom.xml b/repl/pom.xml index edfa1c7f2c29c..03053b4c3b287 100644 --- a/repl/pom.xml +++ b/repl/pom.xml @@ -84,6 +84,11 @@ scalacheck_${scala.binary.version} test + + org.mockito + mockito-all + test + diff --git a/repl/src/main/scala/org/apache/spark/repl/ExecutorClassLoader.scala b/repl/src/main/scala/org/apache/spark/repl/ExecutorClassLoader.scala index 9805609120005..004941d5f50ae 100644 --- a/repl/src/main/scala/org/apache/spark/repl/ExecutorClassLoader.scala +++ b/repl/src/main/scala/org/apache/spark/repl/ExecutorClassLoader.scala @@ -17,9 +17,10 @@ package org.apache.spark.repl -import java.io.{ByteArrayOutputStream, InputStream, FileNotFoundException} -import java.net.{URI, URL, URLEncoder} -import java.util.concurrent.{Executors, ExecutorService} +import java.io.{IOException, ByteArrayOutputStream, InputStream} +import java.net.{HttpURLConnection, URI, URL, URLEncoder} + +import scala.util.control.NonFatal import org.apache.hadoop.fs.{FileSystem, Path} @@ -43,6 +44,9 @@ class ExecutorClassLoader(conf: SparkConf, classUri: String, parent: ClassLoader val parentLoader = new ParentClassLoader(parent) + // Allows HTTP connect and read timeouts to be controlled for testing / debugging purposes + private[repl] var httpUrlConnectionTimeoutMillis: Int = -1 + // Hadoop FileSystem object for our URI, if it isn't using HTTP var fileSystem: FileSystem = { if (Set("http", "https", "ftp").contains(uri.getScheme)) { @@ -71,30 +75,66 @@ class ExecutorClassLoader(conf: SparkConf, classUri: String, parent: ClassLoader } } + private def getClassFileInputStreamFromHttpServer(pathInDirectory: String): InputStream = { + val url = if (SparkEnv.get.securityManager.isAuthenticationEnabled()) { + val uri = new URI(classUri + "/" + urlEncode(pathInDirectory)) + val newuri = Utils.constructURIForAuthentication(uri, SparkEnv.get.securityManager) + newuri.toURL + } else { + new URL(classUri + "/" + urlEncode(pathInDirectory)) + } + val connection: HttpURLConnection = Utils.setupSecureURLConnection(url.openConnection(), + SparkEnv.get.securityManager).asInstanceOf[HttpURLConnection] + // Set the connection timeouts (for testing purposes) + if (httpUrlConnectionTimeoutMillis != -1) { + connection.setConnectTimeout(httpUrlConnectionTimeoutMillis) + connection.setReadTimeout(httpUrlConnectionTimeoutMillis) + } + connection.connect() + try { + if (connection.getResponseCode != 200) { + // Close the error stream so that the connection is eligible for re-use + try { + connection.getErrorStream.close() + } catch { + case ioe: IOException => + logError("Exception while closing error stream", ioe) + } + throw new ClassNotFoundException(s"Class file not found at URL $url") + } else { + connection.getInputStream + } + } catch { + case NonFatal(e) if !e.isInstanceOf[ClassNotFoundException] => + connection.disconnect() + throw e + } + } + + private def getClassFileInputStreamFromFileSystem(pathInDirectory: String): InputStream = { + val path = new Path(directory, pathInDirectory) + if (fileSystem.exists(path)) { + fileSystem.open(path) + } else { + throw new ClassNotFoundException(s"Class file not found at path $path") + } + } + def findClassLocally(name: String): Option[Class[_]] = { + val pathInDirectory = name.replace('.', '/') + ".class" + var inputStream: InputStream = null try { - val pathInDirectory = name.replace('.', '/') + ".class" - val inputStream = { + inputStream = { if (fileSystem != null) { - fileSystem.open(new Path(directory, pathInDirectory)) + getClassFileInputStreamFromFileSystem(pathInDirectory) } else { - val url = if (SparkEnv.get.securityManager.isAuthenticationEnabled()) { - val uri = new URI(classUri + "/" + urlEncode(pathInDirectory)) - val newuri = Utils.constructURIForAuthentication(uri, SparkEnv.get.securityManager) - newuri.toURL - } else { - new URL(classUri + "/" + urlEncode(pathInDirectory)) - } - - Utils.setupSecureURLConnection(url.openConnection(), SparkEnv.get.securityManager) - .getInputStream + getClassFileInputStreamFromHttpServer(pathInDirectory) } } val bytes = readAndTransformClass(name, inputStream) - inputStream.close() Some(defineClass(name, bytes, 0, bytes.length)) } catch { - case e: FileNotFoundException => + case e: ClassNotFoundException => // We did not find the class logDebug(s"Did not load class $name from REPL class server at $uri", e) None @@ -102,6 +142,15 @@ class ExecutorClassLoader(conf: SparkConf, classUri: String, parent: ClassLoader // Something bad happened while checking if the class exists logError(s"Failed to check existence of class $name on REPL class server at $uri", e) None + } finally { + if (inputStream != null) { + try { + inputStream.close() + } catch { + case e: Exception => + logError("Exception while closing inputStream", e) + } + } } } diff --git a/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala b/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala index 6a79e76a34db8..c709cde740748 100644 --- a/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala +++ b/repl/src/test/scala/org/apache/spark/repl/ExecutorClassLoaderSuite.scala @@ -20,13 +20,25 @@ package org.apache.spark.repl import java.io.File import java.net.{URL, URLClassLoader} +import scala.concurrent.duration._ +import scala.language.implicitConversions +import scala.language.postfixOps + import org.scalatest.BeforeAndAfterAll import org.scalatest.FunSuite +import org.scalatest.concurrent.Interruptor +import org.scalatest.concurrent.Timeouts._ +import org.scalatest.mock.MockitoSugar +import org.mockito.Mockito._ -import org.apache.spark.{SparkConf, TestUtils} +import org.apache.spark._ import org.apache.spark.util.Utils -class ExecutorClassLoaderSuite extends FunSuite with BeforeAndAfterAll { +class ExecutorClassLoaderSuite + extends FunSuite + with BeforeAndAfterAll + with MockitoSugar + with Logging { val childClassNames = List("ReplFakeClass1", "ReplFakeClass2") val parentClassNames = List("ReplFakeClass1", "ReplFakeClass2", "ReplFakeClass3") @@ -34,6 +46,7 @@ class ExecutorClassLoaderSuite extends FunSuite with BeforeAndAfterAll { var tempDir2: File = _ var url1: String = _ var urls2: Array[URL] = _ + var classServer: HttpServer = _ override def beforeAll() { super.beforeAll() @@ -47,8 +60,12 @@ class ExecutorClassLoaderSuite extends FunSuite with BeforeAndAfterAll { override def afterAll() { super.afterAll() + if (classServer != null) { + classServer.stop() + } Utils.deleteRecursively(tempDir1) Utils.deleteRecursively(tempDir2) + SparkEnv.set(null) } test("child first") { @@ -83,4 +100,53 @@ class ExecutorClassLoaderSuite extends FunSuite with BeforeAndAfterAll { } } + test("failing to fetch classes from HTTP server should not leak resources (SPARK-6209)") { + // This is a regression test for SPARK-6209, a bug where each failed attempt to load a class + // from the driver's class server would leak a HTTP connection, causing the class server's + // thread / connection pool to be exhausted. + val conf = new SparkConf() + val securityManager = new SecurityManager(conf) + classServer = new HttpServer(conf, tempDir1, securityManager) + classServer.start() + // ExecutorClassLoader uses SparkEnv's SecurityManager, so we need to mock this + val mockEnv = mock[SparkEnv] + when(mockEnv.securityManager).thenReturn(securityManager) + SparkEnv.set(mockEnv) + // Create an ExecutorClassLoader that's configured to load classes from the HTTP server + val parentLoader = new URLClassLoader(Array.empty, null) + val classLoader = new ExecutorClassLoader(conf, classServer.uri, parentLoader, false) + classLoader.httpUrlConnectionTimeoutMillis = 500 + // Check that this class loader can actually load classes that exist + val fakeClass = classLoader.loadClass("ReplFakeClass2").newInstance() + val fakeClassVersion = fakeClass.toString + assert(fakeClassVersion === "1") + // Try to perform a full GC now, since GC during the test might mask resource leaks + System.gc() + // When the original bug occurs, the test thread becomes blocked in a classloading call + // and does not respond to interrupts. Therefore, use a custom ScalaTest interruptor to + // shut down the HTTP server when the test times out + val interruptor: Interruptor = new Interruptor { + override def apply(thread: Thread): Unit = { + classServer.stop() + classServer = null + thread.interrupt() + } + } + def tryAndFailToLoadABunchOfClasses(): Unit = { + // The number of trials here should be much larger than Jetty's thread / connection limit + // in order to expose thread or connection leaks + for (i <- 1 to 1000) { + if (Thread.currentThread().isInterrupted) { + throw new InterruptedException() + } + // Incorporate the iteration number into the class name in order to avoid any response + // caching that might be added in the future + intercept[ClassNotFoundException] { + classLoader.loadClass(s"ReplFakeClassDoesNotExist$i").newInstance() + } + } + } + failAfter(10 seconds)(tryAndFailToLoadABunchOfClasses())(interruptor) + } + } From 73348012d4ce6c9db85dfb48d51026efe5051c73 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Tue, 24 Mar 2015 16:03:55 -0700 Subject: [PATCH 036/591] [SPARK-6428][SQL] Added explicit types for all public methods in catalyst I think after this PR, we can finally turn the rule on. There are still some smaller ones that need to be fixed, but those are easier. Author: Reynold Xin Closes #5162 from rxin/catalyst-explicit-types and squashes the following commits: e7eac03 [Reynold Xin] [SPARK-6428][SQL] Added explicit types for all public methods in catalyst. --- .../sql/catalyst/AbstractSparkSQLParser.scala | 8 +- .../sql/catalyst/analysis/Analyzer.scala | 2 +- .../spark/sql/catalyst/analysis/Catalog.scala | 22 +- .../sql/catalyst/analysis/CheckAnalysis.scala | 2 +- .../catalyst/analysis/FunctionRegistry.scala | 14 +- .../spark/sql/catalyst/analysis/package.scala | 2 +- .../sql/catalyst/analysis/unresolved.scala | 78 +++---- .../spark/sql/catalyst/dsl/package.scala | 202 +++++++++--------- .../catalyst/expressions/AttributeMap.scala | 4 +- .../catalyst/expressions/AttributeSet.scala | 24 ++- .../catalyst/expressions/BoundAttribute.scala | 2 +- .../spark/sql/catalyst/expressions/Cast.scala | 10 +- .../sql/catalyst/expressions/Expression.scala | 14 +- .../sql/catalyst/expressions/Projection.scala | 52 ++--- .../spark/sql/catalyst/expressions/Rand.scala | 13 +- .../sql/catalyst/expressions/ScalaUdf.scala | 4 +- .../sql/catalyst/expressions/SortOrder.scala | 7 +- .../expressions/SpecificMutableRow.scala | 80 +++---- .../sql/catalyst/expressions/aggregates.scala | 165 +++++++------- .../sql/catalyst/expressions/arithmetic.scala | 66 +++--- .../expressions/codegen/CodeGenerator.scala | 2 +- .../catalyst/expressions/complexTypes.scala | 26 +-- .../expressions/decimalFunctions.scala | 12 +- .../sql/catalyst/expressions/generators.scala | 6 +- .../sql/catalyst/expressions/literals.scala | 13 +- .../expressions/namedExpressions.scala | 51 +++-- .../catalyst/expressions/nullFunctions.scala | 21 +- .../sql/catalyst/expressions/predicates.scala | 80 +++---- .../spark/sql/catalyst/expressions/rows.scala | 18 +- .../spark/sql/catalyst/expressions/sets.scala | 35 +-- .../expressions/stringOperations.scala | 37 ++-- .../sql/catalyst/optimizer/Optimizer.scala | 2 +- .../sql/catalyst/planning/patterns.scala | 14 +- .../spark/sql/catalyst/plans/QueryPlan.scala | 6 +- .../plans/logical/basicOperators.scala | 46 ++-- .../catalyst/plans/logical/partitioning.scala | 11 +- .../plans/physical/partitioning.scala | 24 +-- .../spark/sql/catalyst/trees/TreeNode.scala | 27 ++- .../spark/sql/catalyst/trees/package.scala | 4 +- .../spark/sql/catalyst/util/package.scala | 6 +- 40 files changed, 626 insertions(+), 586 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala index 366be00473d1c..3823584287741 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala @@ -26,7 +26,7 @@ import scala.util.parsing.input.CharArrayReader.EofCh import org.apache.spark.sql.catalyst.plans.logical._ private[sql] object KeywordNormalizer { - def apply(str: String) = str.toLowerCase() + def apply(str: String): String = str.toLowerCase() } private[sql] abstract class AbstractSparkSQLParser @@ -42,7 +42,7 @@ private[sql] abstract class AbstractSparkSQLParser } protected case class Keyword(str: String) { - def normalize = KeywordNormalizer(str) + def normalize: String = KeywordNormalizer(str) def parser: Parser[String] = normalize } @@ -81,7 +81,7 @@ private[sql] abstract class AbstractSparkSQLParser class SqlLexical extends StdLexical { case class FloatLit(chars: String) extends Token { - override def toString = chars + override def toString: String = chars } /* This is a work around to support the lazy setting */ @@ -120,7 +120,7 @@ class SqlLexical extends StdLexical { | failure("illegal character") ) - override def identChar = letter | elem('_') + override def identChar: Parser[Elem] = letter | elem('_') override def whitespace: Parser[Any] = ( whitespaceChar diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala index 13d2ae4c6fcb6..44eceb0b372e6 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala @@ -168,7 +168,7 @@ class Analyzer(catalog: Catalog, * Replaces [[UnresolvedRelation]]s with concrete relations from the catalog. */ object ResolveRelations extends Rule[LogicalPlan] { - def getTable(u: UnresolvedRelation) = { + def getTable(u: UnresolvedRelation): LogicalPlan = { try { catalog.lookupRelation(u.tableIdentifier, u.alias) } catch { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala index 9e6e2912e0622..5eb7dff0cede8 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala @@ -86,12 +86,12 @@ class SimpleCatalog(val caseSensitive: Boolean) extends Catalog { tables += ((getDbTableName(tableIdent), plan)) } - override def unregisterTable(tableIdentifier: Seq[String]) = { + override def unregisterTable(tableIdentifier: Seq[String]): Unit = { val tableIdent = processTableIdentifier(tableIdentifier) tables -= getDbTableName(tableIdent) } - override def unregisterAllTables() = { + override def unregisterAllTables(): Unit = { tables.clear() } @@ -147,8 +147,8 @@ trait OverrideCatalog extends Catalog { } abstract override def lookupRelation( - tableIdentifier: Seq[String], - alias: Option[String] = None): LogicalPlan = { + tableIdentifier: Seq[String], + alias: Option[String] = None): LogicalPlan = { val tableIdent = processTableIdentifier(tableIdentifier) val overriddenTable = overrides.get(getDBTable(tableIdent)) val tableWithQualifers = overriddenTable.map(r => Subquery(tableIdent.last, r)) @@ -205,15 +205,15 @@ trait OverrideCatalog extends Catalog { */ object EmptyCatalog extends Catalog { - val caseSensitive: Boolean = true + override val caseSensitive: Boolean = true - def tableExists(tableIdentifier: Seq[String]): Boolean = { + override def tableExists(tableIdentifier: Seq[String]): Boolean = { throw new UnsupportedOperationException } - def lookupRelation( - tableIdentifier: Seq[String], - alias: Option[String] = None) = { + override def lookupRelation( + tableIdentifier: Seq[String], + alias: Option[String] = None): LogicalPlan = { throw new UnsupportedOperationException } @@ -221,11 +221,11 @@ object EmptyCatalog extends Catalog { throw new UnsupportedOperationException } - def registerTable(tableIdentifier: Seq[String], plan: LogicalPlan): Unit = { + override def registerTable(tableIdentifier: Seq[String], plan: LogicalPlan): Unit = { throw new UnsupportedOperationException } - def unregisterTable(tableIdentifier: Seq[String]): Unit = { + override def unregisterTable(tableIdentifier: Seq[String]): Unit = { throw new UnsupportedOperationException } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala index 425e1e41cbf21..40472a1cbb3b4 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala @@ -33,7 +33,7 @@ class CheckAnalysis { */ val extendedCheckRules: Seq[LogicalPlan => Unit] = Nil - def failAnalysis(msg: String) = { + def failAnalysis(msg: String): Nothing = { throw new AnalysisException(msg) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala index 9f334f6d42ad1..c43ea55899695 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala @@ -35,7 +35,7 @@ trait OverrideFunctionRegistry extends FunctionRegistry { val functionBuilders = StringKeyHashMap[FunctionBuilder](caseSensitive) - def registerFunction(name: String, builder: FunctionBuilder) = { + override def registerFunction(name: String, builder: FunctionBuilder): Unit = { functionBuilders.put(name, builder) } @@ -47,7 +47,7 @@ trait OverrideFunctionRegistry extends FunctionRegistry { class SimpleFunctionRegistry(val caseSensitive: Boolean) extends FunctionRegistry { val functionBuilders = StringKeyHashMap[FunctionBuilder](caseSensitive) - def registerFunction(name: String, builder: FunctionBuilder) = { + override def registerFunction(name: String, builder: FunctionBuilder): Unit = { functionBuilders.put(name, builder) } @@ -61,13 +61,15 @@ class SimpleFunctionRegistry(val caseSensitive: Boolean) extends FunctionRegistr * functions are already filled in and the analyser needs only to resolve attribute references. */ object EmptyFunctionRegistry extends FunctionRegistry { - def registerFunction(name: String, builder: FunctionBuilder) = ??? + override def registerFunction(name: String, builder: FunctionBuilder): Unit = { + throw new UnsupportedOperationException + } - def lookupFunction(name: String, children: Seq[Expression]): Expression = { + override def lookupFunction(name: String, children: Seq[Expression]): Expression = { throw new UnsupportedOperationException } - def caseSensitive: Boolean = ??? + override def caseSensitive: Boolean = throw new UnsupportedOperationException } /** @@ -76,7 +78,7 @@ object EmptyFunctionRegistry extends FunctionRegistry { * TODO move this into util folder? */ object StringKeyHashMap { - def apply[T](caseSensitive: Boolean) = caseSensitive match { + def apply[T](caseSensitive: Boolean): StringKeyHashMap[T] = caseSensitive match { case false => new StringKeyHashMap[T](_.toLowerCase) case true => new StringKeyHashMap[T](identity) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/package.scala index a7d3a8ee7deb3..c61c395cb4bb1 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/package.scala @@ -38,7 +38,7 @@ package object analysis { implicit class AnalysisErrorAt(t: TreeNode[_]) { /** Fails the analysis at the point where a specific tree node was parsed. */ - def failAnalysis(msg: String) = { + def failAnalysis(msg: String): Nothing = { throw new AnalysisException(msg, t.origin.line, t.origin.startPosition) } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala index ad5172c0349eb..300e9ba187bc5 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala @@ -22,6 +22,7 @@ import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical.LeafNode import org.apache.spark.sql.catalyst.trees.TreeNode +import org.apache.spark.sql.types.DataType /** * Thrown when an invalid attempt is made to access a property of a tree that has yet to be fully @@ -38,9 +39,10 @@ case class UnresolvedRelation( alias: Option[String] = None) extends LeafNode { /** Returns a `.` separated name for this relation. */ - def tableName = tableIdentifier.mkString(".") + def tableName: String = tableIdentifier.mkString(".") + + override def output: Seq[Attribute] = Nil - override def output = Nil override lazy val resolved = false } @@ -48,16 +50,16 @@ case class UnresolvedRelation( * Holds the name of an attribute that has yet to be resolved. */ case class UnresolvedAttribute(name: String) extends Attribute with trees.LeafNode[Expression] { - override def exprId = throw new UnresolvedException(this, "exprId") - override def dataType = throw new UnresolvedException(this, "dataType") - override def nullable = throw new UnresolvedException(this, "nullable") - override def qualifiers = throw new UnresolvedException(this, "qualifiers") + override def exprId: ExprId = throw new UnresolvedException(this, "exprId") + override def dataType: DataType = throw new UnresolvedException(this, "dataType") + override def nullable: Boolean = throw new UnresolvedException(this, "nullable") + override def qualifiers: Seq[String] = throw new UnresolvedException(this, "qualifiers") override lazy val resolved = false - override def newInstance() = this - override def withNullability(newNullability: Boolean) = this - override def withQualifiers(newQualifiers: Seq[String]) = this - override def withName(newName: String) = UnresolvedAttribute(name) + override def newInstance(): UnresolvedAttribute = this + override def withNullability(newNullability: Boolean): UnresolvedAttribute = this + override def withQualifiers(newQualifiers: Seq[String]): UnresolvedAttribute = this + override def withName(newName: String): UnresolvedAttribute = UnresolvedAttribute(name) // Unresolved attributes are transient at compile time and don't get evaluated during execution. override def eval(input: Row = null): EvaluatedType = @@ -67,16 +69,16 @@ case class UnresolvedAttribute(name: String) extends Attribute with trees.LeafNo } case class UnresolvedFunction(name: String, children: Seq[Expression]) extends Expression { - override def dataType = throw new UnresolvedException(this, "dataType") - override def foldable = throw new UnresolvedException(this, "foldable") - override def nullable = throw new UnresolvedException(this, "nullable") + override def dataType: DataType = throw new UnresolvedException(this, "dataType") + override def foldable: Boolean = throw new UnresolvedException(this, "foldable") + override def nullable: Boolean = throw new UnresolvedException(this, "nullable") override lazy val resolved = false // Unresolved functions are transient at compile time and don't get evaluated during execution. override def eval(input: Row = null): EvaluatedType = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") - override def toString = s"'$name(${children.mkString(",")})" + override def toString: String = s"'$name(${children.mkString(",")})" } /** @@ -86,17 +88,17 @@ case class UnresolvedFunction(name: String, children: Seq[Expression]) extends E trait Star extends Attribute with trees.LeafNode[Expression] { self: Product => - override def name = throw new UnresolvedException(this, "name") - override def exprId = throw new UnresolvedException(this, "exprId") - override def dataType = throw new UnresolvedException(this, "dataType") - override def nullable = throw new UnresolvedException(this, "nullable") - override def qualifiers = throw new UnresolvedException(this, "qualifiers") + override def name: String = throw new UnresolvedException(this, "name") + override def exprId: ExprId = throw new UnresolvedException(this, "exprId") + override def dataType: DataType = throw new UnresolvedException(this, "dataType") + override def nullable: Boolean = throw new UnresolvedException(this, "nullable") + override def qualifiers: Seq[String] = throw new UnresolvedException(this, "qualifiers") override lazy val resolved = false - override def newInstance() = this - override def withNullability(newNullability: Boolean) = this - override def withQualifiers(newQualifiers: Seq[String]) = this - override def withName(newName: String) = this + override def newInstance(): Star = this + override def withNullability(newNullability: Boolean): Star = this + override def withQualifiers(newQualifiers: Seq[String]): Star = this + override def withName(newName: String): Star = this // Star gets expanded at runtime so we never evaluate a Star. override def eval(input: Row = null): EvaluatedType = @@ -129,7 +131,7 @@ case class UnresolvedStar(table: Option[String]) extends Star { } } - override def toString = table.map(_ + ".").getOrElse("") + "*" + override def toString: String = table.map(_ + ".").getOrElse("") + "*" } /** @@ -144,25 +146,25 @@ case class UnresolvedStar(table: Option[String]) extends Star { case class MultiAlias(child: Expression, names: Seq[String]) extends Attribute with trees.UnaryNode[Expression] { - override def name = throw new UnresolvedException(this, "name") + override def name: String = throw new UnresolvedException(this, "name") - override def exprId = throw new UnresolvedException(this, "exprId") + override def exprId: ExprId = throw new UnresolvedException(this, "exprId") - override def dataType = throw new UnresolvedException(this, "dataType") + override def dataType: DataType = throw new UnresolvedException(this, "dataType") - override def nullable = throw new UnresolvedException(this, "nullable") + override def nullable: Boolean = throw new UnresolvedException(this, "nullable") - override def qualifiers = throw new UnresolvedException(this, "qualifiers") + override def qualifiers: Seq[String] = throw new UnresolvedException(this, "qualifiers") override lazy val resolved = false - override def newInstance() = this + override def newInstance(): MultiAlias = this - override def withNullability(newNullability: Boolean) = this + override def withNullability(newNullability: Boolean): MultiAlias = this - override def withQualifiers(newQualifiers: Seq[String]) = this + override def withQualifiers(newQualifiers: Seq[String]): MultiAlias = this - override def withName(newName: String) = this + override def withName(newName: String): MultiAlias = this override def eval(input: Row = null): EvaluatedType = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") @@ -179,17 +181,17 @@ case class MultiAlias(child: Expression, names: Seq[String]) */ case class ResolvedStar(expressions: Seq[NamedExpression]) extends Star { override def expand(input: Seq[Attribute], resolver: Resolver): Seq[NamedExpression] = expressions - override def toString = expressions.mkString("ResolvedStar(", ", ", ")") + override def toString: String = expressions.mkString("ResolvedStar(", ", ", ")") } case class UnresolvedGetField(child: Expression, fieldName: String) extends UnaryExpression { - override def dataType = throw new UnresolvedException(this, "dataType") - override def foldable = throw new UnresolvedException(this, "foldable") - override def nullable = throw new UnresolvedException(this, "nullable") + override def dataType: DataType = throw new UnresolvedException(this, "dataType") + override def foldable: Boolean = throw new UnresolvedException(this, "foldable") + override def nullable: Boolean = throw new UnresolvedException(this, "nullable") override lazy val resolved = false override def eval(input: Row = null): EvaluatedType = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") - override def toString = s"$child.$fieldName" + override def toString: String = s"$child.$fieldName" } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala index 7f5f617812919..145f062dd6817 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala @@ -61,60 +61,60 @@ package object dsl { trait ImplicitOperators { def expr: Expression - def unary_- = UnaryMinus(expr) - def unary_! = Not(expr) - def unary_~ = BitwiseNot(expr) - - def + (other: Expression) = Add(expr, other) - def - (other: Expression) = Subtract(expr, other) - def * (other: Expression) = Multiply(expr, other) - def / (other: Expression) = Divide(expr, other) - def % (other: Expression) = Remainder(expr, other) - def & (other: Expression) = BitwiseAnd(expr, other) - def | (other: Expression) = BitwiseOr(expr, other) - def ^ (other: Expression) = BitwiseXor(expr, other) - - def && (other: Expression) = And(expr, other) - def || (other: Expression) = Or(expr, other) - - def < (other: Expression) = LessThan(expr, other) - def <= (other: Expression) = LessThanOrEqual(expr, other) - def > (other: Expression) = GreaterThan(expr, other) - def >= (other: Expression) = GreaterThanOrEqual(expr, other) - def === (other: Expression) = EqualTo(expr, other) - def <=> (other: Expression) = EqualNullSafe(expr, other) - def !== (other: Expression) = Not(EqualTo(expr, other)) - - def in(list: Expression*) = In(expr, list) - - def like(other: Expression) = Like(expr, other) - def rlike(other: Expression) = RLike(expr, other) - def contains(other: Expression) = Contains(expr, other) - def startsWith(other: Expression) = StartsWith(expr, other) - def endsWith(other: Expression) = EndsWith(expr, other) - def substr(pos: Expression, len: Expression = Literal(Int.MaxValue)) = + def unary_- : Expression= UnaryMinus(expr) + def unary_! : Predicate = Not(expr) + def unary_~ : Expression = BitwiseNot(expr) + + def + (other: Expression): Expression = Add(expr, other) + def - (other: Expression): Expression = Subtract(expr, other) + def * (other: Expression): Expression = Multiply(expr, other) + def / (other: Expression): Expression = Divide(expr, other) + def % (other: Expression): Expression = Remainder(expr, other) + def & (other: Expression): Expression = BitwiseAnd(expr, other) + def | (other: Expression): Expression = BitwiseOr(expr, other) + def ^ (other: Expression): Expression = BitwiseXor(expr, other) + + def && (other: Expression): Predicate = And(expr, other) + def || (other: Expression): Predicate = Or(expr, other) + + def < (other: Expression): Predicate = LessThan(expr, other) + def <= (other: Expression): Predicate = LessThanOrEqual(expr, other) + def > (other: Expression): Predicate = GreaterThan(expr, other) + def >= (other: Expression): Predicate = GreaterThanOrEqual(expr, other) + def === (other: Expression): Predicate = EqualTo(expr, other) + def <=> (other: Expression): Predicate = EqualNullSafe(expr, other) + def !== (other: Expression): Predicate = Not(EqualTo(expr, other)) + + def in(list: Expression*): Expression = In(expr, list) + + def like(other: Expression): Expression = Like(expr, other) + def rlike(other: Expression): Expression = RLike(expr, other) + def contains(other: Expression): Expression = Contains(expr, other) + def startsWith(other: Expression): Expression = StartsWith(expr, other) + def endsWith(other: Expression): Expression = EndsWith(expr, other) + def substr(pos: Expression, len: Expression = Literal(Int.MaxValue)): Expression = Substring(expr, pos, len) - def substring(pos: Expression, len: Expression = Literal(Int.MaxValue)) = + def substring(pos: Expression, len: Expression = Literal(Int.MaxValue)): Expression = Substring(expr, pos, len) - def isNull = IsNull(expr) - def isNotNull = IsNotNull(expr) + def isNull: Predicate = IsNull(expr) + def isNotNull: Predicate = IsNotNull(expr) - def getItem(ordinal: Expression) = GetItem(expr, ordinal) - def getField(fieldName: String) = UnresolvedGetField(expr, fieldName) + def getItem(ordinal: Expression): Expression = GetItem(expr, ordinal) + def getField(fieldName: String): UnresolvedGetField = UnresolvedGetField(expr, fieldName) - def cast(to: DataType) = Cast(expr, to) + def cast(to: DataType): Expression = Cast(expr, to) - def asc = SortOrder(expr, Ascending) - def desc = SortOrder(expr, Descending) + def asc: SortOrder = SortOrder(expr, Ascending) + def desc: SortOrder = SortOrder(expr, Descending) - def as(alias: String) = Alias(expr, alias)() - def as(alias: Symbol) = Alias(expr, alias.name)() + def as(alias: String): NamedExpression = Alias(expr, alias)() + def as(alias: Symbol): NamedExpression = Alias(expr, alias.name)() } trait ExpressionConversions { implicit class DslExpression(e: Expression) extends ImplicitOperators { - def expr = e + def expr: Expression = e } implicit def booleanToLiteral(b: Boolean): Literal = Literal(b) @@ -144,94 +144,100 @@ package object dsl { } } - def sum(e: Expression) = Sum(e) - def sumDistinct(e: Expression) = SumDistinct(e) - def count(e: Expression) = Count(e) - def countDistinct(e: Expression*) = CountDistinct(e) - def approxCountDistinct(e: Expression, rsd: Double = 0.05) = ApproxCountDistinct(e, rsd) - def avg(e: Expression) = Average(e) - def first(e: Expression) = First(e) - def last(e: Expression) = Last(e) - def min(e: Expression) = Min(e) - def max(e: Expression) = Max(e) - def upper(e: Expression) = Upper(e) - def lower(e: Expression) = Lower(e) - def sqrt(e: Expression) = Sqrt(e) - def abs(e: Expression) = Abs(e) - - implicit class DslSymbol(sym: Symbol) extends ImplicitAttribute { def s = sym.name } + def sum(e: Expression): Expression = Sum(e) + def sumDistinct(e: Expression): Expression = SumDistinct(e) + def count(e: Expression): Expression = Count(e) + def countDistinct(e: Expression*): Expression = CountDistinct(e) + def approxCountDistinct(e: Expression, rsd: Double = 0.05): Expression = + ApproxCountDistinct(e, rsd) + def avg(e: Expression): Expression = Average(e) + def first(e: Expression): Expression = First(e) + def last(e: Expression): Expression = Last(e) + def min(e: Expression): Expression = Min(e) + def max(e: Expression): Expression = Max(e) + def upper(e: Expression): Expression = Upper(e) + def lower(e: Expression): Expression = Lower(e) + def sqrt(e: Expression): Expression = Sqrt(e) + def abs(e: Expression): Expression = Abs(e) + + implicit class DslSymbol(sym: Symbol) extends ImplicitAttribute { def s: String = sym.name } // TODO more implicit class for literal? implicit class DslString(val s: String) extends ImplicitOperators { override def expr: Expression = Literal(s) - def attr = analysis.UnresolvedAttribute(s) + def attr: UnresolvedAttribute = analysis.UnresolvedAttribute(s) } abstract class ImplicitAttribute extends ImplicitOperators { def s: String - def expr = attr - def attr = analysis.UnresolvedAttribute(s) + def expr: UnresolvedAttribute = attr + def attr: UnresolvedAttribute = analysis.UnresolvedAttribute(s) /** Creates a new AttributeReference of type boolean */ - def boolean = AttributeReference(s, BooleanType, nullable = true)() + def boolean: AttributeReference = AttributeReference(s, BooleanType, nullable = true)() /** Creates a new AttributeReference of type byte */ - def byte = AttributeReference(s, ByteType, nullable = true)() + def byte: AttributeReference = AttributeReference(s, ByteType, nullable = true)() /** Creates a new AttributeReference of type short */ - def short = AttributeReference(s, ShortType, nullable = true)() + def short: AttributeReference = AttributeReference(s, ShortType, nullable = true)() /** Creates a new AttributeReference of type int */ - def int = AttributeReference(s, IntegerType, nullable = true)() + def int: AttributeReference = AttributeReference(s, IntegerType, nullable = true)() /** Creates a new AttributeReference of type long */ - def long = AttributeReference(s, LongType, nullable = true)() + def long: AttributeReference = AttributeReference(s, LongType, nullable = true)() /** Creates a new AttributeReference of type float */ - def float = AttributeReference(s, FloatType, nullable = true)() + def float: AttributeReference = AttributeReference(s, FloatType, nullable = true)() /** Creates a new AttributeReference of type double */ - def double = AttributeReference(s, DoubleType, nullable = true)() + def double: AttributeReference = AttributeReference(s, DoubleType, nullable = true)() /** Creates a new AttributeReference of type string */ - def string = AttributeReference(s, StringType, nullable = true)() + def string: AttributeReference = AttributeReference(s, StringType, nullable = true)() /** Creates a new AttributeReference of type date */ - def date = AttributeReference(s, DateType, nullable = true)() + def date: AttributeReference = AttributeReference(s, DateType, nullable = true)() /** Creates a new AttributeReference of type decimal */ - def decimal = AttributeReference(s, DecimalType.Unlimited, nullable = true)() + def decimal: AttributeReference = + AttributeReference(s, DecimalType.Unlimited, nullable = true)() /** Creates a new AttributeReference of type decimal */ - def decimal(precision: Int, scale: Int) = + def decimal(precision: Int, scale: Int): AttributeReference = AttributeReference(s, DecimalType(precision, scale), nullable = true)() /** Creates a new AttributeReference of type timestamp */ - def timestamp = AttributeReference(s, TimestampType, nullable = true)() + def timestamp: AttributeReference = AttributeReference(s, TimestampType, nullable = true)() /** Creates a new AttributeReference of type binary */ - def binary = AttributeReference(s, BinaryType, nullable = true)() + def binary: AttributeReference = AttributeReference(s, BinaryType, nullable = true)() /** Creates a new AttributeReference of type array */ - def array(dataType: DataType) = AttributeReference(s, ArrayType(dataType), nullable = true)() + def array(dataType: DataType): AttributeReference = + AttributeReference(s, ArrayType(dataType), nullable = true)() /** Creates a new AttributeReference of type map */ def map(keyType: DataType, valueType: DataType): AttributeReference = map(MapType(keyType, valueType)) - def map(mapType: MapType) = AttributeReference(s, mapType, nullable = true)() + + def map(mapType: MapType): AttributeReference = + AttributeReference(s, mapType, nullable = true)() /** Creates a new AttributeReference of type struct */ def struct(fields: StructField*): AttributeReference = struct(StructType(fields)) - def struct(structType: StructType) = AttributeReference(s, structType, nullable = true)() + def struct(structType: StructType): AttributeReference = + AttributeReference(s, structType, nullable = true)() } implicit class DslAttribute(a: AttributeReference) { - def notNull = a.withNullability(false) - def nullable = a.withNullability(true) + def notNull: AttributeReference = a.withNullability(false) + def nullable: AttributeReference = a.withNullability(true) // Protobuf terminology - def required = a.withNullability(false) + def required: AttributeReference = a.withNullability(false) - def at(ordinal: Int) = BoundReference(ordinal, a.dataType, a.nullable) + def at(ordinal: Int): BoundReference = BoundReference(ordinal, a.dataType, a.nullable) } } @@ -241,23 +247,23 @@ package object dsl { abstract class LogicalPlanFunctions { def logicalPlan: LogicalPlan - def select(exprs: NamedExpression*) = Project(exprs, logicalPlan) + def select(exprs: NamedExpression*): LogicalPlan = Project(exprs, logicalPlan) - def where(condition: Expression) = Filter(condition, logicalPlan) + def where(condition: Expression): LogicalPlan = Filter(condition, logicalPlan) - def limit(limitExpr: Expression) = Limit(limitExpr, logicalPlan) + def limit(limitExpr: Expression): LogicalPlan = Limit(limitExpr, logicalPlan) def join( otherPlan: LogicalPlan, joinType: JoinType = Inner, - condition: Option[Expression] = None) = + condition: Option[Expression] = None): LogicalPlan = Join(logicalPlan, otherPlan, joinType, condition) - def orderBy(sortExprs: SortOrder*) = Sort(sortExprs, true, logicalPlan) + def orderBy(sortExprs: SortOrder*): LogicalPlan = Sort(sortExprs, true, logicalPlan) - def sortBy(sortExprs: SortOrder*) = Sort(sortExprs, false, logicalPlan) + def sortBy(sortExprs: SortOrder*): LogicalPlan = Sort(sortExprs, false, logicalPlan) - def groupBy(groupingExprs: Expression*)(aggregateExprs: Expression*) = { + def groupBy(groupingExprs: Expression*)(aggregateExprs: Expression*): LogicalPlan = { val aliasedExprs = aggregateExprs.map { case ne: NamedExpression => ne case e => Alias(e, e.toString)() @@ -265,27 +271,27 @@ package object dsl { Aggregate(groupingExprs, aliasedExprs, logicalPlan) } - def subquery(alias: Symbol) = Subquery(alias.name, logicalPlan) + def subquery(alias: Symbol): LogicalPlan = Subquery(alias.name, logicalPlan) - def unionAll(otherPlan: LogicalPlan) = Union(logicalPlan, otherPlan) + def unionAll(otherPlan: LogicalPlan): LogicalPlan = Union(logicalPlan, otherPlan) - def sfilter[T1](arg1: Symbol)(udf: (T1) => Boolean) = + def sfilter[T1](arg1: Symbol)(udf: (T1) => Boolean): LogicalPlan = Filter(ScalaUdf(udf, BooleanType, Seq(UnresolvedAttribute(arg1.name))), logicalPlan) def sample( fraction: Double, withReplacement: Boolean = true, - seed: Int = (math.random * 1000).toInt) = + seed: Int = (math.random * 1000).toInt): LogicalPlan = Sample(fraction, withReplacement, seed, logicalPlan) def generate( generator: Generator, join: Boolean = false, outer: Boolean = false, - alias: Option[String] = None) = + alias: Option[String] = None): LogicalPlan = Generate(generator, join, outer, None, logicalPlan) - def insertInto(tableName: String, overwrite: Boolean = false) = + def insertInto(tableName: String, overwrite: Boolean = false): LogicalPlan = InsertIntoTable( analysis.UnresolvedRelation(Seq(tableName)), Map.empty, logicalPlan, overwrite) @@ -294,12 +300,14 @@ package object dsl { object plans { // scalastyle:ignore implicit class DslLogicalPlan(val logicalPlan: LogicalPlan) extends LogicalPlanFunctions { - def writeToFile(path: String) = WriteToFile(path, logicalPlan) + def writeToFile(path: String): LogicalPlan = WriteToFile(path, logicalPlan) } } case class ScalaUdfBuilder[T: TypeTag](f: AnyRef) { - def call(args: Expression*) = ScalaUdf(f, ScalaReflection.schemaFor(typeTag[T]).dataType, args) + def call(args: Expression*): ScalaUdf = { + ScalaUdf(f, ScalaReflection.schemaFor(typeTag[T]).dataType, args) + } } // scalastyle:off diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeMap.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeMap.scala index 82e760b6c6916..96a11e352ec50 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeMap.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeMap.scala @@ -23,7 +23,9 @@ package org.apache.spark.sql.catalyst.expressions * of the name, or the expected nullability). */ object AttributeMap { - def apply[A](kvs: Seq[(Attribute, A)]) = new AttributeMap(kvs.map(kv => (kv._1.exprId, kv)).toMap) + def apply[A](kvs: Seq[(Attribute, A)]): AttributeMap[A] = { + new AttributeMap(kvs.map(kv => (kv._1.exprId, kv)).toMap) + } } class AttributeMap[A](baseMap: Map[ExprId, (Attribute, A)]) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeSet.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeSet.scala index adaeab0b5c027..f9ae85a5cfc1b 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeSet.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeSet.scala @@ -19,27 +19,27 @@ package org.apache.spark.sql.catalyst.expressions protected class AttributeEquals(val a: Attribute) { - override def hashCode() = a match { + override def hashCode(): Int = a match { case ar: AttributeReference => ar.exprId.hashCode() case a => a.hashCode() } - override def equals(other: Any) = (a, other.asInstanceOf[AttributeEquals].a) match { + override def equals(other: Any): Boolean = (a, other.asInstanceOf[AttributeEquals].a) match { case (a1: AttributeReference, a2: AttributeReference) => a1.exprId == a2.exprId case (a1, a2) => a1 == a2 } } object AttributeSet { - def apply(a: Attribute) = - new AttributeSet(Set(new AttributeEquals(a))) + def apply(a: Attribute): AttributeSet = new AttributeSet(Set(new AttributeEquals(a))) /** Constructs a new [[AttributeSet]] given a sequence of [[Expression Expressions]]. */ - def apply(baseSet: Seq[Expression]) = + def apply(baseSet: Seq[Expression]): AttributeSet = { new AttributeSet( baseSet .flatMap(_.references) .map(new AttributeEquals(_)).toSet) + } } /** @@ -57,7 +57,7 @@ class AttributeSet private (val baseSet: Set[AttributeEquals]) extends Traversable[Attribute] with Serializable { /** Returns true if the members of this AttributeSet and other are the same. */ - override def equals(other: Any) = other match { + override def equals(other: Any): Boolean = other match { case otherSet: AttributeSet => baseSet.map(_.a).forall(otherSet.contains) case _ => false } @@ -81,32 +81,34 @@ class AttributeSet private (val baseSet: Set[AttributeEquals]) * Returns true if the [[Attribute Attributes]] in this set are a subset of the Attributes in * `other`. */ - def subsetOf(other: AttributeSet) = baseSet.subsetOf(other.baseSet) + def subsetOf(other: AttributeSet): Boolean = baseSet.subsetOf(other.baseSet) /** * Returns a new [[AttributeSet]] that does not contain any of the [[Attribute Attributes]] found * in `other`. */ - def --(other: Traversable[NamedExpression]) = + def --(other: Traversable[NamedExpression]): AttributeSet = new AttributeSet(baseSet -- other.map(a => new AttributeEquals(a.toAttribute))) /** * Returns a new [[AttributeSet]] that contains all of the [[Attribute Attributes]] found * in `other`. */ - def ++(other: AttributeSet) = new AttributeSet(baseSet ++ other.baseSet) + def ++(other: AttributeSet): AttributeSet = new AttributeSet(baseSet ++ other.baseSet) /** * Returns a new [[AttributeSet]] contain only the [[Attribute Attributes]] where `f` evaluates to * true. */ - override def filter(f: Attribute => Boolean) = new AttributeSet(baseSet.filter(ae => f(ae.a))) + override def filter(f: Attribute => Boolean): AttributeSet = + new AttributeSet(baseSet.filter(ae => f(ae.a))) /** * Returns a new [[AttributeSet]] that only contains [[Attribute Attributes]] that are found in * `this` and `other`. */ - def intersect(other: AttributeSet) = new AttributeSet(baseSet.intersect(other.baseSet)) + def intersect(other: AttributeSet): AttributeSet = + new AttributeSet(baseSet.intersect(other.baseSet)) override def foreach[U](f: (Attribute) => U): Unit = baseSet.map(_.a).foreach(f) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala index 76a9f08dea85f..2225621dbaabd 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala @@ -32,7 +32,7 @@ case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean) type EvaluatedType = Any - override def toString = s"input[$ordinal]" + override def toString: String = s"input[$ordinal]" override def eval(input: Row): Any = input(ordinal) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala index b1bc858478ee1..9bde74ac22669 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala @@ -29,9 +29,9 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w override lazy val resolved = childrenResolved && resolve(child.dataType, dataType) - override def foldable = child.foldable + override def foldable: Boolean = child.foldable - override def nullable = forceNullable(child.dataType, dataType) || child.nullable + override def nullable: Boolean = forceNullable(child.dataType, dataType) || child.nullable private[this] def forceNullable(from: DataType, to: DataType) = (from, to) match { case (StringType, _: NumericType) => true @@ -103,7 +103,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w } } - override def toString = s"CAST($child, $dataType)" + override def toString: String = s"CAST($child, $dataType)" type EvaluatedType = Any @@ -430,14 +430,14 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w object Cast { // `SimpleDateFormat` is not thread-safe. private[sql] val threadLocalTimestampFormat = new ThreadLocal[DateFormat] { - override def initialValue() = { + override def initialValue(): SimpleDateFormat = { new SimpleDateFormat("yyyy-MM-dd HH:mm:ss") } } // `SimpleDateFormat` is not thread-safe. private[sql] val threadLocalDateFormat = new ThreadLocal[DateFormat] { - override def initialValue() = { + override def initialValue(): SimpleDateFormat = { new SimpleDateFormat("yyyy-MM-dd") } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala index 6ad39b8372cfb..4e3bbc06a5b4c 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala @@ -65,7 +65,7 @@ abstract class Expression extends TreeNode[Expression] { * Returns true if all the children of this expression have been resolved to a specific schema * and false if any still contains any unresolved placeholders. */ - def childrenResolved = !children.exists(!_.resolved) + def childrenResolved: Boolean = !children.exists(!_.resolved) /** * Returns a string representation of this expression that does not have developer centric @@ -84,9 +84,9 @@ abstract class BinaryExpression extends Expression with trees.BinaryNode[Express def symbol: String - override def foldable = left.foldable && right.foldable + override def foldable: Boolean = left.foldable && right.foldable - override def toString = s"($left $symbol $right)" + override def toString: String = s"($left $symbol $right)" } abstract class LeafExpression extends Expression with trees.LeafNode[Expression] { @@ -104,8 +104,8 @@ abstract class UnaryExpression extends Expression with trees.UnaryNode[Expressio case class GroupExpression(children: Seq[Expression]) extends Expression { self: Product => type EvaluatedType = Seq[Any] - override def eval(input: Row): EvaluatedType = ??? - override def nullable = false - override def foldable = false - override def dataType = ??? + override def eval(input: Row): EvaluatedType = throw new UnsupportedOperationException + override def nullable: Boolean = false + override def foldable: Boolean = false + override def dataType: DataType = throw new UnsupportedOperationException } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala index db5d897ee569f..c2866cd955409 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala @@ -40,7 +40,7 @@ class InterpretedProjection(expressions: Seq[Expression]) extends Projection { new GenericRow(outputArray) } - override def toString = s"Row => [${exprArray.mkString(",")}]" + override def toString: String = s"Row => [${exprArray.mkString(",")}]" } /** @@ -107,12 +107,12 @@ class JoinedRow extends Row { override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq - override def length = row1.length + row2.length + override def length: Int = row1.length + row2.length - override def apply(i: Int) = + override def apply(i: Int): Any = if (i < row1.length) row1(i) else row2(i - row1.length) - override def isNullAt(i: Int) = + override def isNullAt(i: Int): Boolean = if (i < row1.length) row1.isNullAt(i) else row2.isNullAt(i - row1.length) override def getInt(i: Int): Int = @@ -142,7 +142,7 @@ class JoinedRow extends Row { override def getAs[T](i: Int): T = if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length) - override def copy() = { + override def copy(): Row = { val totalSize = row1.length + row2.length val copiedValues = new Array[Any](totalSize) var i = 0 @@ -153,7 +153,7 @@ class JoinedRow extends Row { new GenericRow(copiedValues) } - override def toString() = { + override def toString: String = { // Make sure toString never throws NullPointerException. if ((row1 eq null) && (row2 eq null)) { "[ empty row ]" @@ -207,12 +207,12 @@ class JoinedRow2 extends Row { override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq - override def length = row1.length + row2.length + override def length: Int = row1.length + row2.length - override def apply(i: Int) = + override def apply(i: Int): Any = if (i < row1.length) row1(i) else row2(i - row1.length) - override def isNullAt(i: Int) = + override def isNullAt(i: Int): Boolean = if (i < row1.length) row1.isNullAt(i) else row2.isNullAt(i - row1.length) override def getInt(i: Int): Int = @@ -242,7 +242,7 @@ class JoinedRow2 extends Row { override def getAs[T](i: Int): T = if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length) - override def copy() = { + override def copy(): Row = { val totalSize = row1.length + row2.length val copiedValues = new Array[Any](totalSize) var i = 0 @@ -253,7 +253,7 @@ class JoinedRow2 extends Row { new GenericRow(copiedValues) } - override def toString() = { + override def toString: String = { // Make sure toString never throws NullPointerException. if ((row1 eq null) && (row2 eq null)) { "[ empty row ]" @@ -301,12 +301,12 @@ class JoinedRow3 extends Row { override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq - override def length = row1.length + row2.length + override def length: Int = row1.length + row2.length - override def apply(i: Int) = + override def apply(i: Int): Any = if (i < row1.length) row1(i) else row2(i - row1.length) - override def isNullAt(i: Int) = + override def isNullAt(i: Int): Boolean = if (i < row1.length) row1.isNullAt(i) else row2.isNullAt(i - row1.length) override def getInt(i: Int): Int = @@ -336,7 +336,7 @@ class JoinedRow3 extends Row { override def getAs[T](i: Int): T = if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length) - override def copy() = { + override def copy(): Row = { val totalSize = row1.length + row2.length val copiedValues = new Array[Any](totalSize) var i = 0 @@ -347,7 +347,7 @@ class JoinedRow3 extends Row { new GenericRow(copiedValues) } - override def toString() = { + override def toString: String = { // Make sure toString never throws NullPointerException. if ((row1 eq null) && (row2 eq null)) { "[ empty row ]" @@ -395,12 +395,12 @@ class JoinedRow4 extends Row { override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq - override def length = row1.length + row2.length + override def length: Int = row1.length + row2.length - override def apply(i: Int) = + override def apply(i: Int): Any = if (i < row1.length) row1(i) else row2(i - row1.length) - override def isNullAt(i: Int) = + override def isNullAt(i: Int): Boolean = if (i < row1.length) row1.isNullAt(i) else row2.isNullAt(i - row1.length) override def getInt(i: Int): Int = @@ -430,7 +430,7 @@ class JoinedRow4 extends Row { override def getAs[T](i: Int): T = if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length) - override def copy() = { + override def copy(): Row = { val totalSize = row1.length + row2.length val copiedValues = new Array[Any](totalSize) var i = 0 @@ -441,7 +441,7 @@ class JoinedRow4 extends Row { new GenericRow(copiedValues) } - override def toString() = { + override def toString: String = { // Make sure toString never throws NullPointerException. if ((row1 eq null) && (row2 eq null)) { "[ empty row ]" @@ -489,12 +489,12 @@ class JoinedRow5 extends Row { override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq - override def length = row1.length + row2.length + override def length: Int = row1.length + row2.length - override def apply(i: Int) = + override def apply(i: Int): Any = if (i < row1.length) row1(i) else row2(i - row1.length) - override def isNullAt(i: Int) = + override def isNullAt(i: Int): Boolean = if (i < row1.length) row1.isNullAt(i) else row2.isNullAt(i - row1.length) override def getInt(i: Int): Int = @@ -524,7 +524,7 @@ class JoinedRow5 extends Row { override def getAs[T](i: Int): T = if (i < row1.length) row1.getAs[T](i) else row2.getAs[T](i - row1.length) - override def copy() = { + override def copy(): Row = { val totalSize = row1.length + row2.length val copiedValues = new Array[Any](totalSize) var i = 0 @@ -535,7 +535,7 @@ class JoinedRow5 extends Row { new GenericRow(copiedValues) } - override def toString() = { + override def toString: String = { // Make sure toString never throws NullPointerException. if ((row1 eq null) && (row2 eq null)) { "[ empty row ]" diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala index b2c6d3029031d..f5fea3f015dc4 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala @@ -18,16 +18,19 @@ package org.apache.spark.sql.catalyst.expressions import java.util.Random -import org.apache.spark.sql.types.DoubleType + +import org.apache.spark.sql.types.{DataType, DoubleType} case object Rand extends LeafExpression { - override def dataType = DoubleType - override def nullable = false + override def dataType: DataType = DoubleType + override def nullable: Boolean = false private[this] lazy val rand = new Random - override def eval(input: Row = null) = rand.nextDouble().asInstanceOf[EvaluatedType] + override def eval(input: Row = null): EvaluatedType = { + rand.nextDouble().asInstanceOf[EvaluatedType] + } - override def toString = "RAND()" + override def toString: String = "RAND()" } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala index 8a36c6810790d..1fd5ce342b2ce 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala @@ -29,9 +29,9 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi type EvaluatedType = Any - def nullable = true + override def nullable: Boolean = true - override def toString = s"scalaUDF(${children.mkString(",")})" + override def toString: String = s"scalaUDF(${children.mkString(",")})" // scalastyle:off diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala index d00b2ac09745c..83074eb1e6310 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala @@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.trees +import org.apache.spark.sql.types.DataType abstract sealed class SortDirection case object Ascending extends SortDirection @@ -31,12 +32,12 @@ case object Descending extends SortDirection case class SortOrder(child: Expression, direction: SortDirection) extends Expression with trees.UnaryNode[Expression] { - override def dataType = child.dataType - override def nullable = child.nullable + override def dataType: DataType = child.dataType + override def nullable: Boolean = child.nullable // SortOrder itself is never evaluated. override def eval(input: Row = null): EvaluatedType = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") - override def toString = s"$child ${if (direction == Ascending) "ASC" else "DESC"}" + override def toString: String = s"$child ${if (direction == Ascending) "ASC" else "DESC"}" } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala index 21d714c9a8c3b..47b6f358ed1b1 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala @@ -62,126 +62,126 @@ abstract class MutableValue extends Serializable { var isNull: Boolean = true def boxed: Any def update(v: Any) - def copy(): this.type + def copy(): MutableValue } final class MutableInt extends MutableValue { var value: Int = 0 - def boxed = if (isNull) null else value - def update(v: Any) = value = { + override def boxed: Any = if (isNull) null else value + override def update(v: Any): Unit = { isNull = false - v.asInstanceOf[Int] + value = v.asInstanceOf[Int] } - def copy() = { + override def copy(): MutableInt = { val newCopy = new MutableInt newCopy.isNull = isNull newCopy.value = value - newCopy.asInstanceOf[this.type] + newCopy.asInstanceOf[MutableInt] } } final class MutableFloat extends MutableValue { var value: Float = 0 - def boxed = if (isNull) null else value - def update(v: Any) = value = { + override def boxed: Any = if (isNull) null else value + override def update(v: Any): Unit = { isNull = false - v.asInstanceOf[Float] + value = v.asInstanceOf[Float] } - def copy() = { + override def copy(): MutableFloat = { val newCopy = new MutableFloat newCopy.isNull = isNull newCopy.value = value - newCopy.asInstanceOf[this.type] + newCopy.asInstanceOf[MutableFloat] } } final class MutableBoolean extends MutableValue { var value: Boolean = false - def boxed = if (isNull) null else value - def update(v: Any) = value = { + override def boxed: Any = if (isNull) null else value + override def update(v: Any): Unit = { isNull = false - v.asInstanceOf[Boolean] + value = v.asInstanceOf[Boolean] } - def copy() = { + override def copy(): MutableBoolean = { val newCopy = new MutableBoolean newCopy.isNull = isNull newCopy.value = value - newCopy.asInstanceOf[this.type] + newCopy.asInstanceOf[MutableBoolean] } } final class MutableDouble extends MutableValue { var value: Double = 0 - def boxed = if (isNull) null else value - def update(v: Any) = value = { + override def boxed: Any = if (isNull) null else value + override def update(v: Any): Unit = { isNull = false - v.asInstanceOf[Double] + value = v.asInstanceOf[Double] } - def copy() = { + override def copy(): MutableDouble = { val newCopy = new MutableDouble newCopy.isNull = isNull newCopy.value = value - newCopy.asInstanceOf[this.type] + newCopy.asInstanceOf[MutableDouble] } } final class MutableShort extends MutableValue { var value: Short = 0 - def boxed = if (isNull) null else value - def update(v: Any) = value = { + override def boxed: Any = if (isNull) null else value + override def update(v: Any): Unit = value = { isNull = false v.asInstanceOf[Short] } - def copy() = { + override def copy(): MutableShort = { val newCopy = new MutableShort newCopy.isNull = isNull newCopy.value = value - newCopy.asInstanceOf[this.type] + newCopy.asInstanceOf[MutableShort] } } final class MutableLong extends MutableValue { var value: Long = 0 - def boxed = if (isNull) null else value - def update(v: Any) = value = { + override def boxed: Any = if (isNull) null else value + override def update(v: Any): Unit = value = { isNull = false v.asInstanceOf[Long] } - def copy() = { + override def copy(): MutableLong = { val newCopy = new MutableLong newCopy.isNull = isNull newCopy.value = value - newCopy.asInstanceOf[this.type] + newCopy.asInstanceOf[MutableLong] } } final class MutableByte extends MutableValue { var value: Byte = 0 - def boxed = if (isNull) null else value - def update(v: Any) = value = { + override def boxed: Any = if (isNull) null else value + override def update(v: Any): Unit = value = { isNull = false v.asInstanceOf[Byte] } - def copy() = { + override def copy(): MutableByte = { val newCopy = new MutableByte newCopy.isNull = isNull newCopy.value = value - newCopy.asInstanceOf[this.type] + newCopy.asInstanceOf[MutableByte] } } final class MutableAny extends MutableValue { var value: Any = _ - def boxed = if (isNull) null else value - def update(v: Any) = value = { + override def boxed: Any = if (isNull) null else value + override def update(v: Any): Unit = { isNull = false - v.asInstanceOf[Any] + value = v.asInstanceOf[Any] } - def copy() = { + override def copy(): MutableAny = { val newCopy = new MutableAny newCopy.isNull = isNull newCopy.value = value - newCopy.asInstanceOf[this.type] + newCopy.asInstanceOf[MutableAny] } } @@ -234,9 +234,9 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR if (value == null) setNullAt(ordinal) else values(ordinal).update(value) } - override def setString(ordinal: Int, value: String) = update(ordinal, value) + override def setString(ordinal: Int, value: String): Unit = update(ordinal, value) - override def getString(ordinal: Int) = apply(ordinal).asInstanceOf[String] + override def getString(ordinal: Int): String = apply(ordinal).asInstanceOf[String] override def setInt(ordinal: Int, value: Int): Unit = { val currentValue = values(ordinal).asInstanceOf[MutableInt] diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala index 5297d1e31246c..30da4faa3f1c6 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala @@ -79,27 +79,29 @@ abstract class AggregateFunction /** Base should return the generic aggregate expression that this function is computing */ val base: AggregateExpression - override def nullable = base.nullable - override def dataType = base.dataType + override def nullable: Boolean = base.nullable + override def dataType: DataType = base.dataType def update(input: Row): Unit // Do we really need this? - override def newInstance() = makeCopy(productIterator.map { case a: AnyRef => a }.toArray) + override def newInstance(): AggregateFunction = { + makeCopy(productIterator.map { case a: AnyRef => a }.toArray) + } } case class Min(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { - override def nullable = true - override def dataType = child.dataType - override def toString = s"MIN($child)" + override def nullable: Boolean = true + override def dataType: DataType = child.dataType + override def toString: String = s"MIN($child)" override def asPartial: SplitEvaluation = { val partialMin = Alias(Min(child), "PartialMin")() SplitEvaluation(Min(partialMin.toAttribute), partialMin :: Nil) } - override def newInstance() = new MinFunction(child, this) + override def newInstance(): MinFunction = new MinFunction(child, this) } case class MinFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction { @@ -121,16 +123,16 @@ case class MinFunction(expr: Expression, base: AggregateExpression) extends Aggr case class Max(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { - override def nullable = true - override def dataType = child.dataType - override def toString = s"MAX($child)" + override def nullable: Boolean = true + override def dataType: DataType = child.dataType + override def toString: String = s"MAX($child)" override def asPartial: SplitEvaluation = { val partialMax = Alias(Max(child), "PartialMax")() SplitEvaluation(Max(partialMax.toAttribute), partialMax :: Nil) } - override def newInstance() = new MaxFunction(child, this) + override def newInstance(): MaxFunction = new MaxFunction(child, this) } case class MaxFunction(expr: Expression, base: AggregateExpression) extends AggregateFunction { @@ -152,29 +154,29 @@ case class MaxFunction(expr: Expression, base: AggregateExpression) extends Aggr case class Count(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { - override def nullable = false - override def dataType = LongType - override def toString = s"COUNT($child)" + override def nullable: Boolean = false + override def dataType: LongType.type = LongType + override def toString: String = s"COUNT($child)" override def asPartial: SplitEvaluation = { val partialCount = Alias(Count(child), "PartialCount")() SplitEvaluation(Coalesce(Seq(Sum(partialCount.toAttribute), Literal(0L))), partialCount :: Nil) } - override def newInstance() = new CountFunction(child, this) + override def newInstance(): CountFunction = new CountFunction(child, this) } case class CountDistinct(expressions: Seq[Expression]) extends PartialAggregate { def this() = this(null) - override def children = expressions + override def children: Seq[Expression] = expressions - override def nullable = false - override def dataType = LongType - override def toString = s"COUNT(DISTINCT ${expressions.mkString(",")})" - override def newInstance() = new CountDistinctFunction(expressions, this) + override def nullable: Boolean = false + override def dataType: DataType = LongType + override def toString: String = s"COUNT(DISTINCT ${expressions.mkString(",")})" + override def newInstance(): CountDistinctFunction = new CountDistinctFunction(expressions, this) - override def asPartial = { + override def asPartial: SplitEvaluation = { val partialSet = Alias(CollectHashSet(expressions), "partialSets")() SplitEvaluation( CombineSetsAndCount(partialSet.toAttribute), @@ -185,11 +187,11 @@ case class CountDistinct(expressions: Seq[Expression]) extends PartialAggregate case class CollectHashSet(expressions: Seq[Expression]) extends AggregateExpression { def this() = this(null) - override def children = expressions - override def nullable = false - override def dataType = ArrayType(expressions.head.dataType) - override def toString = s"AddToHashSet(${expressions.mkString(",")})" - override def newInstance() = new CollectHashSetFunction(expressions, this) + override def children: Seq[Expression] = expressions + override def nullable: Boolean = false + override def dataType: ArrayType = ArrayType(expressions.head.dataType) + override def toString: String = s"AddToHashSet(${expressions.mkString(",")})" + override def newInstance(): CollectHashSetFunction = new CollectHashSetFunction(expressions, this) } case class CollectHashSetFunction( @@ -219,11 +221,13 @@ case class CollectHashSetFunction( case class CombineSetsAndCount(inputSet: Expression) extends AggregateExpression { def this() = this(null) - override def children = inputSet :: Nil - override def nullable = false - override def dataType = LongType - override def toString = s"CombineAndCount($inputSet)" - override def newInstance() = new CombineSetsAndCountFunction(inputSet, this) + override def children: Seq[Expression] = inputSet :: Nil + override def nullable: Boolean = false + override def dataType: DataType = LongType + override def toString: String = s"CombineAndCount($inputSet)" + override def newInstance(): CombineSetsAndCountFunction = { + new CombineSetsAndCountFunction(inputSet, this) + } } case class CombineSetsAndCountFunction( @@ -249,27 +253,31 @@ case class CombineSetsAndCountFunction( case class ApproxCountDistinctPartition(child: Expression, relativeSD: Double) extends AggregateExpression with trees.UnaryNode[Expression] { - override def nullable = false - override def dataType = child.dataType - override def toString = s"APPROXIMATE COUNT(DISTINCT $child)" - override def newInstance() = new ApproxCountDistinctPartitionFunction(child, this, relativeSD) + override def nullable: Boolean = false + override def dataType: DataType = child.dataType + override def toString: String = s"APPROXIMATE COUNT(DISTINCT $child)" + override def newInstance(): ApproxCountDistinctPartitionFunction = { + new ApproxCountDistinctPartitionFunction(child, this, relativeSD) + } } case class ApproxCountDistinctMerge(child: Expression, relativeSD: Double) extends AggregateExpression with trees.UnaryNode[Expression] { - override def nullable = false - override def dataType = LongType - override def toString = s"APPROXIMATE COUNT(DISTINCT $child)" - override def newInstance() = new ApproxCountDistinctMergeFunction(child, this, relativeSD) + override def nullable: Boolean = false + override def dataType: LongType.type = LongType + override def toString: String = s"APPROXIMATE COUNT(DISTINCT $child)" + override def newInstance(): ApproxCountDistinctMergeFunction = { + new ApproxCountDistinctMergeFunction(child, this, relativeSD) + } } case class ApproxCountDistinct(child: Expression, relativeSD: Double = 0.05) extends PartialAggregate with trees.UnaryNode[Expression] { - override def nullable = false - override def dataType = LongType - override def toString = s"APPROXIMATE COUNT(DISTINCT $child)" + override def nullable: Boolean = false + override def dataType: LongType.type = LongType + override def toString: String = s"APPROXIMATE COUNT(DISTINCT $child)" override def asPartial: SplitEvaluation = { val partialCount = @@ -280,14 +288,14 @@ case class ApproxCountDistinct(child: Expression, relativeSD: Double = 0.05) partialCount :: Nil) } - override def newInstance() = new CountDistinctFunction(child :: Nil, this) + override def newInstance(): CountDistinctFunction = new CountDistinctFunction(child :: Nil, this) } case class Average(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { - override def nullable = true + override def nullable: Boolean = true - override def dataType = child.dataType match { + override def dataType: DataType = child.dataType match { case DecimalType.Fixed(precision, scale) => DecimalType(precision + 4, scale + 4) // Add 4 digits after decimal point, like Hive case DecimalType.Unlimited => @@ -296,7 +304,7 @@ case class Average(child: Expression) extends PartialAggregate with trees.UnaryN DoubleType } - override def toString = s"AVG($child)" + override def toString: String = s"AVG($child)" override def asPartial: SplitEvaluation = { child.dataType match { @@ -323,14 +331,14 @@ case class Average(child: Expression) extends PartialAggregate with trees.UnaryN } } - override def newInstance() = new AverageFunction(child, this) + override def newInstance(): AverageFunction = new AverageFunction(child, this) } case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { - override def nullable = true + override def nullable: Boolean = true - override def dataType = child.dataType match { + override def dataType: DataType = child.dataType match { case DecimalType.Fixed(precision, scale) => DecimalType(precision + 10, scale) // Add 10 digits left of decimal point, like Hive case DecimalType.Unlimited => @@ -339,7 +347,7 @@ case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[ child.dataType } - override def toString = s"SUM($child)" + override def toString: String = s"SUM($child)" override def asPartial: SplitEvaluation = { child.dataType match { @@ -357,7 +365,7 @@ case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[ } } - override def newInstance() = new SumFunction(child, this) + override def newInstance(): SumFunction = new SumFunction(child, this) } /** @@ -377,19 +385,19 @@ case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[ case class CombineSum(child: Expression) extends AggregateExpression { def this() = this(null) - override def children = child :: Nil - override def nullable = true - override def dataType = child.dataType - override def toString = s"CombineSum($child)" - override def newInstance() = new CombineSumFunction(child, this) + override def children: Seq[Expression] = child :: Nil + override def nullable: Boolean = true + override def dataType: DataType = child.dataType + override def toString: String = s"CombineSum($child)" + override def newInstance(): CombineSumFunction = new CombineSumFunction(child, this) } case class SumDistinct(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { def this() = this(null) - override def nullable = true - override def dataType = child.dataType match { + override def nullable: Boolean = true + override def dataType: DataType = child.dataType match { case DecimalType.Fixed(precision, scale) => DecimalType(precision + 10, scale) // Add 10 digits left of decimal point, like Hive case DecimalType.Unlimited => @@ -397,10 +405,10 @@ case class SumDistinct(child: Expression) case _ => child.dataType } - override def toString = s"SUM(DISTINCT ${child})" - override def newInstance() = new SumDistinctFunction(child, this) + override def toString: String = s"SUM(DISTINCT $child)" + override def newInstance(): SumDistinctFunction = new SumDistinctFunction(child, this) - override def asPartial = { + override def asPartial: SplitEvaluation = { val partialSet = Alias(CollectHashSet(child :: Nil), "partialSets")() SplitEvaluation( CombineSetsAndSum(partialSet.toAttribute, this), @@ -411,11 +419,13 @@ case class SumDistinct(child: Expression) case class CombineSetsAndSum(inputSet: Expression, base: Expression) extends AggregateExpression { def this() = this(null, null) - override def children = inputSet :: Nil - override def nullable = true - override def dataType = base.dataType - override def toString = s"CombineAndSum($inputSet)" - override def newInstance() = new CombineSetsAndSumFunction(inputSet, this) + override def children: Seq[Expression] = inputSet :: Nil + override def nullable: Boolean = true + override def dataType: DataType = base.dataType + override def toString: String = s"CombineAndSum($inputSet)" + override def newInstance(): CombineSetsAndSumFunction = { + new CombineSetsAndSumFunction(inputSet, this) + } } case class CombineSetsAndSumFunction( @@ -449,9 +459,9 @@ case class CombineSetsAndSumFunction( } case class First(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { - override def nullable = true - override def dataType = child.dataType - override def toString = s"FIRST($child)" + override def nullable: Boolean = true + override def dataType: DataType = child.dataType + override def toString: String = s"FIRST($child)" override def asPartial: SplitEvaluation = { val partialFirst = Alias(First(child), "PartialFirst")() @@ -459,14 +469,14 @@ case class First(child: Expression) extends PartialAggregate with trees.UnaryNod First(partialFirst.toAttribute), partialFirst :: Nil) } - override def newInstance() = new FirstFunction(child, this) + override def newInstance(): FirstFunction = new FirstFunction(child, this) } case class Last(child: Expression) extends PartialAggregate with trees.UnaryNode[Expression] { - override def references = child.references - override def nullable = true - override def dataType = child.dataType - override def toString = s"LAST($child)" + override def references: AttributeSet = child.references + override def nullable: Boolean = true + override def dataType: DataType = child.dataType + override def toString: String = s"LAST($child)" override def asPartial: SplitEvaluation = { val partialLast = Alias(Last(child), "PartialLast")() @@ -474,7 +484,7 @@ case class Last(child: Expression) extends PartialAggregate with trees.UnaryNode Last(partialLast.toAttribute), partialLast :: Nil) } - override def newInstance() = new LastFunction(child, this) + override def newInstance(): LastFunction = new LastFunction(child, this) } case class AverageFunction(expr: Expression, base: AggregateExpression) @@ -713,6 +723,7 @@ case class LastFunction(expr: Expression, base: AggregateExpression) extends Agg result = input } - override def eval(input: Row): Any = if (result != null) expr.eval(result.asInstanceOf[Row]) - else null + override def eval(input: Row): Any = { + if (result != null) expr.eval(result.asInstanceOf[Row]) else null + } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala index 00b0d3c683fe2..1f6526ef66c56 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala @@ -24,10 +24,10 @@ import org.apache.spark.sql.types._ case class UnaryMinus(child: Expression) extends UnaryExpression { type EvaluatedType = Any - def dataType = child.dataType - override def foldable = child.foldable - def nullable = child.nullable - override def toString = s"-$child" + override def dataType: DataType = child.dataType + override def foldable: Boolean = child.foldable + override def nullable: Boolean = child.nullable + override def toString: String = s"-$child" lazy val numeric = dataType match { case n: NumericType => n.numeric.asInstanceOf[Numeric[Any]] @@ -47,10 +47,10 @@ case class UnaryMinus(child: Expression) extends UnaryExpression { case class Sqrt(child: Expression) extends UnaryExpression { type EvaluatedType = Any - def dataType = DoubleType - override def foldable = child.foldable - def nullable = true - override def toString = s"SQRT($child)" + override def dataType: DataType = DoubleType + override def foldable: Boolean = child.foldable + override def nullable: Boolean = true + override def toString: String = s"SQRT($child)" lazy val numeric = child.dataType match { case n: NumericType => n.numeric.asInstanceOf[Numeric[Any]] @@ -74,14 +74,14 @@ abstract class BinaryArithmetic extends BinaryExpression { type EvaluatedType = Any - def nullable = left.nullable || right.nullable + def nullable: Boolean = left.nullable || right.nullable override lazy val resolved = left.resolved && right.resolved && left.dataType == right.dataType && !DecimalType.isFixed(left.dataType) - def dataType = { + def dataType: DataType = { if (!resolved) { throw new UnresolvedException(this, s"datatype. Can not resolve due to differing types ${left.dataType}, ${right.dataType}") @@ -108,7 +108,7 @@ abstract class BinaryArithmetic extends BinaryExpression { } case class Add(left: Expression, right: Expression) extends BinaryArithmetic { - def symbol = "+" + override def symbol: String = "+" lazy val numeric = dataType match { case n: NumericType => n.numeric.asInstanceOf[Numeric[Any]] @@ -131,7 +131,7 @@ case class Add(left: Expression, right: Expression) extends BinaryArithmetic { } case class Subtract(left: Expression, right: Expression) extends BinaryArithmetic { - def symbol = "-" + override def symbol: String = "-" lazy val numeric = dataType match { case n: NumericType => n.numeric.asInstanceOf[Numeric[Any]] @@ -154,7 +154,7 @@ case class Subtract(left: Expression, right: Expression) extends BinaryArithmeti } case class Multiply(left: Expression, right: Expression) extends BinaryArithmetic { - def symbol = "*" + override def symbol: String = "*" lazy val numeric = dataType match { case n: NumericType => n.numeric.asInstanceOf[Numeric[Any]] @@ -177,9 +177,9 @@ case class Multiply(left: Expression, right: Expression) extends BinaryArithmeti } case class Divide(left: Expression, right: Expression) extends BinaryArithmetic { - def symbol = "/" + override def symbol: String = "/" - override def nullable = true + override def nullable: Boolean = true lazy val div: (Any, Any) => Any = dataType match { case ft: FractionalType => ft.fractional.asInstanceOf[Fractional[Any]].div @@ -203,9 +203,9 @@ case class Divide(left: Expression, right: Expression) extends BinaryArithmetic } case class Remainder(left: Expression, right: Expression) extends BinaryArithmetic { - def symbol = "%" + override def symbol: String = "%" - override def nullable = true + override def nullable: Boolean = true lazy val integral = dataType match { case i: IntegralType => i.integral.asInstanceOf[Integral[Any]] @@ -232,7 +232,7 @@ case class Remainder(left: Expression, right: Expression) extends BinaryArithmet * A function that calculates bitwise and(&) of two numbers. */ case class BitwiseAnd(left: Expression, right: Expression) extends BinaryArithmetic { - def symbol = "&" + override def symbol: String = "&" lazy val and: (Any, Any) => Any = dataType match { case ByteType => @@ -253,7 +253,7 @@ case class BitwiseAnd(left: Expression, right: Expression) extends BinaryArithme * A function that calculates bitwise or(|) of two numbers. */ case class BitwiseOr(left: Expression, right: Expression) extends BinaryArithmetic { - def symbol = "|" + override def symbol: String = "|" lazy val or: (Any, Any) => Any = dataType match { case ByteType => @@ -274,7 +274,7 @@ case class BitwiseOr(left: Expression, right: Expression) extends BinaryArithmet * A function that calculates bitwise xor(^) of two numbers. */ case class BitwiseXor(left: Expression, right: Expression) extends BinaryArithmetic { - def symbol = "^" + override def symbol: String = "^" lazy val xor: (Any, Any) => Any = dataType match { case ByteType => @@ -297,10 +297,10 @@ case class BitwiseXor(left: Expression, right: Expression) extends BinaryArithme case class BitwiseNot(child: Expression) extends UnaryExpression { type EvaluatedType = Any - def dataType = child.dataType - override def foldable = child.foldable - def nullable = child.nullable - override def toString = s"~$child" + override def dataType: DataType = child.dataType + override def foldable: Boolean = child.foldable + override def nullable: Boolean = child.nullable + override def toString: String = s"~$child" lazy val not: (Any) => Any = dataType match { case ByteType => @@ -327,17 +327,17 @@ case class BitwiseNot(child: Expression) extends UnaryExpression { case class MaxOf(left: Expression, right: Expression) extends Expression { type EvaluatedType = Any - override def foldable = left.foldable && right.foldable + override def foldable: Boolean = left.foldable && right.foldable - override def nullable = left.nullable && right.nullable + override def nullable: Boolean = left.nullable && right.nullable - override def children = left :: right :: Nil + override def children: Seq[Expression] = left :: right :: Nil override lazy val resolved = left.resolved && right.resolved && left.dataType == right.dataType - override def dataType = { + override def dataType: DataType = { if (!resolved) { throw new UnresolvedException(this, s"datatype. Can not resolve due to differing types ${left.dataType}, ${right.dataType}") @@ -366,7 +366,7 @@ case class MaxOf(left: Expression, right: Expression) extends Expression { } } - override def toString = s"MaxOf($left, $right)" + override def toString: String = s"MaxOf($left, $right)" } /** @@ -375,10 +375,10 @@ case class MaxOf(left: Expression, right: Expression) extends Expression { case class Abs(child: Expression) extends UnaryExpression { type EvaluatedType = Any - def dataType = child.dataType - override def foldable = child.foldable - def nullable = child.nullable - override def toString = s"Abs($child)" + override def dataType: DataType = child.dataType + override def foldable: Boolean = child.foldable + override def nullable: Boolean = child.nullable + override def toString: String = s"Abs($child)" lazy val numeric = dataType match { case n: NumericType => n.numeric.asInstanceOf[Numeric[Any]] diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala index e48b8cde20eda..d1abf3c0b64a5 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala @@ -91,7 +91,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin val startTime = System.nanoTime() val result = create(in) val endTime = System.nanoTime() - def timeMs = (endTime - startTime).toDouble / 1000000 + def timeMs: Double = (endTime - startTime).toDouble / 1000000 logInfo(s"Code generated expression $in in $timeMs ms") result } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala index 68051a2a2007e..3fd78db297462 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala @@ -27,12 +27,12 @@ import org.apache.spark.sql.types._ case class GetItem(child: Expression, ordinal: Expression) extends Expression { type EvaluatedType = Any - val children = child :: ordinal :: Nil + val children: Seq[Expression] = child :: ordinal :: Nil /** `Null` is returned for invalid ordinals. */ - override def nullable = true - override def foldable = child.foldable && ordinal.foldable + override def nullable: Boolean = true + override def foldable: Boolean = child.foldable && ordinal.foldable - def dataType = child.dataType match { + override def dataType: DataType = child.dataType match { case ArrayType(dt, _) => dt case MapType(_, vt, _) => vt } @@ -40,7 +40,7 @@ case class GetItem(child: Expression, ordinal: Expression) extends Expression { childrenResolved && (child.dataType.isInstanceOf[ArrayType] || child.dataType.isInstanceOf[MapType]) - override def toString = s"$child[$ordinal]" + override def toString: String = s"$child[$ordinal]" override def eval(input: Row): Any = { val value = child.eval(input) @@ -75,8 +75,8 @@ trait GetField extends UnaryExpression { self: Product => type EvaluatedType = Any - override def foldable = child.foldable - override def toString = s"$child.${field.name}" + override def foldable: Boolean = child.foldable + override def toString: String = s"$child.${field.name}" def field: StructField } @@ -86,8 +86,8 @@ trait GetField extends UnaryExpression { */ case class StructGetField(child: Expression, field: StructField, ordinal: Int) extends GetField { - def dataType = field.dataType - override def nullable = child.nullable || field.nullable + override def dataType: DataType = field.dataType + override def nullable: Boolean = child.nullable || field.nullable override def eval(input: Row): Any = { val baseValue = child.eval(input).asInstanceOf[Row] @@ -101,8 +101,8 @@ case class StructGetField(child: Expression, field: StructField, ordinal: Int) e case class ArrayGetField(child: Expression, field: StructField, ordinal: Int, containsNull: Boolean) extends GetField { - def dataType = ArrayType(field.dataType, containsNull) - override def nullable = child.nullable + override def dataType: DataType = ArrayType(field.dataType, containsNull) + override def nullable: Boolean = child.nullable override def eval(input: Row): Any = { val baseValue = child.eval(input).asInstanceOf[Seq[Row]] @@ -120,7 +120,7 @@ case class ArrayGetField(child: Expression, field: StructField, ordinal: Int, co case class CreateArray(children: Seq[Expression]) extends Expression { override type EvaluatedType = Any - override def foldable = !children.exists(!_.foldable) + override def foldable: Boolean = !children.exists(!_.foldable) lazy val childTypes = children.map(_.dataType).distinct @@ -140,5 +140,5 @@ case class CreateArray(children: Seq[Expression]) extends Expression { children.map(_.eval(input)) } - override def toString = s"Array(${children.mkString(",")})" + override def toString: String = s"Array(${children.mkString(",")})" } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala index 83d8c1d42bca4..adb94df7d1c7b 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala @@ -24,9 +24,9 @@ case class UnscaledValue(child: Expression) extends UnaryExpression { override type EvaluatedType = Any override def dataType: DataType = LongType - override def foldable = child.foldable - def nullable = child.nullable - override def toString = s"UnscaledValue($child)" + override def foldable: Boolean = child.foldable + override def nullable: Boolean = child.nullable + override def toString: String = s"UnscaledValue($child)" override def eval(input: Row): Any = { val childResult = child.eval(input) @@ -43,9 +43,9 @@ case class MakeDecimal(child: Expression, precision: Int, scale: Int) extends Un override type EvaluatedType = Decimal override def dataType: DataType = DecimalType(precision, scale) - override def foldable = child.foldable - def nullable = child.nullable - override def toString = s"MakeDecimal($child,$precision,$scale)" + override def foldable: Boolean = child.foldable + override def nullable: Boolean = child.nullable + override def toString: String = s"MakeDecimal($child,$precision,$scale)" override def eval(input: Row): Decimal = { val childResult = child.eval(input) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala index 0983d274def3f..860b72fad38b3 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala @@ -45,7 +45,7 @@ abstract class Generator extends Expression { override lazy val dataType = ArrayType(StructType(output.map(a => StructField(a.name, a.dataType, a.nullable, a.metadata)))) - override def nullable = false + override def nullable: Boolean = false /** * Should be overridden by specific generators. Called only once for each instance to ensure @@ -89,7 +89,7 @@ case class UserDefinedGenerator( function(inputRow(input)) } - override def toString = s"UserDefinedGenerator(${children.mkString(",")})" + override def toString: String = s"UserDefinedGenerator(${children.mkString(",")})" } /** @@ -130,5 +130,5 @@ case class Explode(attributeNames: Seq[String], child: Expression) } } - override def toString() = s"explode($child)" + override def toString: String = s"explode($child)" } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala index 9ff66563c8164..19f3fc9c2291a 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala @@ -64,14 +64,13 @@ object IntegerLiteral { case class Literal(value: Any, dataType: DataType) extends LeafExpression { - override def foldable = true - def nullable = value == null + override def foldable: Boolean = true + override def nullable: Boolean = value == null - - override def toString = if (value != null) value.toString else "null" + override def toString: String = if (value != null) value.toString else "null" type EvaluatedType = Any - override def eval(input: Row):Any = value + override def eval(input: Row): Any = value } // TODO: Specialize @@ -79,9 +78,9 @@ case class MutableLiteral(var value: Any, dataType: DataType, nullable: Boolean extends LeafExpression { type EvaluatedType = Any - def update(expression: Expression, input: Row) = { + def update(expression: Expression, input: Row): Unit = { value = expression.eval(input) } - override def eval(input: Row) = value + override def eval(input: Row): Any = value } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala index 08361d043b6ed..bcbcbeb31c7b5 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala @@ -20,11 +20,12 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.catalyst.trees import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute import org.apache.spark.sql.catalyst.errors.TreeNodeException +import org.apache.spark.sql.catalyst.trees.LeafNode import org.apache.spark.sql.types._ object NamedExpression { private val curId = new java.util.concurrent.atomic.AtomicLong() - def newExprId = ExprId(curId.getAndIncrement()) + def newExprId: ExprId = ExprId(curId.getAndIncrement()) def unapply(expr: NamedExpression): Option[(String, DataType)] = Some(expr.name, expr.dataType) } @@ -79,13 +80,13 @@ abstract class NamedExpression extends Expression { abstract class Attribute extends NamedExpression { self: Product => - override def references = AttributeSet(this) + override def references: AttributeSet = AttributeSet(this) def withNullability(newNullability: Boolean): Attribute def withQualifiers(newQualifiers: Seq[String]): Attribute def withName(newName: String): Attribute - def toAttribute = this + def toAttribute: Attribute = this def newInstance(): Attribute } @@ -112,10 +113,10 @@ case class Alias(child: Expression, name: String)( override type EvaluatedType = Any - override def eval(input: Row) = child.eval(input) + override def eval(input: Row): Any = child.eval(input) - override def dataType = child.dataType - override def nullable = child.nullable + override def dataType: DataType = child.dataType + override def nullable: Boolean = child.nullable override def metadata: Metadata = { explicitMetadata.getOrElse { child match { @@ -125,7 +126,7 @@ case class Alias(child: Expression, name: String)( } } - override def toAttribute = { + override def toAttribute: Attribute = { if (resolved) { AttributeReference(name, child.dataType, child.nullable, metadata)(exprId, qualifiers) } else { @@ -135,7 +136,9 @@ case class Alias(child: Expression, name: String)( override def toString: String = s"$child AS $name#${exprId.id}$typeSuffix" - override protected final def otherCopyArgs = exprId :: qualifiers :: explicitMetadata :: Nil + override protected final def otherCopyArgs: Seq[AnyRef] = { + exprId :: qualifiers :: explicitMetadata :: Nil + } override def equals(other: Any): Boolean = other match { case a: Alias => @@ -166,7 +169,7 @@ case class AttributeReference( val exprId: ExprId = NamedExpression.newExprId, val qualifiers: Seq[String] = Nil) extends Attribute with trees.LeafNode[Expression] { - override def equals(other: Any) = other match { + override def equals(other: Any): Boolean = other match { case ar: AttributeReference => name == ar.name && exprId == ar.exprId && dataType == ar.dataType case _ => false } @@ -180,7 +183,7 @@ case class AttributeReference( h } - override def newInstance() = + override def newInstance(): AttributeReference = AttributeReference(name, dataType, nullable, metadata)(qualifiers = qualifiers) /** @@ -205,7 +208,7 @@ case class AttributeReference( /** * Returns a copy of this [[AttributeReference]] with new qualifiers. */ - override def withQualifiers(newQualifiers: Seq[String]) = { + override def withQualifiers(newQualifiers: Seq[String]): AttributeReference = { if (newQualifiers.toSet == qualifiers.toSet) { this } else { @@ -227,20 +230,22 @@ case class AttributeReference( case class PrettyAttribute(name: String) extends Attribute with trees.LeafNode[Expression] { type EvaluatedType = Any - override def toString = name - - override def withNullability(newNullability: Boolean): Attribute = ??? - override def newInstance(): Attribute = ??? - override def withQualifiers(newQualifiers: Seq[String]): Attribute = ??? - override def withName(newName: String): Attribute = ??? - override def qualifiers: Seq[String] = ??? - override def exprId: ExprId = ??? - override def eval(input: Row): EvaluatedType = ??? - override def nullable: Boolean = ??? + override def toString: String = name + + override def withNullability(newNullability: Boolean): Attribute = + throw new UnsupportedOperationException + override def newInstance(): Attribute = throw new UnsupportedOperationException + override def withQualifiers(newQualifiers: Seq[String]): Attribute = + throw new UnsupportedOperationException + override def withName(newName: String): Attribute = throw new UnsupportedOperationException + override def qualifiers: Seq[String] = throw new UnsupportedOperationException + override def exprId: ExprId = throw new UnsupportedOperationException + override def eval(input: Row): EvaluatedType = throw new UnsupportedOperationException + override def nullable: Boolean = throw new UnsupportedOperationException override def dataType: DataType = NullType } object VirtualColumn { - val groupingIdName = "grouping__id" - def newGroupingId = AttributeReference(groupingIdName, IntegerType, false)() + val groupingIdName: String = "grouping__id" + def newGroupingId: AttributeReference = AttributeReference(groupingIdName, IntegerType, false)() } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala index 08b982bc671e7..d1f3d4f4ee9ee 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala @@ -19,22 +19,23 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.catalyst.trees import org.apache.spark.sql.catalyst.analysis.UnresolvedException +import org.apache.spark.sql.types.DataType case class Coalesce(children: Seq[Expression]) extends Expression { type EvaluatedType = Any /** Coalesce is nullable if all of its children are nullable, or if it has no children. */ - def nullable = !children.exists(!_.nullable) + override def nullable: Boolean = !children.exists(!_.nullable) // Coalesce is foldable if all children are foldable. - override def foldable = !children.exists(!_.foldable) + override def foldable: Boolean = !children.exists(!_.foldable) // Only resolved if all the children are of the same type. override lazy val resolved = childrenResolved && (children.map(_.dataType).distinct.size == 1) - override def toString = s"Coalesce(${children.mkString(",")})" + override def toString: String = s"Coalesce(${children.mkString(",")})" - def dataType = if (resolved) { + def dataType: DataType = if (resolved) { children.head.dataType } else { val childTypes = children.map(c => s"$c: ${c.dataType}").mkString(", ") @@ -54,20 +55,20 @@ case class Coalesce(children: Seq[Expression]) extends Expression { } case class IsNull(child: Expression) extends Predicate with trees.UnaryNode[Expression] { - override def foldable = child.foldable - def nullable = false + override def foldable: Boolean = child.foldable + override def nullable: Boolean = false override def eval(input: Row): Any = { child.eval(input) == null } - override def toString = s"IS NULL $child" + override def toString: String = s"IS NULL $child" } case class IsNotNull(child: Expression) extends Predicate with trees.UnaryNode[Expression] { - override def foldable = child.foldable - def nullable = false - override def toString = s"IS NOT NULL $child" + override def foldable: Boolean = child.foldable + override def nullable: Boolean = false + override def toString: String = s"IS NOT NULL $child" override def eval(input: Row): Any = { child.eval(input) != null diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala index 0024ef92c0452..7e47cb3fffe12 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala @@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.catalyst.analysis.UnresolvedException import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan -import org.apache.spark.sql.types.{BinaryType, BooleanType, NativeType} +import org.apache.spark.sql.types.{DataType, BinaryType, BooleanType, NativeType} object InterpretedPredicate { def apply(expression: Expression, inputSchema: Seq[Attribute]): (Row => Boolean) = @@ -34,7 +34,7 @@ object InterpretedPredicate { trait Predicate extends Expression { self: Product => - def dataType = BooleanType + override def dataType: DataType = BooleanType type EvaluatedType = Any } @@ -72,13 +72,13 @@ trait PredicateHelper { abstract class BinaryPredicate extends BinaryExpression with Predicate { self: Product => - def nullable = left.nullable || right.nullable + override def nullable: Boolean = left.nullable || right.nullable } case class Not(child: Expression) extends UnaryExpression with Predicate { - override def foldable = child.foldable - def nullable = child.nullable - override def toString = s"NOT $child" + override def foldable: Boolean = child.foldable + override def nullable: Boolean = child.nullable + override def toString: String = s"NOT $child" override def eval(input: Row): Any = { child.eval(input) match { @@ -92,10 +92,10 @@ case class Not(child: Expression) extends UnaryExpression with Predicate { * Evaluates to `true` if `list` contains `value`. */ case class In(value: Expression, list: Seq[Expression]) extends Predicate { - def children = value +: list + override def children: Seq[Expression] = value +: list - def nullable = true // TODO: Figure out correct nullability semantics of IN. - override def toString = s"$value IN ${list.mkString("(", ",", ")")}" + override def nullable: Boolean = true // TODO: Figure out correct nullability semantics of IN. + override def toString: String = s"$value IN ${list.mkString("(", ",", ")")}" override def eval(input: Row): Any = { val evaluatedValue = value.eval(input) @@ -110,10 +110,10 @@ case class In(value: Expression, list: Seq[Expression]) extends Predicate { case class InSet(value: Expression, hset: Set[Any]) extends Predicate { - def children = value :: Nil + override def children: Seq[Expression] = value :: Nil - def nullable = true // TODO: Figure out correct nullability semantics of IN. - override def toString = s"$value INSET ${hset.mkString("(", ",", ")")}" + override def nullable: Boolean = true // TODO: Figure out correct nullability semantics of IN. + override def toString: String = s"$value INSET ${hset.mkString("(", ",", ")")}" override def eval(input: Row): Any = { hset.contains(value.eval(input)) @@ -121,7 +121,7 @@ case class InSet(value: Expression, hset: Set[Any]) } case class And(left: Expression, right: Expression) extends BinaryPredicate { - def symbol = "&&" + override def symbol: String = "&&" override def eval(input: Row): Any = { val l = left.eval(input) @@ -143,7 +143,7 @@ case class And(left: Expression, right: Expression) extends BinaryPredicate { } case class Or(left: Expression, right: Expression) extends BinaryPredicate { - def symbol = "||" + override def symbol: String = "||" override def eval(input: Row): Any = { val l = left.eval(input) @@ -169,7 +169,8 @@ abstract class BinaryComparison extends BinaryPredicate { } case class EqualTo(left: Expression, right: Expression) extends BinaryComparison { - def symbol = "=" + override def symbol: String = "=" + override def eval(input: Row): Any = { val l = left.eval(input) if (l == null) { @@ -185,8 +186,10 @@ case class EqualTo(left: Expression, right: Expression) extends BinaryComparison } case class EqualNullSafe(left: Expression, right: Expression) extends BinaryComparison { - def symbol = "<=>" - override def nullable = false + override def symbol: String = "<=>" + + override def nullable: Boolean = false + override def eval(input: Row): Any = { val l = left.eval(input) val r = right.eval(input) @@ -201,9 +204,9 @@ case class EqualNullSafe(left: Expression, right: Expression) extends BinaryComp } case class LessThan(left: Expression, right: Expression) extends BinaryComparison { - def symbol = "<" + override def symbol: String = "<" - lazy val ordering = { + lazy val ordering: Ordering[Any] = { if (left.dataType != right.dataType) { throw new TreeNodeException(this, s"Types do not match ${left.dataType} != ${right.dataType}") @@ -216,7 +219,7 @@ case class LessThan(left: Expression, right: Expression) extends BinaryCompariso override def eval(input: Row): Any = { val evalE1 = left.eval(input) - if(evalE1 == null) { + if (evalE1 == null) { null } else { val evalE2 = right.eval(input) @@ -230,9 +233,9 @@ case class LessThan(left: Expression, right: Expression) extends BinaryCompariso } case class LessThanOrEqual(left: Expression, right: Expression) extends BinaryComparison { - def symbol = "<=" + override def symbol: String = "<=" - lazy val ordering = { + lazy val ordering: Ordering[Any] = { if (left.dataType != right.dataType) { throw new TreeNodeException(this, s"Types do not match ${left.dataType} != ${right.dataType}") @@ -245,7 +248,7 @@ case class LessThanOrEqual(left: Expression, right: Expression) extends BinaryCo override def eval(input: Row): Any = { val evalE1 = left.eval(input) - if(evalE1 == null) { + if (evalE1 == null) { null } else { val evalE2 = right.eval(input) @@ -259,9 +262,9 @@ case class LessThanOrEqual(left: Expression, right: Expression) extends BinaryCo } case class GreaterThan(left: Expression, right: Expression) extends BinaryComparison { - def symbol = ">" + override def symbol: String = ">" - lazy val ordering = { + lazy val ordering: Ordering[Any] = { if (left.dataType != right.dataType) { throw new TreeNodeException(this, s"Types do not match ${left.dataType} != ${right.dataType}") @@ -288,9 +291,9 @@ case class GreaterThan(left: Expression, right: Expression) extends BinaryCompar } case class GreaterThanOrEqual(left: Expression, right: Expression) extends BinaryComparison { - def symbol = ">=" + override def symbol: String = ">=" - lazy val ordering = { + lazy val ordering: Ordering[Any] = { if (left.dataType != right.dataType) { throw new TreeNodeException(this, s"Types do not match ${left.dataType} != ${right.dataType}") @@ -303,7 +306,7 @@ case class GreaterThanOrEqual(left: Expression, right: Expression) extends Binar override def eval(input: Row): Any = { val evalE1 = left.eval(input) - if(evalE1 == null) { + if (evalE1 == null) { null } else { val evalE2 = right.eval(input) @@ -317,13 +320,13 @@ case class GreaterThanOrEqual(left: Expression, right: Expression) extends Binar } case class If(predicate: Expression, trueValue: Expression, falseValue: Expression) - extends Expression { + extends Expression { - def children = predicate :: trueValue :: falseValue :: Nil - override def nullable = trueValue.nullable || falseValue.nullable + override def children: Seq[Expression] = predicate :: trueValue :: falseValue :: Nil + override def nullable: Boolean = trueValue.nullable || falseValue.nullable override lazy val resolved = childrenResolved && trueValue.dataType == falseValue.dataType - def dataType = { + override def dataType: DataType = { if (!resolved) { throw new UnresolvedException( this, @@ -342,7 +345,7 @@ case class If(predicate: Expression, trueValue: Expression, falseValue: Expressi } } - override def toString = s"if ($predicate) $trueValue else $falseValue" + override def toString: String = s"if ($predicate) $trueValue else $falseValue" } // scalastyle:off @@ -362,9 +365,10 @@ case class If(predicate: Expression, trueValue: Expression, falseValue: Expressi // scalastyle:on case class CaseWhen(branches: Seq[Expression]) extends Expression { type EvaluatedType = Any - def children = branches - def dataType = { + override def children: Seq[Expression] = branches + + override def dataType: DataType = { if (!resolved) { throw new UnresolvedException(this, "cannot resolve due to differing types in some branches") } @@ -379,12 +383,12 @@ case class CaseWhen(branches: Seq[Expression]) extends Expression { @transient private[this] lazy val elseValue = if (branches.length % 2 == 0) None else Option(branches.last) - override def nullable = { + override def nullable: Boolean = { // If no value is nullable and no elseValue is provided, the whole statement defaults to null. values.exists(_.nullable) || (elseValue.map(_.nullable).getOrElse(true)) } - override lazy val resolved = { + override lazy val resolved: Boolean = { if (!childrenResolved) { false } else { @@ -415,7 +419,7 @@ case class CaseWhen(branches: Seq[Expression]) extends Expression { res } - override def toString = { + override def toString: String = { "CASE" + branches.sliding(2, 2).map { case Seq(cond, value) => s" WHEN $cond THEN $value" case Seq(elseValue) => s" ELSE $elseValue" diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala index f03d6f71a9fae..8bba26bc4cf7f 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala @@ -44,8 +44,8 @@ trait MutableRow extends Row { */ object EmptyRow extends Row { override def apply(i: Int): Any = throw new UnsupportedOperationException - override def toSeq = Seq.empty - override def length = 0 + override def toSeq: Seq[Any] = Seq.empty + override def length: Int = 0 override def isNullAt(i: Int): Boolean = throw new UnsupportedOperationException override def getInt(i: Int): Int = throw new UnsupportedOperationException override def getLong(i: Int): Long = throw new UnsupportedOperationException @@ -56,7 +56,7 @@ object EmptyRow extends Row { override def getByte(i: Int): Byte = throw new UnsupportedOperationException override def getString(i: Int): String = throw new UnsupportedOperationException override def getAs[T](i: Int): T = throw new UnsupportedOperationException - def copy() = this + override def copy(): Row = this } /** @@ -70,13 +70,13 @@ class GenericRow(protected[sql] val values: Array[Any]) extends Row { def this(size: Int) = this(new Array[Any](size)) - override def toSeq = values.toSeq + override def toSeq: Seq[Any] = values.toSeq - override def length = values.length + override def length: Int = values.length - override def apply(i: Int) = values(i) + override def apply(i: Int): Any = values(i) - override def isNullAt(i: Int) = values(i) == null + override def isNullAt(i: Int): Boolean = values(i) == null override def getInt(i: Int): Int = { if (values(i) == null) sys.error("Failed to check null bit for primitive int value.") @@ -167,7 +167,7 @@ class GenericRow(protected[sql] val values: Array[Any]) extends Row { case _ => false } - def copy() = this + override def copy(): Row = this } class GenericRowWithSchema(values: Array[Any], override val schema: StructType) @@ -194,7 +194,7 @@ class GenericMutableRow(v: Array[Any]) extends GenericRow(v) with MutableRow { override def update(ordinal: Int, value: Any): Unit = { values(ordinal) = value } - override def copy() = new GenericRow(values.clone()) + override def copy(): Row = new GenericRow(values.clone()) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala index 3a5bdca1f07c3..35faa00782e80 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala @@ -26,17 +26,17 @@ import org.apache.spark.util.collection.OpenHashSet case class NewSet(elementType: DataType) extends LeafExpression { type EvaluatedType = Any - def nullable = false + override def nullable: Boolean = false // We are currently only using these Expressions internally for aggregation. However, if we ever // expose these to users we'll want to create a proper type instead of hijacking ArrayType. - def dataType = ArrayType(elementType) + override def dataType: DataType = ArrayType(elementType) - def eval(input: Row): Any = { + override def eval(input: Row): Any = { new OpenHashSet[Any]() } - override def toString = s"new Set($dataType)" + override def toString: String = s"new Set($dataType)" } /** @@ -46,12 +46,13 @@ case class NewSet(elementType: DataType) extends LeafExpression { case class AddItemToSet(item: Expression, set: Expression) extends Expression { type EvaluatedType = Any - def children = item :: set :: Nil + override def children: Seq[Expression] = item :: set :: Nil - def nullable = set.nullable + override def nullable: Boolean = set.nullable - def dataType = set.dataType - def eval(input: Row): Any = { + override def dataType: DataType = set.dataType + + override def eval(input: Row): Any = { val itemEval = item.eval(input) val setEval = set.eval(input).asInstanceOf[OpenHashSet[Any]] @@ -67,7 +68,7 @@ case class AddItemToSet(item: Expression, set: Expression) extends Expression { } } - override def toString = s"$set += $item" + override def toString: String = s"$set += $item" } /** @@ -77,13 +78,13 @@ case class AddItemToSet(item: Expression, set: Expression) extends Expression { case class CombineSets(left: Expression, right: Expression) extends BinaryExpression { type EvaluatedType = Any - def nullable = left.nullable || right.nullable + override def nullable: Boolean = left.nullable || right.nullable - def dataType = left.dataType + override def dataType: DataType = left.dataType - def symbol = "++=" + override def symbol: String = "++=" - def eval(input: Row): Any = { + override def eval(input: Row): Any = { val leftEval = left.eval(input).asInstanceOf[OpenHashSet[Any]] if(leftEval != null) { val rightEval = right.eval(input).asInstanceOf[OpenHashSet[Any]] @@ -109,16 +110,16 @@ case class CombineSets(left: Expression, right: Expression) extends BinaryExpres case class CountSet(child: Expression) extends UnaryExpression { type EvaluatedType = Any - def nullable = child.nullable + override def nullable: Boolean = child.nullable - def dataType = LongType + override def dataType: DataType = LongType - def eval(input: Row): Any = { + override def eval(input: Row): Any = { val childEval = child.eval(input).asInstanceOf[OpenHashSet[Any]] if (childEval != null) { childEval.size.toLong } } - override def toString = s"$child.count()" + override def toString: String = s"$child.count()" } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala index f85ee0a9bb6d8..3cdca4e9dd2d1 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala @@ -33,8 +33,8 @@ trait StringRegexExpression { def escape(v: String): String def matches(regex: Pattern, str: String): Boolean - def nullable: Boolean = left.nullable || right.nullable - def dataType: DataType = BooleanType + override def nullable: Boolean = left.nullable || right.nullable + override def dataType: DataType = BooleanType // try cache the pattern for Literal private lazy val cache: Pattern = right match { @@ -98,11 +98,11 @@ trait CaseConversionExpression { case class Like(left: Expression, right: Expression) extends BinaryExpression with StringRegexExpression { - def symbol = "LIKE" + override def symbol: String = "LIKE" // replace the _ with .{1} exactly match 1 time of any character // replace the % with .*, match 0 or more times with any character - override def escape(v: String) = + override def escape(v: String): String = if (!v.isEmpty) { "(?s)" + (' ' +: v.init).zip(v).flatMap { case (prev, '\\') => "" @@ -129,7 +129,7 @@ case class Like(left: Expression, right: Expression) case class RLike(left: Expression, right: Expression) extends BinaryExpression with StringRegexExpression { - def symbol = "RLIKE" + override def symbol: String = "RLIKE" override def escape(v: String): String = v override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).find(0) } @@ -141,7 +141,7 @@ case class Upper(child: Expression) extends UnaryExpression with CaseConversionE override def convert(v: String): String = v.toUpperCase() - override def toString() = s"Upper($child)" + override def toString: String = s"Upper($child)" } /** @@ -151,7 +151,7 @@ case class Lower(child: Expression) extends UnaryExpression with CaseConversionE override def convert(v: String): String = v.toLowerCase() - override def toString() = s"Lower($child)" + override def toString: String = s"Lower($child)" } /** A base trait for functions that compare two strings, returning a boolean. */ @@ -160,7 +160,7 @@ trait StringComparison { type EvaluatedType = Any - def nullable: Boolean = left.nullable || right.nullable + override def nullable: Boolean = left.nullable || right.nullable override def dataType: DataType = BooleanType def compare(l: String, r: String): Boolean @@ -175,9 +175,9 @@ trait StringComparison { } } - def symbol: String = nodeName + override def symbol: String = nodeName - override def toString() = s"$nodeName($left, $right)" + override def toString: String = s"$nodeName($left, $right)" } /** @@ -185,7 +185,7 @@ trait StringComparison { */ case class Contains(left: Expression, right: Expression) extends BinaryExpression with StringComparison { - override def compare(l: String, r: String) = l.contains(r) + override def compare(l: String, r: String): Boolean = l.contains(r) } /** @@ -193,7 +193,7 @@ case class Contains(left: Expression, right: Expression) */ case class StartsWith(left: Expression, right: Expression) extends BinaryExpression with StringComparison { - def compare(l: String, r: String) = l.startsWith(r) + override def compare(l: String, r: String): Boolean = l.startsWith(r) } /** @@ -201,7 +201,7 @@ case class StartsWith(left: Expression, right: Expression) */ case class EndsWith(left: Expression, right: Expression) extends BinaryExpression with StringComparison { - def compare(l: String, r: String) = l.endsWith(r) + override def compare(l: String, r: String): Boolean = l.endsWith(r) } /** @@ -212,17 +212,17 @@ case class Substring(str: Expression, pos: Expression, len: Expression) extends type EvaluatedType = Any - override def foldable = str.foldable && pos.foldable && len.foldable + override def foldable: Boolean = str.foldable && pos.foldable && len.foldable - def nullable: Boolean = str.nullable || pos.nullable || len.nullable - def dataType: DataType = { + override def nullable: Boolean = str.nullable || pos.nullable || len.nullable + override def dataType: DataType = { if (!resolved) { throw new UnresolvedException(this, s"Cannot resolve since $children are not resolved") } if (str.dataType == BinaryType) str.dataType else StringType } - override def children = str :: pos :: len :: Nil + override def children: Seq[Expression] = str :: pos :: len :: Nil @inline def slice[T, C <: Any](str: C, startPos: Int, sliceLen: Int) @@ -267,7 +267,8 @@ case class Substring(str: Expression, pos: Expression, len: Expression) extends } } - override def toString = len match { + override def toString: String = len match { + // TODO: This is broken because max is not an integer value. case max if max == Integer.MAX_VALUE => s"SUBSTR($str, $pos)" case _ => s"SUBSTR($str, $pos, $len)" } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala index 74edaacc4f609..c23d3b61887c6 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala @@ -141,7 +141,7 @@ object ColumnPruning extends Rule[LogicalPlan] { condition.map(_.references).getOrElse(AttributeSet(Seq.empty)) /** Applies a projection only when the child is producing unnecessary attributes */ - def pruneJoinChild(c: LogicalPlan) = prunedChild(c, allReferences) + def pruneJoinChild(c: LogicalPlan): LogicalPlan = prunedChild(c, allReferences) Project(projectList, Join(pruneJoinChild(left), pruneJoinChild(right), joinType, condition)) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala index b4c445b3badf1..9c8c643f7d17a 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala @@ -91,16 +91,18 @@ object PhysicalOperation extends PredicateHelper { (None, Nil, other, Map.empty) } - def collectAliases(fields: Seq[Expression]) = fields.collect { + def collectAliases(fields: Seq[Expression]): Map[Attribute, Expression] = fields.collect { case a @ Alias(child, _) => a.toAttribute.asInstanceOf[Attribute] -> child }.toMap - def substitute(aliases: Map[Attribute, Expression])(expr: Expression) = expr.transform { - case a @ Alias(ref: AttributeReference, name) => - aliases.get(ref).map(Alias(_, name)(a.exprId, a.qualifiers)).getOrElse(a) + def substitute(aliases: Map[Attribute, Expression])(expr: Expression): Expression = { + expr.transform { + case a @ Alias(ref: AttributeReference, name) => + aliases.get(ref).map(Alias(_, name)(a.exprId, a.qualifiers)).getOrElse(a) - case a: AttributeReference => - aliases.get(a).map(Alias(_, a.name)(a.exprId, a.qualifiers)).getOrElse(a) + case a: AttributeReference => + aliases.get(a).map(Alias(_, a.name)(a.exprId, a.qualifiers)).getOrElse(a) + } } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala index bd9291e9ba5d7..02f7c26a8ab6e 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala @@ -71,7 +71,7 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy def transformExpressionsDown(rule: PartialFunction[Expression, Expression]): this.type = { var changed = false - @inline def transformExpressionDown(e: Expression) = { + @inline def transformExpressionDown(e: Expression): Expression = { val newE = e.transformDown(rule) if (newE.fastEquals(e)) { e @@ -104,7 +104,7 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy def transformExpressionsUp(rule: PartialFunction[Expression, Expression]): this.type = { var changed = false - @inline def transformExpressionUp(e: Expression) = { + @inline def transformExpressionUp(e: Expression): Expression = { val newE = e.transformUp(rule) if (newE.fastEquals(e)) { e @@ -165,5 +165,5 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy */ protected def statePrefix = if (missingInput.nonEmpty && children.nonEmpty) "!" else "" - override def simpleString = statePrefix + super.simpleString + override def simpleString: String = statePrefix + super.simpleString } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala index 384fe53a68362..4d9e41a2b5d85 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala @@ -22,7 +22,7 @@ import org.apache.spark.sql.catalyst.plans._ import org.apache.spark.sql.types._ case class Project(projectList: Seq[NamedExpression], child: LogicalPlan) extends UnaryNode { - def output = projectList.map(_.toAttribute) + override def output: Seq[Attribute] = projectList.map(_.toAttribute) override lazy val resolved: Boolean = { val containsAggregatesOrGenerators = projectList.exists ( _.collect { @@ -66,19 +66,19 @@ case class Generate( } } - override def output = + override def output: Seq[Attribute] = if (join) child.output ++ generatorOutput else generatorOutput } case class Filter(condition: Expression, child: LogicalPlan) extends UnaryNode { - override def output = child.output + override def output: Seq[Attribute] = child.output } case class Union(left: LogicalPlan, right: LogicalPlan) extends BinaryNode { // TODO: These aren't really the same attributes as nullability etc might change. - override def output = left.output + override def output: Seq[Attribute] = left.output - override lazy val resolved = + override lazy val resolved: Boolean = childrenResolved && !left.output.zip(right.output).exists { case (l,r) => l.dataType != r.dataType } @@ -94,7 +94,7 @@ case class Join( joinType: JoinType, condition: Option[Expression]) extends BinaryNode { - override def output = { + override def output: Seq[Attribute] = { joinType match { case LeftSemi => left.output @@ -109,7 +109,7 @@ case class Join( } } - def selfJoinResolved = left.outputSet.intersect(right.outputSet).isEmpty + private def selfJoinResolved: Boolean = left.outputSet.intersect(right.outputSet).isEmpty // Joins are only resolved if they don't introduce ambiguious expression ids. override lazy val resolved: Boolean = { @@ -118,7 +118,7 @@ case class Join( } case class Except(left: LogicalPlan, right: LogicalPlan) extends BinaryNode { - def output = left.output + override def output: Seq[Attribute] = left.output } case class InsertIntoTable( @@ -128,10 +128,10 @@ case class InsertIntoTable( overwrite: Boolean) extends LogicalPlan { - override def children = child :: Nil - override def output = child.output + override def children: Seq[LogicalPlan] = child :: Nil + override def output: Seq[Attribute] = child.output - override lazy val resolved = childrenResolved && child.output.zip(table.output).forall { + override lazy val resolved: Boolean = childrenResolved && child.output.zip(table.output).forall { case (childAttr, tableAttr) => DataType.equalsIgnoreCompatibleNullability(childAttr.dataType, tableAttr.dataType) } @@ -143,14 +143,14 @@ case class CreateTableAsSelect[T]( child: LogicalPlan, allowExisting: Boolean, desc: Option[T] = None) extends UnaryNode { - override def output = Seq.empty[Attribute] - override lazy val resolved = databaseName != None && childrenResolved + override def output: Seq[Attribute] = Seq.empty[Attribute] + override lazy val resolved: Boolean = databaseName != None && childrenResolved } case class WriteToFile( path: String, child: LogicalPlan) extends UnaryNode { - override def output = child.output + override def output: Seq[Attribute] = child.output } /** @@ -163,7 +163,7 @@ case class Sort( order: Seq[SortOrder], global: Boolean, child: LogicalPlan) extends UnaryNode { - override def output = child.output + override def output: Seq[Attribute] = child.output } case class Aggregate( @@ -172,7 +172,7 @@ case class Aggregate( child: LogicalPlan) extends UnaryNode { - override def output = aggregateExpressions.map(_.toAttribute) + override def output: Seq[Attribute] = aggregateExpressions.map(_.toAttribute) } /** @@ -199,7 +199,7 @@ trait GroupingAnalytics extends UnaryNode { def groupByExprs: Seq[Expression] def aggregations: Seq[NamedExpression] - override def output = aggregations.map(_.toAttribute) + override def output: Seq[Attribute] = aggregations.map(_.toAttribute) } /** @@ -264,7 +264,7 @@ case class Rollup( gid: AttributeReference = VirtualColumn.newGroupingId) extends GroupingAnalytics case class Limit(limitExpr: Expression, child: LogicalPlan) extends UnaryNode { - override def output = child.output + override def output: Seq[Attribute] = child.output override lazy val statistics: Statistics = { val limit = limitExpr.eval(null).asInstanceOf[Int] @@ -274,21 +274,21 @@ case class Limit(limitExpr: Expression, child: LogicalPlan) extends UnaryNode { } case class Subquery(alias: String, child: LogicalPlan) extends UnaryNode { - override def output = child.output.map(_.withQualifiers(alias :: Nil)) + override def output: Seq[Attribute] = child.output.map(_.withQualifiers(alias :: Nil)) } case class Sample(fraction: Double, withReplacement: Boolean, seed: Long, child: LogicalPlan) extends UnaryNode { - override def output = child.output + override def output: Seq[Attribute] = child.output } case class Distinct(child: LogicalPlan) extends UnaryNode { - override def output = child.output + override def output: Seq[Attribute] = child.output } case object NoRelation extends LeafNode { - override def output = Nil + override def output: Seq[Attribute] = Nil /** * Computes [[Statistics]] for this plan. The default implementation assumes the output @@ -301,5 +301,5 @@ case object NoRelation extends LeafNode { } case class Intersect(left: LogicalPlan, right: LogicalPlan) extends BinaryNode { - override def output = left.output + override def output: Seq[Attribute] = left.output } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala index 72b0c5c8e7a26..e737418d9c3bc 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql.catalyst.plans.logical -import org.apache.spark.sql.catalyst.expressions.{Expression, SortOrder} +import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, SortOrder} /** * Performs a physical redistribution of the data. Used when the consumer of the query @@ -26,14 +26,11 @@ import org.apache.spark.sql.catalyst.expressions.{Expression, SortOrder} abstract class RedistributeData extends UnaryNode { self: Product => - def output = child.output + override def output: Seq[Attribute] = child.output } case class SortPartitions(sortExpressions: Seq[SortOrder], child: LogicalPlan) - extends RedistributeData { -} + extends RedistributeData case class Repartition(partitionExpressions: Seq[Expression], child: LogicalPlan) - extends RedistributeData { -} - + extends RedistributeData diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala index 3c3d7a3119064..288c11f69fe22 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.plans.physical import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.expressions.{Expression, Row, SortOrder} -import org.apache.spark.sql.types.IntegerType +import org.apache.spark.sql.types.{DataType, IntegerType} /** * Specifies how tuples that share common expressions will be distributed when a query is executed @@ -72,7 +72,7 @@ case class OrderedDistribution(ordering: Seq[SortOrder]) extends Distribution { "a single partition.") // TODO: This is not really valid... - def clustering = ordering.map(_.child).toSet + def clustering: Set[Expression] = ordering.map(_.child).toSet } sealed trait Partitioning { @@ -113,7 +113,7 @@ case object SinglePartition extends Partitioning { override def satisfies(required: Distribution): Boolean = true - override def compatibleWith(other: Partitioning) = other match { + override def compatibleWith(other: Partitioning): Boolean = other match { case SinglePartition => true case _ => false } @@ -124,7 +124,7 @@ case object BroadcastPartitioning extends Partitioning { override def satisfies(required: Distribution): Boolean = true - override def compatibleWith(other: Partitioning) = other match { + override def compatibleWith(other: Partitioning): Boolean = other match { case SinglePartition => true case _ => false } @@ -139,9 +139,9 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int) extends Expression with Partitioning { - override def children = expressions - override def nullable = false - override def dataType = IntegerType + override def children: Seq[Expression] = expressions + override def nullable: Boolean = false + override def dataType: DataType = IntegerType private[this] lazy val clusteringSet = expressions.toSet @@ -152,7 +152,7 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int) case _ => false } - override def compatibleWith(other: Partitioning) = other match { + override def compatibleWith(other: Partitioning): Boolean = other match { case BroadcastPartitioning => true case h: HashPartitioning if h == this => true case _ => false @@ -178,9 +178,9 @@ case class RangePartitioning(ordering: Seq[SortOrder], numPartitions: Int) extends Expression with Partitioning { - override def children = ordering - override def nullable = false - override def dataType = IntegerType + override def children: Seq[SortOrder] = ordering + override def nullable: Boolean = false + override def dataType: DataType = IntegerType private[this] lazy val clusteringSet = ordering.map(_.child).toSet @@ -194,7 +194,7 @@ case class RangePartitioning(ordering: Seq[SortOrder], numPartitions: Int) case _ => false } - override def compatibleWith(other: Partitioning) = other match { + override def compatibleWith(other: Partitioning): Boolean = other match { case BroadcastPartitioning => true case r: RangePartitioning if r == this => true case _ => false diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala index 0ae9f6b2965d4..a2df51e598a2b 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala @@ -36,12 +36,12 @@ object CurrentOrigin { override def initialValue: Origin = Origin() } - def get = value.get() - def set(o: Origin) = value.set(o) + def get: Origin = value.get() + def set(o: Origin): Unit = value.set(o) - def reset() = value.set(Origin()) + def reset(): Unit = value.set(Origin()) - def setPosition(line: Int, start: Int) = { + def setPosition(line: Int, start: Int): Unit = { value.set( value.get.copy(line = Some(line), startPosition = Some(start))) } @@ -57,7 +57,7 @@ object CurrentOrigin { abstract class TreeNode[BaseType <: TreeNode[BaseType]] { self: BaseType with Product => - val origin = CurrentOrigin.get + val origin: Origin = CurrentOrigin.get /** Returns a Seq of the children of this node */ def children: Seq[BaseType] @@ -340,12 +340,12 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { } /** Returns the name of this type of TreeNode. Defaults to the class name. */ - def nodeName = getClass.getSimpleName + def nodeName: String = getClass.getSimpleName /** * The arguments that should be included in the arg string. Defaults to the `productIterator`. */ - protected def stringArgs = productIterator + protected def stringArgs: Iterator[Any] = productIterator /** Returns a string representing the arguments to this node, minus any children */ def argString: String = productIterator.flatMap { @@ -357,18 +357,18 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { }.mkString(", ") /** String representation of this node without any children */ - def simpleString = s"$nodeName $argString".trim + def simpleString: String = s"$nodeName $argString".trim override def toString: String = treeString /** Returns a string representation of the nodes in this tree */ - def treeString = generateTreeString(0, new StringBuilder).toString + def treeString: String = generateTreeString(0, new StringBuilder).toString /** * Returns a string representation of the nodes in this tree, where each operator is numbered. * The numbers can be used with [[trees.TreeNode.apply apply]] to easily access specific subtrees. */ - def numberedTreeString = + def numberedTreeString: String = treeString.split("\n").zipWithIndex.map { case (line, i) => f"$i%02d $line" }.mkString("\n") /** @@ -420,14 +420,14 @@ trait BinaryNode[BaseType <: TreeNode[BaseType]] { def left: BaseType def right: BaseType - def children = Seq(left, right) + def children: Seq[BaseType] = Seq(left, right) } /** * A [[TreeNode]] with no children. */ trait LeafNode[BaseType <: TreeNode[BaseType]] { - def children = Nil + def children: Seq[BaseType] = Nil } /** @@ -435,6 +435,5 @@ trait LeafNode[BaseType <: TreeNode[BaseType]] { */ trait UnaryNode[BaseType <: TreeNode[BaseType]] { def child: BaseType - def children = child :: Nil + def children: Seq[BaseType] = child :: Nil } - diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/package.scala index 79a8e06d4b4d4..ea6aa1850db4c 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/package.scala @@ -41,11 +41,11 @@ package object trees extends Logging { * A [[TreeNode]] companion for reference equality for Hash based Collection. */ class TreeNodeRef(val obj: TreeNode[_]) { - override def equals(o: Any) = o match { + override def equals(o: Any): Boolean = o match { case that: TreeNodeRef => that.obj.eq(obj) case _ => false } - override def hashCode = if (obj == null) 0 else obj.hashCode + override def hashCode: Int = if (obj == null) 0 else obj.hashCode } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala index feed50f9a2a2d..c86214a2aa944 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala @@ -23,7 +23,7 @@ import org.apache.spark.util.Utils package object util { - def fileToString(file: File, encoding: String = "UTF-8") = { + def fileToString(file: File, encoding: String = "UTF-8"): String = { val inStream = new FileInputStream(file) val outStream = new ByteArrayOutputStream try { @@ -45,7 +45,7 @@ package object util { def resourceToString( resource:String, encoding: String = "UTF-8", - classLoader: ClassLoader = Utils.getSparkClassLoader) = { + classLoader: ClassLoader = Utils.getSparkClassLoader): String = { val inStream = classLoader.getResourceAsStream(resource) val outStream = new ByteArrayOutputStream try { @@ -93,7 +93,7 @@ package object util { new String(out.toByteArray) } - def stringOrNull(a: AnyRef) = if (a == null) null else a.toString + def stringOrNull(a: AnyRef): String = if (a == null) null else a.toString def benchmark[A](f: => A): A = { val startTime = System.nanoTime() From 6948ab6f8ba836446b005f2cf1cc4abc944c5053 Mon Sep 17 00:00:00 2001 From: Kay Ousterhout Date: Tue, 24 Mar 2015 16:26:43 -0700 Subject: [PATCH 037/591] [SPARK-6088] Correct how tasks that get remote results are shown in UI. It would be great to fix this for 1.3. since the fix is surgical and it helps understandability for users. cc shivaram pwendell Author: Kay Ousterhout Closes #4839 from kayousterhout/SPARK-6088 and squashes the following commits: 3ab012c [Kay Ousterhout] Update getting result time incrementally, correctly set GET_RESULT status f346b49 [Kay Ousterhout] Typos 748ea6b [Kay Ousterhout] Fixed build failure 84d617c [Kay Ousterhout] [SPARK-6088] Correct how tasks that get remote results are shown in the UI. --- .../org/apache/spark/scheduler/TaskInfo.scala | 8 +++--- .../org/apache/spark/ui/jobs/StagePage.scala | 25 +++++++++++++------ 2 files changed, 23 insertions(+), 10 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala index 6fa1f2c880f7a..132a9ced77700 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskInfo.scala @@ -81,9 +81,11 @@ class TaskInfo( def status: String = { if (running) { - "RUNNING" - } else if (gettingResult) { - "GET RESULT" + if (gettingResult) { + "GET RESULT" + } else { + "RUNNING" + } } else if (failed) { "FAILED" } else if (successful) { diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala index e03442894c5cc..797c9404bc449 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala @@ -269,11 +269,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { +: getFormattedTimeQuantiles(serializationTimes) val gettingResultTimes = validTasks.map { case TaskUIData(info, _, _) => - if (info.gettingResultTime > 0) { - (info.finishTime - info.gettingResultTime).toDouble - } else { - 0.0 - } + getGettingResultTime(info).toDouble } val gettingResultQuantiles = @@ -464,7 +460,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { val gcTime = metrics.map(_.jvmGCTime).getOrElse(0L) val taskDeserializationTime = metrics.map(_.executorDeserializeTime).getOrElse(0L) val serializationTime = metrics.map(_.resultSerializationTime).getOrElse(0L) - val gettingResultTime = info.gettingResultTime + val gettingResultTime = getGettingResultTime(info) val maybeAccumulators = info.accumulables val accumulatorsReadable = maybeAccumulators.map{acc => s"${acc.name}: ${acc.update.get}"} @@ -627,6 +623,19 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { {errorSummary}{details} } + private def getGettingResultTime(info: TaskInfo): Long = { + if (info.gettingResultTime > 0) { + if (info.finishTime > 0) { + info.finishTime - info.gettingResultTime + } else { + // The task is still fetching the result. + System.currentTimeMillis - info.gettingResultTime + } + } else { + 0L + } + } + private def getSchedulerDelay(info: TaskInfo, metrics: TaskMetrics): Long = { val totalExecutionTime = if (info.gettingResult) { @@ -638,6 +647,8 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") { } val executorOverhead = (metrics.executorDeserializeTime + metrics.resultSerializationTime) - math.max(0, totalExecutionTime - metrics.executorRunTime - executorOverhead) + math.max( + 0, + totalExecutionTime - metrics.executorRunTime - executorOverhead - getGettingResultTime(info)) } } From d8ccf655f344eed65cdaf5d9252f1b565b8406ca Mon Sep 17 00:00:00 2001 From: Kay Ousterhout Date: Tue, 24 Mar 2015 16:29:40 -0700 Subject: [PATCH 038/591] [SPARK-3570] Include time to open files in shuffle write time. Opening shuffle files can be very significant when the disk is contended, especially when using ext3. While writing data to a file can avoid hitting disk (and instead hit the buffer cache), opening a file always involves writing some metadata about the file to disk, so the open time can be a very significant portion of the shuffle write time. In one job I ran recently, the time to write shuffle data to the file was only 4ms for each task, but the time to open the file was about 100x as long (~400ms). When we add metrics about spilled data (#2504), we should ensure that the file open time is also included there. Author: Kay Ousterhout Closes #4550 from kayousterhout/SPARK-3570 and squashes the following commits: ea3a4ae [Kay Ousterhout] Added comment about excluded open time fdc5185 [Kay Ousterhout] Improved comment 42b7e43 [Kay Ousterhout] Fixed parens for nanotime 2423555 [Kay Ousterhout] [SPARK-3570] Include time to open files in shuffle write time. --- .../org/apache/spark/shuffle/FileShuffleBlockManager.scala | 4 ++++ .../org/apache/spark/shuffle/sort/SortShuffleWriter.scala | 3 +++ .../org/apache/spark/util/collection/ExternalSorter.scala | 5 +++++ 3 files changed, 12 insertions(+) diff --git a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala index 660df00bc32f5..d0178dfde6935 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala @@ -112,6 +112,7 @@ class FileShuffleBlockManager(conf: SparkConf) private val shuffleState = shuffleStates(shuffleId) private var fileGroup: ShuffleFileGroup = null + val openStartTime = System.nanoTime val writers: Array[BlockObjectWriter] = if (consolidateShuffleFiles) { fileGroup = getUnusedFileGroup() Array.tabulate[BlockObjectWriter](numBuckets) { bucketId => @@ -135,6 +136,9 @@ class FileShuffleBlockManager(conf: SparkConf) blockManager.getDiskWriter(blockId, blockFile, serializer, bufferSize, writeMetrics) } } + // Creating the file to write to and creating a disk writer both involve interacting with + // the disk, so should be included in the shuffle write time. + writeMetrics.incShuffleWriteTime(System.nanoTime - openStartTime) override def releaseWriters(success: Boolean) { if (consolidateShuffleFiles) { diff --git a/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala b/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala index fa2e617762f55..55ea0f17b156a 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala @@ -63,6 +63,9 @@ private[spark] class SortShuffleWriter[K, V, C]( sorter.insertAll(records) } + // Don't bother including the time to open the merged output file in the shuffle write time, + // because it just opens a single file, so is typically too fast to measure accurately + // (see SPARK-3570). val outputFile = shuffleBlockManager.getDataFile(dep.shuffleId, mapId) val blockId = shuffleBlockManager.consolidateId(dep.shuffleId, mapId) val partitionLengths = sorter.writePartitionedFile(blockId, context, outputFile) diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala index 3262e670c2030..b962c101c91da 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala @@ -352,6 +352,7 @@ private[spark] class ExternalSorter[K, V, C]( // Create our file writers if we haven't done so yet if (partitionWriters == null) { curWriteMetrics = new ShuffleWriteMetrics() + val openStartTime = System.nanoTime partitionWriters = Array.fill(numPartitions) { // Because these files may be read during shuffle, their compression must be controlled by // spark.shuffle.compress instead of spark.shuffle.spill.compress, so we need to use @@ -359,6 +360,10 @@ private[spark] class ExternalSorter[K, V, C]( val (blockId, file) = diskBlockManager.createTempShuffleBlock() blockManager.getDiskWriter(blockId, file, ser, fileBufferSize, curWriteMetrics).open() } + // Creating the file to write to and creating a disk writer both involve interacting with + // the disk, and can take a long time in aggregate when we open many files, so should be + // included in the shuffle write time. + curWriteMetrics.incShuffleWriteTime(System.nanoTime - openStartTime) } // No need to sort stuff, just write each element out From f7c3668ee62d5b87a0fdf64614aa45c7409ff585 Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Tue, 24 Mar 2015 16:41:31 -0700 Subject: [PATCH 039/591] Revert "[SPARK-5771][UI][hotfix] Change Requested Cores into * if default cores is not set" This reverts commit 12135e90549f957962899487cd5eb95badd8976d. --- .../scala/org/apache/spark/deploy/master/ui/MasterPage.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala index 46509e39c0f23..9ab3c8f8fed9c 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala @@ -218,7 +218,7 @@ private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") { } } - {if (app.requestedCores == Int.MaxValue) "*" else app.requestedCores} + {app.requestedCores} {Utils.megabytesToString(app.desc.memoryPerSlave)} From dd907d1a9df52ffe0a8e1e8dacd837019d11742c Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Tue, 24 Mar 2015 16:49:27 -0700 Subject: [PATCH 040/591] Revert "[SPARK-5771] Number of Cores in Completed Applications of Standalone Master Web Page always be 0 if sc.stop() is called" This reverts commit dd077abf2e2949fdfec31074b760b587f00efcf2. Conflicts: core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala --- .../spark/deploy/master/ApplicationInfo.scala | 2 +- .../spark/deploy/master/ui/MasterPage.scala | 32 ++++--------------- 2 files changed, 7 insertions(+), 27 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala index 536aedb6f9fe9..f979ffa16641a 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala @@ -91,7 +91,7 @@ private[deploy] class ApplicationInfo( } } - private[master] val requestedCores = desc.maxCores.getOrElse(defaultCores) + private val requestedCores = desc.maxCores.getOrElse(defaultCores) private[master] def coresLeft: Int = requestedCores - coresGranted diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala index 9ab3c8f8fed9c..45412a35e9a7d 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala @@ -75,16 +75,12 @@ private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") { val workers = state.workers.sortBy(_.id) val workerTable = UIUtils.listingTable(workerHeaders, workerRow, workers) - val activeAppHeaders = Seq("Application ID", "Name", "Cores in Use", - "Cores Requested", "Memory per Node", "Submitted Time", "User", "State", "Duration") + val appHeaders = Seq("Application ID", "Name", "Cores", "Memory per Node", "Submitted Time", + "User", "State", "Duration") val activeApps = state.activeApps.sortBy(_.startTime).reverse - val activeAppsTable = UIUtils.listingTable(activeAppHeaders, activeAppRow, activeApps) - - val completedAppHeaders = Seq("Application ID", "Name", "Cores Requested", "Memory per Node", - "Submitted Time", "User", "State", "Duration") + val activeAppsTable = UIUtils.listingTable(appHeaders, appRow, activeApps) val completedApps = state.completedApps.sortBy(_.endTime).reverse - val completedAppsTable = UIUtils.listingTable(completedAppHeaders, completeAppRow, - completedApps) + val completedAppsTable = UIUtils.listingTable(appHeaders, appRow, completedApps) val driverHeaders = Seq("Submission ID", "Submitted Time", "Worker", "State", "Cores", "Memory", "Main Class") @@ -191,7 +187,7 @@ private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") { } - private def appRow(app: ApplicationInfo, active: Boolean): Seq[Node] = { + private def appRow(app: ApplicationInfo): Seq[Node] = { val killLink = if (parent.killEnabled && (app.state == ApplicationState.RUNNING || app.state == ApplicationState.WAITING)) { val killLinkUri = s"app/kill?id=${app.id}&terminate=true" @@ -201,7 +197,6 @@ private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") { (kill) } - {app.id} @@ -210,15 +205,8 @@ private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") { {app.desc.name} - { - if (active) { - - {app.coresGranted} - - } - } - {app.requestedCores} + {app.coresGranted} {Utils.megabytesToString(app.desc.memoryPerSlave)} @@ -230,14 +218,6 @@ private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") { } - private def activeAppRow(app: ApplicationInfo): Seq[Node] = { - appRow(app, active = true) - } - - private def completeAppRow(app: ApplicationInfo): Seq[Node] = { - appRow(app, active = false) - } - private def driverRow(driver: DriverInfo): Seq[Node] = { val killLink = if (parent.killEnabled && (driver.state == DriverState.RUNNING || From 05c2214b41f4c0fd17b6f0c62e26398b963efe64 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Christophe=20Pr=C3=A9aud?= Date: Tue, 24 Mar 2015 17:05:49 -0700 Subject: [PATCH 041/591] [SPARK-6469] Improving documentation on YARN local directories usage MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Clarify the local directories usage in YARN Author: Christophe Préaud Closes #5165 from preaudc/yarn-doc-local-dirs and squashes the following commits: 6912b90 [Christophe Préaud] Fix some formatting issues. 4fa8ec2 [Christophe Préaud] Merge remote-tracking branch 'upstream/master' into yarn-doc-local-dirs eaaf519 [Christophe Préaud] Clarify the local directories usage in YARN 436fb7d [Christophe Préaud] Revert "Clarify the local directories usage in YARN" 876ae5e [Christophe Préaud] Clarify the local directories usage in YARN 608dbfa [Christophe Préaud] Merge remote-tracking branch 'upstream/master' a49a2ce [Christophe Préaud] Merge remote-tracking branch 'upstream/master' 9ba89ca [Christophe Préaud] Ensure that files are fetched atomically 54419ae [Christophe Préaud] Merge remote-tracking branch 'upstream/master' c6a5590 [Christophe Préaud] Revert commit 8ea871f8130b2490f1bad7374a819bf56f0ccbbd 7456a33 [Christophe Préaud] Merge remote-tracking branch 'upstream/master' 8ea871f [Christophe Préaud] Ensure that files are fetched atomically --- docs/running-on-yarn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md index 68b1aeb8ebd01..d9f3eb2b74b18 100644 --- a/docs/running-on-yarn.md +++ b/docs/running-on-yarn.md @@ -274,6 +274,6 @@ If you need a reference to the proper location to put log files in the YARN so t # Important notes - Whether core requests are honored in scheduling decisions depends on which scheduler is in use and how it is configured. -- The local directories used by Spark executors will be the local directories configured for YARN (Hadoop YARN config `yarn.nodemanager.local-dirs`). If the user specifies `spark.local.dir`, it will be ignored. +- In `yarn-cluster` mode, the local directories used by the Spark executors and the Spark driver will be the local directories configured for YARN (Hadoop YARN config `yarn.nodemanager.local-dirs`). If the user specifies `spark.local.dir`, it will be ignored. In `yarn-client` mode, the Spark executors will use the local directories configured for YARN while the Spark driver will use those defined in `spark.local.dir`. This is because the Spark driver does not run on the YARN cluster in `yarn-client` mode, only the Spark executors do. - The `--files` and `--archives` options support specifying file names with the # similar to Hadoop. For example you can specify: `--files localtest.txt#appSees.txt` and this will upload the file you have locally named localtest.txt into HDFS but this will be linked to by the name `appSees.txt`, and your application should use the name as `appSees.txt` to reference it when running on YARN. - The `--jars` option allows the `SparkContext.addJar` function to work if you are using it with local files and running in `yarn-cluster` mode. It does not need to be used if you are using it with HDFS, HTTP, HTTPS, or FTP files. From 6930e965e26d39fa6c26ae67a08b4c4d0368d556 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Tue, 24 Mar 2015 17:06:22 -0700 Subject: [PATCH 042/591] [SPARK-6512] add contains to OpenHashMap Add `contains` to test whether a key exists in an OpenHashMap. rxin Author: Xiangrui Meng Closes #5171 from mengxr/openhashmap-contains and squashes the following commits: d6e6f1f [Xiangrui Meng] add contains to primitivekeyopenhashmap 748a69b [Xiangrui Meng] add contains to OpenHashMap --- .../org/apache/spark/util/collection/OpenHashMap.scala | 9 +++++++++ .../util/collection/PrimitiveKeyOpenHashMap.scala | 5 +++++ .../spark/util/collection/OpenHashMapSuite.scala | 10 ++++++++++ .../util/collection/PrimitiveKeyOpenHashMapSuite.scala | 7 +++++++ 4 files changed, 31 insertions(+) diff --git a/core/src/main/scala/org/apache/spark/util/collection/OpenHashMap.scala b/core/src/main/scala/org/apache/spark/util/collection/OpenHashMap.scala index c52591b352340..efc2482c74ddf 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/OpenHashMap.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/OpenHashMap.scala @@ -53,6 +53,15 @@ class OpenHashMap[K : ClassTag, @specialized(Long, Int, Double) V: ClassTag]( override def size: Int = if (haveNullValue) _keySet.size + 1 else _keySet.size + /** Tests whether this map contains a binding for a key. */ + def contains(k: K): Boolean = { + if (k == null) { + haveNullValue + } else { + _keySet.getPos(k) != OpenHashSet.INVALID_POS + } + } + /** Get the value for a given key */ def apply(k: K): V = { if (k == null) { diff --git a/core/src/main/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMap.scala b/core/src/main/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMap.scala index 61e22642761f0..b4ec4ea521253 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMap.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMap.scala @@ -48,6 +48,11 @@ class PrimitiveKeyOpenHashMap[@specialized(Long, Int) K: ClassTag, override def size: Int = _keySet.size + /** Tests whether this map contains a binding for a key. */ + def contains(k: K): Boolean = { + _keySet.getPos(k) != OpenHashSet.INVALID_POS + } + /** Get the value for a given key */ def apply(k: K): V = { val pos = _keySet.getPos(k) diff --git a/core/src/test/scala/org/apache/spark/util/collection/OpenHashMapSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/OpenHashMapSuite.scala index 6a70877356409..ef890d2ba60f3 100644 --- a/core/src/test/scala/org/apache/spark/util/collection/OpenHashMapSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/collection/OpenHashMapSuite.scala @@ -176,4 +176,14 @@ class OpenHashMapSuite extends FunSuite with Matchers { assert(map(i.toString) === i.toString) } } + + test("contains") { + val map = new OpenHashMap[String, Int](2) + map("a") = 1 + assert(map.contains("a")) + assert(!map.contains("b")) + assert(!map.contains(null)) + map(null) = 0 + assert(map.contains(null)) + } } diff --git a/core/src/test/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMapSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMapSuite.scala index 8c7df7d73dcd3..caf378fec8b3e 100644 --- a/core/src/test/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMapSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/collection/PrimitiveKeyOpenHashMapSuite.scala @@ -118,4 +118,11 @@ class PrimitiveKeyOpenHashMapSuite extends FunSuite with Matchers { assert(map(i.toLong) === i.toString) } } + + test("contains") { + val map = new PrimitiveKeyOpenHashMap[Int, Int](1) + map(0) = 0 + assert(map.contains(0)) + assert(!map.contains(1)) + } } From 94598653bc772e71709163db3fed4048aa7f5f75 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Tue, 24 Mar 2015 17:08:25 -0700 Subject: [PATCH 043/591] [SPARK-6428][Streaming] Added explicit types for all public methods. Author: Reynold Xin Closes #5110 from rxin/streaming-explicit-type and squashes the following commits: 2c2db32 [Reynold Xin] [SPARK-6428][Streaming] Added explicit types for all public methods. --- .../apache/spark/streaming/Checkpoint.scala | 6 ++-- .../apache/spark/streaming/DStreamGraph.scala | 6 ++-- .../org/apache/spark/streaming/Duration.scala | 15 ++++----- .../org/apache/spark/streaming/Interval.scala | 10 +++--- .../spark/streaming/StreamingContext.scala | 5 +-- .../streaming/api/java/JavaDStreamLike.scala | 3 +- .../streaming/api/python/PythonDStream.scala | 15 +++++---- .../spark/streaming/dstream/DStream.scala | 20 ++++++----- .../dstream/DStreamCheckpointData.scala | 2 +- .../streaming/dstream/FileInputDStream.scala | 4 +-- .../streaming/dstream/FilteredDStream.scala | 2 +- .../dstream/FlatMapValuedDStream.scala | 2 +- .../streaming/dstream/FlatMappedDStream.scala | 2 +- .../streaming/dstream/ForEachDStream.scala | 2 +- .../streaming/dstream/GlommedDStream.scala | 2 +- .../streaming/dstream/InputDStream.scala | 2 +- .../dstream/MapPartitionedDStream.scala | 2 +- .../streaming/dstream/MapValuedDStream.scala | 2 +- .../streaming/dstream/MappedDStream.scala | 2 +- .../dstream/ReducedWindowedDStream.scala | 4 +-- .../streaming/dstream/ShuffledDStream.scala | 2 +- .../streaming/dstream/StateDStream.scala | 2 +- .../dstream/TransformedDStream.scala | 2 +- .../streaming/dstream/UnionDStream.scala | 6 ++-- .../streaming/dstream/WindowedDStream.scala | 2 +- .../rdd/WriteAheadLogBackedBlockRDD.scala | 6 ++-- .../streaming/receiver/ActorReceiver.scala | 18 +++++----- .../streaming/receiver/BlockGenerator.scala | 2 +- .../spark/streaming/receiver/Receiver.scala | 2 +- .../receiver/ReceiverSupervisor.scala | 4 +-- .../receiver/ReceiverSupervisorImpl.scala | 6 ++-- .../spark/streaming/scheduler/Job.scala | 2 +- .../streaming/scheduler/JobGenerator.scala | 8 ++--- .../streaming/scheduler/JobScheduler.scala | 2 +- .../spark/streaming/scheduler/JobSet.scala | 11 +++---- .../streaming/scheduler/ReceiverTracker.scala | 11 +++---- .../ui/StreamingJobProgressListener.scala | 33 +++++++++++-------- .../spark/streaming/util/RawTextHelper.scala | 14 ++++---- 38 files changed, 127 insertions(+), 114 deletions(-) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala index db64e11e16304..f73b463d07779 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala @@ -67,12 +67,12 @@ object Checkpoint extends Logging { val REGEX = (PREFIX + """([\d]+)([\w\.]*)""").r /** Get the checkpoint file for the given checkpoint time */ - def checkpointFile(checkpointDir: String, checkpointTime: Time) = { + def checkpointFile(checkpointDir: String, checkpointTime: Time): Path = { new Path(checkpointDir, PREFIX + checkpointTime.milliseconds) } /** Get the checkpoint backup file for the given checkpoint time */ - def checkpointBackupFile(checkpointDir: String, checkpointTime: Time) = { + def checkpointBackupFile(checkpointDir: String, checkpointTime: Time): Path = { new Path(checkpointDir, PREFIX + checkpointTime.milliseconds + ".bk") } @@ -232,6 +232,8 @@ object CheckpointReader extends Logging { def read(checkpointDir: String, conf: SparkConf, hadoopConf: Configuration): Option[Checkpoint] = { val checkpointPath = new Path(checkpointDir) + + // TODO(rxin): Why is this a def?! def fs = checkpointPath.getFileSystem(hadoopConf) // Try to find the checkpoint files diff --git a/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala b/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala index 0e285d6088ec1..175140481e5ae 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/DStreamGraph.scala @@ -100,11 +100,11 @@ final private[streaming] class DStreamGraph extends Serializable with Logging { } } - def getInputStreams() = this.synchronized { inputStreams.toArray } + def getInputStreams(): Array[InputDStream[_]] = this.synchronized { inputStreams.toArray } - def getOutputStreams() = this.synchronized { outputStreams.toArray } + def getOutputStreams(): Array[DStream[_]] = this.synchronized { outputStreams.toArray } - def getReceiverInputStreams() = this.synchronized { + def getReceiverInputStreams(): Array[ReceiverInputDStream[_]] = this.synchronized { inputStreams.filter(_.isInstanceOf[ReceiverInputDStream[_]]) .map(_.asInstanceOf[ReceiverInputDStream[_]]) .toArray diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Duration.scala b/streaming/src/main/scala/org/apache/spark/streaming/Duration.scala index a0d8fb5ab93ec..3249bb348981f 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/Duration.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/Duration.scala @@ -55,7 +55,6 @@ case class Duration (private val millis: Long) { def div(that: Duration): Double = this / that - def isMultipleOf(that: Duration): Boolean = (this.millis % that.millis == 0) @@ -71,7 +70,7 @@ case class Duration (private val millis: Long) { def milliseconds: Long = millis - def prettyPrint = Utils.msDurationToString(millis) + def prettyPrint: String = Utils.msDurationToString(millis) } @@ -80,7 +79,7 @@ case class Duration (private val millis: Long) { * a given number of milliseconds. */ object Milliseconds { - def apply(milliseconds: Long) = new Duration(milliseconds) + def apply(milliseconds: Long): Duration = new Duration(milliseconds) } /** @@ -88,7 +87,7 @@ object Milliseconds { * a given number of seconds. */ object Seconds { - def apply(seconds: Long) = new Duration(seconds * 1000) + def apply(seconds: Long): Duration = new Duration(seconds * 1000) } /** @@ -96,7 +95,7 @@ object Seconds { * a given number of minutes. */ object Minutes { - def apply(minutes: Long) = new Duration(minutes * 60000) + def apply(minutes: Long): Duration = new Duration(minutes * 60000) } // Java-friendlier versions of the objects above. @@ -107,16 +106,16 @@ object Durations { /** * @return [[org.apache.spark.streaming.Duration]] representing given number of milliseconds. */ - def milliseconds(milliseconds: Long) = Milliseconds(milliseconds) + def milliseconds(milliseconds: Long): Duration = Milliseconds(milliseconds) /** * @return [[org.apache.spark.streaming.Duration]] representing given number of seconds. */ - def seconds(seconds: Long) = Seconds(seconds) + def seconds(seconds: Long): Duration = Seconds(seconds) /** * @return [[org.apache.spark.streaming.Duration]] representing given number of minutes. */ - def minutes(minutes: Long) = Minutes(minutes) + def minutes(minutes: Long): Duration = Minutes(minutes) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Interval.scala b/streaming/src/main/scala/org/apache/spark/streaming/Interval.scala index ad4f3fdd14ad6..3f5be785e1b1a 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/Interval.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/Interval.scala @@ -39,18 +39,18 @@ class Interval(val beginTime: Time, val endTime: Time) { this.endTime < that.endTime } - def <= (that: Interval) = (this < that || this == that) + def <= (that: Interval): Boolean = (this < that || this == that) - def > (that: Interval) = !(this <= that) + def > (that: Interval): Boolean = !(this <= that) - def >= (that: Interval) = !(this < that) + def >= (that: Interval): Boolean = !(this < that) - override def toString = "[" + beginTime + ", " + endTime + "]" + override def toString: String = "[" + beginTime + ", " + endTime + "]" } private[streaming] object Interval { - def currentInterval(duration: Duration): Interval = { + def currentInterval(duration: Duration): Interval = { val time = new Time(System.currentTimeMillis) val intervalBegin = time.floor(duration) new Interval(intervalBegin, intervalBegin + duration) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala index 543224d4b07bc..f57f295874645 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala @@ -188,7 +188,7 @@ class StreamingContext private[streaming] ( /** * Return the associated Spark context */ - def sparkContext = sc + def sparkContext: SparkContext = sc /** * Set each DStreams in this context to remember RDDs it generated in the last given duration. @@ -596,7 +596,8 @@ object StreamingContext extends Logging { @deprecated("Replaced by implicit functions in the DStream companion object. This is " + "kept here only for backward compatibility.", "1.3.0") def toPairDStreamFunctions[K, V](stream: DStream[(K, V)]) - (implicit kt: ClassTag[K], vt: ClassTag[V], ord: Ordering[K] = null) = { + (implicit kt: ClassTag[K], vt: ClassTag[V], ord: Ordering[K] = null) + : PairDStreamFunctions[K, V] = { DStream.toPairDStreamFunctions(stream)(kt, vt, ord) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala index 2eabdd9387913..73030e15c5661 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala @@ -415,8 +415,9 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T implicit val cmv2: ClassTag[V2] = fakeClassTag implicit val cmw: ClassTag[W] = fakeClassTag - def scalaTransform (inThis: RDD[T], inThat: RDD[(K2, V2)], time: Time): RDD[W] = + def scalaTransform (inThis: RDD[T], inThat: RDD[(K2, V2)], time: Time): RDD[W] = { transformFunc.call(wrapRDD(inThis), other.wrapRDD(inThat), time).rdd + } dstream.transformWith[(K2, V2), W](other.dstream, scalaTransform(_, _, _)) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala index 7053f47ec69a2..4c28654ef6413 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala @@ -176,11 +176,11 @@ private[python] abstract class PythonDStream( val func = new TransformFunction(pfunc) - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration - val asJavaDStream = JavaDStream.fromDStream(this) + val asJavaDStream: JavaDStream[Array[Byte]] = JavaDStream.fromDStream(this) } /** @@ -212,7 +212,7 @@ private[python] class PythonTransformed2DStream( val func = new TransformFunction(pfunc) - override def dependencies = List(parent, parent2) + override def dependencies: List[DStream[_]] = List(parent, parent2) override def slideDuration: Duration = parent.slideDuration @@ -223,7 +223,7 @@ private[python] class PythonTransformed2DStream( func(Some(rdd1), Some(rdd2), validTime) } - val asJavaDStream = JavaDStream.fromDStream(this) + val asJavaDStream: JavaDStream[Array[Byte]] = JavaDStream.fromDStream(this) } /** @@ -260,12 +260,15 @@ private[python] class PythonReducedWindowedDStream( extends PythonDStream(parent, preduceFunc) { super.persist(StorageLevel.MEMORY_ONLY) - override val mustCheckpoint = true - val invReduceFunc = new TransformFunction(pinvReduceFunc) + override val mustCheckpoint: Boolean = true + + val invReduceFunc: TransformFunction = new TransformFunction(pinvReduceFunc) def windowDuration: Duration = _windowDuration + override def slideDuration: Duration = _slideDuration + override def parentRememberDuration: Duration = rememberDuration + windowDuration override def compute(validTime: Time): Option[RDD[Array[Byte]]] = { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala index b874f561c12eb..795c5aa6d585b 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala @@ -104,7 +104,7 @@ abstract class DStream[T: ClassTag] ( private[streaming] def parentRememberDuration = rememberDuration /** Return the StreamingContext associated with this DStream */ - def context = ssc + def context: StreamingContext = ssc /* Set the creation call site */ private[streaming] val creationSite = DStream.getCreationSite() @@ -619,14 +619,16 @@ abstract class DStream[T: ClassTag] ( * operator, so this DStream will be registered as an output stream and there materialized. */ def print(num: Int) { - def foreachFunc = (rdd: RDD[T], time: Time) => { - val firstNum = rdd.take(num + 1) - println ("-------------------------------------------") - println ("Time: " + time) - println ("-------------------------------------------") - firstNum.take(num).foreach(println) - if (firstNum.size > num) println("...") - println() + def foreachFunc: (RDD[T], Time) => Unit = { + (rdd: RDD[T], time: Time) => { + val firstNum = rdd.take(num + 1) + println("-------------------------------------------") + println("Time: " + time) + println("-------------------------------------------") + firstNum.take(num).foreach(println) + if (firstNum.size > num) println("...") + println() + } } new ForEachDStream(this, context.sparkContext.clean(foreachFunc)).register() } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStreamCheckpointData.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStreamCheckpointData.scala index 0dc72790fbdbd..39fd21342813e 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStreamCheckpointData.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStreamCheckpointData.scala @@ -114,7 +114,7 @@ class DStreamCheckpointData[T: ClassTag] (dstream: DStream[T]) } } - override def toString() = { + override def toString: String = { "[\n" + currentCheckpointFiles.size + " checkpoint files \n" + currentCheckpointFiles.mkString("\n") + "\n]" } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala index 22de8c02e63c8..66d519171fd76 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala @@ -298,7 +298,7 @@ class FileInputDStream[K, V, F <: NewInputFormat[K,V]]( private[streaming] class FileInputDStreamCheckpointData extends DStreamCheckpointData(this) { - def hadoopFiles = data.asInstanceOf[mutable.HashMap[Time, Array[String]]] + private def hadoopFiles = data.asInstanceOf[mutable.HashMap[Time, Array[String]]] override def update(time: Time) { hadoopFiles.clear() @@ -320,7 +320,7 @@ class FileInputDStream[K, V, F <: NewInputFormat[K,V]]( } } - override def toString() = { + override def toString: String = { "[\n" + hadoopFiles.size + " file sets\n" + hadoopFiles.map(p => (p._1, p._2.mkString(", "))).mkString("\n") + "\n]" } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FilteredDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FilteredDStream.scala index c81534ae584ea..fcd5216f101af 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FilteredDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FilteredDStream.scala @@ -27,7 +27,7 @@ class FilteredDStream[T: ClassTag]( filterFunc: T => Boolean ) extends DStream[T](parent.ssc) { - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMapValuedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMapValuedDStream.scala index 658623455498c..9d09a3baf37ca 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMapValuedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMapValuedDStream.scala @@ -28,7 +28,7 @@ class FlatMapValuedDStream[K: ClassTag, V: ClassTag, U: ClassTag]( flatMapValueFunc: V => TraversableOnce[U] ) extends DStream[(K, U)](parent.ssc) { - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMappedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMappedDStream.scala index c7bb2833eabb8..475ea2d2d4f38 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMappedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlatMappedDStream.scala @@ -27,7 +27,7 @@ class FlatMappedDStream[T: ClassTag, U: ClassTag]( flatMapFunc: T => Traversable[U] ) extends DStream[U](parent.ssc) { - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ForEachDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ForEachDStream.scala index 1361c30395b57..685a32e1d280d 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ForEachDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ForEachDStream.scala @@ -28,7 +28,7 @@ class ForEachDStream[T: ClassTag] ( foreachFunc: (RDD[T], Time) => Unit ) extends DStream[Unit](parent.ssc) { - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/GlommedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/GlommedDStream.scala index a9bb51f054048..dbb295fe54f71 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/GlommedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/GlommedDStream.scala @@ -25,7 +25,7 @@ private[streaming] class GlommedDStream[T: ClassTag](parent: DStream[T]) extends DStream[Array[T]](parent.ssc) { - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala index aa1993f0580a8..e652702e213ef 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala @@ -61,7 +61,7 @@ abstract class InputDStream[T: ClassTag] (@transient ssc_ : StreamingContext) } } - override def dependencies = List() + override def dependencies: List[DStream[_]] = List() override def slideDuration: Duration = { if (ssc == null) throw new Exception("ssc is null") diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapPartitionedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapPartitionedDStream.scala index 3d8ee29df1e82..5994bc1e23f2b 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapPartitionedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapPartitionedDStream.scala @@ -28,7 +28,7 @@ class MapPartitionedDStream[T: ClassTag, U: ClassTag]( preservePartitioning: Boolean ) extends DStream[U](parent.ssc) { - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapValuedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapValuedDStream.scala index 7aea1f945d9db..954d2eb4a7b00 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapValuedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MapValuedDStream.scala @@ -28,7 +28,7 @@ class MapValuedDStream[K: ClassTag, V: ClassTag, U: ClassTag]( mapValueFunc: V => U ) extends DStream[(K, U)](parent.ssc) { - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MappedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MappedDStream.scala index 02704a8d1c2e0..fa14b2e897c3e 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MappedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/MappedDStream.scala @@ -27,7 +27,7 @@ class MappedDStream[T: ClassTag, U: ClassTag] ( mapFunc: T => U ) extends DStream[U](parent.ssc) { - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala index c0a5af0b65cc3..1385ccbf56ee5 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReducedWindowedDStream.scala @@ -52,7 +52,7 @@ class ReducedWindowedDStream[K: ClassTag, V: ClassTag]( // Reduce each batch of data using reduceByKey which will be further reduced by window // by ReducedWindowedDStream - val reducedStream = parent.reduceByKey(reduceFunc, partitioner) + private val reducedStream = parent.reduceByKey(reduceFunc, partitioner) // Persist RDDs to memory by default as these RDDs are going to be reused. super.persist(StorageLevel.MEMORY_ONLY_SER) @@ -60,7 +60,7 @@ class ReducedWindowedDStream[K: ClassTag, V: ClassTag]( def windowDuration: Duration = _windowDuration - override def dependencies = List(reducedStream) + override def dependencies: List[DStream[_]] = List(reducedStream) override def slideDuration: Duration = _slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ShuffledDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ShuffledDStream.scala index 880a89bc36895..7757ccac09a58 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ShuffledDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ShuffledDStream.scala @@ -33,7 +33,7 @@ class ShuffledDStream[K: ClassTag, V: ClassTag, C: ClassTag]( mapSideCombine: Boolean = true ) extends DStream[(K,C)] (parent.ssc) { - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/StateDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/StateDStream.scala index ebb04dd35b9a2..de8718d0a80fe 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/StateDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/StateDStream.scala @@ -36,7 +36,7 @@ class StateDStream[K: ClassTag, V: ClassTag, S: ClassTag]( super.persist(StorageLevel.MEMORY_ONLY_SER) - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = parent.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/TransformedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/TransformedDStream.scala index 71b61856e23c0..5d46ca0715ffd 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/TransformedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/TransformedDStream.scala @@ -32,7 +32,7 @@ class TransformedDStream[U: ClassTag] ( require(parents.map(_.slideDuration).distinct.size == 1, "Some of the DStreams have different slide durations") - override def dependencies = parents.toList + override def dependencies: List[DStream[_]] = parents.toList override def slideDuration: Duration = parents.head.slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/UnionDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/UnionDStream.scala index abbc40befa95b..9405dbaa12329 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/UnionDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/UnionDStream.scala @@ -33,17 +33,17 @@ class UnionDStream[T: ClassTag](parents: Array[DStream[T]]) require(parents.map(_.slideDuration).distinct.size == 1, "Some of the DStreams have different slide durations") - override def dependencies = parents.toList + override def dependencies: List[DStream[_]] = parents.toList override def slideDuration: Duration = parents.head.slideDuration override def compute(validTime: Time): Option[RDD[T]] = { val rdds = new ArrayBuffer[RDD[T]]() - parents.map(_.getOrCompute(validTime)).foreach(_ match { + parents.map(_.getOrCompute(validTime)).foreach { case Some(rdd) => rdds += rdd case None => throw new Exception("Could not generate RDD from a parent for unifying at time " + validTime) - }) + } if (rdds.size > 0) { Some(new UnionRDD(ssc.sc, rdds)) } else { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala index 775b6bfd065c0..899865a906c27 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala @@ -46,7 +46,7 @@ class WindowedDStream[T: ClassTag]( def windowDuration: Duration = _windowDuration - override def dependencies = List(parent) + override def dependencies: List[DStream[_]] = List(parent) override def slideDuration: Duration = _slideDuration diff --git a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala index dd1e96334952f..93caa4ba35c7f 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala @@ -117,8 +117,8 @@ class WriteAheadLogBackedBlockRDD[T: ClassTag]( override def getPreferredLocations(split: Partition): Seq[String] = { val partition = split.asInstanceOf[WriteAheadLogBackedBlockRDDPartition] val blockLocations = getBlockIdLocations().get(partition.blockId) - def segmentLocations = HdfsUtils.getFileSegmentLocations( - partition.segment.path, partition.segment.offset, partition.segment.length, hadoopConfig) - blockLocations.getOrElse(segmentLocations) + blockLocations.getOrElse( + HdfsUtils.getFileSegmentLocations( + partition.segment.path, partition.segment.offset, partition.segment.length, hadoopConfig)) } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala index a7d63bd4f2dbf..cd309788a7717 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala @@ -17,6 +17,7 @@ package org.apache.spark.streaming.receiver +import java.nio.ByteBuffer import java.util.concurrent.atomic.AtomicInteger import scala.concurrent.duration._ @@ -25,10 +26,10 @@ import scala.reflect.ClassTag import akka.actor._ import akka.actor.SupervisorStrategy.{Escalate, Restart} + import org.apache.spark.{Logging, SparkEnv} -import org.apache.spark.storage.StorageLevel -import java.nio.ByteBuffer import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.storage.StorageLevel /** * :: DeveloperApi :: @@ -149,13 +150,13 @@ private[streaming] class ActorReceiver[T: ClassTag]( class Supervisor extends Actor { override val supervisorStrategy = receiverSupervisorStrategy - val worker = context.actorOf(props, name) + private val worker = context.actorOf(props, name) logInfo("Started receiver worker at:" + worker.path) - val n: AtomicInteger = new AtomicInteger(0) - val hiccups: AtomicInteger = new AtomicInteger(0) + private val n: AtomicInteger = new AtomicInteger(0) + private val hiccups: AtomicInteger = new AtomicInteger(0) - def receive = { + override def receive: PartialFunction[Any, Unit] = { case IteratorData(iterator) => logDebug("received iterator") @@ -189,13 +190,12 @@ private[streaming] class ActorReceiver[T: ClassTag]( } } - def onStart() = { + def onStart(): Unit = { supervisor logInfo("Supervision tree for receivers initialized at:" + supervisor.path) - } - def onStop() = { + def onStop(): Unit = { supervisor ! PoisonPill } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala index ee5e639b26d91..42514d8b47dcf 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala @@ -120,7 +120,7 @@ private[streaming] class BlockGenerator( * `BlockGeneratorListener.onAddData` callback will be called. All received data items * will be periodically pushed into BlockManager. */ - def addDataWithCallback(data: Any, metadata: Any) = synchronized { + def addDataWithCallback(data: Any, metadata: Any): Unit = synchronized { waitToPush() currentBuffer += data listener.onAddData(data, metadata) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala index 5acf8a9a811ee..5b5a3fe648602 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala @@ -245,7 +245,7 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable * Get the unique identifier the receiver input stream that this * receiver is associated with. */ - def streamId = id + def streamId: Int = id /* * ================= diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala index 1f0244c251eba..4943f29395d12 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala @@ -162,13 +162,13 @@ private[streaming] abstract class ReceiverSupervisor( } /** Check if receiver has been marked for stopping */ - def isReceiverStarted() = { + def isReceiverStarted(): Boolean = { logDebug("state = " + receiverState) receiverState == Started } /** Check if receiver has been marked for stopping */ - def isReceiverStopped() = { + def isReceiverStopped(): Boolean = { logDebug("state = " + receiverState) receiverState == Stopped } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala index 7d29ed88cfcb4..8f2f1fef76874 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala @@ -23,7 +23,7 @@ import java.util.concurrent.atomic.AtomicLong import scala.collection.mutable.ArrayBuffer import scala.concurrent.Await -import akka.actor.{Actor, Props} +import akka.actor.{ActorRef, Actor, Props} import akka.pattern.ask import com.google.common.base.Throwables import org.apache.hadoop.conf.Configuration @@ -83,7 +83,7 @@ private[streaming] class ReceiverSupervisorImpl( private val actor = env.actorSystem.actorOf( Props(new Actor { - override def receive() = { + override def receive: PartialFunction[Any, Unit] = { case StopReceiver => logInfo("Received stop signal") stop("Stopped by driver", None) @@ -92,7 +92,7 @@ private[streaming] class ReceiverSupervisorImpl( cleanupOldBlocks(threshTime) } - def ref = self + def ref: ActorRef = self }), "Receiver-" + streamId + "-" + System.currentTimeMillis()) /** Unique block ids if one wants to add blocks directly */ diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala index 7e0f6b2cdfc08..30cf87f5b7dd1 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala @@ -36,5 +36,5 @@ class Job(val time: Time, func: () => _) { id = "streaming job " + time + "." + number } - override def toString = id + override def toString: String = id } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala index 59488dfb0f8c6..4946806d2ee95 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala @@ -82,7 +82,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { if (eventActor != null) return // generator has already been started eventActor = ssc.env.actorSystem.actorOf(Props(new Actor { - def receive = { + override def receive: PartialFunction[Any, Unit] = { case event: JobGeneratorEvent => processEvent(event) } }), "JobGenerator") @@ -111,8 +111,8 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { val pollTime = 100 // To prevent graceful stop to get stuck permanently - def hasTimedOut = { - val timedOut = System.currentTimeMillis() - timeWhenStopStarted > stopTimeout + def hasTimedOut: Boolean = { + val timedOut = (System.currentTimeMillis() - timeWhenStopStarted) > stopTimeout if (timedOut) { logWarning("Timed out while stopping the job generator (timeout = " + stopTimeout + ")") } @@ -133,7 +133,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { logInfo("Stopped generation timer") // Wait for the jobs to complete and checkpoints to be written - def haveAllBatchesBeenProcessed = { + def haveAllBatchesBeenProcessed: Boolean = { lastProcessedBatch != null && lastProcessedBatch.milliseconds == stopTime } logInfo("Waiting for jobs to be processed and checkpoints to be written") diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala index 60bc099b27a4c..d6a93acbe711b 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala @@ -56,7 +56,7 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { logDebug("Starting JobScheduler") eventActor = ssc.env.actorSystem.actorOf(Props(new Actor { - def receive = { + override def receive: PartialFunction[Any, Unit] = { case event: JobSchedulerEvent => processEvent(event) } }), "JobScheduler") diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala index 8c15a75b1b0e0..5b134877d0b2d 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala @@ -28,8 +28,7 @@ private[streaming] case class JobSet( time: Time, jobs: Seq[Job], - receivedBlockInfo: Map[Int, Array[ReceivedBlockInfo]] = Map.empty - ) { + receivedBlockInfo: Map[Int, Array[ReceivedBlockInfo]] = Map.empty) { private val incompleteJobs = new HashSet[Job]() private val submissionTime = System.currentTimeMillis() // when this jobset was submitted @@ -48,17 +47,17 @@ case class JobSet( if (hasCompleted) processingEndTime = System.currentTimeMillis() } - def hasStarted = processingStartTime > 0 + def hasStarted: Boolean = processingStartTime > 0 - def hasCompleted = incompleteJobs.isEmpty + def hasCompleted: Boolean = incompleteJobs.isEmpty // Time taken to process all the jobs from the time they started processing // (i.e. not including the time they wait in the streaming scheduler queue) - def processingDelay = processingEndTime - processingStartTime + def processingDelay: Long = processingEndTime - processingStartTime // Time taken to process all the jobs from the time they were submitted // (i.e. including the time they wait in the streaming scheduler queue) - def totalDelay = { + def totalDelay: Long = { processingEndTime - time.milliseconds } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala index b36aeb341d25e..98900473138fe 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala @@ -72,7 +72,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false private var actor: ActorRef = null /** Start the actor and receiver execution thread. */ - def start() = synchronized { + def start(): Unit = synchronized { if (actor != null) { throw new SparkException("ReceiverTracker already started") } @@ -86,7 +86,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false } /** Stop the receiver execution thread. */ - def stop(graceful: Boolean) = synchronized { + def stop(graceful: Boolean): Unit = synchronized { if (!receiverInputStreams.isEmpty && actor != null) { // First, stop the receivers if (!skipReceiverLaunch) receiverExecutor.stop(graceful) @@ -201,7 +201,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false /** Actor to receive messages from the receivers. */ private class ReceiverTrackerActor extends Actor { - def receive = { + override def receive: PartialFunction[Any, Unit] = { case RegisterReceiver(streamId, typ, host, receiverActor) => registerReceiver(streamId, typ, host, receiverActor, sender) sender ! true @@ -244,16 +244,15 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false if (graceful) { val pollTime = 100 - def done = { receiverInfo.isEmpty && !running } logInfo("Waiting for receiver job to terminate gracefully") - while(!done) { + while (receiverInfo.nonEmpty || running) { Thread.sleep(pollTime) } logInfo("Waited for receiver job to terminate gracefully") } // Check if all the receivers have been deregistered or not - if (!receiverInfo.isEmpty) { + if (receiverInfo.nonEmpty) { logWarning("Not all of the receivers have deregistered, " + receiverInfo) } else { logInfo("All of the receivers have deregistered successfully") diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala index 5ee53a5c5f561..e4bd067cacb77 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala @@ -17,9 +17,10 @@ package org.apache.spark.streaming.ui +import scala.collection.mutable.{Queue, HashMap} + import org.apache.spark.streaming.{Time, StreamingContext} import org.apache.spark.streaming.scheduler._ -import scala.collection.mutable.{Queue, HashMap} import org.apache.spark.streaming.scheduler.StreamingListenerReceiverStarted import org.apache.spark.streaming.scheduler.StreamingListenerBatchStarted import org.apache.spark.streaming.scheduler.BatchInfo @@ -59,11 +60,13 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) } } - override def onBatchSubmitted(batchSubmitted: StreamingListenerBatchSubmitted) = synchronized { - runningBatchInfos(batchSubmitted.batchInfo.batchTime) = batchSubmitted.batchInfo + override def onBatchSubmitted(batchSubmitted: StreamingListenerBatchSubmitted): Unit = { + synchronized { + runningBatchInfos(batchSubmitted.batchInfo.batchTime) = batchSubmitted.batchInfo + } } - override def onBatchStarted(batchStarted: StreamingListenerBatchStarted) = synchronized { + override def onBatchStarted(batchStarted: StreamingListenerBatchStarted): Unit = synchronized { runningBatchInfos(batchStarted.batchInfo.batchTime) = batchStarted.batchInfo waitingBatchInfos.remove(batchStarted.batchInfo.batchTime) @@ -72,19 +75,21 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) } } - override def onBatchCompleted(batchCompleted: StreamingListenerBatchCompleted) = synchronized { - waitingBatchInfos.remove(batchCompleted.batchInfo.batchTime) - runningBatchInfos.remove(batchCompleted.batchInfo.batchTime) - completedaBatchInfos.enqueue(batchCompleted.batchInfo) - if (completedaBatchInfos.size > batchInfoLimit) completedaBatchInfos.dequeue() - totalCompletedBatches += 1L - - batchCompleted.batchInfo.receivedBlockInfo.foreach { case (_, infos) => - totalProcessedRecords += infos.map(_.numRecords).sum + override def onBatchCompleted(batchCompleted: StreamingListenerBatchCompleted): Unit = { + synchronized { + waitingBatchInfos.remove(batchCompleted.batchInfo.batchTime) + runningBatchInfos.remove(batchCompleted.batchInfo.batchTime) + completedaBatchInfos.enqueue(batchCompleted.batchInfo) + if (completedaBatchInfos.size > batchInfoLimit) completedaBatchInfos.dequeue() + totalCompletedBatches += 1L + + batchCompleted.batchInfo.receivedBlockInfo.foreach { case (_, infos) => + totalProcessedRecords += infos.map(_.numRecords).sum + } } } - def numReceivers = synchronized { + def numReceivers: Int = synchronized { ssc.graph.getReceiverInputStreams().size } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextHelper.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextHelper.scala index a73d6f3bf0661..4d968f8bfa7a8 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextHelper.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextHelper.scala @@ -18,9 +18,7 @@ package org.apache.spark.streaming.util import org.apache.spark.SparkContext -import org.apache.spark.SparkContext._ import org.apache.spark.util.collection.OpenHashMap -import scala.collection.JavaConversions.mapAsScalaMap private[streaming] object RawTextHelper { @@ -71,7 +69,7 @@ object RawTextHelper { var count = 0 while(data.hasNext) { - value = data.next + value = data.next() if (value != null) { count += 1 if (len == 0) { @@ -108,9 +106,13 @@ object RawTextHelper { } } - def add(v1: Long, v2: Long) = (v1 + v2) + def add(v1: Long, v2: Long): Long = { + v1 + v2 + } - def subtract(v1: Long, v2: Long) = (v1 - v2) + def subtract(v1: Long, v2: Long): Long = { + v1 - v2 + } - def max(v1: Long, v2: Long) = math.max(v1, v2) + def max(v1: Long, v2: Long): Long = math.max(v1, v2) } From c14ddd97ed662a8429b9b9078bd7c1a5a1dd3d6c Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Tue, 24 Mar 2015 18:58:27 -0700 Subject: [PATCH 044/591] [SPARK-6515] update OpenHashSet impl Though I don't see any bug in the existing code, the update in this PR makes it read better. rxin Author: Xiangrui Meng Closes #5176 from mengxr/SPARK-6515 and squashes the following commits: 134494d [Xiangrui Meng] update OpenHashSet impl --- .../spark/util/collection/OpenHashSet.scala | 22 ++++++++----------- 1 file changed, 9 insertions(+), 13 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala b/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala index c80057f95e0b2..1501111a06655 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/OpenHashSet.scala @@ -122,7 +122,7 @@ class OpenHashSet[@specialized(Long, Int) T: ClassTag]( */ def addWithoutResize(k: T): Int = { var pos = hashcode(hasher.hash(k)) & _mask - var i = 1 + var delta = 1 while (true) { if (!_bitset.get(pos)) { // This is a new key. @@ -134,14 +134,12 @@ class OpenHashSet[@specialized(Long, Int) T: ClassTag]( // Found an existing key. return pos } else { - val delta = i + // quadratic probing with values increase by 1, 2, 3, ... pos = (pos + delta) & _mask - i += 1 + delta += 1 } } - // Never reached here - assert(INVALID_POS != INVALID_POS) - INVALID_POS + throw new RuntimeException("Should never reach here.") } /** @@ -163,21 +161,19 @@ class OpenHashSet[@specialized(Long, Int) T: ClassTag]( */ def getPos(k: T): Int = { var pos = hashcode(hasher.hash(k)) & _mask - var i = 1 - val maxProbe = _data.size - while (i < maxProbe) { + var delta = 1 + while (true) { if (!_bitset.get(pos)) { return INVALID_POS } else if (k == _data(pos)) { return pos } else { - val delta = i + // quadratic probing with values increase by 1, 2, 3, ... pos = (pos + delta) & _mask - i += 1 + delta += 1 } } - // Never reached here - INVALID_POS + throw new RuntimeException("Should never reach here.") } /** Return the value at the specified position. */ From c5cc41468e8709d09c09289bb55bc8edc99404b1 Mon Sep 17 00:00:00 2001 From: Bill Chambers Date: Tue, 24 Mar 2015 22:24:35 -0700 Subject: [PATCH 045/591] [DOCUMENTATION]Fixed Missing Type Import in Documentation Needed to import the types specifically, not the more general pyspark.sql Author: Bill Chambers Author: anabranch Closes #5179 from anabranch/master and squashes the following commits: 8fa67bf [anabranch] Corrected SqlContext Import 603b080 [Bill Chambers] [DOCUMENTATION]Fixed Missing Type Import in Documentation --- docs/sql-programming-guide.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index 6a333fdb562a7..c99a0b03442c4 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -624,7 +624,8 @@ tuples or lists in the RDD created in the step 1. For example: {% highlight python %} # Import SQLContext and data types -from pyspark.sql import * +from pyspark.sql import SQLContext +from pyspark.sql.types import * # sc is an existing SparkContext. sqlContext = SQLContext(sc) From 64262ed99912e780b51f240a14dc98fc3cdf916d Mon Sep 17 00:00:00 2001 From: zzcclp Date: Wed, 25 Mar 2015 19:11:04 +0800 Subject: [PATCH 046/591] [SPARK-6483][SQL]Improve ScalaUdf called performance. As issue [SPARK-6483](https://issues.apache.org/jira/browse/SPARK-6483) description, ScalaUdf is low performance because of calling *asInstanceOf* to convert per record. With this, the performance of ScalaUdf is the same as other case. thank lianhuiwang for telling me how to resolve this problem. Author: zzcclp Closes #5154 from zzcclp/SPARK-6483 and squashes the following commits: 5ac6e09 [zzcclp] Add a newline at the end of source file cc6868e [zzcclp] Fix for fail on unit test. 0a8cdc3 [zzcclp] indention issue b73836a [zzcclp] Access Seq[Expression] element by :: operator, and update the code gen script. 7763848 [zzcclp] rebase from master --- .../sql/catalyst/expressions/ScalaUdf.scala | 1016 +++++++++++------ 1 file changed, 661 insertions(+), 355 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala index 1fd5ce342b2ce..389dc4f745723 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala @@ -39,363 +39,669 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi (1 to 22).map { x => val anys = (1 to x).map(x => "Any").reduce(_ + ", " + _) - val evals = (0 to x - 1).map(x => s" ScalaReflection.convertToScala(children($x).eval(input), children($x).dataType)").reduce(_ + ",\n " + _) - - s""" - case $x => - function.asInstanceOf[($anys) => Any]( - $evals) - """ + val childs = (0 to x - 1).map(x => s"val child$x = children($x)").reduce(_ + "\n " + _) + val evals = (0 to x - 1).map(x => s"ScalaReflection.convertToScala(child$x.eval(input), child$x.dataType)").reduce(_ + ",\n " + _) + + s""" case $x => + val func = function.asInstanceOf[($anys) => Any] + $childs + (input: Row) => { + func( + $evals) + } + """ }.foreach(println) */ - - override def eval(input: Row): Any = { - val result = children.size match { - case 0 => function.asInstanceOf[() => Any]() - case 1 => - function.asInstanceOf[(Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType)) - - - case 2 => - function.asInstanceOf[(Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType)) - - - case 3 => - function.asInstanceOf[(Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType)) - - - case 4 => - function.asInstanceOf[(Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType)) - - - case 5 => - function.asInstanceOf[(Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType)) - - - case 6 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType)) - - - case 7 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType)) - - - case 8 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType)) - - - case 9 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType)) - - - case 10 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType)) - - - case 11 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType)) - - - case 12 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType)) - - - case 13 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType), - ScalaReflection.convertToScala(children(12).eval(input), children(12).dataType)) - - - case 14 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType), - ScalaReflection.convertToScala(children(12).eval(input), children(12).dataType), - ScalaReflection.convertToScala(children(13).eval(input), children(13).dataType)) - - - case 15 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType), - ScalaReflection.convertToScala(children(12).eval(input), children(12).dataType), - ScalaReflection.convertToScala(children(13).eval(input), children(13).dataType), - ScalaReflection.convertToScala(children(14).eval(input), children(14).dataType)) - - - case 16 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType), - ScalaReflection.convertToScala(children(12).eval(input), children(12).dataType), - ScalaReflection.convertToScala(children(13).eval(input), children(13).dataType), - ScalaReflection.convertToScala(children(14).eval(input), children(14).dataType), - ScalaReflection.convertToScala(children(15).eval(input), children(15).dataType)) - - - case 17 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType), - ScalaReflection.convertToScala(children(12).eval(input), children(12).dataType), - ScalaReflection.convertToScala(children(13).eval(input), children(13).dataType), - ScalaReflection.convertToScala(children(14).eval(input), children(14).dataType), - ScalaReflection.convertToScala(children(15).eval(input), children(15).dataType), - ScalaReflection.convertToScala(children(16).eval(input), children(16).dataType)) - - - case 18 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType), - ScalaReflection.convertToScala(children(12).eval(input), children(12).dataType), - ScalaReflection.convertToScala(children(13).eval(input), children(13).dataType), - ScalaReflection.convertToScala(children(14).eval(input), children(14).dataType), - ScalaReflection.convertToScala(children(15).eval(input), children(15).dataType), - ScalaReflection.convertToScala(children(16).eval(input), children(16).dataType), - ScalaReflection.convertToScala(children(17).eval(input), children(17).dataType)) - - - case 19 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType), - ScalaReflection.convertToScala(children(12).eval(input), children(12).dataType), - ScalaReflection.convertToScala(children(13).eval(input), children(13).dataType), - ScalaReflection.convertToScala(children(14).eval(input), children(14).dataType), - ScalaReflection.convertToScala(children(15).eval(input), children(15).dataType), - ScalaReflection.convertToScala(children(16).eval(input), children(16).dataType), - ScalaReflection.convertToScala(children(17).eval(input), children(17).dataType), - ScalaReflection.convertToScala(children(18).eval(input), children(18).dataType)) - - - case 20 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType), - ScalaReflection.convertToScala(children(12).eval(input), children(12).dataType), - ScalaReflection.convertToScala(children(13).eval(input), children(13).dataType), - ScalaReflection.convertToScala(children(14).eval(input), children(14).dataType), - ScalaReflection.convertToScala(children(15).eval(input), children(15).dataType), - ScalaReflection.convertToScala(children(16).eval(input), children(16).dataType), - ScalaReflection.convertToScala(children(17).eval(input), children(17).dataType), - ScalaReflection.convertToScala(children(18).eval(input), children(18).dataType), - ScalaReflection.convertToScala(children(19).eval(input), children(19).dataType)) - - - case 21 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType), - ScalaReflection.convertToScala(children(12).eval(input), children(12).dataType), - ScalaReflection.convertToScala(children(13).eval(input), children(13).dataType), - ScalaReflection.convertToScala(children(14).eval(input), children(14).dataType), - ScalaReflection.convertToScala(children(15).eval(input), children(15).dataType), - ScalaReflection.convertToScala(children(16).eval(input), children(16).dataType), - ScalaReflection.convertToScala(children(17).eval(input), children(17).dataType), - ScalaReflection.convertToScala(children(18).eval(input), children(18).dataType), - ScalaReflection.convertToScala(children(19).eval(input), children(19).dataType), - ScalaReflection.convertToScala(children(20).eval(input), children(20).dataType)) - - - case 22 => - function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any]( - ScalaReflection.convertToScala(children(0).eval(input), children(0).dataType), - ScalaReflection.convertToScala(children(1).eval(input), children(1).dataType), - ScalaReflection.convertToScala(children(2).eval(input), children(2).dataType), - ScalaReflection.convertToScala(children(3).eval(input), children(3).dataType), - ScalaReflection.convertToScala(children(4).eval(input), children(4).dataType), - ScalaReflection.convertToScala(children(5).eval(input), children(5).dataType), - ScalaReflection.convertToScala(children(6).eval(input), children(6).dataType), - ScalaReflection.convertToScala(children(7).eval(input), children(7).dataType), - ScalaReflection.convertToScala(children(8).eval(input), children(8).dataType), - ScalaReflection.convertToScala(children(9).eval(input), children(9).dataType), - ScalaReflection.convertToScala(children(10).eval(input), children(10).dataType), - ScalaReflection.convertToScala(children(11).eval(input), children(11).dataType), - ScalaReflection.convertToScala(children(12).eval(input), children(12).dataType), - ScalaReflection.convertToScala(children(13).eval(input), children(13).dataType), - ScalaReflection.convertToScala(children(14).eval(input), children(14).dataType), - ScalaReflection.convertToScala(children(15).eval(input), children(15).dataType), - ScalaReflection.convertToScala(children(16).eval(input), children(16).dataType), - ScalaReflection.convertToScala(children(17).eval(input), children(17).dataType), - ScalaReflection.convertToScala(children(18).eval(input), children(18).dataType), - ScalaReflection.convertToScala(children(19).eval(input), children(19).dataType), - ScalaReflection.convertToScala(children(20).eval(input), children(20).dataType), - ScalaReflection.convertToScala(children(21).eval(input), children(21).dataType)) - - } - // scalastyle:on - - ScalaReflection.convertToCatalyst(result, dataType) + + val f = children.size match { + case 0 => + val func = function.asInstanceOf[() => Any] + (input: Row) => { + func() + } + + case 1 => + val func = function.asInstanceOf[(Any) => Any] + val child0 = children(0) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType)) + } + + case 2 => + val func = function.asInstanceOf[(Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType)) + } + + case 3 => + val func = function.asInstanceOf[(Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType), + ScalaReflection.convertToScala(child2.eval(input), child2.dataType)) + } + + case 4 => + val func = function.asInstanceOf[(Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType), + ScalaReflection.convertToScala(child2.eval(input), child2.dataType), + ScalaReflection.convertToScala(child3.eval(input), child3.dataType)) + } + + case 5 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType), + ScalaReflection.convertToScala(child2.eval(input), child2.dataType), + ScalaReflection.convertToScala(child3.eval(input), child3.dataType), + ScalaReflection.convertToScala(child4.eval(input), child4.dataType)) + } + + case 6 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType), + ScalaReflection.convertToScala(child2.eval(input), child2.dataType), + ScalaReflection.convertToScala(child3.eval(input), child3.dataType), + ScalaReflection.convertToScala(child4.eval(input), child4.dataType), + ScalaReflection.convertToScala(child5.eval(input), child5.dataType)) + } + + case 7 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType), + ScalaReflection.convertToScala(child2.eval(input), child2.dataType), + ScalaReflection.convertToScala(child3.eval(input), child3.dataType), + ScalaReflection.convertToScala(child4.eval(input), child4.dataType), + ScalaReflection.convertToScala(child5.eval(input), child5.dataType), + ScalaReflection.convertToScala(child6.eval(input), child6.dataType)) + } + + case 8 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType), + ScalaReflection.convertToScala(child2.eval(input), child2.dataType), + ScalaReflection.convertToScala(child3.eval(input), child3.dataType), + ScalaReflection.convertToScala(child4.eval(input), child4.dataType), + ScalaReflection.convertToScala(child5.eval(input), child5.dataType), + ScalaReflection.convertToScala(child6.eval(input), child6.dataType), + ScalaReflection.convertToScala(child7.eval(input), child7.dataType)) + } + + case 9 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType), + ScalaReflection.convertToScala(child2.eval(input), child2.dataType), + ScalaReflection.convertToScala(child3.eval(input), child3.dataType), + ScalaReflection.convertToScala(child4.eval(input), child4.dataType), + ScalaReflection.convertToScala(child5.eval(input), child5.dataType), + ScalaReflection.convertToScala(child6.eval(input), child6.dataType), + ScalaReflection.convertToScala(child7.eval(input), child7.dataType), + ScalaReflection.convertToScala(child8.eval(input), child8.dataType)) + } + + case 10 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType), + ScalaReflection.convertToScala(child2.eval(input), child2.dataType), + ScalaReflection.convertToScala(child3.eval(input), child3.dataType), + ScalaReflection.convertToScala(child4.eval(input), child4.dataType), + ScalaReflection.convertToScala(child5.eval(input), child5.dataType), + ScalaReflection.convertToScala(child6.eval(input), child6.dataType), + ScalaReflection.convertToScala(child7.eval(input), child7.dataType), + ScalaReflection.convertToScala(child8.eval(input), child8.dataType), + ScalaReflection.convertToScala(child9.eval(input), child9.dataType)) + } + + case 11 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType), + ScalaReflection.convertToScala(child2.eval(input), child2.dataType), + ScalaReflection.convertToScala(child3.eval(input), child3.dataType), + ScalaReflection.convertToScala(child4.eval(input), child4.dataType), + ScalaReflection.convertToScala(child5.eval(input), child5.dataType), + ScalaReflection.convertToScala(child6.eval(input), child6.dataType), + ScalaReflection.convertToScala(child7.eval(input), child7.dataType), + ScalaReflection.convertToScala(child8.eval(input), child8.dataType), + ScalaReflection.convertToScala(child9.eval(input), child9.dataType), + ScalaReflection.convertToScala(child10.eval(input), child10.dataType)) + } + + case 12 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType), + ScalaReflection.convertToScala(child2.eval(input), child2.dataType), + ScalaReflection.convertToScala(child3.eval(input), child3.dataType), + ScalaReflection.convertToScala(child4.eval(input), child4.dataType), + ScalaReflection.convertToScala(child5.eval(input), child5.dataType), + ScalaReflection.convertToScala(child6.eval(input), child6.dataType), + ScalaReflection.convertToScala(child7.eval(input), child7.dataType), + ScalaReflection.convertToScala(child8.eval(input), child8.dataType), + ScalaReflection.convertToScala(child9.eval(input), child9.dataType), + ScalaReflection.convertToScala(child10.eval(input), child10.dataType), + ScalaReflection.convertToScala(child11.eval(input), child11.dataType)) + } + + case 13 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + val child12 = children(12) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType), + ScalaReflection.convertToScala(child2.eval(input), child2.dataType), + ScalaReflection.convertToScala(child3.eval(input), child3.dataType), + ScalaReflection.convertToScala(child4.eval(input), child4.dataType), + ScalaReflection.convertToScala(child5.eval(input), child5.dataType), + ScalaReflection.convertToScala(child6.eval(input), child6.dataType), + ScalaReflection.convertToScala(child7.eval(input), child7.dataType), + ScalaReflection.convertToScala(child8.eval(input), child8.dataType), + ScalaReflection.convertToScala(child9.eval(input), child9.dataType), + ScalaReflection.convertToScala(child10.eval(input), child10.dataType), + ScalaReflection.convertToScala(child11.eval(input), child11.dataType), + ScalaReflection.convertToScala(child12.eval(input), child12.dataType)) + } + + case 14 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + val child12 = children(12) + val child13 = children(13) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType), + ScalaReflection.convertToScala(child2.eval(input), child2.dataType), + ScalaReflection.convertToScala(child3.eval(input), child3.dataType), + ScalaReflection.convertToScala(child4.eval(input), child4.dataType), + ScalaReflection.convertToScala(child5.eval(input), child5.dataType), + ScalaReflection.convertToScala(child6.eval(input), child6.dataType), + ScalaReflection.convertToScala(child7.eval(input), child7.dataType), + ScalaReflection.convertToScala(child8.eval(input), child8.dataType), + ScalaReflection.convertToScala(child9.eval(input), child9.dataType), + ScalaReflection.convertToScala(child10.eval(input), child10.dataType), + ScalaReflection.convertToScala(child11.eval(input), child11.dataType), + ScalaReflection.convertToScala(child12.eval(input), child12.dataType), + ScalaReflection.convertToScala(child13.eval(input), child13.dataType)) + } + + case 15 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + val child12 = children(12) + val child13 = children(13) + val child14 = children(14) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType), + ScalaReflection.convertToScala(child2.eval(input), child2.dataType), + ScalaReflection.convertToScala(child3.eval(input), child3.dataType), + ScalaReflection.convertToScala(child4.eval(input), child4.dataType), + ScalaReflection.convertToScala(child5.eval(input), child5.dataType), + ScalaReflection.convertToScala(child6.eval(input), child6.dataType), + ScalaReflection.convertToScala(child7.eval(input), child7.dataType), + ScalaReflection.convertToScala(child8.eval(input), child8.dataType), + ScalaReflection.convertToScala(child9.eval(input), child9.dataType), + ScalaReflection.convertToScala(child10.eval(input), child10.dataType), + ScalaReflection.convertToScala(child11.eval(input), child11.dataType), + ScalaReflection.convertToScala(child12.eval(input), child12.dataType), + ScalaReflection.convertToScala(child13.eval(input), child13.dataType), + ScalaReflection.convertToScala(child14.eval(input), child14.dataType)) + } + + case 16 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + val child12 = children(12) + val child13 = children(13) + val child14 = children(14) + val child15 = children(15) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType), + ScalaReflection.convertToScala(child2.eval(input), child2.dataType), + ScalaReflection.convertToScala(child3.eval(input), child3.dataType), + ScalaReflection.convertToScala(child4.eval(input), child4.dataType), + ScalaReflection.convertToScala(child5.eval(input), child5.dataType), + ScalaReflection.convertToScala(child6.eval(input), child6.dataType), + ScalaReflection.convertToScala(child7.eval(input), child7.dataType), + ScalaReflection.convertToScala(child8.eval(input), child8.dataType), + ScalaReflection.convertToScala(child9.eval(input), child9.dataType), + ScalaReflection.convertToScala(child10.eval(input), child10.dataType), + ScalaReflection.convertToScala(child11.eval(input), child11.dataType), + ScalaReflection.convertToScala(child12.eval(input), child12.dataType), + ScalaReflection.convertToScala(child13.eval(input), child13.dataType), + ScalaReflection.convertToScala(child14.eval(input), child14.dataType), + ScalaReflection.convertToScala(child15.eval(input), child15.dataType)) + } + + case 17 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + val child12 = children(12) + val child13 = children(13) + val child14 = children(14) + val child15 = children(15) + val child16 = children(16) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType), + ScalaReflection.convertToScala(child2.eval(input), child2.dataType), + ScalaReflection.convertToScala(child3.eval(input), child3.dataType), + ScalaReflection.convertToScala(child4.eval(input), child4.dataType), + ScalaReflection.convertToScala(child5.eval(input), child5.dataType), + ScalaReflection.convertToScala(child6.eval(input), child6.dataType), + ScalaReflection.convertToScala(child7.eval(input), child7.dataType), + ScalaReflection.convertToScala(child8.eval(input), child8.dataType), + ScalaReflection.convertToScala(child9.eval(input), child9.dataType), + ScalaReflection.convertToScala(child10.eval(input), child10.dataType), + ScalaReflection.convertToScala(child11.eval(input), child11.dataType), + ScalaReflection.convertToScala(child12.eval(input), child12.dataType), + ScalaReflection.convertToScala(child13.eval(input), child13.dataType), + ScalaReflection.convertToScala(child14.eval(input), child14.dataType), + ScalaReflection.convertToScala(child15.eval(input), child15.dataType), + ScalaReflection.convertToScala(child16.eval(input), child16.dataType)) + } + + case 18 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + val child12 = children(12) + val child13 = children(13) + val child14 = children(14) + val child15 = children(15) + val child16 = children(16) + val child17 = children(17) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType), + ScalaReflection.convertToScala(child2.eval(input), child2.dataType), + ScalaReflection.convertToScala(child3.eval(input), child3.dataType), + ScalaReflection.convertToScala(child4.eval(input), child4.dataType), + ScalaReflection.convertToScala(child5.eval(input), child5.dataType), + ScalaReflection.convertToScala(child6.eval(input), child6.dataType), + ScalaReflection.convertToScala(child7.eval(input), child7.dataType), + ScalaReflection.convertToScala(child8.eval(input), child8.dataType), + ScalaReflection.convertToScala(child9.eval(input), child9.dataType), + ScalaReflection.convertToScala(child10.eval(input), child10.dataType), + ScalaReflection.convertToScala(child11.eval(input), child11.dataType), + ScalaReflection.convertToScala(child12.eval(input), child12.dataType), + ScalaReflection.convertToScala(child13.eval(input), child13.dataType), + ScalaReflection.convertToScala(child14.eval(input), child14.dataType), + ScalaReflection.convertToScala(child15.eval(input), child15.dataType), + ScalaReflection.convertToScala(child16.eval(input), child16.dataType), + ScalaReflection.convertToScala(child17.eval(input), child17.dataType)) + } + + case 19 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + val child12 = children(12) + val child13 = children(13) + val child14 = children(14) + val child15 = children(15) + val child16 = children(16) + val child17 = children(17) + val child18 = children(18) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType), + ScalaReflection.convertToScala(child2.eval(input), child2.dataType), + ScalaReflection.convertToScala(child3.eval(input), child3.dataType), + ScalaReflection.convertToScala(child4.eval(input), child4.dataType), + ScalaReflection.convertToScala(child5.eval(input), child5.dataType), + ScalaReflection.convertToScala(child6.eval(input), child6.dataType), + ScalaReflection.convertToScala(child7.eval(input), child7.dataType), + ScalaReflection.convertToScala(child8.eval(input), child8.dataType), + ScalaReflection.convertToScala(child9.eval(input), child9.dataType), + ScalaReflection.convertToScala(child10.eval(input), child10.dataType), + ScalaReflection.convertToScala(child11.eval(input), child11.dataType), + ScalaReflection.convertToScala(child12.eval(input), child12.dataType), + ScalaReflection.convertToScala(child13.eval(input), child13.dataType), + ScalaReflection.convertToScala(child14.eval(input), child14.dataType), + ScalaReflection.convertToScala(child15.eval(input), child15.dataType), + ScalaReflection.convertToScala(child16.eval(input), child16.dataType), + ScalaReflection.convertToScala(child17.eval(input), child17.dataType), + ScalaReflection.convertToScala(child18.eval(input), child18.dataType)) + } + + case 20 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + val child12 = children(12) + val child13 = children(13) + val child14 = children(14) + val child15 = children(15) + val child16 = children(16) + val child17 = children(17) + val child18 = children(18) + val child19 = children(19) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType), + ScalaReflection.convertToScala(child2.eval(input), child2.dataType), + ScalaReflection.convertToScala(child3.eval(input), child3.dataType), + ScalaReflection.convertToScala(child4.eval(input), child4.dataType), + ScalaReflection.convertToScala(child5.eval(input), child5.dataType), + ScalaReflection.convertToScala(child6.eval(input), child6.dataType), + ScalaReflection.convertToScala(child7.eval(input), child7.dataType), + ScalaReflection.convertToScala(child8.eval(input), child8.dataType), + ScalaReflection.convertToScala(child9.eval(input), child9.dataType), + ScalaReflection.convertToScala(child10.eval(input), child10.dataType), + ScalaReflection.convertToScala(child11.eval(input), child11.dataType), + ScalaReflection.convertToScala(child12.eval(input), child12.dataType), + ScalaReflection.convertToScala(child13.eval(input), child13.dataType), + ScalaReflection.convertToScala(child14.eval(input), child14.dataType), + ScalaReflection.convertToScala(child15.eval(input), child15.dataType), + ScalaReflection.convertToScala(child16.eval(input), child16.dataType), + ScalaReflection.convertToScala(child17.eval(input), child17.dataType), + ScalaReflection.convertToScala(child18.eval(input), child18.dataType), + ScalaReflection.convertToScala(child19.eval(input), child19.dataType)) + } + + case 21 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + val child12 = children(12) + val child13 = children(13) + val child14 = children(14) + val child15 = children(15) + val child16 = children(16) + val child17 = children(17) + val child18 = children(18) + val child19 = children(19) + val child20 = children(20) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType), + ScalaReflection.convertToScala(child2.eval(input), child2.dataType), + ScalaReflection.convertToScala(child3.eval(input), child3.dataType), + ScalaReflection.convertToScala(child4.eval(input), child4.dataType), + ScalaReflection.convertToScala(child5.eval(input), child5.dataType), + ScalaReflection.convertToScala(child6.eval(input), child6.dataType), + ScalaReflection.convertToScala(child7.eval(input), child7.dataType), + ScalaReflection.convertToScala(child8.eval(input), child8.dataType), + ScalaReflection.convertToScala(child9.eval(input), child9.dataType), + ScalaReflection.convertToScala(child10.eval(input), child10.dataType), + ScalaReflection.convertToScala(child11.eval(input), child11.dataType), + ScalaReflection.convertToScala(child12.eval(input), child12.dataType), + ScalaReflection.convertToScala(child13.eval(input), child13.dataType), + ScalaReflection.convertToScala(child14.eval(input), child14.dataType), + ScalaReflection.convertToScala(child15.eval(input), child15.dataType), + ScalaReflection.convertToScala(child16.eval(input), child16.dataType), + ScalaReflection.convertToScala(child17.eval(input), child17.dataType), + ScalaReflection.convertToScala(child18.eval(input), child18.dataType), + ScalaReflection.convertToScala(child19.eval(input), child19.dataType), + ScalaReflection.convertToScala(child20.eval(input), child20.dataType)) + } + + case 22 => + val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] + val child0 = children(0) + val child1 = children(1) + val child2 = children(2) + val child3 = children(3) + val child4 = children(4) + val child5 = children(5) + val child6 = children(6) + val child7 = children(7) + val child8 = children(8) + val child9 = children(9) + val child10 = children(10) + val child11 = children(11) + val child12 = children(12) + val child13 = children(13) + val child14 = children(14) + val child15 = children(15) + val child16 = children(16) + val child17 = children(17) + val child18 = children(18) + val child19 = children(19) + val child20 = children(20) + val child21 = children(21) + (input: Row) => { + func( + ScalaReflection.convertToScala(child0.eval(input), child0.dataType), + ScalaReflection.convertToScala(child1.eval(input), child1.dataType), + ScalaReflection.convertToScala(child2.eval(input), child2.dataType), + ScalaReflection.convertToScala(child3.eval(input), child3.dataType), + ScalaReflection.convertToScala(child4.eval(input), child4.dataType), + ScalaReflection.convertToScala(child5.eval(input), child5.dataType), + ScalaReflection.convertToScala(child6.eval(input), child6.dataType), + ScalaReflection.convertToScala(child7.eval(input), child7.dataType), + ScalaReflection.convertToScala(child8.eval(input), child8.dataType), + ScalaReflection.convertToScala(child9.eval(input), child9.dataType), + ScalaReflection.convertToScala(child10.eval(input), child10.dataType), + ScalaReflection.convertToScala(child11.eval(input), child11.dataType), + ScalaReflection.convertToScala(child12.eval(input), child12.dataType), + ScalaReflection.convertToScala(child13.eval(input), child13.dataType), + ScalaReflection.convertToScala(child14.eval(input), child14.dataType), + ScalaReflection.convertToScala(child15.eval(input), child15.dataType), + ScalaReflection.convertToScala(child16.eval(input), child16.dataType), + ScalaReflection.convertToScala(child17.eval(input), child17.dataType), + ScalaReflection.convertToScala(child18.eval(input), child18.dataType), + ScalaReflection.convertToScala(child19.eval(input), child19.dataType), + ScalaReflection.convertToScala(child20.eval(input), child20.dataType), + ScalaReflection.convertToScala(child21.eval(input), child21.dataType)) + } } + + // scalastyle:on + + override def eval(input: Row): Any = ScalaReflection.convertToCatalyst(f(input), dataType) + } From 10c78607b2724f5a64b0cdb966e9c5805f23919b Mon Sep 17 00:00:00 2001 From: Yanbo Liang Date: Wed, 25 Mar 2015 17:05:56 +0000 Subject: [PATCH 047/591] [SPARK-6496] [MLLIB] GeneralizedLinearAlgorithm.run(input, initialWeights) should initialize numFeatures In GeneralizedLinearAlgorithm ```numFeatures``` is default to -1, we need to update it to correct value when we call run() to train a model. ```LogisticRegressionWithLBFGS.run(input)``` works well, but when we call ```LogisticRegressionWithLBFGS.run(input, initialWeights)``` to train multiclass classification model, it will throw exception due to the numFeatures is not updated. In this PR, we just update numFeatures at the beginning of GeneralizedLinearAlgorithm.run(input, initialWeights) and add test case. Author: Yanbo Liang Closes #5167 from yanboliang/spark-6496 and squashes the following commits: 8131c48 [Yanbo Liang] LogisticRegressionWithLBFGS.run(input, initialWeights) should initialize numFeatures --- .../spark/mllib/regression/GeneralizedLinearAlgorithm.scala | 4 ++++ .../mllib/classification/LogisticRegressionSuite.scala | 6 ++++++ 2 files changed, 10 insertions(+) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala index 45b9ebb4cc0d6..9fd60ff7a0c79 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala @@ -211,6 +211,10 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] */ def run(input: RDD[LabeledPoint], initialWeights: Vector): M = { + if (numFeatures < 0) { + numFeatures = input.map(_.features.size).first() + } + if (input.getStorageLevel == StorageLevel.NONE) { logWarning("The input data is not directly cached, which may hurt performance if its" + " parent RDDs are also uncached.") diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala index aaa81da9e273c..a26c52852c4d7 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala @@ -425,6 +425,12 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext with M val model = lr.run(testRDD) + val numFeatures = testRDD.map(_.features.size).first() + val initialWeights = Vectors.dense(new Array[Double]((numFeatures + 1) * 2)) + val model2 = lr.run(testRDD, initialWeights) + + LogisticRegressionSuite.checkModelsEqual(model, model2) + /** * The following is the instruction to reproduce the model using R's glmnet package. * From 982952f4aebb474823dd886dd2b18f4277bd7c30 Mon Sep 17 00:00:00 2001 From: Augustin Borsu Date: Wed, 25 Mar 2015 10:16:39 -0700 Subject: [PATCH 048/591] [ML][FEATURE] SPARK-5566: RegEx Tokenizer Added a Regex based tokenizer for ml. Currently the regex is fixed but if I could add a regex type paramater to the paramMap, changing the tokenizer regex could be a parameter used in the crossValidation. Also I wonder what would be the best way to add a stop word list. Author: Augustin Borsu Author: Augustin Borsu Author: Augustin Borsu Author: Xiangrui Meng Closes #4504 from aborsu985/master and squashes the following commits: 716d257 [Augustin Borsu] Merge branch 'mengxr-SPARK-5566' cb07021 [Augustin Borsu] Merge branch 'SPARK-5566' of git://github.com/mengxr/spark into mengxr-SPARK-5566 5f09434 [Augustin Borsu] Merge remote-tracking branch 'upstream/master' a164800 [Xiangrui Meng] remove tabs 556aa27 [Xiangrui Meng] Merge branch 'aborsu985-master' into SPARK-5566 9651aec [Xiangrui Meng] update test f96526d [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-5566 2338da5 [Augustin Borsu] Merge remote-tracking branch 'upstream/master' e88d7b8 [Xiangrui Meng] change pattern to a StringParameter; update tests 148126f [Augustin Borsu] Added return type to public functions 12dddb4 [Augustin Borsu] Merge remote-tracking branch 'upstream/master' daf685e [Augustin Borsu] Merge remote-tracking branch 'upstream/master' 6a85982 [Augustin Borsu] Style corrections 38b95a1 [Augustin Borsu] Added Java unit test for RegexTokenizer b66313f [Augustin Borsu] Modified the pattern Param so it is compiled when given to the Tokenizer e262bac [Augustin Borsu] Added unit tests in scala cd6642e [Augustin Borsu] Changed regex to pattern 132b00b [Augustin Borsu] Changed matching to gaps and removed case folding 201a107 [Augustin Borsu] Merge remote-tracking branch 'upstream/master' cb9c9a7 [Augustin Borsu] Merge remote-tracking branch 'upstream/master' d3ef6d3 [Augustin Borsu] Added doc to RegexTokenizer 9082fc3 [Augustin Borsu] Removed stopwords parameters and updated doc 19f9e53 [Augustin Borsu] Merge remote-tracking branch 'upstream/master' f6a5002 [Augustin Borsu] Merge remote-tracking branch 'upstream/master' 7f930bb [Augustin Borsu] Merge remote-tracking branch 'upstream/master' 77ff9ca [Augustin Borsu] Merge remote-tracking branch 'upstream/master' 2e89719 [Augustin Borsu] Merge remote-tracking branch 'upstream/master' 196cd7a [Augustin Borsu] Merge remote-tracking branch 'upstream/master' 11ca50f [Augustin Borsu] Merge remote-tracking branch 'upstream/master' 9f8685a [Augustin Borsu] RegexTokenizer 9e07a78 [Augustin Borsu] Merge remote-tracking branch 'upstream/master' 9547e9d [Augustin Borsu] RegEx Tokenizer 01cd26f [Augustin Borsu] RegExTokenizer --- .../apache/spark/ml/feature/Tokenizer.scala | 66 +++++++++++++- .../spark/ml/feature/JavaTokenizerSuite.java | 71 ++++++++++++++++ .../spark/ml/feature/TokenizerSuite.scala | 85 +++++++++++++++++++ 3 files changed, 221 insertions(+), 1 deletion(-) create mode 100644 mllib/src/test/java/org/apache/spark/ml/feature/JavaTokenizerSuite.java create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/TokenizerSuite.scala diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala index 0b1f90daa7d8e..68401e36950bd 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala @@ -19,7 +19,7 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.UnaryTransformer -import org.apache.spark.ml.param.ParamMap +import org.apache.spark.ml.param.{ParamMap, IntParam, BooleanParam, Param} import org.apache.spark.sql.types.{DataType, StringType, ArrayType} /** @@ -39,3 +39,67 @@ class Tokenizer extends UnaryTransformer[String, Seq[String], Tokenizer] { override protected def outputDataType: DataType = new ArrayType(StringType, false) } + +/** + * :: AlphaComponent :: + * A regex based tokenizer that extracts tokens either by repeatedly matching the regex(default) + * or using it to split the text (set matching to false). Optional parameters also allow to fold + * the text to lowercase prior to it being tokenized and to filer tokens using a minimal length. + * It returns an array of strings that can be empty. + * The default parameters are regex = "\\p{L}+|[^\\p{L}\\s]+", matching = true, + * lowercase = false, minTokenLength = 1 + */ +@AlphaComponent +class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenizer] { + + /** + * param for minimum token length, default is one to avoid returning empty strings + * @group param + */ + val minTokenLength: IntParam = new IntParam(this, "minLength", "minimum token length", Some(1)) + + /** @group setParam */ + def setMinTokenLength(value: Int): this.type = set(minTokenLength, value) + + /** @group getParam */ + def getMinTokenLength: Int = get(minTokenLength) + + /** + * param sets regex as splitting on gaps (true) or matching tokens (false) + * @group param + */ + val gaps: BooleanParam = new BooleanParam( + this, "gaps", "Set regex to match gaps or tokens", Some(false)) + + /** @group setParam */ + def setGaps(value: Boolean): this.type = set(gaps, value) + + /** @group getParam */ + def getGaps: Boolean = get(gaps) + + /** + * param sets regex pattern used by tokenizer + * @group param + */ + val pattern: Param[String] = new Param( + this, "pattern", "regex pattern used for tokenizing", Some("\\p{L}+|[^\\p{L}\\s]+")) + + /** @group setParam */ + def setPattern(value: String): this.type = set(pattern, value) + + /** @group getParam */ + def getPattern: String = get(pattern) + + override protected def createTransformFunc(paramMap: ParamMap): String => Seq[String] = { str => + val re = paramMap(pattern).r + val tokens = if (paramMap(gaps)) re.split(str).toSeq else re.findAllIn(str).toSeq + val minLength = paramMap(minTokenLength) + tokens.filter(_.length >= minLength) + } + + override protected def validateInputType(inputType: DataType): Unit = { + require(inputType == StringType, s"Input type must be string type but got $inputType.") + } + + override protected def outputDataType: DataType = new ArrayType(StringType, false) +} diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaTokenizerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaTokenizerSuite.java new file mode 100644 index 0000000000000..3806f650025b2 --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaTokenizerSuite.java @@ -0,0 +1,71 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature; + +import com.google.common.collect.Lists; +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.SQLContext; + +public class JavaTokenizerSuite { + private transient JavaSparkContext jsc; + private transient SQLContext jsql; + + @Before + public void setUp() { + jsc = new JavaSparkContext("local", "JavaTokenizerSuite"); + jsql = new SQLContext(jsc); + } + + @After + public void tearDown() { + jsc.stop(); + jsc = null; + } + + @Test + public void regexTokenizer() { + RegexTokenizer myRegExTokenizer = new RegexTokenizer() + .setInputCol("rawText") + .setOutputCol("tokens") + .setPattern("\\s") + .setGaps(true) + .setMinTokenLength(3); + + JavaRDD rdd = jsc.parallelize(Lists.newArrayList( + new TokenizerTestData("Test of tok.", new String[] {"Test", "tok."}), + new TokenizerTestData("Te,st. punct", new String[] {"Te,st.", "punct"}) + )); + DataFrame dataset = jsql.createDataFrame(rdd, TokenizerTestData.class); + + Row[] pairs = myRegExTokenizer.transform(dataset) + .select("tokens", "wantedTokens") + .collect(); + + for (Row r : pairs) { + Assert.assertEquals(r.get(0), r.get(1)); + } + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/TokenizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/TokenizerSuite.scala new file mode 100644 index 0000000000000..bf862b912d326 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/TokenizerSuite.scala @@ -0,0 +1,85 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import scala.beans.BeanInfo + +import org.scalatest.FunSuite + +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.sql.{DataFrame, Row, SQLContext} + +@BeanInfo +case class TokenizerTestData(rawText: String, wantedTokens: Seq[String]) { + /** Constructor used in [[org.apache.spark.ml.feature.JavaTokenizerSuite]] */ + def this(rawText: String, wantedTokens: Array[String]) = this(rawText, wantedTokens.toSeq) +} + +class RegexTokenizerSuite extends FunSuite with MLlibTestSparkContext { + import org.apache.spark.ml.feature.RegexTokenizerSuite._ + + @transient var sqlContext: SQLContext = _ + + override def beforeAll(): Unit = { + super.beforeAll() + sqlContext = new SQLContext(sc) + } + + test("RegexTokenizer") { + val tokenizer = new RegexTokenizer() + .setInputCol("rawText") + .setOutputCol("tokens") + + val dataset0 = sqlContext.createDataFrame(Seq( + TokenizerTestData("Test for tokenization.", Seq("Test", "for", "tokenization", ".")), + TokenizerTestData("Te,st. punct", Seq("Te", ",", "st", ".", "punct")) + )) + testRegexTokenizer(tokenizer, dataset0) + + val dataset1 = sqlContext.createDataFrame(Seq( + TokenizerTestData("Test for tokenization.", Seq("Test", "for", "tokenization")), + TokenizerTestData("Te,st. punct", Seq("punct")) + )) + + tokenizer.setMinTokenLength(3) + testRegexTokenizer(tokenizer, dataset1) + + tokenizer + .setPattern("\\s") + .setGaps(true) + .setMinTokenLength(0) + val dataset2 = sqlContext.createDataFrame(Seq( + TokenizerTestData("Test for tokenization.", Seq("Test", "for", "tokenization.")), + TokenizerTestData("Te,st. punct", Seq("Te,st.", "", "punct")) + )) + testRegexTokenizer(tokenizer, dataset2) + } +} + +object RegexTokenizerSuite extends FunSuite { + + def testRegexTokenizer(t: RegexTokenizer, dataset: DataFrame): Unit = { + t.transform(dataset) + .select("tokens", "wantedTokens") + .collect() + .foreach { + case Row(tokens, wantedTokens) => + assert(tokens === wantedTokens) + } + } +} From 968408b345a0e26f7ee9105a6a0c3456cf10576a Mon Sep 17 00:00:00 2001 From: DoingDone9 <799203320@qq.com> Date: Wed, 25 Mar 2015 11:11:52 -0700 Subject: [PATCH 049/591] [SPARK-6409][SQL] It is not necessary that avoid old inteface of hive, because this will make some UDAF can not work. spark avoid old inteface of hive, then some udaf can not work like "org.apache.hadoop.hive.ql.udf.generic.GenericUDAFAverage" Author: DoingDone9 <799203320@qq.com> Closes #5131 from DoingDone9/udaf and squashes the following commits: 9de08d0 [DoingDone9] Update HiveUdfSuite.scala 49c62dc [DoingDone9] Update hiveUdfs.scala 98b134f [DoingDone9] Merge pull request #5 from apache/master 161cae3 [DoingDone9] Merge pull request #4 from apache/master c87e8b6 [DoingDone9] Merge pull request #3 from apache/master cb1852d [DoingDone9] Merge pull request #2 from apache/master c3f046f [DoingDone9] Merge pull request #1 from apache/master --- .../scala/org/apache/spark/sql/hive/hiveUdfs.scala | 3 +-- .../spark/sql/hive/execution/HiveUdfSuite.scala | 11 ++++++++++- 2 files changed, 11 insertions(+), 3 deletions(-) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala index bfe43373d9534..47305571e579e 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala @@ -375,9 +375,8 @@ private[hive] case class HiveUdafFunction( private val returnInspector = function.init(GenericUDAFEvaluator.Mode.COMPLETE, inspectors) - // Cast required to avoid type inference selecting a deprecated Hive API. private val buffer = - function.getNewAggregationBuffer.asInstanceOf[GenericUDAFEvaluator.AbstractAggregationBuffer] + function.getNewAggregationBuffer override def eval(input: Row): Any = unwrap(function.evaluate(buffer), returnInspector) diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala index cb405f56bf53d..d7c5d1a25a82b 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala @@ -22,7 +22,7 @@ import java.util import java.util.Properties import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.hive.ql.udf.generic.GenericUDF +import org.apache.hadoop.hive.ql.udf.generic.{GenericUDAFAverage, GenericUDF} import org.apache.hadoop.hive.ql.udf.generic.GenericUDF.DeferredObject import org.apache.hadoop.hive.serde2.objectinspector.primitive.PrimitiveObjectInspectorFactory import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspector, ObjectInspectorFactory} @@ -93,6 +93,15 @@ class HiveUdfSuite extends QueryTest { sql("DROP TEMPORARY FUNCTION IF EXISTS testUdf") } + test("SPARK-6409 UDAFAverage test") { + sql(s"CREATE TEMPORARY FUNCTION test_avg AS '${classOf[GenericUDAFAverage].getName}'") + checkAnswer( + sql("SELECT test_avg(1), test_avg(substr(value,5)) FROM src"), + Seq(Row(1.0, 260.182))) + sql("DROP TEMPORARY FUNCTION IF EXISTS test_avg") + TestHive.reset() + } + test("SPARK-2693 udaf aggregates test") { checkAnswer(sql("SELECT percentile(key, 1) FROM src LIMIT 1"), sql("SELECT max(key) FROM src").collect().toSeq) From 883b7e9030e1a3948acee17608e51dcd9f4d55e1 Mon Sep 17 00:00:00 2001 From: zsxwing Date: Wed, 25 Mar 2015 12:09:30 -0700 Subject: [PATCH 050/591] [SPARK-6076][Block Manager] Fix a potential OOM issue when StorageLevel is MEMORY_AND_DISK_SER In https://github.com/apache/spark/blob/dcd1e42d6b6ac08d2c0736bf61a15f515a1f222b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala#L538 , when StorageLevel is `MEMORY_AND_DISK_SER`, it will copy the content from file into memory, then put it into MemoryStore. ```scala val copyForMemory = ByteBuffer.allocate(bytes.limit) copyForMemory.put(bytes) memoryStore.putBytes(blockId, copyForMemory, level) bytes.rewind() ``` However, if the file is bigger than the free memory, OOM will happen. A better approach is testing if there is enough memory. If not, copyForMemory should not be created, since this is an optional operation. Author: zsxwing Closes #4827 from zsxwing/SPARK-6076 and squashes the following commits: 7d25545 [zsxwing] Add alias for tryToPut and dropFromMemory 1100a54 [zsxwing] Replace call-by-name with () => T 0cc0257 [zsxwing] Fix a potential OOM issue when StorageLevel is MEMORY_AND_DISK_SER --- .../apache/spark/storage/BlockManager.scala | 23 +++++++--- .../apache/spark/storage/MemoryStore.scala | 43 ++++++++++++++++--- .../spark/storage/BlockManagerSuite.scala | 34 +++++++++++++-- 3 files changed, 85 insertions(+), 15 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala index 80d66e59132da..1dff09a75d038 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala @@ -535,9 +535,14 @@ private[spark] class BlockManager( /* We'll store the bytes in memory if the block's storage level includes * "memory serialized", or if it should be cached as objects in memory * but we only requested its serialized bytes. */ - val copyForMemory = ByteBuffer.allocate(bytes.limit) - copyForMemory.put(bytes) - memoryStore.putBytes(blockId, copyForMemory, level) + memoryStore.putBytes(blockId, bytes.limit, () => { + // https://issues.apache.org/jira/browse/SPARK-6076 + // If the file size is bigger than the free memory, OOM will happen. So if we cannot + // put it into MemoryStore, copyForMemory should not be created. That's why this + // action is put into a `() => ByteBuffer` and created lazily. + val copyForMemory = ByteBuffer.allocate(bytes.limit) + copyForMemory.put(bytes) + }) bytes.rewind() } if (!asBlockResult) { @@ -991,15 +996,23 @@ private[spark] class BlockManager( putIterator(blockId, Iterator(value), level, tellMaster) } + def dropFromMemory( + blockId: BlockId, + data: Either[Array[Any], ByteBuffer]): Option[BlockStatus] = { + dropFromMemory(blockId, () => data) + } + /** * Drop a block from memory, possibly putting it on disk if applicable. Called when the memory * store reaches its limit and needs to free up space. * + * If `data` is not put on disk, it won't be created. + * * Return the block status if the given block has been updated, else None. */ def dropFromMemory( blockId: BlockId, - data: Either[Array[Any], ByteBuffer]): Option[BlockStatus] = { + data: () => Either[Array[Any], ByteBuffer]): Option[BlockStatus] = { logInfo(s"Dropping block $blockId from memory") val info = blockInfo.get(blockId).orNull @@ -1023,7 +1036,7 @@ private[spark] class BlockManager( // Drop to disk, if storage level requires if (level.useDisk && !diskStore.contains(blockId)) { logInfo(s"Writing block $blockId to disk") - data match { + data() match { case Left(elements) => diskStore.putArray(blockId, elements, level, returnValues = false) case Right(bytes) => diff --git a/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala b/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala index 1be860aea63d0..ed609772e6979 100644 --- a/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala +++ b/core/src/main/scala/org/apache/spark/storage/MemoryStore.scala @@ -98,6 +98,26 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long) } } + /** + * Use `size` to test if there is enough space in MemoryStore. If so, create the ByteBuffer and + * put it into MemoryStore. Otherwise, the ByteBuffer won't be created. + * + * The caller should guarantee that `size` is correct. + */ + def putBytes(blockId: BlockId, size: Long, _bytes: () => ByteBuffer): PutResult = { + // Work on a duplicate - since the original input might be used elsewhere. + lazy val bytes = _bytes().duplicate().rewind().asInstanceOf[ByteBuffer] + val putAttempt = tryToPut(blockId, () => bytes, size, deserialized = false) + val data = + if (putAttempt.success) { + assert(bytes.limit == size) + Right(bytes.duplicate()) + } else { + null + } + PutResult(size, data, putAttempt.droppedBlocks) + } + override def putArray( blockId: BlockId, values: Array[Any], @@ -312,11 +332,22 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long) blockId.asRDDId.map(_.rddId) } + private def tryToPut( + blockId: BlockId, + value: Any, + size: Long, + deserialized: Boolean): ResultWithDroppedBlocks = { + tryToPut(blockId, () => value, size, deserialized) + } + /** * Try to put in a set of values, if we can free up enough space. The value should either be * an Array if deserialized is true or a ByteBuffer otherwise. Its (possibly estimated) size * must also be passed by the caller. * + * `value` will be lazily created. If it cannot be put into MemoryStore or disk, `value` won't be + * created to avoid OOM since it may be a big ByteBuffer. + * * Synchronize on `accountingLock` to ensure that all the put requests and its associated block * dropping is done by only on thread at a time. Otherwise while one thread is dropping * blocks to free memory for one block, another thread may use up the freed space for @@ -326,7 +357,7 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long) */ private def tryToPut( blockId: BlockId, - value: Any, + value: () => Any, size: Long, deserialized: Boolean): ResultWithDroppedBlocks = { @@ -345,7 +376,7 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long) droppedBlocks ++= freeSpaceResult.droppedBlocks if (enoughFreeSpace) { - val entry = new MemoryEntry(value, size, deserialized) + val entry = new MemoryEntry(value(), size, deserialized) entries.synchronized { entries.put(blockId, entry) currentMemory += size @@ -357,12 +388,12 @@ private[spark] class MemoryStore(blockManager: BlockManager, maxMemory: Long) } else { // Tell the block manager that we couldn't put it in memory so that it can drop it to // disk if the block allows disk storage. - val data = if (deserialized) { - Left(value.asInstanceOf[Array[Any]]) + lazy val data = if (deserialized) { + Left(value().asInstanceOf[Array[Any]]) } else { - Right(value.asInstanceOf[ByteBuffer].duplicate()) + Right(value().asInstanceOf[ByteBuffer].duplicate()) } - val droppedBlockStatus = blockManager.dropFromMemory(blockId, data) + val droppedBlockStatus = blockManager.dropFromMemory(blockId, () => data) droppedBlockStatus.foreach { status => droppedBlocks += ((blockId, status)) } } // Release the unroll memory used because we no longer need the underlying Array diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala index 3fdbe99b5d02b..ecd1cba5b5abe 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala @@ -170,8 +170,8 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach assert(master.getLocations("a3").size === 0, "master was told about a3") // Drop a1 and a2 from memory; this should be reported back to the master - store.dropFromMemory("a1", null) - store.dropFromMemory("a2", null) + store.dropFromMemory("a1", null: Either[Array[Any], ByteBuffer]) + store.dropFromMemory("a2", null: Either[Array[Any], ByteBuffer]) assert(store.getSingle("a1") === None, "a1 not removed from store") assert(store.getSingle("a2") === None, "a2 not removed from store") assert(master.getLocations("a1").size === 0, "master did not remove a1") @@ -413,8 +413,8 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach t2.join() t3.join() - store.dropFromMemory("a1", null) - store.dropFromMemory("a2", null) + store.dropFromMemory("a1", null: Either[Array[Any], ByteBuffer]) + store.dropFromMemory("a2", null: Either[Array[Any], ByteBuffer]) store.waitForAsyncReregister() } } @@ -1223,4 +1223,30 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach assert(unrollMemoryAfterB6 === unrollMemoryAfterB4) assert(unrollMemoryAfterB7 === unrollMemoryAfterB4) } + + test("lazily create a big ByteBuffer to avoid OOM if it cannot be put into MemoryStore") { + store = makeBlockManager(12000) + val memoryStore = store.memoryStore + val blockId = BlockId("rdd_3_10") + val result = memoryStore.putBytes(blockId, 13000, () => { + fail("A big ByteBuffer that cannot be put into MemoryStore should not be created") + }) + assert(result.size === 13000) + assert(result.data === null) + assert(result.droppedBlocks === Nil) + } + + test("put a small ByteBuffer to MemoryStore") { + store = makeBlockManager(12000) + val memoryStore = store.memoryStore + val blockId = BlockId("rdd_3_10") + var bytes: ByteBuffer = null + val result = memoryStore.putBytes(blockId, 10000, () => { + bytes = ByteBuffer.allocate(10000) + bytes + }) + assert(result.size === 10000) + assert(result.data === Right(bytes)) + assert(result.droppedBlocks === Nil) + } } From acef51defb991bcdc99b76cf2a126afd6d60ec70 Mon Sep 17 00:00:00 2001 From: Kousuke Saruta Date: Wed, 25 Mar 2015 13:27:15 -0700 Subject: [PATCH 051/591] [SPARK-6537] UIWorkloadGenerator: The main thread should not stop SparkContext until all jobs finish The main thread of UIWorkloadGenerator spawn sub threads to launch jobs but the main thread stop SparkContext without waiting for finishing those threads. Author: Kousuke Saruta Closes #5187 from sarutak/SPARK-6537 and squashes the following commits: 4e9307a [Kousuke Saruta] Fixed UIWorkloadGenerator so that the main thread stop SparkContext after all jobs finish --- .../scala/org/apache/spark/ui/UIWorkloadGenerator.scala | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala b/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala index 19ac7a826e306..5fbcd6bb8ad94 100644 --- a/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala +++ b/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala @@ -17,6 +17,8 @@ package org.apache.spark.ui +import java.util.concurrent.Semaphore + import scala.util.Random import org.apache.spark.{SparkConf, SparkContext} @@ -88,6 +90,8 @@ private[spark] object UIWorkloadGenerator { ("Job with delays", baseData.map(x => Thread.sleep(100)).count) ) + val barrier = new Semaphore(-nJobSet * jobs.size + 1) + (1 to nJobSet).foreach { _ => for ((desc, job) <- jobs) { new Thread { @@ -99,12 +103,17 @@ private[spark] object UIWorkloadGenerator { } catch { case e: Exception => println("Job Failed: " + desc) + } finally { + barrier.release() } } }.start Thread.sleep(INTER_JOB_WAIT_MS) } } + + // Waiting for threads. + barrier.acquire() sc.stop() } } From c1b74df6042b33b2b061cb07c2fbd82dba9074bb Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Wed, 25 Mar 2015 13:28:32 -0700 Subject: [PATCH 052/591] [SPARK-5771] Master UI inconsistently displays application cores If the user calls `sc.stop()`, then the number of cores under "Completed Applications" will be 0. If the user does not call `sc.stop()`, then the number of cores will be however many cores were being used before the application exited. This PR makes both cases have the behavior of the latter. Note that there have been a series of PR that attempted to fix this. For the full discussion, please refer to #4841. The unregister event is necessary because of a subtle race condition explained in that PR. Tested this locally with and without calling `sc.stop()`. Author: Andrew Or Closes #5177 from andrewor14/master-ui-cores and squashes the following commits: 62449d1 [Andrew Or] Freeze application state before finishing it --- .../scala/org/apache/spark/deploy/DeployMessage.scala | 2 ++ .../org/apache/spark/deploy/client/AppClient.scala | 1 + .../apache/spark/deploy/master/ApplicationInfo.scala | 4 ++++ .../scala/org/apache/spark/deploy/master/Master.scala | 10 +++++++++- 4 files changed, 16 insertions(+), 1 deletion(-) diff --git a/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala b/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala index 0997507d016f5..9db6fd1ac4dbe 100644 --- a/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala @@ -101,6 +101,8 @@ private[deploy] object DeployMessages { case class RegisterApplication(appDescription: ApplicationDescription) extends DeployMessage + case class UnregisterApplication(appId: String) + case class MasterChangeAcknowledged(appId: String) // Master to AppClient diff --git a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala index 3b729725257ef..4f06d7f96c46e 100644 --- a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala +++ b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala @@ -157,6 +157,7 @@ private[spark] class AppClient( case StopAppClient => markDead("Application has been stopped.") + master ! UnregisterApplication(appId) sender ! true context.stop(self) } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala index f979ffa16641a..bc5b293379f2b 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala @@ -111,6 +111,10 @@ private[deploy] class ApplicationInfo( endTime = System.currentTimeMillis() } + private[master] def isFinished: Boolean = { + state != ApplicationState.WAITING && state != ApplicationState.RUNNING + } + def duration: Long = { if (endTime != -1) { endTime - startTime diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala index 80506621f4d24..9a5d5877da86d 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala @@ -339,7 +339,11 @@ private[master] class Master( if (ExecutorState.isFinished(state)) { // Remove this executor from the worker and app logInfo(s"Removing executor ${exec.fullId} because it is $state") - appInfo.removeExecutor(exec) + // If an application has already finished, preserve its + // state to display its information properly on the UI + if (!appInfo.isFinished) { + appInfo.removeExecutor(exec) + } exec.worker.removeExecutor(exec) val normalExit = exitStatus == Some(0) @@ -428,6 +432,10 @@ private[master] class Master( if (canCompleteRecovery) { completeRecovery() } } + case UnregisterApplication(applicationId) => + logInfo(s"Received unregister request from application $applicationId") + idToApp.get(applicationId).foreach(finishApplication) + case DisassociatedEvent(_, address, _) => { // The disconnected client could've been either a worker or an app; remove whichever it was logInfo(s"$address got disassociated, removing it.") From 435337381f093f95248c8f0204e60c0b366edc81 Mon Sep 17 00:00:00 2001 From: Yanbo Liang Date: Wed, 25 Mar 2015 13:38:33 -0700 Subject: [PATCH 053/591] [SPARK-6256] [MLlib] MLlib Python API parity check for regression MLlib Python API parity check for Regression, major disparities need to be added for Python list following: ```scala LinearRegressionWithSGD setValidateData LassoWithSGD setIntercept setValidateData RidgeRegressionWithSGD setIntercept setValidateData ``` setFeatureScaling is mllib private function which is not needed to expose in pyspark. Author: Yanbo Liang Closes #4997 from yanboliang/spark-6256 and squashes the following commits: 102f498 [Yanbo Liang] fix intercept issue & add doc test 1fb7b4f [Yanbo Liang] change 'intercept' to 'addIntercept' de5ecbc [Yanbo Liang] MLlib Python API parity check for regression --- .../mllib/api/python/PythonMLLibAPI.scala | 16 +++++-- python/pyspark/mllib/regression.py | 43 ++++++++++++++++--- 2 files changed, 49 insertions(+), 10 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index 15ca2547d56a8..e39156734794c 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -111,9 +111,11 @@ private[python] class PythonMLLibAPI extends Serializable { initialWeights: Vector, regParam: Double, regType: String, - intercept: Boolean): JList[Object] = { + intercept: Boolean, + validateData: Boolean): JList[Object] = { val lrAlg = new LinearRegressionWithSGD() lrAlg.setIntercept(intercept) + .setValidateData(validateData) lrAlg.optimizer .setNumIterations(numIterations) .setRegParam(regParam) @@ -135,8 +137,12 @@ private[python] class PythonMLLibAPI extends Serializable { stepSize: Double, regParam: Double, miniBatchFraction: Double, - initialWeights: Vector): JList[Object] = { + initialWeights: Vector, + intercept: Boolean, + validateData: Boolean): JList[Object] = { val lassoAlg = new LassoWithSGD() + lassoAlg.setIntercept(intercept) + .setValidateData(validateData) lassoAlg.optimizer .setNumIterations(numIterations) .setRegParam(regParam) @@ -157,8 +163,12 @@ private[python] class PythonMLLibAPI extends Serializable { stepSize: Double, regParam: Double, miniBatchFraction: Double, - initialWeights: Vector): JList[Object] = { + initialWeights: Vector, + intercept: Boolean, + validateData: Boolean): JList[Object] = { val ridgeAlg = new RidgeRegressionWithSGD() + ridgeAlg.setIntercept(intercept) + .setValidateData(validateData) ridgeAlg.optimizer .setNumIterations(numIterations) .setRegParam(regParam) diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py index 414a0ada80787..209f1ee473b5b 100644 --- a/python/pyspark/mllib/regression.py +++ b/python/pyspark/mllib/regression.py @@ -140,6 +140,13 @@ class LinearRegressionModel(LinearRegressionModelBase): True >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 True + >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=100, step=1.0, + ... miniBatchFraction=1.0, initialWeights=array([1.0]), regParam=0.1, regType="l2", + ... intercept=True, validateData=True) + >>> abs(lrm.predict(array([0.0])) - 0) < 0.5 + True + >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 + True """ def save(self, sc, path): java_model = sc._jvm.org.apache.spark.mllib.regression.LinearRegressionModel( @@ -173,7 +180,8 @@ class LinearRegressionWithSGD(object): @classmethod def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, - initialWeights=None, regParam=0.0, regType=None, intercept=False): + initialWeights=None, regParam=0.0, regType=None, intercept=False, + validateData=True): """ Train a linear regression model on the given data. @@ -195,15 +203,18 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, (default: None) - @param intercept: Boolean parameter which indicates the use + :param intercept: Boolean parameter which indicates the use or not of the augmented representation for training data (i.e. whether bias features are activated or not). (default: False) + :param validateData: Boolean parameter which indicates if the + algorithm should validate data before training. + (default: True) """ def train(rdd, i): return callMLlibFunc("trainLinearRegressionModelWithSGD", rdd, int(iterations), float(step), float(miniBatchFraction), i, float(regParam), - regType, bool(intercept)) + regType, bool(intercept), bool(validateData)) return _regression_train_wrapper(train, LinearRegressionModel, data, initialWeights) @@ -253,6 +264,13 @@ class LassoModel(LinearRegressionModelBase): True >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 True + >>> lrm = LassoWithSGD.train(sc.parallelize(data), iterations=100, step=1.0, + ... regParam=0.01, miniBatchFraction=1.0, initialWeights=array([1.0]), intercept=True, + ... validateData=True) + >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5 + True + >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 + True """ def save(self, sc, path): java_model = sc._jvm.org.apache.spark.mllib.regression.LassoModel( @@ -273,11 +291,13 @@ class LassoWithSGD(object): @classmethod def train(cls, data, iterations=100, step=1.0, regParam=0.01, - miniBatchFraction=1.0, initialWeights=None): + miniBatchFraction=1.0, initialWeights=None, intercept=False, + validateData=True): """Train a Lasso regression model on the given data.""" def train(rdd, i): return callMLlibFunc("trainLassoModelWithSGD", rdd, int(iterations), float(step), - float(regParam), float(miniBatchFraction), i) + float(regParam), float(miniBatchFraction), i, bool(intercept), + bool(validateData)) return _regression_train_wrapper(train, LassoModel, data, initialWeights) @@ -327,6 +347,13 @@ class RidgeRegressionModel(LinearRegressionModelBase): True >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 True + >>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), iterations=100, step=1.0, + ... regParam=0.01, miniBatchFraction=1.0, initialWeights=array([1.0]), intercept=True, + ... validateData=True) + >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5 + True + >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 + True """ def save(self, sc, path): java_model = sc._jvm.org.apache.spark.mllib.regression.RidgeRegressionModel( @@ -347,11 +374,13 @@ class RidgeRegressionWithSGD(object): @classmethod def train(cls, data, iterations=100, step=1.0, regParam=0.01, - miniBatchFraction=1.0, initialWeights=None): + miniBatchFraction=1.0, initialWeights=None, intercept=False, + validateData=True): """Train a ridge regression model on the given data.""" def train(rdd, i): return callMLlibFunc("trainRidgeModelWithSGD", rdd, int(iterations), float(step), - float(regParam), float(miniBatchFraction), i) + float(regParam), float(miniBatchFraction), i, bool(intercept), + bool(validateData)) return _regression_train_wrapper(train, RidgeRegressionModel, data, initialWeights) From 4fc4d0369e8240defe0ee83252426402f1a28a36 Mon Sep 17 00:00:00 2001 From: MechCoder Date: Wed, 25 Mar 2015 14:45:23 -0700 Subject: [PATCH 054/591] [SPARK-5987] [MLlib] Save/load for GaussianMixtureModels Should be self explanatory. Author: MechCoder Closes #4986 from MechCoder/spark-5987 and squashes the following commits: 7d2cd56 [MechCoder] Iterate over dataframe in a better way e7a14cb [MechCoder] Minor 33c84f9 [MechCoder] Store as Array[Data] instead of Data[Array] 505bd57 [MechCoder] Rebased over master and used MatrixUDT 7422bb4 [MechCoder] Store sigmas as Array[Double] instead of Array[Array[Double]] b9794e4 [MechCoder] Minor cb77095 [MechCoder] [SPARK-5987] Save/load for GaussianMixtureModels --- docs/mllib-clustering.md | 8 ++ .../clustering/GaussianMixtureModel.scala | 96 ++++++++++++++++++- .../clustering/GaussianMixtureSuite.scala | 52 +++++++--- 3 files changed, 136 insertions(+), 20 deletions(-) diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md index 0b6db4fcb7b1f..f5aa15b7d9b79 100644 --- a/docs/mllib-clustering.md +++ b/docs/mllib-clustering.md @@ -173,6 +173,7 @@ to the algorithm. We then output the parameters of the mixture model. {% highlight scala %} import org.apache.spark.mllib.clustering.GaussianMixture +import org.apache.spark.mllib.clustering.GaussianMixtureModel import org.apache.spark.mllib.linalg.Vectors // Load and parse the data @@ -182,6 +183,10 @@ val parsedData = data.map(s => Vectors.dense(s.trim.split(' ').map(_.toDouble))) // Cluster the data into two classes using GaussianMixture val gmm = new GaussianMixture().setK(2).run(parsedData) +// Save and load model +gmm.save(sc, "myGMMModel") +val sameModel = GaussianMixtureModel.load(sc, "myGMMModel") + // output parameters of max-likelihood model for (i <- 0 until gmm.k) { println("weight=%f\nmu=%s\nsigma=\n%s\n" format @@ -231,6 +236,9 @@ public class GaussianMixtureExample { // Cluster the data into two classes using GaussianMixture GaussianMixtureModel gmm = new GaussianMixture().setK(2).run(parsedData.rdd()); + // Save and load GaussianMixtureModel + gmm.save(sc, "myGMMModel") + GaussianMixtureModel sameModel = GaussianMixtureModel.load(sc, "myGMMModel") // Output the parameters of the mixture model for(int j=0; j BreezeVector} +import org.json4s.DefaultFormats +import org.json4s.JsonDSL._ +import org.json4s.jackson.JsonMethods._ + +import org.apache.spark.SparkContext import org.apache.spark.annotation.Experimental -import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.linalg.{Vector, Matrices, Matrix} import org.apache.spark.mllib.stat.distribution.MultivariateGaussian -import org.apache.spark.mllib.util.MLUtils +import org.apache.spark.mllib.util.{MLUtils, Loader, Saveable} import org.apache.spark.rdd.RDD +import org.apache.spark.sql.{SQLContext, Row} /** * :: Experimental :: @@ -41,10 +47,16 @@ import org.apache.spark.rdd.RDD @Experimental class GaussianMixtureModel( val weights: Array[Double], - val gaussians: Array[MultivariateGaussian]) extends Serializable { + val gaussians: Array[MultivariateGaussian]) extends Serializable with Saveable{ require(weights.length == gaussians.length, "Length of weight and Gaussian arrays must match") - + + override protected def formatVersion = "1.0" + + override def save(sc: SparkContext, path: String): Unit = { + GaussianMixtureModel.SaveLoadV1_0.save(sc, path, weights, gaussians) + } + /** Number of gaussians in mixture */ def k: Int = weights.length @@ -83,5 +95,79 @@ class GaussianMixtureModel( p(i) /= pSum } p - } + } +} + +@Experimental +object GaussianMixtureModel extends Loader[GaussianMixtureModel] { + + private object SaveLoadV1_0 { + + case class Data(weight: Double, mu: Vector, sigma: Matrix) + + val formatVersionV1_0 = "1.0" + + val classNameV1_0 = "org.apache.spark.mllib.clustering.GaussianMixtureModel" + + def save( + sc: SparkContext, + path: String, + weights: Array[Double], + gaussians: Array[MultivariateGaussian]): Unit = { + + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + + // Create JSON metadata. + val metadata = compact(render + (("class" -> classNameV1_0) ~ ("version" -> formatVersionV1_0) ~ ("k" -> weights.length))) + sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path)) + + // Create Parquet data. + val dataArray = Array.tabulate(weights.length) { i => + Data(weights(i), gaussians(i).mu, gaussians(i).sigma) + } + sc.parallelize(dataArray, 1).toDF().saveAsParquetFile(Loader.dataPath(path)) + } + + def load(sc: SparkContext, path: String): GaussianMixtureModel = { + val dataPath = Loader.dataPath(path) + val sqlContext = new SQLContext(sc) + val dataFrame = sqlContext.parquetFile(dataPath) + val dataArray = dataFrame.select("weight", "mu", "sigma").collect() + + // Check schema explicitly since erasure makes it hard to use match-case for checking. + Loader.checkSchema[Data](dataFrame.schema) + + val (weights, gaussians) = dataArray.map { + case Row(weight: Double, mu: Vector, sigma: Matrix) => + (weight, new MultivariateGaussian(mu, sigma)) + }.unzip + + return new GaussianMixtureModel(weights.toArray, gaussians.toArray) + } + } + + override def load(sc: SparkContext, path: String) : GaussianMixtureModel = { + val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path) + implicit val formats = DefaultFormats + val k = (metadata \ "k").extract[Int] + val classNameV1_0 = SaveLoadV1_0.classNameV1_0 + (loadedClassName, version) match { + case (classNameV1_0, "1.0") => { + val model = SaveLoadV1_0.load(sc, path) + require(model.weights.length == k, + s"GaussianMixtureModel requires weights of length $k " + + s"got weights of length ${model.weights.length}") + require(model.gaussians.length == k, + s"GaussianMixtureModel requires gaussians of length $k" + + s"got gaussians of length ${model.gaussians.length}") + model + } + case _ => throw new Exception( + s"GaussianMixtureModel.load did not recognize model with (className, format version):" + + s"($loadedClassName, $version). Supported:\n" + + s" ($classNameV1_0, 1.0)") + } + } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala index 1b46a4012d731..f356ffa3e3a26 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala @@ -23,6 +23,7 @@ import org.apache.spark.mllib.linalg.{Vectors, Matrices} import org.apache.spark.mllib.stat.distribution.MultivariateGaussian import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.util.Utils class GaussianMixtureSuite extends FunSuite with MLlibTestSparkContext { test("single cluster") { @@ -48,13 +49,7 @@ class GaussianMixtureSuite extends FunSuite with MLlibTestSparkContext { } test("two clusters") { - val data = sc.parallelize(Array( - Vectors.dense(-5.1971), Vectors.dense(-2.5359), Vectors.dense(-3.8220), - Vectors.dense(-5.2211), Vectors.dense(-5.0602), Vectors.dense( 4.7118), - Vectors.dense( 6.8989), Vectors.dense( 3.4592), Vectors.dense( 4.6322), - Vectors.dense( 5.7048), Vectors.dense( 4.6567), Vectors.dense( 5.5026), - Vectors.dense( 4.5605), Vectors.dense( 5.2043), Vectors.dense( 6.2734) - )) + val data = sc.parallelize(GaussianTestData.data) // we set an initial gaussian to induce expected results val initialGmm = new GaussianMixtureModel( @@ -105,14 +100,7 @@ class GaussianMixtureSuite extends FunSuite with MLlibTestSparkContext { } test("two clusters with sparse data") { - val data = sc.parallelize(Array( - Vectors.dense(-5.1971), Vectors.dense(-2.5359), Vectors.dense(-3.8220), - Vectors.dense(-5.2211), Vectors.dense(-5.0602), Vectors.dense( 4.7118), - Vectors.dense( 6.8989), Vectors.dense( 3.4592), Vectors.dense( 4.6322), - Vectors.dense( 5.7048), Vectors.dense( 4.6567), Vectors.dense( 5.5026), - Vectors.dense( 4.5605), Vectors.dense( 5.2043), Vectors.dense( 6.2734) - )) - + val data = sc.parallelize(GaussianTestData.data) val sparseData = data.map(point => Vectors.sparse(1, Array(0), point.toArray)) // we set an initial gaussian to induce expected results val initialGmm = new GaussianMixtureModel( @@ -138,4 +126,38 @@ class GaussianMixtureSuite extends FunSuite with MLlibTestSparkContext { assert(sparseGMM.gaussians(0).sigma ~== Esigma(0) absTol 1E-3) assert(sparseGMM.gaussians(1).sigma ~== Esigma(1) absTol 1E-3) } + + test("model save / load") { + val data = sc.parallelize(GaussianTestData.data) + + val gmm = new GaussianMixture().setK(2).setSeed(0).run(data) + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + + try { + gmm.save(sc, path) + + // TODO: GaussianMixtureModel should implement equals/hashcode directly. + val sameModel = GaussianMixtureModel.load(sc, path) + assert(sameModel.k === gmm.k) + (0 until sameModel.k).foreach { i => + assert(sameModel.gaussians(i).mu === gmm.gaussians(i).mu) + assert(sameModel.gaussians(i).sigma === gmm.gaussians(i).sigma) + } + } finally { + Utils.deleteRecursively(tempDir) + } + } + + object GaussianTestData { + + val data = Array( + Vectors.dense(-5.1971), Vectors.dense(-2.5359), Vectors.dense(-3.8220), + Vectors.dense(-5.2211), Vectors.dense(-5.0602), Vectors.dense( 4.7118), + Vectors.dense( 6.8989), Vectors.dense( 3.4592), Vectors.dense( 4.6322), + Vectors.dense( 5.7048), Vectors.dense( 4.6567), Vectors.dense( 5.5026), + Vectors.dense( 4.5605), Vectors.dense( 5.2043), Vectors.dense( 6.2734) + ) + + } } From d44a3362ed8cf3068f8ff233e13851a39da42219 Mon Sep 17 00:00:00 2001 From: Josh Rosen Date: Wed, 25 Mar 2015 17:40:00 -0700 Subject: [PATCH 055/591] [SPARK-6079] Use index to speed up StatusTracker.getJobIdsForGroup() `StatusTracker.getJobIdsForGroup()` is implemented via a linear scan over a HashMap rather than using an index, which might be an expensive operation if there are many (e.g. thousands) of retained jobs. This patch adds a new map to `JobProgressListener` in order to speed up these lookups. Author: Josh Rosen Closes #4830 from JoshRosen/statustracker-job-group-indexing and squashes the following commits: e39c5c7 [Josh Rosen] Address review feedback 6709fb2 [Josh Rosen] Merge remote-tracking branch 'origin/master' into statustracker-job-group-indexing 2c49614 [Josh Rosen] getOrElse 97275a7 [Josh Rosen] Add jobGroup to jobId index to JobProgressListener --- .../org/apache/spark/SparkStatusTracker.scala | 3 +- .../spark/ui/jobs/JobProgressListener.scala | 23 ++++++++++++-- .../ui/jobs/JobProgressListenerSuite.scala | 31 +++++++++++++++++-- 3 files changed, 51 insertions(+), 6 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/SparkStatusTracker.scala b/core/src/main/scala/org/apache/spark/SparkStatusTracker.scala index edbdda8a0bcb6..34ee3a48f8e74 100644 --- a/core/src/main/scala/org/apache/spark/SparkStatusTracker.scala +++ b/core/src/main/scala/org/apache/spark/SparkStatusTracker.scala @@ -45,8 +45,7 @@ class SparkStatusTracker private[spark] (sc: SparkContext) { */ def getJobIdsForGroup(jobGroup: String): Array[Int] = { jobProgressListener.synchronized { - val jobData = jobProgressListener.jobIdToData.valuesIterator - jobData.filter(_.jobGroup.orNull == jobGroup).map(_.jobId).toArray + jobProgressListener.jobGroupToJobIds.getOrElse(jobGroup, Seq.empty).toArray } } diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala index 949e80d30f5eb..625596885faa1 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala @@ -44,6 +44,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { // These type aliases are public because they're used in the types of public fields: type JobId = Int + type JobGroupId = String type StageId = Int type StageAttemptId = Int type PoolName = String @@ -54,6 +55,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { val completedJobs = ListBuffer[JobUIData]() val failedJobs = ListBuffer[JobUIData]() val jobIdToData = new HashMap[JobId, JobUIData] + val jobGroupToJobIds = new HashMap[JobGroupId, HashSet[JobId]] // Stages: val pendingStages = new HashMap[StageId, StageInfo] @@ -119,7 +121,10 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { Map( "jobIdToData" -> jobIdToData.size, "stageIdToData" -> stageIdToData.size, - "stageIdToStageInfo" -> stageIdToInfo.size + "stageIdToStageInfo" -> stageIdToInfo.size, + "jobGroupToJobIds" -> jobGroupToJobIds.values.map(_.size).sum, + // Since jobGroupToJobIds is map of sets, check that we don't leak keys with empty values: + "jobGroupToJobIds keySet" -> jobGroupToJobIds.keys.size ) } @@ -140,7 +145,19 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { if (jobs.size > retainedJobs) { val toRemove = math.max(retainedJobs / 10, 1) jobs.take(toRemove).foreach { job => - jobIdToData.remove(job.jobId) + // Remove the job's UI data, if it exists + jobIdToData.remove(job.jobId).foreach { removedJob => + // A null jobGroupId is used for jobs that are run without a job group + val jobGroupId = removedJob.jobGroup.orNull + // Remove the job group -> job mapping entry, if it exists + jobGroupToJobIds.get(jobGroupId).foreach { jobsInGroup => + jobsInGroup.remove(job.jobId) + // If this was the last job in this job group, remove the map entry for the job group + if (jobsInGroup.isEmpty) { + jobGroupToJobIds.remove(jobGroupId) + } + } + } } jobs.trimStart(toRemove) } @@ -158,6 +175,8 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { stageIds = jobStart.stageIds, jobGroup = jobGroup, status = JobExecutionStatus.RUNNING) + // A null jobGroupId is used for jobs that are run without a job group + jobGroupToJobIds.getOrElseUpdate(jobGroup.orNull, new HashSet[JobId]).add(jobStart.jobId) jobStart.stageInfos.foreach(x => pendingStages(x.stageId) = x) // Compute (a potential underestimate of) the number of tasks that will be run by this job. // This may be an underestimate because the job start event references all of the result diff --git a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala index 730a4b54f5aa1..c0c28cb60e21d 100644 --- a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala @@ -17,6 +17,8 @@ package org.apache.spark.ui.jobs +import java.util.Properties + import org.scalatest.FunSuite import org.scalatest.Matchers @@ -44,11 +46,19 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc SparkListenerStageCompleted(stageInfo) } - private def createJobStartEvent(jobId: Int, stageIds: Seq[Int]) = { + private def createJobStartEvent( + jobId: Int, + stageIds: Seq[Int], + jobGroup: Option[String] = None): SparkListenerJobStart = { val stageInfos = stageIds.map { stageId => new StageInfo(stageId, 0, stageId.toString, 0, null, "") } - SparkListenerJobStart(jobId, jobSubmissionTime, stageInfos) + val properties: Option[Properties] = jobGroup.map { groupId => + val props = new Properties() + props.setProperty(SparkContext.SPARK_JOB_GROUP_ID, groupId) + props + } + SparkListenerJobStart(jobId, jobSubmissionTime, stageInfos, properties.orNull) } private def createJobEndEvent(jobId: Int, failed: Boolean = false) = { @@ -110,6 +120,23 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc listener.stageIdToActiveJobIds.size should be (0) } + test("test clearing of jobGroupToJobIds") { + val conf = new SparkConf() + conf.set("spark.ui.retainedJobs", 5.toString) + val listener = new JobProgressListener(conf) + + // Run 50 jobs, each with one stage + for (jobId <- 0 to 50) { + listener.onJobStart(createJobStartEvent(jobId, Seq(0), jobGroup = Some(jobId.toString))) + listener.onStageSubmitted(createStageStartEvent(0)) + listener.onStageCompleted(createStageEndEvent(0, failed = false)) + listener.onJobEnd(createJobEndEvent(jobId, false)) + } + assertActiveJobsStateIsEmpty(listener) + // This collection won't become empty, but it should be bounded by spark.ui.retainedJobs + listener.jobGroupToJobIds.size should be (5) + } + test("test LRU eviction of jobs") { val conf = new SparkConf() conf.set("spark.ui.retainedStages", 5.toString) From 8c3b0052f4792d97d23244ade335676e37cb1fae Mon Sep 17 00:00:00 2001 From: Cheng Lian Date: Wed, 25 Mar 2015 17:40:19 -0700 Subject: [PATCH 056/591] [SPARK-6450] [SQL] Fixes metastore Parquet table conversion The `ParquetConversions` analysis rule generates a hash map, which maps from the original `MetastoreRelation` instances to the newly created `ParquetRelation2` instances. However, `MetastoreRelation.equals` doesn't compare output attributes. Thus, if a single metastore Parquet table appears multiple times in a query, only a single entry ends up in the hash map, and the conversion is not correctly performed. Proper fix for this issue should be overriding `equals` and `hashCode` for MetastoreRelation. Unfortunately, this breaks more tests than expected. It's possible that these tests are ill-formed from the very beginning. As 1.3.1 release is approaching, we'd like to make the change more surgical to avoid potential regressions. The proposed fix here is to make both the metastore relations and their output attributes as keys in the hash map used in ParquetConversions. [Review on Reviewable](https://reviewable.io/reviews/apache/spark/5183) Author: Cheng Lian Closes #5183 from liancheng/spark-6450 and squashes the following commits: 3536780 [Cheng Lian] Fixes metastore Parquet table conversion --- .../spark/sql/hive/HiveMetastoreCatalog.scala | 34 ++++++++++--------- .../apache/spark/sql/hive/parquetSuites.scala | 25 ++++++++++++++ 2 files changed, 43 insertions(+), 16 deletions(-) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index 4c5eb48661f7d..d1a99555e90c6 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -459,7 +459,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with relation.tableDesc.getSerdeClassName.toLowerCase.contains("parquet") => val parquetRelation = convertToParquetRelation(relation) val attributedRewrites = relation.output.zip(parquetRelation.output) - (relation, parquetRelation, attributedRewrites) + (relation -> relation.output, parquetRelation, attributedRewrites) // Write path case InsertIntoHiveTable(relation: MetastoreRelation, _, _, _) @@ -470,7 +470,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with relation.tableDesc.getSerdeClassName.toLowerCase.contains("parquet") => val parquetRelation = convertToParquetRelation(relation) val attributedRewrites = relation.output.zip(parquetRelation.output) - (relation, parquetRelation, attributedRewrites) + (relation -> relation.output, parquetRelation, attributedRewrites) // Read path case p @ PhysicalOperation(_, _, relation: MetastoreRelation) @@ -479,33 +479,35 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with relation.tableDesc.getSerdeClassName.toLowerCase.contains("parquet") => val parquetRelation = convertToParquetRelation(relation) val attributedRewrites = relation.output.zip(parquetRelation.output) - (relation, parquetRelation, attributedRewrites) + (relation -> relation.output, parquetRelation, attributedRewrites) } + // Quick fix for SPARK-6450: Notice that we're using both the MetastoreRelation instances and + // their output attributes as the key of the map. This is because MetastoreRelation.equals + // doesn't take output attributes into account, thus multiple MetastoreRelation instances + // pointing to the same table get collapsed into a single entry in the map. A proper fix for + // this should be overriding equals & hashCode in MetastoreRelation. val relationMap = toBeReplaced.map(r => (r._1, r._2)).toMap val attributedRewrites = AttributeMap(toBeReplaced.map(_._3).fold(Nil)(_ ++: _)) // Replaces all `MetastoreRelation`s with corresponding `ParquetRelation2`s, and fixes // attribute IDs referenced in other nodes. plan.transformUp { - case r: MetastoreRelation if relationMap.contains(r) => { - val parquetRelation = relationMap(r) - val withAlias = - r.alias.map(a => Subquery(a, parquetRelation)).getOrElse( - Subquery(r.tableName, parquetRelation)) + case r: MetastoreRelation if relationMap.contains(r -> r.output) => + val parquetRelation = relationMap(r -> r.output) + val alias = r.alias.getOrElse(r.tableName) + Subquery(alias, parquetRelation) - withAlias - } case InsertIntoTable(r: MetastoreRelation, partition, child, overwrite) - if relationMap.contains(r) => { - val parquetRelation = relationMap(r) + if relationMap.contains(r -> r.output) => + val parquetRelation = relationMap(r -> r.output) InsertIntoTable(parquetRelation, partition, child, overwrite) - } + case InsertIntoHiveTable(r: MetastoreRelation, partition, child, overwrite) - if relationMap.contains(r) => { - val parquetRelation = relationMap(r) + if relationMap.contains(r -> r.output) => + val parquetRelation = relationMap(r -> r.output) InsertIntoTable(parquetRelation, partition, child, overwrite) - } + case other => other.transformExpressions { case a: Attribute if a.resolved => attributedRewrites.getOrElse(a, a) } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala index 8a31bd03092d1..432d65a874518 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala @@ -365,6 +365,31 @@ class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase { sql("DROP TABLE IF EXISTS test_insert_parquet") } + + test("SPARK-6450 regression test") { + sql( + """CREATE TABLE IF NOT EXISTS ms_convert (key INT) + |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' + |STORED AS + | INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' + | OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' + """.stripMargin) + + // This shouldn't throw AnalysisException + val analyzed = sql( + """SELECT key FROM ms_convert + |UNION ALL + |SELECT key FROM ms_convert + """.stripMargin).queryExecution.analyzed + + assertResult(2) { + analyzed.collect { + case r @ LogicalRelation(_: ParquetRelation2) => r + }.size + } + + sql("DROP TABLE ms_convert") + } } class ParquetDataSourceOffMetastoreSuite extends ParquetMetastoreSuiteBase { From e6d1406abd55bc24477eb8c6ee72c31e7110435e Mon Sep 17 00:00:00 2001 From: jeanlyn Date: Wed, 25 Mar 2015 17:47:45 -0700 Subject: [PATCH 057/591] [SPARK-5498][SQL]fix query exception when partition schema does not match table schema In hive,the schema of partition may be difference from the table schema.When we use spark-sql to query the data of partition which schema is difference from the table schema,we will get the exceptions as the description of the [jira](https://issues.apache.org/jira/browse/SPARK-5498) .For example: * We take a look of the schema for the partition and the table ```sql DESCRIBE partition_test PARTITION (dt='1'); id int None name string None dt string None # Partition Information # col_name data_type comment dt string None ``` ``` DESCRIBE partition_test; OK id bigint None name string None dt string None # Partition Information # col_name data_type comment dt string None ``` * run the sql ```sql SELECT * FROM partition_test where dt='1'; ``` we will get the cast exception `java.lang.ClassCastException: org.apache.spark.sql.catalyst.expressions.MutableLong cannot be cast to org.apache.spark.sql.catalyst.expressions.MutableInt` Author: jeanlyn Closes #4289 from jeanlyn/schema and squashes the following commits: 9c8da74 [jeanlyn] fix style b41d6b9 [jeanlyn] fix compile errors 07d84b6 [jeanlyn] Merge branch 'master' into schema 535b0b6 [jeanlyn] reduce conflicts d6c93c5 [jeanlyn] fix bug 1e8b30c [jeanlyn] fix code style 0549759 [jeanlyn] fix code style c879aa1 [jeanlyn] clean the code 2a91a87 [jeanlyn] add more test case and clean the code 12d800d [jeanlyn] fix code style 63d170a [jeanlyn] fix compile problem 7470901 [jeanlyn] reduce conflicts afc7da5 [jeanlyn] make getConvertedOI compatible between 0.12.0 and 0.13.1 b1527d5 [jeanlyn] fix type mismatch 10744ca [jeanlyn] Insert a space after the start of the comment 3b27af3 [jeanlyn] SPARK-5498:fix bug when query the data when partition schema does not match table schema --- .../apache/spark/sql/hive/TableReader.scala | 37 +++++++++++----- .../sql/hive/InsertIntoHiveTableSuite.scala | 42 +++++++++++++++++++ .../org/apache/spark/sql/hive/Shim12.scala | 10 ++++- .../org/apache/spark/sql/hive/Shim13.scala | 9 +++- 4 files changed, 84 insertions(+), 14 deletions(-) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala index af309c0c6ce2c..3563472c7ae81 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala @@ -25,7 +25,7 @@ import org.apache.hadoop.hive.ql.exec.Utilities import org.apache.hadoop.hive.ql.metadata.{Partition => HivePartition, Table => HiveTable} import org.apache.hadoop.hive.ql.plan.{PlanUtils, TableDesc} import org.apache.hadoop.hive.serde2.Deserializer -import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector +import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspectorConverters, StructObjectInspector} import org.apache.hadoop.hive.serde2.objectinspector.primitive._ import org.apache.hadoop.io.Writable import org.apache.hadoop.mapred.{FileInputFormat, InputFormat, JobConf} @@ -116,7 +116,7 @@ class HadoopTableReader( val hconf = broadcastedHiveConf.value.value val deserializer = deserializerClass.newInstance() deserializer.initialize(hconf, tableDesc.getProperties) - HadoopTableReader.fillObject(iter, deserializer, attrsWithIndex, mutableRow) + HadoopTableReader.fillObject(iter, deserializer, attrsWithIndex, mutableRow, deserializer) } deserializedHadoopRDD @@ -189,9 +189,13 @@ class HadoopTableReader( val hconf = broadcastedHiveConf.value.value val deserializer = localDeserializer.newInstance() deserializer.initialize(hconf, partProps) + // get the table deserializer + val tableSerDe = tableDesc.getDeserializerClass.newInstance() + tableSerDe.initialize(hconf, tableDesc.getProperties) // fill the non partition key attributes - HadoopTableReader.fillObject(iter, deserializer, nonPartitionKeyAttrs, mutableRow) + HadoopTableReader.fillObject(iter, deserializer, nonPartitionKeyAttrs, + mutableRow, tableSerDe) } }.toSeq @@ -261,25 +265,36 @@ private[hive] object HadoopTableReader extends HiveInspectors { * Transform all given raw `Writable`s into `Row`s. * * @param iterator Iterator of all `Writable`s to be transformed - * @param deserializer The `Deserializer` associated with the input `Writable` + * @param rawDeser The `Deserializer` associated with the input `Writable` * @param nonPartitionKeyAttrs Attributes that should be filled together with their corresponding * positions in the output schema * @param mutableRow A reusable `MutableRow` that should be filled + * @param tableDeser Table Deserializer * @return An `Iterator[Row]` transformed from `iterator` */ def fillObject( iterator: Iterator[Writable], - deserializer: Deserializer, + rawDeser: Deserializer, nonPartitionKeyAttrs: Seq[(Attribute, Int)], - mutableRow: MutableRow): Iterator[Row] = { + mutableRow: MutableRow, + tableDeser: Deserializer): Iterator[Row] = { + + val soi = if (rawDeser.getObjectInspector.equals(tableDeser.getObjectInspector)) { + rawDeser.getObjectInspector.asInstanceOf[StructObjectInspector] + } else { + HiveShim.getConvertedOI( + rawDeser.getObjectInspector, + tableDeser.getObjectInspector).asInstanceOf[StructObjectInspector] + } - val soi = deserializer.getObjectInspector().asInstanceOf[StructObjectInspector] val (fieldRefs, fieldOrdinals) = nonPartitionKeyAttrs.map { case (attr, ordinal) => soi.getStructFieldRef(attr.name) -> ordinal }.unzip - // Builds specific unwrappers ahead of time according to object inspector types to avoid pattern - // matching and branching costs per row. + /** + * Builds specific unwrappers ahead of time according to object inspector + * types to avoid pattern matching and branching costs per row. + */ val unwrappers: Seq[(Any, MutableRow, Int) => Unit] = fieldRefs.map { _.getFieldObjectInspector match { case oi: BooleanObjectInspector => @@ -316,9 +331,11 @@ private[hive] object HadoopTableReader extends HiveInspectors { } } + val converter = ObjectInspectorConverters.getConverter(rawDeser.getObjectInspector, soi) + // Map each tuple to a row object iterator.map { value => - val raw = deserializer.deserialize(value) + val raw = converter.convert(rawDeser.deserialize(value)) var i = 0 while (i < fieldRefs.length) { val fieldValue = soi.getStructFieldData(raw, fieldRefs(i)) diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala index 381cd2a29123e..aa6fb42de7f88 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala @@ -32,9 +32,12 @@ import org.apache.spark.sql.hive.test.TestHive._ case class TestData(key: Int, value: String) +case class ThreeCloumntable(key: Int, value: String, key1: String) + class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter { import org.apache.spark.sql.hive.test.TestHive.implicits._ + val testData = TestHive.sparkContext.parallelize( (1 to 100).map(i => TestData(i, i.toString))).toDF() @@ -186,4 +189,43 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter { sql("DROP TABLE hiveTableWithStructValue") } + + test("SPARK-5498:partition schema does not match table schema") { + val testData = TestHive.sparkContext.parallelize( + (1 to 10).map(i => TestData(i, i.toString))).toDF() + testData.registerTempTable("testData") + + val testDatawithNull = TestHive.sparkContext.parallelize( + (1 to 10).map(i => ThreeCloumntable(i, i.toString,null))).toDF() + + val tmpDir = Files.createTempDir() + sql(s"CREATE TABLE table_with_partition(key int,value string) PARTITIONED by (ds string) location '${tmpDir.toURI.toString}' ") + sql("INSERT OVERWRITE TABLE table_with_partition partition (ds='1') SELECT key,value FROM testData") + + // test schema the same between partition and table + sql("ALTER TABLE table_with_partition CHANGE COLUMN key key BIGINT") + checkAnswer(sql("select key,value from table_with_partition where ds='1' "), + testData.collect.toSeq + ) + + // test difference type of field + sql("ALTER TABLE table_with_partition CHANGE COLUMN key key BIGINT") + checkAnswer(sql("select key,value from table_with_partition where ds='1' "), + testData.collect.toSeq + ) + + // add column to table + sql("ALTER TABLE table_with_partition ADD COLUMNS(key1 string)") + checkAnswer(sql("select key,value,key1 from table_with_partition where ds='1' "), + testDatawithNull.collect.toSeq + ) + + // change column name to table + sql("ALTER TABLE table_with_partition CHANGE COLUMN key keynew BIGINT") + checkAnswer(sql("select keynew,value from table_with_partition where ds='1' "), + testData.collect.toSeq + ) + + sql("DROP TABLE table_with_partition") + } } diff --git a/sql/hive/v0.12.0/src/main/scala/org/apache/spark/sql/hive/Shim12.scala b/sql/hive/v0.12.0/src/main/scala/org/apache/spark/sql/hive/Shim12.scala index 30646ddbc29d8..0ed93c2c5b1fa 100644 --- a/sql/hive/v0.12.0/src/main/scala/org/apache/spark/sql/hive/Shim12.scala +++ b/sql/hive/v0.12.0/src/main/scala/org/apache/spark/sql/hive/Shim12.scala @@ -34,7 +34,7 @@ import org.apache.hadoop.hive.ql.plan.{CreateTableDesc, FileSinkDesc, TableDesc} import org.apache.hadoop.hive.ql.processors._ import org.apache.hadoop.hive.ql.stats.StatsSetupConst import org.apache.hadoop.hive.serde2.{ColumnProjectionUtils, Deserializer, io => hiveIo} -import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspector, PrimitiveObjectInspector} +import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspectorConverters, ObjectInspector, PrimitiveObjectInspector} import org.apache.hadoop.hive.serde2.objectinspector.PrimitiveObjectInspector.PrimitiveCategory import org.apache.hadoop.hive.serde2.objectinspector.primitive.{HiveDecimalObjectInspector, PrimitiveObjectInspectorFactory} import org.apache.hadoop.hive.serde2.typeinfo.{TypeInfo, TypeInfoFactory} @@ -210,7 +210,7 @@ private[hive] object HiveShim { def getDataLocationPath(p: Partition) = p.getPartitionPath - def getAllPartitionsOf(client: Hive, tbl: Table) = client.getAllPartitionsForPruner(tbl) + def getAllPartitionsOf(client: Hive, tbl: Table) = client.getAllPartitionsForPruner(tbl) def compatibilityBlackList = Seq( "decimal_.*", @@ -244,6 +244,12 @@ private[hive] object HiveShim { } } + def getConvertedOI( + inputOI: ObjectInspector, + outputOI: ObjectInspector): ObjectInspector = { + ObjectInspectorConverters.getConvertedOI(inputOI, outputOI, true) + } + def prepareWritable(w: Writable): Writable = { w } diff --git a/sql/hive/v0.13.1/src/main/scala/org/apache/spark/sql/hive/Shim13.scala b/sql/hive/v0.13.1/src/main/scala/org/apache/spark/sql/hive/Shim13.scala index f9fcbdae15745..7577309900209 100644 --- a/sql/hive/v0.13.1/src/main/scala/org/apache/spark/sql/hive/Shim13.scala +++ b/sql/hive/v0.13.1/src/main/scala/org/apache/spark/sql/hive/Shim13.scala @@ -17,6 +17,7 @@ package org.apache.spark.sql.hive +import java.util import java.util.{ArrayList => JArrayList} import java.util.Properties import java.rmi.server.UID @@ -38,7 +39,7 @@ import org.apache.hadoop.hive.ql.processors.CommandProcessorFactory import org.apache.hadoop.hive.serde.serdeConstants import org.apache.hadoop.hive.serde2.typeinfo.{TypeInfo, DecimalTypeInfo, TypeInfoFactory} import org.apache.hadoop.hive.serde2.objectinspector.primitive.{HiveDecimalObjectInspector, PrimitiveObjectInspectorFactory} -import org.apache.hadoop.hive.serde2.objectinspector.{PrimitiveObjectInspector, ObjectInspector} +import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspectorConverters, PrimitiveObjectInspector, ObjectInspector} import org.apache.hadoop.hive.serde2.{Deserializer, ColumnProjectionUtils} import org.apache.hadoop.hive.serde2.{io => hiveIo} import org.apache.hadoop.hive.serde2.avro.AvroGenericRecordWritable @@ -400,7 +401,11 @@ private[hive] object HiveShim { Decimal(hdoi.getPrimitiveJavaObject(data).bigDecimalValue(), hdoi.precision(), hdoi.scale()) } } - + + def getConvertedOI(inputOI: ObjectInspector, outputOI: ObjectInspector): ObjectInspector = { + ObjectInspectorConverters.getConvertedOI(inputOI, outputOI) + } + /* * Bug introduced in hive-0.13. AvroGenericRecordWritable has a member recordReaderID that * is needed to initialize before serialization. From 73d57754dd23d84331c10355338a4240b3ac5fee Mon Sep 17 00:00:00 2001 From: Liang-Chi Hsieh Date: Wed, 25 Mar 2015 17:52:23 -0700 Subject: [PATCH 058/591] [SPARK-6326][SQL] Improve castStruct to be faster Current `castStruct` should be very slow. This pr slightly improves it. Author: Liang-Chi Hsieh Closes #5017 from viirya/faster_caststruct and squashes the following commits: 385d5b0 [Liang-Chi Hsieh] Further improved. 746fcfb [Liang-Chi Hsieh] Make castStruct faster. --- .../spark/sql/catalyst/expressions/Cast.scala | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala index 9bde74ac22669..31f1a5fdc7e53 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala @@ -394,10 +394,17 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w val casts = from.fields.zip(to.fields).map { case (fromField, toField) => cast(fromField.dataType, toField.dataType) } - // TODO: This is very slow! - buildCast[Row](_, row => Row(row.toSeq.zip(casts).map { - case (v, cast) => if (v == null) null else cast(v) - }: _*)) + // TODO: Could be faster? + val newRow = new GenericMutableRow(from.fields.size) + buildCast[Row](_, row => { + var i = 0 + while (i < row.length) { + val v = row(i) + newRow.update(i, if (v == null) null else casts(i)(v)) + i += 1 + } + newRow.copy() + }) } private[this] def cast(from: DataType, to: DataType): Any => Any = to match { From 328daf65f8e320d3bdfd12397a8f85891f1d14c7 Mon Sep 17 00:00:00 2001 From: DoingDone9 <799203320@qq.com> Date: Wed, 25 Mar 2015 18:41:59 -0700 Subject: [PATCH 059/591] [SPARK-6271][SQL] Sort these tokens in alphabetic order to avoid further duplicate in HiveQl Author: DoingDone9 <799203320@qq.com> Closes #4973 from DoingDone9/sort_token and squashes the following commits: 855fa10 [DoingDone9] Update HiveQl.scala c7080b3 [DoingDone9] Sort these tokens in alphabetic order to avoid further duplicate in HiveQl c87e8b6 [DoingDone9] Merge pull request #3 from apache/master cb1852d [DoingDone9] Merge pull request #2 from apache/master c3f046f [DoingDone9] Merge pull request #1 from apache/master --- .../org/apache/spark/sql/hive/HiveQl.scala | 88 ++++++++++--------- 1 file changed, 46 insertions(+), 42 deletions(-) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala index 51775eb4cd6a0..c45c4ad70fae9 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala @@ -55,37 +55,8 @@ private[hive] case object NativePlaceholder extends Command /** Provides a mapping from HiveQL statements to catalyst logical plans and expression trees. */ private[hive] object HiveQl { protected val nativeCommands = Seq( - "TOK_DESCFUNCTION", - "TOK_DESCDATABASE", - "TOK_SHOW_CREATETABLE", - "TOK_SHOWCOLUMNS", - "TOK_SHOW_TABLESTATUS", - "TOK_SHOWDATABASES", - "TOK_SHOWFUNCTIONS", - "TOK_SHOWINDEXES", - "TOK_SHOWINDEXES", - "TOK_SHOWPARTITIONS", - "TOK_SHOW_TBLPROPERTIES", - - "TOK_LOCKTABLE", - "TOK_SHOWLOCKS", - "TOK_UNLOCKTABLE", - - "TOK_SHOW_ROLES", - "TOK_CREATEROLE", - "TOK_DROPROLE", - "TOK_GRANT", - "TOK_GRANT_ROLE", - "TOK_REVOKE", - "TOK_SHOW_GRANT", - "TOK_SHOW_ROLE_GRANT", - "TOK_SHOW_SET_ROLE", - - "TOK_CREATEFUNCTION", - "TOK_DROPFUNCTION", - - "TOK_ALTERDATABASE_PROPERTIES", "TOK_ALTERDATABASE_OWNER", + "TOK_ALTERDATABASE_PROPERTIES", "TOK_ALTERINDEX_PROPERTIES", "TOK_ALTERINDEX_REBUILD", "TOK_ALTERTABLE_ADDCOLS", @@ -102,28 +73,61 @@ private[hive] object HiveQl { "TOK_ALTERTABLE_SKEWED", "TOK_ALTERTABLE_TOUCH", "TOK_ALTERTABLE_UNARCHIVE", - "TOK_CREATEDATABASE", - "TOK_CREATEFUNCTION", - "TOK_CREATEINDEX", - "TOK_DROPDATABASE", - "TOK_DROPINDEX", - "TOK_DROPTABLE_PROPERTIES", - "TOK_MSCK", - "TOK_ALTERVIEW_ADDPARTS", "TOK_ALTERVIEW_AS", "TOK_ALTERVIEW_DROPPARTS", "TOK_ALTERVIEW_PROPERTIES", "TOK_ALTERVIEW_RENAME", + + "TOK_CREATEDATABASE", + "TOK_CREATEFUNCTION", + "TOK_CREATEINDEX", + "TOK_CREATEROLE", "TOK_CREATEVIEW", - "TOK_DROPVIEW_PROPERTIES", + + "TOK_DESCDATABASE", + "TOK_DESCFUNCTION", + + "TOK_DROPDATABASE", + "TOK_DROPFUNCTION", + "TOK_DROPINDEX", + "TOK_DROPROLE", + "TOK_DROPTABLE_PROPERTIES", "TOK_DROPVIEW", - + "TOK_DROPVIEW_PROPERTIES", + "TOK_EXPORT", + + "TOK_GRANT", + "TOK_GRANT_ROLE", + "TOK_IMPORT", + "TOK_LOAD", - - "TOK_SWITCHDATABASE" + + "TOK_LOCKTABLE", + + "TOK_MSCK", + + "TOK_REVOKE", + + "TOK_SHOW_CREATETABLE", + "TOK_SHOW_GRANT", + "TOK_SHOW_ROLE_GRANT", + "TOK_SHOW_ROLES", + "TOK_SHOW_SET_ROLE", + "TOK_SHOW_TABLESTATUS", + "TOK_SHOW_TBLPROPERTIES", + "TOK_SHOWCOLUMNS", + "TOK_SHOWDATABASES", + "TOK_SHOWFUNCTIONS", + "TOK_SHOWINDEXES", + "TOK_SHOWLOCKS", + "TOK_SHOWPARTITIONS", + + "TOK_SWITCHDATABASE", + + "TOK_UNLOCKTABLE" ) // Commands that we do not need to explain. From 5ab6e9f0c06b763b06a0e1396bdb08a823146c32 Mon Sep 17 00:00:00 2001 From: Daoyuan Wang Date: Wed, 25 Mar 2015 18:43:26 -0700 Subject: [PATCH 060/591] [SPARK-6202] [SQL] enable variable substitution on test framework Author: Daoyuan Wang Closes #4930 from adrian-wang/testvs and squashes the following commits: 2ce590f [Daoyuan Wang] add explicit function types b1d68bf [Daoyuan Wang] only substitute for parseSql 9c4a950 [Daoyuan Wang] add a comment explaining 18fb481 [Daoyuan Wang] enable variable substitute on test framework --- .../scala/org/apache/spark/sql/hive/test/TestHive.scala | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala index dc61e9d2e3522..a3497eadd67f6 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala @@ -23,6 +23,7 @@ import java.util.{Set => JavaSet} import org.apache.hadoop.hive.ql.exec.FunctionRegistry import org.apache.hadoop.hive.ql.io.avro.{AvroContainerInputFormat, AvroContainerOutputFormat} import org.apache.hadoop.hive.ql.metadata.Table +import org.apache.hadoop.hive.ql.parse.VariableSubstitution import org.apache.hadoop.hive.ql.processors._ import org.apache.hadoop.hive.serde2.RegexSerDe import org.apache.hadoop.hive.serde2.`lazy`.LazySimpleSerDe @@ -153,8 +154,13 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { val describedTable = "DESCRIBE (\\w+)".r + val vs = new VariableSubstitution() + + // we should substitute variables in hql to pass the text to parseSql() as a parameter. + // Hive parser need substituted text. HiveContext.sql() does this but return a DataFrame, + // while we need a logicalPlan so we cannot reuse that. protected[hive] class HiveQLQueryExecution(hql: String) - extends this.QueryExecution(HiveQl.parseSql(hql)) { + extends this.QueryExecution(HiveQl.parseSql(vs.substitute(hiveconf, hql))) { def hiveExec(): Seq[String] = runSqlHive(hql) override def toString: String = hql + "\n" + super.toString } From e87bf3713e684fa83165a1036d76f7a84f043775 Mon Sep 17 00:00:00 2001 From: KaiXinXiaoLei Date: Wed, 25 Mar 2015 19:15:30 -0700 Subject: [PATCH 061/591] =?UTF-8?q?The=20UT=20test=20of=20spark=20is=20fai?= =?UTF-8?q?led.=20Because=20there=20is=20a=20test=20in=20SQLQuerySuite=20a?= =?UTF-8?q?bout=20creating=20table=20=E2=80=9Ctest=E2=80=9D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit If the tests in "sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala" are running before CachedTableSuite.scala, the test("Drop cached table") will failed. Because the table test is created in SQLQuerySuite.scala ,and this table not droped. So when running "drop cached table", table test already exists. There is error info: 01:18:35.738 ERROR hive.ql.exec.DDLTask: org.apache.hadoop.hive.ql.metadata.HiveException: AlreadyExistsException(message:Table test already exists) at org.apache.hadoop.hive.ql.metadata.Hive.createTable(Hive.java:616) at org.apache.hadoop.hive.ql.exec.DDLTask.createTable(DDLTask.java:4189) at org.apache.hadoop.hive.ql.exec.DDLTask.execute(DDLTask.java:281) at org.apache.hadoop.hive.ql.exec.Task.executeTask(Task.java:153) at org.apache.hadoop.hive.ql.exec.TaskRunner.runSequential(TaskRunner.java:85) at org.apache.hadoop.hive.ql.Driver.launchTask(Driver.java:1503) at org.apache.hadoop.hive.ql.Driver.execute(Driver.java:1270) at org.apache.hadoop.hive.ql.Driver.runInternal(Driver.java:1088) at org.apache.hadoop.hive.ql.Driver.run(Driver.java:911) at org.apache.hadoop.hive.ql.Driver.run(Driver.java:901)test” And the test about "create table test" in "sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala,is: test("SPARK-4825 save join to table") { val testData = sparkContext.parallelize(1 to 10).map(i => TestData(i, i.toString)).toDF() sql("CREATE TABLE test1 (key INT, value STRING)") testData.insertInto("test1") sql("CREATE TABLE test2 (key INT, value STRING)") testData.insertInto("test2") testData.insertInto("test2") sql("CREATE TABLE test AS SELECT COUNT(a.value) FROM test1 a JOIN test2 b ON a.key = b.key") checkAnswer( table("test"), sql("SELECT COUNT(a.value) FROM test1 a JOIN test2 b ON a.key = b.key").collect().toSeq) } Author: KaiXinXiaoLei Closes #5150 from KaiXinXiaoLei/testFailed and squashes the following commits: 7534b02 [KaiXinXiaoLei] The UT test of spark is failed. --- .../org/apache/spark/sql/hive/CachedTableSuite.scala | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala index 44d24273e722a..221a0c263d36c 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala @@ -92,12 +92,12 @@ class CachedTableSuite extends QueryTest { } test("Drop cached table") { - sql("CREATE TABLE test(a INT)") - cacheTable("test") - sql("SELECT * FROM test").collect() - sql("DROP TABLE test") + sql("CREATE TABLE cachedTableTest(a INT)") + cacheTable("cachedTableTest") + sql("SELECT * FROM cachedTableTest").collect() + sql("DROP TABLE cachedTableTest") intercept[AnalysisException] { - sql("SELECT * FROM test").collect() + sql("SELECT * FROM cachedTableTest").collect() } } From 276ef1c3cfd44b5fc082e1a495fff22fbaf6add3 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Wed, 25 Mar 2015 19:21:54 -0700 Subject: [PATCH 062/591] [SPARK-6463][SQL] AttributeSet.equal should compare size Previously this could result in sets compare equals when in fact the right was a subset of the left. Based on #5133 by sisihj Author: sisihj Author: Michael Armbrust Closes #5194 from marmbrus/pr/5133 and squashes the following commits: 5ed4615 [Michael Armbrust] fix imports d4cbbc0 [Michael Armbrust] Add test cases 0a0834f [sisihj] AttributeSet.equal should compare size --- .../catalyst/expressions/AttributeSet.scala | 3 +- .../expressions/AttributeSetSuite.scala | 82 +++++++++++++++++++ 2 files changed, 84 insertions(+), 1 deletion(-) create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/AttributeSetSuite.scala diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeSet.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeSet.scala index f9ae85a5cfc1b..11b4eb5c888be 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeSet.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeSet.scala @@ -58,7 +58,8 @@ class AttributeSet private (val baseSet: Set[AttributeEquals]) /** Returns true if the members of this AttributeSet and other are the same. */ override def equals(other: Any): Boolean = other match { - case otherSet: AttributeSet => baseSet.map(_.a).forall(otherSet.contains) + case otherSet: AttributeSet => + otherSet.size == baseSet.size && baseSet.map(_.a).forall(otherSet.contains) case _ => false } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/AttributeSetSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/AttributeSetSuite.scala new file mode 100644 index 0000000000000..f2f3a84d19380 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/AttributeSetSuite.scala @@ -0,0 +1,82 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.scalatest.FunSuite + +import org.apache.spark.sql.types.IntegerType + +class AttributeSetSuite extends FunSuite { + + val aUpper = AttributeReference("A", IntegerType)(exprId = ExprId(1)) + val aLower = AttributeReference("a", IntegerType)(exprId = ExprId(1)) + val fakeA = AttributeReference("a", IntegerType)(exprId = ExprId(3)) + val aSet = AttributeSet(aLower :: Nil) + + val bUpper = AttributeReference("B", IntegerType)(exprId = ExprId(2)) + val bLower = AttributeReference("b", IntegerType)(exprId = ExprId(2)) + val bSet = AttributeSet(bUpper :: Nil) + + val aAndBSet = AttributeSet(aUpper :: bUpper :: Nil) + + test("sanity check") { + assert(aUpper != aLower) + assert(bUpper != bLower) + } + + test("checks by id not name") { + assert(aSet.contains(aUpper) === true) + assert(aSet.contains(aLower) === true) + assert(aSet.contains(fakeA) === false) + + assert(aSet.contains(bUpper) === false) + assert(aSet.contains(bLower) === false) + } + + test("++ preserves AttributeSet") { + assert((aSet ++ bSet).contains(aUpper) === true) + assert((aSet ++ bSet).contains(aLower) === true) + } + + test("extracts all references references") { + val addSet = AttributeSet(Add(aUpper, Alias(bUpper, "test")()):: Nil) + assert(addSet.contains(aUpper)) + assert(addSet.contains(aLower)) + assert(addSet.contains(bUpper)) + assert(addSet.contains(bLower)) + } + + test("dedups attributes") { + assert(AttributeSet(aUpper :: aLower :: Nil).size === 1) + } + + test("subset") { + assert(aSet.subsetOf(aAndBSet) === true) + assert(aAndBSet.subsetOf(aSet) === false) + } + + test("equality") { + assert(aSet != aAndBSet) + assert(aAndBSet != aSet) + assert(aSet != bSet) + assert(bSet != aSet) + + assert(aSet == aSet) + assert(aSet == AttributeSet(aUpper :: Nil)) + } +} From f535802977c5a3ce45894d89fdf59f8723f023c8 Mon Sep 17 00:00:00 2001 From: Davies Liu Date: Thu, 26 Mar 2015 00:01:24 -0700 Subject: [PATCH 063/591] [SPARK-6536] [PySpark] Column.inSet() in Python ``` >>> df[df.name.inSet("Bob", "Mike")].collect() [Row(age=5, name=u'Bob')] >>> df[df.age.inSet([1, 2, 3])].collect() [Row(age=2, name=u'Alice')] ``` Author: Davies Liu Closes #5190 from davies/in and squashes the following commits: 6b73a47 [Davies Liu] Column.inSet() in Python --- python/pyspark/sql/dataframe.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index 5cb89da7a8ed5..bf7c47b7261a9 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -985,6 +985,23 @@ def substr(self, startPos, length): __getslice__ = substr + def inSet(self, *cols): + """ A boolean expression that is evaluated to true if the value of this + expression is contained by the evaluated values of the arguments. + + >>> df[df.name.inSet("Bob", "Mike")].collect() + [Row(age=5, name=u'Bob')] + >>> df[df.age.inSet([1, 2, 3])].collect() + [Row(age=2, name=u'Alice')] + """ + if len(cols) == 1 and isinstance(cols[0], (list, set)): + cols = cols[0] + cols = [c._jc if isinstance(c, Column) else _create_column_from_literal(c) for c in cols] + sc = SparkContext._active_spark_context + jcols = ListConverter().convert(cols, sc._gateway._gateway_client) + jc = getattr(self._jc, "in")(sc._jvm.PythonUtils.toSeq(jcols)) + return Column(jc) + # order asc = _unary_op("asc", "Returns a sort expression based on the" " ascending order of the given column name.") From 5bbcd1304cfebba31ec6857a80d3825a40d02e83 Mon Sep 17 00:00:00 2001 From: azagrebin Date: Thu, 26 Mar 2015 00:25:04 -0700 Subject: [PATCH 064/591] [SPARK-6117] [SQL] add describe function to DataFrame for summary statis... Please review my solution for SPARK-6117 Author: azagrebin Closes #5073 from azagrebin/SPARK-6117 and squashes the following commits: f9056ac [azagrebin] [SPARK-6117] [SQL] create one aggregation and split it locally into resulting DF, colocate test data with test case ddb3950 [azagrebin] [SPARK-6117] [SQL] simplify implementation, add test for DF without numeric columns 9daf31e [azagrebin] [SPARK-6117] [SQL] add describe function to DataFrame for summary statistics --- .../org/apache/spark/sql/DataFrame.scala | 53 ++++++++++++++++++- .../org/apache/spark/sql/DataFrameSuite.scala | 45 ++++++++++++++++ 2 files changed, 97 insertions(+), 1 deletion(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 5aece166aad22..db561825e676b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -41,7 +41,7 @@ import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, LogicalRDD} import org.apache.spark.sql.jdbc.JDBCWriteDetails import org.apache.spark.sql.json.JsonRDD -import org.apache.spark.sql.types.{NumericType, StructType} +import org.apache.spark.sql.types.{NumericType, StructType, StructField, StringType} import org.apache.spark.sql.sources.{ResolvedDataSource, CreateTableUsingAsSelect} import org.apache.spark.util.Utils @@ -751,6 +751,57 @@ class DataFrame private[sql]( select(colNames :_*) } + /** + * Compute numerical statistics for given columns of this [[DataFrame]]: + * count, mean (avg), stddev (standard deviation), min, max. + * Each row of the resulting [[DataFrame]] contains column with statistic name + * and columns with statistic results for each given column. + * If no columns are given then computes for all numerical columns. + * + * {{{ + * df.describe("age", "height") + * + * // summary age height + * // count 10.0 10.0 + * // mean 53.3 178.05 + * // stddev 11.6 15.7 + * // min 18.0 163.0 + * // max 92.0 192.0 + * }}} + */ + @scala.annotation.varargs + def describe(cols: String*): DataFrame = { + + def stddevExpr(expr: Expression) = + Sqrt(Subtract(Average(Multiply(expr, expr)), Multiply(Average(expr), Average(expr)))) + + val statistics = List[(String, Expression => Expression)]( + "count" -> Count, + "mean" -> Average, + "stddev" -> stddevExpr, + "min" -> Min, + "max" -> Max) + + val aggCols = (if (cols.isEmpty) numericColumns.map(_.prettyString) else cols).toList + + val localAgg = if (aggCols.nonEmpty) { + val aggExprs = statistics.flatMap { case (_, colToAgg) => + aggCols.map(c => Column(colToAgg(Column(c).expr)).as(c)) + } + + agg(aggExprs.head, aggExprs.tail: _*).head().toSeq + .grouped(aggCols.size).toSeq.zip(statistics).map { case (aggregation, (statistic, _)) => + Row(statistic :: aggregation.toList: _*) + } + } else { + statistics.map { case (name, _) => Row(name) } + } + + val schema = StructType(("summary" :: aggCols).map(StructField(_, StringType))) + val rowRdd = sqlContext.sparkContext.parallelize(localAgg) + sqlContext.createDataFrame(rowRdd, schema) + } + /** * Returns the first `n` rows. * @group action diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index c30ed694a62f0..afbedd1e5825d 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -443,6 +443,51 @@ class DataFrameSuite extends QueryTest { assert(df.schema.map(_.name).toSeq === Seq("key", "valueRenamed", "newCol")) } + test("describe") { + + val describeTestData = Seq( + ("Bob", 16, 176), + ("Alice", 32, 164), + ("David", 60, 192), + ("Amy", 24, 180)).toDF("name", "age", "height") + + val describeResult = Seq( + Row("count", 4, 4), + Row("mean", 33.0, 178.0), + Row("stddev", 16.583123951777, 10.0), + Row("min", 16, 164), + Row("max", 60, 192)) + + val emptyDescribeResult = Seq( + Row("count", 0, 0), + Row("mean", null, null), + Row("stddev", null, null), + Row("min", null, null), + Row("max", null, null)) + + def getSchemaAsSeq(df: DataFrame) = df.schema.map(_.name).toSeq + + val describeTwoCols = describeTestData.describe("age", "height") + assert(getSchemaAsSeq(describeTwoCols) === Seq("summary", "age", "height")) + checkAnswer(describeTwoCols, describeResult) + + val describeAllCols = describeTestData.describe() + assert(getSchemaAsSeq(describeAllCols) === Seq("summary", "age", "height")) + checkAnswer(describeAllCols, describeResult) + + val describeOneCol = describeTestData.describe("age") + assert(getSchemaAsSeq(describeOneCol) === Seq("summary", "age")) + checkAnswer(describeOneCol, describeResult.map { case Row(s, d, _) => Row(s, d)} ) + + val describeNoCol = describeTestData.select("name").describe() + assert(getSchemaAsSeq(describeNoCol) === Seq("summary")) + checkAnswer(describeNoCol, describeResult.map { case Row(s, _, _) => Row(s)} ) + + val emptyDescription = describeTestData.limit(0).describe() + assert(getSchemaAsSeq(emptyDescription) === Seq("summary", "age", "height")) + checkAnswer(emptyDescription, emptyDescribeResult) + } + test("apply on query results (SPARK-5462)") { val df = testData.sqlContext.sql("select key from testData") checkAnswer(df.select(df("key")), testData.select('key).collect().toSeq) From 855cba8fe59ffe17b51ed00fbbb5d3d7cf17ade9 Mon Sep 17 00:00:00 2001 From: DoingDone9 <799203320@qq.com> Date: Thu, 26 Mar 2015 17:04:19 +0800 Subject: [PATCH 065/591] [SPARK-6546][Build] Using the wrong code that will make spark compile failed!! wrong code : val tmpDir = Files.createTempDir() not Files should Utils Author: DoingDone9 <799203320@qq.com> Closes #5198 from DoingDone9/FilesBug and squashes the following commits: 6e0140d [DoingDone9] Update InsertIntoHiveTableSuite.scala e57d23f [DoingDone9] Update InsertIntoHiveTableSuite.scala 802261c [DoingDone9] Merge pull request #7 from apache/master d00303b [DoingDone9] Merge pull request #6 from apache/master 98b134f [DoingDone9] Merge pull request #5 from apache/master 161cae3 [DoingDone9] Merge pull request #4 from apache/master c87e8b6 [DoingDone9] Merge pull request #3 from apache/master cb1852d [DoingDone9] Merge pull request #2 from apache/master c3f046f [DoingDone9] Merge pull request #1 from apache/master --- .../org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala index aa6fb42de7f88..8011952e0d535 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala @@ -198,7 +198,7 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter { val testDatawithNull = TestHive.sparkContext.parallelize( (1 to 10).map(i => ThreeCloumntable(i, i.toString,null))).toDF() - val tmpDir = Files.createTempDir() + val tmpDir = Utils.createTempDir() sql(s"CREATE TABLE table_with_partition(key int,value string) PARTITIONED by (ds string) location '${tmpDir.toURI.toString}' ") sql("INSERT OVERWRITE TABLE table_with_partition partition (ds='1') SELECT key,value FROM testData") From f88f51bbd461e0a42ad7021147268509b9c3c56e Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Thu, 26 Mar 2015 18:46:57 +0800 Subject: [PATCH 066/591] [SPARK-6465][SQL] Fix serialization of GenericRowWithSchema using kryo Author: Michael Armbrust Closes #5191 from marmbrus/kryoRowsWithSchema and squashes the following commits: bb83522 [Michael Armbrust] Fix serialization of GenericRowWithSchema using kryo f914f16 [Michael Armbrust] Add no arg constructor to GenericRowWithSchema --- .../spark/sql/catalyst/expressions/rows.scala | 7 +++++-- .../org/apache/spark/sql/types/Metadata.scala | 3 +++ .../org/apache/spark/sql/types/dataTypes.scala | 18 ++++++++++++++++++ .../sql/execution/SparkSqlSerializer.scala | 4 +--- .../scala/org/apache/spark/sql/RowSuite.scala | 12 ++++++++++++ 5 files changed, 39 insertions(+), 5 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala index 8bba26bc4cf7f..a8983df208318 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala @@ -66,7 +66,7 @@ object EmptyRow extends Row { */ class GenericRow(protected[sql] val values: Array[Any]) extends Row { /** No-arg constructor for serialization. */ - def this() = this(null) + protected def this() = this(null) def this(size: Int) = this(new Array[Any](size)) @@ -172,11 +172,14 @@ class GenericRow(protected[sql] val values: Array[Any]) extends Row { class GenericRowWithSchema(values: Array[Any], override val schema: StructType) extends GenericRow(values) { + + /** No-arg constructor for serialization. */ + protected def this() = this(null, null) } class GenericMutableRow(v: Array[Any]) extends GenericRow(v) with MutableRow { /** No-arg constructor for serialization. */ - def this() = this(null) + protected def this() = this(null) def this(size: Int) = this(new Array[Any](size)) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Metadata.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Metadata.scala index e50e9761431f5..6ee24ee0c1913 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Metadata.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Metadata.scala @@ -41,6 +41,9 @@ import org.apache.spark.annotation.DeveloperApi sealed class Metadata private[types] (private[types] val map: Map[String, Any]) extends Serializable { + /** No-arg constructor for kryo. */ + protected def this() = this(null) + /** Tests whether this Metadata contains a binding for a key. */ def contains(key: String): Boolean = map.contains(key) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala index d973144de3468..952cf5c75688d 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala @@ -670,6 +670,10 @@ case class PrecisionInfo(precision: Int, scale: Int) */ @DeveloperApi case class DecimalType(precisionInfo: Option[PrecisionInfo]) extends FractionalType { + + /** No-arg constructor for kryo. */ + protected def this() = this(null) + private[sql] type JvmType = Decimal @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } private[sql] val numeric = Decimal.DecimalIsFractional @@ -819,6 +823,10 @@ object ArrayType { */ @DeveloperApi case class ArrayType(elementType: DataType, containsNull: Boolean) extends DataType { + + /** No-arg constructor for kryo. */ + protected def this() = this(null, false) + private[sql] def buildFormattedString(prefix: String, builder: StringBuilder): Unit = { builder.append( s"$prefix-- element: ${elementType.typeName} (containsNull = $containsNull)\n") @@ -857,6 +865,9 @@ case class StructField( nullable: Boolean = true, metadata: Metadata = Metadata.empty) { + /** No-arg constructor for kryo. */ + protected def this() = this(null, null) + private[sql] def buildFormattedString(prefix: String, builder: StringBuilder): Unit = { builder.append(s"$prefix-- $name: ${dataType.typeName} (nullable = $nullable)\n") DataType.buildFormattedString(dataType, s"$prefix |", builder) @@ -1003,6 +1014,9 @@ object StructType { @DeveloperApi case class StructType(fields: Array[StructField]) extends DataType with Seq[StructField] { + /** No-arg constructor for kryo. */ + protected def this() = this(null) + /** Returns all field names in an array. */ def fieldNames: Array[String] = fields.map(_.name) @@ -1121,6 +1135,10 @@ case class MapType( keyType: DataType, valueType: DataType, valueContainsNull: Boolean) extends DataType { + + /** No-arg constructor for kryo. */ + def this() = this(null, null, false) + private[sql] def buildFormattedString(prefix: String, builder: StringBuilder): Unit = { builder.append(s"$prefix-- key: ${keyType.typeName}\n") builder.append(s"$prefix-- value: ${valueType.typeName} " + diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala index c4534fd5f67e4..967bd76b302d8 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala @@ -39,7 +39,7 @@ import org.apache.spark.sql.catalyst.expressions.codegen.{IntegerHashSet, LongHa private[sql] class SparkSqlSerializer(conf: SparkConf) extends KryoSerializer(conf) { override def newKryo(): Kryo = { - val kryo = new Kryo() + val kryo = super.newKryo() kryo.setRegistrationRequired(false) kryo.register(classOf[MutablePair[_, _]]) kryo.register(classOf[org.apache.spark.sql.catalyst.expressions.GenericRow]) @@ -57,8 +57,6 @@ private[sql] class SparkSqlSerializer(conf: SparkConf) extends KryoSerializer(co kryo.register(classOf[Decimal]) kryo.setReferences(false) - kryo.setClassLoader(Utils.getSparkClassLoader) - new AllScalaRegistrar().apply(kryo) kryo } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala index f5b945f468dad..36465cc2fa11a 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala @@ -17,9 +17,12 @@ package org.apache.spark.sql +import org.apache.spark.sql.execution.SparkSqlSerializer import org.scalatest.FunSuite import org.apache.spark.sql.catalyst.expressions.{GenericMutableRow, SpecificMutableRow} +import org.apache.spark.sql.test.TestSQLContext +import org.apache.spark.sql.test.TestSQLContext.implicits._ import org.apache.spark.sql.types._ class RowSuite extends FunSuite { @@ -50,4 +53,13 @@ class RowSuite extends FunSuite { row(0) = null assert(row.isNullAt(0)) } + + test("serialize w/ kryo") { + val row = Seq((1, Seq(1), Map(1 -> 1), BigDecimal(1))).toDF().first() + val serializer = new SparkSqlSerializer(TestSQLContext.sparkContext.getConf) + val instance = serializer.newInstance() + val ser = instance.serialize(row) + val de = instance.deserialize(ser).asInstanceOf[Row] + assert(de === row) + } } From 0c88ce5416d7687bc806a7655e17009ad5823d30 Mon Sep 17 00:00:00 2001 From: zsxwing Date: Thu, 26 Mar 2015 12:54:48 +0000 Subject: [PATCH 067/591] [SPARK-6468][Block Manager] Fix the race condition of subDirs in DiskBlockManager There are two race conditions of `subDirs` in `DiskBlockManager`: 1. `getAllFiles` does not use correct locks to read the contents in `subDirs`. Although it's designed for testing, it's still worth to add correct locks to eliminate the race condition. 2. The double-check has a race condition in `getFile(filename: String)`. If a thread finds `subDirs(dirId)(subDirId)` is not null out of the `synchronized` block, it may not be able to see the correct content of the File instance pointed by `subDirs(dirId)(subDirId)` according to the Java memory model (there is no volatile variable here). This PR fixed the above race conditions. Author: zsxwing Closes #5136 from zsxwing/SPARK-6468 and squashes the following commits: cbb872b [zsxwing] Fix the race condition of subDirs in DiskBlockManager --- .../spark/storage/DiskBlockManager.scala | 32 +++++++++++-------- 1 file changed, 18 insertions(+), 14 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala index 12cd8ea3bdf1f..2883137872600 100644 --- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala @@ -47,6 +47,8 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon logError("Failed to create any local dir.") System.exit(ExecutorExitCode.DISK_STORE_FAILED_TO_CREATE_DIR) } + // The content of subDirs is immutable but the content of subDirs(i) is mutable. And the content + // of subDirs(i) is protected by the lock of subDirs(i) private val subDirs = Array.fill(localDirs.length)(new Array[File](subDirsPerLocalDir)) private val shutdownHook = addShutdownHook() @@ -61,20 +63,17 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon val subDirId = (hash / localDirs.length) % subDirsPerLocalDir // Create the subdirectory if it doesn't already exist - var subDir = subDirs(dirId)(subDirId) - if (subDir == null) { - subDir = subDirs(dirId).synchronized { - val old = subDirs(dirId)(subDirId) - if (old != null) { - old - } else { - val newDir = new File(localDirs(dirId), "%02x".format(subDirId)) - if (!newDir.exists() && !newDir.mkdir()) { - throw new IOException(s"Failed to create local dir in $newDir.") - } - subDirs(dirId)(subDirId) = newDir - newDir + val subDir = subDirs(dirId).synchronized { + val old = subDirs(dirId)(subDirId) + if (old != null) { + old + } else { + val newDir = new File(localDirs(dirId), "%02x".format(subDirId)) + if (!newDir.exists() && !newDir.mkdir()) { + throw new IOException(s"Failed to create local dir in $newDir.") } + subDirs(dirId)(subDirId) = newDir + newDir } } @@ -91,7 +90,12 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon /** List all the files currently stored on disk by the disk manager. */ def getAllFiles(): Seq[File] = { // Get all the files inside the array of array of directories - subDirs.flatten.filter(_ != null).flatMap { dir => + subDirs.flatMap { dir => + dir.synchronized { + // Copy the content of dir because it may be modified in other threads + dir.clone() + } + }.filter(_ != null).flatMap { dir => val files = dir.listFiles() if (files != null) files else Seq.empty } From 1c05027a143d1b0bf3df192984e6cac752b1e926 Mon Sep 17 00:00:00 2001 From: Yash Datta Date: Thu, 26 Mar 2015 21:13:38 +0800 Subject: [PATCH 068/591] [SQL][SPARK-6471]: Metastore schema should only be a subset of parquet schema to support dropping of columns using replace columns Currently in the parquet relation 2 implementation, error is thrown in case merged schema is not exactly the same as metastore schema. But to support cases like deletion of column using replace column command, we can relax the restriction so that even if metastore schema is a subset of merged parquet schema, the query will work. Author: Yash Datta Closes #5141 from saucam/replace_col and squashes the following commits: e858d5b [Yash Datta] SPARK-6471: Fix test cases, add a new test case for metastore schema to be subset of parquet schema 5f2f467 [Yash Datta] SPARK-6471: Metastore schema should only be a subset of parquet schema to support dropping of columns using replace columns --- .../apache/spark/sql/parquet/newParquet.scala | 5 +++-- .../spark/sql/parquet/ParquetSchemaSuite.scala | 18 ++++++++++++++++-- 2 files changed, 19 insertions(+), 4 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala index 410600b0529d3..3516cfe680c61 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala @@ -758,12 +758,13 @@ private[sql] object ParquetRelation2 extends Logging { |${parquetSchema.prettyJson} """.stripMargin - assert(metastoreSchema.size == parquetSchema.size, schemaConflictMessage) + assert(metastoreSchema.size <= parquetSchema.size, schemaConflictMessage) val ordinalMap = metastoreSchema.zipWithIndex.map { case (field, index) => field.name.toLowerCase -> index }.toMap - val reorderedParquetSchema = parquetSchema.sortBy(f => ordinalMap(f.name.toLowerCase)) + val reorderedParquetSchema = parquetSchema.sortBy(f => + ordinalMap.getOrElse(f.name.toLowerCase, metastoreSchema.size + 1)) StructType(metastoreSchema.zip(reorderedParquetSchema).map { // Uses Parquet field names but retains Metastore data types. diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala index 321832cd43211..8462f9bb2d620 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala @@ -212,8 +212,11 @@ class ParquetSchemaSuite extends FunSuite with ParquetTest { StructField("UPPERCase", IntegerType, nullable = true)))) } - // Conflicting field count - assert(intercept[Throwable] { + // MetaStore schema is subset of parquet schema + assertResult( + StructType(Seq( + StructField("UPPERCase", DoubleType, nullable = false)))) { + ParquetRelation2.mergeMetastoreParquetSchema( StructType(Seq( StructField("uppercase", DoubleType, nullable = false))), @@ -221,6 +224,17 @@ class ParquetSchemaSuite extends FunSuite with ParquetTest { StructType(Seq( StructField("lowerCase", BinaryType), StructField("UPPERCase", IntegerType, nullable = true)))) + } + + // Conflicting field count + assert(intercept[Throwable] { + ParquetRelation2.mergeMetastoreParquetSchema( + StructType(Seq( + StructField("uppercase", DoubleType, nullable = false), + StructField("lowerCase", BinaryType))), + + StructType(Seq( + StructField("UPPERCase", IntegerType, nullable = true)))) }.getMessage.contains("detected conflicting schemas")) // Conflicting field names From 3ddb975faeddeb2674a7e7f7e80cf90dfbd4d6d2 Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Thu, 26 Mar 2015 13:27:05 +0000 Subject: [PATCH 069/591] [MLlib]remove unused import minor thing. Let me know if jira is required. Author: Yuhao Yang Closes #5207 from hhbyyh/adjustImport and squashes the following commits: 2240121 [Yuhao Yang] remove unused import --- .../src/main/scala/org/apache/spark/mllib/clustering/LDA.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala index 5e17c8da61134..9d63a08e211bc 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala @@ -19,7 +19,7 @@ package org.apache.spark.mllib.clustering import java.util.Random -import breeze.linalg.{DenseVector => BDV, normalize, axpy => brzAxpy} +import breeze.linalg.{DenseVector => BDV, normalize} import org.apache.spark.Logging import org.apache.spark.annotation.Experimental From fe15ea976073edd738c006af1eb8d31617a039fc Mon Sep 17 00:00:00 2001 From: Sean Owen Date: Thu, 26 Mar 2015 15:00:23 +0000 Subject: [PATCH 070/591] SPARK-6480 [CORE] histogram() bucket function is wrong in some simple edge cases Fix fastBucketFunction for histogram() to handle edge conditions more correctly. Add a test, and fix existing one accordingly Author: Sean Owen Closes #5148 from srowen/SPARK-6480 and squashes the following commits: 974a0a0 [Sean Owen] Additional test of huge ranges, and a few more comments (and comment fixes) 23ec01e [Sean Owen] Fix fastBucketFunction for histogram() to handle edge conditions more correctly. Add a test, and fix existing one accordingly --- .../apache/spark/rdd/DoubleRDDFunctions.scala | 20 +++++++--------- .../org/apache/spark/rdd/DoubleRDDSuite.scala | 24 +++++++++++++++---- 2 files changed, 29 insertions(+), 15 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala index 03afc289736bb..71e6e300fec5f 100644 --- a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala @@ -191,25 +191,23 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { } } // Determine the bucket function in constant time. Requires that buckets are evenly spaced - def fastBucketFunction(min: Double, increment: Double, count: Int)(e: Double): Option[Int] = { + def fastBucketFunction(min: Double, max: Double, count: Int)(e: Double): Option[Int] = { // If our input is not a number unless the increment is also NaN then we fail fast - if (e.isNaN()) { - return None - } - val bucketNumber = (e - min)/(increment) - // We do this rather than buckets.lengthCompare(bucketNumber) - // because Array[Double] fails to override it (for now). - if (bucketNumber > count || bucketNumber < 0) { + if (e.isNaN || e < min || e > max) { None } else { - Some(bucketNumber.toInt.min(count - 1)) + // Compute ratio of e's distance along range to total range first, for better precision + val bucketNumber = (((e - min) / (max - min)) * count).toInt + // should be less than count, but will equal count if e == max, in which case + // it's part of the last end-range-inclusive bucket, so return count-1 + Some(math.min(bucketNumber, count - 1)) } } // Decide which bucket function to pass to histogramPartition. We decide here - // rather than having a general function so that the decission need only be made + // rather than having a general function so that the decision need only be made // once rather than once per shard val bucketFunction = if (evenBuckets) { - fastBucketFunction(buckets(0), buckets(1)-buckets(0), buckets.length-1) _ + fastBucketFunction(buckets.head, buckets.last, buckets.length - 1) _ } else { basicBucketFunction _ } diff --git a/core/src/test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala index 4cd0f97368ca3..97079382c716f 100644 --- a/core/src/test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala @@ -235,6 +235,12 @@ class DoubleRDDSuite extends FunSuite with SharedSparkContext { assert(histogramBuckets === expectedHistogramBuckets) } + test("WorksWithDoubleValuesAtMinMax") { + val rdd = sc.parallelize(Seq(1, 1, 1, 2, 3, 3)) + assert(Array(3, 0, 1, 2) === rdd.map(_.toDouble).histogram(4)._2) + assert(Array(3, 1, 2) === rdd.map(_.toDouble).histogram(3)._2) + } + test("WorksWithoutBucketsWithMoreRequestedThanElements") { // Verify the basic case of one bucket and all elements in that bucket works val rdd = sc.parallelize(Seq(1, 2)) @@ -248,7 +254,7 @@ class DoubleRDDSuite extends FunSuite with SharedSparkContext { } test("WorksWithoutBucketsForLargerDatasets") { - // Verify the case of slighly larger datasets + // Verify the case of slightly larger datasets val rdd = sc.parallelize(6 to 99) val (histogramBuckets, histogramResults) = rdd.histogram(8) val expectedHistogramResults = @@ -259,17 +265,27 @@ class DoubleRDDSuite extends FunSuite with SharedSparkContext { assert(histogramBuckets === expectedHistogramBuckets) } - test("WorksWithoutBucketsWithIrrationalBucketEdges") { - // Verify the case of buckets with irrational edges. See #SPARK-2862. + test("WorksWithoutBucketsWithNonIntegralBucketEdges") { + // Verify the case of buckets with nonintegral edges. See #SPARK-2862. val rdd = sc.parallelize(6 to 99) val (histogramBuckets, histogramResults) = rdd.histogram(9) + // Buckets are 6.0, 16.333333333333336, 26.666666666666668, 37.0, 47.333333333333336 ... val expectedHistogramResults = - Array(11, 10, 11, 10, 10, 11, 10, 10, 11) + Array(11, 10, 10, 11, 10, 10, 11, 10, 11) assert(histogramResults === expectedHistogramResults) assert(histogramBuckets(0) === 6.0) assert(histogramBuckets(9) === 99.0) } + test("WorksWithHugeRange") { + val rdd = sc.parallelize(Array(0, 1.0e24, 1.0e30)) + val histogramResults = rdd.histogram(1000000)._2 + assert(histogramResults(0) === 1) + assert(histogramResults(1) === 1) + assert(histogramResults.last === 1) + assert((2 to histogramResults.length - 2).forall(i => histogramResults(i) == 0)) + } + // Test the failure mode with an invalid RDD test("ThrowsExceptionOnInvalidRDDs") { // infinity From c3a52a08248db08eade29b265f02483144a282d6 Mon Sep 17 00:00:00 2001 From: Sean Owen Date: Thu, 26 Mar 2015 10:52:31 -0700 Subject: [PATCH 071/591] SPARK-6532 [BUILD] LDAModel.scala fails scalastyle on Windows Use standard UTF-8 source / report encoding for scalastyle Author: Sean Owen Closes #5211 from srowen/SPARK-6532 and squashes the following commits: 16a33e5 [Sean Owen] Use standard UTF-8 source / report encoding for scalastyle --- pom.xml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pom.xml b/pom.xml index 23bb16130b504..b3cecd1893a06 100644 --- a/pom.xml +++ b/pom.xml @@ -1452,7 +1452,8 @@ ${basedir}/src/test/scala scalastyle-config.xml scalastyle-output.xml - UTF-8 + ${project.build.sourceEncoding} + ${project.reporting.outputEncoding} From 784fcd532784fcfd9bf0a1db71c9f71c469ee716 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Thu, 26 Mar 2015 12:26:13 -0700 Subject: [PATCH 072/591] [SPARK-6117] [SQL] Improvements to DataFrame.describe() 1. Slightly modifications to the code to make it more readable. 2. Added Python implementation. 3. Updated the documentation to state that we don't guarantee the output schema for this function and it should only be used for exploratory data analysis. Author: Reynold Xin Closes #5201 from rxin/df-describe and squashes the following commits: 25a7834 [Reynold Xin] Reset run-tests. 6abdfee [Reynold Xin] [SPARK-6117] [SQL] Improvements to DataFrame.describe() --- python/pyspark/sql/dataframe.py | 19 ++++++++ .../org/apache/spark/sql/DataFrame.scala | 46 +++++++++++-------- .../org/apache/spark/sql/DataFrameSuite.scala | 3 +- 3 files changed, 48 insertions(+), 20 deletions(-) diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index bf7c47b7261a9..d51309f7ef5aa 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -520,6 +520,25 @@ def sort(self, *cols): orderBy = sort + def describe(self, *cols): + """Computes statistics for numeric columns. + + This include count, mean, stddev, min, and max. If no columns are + given, this function computes statistics for all numerical columns. + + >>> df.describe().show() + summary age + count 2 + mean 3.5 + stddev 1.5 + min 2 + max 5 + """ + cols = ListConverter().convert(cols, + self.sql_ctx._sc._gateway._gateway_client) + jdf = self._jdf.describe(self.sql_ctx._sc._jvm.PythonUtils.toSeq(cols)) + return DataFrame(jdf, self.sql_ctx) + def head(self, n=None): """ Return the first `n` rows or the first row if n is None. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index db561825e676b..4c80359cf07af 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -33,7 +33,7 @@ import org.apache.spark.api.java.JavaRDD import org.apache.spark.api.python.SerDeUtil import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel -import org.apache.spark.sql.catalyst.{expressions, ScalaReflection, SqlParser} +import org.apache.spark.sql.catalyst.{ScalaReflection, SqlParser} import org.apache.spark.sql.catalyst.analysis.{UnresolvedRelation, ResolvedStar} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.{JoinType, Inner} @@ -41,7 +41,7 @@ import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, LogicalRDD} import org.apache.spark.sql.jdbc.JDBCWriteDetails import org.apache.spark.sql.json.JsonRDD -import org.apache.spark.sql.types.{NumericType, StructType, StructField, StringType} +import org.apache.spark.sql.types._ import org.apache.spark.sql.sources.{ResolvedDataSource, CreateTableUsingAsSelect} import org.apache.spark.util.Utils @@ -752,15 +752,17 @@ class DataFrame private[sql]( } /** - * Compute numerical statistics for given columns of this [[DataFrame]]: - * count, mean (avg), stddev (standard deviation), min, max. - * Each row of the resulting [[DataFrame]] contains column with statistic name - * and columns with statistic results for each given column. - * If no columns are given then computes for all numerical columns. + * Computes statistics for numeric columns, including count, mean, stddev, min, and max. + * If no columns are given, this function computes statistics for all numerical columns. + * + * This function is meant for exploratory data analysis, as we make no guarantee about the + * backward compatibility of the schema of the resulting [[DataFrame]]. If you want to + * programmatically compute summary statistics, use the `agg` function instead. * * {{{ - * df.describe("age", "height") + * df.describe("age", "height").show() * + * // output: * // summary age height * // count 10.0 10.0 * // mean 53.3 178.05 @@ -768,13 +770,17 @@ class DataFrame private[sql]( * // min 18.0 163.0 * // max 92.0 192.0 * }}} + * + * @group action */ @scala.annotation.varargs def describe(cols: String*): DataFrame = { - def stddevExpr(expr: Expression) = + // TODO: Add stddev as an expression, and remove it from here. + def stddevExpr(expr: Expression): Expression = Sqrt(Subtract(Average(Multiply(expr, expr)), Multiply(Average(expr), Average(expr)))) + // The list of summary statistics to compute, in the form of expressions. val statistics = List[(String, Expression => Expression)]( "count" -> Count, "mean" -> Average, @@ -782,24 +788,28 @@ class DataFrame private[sql]( "min" -> Min, "max" -> Max) - val aggCols = (if (cols.isEmpty) numericColumns.map(_.prettyString) else cols).toList + val outputCols = (if (cols.isEmpty) numericColumns.map(_.prettyString) else cols).toList - val localAgg = if (aggCols.nonEmpty) { + val ret: Seq[Row] = if (outputCols.nonEmpty) { val aggExprs = statistics.flatMap { case (_, colToAgg) => - aggCols.map(c => Column(colToAgg(Column(c).expr)).as(c)) + outputCols.map(c => Column(colToAgg(Column(c).expr)).as(c)) } - agg(aggExprs.head, aggExprs.tail: _*).head().toSeq - .grouped(aggCols.size).toSeq.zip(statistics).map { case (aggregation, (statistic, _)) => - Row(statistic :: aggregation.toList: _*) + val row = agg(aggExprs.head, aggExprs.tail: _*).head().toSeq + + // Pivot the data so each summary is one row + row.grouped(outputCols.size).toSeq.zip(statistics).map { + case (aggregation, (statistic, _)) => Row(statistic :: aggregation.toList: _*) } } else { + // If there are no output columns, just output a single column that contains the stats. statistics.map { case (name, _) => Row(name) } } - val schema = StructType(("summary" :: aggCols).map(StructField(_, StringType))) - val rowRdd = sqlContext.sparkContext.parallelize(localAgg) - sqlContext.createDataFrame(rowRdd, schema) + // The first column is string type, and the rest are double type. + val schema = StructType( + StructField("summary", StringType) :: outputCols.map(StructField(_, DoubleType))).toAttributes + LocalRelation(schema, ret) } /** diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index afbedd1e5825d..fbc4065a9666c 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -444,7 +444,6 @@ class DataFrameSuite extends QueryTest { } test("describe") { - val describeTestData = Seq( ("Bob", 16, 176), ("Alice", 32, 164), @@ -465,7 +464,7 @@ class DataFrameSuite extends QueryTest { Row("min", null, null), Row("max", null, null)) - def getSchemaAsSeq(df: DataFrame) = df.schema.map(_.name).toSeq + def getSchemaAsSeq(df: DataFrame): Seq[String] = df.schema.map(_.name) val describeTwoCols = describeTestData.describe("age", "height") assert(getSchemaAsSeq(describeTwoCols) === Seq("summary", "age", "height")) From 71a0d40ebd37c80d8020e184366778b57c762285 Mon Sep 17 00:00:00 2001 From: Cheng Lian Date: Thu, 26 Mar 2015 13:11:37 -0700 Subject: [PATCH 073/591] [SPARK-6554] [SQL] Don't push down predicates which reference partition column(s) There are two cases for the new Parquet data source: 1. Partition columns exist in the Parquet data files We don't need to push-down these predicates since partition pruning already handles them. 1. Partition columns don't exist in the Parquet data files We can't push-down these predicates since they are considered as invalid columns by Parquet. [Review on Reviewable](https://reviewable.io/reviews/apache/spark/5210) Author: Cheng Lian Closes #5210 from liancheng/spark-6554 and squashes the following commits: 4f7ec03 [Cheng Lian] Adds comments e134ced [Cheng Lian] Don't push down predicates which reference partition column(s) --- .../apache/spark/sql/parquet/newParquet.scala | 17 ++++++++++++----- .../spark/sql/parquet/ParquetFilterSuite.scala | 17 +++++++++++++++++ 2 files changed, 29 insertions(+), 5 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala index 3516cfe680c61..0d68810ec6043 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala @@ -435,11 +435,18 @@ private[sql] case class ParquetRelation2( // Push down filters when possible. Notice that not all filters can be converted to Parquet // filter predicate. Here we try to convert each individual predicate and only collect those // convertible ones. - predicates - .flatMap(ParquetFilters.createFilter) - .reduceOption(FilterApi.and) - .filter(_ => sqlContext.conf.parquetFilterPushDown) - .foreach(ParquetInputFormat.setFilterPredicate(jobConf, _)) + if (sqlContext.conf.parquetFilterPushDown) { + predicates + // Don't push down predicates which reference partition columns + .filter { pred => + val partitionColNames = partitionColumns.map(_.name).toSet + val referencedColNames = pred.references.map(_.name).toSet + referencedColNames.intersect(partitionColNames).isEmpty + } + .flatMap(ParquetFilters.createFilter) + .reduceOption(FilterApi.and) + .foreach(ParquetInputFormat.setFilterPredicate(jobConf, _)) + } if (isPartitioned) { logInfo { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala index 4d32e84fc1115..6a2c2a7c4080a 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala @@ -321,6 +321,23 @@ class ParquetDataSourceOnFilterSuite extends ParquetFilterSuiteBase with BeforeA override protected def afterAll(): Unit = { sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) } + + test("SPARK-6554: don't push down predicates which reference partition columns") { + import sqlContext.implicits._ + + withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED -> "true") { + withTempPath { dir => + val path = s"${dir.getCanonicalPath}/part=1" + (1 to 3).map(i => (i, i.toString)).toDF("a", "b").saveAsParquetFile(path) + + // If the "part = 1" filter gets pushed down, this query will throw an exception since + // "part" is not a valid column in the actual Parquet file + checkAnswer( + sqlContext.parquetFile(path).filter("part = 1"), + (1 to 3).map(i => Row(i, i.toString, 1))) + } + } + } } class ParquetDataSourceOffFilterSuite extends ParquetFilterSuiteBase with BeforeAndAfterAll { From aad00322765d6041e817a6bd3fcff2187d212057 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Thu, 26 Mar 2015 14:51:46 -0700 Subject: [PATCH 074/591] [DOCS][SQL] Fix JDBC example Author: Michael Armbrust Closes #5192 from marmbrus/fixJDBCDocs and squashes the following commits: b48a33d [Michael Armbrust] [DOCS][SQL] Fix JDBC example --- docs/sql-programming-guide.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index c99a0b03442c4..4441d6a000a02 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -1406,7 +1406,7 @@ DataFrame jdbcDF = sqlContext.load("jdbc", options) {% highlight python %} -df = sqlContext.load("jdbc", url="jdbc:postgresql:dbserver", dbtable="schema.tablename") +df = sqlContext.load(source="jdbc", url="jdbc:postgresql:dbserver", dbtable="schema.tablename") {% endhighlight %} From 39fb57968352549f2276ac4fcd2b92988ed6fe42 Mon Sep 17 00:00:00 2001 From: Brennon York Date: Thu, 26 Mar 2015 19:08:09 -0700 Subject: [PATCH 075/591] [SPARK-6510][GraphX]: Add Graph#minus method to act as Set#difference Adds a `Graph#minus` method which will return only unique `VertexId`'s from the calling `VertexRDD`. To demonstrate a basic example with pseudocode: ``` Set((0L,0),(1L,1)).minus(Set((1L,1),(2L,2))) > Set((0L,0)) ``` Author: Brennon York Closes #5175 from brennonyork/SPARK-6510 and squashes the following commits: 248d5c8 [Brennon York] added minus(VertexRDD[VD]) method to avoid createUsingIndex and updated the mask operations to simplify with andNot call 3fb7cce [Brennon York] updated graphx doc to reflect the addition of minus method 6575d92 [Brennon York] updated mima exclude aaa030b [Brennon York] completed graph#minus functionality 7227c0f [Brennon York] beginning work on minus functionality --- docs/graphx-programming-guide.md | 2 ++ .../org/apache/spark/graphx/VertexRDD.scala | 16 +++++++++ .../graphx/impl/VertexPartitionBaseOps.scala | 15 +++++++++ .../spark/graphx/impl/VertexRDDImpl.scala | 25 ++++++++++++++ .../apache/spark/graphx/VertexRDDSuite.scala | 33 +++++++++++++++++-- project/MimaExcludes.scala | 3 ++ 6 files changed, 92 insertions(+), 2 deletions(-) diff --git a/docs/graphx-programming-guide.md b/docs/graphx-programming-guide.md index c601d793a2e9a..3f10cb2dc3d2a 100644 --- a/docs/graphx-programming-guide.md +++ b/docs/graphx-programming-guide.md @@ -899,6 +899,8 @@ class VertexRDD[VD] extends RDD[(VertexID, VD)] { // Transform the values without changing the ids (preserves the internal index) def mapValues[VD2](map: VD => VD2): VertexRDD[VD2] def mapValues[VD2](map: (VertexId, VD) => VD2): VertexRDD[VD2] + // Show only vertices unique to this set based on their VertexId's + def minus(other: RDD[(VertexId, VD)]) // Remove vertices from this set that appear in the other set def diff(other: VertexRDD[VD]): VertexRDD[VD] // Join operators that take advantage of the internal indexing to accelerate joins (substantially) diff --git a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala index ad4bfe077293a..a9f04b559c3d1 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala @@ -121,6 +121,22 @@ abstract class VertexRDD[VD]( */ def mapValues[VD2: ClassTag](f: (VertexId, VD) => VD2): VertexRDD[VD2] + /** + * For each VertexId present in both `this` and `other`, minus will act as a set difference + * operation returning only those unique VertexId's present in `this`. + * + * @param other an RDD to run the set operation against + */ + def minus(other: RDD[(VertexId, VD)]): VertexRDD[VD] + + /** + * For each VertexId present in both `this` and `other`, minus will act as a set difference + * operation returning only those unique VertexId's present in `this`. + * + * @param other a VertexRDD to run the set operation against + */ + def minus(other: VertexRDD[VD]): VertexRDD[VD] + /** * For each vertex present in both `this` and `other`, `diff` returns only those vertices with * differing values; for values that are different, keeps the values from `other`. This is diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala index 4fd2548b7faf6..b90f9fa327052 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexPartitionBaseOps.scala @@ -88,6 +88,21 @@ private[graphx] abstract class VertexPartitionBaseOps this.withMask(newMask) } + /** Hides the VertexId's that are the same between `this` and `other`. */ + def minus(other: Self[VD]): Self[VD] = { + if (self.index != other.index) { + logWarning("Minus operations on two VertexPartitions with different indexes is slow.") + minus(createUsingIndex(other.iterator)) + } else { + self.withMask(self.mask.andNot(other.mask)) + } + } + + /** Hides the VertexId's that are the same between `this` and `other`. */ + def minus(other: Iterator[(VertexId, VD)]): Self[VD] = { + minus(createUsingIndex(other)) + } + /** * Hides vertices that are the same between this and other. For vertices that are different, keeps * the values from `other`. The indices of `this` and `other` must be the same. diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala index 125692ddaad83..349c8545bf201 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala @@ -103,6 +103,31 @@ class VertexRDDImpl[VD] private[graphx] ( override def mapValues[VD2: ClassTag](f: (VertexId, VD) => VD2): VertexRDD[VD2] = this.mapVertexPartitions(_.map(f)) + override def minus(other: RDD[(VertexId, VD)]): VertexRDD[VD] = { + minus(this.aggregateUsingIndex(other, (a: VD, b: VD) => a)) + } + + override def minus (other: VertexRDD[VD]): VertexRDD[VD] = { + other match { + case other: VertexRDD[_] if this.partitioner == other.partitioner => + this.withPartitionsRDD[VD]( + partitionsRDD.zipPartitions( + other.partitionsRDD, preservesPartitioning = true) { + (thisIter, otherIter) => + val thisPart = thisIter.next() + val otherPart = otherIter.next() + Iterator(thisPart.minus(otherPart)) + }) + case _ => + this.withPartitionsRDD[VD]( + partitionsRDD.zipPartitions( + other.partitionBy(this.partitioner.get), preservesPartitioning = true) { + (partIter, msgs) => partIter.map(_.minus(msgs)) + } + ) + } + } + override def diff(other: RDD[(VertexId, VD)]): VertexRDD[VD] = { diff(this.aggregateUsingIndex(other, (a: VD, b: VD) => a)) } diff --git a/graphx/src/test/scala/org/apache/spark/graphx/VertexRDDSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/VertexRDDSuite.scala index 4f7a442ab503d..c9443d11c76cf 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/VertexRDDSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/VertexRDDSuite.scala @@ -47,6 +47,35 @@ class VertexRDDSuite extends FunSuite with LocalSparkContext { } } + test("minus") { + withSpark { sc => + val vertexA = VertexRDD(sc.parallelize(0 until 75, 2).map(i => (i.toLong, 0))).cache() + val vertexB = VertexRDD(sc.parallelize(25 until 100, 2).map(i => (i.toLong, 1))).cache() + val vertexC = vertexA.minus(vertexB) + assert(vertexC.map(_._1).collect.toSet === (0 until 25).toSet) + } + } + + test("minus with RDD[(VertexId, VD)]") { + withSpark { sc => + val vertexA = VertexRDD(sc.parallelize(0 until 75, 2).map(i => (i.toLong, 0))).cache() + val vertexB: RDD[(VertexId, Int)] = + sc.parallelize(25 until 100, 2).map(i => (i.toLong, 1)).cache() + val vertexC = vertexA.minus(vertexB) + assert(vertexC.map(_._1).collect.toSet === (0 until 25).toSet) + } + } + + test("minus with non-equal number of partitions") { + withSpark { sc => + val vertexA = VertexRDD(sc.parallelize(0 until 75, 5).map(i => (i.toLong, 0))) + val vertexB = VertexRDD(sc.parallelize(50 until 100, 2).map(i => (i.toLong, 1))) + assert(vertexA.partitions.size != vertexB.partitions.size) + val vertexC = vertexA.minus(vertexB) + assert(vertexC.map(_._1).collect.toSet === (0 until 50).toSet) + } + } + test("diff") { withSpark { sc => val n = 100 @@ -71,7 +100,7 @@ class VertexRDDSuite extends FunSuite with LocalSparkContext { } } - test("diff vertices with the non-equal number of partitions") { + test("diff vertices with non-equal number of partitions") { withSpark { sc => val vertexA = VertexRDD(sc.parallelize(0 until 24, 3).map(i => (i.toLong, 0))) val vertexB = VertexRDD(sc.parallelize(8 until 16, 2).map(i => (i.toLong, 1))) @@ -96,7 +125,7 @@ class VertexRDDSuite extends FunSuite with LocalSparkContext { } } - test("leftJoin vertices with the non-equal number of partitions") { + test("leftJoin vertices with non-equal number of partitions") { withSpark { sc => val vertexA = VertexRDD(sc.parallelize(0 until 100, 2).map(i => (i.toLong, 1))) val vertexB = VertexRDD( diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala index 56f5dbe53fad4..b9f40046e15a2 100644 --- a/project/MimaExcludes.scala +++ b/project/MimaExcludes.scala @@ -51,6 +51,9 @@ object MimaExcludes { "org.apache.spark.broadcast.HttpBroadcastFactory.newBroadcast"), ProblemFilters.exclude[IncompatibleResultTypeProblem]( "org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast") + ) ++ Seq( + // SPARK-6510 Add a Graph#minus method acting as Set#difference + ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.minus") ) case v if v.startsWith("1.3") => From 49d2ec63eccec8a3a78b15b583c36f84310fc6f0 Mon Sep 17 00:00:00 2001 From: mcheah Date: Thu, 26 Mar 2015 22:48:42 -0700 Subject: [PATCH 076/591] [SPARK-6405] Limiting the maximum Kryo buffer size to be 2GB. Kryo buffers are backed by byte arrays, but primitive arrays can only be up to 2GB in size. It is misleading to allow users to set buffers past this size. Author: mcheah Closes #5218 from mccheah/feature/limit-kryo-buffer and squashes the following commits: 1d6d1be [mcheah] Fixing numeric typo e2e30ce [mcheah] Removing explicit int and double type to match style 09fd80b [mcheah] Should be >= not >. Slightly more consistent error message. 60634f9 [mcheah] [SPARK-6405] Limiting the maximum Kryo buffer size to be 2GB. --- .../apache/spark/serializer/KryoSerializer.scala | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala index f83bcaa5cc09e..579fb6624e692 100644 --- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala +++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala @@ -49,10 +49,20 @@ class KryoSerializer(conf: SparkConf) with Logging with Serializable { - private val bufferSize = - (conf.getDouble("spark.kryoserializer.buffer.mb", 0.064) * 1024 * 1024).toInt + private val bufferSizeMb = conf.getDouble("spark.kryoserializer.buffer.mb", 0.064) + if (bufferSizeMb >= 2048) { + throw new IllegalArgumentException("spark.kryoserializer.buffer.mb must be less than " + + s"2048 mb, got: + $bufferSizeMb mb.") + } + private val bufferSize = (bufferSizeMb * 1024 * 1024).toInt + + val maxBufferSizeMb = conf.getInt("spark.kryoserializer.buffer.max.mb", 64) + if (maxBufferSizeMb >= 2048) { + throw new IllegalArgumentException("spark.kryoserializer.buffer.max.mb must be less than " + + s"2048 mb, got: + $maxBufferSizeMb mb.") + } + private val maxBufferSize = maxBufferSizeMb * 1024 * 1024 - private val maxBufferSize = conf.getInt("spark.kryoserializer.buffer.max.mb", 64) * 1024 * 1024 private val referenceTracking = conf.getBoolean("spark.kryo.referenceTracking", true) private val registrationRequired = conf.getBoolean("spark.kryo.registrationRequired", false) private val userRegistrator = conf.getOption("spark.kryo.registrator") From f43a61031fd7d9d4fab3d8ac584e7b4c7c5e1035 Mon Sep 17 00:00:00 2001 From: Yu ISHIKAWA Date: Fri, 27 Mar 2015 00:15:02 -0700 Subject: [PATCH 077/591] [SPARK-6341][mllib] Upgrade breeze from 0.11.1 to 0.11.2 There are any bugs of breeze's SparseVector at 0.11.1. You know, Spark 1.3 depends on breeze 0.11.1. So I think we should upgrade it to 0.11.2. https://issues.apache.org/jira/browse/SPARK-6341 And thanks you for your great cooperation, David Hall(dlwh) Author: Yu ISHIKAWA Closes #5222 from yu-iskw/upgrade-breeze and squashes the following commits: ad8a688 [Yu ISHIKAWA] Upgrade breeze from 0.11.1 to 0.11.2 because of a bug of SparseVector. Thanks you for your great cooperation, David Hall(@dlwh) --- mllib/pom.xml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mllib/pom.xml b/mllib/pom.xml index 4c183543e3fa8..5dfab36c76907 100644 --- a/mllib/pom.xml +++ b/mllib/pom.xml @@ -64,7 +64,7 @@ org.scalanlp breeze_${scala.binary.version} - 0.11.1 + 0.11.2 From da546b7ba03d84d7f6af97fe04471b12f5b3392f Mon Sep 17 00:00:00 2001 From: zsxwing Date: Fri, 27 Mar 2015 12:31:06 +0000 Subject: [PATCH 078/591] [SPARK-6556][Core] Fix wrong parsing logic of executorTimeoutMs and checkTimeoutIntervalMs in HeartbeatReceiver The current reading logic of `executorTimeoutMs` is: ```Scala private val executorTimeoutMs = sc.conf.getLong("spark.network.timeout", sc.conf.getLong("spark.storage.blockManagerSlaveTimeoutMs", 120)) * 1000 ``` So if `spark.storage.blockManagerSlaveTimeoutMs` is 10000 and `spark.network.timeout` is not set, executorTimeoutMs will be 10000 * 1000. But the correct value should have been 10000. `checkTimeoutIntervalMs` has the same issue. This PR fixes them. Author: zsxwing Closes #5209 from zsxwing/SPARK-6556 and squashes the following commits: 6a0a411 [zsxwing] Fix docs c7d5422 [zsxwing] Add comments for executorTimeoutMs and checkTimeoutIntervalMs ccd5147 [zsxwing] Fix wrong parsing logic of executorTimeoutMs and checkTimeoutIntervalMs in HeartbeatReceiver --- .../org/apache/spark/HeartbeatReceiver.scala | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala index 715f292f03469..548dcb93c3358 100644 --- a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala +++ b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala @@ -49,12 +49,17 @@ private[spark] class HeartbeatReceiver(sc: SparkContext, scheduler: TaskSchedule // executor ID -> timestamp of when the last heartbeat from this executor was received private val executorLastSeen = new mutable.HashMap[String, Long] - - private val executorTimeoutMs = sc.conf.getLong("spark.network.timeout", - sc.conf.getLong("spark.storage.blockManagerSlaveTimeoutMs", 120)) * 1000 - - private val checkTimeoutIntervalMs = sc.conf.getLong("spark.network.timeoutInterval", - sc.conf.getLong("spark.storage.blockManagerTimeoutIntervalMs", 60)) * 1000 + + // "spark.network.timeout" uses "seconds", while `spark.storage.blockManagerSlaveTimeoutMs` uses + // "milliseconds" + private val executorTimeoutMs = sc.conf.getOption("spark.network.timeout").map(_.toLong * 1000). + getOrElse(sc.conf.getLong("spark.storage.blockManagerSlaveTimeoutMs", 120000)) + + // "spark.network.timeoutInterval" uses "seconds", while + // "spark.storage.blockManagerTimeoutIntervalMs" uses "milliseconds" + private val checkTimeoutIntervalMs = + sc.conf.getOption("spark.network.timeoutInterval").map(_.toLong * 1000). + getOrElse(sc.conf.getLong("spark.storage.blockManagerTimeoutIntervalMs", 60000)) private var timeoutCheckingTask: Cancellable = null From aa2b9917489f9bbb02c8acea5ff43335042e2705 Mon Sep 17 00:00:00 2001 From: Dean Chen Date: Fri, 27 Mar 2015 14:32:51 +0000 Subject: [PATCH 079/591] [SPARK-6544][build] Increment Avro version from 1.7.6 to 1.7.7 Fixes bug causing Kryo serialization to fail with Avro files in between stages. https://issues.apache.org/jira/browse/AVRO-1476?focusedCommentId=13999249&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-13999249 Author: Dean Chen Closes #5193 from deanchen/SPARK-6544 and squashes the following commits: 813d4c5 [Dean Chen] [SPARK-6544][build] Increment Avro version from 1.7.6 to 1.7.7 --- pom.xml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pom.xml b/pom.xml index b3cecd1893a06..3eb3da2cd8af3 100644 --- a/pom.xml +++ b/pom.xml @@ -141,7 +141,7 @@ 2.4.0 2.0.8 3.1.0 - 1.7.6 + 1.7.7 0.7.1 1.8.3 From 5d9c37c23d1edd91e6c5561780006b762cde5f66 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Fri, 27 Mar 2015 11:40:00 -0700 Subject: [PATCH 080/591] [SPARK-6550][SQL] Use analyzed plan in DataFrame This is based on bug and test case proposed by viirya. See #5203 for a excellent description of the problem. TLDR; The problem occurs because the function `groupBy(String)` calls `resolve`, which returns an `AttributeReference`. However, this `AttributeReference` is based on an analyzed plan which is thrown away. At execution time, we once again analyze the plan. However, in the case of self-joins, each call to analyze will produce a new tree for the left side of the join, rendering the previously returned `AttributeReference` invalid. As a fix, I propose we keep the analyzed plan instead of the unresolved plan inside of a `DataFrame`. Author: Michael Armbrust Closes #5217 from marmbrus/preanalyzer and squashes the following commits: 1f98e2d [Michael Armbrust] revert change dd4dec1 [Michael Armbrust] Use the analyzed plan in DataFrame 089c52e [Michael Armbrust] WIP --- sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala | 2 +- .../src/test/scala/org/apache/spark/sql/DataFrameSuite.scala | 4 ++++ 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 4c80359cf07af..423ef3912bc89 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -146,7 +146,7 @@ class DataFrame private[sql]( _: WriteToFile => LogicalRDD(queryExecution.analyzed.output, queryExecution.toRdd)(sqlContext) case _ => - queryExecution.logical + queryExecution.analyzed } /** diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index fbc4065a9666c..5f03805d70416 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -113,6 +113,10 @@ class DataFrameSuite extends QueryTest { checkAnswer( df.as('x).join(df.as('y), $"x.str" === $"y.str").groupBy("x.str").count(), Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil) + + checkAnswer( + df.as('x).join(df.as('y), $"x.str" === $"y.str").groupBy("y.str").count(), + Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil) } test("explode") { From 887e1b72dfa5965f8ab1aad212fb33bb365b0e1b Mon Sep 17 00:00:00 2001 From: Davies Liu Date: Fri, 27 Mar 2015 11:42:26 -0700 Subject: [PATCH 081/591] [SPARK-6574] [PySpark] fix sql example Fix the import in sql example. Author: Davies Liu Closes #5230 from davies/fix_sql_example and squashes the following commits: 7ecc5f4 [Davies Liu] fix sql example --- examples/src/main/python/sql.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/src/main/python/sql.py b/examples/src/main/python/sql.py index 47202fde7510b..d89361f324917 100644 --- a/examples/src/main/python/sql.py +++ b/examples/src/main/python/sql.py @@ -19,7 +19,7 @@ from pyspark import SparkContext from pyspark.sql import SQLContext -from pyspark.sql import Row, StructField, StructType, StringType, IntegerType +from pyspark.sql.types import Row, StructField, StructType, StringType, IntegerType if __name__ == "__main__": From d5497ab1343e4d1b2a1c336f2e3520d74c6674a1 Mon Sep 17 00:00:00 2001 From: Xusen Yin Date: Fri, 27 Mar 2015 13:29:10 -0700 Subject: [PATCH 082/591] [SPARK-6526][ML] Add Normalizer transformer in ML package See [SPARK-6526](https://issues.apache.org/jira/browse/SPARK-6526). mengxr Should we add test suite for this transformer? There is no test suite for all feature transformers in ML package now. Author: Xusen Yin Closes #5181 from yinxusen/SPARK-6526 and squashes the following commits: 6faa7bf [Xusen Yin] fix style 8a462da [Xusen Yin] remove duplications ab35ab0 [Xusen Yin] add test suite bc8cd0f [Xusen Yin] fix comment 79774c9 [Xusen Yin] add Normalizer transformer in ML package --- .../apache/spark/ml/feature/Normalizer.scala | 53 +++++++++ .../spark/ml/feature/NormalizerSuite.scala | 109 ++++++++++++++++++ 2 files changed, 162 insertions(+) create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala new file mode 100644 index 0000000000000..05f91dc9105fe --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala @@ -0,0 +1,53 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.UnaryTransformer +import org.apache.spark.ml.param.{DoubleParam, ParamMap} +import org.apache.spark.mllib.feature +import org.apache.spark.mllib.linalg.{VectorUDT, Vector} +import org.apache.spark.sql.types.DataType + +/** + * :: AlphaComponent :: + * Normalize a vector to have unit norm using the given p-norm. + */ +@AlphaComponent +class Normalizer extends UnaryTransformer[Vector, Vector, Normalizer] { + + /** + * Normalization in L^p^ space, p = 2 by default. + * @group param + */ + val p = new DoubleParam(this, "p", "the p norm value", Some(2)) + + /** @group getParam */ + def getP: Double = get(p) + + /** @group setParam */ + def setP(value: Double): this.type = set(p, value) + + override protected def createTransformFunc(paramMap: ParamMap): Vector => Vector = { + val normalizer = new feature.Normalizer(paramMap(p)) + normalizer.transform + } + + override protected def outputDataType: DataType = new VectorUDT() +} + diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala new file mode 100644 index 0000000000000..a18c335952b96 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala @@ -0,0 +1,109 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.scalatest.FunSuite + +import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.sql.{DataFrame, Row, SQLContext} + +private case class DataSet(features: Vector) + +class NormalizerSuite extends FunSuite with MLlibTestSparkContext { + + @transient var data: Array[Vector] = _ + @transient var dataFrame: DataFrame = _ + @transient var normalizer: Normalizer = _ + @transient var l1Normalized: Array[Vector] = _ + @transient var l2Normalized: Array[Vector] = _ + + override def beforeAll(): Unit = { + super.beforeAll() + + data = Array( + Vectors.sparse(3, Seq((0, -2.0), (1, 2.3))), + Vectors.dense(0.0, 0.0, 0.0), + Vectors.dense(0.6, -1.1, -3.0), + Vectors.sparse(3, Seq((1, 0.91), (2, 3.2))), + Vectors.sparse(3, Seq((0, 5.7), (1, 0.72), (2, 2.7))), + Vectors.sparse(3, Seq()) + ) + l1Normalized = Array( + Vectors.sparse(3, Seq((0, -0.465116279), (1, 0.53488372))), + Vectors.dense(0.0, 0.0, 0.0), + Vectors.dense(0.12765957, -0.23404255, -0.63829787), + Vectors.sparse(3, Seq((1, 0.22141119), (2, 0.7785888))), + Vectors.dense(0.625, 0.07894737, 0.29605263), + Vectors.sparse(3, Seq()) + ) + l2Normalized = Array( + Vectors.sparse(3, Seq((0, -0.65617871), (1, 0.75460552))), + Vectors.dense(0.0, 0.0, 0.0), + Vectors.dense(0.184549876, -0.3383414, -0.922749378), + Vectors.sparse(3, Seq((1, 0.27352993), (2, 0.96186349))), + Vectors.dense(0.897906166, 0.113419726, 0.42532397), + Vectors.sparse(3, Seq()) + ) + + val sqlContext = new SQLContext(sc) + dataFrame = sqlContext.createDataFrame(sc.parallelize(data, 2).map(DataSet)) + normalizer = new Normalizer() + .setInputCol("features") + .setOutputCol("normalized_features") + } + + def collectResult(result: DataFrame): Array[Vector] = { + result.select("normalized_features").collect().map { + case Row(features: Vector) => features + } + } + + def assertTypeOfVector(lhs: Array[Vector], rhs: Array[Vector]): Unit = { + assert((lhs, rhs).zipped.forall { + case (v1: DenseVector, v2: DenseVector) => true + case (v1: SparseVector, v2: SparseVector) => true + case _ => false + }, "The vector type should be preserved after normalization.") + } + + def assertValues(lhs: Array[Vector], rhs: Array[Vector]): Unit = { + assert((lhs, rhs).zipped.forall { (vector1, vector2) => + vector1 ~== vector2 absTol 1E-5 + }, "The vector value is not correct after normalization.") + } + + test("Normalization with default parameter") { + val result = collectResult(normalizer.transform(dataFrame)) + + assertTypeOfVector(data, result) + + assertValues(result, l2Normalized) + } + + test("Normalization with setter") { + normalizer.setP(1) + + val result = collectResult(normalizer.transform(dataFrame)) + + assertTypeOfVector(data, result) + + assertValues(result, l1Normalized) + } +} From 3af7334304341fba091aa39ce2efbdfd167c697b Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Fri, 27 Mar 2015 14:56:57 -0700 Subject: [PATCH 083/591] [SPARK-6564][SQL] SQLContext.emptyDataFrame should contain 0 row, not 1 row Author: Reynold Xin Closes #5226 from rxin/empty-df and squashes the following commits: 1306d88 [Reynold Xin] Proper fix. e135bb9 [Reynold Xin] [SPARK-6564][SQL] SQLContext.emptyDataFrame should contain 0 rows, not 1 row. --- .../main/scala/org/apache/spark/sql/catalyst/SqlParser.scala | 2 +- .../spark/sql/catalyst/plans/logical/basicOperators.scala | 5 ++++- .../sql/catalyst/optimizer/ExpressionOptimizationSuite.scala | 2 +- .../scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala | 4 ++-- .../src/main/scala/org/apache/spark/sql/SQLContext.scala | 4 ++-- .../org/apache/spark/sql/execution/SparkStrategies.scala | 2 +- .../src/test/scala/org/apache/spark/sql/DataFrameSuite.scala | 5 +++++ .../src/main/scala/org/apache/spark/sql/hive/HiveQl.scala | 4 ++-- 8 files changed, 18 insertions(+), 10 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala index ea7d44a3723d1..b176f7e729a42 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala @@ -139,7 +139,7 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser { sortType.? ~ (LIMIT ~> expression).? ^^ { case d ~ p ~ r ~ f ~ g ~ h ~ o ~ l => - val base = r.getOrElse(NoRelation) + val base = r.getOrElse(OneRowRelation) val withFilter = f.map(Filter(_, base)).getOrElse(base) val withProjection = g .map(Aggregate(_, assignAliases(p), withFilter)) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala index 4d9e41a2b5d85..190209238a4a5 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala @@ -287,7 +287,10 @@ case class Distinct(child: LogicalPlan) extends UnaryNode { override def output: Seq[Attribute] = child.output } -case object NoRelation extends LeafNode { +/** + * A relation with one row. This is used in "SELECT ..." without a from clause. + */ +case object OneRowRelation extends LeafNode { override def output: Seq[Attribute] = Nil /** diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ExpressionOptimizationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ExpressionOptimizationSuite.scala index ae99a3f9ba287..2f3704be59a9d 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ExpressionOptimizationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ExpressionOptimizationSuite.scala @@ -29,7 +29,7 @@ class ExpressionOptimizationSuite extends ExpressionEvaluationSuite { expression: Expression, expected: Any, inputRow: Row = EmptyRow): Unit = { - val plan = Project(Alias(expression, s"Optimized($expression)")() :: Nil, NoRelation) + val plan = Project(Alias(expression, s"Optimized($expression)")() :: Nil, OneRowRelation) val optimizedPlan = DefaultOptimizer(plan) super.checkEvaluation(optimizedPlan.expressions.head, expected, inputRow) } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala index 48884040bfce7..129d091ca03e3 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala @@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst.plans import org.scalatest.FunSuite import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.plans.logical.{NoRelation, Filter, LogicalPlan} +import org.apache.spark.sql.catalyst.plans.logical.{OneRowRelation, Filter, LogicalPlan} import org.apache.spark.sql.catalyst.util._ /** @@ -55,6 +55,6 @@ class PlanTest extends FunSuite { /** Fails the test if the two expressions do not match */ protected def compareExpressions(e1: Expression, e2: Expression): Unit = { - comparePlans(Filter(e1, NoRelation), Filter(e2, NoRelation)) + comparePlans(Filter(e1, OneRowRelation), Filter(e2, OneRowRelation)) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala index e59cf9b9e037b..b8100782ec937 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala @@ -31,7 +31,7 @@ import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.analysis._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.optimizer.{DefaultOptimizer, Optimizer} -import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan, NoRelation} +import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan, OneRowRelation} import org.apache.spark.sql.catalyst.rules.RuleExecutor import org.apache.spark.sql.catalyst.{ScalaReflection, expressions} import org.apache.spark.sql.execution.{Filter, _} @@ -177,7 +177,7 @@ class SQLContext(@transient val sparkContext: SparkContext) */ @Experimental @transient - lazy val emptyDataFrame = DataFrame(this, NoRelation) + lazy val emptyDataFrame: DataFrame = createDataFrame(sparkContext.emptyRDD[Row], StructType(Nil)) /** * A collection of methods for registering user-defined functions (UDF). diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala index 2b581152e5f77..f754fa770d1b5 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala @@ -296,7 +296,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { execution.Intersect(planLater(left), planLater(right)) :: Nil case logical.Generate(generator, join, outer, _, child) => execution.Generate(generator, join = join, outer = outer, planLater(child)) :: Nil - case logical.NoRelation => + case logical.OneRowRelation => execution.PhysicalRDD(Nil, singleRowRdd) :: Nil case logical.Repartition(expressions, child) => execution.Exchange(HashPartitioning(expressions, numPartitions), planLater(child)) :: Nil diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index 5f03805d70416..6761d996fd975 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -84,6 +84,11 @@ class DataFrameSuite extends QueryTest { testData.collect().toSeq) } + test("empty data frame") { + assert(TestSQLContext.emptyDataFrame.columns.toSeq === Seq.empty[String]) + assert(TestSQLContext.emptyDataFrame.count() === 0) + } + test("head and take") { assert(testData.take(2) === testData.collect().take(2)) assert(testData.head(2) === testData.collect().take(2)) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala index c45c4ad70fae9..cd8e7c09eea5b 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala @@ -479,7 +479,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C // Just fake explain for any of the native commands. case Token("TOK_EXPLAIN", explainArgs) if noExplainCommands.contains(explainArgs.head.getText) => - ExplainCommand(NoRelation) + ExplainCommand(OneRowRelation) case Token("TOK_EXPLAIN", explainArgs) if "TOK_CREATETABLE" == explainArgs.head.getText => val Some(crtTbl) :: _ :: extended :: Nil = @@ -622,7 +622,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C val relations = fromClause match { case Some(f) => nodeToRelation(f) - case None => NoRelation + case None => OneRowRelation } val withWhere = whereClause.map { whereNode => From 5909f0973de15f685836c2828e6d4c38f57d2c19 Mon Sep 17 00:00:00 2001 From: Adam Budde Date: Sat, 28 Mar 2015 09:14:09 +0800 Subject: [PATCH 084/591] [SPARK-6538][SQL] Add missing nullable Metastore fields when merging a Parquet schema Opening to replace #5188. When Spark SQL infers a schema for a DataFrame, it will take the union of all field types present in the structured source data (e.g. an RDD of JSON data). When the source data for a row doesn't define a particular field on the DataFrame's schema, a null value will simply be assumed for this field. This workflow makes it very easy to construct tables and query over a set of structured data with a nonuniform schema. However, this behavior is not consistent in some cases when dealing with Parquet files and an external table managed by an external Hive metastore. In our particular usecase, we use Spark Streaming to parse and transform our input data and then apply a window function to save an arbitrary-sized batch of data as a Parquet file, which itself will be added as a partition to an external Hive table via an *"ALTER TABLE... ADD PARTITION..."* statement. Since our input data is nonuniform, it is expected that not every partition batch will contain every field present in the table's schema obtained from the Hive metastore. As such, we expect that the schema of some of our Parquet files may not contain the same set fields present in the full metastore schema. In such cases, it seems natural that Spark SQL would simply assume null values for any missing fields in the partition's Parquet file, assuming these fields are specified as nullable by the metastore schema. This is not the case in the current implementation of ParquetRelation2. The **mergeMetastoreParquetSchema()** method used to reconcile differences between a Parquet file's schema and a schema retrieved from the Hive metastore will raise an exception if the Parquet file doesn't match the same set of fields specified by the metastore. This pull requests alters the behavior of **mergeMetastoreParquetSchema()** by having it first add any nullable fields from the metastore schema to the Parquet file schema if they aren't already present there. Author: Adam Budde Closes #5214 from budde/nullable-fields and squashes the following commits: a52d378 [Adam Budde] Refactor ParquetSchemaSuite.scala for cases now permitted by SPARK-6471 and SPARK-6538 9041bfa [Adam Budde] Add missing nullable Metastore fields when merging a Parquet schema --- .../apache/spark/sql/parquet/newParquet.scala | 32 ++++++++++++++- .../sql/parquet/ParquetSchemaSuite.scala | 40 +++++++++++++++++-- 2 files changed, 66 insertions(+), 6 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala index 0d68810ec6043..53f765ee26a13 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala @@ -765,12 +765,14 @@ private[sql] object ParquetRelation2 extends Logging { |${parquetSchema.prettyJson} """.stripMargin - assert(metastoreSchema.size <= parquetSchema.size, schemaConflictMessage) + val mergedParquetSchema = mergeMissingNullableFields(metastoreSchema, parquetSchema) + + assert(metastoreSchema.size <= mergedParquetSchema.size, schemaConflictMessage) val ordinalMap = metastoreSchema.zipWithIndex.map { case (field, index) => field.name.toLowerCase -> index }.toMap - val reorderedParquetSchema = parquetSchema.sortBy(f => + val reorderedParquetSchema = mergedParquetSchema.sortBy(f => ordinalMap.getOrElse(f.name.toLowerCase, metastoreSchema.size + 1)) StructType(metastoreSchema.zip(reorderedParquetSchema).map { @@ -782,6 +784,32 @@ private[sql] object ParquetRelation2 extends Logging { }) } + /** + * Returns the original schema from the Parquet file with any missing nullable fields from the + * Hive Metastore schema merged in. + * + * When constructing a DataFrame from a collection of structured data, the resulting object has + * a schema corresponding to the union of the fields present in each element of the collection. + * Spark SQL simply assigns a null value to any field that isn't present for a particular row. + * In some cases, it is possible that a given table partition stored as a Parquet file doesn't + * contain a particular nullable field in its schema despite that field being present in the + * table schema obtained from the Hive Metastore. This method returns a schema representing the + * Parquet file schema along with any additional nullable fields from the Metastore schema + * merged in. + */ + private[parquet] def mergeMissingNullableFields( + metastoreSchema: StructType, + parquetSchema: StructType): StructType = { + val fieldMap = metastoreSchema.map(f => f.name.toLowerCase -> f).toMap + val missingFields = metastoreSchema + .map(_.name.toLowerCase) + .diff(parquetSchema.map(_.name.toLowerCase)) + .map(fieldMap(_)) + .filter(_.nullable) + StructType(parquetSchema ++ missingFields) + } + + // TODO Data source implementations shouldn't touch Catalyst types (`Literal`). // However, we are already using Catalyst expressions for partition pruning and predicate // push-down here... diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala index 8462f9bb2d620..61f1cf347ab0f 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala @@ -226,22 +226,54 @@ class ParquetSchemaSuite extends FunSuite with ParquetTest { StructField("UPPERCase", IntegerType, nullable = true)))) } - // Conflicting field count + // Metastore schema contains additional non-nullable fields. assert(intercept[Throwable] { ParquetRelation2.mergeMetastoreParquetSchema( StructType(Seq( StructField("uppercase", DoubleType, nullable = false), - StructField("lowerCase", BinaryType))), + StructField("lowerCase", BinaryType, nullable = false))), StructType(Seq( StructField("UPPERCase", IntegerType, nullable = true)))) }.getMessage.contains("detected conflicting schemas")) - // Conflicting field names + // Conflicting non-nullable field names intercept[Throwable] { ParquetRelation2.mergeMetastoreParquetSchema( - StructType(Seq(StructField("lower", StringType))), + StructType(Seq(StructField("lower", StringType, nullable = false))), StructType(Seq(StructField("lowerCase", BinaryType)))) } } + + test("merge missing nullable fields from Metastore schema") { + // Standard case: Metastore schema contains additional nullable fields not present + // in the Parquet file schema. + assertResult( + StructType(Seq( + StructField("firstField", StringType, nullable = true), + StructField("secondField", StringType, nullable = true), + StructField("thirdfield", StringType, nullable = true)))) { + ParquetRelation2.mergeMetastoreParquetSchema( + StructType(Seq( + StructField("firstfield", StringType, nullable = true), + StructField("secondfield", StringType, nullable = true), + StructField("thirdfield", StringType, nullable = true))), + StructType(Seq( + StructField("firstField", StringType, nullable = true), + StructField("secondField", StringType, nullable = true)))) + } + + // Merge should fail if the Metastore contains any additional fields that are not + // nullable. + assert(intercept[Throwable] { + ParquetRelation2.mergeMetastoreParquetSchema( + StructType(Seq( + StructField("firstfield", StringType, nullable = true), + StructField("secondfield", StringType, nullable = true), + StructField("thirdfield", StringType, nullable = false))), + StructType(Seq( + StructField("firstField", StringType, nullable = true), + StructField("secondField", StringType, nullable = true)))) + }.getMessage.contains("detected conflicting schemas")) + } } From 99631438c0ec777d6a77974b148dbbd3e890260e Mon Sep 17 00:00:00 2001 From: WangTaoTheTonic Date: Sat, 28 Mar 2015 12:32:35 +0000 Subject: [PATCH 085/591] [SPARK-6552][Deploy][Doc]expose start-slave.sh to user and update outdated doc https://issues.apache.org/jira/browse/SPARK-6552 /cc srowen Author: WangTaoTheTonic Closes #5205 from WangTaoTheTonic/SPARK-6552 and squashes the following commits: b02263c [WangTaoTheTonic] use less than rather than less equal f0fa408 [WangTaoTheTonic] expose start-slave.sh --- docs/spark-standalone.md | 3 ++- sbin/start-slave.sh | 10 ++++++++-- sbin/start-slaves.sh | 2 ++ 3 files changed, 12 insertions(+), 3 deletions(-) diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md index 74d8653a8b845..0eed9adacf123 100644 --- a/docs/spark-standalone.md +++ b/docs/spark-standalone.md @@ -24,7 +24,7 @@ the master's web UI, which is [http://localhost:8080](http://localhost:8080) by Similarly, you can start one or more workers and connect them to the master via: - ./bin/spark-class org.apache.spark.deploy.worker.Worker spark://IP:PORT + ./sbin/start-slave.sh Once you have started a worker, look at the master's web UI ([http://localhost:8080](http://localhost:8080) by default). You should see the new node listed there, along with its number of CPUs and memory (minus one gigabyte left for the OS). @@ -81,6 +81,7 @@ Once you've set up this file, you can launch or stop your cluster with the follo - `sbin/start-master.sh` - Starts a master instance on the machine the script is executed on. - `sbin/start-slaves.sh` - Starts a slave instance on each machine specified in the `conf/slaves` file. +- `sbin/start-slave.sh` - Starts a slave instance on the machine the script is executed on. - `sbin/start-all.sh` - Starts both a master and a number of slaves as described above. - `sbin/stop-master.sh` - Stops the master that was started via the `bin/start-master.sh` script. - `sbin/stop-slaves.sh` - Stops all slave instances on the machines specified in the `conf/slaves` file. diff --git a/sbin/start-slave.sh b/sbin/start-slave.sh index 2fc35309f4ca5..c0155384f7395 100755 --- a/sbin/start-slave.sh +++ b/sbin/start-slave.sh @@ -17,8 +17,14 @@ # limitations under the License. # -# Usage: start-slave.sh -# where is like "spark://localhost:7077" +# Starts a slave on the machine this script is executed on. + +usage="Usage: start-slave.sh where is like "spark://localhost:7077" + +if [ $# -lt 2 ]; then + echo $usage + exit 1 +fi sbin="`dirname "$0"`" sbin="`cd "$sbin"; pwd`" diff --git a/sbin/start-slaves.sh b/sbin/start-slaves.sh index 76316a3067c93..4356c03657109 100755 --- a/sbin/start-slaves.sh +++ b/sbin/start-slaves.sh @@ -17,6 +17,8 @@ # limitations under the License. # +# Starts a slave instance on each machine specified in the conf/slaves file. + sbin="`dirname "$0"`" sbin="`cd "$sbin"; pwd`" From f75f633b21faaf911f04aeff847f25749b1ecd89 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Sat, 28 Mar 2015 15:08:05 -0700 Subject: [PATCH 086/591] [SPARK-6571][MLLIB] use wrapper in MatrixFactorizationModel.load This fixes `predictAll` after load. jkbradley Author: Xiangrui Meng Closes #5243 from mengxr/SPARK-6571 and squashes the following commits: 82dcaa7 [Xiangrui Meng] use wrapper in MatrixFactorizationModel.load --- .../MatrixFactorizationModelWrapper.scala | 40 +++++++++++++++++++ .../mllib/api/python/PythonMLLibAPI.scala | 18 --------- python/pyspark/mllib/recommendation.py | 8 ++++ 3 files changed, 48 insertions(+), 18 deletions(-) create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/api/python/MatrixFactorizationModelWrapper.scala diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/MatrixFactorizationModelWrapper.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/MatrixFactorizationModelWrapper.scala new file mode 100644 index 0000000000000..ecd3b16598438 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/MatrixFactorizationModelWrapper.scala @@ -0,0 +1,40 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.api.python + +import org.apache.spark.api.java.JavaRDD +import org.apache.spark.mllib.recommendation.{MatrixFactorizationModel, Rating} +import org.apache.spark.rdd.RDD + +/** + * A Wrapper of MatrixFactorizationModel to provide helper method for Python. + */ +private[python] class MatrixFactorizationModelWrapper(model: MatrixFactorizationModel) + extends MatrixFactorizationModel(model.rank, model.userFeatures, model.productFeatures) { + + def predict(userAndProducts: JavaRDD[Array[Any]]): RDD[Rating] = + predict(SerDe.asTupleRDD(userAndProducts.rdd)) + + def getUserFeatures: RDD[Array[Any]] = { + SerDe.fromTuple2RDD(userFeatures.asInstanceOf[RDD[(Any, Any)]]) + } + + def getProductFeatures: RDD[Array[Any]] = { + SerDe.fromTuple2RDD(productFeatures.asInstanceOf[RDD[(Any, Any)]]) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index e39156734794c..22fa684fd2895 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -58,7 +58,6 @@ import org.apache.spark.util.Utils */ private[python] class PythonMLLibAPI extends Serializable { - /** * Loads and serializes labeled points saved with `RDD#saveAsTextFile`. * @param jsc Java SparkContext @@ -346,24 +345,7 @@ private[python] class PythonMLLibAPI extends Serializable { model.predictSoft(data) } - /** - * A Wrapper of MatrixFactorizationModel to provide helpfer method for Python - */ - private[python] class MatrixFactorizationModelWrapper(model: MatrixFactorizationModel) - extends MatrixFactorizationModel(model.rank, model.userFeatures, model.productFeatures) { - def predict(userAndProducts: JavaRDD[Array[Any]]): RDD[Rating] = - predict(SerDe.asTupleRDD(userAndProducts.rdd)) - - def getUserFeatures: RDD[Array[Any]] = { - SerDe.fromTuple2RDD(userFeatures.asInstanceOf[RDD[(Any, Any)]]) - } - - def getProductFeatures: RDD[Array[Any]] = { - SerDe.fromTuple2RDD(productFeatures.asInstanceOf[RDD[(Any, Any)]]) - } - - } /** * Java stub for Python mllib ALS.train(). This stub returns a handle diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py index 1a4527b12cef2..b094e50856f70 100644 --- a/python/pyspark/mllib/recommendation.py +++ b/python/pyspark/mllib/recommendation.py @@ -90,6 +90,8 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): >>> sameModel = MatrixFactorizationModel.load(sc, path) >>> sameModel.predict(2,2) 0.43... + >>> sameModel.predictAll(testset).collect() + [Rating(... >>> try: ... os.removedirs(path) ... except OSError: @@ -111,6 +113,12 @@ def userFeatures(self): def productFeatures(self): return self.call("getProductFeatures") + @classmethod + def load(cls, sc, path): + model = cls._load_java(sc, path) + wrapper = sc._jvm.MatrixFactorizationModelWrapper(model) + return MatrixFactorizationModel(wrapper) + class ALS(object): From 5eef00d0c6c7cc5448aca7b1c2a2e289a4c43eb0 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Sat, 28 Mar 2015 23:59:27 -0700 Subject: [PATCH 087/591] [DOC] Improvements to Python docs. Author: Reynold Xin Closes #5238 from rxin/pyspark-docs and squashes the following commits: c285951 [Reynold Xin] Reset deprecation warning. 8c1031e [Reynold Xin] inferSchema dd91b1a [Reynold Xin] [DOC] Improvements to Python docs. --- python/docs/index.rst | 8 ++++++++ python/pyspark/sql/__init__.py | 14 ++++++++------ python/pyspark/sql/dataframe.py | 9 +-------- 3 files changed, 17 insertions(+), 14 deletions(-) diff --git a/python/docs/index.rst b/python/docs/index.rst index d150de9d5c502..f7eede9c3c82a 100644 --- a/python/docs/index.rst +++ b/python/docs/index.rst @@ -29,6 +29,14 @@ Core classes: A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. + :class:`pyspark.sql.SQLContext` + + Main entry point for DataFrame and SQL functionality. + + :class:`pyspark.sql.DataFrame` + + A distributed collection of data grouped into named columns. + Indices and tables ================== diff --git a/python/pyspark/sql/__init__.py b/python/pyspark/sql/__init__.py index b9ffd6945ea7e..54a01631d8899 100644 --- a/python/pyspark/sql/__init__.py +++ b/python/pyspark/sql/__init__.py @@ -19,17 +19,19 @@ public classes of Spark SQL: - L{SQLContext} - Main entry point for SQL functionality. + Main entry point for :class:`DataFrame` and SQL functionality. - L{DataFrame} - A Resilient Distributed Dataset (RDD) with Schema information for the data contained. In - addition to normal RDD operations, DataFrames also support SQL. + A distributed collection of data grouped into named columns. - L{GroupedData} + Aggregation methods, returned by :func:`DataFrame.groupBy`. - L{Column} - Column is a DataFrame with a single column. + A column expression in a :class:`DataFrame`. - L{Row} - A Row of data returned by a Spark SQL query. + A row of data in a :class:`DataFrame`. - L{HiveContext} - Main entry point for accessing data stored in Apache Hive.. + Main entry point for accessing data stored in Apache Hive. + - L{functions} + List of built-in functions available for :class:`DataFrame`. """ from pyspark.sql.context import SQLContext, HiveContext diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index d51309f7ef5aa..23c0e63e77812 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -50,13 +50,6 @@ class DataFrame(object): ageCol = people.age - Note that the :class:`Column` type can also be manipulated - through its various functions:: - - # The following creates a new column that increases everybody's age by 10. - people.age + 10 - - A more concrete example:: # To create DataFrame using SQLContext @@ -77,7 +70,7 @@ def __init__(self, jdf, sql_ctx): @property def rdd(self): """ - Return the content of the :class:`DataFrame` as an :class:`RDD` + Return the content of the :class:`DataFrame` as an :class:`pyspark.RDD` of :class:`Row` s. """ if not hasattr(self, '_lazy_rdd'): From 55153f5c14fad10607b44fbb8eebd9636a6bc2e1 Mon Sep 17 00:00:00 2001 From: Brennon York Date: Sun, 29 Mar 2015 12:37:53 +0100 Subject: [PATCH 088/591] [SPARK-4123][Project Infra]: Show new dependencies added in pull requests Starting work on this, but need to find a way to ensure that, after doing a checkout from `apache/master`, we can successfully return to the current checkout. I believe that `git rev-parse HEAD` will get me what I want, but pushing this PR up to test what the Jenkins boxes are seeing. Author: Brennon York Closes #5093 from brennonyork/SPARK-4123 and squashes the following commits: 42e243e [Brennon York] moved starting test output to before pr tests, fixed indentation, changed mvn call to build/mvn dadd941 [Brennon York] reverted assembly pom, put the regular test suite back in play 7aa1dee [Brennon York] set new dendencies into a block, removed the bash debugging flag 0074566 [Brennon York] fixed minor echo issue with quotes e229802 [Brennon York] updated to print the new dependency found 27bb9b5 [Brennon York] changed the assembly pom to test whether the pr test will pick up new deps 5375ad8 [Brennon York] git output to dev null 9bce980 [Brennon York] ensure both gate files exist 8f3c4b4 [Brennon York] updated to reflect the correct pushed in HEAD variable 2bc7b27 [Brennon York] added a pom gate check a18db71 [Brennon York] full test of new deps script ea170de [Brennon York] dont let mvn execute tests f70d8cd [Brennon York] testing mvn with package 62ffd65 [Brennon York] updated dependency output message and changed compile to package given the jenkins failure output 04747e4 [Brennon York] adding simple mvn statement to see if command executes and prints compile output 87f9bea [Brennon York] added -x flag with bash to get insight into what is executing and what isnt 9e87208 [Brennon York] added set blocks to catch any non-zero exit codes and updated output 6b3042b [Brennon York] removed excess git checkout print statements 4077d46 [Brennon York] Merge remote-tracking branch 'upstream/master' into SPARK-4123 2bb5527 [Brennon York] added echo statement so jenkins logs which pr tests are running d027f8f [Brennon York] proper piping of unnecessary stderr and stdout 6e2890d [Brennon York] updated test output newlines d9f6f7f [Brennon York] removed echo bad9a3a [Brennon York] added back the new deps test e9e3ad1 [Brennon York] removed escapes for quotes 97e5cfb [Brennon York] commenting out new deps script 17379a5 [Brennon York] Merge remote-tracking branch 'upstream/master' into SPARK-4123 56f74a8 [Brennon York] updated the unop for ensuring a test is available f2abc8c [Brennon York] removed the git checkout 6912584 [Brennon York] added this_mssg echo output c610d42 [Brennon York] removed the error to dev/null b98f78c [Brennon York] added the removed deps and echo output for jenkins testing 291a8fe [Brennon York] updated location of maven binary 126ce61 [Brennon York] removing new deps test to isolate why jenkins isn't posting messages f8011d8 [Brennon York] minor updates and style changes 63a35c9 [Brennon York] updated new dependencies test dae7ba8 [Brennon York] Capturing output directly from dependency builds 94d3547 [Brennon York] adding the new dependencies script into the test mix 2bca3c3 [Brennon York] added a git checkout 'git rev-parse HEAD' to the end of each pr test ae83b90 [Brennon York] removed jenkins tests to grab some values from the jenkins box 4110993 [Brennon York] beginning work on pr test to add new dependencies --- dev/run-tests-jenkins | 41 ++++++----- dev/tests/pr_new_dependencies.sh | 117 +++++++++++++++++++++++++++++++ 2 files changed, 140 insertions(+), 18 deletions(-) create mode 100755 dev/tests/pr_new_dependencies.sh diff --git a/dev/run-tests-jenkins b/dev/run-tests-jenkins index 3a937b637e003..f10aa6b59e1af 100755 --- a/dev/run-tests-jenkins +++ b/dev/run-tests-jenkins @@ -55,13 +55,14 @@ TESTS_TIMEOUT="120m" # format: http://linux.die.net/man/1/timeout # To write a PR test: #+ * the file must reside within the dev/tests directory #+ * be an executable bash script -#+ * accept two arguments on the command line, the first being the Github PR long commit -#+ hash and the second the Github SHA1 hash +#+ * accept three arguments on the command line, the first being the Github PR long commit +#+ hash, the second the Github SHA1 hash, and the final the current PR hash #+ * and, lastly, return string output to be included in the pr message output that will #+ be posted to Github PR_TESTS=( "pr_merge_ability" "pr_public_classes" + "pr_new_dependencies" ) function post_message () { @@ -146,34 +147,38 @@ function send_archived_logs () { fi } +# post start message +{ + start_message="\ + [Test build ${BUILD_DISPLAY_NAME} has started](${BUILD_URL}consoleFull) for \ + PR $ghprbPullId at commit [\`${SHORT_COMMIT_HASH}\`](${COMMIT_URL})." + + post_message "$start_message" +} + # Environment variable to capture PR test output pr_message="" +# Ensure we save off the current HEAD to revert to +current_pr_head="`git rev-parse HEAD`" # Run pull request tests for t in "${PR_TESTS[@]}"; do this_test="${FWDIR}/dev/tests/${t}.sh" - # Ensure the test is a file and is executable - if [ -x "$this_test" ]; then - echo "ghprb: $ghprbActualCommit sha1: $sha1" - this_mssg="`bash \"${this_test}\" \"${ghprbActualCommit}\" \"${sha1}\" 2>/dev/null`" + # Ensure the test can be found and is a file + if [ -f "${this_test}" ]; then + echo "Running test: $t" + this_mssg="$(bash "${this_test}" "${ghprbActualCommit}" "${sha1}" "${current_pr_head}")" # Check if this is the merge test as we submit that note *before* and *after* # the tests run [ "$t" == "pr_merge_ability" ] && merge_note="${this_mssg}" pr_message="${pr_message}\n${this_mssg}" + # Ensure, after each test, that we're back on the current PR + git checkout -f "${current_pr_head}" &>/dev/null + else + echo "Cannot find test ${this_test}." fi done -# post start message -{ - start_message="\ - [Test build ${BUILD_DISPLAY_NAME} has started](${BUILD_URL}consoleFull) for \ - PR $ghprbPullId at commit [\`${SHORT_COMMIT_HASH}\`](${COMMIT_URL})." - - start_message="${start_message}\n${merge_note}" - - post_message "$start_message" -} - # run tests { timeout "${TESTS_TIMEOUT}" ./dev/run-tests @@ -222,7 +227,7 @@ done PR $ghprbPullId at commit [\`${SHORT_COMMIT_HASH}\`](${COMMIT_URL})." result_message="${result_message}\n${test_result_note}" - result_message="${result_message}\n${pr_message}" + result_message="${result_message}${pr_message}" post_message "$result_message" } diff --git a/dev/tests/pr_new_dependencies.sh b/dev/tests/pr_new_dependencies.sh new file mode 100755 index 0000000000000..115a5cd1354f0 --- /dev/null +++ b/dev/tests/pr_new_dependencies.sh @@ -0,0 +1,117 @@ +#!/usr/bin/env bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# +# This script follows the base format for testing pull requests against +# another branch and returning results to be published. More details can be +# found at dev/run-tests-jenkins. +# +# Arg1: The Github Pull Request Actual Commit +#+ known as `ghprbActualCommit` in `run-tests-jenkins` +# Arg2: The SHA1 hash +#+ known as `sha1` in `run-tests-jenkins` +# Arg3: Current PR Commit Hash +#+ the PR hash for the current commit +# + +ghprbActualCommit="$1" +sha1="$2" +current_pr_head="$3" + +MVN_BIN="build/mvn" +CURR_CP_FILE="my-classpath.txt" +MASTER_CP_FILE="master-classpath.txt" + +# First switch over to the master branch +git checkout master &>/dev/null +# Find and copy all pom.xml files into a *.gate file that we can check +# against through various `git` changes +find -name "pom.xml" -exec cp {} {}.gate \; +# Switch back to the current PR +git checkout "${current_pr_head}" &>/dev/null + +# Check if any *.pom files from the current branch are different from the master +difference_q="" +for p in $(find -name "pom.xml"); do + [[ -f "${p}" && -f "${p}.gate" ]] && \ + difference_q="${difference_q}$(diff $p.gate $p)" +done + +# If no pom files were changed we can easily say no new dependencies were added +if [ -z "${difference_q}" ]; then + echo " * This patch does not change any dependencies." +else + # Else we need to manually build spark to determine what, if any, dependencies + # were added into the Spark assembly jar + ${MVN_BIN} clean package dependency:build-classpath -DskipTests 2>/dev/null | \ + sed -n -e '/Building Spark Project Assembly/,$p' | \ + grep --context=1 -m 2 "Dependencies classpath:" | \ + head -n 3 | \ + tail -n 1 | \ + tr ":" "\n" | \ + rev | \ + cut -d "/" -f 1 | \ + rev | \ + sort > ${CURR_CP_FILE} + + # Checkout the master branch to compare against + git checkout master &>/dev/null + + ${MVN_BIN} clean package dependency:build-classpath -DskipTests 2>/dev/null | \ + sed -n -e '/Building Spark Project Assembly/,$p' | \ + grep --context=1 -m 2 "Dependencies classpath:" | \ + head -n 3 | \ + tail -n 1 | \ + tr ":" "\n" | \ + rev | \ + cut -d "/" -f 1 | \ + rev | \ + sort > ${MASTER_CP_FILE} + + DIFF_RESULTS="`diff my-classpath.txt master-classpath.txt`" + + if [ -z "${DIFF_RESULTS}" ]; then + echo " * This patch does not change any dependencies." + else + # Pretty print the new dependencies + added_deps=$(echo "${DIFF_RESULTS}" | grep "<" | cut -d' ' -f2 | awk '{print " * \`"$1"\`"}') + removed_deps=$(echo "${DIFF_RESULTS}" | grep ">" | cut -d' ' -f2 | awk '{print " * \`"$1"\`"}') + added_deps_text=" * This patch **adds the following new dependencies:**\n${added_deps}" + removed_deps_text=" * This patch **removes the following dependencies:**\n${removed_deps}" + + # Construct the final returned message with proper + return_mssg="" + [ -n "${added_deps}" ] && return_mssg="${added_deps_text}" + if [ -n "${removed_deps}" ]; then + if [ -n "${return_mssg}" ]; then + return_mssg="${return_mssg}\n${removed_deps_text}" + else + return_mssg="${removed_deps_text}" + fi + fi + echo "${return_mssg}" + fi + + # Remove the files we've left over + [ -f "${CURR_CP_FILE}" ] && rm -f "${CURR_CP_FILE}" + [ -f "${MASTER_CP_FILE}" ] && rm -f "${MASTER_CP_FILE}" + + # Clean up our mess from the Maven builds just in case + ${MVN_BIN} clean &>/dev/null +fi From e3eb393961051a48ed1cac756ac1928156aa161f Mon Sep 17 00:00:00 2001 From: Nishkam Ravi Date: Sun, 29 Mar 2015 12:40:37 +0100 Subject: [PATCH 089/591] [SPARK-6406] Launch Spark using assembly jar instead of a separate launcher jar Author: Nishkam Ravi Author: nishkamravi2 Author: nravi Closes #5085 from nishkamravi2/master_nravi and squashes the following commits: bad4349 [nishkamravi2] Update Main.java 36a6f87 [Nishkam Ravi] Minor changes and bug fixes b7f4ae7 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 4a45d6a [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 458af39 [Nishkam Ravi] Locate the jar using getLocation, obviates the need to pass assembly path as an argument d9658d6 [Nishkam Ravi] Changes for SPARK-6406 ccdc334 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 3faa7a4 [Nishkam Ravi] Launcher library changes (SPARK-6406) 345206a [Nishkam Ravi] spark-class merge Merge branch 'master_nravi' of https://github.com/nishkamravi2/spark into master_nravi ac58975 [Nishkam Ravi] spark-class changes 06bfeb0 [nishkamravi2] Update spark-class 35af990 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 32c3ab3 [nishkamravi2] Update AbstractCommandBuilder.java 4bd4489 [nishkamravi2] Update AbstractCommandBuilder.java 746f35b [Nishkam Ravi] "hadoop" string in the assembly name should not be mandatory (everywhere else in spark we mandate spark-assembly*hadoop*.jar) bfe96e0 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi ee902fa [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi d453197 [nishkamravi2] Update NewHadoopRDD.scala 6f41a1d [nishkamravi2] Update NewHadoopRDD.scala 0ce2c32 [nishkamravi2] Update HadoopRDD.scala f7e33c2 [Nishkam Ravi] Merge branch 'master_nravi' of https://github.com/nishkamravi2/spark into master_nravi ba1eb8b [Nishkam Ravi] Try-catch block around the two occurrences of removeShutDownHook. Deletion of semi-redundant occurrences of expensive operation inShutDown. 71d0e17 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 494d8c0 [nishkamravi2] Update DiskBlockManager.scala 3c5ddba [nishkamravi2] Update DiskBlockManager.scala f0d12de [Nishkam Ravi] Workaround for IllegalStateException caused by recent changes to BlockManager.stop 79ea8b4 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi b446edc [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 5c9a4cb [nishkamravi2] Update TaskSetManagerSuite.scala 535295a [nishkamravi2] Update TaskSetManager.scala 3e1b616 [Nishkam Ravi] Modify test for maxResultSize 9f6583e [Nishkam Ravi] Changes to maxResultSize code (improve error message and add condition to check if maxResultSize > 0) 5f8f9ed [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 636a9ff [nishkamravi2] Update YarnAllocator.scala 8f76c8b [Nishkam Ravi] Doc change for yarn memory overhead 35daa64 [Nishkam Ravi] Slight change in the doc for yarn memory overhead 5ac2ec1 [Nishkam Ravi] Remove out dac1047 [Nishkam Ravi] Additional documentation for yarn memory overhead issue 42c2c3d [Nishkam Ravi] Additional changes for yarn memory overhead issue 362da5e [Nishkam Ravi] Additional changes for yarn memory overhead c726bd9 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi f00fa31 [Nishkam Ravi] Improving logging for AM memoryOverhead 1cf2d1e [nishkamravi2] Update YarnAllocator.scala ebcde10 [Nishkam Ravi] Modify default YARN memory_overhead-- from an additive constant to a multiplier (redone to resolve merge conflicts) 2e69f11 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi efd688a [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark 2b630f9 [nravi] Accept memory input as "30g", "512M" instead of an int value, to be consistent with rest of Spark 3bf8fad [nravi] Merge branch 'master' of https://github.com/apache/spark 5423a03 [nravi] Merge branch 'master' of https://github.com/apache/spark eb663ca [nravi] Merge branch 'master' of https://github.com/apache/spark df2aeb1 [nravi] Improved fix for ConcurrentModificationIssue (Spark-1097, Hadoop-10456) 6b840f0 [nravi] Undo the fix for SPARK-1758 (the problem is fixed) 5108700 [nravi] Fix in Spark for the Concurrent thread modification issue (SPARK-1097, HADOOP-10456) 681b36f [nravi] Fix for SPARK-1758: failing test org.apache.spark.JavaAPISuite.wholeTextFiles --- bin/spark-class | 61 +++++++----- bin/spark-class2.cmd | 33 +++---- .../launcher/AbstractCommandBuilder.java | 99 +++++-------------- make-distribution.sh | 1 - 4 files changed, 69 insertions(+), 125 deletions(-) diff --git a/bin/spark-class b/bin/spark-class index e29b234afaf96..c03946d92e2e4 100755 --- a/bin/spark-class +++ b/bin/spark-class @@ -40,35 +40,46 @@ else fi fi -# Look for the launcher. In non-release mode, add the compiled classes directly to the classpath -# instead of looking for a jar file. -SPARK_LAUNCHER_CP= -if [ -f $SPARK_HOME/RELEASE ]; then - LAUNCHER_DIR="$SPARK_HOME/lib" - num_jars="$(ls -1 "$LAUNCHER_DIR" | grep "^spark-launcher.*\.jar$" | wc -l)" - if [ "$num_jars" -eq "0" -a -z "$SPARK_LAUNCHER_CP" ]; then - echo "Failed to find Spark launcher in $LAUNCHER_DIR." 1>&2 - echo "You need to build Spark before running this program." 1>&2 - exit 1 - fi +# Find assembly jar +SPARK_ASSEMBLY_JAR= +if [ -f "$SPARK_HOME/RELEASE" ]; then + ASSEMBLY_DIR="$SPARK_HOME/lib" +else + ASSEMBLY_DIR="$SPARK_HOME/assembly/target/scala-$SPARK_SCALA_VERSION" +fi - LAUNCHER_JARS="$(ls -1 "$LAUNCHER_DIR" | grep "^spark-launcher.*\.jar$" || true)" - if [ "$num_jars" -gt "1" ]; then - echo "Found multiple Spark launcher jars in $LAUNCHER_DIR:" 1>&2 - echo "$LAUNCHER_JARS" 1>&2 - echo "Please remove all but one jar." 1>&2 - exit 1 - fi +num_jars="$(ls -1 "$ASSEMBLY_DIR" | grep "^spark-assembly.*hadoop.*\.jar$" | wc -l)" +if [ "$num_jars" -eq "0" -a -z "$SPARK_ASSEMBLY_JAR" ]; then + echo "Failed to find Spark assembly in $ASSEMBLY_DIR." 1>&2 + echo "You need to build Spark before running this program." 1>&2 + exit 1 +fi +ASSEMBLY_JARS="$(ls -1 "$ASSEMBLY_DIR" | grep "^spark-assembly.*hadoop.*\.jar$" || true)" +if [ "$num_jars" -gt "1" ]; then + echo "Found multiple Spark assembly jars in $ASSEMBLY_DIR:" 1>&2 + echo "$ASSEMBLY_JARS" 1>&2 + echo "Please remove all but one jar." 1>&2 + exit 1 +fi - SPARK_LAUNCHER_CP="${LAUNCHER_DIR}/${LAUNCHER_JARS}" +SPARK_ASSEMBLY_JAR="${ASSEMBLY_DIR}/${ASSEMBLY_JARS}" + +# Verify that versions of java used to build the jars and run Spark are compatible +if [ -n "$JAVA_HOME" ]; then + JAR_CMD="$JAVA_HOME/bin/jar" else - LAUNCHER_DIR="$SPARK_HOME/launcher/target/scala-$SPARK_SCALA_VERSION" - if [ ! -d "$LAUNCHER_DIR/classes" ]; then - echo "Failed to find Spark launcher classes in $LAUNCHER_DIR." 1>&2 - echo "You need to build Spark before running this program." 1>&2 + JAR_CMD="jar" +fi + +if [ $(command -v "$JAR_CMD") ] ; then + jar_error_check=$("$JAR_CMD" -tf "$SPARK_ASSEMBLY_JAR" nonexistent/class/path 2>&1) + if [[ "$jar_error_check" =~ "invalid CEN header" ]]; then + echo "Loading Spark jar with '$JAR_CMD' failed. " 1>&2 + echo "This is likely because Spark was compiled with Java 7 and run " 1>&2 + echo "with Java 6. (see SPARK-1703). Please use Java 7 to run Spark " 1>&2 + echo "or build Spark with Java 6." 1>&2 exit 1 fi - SPARK_LAUNCHER_CP="$LAUNCHER_DIR/classes" fi # The launcher library will print arguments separated by a NULL character, to allow arguments with @@ -77,7 +88,7 @@ fi CMD=() while IFS= read -d '' -r ARG; do CMD+=("$ARG") -done < <("$RUNNER" -cp "$SPARK_LAUNCHER_CP" org.apache.spark.launcher.Main "$@") +done < <("$RUNNER" -cp "$SPARK_ASSEMBLY_JAR" org.apache.spark.launcher.Main "$@") if [ "${CMD[0]}" = "usage" ]; then "${CMD[@]}" diff --git a/bin/spark-class2.cmd b/bin/spark-class2.cmd index 37d22215a0e7e..4ce727bc99128 100644 --- a/bin/spark-class2.cmd +++ b/bin/spark-class2.cmd @@ -29,31 +29,20 @@ if "x%1"=="x" ( exit /b 1 ) -set LAUNCHER_CP=0 -if exist %SPARK_HOME%\RELEASE goto find_release_launcher +rem Find assembly jar +set SPARK_ASSEMBLY_JAR=0 -rem Look for the Spark launcher in both Scala build directories. The launcher doesn't use Scala so -rem it doesn't really matter which one is picked up. Add the compiled classes directly to the -rem classpath instead of looking for a jar file, since it's very common for people using sbt to use -rem the "assembly" target instead of "package". -set LAUNCHER_CLASSES=%SPARK_HOME%\launcher\target\scala-2.10\classes -if exist %LAUNCHER_CLASSES% ( - set LAUNCHER_CP=%LAUNCHER_CLASSES% +if exist "%SPARK_HOME%\RELEASE" ( + set ASSEMBLY_DIR=%SPARK_HOME%\lib +) else ( + set ASSEMBLY_DIR=%SPARK_HOME%\assembly\target\scala-%SPARK_SCALA_VERSION% ) -set LAUNCHER_CLASSES=%SPARK_HOME%\launcher\target\scala-2.11\classes -if exist %LAUNCHER_CLASSES% ( - set LAUNCHER_CP=%LAUNCHER_CLASSES% -) -goto check_launcher -:find_release_launcher -for %%d in (%SPARK_HOME%\lib\spark-launcher*.jar) do ( - set LAUNCHER_CP=%%d +for %%d in (%ASSEMBLY_DIR%\spark-assembly*hadoop*.jar) do ( + set SPARK_ASSEMBLY_JAR=%%d ) - -:check_launcher -if "%LAUNCHER_CP%"=="0" ( - echo Failed to find Spark launcher JAR. +if "%SPARK_ASSEMBLY_JAR%"=="0" ( + echo Failed to find Spark assembly JAR. echo You need to build Spark before running this program. exit /b 1 ) @@ -64,7 +53,7 @@ if not "x%JAVA_HOME%"=="x" set RUNNER=%JAVA_HOME%\bin\java rem The launcher library prints the command to be executed in a single line suitable for being rem executed by the batch interpreter. So read all the output of the launcher into a variable. -for /f "tokens=*" %%i in ('cmd /C ""%RUNNER%" -cp %LAUNCHER_CP% org.apache.spark.launcher.Main %*"') do ( +for /f "tokens=*" %%i in ('cmd /C ""%RUNNER%" -cp %SPARK_ASSEMBLY_JAR% org.apache.spark.launcher.Main %*"') do ( set SPARK_CMD=%%i ) %SPARK_CMD% diff --git a/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java index 2da5f7278729e..d8279145d8e90 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java +++ b/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java @@ -86,10 +86,14 @@ public AbstractCommandBuilder() { */ List buildJavaCommand(String extraClassPath) throws IOException { List cmd = new ArrayList(); - if (javaHome == null) { - cmd.add(join(File.separator, System.getProperty("java.home"), "bin", "java")); - } else { + String envJavaHome; + + if (javaHome != null) { cmd.add(join(File.separator, javaHome, "bin", "java")); + } else if ((envJavaHome = System.getenv("JAVA_HOME")) != null) { + cmd.add(join(File.separator, envJavaHome, "bin", "java")); + } else { + cmd.add(join(File.separator, System.getProperty("java.home"), "bin", "java")); } // Load extra JAVA_OPTS from conf/java-opts, if it exists. @@ -182,59 +186,25 @@ List buildClassPath(String appClassPath) throws IOException { addToClassPath(cp, String.format("%s/core/target/jars/*", sparkHome)); } - String assembly = findAssembly(); + final String assembly = AbstractCommandBuilder.class.getProtectionDomain().getCodeSource(). + getLocation().getPath(); addToClassPath(cp, assembly); - // When Hive support is needed, Datanucleus jars must be included on the classpath. Datanucleus - // jars do not work if only included in the uber jar as plugin.xml metadata is lost. Both sbt - // and maven will populate "lib_managed/jars/" with the datanucleus jars when Spark is built - // with Hive, so first check if the datanucleus jars exist, and then ensure the current Spark - // assembly is built for Hive, before actually populating the CLASSPATH with the jars. - // - // This block also serves as a check for SPARK-1703, when the assembly jar is built with - // Java 7 and ends up with too many files, causing issues with other JDK versions. - boolean needsDataNucleus = false; - JarFile assemblyJar = null; - try { - assemblyJar = new JarFile(assembly); - needsDataNucleus = assemblyJar.getEntry("org/apache/hadoop/hive/ql/exec/") != null; - } catch (IOException ioe) { - if (ioe.getMessage().indexOf("invalid CEN header") >= 0) { - System.err.println( - "Loading Spark jar failed.\n" + - "This is likely because Spark was compiled with Java 7 and run\n" + - "with Java 6 (see SPARK-1703). Please use Java 7 to run Spark\n" + - "or build Spark with Java 6."); - System.exit(1); - } else { - throw ioe; - } - } finally { - if (assemblyJar != null) { - try { - assemblyJar.close(); - } catch (IOException e) { - // Ignore. - } - } + // Datanucleus jars must be included on the classpath. Datanucleus jars do not work if only + // included in the uber jar as plugin.xml metadata is lost. Both sbt and maven will populate + // "lib_managed/jars/" with the datanucleus jars when Spark is built with Hive + File libdir; + if (new File(sparkHome, "RELEASE").isFile()) { + libdir = new File(sparkHome, "lib"); + } else { + libdir = new File(sparkHome, "lib_managed/jars"); } - if (needsDataNucleus) { - System.err.println("Spark assembly has been built with Hive, including Datanucleus jars " + - "in classpath."); - File libdir; - if (new File(sparkHome, "RELEASE").isFile()) { - libdir = new File(sparkHome, "lib"); - } else { - libdir = new File(sparkHome, "lib_managed/jars"); - } - - checkState(libdir.isDirectory(), "Library directory '%s' does not exist.", - libdir.getAbsolutePath()); - for (File jar : libdir.listFiles()) { - if (jar.getName().startsWith("datanucleus-")) { - addToClassPath(cp, jar.getAbsolutePath()); - } + checkState(libdir.isDirectory(), "Library directory '%s' does not exist.", + libdir.getAbsolutePath()); + for (File jar : libdir.listFiles()) { + if (jar.getName().startsWith("datanucleus-")) { + addToClassPath(cp, jar.getAbsolutePath()); } } @@ -270,7 +240,6 @@ String getScalaVersion() { if (scala != null) { return scala; } - String sparkHome = getSparkHome(); File scala210 = new File(sparkHome, "assembly/target/scala-2.10"); File scala211 = new File(sparkHome, "assembly/target/scala-2.11"); @@ -330,30 +299,6 @@ String getenv(String key) { return firstNonEmpty(childEnv.get(key), System.getenv(key)); } - private String findAssembly() { - String sparkHome = getSparkHome(); - File libdir; - if (new File(sparkHome, "RELEASE").isFile()) { - libdir = new File(sparkHome, "lib"); - checkState(libdir.isDirectory(), "Library directory '%s' does not exist.", - libdir.getAbsolutePath()); - } else { - libdir = new File(sparkHome, String.format("assembly/target/scala-%s", getScalaVersion())); - } - - final Pattern re = Pattern.compile("spark-assembly.*hadoop.*\\.jar"); - FileFilter filter = new FileFilter() { - @Override - public boolean accept(File file) { - return file.isFile() && re.matcher(file.getName()).matches(); - } - }; - File[] assemblies = libdir.listFiles(filter); - checkState(assemblies != null && assemblies.length > 0, "No assemblies found in '%s'.", libdir); - checkState(assemblies.length == 1, "Multiple assemblies found in '%s'.", libdir); - return assemblies[0].getAbsolutePath(); - } - private String getConfDir() { String confDir = getenv("SPARK_CONF_DIR"); return confDir != null ? confDir : join(File.separator, getSparkHome(), "conf"); diff --git a/make-distribution.sh b/make-distribution.sh index 9ed1abfe8c598..738a9c4d69601 100755 --- a/make-distribution.sh +++ b/make-distribution.sh @@ -199,7 +199,6 @@ echo "Build flags: $@" >> "$DISTDIR/RELEASE" # Copy jars cp "$SPARK_HOME"/assembly/target/scala*/*assembly*hadoop*.jar "$DISTDIR/lib/" cp "$SPARK_HOME"/examples/target/scala*/spark-examples*.jar "$DISTDIR/lib/" -cp "$SPARK_HOME"/launcher/target/spark-launcher_$SCALA_VERSION-$VERSION.jar "$DISTDIR/lib/" # This will fail if the -Pyarn profile is not provided # In this case, silence the error and ignore the return code of this command cp "$SPARK_HOME"/network/yarn/target/scala*/spark-*-yarn-shuffle.jar "$DISTDIR/lib/" &> /dev/null || : From 52ece26b8fb9769f6ed9167e3dffc8b1d7c61b02 Mon Sep 17 00:00:00 2001 From: Thomas Graves Date: Sun, 29 Mar 2015 12:43:30 +0100 Subject: [PATCH 090/591] [SPARK-6558] Utils.getCurrentUserName returns the full principal name instead of login name Utils.getCurrentUserName returns UserGroupInformation.getCurrentUser().getUserName() when SPARK_USER isn't set. It should return UserGroupInformation.getCurrentUser().getShortUserName() getUserName() returns the users full principal name (ie user1CORP.COM). getShortUserName() returns just the users login name (user1). This just happens to work on YARN because the Client code sets: env("SPARK_USER") = UserGroupInformation.getCurrentUser().getShortUserName() Author: Thomas Graves Closes #5229 from tgravescs/SPARK-6558 and squashes the following commits: 24830bf [Thomas Graves] Utils.getCurrentUserName returns the full principal name instead of login name --- core/src/main/scala/org/apache/spark/util/Utils.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index 0b5a914e7dbbf..bb8bd1015668a 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -2055,7 +2055,7 @@ private[spark] object Utils extends Logging { */ def getCurrentUserName(): String = { Option(System.getenv("SPARK_USER")) - .getOrElse(UserGroupInformation.getCurrentUser().getUserName()) + .getOrElse(UserGroupInformation.getCurrentUser().getShortUserName()) } } From 0e2753ff14e0d3f2433272c13ce26f67dc89767f Mon Sep 17 00:00:00 2001 From: "June.He" Date: Sun, 29 Mar 2015 12:47:22 +0100 Subject: [PATCH 091/591] [SPARK-6585][Tests]Fix FileServerSuite testcase in some Env. Change FileServerSuite.test("HttpFileServer should not work with SSL when the server is untrusted") catch SSLException Author: June.He Closes #5239 from sisihj/SPARK-6585 and squashes the following commits: cb19ae3 [June.He] Change FileServerSuite.test("HttpFileServer should not work with SSL when the server is untrusted") catch SSLException --- core/src/test/scala/org/apache/spark/FileServerSuite.scala | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/core/src/test/scala/org/apache/spark/FileServerSuite.scala b/core/src/test/scala/org/apache/spark/FileServerSuite.scala index 5fdf6bc2777e3..a69e9b761f9a7 100644 --- a/core/src/test/scala/org/apache/spark/FileServerSuite.scala +++ b/core/src/test/scala/org/apache/spark/FileServerSuite.scala @@ -20,7 +20,7 @@ package org.apache.spark import java.io._ import java.net.URI import java.util.jar.{JarEntry, JarOutputStream} -import javax.net.ssl.SSLHandshakeException +import javax.net.ssl.SSLException import com.google.common.io.ByteStreams import org.apache.commons.io.{FileUtils, IOUtils} @@ -228,7 +228,7 @@ class FileServerSuite extends FunSuite with LocalSparkContext { try { server.initialize() - intercept[SSLHandshakeException] { + intercept[SSLException] { fileTransferTest(server) } } finally { From a8d53afb4e119788fa0d9dd6b3e3ca94cea98581 Mon Sep 17 00:00:00 2001 From: zsxwing Date: Sun, 29 Mar 2015 21:25:09 -0700 Subject: [PATCH 092/591] [SPARK-5124][Core] A standard RPC interface and an Akka implementation This PR added a standard internal RPC interface for Spark and an Akka implementation. See [the design document](https://issues.apache.org/jira/secure/attachment/12698710/Pluggable%20RPC%20-%20draft%202.pdf) for more details. I will split the whole work into multiple PRs to make it easier for code review. This is the first PR and avoid to touch too many files. Author: zsxwing Closes #4588 from zsxwing/rpc-part1 and squashes the following commits: fe3df4c [zsxwing] Move registerEndpoint and use actorSystem.dispatcher in asyncSetupEndpointRefByURI f6f3287 [zsxwing] Remove RpcEndpointRef.toURI 8bd1097 [zsxwing] Fix docs and the code style f459380 [zsxwing] Add RpcAddress.fromURI and rename urls to uris b221398 [zsxwing] Move send methods above ask methods 15cfd7b [zsxwing] Merge branch 'master' into rpc-part1 9ffa997 [zsxwing] Fix MiMa tests 78a1733 [zsxwing] Merge remote-tracking branch 'origin/master' into rpc-part1 385b9c3 [zsxwing] Fix the code style and add docs 2cc3f78 [zsxwing] Add an asynchronous version of setupEndpointRefByUrl e8dfec3 [zsxwing] Remove 'sendWithReply(message: Any, sender: RpcEndpointRef): Unit' 08564ae [zsxwing] Add RpcEnvFactory to create RpcEnv e5df4ca [zsxwing] Handle AkkaFailure(e) in Actor ec7c5b0 [zsxwing] Fix docs 7fc95e1 [zsxwing] Implement askWithReply in RpcEndpointRef 9288406 [zsxwing] Document thread-safety for setupThreadSafeEndpoint 3007c09 [zsxwing] Move setupDriverEndpointRef to RpcUtils and rename to makeDriverRef c425022 [zsxwing] Fix the code style 5f87700 [zsxwing] Move the logical of processing message to a private function 3e56123 [zsxwing] Use lazy to eliminate CountDownLatch 07f128f [zsxwing] Remove ActionScheduler.scala 4d34191 [zsxwing] Remove scheduler from RpcEnv 7cdd95e [zsxwing] Add docs for RpcEnv 51e6667 [zsxwing] Add 'sender' to RpcCallContext and rename the parameter of receiveAndReply to 'context' ffc1280 [zsxwing] Rename 'fail' to 'sendFailure' and other minor code style changes 28e6d0f [zsxwing] Add onXXX for network events and remove the companion objects of network events 3751c97 [zsxwing] Rename RpcResponse to RpcCallContext fe7d1ff [zsxwing] Add explicit reply in rpc 7b9e0c9 [zsxwing] Fix the indentation 04a106e [zsxwing] Remove NopCancellable and add a const NOP in object SettableCancellable 2a579f4 [zsxwing] Remove RpcEnv.systemName 155b987 [zsxwing] Change newURI to uriOf and add some comments 45b2317 [zsxwing] A standard RPC interface and An Akka implementation --- .../scala/org/apache/spark/SparkEnv.scala | 42 +- .../spark/deploy/worker/DriverWrapper.scala | 11 +- .../spark/deploy/worker/WorkerWatcher.scala | 59 +- .../CoarseGrainedExecutorBackend.scala | 2 +- .../scala/org/apache/spark/rpc/RpcEnv.scala | 429 ++++++++++++++ .../apache/spark/rpc/akka/AkkaRpcEnv.scala | 318 +++++++++++ .../scheduler/OutputCommitCoordinator.scala | 37 +- .../org/apache/spark/util/AkkaUtils.scala | 2 +- .../org/apache/spark/util/RpcUtils.scala | 35 ++ .../deploy/worker/WorkerWatcherSuite.scala | 38 +- .../org/apache/spark/rpc/RpcEnvSuite.scala | 525 ++++++++++++++++++ .../spark/rpc/akka/AkkaRpcEnvSuite.scala | 50 ++ project/MimaExcludes.scala | 4 +- 13 files changed, 1466 insertions(+), 86 deletions(-) create mode 100644 core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala create mode 100644 core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala create mode 100644 core/src/main/scala/org/apache/spark/util/RpcUtils.scala create mode 100644 core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala create mode 100644 core/src/test/scala/org/apache/spark/rpc/akka/AkkaRpcEnvSuite.scala diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala index 2a0c7e756dd3a..4a2ed82a40dec 100644 --- a/core/src/main/scala/org/apache/spark/SparkEnv.scala +++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala @@ -34,12 +34,14 @@ import org.apache.spark.metrics.MetricsSystem import org.apache.spark.network.BlockTransferService import org.apache.spark.network.netty.NettyBlockTransferService import org.apache.spark.network.nio.NioBlockTransferService +import org.apache.spark.rpc.{RpcEndpointRef, RpcEndpoint, RpcEnv} +import org.apache.spark.rpc.akka.AkkaRpcEnv import org.apache.spark.scheduler.{OutputCommitCoordinator, LiveListenerBus} -import org.apache.spark.scheduler.OutputCommitCoordinator.OutputCommitCoordinatorActor +import org.apache.spark.scheduler.OutputCommitCoordinator.OutputCommitCoordinatorEndpoint import org.apache.spark.serializer.Serializer import org.apache.spark.shuffle.{ShuffleMemoryManager, ShuffleManager} import org.apache.spark.storage._ -import org.apache.spark.util.{AkkaUtils, Utils} +import org.apache.spark.util.{AkkaUtils, RpcUtils, Utils} /** * :: DeveloperApi :: @@ -54,7 +56,7 @@ import org.apache.spark.util.{AkkaUtils, Utils} @DeveloperApi class SparkEnv ( val executorId: String, - val actorSystem: ActorSystem, + private[spark] val rpcEnv: RpcEnv, val serializer: Serializer, val closureSerializer: Serializer, val cacheManager: CacheManager, @@ -71,6 +73,9 @@ class SparkEnv ( val outputCommitCoordinator: OutputCommitCoordinator, val conf: SparkConf) extends Logging { + // TODO Remove actorSystem + val actorSystem = rpcEnv.asInstanceOf[AkkaRpcEnv].actorSystem + private[spark] var isStopped = false private val pythonWorkers = mutable.HashMap[(String, Map[String, String]), PythonWorkerFactory]() @@ -91,7 +96,8 @@ class SparkEnv ( blockManager.master.stop() metricsSystem.stop() outputCommitCoordinator.stop() - actorSystem.shutdown() + rpcEnv.shutdown() + // Unfortunately Akka's awaitTermination doesn't actually wait for the Netty server to shut // down, but let's call it anyway in case it gets fixed in a later release // UPDATE: In Akka 2.1.x, this hangs if there are remote actors, so we can't call it. @@ -236,16 +242,15 @@ object SparkEnv extends Logging { val securityManager = new SecurityManager(conf) // Create the ActorSystem for Akka and get the port it binds to. - val (actorSystem, boundPort) = { - val actorSystemName = if (isDriver) driverActorSystemName else executorActorSystemName - AkkaUtils.createActorSystem(actorSystemName, hostname, port, conf, securityManager) - } + val actorSystemName = if (isDriver) driverActorSystemName else executorActorSystemName + val rpcEnv = RpcEnv.create(actorSystemName, hostname, port, conf, securityManager) + val actorSystem = rpcEnv.asInstanceOf[AkkaRpcEnv].actorSystem // Figure out which port Akka actually bound to in case the original port is 0 or occupied. if (isDriver) { - conf.set("spark.driver.port", boundPort.toString) + conf.set("spark.driver.port", rpcEnv.address.port.toString) } else { - conf.set("spark.executor.port", boundPort.toString) + conf.set("spark.executor.port", rpcEnv.address.port.toString) } // Create an instance of the class with the given name, possibly initializing it with our conf @@ -290,6 +295,15 @@ object SparkEnv extends Logging { } } + def registerOrLookupEndpoint(name: String, endpointCreator: => RpcEndpoint): RpcEndpointRef = { + if (isDriver) { + logInfo("Registering " + name) + rpcEnv.setupEndpoint(name, endpointCreator) + } else { + RpcUtils.makeDriverRef(name, conf, rpcEnv) + } + } + val mapOutputTracker = if (isDriver) { new MapOutputTrackerMaster(conf) } else { @@ -377,13 +391,13 @@ object SparkEnv extends Logging { val outputCommitCoordinator = mockOutputCommitCoordinator.getOrElse { new OutputCommitCoordinator(conf) } - val outputCommitCoordinatorActor = registerOrLookup("OutputCommitCoordinator", - new OutputCommitCoordinatorActor(outputCommitCoordinator)) - outputCommitCoordinator.coordinatorActor = Some(outputCommitCoordinatorActor) + val outputCommitCoordinatorRef = registerOrLookupEndpoint("OutputCommitCoordinator", + new OutputCommitCoordinatorEndpoint(rpcEnv, outputCommitCoordinator)) + outputCommitCoordinator.coordinatorRef = Some(outputCommitCoordinatorRef) val envInstance = new SparkEnv( executorId, - actorSystem, + rpcEnv, serializer, closureSerializer, cacheManager, diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala b/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala index deef6ef9043c6..d1a12b01e78f7 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala @@ -19,10 +19,9 @@ package org.apache.spark.deploy.worker import java.io.File -import akka.actor._ - import org.apache.spark.{SecurityManager, SparkConf} -import org.apache.spark.util.{AkkaUtils, ChildFirstURLClassLoader, MutableURLClassLoader, Utils} +import org.apache.spark.rpc.RpcEnv +import org.apache.spark.util.{ChildFirstURLClassLoader, MutableURLClassLoader, Utils} /** * Utility object for launching driver programs such that they share fate with the Worker process. @@ -39,9 +38,9 @@ object DriverWrapper { */ case workerUrl :: userJar :: mainClass :: extraArgs => val conf = new SparkConf() - val (actorSystem, _) = AkkaUtils.createActorSystem("Driver", + val rpcEnv = RpcEnv.create("Driver", Utils.localHostName(), 0, conf, new SecurityManager(conf)) - actorSystem.actorOf(Props(classOf[WorkerWatcher], workerUrl), name = "workerWatcher") + rpcEnv.setupEndpoint("workerWatcher", new WorkerWatcher(rpcEnv, workerUrl)) val currentLoader = Thread.currentThread.getContextClassLoader val userJarUrl = new File(userJar).toURI().toURL() @@ -58,7 +57,7 @@ object DriverWrapper { val mainMethod = clazz.getMethod("main", classOf[Array[String]]) mainMethod.invoke(null, extraArgs.toArray[String]) - actorSystem.shutdown() + rpcEnv.shutdown() case _ => System.err.println("Usage: DriverWrapper [options]") diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala index e0790274d7d3e..83fb991891a41 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala @@ -17,58 +17,63 @@ package org.apache.spark.deploy.worker -import akka.actor.{Actor, Address, AddressFromURIString} -import akka.remote.{AssociatedEvent, AssociationErrorEvent, AssociationEvent, DisassociatedEvent, RemotingLifecycleEvent} - import org.apache.spark.Logging import org.apache.spark.deploy.DeployMessages.SendHeartbeat -import org.apache.spark.util.ActorLogReceive +import org.apache.spark.rpc._ /** * Actor which connects to a worker process and terminates the JVM if the connection is severed. * Provides fate sharing between a worker and its associated child processes. */ -private[spark] class WorkerWatcher(workerUrl: String) - extends Actor with ActorLogReceive with Logging { - - override def preStart() { - context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) +private[spark] class WorkerWatcher(override val rpcEnv: RpcEnv, workerUrl: String) + extends RpcEndpoint with Logging { + override def onStart() { logInfo(s"Connecting to worker $workerUrl") - val worker = context.actorSelection(workerUrl) - worker ! SendHeartbeat // need to send a message here to initiate connection + if (!isTesting) { + rpcEnv.asyncSetupEndpointRefByURI(workerUrl) + } } // Used to avoid shutting down JVM during tests + // In the normal case, exitNonZero will call `System.exit(-1)` to shutdown the JVM. In the unit + // test, the user should call `setTesting(true)` so that `exitNonZero` will set `isShutDown` to + // true rather than calling `System.exit`. The user can check `isShutDown` to know if + // `exitNonZero` is called. private[deploy] var isShutDown = false private[deploy] def setTesting(testing: Boolean) = isTesting = testing private var isTesting = false // Lets us filter events only from the worker's actor system - private val expectedHostPort = AddressFromURIString(workerUrl).hostPort - private def isWorker(address: Address) = address.hostPort == expectedHostPort + private val expectedAddress = RpcAddress.fromURIString(workerUrl) + private def isWorker(address: RpcAddress) = expectedAddress == address private def exitNonZero() = if (isTesting) isShutDown = true else System.exit(-1) - override def receiveWithLogging: PartialFunction[Any, Unit] = { - case AssociatedEvent(localAddress, remoteAddress, inbound) if isWorker(remoteAddress) => - logInfo(s"Successfully connected to $workerUrl") + override def receive: PartialFunction[Any, Unit] = { + case e => logWarning(s"Received unexpected message: $e") + } - case AssociationErrorEvent(cause, localAddress, remoteAddress, inbound, _) - if isWorker(remoteAddress) => - // These logs may not be seen if the worker (and associated pipe) has died - logError(s"Could not initialize connection to worker $workerUrl. Exiting.") - logError(s"Error was: $cause") - exitNonZero() + override def onConnected(remoteAddress: RpcAddress): Unit = { + if (isWorker(remoteAddress)) { + logInfo(s"Successfully connected to $workerUrl") + } + } - case DisassociatedEvent(localAddress, remoteAddress, inbound) if isWorker(remoteAddress) => + override def onDisconnected(remoteAddress: RpcAddress): Unit = { + if (isWorker(remoteAddress)) { // This log message will never be seen logError(s"Lost connection to worker actor $workerUrl. Exiting.") exitNonZero() + } + } - case e: AssociationEvent => - // pass through association events relating to other remote actor systems - - case e => logWarning(s"Received unexpected actor system event: $e") + override def onNetworkError(cause: Throwable, remoteAddress: RpcAddress): Unit = { + if (isWorker(remoteAddress)) { + // These logs may not be seen if the worker (and associated pipe) has died + logError(s"Could not initialize connection to worker $workerUrl. Exiting.") + logError(s"Error was: $cause") + exitNonZero() + } } } diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala index b5205d4e997ae..900e678ee02ef 100644 --- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala +++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala @@ -169,7 +169,7 @@ private[spark] object CoarseGrainedExecutorBackend extends Logging { driverUrl, executorId, sparkHostPort, cores, userClassPath, env), name = "Executor") workerUrl.foreach { url => - env.actorSystem.actorOf(Props(classOf[WorkerWatcher], url), name = "WorkerWatcher") + env.rpcEnv.setupEndpoint("WorkerWatcher", new WorkerWatcher(env.rpcEnv, url)) } env.actorSystem.awaitTermination() } diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala new file mode 100644 index 0000000000000..7985941d949c0 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala @@ -0,0 +1,429 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rpc + +import java.net.URI + +import scala.concurrent.{Await, Future} +import scala.concurrent.duration._ +import scala.language.postfixOps +import scala.reflect.ClassTag + +import org.apache.spark.{Logging, SparkException, SecurityManager, SparkConf} +import org.apache.spark.util.{AkkaUtils, Utils} + +/** + * An RPC environment. [[RpcEndpoint]]s need to register itself with a name to [[RpcEnv]] to + * receives messages. Then [[RpcEnv]] will process messages sent from [[RpcEndpointRef]] or remote + * nodes, and deliver them to corresponding [[RpcEndpoint]]s. + * + * [[RpcEnv]] also provides some methods to retrieve [[RpcEndpointRef]]s given name or uri. + */ +private[spark] abstract class RpcEnv(conf: SparkConf) { + + private[spark] val defaultLookupTimeout = AkkaUtils.lookupTimeout(conf) + + /** + * Return RpcEndpointRef of the registered [[RpcEndpoint]]. Will be used to implement + * [[RpcEndpoint.self]]. + * + * Note: This method won't return null. `IllegalArgumentException` will be thrown if calling this + * on a non-existent endpoint. + */ + private[rpc] def endpointRef(endpoint: RpcEndpoint): RpcEndpointRef + + /** + * Return the address that [[RpcEnv]] is listening to. + */ + def address: RpcAddress + + /** + * Register a [[RpcEndpoint]] with a name and return its [[RpcEndpointRef]]. [[RpcEnv]] does not + * guarantee thread-safety. + */ + def setupEndpoint(name: String, endpoint: RpcEndpoint): RpcEndpointRef + + /** + * Register a [[RpcEndpoint]] with a name and return its [[RpcEndpointRef]]. [[RpcEnv]] should + * make sure thread-safely sending messages to [[RpcEndpoint]]. + * + * Thread-safety means processing of one message happens before processing of the next message by + * the same [[RpcEndpoint]]. In the other words, changes to internal fields of a [[RpcEndpoint]] + * are visible when processing the next message, and fields in the [[RpcEndpoint]] need not be + * volatile or equivalent. + * + * However, there is no guarantee that the same thread will be executing the same [[RpcEndpoint]] + * for different messages. + */ + def setupThreadSafeEndpoint(name: String, endpoint: RpcEndpoint): RpcEndpointRef + + /** + * Retrieve the [[RpcEndpointRef]] represented by `uri` asynchronously. + */ + def asyncSetupEndpointRefByURI(uri: String): Future[RpcEndpointRef] + + /** + * Retrieve the [[RpcEndpointRef]] represented by `uri`. This is a blocking action. + */ + def setupEndpointRefByURI(uri: String): RpcEndpointRef = { + Await.result(asyncSetupEndpointRefByURI(uri), defaultLookupTimeout) + } + + /** + * Retrieve the [[RpcEndpointRef]] represented by `systemName`, `address` and `endpointName` + * asynchronously. + */ + def asyncSetupEndpointRef( + systemName: String, address: RpcAddress, endpointName: String): Future[RpcEndpointRef] = { + asyncSetupEndpointRefByURI(uriOf(systemName, address, endpointName)) + } + + /** + * Retrieve the [[RpcEndpointRef]] represented by `systemName`, `address` and `endpointName`. + * This is a blocking action. + */ + def setupEndpointRef( + systemName: String, address: RpcAddress, endpointName: String): RpcEndpointRef = { + setupEndpointRefByURI(uriOf(systemName, address, endpointName)) + } + + /** + * Stop [[RpcEndpoint]] specified by `endpoint`. + */ + def stop(endpoint: RpcEndpointRef): Unit + + /** + * Shutdown this [[RpcEnv]] asynchronously. If need to make sure [[RpcEnv]] exits successfully, + * call [[awaitTermination()]] straight after [[shutdown()]]. + */ + def shutdown(): Unit + + /** + * Wait until [[RpcEnv]] exits. + * + * TODO do we need a timeout parameter? + */ + def awaitTermination(): Unit + + /** + * Create a URI used to create a [[RpcEndpointRef]]. Use this one to create the URI instead of + * creating it manually because different [[RpcEnv]] may have different formats. + */ + def uriOf(systemName: String, address: RpcAddress, endpointName: String): String +} + +private[spark] case class RpcEnvConfig( + conf: SparkConf, + name: String, + host: String, + port: Int, + securityManager: SecurityManager) + +/** + * A RpcEnv implementation must have a [[RpcEnvFactory]] implementation with an empty constructor + * so that it can be created via Reflection. + */ +private[spark] object RpcEnv { + + private def getRpcEnvFactory(conf: SparkConf): RpcEnvFactory = { + // Add more RpcEnv implementations here + val rpcEnvNames = Map("akka" -> "org.apache.spark.rpc.akka.AkkaRpcEnvFactory") + val rpcEnvName = conf.get("spark.rpc", "akka") + val rpcEnvFactoryClassName = rpcEnvNames.getOrElse(rpcEnvName.toLowerCase, rpcEnvName) + Class.forName(rpcEnvFactoryClassName, true, Utils.getContextOrSparkClassLoader). + newInstance().asInstanceOf[RpcEnvFactory] + } + + def create( + name: String, + host: String, + port: Int, + conf: SparkConf, + securityManager: SecurityManager): RpcEnv = { + // Using Reflection to create the RpcEnv to avoid to depend on Akka directly + val config = RpcEnvConfig(conf, name, host, port, securityManager) + getRpcEnvFactory(conf).create(config) + } + +} + +/** + * A factory class to create the [[RpcEnv]]. It must have an empty constructor so that it can be + * created using Reflection. + */ +private[spark] trait RpcEnvFactory { + + def create(config: RpcEnvConfig): RpcEnv +} + +/** + * An end point for the RPC that defines what functions to trigger given a message. + * + * It is guaranteed that `onStart`, `receive` and `onStop` will be called in sequence. + * + * The lift-cycle will be: + * + * constructor onStart receive* onStop + * + * Note: `receive` can be called concurrently. If you want `receive` is thread-safe, please use + * [[RpcEnv.setupThreadSafeEndpoint]] + * + * If any error is thrown from one of [[RpcEndpoint]] methods except `onError`, `onError` will be + * invoked with the cause. If `onError` throws an error, [[RpcEnv]] will ignore it. + */ +private[spark] trait RpcEndpoint { + + /** + * The [[RpcEnv]] that this [[RpcEndpoint]] is registered to. + */ + val rpcEnv: RpcEnv + + /** + * The [[RpcEndpointRef]] of this [[RpcEndpoint]]. `self` will become valid when `onStart` is + * called. + * + * Note: Because before `onStart`, [[RpcEndpoint]] has not yet been registered and there is not + * valid [[RpcEndpointRef]] for it. So don't call `self` before `onStart` is called. + */ + final def self: RpcEndpointRef = { + require(rpcEnv != null, "rpcEnv has not been initialized") + rpcEnv.endpointRef(this) + } + + /** + * Process messages from [[RpcEndpointRef.send]] or [[RpcCallContext.reply)]]. If receiving a + * unmatched message, [[SparkException]] will be thrown and sent to `onError`. + */ + def receive: PartialFunction[Any, Unit] = { + case _ => throw new SparkException(self + " does not implement 'receive'") + } + + /** + * Process messages from [[RpcEndpointRef.sendWithReply]]. If receiving a unmatched message, + * [[SparkException]] will be thrown and sent to `onError`. + */ + def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case _ => context.sendFailure(new SparkException(self + " won't reply anything")) + } + + /** + * Call onError when any exception is thrown during handling messages. + * + * @param cause + */ + def onError(cause: Throwable): Unit = { + // By default, throw e and let RpcEnv handle it + throw cause + } + + /** + * Invoked before [[RpcEndpoint]] starts to handle any message. + */ + def onStart(): Unit = { + // By default, do nothing. + } + + /** + * Invoked when [[RpcEndpoint]] is stopping. + */ + def onStop(): Unit = { + // By default, do nothing. + } + + /** + * Invoked when `remoteAddress` is connected to the current node. + */ + def onConnected(remoteAddress: RpcAddress): Unit = { + // By default, do nothing. + } + + /** + * Invoked when `remoteAddress` is lost. + */ + def onDisconnected(remoteAddress: RpcAddress): Unit = { + // By default, do nothing. + } + + /** + * Invoked when some network error happens in the connection between the current node and + * `remoteAddress`. + */ + def onNetworkError(cause: Throwable, remoteAddress: RpcAddress): Unit = { + // By default, do nothing. + } + + /** + * A convenient method to stop [[RpcEndpoint]]. + */ + final def stop(): Unit = { + val _self = self + if (_self != null) { + rpcEnv.stop(self) + } + } +} + +/** + * A reference for a remote [[RpcEndpoint]]. [[RpcEndpointRef]] is thread-safe. + */ +private[spark] abstract class RpcEndpointRef(@transient conf: SparkConf) + extends Serializable with Logging { + + private[this] val maxRetries = conf.getInt("spark.akka.num.retries", 3) + private[this] val retryWaitMs = conf.getLong("spark.akka.retry.wait", 3000) + private[this] val defaultTimeout = conf.getLong("spark.akka.lookupTimeout", 30) seconds + + /** + * return the address for the [[RpcEndpointRef]] + */ + def address: RpcAddress + + def name: String + + /** + * Sends a one-way asynchronous message. Fire-and-forget semantics. + */ + def send(message: Any): Unit + + /** + * Send a message to the corresponding [[RpcEndpoint.receiveAndReply)]] and return a `Future` to + * receive the reply within a default timeout. + * + * This method only sends the message once and never retries. + */ + def sendWithReply[T: ClassTag](message: Any): Future[T] = sendWithReply(message, defaultTimeout) + + /** + * Send a message to the corresponding [[RpcEndpoint.receiveAndReply)]] and return a `Future` to + * receive the reply within the specified timeout. + * + * This method only sends the message once and never retries. + */ + def sendWithReply[T: ClassTag](message: Any, timeout: FiniteDuration): Future[T] + + /** + * Send a message to the corresponding [[RpcEndpoint]] and get its result within a default + * timeout, or throw a SparkException if this fails even after the default number of retries. + * The default `timeout` will be used in every trial of calling `sendWithReply`. Because this + * method retries, the message handling in the receiver side should be idempotent. + * + * Note: this is a blocking action which may cost a lot of time, so don't call it in an message + * loop of [[RpcEndpoint]]. + * + * @param message the message to send + * @tparam T type of the reply message + * @return the reply message from the corresponding [[RpcEndpoint]] + */ + def askWithReply[T: ClassTag](message: Any): T = askWithReply(message, defaultTimeout) + + /** + * Send a message to the corresponding [[RpcEndpoint.receive]] and get its result within a + * specified timeout, throw a SparkException if this fails even after the specified number of + * retries. `timeout` will be used in every trial of calling `sendWithReply`. Because this method + * retries, the message handling in the receiver side should be idempotent. + * + * Note: this is a blocking action which may cost a lot of time, so don't call it in an message + * loop of [[RpcEndpoint]]. + * + * @param message the message to send + * @param timeout the timeout duration + * @tparam T type of the reply message + * @return the reply message from the corresponding [[RpcEndpoint]] + */ + def askWithReply[T: ClassTag](message: Any, timeout: FiniteDuration): T = { + // TODO: Consider removing multiple attempts + var attempts = 0 + var lastException: Exception = null + while (attempts < maxRetries) { + attempts += 1 + try { + val future = sendWithReply[T](message, timeout) + val result = Await.result(future, timeout) + if (result == null) { + throw new SparkException("Actor returned null") + } + return result + } catch { + case ie: InterruptedException => throw ie + case e: Exception => + lastException = e + logWarning(s"Error sending message [message = $message] in $attempts attempts", e) + } + Thread.sleep(retryWaitMs) + } + + throw new SparkException( + s"Error sending message [message = $message]", lastException) + } + +} + +/** + * Represent a host with a port + */ +private[spark] case class RpcAddress(host: String, port: Int) { + // TODO do we need to add the type of RpcEnv in the address? + + val hostPort: String = host + ":" + port + + override val toString: String = hostPort +} + +private[spark] object RpcAddress { + + /** + * Return the [[RpcAddress]] represented by `uri`. + */ + def fromURI(uri: URI): RpcAddress = { + RpcAddress(uri.getHost, uri.getPort) + } + + /** + * Return the [[RpcAddress]] represented by `uri`. + */ + def fromURIString(uri: String): RpcAddress = { + fromURI(new java.net.URI(uri)) + } + + def fromSparkURL(sparkUrl: String): RpcAddress = { + val (host, port) = Utils.extractHostPortFromSparkUrl(sparkUrl) + RpcAddress(host, port) + } +} + +/** + * A callback that [[RpcEndpoint]] can use it to send back a message or failure. + */ +private[spark] trait RpcCallContext { + + /** + * Reply a message to the sender. If the sender is [[RpcEndpoint]], its [[RpcEndpoint.receive]] + * will be called. + */ + def reply(response: Any): Unit + + /** + * Report a failure to the sender. + */ + def sendFailure(e: Throwable): Unit + + /** + * The sender of this message. + */ + def sender: RpcEndpointRef +} diff --git a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala new file mode 100644 index 0000000000000..769d59b7b3343 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala @@ -0,0 +1,318 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rpc.akka + +import java.net.URI +import java.util.concurrent.ConcurrentHashMap + +import scala.concurrent.{Await, Future} +import scala.concurrent.duration._ +import scala.language.postfixOps +import scala.reflect.ClassTag +import scala.util.control.NonFatal + +import akka.actor.{ActorSystem, ExtendedActorSystem, Actor, ActorRef, Props, Address} +import akka.pattern.{ask => akkaAsk} +import akka.remote.{AssociationEvent, AssociatedEvent, DisassociatedEvent, AssociationErrorEvent} +import org.apache.spark.{SparkException, Logging, SparkConf} +import org.apache.spark.rpc._ +import org.apache.spark.util.{ActorLogReceive, AkkaUtils} + +/** + * A RpcEnv implementation based on Akka. + * + * TODO Once we remove all usages of Akka in other place, we can move this file to a new project and + * remove Akka from the dependencies. + * + * @param actorSystem + * @param conf + * @param boundPort + */ +private[spark] class AkkaRpcEnv private[akka] ( + val actorSystem: ActorSystem, conf: SparkConf, boundPort: Int) + extends RpcEnv(conf) with Logging { + + private val defaultAddress: RpcAddress = { + val address = actorSystem.asInstanceOf[ExtendedActorSystem].provider.getDefaultAddress + // In some test case, ActorSystem doesn't bind to any address. + // So just use some default value since they are only some unit tests + RpcAddress(address.host.getOrElse("localhost"), address.port.getOrElse(boundPort)) + } + + override val address: RpcAddress = defaultAddress + + /** + * A lookup table to search a [[RpcEndpointRef]] for a [[RpcEndpoint]]. We need it to make + * [[RpcEndpoint.self]] work. + */ + private val endpointToRef = new ConcurrentHashMap[RpcEndpoint, RpcEndpointRef]() + + /** + * Need this map to remove `RpcEndpoint` from `endpointToRef` via a `RpcEndpointRef` + */ + private val refToEndpoint = new ConcurrentHashMap[RpcEndpointRef, RpcEndpoint]() + + private def registerEndpoint(endpoint: RpcEndpoint, endpointRef: RpcEndpointRef): Unit = { + endpointToRef.put(endpoint, endpointRef) + refToEndpoint.put(endpointRef, endpoint) + } + + private def unregisterEndpoint(endpointRef: RpcEndpointRef): Unit = { + val endpoint = refToEndpoint.remove(endpointRef) + if (endpoint != null) { + endpointToRef.remove(endpoint) + } + } + + /** + * Retrieve the [[RpcEndpointRef]] of `endpoint`. + */ + override def endpointRef(endpoint: RpcEndpoint): RpcEndpointRef = { + val endpointRef = endpointToRef.get(endpoint) + require(endpointRef != null, s"Cannot find RpcEndpointRef of ${endpoint} in ${this}") + endpointRef + } + + override def setupEndpoint(name: String, endpoint: RpcEndpoint): RpcEndpointRef = { + setupThreadSafeEndpoint(name, endpoint) + } + + override def setupThreadSafeEndpoint(name: String, endpoint: RpcEndpoint): RpcEndpointRef = { + @volatile var endpointRef: AkkaRpcEndpointRef = null + // Use lazy because the Actor needs to use `endpointRef`. + // So `actorRef` should be created after assigning `endpointRef`. + lazy val actorRef = actorSystem.actorOf(Props(new Actor with ActorLogReceive with Logging { + + assert(endpointRef != null) + + override def preStart(): Unit = { + // Listen for remote client network events + context.system.eventStream.subscribe(self, classOf[AssociationEvent]) + safelyCall(endpoint) { + endpoint.onStart() + } + } + + override def receiveWithLogging: Receive = { + case AssociatedEvent(_, remoteAddress, _) => + safelyCall(endpoint) { + endpoint.onConnected(akkaAddressToRpcAddress(remoteAddress)) + } + + case DisassociatedEvent(_, remoteAddress, _) => + safelyCall(endpoint) { + endpoint.onDisconnected(akkaAddressToRpcAddress(remoteAddress)) + } + + case AssociationErrorEvent(cause, localAddress, remoteAddress, inbound, _) => + safelyCall(endpoint) { + endpoint.onNetworkError(cause, akkaAddressToRpcAddress(remoteAddress)) + } + + case e: AssociationEvent => + // TODO ignore? + + case m: AkkaMessage => + logDebug(s"Received RPC message: $m") + safelyCall(endpoint) { + processMessage(endpoint, m, sender) + } + + case AkkaFailure(e) => + safelyCall(endpoint) { + throw e + } + + case message: Any => { + logWarning(s"Unknown message: $message") + } + + } + + override def postStop(): Unit = { + unregisterEndpoint(endpoint.self) + safelyCall(endpoint) { + endpoint.onStop() + } + } + + }), name = name) + endpointRef = new AkkaRpcEndpointRef(defaultAddress, actorRef, conf, initInConstructor = false) + registerEndpoint(endpoint, endpointRef) + // Now actorRef can be created safely + endpointRef.init() + endpointRef + } + + private def processMessage(endpoint: RpcEndpoint, m: AkkaMessage, _sender: ActorRef): Unit = { + val message = m.message + val needReply = m.needReply + val pf: PartialFunction[Any, Unit] = + if (needReply) { + endpoint.receiveAndReply(new RpcCallContext { + override def sendFailure(e: Throwable): Unit = { + _sender ! AkkaFailure(e) + } + + override def reply(response: Any): Unit = { + _sender ! AkkaMessage(response, false) + } + + // Some RpcEndpoints need to know the sender's address + override val sender: RpcEndpointRef = + new AkkaRpcEndpointRef(defaultAddress, _sender, conf) + }) + } else { + endpoint.receive + } + try { + pf.applyOrElse[Any, Unit](message, { message => + throw new SparkException(s"Unmatched message $message from ${_sender}") + }) + } catch { + case NonFatal(e) => + if (needReply) { + // If the sender asks a reply, we should send the error back to the sender + _sender ! AkkaFailure(e) + } else { + throw e + } + } + } + + /** + * Run `action` safely to avoid to crash the thread. If any non-fatal exception happens, it will + * call `endpoint.onError`. If `endpoint.onError` throws any non-fatal exception, just log it. + */ + private def safelyCall(endpoint: RpcEndpoint)(action: => Unit): Unit = { + try { + action + } catch { + case NonFatal(e) => { + try { + endpoint.onError(e) + } catch { + case NonFatal(e) => logError(s"Ignore error: ${e.getMessage}", e) + } + } + } + } + + private def akkaAddressToRpcAddress(address: Address): RpcAddress = { + RpcAddress(address.host.getOrElse(defaultAddress.host), + address.port.getOrElse(defaultAddress.port)) + } + + override def asyncSetupEndpointRefByURI(uri: String): Future[RpcEndpointRef] = { + import actorSystem.dispatcher + actorSystem.actorSelection(uri).resolveOne(defaultLookupTimeout). + map(new AkkaRpcEndpointRef(defaultAddress, _, conf)) + } + + override def uriOf(systemName: String, address: RpcAddress, endpointName: String): String = { + AkkaUtils.address( + AkkaUtils.protocol(actorSystem), systemName, address.host, address.port, endpointName) + } + + override def shutdown(): Unit = { + actorSystem.shutdown() + } + + override def stop(endpoint: RpcEndpointRef): Unit = { + require(endpoint.isInstanceOf[AkkaRpcEndpointRef]) + actorSystem.stop(endpoint.asInstanceOf[AkkaRpcEndpointRef].actorRef) + } + + override def awaitTermination(): Unit = { + actorSystem.awaitTermination() + } + + override def toString: String = s"${getClass.getSimpleName}($actorSystem)" +} + +private[spark] class AkkaRpcEnvFactory extends RpcEnvFactory { + + def create(config: RpcEnvConfig): RpcEnv = { + val (actorSystem, boundPort) = AkkaUtils.createActorSystem( + config.name, config.host, config.port, config.conf, config.securityManager) + new AkkaRpcEnv(actorSystem, config.conf, boundPort) + } +} + +private[akka] class AkkaRpcEndpointRef( + @transient defaultAddress: RpcAddress, + @transient _actorRef: => ActorRef, + @transient conf: SparkConf, + @transient initInConstructor: Boolean = true) + extends RpcEndpointRef(conf) with Logging { + + lazy val actorRef = _actorRef + + override lazy val address: RpcAddress = { + val akkaAddress = actorRef.path.address + RpcAddress(akkaAddress.host.getOrElse(defaultAddress.host), + akkaAddress.port.getOrElse(defaultAddress.port)) + } + + override lazy val name: String = actorRef.path.name + + private[akka] def init(): Unit = { + // Initialize the lazy vals + actorRef + address + name + } + + if (initInConstructor) { + init() + } + + override def send(message: Any): Unit = { + actorRef ! AkkaMessage(message, false) + } + + override def sendWithReply[T: ClassTag](message: Any, timeout: FiniteDuration): Future[T] = { + import scala.concurrent.ExecutionContext.Implicits.global + actorRef.ask(AkkaMessage(message, true))(timeout).flatMap { + case msg @ AkkaMessage(message, reply) => + if (reply) { + logError(s"Receive $msg but the sender cannot reply") + Future.failed(new SparkException(s"Receive $msg but the sender cannot reply")) + } else { + Future.successful(message) + } + case AkkaFailure(e) => + Future.failed(e) + }.mapTo[T] + } + + override def toString: String = s"${getClass.getSimpleName}($actorRef)" + +} + +/** + * A wrapper to `message` so that the receiver knows if the sender expects a reply. + * @param message + * @param needReply if the sender expects a reply message + */ +private[akka] case class AkkaMessage(message: Any, needReply: Boolean) + +/** + * A reply with the failure error from the receiver to the sender + */ +private[akka] case class AkkaFailure(e: Throwable) diff --git a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala index a3caa9f000c89..f748f394d1347 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala @@ -19,10 +19,8 @@ package org.apache.spark.scheduler import scala.collection.mutable -import akka.actor.{ActorRef, Actor} - import org.apache.spark._ -import org.apache.spark.util.{AkkaUtils, ActorLogReceive} +import org.apache.spark.rpc.{RpcCallContext, RpcEndpointRef, RpcEnv, RpcEndpoint} private sealed trait OutputCommitCoordinationMessage extends Serializable @@ -34,8 +32,8 @@ private case class AskPermissionToCommitOutput(stage: Int, task: Long, taskAttem * policy. * * OutputCommitCoordinator is instantiated in both the drivers and executors. On executors, it is - * configured with a reference to the driver's OutputCommitCoordinatorActor, so requests to commit - * output will be forwarded to the driver's OutputCommitCoordinator. + * configured with a reference to the driver's OutputCommitCoordinatorEndpoint, so requests to + * commit output will be forwarded to the driver's OutputCommitCoordinator. * * This class was introduced in SPARK-4879; see that JIRA issue (and the associated pull requests) * for an extensive design discussion. @@ -43,10 +41,7 @@ private case class AskPermissionToCommitOutput(stage: Int, task: Long, taskAttem private[spark] class OutputCommitCoordinator(conf: SparkConf) extends Logging { // Initialized by SparkEnv - var coordinatorActor: Option[ActorRef] = None - private val timeout = AkkaUtils.askTimeout(conf) - private val maxAttempts = AkkaUtils.numRetries(conf) - private val retryInterval = AkkaUtils.retryWaitMs(conf) + var coordinatorRef: Option[RpcEndpointRef] = None private type StageId = Int private type PartitionId = Long @@ -81,9 +76,9 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf) extends Logging { partition: PartitionId, attempt: TaskAttemptId): Boolean = { val msg = AskPermissionToCommitOutput(stage, partition, attempt) - coordinatorActor match { - case Some(actor) => - AkkaUtils.askWithReply[Boolean](msg, actor, maxAttempts, retryInterval, timeout) + coordinatorRef match { + case Some(endpointRef) => + endpointRef.askWithReply[Boolean](msg) case None => logError( "canCommit called after coordinator was stopped (is SparkEnv shutdown in progress)?") @@ -125,8 +120,8 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf) extends Logging { } def stop(): Unit = synchronized { - coordinatorActor.foreach(_ ! StopCoordinator) - coordinatorActor = None + coordinatorRef.foreach(_ send StopCoordinator) + coordinatorRef = None authorizedCommittersByStage.clear() } @@ -157,16 +152,18 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf) extends Logging { private[spark] object OutputCommitCoordinator { // This actor is used only for RPC - class OutputCommitCoordinatorActor(outputCommitCoordinator: OutputCommitCoordinator) - extends Actor with ActorLogReceive with Logging { + private[spark] class OutputCommitCoordinatorEndpoint( + override val rpcEnv: RpcEnv, outputCommitCoordinator: OutputCommitCoordinator) + extends RpcEndpoint with Logging { - override def receiveWithLogging: PartialFunction[Any, Unit] = { + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case AskPermissionToCommitOutput(stage, partition, taskAttempt) => - sender ! outputCommitCoordinator.handleAskPermissionToCommit(stage, partition, taskAttempt) + context.reply( + outputCommitCoordinator.handleAskPermissionToCommit(stage, partition, taskAttempt)) case StopCoordinator => logInfo("OutputCommitCoordinator stopped!") - context.stop(self) - sender ! true + context.reply(true) + stop() } } } diff --git a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala index 48a6ede05e17b..6c2c5261306e7 100644 --- a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala +++ b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala @@ -179,7 +179,7 @@ private[spark] object AkkaUtils extends Logging { message: Any, actor: ActorRef, maxAttempts: Int, - retryInterval: Int, + retryInterval: Long, timeout: FiniteDuration): T = { // TODO: Consider removing multiple attempts if (actor == null) { diff --git a/core/src/main/scala/org/apache/spark/util/RpcUtils.scala b/core/src/main/scala/org/apache/spark/util/RpcUtils.scala new file mode 100644 index 0000000000000..6665b17c3d5df --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/RpcUtils.scala @@ -0,0 +1,35 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util + +import org.apache.spark.{SparkEnv, SparkConf} +import org.apache.spark.rpc.{RpcAddress, RpcEndpointRef, RpcEnv} + +object RpcUtils { + + /** + * Retrieve a [[RpcEndpointRef]] which is located in the driver via its name. + */ + def makeDriverRef(name: String, conf: SparkConf, rpcEnv: RpcEnv): RpcEndpointRef = { + val driverActorSystemName = SparkEnv.driverActorSystemName + val driverHost: String = conf.get("spark.driver.host", "localhost") + val driverPort: Int = conf.getInt("spark.driver.port", 7077) + Utils.checkHost(driverHost, "Expected hostname") + rpcEnv.setupEndpointRef(driverActorSystemName, RpcAddress(driverHost, driverPort), name) + } +} diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala index 5e538d6fab2a1..6a6f29dd613cd 100644 --- a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala @@ -17,32 +17,38 @@ package org.apache.spark.deploy.worker -import akka.actor.{ActorSystem, AddressFromURIString, Props} -import akka.testkit.TestActorRef -import akka.remote.DisassociatedEvent +import akka.actor.AddressFromURIString +import org.apache.spark.SparkConf +import org.apache.spark.SecurityManager +import org.apache.spark.rpc.{RpcAddress, RpcEnv} import org.scalatest.FunSuite class WorkerWatcherSuite extends FunSuite { test("WorkerWatcher shuts down on valid disassociation") { - val actorSystem = ActorSystem("test") - val targetWorkerUrl = "akka://1.2.3.4/user/Worker" + val conf = new SparkConf() + val rpcEnv = RpcEnv.create("test", "localhost", 12345, conf, new SecurityManager(conf)) + val targetWorkerUrl = "akka://test@1.2.3.4:1234/user/Worker" val targetWorkerAddress = AddressFromURIString(targetWorkerUrl) - val actorRef = TestActorRef[WorkerWatcher](Props(classOf[WorkerWatcher], targetWorkerUrl))(actorSystem) - val workerWatcher = actorRef.underlyingActor + val workerWatcher = new WorkerWatcher(rpcEnv, targetWorkerUrl) workerWatcher.setTesting(testing = true) - actorRef.underlyingActor.receive(new DisassociatedEvent(null, targetWorkerAddress, false)) - assert(actorRef.underlyingActor.isShutDown) + rpcEnv.setupEndpoint("worker-watcher", workerWatcher) + workerWatcher.onDisconnected( + RpcAddress(targetWorkerAddress.host.get, targetWorkerAddress.port.get)) + assert(workerWatcher.isShutDown) + rpcEnv.shutdown() } test("WorkerWatcher stays alive on invalid disassociation") { - val actorSystem = ActorSystem("test") - val targetWorkerUrl = "akka://1.2.3.4/user/Worker" - val otherAkkaURL = "akka://4.3.2.1/user/OtherActor" + val conf = new SparkConf() + val rpcEnv = RpcEnv.create("test", "localhost", 12345, conf, new SecurityManager(conf)) + val targetWorkerUrl = "akka://test@1.2.3.4:1234/user/Worker" + val otherAkkaURL = "akka://test@4.3.2.1:1234/user/OtherActor" val otherAkkaAddress = AddressFromURIString(otherAkkaURL) - val actorRef = TestActorRef[WorkerWatcher](Props(classOf[WorkerWatcher], targetWorkerUrl))(actorSystem) - val workerWatcher = actorRef.underlyingActor + val workerWatcher = new WorkerWatcher(rpcEnv, targetWorkerUrl) workerWatcher.setTesting(testing = true) - actorRef.underlyingActor.receive(new DisassociatedEvent(null, otherAkkaAddress, false)) - assert(!actorRef.underlyingActor.isShutDown) + rpcEnv.setupEndpoint("worker-watcher", workerWatcher) + workerWatcher.onDisconnected(RpcAddress(otherAkkaAddress.host.get, otherAkkaAddress.port.get)) + assert(!workerWatcher.isShutDown) + rpcEnv.shutdown() } } diff --git a/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala b/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala new file mode 100644 index 0000000000000..e07bdb9637575 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala @@ -0,0 +1,525 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rpc + +import java.util.concurrent.{TimeUnit, CountDownLatch, TimeoutException} + +import scala.collection.mutable +import scala.concurrent.Await +import scala.concurrent.duration._ +import scala.language.postfixOps + +import org.scalatest.{BeforeAndAfterAll, FunSuite} +import org.scalatest.concurrent.Eventually._ + +import org.apache.spark.{SparkException, SparkConf} + +/** + * Common tests for an RpcEnv implementation. + */ +abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { + + var env: RpcEnv = _ + + override def beforeAll(): Unit = { + val conf = new SparkConf() + env = createRpcEnv(conf, "local", 12345) + } + + override def afterAll(): Unit = { + if(env != null) { + env.shutdown() + } + } + + def createRpcEnv(conf: SparkConf, name: String, port: Int): RpcEnv + + test("send a message locally") { + @volatile var message: String = null + val rpcEndpointRef = env.setupEndpoint("send-locally", new RpcEndpoint { + override val rpcEnv = env + + override def receive = { + case msg: String => message = msg + } + }) + rpcEndpointRef.send("hello") + eventually(timeout(5 seconds), interval(10 millis)) { + assert("hello" === message) + } + } + + test("send a message remotely") { + @volatile var message: String = null + // Set up a RpcEndpoint using env + env.setupEndpoint("send-remotely", new RpcEndpoint { + override val rpcEnv = env + + override def receive = { + case msg: String => message = msg + } + }) + + val anotherEnv = createRpcEnv(new SparkConf(), "remote" ,13345) + // Use anotherEnv to find out the RpcEndpointRef + val rpcEndpointRef = anotherEnv.setupEndpointRef("local", env.address, "send-remotely") + try { + rpcEndpointRef.send("hello") + eventually(timeout(5 seconds), interval(10 millis)) { + assert("hello" === message) + } + } finally { + anotherEnv.shutdown() + anotherEnv.awaitTermination() + } + } + + test("send a RpcEndpointRef") { + val endpoint = new RpcEndpoint { + override val rpcEnv = env + + override def receiveAndReply(context: RpcCallContext) = { + case "Hello" => context.reply(self) + case "Echo" => context.reply("Echo") + } + } + val rpcEndpointRef = env.setupEndpoint("send-ref", endpoint) + + val newRpcEndpointRef = rpcEndpointRef.askWithReply[RpcEndpointRef]("Hello") + val reply = newRpcEndpointRef.askWithReply[String]("Echo") + assert("Echo" === reply) + } + + test("ask a message locally") { + val rpcEndpointRef = env.setupEndpoint("ask-locally", new RpcEndpoint { + override val rpcEnv = env + + override def receiveAndReply(context: RpcCallContext) = { + case msg: String => { + context.reply(msg) + } + } + }) + val reply = rpcEndpointRef.askWithReply[String]("hello") + assert("hello" === reply) + } + + test("ask a message remotely") { + env.setupEndpoint("ask-remotely", new RpcEndpoint { + override val rpcEnv = env + + override def receiveAndReply(context: RpcCallContext) = { + case msg: String => { + context.reply(msg) + } + } + }) + + val anotherEnv = createRpcEnv(new SparkConf(), "remote", 13345) + // Use anotherEnv to find out the RpcEndpointRef + val rpcEndpointRef = anotherEnv.setupEndpointRef("local", env.address, "ask-remotely") + try { + val reply = rpcEndpointRef.askWithReply[String]("hello") + assert("hello" === reply) + } finally { + anotherEnv.shutdown() + anotherEnv.awaitTermination() + } + } + + test("ask a message timeout") { + env.setupEndpoint("ask-timeout", new RpcEndpoint { + override val rpcEnv = env + + override def receiveAndReply(context: RpcCallContext) = { + case msg: String => { + Thread.sleep(100) + context.reply(msg) + } + } + }) + + val conf = new SparkConf() + conf.set("spark.akka.retry.wait", "0") + conf.set("spark.akka.num.retries", "1") + val anotherEnv = createRpcEnv(conf, "remote", 13345) + // Use anotherEnv to find out the RpcEndpointRef + val rpcEndpointRef = anotherEnv.setupEndpointRef("local", env.address, "ask-timeout") + try { + val e = intercept[Exception] { + rpcEndpointRef.askWithReply[String]("hello", 1 millis) + } + assert(e.isInstanceOf[TimeoutException] || e.getCause.isInstanceOf[TimeoutException]) + } finally { + anotherEnv.shutdown() + anotherEnv.awaitTermination() + } + } + + test("onStart and onStop") { + val stopLatch = new CountDownLatch(1) + val calledMethods = mutable.ArrayBuffer[String]() + + val endpoint = new RpcEndpoint { + override val rpcEnv = env + + override def onStart(): Unit = { + calledMethods += "start" + } + + override def receive = { + case msg: String => + } + + override def onStop(): Unit = { + calledMethods += "stop" + stopLatch.countDown() + } + } + val rpcEndpointRef = env.setupEndpoint("start-stop-test", endpoint) + env.stop(rpcEndpointRef) + stopLatch.await(10, TimeUnit.SECONDS) + assert(List("start", "stop") === calledMethods) + } + + test("onError: error in onStart") { + @volatile var e: Throwable = null + env.setupEndpoint("onError-onStart", new RpcEndpoint { + override val rpcEnv = env + + override def onStart(): Unit = { + throw new RuntimeException("Oops!") + } + + override def receive = { + case m => + } + + override def onError(cause: Throwable): Unit = { + e = cause + } + }) + + eventually(timeout(5 seconds), interval(10 millis)) { + assert(e.getMessage === "Oops!") + } + } + + test("onError: error in onStop") { + @volatile var e: Throwable = null + val endpointRef = env.setupEndpoint("onError-onStop", new RpcEndpoint { + override val rpcEnv = env + + override def receive = { + case m => + } + + override def onError(cause: Throwable): Unit = { + e = cause + } + + override def onStop(): Unit = { + throw new RuntimeException("Oops!") + } + }) + + env.stop(endpointRef) + + eventually(timeout(5 seconds), interval(10 millis)) { + assert(e.getMessage === "Oops!") + } + } + + test("onError: error in receive") { + @volatile var e: Throwable = null + val endpointRef = env.setupEndpoint("onError-receive", new RpcEndpoint { + override val rpcEnv = env + + override def receive = { + case m => throw new RuntimeException("Oops!") + } + + override def onError(cause: Throwable): Unit = { + e = cause + } + }) + + endpointRef.send("Foo") + + eventually(timeout(5 seconds), interval(10 millis)) { + assert(e.getMessage === "Oops!") + } + } + + test("self: call in onStart") { + @volatile var callSelfSuccessfully = false + + env.setupEndpoint("self-onStart", new RpcEndpoint { + override val rpcEnv = env + + override def onStart(): Unit = { + self + callSelfSuccessfully = true + } + + override def receive = { + case m => + } + }) + + eventually(timeout(5 seconds), interval(10 millis)) { + // Calling `self` in `onStart` is fine + assert(callSelfSuccessfully === true) + } + } + + test("self: call in receive") { + @volatile var callSelfSuccessfully = false + + val endpointRef = env.setupEndpoint("self-receive", new RpcEndpoint { + override val rpcEnv = env + + override def receive = { + case m => { + self + callSelfSuccessfully = true + } + } + }) + + endpointRef.send("Foo") + + eventually(timeout(5 seconds), interval(10 millis)) { + // Calling `self` in `receive` is fine + assert(callSelfSuccessfully === true) + } + } + + test("self: call in onStop") { + @volatile var e: Throwable = null + + val endpointRef = env.setupEndpoint("self-onStop", new RpcEndpoint { + override val rpcEnv = env + + override def receive = { + case m => + } + + override def onStop(): Unit = { + self + } + + override def onError(cause: Throwable): Unit = { + e = cause + } + }) + + env.stop(endpointRef) + + eventually(timeout(5 seconds), interval(10 millis)) { + // Calling `self` in `onStop` is invalid + assert(e != null) + assert(e.getMessage.contains("Cannot find RpcEndpointRef")) + } + } + + test("call receive in sequence") { + // If a RpcEnv implementation breaks the `receive` contract, hope this test can expose it + for(i <- 0 until 100) { + @volatile var result = 0 + val endpointRef = env.setupThreadSafeEndpoint(s"receive-in-sequence-$i", new RpcEndpoint { + override val rpcEnv = env + + override def receive = { + case m => result += 1 + } + + }) + + (0 until 10) foreach { _ => + new Thread { + override def run() { + (0 until 100) foreach { _ => + endpointRef.send("Hello") + } + } + }.start() + } + + eventually(timeout(5 seconds), interval(5 millis)) { + assert(result == 1000) + } + + env.stop(endpointRef) + } + } + + test("stop(RpcEndpointRef) reentrant") { + @volatile var onStopCount = 0 + val endpointRef = env.setupEndpoint("stop-reentrant", new RpcEndpoint { + override val rpcEnv = env + + override def receive = { + case m => + } + + override def onStop(): Unit = { + onStopCount += 1 + } + }) + + env.stop(endpointRef) + env.stop(endpointRef) + + eventually(timeout(5 seconds), interval(5 millis)) { + // Calling stop twice should only trigger onStop once. + assert(onStopCount == 1) + } + } + + test("sendWithReply") { + val endpointRef = env.setupEndpoint("sendWithReply", new RpcEndpoint { + override val rpcEnv = env + + override def receiveAndReply(context: RpcCallContext) = { + case m => context.reply("ack") + } + }) + + val f = endpointRef.sendWithReply[String]("Hi") + val ack = Await.result(f, 5 seconds) + assert("ack" === ack) + + env.stop(endpointRef) + } + + test("sendWithReply: remotely") { + env.setupEndpoint("sendWithReply-remotely", new RpcEndpoint { + override val rpcEnv = env + + override def receiveAndReply(context: RpcCallContext) = { + case m => context.reply("ack") + } + }) + + val anotherEnv = createRpcEnv(new SparkConf(), "remote", 13345) + // Use anotherEnv to find out the RpcEndpointRef + val rpcEndpointRef = anotherEnv.setupEndpointRef("local", env.address, "sendWithReply-remotely") + try { + val f = rpcEndpointRef.sendWithReply[String]("hello") + val ack = Await.result(f, 5 seconds) + assert("ack" === ack) + } finally { + anotherEnv.shutdown() + anotherEnv.awaitTermination() + } + } + + test("sendWithReply: error") { + val endpointRef = env.setupEndpoint("sendWithReply-error", new RpcEndpoint { + override val rpcEnv = env + + override def receiveAndReply(context: RpcCallContext) = { + case m => context.sendFailure(new SparkException("Oops")) + } + }) + + val f = endpointRef.sendWithReply[String]("Hi") + val e = intercept[SparkException] { + Await.result(f, 5 seconds) + } + assert("Oops" === e.getMessage) + + env.stop(endpointRef) + } + + test("sendWithReply: remotely error") { + env.setupEndpoint("sendWithReply-remotely-error", new RpcEndpoint { + override val rpcEnv = env + + override def receiveAndReply(context: RpcCallContext) = { + case msg: String => context.sendFailure(new SparkException("Oops")) + } + }) + + val anotherEnv = createRpcEnv(new SparkConf(), "remote", 13345) + // Use anotherEnv to find out the RpcEndpointRef + val rpcEndpointRef = anotherEnv.setupEndpointRef( + "local", env.address, "sendWithReply-remotely-error") + try { + val f = rpcEndpointRef.sendWithReply[String]("hello") + val e = intercept[SparkException] { + Await.result(f, 5 seconds) + } + assert("Oops" === e.getMessage) + } finally { + anotherEnv.shutdown() + anotherEnv.awaitTermination() + } + } + + test("network events") { + val events = new mutable.ArrayBuffer[(Any, Any)] with mutable.SynchronizedBuffer[(Any, Any)] + env.setupThreadSafeEndpoint("network-events", new RpcEndpoint { + override val rpcEnv = env + + override def receive = { + case "hello" => + case m => events += "receive" -> m + } + + override def onConnected(remoteAddress: RpcAddress): Unit = { + events += "onConnected" -> remoteAddress + } + + override def onDisconnected(remoteAddress: RpcAddress): Unit = { + events += "onDisconnected" -> remoteAddress + } + + override def onNetworkError(cause: Throwable, remoteAddress: RpcAddress): Unit = { + events += "onNetworkError" -> remoteAddress + } + + }) + + val anotherEnv = createRpcEnv(new SparkConf(), "remote", 13345) + // Use anotherEnv to find out the RpcEndpointRef + val rpcEndpointRef = anotherEnv.setupEndpointRef( + "local", env.address, "network-events") + val remoteAddress = anotherEnv.address + rpcEndpointRef.send("hello") + eventually(timeout(5 seconds), interval(5 millis)) { + assert(events === List(("onConnected", remoteAddress))) + } + + anotherEnv.shutdown() + anotherEnv.awaitTermination() + eventually(timeout(5 seconds), interval(5 millis)) { + assert(events === List( + ("onConnected", remoteAddress), + ("onNetworkError", remoteAddress), + ("onDisconnected", remoteAddress))) + } + } +} + +case object Start + +case class Ping(id: Int) + +case class Pong(id: Int) diff --git a/core/src/test/scala/org/apache/spark/rpc/akka/AkkaRpcEnvSuite.scala b/core/src/test/scala/org/apache/spark/rpc/akka/AkkaRpcEnvSuite.scala new file mode 100644 index 0000000000000..58214c0637235 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/rpc/akka/AkkaRpcEnvSuite.scala @@ -0,0 +1,50 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rpc.akka + +import org.apache.spark.rpc._ +import org.apache.spark.{SecurityManager, SparkConf} + +class AkkaRpcEnvSuite extends RpcEnvSuite { + + override def createRpcEnv(conf: SparkConf, name: String, port: Int): RpcEnv = { + new AkkaRpcEnvFactory().create( + RpcEnvConfig(conf, name, "localhost", port, new SecurityManager(conf))) + } + + test("setupEndpointRef: systemName, address, endpointName") { + val ref = env.setupEndpoint("test_endpoint", new RpcEndpoint { + override val rpcEnv = env + + override def receive = { + case _ => + } + }) + val conf = new SparkConf() + val newRpcEnv = new AkkaRpcEnvFactory().create( + RpcEnvConfig(conf, "test", "localhost", 12346, new SecurityManager(conf))) + try { + val newRef = newRpcEnv.setupEndpointRef("local", ref.address, "test_endpoint") + assert("akka.tcp://local@localhost:12345/user/test_endpoint" === + newRef.asInstanceOf[AkkaRpcEndpointRef].actorRef.path.toString) + } finally { + newRpcEnv.shutdown() + } + } + +} diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala index b9f40046e15a2..efd59a7e5470f 100644 --- a/project/MimaExcludes.scala +++ b/project/MimaExcludes.scala @@ -50,7 +50,9 @@ object MimaExcludes { ProblemFilters.exclude[IncompatibleResultTypeProblem]( "org.apache.spark.broadcast.HttpBroadcastFactory.newBroadcast"), ProblemFilters.exclude[IncompatibleResultTypeProblem]( - "org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast") + "org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast"), + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.scheduler.OutputCommitCoordinator$OutputCommitCoordinatorActor") ) ++ Seq( // SPARK-6510 Add a Graph#minus method acting as Set#difference ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.minus") From 01dc9f50d1aae1f24021062291d73182a2622f2c Mon Sep 17 00:00:00 2001 From: Li Zhihui Date: Sun, 29 Mar 2015 21:30:37 -0700 Subject: [PATCH 093/591] Fix string interpolator error in HeartbeatReceiver Error log before fixed 15/03/29 10:07:25 ERROR YarnScheduler: Lost an executor 24 (already removed): Executor heartbeat timed out after ${now - lastSeenMs} ms Author: Li Zhihui Closes #5255 from li-zhihui/fixstringinterpolator and squashes the following commits: c93f2b7 [Li Zhihui] Fix string interpolator error in HeartbeatReceiver --- core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala index 548dcb93c3358..8435e1ea2611c 100644 --- a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala +++ b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala @@ -89,7 +89,7 @@ private[spark] class HeartbeatReceiver(sc: SparkContext, scheduler: TaskSchedule logWarning(s"Removing executor $executorId with no recent heartbeats: " + s"${now - lastSeenMs} ms exceeds timeout $executorTimeoutMs ms") scheduler.executorLost(executorId, SlaveLost("Executor heartbeat " + - "timed out after ${now - lastSeenMs} ms")) + s"timed out after ${now - lastSeenMs} ms")) if (sc.supportDynamicAllocation) { sc.killExecutor(executorId) } From 17b13c53ec9d8579a7fb801ab781bce43809db6a Mon Sep 17 00:00:00 2001 From: Eran Medan Date: Mon, 30 Mar 2015 00:02:52 -0700 Subject: [PATCH 094/591] [spark-sql] a better exception message than "scala.MatchError" for unsupported types in Schema creation Currently if trying to register an RDD (or DataFrame in 1.3) as a table that has types that have no supported Schema representation (e.g. type "Any") - it would throw a match error. e.g. scala.MatchError: Any (of class scala.reflect.internal.Types$ClassNoArgsTypeRef) This fix is just to have a nicer error message than a MatchError Author: Eran Medan Closes #5235 from eranation/patch-2 and squashes the following commits: af4b1a2 [Eran Medan] Line should be under 100 chars 0c69e9d [Eran Medan] Change from sys.error UnsupportedOperationException 524be86 [Eran Medan] better exception than scala.MatchError: Any --- .../scala/org/apache/spark/sql/catalyst/ScalaReflection.scala | 2 ++ 1 file changed, 2 insertions(+) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala index d6126c24fc50d..2220970085462 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala @@ -179,6 +179,8 @@ trait ScalaReflection { case t if t <:< definitions.ShortTpe => Schema(ShortType, nullable = false) case t if t <:< definitions.ByteTpe => Schema(ByteType, nullable = false) case t if t <:< definitions.BooleanTpe => Schema(BooleanType, nullable = false) + case other => + throw new UnsupportedOperationException(s"Schema for type $other is not supported") } } From de6733036e060e18b0d1f21f9365bda81132a1a2 Mon Sep 17 00:00:00 2001 From: CodingCat Date: Mon, 30 Mar 2015 11:41:43 +0100 Subject: [PATCH 095/591] [SPARK-6596] fix the instruction on building scaladoc In README.md under docs/ directory, it says that > You can build just the Spark scaladoc by running build/sbt doc from the SPARK_PROJECT_ROOT directory. I guess the right approach is build/sbt unidoc Author: CodingCat Closes #5253 from CodingCat/SPARK-6596 and squashes the following commits: af379ed [CodingCat] fix the instruction on building scaladoc --- docs/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/README.md b/docs/README.md index 8a54724c4beae..3773ea25c8b67 100644 --- a/docs/README.md +++ b/docs/README.md @@ -60,7 +60,7 @@ We use Sphinx to generate Python API docs, so you will need to install it by run ## API Docs (Scaladoc and Sphinx) -You can build just the Spark scaladoc by running `build/sbt doc` from the SPARK_PROJECT_ROOT directory. +You can build just the Spark scaladoc by running `build/sbt unidoc` from the SPARK_PROJECT_ROOT directory. Similarly, you can build just the PySpark docs by running `make html` from the SPARK_PROJECT_ROOT/python/docs directory. Documentation is only generated for classes that are listed as @@ -68,7 +68,7 @@ public in `__init__.py`. When you run `jekyll` in the `docs` directory, it will also copy over the scaladoc for the various Spark subprojects into the `docs` directory (and then also into the `_site` directory). We use a -jekyll plugin to run `build/sbt doc` before building the site so if you haven't run it (recently) it +jekyll plugin to run `build/sbt unidoc` before building the site so if you haven't run it (recently) it may take some time as it generates all of the scaladoc. The jekyll plugin also generates the PySpark docs [Sphinx](http://sphinx-doc.org/). From 4bdfb7bab3b9d20167571d9b6888a2a44d9d43fc Mon Sep 17 00:00:00 2001 From: Ilya Ganelin Date: Mon, 30 Mar 2015 11:52:02 +0100 Subject: [PATCH 096/591] [SPARK-5750][SPARK-3441][SPARK-5836][CORE] Added documentation explaining shuffle I've updated the Spark Programming Guide to add a section on the shuffle operation providing some background on what it does. I've also addressed some of its performance impacts. I've included documentation to address the following issues: https://issues.apache.org/jira/browse/SPARK-5836 https://issues.apache.org/jira/browse/SPARK-3441 https://issues.apache.org/jira/browse/SPARK-5750 https://issues.apache.org/jira/browse/SPARK-4227 is related but can be addressed in a separate PR since it involves updates to the Spark Configuration Guide. Author: Ilya Ganelin Author: Ilya Ganelin Closes #5074 from ilganeli/SPARK-5750 and squashes the following commits: 6178e24 [Ilya Ganelin] Update programming-guide.md 7a0b96f [Ilya Ganelin] Update programming-guide.md 2c5df08 [Ilya Ganelin] Merge branch 'SPARK-5750' of github.com:ilganeli/spark into SPARK-5750 dffbd2d [Ilya Ganelin] [SPARK-5750] Slight wording update 1ff4eb4 [Ilya Ganelin] Merge remote-tracking branch 'upstream/master' into SPARK-5750 85f9c6e [Ilya Ganelin] Update programming-guide.md 349d1fa [Ilya Ganelin] Added cross linkf or configuration page eeb5a7a [Ilya Ganelin] [SPARK-5750] Added some minor fixes dd5cc9d [Ilya Ganelin] [SPARK-5750] Fixed some factual inaccuracies with regards to shuffle internals. a8adb57 [Ilya Ganelin] [SPARK-5750] Incoporated feedback from Sean Owen 9954bbe [Ilya Ganelin] Merge remote-tracking branch 'upstream/master' into SPARK-5750 159dd1c [Ilya Ganelin] [SPARK-5750] Style fixes from rxin. 75ef67b [Ilya Ganelin] [SPARK-5750][SPARK-3441][SPARK-5836] Added documentation explaining the shuffle operation and included errata from a number of other JIRAs --- docs/programming-guide.md | 83 +++++++++++++++++++++++++++++++++------ 1 file changed, 72 insertions(+), 11 deletions(-) diff --git a/docs/programming-guide.md b/docs/programming-guide.md index f5b775da7930a..f4fabb0927b66 100644 --- a/docs/programming-guide.md +++ b/docs/programming-guide.md @@ -937,7 +937,7 @@ for details. Similar to map, but each input item can be mapped to 0 or more output items (so func should return a Seq rather than a single item). - mapPartitions(func) + mapPartitions(func) Similar to map, but runs separately on each partition (block) of the RDD, so func must be of type Iterator<T> => Iterator<U> when running on an RDD of type T. @@ -964,7 +964,7 @@ for details. Return a new dataset that contains the distinct elements of the source dataset. - groupByKey([numTasks]) + groupByKey([numTasks]) When called on a dataset of (K, V) pairs, returns a dataset of (K, Iterable<V>) pairs.
Note: If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using reduceByKey or aggregateByKey will yield much better @@ -975,25 +975,25 @@ for details. - reduceByKey(func, [numTasks]) + reduceByKey(func, [numTasks]) When called on a dataset of (K, V) pairs, returns a dataset of (K, V) pairs where the values for each key are aggregated using the given reduce function func, which must be of type (V,V) => V. Like in groupByKey, the number of reduce tasks is configurable through an optional second argument. - aggregateByKey(zeroValue)(seqOp, combOp, [numTasks]) + aggregateByKey(zeroValue)(seqOp, combOp, [numTasks]) When called on a dataset of (K, V) pairs, returns a dataset of (K, U) pairs where the values for each key are aggregated using the given combine functions and a neutral "zero" value. Allows an aggregated value type that is different than the input value type, while avoiding unnecessary allocations. Like in groupByKey, the number of reduce tasks is configurable through an optional second argument. - sortByKey([ascending], [numTasks]) + sortByKey([ascending], [numTasks]) When called on a dataset of (K, V) pairs where K implements Ordered, returns a dataset of (K, V) pairs sorted by keys in ascending or descending order, as specified in the boolean ascending argument. - join(otherDataset, [numTasks]) + join(otherDataset, [numTasks]) When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (V, W)) pairs with all pairs of elements for each key. Outer joins are supported through leftOuterJoin, rightOuterJoin, and fullOuterJoin. - cogroup(otherDataset, [numTasks]) + cogroup(otherDataset, [numTasks]) When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (Iterable<V>, Iterable<W>)) tuples. This operation is also called groupWith. @@ -1006,17 +1006,17 @@ for details. process's stdin and lines output to its stdout are returned as an RDD of strings. - coalesce(numPartitions) + coalesce(numPartitions) Decrease the number of partitions in the RDD to numPartitions. Useful for running operations more efficiently after filtering down a large dataset. repartition(numPartitions) Reshuffle the data in the RDD randomly to create either more or fewer partitions and balance it across them. - This always shuffles all data over the network. + This always shuffles all data over the network. - repartitionAndSortWithinPartitions(partitioner) + repartitionAndSortWithinPartitions(partitioner) Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys. This is more efficient than calling repartition and then sorting within each partition because it can push the sorting down into the shuffle machinery. @@ -1080,7 +1080,7 @@ for details. SparkContext.objectFile(). - countByKey() + countByKey() Only available on RDDs of type (K, V). Returns a hashmap of (K, Int) pairs with the count of each key. @@ -1090,6 +1090,67 @@ for details. +### Shuffle operations + +Certain operations within Spark trigger an event known as the shuffle. The shuffle is Spark's +mechanism for re-distributing data so that is grouped differently across partitions. This typically +involves copying data across executors and machines, making the shuffle a complex and +costly operation. + +#### Background + +To understand what happens during the shuffle we can consider the example of the +[`reduceByKey`](#ReduceByLink) operation. The `reduceByKey` operation generates a new RDD where all +values for a single key are combined into a tuple - the key and the result of executing a reduce +function against all values associated with that key. The challenge is that not all values for a +single key necessarily reside on the same partition, or even the same machine, but they must be +co-located to compute the result. + +In Spark, data is generally not distributed across partitions to be in the necessary place for a +specific operation. During computations, a single task will operate on a single partition - thus, to +organize all the data for a single `reduceByKey` reduce task to execute, Spark needs to perform an +all-to-all operation. It must read from all partitions to find all the values for all keys, +and then bring together values across partitions to compute the final result for each key - +this is called the **shuffle**. + +Although the set of elements in each partition of newly shuffled data will be deterministic, and so +is the ordering of partitions themselves, the ordering of these elements is not. If one desires predictably +ordered data following shuffle then it's possible to use: + +* `mapPartitions` to sort each partition using, for example, `.sorted` +* `repartitionAndSortWithinPartitions` to efficiently sort partitions while simultaneously repartitioning +* `sortBy` to make a globally ordered RDD + +Operations which can cause a shuffle include **repartition** operations like +[`repartition`](#RepartitionLink), and [`coalesce`](#CoalesceLink), **'ByKey** operations +(except for counting) like [`groupByKey`](#GroupByLink) and [`reduceByKey`](#ReduceByLink), and +**join** operations like [`cogroup`](#CogroupLink) and [`join`](#JoinLink). + +#### Performance Impact +The **Shuffle** is an expensive operation since it involves disk I/O, data serialization, and +network I/O. To organize data for the shuffle, Spark generates sets of tasks - *map* tasks to +organize the data, and a set of *reduce* tasks to aggregate it. This nomenclature comes from +MapReduce and does not directly relate to Spark's `map` and `reduce` operations. + +Internally, results from individual map tasks are kept in memory until they can't fit. Then, these +are sorted based on the target partition and written to a single file. On the reduce side, tasks +read the relevant sorted blocks. + +Certain shuffle operations can consume significant amounts of heap memory since they employ +in-memory data structures to organize records before or after transferring them. Specifically, +`reduceByKey` and `aggregateByKey` create these structures on the map side and `'ByKey` operations +generate these on the reduce side. When data does not fit in memory Spark will spill these tables +to disk, incurring the additional overhead of disk I/O and increased garbage collection. + +Shuffle also generates a large number of intermediate files on disk. As of Spark 1.3, these files +are not cleaned up from Spark's temporary storage until Spark is stopped, which means that +long-running Spark jobs may consume available disk space. This is done so the shuffle doesn't need +to be re-computed if the lineage is re-computed. The temporary storage directory is specified by the +`spark.local.dir` configuration parameter when configuring the Spark context. + +Shuffle behavior can be tuned by adjusting a variety of configuration parameters. See the +'Shuffle Behavior' section within the [Spark Configuration Guide](configuration.html). + ## RDD Persistence One of the most important capabilities in Spark is *persisting* (or *caching*) a dataset in memory From 19d4c392fa1738e5dd04418cb008abc8810b8122 Mon Sep 17 00:00:00 2001 From: Jose Manuel Gomez Date: Mon, 30 Mar 2015 14:59:08 +0100 Subject: [PATCH 097/591] [HOTFIX] Update start-slave.sh wihtout this change the below error happens when I execute sbin/start-all.sh localhost: /spark-1.3/sbin/start-slave.sh: line 32: unexpected EOF while looking for matching `"' localhost: /spark-1.3/sbin/start-slave.sh: line 33: syntax error: unexpected end of file my operating system is Linux Mint 17.1 Rebecca Author: Jose Manuel Gomez Closes #5262 from josegom/patch-2 and squashes the following commits: 453af8b [Jose Manuel Gomez] Update start-slave.sh 2c456bd [Jose Manuel Gomez] Update start-slave.sh --- sbin/start-slave.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sbin/start-slave.sh b/sbin/start-slave.sh index c0155384f7395..5a6de11afdd3d 100755 --- a/sbin/start-slave.sh +++ b/sbin/start-slave.sh @@ -19,7 +19,7 @@ # Starts a slave on the machine this script is executed on. -usage="Usage: start-slave.sh where is like "spark://localhost:7077" +usage="Usage: start-slave.sh where is like spark://localhost:7077" if [ $# -lt 2 ]; then echo $usage From fe81f6c779213a91369ec61cf5489ad5c66cc49c Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Mon, 30 Mar 2015 22:24:12 +0800 Subject: [PATCH 098/591] [SPARK-6595][SQL] MetastoreRelation should be a MultiInstanceRelation Now that we have `DataFrame`s it is possible to have multiple copies in a single query plan. As such, it needs to inherit from `MultiInstanceRelation` or self joins will break. I also add better debugging errors when our self join handling fails in case there are future bugs. Author: Michael Armbrust Closes #5251 from marmbrus/multiMetaStore and squashes the following commits: 4272f6d [Michael Armbrust] [SPARK-6595][SQL] MetastoreRelation should be MuliInstanceRelation --- .../apache/spark/sql/catalyst/analysis/Analyzer.scala | 10 +++++++++- .../sql/catalyst/analysis/MultiInstanceRelation.scala | 2 +- .../apache/spark/sql/hive/HiveMetastoreCatalog.scala | 11 +++++++++-- .../spark/sql/hive/HiveMetastoreCatalogSuite.scala | 8 ++++++++ 4 files changed, 27 insertions(+), 4 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala index 44eceb0b372e6..ba1ac141b9fab 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala @@ -252,7 +252,15 @@ class Analyzer(catalog: Catalog, case oldVersion @ Aggregate(_, aggregateExpressions, _) if findAliases(aggregateExpressions).intersect(conflictingAttributes).nonEmpty => (oldVersion, oldVersion.copy(aggregateExpressions = newAliases(aggregateExpressions))) - }.head // Only handle first case found, others will be fixed on the next pass. + }.headOption.getOrElse { // Only handle first case, others will be fixed on the next pass. + sys.error( + s""" + |Failure when resolving conflicting references in Join: + |$plan + | + |Conflicting attributes: ${conflictingAttributes.mkString(",")} + """.stripMargin) + } val attributeRewrites = AttributeMap(oldRelation.output.zip(newRelation.output)) val newRight = right transformUp { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/MultiInstanceRelation.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/MultiInstanceRelation.scala index 894c3500cf533..35b74024a4cab 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/MultiInstanceRelation.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/MultiInstanceRelation.scala @@ -30,5 +30,5 @@ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan * of itself with globally unique expression ids. */ trait MultiInstanceRelation { - def newInstance(): this.type + def newInstance(): LogicalPlan } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index d1a99555e90c6..203164ea84292 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -32,7 +32,7 @@ import org.apache.hadoop.util.ReflectionUtils import org.apache.spark.Logging import org.apache.spark.sql.{SaveMode, AnalysisException, SQLContext} -import org.apache.spark.sql.catalyst.analysis.{NoSuchTableException, Catalog, OverrideCatalog} +import org.apache.spark.sql.catalyst.analysis.{MultiInstanceRelation, NoSuchTableException, Catalog, OverrideCatalog} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.planning.PhysicalOperation import org.apache.spark.sql.catalyst.plans.logical @@ -697,7 +697,7 @@ private[hive] case class MetastoreRelation (databaseName: String, tableName: String, alias: Option[String]) (val table: TTable, val partitions: Seq[TPartition]) (@transient sqlContext: SQLContext) - extends LeafNode { + extends LeafNode with MultiInstanceRelation { self: Product => @@ -778,6 +778,13 @@ private[hive] case class MetastoreRelation /** An attribute map for determining the ordinal for non-partition columns. */ val columnOrdinals = AttributeMap(attributes.zipWithIndex) + + override def newInstance() = { + val newCopy = MetastoreRelation(databaseName, tableName, alias)(table, partitions)(sqlContext) + // The project here is an ugly hack to work around the fact that MetastoreRelation's + // equals method is broken. Please remove this when SPARK-6555 is fixed. + Project(newCopy.output, newCopy) + } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala index aad48ada52642..fa8e11ffec2b4 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala @@ -17,6 +17,7 @@ package org.apache.spark.sql.hive +import org.apache.spark.sql.hive.test.TestHive import org.scalatest.FunSuite import org.apache.spark.sql.test.ExamplePointUDT @@ -36,4 +37,11 @@ class HiveMetastoreCatalogSuite extends FunSuite { assert(HiveMetastoreTypes.toMetastoreType(udt) === HiveMetastoreTypes.toMetastoreType(udt.sqlType)) } + + test("duplicated metastore relations") { + import TestHive.implicits._ + val df = TestHive.sql("SELECT * FROM src") + println(df.queryExecution) + df.as('a).join(df.as('b), $"a.key" === $"b.key") + } } From 32259c671ab419f4c8a6ba8e2f7d676c5dfd0f4f Mon Sep 17 00:00:00 2001 From: CodingCat Date: Mon, 30 Mar 2015 11:54:44 -0700 Subject: [PATCH 099/591] [SPARK-6592][SQL] fix filter for scaladoc to generate API doc for Row class under catalyst dir https://issues.apache.org/jira/browse/SPARK-6592 The current impl in SparkBuild.scala filter all classes under catalyst directory, however, we have a corner case that Row class is a public API under that directory we need to include Row into the scaladoc while still excluding other classes of catalyst project Thanks for the help on this patch from rxin and liancheng Author: CodingCat Closes #5252 from CodingCat/SPARK-6592 and squashes the following commits: 02098a4 [CodingCat] ignore collection, enable types (except those protected classes) f7af2cb [CodingCat] commit 3ab4403 [CodingCat] fix filter for scaladoc to generate API doc for Row.scala under catalyst directory --- project/SparkBuild.scala | 16 ++++++++-------- .../spark/sql/types/DataTypeConversions.scala | 2 +- .../apache/spark/sql/types/DataTypeParser.scala | 2 +- 3 files changed, 10 insertions(+), 10 deletions(-) diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index ac37c605de4b6..d3faa551a4b14 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -360,15 +360,15 @@ object Unidoc { packages .map(_.filterNot(_.getName.contains("$"))) .map(_.filterNot(_.getCanonicalPath.contains("akka"))) - .map(_.filterNot(_.getCanonicalPath.contains("deploy"))) - .map(_.filterNot(_.getCanonicalPath.contains("network"))) - .map(_.filterNot(_.getCanonicalPath.contains("shuffle"))) - .map(_.filterNot(_.getCanonicalPath.contains("executor"))) + .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/deploy"))) + .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/network"))) + .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/shuffle"))) + .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/executor"))) .map(_.filterNot(_.getCanonicalPath.contains("python"))) - .map(_.filterNot(_.getCanonicalPath.contains("collection"))) - .map(_.filterNot(_.getCanonicalPath.contains("sql/catalyst"))) - .map(_.filterNot(_.getCanonicalPath.contains("sql/execution"))) - .map(_.filterNot(_.getCanonicalPath.contains("sql/hive/test"))) + .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/util/collection"))) + .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/sql/catalyst"))) + .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/sql/execution"))) + .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/sql/hive/test"))) } lazy val settings = scalaJavaUnidocSettings ++ Seq ( diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeConversions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeConversions.scala index c243be07a91b6..a9d63e784963d 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeConversions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeConversions.scala @@ -24,7 +24,7 @@ import org.apache.spark.sql.catalyst.ScalaReflection import org.apache.spark.sql.catalyst.expressions.GenericMutableRow -protected[sql] object DataTypeConversions { +private[sql] object DataTypeConversions { def productToRow(product: Product, schema: StructType): Row = { val mutableRow = new GenericMutableRow(product.productArity) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala index 89278f7dbc806..34270d0ca7cd7 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala @@ -112,4 +112,4 @@ private[sql] object DataTypeParser { } /** The exception thrown from the [[DataTypeParser]]. */ -protected[sql] class DataTypeException(message: String) extends Exception(message) +private[sql] class DataTypeException(message: String) extends Exception(message) From df3550084c9975f999ed370dd9f7c495181a68ba Mon Sep 17 00:00:00 2001 From: Brennon York Date: Mon, 30 Mar 2015 12:48:26 -0700 Subject: [PATCH 100/591] [HOTFIX][SPARK-4123]: Updated to fix bug where multiple dependencies added breaks Github output Currently there is a bug whereby if a new patch introduces more than one new dependency (or removes more than one) it breaks the Github post output (see [this build](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/29399/consoleFull)). This hotfix will remove `awk` print statements in place of `printf` so as not to automatically add the newline character which is then escaped and added directly at the end of the `awk` statement. This should take a failed build output such as: ```json data: {"body": " [Test build #29400 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/29400/consoleFull) for PR 5266 at commit [`2aa4be0`](https://github.com/apache/spark/commit/2aa4be0e1d7ce052f8c901c6d9462c611c3a920a).\n * This patch **passes all tests**.\n * This patch merges cleanly.\n * This patch adds the following public classes _(experimental)_:\n * `class IDF extends Estimator[IDFModel] with IDFParams `\n * `class Normalizer extends UnaryTransformer[Vector, Vector, Normalizer] `\n\n * This patch **adds the following new dependencies:**\n * `avro-1.7.7.jar` * `breeze-macros_2.10-0.11.2.jar` * `breeze_2.10-0.11.2.jar`\n * This patch **removes the following dependencies:**\n * `avro-1.7.6.jar` * `breeze-macros_2.10-0.11.1.jar` * `breeze_2.10-0.11.1.jar`"} ``` and turn it into: ```json data: {"body": " [Test build #29400 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/29400/consoleFull) for PR 5266 at commit [`2aa4be0`](https://github.com/apache/spark/commit/2aa4be0e1d7ce052f8c901c6d9462c611c3a920a).\n * This patch **passes all tests**.\n * This patch merges cleanly.\n * This patch adds the following public classes _(experimental)_:\n * `class IDF extends Estimator[IDFModel] with IDFParams `\n * `class Normalizer extends UnaryTransformer[Vector, Vector, Normalizer] `\n\n * This patch **adds the following new dependencies:**\n * `avro-1.7.7.jar`\n * `breeze-macros_2.10-0.11.2.jar`\n * `breeze_2.10-0.11.2.jar`\n * This patch **removes the following dependencies:**\n * `avro-1.7.6.jar`\n * `breeze-macros_2.10-0.11.1.jar`\n * `breeze_2.10-0.11.1.jar`"} ``` I've tested this locally and all worked. /cc srowen pwendell nchammas Author: Brennon York Closes #5269 from brennonyork/HOTFIX-SPARK-4123 and squashes the following commits: a441068 [Brennon York] Updated awk to use printf and to manually insert newlines so that the JSON github string when posted is corrected --- dev/tests/pr_new_dependencies.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/dev/tests/pr_new_dependencies.sh b/dev/tests/pr_new_dependencies.sh index 115a5cd1354f0..370c7cc737bbd 100755 --- a/dev/tests/pr_new_dependencies.sh +++ b/dev/tests/pr_new_dependencies.sh @@ -90,8 +90,8 @@ else echo " * This patch does not change any dependencies." else # Pretty print the new dependencies - added_deps=$(echo "${DIFF_RESULTS}" | grep "<" | cut -d' ' -f2 | awk '{print " * \`"$1"\`"}') - removed_deps=$(echo "${DIFF_RESULTS}" | grep ">" | cut -d' ' -f2 | awk '{print " * \`"$1"\`"}') + added_deps=$(echo "${DIFF_RESULTS}" | grep "<" | cut -d' ' -f2 | awk '{printf " * \`"$1"\`\\n"}') + removed_deps=$(echo "${DIFF_RESULTS}" | grep ">" | cut -d' ' -f2 | awk '{printf " * \`"$1"\`\\n"}') added_deps_text=" * This patch **adds the following new dependencies:**\n${added_deps}" removed_deps_text=" * This patch **removes the following dependencies:**\n${removed_deps}" From f76d2e55b1a67bf5576e1aa001a0b872b9b3895a Mon Sep 17 00:00:00 2001 From: Davies Liu Date: Mon, 30 Mar 2015 15:47:00 -0700 Subject: [PATCH 101/591] [SPARK-6603] [PySpark] [SQL] add SQLContext.udf and deprecate inferSchema() and applySchema This PR create an alias for `registerFunction` as `udf.register`, to be consistent with Scala API. It also deprecated inferSchema() and applySchema(), show an warning for them. cc rxin Author: Davies Liu Closes #5273 from davies/udf and squashes the following commits: 476e947 [Davies Liu] address comments c096fdb [Davies Liu] add SQLContext.udf and deprecate inferSchema() and applySchema --- python/pyspark/sql/context.py | 87 ++++++++++++++++++++++++----------- 1 file changed, 60 insertions(+), 27 deletions(-) diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py index 795ef0dbc4c47..80939a1f8ab1e 100644 --- a/python/pyspark/sql/context.py +++ b/python/pyspark/sql/context.py @@ -34,7 +34,7 @@ except ImportError: has_pandas = False -__all__ = ["SQLContext", "HiveContext"] +__all__ = ["SQLContext", "HiveContext", "UDFRegistration"] def _monkey_patch_RDD(sqlCtx): @@ -56,6 +56,31 @@ def toDF(self, schema=None, sampleRatio=None): RDD.toDF = toDF +class UDFRegistration(object): + """Wrapper for register UDF""" + + def __init__(self, sqlCtx): + self.sqlCtx = sqlCtx + + def register(self, name, f, returnType=StringType()): + """Registers a lambda function as a UDF so it can be used in SQL statements. + + In addition to a name and the function itself, the return type can be optionally specified. + When the return type is not given it default to a string and conversion will automatically + be done. For any other return type, the produced object must match the specified type. + + >>> sqlCtx.udf.register("stringLengthString", lambda x: len(x)) + >>> sqlCtx.sql("SELECT stringLengthString('test')").collect() + [Row(c0=u'4')] + + >>> from pyspark.sql.types import IntegerType + >>> sqlCtx.udf.register("stringLengthInt", lambda x: len(x), IntegerType()) + >>> sqlCtx.sql("SELECT stringLengthInt('test')").collect() + [Row(c0=4)] + """ + return self.sqlCtx.registerFunction(name, f, returnType) + + class SQLContext(object): """Main entry point for Spark SQL functionality. @@ -118,6 +143,11 @@ def getConf(self, key, defaultValue): """ return self._ssql_ctx.getConf(key, defaultValue) + @property + def udf(self): + """Wrapper for register Python function as UDF """ + return UDFRegistration(self) + def registerFunction(self, name, f, returnType=StringType()): """Registers a lambda function as a UDF so it can be used in SQL statements. @@ -198,14 +228,12 @@ def inferSchema(self, rdd, samplingRatio=None): >>> df.collect()[0] Row(field1=1, field2=u'row1') """ + warnings.warn("inferSchema is deprecated, please use createDataFrame instead") if isinstance(rdd, DataFrame): raise TypeError("Cannot apply schema to DataFrame") - schema = self._inferSchema(rdd, samplingRatio) - converter = _create_converter(schema) - rdd = rdd.map(converter) - return self.applySchema(rdd, schema) + return self.createDataFrame(rdd, None, samplingRatio) def applySchema(self, rdd, schema): """ @@ -230,6 +258,7 @@ def applySchema(self, rdd, schema): >>> df.collect() [Row(field1=1, field2=u'row1'),..., Row(field1=3, field2=u'row3')] """ + warnings.warn("applySchema is deprecated, please use createDataFrame instead") if isinstance(rdd, DataFrame): raise TypeError("Cannot apply schema to DataFrame") @@ -237,23 +266,7 @@ def applySchema(self, rdd, schema): if not isinstance(schema, StructType): raise TypeError("schema should be StructType, but got %s" % schema) - # take the first few rows to verify schema - rows = rdd.take(10) - # Row() cannot been deserialized by Pyrolite - if rows and isinstance(rows[0], tuple) and rows[0].__class__.__name__ == 'Row': - rdd = rdd.map(tuple) - rows = rdd.take(10) - - for row in rows: - _verify_type(row, schema) - - # convert python objects to sql data - converter = _python_to_sql_converter(schema) - rdd = rdd.map(converter) - - jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd()) - df = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), schema.json()) - return DataFrame(df, self) + return self.createDataFrame(rdd, schema) def createDataFrame(self, data, schema=None, samplingRatio=None): """ @@ -323,22 +336,42 @@ def createDataFrame(self, data, schema=None, samplingRatio=None): if not isinstance(data, RDD): try: # data could be list, tuple, generator ... - data = self._sc.parallelize(data) + rdd = self._sc.parallelize(data) except Exception: raise ValueError("cannot create an RDD from type: %s" % type(data)) + else: + rdd = data if schema is None: - return self.inferSchema(data, samplingRatio) + schema = self._inferSchema(rdd, samplingRatio) + converter = _create_converter(schema) + rdd = rdd.map(converter) if isinstance(schema, (list, tuple)): - first = data.first() + first = rdd.first() if not isinstance(first, (list, tuple)): raise ValueError("each row in `rdd` should be list or tuple, " "but got %r" % type(first)) row_cls = Row(*schema) - schema = self._inferSchema(data.map(lambda r: row_cls(*r)), samplingRatio) + schema = self._inferSchema(rdd.map(lambda r: row_cls(*r)), samplingRatio) - return self.applySchema(data, schema) + # take the first few rows to verify schema + rows = rdd.take(10) + # Row() cannot been deserialized by Pyrolite + if rows and isinstance(rows[0], tuple) and rows[0].__class__.__name__ == 'Row': + rdd = rdd.map(tuple) + rows = rdd.take(10) + + for row in rows: + _verify_type(row, schema) + + # convert python objects to sql data + converter = _python_to_sql_converter(schema) + rdd = rdd.map(converter) + + jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd()) + df = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), schema.json()) + return DataFrame(df, self) def registerDataFrameAsTable(self, rdd, tableName): """Registers the given RDD as a temporary table in the catalog. From fde6945417355ae57500b67d034c9cad4f20d240 Mon Sep 17 00:00:00 2001 From: Cheng Lian Date: Tue, 31 Mar 2015 07:48:37 +0800 Subject: [PATCH 102/591] [SPARK-6369] [SQL] Uses commit coordinator to help committing Hive and Parquet tables This PR leverages the output commit coordinator introduced in #4066 to help committing Hive and Parquet tables. This PR extracts output commit code in `SparkHadoopWriter.commit` to `SparkHadoopMapRedUtil.commitTask`, and reuses it for committing Parquet and Hive tables on executor side. TODO - [ ] Add tests [Review on Reviewable](https://reviewable.io/reviews/apache/spark/5139) Author: Cheng Lian Closes #5139 from liancheng/spark-6369 and squashes the following commits: 72eb628 [Cheng Lian] Fixes typo in javadoc 9a4b82b [Cheng Lian] Adds javadoc and addresses @aarondav's comments dfdf3ef [Cheng Lian] Uses commit coordinator to help committing Hive and Parquet tables --- .../org/apache/spark/SparkHadoopWriter.scala | 52 +---------- .../spark/mapred/SparkHadoopMapRedUtil.scala | 91 ++++++++++++++++++- .../sql/parquet/ParquetTableOperations.scala | 11 ++- .../apache/spark/sql/parquet/newParquet.scala | 4 +- .../hive/execution/InsertIntoHiveTable.scala | 1 - .../spark/sql/hive/hiveWriterContainers.scala | 17 +--- 6 files changed, 103 insertions(+), 73 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala b/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala index 6eb4537d10477..2ec42d3aea169 100644 --- a/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala +++ b/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala @@ -26,7 +26,6 @@ import org.apache.hadoop.mapred._ import org.apache.hadoop.fs.FileSystem import org.apache.hadoop.fs.Path -import org.apache.spark.executor.CommitDeniedException import org.apache.spark.mapred.SparkHadoopMapRedUtil import org.apache.spark.rdd.HadoopRDD @@ -104,55 +103,8 @@ class SparkHadoopWriter(@transient jobConf: JobConf) } def commit() { - val taCtxt = getTaskContext() - val cmtr = getOutputCommitter() - - // Called after we have decided to commit - def performCommit(): Unit = { - try { - cmtr.commitTask(taCtxt) - logInfo (s"$taID: Committed") - } catch { - case e: IOException => - logError("Error committing the output of task: " + taID.value, e) - cmtr.abortTask(taCtxt) - throw e - } - } - - // First, check whether the task's output has already been committed by some other attempt - if (cmtr.needsTaskCommit(taCtxt)) { - // The task output needs to be committed, but we don't know whether some other task attempt - // might be racing to commit the same output partition. Therefore, coordinate with the driver - // in order to determine whether this attempt can commit (see SPARK-4879). - val shouldCoordinateWithDriver: Boolean = { - val sparkConf = SparkEnv.get.conf - // We only need to coordinate with the driver if there are multiple concurrent task - // attempts, which should only occur if speculation is enabled - val speculationEnabled = sparkConf.getBoolean("spark.speculation", false) - // This (undocumented) setting is an escape-hatch in case the commit code introduces bugs - sparkConf.getBoolean("spark.hadoop.outputCommitCoordination.enabled", speculationEnabled) - } - if (shouldCoordinateWithDriver) { - val outputCommitCoordinator = SparkEnv.get.outputCommitCoordinator - val canCommit = outputCommitCoordinator.canCommit(jobID, splitID, attemptID) - if (canCommit) { - performCommit() - } else { - val msg = s"$taID: Not committed because the driver did not authorize commit" - logInfo(msg) - // We need to abort the task so that the driver can reschedule new attempts, if necessary - cmtr.abortTask(taCtxt) - throw new CommitDeniedException(msg, jobID, splitID, attemptID) - } - } else { - // Speculation is disabled or a user has chosen to manually bypass the commit coordination - performCommit() - } - } else { - // Some other attempt committed the output, so we do nothing and signal success - logInfo(s"No need to commit output of task because needsTaskCommit=false: ${taID.value}") - } + SparkHadoopMapRedUtil.commitTask( + getOutputCommitter(), getTaskContext(), jobID, splitID, attemptID) } def commitJob() { diff --git a/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala b/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala index 87c2aa481095d..818f7a4c8d422 100644 --- a/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala +++ b/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala @@ -17,9 +17,15 @@ package org.apache.spark.mapred +import java.io.IOException import java.lang.reflect.Modifier -import org.apache.hadoop.mapred.{TaskAttemptID, JobID, JobConf, JobContext, TaskAttemptContext} +import org.apache.hadoop.mapred._ +import org.apache.hadoop.mapreduce.{TaskAttemptContext => MapReduceTaskAttemptContext} +import org.apache.hadoop.mapreduce.{OutputCommitter => MapReduceOutputCommitter} + +import org.apache.spark.executor.CommitDeniedException +import org.apache.spark.{Logging, SparkEnv, TaskContext} private[spark] trait SparkHadoopMapRedUtil { @@ -65,3 +71,86 @@ trait SparkHadoopMapRedUtil { } } } + +object SparkHadoopMapRedUtil extends Logging { + /** + * Commits a task output. Before committing the task output, we need to know whether some other + * task attempt might be racing to commit the same output partition. Therefore, coordinate with + * the driver in order to determine whether this attempt can commit (please see SPARK-4879 for + * details). + * + * Output commit coordinator is only contacted when the following two configurations are both set + * to `true`: + * + * - `spark.speculation` + * - `spark.hadoop.outputCommitCoordination.enabled` + */ + def commitTask( + committer: MapReduceOutputCommitter, + mrTaskContext: MapReduceTaskAttemptContext, + jobId: Int, + splitId: Int, + attemptId: Int): Unit = { + + val mrTaskAttemptID = mrTaskContext.getTaskAttemptID + + // Called after we have decided to commit + def performCommit(): Unit = { + try { + committer.commitTask(mrTaskContext) + logInfo(s"$mrTaskAttemptID: Committed") + } catch { + case cause: IOException => + logError(s"Error committing the output of task: $mrTaskAttemptID", cause) + committer.abortTask(mrTaskContext) + throw cause + } + } + + // First, check whether the task's output has already been committed by some other attempt + if (committer.needsTaskCommit(mrTaskContext)) { + val shouldCoordinateWithDriver: Boolean = { + val sparkConf = SparkEnv.get.conf + // We only need to coordinate with the driver if there are multiple concurrent task + // attempts, which should only occur if speculation is enabled + val speculationEnabled = sparkConf.getBoolean("spark.speculation", defaultValue = false) + // This (undocumented) setting is an escape-hatch in case the commit code introduces bugs + sparkConf.getBoolean("spark.hadoop.outputCommitCoordination.enabled", speculationEnabled) + } + + if (shouldCoordinateWithDriver) { + val outputCommitCoordinator = SparkEnv.get.outputCommitCoordinator + val canCommit = outputCommitCoordinator.canCommit(jobId, splitId, attemptId) + + if (canCommit) { + performCommit() + } else { + val message = + s"$mrTaskAttemptID: Not committed because the driver did not authorize commit" + logInfo(message) + // We need to abort the task so that the driver can reschedule new attempts, if necessary + committer.abortTask(mrTaskContext) + throw new CommitDeniedException(message, jobId, splitId, attemptId) + } + } else { + // Speculation is disabled or a user has chosen to manually bypass the commit coordination + performCommit() + } + } else { + // Some other attempt committed the output, so we do nothing and signal success + logInfo(s"No need to commit output of task because needsTaskCommit=false: $mrTaskAttemptID") + } + } + + def commitTask( + committer: MapReduceOutputCommitter, + mrTaskContext: MapReduceTaskAttemptContext, + sparkTaskContext: TaskContext): Unit = { + commitTask( + committer, + mrTaskContext, + sparkTaskContext.stageId(), + sparkTaskContext.partitionId(), + sparkTaskContext.attemptNumber()) + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala index 5130d8ad5e003..1c868da23e060 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala @@ -19,10 +19,9 @@ package org.apache.spark.sql.parquet import java.io.IOException import java.lang.{Long => JLong} -import java.text.SimpleDateFormat -import java.text.NumberFormat +import java.text.{NumberFormat, SimpleDateFormat} import java.util.concurrent.{Callable, TimeUnit} -import java.util.{ArrayList, Collections, Date, List => JList} +import java.util.{Date, List => JList} import scala.collection.JavaConversions._ import scala.collection.mutable @@ -43,12 +42,13 @@ import parquet.io.ParquetDecodingException import parquet.schema.MessageType import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.mapred.SparkHadoopMapRedUtil import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil import org.apache.spark.rdd.RDD import org.apache.spark.sql.SQLConf import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, Row, _} import org.apache.spark.sql.execution.{LeafNode, SparkPlan, UnaryNode} -import org.apache.spark.sql.types.{DataType, StructType} +import org.apache.spark.sql.types.StructType import org.apache.spark.{Logging, SerializableWritable, TaskContext} /** @@ -356,7 +356,7 @@ private[sql] case class InsertIntoParquetTable( } finally { writer.close(hadoopContext) } - committer.commitTask(hadoopContext) + SparkHadoopMapRedUtil.commitTask(committer, hadoopContext, context) 1 } val jobFormat = new AppendingParquetOutputFormat(taskIdOffset) @@ -512,6 +512,7 @@ private[parquet] class FilteringParquetRowInputFormat import parquet.filter2.compat.FilterCompat.Filter import parquet.filter2.compat.RowGroupFilter + import org.apache.spark.sql.parquet.FilteringParquetRowInputFormat.blockLocationCache val cacheMetadata = configuration.getBoolean(SQLConf.PARQUET_CACHE_METADATA, true) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala index 53f765ee26a13..19800ad88c031 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala @@ -42,6 +42,7 @@ import parquet.hadoop.{ParquetInputFormat, _} import org.apache.spark.annotation.DeveloperApi import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.mapred.SparkHadoopMapRedUtil import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil import org.apache.spark.rdd.{NewHadoopPartition, NewHadoopRDD, RDD} import org.apache.spark.sql.catalyst.expressions @@ -669,7 +670,8 @@ private[sql] case class ParquetRelation2( } finally { writer.close(hadoopContext) } - committer.commitTask(hadoopContext) + + SparkHadoopMapRedUtil.commitTask(committer, hadoopContext, context) } val jobFormat = new AppendingParquetOutputFormat(taskIdOffset) /* apparently we need a TaskAttemptID to construct an OutputCommitter; diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala index da53d30354551..cdf012b5117be 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala @@ -72,7 +72,6 @@ case class InsertIntoHiveTable( val outputFileFormatClassName = fileSinkConf.getTableInfo.getOutputFileFormatClassName assert(outputFileFormatClassName != null, "Output format class not set") conf.value.set("mapred.output.format.class", outputFileFormatClassName) - conf.value.setOutputCommitter(classOf[FileOutputCommitter]) FileOutputFormat.setOutputPath( conf.value, diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala index ba2bf67aed684..8398da268174d 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala @@ -17,7 +17,6 @@ package org.apache.spark.sql.hive -import java.io.IOException import java.text.NumberFormat import java.util.Date @@ -118,19 +117,7 @@ private[hive] class SparkHiveWriterContainer( } protected def commit() { - if (committer.needsTaskCommit(taskContext)) { - try { - committer.commitTask(taskContext) - logInfo (taID + ": Committed") - } catch { - case e: IOException => - logError("Error committing the output of task: " + taID.value, e) - committer.abortTask(taskContext) - throw e - } - } else { - logInfo("No need to commit output of task: " + taID.value) - } + SparkHadoopMapRedUtil.commitTask(committer, taskContext, jobID, splitID, attemptID) } private def setIDs(jobId: Int, splitId: Int, attemptId: Int) { @@ -213,7 +200,7 @@ private[spark] class SparkHiveDynamicPartitionWriterContainer( .zip(row.toSeq.takeRight(dynamicPartColNames.length)) .map { case (col, rawVal) => val string = if (rawVal == null) null else String.valueOf(rawVal) - val colString = + val colString = if (string == null || string.isEmpty) { defaultPartName } else { From b8ff2bc61c9835867f56afa1860ab5eb727c4a58 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Mon, 30 Mar 2015 20:47:10 -0700 Subject: [PATCH 103/591] [SPARK-6119][SQL] DataFrame support for missing data handling This pull request adds variants of DataFrame.na.drop and DataFrame.na.fill to the Scala/Java API, and DataFrame.fillna and DataFrame.dropna to the Python API. Author: Reynold Xin Closes #5274 from rxin/df-missing-value and squashes the following commits: 4ee1b98 [Reynold Xin] Improve error reporting in Python. 33a330c [Reynold Xin] Remove replace for now. bc4fdbb [Reynold Xin] Added documentation for replace. d56f5a5 [Reynold Xin] Added replace for Scala/Java. 2385d00 [Reynold Xin] Feedback from Xiangrui on "how". 914a374 [Reynold Xin] fill with map. 185c67e [Reynold Xin] Allow specifying column subsets in fill. 749eb47 [Reynold Xin] fillna 249b94e [Reynold Xin] Removing undefined functions. 6a73c68 [Reynold Xin] Missing file. 67d7003 [Reynold Xin] [SPARK-6119][SQL] DataFrame.na.drop (Scala/Java) and DataFrame.dropna (Python) --- python/pyspark/sql/dataframe.py | 86 +++++++ python/pyspark/sql/tests.py | 96 ++++++++ .../catalyst/expressions/nullFunctions.scala | 25 +- .../org/apache/spark/sql/DataFrame.scala | 15 +- .../spark/sql/DataFrameNaFunctions.scala | 228 ++++++++++++++++++ .../org/apache/spark/sql/GroupedData.scala | 5 +- .../org/apache/spark/sql/json/JsonRDD.scala | 2 +- .../spark/sql/DataFrameNaFunctionsSuite.scala | 157 ++++++++++++ 8 files changed, 606 insertions(+), 8 deletions(-) create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index 23c0e63e77812..4f174de811697 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -690,6 +690,86 @@ def subtract(self, other): """ return DataFrame(getattr(self._jdf, "except")(other._jdf), self.sql_ctx) + def dropna(self, how='any', thresh=None, subset=None): + """Returns a new :class:`DataFrame` omitting rows with null values. + + :param how: 'any' or 'all'. + If 'any', drop a row if it contains any nulls. + If 'all', drop a row only if all its values are null. + :param thresh: int, default None + If specified, drop rows that have less than `thresh` non-null values. + This overwrites the `how` parameter. + :param subset: optional list of column names to consider. + + >>> df4.dropna().show() + age height name + 10 80 Alice + """ + if how is not None and how not in ['any', 'all']: + raise ValueError("how ('" + how + "') should be 'any' or 'all'") + + if subset is None: + subset = self.columns + elif isinstance(subset, basestring): + subset = [subset] + elif not isinstance(subset, (list, tuple)): + raise ValueError("subset should be a list or tuple of column names") + + if thresh is None: + thresh = len(subset) if how == 'any' else 1 + + cols = ListConverter().convert(subset, self.sql_ctx._sc._gateway._gateway_client) + cols = self.sql_ctx._sc._jvm.PythonUtils.toSeq(cols) + return DataFrame(self._jdf.na().drop(thresh, cols), self.sql_ctx) + + def fillna(self, value, subset=None): + """Replace null values. + + :param value: int, long, float, string, or dict. + Value to replace null values with. + If the value is a dict, then `subset` is ignored and `value` must be a mapping + from column name (string) to replacement value. The replacement value must be + an int, long, float, or string. + :param subset: optional list of column names to consider. + Columns specified in subset that do not have matching data type are ignored. + For example, if `value` is a string, and subset contains a non-string column, + then the non-string column is simply ignored. + + >>> df4.fillna(50).show() + age height name + 10 80 Alice + 5 50 Bob + 50 50 Tom + 50 50 null + + >>> df4.fillna({'age': 50, 'name': 'unknown'}).show() + age height name + 10 80 Alice + 5 null Bob + 50 null Tom + 50 null unknown + """ + if not isinstance(value, (float, int, long, basestring, dict)): + raise ValueError("value should be a float, int, long, string, or dict") + + if isinstance(value, (int, long)): + value = float(value) + + if isinstance(value, dict): + value = MapConverter().convert(value, self.sql_ctx._sc._gateway._gateway_client) + return DataFrame(self._jdf.na().fill(value), self.sql_ctx) + elif subset is None: + return DataFrame(self._jdf.na().fill(value), self.sql_ctx) + else: + if isinstance(subset, basestring): + subset = [subset] + elif not isinstance(subset, (list, tuple)): + raise ValueError("subset should be a list or tuple of column names") + + cols = ListConverter().convert(subset, self.sql_ctx._sc._gateway._gateway_client) + cols = self.sql_ctx._sc._jvm.PythonUtils.toSeq(cols) + return DataFrame(self._jdf.na().fill(value, cols), self.sql_ctx) + def withColumn(self, colName, col): """ Return a new :class:`DataFrame` by adding a column. @@ -1069,6 +1149,12 @@ def _test(): globs['df2'] = sc.parallelize([Row(name='Tom', height=80), Row(name='Bob', height=85)]).toDF() globs['df3'] = sc.parallelize([Row(name='Alice', age=2, height=80), Row(name='Bob', age=5, height=85)]).toDF() + + globs['df4'] = sc.parallelize([Row(name='Alice', age=10, height=80), + Row(name='Bob', age=5, height=None), + Row(name='Tom', age=None, height=None), + Row(name=None, age=None, height=None)]).toDF() + (failure_count, test_count) = doctest.testmod( pyspark.sql.dataframe, globs=globs, optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF) diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index 2720439416682..258464b7f230d 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -415,6 +415,102 @@ def test_infer_long_type(self): self.assertEqual(_infer_type(2**61), LongType()) self.assertEqual(_infer_type(2**71), LongType()) + def test_dropna(self): + schema = StructType([ + StructField("name", StringType(), True), + StructField("age", IntegerType(), True), + StructField("height", DoubleType(), True)]) + + # shouldn't drop a non-null row + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', 50, 80.1)], schema).dropna().count(), + 1) + + # dropping rows with a single null value + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', None, 80.1)], schema).dropna().count(), + 0) + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', None, 80.1)], schema).dropna(how='any').count(), + 0) + + # if how = 'all', only drop rows if all values are null + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', None, 80.1)], schema).dropna(how='all').count(), + 1) + self.assertEqual(self.sqlCtx.createDataFrame( + [(None, None, None)], schema).dropna(how='all').count(), + 0) + + # how and subset + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', 50, None)], schema).dropna(how='any', subset=['name', 'age']).count(), + 1) + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', None, None)], schema).dropna(how='any', subset=['name', 'age']).count(), + 0) + + # threshold + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', None, 80.1)], schema).dropna(thresh=2).count(), + 1) + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', None, None)], schema).dropna(thresh=2).count(), + 0) + + # threshold and subset + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', 50, None)], schema).dropna(thresh=2, subset=['name', 'age']).count(), + 1) + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', None, 180.9)], schema).dropna(thresh=2, subset=['name', 'age']).count(), + 0) + + # thresh should take precedence over how + self.assertEqual(self.sqlCtx.createDataFrame( + [(u'Alice', 50, None)], schema).dropna( + how='any', thresh=2, subset=['name', 'age']).count(), + 1) + + def test_fillna(self): + schema = StructType([ + StructField("name", StringType(), True), + StructField("age", IntegerType(), True), + StructField("height", DoubleType(), True)]) + + # fillna shouldn't change non-null values + row = self.sqlCtx.createDataFrame([(u'Alice', 10, 80.1)], schema).fillna(50).first() + self.assertEqual(row.age, 10) + + # fillna with int + row = self.sqlCtx.createDataFrame([(u'Alice', None, None)], schema).fillna(50).first() + self.assertEqual(row.age, 50) + self.assertEqual(row.height, 50.0) + + # fillna with double + row = self.sqlCtx.createDataFrame([(u'Alice', None, None)], schema).fillna(50.1).first() + self.assertEqual(row.age, 50) + self.assertEqual(row.height, 50.1) + + # fillna with string + row = self.sqlCtx.createDataFrame([(None, None, None)], schema).fillna("hello").first() + self.assertEqual(row.name, u"hello") + self.assertEqual(row.age, None) + + # fillna with subset specified for numeric cols + row = self.sqlCtx.createDataFrame( + [(None, None, None)], schema).fillna(50, subset=['name', 'age']).first() + self.assertEqual(row.name, None) + self.assertEqual(row.age, 50) + self.assertEqual(row.height, None) + + # fillna with subset specified for numeric cols + row = self.sqlCtx.createDataFrame( + [(None, None, None)], schema).fillna("haha", subset=['name', 'age']).first() + self.assertEqual(row.name, "haha") + self.assertEqual(row.age, None) + self.assertEqual(row.height, None) + class HiveContextSQLTests(ReusedPySparkTestCase): diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala index d1f3d4f4ee9ee..f9161cf34f0c9 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala @@ -35,7 +35,7 @@ case class Coalesce(children: Seq[Expression]) extends Expression { override def toString: String = s"Coalesce(${children.mkString(",")})" - def dataType: DataType = if (resolved) { + override def dataType: DataType = if (resolved) { children.head.dataType } else { val childTypes = children.map(c => s"$c: ${c.dataType}").mkString(", ") @@ -74,3 +74,26 @@ case class IsNotNull(child: Expression) extends Predicate with trees.UnaryNode[E child.eval(input) != null } } + +/** + * A predicate that is evaluated to be true if there are at least `n` non-null values. + */ +case class AtLeastNNonNulls(n: Int, children: Seq[Expression]) extends Predicate { + override def nullable: Boolean = false + override def foldable: Boolean = false + override def toString: String = s"AtLeastNNulls(n, ${children.mkString(",")})" + + private[this] val childrenArray = children.toArray + + override def eval(input: Row): Boolean = { + var numNonNulls = 0 + var i = 0 + while (i < childrenArray.length && numNonNulls < n) { + if (childrenArray(i).eval(input) != null) { + numNonNulls += 1 + } + i += 1 + } + numNonNulls >= n + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 423ef3912bc89..5cd0a18ff688c 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -237,8 +237,8 @@ class DataFrame private[sql]( def toDF(colNames: String*): DataFrame = { require(schema.size == colNames.size, "The number of columns doesn't match.\n" + - "Old column names: " + schema.fields.map(_.name).mkString(", ") + "\n" + - "New column names: " + colNames.mkString(", ")) + s"Old column names (${schema.size}): " + schema.fields.map(_.name).mkString(", ") + "\n" + + s"New column names (${colNames.size}): " + colNames.mkString(", ")) val newCols = schema.fieldNames.zip(colNames).map { case (oldName, newName) => apply(oldName).as(newName) @@ -319,6 +319,17 @@ class DataFrame private[sql]( */ def show(): Unit = show(20) + /** + * Returns a [[DataFrameNaFunctions]] for working with missing data. + * {{{ + * // Dropping rows containing any null values. + * df.na.drop() + * }}} + * + * @group dfops + */ + def na: DataFrameNaFunctions = new DataFrameNaFunctions(this) + /** * Cartesian join with another [[DataFrame]]. * diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala new file mode 100644 index 0000000000000..3a3dc70f7285c --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala @@ -0,0 +1,228 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql + +import java.{lang => jl} + +import scala.collection.JavaConversions._ + +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types._ + + +/** + * Functionality for working with missing data in [[DataFrame]]s. + */ +final class DataFrameNaFunctions private[sql](df: DataFrame) { + + /** + * Returns a new [[DataFrame]] that drops rows containing any null values. + */ + def drop(): DataFrame = drop("any", df.columns) + + /** + * Returns a new [[DataFrame]] that drops rows containing null values. + * + * If `how` is "any", then drop rows containing any null values. + * If `how` is "all", then drop rows only if every column is null for that row. + */ + def drop(how: String): DataFrame = drop(how, df.columns) + + /** + * Returns a new [[DataFrame]] that drops rows containing any null values + * in the specified columns. + */ + def drop(cols: Array[String]): DataFrame = drop(cols.toSeq) + + /** + * (Scala-specific) Returns a new [[DataFrame ]] that drops rows containing any null values + * in the specified columns. + */ + def drop(cols: Seq[String]): DataFrame = drop(cols.size, cols) + + /** + * Returns a new [[DataFrame]] that drops rows containing null values + * in the specified columns. + * + * If `how` is "any", then drop rows containing any null values in the specified columns. + * If `how` is "all", then drop rows only if every specified column is null for that row. + */ + def drop(how: String, cols: Array[String]): DataFrame = drop(how, cols.toSeq) + + /** + * (Scala-specific) Returns a new [[DataFrame]] that drops rows containing null values + * in the specified columns. + * + * If `how` is "any", then drop rows containing any null values in the specified columns. + * If `how` is "all", then drop rows only if every specified column is null for that row. + */ + def drop(how: String, cols: Seq[String]): DataFrame = { + how.toLowerCase match { + case "any" => drop(cols.size, cols) + case "all" => drop(1, cols) + case _ => throw new IllegalArgumentException(s"how ($how) must be 'any' or 'all'") + } + } + + /** + * Returns a new [[DataFrame]] that drops rows containing less than `minNonNulls` non-null values. + */ + def drop(minNonNulls: Int): DataFrame = drop(minNonNulls, df.columns) + + /** + * Returns a new [[DataFrame]] that drops rows containing less than `minNonNulls` non-null + * values in the specified columns. + */ + def drop(minNonNulls: Int, cols: Array[String]): DataFrame = drop(minNonNulls, cols.toSeq) + + /** + * (Scala-specific) Returns a new [[DataFrame]] that drops rows containing less than + * `minNonNulls` non-null values in the specified columns. + */ + def drop(minNonNulls: Int, cols: Seq[String]): DataFrame = { + // Filtering condition -- only keep the row if it has at least `minNonNulls` non-null values. + val predicate = AtLeastNNonNulls(minNonNulls, cols.map(name => df.resolve(name))) + df.filter(Column(predicate)) + } + + /** + * Returns a new [[DataFrame]] that replaces null values in numeric columns with `value`. + */ + def fill(value: Double): DataFrame = fill(value, df.columns) + + /** + * Returns a new [[DataFrame ]] that replaces null values in string columns with `value`. + */ + def fill(value: String): DataFrame = fill(value, df.columns) + + /** + * Returns a new [[DataFrame]] that replaces null values in specified numeric columns. + * If a specified column is not a numeric column, it is ignored. + */ + def fill(value: Double, cols: Array[String]): DataFrame = fill(value, cols.toSeq) + + /** + * (Scala-specific) Returns a new [[DataFrame]] that replaces null values in specified + * numeric columns. If a specified column is not a numeric column, it is ignored. + */ + def fill(value: Double, cols: Seq[String]): DataFrame = { + val columnEquals = df.sqlContext.analyzer.resolver + val projections = df.schema.fields.map { f => + // Only fill if the column is part of the cols list. + if (f.dataType.isInstanceOf[NumericType] && cols.exists(col => columnEquals(f.name, col))) { + fillCol[Double](f, value) + } else { + df.col(f.name) + } + } + df.select(projections : _*) + } + + /** + * Returns a new [[DataFrame]] that replaces null values in specified string columns. + * If a specified column is not a string column, it is ignored. + */ + def fill(value: String, cols: Array[String]): DataFrame = fill(value, cols.toSeq) + + /** + * (Scala-specific) Returns a new [[DataFrame]] that replaces null values in + * specified string columns. If a specified column is not a string column, it is ignored. + */ + def fill(value: String, cols: Seq[String]): DataFrame = { + val columnEquals = df.sqlContext.analyzer.resolver + val projections = df.schema.fields.map { f => + // Only fill if the column is part of the cols list. + if (f.dataType.isInstanceOf[StringType] && cols.exists(col => columnEquals(f.name, col))) { + fillCol[String](f, value) + } else { + df.col(f.name) + } + } + df.select(projections : _*) + } + + /** + * Returns a new [[DataFrame]] that replaces null values. + * + * The key of the map is the column name, and the value of the map is the replacement value. + * The value must be of the following type: `Integer`, `Long`, `Float`, `Double`, `String`. + * + * For example, the following replaces null values in column "A" with string "unknown", and + * null values in column "B" with numeric value 1.0. + * {{{ + * import com.google.common.collect.ImmutableMap; + * df.na.fill(ImmutableMap.of("A", "unknown", "B", 1.0)); + * }}} + */ + def fill(valueMap: java.util.Map[String, Any]): DataFrame = fill0(valueMap.toSeq) + + /** + * (Scala-specific) Returns a new [[DataFrame]] that replaces null values. + * + * The key of the map is the column name, and the value of the map is the replacement value. + * The value must be of the following type: `Int`, `Long`, `Float`, `Double`, `String`. + * + * For example, the following replaces null values in column "A" with string "unknown", and + * null values in column "B" with numeric value 1.0. + * {{{ + * df.na.fill(Map( + * "A" -> "unknown", + * "B" -> 1.0 + * )) + * }}} + */ + def fill(valueMap: Map[String, Any]): DataFrame = fill0(valueMap.toSeq) + + private def fill0(values: Seq[(String, Any)]): DataFrame = { + // Error handling + values.foreach { case (colName, replaceValue) => + // Check column name exists + df.resolve(colName) + + // Check data type + replaceValue match { + case _: jl.Double | _: jl.Float | _: jl.Integer | _: jl.Long | _: String => + // This is good + case _ => throw new IllegalArgumentException( + s"Unsupported value type ${replaceValue.getClass.getName} ($replaceValue).") + } + } + + val columnEquals = df.sqlContext.analyzer.resolver + val projections = df.schema.fields.map { f => + values.find { case (k, _) => columnEquals(k, f.name) }.map { case (_, v) => + v match { + case v: jl.Float => fillCol[Double](f, v.toDouble) + case v: jl.Double => fillCol[Double](f, v) + case v: jl.Long => fillCol[Double](f, v.toDouble) + case v: jl.Integer => fillCol[Double](f, v.toDouble) + case v: String => fillCol[String](f, v) + } + }.getOrElse(df.col(f.name)) + } + df.select(projections : _*) + } + + /** + * Returns a [[Column]] expression that replaces null value in `col` with `replacement`. + */ + private def fillCol[T](col: StructField, replacement: T): Column = { + coalesce(df.col(col.name), lit(replacement).cast(col.dataType)).as(col.name) + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala index 45a63ae26ed71..a5e6b638d2150 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala @@ -127,10 +127,7 @@ class GroupedData protected[sql](df: DataFrame, groupingExprs: Seq[Expression]) * {{{ * // Selects the age of the oldest employee and the aggregate expense for each department * import com.google.common.collect.ImmutableMap; - * df.groupBy("department").agg(ImmutableMap.builder() - * .put("age", "max") - * .put("expense", "sum") - * .build()); + * df.groupBy("department").agg(ImmutableMap.of("age", "max", "expense", "sum")); * }}} */ def agg(exprs: java.util.Map[String, String]): DataFrame = { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala index 2b0358c4e2a1e..0b770f2251943 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala @@ -49,7 +49,7 @@ private[sql] object JsonRDD extends Logging { val schemaData = if (samplingRatio > 0.99) json else json.sample(false, samplingRatio, 1) val allKeys = if (schemaData.isEmpty()) { - Set.empty[(String,DataType)] + Set.empty[(String, DataType)] } else { parseJson(schemaData, columnNameOfCorruptRecords).map(allKeysWithValueTypes).reduce(_ ++ _) } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala new file mode 100644 index 0000000000000..0896f175c056f --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala @@ -0,0 +1,157 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import scala.collection.JavaConversions._ + +import org.apache.spark.sql.test.TestSQLContext.implicits._ + + +class DataFrameNaFunctionsSuite extends QueryTest { + + def createDF(): DataFrame = { + Seq[(String, java.lang.Integer, java.lang.Double)]( + ("Bob", 16, 176.5), + ("Alice", null, 164.3), + ("David", 60, null), + ("Amy", null, null), + (null, null, null)).toDF("name", "age", "height") + } + + test("drop") { + val input = createDF() + val rows = input.collect() + + checkAnswer( + input.na.drop("name" :: Nil), + rows(0) :: rows(1) :: rows(2) :: rows(3) :: Nil) + + checkAnswer( + input.na.drop("age" :: Nil), + rows(0) :: rows(2) :: Nil) + + checkAnswer( + input.na.drop("age" :: "height" :: Nil), + rows(0) :: Nil) + + checkAnswer( + input.na.drop(), + rows(0)) + + // dropna on an a dataframe with no column should return an empty data frame. + val empty = input.sqlContext.emptyDataFrame.select() + assert(empty.na.drop().count() === 0L) + + // Make sure the columns are properly named. + assert(input.na.drop().columns.toSeq === input.columns.toSeq) + } + + test("drop with how") { + val input = createDF() + val rows = input.collect() + + checkAnswer( + input.na.drop("all"), + rows(0) :: rows(1) :: rows(2) :: rows(3) :: Nil) + + checkAnswer( + input.na.drop("any"), + rows(0) :: Nil) + + checkAnswer( + input.na.drop("any", Seq("age", "height")), + rows(0) :: Nil) + + checkAnswer( + input.na.drop("all", Seq("age", "height")), + rows(0) :: rows(1) :: rows(2) :: Nil) + } + + test("drop with threshold") { + val input = createDF() + val rows = input.collect() + + checkAnswer( + input.na.drop(2, Seq("age", "height")), + rows(0) :: Nil) + + checkAnswer( + input.na.drop(3, Seq("name", "age", "height")), + rows(0)) + + // Make sure the columns are properly named. + assert(input.na.drop(2, Seq("age", "height")).columns.toSeq === input.columns.toSeq) + } + + test("fill") { + val input = createDF() + + val fillNumeric = input.na.fill(50.6) + checkAnswer( + fillNumeric, + Row("Bob", 16, 176.5) :: + Row("Alice", 50, 164.3) :: + Row("David", 60, 50.6) :: + Row("Amy", 50, 50.6) :: + Row(null, 50, 50.6) :: Nil) + + // Make sure the columns are properly named. + assert(fillNumeric.columns.toSeq === input.columns.toSeq) + + // string + checkAnswer( + input.na.fill("unknown").select("name"), + Row("Bob") :: Row("Alice") :: Row("David") :: Row("Amy") :: Row("unknown") :: Nil) + assert(input.na.fill("unknown").columns.toSeq === input.columns.toSeq) + + // fill double with subset columns + checkAnswer( + input.na.fill(50.6, "age" :: Nil), + Row("Bob", 16, 176.5) :: + Row("Alice", 50, 164.3) :: + Row("David", 60, null) :: + Row("Amy", 50, null) :: + Row(null, 50, null) :: Nil) + + // fill string with subset columns + checkAnswer( + Seq[(String, String)]((null, null)).toDF("col1", "col2").na.fill("test", "col1" :: Nil), + Row("test", null)) + } + + test("fill with map") { + val df = Seq[(String, String, java.lang.Long, java.lang.Double)]( + (null, null, null, null)).toDF("a", "b", "c", "d") + checkAnswer( + df.na.fill(Map( + "a" -> "test", + "c" -> 1, + "d" -> 2.2 + )), + Row("test", null, 1, 2.2)) + + // Test Java version + checkAnswer( + df.na.fill(mapAsJavaMap(Map( + "a" -> "test", + "c" -> 1, + "d" -> 2.2 + ))), + Row("test", null, 1, 2.2)) + } +} From 56775571cb938c819e5f7c3d49c5dd416ed034cb Mon Sep 17 00:00:00 2001 From: zsxwing Date: Mon, 30 Mar 2015 22:10:49 -0700 Subject: [PATCH 104/591] [SPARK-5124][Core] Move StopCoordinator to the receive method since it does not require a reply Hotfix for #4588 cc rxin Author: zsxwing Closes #5283 from zsxwing/hotfix and squashes the following commits: cf3e5a7 [zsxwing] Move StopCoordinator to the receive method since it does not require a reply --- .../spark/scheduler/OutputCommitCoordinator.scala | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala index f748f394d1347..17055e2f22d0d 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala @@ -156,14 +156,16 @@ private[spark] object OutputCommitCoordinator { override val rpcEnv: RpcEnv, outputCommitCoordinator: OutputCommitCoordinator) extends RpcEndpoint with Logging { + override def receive: PartialFunction[Any, Unit] = { + case StopCoordinator => + logInfo("OutputCommitCoordinator stopped!") + stop() + } + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case AskPermissionToCommitOutput(stage, partition, taskAttempt) => context.reply( outputCommitCoordinator.handleAskPermissionToCommit(stage, partition, taskAttempt)) - case StopCoordinator => - logInfo("OutputCommitCoordinator stopped!") - context.reply(true) - stop() } } } From f07e714062f02feadff10a45f9b9061444bb8ec5 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Tue, 31 Mar 2015 00:19:51 -0700 Subject: [PATCH 105/591] [SPARK-6625][SQL] Add common string filters to data sources. Filters such as startsWith, endsWith, contains will be very useful for data sources that provide search functionality, e.g. Succinct, Elastic Search, Solr. I also took this chance to improve documentation for the data source filters. Author: Reynold Xin Closes #5285 from rxin/ds-string-filters and squashes the following commits: f021727 [Reynold Xin] Fixed grammar. 7695a52 [Reynold Xin] [SPARK-6625][SQL] Add common string filters to data sources. --- .../sql/sources/DataSourceStrategy.scala | 10 +++ .../apache/spark/sql/sources/filters.scala | 69 ++++++++++++++++++ .../apache/spark/sql/sources/interfaces.scala | 3 + .../spark/sql/sources/FilteredScanSuite.scala | 73 +++++++++++++------ 4 files changed, 133 insertions(+), 22 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala index 67f3507c61ab6..83b603a4bb245 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala @@ -23,6 +23,7 @@ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.planning.PhysicalOperation import org.apache.spark.sql.catalyst.plans.logical import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan +import org.apache.spark.sql.types.StringType import org.apache.spark.sql.{Row, Strategy, execution, sources} /** @@ -166,6 +167,15 @@ private[sql] object DataSourceStrategy extends Strategy { case expressions.Not(child) => translate(child).map(sources.Not) + case expressions.StartsWith(a: Attribute, Literal(v: String, StringType)) => + Some(sources.StringStartsWith(a.name, v)) + + case expressions.EndsWith(a: Attribute, Literal(v: String, StringType)) => + Some(sources.StringEndsWith(a.name, v)) + + case expressions.EndsWith(a: Attribute, Literal(v: String, StringType)) => + Some(sources.StringContains(a.name, v)) + case _ => None } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala index 1e4505e36d2f0..791046e0079d6 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala @@ -17,16 +17,85 @@ package org.apache.spark.sql.sources +/** + * A filter predicate for data sources. + */ abstract class Filter +/** + * A filter that evaluates to `true` iff the attribute evaluates to a value + * equal to `value`. + */ case class EqualTo(attribute: String, value: Any) extends Filter + +/** + * A filter that evaluates to `true` iff the attribute evaluates to a value + * greater than `value`. + */ case class GreaterThan(attribute: String, value: Any) extends Filter + +/** + * A filter that evaluates to `true` iff the attribute evaluates to a value + * greater than or equal to `value`. + */ case class GreaterThanOrEqual(attribute: String, value: Any) extends Filter + +/** + * A filter that evaluates to `true` iff the attribute evaluates to a value + * less than `value`. + */ case class LessThan(attribute: String, value: Any) extends Filter + +/** + * A filter that evaluates to `true` iff the attribute evaluates to a value + * less than or equal to `value`. + */ case class LessThanOrEqual(attribute: String, value: Any) extends Filter + +/** + * A filter that evaluates to `true` iff the attribute evaluates to one of the values in the array. + */ case class In(attribute: String, values: Array[Any]) extends Filter + +/** + * A filter that evaluates to `true` iff the attribute evaluates to null. + */ case class IsNull(attribute: String) extends Filter + +/** + * A filter that evaluates to `true` iff the attribute evaluates to a non-null value. + */ case class IsNotNull(attribute: String) extends Filter + +/** + * A filter that evaluates to `true` iff both `left` or `right` evaluate to `true`. + */ case class And(left: Filter, right: Filter) extends Filter + +/** + * A filter that evaluates to `true` iff at least one of `left` or `right` evaluates to `true`. + */ case class Or(left: Filter, right: Filter) extends Filter + +/** + * A filter that evaluates to `true` iff `child` is evaluated to `false`. + */ case class Not(child: Filter) extends Filter + +/** + * A filter that evaluates to `true` iff the attribute evaluates to + * a string that starts with `value`. + */ +case class StringStartsWith(attribute: String, value: String) extends Filter + +/** + * A filter that evaluates to `true` iff the attribute evaluates to + * a string that starts with `value`. + */ +case class StringEndsWith(attribute: String, value: String) extends Filter + +/** + * A filter that evaluates to `true` iff the attribute evaluates to + * a string that contains the string `value`. + */ +case class StringContains(attribute: String, value: String) extends Filter diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala index a046a48c1733d..8f9946a5a801e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala @@ -152,6 +152,9 @@ trait PrunedScan { * A BaseRelation that can eliminate unneeded columns and filter using selected * predicates before producing an RDD containing all matching tuples as Row objects. * + * The actual filter should be the conjunction of all `filters`, + * i.e. they should be "and" together. + * * The pushed down filters are currently purely an optimization as they will all be evaluated * again. This means it is safe to use them with methods that produce false positives such * as filtering partitions based on a bloom filter. diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala index ffeccf0b69394..72ddc0ea2c8cb 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala @@ -35,20 +35,23 @@ case class SimpleFilteredScan(from: Int, to: Int)(@transient val sqlContext: SQL extends BaseRelation with PrunedFilteredScan { - override def schema = + override def schema: StructType = StructType( StructField("a", IntegerType, nullable = false) :: - StructField("b", IntegerType, nullable = false) :: Nil) + StructField("b", IntegerType, nullable = false) :: + StructField("c", StringType, nullable = false) :: Nil) override def buildScan(requiredColumns: Array[String], filters: Array[Filter]) = { val rowBuilders = requiredColumns.map { case "a" => (i: Int) => Seq(i) case "b" => (i: Int) => Seq(i * 2) + case "c" => (i: Int) => Seq((i - 1 + 'a').toChar.toString * 10) } FiltersPushed.list = filters - def translateFilter(filter: Filter): Int => Boolean = filter match { + // Predicate test on integer column + def translateFilterOnA(filter: Filter): Int => Boolean = filter match { case EqualTo("a", v) => (a: Int) => a == v case LessThan("a", v: Int) => (a: Int) => a < v case LessThanOrEqual("a", v: Int) => (a: Int) => a <= v @@ -57,13 +60,27 @@ case class SimpleFilteredScan(from: Int, to: Int)(@transient val sqlContext: SQL case In("a", values) => (a: Int) => values.map(_.asInstanceOf[Int]).toSet.contains(a) case IsNull("a") => (a: Int) => false // Int can't be null case IsNotNull("a") => (a: Int) => true - case Not(pred) => (a: Int) => !translateFilter(pred)(a) - case And(left, right) => (a: Int) => translateFilter(left)(a) && translateFilter(right)(a) - case Or(left, right) => (a: Int) => translateFilter(left)(a) || translateFilter(right)(a) + case Not(pred) => (a: Int) => !translateFilterOnA(pred)(a) + case And(left, right) => (a: Int) => + translateFilterOnA(left)(a) && translateFilterOnA(right)(a) + case Or(left, right) => (a: Int) => + translateFilterOnA(left)(a) || translateFilterOnA(right)(a) case _ => (a: Int) => true } - def eval(a: Int) = !filters.map(translateFilter(_)(a)).contains(false) + // Predicate test on string column + def translateFilterOnC(filter: Filter): String => Boolean = filter match { + case StringStartsWith("c", v) => _.startsWith(v) + case StringEndsWith("c", v) => _.endsWith(v) + case StringContains("c", v) => _.contains(v) + case _ => (c: String) => true + } + + def eval(a: Int) = { + val c = (a - 1 + 'a').toChar.toString * 10 + !filters.map(translateFilterOnA(_)(a)).contains(false) && + !filters.map(translateFilterOnC(_)(c)).contains(false) + } sqlContext.sparkContext.parallelize(from to to).filter(eval).map(i => Row.fromSeq(rowBuilders.map(_(i)).reduceOption(_ ++ _).getOrElse(Seq.empty))) @@ -93,7 +110,7 @@ class FilteredScanSuite extends DataSourceTest { sqlTest( "SELECT * FROM oneToTenFiltered", - (1 to 10).map(i => Row(i, i * 2)).toSeq) + (1 to 10).map(i => Row(i, i * 2, (i - 1 + 'a').toChar.toString * 10)).toSeq) sqlTest( "SELECT a, b FROM oneToTenFiltered", @@ -128,41 +145,53 @@ class FilteredScanSuite extends DataSourceTest { (2 to 10 by 2).map(i => Row(i, i)).toSeq) sqlTest( - "SELECT * FROM oneToTenFiltered WHERE a = 1", - Seq(1).map(i => Row(i, i * 2)).toSeq) + "SELECT a, b FROM oneToTenFiltered WHERE a = 1", + Seq(1).map(i => Row(i, i * 2))) sqlTest( - "SELECT * FROM oneToTenFiltered WHERE a IN (1,3,5)", - Seq(1,3,5).map(i => Row(i, i * 2)).toSeq) + "SELECT a, b FROM oneToTenFiltered WHERE a IN (1,3,5)", + Seq(1,3,5).map(i => Row(i, i * 2))) sqlTest( - "SELECT * FROM oneToTenFiltered WHERE A = 1", - Seq(1).map(i => Row(i, i * 2)).toSeq) + "SELECT a, b FROM oneToTenFiltered WHERE A = 1", + Seq(1).map(i => Row(i, i * 2))) sqlTest( - "SELECT * FROM oneToTenFiltered WHERE b = 2", - Seq(1).map(i => Row(i, i * 2)).toSeq) + "SELECT a, b FROM oneToTenFiltered WHERE b = 2", + Seq(1).map(i => Row(i, i * 2))) sqlTest( - "SELECT * FROM oneToTenFiltered WHERE a IS NULL", + "SELECT a, b FROM oneToTenFiltered WHERE a IS NULL", Seq.empty[Row]) sqlTest( - "SELECT * FROM oneToTenFiltered WHERE a IS NOT NULL", + "SELECT a, b FROM oneToTenFiltered WHERE a IS NOT NULL", (1 to 10).map(i => Row(i, i * 2)).toSeq) sqlTest( - "SELECT * FROM oneToTenFiltered WHERE a < 5 AND a > 1", + "SELECT a, b FROM oneToTenFiltered WHERE a < 5 AND a > 1", (2 to 4).map(i => Row(i, i * 2)).toSeq) sqlTest( - "SELECT * FROM oneToTenFiltered WHERE a < 3 OR a > 8", - Seq(1, 2, 9, 10).map(i => Row(i, i * 2)).toSeq) + "SELECT a, b FROM oneToTenFiltered WHERE a < 3 OR a > 8", + Seq(1, 2, 9, 10).map(i => Row(i, i * 2))) sqlTest( - "SELECT * FROM oneToTenFiltered WHERE NOT (a < 6)", + "SELECT a, b FROM oneToTenFiltered WHERE NOT (a < 6)", (6 to 10).map(i => Row(i, i * 2)).toSeq) + sqlTest( + "SELECT a, b, c FROM oneToTenFiltered WHERE c like 'c%'", + Seq(Row(3, 3 * 2, "c" * 10))) + + sqlTest( + "SELECT a, b, c FROM oneToTenFiltered WHERE c like 'd%'", + Seq(Row(4, 4 * 2, "d" * 10))) + + sqlTest( + "SELECT a, b, c FROM oneToTenFiltered WHERE c like '%e%'", + Seq(Row(5, 5 * 2, "e" * 10))) + testPushDown("SELECT * FROM oneToTenFiltered WHERE A = 1", 1) testPushDown("SELECT a FROM oneToTenFiltered WHERE A = 1", 1) testPushDown("SELECT b FROM oneToTenFiltered WHERE A = 1", 1) From b80a030e90d790e27e89b26f536565c582dbf3d5 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Tue, 31 Mar 2015 00:25:23 -0700 Subject: [PATCH 106/591] [SPARK-6623][SQL] Alias DataFrame.na.drop and DataFrame.na.fill in Python. To maintain consistency with the Scala API. Author: Reynold Xin Closes #5284 from rxin/df-na-alias and squashes the following commits: 19f46b7 [Reynold Xin] Show DataFrameNaFunctions in docs. 6618118 [Reynold Xin] [SPARK-6623][SQL] Alias DataFrame.na.drop and DataFrame.na.fill in Python. --- python/pyspark/sql/__init__.py | 10 ++++---- python/pyspark/sql/dataframe.py | 41 +++++++++++++++++++++++++++++++-- 2 files changed, 45 insertions(+), 6 deletions(-) diff --git a/python/pyspark/sql/__init__.py b/python/pyspark/sql/__init__.py index 54a01631d8899..9d39e5d9c2449 100644 --- a/python/pyspark/sql/__init__.py +++ b/python/pyspark/sql/__init__.py @@ -22,22 +22,24 @@ Main entry point for :class:`DataFrame` and SQL functionality. - L{DataFrame} A distributed collection of data grouped into named columns. - - L{GroupedData} - Aggregation methods, returned by :func:`DataFrame.groupBy`. - L{Column} A column expression in a :class:`DataFrame`. - L{Row} A row of data in a :class:`DataFrame`. - L{HiveContext} Main entry point for accessing data stored in Apache Hive. + - L{GroupedData} + Aggregation methods, returned by :func:`DataFrame.groupBy`. + - L{DataFrameNaFunctions} + Methods for handling missing data (null values). - L{functions} List of built-in functions available for :class:`DataFrame`. """ from pyspark.sql.context import SQLContext, HiveContext from pyspark.sql.types import Row -from pyspark.sql.dataframe import DataFrame, GroupedData, Column, SchemaRDD +from pyspark.sql.dataframe import DataFrame, GroupedData, Column, SchemaRDD, DataFrameNaFunctions __all__ = [ - 'SQLContext', 'HiveContext', 'DataFrame', 'GroupedData', 'Column', 'Row', + 'SQLContext', 'HiveContext', 'DataFrame', 'GroupedData', 'Column', 'Row', 'DataFrameNaFunctions' ] diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index 4f174de811697..15508023326cc 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -31,7 +31,7 @@ from pyspark.sql.types import _create_cls, _parse_datatype_json_string -__all__ = ["DataFrame", "GroupedData", "Column", "SchemaRDD"] +__all__ = ["DataFrame", "GroupedData", "Column", "SchemaRDD", "DataFrameNaFunctions"] class DataFrame(object): @@ -86,6 +86,12 @@ def applySchema(it): return self._lazy_rdd + @property + def na(self): + """Returns a :class:`DataFrameNaFunctions` for handling missing values. + """ + return DataFrameNaFunctions(self) + def toJSON(self, use_unicode=False): """Convert a :class:`DataFrame` into a MappedRDD of JSON documents; one document per row. @@ -693,6 +699,8 @@ def subtract(self, other): def dropna(self, how='any', thresh=None, subset=None): """Returns a new :class:`DataFrame` omitting rows with null values. + This is an alias for `na.drop`. + :param how: 'any' or 'all'. If 'any', drop a row if it contains any nulls. If 'all', drop a row only if all its values are null. @@ -704,6 +712,10 @@ def dropna(self, how='any', thresh=None, subset=None): >>> df4.dropna().show() age height name 10 80 Alice + + >>> df4.na.drop().show() + age height name + 10 80 Alice """ if how is not None and how not in ['any', 'all']: raise ValueError("how ('" + how + "') should be 'any' or 'all'") @@ -723,7 +735,7 @@ def dropna(self, how='any', thresh=None, subset=None): return DataFrame(self._jdf.na().drop(thresh, cols), self.sql_ctx) def fillna(self, value, subset=None): - """Replace null values. + """Replace null values, alias for `na.fill`. :param value: int, long, float, string, or dict. Value to replace null values with. @@ -748,6 +760,13 @@ def fillna(self, value, subset=None): 5 null Bob 50 null Tom 50 null unknown + + >>> df4.na.fill({'age': 50, 'name': 'unknown'}).show() + age height name + 10 80 Alice + 5 null Bob + 50 null Tom + 50 null unknown """ if not isinstance(value, (float, int, long, basestring, dict)): raise ValueError("value should be a float, int, long, string, or dict") @@ -1134,6 +1153,24 @@ def __repr__(self): return 'Column<%s>' % self._jc.toString().encode('utf8') +class DataFrameNaFunctions(object): + """Functionality for working with missing data in :class:`DataFrame`. + """ + + def __init__(self, df): + self.df = df + + def drop(self, how='any', thresh=None, subset=None): + return self.df.dropna(how=how, thresh=thresh, subset=subset) + + drop.__doc__ = DataFrame.dropna.__doc__ + + def fill(self, value, subset=None): + return self.df.fillna(value=value, subset=subset) + + fill.__doc__ = DataFrame.fillna.__doc__ + + def _test(): import doctest from pyspark.context import SparkContext From 314afd0e2f08dd8d3333d3143712c2c79fa40d1e Mon Sep 17 00:00:00 2001 From: Yin Huai Date: Tue, 31 Mar 2015 16:28:40 +0800 Subject: [PATCH 107/591] [SPARK-6618][SQL] HiveMetastoreCatalog.lookupRelation should use fine-grained lock JIRA: https://issues.apache.org/jira/browse/SPARK-6618 Author: Yin Huai Closes #5281 from yhuai/lookupRelationLock and squashes the following commits: 591b4be [Yin Huai] A test? b3a9625 [Yin Huai] Just protect client. --- .../apache/spark/sql/hive/HiveMetastoreCatalog.scala | 12 +++++++++--- .../spark/sql/hive/execution/SQLQuerySuite.scala | 11 +++++++++++ 2 files changed, 20 insertions(+), 3 deletions(-) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index 203164ea84292..6a01a23124d95 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -172,12 +172,16 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with def lookupRelation( tableIdentifier: Seq[String], - alias: Option[String]): LogicalPlan = synchronized { + alias: Option[String]): LogicalPlan = { val tableIdent = processTableIdentifier(tableIdentifier) val databaseName = tableIdent.lift(tableIdent.size - 2).getOrElse( hive.sessionState.getCurrentDatabase) val tblName = tableIdent.last - val table = try client.getTable(databaseName, tblName) catch { + val table = try { + synchronized { + client.getTable(databaseName, tblName) + } + } catch { case te: org.apache.hadoop.hive.ql.metadata.InvalidTableException => throw new NoSuchTableException } @@ -199,7 +203,9 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with } else { val partitions: Seq[Partition] = if (table.isPartitioned) { - HiveShim.getAllPartitionsOf(client, table).toSeq + synchronized { + HiveShim.getAllPartitionsOf(client, table).toSeq + } } else { Nil } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala index 1187228f4c3db..2f50a33448462 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala @@ -433,4 +433,15 @@ class SQLQuerySuite extends QueryTest { dropTempTable("data") setConf("spark.sql.hive.convertCTAS", originalConf) } + + test("sanity test for SPARK-6618") { + (1 to 100).par.map { i => + val tableName = s"SPARK_6618_table_$i" + sql(s"CREATE TABLE $tableName (col1 string)") + catalog.lookupRelation(Seq(tableName)) + table(tableName) + tables() + sql(s"DROP TABLE $tableName") + } + } } From a05835b89fe2086e460f0b80f7c22e284c0c32d0 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Tue, 31 Mar 2015 17:05:23 +0800 Subject: [PATCH 108/591] [SPARK-6542][SQL] add CreateStruct Similar to `CreateArray`, we can add `CreateStruct` to create nested columns. marmbrus Author: Xiangrui Meng Closes #5195 from mengxr/SPARK-6542 and squashes the following commits: 3795c57 [Xiangrui Meng] update error message ae7ac3e [Xiangrui Meng] move unit test to a separate suite 85dd559 [Xiangrui Meng] use NamedExpr c78e31a [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-6542 85f3106 [Xiangrui Meng] add CreateStruct --- .../sql/catalyst/analysis/Analyzer.scala | 6 ++ .../catalyst/expressions/complexTypes.scala | 29 ++++++++- .../ExpressionEvaluationSuite.scala | 61 ++++++++++++------- 3 files changed, 73 insertions(+), 23 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala index ba1ac141b9fab..dc14f49e6ee99 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala @@ -212,6 +212,12 @@ class Analyzer(catalog: Catalog, case o => o :: Nil } Alias(c.copy(children = expandedArgs), name)() :: Nil + case Alias(c @ CreateStruct(args), name) if containsStar(args) => + val expandedArgs = args.flatMap { + case s: Star => s.expand(child.output, resolver) + case o => o :: Nil + } + Alias(c.copy(children = expandedArgs), name)() :: Nil case o => o :: Nil }, child) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala index 3fd78db297462..3b2b9211268a9 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala @@ -120,7 +120,7 @@ case class ArrayGetField(child: Expression, field: StructField, ordinal: Int, co case class CreateArray(children: Seq[Expression]) extends Expression { override type EvaluatedType = Any - override def foldable: Boolean = !children.exists(!_.foldable) + override def foldable: Boolean = children.forall(_.foldable) lazy val childTypes = children.map(_.dataType).distinct @@ -142,3 +142,30 @@ case class CreateArray(children: Seq[Expression]) extends Expression { override def toString: String = s"Array(${children.mkString(",")})" } + +/** + * Returns a Row containing the evaluation of all children expressions. + * TODO: [[CreateStruct]] does not support codegen. + */ +case class CreateStruct(children: Seq[NamedExpression]) extends Expression { + override type EvaluatedType = Row + + override def foldable: Boolean = children.forall(_.foldable) + + override lazy val resolved: Boolean = childrenResolved + + override lazy val dataType: StructType = { + assert(resolved, + s"CreateStruct contains unresolvable children: ${children.filterNot(_.resolved)}.") + val fields = children.map { child => + StructField(child.name, child.dataType, child.nullable, child.metadata) + } + StructType(fields) + } + + override def nullable: Boolean = false + + override def eval(input: Row): EvaluatedType = { + Row(children.map(_.eval(input)): _*) + } +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala index dcfd8b28cb02a..1183a0d899dda 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala @@ -30,7 +30,34 @@ import org.apache.spark.sql.catalyst.analysis.UnresolvedGetField import org.apache.spark.sql.types._ -class ExpressionEvaluationSuite extends FunSuite { +class ExpressionEvaluationBaseSuite extends FunSuite { + + def evaluate(expression: Expression, inputRow: Row = EmptyRow): Any = { + expression.eval(inputRow) + } + + def checkEvaluation(expression: Expression, expected: Any, inputRow: Row = EmptyRow): Unit = { + val actual = try evaluate(expression, inputRow) catch { + case e: Exception => fail(s"Exception evaluating $expression", e) + } + if(actual != expected) { + val input = if(inputRow == EmptyRow) "" else s", input: $inputRow" + fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expected$input") + } + } + + def checkDoubleEvaluation( + expression: Expression, + expected: Spread[Double], + inputRow: Row = EmptyRow): Unit = { + val actual = try evaluate(expression, inputRow) catch { + case e: Exception => fail(s"Exception evaluating $expression", e) + } + actual.asInstanceOf[Double] shouldBe expected + } +} + +class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { test("literals") { checkEvaluation(Literal(1), 1) @@ -134,27 +161,6 @@ class ExpressionEvaluationSuite extends FunSuite { } } - def evaluate(expression: Expression, inputRow: Row = EmptyRow): Any = { - expression.eval(inputRow) - } - - def checkEvaluation(expression: Expression, expected: Any, inputRow: Row = EmptyRow): Unit = { - val actual = try evaluate(expression, inputRow) catch { - case e: Exception => fail(s"Exception evaluating $expression", e) - } - if(actual != expected) { - val input = if(inputRow == EmptyRow) "" else s", input: $inputRow" - fail(s"Incorrect Evaluation: $expression, actual: $actual, expected: $expected$input") - } - } - - def checkDoubleEvaluation(expression: Expression, expected: Spread[Double], inputRow: Row = EmptyRow): Unit = { - val actual = try evaluate(expression, inputRow) catch { - case e: Exception => fail(s"Exception evaluating $expression", e) - } - actual.asInstanceOf[Double] shouldBe expected - } - test("IN") { checkEvaluation(In(Literal(1), Seq(Literal(1), Literal(2))), true) checkEvaluation(In(Literal(2), Seq(Literal(1), Literal(2))), true) @@ -1081,3 +1087,14 @@ class ExpressionEvaluationSuite extends FunSuite { checkEvaluation(~c1, -2, row) } } + +// TODO: Make the tests work with codegen. +class ExpressionEvaluationWithoutCodeGenSuite extends ExpressionEvaluationBaseSuite { + + test("CreateStruct") { + val row = Row(1, 2, 3) + val c1 = 'a.int.at(0).as("a") + val c3 = 'c.int.at(2).as("c") + checkEvaluation(CreateStruct(Seq(c1, c3)), Row(1, 3), row) + } +} From d01a6d8c33fc5c8325b0cc4b51395dba5eb3462c Mon Sep 17 00:00:00 2001 From: leahmcguire Date: Tue, 31 Mar 2015 11:16:55 -0700 Subject: [PATCH 109/591] [SPARK-4894][mllib] Added Bernoulli option to NaiveBayes model in mllib Added optional model type parameter for NaiveBayes training. Can be either Multinomial or Bernoulli. When Bernoulli is given the Bernoulli smoothing is used for fitting and for prediction as per: http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html. Default for model is original Multinomial fit and predict. Added additional testing for Bernoulli and Multinomial models. Author: leahmcguire Author: Joseph K. Bradley Author: Leah McGuire Closes #4087 from leahmcguire/master and squashes the following commits: f3c8994 [leahmcguire] changed checks on model type to requires acb69af [leahmcguire] removed enum type and replaces all modelType parameters with strings 2224b15 [Leah McGuire] Merge pull request #2 from jkbradley/leahmcguire-master 9ad89ca [Joseph K. Bradley] removed old code 6a8f383 [Joseph K. Bradley] Added new model save/load format 2.0 for NaiveBayesModel after modelType parameter was added. Updated tests. Also updated ModelType enum-like type. 852a727 [leahmcguire] merged with upstream master a22d670 [leahmcguire] changed NaiveBayesModel modelType parameter back to NaiveBayes.ModelType, made NaiveBayes.ModelType serializable, fixed getter method in NavieBayes 18f3219 [leahmcguire] removed private from naive bayes constructor for lambda only bea62af [leahmcguire] put back in constructor for NaiveBayes 01baad7 [leahmcguire] made fixes from code review fb0a5c7 [leahmcguire] removed typo e2d925e [leahmcguire] fixed nonserializable error that was causing naivebayes test failures 2d0c1ba [leahmcguire] fixed typo in NaiveBayes c298e78 [leahmcguire] fixed scala style errors b85b0c9 [leahmcguire] Merge remote-tracking branch 'upstream/master' 900b586 [leahmcguire] fixed model call so that uses type argument ea09b28 [leahmcguire] Merge remote-tracking branch 'upstream/master' e016569 [leahmcguire] updated test suite with model type fix 85f298f [leahmcguire] Merge remote-tracking branch 'upstream/master' dc65374 [leahmcguire] integrated model type fix 7622b0c [leahmcguire] added comments and fixed style as per rb b93aaf6 [Leah McGuire] Merge pull request #1 from jkbradley/nb-model-type 3730572 [Joseph K. Bradley] modified NB model type to be more Java-friendly b61b5e2 [leahmcguire] added back compatable constructor to NaiveBayesModel to fix MIMA test failure 5a4a534 [leahmcguire] fixed scala style error in NaiveBayes 3891bf2 [leahmcguire] synced with apache spark and resolved merge conflict d9477ed [leahmcguire] removed old inaccurate comment from test suite for mllib naive bayes 76e5b0f [leahmcguire] removed unnecessary sort from test 0313c0c [leahmcguire] fixed style error in NaiveBayes.scala 4a3676d [leahmcguire] Updated changes re-comments. Got rid of verbose populateMatrix method. Public api now has string instead of enumeration. Docs are updated." ce73c63 [leahmcguire] added Bernoulli option to niave bayes model in mllib, added optional model type parameter for training. When Bernoulli is given the Bernoulli smoothing is used for fitting and for prediction http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html --- docs/mllib-naive-bayes.md | 17 +- .../mllib/classification/NaiveBayes.scala | 225 ++++++++++++++---- .../classification/JavaNaiveBayesSuite.java | 23 +- .../classification/NaiveBayesSuite.scala | 148 +++++++++--- 4 files changed, 322 insertions(+), 91 deletions(-) diff --git a/docs/mllib-naive-bayes.md b/docs/mllib-naive-bayes.md index a83472f5be52e..9780ea52c4994 100644 --- a/docs/mllib-naive-bayes.md +++ b/docs/mllib-naive-bayes.md @@ -13,12 +13,15 @@ compute the conditional probability distribution of label given an observation and use it for prediction. MLlib supports [multinomial naive -Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bayes), -which is typically used for [document -classification](http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html). +Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bayes) +and [Bernoulli naive Bayes] (http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html). +These models are typically used for [document classification] +(http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html). Within that context, each observation is a document and each -feature represents a term whose value is the frequency of the term. -Feature values must be nonnegative to represent term frequencies. +feature represents a term whose value is the frequency of the term (in multinomial naive Bayes) or +a zero or one indicating whether the term was found in the document (in Bernoulli naive Bayes). +Feature values must be nonnegative. The model type is selected with an optional parameter +"Multinomial" or "Bernoulli" with "Multinomial" as the default. [Additive smoothing](http://en.wikipedia.org/wiki/Lidstone_smoothing) can be used by setting the parameter $\lambda$ (default to $1.0$). For document classification, the input feature vectors are usually sparse, and sparse vectors should be supplied as input to take advantage of @@ -32,7 +35,7 @@ sparsity. Since the training data is only used once, it is not necessary to cach [NaiveBayes](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements multinomial naive Bayes. It takes an RDD of [LabeledPoint](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an optional -smoothing parameter `lambda` as input, and output a +smoothing parameter `lambda` as input, an optional model type parameter (default is Multinomial), and outputs a [NaiveBayesModel](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which can be used for evaluation and prediction. @@ -51,7 +54,7 @@ val splits = parsedData.randomSplit(Array(0.6, 0.4), seed = 11L) val training = splits(0) val test = splits(1) -val model = NaiveBayes.train(training, lambda = 1.0) +val model = NaiveBayes.train(training, lambda = 1.0, model = "Multinomial") val predictionAndLabel = test.map(p => (model.predict(p.features), p.label)) val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test.count() diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala index d60e82c410979..c9b3ff0172e2e 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala @@ -21,9 +21,12 @@ import java.lang.{Iterable => JIterable} import scala.collection.JavaConverters._ -import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax => brzArgmax, sum => brzSum} +import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax => brzArgmax, sum => brzSum, Axis} +import breeze.numerics.{exp => brzExp, log => brzLog} + import org.json4s.JsonDSL._ import org.json4s.jackson.JsonMethods._ +import org.json4s.{DefaultFormats, JValue} import org.apache.spark.{Logging, SparkContext, SparkException} import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector} @@ -32,6 +35,7 @@ import org.apache.spark.mllib.util.{Loader, Saveable} import org.apache.spark.rdd.RDD import org.apache.spark.sql.{DataFrame, SQLContext} + /** * Model for Naive Bayes Classifiers. * @@ -39,11 +43,17 @@ import org.apache.spark.sql.{DataFrame, SQLContext} * @param pi log of class priors, whose dimension is C, number of labels * @param theta log of class conditional probabilities, whose dimension is C-by-D, * where D is number of features + * @param modelType The type of NB model to fit can be "Multinomial" or "Bernoulli" */ class NaiveBayesModel private[mllib] ( val labels: Array[Double], val pi: Array[Double], - val theta: Array[Array[Double]]) extends ClassificationModel with Serializable with Saveable { + val theta: Array[Array[Double]], + val modelType: String) + extends ClassificationModel with Serializable with Saveable { + + private[mllib] def this(labels: Array[Double], pi: Array[Double], theta: Array[Array[Double]]) = + this(labels, pi, theta, "Multinomial") /** A Java-friendly constructor that takes three Iterable parameters. */ private[mllib] def this( @@ -53,19 +63,19 @@ class NaiveBayesModel private[mllib] ( this(labels.asScala.toArray, pi.asScala.toArray, theta.asScala.toArray.map(_.asScala.toArray)) private val brzPi = new BDV[Double](pi) - private val brzTheta = new BDM[Double](theta.length, theta(0).length) - - { - // Need to put an extra pair of braces to prevent Scala treating `i` as a member. - var i = 0 - while (i < theta.length) { - var j = 0 - while (j < theta(i).length) { - brzTheta(i, j) = theta(i)(j) - j += 1 - } - i += 1 - } + private val brzTheta = new BDM(theta(0).length, theta.length, theta.flatten).t + + // Bernoulli scoring requires log(condprob) if 1, log(1-condprob) if 0. + // This precomputes log(1.0 - exp(theta)) and its sum which are used for the linear algebra + // application of this condition (in predict function). + private val (brzNegTheta, brzNegThetaSum) = modelType match { + case "Multinomial" => (None, None) + case "Bernoulli" => + val negTheta = brzLog((brzExp(brzTheta.copy) :*= (-1.0)) :+= 1.0) // log(1.0 - exp(x)) + (Option(negTheta), Option(brzSum(negTheta, Axis._1))) + case _ => + // This should never happen. + throw new UnknownError(s"NaiveBayesModel was created with an unknown ModelType: $modelType") } override def predict(testData: RDD[Vector]): RDD[Double] = { @@ -77,22 +87,78 @@ class NaiveBayesModel private[mllib] ( } override def predict(testData: Vector): Double = { - labels(brzArgmax(brzPi + brzTheta * testData.toBreeze)) + modelType match { + case "Multinomial" => + labels (brzArgmax (brzPi + brzTheta * testData.toBreeze) ) + case "Bernoulli" => + labels (brzArgmax (brzPi + + (brzTheta - brzNegTheta.get) * testData.toBreeze + brzNegThetaSum.get)) + case _ => + // This should never happen. + throw new UnknownError(s"NaiveBayesModel was created with an unknown ModelType: $modelType") + } } override def save(sc: SparkContext, path: String): Unit = { - val data = NaiveBayesModel.SaveLoadV1_0.Data(labels, pi, theta) - NaiveBayesModel.SaveLoadV1_0.save(sc, path, data) + val data = NaiveBayesModel.SaveLoadV2_0.Data(labels, pi, theta, modelType) + NaiveBayesModel.SaveLoadV2_0.save(sc, path, data) } - override protected def formatVersion: String = "1.0" + override protected def formatVersion: String = "2.0" } object NaiveBayesModel extends Loader[NaiveBayesModel] { import org.apache.spark.mllib.util.Loader._ - private object SaveLoadV1_0 { + private[mllib] object SaveLoadV2_0 { + + def thisFormatVersion: String = "2.0" + + /** Hard-code class name string in case it changes in the future */ + def thisClassName: String = "org.apache.spark.mllib.classification.NaiveBayesModel" + + /** Model data for model import/export */ + case class Data( + labels: Array[Double], + pi: Array[Double], + theta: Array[Array[Double]], + modelType: String) + + def save(sc: SparkContext, path: String, data: Data): Unit = { + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + + // Create JSON metadata. + val metadata = compact(render( + ("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~ + ("numFeatures" -> data.theta(0).length) ~ ("numClasses" -> data.pi.length))) + sc.parallelize(Seq(metadata), 1).saveAsTextFile(metadataPath(path)) + + // Create Parquet data. + val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF() + dataRDD.saveAsParquetFile(dataPath(path)) + } + + def load(sc: SparkContext, path: String): NaiveBayesModel = { + val sqlContext = new SQLContext(sc) + // Load Parquet data. + val dataRDD = sqlContext.parquetFile(dataPath(path)) + // Check schema explicitly since erasure makes it hard to use match-case for checking. + checkSchema[Data](dataRDD.schema) + val dataArray = dataRDD.select("labels", "pi", "theta", "modelType").take(1) + assert(dataArray.size == 1, s"Unable to load NaiveBayesModel data from: ${dataPath(path)}") + val data = dataArray(0) + val labels = data.getAs[Seq[Double]](0).toArray + val pi = data.getAs[Seq[Double]](1).toArray + val theta = data.getAs[Seq[Seq[Double]]](2).map(_.toArray).toArray + val modelType = data.getString(3) + new NaiveBayesModel(labels, pi, theta, modelType) + } + + } + + private[mllib] object SaveLoadV1_0 { def thisFormatVersion: String = "1.0" @@ -100,7 +166,10 @@ object NaiveBayesModel extends Loader[NaiveBayesModel] { def thisClassName: String = "org.apache.spark.mllib.classification.NaiveBayesModel" /** Model data for model import/export */ - case class Data(labels: Array[Double], pi: Array[Double], theta: Array[Array[Double]]) + case class Data( + labels: Array[Double], + pi: Array[Double], + theta: Array[Array[Double]]) def save(sc: SparkContext, path: String, data: Data): Unit = { val sqlContext = new SQLContext(sc) @@ -136,26 +205,32 @@ object NaiveBayesModel extends Loader[NaiveBayesModel] { override def load(sc: SparkContext, path: String): NaiveBayesModel = { val (loadedClassName, version, metadata) = loadMetadata(sc, path) val classNameV1_0 = SaveLoadV1_0.thisClassName - (loadedClassName, version) match { + val classNameV2_0 = SaveLoadV2_0.thisClassName + val (model, numFeatures, numClasses) = (loadedClassName, version) match { case (className, "1.0") if className == classNameV1_0 => val (numFeatures, numClasses) = ClassificationModel.getNumFeaturesClasses(metadata) val model = SaveLoadV1_0.load(sc, path) - assert(model.pi.size == numClasses, - s"NaiveBayesModel.load expected $numClasses classes," + - s" but class priors vector pi had ${model.pi.size} elements") - assert(model.theta.size == numClasses, - s"NaiveBayesModel.load expected $numClasses classes," + - s" but class conditionals array theta had ${model.theta.size} elements") - assert(model.theta.forall(_.size == numFeatures), - s"NaiveBayesModel.load expected $numFeatures features," + - s" but class conditionals array theta had elements of size:" + - s" ${model.theta.map(_.size).mkString(",")}") - model + (model, numFeatures, numClasses) + case (className, "2.0") if className == classNameV2_0 => + val (numFeatures, numClasses) = ClassificationModel.getNumFeaturesClasses(metadata) + val model = SaveLoadV2_0.load(sc, path) + (model, numFeatures, numClasses) case _ => throw new Exception( s"NaiveBayesModel.load did not recognize model with (className, format version):" + s"($loadedClassName, $version). Supported:\n" + s" ($classNameV1_0, 1.0)") } + assert(model.pi.size == numClasses, + s"NaiveBayesModel.load expected $numClasses classes," + + s" but class priors vector pi had ${model.pi.size} elements") + assert(model.theta.size == numClasses, + s"NaiveBayesModel.load expected $numClasses classes," + + s" but class conditionals array theta had ${model.theta.size} elements") + assert(model.theta.forall(_.size == numFeatures), + s"NaiveBayesModel.load expected $numFeatures features," + + s" but class conditionals array theta had elements of size:" + + s" ${model.theta.map(_.size).mkString(",")}") + model } } @@ -167,9 +242,14 @@ object NaiveBayesModel extends Loader[NaiveBayesModel] { * document classification. By making every vector a 0-1 vector, it can also be used as * Bernoulli NB ([[http://tinyurl.com/p7c96j6]]). The input feature values must be nonnegative. */ -class NaiveBayes private (private var lambda: Double) extends Serializable with Logging { - def this() = this(1.0) +class NaiveBayes private ( + private var lambda: Double, + private var modelType: String) extends Serializable with Logging { + + def this(lambda: Double) = this(lambda, "Multinomial") + + def this() = this(1.0, "Multinomial") /** Set the smoothing parameter. Default: 1.0. */ def setLambda(lambda: Double): NaiveBayes = { @@ -177,9 +257,24 @@ class NaiveBayes private (private var lambda: Double) extends Serializable with this } - /** Get the smoothing parameter. Default: 1.0. */ + /** Get the smoothing parameter. */ def getLambda: Double = lambda + /** + * Set the model type using a string (case-sensitive). + * Supported options: "Multinomial" and "Bernoulli". + * (default: Multinomial) + */ + def setModelType(modelType:String): NaiveBayes = { + require(NaiveBayes.supportedModelTypes.contains(modelType), + s"NaiveBayes was created with an unknown ModelType: $modelType") + this.modelType = modelType + this + } + + /** Get the model type. */ + def getModelType: String = this.modelType + /** * Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries. * @@ -213,21 +308,30 @@ class NaiveBayes private (private var lambda: Double) extends Serializable with mergeCombiners = (c1: (Long, BDV[Double]), c2: (Long, BDV[Double])) => (c1._1 + c2._1, c1._2 += c2._2) ).collect() + val numLabels = aggregated.length var numDocuments = 0L aggregated.foreach { case (_, (n, _)) => numDocuments += n } val numFeatures = aggregated.head match { case (_, (_, v)) => v.size } + val labels = new Array[Double](numLabels) val pi = new Array[Double](numLabels) val theta = Array.fill(numLabels)(new Array[Double](numFeatures)) + val piLogDenom = math.log(numDocuments + numLabels * lambda) var i = 0 aggregated.foreach { case (label, (n, sumTermFreqs)) => labels(i) = label - val thetaLogDenom = math.log(brzSum(sumTermFreqs) + numFeatures * lambda) pi(i) = math.log(n + lambda) - piLogDenom + val thetaLogDenom = modelType match { + case "Multinomial" => math.log(brzSum(sumTermFreqs) + numFeatures * lambda) + case "Bernoulli" => math.log(n + 2.0 * lambda) + case _ => + // This should never happen. + throw new UnknownError(s"NaiveBayes was created with an unknown ModelType: $modelType") + } var j = 0 while (j < numFeatures) { theta(i)(j) = math.log(sumTermFreqs(j) + lambda) - thetaLogDenom @@ -236,7 +340,7 @@ class NaiveBayes private (private var lambda: Double) extends Serializable with i += 1 } - new NaiveBayesModel(labels, pi, theta) + new NaiveBayesModel(labels, pi, theta, modelType) } } @@ -244,13 +348,16 @@ class NaiveBayes private (private var lambda: Double) extends Serializable with * Top-level methods for calling naive Bayes. */ object NaiveBayes { + + /* Set of modelTypes that NaiveBayes supports */ + private[mllib] val supportedModelTypes = Set("Multinomial", "Bernoulli") + /** * Trains a Naive Bayes model given an RDD of `(label, features)` pairs. * - * This is the Multinomial NB ([[http://tinyurl.com/lsdw6p]]) which can handle all kinds of - * discrete data. For example, by converting documents into TF-IDF vectors, it can be used for - * document classification. By making every vector a 0-1 vector, it can also be used as - * Bernoulli NB ([[http://tinyurl.com/p7c96j6]]). + * This is the default Multinomial NB ([[http://tinyurl.com/lsdw6p]]) which can handle all + * kinds of discrete data. For example, by converting documents into TF-IDF vectors, it + * can be used for document classification. * * This version of the method uses a default smoothing parameter of 1.0. * @@ -264,16 +371,40 @@ object NaiveBayes { /** * Trains a Naive Bayes model given an RDD of `(label, features)` pairs. * - * This is the Multinomial NB ([[http://tinyurl.com/lsdw6p]]) which can handle all kinds of - * discrete data. For example, by converting documents into TF-IDF vectors, it can be used for - * document classification. By making every vector a 0-1 vector, it can also be used as - * Bernoulli NB ([[http://tinyurl.com/p7c96j6]]). + * This is the default Multinomial NB ([[http://tinyurl.com/lsdw6p]]) which can handle all + * kinds of discrete data. For example, by converting documents into TF-IDF vectors, it + * can be used for document classification. * * @param input RDD of `(label, array of features)` pairs. Every vector should be a frequency * vector or a count vector. * @param lambda The smoothing parameter */ def train(input: RDD[LabeledPoint], lambda: Double): NaiveBayesModel = { - new NaiveBayes(lambda).run(input) + new NaiveBayes(lambda, "Multinomial").run(input) + } + + /** + * Trains a Naive Bayes model given an RDD of `(label, features)` pairs. + * + * The model type can be set to either Multinomial NB ([[http://tinyurl.com/lsdw6p]]) + * or Bernoulli NB ([[http://tinyurl.com/p7c96j6]]). The Multinomial NB can handle + * discrete count data and can be called by setting the model type to "multinomial". + * For example, it can be used with word counts or TF_IDF vectors of documents. + * The Bernoulli model fits presence or absence (0-1) counts. By making every vector a + * 0-1 vector and setting the model type to "bernoulli", the fits and predicts as + * Bernoulli NB. + * + * @param input RDD of `(label, array of features)` pairs. Every vector should be a frequency + * vector or a count vector. + * @param lambda The smoothing parameter + * + * @param modelType The type of NB model to fit from the enumeration NaiveBayesModels, can be + * multinomial or bernoulli + */ + def train(input: RDD[LabeledPoint], lambda: Double, modelType: String): NaiveBayesModel = { + require(supportedModelTypes.contains(modelType), + s"NaiveBayes was created with an unknown ModelType: $modelType") + new NaiveBayes(lambda, modelType).run(input) } + } diff --git a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java index 1c90522a0714a..71fb7f13c39c2 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java @@ -17,20 +17,22 @@ package org.apache.spark.mllib.classification; +import java.io.Serializable; +import java.util.Arrays; +import java.util.List; + +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.api.java.function.Function; import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.mllib.linalg.Vectors; import org.apache.spark.mllib.regression.LabeledPoint; -import org.junit.After; -import org.junit.Assert; -import org.junit.Before; -import org.junit.Test; -import java.io.Serializable; -import java.util.Arrays; -import java.util.List; public class JavaNaiveBayesSuite implements Serializable { private transient JavaSparkContext sc; @@ -102,4 +104,11 @@ public Vector call(LabeledPoint v) throws Exception { // Should be able to get the first prediction. predictions.first(); } + + @Test + public void testModelTypeSetters() { + NaiveBayes nb = new NaiveBayes() + .setModelType("Bernoulli") + .setModelType("Multinomial"); + } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala index 5a27c7d2309c5..f9fe3e006ccb8 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala @@ -19,6 +19,9 @@ package org.apache.spark.mllib.classification import scala.util.Random +import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax => brzArgmax, sum => brzSum, Axis} +import breeze.stats.distributions.{Multinomial => BrzMultinomial} + import org.scalatest.FunSuite import org.apache.spark.SparkException @@ -41,37 +44,48 @@ object NaiveBayesSuite { // Generate input of the form Y = (theta * x).argmax() def generateNaiveBayesInput( - pi: Array[Double], // 1XC - theta: Array[Array[Double]], // CXD - nPoints: Int, - seed: Int): Seq[LabeledPoint] = { + pi: Array[Double], // 1XC + theta: Array[Array[Double]], // CXD + nPoints: Int, + seed: Int, + modelType: String = "Multinomial", + sample: Int = 10): Seq[LabeledPoint] = { val D = theta(0).length val rnd = new Random(seed) - val _pi = pi.map(math.pow(math.E, _)) val _theta = theta.map(row => row.map(math.pow(math.E, _))) for (i <- 0 until nPoints) yield { val y = calcLabel(rnd.nextDouble(), _pi) - val xi = Array.tabulate[Double](D) { j => - if (rnd.nextDouble() < _theta(y)(j)) 1 else 0 + val xi = modelType match { + case "Bernoulli" => Array.tabulate[Double] (D) { j => + if (rnd.nextDouble () < _theta(y)(j) ) 1 else 0 + } + case "Multinomial" => + val mult = BrzMultinomial(BDV(_theta(y))) + val emptyMap = (0 until D).map(x => (x, 0.0)).toMap + val counts = emptyMap ++ mult.sample(sample).groupBy(x => x).map { + case (index, reps) => (index, reps.size.toDouble) + } + counts.toArray.sortBy(_._1).map(_._2) + case _ => + // This should never happen. + throw new UnknownError(s"NaiveBayesSuite found unknown ModelType: $modelType") } LabeledPoint(y, Vectors.dense(xi)) } } - private val smallPi = Array(0.5, 0.3, 0.2).map(math.log) + /** Bernoulli NaiveBayes with binary labels, 3 features */ + private val binaryBernoulliModel = new NaiveBayesModel(labels = Array(0.0, 1.0), + pi = Array(0.2, 0.8), theta = Array(Array(0.1, 0.3, 0.6), Array(0.2, 0.4, 0.4)), + "Bernoulli") - private val smallTheta = Array( - Array(0.91, 0.03, 0.03, 0.03), // label 0 - Array(0.03, 0.91, 0.03, 0.03), // label 1 - Array(0.03, 0.03, 0.91, 0.03) // label 2 - ).map(_.map(math.log)) - - /** Binary labels, 3 features */ - private val binaryModel = new NaiveBayesModel(labels = Array(0.0, 1.0), pi = Array(0.2, 0.8), - theta = Array(Array(0.1, 0.3, 0.6), Array(0.2, 0.4, 0.4))) + /** Multinomial NaiveBayes with binary labels, 3 features */ + private val binaryMultinomialModel = new NaiveBayesModel(labels = Array(0.0, 1.0), + pi = Array(0.2, 0.8), theta = Array(Array(0.1, 0.3, 0.6), Array(0.2, 0.4, 0.4)), + "Multinomial") } class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext { @@ -85,6 +99,24 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext { assert(numOfPredictions < input.length / 5) } + def validateModelFit( + piData: Array[Double], + thetaData: Array[Array[Double]], + model: NaiveBayesModel) = { + def closeFit(d1: Double, d2: Double, precision: Double): Boolean = { + (d1 - d2).abs <= precision + } + val modelIndex = (0 until piData.length).zip(model.labels.map(_.toInt)) + for (i <- modelIndex) { + assert(closeFit(math.exp(piData(i._2)), math.exp(model.pi(i._1)), 0.05)) + } + for (i <- modelIndex) { + for (j <- 0 until thetaData(i._2).length) { + assert(closeFit(math.exp(thetaData(i._2)(j)), math.exp(model.theta(i._1)(j)), 0.05)) + } + } + } + test("get, set params") { val nb = new NaiveBayes() nb.setLambda(2.0) @@ -93,19 +125,53 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext { assert(nb.getLambda === 3.0) } - test("Naive Bayes") { - val nPoints = 10000 + test("Naive Bayes Multinomial") { + val nPoints = 1000 + val pi = Array(0.5, 0.1, 0.4).map(math.log) + val theta = Array( + Array(0.70, 0.10, 0.10, 0.10), // label 0 + Array(0.10, 0.70, 0.10, 0.10), // label 1 + Array(0.10, 0.10, 0.70, 0.10) // label 2 + ).map(_.map(math.log)) + + val testData = NaiveBayesSuite.generateNaiveBayesInput( + pi, theta, nPoints, 42, "Multinomial") + val testRDD = sc.parallelize(testData, 2) + testRDD.cache() + + val model = NaiveBayes.train(testRDD, 1.0, "Multinomial") + validateModelFit(pi, theta, model) + + val validationData = NaiveBayesSuite.generateNaiveBayesInput( + pi, theta, nPoints, 17, "Multinomial") + val validationRDD = sc.parallelize(validationData, 2) + + // Test prediction on RDD. + validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData) - val pi = NaiveBayesSuite.smallPi - val theta = NaiveBayesSuite.smallTheta + // Test prediction on Array. + validatePrediction(validationData.map(row => model.predict(row.features)), validationData) + } - val testData = NaiveBayesSuite.generateNaiveBayesInput(pi, theta, nPoints, 42) + test("Naive Bayes Bernoulli") { + val nPoints = 10000 + val pi = Array(0.5, 0.3, 0.2).map(math.log) + val theta = Array( + Array(0.50, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.40), // label 0 + Array(0.02, 0.70, 0.10, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02), // label 1 + Array(0.02, 0.02, 0.60, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.30) // label 2 + ).map(_.map(math.log)) + + val testData = NaiveBayesSuite.generateNaiveBayesInput( + pi, theta, nPoints, 45, "Bernoulli") val testRDD = sc.parallelize(testData, 2) testRDD.cache() - val model = NaiveBayes.train(testRDD) + val model = NaiveBayes.train(testRDD, 1.0, "Bernoulli") + validateModelFit(pi, theta, model) - val validationData = NaiveBayesSuite.generateNaiveBayesInput(pi, theta, nPoints, 17) + val validationData = NaiveBayesSuite.generateNaiveBayesInput( + pi, theta, nPoints, 20, "Bernoulli") val validationRDD = sc.parallelize(validationData, 2) // Test prediction on RDD. @@ -142,19 +208,41 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext { } } - test("model save/load") { - val model = NaiveBayesSuite.binaryModel + test("model save/load: 2.0 to 2.0") { + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + + Seq(NaiveBayesSuite.binaryBernoulliModel, NaiveBayesSuite.binaryMultinomialModel).map { + model => + // Save model, load it back, and compare. + try { + model.save(sc, path) + val sameModel = NaiveBayesModel.load(sc, path) + assert(model.labels === sameModel.labels) + assert(model.pi === sameModel.pi) + assert(model.theta === sameModel.theta) + assert(model.modelType === sameModel.modelType) + } finally { + Utils.deleteRecursively(tempDir) + } + } + } + + test("model save/load: 1.0 to 2.0") { + val model = NaiveBayesSuite.binaryMultinomialModel val tempDir = Utils.createTempDir() val path = tempDir.toURI.toString - // Save model, load it back, and compare. + // Save model as version 1.0, load it back, and compare. try { - model.save(sc, path) + val data = NaiveBayesModel.SaveLoadV1_0.Data(model.labels, model.pi, model.theta) + NaiveBayesModel.SaveLoadV1_0.save(sc, path, data) val sameModel = NaiveBayesModel.load(sc, path) assert(model.labels === sameModel.labels) assert(model.pi === sameModel.pi) assert(model.theta === sameModel.theta) + assert(model.modelType === "Multinomial") } finally { Utils.deleteRecursively(tempDir) } @@ -172,8 +260,8 @@ class NaiveBayesClusterSuite extends FunSuite with LocalClusterSparkContext { LabeledPoint(random.nextInt(2), Vectors.dense(Array.fill(n)(random.nextDouble()))) } } - // If we serialize data directly in the task closure, the size of the serialized task would be - // greater than 1MB and hence Spark would throw an error. + // If we serialize data directly in the task closure, the size of the serialized task + // would be greater than 1MB and hence Spark would throw an error. val model = NaiveBayes.train(examples) val predictions = model.predict(examples.map(_.features)) } From a7992ffaf1e8adc9d2c225a986fa3162e8e130eb Mon Sep 17 00:00:00 2001 From: Cheng Lian Date: Tue, 31 Mar 2015 11:18:25 -0700 Subject: [PATCH 110/591] [SPARK-6555] [SQL] Overrides equals() and hashCode() for MetastoreRelation Also removes temporary workarounds made in #5183 and #5251. [Review on Reviewable](https://reviewable.io/reviews/apache/spark/5289) Author: Cheng Lian Closes #5289 from liancheng/spark-6555 and squashes the following commits: d0095ac [Cheng Lian] Removes unused imports cfafeeb [Cheng Lian] Removes outdated comment 75a2746 [Cheng Lian] Overrides equals() and hashCode() for MetastoreRelation --- .../spark/sql/hive/HiveMetastoreCatalog.scala | 42 +++++++++++-------- .../sql/hive/execution/HivePlanTest.scala | 6 ++- 2 files changed, 28 insertions(+), 20 deletions(-) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index 6a01a23124d95..f20f0ad99f865 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -20,6 +20,7 @@ package org.apache.spark.sql.hive import java.io.IOException import java.util.{List => JList} +import com.google.common.base.Objects import com.google.common.cache.{CacheBuilder, CacheLoader, LoadingCache} import org.apache.hadoop.hive.metastore.api.{FieldSchema, Partition => TPartition, Table => TTable} import org.apache.hadoop.hive.metastore.{TableType, Warehouse} @@ -465,7 +466,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with relation.tableDesc.getSerdeClassName.toLowerCase.contains("parquet") => val parquetRelation = convertToParquetRelation(relation) val attributedRewrites = relation.output.zip(parquetRelation.output) - (relation -> relation.output, parquetRelation, attributedRewrites) + (relation, parquetRelation, attributedRewrites) // Write path case InsertIntoHiveTable(relation: MetastoreRelation, _, _, _) @@ -476,7 +477,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with relation.tableDesc.getSerdeClassName.toLowerCase.contains("parquet") => val parquetRelation = convertToParquetRelation(relation) val attributedRewrites = relation.output.zip(parquetRelation.output) - (relation -> relation.output, parquetRelation, attributedRewrites) + (relation, parquetRelation, attributedRewrites) // Read path case p @ PhysicalOperation(_, _, relation: MetastoreRelation) @@ -485,33 +486,28 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with relation.tableDesc.getSerdeClassName.toLowerCase.contains("parquet") => val parquetRelation = convertToParquetRelation(relation) val attributedRewrites = relation.output.zip(parquetRelation.output) - (relation -> relation.output, parquetRelation, attributedRewrites) + (relation, parquetRelation, attributedRewrites) } - // Quick fix for SPARK-6450: Notice that we're using both the MetastoreRelation instances and - // their output attributes as the key of the map. This is because MetastoreRelation.equals - // doesn't take output attributes into account, thus multiple MetastoreRelation instances - // pointing to the same table get collapsed into a single entry in the map. A proper fix for - // this should be overriding equals & hashCode in MetastoreRelation. val relationMap = toBeReplaced.map(r => (r._1, r._2)).toMap val attributedRewrites = AttributeMap(toBeReplaced.map(_._3).fold(Nil)(_ ++: _)) // Replaces all `MetastoreRelation`s with corresponding `ParquetRelation2`s, and fixes // attribute IDs referenced in other nodes. plan.transformUp { - case r: MetastoreRelation if relationMap.contains(r -> r.output) => - val parquetRelation = relationMap(r -> r.output) + case r: MetastoreRelation if relationMap.contains(r) => + val parquetRelation = relationMap(r) val alias = r.alias.getOrElse(r.tableName) Subquery(alias, parquetRelation) case InsertIntoTable(r: MetastoreRelation, partition, child, overwrite) - if relationMap.contains(r -> r.output) => - val parquetRelation = relationMap(r -> r.output) + if relationMap.contains(r) => + val parquetRelation = relationMap(r) InsertIntoTable(parquetRelation, partition, child, overwrite) case InsertIntoHiveTable(r: MetastoreRelation, partition, child, overwrite) - if relationMap.contains(r -> r.output) => - val parquetRelation = relationMap(r -> r.output) + if relationMap.contains(r) => + val parquetRelation = relationMap(r) InsertIntoTable(parquetRelation, partition, child, overwrite) case other => other.transformExpressions { @@ -707,6 +703,19 @@ private[hive] case class MetastoreRelation self: Product => + override def equals(other: scala.Any): Boolean = other match { + case relation: MetastoreRelation => + databaseName == relation.databaseName && + tableName == relation.tableName && + alias == relation.alias && + output == relation.output + case _ => false + } + + override def hashCode(): Int = { + Objects.hashCode(databaseName, tableName, alias, output) + } + // TODO: Can we use org.apache.hadoop.hive.ql.metadata.Table as the type of table and // use org.apache.hadoop.hive.ql.metadata.Partition as the type of elements of partitions. // Right now, using org.apache.hadoop.hive.ql.metadata.Table and @@ -786,10 +795,7 @@ private[hive] case class MetastoreRelation val columnOrdinals = AttributeMap(attributes.zipWithIndex) override def newInstance() = { - val newCopy = MetastoreRelation(databaseName, tableName, alias)(table, partitions)(sqlContext) - // The project here is an ugly hack to work around the fact that MetastoreRelation's - // equals method is broken. Please remove this when SPARK-6555 is fixed. - Project(newCopy.output, newCopy) + MetastoreRelation(databaseName, tableName, alias)(table, partitions)(sqlContext) } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HivePlanTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HivePlanTest.scala index c939e6e99d28a..bdb53ddf59c19 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HivePlanTest.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HivePlanTest.scala @@ -22,10 +22,12 @@ import org.apache.spark.sql.hive.test.TestHive class HivePlanTest extends QueryTest { import TestHive._ + import TestHive.implicits._ test("udf constant folding") { - val optimized = sql("SELECT cos(null) FROM src").queryExecution.optimizedPlan - val correctAnswer = sql("SELECT cast(null as double) FROM src").queryExecution.optimizedPlan + Seq.empty[Tuple1[Int]].toDF("a").registerTempTable("t") + val optimized = sql("SELECT cos(null) FROM t").queryExecution.optimizedPlan + val correctAnswer = sql("SELECT cast(null as double) FROM t").queryExecution.optimizedPlan comparePlans(optimized, correctAnswer) } From 81020144708773ba3af4932288ffa09ef901269e Mon Sep 17 00:00:00 2001 From: Cheng Lian Date: Tue, 31 Mar 2015 11:21:15 -0700 Subject: [PATCH 111/591] [SPARK-6575] [SQL] Adds configuration to disable schema merging while converting metastore Parquet tables Consider a metastore Parquet table that 1. doesn't have schema evolution issue 2. has lots of data files and/or partitions In this case, driver schema merging can be both slow and unnecessary. Would be good to have a configuration to let the use disable schema merging when converting such a metastore Parquet table. [Review on Reviewable](https://reviewable.io/reviews/apache/spark/5231) Author: Cheng Lian Closes #5231 from liancheng/spark-6575 and squashes the following commits: cd96159 [Cheng Lian] Adds configuration to disable schema merging while converting metastore Parquet tables --- .../org/apache/spark/sql/hive/HiveContext.scala | 9 +++++++++ .../spark/sql/hive/HiveMetastoreCatalog.scala | 16 ++++++---------- 2 files changed, 15 insertions(+), 10 deletions(-) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala index c06c2e396bbc1..6bb1c47dba920 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala @@ -57,6 +57,15 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { protected[sql] def convertMetastoreParquet: Boolean = getConf("spark.sql.hive.convertMetastoreParquet", "true") == "true" + /** + * When true, also tries to merge possibly different but compatible Parquet schemas in different + * Parquet data files. + * + * This configuration is only effective when "spark.sql.hive.convertMetastoreParquet" is true. + */ + protected[sql] def convertMetastoreParquetWithSchemaMerging: Boolean = + getConf("spark.sql.hive.convertMetastoreParquet.mergeSchema", "false") == "true" + /** * When true, a table created by a Hive CTAS statement (no USING clause) will be * converted to a data source table, using the data source set by spark.sql.sources.default. diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index f20f0ad99f865..2b5d031741a63 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -218,6 +218,10 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with private def convertToParquetRelation(metastoreRelation: MetastoreRelation): LogicalRelation = { val metastoreSchema = StructType.fromAttributes(metastoreRelation.output) + val mergeSchema = hive.convertMetastoreParquetWithSchemaMerging + val parquetOptions = Map( + ParquetRelation2.METASTORE_SCHEMA -> metastoreSchema.json, + ParquetRelation2.MERGE_SCHEMA -> mergeSchema.toString) // NOTE: Instead of passing Metastore schema directly to `ParquetRelation2`, we have to // serialize the Metastore schema to JSON and pass it as a data source option because of the @@ -234,18 +238,10 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with } val partitionSpec = PartitionSpec(partitionSchema, partitions) val paths = partitions.map(_.path) - LogicalRelation( - ParquetRelation2( - paths, - Map(ParquetRelation2.METASTORE_SCHEMA -> metastoreSchema.json), - None, - Some(partitionSpec))(hive)) + LogicalRelation(ParquetRelation2(paths, parquetOptions, None, Some(partitionSpec))(hive)) } else { val paths = Seq(metastoreRelation.hiveQlTable.getDataLocation.toString) - LogicalRelation( - ParquetRelation2( - paths, - Map(ParquetRelation2.METASTORE_SCHEMA -> metastoreSchema.json))(hive)) + LogicalRelation(ParquetRelation2(paths, parquetOptions)(hive)) } } From cd48ca50129e8952f487051796244e7569275416 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Tue, 31 Mar 2015 11:23:18 -0700 Subject: [PATCH 112/591] [SPARK-6145][SQL] fix ORDER BY on nested fields This PR is based on work by cloud-fan in #4904, but with two differences: - We isolate the logic for Sort's special handling into `ResolveSortReferences` - We avoid creating UnresolvedGetField expressions during resolution. Instead we either resolve GetField or we return None. This avoids us going down the wrong path early on. Author: Michael Armbrust Closes #5189 from marmbrus/nestedOrderBy and squashes the following commits: b8cae45 [Michael Armbrust] fix another test 0f36a11 [Michael Armbrust] WIP 91820cd [Michael Armbrust] Fix bug. --- .../sql/catalyst/analysis/Analyzer.scala | 76 ++++++++++++++----- .../sql/catalyst/analysis/CheckAnalysis.scala | 12 ++- .../catalyst/expressions/AttributeSet.scala | 2 +- .../catalyst/plans/logical/LogicalPlan.scala | 76 +++++++++++++++---- .../sql/catalyst/analysis/AnalysisSuite.scala | 39 +++++++++- .../org/apache/spark/sql/SQLContext.scala | 14 ++-- .../org/apache/spark/sql/SQLQuerySuite.scala | 19 +++-- .../spark/sql/sources/DataSourceTest.scala | 4 + 8 files changed, 185 insertions(+), 57 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala index dc14f49e6ee99..c578d084a45b6 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala @@ -37,11 +37,12 @@ object SimpleAnalyzer extends Analyzer(EmptyCatalog, EmptyFunctionRegistry, true * [[UnresolvedRelation]]s into fully typed objects using information in a schema [[Catalog]] and * a [[FunctionRegistry]]. */ -class Analyzer(catalog: Catalog, - registry: FunctionRegistry, - caseSensitive: Boolean, - maxIterations: Int = 100) - extends RuleExecutor[LogicalPlan] with HiveTypeCoercion { +class Analyzer( + catalog: Catalog, + registry: FunctionRegistry, + caseSensitive: Boolean, + maxIterations: Int = 100) + extends RuleExecutor[LogicalPlan] with HiveTypeCoercion with CheckAnalysis { val resolver = if (caseSensitive) caseSensitiveResolution else caseInsensitiveResolution @@ -354,19 +355,16 @@ class Analyzer(catalog: Catalog, def apply(plan: LogicalPlan): LogicalPlan = plan transformUp { case s @ Sort(ordering, global, p @ Project(projectList, child)) if !s.resolved && p.resolved => - val unresolved = ordering.flatMap(_.collect { case UnresolvedAttribute(name) => name }) - val resolved = unresolved.flatMap(child.resolve(_, resolver)) - val requiredAttributes = - AttributeSet(resolved.flatMap(_.collect { case a: Attribute => a })) + val (resolvedOrdering, missing) = resolveAndFindMissing(ordering, p, child) - val missingInProject = requiredAttributes -- p.output - if (missingInProject.nonEmpty) { + // If this rule was not a no-op, return the transformed plan, otherwise return the original. + if (missing.nonEmpty) { // Add missing attributes and then project them away after the sort. - Project(projectList.map(_.toAttribute), - Sort(ordering, global, - Project(projectList ++ missingInProject, child))) + Project(p.output, + Sort(resolvedOrdering, global, + Project(projectList ++ missing, child))) } else { - logDebug(s"Failed to find $missingInProject in ${p.output.mkString(", ")}") + logDebug(s"Failed to find $missing in ${p.output.mkString(", ")}") s // Nothing we can do here. Return original plan. } case s @ Sort(ordering, global, a @ Aggregate(grouping, aggs, child)) @@ -378,18 +376,54 @@ class Analyzer(catalog: Catalog, grouping.collect { case ne: NamedExpression => ne.toAttribute } ) - logDebug(s"Grouping expressions: $groupingRelation") - val resolved = unresolved.flatMap(groupingRelation.resolve(_, resolver)) - val missingInAggs = resolved.filterNot(a.outputSet.contains) - logDebug(s"Resolved: $resolved Missing in aggs: $missingInAggs") - if (missingInAggs.nonEmpty) { + val (resolvedOrdering, missing) = resolveAndFindMissing(ordering, a, groupingRelation) + + if (missing.nonEmpty) { // Add missing grouping exprs and then project them away after the sort. Project(a.output, - Sort(ordering, global, Aggregate(grouping, aggs ++ missingInAggs, child))) + Sort(resolvedOrdering, global, + Aggregate(grouping, aggs ++ missing, child))) } else { s // Nothing we can do here. Return original plan. } } + + /** + * Given a child and a grandchild that are present beneath a sort operator, returns + * a resolved sort ordering and a list of attributes that are missing from the child + * but are present in the grandchild. + */ + def resolveAndFindMissing( + ordering: Seq[SortOrder], + child: LogicalPlan, + grandchild: LogicalPlan): (Seq[SortOrder], Seq[Attribute]) = { + // Find any attributes that remain unresolved in the sort. + val unresolved: Seq[String] = + ordering.flatMap(_.collect { case UnresolvedAttribute(name) => name }) + + // Create a map from name, to resolved attributes, when the desired name can be found + // prior to the projection. + val resolved: Map[String, NamedExpression] = + unresolved.flatMap(u => grandchild.resolve(u, resolver).map(a => u -> a)).toMap + + // Construct a set that contains all of the attributes that we need to evaluate the + // ordering. + val requiredAttributes = AttributeSet(resolved.values) + + // Figure out which ones are missing from the projection, so that we can add them and + // remove them after the sort. + val missingInProject = requiredAttributes -- child.output + + // Now that we have all the attributes we need, reconstruct a resolved ordering. + // It is important to do it here, instead of waiting for the standard resolved as adding + // attributes to the project below can actually introduce ambiquity that was not present + // before. + val resolvedOrdering = ordering.map(_ transform { + case u @ UnresolvedAttribute(name) => resolved.getOrElse(name, u) + }).asInstanceOf[Seq[SortOrder]] + + (resolvedOrdering, missingInProject.toSeq) + } } /** diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala index 40472a1cbb3b4..fa02111385c06 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala @@ -25,7 +25,8 @@ import org.apache.spark.sql.types._ /** * Throws user facing errors when passed invalid queries that fail to analyze. */ -class CheckAnalysis { +trait CheckAnalysis { + self: Analyzer => /** * Override to provide additional checks for correct analysis. @@ -33,17 +34,22 @@ class CheckAnalysis { */ val extendedCheckRules: Seq[LogicalPlan => Unit] = Nil - def failAnalysis(msg: String): Nothing = { + protected def failAnalysis(msg: String): Nothing = { throw new AnalysisException(msg) } - def apply(plan: LogicalPlan): Unit = { + def checkAnalysis(plan: LogicalPlan): Unit = { // We transform up and order the rules so as to catch the first possible failure instead // of the result of cascading resolution failures. plan.foreachUp { case operator: LogicalPlan => operator transformExpressionsUp { case a: Attribute if !a.resolved => + if (operator.childrenResolved) { + // Throw errors for specific problems with get field. + operator.resolveChildren(a.name, resolver, throwErrors = true) + } + val from = operator.inputSet.map(_.name).mkString(", ") a.failAnalysis(s"cannot resolve '${a.prettyString}' given input columns $from") diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeSet.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeSet.scala index 11b4eb5c888be..5345696570b41 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeSet.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/AttributeSet.scala @@ -34,7 +34,7 @@ object AttributeSet { def apply(a: Attribute): AttributeSet = new AttributeSet(Set(new AttributeEquals(a))) /** Constructs a new [[AttributeSet]] given a sequence of [[Expression Expressions]]. */ - def apply(baseSet: Seq[Expression]): AttributeSet = { + def apply(baseSet: Iterable[Expression]): AttributeSet = { new AttributeSet( baseSet .flatMap(_.references) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala index b01a61d7bf8d6..2e9f3aa4ec4ad 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala @@ -24,6 +24,7 @@ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.QueryPlan import org.apache.spark.sql.catalyst.trees.TreeNode import org.apache.spark.sql.catalyst.trees +import org.apache.spark.sql.types.{ArrayType, StructType, StructField} abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { @@ -109,16 +110,22 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { * nodes of this LogicalPlan. The attribute is expressed as * as string in the following form: `[scope].AttributeName.[nested].[fields]...`. */ - def resolveChildren(name: String, resolver: Resolver): Option[NamedExpression] = - resolve(name, children.flatMap(_.output), resolver) + def resolveChildren( + name: String, + resolver: Resolver, + throwErrors: Boolean = false): Option[NamedExpression] = + resolve(name, children.flatMap(_.output), resolver, throwErrors) /** * Optionally resolves the given string to a [[NamedExpression]] based on the output of this * LogicalPlan. The attribute is expressed as string in the following form: * `[scope].AttributeName.[nested].[fields]...`. */ - def resolve(name: String, resolver: Resolver): Option[NamedExpression] = - resolve(name, output, resolver) + def resolve( + name: String, + resolver: Resolver, + throwErrors: Boolean = false): Option[NamedExpression] = + resolve(name, output, resolver, throwErrors) /** * Resolve the given `name` string against the given attribute, returning either 0 or 1 match. @@ -162,7 +169,8 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { protected def resolve( name: String, input: Seq[Attribute], - resolver: Resolver): Option[NamedExpression] = { + resolver: Resolver, + throwErrors: Boolean): Option[NamedExpression] = { val parts = name.split("\\.") @@ -196,14 +204,19 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { // One match, but we also need to extract the requested nested field. case Seq((a, nestedFields)) => - // The foldLeft adds UnresolvedGetField for every remaining parts of the name, - // and aliased it with the last part of the name. - // For example, consider name "a.b.c", where "a" is resolved to an existing attribute. - // Then this will add UnresolvedGetField("b") and UnresolvedGetField("c"), and alias - // the final expression as "c". - val fieldExprs = nestedFields.foldLeft(a: Expression)(UnresolvedGetField) - val aliasName = nestedFields.last - Some(Alias(fieldExprs, aliasName)()) + try { + + // The foldLeft adds UnresolvedGetField for every remaining parts of the name, + // and aliased it with the last part of the name. + // For example, consider name "a.b.c", where "a" is resolved to an existing attribute. + // Then this will add UnresolvedGetField("b") and UnresolvedGetField("c"), and alias + // the final expression as "c". + val fieldExprs = nestedFields.foldLeft(a: Expression)(resolveGetField(_, _, resolver)) + val aliasName = nestedFields.last + Some(Alias(fieldExprs, aliasName)()) + } catch { + case a: AnalysisException if !throwErrors => None + } // No matches. case Seq() => @@ -212,11 +225,46 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { // More than one match. case ambiguousReferences => - val referenceNames = ambiguousReferences.map(_._1.qualifiedName).mkString(", ") + val referenceNames = ambiguousReferences.map(_._1).mkString(", ") throw new AnalysisException( s"Reference '$name' is ambiguous, could be: $referenceNames.") } } + + /** + * Returns the resolved `GetField`, and report error if no desired field or over one + * desired fields are found. + * + * TODO: this code is duplicated from Analyzer and should be refactored to avoid this. + */ + protected def resolveGetField( + expr: Expression, + fieldName: String, + resolver: Resolver): Expression = { + def findField(fields: Array[StructField]): Int = { + val checkField = (f: StructField) => resolver(f.name, fieldName) + val ordinal = fields.indexWhere(checkField) + if (ordinal == -1) { + throw new AnalysisException( + s"No such struct field $fieldName in ${fields.map(_.name).mkString(", ")}") + } else if (fields.indexWhere(checkField, ordinal + 1) != -1) { + throw new AnalysisException( + s"Ambiguous reference to fields ${fields.filter(checkField).mkString(", ")}") + } else { + ordinal + } + } + expr.dataType match { + case StructType(fields) => + val ordinal = findField(fields) + StructGetField(expr, fields(ordinal), ordinal) + case ArrayType(StructType(fields), containsNull) => + val ordinal = findField(fields) + ArrayGetField(expr, fields(ordinal), ordinal, containsNull) + case otherType => + throw new AnalysisException(s"GetField is not valid on fields of type $otherType") + } + } } /** diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala index 756cd36f05c8c..ee7b14c7a157c 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala @@ -40,14 +40,12 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { override val extendedResolutionRules = EliminateSubQueries :: Nil } - val checkAnalysis = new CheckAnalysis - def caseSensitiveAnalyze(plan: LogicalPlan) = - checkAnalysis(caseSensitiveAnalyzer(plan)) + caseSensitiveAnalyzer.checkAnalysis(caseSensitiveAnalyzer(plan)) def caseInsensitiveAnalyze(plan: LogicalPlan) = - checkAnalysis(caseInsensitiveAnalyzer(plan)) + caseInsensitiveAnalyzer.checkAnalysis(caseInsensitiveAnalyzer(plan)) val testRelation = LocalRelation(AttributeReference("a", IntegerType, nullable = true)()) val testRelation2 = LocalRelation( @@ -57,6 +55,21 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { AttributeReference("d", DecimalType.Unlimited)(), AttributeReference("e", ShortType)()) + val nestedRelation = LocalRelation( + AttributeReference("top", StructType( + StructField("duplicateField", StringType) :: + StructField("duplicateField", StringType) :: + StructField("differentCase", StringType) :: + StructField("differentcase", StringType) :: Nil + ))()) + + val nestedRelation2 = LocalRelation( + AttributeReference("top", StructType( + StructField("aField", StringType) :: + StructField("bField", StringType) :: + StructField("cField", StringType) :: Nil + ))()) + before { caseSensitiveCatalog.registerTable(Seq("TaBlE"), testRelation) caseInsensitiveCatalog.registerTable(Seq("TaBlE"), testRelation) @@ -169,6 +182,24 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { "'b'" :: "group by" :: Nil ) + errorTest( + "ambiguous field", + nestedRelation.select($"top.duplicateField"), + "Ambiguous reference to fields" :: "duplicateField" :: Nil, + caseSensitive = false) + + errorTest( + "ambiguous field due to case insensitivity", + nestedRelation.select($"top.differentCase"), + "Ambiguous reference to fields" :: "differentCase" :: "differentcase" :: Nil, + caseSensitive = false) + + errorTest( + "missing field", + nestedRelation2.select($"top.c"), + "No such struct field" :: "aField" :: "bField" :: "cField" :: Nil, + caseSensitive = false) + case class UnresolvedTestPlan() extends LeafNode { override lazy val resolved = false override def output = Nil diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala index b8100782ec937..1794936a52c6d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala @@ -120,6 +120,10 @@ class SQLContext(@transient val sparkContext: SparkContext) ExtractPythonUdfs :: sources.PreInsertCastAndRename :: Nil + + override val extendedCheckRules = Seq( + sources.PreWriteCheck(catalog) + ) } @transient @@ -1065,14 +1069,6 @@ class SQLContext(@transient val sparkContext: SparkContext) Batch("Add exchange", Once, AddExchange(self)) :: Nil } - @transient - protected[sql] lazy val checkAnalysis = new CheckAnalysis { - override val extendedCheckRules = Seq( - sources.PreWriteCheck(catalog) - ) - } - - protected[sql] def openSession(): SQLSession = { detachSession() val session = createSession() @@ -1105,7 +1101,7 @@ class SQLContext(@transient val sparkContext: SparkContext) */ @DeveloperApi protected[sql] class QueryExecution(val logical: LogicalPlan) { - def assertAnalyzed(): Unit = checkAnalysis(analyzed) + def assertAnalyzed(): Unit = analyzer.checkAnalysis(analyzed) lazy val analyzed: LogicalPlan = analyzer(logical) lazy val withCachedData: LogicalPlan = { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala index a3c0076e16d6c..87e7cf8c8af9f 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala @@ -1084,10 +1084,19 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { test("SPARK-6145: ORDER BY test for nested fields") { jsonRDD(sparkContext.makeRDD( """{"a": {"b": 1, "a": {"a": 1}}, "c": [{"d": 1}]}""" :: Nil)).registerTempTable("nestedOrder") - // These should be successfully analyzed - sql("SELECT 1 FROM nestedOrder ORDER BY a.b").queryExecution.analyzed - sql("SELECT a.b FROM nestedOrder ORDER BY a.b").queryExecution.analyzed - sql("SELECT 1 FROM nestedOrder ORDER BY a.a.a").queryExecution.analyzed - sql("SELECT 1 FROM nestedOrder ORDER BY c[0].d").queryExecution.analyzed + + checkAnswer(sql("SELECT 1 FROM nestedOrder ORDER BY a.b"), Row(1)) + checkAnswer(sql("SELECT a.b FROM nestedOrder ORDER BY a.b"), Row(1)) + checkAnswer(sql("SELECT 1 FROM nestedOrder ORDER BY a.a.a"), Row(1)) + checkAnswer(sql("SELECT a.a.a FROM nestedOrder ORDER BY a.a.a"), Row(1)) + checkAnswer(sql("SELECT 1 FROM nestedOrder ORDER BY c[0].d"), Row(1)) + checkAnswer(sql("SELECT c[0].d FROM nestedOrder ORDER BY c[0].d"), Row(1)) + } + + test("SPARK-6145: special cases") { + jsonRDD(sparkContext.makeRDD( + """{"a": {"b": [1]}, "b": [{"a": 1}], "c0": {"a": 1}}""" :: Nil)).registerTempTable("t") + checkAnswer(sql("SELECT a.b[0] FROM t ORDER BY c0.a"), Row(1)) + checkAnswer(sql("SELECT b[0].a FROM t ORDER BY c0.a"), Row(1)) } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala index 91c6367371f15..33c67355967dd 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala @@ -32,6 +32,10 @@ abstract class DataSourceTest extends QueryTest with BeforeAndAfter { override val extendedResolutionRules = PreInsertCastAndRename :: Nil + + override val extendedCheckRules = Seq( + sources.PreWriteCheck(catalog) + ) } } } From 46de6c05e0619250346f0988e296849f8f93d2b1 Mon Sep 17 00:00:00 2001 From: lewuathe Date: Tue, 31 Mar 2015 11:25:21 -0700 Subject: [PATCH 113/591] [SPARK-6598][MLLIB] Python API for IDFModel This is the sub-task of SPARK-6254. Wrapping IDFModel `idf` member function for pyspark. Author: lewuathe Closes #5264 from Lewuathe/SPARK-6598 and squashes the following commits: 1dc522c [lewuathe] [SPARK-6598] Python API for IDFModel --- python/pyspark/mllib/feature.py | 6 ++++++ python/pyspark/mllib/tests.py | 14 ++++++++++++++ 2 files changed, 20 insertions(+) diff --git a/python/pyspark/mllib/feature.py b/python/pyspark/mllib/feature.py index 0ffe092a07365..4bfe3014ef748 100644 --- a/python/pyspark/mllib/feature.py +++ b/python/pyspark/mllib/feature.py @@ -244,6 +244,12 @@ def transform(self, x): x = _convert_to_vector(x) return JavaVectorTransformer.transform(self, x) + def idf(self): + """ + Returns the current IDF vector. + """ + return self.call('idf') + class IDF(object): """ diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 155019638f806..3bb0f0ca68128 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -41,6 +41,7 @@ from pyspark.mllib.regression import LabeledPoint from pyspark.mllib.random import RandomRDDs from pyspark.mllib.stat import Statistics +from pyspark.mllib.feature import IDF from pyspark.serializers import PickleSerializer from pyspark.sql import SQLContext from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase @@ -620,6 +621,19 @@ def test_right_number_of_results(self): self.assertEqual(len(chi), num_cols) self.assertIsNotNone(chi[1000]) + +class FeatureTest(PySparkTestCase): + def test_idf_model(self): + data = [ + Vectors.dense([1, 2, 6, 0, 2, 3, 1, 1, 0, 0, 3]), + Vectors.dense([1, 3, 0, 1, 3, 0, 0, 2, 0, 0, 1]), + Vectors.dense([1, 4, 1, 0, 0, 4, 9, 0, 1, 2, 0]), + Vectors.dense([2, 1, 0, 3, 0, 0, 5, 0, 2, 3, 9]) + ] + model = IDF().fit(self.sc.parallelize(data, 2)) + idf = model.idf() + self.assertEqual(len(idf), 11) + if __name__ == "__main__": if not _have_scipy: print "NOTE: Skipping SciPy tests as it does not seem to be installed" From b5bd75d90a761199c3f9cb583c1fe48c8fda7780 Mon Sep 17 00:00:00 2001 From: Yanbo Liang Date: Tue, 31 Mar 2015 11:32:14 -0700 Subject: [PATCH 114/591] [SPARK-6255] [MLLIB] Support multiclass classification in Python API Python API parity check for classification and multiclass classification support, major disparities need to be added for Python: ```scala LogisticRegressionWithLBFGS setNumClasses setValidateData LogisticRegressionModel getThreshold numClasses numFeatures SVMWithSGD setValidateData SVMModel getThreshold ``` For users the greatest benefit in this PR is multiclass classification was supported by Python API. Users can train multiclass classification model and use it to predict in pyspark. Author: Yanbo Liang Closes #5137 from yanboliang/spark-6255 and squashes the following commits: 0bd531e [Yanbo Liang] address comments 444d5e2 [Yanbo Liang] LogisticRegressionModel.predict() optimization fc7990b [Yanbo Liang] address comments b0d9c63 [Yanbo Liang] Support Mulinomial LR model predict in Python API ded847c [Yanbo Liang] Python API parity check for classification (support multiclass classification) --- .../mllib/api/python/PythonMLLibAPI.scala | 22 ++- python/pyspark/mllib/classification.py | 134 ++++++++++++++---- python/pyspark/mllib/regression.py | 10 +- 3 files changed, 134 insertions(+), 32 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index 22fa684fd2895..662ec5fbed453 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -77,7 +77,13 @@ private[python] class PythonMLLibAPI extends Serializable { initialWeights: Vector): JList[Object] = { try { val model = learner.run(data.rdd.persist(StorageLevel.MEMORY_AND_DISK), initialWeights) - List(model.weights, model.intercept).map(_.asInstanceOf[Object]).asJava + if (model.isInstanceOf[LogisticRegressionModel]) { + val lrModel = model.asInstanceOf[LogisticRegressionModel] + List(lrModel.weights, lrModel.intercept, lrModel.numFeatures, lrModel.numClasses) + .map(_.asInstanceOf[Object]).asJava + } else { + List(model.weights, model.intercept).map(_.asInstanceOf[Object]).asJava + } } finally { data.rdd.unpersist(blocking = false) } @@ -190,9 +196,11 @@ private[python] class PythonMLLibAPI extends Serializable { miniBatchFraction: Double, initialWeights: Vector, regType: String, - intercept: Boolean): JList[Object] = { + intercept: Boolean, + validateData: Boolean): JList[Object] = { val SVMAlg = new SVMWithSGD() SVMAlg.setIntercept(intercept) + .setValidateData(validateData) SVMAlg.optimizer .setNumIterations(numIterations) .setRegParam(regParam) @@ -216,9 +224,11 @@ private[python] class PythonMLLibAPI extends Serializable { initialWeights: Vector, regParam: Double, regType: String, - intercept: Boolean): JList[Object] = { + intercept: Boolean, + validateData: Boolean): JList[Object] = { val LogRegAlg = new LogisticRegressionWithSGD() LogRegAlg.setIntercept(intercept) + .setValidateData(validateData) LogRegAlg.optimizer .setNumIterations(numIterations) .setRegParam(regParam) @@ -242,9 +252,13 @@ private[python] class PythonMLLibAPI extends Serializable { regType: String, intercept: Boolean, corrections: Int, - tolerance: Double): JList[Object] = { + tolerance: Double, + validateData: Boolean, + numClasses: Int): JList[Object] = { val LogRegAlg = new LogisticRegressionWithLBFGS() LogRegAlg.setIntercept(intercept) + .setValidateData(validateData) + .setNumClasses(numClasses) LogRegAlg.optimizer .setNumIterations(numIterations) .setRegParam(regParam) diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py index 6766f3ebb8894..2466e8ac43458 100644 --- a/python/pyspark/mllib/classification.py +++ b/python/pyspark/mllib/classification.py @@ -22,7 +22,7 @@ from pyspark import RDD from pyspark.mllib.common import callMLlibFunc, _py2java, _java2py -from pyspark.mllib.linalg import SparseVector, _convert_to_vector +from pyspark.mllib.linalg import DenseVector, SparseVector, _convert_to_vector from pyspark.mllib.regression import LabeledPoint, LinearModel, _regression_train_wrapper from pyspark.mllib.util import Saveable, Loader, inherit_doc @@ -31,13 +31,13 @@ 'SVMModel', 'SVMWithSGD', 'NaiveBayesModel', 'NaiveBayes'] -class LinearBinaryClassificationModel(LinearModel): +class LinearClassificationModel(LinearModel): """ - Represents a linear binary classification model that predicts to whether an - example is positive (1.0) or negative (0.0). + A private abstract class representing a multiclass classification model. + The categories are represented by int values: 0, 1, 2, etc. """ def __init__(self, weights, intercept): - super(LinearBinaryClassificationModel, self).__init__(weights, intercept) + super(LinearClassificationModel, self).__init__(weights, intercept) self._threshold = None def setThreshold(self, value): @@ -47,14 +47,26 @@ def setThreshold(self, value): Sets the threshold that separates positive predictions from negative predictions. An example with prediction score greater than or equal to this threshold is identified as an positive, and negative otherwise. + It is used for binary classification only. """ self._threshold = value + @property + def threshold(self): + """ + .. note:: Experimental + + Returns the threshold (if any) used for converting raw prediction scores + into 0/1 predictions. It is used for binary classification only. + """ + return self._threshold + def clearThreshold(self): """ .. note:: Experimental Clears the threshold so that `predict` will output raw prediction scores. + It is used for binary classification only. """ self._threshold = None @@ -66,7 +78,7 @@ def predict(self, test): raise NotImplementedError -class LogisticRegressionModel(LinearBinaryClassificationModel): +class LogisticRegressionModel(LinearClassificationModel): """A linear binary classification model derived from logistic regression. @@ -112,10 +124,39 @@ class LogisticRegressionModel(LinearBinaryClassificationModel): ... os.removedirs(path) ... except: ... pass + >>> multi_class_data = [ + ... LabeledPoint(0.0, [0.0, 1.0, 0.0]), + ... LabeledPoint(1.0, [1.0, 0.0, 0.0]), + ... LabeledPoint(2.0, [0.0, 0.0, 1.0]) + ... ] + >>> mcm = LogisticRegressionWithLBFGS.train(data=sc.parallelize(multi_class_data), numClasses=3) + >>> mcm.predict([0.0, 0.5, 0.0]) + 0 + >>> mcm.predict([0.8, 0.0, 0.0]) + 1 + >>> mcm.predict([0.0, 0.0, 0.3]) + 2 """ - def __init__(self, weights, intercept): + def __init__(self, weights, intercept, numFeatures, numClasses): super(LogisticRegressionModel, self).__init__(weights, intercept) + self._numFeatures = int(numFeatures) + self._numClasses = int(numClasses) self._threshold = 0.5 + if self._numClasses == 2: + self._dataWithBiasSize = None + self._weightsMatrix = None + else: + self._dataWithBiasSize = self._coeff.size / (self._numClasses - 1) + self._weightsMatrix = self._coeff.toArray().reshape(self._numClasses - 1, + self._dataWithBiasSize) + + @property + def numFeatures(self): + return self._numFeatures + + @property + def numClasses(self): + return self._numClasses def predict(self, x): """ @@ -126,20 +167,38 @@ def predict(self, x): return x.map(lambda v: self.predict(v)) x = _convert_to_vector(x) - margin = self.weights.dot(x) + self._intercept - if margin > 0: - prob = 1 / (1 + exp(-margin)) + if self.numClasses == 2: + margin = self.weights.dot(x) + self._intercept + if margin > 0: + prob = 1 / (1 + exp(-margin)) + else: + exp_margin = exp(margin) + prob = exp_margin / (1 + exp_margin) + if self._threshold is None: + return prob + else: + return 1 if prob > self._threshold else 0 else: - exp_margin = exp(margin) - prob = exp_margin / (1 + exp_margin) - if self._threshold is None: - return prob - else: - return 1 if prob > self._threshold else 0 + best_class = 0 + max_margin = 0.0 + if x.size + 1 == self._dataWithBiasSize: + for i in range(0, self._numClasses - 1): + margin = x.dot(self._weightsMatrix[i][0:x.size]) + \ + self._weightsMatrix[i][x.size] + if margin > max_margin: + max_margin = margin + best_class = i + 1 + else: + for i in range(0, self._numClasses - 1): + margin = x.dot(self._weightsMatrix[i]) + if margin > max_margin: + max_margin = margin + best_class = i + 1 + return best_class def save(self, sc, path): java_model = sc._jvm.org.apache.spark.mllib.classification.LogisticRegressionModel( - _py2java(sc, self._coeff), self.intercept) + _py2java(sc, self._coeff), self.intercept, self.numFeatures, self.numClasses) java_model.save(sc._jsc.sc(), path) @classmethod @@ -148,8 +207,10 @@ def load(cls, sc, path): sc._jsc.sc(), path) weights = _java2py(sc, java_model.weights()) intercept = java_model.intercept() + numFeatures = java_model.numFeatures() + numClasses = java_model.numClasses() threshold = java_model.getThreshold().get() - model = LogisticRegressionModel(weights, intercept) + model = LogisticRegressionModel(weights, intercept, numFeatures, numClasses) model.setThreshold(threshold) return model @@ -158,7 +219,8 @@ class LogisticRegressionWithSGD(object): @classmethod def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, - initialWeights=None, regParam=0.01, regType="l2", intercept=False): + initialWeights=None, regParam=0.01, regType="l2", intercept=False, + validateData=True): """ Train a logistic regression model on the given data. @@ -184,11 +246,14 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0, or not of the augmented representation for training data (i.e. whether bias features are activated or not). + :param validateData: Boolean parameter which indicates if the + algorithm should validate data before training. + (default: True) """ def train(rdd, i): return callMLlibFunc("trainLogisticRegressionModelWithSGD", rdd, int(iterations), float(step), float(miniBatchFraction), i, float(regParam), regType, - bool(intercept)) + bool(intercept), bool(validateData)) return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights) @@ -197,7 +262,7 @@ class LogisticRegressionWithLBFGS(object): @classmethod def train(cls, data, iterations=100, initialWeights=None, regParam=0.01, regType="l2", - intercept=False, corrections=10, tolerance=1e-4): + intercept=False, corrections=10, tolerance=1e-4, validateData=True, numClasses=2): """ Train a logistic regression model on the given data. @@ -223,6 +288,11 @@ def train(cls, data, iterations=100, initialWeights=None, regParam=0.01, regType update (default: 10). :param tolerance: The convergence tolerance of iterations for L-BFGS (default: 1e-4). + :param validateData: Boolean parameter which indicates if the + algorithm should validate data before training. + (default: True) + :param numClasses: The number of classes (i.e., outcomes) a label can take + in Multinomial Logistic Regression (default: 2). >>> data = [ ... LabeledPoint(0.0, [0.0, 1.0]), @@ -237,12 +307,20 @@ def train(cls, data, iterations=100, initialWeights=None, regParam=0.01, regType def train(rdd, i): return callMLlibFunc("trainLogisticRegressionModelWithLBFGS", rdd, int(iterations), i, float(regParam), regType, bool(intercept), int(corrections), - float(tolerance)) - + float(tolerance), bool(validateData), int(numClasses)) + + if initialWeights is None: + if numClasses == 2: + initialWeights = [0.0] * len(data.first().features) + else: + if intercept: + initialWeights = [0.0] * (len(data.first().features) + 1) * (numClasses - 1) + else: + initialWeights = [0.0] * len(data.first().features) * (numClasses - 1) return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights) -class SVMModel(LinearBinaryClassificationModel): +class SVMModel(LinearClassificationModel): """A support vector machine. @@ -325,7 +403,8 @@ class SVMWithSGD(object): @classmethod def train(cls, data, iterations=100, step=1.0, regParam=0.01, - miniBatchFraction=1.0, initialWeights=None, regType="l2", intercept=False): + miniBatchFraction=1.0, initialWeights=None, regType="l2", + intercept=False, validateData=True): """ Train a support vector machine on the given data. @@ -351,11 +430,14 @@ def train(cls, data, iterations=100, step=1.0, regParam=0.01, or not of the augmented representation for training data (i.e. whether bias features are activated or not). + :param validateData: Boolean parameter which indicates if the + algorithm should validate data before training. + (default: True) """ def train(rdd, i): return callMLlibFunc("trainSVMModelWithSGD", rdd, int(iterations), float(step), float(regParam), float(miniBatchFraction), i, regType, - bool(intercept)) + bool(intercept), bool(validateData)) return _regression_train_wrapper(train, SVMModel, data, initialWeights) diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py index 209f1ee473b5b..cd7310a64f4ae 100644 --- a/python/pyspark/mllib/regression.py +++ b/python/pyspark/mllib/regression.py @@ -167,13 +167,19 @@ def load(cls, sc, path): # return the result of a call to the appropriate JVM stub. # _regression_train_wrapper is responsible for setup and error checking. def _regression_train_wrapper(train_func, modelClass, data, initial_weights): + from pyspark.mllib.classification import LogisticRegressionModel first = data.first() if not isinstance(first, LabeledPoint): raise ValueError("data should be an RDD of LabeledPoint, but got %s" % first) if initial_weights is None: initial_weights = [0.0] * len(data.first().features) - weights, intercept = train_func(data, _convert_to_vector(initial_weights)) - return modelClass(weights, intercept) + if (modelClass == LogisticRegressionModel): + weights, intercept, numFeatures, numClasses = train_func( + data, _convert_to_vector(initial_weights)) + return modelClass(weights, intercept, numFeatures, numClasses) + else: + weights, intercept = train_func(data, _convert_to_vector(initial_weights)) + return modelClass(weights, intercept) class LinearRegressionWithSGD(object): From beebb7ffc21c66ae3e4c615555194d1e19ede1bb Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Tue, 31 Mar 2015 11:34:29 -0700 Subject: [PATCH 115/591] [SPARK-5371][SQL] Propagate types after function conversion, before futher resolution Before it was possible for a query to flip back and forth from a resolved state, allowing resolution to propagate up before coercion had stabilized. The issue was that `ResolvedReferences` would run after `FunctionArgumentConversion`, but before `PropagateTypes` had run. This PR ensures we correctly `PropagateTypes` after any coercion has applied. Author: Michael Armbrust Closes #5278 from marmbrus/unionNull and squashes the following commits: dc3581a [Michael Armbrust] [SPARK-5371][SQL] Propogate types after function conversion / before futher resolution --- .../catalyst/analysis/HiveTypeCoercion.scala | 1 + .../plans/logical/basicOperators.scala | 2 +- .../sql/hive/execution/SQLQuerySuite.scala | 26 ++++++++++++++++++- 3 files changed, 27 insertions(+), 2 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala index 34ef7d28cc7f2..3c7b46e0702a2 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala @@ -78,6 +78,7 @@ trait HiveTypeCoercion { FunctionArgumentConversion :: CaseWhenCoercion :: Division :: + PropagateTypes :: Nil /** diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala index 190209238a4a5..8633e06093cf3 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala @@ -80,7 +80,7 @@ case class Union(left: LogicalPlan, right: LogicalPlan) extends BinaryNode { override lazy val resolved: Boolean = childrenResolved && - !left.output.zip(right.output).exists { case (l,r) => l.dataType != r.dataType } + left.output.zip(right.output).forall { case (l,r) => l.dataType == r.dataType } override def statistics: Statistics = { val sizeInBytes = left.statistics.sizeInBytes + right.statistics.sizeInBytes diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala index 2f50a33448462..2065f0d60d92f 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala @@ -41,8 +41,32 @@ case class NestedArray1(a: NestedArray2) */ class SQLQuerySuite extends QueryTest { + test("SPARK-5371: union with null and sum") { + val df = Seq((1, 1)).toDF("c1", "c2") + df.registerTempTable("table1") + + val query = sql( + """ + |SELECT + | MIN(c1), + | MIN(c2) + |FROM ( + | SELECT + | SUM(c1) c1, + | NULL c2 + | FROM table1 + | UNION ALL + | SELECT + | NULL c1, + | SUM(c2) c2 + | FROM table1 + |) a + """.stripMargin) + checkAnswer(query, Row(1, 1) :: Nil) + } + test("explode nested Field") { - Seq(NestedArray1(NestedArray2(Seq(1,2,3)))).toDF.registerTempTable("nestedArray") + Seq(NestedArray1(NestedArray2(Seq(1, 2, 3)))).toDF.registerTempTable("nestedArray") checkAnswer( sql("SELECT ints FROM nestedArray LATERAL VIEW explode(a.b) a AS ints"), Row(1) :: Row(2) :: Row(3) :: Nil) From 2036bc5993022da550f0cb1c0485ae92ec3e6fb0 Mon Sep 17 00:00:00 2001 From: Liang-Chi Hsieh Date: Tue, 31 Mar 2015 13:18:07 -0700 Subject: [PATCH 116/591] [SPARK-6633][SQL] Should be "Contains" instead of "EndsWith" when constructing sources.StringContains Author: Liang-Chi Hsieh Closes #5299 from viirya/stringcontains and squashes the following commits: c1ece4c [Liang-Chi Hsieh] Should be Contains instead of EndsWith. --- .../scala/org/apache/spark/sql/sources/DataSourceStrategy.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala index 83b603a4bb245..e13759b7feb7b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala @@ -173,7 +173,7 @@ private[sql] object DataSourceStrategy extends Strategy { case expressions.EndsWith(a: Attribute, Literal(v: String, StringType)) => Some(sources.StringEndsWith(a.name, v)) - case expressions.EndsWith(a: Attribute, Literal(v: String, StringType)) => + case expressions.Contains(a: Attribute, Literal(v: String, StringType)) => Some(sources.StringContains(a.name, v)) case _ => None From 0e00f12d33d28d064c166262b14e012a1aeaa7b0 Mon Sep 17 00:00:00 2001 From: MechCoder Date: Tue, 31 Mar 2015 16:01:08 -0700 Subject: [PATCH 117/591] [SPARK-5692] [MLlib] Word2Vec save/load Word2Vec model now supports saving and loading. a] The Metadata stored in JSON format consists of "version", "classname", "vectorSize" and "numWords" b] The data stored in Parquet file format consists of an Array of rows with each row consisting of 2 columns, first being the word: String and the second, an Array of Floats. Author: MechCoder Closes #5291 from MechCoder/spark-5692 and squashes the following commits: 1142f3a [MechCoder] Add numWords to metaData bfe4c39 [MechCoder] [SPARK-5692] Word2Vec save/load --- .../apache/spark/mllib/feature/Word2Vec.scala | 87 ++++++++++++++++++- .../spark/mllib/feature/Word2VecSuite.scala | 26 ++++++ 2 files changed, 110 insertions(+), 3 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala index 59a79e5c6a4ac..9ee7e4a66b535 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala @@ -25,14 +25,21 @@ import scala.collection.mutable.ArrayBuilder import com.github.fommil.netlib.BLAS.{getInstance => blas} +import org.json4s.DefaultFormats +import org.json4s.JsonDSL._ +import org.json4s.jackson.JsonMethods._ + import org.apache.spark.Logging +import org.apache.spark.SparkContext import org.apache.spark.SparkContext._ import org.apache.spark.annotation.Experimental import org.apache.spark.api.java.JavaRDD import org.apache.spark.mllib.linalg.{Vector, Vectors} +import org.apache.spark.mllib.util.{Loader, Saveable} import org.apache.spark.rdd._ import org.apache.spark.util.Utils import org.apache.spark.util.random.XORShiftRandom +import org.apache.spark.sql.{SQLContext, Row} /** * Entry in vocabulary @@ -422,7 +429,7 @@ class Word2Vec extends Serializable with Logging { */ @Experimental class Word2VecModel private[mllib] ( - private val model: Map[String, Array[Float]]) extends Serializable { + private val model: Map[String, Array[Float]]) extends Serializable with Saveable { private def cosineSimilarity(v1: Array[Float], v2: Array[Float]): Double = { require(v1.length == v2.length, "Vectors should have the same length") @@ -432,7 +439,13 @@ class Word2VecModel private[mllib] ( if (norm1 == 0 || norm2 == 0) return 0.0 blas.sdot(n, v1, 1, v2,1) / norm1 / norm2 } - + + override protected def formatVersion = "1.0" + + def save(sc: SparkContext, path: String): Unit = { + Word2VecModel.SaveLoadV1_0.save(sc, path, model) + } + /** * Transforms a word to its vector representation * @param word a word @@ -475,7 +488,7 @@ class Word2VecModel private[mllib] ( .tail .toArray } - + /** * Returns a map of words to their vector representations. */ @@ -483,3 +496,71 @@ class Word2VecModel private[mllib] ( model } } + +@Experimental +object Word2VecModel extends Loader[Word2VecModel] { + + private object SaveLoadV1_0 { + + val formatVersionV1_0 = "1.0" + + val classNameV1_0 = "org.apache.spark.mllib.feature.Word2VecModel" + + case class Data(word: String, vector: Array[Float]) + + def load(sc: SparkContext, path: String): Word2VecModel = { + val dataPath = Loader.dataPath(path) + val sqlContext = new SQLContext(sc) + val dataFrame = sqlContext.parquetFile(dataPath) + + val dataArray = dataFrame.select("word", "vector").collect() + + // Check schema explicitly since erasure makes it hard to use match-case for checking. + Loader.checkSchema[Data](dataFrame.schema) + + val word2VecMap = dataArray.map(i => (i.getString(0), i.getSeq[Float](1).toArray)).toMap + new Word2VecModel(word2VecMap) + } + + def save(sc: SparkContext, path: String, model: Map[String, Array[Float]]) = { + + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + + val vectorSize = model.values.head.size + val numWords = model.size + val metadata = compact(render + (("class" -> classNameV1_0) ~ ("version" -> formatVersionV1_0) ~ + ("vectorSize" -> vectorSize) ~ ("numWords" -> numWords))) + sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path)) + + val dataArray = model.toSeq.map { case (w, v) => Data(w, v) } + sc.parallelize(dataArray.toSeq, 1).toDF().saveAsParquetFile(Loader.dataPath(path)) + } + } + + override def load(sc: SparkContext, path: String): Word2VecModel = { + + val (loadedClassName, loadedVersion, metadata) = Loader.loadMetadata(sc, path) + implicit val formats = DefaultFormats + val expectedVectorSize = (metadata \ "vectorSize").extract[Int] + val expectedNumWords = (metadata \ "numWords").extract[Int] + val classNameV1_0 = SaveLoadV1_0.classNameV1_0 + (loadedClassName, loadedVersion) match { + case (classNameV1_0, "1.0") => + val model = SaveLoadV1_0.load(sc, path) + val vectorSize = model.getVectors.values.head.size + val numWords = model.getVectors.size + require(expectedVectorSize == vectorSize, + s"Word2VecModel requires each word to be mapped to a vector of size " + + s"$expectedVectorSize, got vector of size $vectorSize") + require(expectedNumWords == numWords, + s"Word2VecModel requires $expectedNumWords words, but got $numWords") + model + case _ => throw new Exception( + s"Word2VecModel.load did not recognize model with (className, format version):" + + s"($loadedClassName, $loadedVersion). Supported:\n" + + s" ($classNameV1_0, 1.0)") + } + } +} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala index 52278690dbd89..98a98a7599bcb 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/feature/Word2VecSuite.scala @@ -21,6 +21,9 @@ import org.scalatest.FunSuite import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.util.Utils + class Word2VecSuite extends FunSuite with MLlibTestSparkContext { // TODO: add more tests @@ -51,4 +54,27 @@ class Word2VecSuite extends FunSuite with MLlibTestSparkContext { assert(syms(0)._1 == "taiwan") assert(syms(1)._1 == "japan") } + + test("model load / save") { + + val word2VecMap = Map( + ("china", Array(0.50f, 0.50f, 0.50f, 0.50f)), + ("japan", Array(0.40f, 0.50f, 0.50f, 0.50f)), + ("taiwan", Array(0.60f, 0.50f, 0.50f, 0.50f)), + ("korea", Array(0.45f, 0.60f, 0.60f, 0.60f)) + ) + val model = new Word2VecModel(word2VecMap) + + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + + try { + model.save(sc, path) + val sameModel = Word2VecModel.load(sc, path) + assert(sameModel.getVectors.mapValues(_.toSeq) === model.getVectors.mapValues(_.toSeq)) + } finally { + Utils.deleteRecursively(tempDir) + } + + } } From 37326079d818fdb140415a65653767d997613dac Mon Sep 17 00:00:00 2001 From: Josh Rosen Date: Tue, 31 Mar 2015 16:18:39 -0700 Subject: [PATCH 118/591] [SPARK-6614] OutputCommitCoordinator should clear authorized committer only after authorized committer fails, not after any failure In OutputCommitCoordinator, there is some logic to clear the authorized committer's lock on committing in case that task fails. However, it looks like the current code also clears this lock if other non-authorized tasks fail, which is an obvious bug. In theory, it's possible that this could allow a new committer to start, run to completion, and commit output before the authorized committer finished, but it's unlikely that this race occurs often in practice due to the complex combination of failure and timing conditions that would be required to expose it. This patch addresses this issue and adds a regression test. Thanks to aarondav for spotting this issue. Author: Josh Rosen Closes #5276 from JoshRosen/SPARK-6614 and squashes the following commits: d532ba7 [Josh Rosen] Check whether failed task was authorized committer cbb3784 [Josh Rosen] Add regression test for SPARK-6614 --- .../scheduler/OutputCommitCoordinator.scala | 8 +++--- .../OutputCommitCoordinatorSuite.scala | 25 +++++++++++++++++++ 2 files changed, 30 insertions(+), 3 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala index 17055e2f22d0d..9e29fd13821dc 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala @@ -113,9 +113,11 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf) extends Logging { logInfo( s"Task was denied committing, stage: $stage, partition: $partition, attempt: $attempt") case otherReason => - logDebug(s"Authorized committer $attempt (stage=$stage, partition=$partition) failed;" + - s" clearing lock") - authorizedCommitters.remove(partition) + if (authorizedCommitters.get(partition).exists(_ == attempt)) { + logDebug(s"Authorized committer $attempt (stage=$stage, partition=$partition) failed;" + + s" clearing lock") + authorizedCommitters.remove(partition) + } } } diff --git a/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorSuite.scala index c8c957856247a..cf97707946706 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/OutputCommitCoordinatorSuite.scala @@ -161,6 +161,31 @@ class OutputCommitCoordinatorSuite extends FunSuite with BeforeAndAfter { } assert(tempDir.list().size === 0) } + + test("Only authorized committer failures can clear the authorized committer lock (SPARK-6614)") { + val stage: Int = 1 + val partition: Long = 2 + val authorizedCommitter: Long = 3 + val nonAuthorizedCommitter: Long = 100 + outputCommitCoordinator.stageStart(stage) + assert(outputCommitCoordinator.canCommit(stage, partition, attempt = authorizedCommitter)) + assert(!outputCommitCoordinator.canCommit(stage, partition, attempt = nonAuthorizedCommitter)) + // The non-authorized committer fails + outputCommitCoordinator.taskCompleted( + stage, partition, attempt = nonAuthorizedCommitter, reason = TaskKilled) + // New tasks should still not be able to commit because the authorized committer has not failed + assert( + !outputCommitCoordinator.canCommit(stage, partition, attempt = nonAuthorizedCommitter + 1)) + // The authorized committer now fails, clearing the lock + outputCommitCoordinator.taskCompleted( + stage, partition, attempt = authorizedCommitter, reason = TaskKilled) + // A new task should now be allowed to become the authorized committer + assert( + outputCommitCoordinator.canCommit(stage, partition, attempt = nonAuthorizedCommitter + 2)) + // There can only be one authorized committer + assert( + !outputCommitCoordinator.canCommit(stage, partition, attempt = nonAuthorizedCommitter + 3)) + } } /** From 305abe1e57450f49e3ec4dffb073c5adf17cadef Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Tue, 31 Mar 2015 18:31:36 -0700 Subject: [PATCH 119/591] [Doc] Improve Python DataFrame documentation Author: Reynold Xin Closes #5287 from rxin/pyspark-df-doc-cleanup-context and squashes the following commits: 1841b60 [Reynold Xin] Lint. f2007f1 [Reynold Xin] functions and types. bc3b72b [Reynold Xin] More improvements to DataFrame Python doc. ac1d4c0 [Reynold Xin] Bug fix. b163365 [Reynold Xin] Python fix. Added Experimental flag to DataFrameNaFunctions. 608422d [Reynold Xin] [Doc] Cleanup context.py Python docs. --- python/pyspark/sql/__init__.py | 4 +- python/pyspark/sql/context.py | 227 ++++++---------- python/pyspark/sql/dataframe.py | 249 +++++++++--------- python/pyspark/sql/functions.py | 6 +- python/pyspark/sql/types.py | 154 +++-------- .../spark/sql/DataFrameNaFunctions.scala | 3 + 6 files changed, 253 insertions(+), 390 deletions(-) diff --git a/python/pyspark/sql/__init__.py b/python/pyspark/sql/__init__.py index 9d39e5d9c2449..65abb24eed823 100644 --- a/python/pyspark/sql/__init__.py +++ b/python/pyspark/sql/__init__.py @@ -16,7 +16,7 @@ # """ -public classes of Spark SQL: +Important classes of Spark SQL and DataFrames: - L{SQLContext} Main entry point for :class:`DataFrame` and SQL functionality. @@ -34,6 +34,8 @@ Methods for handling missing data (null values). - L{functions} List of built-in functions available for :class:`DataFrame`. + - L{types} + List of data types available. """ from pyspark.sql.context import SQLContext, HiveContext diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py index 80939a1f8ab1e..c2d81ba804110 100644 --- a/python/pyspark/sql/context.py +++ b/python/pyspark/sql/context.py @@ -40,9 +40,9 @@ def _monkey_patch_RDD(sqlCtx): def toDF(self, schema=None, sampleRatio=None): """ - Convert current :class:`RDD` into a :class:`DataFrame` + Converts current :class:`RDD` into a :class:`DataFrame` - This is a shorthand for `sqlCtx.createDataFrame(rdd, schema, sampleRatio)` + This is a shorthand for ``sqlCtx.createDataFrame(rdd, schema, sampleRatio)`` :param schema: a StructType or list of names of columns :param samplingRatio: the sample ratio of rows used for inferring @@ -56,49 +56,23 @@ def toDF(self, schema=None, sampleRatio=None): RDD.toDF = toDF -class UDFRegistration(object): - """Wrapper for register UDF""" - - def __init__(self, sqlCtx): - self.sqlCtx = sqlCtx - - def register(self, name, f, returnType=StringType()): - """Registers a lambda function as a UDF so it can be used in SQL statements. - - In addition to a name and the function itself, the return type can be optionally specified. - When the return type is not given it default to a string and conversion will automatically - be done. For any other return type, the produced object must match the specified type. - - >>> sqlCtx.udf.register("stringLengthString", lambda x: len(x)) - >>> sqlCtx.sql("SELECT stringLengthString('test')").collect() - [Row(c0=u'4')] - - >>> from pyspark.sql.types import IntegerType - >>> sqlCtx.udf.register("stringLengthInt", lambda x: len(x), IntegerType()) - >>> sqlCtx.sql("SELECT stringLengthInt('test')").collect() - [Row(c0=4)] - """ - return self.sqlCtx.registerFunction(name, f, returnType) - - class SQLContext(object): - """Main entry point for Spark SQL functionality. - A SQLContext can be used create L{DataFrame}, register L{DataFrame} as + A SQLContext can be used create :class:`DataFrame`, register :class:`DataFrame` as tables, execute SQL over tables, cache tables, and read parquet files. - """ - def __init__(self, sparkContext, sqlContext=None): - """Create a new SQLContext. - - It will add a method called `toDF` to :class:`RDD`, which could be - used to convert an RDD into a DataFrame, it's a shorthand for - :func:`SQLContext.createDataFrame`. + When created, :class:`SQLContext` adds a method called ``toDF`` to :class:`RDD`, + which could be used to convert an RDD into a DataFrame, it's a shorthand for + :func:`SQLContext.createDataFrame`. - :param sparkContext: The SparkContext to wrap. - :param sqlContext: An optional JVM Scala SQLContext. If set, we do not instatiate a new + :param sparkContext: The :class:`SparkContext` backing this SQLContext. + :param sqlContext: An optional JVM Scala SQLContext. If set, we do not instantiate a new SQLContext in the JVM, instead we make all calls to this object. + """ + + def __init__(self, sparkContext, sqlContext=None): + """Creates a new SQLContext. >>> from datetime import datetime >>> sqlCtx = SQLContext(sc) @@ -145,7 +119,7 @@ def getConf(self, key, defaultValue): @property def udf(self): - """Wrapper for register Python function as UDF """ + """Returns a :class:`UDFRegistration` for UDF registration.""" return UDFRegistration(self) def registerFunction(self, name, f, returnType=StringType()): @@ -155,6 +129,10 @@ def registerFunction(self, name, f, returnType=StringType()): When the return type is not given it default to a string and conversion will automatically be done. For any other return type, the produced object must match the specified type. + :param name: name of the UDF + :param samplingRatio: lambda function + :param returnType: a :class:`DataType` object + >>> sqlCtx.registerFunction("stringLengthString", lambda x: len(x)) >>> sqlCtx.sql("SELECT stringLengthString('test')").collect() [Row(c0=u'4')] @@ -163,6 +141,11 @@ def registerFunction(self, name, f, returnType=StringType()): >>> sqlCtx.registerFunction("stringLengthInt", lambda x: len(x), IntegerType()) >>> sqlCtx.sql("SELECT stringLengthInt('test')").collect() [Row(c0=4)] + + >>> from pyspark.sql.types import IntegerType + >>> sqlCtx.udf.register("stringLengthInt", lambda x: len(x), IntegerType()) + >>> sqlCtx.sql("SELECT stringLengthInt('test')").collect() + [Row(c0=4)] """ func = lambda _, it: imap(lambda x: f(*x), it) ser = AutoBatchedSerializer(PickleSerializer()) @@ -203,30 +186,7 @@ def _inferSchema(self, rdd, samplingRatio=None): return schema def inferSchema(self, rdd, samplingRatio=None): - """Infer and apply a schema to an RDD of L{Row}. - - ::note: - Deprecated in 1.3, use :func:`createDataFrame` instead - - When samplingRatio is specified, the schema is inferred by looking - at the types of each row in the sampled dataset. Otherwise, the - first 100 rows of the RDD are inspected. Nested collections are - supported, which can include array, dict, list, Row, tuple, - namedtuple, or object. - - Each row could be L{pyspark.sql.Row} object or namedtuple or objects. - Using top level dicts is deprecated, as dict is used to represent Maps. - - If a single column has multiple distinct inferred types, it may cause - runtime exceptions. - - >>> rdd = sc.parallelize( - ... [Row(field1=1, field2="row1"), - ... Row(field1=2, field2="row2"), - ... Row(field1=3, field2="row3")]) - >>> df = sqlCtx.inferSchema(rdd) - >>> df.collect()[0] - Row(field1=1, field2=u'row1') + """::note: Deprecated in 1.3, use :func:`createDataFrame` instead. """ warnings.warn("inferSchema is deprecated, please use createDataFrame instead") @@ -236,27 +196,7 @@ def inferSchema(self, rdd, samplingRatio=None): return self.createDataFrame(rdd, None, samplingRatio) def applySchema(self, rdd, schema): - """ - Applies the given schema to the given RDD of L{tuple} or L{list}. - - ::note: - Deprecated in 1.3, use :func:`createDataFrame` instead - - These tuples or lists can contain complex nested structures like - lists, maps or nested rows. - - The schema should be a StructType. - - It is important that the schema matches the types of the objects - in each row or exceptions could be thrown at runtime. - - >>> from pyspark.sql.types import * - >>> rdd2 = sc.parallelize([(1, "row1"), (2, "row2"), (3, "row3")]) - >>> schema = StructType([StructField("field1", IntegerType(), False), - ... StructField("field2", StringType(), False)]) - >>> df = sqlCtx.applySchema(rdd2, schema) - >>> df.collect() - [Row(field1=1, field2=u'row1'),..., Row(field1=3, field2=u'row3')] + """::note: Deprecated in 1.3, use :func:`createDataFrame` instead. """ warnings.warn("applySchema is deprecated, please use createDataFrame instead") @@ -270,25 +210,23 @@ def applySchema(self, rdd, schema): def createDataFrame(self, data, schema=None, samplingRatio=None): """ - Create a DataFrame from an RDD of tuple/list, list or pandas.DataFrame. + Creates a :class:`DataFrame` from an :class:`RDD` of :class:`tuple`/:class:`list`, + list or :class:`pandas.DataFrame`. - `schema` could be :class:`StructType` or a list of column names. + When ``schema`` is a list of column names, the type of each column + will be inferred from ``data``. - When `schema` is a list of column names, the type of each column - will be inferred from `rdd`. + When ``schema`` is ``None``, it will try to infer the schema (column names and types) + from ``data``, which should be an RDD of :class:`Row`, + or :class:`namedtuple`, or :class:`dict`. - When `schema` is None, it will try to infer the column name and type - from `rdd`, which should be an RDD of :class:`Row`, or namedtuple, - or dict. + If schema inference is needed, ``samplingRatio`` is used to determined the ratio of + rows used for schema inference. The first row will be used if ``samplingRatio`` is ``None``. - If referring needed, `samplingRatio` is used to determined how many - rows will be used to do referring. The first row will be used if - `samplingRatio` is None. - - :param data: an RDD of Row/tuple/list/dict, list, or pandas.DataFrame - :param schema: a StructType or list of names of columns + :param data: an RDD of :class:`Row`/:class:`tuple`/:class:`list`/:class:`dict`, + :class:`list`, or :class:`pandas.DataFrame`. + :param schema: a :class:`StructType` or list of column names. default None. :param samplingRatio: the sample ratio of rows used for inferring - :return: a DataFrame >>> l = [('Alice', 1)] >>> sqlCtx.createDataFrame(l).collect() @@ -373,22 +311,20 @@ def createDataFrame(self, data, schema=None, samplingRatio=None): df = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), schema.json()) return DataFrame(df, self) - def registerDataFrameAsTable(self, rdd, tableName): - """Registers the given RDD as a temporary table in the catalog. + def registerDataFrameAsTable(self, df, tableName): + """Registers the given :class:`DataFrame` as a temporary table in the catalog. - Temporary tables exist only during the lifetime of this instance of - SQLContext. + Temporary tables exist only during the lifetime of this instance of :class:`SQLContext`. >>> sqlCtx.registerDataFrameAsTable(df, "table1") """ - if (rdd.__class__ is DataFrame): - df = rdd._jdf - self._ssql_ctx.registerDataFrameAsTable(df, tableName) + if (df.__class__ is DataFrame): + self._ssql_ctx.registerDataFrameAsTable(df._jdf, tableName) else: raise ValueError("Can only register DataFrame as table") def parquetFile(self, *paths): - """Loads a Parquet file, returning the result as a L{DataFrame}. + """Loads a Parquet file, returning the result as a :class:`DataFrame`. >>> import tempfile, shutil >>> parquetFile = tempfile.mkdtemp() @@ -406,15 +342,10 @@ def parquetFile(self, *paths): return DataFrame(jdf, self) def jsonFile(self, path, schema=None, samplingRatio=1.0): - """ - Loads a text file storing one JSON object per line as a - L{DataFrame}. + """Loads a text file storing one JSON object per line as a :class:`DataFrame`. - If the schema is provided, applies the given schema to this - JSON dataset. - - Otherwise, it samples the dataset with ratio `samplingRatio` to - determine the schema. + If the schema is provided, applies the given schema to this JSON dataset. + Otherwise, it samples the dataset with ratio ``samplingRatio`` to determine the schema. >>> import tempfile, shutil >>> jsonFile = tempfile.mkdtemp() @@ -450,13 +381,10 @@ def jsonFile(self, path, schema=None, samplingRatio=1.0): return DataFrame(df, self) def jsonRDD(self, rdd, schema=None, samplingRatio=1.0): - """Loads an RDD storing one JSON object per string as a L{DataFrame}. - - If the schema is provided, applies the given schema to this - JSON dataset. + """Loads an RDD storing one JSON object per string as a :class:`DataFrame`. - Otherwise, it samples the dataset with ratio `samplingRatio` to - determine the schema. + If the schema is provided, applies the given schema to this JSON dataset. + Otherwise, it samples the dataset with ratio ``samplingRatio`` to determine the schema. >>> df1 = sqlCtx.jsonRDD(json) >>> df1.first() @@ -475,7 +403,6 @@ def jsonRDD(self, rdd, schema=None, samplingRatio=1.0): >>> df3 = sqlCtx.jsonRDD(json, schema) >>> df3.first() Row(field2=u'row1', field3=Row(field5=None)) - """ def func(iterator): @@ -496,11 +423,11 @@ def func(iterator): return DataFrame(df, self) def load(self, path=None, source=None, schema=None, **options): - """Returns the dataset in a data source as a DataFrame. + """Returns the dataset in a data source as a :class:`DataFrame`. - The data source is specified by the `source` and a set of `options`. - If `source` is not specified, the default data source configured by - spark.sql.sources.default will be used. + The data source is specified by the ``source`` and a set of ``options``. + If ``source`` is not specified, the default data source configured by + ``spark.sql.sources.default`` will be used. Optionally, a schema can be provided as the schema of the returned DataFrame. """ @@ -526,11 +453,11 @@ def createExternalTable(self, tableName, path=None, source=None, It returns the DataFrame associated with the external table. - The data source is specified by the `source` and a set of `options`. - If `source` is not specified, the default data source configured by - spark.sql.sources.default will be used. + The data source is specified by the ``source`` and a set of ``options``. + If ``source`` is not specified, the default data source configured by + ``spark.sql.sources.default`` will be used. - Optionally, a schema can be provided as the schema of the returned DataFrame and + Optionally, a schema can be provided as the schema of the returned :class:`DataFrame` and created external table. """ if path is not None: @@ -551,7 +478,7 @@ def createExternalTable(self, tableName, path=None, source=None, return DataFrame(df, self) def sql(self, sqlQuery): - """Return a L{DataFrame} representing the result of the given query. + """Returns a :class:`DataFrame` representing the result of the given query. >>> sqlCtx.registerDataFrameAsTable(df, "table1") >>> df2 = sqlCtx.sql("SELECT field1 AS f1, field2 as f2 from table1") @@ -561,7 +488,7 @@ def sql(self, sqlQuery): return DataFrame(self._ssql_ctx.sql(sqlQuery), self) def table(self, tableName): - """Returns the specified table as a L{DataFrame}. + """Returns the specified table as a :class:`DataFrame`. >>> sqlCtx.registerDataFrameAsTable(df, "table1") >>> df2 = sqlCtx.table("table1") @@ -571,12 +498,12 @@ def table(self, tableName): return DataFrame(self._ssql_ctx.table(tableName), self) def tables(self, dbName=None): - """Returns a DataFrame containing names of tables in the given database. + """Returns a :class:`DataFrame` containing names of tables in the given database. - If `dbName` is not specified, the current database will be used. + If ``dbName`` is not specified, the current database will be used. - The returned DataFrame has two columns, tableName and isTemporary - (a column with BooleanType indicating if a table is a temporary one or not). + The returned DataFrame has two columns: ``tableName`` and ``isTemporary`` + (a column with :class:`BooleanType` indicating if a table is a temporary one or not). >>> sqlCtx.registerDataFrameAsTable(df, "table1") >>> df2 = sqlCtx.tables() @@ -589,9 +516,9 @@ def tables(self, dbName=None): return DataFrame(self._ssql_ctx.tables(dbName), self) def tableNames(self, dbName=None): - """Returns a list of names of tables in the database `dbName`. + """Returns a list of names of tables in the database ``dbName``. - If `dbName` is not specified, the current database will be used. + If ``dbName`` is not specified, the current database will be used. >>> sqlCtx.registerDataFrameAsTable(df, "table1") >>> "table1" in sqlCtx.tableNames() @@ -618,22 +545,18 @@ def clearCache(self): class HiveContext(SQLContext): - """A variant of Spark SQL that integrates with data stored in Hive. - Configuration for Hive is read from hive-site.xml on the classpath. + Configuration for Hive is read from ``hive-site.xml`` on the classpath. It supports running both SQL and HiveQL commands. + + :param sparkContext: The SparkContext to wrap. + :param hiveContext: An optional JVM Scala HiveContext. If set, we do not instantiate a new + :class:`HiveContext` in the JVM, instead we make all calls to this object. """ def __init__(self, sparkContext, hiveContext=None): - """Create a new HiveContext. - - :param sparkContext: The SparkContext to wrap. - :param hiveContext: An optional JVM Scala HiveContext. If set, we do not instatiate a new - HiveContext in the JVM, instead we make all calls to this object. - """ SQLContext.__init__(self, sparkContext) - if hiveContext: self._scala_HiveContext = hiveContext @@ -652,6 +575,18 @@ def _get_hive_ctx(self): return self._jvm.HiveContext(self._jsc.sc()) +class UDFRegistration(object): + """Wrapper for user-defined function registration.""" + + def __init__(self, sqlCtx): + self.sqlCtx = sqlCtx + + def register(self, name, f, returnType=StringType()): + return self.sqlCtx.registerFunction(name, f, returnType) + + register.__doc__ = SQLContext.registerFunction.__doc__ + + def _test(): import doctest from pyspark.context import SparkContext diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index 15508023326cc..c30326ebd133e 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -35,8 +35,7 @@ class DataFrame(object): - - """A collection of rows that have the same columns. + """A distributed collection of data grouped into named columns. A :class:`DataFrame` is equivalent to a relational table in Spark SQL, and can be created using various functions in :class:`SQLContext`:: @@ -69,9 +68,7 @@ def __init__(self, jdf, sql_ctx): @property def rdd(self): - """ - Return the content of the :class:`DataFrame` as an :class:`pyspark.RDD` - of :class:`Row` s. + """Returns the content as an :class:`pyspark.RDD` of :class:`Row`. """ if not hasattr(self, '_lazy_rdd'): jrdd = self._jdf.javaToPython() @@ -93,7 +90,9 @@ def na(self): return DataFrameNaFunctions(self) def toJSON(self, use_unicode=False): - """Convert a :class:`DataFrame` into a MappedRDD of JSON documents; one document per row. + """Converts a :class:`DataFrame` into a :class:`RDD` of string. + + Each row is turned into a JSON document as one element in the returned RDD. >>> df.toJSON().first() '{"age":2,"name":"Alice"}' @@ -102,10 +101,10 @@ def toJSON(self, use_unicode=False): return RDD(rdd.toJavaRDD(), self._sc, UTF8Deserializer(use_unicode)) def saveAsParquetFile(self, path): - """Save the contents as a Parquet file, preserving the schema. + """Saves the contents as a Parquet file, preserving the schema. Files that are written out using this method can be read back in as - a :class:`DataFrame` using the L{SQLContext.parquetFile} method. + a :class:`DataFrame` using :func:`SQLContext.parquetFile`. >>> import tempfile, shutil >>> parquetFile = tempfile.mkdtemp() @@ -120,8 +119,8 @@ def saveAsParquetFile(self, path): def registerTempTable(self, name): """Registers this RDD as a temporary table using the given name. - The lifetime of this temporary table is tied to the L{SQLContext} - that was used to create this DataFrame. + The lifetime of this temporary table is tied to the :class:`SQLContext` + that was used to create this :class:`DataFrame`. >>> df.registerTempTable("people") >>> df2 = sqlCtx.sql("select * from people") @@ -131,7 +130,7 @@ def registerTempTable(self, name): self._jdf.registerTempTable(name) def registerAsTable(self, name): - """DEPRECATED: use registerTempTable() instead""" + """DEPRECATED: use :func:`registerTempTable` instead""" warnings.warn("Use registerTempTable instead of registerAsTable.", DeprecationWarning) self.registerTempTable(name) @@ -162,22 +161,19 @@ def _java_save_mode(self, mode): return jmode def saveAsTable(self, tableName, source=None, mode="error", **options): - """Saves the contents of the :class:`DataFrame` to a data source as a table. + """Saves the contents of this :class:`DataFrame` to a data source as a table. - The data source is specified by the `source` and a set of `options`. - If `source` is not specified, the default data source configured by - spark.sql.sources.default will be used. + The data source is specified by the ``source`` and a set of ``options``. + If ``source`` is not specified, the default data source configured by + ``spark.sql.sources.default`` will be used. Additionally, mode is used to specify the behavior of the saveAsTable operation when table already exists in the data source. There are four modes: - * append: Contents of this :class:`DataFrame` are expected to be appended \ - to existing table. - * overwrite: Data in the existing table is expected to be overwritten by \ - the contents of this DataFrame. - * error: An exception is expected to be thrown. - * ignore: The save operation is expected to not save the contents of the \ - :class:`DataFrame` and to not change the existing table. + * `append`: Append contents of this :class:`DataFrame` to existing data. + * `overwrite`: Overwrite existing data. + * `error`: Throw an exception if data already exists. + * `ignore`: Silently ignore this operation if data already exists. """ if source is None: source = self.sql_ctx.getConf("spark.sql.sources.default", @@ -190,18 +186,17 @@ def saveAsTable(self, tableName, source=None, mode="error", **options): def save(self, path=None, source=None, mode="error", **options): """Saves the contents of the :class:`DataFrame` to a data source. - The data source is specified by the `source` and a set of `options`. - If `source` is not specified, the default data source configured by - spark.sql.sources.default will be used. + The data source is specified by the ``source`` and a set of ``options``. + If ``source`` is not specified, the default data source configured by + ``spark.sql.sources.default`` will be used. Additionally, mode is used to specify the behavior of the save operation when data already exists in the data source. There are four modes: - * append: Contents of this :class:`DataFrame` are expected to be appended to existing data. - * overwrite: Existing data is expected to be overwritten by the contents of this DataFrame. - * error: An exception is expected to be thrown. - * ignore: The save operation is expected to not save the contents of \ - the :class:`DataFrame` and to not change the existing data. + * `append`: Append contents of this :class:`DataFrame` to existing data. + * `overwrite`: Overwrite existing data. + * `error`: Throw an exception if data already exists. + * `ignore`: Silently ignore this operation if data already exists. """ if path is not None: options["path"] = path @@ -215,8 +210,7 @@ def save(self, path=None, source=None, mode="error", **options): @property def schema(self): - """Returns the schema of this :class:`DataFrame` (represented by - a L{StructType}). + """Returns the schema of this :class:`DataFrame` as a :class:`types.StructType`. >>> df.schema StructType(List(StructField(age,IntegerType,true),StructField(name,StringType,true))) @@ -237,11 +231,9 @@ def printSchema(self): print (self._jdf.schema().treeString()) def explain(self, extended=False): - """ - Prints the plans (logical and physical) to the console for - debugging purpose. + """Prints the (logical and physical) plans to the console for debugging purpose. - If extended is False, only prints the physical plan. + :param extended: boolean, default ``False``. If ``False``, prints only the physical plan. >>> df.explain() PhysicalRDD [age#0,name#1], MapPartitionsRDD[...] at mapPartitions at SQLContext.scala:... @@ -263,15 +255,13 @@ def explain(self, extended=False): print self._jdf.queryExecution().executedPlan().toString() def isLocal(self): - """ - Returns True if the `collect` and `take` methods can be run locally + """Returns ``True`` if the :func:`collect` and :func:`take` methods can be run locally (without any Spark executors). """ return self._jdf.isLocal() def show(self, n=20): - """ - Print the first n rows. + """Prints the first ``n`` rows to the console. >>> df DataFrame[age: int, name: string] @@ -286,11 +276,7 @@ def __repr__(self): return "DataFrame[%s]" % (", ".join("%s: %s" % c for c in self.dtypes)) def count(self): - """Return the number of elements in this RDD. - - Unlike the base RDD implementation of count, this implementation - leverages the query optimizer to compute the count on the DataFrame, - which supports features such as filter pushdown. + """Returns the number of rows in this :class:`DataFrame`. >>> df.count() 2L @@ -298,10 +284,7 @@ def count(self): return self._jdf.count() def collect(self): - """Return a list that contains all of the rows. - - Each object in the list is a Row, the fields can be accessed as - attributes. + """Returns all the records as a list of :class:`Row`. >>> df.collect() [Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')] @@ -313,7 +296,7 @@ def collect(self): return [cls(r) for r in rs] def limit(self, num): - """Limit the result count to the number specified. + """Limits the result count to the number specified. >>> df.limit(1).collect() [Row(age=2, name=u'Alice')] @@ -324,10 +307,7 @@ def limit(self, num): return DataFrame(jdf, self.sql_ctx) def take(self, num): - """Take the first num rows of the RDD. - - Each object in the list is a Row, the fields can be accessed as - attributes. + """Returns the first ``num`` rows as a :class:`list` of :class:`Row`. >>> df.take(2) [Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')] @@ -335,9 +315,9 @@ def take(self, num): return self.limit(num).collect() def map(self, f): - """ Return a new RDD by applying a function to each Row + """ Returns a new :class:`RDD` by applying a the ``f`` function to each :class:`Row`. - It's a shorthand for df.rdd.map() + This is a shorthand for ``df.rdd.map()``. >>> df.map(lambda p: p.name).collect() [u'Alice', u'Bob'] @@ -345,10 +325,10 @@ def map(self, f): return self.rdd.map(f) def flatMap(self, f): - """ Return a new RDD by first applying a function to all elements of this, + """ Returns a new :class:`RDD` by first applying the ``f`` function to each :class:`Row`, and then flattening the results. - It's a shorthand for df.rdd.flatMap() + This is a shorthand for ``df.rdd.flatMap()``. >>> df.flatMap(lambda p: p.name).collect() [u'A', u'l', u'i', u'c', u'e', u'B', u'o', u'b'] @@ -356,10 +336,9 @@ def flatMap(self, f): return self.rdd.flatMap(f) def mapPartitions(self, f, preservesPartitioning=False): - """ - Return a new RDD by applying a function to each partition. + """Returns a new :class:`RDD` by applying the ``f`` function to each partition. - It's a shorthand for df.rdd.mapPartitions() + This is a shorthand for ``df.rdd.mapPartitions()``. >>> rdd = sc.parallelize([1, 2, 3, 4], 4) >>> def f(iterator): yield 1 @@ -369,10 +348,9 @@ def mapPartitions(self, f, preservesPartitioning=False): return self.rdd.mapPartitions(f, preservesPartitioning) def foreach(self, f): - """ - Applies a function to all rows of this DataFrame. + """Applies the ``f`` function to all :class:`Row` of this :class:`DataFrame`. - It's a shorthand for df.rdd.foreach() + This is a shorthand for ``df.rdd.foreach()``. >>> def f(person): ... print person.name @@ -381,10 +359,9 @@ def foreach(self, f): return self.rdd.foreach(f) def foreachPartition(self, f): - """ - Applies a function to each partition of this DataFrame. + """Applies the ``f`` function to each partition of this :class:`DataFrame`. - It's a shorthand for df.rdd.foreachPartition() + This a shorthand for ``df.rdd.foreachPartition()``. >>> def f(people): ... for person in people: @@ -394,14 +371,14 @@ def foreachPartition(self, f): return self.rdd.foreachPartition(f) def cache(self): - """ Persist with the default storage level (C{MEMORY_ONLY_SER}). + """ Persists with the default storage level (C{MEMORY_ONLY_SER}). """ self.is_cached = True self._jdf.cache() return self def persist(self, storageLevel=StorageLevel.MEMORY_ONLY_SER): - """ Set the storage level to persist its values across operations + """Sets the storage level to persist its values across operations after the first time it is computed. This can only be used to assign a new storage level if the RDD does not have a storage level set yet. If no storage level is specified defaults to (C{MEMORY_ONLY_SER}). @@ -412,7 +389,7 @@ def persist(self, storageLevel=StorageLevel.MEMORY_ONLY_SER): return self def unpersist(self, blocking=True): - """ Mark it as non-persistent, and remove all blocks for it from + """Marks the :class:`DataFrame` as non-persistent, and remove all blocks for it from memory and disk. """ self.is_cached = False @@ -424,8 +401,7 @@ def unpersist(self, blocking=True): # return DataFrame(rdd, self.sql_ctx) def repartition(self, numPartitions): - """ Return a new :class:`DataFrame` that has exactly `numPartitions` - partitions. + """Returns a new :class:`DataFrame` that has exactly ``numPartitions`` partitions. >>> df.repartition(10).rdd.getNumPartitions() 10 @@ -433,8 +409,7 @@ def repartition(self, numPartitions): return DataFrame(self._jdf.repartition(numPartitions), self.sql_ctx) def distinct(self): - """ - Return a new :class:`DataFrame` containing the distinct rows in this DataFrame. + """Returns a new :class:`DataFrame` containing the distinct rows in this :class:`DataFrame`. >>> df.distinct().count() 2L @@ -442,8 +417,7 @@ def distinct(self): return DataFrame(self._jdf.distinct(), self.sql_ctx) def sample(self, withReplacement, fraction, seed=None): - """ - Return a sampled subset of this DataFrame. + """Returns a sampled subset of this :class:`DataFrame`. >>> df.sample(False, 0.5, 97).count() 1L @@ -455,7 +429,7 @@ def sample(self, withReplacement, fraction, seed=None): @property def dtypes(self): - """Return all column names and their data types as a list. + """Returns all column names and their data types as a list. >>> df.dtypes [('age', 'int'), ('name', 'string')] @@ -464,7 +438,7 @@ def dtypes(self): @property def columns(self): - """ Return all column names as a list. + """Returns all column names as a list. >>> df.columns [u'age', u'name'] @@ -472,13 +446,14 @@ def columns(self): return [f.name for f in self.schema.fields] def join(self, other, joinExprs=None, joinType=None): - """ - Join with another :class:`DataFrame`, using the given join expression. - The following performs a full outer join between `df1` and `df2`. + """Joins with another :class:`DataFrame`, using the given join expression. + + The following performs a full outer join between ``df1`` and ``df2``. :param other: Right side of the join :param joinExprs: Join expression - :param joinType: One of `inner`, `outer`, `left_outer`, `right_outer`, `semijoin`. + :param joinType: str, default 'inner'. + One of `inner`, `outer`, `left_outer`, `right_outer`, `semijoin`. >>> df.join(df2, df.name == df2.name, 'outer').select(df.name, df2.height).collect() [Row(name=None, height=80), Row(name=u'Bob', height=85), Row(name=u'Alice', height=None)] @@ -496,9 +471,9 @@ def join(self, other, joinExprs=None, joinType=None): return DataFrame(jdf, self.sql_ctx) def sort(self, *cols): - """ Return a new :class:`DataFrame` sorted by the specified column(s). + """Returns a new :class:`DataFrame` sorted by the specified column(s). - :param cols: The columns or expressions used for sorting + :param cols: list of :class:`Column` to sort by. >>> df.sort(df.age.desc()).collect() [Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')] @@ -539,7 +514,9 @@ def describe(self, *cols): return DataFrame(jdf, self.sql_ctx) def head(self, n=None): - """ Return the first `n` rows or the first row if n is None. + """ + Returns the first ``n`` rows as a list of :class:`Row`, + or the first :class:`Row` if ``n`` is ``None.`` >>> df.head() Row(age=2, name=u'Alice') @@ -552,7 +529,7 @@ def head(self, n=None): return self.take(n) def first(self): - """ Return the first row. + """Returns the first row as a :class:`Row`. >>> df.first() Row(age=2, name=u'Alice') @@ -560,7 +537,7 @@ def first(self): return self.head() def __getitem__(self, item): - """ Return the column by given name + """Returns the column as a :class:`Column`. >>> df.select(df['age']).collect() [Row(age=2), Row(age=5)] @@ -580,7 +557,7 @@ def __getitem__(self, item): raise IndexError("unexpected index: %s" % item) def __getattr__(self, name): - """ Return the column by given name + """Returns the :class:`Column` denoted by ``name``. >>> df.select(df.age).collect() [Row(age=2), Row(age=5)] @@ -591,7 +568,11 @@ def __getattr__(self, name): return Column(jc) def select(self, *cols): - """ Selecting a set of expressions. + """Projects a set of expressions and returns a new :class:`DataFrame`. + + :param cols: list of column names (string) or expressions (:class:`Column`). + If one of the column names is '*', that column is expanded to include all columns + in the current DataFrame. >>> df.select('*').collect() [Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')] @@ -606,9 +587,9 @@ def select(self, *cols): return DataFrame(jdf, self.sql_ctx) def selectExpr(self, *expr): - """ - Selects a set of SQL expressions. This is a variant of - `select` that accepts SQL expressions. + """Projects a set of SQL expressions and returns a new :class:`DataFrame`. + + This is a variant of :func:`select` that accepts SQL expressions. >>> df.selectExpr("age * 2", "abs(age)").collect() [Row((age * 2)=4, Abs(age)=2), Row((age * 2)=10, Abs(age)=5)] @@ -618,10 +599,12 @@ def selectExpr(self, *expr): return DataFrame(jdf, self.sql_ctx) def filter(self, condition): - """ Filtering rows using the given condition, which could be - :class:`Column` expression or string of SQL expression. + """Filters rows using the given condition. + + :func:`where` is an alias for :func:`filter`. - where() is an alias for filter(). + :param condition: a :class:`Column` of :class:`types.BooleanType` + or a string of SQL expression. >>> df.filter(df.age > 3).collect() [Row(age=5, name=u'Bob')] @@ -644,10 +627,13 @@ def filter(self, condition): where = filter def groupBy(self, *cols): - """ Group the :class:`DataFrame` using the specified columns, + """Groups the :class:`DataFrame` using the specified columns, so we can run aggregation on them. See :class:`GroupedData` for all the available aggregate functions. + :param cols: list of columns to group by. + Each element should be a column name (string) or an expression (:class:`Column`). + >>> df.groupBy().avg().collect() [Row(AVG(age)=3.5)] >>> df.groupBy('name').agg({'age': 'mean'}).collect() @@ -662,7 +648,7 @@ def groupBy(self, *cols): def agg(self, *exprs): """ Aggregate on the entire :class:`DataFrame` without groups - (shorthand for df.groupBy.agg()). + (shorthand for ``df.groupBy.agg()``). >>> df.agg({"age": "max"}).collect() [Row(MAX(age)=5)] @@ -699,7 +685,7 @@ def subtract(self, other): def dropna(self, how='any', thresh=None, subset=None): """Returns a new :class:`DataFrame` omitting rows with null values. - This is an alias for `na.drop`. + This is an alias for ``na.drop()``. :param how: 'any' or 'all'. If 'any', drop a row if it contains any nulls. @@ -735,7 +721,7 @@ def dropna(self, how='any', thresh=None, subset=None): return DataFrame(self._jdf.na().drop(thresh, cols), self.sql_ctx) def fillna(self, value, subset=None): - """Replace null values, alias for `na.fill`. + """Replace null values, alias for ``na.fill()``. :param value: int, long, float, string, or dict. Value to replace null values with. @@ -790,7 +776,10 @@ def fillna(self, value, subset=None): return DataFrame(self._jdf.na().fill(value, cols), self.sql_ctx) def withColumn(self, colName, col): - """ Return a new :class:`DataFrame` by adding a column. + """Returns a new :class:`DataFrame` by adding a column. + + :param colName: string, name of the new column. + :param col: a :class:`Column` expression for the new column. >>> df.withColumn('age2', df.age + 2).collect() [Row(age=2, name=u'Alice', age2=4), Row(age=5, name=u'Bob', age2=7)] @@ -798,7 +787,10 @@ def withColumn(self, colName, col): return self.select('*', col.alias(colName)) def withColumnRenamed(self, existing, new): - """ Rename an existing column to a new name + """REturns a new :class:`DataFrame` by renaming an existing column. + + :param existing: string, name of the existing column to rename. + :param col: string, new name of the column. >>> df.withColumnRenamed('age', 'age2').collect() [Row(age2=2, name=u'Alice'), Row(age2=5, name=u'Bob')] @@ -809,8 +801,9 @@ def withColumnRenamed(self, existing, new): return self.select(*cols) def toPandas(self): - """ - Collect all the rows and return a `pandas.DataFrame`. + """Returns the contents of this :class:`DataFrame` as Pandas ``pandas.DataFrame``. + + This is only available if Pandas is installed and available. >>> df.toPandas() # doctest: +SKIP age name @@ -823,8 +816,7 @@ def toPandas(self): # Having SchemaRDD for backward compatibility (for docs) class SchemaRDD(DataFrame): - """ - SchemaRDD is deprecated, please use DataFrame + """SchemaRDD is deprecated, please use :class:`DataFrame`. """ @@ -851,10 +843,9 @@ def _api(self, *args): class GroupedData(object): - """ A set of methods for aggregations on a :class:`DataFrame`, - created by DataFrame.groupBy(). + created by :func:`DataFrame.groupBy`. """ def __init__(self, jdf, sql_ctx): @@ -862,14 +853,17 @@ def __init__(self, jdf, sql_ctx): self.sql_ctx = sql_ctx def agg(self, *exprs): - """ Compute aggregates by specifying a map from column name - to aggregate methods. + """Compute aggregates and returns the result as a :class:`DataFrame`. + + The available aggregate functions are `avg`, `max`, `min`, `sum`, `count`. + + If ``exprs`` is a single :class:`dict` mapping from string to string, then the key + is the column to perform aggregation on, and the value is the aggregate function. - The available aggregate methods are `avg`, `max`, `min`, - `sum`, `count`. + Alternatively, ``exprs`` can also be a list of aggregate :class:`Column` expressions. - :param exprs: list or aggregate columns or a map from column - name to aggregate methods. + :param exprs: a dict mapping from column name (string) to aggregate functions (string), + or a list of :class:`Column`. >>> gdf = df.groupBy(df.name) >>> gdf.agg({"*": "count"}).collect() @@ -894,7 +888,7 @@ def agg(self, *exprs): @dfapi def count(self): - """ Count the number of rows for each group. + """Counts the number of records for each group. >>> df.groupBy(df.age).count().collect() [Row(age=2, count=1), Row(age=5, count=1)] @@ -902,8 +896,11 @@ def count(self): @df_varargs_api def mean(self, *cols): - """Compute the average value for each numeric columns - for each group. This is an alias for `avg`. + """Computes average values for each numeric columns for each group. + + :func:`mean` is an alias for :func:`avg`. + + :param cols: list of column names (string). Non-numeric columns are ignored. >>> df.groupBy().mean('age').collect() [Row(AVG(age)=3.5)] @@ -913,8 +910,11 @@ def mean(self, *cols): @df_varargs_api def avg(self, *cols): - """Compute the average value for each numeric columns - for each group. + """Computes average values for each numeric columns for each group. + + :func:`mean` is an alias for :func:`avg`. + + :param cols: list of column names (string). Non-numeric columns are ignored. >>> df.groupBy().avg('age').collect() [Row(AVG(age)=3.5)] @@ -924,8 +924,7 @@ def avg(self, *cols): @df_varargs_api def max(self, *cols): - """Compute the max value for each numeric columns for - each group. + """Computes the max value for each numeric columns for each group. >>> df.groupBy().max('age').collect() [Row(MAX(age)=5)] @@ -935,8 +934,9 @@ def max(self, *cols): @df_varargs_api def min(self, *cols): - """Compute the min value for each numeric column for - each group. + """Computes the min value for each numeric column for each group. + + :param cols: list of column names (string). Non-numeric columns are ignored. >>> df.groupBy().min('age').collect() [Row(MIN(age)=2)] @@ -946,8 +946,9 @@ def min(self, *cols): @df_varargs_api def sum(self, *cols): - """Compute the sum for each numeric columns for each - group. + """Compute the sum for each numeric columns for each group. + + :param cols: list of column names (string). Non-numeric columns are ignored. >>> df.groupBy().sum('age').collect() [Row(SUM(age)=7)] diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py index 5873f09ae3275..8a478fddf0e95 100644 --- a/python/pyspark/sql/functions.py +++ b/python/pyspark/sql/functions.py @@ -76,7 +76,7 @@ def _(col): def countDistinct(col, *cols): - """ Return a new Column for distinct count of `col` or `cols` + """Returns a new :class:`Column` for distinct count of ``col`` or ``cols``. >>> df.agg(countDistinct(df.age, df.name).alias('c')).collect() [Row(c=2)] @@ -91,7 +91,7 @@ def countDistinct(col, *cols): def approxCountDistinct(col, rsd=None): - """ Return a new Column for approximate distinct count of `col` + """Returns a new :class:`Column` for approximate distinct count of ``col``. >>> df.agg(approxCountDistinct(df.age).alias('c')).collect() [Row(c=2)] @@ -142,7 +142,7 @@ def __call__(self, *cols): def udf(f, returnType=StringType()): - """Create a user defined function (UDF) + """Creates a :class:`Column` expression representing a user defined function (UDF). >>> from pyspark.sql.types import IntegerType >>> slen = udf(lambda s: len(s), IntegerType()) diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py index 0169028ccc4eb..45eb8b945dcb0 100644 --- a/python/pyspark/sql/types.py +++ b/python/pyspark/sql/types.py @@ -33,8 +33,7 @@ class DataType(object): - - """Spark SQL DataType""" + """Base class for data types.""" def __repr__(self): return self.__class__.__name__ @@ -67,7 +66,6 @@ def json(self): # This singleton pattern does not work with pickle, you will get # another object after pickle and unpickle class PrimitiveTypeSingleton(type): - """Metaclass for PrimitiveType""" _instances = {} @@ -79,66 +77,45 @@ def __call__(cls): class PrimitiveType(DataType): - """Spark SQL PrimitiveType""" __metaclass__ = PrimitiveTypeSingleton class NullType(PrimitiveType): + """Null type. - """Spark SQL NullType - - The data type representing None, used for the types which has not - been inferred. + The data type representing None, used for the types that cannot be inferred. """ class StringType(PrimitiveType): - - """Spark SQL StringType - - The data type representing string values. + """String data type. """ class BinaryType(PrimitiveType): - - """Spark SQL BinaryType - - The data type representing bytearray values. + """Binary (byte array) data type. """ class BooleanType(PrimitiveType): - - """Spark SQL BooleanType - - The data type representing bool values. + """Boolean data type. """ class DateType(PrimitiveType): - - """Spark SQL DateType - - The data type representing datetime.date values. + """Date (datetime.date) data type. """ class TimestampType(PrimitiveType): - - """Spark SQL TimestampType - - The data type representing datetime.datetime values. + """Timestamp (datetime.datetime) data type. """ class DecimalType(DataType): - - """Spark SQL DecimalType - - The data type representing decimal.Decimal values. + """Decimal (decimal.Decimal) data type. """ def __init__(self, precision=None, scale=None): @@ -166,80 +143,55 @@ def __repr__(self): class DoubleType(PrimitiveType): - - """Spark SQL DoubleType - - The data type representing float values. + """Double data type, representing double precision floats. """ class FloatType(PrimitiveType): - - """Spark SQL FloatType - - The data type representing single precision floating-point values. + """Float data type, representing single precision floats. """ class ByteType(PrimitiveType): - - """Spark SQL ByteType - - The data type representing int values with 1 singed byte. + """Byte data type, i.e. a signed integer in a single byte. """ def simpleString(self): return 'tinyint' class IntegerType(PrimitiveType): - - """Spark SQL IntegerType - - The data type representing int values. + """Int data type, i.e. a signed 32-bit integer. """ def simpleString(self): return 'int' class LongType(PrimitiveType): + """Long data type, i.e. a signed 64-bit integer. - """Spark SQL LongType - - The data type representing long values. If the any value is - beyond the range of [-9223372036854775808, 9223372036854775807], - please use DecimalType. + If the values are beyond the range of [-9223372036854775808, 9223372036854775807], + please use :class:`DecimalType`. """ def simpleString(self): return 'bigint' class ShortType(PrimitiveType): - - """Spark SQL ShortType - - The data type representing int values with 2 signed bytes. + """Short data type, i.e. a signed 16-bit integer. """ def simpleString(self): return 'smallint' class ArrayType(DataType): + """Array data type. - """Spark SQL ArrayType - - The data type representing list values. An ArrayType object - comprises two fields, elementType (a DataType) and containsNull (a bool). - The field of elementType is used to specify the type of array elements. - The field of containsNull is used to specify if the array has None values. - + :param elementType: :class:`DataType` of each element in the array. + :param containsNull: boolean, whether the array can contain null (None) values. """ def __init__(self, elementType, containsNull=True): - """Creates an ArrayType - - :param elementType: the data type of elements. - :param containsNull: indicates whether the list contains None values. - + """ >>> ArrayType(StringType()) == ArrayType(StringType(), True) True >>> ArrayType(StringType(), False) == ArrayType(StringType()) @@ -268,29 +220,17 @@ def fromJson(cls, json): class MapType(DataType): + """Map data type. - """Spark SQL MapType - - The data type representing dict values. A MapType object comprises - three fields, keyType (a DataType), valueType (a DataType) and - valueContainsNull (a bool). - - The field of keyType is used to specify the type of keys in the map. - The field of valueType is used to specify the type of values in the map. - The field of valueContainsNull is used to specify if values of this - map has None values. - - For values of a MapType column, keys are not allowed to have None values. + :param keyType: :class:`DataType` of the keys in the map. + :param valueType: :class:`DataType` of the values in the map. + :param valueContainsNull: indicates whether values can contain null (None) values. + Keys in a map data type are not allowed to be null (None). """ def __init__(self, keyType, valueType, valueContainsNull=True): - """Creates a MapType - :param keyType: the data type of keys. - :param valueType: the data type of values. - :param valueContainsNull: indicates whether values contains - null values. - + """ >>> (MapType(StringType(), IntegerType()) ... == MapType(StringType(), IntegerType(), True)) True @@ -325,30 +265,16 @@ def fromJson(cls, json): class StructField(DataType): + """A field in :class:`StructType`. - """Spark SQL StructField - - Represents a field in a StructType. - A StructField object comprises three fields, name (a string), - dataType (a DataType) and nullable (a bool). The field of name - is the name of a StructField. The field of dataType specifies - the data type of a StructField. - - The field of nullable specifies if values of a StructField can - contain None values. - + :param name: string, name of the field. + :param dataType: :class:`DataType` of the field. + :param nullable: boolean, whether the field can be null (None) or not. + :param metadata: a dict from string to simple type that can be serialized to JSON automatically """ def __init__(self, name, dataType, nullable=True, metadata=None): - """Creates a StructField - :param name: the name of this field. - :param dataType: the data type of this field. - :param nullable: indicates whether values of this field - can be null. - :param metadata: metadata of this field, which is a map from string - to simple type that can be serialized to JSON - automatically - + """ >>> (StructField("f1", StringType(), True) ... == StructField("f1", StringType(), True)) True @@ -384,17 +310,13 @@ def fromJson(cls, json): class StructType(DataType): + """Struct type, consisting of a list of :class:`StructField`. - """Spark SQL StructType - - The data type representing rows. - A StructType object comprises a list of L{StructField}. - + This is the data type representing a :class:`Row`. """ def __init__(self, fields): - """Creates a StructType - + """ >>> struct1 = StructType([StructField("f1", StringType(), True)]) >>> struct2 = StructType([StructField("f1", StringType(), True)]) >>> struct1 == struct2 @@ -425,9 +347,9 @@ def fromJson(cls, json): class UserDefinedType(DataType): - """ + """User-defined type (UDT). + .. note:: WARN: Spark Internal Use Only - SQL User-Defined Type (UDT). """ @classmethod diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala index 3a3dc70f7285c..bf3c3fe876873 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala @@ -21,14 +21,17 @@ import java.{lang => jl} import scala.collection.JavaConversions._ +import org.apache.spark.annotation.Experimental import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.functions._ import org.apache.spark.sql.types._ /** + * :: Experimental :: * Functionality for working with missing data in [[DataFrame]]s. */ +@Experimental final class DataFrameNaFunctions private[sql](df: DataFrame) { /** From ff1915e12edc4d23e0b4e88933429c2d3470f3d9 Mon Sep 17 00:00:00 2001 From: Ilya Ganelin Date: Wed, 1 Apr 2015 11:09:00 +0100 Subject: [PATCH 120/591] [SPARK-4655][Core] Split Stage into ShuffleMapStage and ResultStage subclasses Hi all - this patch changes the Stage class to an abstract class and introduces two new classes that extend it: ShuffleMapStage and ResultStage - with the goal of increasing readability of the DAGScheduler class. Their usage is updated within DAGScheduler. Author: Ilya Ganelin Author: Ilya Ganelin Closes #4708 from ilganeli/SPARK-4655 and squashes the following commits: c248924 [Ilya Ganelin] Merge branch 'SPARK-4655' of github.com:ilganeli/spark into SPARK-4655 d930385 [Ilya Ganelin] Fixed merge conflict from a9a765f [Ilya Ganelin] Update DAGScheduler.scala c03563c [Ilya Ganelin] Minor fixeS c39e971 [Ilya Ganelin] Added return typing for public methods 845bc87 [Ilya Ganelin] Merge branch 'SPARK-4655' of github.com:ilganeli/spark into SPARK-4655 e8031d8 [Ilya Ganelin] Minor string fixes 4ec53ac [Ilya Ganelin] Merge remote-tracking branch 'upstream/master' into SPARK-4655 c004f62 [Ilya Ganelin] Update DAGScheduler.scala a2cb03f [Ilya Ganelin] [SPARK-4655] Replaced usages of Nil and eliminated some code reuse 3d5cf20 [Ilya Ganelin] [SPARK-4655] Moved mima exclude to 1.4 6912c55 [Ilya Ganelin] Resolved merge conflict 4bff208 [Ilya Ganelin] Minor stylistic fixes c6fffbb [Ilya Ganelin] newline 41402ad [Ilya Ganelin] Style fixes 02c6981 [Ilya Ganelin] Merge branch 'SPARK-4655' of github.com:ilganeli/spark into SPARK-4655 c755a09 [Ilya Ganelin] Some more stylistic updates and minor refactoring b6257a0 [Ilya Ganelin] Update MimaExcludes.scala 0f0c624 [Ilya Ganelin] Fixed merge conflict 2eba262 [Ilya Ganelin] Merge remote-tracking branch 'upstream/master' into SPARK-4655 6b43d7b [Ilya Ganelin] Got rid of some spaces 6f1a5db [Ilya Ganelin] Revert "More minor formatting and refactoring" 1b3471b [Ilya Ganelin] Merge remote-tracking branch 'upstream/master' into SPARK-4655 c9288e2 [Ilya Ganelin] More minor formatting and refactoring d548caf [Ilya Ganelin] Formatting fix c3ae5c2 [Ilya Ganelin] Explicit typing 0dacaf3 [Ilya Ganelin] Got rid of stale import 6da3a71 [Ilya Ganelin] Trailing whitespace b85c5fe [Ilya Ganelin] Added minor fixes a57dfcd [Ilya Ganelin] Added MiMA exclusion to get around binary compatibility check 83ed849 [Ilya Ganelin] moved braces for consistency 96dd161 [Ilya Ganelin] Fixed minor style error cfd6f10 [Ilya Ganelin] Updated DAGScheduler to use new ResultStage and ShuffleMapStage classes 83494e9 [Ilya Ganelin] Added new Stage classes --- .../apache/spark/scheduler/ActiveJob.scala | 2 +- .../apache/spark/scheduler/DAGScheduler.scala | 297 ++++++++++-------- .../apache/spark/scheduler/ResultStage.scala | 40 +++ .../spark/scheduler/ShuffleMapStage.scala | 84 +++++ .../org/apache/spark/scheduler/Stage.scala | 65 +--- project/MimaExcludes.scala | 6 +- 6 files changed, 298 insertions(+), 196 deletions(-) create mode 100644 core/src/main/scala/org/apache/spark/scheduler/ResultStage.scala create mode 100644 core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala diff --git a/core/src/main/scala/org/apache/spark/scheduler/ActiveJob.scala b/core/src/main/scala/org/apache/spark/scheduler/ActiveJob.scala index b755d8fb15757..50a69379412d2 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ActiveJob.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ActiveJob.scala @@ -27,7 +27,7 @@ import org.apache.spark.util.CallSite */ private[spark] class ActiveJob( val jobId: Int, - val finalStage: Stage, + val finalStage: ResultStage, val func: (TaskContext, Iterator[_]) => _, val partitions: Array[Int], val callSite: CallSite, diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala index b405bd3338e7c..d35b4f9dbaf88 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -83,7 +83,7 @@ class DAGScheduler( private[scheduler] val jobIdToStageIds = new HashMap[Int, HashSet[Int]] private[scheduler] val stageIdToStage = new HashMap[Int, Stage] - private[scheduler] val shuffleToMapStage = new HashMap[Int, Stage] + private[scheduler] val shuffleToMapStage = new HashMap[Int, ShuffleMapStage] private[scheduler] val jobIdToActiveJob = new HashMap[Int, ActiveJob] // Stages we need to run whose parents aren't done @@ -150,7 +150,7 @@ class DAGScheduler( result: Any, accumUpdates: Map[Long, Any], taskInfo: TaskInfo, - taskMetrics: TaskMetrics) { + taskMetrics: TaskMetrics): Unit = { eventProcessLoop.post( CompletionEvent(task, reason, result, accumUpdates, taskInfo, taskMetrics)) } @@ -173,18 +173,18 @@ class DAGScheduler( } // Called by TaskScheduler when an executor fails. - def executorLost(execId: String) { + def executorLost(execId: String): Unit = { eventProcessLoop.post(ExecutorLost(execId)) } // Called by TaskScheduler when a host is added - def executorAdded(execId: String, host: String) { + def executorAdded(execId: String, host: String): Unit = { eventProcessLoop.post(ExecutorAdded(execId, host)) } // Called by TaskScheduler to cancel an entire TaskSet due to either repeated failures or // cancellation of the job itself. - def taskSetFailed(taskSet: TaskSet, reason: String) { + def taskSetFailed(taskSet: TaskSet, reason: String): Unit = { eventProcessLoop.post(TaskSetFailed(taskSet, reason)) } @@ -210,40 +210,65 @@ class DAGScheduler( * The jobId value passed in will be used if the stage doesn't already exist with * a lower jobId (jobId always increases across jobs.) */ - private def getShuffleMapStage(shuffleDep: ShuffleDependency[_, _, _], jobId: Int): Stage = { + private def getShuffleMapStage( + shuffleDep: ShuffleDependency[_, _, _], + jobId: Int): ShuffleMapStage = { shuffleToMapStage.get(shuffleDep.shuffleId) match { case Some(stage) => stage case None => // We are going to register ancestor shuffle dependencies registerShuffleDependencies(shuffleDep, jobId) // Then register current shuffleDep - val stage = - newOrUsedStage( - shuffleDep.rdd, shuffleDep.rdd.partitions.size, shuffleDep, jobId, - shuffleDep.rdd.creationSite) + val stage = newOrUsedShuffleStage(shuffleDep, jobId) shuffleToMapStage(shuffleDep.shuffleId) = stage - + stage } } /** - * Create a Stage -- either directly for use as a result stage, or as part of the (re)-creation - * of a shuffle map stage in newOrUsedStage. The stage will be associated with the provided - * jobId. Production of shuffle map stages should always use newOrUsedStage, not newStage - * directly. + * Helper function to eliminate some code re-use when creating new stages. */ - private def newStage( + private def getParentStagesAndId(rdd: RDD[_], jobId: Int): (List[Stage], Int) = { + val parentStages = getParentStages(rdd, jobId) + val id = nextStageId.getAndIncrement() + (parentStages, id) + } + + /** + * Create a ShuffleMapStage as part of the (re)-creation of a shuffle map stage in + * newOrUsedShuffleStage. The stage will be associated with the provided jobId. + * Production of shuffle map stages should always use newOrUsedShuffleStage, not + * newShuffleMapStage directly. + */ + private def newShuffleMapStage( rdd: RDD[_], numTasks: Int, - shuffleDep: Option[ShuffleDependency[_, _, _]], + shuffleDep: ShuffleDependency[_, _, _], jobId: Int, - callSite: CallSite) - : Stage = - { - val parentStages = getParentStages(rdd, jobId) - val id = nextStageId.getAndIncrement() - val stage = new Stage(id, rdd, numTasks, shuffleDep, parentStages, jobId, callSite) + callSite: CallSite): ShuffleMapStage = { + val (parentStages: List[Stage], id: Int) = getParentStagesAndId(rdd, jobId) + val stage: ShuffleMapStage = new ShuffleMapStage(id, rdd, numTasks, parentStages, + jobId, callSite, shuffleDep) + + stageIdToStage(id) = stage + updateJobIdStageIdMaps(jobId, stage) + stage + } + + /** + * Create a ResultStage -- either directly for use as a result stage, or as part of the + * (re)-creation of a shuffle map stage in newOrUsedShuffleStage. The stage will be associated + * with the provided jobId. + */ + private def newResultStage( + rdd: RDD[_], + numTasks: Int, + jobId: Int, + callSite: CallSite): ResultStage = { + val (parentStages: List[Stage], id: Int) = getParentStagesAndId(rdd, jobId) + val stage: ResultStage = new ResultStage(id, rdd, numTasks, parentStages, jobId, callSite) + stageIdToStage(id) = stage updateJobIdStageIdMaps(jobId, stage) stage @@ -255,20 +280,17 @@ class DAGScheduler( * present in the MapOutputTracker, then the number and location of available outputs are * recovered from the MapOutputTracker */ - private def newOrUsedStage( - rdd: RDD[_], - numTasks: Int, + private def newOrUsedShuffleStage( shuffleDep: ShuffleDependency[_, _, _], - jobId: Int, - callSite: CallSite) - : Stage = - { - val stage = newStage(rdd, numTasks, Some(shuffleDep), jobId, callSite) + jobId: Int): ShuffleMapStage = { + val rdd = shuffleDep.rdd + val numTasks = rdd.partitions.size + val stage = newShuffleMapStage(rdd, numTasks, shuffleDep, jobId, rdd.creationSite) if (mapOutputTracker.containsShuffle(shuffleDep.shuffleId)) { val serLocs = mapOutputTracker.getSerializedMapOutputStatuses(shuffleDep.shuffleId) val locs = MapOutputTracker.deserializeMapStatuses(serLocs) for (i <- 0 until locs.size) { - stage.outputLocs(i) = Option(locs(i)).toList // locs(i) will be null if missing + stage.outputLocs(i) = Option(locs(i)).toList // locs(i) will be null if missing } stage.numAvailableOutputs = locs.count(_ != null) } else { @@ -306,26 +328,23 @@ class DAGScheduler( } } waitingForVisit.push(rdd) - while (!waitingForVisit.isEmpty) { + while (waitingForVisit.nonEmpty) { visit(waitingForVisit.pop()) } parents.toList } - // Find ancestor missing shuffle dependencies and register into shuffleToMapStage - private def registerShuffleDependencies(shuffleDep: ShuffleDependency[_, _, _], jobId: Int) = { + /** Find ancestor missing shuffle dependencies and register into shuffleToMapStage */ + private def registerShuffleDependencies(shuffleDep: ShuffleDependency[_, _, _], jobId: Int) { val parentsWithNoMapStage = getAncestorShuffleDependencies(shuffleDep.rdd) - while (!parentsWithNoMapStage.isEmpty) { + while (parentsWithNoMapStage.nonEmpty) { val currentShufDep = parentsWithNoMapStage.pop() - val stage = - newOrUsedStage( - currentShufDep.rdd, currentShufDep.rdd.partitions.size, currentShufDep, jobId, - currentShufDep.rdd.creationSite) + val stage = newOrUsedShuffleStage(currentShufDep, jobId) shuffleToMapStage(currentShufDep.shuffleId) = stage } } - // Find ancestor shuffle dependencies that are not registered in shuffleToMapStage yet + /** Find ancestor shuffle dependencies that are not registered in shuffleToMapStage yet */ private def getAncestorShuffleDependencies(rdd: RDD[_]): Stack[ShuffleDependency[_, _, _]] = { val parents = new Stack[ShuffleDependency[_, _, _]] val visited = new HashSet[RDD[_]] @@ -351,7 +370,7 @@ class DAGScheduler( } waitingForVisit.push(rdd) - while (!waitingForVisit.isEmpty) { + while (waitingForVisit.nonEmpty) { visit(waitingForVisit.pop()) } parents @@ -382,7 +401,7 @@ class DAGScheduler( } } waitingForVisit.push(stage.rdd) - while (!waitingForVisit.isEmpty) { + while (waitingForVisit.nonEmpty) { visit(waitingForVisit.pop()) } missing.toList @@ -392,7 +411,7 @@ class DAGScheduler( * Registers the given jobId among the jobs that need the given stage and * all of that stage's ancestors. */ - private def updateJobIdStageIdMaps(jobId: Int, stage: Stage) { + private def updateJobIdStageIdMaps(jobId: Int, stage: Stage): Unit = { def updateJobIdStageIdMapsList(stages: List[Stage]) { if (stages.nonEmpty) { val s = stages.head @@ -412,7 +431,7 @@ class DAGScheduler( * * @param job The job whose state to cleanup. */ - private def cleanupStateForJobAndIndependentStages(job: ActiveJob) { + private def cleanupStateForJobAndIndependentStages(job: ActiveJob): Unit = { val registeredStages = jobIdToStageIds.get(job.jobId) if (registeredStages.isEmpty || registeredStages.get.isEmpty) { logError("No stages registered for job " + job.jobId) @@ -474,8 +493,7 @@ class DAGScheduler( callSite: CallSite, allowLocal: Boolean, resultHandler: (Int, U) => Unit, - properties: Properties = null): JobWaiter[U] = - { + properties: Properties = null): JobWaiter[U] = { // Check to make sure we are not launching a task on a partition that does not exist. val maxPartitions = rdd.partitions.length partitions.find(p => p >= maxPartitions || p < 0).foreach { p => @@ -504,15 +522,13 @@ class DAGScheduler( callSite: CallSite, allowLocal: Boolean, resultHandler: (Int, U) => Unit, - properties: Properties = null) - { + properties: Properties = null): Unit = { val start = System.nanoTime val waiter = submitJob(rdd, func, partitions, callSite, allowLocal, resultHandler, properties) waiter.awaitResult() match { - case JobSucceeded => { + case JobSucceeded => logInfo("Job %d finished: %s, took %f s".format (waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9)) - } case JobFailed(exception: Exception) => logInfo("Job %d failed: %s, took %f s".format (waiter.jobId, callSite.shortForm, (System.nanoTime - start) / 1e9)) @@ -526,9 +542,7 @@ class DAGScheduler( evaluator: ApproximateEvaluator[U, R], callSite: CallSite, timeout: Long, - properties: Properties = null) - : PartialResult[R] = - { + properties: Properties = null): PartialResult[R] = { val listener = new ApproximateActionListener(rdd, func, evaluator, timeout) val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _] val partitions = (0 until rdd.partitions.size).toArray @@ -541,12 +555,12 @@ class DAGScheduler( /** * Cancel a job that is running or waiting in the queue. */ - def cancelJob(jobId: Int) { + def cancelJob(jobId: Int): Unit = { logInfo("Asked to cancel job " + jobId) eventProcessLoop.post(JobCancelled(jobId)) } - def cancelJobGroup(groupId: String) { + def cancelJobGroup(groupId: String): Unit = { logInfo("Asked to cancel job group " + groupId) eventProcessLoop.post(JobGroupCancelled(groupId)) } @@ -554,7 +568,7 @@ class DAGScheduler( /** * Cancel all jobs that are running or waiting in the queue. */ - def cancelAllJobs() { + def cancelAllJobs(): Unit = { eventProcessLoop.post(AllJobsCancelled) } @@ -722,13 +736,12 @@ class DAGScheduler( allowLocal: Boolean, callSite: CallSite, listener: JobListener, - properties: Properties = null) - { - var finalStage: Stage = null + properties: Properties = null) { + var finalStage: ResultStage = null try { // New stage creation may throw an exception if, for example, jobs are run on a // HadoopRDD whose underlying HDFS files have been deleted. - finalStage = newStage(finalRDD, partitions.size, None, jobId, callSite) + finalStage = newResultStage(finalRDD, partitions.size, jobId, callSite) } catch { case e: Exception => logWarning("Creating new stage failed due to exception - job: " + jobId, e) @@ -773,7 +786,7 @@ class DAGScheduler( if (!waitingStages(stage) && !runningStages(stage) && !failedStages(stage)) { val missing = getMissingParentStages(stage).sortBy(_.id) logDebug("missing: " + missing) - if (missing == Nil) { + if (missing.isEmpty) { logInfo("Submitting " + stage + " (" + stage.rdd + "), which has no missing parents") submitMissingTasks(stage, jobId.get) } else { @@ -794,13 +807,15 @@ class DAGScheduler( // Get our pending tasks and remember them in our pendingTasks entry stage.pendingTasks.clear() + // First figure out the indexes of partition ids to compute. val partitionsToCompute: Seq[Int] = { - if (stage.isShuffleMap) { - (0 until stage.numPartitions).filter(id => stage.outputLocs(id) == Nil) - } else { - val job = stage.resultOfJob.get - (0 until job.numPartitions).filter(id => !job.finished(id)) + stage match { + case stage: ShuffleMapStage => + (0 until stage.numPartitions).filter(id => stage.outputLocs(id).isEmpty) + case stage: ResultStage => + val job = stage.resultOfJob.get + (0 until job.numPartitions).filter(id => !job.finished(id)) } } @@ -830,18 +845,21 @@ class DAGScheduler( try { // For ShuffleMapTask, serialize and broadcast (rdd, shuffleDep). // For ResultTask, serialize and broadcast (rdd, func). - val taskBinaryBytes: Array[Byte] = - if (stage.isShuffleMap) { - closureSerializer.serialize((stage.rdd, stage.shuffleDep.get) : AnyRef).array() - } else { - closureSerializer.serialize((stage.rdd, stage.resultOfJob.get.func) : AnyRef).array() - } + val taskBinaryBytes: Array[Byte] = stage match { + case stage: ShuffleMapStage => + closureSerializer.serialize((stage.rdd, stage.shuffleDep): AnyRef).array() + case stage: ResultStage => + closureSerializer.serialize((stage.rdd, stage.resultOfJob.get.func): AnyRef).array() + } + taskBinary = sc.broadcast(taskBinaryBytes) } catch { // In the case of a failure during serialization, abort the stage. case e: NotSerializableException => abortStage(stage, "Task not serializable: " + e.toString) runningStages -= stage + + // Abort execution return case NonFatal(e) => abortStage(stage, s"Task serialization failed: $e\n${e.getStackTraceString}") @@ -849,20 +867,22 @@ class DAGScheduler( return } - val tasks: Seq[Task[_]] = if (stage.isShuffleMap) { - partitionsToCompute.map { id => - val locs = getPreferredLocs(stage.rdd, id) - val part = stage.rdd.partitions(id) - new ShuffleMapTask(stage.id, taskBinary, part, locs) - } - } else { - val job = stage.resultOfJob.get - partitionsToCompute.map { id => - val p: Int = job.partitions(id) - val part = stage.rdd.partitions(p) - val locs = getPreferredLocs(stage.rdd, p) - new ResultTask(stage.id, taskBinary, part, locs, id) - } + val tasks: Seq[Task[_]] = stage match { + case stage: ShuffleMapStage => + partitionsToCompute.map { id => + val locs = getPreferredLocs(stage.rdd, id) + val part = stage.rdd.partitions(id) + new ShuffleMapTask(stage.id, taskBinary, part, locs) + } + + case stage: ResultStage => + val job = stage.resultOfJob.get + partitionsToCompute.map { id => + val p: Int = job.partitions(id) + val part = stage.rdd.partitions(p) + val locs = getPreferredLocs(stage.rdd, p) + new ResultTask(stage.id, taskBinary, part, locs, id) + } } if (tasks.size > 0) { @@ -877,8 +897,17 @@ class DAGScheduler( // SparkListenerStageCompleted here in case there are no tasks to run. outputCommitCoordinator.stageEnd(stage.id) listenerBus.post(SparkListenerStageCompleted(stage.latestInfo)) - logDebug("Stage " + stage + " is actually done; %b %d %d".format( - stage.isAvailable, stage.numAvailableOutputs, stage.numPartitions)) + + val debugString = stage match { + case stage: ShuffleMapStage => + s"Stage ${stage} is actually done; " + + s"(available: ${stage.isAvailable}," + + s"available outputs: ${stage.numAvailableOutputs}," + + s"partitions: ${stage.numPartitions})" + case stage : ResultStage => + s"Stage ${stage} is actually done; (partitions: ${stage.numPartitions})" + } + logDebug(debugString) runningStages -= stage } } @@ -968,7 +997,10 @@ class DAGScheduler( stage.pendingTasks -= task task match { case rt: ResultTask[_, _] => - stage.resultOfJob match { + // Cast to ResultStage here because it's part of the ResultTask + // TODO Refactor this out to a function that accepts a ResultStage + val resultStage = stage.asInstanceOf[ResultStage] + resultStage.resultOfJob match { case Some(job) => if (!job.finished(rt.outputId)) { updateAccumulators(event) @@ -976,7 +1008,7 @@ class DAGScheduler( job.numFinished += 1 // If the whole job has finished, remove it if (job.numFinished == job.numPartitions) { - markStageAsFinished(stage) + markStageAsFinished(resultStage) cleanupStateForJobAndIndependentStages(job) listenerBus.post( SparkListenerJobEnd(job.jobId, clock.getTimeMillis(), JobSucceeded)) @@ -988,7 +1020,7 @@ class DAGScheduler( job.listener.taskSucceeded(rt.outputId, event.result) } catch { case e: Exception => - // TODO: Perhaps we want to mark the stage as failed? + // TODO: Perhaps we want to mark the resultStage as failed? job.listener.jobFailed(new SparkDriverExecutionException(e)) } } @@ -997,6 +1029,7 @@ class DAGScheduler( } case smt: ShuffleMapTask => + val shuffleStage = stage.asInstanceOf[ShuffleMapStage] updateAccumulators(event) val status = event.result.asInstanceOf[MapStatus] val execId = status.location.executorId @@ -1004,50 +1037,54 @@ class DAGScheduler( if (failedEpoch.contains(execId) && smt.epoch <= failedEpoch(execId)) { logInfo("Ignoring possibly bogus ShuffleMapTask completion from " + execId) } else { - stage.addOutputLoc(smt.partitionId, status) + shuffleStage.addOutputLoc(smt.partitionId, status) } - if (runningStages.contains(stage) && stage.pendingTasks.isEmpty) { - markStageAsFinished(stage) + if (runningStages.contains(shuffleStage) && shuffleStage.pendingTasks.isEmpty) { + markStageAsFinished(shuffleStage) logInfo("looking for newly runnable stages") logInfo("running: " + runningStages) logInfo("waiting: " + waitingStages) logInfo("failed: " + failedStages) - if (stage.shuffleDep.isDefined) { - // We supply true to increment the epoch number here in case this is a - // recomputation of the map outputs. In that case, some nodes may have cached - // locations with holes (from when we detected the error) and will need the - // epoch incremented to refetch them. - // TODO: Only increment the epoch number if this is not the first time - // we registered these map outputs. - mapOutputTracker.registerMapOutputs( - stage.shuffleDep.get.shuffleId, - stage.outputLocs.map(list => if (list.isEmpty) null else list.head).toArray, - changeEpoch = true) - } + + // We supply true to increment the epoch number here in case this is a + // recomputation of the map outputs. In that case, some nodes may have cached + // locations with holes (from when we detected the error) and will need the + // epoch incremented to refetch them. + // TODO: Only increment the epoch number if this is not the first time + // we registered these map outputs. + mapOutputTracker.registerMapOutputs( + shuffleStage.shuffleDep.shuffleId, + shuffleStage.outputLocs.map(list => if (list.isEmpty) null else list.head).toArray, + changeEpoch = true) + clearCacheLocs() - if (stage.outputLocs.exists(_ == Nil)) { - // Some tasks had failed; let's resubmit this stage + if (shuffleStage.outputLocs.contains(Nil)) { + // Some tasks had failed; let's resubmit this shuffleStage // TODO: Lower-level scheduler should also deal with this - logInfo("Resubmitting " + stage + " (" + stage.name + + logInfo("Resubmitting " + shuffleStage + " (" + shuffleStage.name + ") because some of its tasks had failed: " + - stage.outputLocs.zipWithIndex.filter(_._1 == Nil).map(_._2).mkString(", ")) - submitStage(stage) + shuffleStage.outputLocs.zipWithIndex.filter(_._1.isEmpty) + .map(_._2).mkString(", ")) + submitStage(shuffleStage) } else { val newlyRunnable = new ArrayBuffer[Stage] - for (stage <- waitingStages) { - logInfo("Missing parents for " + stage + ": " + getMissingParentStages(stage)) + for (shuffleStage <- waitingStages) { + logInfo("Missing parents for " + shuffleStage + ": " + + getMissingParentStages(shuffleStage)) } - for (stage <- waitingStages if getMissingParentStages(stage) == Nil) { - newlyRunnable += stage + for (shuffleStage <- waitingStages if getMissingParentStages(shuffleStage).isEmpty) + { + newlyRunnable += shuffleStage } waitingStages --= newlyRunnable runningStages ++= newlyRunnable for { - stage <- newlyRunnable.sortBy(_.id) - jobId <- activeJobForStage(stage) + shuffleStage <- newlyRunnable.sortBy(_.id) + jobId <- activeJobForStage(shuffleStage) } { - logInfo("Submitting " + stage + " (" + stage.rdd + "), which is now runnable") - submitMissingTasks(stage, jobId) + logInfo("Submitting " + shuffleStage + " (" + + shuffleStage.rdd + "), which is now runnable") + submitMissingTasks(shuffleStage, jobId) } } } @@ -1204,9 +1241,7 @@ class DAGScheduler( } } - /** - * Fails a job and all stages that are only used by that job, and cleans up relevant state. - */ + /** Fails a job and all stages that are only used by that job, and cleans up relevant state. */ private def failJobAndIndependentStages(job: ActiveJob, failureReason: String) { val error = new SparkException(failureReason) var ableToCancelStages = true @@ -1254,9 +1289,7 @@ class DAGScheduler( } } - /** - * Return true if one of stage's ancestors is target. - */ + /** Return true if one of stage's ancestors is target. */ private def stageDependsOn(stage: Stage, target: Stage): Boolean = { if (stage == target) { return true @@ -1282,7 +1315,7 @@ class DAGScheduler( } } waitingForVisit.push(stage.rdd) - while (!waitingForVisit.isEmpty) { + while (waitingForVisit.nonEmpty) { visit(waitingForVisit.pop()) } visitedRdds.contains(target.rdd) @@ -1312,9 +1345,7 @@ class DAGScheduler( private def getPreferredLocsInternal( rdd: RDD[_], partition: Int, - visited: HashSet[(RDD[_],Int)]) - : Seq[TaskLocation] = - { + visited: HashSet[(RDD[_],Int)]): Seq[TaskLocation] = { // If the partition has already been visited, no need to re-visit. // This avoids exponential path exploration. SPARK-695 if (!visited.add((rdd,partition))) { @@ -1323,12 +1354,12 @@ class DAGScheduler( } // If the partition is cached, return the cache locations val cached = getCacheLocs(rdd)(partition) - if (!cached.isEmpty) { + if (cached.nonEmpty) { return cached } // If the RDD has some placement preferences (as is the case for input RDDs), get those val rddPrefs = rdd.preferredLocations(rdd.partitions(partition)).toList - if (!rddPrefs.isEmpty) { + if (rddPrefs.nonEmpty) { return rddPrefs.map(TaskLocation(_)) } // If the RDD has narrow dependencies, pick the first partition of the first narrow dep @@ -1412,7 +1443,7 @@ private[scheduler] class DAGSchedulerEventProcessLoop(dagScheduler: DAGScheduler dagScheduler.sc.stop() } - override def onStop() { + override def onStop(): Unit = { // Cancel any active jobs in postStop hook dagScheduler.cleanUpAfterSchedulerStop() } diff --git a/core/src/main/scala/org/apache/spark/scheduler/ResultStage.scala b/core/src/main/scala/org/apache/spark/scheduler/ResultStage.scala new file mode 100644 index 0000000000000..c0f3d5a13d623 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/scheduler/ResultStage.scala @@ -0,0 +1,40 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler + +import org.apache.spark.rdd.RDD +import org.apache.spark.util.CallSite + +/** + * The ResultStage represents the final stage in a job. + */ +private[spark] class ResultStage( + id: Int, + rdd: RDD[_], + numTasks: Int, + parents: List[Stage], + jobId: Int, + callSite: CallSite) + extends Stage(id, rdd, numTasks, parents, jobId, callSite) { + + // The active job for this result stage. Will be empty if the job has already finished + // (e.g., because the job was cancelled). + var resultOfJob: Option[ActiveJob] = None + + override def toString: String = "ResultStage " + id +} diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala new file mode 100644 index 0000000000000..d02210743484c --- /dev/null +++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala @@ -0,0 +1,84 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler + +import org.apache.spark.ShuffleDependency +import org.apache.spark.rdd.RDD +import org.apache.spark.storage.BlockManagerId +import org.apache.spark.util.CallSite + +/** + * The ShuffleMapStage represents the intermediate stages in a job. + */ +private[spark] class ShuffleMapStage( + id: Int, + rdd: RDD[_], + numTasks: Int, + parents: List[Stage], + jobId: Int, + callSite: CallSite, + val shuffleDep: ShuffleDependency[_, _, _]) + extends Stage(id, rdd, numTasks, parents, jobId, callSite) { + + override def toString: String = "ShuffleMapStage " + id + + var numAvailableOutputs: Long = 0 + + def isAvailable: Boolean = numAvailableOutputs == numPartitions + + val outputLocs = Array.fill[List[MapStatus]](numPartitions)(Nil) + + def addOutputLoc(partition: Int, status: MapStatus): Unit = { + val prevList = outputLocs(partition) + outputLocs(partition) = status :: prevList + if (prevList == Nil) { + numAvailableOutputs += 1 + } + } + + def removeOutputLoc(partition: Int, bmAddress: BlockManagerId): Unit = { + val prevList = outputLocs(partition) + val newList = prevList.filterNot(_.location == bmAddress) + outputLocs(partition) = newList + if (prevList != Nil && newList == Nil) { + numAvailableOutputs -= 1 + } + } + + /** + * Removes all shuffle outputs associated with this executor. Note that this will also remove + * outputs which are served by an external shuffle server (if one exists), as they are still + * registered with this execId. + */ + def removeOutputsOnExecutor(execId: String): Unit = { + var becameUnavailable = false + for (partition <- 0 until numPartitions) { + val prevList = outputLocs(partition) + val newList = prevList.filterNot(_.location.executorId == execId) + outputLocs(partition) = newList + if (prevList != Nil && newList == Nil) { + becameUnavailable = true + numAvailableOutputs -= 1 + } + } + if (becameUnavailable) { + logInfo("%s is now unavailable on executor %s (%d/%d, %s)".format( + this, execId, numAvailableOutputs, numPartitions, isAvailable)) + } + } +} diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala index 4cbc6e84a6bdd..5d0ddb8377c33 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala @@ -21,7 +21,6 @@ import scala.collection.mutable.HashSet import org.apache.spark._ import org.apache.spark.rdd.RDD -import org.apache.spark.storage.BlockManagerId import org.apache.spark.util.CallSite /** @@ -47,29 +46,23 @@ import org.apache.spark.util.CallSite * be updated for each attempt. * */ -private[spark] class Stage( +private[spark] abstract class Stage( val id: Int, val rdd: RDD[_], val numTasks: Int, - val shuffleDep: Option[ShuffleDependency[_, _, _]], // Output shuffle if stage is a map stage val parents: List[Stage], val jobId: Int, val callSite: CallSite) extends Logging { - val isShuffleMap = shuffleDep.isDefined val numPartitions = rdd.partitions.size - val outputLocs = Array.fill[List[MapStatus]](numPartitions)(Nil) - var numAvailableOutputs = 0 /** Set of jobs that this stage belongs to. */ val jobIds = new HashSet[Int] - /** For stages that are the final (consists of only ResultTasks), link to the ActiveJob. */ - var resultOfJob: Option[ActiveJob] = None var pendingTasks = new HashSet[Task[_]] - private var nextAttemptId = 0 + private var nextAttemptId: Int = 0 val name = callSite.shortForm val details = callSite.longForm @@ -77,53 +70,6 @@ private[spark] class Stage( /** Pointer to the latest [StageInfo] object, set by DAGScheduler. */ var latestInfo: StageInfo = StageInfo.fromStage(this) - def isAvailable: Boolean = { - if (!isShuffleMap) { - true - } else { - numAvailableOutputs == numPartitions - } - } - - def addOutputLoc(partition: Int, status: MapStatus) { - val prevList = outputLocs(partition) - outputLocs(partition) = status :: prevList - if (prevList == Nil) { - numAvailableOutputs += 1 - } - } - - def removeOutputLoc(partition: Int, bmAddress: BlockManagerId) { - val prevList = outputLocs(partition) - val newList = prevList.filterNot(_.location == bmAddress) - outputLocs(partition) = newList - if (prevList != Nil && newList == Nil) { - numAvailableOutputs -= 1 - } - } - - /** - * Removes all shuffle outputs associated with this executor. Note that this will also remove - * outputs which are served by an external shuffle server (if one exists), as they are still - * registered with this execId. - */ - def removeOutputsOnExecutor(execId: String) { - var becameUnavailable = false - for (partition <- 0 until numPartitions) { - val prevList = outputLocs(partition) - val newList = prevList.filterNot(_.location.executorId == execId) - outputLocs(partition) = newList - if (prevList != Nil && newList == Nil) { - becameUnavailable = true - numAvailableOutputs -= 1 - } - } - if (becameUnavailable) { - logInfo("%s is now unavailable on executor %s (%d/%d, %s)".format( - this, execId, numAvailableOutputs, numPartitions, isAvailable)) - } - } - /** Return a new attempt id, starting with 0. */ def newAttemptId(): Int = { val id = nextAttemptId @@ -133,11 +79,8 @@ private[spark] class Stage( def attemptId: Int = nextAttemptId - override def toString: String = "Stage " + id - - override def hashCode(): Int = id - - override def equals(other: Any): Boolean = other match { + override final def hashCode(): Int = id + override final def equals(other: Any): Boolean = other match { case stage: Stage => stage != null && stage.id == id case _ => false } diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala index efd59a7e5470f..54500f7c2701f 100644 --- a/project/MimaExcludes.scala +++ b/project/MimaExcludes.scala @@ -54,7 +54,11 @@ object MimaExcludes { ProblemFilters.exclude[MissingClassProblem]( "org.apache.spark.scheduler.OutputCommitCoordinator$OutputCommitCoordinatorActor") ) ++ Seq( - // SPARK-6510 Add a Graph#minus method acting as Set#difference + // SPARK-4655 - Making Stage an Abstract class broke binary compatility even though + // the stage class is defined as private[spark] + ProblemFilters.exclude[AbstractClassProblem]("org.apache.spark.scheduler.Stage") + ) ++ Seq( + // SPARK-6510 Add a Graph#minus method acting as Set#difference ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.minus") ) From 412262346f6f48e641bd6899c703efa31aeaba1e Mon Sep 17 00:00:00 2001 From: Florian Verhein Date: Wed, 1 Apr 2015 11:10:43 +0100 Subject: [PATCH 121/591] [EC2] [SPARK-6600] Open ports in ec2/spark_ec2.py to allow HDFS NFS gateway Authorizes incoming access to master on the ports required to use the hadoop hdfs nfs gateway from outside the cluster. Author: Florian Verhein Closes #5257 from florianverhein/master and squashes the following commits: 72a586a [Florian Verhein] [EC2] [SPARK-6600] initial impl --- ec2/spark_ec2.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py index c467cd08ed742..5507a9c5a4733 100755 --- a/ec2/spark_ec2.py +++ b/ec2/spark_ec2.py @@ -456,6 +456,13 @@ def launch_cluster(conn, opts, cluster_name): master_group.authorize('tcp', 50070, 50070, authorized_address) master_group.authorize('tcp', 60070, 60070, authorized_address) master_group.authorize('tcp', 4040, 4045, authorized_address) + # HDFS NFS gateway requires 111,2049,4242 for tcp & udp + master_group.authorize('tcp', 111, 111, authorized_address) + master_group.authorize('udp', 111, 111, authorized_address) + master_group.authorize('tcp', 2049, 2049, authorized_address) + master_group.authorize('udp', 2049, 2049, authorized_address) + master_group.authorize('tcp', 4242, 4242, authorized_address) + master_group.authorize('udp', 4242, 4242, authorized_address) if opts.ganglia: master_group.authorize('tcp', 5080, 5080, authorized_address) if slave_group.rules == []: # Group was just now created From d824c11c9fe8af1ca1d7c694b2fb81289eb83f97 Mon Sep 17 00:00:00 2001 From: Kousuke Saruta Date: Wed, 1 Apr 2015 11:11:56 +0100 Subject: [PATCH 122/591] [SPARK-6597][Minor] Replace `input:checkbox` with `input[type="checkbox"]` in additional-metrics.js In additional-metrics.js, there are some selector notation like `input:checkbox` but JQuery's official document says `input[type="checkbox"]` is better. https://api.jquery.com/checkbox-selector/ Author: Kousuke Saruta Closes #5254 from sarutak/SPARK-6597 and squashes the following commits: a253bc4 [Kousuke Saruta] Replaced input:checkbox with input[type="checkbox"] --- .../org/apache/spark/ui/static/additional-metrics.js | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/core/src/main/resources/org/apache/spark/ui/static/additional-metrics.js b/core/src/main/resources/org/apache/spark/ui/static/additional-metrics.js index 14ba37d7c9bd9..013db8df9b363 100644 --- a/core/src/main/resources/org/apache/spark/ui/static/additional-metrics.js +++ b/core/src/main/resources/org/apache/spark/ui/static/additional-metrics.js @@ -30,7 +30,7 @@ $(function() { stripeSummaryTable(); - $("input:checkbox").click(function() { + $('input[type="checkbox"]').click(function() { var column = "table ." + $(this).attr("name"); $(column).toggle(); stripeSummaryTable(); @@ -39,15 +39,15 @@ $(function() { $("#select-all-metrics").click(function() { if (this.checked) { // Toggle all un-checked options. - $('input:checkbox:not(:checked)').trigger('click'); + $('input[type="checkbox"]:not(:checked)').trigger('click'); } else { // Toggle all checked options. - $('input:checkbox:checked').trigger('click'); + $('input[type="checkbox"]:checked').trigger('click'); } }); // Trigger a click on the checkbox if a user clicks the label next to it. $("span.additional-metric-title").click(function() { - $(this).parent().find('input:checkbox').trigger('click'); + $(this).parent().find('input[type="checkbox"]').trigger('click'); }); }); From 0358b08db85b3ee4ae70834626e7a42311bcc635 Mon Sep 17 00:00:00 2001 From: jayson Date: Wed, 1 Apr 2015 11:12:55 +0100 Subject: [PATCH 123/591] SPARK-6626 [DOCS]: Corrected Scala:TwitterUtils parameters Per Sean Owen's request, here is the update call for TwitterUtils using Scala :) Author: jayson Closes #5295 from JaysonSunshine/master and squashes the following commits: df1d056 [jayson] Corrected Scala:TwitterUtils parameters --- docs/streaming-programming-guide.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md index 6d6229625f3f9..262512a639046 100644 --- a/docs/streaming-programming-guide.md +++ b/docs/streaming-programming-guide.md @@ -704,7 +704,7 @@ create a DStream using data from Twitter's stream of tweets, you have to do the {% highlight scala %} import org.apache.spark.streaming.twitter._ -TwitterUtils.createStream(ssc) +TwitterUtils.createStream(ssc, None) {% endhighlight %}
From d36c5fca7b9227c4c6e1b0c1455269b5fd8d4852 Mon Sep 17 00:00:00 2001 From: Cheng Lian Date: Wed, 1 Apr 2015 21:34:45 +0800 Subject: [PATCH 124/591] [SPARK-6608] [SQL] Makes DataFrame.rdd a lazy val Before 1.3.0, `SchemaRDD.id` works as a unique identifier of each `SchemaRDD`. In 1.3.0, unlike `SchemaRDD`, `DataFrame` is no longer an RDD, and `DataFrame.rdd` is actually a function which always returns a new RDD instance. Making `DataFrame.rdd` a lazy val should bring the unique identifier back. [Review on Reviewable](https://reviewable.io/reviews/apache/spark/5265) Author: Cheng Lian Closes #5265 from liancheng/spark-6608 and squashes the following commits: 7500968 [Cheng Lian] Updates javadoc 7f37d21 [Cheng Lian] Makes DataFrame.rdd a lazy val --- .../src/main/scala/org/apache/spark/sql/DataFrame.scala | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 5cd0a18ff688c..19cfa15f27b09 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -952,10 +952,12 @@ class DataFrame private[sql]( ///////////////////////////////////////////////////////////////////////////// /** - * Returns the content of the [[DataFrame]] as an [[RDD]] of [[Row]]s. + * Represents the content of the [[DataFrame]] as an [[RDD]] of [[Row]]s. Note that the RDD is + * memoized. Once called, it won't change even if you change any query planning related Spark SQL + * configurations (e.g. `spark.sql.shuffle.partitions`). * @group rdd */ - def rdd: RDD[Row] = { + lazy val rdd: RDD[Row] = { // use a local variable to make sure the map closure doesn't capture the whole DataFrame val schema = this.schema queryExecution.executedPlan.execute().map(ScalaReflection.convertRowToScala(_, schema)) From ee11be258251adf900680927ba200bf46512cc04 Mon Sep 17 00:00:00 2001 From: Steve Loughran Date: Wed, 1 Apr 2015 16:26:54 +0100 Subject: [PATCH 125/591] SPARK-6433 hive tests to import spark-sql test JAR for QueryTest access 1. Test JARs are built & published 1. log4j.resources is explicitly excluded. Without this, downstream test run logging depends on the order the JARs are listed/loaded 1. sql/hive pulls in spark-sql &...spark-catalyst for its test runs 1. The copied in test classes were rm'd, and a test edited to remove its now duplicate assert method 1. Spark streaming is now build with the same plugin/phase as the rest, but its shade plugin declaration is kept in (so different from the rest of the test plugins). Due to (#2), this means the test JAR no longer includes its log4j file. Outstanding issues: * should the JARs be shaded? `spark-streaming-test.jar` does, but given these are test jars for developers only, especially in the same spark source tree, it's hard to justify. * `maven-jar-plugin` v 2.6 was explicitly selected; without this the apache-1.4 parent template JAR version (2.4) chosen. * Are there any other resources to exclude? Author: Steve Loughran Closes #5119 from steveloughran/stevel/patches/SPARK-6433-test-jars and squashes the following commits: 81ceb01 [Steve Loughran] SPARK-6433 add a clearer comment explaining what the plugin is doing & why a6dca33 [Steve Loughran] SPARK-6433 : pull configuration section form archive plugin c2b5f89 [Steve Loughran] SPARK-6433 omit "jar" goal from jar plugin fdac51b [Steve Loughran] SPARK-6433 -002; indentation & delegate plugin version to parent 650f442 [Steve Loughran] SPARK-6433 patch 001: test JARs are built; sql/hive pulls in spark-sql & spark-catalyst for its test runs --- pom.xml | 20 +++ sql/hive/pom.xml | 14 ++ .../org/apache/spark/sql/QueryTest.scala | 140 ------------------ .../spark/sql/catalyst/plans/PlanTest.scala | 57 ------- .../spark/sql/hive/CachedTableSuite.scala | 15 -- streaming/pom.xml | 28 ---- 6 files changed, 34 insertions(+), 240 deletions(-) delete mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/QueryTest.scala delete mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala diff --git a/pom.xml b/pom.xml index 3eb3da2cd8af3..42bd926a2fcb8 100644 --- a/pom.xml +++ b/pom.xml @@ -1265,6 +1265,7 @@ create-source-jar jar-no-fork + test-jar-no-fork @@ -1473,6 +1474,25 @@ org.scalatest scalatest-maven-plugin + + + org.apache.maven.plugins + maven-jar-plugin + + + prepare-test-jar + prepare-package + + test-jar + + + + log4j.properties + + + + + diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml index a9816f6c38cd2..04440076a26a3 100644 --- a/sql/hive/pom.xml +++ b/sql/hive/pom.xml @@ -89,6 +89,20 @@ junit test + + org.apache.spark + spark-sql_${scala.binary.version} + test-jar + ${project.version} + test + + + org.apache.spark + spark-catalyst_${scala.binary.version} + test-jar + ${project.version} + test + diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/QueryTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/QueryTest.scala deleted file mode 100644 index 0270e63557963..0000000000000 --- a/sql/hive/src/test/scala/org/apache/spark/sql/QueryTest.scala +++ /dev/null @@ -1,140 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql - -import scala.collection.JavaConversions._ - -import org.apache.spark.sql.catalyst.plans._ -import org.apache.spark.sql.catalyst.util._ - - -/** - * *** DUPLICATED FROM sql/core. *** - * - * It is hard to have maven allow one subproject depend on another subprojects test code. - * So, we duplicate this code here. - */ -class QueryTest extends PlanTest { - - /** - * Runs the plan and makes sure the answer contains all of the keywords, or the - * none of keywords are listed in the answer - * @param rdd the [[DataFrame]] to be executed - * @param exists true for make sure the keywords are listed in the output, otherwise - * to make sure none of the keyword are not listed in the output - * @param keywords keyword in string array - */ - def checkExistence(rdd: DataFrame, exists: Boolean, keywords: String*) { - val outputs = rdd.collect().map(_.mkString).mkString - for (key <- keywords) { - if (exists) { - assert(outputs.contains(key), s"Failed for $rdd ($key doens't exist in result)") - } else { - assert(!outputs.contains(key), s"Failed for $rdd ($key existed in the result)") - } - } - } - - /** - * Runs the plan and makes sure the answer matches the expected result. - * @param rdd the [[DataFrame]] to be executed - * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s. - */ - protected def checkAnswer(rdd: DataFrame, expectedAnswer: Seq[Row]): Unit = { - QueryTest.checkAnswer(rdd, expectedAnswer) match { - case Some(errorMessage) => fail(errorMessage) - case None => - } - } - - protected def checkAnswer(rdd: DataFrame, expectedAnswer: Row): Unit = { - checkAnswer(rdd, Seq(expectedAnswer)) - } - - def sqlTest(sqlString: String, expectedAnswer: Seq[Row])(implicit sqlContext: SQLContext): Unit = { - test(sqlString) { - checkAnswer(sqlContext.sql(sqlString), expectedAnswer) - } - } -} - -object QueryTest { - /** - * Runs the plan and makes sure the answer matches the expected result. - * If there was exception during the execution or the contents of the DataFrame does not - * match the expected result, an error message will be returned. Otherwise, a [[None]] will - * be returned. - * @param rdd the [[DataFrame]] to be executed - * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s. - */ - def checkAnswer(rdd: DataFrame, expectedAnswer: Seq[Row]): Option[String] = { - val isSorted = rdd.logicalPlan.collect { case s: logical.Sort => s }.nonEmpty - def prepareAnswer(answer: Seq[Row]): Seq[Row] = { - // Converts data to types that we can do equality comparison using Scala collections. - // For BigDecimal type, the Scala type has a better definition of equality test (similar to - // Java's java.math.BigDecimal.compareTo). - val converted: Seq[Row] = answer.map { s => - Row.fromSeq(s.toSeq.map { - case d: java.math.BigDecimal => BigDecimal(d) - case o => o - }) - } - if (!isSorted) converted.sortBy(_.toString) else converted - } - val sparkAnswer = try rdd.collect().toSeq catch { - case e: Exception => - val errorMessage = - s""" - |Exception thrown while executing query: - |${rdd.queryExecution} - |== Exception == - |$e - |${org.apache.spark.sql.catalyst.util.stackTraceToString(e)} - """.stripMargin - return Some(errorMessage) - } - - if (prepareAnswer(expectedAnswer) != prepareAnswer(sparkAnswer)) { - val errorMessage = - s""" - |Results do not match for query: - |${rdd.logicalPlan} - |== Analyzed Plan == - |${rdd.queryExecution.analyzed} - |== Physical Plan == - |${rdd.queryExecution.executedPlan} - |== Results == - |${sideBySide( - s"== Correct Answer - ${expectedAnswer.size} ==" +: - prepareAnswer(expectedAnswer).map(_.toString), - s"== Spark Answer - ${sparkAnswer.size} ==" +: - prepareAnswer(sparkAnswer).map(_.toString)).mkString("\n")} - """.stripMargin - return Some(errorMessage) - } - - return None - } - - def checkAnswer(rdd: DataFrame, expectedAnswer: java.util.List[Row]): String = { - checkAnswer(rdd, expectedAnswer.toSeq) match { - case Some(errorMessage) => errorMessage - case None => null - } - } -} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala deleted file mode 100644 index 98f1c0e69e29d..0000000000000 --- a/sql/hive/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala +++ /dev/null @@ -1,57 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.catalyst.plans - -import org.apache.spark.sql.catalyst.expressions.{Alias, AttributeReference, ExprId} -import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan -import org.apache.spark.sql.catalyst.util._ -import org.scalatest.FunSuite - -/** - * *** DUPLICATED FROM sql/catalyst/plans. *** - * - * It is hard to have maven allow one subproject depend on another subprojects test code. - * So, we duplicate this code here. - */ -class PlanTest extends FunSuite { - - /** - * Since attribute references are given globally unique ids during analysis, - * we must normalize them to check if two different queries are identical. - */ - protected def normalizeExprIds(plan: LogicalPlan) = { - plan transformAllExpressions { - case a: AttributeReference => - AttributeReference(a.name, a.dataType, a.nullable)(exprId = ExprId(0)) - case a: Alias => - Alias(a.child, a.name)(exprId = ExprId(0)) - } - } - - /** Fails the test if the two plans do not match */ - protected def comparePlans(plan1: LogicalPlan, plan2: LogicalPlan) { - val normalized1 = normalizeExprIds(plan1) - val normalized2 = normalizeExprIds(plan2) - if (normalized1 != normalized2) - fail( - s""" - |== FAIL: Plans do not match === - |${sideBySide(normalized1.treeString, normalized2.treeString).mkString("\n")} - """.stripMargin) - } -} diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala index 221a0c263d36c..c188264072a84 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala @@ -24,21 +24,6 @@ import org.apache.spark.sql.{AnalysisException, DataFrame, QueryTest} import org.apache.spark.storage.RDDBlockId class CachedTableSuite extends QueryTest { - /** - * Throws a test failed exception when the number of cached tables differs from the expected - * number. - */ - def assertCached(query: DataFrame, numCachedTables: Int = 1): Unit = { - val planWithCaching = query.queryExecution.withCachedData - val cachedData = planWithCaching collect { - case cached: InMemoryRelation => cached - } - - assert( - cachedData.size == numCachedTables, - s"Expected query to contain $numCachedTables, but it actually had ${cachedData.size}\n" + - planWithCaching) - } def rddIdOf(tableName: String): Int = { val executedPlan = table(tableName).queryExecution.executedPlan diff --git a/streaming/pom.xml b/streaming/pom.xml index 23a8358d45c2a..5ca55a4f680bb 100644 --- a/streaming/pom.xml +++ b/streaming/pom.xml @@ -97,34 +97,6 @@ target/scala-${scala.binary.version}/classes target/scala-${scala.binary.version}/test-classes - - - org.apache.maven.plugins - maven-jar-plugin - - - - test-jar - - - - test-jar-on-test-compile - test-compile - - test-jar - - - - - org.apache.maven.plugins maven-shade-plugin From 2275acce7ba5fac83c58554d7ee9f4c7f3e866cf Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Wed, 1 Apr 2015 13:29:04 -0700 Subject: [PATCH 126/591] [SPARK-6651][MLLIB] delegate dense vector arithmetics to the underlying numpy array Users should be able to use numpy operators directly on dense vectors. davies atalwalkar Author: Xiangrui Meng Closes #5312 from mengxr/SPARK-6651 and squashes the following commits: e665c5c [Xiangrui Meng] wrap the result in a dense vector 23dfca3 [Xiangrui Meng] delegate dense vector arithmetics to the underlying numpy array --- python/pyspark/mllib/linalg.py | 38 +++++++++++++++++++++++++++++++++- 1 file changed, 37 insertions(+), 1 deletion(-) diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py index f5aad28afda0f..8b791ff6a7877 100644 --- a/python/pyspark/mllib/linalg.py +++ b/python/pyspark/mllib/linalg.py @@ -173,7 +173,24 @@ def toArray(self): class DenseVector(Vector): """ - A dense vector represented by a value array. + A dense vector represented by a value array. We use numpy array for + storage and arithmetics will be delegated to the underlying numpy + array. + + >>> v = Vectors.dense([1.0, 2.0]) + >>> u = Vectors.dense([3.0, 4.0]) + >>> v + u + DenseVector([4.0, 6.0]) + >>> 2 - v + DenseVector([1.0, 0.0]) + >>> v / 2 + DenseVector([0.5, 1.0]) + >>> v * u + DenseVector([3.0, 8.0]) + >>> u / v + DenseVector([3.0, 2.0]) + >>> u % 2 + DenseVector([1.0, 0.0]) """ def __init__(self, ar): if isinstance(ar, basestring): @@ -292,6 +309,25 @@ def __ne__(self, other): def __getattr__(self, item): return getattr(self.array, item) + def _delegate(op): + def func(self, other): + if isinstance(other, DenseVector): + other = other.array + return DenseVector(getattr(self.array, op)(other)) + return func + + __neg__ = _delegate("__neg__") + __add__ = _delegate("__add__") + __sub__ = _delegate("__sub__") + __mul__ = _delegate("__mul__") + __div__ = _delegate("__div__") + __mod__ = _delegate("__mod__") + __radd__ = _delegate("__radd__") + __rsub__ = _delegate("__rsub__") + __rmul__ = _delegate("__rmul__") + __rdiv__ = _delegate("__rdiv__") + __rmod__ = _delegate("__rmod__") + class SparseVector(Vector): """ From fb25e8c7f45b4f96561e3f7434a0f4dfce8ddefe Mon Sep 17 00:00:00 2001 From: "Joseph K. Bradley" Date: Wed, 1 Apr 2015 15:15:47 -0700 Subject: [PATCH 127/591] [SPARK-6657] [Python] [Docs] fixed python doc build warnings fixed python doc build warnings CC whomever wants to review: rxin mengxr davies Author: Joseph K. Bradley Closes #5317 from jkbradley/python-doc-warnings and squashes the following commits: 4cd43c2 [Joseph K. Bradley] fixed python doc build warnings --- python/docs/pyspark.streaming.rst | 2 +- python/pyspark/mllib/tree.py | 26 ++++++++++---------------- 2 files changed, 11 insertions(+), 17 deletions(-) diff --git a/python/docs/pyspark.streaming.rst b/python/docs/pyspark.streaming.rst index 7890d9dcaac21..50822c93faba1 100644 --- a/python/docs/pyspark.streaming.rst +++ b/python/docs/pyspark.streaming.rst @@ -10,7 +10,7 @@ Module contents :show-inheritance: pyspark.streaming.kafka module ----------------------------- +------------------------------ .. automodule:: pyspark.streaming.kafka :members: :undoc-members: diff --git a/python/pyspark/mllib/tree.py b/python/pyspark/mllib/tree.py index bf288d76447bd..a7a4d2aaf855b 100644 --- a/python/pyspark/mllib/tree.py +++ b/python/pyspark/mllib/tree.py @@ -286,21 +286,18 @@ def trainClassifier(cls, data, numClasses, categoricalFeaturesInfo, numTrees, :param numTrees: Number of trees in the random forest. :param featureSubsetStrategy: Number of features to consider for splits at each node. - Supported: "auto" (default), "all", "sqrt", "log2", - "onethird". - If "auto" is set, this parameter is set based on - numTrees: - if numTrees == 1, set to "all"; - if numTrees > 1 (forest) set to "sqrt". - :param impurity: Criterion used for information gain - calculation. + Supported: "auto" (default), "all", "sqrt", "log2", "onethird". + If "auto" is set, this parameter is set based on numTrees: + if numTrees == 1, set to "all"; + if numTrees > 1 (forest) set to "sqrt". + :param impurity: Criterion used for information gain calculation. Supported values: "gini" (recommended) or "entropy". :param maxDepth: Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. (default: 4) :param maxBins: maximum number of bins used for splitting features - (default: 100) + (default: 100) :param seed: Random seed for bootstrapping and choosing feature subsets. :return: RandomForestModel that can be used for prediction @@ -365,13 +362,10 @@ def trainRegressor(cls, data, categoricalFeaturesInfo, numTrees, featureSubsetSt :param numTrees: Number of trees in the random forest. :param featureSubsetStrategy: Number of features to consider for splits at each node. - Supported: "auto" (default), "all", "sqrt", "log2", - "onethird". - If "auto" is set, this parameter is set based on - numTrees: - if numTrees == 1, set to "all"; - if numTrees > 1 (forest) set to "onethird" for - regression. + Supported: "auto" (default), "all", "sqrt", "log2", "onethird". + If "auto" is set, this parameter is set based on numTrees: + if numTrees == 1, set to "all"; + if numTrees > 1 (forest) set to "onethird" for regression. :param impurity: Criterion used for information gain calculation. Supported values: "variance". From f084c5de14eb10a6aba82a39e03e7877926ebb9e Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Wed, 1 Apr 2015 16:06:11 -0700 Subject: [PATCH 128/591] [SPARK-6578] [core] Fix thread-safety issue in outbound path of network library. While the inbound path of a netty pipeline is thread-safe, the outbound path is not. That means that multiple threads can compete to write messages to the next stage of the pipeline. The network library sometimes breaks a single RPC message into multiple buffers internally to avoid copying data (see MessageEncoder). This can result in the following scenario (where "FxBy" means "frame x, buffer y"): T1 F1B1 F1B2 \ \ \ \ socket F1B1 F2B1 F1B2 F2B2 / / / / T2 F2B1 F2B2 And the frames now cannot be rebuilt on the receiving side because the different messages have been mixed up on the wire. The fix wraps these multi-buffer messages into a `FileRegion` object so that these messages are written "atomically" to the next pipeline handler. Author: Marcelo Vanzin Closes #5234 from vanzin/SPARK-6578 and squashes the following commits: 16b2d70 [Marcelo Vanzin] Forgot to update a type. c9c2e4e [Marcelo Vanzin] Review comments: simplify some code. 9c888ac [Marcelo Vanzin] Small style nits. 8474bab [Marcelo Vanzin] Fix multiple calls to MessageWithHeader.transferTo(). e26509f [Marcelo Vanzin] Merge branch 'master' into SPARK-6578 c503f6c [Marcelo Vanzin] Implement a custom FileRegion instead of using locks. 84aa7ce [Marcelo Vanzin] Rename handler to the correct name. 432f3bd [Marcelo Vanzin] Remove unneeded method. 8d70e60 [Marcelo Vanzin] Fix thread-safety issue in outbound path of network library. --- network/common/pom.xml | 5 + .../network/protocol/MessageEncoder.java | 6 +- .../network/protocol/MessageWithHeader.java | 106 ++++++++++++++ .../network/ByteArrayWritableChannel.java | 55 ++++++++ .../apache/spark/network/ProtocolSuite.java | 46 +++++-- .../protocol/MessageWithHeaderSuite.java | 129 ++++++++++++++++++ .../src/test/resources/log4j.properties | 27 ++++ 7 files changed, 364 insertions(+), 10 deletions(-) create mode 100644 network/common/src/main/java/org/apache/spark/network/protocol/MessageWithHeader.java create mode 100644 network/common/src/test/java/org/apache/spark/network/ByteArrayWritableChannel.java create mode 100644 network/common/src/test/java/org/apache/spark/network/protocol/MessageWithHeaderSuite.java create mode 100644 network/common/src/test/resources/log4j.properties diff --git a/network/common/pom.xml b/network/common/pom.xml index 7b51845206f4a..22c738bde6d42 100644 --- a/network/common/pom.xml +++ b/network/common/pom.xml @@ -80,6 +80,11 @@ mockito-all test + + org.slf4j + slf4j-log4j12 + test + diff --git a/network/common/src/main/java/org/apache/spark/network/protocol/MessageEncoder.java b/network/common/src/main/java/org/apache/spark/network/protocol/MessageEncoder.java index 91d1e8a538a77..0f999f5dfe8d8 100644 --- a/network/common/src/main/java/org/apache/spark/network/protocol/MessageEncoder.java +++ b/network/common/src/main/java/org/apache/spark/network/protocol/MessageEncoder.java @@ -72,9 +72,11 @@ public void encode(ChannelHandlerContext ctx, Message in, List out) { in.encode(header); assert header.writableBytes() == 0; - out.add(header); if (body != null && bodyLength > 0) { - out.add(body); + out.add(new MessageWithHeader(header, body, bodyLength)); + } else { + out.add(header); } } + } diff --git a/network/common/src/main/java/org/apache/spark/network/protocol/MessageWithHeader.java b/network/common/src/main/java/org/apache/spark/network/protocol/MessageWithHeader.java new file mode 100644 index 0000000000000..215a8517e8608 --- /dev/null +++ b/network/common/src/main/java/org/apache/spark/network/protocol/MessageWithHeader.java @@ -0,0 +1,106 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.network.protocol; + +import java.io.IOException; +import java.nio.channels.WritableByteChannel; + +import com.google.common.base.Preconditions; +import com.google.common.primitives.Ints; +import io.netty.buffer.ByteBuf; +import io.netty.channel.FileRegion; +import io.netty.util.AbstractReferenceCounted; +import io.netty.util.ReferenceCountUtil; + +/** + * A wrapper message that holds two separate pieces (a header and a body) to avoid + * copying the body's content. + */ +class MessageWithHeader extends AbstractReferenceCounted implements FileRegion { + + private final ByteBuf header; + private final int headerLength; + private final Object body; + private final long bodyLength; + private long totalBytesTransferred; + + MessageWithHeader(ByteBuf header, Object body, long bodyLength) { + Preconditions.checkArgument(body instanceof ByteBuf || body instanceof FileRegion, + "Body must be a ByteBuf or a FileRegion."); + this.header = header; + this.headerLength = header.readableBytes(); + this.body = body; + this.bodyLength = bodyLength; + } + + @Override + public long count() { + return headerLength + bodyLength; + } + + @Override + public long position() { + return 0; + } + + @Override + public long transfered() { + return totalBytesTransferred; + } + + @Override + public long transferTo(WritableByteChannel target, long position) throws IOException { + Preconditions.checkArgument(position == totalBytesTransferred, "Invalid position."); + long written = 0; + + if (position < headerLength) { + written += copyByteBuf(header, target); + if (header.readableBytes() > 0) { + totalBytesTransferred += written; + return written; + } + } + + if (body instanceof FileRegion) { + // Adjust the position. If the write is happening as part of the same call where the header + // (or some part of it) is written, `position` will be less than the header size, so we want + // to start from position 0 in the FileRegion object. Otherwise, we start from the position + // requested by the caller. + long bodyPos = position > headerLength ? position - headerLength : 0; + written += ((FileRegion)body).transferTo(target, bodyPos); + } else if (body instanceof ByteBuf) { + written += copyByteBuf((ByteBuf) body, target); + } + + totalBytesTransferred += written; + return written; + } + + @Override + protected void deallocate() { + header.release(); + ReferenceCountUtil.release(body); + } + + private int copyByteBuf(ByteBuf buf, WritableByteChannel target) throws IOException { + int written = target.write(buf.nioBuffer()); + buf.skipBytes(written); + return written; + } + +} diff --git a/network/common/src/test/java/org/apache/spark/network/ByteArrayWritableChannel.java b/network/common/src/test/java/org/apache/spark/network/ByteArrayWritableChannel.java new file mode 100644 index 0000000000000..b525ed69fc9fb --- /dev/null +++ b/network/common/src/test/java/org/apache/spark/network/ByteArrayWritableChannel.java @@ -0,0 +1,55 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.network; + +import java.nio.ByteBuffer; +import java.nio.channels.WritableByteChannel; + +public class ByteArrayWritableChannel implements WritableByteChannel { + + private final byte[] data; + private int offset; + + public ByteArrayWritableChannel(int size) { + this.data = new byte[size]; + this.offset = 0; + } + + public byte[] getData() { + return data; + } + + @Override + public int write(ByteBuffer src) { + int available = src.remaining(); + src.get(data, offset, available); + offset += available; + return available; + } + + @Override + public void close() { + + } + + @Override + public boolean isOpen() { + return true; + } + +} diff --git a/network/common/src/test/java/org/apache/spark/network/ProtocolSuite.java b/network/common/src/test/java/org/apache/spark/network/ProtocolSuite.java index 43dc0cf8c7194..860dd6d9b3915 100644 --- a/network/common/src/test/java/org/apache/spark/network/ProtocolSuite.java +++ b/network/common/src/test/java/org/apache/spark/network/ProtocolSuite.java @@ -17,26 +17,34 @@ package org.apache.spark.network; +import java.util.List; + +import com.google.common.primitives.Ints; +import io.netty.buffer.Unpooled; +import io.netty.channel.ChannelHandlerContext; +import io.netty.channel.FileRegion; import io.netty.channel.embedded.EmbeddedChannel; +import io.netty.handler.codec.MessageToMessageEncoder; import org.junit.Test; import static org.junit.Assert.assertEquals; -import org.apache.spark.network.protocol.Message; -import org.apache.spark.network.protocol.StreamChunkId; -import org.apache.spark.network.protocol.ChunkFetchRequest; import org.apache.spark.network.protocol.ChunkFetchFailure; +import org.apache.spark.network.protocol.ChunkFetchRequest; import org.apache.spark.network.protocol.ChunkFetchSuccess; -import org.apache.spark.network.protocol.RpcRequest; -import org.apache.spark.network.protocol.RpcFailure; -import org.apache.spark.network.protocol.RpcResponse; +import org.apache.spark.network.protocol.Message; import org.apache.spark.network.protocol.MessageDecoder; import org.apache.spark.network.protocol.MessageEncoder; +import org.apache.spark.network.protocol.RpcFailure; +import org.apache.spark.network.protocol.RpcRequest; +import org.apache.spark.network.protocol.RpcResponse; +import org.apache.spark.network.protocol.StreamChunkId; import org.apache.spark.network.util.NettyUtils; public class ProtocolSuite { private void testServerToClient(Message msg) { - EmbeddedChannel serverChannel = new EmbeddedChannel(new MessageEncoder()); + EmbeddedChannel serverChannel = new EmbeddedChannel(new FileRegionEncoder(), + new MessageEncoder()); serverChannel.writeOutbound(msg); EmbeddedChannel clientChannel = new EmbeddedChannel( @@ -51,7 +59,8 @@ private void testServerToClient(Message msg) { } private void testClientToServer(Message msg) { - EmbeddedChannel clientChannel = new EmbeddedChannel(new MessageEncoder()); + EmbeddedChannel clientChannel = new EmbeddedChannel(new FileRegionEncoder(), + new MessageEncoder()); clientChannel.writeOutbound(msg); EmbeddedChannel serverChannel = new EmbeddedChannel( @@ -83,4 +92,25 @@ public void responses() { testServerToClient(new RpcFailure(0, "this is an error")); testServerToClient(new RpcFailure(0, "")); } + + /** + * Handler to transform a FileRegion into a byte buffer. EmbeddedChannel doesn't actually transfer + * bytes, but messages, so this is needed so that the frame decoder on the receiving side can + * understand what MessageWithHeader actually contains. + */ + private static class FileRegionEncoder extends MessageToMessageEncoder { + + @Override + public void encode(ChannelHandlerContext ctx, FileRegion in, List out) + throws Exception { + + ByteArrayWritableChannel channel = new ByteArrayWritableChannel(Ints.checkedCast(in.count())); + while (in.transfered() < in.count()) { + in.transferTo(channel, in.transfered()); + } + out.add(Unpooled.wrappedBuffer(channel.getData())); + } + + } + } diff --git a/network/common/src/test/java/org/apache/spark/network/protocol/MessageWithHeaderSuite.java b/network/common/src/test/java/org/apache/spark/network/protocol/MessageWithHeaderSuite.java new file mode 100644 index 0000000000000..ff985096d72d5 --- /dev/null +++ b/network/common/src/test/java/org/apache/spark/network/protocol/MessageWithHeaderSuite.java @@ -0,0 +1,129 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.network.protocol; + +import java.io.IOException; +import java.nio.ByteBuffer; +import java.nio.channels.WritableByteChannel; + +import io.netty.buffer.ByteBuf; +import io.netty.buffer.Unpooled; +import io.netty.channel.FileRegion; +import io.netty.util.AbstractReferenceCounted; +import org.junit.Test; + +import static org.junit.Assert.*; + +import org.apache.spark.network.ByteArrayWritableChannel; + +public class MessageWithHeaderSuite { + + @Test + public void testSingleWrite() throws Exception { + testFileRegionBody(8, 8); + } + + @Test + public void testShortWrite() throws Exception { + testFileRegionBody(8, 1); + } + + @Test + public void testByteBufBody() throws Exception { + ByteBuf header = Unpooled.copyLong(42); + ByteBuf body = Unpooled.copyLong(84); + MessageWithHeader msg = new MessageWithHeader(header, body, body.readableBytes()); + + ByteBuf result = doWrite(msg, 1); + assertEquals(msg.count(), result.readableBytes()); + assertEquals(42, result.readLong()); + assertEquals(84, result.readLong()); + } + + private void testFileRegionBody(int totalWrites, int writesPerCall) throws Exception { + ByteBuf header = Unpooled.copyLong(42); + int headerLength = header.readableBytes(); + TestFileRegion region = new TestFileRegion(totalWrites, writesPerCall); + MessageWithHeader msg = new MessageWithHeader(header, region, region.count()); + + ByteBuf result = doWrite(msg, totalWrites / writesPerCall); + assertEquals(headerLength + region.count(), result.readableBytes()); + assertEquals(42, result.readLong()); + for (long i = 0; i < 8; i++) { + assertEquals(i, result.readLong()); + } + } + + private ByteBuf doWrite(MessageWithHeader msg, int minExpectedWrites) throws Exception { + int writes = 0; + ByteArrayWritableChannel channel = new ByteArrayWritableChannel((int) msg.count()); + while (msg.transfered() < msg.count()) { + msg.transferTo(channel, msg.transfered()); + writes++; + } + assertTrue("Not enough writes!", minExpectedWrites <= writes); + return Unpooled.wrappedBuffer(channel.getData()); + } + + private static class TestFileRegion extends AbstractReferenceCounted implements FileRegion { + + private final int writeCount; + private final int writesPerCall; + private int written; + + TestFileRegion(int totalWrites, int writesPerCall) { + this.writeCount = totalWrites; + this.writesPerCall = writesPerCall; + } + + @Override + public long count() { + return 8 * writeCount; + } + + @Override + public long position() { + return 0; + } + + @Override + public long transfered() { + return 8 * written; + } + + @Override + public long transferTo(WritableByteChannel target, long position) throws IOException { + for (int i = 0; i < writesPerCall; i++) { + ByteBuf buf = Unpooled.copyLong((position / 8) + i); + ByteBuffer nio = buf.nioBuffer(); + while (nio.remaining() > 0) { + target.write(nio); + } + buf.release(); + written++; + } + return 8 * writesPerCall; + } + + @Override + protected void deallocate() { + } + + } + +} diff --git a/network/common/src/test/resources/log4j.properties b/network/common/src/test/resources/log4j.properties new file mode 100644 index 0000000000000..e8da774f7ca9e --- /dev/null +++ b/network/common/src/test/resources/log4j.properties @@ -0,0 +1,27 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Set everything to be logged to the file target/unit-tests.log +log4j.rootCategory=DEBUG, file +log4j.appender.file=org.apache.log4j.FileAppender +log4j.appender.file.append=true +log4j.appender.file.file=target/unit-tests.log +log4j.appender.file.layout=org.apache.log4j.PatternLayout +log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n + +# Silence verbose logs from 3rd-party libraries. +log4j.logger.io.netty=INFO From ccafd757eda478913f783f3127be715bf6413740 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Wed, 1 Apr 2015 16:47:18 -0700 Subject: [PATCH 129/591] [SPARK-6642][MLLIB] use 1.2 lambda scaling and remove addImplicit from NormalEquation This PR changes lambda scaling from number of users/items to number of explicit ratings. The latter is the behavior in 1.2. Slight refactor of NormalEquation to make it independent of ALS models. srowen codexiang Author: Xiangrui Meng Closes #5314 from mengxr/SPARK-6642 and squashes the following commits: dc655a1 [Xiangrui Meng] relax python tests f410df2 [Xiangrui Meng] use 1.2 scaling and remove addImplicit from NormalEquation --- .../apache/spark/ml/recommendation/ALS.scala | 67 +++++++++-------- .../spark/ml/recommendation/ALSSuite.scala | 71 +++++++------------ python/pyspark/mllib/recommendation.py | 6 +- 3 files changed, 60 insertions(+), 84 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala index 514b4ef98dc5b..52c9e95d6012f 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala @@ -320,7 +320,7 @@ object ALS extends Logging { /** Trait for least squares solvers applied to the normal equation. */ private[recommendation] trait LeastSquaresNESolver extends Serializable { - /** Solves a least squares problem (possibly with other constraints). */ + /** Solves a least squares problem with regularization (possibly with other constraints). */ def solve(ne: NormalEquation, lambda: Double): Array[Float] } @@ -332,20 +332,19 @@ object ALS extends Logging { /** * Solves a least squares problem with L2 regularization: * - * min norm(A x - b)^2^ + lambda * n * norm(x)^2^ + * min norm(A x - b)^2^ + lambda * norm(x)^2^ * * @param ne a [[NormalEquation]] instance that contains AtA, Atb, and n (number of instances) - * @param lambda regularization constant, which will be scaled by n + * @param lambda regularization constant * @return the solution x */ override def solve(ne: NormalEquation, lambda: Double): Array[Float] = { val k = ne.k // Add scaled lambda to the diagonals of AtA. - val scaledlambda = lambda * ne.n var i = 0 var j = 2 while (i < ne.triK) { - ne.ata(i) += scaledlambda + ne.ata(i) += lambda i += j j += 1 } @@ -391,7 +390,7 @@ object ALS extends Logging { override def solve(ne: NormalEquation, lambda: Double): Array[Float] = { val rank = ne.k initialize(rank) - fillAtA(ne.ata, lambda * ne.n) + fillAtA(ne.ata, lambda) val x = NNLS.solve(ata, ne.atb, workspace) ne.reset() x.map(x => x.toFloat) @@ -420,7 +419,15 @@ object ALS extends Logging { } } - /** Representing a normal equation (ALS' subproblem). */ + /** + * Representing a normal equation to solve the following weighted least squares problem: + * + * minimize \sum,,i,, c,,i,, (a,,i,,^T^ x - b,,i,,)^2^ + lambda * x^T^ x. + * + * Its normal equation is given by + * + * \sum,,i,, c,,i,, (a,,i,, a,,i,,^T^ x - b,,i,, a,,i,,) + lambda * x = 0. + */ private[recommendation] class NormalEquation(val k: Int) extends Serializable { /** Number of entries in the upper triangular part of a k-by-k matrix. */ @@ -429,8 +436,6 @@ object ALS extends Logging { val ata = new Array[Double](triK) /** A^T^ * b */ val atb = new Array[Double](k) - /** Number of observations. */ - var n = 0 private val da = new Array[Double](k) private val upper = "U" @@ -444,28 +449,13 @@ object ALS extends Logging { } /** Adds an observation. */ - def add(a: Array[Float], b: Float): this.type = { - require(a.length == k) - copyToDouble(a) - blas.dspr(upper, k, 1.0, da, 1, ata) - blas.daxpy(k, b.toDouble, da, 1, atb, 1) - n += 1 - this - } - - /** - * Adds an observation with implicit feedback. Note that this does not increment the counter. - */ - def addImplicit(a: Array[Float], b: Float, alpha: Double): this.type = { + def add(a: Array[Float], b: Double, c: Double = 1.0): this.type = { + require(c >= 0.0) require(a.length == k) - // Extension to the original paper to handle b < 0. confidence is a function of |b| instead - // so that it is never negative. - val confidence = 1.0 + alpha * math.abs(b) copyToDouble(a) - blas.dspr(upper, k, confidence - 1.0, da, 1, ata) - // For b <= 0, the corresponding preference is 0. So the term below is only added for b > 0. - if (b > 0) { - blas.daxpy(k, confidence, da, 1, atb, 1) + blas.dspr(upper, k, c, da, 1, ata) + if (b != 0.0) { + blas.daxpy(k, c * b, da, 1, atb, 1) } this } @@ -475,7 +465,6 @@ object ALS extends Logging { require(other.k == k) blas.daxpy(ata.length, 1.0, other.ata, 1, ata, 1) blas.daxpy(atb.length, 1.0, other.atb, 1, atb, 1) - n += other.n this } @@ -483,7 +472,6 @@ object ALS extends Logging { def reset(): Unit = { ju.Arrays.fill(ata, 0.0) ju.Arrays.fill(atb, 0.0) - n = 0 } } @@ -1114,6 +1102,7 @@ object ALS extends Logging { ls.merge(YtY.get) } var i = srcPtrs(j) + var numExplicits = 0 while (i < srcPtrs(j + 1)) { val encoded = srcEncodedIndices(i) val blockId = srcEncoder.blockId(encoded) @@ -1121,13 +1110,23 @@ object ALS extends Logging { val srcFactor = sortedSrcFactors(blockId)(localIndex) val rating = ratings(i) if (implicitPrefs) { - ls.addImplicit(srcFactor, rating, alpha) + // Extension to the original paper to handle b < 0. confidence is a function of |b| + // instead so that it is never negative. c1 is confidence - 1.0. + val c1 = alpha * math.abs(rating) + // For rating <= 0, the corresponding preference is 0. So the term below is only added + // for rating > 0. Because YtY is already added, we need to adjust the scaling here. + if (rating > 0) { + numExplicits += 1 + ls.add(srcFactor, (c1 + 1.0) / c1, c1) + } } else { ls.add(srcFactor, rating) + numExplicits += 1 } i += 1 } - dstFactors(j) = solver.solve(ls, regParam) + // Weight lambda by the number of explicit ratings based on the ALS-WR paper. + dstFactors(j) = solver.solve(ls, numExplicits * regParam) j += 1 } dstFactors @@ -1141,7 +1140,7 @@ object ALS extends Logging { private def computeYtY(factorBlocks: RDD[(Int, FactorBlock)], rank: Int): NormalEquation = { factorBlocks.values.aggregate(new NormalEquation(rank))( seqOp = (ne, factors) => { - factors.foreach(ne.add(_, 0.0f)) + factors.foreach(ne.add(_, 0.0)) ne }, combOp = (ne1, ne2) => ne1.merge(ne2)) diff --git a/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala index 0bb06e9e8ac9c..29d4ec5f85c1e 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala @@ -68,39 +68,42 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging { } } - test("normal equation construction with explict feedback") { + test("normal equation construction") { val k = 2 val ne0 = new NormalEquation(k) - .add(Array(1.0f, 2.0f), 3.0f) - .add(Array(4.0f, 5.0f), 6.0f) + .add(Array(1.0f, 2.0f), 3.0) + .add(Array(4.0f, 5.0f), 6.0, 2.0) // weighted assert(ne0.k === k) assert(ne0.triK === k * (k + 1) / 2) - assert(ne0.n === 2) // NumPy code that computes the expected values: // A = np.matrix("1 2; 4 5") // b = np.matrix("3; 6") - // ata = A.transpose() * A - // atb = A.transpose() * b - assert(Vectors.dense(ne0.ata) ~== Vectors.dense(17.0, 22.0, 29.0) relTol 1e-8) - assert(Vectors.dense(ne0.atb) ~== Vectors.dense(27.0, 36.0) relTol 1e-8) + // C = np.matrix(np.diag([1, 2])) + // ata = A.transpose() * C * A + // atb = A.transpose() * C * b + assert(Vectors.dense(ne0.ata) ~== Vectors.dense(33.0, 42.0, 54.0) relTol 1e-8) + assert(Vectors.dense(ne0.atb) ~== Vectors.dense(51.0, 66.0) relTol 1e-8) val ne1 = new NormalEquation(2) - .add(Array(7.0f, 8.0f), 9.0f) + .add(Array(7.0f, 8.0f), 9.0) ne0.merge(ne1) - assert(ne0.n === 3) // NumPy code that computes the expected values: // A = np.matrix("1 2; 4 5; 7 8") // b = np.matrix("3; 6; 9") - // ata = A.transpose() * A - // atb = A.transpose() * b - assert(Vectors.dense(ne0.ata) ~== Vectors.dense(66.0, 78.0, 93.0) relTol 1e-8) - assert(Vectors.dense(ne0.atb) ~== Vectors.dense(90.0, 108.0) relTol 1e-8) + // C = np.matrix(np.diag([1, 2, 1])) + // ata = A.transpose() * C * A + // atb = A.transpose() * C * b + assert(Vectors.dense(ne0.ata) ~== Vectors.dense(82.0, 98.0, 118.0) relTol 1e-8) + assert(Vectors.dense(ne0.atb) ~== Vectors.dense(114.0, 138.0) relTol 1e-8) intercept[IllegalArgumentException] { - ne0.add(Array(1.0f), 2.0f) + ne0.add(Array(1.0f), 2.0) } intercept[IllegalArgumentException] { - ne0.add(Array(1.0f, 2.0f, 3.0f), 4.0f) + ne0.add(Array(1.0f, 2.0f, 3.0f), 4.0) + } + intercept[IllegalArgumentException] { + ne0.add(Array(1.0f, 2.0f), 0.0, -1.0) } intercept[IllegalArgumentException] { val ne2 = new NormalEquation(3) @@ -108,41 +111,16 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging { } ne0.reset() - assert(ne0.n === 0) assert(ne0.ata.forall(_ == 0.0)) assert(ne0.atb.forall(_ == 0.0)) } - test("normal equation construction with implicit feedback") { - val k = 2 - val alpha = 0.5 - val ne0 = new NormalEquation(k) - .addImplicit(Array(-5.0f, -4.0f), -3.0f, alpha) - .addImplicit(Array(-2.0f, -1.0f), 0.0f, alpha) - .addImplicit(Array(1.0f, 2.0f), 3.0f, alpha) - assert(ne0.k === k) - assert(ne0.triK === k * (k + 1) / 2) - assert(ne0.n === 0) // addImplicit doesn't increase the count. - // NumPy code that computes the expected values: - // alpha = 0.5 - // A = np.matrix("-5 -4; -2 -1; 1 2") - // b = np.matrix("-3; 0; 3") - // b1 = b > 0 - // c = 1.0 + alpha * np.abs(b) - // C = np.diag(c.A1) - // I = np.eye(3) - // ata = A.transpose() * (C - I) * A - // atb = A.transpose() * C * b1 - assert(Vectors.dense(ne0.ata) ~== Vectors.dense(39.0, 33.0, 30.0) relTol 1e-8) - assert(Vectors.dense(ne0.atb) ~== Vectors.dense(2.5, 5.0) relTol 1e-8) - } - test("CholeskySolver") { val k = 2 val ne0 = new NormalEquation(k) - .add(Array(1.0f, 2.0f), 4.0f) - .add(Array(1.0f, 3.0f), 9.0f) - .add(Array(1.0f, 4.0f), 16.0f) + .add(Array(1.0f, 2.0f), 4.0) + .add(Array(1.0f, 3.0f), 9.0) + .add(Array(1.0f, 4.0f), 16.0) val ne1 = new NormalEquation(k) .merge(ne0) @@ -154,13 +132,12 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext with Logging { // x0 = np.linalg.lstsq(A, b)[0] assert(Vectors.dense(x0) ~== Vectors.dense(-8.333333, 6.0) relTol 1e-6) - assert(ne0.n === 0) assert(ne0.ata.forall(_ == 0.0)) assert(ne0.atb.forall(_ == 0.0)) - val x1 = chol.solve(ne1, 0.5).map(_.toDouble) + val x1 = chol.solve(ne1, 1.5).map(_.toDouble) // NumPy code that computes the expected solution, where lambda is scaled by n: - // x0 = np.linalg.solve(A.transpose() * A + 0.5 * 3 * np.eye(2), A.transpose() * b) + // x0 = np.linalg.solve(A.transpose() * A + 1.5 * np.eye(2), A.transpose() * b) assert(Vectors.dense(x1) ~== Vectors.dense(-0.1155556, 3.28) relTol 1e-6) } diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py index b094e50856f70..c5c4c13dae105 100644 --- a/python/pyspark/mllib/recommendation.py +++ b/python/pyspark/mllib/recommendation.py @@ -52,7 +52,7 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): >>> ratings = sc.parallelize([r1, r2, r3]) >>> model = ALS.trainImplicit(ratings, 1, seed=10) >>> model.predict(2, 2) - 0.43... + 0.4... >>> testset = sc.parallelize([(1, 2), (1, 1)]) >>> model = ALS.train(ratings, 2, seed=0) @@ -82,14 +82,14 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): >>> model = ALS.trainImplicit(ratings, 1, nonnegative=True, seed=10) >>> model.predict(2,2) - 0.43... + 0.4... >>> import os, tempfile >>> path = tempfile.mkdtemp() >>> model.save(sc, path) >>> sameModel = MatrixFactorizationModel.load(sc, path) >>> sameModel.predict(2,2) - 0.43... + 0.4... >>> sameModel.predictAll(testset).collect() [Rating(... >>> try: From 2fa3b47dbf38aae58514473932c69bbd35de4e4c Mon Sep 17 00:00:00 2001 From: MechCoder Date: Wed, 1 Apr 2015 17:03:39 -0700 Subject: [PATCH 130/591] [SPARK-6576] [MLlib] [PySpark] DenseMatrix in PySpark should support indexing Support indexing in DenseMatrices in PySpark Author: MechCoder Closes #5232 from MechCoder/SPARK-6576 and squashes the following commits: a735078 [MechCoder] Change bounds a062025 [MechCoder] Matrices are stored in column order 7917bc1 [MechCoder] [SPARK-6576] DenseMatrix in PySpark should support indexing --- python/pyspark/mllib/linalg.py | 10 ++++++++++ python/pyspark/mllib/tests.py | 7 +++++++ 2 files changed, 17 insertions(+) diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py index 8b791ff6a7877..51c1490b1618d 100644 --- a/python/pyspark/mllib/linalg.py +++ b/python/pyspark/mllib/linalg.py @@ -670,6 +670,16 @@ def toArray(self): """ return self.values.reshape((self.numRows, self.numCols), order='F') + def __getitem__(self, indices): + i, j = indices + if i < 0 or i >= self.numRows: + raise ValueError("Row index %d is out of range [0, %d)" + % (i, self.numRows)) + if j >= self.numCols or j < 0: + raise ValueError("Column index %d is out of range [0, %d)" + % (j, self.numCols)) + return self.values[i + j * self.numRows] + def __eq__(self, other): return (isinstance(other, DenseMatrix) and self.numRows == other.numRows and diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 3bb0f0ca68128..893fc6f491ab3 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -135,6 +135,13 @@ def test_sparse_vector_indexing(self): for ind in [4, -5, 7.8]: self.assertRaises(ValueError, sv.__getitem__, ind) + def test_matrix_indexing(self): + mat = DenseMatrix(3, 2, [0, 1, 4, 6, 8, 10]) + expected = [[0, 6], [1, 8], [4, 10]] + for i in range(3): + for j in range(2): + self.assertEquals(mat[i, j], expected[i][j]) + class ListTests(PySparkTestCase): From 86b43993517104e6d5ad0785704ceec6db8acc20 Mon Sep 17 00:00:00 2001 From: Yanbo Liang Date: Wed, 1 Apr 2015 17:19:36 -0700 Subject: [PATCH 131/591] [SPARK-6580] [MLLIB] Optimize LogisticRegressionModel.predictPoint https://issues.apache.org/jira/browse/SPARK-6580 Author: Yanbo Liang Closes #5249 from yanboliang/spark-6580 and squashes the following commits: 6f47f21 [Yanbo Liang] address comments 4e0bd0f [Yanbo Liang] fix typos 04e2e2a [Yanbo Liang] trigger jenkins cad5bcd [Yanbo Liang] Optimize LogisticRegressionModel.predictPoint --- .../classification/LogisticRegression.scala | 55 +++++++++---------- 1 file changed, 26 insertions(+), 29 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala index e7c3599ff619c..057b628c6a586 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala @@ -62,6 +62,15 @@ class LogisticRegressionModel ( s" but was given weights of length ${weights.size}") } + private val dataWithBiasSize: Int = weights.size / (numClasses - 1) + + private val weightsArray: Array[Double] = weights match { + case dv: DenseVector => dv.values + case _ => + throw new IllegalArgumentException( + s"weights only supports dense vector but got type ${weights.getClass}.") + } + /** * Constructs a [[LogisticRegressionModel]] with weights and intercept for binary classification. */ @@ -74,6 +83,7 @@ class LogisticRegressionModel ( * Sets the threshold that separates positive predictions from negative predictions * in Binary Logistic Regression. An example with prediction score greater than or equal to * this threshold is identified as an positive, and negative otherwise. The default value is 0.5. + * It is only used for binary classification. */ @Experimental def setThreshold(threshold: Double): this.type = { @@ -84,6 +94,7 @@ class LogisticRegressionModel ( /** * :: Experimental :: * Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions. + * It is only used for binary classification. */ @Experimental def getThreshold: Option[Double] = threshold @@ -91,6 +102,7 @@ class LogisticRegressionModel ( /** * :: Experimental :: * Clears the threshold so that `predict` will output raw prediction scores. + * It is only used for binary classification. */ @Experimental def clearThreshold(): this.type = { @@ -106,7 +118,6 @@ class LogisticRegressionModel ( // If dataMatrix and weightMatrix have the same dimension, it's binary logistic regression. if (numClasses == 2) { - require(numFeatures == weightMatrix.size) val margin = dot(weightMatrix, dataMatrix) + intercept val score = 1.0 / (1.0 + math.exp(-margin)) threshold match { @@ -114,30 +125,9 @@ class LogisticRegressionModel ( case None => score } } else { - val dataWithBiasSize = weightMatrix.size / (numClasses - 1) - - val weightsArray = weightMatrix match { - case dv: DenseVector => dv.values - case _ => - throw new IllegalArgumentException( - s"weights only supports dense vector but got type ${weightMatrix.getClass}.") - } - - val margins = (0 until numClasses - 1).map { i => - var margin = 0.0 - dataMatrix.foreachActive { (index, value) => - if (value != 0.0) margin += value * weightsArray((i * dataWithBiasSize) + index) - } - // Intercept is required to be added into margin. - if (dataMatrix.size + 1 == dataWithBiasSize) { - margin += weightsArray((i * dataWithBiasSize) + dataMatrix.size) - } - margin - } - /** - * Find the one with maximum margins. If the maxMargin is negative, then the prediction - * result will be the first class. + * Compute and find the one with maximum margins. If the maxMargin is negative, then the + * prediction result will be the first class. * * PS, if you want to compute the probabilities for each outcome instead of the outcome * with maximum probability, remember to subtract the maxMargin from margins if maxMargin @@ -145,13 +135,20 @@ class LogisticRegressionModel ( */ var bestClass = 0 var maxMargin = 0.0 - var i = 0 - while(i < margins.size) { - if (margins(i) > maxMargin) { - maxMargin = margins(i) + val withBias = dataMatrix.size + 1 == dataWithBiasSize + (0 until numClasses - 1).foreach { i => + var margin = 0.0 + dataMatrix.foreachActive { (index, value) => + if (value != 0.0) margin += value * weightsArray((i * dataWithBiasSize) + index) + } + // Intercept is required to be added into margin. + if (withBias) { + margin += weightsArray((i * dataWithBiasSize) + dataMatrix.size) + } + if (margin > maxMargin) { + maxMargin = margin bestClass = i + 1 } - i += 1 } bestClass.toDouble } From 757b2e91756ba49d7d1ab89abf19b00c7f5fd721 Mon Sep 17 00:00:00 2001 From: ksonj Date: Wed, 1 Apr 2015 17:23:57 -0700 Subject: [PATCH 132/591] [SPARK-6553] [pyspark] Support functools.partial as UDF Use `f.__repr__()` instead of `f.__name__` when instantiating `UserDefinedFunction`s, so `functools.partial`s may be used. Author: ksonj Closes #5206 from ksonj/partials and squashes the following commits: ea66f3d [ksonj] Inserted blank lines for PEP8 compliance d81b02b [ksonj] added tests for udf with partial function and callable object 2c76100 [ksonj] Makes UDFs work with all types of callables b814a12 [ksonj] support functools.partial as udf (cherry picked from commit 98f72dfc17853b570d05c20e97c78919682b6df6) Signed-off-by: Josh Rosen --- python/pyspark/sql/functions.py | 3 ++- python/pyspark/sql/tests.py | 31 +++++++++++++++++++++++++++++++ 2 files changed, 33 insertions(+), 1 deletion(-) diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py index 8a478fddf0e95..146ba6f3e0d98 100644 --- a/python/pyspark/sql/functions.py +++ b/python/pyspark/sql/functions.py @@ -123,7 +123,8 @@ def _create_judf(self): pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command, self) ssql_ctx = sc._jvm.SQLContext(sc._jsc.sc()) jdt = ssql_ctx.parseDataType(self.returnType.json()) - judf = sc._jvm.UserDefinedPythonFunction(f.__name__, bytearray(pickled_command), env, + fname = f.__name__ if hasattr(f, '__name__') else f.__class__.__name__ + judf = sc._jvm.UserDefinedPythonFunction(fname, bytearray(pickled_command), env, includes, sc.pythonExec, broadcast_vars, sc._javaAccumulator, jdt) return judf diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index 258464b7f230d..b3a6a2c6a9229 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -25,6 +25,7 @@ import shutil import tempfile import pickle +import functools import py4j @@ -41,6 +42,7 @@ from pyspark.sql.types import * from pyspark.sql.types import UserDefinedType, _infer_type from pyspark.tests import ReusedPySparkTestCase +from pyspark.sql.functions import UserDefinedFunction class ExamplePointUDT(UserDefinedType): @@ -114,6 +116,35 @@ def tearDownClass(cls): ReusedPySparkTestCase.tearDownClass() shutil.rmtree(cls.tempdir.name, ignore_errors=True) + def test_udf_with_callable(self): + d = [Row(number=i, squared=i**2) for i in range(10)] + rdd = self.sc.parallelize(d) + data = self.sqlCtx.createDataFrame(rdd) + + class PlusFour: + def __call__(self, col): + if col is not None: + return col + 4 + + call = PlusFour() + pudf = UserDefinedFunction(call, LongType()) + res = data.select(pudf(data['number']).alias('plus_four')) + self.assertEqual(res.agg({'plus_four': 'sum'}).collect()[0][0], 85) + + def test_udf_with_partial_function(self): + d = [Row(number=i, squared=i**2) for i in range(10)] + rdd = self.sc.parallelize(d) + data = self.sqlCtx.createDataFrame(rdd) + + def some_func(col, param): + if col is not None: + return col + param + + pfunc = functools.partial(some_func, param=4) + pudf = UserDefinedFunction(pfunc, LongType()) + res = data.select(pudf(data['number']).alias('plus_four')) + self.assertEqual(res.agg({'plus_four': 'sum'}).collect()[0][0], 85) + def test_udf(self): self.sqlCtx.registerFunction("twoArgs", lambda x, y: len(x) + y, IntegerType()) [row] = self.sqlCtx.sql("SELECT twoArgs('test', 1)").collect() From 4815bc2128c7f6d4d21da730b8c72da087233b34 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Wed, 1 Apr 2015 18:17:07 -0700 Subject: [PATCH 133/591] [SPARK-6660][MLLIB] pythonToJava doesn't recognize object arrays davies Author: Xiangrui Meng Closes #5318 from mengxr/SPARK-6660 and squashes the following commits: 0f66ec2 [Xiangrui Meng] recognize object arrays ad8c42f [Xiangrui Meng] add a test for SPARK-6660 --- .../apache/spark/mllib/api/python/PythonMLLibAPI.scala | 5 ++++- python/pyspark/mllib/tests.py | 8 ++++++++ 2 files changed, 12 insertions(+), 1 deletion(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index 662ec5fbed453..5995d6df97c15 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -1113,7 +1113,10 @@ private[spark] object SerDe extends Serializable { iter.flatMap { row => val obj = unpickle.loads(row) if (batched) { - obj.asInstanceOf[JArrayList[_]].asScala + obj match { + case list: JArrayList[_] => list.asScala + case arr: Array[_] => arr + } } else { Seq(obj) } diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 893fc6f491ab3..6e9c68ec8a5c1 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -36,6 +36,7 @@ else: import unittest +from pyspark.mllib.common import _to_java_object_rdd from pyspark.mllib.linalg import Vector, SparseVector, DenseVector, VectorUDT, _convert_to_vector,\ DenseMatrix, Vectors, Matrices from pyspark.mllib.regression import LabeledPoint @@ -641,6 +642,13 @@ def test_idf_model(self): idf = model.idf() self.assertEqual(len(idf), 11) + +class SerDeTest(PySparkTestCase): + def test_to_java_object_rdd(self): # SPARK-6660 + data = RandomRDDs.uniformRDD(self.sc, 10, 5, seed=0L) + self.assertEqual(_to_java_object_rdd(data).count(), 10) + + if __name__ == "__main__": if not _have_scipy: print "NOTE: Skipping SciPy tests as it does not seem to be installed" From 899ebcb1448126f40be784ce42e69218e9a1ead7 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Wed, 1 Apr 2015 18:36:06 -0700 Subject: [PATCH 134/591] [SPARK-6578] Small rewrite to make the logic more clear in MessageWithHeader.transferTo. Author: Reynold Xin Closes #5319 from rxin/SPARK-6578 and squashes the following commits: 7c62a64 [Reynold Xin] Small rewrite to make the logic more clear in transferTo. --- .../network/protocol/MessageWithHeader.java | 43 ++++++++++--------- 1 file changed, 23 insertions(+), 20 deletions(-) diff --git a/network/common/src/main/java/org/apache/spark/network/protocol/MessageWithHeader.java b/network/common/src/main/java/org/apache/spark/network/protocol/MessageWithHeader.java index 215a8517e8608..d686a951467cf 100644 --- a/network/common/src/main/java/org/apache/spark/network/protocol/MessageWithHeader.java +++ b/network/common/src/main/java/org/apache/spark/network/protocol/MessageWithHeader.java @@ -21,15 +21,15 @@ import java.nio.channels.WritableByteChannel; import com.google.common.base.Preconditions; -import com.google.common.primitives.Ints; import io.netty.buffer.ByteBuf; import io.netty.channel.FileRegion; import io.netty.util.AbstractReferenceCounted; import io.netty.util.ReferenceCountUtil; /** - * A wrapper message that holds two separate pieces (a header and a body) to avoid - * copying the body's content. + * A wrapper message that holds two separate pieces (a header and a body). + * + * The header must be a ByteBuf, while the body can be a ByteBuf or a FileRegion. */ class MessageWithHeader extends AbstractReferenceCounted implements FileRegion { @@ -63,32 +63,36 @@ public long transfered() { return totalBytesTransferred; } + /** + * This code is more complicated than you would think because we might require multiple + * transferTo invocations in order to transfer a single MessageWithHeader to avoid busy waiting. + * + * The contract is that the caller will ensure position is properly set to the total number + * of bytes transferred so far (i.e. value returned by transfered()). + */ @Override - public long transferTo(WritableByteChannel target, long position) throws IOException { + public long transferTo(final WritableByteChannel target, final long position) throws IOException { Preconditions.checkArgument(position == totalBytesTransferred, "Invalid position."); - long written = 0; - - if (position < headerLength) { - written += copyByteBuf(header, target); + // Bytes written for header in this call. + long writtenHeader = 0; + if (header.readableBytes() > 0) { + writtenHeader = copyByteBuf(header, target); + totalBytesTransferred += writtenHeader; if (header.readableBytes() > 0) { - totalBytesTransferred += written; - return written; + return writtenHeader; } } + // Bytes written for body in this call. + long writtenBody = 0; if (body instanceof FileRegion) { - // Adjust the position. If the write is happening as part of the same call where the header - // (or some part of it) is written, `position` will be less than the header size, so we want - // to start from position 0 in the FileRegion object. Otherwise, we start from the position - // requested by the caller. - long bodyPos = position > headerLength ? position - headerLength : 0; - written += ((FileRegion)body).transferTo(target, bodyPos); + writtenBody = ((FileRegion) body).transferTo(target, totalBytesTransferred - headerLength); } else if (body instanceof ByteBuf) { - written += copyByteBuf((ByteBuf) body, target); + writtenBody = copyByteBuf((ByteBuf) body, target); } + totalBytesTransferred += writtenBody; - totalBytesTransferred += written; - return written; + return writtenHeader + writtenBody; } @Override @@ -102,5 +106,4 @@ private int copyByteBuf(ByteBuf buf, WritableByteChannel target) throws IOExcept buf.skipBytes(written); return written; } - } From 191524e7401fcdfae46dc7e6a64c28907b1b1c20 Mon Sep 17 00:00:00 2001 From: Chet Mancini Date: Wed, 1 Apr 2015 21:39:46 -0700 Subject: [PATCH 135/591] [SPARK-6658][SQL] Update DataFrame documentation to fix type references. First contribution here; would love to be getting some code contributions in soon. Let me know if there's anything about contribution process I should improve. Author: Chet Mancini Closes #5316 from chetmancini/SPARK_6658_dataframe_doc and squashes the following commits: 53b627a [Chet Mancini] [SQL] SPARK-6658: Update DataFrame documentation to refer to correct types --- .../main/scala/org/apache/spark/sql/DataFrame.scala | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 19cfa15f27b09..ce0890906bf1b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -273,7 +273,7 @@ class DataFrame private[sql]( def printSchema(): Unit = println(schema.treeString) /** - * Prints the plans (logical and physical) to the console for debugging purpose. + * Prints the plans (logical and physical) to the console for debugging purposes. * @group basic */ def explain(extended: Boolean): Unit = { @@ -285,7 +285,7 @@ class DataFrame private[sql]( } /** - * Only prints the physical plan to the console for debugging purpose. + * Only prints the physical plan to the console for debugging purposes. * @group basic */ def explain(): Unit = explain(extended = false) @@ -976,8 +976,8 @@ class DataFrame private[sql]( def javaRDD: JavaRDD[Row] = toJavaRDD /** - * Registers this RDD as a temporary table using the given name. The lifetime of this temporary - * table is tied to the [[SQLContext]] that was used to create this DataFrame. + * Registers this [[DataFrame]] as a temporary table using the given name. The lifetime of this + * temporary table is tied to the [[SQLContext]] that was used to create this DataFrame. * * @group basic */ @@ -1252,7 +1252,7 @@ class DataFrame private[sql]( //////////////////////////////////////////////////////////////////////////// /** - * Save this RDD to a JDBC database at `url` under the table name `table`. + * Save this [[DataFrame]] to a JDBC database at `url` under the table name `table`. * This will run a `CREATE TABLE` and a bunch of `INSERT INTO` statements. * If you pass `true` for `allowExisting`, it will drop any table with the * given name; if you pass `false`, it will throw if the table already @@ -1276,7 +1276,7 @@ class DataFrame private[sql]( } /** - * Save this RDD to a JDBC database at `url` under the table name `table`. + * Save this [[DataFrame]] to a JDBC database at `url` under the table name `table`. * Assumes the table already exists and has a compatible schema. If you * pass `true` for `overwrite`, it will `TRUNCATE` the table before * performing the `INSERT`s. From 2bc7fe7f7eb31b8f0591611b1e66b601bba8a4b7 Mon Sep 17 00:00:00 2001 From: Cheng Lian Date: Thu, 2 Apr 2015 12:56:34 +0800 Subject: [PATCH 136/591] Revert "[SPARK-6618][SQL] HiveMetastoreCatalog.lookupRelation should use fine-grained lock" This reverts commit 314afd0e2f08dd8d3333d3143712c2c79fa40d1e. --- .../apache/spark/sql/hive/HiveMetastoreCatalog.scala | 12 +++--------- .../spark/sql/hive/execution/SQLQuerySuite.scala | 11 ----------- 2 files changed, 3 insertions(+), 20 deletions(-) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index 2b5d031741a63..f0076cef13777 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -173,16 +173,12 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with def lookupRelation( tableIdentifier: Seq[String], - alias: Option[String]): LogicalPlan = { + alias: Option[String]): LogicalPlan = synchronized { val tableIdent = processTableIdentifier(tableIdentifier) val databaseName = tableIdent.lift(tableIdent.size - 2).getOrElse( hive.sessionState.getCurrentDatabase) val tblName = tableIdent.last - val table = try { - synchronized { - client.getTable(databaseName, tblName) - } - } catch { + val table = try client.getTable(databaseName, tblName) catch { case te: org.apache.hadoop.hive.ql.metadata.InvalidTableException => throw new NoSuchTableException } @@ -204,9 +200,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with } else { val partitions: Seq[Partition] = if (table.isPartitioned) { - synchronized { - HiveShim.getAllPartitionsOf(client, table).toSeq - } + HiveShim.getAllPartitionsOf(client, table).toSeq } else { Nil } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala index 2065f0d60d92f..310c2bfdf1011 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala @@ -457,15 +457,4 @@ class SQLQuerySuite extends QueryTest { dropTempTable("data") setConf("spark.sql.hive.convertCTAS", originalConf) } - - test("sanity test for SPARK-6618") { - (1 to 100).par.map { i => - val tableName = s"SPARK_6618_table_$i" - sql(s"CREATE TABLE $tableName (col1 string)") - catalog.lookupRelation(Seq(tableName)) - table(tableName) - tables() - sql(s"DROP TABLE $tableName") - } - } } From 40df5d49bb5c80cd3a1e2d7c853c0b5ea901adf3 Mon Sep 17 00:00:00 2001 From: Davies Liu Date: Wed, 1 Apr 2015 23:11:38 -0700 Subject: [PATCH 137/591] [SPARK-6663] [SQL] use Literal.create instread of constructor In order to do inbound checking and type conversion, we should use Literal.create() instead of constructor. Author: Davies Liu Closes #5320 from davies/literal and squashes the following commits: 1667604 [Davies Liu] fix style and add comment 5f8c0fd [Davies Liu] use Literal.create instread of constructor --- .../apache/spark/sql/catalyst/SqlParser.scala | 8 +- .../sql/catalyst/analysis/Analyzer.scala | 4 +- .../catalyst/analysis/HiveTypeCoercion.scala | 2 +- .../sql/catalyst/expressions/aggregates.scala | 3 +- .../sql/catalyst/expressions/literals.scala | 7 +- .../sql/catalyst/optimizer/Optimizer.scala | 42 ++-- .../analysis/HiveTypeCoercionSuite.scala | 4 +- .../ExpressionEvaluationSuite.scala | 204 +++++++++--------- .../optimizer/ConstantFoldingSuite.scala | 70 +++--- .../sql/catalyst/trees/TreeNodeSuite.scala | 2 +- .../sql/execution/GeneratedAggregate.scala | 8 +- .../apache/spark/sql/parquet/newParquet.scala | 21 +- .../ParquetPartitionDiscoverySuite.scala | 20 +- .../spark/sql/hive/HiveInspectors.scala | 2 +- .../org/apache/spark/sql/hive/HiveQl.scala | 20 +- .../spark/sql/hive/HiveInspectorSuite.scala | 16 +- 16 files changed, 220 insertions(+), 213 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala index b176f7e729a42..89f4a19add1c6 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala @@ -316,13 +316,13 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser { protected lazy val literal: Parser[Literal] = ( numericLiteral | booleanLiteral - | stringLit ^^ {case s => Literal(s, StringType) } - | NULL ^^^ Literal(null, NullType) + | stringLit ^^ {case s => Literal.create(s, StringType) } + | NULL ^^^ Literal.create(null, NullType) ) protected lazy val booleanLiteral: Parser[Literal] = - ( TRUE ^^^ Literal(true, BooleanType) - | FALSE ^^^ Literal(false, BooleanType) + ( TRUE ^^^ Literal.create(true, BooleanType) + | FALSE ^^^ Literal.create(false, BooleanType) ) protected lazy val numericLiteral: Parser[Literal] = diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala index c578d084a45b6..119cb9c3a4400 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala @@ -140,10 +140,10 @@ class Analyzer( case x: Expression if nonSelectedGroupExprSet.contains(x) => // if the input attribute in the Invalid Grouping Expression set of for this group // replace it with constant null - Literal(null, expr.dataType) + Literal.create(null, expr.dataType) case x if x == g.gid => // replace the groupingId with concrete value (the bit mask) - Literal(bitmask, IntegerType) + Literal.create(bitmask, IntegerType) }) result += GroupExpression(substitution) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala index 3c7b46e0702a2..9a33eb145273e 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala @@ -115,7 +115,7 @@ trait HiveTypeCoercion { * the appropriate numeric equivalent. */ object ConvertNaNs extends Rule[LogicalPlan] { - val stringNaN = Literal("NaN", StringType) + val stringNaN = Literal.create("NaN", StringType) def apply(plan: LogicalPlan): LogicalPlan = plan transform { case q: LogicalPlan => q transformExpressions { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala index 30da4faa3f1c6..406de38d1c483 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala @@ -505,7 +505,8 @@ case class AverageFunction(expr: Expression, base: AggregateExpression) private var count: Long = _ private val sum = MutableLiteral(zero.eval(null), calcType) - private def addFunction(value: Any) = Add(sum, Cast(Literal(value, expr.dataType), calcType)) + private def addFunction(value: Any) = Add(sum, + Cast(Literal.create(value, expr.dataType), calcType)) override def eval(input: Row): Any = { if (count == 0L) { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala index 19f3fc9c2291a..0e2d593e94124 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala @@ -41,6 +41,8 @@ object Literal { case _ => throw new RuntimeException("Unsupported literal type " + v.getClass + " " + v) } + + def create(v: Any, dataType: DataType): Literal = Literal(v, dataType) } /** @@ -62,7 +64,10 @@ object IntegerLiteral { } } -case class Literal(value: Any, dataType: DataType) extends LeafExpression { +/** + * In order to do type checking, use Literal.create() instead of constructor + */ +case class Literal protected (value: Any, dataType: DataType) extends LeafExpression { override def foldable: Boolean = true override def nullable: Boolean = value == null diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala index c23d3b61887c6..93e69d409cb91 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala @@ -218,12 +218,12 @@ object NullPropagation extends Rule[LogicalPlan] { def apply(plan: LogicalPlan): LogicalPlan = plan transform { case q: LogicalPlan => q transformExpressionsUp { case e @ Count(Literal(null, _)) => Cast(Literal(0L), e.dataType) - case e @ IsNull(c) if !c.nullable => Literal(false, BooleanType) - case e @ IsNotNull(c) if !c.nullable => Literal(true, BooleanType) - case e @ GetItem(Literal(null, _), _) => Literal(null, e.dataType) - case e @ GetItem(_, Literal(null, _)) => Literal(null, e.dataType) - case e @ StructGetField(Literal(null, _), _, _) => Literal(null, e.dataType) - case e @ ArrayGetField(Literal(null, _), _, _, _) => Literal(null, e.dataType) + case e @ IsNull(c) if !c.nullable => Literal.create(false, BooleanType) + case e @ IsNotNull(c) if !c.nullable => Literal.create(true, BooleanType) + case e @ GetItem(Literal(null, _), _) => Literal.create(null, e.dataType) + case e @ GetItem(_, Literal(null, _)) => Literal.create(null, e.dataType) + case e @ StructGetField(Literal(null, _), _, _) => Literal.create(null, e.dataType) + case e @ ArrayGetField(Literal(null, _), _, _, _) => Literal.create(null, e.dataType) case e @ EqualNullSafe(Literal(null, _), r) => IsNull(r) case e @ EqualNullSafe(l, Literal(null, _)) => IsNull(l) case e @ Count(expr) if !expr.nullable => Count(Literal(1)) @@ -235,36 +235,36 @@ object NullPropagation extends Rule[LogicalPlan] { case _ => true } if (newChildren.length == 0) { - Literal(null, e.dataType) + Literal.create(null, e.dataType) } else if (newChildren.length == 1) { newChildren(0) } else { Coalesce(newChildren) } - case e @ Substring(Literal(null, _), _, _) => Literal(null, e.dataType) - case e @ Substring(_, Literal(null, _), _) => Literal(null, e.dataType) - case e @ Substring(_, _, Literal(null, _)) => Literal(null, e.dataType) + case e @ Substring(Literal(null, _), _, _) => Literal.create(null, e.dataType) + case e @ Substring(_, Literal(null, _), _) => Literal.create(null, e.dataType) + case e @ Substring(_, _, Literal(null, _)) => Literal.create(null, e.dataType) // Put exceptional cases above if any case e: BinaryArithmetic => e.children match { - case Literal(null, _) :: right :: Nil => Literal(null, e.dataType) - case left :: Literal(null, _) :: Nil => Literal(null, e.dataType) + case Literal(null, _) :: right :: Nil => Literal.create(null, e.dataType) + case left :: Literal(null, _) :: Nil => Literal.create(null, e.dataType) case _ => e } case e: BinaryComparison => e.children match { - case Literal(null, _) :: right :: Nil => Literal(null, e.dataType) - case left :: Literal(null, _) :: Nil => Literal(null, e.dataType) + case Literal(null, _) :: right :: Nil => Literal.create(null, e.dataType) + case left :: Literal(null, _) :: Nil => Literal.create(null, e.dataType) case _ => e } case e: StringRegexExpression => e.children match { - case Literal(null, _) :: right :: Nil => Literal(null, e.dataType) - case left :: Literal(null, _) :: Nil => Literal(null, e.dataType) + case Literal(null, _) :: right :: Nil => Literal.create(null, e.dataType) + case left :: Literal(null, _) :: Nil => Literal.create(null, e.dataType) case _ => e } case e: StringComparison => e.children match { - case Literal(null, _) :: right :: Nil => Literal(null, e.dataType) - case left :: Literal(null, _) :: Nil => Literal(null, e.dataType) + case Literal(null, _) :: right :: Nil => Literal.create(null, e.dataType) + case left :: Literal(null, _) :: Nil => Literal.create(null, e.dataType) case _ => e } } @@ -284,13 +284,13 @@ object ConstantFolding extends Rule[LogicalPlan] { case l: Literal => l // Fold expressions that are foldable. - case e if e.foldable => Literal(e.eval(null), e.dataType) + case e if e.foldable => Literal.create(e.eval(null), e.dataType) // Fold "literal in (item1, item2, ..., literal, ...)" into true directly. case In(Literal(v, _), list) if list.exists { case Literal(candidate, _) if candidate == v => true case _ => false - } => Literal(true, BooleanType) + } => Literal.create(true, BooleanType) } } } @@ -647,7 +647,7 @@ object DecimalAggregates extends Rule[LogicalPlan] { case Average(e @ DecimalType.Expression(prec, scale)) if prec + 4 <= MAX_DOUBLE_DIGITS => Cast( - Divide(Average(UnscaledValue(e)), Literal(math.pow(10.0, scale), DoubleType)), + Divide(Average(UnscaledValue(e)), Literal.create(math.pow(10.0, scale), DoubleType)), DecimalType(prec + 4, scale + 4)) } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala index ecbb54218d457..70aef1cac421a 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala @@ -127,11 +127,11 @@ class HiveTypeCoercionSuite extends PlanTest { ruleTest( Coalesce(Literal(1.0) :: Literal(1) - :: Literal(1.0, FloatType) + :: Literal.create(1.0, FloatType) :: Nil), Coalesce(Cast(Literal(1.0), DoubleType) :: Cast(Literal(1), DoubleType) - :: Cast(Literal(1.0, FloatType), DoubleType) + :: Cast(Literal.create(1.0, FloatType), DoubleType) :: Nil)) ruleTest( Coalesce(Literal(1L) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala index 1183a0d899dda..3dbefa40d2808 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala @@ -111,7 +111,7 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { test("3VL Not") { notTrueTable.foreach { case (v, answer) => - checkEvaluation(!Literal(v, BooleanType), answer) + checkEvaluation(!Literal.create(v, BooleanType), answer) } } @@ -155,7 +155,7 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { test(s"3VL $name") { truthTable.foreach { case (l,r,answer) => - val expr = op(Literal(l, BooleanType), Literal(r, BooleanType)) + val expr = op(Literal.create(l, BooleanType), Literal.create(r, BooleanType)) checkEvaluation(expr, answer) } } @@ -175,12 +175,12 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(Divide(Literal(1), Literal(0)), null) checkEvaluation(Divide(Literal(1.0), Literal(0.0)), null) checkEvaluation(Divide(Literal(0.0), Literal(0.0)), null) - checkEvaluation(Divide(Literal(0), Literal(null, IntegerType)), null) - checkEvaluation(Divide(Literal(1), Literal(null, IntegerType)), null) - checkEvaluation(Divide(Literal(null, IntegerType), Literal(0)), null) - checkEvaluation(Divide(Literal(null, DoubleType), Literal(0.0)), null) - checkEvaluation(Divide(Literal(null, IntegerType), Literal(1)), null) - checkEvaluation(Divide(Literal(null, IntegerType), Literal(null, IntegerType)), null) + checkEvaluation(Divide(Literal(0), Literal.create(null, IntegerType)), null) + checkEvaluation(Divide(Literal(1), Literal.create(null, IntegerType)), null) + checkEvaluation(Divide(Literal.create(null, IntegerType), Literal(0)), null) + checkEvaluation(Divide(Literal.create(null, DoubleType), Literal(0.0)), null) + checkEvaluation(Divide(Literal.create(null, IntegerType), Literal(1)), null) + checkEvaluation(Divide(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null) } test("Remainder") { @@ -190,12 +190,12 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(Remainder(Literal(1), Literal(0)), null) checkEvaluation(Remainder(Literal(1.0), Literal(0.0)), null) checkEvaluation(Remainder(Literal(0.0), Literal(0.0)), null) - checkEvaluation(Remainder(Literal(0), Literal(null, IntegerType)), null) - checkEvaluation(Remainder(Literal(1), Literal(null, IntegerType)), null) - checkEvaluation(Remainder(Literal(null, IntegerType), Literal(0)), null) - checkEvaluation(Remainder(Literal(null, DoubleType), Literal(0.0)), null) - checkEvaluation(Remainder(Literal(null, IntegerType), Literal(1)), null) - checkEvaluation(Remainder(Literal(null, IntegerType), Literal(null, IntegerType)), null) + checkEvaluation(Remainder(Literal(0), Literal.create(null, IntegerType)), null) + checkEvaluation(Remainder(Literal(1), Literal.create(null, IntegerType)), null) + checkEvaluation(Remainder(Literal.create(null, IntegerType), Literal(0)), null) + checkEvaluation(Remainder(Literal.create(null, DoubleType), Literal(0.0)), null) + checkEvaluation(Remainder(Literal.create(null, IntegerType), Literal(1)), null) + checkEvaluation(Remainder(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null) } test("INSET") { @@ -222,14 +222,14 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(MaxOf(1L, 2L), 2L) checkEvaluation(MaxOf(2L, 1L), 2L) - checkEvaluation(MaxOf(Literal(null, IntegerType), 2), 2) - checkEvaluation(MaxOf(2, Literal(null, IntegerType)), 2) + checkEvaluation(MaxOf(Literal.create(null, IntegerType), 2), 2) + checkEvaluation(MaxOf(2, Literal.create(null, IntegerType)), 2) } test("LIKE literal Regular Expression") { - checkEvaluation(Literal(null, StringType).like("a"), null) - checkEvaluation(Literal("a", StringType).like(Literal(null, StringType)), null) - checkEvaluation(Literal(null, StringType).like(Literal(null, StringType)), null) + checkEvaluation(Literal.create(null, StringType).like("a"), null) + checkEvaluation(Literal.create("a", StringType).like(Literal.create(null, StringType)), null) + checkEvaluation(Literal.create(null, StringType).like(Literal.create(null, StringType)), null) checkEvaluation("abdef" like "abdef", true) checkEvaluation("a_%b" like "a\\__b", true) checkEvaluation("addb" like "a_%b", true) @@ -264,13 +264,13 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation("ab" like regEx, true, new GenericRow(Array[Any]("a%b"))) checkEvaluation("a\nb" like regEx, true, new GenericRow(Array[Any]("a%b"))) - checkEvaluation(Literal(null, StringType) like regEx, null, new GenericRow(Array[Any]("bc%"))) + checkEvaluation(Literal.create(null, StringType) like regEx, null, new GenericRow(Array[Any]("bc%"))) } test("RLIKE literal Regular Expression") { - checkEvaluation(Literal(null, StringType) rlike "abdef", null) - checkEvaluation("abdef" rlike Literal(null, StringType), null) - checkEvaluation(Literal(null, StringType) rlike Literal(null, StringType), null) + checkEvaluation(Literal.create(null, StringType) rlike "abdef", null) + checkEvaluation("abdef" rlike Literal.create(null, StringType), null) + checkEvaluation(Literal.create(null, StringType) rlike Literal.create(null, StringType), null) checkEvaluation("abdef" rlike "abdef", true) checkEvaluation("abbbbc" rlike "a.*c", true) @@ -381,7 +381,7 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { assert(("abcdef" cast DoubleType).nullable === true) assert(("abcdef" cast FloatType).nullable === true) - checkEvaluation(Cast(Literal(null, IntegerType), ShortType), null) + checkEvaluation(Cast(Literal.create(null, IntegerType), ShortType), null) } test("date") { @@ -507,8 +507,8 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { } test("array casting") { - val array = Literal(Seq("123", "abc", "", null), ArrayType(StringType, containsNull = true)) - val array_notNull = Literal(Seq("123", "abc", ""), ArrayType(StringType, containsNull = false)) + val array = Literal.create(Seq("123", "abc", "", null), ArrayType(StringType, containsNull = true)) + val array_notNull = Literal.create(Seq("123", "abc", ""), ArrayType(StringType, containsNull = false)) { val cast = Cast(array, ArrayType(IntegerType, containsNull = true)) @@ -556,10 +556,10 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { } test("map casting") { - val map = Literal( + val map = Literal.create( Map("a" -> "123", "b" -> "abc", "c" -> "", "d" -> null), MapType(StringType, StringType, valueContainsNull = true)) - val map_notNull = Literal( + val map_notNull = Literal.create( Map("a" -> "123", "b" -> "abc", "c" -> ""), MapType(StringType, StringType, valueContainsNull = false)) @@ -617,14 +617,14 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { } test("struct casting") { - val struct = Literal( + val struct = Literal.create( Row("123", "abc", "", null), StructType(Seq( StructField("a", StringType, nullable = true), StructField("b", StringType, nullable = true), StructField("c", StringType, nullable = true), StructField("d", StringType, nullable = true)))) - val struct_notNull = Literal( + val struct_notNull = Literal.create( Row("123", "abc", ""), StructType(Seq( StructField("a", StringType, nullable = false), @@ -712,7 +712,7 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { } test("complex casting") { - val complex = Literal( + val complex = Literal.create( Row( Seq("123", "abc", ""), Map("a" -> "123", "b" -> "abc", "c" -> ""), @@ -755,30 +755,30 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(c2.isNull, true, row) checkEvaluation(c2.isNotNull, false, row) - checkEvaluation(Literal(1, ShortType).isNull, false) - checkEvaluation(Literal(1, ShortType).isNotNull, true) + checkEvaluation(Literal.create(1, ShortType).isNull, false) + checkEvaluation(Literal.create(1, ShortType).isNotNull, true) - checkEvaluation(Literal(null, ShortType).isNull, true) - checkEvaluation(Literal(null, ShortType).isNotNull, false) + checkEvaluation(Literal.create(null, ShortType).isNull, true) + checkEvaluation(Literal.create(null, ShortType).isNotNull, false) checkEvaluation(Coalesce(c1 :: c2 :: Nil), "^Ba*n", row) - checkEvaluation(Coalesce(Literal(null, StringType) :: Nil), null, row) - checkEvaluation(Coalesce(Literal(null, StringType) :: c1 :: c2 :: Nil), "^Ba*n", row) + checkEvaluation(Coalesce(Literal.create(null, StringType) :: Nil), null, row) + checkEvaluation(Coalesce(Literal.create(null, StringType) :: c1 :: c2 :: Nil), "^Ba*n", row) - checkEvaluation(If(c3, Literal("a", StringType), Literal("b", StringType)), "a", row) + checkEvaluation(If(c3, Literal.create("a", StringType), Literal.create("b", StringType)), "a", row) checkEvaluation(If(c3, c1, c2), "^Ba*n", row) checkEvaluation(If(c4, c2, c1), "^Ba*n", row) - checkEvaluation(If(Literal(null, BooleanType), c2, c1), "^Ba*n", row) - checkEvaluation(If(Literal(true, BooleanType), c1, c2), "^Ba*n", row) - checkEvaluation(If(Literal(false, BooleanType), c2, c1), "^Ba*n", row) - checkEvaluation(If(Literal(false, BooleanType), - Literal("a", StringType), Literal("b", StringType)), "b", row) + checkEvaluation(If(Literal.create(null, BooleanType), c2, c1), "^Ba*n", row) + checkEvaluation(If(Literal.create(true, BooleanType), c1, c2), "^Ba*n", row) + checkEvaluation(If(Literal.create(false, BooleanType), c2, c1), "^Ba*n", row) + checkEvaluation(If(Literal.create(false, BooleanType), + Literal.create("a", StringType), Literal.create("b", StringType)), "b", row) checkEvaluation(c1 in (c1, c2), true, row) checkEvaluation( - Literal("^Ba*n", StringType) in (Literal("^Ba*n", StringType)), true, row) + Literal.create("^Ba*n", StringType) in (Literal.create("^Ba*n", StringType)), true, row) checkEvaluation( - Literal("^Ba*n", StringType) in (Literal("^Ba*n", StringType), c2), true, row) + Literal.create("^Ba*n", StringType) in (Literal.create("^Ba*n", StringType), c2), true, row) } test("case when") { @@ -793,9 +793,9 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(CaseWhen(Seq(c1, c4, c6)), "c", row) checkEvaluation(CaseWhen(Seq(c2, c4, c6)), "c", row) checkEvaluation(CaseWhen(Seq(c3, c4, c6)), "a", row) - checkEvaluation(CaseWhen(Seq(Literal(null, BooleanType), c4, c6)), "c", row) - checkEvaluation(CaseWhen(Seq(Literal(false, BooleanType), c4, c6)), "c", row) - checkEvaluation(CaseWhen(Seq(Literal(true, BooleanType), c4, c6)), "a", row) + checkEvaluation(CaseWhen(Seq(Literal.create(null, BooleanType), c4, c6)), "c", row) + checkEvaluation(CaseWhen(Seq(Literal.create(false, BooleanType), c4, c6)), "c", row) + checkEvaluation(CaseWhen(Seq(Literal.create(true, BooleanType), c4, c6)), "a", row) checkEvaluation(CaseWhen(Seq(c3, c4, c2, c5, c6)), "a", row) checkEvaluation(CaseWhen(Seq(c2, c4, c3, c5, c6)), "b", row) @@ -841,17 +841,17 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(GetItem(BoundReference(3, typeMap, true), Literal("aa")), "bb", row) - checkEvaluation(GetItem(Literal(null, typeMap), Literal("aa")), null, row) - checkEvaluation(GetItem(Literal(null, typeMap), Literal(null, StringType)), null, row) + checkEvaluation(GetItem(Literal.create(null, typeMap), Literal("aa")), null, row) + checkEvaluation(GetItem(Literal.create(null, typeMap), Literal.create(null, StringType)), null, row) checkEvaluation(GetItem(BoundReference(3, typeMap, true), - Literal(null, StringType)), null, row) + Literal.create(null, StringType)), null, row) checkEvaluation(GetItem(BoundReference(4, typeArray, true), Literal(1)), "bb", row) - checkEvaluation(GetItem(Literal(null, typeArray), Literal(1)), null, row) - checkEvaluation(GetItem(Literal(null, typeArray), Literal(null, IntegerType)), null, row) + checkEvaluation(GetItem(Literal.create(null, typeArray), Literal(1)), null, row) + checkEvaluation(GetItem(Literal.create(null, typeArray), Literal.create(null, IntegerType)), null, row) checkEvaluation(GetItem(BoundReference(4, typeArray, true), - Literal(null, IntegerType)), null, row) + Literal.create(null, IntegerType)), null, row) def quickBuildGetField(expr: Expression, fieldName: String) = { expr.dataType match { @@ -864,7 +864,7 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { def quickResolve(u: UnresolvedGetField) = quickBuildGetField(u.child, u.fieldName) checkEvaluation(quickBuildGetField(BoundReference(2, typeS, nullable = true), "a"), "aa", row) - checkEvaluation(quickBuildGetField(Literal(null, typeS), "a"), null, row) + checkEvaluation(quickBuildGetField(Literal.create(null, typeS), "a"), null, row) val typeS_notNullable = StructType( StructField("a", StringType, nullable = false) @@ -874,8 +874,8 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { assert(quickBuildGetField(BoundReference(2,typeS, nullable = true), "a").nullable === true) assert(quickBuildGetField(BoundReference(2, typeS_notNullable, nullable = false), "a").nullable === false) - assert(quickBuildGetField(Literal(null, typeS), "a").nullable === true) - assert(quickBuildGetField(Literal(null, typeS_notNullable), "a").nullable === true) + assert(quickBuildGetField(Literal.create(null, typeS), "a").nullable === true) + assert(quickBuildGetField(Literal.create(null, typeS_notNullable), "a").nullable === true) checkEvaluation('c.map(typeMap).at(3).getItem("aa"), "bb", row) checkEvaluation('c.array(typeArray.elementType).at(4).getItem(1), "bb", row) @@ -890,13 +890,13 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { val c4 = 'a.int.at(3) checkEvaluation(UnaryMinus(c1), -1, row) - checkEvaluation(UnaryMinus(Literal(100, IntegerType)), -100) + checkEvaluation(UnaryMinus(Literal.create(100, IntegerType)), -100) checkEvaluation(Add(c1, c4), null, row) checkEvaluation(Add(c1, c2), 3, row) - checkEvaluation(Add(c1, Literal(null, IntegerType)), null, row) - checkEvaluation(Add(Literal(null, IntegerType), c2), null, row) - checkEvaluation(Add(Literal(null, IntegerType), Literal(null, IntegerType)), null, row) + checkEvaluation(Add(c1, Literal.create(null, IntegerType)), null, row) + checkEvaluation(Add(Literal.create(null, IntegerType), c2), null, row) + checkEvaluation(Add(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) checkEvaluation(-c1, -1, row) checkEvaluation(c1 + c2, 3, row) @@ -914,12 +914,12 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { val c4 = 'a.double.at(3) checkEvaluation(UnaryMinus(c1), -1.1, row) - checkEvaluation(UnaryMinus(Literal(100.0, DoubleType)), -100.0) + checkEvaluation(UnaryMinus(Literal.create(100.0, DoubleType)), -100.0) checkEvaluation(Add(c1, c4), null, row) checkEvaluation(Add(c1, c2), 3.1, row) - checkEvaluation(Add(c1, Literal(null, DoubleType)), null, row) - checkEvaluation(Add(Literal(null, DoubleType), c2), null, row) - checkEvaluation(Add(Literal(null, DoubleType), Literal(null, DoubleType)), null, row) + checkEvaluation(Add(c1, Literal.create(null, DoubleType)), null, row) + checkEvaluation(Add(Literal.create(null, DoubleType), c2), null, row) + checkEvaluation(Add(Literal.create(null, DoubleType), Literal.create(null, DoubleType)), null, row) checkEvaluation(-c1, -1.1, row) checkEvaluation(c1 + c2, 3.1, row) @@ -940,9 +940,9 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(LessThan(c1, c4), null, row) checkEvaluation(LessThan(c1, c2), true, row) - checkEvaluation(LessThan(c1, Literal(null, IntegerType)), null, row) - checkEvaluation(LessThan(Literal(null, IntegerType), c2), null, row) - checkEvaluation(LessThan(Literal(null, IntegerType), Literal(null, IntegerType)), null, row) + checkEvaluation(LessThan(c1, Literal.create(null, IntegerType)), null, row) + checkEvaluation(LessThan(Literal.create(null, IntegerType), c2), null, row) + checkEvaluation(LessThan(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) checkEvaluation(c1 < c2, true, row) checkEvaluation(c1 <= c2, true, row) @@ -954,8 +954,8 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(c1 <=> c4, false, row) checkEvaluation(c4 <=> c6, true, row) checkEvaluation(c3 <=> c5, true, row) - checkEvaluation(Literal(true) <=> Literal(null, BooleanType), false, row) - checkEvaluation(Literal(null, BooleanType) <=> Literal(true), false, row) + checkEvaluation(Literal(true) <=> Literal.create(null, BooleanType), false, row) + checkEvaluation(Literal.create(null, BooleanType) <=> Literal(true), false, row) } test("StringComparison") { @@ -966,17 +966,17 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(c1 contains "b", true, row) checkEvaluation(c1 contains "x", false, row) checkEvaluation(c2 contains "b", null, row) - checkEvaluation(c1 contains Literal(null, StringType), null, row) + checkEvaluation(c1 contains Literal.create(null, StringType), null, row) checkEvaluation(c1 startsWith "a", true, row) checkEvaluation(c1 startsWith "b", false, row) checkEvaluation(c2 startsWith "a", null, row) - checkEvaluation(c1 startsWith Literal(null, StringType), null, row) + checkEvaluation(c1 startsWith Literal.create(null, StringType), null, row) checkEvaluation(c1 endsWith "c", true, row) checkEvaluation(c1 endsWith "b", false, row) checkEvaluation(c2 endsWith "b", null, row) - checkEvaluation(c1 endsWith Literal(null, StringType), null, row) + checkEvaluation(c1 endsWith Literal.create(null, StringType), null, row) } test("Substring") { @@ -985,54 +985,54 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { val s = 'a.string.at(0) // substring from zero position with less-than-full length - checkEvaluation(Substring(s, Literal(0, IntegerType), Literal(2, IntegerType)), "ex", row) - checkEvaluation(Substring(s, Literal(1, IntegerType), Literal(2, IntegerType)), "ex", row) + checkEvaluation(Substring(s, Literal.create(0, IntegerType), Literal.create(2, IntegerType)), "ex", row) + checkEvaluation(Substring(s, Literal.create(1, IntegerType), Literal.create(2, IntegerType)), "ex", row) // substring from zero position with full length - checkEvaluation(Substring(s, Literal(0, IntegerType), Literal(7, IntegerType)), "example", row) - checkEvaluation(Substring(s, Literal(1, IntegerType), Literal(7, IntegerType)), "example", row) + checkEvaluation(Substring(s, Literal.create(0, IntegerType), Literal.create(7, IntegerType)), "example", row) + checkEvaluation(Substring(s, Literal.create(1, IntegerType), Literal.create(7, IntegerType)), "example", row) // substring from zero position with greater-than-full length - checkEvaluation(Substring(s, Literal(0, IntegerType), Literal(100, IntegerType)), "example", row) - checkEvaluation(Substring(s, Literal(1, IntegerType), Literal(100, IntegerType)), "example", row) + checkEvaluation(Substring(s, Literal.create(0, IntegerType), Literal.create(100, IntegerType)), "example", row) + checkEvaluation(Substring(s, Literal.create(1, IntegerType), Literal.create(100, IntegerType)), "example", row) // substring from nonzero position with less-than-full length - checkEvaluation(Substring(s, Literal(2, IntegerType), Literal(2, IntegerType)), "xa", row) + checkEvaluation(Substring(s, Literal.create(2, IntegerType), Literal.create(2, IntegerType)), "xa", row) // substring from nonzero position with full length - checkEvaluation(Substring(s, Literal(2, IntegerType), Literal(6, IntegerType)), "xample", row) + checkEvaluation(Substring(s, Literal.create(2, IntegerType), Literal.create(6, IntegerType)), "xample", row) // substring from nonzero position with greater-than-full length - checkEvaluation(Substring(s, Literal(2, IntegerType), Literal(100, IntegerType)), "xample", row) + checkEvaluation(Substring(s, Literal.create(2, IntegerType), Literal.create(100, IntegerType)), "xample", row) // zero-length substring (within string bounds) - checkEvaluation(Substring(s, Literal(0, IntegerType), Literal(0, IntegerType)), "", row) + checkEvaluation(Substring(s, Literal.create(0, IntegerType), Literal.create(0, IntegerType)), "", row) // zero-length substring (beyond string bounds) - checkEvaluation(Substring(s, Literal(100, IntegerType), Literal(4, IntegerType)), "", row) + checkEvaluation(Substring(s, Literal.create(100, IntegerType), Literal.create(4, IntegerType)), "", row) // substring(null, _, _) -> null - checkEvaluation(Substring(s, Literal(100, IntegerType), Literal(4, IntegerType)), null, new GenericRow(Array[Any](null))) + checkEvaluation(Substring(s, Literal.create(100, IntegerType), Literal.create(4, IntegerType)), null, new GenericRow(Array[Any](null))) // substring(_, null, _) -> null - checkEvaluation(Substring(s, Literal(null, IntegerType), Literal(4, IntegerType)), null, row) + checkEvaluation(Substring(s, Literal.create(null, IntegerType), Literal.create(4, IntegerType)), null, row) // substring(_, _, null) -> null - checkEvaluation(Substring(s, Literal(100, IntegerType), Literal(null, IntegerType)), null, row) + checkEvaluation(Substring(s, Literal.create(100, IntegerType), Literal.create(null, IntegerType)), null, row) // 2-arg substring from zero position - checkEvaluation(Substring(s, Literal(0, IntegerType), Literal(Integer.MAX_VALUE, IntegerType)), "example", row) - checkEvaluation(Substring(s, Literal(1, IntegerType), Literal(Integer.MAX_VALUE, IntegerType)), "example", row) + checkEvaluation(Substring(s, Literal.create(0, IntegerType), Literal.create(Integer.MAX_VALUE, IntegerType)), "example", row) + checkEvaluation(Substring(s, Literal.create(1, IntegerType), Literal.create(Integer.MAX_VALUE, IntegerType)), "example", row) // 2-arg substring from nonzero position - checkEvaluation(Substring(s, Literal(2, IntegerType), Literal(Integer.MAX_VALUE, IntegerType)), "xample", row) + checkEvaluation(Substring(s, Literal.create(2, IntegerType), Literal.create(Integer.MAX_VALUE, IntegerType)), "xample", row) val s_notNull = 'a.string.notNull.at(0) - assert(Substring(s, Literal(0, IntegerType), Literal(2, IntegerType)).nullable === true) - assert(Substring(s_notNull, Literal(0, IntegerType), Literal(2, IntegerType)).nullable === false) - assert(Substring(s_notNull, Literal(null, IntegerType), Literal(2, IntegerType)).nullable === true) - assert(Substring(s_notNull, Literal(0, IntegerType), Literal(null, IntegerType)).nullable === true) + assert(Substring(s, Literal.create(0, IntegerType), Literal.create(2, IntegerType)).nullable === true) + assert(Substring(s_notNull, Literal.create(0, IntegerType), Literal.create(2, IntegerType)).nullable === false) + assert(Substring(s_notNull, Literal.create(null, IntegerType), Literal.create(2, IntegerType)).nullable === true) + assert(Substring(s_notNull, Literal.create(0, IntegerType), Literal.create(null, IntegerType)).nullable === true) checkEvaluation(s.substr(0, 2), "ex", row) checkEvaluation(s.substr(0), "example", row) @@ -1050,7 +1050,7 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(Sqrt(d), expected, row) } - checkEvaluation(Sqrt(Literal(null, DoubleType)), null, new GenericRow(Array[Any](null))) + checkEvaluation(Sqrt(Literal.create(null, DoubleType)), null, new GenericRow(Array[Any](null))) checkEvaluation(Sqrt(-1), null, EmptyRow) checkEvaluation(Sqrt(-1.5), null, EmptyRow) } @@ -1064,22 +1064,22 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(BitwiseAnd(c1, c4), null, row) checkEvaluation(BitwiseAnd(c1, c2), 0, row) - checkEvaluation(BitwiseAnd(c1, Literal(null, IntegerType)), null, row) - checkEvaluation(BitwiseAnd(Literal(null, IntegerType), Literal(null, IntegerType)), null, row) + checkEvaluation(BitwiseAnd(c1, Literal.create(null, IntegerType)), null, row) + checkEvaluation(BitwiseAnd(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) checkEvaluation(BitwiseOr(c1, c4), null, row) checkEvaluation(BitwiseOr(c1, c2), 3, row) - checkEvaluation(BitwiseOr(c1, Literal(null, IntegerType)), null, row) - checkEvaluation(BitwiseOr(Literal(null, IntegerType), Literal(null, IntegerType)), null, row) + checkEvaluation(BitwiseOr(c1, Literal.create(null, IntegerType)), null, row) + checkEvaluation(BitwiseOr(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) checkEvaluation(BitwiseXor(c1, c4), null, row) checkEvaluation(BitwiseXor(c1, c2), 3, row) - checkEvaluation(BitwiseXor(c1, Literal(null, IntegerType)), null, row) - checkEvaluation(BitwiseXor(Literal(null, IntegerType), Literal(null, IntegerType)), null, row) + checkEvaluation(BitwiseXor(c1, Literal.create(null, IntegerType)), null, row) + checkEvaluation(BitwiseXor(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) checkEvaluation(BitwiseNot(c4), null, row) checkEvaluation(BitwiseNot(c1), -2, row) - checkEvaluation(BitwiseNot(Literal(null, IntegerType)), null, row) + checkEvaluation(BitwiseNot(Literal.create(null, IntegerType)), null, row) checkEvaluation(c1 & c2, 0, row) checkEvaluation(c1 | c2, 3, row) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala index ef10c0aece716..a0efe9e2e7f6b 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala @@ -182,33 +182,33 @@ class ConstantFoldingSuite extends PlanTest { IsNull(Literal(null)) as 'c1, IsNotNull(Literal(null)) as 'c2, - GetItem(Literal(null, ArrayType(IntegerType)), 1) as 'c3, - GetItem(Literal(Seq(1), ArrayType(IntegerType)), Literal(null, IntegerType)) as 'c4, + GetItem(Literal.create(null, ArrayType(IntegerType)), 1) as 'c3, + GetItem(Literal.create(Seq(1), ArrayType(IntegerType)), Literal.create(null, IntegerType)) as 'c4, UnresolvedGetField( - Literal(null, StructType(Seq(StructField("a", IntegerType, true)))), + Literal.create(null, StructType(Seq(StructField("a", IntegerType, true)))), "a") as 'c5, - UnaryMinus(Literal(null, IntegerType)) as 'c6, + UnaryMinus(Literal.create(null, IntegerType)) as 'c6, Cast(Literal(null), IntegerType) as 'c7, - Not(Literal(null, BooleanType)) as 'c8, + Not(Literal.create(null, BooleanType)) as 'c8, - Add(Literal(null, IntegerType), 1) as 'c9, - Add(1, Literal(null, IntegerType)) as 'c10, + Add(Literal.create(null, IntegerType), 1) as 'c9, + Add(1, Literal.create(null, IntegerType)) as 'c10, - EqualTo(Literal(null, IntegerType), 1) as 'c11, - EqualTo(1, Literal(null, IntegerType)) as 'c12, + EqualTo(Literal.create(null, IntegerType), 1) as 'c11, + EqualTo(1, Literal.create(null, IntegerType)) as 'c12, - Like(Literal(null, StringType), "abc") as 'c13, - Like("abc", Literal(null, StringType)) as 'c14, + Like(Literal.create(null, StringType), "abc") as 'c13, + Like("abc", Literal.create(null, StringType)) as 'c14, - Upper(Literal(null, StringType)) as 'c15, + Upper(Literal.create(null, StringType)) as 'c15, - Substring(Literal(null, StringType), 0, 1) as 'c16, - Substring("abc", Literal(null, IntegerType), 1) as 'c17, - Substring("abc", 0, Literal(null, IntegerType)) as 'c18, + Substring(Literal.create(null, StringType), 0, 1) as 'c16, + Substring("abc", Literal.create(null, IntegerType), 1) as 'c17, + Substring("abc", 0, Literal.create(null, IntegerType)) as 'c18, - Contains(Literal(null, StringType), "abc") as 'c19, - Contains("abc", Literal(null, StringType)) as 'c20 + Contains(Literal.create(null, StringType), "abc") as 'c19, + Contains("abc", Literal.create(null, StringType)) as 'c20 ) val optimized = Optimize(originalQuery.analyze) @@ -219,31 +219,31 @@ class ConstantFoldingSuite extends PlanTest { Literal(true) as 'c1, Literal(false) as 'c2, - Literal(null, IntegerType) as 'c3, - Literal(null, IntegerType) as 'c4, - Literal(null, IntegerType) as 'c5, + Literal.create(null, IntegerType) as 'c3, + Literal.create(null, IntegerType) as 'c4, + Literal.create(null, IntegerType) as 'c5, - Literal(null, IntegerType) as 'c6, - Literal(null, IntegerType) as 'c7, - Literal(null, BooleanType) as 'c8, + Literal.create(null, IntegerType) as 'c6, + Literal.create(null, IntegerType) as 'c7, + Literal.create(null, BooleanType) as 'c8, - Literal(null, IntegerType) as 'c9, - Literal(null, IntegerType) as 'c10, + Literal.create(null, IntegerType) as 'c9, + Literal.create(null, IntegerType) as 'c10, - Literal(null, BooleanType) as 'c11, - Literal(null, BooleanType) as 'c12, + Literal.create(null, BooleanType) as 'c11, + Literal.create(null, BooleanType) as 'c12, - Literal(null, BooleanType) as 'c13, - Literal(null, BooleanType) as 'c14, + Literal.create(null, BooleanType) as 'c13, + Literal.create(null, BooleanType) as 'c14, - Literal(null, StringType) as 'c15, + Literal.create(null, StringType) as 'c15, - Literal(null, StringType) as 'c16, - Literal(null, StringType) as 'c17, - Literal(null, StringType) as 'c18, + Literal.create(null, StringType) as 'c16, + Literal.create(null, StringType) as 'c17, + Literal.create(null, StringType) as 'c18, - Literal(null, BooleanType) as 'c19, - Literal(null, BooleanType) as 'c20 + Literal.create(null, BooleanType) as 'c19, + Literal.create(null, BooleanType) as 'c20 ).analyze comparePlans(optimized, correctAnswer) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala index e7ce92a2160b6..274f3ede0045c 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala @@ -90,7 +90,7 @@ class TreeNodeSuite extends FunSuite { } test("transform works on nodes with Option children") { - val dummy1 = Dummy(Some(Literal("1", StringType))) + val dummy1 = Dummy(Some(Literal.create("1", StringType))) val dummy2 = Dummy(None) val toZero: PartialFunction[Expression, Expression] = { case Literal(_, _) => Literal(0) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala index 89682d25ca7dc..a8018b9213f2b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala @@ -93,7 +93,7 @@ case class GeneratedAggregate( } val currentSum = AttributeReference("currentSum", calcType, nullable = true)() - val initialValue = Literal(null, calcType) + val initialValue = Literal.create(null, calcType) // Coalasce avoids double calculation... // but really, common sub expression elimination would be better.... @@ -137,13 +137,13 @@ case class GeneratedAggregate( expr.dataType match { case DecimalType.Fixed(_, _) => If(EqualTo(currentCount, Literal(0L)), - Literal(null, a.dataType), + Literal.create(null, a.dataType), Cast(Divide( Cast(currentSum, DecimalType.Unlimited), Cast(currentCount, DecimalType.Unlimited)), a.dataType)) case _ => If(EqualTo(currentCount, Literal(0L)), - Literal(null, a.dataType), + Literal.create(null, a.dataType), Divide(Cast(currentSum, a.dataType), Cast(currentCount, a.dataType))) } @@ -156,7 +156,7 @@ case class GeneratedAggregate( case m @ Max(expr) => val currentMax = AttributeReference("currentMax", expr.dataType, nullable = true)() - val initialValue = Literal(null, expr.dataType) + val initialValue = Literal.create(null, expr.dataType) val updateMax = MaxOf(currentMax, expr) AggregateEvaluation( diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala index 19800ad88c031..43f260d3ef8d3 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala @@ -872,9 +872,9 @@ private[sql] object ParquetRelation2 extends Logging { * PartitionValues( * Seq("a", "b", "c"), * Seq( - * Literal(42, IntegerType), - * Literal("hello", StringType), - * Literal(3.14, FloatType))) + * Literal.create(42, IntegerType), + * Literal.create("hello", StringType), + * Literal.create(3.14, FloatType))) * }}} */ private[parquet] def parsePartition( @@ -953,15 +953,16 @@ private[sql] object ParquetRelation2 extends Logging { raw: String, defaultPartitionName: String): Literal = { // First tries integral types - Try(Literal(Integer.parseInt(raw), IntegerType)) - .orElse(Try(Literal(JLong.parseLong(raw), LongType))) + Try(Literal.create(Integer.parseInt(raw), IntegerType)) + .orElse(Try(Literal.create(JLong.parseLong(raw), LongType))) // Then falls back to fractional types - .orElse(Try(Literal(JFloat.parseFloat(raw), FloatType))) - .orElse(Try(Literal(JDouble.parseDouble(raw), DoubleType))) - .orElse(Try(Literal(new JBigDecimal(raw), DecimalType.Unlimited))) + .orElse(Try(Literal.create(JFloat.parseFloat(raw), FloatType))) + .orElse(Try(Literal.create(JDouble.parseDouble(raw), DoubleType))) + .orElse(Try(Literal.create(new JBigDecimal(raw), DecimalType.Unlimited))) // Then falls back to string .getOrElse { - if (raw == defaultPartitionName) Literal(null, NullType) else Literal(raw, StringType) + if (raw == defaultPartitionName) Literal.create(null, NullType) + else Literal.create(raw, StringType) } } @@ -980,7 +981,7 @@ private[sql] object ParquetRelation2 extends Logging { } literals.map { case l @ Literal(_, dataType) => - Literal(Cast(l, desiredType).eval(), desiredType) + Literal.create(Cast(l, desiredType).eval(), desiredType) } } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala index adb3c9391f6c2..b7561ce7298cb 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala @@ -45,11 +45,11 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest { assert(inferPartitionColumnValue(raw, defaultPartitionName) === literal) } - check("10", Literal(10, IntegerType)) - check("1000000000000000", Literal(1000000000000000L, LongType)) - check("1.5", Literal(1.5, FloatType)) - check("hello", Literal("hello", StringType)) - check(defaultPartitionName, Literal(null, NullType)) + check("10", Literal.create(10, IntegerType)) + check("1000000000000000", Literal.create(1000000000000000L, LongType)) + check("1.5", Literal.create(1.5, FloatType)) + check("hello", Literal.create("hello", StringType)) + check(defaultPartitionName, Literal.create(null, NullType)) } test("parse partition") { @@ -75,22 +75,22 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest { "file://path/a=10", PartitionValues( ArrayBuffer("a"), - ArrayBuffer(Literal(10, IntegerType)))) + ArrayBuffer(Literal.create(10, IntegerType)))) check( "file://path/a=10/b=hello/c=1.5", PartitionValues( ArrayBuffer("a", "b", "c"), ArrayBuffer( - Literal(10, IntegerType), - Literal("hello", StringType), - Literal(1.5, FloatType)))) + Literal.create(10, IntegerType), + Literal.create("hello", StringType), + Literal.create(1.5, FloatType)))) check( "file://path/a=10/b_hello/c=1.5", PartitionValues( ArrayBuffer("c"), - ArrayBuffer(Literal(1.5, FloatType)))) + ArrayBuffer(Literal.create(1.5, FloatType)))) checkThrows[AssertionError]("file://path/=10", "Empty partition column name") checkThrows[AssertionError]("file://path/a=", "Empty partition column value") diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala index 4afa2e71d77cc..921c6194c7b76 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala @@ -593,7 +593,7 @@ private[hive] trait HiveInspectors { case Literal(_, dt) => sys.error(s"Hive doesn't support the constant type [$dt].") // ideally, we don't test the foldable here(but in optimizer), however, some of the // Hive UDF / UDAF requires its argument to be constant objectinspector, we do it eagerly. - case _ if expr.foldable => toInspector(Literal(expr.eval(), expr.dataType)) + case _ if expr.foldable => toInspector(Literal.create(expr.eval(), expr.dataType)) // For those non constant expression, map to object inspector according to its data type case _ => toInspector(expr.dataType) } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala index cd8e7c09eea5b..5be09a11ad641 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala @@ -1201,7 +1201,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C CreateArray(children.map(nodeToExpr)) case Token("TOK_FUNCTION", Token(RAND(), Nil) :: Nil) => Rand case Token("TOK_FUNCTION", Token(SUBSTR(), Nil) :: string :: pos :: Nil) => - Substring(nodeToExpr(string), nodeToExpr(pos), Literal(Integer.MAX_VALUE, IntegerType)) + Substring(nodeToExpr(string), nodeToExpr(pos), Literal.create(Integer.MAX_VALUE, IntegerType)) case Token("TOK_FUNCTION", Token(SUBSTR(), Nil) :: string :: pos :: length :: Nil) => Substring(nodeToExpr(string), nodeToExpr(pos), nodeToExpr(length)) case Token("TOK_FUNCTION", Token(COALESCE(), Nil) :: list) => Coalesce(list.map(nodeToExpr)) @@ -1213,9 +1213,9 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C UnresolvedFunction(name, UnresolvedStar(None) :: Nil) /* Literals */ - case Token("TOK_NULL", Nil) => Literal(null, NullType) - case Token(TRUE(), Nil) => Literal(true, BooleanType) - case Token(FALSE(), Nil) => Literal(false, BooleanType) + case Token("TOK_NULL", Nil) => Literal.create(null, NullType) + case Token(TRUE(), Nil) => Literal.create(true, BooleanType) + case Token(FALSE(), Nil) => Literal.create(false, BooleanType) case Token("TOK_STRINGLITERALSEQUENCE", strings) => Literal(strings.map(s => BaseSemanticAnalyzer.unescapeSQLString(s.getText)).mkString) @@ -1226,21 +1226,21 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C try { if (ast.getText.endsWith("L")) { // Literal bigint. - v = Literal(ast.getText.substring(0, ast.getText.length() - 1).toLong, LongType) + v = Literal.create(ast.getText.substring(0, ast.getText.length() - 1).toLong, LongType) } else if (ast.getText.endsWith("S")) { // Literal smallint. - v = Literal(ast.getText.substring(0, ast.getText.length() - 1).toShort, ShortType) + v = Literal.create(ast.getText.substring(0, ast.getText.length() - 1).toShort, ShortType) } else if (ast.getText.endsWith("Y")) { // Literal tinyint. - v = Literal(ast.getText.substring(0, ast.getText.length() - 1).toByte, ByteType) + v = Literal.create(ast.getText.substring(0, ast.getText.length() - 1).toByte, ByteType) } else if (ast.getText.endsWith("BD") || ast.getText.endsWith("D")) { // Literal decimal val strVal = ast.getText.stripSuffix("D").stripSuffix("B") v = Literal(Decimal(strVal)) } else { - v = Literal(ast.getText.toDouble, DoubleType) - v = Literal(ast.getText.toLong, LongType) - v = Literal(ast.getText.toInt, IntegerType) + v = Literal.create(ast.getText.toDouble, DoubleType) + v = Literal.create(ast.getText.toLong, LongType) + v = Literal.create(ast.getText.toInt, IntegerType) } } catch { case nfe: NumberFormatException => // Do nothing diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala index 3181cfe40016c..c482c6de8a736 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala @@ -79,9 +79,9 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { Literal(Decimal(BigDecimal(123.123))) :: Literal(new java.sql.Timestamp(123123)) :: Literal(Array[Byte](1,2,3)) :: - Literal(Seq[Int](1,2,3), ArrayType(IntegerType)) :: - Literal(Map[Int, Int](1->2, 2->1), MapType(IntegerType, IntegerType)) :: - Literal(Row(1,2.0d,3.0f), + Literal.create(Seq[Int](1,2,3), ArrayType(IntegerType)) :: + Literal.create(Map[Int, Int](1->2, 2->1), MapType(IntegerType, IntegerType)) :: + Literal.create(Row(1,2.0d,3.0f), StructType(StructField("c1", IntegerType) :: StructField("c2", DoubleType) :: StructField("c3", FloatType) :: Nil)) :: @@ -166,7 +166,7 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { val constantData = constantExprs.map(_.eval()) val constantNullData = constantData.map(_ => null) val constantWritableOIs = constantExprs.map(e => toWritableInspector(e.dataType)) - val constantNullWritableOIs = constantExprs.map(e => toInspector(Literal(null, e.dataType))) + val constantNullWritableOIs = constantExprs.map(e => toInspector(Literal.create(null, e.dataType))) checkValues(constantData, constantData.zip(constantWritableOIs).map { case (d, oi) => unwrap(wrap(d, oi), oi) @@ -212,8 +212,8 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { val d = row(0) :: row(0) :: Nil checkValue(d, unwrap(wrap(d, toInspector(dt)), toInspector(dt))) checkValue(null, unwrap(wrap(null, toInspector(dt)), toInspector(dt))) - checkValue(d, unwrap(wrap(d, toInspector(Literal(d, dt))), toInspector(Literal(d, dt)))) - checkValue(d, unwrap(wrap(null, toInspector(Literal(d, dt))), toInspector(Literal(d, dt)))) + checkValue(d, unwrap(wrap(d, toInspector(Literal.create(d, dt))), toInspector(Literal.create(d, dt)))) + checkValue(d, unwrap(wrap(null, toInspector(Literal.create(d, dt))), toInspector(Literal.create(d, dt)))) } test("wrap / unwrap Map Type") { @@ -222,7 +222,7 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { val d = Map(row(0) -> row(1)) checkValue(d, unwrap(wrap(d, toInspector(dt)), toInspector(dt))) checkValue(null, unwrap(wrap(null, toInspector(dt)), toInspector(dt))) - checkValue(d, unwrap(wrap(d, toInspector(Literal(d, dt))), toInspector(Literal(d, dt)))) - checkValue(d, unwrap(wrap(null, toInspector(Literal(d, dt))), toInspector(Literal(d, dt)))) + checkValue(d, unwrap(wrap(d, toInspector(Literal.create(d, dt))), toInspector(Literal.create(d, dt)))) + checkValue(d, unwrap(wrap(null, toInspector(Literal.create(d, dt))), toInspector(Literal.create(d, dt)))) } } From 6562787b963204763a33e1c4e9d192db913af1fc Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Wed, 1 Apr 2015 23:42:09 -0700 Subject: [PATCH 138/591] [SPARK-6627] Some clean-up in shuffle code. Before diving into review #4450 I did a look through the existing shuffle code to learn how it works. Unfortunately, there are some very confusing things in this code. This patch makes a few small changes to simplify things. It is not easily to concisely describe the changes because of how convoluted the issues were, but they are fairly small logically: 1. There is a trait named `ShuffleBlockManager` that only deals with one logical function which is retrieving shuffle block data given shuffle block coordinates. This trait has two implementors FileShuffleBlockManager and IndexShuffleBlockManager. Confusingly the vast majority of those implementations have nothing to do with this particular functionality. So I've renamed the trait to ShuffleBlockResolver and documented it. 2. The aforementioned trait had two almost identical methods, for no good reason. I removed one method (getBytes) and modified callers to use the other one. I think the behavior is preserved in all cases. 3. The sort shuffle code uses an identifier "0" in the reduce slot of a BlockID as a placeholder. I made it into a constant since it needs to be consistent across multiple places. I think for (3) there is actually a better solution that would avoid the need to do this type of workaround/hack in the first place, but it's more complex so I'm punting it for now. Author: Patrick Wendell Closes #5286 from pwendell/cleanup and squashes the following commits: c71fbc7 [Patrick Wendell] Open interface back up for testing f36edd5 [Patrick Wendell] Code review feedback d1c0494 [Patrick Wendell] Style fix a406079 [Patrick Wendell] [HOTFIX] Some clean-up in shuffle code. --- .../shuffle/FileShuffleBlockManager.scala | 7 +---- .../shuffle/IndexShuffleBlockManager.scala | 27 +++++++++---------- ...nager.scala => ShuffleBlockResolver.scala} | 14 ++++++---- .../apache/spark/shuffle/ShuffleManager.scala | 5 +++- .../apache/spark/shuffle/ShuffleWriter.scala | 2 +- .../shuffle/hash/HashShuffleManager.scala | 8 +++--- .../shuffle/sort/SortShuffleManager.scala | 9 ++++--- .../shuffle/sort/SortShuffleWriter.scala | 6 ++--- .../apache/spark/storage/BlockManager.scala | 14 ++++------ .../util/collection/ExternalSorter.scala | 6 +++-- .../hash/HashShuffleManagerSuite.scala | 2 +- .../spark/tools/StoragePerfTester.scala | 2 +- 12 files changed, 51 insertions(+), 51 deletions(-) rename core/src/main/scala/org/apache/spark/shuffle/{ShuffleBlockManager.scala => ShuffleBlockResolver.scala} (68%) diff --git a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala index d0178dfde6935..5be3ed771e534 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala @@ -67,7 +67,7 @@ private[spark] trait ShuffleWriterGroup { // org.apache.spark.network.shuffle.StandaloneShuffleBlockManager#getHashBasedShuffleBlockData(). private[spark] class FileShuffleBlockManager(conf: SparkConf) - extends ShuffleBlockManager with Logging { + extends ShuffleBlockResolver with Logging { private val transportConf = SparkTransportConf.fromSparkConf(conf) @@ -175,11 +175,6 @@ class FileShuffleBlockManager(conf: SparkConf) } } - override def getBytes(blockId: ShuffleBlockId): Option[ByteBuffer] = { - val segment = getBlockData(blockId) - Some(segment.nioByteBuffer()) - } - override def getBlockData(blockId: ShuffleBlockId): ManagedBuffer = { if (consolidateShuffleFiles) { // Search all file groups associated with this shuffle. diff --git a/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala index 87fd161e06c85..50edb5a34e333 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala @@ -27,6 +27,8 @@ import org.apache.spark.network.buffer.{FileSegmentManagedBuffer, ManagedBuffer} import org.apache.spark.network.netty.SparkTransportConf import org.apache.spark.storage._ +import IndexShuffleBlockManager.NOOP_REDUCE_ID + /** * Create and maintain the shuffle blocks' mapping between logic block and physical file location. * Data of shuffle blocks from the same map task are stored in a single consolidated data file. @@ -39,25 +41,18 @@ import org.apache.spark.storage._ // Note: Changes to the format in this file should be kept in sync with // org.apache.spark.network.shuffle.StandaloneShuffleBlockManager#getSortBasedShuffleBlockData(). private[spark] -class IndexShuffleBlockManager(conf: SparkConf) extends ShuffleBlockManager { +class IndexShuffleBlockManager(conf: SparkConf) extends ShuffleBlockResolver { private lazy val blockManager = SparkEnv.get.blockManager private val transportConf = SparkTransportConf.fromSparkConf(conf) - /** - * Mapping to a single shuffleBlockId with reduce ID 0. - * */ - def consolidateId(shuffleId: Int, mapId: Int): ShuffleBlockId = { - ShuffleBlockId(shuffleId, mapId, 0) - } - def getDataFile(shuffleId: Int, mapId: Int): File = { - blockManager.diskBlockManager.getFile(ShuffleDataBlockId(shuffleId, mapId, 0)) + blockManager.diskBlockManager.getFile(ShuffleDataBlockId(shuffleId, mapId, NOOP_REDUCE_ID)) } private def getIndexFile(shuffleId: Int, mapId: Int): File = { - blockManager.diskBlockManager.getFile(ShuffleIndexBlockId(shuffleId, mapId, 0)) + blockManager.diskBlockManager.getFile(ShuffleIndexBlockId(shuffleId, mapId, NOOP_REDUCE_ID)) } /** @@ -97,10 +92,6 @@ class IndexShuffleBlockManager(conf: SparkConf) extends ShuffleBlockManager { } } - override def getBytes(blockId: ShuffleBlockId): Option[ByteBuffer] = { - Some(getBlockData(blockId).nioByteBuffer()) - } - override def getBlockData(blockId: ShuffleBlockId): ManagedBuffer = { // The block is actually going to be a range of a single map output file for this map, so // find out the consolidated file, then the offset within that from our index @@ -123,3 +114,11 @@ class IndexShuffleBlockManager(conf: SparkConf) extends ShuffleBlockManager { override def stop(): Unit = {} } + +private[spark] object IndexShuffleBlockManager { + // No-op reduce ID used in interactions with disk store and BlockObjectWriter. + // The disk store currently expects puts to relate to a (map, reduce) pair, but in the sort + // shuffle outputs for several reduces are glommed into a single file. + // TODO: Avoid this entirely by having the DiskBlockObjectWriter not require a BlockId. + val NOOP_REDUCE_ID = 0 +} diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleBlockResolver.scala similarity index 68% rename from core/src/main/scala/org/apache/spark/shuffle/ShuffleBlockManager.scala rename to core/src/main/scala/org/apache/spark/shuffle/ShuffleBlockResolver.scala index b521f0c7fc77e..4342b0d598b16 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/ShuffleBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleBlockResolver.scala @@ -22,15 +22,19 @@ import org.apache.spark.network.buffer.ManagedBuffer import org.apache.spark.storage.ShuffleBlockId private[spark] -trait ShuffleBlockManager { +/** + * Implementers of this trait understand how to retrieve block data for a logical shuffle block + * identifier (i.e. map, reduce, and shuffle). Implementations may use files or file segments to + * encapsulate shuffle data. This is used by the BlockStore to abstract over different shuffle + * implementations when shuffle data is retrieved. + */ +trait ShuffleBlockResolver { type ShuffleId = Int /** - * Get shuffle block data managed by the local ShuffleBlockManager. - * @return Some(ByteBuffer) if block found, otherwise None. + * Retrieve the data for the specified block. If the data for that block is not available, + * throws an unspecified exception. */ - def getBytes(blockId: ShuffleBlockId): Option[ByteBuffer] - def getBlockData(blockId: ShuffleBlockId): ManagedBuffer def stop(): Unit diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleManager.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleManager.scala index a44a8e1249256..978366d1a1d1b 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/ShuffleManager.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleManager.scala @@ -55,7 +55,10 @@ private[spark] trait ShuffleManager { */ def unregisterShuffle(shuffleId: Int): Boolean - def shuffleBlockManager: ShuffleBlockManager + /** + * Return a resolver capable of retrieving shuffle block data based on block coordinates. + */ + def shuffleBlockResolver: ShuffleBlockResolver /** Shut down this ShuffleManager. */ def stop(): Unit diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleWriter.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleWriter.scala index b934480cfb9be..f6e6fe5defe09 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/ShuffleWriter.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleWriter.scala @@ -23,7 +23,7 @@ import org.apache.spark.scheduler.MapStatus * Obtained inside a map task to write out records to the shuffle system. */ private[spark] trait ShuffleWriter[K, V] { - /** Write a bunch of records to this task's output */ + /** Write a sequence of records to this task's output */ def write(records: Iterator[_ <: Product2[K, V]]): Unit /** Close this writer, passing along whether the map completed */ diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleManager.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleManager.scala index 62e0629b34400..2a7df8dd5bd83 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleManager.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleManager.scala @@ -53,20 +53,20 @@ private[spark] class HashShuffleManager(conf: SparkConf) extends ShuffleManager override def getWriter[K, V](handle: ShuffleHandle, mapId: Int, context: TaskContext) : ShuffleWriter[K, V] = { new HashShuffleWriter( - shuffleBlockManager, handle.asInstanceOf[BaseShuffleHandle[K, V, _]], mapId, context) + shuffleBlockResolver, handle.asInstanceOf[BaseShuffleHandle[K, V, _]], mapId, context) } /** Remove a shuffle's metadata from the ShuffleManager. */ override def unregisterShuffle(shuffleId: Int): Boolean = { - shuffleBlockManager.removeShuffle(shuffleId) + shuffleBlockResolver.removeShuffle(shuffleId) } - override def shuffleBlockManager: FileShuffleBlockManager = { + override def shuffleBlockResolver: FileShuffleBlockManager = { fileShuffleBlockManager } /** Shut down this ShuffleManager. */ override def stop(): Unit = { - shuffleBlockManager.stop() + shuffleBlockResolver.stop() } } diff --git a/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleManager.scala b/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleManager.scala index bda30a56d808e..0497036192154 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleManager.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleManager.scala @@ -58,7 +58,7 @@ private[spark] class SortShuffleManager(conf: SparkConf) extends ShuffleManager val baseShuffleHandle = handle.asInstanceOf[BaseShuffleHandle[K, V, _]] shuffleMapNumber.putIfAbsent(baseShuffleHandle.shuffleId, baseShuffleHandle.numMaps) new SortShuffleWriter( - shuffleBlockManager, baseShuffleHandle, mapId, context) + shuffleBlockResolver, baseShuffleHandle, mapId, context) } /** Remove a shuffle's metadata from the ShuffleManager. */ @@ -66,18 +66,19 @@ private[spark] class SortShuffleManager(conf: SparkConf) extends ShuffleManager if (shuffleMapNumber.containsKey(shuffleId)) { val numMaps = shuffleMapNumber.remove(shuffleId) (0 until numMaps).map{ mapId => - shuffleBlockManager.removeDataByMap(shuffleId, mapId) + shuffleBlockResolver.removeDataByMap(shuffleId, mapId) } } true } - override def shuffleBlockManager: IndexShuffleBlockManager = { + override def shuffleBlockResolver: IndexShuffleBlockManager = { indexShuffleBlockManager } /** Shut down this ShuffleManager. */ override def stop(): Unit = { - shuffleBlockManager.stop() + shuffleBlockResolver.stop() } } + diff --git a/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala b/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala index 55ea0f17b156a..a066435df6fb0 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/sort/SortShuffleWriter.scala @@ -58,8 +58,7 @@ private[spark] class SortShuffleWriter[K, V, C]( // In this case we pass neither an aggregator nor an ordering to the sorter, because we don't // care whether the keys get sorted in each partition; that will be done on the reduce side // if the operation being run is sortByKey. - sorter = new ExternalSorter[K, V, V]( - None, Some(dep.partitioner), None, dep.serializer) + sorter = new ExternalSorter[K, V, V](None, Some(dep.partitioner), None, dep.serializer) sorter.insertAll(records) } @@ -67,7 +66,7 @@ private[spark] class SortShuffleWriter[K, V, C]( // because it just opens a single file, so is typically too fast to measure accurately // (see SPARK-3570). val outputFile = shuffleBlockManager.getDataFile(dep.shuffleId, mapId) - val blockId = shuffleBlockManager.consolidateId(dep.shuffleId, mapId) + val blockId = ShuffleBlockId(dep.shuffleId, mapId, IndexShuffleBlockManager.NOOP_REDUCE_ID) val partitionLengths = sorter.writePartitionedFile(blockId, context, outputFile) shuffleBlockManager.writeIndexFile(dep.shuffleId, mapId, partitionLengths) @@ -100,3 +99,4 @@ private[spark] class SortShuffleWriter[K, V, C]( } } } + diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala index 1dff09a75d038..fc31296f4deb3 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala @@ -301,7 +301,7 @@ private[spark] class BlockManager( */ override def getBlockData(blockId: BlockId): ManagedBuffer = { if (blockId.isShuffle) { - shuffleManager.shuffleBlockManager.getBlockData(blockId.asInstanceOf[ShuffleBlockId]) + shuffleManager.shuffleBlockResolver.getBlockData(blockId.asInstanceOf[ShuffleBlockId]) } else { val blockBytesOpt = doGetLocal(blockId, asBlockResult = false) .asInstanceOf[Option[ByteBuffer]] @@ -439,14 +439,10 @@ private[spark] class BlockManager( // As an optimization for map output fetches, if the block is for a shuffle, return it // without acquiring a lock; the disk store never deletes (recent) items so this should work if (blockId.isShuffle) { - val shuffleBlockManager = shuffleManager.shuffleBlockManager - shuffleBlockManager.getBytes(blockId.asInstanceOf[ShuffleBlockId]) match { - case Some(bytes) => - Some(bytes) - case None => - throw new BlockException( - blockId, s"Block $blockId not found on disk, though it should be") - } + val shuffleBlockManager = shuffleManager.shuffleBlockResolver + // TODO: This should gracefully handle case where local block is not available. Currently + // downstream code will throw an exception. + Option(shuffleBlockManager.getBlockData(blockId.asInstanceOf[ShuffleBlockId]).nioByteBuffer()) } else { doGetLocal(blockId, asBlockResult = false).asInstanceOf[Option[ByteBuffer]] } diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala index b962c101c91da..7bd3c7852a6b2 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala @@ -664,6 +664,8 @@ private[spark] class ExternalSorter[K, V, C]( } /** + * Exposed for testing purposes. + * * Return an iterator over all the data written to this object, grouped by partition and * aggregated by the requested aggregator. For each partition we then have an iterator over its * contents, and these are expected to be accessed in order (you can't "skip ahead" to one @@ -673,7 +675,7 @@ private[spark] class ExternalSorter[K, V, C]( * For now, we just merge all the spilled files in once pass, but this can be modified to * support hierarchical merging. */ - def partitionedIterator: Iterator[(Int, Iterator[Product2[K, C]])] = { + def partitionedIterator: Iterator[(Int, Iterator[Product2[K, C]])] = { val usingMap = aggregator.isDefined val collection: SizeTrackingPairCollection[(Int, K), C] = if (usingMap) map else buffer if (spills.isEmpty && partitionWriters == null) { @@ -781,7 +783,7 @@ private[spark] class ExternalSorter[K, V, C]( /** * Read a partition file back as an iterator (used in our iterator method) */ - def readPartitionFile(writer: BlockObjectWriter): Iterator[Product2[K, C]] = { + private def readPartitionFile(writer: BlockObjectWriter): Iterator[Product2[K, C]] = { if (writer.isOpen) { writer.commitAndClose() } diff --git a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala index 6790388f96603..b834dc0e735eb 100644 --- a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala @@ -54,7 +54,7 @@ class HashShuffleManagerSuite extends FunSuite with LocalSparkContext { sc = new SparkContext("local", "test", conf) val shuffleBlockManager = - SparkEnv.get.shuffleManager.shuffleBlockManager.asInstanceOf[FileShuffleBlockManager] + SparkEnv.get.shuffleManager.shuffleBlockResolver.asInstanceOf[FileShuffleBlockManager] val shuffle1 = shuffleBlockManager.forMapTask(1, 1, 1, new JavaSerializer(conf), new ShuffleWriteMetrics) diff --git a/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala b/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala index 15ee95070a3d3..6b666a0384879 100644 --- a/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala +++ b/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala @@ -59,7 +59,7 @@ object StoragePerfTester { val hashShuffleManager = sc.env.shuffleManager.asInstanceOf[HashShuffleManager] def writeOutputBytes(mapId: Int, total: AtomicLong) = { - val shuffle = hashShuffleManager.shuffleBlockManager.forMapTask(1, mapId, numOutputSplits, + val shuffle = hashShuffleManager.shuffleBlockResolver.forMapTask(1, mapId, numOutputSplits, new KryoSerializer(sc.conf), new ShuffleWriteMetrics()) val writers = shuffle.writers for (i <- 1 to recordsPerMap) { From 424e987dfebbbaa37f4496d44090d469a931ce76 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Thu, 2 Apr 2015 17:57:01 +0800 Subject: [PATCH 139/591] [SPARK-6672][SQL] convert row to catalyst in createDataFrame(RDD[Row], ...) We assume that `RDD[Row]` contains Scala types. So we need to convert them into catalyst types in createDataFrame. liancheng Author: Xiangrui Meng Closes #5329 from mengxr/SPARK-6672 and squashes the following commits: 2d52644 [Xiangrui Meng] set needsConversion = false in jsonRDD 06896e4 [Xiangrui Meng] add createDataFrame without conversion 4a3767b [Xiangrui Meng] convert Row to catalyst --- .../spark/sql/catalyst/ScalaReflection.scala | 5 +++++ .../org/apache/spark/sql/DataFrame.scala | 3 ++- .../org/apache/spark/sql/SQLContext.scala | 20 ++++++++++++++++--- .../apache/spark/sql/parquet/newParquet.scala | 3 ++- .../apache/spark/sql/sources/commands.scala | 3 ++- .../spark/sql/test/ExamplePointUDT.scala | 2 +- .../org/apache/spark/sql/DataFrameSuite.scala | 9 ++++++++- 7 files changed, 37 insertions(+), 8 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala index 2220970085462..8bfd0471d9c7a 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala @@ -72,6 +72,11 @@ trait ScalaReflection { case (d: BigDecimal, _) => Decimal(d) case (d: java.math.BigDecimal, _) => Decimal(d) case (d: java.sql.Date, _) => DateUtils.fromJavaDate(d) + case (r: Row, structType: StructType) => + new GenericRow( + r.toSeq.zip(structType.fields).map { case (elem, field) => + convertToCatalyst(elem, field.dataType) + }.toArray) case (other, _) => other } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index ce0890906bf1b..34be17325b2b0 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -904,7 +904,8 @@ class DataFrame private[sql]( */ override def repartition(numPartitions: Int): DataFrame = { sqlContext.createDataFrame( - queryExecution.toRdd.map(_.copy()).repartition(numPartitions), schema) + queryExecution.toRdd.map(_.copy()).repartition(numPartitions), + schema, needsConversion = false) } /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala index 1794936a52c6d..39dd14e796f06 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala @@ -392,9 +392,23 @@ class SQLContext(@transient val sparkContext: SparkContext) */ @DeveloperApi def createDataFrame(rowRDD: RDD[Row], schema: StructType): DataFrame = { + createDataFrame(rowRDD, schema, needsConversion = true) + } + + /** + * Creates a DataFrame from an RDD[Row]. User can specify whether the input rows should be + * converted to Catalyst rows. + */ + private[sql] + def createDataFrame(rowRDD: RDD[Row], schema: StructType, needsConversion: Boolean) = { // TODO: use MutableProjection when rowRDD is another DataFrame and the applied // schema differs from the existing schema on any field data type. - val logicalPlan = LogicalRDD(schema.toAttributes, rowRDD)(self) + val catalystRows = if (needsConversion) { + rowRDD.map(ScalaReflection.convertToCatalyst(_, schema).asInstanceOf[Row]) + } else { + rowRDD + } + val logicalPlan = LogicalRDD(schema.toAttributes, catalystRows)(self) DataFrame(this, logicalPlan) } @@ -604,7 +618,7 @@ class SQLContext(@transient val sparkContext: SparkContext) JsonRDD.nullTypeToStringType( JsonRDD.inferSchema(json, 1.0, columnNameOfCorruptJsonRecord))) val rowRDD = JsonRDD.jsonStringToRow(json, appliedSchema, columnNameOfCorruptJsonRecord) - createDataFrame(rowRDD, appliedSchema) + createDataFrame(rowRDD, appliedSchema, needsConversion = false) } /** @@ -633,7 +647,7 @@ class SQLContext(@transient val sparkContext: SparkContext) JsonRDD.nullTypeToStringType( JsonRDD.inferSchema(json, samplingRatio, columnNameOfCorruptJsonRecord)) val rowRDD = JsonRDD.jsonStringToRow(json, appliedSchema, columnNameOfCorruptJsonRecord) - createDataFrame(rowRDD, appliedSchema) + createDataFrame(rowRDD, appliedSchema, needsConversion = false) } /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala index 43f260d3ef8d3..e12531480ce92 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala @@ -122,7 +122,8 @@ private[sql] class DefaultSource val df = sqlContext.createDataFrame( data.queryExecution.toRdd, - data.schema.asNullable) + data.schema.asNullable, + needsConversion = false) val createdRelation = createRelation(sqlContext, parameters, df.schema).asInstanceOf[ParquetRelation2] createdRelation.insert(df, overwrite = mode == SaveMode.Overwrite) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala index 9bbe06e59ba30..dbdb0d39c26a1 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/commands.scala @@ -31,7 +31,8 @@ private[sql] case class InsertIntoDataSource( val relation = logicalRelation.relation.asInstanceOf[InsertableRelation] val data = DataFrame(sqlContext, query) // Apply the schema of the existing table to the new data. - val df = sqlContext.createDataFrame(data.queryExecution.toRdd, logicalRelation.schema) + val df = sqlContext.createDataFrame( + data.queryExecution.toRdd, logicalRelation.schema, needsConversion = false) relation.insert(df, overwrite) // Invalidate the cache. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/test/ExamplePointUDT.scala b/sql/core/src/main/scala/org/apache/spark/sql/test/ExamplePointUDT.scala index c11d0ae5bf1cc..2fdd798b44bb6 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/test/ExamplePointUDT.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/test/ExamplePointUDT.scala @@ -28,7 +28,7 @@ import org.apache.spark.sql.types._ * @param y y coordinate */ @SQLUserDefinedType(udt = classOf[ExamplePointUDT]) -private[sql] class ExamplePoint(val x: Double, val y: Double) +private[sql] class ExamplePoint(val x: Double, val y: Double) extends Serializable /** * User-defined type for [[ExamplePoint]]. diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index 6761d996fd975..5297cc01eddfc 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -21,7 +21,7 @@ import scala.language.postfixOps import org.apache.spark.sql.functions._ import org.apache.spark.sql.types._ -import org.apache.spark.sql.test.TestSQLContext +import org.apache.spark.sql.test.{ExamplePointUDT, ExamplePoint, TestSQLContext} import org.apache.spark.sql.test.TestSQLContext.logicalPlanToSparkQuery import org.apache.spark.sql.test.TestSQLContext.implicits._ import org.apache.spark.sql.test.TestSQLContext.sql @@ -506,4 +506,11 @@ class DataFrameSuite extends QueryTest { testData.select($"*").show() testData.select($"*").show(1000) } + + test("createDataFrame(RDD[Row], StructType) should convert UDTs (SPARK-6672)") { + val rowRDD = TestSQLContext.sparkContext.parallelize(Seq(Row(new ExamplePoint(1.0, 2.0)))) + val schema = StructType(Array(StructField("point", new ExamplePointUDT(), false))) + val df = TestSQLContext.createDataFrame(rowRDD, schema) + df.rdd.collect() + } } From 0cce5451adfc6bf4661bcf67aca3db26376455fe Mon Sep 17 00:00:00 2001 From: Davies Liu Date: Thu, 2 Apr 2015 12:18:33 -0700 Subject: [PATCH 140/591] [SPARK-6667] [PySpark] remove setReuseAddress The reused address on server side had caused the server can not acknowledge the connected connections, remove it. This PR will retry once after timeout, it also add a timeout at client side. Author: Davies Liu Closes #5324 from davies/collect_hang and squashes the following commits: e5a51a2 [Davies Liu] remove setReuseAddress 7977c2f [Davies Liu] do retry on client side b838f35 [Davies Liu] retry after timeout --- core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala | 1 - python/pyspark/rdd.py | 1 + 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala index 19f4c95fcad74..36cf2af0857dd 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala @@ -605,7 +605,6 @@ private[spark] object PythonRDD extends Logging { */ private def serveIterator[T](items: Iterator[T], threadName: String): Int = { val serverSocket = new ServerSocket(0, 1) - serverSocket.setReuseAddress(true) // Close the socket if no connection in 3 seconds serverSocket.setSoTimeout(3000) diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py index c337a43c8a7fc..2d05611321ed6 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.py @@ -113,6 +113,7 @@ def _parse_memory(s): def _load_from_socket(port, serializer): sock = socket.socket() + sock.settimeout(3) try: sock.connect(("localhost", port)) rf = sock.makefile("rb", 65536) From e3202aa2e9bd140effbcf2a7a02b90cb077e760b Mon Sep 17 00:00:00 2001 From: Hung Lin Date: Thu, 2 Apr 2015 14:01:43 -0700 Subject: [PATCH 141/591] SPARK-6414: Spark driver failed with NPE on job cancelation Use Option for ActiveJob.properties to avoid NPE bug Author: Hung Lin Closes #5124 from hunglin/SPARK-6414 and squashes the following commits: 2290b6b [Hung Lin] [SPARK-6414][core] Fix NPE in SparkContext.cancelJobGroup() --- .../scala/org/apache/spark/SparkContext.scala | 4 +--- .../apache/spark/scheduler/DAGScheduler.scala | 10 +++++----- .../org/apache/spark/SparkContextSuite.scala | 20 ++++++++++++++++++- 3 files changed, 25 insertions(+), 9 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index a70be16f77eeb..3904f7d1060c5 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -433,6 +433,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli // Thread Local variable that can be used by users to pass information down the stack private val localProperties = new InheritableThreadLocal[Properties] { override protected def childValue(parent: Properties): Properties = new Properties(parent) + override protected def initialValue(): Properties = new Properties() } /** @@ -474,9 +475,6 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * Spark fair scheduler pool. */ def setLocalProperty(key: String, value: String) { - if (localProperties.get() == null) { - localProperties.set(new Properties()) - } if (value == null) { localProperties.get.remove(key) } else { diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala index d35b4f9dbaf88..7227fa9da4317 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -493,7 +493,7 @@ class DAGScheduler( callSite: CallSite, allowLocal: Boolean, resultHandler: (Int, U) => Unit, - properties: Properties = null): JobWaiter[U] = { + properties: Properties): JobWaiter[U] = { // Check to make sure we are not launching a task on a partition that does not exist. val maxPartitions = rdd.partitions.length partitions.find(p => p >= maxPartitions || p < 0).foreach { p => @@ -522,7 +522,7 @@ class DAGScheduler( callSite: CallSite, allowLocal: Boolean, resultHandler: (Int, U) => Unit, - properties: Properties = null): Unit = { + properties: Properties): Unit = { val start = System.nanoTime val waiter = submitJob(rdd, func, partitions, callSite, allowLocal, resultHandler, properties) waiter.awaitResult() match { @@ -542,7 +542,7 @@ class DAGScheduler( evaluator: ApproximateEvaluator[U, R], callSite: CallSite, timeout: Long, - properties: Properties = null): PartialResult[R] = { + properties: Properties): PartialResult[R] = { val listener = new ApproximateActionListener(rdd, func, evaluator, timeout) val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _] val partitions = (0 until rdd.partitions.size).toArray @@ -689,7 +689,7 @@ class DAGScheduler( // Cancel all jobs belonging to this job group. // First finds all active jobs with this group id, and then kill stages for them. val activeInGroup = activeJobs.filter(activeJob => - groupId == activeJob.properties.get(SparkContext.SPARK_JOB_GROUP_ID)) + Option(activeJob.properties).exists(_.get(SparkContext.SPARK_JOB_GROUP_ID) == groupId)) val jobIds = activeInGroup.map(_.jobId) jobIds.foreach(handleJobCancellation(_, "part of cancelled job group %s".format(groupId))) submitWaitingStages() @@ -736,7 +736,7 @@ class DAGScheduler( allowLocal: Boolean, callSite: CallSite, listener: JobListener, - properties: Properties = null) { + properties: Properties) { var finalStage: ResultStage = null try { // New stage creation may throw an exception if, for example, jobs are run on a diff --git a/core/src/test/scala/org/apache/spark/SparkContextSuite.scala b/core/src/test/scala/org/apache/spark/SparkContextSuite.scala index b07c4d93db4e6..c7301a30d8b11 100644 --- a/core/src/test/scala/org/apache/spark/SparkContextSuite.scala +++ b/core/src/test/scala/org/apache/spark/SparkContextSuite.scala @@ -18,6 +18,7 @@ package org.apache.spark import java.io.File +import java.util.concurrent.TimeUnit import com.google.common.base.Charsets._ import com.google.common.io.Files @@ -25,9 +26,11 @@ import com.google.common.io.Files import org.scalatest.FunSuite import org.apache.hadoop.io.BytesWritable - import org.apache.spark.util.Utils +import scala.concurrent.Await +import scala.concurrent.duration.Duration + class SparkContextSuite extends FunSuite with LocalSparkContext { test("Only one SparkContext may be active at a time") { @@ -173,4 +176,19 @@ class SparkContextSuite extends FunSuite with LocalSparkContext { sc.stop() } } + + test("Cancelling job group should not cause SparkContext to shutdown (SPARK-6414)") { + try { + sc = new SparkContext(new SparkConf().setAppName("test").setMaster("local")) + val future = sc.parallelize(Seq(0)).foreachAsync(_ => {Thread.sleep(1000L)}) + sc.cancelJobGroup("nonExistGroupId") + Await.ready(future, Duration(2, TimeUnit.SECONDS)) + + // In SPARK-6414, sc.cancelJobGroup will cause NullPointerException and cause + // SparkContext to shutdown, so the following assertion will fail. + assert(sc.parallelize(1 to 10).count() == 10L) + } finally { + sc.stop() + } + } } From 4214e50fc32de1478584d8edfa3a35576c12c025 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Thu, 2 Apr 2015 16:01:03 -0700 Subject: [PATCH 142/591] [SQL] Throw UnsupportedOperationException instead of NotImplementedError NotImplementedError in scala 2.10 is a fatal exception, which is not very nice to throw when not actually fatal. Author: Michael Armbrust Closes #5315 from marmbrus/throwUnsupported and squashes the following commits: c29e03b [Michael Armbrust] [SQL] Throw UnsupportedOperationException instead of NotImplementedError 052e05b [Michael Armbrust] [SQL] Throw UnsupportedOperationException instead of NotImplementedError --- .../main/scala/org/apache/spark/sql/hive/HiveContext.scala | 5 ++--- .../scala/org/apache/spark/sql/hive/StatisticsSuite.scala | 2 +- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala index 6bb1c47dba920..46991fbd68cde 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala @@ -184,9 +184,8 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { catalog.client.alterTable(tableFullName, new Table(hiveTTable)) } case otherRelation => - throw new NotImplementedError( - s"Analyze has only implemented for Hive tables, " + - s"but $tableName is a ${otherRelation.nodeName}") + throw new UnsupportedOperationException( + s"Analyze only works for Hive tables, but $tableName is a ${otherRelation.nodeName}") } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala index 1e05a024b8807..ccd0e5aa51f95 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala @@ -120,7 +120,7 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll { // Try to analyze a temp table sql("""SELECT * FROM src""").registerTempTable("tempTable") - intercept[NotImplementedError] { + intercept[UnsupportedOperationException] { analyze("tempTable") } catalog.unregisterTable(Seq("tempTable")) From 251698fb7335a3bb465f1cd0c29e7e74e0361f4a Mon Sep 17 00:00:00 2001 From: Yin Huai Date: Thu, 2 Apr 2015 16:02:31 -0700 Subject: [PATCH 143/591] [SPARK-6655][SQL] We need to read the schema of a data source table stored in spark.sql.sources.schema property https://issues.apache.org/jira/browse/SPARK-6655 Author: Yin Huai Closes #5313 from yhuai/SPARK-6655 and squashes the following commits: 1e00c03 [Yin Huai] Unnecessary change. f131bd9 [Yin Huai] Fix. f1218c1 [Yin Huai] Failed test. --- .../spark/sql/hive/HiveMetastoreCatalog.scala | 18 +++++++++++---- .../sql/hive/MetastoreDataSourcesSuite.scala | 23 +++++++++++++++++++ 2 files changed, 37 insertions(+), 4 deletions(-) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index f0076cef13777..14cdb420731cd 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -70,7 +70,8 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with val table = synchronized { client.getTable(in.database, in.name) } - val userSpecifiedSchema = + + def schemaStringFromParts: Option[String] = { Option(table.getProperty("spark.sql.sources.schema.numParts")).map { numParts => val parts = (0 until numParts.toInt).map { index => val part = table.getProperty(s"spark.sql.sources.schema.part.${index}") @@ -82,10 +83,19 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with part } - // Stick all parts back to a single schema string in the JSON representation - // and convert it back to a StructType. - DataType.fromJson(parts.mkString).asInstanceOf[StructType] + // Stick all parts back to a single schema string. + parts.mkString } + } + + // Originally, we used spark.sql.sources.schema to store the schema of a data source table. + // After SPARK-6024, we removed this flag. + // Although we are not using spark.sql.sources.schema any more, we need to still support. + val schemaString = + Option(table.getProperty("spark.sql.sources.schema")).orElse(schemaStringFromParts) + + val userSpecifiedSchema = + schemaString.map(s => DataType.fromJson(s).asInstanceOf[StructType]) // It does not appear that the ql client for the metastore has a way to enumerate all the // SerDe properties directly... diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala index e5ad0bf552073..e09c702c8969e 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala @@ -25,6 +25,8 @@ import org.scalatest.BeforeAndAfterEach import org.apache.commons.io.FileUtils import org.apache.hadoop.fs.Path +import org.apache.hadoop.hive.metastore.TableType +import org.apache.hadoop.hive.ql.metadata.Table import org.apache.hadoop.mapred.InvalidInputException import org.apache.spark.sql._ @@ -682,6 +684,27 @@ class MetastoreDataSourcesSuite extends QueryTest with BeforeAndAfterEach { assert(schema === actualSchema) } + test("SPARK-6655 still support a schema stored in spark.sql.sources.schema") { + val tableName = "spark6655" + val schema = StructType(StructField("int", IntegerType, true) :: Nil) + // Manually create the metadata in metastore. + val tbl = new Table("default", tableName) + tbl.setProperty("spark.sql.sources.provider", "json") + tbl.setProperty("spark.sql.sources.schema", schema.json) + tbl.setProperty("EXTERNAL", "FALSE") + tbl.setTableType(TableType.MANAGED_TABLE) + tbl.setSerdeParam("path", catalog.hiveDefaultTableFilePath(tableName)) + catalog.synchronized { + catalog.client.createTable(tbl) + } + + invalidateTable(tableName) + val actualSchema = table(tableName).schema + assert(schema === actualSchema) + sql(s"drop table $tableName") + } + + test("insert into a table") { def createDF(from: Int, to: Int): DataFrame = createDataFrame((from to to).map(i => Tuple2(i, s"str$i"))).toDF("c1", "c2") From d3944b6f2aeb36629bf89207629cc5e55d327241 Mon Sep 17 00:00:00 2001 From: Cheng Lian Date: Thu, 2 Apr 2015 16:15:34 -0700 Subject: [PATCH 144/591] [Minor] [SQL] Follow-up of PR #5210 This PR addresses rxin's comments in PR #5210. [Review on Reviewable](https://reviewable.io/reviews/apache/spark/5219) Author: Cheng Lian Closes #5219 from liancheng/spark-6554-followup and squashes the following commits: 41f3a09 [Cheng Lian] Addresses comments in #5210 --- .../scala/org/apache/spark/sql/parquet/newParquet.scala | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala index e12531480ce92..583bac42fdcce 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala @@ -434,17 +434,18 @@ private[sql] case class ParquetRelation2( FileInputFormat.setInputPaths(job, selectedFiles.map(_.getPath): _*) } - // Push down filters when possible. Notice that not all filters can be converted to Parquet - // filter predicate. Here we try to convert each individual predicate and only collect those - // convertible ones. + // Try to push down filters when filter push-down is enabled. if (sqlContext.conf.parquetFilterPushDown) { + val partitionColNames = partitionColumns.map(_.name).toSet predicates // Don't push down predicates which reference partition columns .filter { pred => - val partitionColNames = partitionColumns.map(_.name).toSet val referencedColNames = pred.references.map(_.name).toSet referencedColNames.intersect(partitionColNames).isEmpty } + // Collects all converted Parquet filter predicates. Notice that not all predicates can be + // converted (`ParquetFilters.createFilter` returns an `Option`). That's why a `flatMap` + // is used here. .flatMap(ParquetFilters.createFilter) .reduceOption(FilterApi.and) .foreach(ParquetInputFormat.setFilterPredicate(jobConf, _)) From 5db89127e72630aec7c5552f2c84018ae18d03fe Mon Sep 17 00:00:00 2001 From: Yin Huai Date: Thu, 2 Apr 2015 16:46:50 -0700 Subject: [PATCH 145/591] [SPARK-6618][SPARK-6669][SQL] Lock Hive metastore client correctly. Author: Yin Huai Author: Michael Armbrust Closes #5333 from yhuai/lookupRelationLock and squashes the following commits: 59c884f [Michael Armbrust] [SQL] Lock metastore client in analyzeTable 7667030 [Yin Huai] Merge pull request #2 from marmbrus/pr/5333 e4a9b0b [Michael Armbrust] Correctly lock on MetastoreCatalog d6fc32f [Yin Huai] Missing `)`. 1e241af [Yin Huai] Protect InsertIntoHive. fee7e9c [Yin Huai] A test? 5416b0f [Yin Huai] Just protect client. --- .../apache/spark/sql/hive/HiveContext.scala | 4 +- .../spark/sql/hive/HiveMetastoreCatalog.scala | 14 +++-- .../hive/execution/InsertIntoHiveTable.scala | 51 +++++++++++-------- .../sql/hive/execution/SQLQuerySuite.scala | 11 ++++ 4 files changed, 53 insertions(+), 27 deletions(-) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala index 46991fbd68cde..7c6a7df2bd01e 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala @@ -181,7 +181,9 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { val tableFullName = relation.hiveQlTable.getDbName + "." + relation.hiveQlTable.getTableName - catalog.client.alterTable(tableFullName, new Table(hiveTTable)) + catalog.synchronized { + catalog.client.alterTable(tableFullName, new Table(hiveTTable)) + } } case otherRelation => throw new UnsupportedOperationException( diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index 14cdb420731cd..bbd920a4051de 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -67,7 +67,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with val cacheLoader = new CacheLoader[QualifiedTableName, LogicalPlan]() { override def load(in: QualifiedTableName): LogicalPlan = { logDebug(s"Creating new cached data source for $in") - val table = synchronized { + val table = HiveMetastoreCatalog.this.synchronized { client.getTable(in.database, in.name) } @@ -183,12 +183,16 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with def lookupRelation( tableIdentifier: Seq[String], - alias: Option[String]): LogicalPlan = synchronized { + alias: Option[String]): LogicalPlan = { val tableIdent = processTableIdentifier(tableIdentifier) val databaseName = tableIdent.lift(tableIdent.size - 2).getOrElse( hive.sessionState.getCurrentDatabase) val tblName = tableIdent.last - val table = try client.getTable(databaseName, tblName) catch { + val table = try { + synchronized { + client.getTable(databaseName, tblName) + } + } catch { case te: org.apache.hadoop.hive.ql.metadata.InvalidTableException => throw new NoSuchTableException } @@ -210,7 +214,9 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with } else { val partitions: Seq[Partition] = if (table.isPartitioned) { - HiveShim.getAllPartitionsOf(client, table).toSeq + synchronized { + HiveShim.getAllPartitionsOf(client, table).toSeq + } } else { Nil } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala index cdf012b5117be..6c96747439683 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala @@ -50,7 +50,7 @@ case class InsertIntoHiveTable( @transient val sc: HiveContext = sqlContext.asInstanceOf[HiveContext] @transient lazy val outputClass = newSerializer(table.tableDesc).getSerializedClass @transient private lazy val hiveContext = new Context(sc.hiveconf) - @transient private lazy val db = Hive.get(sc.hiveconf) + @transient private lazy val catalog = sc.catalog private def newSerializer(tableDesc: TableDesc): Serializer = { val serializer = tableDesc.getDeserializerClass.newInstance().asInstanceOf[Serializer] @@ -199,38 +199,45 @@ case class InsertIntoHiveTable( orderedPartitionSpec.put(entry.getName,partitionSpec.get(entry.getName).getOrElse("")) } val partVals = MetaStoreUtils.getPvals(table.hiveQlTable.getPartCols, partitionSpec) - db.validatePartitionNameCharacters(partVals) + catalog.synchronized { + catalog.client.validatePartitionNameCharacters(partVals) + } // inheritTableSpecs is set to true. It should be set to false for a IMPORT query // which is currently considered as a Hive native command. val inheritTableSpecs = true // TODO: Correctly set isSkewedStoreAsSubdir. val isSkewedStoreAsSubdir = false if (numDynamicPartitions > 0) { - db.loadDynamicPartitions( - outputPath, - qualifiedTableName, - orderedPartitionSpec, - overwrite, - numDynamicPartitions, - holdDDLTime, - isSkewedStoreAsSubdir - ) + catalog.synchronized { + catalog.client.loadDynamicPartitions( + outputPath, + qualifiedTableName, + orderedPartitionSpec, + overwrite, + numDynamicPartitions, + holdDDLTime, + isSkewedStoreAsSubdir) + } } else { - db.loadPartition( + catalog.synchronized { + catalog.client.loadPartition( + outputPath, + qualifiedTableName, + orderedPartitionSpec, + overwrite, + holdDDLTime, + inheritTableSpecs, + isSkewedStoreAsSubdir) + } + } + } else { + catalog.synchronized { + catalog.client.loadTable( outputPath, qualifiedTableName, - orderedPartitionSpec, overwrite, - holdDDLTime, - inheritTableSpecs, - isSkewedStoreAsSubdir) + holdDDLTime) } - } else { - db.loadTable( - outputPath, - qualifiedTableName, - overwrite, - holdDDLTime) } // Invalidate the cache. diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala index 310c2bfdf1011..2065f0d60d92f 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala @@ -457,4 +457,15 @@ class SQLQuerySuite extends QueryTest { dropTempTable("data") setConf("spark.sql.hive.convertCTAS", originalConf) } + + test("sanity test for SPARK-6618") { + (1 to 100).par.map { i => + val tableName = s"SPARK_6618_table_$i" + sql(s"CREATE TABLE $tableName (col1 string)") + catalog.lookupRelation(Seq(tableName)) + table(tableName) + tables() + sql(s"DROP TABLE $tableName") + } + } } From dfd2982bc7047732197f1d9ad77221e9c6076fc2 Mon Sep 17 00:00:00 2001 From: Cheng Hao Date: Thu, 2 Apr 2015 17:20:31 -0700 Subject: [PATCH 146/591] [SQL][Minor] Use analyzed logical instead of unresolved in HiveComparisonTest Some internal unit test failed due to the logical plan node in pattern matching in `HiveComparisonTest`, e.g. https://github.com/apache/spark/blob/master/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala#L137 Which will may call the `output` function on an unresolved logical plan. Author: Cheng Hao Closes #4946 from chenghao-intel/logical and squashes the following commits: 432ecb3 [Cheng Hao] Use analyzed instead of logical in HiveComparisonTest --- .../apache/spark/sql/hive/execution/HiveComparisonTest.scala | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala index 8f3285242091c..a5ec312ee430c 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala @@ -138,7 +138,7 @@ abstract class HiveComparisonTest case _ => plan.children.iterator.exists(isSorted) } - val orderedAnswer = hiveQuery.logical match { + val orderedAnswer = hiveQuery.analyzed match { // Clean out non-deterministic time schema info. // Hack: Hive simply prints the result of a SET command to screen, // and does not return it as a query answer. @@ -299,7 +299,7 @@ abstract class HiveComparisonTest val hiveQueries = queryList.map(new TestHive.HiveQLQueryExecution(_)) // Make sure we can at least parse everything before attempting hive execution. - hiveQueries.foreach(_.logical) + hiveQueries.foreach(_.analyzed) val computedResults = (queryList.zipWithIndex, hiveQueries, hiveCacheFiles).zipped.map { case ((queryString, i), hiveQuery, cachedAnswerFile)=> try { From 947802cb0de581e51f8141f6663e896de3d753ce Mon Sep 17 00:00:00 2001 From: DoingDone9 <799203320@qq.com> Date: Thu, 2 Apr 2015 17:23:51 -0700 Subject: [PATCH 147/591] [SPARK-6243][SQL] The Operation of match did not conside the scenarios that order.dataType does not match NativeType It did not conside that order.dataType does not match NativeType. So i add "case other => ..." for other cenarios. Author: DoingDone9 <799203320@qq.com> Closes #4959 from DoingDone9/case_ and squashes the following commits: 6278846 [DoingDone9] Update rows.scala cb1852d [DoingDone9] Merge pull request #2 from apache/master c3f046f [DoingDone9] Merge pull request #1 from apache/master --- .../scala/org/apache/spark/sql/catalyst/expressions/rows.scala | 1 + 1 file changed, 1 insertion(+) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala index a8983df208318..0a275b84086cf 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala @@ -224,6 +224,7 @@ class RowOrdering(ordering: Seq[SortOrder]) extends Ordering[Row] { n.ordering.asInstanceOf[Ordering[Any]].compare(left, right) case n: NativeType if order.direction == Descending => n.ordering.asInstanceOf[Ordering[Any]].reverse.compare(left, right) + case other => sys.error(s"Type $other does not support ordered operations") } if (comparison != 0) return comparison } From 052dee0707830cfd3cd8821ecc3471a37ede294a Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Thu, 2 Apr 2015 18:30:55 -0700 Subject: [PATCH 148/591] [SPARK-6686][SQL] Use resolved output instead of names for toDF rename This is a workaround for a problem reported on the user list. This doesn't fix the core problem, but in general is a more robust way to do renames. Author: Michael Armbrust Closes #5337 from marmbrus/toDFrename and squashes the following commits: 6a3159d [Michael Armbrust] [SPARK-6686][SQL] Use resolved output instead of names for toDF rename --- .../src/main/scala/org/apache/spark/sql/DataFrame.scala | 4 ++-- .../test/scala/org/apache/spark/sql/DataFrameSuite.scala | 8 ++++++++ 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 34be17325b2b0..5c6016a4a2ce2 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -240,8 +240,8 @@ class DataFrame private[sql]( s"Old column names (${schema.size}): " + schema.fields.map(_.name).mkString(", ") + "\n" + s"New column names (${colNames.size}): " + colNames.mkString(", ")) - val newCols = schema.fieldNames.zip(colNames).map { case (oldName, newName) => - apply(oldName).as(newName) + val newCols = logicalPlan.output.zip(colNames).map { case (oldAttribute, newName) => + Column(oldAttribute).as(newName) } select(newCols :_*) } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index 5297cc01eddfc..1db0cf7daac03 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -60,6 +60,14 @@ class DataFrameSuite extends QueryTest { assert($"test".toString === "test") } + test("rename nested groupby") { + val df = Seq((1,(1,1))).toDF() + + checkAnswer( + df.groupBy("_1").agg(col("_1"), sum("_2._1")).toDF("key", "total"), + Row(1, 1) :: Nil) + } + test("invalid plan toString, debug mode") { val oldSetting = TestSQLContext.conf.dataFrameEagerAnalysis TestSQLContext.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, "true") From 45134ec920c3766c22aefd4366b4b60ec99bd810 Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Thu, 2 Apr 2015 19:48:55 -0700 Subject: [PATCH 149/591] [SPARK-6650] [core] Stop ExecutorAllocationManager when context stops. This fixes the thread leak. I also changed the unit test to keep track of allocated contexts and make sure they're closed after tests are run; this is needed since some tests use this pattern: val sc = createContext() doSomethingThatMayThrow() sc.stop() Author: Marcelo Vanzin Closes #5311 from vanzin/SPARK-6650 and squashes the following commits: 652c73b [Marcelo Vanzin] Nits. 5711512 [Marcelo Vanzin] More exception safety. cc5a744 [Marcelo Vanzin] Stop alloc manager before scheduler. 9886f69 [Marcelo Vanzin] [SPARK-6650] [core] Stop ExecutorAllocationManager when context stops. --- .../spark/ExecutorAllocationManager.scala | 38 ++++++++-------- .../scala/org/apache/spark/SparkContext.scala | 3 +- .../ExecutorAllocationManagerSuite.scala | 44 ++++++++++++------- 3 files changed, 49 insertions(+), 36 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala index 21c6e6ffa6666..9385f557c4614 100644 --- a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala +++ b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala @@ -17,10 +17,12 @@ package org.apache.spark +import java.util.concurrent.{Executors, TimeUnit} + import scala.collection.mutable import org.apache.spark.scheduler._ -import org.apache.spark.util.{SystemClock, Clock} +import org.apache.spark.util.{Clock, SystemClock, Utils} /** * An agent that dynamically allocates and removes executors based on the workload. @@ -129,6 +131,10 @@ private[spark] class ExecutorAllocationManager( // Listener for Spark events that impact the allocation policy private val listener = new ExecutorAllocationListener + // Executor that handles the scheduling task. + private val executor = Executors.newSingleThreadScheduledExecutor( + Utils.namedThreadFactory("spark-dynamic-executor-allocation")) + /** * Verify that the settings specified through the config are valid. * If not, throw an appropriate exception. @@ -173,32 +179,24 @@ private[spark] class ExecutorAllocationManager( } /** - * Register for scheduler callbacks to decide when to add and remove executors. + * Register for scheduler callbacks to decide when to add and remove executors, and start + * the scheduling task. */ def start(): Unit = { listenerBus.addListener(listener) - startPolling() + + val scheduleTask = new Runnable() { + override def run(): Unit = Utils.logUncaughtExceptions(schedule()) + } + executor.scheduleAtFixedRate(scheduleTask, 0, intervalMillis, TimeUnit.MILLISECONDS) } /** - * Start the main polling thread that keeps track of when to add and remove executors. + * Stop the allocation manager. */ - private def startPolling(): Unit = { - val t = new Thread { - override def run(): Unit = { - while (true) { - try { - schedule() - } catch { - case e: Exception => logError("Exception in dynamic executor allocation thread!", e) - } - Thread.sleep(intervalMillis) - } - } - } - t.setName("spark-dynamic-executor-allocation") - t.setDaemon(true) - t.start() + def stop(): Unit = { + executor.shutdown() + executor.awaitTermination(10, TimeUnit.SECONDS) } /** diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 3904f7d1060c5..5b3778ead6994 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -1136,7 +1136,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * Return whether dynamically adjusting the amount of resources allocated to * this application is supported. This is currently only available for YARN. */ - private[spark] def supportDynamicAllocation = + private[spark] def supportDynamicAllocation = master.contains("yarn") || dynamicAllocationTesting /** @@ -1400,6 +1400,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli env.metricsSystem.report() metadataCleaner.cancel() cleaner.foreach(_.stop()) + executorAllocationManager.foreach(_.stop()) dagScheduler.stop() dagScheduler = null listenerBus.stop() diff --git a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala index abfcee75728dc..3ded1e4af8742 100644 --- a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala @@ -19,7 +19,7 @@ package org.apache.spark import scala.collection.mutable -import org.scalatest.{FunSuite, PrivateMethodTester} +import org.scalatest.{BeforeAndAfter, FunSuite, PrivateMethodTester} import org.apache.spark.executor.TaskMetrics import org.apache.spark.scheduler._ import org.apache.spark.scheduler.cluster.ExecutorInfo @@ -28,10 +28,20 @@ import org.apache.spark.util.ManualClock /** * Test add and remove behavior of ExecutorAllocationManager. */ -class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext { +class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext with BeforeAndAfter { import ExecutorAllocationManager._ import ExecutorAllocationManagerSuite._ + private val contexts = new mutable.ListBuffer[SparkContext]() + + before { + contexts.clear() + } + + after { + contexts.foreach(_.stop()) + } + test("verify min/max executors") { val conf = new SparkConf() .setMaster("local") @@ -39,18 +49,19 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext { .set("spark.dynamicAllocation.enabled", "true") .set("spark.dynamicAllocation.testing", "true") val sc0 = new SparkContext(conf) + contexts += sc0 assert(sc0.executorAllocationManager.isDefined) sc0.stop() // Min < 0 val conf1 = conf.clone().set("spark.dynamicAllocation.minExecutors", "-1") - intercept[SparkException] { new SparkContext(conf1) } + intercept[SparkException] { contexts += new SparkContext(conf1) } SparkEnv.get.stop() SparkContext.clearActiveContext() // Max < 0 val conf2 = conf.clone().set("spark.dynamicAllocation.maxExecutors", "-1") - intercept[SparkException] { new SparkContext(conf2) } + intercept[SparkException] { contexts += new SparkContext(conf2) } SparkEnv.get.stop() SparkContext.clearActiveContext() @@ -665,16 +676,6 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext { assert(removeTimes(manager).contains("executor-2")) assert(!removeTimes(manager).contains("executor-1")) } -} - -/** - * Helper methods for testing ExecutorAllocationManager. - * This includes methods to access private methods and fields in ExecutorAllocationManager. - */ -private object ExecutorAllocationManagerSuite extends PrivateMethodTester { - private val schedulerBacklogTimeout = 1L - private val sustainedSchedulerBacklogTimeout = 2L - private val executorIdleTimeout = 3L private def createSparkContext(minExecutors: Int = 1, maxExecutors: Int = 5): SparkContext = { val conf = new SparkConf() @@ -688,9 +689,22 @@ private object ExecutorAllocationManagerSuite extends PrivateMethodTester { sustainedSchedulerBacklogTimeout.toString) .set("spark.dynamicAllocation.executorIdleTimeout", executorIdleTimeout.toString) .set("spark.dynamicAllocation.testing", "true") - new SparkContext(conf) + val sc = new SparkContext(conf) + contexts += sc + sc } +} + +/** + * Helper methods for testing ExecutorAllocationManager. + * This includes methods to access private methods and fields in ExecutorAllocationManager. + */ +private object ExecutorAllocationManagerSuite extends PrivateMethodTester { + private val schedulerBacklogTimeout = 1L + private val sustainedSchedulerBacklogTimeout = 2L + private val executorIdleTimeout = 3L + private def createStageInfo(stageId: Int, numTasks: Int): StageInfo = { new StageInfo(stageId, 0, "name", numTasks, Seq.empty, "no details") } From 4b82bd730a24f96d94dfea87420cfaa4253a5ccb Mon Sep 17 00:00:00 2001 From: Yin Huai Date: Thu, 2 Apr 2015 20:23:08 -0700 Subject: [PATCH 150/591] [SPARK-6575][SQL] Converted Parquet Metastore tables no longer cache metadata https://issues.apache.org/jira/browse/SPARK-6575 Author: Yin Huai Closes #5339 from yhuai/parquetRelationCache and squashes the following commits: 83d9846 [Yin Huai] Remove unnecessary change. c0dc7a4 [Yin Huai] Cache converted parquet relations. --- .../spark/sql/hive/HiveMetastoreCatalog.scala | 61 +++++++++- .../apache/spark/sql/hive/parquetSuites.scala | 112 ++++++++++++++++++ 2 files changed, 167 insertions(+), 6 deletions(-) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index bbd920a4051de..76d329a3ddcdf 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -116,7 +116,9 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with } override def refreshTable(databaseName: String, tableName: String): Unit = { - cachedDataSourceTables.refresh(QualifiedTableName(databaseName, tableName).toLowerCase) + // refresh table does not eagerly reload the cache. It just invalidate the cache. + // Next time when we use the table, it will be populated in the cache. + invalidateTable(databaseName, tableName) } def invalidateTable(databaseName: String, tableName: String): Unit = { @@ -229,13 +231,42 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with private def convertToParquetRelation(metastoreRelation: MetastoreRelation): LogicalRelation = { val metastoreSchema = StructType.fromAttributes(metastoreRelation.output) val mergeSchema = hive.convertMetastoreParquetWithSchemaMerging - val parquetOptions = Map( - ParquetRelation2.METASTORE_SCHEMA -> metastoreSchema.json, - ParquetRelation2.MERGE_SCHEMA -> mergeSchema.toString) // NOTE: Instead of passing Metastore schema directly to `ParquetRelation2`, we have to // serialize the Metastore schema to JSON and pass it as a data source option because of the // evil case insensitivity issue, which is reconciled within `ParquetRelation2`. + val parquetOptions = Map( + ParquetRelation2.METASTORE_SCHEMA -> metastoreSchema.json, + ParquetRelation2.MERGE_SCHEMA -> mergeSchema.toString) + val tableIdentifier = + QualifiedTableName(metastoreRelation.databaseName, metastoreRelation.tableName) + + def getCached( + tableIdentifier: QualifiedTableName, + pathsInMetastore: Seq[String], + schemaInMetastore: StructType, + partitionSpecInMetastore: Option[PartitionSpec]): Option[LogicalRelation] = { + cachedDataSourceTables.getIfPresent(tableIdentifier) match { + case null => None // Cache miss + case logical @ LogicalRelation(parquetRelation: ParquetRelation2) => + // If we have the same paths, same schema, and same partition spec, + // we will use the cached Parquet Relation. + val useCached = + parquetRelation.paths == pathsInMetastore && + logical.schema.sameType(metastoreSchema) && + parquetRelation.maybePartitionSpec == partitionSpecInMetastore + + if (useCached) Some(logical) else None + case other => + logWarning( + s"${metastoreRelation.databaseName}.${metastoreRelation.tableName} shold be stored " + + s"as Parquet. However, we are getting a ${other} from the metastore cache. " + + s"This cached entry will be invalidated.") + cachedDataSourceTables.invalidate(tableIdentifier) + None + } + } + if (metastoreRelation.hiveQlTable.isPartitioned) { val partitionSchema = StructType.fromAttributes(metastoreRelation.partitionKeys) val partitionColumnDataTypes = partitionSchema.map(_.dataType) @@ -248,10 +279,28 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with } val partitionSpec = PartitionSpec(partitionSchema, partitions) val paths = partitions.map(_.path) - LogicalRelation(ParquetRelation2(paths, parquetOptions, None, Some(partitionSpec))(hive)) + + val cached = getCached(tableIdentifier, paths, metastoreSchema, Some(partitionSpec)) + val parquetRelation = cached.getOrElse { + val created = + LogicalRelation(ParquetRelation2(paths, parquetOptions, None, Some(partitionSpec))(hive)) + cachedDataSourceTables.put(tableIdentifier, created) + created + } + + parquetRelation } else { val paths = Seq(metastoreRelation.hiveQlTable.getDataLocation.toString) - LogicalRelation(ParquetRelation2(paths, parquetOptions)(hive)) + + val cached = getCached(tableIdentifier, paths, metastoreSchema, None) + val parquetRelation = cached.getOrElse { + val created = + LogicalRelation(ParquetRelation2(paths, parquetOptions)(hive)) + cachedDataSourceTables.put(tableIdentifier, created) + created + } + + parquetRelation } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala index 432d65a874518..2ad6e867262b1 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala @@ -26,8 +26,10 @@ import org.apache.spark.sql.{QueryTest, SQLConf, SaveMode} import org.apache.spark.sql.catalyst.expressions.Row import org.apache.spark.sql.execution.{ExecutedCommand, PhysicalRDD} import org.apache.spark.sql.hive.execution.HiveTableScan +import org.apache.spark.sql.hive.test.TestHive import org.apache.spark.sql.hive.test.TestHive._ import org.apache.spark.sql.hive.test.TestHive.implicits._ +import org.apache.spark.sql.json.JSONRelation import org.apache.spark.sql.sources.{InsertIntoDataSource, LogicalRelation} import org.apache.spark.sql.parquet.{ParquetRelation2, ParquetTableScan} import org.apache.spark.sql.SaveMode @@ -390,6 +392,116 @@ class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase { sql("DROP TABLE ms_convert") } + + test("Caching converted data source Parquet Relations") { + def checkCached(tableIdentifer: catalog.QualifiedTableName): Unit = { + // Converted test_parquet should be cached. + catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) match { + case null => fail("Converted test_parquet should be cached in the cache.") + case logical @ LogicalRelation(parquetRelation: ParquetRelation2) => // OK + case other => + fail( + "The cached test_parquet should be a Parquet Relation. " + + s"However, $other is returned form the cache.") + } + } + + sql("DROP TABLE IF EXISTS test_insert_parquet") + sql("DROP TABLE IF EXISTS test_parquet_partitioned_cache_test") + + sql( + """ + |create table test_insert_parquet + |( + | intField INT, + | stringField STRING + |) + |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' + |STORED AS + | INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' + | OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' + """.stripMargin) + + var tableIdentifer = catalog.QualifiedTableName("default", "test_insert_parquet") + + // First, make sure the converted test_parquet is not cached. + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + // Table lookup will make the table cached. + table("test_insert_parquet") + checkCached(tableIdentifer) + // For insert into non-partitioned table, we will do the conversion, + // so the converted test_insert_parquet should be cached. + invalidateTable("test_insert_parquet") + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + sql( + """ + |INSERT INTO TABLE test_insert_parquet + |select a, b from jt + """.stripMargin) + checkCached(tableIdentifer) + // Make sure we can read the data. + checkAnswer( + sql("select * from test_insert_parquet"), + sql("select a, b from jt").collect()) + // Invalidate the cache. + invalidateTable("test_insert_parquet") + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + + // Create a partitioned table. + sql( + """ + |create table test_parquet_partitioned_cache_test + |( + | intField INT, + | stringField STRING + |) + |PARTITIONED BY (date string) + |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe' + |STORED AS + | INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' + | OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat' + """.stripMargin) + + tableIdentifer = catalog.QualifiedTableName("default", "test_parquet_partitioned_cache_test") + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + sql( + """ + |INSERT INTO TABLE test_parquet_partitioned_cache_test + |PARTITION (date='2015-04-01') + |select a, b from jt + """.stripMargin) + // Right now, insert into a partitioned Parquet is not supported in data source Parquet. + // So, we expect it is not cached. + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "false") + sql( + """ + |INSERT INTO TABLE test_parquet_partitioned_cache_test + |PARTITION (date='2015-04-02') + |select a, b from jt + """.stripMargin) + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "true") + + // Make sure we can cache the partitioned table. + table("test_parquet_partitioned_cache_test") + checkCached(tableIdentifer) + // Make sure we can read the data. + checkAnswer( + sql("select STRINGField, date, intField from test_parquet_partitioned_cache_test"), + sql( + """ + |select b, '2015-04-01', a FROM jt + |UNION ALL + |select b, '2015-04-02', a FROM jt + """.stripMargin).collect()) + + invalidateTable("test_parquet_partitioned_cache_test") + assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) + + sql("DROP TABLE test_insert_parquet") + sql("DROP TABLE test_parquet_partitioned_cache_test") + } } class ParquetDataSourceOffMetastoreSuite extends ParquetMetastoreSuiteBase { From 8a0aa81ca37d337423db60edb09cf264cc2c6498 Mon Sep 17 00:00:00 2001 From: KaiXinXiaoLei Date: Thu, 2 Apr 2015 20:24:31 -0700 Subject: [PATCH 151/591] [CORE] The descriptionof jobHistory config should be spark.history.fs.logDirectory The config option is spark.history.fs.logDirectory, not spark.fs.history.logDirectory. So the descriptionof should be changed. Thanks. Author: KaiXinXiaoLei Closes #5332 from KaiXinXiaoLei/historyConfig and squashes the following commits: 5ffbfb5 [KaiXinXiaoLei] the describe of jobHistory config is error --- .../org/apache/spark/deploy/history/FsHistoryProvider.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala index 80c9c13ddec1e..9d40d8c8fd7a8 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala @@ -118,7 +118,7 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis if (!fs.exists(path)) { var msg = s"Log directory specified does not exist: $logDir." if (logDir == DEFAULT_LOG_DIR) { - msg += " Did you configure the correct one through spark.fs.history.logDirectory?" + msg += " Did you configure the correct one through spark.history.fs.logDirectory?" } throw new IllegalArgumentException(msg) } From 6e1c1ec67bc4d7e5700f523ec08db6bb25bd2302 Mon Sep 17 00:00:00 2001 From: freeman Date: Thu, 2 Apr 2015 21:37:44 -0700 Subject: [PATCH 152/591] [SPARK-6345][STREAMING][MLLIB] Fix for training with prediction This patch fixes a reported bug causing model updates to not properly propagate to model predictions during streaming regression. These minor changes in model declaration fix the problem, and I expanded the tests to include the scenario in which the bug was arising. The two new tests failed prior to the patch and now pass. cc mengxr Author: freeman Closes #5037 from freeman-lab/train-predict-fix and squashes the following commits: 3af953e [freeman] Expand test coverage to include combined training and prediction 8f84fc8 [freeman] Move model declaration --- .../StreamingLogisticRegressionWithSGD.scala | 2 ++ .../regression/StreamingLinearAlgorithm.scala | 6 ++-- .../StreamingLinearRegressionWithSGD.scala | 2 ++ .../StreamingLogisticRegressionSuite.scala | 27 ++++++++++++++++++ .../StreamingLinearRegressionSuite.scala | 28 +++++++++++++++++++ 5 files changed, 62 insertions(+), 3 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala index b89f38cf5aba4..7d33df3221fbf 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala @@ -63,6 +63,8 @@ class StreamingLogisticRegressionWithSGD private[mllib] ( protected val algorithm = new LogisticRegressionWithSGD( stepSize, numIterations, regParam, miniBatchFraction) + protected var model: Option[LogisticRegressionModel] = None + /** Set the step size for gradient descent. Default: 0.1. */ def setStepSize(stepSize: Double): this.type = { this.algorithm.optimizer.setStepSize(stepSize) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala index ce95c063db970..cea8f3f47307b 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala @@ -60,7 +60,7 @@ abstract class StreamingLinearAlgorithm[ A <: GeneralizedLinearAlgorithm[M]] extends Logging { /** The model to be updated and used for prediction. */ - protected var model: Option[M] = None + protected var model: Option[M] /** The algorithm to use for updating. */ protected val algorithm: A @@ -114,7 +114,7 @@ abstract class StreamingLinearAlgorithm[ if (model.isEmpty) { throw new IllegalArgumentException("Model must be initialized before starting prediction.") } - data.map(model.get.predict) + data.map{x => model.get.predict(x)} } /** Java-friendly version of `predictOn`. */ @@ -132,7 +132,7 @@ abstract class StreamingLinearAlgorithm[ if (model.isEmpty) { throw new IllegalArgumentException("Model must be initialized before starting prediction") } - data.mapValues(model.get.predict) + data.mapValues{x => model.get.predict(x)} } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala index e5e6301127a28..a49153bf73c0d 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala @@ -59,6 +59,8 @@ class StreamingLinearRegressionWithSGD private[mllib] ( val algorithm = new LinearRegressionWithSGD(stepSize, numIterations, miniBatchFraction) + protected var model: Option[LinearRegressionModel] = None + /** Set the step size for gradient descent. Default: 0.1. */ def setStepSize(stepSize: Double): this.type = { this.algorithm.optimizer.setStepSize(stepSize) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala index 8b3e6e5ce9249..d50c43d439187 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala @@ -132,4 +132,31 @@ class StreamingLogisticRegressionSuite extends FunSuite with TestSuiteBase { assert(errors.forall(x => x <= 0.4)) } + // Test training combined with prediction + test("training and prediction") { + // create model initialized with zero weights + val model = new StreamingLogisticRegressionWithSGD() + .setInitialWeights(Vectors.dense(-0.1)) + .setStepSize(0.01) + .setNumIterations(10) + + // generate sequence of simulated data for testing + val numBatches = 10 + val nPoints = 100 + val testInput = (0 until numBatches).map { i => + LogisticRegressionSuite.generateLogisticInput(0.0, 5.0, nPoints, 42 * (i + 1)) + } + + // train and predict + val ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => { + model.trainOn(inputDStream) + model.predictOnValues(inputDStream.map(x => (x.label, x.features))) + }) + + val output: Seq[Seq[(Double, Double)]] = runStreams(ssc, numBatches, numBatches) + + // assert that prediction error improves, ensuring that the updated model is being used + val error = output.map(batch => batch.map(p => math.abs(p._1 - p._2)).sum / nPoints).toList + assert(error.head > 0.8 & error.last < 0.2) + } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala index 70b43ddb7daf5..24fd8df691817 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala @@ -139,4 +139,32 @@ class StreamingLinearRegressionSuite extends FunSuite with TestSuiteBase { val errors = output.map(batch => batch.map(p => math.abs(p._1 - p._2)).sum / nPoints) assert(errors.forall(x => x <= 0.1)) } + + // Test training combined with prediction + test("training and prediction") { + // create model initialized with zero weights + val model = new StreamingLinearRegressionWithSGD() + .setInitialWeights(Vectors.dense(0.0, 0.0)) + .setStepSize(0.2) + .setNumIterations(25) + + // generate sequence of simulated data for testing + val numBatches = 10 + val nPoints = 100 + val testInput = (0 until numBatches).map { i => + LinearDataGenerator.generateLinearInput(0.0, Array(10.0, 10.0), nPoints, 42 * (i + 1)) + } + + // train and predict + val ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => { + model.trainOn(inputDStream) + model.predictOnValues(inputDStream.map(x => (x.label, x.features))) + }) + + val output: Seq[Seq[(Double, Double)]] = runStreams(ssc, numBatches, numBatches) + + // assert that prediction error improves, ensuring that the updated model is being used + val error = output.map(batch => batch.map(p => math.abs(p._1 - p._2)).sum / nPoints).toList + assert((error.head - error.last) > 2) + } } From 440ea31b76aa7e813436271fd63880c7bcd69157 Mon Sep 17 00:00:00 2001 From: zsxwing Date: Thu, 2 Apr 2015 22:54:30 -0700 Subject: [PATCH 153/591] [SPARK-6621][Core] Fix the bug that calling EventLoop.stop in EventLoop.onReceive/onError/onStart doesn't call onStop Author: zsxwing Closes #5280 from zsxwing/SPARK-6621 and squashes the following commits: 521125e [zsxwing] Fix the bug that calling EventLoop.stop in EventLoop.onReceive and EventLoop.onError doesn't call onStop --- .../org/apache/spark/util/EventLoop.scala | 18 ++++- .../apache/spark/util/EventLoopSuite.scala | 72 +++++++++++++++++++ 2 files changed, 87 insertions(+), 3 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/util/EventLoop.scala b/core/src/main/scala/org/apache/spark/util/EventLoop.scala index b0ed908b84424..e9b2b8d24b476 100644 --- a/core/src/main/scala/org/apache/spark/util/EventLoop.scala +++ b/core/src/main/scala/org/apache/spark/util/EventLoop.scala @@ -76,9 +76,21 @@ private[spark] abstract class EventLoop[E](name: String) extends Logging { def stop(): Unit = { if (stopped.compareAndSet(false, true)) { eventThread.interrupt() - eventThread.join() - // Call onStop after the event thread exits to make sure onReceive happens before onStop - onStop() + var onStopCalled = false + try { + eventThread.join() + // Call onStop after the event thread exits to make sure onReceive happens before onStop + onStopCalled = true + onStop() + } catch { + case ie: InterruptedException => + Thread.currentThread().interrupt() + if (!onStopCalled) { + // ie is thrown from `eventThread.join()`. Otherwise, we should not call `onStop` since + // it's already called. + onStop() + } + } } else { // Keep quiet to allow calling `stop` multiple times. } diff --git a/core/src/test/scala/org/apache/spark/util/EventLoopSuite.scala b/core/src/test/scala/org/apache/spark/util/EventLoopSuite.scala index 1026cb2aa7cae..47b535206c949 100644 --- a/core/src/test/scala/org/apache/spark/util/EventLoopSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/EventLoopSuite.scala @@ -203,4 +203,76 @@ class EventLoopSuite extends FunSuite with Timeouts { assert(!eventLoop.isActive) } } + + test("EventLoop: stop() in onStart should call onStop") { + @volatile var onStopCalled: Boolean = false + val eventLoop = new EventLoop[Int]("test") { + + override def onStart(): Unit = { + stop() + } + + override def onReceive(event: Int): Unit = { + } + + override def onError(e: Throwable): Unit = { + } + + override def onStop(): Unit = { + onStopCalled = true + } + } + eventLoop.start() + eventually(timeout(5 seconds), interval(5 millis)) { + assert(!eventLoop.isActive) + } + assert(onStopCalled) + } + + test("EventLoop: stop() in onReceive should call onStop") { + @volatile var onStopCalled: Boolean = false + val eventLoop = new EventLoop[Int]("test") { + + override def onReceive(event: Int): Unit = { + stop() + } + + override def onError(e: Throwable): Unit = { + } + + override def onStop(): Unit = { + onStopCalled = true + } + } + eventLoop.start() + eventLoop.post(1) + eventually(timeout(5 seconds), interval(5 millis)) { + assert(!eventLoop.isActive) + } + assert(onStopCalled) + } + + test("EventLoop: stop() in onError should call onStop") { + @volatile var onStopCalled: Boolean = false + val eventLoop = new EventLoop[Int]("test") { + + override def onReceive(event: Int): Unit = { + throw new RuntimeException("Oops") + } + + override def onError(e: Throwable): Unit = { + stop() + } + + override def onStop(): Unit = { + onStopCalled = true + } + } + eventLoop.start() + eventLoop.post(1) + eventually(timeout(5 seconds), interval(5 millis)) { + assert(!eventLoop.isActive) + } + assert(onStopCalled) + } } From c42c3fc7f7b79a1f6ce990d39b5d9d14ab19fcf0 Mon Sep 17 00:00:00 2001 From: Yin Huai Date: Fri, 3 Apr 2015 14:40:36 +0800 Subject: [PATCH 154/591] [SPARK-6575][SQL] Converted Parquet Metastore tables no longer cache metadata https://issues.apache.org/jira/browse/SPARK-6575 Author: Yin Huai This patch had conflicts when merged, resolved by Committer: Cheng Lian Closes #5339 from yhuai/parquetRelationCache and squashes the following commits: b0e1a42 [Yin Huai] Address comments. 83d9846 [Yin Huai] Remove unnecessary change. c0dc7a4 [Yin Huai] Cache converted parquet relations. --- .../spark/sql/hive/HiveMetastoreCatalog.scala | 28 +++++++++++++------ .../spark/sql/hive/execution/commands.scala | 5 ++-- .../apache/spark/sql/hive/parquetSuites.scala | 2 -- 3 files changed, 23 insertions(+), 12 deletions(-) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index 76d329a3ddcdf..c4da34ae645b8 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -116,8 +116,14 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with } override def refreshTable(databaseName: String, tableName: String): Unit = { - // refresh table does not eagerly reload the cache. It just invalidate the cache. + // refreshTable does not eagerly reload the cache. It just invalidate the cache. // Next time when we use the table, it will be populated in the cache. + // Since we also cache ParquetRealtions converted from Hive Parquet tables and + // adding converted ParquetRealtions into the cache is not defined in the load function + // of the cache (instead, we add the cache entry in convertToParquetRelation), + // it is better at here to invalidate the cache to avoid confusing waring logs from the + // cache loader (e.g. cannot find data source provider, which is only defined for + // data source table.). invalidateTable(databaseName, tableName) } @@ -242,21 +248,27 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with QualifiedTableName(metastoreRelation.databaseName, metastoreRelation.tableName) def getCached( - tableIdentifier: QualifiedTableName, - pathsInMetastore: Seq[String], - schemaInMetastore: StructType, - partitionSpecInMetastore: Option[PartitionSpec]): Option[LogicalRelation] = { + tableIdentifier: QualifiedTableName, + pathsInMetastore: Seq[String], + schemaInMetastore: StructType, + partitionSpecInMetastore: Option[PartitionSpec]): Option[LogicalRelation] = { cachedDataSourceTables.getIfPresent(tableIdentifier) match { case null => None // Cache miss - case logical @ LogicalRelation(parquetRelation: ParquetRelation2) => + case logical@LogicalRelation(parquetRelation: ParquetRelation2) => // If we have the same paths, same schema, and same partition spec, // we will use the cached Parquet Relation. val useCached = - parquetRelation.paths == pathsInMetastore && + parquetRelation.paths.toSet == pathsInMetastore.toSet && logical.schema.sameType(metastoreSchema) && parquetRelation.maybePartitionSpec == partitionSpecInMetastore - if (useCached) Some(logical) else None + if (useCached) { + Some(logical) + } else { + // If the cached relation is not updated, we invalidate it right away. + cachedDataSourceTables.invalidate(tableIdentifier) + None + } case other => logWarning( s"${metastoreRelation.databaseName}.${metastoreRelation.tableName} shold be stored " + diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala index 4345ffbf30f77..99dc58646ddd6 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala @@ -58,12 +58,13 @@ case class DropTable( try { hiveContext.cacheManager.tryUncacheQuery(hiveContext.table(tableName)) } catch { - // This table's metadata is not in + // This table's metadata is not in Hive metastore (e.g. the table does not exist). case _: org.apache.hadoop.hive.ql.metadata.InvalidTableException => + case _: org.apache.spark.sql.catalyst.analysis.NoSuchTableException => // Other Throwables can be caused by users providing wrong parameters in OPTIONS // (e.g. invalid paths). We catch it and log a warning message. // Users should be able to drop such kinds of tables regardless if there is an error. - case e: Throwable => log.warn(s"${e.getMessage}") + case e: Throwable => log.warn(s"${e.getMessage}", e) } hiveContext.invalidateTable(tableName) hiveContext.runSqlHive(s"DROP TABLE $ifExistsClause$tableName") diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala index 2ad6e867262b1..1319c81dfc131 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala @@ -473,7 +473,6 @@ class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase { // Right now, insert into a partitioned Parquet is not supported in data source Parquet. // So, we expect it is not cached. assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) - conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "false") sql( """ |INSERT INTO TABLE test_parquet_partitioned_cache_test @@ -481,7 +480,6 @@ class ParquetDataSourceOnMetastoreSuite extends ParquetMetastoreSuiteBase { |select a, b from jt """.stripMargin) assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifer) === null) - conf.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, "true") // Make sure we can cache the partitioned table. table("test_parquet_partitioned_cache_test") From 82701ee25fda64f03899713bc56f82ca6f278151 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Fri, 3 Apr 2015 01:25:02 -0700 Subject: [PATCH 155/591] [SPARK-6428] Turn on explicit type checking for public methods. This builds on my earlier pull requests and turns on the explicit type checking in scalastyle. Author: Reynold Xin Closes #5342 from rxin/SPARK-6428 and squashes the following commits: 7b531ab [Reynold Xin] import ordering 2d9a8a5 [Reynold Xin] jl e668b1c [Reynold Xin] override 9b9e119 [Reynold Xin] Parenthesis. 82e0cf5 [Reynold Xin] [SPARK-6428] Turn on explicit type checking for public methods. --- .../apache/spark/api/java/JavaPairRDD.scala | 2 +- .../org/apache/spark/api/java/JavaRDD.scala | 2 +- .../apache/spark/api/java/JavaRDDLike.scala | 53 ++++++++++------ .../apache/spark/examples/LocalKMeans.scala | 4 +- .../org/apache/spark/examples/LocalLR.scala | 4 +- .../org/apache/spark/examples/LogQuery.scala | 4 +- .../org/apache/spark/examples/SparkLR.scala | 4 +- .../org/apache/spark/examples/SparkTC.scala | 2 +- .../spark/examples/bagel/PageRankUtils.scala | 2 +- .../spark/examples/mllib/MovieLensALS.scala | 4 +- .../examples/streaming/ActorWordCount.scala | 6 +- .../RecoverableNetworkWordCount.scala | 3 +- .../examples/streaming/ZeroMQWordCount.scala | 6 +- .../clickstream/PageViewGenerator.scala | 2 +- .../streaming/flume/FlumeInputDStream.scala | 12 ++-- .../kafka/DirectKafkaInputDStream.scala | 5 +- .../spark/streaming/kafka/KafkaRDD.scala | 4 +- .../twitter/TwitterInputDStream.scala | 2 +- .../streaming/zeromq/ZeroMQReceiver.scala | 13 ++-- .../org/apache/spark/graphx/EdgeContext.scala | 3 +- .../apache/spark/graphx/EdgeDirection.scala | 12 ++-- .../org/apache/spark/graphx/EdgeTriplet.scala | 2 +- .../spark/graphx/impl/EdgePartition.scala | 14 ++--- .../spark/graphx/impl/EdgeRDDImpl.scala | 4 +- .../graphx/impl/ReplicatedVertexView.scala | 2 +- .../spark/graphx/impl/VertexRDDImpl.scala | 4 +- .../graphx/lib/ConnectedComponents.scala | 2 +- .../spark/graphx/lib/LabelPropagation.scala | 4 +- .../apache/spark/graphx/lib/PageRank.scala | 2 +- .../GraphXPrimitiveKeyOpenHashMap.scala | 8 +-- .../apache/spark/mllib/feature/Word2Vec.scala | 2 +- scalastyle-config.xml | 2 +- .../apache/spark/sql/AnalysisException.scala | 2 +- .../spark/sql/catalyst/analysis/package.scala | 2 +- .../org/apache/spark/sql/sources/ddl.scala | 2 +- .../spark/sql/hive/HiveMetastoreCatalog.scala | 2 +- .../org/apache/spark/sql/hive/HiveQl.scala | 3 +- .../apache/spark/streaming/Checkpoint.scala | 2 +- .../streaming/api/java/JavaDStreamLike.scala | 12 ++-- .../streaming/api/java/JavaPairDStream.scala | 2 +- .../api/java/JavaStreamingContext.scala | 10 +-- .../spark/streaming/dstream/DStream.scala | 2 +- .../tools/JavaAPICompletenessChecker.scala | 4 +- .../spark/tools/StoragePerfTester.scala | 6 +- .../spark/deploy/yarn/ApplicationMaster.scala | 62 ++++++++++--------- .../spark/deploy/yarn/ExecutorRunnable.scala | 6 +- 46 files changed, 170 insertions(+), 142 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala index a023712be1166..8441bb3a3047e 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala @@ -661,7 +661,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)]) */ def flatMapValues[U](f: JFunction[V, java.lang.Iterable[U]]): JavaPairRDD[K, U] = { import scala.collection.JavaConverters._ - def fn = (x: V) => f.call(x).asScala + def fn: (V) => Iterable[U] = (x: V) => f.call(x).asScala implicit val ctag: ClassTag[U] = fakeClassTag fromRDD(rdd.flatMapValues(fn)) } diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala index 18ccd625fc8d1..db4e996feb31c 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala @@ -192,7 +192,7 @@ class JavaRDD[T](val rdd: RDD[T])(implicit val classTag: ClassTag[T]) */ def sortBy[S](f: JFunction[T, S], ascending: Boolean, numPartitions: Int): JavaRDD[T] = { import scala.collection.JavaConverters._ - def fn = (x: T) => f.call(x) + def fn: (T) => S = (x: T) => f.call(x) import com.google.common.collect.Ordering // shadows scala.math.Ordering implicit val ordering = Ordering.natural().asInstanceOf[Ordering[S]] implicit val ctag: ClassTag[S] = fakeClassTag diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala index 8da42934a7d96..8bf0627fc420d 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala @@ -17,8 +17,9 @@ package org.apache.spark.api.java -import java.util.{Comparator, List => JList, Iterator => JIterator} +import java.{lang => jl} import java.lang.{Iterable => JIterable, Long => JLong} +import java.util.{Comparator, List => JList, Iterator => JIterator} import scala.collection.JavaConversions._ import scala.collection.JavaConverters._ @@ -93,7 +94,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { * of the original partition. */ def mapPartitionsWithIndex[R]( - f: JFunction2[java.lang.Integer, java.util.Iterator[T], java.util.Iterator[R]], + f: JFunction2[jl.Integer, java.util.Iterator[T], java.util.Iterator[R]], preservesPartitioning: Boolean = false): JavaRDD[R] = new JavaRDD(rdd.mapPartitionsWithIndex(((a,b) => f(a,asJavaIterator(b))), preservesPartitioning)(fakeClassTag))(fakeClassTag) @@ -109,7 +110,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { * Return a new RDD by applying a function to all elements of this RDD. */ def mapToPair[K2, V2](f: PairFunction[T, K2, V2]): JavaPairRDD[K2, V2] = { - def cm = implicitly[ClassTag[(K2, V2)]] + def cm: ClassTag[(K2, V2)] = implicitly[ClassTag[(K2, V2)]] new JavaPairRDD(rdd.map[(K2, V2)](f)(cm))(fakeClassTag[K2], fakeClassTag[V2]) } @@ -119,7 +120,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { */ def flatMap[U](f: FlatMapFunction[T, U]): JavaRDD[U] = { import scala.collection.JavaConverters._ - def fn = (x: T) => f.call(x).asScala + def fn: (T) => Iterable[U] = (x: T) => f.call(x).asScala JavaRDD.fromRDD(rdd.flatMap(fn)(fakeClassTag[U]))(fakeClassTag[U]) } @@ -129,8 +130,8 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { */ def flatMapToDouble(f: DoubleFlatMapFunction[T]): JavaDoubleRDD = { import scala.collection.JavaConverters._ - def fn = (x: T) => f.call(x).asScala - new JavaDoubleRDD(rdd.flatMap(fn).map((x: java.lang.Double) => x.doubleValue())) + def fn: (T) => Iterable[jl.Double] = (x: T) => f.call(x).asScala + new JavaDoubleRDD(rdd.flatMap(fn).map((x: jl.Double) => x.doubleValue())) } /** @@ -139,8 +140,8 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { */ def flatMapToPair[K2, V2](f: PairFlatMapFunction[T, K2, V2]): JavaPairRDD[K2, V2] = { import scala.collection.JavaConverters._ - def fn = (x: T) => f.call(x).asScala - def cm = implicitly[ClassTag[(K2, V2)]] + def fn: (T) => Iterable[(K2, V2)] = (x: T) => f.call(x).asScala + def cm: ClassTag[(K2, V2)] = implicitly[ClassTag[(K2, V2)]] JavaPairRDD.fromRDD(rdd.flatMap(fn)(cm))(fakeClassTag[K2], fakeClassTag[V2]) } @@ -148,7 +149,9 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { * Return a new RDD by applying a function to each partition of this RDD. */ def mapPartitions[U](f: FlatMapFunction[java.util.Iterator[T], U]): JavaRDD[U] = { - def fn = (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + def fn: (Iterator[T]) => Iterator[U] = { + (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + } JavaRDD.fromRDD(rdd.mapPartitions(fn)(fakeClassTag[U]))(fakeClassTag[U]) } @@ -157,7 +160,9 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { */ def mapPartitions[U](f: FlatMapFunction[java.util.Iterator[T], U], preservesPartitioning: Boolean): JavaRDD[U] = { - def fn = (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + def fn: (Iterator[T]) => Iterator[U] = { + (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + } JavaRDD.fromRDD( rdd.mapPartitions(fn, preservesPartitioning)(fakeClassTag[U]))(fakeClassTag[U]) } @@ -166,8 +171,10 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { * Return a new RDD by applying a function to each partition of this RDD. */ def mapPartitionsToDouble(f: DoubleFlatMapFunction[java.util.Iterator[T]]): JavaDoubleRDD = { - def fn = (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) - new JavaDoubleRDD(rdd.mapPartitions(fn).map((x: java.lang.Double) => x.doubleValue())) + def fn: (Iterator[T]) => Iterator[jl.Double] = { + (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + } + new JavaDoubleRDD(rdd.mapPartitions(fn).map((x: jl.Double) => x.doubleValue())) } /** @@ -175,7 +182,9 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { */ def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[java.util.Iterator[T], K2, V2]): JavaPairRDD[K2, V2] = { - def fn = (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + def fn: (Iterator[T]) => Iterator[(K2, V2)] = { + (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + } JavaPairRDD.fromRDD(rdd.mapPartitions(fn))(fakeClassTag[K2], fakeClassTag[V2]) } @@ -184,7 +193,9 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { */ def mapPartitionsToDouble(f: DoubleFlatMapFunction[java.util.Iterator[T]], preservesPartitioning: Boolean): JavaDoubleRDD = { - def fn = (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + def fn: (Iterator[T]) => Iterator[jl.Double] = { + (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + } new JavaDoubleRDD(rdd.mapPartitions(fn, preservesPartitioning) .map(x => x.doubleValue())) } @@ -194,7 +205,9 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { */ def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[java.util.Iterator[T], K2, V2], preservesPartitioning: Boolean): JavaPairRDD[K2, V2] = { - def fn = (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + def fn: (Iterator[T]) => Iterator[(K2, V2)] = { + (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + } JavaPairRDD.fromRDD( rdd.mapPartitions(fn, preservesPartitioning))(fakeClassTag[K2], fakeClassTag[V2]) } @@ -277,8 +290,10 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { def zipPartitions[U, V]( other: JavaRDDLike[U, _], f: FlatMapFunction2[java.util.Iterator[T], java.util.Iterator[U], V]): JavaRDD[V] = { - def fn = (x: Iterator[T], y: Iterator[U]) => asScalaIterator( - f.call(asJavaIterator(x), asJavaIterator(y)).iterator()) + def fn: (Iterator[T], Iterator[U]) => Iterator[V] = { + (x: Iterator[T], y: Iterator[U]) => asScalaIterator( + f.call(asJavaIterator(x), asJavaIterator(y)).iterator()) + } JavaRDD.fromRDD( rdd.zipPartitions(other.rdd)(fn)(other.classTag, fakeClassTag[V]))(fakeClassTag[V]) } @@ -441,8 +456,8 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { * Return the count of each unique value in this RDD as a map of (value, count) pairs. The final * combine step happens locally on the master, equivalent to running a single reduce task. */ - def countByValue(): java.util.Map[T, java.lang.Long] = - mapAsSerializableJavaMap(rdd.countByValue().map((x => (x._1, new java.lang.Long(x._2))))) + def countByValue(): java.util.Map[T, jl.Long] = + mapAsSerializableJavaMap(rdd.countByValue().map((x => (x._1, new jl.Long(x._2))))) /** * (Experimental) Approximate version of countByValue(). diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala index 17624c20cff3d..f73eac1e2b906 100644 --- a/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala +++ b/examples/src/main/scala/org/apache/spark/examples/LocalKMeans.scala @@ -40,8 +40,8 @@ object LocalKMeans { val convergeDist = 0.001 val rand = new Random(42) - def generateData = { - def generatePoint(i: Int) = { + def generateData: Array[DenseVector[Double]] = { + def generatePoint(i: Int): DenseVector[Double] = { DenseVector.fill(D){rand.nextDouble * R} } Array.tabulate(N)(generatePoint) diff --git a/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala b/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala index 92a683ad57ea1..a55e0dc8d36c2 100644 --- a/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala +++ b/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala @@ -37,8 +37,8 @@ object LocalLR { case class DataPoint(x: Vector[Double], y: Double) - def generateData = { - def generatePoint(i: Int) = { + def generateData: Array[DataPoint] = { + def generatePoint(i: Int): DataPoint = { val y = if(i % 2 == 0) -1 else 1 val x = DenseVector.fill(D){rand.nextGaussian + y * R} DataPoint(x, y) diff --git a/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala b/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala index 74620ad007d83..32e02eab8b031 100644 --- a/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala +++ b/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala @@ -54,8 +54,8 @@ object LogQuery { // scalastyle:on /** Tracks the total query count and number of aggregate bytes for a particular group. */ class Stats(val count: Int, val numBytes: Int) extends Serializable { - def merge(other: Stats) = new Stats(count + other.count, numBytes + other.numBytes) - override def toString = "bytes=%s\tn=%s".format(numBytes, count) + def merge(other: Stats): Stats = new Stats(count + other.count, numBytes + other.numBytes) + override def toString: String = "bytes=%s\tn=%s".format(numBytes, count) } def extractKey(line: String): (String, String, String) = { diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala index 257a7d29f922a..8c01a60844620 100644 --- a/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala +++ b/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala @@ -42,8 +42,8 @@ object SparkLR { case class DataPoint(x: Vector[Double], y: Double) - def generateData = { - def generatePoint(i: Int) = { + def generateData: Array[DataPoint] = { + def generatePoint(i: Int): DataPoint = { val y = if(i % 2 == 0) -1 else 1 val x = DenseVector.fill(D){rand.nextGaussian + y * R} DataPoint(x, y) diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala b/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala index f7f83086df3db..772cd897f5140 100644 --- a/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala +++ b/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala @@ -31,7 +31,7 @@ object SparkTC { val numVertices = 100 val rand = new Random(42) - def generateGraph = { + def generateGraph: Seq[(Int, Int)] = { val edges: mutable.Set[(Int, Int)] = mutable.Set.empty while (edges.size < numEdges) { val from = rand.nextInt(numVertices) diff --git a/examples/src/main/scala/org/apache/spark/examples/bagel/PageRankUtils.scala b/examples/src/main/scala/org/apache/spark/examples/bagel/PageRankUtils.scala index e322d4ce5a745..ab6e63deb3c95 100644 --- a/examples/src/main/scala/org/apache/spark/examples/bagel/PageRankUtils.scala +++ b/examples/src/main/scala/org/apache/spark/examples/bagel/PageRankUtils.scala @@ -90,7 +90,7 @@ class PRMessage() extends Message[String] with Serializable { } class CustomPartitioner(partitions: Int) extends Partitioner { - def numPartitions = partitions + def numPartitions: Int = partitions def getPartition(key: Any): Int = { val hash = key match { diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala index 1f4ca4fbe7778..0bc36ea65e1ab 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala @@ -178,7 +178,9 @@ object MovieLensALS { def computeRmse(model: MatrixFactorizationModel, data: RDD[Rating], implicitPrefs: Boolean) : Double = { - def mapPredictedRating(r: Double) = if (implicitPrefs) math.max(math.min(r, 1.0), 0.0) else r + def mapPredictedRating(r: Double): Double = { + if (implicitPrefs) math.max(math.min(r, 1.0), 0.0) else r + } val predictions: RDD[Rating] = model.predict(data.map(x => (x.user, x.product))) val predictionsAndRatings = predictions.map{ x => diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala index b433082dce1a2..92867b44be138 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala @@ -85,13 +85,13 @@ extends Actor with ActorHelper { lazy private val remotePublisher = context.actorSelection(urlOfPublisher) - override def preStart = remotePublisher ! SubscribeReceiver(context.self) + override def preStart(): Unit = remotePublisher ! SubscribeReceiver(context.self) - def receive = { + def receive: PartialFunction[Any, Unit] = { case msg => store(msg.asInstanceOf[T]) } - override def postStop() = remotePublisher ! UnsubscribeReceiver(context.self) + override def postStop(): Unit = remotePublisher ! UnsubscribeReceiver(context.self) } diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala index c3a05c89d817e..751b30ea15782 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala @@ -55,7 +55,8 @@ import org.apache.spark.util.IntParam */ object RecoverableNetworkWordCount { - def createContext(ip: String, port: Int, outputPath: String, checkpointDirectory: String) = { + def createContext(ip: String, port: Int, outputPath: String, checkpointDirectory: String) + : StreamingContext = { // If you do not see this printed, that means the StreamingContext has been loaded // from the new checkpoint diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala index 6510c70bd1866..e99d1baa72b9f 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala @@ -35,7 +35,7 @@ import org.apache.spark.SparkConf */ object SimpleZeroMQPublisher { - def main(args: Array[String]) = { + def main(args: Array[String]): Unit = { if (args.length < 2) { System.err.println("Usage: SimpleZeroMQPublisher ") System.exit(1) @@ -45,7 +45,7 @@ object SimpleZeroMQPublisher { val acs: ActorSystem = ActorSystem() val pubSocket = ZeroMQExtension(acs).newSocket(SocketType.Pub, Bind(url)) - implicit def stringToByteString(x: String) = ByteString(x) + implicit def stringToByteString(x: String): ByteString = ByteString(x) val messages: List[ByteString] = List("words ", "may ", "count ") while (true) { Thread.sleep(1000) @@ -86,7 +86,7 @@ object ZeroMQWordCount { // Create the context and set the batch size val ssc = new StreamingContext(sparkConf, Seconds(2)) - def bytesToStringIterator(x: Seq[ByteString]) = (x.map(_.utf8String)).iterator + def bytesToStringIterator(x: Seq[ByteString]): Iterator[String] = x.map(_.utf8String).iterator // For this stream, a zeroMQ publisher should be running. val lines = ZeroMQUtils.createStream(ssc, url, Subscribe(topic), bytesToStringIterator _) diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala index 8402491b62671..54d996b8ac990 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/clickstream/PageViewGenerator.scala @@ -94,7 +94,7 @@ object PageViewGenerator { while (true) { val socket = listener.accept() new Thread() { - override def run = { + override def run(): Unit = { println("Got client connected from: " + socket.getInetAddress) val out = new PrintWriter(socket.getOutputStream(), true) diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala index 2de2a7926bfd1..60e2994431b38 100644 --- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala +++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala @@ -37,8 +37,7 @@ import org.apache.spark.streaming.dstream._ import org.apache.spark.streaming.StreamingContext import org.apache.spark.streaming.receiver.Receiver -import org.jboss.netty.channel.ChannelPipelineFactory -import org.jboss.netty.channel.Channels +import org.jboss.netty.channel.{ChannelPipeline, ChannelPipelineFactory, Channels} import org.jboss.netty.channel.socket.nio.NioServerSocketChannelFactory import org.jboss.netty.handler.codec.compression._ @@ -187,8 +186,8 @@ class FlumeReceiver( logInfo("Flume receiver stopped") } - override def preferredLocation = Some(host) - + override def preferredLocation: Option[String] = Option(host) + /** A Netty Pipeline factory that will decompress incoming data from * and the Netty client and compress data going back to the client. * @@ -198,13 +197,12 @@ class FlumeReceiver( */ private[streaming] class CompressionChannelPipelineFactory extends ChannelPipelineFactory { - - def getPipeline() = { + def getPipeline(): ChannelPipeline = { val pipeline = Channels.pipeline() val encoder = new ZlibEncoder(6) pipeline.addFirst("deflater", encoder) pipeline.addFirst("inflater", new ZlibDecoder()) pipeline + } } } -} diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala index 04e65cb3d708c..1b1fc8051d052 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala @@ -129,8 +129,9 @@ class DirectKafkaInputDStream[ private[streaming] class DirectKafkaInputDStreamCheckpointData extends DStreamCheckpointData(this) { - def batchForTime = data.asInstanceOf[mutable.HashMap[ - Time, Array[OffsetRange.OffsetRangeTuple]]] + def batchForTime: mutable.HashMap[Time, Array[(String, Int, Long, Long)]] = { + data.asInstanceOf[mutable.HashMap[Time, Array[OffsetRange.OffsetRangeTuple]]] + } override def update(time: Time) { batchForTime.clear() diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala index 6d465bcb6bfc0..4a83b715fa89d 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala @@ -155,7 +155,7 @@ class KafkaRDD[ .dropWhile(_.offset < requestOffset) } - override def close() = consumer.close() + override def close(): Unit = consumer.close() override def getNext(): R = { if (iter == null || !iter.hasNext) { @@ -207,7 +207,7 @@ object KafkaRDD { fromOffsets: Map[TopicAndPartition, Long], untilOffsets: Map[TopicAndPartition, LeaderOffset], messageHandler: MessageAndMetadata[K, V] => R - ): KafkaRDD[K, V, U, T, R] = { + ): KafkaRDD[K, V, U, T, R] = { val leaders = untilOffsets.map { case (tp, lo) => tp -> (lo.host, lo.port) }.toMap diff --git a/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala index 4eacc47da5699..7cf02d85d73d3 100644 --- a/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala +++ b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala @@ -70,7 +70,7 @@ class TwitterReceiver( try { val newTwitterStream = new TwitterStreamFactory().getInstance(twitterAuth) newTwitterStream.addListener(new StatusListener { - def onStatus(status: Status) = { + def onStatus(status: Status): Unit = { store(status) } // Unimplemented diff --git a/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQReceiver.scala b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQReceiver.scala index 554705878ee78..588e6bac7b14a 100644 --- a/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQReceiver.scala +++ b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQReceiver.scala @@ -29,13 +29,16 @@ import org.apache.spark.streaming.receiver.ActorHelper /** * A receiver to subscribe to ZeroMQ stream. */ -private[streaming] class ZeroMQReceiver[T: ClassTag](publisherUrl: String, - subscribe: Subscribe, - bytesToObjects: Seq[ByteString] => Iterator[T]) +private[streaming] class ZeroMQReceiver[T: ClassTag]( + publisherUrl: String, + subscribe: Subscribe, + bytesToObjects: Seq[ByteString] => Iterator[T]) extends Actor with ActorHelper with Logging { - override def preStart() = ZeroMQExtension(context.system) - .newSocket(SocketType.Sub, Listener(self), Connect(publisherUrl), subscribe) + override def preStart(): Unit = { + ZeroMQExtension(context.system) + .newSocket(SocketType.Sub, Listener(self), Connect(publisherUrl), subscribe) + } def receive: Receive = { diff --git a/graphx/src/main/scala/org/apache/spark/graphx/EdgeContext.scala b/graphx/src/main/scala/org/apache/spark/graphx/EdgeContext.scala index d8be02e2023d5..23430179f12ec 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/EdgeContext.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/EdgeContext.scala @@ -62,7 +62,6 @@ object EdgeContext { * , _ + _) * }}} */ - def unapply[VD, ED, A](edge: EdgeContext[VD, ED, A]) = + def unapply[VD, ED, A](edge: EdgeContext[VD, ED, A]): Some[(VertexId, VertexId, VD, VD, ED)] = Some(edge.srcId, edge.dstId, edge.srcAttr, edge.dstAttr, edge.attr) } - diff --git a/graphx/src/main/scala/org/apache/spark/graphx/EdgeDirection.scala b/graphx/src/main/scala/org/apache/spark/graphx/EdgeDirection.scala index 6f03eb1439773..058c8c8aa1b24 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/EdgeDirection.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/EdgeDirection.scala @@ -34,12 +34,12 @@ class EdgeDirection private (private val name: String) extends Serializable { override def toString: String = "EdgeDirection." + name - override def equals(o: Any) = o match { + override def equals(o: Any): Boolean = o match { case other: EdgeDirection => other.name == name case _ => false } - override def hashCode = name.hashCode + override def hashCode: Int = name.hashCode } @@ -48,14 +48,14 @@ class EdgeDirection private (private val name: String) extends Serializable { */ object EdgeDirection { /** Edges arriving at a vertex. */ - final val In = new EdgeDirection("In") + final val In: EdgeDirection = new EdgeDirection("In") /** Edges originating from a vertex. */ - final val Out = new EdgeDirection("Out") + final val Out: EdgeDirection = new EdgeDirection("Out") /** Edges originating from *or* arriving at a vertex of interest. */ - final val Either = new EdgeDirection("Either") + final val Either: EdgeDirection = new EdgeDirection("Either") /** Edges originating from *and* arriving at a vertex of interest. */ - final val Both = new EdgeDirection("Both") + final val Both: EdgeDirection = new EdgeDirection("Both") } diff --git a/graphx/src/main/scala/org/apache/spark/graphx/EdgeTriplet.scala b/graphx/src/main/scala/org/apache/spark/graphx/EdgeTriplet.scala index 9d473d5ebda44..c8790cac3d8a0 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/EdgeTriplet.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/EdgeTriplet.scala @@ -62,7 +62,7 @@ class EdgeTriplet[VD, ED] extends Edge[ED] { def vertexAttr(vid: VertexId): VD = if (srcId == vid) srcAttr else { assert(dstId == vid); dstAttr } - override def toString = ((srcId, srcAttr), (dstId, dstAttr), attr).toString() + override def toString: String = ((srcId, srcAttr), (dstId, dstAttr), attr).toString() def toTuple: ((VertexId, VD), (VertexId, VD), ED) = ((srcId, srcAttr), (dstId, dstAttr), attr) } diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala index 373af75448374..c561570809253 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgePartition.scala @@ -324,7 +324,7 @@ class EdgePartition[ * * @return an iterator over edges in the partition */ - def iterator = new Iterator[Edge[ED]] { + def iterator: Iterator[Edge[ED]] = new Iterator[Edge[ED]] { private[this] val edge = new Edge[ED] private[this] var pos = 0 @@ -351,7 +351,7 @@ class EdgePartition[ override def hasNext: Boolean = pos < EdgePartition.this.size - override def next() = { + override def next(): EdgeTriplet[VD, ED] = { val triplet = new EdgeTriplet[VD, ED] val localSrcId = localSrcIds(pos) val localDstId = localDstIds(pos) @@ -518,11 +518,11 @@ private class AggregatingEdgeContext[VD, ED, A]( _attr = attr } - override def srcId = _srcId - override def dstId = _dstId - override def srcAttr = _srcAttr - override def dstAttr = _dstAttr - override def attr = _attr + override def srcId: VertexId = _srcId + override def dstId: VertexId = _dstId + override def srcAttr: VD = _srcAttr + override def dstAttr: VD = _dstAttr + override def attr: ED = _attr override def sendToSrc(msg: A) { send(_localSrcId, msg) diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeRDDImpl.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeRDDImpl.scala index 43a3aea0f6196..c88b2f65a86cd 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeRDDImpl.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeRDDImpl.scala @@ -70,9 +70,9 @@ class EdgeRDDImpl[ED: ClassTag, VD: ClassTag] private[graphx] ( this } - override def getStorageLevel = partitionsRDD.getStorageLevel + override def getStorageLevel: StorageLevel = partitionsRDD.getStorageLevel - override def checkpoint() = { + override def checkpoint(): Unit = { partitionsRDD.checkpoint() } diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/ReplicatedVertexView.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/ReplicatedVertexView.scala index 8ab255bd4038c..1df86449fa0c2 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/impl/ReplicatedVertexView.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/ReplicatedVertexView.scala @@ -50,7 +50,7 @@ class ReplicatedVertexView[VD: ClassTag, ED: ClassTag]( * Return a new `ReplicatedVertexView` where edges are reversed and shipping levels are swapped to * match. */ - def reverse() = { + def reverse(): ReplicatedVertexView[VD, ED] = { val newEdges = edges.mapEdgePartitions((pid, part) => part.reverse) new ReplicatedVertexView(newEdges, hasDstId, hasSrcId) } diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala index 349c8545bf201..33ac7b0ed6095 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala @@ -71,9 +71,9 @@ class VertexRDDImpl[VD] private[graphx] ( this } - override def getStorageLevel = partitionsRDD.getStorageLevel + override def getStorageLevel: StorageLevel = partitionsRDD.getStorageLevel - override def checkpoint() = { + override def checkpoint(): Unit = { partitionsRDD.checkpoint() } diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/ConnectedComponents.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/ConnectedComponents.scala index e2f6cc138958e..859f896039047 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/lib/ConnectedComponents.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/ConnectedComponents.scala @@ -37,7 +37,7 @@ object ConnectedComponents { */ def run[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]): Graph[VertexId, ED] = { val ccGraph = graph.mapVertices { case (vid, _) => vid } - def sendMessage(edge: EdgeTriplet[VertexId, ED]) = { + def sendMessage(edge: EdgeTriplet[VertexId, ED]): Iterator[(VertexId, VertexId)] = { if (edge.srcAttr < edge.dstAttr) { Iterator((edge.dstId, edge.srcAttr)) } else if (edge.srcAttr > edge.dstAttr) { diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala index 82e9e06515179..2bcf8684b8b8e 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala @@ -43,7 +43,7 @@ object LabelPropagation { */ def run[VD, ED: ClassTag](graph: Graph[VD, ED], maxSteps: Int): Graph[VertexId, ED] = { val lpaGraph = graph.mapVertices { case (vid, _) => vid } - def sendMessage(e: EdgeTriplet[VertexId, ED]) = { + def sendMessage(e: EdgeTriplet[VertexId, ED]): Iterator[(VertexId, Map[VertexId, VertexId])] = { Iterator((e.srcId, Map(e.dstAttr -> 1L)), (e.dstId, Map(e.srcAttr -> 1L))) } def mergeMessage(count1: Map[VertexId, Long], count2: Map[VertexId, Long]) @@ -54,7 +54,7 @@ object LabelPropagation { i -> (count1Val + count2Val) }.toMap } - def vertexProgram(vid: VertexId, attr: Long, message: Map[VertexId, Long]) = { + def vertexProgram(vid: VertexId, attr: Long, message: Map[VertexId, Long]): VertexId = { if (message.isEmpty) attr else message.maxBy(_._2)._1 } val initialMessage = Map[VertexId, Long]() diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala index 570440ba4441f..042e366a29f58 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala @@ -156,7 +156,7 @@ object PageRank extends Logging { (newPR, newPR - oldPR) } - def sendMessage(edge: EdgeTriplet[(Double, Double), Double]) = { + def sendMessage(edge: EdgeTriplet[(Double, Double), Double]): Iterator[(VertexId, Double)] = { if (edge.srcAttr._2 > tol) { Iterator((edge.dstId, edge.srcAttr._2 * edge.attr)) } else { diff --git a/graphx/src/main/scala/org/apache/spark/graphx/util/collection/GraphXPrimitiveKeyOpenHashMap.scala b/graphx/src/main/scala/org/apache/spark/graphx/util/collection/GraphXPrimitiveKeyOpenHashMap.scala index 57b01b6f2e1fb..e2754ea699da9 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/util/collection/GraphXPrimitiveKeyOpenHashMap.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/util/collection/GraphXPrimitiveKeyOpenHashMap.scala @@ -56,7 +56,7 @@ class GraphXPrimitiveKeyOpenHashMap[@specialized(Long, Int) K: ClassTag, private var _oldValues: Array[V] = null - override def size = keySet.size + override def size: Int = keySet.size /** Get the value for a given key */ def apply(k: K): V = { @@ -112,7 +112,7 @@ class GraphXPrimitiveKeyOpenHashMap[@specialized(Long, Int) K: ClassTag, } } - override def iterator = new Iterator[(K, V)] { + override def iterator: Iterator[(K, V)] = new Iterator[(K, V)] { var pos = 0 var nextPair: (K, V) = computeNextPair() @@ -128,9 +128,9 @@ class GraphXPrimitiveKeyOpenHashMap[@specialized(Long, Int) K: ClassTag, } } - def hasNext = nextPair != null + def hasNext: Boolean = nextPair != null - def next() = { + def next(): (K, V) = { val pair = nextPair nextPair = computeNextPair() pair diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala index 9ee7e4a66b535..b2d9053f70145 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala @@ -522,7 +522,7 @@ object Word2VecModel extends Loader[Word2VecModel] { new Word2VecModel(word2VecMap) } - def save(sc: SparkContext, path: String, model: Map[String, Array[Float]]) = { + def save(sc: SparkContext, path: String, model: Map[String, Array[Float]]): Unit = { val sqlContext = new SQLContext(sc) import sqlContext.implicits._ diff --git a/scalastyle-config.xml b/scalastyle-config.xml index 459a5035d4984..7168d5b2a8e26 100644 --- a/scalastyle-config.xml +++ b/scalastyle-config.xml @@ -137,7 +137,7 @@ - + diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/AnalysisException.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/AnalysisException.scala index 34fedead44db3..f9992185a4563 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/AnalysisException.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/AnalysisException.scala @@ -30,7 +30,7 @@ class AnalysisException protected[sql] ( val startPosition: Option[Int] = None) extends Exception with Serializable { - def withPosition(line: Option[Int], startPosition: Option[Int]) = { + def withPosition(line: Option[Int], startPosition: Option[Int]): AnalysisException = { val newException = new AnalysisException(message, line, startPosition) newException.setStackTrace(getStackTrace) newException diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/package.scala index c61c395cb4bb1..7731336d247db 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/package.scala @@ -44,7 +44,7 @@ package object analysis { } /** Catches any AnalysisExceptions thrown by `f` and attaches `t`'s position if any. */ - def withPosition[A](t: TreeNode[_])(f: => A) = { + def withPosition[A](t: TreeNode[_])(f: => A): A = { try f catch { case a: AnalysisException => throw a.withPosition(t.origin.line, t.origin.startPosition) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala index eb46b46ca5bf4..319de710fbc3e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala @@ -204,7 +204,7 @@ private[sql] object ResolvedDataSource { provider: String, options: Map[String, String]): ResolvedDataSource = { val clazz: Class[_] = lookupDataSource(provider) - def className = clazz.getCanonicalName + def className: String = clazz.getCanonicalName val relation = userSpecifiedSchema match { case Some(schema: StructType) => clazz.newInstance() match { case dataSource: SchemaRelationProvider => diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index c4da34ae645b8..ae5ce4cf4c7e7 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -861,7 +861,7 @@ private[hive] case class MetastoreRelation /** An attribute map for determining the ordinal for non-partition columns. */ val columnOrdinals = AttributeMap(attributes.zipWithIndex) - override def newInstance() = { + override def newInstance(): MetastoreRelation = { MetastoreRelation(databaseName, tableName, alias)(table, partitions)(sqlContext) } } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala index 5be09a11ad641..077e64133faad 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala @@ -659,7 +659,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C AttributeReference("value", StringType)()), true) } - def matchSerDe(clause: Seq[ASTNode]) = clause match { + def matchSerDe(clause: Seq[ASTNode]) + : (Seq[(String, String)], String, Seq[(String, String)]) = clause match { case Token("TOK_SERDEPROPS", propsClause) :: Nil => val rowFormat = propsClause.map { case Token(name, Token(value, Nil) :: Nil) => (name, value) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala index f73b463d07779..28703ef8129b3 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala @@ -234,7 +234,7 @@ object CheckpointReader extends Logging { val checkpointPath = new Path(checkpointDir) // TODO(rxin): Why is this a def?! - def fs = checkpointPath.getFileSystem(hadoopConf) + def fs: FileSystem = checkpointPath.getFileSystem(hadoopConf) // Try to find the checkpoint files val checkpointFiles = Checkpoint.getCheckpointFiles(checkpointDir, fs).reverse diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala index 73030e15c5661..808dcc174cf9a 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala @@ -169,7 +169,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T */ def flatMap[U](f: FlatMapFunction[T, U]): JavaDStream[U] = { import scala.collection.JavaConverters._ - def fn = (x: T) => f.call(x).asScala + def fn: (T) => Iterable[U] = (x: T) => f.call(x).asScala new JavaDStream(dstream.flatMap(fn)(fakeClassTag[U]))(fakeClassTag[U]) } @@ -179,7 +179,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T */ def flatMapToPair[K2, V2](f: PairFlatMapFunction[T, K2, V2]): JavaPairDStream[K2, V2] = { import scala.collection.JavaConverters._ - def fn = (x: T) => f.call(x).asScala + def fn: (T) => Iterable[(K2, V2)] = (x: T) => f.call(x).asScala def cm: ClassTag[(K2, V2)] = fakeClassTag new JavaPairDStream(dstream.flatMap(fn)(cm))(fakeClassTag[K2], fakeClassTag[V2]) } @@ -190,7 +190,9 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T * of the RDD. */ def mapPartitions[U](f: FlatMapFunction[java.util.Iterator[T], U]): JavaDStream[U] = { - def fn = (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + def fn: (Iterator[T]) => Iterator[U] = { + (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + } new JavaDStream(dstream.mapPartitions(fn)(fakeClassTag[U]))(fakeClassTag[U]) } @@ -201,7 +203,9 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T */ def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[java.util.Iterator[T], K2, V2]) : JavaPairDStream[K2, V2] = { - def fn = (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + def fn: (Iterator[T]) => Iterator[(K2, V2)] = { + (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator()) + } new JavaPairDStream(dstream.mapPartitions(fn))(fakeClassTag[K2], fakeClassTag[V2]) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala index f94f2d0e8bd31..93baad19e3ee1 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala @@ -526,7 +526,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( */ def flatMapValues[U](f: JFunction[V, java.lang.Iterable[U]]): JavaPairDStream[K, U] = { import scala.collection.JavaConverters._ - def fn = (x: V) => f.apply(x).asScala + def fn: (V) => Iterable[U] = (x: V) => f.apply(x).asScala implicit val cm: ClassTag[U] = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[U]] dstream.flatMapValues(fn) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala index e3db01c1e12c6..4095a7cc84946 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala @@ -192,7 +192,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable { converter: JFunction[InputStream, java.lang.Iterable[T]], storageLevel: StorageLevel) : JavaReceiverInputDStream[T] = { - def fn = (x: InputStream) => converter.call(x).toIterator + def fn: (InputStream) => Iterator[T] = (x: InputStream) => converter.call(x).toIterator implicit val cmt: ClassTag[T] = implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] ssc.socketStream(hostname, port, fn, storageLevel) @@ -313,7 +313,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable { implicit val cmk: ClassTag[K] = ClassTag(kClass) implicit val cmv: ClassTag[V] = ClassTag(vClass) implicit val cmf: ClassTag[F] = ClassTag(fClass) - def fn = (x: Path) => filter.call(x).booleanValue() + def fn: (Path) => Boolean = (x: Path) => filter.call(x).booleanValue() ssc.fileStream[K, V, F](directory, fn, newFilesOnly) } @@ -344,7 +344,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable { implicit val cmk: ClassTag[K] = ClassTag(kClass) implicit val cmv: ClassTag[V] = ClassTag(vClass) implicit val cmf: ClassTag[F] = ClassTag(fClass) - def fn = (x: Path) => filter.call(x).booleanValue() + def fn: (Path) => Boolean = (x: Path) => filter.call(x).booleanValue() ssc.fileStream[K, V, F](directory, fn, newFilesOnly, conf) } @@ -625,7 +625,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable { * Stop the execution of the streams. * @param stopSparkContext Stop the associated SparkContext or not */ - def stop(stopSparkContext: Boolean) = ssc.stop(stopSparkContext) + def stop(stopSparkContext: Boolean): Unit = ssc.stop(stopSparkContext) /** * Stop the execution of the streams. @@ -633,7 +633,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable { * @param stopGracefully Stop gracefully by waiting for the processing of all * received data to be completed */ - def stop(stopSparkContext: Boolean, stopGracefully: Boolean) = { + def stop(stopSparkContext: Boolean, stopGracefully: Boolean): Unit = { ssc.stop(stopSparkContext, stopGracefully) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala index 795c5aa6d585b..24f99a2b929f5 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala @@ -839,7 +839,7 @@ object DStream { /** Filtering function that excludes non-user classes for a streaming application */ def streamingExclustionFunction(className: String): Boolean = { - def doesMatch(r: Regex) = r.findFirstIn(className).isDefined + def doesMatch(r: Regex): Boolean = r.findFirstIn(className).isDefined val isSparkClass = doesMatch(SPARK_CLASS_REGEX) val isSparkExampleClass = doesMatch(SPARK_EXAMPLES_CLASS_REGEX) val isSparkStreamingTestClass = doesMatch(SPARK_STREAMING_TESTCLASS_REGEX) diff --git a/tools/src/main/scala/org/apache/spark/tools/JavaAPICompletenessChecker.scala b/tools/src/main/scala/org/apache/spark/tools/JavaAPICompletenessChecker.scala index 8d0f09933c8d3..583823c90c5c6 100644 --- a/tools/src/main/scala/org/apache/spark/tools/JavaAPICompletenessChecker.scala +++ b/tools/src/main/scala/org/apache/spark/tools/JavaAPICompletenessChecker.scala @@ -17,7 +17,7 @@ package org.apache.spark.tools -import java.lang.reflect.Method +import java.lang.reflect.{Type, Method} import scala.collection.mutable.ArrayBuffer import scala.language.existentials @@ -302,7 +302,7 @@ object JavaAPICompletenessChecker { private def isExcludedByInterface(method: Method): Boolean = { val excludedInterfaces = Set("org.apache.spark.Logging", "org.apache.hadoop.mapreduce.HadoopMapReduceUtil") - def toComparisionKey(method: Method) = + def toComparisionKey(method: Method): (Class[_], String, Type) = (method.getReturnType, method.getName, method.getGenericReturnType) val interfaces = method.getDeclaringClass.getInterfaces.filter { i => excludedInterfaces.contains(i.getName) diff --git a/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala b/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala index 6b666a0384879..f2d135397ce2f 100644 --- a/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala +++ b/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala @@ -32,7 +32,7 @@ import org.apache.spark.util.Utils * Writes simulated shuffle output from several threads and records the observed throughput. */ object StoragePerfTester { - def main(args: Array[String]) = { + def main(args: Array[String]): Unit = { /** Total amount of data to generate. Distributed evenly amongst maps and reduce splits. */ val dataSizeMb = Utils.memoryStringToMb(sys.env.getOrElse("OUTPUT_DATA", "1g")) @@ -58,7 +58,7 @@ object StoragePerfTester { val sc = new SparkContext("local[4]", "Write Tester", conf) val hashShuffleManager = sc.env.shuffleManager.asInstanceOf[HashShuffleManager] - def writeOutputBytes(mapId: Int, total: AtomicLong) = { + def writeOutputBytes(mapId: Int, total: AtomicLong): Unit = { val shuffle = hashShuffleManager.shuffleBlockResolver.forMapTask(1, mapId, numOutputSplits, new KryoSerializer(sc.conf), new ShuffleWriteMetrics()) val writers = shuffle.writers @@ -78,7 +78,7 @@ object StoragePerfTester { val totalBytes = new AtomicLong() for (task <- 1 to numMaps) { executor.submit(new Runnable() { - override def run() = { + override def run(): Unit = { try { writeOutputBytes(task, totalBytes) latch.countDown() diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala index 3d18690cd9cbf..455554eea0597 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -162,7 +162,7 @@ private[spark] class ApplicationMaster( * status to SUCCEEDED in cluster mode to handle if the user calls System.exit * from the application code. */ - final def getDefaultFinalStatus() = { + final def getDefaultFinalStatus(): FinalApplicationStatus = { if (isClusterMode) { FinalApplicationStatus.SUCCEEDED } else { @@ -175,31 +175,35 @@ private[spark] class ApplicationMaster( * This means the ResourceManager will not retry the application attempt on your behalf if * a failure occurred. */ - final def unregister(status: FinalApplicationStatus, diagnostics: String = null) = synchronized { - if (!unregistered) { - logInfo(s"Unregistering ApplicationMaster with $status" + - Option(diagnostics).map(msg => s" (diag message: $msg)").getOrElse("")) - unregistered = true - client.unregister(status, Option(diagnostics).getOrElse("")) + final def unregister(status: FinalApplicationStatus, diagnostics: String = null): Unit = { + synchronized { + if (!unregistered) { + logInfo(s"Unregistering ApplicationMaster with $status" + + Option(diagnostics).map(msg => s" (diag message: $msg)").getOrElse("")) + unregistered = true + client.unregister(status, Option(diagnostics).getOrElse("")) + } } } - final def finish(status: FinalApplicationStatus, code: Int, msg: String = null) = synchronized { - if (!finished) { - val inShutdown = Utils.inShutdown() - logInfo(s"Final app status: ${status}, exitCode: ${code}" + - Option(msg).map(msg => s", (reason: $msg)").getOrElse("")) - exitCode = code - finalStatus = status - finalMsg = msg - finished = true - if (!inShutdown && Thread.currentThread() != reporterThread && reporterThread != null) { - logDebug("shutting down reporter thread") - reporterThread.interrupt() - } - if (!inShutdown && Thread.currentThread() != userClassThread && userClassThread != null) { - logDebug("shutting down user thread") - userClassThread.interrupt() + final def finish(status: FinalApplicationStatus, code: Int, msg: String = null): Unit = { + synchronized { + if (!finished) { + val inShutdown = Utils.inShutdown() + logInfo(s"Final app status: $status, exitCode: $code" + + Option(msg).map(msg => s", (reason: $msg)").getOrElse("")) + exitCode = code + finalStatus = status + finalMsg = msg + finished = true + if (!inShutdown && Thread.currentThread() != reporterThread && reporterThread != null) { + logDebug("shutting down reporter thread") + reporterThread.interrupt() + } + if (!inShutdown && Thread.currentThread() != userClassThread && userClassThread != null) { + logDebug("shutting down user thread") + userClassThread.interrupt() + } } } } @@ -506,7 +510,7 @@ private[spark] class ApplicationMaster( private class AMActor(driverUrl: String, isClusterMode: Boolean) extends Actor { var driver: ActorSelection = _ - override def preStart() = { + override def preStart(): Unit = { logInfo("Listen to driver: " + driverUrl) driver = context.actorSelection(driverUrl) // Send a hello message to establish the connection, after which @@ -520,7 +524,7 @@ private[spark] class ApplicationMaster( } } - override def receive = { + override def receive: PartialFunction[Any, Unit] = { case x: DisassociatedEvent => logInfo(s"Driver terminated or disconnected! Shutting down. $x") // In cluster mode, do not rely on the disassociated event to exit @@ -567,7 +571,7 @@ object ApplicationMaster extends Logging { private var master: ApplicationMaster = _ - def main(args: Array[String]) = { + def main(args: Array[String]): Unit = { SignalLogger.register(log) val amArgs = new ApplicationMasterArguments(args) SparkHadoopUtil.get.runAsSparkUser { () => @@ -576,11 +580,11 @@ object ApplicationMaster extends Logging { } } - private[spark] def sparkContextInitialized(sc: SparkContext) = { + private[spark] def sparkContextInitialized(sc: SparkContext): Unit = { master.sparkContextInitialized(sc) } - private[spark] def sparkContextStopped(sc: SparkContext) = { + private[spark] def sparkContextStopped(sc: SparkContext): Boolean = { master.sparkContextStopped(sc) } @@ -592,7 +596,7 @@ object ApplicationMaster extends Logging { */ object ExecutorLauncher { - def main(args: Array[String]) = { + def main(args: Array[String]): Unit = { ApplicationMaster.main(args) } diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala index c1d3f7320f53c..1ce10d906ab23 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala @@ -59,15 +59,15 @@ class ExecutorRunnable( val yarnConf: YarnConfiguration = new YarnConfiguration(conf) lazy val env = prepareEnvironment(container) - def run = { + override def run(): Unit = { logInfo("Starting Executor Container") nmClient = NMClient.createNMClient() nmClient.init(yarnConf) nmClient.start() - startContainer + startContainer() } - def startContainer = { + def startContainer(): java.util.Map[String, ByteBuffer] = { logInfo("Setting up ContainerLaunchContext") val ctx = Records.newRecord(classOf[ContainerLaunchContext]) From b0d884f044fea1c954da77073f3556cd9ab1e922 Mon Sep 17 00:00:00 2001 From: Stephen Haberman Date: Fri, 3 Apr 2015 09:48:37 +0100 Subject: [PATCH 156/591] [SPARK-6560][CORE] Do not suppress exceptions from writer.write. If there is a failure in the Hadoop backend while calling writer.write, we should remember this original exception, and try to call writer.close(), but if that fails as well, still report the original exception. Note that, if writer.write fails, it is likely that writer was left in an invalid state, and so actually makes it more likely that writer.close will also fail. Which just increases the chances for writer.write's exception to be suppressed. This patch introduces an admittedly potentially too cute Utils.tryWithSafeFinally method to handle the try/finally gyrations. Author: Stephen Haberman Closes #5223 from stephenh/do_not_suppress_writer_exception and squashes the following commits: c7ad53f [Stephen Haberman] [SPARK-6560][CORE] Do not suppress exceptions from writer.write. --- .../org/apache/spark/MapOutputTracker.scala | 11 +++-- .../apache/spark/api/python/PythonRDD.scala | 8 ++-- .../spark/broadcast/HttpBroadcast.scala | 19 +++++--- .../master/FileSystemPersistenceEngine.scala | 5 +- .../deploy/rest/StandaloneRestClient.scala | 8 +++- .../org/apache/spark/rdd/CheckpointRDD.scala | 8 +++- .../apache/spark/rdd/PairRDDFunctions.scala | 9 ++-- .../shuffle/IndexShuffleBlockManager.scala | 6 +-- .../spark/storage/BlockObjectWriter.scala | 16 ++++--- .../org/apache/spark/storage/DiskStore.scala | 18 ++++---- .../scala/org/apache/spark/util/Utils.scala | 46 +++++++++++++++++-- .../util/collection/ExternalSorter.scala | 26 ++++------- 12 files changed, 118 insertions(+), 62 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala index c9426c5de23a2..5718951451afc 100644 --- a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala +++ b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala @@ -356,11 +356,14 @@ private[spark] object MapOutputTracker extends Logging { def serializeMapStatuses(statuses: Array[MapStatus]): Array[Byte] = { val out = new ByteArrayOutputStream val objOut = new ObjectOutputStream(new GZIPOutputStream(out)) - // Since statuses can be modified in parallel, sync on it - statuses.synchronized { - objOut.writeObject(statuses) + Utils.tryWithSafeFinally { + // Since statuses can be modified in parallel, sync on it + statuses.synchronized { + objOut.writeObject(statuses) + } + } { + objOut.close() } - objOut.close() out.toByteArray } diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala index 36cf2af0857dd..b1ffba4c546bf 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala @@ -614,9 +614,9 @@ private[spark] object PythonRDD extends Logging { try { val sock = serverSocket.accept() val out = new DataOutputStream(new BufferedOutputStream(sock.getOutputStream)) - try { + Utils.tryWithSafeFinally { writeIteratorToStream(items, out) - } finally { + } { out.close() } } catch { @@ -862,9 +862,9 @@ private[spark] class PythonBroadcast(@transient var path: String) extends Serial val file = File.createTempFile("broadcast", "", dir) path = file.getAbsolutePath val out = new FileOutputStream(file) - try { + Utils.tryWithSafeFinally { Utils.copyStream(in, out) - } finally { + } { out.close() } } diff --git a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala index 74ccfa6d3c9a3..4457c75e8b0fc 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala @@ -165,7 +165,7 @@ private[broadcast] object HttpBroadcast extends Logging { private def write(id: Long, value: Any) { val file = getFile(id) val fileOutputStream = new FileOutputStream(file) - try { + Utils.tryWithSafeFinally { val out: OutputStream = { if (compress) { compressionCodec.compressedOutputStream(fileOutputStream) @@ -175,10 +175,13 @@ private[broadcast] object HttpBroadcast extends Logging { } val ser = SparkEnv.get.serializer.newInstance() val serOut = ser.serializeStream(out) - serOut.writeObject(value) - serOut.close() + Utils.tryWithSafeFinally { + serOut.writeObject(value) + } { + serOut.close() + } files += file - } finally { + } { fileOutputStream.close() } } @@ -212,9 +215,11 @@ private[broadcast] object HttpBroadcast extends Logging { } val ser = SparkEnv.get.serializer.newInstance() val serIn = ser.deserializeStream(in) - val obj = serIn.readObject[T]() - serIn.close() - obj + Utils.tryWithSafeFinally { + serIn.readObject[T]() + } { + serIn.close() + } } /** diff --git a/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala index 32499b3a784a1..f459ed5b3a1a1 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala @@ -24,6 +24,7 @@ import scala.reflect.ClassTag import akka.serialization.Serialization import org.apache.spark.Logging +import org.apache.spark.util.Utils /** @@ -59,9 +60,9 @@ private[master] class FileSystemPersistenceEngine( val serializer = serialization.findSerializerFor(value) val serialized = serializer.toBinary(value) val out = new FileOutputStream(file) - try { + Utils.tryWithSafeFinally { out.write(serialized) - } finally { + } { out.close() } } diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestClient.scala b/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestClient.scala index 420442f7564cc..a3539e44bd2f9 100644 --- a/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestClient.scala +++ b/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestClient.scala @@ -27,6 +27,7 @@ import com.fasterxml.jackson.core.JsonProcessingException import com.google.common.base.Charsets import org.apache.spark.{Logging, SparkConf, SPARK_VERSION => sparkVersion} +import org.apache.spark.util.Utils /** * A client that submits applications to the standalone Master using a REST protocol. @@ -148,8 +149,11 @@ private[deploy] class StandaloneRestClient extends Logging { conn.setRequestProperty("charset", "utf-8") conn.setDoOutput(true) val out = new DataOutputStream(conn.getOutputStream) - out.write(json.getBytes(Charsets.UTF_8)) - out.close() + Utils.tryWithSafeFinally { + out.write(json.getBytes(Charsets.UTF_8)) + } { + out.close() + } readResponse(conn) } diff --git a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala index 1c13e2c372845..760c0fa3ac96a 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala @@ -27,6 +27,7 @@ import org.apache.hadoop.fs.Path import org.apache.spark._ import org.apache.spark.broadcast.Broadcast import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.util.Utils private[spark] class CheckpointRDDPartition(val index: Int) extends Partition {} @@ -112,8 +113,11 @@ private[spark] object CheckpointRDD extends Logging { } val serializer = env.serializer.newInstance() val serializeStream = serializer.serializeStream(fileOutputStream) - serializeStream.writeAll(iterator) - serializeStream.close() + Utils.tryWithSafeFinally { + serializeStream.writeAll(iterator) + } { + serializeStream.close() + } if (!fs.rename(tempOutputPath, finalOutputPath)) { if (!fs.exists(finalOutputPath)) { diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala index 6b4f097ea9ae5..bf1303d39592d 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala @@ -995,7 +995,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) val writer = format.getRecordWriter(hadoopContext).asInstanceOf[NewRecordWriter[K,V]] require(writer != null, "Unable to obtain RecordWriter") var recordsWritten = 0L - try { + Utils.tryWithSafeFinally { while (iter.hasNext) { val pair = iter.next() writer.write(pair._1, pair._2) @@ -1004,7 +1004,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) maybeUpdateOutputMetrics(bytesWrittenCallback, outputMetrics, recordsWritten) recordsWritten += 1 } - } finally { + } { writer.close(hadoopContext) } committer.commitTask(hadoopContext) @@ -1068,7 +1068,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) writer.setup(context.stageId, context.partitionId, taskAttemptId) writer.open() var recordsWritten = 0L - try { + + Utils.tryWithSafeFinally { while (iter.hasNext) { val record = iter.next() writer.write(record._1.asInstanceOf[AnyRef], record._2.asInstanceOf[AnyRef]) @@ -1077,7 +1078,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) maybeUpdateOutputMetrics(bytesWrittenCallback, outputMetrics, recordsWritten) recordsWritten += 1 } - } finally { + } { writer.close() } writer.commit() diff --git a/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala index 50edb5a34e333..a1741e2875c16 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockManager.scala @@ -26,6 +26,7 @@ import org.apache.spark.{SparkConf, SparkEnv} import org.apache.spark.network.buffer.{FileSegmentManagedBuffer, ManagedBuffer} import org.apache.spark.network.netty.SparkTransportConf import org.apache.spark.storage._ +import org.apache.spark.util.Utils import IndexShuffleBlockManager.NOOP_REDUCE_ID @@ -78,16 +79,15 @@ class IndexShuffleBlockManager(conf: SparkConf) extends ShuffleBlockResolver { def writeIndexFile(shuffleId: Int, mapId: Int, lengths: Array[Long]): Unit = { val indexFile = getIndexFile(shuffleId, mapId) val out = new DataOutputStream(new BufferedOutputStream(new FileOutputStream(indexFile))) - try { + Utils.tryWithSafeFinally { // We take in lengths of each block, need to convert it to offsets. var offset = 0L out.writeLong(offset) - for (length <- lengths) { offset += length out.writeLong(offset) } - } finally { + } { out.close() } } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala index f703e50b6b0ac..0dfc91dfaff85 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala @@ -23,6 +23,7 @@ import java.nio.channels.FileChannel import org.apache.spark.Logging import org.apache.spark.serializer.{SerializationStream, Serializer} import org.apache.spark.executor.ShuffleWriteMetrics +import org.apache.spark.util.Utils /** * An interface for writing JVM objects to some underlying storage. This interface allows @@ -140,14 +141,17 @@ private[spark] class DiskBlockObjectWriter( override def close() { if (initialized) { - if (syncWrites) { - // Force outstanding writes to disk and track how long it takes - objOut.flush() - callWithTiming { - fos.getFD.sync() + Utils.tryWithSafeFinally { + if (syncWrites) { + // Force outstanding writes to disk and track how long it takes + objOut.flush() + callWithTiming { + fos.getFD.sync() + } } + } { + objOut.close() } - objOut.close() channel = null bs = null diff --git a/core/src/main/scala/org/apache/spark/storage/DiskStore.scala b/core/src/main/scala/org/apache/spark/storage/DiskStore.scala index 61ef5ff168791..4b232ae7d3180 100644 --- a/core/src/main/scala/org/apache/spark/storage/DiskStore.scala +++ b/core/src/main/scala/org/apache/spark/storage/DiskStore.scala @@ -46,10 +46,13 @@ private[spark] class DiskStore(blockManager: BlockManager, diskManager: DiskBloc val startTime = System.currentTimeMillis val file = diskManager.getFile(blockId) val channel = new FileOutputStream(file).getChannel - while (bytes.remaining > 0) { - channel.write(bytes) + Utils.tryWithSafeFinally { + while (bytes.remaining > 0) { + channel.write(bytes) + } + } { + channel.close() } - channel.close() val finishTime = System.currentTimeMillis logDebug("Block %s stored as %s file on disk in %d ms".format( file.getName, Utils.bytesToString(bytes.limit), finishTime - startTime)) @@ -75,9 +78,9 @@ private[spark] class DiskStore(blockManager: BlockManager, diskManager: DiskBloc val file = diskManager.getFile(blockId) val outputStream = new FileOutputStream(file) try { - try { + Utils.tryWithSafeFinally { blockManager.dataSerializeStream(blockId, outputStream, values) - } finally { + } { // Close outputStream here because it should be closed before file is deleted. outputStream.close() } @@ -106,8 +109,7 @@ private[spark] class DiskStore(blockManager: BlockManager, diskManager: DiskBloc private def getBytes(file: File, offset: Long, length: Long): Option[ByteBuffer] = { val channel = new RandomAccessFile(file, "r").getChannel - - try { + Utils.tryWithSafeFinally { // For small files, directly read rather than memory map if (length < minMemoryMapBytes) { val buf = ByteBuffer.allocate(length.toInt) @@ -123,7 +125,7 @@ private[spark] class DiskStore(blockManager: BlockManager, diskManager: DiskBloc } else { Some(channel.map(MapMode.READ_ONLY, offset, length)) } - } finally { + } { channel.close() } } diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index bb8bd1015668a..7c85e28679f1d 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -313,7 +313,7 @@ private[spark] object Utils extends Logging { transferToEnabled: Boolean = false): Long = { var count = 0L - try { + tryWithSafeFinally { if (in.isInstanceOf[FileInputStream] && out.isInstanceOf[FileOutputStream] && transferToEnabled) { // When both streams are File stream, use transferTo to improve copy performance. @@ -353,7 +353,7 @@ private[spark] object Utils extends Logging { } } count - } finally { + } { if (closeStreams) { try { in.close() @@ -1214,6 +1214,44 @@ private[spark] object Utils extends Logging { } } + /** + * Execute a block of code, then a finally block, but if exceptions happen in + * the finally block, do not suppress the original exception. + * + * This is primarily an issue with `finally { out.close() }` blocks, where + * close needs to be called to clean up `out`, but if an exception happened + * in `out.write`, it's likely `out` may be corrupted and `out.close` will + * fail as well. This would then suppress the original/likely more meaningful + * exception from the original `out.write` call. + */ + def tryWithSafeFinally[T](block: => T)(finallyBlock: => Unit): T = { + // It would be nice to find a method on Try that did this + var originalThrowable: Throwable = null + try { + block + } catch { + case t: Throwable => + // Purposefully not using NonFatal, because even fatal exceptions + // we don't want to have our finallyBlock suppress + originalThrowable = t + throw originalThrowable + } finally { + try { + finallyBlock + } catch { + case t: Throwable => + if (originalThrowable != null) { + // We could do originalThrowable.addSuppressed(t), but it's + // not available in JDK 1.6. + logWarning(s"Suppressing exception in finally: " + t.getMessage, t) + throw originalThrowable + } else { + throw t + } + } + } + } + /** Default filtering function for finding call sites using `getCallSite`. */ private def coreExclusionFunction(className: String): Boolean = { // A regular expression to match classes of the "core" Spark API that we want to skip when @@ -2074,7 +2112,7 @@ private[spark] class RedirectThread( override def run() { scala.util.control.Exception.ignoring(classOf[IOException]) { // FIXME: We copy the stream on the level of bytes to avoid encoding problems. - try { + Utils.tryWithSafeFinally { val buf = new Array[Byte](1024) var len = in.read(buf) while (len != -1) { @@ -2082,7 +2120,7 @@ private[spark] class RedirectThread( out.flush() len = in.read(buf) } - } finally { + } { if (propagateEof) { out.close() } diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala index 7bd3c7852a6b2..035f3767ff554 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala @@ -728,25 +728,19 @@ private[spark] class ExternalSorter[K, V, C]( // this simple we spill out the current in-memory collection so that everything is in files. spillToPartitionFiles(if (aggregator.isDefined) map else buffer) partitionWriters.foreach(_.commitAndClose()) - var out: FileOutputStream = null - var in: FileInputStream = null + val out = new FileOutputStream(outputFile, true) val writeStartTime = System.nanoTime - try { - out = new FileOutputStream(outputFile, true) + util.Utils.tryWithSafeFinally { for (i <- 0 until numPartitions) { - in = new FileInputStream(partitionWriters(i).fileSegment().file) - val size = org.apache.spark.util.Utils.copyStream(in, out, false, transferToEnabled) - in.close() - in = null - lengths(i) = size - } - } finally { - if (out != null) { - out.close() - } - if (in != null) { - in.close() + val in = new FileInputStream(partitionWriters(i).fileSegment().file) + util.Utils.tryWithSafeFinally { + lengths(i) = org.apache.spark.util.Utils.copyStream(in, out, false, transferToEnabled) + } { + in.close() + } } + } { + out.close() context.taskMetrics.shuffleWriteMetrics.foreach( _.incShuffleWriteTime(System.nanoTime - writeStartTime)) } From b52c7f9fc87a1b9a039724e1dac8b30554f75196 Mon Sep 17 00:00:00 2001 From: Omede Firouz Date: Fri, 3 Apr 2015 10:26:43 +0100 Subject: [PATCH 157/591] [MLLIB] Remove println in LogisticRegression.scala There's no corresponding printing in linear regression. Here was my previous PR (something weird happened and I can't reopen it) https://github.com/apache/spark/pull/5272 Author: Omede Firouz Closes #5338 from oefirouz/println and squashes the following commits: 3f3dbf4 [Omede Firouz] [MLLIB] Remove println --- .../org/apache/spark/ml/classification/LogisticRegression.scala | 1 - 1 file changed, 1 deletion(-) diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala index 21f61d80dd95a..49c00f77480e8 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala @@ -180,7 +180,6 @@ class LogisticRegressionModel private[ml] ( * The behavior of this can be adjusted using [[threshold]]. */ override protected def predict(features: Vector): Double = { - println(s"LR.predict with threshold: ${paramMap(threshold)}") if (score(features) > paramMap(threshold)) 1 else 0 } From 512a2f191a6b53699373b6588f316b4437050425 Mon Sep 17 00:00:00 2001 From: lewuathe Date: Fri, 3 Apr 2015 09:49:50 -0700 Subject: [PATCH 158/591] [SPARK-6615][MLLIB] Python API for Word2Vec This is the sub-task of SPARK-6254. Wrap missing method for `Word2Vec` and `Word2VecModel`. Author: lewuathe Closes #5296 from Lewuathe/SPARK-6615 and squashes the following commits: f14c304 [lewuathe] Reorder tests 1d326b9 [lewuathe] Merge master e2bedfb [lewuathe] Modify test cases afb866d [lewuathe] [SPARK-6615] Python API for Word2Vec --- .../mllib/api/python/PythonMLLibAPI.scala | 8 +++- python/pyspark/mllib/feature.py | 18 +++++++- python/pyspark/mllib/tests.py | 45 ++++++++++++++++--- 3 files changed, 64 insertions(+), 7 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index 5995d6df97c15..6c386cacfb7ca 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -476,13 +476,15 @@ private[python] class PythonMLLibAPI extends Serializable { learningRate: Double, numPartitions: Int, numIterations: Int, - seed: Long): Word2VecModelWrapper = { + seed: Long, + minCount: Int): Word2VecModelWrapper = { val word2vec = new Word2Vec() .setVectorSize(vectorSize) .setLearningRate(learningRate) .setNumPartitions(numPartitions) .setNumIterations(numIterations) .setSeed(seed) + .setMinCount(minCount) try { val model = word2vec.fit(dataJRDD.rdd.persist(StorageLevel.MEMORY_AND_DISK_SER)) new Word2VecModelWrapper(model) @@ -516,6 +518,10 @@ private[python] class PythonMLLibAPI extends Serializable { val words = result.map(_._1) List(words, similarity).map(_.asInstanceOf[Object]).asJava } + + def getVectors: JMap[String, JList[Float]] = { + model.getVectors.map({case (k, v) => (k, v.toList.asJava)}).asJava + } } /** diff --git a/python/pyspark/mllib/feature.py b/python/pyspark/mllib/feature.py index 4bfe3014ef748..3cda1205e1391 100644 --- a/python/pyspark/mllib/feature.py +++ b/python/pyspark/mllib/feature.py @@ -337,6 +337,12 @@ def findSynonyms(self, word, num): words, similarity = self.call("findSynonyms", word, num) return zip(words, similarity) + def getVectors(self): + """ + Returns a map of words to their vector representations. + """ + return self.call("getVectors") + class Word2Vec(object): """ @@ -379,6 +385,7 @@ def __init__(self): self.numPartitions = 1 self.numIterations = 1 self.seed = random.randint(0, sys.maxint) + self.minCount = 5 def setVectorSize(self, vectorSize): """ @@ -417,6 +424,14 @@ def setSeed(self, seed): self.seed = seed return self + def setMinCount(self, minCount): + """ + Sets minCount, the minimum number of times a token must appear + to be included in the word2vec model's vocabulary (default: 5). + """ + self.minCount = minCount + return self + def fit(self, data): """ Computes the vector representation of each word in vocabulary. @@ -428,7 +443,8 @@ def fit(self, data): raise TypeError("data should be an RDD of list of string") jmodel = callMLlibFunc("trainWord2Vec", data, int(self.vectorSize), float(self.learningRate), int(self.numPartitions), - int(self.numIterations), long(self.seed)) + int(self.numIterations), long(self.seed), + int(self.minCount)) return Word2VecModel(jmodel) diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 6e9c68ec8a5c1..dd3b66ce67457 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -42,6 +42,7 @@ from pyspark.mllib.regression import LabeledPoint from pyspark.mllib.random import RandomRDDs from pyspark.mllib.stat import Statistics +from pyspark.mllib.feature import Word2Vec from pyspark.mllib.feature import IDF from pyspark.serializers import PickleSerializer from pyspark.sql import SQLContext @@ -630,6 +631,12 @@ def test_right_number_of_results(self): self.assertIsNotNone(chi[1000]) +class SerDeTest(PySparkTestCase): + def test_to_java_object_rdd(self): # SPARK-6660 + data = RandomRDDs.uniformRDD(self.sc, 10, 5, seed=0L) + self.assertEqual(_to_java_object_rdd(data).count(), 10) + + class FeatureTest(PySparkTestCase): def test_idf_model(self): data = [ @@ -643,11 +650,39 @@ def test_idf_model(self): self.assertEqual(len(idf), 11) -class SerDeTest(PySparkTestCase): - def test_to_java_object_rdd(self): # SPARK-6660 - data = RandomRDDs.uniformRDD(self.sc, 10, 5, seed=0L) - self.assertEqual(_to_java_object_rdd(data).count(), 10) - +class Word2VecTests(PySparkTestCase): + def test_word2vec_setters(self): + data = [ + ["I", "have", "a", "pen"], + ["I", "like", "soccer", "very", "much"], + ["I", "live", "in", "Tokyo"] + ] + model = Word2Vec() \ + .setVectorSize(2) \ + .setLearningRate(0.01) \ + .setNumPartitions(2) \ + .setNumIterations(10) \ + .setSeed(1024) \ + .setMinCount(3) + self.assertEquals(model.vectorSize, 2) + self.assertTrue(model.learningRate < 0.02) + self.assertEquals(model.numPartitions, 2) + self.assertEquals(model.numIterations, 10) + self.assertEquals(model.seed, 1024) + self.assertEquals(model.minCount, 3) + + def test_word2vec_get_vectors(self): + data = [ + ["a", "b", "c", "d", "e", "f", "g"], + ["a", "b", "c", "d", "e", "f"], + ["a", "b", "c", "d", "e"], + ["a", "b", "c", "d"], + ["a", "b", "c"], + ["a", "b"], + ["a"] + ] + model = Word2Vec().fit(self.sc.parallelize(data)) + self.assertEquals(len(model.getVectors()), 3) if __name__ == "__main__": if not _have_scipy: From dc6dff248d8f5d7de22af64b0586dfe3885731df Mon Sep 17 00:00:00 2001 From: Liang-Chi Hsieh Date: Fri, 3 Apr 2015 18:31:48 +0100 Subject: [PATCH 159/591] [Minor][SQL] Fix typo Just fix a typo. Author: Liang-Chi Hsieh Closes #5352 from viirya/fix_a_typo and squashes the following commits: 303b2d2 [Liang-Chi Hsieh] Fix typo. --- .../scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index ae5ce4cf4c7e7..315fab673da5c 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -271,7 +271,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with } case other => logWarning( - s"${metastoreRelation.databaseName}.${metastoreRelation.tableName} shold be stored " + + s"${metastoreRelation.databaseName}.${metastoreRelation.tableName} should be stored " + s"as Parquet. However, we are getting a ${other} from the metastore cache. " + s"This cached entry will be invalidated.") cachedDataSourceTables.invalidate(tableIdentifier) From c23ba81b8cf86c3a085de8ddfef9403ff6fcd87f Mon Sep 17 00:00:00 2001 From: guowei2 Date: Sat, 4 Apr 2015 02:02:30 +0800 Subject: [PATCH 160/591] [SPARK-5203][SQL] fix union with different decimal type When union non-decimal types with decimals, we use the following rules: - FIRST `intTypeToFixed`, then fixed union decimals with precision/scale p1/s2 and p2/s2 will be promoted to DecimalType(max(p1, p2), max(s1, s2)) - FLOAT and DOUBLE cause fixed-length decimals to turn into DOUBLE (this is the same as Hive, but note that unlimited decimals are considered bigger than doubles in WidenTypes) Author: guowei2 Closes #4004 from guowei2/SPARK-5203 and squashes the following commits: ff50f5f [guowei2] fix code style 11df1bf [guowei2] fix decimal union with double, double->Decimal(15,15) 0f345f9 [guowei2] fix structType merge with decimal 101ed4d [guowei2] fix build error after rebase 0b196e4 [guowei2] code style fe2c2ca [guowei2] handle union decimal precision in 'DecimalPrecision' 421d840 [guowei2] fix union types for decimal precision ef2c661 [guowei2] fix union with different decimal type --- .../catalyst/analysis/HiveTypeCoercion.scala | 190 ++++++++++++------ .../apache/spark/sql/types/dataTypes.scala | 5 +- .../analysis/DecimalPrecisionSuite.scala | 30 ++- .../sql/hive/execution/SQLQuerySuite.scala | 11 + 4 files changed, 167 insertions(+), 69 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala index 9a33eb145273e..3aeb964994d37 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala @@ -285,6 +285,7 @@ trait HiveTypeCoercion { * Calculates and propagates precision for fixed-precision decimals. Hive has a number of * rules for this based on the SQL standard and MS SQL: * https://cwiki.apache.org/confluence/download/attachments/27362075/Hive_Decimal_Precision_Scale_Support.pdf + * https://msdn.microsoft.com/en-us/library/ms190476.aspx * * In particular, if we have expressions e1 and e2 with precision/scale p1/s2 and p2/s2 * respectively, then the following operations have the following precision / scale: @@ -296,6 +297,7 @@ trait HiveTypeCoercion { * e1 * e2 p1 + p2 + 1 s1 + s2 * e1 / e2 p1 - s1 + s2 + max(6, s1 + p2 + 1) max(6, s1 + p2 + 1) * e1 % e2 min(p1-s1, p2-s2) + max(s1, s2) max(s1, s2) + * e1 union e2 max(s1, s2) + max(p1-s1, p2-s2) max(s1, s2) * sum(e1) p1 + 10 s1 * avg(e1) p1 + 4 s1 + 4 * @@ -311,7 +313,12 @@ trait HiveTypeCoercion { * - SHORT gets turned into DECIMAL(5, 0) * - INT gets turned into DECIMAL(10, 0) * - LONG gets turned into DECIMAL(20, 0) - * - FLOAT and DOUBLE cause fixed-length decimals to turn into DOUBLE (this is the same as Hive, + * - FLOAT and DOUBLE + * 1. Union operation: + * FLOAT gets turned into DECIMAL(7, 7), DOUBLE gets turned into DECIMAL(15, 15) (this is the + * same as Hive) + * 2. Other operation: + * FLOAT and DOUBLE cause fixed-length decimals to turn into DOUBLE (this is the same as Hive, * but note that unlimited decimals are considered bigger than doubles in WidenTypes) */ // scalastyle:on @@ -328,76 +335,127 @@ trait HiveTypeCoercion { def isFloat(t: DataType): Boolean = t == FloatType || t == DoubleType - def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions { - // Skip nodes whose children have not been resolved yet - case e if !e.childrenResolved => e + // Conversion rules for float and double into fixed-precision decimals + val floatTypeToFixed: Map[DataType, DecimalType] = Map( + FloatType -> DecimalType(7, 7), + DoubleType -> DecimalType(15, 15) + ) - case Add(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => - Cast( - Add(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)), - DecimalType(max(s1, s2) + max(p1 - s1, p2 - s2) + 1, max(s1, s2)) - ) - - case Subtract(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => - Cast( - Subtract(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)), - DecimalType(max(s1, s2) + max(p1 - s1, p2 - s2) + 1, max(s1, s2)) - ) - - case Multiply(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => - Cast( - Multiply(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)), - DecimalType(p1 + p2 + 1, s1 + s2) - ) - - case Divide(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => - Cast( - Divide(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)), - DecimalType(p1 - s1 + s2 + max(6, s1 + p2 + 1), max(6, s1 + p2 + 1)) - ) - - case Remainder(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => - Cast( - Remainder(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)), - DecimalType(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2)) - ) - - case LessThan(e1 @ DecimalType.Expression(p1, s1), - e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => - LessThan(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) - - case LessThanOrEqual(e1 @ DecimalType.Expression(p1, s1), - e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => - LessThanOrEqual(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) - - case GreaterThan(e1 @ DecimalType.Expression(p1, s1), - e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => - GreaterThan(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) - - case GreaterThanOrEqual(e1 @ DecimalType.Expression(p1, s1), - e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => - GreaterThanOrEqual(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) - - // Promote integers inside a binary expression with fixed-precision decimals to decimals, - // and fixed-precision decimals in an expression with floats / doubles to doubles - case b: BinaryExpression if b.left.dataType != b.right.dataType => - (b.left.dataType, b.right.dataType) match { - case (t, DecimalType.Fixed(p, s)) if intTypeToFixed.contains(t) => - b.makeCopy(Array(Cast(b.left, intTypeToFixed(t)), b.right)) - case (DecimalType.Fixed(p, s), t) if intTypeToFixed.contains(t) => - b.makeCopy(Array(b.left, Cast(b.right, intTypeToFixed(t)))) - case (t, DecimalType.Fixed(p, s)) if isFloat(t) => - b.makeCopy(Array(b.left, Cast(b.right, DoubleType))) - case (DecimalType.Fixed(p, s), t) if isFloat(t) => - b.makeCopy(Array(Cast(b.left, DoubleType), b.right)) - case _ => - b + def apply(plan: LogicalPlan): LogicalPlan = plan transform { + // fix decimal precision for union + case u @ Union(left, right) if u.childrenResolved && !u.resolved => + val castedInput = left.output.zip(right.output).map { + case (l, r) if l.dataType != r.dataType => + (l.dataType, r.dataType) match { + case (DecimalType.Fixed(p1, s1), DecimalType.Fixed(p2, s2)) => + // Union decimals with precision/scale p1/s2 and p2/s2 will be promoted to + // DecimalType(max(s1, s2) + max(p1-s1, p2-s2), max(s1, s2)) + val fixedType = DecimalType(max(s1, s2) + max(p1 - s1, p2 - s2), max(s1, s2)) + (Alias(Cast(l, fixedType), l.name)(), Alias(Cast(r, fixedType), r.name)()) + case (t, DecimalType.Fixed(p, s)) if intTypeToFixed.contains(t) => + (Alias(Cast(l, intTypeToFixed(t)), l.name)(), r) + case (DecimalType.Fixed(p, s), t) if intTypeToFixed.contains(t) => + (l, Alias(Cast(r, intTypeToFixed(t)), r.name)()) + case (t, DecimalType.Fixed(p, s)) if floatTypeToFixed.contains(t) => + (Alias(Cast(l, floatTypeToFixed(t)), l.name)(), r) + case (DecimalType.Fixed(p, s), t) if floatTypeToFixed.contains(t) => + (l, Alias(Cast(r, floatTypeToFixed(t)), r.name)()) + case _ => (l, r) + } + case other => other } - // TODO: MaxOf, MinOf, etc might want other rules + val (castedLeft, castedRight) = castedInput.unzip - // SUM and AVERAGE are handled by the implementations of those expressions + val newLeft = + if (castedLeft.map(_.dataType) != left.output.map(_.dataType)) { + Project(castedLeft, left) + } else { + left + } + + val newRight = + if (castedRight.map(_.dataType) != right.output.map(_.dataType)) { + Project(castedRight, right) + } else { + right + } + + Union(newLeft, newRight) + + // fix decimal precision for expressions + case q => q.transformExpressions { + // Skip nodes whose children have not been resolved yet + case e if !e.childrenResolved => e + + case Add(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => + Cast( + Add(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)), + DecimalType(max(s1, s2) + max(p1 - s1, p2 - s2) + 1, max(s1, s2)) + ) + + case Subtract(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => + Cast( + Subtract(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)), + DecimalType(max(s1, s2) + max(p1 - s1, p2 - s2) + 1, max(s1, s2)) + ) + + case Multiply(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => + Cast( + Multiply(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)), + DecimalType(p1 + p2 + 1, s1 + s2) + ) + + case Divide(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => + Cast( + Divide(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)), + DecimalType(p1 - s1 + s2 + max(6, s1 + p2 + 1), max(6, s1 + p2 + 1)) + ) + + case Remainder(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) => + Cast( + Remainder(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)), + DecimalType(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2)) + ) + + case LessThan(e1 @ DecimalType.Expression(p1, s1), + e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => + LessThan(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) + + case LessThanOrEqual(e1 @ DecimalType.Expression(p1, s1), + e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => + LessThanOrEqual(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) + + case GreaterThan(e1 @ DecimalType.Expression(p1, s1), + e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => + GreaterThan(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) + + case GreaterThanOrEqual(e1 @ DecimalType.Expression(p1, s1), + e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 => + GreaterThanOrEqual(Cast(e1, DecimalType.Unlimited), Cast(e2, DecimalType.Unlimited)) + + // Promote integers inside a binary expression with fixed-precision decimals to decimals, + // and fixed-precision decimals in an expression with floats / doubles to doubles + case b: BinaryExpression if b.left.dataType != b.right.dataType => + (b.left.dataType, b.right.dataType) match { + case (t, DecimalType.Fixed(p, s)) if intTypeToFixed.contains(t) => + b.makeCopy(Array(Cast(b.left, intTypeToFixed(t)), b.right)) + case (DecimalType.Fixed(p, s), t) if intTypeToFixed.contains(t) => + b.makeCopy(Array(b.left, Cast(b.right, intTypeToFixed(t)))) + case (t, DecimalType.Fixed(p, s)) if isFloat(t) => + b.makeCopy(Array(b.left, Cast(b.right, DoubleType))) + case (DecimalType.Fixed(p, s), t) if isFloat(t) => + b.makeCopy(Array(Cast(b.left, DoubleType), b.right)) + case _ => + b + } + + // TODO: MaxOf, MinOf, etc might want other rules + + // SUM and AVERAGE are handled by the implementations of those expressions + } } + } /** diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala index 952cf5c75688d..cdf2bc68d9c5e 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala @@ -20,6 +20,7 @@ package org.apache.spark.sql.types import java.sql.Timestamp import scala.collection.mutable.ArrayBuffer +import scala.math._ import scala.math.Numeric.{FloatAsIfIntegral, DoubleAsIfIntegral} import scala.reflect.ClassTag import scala.reflect.runtime.universe.{TypeTag, runtimeMirror, typeTag} @@ -934,7 +935,9 @@ object StructType { case (DecimalType.Fixed(leftPrecision, leftScale), DecimalType.Fixed(rightPrecision, rightScale)) => - DecimalType(leftPrecision.max(rightPrecision), leftScale.max(rightScale)) + DecimalType( + max(leftScale, rightScale) + max(leftPrecision - leftScale, rightPrecision - rightScale), + max(leftScale, rightScale)) case (leftUdt: UserDefinedType[_], rightUdt: UserDefinedType[_]) if leftUdt.userClass == rightUdt.userClass => leftUdt diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala index bc2ec754d5865..67bec999dfbd1 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala @@ -18,7 +18,7 @@ package org.apache.spark.sql.catalyst.analysis import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.plans.logical.{Project, LocalRelation} +import org.apache.spark.sql.catalyst.plans.logical.{Union, Project, LocalRelation} import org.apache.spark.sql.types._ import org.scalatest.{BeforeAndAfter, FunSuite} @@ -31,7 +31,8 @@ class DecimalPrecisionSuite extends FunSuite with BeforeAndAfter { AttributeReference("d1", DecimalType(2, 1))(), AttributeReference("d2", DecimalType(5, 2))(), AttributeReference("u", DecimalType.Unlimited)(), - AttributeReference("f", FloatType)() + AttributeReference("f", FloatType)(), + AttributeReference("b", DoubleType)() ) val i: Expression = UnresolvedAttribute("i") @@ -39,6 +40,7 @@ class DecimalPrecisionSuite extends FunSuite with BeforeAndAfter { val d2: Expression = UnresolvedAttribute("d2") val u: Expression = UnresolvedAttribute("u") val f: Expression = UnresolvedAttribute("f") + val b: Expression = UnresolvedAttribute("b") before { catalog.registerTable(Seq("table"), relation) @@ -58,6 +60,17 @@ class DecimalPrecisionSuite extends FunSuite with BeforeAndAfter { assert(comparison.right.dataType === expectedType) } + private def checkUnion(left: Expression, right: Expression, expectedType: DataType): Unit = { + val plan = + Union(Project(Seq(Alias(left, "l")()), relation), + Project(Seq(Alias(right, "r")()), relation)) + val (l, r) = analyzer(plan).collect { + case Union(left, right) => (left.output.head, right.output.head) + }.head + assert(l.dataType === expectedType) + assert(r.dataType === expectedType) + } + test("basic operations") { checkType(Add(d1, d2), DecimalType(6, 2)) checkType(Subtract(d1, d2), DecimalType(6, 2)) @@ -82,6 +95,19 @@ class DecimalPrecisionSuite extends FunSuite with BeforeAndAfter { checkComparison(GreaterThan(d2, d2), DecimalType(5, 2)) } + test("decimal precision for union") { + checkUnion(d1, i, DecimalType(11, 1)) + checkUnion(i, d2, DecimalType(12, 2)) + checkUnion(d1, d2, DecimalType(5, 2)) + checkUnion(d2, d1, DecimalType(5, 2)) + checkUnion(d1, f, DecimalType(8, 7)) + checkUnion(f, d2, DecimalType(10, 7)) + checkUnion(d1, b, DecimalType(16, 15)) + checkUnion(b, d2, DecimalType(18, 15)) + checkUnion(d1, u, DecimalType.Unlimited) + checkUnion(u, d2, DecimalType.Unlimited) + } + test("bringing in primitive types") { checkType(Add(d1, i), DecimalType(12, 1)) checkType(Add(d1, f), DoubleType) diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala index 2065f0d60d92f..817b9dcb8f505 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala @@ -468,4 +468,15 @@ class SQLQuerySuite extends QueryTest { sql(s"DROP TABLE $tableName") } } + + test("SPARK-5203 union with different decimal precision") { + Seq.empty[(Decimal, Decimal)] + .toDF("d1", "d2") + .select($"d1".cast(DecimalType(10, 15)).as("d")) + .registerTempTable("dn") + + sql("select d from dn union all select d * 2 from dn") + .queryExecution.analyzed + } + } From 2c43ea38ee0db6b292c14baf6bc6f8d16f509c9d Mon Sep 17 00:00:00 2001 From: Ilya Ganelin Date: Fri, 3 Apr 2015 19:23:11 +0100 Subject: [PATCH 161/591] [SPARK-6492][CORE] SparkContext.stop() can deadlock when DAGSchedulerEventProcessLoop dies I've added a timeout and retry loop around the SparkContext shutdown code that should fix this deadlock. If a SparkContext shutdown is in progress when another thread comes knocking, it will wait for 10 seconds for the lock, then fall through where the outer loop will re-submit the request. Author: Ilya Ganelin Closes #5277 from ilganeli/SPARK-6492 and squashes the following commits: 8617a7e [Ilya Ganelin] Resolved merge conflict 2fbab66 [Ilya Ganelin] Added MIMA Exclude a0e2c70 [Ilya Ganelin] Deleted stale imports fa28ce7 [Ilya Ganelin] reverted to just having a single stopped 76fc825 [Ilya Ganelin] Updated to use atomic booleans instead of the synchronized vars 6e8a7f7 [Ilya Ganelin] Removing unecessary null check for now since i'm not fixing stop ordering yet cdf7073 [Ilya Ganelin] [SPARK-6492] Moved stopped=true back to the start of the shutdown sequence so this can be addressed in a seperate PR 7fb795b [Ilya Ganelin] Spacing b7a0c5c [Ilya Ganelin] Import ordering df8224f [Ilya Ganelin] Added comment for added lock 343cb94 [Ilya Ganelin] [SPARK-6492] Added timeout/retry logic to fix a deadlock in SparkContext shutdown --- .../scala/org/apache/spark/SparkContext.scala | 59 ++++++++++--------- project/MimaExcludes.scala | 4 ++ 2 files changed, 34 insertions(+), 29 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 5b3778ead6994..abf81e312d8e6 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -23,7 +23,7 @@ import java.io._ import java.lang.reflect.Constructor import java.net.URI import java.util.{Arrays, Properties, UUID} -import java.util.concurrent.atomic.AtomicInteger +import java.util.concurrent.atomic.{AtomicBoolean, AtomicInteger} import java.util.UUID.randomUUID import scala.collection.{Map, Set} @@ -95,10 +95,10 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli val startTime = System.currentTimeMillis() - @volatile private var stopped: Boolean = false + private val stopped: AtomicBoolean = new AtomicBoolean(false) private def assertNotStopped(): Unit = { - if (stopped) { + if (stopped.get()) { throw new IllegalStateException("Cannot call methods on a stopped SparkContext") } } @@ -1390,33 +1390,34 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli addedJars.clear() } - /** Shut down the SparkContext. */ + // Shut down the SparkContext. def stop() { - SparkContext.SPARK_CONTEXT_CONSTRUCTOR_LOCK.synchronized { - if (!stopped) { - stopped = true - postApplicationEnd() - ui.foreach(_.stop()) - env.metricsSystem.report() - metadataCleaner.cancel() - cleaner.foreach(_.stop()) - executorAllocationManager.foreach(_.stop()) - dagScheduler.stop() - dagScheduler = null - listenerBus.stop() - eventLogger.foreach(_.stop()) - env.actorSystem.stop(heartbeatReceiver) - progressBar.foreach(_.stop()) - taskScheduler = null - // TODO: Cache.stop()? - env.stop() - SparkEnv.set(null) - logInfo("Successfully stopped SparkContext") - SparkContext.clearActiveContext() - } else { - logInfo("SparkContext already stopped") - } + // Use the stopping variable to ensure no contention for the stop scenario. + // Still track the stopped variable for use elsewhere in the code. + + if (!stopped.compareAndSet(false, true)) { + logInfo("SparkContext already stopped.") + return } + + postApplicationEnd() + ui.foreach(_.stop()) + env.metricsSystem.report() + metadataCleaner.cancel() + cleaner.foreach(_.stop()) + executorAllocationManager.foreach(_.stop()) + dagScheduler.stop() + dagScheduler = null + listenerBus.stop() + eventLogger.foreach(_.stop()) + env.actorSystem.stop(heartbeatReceiver) + progressBar.foreach(_.stop()) + taskScheduler = null + // TODO: Cache.stop()? + env.stop() + SparkEnv.set(null) + SparkContext.clearActiveContext() + logInfo("Successfully stopped SparkContext") } @@ -1478,7 +1479,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli partitions: Seq[Int], allowLocal: Boolean, resultHandler: (Int, U) => Unit) { - if (stopped) { + if (stopped.get()) { throw new IllegalStateException("SparkContext has been shutdown") } val callSite = getCallSite diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala index 54500f7c2701f..c2d828f982fe0 100644 --- a/project/MimaExcludes.scala +++ b/project/MimaExcludes.scala @@ -60,6 +60,10 @@ object MimaExcludes { ) ++ Seq( // SPARK-6510 Add a Graph#minus method acting as Set#difference ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.minus") + ) ++ Seq( + // SPARK-6492 Fix deadlock in SparkContext.stop() + ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.SparkContext.org$" + + "apache$spark$SparkContext$$SPARK_CONTEXT_CONSTRUCTOR_LOCK") ) case v if v.startsWith("1.3") => From 88504b75ee610e14d7ceed8b038fa698a3d14f81 Mon Sep 17 00:00:00 2001 From: zsxwing Date: Fri, 3 Apr 2015 11:44:27 -0700 Subject: [PATCH 162/591] [SPARK-6640][Core] Fix the race condition of creating HeartbeatReceiver and retrieving HeartbeatReceiver This PR moved the code of creating `HeartbeatReceiver` above the code of creating `schedulerBackend` to resolve the race condition. Author: zsxwing Closes #5306 from zsxwing/SPARK-6640 and squashes the following commits: 840399d [zsxwing] Don't send TaskScheduler through Akka a90616a [zsxwing] Fix docs dd202c7 [zsxwing] Fix typo d7c250d [zsxwing] Fix the race condition of creating HeartbeatReceiver and retrieving HeartbeatReceiver --- .../org/apache/spark/HeartbeatReceiver.scala | 32 +++++++++++++++---- .../scala/org/apache/spark/SparkContext.scala | 10 ++++-- 2 files changed, 33 insertions(+), 9 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala index 8435e1ea2611c..9f8ad03b91e85 100644 --- a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala +++ b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala @@ -37,6 +37,12 @@ private[spark] case class Heartbeat( taskMetrics: Array[(Long, TaskMetrics)], // taskId -> TaskMetrics blockManagerId: BlockManagerId) +/** + * An event that SparkContext uses to notify HeartbeatReceiver that SparkContext.taskScheduler is + * created. + */ +private[spark] case object TaskSchedulerIsSet + private[spark] case object ExpireDeadHosts private[spark] case class HeartbeatResponse(reregisterBlockManager: Boolean) @@ -44,9 +50,11 @@ private[spark] case class HeartbeatResponse(reregisterBlockManager: Boolean) /** * Lives in the driver to receive heartbeats from executors.. */ -private[spark] class HeartbeatReceiver(sc: SparkContext, scheduler: TaskScheduler) +private[spark] class HeartbeatReceiver(sc: SparkContext) extends Actor with ActorLogReceive with Logging { + private var scheduler: TaskScheduler = null + // executor ID -> timestamp of when the last heartbeat from this executor was received private val executorLastSeen = new mutable.HashMap[String, Long] @@ -71,12 +79,22 @@ private[spark] class HeartbeatReceiver(sc: SparkContext, scheduler: TaskSchedule } override def receiveWithLogging: PartialFunction[Any, Unit] = { - case Heartbeat(executorId, taskMetrics, blockManagerId) => - val unknownExecutor = !scheduler.executorHeartbeatReceived( - executorId, taskMetrics, blockManagerId) - val response = HeartbeatResponse(reregisterBlockManager = unknownExecutor) - executorLastSeen(executorId) = System.currentTimeMillis() - sender ! response + case TaskSchedulerIsSet => + scheduler = sc.taskScheduler + case heartbeat @ Heartbeat(executorId, taskMetrics, blockManagerId) => + if (scheduler != null) { + val unknownExecutor = !scheduler.executorHeartbeatReceived( + executorId, taskMetrics, blockManagerId) + val response = HeartbeatResponse(reregisterBlockManager = unknownExecutor) + executorLastSeen(executorId) = System.currentTimeMillis() + sender ! response + } else { + // Because Executor will sleep several seconds before sending the first "Heartbeat", this + // case rarely happens. However, if it really happens, log it and ask the executor to + // register itself again. + logWarning(s"Dropping $heartbeat because TaskScheduler is not ready yet") + sender ! HeartbeatResponse(reregisterBlockManager = true) + } case ExpireDeadHosts => expireDeadHosts() } diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index abf81e312d8e6..fd1838976ee22 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -356,11 +356,17 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli val sparkUser = Utils.getCurrentUserName() executorEnvs("SPARK_USER") = sparkUser + // We need to register "HeartbeatReceiver" before "createTaskScheduler" because Executor will + // retrieve "HeartbeatReceiver" in the constructor. (SPARK-6640) + private val heartbeatReceiver = env.actorSystem.actorOf( + Props(new HeartbeatReceiver(this)), "HeartbeatReceiver") + // Create and start the scheduler private[spark] var (schedulerBackend, taskScheduler) = SparkContext.createTaskScheduler(this, master) - private val heartbeatReceiver = env.actorSystem.actorOf( - Props(new HeartbeatReceiver(this, taskScheduler)), "HeartbeatReceiver") + + heartbeatReceiver ! TaskSchedulerIsSet + @volatile private[spark] var dagScheduler: DAGScheduler = _ try { dagScheduler = new DAGScheduler(this) From ffe8cc9a25454ee4f451f6ee3ec6d1e934b47ca2 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Fri, 3 Apr 2015 11:53:07 -0700 Subject: [PATCH 163/591] Closes #3158 From 14632b7942c02a332c4d3814fb6b2611e3f76fc7 Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Fri, 3 Apr 2015 11:54:31 -0700 Subject: [PATCH 164/591] [SPARK-6688] [core] Always use resolved URIs in EventLoggingListener. Author: Marcelo Vanzin Closes #5340 from vanzin/SPARK-6688 and squashes the following commits: ccfddd9 [Marcelo Vanzin] Resolve at the source. 20d2a34 [Marcelo Vanzin] [SPARK-6688] [core] Always use resolved URIs in EventLoggingListener. --- .../scala/org/apache/spark/SparkContext.scala | 6 +++-- .../spark/deploy/ApplicationDescription.scala | 6 +++-- .../scheduler/EventLoggingListener.scala | 10 ++++----- .../history/FsHistoryProviderSuite.scala | 2 +- .../scheduler/EventLoggingListenerSuite.scala | 22 ++++++++++++------- .../spark/scheduler/ReplayListenerSuite.scala | 3 ++- 6 files changed, 30 insertions(+), 19 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index fd1838976ee22..3b73a8a8fd850 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -227,9 +227,11 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli val appName = conf.get("spark.app.name") private[spark] val isEventLogEnabled = conf.getBoolean("spark.eventLog.enabled", false) - private[spark] val eventLogDir: Option[String] = { + private[spark] val eventLogDir: Option[URI] = { if (isEventLogEnabled) { - Some(conf.get("spark.eventLog.dir", EventLoggingListener.DEFAULT_LOG_DIR).stripSuffix("/")) + val unresolvedDir = conf.get("spark.eventLog.dir", EventLoggingListener.DEFAULT_LOG_DIR) + .stripSuffix("/") + Some(Utils.resolveURI(unresolvedDir)) } else { None } diff --git a/core/src/main/scala/org/apache/spark/deploy/ApplicationDescription.scala b/core/src/main/scala/org/apache/spark/deploy/ApplicationDescription.scala index 3d0d68de8f495..b7ae9c1fc0a23 100644 --- a/core/src/main/scala/org/apache/spark/deploy/ApplicationDescription.scala +++ b/core/src/main/scala/org/apache/spark/deploy/ApplicationDescription.scala @@ -17,13 +17,15 @@ package org.apache.spark.deploy +import java.net.URI + private[spark] class ApplicationDescription( val name: String, val maxCores: Option[Int], val memoryPerSlave: Int, val command: Command, var appUiUrl: String, - val eventLogDir: Option[String] = None, + val eventLogDir: Option[URI] = None, // short name of compression codec used when writing event logs, if any (e.g. lzf) val eventLogCodec: Option[String] = None) extends Serializable { @@ -36,7 +38,7 @@ private[spark] class ApplicationDescription( memoryPerSlave: Int = memoryPerSlave, command: Command = command, appUiUrl: String = appUiUrl, - eventLogDir: Option[String] = eventLogDir, + eventLogDir: Option[URI] = eventLogDir, eventLogCodec: Option[String] = eventLogCodec): ApplicationDescription = new ApplicationDescription( name, maxCores, memoryPerSlave, command, appUiUrl, eventLogDir, eventLogCodec) diff --git a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala index c0d889360ae99..08e7727db2fde 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala @@ -47,21 +47,21 @@ import org.apache.spark.util.{JsonProtocol, Utils} */ private[spark] class EventLoggingListener( appId: String, - logBaseDir: String, + logBaseDir: URI, sparkConf: SparkConf, hadoopConf: Configuration) extends SparkListener with Logging { import EventLoggingListener._ - def this(appId: String, logBaseDir: String, sparkConf: SparkConf) = + def this(appId: String, logBaseDir: URI, sparkConf: SparkConf) = this(appId, logBaseDir, sparkConf, SparkHadoopUtil.get.newConfiguration(sparkConf)) private val shouldCompress = sparkConf.getBoolean("spark.eventLog.compress", false) private val shouldOverwrite = sparkConf.getBoolean("spark.eventLog.overwrite", false) private val testing = sparkConf.getBoolean("spark.eventLog.testing", false) private val outputBufferSize = sparkConf.getInt("spark.eventLog.buffer.kb", 100) * 1024 - private val fileSystem = Utils.getHadoopFileSystem(new URI(logBaseDir), hadoopConf) + private val fileSystem = Utils.getHadoopFileSystem(logBaseDir, hadoopConf) private val compressionCodec = if (shouldCompress) { Some(CompressionCodec.createCodec(sparkConf)) @@ -259,13 +259,13 @@ private[spark] object EventLoggingListener extends Logging { * @return A path which consists of file-system-safe characters. */ def getLogPath( - logBaseDir: String, + logBaseDir: URI, appId: String, compressionCodecName: Option[String] = None): String = { val sanitizedAppId = appId.replaceAll("[ :/]", "-").replaceAll("[.${}'\"]", "_").toLowerCase // e.g. app_123, app_123.lzf val logName = sanitizedAppId + compressionCodecName.map { "." + _ }.getOrElse("") - Utils.resolveURI(logBaseDir).toString.stripSuffix("/") + "/" + logName + logBaseDir.toString.stripSuffix("/") + "/" + logName } /** diff --git a/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala b/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala index e908ba604ebed..fcae603c7d18e 100644 --- a/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala @@ -50,7 +50,7 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers inProgress: Boolean, codec: Option[String] = None): File = { val ip = if (inProgress) EventLoggingListener.IN_PROGRESS else "" - val logUri = EventLoggingListener.getLogPath(testDir.getAbsolutePath, appId) + val logUri = EventLoggingListener.getLogPath(testDir.toURI, appId) val logPath = new URI(logUri).getPath + ip new File(logPath) } diff --git a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala index 448258a754153..30ee63e78d9d8 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala @@ -61,7 +61,7 @@ class EventLoggingListenerSuite extends FunSuite with LocalSparkContext with Bef test("Verify log file exist") { // Verify logging directory exists val conf = getLoggingConf(testDirPath) - val eventLogger = new EventLoggingListener("test", testDirPath.toUri().toString(), conf) + val eventLogger = new EventLoggingListener("test", testDirPath.toUri(), conf) eventLogger.start() val logPath = new Path(eventLogger.logPath + EventLoggingListener.IN_PROGRESS) @@ -95,7 +95,7 @@ class EventLoggingListenerSuite extends FunSuite with LocalSparkContext with Bef } test("Log overwriting") { - val logUri = EventLoggingListener.getLogPath(testDir.getAbsolutePath, "test") + val logUri = EventLoggingListener.getLogPath(testDir.toURI, "test") val logPath = new URI(logUri).getPath // Create file before writing the event log new FileOutputStream(new File(logPath)).close() @@ -107,16 +107,19 @@ class EventLoggingListenerSuite extends FunSuite with LocalSparkContext with Bef test("Event log name") { // without compression - assert(s"file:/base-dir/app1" === EventLoggingListener.getLogPath("/base-dir", "app1")) + assert(s"file:/base-dir/app1" === EventLoggingListener.getLogPath( + Utils.resolveURI("/base-dir"), "app1")) // with compression assert(s"file:/base-dir/app1.lzf" === - EventLoggingListener.getLogPath("/base-dir", "app1", Some("lzf"))) + EventLoggingListener.getLogPath(Utils.resolveURI("/base-dir"), "app1", Some("lzf"))) // illegal characters in app ID assert(s"file:/base-dir/a-fine-mind_dollar_bills__1" === - EventLoggingListener.getLogPath("/base-dir", "a fine:mind$dollar{bills}.1")) + EventLoggingListener.getLogPath(Utils.resolveURI("/base-dir"), + "a fine:mind$dollar{bills}.1")) // illegal characters in app ID with compression assert(s"file:/base-dir/a-fine-mind_dollar_bills__1.lz4" === - EventLoggingListener.getLogPath("/base-dir", "a fine:mind$dollar{bills}.1", Some("lz4"))) + EventLoggingListener.getLogPath(Utils.resolveURI("/base-dir"), + "a fine:mind$dollar{bills}.1", Some("lz4"))) } /* ----------------- * @@ -137,7 +140,7 @@ class EventLoggingListenerSuite extends FunSuite with LocalSparkContext with Bef val conf = getLoggingConf(testDirPath, compressionCodec) extraConf.foreach { case (k, v) => conf.set(k, v) } val logName = compressionCodec.map("test-" + _).getOrElse("test") - val eventLogger = new EventLoggingListener(logName, testDirPath.toUri().toString(), conf) + val eventLogger = new EventLoggingListener(logName, testDirPath.toUri(), conf) val listenerBus = new LiveListenerBus val applicationStart = SparkListenerApplicationStart("Greatest App (N)ever", None, 125L, "Mickey") @@ -173,12 +176,15 @@ class EventLoggingListenerSuite extends FunSuite with LocalSparkContext with Bef * This runs a simple Spark job and asserts that the expected events are logged when expected. */ private def testApplicationEventLogging(compressionCodec: Option[String] = None) { + // Set defaultFS to something that would cause an exception, to make sure we don't run + // into SPARK-6688. val conf = getLoggingConf(testDirPath, compressionCodec) + .set("spark.hadoop.fs.defaultFS", "unsupported://example.com") val sc = new SparkContext("local-cluster[2,2,512]", "test", conf) assert(sc.eventLogger.isDefined) val eventLogger = sc.eventLogger.get val eventLogPath = eventLogger.logPath - val expectedLogDir = testDir.toURI().toString() + val expectedLogDir = testDir.toURI() assert(eventLogPath === EventLoggingListener.getLogPath( expectedLogDir, sc.applicationId, compressionCodec.map(CompressionCodec.getShortName))) diff --git a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala index 601694f57aad0..6de6d2fec622a 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala @@ -18,6 +18,7 @@ package org.apache.spark.scheduler import java.io.{File, PrintWriter} +import java.net.URI import org.json4s.jackson.JsonMethods._ import org.scalatest.{BeforeAndAfter, FunSuite} @@ -145,7 +146,7 @@ class ReplayListenerSuite extends FunSuite with BeforeAndAfter { * log the events. */ private class EventMonster(conf: SparkConf) - extends EventLoggingListener("test", "testdir", conf) { + extends EventLoggingListener("test", new URI("testdir"), conf) { override def start() { } From 26b415e15970d02523f0df459557b09ffda0c8c1 Mon Sep 17 00:00:00 2001 From: Liang-Chi Hsieh Date: Fri, 3 Apr 2015 12:35:00 -0700 Subject: [PATCH 165/591] [SPARK-6647][SQL] Make trait StringComparison as BinaryPredicate and fix unit tests of string data source Filter Now trait `StringComparison` is a `BinaryExpression`. In fact, it should be a `BinaryPredicate`. By making `StringComparison` as `BinaryPredicate`, we can throw error when a `expressions.Predicate` can't translate to a data source `Filter` in function `selectFilters`. Without this modification, because we will wrap a `Filter` outside the scanned results in `pruneFilterProjectRaw`, we can't detect about something is wrong in translating predicates to filters in `selectFilters`. The unit test of #5285 demonstrates such problem. In that pr, even `expressions.Contains` is not properly translated to `sources.StringContains`, the filtering is still performed by the `Filter` and so the test passes. Of course, by doing this modification, all `expressions.Predicate` classes need to have its data source `Filter` correspondingly. There is a small bug in `FilteredScanSuite` for doing `StringEndsWith` filter. This pr also fixes it. Author: Liang-Chi Hsieh Closes #5309 from viirya/translate_predicate and squashes the following commits: b176385 [Liang-Chi Hsieh] Address comment. 275a493 [Liang-Chi Hsieh] More properly test for StringStartsWith, StringEndsWith and StringContains. caf2347 [Liang-Chi Hsieh] Make trait StringComparison as BinaryPredicate and throw error when Predicate can't translate to data source Filter. --- .../expressions/stringOperations.scala | 11 ++++---- .../spark/sql/sources/FilteredScanSuite.scala | 28 +++++++++++++------ 2 files changed, 25 insertions(+), 14 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala index 3cdca4e9dd2d1..acfbbace608ef 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala @@ -156,12 +156,11 @@ case class Lower(child: Expression) extends UnaryExpression with CaseConversionE /** A base trait for functions that compare two strings, returning a boolean. */ trait StringComparison { - self: BinaryExpression => + self: BinaryPredicate => - type EvaluatedType = Any + override type EvaluatedType = Any override def nullable: Boolean = left.nullable || right.nullable - override def dataType: DataType = BooleanType def compare(l: String, r: String): Boolean @@ -184,7 +183,7 @@ trait StringComparison { * A function that returns true if the string `left` contains the string `right`. */ case class Contains(left: Expression, right: Expression) - extends BinaryExpression with StringComparison { + extends BinaryPredicate with StringComparison { override def compare(l: String, r: String): Boolean = l.contains(r) } @@ -192,7 +191,7 @@ case class Contains(left: Expression, right: Expression) * A function that returns true if the string `left` starts with the string `right`. */ case class StartsWith(left: Expression, right: Expression) - extends BinaryExpression with StringComparison { + extends BinaryPredicate with StringComparison { override def compare(l: String, r: String): Boolean = l.startsWith(r) } @@ -200,7 +199,7 @@ case class StartsWith(left: Expression, right: Expression) * A function that returns true if the string `left` ends with the string `right`. */ case class EndsWith(left: Expression, right: Expression) - extends BinaryExpression with StringComparison { + extends BinaryPredicate with StringComparison { override def compare(l: String, r: String): Boolean = l.endsWith(r) } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala index 72ddc0ea2c8cb..773bd1602d5e5 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala @@ -45,7 +45,9 @@ case class SimpleFilteredScan(from: Int, to: Int)(@transient val sqlContext: SQL val rowBuilders = requiredColumns.map { case "a" => (i: Int) => Seq(i) case "b" => (i: Int) => Seq(i * 2) - case "c" => (i: Int) => Seq((i - 1 + 'a').toChar.toString * 10) + case "c" => (i: Int) => + val c = (i - 1 + 'a').toChar.toString + Seq(c * 5 + c.toUpperCase() * 5) } FiltersPushed.list = filters @@ -77,7 +79,7 @@ case class SimpleFilteredScan(from: Int, to: Int)(@transient val sqlContext: SQL } def eval(a: Int) = { - val c = (a - 1 + 'a').toChar.toString * 10 + val c = (a - 1 + 'a').toChar.toString * 5 + (a - 1 + 'a').toChar.toString.toUpperCase() * 5 !filters.map(translateFilterOnA(_)(a)).contains(false) && !filters.map(translateFilterOnC(_)(c)).contains(false) } @@ -110,7 +112,8 @@ class FilteredScanSuite extends DataSourceTest { sqlTest( "SELECT * FROM oneToTenFiltered", - (1 to 10).map(i => Row(i, i * 2, (i - 1 + 'a').toChar.toString * 10)).toSeq) + (1 to 10).map(i => Row(i, i * 2, (i - 1 + 'a').toChar.toString * 5 + + (i - 1 + 'a').toChar.toString.toUpperCase() * 5)).toSeq) sqlTest( "SELECT a, b FROM oneToTenFiltered", @@ -182,15 +185,15 @@ class FilteredScanSuite extends DataSourceTest { sqlTest( "SELECT a, b, c FROM oneToTenFiltered WHERE c like 'c%'", - Seq(Row(3, 3 * 2, "c" * 10))) + Seq(Row(3, 3 * 2, "c" * 5 + "C" * 5))) sqlTest( - "SELECT a, b, c FROM oneToTenFiltered WHERE c like 'd%'", - Seq(Row(4, 4 * 2, "d" * 10))) + "SELECT a, b, c FROM oneToTenFiltered WHERE c like '%D'", + Seq(Row(4, 4 * 2, "d" * 5 + "D" * 5))) sqlTest( - "SELECT a, b, c FROM oneToTenFiltered WHERE c like '%e%'", - Seq(Row(5, 5 * 2, "e" * 10))) + "SELECT a, b, c FROM oneToTenFiltered WHERE c like '%eE%'", + Seq(Row(5, 5 * 2, "e" * 5 + "E" * 5))) testPushDown("SELECT * FROM oneToTenFiltered WHERE A = 1", 1) testPushDown("SELECT a FROM oneToTenFiltered WHERE A = 1", 1) @@ -222,6 +225,15 @@ class FilteredScanSuite extends DataSourceTest { testPushDown("SELECT * FROM oneToTenFiltered WHERE a < 3 OR a > 8", 4) testPushDown("SELECT * FROM oneToTenFiltered WHERE NOT (a < 6)", 5) + testPushDown("SELECT a, b, c FROM oneToTenFiltered WHERE c like 'c%'", 1) + testPushDown("SELECT a, b, c FROM oneToTenFiltered WHERE c like 'C%'", 0) + + testPushDown("SELECT a, b, c FROM oneToTenFiltered WHERE c like '%D'", 1) + testPushDown("SELECT a, b, c FROM oneToTenFiltered WHERE c like '%d'", 0) + + testPushDown("SELECT a, b, c FROM oneToTenFiltered WHERE c like '%eE%'", 1) + testPushDown("SELECT a, b, c FROM oneToTenFiltered WHERE c like '%Ee%'", 0) + def testPushDown(sqlString: String, expectedCount: Int): Unit = { test(s"PushDown Returns $expectedCount: $sqlString") { val queryExecution = sql(sqlString).queryExecution From 9b40c17ab161b64933539abeefde443cb4f98673 Mon Sep 17 00:00:00 2001 From: Davies Liu Date: Fri, 3 Apr 2015 15:22:21 -0700 Subject: [PATCH 166/591] [SPARK-6700] disable flaky test Author: Davies Liu Closes #5356 from davies/flaky and squashes the following commits: 08955f4 [Davies Liu] disable flaky test --- .../scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala index 0e37276ba724b..c06c0105670c0 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala @@ -143,7 +143,8 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit } } - test("run Python application in yarn-cluster mode") { + // Enable this once fix SPARK-6700 + ignore("run Python application in yarn-cluster mode") { val primaryPyFile = new File(tempDir, "test.py") Files.write(TEST_PYFILE, primaryPyFile, UTF_8) val pyFile = new File(tempDir, "test2.py") From da25c86d64ff9ce80f88186ba083f6c21dd9a568 Mon Sep 17 00:00:00 2001 From: Yin Huai Date: Sat, 4 Apr 2015 23:26:10 +0800 Subject: [PATCH 167/591] [SQL] Use path.makeQualified in newParquet. Author: Yin Huai Closes #5353 from yhuai/wrongFS and squashes the following commits: 849603b [Yin Huai] Not use deprecated method. 6d6ae34 [Yin Huai] Use path.makeQualified. --- .../main/scala/org/apache/spark/sql/parquet/newParquet.scala | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala index 583bac42fdcce..0dce3623a66df 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala @@ -268,7 +268,8 @@ private[sql] case class ParquetRelation2( // containing Parquet files (e.g. partitioned Parquet table). val baseStatuses = paths.distinct.map { p => val fs = FileSystem.get(URI.create(p), sparkContext.hadoopConfiguration) - val qualified = fs.makeQualified(new Path(p)) + val path = new Path(p) + val qualified = path.makeQualified(fs.getUri, fs.getWorkingDirectory) if (!fs.exists(qualified) && maybeSchema.isDefined) { fs.mkdirs(qualified) From 7bca62f79056e592cf07b49d8b8d04c59dea25fc Mon Sep 17 00:00:00 2001 From: Liang-Chi Hsieh Date: Sun, 5 Apr 2015 00:20:43 +0800 Subject: [PATCH 168/591] [SPARK-6607][SQL] Check invalid characters for Parquet schema and show error messages '(' and ')' are special characters used in Parquet schema for type annotation. When we run an aggregation query, we will obtain attribute name such as "MAX(a)". If we directly store the generated DataFrame as Parquet file, it causes failure when reading and parsing the stored schema string. Several methods can be adopted to solve this. This pr uses a simplest one to just replace attribute names before generating Parquet schema based on these attributes. Another possible method might be modifying all aggregation expression names from "func(column)" to "func[column]". Author: Liang-Chi Hsieh Closes #5263 from viirya/parquet_aggregation_name and squashes the following commits: 2d70542 [Liang-Chi Hsieh] Address comment. 463dff4 [Liang-Chi Hsieh] Instead of replacing special chars, showing error message to user to suggest using Alias. 1de001d [Liang-Chi Hsieh] Replace special characters '(' and ')' of Parquet schema. --- .../apache/spark/sql/parquet/ParquetTypes.scala | 14 ++++++++++++++ .../apache/spark/sql/hive/parquetSuites.scala | 16 ++++++++++++++++ 2 files changed, 30 insertions(+) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala index da668f068613b..60e1bec4db8e5 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala @@ -390,6 +390,7 @@ private[parquet] object ParquetTypesConverter extends Logging { def convertFromAttributes(attributes: Seq[Attribute], toThriftSchemaNames: Boolean = false): MessageType = { + checkSpecialCharacters(attributes) val fields = attributes.map( attribute => fromDataType(attribute.dataType, attribute.name, attribute.nullable, @@ -404,7 +405,20 @@ private[parquet] object ParquetTypesConverter extends Logging { } } + private def checkSpecialCharacters(schema: Seq[Attribute]) = { + // ,;{}()\n\t= and space character are special characters in Parquet schema + schema.map(_.name).foreach { name => + if (name.matches(".*[ ,;{}()\n\t=].*")) { + sys.error( + s"""Attribute name "$name" contains invalid character(s) among " ,;{}()\n\t=". + |Please use alias to rename it. + """.stripMargin.split("\n").mkString(" ")) + } + } + } + def convertToString(schema: Seq[Attribute]): String = { + checkSpecialCharacters(schema) StructType.fromAttributes(schema).json } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala index 1319c81dfc131..5f71e1bbc2d2e 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala @@ -688,6 +688,22 @@ class ParquetDataSourceOnSourceSuite extends ParquetSourceSuiteBase { sql("DROP TABLE alwaysNullable") } + + test("Aggregation attribute names can't contain special chars \" ,;{}()\\n\\t=\"") { + val tempDir = Utils.createTempDir() + val filePath = new File(tempDir, "testParquet").getCanonicalPath + val filePath2 = new File(tempDir, "testParquet2").getCanonicalPath + + val df = Seq(1,2,3).map(i => (i, i.toString)).toDF("int", "str") + val df2 = df.as('x).join(df.as('y), $"x.str" === $"y.str").groupBy("y.str").max("y.int") + intercept[RuntimeException](df2.saveAsParquetFile(filePath)) + + val df3 = df2.toDF("str", "max_int") + df3.saveAsParquetFile(filePath2) + val df4 = parquetFile(filePath2) + checkAnswer(df4, Row("1", 1) :: Row("2", 2) :: Row("3", 3) :: Nil) + assert(df4.columns === Array("str", "max_int")) + } } class ParquetDataSourceOffSourceSuite extends ParquetSourceSuiteBase { From f15806a8f8ca34288ddb2d74b9ff1972c8374b59 Mon Sep 17 00:00:00 2001 From: zsxwing Date: Sat, 4 Apr 2015 11:52:05 -0700 Subject: [PATCH 169/591] [SPARK-6602][Core] Replace direct use of Akka with Spark RPC interface - part 1 This PR replaced the following `Actor`s to `RpcEndpoint`: 1. HeartbeatReceiver 1. ExecutorActor 1. BlockManagerMasterActor 1. BlockManagerSlaveActor 1. CoarseGrainedExecutorBackend and subclasses 1. CoarseGrainedSchedulerBackend.DriverActor This is the first PR. I will split the work of SPARK-6602 to several PRs for code review. Author: zsxwing Closes #5268 from zsxwing/rpc-rewrite and squashes the following commits: 287e9f8 [zsxwing] Fix the code style 26c56b7 [zsxwing] Merge branch 'master' into rpc-rewrite 9cc825a [zsxwing] Rmove setupThreadSafeEndpoint and add ThreadSafeRpcEndpoint 30a9036 [zsxwing] Make self return null after stopping RpcEndpointRef; fix docs and error messages 705245d [zsxwing] Fix some bugs after rebasing the changes on the master 003cf80 [zsxwing] Update CoarseGrainedExecutorBackend and CoarseGrainedSchedulerBackend to use RpcEndpoint 7d0e6dc [zsxwing] Update BlockManagerSlaveActor to use RpcEndpoint f5d6543 [zsxwing] Update BlockManagerMaster to use RpcEndpoint 30e3f9f [zsxwing] Update ExecutorActor to use RpcEndpoint 478b443 [zsxwing] Update HeartbeatReceiver to use RpcEndpoint --- .../org/apache/spark/HeartbeatReceiver.scala | 66 +++++--- .../scala/org/apache/spark/SparkContext.scala | 23 +-- .../scala/org/apache/spark/SparkEnv.scala | 13 +- .../CoarseGrainedExecutorBackend.scala | 79 +++++----- .../org/apache/spark/executor/Executor.scala | 18 +-- ...utorActor.scala => ExecutorEndpoint.scala} | 18 ++- .../scala/org/apache/spark/rpc/RpcEnv.scala | 39 +++-- .../apache/spark/rpc/akka/AkkaRpcEnv.scala | 10 +- .../apache/spark/scheduler/DAGScheduler.scala | 11 +- .../cluster/CoarseGrainedClusterMessage.scala | 6 +- .../CoarseGrainedSchedulerBackend.scala | 148 +++++++++--------- .../scheduler/cluster/ExecutorData.scala | 8 +- .../cluster/SimrSchedulerBackend.scala | 13 +- .../cluster/SparkDeploySchedulerBackend.scala | 14 +- .../cluster/YarnSchedulerBackend.scala | 93 +++++------ .../mesos/CoarseMesosSchedulerBackend.scala | 4 +- .../spark/scheduler/local/LocalBackend.scala | 48 +++--- .../apache/spark/storage/BlockManager.scala | 22 +-- .../spark/storage/BlockManagerMaster.scala | 72 ++++----- ...scala => BlockManagerMasterEndpoint.scala} | 119 +++++++------- .../spark/storage/BlockManagerMessages.scala | 7 +- ....scala => BlockManagerSlaveEndpoint.scala} | 44 +++--- .../scala/org/apache/spark/util/Utils.scala | 10 ++ .../apache/spark/HeartbeatReceiverSuite.scala | 81 ++++++++++ .../org/apache/spark/rpc/RpcEnvSuite.scala | 14 +- .../BlockManagerReplicationSuite.scala | 28 ++-- .../spark/storage/BlockManagerSuite.scala | 37 ++--- .../streaming/ReceivedBlockHandlerSuite.scala | 25 ++- .../spark/deploy/yarn/ApplicationMaster.scala | 86 +++++----- .../spark/deploy/yarn/YarnAllocator.scala | 2 +- 30 files changed, 616 insertions(+), 542 deletions(-) rename core/src/main/scala/org/apache/spark/executor/{ExecutorActor.scala => ExecutorEndpoint.scala} (67%) rename core/src/main/scala/org/apache/spark/storage/{BlockManagerMasterActor.scala => BlockManagerMasterEndpoint.scala} (83%) rename core/src/main/scala/org/apache/spark/storage/{BlockManagerSlaveActor.scala => BlockManagerSlaveEndpoint.scala} (61%) create mode 100644 core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala diff --git a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala index 9f8ad03b91e85..5871b8c869f03 100644 --- a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala +++ b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala @@ -17,15 +17,15 @@ package org.apache.spark -import scala.concurrent.duration._ -import scala.collection.mutable +import java.util.concurrent.{ScheduledFuture, TimeUnit, Executors} -import akka.actor.{Actor, Cancellable} +import scala.collection.mutable import org.apache.spark.executor.TaskMetrics +import org.apache.spark.rpc.{ThreadSafeRpcEndpoint, RpcEnv, RpcCallContext} import org.apache.spark.storage.BlockManagerId import org.apache.spark.scheduler.{SlaveLost, TaskScheduler} -import org.apache.spark.util.ActorLogReceive +import org.apache.spark.util.Utils /** * A heartbeat from executors to the driver. This is a shared message used by several internal @@ -51,9 +51,11 @@ private[spark] case class HeartbeatResponse(reregisterBlockManager: Boolean) * Lives in the driver to receive heartbeats from executors.. */ private[spark] class HeartbeatReceiver(sc: SparkContext) - extends Actor with ActorLogReceive with Logging { + extends ThreadSafeRpcEndpoint with Logging { + + override val rpcEnv: RpcEnv = sc.env.rpcEnv - private var scheduler: TaskScheduler = null + private[spark] var scheduler: TaskScheduler = null // executor ID -> timestamp of when the last heartbeat from this executor was received private val executorLastSeen = new mutable.HashMap[String, Long] @@ -69,34 +71,44 @@ private[spark] class HeartbeatReceiver(sc: SparkContext) sc.conf.getOption("spark.network.timeoutInterval").map(_.toLong * 1000). getOrElse(sc.conf.getLong("spark.storage.blockManagerTimeoutIntervalMs", 60000)) - private var timeoutCheckingTask: Cancellable = null - - override def preStart(): Unit = { - import context.dispatcher - timeoutCheckingTask = context.system.scheduler.schedule(0.seconds, - checkTimeoutIntervalMs.milliseconds, self, ExpireDeadHosts) - super.preStart() + private var timeoutCheckingTask: ScheduledFuture[_] = null + + private val timeoutCheckingThread = Executors.newSingleThreadScheduledExecutor( + Utils.namedThreadFactory("heartbeat-timeout-checking-thread")) + + private val killExecutorThread = Executors.newSingleThreadExecutor( + Utils.namedThreadFactory("kill-executor-thread")) + + override def onStart(): Unit = { + timeoutCheckingTask = timeoutCheckingThread.scheduleAtFixedRate(new Runnable { + override def run(): Unit = Utils.tryLogNonFatalError { + Option(self).foreach(_.send(ExpireDeadHosts)) + } + }, 0, checkTimeoutIntervalMs, TimeUnit.MILLISECONDS) } - - override def receiveWithLogging: PartialFunction[Any, Unit] = { + + override def receive: PartialFunction[Any, Unit] = { + case ExpireDeadHosts => + expireDeadHosts() case TaskSchedulerIsSet => scheduler = sc.taskScheduler + } + + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case heartbeat @ Heartbeat(executorId, taskMetrics, blockManagerId) => if (scheduler != null) { val unknownExecutor = !scheduler.executorHeartbeatReceived( executorId, taskMetrics, blockManagerId) val response = HeartbeatResponse(reregisterBlockManager = unknownExecutor) executorLastSeen(executorId) = System.currentTimeMillis() - sender ! response + context.reply(response) } else { // Because Executor will sleep several seconds before sending the first "Heartbeat", this // case rarely happens. However, if it really happens, log it and ask the executor to // register itself again. logWarning(s"Dropping $heartbeat because TaskScheduler is not ready yet") - sender ! HeartbeatResponse(reregisterBlockManager = true) + context.reply(HeartbeatResponse(reregisterBlockManager = true)) } - case ExpireDeadHosts => - expireDeadHosts() } private def expireDeadHosts(): Unit = { @@ -109,17 +121,25 @@ private[spark] class HeartbeatReceiver(sc: SparkContext) scheduler.executorLost(executorId, SlaveLost("Executor heartbeat " + s"timed out after ${now - lastSeenMs} ms")) if (sc.supportDynamicAllocation) { - sc.killExecutor(executorId) + // Asynchronously kill the executor to avoid blocking the current thread + killExecutorThread.submit(new Runnable { + override def run(): Unit = sc.killExecutor(executorId) + }) } executorLastSeen.remove(executorId) } } } - override def postStop(): Unit = { + override def onStop(): Unit = { if (timeoutCheckingTask != null) { - timeoutCheckingTask.cancel() + timeoutCheckingTask.cancel(true) } - super.postStop() + timeoutCheckingThread.shutdownNow() + killExecutorThread.shutdownNow() } } + +object HeartbeatReceiver { + val ENDPOINT_NAME = "HeartbeatReceiver" +} diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 3b73a8a8fd850..942c5975ece6d 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -32,8 +32,6 @@ import scala.collection.generic.Growable import scala.collection.mutable.HashMap import scala.reflect.{ClassTag, classTag} -import akka.actor.Props - import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.apache.hadoop.io.{ArrayWritable, BooleanWritable, BytesWritable, DoubleWritable, @@ -48,12 +46,13 @@ import org.apache.mesos.MesosNativeLibrary import org.apache.spark.annotation.{DeveloperApi, Experimental} import org.apache.spark.broadcast.Broadcast import org.apache.spark.deploy.{LocalSparkCluster, SparkHadoopUtil} -import org.apache.spark.executor.TriggerThreadDump +import org.apache.spark.executor.{ExecutorEndpoint, TriggerThreadDump} import org.apache.spark.input.{StreamInputFormat, PortableDataStream, WholeTextFileInputFormat, FixedLengthBinaryInputFormat} import org.apache.spark.io.CompressionCodec import org.apache.spark.partial.{ApproximateEvaluator, PartialResult} import org.apache.spark.rdd._ +import org.apache.spark.rpc.RpcAddress import org.apache.spark.scheduler._ import org.apache.spark.scheduler.cluster.{CoarseGrainedSchedulerBackend, SparkDeploySchedulerBackend, SimrSchedulerBackend} @@ -360,14 +359,14 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli // We need to register "HeartbeatReceiver" before "createTaskScheduler" because Executor will // retrieve "HeartbeatReceiver" in the constructor. (SPARK-6640) - private val heartbeatReceiver = env.actorSystem.actorOf( - Props(new HeartbeatReceiver(this)), "HeartbeatReceiver") + private val heartbeatReceiver = env.rpcEnv.setupEndpoint( + HeartbeatReceiver.ENDPOINT_NAME, new HeartbeatReceiver(this)) // Create and start the scheduler private[spark] var (schedulerBackend, taskScheduler) = SparkContext.createTaskScheduler(this, master) - heartbeatReceiver ! TaskSchedulerIsSet + heartbeatReceiver.send(TaskSchedulerIsSet) @volatile private[spark] var dagScheduler: DAGScheduler = _ try { @@ -455,10 +454,12 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli if (executorId == SparkContext.DRIVER_IDENTIFIER) { Some(Utils.getThreadDump()) } else { - val (host, port) = env.blockManager.master.getActorSystemHostPortForExecutor(executorId).get - val actorRef = AkkaUtils.makeExecutorRef("ExecutorActor", conf, host, port, env.actorSystem) - Some(AkkaUtils.askWithReply[Array[ThreadStackTrace]](TriggerThreadDump, actorRef, - AkkaUtils.numRetries(conf), AkkaUtils.retryWaitMs(conf), AkkaUtils.askTimeout(conf))) + val (host, port) = env.blockManager.master.getRpcHostPortForExecutor(executorId).get + val endpointRef = env.rpcEnv.setupEndpointRef( + SparkEnv.executorActorSystemName, + RpcAddress(host, port), + ExecutorEndpoint.EXECUTOR_ENDPOINT_NAME) + Some(endpointRef.askWithReply[Array[ThreadStackTrace]](TriggerThreadDump)) } } catch { case e: Exception => @@ -1418,7 +1419,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli dagScheduler = null listenerBus.stop() eventLogger.foreach(_.stop()) - env.actorSystem.stop(heartbeatReceiver) + env.rpcEnv.stop(heartbeatReceiver) progressBar.foreach(_.stop()) taskScheduler = null // TODO: Cache.stop()? diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala index 4a2ed82a40dec..55be0a59fedd9 100644 --- a/core/src/main/scala/org/apache/spark/SparkEnv.scala +++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala @@ -295,7 +295,9 @@ object SparkEnv extends Logging { } } - def registerOrLookupEndpoint(name: String, endpointCreator: => RpcEndpoint): RpcEndpointRef = { + def registerOrLookupEndpoint( + name: String, endpointCreator: => RpcEndpoint): + RpcEndpointRef = { if (isDriver) { logInfo("Registering " + name) rpcEnv.setupEndpoint(name, endpointCreator) @@ -334,12 +336,13 @@ object SparkEnv extends Logging { new NioBlockTransferService(conf, securityManager) } - val blockManagerMaster = new BlockManagerMaster(registerOrLookup( - "BlockManagerMaster", - new BlockManagerMasterActor(isLocal, conf, listenerBus)), conf, isDriver) + val blockManagerMaster = new BlockManagerMaster(registerOrLookupEndpoint( + BlockManagerMaster.DRIVER_ENDPOINT_NAME, + new BlockManagerMasterEndpoint(rpcEnv, isLocal, conf, listenerBus)), + conf, isDriver) // NB: blockManager is not valid until initialize() is called later. - val blockManager = new BlockManager(executorId, actorSystem, blockManagerMaster, + val blockManager = new BlockManager(executorId, rpcEnv, blockManagerMaster, serializer, conf, mapOutputTracker, shuffleManager, blockTransferService, securityManager, numUsableCores) diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala index 900e678ee02ef..8300f9f2190b9 100644 --- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala +++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala @@ -21,39 +21,45 @@ import java.net.URL import java.nio.ByteBuffer import scala.collection.mutable -import scala.concurrent.Await +import scala.util.{Failure, Success} -import akka.actor.{Actor, ActorSelection, Props} -import akka.pattern.Patterns -import akka.remote.{RemotingLifecycleEvent, DisassociatedEvent} - -import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkEnv} +import org.apache.spark.rpc._ +import org.apache.spark._ import org.apache.spark.TaskState.TaskState import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.deploy.worker.WorkerWatcher import org.apache.spark.scheduler.TaskDescription import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ -import org.apache.spark.util.{ActorLogReceive, AkkaUtils, SignalLogger, Utils} +import org.apache.spark.util.{SignalLogger, Utils} private[spark] class CoarseGrainedExecutorBackend( + override val rpcEnv: RpcEnv, driverUrl: String, executorId: String, hostPort: String, cores: Int, userClassPath: Seq[URL], env: SparkEnv) - extends Actor with ActorLogReceive with ExecutorBackend with Logging { + extends ThreadSafeRpcEndpoint with ExecutorBackend with Logging { Utils.checkHostPort(hostPort, "Expected hostport") var executor: Executor = null - var driver: ActorSelection = null + @volatile var driver: Option[RpcEndpointRef] = None - override def preStart() { + override def onStart() { + import scala.concurrent.ExecutionContext.Implicits.global logInfo("Connecting to driver: " + driverUrl) - driver = context.actorSelection(driverUrl) - driver ! RegisterExecutor(executorId, hostPort, cores, extractLogUrls) - context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) + rpcEnv.asyncSetupEndpointRefByURI(driverUrl).flatMap { ref => + driver = Some(ref) + ref.sendWithReply[RegisteredExecutor.type]( + RegisterExecutor(executorId, self, hostPort, cores, extractLogUrls)) + } onComplete { + case Success(msg) => Utils.tryLogNonFatalError { + Option(self).foreach(_.send(msg)) // msg must be RegisteredExecutor + } + case Failure(e) => logError(s"Cannot register with driver: $driverUrl", e) + } } def extractLogUrls: Map[String, String] = { @@ -62,7 +68,7 @@ private[spark] class CoarseGrainedExecutorBackend( .map(e => (e._1.substring(prefix.length).toLowerCase, e._2)) } - override def receiveWithLogging: PartialFunction[Any, Unit] = { + override def receive: PartialFunction[Any, Unit] = { case RegisteredExecutor => logInfo("Successfully registered with driver") val (hostname, _) = Utils.parseHostPort(hostPort) @@ -92,23 +98,28 @@ private[spark] class CoarseGrainedExecutorBackend( executor.killTask(taskId, interruptThread) } - case x: DisassociatedEvent => - if (x.remoteAddress == driver.anchorPath.address) { - logError(s"Driver $x disassociated! Shutting down.") - System.exit(1) - } else { - logWarning(s"Received irrelevant DisassociatedEvent $x") - } - case StopExecutor => logInfo("Driver commanded a shutdown") executor.stop() - context.stop(self) - context.system.shutdown() + stop() + rpcEnv.shutdown() + } + + override def onDisconnected(remoteAddress: RpcAddress): Unit = { + if (driver.exists(_.address == remoteAddress)) { + logError(s"Driver $remoteAddress disassociated! Shutting down.") + System.exit(1) + } else { + logWarning(s"An unknown ($remoteAddress) driver disconnected.") + } } override def statusUpdate(taskId: Long, state: TaskState, data: ByteBuffer) { - driver ! StatusUpdate(executorId, taskId, state, data) + val msg = StatusUpdate(executorId, taskId, state, data) + driver match { + case Some(driverRef) => driverRef.send(msg) + case None => logWarning(s"Drop $msg because has not yet connected to driver") + } } } @@ -132,16 +143,14 @@ private[spark] object CoarseGrainedExecutorBackend extends Logging { // Bootstrap to fetch the driver's Spark properties. val executorConf = new SparkConf val port = executorConf.getInt("spark.executor.port", 0) - val (fetcher, _) = AkkaUtils.createActorSystem( + val fetcher = RpcEnv.create( "driverPropsFetcher", hostname, port, executorConf, new SecurityManager(executorConf)) - val driver = fetcher.actorSelection(driverUrl) - val timeout = AkkaUtils.askTimeout(executorConf) - val fut = Patterns.ask(driver, RetrieveSparkProps, timeout) - val props = Await.result(fut, timeout).asInstanceOf[Seq[(String, String)]] ++ + val driver = fetcher.setupEndpointRefByURI(driverUrl) + val props = driver.askWithReply[Seq[(String, String)]](RetrieveSparkProps) ++ Seq[(String, String)](("spark.app.id", appId)) fetcher.shutdown() @@ -162,16 +171,14 @@ private[spark] object CoarseGrainedExecutorBackend extends Logging { val boundPort = env.conf.getInt("spark.executor.port", 0) assert(boundPort != 0) - // Start the CoarseGrainedExecutorBackend actor. + // Start the CoarseGrainedExecutorBackend endpoint. val sparkHostPort = hostname + ":" + boundPort - env.actorSystem.actorOf( - Props(classOf[CoarseGrainedExecutorBackend], - driverUrl, executorId, sparkHostPort, cores, userClassPath, env), - name = "Executor") + env.rpcEnv.setupEndpoint("Executor", new CoarseGrainedExecutorBackend( + env.rpcEnv, driverUrl, executorId, sparkHostPort, cores, userClassPath, env)) workerUrl.foreach { url => env.rpcEnv.setupEndpoint("WorkerWatcher", new WorkerWatcher(env.rpcEnv, url)) } - env.actorSystem.awaitTermination() + env.rpcEnv.awaitTermination() } } diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala index bf3135ef081c1..14f99a464b6e9 100644 --- a/core/src/main/scala/org/apache/spark/executor/Executor.scala +++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala @@ -27,8 +27,6 @@ import scala.collection.JavaConversions._ import scala.collection.mutable.{ArrayBuffer, HashMap} import scala.util.control.NonFatal -import akka.actor.Props - import org.apache.spark._ import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.scheduler.{DirectTaskResult, IndirectTaskResult, Task} @@ -88,9 +86,9 @@ private[spark] class Executor( env.blockManager.initialize(conf.getAppId) } - // Create an actor for receiving RPCs from the driver - private val executorActor = env.actorSystem.actorOf( - Props(new ExecutorActor(executorId)), "ExecutorActor") + // Create an RpcEndpoint for receiving RPCs from the driver + private val executorEndpoint = env.rpcEnv.setupEndpoint( + ExecutorEndpoint.EXECUTOR_ENDPOINT_NAME, new ExecutorEndpoint(env.rpcEnv, executorId)) // Whether to load classes in user jars before those in Spark jars private val userClassPathFirst: Boolean = { @@ -139,7 +137,7 @@ private[spark] class Executor( def stop(): Unit = { env.metricsSystem.report() - env.actorSystem.stop(executorActor) + env.rpcEnv.stop(executorEndpoint) isStopped = true threadPool.shutdown() if (!isLocal) { @@ -391,11 +389,8 @@ private[spark] class Executor( } } - private val timeout = AkkaUtils.lookupTimeout(conf) - private val retryAttempts = AkkaUtils.numRetries(conf) - private val retryIntervalMs = AkkaUtils.retryWaitMs(conf) private val heartbeatReceiverRef = - AkkaUtils.makeDriverRef("HeartbeatReceiver", conf, env.actorSystem) + RpcUtils.makeDriverRef(HeartbeatReceiver.ENDPOINT_NAME, conf, env.rpcEnv) /** Reports heartbeat and metrics for active tasks to the driver. */ private def reportHeartBeat(): Unit = { @@ -426,8 +421,7 @@ private[spark] class Executor( val message = Heartbeat(executorId, tasksMetrics.toArray, env.blockManager.blockManagerId) try { - val response = AkkaUtils.askWithReply[HeartbeatResponse](message, heartbeatReceiverRef, - retryAttempts, retryIntervalMs, timeout) + val response = heartbeatReceiverRef.askWithReply[HeartbeatResponse](message) if (response.reregisterBlockManager) { logWarning("Told to re-register on heartbeat") env.blockManager.reregister() diff --git a/core/src/main/scala/org/apache/spark/executor/ExecutorActor.scala b/core/src/main/scala/org/apache/spark/executor/ExecutorEndpoint.scala similarity index 67% rename from core/src/main/scala/org/apache/spark/executor/ExecutorActor.scala rename to core/src/main/scala/org/apache/spark/executor/ExecutorEndpoint.scala index 3e47d13f7545d..cf362f8464735 100644 --- a/core/src/main/scala/org/apache/spark/executor/ExecutorActor.scala +++ b/core/src/main/scala/org/apache/spark/executor/ExecutorEndpoint.scala @@ -17,10 +17,8 @@ package org.apache.spark.executor -import akka.actor.Actor -import org.apache.spark.Logging - -import org.apache.spark.util.{Utils, ActorLogReceive} +import org.apache.spark.rpc.{RpcEnv, RpcCallContext, RpcEndpoint} +import org.apache.spark.util.Utils /** * Driver -> Executor message to trigger a thread dump. @@ -28,14 +26,18 @@ import org.apache.spark.util.{Utils, ActorLogReceive} private[spark] case object TriggerThreadDump /** - * Actor that runs inside of executors to enable driver -> executor RPC. + * [[RpcEndpoint]] that runs inside of executors to enable driver -> executor RPC. */ private[spark] -class ExecutorActor(executorId: String) extends Actor with ActorLogReceive with Logging { +class ExecutorEndpoint(override val rpcEnv: RpcEnv, executorId: String) extends RpcEndpoint { - override def receiveWithLogging: PartialFunction[Any, Unit] = { + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case TriggerThreadDump => - sender ! Utils.getThreadDump() + context.reply(Utils.getThreadDump()) } } + +object ExecutorEndpoint { + val EXECUTOR_ENDPOINT_NAME = "ExecutorEndpoint" +} diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala index 7985941d949c0..d47e41abcfa50 100644 --- a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala +++ b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala @@ -40,10 +40,7 @@ private[spark] abstract class RpcEnv(conf: SparkConf) { /** * Return RpcEndpointRef of the registered [[RpcEndpoint]]. Will be used to implement - * [[RpcEndpoint.self]]. - * - * Note: This method won't return null. `IllegalArgumentException` will be thrown if calling this - * on a non-existent endpoint. + * [[RpcEndpoint.self]]. Return `null` if the corresponding [[RpcEndpointRef]] does not exist. */ private[rpc] def endpointRef(endpoint: RpcEndpoint): RpcEndpointRef @@ -58,20 +55,6 @@ private[spark] abstract class RpcEnv(conf: SparkConf) { */ def setupEndpoint(name: String, endpoint: RpcEndpoint): RpcEndpointRef - /** - * Register a [[RpcEndpoint]] with a name and return its [[RpcEndpointRef]]. [[RpcEnv]] should - * make sure thread-safely sending messages to [[RpcEndpoint]]. - * - * Thread-safety means processing of one message happens before processing of the next message by - * the same [[RpcEndpoint]]. In the other words, changes to internal fields of a [[RpcEndpoint]] - * are visible when processing the next message, and fields in the [[RpcEndpoint]] need not be - * volatile or equivalent. - * - * However, there is no guarantee that the same thread will be executing the same [[RpcEndpoint]] - * for different messages. - */ - def setupThreadSafeEndpoint(name: String, endpoint: RpcEndpoint): RpcEndpointRef - /** * Retrieve the [[RpcEndpointRef]] represented by `uri` asynchronously. */ @@ -181,7 +164,7 @@ private[spark] trait RpcEnvFactory { * constructor onStart receive* onStop * * Note: `receive` can be called concurrently. If you want `receive` is thread-safe, please use - * [[RpcEnv.setupThreadSafeEndpoint]] + * [[ThreadSafeRpcEndpoint]] * * If any error is thrown from one of [[RpcEndpoint]] methods except `onError`, `onError` will be * invoked with the cause. If `onError` throws an error, [[RpcEnv]] will ignore it. @@ -195,7 +178,7 @@ private[spark] trait RpcEndpoint { /** * The [[RpcEndpointRef]] of this [[RpcEndpoint]]. `self` will become valid when `onStart` is - * called. + * called. And `self` will become `null` when `onStop` is called. * * Note: Because before `onStart`, [[RpcEndpoint]] has not yet been registered and there is not * valid [[RpcEndpointRef]] for it. So don't call `self` before `onStart` is called. @@ -278,6 +261,19 @@ private[spark] trait RpcEndpoint { } } +/** + * A trait that requires RpcEnv thread-safely sending messages to it. + * + * Thread-safety means processing of one message happens before processing of the next message by + * the same [[ThreadSafeRpcEndpoint]]. In the other words, changes to internal fields of a + * [[ThreadSafeRpcEndpoint]] are visible when processing the next message, and fields in the + * [[ThreadSafeRpcEndpoint]] need not be volatile or equivalent. + * + * However, there is no guarantee that the same thread will be executing the same + * [[ThreadSafeRpcEndpoint]] for different messages. + */ +trait ThreadSafeRpcEndpoint extends RpcEndpoint + /** * A reference for a remote [[RpcEndpoint]]. [[RpcEndpointRef]] is thread-safe. */ @@ -407,7 +403,8 @@ private[spark] object RpcAddress { } /** - * A callback that [[RpcEndpoint]] can use it to send back a message or failure. + * A callback that [[RpcEndpoint]] can use it to send back a message or failure. It's thread-safe + * and can be called in any thread. */ private[spark] trait RpcCallContext { diff --git a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala index 769d59b7b3343..9e06147dff1ed 100644 --- a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala +++ b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala @@ -82,17 +82,9 @@ private[spark] class AkkaRpcEnv private[akka] ( /** * Retrieve the [[RpcEndpointRef]] of `endpoint`. */ - override def endpointRef(endpoint: RpcEndpoint): RpcEndpointRef = { - val endpointRef = endpointToRef.get(endpoint) - require(endpointRef != null, s"Cannot find RpcEndpointRef of ${endpoint} in ${this}") - endpointRef - } + override def endpointRef(endpoint: RpcEndpoint): RpcEndpointRef = endpointToRef.get(endpoint) override def setupEndpoint(name: String, endpoint: RpcEndpoint): RpcEndpointRef = { - setupThreadSafeEndpoint(name, endpoint) - } - - override def setupThreadSafeEndpoint(name: String, endpoint: RpcEndpoint): RpcEndpointRef = { @volatile var endpointRef: AkkaRpcEndpointRef = null // Use lazy because the Actor needs to use `endpointRef`. // So `actorRef` should be created after assigning `endpointRef`. diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala index 7227fa9da4317..917cce1f9686c 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -23,14 +23,10 @@ import java.util.concurrent.{TimeUnit, Executors} import java.util.concurrent.atomic.AtomicInteger import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, Map, Stack} -import scala.concurrent.Await import scala.concurrent.duration._ import scala.language.postfixOps import scala.util.control.NonFatal -import akka.pattern.ask -import akka.util.Timeout - import org.apache.spark._ import org.apache.spark.broadcast.Broadcast import org.apache.spark.executor.TaskMetrics @@ -165,11 +161,8 @@ class DAGScheduler( taskMetrics: Array[(Long, Int, Int, TaskMetrics)], // (taskId, stageId, stateAttempt, metrics) blockManagerId: BlockManagerId): Boolean = { listenerBus.post(SparkListenerExecutorMetricsUpdate(execId, taskMetrics)) - implicit val timeout = Timeout(600 seconds) - - Await.result( - blockManagerMaster.driverActor ? BlockManagerHeartbeat(blockManagerId), - timeout.duration).asInstanceOf[Boolean] + blockManagerMaster.driverEndpoint.askWithReply[Boolean]( + BlockManagerHeartbeat(blockManagerId), 600 seconds) } // Called by TaskScheduler when an executor fails. diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala index 9bf74f4be198d..70364cea62a80 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala @@ -20,6 +20,7 @@ package org.apache.spark.scheduler.cluster import java.nio.ByteBuffer import org.apache.spark.TaskState.TaskState +import org.apache.spark.rpc.RpcEndpointRef import org.apache.spark.util.{SerializableBuffer, Utils} private[spark] sealed trait CoarseGrainedClusterMessage extends Serializable @@ -41,6 +42,7 @@ private[spark] object CoarseGrainedClusterMessages { // Executors to driver case class RegisterExecutor( executorId: String, + executorRef: RpcEndpointRef, hostPort: String, cores: Int, logUrls: Map[String, String]) @@ -70,6 +72,8 @@ private[spark] object CoarseGrainedClusterMessages { case class RemoveExecutor(executorId: String, reason: String) extends CoarseGrainedClusterMessage + case class SetupDriver(driver: RpcEndpointRef) extends CoarseGrainedClusterMessage + // Exchanged between the driver and the AM in Yarn client mode case class AddWebUIFilter(filterName:String, filterParams: Map[String, String], proxyBase: String) extends CoarseGrainedClusterMessage @@ -77,7 +81,7 @@ private[spark] object CoarseGrainedClusterMessages { // Messages exchanged between the driver and the cluster manager for executor allocation // In Yarn mode, these are exchanged between the driver and the AM - case object RegisterClusterManager extends CoarseGrainedClusterMessage + case class RegisterClusterManager(am: RpcEndpointRef) extends CoarseGrainedClusterMessage // Request executors by specifying the new total number of executors desired // This includes executors already pending or running diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala index 5d258d9da4d1a..4c49da87af9dc 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala @@ -17,20 +17,16 @@ package org.apache.spark.scheduler.cluster +import java.util.concurrent.{TimeUnit, Executors} import java.util.concurrent.atomic.AtomicInteger import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet} -import scala.concurrent.Await -import scala.concurrent.duration._ - -import akka.actor._ -import akka.pattern.ask -import akka.remote.{DisassociatedEvent, RemotingLifecycleEvent} +import org.apache.spark.rpc._ import org.apache.spark.{ExecutorAllocationClient, Logging, SparkEnv, SparkException, TaskState} import org.apache.spark.scheduler._ import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ -import org.apache.spark.util.{ActorLogReceive, SerializableBuffer, AkkaUtils, Utils} +import org.apache.spark.util.{SerializableBuffer, AkkaUtils, Utils} /** * A scheduler backend that waits for coarse grained executors to connect to it through Akka. @@ -41,7 +37,7 @@ import org.apache.spark.util.{ActorLogReceive, SerializableBuffer, AkkaUtils, Ut * (spark.deploy.*). */ private[spark] -class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSystem: ActorSystem) +class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: RpcEnv) extends ExecutorAllocationClient with SchedulerBackend with Logging { // Use an atomic variable to track total number of cores in the cluster for simplicity and speed @@ -49,7 +45,6 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste // Total number of executors that are currently registered var totalRegisteredExecutors = new AtomicInteger(0) val conf = scheduler.sc.conf - private val timeout = AkkaUtils.askTimeout(conf) private val akkaFrameSize = AkkaUtils.maxFrameSizeBytes(conf) // Submit tasks only after (registered resources / total expected resources) // is equal to at least this value, that is double between 0 and 1. @@ -71,48 +66,26 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste // Executors we have requested the cluster manager to kill that have not died yet private val executorsPendingToRemove = new HashSet[String] - class DriverActor(sparkProperties: Seq[(String, String)]) extends Actor with ActorLogReceive { + class DriverEndpoint(override val rpcEnv: RpcEnv, sparkProperties: Seq[(String, String)]) + extends ThreadSafeRpcEndpoint with Logging { override protected def log = CoarseGrainedSchedulerBackend.this.log - private val addressToExecutorId = new HashMap[Address, String] - override def preStart() { - // Listen for remote client disconnection events, since they don't go through Akka's watch() - context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) + private val addressToExecutorId = new HashMap[RpcAddress, String] + + private val reviveThread = + Executors.newSingleThreadScheduledExecutor(Utils.namedThreadFactory("driver-revive-thread")) + override def onStart() { // Periodically revive offers to allow delay scheduling to work val reviveInterval = conf.getLong("spark.scheduler.revive.interval", 1000) - import context.dispatcher - context.system.scheduler.schedule(0.millis, reviveInterval.millis, self, ReviveOffers) - } - - def receiveWithLogging: PartialFunction[Any, Unit] = { - case RegisterExecutor(executorId, hostPort, cores, logUrls) => - Utils.checkHostPort(hostPort, "Host port expected " + hostPort) - if (executorDataMap.contains(executorId)) { - sender ! RegisterExecutorFailed("Duplicate executor ID: " + executorId) - } else { - logInfo("Registered executor: " + sender + " with ID " + executorId) - sender ! RegisteredExecutor - - addressToExecutorId(sender.path.address) = executorId - totalCoreCount.addAndGet(cores) - totalRegisteredExecutors.addAndGet(1) - val (host, _) = Utils.parseHostPort(hostPort) - val data = new ExecutorData(sender, sender.path.address, host, cores, cores, logUrls) - // This must be synchronized because variables mutated - // in this block are read when requesting executors - CoarseGrainedSchedulerBackend.this.synchronized { - executorDataMap.put(executorId, data) - if (numPendingExecutors > 0) { - numPendingExecutors -= 1 - logDebug(s"Decremented number of pending executors ($numPendingExecutors left)") - } - } - listenerBus.post( - SparkListenerExecutorAdded(System.currentTimeMillis(), executorId, data)) - makeOffers() + reviveThread.scheduleAtFixedRate(new Runnable { + override def run(): Unit = Utils.tryLogNonFatalError { + Option(self).foreach(_.send(ReviveOffers)) } + }, 0, reviveInterval, TimeUnit.MILLISECONDS) + } + override def receive: PartialFunction[Any, Unit] = { case StatusUpdate(executorId, taskId, state, data) => scheduler.statusUpdate(taskId, state, data.value) if (TaskState.isFinished(state)) { @@ -133,33 +106,58 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste case KillTask(taskId, executorId, interruptThread) => executorDataMap.get(executorId) match { case Some(executorInfo) => - executorInfo.executorActor ! KillTask(taskId, executorId, interruptThread) + executorInfo.executorEndpoint.send(KillTask(taskId, executorId, interruptThread)) case None => // Ignoring the task kill since the executor is not registered. logWarning(s"Attempted to kill task $taskId for unknown executor $executorId.") } + } + + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case RegisterExecutor(executorId, executorRef, hostPort, cores, logUrls) => + Utils.checkHostPort(hostPort, "Host port expected " + hostPort) + if (executorDataMap.contains(executorId)) { + context.reply(RegisterExecutorFailed("Duplicate executor ID: " + executorId)) + } else { + logInfo("Registered executor: " + executorRef + " with ID " + executorId) + context.reply(RegisteredExecutor) + + addressToExecutorId(executorRef.address) = executorId + totalCoreCount.addAndGet(cores) + totalRegisteredExecutors.addAndGet(1) + val (host, _) = Utils.parseHostPort(hostPort) + val data = new ExecutorData(executorRef, executorRef.address, host, cores, cores, logUrls) + // This must be synchronized because variables mutated + // in this block are read when requesting executors + CoarseGrainedSchedulerBackend.this.synchronized { + executorDataMap.put(executorId, data) + if (numPendingExecutors > 0) { + numPendingExecutors -= 1 + logDebug(s"Decremented number of pending executors ($numPendingExecutors left)") + } + } + listenerBus.post( + SparkListenerExecutorAdded(System.currentTimeMillis(), executorId, data)) + makeOffers() + } case StopDriver => - sender ! true - context.stop(self) + context.reply(true) + stop() case StopExecutors => logInfo("Asking each executor to shut down") for ((_, executorData) <- executorDataMap) { - executorData.executorActor ! StopExecutor + executorData.executorEndpoint.send(StopExecutor) } - sender ! true + context.reply(true) case RemoveExecutor(executorId, reason) => removeExecutor(executorId, reason) - sender ! true - - case DisassociatedEvent(_, address, _) => - addressToExecutorId.get(address).foreach(removeExecutor(_, - "remote Akka client disassociated")) + context.reply(true) case RetrieveSparkProps => - sender ! sparkProperties + context.reply(sparkProperties) } // Make fake resource offers on all executors @@ -169,6 +167,11 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste }.toSeq)) } + override def onDisconnected(remoteAddress: RpcAddress): Unit = { + addressToExecutorId.get(remoteAddress).foreach(removeExecutor(_, + "remote Rpc client disassociated")) + } + // Make fake resource offers on just one executor def makeOffers(executorId: String) { val executorData = executorDataMap(executorId) @@ -199,7 +202,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste else { val executorData = executorDataMap(task.executorId) executorData.freeCores -= scheduler.CPUS_PER_TASK - executorData.executorActor ! LaunchTask(new SerializableBuffer(serializedTask)) + executorData.executorEndpoint.send(LaunchTask(new SerializableBuffer(serializedTask))) } } } @@ -223,9 +226,13 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste case None => logError(s"Asked to remove non-existent executor $executorId") } } + + override def onStop() { + reviveThread.shutdownNow() + } } - var driverActor: ActorRef = null + var driverEndpoint: RpcEndpointRef = null val taskIdsOnSlave = new HashMap[String, HashSet[String]] override def start() { @@ -236,16 +243,15 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste } } // TODO (prashant) send conf instead of properties - driverActor = actorSystem.actorOf( - Props(new DriverActor(properties)), name = CoarseGrainedSchedulerBackend.ACTOR_NAME) + driverEndpoint = rpcEnv.setupEndpoint( + CoarseGrainedSchedulerBackend.ENDPOINT_NAME, new DriverEndpoint(rpcEnv, properties)) } def stopExecutors() { try { - if (driverActor != null) { + if (driverEndpoint != null) { logInfo("Shutting down all executors") - val future = driverActor.ask(StopExecutors)(timeout) - Await.ready(future, timeout) + driverEndpoint.askWithReply[Boolean](StopExecutors) } } catch { case e: Exception => @@ -256,22 +262,21 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste override def stop() { stopExecutors() try { - if (driverActor != null) { - val future = driverActor.ask(StopDriver)(timeout) - Await.ready(future, timeout) + if (driverEndpoint != null) { + driverEndpoint.askWithReply[Boolean](StopDriver) } } catch { case e: Exception => - throw new SparkException("Error stopping standalone scheduler's driver actor", e) + throw new SparkException("Error stopping standalone scheduler's driver endpoint", e) } } override def reviveOffers() { - driverActor ! ReviveOffers + driverEndpoint.send(ReviveOffers) } override def killTask(taskId: Long, executorId: String, interruptThread: Boolean) { - driverActor ! KillTask(taskId, executorId, interruptThread) + driverEndpoint.send(KillTask(taskId, executorId, interruptThread)) } override def defaultParallelism(): Int = { @@ -281,11 +286,10 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste // Called by subclasses when notified of a lost worker def removeExecutor(executorId: String, reason: String) { try { - val future = driverActor.ask(RemoveExecutor(executorId, reason))(timeout) - Await.ready(future, timeout) + driverEndpoint.askWithReply[Boolean](RemoveExecutor(executorId, reason)) } catch { case e: Exception => - throw new SparkException("Error notifying standalone scheduler's driver actor", e) + throw new SparkException("Error notifying standalone scheduler's driver endpoint", e) } } @@ -391,5 +395,5 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val actorSyste } private[spark] object CoarseGrainedSchedulerBackend { - val ACTOR_NAME = "CoarseGrainedScheduler" + val ENDPOINT_NAME = "CoarseGrainedScheduler" } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/ExecutorData.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/ExecutorData.scala index 5e571efe76720..26e72c0bff38d 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/ExecutorData.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/ExecutorData.scala @@ -17,20 +17,20 @@ package org.apache.spark.scheduler.cluster -import akka.actor.{Address, ActorRef} +import org.apache.spark.rpc.{RpcEndpointRef, RpcAddress} /** * Grouping of data for an executor used by CoarseGrainedSchedulerBackend. * - * @param executorActor The ActorRef representing this executor + * @param executorEndpoint The ActorRef representing this executor * @param executorAddress The network address of this executor * @param executorHost The hostname that this executor is running on * @param freeCores The current number of cores available for work on the executor * @param totalCores The total number of cores available to the executor */ private[cluster] class ExecutorData( - val executorActor: ActorRef, - val executorAddress: Address, + val executorEndpoint: RpcEndpointRef, + val executorAddress: RpcAddress, override val executorHost: String, var freeCores: Int, override val totalCores: Int, diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala index 06786a59524e7..0324c9dab910b 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala @@ -19,16 +19,16 @@ package org.apache.spark.scheduler.cluster import org.apache.hadoop.fs.{Path, FileSystem} +import org.apache.spark.rpc.RpcAddress import org.apache.spark.{Logging, SparkContext, SparkEnv} import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.scheduler.TaskSchedulerImpl -import org.apache.spark.util.AkkaUtils private[spark] class SimrSchedulerBackend( scheduler: TaskSchedulerImpl, sc: SparkContext, driverFilePath: String) - extends CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem) + extends CoarseGrainedSchedulerBackend(scheduler, sc.env.rpcEnv) with Logging { val tmpPath = new Path(driverFilePath + "_tmp") @@ -39,12 +39,9 @@ private[spark] class SimrSchedulerBackend( override def start() { super.start() - val driverUrl = AkkaUtils.address( - AkkaUtils.protocol(actorSystem), - SparkEnv.driverActorSystemName, - sc.conf.get("spark.driver.host"), - sc.conf.get("spark.driver.port"), - CoarseGrainedSchedulerBackend.ACTOR_NAME) + val driverUrl = rpcEnv.uriOf(SparkEnv.driverActorSystemName, + RpcAddress(sc.conf.get("spark.driver.host"), sc.conf.get("spark.driver.port").toInt), + CoarseGrainedSchedulerBackend.ENDPOINT_NAME) val conf = SparkHadoopUtil.get.newConfiguration(sc.conf) val fs = FileSystem.get(conf) diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala index ffd4825705755..7eb3fdc19b5b8 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala @@ -19,17 +19,18 @@ package org.apache.spark.scheduler.cluster import java.util.concurrent.Semaphore +import org.apache.spark.rpc.RpcAddress import org.apache.spark.{Logging, SparkConf, SparkContext, SparkEnv} import org.apache.spark.deploy.{ApplicationDescription, Command} import org.apache.spark.deploy.client.{AppClient, AppClientListener} import org.apache.spark.scheduler.{ExecutorExited, ExecutorLossReason, SlaveLost, TaskSchedulerImpl} -import org.apache.spark.util.{AkkaUtils, Utils} +import org.apache.spark.util.Utils private[spark] class SparkDeploySchedulerBackend( scheduler: TaskSchedulerImpl, sc: SparkContext, masters: Array[String]) - extends CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem) + extends CoarseGrainedSchedulerBackend(scheduler, sc.env.rpcEnv) with AppClientListener with Logging { @@ -48,12 +49,9 @@ private[spark] class SparkDeploySchedulerBackend( super.start() // The endpoint for executors to talk to us - val driverUrl = AkkaUtils.address( - AkkaUtils.protocol(actorSystem), - SparkEnv.driverActorSystemName, - conf.get("spark.driver.host"), - conf.get("spark.driver.port"), - CoarseGrainedSchedulerBackend.ACTOR_NAME) + val driverUrl = rpcEnv.uriOf(SparkEnv.driverActorSystemName, + RpcAddress(sc.conf.get("spark.driver.host"), sc.conf.get("spark.driver.port").toInt), + CoarseGrainedSchedulerBackend.ENDPOINT_NAME) val args = Seq( "--driver-url", driverUrl, "--executor-id", "{{EXECUTOR_ID}}", diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala index 5a38ad9f2b12c..f72566c370a6f 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala @@ -19,10 +19,8 @@ package org.apache.spark.scheduler.cluster import scala.concurrent.{Future, ExecutionContext} -import akka.actor.{Actor, ActorRef, Props} -import akka.remote.{DisassociatedEvent, RemotingLifecycleEvent} - -import org.apache.spark.SparkContext +import org.apache.spark.{Logging, SparkContext} +import org.apache.spark.rpc._ import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ import org.apache.spark.scheduler.TaskSchedulerImpl import org.apache.spark.ui.JettyUtils @@ -37,7 +35,7 @@ import scala.util.control.NonFatal private[spark] abstract class YarnSchedulerBackend( scheduler: TaskSchedulerImpl, sc: SparkContext) - extends CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem) { + extends CoarseGrainedSchedulerBackend(scheduler, sc.env.rpcEnv) { if (conf.getOption("spark.scheduler.minRegisteredResourcesRatio").isEmpty) { minRegisteredRatio = 0.8 @@ -45,10 +43,8 @@ private[spark] abstract class YarnSchedulerBackend( protected var totalExpectedExecutors = 0 - private val yarnSchedulerActor: ActorRef = - actorSystem.actorOf( - Props(new YarnSchedulerActor), - name = YarnSchedulerBackend.ACTOR_NAME) + private val yarnSchedulerEndpoint = rpcEnv.setupEndpoint( + YarnSchedulerBackend.ENDPOINT_NAME, new YarnSchedulerEndpoint(rpcEnv)) private implicit val askTimeout = AkkaUtils.askTimeout(sc.conf) @@ -57,16 +53,14 @@ private[spark] abstract class YarnSchedulerBackend( * This includes executors already pending or running. */ override def doRequestTotalExecutors(requestedTotal: Int): Boolean = { - AkkaUtils.askWithReply[Boolean]( - RequestExecutors(requestedTotal), yarnSchedulerActor, askTimeout) + yarnSchedulerEndpoint.askWithReply[Boolean](RequestExecutors(requestedTotal)) } /** * Request that the ApplicationMaster kill the specified executors. */ override def doKillExecutors(executorIds: Seq[String]): Boolean = { - AkkaUtils.askWithReply[Boolean]( - KillExecutors(executorIds), yarnSchedulerActor, askTimeout) + yarnSchedulerEndpoint.askWithReply[Boolean](KillExecutors(executorIds)) } override def sufficientResourcesRegistered(): Boolean = { @@ -96,64 +90,71 @@ private[spark] abstract class YarnSchedulerBackend( } /** - * An actor that communicates with the ApplicationMaster. + * An [[RpcEndpoint]] that communicates with the ApplicationMaster. */ - private class YarnSchedulerActor extends Actor { - private var amActor: Option[ActorRef] = None - - implicit val askAmActorExecutor = ExecutionContext.fromExecutor( - Utils.newDaemonCachedThreadPool("yarn-scheduler-ask-am-executor")) + private class YarnSchedulerEndpoint(override val rpcEnv: RpcEnv) + extends ThreadSafeRpcEndpoint with Logging { + private var amEndpoint: Option[RpcEndpointRef] = None - override def preStart(): Unit = { - // Listen for disassociation events - context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) - } + private val askAmThreadPool = + Utils.newDaemonCachedThreadPool("yarn-scheduler-ask-am-thread-pool") + implicit val askAmExecutor = ExecutionContext.fromExecutor(askAmThreadPool) override def receive: PartialFunction[Any, Unit] = { - case RegisterClusterManager => - logInfo(s"ApplicationMaster registered as $sender") - amActor = Some(sender) + case RegisterClusterManager(am) => + logInfo(s"ApplicationMaster registered as $am") + amEndpoint = Some(am) + + case AddWebUIFilter(filterName, filterParams, proxyBase) => + addWebUIFilter(filterName, filterParams, proxyBase) + + } + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case r: RequestExecutors => - amActor match { - case Some(actor) => - val driverActor = sender + amEndpoint match { + case Some(am) => Future { - driverActor ! AkkaUtils.askWithReply[Boolean](r, actor, askTimeout) + context.reply(am.askWithReply[Boolean](r)) } onFailure { - case NonFatal(e) => logError(s"Sending $r to AM was unsuccessful", e) + case NonFatal(e) => + logError(s"Sending $r to AM was unsuccessful", e) + context.sendFailure(e) } case None => logWarning("Attempted to request executors before the AM has registered!") - sender ! false + context.reply(false) } case k: KillExecutors => - amActor match { - case Some(actor) => - val driverActor = sender + amEndpoint match { + case Some(am) => Future { - driverActor ! AkkaUtils.askWithReply[Boolean](k, actor, askTimeout) + context.reply(am.askWithReply[Boolean](k)) } onFailure { - case NonFatal(e) => logError(s"Sending $k to AM was unsuccessful", e) + case NonFatal(e) => + logError(s"Sending $k to AM was unsuccessful", e) + context.sendFailure(e) } case None => logWarning("Attempted to kill executors before the AM has registered!") - sender ! false + context.reply(false) } - case AddWebUIFilter(filterName, filterParams, proxyBase) => - addWebUIFilter(filterName, filterParams, proxyBase) - sender ! true + } - case d: DisassociatedEvent => - if (amActor.isDefined && sender == amActor.get) { - logWarning(s"ApplicationMaster has disassociated: $d") - } + override def onDisconnected(remoteAddress: RpcAddress): Unit = { + if (amEndpoint.exists(_.address == remoteAddress)) { + logWarning(s"ApplicationMaster has disassociated: $remoteAddress") + } + } + + override def onStop(): Unit ={ + askAmThreadPool.shutdownNow() } } } private[spark] object YarnSchedulerBackend { - val ACTOR_NAME = "YarnScheduler" + val ENDPOINT_NAME = "YarnScheduler" } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala index e13de0f46ef89..b037a4966ced0 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala @@ -47,7 +47,7 @@ private[spark] class CoarseMesosSchedulerBackend( scheduler: TaskSchedulerImpl, sc: SparkContext, master: String) - extends CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem) + extends CoarseGrainedSchedulerBackend(scheduler, sc.env.rpcEnv) with MScheduler with Logging { @@ -148,7 +148,7 @@ private[spark] class CoarseMesosSchedulerBackend( SparkEnv.driverActorSystemName, conf.get("spark.driver.host"), conf.get("spark.driver.port"), - CoarseGrainedSchedulerBackend.ACTOR_NAME) + CoarseGrainedSchedulerBackend.ENDPOINT_NAME) val uri = conf.get("spark.executor.uri", null) if (uri == null) { diff --git a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala index eb3f999b5b375..70a477a6895cc 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala @@ -18,17 +18,14 @@ package org.apache.spark.scheduler.local import java.nio.ByteBuffer +import java.util.concurrent.{Executors, TimeUnit} -import scala.concurrent.duration._ -import scala.language.postfixOps - -import akka.actor.{Actor, ActorRef, Props} - +import org.apache.spark.rpc.{ThreadSafeRpcEndpoint, RpcEndpointRef, RpcEnv} +import org.apache.spark.util.Utils import org.apache.spark.{Logging, SparkContext, SparkEnv, TaskState} import org.apache.spark.TaskState.TaskState import org.apache.spark.executor.{Executor, ExecutorBackend} import org.apache.spark.scheduler.{SchedulerBackend, TaskSchedulerImpl, WorkerOffer} -import org.apache.spark.util.ActorLogReceive private case class ReviveOffers() @@ -39,17 +36,19 @@ private case class KillTask(taskId: Long, interruptThread: Boolean) private case class StopExecutor() /** - * Calls to LocalBackend are all serialized through LocalActor. Using an actor makes the calls on - * LocalBackend asynchronous, which is necessary to prevent deadlock between LocalBackend + * Calls to LocalBackend are all serialized through LocalEndpoint. Using an RpcEndpoint makes the + * calls on LocalBackend asynchronous, which is necessary to prevent deadlock between LocalBackend * and the TaskSchedulerImpl. */ -private[spark] class LocalActor( +private[spark] class LocalEndpoint( + override val rpcEnv: RpcEnv, scheduler: TaskSchedulerImpl, executorBackend: LocalBackend, private val totalCores: Int) - extends Actor with ActorLogReceive with Logging { + extends ThreadSafeRpcEndpoint with Logging { - import context.dispatcher // to use Akka's scheduler.scheduleOnce() + private val reviveThread = Executors.newSingleThreadScheduledExecutor( + Utils.namedThreadFactory("local-revive-thread")) private var freeCores = totalCores @@ -59,7 +58,7 @@ private[spark] class LocalActor( private val executor = new Executor( localExecutorId, localExecutorHostname, SparkEnv.get, isLocal = true) - override def receiveWithLogging: PartialFunction[Any, Unit] = { + override def receive: PartialFunction[Any, Unit] = { case ReviveOffers => reviveOffers() @@ -87,9 +86,17 @@ private[spark] class LocalActor( } if (tasks.isEmpty && scheduler.activeTaskSets.nonEmpty) { // Try to reviveOffer after 1 second, because scheduler may wait for locality timeout - context.system.scheduler.scheduleOnce(1000 millis, self, ReviveOffers) + reviveThread.schedule(new Runnable { + override def run(): Unit = Utils.tryLogNonFatalError { + Option(self).foreach(_.send(ReviveOffers)) + } + }, 1000, TimeUnit.MILLISECONDS) } } + + override def onStop(): Unit = { + reviveThread.shutdownNow() + } } /** @@ -101,31 +108,30 @@ private[spark] class LocalBackend(scheduler: TaskSchedulerImpl, val totalCores: extends SchedulerBackend with ExecutorBackend { private val appId = "local-" + System.currentTimeMillis - var localActor: ActorRef = null + var localEndpoint: RpcEndpointRef = null override def start() { - localActor = SparkEnv.get.actorSystem.actorOf( - Props(new LocalActor(scheduler, this, totalCores)), - "LocalBackendActor") + localEndpoint = SparkEnv.get.rpcEnv.setupEndpoint( + "LocalBackendEndpoint", new LocalEndpoint(SparkEnv.get.rpcEnv, scheduler, this, totalCores)) } override def stop() { - localActor ! StopExecutor + localEndpoint.send(StopExecutor) } override def reviveOffers() { - localActor ! ReviveOffers + localEndpoint.send(ReviveOffers) } override def defaultParallelism(): Int = scheduler.conf.getInt("spark.default.parallelism", totalCores) override def killTask(taskId: Long, executorId: String, interruptThread: Boolean) { - localActor ! KillTask(taskId, interruptThread) + localEndpoint.send(KillTask(taskId, interruptThread)) } override def statusUpdate(taskId: Long, state: TaskState, serializedData: ByteBuffer) { - localActor ! StatusUpdate(taskId, state, serializedData) + localEndpoint.send(StatusUpdate(taskId, state, serializedData)) } override def applicationId(): String = appId diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala index fc31296f4deb3..1aa0ef18de118 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala @@ -26,7 +26,6 @@ import scala.concurrent.ExecutionContext.Implicits.global import scala.concurrent.duration._ import scala.util.Random -import akka.actor.{ActorSystem, Props} import sun.nio.ch.DirectBuffer import org.apache.spark._ @@ -37,6 +36,7 @@ import org.apache.spark.network.buffer.{ManagedBuffer, NioManagedBuffer} import org.apache.spark.network.netty.SparkTransportConf import org.apache.spark.network.shuffle.ExternalShuffleClient import org.apache.spark.network.shuffle.protocol.ExecutorShuffleInfo +import org.apache.spark.rpc.RpcEnv import org.apache.spark.serializer.Serializer import org.apache.spark.shuffle.ShuffleManager import org.apache.spark.shuffle.hash.HashShuffleManager @@ -64,7 +64,7 @@ private[spark] class BlockResult( */ private[spark] class BlockManager( executorId: String, - actorSystem: ActorSystem, + rpcEnv: RpcEnv, val master: BlockManagerMaster, defaultSerializer: Serializer, maxMemory: Long, @@ -136,9 +136,9 @@ private[spark] class BlockManager( // Whether to compress shuffle output temporarily spilled to disk private val compressShuffleSpill = conf.getBoolean("spark.shuffle.spill.compress", true) - private val slaveActor = actorSystem.actorOf( - Props(new BlockManagerSlaveActor(this, mapOutputTracker)), - name = "BlockManagerActor" + BlockManager.ID_GENERATOR.next) + private val slaveEndpoint = rpcEnv.setupEndpoint( + "BlockManagerEndpoint" + BlockManager.ID_GENERATOR.next, + new BlockManagerSlaveEndpoint(rpcEnv, this, mapOutputTracker)) // Pending re-registration action being executed asynchronously or null if none is pending. // Accesses should synchronize on asyncReregisterLock. @@ -167,7 +167,7 @@ private[spark] class BlockManager( */ def this( execId: String, - actorSystem: ActorSystem, + rpcEnv: RpcEnv, master: BlockManagerMaster, serializer: Serializer, conf: SparkConf, @@ -176,7 +176,7 @@ private[spark] class BlockManager( blockTransferService: BlockTransferService, securityManager: SecurityManager, numUsableCores: Int) = { - this(execId, actorSystem, master, serializer, BlockManager.getMaxMemory(conf), + this(execId, rpcEnv, master, serializer, BlockManager.getMaxMemory(conf), conf, mapOutputTracker, shuffleManager, blockTransferService, securityManager, numUsableCores) } @@ -186,7 +186,7 @@ private[spark] class BlockManager( * where it is only learned after registration with the TaskScheduler). * * This method initializes the BlockTransferService and ShuffleClient, registers with the - * BlockManagerMaster, starts the BlockManagerWorker actor, and registers with a local shuffle + * BlockManagerMaster, starts the BlockManagerWorker endpoint, and registers with a local shuffle * service if configured. */ def initialize(appId: String): Unit = { @@ -202,7 +202,7 @@ private[spark] class BlockManager( blockManagerId } - master.registerBlockManager(blockManagerId, maxMemory, slaveActor) + master.registerBlockManager(blockManagerId, maxMemory, slaveEndpoint) // Register Executors' configuration with the local shuffle service, if one should exist. if (externalShuffleServiceEnabled && !blockManagerId.isDriver) { @@ -265,7 +265,7 @@ private[spark] class BlockManager( def reregister(): Unit = { // TODO: We might need to rate limit re-registering. logInfo("BlockManager re-registering with master") - master.registerBlockManager(blockManagerId, maxMemory, slaveActor) + master.registerBlockManager(blockManagerId, maxMemory, slaveEndpoint) reportAllBlocks() } @@ -1215,7 +1215,7 @@ private[spark] class BlockManager( shuffleClient.close() } diskBlockManager.stop() - actorSystem.stop(slaveActor) + rpcEnv.stop(slaveEndpoint) blockInfo.clear() memoryStore.clear() diskStore.clear() diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala index 061964826f08b..ceacf043029f3 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala @@ -20,35 +20,31 @@ package org.apache.spark.storage import scala.concurrent.{Await, Future} import scala.concurrent.ExecutionContext.Implicits.global -import akka.actor._ - +import org.apache.spark.rpc.RpcEndpointRef import org.apache.spark.{Logging, SparkConf, SparkException} import org.apache.spark.storage.BlockManagerMessages._ import org.apache.spark.util.AkkaUtils private[spark] class BlockManagerMaster( - var driverActor: ActorRef, + var driverEndpoint: RpcEndpointRef, conf: SparkConf, isDriver: Boolean) extends Logging { - private val AKKA_RETRY_ATTEMPTS: Int = AkkaUtils.numRetries(conf) - private val AKKA_RETRY_INTERVAL_MS: Int = AkkaUtils.retryWaitMs(conf) - - val DRIVER_AKKA_ACTOR_NAME = "BlockManagerMaster" val timeout = AkkaUtils.askTimeout(conf) - /** Remove a dead executor from the driver actor. This is only called on the driver side. */ + /** Remove a dead executor from the driver endpoint. This is only called on the driver side. */ def removeExecutor(execId: String) { tell(RemoveExecutor(execId)) logInfo("Removed " + execId + " successfully in removeExecutor") } /** Register the BlockManager's id with the driver. */ - def registerBlockManager(blockManagerId: BlockManagerId, maxMemSize: Long, slaveActor: ActorRef) { + def registerBlockManager( + blockManagerId: BlockManagerId, maxMemSize: Long, slaveEndpoint: RpcEndpointRef): Unit = { logInfo("Trying to register BlockManager") - tell(RegisterBlockManager(blockManagerId, maxMemSize, slaveActor)) + tell(RegisterBlockManager(blockManagerId, maxMemSize, slaveEndpoint)) logInfo("Registered BlockManager") } @@ -59,7 +55,7 @@ class BlockManagerMaster( memSize: Long, diskSize: Long, tachyonSize: Long): Boolean = { - val res = askDriverWithReply[Boolean]( + val res = driverEndpoint.askWithReply[Boolean]( UpdateBlockInfo(blockManagerId, blockId, storageLevel, memSize, diskSize, tachyonSize)) logDebug(s"Updated info of block $blockId") res @@ -67,12 +63,12 @@ class BlockManagerMaster( /** Get locations of the blockId from the driver */ def getLocations(blockId: BlockId): Seq[BlockManagerId] = { - askDriverWithReply[Seq[BlockManagerId]](GetLocations(blockId)) + driverEndpoint.askWithReply[Seq[BlockManagerId]](GetLocations(blockId)) } /** Get locations of multiple blockIds from the driver */ def getLocations(blockIds: Array[BlockId]): Seq[Seq[BlockManagerId]] = { - askDriverWithReply[Seq[Seq[BlockManagerId]]](GetLocationsMultipleBlockIds(blockIds)) + driverEndpoint.askWithReply[Seq[Seq[BlockManagerId]]](GetLocationsMultipleBlockIds(blockIds)) } /** @@ -85,11 +81,11 @@ class BlockManagerMaster( /** Get ids of other nodes in the cluster from the driver */ def getPeers(blockManagerId: BlockManagerId): Seq[BlockManagerId] = { - askDriverWithReply[Seq[BlockManagerId]](GetPeers(blockManagerId)) + driverEndpoint.askWithReply[Seq[BlockManagerId]](GetPeers(blockManagerId)) } - def getActorSystemHostPortForExecutor(executorId: String): Option[(String, Int)] = { - askDriverWithReply[Option[(String, Int)]](GetActorSystemHostPortForExecutor(executorId)) + def getRpcHostPortForExecutor(executorId: String): Option[(String, Int)] = { + driverEndpoint.askWithReply[Option[(String, Int)]](GetRpcHostPortForExecutor(executorId)) } /** @@ -97,12 +93,12 @@ class BlockManagerMaster( * blocks that the driver knows about. */ def removeBlock(blockId: BlockId) { - askDriverWithReply(RemoveBlock(blockId)) + driverEndpoint.askWithReply[Boolean](RemoveBlock(blockId)) } /** Remove all blocks belonging to the given RDD. */ def removeRdd(rddId: Int, blocking: Boolean) { - val future = askDriverWithReply[Future[Seq[Int]]](RemoveRdd(rddId)) + val future = driverEndpoint.askWithReply[Future[Seq[Int]]](RemoveRdd(rddId)) future.onFailure { case e: Exception => logWarning(s"Failed to remove RDD $rddId - ${e.getMessage}}") @@ -114,7 +110,7 @@ class BlockManagerMaster( /** Remove all blocks belonging to the given shuffle. */ def removeShuffle(shuffleId: Int, blocking: Boolean) { - val future = askDriverWithReply[Future[Seq[Boolean]]](RemoveShuffle(shuffleId)) + val future = driverEndpoint.askWithReply[Future[Seq[Boolean]]](RemoveShuffle(shuffleId)) future.onFailure { case e: Exception => logWarning(s"Failed to remove shuffle $shuffleId - ${e.getMessage}}") @@ -126,7 +122,7 @@ class BlockManagerMaster( /** Remove all blocks belonging to the given broadcast. */ def removeBroadcast(broadcastId: Long, removeFromMaster: Boolean, blocking: Boolean) { - val future = askDriverWithReply[Future[Seq[Int]]]( + val future = driverEndpoint.askWithReply[Future[Seq[Int]]]( RemoveBroadcast(broadcastId, removeFromMaster)) future.onFailure { case e: Exception => @@ -145,11 +141,11 @@ class BlockManagerMaster( * amount of remaining memory. */ def getMemoryStatus: Map[BlockManagerId, (Long, Long)] = { - askDriverWithReply[Map[BlockManagerId, (Long, Long)]](GetMemoryStatus) + driverEndpoint.askWithReply[Map[BlockManagerId, (Long, Long)]](GetMemoryStatus) } def getStorageStatus: Array[StorageStatus] = { - askDriverWithReply[Array[StorageStatus]](GetStorageStatus) + driverEndpoint.askWithReply[Array[StorageStatus]](GetStorageStatus) } /** @@ -165,11 +161,12 @@ class BlockManagerMaster( askSlaves: Boolean = true): Map[BlockManagerId, BlockStatus] = { val msg = GetBlockStatus(blockId, askSlaves) /* - * To avoid potential deadlocks, the use of Futures is necessary, because the master actor + * To avoid potential deadlocks, the use of Futures is necessary, because the master endpoint * should not block on waiting for a block manager, which can in turn be waiting for the - * master actor for a response to a prior message. + * master endpoint for a response to a prior message. */ - val response = askDriverWithReply[Map[BlockManagerId, Future[Option[BlockStatus]]]](msg) + val response = driverEndpoint. + askWithReply[Map[BlockManagerId, Future[Option[BlockStatus]]]](msg) val (blockManagerIds, futures) = response.unzip val result = Await.result(Future.sequence(futures), timeout) if (result == null) { @@ -193,33 +190,28 @@ class BlockManagerMaster( filter: BlockId => Boolean, askSlaves: Boolean): Seq[BlockId] = { val msg = GetMatchingBlockIds(filter, askSlaves) - val future = askDriverWithReply[Future[Seq[BlockId]]](msg) + val future = driverEndpoint.askWithReply[Future[Seq[BlockId]]](msg) Await.result(future, timeout) } - /** Stop the driver actor, called only on the Spark driver node */ + /** Stop the driver endpoint, called only on the Spark driver node */ def stop() { - if (driverActor != null && isDriver) { + if (driverEndpoint != null && isDriver) { tell(StopBlockManagerMaster) - driverActor = null + driverEndpoint = null logInfo("BlockManagerMaster stopped") } } - /** Send a one-way message to the master actor, to which we expect it to reply with true. */ + /** Send a one-way message to the master endpoint, to which we expect it to reply with true. */ private def tell(message: Any) { - if (!askDriverWithReply[Boolean](message)) { - throw new SparkException("BlockManagerMasterActor returned false, expected true.") + if (!driverEndpoint.askWithReply[Boolean](message)) { + throw new SparkException("BlockManagerMasterEndpoint returned false, expected true.") } } - /** - * Send a message to the driver actor and get its result within a default timeout, or - * throw a SparkException if this fails. - */ - private def askDriverWithReply[T](message: Any): T = { - AkkaUtils.askWithReply(message, driverActor, AKKA_RETRY_ATTEMPTS, AKKA_RETRY_INTERVAL_MS, - timeout) - } +} +private[spark] object BlockManagerMaster { + val DRIVER_ENDPOINT_NAME = "BlockManagerMaster" } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala similarity index 83% rename from core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala rename to core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala index 5b5328016124e..28c73a7d543ff 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala @@ -21,25 +21,26 @@ import java.util.{HashMap => JHashMap} import scala.collection.mutable import scala.collection.JavaConversions._ -import scala.concurrent.Future -import scala.concurrent.duration._ +import scala.concurrent.{ExecutionContext, Future} -import akka.actor.{Actor, ActorRef} -import akka.pattern.ask - -import org.apache.spark.{Logging, SparkConf, SparkException} +import org.apache.spark.rpc.{RpcEndpointRef, RpcEnv, RpcCallContext, ThreadSafeRpcEndpoint} +import org.apache.spark.{Logging, SparkConf} import org.apache.spark.annotation.DeveloperApi import org.apache.spark.scheduler._ import org.apache.spark.storage.BlockManagerMessages._ -import org.apache.spark.util.{ActorLogReceive, AkkaUtils, Utils} +import org.apache.spark.util.Utils /** - * BlockManagerMasterActor is an actor on the master node to track statuses of - * all slaves' block managers. + * BlockManagerMasterEndpoint is an [[ThreadSafeRpcEndpoint]] on the master node to track statuses + * of all slaves' block managers. */ private[spark] -class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus: LiveListenerBus) - extends Actor with ActorLogReceive with Logging { +class BlockManagerMasterEndpoint( + override val rpcEnv: RpcEnv, + val isLocal: Boolean, + conf: SparkConf, + listenerBus: LiveListenerBus) + extends ThreadSafeRpcEndpoint with Logging { // Mapping from block manager id to the block manager's information. private val blockManagerInfo = new mutable.HashMap[BlockManagerId, BlockManagerInfo] @@ -50,68 +51,67 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus // Mapping from block id to the set of block managers that have the block. private val blockLocations = new JHashMap[BlockId, mutable.HashSet[BlockManagerId]] - private val akkaTimeout = AkkaUtils.askTimeout(conf) + private val askThreadPool = Utils.newDaemonCachedThreadPool("block-manager-ask-thread-pool") + private implicit val askExecutionContext = ExecutionContext.fromExecutorService(askThreadPool) - override def receiveWithLogging: PartialFunction[Any, Unit] = { - case RegisterBlockManager(blockManagerId, maxMemSize, slaveActor) => - register(blockManagerId, maxMemSize, slaveActor) - sender ! true + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case RegisterBlockManager(blockManagerId, maxMemSize, slaveEndpoint) => + register(blockManagerId, maxMemSize, slaveEndpoint) + context.reply(true) case UpdateBlockInfo( blockManagerId, blockId, storageLevel, deserializedSize, size, tachyonSize) => - sender ! updateBlockInfo( - blockManagerId, blockId, storageLevel, deserializedSize, size, tachyonSize) + context.reply(updateBlockInfo( + blockManagerId, blockId, storageLevel, deserializedSize, size, tachyonSize)) case GetLocations(blockId) => - sender ! getLocations(blockId) + context.reply(getLocations(blockId)) case GetLocationsMultipleBlockIds(blockIds) => - sender ! getLocationsMultipleBlockIds(blockIds) + context.reply(getLocationsMultipleBlockIds(blockIds)) case GetPeers(blockManagerId) => - sender ! getPeers(blockManagerId) + context.reply(getPeers(blockManagerId)) - case GetActorSystemHostPortForExecutor(executorId) => - sender ! getActorSystemHostPortForExecutor(executorId) + case GetRpcHostPortForExecutor(executorId) => + context.reply(getRpcHostPortForExecutor(executorId)) case GetMemoryStatus => - sender ! memoryStatus + context.reply(memoryStatus) case GetStorageStatus => - sender ! storageStatus + context.reply(storageStatus) case GetBlockStatus(blockId, askSlaves) => - sender ! blockStatus(blockId, askSlaves) + context.reply(blockStatus(blockId, askSlaves)) case GetMatchingBlockIds(filter, askSlaves) => - sender ! getMatchingBlockIds(filter, askSlaves) + context.reply(getMatchingBlockIds(filter, askSlaves)) case RemoveRdd(rddId) => - sender ! removeRdd(rddId) + context.reply(removeRdd(rddId)) case RemoveShuffle(shuffleId) => - sender ! removeShuffle(shuffleId) + context.reply(removeShuffle(shuffleId)) case RemoveBroadcast(broadcastId, removeFromDriver) => - sender ! removeBroadcast(broadcastId, removeFromDriver) + context.reply(removeBroadcast(broadcastId, removeFromDriver)) case RemoveBlock(blockId) => removeBlockFromWorkers(blockId) - sender ! true + context.reply(true) case RemoveExecutor(execId) => removeExecutor(execId) - sender ! true + context.reply(true) case StopBlockManagerMaster => - sender ! true - context.stop(self) + context.reply(true) + stop() case BlockManagerHeartbeat(blockManagerId) => - sender ! heartbeatReceived(blockManagerId) + context.reply(heartbeatReceived(blockManagerId)) - case other => - logWarning("Got unknown message: " + other) } private def removeRdd(rddId: Int): Future[Seq[Int]] = { @@ -129,22 +129,20 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus // Ask the slaves to remove the RDD, and put the result in a sequence of Futures. // The dispatcher is used as an implicit argument into the Future sequence construction. - import context.dispatcher val removeMsg = RemoveRdd(rddId) Future.sequence( blockManagerInfo.values.map { bm => - bm.slaveActor.ask(removeMsg)(akkaTimeout).mapTo[Int] + bm.slaveEndpoint.sendWithReply[Int](removeMsg) }.toSeq ) } private def removeShuffle(shuffleId: Int): Future[Seq[Boolean]] = { - // Nothing to do in the BlockManagerMasterActor data structures - import context.dispatcher + // Nothing to do in the BlockManagerMasterEndpoint data structures val removeMsg = RemoveShuffle(shuffleId) Future.sequence( blockManagerInfo.values.map { bm => - bm.slaveActor.ask(removeMsg)(akkaTimeout).mapTo[Boolean] + bm.slaveEndpoint.sendWithReply[Boolean](removeMsg) }.toSeq ) } @@ -155,14 +153,13 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus * from the executors, but not from the driver. */ private def removeBroadcast(broadcastId: Long, removeFromDriver: Boolean): Future[Seq[Int]] = { - import context.dispatcher val removeMsg = RemoveBroadcast(broadcastId, removeFromDriver) val requiredBlockManagers = blockManagerInfo.values.filter { info => removeFromDriver || !info.blockManagerId.isDriver } Future.sequence( requiredBlockManagers.map { bm => - bm.slaveActor.ask(removeMsg)(akkaTimeout).mapTo[Int] + bm.slaveEndpoint.sendWithReply[Int](removeMsg) }.toSeq ) } @@ -217,7 +214,7 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus // Remove the block from the slave's BlockManager. // Doesn't actually wait for a confirmation and the message might get lost. // If message loss becomes frequent, we should add retry logic here. - blockManager.get.slaveActor.ask(RemoveBlock(blockId))(akkaTimeout) + blockManager.get.slaveEndpoint.sendWithReply[Boolean](RemoveBlock(blockId)) } } } @@ -247,17 +244,16 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus private def blockStatus( blockId: BlockId, askSlaves: Boolean): Map[BlockManagerId, Future[Option[BlockStatus]]] = { - import context.dispatcher val getBlockStatus = GetBlockStatus(blockId) /* - * Rather than blocking on the block status query, master actor should simply return + * Rather than blocking on the block status query, master endpoint should simply return * Futures to avoid potential deadlocks. This can arise if there exists a block manager - * that is also waiting for this master actor's response to a previous message. + * that is also waiting for this master endpoint's response to a previous message. */ blockManagerInfo.values.map { info => val blockStatusFuture = if (askSlaves) { - info.slaveActor.ask(getBlockStatus)(akkaTimeout).mapTo[Option[BlockStatus]] + info.slaveEndpoint.sendWithReply[Option[BlockStatus]](getBlockStatus) } else { Future { info.getStatus(blockId) } } @@ -276,13 +272,12 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus private def getMatchingBlockIds( filter: BlockId => Boolean, askSlaves: Boolean): Future[Seq[BlockId]] = { - import context.dispatcher val getMatchingBlockIds = GetMatchingBlockIds(filter) Future.sequence( blockManagerInfo.values.map { info => val future = if (askSlaves) { - info.slaveActor.ask(getMatchingBlockIds)(akkaTimeout).mapTo[Seq[BlockId]] + info.slaveEndpoint.sendWithReply[Seq[BlockId]](getMatchingBlockIds) } else { Future { info.blocks.keys.filter(filter).toSeq } } @@ -291,7 +286,7 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus ).map(_.flatten.toSeq) } - private def register(id: BlockManagerId, maxMemSize: Long, slaveActor: ActorRef) { + private def register(id: BlockManagerId, maxMemSize: Long, slaveEndpoint: RpcEndpointRef) { val time = System.currentTimeMillis() if (!blockManagerInfo.contains(id)) { blockManagerIdByExecutor.get(id.executorId) match { @@ -308,7 +303,7 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus blockManagerIdByExecutor(id.executorId) = id blockManagerInfo(id) = new BlockManagerInfo( - id, System.currentTimeMillis(), maxMemSize, slaveActor) + id, System.currentTimeMillis(), maxMemSize, slaveEndpoint) } listenerBus.post(SparkListenerBlockManagerAdded(time, id, maxMemSize)) } @@ -379,19 +374,21 @@ class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf, listenerBus } /** - * Returns the hostname and port of an executor's actor system, based on the Akka address of its - * BlockManagerSlaveActor. + * Returns the hostname and port of an executor, based on the [[RpcEnv]] address of its + * [[BlockManagerSlaveEndpoint]]. */ - private def getActorSystemHostPortForExecutor(executorId: String): Option[(String, Int)] = { + private def getRpcHostPortForExecutor(executorId: String): Option[(String, Int)] = { for ( blockManagerId <- blockManagerIdByExecutor.get(executorId); - info <- blockManagerInfo.get(blockManagerId); - host <- info.slaveActor.path.address.host; - port <- info.slaveActor.path.address.port + info <- blockManagerInfo.get(blockManagerId) ) yield { - (host, port) + (info.slaveEndpoint.address.host, info.slaveEndpoint.address.port) } } + + override def onStop(): Unit = { + askThreadPool.shutdownNow() + } } @DeveloperApi @@ -412,7 +409,7 @@ private[spark] class BlockManagerInfo( val blockManagerId: BlockManagerId, timeMs: Long, val maxMem: Long, - val slaveActor: ActorRef) + val slaveEndpoint: RpcEndpointRef) extends Logging { private var _lastSeenMs: Long = timeMs diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala index 48247453edef0..f89d8d7493f7c 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala @@ -19,8 +19,7 @@ package org.apache.spark.storage import java.io.{Externalizable, ObjectInput, ObjectOutput} -import akka.actor.ActorRef - +import org.apache.spark.rpc.RpcEndpointRef import org.apache.spark.util.Utils private[spark] object BlockManagerMessages { @@ -52,7 +51,7 @@ private[spark] object BlockManagerMessages { case class RegisterBlockManager( blockManagerId: BlockManagerId, maxMemSize: Long, - sender: ActorRef) + sender: RpcEndpointRef) extends ToBlockManagerMaster case class UpdateBlockInfo( @@ -92,7 +91,7 @@ private[spark] object BlockManagerMessages { case class GetPeers(blockManagerId: BlockManagerId) extends ToBlockManagerMaster - case class GetActorSystemHostPortForExecutor(executorId: String) extends ToBlockManagerMaster + case class GetRpcHostPortForExecutor(executorId: String) extends ToBlockManagerMaster case class RemoveExecutor(execId: String) extends ToBlockManagerMaster diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveEndpoint.scala similarity index 61% rename from core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala rename to core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveEndpoint.scala index 52fb896c4e21f..8980fa8eb70e2 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveActor.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveEndpoint.scala @@ -17,41 +17,43 @@ package org.apache.spark.storage -import scala.concurrent.Future - -import akka.actor.{ActorRef, Actor} +import scala.concurrent.{ExecutionContext, Future} +import org.apache.spark.rpc.{RpcEnv, RpcCallContext, RpcEndpoint} +import org.apache.spark.util.Utils import org.apache.spark.{Logging, MapOutputTracker, SparkEnv} import org.apache.spark.storage.BlockManagerMessages._ -import org.apache.spark.util.ActorLogReceive /** - * An actor to take commands from the master to execute options. For example, + * An RpcEndpoint to take commands from the master to execute options. For example, * this is used to remove blocks from the slave's BlockManager. */ private[storage] -class BlockManagerSlaveActor( +class BlockManagerSlaveEndpoint( + override val rpcEnv: RpcEnv, blockManager: BlockManager, mapOutputTracker: MapOutputTracker) - extends Actor with ActorLogReceive with Logging { + extends RpcEndpoint with Logging { - import context.dispatcher + private val asyncThreadPool = + Utils.newDaemonCachedThreadPool("block-manager-slave-async-thread-pool") + private implicit val asyncExecutionContext = ExecutionContext.fromExecutorService(asyncThreadPool) // Operations that involve removing blocks may be slow and should be done asynchronously - override def receiveWithLogging: PartialFunction[Any, Unit] = { + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case RemoveBlock(blockId) => - doAsync[Boolean]("removing block " + blockId, sender) { + doAsync[Boolean]("removing block " + blockId, context) { blockManager.removeBlock(blockId) true } case RemoveRdd(rddId) => - doAsync[Int]("removing RDD " + rddId, sender) { + doAsync[Int]("removing RDD " + rddId, context) { blockManager.removeRdd(rddId) } case RemoveShuffle(shuffleId) => - doAsync[Boolean]("removing shuffle " + shuffleId, sender) { + doAsync[Boolean]("removing shuffle " + shuffleId, context) { if (mapOutputTracker != null) { mapOutputTracker.unregisterShuffle(shuffleId) } @@ -59,30 +61,34 @@ class BlockManagerSlaveActor( } case RemoveBroadcast(broadcastId, _) => - doAsync[Int]("removing broadcast " + broadcastId, sender) { + doAsync[Int]("removing broadcast " + broadcastId, context) { blockManager.removeBroadcast(broadcastId, tellMaster = true) } case GetBlockStatus(blockId, _) => - sender ! blockManager.getStatus(blockId) + context.reply(blockManager.getStatus(blockId)) case GetMatchingBlockIds(filter, _) => - sender ! blockManager.getMatchingBlockIds(filter) + context.reply(blockManager.getMatchingBlockIds(filter)) } - private def doAsync[T](actionMessage: String, responseActor: ActorRef)(body: => T) { + private def doAsync[T](actionMessage: String, context: RpcCallContext)(body: => T) { val future = Future { logDebug(actionMessage) body } future.onSuccess { case response => logDebug("Done " + actionMessage + ", response is " + response) - responseActor ! response - logDebug("Sent response: " + response + " to " + responseActor) + context.reply(response) + logDebug("Sent response: " + response + " to " + context.sender) } future.onFailure { case t: Throwable => logError("Error in " + actionMessage, t) - responseActor ! null.asInstanceOf[T] + context.sendFailure(t) } } + + override def onStop(): Unit = { + asyncThreadPool.shutdownNow() + } } diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index 7c85e28679f1d..0fdfaf300e95d 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -1214,6 +1214,16 @@ private[spark] object Utils extends Logging { } } + /** Executes the given block. Log non-fatal errors if any, and only throw fatal errors */ + def tryLogNonFatalError(block: => Unit) { + try { + block + } catch { + case NonFatal(t) => + logError(s"Uncaught exception in thread ${Thread.currentThread().getName}", t) + } + } + /** * Execute a block of code, then a finally block, but if exceptions happen in * the finally block, do not suppress the original exception. diff --git a/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala b/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala new file mode 100644 index 0000000000000..0fd570e5297d9 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala @@ -0,0 +1,81 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark + +import scala.concurrent.duration._ +import scala.language.postfixOps + +import org.apache.spark.executor.TaskMetrics +import org.apache.spark.storage.BlockManagerId +import org.scalatest.FunSuite +import org.mockito.Mockito.{mock, spy, verify, when} +import org.mockito.Matchers +import org.mockito.Matchers._ + +import org.apache.spark.scheduler.TaskScheduler +import org.apache.spark.util.RpcUtils +import org.scalatest.concurrent.Eventually._ + +class HeartbeatReceiverSuite extends FunSuite with LocalSparkContext { + + test("HeartbeatReceiver") { + sc = spy(new SparkContext("local[2]", "test")) + val scheduler = mock(classOf[TaskScheduler]) + when(scheduler.executorHeartbeatReceived(any(), any(), any())).thenReturn(true) + when(sc.taskScheduler).thenReturn(scheduler) + + val heartbeatReceiver = new HeartbeatReceiver(sc) + sc.env.rpcEnv.setupEndpoint("heartbeat", heartbeatReceiver).send(TaskSchedulerIsSet) + eventually(timeout(5 seconds), interval(5 millis)) { + assert(heartbeatReceiver.scheduler != null) + } + val receiverRef = RpcUtils.makeDriverRef("heartbeat", sc.conf, sc.env.rpcEnv) + + val metrics = new TaskMetrics + val blockManagerId = BlockManagerId("executor-1", "localhost", 12345) + val response = receiverRef.askWithReply[HeartbeatResponse]( + Heartbeat("executor-1", Array(1L -> metrics), blockManagerId)) + + verify(scheduler).executorHeartbeatReceived( + Matchers.eq("executor-1"), Matchers.eq(Array(1L -> metrics)), Matchers.eq(blockManagerId)) + assert(false === response.reregisterBlockManager) + } + + test("HeartbeatReceiver re-register") { + sc = spy(new SparkContext("local[2]", "test")) + val scheduler = mock(classOf[TaskScheduler]) + when(scheduler.executorHeartbeatReceived(any(), any(), any())).thenReturn(false) + when(sc.taskScheduler).thenReturn(scheduler) + + val heartbeatReceiver = new HeartbeatReceiver(sc) + sc.env.rpcEnv.setupEndpoint("heartbeat", heartbeatReceiver).send(TaskSchedulerIsSet) + eventually(timeout(5 seconds), interval(5 millis)) { + assert(heartbeatReceiver.scheduler != null) + } + val receiverRef = RpcUtils.makeDriverRef("heartbeat", sc.conf, sc.env.rpcEnv) + + val metrics = new TaskMetrics + val blockManagerId = BlockManagerId("executor-1", "localhost", 12345) + val response = receiverRef.askWithReply[HeartbeatResponse]( + Heartbeat("executor-1", Array(1L -> metrics), blockManagerId)) + + verify(scheduler).executorHeartbeatReceived( + Matchers.eq("executor-1"), Matchers.eq(Array(1L -> metrics)), Matchers.eq(blockManagerId)) + assert(true === response.reregisterBlockManager) + } +} diff --git a/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala b/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala index e07bdb9637575..4f19c4f2110d2 100644 --- a/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala +++ b/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala @@ -311,7 +311,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { } test("self: call in onStop") { - @volatile var e: Throwable = null + @volatile var selfOption: Option[RpcEndpointRef] = null val endpointRef = env.setupEndpoint("self-onStop", new RpcEndpoint { override val rpcEnv = env @@ -321,20 +321,18 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { } override def onStop(): Unit = { - self + selfOption = Option(self) } override def onError(cause: Throwable): Unit = { - e = cause } }) env.stop(endpointRef) eventually(timeout(5 seconds), interval(10 millis)) { - // Calling `self` in `onStop` is invalid - assert(e != null) - assert(e.getMessage.contains("Cannot find RpcEndpointRef")) + // Calling `self` in `onStop` will return null, so selfOption will be None + assert(selfOption == None) } } @@ -342,7 +340,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { // If a RpcEnv implementation breaks the `receive` contract, hope this test can expose it for(i <- 0 until 100) { @volatile var result = 0 - val endpointRef = env.setupThreadSafeEndpoint(s"receive-in-sequence-$i", new RpcEndpoint { + val endpointRef = env.setupEndpoint(s"receive-in-sequence-$i", new ThreadSafeRpcEndpoint { override val rpcEnv = env override def receive = { @@ -475,7 +473,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { test("network events") { val events = new mutable.ArrayBuffer[(Any, Any)] with mutable.SynchronizedBuffer[(Any, Any)] - env.setupThreadSafeEndpoint("network-events", new RpcEndpoint { + env.setupEndpoint("network-events", new ThreadSafeRpcEndpoint { override val rpcEnv = env override def receive = { diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala index c2903c8597997..b4de90b65d545 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala @@ -22,11 +22,11 @@ import scala.concurrent.duration._ import scala.language.implicitConversions import scala.language.postfixOps -import akka.actor.{ActorSystem, Props} import org.mockito.Mockito.{mock, when} -import org.scalatest.{BeforeAndAfter, FunSuite, Matchers, PrivateMethodTester} +import org.scalatest.{BeforeAndAfter, FunSuite, Matchers} import org.scalatest.concurrent.Eventually._ +import org.apache.spark.rpc.RpcEnv import org.apache.spark.{MapOutputTrackerMaster, SparkConf, SparkContext, SecurityManager} import org.apache.spark.network.BlockTransferService import org.apache.spark.network.nio.NioBlockTransferService @@ -34,13 +34,12 @@ import org.apache.spark.scheduler.LiveListenerBus import org.apache.spark.serializer.KryoSerializer import org.apache.spark.shuffle.hash.HashShuffleManager import org.apache.spark.storage.StorageLevel._ -import org.apache.spark.util.{AkkaUtils, SizeEstimator} /** Testsuite that tests block replication in BlockManager */ class BlockManagerReplicationSuite extends FunSuite with Matchers with BeforeAndAfter { private val conf = new SparkConf(false) - var actorSystem: ActorSystem = null + var rpcEnv: RpcEnv = null var master: BlockManagerMaster = null val securityMgr = new SecurityManager(conf) val mapOutputTracker = new MapOutputTrackerMaster(conf) @@ -61,7 +60,7 @@ class BlockManagerReplicationSuite extends FunSuite with Matchers with BeforeAnd maxMem: Long, name: String = SparkContext.DRIVER_IDENTIFIER): BlockManager = { val transfer = new NioBlockTransferService(conf, securityMgr) - val store = new BlockManager(name, actorSystem, master, serializer, maxMem, conf, + val store = new BlockManager(name, rpcEnv, master, serializer, maxMem, conf, mapOutputTracker, shuffleManager, transfer, securityMgr, 0) store.initialize("app-id") allStores += store @@ -69,12 +68,10 @@ class BlockManagerReplicationSuite extends FunSuite with Matchers with BeforeAnd } before { - val (actorSystem, boundPort) = AkkaUtils.createActorSystem( - "test", "localhost", 0, conf = conf, securityManager = securityMgr) - this.actorSystem = actorSystem + rpcEnv = RpcEnv.create("test", "localhost", 0, conf, securityMgr) conf.set("spark.authenticate", "false") - conf.set("spark.driver.port", boundPort.toString) + conf.set("spark.driver.port", rpcEnv.address.port.toString) conf.set("spark.storage.unrollFraction", "0.4") conf.set("spark.storage.unrollMemoryThreshold", "512") @@ -83,18 +80,17 @@ class BlockManagerReplicationSuite extends FunSuite with Matchers with BeforeAnd // to make cached peers refresh frequently conf.set("spark.storage.cachedPeersTtl", "10") - master = new BlockManagerMaster( - actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf, new LiveListenerBus))), - conf, true) + master = new BlockManagerMaster(rpcEnv.setupEndpoint("blockmanager", + new BlockManagerMasterEndpoint(rpcEnv, true, conf, new LiveListenerBus)), conf, true) allStores.clear() } after { allStores.foreach { _.stop() } allStores.clear() - actorSystem.shutdown() - actorSystem.awaitTermination() - actorSystem = null + rpcEnv.shutdown() + rpcEnv.awaitTermination() + rpcEnv = null master = null } @@ -262,7 +258,7 @@ class BlockManagerReplicationSuite extends FunSuite with Matchers with BeforeAnd val failableTransfer = mock(classOf[BlockTransferService]) // this wont actually work when(failableTransfer.hostName).thenReturn("some-hostname") when(failableTransfer.port).thenReturn(1000) - val failableStore = new BlockManager("failable-store", actorSystem, master, serializer, + val failableStore = new BlockManager("failable-store", rpcEnv, master, serializer, 10000, conf, mapOutputTracker, shuffleManager, failableTransfer, securityMgr, 0) failableStore.initialize("app-id") allStores += failableStore // so that this gets stopped after test diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala index ecd1cba5b5abe..283090e3bdb1f 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala @@ -19,24 +19,18 @@ package org.apache.spark.storage import java.nio.{ByteBuffer, MappedByteBuffer} import java.util.Arrays -import java.util.concurrent.TimeUnit import scala.collection.mutable.ArrayBuffer -import scala.concurrent.Await import scala.concurrent.duration._ import scala.language.implicitConversions import scala.language.postfixOps -import akka.actor._ -import akka.pattern.ask -import akka.util.Timeout - import org.mockito.Mockito.{mock, when} - import org.scalatest._ import org.scalatest.concurrent.Eventually._ import org.scalatest.concurrent.Timeouts._ +import org.apache.spark.rpc.RpcEnv import org.apache.spark.{MapOutputTrackerMaster, SparkConf, SparkContext, SecurityManager} import org.apache.spark.executor.DataReadMethod import org.apache.spark.network.nio.NioBlockTransferService @@ -53,7 +47,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach private val conf = new SparkConf(false) var store: BlockManager = null var store2: BlockManager = null - var actorSystem: ActorSystem = null + var rpcEnv: RpcEnv = null var master: BlockManagerMaster = null conf.set("spark.authenticate", "false") val securityMgr = new SecurityManager(conf) @@ -72,28 +66,25 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach maxMem: Long, name: String = SparkContext.DRIVER_IDENTIFIER): BlockManager = { val transfer = new NioBlockTransferService(conf, securityMgr) - val manager = new BlockManager(name, actorSystem, master, serializer, maxMem, conf, + val manager = new BlockManager(name, rpcEnv, master, serializer, maxMem, conf, mapOutputTracker, shuffleManager, transfer, securityMgr, 0) manager.initialize("app-id") manager } override def beforeEach(): Unit = { - val (actorSystem, boundPort) = AkkaUtils.createActorSystem( - "test", "localhost", 0, conf = conf, securityManager = securityMgr) - this.actorSystem = actorSystem + rpcEnv = RpcEnv.create("test", "localhost", 0, conf, securityMgr) // Set the arch to 64-bit and compressedOops to true to get a deterministic test-case System.setProperty("os.arch", "amd64") conf.set("os.arch", "amd64") conf.set("spark.test.useCompressedOops", "true") - conf.set("spark.driver.port", boundPort.toString) + conf.set("spark.driver.port", rpcEnv.address.port.toString) conf.set("spark.storage.unrollFraction", "0.4") conf.set("spark.storage.unrollMemoryThreshold", "512") - master = new BlockManagerMaster( - actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf, new LiveListenerBus))), - conf, true) + master = new BlockManagerMaster(rpcEnv.setupEndpoint("blockmanager", + new BlockManagerMasterEndpoint(rpcEnv, true, conf, new LiveListenerBus)), conf, true) val initialize = PrivateMethod[Unit]('initialize) SizeEstimator invokePrivate initialize() @@ -108,9 +99,9 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach store2.stop() store2 = null } - actorSystem.shutdown() - actorSystem.awaitTermination() - actorSystem = null + rpcEnv.shutdown() + rpcEnv.awaitTermination() + rpcEnv = null master = null } @@ -357,10 +348,8 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach master.removeExecutor(store.blockManagerId.executorId) assert(master.getLocations("a1").size == 0, "a1 was not removed from master") - implicit val timeout = Timeout(30, TimeUnit.SECONDS) - val reregister = !Await.result( - master.driverActor ? BlockManagerHeartbeat(store.blockManagerId), - timeout.duration).asInstanceOf[Boolean] + val reregister = !master.driverEndpoint.askWithReply[Boolean]( + BlockManagerHeartbeat(store.blockManagerId)) assert(reregister == true) } @@ -785,7 +774,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach test("block store put failure") { // Use Java serializer so we can create an unserializable error. val transfer = new NioBlockTransferService(conf, securityMgr) - store = new BlockManager(SparkContext.DRIVER_IDENTIFIER, actorSystem, master, + store = new BlockManager(SparkContext.DRIVER_IDENTIFIER, rpcEnv, master, new JavaSerializer(conf), 1200, conf, mapOutputTracker, shuffleManager, transfer, securityMgr, 0) diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala index 18a477f92094d..ef4873de2f5a9 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala @@ -24,20 +24,20 @@ import scala.collection.mutable.ArrayBuffer import scala.concurrent.duration._ import scala.language.postfixOps -import akka.actor.{ActorSystem, Props} import org.apache.hadoop.conf.Configuration import org.scalatest.{BeforeAndAfter, FunSuite, Matchers} import org.scalatest.concurrent.Eventually._ import org.apache.spark._ import org.apache.spark.network.nio.NioBlockTransferService +import org.apache.spark.rpc.RpcEnv import org.apache.spark.scheduler.LiveListenerBus import org.apache.spark.serializer.KryoSerializer import org.apache.spark.shuffle.hash.HashShuffleManager import org.apache.spark.storage._ import org.apache.spark.streaming.receiver._ import org.apache.spark.streaming.util._ -import org.apache.spark.util.{AkkaUtils, ManualClock, Utils} +import org.apache.spark.util.{ManualClock, Utils} import WriteAheadLogBasedBlockHandler._ import WriteAheadLogSuite._ @@ -54,22 +54,19 @@ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matche val manualClock = new ManualClock val blockManagerSize = 10000000 - var actorSystem: ActorSystem = null + var rpcEnv: RpcEnv = null var blockManagerMaster: BlockManagerMaster = null var blockManager: BlockManager = null var tempDirectory: File = null before { - val (actorSystem, boundPort) = AkkaUtils.createActorSystem( - "test", "localhost", 0, conf = conf, securityManager = securityMgr) - this.actorSystem = actorSystem - conf.set("spark.driver.port", boundPort.toString) + rpcEnv = RpcEnv.create("test", "localhost", 0, conf, securityMgr) + conf.set("spark.driver.port", rpcEnv.address.port.toString) - blockManagerMaster = new BlockManagerMaster( - actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf, new LiveListenerBus))), - conf, true) + blockManagerMaster = new BlockManagerMaster(rpcEnv.setupEndpoint("blockmanager", + new BlockManagerMasterEndpoint(rpcEnv, true, conf, new LiveListenerBus)), conf, true) - blockManager = new BlockManager("bm", actorSystem, blockManagerMaster, serializer, + blockManager = new BlockManager("bm", rpcEnv, blockManagerMaster, serializer, blockManagerSize, conf, mapOutputTracker, shuffleManager, new NioBlockTransferService(conf, securityMgr), securityMgr, 0) blockManager.initialize("app-id") @@ -87,9 +84,9 @@ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matche blockManagerMaster.stop() blockManagerMaster = null } - actorSystem.shutdown() - actorSystem.awaitTermination() - actorSystem = null + rpcEnv.shutdown() + rpcEnv.awaitTermination() + rpcEnv = null Utils.deleteRecursively(tempDirectory) } diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala index 455554eea0597..24a1e02795218 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -24,22 +24,20 @@ import java.lang.reflect.InvocationTargetException import java.net.{Socket, URL} import java.util.concurrent.atomic.AtomicReference -import akka.actor._ -import akka.remote._ import org.apache.hadoop.fs.{FileSystem, Path} import org.apache.hadoop.util.ShutdownHookManager import org.apache.hadoop.yarn.api._ import org.apache.hadoop.yarn.api.records._ import org.apache.hadoop.yarn.conf.YarnConfiguration +import org.apache.spark.rpc._ import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext, SparkEnv} import org.apache.spark.SparkException import org.apache.spark.deploy.{PythonRunner, SparkHadoopUtil} import org.apache.spark.deploy.history.HistoryServer import org.apache.spark.scheduler.cluster.YarnSchedulerBackend import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ -import org.apache.spark.util.{AkkaUtils, ChildFirstURLClassLoader, MutableURLClassLoader, - SignalLogger, Utils} +import org.apache.spark.util._ /** * Common application master functionality for Spark on Yarn. @@ -72,8 +70,8 @@ private[spark] class ApplicationMaster( @volatile private var allocator: YarnAllocator = _ // Fields used in client mode. - private var actorSystem: ActorSystem = null - private var actor: ActorRef = _ + private var rpcEnv: RpcEnv = null + private var amEndpoint: RpcEndpointRef = _ // Fields used in cluster mode. private val sparkContextRef = new AtomicReference[SparkContext](null) @@ -240,22 +238,21 @@ private[spark] class ApplicationMaster( } /** - * Create an actor that communicates with the driver. + * Create an [[RpcEndpoint]] that communicates with the driver. * * In cluster mode, the AM and the driver belong to same process - * so the AM actor need not monitor lifecycle of the driver. + * so the AMEndpoint need not monitor lifecycle of the driver. */ - private def runAMActor( + private def runAMEndpoint( host: String, port: String, isClusterMode: Boolean): Unit = { - val driverUrl = AkkaUtils.address( - AkkaUtils.protocol(actorSystem), + val driverEndpont = rpcEnv.setupEndpointRef( SparkEnv.driverActorSystemName, - host, - port, - YarnSchedulerBackend.ACTOR_NAME) - actor = actorSystem.actorOf(Props(new AMActor(driverUrl, isClusterMode)), name = "YarnAM") + RpcAddress(host, port.toInt), + YarnSchedulerBackend.ENDPOINT_NAME) + amEndpoint = + rpcEnv.setupEndpoint("YarnAM", new AMEndpoint(rpcEnv, driverEndpont, isClusterMode)) } private def runDriver(securityMgr: SecurityManager): Unit = { @@ -272,8 +269,8 @@ private[spark] class ApplicationMaster( ApplicationMaster.EXIT_SC_NOT_INITED, "Timed out waiting for SparkContext.") } else { - actorSystem = sc.env.actorSystem - runAMActor( + rpcEnv = sc.env.rpcEnv + runAMEndpoint( sc.getConf.get("spark.driver.host"), sc.getConf.get("spark.driver.port"), isClusterMode = true) @@ -283,8 +280,7 @@ private[spark] class ApplicationMaster( } private def runExecutorLauncher(securityMgr: SecurityManager): Unit = { - actorSystem = AkkaUtils.createActorSystem("sparkYarnAM", Utils.localHostName, 0, - conf = sparkConf, securityManager = securityMgr)._1 + rpcEnv = RpcEnv.create("sparkYarnAM", Utils.localHostName, 0, sparkConf, securityMgr) waitForSparkDriver() addAmIpFilter() registerAM(sparkConf.get("spark.driver.appUIAddress", ""), securityMgr) @@ -431,7 +427,7 @@ private[spark] class ApplicationMaster( sparkConf.set("spark.driver.host", driverHost) sparkConf.set("spark.driver.port", driverPort.toString) - runAMActor(driverHost, driverPort.toString, isClusterMode = false) + runAMEndpoint(driverHost, driverPort.toString, isClusterMode = false) } /** Add the Yarn IP filter that is required for properly securing the UI. */ @@ -443,7 +439,7 @@ private[spark] class ApplicationMaster( System.setProperty("spark.ui.filters", amFilter) params.foreach { case (k, v) => System.setProperty(s"spark.$amFilter.param.$k", v) } } else { - actor ! AddWebUIFilter(amFilter, params.toMap, proxyBase) + amEndpoint.send(AddWebUIFilter(amFilter, params.toMap, proxyBase)) } } @@ -505,44 +501,29 @@ private[spark] class ApplicationMaster( } /** - * An actor that communicates with the driver's scheduler backend. + * An [[RpcEndpoint]] that communicates with the driver's scheduler backend. */ - private class AMActor(driverUrl: String, isClusterMode: Boolean) extends Actor { - var driver: ActorSelection = _ - - override def preStart(): Unit = { - logInfo("Listen to driver: " + driverUrl) - driver = context.actorSelection(driverUrl) - // Send a hello message to establish the connection, after which - // we can monitor Lifecycle Events. - driver ! "Hello" - driver ! RegisterClusterManager - // In cluster mode, the AM can directly monitor the driver status instead - // of trying to deduce it from the lifecycle of the driver's actor - if (!isClusterMode) { - context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) - } + private class AMEndpoint( + override val rpcEnv: RpcEnv, driver: RpcEndpointRef, isClusterMode: Boolean) + extends RpcEndpoint with Logging { + + override def onStart(): Unit = { + driver.send(RegisterClusterManager(self)) } override def receive: PartialFunction[Any, Unit] = { - case x: DisassociatedEvent => - logInfo(s"Driver terminated or disconnected! Shutting down. $x") - // In cluster mode, do not rely on the disassociated event to exit - // This avoids potentially reporting incorrect exit codes if the driver fails - if (!isClusterMode) { - finish(FinalApplicationStatus.SUCCEEDED, ApplicationMaster.EXIT_SUCCESS) - } - case x: AddWebUIFilter => logInfo(s"Add WebUI Filter. $x") - driver ! x + driver.send(x) + } + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case RequestExecutors(requestedTotal) => Option(allocator) match { case Some(a) => a.requestTotalExecutors(requestedTotal) case None => logWarning("Container allocator is not ready to request executors yet.") } - sender ! true + context.reply(true) case KillExecutors(executorIds) => logInfo(s"Driver requested to kill executor(s) ${executorIds.mkString(", ")}.") @@ -550,7 +531,16 @@ private[spark] class ApplicationMaster( case Some(a) => executorIds.foreach(a.killExecutor) case None => logWarning("Container allocator is not ready to kill executors yet.") } - sender ! true + context.reply(true) + } + + override def onDisconnected(remoteAddress: RpcAddress): Unit = { + logInfo(s"Driver terminated or disconnected! Shutting down. $remoteAddress") + // In cluster mode, do not rely on the disassociated event to exit + // This avoids potentially reporting incorrect exit codes if the driver fails + if (!isClusterMode) { + finish(FinalApplicationStatus.SUCCEEDED, ApplicationMaster.EXIT_SUCCESS) + } } } diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala index c98763e15b58f..b8f42dadcb464 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala @@ -112,7 +112,7 @@ private[yarn] class YarnAllocator( SparkEnv.driverActorSystemName, sparkConf.get("spark.driver.host"), sparkConf.get("spark.driver.port"), - CoarseGrainedSchedulerBackend.ACTOR_NAME) + CoarseGrainedSchedulerBackend.ENDPOINT_NAME) // For testing private val launchContainers = sparkConf.getBoolean("spark.yarn.launchContainers", true) From acffc43455d7b3e4000be4ff0175b8ea19cd280b Mon Sep 17 00:00:00 2001 From: lewuathe Date: Sun, 5 Apr 2015 16:13:31 -0700 Subject: [PATCH 170/591] [SPARK-6262][MLLIB]Implement missing methods for MultivariateStatisticalSummary Add below methods in pyspark for MultivariateStatisticalSummary - normL1 - normL2 Author: lewuathe Closes #5359 from Lewuathe/SPARK-6262 and squashes the following commits: cbe439e [lewuathe] Implement missing methods for MultivariateStatisticalSummary --- python/pyspark/mllib/stat/_statistics.py | 6 ++++++ python/pyspark/mllib/tests.py | 6 ++++++ 2 files changed, 12 insertions(+) diff --git a/python/pyspark/mllib/stat/_statistics.py b/python/pyspark/mllib/stat/_statistics.py index 218ac148ca992..1d83e9d483f8e 100644 --- a/python/pyspark/mllib/stat/_statistics.py +++ b/python/pyspark/mllib/stat/_statistics.py @@ -49,6 +49,12 @@ def max(self): def min(self): return self.call("min").toArray() + def normL1(self): + return self.call("normL1").toArray() + + def normL2(self): + return self.call("normL2").toArray() + class Statistics(object): diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index dd3b66ce67457..47dad7d12e4e4 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -357,6 +357,12 @@ def test_col_with_different_rdds(self): summary = Statistics.colStats(data) self.assertEqual(10, summary.count()) + def test_col_norms(self): + data = RandomRDDs.normalVectorRDD(self.sc, 1000, 10, 10) + summary = Statistics.colStats(data) + self.assertEqual(10, len(summary.normL1())) + self.assertEqual(10, len(summary.normL2())) + class VectorUDTTests(PySparkTestCase): From 0b5d028a93b7d5adb148fbf3a576257bb3a6d8cb Mon Sep 17 00:00:00 2001 From: zsxwing Date: Sun, 5 Apr 2015 21:57:15 -0700 Subject: [PATCH 171/591] [SPARK-6602][Core] Update MapOutputTrackerMasterActor to MapOutputTrackerMasterEndpoint This is the second PR for [SPARK-6602]. It updated MapOutputTrackerMasterActor and its unit tests. cc rxin Author: zsxwing Closes #5371 from zsxwing/rpc-rewrite-part2 and squashes the following commits: fcf3816 [zsxwing] Fix the code style 4013a22 [zsxwing] Add doc for uncaught exceptions in RpcEnv 93c6c20 [zsxwing] Add an example of UnserializableException and add ErrorMonitor to monitor errors from Akka 134fe7b [zsxwing] Update MapOutputTrackerMasterActor to MapOutputTrackerMasterEndpoint --- .../org/apache/spark/MapOutputTracker.scala | 61 +++--- .../scala/org/apache/spark/SparkEnv.scala | 18 +- .../scala/org/apache/spark/rpc/RpcEnv.scala | 4 +- .../apache/spark/rpc/akka/AkkaRpcEnv.scala | 19 +- .../apache/spark/MapOutputTrackerSuite.scala | 100 +++++---- .../org/apache/spark/rpc/RpcEnvSuite.scala | 33 ++- .../apache/spark/util/AkkaUtilsSuite.scala | 198 ++++++++---------- 7 files changed, 221 insertions(+), 212 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala index 5718951451afc..d65c94e410662 100644 --- a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala +++ b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala @@ -21,13 +21,11 @@ import java.io._ import java.util.concurrent.ConcurrentHashMap import java.util.zip.{GZIPInputStream, GZIPOutputStream} -import scala.collection.mutable.{HashSet, HashMap, Map} -import scala.concurrent.Await +import scala.collection.mutable.{HashSet, Map} import scala.collection.JavaConversions._ +import scala.reflect.ClassTag -import akka.actor._ -import akka.pattern.ask - +import org.apache.spark.rpc.{RpcEndpointRef, RpcEnv, RpcCallContext, RpcEndpoint} import org.apache.spark.scheduler.MapStatus import org.apache.spark.shuffle.MetadataFetchFailedException import org.apache.spark.storage.BlockManagerId @@ -38,14 +36,15 @@ private[spark] case class GetMapOutputStatuses(shuffleId: Int) extends MapOutputTrackerMessage private[spark] case object StopMapOutputTracker extends MapOutputTrackerMessage -/** Actor class for MapOutputTrackerMaster */ -private[spark] class MapOutputTrackerMasterActor(tracker: MapOutputTrackerMaster, conf: SparkConf) - extends Actor with ActorLogReceive with Logging { +/** RpcEndpoint class for MapOutputTrackerMaster */ +private[spark] class MapOutputTrackerMasterEndpoint( + override val rpcEnv: RpcEnv, tracker: MapOutputTrackerMaster, conf: SparkConf) + extends RpcEndpoint with Logging { val maxAkkaFrameSize = AkkaUtils.maxFrameSizeBytes(conf) - override def receiveWithLogging: PartialFunction[Any, Unit] = { + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case GetMapOutputStatuses(shuffleId: Int) => - val hostPort = sender.path.address.hostPort + val hostPort = context.sender.address.hostPort logInfo("Asked to send map output locations for shuffle " + shuffleId + " to " + hostPort) val mapOutputStatuses = tracker.getSerializedMapOutputStatuses(shuffleId) val serializedSize = mapOutputStatuses.size @@ -53,19 +52,19 @@ private[spark] class MapOutputTrackerMasterActor(tracker: MapOutputTrackerMaster val msg = s"Map output statuses were $serializedSize bytes which " + s"exceeds spark.akka.frameSize ($maxAkkaFrameSize bytes)." - /* For SPARK-1244 we'll opt for just logging an error and then throwing an exception. - * Note that on exception the actor will just restart. A bigger refactoring (SPARK-1239) - * will ultimately remove this entire code path. */ + /* For SPARK-1244 we'll opt for just logging an error and then sending it to the sender. + * A bigger refactoring (SPARK-1239) will ultimately remove this entire code path. */ val exception = new SparkException(msg) logError(msg, exception) - throw exception + context.sendFailure(exception) + } else { + context.reply(mapOutputStatuses) } - sender ! mapOutputStatuses case StopMapOutputTracker => - logInfo("MapOutputTrackerActor stopped!") - sender ! true - context.stop(self) + logInfo("MapOutputTrackerMasterEndpoint stopped!") + context.reply(true) + stop() } } @@ -75,12 +74,9 @@ private[spark] class MapOutputTrackerMasterActor(tracker: MapOutputTrackerMaster * (driver and executor) use different HashMap to store its metadata. */ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging { - private val timeout = AkkaUtils.askTimeout(conf) - private val retryAttempts = AkkaUtils.numRetries(conf) - private val retryIntervalMs = AkkaUtils.retryWaitMs(conf) - /** Set to the MapOutputTrackerActor living on the driver. */ - var trackerActor: ActorRef = _ + /** Set to the MapOutputTrackerMasterEndpoint living on the driver. */ + var trackerEndpoint: RpcEndpointRef = _ /** * This HashMap has different behavior for the driver and the executors. @@ -105,12 +101,12 @@ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging private val fetching = new HashSet[Int] /** - * Send a message to the trackerActor and get its result within a default timeout, or + * Send a message to the trackerEndpoint and get its result within a default timeout, or * throw a SparkException if this fails. */ - protected def askTracker(message: Any): Any = { + protected def askTracker[T: ClassTag](message: Any): T = { try { - AkkaUtils.askWithReply(message, trackerActor, retryAttempts, retryIntervalMs, timeout) + trackerEndpoint.askWithReply[T](message) } catch { case e: Exception => logError("Error communicating with MapOutputTracker", e) @@ -118,9 +114,9 @@ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging } } - /** Send a one-way message to the trackerActor, to which we expect it to reply with true. */ + /** Send a one-way message to the trackerEndpoint, to which we expect it to reply with true. */ protected def sendTracker(message: Any) { - val response = askTracker(message) + val response = askTracker[Boolean](message) if (response != true) { throw new SparkException( "Error reply received from MapOutputTracker. Expecting true, got " + response.toString) @@ -157,11 +153,10 @@ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging if (fetchedStatuses == null) { // We won the race to fetch the output locs; do so - logInfo("Doing the fetch; tracker actor = " + trackerActor) + logInfo("Doing the fetch; tracker endpoint = " + trackerEndpoint) // This try-finally prevents hangs due to timeouts: try { - val fetchedBytes = - askTracker(GetMapOutputStatuses(shuffleId)).asInstanceOf[Array[Byte]] + val fetchedBytes = askTracker[Array[Byte]](GetMapOutputStatuses(shuffleId)) fetchedStatuses = MapOutputTracker.deserializeMapStatuses(fetchedBytes) logInfo("Got the output locations") mapStatuses.put(shuffleId, fetchedStatuses) @@ -328,7 +323,7 @@ private[spark] class MapOutputTrackerMaster(conf: SparkConf) override def stop() { sendTracker(StopMapOutputTracker) mapStatuses.clear() - trackerActor = null + trackerEndpoint = null metadataCleaner.cancel() cachedSerializedStatuses.clear() } @@ -350,6 +345,8 @@ private[spark] class MapOutputTrackerWorker(conf: SparkConf) extends MapOutputTr private[spark] object MapOutputTracker extends Logging { + val ENDPOINT_NAME = "MapOutputTracker" + // Serialize an array of map output locations into an efficient byte format so that we can send // it to reduce tasks. We do this by compressing the serialized bytes using GZIP. They will // generally be pretty compressible because many map outputs will be on the same hostname. diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala index 55be0a59fedd9..0171488e09562 100644 --- a/core/src/main/scala/org/apache/spark/SparkEnv.scala +++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala @@ -24,7 +24,6 @@ import scala.collection.JavaConversions._ import scala.collection.mutable import scala.util.Properties -import akka.actor._ import com.google.common.collect.MapMaker import org.apache.spark.annotation.DeveloperApi @@ -41,7 +40,7 @@ import org.apache.spark.scheduler.OutputCommitCoordinator.OutputCommitCoordinato import org.apache.spark.serializer.Serializer import org.apache.spark.shuffle.{ShuffleMemoryManager, ShuffleManager} import org.apache.spark.storage._ -import org.apache.spark.util.{AkkaUtils, RpcUtils, Utils} +import org.apache.spark.util.{RpcUtils, Utils} /** * :: DeveloperApi :: @@ -286,15 +285,6 @@ object SparkEnv extends Logging { val closureSerializer = instantiateClassFromConf[Serializer]( "spark.closure.serializer", "org.apache.spark.serializer.JavaSerializer") - def registerOrLookup(name: String, newActor: => Actor): ActorRef = { - if (isDriver) { - logInfo("Registering " + name) - actorSystem.actorOf(Props(newActor), name = name) - } else { - AkkaUtils.makeDriverRef(name, conf, actorSystem) - } - } - def registerOrLookupEndpoint( name: String, endpointCreator: => RpcEndpoint): RpcEndpointRef = { @@ -314,9 +304,9 @@ object SparkEnv extends Logging { // Have to assign trackerActor after initialization as MapOutputTrackerActor // requires the MapOutputTracker itself - mapOutputTracker.trackerActor = registerOrLookup( - "MapOutputTracker", - new MapOutputTrackerMasterActor(mapOutputTracker.asInstanceOf[MapOutputTrackerMaster], conf)) + mapOutputTracker.trackerEndpoint = registerOrLookupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint( + rpcEnv, mapOutputTracker.asInstanceOf[MapOutputTrackerMaster], conf)) // Let the user specify short names for shuffle managers val shortShuffleMgrNames = Map( diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala index d47e41abcfa50..e259867c14040 100644 --- a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala +++ b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala @@ -30,7 +30,9 @@ import org.apache.spark.util.{AkkaUtils, Utils} /** * An RPC environment. [[RpcEndpoint]]s need to register itself with a name to [[RpcEnv]] to * receives messages. Then [[RpcEnv]] will process messages sent from [[RpcEndpointRef]] or remote - * nodes, and deliver them to corresponding [[RpcEndpoint]]s. + * nodes, and deliver them to corresponding [[RpcEndpoint]]s. For uncaught exceptions caught by + * [[RpcEnv]], [[RpcEnv]] will use [[RpcCallContext.sendFailure]] to send exceptions back to the + * sender, or logging them if no such sender or `NotSerializableException`. * * [[RpcEnv]] also provides some methods to retrieve [[RpcEndpointRef]]s given name or uri. */ diff --git a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala index 9e06147dff1ed..652e52f2b2e73 100644 --- a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala +++ b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala @@ -17,16 +17,16 @@ package org.apache.spark.rpc.akka -import java.net.URI import java.util.concurrent.ConcurrentHashMap -import scala.concurrent.{Await, Future} +import scala.concurrent.Future import scala.concurrent.duration._ import scala.language.postfixOps import scala.reflect.ClassTag import scala.util.control.NonFatal import akka.actor.{ActorSystem, ExtendedActorSystem, Actor, ActorRef, Props, Address} +import akka.event.Logging.Error import akka.pattern.{ask => akkaAsk} import akka.remote.{AssociationEvent, AssociatedEvent, DisassociatedEvent, AssociationErrorEvent} import org.apache.spark.{SparkException, Logging, SparkConf} @@ -242,10 +242,25 @@ private[spark] class AkkaRpcEnvFactory extends RpcEnvFactory { def create(config: RpcEnvConfig): RpcEnv = { val (actorSystem, boundPort) = AkkaUtils.createActorSystem( config.name, config.host, config.port, config.conf, config.securityManager) + actorSystem.actorOf(Props(classOf[ErrorMonitor]), "ErrorMonitor") new AkkaRpcEnv(actorSystem, config.conf, boundPort) } } +/** + * Monitor errors reported by Akka and log them. + */ +private[akka] class ErrorMonitor extends Actor with ActorLogReceive with Logging { + + override def preStart(): Unit = { + context.system.eventStream.subscribe(self, classOf[Error]) + } + + override def receiveWithLogging: Actor.Receive = { + case Error(cause: Throwable, _, _, message: String) => logError(message, cause) + } +} + private[akka] class AkkaRpcEndpointRef( @transient defaultAddress: RpcAddress, @transient _actorRef: => ActorRef, diff --git a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala index ccfe0678cb1c3..6295d34be5ca9 100644 --- a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala +++ b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala @@ -17,34 +17,37 @@ package org.apache.spark -import scala.concurrent.Await - -import akka.actor._ -import akka.testkit.TestActorRef +import org.mockito.Mockito._ +import org.mockito.Matchers.{any, isA} import org.scalatest.FunSuite +import org.apache.spark.rpc.{RpcAddress, RpcEndpointRef, RpcCallContext, RpcEnv} import org.apache.spark.scheduler.{CompressedMapStatus, MapStatus} import org.apache.spark.shuffle.FetchFailedException import org.apache.spark.storage.BlockManagerId -import org.apache.spark.util.AkkaUtils class MapOutputTrackerSuite extends FunSuite { private val conf = new SparkConf + def createRpcEnv(name: String, host: String = "localhost", port: Int = 0, + securityManager: SecurityManager = new SecurityManager(conf)): RpcEnv = { + RpcEnv.create(name, host, port, conf, securityManager) + } + test("master start and stop") { - val actorSystem = ActorSystem("test") + val rpcEnv = createRpcEnv("test") val tracker = new MapOutputTrackerMaster(conf) - tracker.trackerActor = - actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker, conf))) + tracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, tracker, conf)) tracker.stop() - actorSystem.shutdown() + rpcEnv.shutdown() } test("master register shuffle and fetch") { - val actorSystem = ActorSystem("test") + val rpcEnv = createRpcEnv("test") val tracker = new MapOutputTrackerMaster(conf) - tracker.trackerActor = - actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker, conf))) + tracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, tracker, conf)) tracker.registerShuffle(10, 2) assert(tracker.containsShuffle(10)) val size1000 = MapStatus.decompressSize(MapStatus.compressSize(1000L)) @@ -57,13 +60,14 @@ class MapOutputTrackerSuite extends FunSuite { assert(statuses.toSeq === Seq((BlockManagerId("a", "hostA", 1000), size1000), (BlockManagerId("b", "hostB", 1000), size10000))) tracker.stop() - actorSystem.shutdown() + rpcEnv.shutdown() } test("master register and unregister shuffle") { - val actorSystem = ActorSystem("test") + val rpcEnv = createRpcEnv("test") val tracker = new MapOutputTrackerMaster(conf) - tracker.trackerActor = actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker, conf))) + tracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, tracker, conf)) tracker.registerShuffle(10, 2) val compressedSize1000 = MapStatus.compressSize(1000L) val compressedSize10000 = MapStatus.compressSize(10000L) @@ -78,14 +82,14 @@ class MapOutputTrackerSuite extends FunSuite { assert(tracker.getServerStatuses(10, 0).isEmpty) tracker.stop() - actorSystem.shutdown() + rpcEnv.shutdown() } test("master register shuffle and unregister map output and fetch") { - val actorSystem = ActorSystem("test") + val rpcEnv = createRpcEnv("test") val tracker = new MapOutputTrackerMaster(conf) - tracker.trackerActor = - actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker, conf))) + tracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, tracker, conf)) tracker.registerShuffle(10, 2) val compressedSize1000 = MapStatus.compressSize(1000L) val compressedSize10000 = MapStatus.compressSize(10000L) @@ -104,25 +108,21 @@ class MapOutputTrackerSuite extends FunSuite { intercept[FetchFailedException] { tracker.getServerStatuses(10, 1) } tracker.stop() - actorSystem.shutdown() + rpcEnv.shutdown() } test("remote fetch") { val hostname = "localhost" - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, conf = conf, - securityManager = new SecurityManager(conf)) + val rpcEnv = createRpcEnv("spark", hostname, 0, new SecurityManager(conf)) val masterTracker = new MapOutputTrackerMaster(conf) - masterTracker.trackerActor = actorSystem.actorOf( - Props(new MapOutputTrackerMasterActor(masterTracker, conf)), "MapOutputTracker") + masterTracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, conf)) - val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, conf = conf, - securityManager = new SecurityManager(conf)) + val slaveRpcEnv = createRpcEnv("spark-slave", hostname, 0, new SecurityManager(conf)) val slaveTracker = new MapOutputTrackerWorker(conf) - val selection = slaveSystem.actorSelection( - AkkaUtils.address(AkkaUtils.protocol(slaveSystem), "spark", "localhost", boundPort, "MapOutputTracker")) - val timeout = AkkaUtils.lookupTimeout(conf) - slaveTracker.trackerActor = Await.result(selection.resolveOne(timeout), timeout) + slaveTracker.trackerEndpoint = + slaveRpcEnv.setupEndpointRef("spark", rpcEnv.address, MapOutputTracker.ENDPOINT_NAME) masterTracker.registerShuffle(10, 1) masterTracker.incrementEpoch() @@ -147,8 +147,8 @@ class MapOutputTrackerSuite extends FunSuite { masterTracker.stop() slaveTracker.stop() - actorSystem.shutdown() - slaveSystem.shutdown() + rpcEnv.shutdown() + slaveRpcEnv.shutdown() } test("remote fetch below akka frame size") { @@ -157,19 +157,24 @@ class MapOutputTrackerSuite extends FunSuite { newConf.set("spark.akka.askTimeout", "1") // Fail fast val masterTracker = new MapOutputTrackerMaster(conf) - val actorSystem = ActorSystem("test") - val actorRef = TestActorRef[MapOutputTrackerMasterActor]( - Props(new MapOutputTrackerMasterActor(masterTracker, newConf)))(actorSystem) - val masterActor = actorRef.underlyingActor + val rpcEnv = createRpcEnv("spark") + val masterEndpoint = new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, newConf) + rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, masterEndpoint) // Frame size should be ~123B, and no exception should be thrown masterTracker.registerShuffle(10, 1) masterTracker.registerMapOutput(10, 0, MapStatus( BlockManagerId("88", "mph", 1000), Array.fill[Long](10)(0))) - masterActor.receive(GetMapOutputStatuses(10)) + val sender = mock(classOf[RpcEndpointRef]) + when(sender.address).thenReturn(RpcAddress("localhost", 12345)) + val rpcCallContext = mock(classOf[RpcCallContext]) + when(rpcCallContext.sender).thenReturn(sender) + masterEndpoint.receiveAndReply(rpcCallContext)(GetMapOutputStatuses(10)) + verify(rpcCallContext).reply(any()) + verify(rpcCallContext, never()).sendFailure(any()) // masterTracker.stop() // this throws an exception - actorSystem.shutdown() + rpcEnv.shutdown() } test("remote fetch exceeds akka frame size") { @@ -178,12 +183,11 @@ class MapOutputTrackerSuite extends FunSuite { newConf.set("spark.akka.askTimeout", "1") // Fail fast val masterTracker = new MapOutputTrackerMaster(conf) - val actorSystem = ActorSystem("test") - val actorRef = TestActorRef[MapOutputTrackerMasterActor]( - Props(new MapOutputTrackerMasterActor(masterTracker, newConf)))(actorSystem) - val masterActor = actorRef.underlyingActor + val rpcEnv = createRpcEnv("test") + val masterEndpoint = new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, newConf) + rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, masterEndpoint) - // Frame size should be ~1.1MB, and MapOutputTrackerMasterActor should throw exception. + // Frame size should be ~1.1MB, and MapOutputTrackerMasterEndpoint should throw exception. // Note that the size is hand-selected here because map output statuses are compressed before // being sent. masterTracker.registerShuffle(20, 100) @@ -191,9 +195,15 @@ class MapOutputTrackerSuite extends FunSuite { masterTracker.registerMapOutput(20, i, new CompressedMapStatus( BlockManagerId("999", "mps", 1000), Array.fill[Long](4000000)(0))) } - intercept[SparkException] { masterActor.receive(GetMapOutputStatuses(20)) } + val sender = mock(classOf[RpcEndpointRef]) + when(sender.address).thenReturn(RpcAddress("localhost", 12345)) + val rpcCallContext = mock(classOf[RpcCallContext]) + when(rpcCallContext.sender).thenReturn(sender) + masterEndpoint.receiveAndReply(rpcCallContext)(GetMapOutputStatuses(20)) + verify(rpcCallContext, never()).reply(any()) + verify(rpcCallContext).sendFailure(isA(classOf[SparkException])) // masterTracker.stop() // this throws an exception - actorSystem.shutdown() + rpcEnv.shutdown() } } diff --git a/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala b/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala index 4f19c4f2110d2..5a734ec5ba5ec 100644 --- a/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala +++ b/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala @@ -514,10 +514,35 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { ("onDisconnected", remoteAddress))) } } -} -case object Start + test("sendWithReply: unserializable error") { + env.setupEndpoint("sendWithReply-unserializable-error", new RpcEndpoint { + override val rpcEnv = env -case class Ping(id: Int) + override def receiveAndReply(context: RpcCallContext) = { + case msg: String => context.sendFailure(new UnserializableException) + } + }) -case class Pong(id: Int) + val anotherEnv = createRpcEnv(new SparkConf(), "remote", 13345) + // Use anotherEnv to find out the RpcEndpointRef + val rpcEndpointRef = anotherEnv.setupEndpointRef( + "local", env.address, "sendWithReply-unserializable-error") + try { + val f = rpcEndpointRef.sendWithReply[String]("hello") + intercept[TimeoutException] { + Await.result(f, 1 seconds) + } + } finally { + anotherEnv.shutdown() + anotherEnv.awaitTermination() + } + } + +} + +class UnserializableClass + +class UnserializableException extends Exception { + private val unserializableField = new UnserializableClass +} diff --git a/core/src/test/scala/org/apache/spark/util/AkkaUtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/AkkaUtilsSuite.scala index 6250d50fb7036..bec79fc4dc8f7 100644 --- a/core/src/test/scala/org/apache/spark/util/AkkaUtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/AkkaUtilsSuite.scala @@ -19,14 +19,11 @@ package org.apache.spark.util import java.util.concurrent.TimeoutException -import scala.concurrent.Await -import scala.util.{Failure, Try} - -import akka.actor._ - +import akka.actor.ActorNotFound import org.scalatest.FunSuite import org.apache.spark._ +import org.apache.spark.rpc.RpcEnv import org.apache.spark.scheduler.MapStatus import org.apache.spark.storage.BlockManagerId import org.apache.spark.SSLSampleConfigs._ @@ -39,39 +36,37 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro test("remote fetch security bad password") { val conf = new SparkConf + conf.set("spark.rpc", "akka") conf.set("spark.authenticate", "true") conf.set("spark.authenticate.secret", "good") val securityManager = new SecurityManager(conf) val hostname = "localhost" - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, - conf = conf, securityManager = securityManager) - System.setProperty("spark.hostPort", hostname + ":" + boundPort) + val rpcEnv = RpcEnv.create("spark", hostname, 0, conf, securityManager) + System.setProperty("spark.hostPort", rpcEnv.address.hostPort) assert(securityManager.isAuthenticationEnabled() === true) val masterTracker = new MapOutputTrackerMaster(conf) - masterTracker.trackerActor = actorSystem.actorOf( - Props(new MapOutputTrackerMasterActor(masterTracker, conf)), "MapOutputTracker") + masterTracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, conf)) val badconf = new SparkConf + badconf.set("spark.rpc", "akka") badconf.set("spark.authenticate", "true") badconf.set("spark.authenticate.secret", "bad") val securityManagerBad = new SecurityManager(badconf) assert(securityManagerBad.isAuthenticationEnabled() === true) - val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, - conf = conf, securityManager = securityManagerBad) + val slaveRpcEnv = RpcEnv.create("spark-slave", hostname, 0, conf, securityManagerBad) val slaveTracker = new MapOutputTrackerWorker(conf) - val selection = slaveSystem.actorSelection( - AkkaUtils.address(AkkaUtils.protocol(slaveSystem), "spark", "localhost", boundPort, "MapOutputTracker")) - val timeout = AkkaUtils.lookupTimeout(conf) intercept[akka.actor.ActorNotFound] { - slaveTracker.trackerActor = Await.result(selection.resolveOne(timeout), timeout) + slaveTracker.trackerEndpoint = + slaveRpcEnv.setupEndpointRef("spark", rpcEnv.address, MapOutputTracker.ENDPOINT_NAME) } - actorSystem.shutdown() - slaveSystem.shutdown() + rpcEnv.shutdown() + slaveRpcEnv.shutdown() } test("remote fetch security off") { @@ -81,28 +76,24 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro val securityManager = new SecurityManager(conf) val hostname = "localhost" - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, - conf = conf, securityManager = securityManager) - System.setProperty("spark.hostPort", hostname + ":" + boundPort) + val rpcEnv = RpcEnv.create("spark", hostname, 0, conf, securityManager) + System.setProperty("spark.hostPort", rpcEnv.address.hostPort) assert(securityManager.isAuthenticationEnabled() === false) val masterTracker = new MapOutputTrackerMaster(conf) - masterTracker.trackerActor = actorSystem.actorOf( - Props(new MapOutputTrackerMasterActor(masterTracker, conf)), "MapOutputTracker") + masterTracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, conf)) val badconf = new SparkConf badconf.set("spark.authenticate", "false") badconf.set("spark.authenticate.secret", "good") val securityManagerBad = new SecurityManager(badconf) - val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, - conf = badconf, securityManager = securityManagerBad) + val slaveRpcEnv = RpcEnv.create("spark-slave", hostname, 0, badconf, securityManagerBad) val slaveTracker = new MapOutputTrackerWorker(conf) - val selection = slaveSystem.actorSelection( - AkkaUtils.address(AkkaUtils.protocol(slaveSystem), "spark", "localhost", boundPort, "MapOutputTracker")) - val timeout = AkkaUtils.lookupTimeout(conf) - slaveTracker.trackerActor = Await.result(selection.resolveOne(timeout), timeout) + slaveTracker.trackerEndpoint = + slaveRpcEnv.setupEndpointRef("spark", rpcEnv.address, MapOutputTracker.ENDPOINT_NAME) assert(securityManagerBad.isAuthenticationEnabled() === false) @@ -120,8 +111,8 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro assert(slaveTracker.getServerStatuses(10, 0).toSeq === Seq((BlockManagerId("a", "hostA", 1000), size1000))) - actorSystem.shutdown() - slaveSystem.shutdown() + rpcEnv.shutdown() + slaveRpcEnv.shutdown() } test("remote fetch security pass") { @@ -131,15 +122,14 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro val securityManager = new SecurityManager(conf) val hostname = "localhost" - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, - conf = conf, securityManager = securityManager) - System.setProperty("spark.hostPort", hostname + ":" + boundPort) + val rpcEnv = RpcEnv.create("spark", hostname, 0, conf, securityManager) + System.setProperty("spark.hostPort", rpcEnv.address.hostPort) assert(securityManager.isAuthenticationEnabled() === true) val masterTracker = new MapOutputTrackerMaster(conf) - masterTracker.trackerActor = actorSystem.actorOf( - Props(new MapOutputTrackerMasterActor(masterTracker, conf)), "MapOutputTracker") + masterTracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, conf)) val goodconf = new SparkConf goodconf.set("spark.authenticate", "true") @@ -148,13 +138,10 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro assert(securityManagerGood.isAuthenticationEnabled() === true) - val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, - conf = goodconf, securityManager = securityManagerGood) + val slaveRpcEnv =RpcEnv.create("spark-slave", hostname, 0, goodconf, securityManagerGood) val slaveTracker = new MapOutputTrackerWorker(conf) - val selection = slaveSystem.actorSelection( - AkkaUtils.address(AkkaUtils.protocol(slaveSystem), "spark", "localhost", boundPort, "MapOutputTracker")) - val timeout = AkkaUtils.lookupTimeout(conf) - slaveTracker.trackerActor = Await.result(selection.resolveOne(timeout), timeout) + slaveTracker.trackerEndpoint = + slaveRpcEnv.setupEndpointRef("spark", rpcEnv.address, MapOutputTracker.ENDPOINT_NAME) masterTracker.registerShuffle(10, 1) masterTracker.incrementEpoch() @@ -170,47 +157,45 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro assert(slaveTracker.getServerStatuses(10, 0).toSeq === Seq((BlockManagerId("a", "hostA", 1000), size1000))) - actorSystem.shutdown() - slaveSystem.shutdown() + rpcEnv.shutdown() + slaveRpcEnv.shutdown() } test("remote fetch security off client") { val conf = new SparkConf + conf.set("spark.rpc", "akka") conf.set("spark.authenticate", "true") conf.set("spark.authenticate.secret", "good") val securityManager = new SecurityManager(conf) val hostname = "localhost" - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, - conf = conf, securityManager = securityManager) - System.setProperty("spark.hostPort", hostname + ":" + boundPort) + val rpcEnv = RpcEnv.create("spark", hostname, 0, conf, securityManager) + System.setProperty("spark.hostPort", rpcEnv.address.hostPort) assert(securityManager.isAuthenticationEnabled() === true) val masterTracker = new MapOutputTrackerMaster(conf) - masterTracker.trackerActor = actorSystem.actorOf( - Props(new MapOutputTrackerMasterActor(masterTracker, conf)), "MapOutputTracker") + masterTracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, conf)) val badconf = new SparkConf + badconf.set("spark.rpc", "akka") badconf.set("spark.authenticate", "false") badconf.set("spark.authenticate.secret", "bad") val securityManagerBad = new SecurityManager(badconf) assert(securityManagerBad.isAuthenticationEnabled() === false) - val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, - conf = badconf, securityManager = securityManagerBad) + val slaveRpcEnv = RpcEnv.create("spark-slave", hostname, 0, badconf, securityManagerBad) val slaveTracker = new MapOutputTrackerWorker(conf) - val selection = slaveSystem.actorSelection( - AkkaUtils.address(AkkaUtils.protocol(slaveSystem), "spark", "localhost", boundPort, "MapOutputTracker")) - val timeout = AkkaUtils.lookupTimeout(conf) intercept[akka.actor.ActorNotFound] { - slaveTracker.trackerActor = Await.result(selection.resolveOne(timeout), timeout) + slaveTracker.trackerEndpoint = + slaveRpcEnv.setupEndpointRef("spark", rpcEnv.address, MapOutputTracker.ENDPOINT_NAME) } - actorSystem.shutdown() - slaveSystem.shutdown() + rpcEnv.shutdown() + slaveRpcEnv.shutdown() } test("remote fetch ssl on") { @@ -218,26 +203,22 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro val securityManager = new SecurityManager(conf) val hostname = "localhost" - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, - conf = conf, securityManager = securityManager) - System.setProperty("spark.hostPort", hostname + ":" + boundPort) + val rpcEnv = RpcEnv.create("spark", hostname, 0, conf, securityManager) + System.setProperty("spark.hostPort", rpcEnv.address.hostPort) assert(securityManager.isAuthenticationEnabled() === false) val masterTracker = new MapOutputTrackerMaster(conf) - masterTracker.trackerActor = actorSystem.actorOf( - Props(new MapOutputTrackerMasterActor(masterTracker, conf)), "MapOutputTracker") + masterTracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, conf)) val slaveConf = sparkSSLConfig() val securityManagerBad = new SecurityManager(slaveConf) - val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, - conf = slaveConf, securityManager = securityManagerBad) + val slaveRpcEnv = RpcEnv.create("spark-slaves", hostname, 0, slaveConf, securityManagerBad) val slaveTracker = new MapOutputTrackerWorker(conf) - val selection = slaveSystem.actorSelection( - AkkaUtils.address(AkkaUtils.protocol(slaveSystem), "spark", "localhost", boundPort, "MapOutputTracker")) - val timeout = AkkaUtils.lookupTimeout(conf) - slaveTracker.trackerActor = Await.result(selection.resolveOne(timeout), timeout) + slaveTracker.trackerEndpoint = + slaveRpcEnv.setupEndpointRef("spark", rpcEnv.address, MapOutputTracker.ENDPOINT_NAME) assert(securityManagerBad.isAuthenticationEnabled() === false) @@ -255,8 +236,8 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro assert(slaveTracker.getServerStatuses(10, 0).toSeq === Seq((BlockManagerId("a", "hostA", 1000), size1000))) - actorSystem.shutdown() - slaveSystem.shutdown() + rpcEnv.shutdown() + slaveRpcEnv.shutdown() } @@ -267,28 +248,24 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro val securityManager = new SecurityManager(conf) val hostname = "localhost" - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, - conf = conf, securityManager = securityManager) - System.setProperty("spark.hostPort", hostname + ":" + boundPort) + val rpcEnv = RpcEnv.create("spark", hostname, 0, conf, securityManager) + System.setProperty("spark.hostPort", rpcEnv.address.hostPort) assert(securityManager.isAuthenticationEnabled() === true) val masterTracker = new MapOutputTrackerMaster(conf) - masterTracker.trackerActor = actorSystem.actorOf( - Props(new MapOutputTrackerMasterActor(masterTracker, conf)), "MapOutputTracker") + masterTracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, conf)) val slaveConf = sparkSSLConfig() slaveConf.set("spark.authenticate", "true") slaveConf.set("spark.authenticate.secret", "good") val securityManagerBad = new SecurityManager(slaveConf) - val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, - conf = slaveConf, securityManager = securityManagerBad) + val slaveRpcEnv = RpcEnv.create("spark-slave", hostname, 0, slaveConf, securityManagerBad) val slaveTracker = new MapOutputTrackerWorker(conf) - val selection = slaveSystem.actorSelection( - AkkaUtils.address(AkkaUtils.protocol(slaveSystem), "spark", "localhost", boundPort, "MapOutputTracker")) - val timeout = AkkaUtils.lookupTimeout(conf) - slaveTracker.trackerActor = Await.result(selection.resolveOne(timeout), timeout) + slaveTracker.trackerEndpoint = + slaveRpcEnv.setupEndpointRef("spark", rpcEnv.address, MapOutputTracker.ENDPOINT_NAME) assert(securityManagerBad.isAuthenticationEnabled() === true) @@ -305,45 +282,43 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro assert(slaveTracker.getServerStatuses(10, 0).toSeq === Seq((BlockManagerId("a", "hostA", 1000), size1000))) - actorSystem.shutdown() - slaveSystem.shutdown() + rpcEnv.shutdown() + slaveRpcEnv.shutdown() } test("remote fetch ssl on and security enabled - bad credentials") { val conf = sparkSSLConfig() + conf.set("spark.rpc", "akka") conf.set("spark.authenticate", "true") conf.set("spark.authenticate.secret", "good") val securityManager = new SecurityManager(conf) val hostname = "localhost" - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, - conf = conf, securityManager = securityManager) - System.setProperty("spark.hostPort", hostname + ":" + boundPort) + val rpcEnv = RpcEnv.create("spark", hostname, 0, conf, securityManager) + System.setProperty("spark.hostPort", rpcEnv.address.hostPort) assert(securityManager.isAuthenticationEnabled() === true) val masterTracker = new MapOutputTrackerMaster(conf) - masterTracker.trackerActor = actorSystem.actorOf( - Props(new MapOutputTrackerMasterActor(masterTracker, conf)), "MapOutputTracker") + masterTracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, conf)) val slaveConf = sparkSSLConfig() + slaveConf.set("spark.rpc", "akka") slaveConf.set("spark.authenticate", "true") slaveConf.set("spark.authenticate.secret", "bad") val securityManagerBad = new SecurityManager(slaveConf) - val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, - conf = slaveConf, securityManager = securityManagerBad) + val slaveRpcEnv = RpcEnv.create("spark-slave", hostname, 0, slaveConf, securityManagerBad) val slaveTracker = new MapOutputTrackerWorker(conf) - val selection = slaveSystem.actorSelection( - AkkaUtils.address(AkkaUtils.protocol(slaveSystem), "spark", "localhost", boundPort, "MapOutputTracker")) - val timeout = AkkaUtils.lookupTimeout(conf) intercept[akka.actor.ActorNotFound] { - slaveTracker.trackerActor = Await.result(selection.resolveOne(timeout), timeout) + slaveTracker.trackerEndpoint = + slaveRpcEnv.setupEndpointRef("spark", rpcEnv.address, MapOutputTracker.ENDPOINT_NAME) } - actorSystem.shutdown() - slaveSystem.shutdown() + rpcEnv.shutdown() + slaveRpcEnv.shutdown() } @@ -352,35 +327,30 @@ class AkkaUtilsSuite extends FunSuite with LocalSparkContext with ResetSystemPro val securityManager = new SecurityManager(conf) val hostname = "localhost" - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, - conf = conf, securityManager = securityManager) - System.setProperty("spark.hostPort", hostname + ":" + boundPort) + val rpcEnv = RpcEnv.create("spark", hostname, 0, conf, securityManager) + System.setProperty("spark.hostPort", rpcEnv.address.hostPort) assert(securityManager.isAuthenticationEnabled() === false) val masterTracker = new MapOutputTrackerMaster(conf) - masterTracker.trackerActor = actorSystem.actorOf( - Props(new MapOutputTrackerMasterActor(masterTracker, conf)), "MapOutputTracker") + masterTracker.trackerEndpoint = rpcEnv.setupEndpoint(MapOutputTracker.ENDPOINT_NAME, + new MapOutputTrackerMasterEndpoint(rpcEnv, masterTracker, conf)) val slaveConf = sparkSSLConfig() val securityManagerBad = new SecurityManager(slaveConf) - val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, - conf = slaveConf, securityManager = securityManagerBad) + val slaveRpcEnv = RpcEnv.create("spark-slave", hostname, 0, slaveConf, securityManagerBad) val slaveTracker = new MapOutputTrackerWorker(conf) - val selection = slaveSystem.actorSelection( - AkkaUtils.address(AkkaUtils.protocol(slaveSystem), "spark", "localhost", boundPort, "MapOutputTracker")) - val timeout = AkkaUtils.lookupTimeout(conf) - val result = Try(Await.result(selection.resolveOne(timeout * 2), timeout)) - - result match { - case Failure(ex: ActorNotFound) => - case Failure(ex: TimeoutException) => - case r => fail(s"$r is neither Failure(ActorNotFound) nor Failure(TimeoutException)") + try { + slaveRpcEnv.setupEndpointRef("spark", rpcEnv.address, MapOutputTracker.ENDPOINT_NAME) + fail("should receive either ActorNotFound or TimeoutException") + } catch { + case e: ActorNotFound => + case e: TimeoutException => } - actorSystem.shutdown() - slaveSystem.shutdown() + rpcEnv.shutdown() + slaveRpcEnv.shutdown() } } From 49f38824a4770fc9017e6cc9b1803c4543b0c081 Mon Sep 17 00:00:00 2001 From: Masayoshi TSUZUKI Date: Mon, 6 Apr 2015 10:11:20 +0100 Subject: [PATCH 172/591] [SPARK-6673] spark-shell.cmd can't start in Windows even when spark was built added equivalent script to load-spark-env.sh Author: Masayoshi TSUZUKI Closes #5328 from tsudukim/feature/SPARK-6673 and squashes the following commits: aaefb19 [Masayoshi TSUZUKI] removed dust. be3405e [Masayoshi TSUZUKI] [SPARK-6673] spark-shell.cmd can't start in Windows even when spark was built --- bin/load-spark-env.cmd | 59 ++++++++++++++++++++++++++++++++++++++++++ bin/pyspark2.cmd | 3 +-- bin/run-example2.cmd | 3 +-- bin/spark-class2.cmd | 3 +-- 4 files changed, 62 insertions(+), 6 deletions(-) create mode 100644 bin/load-spark-env.cmd diff --git a/bin/load-spark-env.cmd b/bin/load-spark-env.cmd new file mode 100644 index 0000000000000..36d932c453b6f --- /dev/null +++ b/bin/load-spark-env.cmd @@ -0,0 +1,59 @@ +@echo off + +rem +rem Licensed to the Apache Software Foundation (ASF) under one or more +rem contributor license agreements. See the NOTICE file distributed with +rem this work for additional information regarding copyright ownership. +rem The ASF licenses this file to You under the Apache License, Version 2.0 +rem (the "License"); you may not use this file except in compliance with +rem the License. You may obtain a copy of the License at +rem +rem http://www.apache.org/licenses/LICENSE-2.0 +rem +rem Unless required by applicable law or agreed to in writing, software +rem distributed under the License is distributed on an "AS IS" BASIS, +rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +rem See the License for the specific language governing permissions and +rem limitations under the License. +rem + +rem This script loads spark-env.cmd if it exists, and ensures it is only loaded once. +rem spark-env.cmd is loaded from SPARK_CONF_DIR if set, or within the current directory's +rem conf/ subdirectory. + +if [%SPARK_ENV_LOADED%] == [] ( + set SPARK_ENV_LOADED=1 + + if not [%SPARK_CONF_DIR%] == [] ( + set user_conf_dir=%SPARK_CONF_DIR% + ) else ( + set user_conf_dir=%~dp0..\..\conf + ) + + call :LoadSparkEnv +) + +rem Setting SPARK_SCALA_VERSION if not already set. + +set ASSEMBLY_DIR2=%SPARK_HOME%/assembly/target/scala-2.11 +set ASSEMBLY_DIR1=%SPARK_HOME%/assembly/target/scala-2.10 + +if [%SPARK_SCALA_VERSION%] == [] ( + + if exist %ASSEMBLY_DIR2% if exist %ASSEMBLY_DIR1% ( + echo "Presence of build for both scala versions(SCALA 2.10 and SCALA 2.11) detected." + echo "Either clean one of them or, set SPARK_SCALA_VERSION=2.11 in spark-env.cmd." + exit 1 + ) + if exist %ASSEMBLY_DIR2% ( + set SPARK_SCALA_VERSION=2.11 + ) else ( + set SPARK_SCALA_VERSION=2.10 + ) +) +exit /b 0 + +:LoadSparkEnv +if exist "%user_conf_dir%\spark-env.cmd" ( + call "%user_conf_dir%\spark-env.cmd" +) diff --git a/bin/pyspark2.cmd b/bin/pyspark2.cmd index 4f5eb5e20614d..09b4149c2a439 100644 --- a/bin/pyspark2.cmd +++ b/bin/pyspark2.cmd @@ -20,8 +20,7 @@ rem rem Figure out where the Spark framework is installed set SPARK_HOME=%~dp0.. -rem Load environment variables from conf\spark-env.cmd, if it exists -if exist "%SPARK_HOME%\conf\spark-env.cmd" call "%SPARK_HOME%\conf\spark-env.cmd" +call %SPARK_HOME%\bin\load-spark-env.cmd rem Figure out which Python to use. if "x%PYSPARK_DRIVER_PYTHON%"=="x" ( diff --git a/bin/run-example2.cmd b/bin/run-example2.cmd index b49d0dcb4ff2d..c3e0221fb62e3 100644 --- a/bin/run-example2.cmd +++ b/bin/run-example2.cmd @@ -25,8 +25,7 @@ set FWDIR=%~dp0..\ rem Export this as SPARK_HOME set SPARK_HOME=%FWDIR% -rem Load environment variables from conf\spark-env.cmd, if it exists -if exist "%FWDIR%conf\spark-env.cmd" call "%FWDIR%conf\spark-env.cmd" +call %SPARK_HOME%\bin\load-spark-env.cmd rem Test that an argument was given if not "x%1"=="x" goto arg_given diff --git a/bin/spark-class2.cmd b/bin/spark-class2.cmd index 4ce727bc99128..4b3401d745f2a 100644 --- a/bin/spark-class2.cmd +++ b/bin/spark-class2.cmd @@ -20,8 +20,7 @@ rem rem Figure out where the Spark framework is installed set SPARK_HOME=%~dp0.. -rem Load environment variables from conf\spark-env.cmd, if it exists -if exist "%SPARK_HOME%\conf\spark-env.cmd" call "%SPARK_HOME%\conf\spark-env.cmd" +call %SPARK_HOME%\bin\load-spark-env.cmd rem Test that an argument was given if "x%1"=="x" ( From 9fe41252198df71f4629843d363db8c83f36440c Mon Sep 17 00:00:00 2001 From: Sean Owen Date: Mon, 6 Apr 2015 10:18:56 +0100 Subject: [PATCH 173/591] SPARK-6569 [STREAMING] Down-grade same-offset message in Kafka streaming to INFO Reduce "is the same as ending offset" message to INFO level per JIRA discussion Author: Sean Owen Closes #5366 from srowen/SPARK-6569 and squashes the following commits: 8a5b992 [Sean Owen] Reduce "is the same as ending offset" message to INFO level per JIRA discussion --- .../main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala index 4a83b715fa89d..a0b8a0c565210 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala @@ -86,7 +86,7 @@ class KafkaRDD[ val part = thePart.asInstanceOf[KafkaRDDPartition] assert(part.fromOffset <= part.untilOffset, errBeginAfterEnd(part)) if (part.fromOffset == part.untilOffset) { - log.warn(s"Beginning offset ${part.fromOffset} is the same as ending offset " + + log.info(s"Beginning offset ${part.fromOffset} is the same as ending offset " + s"skipping ${part.topic} ${part.partition}") Iterator.empty } else { From 30363ede8635f2548e444697dbcf60a795b61a84 Mon Sep 17 00:00:00 2001 From: Reza Zadeh Date: Mon, 6 Apr 2015 13:15:01 -0700 Subject: [PATCH 174/591] [MLlib] [SPARK-6713] Iterators in columnSimilarities for mapPartitionsWithIndex Use Iterators in columnSimilarities to allow mapPartitionsWithIndex to spill to disk. This could happen in a dense and large column - this way Spark can spill the pairs onto disk instead of building all the pairs before handing them to Spark. Another PR coming to update documentation. Author: Reza Zadeh Closes #5364 from rezazadeh/optmemsim and squashes the following commits: 47c90ba [Reza Zadeh] Iterators in columnSimilarities for flatMap --- .../mllib/linalg/distributed/RowMatrix.scala | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala index 961111507f2c2..9a89a6f3a515f 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala @@ -531,7 +531,6 @@ class RowMatrix( val rand = new XORShiftRandom(indx) val scaled = new Array[Double](p.size) iter.flatMap { row => - val buf = new ListBuffer[((Int, Int), Double)]() row match { case SparseVector(size, indices, values) => val nnz = indices.size @@ -540,8 +539,9 @@ class RowMatrix( scaled(k) = values(k) / q(indices(k)) k += 1 } - k = 0 - while (k < nnz) { + + Iterator.tabulate (nnz) { k => + val buf = new ListBuffer[((Int, Int), Double)]() val i = indices(k) val iVal = scaled(k) if (iVal != 0 && rand.nextDouble() < p(i)) { @@ -555,8 +555,8 @@ class RowMatrix( l += 1 } } - k += 1 - } + buf + }.flatten case DenseVector(values) => val n = values.size var i = 0 @@ -564,8 +564,8 @@ class RowMatrix( scaled(i) = values(i) / q(i) i += 1 } - i = 0 - while (i < n) { + Iterator.tabulate (n) { i => + val buf = new ListBuffer[((Int, Int), Double)]() val iVal = scaled(i) if (iVal != 0 && rand.nextDouble() < p(i)) { var j = i + 1 @@ -577,10 +577,9 @@ class RowMatrix( j += 1 } } - i += 1 - } + buf + }.flatten } - buf } }.reduceByKey(_ + _).map { case ((i, j), sim) => MatrixEntry(i.toLong, j.toLong, sim) From e40ea8742a8771ecd46b182f45b5fcd8bd6dd725 Mon Sep 17 00:00:00 2001 From: Volodymyr Lyubinets Date: Mon, 6 Apr 2015 18:00:51 -0700 Subject: [PATCH 175/591] [Minor] [SQL] [SPARK-6729] Minor fix for DriverQuirks get The function uses .substring(0, X), which will trigger OutOfBoundsException if string length is less than X. A better way to do this is to use startsWith, which won't error out in this case. Author: Volodymyr Lyubinets Closes #5378 from vlyubin/quirks and squashes the following commits: 504e8e0 [Volodymyr Lyubinets] Minor fix for DriverQuirks get --- .../main/scala/org/apache/spark/sql/jdbc/DriverQuirks.scala | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/DriverQuirks.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/DriverQuirks.scala index 1704be7fcbd30..0feabc4282f4a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/DriverQuirks.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/DriverQuirks.scala @@ -49,9 +49,9 @@ private[sql] object DriverQuirks { * Fetch the DriverQuirks class corresponding to a given database url. */ def get(url: String): DriverQuirks = { - if (url.substring(0, 10).equals("jdbc:mysql")) { + if (url.startsWith("jdbc:mysql")) { new MySQLQuirks() - } else if (url.substring(0, 15).equals("jdbc:postgresql")) { + } else if (url.startsWith("jdbc:postgresql")) { new PostgresQuirks() } else { new NoQuirks() From a0846c4b635eac8d8637c83d490177f881952d27 Mon Sep 17 00:00:00 2001 From: Josh Rosen Date: Mon, 6 Apr 2015 23:33:16 -0700 Subject: [PATCH 176/591] [SPARK-6716] Change SparkContext.DRIVER_IDENTIFIER from to driver Currently, the driver's executorId is set to ``. This choice of ID was present in older Spark versions, but it has started to cause problems now that executorIds are used in more contexts, such as Ganglia metric names or driver thread-dump links the web UI. The angle brackets must be escaped when embedding this ID in XML or as part of URLs and this has led to multiple problems: - https://issues.apache.org/jira/browse/SPARK-6484 - https://issues.apache.org/jira/browse/SPARK-4313 The simplest solution seems to be to change this id to something that does not contain any special characters, such as `driver`. I'm not sure whether we can perform this change in a patch release, since this ID may be considered a stable API by metrics users, but it's probably okay to do this in a major release as long as we document it in the release notes. Author: Josh Rosen Closes #5372 from JoshRosen/driver-id-fix and squashes the following commits: 42d3c10 [Josh Rosen] Clarify comment 0c5d04b [Josh Rosen] Add backwards-compatibility in BlockManagerId.isDriver 7ff12e0 [Josh Rosen] Change SparkContext.DRIVER_IDENTIFIER from to driver --- .../main/scala/org/apache/spark/SparkContext.scala | 12 +++++++++++- .../org/apache/spark/storage/BlockManagerId.scala | 5 ++++- .../org/apache/spark/storage/BlockManagerSuite.scala | 6 ++++++ 3 files changed, 21 insertions(+), 2 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 942c5975ece6d..3f1a7dd99d635 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -1901,7 +1901,17 @@ object SparkContext extends Logging { private[spark] val SPARK_JOB_INTERRUPT_ON_CANCEL = "spark.job.interruptOnCancel" - private[spark] val DRIVER_IDENTIFIER = "" + /** + * Executor id for the driver. In earlier versions of Spark, this was ``, but this was + * changed to `driver` because the angle brackets caused escaping issues in URLs and XML (see + * SPARK-6716 for more details). + */ + private[spark] val DRIVER_IDENTIFIER = "driver" + + /** + * Legacy version of DRIVER_IDENTIFIER, retained for backwards-compatibility. + */ + private[spark] val LEGACY_DRIVER_IDENTIFIER = "" // The following deprecated objects have already been copied to `object AccumulatorParam` to // make the compiler find them automatically. They are duplicate codes only for backward diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala index a6f1ebf325a7c..69ac37511e730 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerId.scala @@ -60,7 +60,10 @@ class BlockManagerId private ( def port: Int = port_ - def isDriver: Boolean = { executorId == SparkContext.DRIVER_IDENTIFIER } + def isDriver: Boolean = { + executorId == SparkContext.DRIVER_IDENTIFIER || + executorId == SparkContext.LEGACY_DRIVER_IDENTIFIER + } override def writeExternal(out: ObjectOutput): Unit = Utils.tryOrIOException { out.writeUTF(executorId_) diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala index 283090e3bdb1f..6dc5bc4cb08c4 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala @@ -139,6 +139,12 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach assert(id2_.eq(id1), "Deserialized id2 is not the same object as original id1") } + test("BlockManagerId.isDriver() backwards-compatibility with legacy driver ids (SPARK-6716)") { + assert(BlockManagerId(SparkContext.DRIVER_IDENTIFIER, "XXX", 1).isDriver) + assert(BlockManagerId(SparkContext.LEGACY_DRIVER_IDENTIFIER, "XXX", 1).isDriver) + assert(!BlockManagerId("notADriverIdentifier", "XXX", 1).isDriver) + } + test("master + 1 manager interaction") { store = makeBlockManager(20000) val a1 = new Array[Byte](4000) From 6f0d55d76f758d217fd18ffa0ccf273d7ab0377b Mon Sep 17 00:00:00 2001 From: Matt Aasted Date: Mon, 6 Apr 2015 23:50:48 -0700 Subject: [PATCH 177/591] [SPARK-6636] Use public DNS hostname everywhere in spark_ec2.py The spark_ec2.py script uses public_dns_name everywhere in the script except for testing ssh availability, which is done using the public ip address of the instances. This breaks the script for users who are deploying the cluster with a private-network-only security group. The fix is to use public_dns_name in the remaining place. Author: Matt Aasted Closes #5302 from aasted/master and squashes the following commits: 60cf6ee [Matt Aasted] [SPARK-6636] Use public DNS hostname everywhere in spark_ec2.py --- ec2/spark_ec2.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py index 5507a9c5a4733..879a52cef8ff0 100755 --- a/ec2/spark_ec2.py +++ b/ec2/spark_ec2.py @@ -809,7 +809,7 @@ def is_cluster_ssh_available(cluster_instances, opts): Check if SSH is available on all the instances in a cluster. """ for i in cluster_instances: - if not is_ssh_available(host=i.ip_address, opts=opts): + if not is_ssh_available(host=i.public_dns_name, opts=opts): return False else: return True From ae980eb41c00b5f1f64c650f267b884e864693f0 Mon Sep 17 00:00:00 2001 From: Sasaki Toru Date: Tue, 7 Apr 2015 01:55:32 -0700 Subject: [PATCH 178/591] [SPARK-6736][GraphX][Doc]Example of Graph#aggregateMessages has error Example of Graph#aggregateMessages has error. Since aggregateMessages is a method of Graph, It should be written "rawGraph.aggregateMessages" Author: Sasaki Toru Closes #5388 from sasakitoa/aggregateMessagesExample and squashes the following commits: b1d631b [Sasaki Toru] Example of Graph#aggregateMessages has error --- graphx/src/main/scala/org/apache/spark/graphx/Graph.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala index 8494d06b1cdb7..36dc7b0f86c89 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala @@ -409,7 +409,7 @@ abstract class Graph[VD: ClassTag, ED: ClassTag] protected () extends Serializab * {{{ * val rawGraph: Graph[_, _] = Graph.textFile("twittergraph") * val inDeg: RDD[(VertexId, Int)] = - * aggregateMessages[Int](ctx => ctx.sendToDst(1), _ + _) + * rawGraph.aggregateMessages[Int](ctx => ctx.sendToDst(1), _ + _) * }}} * * @note By expressing computation at the edge level we achieve From b65bad65c3500475b974ca0219f218eef296db2c Mon Sep 17 00:00:00 2001 From: WangTaoTheTonic Date: Tue, 7 Apr 2015 08:36:25 -0500 Subject: [PATCH 179/591] [SPARK-3591][YARN]fire and forget for YARN cluster mode https://issues.apache.org/jira/browse/SPARK-3591 The output after this patch: >doggie153:/opt/oss/spark-1.3.0-bin-hadoop2.4/bin # ./spark-submit --class org.apache.spark.examples.SparkPi --master yarn-cluster ../lib/spark-examples*.jar 15/03/31 21:15:25 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 15/03/31 21:15:25 INFO RMProxy: Connecting to ResourceManager at doggie153/10.177.112.153:8032 15/03/31 21:15:25 INFO Client: Requesting a new application from cluster with 4 NodeManagers 15/03/31 21:15:25 INFO Client: Verifying our application has not requested more than the maximum memory capability of the cluster (8192 MB per container) 15/03/31 21:15:25 INFO Client: Will allocate AM container, with 896 MB memory including 384 MB overhead 15/03/31 21:15:25 INFO Client: Setting up container launch context for our AM 15/03/31 21:15:25 INFO Client: Preparing resources for our AM container 15/03/31 21:15:26 INFO Client: Uploading resource file:/opt/oss/spark-1.3.0-bin-hadoop2.4/lib/spark-assembly-1.4.0-SNAPSHOT-hadoop2.4.1.jar -> hdfs://doggie153:9000/user/root/.sparkStaging/application_1427257505534_0016/spark-assembly-1.4.0-SNAPSHOT-hadoop2.4.1.jar 15/03/31 21:15:27 INFO Client: Uploading resource file:/opt/oss/spark-1.3.0-bin-hadoop2.4/lib/spark-examples-1.3.0-hadoop2.4.0.jar -> hdfs://doggie153:9000/user/root/.sparkStaging/application_1427257505534_0016/spark-examples-1.3.0-hadoop2.4.0.jar 15/03/31 21:15:28 INFO Client: Setting up the launch environment for our AM container 15/03/31 21:15:28 INFO SecurityManager: Changing view acls to: root 15/03/31 21:15:28 INFO SecurityManager: Changing modify acls to: root 15/03/31 21:15:28 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(root); users with modify permissions: Set(root) 15/03/31 21:15:28 INFO Client: Submitting application 16 to ResourceManager 15/03/31 21:15:28 INFO YarnClientImpl: Submitted application application_1427257505534_0016 15/03/31 21:15:28 INFO Client: ... waiting before polling ResourceManager for application state 15/03/31 21:15:33 INFO Client: ... polling ResourceManager for application state 15/03/31 21:15:33 INFO Client: Application report for application_1427257505534_0016 (state: RUNNING) 15/03/31 21:15:33 INFO Client: client token: N/A diagnostics: N/A ApplicationMaster host: doggie157 ApplicationMaster RPC port: 0 queue: default start time: 1427807728307 final status: UNDEFINED tracking URL: http://doggie153:8088/proxy/application_1427257505534_0016/ user: root /cc andrewor14 Author: WangTaoTheTonic Closes #5297 from WangTaoTheTonic/SPARK-3591 and squashes the following commits: c76d232 [WangTaoTheTonic] wrap lines 16c90a8 [WangTaoTheTonic] move up lines to avoid duplicate fea390d [WangTaoTheTonic] log failed/killed report, style and comment be1cc2e [WangTaoTheTonic] reword f0bc54f [WangTaoTheTonic] minor: expose appid in excepiton messages ba9b22b [WangTaoTheTonic] wrong config name e1a4013 [WangTaoTheTonic] revert to the old version and do some robust 19706c0 [WangTaoTheTonic] add a config to control whether to forget 0cbdce8 [WangTaoTheTonic] fire and forget for YARN cluster mode --- .../org/apache/spark/deploy/Client.scala | 2 +- .../deploy/rest/StandaloneRestClient.scala | 2 +- docs/running-on-yarn.md | 9 ++ .../org/apache/spark/deploy/yarn/Client.scala | 83 +++++++++++-------- 4 files changed, 61 insertions(+), 35 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/deploy/Client.scala b/core/src/main/scala/org/apache/spark/deploy/Client.scala index 65238af2caa24..8d13b2a2cd4f3 100644 --- a/core/src/main/scala/org/apache/spark/deploy/Client.scala +++ b/core/src/main/scala/org/apache/spark/deploy/Client.scala @@ -89,7 +89,7 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) /* Find out driver status then exit the JVM */ def pollAndReportStatus(driverId: String) { - println(s"... waiting before polling master for driver state") + println("... waiting before polling master for driver state") Thread.sleep(5000) println("... polling master for driver state") val statusFuture = (masterActor ? RequestDriverStatus(driverId))(timeout) diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestClient.scala b/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestClient.scala index a3539e44bd2f9..b8fd406fb6f9a 100644 --- a/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestClient.scala +++ b/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestClient.scala @@ -245,7 +245,7 @@ private[deploy] class StandaloneRestClient extends Logging { } } else { val failMessage = Option(submitResponse.message).map { ": " + _ }.getOrElse("") - logError("Application submission failed" + failMessage) + logError(s"Application submission failed$failMessage") } } diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md index d9f3eb2b74b18..b7e68d4f71714 100644 --- a/docs/running-on-yarn.md +++ b/docs/running-on-yarn.md @@ -196,6 +196,15 @@ Most of the configs are the same for Spark on YARN as for other deployment modes It should be no larger than the global number of max attempts in the YARN configuration. + + spark.yarn.submit.waitAppCompletion + true + + In YARN cluster mode, controls whether the client waits to exit until the application completes. + If set to true, the client process will stay alive reporting the application's status. + Otherwise, the client process will exit after submission. + + # Launching Spark on YARN diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 61f8fc3f5a014..79d55a09eb671 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -66,6 +66,8 @@ private[spark] class Client( private val executorMemoryOverhead = args.executorMemoryOverhead // MB private val distCacheMgr = new ClientDistributedCacheManager() private val isClusterMode = args.isClusterMode + private val fireAndForget = isClusterMode && + !sparkConf.getBoolean("spark.yarn.submit.waitAppCompletion", true) def stop(): Unit = yarnClient.stop() @@ -564,31 +566,13 @@ private[spark] class Client( if (logApplicationReport) { logInfo(s"Application report for $appId (state: $state)") - val details = Seq[(String, String)]( - ("client token", getClientToken(report)), - ("diagnostics", report.getDiagnostics), - ("ApplicationMaster host", report.getHost), - ("ApplicationMaster RPC port", report.getRpcPort.toString), - ("queue", report.getQueue), - ("start time", report.getStartTime.toString), - ("final status", report.getFinalApplicationStatus.toString), - ("tracking URL", report.getTrackingUrl), - ("user", report.getUser) - ) - - // Use more loggable format if value is null or empty - val formattedDetails = details - .map { case (k, v) => - val newValue = Option(v).filter(_.nonEmpty).getOrElse("N/A") - s"\n\t $k: $newValue" } - .mkString("") // If DEBUG is enabled, log report details every iteration // Otherwise, log them every time the application changes state if (log.isDebugEnabled) { - logDebug(formattedDetails) + logDebug(formatReportDetails(report)) } else if (lastState != state) { - logInfo(formattedDetails) + logInfo(formatReportDetails(report)) } } @@ -609,24 +593,57 @@ private[spark] class Client( throw new SparkException("While loop is depleted! This should never happen...") } + private def formatReportDetails(report: ApplicationReport): String = { + val details = Seq[(String, String)]( + ("client token", getClientToken(report)), + ("diagnostics", report.getDiagnostics), + ("ApplicationMaster host", report.getHost), + ("ApplicationMaster RPC port", report.getRpcPort.toString), + ("queue", report.getQueue), + ("start time", report.getStartTime.toString), + ("final status", report.getFinalApplicationStatus.toString), + ("tracking URL", report.getTrackingUrl), + ("user", report.getUser) + ) + + // Use more loggable format if value is null or empty + details.map { case (k, v) => + val newValue = Option(v).filter(_.nonEmpty).getOrElse("N/A") + s"\n\t $k: $newValue" + }.mkString("") + } + /** - * Submit an application to the ResourceManager and monitor its state. - * This continues until the application has exited for any reason. + * Submit an application to the ResourceManager. + * If set spark.yarn.submit.waitAppCompletion to true, it will stay alive + * reporting the application's status until the application has exited for any reason. + * Otherwise, the client process will exit after submission. * If the application finishes with a failed, killed, or undefined status, * throw an appropriate SparkException. */ def run(): Unit = { - val (yarnApplicationState, finalApplicationStatus) = monitorApplication(submitApplication()) - if (yarnApplicationState == YarnApplicationState.FAILED || - finalApplicationStatus == FinalApplicationStatus.FAILED) { - throw new SparkException("Application finished with failed status") - } - if (yarnApplicationState == YarnApplicationState.KILLED || - finalApplicationStatus == FinalApplicationStatus.KILLED) { - throw new SparkException("Application is killed") - } - if (finalApplicationStatus == FinalApplicationStatus.UNDEFINED) { - throw new SparkException("The final status of application is undefined") + val appId = submitApplication() + if (fireAndForget) { + val report = getApplicationReport(appId) + val state = report.getYarnApplicationState + logInfo(s"Application report for $appId (state: $state)") + logInfo(formatReportDetails(report)) + if (state == YarnApplicationState.FAILED || state == YarnApplicationState.KILLED) { + throw new SparkException(s"Application $appId finished with status: $state") + } + } else { + val (yarnApplicationState, finalApplicationStatus) = monitorApplication(appId) + if (yarnApplicationState == YarnApplicationState.FAILED || + finalApplicationStatus == FinalApplicationStatus.FAILED) { + throw new SparkException(s"Application $appId finished with failed status") + } + if (yarnApplicationState == YarnApplicationState.KILLED || + finalApplicationStatus == FinalApplicationStatus.KILLED) { + throw new SparkException(s"Application $appId is killed") + } + if (finalApplicationStatus == FinalApplicationStatus.UNDEFINED) { + throw new SparkException(s"The final status of application $appId is undefined") + } } } } From 7162ecf88624615c78a332de482f5defd297e415 Mon Sep 17 00:00:00 2001 From: Vinod K C Date: Tue, 7 Apr 2015 10:42:08 -0700 Subject: [PATCH 180/591] [SPARK-6733][ Scheduler]Added scala.language.existentials Author: Vinod K C Closes #5384 from vinodkc/Suppression_Scala_existential_code and squashes the following commits: 82a3a1f [Vinod K C] Added scala.language.existentials --- .../src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala | 1 + .../test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala | 1 + 2 files changed, 2 insertions(+) diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala index 917cce1f9686c..c82ae4baa3630 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -24,6 +24,7 @@ import java.util.concurrent.atomic.AtomicInteger import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, Map, Stack} import scala.concurrent.duration._ +import scala.language.existentials import scala.language.postfixOps import scala.util.control.NonFatal diff --git a/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala index 29d4ec5f85c1e..fc7349330cf86 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala @@ -22,6 +22,7 @@ import java.util.Random import scala.collection.mutable import scala.collection.mutable.ArrayBuffer +import scala.language.existentials import com.github.fommil.netlib.BLAS.{getInstance => blas} import org.scalatest.FunSuite From 2c32bef1790dac6f77ef9674f6106c2e24ea0338 Mon Sep 17 00:00:00 2001 From: sksamuel Date: Tue, 7 Apr 2015 10:43:22 -0700 Subject: [PATCH 181/591] Replace use of .size with .length for Arrays Invoking .size on arrays is valid, but requires an implicit conversion to SeqLike. This incurs a compile time overhead and more importantly a runtime overhead, as the Array must be wrapped before the method can be invoked. For example, the difference in generated byte code is: public int withSize(); Code: 0: getstatic #23 // Field scala/Predef$.MODULE$:Lscala/Predef$; 3: aload_0 4: invokevirtual #25 // Method array:()[I 7: invokevirtual #29 // Method scala/Predef$.intArrayOps:([I)Lscala/collection/mutable/ArrayOps; 10: invokeinterface #34, 1 // InterfaceMethod scala/collection/mutable/ArrayOps.size:()I 15: ireturn public int withLength(); Code: 0: aload_0 1: invokevirtual #25 // Method array:()[I 4: arraylength 5: ireturn Author: sksamuel Closes #5376 from sksamuel/master and squashes the following commits: 77ec261 [sksamuel] Replace use of .size with .length for Arrays. --- .../apache/spark/network/nio/Connection.scala | 2 +- .../apache/spark/rdd/AsyncRDDActions.scala | 10 ++++----- .../scala/org/apache/spark/rdd/BlockRDD.scala | 2 +- .../org/apache/spark/rdd/CartesianRDD.scala | 4 ++-- .../org/apache/spark/rdd/CheckpointRDD.scala | 2 +- .../org/apache/spark/rdd/CoGroupedRDD.scala | 4 ++-- .../org/apache/spark/rdd/CoalescedRDD.scala | 2 +- .../apache/spark/rdd/DoubleRDDFunctions.scala | 4 ++-- .../spark/rdd/OrderedRDDFunctions.scala | 2 +- .../apache/spark/rdd/PairRDDFunctions.scala | 2 +- .../main/scala/org/apache/spark/rdd/RDD.scala | 22 +++++++++---------- .../apache/spark/rdd/RDDCheckpointData.scala | 6 ++--- .../org/apache/spark/rdd/SubtractedRDD.scala | 2 +- .../scala/org/apache/spark/rdd/UnionRDD.scala | 6 ++--- .../spark/rdd/ZippedPartitionsRDD.scala | 4 ++-- .../apache/spark/rdd/ZippedWithIndexRDD.scala | 2 +- .../org/apache/spark/storage/RDDInfo.scala | 2 +- .../apache/spark/ui/ConsoleProgressBar.scala | 4 ++-- .../apache/spark/util/collection/BitSet.scala | 2 +- 19 files changed, 42 insertions(+), 42 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/network/nio/Connection.scala b/core/src/main/scala/org/apache/spark/network/nio/Connection.scala index 04eb2bf9ba4ab..6b898bd4bfc1b 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/Connection.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/Connection.scala @@ -181,7 +181,7 @@ abstract class Connection(val channel: SocketChannel, val selector: Selector, buffer.get(bytes) bytes.foreach(x => print(x + " ")) buffer.position(curPosition) - print(" (" + bytes.size + ")") + print(" (" + bytes.length + ")") } def printBuffer(buffer: ByteBuffer, position: Int, length: Int) { diff --git a/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala b/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala index 646df283ac069..3406a7e97e368 100644 --- a/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala @@ -45,7 +45,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi } result }, - Range(0, self.partitions.size), + Range(0, self.partitions.length), (index: Int, data: Long) => totalCount.addAndGet(data), totalCount.get()) } @@ -54,8 +54,8 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi * Returns a future for retrieving all elements of this RDD. */ def collectAsync(): FutureAction[Seq[T]] = { - val results = new Array[Array[T]](self.partitions.size) - self.context.submitJob[T, Array[T], Seq[T]](self, _.toArray, Range(0, self.partitions.size), + val results = new Array[Array[T]](self.partitions.length) + self.context.submitJob[T, Array[T], Seq[T]](self, _.toArray, Range(0, self.partitions.length), (index, data) => results(index) = data, results.flatten.toSeq) } @@ -111,7 +111,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi */ def foreachAsync(f: T => Unit): FutureAction[Unit] = { val cleanF = self.context.clean(f) - self.context.submitJob[T, Unit, Unit](self, _.foreach(cleanF), Range(0, self.partitions.size), + self.context.submitJob[T, Unit, Unit](self, _.foreach(cleanF), Range(0, self.partitions.length), (index, data) => Unit, Unit) } @@ -119,7 +119,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi * Applies a function f to each partition of this RDD. */ def foreachPartitionAsync(f: Iterator[T] => Unit): FutureAction[Unit] = { - self.context.submitJob[T, Unit, Unit](self, f, Range(0, self.partitions.size), + self.context.submitJob[T, Unit, Unit](self, f, Range(0, self.partitions.length), (index, data) => Unit, Unit) } } diff --git a/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala b/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala index fffa1911f5bc2..71578d1210fde 100644 --- a/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala @@ -36,7 +36,7 @@ class BlockRDD[T: ClassTag](@transient sc: SparkContext, @transient val blockIds override def getPartitions: Array[Partition] = { assertValid() - (0 until blockIds.size).map(i => { + (0 until blockIds.length).map(i => { new BlockRDDPartition(blockIds(i), i).asInstanceOf[Partition] }).toArray } diff --git a/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala index 9059eb13bb5d8..c1d6971787572 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala @@ -53,11 +53,11 @@ class CartesianRDD[T: ClassTag, U: ClassTag]( extends RDD[Pair[T, U]](sc, Nil) with Serializable { - val numPartitionsInRdd2 = rdd2.partitions.size + val numPartitionsInRdd2 = rdd2.partitions.length override def getPartitions: Array[Partition] = { // create the cross product split - val array = new Array[Partition](rdd1.partitions.size * rdd2.partitions.size) + val array = new Array[Partition](rdd1.partitions.length * rdd2.partitions.length) for (s1 <- rdd1.partitions; s2 <- rdd2.partitions) { val idx = s1.index * numPartitionsInRdd2 + s2.index array(idx) = new CartesianPartition(idx, rdd1, rdd2, s1.index, s2.index) diff --git a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala index 760c0fa3ac96a..0d130dd4c7a60 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala @@ -49,7 +49,7 @@ class CheckpointRDD[T: ClassTag](sc: SparkContext, val checkpointPath: String) if (fs.exists(cpath)) { val dirContents = fs.listStatus(cpath).map(_.getPath) val partitionFiles = dirContents.filter(_.getName.startsWith("part-")).map(_.toString).sorted - val numPart = partitionFiles.size + val numPart = partitionFiles.length if (numPart > 0 && (! partitionFiles(0).endsWith(CheckpointRDD.splitIdToFile(0)) || ! partitionFiles(numPart-1).endsWith(CheckpointRDD.splitIdToFile(numPart-1)))) { throw new SparkException("Invalid checkpoint directory: " + checkpointPath) diff --git a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala index 07398a6fa62f6..7021a339e879b 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala @@ -99,7 +99,7 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part: override def getPartitions: Array[Partition] = { val array = new Array[Partition](part.numPartitions) - for (i <- 0 until array.size) { + for (i <- 0 until array.length) { // Each CoGroupPartition will have a dependency per contributing RDD array(i) = new CoGroupPartition(i, rdds.zipWithIndex.map { case (rdd, j) => // Assume each RDD contributed a single dependency, and get it @@ -120,7 +120,7 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part: val sparkConf = SparkEnv.get.conf val externalSorting = sparkConf.getBoolean("spark.shuffle.spill", true) val split = s.asInstanceOf[CoGroupPartition] - val numRdds = split.deps.size + val numRdds = split.deps.length // A list of (rdd iterator, dependency number) pairs val rddIterators = new ArrayBuffer[(Iterator[Product2[K, Any]], Int)] diff --git a/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala index 5117ccfabfcc2..0c1b02c07d09f 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CoalescedRDD.scala @@ -166,7 +166,7 @@ private class PartitionCoalescer(maxPartitions: Int, prev: RDD[_], balanceSlack: // determines the tradeoff between load-balancing the partitions sizes and their locality // e.g. balanceSlack=0.10 means that it allows up to 10% imbalance in favor of locality - val slack = (balanceSlack * prev.partitions.size).toInt + val slack = (balanceSlack * prev.partitions.length).toInt var noLocality = true // if true if no preferredLocations exists for parent RDD diff --git a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala index 71e6e300fec5f..29ca3e9c4bd04 100644 --- a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala @@ -70,7 +70,7 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { @Experimental def meanApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = { val processPartition = (ctx: TaskContext, ns: Iterator[Double]) => StatCounter(ns) - val evaluator = new MeanEvaluator(self.partitions.size, confidence) + val evaluator = new MeanEvaluator(self.partitions.length, confidence) self.context.runApproximateJob(self, processPartition, evaluator, timeout) } @@ -81,7 +81,7 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { @Experimental def sumApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = { val processPartition = (ctx: TaskContext, ns: Iterator[Double]) => StatCounter(ns) - val evaluator = new SumEvaluator(self.partitions.size, confidence) + val evaluator = new SumEvaluator(self.partitions.length, confidence) self.context.runApproximateJob(self, processPartition, evaluator, timeout) } diff --git a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala index 6fdfdb734d1b8..6afe50161dacd 100644 --- a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala @@ -56,7 +56,7 @@ class OrderedRDDFunctions[K : Ordering : ClassTag, * order of the keys). */ // TODO: this currently doesn't work on P other than Tuple2! - def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.size) + def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.length) : RDD[(K, V)] = { val part = new RangePartitioner(numPartitions, self, ascending) diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala index bf1303d39592d..05351ba4ff76b 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala @@ -823,7 +823,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * RDD will be <= us. */ def subtractByKey[W: ClassTag](other: RDD[(K, W)]): RDD[(K, V)] = - subtractByKey(other, self.partitioner.getOrElse(new HashPartitioner(self.partitions.size))) + subtractByKey(other, self.partitioner.getOrElse(new HashPartitioner(self.partitions.length))) /** Return an RDD with the pairs from `this` whose keys are not in `other`. */ def subtractByKey[W: ClassTag](other: RDD[(K, W)], numPartitions: Int): RDD[(K, V)] = diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala index ddbfd5624e741..d80d94a588346 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -316,7 +316,7 @@ abstract class RDD[T: ClassTag]( /** * Return a new RDD containing the distinct elements in this RDD. */ - def distinct(): RDD[T] = distinct(partitions.size) + def distinct(): RDD[T] = distinct(partitions.length) /** * Return a new RDD that has exactly numPartitions partitions. @@ -488,7 +488,7 @@ abstract class RDD[T: ClassTag]( def sortBy[K]( f: (T) => K, ascending: Boolean = true, - numPartitions: Int = this.partitions.size) + numPartitions: Int = this.partitions.length) (implicit ord: Ordering[K], ctag: ClassTag[K]): RDD[T] = this.keyBy[K](f) .sortByKey(ascending, numPartitions) @@ -852,7 +852,7 @@ abstract class RDD[T: ClassTag]( * RDD will be <= us. */ def subtract(other: RDD[T]): RDD[T] = - subtract(other, partitioner.getOrElse(new HashPartitioner(partitions.size))) + subtract(other, partitioner.getOrElse(new HashPartitioner(partitions.length))) /** * Return an RDD with the elements from `this` that are not in `other`. @@ -986,14 +986,14 @@ abstract class RDD[T: ClassTag]( combOp: (U, U) => U, depth: Int = 2): U = { require(depth >= 1, s"Depth must be greater than or equal to 1 but got $depth.") - if (partitions.size == 0) { + if (partitions.length == 0) { return Utils.clone(zeroValue, context.env.closureSerializer.newInstance()) } val cleanSeqOp = context.clean(seqOp) val cleanCombOp = context.clean(combOp) val aggregatePartition = (it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp, cleanCombOp) var partiallyAggregated = mapPartitions(it => Iterator(aggregatePartition(it))) - var numPartitions = partiallyAggregated.partitions.size + var numPartitions = partiallyAggregated.partitions.length val scale = math.max(math.ceil(math.pow(numPartitions, 1.0 / depth)).toInt, 2) // If creating an extra level doesn't help reduce the wall-clock time, we stop tree aggregation. while (numPartitions > scale + numPartitions / scale) { @@ -1026,7 +1026,7 @@ abstract class RDD[T: ClassTag]( } result } - val evaluator = new CountEvaluator(partitions.size, confidence) + val evaluator = new CountEvaluator(partitions.length, confidence) sc.runApproximateJob(this, countElements, evaluator, timeout) } @@ -1061,7 +1061,7 @@ abstract class RDD[T: ClassTag]( } map } - val evaluator = new GroupedCountEvaluator[T](partitions.size, confidence) + val evaluator = new GroupedCountEvaluator[T](partitions.length, confidence) sc.runApproximateJob(this, countPartition, evaluator, timeout) } @@ -1140,7 +1140,7 @@ abstract class RDD[T: ClassTag]( * the same index assignments, you should sort the RDD with sortByKey() or save it to a file. */ def zipWithUniqueId(): RDD[(T, Long)] = { - val n = this.partitions.size.toLong + val n = this.partitions.length.toLong this.mapPartitionsWithIndex { case (k, iter) => iter.zipWithIndex.map { case (item, i) => (item, i * n + k) @@ -1243,7 +1243,7 @@ abstract class RDD[T: ClassTag]( queue ++= util.collection.Utils.takeOrdered(items, num)(ord) Iterator.single(queue) } - if (mapRDDs.partitions.size == 0) { + if (mapRDDs.partitions.length == 0) { Array.empty } else { mapRDDs.reduce { (queue1, queue2) => @@ -1489,7 +1489,7 @@ abstract class RDD[T: ClassTag]( } // The first RDD in the dependency stack has no parents, so no need for a +- def firstDebugString(rdd: RDD[_]): Seq[String] = { - val partitionStr = "(" + rdd.partitions.size + ")" + val partitionStr = "(" + rdd.partitions.length + ")" val leftOffset = (partitionStr.length - 1) / 2 val nextPrefix = (" " * leftOffset) + "|" + (" " * (partitionStr.length - leftOffset)) @@ -1499,7 +1499,7 @@ abstract class RDD[T: ClassTag]( } ++ debugChildren(rdd, nextPrefix) } def shuffleDebugString(rdd: RDD[_], prefix: String = "", isLastChild: Boolean): Seq[String] = { - val partitionStr = "(" + rdd.partitions.size + ")" + val partitionStr = "(" + rdd.partitions.length + ")" val leftOffset = (partitionStr.length - 1) / 2 val thisPrefix = prefix.replaceAll("\\|\\s+$", "") val nextPrefix = ( diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala index f67e5f1857979..6afd63d537d75 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala @@ -94,10 +94,10 @@ private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T]) new SerializableWritable(rdd.context.hadoopConfiguration)) rdd.context.runJob(rdd, CheckpointRDD.writeToFile[T](path.toString, broadcastedConf) _) val newRDD = new CheckpointRDD[T](rdd.context, path.toString) - if (newRDD.partitions.size != rdd.partitions.size) { + if (newRDD.partitions.length != rdd.partitions.length) { throw new SparkException( - "Checkpoint RDD " + newRDD + "(" + newRDD.partitions.size + ") has different " + - "number of partitions than original RDD " + rdd + "(" + rdd.partitions.size + ")") + "Checkpoint RDD " + newRDD + "(" + newRDD.partitions.length + ") has different " + + "number of partitions than original RDD " + rdd + "(" + rdd.partitions.length + ")") } // Change the dependencies and partitions of the RDD diff --git a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala index c27f435eb9c5a..e9d745588ee9a 100644 --- a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala @@ -76,7 +76,7 @@ private[spark] class SubtractedRDD[K: ClassTag, V: ClassTag, W: ClassTag]( override def getPartitions: Array[Partition] = { val array = new Array[Partition](part.numPartitions) - for (i <- 0 until array.size) { + for (i <- 0 until array.length) { // Each CoGroupPartition will depend on rdd1 and rdd2 array(i) = new CoGroupPartition(i, Seq(rdd1, rdd2).zipWithIndex.map { case (rdd, j) => dependencies(j) match { diff --git a/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala b/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala index 4239e7e22af89..3986645350a82 100644 --- a/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala @@ -63,7 +63,7 @@ class UnionRDD[T: ClassTag]( extends RDD[T](sc, Nil) { // Nil since we implement getDependencies override def getPartitions: Array[Partition] = { - val array = new Array[Partition](rdds.map(_.partitions.size).sum) + val array = new Array[Partition](rdds.map(_.partitions.length).sum) var pos = 0 for ((rdd, rddIndex) <- rdds.zipWithIndex; split <- rdd.partitions) { array(pos) = new UnionPartition(pos, rdd, rddIndex, split.index) @@ -76,8 +76,8 @@ class UnionRDD[T: ClassTag]( val deps = new ArrayBuffer[Dependency[_]] var pos = 0 for (rdd <- rdds) { - deps += new RangeDependency(rdd, 0, pos, rdd.partitions.size) - pos += rdd.partitions.size + deps += new RangeDependency(rdd, 0, pos, rdd.partitions.length) + pos += rdd.partitions.length } deps } diff --git a/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala index d0be304762e1f..a96b6c3d23454 100644 --- a/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala @@ -52,8 +52,8 @@ private[spark] abstract class ZippedPartitionsBaseRDD[V: ClassTag]( if (preservesPartitioning) firstParent[Any].partitioner else None override def getPartitions: Array[Partition] = { - val numParts = rdds.head.partitions.size - if (!rdds.forall(rdd => rdd.partitions.size == numParts)) { + val numParts = rdds.head.partitions.length + if (!rdds.forall(rdd => rdd.partitions.length == numParts)) { throw new IllegalArgumentException("Can't zip RDDs with unequal numbers of partitions") } Array.tabulate[Partition](numParts) { i => diff --git a/core/src/main/scala/org/apache/spark/rdd/ZippedWithIndexRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ZippedWithIndexRDD.scala index 8c43a559409f2..523aaf2b860b5 100644 --- a/core/src/main/scala/org/apache/spark/rdd/ZippedWithIndexRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/ZippedWithIndexRDD.scala @@ -41,7 +41,7 @@ class ZippedWithIndexRDD[T: ClassTag](@transient prev: RDD[T]) extends RDD[(T, L /** The start index of each partition. */ @transient private val startIndices: Array[Long] = { - val n = prev.partitions.size + val n = prev.partitions.length if (n == 0) { Array[Long]() } else if (n == 1) { diff --git a/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala b/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala index 0186eb30a1905..034525b56f59c 100644 --- a/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala +++ b/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala @@ -52,6 +52,6 @@ class RDDInfo( private[spark] object RDDInfo { def fromRdd(rdd: RDD[_]): RDDInfo = { val rddName = Option(rdd.name).getOrElse(rdd.id.toString) - new RDDInfo(rdd.id, rddName, rdd.partitions.size, rdd.getStorageLevel) + new RDDInfo(rdd.id, rddName, rdd.partitions.length, rdd.getStorageLevel) } } diff --git a/core/src/main/scala/org/apache/spark/ui/ConsoleProgressBar.scala b/core/src/main/scala/org/apache/spark/ui/ConsoleProgressBar.scala index 67f572e79314d..77c0bc8b5360a 100644 --- a/core/src/main/scala/org/apache/spark/ui/ConsoleProgressBar.scala +++ b/core/src/main/scala/org/apache/spark/ui/ConsoleProgressBar.scala @@ -65,7 +65,7 @@ private[spark] class ConsoleProgressBar(sc: SparkContext) extends Logging { val stageIds = sc.statusTracker.getActiveStageIds() val stages = stageIds.map(sc.statusTracker.getStageInfo).flatten.filter(_.numTasks() > 1) .filter(now - _.submissionTime() > FIRST_DELAY).sortBy(_.stageId()) - if (stages.size > 0) { + if (stages.length > 0) { show(now, stages.take(3)) // display at most 3 stages in same time } } @@ -81,7 +81,7 @@ private[spark] class ConsoleProgressBar(sc: SparkContext) extends Logging { val total = s.numTasks() val header = s"[Stage ${s.stageId()}:" val tailer = s"(${s.numCompletedTasks()} + ${s.numActiveTasks()}) / $total]" - val w = width - header.size - tailer.size + val w = width - header.length - tailer.length val bar = if (w > 0) { val percent = w * s.numCompletedTasks() / total (0 until w).map { i => diff --git a/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala b/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala index f79e8e0491ea1..41cb8cfe2afa3 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala @@ -39,7 +39,7 @@ class BitSet(numBits: Int) extends Serializable { val wordIndex = bitIndex >> 6 // divide by 64 var i = 0 while(i < wordIndex) { words(i) = -1; i += 1 } - if(wordIndex < words.size) { + if(wordIndex < words.length) { // Set the remaining bits (note that the mask could still be zero) val mask = ~(-1L << (bitIndex & 0x3f)) words(wordIndex) |= mask From 12322159147581602978f7f5a6b33b887ef781a1 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Tue, 7 Apr 2015 12:37:33 -0700 Subject: [PATCH 182/591] [SPARK-6750] Upgrade ScalaStyle to 0.7. 0.7 fixes a bug that's pretty useful, i.e. inline functions no longer return explicit type definition. Author: Reynold Xin Closes #5399 from rxin/style0.7 and squashes the following commits: 54c41b2 [Reynold Xin] Actually update the version. 09c759c [Reynold Xin] [SPARK-6750] Upgrade ScalaStyle to 0.7. --- project/plugins.sbt | 2 +- project/project/SparkPluginBuild.scala | 16 +------- .../scalastyle/NonASCIICharacterChecker.scala | 39 ------------------- 3 files changed, 2 insertions(+), 55 deletions(-) delete mode 100644 project/spark-style/src/main/scala/org/apache/spark/scalastyle/NonASCIICharacterChecker.scala diff --git a/project/plugins.sbt b/project/plugins.sbt index ee45b6a51905e..7096b0d3ee7de 100644 --- a/project/plugins.sbt +++ b/project/plugins.sbt @@ -19,7 +19,7 @@ addSbtPlugin("com.github.mpeltonen" % "sbt-idea" % "1.6.0") addSbtPlugin("net.virtual-void" % "sbt-dependency-graph" % "0.7.4") -addSbtPlugin("org.scalastyle" %% "scalastyle-sbt-plugin" % "0.6.0") +addSbtPlugin("org.scalastyle" %% "scalastyle-sbt-plugin" % "0.7.0") addSbtPlugin("com.typesafe" % "sbt-mima-plugin" % "0.1.6") diff --git a/project/project/SparkPluginBuild.scala b/project/project/SparkPluginBuild.scala index 8863f272da415..471d00bd8223f 100644 --- a/project/project/SparkPluginBuild.scala +++ b/project/project/SparkPluginBuild.scala @@ -24,20 +24,6 @@ import sbt.Keys._ * becomes available for scalastyle sbt plugin. */ object SparkPluginDef extends Build { - lazy val root = Project("plugins", file(".")) dependsOn(sparkStyle, sbtPomReader) - lazy val sparkStyle = Project("spark-style", file("spark-style"), settings = styleSettings) + lazy val root = Project("plugins", file(".")) dependsOn(sbtPomReader) lazy val sbtPomReader = uri("https://github.com/ScrapCodes/sbt-pom-reader.git#ignore_artifact_id") - - // There is actually no need to publish this artifact. - def styleSettings = Defaults.defaultSettings ++ Seq ( - name := "spark-style", - organization := "org.apache.spark", - scalaVersion := "2.10.4", - scalacOptions := Seq("-unchecked", "-deprecation"), - libraryDependencies ++= Dependencies.scalaStyle - ) - - object Dependencies { - val scalaStyle = Seq("org.scalastyle" %% "scalastyle" % "0.4.0") - } } diff --git a/project/spark-style/src/main/scala/org/apache/spark/scalastyle/NonASCIICharacterChecker.scala b/project/spark-style/src/main/scala/org/apache/spark/scalastyle/NonASCIICharacterChecker.scala deleted file mode 100644 index 3d43c35299555..0000000000000 --- a/project/spark-style/src/main/scala/org/apache/spark/scalastyle/NonASCIICharacterChecker.scala +++ /dev/null @@ -1,39 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - - -package org.apache.spark.scalastyle - -import java.util.regex.Pattern - -import org.scalastyle.{PositionError, ScalariformChecker, ScalastyleError} - -import scalariform.lexer.Token -import scalariform.parser.CompilationUnit - -class NonASCIICharacterChecker extends ScalariformChecker { - val errorKey: String = "non.ascii.character.disallowed" - - override def verify(ast: CompilationUnit): List[ScalastyleError] = { - ast.tokens.filter(hasNonAsciiChars).map(x => PositionError(x.offset)).toList - } - - private def hasNonAsciiChars(x: Token) = - x.rawText.trim.nonEmpty && !Pattern.compile( """\p{ASCII}+""", Pattern.DOTALL) - .matcher(x.text.trim).matches() - -} From 596ba77c5fdca79486396989e549632153055caf Mon Sep 17 00:00:00 2001 From: Masayoshi TSUZUKI Date: Tue, 7 Apr 2015 14:29:53 -0700 Subject: [PATCH 183/591] [SPARK-6568] spark-shell.cmd --jars option does not accept the jar that has space in its path escape spaces in the arguments. Author: Masayoshi TSUZUKI Closes #5347 from tsudukim/feature/SPARK-6568 and squashes the following commits: 9180aaf [Masayoshi TSUZUKI] [SPARK-6568] spark-shell.cmd --jars option does not accept the jar that has space in its path --- core/src/main/scala/org/apache/spark/util/Utils.scala | 2 +- core/src/test/scala/org/apache/spark/util/UtilsSuite.scala | 6 ++++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index 0fdfaf300e95d..25ae6ee579ab3 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -1661,7 +1661,7 @@ private[spark] object Utils extends Logging { /** * Format a Windows path such that it can be safely passed to a URI. */ - def formatWindowsPath(path: String): String = path.replace("\\", "/") + def formatWindowsPath(path: String): String = path.replace("\\", "/").replace(" ", "%20") /** * Indicates whether Spark is currently running unit tests. diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala index 5d93086082189..b7cc84078983a 100644 --- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala @@ -241,6 +241,7 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { assertResolves("C:/path/to/file.txt", "file:/C:/path/to/file.txt", testWindows = true) assertResolves("C:\\path\\to\\file.txt", "file:/C:/path/to/file.txt", testWindows = true) assertResolves("file:/C:/path/to/file.txt", "file:/C:/path/to/file.txt", testWindows = true) + assertResolves("file:/C:/path to/file.txt", "file:/C:/path%20to/file.txt", testWindows = true) assertResolves("file:///C:/path/to/file.txt", "file:/C:/path/to/file.txt", testWindows = true) assertResolves("file:/C:/file.txt#alias.txt", "file:/C:/file.txt#alias.txt", testWindows = true) intercept[IllegalArgumentException] { Utils.resolveURI("file:foo") } @@ -264,8 +265,9 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { assertResolves("hdfs:/jar1,file:/jar2,jar3", s"hdfs:/jar1,file:/jar2,file:$cwd/jar3") assertResolves("hdfs:/jar1,file:/jar2,jar3,jar4#jar5", s"hdfs:/jar1,file:/jar2,file:$cwd/jar3,file:$cwd/jar4#jar5") - assertResolves("hdfs:/jar1,file:/jar2,jar3,C:\\pi.py#py.pi", - s"hdfs:/jar1,file:/jar2,file:$cwd/jar3,file:/C:/pi.py#py.pi", testWindows = true) + assertResolves("""hdfs:/jar1,file:/jar2,jar3,C:\pi.py#py.pi,C:\path to\jar4.jar""", + s"hdfs:/jar1,file:/jar2,file:$cwd/jar3,file:/C:/pi.py#py.pi,file:/C:/path%20to/jar4.jar", + testWindows = true) } test("nonLocalPaths") { From e6f08fb42fda35952ea8b005170750ae551dc7d9 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Tue, 7 Apr 2015 14:34:15 -0700 Subject: [PATCH 184/591] Revert "[SPARK-6568] spark-shell.cmd --jars option does not accept the jar that has space in its path" This reverts commit 596ba77c5fdca79486396989e549632153055caf. --- core/src/main/scala/org/apache/spark/util/Utils.scala | 2 +- core/src/test/scala/org/apache/spark/util/UtilsSuite.scala | 6 ++---- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index 25ae6ee579ab3..0fdfaf300e95d 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -1661,7 +1661,7 @@ private[spark] object Utils extends Logging { /** * Format a Windows path such that it can be safely passed to a URI. */ - def formatWindowsPath(path: String): String = path.replace("\\", "/").replace(" ", "%20") + def formatWindowsPath(path: String): String = path.replace("\\", "/") /** * Indicates whether Spark is currently running unit tests. diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala index b7cc84078983a..5d93086082189 100644 --- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala @@ -241,7 +241,6 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { assertResolves("C:/path/to/file.txt", "file:/C:/path/to/file.txt", testWindows = true) assertResolves("C:\\path\\to\\file.txt", "file:/C:/path/to/file.txt", testWindows = true) assertResolves("file:/C:/path/to/file.txt", "file:/C:/path/to/file.txt", testWindows = true) - assertResolves("file:/C:/path to/file.txt", "file:/C:/path%20to/file.txt", testWindows = true) assertResolves("file:///C:/path/to/file.txt", "file:/C:/path/to/file.txt", testWindows = true) assertResolves("file:/C:/file.txt#alias.txt", "file:/C:/file.txt#alias.txt", testWindows = true) intercept[IllegalArgumentException] { Utils.resolveURI("file:foo") } @@ -265,9 +264,8 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { assertResolves("hdfs:/jar1,file:/jar2,jar3", s"hdfs:/jar1,file:/jar2,file:$cwd/jar3") assertResolves("hdfs:/jar1,file:/jar2,jar3,jar4#jar5", s"hdfs:/jar1,file:/jar2,file:$cwd/jar3,file:$cwd/jar4#jar5") - assertResolves("""hdfs:/jar1,file:/jar2,jar3,C:\pi.py#py.pi,C:\path to\jar4.jar""", - s"hdfs:/jar1,file:/jar2,file:$cwd/jar3,file:/C:/pi.py#py.pi,file:/C:/path%20to/jar4.jar", - testWindows = true) + assertResolves("hdfs:/jar1,file:/jar2,jar3,C:\\pi.py#py.pi", + s"hdfs:/jar1,file:/jar2,file:$cwd/jar3,file:/C:/pi.py#py.pi", testWindows = true) } test("nonLocalPaths") { From fc957dc78138e72036dbbadc9a54f155d318c038 Mon Sep 17 00:00:00 2001 From: lewuathe Date: Tue, 7 Apr 2015 14:36:57 -0700 Subject: [PATCH 185/591] [SPARK-6720][MLLIB] PySpark MultivariateStatisticalSummary unit test for normL1... ... and normL2. Add test cases to insufficient unit test for `normL1` and `normL2`. Ref: https://github.com/apache/spark/pull/5359 Author: lewuathe Closes #5374 from Lewuathe/SPARK-6720 and squashes the following commits: 5541b24 [lewuathe] More accurate tests dc5718c [lewuathe] [SPARK-6720] PySpark MultivariateStatisticalSummary unit test for normL1 and normL2 --- python/pyspark/mllib/tests.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 47dad7d12e4e4..61ef398487c0c 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -363,6 +363,13 @@ def test_col_norms(self): self.assertEqual(10, len(summary.normL1())) self.assertEqual(10, len(summary.normL2())) + data2 = self.sc.parallelize(xrange(10)).map(lambda x: Vectors.dense(x)) + summary2 = Statistics.colStats(data2) + self.assertEqual(array([45.0]), summary2.normL1()) + import math + expectedNormL2 = math.sqrt(sum(map(lambda x: x*x, xrange(10)))) + self.assertTrue(math.fabs(summary2.normL2()[0] - expectedNormL2) < 1e-14) + class VectorUDTTests(PySparkTestCase): From 77bcceb9f01e97cb6f41791f2167b40c4311f701 Mon Sep 17 00:00:00 2001 From: Cheng Lian Date: Wed, 8 Apr 2015 07:00:56 +0800 Subject: [PATCH 186/591] [SPARK-6748] [SQL] Makes QueryPlan.schema a lazy val `DataFrame.collect()` calls `SparkPlan.executeCollect()`, which consists of a single line: ```scala execute().map(ScalaReflection.convertRowToScala(_, schema)).collect() ``` The problem is that, `QueryPlan.schema` is a function. And since 1.3.0, `convertRowToScala` starts returning a `GenericRowWithSchema`. Thus, every `GenericRowWithSchema` instance holds a separate copy of the schema object. Also, YJP profiling result of the following simple micro benchmark (executed in Spark shell) shows that constructing the schema object takes up to ~35% CPU time. ```scala sc.parallelize(1 to 10000000). map(i => (i, s"val_$i")). toDF("key", "value"). saveAsParquetFile("file:///tmp/src.parquet") // Profiling started from this line sqlContext.parquetFile("file:///tmp/src.parquet").collect() ``` [Review on Reviewable](https://reviewable.io/reviews/apache/spark/5398) Author: Cheng Lian Closes #5398 from liancheng/spark-6748 and squashes the following commits: 3159469 [Cheng Lian] Makes QueryPlan.schema a lazy val --- .../scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala index 02f7c26a8ab6e..7967189cacb24 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala @@ -150,7 +150,7 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy }.toSeq } - def schema: StructType = StructType.fromAttributes(output) + lazy val schema: StructType = StructType.fromAttributes(output) /** Returns the output schema in the tree format. */ def schemaString: String = schema.treeString From c83e03948b184ffb3a9418fecc4d2c26ae33b057 Mon Sep 17 00:00:00 2001 From: Josh Rosen Date: Tue, 7 Apr 2015 16:18:55 -0700 Subject: [PATCH 187/591] [SPARK-6737] Fix memory leak in OutputCommitCoordinator This patch fixes a memory leak in the DAGScheduler, which caused us to leak a map entry per submitted stage. The problem is that the OutputCommitCoordinator needs to be informed when stages end in order to remove entries from its `authorizedCommitters` map, but the DAGScheduler only called it in one of the four code paths that are used to mark stages as completed. This patch fixes this issue by consolidating the processing of stage completion into a new `markStageAsFinished` method and updates DAGSchedulerSuite's `assertDataStructuresEmpty` assertion to also check the OutputCommitCoordinator data structures. I've also added a comment at the top of DAGScheduler so that we remember to update this test when adding new data structures. Author: Josh Rosen Closes #5397 from JoshRosen/SPARK-6737 and squashes the following commits: af3b02f [Josh Rosen] Consolidate stage completion handling code in a single method. e96ce3a [Josh Rosen] Consolidate stage completion handling code in a single method. 3052aea [Josh Rosen] Comment update 7896899 [Josh Rosen] Fix SPARK-6737 by informing OutputCommitCoordinator of all stage end events. 4ead1dc [Josh Rosen] Add regression tests for SPARK-6737 --- .../apache/spark/scheduler/DAGScheduler.scala | 63 ++++++++++--------- .../scheduler/OutputCommitCoordinator.scala | 7 +++ .../spark/scheduler/DAGSchedulerSuite.scala | 1 + 3 files changed, 42 insertions(+), 29 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala index c82ae4baa3630..c912520fded3b 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -50,6 +50,10 @@ import org.apache.spark.storage.BlockManagerMessages.BlockManagerHeartbeat * not caused by shuffle file loss are handled by the TaskScheduler, which will retry each task * a small number of times before cancelling the whole stage. * + * Here's a checklist to use when making or reviewing changes to this class: + * + * - When adding a new data structure, update `DAGSchedulerSuite.assertDataStructuresEmpty` to + * include the new structure. This will help to catch memory leaks. */ private[spark] class DAGScheduler( @@ -111,6 +115,8 @@ class DAGScheduler( // stray messages to detect. private val failedEpoch = new HashMap[String, Long] + private [scheduler] val outputCommitCoordinator = env.outputCommitCoordinator + // A closure serializer that we reuse. // This is only safe because DAGScheduler runs in a single thread. private val closureSerializer = SparkEnv.get.closureSerializer.newInstance() @@ -128,8 +134,6 @@ class DAGScheduler( private[scheduler] val eventProcessLoop = new DAGSchedulerEventProcessLoop(this) taskScheduler.setDAGScheduler(this) - private val outputCommitCoordinator = env.outputCommitCoordinator - // Called by TaskScheduler to report task's starting. def taskStarted(task: Task[_], taskInfo: TaskInfo) { eventProcessLoop.post(BeginEvent(task, taskInfo)) @@ -710,9 +714,10 @@ class DAGScheduler( // cancelling the stages because if the DAG scheduler is stopped, the entire application // is in the process of getting stopped. val stageFailedMessage = "Stage cancelled because SparkContext was shut down" - runningStages.foreach { stage => - stage.latestInfo.stageFailed(stageFailedMessage) - listenerBus.post(SparkListenerStageCompleted(stage.latestInfo)) + // The `toArray` here is necessary so that we don't iterate over `runningStages` while + // mutating it. + runningStages.toArray.foreach { stage => + markStageAsFinished(stage, Some(stageFailedMessage)) } listenerBus.post(SparkListenerJobEnd(job.jobId, clock.getTimeMillis(), JobFailed(error))) } @@ -887,10 +892,9 @@ class DAGScheduler( new TaskSet(tasks.toArray, stage.id, stage.newAttemptId(), stage.jobId, properties)) stage.latestInfo.submissionTime = Some(clock.getTimeMillis()) } else { - // Because we posted SparkListenerStageSubmitted earlier, we should post - // SparkListenerStageCompleted here in case there are no tasks to run. - outputCommitCoordinator.stageEnd(stage.id) - listenerBus.post(SparkListenerStageCompleted(stage.latestInfo)) + // Because we posted SparkListenerStageSubmitted earlier, we should mark + // the stage as completed here in case there are no tasks to run + markStageAsFinished(stage, None) val debugString = stage match { case stage: ShuffleMapStage => @@ -902,7 +906,6 @@ class DAGScheduler( s"Stage ${stage} is actually done; (partitions: ${stage.numPartitions})" } logDebug(debugString) - runningStages -= stage } } @@ -968,22 +971,6 @@ class DAGScheduler( } val stage = stageIdToStage(task.stageId) - - def markStageAsFinished(stage: Stage, errorMessage: Option[String] = None): Unit = { - val serviceTime = stage.latestInfo.submissionTime match { - case Some(t) => "%.03f".format((clock.getTimeMillis() - t) / 1000.0) - case _ => "Unknown" - } - if (errorMessage.isEmpty) { - logInfo("%s (%s) finished in %s s".format(stage, stage.name, serviceTime)) - stage.latestInfo.completionTime = Some(clock.getTimeMillis()) - } else { - stage.latestInfo.stageFailed(errorMessage.get) - logInfo("%s (%s) failed in %s s".format(stage, stage.name, serviceTime)) - } - listenerBus.post(SparkListenerStageCompleted(stage.latestInfo)) - runningStages -= stage - } event.reason match { case Success => listenerBus.post(SparkListenerTaskEnd(stageId, stage.latestInfo.attemptId, taskType, @@ -1099,7 +1086,6 @@ class DAGScheduler( logInfo(s"Marking $failedStage (${failedStage.name}) as failed " + s"due to a fetch failure from $mapStage (${mapStage.name})") markStageAsFinished(failedStage, Some(failureMessage)) - runningStages -= failedStage } if (disallowStageRetryForTest) { @@ -1215,6 +1201,26 @@ class DAGScheduler( submitWaitingStages() } + /** + * Marks a stage as finished and removes it from the list of running stages. + */ + private def markStageAsFinished(stage: Stage, errorMessage: Option[String] = None): Unit = { + val serviceTime = stage.latestInfo.submissionTime match { + case Some(t) => "%.03f".format((clock.getTimeMillis() - t) / 1000.0) + case _ => "Unknown" + } + if (errorMessage.isEmpty) { + logInfo("%s (%s) finished in %s s".format(stage, stage.name, serviceTime)) + stage.latestInfo.completionTime = Some(clock.getTimeMillis()) + } else { + stage.latestInfo.stageFailed(errorMessage.get) + logInfo("%s (%s) failed in %s s".format(stage, stage.name, serviceTime)) + } + outputCommitCoordinator.stageEnd(stage.id) + listenerBus.post(SparkListenerStageCompleted(stage.latestInfo)) + runningStages -= stage + } + /** * Aborts all jobs depending on a particular Stage. This is called in response to a task set * being canceled by the TaskScheduler. Use taskSetFailed() to inject this event from outside. @@ -1264,8 +1270,7 @@ class DAGScheduler( if (runningStages.contains(stage)) { try { // cancelTasks will fail if a SchedulerBackend does not implement killTask taskScheduler.cancelTasks(stageId, shouldInterruptThread) - stage.latestInfo.stageFailed(failureReason) - listenerBus.post(SparkListenerStageCompleted(stage.latestInfo)) + markStageAsFinished(stage, Some(failureReason)) } catch { case e: UnsupportedOperationException => logInfo(s"Could not cancel tasks for stage $stageId", e) diff --git a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala index 9e29fd13821dc..7c184b1dcb308 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala @@ -59,6 +59,13 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf) extends Logging { private val authorizedCommittersByStage: CommittersByStageMap = mutable.Map() private type CommittersByStageMap = mutable.Map[StageId, mutable.Map[PartitionId, TaskAttemptId]] + /** + * Returns whether the OutputCommitCoordinator's internal data structures are all empty. + */ + def isEmpty: Boolean = { + authorizedCommittersByStage.isEmpty + } + /** * Called by tasks to ask whether they can commit their output to HDFS. * diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala index 63360a0f189a3..eb759f0807a17 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala @@ -783,6 +783,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar assert(scheduler.runningStages.isEmpty) assert(scheduler.shuffleToMapStage.isEmpty) assert(scheduler.waitingStages.isEmpty) + assert(scheduler.outputCommitCoordinator.isEmpty) } // Nothing in this test should break if the task info's fields are null, but From d138aa8ee23f4450242da3ac70a493229a90c76b Mon Sep 17 00:00:00 2001 From: Omede Firouz Date: Tue, 7 Apr 2015 23:36:31 -0400 Subject: [PATCH 188/591] [SPARK-6705][MLLIB] Add fit intercept api to ml logisticregression I have the fit intercept enabled by default for logistic regression, I wonder what others think here. I understand that it enables allocation by default which is undesirable, but one needs to have a very strong reason for not having an intercept term enabled so it is the safer default from a statistical sense. Explicitly modeling the intercept by adding a column of all 1s does not work. I believe the reason is that since the API for LogisticRegressionWithLBFGS forces column normalization, and a column of all 1s has 0 variance so dividing by 0 kills it. Author: Omede Firouz Closes #5301 from oefirouz/addIntercept and squashes the following commits: 9f1286b [Omede Firouz] [SPARK-6705][MLLIB] Add fitInterceptTerm to LogisticRegression 1d6bd6f [Omede Firouz] [SPARK-6705][MLLIB] Add a fit intercept term to ML LogisticRegression 9963509 [Omede Firouz] [MLLIB] Add fitIntercept to LogisticRegression 2257fca [Omede Firouz] [MLLIB] Add fitIntercept param to logistic regression 329c1e2 [Omede Firouz] [MLLIB] Add fit intercept term bd9663c [Omede Firouz] [MLLIB] Add fit intercept api to ml logisticregression --- .../spark/ml/classification/LogisticRegression.scala | 8 ++++++-- .../org/apache/spark/ml/param/sharedParams.scala | 12 ++++++++++++ .../ml/classification/LogisticRegressionSuite.scala | 9 +++++++++ 3 files changed, 27 insertions(+), 2 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala index 49c00f77480e8..34625745dd0a8 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala @@ -31,7 +31,7 @@ import org.apache.spark.storage.StorageLevel * Params for logistic regression. */ private[classification] trait LogisticRegressionParams extends ProbabilisticClassifierParams - with HasRegParam with HasMaxIter with HasThreshold + with HasRegParam with HasMaxIter with HasFitIntercept with HasThreshold /** @@ -55,6 +55,9 @@ class LogisticRegression /** @group setParam */ def setMaxIter(value: Int): this.type = set(maxIter, value) + /** @group setParam */ + def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value) + /** @group setParam */ def setThreshold(value: Double): this.type = set(threshold, value) @@ -67,7 +70,8 @@ class LogisticRegression } // Train model - val lr = new LogisticRegressionWithLBFGS + val lr = new LogisticRegressionWithLBFGS() + .setIntercept(paramMap(fitIntercept)) lr.optimizer .setRegParam(paramMap(regParam)) .setNumIterations(paramMap(maxIter)) diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala index 5d660d1e151a7..0739fdbfcbaae 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala @@ -106,6 +106,18 @@ private[ml] trait HasProbabilityCol extends Params { def getProbabilityCol: String = get(probabilityCol) } +private[ml] trait HasFitIntercept extends Params { + /** + * param for fitting the intercept term, defaults to true + * @group param + */ + val fitIntercept: BooleanParam = + new BooleanParam(this, "fitIntercept", "indicates whether to fit an intercept term", Some(true)) + + /** @group getParam */ + def getFitIntercept: Boolean = get(fitIntercept) +} + private[ml] trait HasThreshold extends Params { /** * param for threshold in (binary) prediction diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala index b3d1bfcfbee0f..35d8c2e16c6cd 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala @@ -46,6 +46,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { assert(lr.getPredictionCol == "prediction") assert(lr.getRawPredictionCol == "rawPrediction") assert(lr.getProbabilityCol == "probability") + assert(lr.getFitIntercept == true) val model = lr.fit(dataset) model.transform(dataset) .select("label", "probability", "prediction", "rawPrediction") @@ -55,6 +56,14 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { assert(model.getPredictionCol == "prediction") assert(model.getRawPredictionCol == "rawPrediction") assert(model.getProbabilityCol == "probability") + assert(model.intercept !== 0.0) + } + + test("logistic regression doesn't fit intercept when fitIntercept is off") { + val lr = new LogisticRegression + lr.setFitIntercept(false) + val model = lr.fit(dataset) + assert(model.intercept === 0.0) } test("logistic regression with setters") { From 8d2a36c0fdfbea9f58271ef6aeb89bb79b22cf62 Mon Sep 17 00:00:00 2001 From: Kay Ousterhout Date: Tue, 7 Apr 2015 22:40:42 -0700 Subject: [PATCH 189/591] [SPARK-6754] Remove unnecessary TaskContextHelper The TaskContextHelper was originally necessary because TaskContext was written in Java, which does not have a way to specify that classes are package-private, so TaskContextHelper existed to work around this. Now that TaskContext has been re-written in Scala, this class is no longer necessary. rxin can you look at this? It looks like you missed this bit of cleanup when you moved TaskContext from Java to Scala in #4324 cc ScrapCodes and pwendell who added this originally. Author: Kay Ousterhout Closes #5402 from kayousterhout/SPARK-6754 and squashes the following commits: f089800 [Kay Ousterhout] [SPARK-6754] Remove unnecessary TaskContextHelper --- .../org/apache/spark/TaskContextHelper.scala | 29 ------------------- .../apache/spark/scheduler/DAGScheduler.scala | 4 +-- .../org/apache/spark/scheduler/Task.scala | 6 ++-- 3 files changed, 5 insertions(+), 34 deletions(-) delete mode 100644 core/src/main/scala/org/apache/spark/TaskContextHelper.scala diff --git a/core/src/main/scala/org/apache/spark/TaskContextHelper.scala b/core/src/main/scala/org/apache/spark/TaskContextHelper.scala deleted file mode 100644 index 4636c4600a01a..0000000000000 --- a/core/src/main/scala/org/apache/spark/TaskContextHelper.scala +++ /dev/null @@ -1,29 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark - -/** - * This class exists to restrict the visibility of TaskContext setters. - */ -private [spark] object TaskContextHelper { - - def setTaskContext(tc: TaskContext): Unit = TaskContext.setTaskContext(tc) - - def unset(): Unit = TaskContext.unset() - -} diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala index c912520fded3b..508fe7b3303ca 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -645,13 +645,13 @@ class DAGScheduler( val split = rdd.partitions(job.partitions(0)) val taskContext = new TaskContextImpl(job.finalStage.id, job.partitions(0), taskAttemptId = 0, attemptNumber = 0, runningLocally = true) - TaskContextHelper.setTaskContext(taskContext) + TaskContext.setTaskContext(taskContext) try { val result = job.func(taskContext, rdd.iterator(split, taskContext)) job.listener.taskSucceeded(0, result) } finally { taskContext.markTaskCompleted() - TaskContextHelper.unset() + TaskContext.unset() } } catch { case e: Exception => diff --git a/core/src/main/scala/org/apache/spark/scheduler/Task.scala b/core/src/main/scala/org/apache/spark/scheduler/Task.scala index 4d9f940813b8e..8b592867ee31d 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/Task.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/Task.scala @@ -22,7 +22,7 @@ import java.nio.ByteBuffer import scala.collection.mutable.HashMap -import org.apache.spark.{TaskContextHelper, TaskContextImpl, TaskContext} +import org.apache.spark.{TaskContextImpl, TaskContext} import org.apache.spark.executor.TaskMetrics import org.apache.spark.serializer.SerializerInstance import org.apache.spark.util.ByteBufferInputStream @@ -54,7 +54,7 @@ private[spark] abstract class Task[T](val stageId: Int, var partitionId: Int) ex final def run(taskAttemptId: Long, attemptNumber: Int): T = { context = new TaskContextImpl(stageId = stageId, partitionId = partitionId, taskAttemptId = taskAttemptId, attemptNumber = attemptNumber, runningLocally = false) - TaskContextHelper.setTaskContext(context) + TaskContext.setTaskContext(context) context.taskMetrics.setHostname(Utils.localHostName()) taskThread = Thread.currentThread() if (_killed) { @@ -64,7 +64,7 @@ private[spark] abstract class Task[T](val stageId: Int, var partitionId: Int) ex runTask(context) } finally { context.markTaskCompleted() - TaskContextHelper.unset() + TaskContext.unset() } } From 15e0d2bd1304db62fad286c1bb687e87c361e16c Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Wed, 8 Apr 2015 00:24:59 -0700 Subject: [PATCH 190/591] [SPARK-6765] Fix test code style for streaming. So we can turn style checker on for test code. Author: Reynold Xin Closes #5409 from rxin/test-style-streaming and squashes the following commits: 7aea69b [Reynold Xin] [SPARK-6765] Fix test code style for streaming. --- .../flume/FlumePollingStreamSuite.scala | 29 ++++++------ .../streaming/flume/FlumeStreamSuite.scala | 4 +- .../streaming/mqtt/MQTTStreamSuite.scala | 3 +- .../streaming/BasicOperationsSuite.scala | 6 ++- .../spark/streaming/CheckpointSuite.scala | 45 ++++++++++++++----- .../apache/spark/streaming/FailureSuite.scala | 4 +- .../spark/streaming/InputStreamsSuite.scala | 15 ++++--- .../streaming/ReceivedBlockHandlerSuite.scala | 4 +- .../streaming/ReceivedBlockTrackerSuite.scala | 6 ++- .../spark/streaming/ReceiverSuite.scala | 11 ++--- .../streaming/StreamingContextSuite.scala | 5 ++- .../streaming/StreamingListenerSuite.scala | 4 +- .../spark/streaming/TestSuiteBase.scala | 28 +++++++----- .../spark/streaming/UISeleniumSuite.scala | 3 +- .../streaming/WindowOperationsSuite.scala | 4 +- .../WriteAheadLogBackedBlockRDDSuite.scala | 12 +++-- .../scheduler/JobGeneratorSuite.scala | 2 +- .../streaming/util/WriteAheadLogSuite.scala | 2 +- .../spark/streamingtest/ImplicitSuite.scala | 3 +- 19 files changed, 115 insertions(+), 75 deletions(-) diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala index e04d4088df7dc..2edea9b5b69ba 100644 --- a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala +++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala @@ -1,21 +1,20 @@ /* - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at * - * http://www.apache.org/licenses/LICENSE-2.0 + * http://www.apache.org/licenses/LICENSE-2.0 * - * Unless required by applicable law or agreed to in writing, - * software distributed under the License is distributed on an - * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY - * KIND, either express or implied. See the License for the - * specific language governing permissions and limitations - * under the License. + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. */ + package org.apache.spark.streaming.flume import java.net.InetSocketAddress @@ -213,7 +212,7 @@ class FlumePollingStreamSuite extends FunSuite with BeforeAndAfter with Logging assert(counter === totalEventsPerChannel * channels.size) } - def assertChannelIsEmpty(channel: MemoryChannel) = { + def assertChannelIsEmpty(channel: MemoryChannel): Unit = { val queueRemaining = channel.getClass.getDeclaredField("queueRemaining") queueRemaining.setAccessible(true) val m = queueRemaining.get(channel).getClass.getDeclaredMethod("availablePermits") diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala index 51d273af8da84..39e6754c81dbf 100644 --- a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala +++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala @@ -151,7 +151,9 @@ class FlumeStreamSuite extends FunSuite with BeforeAndAfter with Matchers with L } /** Class to create socket channel with compression */ - private class CompressionChannelFactory(compressionLevel: Int) extends NioClientSocketChannelFactory { + private class CompressionChannelFactory(compressionLevel: Int) + extends NioClientSocketChannelFactory { + override def newChannel(pipeline: ChannelPipeline): SocketChannel = { val encoder = new ZlibEncoder(compressionLevel) pipeline.addFirst("deflater", encoder) diff --git a/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala index 24d78ecb3a97d..a19a72c58a705 100644 --- a/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala +++ b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala @@ -139,7 +139,8 @@ class MQTTStreamSuite extends FunSuite with Eventually with BeforeAndAfter { msgTopic.publish(message) } catch { case e: MqttException if e.getReasonCode == MqttException.REASON_CODE_MAX_INFLIGHT => - Thread.sleep(50) // wait for Spark streaming to consume something from the message queue + // wait for Spark streaming to consume something from the message queue + Thread.sleep(50) } } } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala index cf191715d29d6..87bc20f79c3cd 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala @@ -171,7 +171,9 @@ class BasicOperationsSuite extends TestSuiteBase { test("flatMapValues") { testOperation( Seq( Seq("a", "a", "b"), Seq("", ""), Seq() ), - (s: DStream[String]) => s.map(x => (x, 1)).reduceByKey(_ + _).flatMapValues(x => Seq(x, x + 10)), + (s: DStream[String]) => { + s.map(x => (x, 1)).reduceByKey(_ + _).flatMapValues(x => Seq(x, x + 10)) + }, Seq( Seq(("a", 2), ("a", 12), ("b", 1), ("b", 11)), Seq(("", 2), ("", 12)), Seq() ), true ) @@ -474,7 +476,7 @@ class BasicOperationsSuite extends TestSuiteBase { stream.foreachRDD(_ => {}) // Dummy output stream ssc.start() Thread.sleep(2000) - def getInputFromSlice(fromMillis: Long, toMillis: Long) = { + def getInputFromSlice(fromMillis: Long, toMillis: Long): Set[Int] = { stream.slice(new Time(fromMillis), new Time(toMillis)).flatMap(_.collect()).toSet } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala index 91a2b2bba461d..54c30440a6e8d 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala @@ -43,7 +43,7 @@ class CheckpointSuite extends TestSuiteBase { var ssc: StreamingContext = null - override def batchDuration = Milliseconds(500) + override def batchDuration: Duration = Milliseconds(500) override def beforeFunction() { super.beforeFunction() @@ -72,7 +72,7 @@ class CheckpointSuite extends TestSuiteBase { val input = (1 to 10).map(_ => Seq("a")).toSeq val operation = (st: DStream[String]) => { val updateFunc = (values: Seq[Int], state: Option[Int]) => { - Some((values.sum + state.getOrElse(0))) + Some(values.sum + state.getOrElse(0)) } st.map(x => (x, 1)) .updateStateByKey(updateFunc) @@ -199,7 +199,12 @@ class CheckpointSuite extends TestSuiteBase { testCheckpointedOperation( Seq( Seq("a", "a", "b"), Seq("", ""), Seq(), Seq("a", "a", "b"), Seq("", ""), Seq() ), (s: DStream[String]) => s.map(x => (x, 1)).reduceByKey(_ + _), - Seq( Seq(("a", 2), ("b", 1)), Seq(("", 2)), Seq(), Seq(("a", 2), ("b", 1)), Seq(("", 2)), Seq() ), + Seq( + Seq(("a", 2), ("b", 1)), + Seq(("", 2)), + Seq(), + Seq(("a", 2), ("b", 1)), + Seq(("", 2)), Seq() ), 3 ) } @@ -212,7 +217,8 @@ class CheckpointSuite extends TestSuiteBase { val n = 10 val w = 4 val input = (1 to n).map(_ => Seq("a")).toSeq - val output = Seq(Seq(("a", 1)), Seq(("a", 2)), Seq(("a", 3))) ++ (1 to (n - w + 1)).map(x => Seq(("a", 4))) + val output = Seq( + Seq(("a", 1)), Seq(("a", 2)), Seq(("a", 3))) ++ (1 to (n - w + 1)).map(x => Seq(("a", 4))) val operation = (st: DStream[String]) => { st.map(x => (x, 1)) .reduceByKeyAndWindow(_ + _, _ - _, batchDuration * w, batchDuration) @@ -236,7 +242,13 @@ class CheckpointSuite extends TestSuiteBase { classOf[TextOutputFormat[Text, IntWritable]]) output }, - Seq(Seq(("a", 2), ("b", 1)), Seq(("", 2)), Seq(), Seq(("a", 2), ("b", 1)), Seq(("", 2)), Seq()), + Seq( + Seq(("a", 2), ("b", 1)), + Seq(("", 2)), + Seq(), + Seq(("a", 2), ("b", 1)), + Seq(("", 2)), + Seq()), 3 ) } finally { @@ -259,7 +271,13 @@ class CheckpointSuite extends TestSuiteBase { classOf[NewTextOutputFormat[Text, IntWritable]]) output }, - Seq(Seq(("a", 2), ("b", 1)), Seq(("", 2)), Seq(), Seq(("a", 2), ("b", 1)), Seq(("", 2)), Seq()), + Seq( + Seq(("a", 2), ("b", 1)), + Seq(("", 2)), + Seq(), + Seq(("a", 2), ("b", 1)), + Seq(("", 2)), + Seq()), 3 ) } finally { @@ -298,7 +316,13 @@ class CheckpointSuite extends TestSuiteBase { output } }, - Seq(Seq(("a", 2), ("b", 1)), Seq(("", 2)), Seq(), Seq(("a", 2), ("b", 1)), Seq(("", 2)), Seq()), + Seq( + Seq(("a", 2), ("b", 1)), + Seq(("", 2)), + Seq(), + Seq(("a", 2), ("b", 1)), + Seq(("", 2)), + Seq()), 3 ) } finally { @@ -533,7 +557,8 @@ class CheckpointSuite extends TestSuiteBase { * Advances the manual clock on the streaming scheduler by given number of batches. * It also waits for the expected amount of time for each batch. */ - def advanceTimeWithRealDelay[V: ClassTag](ssc: StreamingContext, numBatches: Long): Seq[Seq[V]] = { + def advanceTimeWithRealDelay[V: ClassTag](ssc: StreamingContext, numBatches: Long): Seq[Seq[V]] = + { val clock = ssc.scheduler.clock.asInstanceOf[ManualClock] logInfo("Manual clock before advancing = " + clock.getTimeMillis()) for (i <- 1 to numBatches.toInt) { @@ -543,7 +568,7 @@ class CheckpointSuite extends TestSuiteBase { logInfo("Manual clock after advancing = " + clock.getTimeMillis()) Thread.sleep(batchDuration.milliseconds) - val outputStream = ssc.graph.getOutputStreams.filter { dstream => + val outputStream = ssc.graph.getOutputStreams().filter { dstream => dstream.isInstanceOf[TestOutputStreamWithPartitions[V]] }.head.asInstanceOf[TestOutputStreamWithPartitions[V]] outputStream.output.map(_.flatten) @@ -552,4 +577,4 @@ class CheckpointSuite extends TestSuiteBase { private object CheckpointSuite extends Serializable { var batchThreeShouldBlockIndefinitely: Boolean = true -} \ No newline at end of file +} diff --git a/streaming/src/test/scala/org/apache/spark/streaming/FailureSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/FailureSuite.scala index 26435d8515815..0c4c06534a693 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/FailureSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/FailureSuite.scala @@ -29,9 +29,9 @@ class FailureSuite extends TestSuiteBase with Logging { val directory = Utils.createTempDir() val numBatches = 30 - override def batchDuration = Milliseconds(1000) + override def batchDuration: Duration = Milliseconds(1000) - override def useManualClock = false + override def useManualClock: Boolean = false override def afterFunction() { Utils.deleteRecursively(directory) diff --git a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala index 7ed6320a3d0bc..e6ac4975c5e68 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala @@ -52,7 +52,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { "localhost", testServer.port, StorageLevel.MEMORY_AND_DISK) val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]] val outputStream = new TestOutputStream(networkStream, outputBuffer) - def output = outputBuffer.flatMap(x => x) + def output: ArrayBuffer[String] = outputBuffer.flatMap(x => x) outputStream.register() ssc.start() @@ -164,7 +164,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { val countStream = networkStream.count val outputBuffer = new ArrayBuffer[Seq[Long]] with SynchronizedBuffer[Seq[Long]] val outputStream = new TestOutputStream(countStream, outputBuffer) - def output = outputBuffer.flatMap(x => x) + def output: ArrayBuffer[Long] = outputBuffer.flatMap(x => x) outputStream.register() ssc.start() @@ -196,7 +196,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { val queueStream = ssc.queueStream(queue, oneAtATime = true) val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]] val outputStream = new TestOutputStream(queueStream, outputBuffer) - def output = outputBuffer.filter(_.size > 0) + def output: ArrayBuffer[Seq[String]] = outputBuffer.filter(_.size > 0) outputStream.register() ssc.start() @@ -204,7 +204,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { val clock = ssc.scheduler.clock.asInstanceOf[ManualClock] val input = Seq("1", "2", "3", "4", "5") val expectedOutput = input.map(Seq(_)) - //Thread.sleep(1000) + val inputIterator = input.toIterator for (i <- 0 until input.size) { // Enqueue more than 1 item per tick but they should dequeue one at a time @@ -239,7 +239,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { val queueStream = ssc.queueStream(queue, oneAtATime = false) val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]] val outputStream = new TestOutputStream(queueStream, outputBuffer) - def output = outputBuffer.filter(_.size > 0) + def output: ArrayBuffer[Seq[String]] = outputBuffer.filter(_.size > 0) outputStream.register() ssc.start() @@ -352,7 +352,8 @@ class TestServer(portToBind: Int = 0) extends Logging { logInfo("New connection") try { clientSocket.setTcpNoDelay(true) - val outputStream = new BufferedWriter(new OutputStreamWriter(clientSocket.getOutputStream)) + val outputStream = new BufferedWriter( + new OutputStreamWriter(clientSocket.getOutputStream)) while(clientSocket.isConnected) { val msg = queue.poll(100, TimeUnit.MILLISECONDS) @@ -384,7 +385,7 @@ class TestServer(portToBind: Int = 0) extends Logging { def stop() { servingThread.interrupt() } - def port = serverSocket.getLocalPort + def port: Int = serverSocket.getLocalPort } /** This is a receiver to test multiple threads inserting data using block generator */ diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala index ef4873de2f5a9..c090eaec2928d 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala @@ -96,7 +96,7 @@ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matche testBlockStoring(handler) { case (data, blockIds, storeResults) => // Verify the data in block manager is correct val storedData = blockIds.flatMap { blockId => - blockManager.getLocal(blockId).map { _.data.map {_.toString}.toList }.getOrElse(List.empty) + blockManager.getLocal(blockId).map(_.data.map(_.toString).toList).getOrElse(List.empty) }.toList storedData shouldEqual data @@ -120,7 +120,7 @@ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matche testBlockStoring(handler) { case (data, blockIds, storeResults) => // Verify the data in block manager is correct val storedData = blockIds.flatMap { blockId => - blockManager.getLocal(blockId).map { _.data.map {_.toString}.toList }.getOrElse(List.empty) + blockManager.getLocal(blockId).map(_.data.map(_.toString).toList).getOrElse(List.empty) }.toList storedData shouldEqual data diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala index 42fad769f0c1a..b63b37d9f9cef 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala @@ -228,7 +228,8 @@ class ReceivedBlockTrackerSuite * Get all the data written in the given write ahead log files. By default, it will read all * files in the test log directory. */ - def getWrittenLogData(logFiles: Seq[String] = getWriteAheadLogFiles): Seq[ReceivedBlockTrackerLogEvent] = { + def getWrittenLogData(logFiles: Seq[String] = getWriteAheadLogFiles) + : Seq[ReceivedBlockTrackerLogEvent] = { logFiles.flatMap { file => new WriteAheadLogReader(file, hadoopConf).toSeq }.map { byteBuffer => @@ -244,7 +245,8 @@ class ReceivedBlockTrackerSuite } /** Create batch allocation object from the given info */ - def createBatchAllocation(time: Long, blockInfos: Seq[ReceivedBlockInfo]): BatchAllocationEvent = { + def createBatchAllocation(time: Long, blockInfos: Seq[ReceivedBlockInfo]) + : BatchAllocationEvent = { BatchAllocationEvent(time, AllocatedBlocks(Map((streamId -> blockInfos)))) } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala index aa20ad0b5374e..10c35cba8dc53 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala @@ -308,7 +308,7 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable { val errors = new ArrayBuffer[Throwable] /** Check if all data structures are clean */ - def isAllEmpty = { + def isAllEmpty: Boolean = { singles.isEmpty && byteBuffers.isEmpty && iterators.isEmpty && arrayBuffers.isEmpty && errors.isEmpty } @@ -320,24 +320,21 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable { def pushBytes( bytes: ByteBuffer, optionalMetadata: Option[Any], - optionalBlockId: Option[StreamBlockId] - ) { + optionalBlockId: Option[StreamBlockId]) { byteBuffers += bytes } def pushIterator( iterator: Iterator[_], optionalMetadata: Option[Any], - optionalBlockId: Option[StreamBlockId] - ) { + optionalBlockId: Option[StreamBlockId]) { iterators += iterator } def pushArrayBuffer( arrayBuffer: ArrayBuffer[_], optionalMetadata: Option[Any], - optionalBlockId: Option[StreamBlockId] - ) { + optionalBlockId: Option[StreamBlockId]) { arrayBuffers += arrayBuffer } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala index 2e5005ef6ff14..d1bbf39dc7897 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala @@ -213,7 +213,7 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w ssc = new StreamingContext(sc, Milliseconds(100)) var runningCount = 0 SlowTestReceiver.receivedAllRecords = false - //Create test receiver that sleeps in onStop() + // Create test receiver that sleeps in onStop() val totalNumRecords = 15 val recordsPerSecond = 1 val input = ssc.receiverStream(new SlowTestReceiver(totalNumRecords, recordsPerSecond)) @@ -370,7 +370,8 @@ object TestReceiver { } /** Custom receiver for testing whether a slow receiver can be shutdown gracefully or not */ -class SlowTestReceiver(totalRecords: Int, recordsPerSecond: Int) extends Receiver[Int](StorageLevel.MEMORY_ONLY) with Logging { +class SlowTestReceiver(totalRecords: Int, recordsPerSecond: Int) + extends Receiver[Int](StorageLevel.MEMORY_ONLY) with Logging { var receivingThreadOption: Option[Thread] = None diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala index f52562b0a0f73..852e8bb71d4f6 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala @@ -38,8 +38,8 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers { // To make sure that the processing start and end times in collected // information are different for successive batches - override def batchDuration = Milliseconds(100) - override def actuallyWait = true + override def batchDuration: Duration = Milliseconds(100) + override def actuallyWait: Boolean = true test("batch info reporting") { val ssc = setupStreams(input, operation) diff --git a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala index 3565d621e8a6c..c3cae8aeb6d15 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala @@ -53,8 +53,9 @@ class TestInputStream[T: ClassTag](ssc_ : StreamingContext, input: Seq[Seq[T]], val selectedInput = if (index < input.size) input(index) else Seq[T]() // lets us test cases where RDDs are not created - if (selectedInput == null) + if (selectedInput == null) { return None + } val rdd = ssc.sc.makeRDD(selectedInput, numPartitions) logInfo("Created RDD " + rdd.id + " with " + selectedInput) @@ -104,7 +105,9 @@ class TestOutputStreamWithPartitions[T: ClassTag](parent: DStream[T], output.clear() } - def toTestOutputStream = new TestOutputStream[T](this.parent, this.output.map(_.flatten)) + def toTestOutputStream: TestOutputStream[T] = { + new TestOutputStream[T](this.parent, this.output.map(_.flatten)) + } } /** @@ -148,34 +151,34 @@ class BatchCounter(ssc: StreamingContext) { trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { // Name of the framework for Spark context - def framework = this.getClass.getSimpleName + def framework: String = this.getClass.getSimpleName // Master for Spark context - def master = "local[2]" + def master: String = "local[2]" // Batch duration - def batchDuration = Seconds(1) + def batchDuration: Duration = Seconds(1) // Directory where the checkpoint data will be saved - lazy val checkpointDir = { + lazy val checkpointDir: String = { val dir = Utils.createTempDir() logDebug(s"checkpointDir: $dir") dir.toString } // Number of partitions of the input parallel collections created for testing - def numInputPartitions = 2 + def numInputPartitions: Int = 2 // Maximum time to wait before the test times out - def maxWaitTimeMillis = 10000 + def maxWaitTimeMillis: Int = 10000 // Whether to use manual clock or not - def useManualClock = true + def useManualClock: Boolean = true // Whether to actually wait in real time before changing manual clock - def actuallyWait = false + def actuallyWait: Boolean = false - //// A SparkConf to use in tests. Can be modified before calling setupStreams to configure things. + // A SparkConf to use in tests. Can be modified before calling setupStreams to configure things. val conf = new SparkConf() .setMaster(master) .setAppName(framework) @@ -346,7 +349,8 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { // Wait until expected number of output items have been generated val startTime = System.currentTimeMillis() - while (output.size < numExpectedOutput && System.currentTimeMillis() - startTime < maxWaitTimeMillis) { + while (output.size < numExpectedOutput && + System.currentTimeMillis() - startTime < maxWaitTimeMillis) { logInfo("output.size = " + output.size + ", numExpectedOutput = " + numExpectedOutput) ssc.awaitTerminationOrTimeout(50) } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala index 87a0395efbf2a..998426ebb82e5 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala @@ -32,7 +32,8 @@ import org.apache.spark._ /** * Selenium tests for the Spark Web UI. */ -class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with BeforeAndAfterAll with TestSuiteBase { +class UISeleniumSuite + extends FunSuite with WebBrowser with Matchers with BeforeAndAfterAll with TestSuiteBase { implicit var webDriver: WebDriver = _ diff --git a/streaming/src/test/scala/org/apache/spark/streaming/WindowOperationsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/WindowOperationsSuite.scala index a5d2bb2fde16c..c39ad05f41520 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/WindowOperationsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/WindowOperationsSuite.scala @@ -22,9 +22,9 @@ import org.apache.spark.storage.StorageLevel class WindowOperationsSuite extends TestSuiteBase { - override def maxWaitTimeMillis = 20000 // large window tests can sometimes take longer + override def maxWaitTimeMillis: Int = 20000 // large window tests can sometimes take longer - override def batchDuration = Seconds(1) // making sure its visible in this class + override def batchDuration: Duration = Seconds(1) // making sure its visible in this class val largerSlideInput = Seq( Seq(("a", 1)), diff --git a/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala index 7a6a2f3e577dd..c3602a5b73732 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala @@ -28,10 +28,13 @@ import org.apache.spark.storage.{BlockId, BlockManager, StorageLevel, StreamBloc import org.apache.spark.streaming.util.{WriteAheadLogFileSegment, WriteAheadLogWriter} import org.apache.spark.util.Utils -class WriteAheadLogBackedBlockRDDSuite extends FunSuite with BeforeAndAfterAll with BeforeAndAfterEach { +class WriteAheadLogBackedBlockRDDSuite + extends FunSuite with BeforeAndAfterAll with BeforeAndAfterEach { + val conf = new SparkConf() .setMaster("local[2]") .setAppName(this.getClass.getSimpleName) + val hadoopConf = new Configuration() var sparkContext: SparkContext = null @@ -86,7 +89,8 @@ class WriteAheadLogBackedBlockRDDSuite extends FunSuite with BeforeAndAfterAll w * @param numPartitionsInWAL Number of partitions to write to the Write Ahead Log * @param testStoreInBM Test whether blocks read from log are stored back into block manager */ - private def testRDD(numPartitionsInBM: Int, numPartitionsInWAL: Int, testStoreInBM: Boolean = false) { + private def testRDD( + numPartitionsInBM: Int, numPartitionsInWAL: Int, testStoreInBM: Boolean = false) { val numBlocks = numPartitionsInBM + numPartitionsInWAL val data = Seq.fill(numBlocks, 10)(scala.util.Random.nextString(50)) @@ -110,7 +114,7 @@ class WriteAheadLogBackedBlockRDDSuite extends FunSuite with BeforeAndAfterAll w "Unexpected blocks in BlockManager" ) - // Make sure that the right `numPartitionsInWAL` blocks are in write ahead logs, and other are not + // Make sure that the right `numPartitionsInWAL` blocks are in WALs, and other are not require( segments.takeRight(numPartitionsInWAL).forall(s => new File(s.path.stripPrefix("file://")).exists()), @@ -152,6 +156,6 @@ class WriteAheadLogBackedBlockRDDSuite extends FunSuite with BeforeAndAfterAll w } private def generateFakeSegments(count: Int): Seq[WriteAheadLogFileSegment] = { - Array.fill(count)(new WriteAheadLogFileSegment("random", 0l, 0)) + Array.fill(count)(new WriteAheadLogFileSegment("random", 0L, 0)) } } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/JobGeneratorSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/JobGeneratorSuite.scala index 4150b60635ed6..7865b06c2e3c2 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/JobGeneratorSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/JobGeneratorSuite.scala @@ -90,7 +90,7 @@ class JobGeneratorSuite extends TestSuiteBase { val receiverTracker = ssc.scheduler.receiverTracker // Get the blocks belonging to a batch - def getBlocksOfBatch(batchTime: Long) = { + def getBlocksOfBatch(batchTime: Long): Seq[ReceivedBlockInfo] = { receiverTracker.getBlocksOfBatchAndStream(Time(batchTime), inputStream.id) } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala index 8335659667f22..a3919c43b95b4 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala @@ -291,7 +291,7 @@ object WriteAheadLogSuite { manager } - /** Read data from a segments of a log file directly and return the list of byte buffers.*/ + /** Read data from a segments of a log file directly and return the list of byte buffers. */ def readDataManually(segments: Seq[WriteAheadLogFileSegment]): Seq[String] = { segments.map { segment => val reader = HdfsUtils.getInputStream(segment.path, hadoopConf) diff --git a/streaming/src/test/scala/org/apache/spark/streamingtest/ImplicitSuite.scala b/streaming/src/test/scala/org/apache/spark/streamingtest/ImplicitSuite.scala index d0bf328f2b74d..d66750463033a 100644 --- a/streaming/src/test/scala/org/apache/spark/streamingtest/ImplicitSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streamingtest/ImplicitSuite.scala @@ -25,7 +25,8 @@ package org.apache.spark.streamingtest */ class ImplicitSuite { - // We only want to test if `implict` works well with the compiler, so we don't need a real DStream. + // We only want to test if `implicit` works well with the compiler, + // so we don't need a real DStream. def mockDStream[T]: org.apache.spark.streaming.dstream.DStream[T] = null def testToPairDStreamFunctions(): Unit = { From f7e21dd1ec4541be54eb01d8b15cfcc6714feed0 Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Wed, 8 Apr 2015 10:14:52 -0700 Subject: [PATCH 191/591] [SPARK-6506] [pyspark] Do not try to retrieve SPARK_HOME when not needed... .... In particular, this makes pyspark in yarn-cluster mode fail unless SPARK_HOME is set, when it's not really needed. Author: Marcelo Vanzin Closes #5405 from vanzin/SPARK-6506 and squashes the following commits: e184507 [Marcelo Vanzin] [SPARK-6506] [pyspark] Do not try to retrieve SPARK_HOME when not needed. --- python/pyspark/java_gateway.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py index 0a16cbd8bff62..2a5e84a7dfdb4 100644 --- a/python/pyspark/java_gateway.py +++ b/python/pyspark/java_gateway.py @@ -29,11 +29,10 @@ def launch_gateway(): - SPARK_HOME = os.environ["SPARK_HOME"] - if "PYSPARK_GATEWAY_PORT" in os.environ: gateway_port = int(os.environ["PYSPARK_GATEWAY_PORT"]) else: + SPARK_HOME = os.environ["SPARK_HOME"] # Launch the Py4j gateway using Spark's run command so that we pick up the # proper classpath and settings from spark-env.sh on_windows = platform.system() == "Windows" From 9d44ddce1d1e19011026605549c37d0db6d6afa1 Mon Sep 17 00:00:00 2001 From: Kay Ousterhout Date: Wed, 8 Apr 2015 10:26:45 -0700 Subject: [PATCH 192/591] [SPARK-6753] Clone SparkConf in ShuffleSuite tests Prior to this change, the unit test for SPARK-3426 did not clone the original SparkConf, which meant that that test did not use the options set by suites that subclass ShuffleSuite.scala. This commit fixes that problem. JoshRosen would be great if you could take a look at this, since you wrote this test originally. Author: Kay Ousterhout Closes #5401 from kayousterhout/SPARK-6753 and squashes the following commits: 368c540 [Kay Ousterhout] [SPARK-6753] Clone SparkConf in ShuffleSuite tests --- core/src/test/scala/org/apache/spark/ShuffleSuite.scala | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala index f57921b768310..30b6184c77839 100644 --- a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala +++ b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala @@ -242,14 +242,14 @@ abstract class ShuffleSuite extends FunSuite with Matchers with LocalSparkContex shuffleSpillCompress <- Set(true, false); shuffleCompress <- Set(true, false) ) { - val conf = new SparkConf() + val myConf = conf.clone() .setAppName("test") .setMaster("local") .set("spark.shuffle.spill.compress", shuffleSpillCompress.toString) .set("spark.shuffle.compress", shuffleCompress.toString) .set("spark.shuffle.memoryFraction", "0.001") resetSparkContext() - sc = new SparkContext(conf) + sc = new SparkContext(myConf) try { sc.parallelize(0 until 100000).map(i => (i / 4, i)).groupByKey().collect() } catch { From 8d812f9986f2edf420a18ca822711c9765f480e2 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Wed, 8 Apr 2015 11:31:48 -0700 Subject: [PATCH 193/591] [SPARK-6765] Fix test code style for graphx. So we can turn style checker on for test code. Author: Reynold Xin Closes #5410 from rxin/test-style-graphx and squashes the following commits: 89e253a [Reynold Xin] [SPARK-6765] Fix test code style for graphx. --- .../org/apache/spark/graphx/GraphSuite.scala | 71 ++++++++++--------- .../spark/graphx/LocalSparkContext.scala | 2 +- .../apache/spark/graphx/VertexRDDSuite.scala | 26 +++---- .../graphx/lib/ConnectedComponentsSuite.scala | 18 ++--- .../spark/graphx/lib/PageRankSuite.scala | 33 ++++----- .../StronglyConnectedComponentsSuite.scala | 23 +++--- 6 files changed, 88 insertions(+), 85 deletions(-) diff --git a/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala index 8d15150458d26..a570e4ed75fc3 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/GraphSuite.scala @@ -38,12 +38,12 @@ class GraphSuite extends FunSuite with LocalSparkContext { val doubleRing = ring ++ ring val graph = Graph.fromEdgeTuples(sc.parallelize(doubleRing), 1) assert(graph.edges.count() === doubleRing.size) - assert(graph.edges.collect.forall(e => e.attr == 1)) + assert(graph.edges.collect().forall(e => e.attr == 1)) // uniqueEdges option should uniquify edges and store duplicate count in edge attributes val uniqueGraph = Graph.fromEdgeTuples(sc.parallelize(doubleRing), 1, Some(RandomVertexCut)) assert(uniqueGraph.edges.count() === ring.size) - assert(uniqueGraph.edges.collect.forall(e => e.attr == 2)) + assert(uniqueGraph.edges.collect().forall(e => e.attr == 2)) } } @@ -64,7 +64,7 @@ class GraphSuite extends FunSuite with LocalSparkContext { assert( graph.edges.count() === rawEdges.size ) // Vertices not explicitly provided but referenced by edges should be created automatically assert( graph.vertices.count() === 100) - graph.triplets.collect.map { et => + graph.triplets.collect().map { et => assert((et.srcId < 10 && et.srcAttr) || (et.srcId >= 10 && !et.srcAttr)) assert((et.dstId < 10 && et.dstAttr) || (et.dstId >= 10 && !et.dstAttr)) } @@ -75,15 +75,17 @@ class GraphSuite extends FunSuite with LocalSparkContext { withSpark { sc => val n = 5 val star = starGraph(sc, n) - assert(star.triplets.map(et => (et.srcId, et.dstId, et.srcAttr, et.dstAttr)).collect.toSet === - (1 to n).map(x => (0: VertexId, x: VertexId, "v", "v")).toSet) + assert(star.triplets.map(et => (et.srcId, et.dstId, et.srcAttr, et.dstAttr)).collect().toSet + === (1 to n).map(x => (0: VertexId, x: VertexId, "v", "v")).toSet) } } test("partitionBy") { withSpark { sc => - def mkGraph(edges: List[(Long, Long)]) = Graph.fromEdgeTuples(sc.parallelize(edges, 2), 0) - def nonemptyParts(graph: Graph[Int, Int]) = { + def mkGraph(edges: List[(Long, Long)]): Graph[Int, Int] = { + Graph.fromEdgeTuples(sc.parallelize(edges, 2), 0) + } + def nonemptyParts(graph: Graph[Int, Int]): RDD[List[Edge[Int]]] = { graph.edges.partitionsRDD.mapPartitions { iter => Iterator(iter.next()._2.iterator.toList) }.filter(_.nonEmpty) @@ -102,7 +104,8 @@ class GraphSuite extends FunSuite with LocalSparkContext { assert(nonemptyParts(mkGraph(sameSrcEdges).partitionBy(EdgePartition1D)).count === 1) // partitionBy(CanonicalRandomVertexCut) puts edges that are identical modulo direction into // the same partition - assert(nonemptyParts(mkGraph(canonicalEdges).partitionBy(CanonicalRandomVertexCut)).count === 1) + assert( + nonemptyParts(mkGraph(canonicalEdges).partitionBy(CanonicalRandomVertexCut)).count === 1) // partitionBy(EdgePartition2D) puts identical edges in the same partition assert(nonemptyParts(mkGraph(identicalEdges).partitionBy(EdgePartition2D)).count === 1) @@ -140,10 +143,10 @@ class GraphSuite extends FunSuite with LocalSparkContext { val g = Graph( sc.parallelize(List((0L, "a"), (1L, "b"), (2L, "c"))), sc.parallelize(List(Edge(0L, 1L, 1), Edge(0L, 2L, 1)), 2)) - assert(g.triplets.collect.map(_.toTuple).toSet === + assert(g.triplets.collect().map(_.toTuple).toSet === Set(((0L, "a"), (1L, "b"), 1), ((0L, "a"), (2L, "c"), 1))) val gPart = g.partitionBy(EdgePartition2D) - assert(gPart.triplets.collect.map(_.toTuple).toSet === + assert(gPart.triplets.collect().map(_.toTuple).toSet === Set(((0L, "a"), (1L, "b"), 1), ((0L, "a"), (2L, "c"), 1))) } } @@ -154,10 +157,10 @@ class GraphSuite extends FunSuite with LocalSparkContext { val star = starGraph(sc, n) // mapVertices preserving type val mappedVAttrs = star.mapVertices((vid, attr) => attr + "2") - assert(mappedVAttrs.vertices.collect.toSet === (0 to n).map(x => (x: VertexId, "v2")).toSet) + assert(mappedVAttrs.vertices.collect().toSet === (0 to n).map(x => (x: VertexId, "v2")).toSet) // mapVertices changing type val mappedVAttrs2 = star.mapVertices((vid, attr) => attr.length) - assert(mappedVAttrs2.vertices.collect.toSet === (0 to n).map(x => (x: VertexId, 1)).toSet) + assert(mappedVAttrs2.vertices.collect().toSet === (0 to n).map(x => (x: VertexId, 1)).toSet) } } @@ -177,12 +180,12 @@ class GraphSuite extends FunSuite with LocalSparkContext { // Trigger initial vertex replication graph0.triplets.foreach(x => {}) // Change type of replicated vertices, but preserve erased type - val graph1 = graph0.mapVertices { - case (vid, integerOpt) => integerOpt.map((x: java.lang.Integer) => (x.toDouble): java.lang.Double) + val graph1 = graph0.mapVertices { case (vid, integerOpt) => + integerOpt.map((x: java.lang.Integer) => x.toDouble: java.lang.Double) } // Access replicated vertices, exposing the erased type val graph2 = graph1.mapTriplets(t => t.srcAttr.get) - assert(graph2.edges.map(_.attr).collect.toSet === Set[java.lang.Double](1.0, 2.0, 3.0)) + assert(graph2.edges.map(_.attr).collect().toSet === Set[java.lang.Double](1.0, 2.0, 3.0)) } } @@ -202,7 +205,7 @@ class GraphSuite extends FunSuite with LocalSparkContext { withSpark { sc => val n = 5 val star = starGraph(sc, n) - assert(star.mapTriplets(et => et.srcAttr + et.dstAttr).edges.collect.toSet === + assert(star.mapTriplets(et => et.srcAttr + et.dstAttr).edges.collect().toSet === (1L to n).map(x => Edge(0, x, "vv")).toSet) } } @@ -211,7 +214,7 @@ class GraphSuite extends FunSuite with LocalSparkContext { withSpark { sc => val n = 5 val star = starGraph(sc, n) - assert(star.reverse.outDegrees.collect.toSet === (1 to n).map(x => (x: VertexId, 1)).toSet) + assert(star.reverse.outDegrees.collect().toSet === (1 to n).map(x => (x: VertexId, 1)).toSet) } } @@ -221,7 +224,7 @@ class GraphSuite extends FunSuite with LocalSparkContext { val edges: RDD[Edge[Int]] = sc.parallelize(Array(Edge(1L, 2L, 0))) val graph = Graph(vertices, edges).reverse val result = graph.mapReduceTriplets[Int](et => Iterator((et.dstId, et.srcAttr)), _ + _) - assert(result.collect.toSet === Set((1L, 2))) + assert(result.collect().toSet === Set((1L, 2))) } } @@ -237,7 +240,8 @@ class GraphSuite extends FunSuite with LocalSparkContext { assert(subgraph.vertices.collect().toSet === (0 to n by 2).map(x => (x, "v")).toSet) // And 4 edges. - assert(subgraph.edges.map(_.copy()).collect().toSet === (2 to n by 2).map(x => Edge(0, x, 1)).toSet) + assert(subgraph.edges.map(_.copy()).collect().toSet === + (2 to n by 2).map(x => Edge(0, x, 1)).toSet) } } @@ -273,9 +277,9 @@ class GraphSuite extends FunSuite with LocalSparkContext { sc.parallelize((1 to n).flatMap(x => List((0: VertexId, x: VertexId), (0: VertexId, x: VertexId))), 1), "v") val star2 = doubleStar.groupEdges { (a, b) => a} - assert(star2.edges.collect.toArray.sorted(Edge.lexicographicOrdering[Int]) === - star.edges.collect.toArray.sorted(Edge.lexicographicOrdering[Int])) - assert(star2.vertices.collect.toSet === star.vertices.collect.toSet) + assert(star2.edges.collect().toArray.sorted(Edge.lexicographicOrdering[Int]) === + star.edges.collect().toArray.sorted(Edge.lexicographicOrdering[Int])) + assert(star2.vertices.collect().toSet === star.vertices.collect().toSet) } } @@ -300,21 +304,23 @@ class GraphSuite extends FunSuite with LocalSparkContext { throw new Exception("map ran on edge with dst vid %d, which is odd".format(et.dstId)) } Iterator((et.srcId, 1)) - }, (a: Int, b: Int) => a + b, Some((active, EdgeDirection.In))).collect.toSet + }, (a: Int, b: Int) => a + b, Some((active, EdgeDirection.In))).collect().toSet assert(numEvenNeighbors === (1 to n).map(x => (x: VertexId, n / 2)).toSet) // outerJoinVertices followed by mapReduceTriplets(activeSetOpt) - val ringEdges = sc.parallelize((0 until n).map(x => (x: VertexId, (x+1) % n: VertexId)), 3) + val ringEdges = sc.parallelize((0 until n).map(x => (x: VertexId, (x + 1) % n: VertexId)), 3) val ring = Graph.fromEdgeTuples(ringEdges, 0) .mapVertices((vid, attr) => vid).cache() val changed = ring.vertices.filter { case (vid, attr) => attr % 2 == 1 }.mapValues(-_).cache() - val changedGraph = ring.outerJoinVertices(changed) { (vid, old, newOpt) => newOpt.getOrElse(old) } + val changedGraph = ring.outerJoinVertices(changed) { (vid, old, newOpt) => + newOpt.getOrElse(old) + } val numOddNeighbors = changedGraph.mapReduceTriplets(et => { // Map function should only run on edges with source in the active set if (et.srcId % 2 != 1) { throw new Exception("map ran on edge with src vid %d, which is even".format(et.dstId)) } Iterator((et.dstId, 1)) - }, (a: Int, b: Int) => a + b, Some(changed, EdgeDirection.Out)).collect.toSet + }, (a: Int, b: Int) => a + b, Some(changed, EdgeDirection.Out)).collect().toSet assert(numOddNeighbors === (2 to n by 2).map(x => (x: VertexId, 1)).toSet) } @@ -340,17 +346,18 @@ class GraphSuite extends FunSuite with LocalSparkContext { val n = 5 val reverseStar = starGraph(sc, n).reverse.cache() // outerJoinVertices changing type - val reverseStarDegrees = - reverseStar.outerJoinVertices(reverseStar.outDegrees) { (vid, a, bOpt) => bOpt.getOrElse(0) } + val reverseStarDegrees = reverseStar.outerJoinVertices(reverseStar.outDegrees) { + (vid, a, bOpt) => bOpt.getOrElse(0) + } val neighborDegreeSums = reverseStarDegrees.mapReduceTriplets( et => Iterator((et.srcId, et.dstAttr), (et.dstId, et.srcAttr)), - (a: Int, b: Int) => a + b).collect.toSet + (a: Int, b: Int) => a + b).collect().toSet assert(neighborDegreeSums === Set((0: VertexId, n)) ++ (1 to n).map(x => (x: VertexId, 0))) // outerJoinVertices preserving type val messages = reverseStar.vertices.mapValues { (vid, attr) => vid.toString } val newReverseStar = reverseStar.outerJoinVertices(messages) { (vid, a, bOpt) => a + bOpt.getOrElse("") } - assert(newReverseStar.vertices.map(_._2).collect.toSet === + assert(newReverseStar.vertices.map(_._2).collect().toSet === (0 to n).map(x => "v%d".format(x)).toSet) } } @@ -361,7 +368,7 @@ class GraphSuite extends FunSuite with LocalSparkContext { val edges = sc.parallelize(List(Edge(1, 2, 0), Edge(2, 1, 0)), 2) val graph = Graph(verts, edges) val triplets = graph.triplets.map(et => (et.srcId, et.dstId, et.srcAttr, et.dstAttr)) - .collect.toSet + .collect().toSet assert(triplets === Set((1: VertexId, 2: VertexId, "a", "b"), (2: VertexId, 1: VertexId, "b", "a"))) } @@ -417,7 +424,7 @@ class GraphSuite extends FunSuite with LocalSparkContext { val graph = Graph.fromEdgeTuples(edges, 1) val neighborAttrSums = graph.mapReduceTriplets[Int]( et => Iterator((et.dstId, et.srcAttr)), _ + _) - assert(neighborAttrSums.collect.toSet === Set((0: VertexId, n))) + assert(neighborAttrSums.collect().toSet === Set((0: VertexId, n))) } finally { sc.stop() } diff --git a/graphx/src/test/scala/org/apache/spark/graphx/LocalSparkContext.scala b/graphx/src/test/scala/org/apache/spark/graphx/LocalSparkContext.scala index a3e28efc75a98..d2ad9be555770 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/LocalSparkContext.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/LocalSparkContext.scala @@ -26,7 +26,7 @@ import org.apache.spark.SparkContext */ trait LocalSparkContext { /** Runs `f` on a new SparkContext and ensures that it is stopped afterwards. */ - def withSpark[T](f: SparkContext => T) = { + def withSpark[T](f: SparkContext => T): T = { val conf = new SparkConf() GraphXUtils.registerKryoClasses(conf) val sc = new SparkContext("local", "test", conf) diff --git a/graphx/src/test/scala/org/apache/spark/graphx/VertexRDDSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/VertexRDDSuite.scala index c9443d11c76cf..d0a7198d691d7 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/VertexRDDSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/VertexRDDSuite.scala @@ -25,7 +25,7 @@ import org.apache.spark.storage.StorageLevel class VertexRDDSuite extends FunSuite with LocalSparkContext { - def vertices(sc: SparkContext, n: Int) = { + private def vertices(sc: SparkContext, n: Int) = { VertexRDD(sc.parallelize((0 to n).map(x => (x.toLong, x)), 5)) } @@ -52,7 +52,7 @@ class VertexRDDSuite extends FunSuite with LocalSparkContext { val vertexA = VertexRDD(sc.parallelize(0 until 75, 2).map(i => (i.toLong, 0))).cache() val vertexB = VertexRDD(sc.parallelize(25 until 100, 2).map(i => (i.toLong, 1))).cache() val vertexC = vertexA.minus(vertexB) - assert(vertexC.map(_._1).collect.toSet === (0 until 25).toSet) + assert(vertexC.map(_._1).collect().toSet === (0 until 25).toSet) } } @@ -62,7 +62,7 @@ class VertexRDDSuite extends FunSuite with LocalSparkContext { val vertexB: RDD[(VertexId, Int)] = sc.parallelize(25 until 100, 2).map(i => (i.toLong, 1)).cache() val vertexC = vertexA.minus(vertexB) - assert(vertexC.map(_._1).collect.toSet === (0 until 25).toSet) + assert(vertexC.map(_._1).collect().toSet === (0 until 25).toSet) } } @@ -72,7 +72,7 @@ class VertexRDDSuite extends FunSuite with LocalSparkContext { val vertexB = VertexRDD(sc.parallelize(50 until 100, 2).map(i => (i.toLong, 1))) assert(vertexA.partitions.size != vertexB.partitions.size) val vertexC = vertexA.minus(vertexB) - assert(vertexC.map(_._1).collect.toSet === (0 until 50).toSet) + assert(vertexC.map(_._1).collect().toSet === (0 until 50).toSet) } } @@ -106,7 +106,7 @@ class VertexRDDSuite extends FunSuite with LocalSparkContext { val vertexB = VertexRDD(sc.parallelize(8 until 16, 2).map(i => (i.toLong, 1))) assert(vertexA.partitions.size != vertexB.partitions.size) val vertexC = vertexA.diff(vertexB) - assert(vertexC.map(_._1).collect.toSet === (8 until 16).toSet) + assert(vertexC.map(_._1).collect().toSet === (8 until 16).toSet) } } @@ -116,11 +116,11 @@ class VertexRDDSuite extends FunSuite with LocalSparkContext { val verts = vertices(sc, n).cache() val evens = verts.filter(q => ((q._2 % 2) == 0)).cache() // leftJoin with another VertexRDD - assert(verts.leftJoin(evens) { (id, a, bOpt) => a - bOpt.getOrElse(0) }.collect.toSet === + assert(verts.leftJoin(evens) { (id, a, bOpt) => a - bOpt.getOrElse(0) }.collect().toSet === (0 to n by 2).map(x => (x.toLong, 0)).toSet ++ (1 to n by 2).map(x => (x.toLong, x)).toSet) // leftJoin with an RDD val evensRDD = evens.map(identity) - assert(verts.leftJoin(evensRDD) { (id, a, bOpt) => a - bOpt.getOrElse(0) }.collect.toSet === + assert(verts.leftJoin(evensRDD) { (id, a, bOpt) => a - bOpt.getOrElse(0) }.collect().toSet === (0 to n by 2).map(x => (x.toLong, 0)).toSet ++ (1 to n by 2).map(x => (x.toLong, x)).toSet) } } @@ -134,7 +134,7 @@ class VertexRDDSuite extends FunSuite with LocalSparkContext { val vertexC = vertexA.leftJoin(vertexB) { (vid, old, newOpt) => old - newOpt.getOrElse(0) } - assert(vertexC.filter(v => v._2 != 0).map(_._1).collect.toSet == (1 to 99 by 2).toSet) + assert(vertexC.filter(v => v._2 != 0).map(_._1).collect().toSet == (1 to 99 by 2).toSet) } } @@ -144,11 +144,11 @@ class VertexRDDSuite extends FunSuite with LocalSparkContext { val verts = vertices(sc, n).cache() val evens = verts.filter(q => ((q._2 % 2) == 0)).cache() // innerJoin with another VertexRDD - assert(verts.innerJoin(evens) { (id, a, b) => a - b }.collect.toSet === + assert(verts.innerJoin(evens) { (id, a, b) => a - b }.collect().toSet === (0 to n by 2).map(x => (x.toLong, 0)).toSet) // innerJoin with an RDD val evensRDD = evens.map(identity) - assert(verts.innerJoin(evensRDD) { (id, a, b) => a - b }.collect.toSet === + assert(verts.innerJoin(evensRDD) { (id, a, b) => a - b }.collect().toSet === (0 to n by 2).map(x => (x.toLong, 0)).toSet) } } @@ -161,7 +161,7 @@ class VertexRDDSuite extends FunSuite with LocalSparkContext { val vertexC = vertexA.innerJoin(vertexB) { (vid, old, newVal) => old - newVal } - assert(vertexC.filter(v => v._2 == 0).map(_._1).collect.toSet == (0 to 98 by 2).toSet) + assert(vertexC.filter(v => v._2 == 0).map(_._1).collect().toSet == (0 to 98 by 2).toSet) } } @@ -171,7 +171,7 @@ class VertexRDDSuite extends FunSuite with LocalSparkContext { val verts = vertices(sc, n) val messageTargets = (0 to n) ++ (0 to n by 2) val messages = sc.parallelize(messageTargets.map(x => (x.toLong, 1))) - assert(verts.aggregateUsingIndex[Int](messages, _ + _).collect.toSet === + assert(verts.aggregateUsingIndex[Int](messages, _ + _).collect().toSet === (0 to n).map(x => (x.toLong, if (x % 2 == 0) 2 else 1)).toSet) } } @@ -183,7 +183,7 @@ class VertexRDDSuite extends FunSuite with LocalSparkContext { val edges = EdgeRDD.fromEdges(sc.parallelize(List.empty[Edge[Int]])) val rdd = VertexRDD(verts, edges, 0, (a: Int, b: Int) => a + b) // test merge function - assert(rdd.collect.toSet == Set((0L, 0), (1L, 3), (2L, 9))) + assert(rdd.collect().toSet == Set((0L, 0), (1L, 3), (2L, 9))) } } diff --git a/graphx/src/test/scala/org/apache/spark/graphx/lib/ConnectedComponentsSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/lib/ConnectedComponentsSuite.scala index 3915be15b3434..4cc30a96408f8 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/lib/ConnectedComponentsSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/lib/ConnectedComponentsSuite.scala @@ -32,7 +32,7 @@ class ConnectedComponentsSuite extends FunSuite with LocalSparkContext { withSpark { sc => val gridGraph = GraphGenerators.gridGraph(sc, 10, 10) val ccGraph = gridGraph.connectedComponents() - val maxCCid = ccGraph.vertices.map { case (vid, ccId) => ccId }.sum + val maxCCid = ccGraph.vertices.map { case (vid, ccId) => ccId }.sum() assert(maxCCid === 0) } } // end of Grid connected components @@ -42,7 +42,7 @@ class ConnectedComponentsSuite extends FunSuite with LocalSparkContext { withSpark { sc => val gridGraph = GraphGenerators.gridGraph(sc, 10, 10).reverse val ccGraph = gridGraph.connectedComponents() - val maxCCid = ccGraph.vertices.map { case (vid, ccId) => ccId }.sum + val maxCCid = ccGraph.vertices.map { case (vid, ccId) => ccId }.sum() assert(maxCCid === 0) } } // end of Grid connected components @@ -50,8 +50,8 @@ class ConnectedComponentsSuite extends FunSuite with LocalSparkContext { test("Chain Connected Components") { withSpark { sc => - val chain1 = (0 until 9).map(x => (x, x+1) ) - val chain2 = (10 until 20).map(x => (x, x+1) ) + val chain1 = (0 until 9).map(x => (x, x + 1)) + val chain2 = (10 until 20).map(x => (x, x + 1)) val rawEdges = sc.parallelize(chain1 ++ chain2, 3).map { case (s,d) => (s.toLong, d.toLong) } val twoChains = Graph.fromEdgeTuples(rawEdges, 1.0) val ccGraph = twoChains.connectedComponents() @@ -73,12 +73,12 @@ class ConnectedComponentsSuite extends FunSuite with LocalSparkContext { test("Reverse Chain Connected Components") { withSpark { sc => - val chain1 = (0 until 9).map(x => (x, x+1) ) - val chain2 = (10 until 20).map(x => (x, x+1) ) + val chain1 = (0 until 9).map(x => (x, x + 1)) + val chain2 = (10 until 20).map(x => (x, x + 1)) val rawEdges = sc.parallelize(chain1 ++ chain2, 3).map { case (s,d) => (s.toLong, d.toLong) } val twoChains = Graph.fromEdgeTuples(rawEdges, true).reverse val ccGraph = twoChains.connectedComponents() - val vertices = ccGraph.vertices.collect + val vertices = ccGraph.vertices.collect() for ( (id, cc) <- vertices ) { if (id < 10) { assert(cc === 0) @@ -120,9 +120,9 @@ class ConnectedComponentsSuite extends FunSuite with LocalSparkContext { // Build the initial Graph val graph = Graph(users, relationships, defaultUser) val ccGraph = graph.connectedComponents() - val vertices = ccGraph.vertices.collect + val vertices = ccGraph.vertices.collect() for ( (id, cc) <- vertices ) { - assert(cc == 0) + assert(cc === 0) } } } // end of toy connected components diff --git a/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala index fc491ae327c2a..95804b07b1db0 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala @@ -19,15 +19,12 @@ package org.apache.spark.graphx.lib import org.scalatest.FunSuite -import org.apache.spark.SparkContext -import org.apache.spark.SparkContext._ import org.apache.spark.graphx._ -import org.apache.spark.graphx.lib._ import org.apache.spark.graphx.util.GraphGenerators -import org.apache.spark.rdd._ + object GridPageRank { - def apply(nRows: Int, nCols: Int, nIter: Int, resetProb: Double) = { + def apply(nRows: Int, nCols: Int, nIter: Int, resetProb: Double): Seq[(VertexId, Double)] = { val inNbrs = Array.fill(nRows * nCols)(collection.mutable.MutableList.empty[Int]) val outDegree = Array.fill(nRows * nCols)(0) // Convert row column address into vertex ids (row major order) @@ -35,13 +32,13 @@ object GridPageRank { // Make the grid graph for (r <- 0 until nRows; c <- 0 until nCols) { val ind = sub2ind(r,c) - if (r+1 < nRows) { + if (r + 1 < nRows) { outDegree(ind) += 1 - inNbrs(sub2ind(r+1,c)) += ind + inNbrs(sub2ind(r + 1,c)) += ind } - if (c+1 < nCols) { + if (c + 1 < nCols) { outDegree(ind) += 1 - inNbrs(sub2ind(r,c+1)) += ind + inNbrs(sub2ind(r,c + 1)) += ind } } // compute the pagerank @@ -64,7 +61,7 @@ class PageRankSuite extends FunSuite with LocalSparkContext { def compareRanks(a: VertexRDD[Double], b: VertexRDD[Double]): Double = { a.leftJoin(b) { case (id, a, bOpt) => (a - bOpt.getOrElse(0.0)) * (a - bOpt.getOrElse(0.0)) } - .map { case (id, error) => error }.sum + .map { case (id, error) => error }.sum() } test("Star PageRank") { @@ -80,12 +77,12 @@ class PageRankSuite extends FunSuite with LocalSparkContext { // Static PageRank should only take 2 iterations to converge val notMatching = staticRanks1.innerZipJoin(staticRanks2) { (vid, pr1, pr2) => if (pr1 != pr2) 1 else 0 - }.map { case (vid, test) => test }.sum + }.map { case (vid, test) => test }.sum() assert(notMatching === 0) val staticErrors = staticRanks2.map { case (vid, pr) => - val correct = (vid > 0 && pr == resetProb) || - (vid == 0 && math.abs(pr - (resetProb + (1.0 - resetProb) * (resetProb * (nVertices - 1)) )) < 1.0E-5) + val p = math.abs(pr - (resetProb + (1.0 - resetProb) * (resetProb * (nVertices - 1)) )) + val correct = (vid > 0 && pr == resetProb) || (vid == 0L && p < 1.0E-5) if (!correct) 1 else 0 } assert(staticErrors.sum === 0) @@ -95,8 +92,6 @@ class PageRankSuite extends FunSuite with LocalSparkContext { } } // end of test Star PageRank - - test("Grid PageRank") { withSpark { sc => val rows = 10 @@ -109,18 +104,18 @@ class PageRankSuite extends FunSuite with LocalSparkContext { val staticRanks = gridGraph.staticPageRank(numIter, resetProb).vertices.cache() val dynamicRanks = gridGraph.pageRank(tol, resetProb).vertices.cache() - val referenceRanks = VertexRDD(sc.parallelize(GridPageRank(rows, cols, numIter, resetProb))).cache() + val referenceRanks = VertexRDD( + sc.parallelize(GridPageRank(rows, cols, numIter, resetProb))).cache() assert(compareRanks(staticRanks, referenceRanks) < errorTol) assert(compareRanks(dynamicRanks, referenceRanks) < errorTol) } } // end of Grid PageRank - test("Chain PageRank") { withSpark { sc => - val chain1 = (0 until 9).map(x => (x, x+1) ) - val rawEdges = sc.parallelize(chain1, 1).map { case (s,d) => (s.toLong, d.toLong) } + val chain1 = (0 until 9).map(x => (x, x + 1)) + val rawEdges = sc.parallelize(chain1, 1).map { case (s, d) => (s.toLong, d.toLong) } val chain = Graph.fromEdgeTuples(rawEdges, 1.0).cache() val resetProb = 0.15 val tol = 0.0001 diff --git a/graphx/src/test/scala/org/apache/spark/graphx/lib/StronglyConnectedComponentsSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/lib/StronglyConnectedComponentsSuite.scala index df54aa37cad68..1f658c371ffcf 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/lib/StronglyConnectedComponentsSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/lib/StronglyConnectedComponentsSuite.scala @@ -34,8 +34,8 @@ class StronglyConnectedComponentsSuite extends FunSuite with LocalSparkContext { val edges = sc.parallelize(Seq.empty[Edge[Int]]) val graph = Graph(vertices, edges) val sccGraph = graph.stronglyConnectedComponents(5) - for ((id, scc) <- sccGraph.vertices.collect) { - assert(id == scc) + for ((id, scc) <- sccGraph.vertices.collect()) { + assert(id === scc) } } } @@ -45,8 +45,8 @@ class StronglyConnectedComponentsSuite extends FunSuite with LocalSparkContext { val rawEdges = sc.parallelize((0L to 6L).map(x => (x, (x + 1) % 7))) val graph = Graph.fromEdgeTuples(rawEdges, -1) val sccGraph = graph.stronglyConnectedComponents(20) - for ((id, scc) <- sccGraph.vertices.collect) { - assert(0L == scc) + for ((id, scc) <- sccGraph.vertices.collect()) { + assert(0L === scc) } } } @@ -60,13 +60,14 @@ class StronglyConnectedComponentsSuite extends FunSuite with LocalSparkContext { val rawEdges = sc.parallelize(edges) val graph = Graph.fromEdgeTuples(rawEdges, -1) val sccGraph = graph.stronglyConnectedComponents(20) - for ((id, scc) <- sccGraph.vertices.collect) { - if (id < 3) - assert(0L == scc) - else if (id < 6) - assert(3L == scc) - else - assert(id == scc) + for ((id, scc) <- sccGraph.vertices.collect()) { + if (id < 3) { + assert(0L === scc) + } else if (id < 6) { + assert(3L === scc) + } else { + assert(id === scc) + } } } } From 66159c35010af35098dd1ec75475bb5d4d0fd6ca Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Wed, 8 Apr 2015 11:32:44 -0700 Subject: [PATCH 194/591] [SPARK-6765] Fix test code style for mllib. So we can turn style checker on for test code. Author: Reynold Xin Closes #5411 from rxin/test-style-mllib and squashes the following commits: d8a2569 [Reynold Xin] [SPARK-6765] Fix test code style for mllib. --- .../org/apache/spark/ml/param/TestParams.scala | 2 +- .../mllib/classification/NaiveBayesSuite.scala | 2 +- .../StreamingLogisticRegressionSuite.scala | 2 +- .../spark/mllib/clustering/KMeansSuite.scala | 10 +++++++--- .../spark/mllib/clustering/LDASuite.scala | 4 ++-- .../clustering/StreamingKMeansSuite.scala | 4 ++-- .../spark/mllib/random/RandomRDDsSuite.scala | 5 +++-- .../spark/mllib/recommendation/ALSSuite.scala | 3 +++ .../regression/RidgeRegressionSuite.scala | 2 +- .../StreamingLinearRegressionSuite.scala | 2 +- .../apache/spark/mllib/util/TestingUtils.scala | 14 +++++++------- .../spark/mllib/util/TestingUtilsSuite.scala | 18 +++++++++++------- 12 files changed, 40 insertions(+), 28 deletions(-) diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala index 1a65883d78a71..ce52f2f230085 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala @@ -28,7 +28,7 @@ class TestParams extends Params { def setInputCol(value: String): this.type = { set(inputCol, value); this } def getInputCol: String = get(inputCol) - override def validate(paramMap: ParamMap) = { + override def validate(paramMap: ParamMap): Unit = { val m = this.paramMap ++ paramMap require(m(maxIter) >= 0) require(m.contains(inputCol)) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala index f9fe3e006ccb8..ea89b17b7c08f 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/NaiveBayesSuite.scala @@ -102,7 +102,7 @@ class NaiveBayesSuite extends FunSuite with MLlibTestSparkContext { def validateModelFit( piData: Array[Double], thetaData: Array[Array[Double]], - model: NaiveBayesModel) = { + model: NaiveBayesModel): Unit = { def closeFit(d1: Double, d2: Double, precision: Double): Boolean = { (d1 - d2).abs <= precision } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala index d50c43d439187..5683b55e8500a 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala @@ -30,7 +30,7 @@ import org.apache.spark.streaming.TestSuiteBase class StreamingLogisticRegressionSuite extends FunSuite with TestSuiteBase { // use longer wait time to ensure job completion - override def maxWaitTimeMillis = 30000 + override def maxWaitTimeMillis: Int = 30000 // Test if we can accurately learn B for Y = logistic(BX) on streaming data test("parameter accuracy") { diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala index 7bf250eb5a383..0f2b26d462ad2 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala @@ -199,9 +199,13 @@ class KMeansSuite extends FunSuite with MLlibTestSparkContext { test("k-means|| initialization") { case class VectorWithCompare(x: Vector) extends Ordered[VectorWithCompare] { - @Override def compare(that: VectorWithCompare): Int = { - if(this.x.toArray.foldLeft[Double](0.0)((acc, x) => acc + x * x) > - that.x.toArray.foldLeft[Double](0.0)((acc, x) => acc + x * x)) -1 else 1 + override def compare(that: VectorWithCompare): Int = { + if (this.x.toArray.foldLeft[Double](0.0)((acc, x) => acc + x * x) > + that.x.toArray.foldLeft[Double](0.0)((acc, x) => acc + x * x)) { + -1 + } else { + 1 + } } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala index 302d751eb8a94..15de10fd13a19 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala @@ -19,7 +19,7 @@ package org.apache.spark.mllib.clustering import org.scalatest.FunSuite -import org.apache.spark.mllib.linalg.{DenseMatrix, Matrix, Vectors} +import org.apache.spark.mllib.linalg.{Vector, DenseMatrix, Matrix, Vectors} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ @@ -141,7 +141,7 @@ private[clustering] object LDASuite { (terms.toArray, termWeights.toArray) } - def tinyCorpus = Array( + def tinyCorpus: Array[(Long, Vector)] = Array( Vectors.dense(1, 3, 0, 2, 8), Vectors.dense(0, 2, 1, 0, 4), Vectors.dense(2, 3, 12, 3, 1), diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala index 850c9fce507cd..f90025d535e45 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala @@ -27,7 +27,7 @@ import org.apache.spark.util.random.XORShiftRandom class StreamingKMeansSuite extends FunSuite with TestSuiteBase { - override def maxWaitTimeMillis = 30000 + override def maxWaitTimeMillis: Int = 30000 test("accuracy for single center and equivalence to grand average") { // set parameters @@ -59,7 +59,7 @@ class StreamingKMeansSuite extends FunSuite with TestSuiteBase { // estimated center from streaming should exactly match the arithmetic mean of all data points // because the decay factor is set to 1.0 val grandMean = - input.flatten.map(x => x.toBreeze).reduce(_+_) / (numBatches * numPoints).toDouble + input.flatten.map(x => x.toBreeze).reduce(_ + _) / (numBatches * numPoints).toDouble assert(model.latestModel().clusterCenters(0) ~== Vectors.dense(grandMean.toArray) absTol 1E-5) } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDsSuite.scala index 6395188a0842a..63f2ea916d457 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/random/RandomRDDsSuite.scala @@ -181,7 +181,8 @@ class RandomRDDsSuite extends FunSuite with MLlibTestSparkContext with Serializa val poisson = RandomRDDs.poissonVectorRDD(sc, poissonMean, rows, cols, parts, seed) testGeneratedVectorRDD(poisson, rows, cols, parts, poissonMean, math.sqrt(poissonMean), 0.1) - val exponential = RandomRDDs.exponentialVectorRDD(sc, exponentialMean, rows, cols, parts, seed) + val exponential = + RandomRDDs.exponentialVectorRDD(sc, exponentialMean, rows, cols, parts, seed) testGeneratedVectorRDD(exponential, rows, cols, parts, exponentialMean, exponentialMean, 0.1) val gamma = RandomRDDs.gammaVectorRDD(sc, gammaShape, gammaScale, rows, cols, parts, seed) @@ -197,7 +198,7 @@ private[random] class MockDistro extends RandomDataGenerator[Double] { // This allows us to check that each partition has a different seed override def nextValue(): Double = seed.toDouble - override def setSeed(seed: Long) = this.seed = seed + override def setSeed(seed: Long): Unit = this.seed = seed override def copy(): MockDistro = new MockDistro } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala index 8775c0ca9df84..b3798940ddc38 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala @@ -203,6 +203,7 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext { * @param numProductBlocks number of product blocks to partition products into * @param negativeFactors whether the generated user/product factors can have negative entries */ + // scalastyle:off def testALS( users: Int, products: Int, @@ -216,6 +217,8 @@ class ALSSuite extends FunSuite with MLlibTestSparkContext { numUserBlocks: Int = -1, numProductBlocks: Int = -1, negativeFactors: Boolean = true) { + // scalastyle:on + val (sampledRatings, trueRatings, truePrefs) = ALSSuite.generateRatings(users, products, features, samplingRate, implicitPrefs, negativeWeights, negativeFactors) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala index 43d61151e2471..d6c93cc0e49cd 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/RidgeRegressionSuite.scala @@ -35,7 +35,7 @@ private object RidgeRegressionSuite { class RidgeRegressionSuite extends FunSuite with MLlibTestSparkContext { - def predictionError(predictions: Seq[Double], input: Seq[LabeledPoint]) = { + def predictionError(predictions: Seq[Double], input: Seq[LabeledPoint]): Double = { predictions.zip(input).map { case (prediction, expected) => (prediction - expected.label) * (prediction - expected.label) }.reduceLeft(_ + _) / predictions.size diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala index 24fd8df691817..26604dbe6c1ef 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala @@ -29,7 +29,7 @@ import org.apache.spark.streaming.TestSuiteBase class StreamingLinearRegressionSuite extends FunSuite with TestSuiteBase { // use longer wait time to ensure job completion - override def maxWaitTimeMillis = 20000 + override def maxWaitTimeMillis: Int = 20000 // Assert that two values are equal within tolerance epsilon def assertEqual(v1: Double, v2: Double, epsilon: Double) { diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala index e957fa5d25f4c..352193a67860c 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtils.scala @@ -95,16 +95,16 @@ object TestingUtils { /** * Comparison using absolute tolerance. */ - def absTol(eps: Double): CompareDoubleRightSide = CompareDoubleRightSide(AbsoluteErrorComparison, - x, eps, ABS_TOL_MSG) + def absTol(eps: Double): CompareDoubleRightSide = + CompareDoubleRightSide(AbsoluteErrorComparison, x, eps, ABS_TOL_MSG) /** * Comparison using relative tolerance. */ - def relTol(eps: Double): CompareDoubleRightSide = CompareDoubleRightSide(RelativeErrorComparison, - x, eps, REL_TOL_MSG) + def relTol(eps: Double): CompareDoubleRightSide = + CompareDoubleRightSide(RelativeErrorComparison, x, eps, REL_TOL_MSG) - override def toString = x.toString + override def toString: String = x.toString } case class CompareVectorRightSide( @@ -166,7 +166,7 @@ object TestingUtils { x.toArray.zip(y.toArray).forall(x => x._1 ~= x._2 relTol eps) }, x, eps, REL_TOL_MSG) - override def toString = x.toString + override def toString: String = x.toString } case class CompareMatrixRightSide( @@ -229,7 +229,7 @@ object TestingUtils { x.toArray.zip(y.toArray).forall(x => x._1 ~= x._2 relTol eps) }, x, eps, REL_TOL_MSG) - override def toString = x.toString + override def toString: String = x.toString } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtilsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtilsSuite.scala index b0ecb33c28483..59e6c778806f4 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtilsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/util/TestingUtilsSuite.scala @@ -88,16 +88,20 @@ class TestingUtilsSuite extends FunSuite { assert(!(17.8 ~= 17.59 absTol 0.2)) // Comparisons of numbers very close to zero, and both side of zeros - assert(Double.MinPositiveValue ~== 4 * Double.MinPositiveValue absTol 5 * Double.MinPositiveValue) - assert(Double.MinPositiveValue !~== 6 * Double.MinPositiveValue absTol 5 * Double.MinPositiveValue) - - assert(-Double.MinPositiveValue ~== 3 * Double.MinPositiveValue absTol 5 * Double.MinPositiveValue) - assert(Double.MinPositiveValue !~== -4 * Double.MinPositiveValue absTol 5 * Double.MinPositiveValue) + assert( + Double.MinPositiveValue ~== 4 * Double.MinPositiveValue absTol 5 * Double.MinPositiveValue) + assert( + Double.MinPositiveValue !~== 6 * Double.MinPositiveValue absTol 5 * Double.MinPositiveValue) + + assert( + -Double.MinPositiveValue ~== 3 * Double.MinPositiveValue absTol 5 * Double.MinPositiveValue) + assert( + Double.MinPositiveValue !~== -4 * Double.MinPositiveValue absTol 5 * Double.MinPositiveValue) } test("Comparing vectors using relative error.") { - //Comparisons of two dense vectors + // Comparisons of two dense vectors assert(Vectors.dense(Array(3.1, 3.5)) ~== Vectors.dense(Array(3.130, 3.534)) relTol 0.01) assert(Vectors.dense(Array(3.1, 3.5)) !~== Vectors.dense(Array(3.135, 3.534)) relTol 0.01) assert(Vectors.dense(Array(3.1, 3.5)) ~= Vectors.dense(Array(3.130, 3.534)) relTol 0.01) @@ -130,7 +134,7 @@ class TestingUtilsSuite extends FunSuite { test("Comparing vectors using absolute error.") { - //Comparisons of two dense vectors + // Comparisons of two dense vectors assert(Vectors.dense(Array(3.1, 3.5, 0.0)) ~== Vectors.dense(Array(3.1 + 1E-8, 3.5 + 2E-7, 1E-8)) absTol 1E-6) From 6ada4f6f52cf1d992c7ab0c32318790cf08b0a0d Mon Sep 17 00:00:00 2001 From: Davies Liu Date: Wed, 8 Apr 2015 13:31:45 -0700 Subject: [PATCH 195/591] [SPARK-6781] [SQL] use sqlContext in python shell Use `sqlContext` in PySpark shell, make it consistent with SQL programming guide. `sqlCtx` is also kept for compatibility. Author: Davies Liu Closes #5425 from davies/sqlCtx and squashes the following commits: af67340 [Davies Liu] sqlCtx -> sqlContext 15a278f [Davies Liu] use sqlContext in python shell --- docs/ml-guide.md | 2 +- docs/sql-programming-guide.md | 4 +- .../spark/examples/sql/JavaSparkSQL.java | 20 ++--- .../ml/simple_text_classification_pipeline.py | 2 +- .../src/main/python/mllib/dataset_example.py | 6 +- python/pyspark/ml/classification.py | 4 +- python/pyspark/ml/feature.py | 4 +- python/pyspark/shell.py | 6 +- python/pyspark/sql/context.py | 79 +++++++++---------- python/pyspark/sql/dataframe.py | 6 +- python/pyspark/sql/functions.py | 2 +- python/pyspark/sql/types.py | 4 +- 12 files changed, 69 insertions(+), 70 deletions(-) diff --git a/docs/ml-guide.md b/docs/ml-guide.md index c08c76d226713..771a07183e26f 100644 --- a/docs/ml-guide.md +++ b/docs/ml-guide.md @@ -493,7 +493,7 @@ from pyspark.ml.feature import HashingTF, Tokenizer from pyspark.sql import Row, SQLContext sc = SparkContext(appName="SimpleTextClassificationPipeline") -sqlCtx = SQLContext(sc) +sqlContext = SQLContext(sc) # Prepare training documents, which are labeled. LabeledDocument = Row("id", "text", "label") diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index 4441d6a000a02..663f656883721 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -1642,7 +1642,7 @@ moved into the udf object in `SQLContext`.
{% highlight java %} -sqlCtx.udf.register("strLen", (s: String) => s.length()) +sqlContext.udf.register("strLen", (s: String) => s.length()) {% endhighlight %}
@@ -1650,7 +1650,7 @@ sqlCtx.udf.register("strLen", (s: String) => s.length())
{% highlight java %} -sqlCtx.udf().register("strLen", (String s) -> { s.length(); }); +sqlContext.udf().register("strLen", (String s) -> { s.length(); }); {% endhighlight %}
diff --git a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java index dee794840a3e1..8159ffbe2d269 100644 --- a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java +++ b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java @@ -55,7 +55,7 @@ public void setAge(int age) { public static void main(String[] args) throws Exception { SparkConf sparkConf = new SparkConf().setAppName("JavaSparkSQL"); JavaSparkContext ctx = new JavaSparkContext(sparkConf); - SQLContext sqlCtx = new SQLContext(ctx); + SQLContext sqlContext = new SQLContext(ctx); System.out.println("=== Data source: RDD ==="); // Load a text file and convert each line to a Java Bean. @@ -74,11 +74,11 @@ public Person call(String line) { }); // Apply a schema to an RDD of Java Beans and register it as a table. - DataFrame schemaPeople = sqlCtx.createDataFrame(people, Person.class); + DataFrame schemaPeople = sqlContext.createDataFrame(people, Person.class); schemaPeople.registerTempTable("people"); // SQL can be run over RDDs that have been registered as tables. - DataFrame teenagers = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19"); + DataFrame teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19"); // The results of SQL queries are DataFrames and support all the normal RDD operations. // The columns of a row in the result can be accessed by ordinal. @@ -99,12 +99,12 @@ public String call(Row row) { // Read in the parquet file created above. // Parquet files are self-describing so the schema is preserved. // The result of loading a parquet file is also a DataFrame. - DataFrame parquetFile = sqlCtx.parquetFile("people.parquet"); + DataFrame parquetFile = sqlContext.parquetFile("people.parquet"); //Parquet files can also be registered as tables and then used in SQL statements. parquetFile.registerTempTable("parquetFile"); DataFrame teenagers2 = - sqlCtx.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19"); + sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19"); teenagerNames = teenagers2.toJavaRDD().map(new Function() { @Override public String call(Row row) { @@ -120,7 +120,7 @@ public String call(Row row) { // The path can be either a single text file or a directory storing text files. String path = "examples/src/main/resources/people.json"; // Create a DataFrame from the file(s) pointed by path - DataFrame peopleFromJsonFile = sqlCtx.jsonFile(path); + DataFrame peopleFromJsonFile = sqlContext.jsonFile(path); // Because the schema of a JSON dataset is automatically inferred, to write queries, // it is better to take a look at what is the schema. @@ -133,8 +133,8 @@ public String call(Row row) { // Register this DataFrame as a table. peopleFromJsonFile.registerTempTable("people"); - // SQL statements can be run by using the sql methods provided by sqlCtx. - DataFrame teenagers3 = sqlCtx.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19"); + // SQL statements can be run by using the sql methods provided by sqlContext. + DataFrame teenagers3 = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19"); // The results of SQL queries are DataFrame and support all the normal RDD operations. // The columns of a row in the result can be accessed by ordinal. @@ -151,7 +151,7 @@ public String call(Row row) { List jsonData = Arrays.asList( "{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}"); JavaRDD anotherPeopleRDD = ctx.parallelize(jsonData); - DataFrame peopleFromJsonRDD = sqlCtx.jsonRDD(anotherPeopleRDD.rdd()); + DataFrame peopleFromJsonRDD = sqlContext.jsonRDD(anotherPeopleRDD.rdd()); // Take a look at the schema of this new DataFrame. peopleFromJsonRDD.printSchema(); @@ -164,7 +164,7 @@ public String call(Row row) { peopleFromJsonRDD.registerTempTable("people2"); - DataFrame peopleWithCity = sqlCtx.sql("SELECT name, address.city FROM people2"); + DataFrame peopleWithCity = sqlContext.sql("SELECT name, address.city FROM people2"); List nameAndCity = peopleWithCity.toJavaRDD().map(new Function() { @Override public String call(Row row) { diff --git a/examples/src/main/python/ml/simple_text_classification_pipeline.py b/examples/src/main/python/ml/simple_text_classification_pipeline.py index d281f4fa44282..c73edb7fd6b20 100644 --- a/examples/src/main/python/ml/simple_text_classification_pipeline.py +++ b/examples/src/main/python/ml/simple_text_classification_pipeline.py @@ -33,7 +33,7 @@ if __name__ == "__main__": sc = SparkContext(appName="SimpleTextClassificationPipeline") - sqlCtx = SQLContext(sc) + sqlContext = SQLContext(sc) # Prepare training documents, which are labeled. LabeledDocument = Row("id", "text", "label") diff --git a/examples/src/main/python/mllib/dataset_example.py b/examples/src/main/python/mllib/dataset_example.py index b5a70db2b9a3c..fcbf56cbf0c52 100644 --- a/examples/src/main/python/mllib/dataset_example.py +++ b/examples/src/main/python/mllib/dataset_example.py @@ -44,19 +44,19 @@ def summarize(dataset): print >> sys.stderr, "Usage: dataset_example.py " exit(-1) sc = SparkContext(appName="DatasetExample") - sqlCtx = SQLContext(sc) + sqlContext = SQLContext(sc) if len(sys.argv) == 2: input = sys.argv[1] else: input = "data/mllib/sample_libsvm_data.txt" points = MLUtils.loadLibSVMFile(sc, input) - dataset0 = sqlCtx.inferSchema(points).setName("dataset0").cache() + dataset0 = sqlContext.inferSchema(points).setName("dataset0").cache() summarize(dataset0) tempdir = tempfile.NamedTemporaryFile(delete=False).name os.unlink(tempdir) print "Save dataset as a Parquet file to %s." % tempdir dataset0.saveAsParquetFile(tempdir) print "Load it back and summarize it again." - dataset1 = sqlCtx.parquetFile(tempdir).setName("dataset1").cache() + dataset1 = sqlContext.parquetFile(tempdir).setName("dataset1").cache() summarize(dataset1) shutil.rmtree(tempdir) diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py index 4ff7463498cce..7f42de531f3b4 100644 --- a/python/pyspark/ml/classification.py +++ b/python/pyspark/ml/classification.py @@ -91,9 +91,9 @@ class LogisticRegressionModel(JavaModel): # The small batch size here ensures that we see multiple batches, # even in these small test examples: sc = SparkContext("local[2]", "ml.feature tests") - sqlCtx = SQLContext(sc) + sqlContext = SQLContext(sc) globs['sc'] = sc - globs['sqlCtx'] = sqlCtx + globs['sqlContext'] = sqlContext (failure_count, test_count) = doctest.testmod( globs=globs, optionflags=doctest.ELLIPSIS) sc.stop() diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py index 433b4fb5d22bf..1cfcd019dfb18 100644 --- a/python/pyspark/ml/feature.py +++ b/python/pyspark/ml/feature.py @@ -117,9 +117,9 @@ def setParams(self, numFeatures=1 << 18, inputCol="input", outputCol="output"): # The small batch size here ensures that we see multiple batches, # even in these small test examples: sc = SparkContext("local[2]", "ml.feature tests") - sqlCtx = SQLContext(sc) + sqlContext = SQLContext(sc) globs['sc'] = sc - globs['sqlCtx'] = sqlCtx + globs['sqlContext'] = sqlContext (failure_count, test_count) = doctest.testmod( globs=globs, optionflags=doctest.ELLIPSIS) sc.stop() diff --git a/python/pyspark/shell.py b/python/pyspark/shell.py index 1a02fece9c5a5..81aa970a32f76 100644 --- a/python/pyspark/shell.py +++ b/python/pyspark/shell.py @@ -53,9 +53,9 @@ try: # Try to access HiveConf, it will raise exception if Hive is not added sc._jvm.org.apache.hadoop.hive.conf.HiveConf() - sqlCtx = HiveContext(sc) + sqlCtx = sqlContext = HiveContext(sc) except py4j.protocol.Py4JError: - sqlCtx = SQLContext(sc) + sqlCtx = sqlContext = SQLContext(sc) print("""Welcome to ____ __ @@ -68,7 +68,7 @@ platform.python_version(), platform.python_build()[0], platform.python_build()[1])) -print("SparkContext available as sc, %s available as sqlCtx." % sqlCtx.__class__.__name__) +print("SparkContext available as sc, %s available as sqlContext." % sqlContext.__class__.__name__) if add_files is not None: print("Warning: ADD_FILES environment variable is deprecated, use --py-files argument instead") diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py index c2d81ba804110..93e2d176a5b6f 100644 --- a/python/pyspark/sql/context.py +++ b/python/pyspark/sql/context.py @@ -37,12 +37,12 @@ __all__ = ["SQLContext", "HiveContext", "UDFRegistration"] -def _monkey_patch_RDD(sqlCtx): +def _monkey_patch_RDD(sqlContext): def toDF(self, schema=None, sampleRatio=None): """ Converts current :class:`RDD` into a :class:`DataFrame` - This is a shorthand for ``sqlCtx.createDataFrame(rdd, schema, sampleRatio)`` + This is a shorthand for ``sqlContext.createDataFrame(rdd, schema, sampleRatio)`` :param schema: a StructType or list of names of columns :param samplingRatio: the sample ratio of rows used for inferring @@ -51,7 +51,7 @@ def toDF(self, schema=None, sampleRatio=None): >>> rdd.toDF().collect() [Row(name=u'Alice', age=1)] """ - return sqlCtx.createDataFrame(self, schema, sampleRatio) + return sqlContext.createDataFrame(self, schema, sampleRatio) RDD.toDF = toDF @@ -75,13 +75,13 @@ def __init__(self, sparkContext, sqlContext=None): """Creates a new SQLContext. >>> from datetime import datetime - >>> sqlCtx = SQLContext(sc) + >>> sqlContext = SQLContext(sc) >>> allTypes = sc.parallelize([Row(i=1, s="string", d=1.0, l=1L, ... b=True, list=[1, 2, 3], dict={"s": 0}, row=Row(a=1), ... time=datetime(2014, 8, 1, 14, 1, 5))]) >>> df = allTypes.toDF() >>> df.registerTempTable("allTypes") - >>> sqlCtx.sql('select i+1, d+1, not b, list[1], dict["s"], time, row.a ' + >>> sqlContext.sql('select i+1, d+1, not b, list[1], dict["s"], time, row.a ' ... 'from allTypes where b and i > 0').collect() [Row(c0=2, c1=2.0, c2=False, c3=2, c4=0...8, 1, 14, 1, 5), a=1)] >>> df.map(lambda x: (x.i, x.s, x.d, x.l, x.b, x.time, @@ -133,18 +133,18 @@ def registerFunction(self, name, f, returnType=StringType()): :param samplingRatio: lambda function :param returnType: a :class:`DataType` object - >>> sqlCtx.registerFunction("stringLengthString", lambda x: len(x)) - >>> sqlCtx.sql("SELECT stringLengthString('test')").collect() + >>> sqlContext.registerFunction("stringLengthString", lambda x: len(x)) + >>> sqlContext.sql("SELECT stringLengthString('test')").collect() [Row(c0=u'4')] >>> from pyspark.sql.types import IntegerType - >>> sqlCtx.registerFunction("stringLengthInt", lambda x: len(x), IntegerType()) - >>> sqlCtx.sql("SELECT stringLengthInt('test')").collect() + >>> sqlContext.registerFunction("stringLengthInt", lambda x: len(x), IntegerType()) + >>> sqlContext.sql("SELECT stringLengthInt('test')").collect() [Row(c0=4)] >>> from pyspark.sql.types import IntegerType - >>> sqlCtx.udf.register("stringLengthInt", lambda x: len(x), IntegerType()) - >>> sqlCtx.sql("SELECT stringLengthInt('test')").collect() + >>> sqlContext.udf.register("stringLengthInt", lambda x: len(x), IntegerType()) + >>> sqlContext.sql("SELECT stringLengthInt('test')").collect() [Row(c0=4)] """ func = lambda _, it: imap(lambda x: f(*x), it) @@ -229,26 +229,26 @@ def createDataFrame(self, data, schema=None, samplingRatio=None): :param samplingRatio: the sample ratio of rows used for inferring >>> l = [('Alice', 1)] - >>> sqlCtx.createDataFrame(l).collect() + >>> sqlContext.createDataFrame(l).collect() [Row(_1=u'Alice', _2=1)] - >>> sqlCtx.createDataFrame(l, ['name', 'age']).collect() + >>> sqlContext.createDataFrame(l, ['name', 'age']).collect() [Row(name=u'Alice', age=1)] >>> d = [{'name': 'Alice', 'age': 1}] - >>> sqlCtx.createDataFrame(d).collect() + >>> sqlContext.createDataFrame(d).collect() [Row(age=1, name=u'Alice')] >>> rdd = sc.parallelize(l) - >>> sqlCtx.createDataFrame(rdd).collect() + >>> sqlContext.createDataFrame(rdd).collect() [Row(_1=u'Alice', _2=1)] - >>> df = sqlCtx.createDataFrame(rdd, ['name', 'age']) + >>> df = sqlContext.createDataFrame(rdd, ['name', 'age']) >>> df.collect() [Row(name=u'Alice', age=1)] >>> from pyspark.sql import Row >>> Person = Row('name', 'age') >>> person = rdd.map(lambda r: Person(*r)) - >>> df2 = sqlCtx.createDataFrame(person) + >>> df2 = sqlContext.createDataFrame(person) >>> df2.collect() [Row(name=u'Alice', age=1)] @@ -256,11 +256,11 @@ def createDataFrame(self, data, schema=None, samplingRatio=None): >>> schema = StructType([ ... StructField("name", StringType(), True), ... StructField("age", IntegerType(), True)]) - >>> df3 = sqlCtx.createDataFrame(rdd, schema) + >>> df3 = sqlContext.createDataFrame(rdd, schema) >>> df3.collect() [Row(name=u'Alice', age=1)] - >>> sqlCtx.createDataFrame(df.toPandas()).collect() # doctest: +SKIP + >>> sqlContext.createDataFrame(df.toPandas()).collect() # doctest: +SKIP [Row(name=u'Alice', age=1)] """ if isinstance(data, DataFrame): @@ -316,7 +316,7 @@ def registerDataFrameAsTable(self, df, tableName): Temporary tables exist only during the lifetime of this instance of :class:`SQLContext`. - >>> sqlCtx.registerDataFrameAsTable(df, "table1") + >>> sqlContext.registerDataFrameAsTable(df, "table1") """ if (df.__class__ is DataFrame): self._ssql_ctx.registerDataFrameAsTable(df._jdf, tableName) @@ -330,7 +330,7 @@ def parquetFile(self, *paths): >>> parquetFile = tempfile.mkdtemp() >>> shutil.rmtree(parquetFile) >>> df.saveAsParquetFile(parquetFile) - >>> df2 = sqlCtx.parquetFile(parquetFile) + >>> df2 = sqlContext.parquetFile(parquetFile) >>> sorted(df.collect()) == sorted(df2.collect()) True """ @@ -352,7 +352,7 @@ def jsonFile(self, path, schema=None, samplingRatio=1.0): >>> shutil.rmtree(jsonFile) >>> with open(jsonFile, 'w') as f: ... f.writelines(jsonStrings) - >>> df1 = sqlCtx.jsonFile(jsonFile) + >>> df1 = sqlContext.jsonFile(jsonFile) >>> df1.printSchema() root |-- field1: long (nullable = true) @@ -365,7 +365,7 @@ def jsonFile(self, path, schema=None, samplingRatio=1.0): ... StructField("field2", StringType()), ... StructField("field3", ... StructType([StructField("field5", ArrayType(IntegerType()))]))]) - >>> df2 = sqlCtx.jsonFile(jsonFile, schema) + >>> df2 = sqlContext.jsonFile(jsonFile, schema) >>> df2.printSchema() root |-- field2: string (nullable = true) @@ -386,11 +386,11 @@ def jsonRDD(self, rdd, schema=None, samplingRatio=1.0): If the schema is provided, applies the given schema to this JSON dataset. Otherwise, it samples the dataset with ratio ``samplingRatio`` to determine the schema. - >>> df1 = sqlCtx.jsonRDD(json) + >>> df1 = sqlContext.jsonRDD(json) >>> df1.first() Row(field1=1, field2=u'row1', field3=Row(field4=11, field5=None), field6=None) - >>> df2 = sqlCtx.jsonRDD(json, df1.schema) + >>> df2 = sqlContext.jsonRDD(json, df1.schema) >>> df2.first() Row(field1=1, field2=u'row1', field3=Row(field4=11, field5=None), field6=None) @@ -400,7 +400,7 @@ def jsonRDD(self, rdd, schema=None, samplingRatio=1.0): ... StructField("field3", ... StructType([StructField("field5", ArrayType(IntegerType()))])) ... ]) - >>> df3 = sqlCtx.jsonRDD(json, schema) + >>> df3 = sqlContext.jsonRDD(json, schema) >>> df3.first() Row(field2=u'row1', field3=Row(field5=None)) """ @@ -480,8 +480,8 @@ def createExternalTable(self, tableName, path=None, source=None, def sql(self, sqlQuery): """Returns a :class:`DataFrame` representing the result of the given query. - >>> sqlCtx.registerDataFrameAsTable(df, "table1") - >>> df2 = sqlCtx.sql("SELECT field1 AS f1, field2 as f2 from table1") + >>> sqlContext.registerDataFrameAsTable(df, "table1") + >>> df2 = sqlContext.sql("SELECT field1 AS f1, field2 as f2 from table1") >>> df2.collect() [Row(f1=1, f2=u'row1'), Row(f1=2, f2=u'row2'), Row(f1=3, f2=u'row3')] """ @@ -490,8 +490,8 @@ def sql(self, sqlQuery): def table(self, tableName): """Returns the specified table as a :class:`DataFrame`. - >>> sqlCtx.registerDataFrameAsTable(df, "table1") - >>> df2 = sqlCtx.table("table1") + >>> sqlContext.registerDataFrameAsTable(df, "table1") + >>> df2 = sqlContext.table("table1") >>> sorted(df.collect()) == sorted(df2.collect()) True """ @@ -505,8 +505,8 @@ def tables(self, dbName=None): The returned DataFrame has two columns: ``tableName`` and ``isTemporary`` (a column with :class:`BooleanType` indicating if a table is a temporary one or not). - >>> sqlCtx.registerDataFrameAsTable(df, "table1") - >>> df2 = sqlCtx.tables() + >>> sqlContext.registerDataFrameAsTable(df, "table1") + >>> df2 = sqlContext.tables() >>> df2.filter("tableName = 'table1'").first() Row(tableName=u'table1', isTemporary=True) """ @@ -520,10 +520,10 @@ def tableNames(self, dbName=None): If ``dbName`` is not specified, the current database will be used. - >>> sqlCtx.registerDataFrameAsTable(df, "table1") - >>> "table1" in sqlCtx.tableNames() + >>> sqlContext.registerDataFrameAsTable(df, "table1") + >>> "table1" in sqlContext.tableNames() True - >>> "table1" in sqlCtx.tableNames("db") + >>> "table1" in sqlContext.tableNames("db") True """ if dbName is None: @@ -578,11 +578,11 @@ def _get_hive_ctx(self): class UDFRegistration(object): """Wrapper for user-defined function registration.""" - def __init__(self, sqlCtx): - self.sqlCtx = sqlCtx + def __init__(self, sqlContext): + self.sqlContext = sqlContext def register(self, name, f, returnType=StringType()): - return self.sqlCtx.registerFunction(name, f, returnType) + return self.sqlContext.registerFunction(name, f, returnType) register.__doc__ = SQLContext.registerFunction.__doc__ @@ -595,13 +595,12 @@ def _test(): globs = pyspark.sql.context.__dict__.copy() sc = SparkContext('local[4]', 'PythonTest') globs['sc'] = sc - globs['sqlCtx'] = sqlCtx = SQLContext(sc) + globs['sqlContext'] = SQLContext(sc) globs['rdd'] = rdd = sc.parallelize( [Row(field1=1, field2="row1"), Row(field1=2, field2="row2"), Row(field1=3, field2="row3")] ) - _monkey_patch_RDD(sqlCtx) globs['df'] = rdd.toDF() jsonStrings = [ '{"field1": 1, "field2": "row1", "field3":{"field4":11}}', diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index c30326ebd133e..ef91a9c4f522d 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -110,7 +110,7 @@ def saveAsParquetFile(self, path): >>> parquetFile = tempfile.mkdtemp() >>> shutil.rmtree(parquetFile) >>> df.saveAsParquetFile(parquetFile) - >>> df2 = sqlCtx.parquetFile(parquetFile) + >>> df2 = sqlContext.parquetFile(parquetFile) >>> sorted(df2.collect()) == sorted(df.collect()) True """ @@ -123,7 +123,7 @@ def registerTempTable(self, name): that was used to create this :class:`DataFrame`. >>> df.registerTempTable("people") - >>> df2 = sqlCtx.sql("select * from people") + >>> df2 = sqlContext.sql("select * from people") >>> sorted(df.collect()) == sorted(df2.collect()) True """ @@ -1180,7 +1180,7 @@ def _test(): globs = pyspark.sql.dataframe.__dict__.copy() sc = SparkContext('local[4]', 'PythonTest') globs['sc'] = sc - globs['sqlCtx'] = SQLContext(sc) + globs['sqlContext'] = SQLContext(sc) globs['df'] = sc.parallelize([(2, 'Alice'), (5, 'Bob')])\ .toDF(StructType([StructField('age', IntegerType()), StructField('name', StringType())])) diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py index 146ba6f3e0d98..daeb6916b58bc 100644 --- a/python/pyspark/sql/functions.py +++ b/python/pyspark/sql/functions.py @@ -161,7 +161,7 @@ def _test(): globs = pyspark.sql.functions.__dict__.copy() sc = SparkContext('local[4]', 'PythonTest') globs['sc'] = sc - globs['sqlCtx'] = SQLContext(sc) + globs['sqlContext'] = SQLContext(sc) globs['df'] = sc.parallelize([Row(name='Alice', age=2), Row(name='Bob', age=5)]).toDF() (failure_count, test_count) = doctest.testmod( pyspark.sql.functions, globs=globs, diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py index 45eb8b945dcb0..7e0124b13671b 100644 --- a/python/pyspark/sql/types.py +++ b/python/pyspark/sql/types.py @@ -434,7 +434,7 @@ def _parse_datatype_json_string(json_string): >>> def check_datatype(datatype): ... pickled = pickle.loads(pickle.dumps(datatype)) ... assert datatype == pickled - ... scala_datatype = sqlCtx._ssql_ctx.parseDataType(datatype.json()) + ... scala_datatype = sqlContext._ssql_ctx.parseDataType(datatype.json()) ... python_datatype = _parse_datatype_json_string(scala_datatype.json()) ... assert datatype == python_datatype >>> for cls in _all_primitive_types.values(): @@ -1237,7 +1237,7 @@ def _test(): globs = pyspark.sql.types.__dict__.copy() sc = SparkContext('local[4]', 'PythonTest') globs['sc'] = sc - globs['sqlCtx'] = sqlCtx = SQLContext(sc) + globs['sqlContext'] = SQLContext(sc) globs['ExamplePoint'] = ExamplePoint globs['ExamplePointUDT'] = ExamplePointUDT (failure_count, test_count) = doctest.testmod( From 2f482d706b9d38820472c3152dbd1612c98729bd Mon Sep 17 00:00:00 2001 From: Tijo Thomas Date: Wed, 8 Apr 2015 13:42:29 -0700 Subject: [PATCH 196/591] [SPARK-6767][SQL] Fixed Query DSL error in spark sql Readme Fixed the following error query.where('key > 30).select(avg('key)).collect() :43: error: value > is not a member of Symbol query.where('key > 30).select(avg('key)).collect() Author: Tijo Thomas Closes #5415 from tijoparacka/ERROR_SQL_DATAFRAME_EXAMPLE and squashes the following commits: 234751e [Tijo Thomas] Fixed Query DSL error in spark sql Readme --- sql/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sql/README.md b/sql/README.md index fbb3200a3a4b4..237620e3fa808 100644 --- a/sql/README.md +++ b/sql/README.md @@ -56,6 +56,6 @@ res2: Array[org.apache.spark.sql.Row] = Array([238,val_238], [86,val_86], [311,v You can also build further queries on top of these `DataFrames` using the query DSL. ``` -scala> query.where('key > 30).select(avg('key)).collect() +scala> query.where(query("key") > 30).select(avg(query("key"))).collect() res3: Array[org.apache.spark.sql.Row] = Array([274.79025423728814]) ``` From 86403f5525782bc9656ab11790f7020baa6b2c1f Mon Sep 17 00:00:00 2001 From: Michelangelo D'Agostino Date: Wed, 8 Apr 2015 16:48:45 -0400 Subject: [PATCH 197/591] [SPARK-5242]: Add --private-ips flag to EC2 script The `spark_ec2.py` script currently references the `ip_address` and `public_dns_name` attributes of an instance. On private networks, these fields aren't set, so we have problems. This PR introduces a `--private-ips` flag that instead refers to the `private_ip_address` attribute in both cases. Author: Michelangelo D'Agostino Closes #5244 from mdagost/ec2_private_nets and squashes the following commits: b684c67 [Michelangelo D'Agostino] STY: A few python lint changes. a4a2eac [Michelangelo D'Agostino] ENH: Fix IP's typo and refactor conditional logic into functions. c004604 [Michelangelo D'Agostino] ENH: Add --private-ips flag. --- ec2/spark_ec2.py | 64 +++++++++++++++++++++++++++++++++++------------- 1 file changed, 47 insertions(+), 17 deletions(-) diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py index 879a52cef8ff0..0c1f24761d0de 100755 --- a/ec2/spark_ec2.py +++ b/ec2/spark_ec2.py @@ -282,6 +282,10 @@ def parse_args(): parser.add_option( "--vpc-id", default=None, help="VPC to launch instances in") + parser.add_option( + "--private-ips", action="store_true", default=False, + help="Use private IPs for instances rather than public if VPC/subnet " + + "requires that.") (opts, args) = parser.parse_args() if len(args) != 2: @@ -707,7 +711,7 @@ def get_instances(group_names): # Deploy configuration files and run setup scripts on a newly launched # or started EC2 cluster. def setup_cluster(conn, master_nodes, slave_nodes, opts, deploy_ssh_key): - master = master_nodes[0].public_dns_name + master = get_dns_name(master_nodes[0], opts.private_ips) if deploy_ssh_key: print "Generating cluster's SSH key on master..." key_setup = """ @@ -719,8 +723,9 @@ def setup_cluster(conn, master_nodes, slave_nodes, opts, deploy_ssh_key): dot_ssh_tar = ssh_read(master, opts, ['tar', 'c', '.ssh']) print "Transferring cluster's SSH key to slaves..." for slave in slave_nodes: - print slave.public_dns_name - ssh_write(slave.public_dns_name, opts, ['tar', 'x'], dot_ssh_tar) + slave_address = get_dns_name(slave, opts.private_ips) + print slave_address + ssh_write(slave_address, opts, ['tar', 'x'], dot_ssh_tar) modules = ['spark', 'ephemeral-hdfs', 'persistent-hdfs', 'mapreduce', 'spark-standalone', 'tachyon'] @@ -809,7 +814,8 @@ def is_cluster_ssh_available(cluster_instances, opts): Check if SSH is available on all the instances in a cluster. """ for i in cluster_instances: - if not is_ssh_available(host=i.public_dns_name, opts=opts): + dns_name = get_dns_name(i, opts.private_ips) + if not is_ssh_available(host=dns_name, opts=opts): return False else: return True @@ -923,7 +929,7 @@ def get_num_disks(instance_type): # # root_dir should be an absolute path to the directory with the files we want to deploy. def deploy_files(conn, root_dir, opts, master_nodes, slave_nodes, modules): - active_master = master_nodes[0].public_dns_name + active_master = get_dns_name(master_nodes[0], opts.private_ips) num_disks = get_num_disks(opts.instance_type) hdfs_data_dirs = "/mnt/ephemeral-hdfs/data" @@ -948,10 +954,12 @@ def deploy_files(conn, root_dir, opts, master_nodes, slave_nodes, modules): print "Deploying Spark via git hash; Tachyon won't be set up" modules = filter(lambda x: x != "tachyon", modules) + master_addresses = [get_dns_name(i, opts.private_ips) for i in master_nodes] + slave_addresses = [get_dns_name(i, opts.private_ips) for i in slave_nodes] template_vars = { - "master_list": '\n'.join([i.public_dns_name for i in master_nodes]), + "master_list": '\n'.join(master_addresses), "active_master": active_master, - "slave_list": '\n'.join([i.public_dns_name for i in slave_nodes]), + "slave_list": '\n'.join(slave_addresses), "cluster_url": cluster_url, "hdfs_data_dirs": hdfs_data_dirs, "mapred_local_dirs": mapred_local_dirs, @@ -1011,7 +1019,7 @@ def deploy_files(conn, root_dir, opts, master_nodes, slave_nodes, modules): # # root_dir should be an absolute path. def deploy_user_files(root_dir, opts, master_nodes): - active_master = master_nodes[0].public_dns_name + active_master = get_dns_name(master_nodes[0], opts.private_ips) command = [ 'rsync', '-rv', '-e', stringify_command(ssh_command(opts)), @@ -1122,6 +1130,20 @@ def get_partition(total, num_partitions, current_partitions): return num_slaves_this_zone +# Gets the IP address, taking into account the --private-ips flag +def get_ip_address(instance, private_ips=False): + ip = instance.ip_address if not private_ips else \ + instance.private_ip_address + return ip + + +# Gets the DNS name, taking into account the --private-ips flag +def get_dns_name(instance, private_ips=False): + dns = instance.public_dns_name if not private_ips else \ + instance.private_ip_address + return dns + + def real_main(): (opts, action, cluster_name) = parse_args() @@ -1230,7 +1252,7 @@ def real_main(): if any(master_nodes + slave_nodes): print "The following instances will be terminated:" for inst in master_nodes + slave_nodes: - print "> %s" % inst.public_dns_name + print "> %s" % get_dns_name(inst, opts.private_ips) print "ALL DATA ON ALL NODES WILL BE LOST!!" msg = "Are you sure you want to destroy the cluster {c}? (y/N) ".format(c=cluster_name) @@ -1294,13 +1316,17 @@ def real_main(): elif action == "login": (master_nodes, slave_nodes) = get_existing_cluster(conn, opts, cluster_name) - master = master_nodes[0].public_dns_name - print "Logging into master " + master + "..." - proxy_opt = [] - if opts.proxy_port is not None: - proxy_opt = ['-D', opts.proxy_port] - subprocess.check_call( - ssh_command(opts) + proxy_opt + ['-t', '-t', "%s@%s" % (opts.user, master)]) + if not master_nodes[0].public_dns_name and not opts.private_ips: + print "Master has no public DNS name. Maybe you meant to specify " \ + "--private-ips?" + else: + master = get_dns_name(master_nodes[0], opts.private_ips) + print "Logging into master " + master + "..." + proxy_opt = [] + if opts.proxy_port is not None: + proxy_opt = ['-D', opts.proxy_port] + subprocess.check_call( + ssh_command(opts) + proxy_opt + ['-t', '-t', "%s@%s" % (opts.user, master)]) elif action == "reboot-slaves": response = raw_input( @@ -1318,7 +1344,11 @@ def real_main(): elif action == "get-master": (master_nodes, slave_nodes) = get_existing_cluster(conn, opts, cluster_name) - print master_nodes[0].public_dns_name + if not master_nodes[0].public_dns_name and not opts.private_ips: + print "Master has no public DNS name. Maybe you meant to specify " \ + "--private-ips?" + else: + print get_dns_name(master_nodes[0], opts.private_ips) elif action == "stop": response = raw_input( From 55a92ef34c0b57b6e379523d5d79baa05392de37 Mon Sep 17 00:00:00 2001 From: unknown Date: Wed, 8 Apr 2015 13:56:42 -0700 Subject: [PATCH 198/591] [SPARK-4346][SPARK-3596][YARN] Commonize the monitor logic 1. YarnClientSchedulerBack.asyncMonitorApplication use Client.monitorApplication so that commonize the monitor logic 2. Support changing the yarn client monitor interval, see #5292 3. More details see discussion on https://github.com/apache/spark/pull/3143 Author: unknown Author: Sephiroth-Lin Closes #5305 from Sephiroth-Lin/SPARK-4346_3596 and squashes the following commits: 47c0014 [unknown] Edit conflicts 52b29fe [unknown] Interrupt thread when we call stop() d4298a1 [unknown] Unused, don't push aaacb42 [Sephiroth-Lin] don't wrap the entire block in the try ee2b2fd [Sephiroth-Lin] update 6483a2a [unknown] Catch exception 6b47ff7 [unknown] Update code 568f46f [unknown] YarnClientSchedulerBack.asyncMonitorApplication should be common with Client.monitorApplication --- .../org/apache/spark/deploy/yarn/Client.scala | 10 +++++- .../cluster/YarnClientSchedulerBackend.scala | 32 ++++++------------- 2 files changed, 18 insertions(+), 24 deletions(-) diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 79d55a09eb671..7219852c0a752 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -40,6 +40,7 @@ import org.apache.hadoop.yarn.api.protocolrecords._ import org.apache.hadoop.yarn.api.records._ import org.apache.hadoop.yarn.client.api.{YarnClient, YarnClientApplication} import org.apache.hadoop.yarn.conf.YarnConfiguration +import org.apache.hadoop.yarn.exceptions.ApplicationNotFoundException import org.apache.hadoop.yarn.util.Records import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext, SparkException} @@ -561,7 +562,14 @@ private[spark] class Client( var lastState: YarnApplicationState = null while (true) { Thread.sleep(interval) - val report = getApplicationReport(appId) + val report: ApplicationReport = + try { + getApplicationReport(appId) + } catch { + case e: ApplicationNotFoundException => + logError(s"Application $appId not found.") + return (YarnApplicationState.KILLED, FinalApplicationStatus.KILLED) + } val state = report.getYarnApplicationState if (logApplicationReport) { diff --git a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala index 8abdc26b43806..407dc1ac4d37d 100644 --- a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala +++ b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala @@ -34,7 +34,7 @@ private[spark] class YarnClientSchedulerBackend( private var client: Client = null private var appId: ApplicationId = null - @volatile private var stopping: Boolean = false + private var monitorThread: Thread = null /** * Create a Yarn client to submit an application to the ResourceManager. @@ -57,7 +57,8 @@ private[spark] class YarnClientSchedulerBackend( client = new Client(args, conf) appId = client.submitApplication() waitForApplication() - asyncMonitorApplication() + monitorThread = asyncMonitorApplication() + monitorThread.start() } /** @@ -123,34 +124,19 @@ private[spark] class YarnClientSchedulerBackend( * If the application has exited for any reason, stop the SparkContext. * This assumes both `client` and `appId` have already been set. */ - private def asyncMonitorApplication(): Unit = { + private def asyncMonitorApplication(): Thread = { assert(client != null && appId != null, "Application has not been submitted yet!") val t = new Thread { override def run() { - while (!stopping) { - var state: YarnApplicationState = null - try { - val report = client.getApplicationReport(appId) - state = report.getYarnApplicationState() - } catch { - case e: ApplicationNotFoundException => - state = YarnApplicationState.KILLED - } - if (state == YarnApplicationState.FINISHED || - state == YarnApplicationState.KILLED || - state == YarnApplicationState.FAILED) { - logError(s"Yarn application has already exited with state $state!") - sc.stop() - stopping = true - } - Thread.sleep(1000L) - } + val (state, _) = client.monitorApplication(appId, logApplicationReport = false) + logError(s"Yarn application has already exited with state $state!") + sc.stop() Thread.currentThread().interrupt() } } t.setName("Yarn application state monitor") t.setDaemon(true) - t.start() + t } /** @@ -158,7 +144,7 @@ private[spark] class YarnClientSchedulerBackend( */ override def stop() { assert(client != null, "Attempted to stop this scheduler before starting it!") - stopping = true + monitorThread.interrupt() super.stop() client.stop() logInfo("Stopped") From 9418280547f962eaf309bfff9986cdd848409643 Mon Sep 17 00:00:00 2001 From: Wenchen Fan Date: Wed, 8 Apr 2015 13:57:01 -0700 Subject: [PATCH 199/591] [SQL][minor] remove duplicated resolveGetField and update comment It's after https://github.com/apache/spark/pull/5189 Author: Wenchen Fan Closes #5304 from cloud-fan/tmp and squashes the following commits: c58c9b3 [Wenchen Fan] remove duplicated code and update comment --- .../sql/catalyst/analysis/Analyzer.scala | 32 +------------------ .../catalyst/plans/logical/LogicalPlan.scala | 13 +++----- 2 files changed, 6 insertions(+), 39 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala index 119cb9c3a4400..b3aba4f68ddf9 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala @@ -293,7 +293,7 @@ class Analyzer( logDebug(s"Resolving $u to $result") result case UnresolvedGetField(child, fieldName) if child.resolved => - resolveGetField(child, fieldName) + q.resolveGetField(child, fieldName, resolver) } } @@ -313,36 +313,6 @@ class Analyzer( */ protected def containsStar(exprs: Seq[Expression]): Boolean = exprs.exists(_.collect { case _: Star => true }.nonEmpty) - - /** - * Returns the resolved `GetField`, and report error if no desired field or over one - * desired fields are found. - */ - protected def resolveGetField(expr: Expression, fieldName: String): Expression = { - def findField(fields: Array[StructField]): Int = { - val checkField = (f: StructField) => resolver(f.name, fieldName) - val ordinal = fields.indexWhere(checkField) - if (ordinal == -1) { - throw new AnalysisException( - s"No such struct field $fieldName in ${fields.map(_.name).mkString(", ")}") - } else if (fields.indexWhere(checkField, ordinal + 1) != -1) { - throw new AnalysisException( - s"Ambiguous reference to fields ${fields.filter(checkField).mkString(", ")}") - } else { - ordinal - } - } - expr.dataType match { - case StructType(fields) => - val ordinal = findField(fields) - StructGetField(expr, fields(ordinal), ordinal) - case ArrayType(StructType(fields), containsNull) => - val ordinal = findField(fields) - ArrayGetField(expr, fields(ordinal), ordinal, containsNull) - case otherType => - throw new AnalysisException(s"GetField is not valid on fields of type $otherType") - } - } } /** diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala index 2e9f3aa4ec4ad..d8f5858f5033e 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala @@ -205,11 +205,10 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { // One match, but we also need to extract the requested nested field. case Seq((a, nestedFields)) => try { - - // The foldLeft adds UnresolvedGetField for every remaining parts of the name, - // and aliased it with the last part of the name. - // For example, consider name "a.b.c", where "a" is resolved to an existing attribute. - // Then this will add UnresolvedGetField("b") and UnresolvedGetField("c"), and alias + // The foldLeft adds GetFields for every remaining parts of the identifier, + // and aliases it with the last part of the identifier. + // For example, consider "a.b.c", where "a" is resolved to an existing attribute. + // Then this will add GetField("c", GetField("b", a)), and alias // the final expression as "c". val fieldExprs = nestedFields.foldLeft(a: Expression)(resolveGetField(_, _, resolver)) val aliasName = nestedFields.last @@ -234,10 +233,8 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { /** * Returns the resolved `GetField`, and report error if no desired field or over one * desired fields are found. - * - * TODO: this code is duplicated from Analyzer and should be refactored to avoid this. */ - protected def resolveGetField( + def resolveGetField( expr: Expression, fieldName: String, resolver: Resolver): Expression = { From 7d7384c781ea72e1eabab3daca2e237e3b0fc666 Mon Sep 17 00:00:00 2001 From: Venkata Ramana Gollamudi Date: Wed, 8 Apr 2015 18:42:34 -0700 Subject: [PATCH 200/591] [SPARK-6451][SQL] supported code generation for CombineSum Author: Venkata Ramana Gollamudi Closes #5138 from gvramana/sum_fix_codegen and squashes the following commits: 95f5fe4 [Venkata Ramana Gollamudi] rebase merge changes 12f45a5 [Venkata Ramana Gollamudi] Combined and added code generations tests as per comment d6a76ac [Venkata Ramana Gollamudi] added support for codegeneration for CombineSum and tests --- .../sql/execution/GeneratedAggregate.scala | 44 ++++++++- .../spark/sql/execution/SparkStrategies.scala | 2 +- .../org/apache/spark/sql/SQLQuerySuite.scala | 92 ++++++++++++++++++- 3 files changed, 133 insertions(+), 5 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala index a8018b9213f2b..861a2c21ad9a0 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala @@ -99,7 +99,10 @@ case class GeneratedAggregate( // but really, common sub expression elimination would be better.... val zero = Cast(Literal(0), calcType) val updateFunction = Coalesce( - Add(Coalesce(currentSum :: zero :: Nil), Cast(expr, calcType)) :: currentSum :: Nil) + Add( + Coalesce(currentSum :: zero :: Nil), + Cast(expr, calcType) + ) :: currentSum :: zero :: Nil) val result = expr.dataType match { case DecimalType.Fixed(_, _) => @@ -109,6 +112,45 @@ case class GeneratedAggregate( AggregateEvaluation(currentSum :: Nil, initialValue :: Nil, updateFunction :: Nil, result) + case cs @ CombineSum(expr) => + val calcType = expr.dataType + expr.dataType match { + case DecimalType.Fixed(_, _) => + DecimalType.Unlimited + case _ => + expr.dataType + } + + val currentSum = AttributeReference("currentSum", calcType, nullable = true)() + val initialValue = Literal.create(null, calcType) + + // Coalasce avoids double calculation... + // but really, common sub expression elimination would be better.... + val zero = Cast(Literal(0), calcType) + // If we're evaluating UnscaledValue(x), we can do Count on x directly, since its + // UnscaledValue will be null if and only if x is null; helps with Average on decimals + val actualExpr = expr match { + case UnscaledValue(e) => e + case _ => expr + } + // partial sum result can be null only when no input rows present + val updateFunction = If( + IsNotNull(actualExpr), + Coalesce( + Add( + Coalesce(currentSum :: zero :: Nil), + Cast(expr, calcType)) :: currentSum :: zero :: Nil), + currentSum) + + val result = + expr.dataType match { + case DecimalType.Fixed(_, _) => + Cast(currentSum, cs.dataType) + case _ => currentSum + } + + AggregateEvaluation(currentSum :: Nil, initialValue :: Nil, updateFunction :: Nil, result) + case a @ Average(expr) => val calcType = expr.dataType match { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala index f754fa770d1b5..23f7e5609414b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala @@ -155,7 +155,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { } def canBeCodeGened(aggs: Seq[AggregateExpression]): Boolean = !aggs.exists { - case _: Sum | _: Count | _: Max | _: CombineSetsAndCount => false + case _: CombineSum | _: Sum | _: Count | _: Max | _: CombineSetsAndCount => false // The generated set implementation is pretty limited ATM. case CollectHashSet(exprs) if exprs.size == 1 && Seq(IntegerType, LongType).contains(exprs.head.dataType) => false diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala index 87e7cf8c8af9f..1ad92a3941187 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala @@ -20,6 +20,7 @@ package org.apache.spark.sql import org.apache.spark.sql.test.TestSQLContext import org.scalatest.BeforeAndAfterAll +import org.apache.spark.sql.execution.GeneratedAggregate import org.apache.spark.sql.functions._ import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan @@ -102,14 +103,99 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { sql("SELECT ABS(2.5)"), Row(2.5)) } - + test("aggregation with codegen") { val originalValue = conf.codegenEnabled setConf(SQLConf.CODEGEN_ENABLED, "true") - sql("SELECT key FROM testData GROUP BY key").collect() + // Prepare a table that we can group some rows. + table("testData") + .unionAll(table("testData")) + .unionAll(table("testData")) + .registerTempTable("testData3x") + + def testCodeGen(sqlText: String, expectedResults: Seq[Row]): Unit = { + val df = sql(sqlText) + // First, check if we have GeneratedAggregate. + var hasGeneratedAgg = false + df.queryExecution.executedPlan.foreach { + case generatedAgg: GeneratedAggregate => hasGeneratedAgg = true + case _ => + } + if (!hasGeneratedAgg) { + fail( + s""" + |Codegen is enabled, but query $sqlText does not have GeneratedAggregate in the plan. + |${df.queryExecution.simpleString} + """.stripMargin) + } + // Then, check results. + checkAnswer(df, expectedResults) + } + + // Just to group rows. + testCodeGen( + "SELECT key FROM testData3x GROUP BY key", + (1 to 100).map(Row(_))) + // COUNT + testCodeGen( + "SELECT key, count(value) FROM testData3x GROUP BY key", + (1 to 100).map(i => Row(i, 3))) + testCodeGen( + "SELECT count(key) FROM testData3x", + Row(300) :: Nil) + // COUNT DISTINCT ON int + testCodeGen( + "SELECT value, count(distinct key) FROM testData3x GROUP BY value", + (1 to 100).map(i => Row(i.toString, 1))) + testCodeGen( + "SELECT count(distinct key) FROM testData3x", + Row(100) :: Nil) + // SUM + testCodeGen( + "SELECT value, sum(key) FROM testData3x GROUP BY value", + (1 to 100).map(i => Row(i.toString, 3 * i))) + testCodeGen( + "SELECT sum(key), SUM(CAST(key as Double)) FROM testData3x", + Row(5050 * 3, 5050 * 3.0) :: Nil) + // AVERAGE + testCodeGen( + "SELECT value, avg(key) FROM testData3x GROUP BY value", + (1 to 100).map(i => Row(i.toString, i))) + testCodeGen( + "SELECT avg(key) FROM testData3x", + Row(50.5) :: Nil) + // MAX + testCodeGen( + "SELECT value, max(key) FROM testData3x GROUP BY value", + (1 to 100).map(i => Row(i.toString, i))) + testCodeGen( + "SELECT max(key) FROM testData3x", + Row(100) :: Nil) + // Some combinations. + testCodeGen( + """ + |SELECT + | value, + | sum(key), + | max(key), + | avg(key), + | count(key), + | count(distinct key) + |FROM testData3x + |GROUP BY value + """.stripMargin, + (1 to 100).map(i => Row(i.toString, i*3, i, i, 3, 1))) + testCodeGen( + "SELECT max(key), avg(key), count(key), count(distinct key) FROM testData3x", + Row(100, 50.5, 300, 100) :: Nil) + // Aggregate with Code generation handling all null values + testCodeGen( + "SELECT sum('a'), avg('a'), count(null) FROM testData", + Row(0, null, 0) :: Nil) + + dropTempTable("testData3x") setConf(SQLConf.CODEGEN_ENABLED, originalValue.toString) } - test("Add Parser of SQL COALESCE()") { checkAnswer( sql("""SELECT COALESCE(1, 2)"""), From 891ada5be1e7fdd796380e2626d80843f2ef6017 Mon Sep 17 00:00:00 2001 From: Cheng Lian Date: Wed, 8 Apr 2015 18:47:39 -0700 Subject: [PATCH 201/591] [SPARK-6696] [SQL] Adds HiveContext.refreshTable to PySpark [Review on Reviewable](https://reviewable.io/reviews/apache/spark/5349) Author: Cheng Lian Closes #5349 from liancheng/py-refresh-table and squashes the following commits: 004bec0 [Cheng Lian] Adds HiveContext.refreshTable to PySpark --- python/pyspark/sql/context.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py index 93e2d176a5b6f..e8529a8f8e3a4 100644 --- a/python/pyspark/sql/context.py +++ b/python/pyspark/sql/context.py @@ -574,6 +574,15 @@ def _ssql_ctx(self): def _get_hive_ctx(self): return self._jvm.HiveContext(self._jsc.sc()) + def refreshTable(self, tableName): + """Invalidate and refresh all the cached the metadata of the given + table. For performance reasons, Spark SQL or the external data source + library it uses might cache certain metadata about a table, such as the + location of blocks. When those change outside of Spark SQL, users should + call this function to invalidate the cache. + """ + self._ssql_ctx.refreshTable(tableName) + class UDFRegistration(object): """Wrapper for user-defined function registration.""" From 1b2aab8d5b9cc2ff702506038bd71aa8debe7ca0 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Wed, 8 Apr 2015 20:35:29 -0700 Subject: [PATCH 202/591] [SPARK-6765] Fix test code style for SQL So we can turn style checker on for test code. Author: Reynold Xin Closes #5412 from rxin/test-style-sql and squashes the following commits: 9098a31 [Reynold Xin] One more compilation error ... 8c7250a [Reynold Xin] Fix compilation. 82d0944 [Reynold Xin] Indentation. 0b03fbb [Reynold Xin] code review. f2f4348 [Reynold Xin] oops. ef4ec48 [Reynold Xin] Hive module. 7e0db5e [Reynold Xin] sql module 04ec7ac [Reynold Xin] catalyst module --- .../sql/catalyst/DistributionSuite.scala | 3 +- .../sql/catalyst/analysis/AnalysisSuite.scala | 10 +- .../analysis/HiveTypeCoercionSuite.scala | 8 +- .../ExpressionEvaluationSuite.scala | 134 ++++++++++++------ .../optimizer/ConstantFoldingSuite.scala | 51 ++++--- .../optimizer/FilterPushdownSuite.scala | 3 +- .../catalyst/optimizer/OptimizeInSuite.scala | 2 +- .../spark/sql/catalyst/plans/PlanTest.scala | 5 +- .../sql/catalyst/plans/SameResultSuite.scala | 2 +- .../sql/catalyst/trees/TreeNodeSuite.scala | 8 +- .../apache/spark/sql/CachedTableSuite.scala | 3 +- .../org/apache/spark/sql/DataFrameSuite.scala | 3 +- .../org/apache/spark/sql/QueryTest.scala | 2 +- .../org/apache/spark/sql/SQLQuerySuite.scala | 30 ++-- .../sql/ScalaReflectionRelationSuite.scala | 5 +- .../spark/sql/UserDefinedTypeSuite.scala | 2 +- .../sql/columnar/ColumnarTestUtils.scala | 4 +- .../NullableColumnAccessorSuite.scala | 3 +- .../columnar/NullableColumnBuilderSuite.scala | 3 +- .../TestCompressibleColumnBuilder.scala | 2 +- .../sql/execution/debug/DebuggingSuite.scala | 2 +- .../org/apache/spark/sql/jdbc/JDBCSuite.scala | 98 ++++++------- .../org/apache/spark/sql/json/JsonSuite.scala | 17 ++- .../spark/sql/parquet/ParquetIOSuite.scala | 2 +- .../sql/parquet/ParquetSchemaSuite.scala | 2 + .../spark/sql/sources/DDLTestSuite.scala | 8 +- .../spark/sql/sources/FilteredScanSuite.scala | 3 +- .../spark/sql/sources/PrunedScanSuite.scala | 5 +- .../spark/sql/sources/SaveLoadSuite.scala | 2 +- .../spark/sql/sources/TableScanSuite.scala | 9 +- .../spark/sql/hive/ErrorPositionSuite.scala | 2 +- .../spark/sql/hive/HiveInspectorSuite.scala | 33 +++-- .../sql/hive/InsertIntoHiveTableSuite.scala | 57 ++++++-- .../spark/sql/hive/StatisticsSuite.scala | 2 +- .../execution/BigDataBenchmarkSuite.scala | 12 +- .../hive/execution/HiveComparisonTest.scala | 27 ++-- .../hive/execution/HiveQueryFileTest.scala | 11 +- .../sql/hive/execution/HiveQuerySuite.scala | 13 +- .../hive/execution/HiveResolutionSuite.scala | 3 +- .../sql/hive/execution/HiveSerDeSuite.scala | 3 +- .../execution/HiveTypeCoercionSuite.scala | 6 +- .../sql/hive/execution/HiveUdfSuite.scala | 16 ++- .../sql/hive/execution/PruningSuite.scala | 2 +- .../sql/hive/execution/SQLQuerySuite.scala | 4 +- .../apache/spark/sql/hive/parquetSuites.scala | 7 +- 45 files changed, 395 insertions(+), 234 deletions(-) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/DistributionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/DistributionSuite.scala index 46b2250aab231..ea82cd2622de9 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/DistributionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/DistributionSuite.scala @@ -30,7 +30,7 @@ class DistributionSuite extends FunSuite { inputPartitioning: Partitioning, requiredDistribution: Distribution, satisfied: Boolean) { - if (inputPartitioning.satisfies(requiredDistribution) != satisfied) + if (inputPartitioning.satisfies(requiredDistribution) != satisfied) { fail( s""" |== Input Partitioning == @@ -40,6 +40,7 @@ class DistributionSuite extends FunSuite { |== Does input partitioning satisfy required distribution? == |Expected $satisfied got ${inputPartitioning.satisfies(requiredDistribution)} """.stripMargin) + } } test("HashPartitioning is the output partitioning") { diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala index ee7b14c7a157c..6e3d6b9263e86 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala @@ -27,6 +27,8 @@ import org.apache.spark.sql.types._ import org.apache.spark.sql.catalyst.dsl.expressions._ import org.apache.spark.sql.catalyst.dsl.plans._ +import scala.collection.immutable + class AnalysisSuite extends FunSuite with BeforeAndAfter { val caseSensitiveCatalog = new SimpleCatalog(true) val caseInsensitiveCatalog = new SimpleCatalog(false) @@ -41,10 +43,10 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { } - def caseSensitiveAnalyze(plan: LogicalPlan) = + def caseSensitiveAnalyze(plan: LogicalPlan): Unit = caseSensitiveAnalyzer.checkAnalysis(caseSensitiveAnalyzer(plan)) - def caseInsensitiveAnalyze(plan: LogicalPlan) = + def caseInsensitiveAnalyze(plan: LogicalPlan): Unit = caseInsensitiveAnalyzer.checkAnalysis(caseInsensitiveAnalyzer(plan)) val testRelation = LocalRelation(AttributeReference("a", IntegerType, nullable = true)()) @@ -147,7 +149,7 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { name: String, plan: LogicalPlan, errorMessages: Seq[String], - caseSensitive: Boolean = true) = { + caseSensitive: Boolean = true): Unit = { test(name) { val error = intercept[AnalysisException] { if(caseSensitive) { @@ -202,7 +204,7 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { case class UnresolvedTestPlan() extends LeafNode { override lazy val resolved = false - override def output = Nil + override def output: Seq[Attribute] = Nil } errorTest( diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala index 70aef1cac421a..fcd745f43cfbf 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala @@ -96,7 +96,9 @@ class HiveTypeCoercionSuite extends PlanTest { widenTest(StringType, TimestampType, None) // ComplexType - widenTest(NullType, MapType(IntegerType, StringType, false), Some(MapType(IntegerType, StringType, false))) + widenTest(NullType, + MapType(IntegerType, StringType, false), + Some(MapType(IntegerType, StringType, false))) widenTest(NullType, StructType(Seq()), Some(StructType(Seq()))) widenTest(StringType, MapType(IntegerType, StringType, true), None) widenTest(ArrayType(IntegerType), StructType(Seq()), None) @@ -113,7 +115,9 @@ class HiveTypeCoercionSuite extends PlanTest { // Remove superflous boolean -> boolean casts. ruleTest(Cast(Literal(true), BooleanType), Literal(true)) // Stringify boolean when casting to string. - ruleTest(Cast(Literal(false), StringType), If(Literal(false), Literal("true"), Literal("false"))) + ruleTest( + Cast(Literal(false), StringType), + If(Literal(false), Literal("true"), Literal("false"))) } test("coalesce casts") { diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala index 3dbefa40d2808..d2b1090a0cdd5 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala @@ -82,10 +82,13 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { assert(BitwiseNot(1.toByte).eval(EmptyRow).isInstanceOf[Byte]) } + // scalastyle:off /** * Checks for three-valued-logic. Based on: * http://en.wikipedia.org/wiki/Null_(SQL)#Comparisons_with_NULL_and_the_three-valued_logic_.283VL.29 - * I.e. in flat cpo "False -> Unknown -> True", OR is lowest upper bound, AND is greatest lower bound. + * I.e. in flat cpo "False -> Unknown -> True", + * OR is lowest upper bound, + * AND is greatest lower bound. * p q p OR q p AND q p = q * True True True True True * True False True False False @@ -102,7 +105,7 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { * False True * Unknown Unknown */ - + // scalastyle:on val notTrueTable = (true, false) :: (false, true) :: @@ -165,7 +168,9 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(In(Literal(1), Seq(Literal(1), Literal(2))), true) checkEvaluation(In(Literal(2), Seq(Literal(1), Literal(2))), true) checkEvaluation(In(Literal(3), Seq(Literal(1), Literal(2))), false) - checkEvaluation(In(Literal(1), Seq(Literal(1), Literal(2))) && In(Literal(2), Seq(Literal(1), Literal(2))), true) + checkEvaluation( + In(Literal(1), Seq(Literal(1), Literal(2))) && In(Literal(2), Seq(Literal(1), Literal(2))), + true) } test("Divide") { @@ -180,7 +185,8 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(Divide(Literal.create(null, IntegerType), Literal(0)), null) checkEvaluation(Divide(Literal.create(null, DoubleType), Literal(0.0)), null) checkEvaluation(Divide(Literal.create(null, IntegerType), Literal(1)), null) - checkEvaluation(Divide(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null) + checkEvaluation(Divide(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), + null) } test("Remainder") { @@ -195,7 +201,8 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(Remainder(Literal.create(null, IntegerType), Literal(0)), null) checkEvaluation(Remainder(Literal.create(null, DoubleType), Literal(0.0)), null) checkEvaluation(Remainder(Literal.create(null, IntegerType), Literal(1)), null) - checkEvaluation(Remainder(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null) + checkEvaluation(Remainder(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), + null) } test("INSET") { @@ -264,7 +271,8 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation("ab" like regEx, true, new GenericRow(Array[Any]("a%b"))) checkEvaluation("a\nb" like regEx, true, new GenericRow(Array[Any]("a%b"))) - checkEvaluation(Literal.create(null, StringType) like regEx, null, new GenericRow(Array[Any]("bc%"))) + checkEvaluation(Literal.create(null, StringType) like regEx, null, + new GenericRow(Array[Any]("bc%"))) } test("RLIKE literal Regular Expression") { @@ -507,8 +515,10 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { } test("array casting") { - val array = Literal.create(Seq("123", "abc", "", null), ArrayType(StringType, containsNull = true)) - val array_notNull = Literal.create(Seq("123", "abc", ""), ArrayType(StringType, containsNull = false)) + val array = Literal.create(Seq("123", "abc", "", null), + ArrayType(StringType, containsNull = true)) + val array_notNull = Literal.create(Seq("123", "abc", ""), + ArrayType(StringType, containsNull = false)) { val cast = Cast(array, ArrayType(IntegerType, containsNull = true)) @@ -765,7 +775,8 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(Coalesce(Literal.create(null, StringType) :: Nil), null, row) checkEvaluation(Coalesce(Literal.create(null, StringType) :: c1 :: c2 :: Nil), "^Ba*n", row) - checkEvaluation(If(c3, Literal.create("a", StringType), Literal.create("b", StringType)), "a", row) + checkEvaluation( + If(c3, Literal.create("a", StringType), Literal.create("b", StringType)), "a", row) checkEvaluation(If(c3, c1, c2), "^Ba*n", row) checkEvaluation(If(c4, c2, c1), "^Ba*n", row) checkEvaluation(If(Literal.create(null, BooleanType), c2, c1), "^Ba*n", row) @@ -842,18 +853,20 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(GetItem(BoundReference(3, typeMap, true), Literal("aa")), "bb", row) checkEvaluation(GetItem(Literal.create(null, typeMap), Literal("aa")), null, row) - checkEvaluation(GetItem(Literal.create(null, typeMap), Literal.create(null, StringType)), null, row) + checkEvaluation( + GetItem(Literal.create(null, typeMap), Literal.create(null, StringType)), null, row) checkEvaluation(GetItem(BoundReference(3, typeMap, true), Literal.create(null, StringType)), null, row) checkEvaluation(GetItem(BoundReference(4, typeArray, true), Literal(1)), "bb", row) checkEvaluation(GetItem(Literal.create(null, typeArray), Literal(1)), null, row) - checkEvaluation(GetItem(Literal.create(null, typeArray), Literal.create(null, IntegerType)), null, row) + checkEvaluation( + GetItem(Literal.create(null, typeArray), Literal.create(null, IntegerType)), null, row) checkEvaluation(GetItem(BoundReference(4, typeArray, true), Literal.create(null, IntegerType)), null, row) - def quickBuildGetField(expr: Expression, fieldName: String) = { + def quickBuildGetField(expr: Expression, fieldName: String): StructGetField = { expr.dataType match { case StructType(fields) => val field = fields.find(_.name == fieldName).get @@ -861,7 +874,9 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { } } - def quickResolve(u: UnresolvedGetField) = quickBuildGetField(u.child, u.fieldName) + def quickResolve(u: UnresolvedGetField): StructGetField = { + quickBuildGetField(u.child, u.fieldName) + } checkEvaluation(quickBuildGetField(BoundReference(2, typeS, nullable = true), "a"), "aa", row) checkEvaluation(quickBuildGetField(Literal.create(null, typeS), "a"), null, row) @@ -872,7 +887,8 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { ) assert(quickBuildGetField(BoundReference(2,typeS, nullable = true), "a").nullable === true) - assert(quickBuildGetField(BoundReference(2, typeS_notNullable, nullable = false), "a").nullable === false) + assert(quickBuildGetField(BoundReference(2, typeS_notNullable, nullable = false), "a").nullable + === false) assert(quickBuildGetField(Literal.create(null, typeS), "a").nullable === true) assert(quickBuildGetField(Literal.create(null, typeS_notNullable), "a").nullable === true) @@ -896,7 +912,8 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(Add(c1, c2), 3, row) checkEvaluation(Add(c1, Literal.create(null, IntegerType)), null, row) checkEvaluation(Add(Literal.create(null, IntegerType), c2), null, row) - checkEvaluation(Add(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) + checkEvaluation( + Add(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) checkEvaluation(-c1, -1, row) checkEvaluation(c1 + c2, 3, row) @@ -919,7 +936,8 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(Add(c1, c2), 3.1, row) checkEvaluation(Add(c1, Literal.create(null, DoubleType)), null, row) checkEvaluation(Add(Literal.create(null, DoubleType), c2), null, row) - checkEvaluation(Add(Literal.create(null, DoubleType), Literal.create(null, DoubleType)), null, row) + checkEvaluation( + Add(Literal.create(null, DoubleType), Literal.create(null, DoubleType)), null, row) checkEvaluation(-c1, -1.1, row) checkEvaluation(c1 + c2, 3.1, row) @@ -942,7 +960,8 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(LessThan(c1, c2), true, row) checkEvaluation(LessThan(c1, Literal.create(null, IntegerType)), null, row) checkEvaluation(LessThan(Literal.create(null, IntegerType), c2), null, row) - checkEvaluation(LessThan(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) + checkEvaluation( + LessThan(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) checkEvaluation(c1 < c2, true, row) checkEvaluation(c1 <= c2, true, row) @@ -985,54 +1004,84 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { val s = 'a.string.at(0) // substring from zero position with less-than-full length - checkEvaluation(Substring(s, Literal.create(0, IntegerType), Literal.create(2, IntegerType)), "ex", row) - checkEvaluation(Substring(s, Literal.create(1, IntegerType), Literal.create(2, IntegerType)), "ex", row) + checkEvaluation( + Substring(s, Literal.create(0, IntegerType), Literal.create(2, IntegerType)), "ex", row) + checkEvaluation( + Substring(s, Literal.create(1, IntegerType), Literal.create(2, IntegerType)), "ex", row) // substring from zero position with full length - checkEvaluation(Substring(s, Literal.create(0, IntegerType), Literal.create(7, IntegerType)), "example", row) - checkEvaluation(Substring(s, Literal.create(1, IntegerType), Literal.create(7, IntegerType)), "example", row) + checkEvaluation( + Substring(s, Literal.create(0, IntegerType), Literal.create(7, IntegerType)), "example", row) + checkEvaluation( + Substring(s, Literal.create(1, IntegerType), Literal.create(7, IntegerType)), "example", row) // substring from zero position with greater-than-full length - checkEvaluation(Substring(s, Literal.create(0, IntegerType), Literal.create(100, IntegerType)), "example", row) - checkEvaluation(Substring(s, Literal.create(1, IntegerType), Literal.create(100, IntegerType)), "example", row) + checkEvaluation(Substring(s, Literal.create(0, IntegerType), Literal.create(100, IntegerType)), + "example", row) + checkEvaluation(Substring(s, Literal.create(1, IntegerType), Literal.create(100, IntegerType)), + "example", row) // substring from nonzero position with less-than-full length - checkEvaluation(Substring(s, Literal.create(2, IntegerType), Literal.create(2, IntegerType)), "xa", row) + checkEvaluation(Substring(s, Literal.create(2, IntegerType), Literal.create(2, IntegerType)), + "xa", row) // substring from nonzero position with full length - checkEvaluation(Substring(s, Literal.create(2, IntegerType), Literal.create(6, IntegerType)), "xample", row) + checkEvaluation(Substring(s, Literal.create(2, IntegerType), Literal.create(6, IntegerType)), + "xample", row) // substring from nonzero position with greater-than-full length - checkEvaluation(Substring(s, Literal.create(2, IntegerType), Literal.create(100, IntegerType)), "xample", row) + checkEvaluation(Substring(s, Literal.create(2, IntegerType), Literal.create(100, IntegerType)), + "xample", row) // zero-length substring (within string bounds) - checkEvaluation(Substring(s, Literal.create(0, IntegerType), Literal.create(0, IntegerType)), "", row) + checkEvaluation(Substring(s, Literal.create(0, IntegerType), Literal.create(0, IntegerType)), + "", row) // zero-length substring (beyond string bounds) - checkEvaluation(Substring(s, Literal.create(100, IntegerType), Literal.create(4, IntegerType)), "", row) + checkEvaluation(Substring(s, Literal.create(100, IntegerType), Literal.create(4, IntegerType)), + "", row) // substring(null, _, _) -> null - checkEvaluation(Substring(s, Literal.create(100, IntegerType), Literal.create(4, IntegerType)), null, new GenericRow(Array[Any](null))) + checkEvaluation(Substring(s, Literal.create(100, IntegerType), Literal.create(4, IntegerType)), + null, new GenericRow(Array[Any](null))) // substring(_, null, _) -> null - checkEvaluation(Substring(s, Literal.create(null, IntegerType), Literal.create(4, IntegerType)), null, row) + checkEvaluation(Substring(s, Literal.create(null, IntegerType), Literal.create(4, IntegerType)), + null, row) // substring(_, _, null) -> null - checkEvaluation(Substring(s, Literal.create(100, IntegerType), Literal.create(null, IntegerType)), null, row) + checkEvaluation( + Substring(s, Literal.create(100, IntegerType), Literal.create(null, IntegerType)), + null, + row) // 2-arg substring from zero position - checkEvaluation(Substring(s, Literal.create(0, IntegerType), Literal.create(Integer.MAX_VALUE, IntegerType)), "example", row) - checkEvaluation(Substring(s, Literal.create(1, IntegerType), Literal.create(Integer.MAX_VALUE, IntegerType)), "example", row) + checkEvaluation( + Substring(s, Literal.create(0, IntegerType), Literal.create(Integer.MAX_VALUE, IntegerType)), + "example", + row) + checkEvaluation( + Substring(s, Literal.create(1, IntegerType), Literal.create(Integer.MAX_VALUE, IntegerType)), + "example", + row) // 2-arg substring from nonzero position - checkEvaluation(Substring(s, Literal.create(2, IntegerType), Literal.create(Integer.MAX_VALUE, IntegerType)), "xample", row) + checkEvaluation( + Substring(s, Literal.create(2, IntegerType), Literal.create(Integer.MAX_VALUE, IntegerType)), + "xample", + row) val s_notNull = 'a.string.notNull.at(0) - assert(Substring(s, Literal.create(0, IntegerType), Literal.create(2, IntegerType)).nullable === true) - assert(Substring(s_notNull, Literal.create(0, IntegerType), Literal.create(2, IntegerType)).nullable === false) - assert(Substring(s_notNull, Literal.create(null, IntegerType), Literal.create(2, IntegerType)).nullable === true) - assert(Substring(s_notNull, Literal.create(0, IntegerType), Literal.create(null, IntegerType)).nullable === true) + assert(Substring(s, Literal.create(0, IntegerType), Literal.create(2, IntegerType)).nullable + === true) + assert( + Substring(s_notNull, Literal.create(0, IntegerType), Literal.create(2, IntegerType)).nullable + === false) + assert(Substring(s_notNull, + Literal.create(null, IntegerType), Literal.create(2, IntegerType)).nullable === true) + assert(Substring(s_notNull, + Literal.create(0, IntegerType), Literal.create(null, IntegerType)).nullable === true) checkEvaluation(s.substr(0, 2), "ex", row) checkEvaluation(s.substr(0), "example", row) @@ -1065,17 +1114,20 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(BitwiseAnd(c1, c4), null, row) checkEvaluation(BitwiseAnd(c1, c2), 0, row) checkEvaluation(BitwiseAnd(c1, Literal.create(null, IntegerType)), null, row) - checkEvaluation(BitwiseAnd(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) + checkEvaluation( + BitwiseAnd(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) checkEvaluation(BitwiseOr(c1, c4), null, row) checkEvaluation(BitwiseOr(c1, c2), 3, row) checkEvaluation(BitwiseOr(c1, Literal.create(null, IntegerType)), null, row) - checkEvaluation(BitwiseOr(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) + checkEvaluation( + BitwiseOr(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) checkEvaluation(BitwiseXor(c1, c4), null, row) checkEvaluation(BitwiseXor(c1, c2), 3, row) checkEvaluation(BitwiseXor(c1, Literal.create(null, IntegerType)), null, row) - checkEvaluation(BitwiseXor(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) + checkEvaluation( + BitwiseXor(Literal.create(null, IntegerType), Literal.create(null, IntegerType)), null, row) checkEvaluation(BitwiseNot(c4), null, row) checkEvaluation(BitwiseNot(c1), -2, row) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala index a0efe9e2e7f6b..4396bd0dda9a9 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala @@ -176,40 +176,39 @@ class ConstantFoldingSuite extends PlanTest { } test("Constant folding test: expressions have null literals") { - val originalQuery = - testRelation - .select( - IsNull(Literal(null)) as 'c1, - IsNotNull(Literal(null)) as 'c2, + val originalQuery = testRelation.select( + IsNull(Literal(null)) as 'c1, + IsNotNull(Literal(null)) as 'c2, - GetItem(Literal.create(null, ArrayType(IntegerType)), 1) as 'c3, - GetItem(Literal.create(Seq(1), ArrayType(IntegerType)), Literal.create(null, IntegerType)) as 'c4, - UnresolvedGetField( - Literal.create(null, StructType(Seq(StructField("a", IntegerType, true)))), - "a") as 'c5, + GetItem(Literal.create(null, ArrayType(IntegerType)), 1) as 'c3, + GetItem( + Literal.create(Seq(1), ArrayType(IntegerType)), Literal.create(null, IntegerType)) as 'c4, + UnresolvedGetField( + Literal.create(null, StructType(Seq(StructField("a", IntegerType, true)))), + "a") as 'c5, - UnaryMinus(Literal.create(null, IntegerType)) as 'c6, - Cast(Literal(null), IntegerType) as 'c7, - Not(Literal.create(null, BooleanType)) as 'c8, + UnaryMinus(Literal.create(null, IntegerType)) as 'c6, + Cast(Literal(null), IntegerType) as 'c7, + Not(Literal.create(null, BooleanType)) as 'c8, - Add(Literal.create(null, IntegerType), 1) as 'c9, - Add(1, Literal.create(null, IntegerType)) as 'c10, + Add(Literal.create(null, IntegerType), 1) as 'c9, + Add(1, Literal.create(null, IntegerType)) as 'c10, - EqualTo(Literal.create(null, IntegerType), 1) as 'c11, - EqualTo(1, Literal.create(null, IntegerType)) as 'c12, + EqualTo(Literal.create(null, IntegerType), 1) as 'c11, + EqualTo(1, Literal.create(null, IntegerType)) as 'c12, - Like(Literal.create(null, StringType), "abc") as 'c13, - Like("abc", Literal.create(null, StringType)) as 'c14, + Like(Literal.create(null, StringType), "abc") as 'c13, + Like("abc", Literal.create(null, StringType)) as 'c14, - Upper(Literal.create(null, StringType)) as 'c15, + Upper(Literal.create(null, StringType)) as 'c15, - Substring(Literal.create(null, StringType), 0, 1) as 'c16, - Substring("abc", Literal.create(null, IntegerType), 1) as 'c17, - Substring("abc", 0, Literal.create(null, IntegerType)) as 'c18, + Substring(Literal.create(null, StringType), 0, 1) as 'c16, + Substring("abc", Literal.create(null, IntegerType), 1) as 'c17, + Substring("abc", 0, Literal.create(null, IntegerType)) as 'c18, - Contains(Literal.create(null, StringType), "abc") as 'c19, - Contains("abc", Literal.create(null, StringType)) as 'c20 - ) + Contains(Literal.create(null, StringType), "abc") as 'c19, + Contains("abc", Literal.create(null, StringType)) as 'c20 + ) val optimized = Optimize(originalQuery.analyze) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala index 55c6766520a1e..1448098c770aa 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala @@ -432,7 +432,8 @@ class FilterPushdownSuite extends PlanTest { val originalQuery = { z.join(x.join(y)) - .where(("x.b".attr === "y.b".attr) && ("x.a".attr === 1) && ("z.a".attr >= 3) && ("z.a".attr === "x.b".attr)) + .where(("x.b".attr === "y.b".attr) && ("x.a".attr === 1) && + ("z.a".attr >= 3) && ("z.a".attr === "x.b".attr)) } val optimized = Optimize(originalQuery.analyze) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala index 233e329cb2038..966bc9ada1e6e 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala @@ -52,7 +52,7 @@ class OptimizeInSuite extends PlanTest { val optimized = Optimize(originalQuery.analyze) val correctAnswer = testRelation - .where(InSet(UnresolvedAttribute("a"), HashSet[Any]()+1+2)) + .where(InSet(UnresolvedAttribute("a"), HashSet[Any]() + 1 + 2)) .analyze comparePlans(optimized, correctAnswer) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala index 129d091ca03e3..e7cafcc96de87 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala @@ -45,12 +45,13 @@ class PlanTest extends FunSuite { protected def comparePlans(plan1: LogicalPlan, plan2: LogicalPlan) { val normalized1 = normalizeExprIds(plan1) val normalized2 = normalizeExprIds(plan2) - if (normalized1 != normalized2) + if (normalized1 != normalized2) { fail( s""" |== FAIL: Plans do not match === |${sideBySide(normalized1.treeString, normalized2.treeString).mkString("\n")} - """.stripMargin) + """.stripMargin) + } } /** Fails the test if the two expressions do not match */ diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/SameResultSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/SameResultSuite.scala index 11e6831b24768..1273921f6394c 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/SameResultSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/SameResultSuite.scala @@ -32,7 +32,7 @@ class SameResultSuite extends FunSuite { val testRelation = LocalRelation('a.int, 'b.int, 'c.int) val testRelation2 = LocalRelation('a.int, 'b.int, 'c.int) - def assertSameResult(a: LogicalPlan, b: LogicalPlan, result: Boolean = true) = { + def assertSameResult(a: LogicalPlan, b: LogicalPlan, result: Boolean = true): Unit = { val aAnalyzed = a.analyze val bAnalyzed = b.analyze diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala index 274f3ede0045c..4eb8708335dcf 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala @@ -25,12 +25,12 @@ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.types.{StringType, NullType} case class Dummy(optKey: Option[Expression]) extends Expression { - def children = optKey.toSeq - def nullable = true - def dataType = NullType + def children: Seq[Expression] = optKey.toSeq + def nullable: Boolean = true + def dataType: NullType = NullType override lazy val resolved = true type EvaluatedType = Any - def eval(input: Row) = null.asInstanceOf[Any] + def eval(input: Row): Any = null.asInstanceOf[Any] } class TreeNodeSuite extends FunSuite { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala index c240f2be955ca..f7b5f08beb92f 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala @@ -92,7 +92,8 @@ class CachedTableSuite extends QueryTest { test("too big for memory") { val data = "*" * 10000 - sparkContext.parallelize(1 to 200000, 1).map(_ => BigData(data)).toDF().registerTempTable("bigData") + sparkContext.parallelize(1 to 200000, 1).map(_ => BigData(data)).toDF() + .registerTempTable("bigData") table("bigData").persist(StorageLevel.MEMORY_AND_DISK) assert(table("bigData").count() === 200000L) table("bigData").unpersist(blocking = true) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index 1db0cf7daac03..f5df8c6a59f10 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -329,8 +329,9 @@ class DataFrameSuite extends QueryTest { checkAnswer( decimalData.agg(avg('a cast DecimalType(10, 2))), Row(new java.math.BigDecimal(2.0))) + // non-partial checkAnswer( - decimalData.agg(avg('a cast DecimalType(10, 2)), sumDistinct('a cast DecimalType(10, 2))), // non-partial + decimalData.agg(avg('a cast DecimalType(10, 2)), sumDistinct('a cast DecimalType(10, 2))), Row(new java.math.BigDecimal(2.0), new java.math.BigDecimal(6)) :: Nil) } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala index 9b4dd6c620fec..9a81fc5d72819 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala @@ -67,7 +67,7 @@ class QueryTest extends PlanTest { checkAnswer(df, Seq(expectedAnswer)) } - def sqlTest(sqlString: String, expectedAnswer: Seq[Row])(implicit sqlContext: SQLContext): Unit = { + def sqlTest(sqlString: String, expectedAnswer: Seq[Row])(implicit sqlContext: SQLContext) { test(sqlString) { checkAnswer(sqlContext.sql(sqlString), expectedAnswer) } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala index 1ad92a3941187..1392b4819131b 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala @@ -268,7 +268,10 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { Row(java.sql.Timestamp.valueOf("1969-12-31 16:00:00.002"))) checkAnswer(sql( - "SELECT time FROM timestamps WHERE time IN ('1969-12-31 16:00:00.001','1969-12-31 16:00:00.002')"), + """ + |SELECT time FROM timestamps + |WHERE time IN ('1969-12-31 16:00:00.001','1969-12-31 16:00:00.002') + """.stripMargin), Seq(Row(java.sql.Timestamp.valueOf("1969-12-31 16:00:00.001")), Row(java.sql.Timestamp.valueOf("1969-12-31 16:00:00.002")))) @@ -334,7 +337,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { Row("1")) } - def sortTest() = { + def sortTest(): Unit = { checkAnswer( sql("SELECT * FROM testData2 ORDER BY a ASC, b ASC"), Seq(Row(1,1), Row(1,2), Row(2,1), Row(2,2), Row(3,1), Row(3,2))) @@ -413,7 +416,10 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { test("from follow multiple brackets") { checkAnswer(sql( - "select key from ((select * from testData limit 1) union all (select * from testData limit 1)) x limit 1"), + """ + |select key from ((select * from testData limit 1) + | union all (select * from testData limit 1)) x limit 1 + """.stripMargin), Row(1) ) @@ -423,7 +429,11 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { ) checkAnswer(sql( - "select key from (select * from testData limit 1 union all select * from testData limit 1) x limit 1"), + """ + |select key from + | (select * from testData limit 1 union all select * from testData limit 1) x + | limit 1 + """.stripMargin), Row(1) ) } @@ -470,7 +480,10 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { Seq(Row(1, 0), Row(2, 1))) checkAnswer( - sql("SELECT COUNT(a), COUNT(b), COUNT(1), COUNT(DISTINCT a), COUNT(DISTINCT b) FROM testData3"), + sql( + """ + |SELECT COUNT(a), COUNT(b), COUNT(1), COUNT(DISTINCT a), COUNT(DISTINCT b) FROM testData3 + """.stripMargin), Row(2, 1, 2, 2, 1)) } @@ -1083,7 +1096,8 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { } test("SPARK-3483 Special chars in column names") { - val data = sparkContext.parallelize(Seq("""{"key?number1": "value1", "key.number2": "value2"}""")) + val data = sparkContext.parallelize( + Seq("""{"key?number1": "value1", "key.number2": "value2"}""")) jsonRDD(data).registerTempTable("records") sql("SELECT `key?number1` FROM records") } @@ -1168,8 +1182,8 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { } test("SPARK-6145: ORDER BY test for nested fields") { - jsonRDD(sparkContext.makeRDD( - """{"a": {"b": 1, "a": {"a": 1}}, "c": [{"d": 1}]}""" :: Nil)).registerTempTable("nestedOrder") + jsonRDD(sparkContext.makeRDD("""{"a": {"b": 1, "a": {"a": 1}}, "c": [{"d": 1}]}""" :: Nil)) + .registerTempTable("nestedOrder") checkAnswer(sql("SELECT 1 FROM nestedOrder ORDER BY a.b"), Row(1)) checkAnswer(sql("SELECT a.b FROM nestedOrder ORDER BY a.b"), Row(1)) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala index 17e923ca48502..3fa00fd9d0ccb 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala @@ -80,7 +80,7 @@ class ScalaReflectionRelationSuite extends FunSuite { test("query case class RDD") { val data = ReflectData("a", 1, 1L, 1.toFloat, 1.toDouble, 1.toShort, 1.toByte, true, - new java.math.BigDecimal(1), new Date(12345), new Timestamp(12345), Seq(1,2,3)) + new java.math.BigDecimal(1), new Date(12345), new Timestamp(12345), Seq(1,2,3)) val rdd = sparkContext.parallelize(data :: Nil) rdd.toDF().registerTempTable("reflectData") @@ -103,7 +103,8 @@ class ScalaReflectionRelationSuite extends FunSuite { val rdd = sparkContext.parallelize(data :: Nil) rdd.toDF().registerTempTable("reflectOptionalData") - assert(sql("SELECT * FROM reflectOptionalData").collect().head === Row.fromSeq(Seq.fill(7)(null))) + assert(sql("SELECT * FROM reflectOptionalData").collect().head === + Row.fromSeq(Seq.fill(7)(null))) } // Equality is broken for Arrays, so we test that separately. diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala index fe618e0e8e767..902da5c3ba6d6 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala @@ -63,7 +63,7 @@ private[sql] class MyDenseVectorUDT extends UserDefinedType[MyDenseVector] { } } - override def userClass = classOf[MyDenseVector] + override def userClass: Class[MyDenseVector] = classOf[MyDenseVector] private[spark] override def asNullable: MyDenseVectorUDT = this } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala index c7a40845db16c..b301818a008e7 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala @@ -27,7 +27,7 @@ import org.apache.spark.sql.catalyst.expressions.GenericMutableRow import org.apache.spark.sql.types.{Decimal, DataType, NativeType} object ColumnarTestUtils { - def makeNullRow(length: Int) = { + def makeNullRow(length: Int): GenericMutableRow = { val row = new GenericMutableRow(length) (0 until length).foreach(row.setNullAt) row @@ -93,7 +93,7 @@ object ColumnarTestUtils { def makeUniqueValuesAndSingleValueRows[T <: NativeType]( columnType: NativeColumnType[T], - count: Int) = { + count: Int): (Seq[T#JvmType], Seq[GenericMutableRow]) = { val values = makeUniqueRandomValues(columnType, count) val rows = values.map { value => diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala index bb305355276bf..a0702144f942c 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnAccessorSuite.scala @@ -31,7 +31,8 @@ class TestNullableColumnAccessor[T <: DataType, JvmType]( with NullableColumnAccessor object TestNullableColumnAccessor { - def apply[T <: DataType, JvmType](buffer: ByteBuffer, columnType: ColumnType[T, JvmType]) = { + def apply[T <: DataType, JvmType](buffer: ByteBuffer, columnType: ColumnType[T, JvmType]) + : TestNullableColumnAccessor[T, JvmType] = { // Skips the column type ID buffer.getInt() new TestNullableColumnAccessor(buffer, columnType) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala index 75a47498683f4..3a5605d2335d7 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/NullableColumnBuilderSuite.scala @@ -27,7 +27,8 @@ class TestNullableColumnBuilder[T <: DataType, JvmType](columnType: ColumnType[T with NullableColumnBuilder object TestNullableColumnBuilder { - def apply[T <: DataType, JvmType](columnType: ColumnType[T, JvmType], initialSize: Int = 0) = { + def apply[T <: DataType, JvmType](columnType: ColumnType[T, JvmType], initialSize: Int = 0) + : TestNullableColumnBuilder[T, JvmType] = { val builder = new TestNullableColumnBuilder(columnType) builder.initialize(initialSize) builder diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala index 0b18b4119268f..fc8ff3b41d0e6 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala @@ -35,7 +35,7 @@ object TestCompressibleColumnBuilder { def apply[T <: NativeType]( columnStats: ColumnStats, columnType: NativeColumnType[T], - scheme: CompressionScheme) = { + scheme: CompressionScheme): TestCompressibleColumnBuilder[T] = { val builder = new TestCompressibleColumnBuilder(columnStats, columnType, Seq(scheme)) builder.initialize(0, "", useCompression = true) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala index 4e9472c60249e..358d8cf06e463 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala @@ -30,4 +30,4 @@ class DebuggingSuite extends FunSuite { test("DataFrame.typeCheck()") { testData.typeCheck() } -} \ No newline at end of file +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala index 592ed4b23b7d3..3596b183d4328 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala @@ -45,10 +45,12 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { conn = DriverManager.getConnection(url, properties) conn.prepareStatement("create schema test").executeUpdate() - conn.prepareStatement("create table test.people (name TEXT(32) NOT NULL, theid INTEGER NOT NULL)").executeUpdate() + conn.prepareStatement( + "create table test.people (name TEXT(32) NOT NULL, theid INTEGER NOT NULL)").executeUpdate() conn.prepareStatement("insert into test.people values ('fred', 1)").executeUpdate() conn.prepareStatement("insert into test.people values ('mary', 2)").executeUpdate() - conn.prepareStatement("insert into test.people values ('joe ''foo'' \"bar\"', 3)").executeUpdate() + conn.prepareStatement( + "insert into test.people values ('joe ''foo'' \"bar\"', 3)").executeUpdate() conn.commit() sql( @@ -132,25 +134,25 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { } test("SELECT *") { - assert(sql("SELECT * FROM foobar").collect().size == 3) + assert(sql("SELECT * FROM foobar").collect().size === 3) } test("SELECT * WHERE (simple predicates)") { - assert(sql("SELECT * FROM foobar WHERE THEID < 1").collect().size == 0) - assert(sql("SELECT * FROM foobar WHERE THEID != 2").collect().size == 2) - assert(sql("SELECT * FROM foobar WHERE THEID = 1").collect().size == 1) - assert(sql("SELECT * FROM foobar WHERE NAME = 'fred'").collect().size == 1) - assert(sql("SELECT * FROM foobar WHERE NAME > 'fred'").collect().size == 2) - assert(sql("SELECT * FROM foobar WHERE NAME != 'fred'").collect().size == 2) + assert(sql("SELECT * FROM foobar WHERE THEID < 1").collect().size === 0) + assert(sql("SELECT * FROM foobar WHERE THEID != 2").collect().size === 2) + assert(sql("SELECT * FROM foobar WHERE THEID = 1").collect().size === 1) + assert(sql("SELECT * FROM foobar WHERE NAME = 'fred'").collect().size === 1) + assert(sql("SELECT * FROM foobar WHERE NAME > 'fred'").collect().size === 2) + assert(sql("SELECT * FROM foobar WHERE NAME != 'fred'").collect().size === 2) } test("SELECT * WHERE (quoted strings)") { - assert(sql("select * from foobar").where('NAME === "joe 'foo' \"bar\"").collect().size == 1) + assert(sql("select * from foobar").where('NAME === "joe 'foo' \"bar\"").collect().size === 1) } test("SELECT first field") { val names = sql("SELECT NAME FROM foobar").collect().map(x => x.getString(0)).sortWith(_ < _) - assert(names.size == 3) + assert(names.size === 3) assert(names(0).equals("fred")) assert(names(1).equals("joe 'foo' \"bar\"")) assert(names(2).equals("mary")) @@ -158,10 +160,10 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { test("SELECT second field") { val ids = sql("SELECT THEID FROM foobar").collect().map(x => x.getInt(0)).sortWith(_ < _) - assert(ids.size == 3) - assert(ids(0) == 1) - assert(ids(1) == 2) - assert(ids(2) == 3) + assert(ids.size === 3) + assert(ids(0) === 1) + assert(ids(1) === 2) + assert(ids(2) === 3) } test("SELECT * partitioned") { @@ -169,46 +171,46 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { } test("SELECT WHERE (simple predicates) partitioned") { - assert(sql("SELECT * FROM parts WHERE THEID < 1").collect().size == 0) - assert(sql("SELECT * FROM parts WHERE THEID != 2").collect().size == 2) - assert(sql("SELECT THEID FROM parts WHERE THEID = 1").collect().size == 1) + assert(sql("SELECT * FROM parts WHERE THEID < 1").collect().size === 0) + assert(sql("SELECT * FROM parts WHERE THEID != 2").collect().size === 2) + assert(sql("SELECT THEID FROM parts WHERE THEID = 1").collect().size === 1) } test("SELECT second field partitioned") { val ids = sql("SELECT THEID FROM parts").collect().map(x => x.getInt(0)).sortWith(_ < _) - assert(ids.size == 3) - assert(ids(0) == 1) - assert(ids(1) == 2) - assert(ids(2) == 3) + assert(ids.size === 3) + assert(ids(0) === 1) + assert(ids(1) === 2) + assert(ids(2) === 3) } test("Basic API") { - assert(TestSQLContext.jdbc(urlWithUserAndPass, "TEST.PEOPLE").collect.size == 3) + assert(TestSQLContext.jdbc(urlWithUserAndPass, "TEST.PEOPLE").collect().size === 3) } test("Partitioning via JDBCPartitioningInfo API") { assert(TestSQLContext.jdbc(urlWithUserAndPass, "TEST.PEOPLE", "THEID", 0, 4, 3) - .collect.size == 3) + .collect.size === 3) } test("Partitioning via list-of-where-clauses API") { val parts = Array[String]("THEID < 2", "THEID >= 2") - assert(TestSQLContext.jdbc(urlWithUserAndPass, "TEST.PEOPLE", parts).collect.size == 3) + assert(TestSQLContext.jdbc(urlWithUserAndPass, "TEST.PEOPLE", parts).collect().size === 3) } test("H2 integral types") { val rows = sql("SELECT * FROM inttypes WHERE A IS NOT NULL").collect() - assert(rows.size == 1) - assert(rows(0).getInt(0) == 1) - assert(rows(0).getBoolean(1) == false) - assert(rows(0).getInt(2) == 3) - assert(rows(0).getInt(3) == 4) - assert(rows(0).getLong(4) == 1234567890123L) + assert(rows.size === 1) + assert(rows(0).getInt(0) === 1) + assert(rows(0).getBoolean(1) === false) + assert(rows(0).getInt(2) === 3) + assert(rows(0).getInt(3) === 4) + assert(rows(0).getLong(4) === 1234567890123L) } test("H2 null entries") { val rows = sql("SELECT * FROM inttypes WHERE A IS NULL").collect() - assert(rows.size == 1) + assert(rows.size === 1) assert(rows(0).isNullAt(0)) assert(rows(0).isNullAt(1)) assert(rows(0).isNullAt(2)) @@ -230,27 +232,27 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { val rows = sql("SELECT * FROM timetypes").collect() val cal = new GregorianCalendar(java.util.Locale.ROOT) cal.setTime(rows(0).getAs[java.sql.Timestamp](0)) - assert(cal.get(Calendar.HOUR_OF_DAY) == 12) - assert(cal.get(Calendar.MINUTE) == 34) - assert(cal.get(Calendar.SECOND) == 56) + assert(cal.get(Calendar.HOUR_OF_DAY) === 12) + assert(cal.get(Calendar.MINUTE) === 34) + assert(cal.get(Calendar.SECOND) === 56) cal.setTime(rows(0).getAs[java.sql.Timestamp](1)) - assert(cal.get(Calendar.YEAR) == 1996) - assert(cal.get(Calendar.MONTH) == 0) - assert(cal.get(Calendar.DAY_OF_MONTH) == 1) + assert(cal.get(Calendar.YEAR) === 1996) + assert(cal.get(Calendar.MONTH) === 0) + assert(cal.get(Calendar.DAY_OF_MONTH) === 1) cal.setTime(rows(0).getAs[java.sql.Timestamp](2)) - assert(cal.get(Calendar.YEAR) == 2002) - assert(cal.get(Calendar.MONTH) == 1) - assert(cal.get(Calendar.DAY_OF_MONTH) == 20) - assert(cal.get(Calendar.HOUR) == 11) - assert(cal.get(Calendar.MINUTE) == 22) - assert(cal.get(Calendar.SECOND) == 33) - assert(rows(0).getAs[java.sql.Timestamp](2).getNanos == 543543543) + assert(cal.get(Calendar.YEAR) === 2002) + assert(cal.get(Calendar.MONTH) === 1) + assert(cal.get(Calendar.DAY_OF_MONTH) === 20) + assert(cal.get(Calendar.HOUR) === 11) + assert(cal.get(Calendar.MINUTE) === 22) + assert(cal.get(Calendar.SECOND) === 33) + assert(rows(0).getAs[java.sql.Timestamp](2).getNanos === 543543543) } test("H2 floating-point types") { val rows = sql("SELECT * FROM flttypes").collect() - assert(rows(0).getDouble(0) == 1.00000000000000022) // Yes, I meant ==. - assert(rows(0).getDouble(1) == 1.00000011920928955) // Yes, I meant ==. + assert(rows(0).getDouble(0) === 1.00000000000000022) // Yes, I meant ==. + assert(rows(0).getDouble(1) === 1.00000011920928955) // Yes, I meant ==. assert(rows(0).getAs[BigDecimal](2) .equals(new BigDecimal("123456789012345.54321543215432100000"))) } @@ -264,7 +266,7 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { | user 'testUser', password 'testPass') """.stripMargin.replaceAll("\n", " ")) val rows = sql("SELECT * FROM hack").collect() - assert(rows(0).getDouble(0) == 1.00000011920928955) // Yes, I meant ==. + assert(rows(0).getDouble(0) === 1.00000011920928955) // Yes, I meant ==. // For some reason, H2 computes this square incorrectly... assert(math.abs(rows(0).getDouble(1) - 1.00000023841859331) < 1e-12) } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala index 706c966ee05f5..1fe0b76c00be3 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala @@ -380,8 +380,10 @@ class JsonSuite extends QueryTest { sql("select * from jsonTable"), Row("true", 11L, null, 1.1, "13.1", "str1") :: Row("12", null, new java.math.BigDecimal("21474836470.9"), null, null, "true") :: - Row("false", 21474836470L, new java.math.BigDecimal("92233720368547758070"), 100, "str1", "false") :: - Row(null, 21474836570L, new java.math.BigDecimal("1.1"), 21474836470L, "92233720368547758070", null) :: Nil + Row("false", 21474836470L, + new java.math.BigDecimal("92233720368547758070"), 100, "str1", "false") :: + Row(null, 21474836570L, + new java.math.BigDecimal("1.1"), 21474836470L, "92233720368547758070", null) :: Nil ) // Number and Boolean conflict: resolve the type as number in this query. @@ -404,7 +406,8 @@ class JsonSuite extends QueryTest { // Widening to DecimalType checkAnswer( sql("select num_num_2 + 1.2 from jsonTable where num_num_2 > 1.1"), - Row(new java.math.BigDecimal("21474836472.1")) :: Row(new java.math.BigDecimal("92233720368547758071.2")) :: Nil + Row(new java.math.BigDecimal("21474836472.1")) :: + Row(new java.math.BigDecimal("92233720368547758071.2")) :: Nil ) // Widening to DoubleType @@ -913,8 +916,10 @@ class JsonSuite extends QueryTest { df1.registerTempTable("applySchema1") val df2 = df1.toDF val result = df2.toJSON.collect() + // scalastyle:off assert(result(0) === "{\"f1\":1,\"f2\":\"A1\",\"f3\":true,\"f4\":[\"1\",\" A1\",\" true\",\" null\"]}") assert(result(3) === "{\"f1\":4,\"f2\":\"D4\",\"f3\":true,\"f4\":[\"4\",\" D4\",\" true\",\" 2147483644\"],\"f5\":2147483644}") + // scalastyle:on val schema2 = StructType( StructField("f1", StructType( @@ -968,7 +973,8 @@ class JsonSuite extends QueryTest { // Access elements of a BigInteger array (we use DecimalType internally). checkAnswer( - sql("select arrayOfBigInteger[0], arrayOfBigInteger[1], arrayOfBigInteger[2] from complexTable"), + sql("select arrayOfBigInteger[0], arrayOfBigInteger[1], arrayOfBigInteger[2] " + + " from complexTable"), Row(new java.math.BigDecimal("922337203685477580700"), new java.math.BigDecimal("-922337203685477580800"), null) ) @@ -1008,7 +1014,8 @@ class JsonSuite extends QueryTest { // Access elements of an array field of a struct. checkAnswer( - sql("select structWithArrayFields.field1[1], structWithArrayFields.field2[3] from complexTable"), + sql("select structWithArrayFields.field1[1], structWithArrayFields.field2[3] " + + "from complexTable"), Row(5, null) ) } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala index 203bc79f153dd..97c0f439acf13 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala @@ -218,7 +218,7 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest { } test("compression codec") { - def compressionCodecFor(path: String) = { + def compressionCodecFor(path: String): String = { val codecs = ParquetTypesConverter .readMetaData(new Path(path), Some(configuration)) .getBlocks diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala index 61f1cf347ab0f..c964b6d984557 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala @@ -180,10 +180,12 @@ class ParquetSchemaSuite extends FunSuite with ParquetTest { val caseClassString = "StructType(List(StructField(c1,IntegerType,false), StructField(c2,BinaryType,true)))" + // scalastyle:off val jsonString = """ |{"type":"struct","fields":[{"name":"c1","type":"integer","nullable":false,"metadata":{}},{"name":"c2","type":"binary","nullable":true,"metadata":{}}]} """.stripMargin + // scalastyle:on val fromCaseClassString = ParquetTypesConverter.convertFromString(caseClassString) val fromJson = ParquetTypesConverter.convertFromString(jsonString) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala index 54af50c6e10ad..3f24a497390c1 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala @@ -17,6 +17,7 @@ package org.apache.spark.sql.sources +import org.apache.spark.rdd.RDD import org.apache.spark.sql._ import org.apache.spark.sql.types._ @@ -31,7 +32,7 @@ class DDLScanSource extends RelationProvider { case class SimpleDDLScan(from: Int, to: Int)(@transient val sqlContext: SQLContext) extends BaseRelation with TableScan { - override def schema = + override def schema: StructType = StructType(Seq( StructField("intType", IntegerType, nullable = false, new MetadataBuilder().putString("comment", "test comment").build()), @@ -57,8 +58,9 @@ case class SimpleDDLScan(from: Int, to: Int)(@transient val sqlContext: SQLConte )) - override def buildScan() = sqlContext.sparkContext.parallelize(from to to). - map(e => Row(s"people$e", e * 2)) + override def buildScan(): RDD[Row] = { + sqlContext.sparkContext.parallelize(from to to).map(e => Row(s"people$e", e * 2)) + } } class DDLTestSuite extends DataSourceTest { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala index 773bd1602d5e5..cb5e5147ff189 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala @@ -19,6 +19,7 @@ package org.apache.spark.sql.sources import scala.language.existentials +import org.apache.spark.rdd.RDD import org.apache.spark.sql._ import org.apache.spark.sql.types._ @@ -41,7 +42,7 @@ case class SimpleFilteredScan(from: Int, to: Int)(@transient val sqlContext: SQL StructField("b", IntegerType, nullable = false) :: StructField("c", StringType, nullable = false) :: Nil) - override def buildScan(requiredColumns: Array[String], filters: Array[Filter]) = { + override def buildScan(requiredColumns: Array[String], filters: Array[Filter]): RDD[Row] = { val rowBuilders = requiredColumns.map { case "a" => (i: Int) => Seq(i) case "b" => (i: Int) => Seq(i * 2) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala index 08fb5380dc026..6a1ddf2f8e98b 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala @@ -19,6 +19,7 @@ package org.apache.spark.sql.sources import scala.language.existentials +import org.apache.spark.rdd.RDD import org.apache.spark.sql._ import org.apache.spark.sql.types._ @@ -34,12 +35,12 @@ case class SimplePrunedScan(from: Int, to: Int)(@transient val sqlContext: SQLCo extends BaseRelation with PrunedScan { - override def schema = + override def schema: StructType = StructType( StructField("a", IntegerType, nullable = false) :: StructField("b", IntegerType, nullable = false) :: Nil) - override def buildScan(requiredColumns: Array[String]) = { + override def buildScan(requiredColumns: Array[String]): RDD[Row] = { val rowBuilders = requiredColumns.map { case "a" => (i: Int) => Seq(i) case "b" => (i: Int) => Seq(i * 2) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala index 43bc8eb2d11a7..cb287ba85c1f8 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala @@ -114,4 +114,4 @@ class SaveLoadSuite extends DataSourceTest with BeforeAndAfterAll { message.contains("Append mode is not supported"), "We should complain that 'Append mode is not supported' for JSON source.") } -} \ No newline at end of file +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala index 7928600ac2fb5..60c8c00bda4d5 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala @@ -19,6 +19,7 @@ package org.apache.spark.sql.sources import java.sql.{Timestamp, Date} +import org.apache.spark.rdd.RDD import org.apache.spark.sql._ import org.apache.spark.sql.types._ @@ -35,10 +36,10 @@ class SimpleScanSource extends RelationProvider { case class SimpleScan(from: Int, to: Int)(@transient val sqlContext: SQLContext) extends BaseRelation with TableScan { - override def schema = + override def schema: StructType = StructType(StructField("i", IntegerType, nullable = false) :: Nil) - override def buildScan() = sqlContext.sparkContext.parallelize(from to to).map(Row(_)) + override def buildScan(): RDD[Row] = sqlContext.sparkContext.parallelize(from to to).map(Row(_)) } class AllDataTypesScanSource extends SchemaRelationProvider { @@ -57,9 +58,9 @@ case class AllDataTypesScan( extends BaseRelation with TableScan { - override def schema = userSpecifiedSchema + override def schema: StructType = userSpecifiedSchema - override def buildScan() = { + override def buildScan(): RDD[Row] = { sqlContext.sparkContext.parallelize(from to to).map { i => Row( s"str_$i", diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala index 968557c9c4686..d960a30e00738 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala @@ -136,7 +136,7 @@ class ErrorPositionSuite extends QueryTest with BeforeAndAfter { * @param query the query to analyze * @param token a unique token in the string that should be indicated by the exception */ - def positionTest(name: String, query: String, token: String) = { + def positionTest(name: String, query: String, token: String): Unit = { def parseTree = Try(quietly(HiveQl.dumpTree(HiveQl.getAst(query)))).getOrElse("") diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala index c482c6de8a736..2a7374cc172b7 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala @@ -116,21 +116,20 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { } def checkDataType(dt1: Seq[DataType], dt2: Seq[DataType]): Unit = { - dt1.zip(dt2).map { - case (dd1, dd2) => - assert(dd1.getClass === dd2.getClass) // DecimalType doesn't has the default precision info + dt1.zip(dt2).foreach { case (dd1, dd2) => + assert(dd1.getClass === dd2.getClass) // DecimalType doesn't has the default precision info } } def checkValues(row1: Seq[Any], row2: Seq[Any]): Unit = { - row1.zip(row2).map { - case (r1, r2) => checkValue(r1, r2) + row1.zip(row2).foreach { case (r1, r2) => + checkValue(r1, r2) } } def checkValues(row1: Seq[Any], row2: Row): Unit = { - row1.zip(row2.toSeq).map { - case (r1, r2) => checkValue(r1, r2) + row1.zip(row2.toSeq).foreach { case (r1, r2) => + checkValue(r1, r2) } } @@ -141,7 +140,7 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { assert(r1.compare(r2) === 0) case (r1: Array[Byte], r2: Array[Byte]) if r1 != null && r2 != null && r1.length == r2.length => - r1.zip(r2).map { case (b1, b2) => assert(b1 === b2) } + r1.zip(r2).foreach { case (b1, b2) => assert(b1 === b2) } case (r1, r2) => assert(r1 === r2) } } @@ -166,7 +165,8 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { val constantData = constantExprs.map(_.eval()) val constantNullData = constantData.map(_ => null) val constantWritableOIs = constantExprs.map(e => toWritableInspector(e.dataType)) - val constantNullWritableOIs = constantExprs.map(e => toInspector(Literal.create(null, e.dataType))) + val constantNullWritableOIs = + constantExprs.map(e => toInspector(Literal.create(null, e.dataType))) checkValues(constantData, constantData.zip(constantWritableOIs).map { case (d, oi) => unwrap(wrap(d, oi), oi) @@ -202,7 +202,8 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { case (t, idx) => StructField(s"c_$idx", t) }) - checkValues(row, unwrap(wrap(Row.fromSeq(row), toInspector(dt)), toInspector(dt)).asInstanceOf[Row]) + checkValues(row, + unwrap(wrap(Row.fromSeq(row), toInspector(dt)), toInspector(dt)).asInstanceOf[Row]) checkValue(null, unwrap(wrap(null, toInspector(dt)), toInspector(dt))) } @@ -212,8 +213,10 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { val d = row(0) :: row(0) :: Nil checkValue(d, unwrap(wrap(d, toInspector(dt)), toInspector(dt))) checkValue(null, unwrap(wrap(null, toInspector(dt)), toInspector(dt))) - checkValue(d, unwrap(wrap(d, toInspector(Literal.create(d, dt))), toInspector(Literal.create(d, dt)))) - checkValue(d, unwrap(wrap(null, toInspector(Literal.create(d, dt))), toInspector(Literal.create(d, dt)))) + checkValue(d, + unwrap(wrap(d, toInspector(Literal.create(d, dt))), toInspector(Literal.create(d, dt)))) + checkValue(d, + unwrap(wrap(null, toInspector(Literal.create(d, dt))), toInspector(Literal.create(d, dt)))) } test("wrap / unwrap Map Type") { @@ -222,7 +225,9 @@ class HiveInspectorSuite extends FunSuite with HiveInspectors { val d = Map(row(0) -> row(1)) checkValue(d, unwrap(wrap(d, toInspector(dt)), toInspector(dt))) checkValue(null, unwrap(wrap(null, toInspector(dt)), toInspector(dt))) - checkValue(d, unwrap(wrap(d, toInspector(Literal.create(d, dt))), toInspector(Literal.create(d, dt)))) - checkValue(d, unwrap(wrap(null, toInspector(Literal.create(d, dt))), toInspector(Literal.create(d, dt)))) + checkValue(d, + unwrap(wrap(d, toInspector(Literal.create(d, dt))), toInspector(Literal.create(d, dt)))) + checkValue(d, + unwrap(wrap(null, toInspector(Literal.create(d, dt))), toInspector(Literal.create(d, dt)))) } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala index 8011952e0d535..ecb990e8aac91 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala @@ -115,11 +115,36 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter { test("SPARK-4203:random partition directory order") { sql("CREATE TABLE tmp_table (key int, value string)") val tmpDir = Utils.createTempDir() - sql(s"CREATE TABLE table_with_partition(c1 string) PARTITIONED by (p1 string,p2 string,p3 string,p4 string,p5 string) location '${tmpDir.toURI.toString}' ") - sql("INSERT OVERWRITE TABLE table_with_partition partition (p1='a',p2='b',p3='c',p4='c',p5='1') SELECT 'blarr' FROM tmp_table") - sql("INSERT OVERWRITE TABLE table_with_partition partition (p1='a',p2='b',p3='c',p4='c',p5='2') SELECT 'blarr' FROM tmp_table") - sql("INSERT OVERWRITE TABLE table_with_partition partition (p1='a',p2='b',p3='c',p4='c',p5='3') SELECT 'blarr' FROM tmp_table") - sql("INSERT OVERWRITE TABLE table_with_partition partition (p1='a',p2='b',p3='c',p4='c',p5='4') SELECT 'blarr' FROM tmp_table") + sql( + s""" + |CREATE TABLE table_with_partition(c1 string) + |PARTITIONED by (p1 string,p2 string,p3 string,p4 string,p5 string) + |location '${tmpDir.toURI.toString}' + """.stripMargin) + sql( + """ + |INSERT OVERWRITE TABLE table_with_partition + |partition (p1='a',p2='b',p3='c',p4='c',p5='1') + |SELECT 'blarr' FROM tmp_table + """.stripMargin) + sql( + """ + |INSERT OVERWRITE TABLE table_with_partition + |partition (p1='a',p2='b',p3='c',p4='c',p5='2') + |SELECT 'blarr' FROM tmp_table + """.stripMargin) + sql( + """ + |INSERT OVERWRITE TABLE table_with_partition + |partition (p1='a',p2='b',p3='c',p4='c',p5='3') + |SELECT 'blarr' FROM tmp_table + """.stripMargin) + sql( + """ + |INSERT OVERWRITE TABLE table_with_partition + |partition (p1='a',p2='b',p3='c',p4='c',p5='4') + |SELECT 'blarr' FROM tmp_table + """.stripMargin) def listFolders(path: File, acc: List[String]): List[List[String]] = { val dir = path.listFiles() val folders = dir.filter(_.isDirectory).toList @@ -196,34 +221,42 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter { testData.registerTempTable("testData") val testDatawithNull = TestHive.sparkContext.parallelize( - (1 to 10).map(i => ThreeCloumntable(i, i.toString,null))).toDF() + (1 to 10).map(i => ThreeCloumntable(i, i.toString, null))).toDF() val tmpDir = Utils.createTempDir() - sql(s"CREATE TABLE table_with_partition(key int,value string) PARTITIONED by (ds string) location '${tmpDir.toURI.toString}' ") - sql("INSERT OVERWRITE TABLE table_with_partition partition (ds='1') SELECT key,value FROM testData") + sql( + s""" + |CREATE TABLE table_with_partition(key int,value string) + |PARTITIONED by (ds string) location '${tmpDir.toURI.toString}' + """.stripMargin) + sql( + """ + |INSERT OVERWRITE TABLE table_with_partition + |partition (ds='1') SELECT key,value FROM testData + """.stripMargin) // test schema the same between partition and table sql("ALTER TABLE table_with_partition CHANGE COLUMN key key BIGINT") checkAnswer(sql("select key,value from table_with_partition where ds='1' "), - testData.collect.toSeq + testData.collect().toSeq ) // test difference type of field sql("ALTER TABLE table_with_partition CHANGE COLUMN key key BIGINT") checkAnswer(sql("select key,value from table_with_partition where ds='1' "), - testData.collect.toSeq + testData.collect().toSeq ) // add column to table sql("ALTER TABLE table_with_partition ADD COLUMNS(key1 string)") checkAnswer(sql("select key,value,key1 from table_with_partition where ds='1' "), - testDatawithNull.collect.toSeq + testDatawithNull.collect().toSeq ) // change column name to table sql("ALTER TABLE table_with_partition CHANGE COLUMN key keynew BIGINT") checkAnswer(sql("select keynew,value from table_with_partition where ds='1' "), - testData.collect.toSeq + testData.collect().toSeq ) sql("DROP TABLE table_with_partition") diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala index ccd0e5aa51f95..00a69de9e4262 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala @@ -142,7 +142,7 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll { after: () => Unit, query: String, expectedAnswer: Seq[Row], - ct: ClassTag[_]) = { + ct: ClassTag[_]): Unit = { before() var df = sql(query) diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/BigDataBenchmarkSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/BigDataBenchmarkSuite.scala index 42a82c1fbf5c7..a3f5921a0cb23 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/BigDataBenchmarkSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/BigDataBenchmarkSuite.scala @@ -28,6 +28,7 @@ import org.apache.spark.sql.hive.test.TestHive._ class BigDataBenchmarkSuite extends HiveComparisonTest { val testDataDirectory = new File("target" + File.separator + "big-data-benchmark-testdata") + val userVisitPath = new File(testDataDirectory, "uservisits").getCanonicalPath val testTables = Seq( TestTable( "rankings", @@ -63,7 +64,7 @@ class BigDataBenchmarkSuite extends HiveComparisonTest { | searchWord STRING, | duration INT) | ROW FORMAT DELIMITED FIELDS TERMINATED BY "," - | STORED AS TEXTFILE LOCATION "${new File(testDataDirectory, "uservisits").getCanonicalPath}" + | STORED AS TEXTFILE LOCATION "$userVisitPath" """.stripMargin.cmd), TestTable( "documents", @@ -83,7 +84,10 @@ class BigDataBenchmarkSuite extends HiveComparisonTest { "SELECT pageURL, pageRank FROM rankings WHERE pageRank > 1") createQueryTest("query2", - "SELECT SUBSTR(sourceIP, 1, 10), SUM(adRevenue) FROM uservisits GROUP BY SUBSTR(sourceIP, 1, 10)") + """ + |SELECT SUBSTR(sourceIP, 1, 10), SUM(adRevenue) FROM uservisits + |GROUP BY SUBSTR(sourceIP, 1, 10) + """.stripMargin) createQueryTest("query3", """ @@ -113,8 +117,8 @@ class BigDataBenchmarkSuite extends HiveComparisonTest { |CREATE TABLE url_counts_total AS | SELECT SUM(count) AS totalCount, destpage | FROM url_counts_partial GROUP BY destpage - |-- The following queries run, but generate different results in HIVE likely because the UDF is not deterministic - |-- given different input splits. + |-- The following queries run, but generate different results in HIVE + |-- likely because the UDF is not deterministic given different input splits. |-- SELECT CAST(SUM(count) AS INT) FROM url_counts_partial |-- SELECT COUNT(*) FROM url_counts_partial |-- SELECT * FROM url_counts_partial diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala index a5ec312ee430c..027056d4b865f 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala @@ -255,8 +255,9 @@ abstract class HiveComparisonTest .filterNot(_ contains "hive.outerjoin.supports.filters") .filterNot(_ contains "hive.exec.post.hooks") - if (allQueries != queryList) + if (allQueries != queryList) { logWarning(s"Simplifications made on unsupported operations for test $testCaseName") + } lazy val consoleTestCase = { val quotes = "\"\"\"" @@ -305,13 +306,16 @@ abstract class HiveComparisonTest try { // Hooks often break the harness and don't really affect our test anyway, don't // even try running them. - if (installHooksCommand.findAllMatchIn(queryString).nonEmpty) + if (installHooksCommand.findAllMatchIn(queryString).nonEmpty) { sys.error("hive exec hooks not supported for tests.") + } - logWarning(s"Running query ${i+1}/${queryList.size} with hive.") + logWarning(s"Running query ${i + 1}/${queryList.size} with hive.") // Analyze the query with catalyst to ensure test tables are loaded. val answer = hiveQuery.analyzed match { - case _: ExplainCommand => Nil // No need to execute EXPLAIN queries as we don't check the output. + case _: ExplainCommand => + // No need to execute EXPLAIN queries as we don't check the output. + Nil case _ => TestHive.runSqlHive(queryString) } @@ -394,21 +398,24 @@ abstract class HiveComparisonTest case tf: org.scalatest.exceptions.TestFailedException => throw tf case originalException: Exception => if (System.getProperty("spark.hive.canarytest") != null) { - // When we encounter an error we check to see if the environment is still okay by running a simple query. - // If this fails then we halt testing since something must have gone seriously wrong. + // When we encounter an error we check to see if the environment is still + // okay by running a simple query. If this fails then we halt testing since + // something must have gone seriously wrong. try { new TestHive.HiveQLQueryExecution("SELECT key FROM src").stringResult() TestHive.runSqlHive("SELECT key FROM src") } catch { case e: Exception => - logError(s"FATAL ERROR: Canary query threw $e This implies that the testing environment has likely been corrupted.") - // The testing setup traps exits so wait here for a long time so the developer can see when things started - // to go wrong. + logError(s"FATAL ERROR: Canary query threw $e This implies that the " + + "testing environment has likely been corrupted.") + // The testing setup traps exits so wait here for a long time so the developer + // can see when things started to go wrong. Thread.sleep(1000000) } } - // If the canary query didn't fail then the environment is still okay, so just throw the original exception. + // If the canary query didn't fail then the environment is still okay, + // so just throw the original exception. throw originalException } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQueryFileTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQueryFileTest.scala index 02518d516261b..f7b37dae0a5f3 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQueryFileTest.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQueryFileTest.scala @@ -24,8 +24,9 @@ import org.apache.spark.sql.catalyst.util._ /** * A framework for running the query tests that are listed as a set of text files. * - * TestSuites that derive from this class must provide a map of testCaseName -> testCaseFiles that should be included. - * Additionally, there is support for whitelisting and blacklisting tests as development progresses. + * TestSuites that derive from this class must provide a map of testCaseName -> testCaseFiles + * that should be included. Additionally, there is support for whitelisting and blacklisting + * tests as development progresses. */ abstract class HiveQueryFileTest extends HiveComparisonTest { /** A list of tests deemed out of scope and thus completely disregarded */ @@ -54,15 +55,17 @@ abstract class HiveQueryFileTest extends HiveComparisonTest { case (testCaseName, testCaseFile) => if (blackList.map(_.r.pattern.matcher(testCaseName).matches()).reduceLeft(_||_)) { logDebug(s"Blacklisted test skipped $testCaseName") - } else if (realWhiteList.map(_.r.pattern.matcher(testCaseName).matches()).reduceLeft(_||_) || runAll) { + } else if (realWhiteList.map(_.r.pattern.matcher(testCaseName).matches()).reduceLeft(_||_) || + runAll) { // Build a test case and submit it to scala test framework... val queriesString = fileToString(testCaseFile) createQueryTest(testCaseName, queriesString) } else { // Only output warnings for the built in whitelist as this clutters the output when the user // trying to execute a single test from the commandline. - if(System.getProperty(whiteListProperty) == null && !runAll) + if (System.getProperty(whiteListProperty) == null && !runAll) { ignore(testCaseName) {} + } } } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala index de140fc72a2c3..af781a502e9f3 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala @@ -37,7 +37,8 @@ import org.apache.spark.sql.hive.test.TestHive._ case class TestData(a: Int, b: String) /** - * A set of test cases expressed in Hive QL that are not covered by the tests included in the hive distribution. + * A set of test cases expressed in Hive QL that are not covered by the tests + * included in the hive distribution. */ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { private val originalTimeZone = TimeZone.getDefault @@ -237,7 +238,8 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { } createQueryTest("modulus", - "SELECT 11 % 10, IF((101.1 % 100.0) BETWEEN 1.01 AND 1.11, \"true\", \"false\"), (101 / 2) % 10 FROM src LIMIT 1") + "SELECT 11 % 10, IF((101.1 % 100.0) BETWEEN 1.01 AND 1.11, \"true\", \"false\"), " + + "(101 / 2) % 10 FROM src LIMIT 1") test("Query expressed in SQL") { setConf("spark.sql.dialect", "sql") @@ -309,7 +311,8 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { "SELECT * FROM src a JOIN src b ON a.key = b.key") createQueryTest("small.cartesian", - "SELECT a.key, b.key FROM (SELECT key FROM src WHERE key < 1) a JOIN (SELECT key FROM src WHERE key = 2) b") + "SELECT a.key, b.key FROM (SELECT key FROM src WHERE key < 1) a JOIN " + + "(SELECT key FROM src WHERE key = 2) b") createQueryTest("length.udf", "SELECT length(\"test\") FROM src LIMIT 1") @@ -457,6 +460,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { createQueryTest("lateral view3", "FROM src SELECT key, D.* lateral view explode(array(key+3, key+4)) D as CX") + // scalastyle:off createQueryTest("lateral view4", """ |create table src_lv1 (key string, value string); @@ -466,6 +470,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { |insert overwrite table src_lv1 SELECT key, D.* lateral view explode(array(key+3, key+4)) D as CX |insert overwrite table src_lv2 SELECT key, D.* lateral view explode(array(key+3, key+4)) D as CX """.stripMargin) + // scalastyle:on createQueryTest("lateral view5", "FROM src SELECT explode(array(key+3, key+4))") @@ -584,7 +589,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { } } - def isExplanation(result: DataFrame) = { + def isExplanation(result: DataFrame): Boolean = { val explanation = result.select('plan).collect().map { case Row(plan: String) => plan } explanation.contains("== Physical Plan ==") } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala index f4440e5b7846a..8ad3627504229 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveResolutionSuite.scala @@ -25,7 +25,8 @@ case class Nested(a: Int, B: Int) case class Data(a: Int, B: Int, n: Nested, nestedArray: Seq[Nested]) /** - * A set of test cases expressed in Hive QL that are not covered by the tests included in the hive distribution. + * A set of test cases expressed in Hive QL that are not covered by the tests + * included in the hive distribution. */ class HiveResolutionSuite extends HiveComparisonTest { diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala index 7486bfa82b00b..d05e11fcf281b 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala @@ -26,8 +26,9 @@ import org.apache.spark.sql.hive.test.TestHive */ class HiveSerDeSuite extends HiveComparisonTest with BeforeAndAfterAll { - override def beforeAll() = { + override def beforeAll(): Unit = { TestHive.cacheTables = false + super.beforeAll() } createQueryTest( diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala index ab0e0443c7faa..f0f04f8c73fb4 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveTypeCoercionSuite.scala @@ -35,8 +35,10 @@ class HiveTypeCoercionSuite extends HiveComparisonTest { val nullVal = "null" baseTypes.init.foreach { i => - createQueryTest(s"case when then $i else $nullVal end ", s"SELECT case when true then $i else $nullVal end FROM src limit 1") - createQueryTest(s"case when then $nullVal else $i end ", s"SELECT case when true then $nullVal else $i end FROM src limit 1") + createQueryTest(s"case when then $i else $nullVal end ", + s"SELECT case when true then $i else $nullVal end FROM src limit 1") + createQueryTest(s"case when then $nullVal else $i end ", + s"SELECT case when true then $nullVal else $i end FROM src limit 1") } test("[SPARK-2210] boolean cast on boolean value should be removed") { diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala index d7c5d1a25a82b..7f49eac490572 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUdfSuite.scala @@ -123,9 +123,10 @@ class HiveUdfSuite extends QueryTest { IntegerCaseClass(1) :: IntegerCaseClass(2) :: Nil).toDF() testData.registerTempTable("integerTable") - sql(s"CREATE TEMPORARY FUNCTION testUDFIntegerToString AS '${classOf[UDFIntegerToString].getName}'") + val udfName = classOf[UDFIntegerToString].getName + sql(s"CREATE TEMPORARY FUNCTION testUDFIntegerToString AS '$udfName'") checkAnswer( - sql("SELECT testUDFIntegerToString(i) FROM integerTable"), //.collect(), + sql("SELECT testUDFIntegerToString(i) FROM integerTable"), Seq(Row("1"), Row("2"))) sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFIntegerToString") @@ -141,7 +142,7 @@ class HiveUdfSuite extends QueryTest { sql(s"CREATE TEMPORARY FUNCTION testUDFListListInt AS '${classOf[UDFListListInt].getName}'") checkAnswer( - sql("SELECT testUDFListListInt(lli) FROM listListIntTable"), //.collect(), + sql("SELECT testUDFListListInt(lli) FROM listListIntTable"), Seq(Row(0), Row(2), Row(13))) sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFListListInt") @@ -156,7 +157,7 @@ class HiveUdfSuite extends QueryTest { sql(s"CREATE TEMPORARY FUNCTION testUDFListString AS '${classOf[UDFListString].getName}'") checkAnswer( - sql("SELECT testUDFListString(l) FROM listStringTable"), //.collect(), + sql("SELECT testUDFListString(l) FROM listStringTable"), Seq(Row("a,b,c"), Row("d,e"))) sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFListString") @@ -170,7 +171,7 @@ class HiveUdfSuite extends QueryTest { sql(s"CREATE TEMPORARY FUNCTION testStringStringUdf AS '${classOf[UDFStringString].getName}'") checkAnswer( - sql("SELECT testStringStringUdf(\"hello\", s) FROM stringTable"), //.collect(), + sql("SELECT testStringStringUdf(\"hello\", s) FROM stringTable"), Seq(Row("hello world"), Row("hello goodbye"))) sql("DROP TEMPORARY FUNCTION IF EXISTS testStringStringUdf") @@ -187,7 +188,7 @@ class HiveUdfSuite extends QueryTest { sql(s"CREATE TEMPORARY FUNCTION testUDFTwoListList AS '${classOf[UDFTwoListList].getName}'") checkAnswer( - sql("SELECT testUDFTwoListList(lli, lli) FROM TwoListTable"), //.collect(), + sql("SELECT testUDFTwoListList(lli, lli) FROM TwoListTable"), Seq(Row("0, 0"), Row("2, 2"), Row("13, 13"))) sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFTwoListList") @@ -247,7 +248,8 @@ class PairUdf extends GenericUDF { override def initialize(p1: Array[ObjectInspector]): ObjectInspector = ObjectInspectorFactory.getStandardStructObjectInspector( Seq("id", "value"), - Seq(PrimitiveObjectInspectorFactory.javaIntObjectInspector, PrimitiveObjectInspectorFactory.javaIntObjectInspector) + Seq(PrimitiveObjectInspectorFactory.javaIntObjectInspector, + PrimitiveObjectInspectorFactory.javaIntObjectInspector) ) override def evaluate(args: Array[DeferredObject]): AnyRef = { diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala index 8474d850c9c6c..067b577f1560e 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala @@ -143,7 +143,7 @@ class PruningSuite extends HiveComparisonTest with BeforeAndAfter { sql: String, expectedOutputColumns: Seq[String], expectedScannedColumns: Seq[String], - expectedPartValues: Seq[Seq[String]]) = { + expectedPartValues: Seq[Seq[String]]): Unit = { test(s"$testCaseName - pruning test") { val plan = new TestHive.HiveQLQueryExecution(sql).executedPlan val actualOutputColumns = plan.output.map(_.name) diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala index 817b9dcb8f505..7811bd2e9effb 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala @@ -422,7 +422,7 @@ class SQLQuerySuite extends QueryTest { } test("resolve udtf with single alias") { - val rdd = sparkContext.makeRDD((1 to 5).map(i => s"""{"a":[$i, ${i+1}]}""")) + val rdd = sparkContext.makeRDD((1 to 5).map(i => s"""{"a":[$i, ${i + 1}]}""")) jsonRDD(rdd).registerTempTable("data") val df = sql("SELECT explode(a) AS val FROM data") val col = df("val") @@ -435,7 +435,7 @@ class SQLQuerySuite extends QueryTest { // is not in a valid state (cannot be executed). Because of this bug, the analysis rule of // PreInsertionCasts will actually start to work before ImplicitGenerate and then // generates an invalid query plan. - val rdd = sparkContext.makeRDD((1 to 5).map(i => s"""{"a":[$i, ${i+1}]}""")) + val rdd = sparkContext.makeRDD((1 to 5).map(i => s"""{"a":[$i, ${i + 1}]}""")) jsonRDD(rdd).registerTempTable("data") val originalConf = getConf("spark.sql.hive.convertCTAS", "false") setConf("spark.sql.hive.convertCTAS", "false") diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala index 5f71e1bbc2d2e..d5dd0bf58e702 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala @@ -1,4 +1,3 @@ - /* * Licensed to the Apache Software Foundation (ASF) under one or more * contributor license agreements. See the NOTICE file distributed with @@ -887,7 +886,11 @@ abstract class ParquetPartitioningTest extends QueryTest with BeforeAndAfterAll test(s"SPARK-5775 read struct from $table") { checkAnswer( - sql(s"SELECT p, structField.intStructField, structField.stringStructField FROM $table WHERE p = 1"), + sql( + s""" + |SELECT p, structField.intStructField, structField.stringStructField + |FROM $table WHERE p = 1 + """.stripMargin), (1 to 10).map(i => Row(1, i, f"${i}_string"))) } From 2fe0a1aaeebbf7f60bd4130847d738c29f1e3d53 Mon Sep 17 00:00:00 2001 From: Shivaram Venkataraman Date: Wed, 8 Apr 2015 22:45:40 -0700 Subject: [PATCH 203/591] [SPARK-5654] Integrate SparkR This pull requests integrates SparkR, an R frontend for Spark. The SparkR package contains both RDD and DataFrame APIs in R and is integrated with Spark's submission scripts to work on different cluster managers. Some integration points that would be great to get feedback on: 1. Build procedure: SparkR requires R to be installed on the machine to be built. Right now we have a new Maven profile `-PsparkR` that can be used to enable SparkR builds 2. YARN cluster mode: The R package that is built needs to be present on the driver and all the worker nodes during execution. The R package location is currently set using SPARK_HOME, but this might not work on YARN cluster mode. The SparkR package represents the work of many contributors and attached below is a list of people along with areas they worked on edwardt (edwart) - Documentation improvements Felix Cheung (felixcheung) - Documentation improvements Hossein Falaki (falaki) - Documentation improvements Chris Freeman (cafreeman) - DataFrame API, Programming Guide Todd Gao (7c00) - R worker Internals Ryan Hafen (hafen) - SparkR Internals Qian Huang (hqzizania) - RDD API Hao Lin (hlin09) - RDD API, Closure cleaner Evert Lammerts (evertlammerts) - DataFrame API Davies Liu (davies) - DataFrame API, R worker internals, Merging with Spark Yi Lu (lythesia) - RDD API, Worker internals Matt Massie (massie) - Jenkins build Harihar Nahak (hnahak87) - SparkR examples Oscar Olmedo (oscaroboto) - Spark configuration Antonio Piccolboni (piccolbo) - SparkR examples, Namespace bug fixes Dan Putler (dputler) - Dataframe API, SparkR Install Guide Ashutosh Raina (ashutoshraina) - Build improvements Josh Rosen (joshrosen) - Travis CI build Sun Rui (sun-rui)- RDD API, JVM Backend, Shuffle improvements Shivaram Venkataraman (shivaram) - RDD API, JVM Backend, Worker Internals Zongheng Yang (concretevitamin) - RDD API, Pipelined RDDs, Examples and EC2 guide Author: Shivaram Venkataraman Author: Shivaram Venkataraman Author: Zongheng Yang Author: cafreeman Author: Shivaram Venkataraman Author: Davies Liu Author: Davies Liu Author: hlin09 Author: Sun Rui Author: lythesia Author: oscaroboto Author: Antonio Piccolboni Author: root Author: edwardt Author: hqzizania Author: dputler Author: Todd Gao Author: Chris Freeman Author: Felix Cheung Author: Hossein Author: Evert Lammerts Author: Felix Cheung Author: felixcheung Author: Ryan Hafen Author: Ashutosh Raina Author: Oscar Olmedo Author: Josh Rosen Author: Yi Lu Author: Harihar Nahak Closes #5096 from shivaram/R and squashes the following commits: da64742 [Davies Liu] fix Date serialization 59266d1 [Davies Liu] check exclusive of primary-py-file and primary-r-file 55808e4 [Davies Liu] fix tests 5581c75 [Davies Liu] update author of SparkR f731b48 [Shivaram Venkataraman] Only run SparkR tests if R is installed 64eda24 [Shivaram Venkataraman] Merge branch 'R' of https://github.com/amplab-extras/spark into R d7c3f22 [Shivaram Venkataraman] Address code review comments Changes include 1. Adding SparkR docs to API docs generated 2. Style fixes in SparkR scala files 3. Clean up of shell scripts and explanation of install-dev.sh 377151f [Shivaram Venkataraman] Merge remote-tracking branch 'apache/master' into R eb5da53 [Shivaram Venkataraman] Merge pull request #3 from davies/R2 a18ff5c [Davies Liu] Update sparkR.R 5133f3a [Shivaram Venkataraman] Merge pull request #7 from hqzizania/R3 940b631 [hqzizania] [SPARKR-92] Phase 2: implement sum(rdd) 0e788c0 [Shivaram Venkataraman] Merge pull request #5 from hlin09/doc-fix 3487461 [hlin09] Add tests log in .gitignore. 1d1802e [Shivaram Venkataraman] Merge pull request #4 from felixcheung/r-require 11981b7 [felixcheung] Update R to fail early if SparkR package is missing c300e08 [Davies Liu] remove duplicated file b045701 [Davies Liu] Merge branch 'remote_r' into R 19c9368 [Davies Liu] Merge branch 'sparkr-sql' of github.com:amplab-extras/SparkR-pkg into remote_r f8fa8af [Davies Liu] mute logging when start/stop context e7104b6 [Davies Liu] remove ::: in SparkR a1777eb [Davies Liu] move rules into R/.gitignore e88b649 [Davies Liu] Merge branch 'R' of github.com:amplab-extras/spark into R 6e20e71 [Davies Liu] address comments b433817 [Davies Liu] Merge branch 'master' of github.com:apache/spark into R a1cedad [Shivaram Venkataraman] Merge pull request #228 from felixcheung/doc e089151 [Davies Liu] Merge pull request #225 from sun-rui/SPARKR-154_2 463e28c [Davies Liu] Merge pull request #2 from shivaram/doc-fixes bc2d6d8 [Shivaram Venkataraman] Remove arg from sparkR.stop and update docs d425363 [Shivaram Venkataraman] Some doc fixes for column, generics, group 1f1a7e0 [Shivaram Venkataraman] Some fixes to DataFrame, RDD, SQLContext docs 104ad4e [Shivaram Venkataraman] Check the right env in exists cf5cd99 [Shivaram Venkataraman] Remove unused numCols argument 85a50ec [Shivaram Venkataraman] Merge pull request #226 from RevolutionAnalytics/master 3eacfc0 [Davies Liu] fix flaky test 733380d [Davies Liu] update R examples (remove master from args) b21a0da [Davies Liu] Merge pull request #1 from shivaram/log4j-tests a1493d7 [Shivaram Venkataraman] Address comments e1f83ab [Shivaram Venkataraman] Send Spark INFO logs to a file in SparkR tests 58276f5 [Shivaram Venkataraman] Merge branch 'R' of https://github.com/amplab-extras/spark into R 52cc92d [Shivaram Venkataraman] Add license to create-docs.sh 6ff5ea2 [Shivaram Venkataraman] Add instructions to generate docs 1f478c5 [Shivaram Venkataraman] Merge branch 'R' of https://github.com/amplab-extras/spark into R 02b4833 [Shivaram Venkataraman] Add a script to generate R docs (Rd, html) Also fix some issues with our documentation d6d3729 [Davies Liu] enable spark and pyspark tests 0e5a83f [Davies Liu] fix code style afd8a77 [Davies Liu] Merge branch 'R' of github.com:amplab-extras/spark into R d87a181 [Davies Liu] fix flaky tests 7100fb9 [Shivaram Venkataraman] Fix libPaths in README bdf3a14 [Davies Liu] Merge branch 'R' of github.com:amplab-extras/spark into R 05e7375 [Davies Liu] sort generics b44e371 [Shivaram Venkataraman] Include RStudio instructions in README 855537f [Davies Liu] Merge branch 'R' of github.com:amplab-extras/spark into R 9fb6af3 [Davies Liu] mark R classes/objects are private 423ea3c [Shivaram Venkataraman] Ignore unknown jobj in cleanup 974e4ea [Davies Liu] fix flaky test 410ec18 [Davies Liu] fix zipRDD() tests d8b24fc [Davies Liu] disable spark and python tests temporary ce3ca62 [Davies Liu] fix license check 7da0049 [Davies Liu] fix build 2892e29 [Davies Liu] support R in YARN cluster ebd4d07 [Davies Liu] Merge branch 'R' of github.com:amplab-extras/spark into R 38cbf59 [Davies Liu] fix test of zipRDD() 756ece0 [Shivaram Venkataraman] Update README remove outdated TODO d436f26 [Davies Liu] add missing files 40d193a [Shivaram Venkataraman] Merge pull request #224 from sun-rui/SPARKR-224-new 1a16cd6 [Davies Liu] rm PROJECT_HOME 56670ef [Davies Liu] rm man page ba4b80b [Davies Liu] Merge branch 'remote_r' into R f04080c [Davies Liu] Merge branch 'sparkr-sql' of github.com:amplab-extras/SparkR-pkg into remote_r 028cbfb [Davies Liu] fix exit code of sparkr unit test 42d8b4c [Davies Liu] Merge branch 'R' of github.com:amplab-extras/spark into R ef26015 [Davies Liu] Merge branch 'R' of github.com:amplab-extras/spark into R a1870e8 [Shivaram Venkataraman] Merge pull request #214 from sun-rui/SPARKR-156_3 cb6e5e3 [Shivaram Venkataraman] Add scripts to start SparkR on windows 8030847 [Shivaram Venkataraman] Set windows file separators, install dirs 05afef0 [Shivaram Venkataraman] Only stop backend JVM if R launched it 95d2de3 [Davies Liu] fix spark-submit with R scripot baefd9e [Shivaram Venkataraman] Make bin/sparkR use spark-submit As a part of this move the R initialization functions into first.R and first-submit.R d6f2bdd [Shivaram Venkataraman] Fix run-tests path ea90fab [Davies Liu] fix spark-submit with R path and sparkR -h 0e2412c [Davies Liu] fix bin/sparkR 9f6aa1f [Davies Liu] Merge branch 'R' of github.com:amplab-extras/spark into R 479e3fe [Davies Liu] change println() to logging 52ca6e5 [Shivaram Venkataraman] Add missing comma 716b16f [Shivaram Venkataraman] Merge branch 'R' of https://github.com/amplab-extras/spark into R 2d235d4 [Shivaram Venkataraman] Build SparkR with Maven profile aae881b [Davies Liu] fix rat ff776aa [Shivaram Venkataraman] Fix style e4f1937 [Shivaram Venkataraman] Remove DFC example f7b6936 [Davies Liu] remove Spark prefix for class 043959e [Davies Liu] cleanup ba53b09 [Davies Liu] support R in spark-submit f403b4a [Davies Liu] rm .travis.yml c4a5bdf [Davies Liu] run sparkr tests in Spark e8fc7ca [Davies Liu] fix .gitignore 35e5755 [Davies Liu] reduce size of example data 50bff63 [Davies Liu] add LICENSE header for R sources facb6e0 [Davies Liu] add .gitignore for .o, .so, .Rd 18e5eed [Davies Liu] update docs 0a0e632 [Davies Liu] move sparkR into bin/ a76472f [Davies Liu] fix path of assembly jar df3eeea [Davies Liu] move R/examples into examples/src/main/r 3415cc7 [Davies Liu] move Scala source into core/ and sql/ 180fc9c [Davies Liu] move scala 014d253 [Davies Liu] delete man pages 49a8133 [Davies Liu] Merge branch 'remote_r' into R 44994c2 [Davies Liu] Moved files to R/ 2fc553f [Shivaram Venkataraman] Merge pull request #222 from davies/column2 b043876 [Davies Liu] fix test 5e610cb [Davies Liu] add more API for Column 6f95d49 [Shivaram Venkataraman] Merge pull request #221 from shivaram/sparkr-stop-start 3214c6d [Shivaram Venkataraman] Merge pull request #217 from hlin09/cleanClosureFix f5d3355 [Shivaram Venkataraman] Merge pull request #218 from davies/merge 70f620c [Davies Liu] address comments 4b1628d [Davies Liu] Merge branch 'sparkr-sql' of github.com:amplab-extras/SparkR-pkg into merge 3139325 [Shivaram Venkataraman] Merge pull request #212 from davies/toDF 6122e0e [Davies Liu] handle NULL bc2ff38 [Davies Liu] handle NULL 7f5e70c [Davies Liu] Update SerDe.scala 46454e4 [Davies Liu] address comments dd52cbc [Shivaram Venkataraman] Merge pull request #220 from shivaram/sparkr-utils-include 662938a [Shivaram Venkataraman] Include utils before SparkR for `head` to work Before this change calling `head` on a DataFrame would not work from the sparkR script as utils would be loaded after SparkR and placed ahead in the search list. This change requires utils to be loaded before SparkR 1bc2998 [Shivaram Venkataraman] Merge pull request #179 from evertlammerts/sparkr-sql 7695d36 [Evert Lammerts] added tests 8190127 [Evert Lammerts] fixed parquetFile signature d8c8fcc [Shivaram Venkataraman] Merge pull request #219 from shivaram/sparkr-build-final 963c7ee [Davies Liu] Merge branch 'master' into merge 8bff523 [Shivaram Venkataraman] Remove staging repo now that 1.3 is released e52258f [Davies Liu] Merge branch 'sparkr-sql' of github.com:amplab-extras/SparkR-pkg into toDF 05b9126 [Shivaram Venkataraman] Merge pull request #215 from davies/agg 8e1497d [Davies Liu] Update DataFrame.R 72adb14 [Davies Liu] Update SQLContext.R 66cc92a [Davies Liu] address commets 55c38bc [Shivaram Venkataraman] Merge pull request #216 from davies/select2 3e0555d [Shivaram Venkataraman] Merge pull request #193 from davies/daemon 0467474 [Davies Liu] add more selecter for DataFrame 9a6be74 [Davies Liu] include grouping columns in agg() e87bb98 [Davies Liu] improve comment and logging a6dc435 [Davies Liu] remove dependency of jsonlite 26a3621 [Davies Liu] support date.frame and Date/Time 4e4908a [Davies Liu] createDataFrame from rdd 5757b95 [Shivaram Venkataraman] Merge pull request #196 from davies/die 90f2692 [Shivaram Venkataraman] Merge pull request #211 from hlin09/generics 8583968 [Davies Liu] readFully() 46cea3d [Davies Liu] retry 01aa5ee [Davies Liu] add config for using daemon, refactor ff948db [hlin09] Remove missingOrInteger. ecdfda1 [hlin09] Remove duplication. 411b751 [Davies Liu] make RStudio happy 8f8813f [Davies Liu] switch back to use parallel 6bccbbf [hlin09] Move roxygen doc back to implementation. ffd6e8e [Shivaram Venkataraman] Merge pull request #210 from hlin09/hlin09 471c794 [hlin09] Move getJRDD and broadcast's value to 00-generic.R. 89b886d [hlin09] Move setGeneric() to 00-generics.R. 97dde1a [hlin09] Add a test for access operators. 09ff163 [Shivaram Venkataraman] Merge pull request #204 from cafreeman/sparkr-sql 15a713f [cafreeman] Fix example for `dropTempTable` dc1291b [hlin09] Add checks for namespace access operators in cleanClosure. b4c0b2e [Davies Liu] use fork package 3db5649 [cafreeman] Merge branch 'sparkr-sql' of https://github.com/amplab-extras/SparkR-pkg into sparkr-sql 789be97 [Shivaram Venkataraman] Merge pull request #207 from shivaram/err-remove e60578a [cafreeman] update tests to guarantee row order 5eec6fc [Shivaram Venkataraman] Merge pull request #206 from sun-rui/SPARKR-156_2 3f7aed6 [Sun Rui] Fix minor typos in the function description. a8cebf0 [Shivaram Venkataraman] Remove print statement in SparkRBackendHandler This print statement is noisy for SQL methods which have multiple APIs (like loadDF). We already have a better error message when no valid methods are found 5e3a576 [Sun Rui] Fix indentation. f3d99a6 [Sun Rui] [SPARKR-156] phase 2: implement zipWithIndex() of the RDD class. a582810 [cafreeman] Merge branch 'dfMethods' into sparkr-sql 7a5d6fd [cafreeman] `withColumn` and `withColumnRenamed` c5fa3b9 [cafreeman] New `select` method bcb0bf5 [Shivaram Venkataraman] Merge pull request #180 from davies/group 9dd6a5a [Davies Liu] Update SparkRBackendHandler.scala e6fb8d8 [Davies Liu] improve logging 428a99a [Davies Liu] remove test, catch exception fef99de [cafreeman] `intersect`, `subtract`, `unionAll` befbd32 [cafreeman] `insertInto` 9d01bcd [cafreeman] `dropTempTable` d8c1c09 [Davies Liu] add test to start and stop context multiple times 18c6004 [Shivaram Venkataraman] Merge pull request #201 from sun-rui/SPARKR-156_1 dfb399a [Davies Liu] address comments f06ccec [Sun Rui] Use mapply() instead of for statement. 3c7674f [Davies Liu] Merge branch 'die' of github.com:davies/SparkR-pkg into die ac8a852 [Davies Liu] close monitor connection in sparkR.stop() 4d0fb56 [Shivaram Venkataraman] Merge pull request #203 from shivaram/sparkr-hive-fix 62b0760 [Shivaram Venkataraman] Fix test hive context package name 47a613f [Shivaram Venkataraman] Fix HiveContext package name fb3b139 [Davies Liu] fix tests d0d4626 [Shivaram Venkataraman] Merge pull request #199 from davies/load 8b7fb67 [Davies Liu] fix HiveContext bb46832 [Davies Liu] Merge branch 'sparkr-sql' of github.com:amplab-extras/SparkR-pkg into load e9e2a03 [Davies Liu] Merge branch 'sparkr-sql' of github.com:amplab-extras/SparkR-pkg into group b875b4f [Davies Liu] fix style de2abfa [Shivaram Venkataraman] Merge pull request #202 from cafreeman/sparkr-sql 3675fcf [cafreeman] Update `explain` and fixed doc for `toJSON` 5fd9575 [Davies Liu] Merge branch 'sparkr-sql' of github.com:amplab-extras/SparkR-pkg into load 6fac596 [Davies Liu] support Column expression in agg() f10a24e [Davies Liu] address comments ff8b005 [cafreeman] 'saveAsParquetFile` a5c2887 [cafreeman] fix test 3fab0f8 [cafreeman] `showDF` 779c102 [cafreeman] `isLocal` 68b11cf [cafreeman] `toJSON` 0ac4abc [cafreeman] 'explain` 20242c4 [cafreeman] clean up docs 6a1fe64 [Shivaram Venkataraman] Merge pull request #198 from cafreeman/sparkr-sql 198c130 [Shivaram Venkataraman] Merge pull request #200 from shivaram/sparkr-sql-build 870acd4 [Shivaram Venkataraman] Use rc2 explicitly 8b9a963 [cafreeman] Merge branch 'sparkr-sql' of https://github.com/amplab-extras/SparkR-pkg into sparkr-sql bc90115 [cafreeman] Fixed docs 3865f39 [Sun Rui] [SPARKR-156] phase 1: implement zipWithUniqueId() of the RDD class. a37fd80 [Davies Liu] Update sparkR.R d18f9d3 [Shivaram Venkataraman] Remove SparkR snapshot build We now have 1.3.0 RC2 on Apache Staging 8de958d [Davies Liu] Update SparkRBackend.scala 4e0becc [Shivaram Venkataraman] Merge pull request #194 from davies/api 197a79b [Davies Liu] add HiveContext (commented) 32aa01d [Shivaram Venkataraman] Merge pull request #191 from felixcheung/doc 5073e07 [Davies Liu] Merge branch 'sparkr-sql' of github.com:amplab-extras/SparkR-pkg into load 7918634 [cafreeman] Fix test acea146 [cafreeman] remove extra line 74269f3 [cafreeman] Merge branch 'dfMethods' into sparkr-sql cd7ac8a [Shivaram Venkataraman] Merge pull request #197 from cafreeman/sparkr-sql 494a4dd [cafreeman] update export e14c328 [cafreeman] `selectExpr` 32b37d1 [cafreeman] Fixed indent in `join` test. 2e7b190 [Felix Cheung] small update on yarn deploy mode. 8ff29d6 [Davies Liu] fix tests 12a6db2 [Davies Liu] Merge branch 'sparkr-sql' of github.com:amplab-extras/SparkR-pkg into api 294ca4a [cafreeman] `join`, `sort`, and `filter` 4fa6343 [cafreeman] Refactor `join` generic for use with `DataFrame` 3f22c8d [Shivaram Venkataraman] Merge pull request #195 from cafreeman/sparkr-sql 2b6f980 [Davies Liu] shutdown the JVM after R process die e8639c3 [cafreeman] New 1.3 repo and updates to `column.R` ed9a89f [Davies Liu] address comments 03bcf20 [Davies Liu] Merge branch 'group' of github.com:davies/SparkR-pkg into group 39c253d [Davies Liu] Merge branch 'sparkr-sql' of github.com:amplab-extras/SparkR-pkg into group 98cc97a [Davies Liu] fix test and docs e2d144a [Felix Cheung] Fixed small typos 3beadcf [Davies Liu] Merge branch 'sparkr-sql' of github.com:amplab-extras/SparkR-pkg into api 06cbc2d [Davies Liu] launch R worker by a daemon 8a676b1 [Shivaram Venkataraman] Merge pull request #188 from davies/column 524c122 [Davies Liu] Merge branch 'sparkr-sql' of github.com:amplab-extras/SparkR-pkg into column f798402 [Davies Liu] Update column.R 1d0f2ae [Davies Liu] Update DataFrame.R 03402eb [Felix Cheung] Updates as per feedback on sparkR-submit 76cf2e0 [Shivaram Venkataraman] Merge pull request #192 from cafreeman/sparkr-sql 1955a09 [cafreeman] return object instead of a list of one object f585929 [cafreeman] Fix brackets e998356 [cafreeman] define generic for 'first' in RDD API 71d66a1 [Davies Liu] fix first(0 8ec21af [Davies Liu] fix signature acae527 [Davies Liu] refactor d7b17a4 [Davies Liu] fix approxCountDistinct 7dfe27d [Davies Liu] fix cyclic namespace dependency 8caf5bb [Davies Liu] use S4 methods 5c0bb24 [Felix Cheung] Doc updates: build and running on YARN 773baf0 [Zongheng Yang] Merge pull request #178 from davies/random 862f07c [Shivaram Venkataraman] Merge pull request #190 from shivaram/SPARKR-79 b457833 [Shivaram Venkataraman] Merge pull request #189 from shivaram/stdErrFix f7caeb8 [Davies Liu] Update SparkRBackend.scala 8c4deae [Shivaram Venkataraman] Remove unused function 6e51c7f [Shivaram Venkataraman] Fix stderr redirection on executors 7afa4c9 [Shivaram Venkataraman] Merge pull request #186 from hlin09/funcDep3 4d36ab1 [hlin09] Add tests for broadcast variables. 3f57e56 [hlin09] Fix comments. 7b72487 [hlin09] Fix comments. ae05bf1 [Davies Liu] Merge branch 'sparkr-sql' of github.com:amplab-extras/SparkR-pkg into column abb4bb9 [Davies Liu] add Column and expression eb8ac11 [Shivaram Venkataraman] Set Spark version 1.3.0 in Windows build 5c72e73 [Davies Liu] wait atmost 100 seconds e425437 [Shivaram Venkataraman] Merge pull request #177 from lythesia/master a00f502 [lythesia] fix indents 0346e5f [Davies Liu] address comment 6134649 [Shivaram Venkataraman] Merge pull request #187 from cafreeman/sparkr-sql ad0935e [lythesia] minor fixes b0e7f73 [cafreeman] Update `sampleDF` test 7b0d070 [lythesia] keep partitions check 889c265 [cafreeman] numToInt utility function 27dd3a0 [lythesia] modify tests for repartition cad0f0c [cafreeman] Fix docs and indents 2808dcf [cafreeman] Three more DataFrame methods 5ef66fb [Davies Liu] send back the port via temporary file 3b46429 [Davies Liu] Merge branch 'master' of github.com:amplab-extras/SparkR-pkg into random 798f453 [cafreeman] Merge branch 'sparkr-sql' into dev 9aa4acf [Shivaram Venkataraman] Merge pull request #184 from davies/socket 020bce8 [Shivaram Venkataraman] Merge pull request #183 from cafreeman/sparkr-sql 222e06b [cafreeman] Lazy evaluation and formatting changes e776324 [Davies Liu] fix import 211cc15 [cafreeman] Merge branch 'sparkr-sql' into dev 3351afd [hlin09] Replaces getDependencies with cleanClosure, to serialize UDFs to workers. e7c56d6 [lythesia] fix random partition key 50c74b1 [Davies Liu] address comments 083c89f [cafreeman] Remove commented lines an unused import dfa119b [hlin09] Improve the coverage of processClosure. a41c9b9 [cafreeman] Merge branch 'wrapper' into sparkr-sql 1cd714f [cafreeman] Wrapper function docs. db0cd9e [cafreeman] Clean up for wrapper functions 818c19f [cafreeman] Update schema-related functions a57884e [cafreeman] Remove unused import d72e830 [cafreeman] Add wrapper for `StructField` and `StructType` 2ea2ecf [lythesia] use generic arg 09b9512 [hlin09] add docs f4f077c [hlin09] Add recursive cleanClosure for function access. f84ad27 [hlin09] Merge remote-tracking branch 'upstream/master' into funcDep2 5300766 [Shivaram Venkataraman] Merge pull request #185 from hlin09/hlin09 07aa7c0 [hlin09] Unifies the implementation of lapply with lapplyParitionsWithIndex. f4dbb0b [Davies Liu] use socket in worker 8282c59 [Davies Liu] Update DataFrame.R ba495a8 [Davies Liu] Update NAMESPACE 36dffb3 [cafreeman] Add 'head` and `first` 534a95f [cafreeman] Schema-related methods 64f488d [cafreeman] Cache and Persist Methods 30d71fd [cafreeman] Standardize method arguments for DataFrame methods 785898b [Shivaram Venkataraman] Merge pull request #182 from cafreeman/sparkr-sql 2619003 [Shivaram Venkataraman] Merge pull request #181 from cafreeman/master a9bbe0b [cafreeman] Update existing SparkSQL functions 8c241a3 [cafreeman] Merge with master, include changes to method args 68d6de4 [cafreeman] Fix typos 8d2ec6e [Davies Liu] add sum/max/min/avg/mean 774e687 [Davies Liu] add missing API in SQLContext 1e72b4b [Davies Liu] missing API in SQLContext 3294949 [Chris Freeman] Restore `rdd` argument to `getJRDD` 3a58ebc [Davies Liu] rm unrelated file 8bd93b5 [Davies Liu] fix signature c652b4c [cafreeman] Update method signatures to use generic arg 48c8827 [Davies Liu] update NAMESPACE 84e2d8c [Davies Liu] groupBy and agg() 7c3ddbd [Davies Liu] create jmode in JVM 9465426 [Davies Liu] load and save 982f342 [lythesia] fix numeric issue 7651d84 [lythesia] fix coalesce 4e712e1 [Davies Liu] use random port in backend 041d22b [Shivaram Venkataraman] Merge pull request #172 from cafreeman/sparkr-sql 0d07770 [cafreeman] Added `limit` and updated `take` 301d8e5 [cafreeman] Remove extraneous map functions 0387db2 [cafreeman] Remove colNames 04c4b65 [lythesia] add repartition/coalesce 231deab [cafreeman] Change reserialize to serializeToBytes acf7e1a [cafreeman] Rework the Scala to R DataFrame Conversion 481ae37 [cafreeman] Updated stale comments and standardized arg names 21d4a97 [hlin09] Adds cleanClosure to capture the function closures. d24ffb4 [hlin09] Merge remote-tracking branch 'upstream/master' into funcDep2 8be02de [hlin09] Revert "loop 1-12 test pass." fddb9cc [hlin09] Revert "add docs" f8ef0ab [hlin09] Revert "More docs" 8e4b3da [hlin09] Revert "More docs" 57e005b [hlin09] Revert "fix tests." c10148e [Shivaram Venkataraman] Merge pull request #174 from shivaram/sparkr-runner 910e3be [Shivaram Venkataraman] Add a timeout for initialization Also move sparkRBackend.stop into a finally block bf52b17 [Shivaram Venkataraman] Merge remote-tracking branch 'amplab-sparkr/master' into sparkr-runner 08102b0 [Shivaram Venkataraman] Merge pull request #176 from lythesia/master 9c77b20 [Chris Freeman] Merge pull request #2 from shivaram/sparkr-sql 179ab38 [lythesia] add try counts and increase time interval 71a73b2 [Shivaram Venkataraman] Use a getter for serialization mode This change encapsulates the semantics of serialization mode for RDDs inside a getter function. For PipelinedRDDs if a backing JavaRDD is available we use that else we fall back to a default serialization mode 06bf250 [Shivaram Venkataraman] Merge pull request #173 from shivaram/windows-space-fix 88bf97f [Shivaram Venkataraman] Create SparkContext for R shell launch f9268d9 [Shivaram Venkataraman] Fix code review comments e6ad12d [Shivaram Venkataraman] Update comment describing sparkR-submit 17eda4c [Shivaram Venkataraman] Merge pull request #175 from falaki/docfix ba2b72b [Hossein] Spark 1.1.0 is default 4cd7d3f [lythesia] retry backend connection 749e2d0 [Hossein] Updated README bc04cf4 [Shivaram Venkataraman] Use SPARKR_BACKEND_PORT in sparkR.R as default Change SparkRRunner to use EXISTING_SPARKR_BACKEND_PORT to differentiate between the two 22a19ac [Shivaram Venkataraman] Use a semaphore to wait for backend to initalize Also pick a random port to avoid collisions 7f1f0f8 [cafreeman] Move comments to fit 100 char line length 8b84e4e [cafreeman] Make if statements more explicit ce5d5ab [cafreeman] New tests for Union and Object File b063320 [cafreeman] Changed 'serialized' to 'serializedMode' 0981dff [Zongheng Yang] Merge pull request #168 from sun-rui/SPARKR-153_2 86fc639 [Shivaram Venkataraman] Move sparkR-submit into pkg/inst fd8f8a9 [Shivaram Venkataraman] Merge branch 'hqzizania-master' a33dbea [Shivaram Venkataraman] Merge branch 'master' of https://github.com/hqzizania/SparkR-pkg into hqzizania-master 384e6e2 [Shivaram Venkataraman] Merge pull request #171 from hlin09/hlin09 1f5a6ac [hlin09] fixed comments 7f7596a [cafreeman] Additional handling for "row" serialization 8c3b8c5 [cafreeman] Add test for UnionRDD on "row" serialization b1141f8 [cafreeman] Fixed formatting issues. 5db30bf [cafreeman] Changed serialized from bool to string 2f0c0b8 [cafreeman] Add check for serialized type d243dfb [cafreeman] Clean up code 5ff63a2 [cafreeman] Change test from boolean to string 77fec1a [cafreeman] Updated .Rd files 9224989 [cafreeman] Various updates for DataFrame to RRDD 26af62b [cafreeman] DataFrame to RRDD e004481 [cafreeman] Update UnionRDD test 5292be7 [hlin09] Adds support of pipeRDD(). e2a7560 [Shivaram Venkataraman] Merge pull request #170 from cafreeman/sparkr-sql 5d537f4 [cafreeman] Add pairRDD to Description b6fa88e [cafreeman] Updating to current master 0cda231 [Sun Rui] [SPARKR-153] phase 2: implement aggregateByKey() and foldByKey(). 95ee6b4 [Shivaram Venkataraman] Merge remote-tracking branch 'amplab-sparkr/master' into sparkr-runner 67fbc60 [Shivaram Venkataraman] Add support for SparkR shell to use spark-submit This ensures that SparkConf options are read in both in batch and interactive modes 2271030 [Shivaram Venkataraman] Merge pull request #167 from sun-rui/removePartionByInRDD 7fcb46a [Sun Rui] Remove partitionBy() in RDD. 52f94c4 [Shivaram Venkataraman] Merge pull request #160 from lythesia/master 59e2d54 [lythesia] merge with upstream 5836650 [Zongheng Yang] Merge pull request #163 from sun-rui/SPARKR-153_1 141723e [Sun Rui] fix comments. f73a07e [Shivaram Venkataraman] Merge pull request #165 from shivaram/sparkr-sql-build 10ffc6d [Shivaram Venkataraman] Set Spark version to 1.3 using staging dependency Also fix the maven build c91ede2 [Shivaram Venkataraman] Merge pull request #164 from hlin09/hlin09 9d335a9 [hlin09] Makes git to ignore Eclipse meta files. 94066bf [Sun Rui] [SPARKR-153] phase 1: implement fold() and aggregate(). 9c391c7 [hqzizania] Merge remote-tracking branch 'upstream/master' 5f29551 [hqzizania] modified: pkg/R/RDD.R modified: pkg/R/context.R d968664 [lythesia] fix comment 7972858 [Shivaram Venkataraman] Merge pull request #159 from sun-rui/SPARKR-150_2 7690878 [lythesia] separate out pair RDD functions f4573c1 [Sun Rui] Use reduce() instead of sortBy().take() to get the ordered elements. 63e62ed [Sun Rui] [SPARKR-150] phase 2: implement takeOrdered() and top(). 050390b [Shivaram Venkataraman] Fix bugs in inferring R file 8398f2e [Shivaram Venkataraman] Add sparkR-submit helper script Also adjust R file path for YARN cluster mode bd6705b [Zongheng Yang] Merge pull request #154 from sun-rui/SPARKR-150 c7964c9 [Sun Rui] Merge with upstream master. 7feac38 [Sun Rui] Use default arguments for sortBy() and sortKeyBy(). de2bfb3 [Sun Rui] Fix minor comments and add more test cases. 0c6e071 [Zongheng Yang] Merge pull request #157 from lythesia/master f5038c0 [lythesia] pull out anonymous functions in groupByKey ba6f044 [lythesia] fixes for reduceByKeyLocally 343b6ab [Oscar Olmedo] Export sparkR.stop Closes #156 from oscaroboto/master 25639cf [Shivaram Venkataraman] Replace tabs with spaces bb25920 [Shivaram Venkataraman] Merge branch 'dputler-master' fd836db [hlin09] fix tests. 24a7f13 [hlin09] More docs a465165 [hlin09] More docs 6ad4fc3 [hlin09] add docs b082a35 [lythesia] add reduceByKeyLocally 7ca6512 [Shivaram Venkataraman] First cut of SparkRRunner 193f5fe [hlin09] loop 1-12 test pass. 345f1b8 [dputler] [SPARKR-195] Implemented project style guidelines for if-else statements 8043559 [Sun Rui] Add a TODO to use binary search in the range partitioner. 91b2fd6 [Sun Rui] Add more test cases. e8ebbe4 [Shivaram Venkataraman] Merge pull request #152 from cafreeman/sparkr-sql 0c53d6c [dputler] Data frames now coerced to lists, and messages issued for a data frame or matrix on how they are parallelized 6d57ec0 [cafreeman] Remove json test file since we're using a temp ac1ef09 [cafreeman] Update registerTempTable test d9da451 [Sun Rui] [SPARKR-150] phase 1: implement sortBy() and sortByKey(). 08ff30b [Shivaram Venkataraman] Merge pull request #153 from hqzizania/master 9767e8e [hqzizania] modified: pkg/man/collect-methods.Rd 5d69f0a [hqzizania] modified: pkg/R/RDD.R 4914091 [hqzizania] modified: pkg/inst/tests/test_rdd.R 742a68b [cafreeman] Update test_sparkRSQL.R a95823e [hqzizania] modified: pkg/R/RDD.R 2d04526 [cafreeman] Formatting fae9bdd [cafreeman] Renamed to SQLUtils.scala 39888ea [Chris Freeman] Update test_sparkSQL.R fce2453 [cafreeman] Updated documentation for SQLContext 13fbf12 [cafreeman] Regenerated .Rd files 51ecf41 [cafreeman] Updated Scala object 30d7337 [cafreeman] Added SparkSQL test 74b3ed6 [cafreeman] Incorporate code feedback 554bda0 [Zongheng Yang] Merge pull request #147 from shivaram/sparkr-ec2-fixes a5f4f8f [cafreeman] Squashed commit of the following: f34bb88 [Shivaram Venkataraman] Remove profiling information from this PR c662f29 [Zongheng Yang] Merge pull request #146 from shivaram/spark-1.2-build 21e9b74 [Zongheng Yang] Merge pull request #145 from lythesia/master 76f6b9e [Shivaram Venkataraman] Merge pull request #149 from hqzizania/master 1c2dbec [lythesia] minor fix for refactoring join code 5b380d3 [hqzizania] modified: pkg/man/combineByKey.Rd modified: pkg/man/groupByKey.Rd modified: pkg/man/partitionBy.Rd modified: pkg/man/reduceByKey.Rd 98794fe [hqzizania] modified: pkg/R/RDD.R b66534d [Zongheng Yang] Merge pull request #144 from shivaram/fix-rd-files 60da1df [Shivaram Venkataraman] Initialize timing variables 179aa75 [Shivaram Venkataraman] Bunch of fixes for longer running jobs 1. Increase the timeout for socket connection to wait for long jobs 2. Add some profiling information in worker.R 3. Put temp file writes before stdin writes in RRDD.scala 06d99f0 [Shivaram Venkataraman] Fix URI to have right number of slashes add97f5 [Shivaram Venkataraman] Use URL encode to create valid URIs for jars 4eec962 [lythesia] refactor join functions 73430c6 [Shivaram Venkataraman] Make SparkR work on paths with spaces on Windows aaf8f47 [Shivaram Venkataraman] Exclude hadoop client from Spark dependency 227ee42 [Zongheng Yang] Merge pull request #141 from shivaram/SPARKR-140 ac5ceb1 [Shivaram Venkataraman] Fix code review comments 32394de [Shivaram Venkataraman] Regenerate Rd files for SparkR This fixes a number of issues in SparkR man pages. The main changes are 1. Don't export or generate docs for PipelineRDD 2. Fix variable names for Filter, count to match base methods 3. Document missing arguments for sparkR.init, print.jobj etc. e157bf6 [Shivaram Venkataraman] Use prev_serialized to track if JRDD is serialized This changes introduces a new variable in PipelineRDD environment to track if the prev_jrdd is serialized or not. 7428a7e [Zongheng Yang] Merge pull request #143 from shivaram/SPARKR-181 7dd1797 [Shivaram Venkataraman] Address code review comments 8f81c45 [Shivaram Venkataraman] Remove roxygen export for PipelinedRDD 0cb90f1 [Zongheng Yang] Merge pull request #142 from shivaram/SPARKR-169 d1c6e6c [Shivaram Venkataraman] Buffer stderr from R and return it on Exception This change buffers the last 100 lines from R process and passes these lines back to the driver if we have an exception. This will help users debug why their tasks failed on the cluster d6c1393 [Shivaram Venkataraman] Suppress warnings from normalizePath a382835 [Shivaram Venkataraman] Fix serialization tracking in pipelined RDDs When creating a pipeline RDD, we need to check if the JavaRDD belonging to the parent is serialized. da39529 [Zongheng Yang] Merge pull request #140 from sun-rui/SPARKR-183 2814caa [Sun Rui] Merge with upstream master. cd2a5b3 [Sun Rui] Add reference to Nagle's algorithm and clean code. 52356b6 [Shivaram Venkataraman] Merge pull request #139 from shivaram/fix-backend-exit 97e5a1f [Sun Rui] [SPARKR-183] Fix the issue that parallelize collect tests are slow. a9f8e8e [Shivaram Venkataraman] Merge pull request #138 from concretevitamin/fix-collect-test 125ae43 [Shivaram Venkataraman] Fix SparkR backend to exit in more cases This change has two fixes 1. When the workspace is saved (from R or RStudio) the backend connection seems to be closed before the finalizer is run. In such cases we reopen the connection and stop the backend 2. With RStudio when R is restarted, there are port-conflicts which appear due to a race condition between the JVM and rsession restart. This change adds a 1 sec sleep to avoid this race. 12c102a [Zongheng Yang] Simplify a unit test. 9c0637a [Zongheng Yang] Merge pull request #137 from shivaram/fix-docs 0df0e18 [Shivaram Venkataraman] Fix documentation for includePackage 7549f88 [Zongheng Yang] Merge pull request #136 from shivaram/man-updates 7edbe46 [Shivaram Venkataraman] Add missing man pages 9cb9567 [Shivaram Venkataraman] Merge pull request #131 from shivaram/rJavaExpt 1fa722e [Shivaram Venkataraman] Rename to SerDe now 2fcb051 [Shivaram Venkataraman] Rename to SerDeJVMR d112cf0 [Shivaram Venkataraman] Style fixes 9fd01cc [Shivaram Venkataraman] Remove unnecessary braces 0881931 [Shivaram Venkataraman] Some more style fixes f00b531 [Shivaram Venkataraman] Address code review comments. Big changes include style fixes throughout for named arguments c09ba05 [Shivaram Venkataraman] Change jobj id to be just an integer Add a new print.jobj that gets the class name and prints it Also add a utility function isInstanceOf be05b16 [Shivaram Venkataraman] Check if context, connection exist before stopping d596a23 [Shivaram Venkataraman] Address code review comments 396e7ac [Shivaram Venkataraman] Changes to make new backend work on Windows This change uses file.path to construct the Java binary path in a OS agnostic way and uses system2 to handle quoting binary paths correctly. Tests pass on Mac OSX and a Windows EC2 instance. e7a4e03 [Shivaram Venkataraman] Remove unused file BACKEND.md 62f380b [Shivaram Venkataraman] Update worker.R to use new deserialization call 8b9c4e6 [Shivaram Venkataraman] Change RDD name, setName to use new backend 6dcd5c5 [Shivaram Venkataraman] Merge branch 'master' of https://github.com/amplab-extras/SparkR-pkg into rJavaExpt 0873397 [Shivaram Venkataraman] Refactor java object tracking into a new singleton. Also add comments describing each class 95db964 [Shivaram Venkataraman] Add comments, cleanup new R code bcd4258 [Zongheng Yang] Merge pull request #130 from lythesia/master 74dbc5e [Sun Rui] Match method using parameter types. 7ad4a4d [Sun Rui] Use 1 char to represent types on the backend->client direction. bace887 [Sun Rui] Use an integer count for the backend java object ID because Uniqueness isn't guaranteed by System.identityHashCode(). b38d04f [Sun Rui] Use 1 char to represent types on the client -> backend direction. f88bc68 [lythesia] Merge branch 'master' of github.com:lythesia/SparkR-pkg 71d41f5 [lythesia] add test case for fullOuterJoin eb4f423 [lythesia] --amend cffecc5 [lythesia] add test case for fullOuterJoin a547dd2 [Shivaram Venkataraman] Move classTag, rddRef into newJObject call This avoids them getting eagerly garbage collected 1255391 [Shivaram Venkataraman] Add a finalizer for jobj objects This enables Java objects to be garbage collected on the backend when they are no longer referenced in R. Also rename newJava to newJObject to be more consistent with callJMethod 70fa409 [Sun Rui] Add YARN Conf Dir to the class path when launching the backend. a1108ca [lythesia] add fullOuterJoin in RDD.R 2152727 [Shivaram Venkataraman] Remove empty file cd08bee [Shivaram Venkataraman] Update all functions to use new backend All unit tests pass. 9de49b7 [Shivaram Venkataraman] Add high level calls for methods, constructors Also update BACKEND.md 5a97ea4 [Shivaram Venkataraman] Add jobj S3 class that holds backend refs e071d3e [Shivaram Venkataraman] Change SparkRBackend to use general method calls This change uses a custom protocl + JNI to invoke any method on a given object type. Also update serializers, deserializers to make code more concise 49f0404 [Shivaram Venkataraman] Merge pull request #129 from lythesia/master 7f8cd82 [lythesia] update man 4715ed2 [Yi Lu] Update RDD.R 5a53801 [lythesia] fix name,setName 4f3870b [lythesia] add name,setName in RDD.R 1c25700 [Shivaram Venkataraman] Merge pull request #128 from sun-rui/SPARKR-165 c8507d8 [Sun Rui] [SPARKR-165] IS_SCALAR is not present in R before 3.1 2cff2bd [Sun Rui] Add function to invoke Java method. 7a31da1 [Shivaram Venkataraman] Merge branch 'dputler-master'. Closes #119 0ceba82 [Shivaram Venkataraman] Merge branch 'master' of https://github.com/dputler/SparkR-pkg into dputler-master 735f70c [Shivaram Venkataraman] Merge pull request #125 from 7c00/rawcon fccfe6c [Shivaram Venkataraman] Merge pull request #127 from sun-rui/SPARKR-164 387bd57 [Sun Rui] [SPARKR-164] Temporary files used by SparkR accumulat as time goes on. 5f2268f [Shivaram Venkataraman] Add support to stop backend 5f745c0 [Shivaram Venkataraman] Update notes in backend 22015c1 [Shivaram Venkataraman] Add first cut of SparkR Backend 52821da [Todd Gao] switch the order of packages and function deps d7b0007 [Todd Gao] remove memCompress cb6873e [Shivaram Venkataraman] Merge pull request #126 from sun-rui/SPARKR-147 c5962eb [Todd Gao] further optimize using rawConnection f04c6e0 [Sun Rui] [SPARKR-147] Support multiple directories as input to textFile. b7de604 [Todd Gao] optimize execFunctionDeps loading in worker.R 4d4fc30 [Shivaram Venkataraman] Merge pull request #122 from cafreeman/master b508877 [cafreeman] Update SparkR_IDE_Setup.sh 21ed9d7 [cafreeman] Update build.sbt f73ec16 [cafreeman] Delete SparkR_IDE_Setup_Guide.md d63b026 [cafreeman] Delete SparkR_Quick_Start_Guide.md 6e6cb62 [cafreeman] Update SparkR_IDE_Setup.sh bc6042b [cafreeman] Update build.sbt a8197d5 [cafreeman] Merge remote-tracking branch 'upstream/master' d671564 [Zongheng Yang] Merge pull request #123 from shivaram/jcheck-void 76b8d00 [Zongheng Yang] Merge pull request #124 from shivaram/master b690d58 [Shivaram Venkataraman] Specify how to change Spark versions in README 0fb003d [Shivaram Venkataraman] Merge branch 'master' of https://github.com/amplab-extras/SparkR-pkg into jcheck-void 1c227b4 [Shivaram Venkataraman] Also add a check in context.R 96812b6 [Shivaram Venkataraman] Check for exceptions after void method calls f5c216d [cafreeman] Merge remote-tracking branch 'upstream/master' 90c8933 [Zongheng Yang] Merge pull request #121 from shivaram/fix-sort-order bd0e3b4 [Shivaram Venkataraman] Fix saveAsTextFile test case 2e55f67 [Shivaram Venkataraman] Merge branch 'master' of https://github.com/amplab-extras/SparkR-pkg into fix-sort-order f10c607 [Shivaram Venkataraman] Merge pull request #118 from sun-rui/saveAsTextFile 6c9bfc0 [Sun Rui] Merge remote-tracking branch 'SparkR_upstream/master' into saveAsTextFile 6faedbe [cafreeman] Update SparkR_IDE_Setup_Guide.md 57008bc [cafreeman] Update SparkR_IDE_Setup.sh bb1c17d [cafreeman] Update SparkR_IDE_Setup.sh 538bfdb [cafreeman] Update SparkR_Quick_Start_Guide.md 31322c6 [cafreeman] Update SparkR_IDE_Setup.sh ca3f593 [Sun Rui] Refactor RRDD code. df58d95 [cafreeman] Update SparkR_Quick_Start_Guide.md b488c88 [cafreeman] Rename Spark_IDE_Setup.sh to SparkR_IDE_Setup.sh b2545a4 [cafreeman] Added IDE Setup Guide 0ffb5de [cafreeman] Merge branch 'master' of https://github.com/cafreeman/SparkR-pkg bd8fbfb [cafreeman] Merge remote-tracking branch 'upstream/master' 98efa5b [cafreeman] Added Quick Start Guide 3cf88f2 [Shivaram Venkataraman] Sort lists before comparing in unit tests Since Spark doesn't guarantee that shuffle results will always be in the same order, we need to sort the results before comparing for deterministic behavior d621dbc [Shivaram Venkataraman] Merge pull request #120 from sun-rui/objectFile c4a44d7 [Sun Rui] Add @seealso in comments and extract some common code into a function. 724e3a4 [cafreeman] Update Spark_IDE_Setup.sh 8153e5a [Sun Rui] [SPARKR-146] Support read/save object files in SparkR. 17f9909 [cafreeman] Update Spark_IDE_Setup.sh a9eb080 [cafreeman] IDE Shell Script 64d800c [dputler] Merge remote branch 'upstream/master' 1fbdb2e [dputler] Added the ability for the user to specify a text file location throught the use of tilde expansion or just the file name if it is in the working directory. d83c017 [Shivaram Venkataraman] Merge pull request #113 from sun-rui/stringHashCodeInC a7d9cdb [Sun Rui] Fix build on Windows. 7d81b05 [Shivaram Venkataraman] Merge pull request #114 from hlin09/hlin09 47c4bb7 [hlin09] fix reviews a457f7f [Shivaram Venkataraman] Merge pull request #116 from dputler/master 0fa48d1 [Shivaram Venkataraman] Merge pull request #117 from sun-rui/keyBy 85cfeb4 [Sun Rui] [SPARKR-144] Implement saveAsTextFile() in the RDD class. 09083d9 [Sun Rui] Add keyBy() to the RDD class. caad5d7 [dputler] Adding the script to install software on the Cloudera Quick Start VM. dca3d05 [hlin09] Minor fix. ece5f7d [hlin09] Merge remote-tracking branch 'upstream/master' into hlin09 a40874b [hlin09] Use extendible accumulators aggregate the cogroup values. d0347ce [Zongheng Yang] Merge pull request #112 from sun-rui/outer_join 492f76e [Sun Rui] Refine code and add description. ba01358 [Shivaram Venkataraman] Merge pull request #115 from sun-rui/SPARKR-130 5c8e46e [Sun Rui] Fix per the review comments. 7190a2c [Sun Rui] Update comment to add a reference to storage levels. 1da705e [hlin09] Fix the review comments. c4b77be [Sun Rui] [SPARKR-130] Add persist(storageLevel) API to RDD. b424a1a [hlin09] Add function cogroup(). 9770312 [Shivaram Venkataraman] Merge pull request #111 from hlin09/hlin09 cead7df [hlin09] fix review comments. 54f712e [Sun Rui] Implement string hash code in C. 425f0c6 [Sun Rui] Add leftOuterJoin() and rightOuterJoin() to the RDD class. 39509c7 [hlin09] add Rd file for foreach and foreachPartition. 63d6ac7 [hlin09] Adds function foreach() and foreachPartition(). 9c954df [Zongheng Yang] Merge pull request #105 from sun-rui/join c71228d [Sun Rui] Pre-allocate list with fixed length. Add test case for join() using string key. bc3e9f6 [Shivaram Venkataraman] Merge pull request #108 from concretevitamin/take-optimize c06fc90 [Zongheng Yang] Fix: only optimize for unserialized dataset case. d399aeb [Zongheng Yang] Apply size-capping on logical representation instead of physical. e4217dd [Zongheng Yang] Merge pull request #107 from shivaram/master 7952180 [Shivaram Venkataraman] Copy, use getLocalDirs from Spark Utils.scala 08e24c3 [Zongheng Yang] Merge pull request #109 from hlin09/hlin09 97d4e02 [Zongheng Yang] Min() upper-bound size with actual size. bb779bf [hlin09] Rename the filter function to filterRDD to follow the API consistency. Filter() is also kept. ce1661f [Zongheng Yang] Fix slow take(): deserialize only up to necessary # of elements. 4dca9b1 [Shivaram Venkataraman] Merge pull request #106 from hlin09/hlin09 1220d92 [hlin09] Adds function numPartitions(). 2326a65 [Shivaram Venkataraman] Use SPARK_LOCAL_DIRS to create tmp files e119757 [hlin09] Minor fix. 9c24c8b [hlin09] Adds function countByKey(). 48fce67 [hlin09] Adds countByValue(). 6679eef [Sun Rui] Update documentation for join(). 70586b4 [Sun Rui] Add join() to the RDD class. e6fb999 [Zongheng Yang] Merge pull request #103 from shivaram/rlibdir-fix a21f146 [Shivaram Venkataraman] Merge pull request #102 from hlin09/hlin09 32eb619 [Shivaram Venkataraman] Merge pull request #104 from sun-rui/add_keys_values d8692e9 [Sun Rui] Add keys() and values() for the RDD class. 18b9be1 [Shivaram Venkataraman] Allow users to set where SparkR is installed This also adds a warning if somebody tries to call sparkR.init multiple times. a17f135 [hlin09] Adds tests for flatMap and flatMapValues. 4bcf59b [hlin09] Adds function flatMapValues. 4a193ef [Zongheng Yang] Merge pull request #101 from ashutoshraina/master 60d22f2 [Ashutosh Raina] changed sbt version 5400793 [Zongheng Yang] Merge pull request #98 from shivaram/windows-fixes-build 36d61a7 [Shivaram Venkataraman] Merge pull request #97 from hlin09/hlin09 f7d7d89 [hlin09] Remove redundant code in test. 6bbe823 [hlin09] minor style fix. 9b47f3a [Shivaram Venkataraman] Merge pull request #100 from hnahak87/patch-1 7f6e4ea [Harihar Nahak] Update logistic_regression.R a605047 [Shivaram Venkataraman] Merge pull request #99 from hlin09/makefile 323151d [hlin09] Fix yar flag in Makefile to remove build error in Maven. 8911897 [hlin09] Make reserialize() private function in package. 79aee73 [Shivaram Venkataraman] Add notes on how to build SparkR on windows 49a99e7 [Shivaram Venkataraman] Clean up some commented code ddc271b [Shivaram Venkataraman] Only append file:/// to non empty jar paths a53952e [Shivaram Venkataraman] Add windows build scripts 325b179 [hlin09] Merge remote-tracking branch 'upstream/master' into hlin09 daf5040 [hlin09] Add reserialize() before union if two RDDs are not both serialized. 536afb1 [hlin09] Add new function of union(). 7044677 [Shivaram Venkataraman] Merge branch 'master' of https://github.com/amplab-extras/SparkR-pkg into windows-fixes d22a02d [Zongheng Yang] Merge pull request #94 from shivaram/windows-fixes-stdin 51924f7 [Shivaram Venkataraman] Merge pull request #90 from oscaroboto/master eb97d85 [Shivaram Venkataraman] Merge pull request #96 from sun-rui/add_clarification_readme 5a128f4 [Sun Rui] Add clarification on setting Spark master when launching the SparkR shell. 187526a [oscaroboto] Update sparkR.R 32c567b [Shivaram Venkataraman] Merge pull request #95 from concretevitamin/master 4cd2d5e [Zongheng Yang] Notes about spark-ec2. 1c28e3b [Shivaram Venkataraman] Merge branch 'master' of https://github.com/amplab-extras/SparkR-pkg into windows-fixes 8e8a029 [Zongheng Yang] Merge pull request #92 from shivaram/sparkr-yarn 721043b [Zongheng Yang] Update README.md with YARN instructions. 1681f58 [Shivaram Venkataraman] Use temporary files for input instead of stdin This fixes a bug for Windows where stdin would get truncated b084314 [oscaroboto] removed ... from example 44c93d4 [oscaroboto] Added example to SparkR.R be82dcc [Shivaram Venkataraman] Merge pull request #93 from hlin09/hlin09 868554d [oscaroboto] Update sparkR.R 488ac47 [hlin09] Add generated Rd file of previous added functions, distinct() and mapValues(). b2740ad [hlin09] Add test for filter all elements. Add filter() as alias. 08d3631 [hlin09] Minor style fixes. 2c0e34f [hlin09] Adds function Filter(), which extracts the elements that satisfy a predicate. 5951d3b [Shivaram Venkataraman] Remove SBT plugin 4e70ced [oscaroboto] changed ExecutorEnv to sparkExecutorEnvMap, to make it consistent with sparkEnvirMap 903d18a [oscaroboto] changed executorEnv to sparkExecutorEnvMap, will do the same in R f97346e [oscaroboto] executorEnv to lower-case e 88a524e [oscaroboto] Added LD_LIBRARY_PATH to the ExecutorEnv. This is need so that the nodes can find libjvm.so, or if the master has a different LD_LIBRARY_PATH then the nodes. Make sure to export LD_LIBRARY_PATH that includes the path to libjvm.so in the nodes. 1d208ae [oscaroboto] added the YARN_CONF_DIR to the classpath 8a9b75c [oscaroboto] forgot to change hm and ee inside the for loops 579db58 [Shivaram Venkataraman] Merge pull request #91 from sun-rui/add_max_min 4381efa [Sun Rui] use reduce() to implemement max() and min(). a5459c5 [Shivaram Venkataraman] Consolidate yarn flags 86b04eb [Shivaram Venkataraman] Don't use quotes around yarn bf0797f [Shivaram Venkataraman] Add dependency on spark yarn module af5fe77 [Shivaram Venkataraman] Fix SBT build, add dependency tree plugin 4917607 [Sun Rui] Add maximum() and minimum() API to RDD. 51bbbe4 [Shivaram Venkataraman] Changes to make SparkR work with YARN 9d5e3ab [oscaroboto] a few stylistic changes. Also change vars to sparkEnvirMap and eevars to ExecutorEnv, to match sparkR.R 578f545 [oscaroboto] a few stylistic changes 39eea2f [oscaroboto] Modification to dynamically create a sparkContext with YARN. Added .setExecutorEnv to the sparkConf in createSparkContext within the RRDD object. This modification was made together with sparkR.R 17ec42e [oscaroboto] A modification to dynamically create a sparkContext with YARN. sparkR.R modified to pass custom Jar file names and EnvironmentEnv to the sparkConf. RRDD.scala was also modified to accept the new inputs to creatSparkContext. 624ac9d [Shivaram Venkataraman] Merge pull request #87 from sun-rui/SPARKR-125 4f213db [Shivaram Venkataraman] Merge pull request #89 from sun-rui/SPARKR-108 eb833c5 [Shivaram Venkataraman] Merge pull request #88 from hlin09/hlin09 07bf971 [Sun Rui] [SPARKR-108] Implement map-side reduction for reduceByKey(). 4accba1 [hlin09] Fixes style and adds an optional param 'numPartition' in distinct(). 80d303a [hlin09] typo fixed. e37a9b5 [hlin09] Adds function distinct() and mapValues(). 08dac06 [Sun Rui] [SPARKR-125] Get the iterator of the parent RDD before launching a R worker process in compute() of RRDD/PairwiseRRDD c4ba53c [Shivaram Venkataraman] Merge pull request #85 from edwardt/master 72a9d27 [root] reorder to keep relative ordering the same f3fcb10 [root] fix up build.sbt also to match pom.xml 5ecbe3e [root] Make spark verison configurable in build script per ISSUE122 a44e63d [Shivaram Venkataraman] Merge pull request #84 from sun-rui/SPARKR-94 fbb5663 [Sun Rui] Add {} to one-line functions and add a test case for lookup where no match is found. 95beb4e [Shivaram Venkataraman] Merge pull request #82 from edwardt/master 36776c5 [edwardt] missed one 0.9.0 revert b26deec [Sun Rui] [SPARKR-94] Add a method to get an element of a pair RDD object by key. 1ba256e [edwardt] Keep 0.9.0 and says uses 1.1.0 by default 5380c43 [root] missed one version 21f74da [root] upgrade to spark version 1.1.0 to match lastest merge list ddfcde9 [root] merge 67d067a [Shivaram Venkataraman] Merge pull request #81 from sun-rui/SparkR-117 993868f [Sun Rui] [SPARKR-117] Update Spark dependency to 1.1.0 d20661a [Zongheng Yang] Merge pull request #80 from sun-rui/master 0b2da9f [Sun Rui] Update Rd file and add a test case for mapPartitions. 5879648 [Sun Rui] Add mapPartitions() method to RDD for API consistency. c033461 [Shivaram Venkataraman] Merge pull request #79 from sun-rui/fix-kmeans f62b77e [Sun Rui] Adjust coding style. b40911d [Sun Rui] Fix syntax error in examples/kmeans.R. 5304451 [Shivaram Venkataraman] Merge pull request #78 from sun-rui/master 70ffbfb [Sun Rui] Fix a bug that modifications to build.sbt won't trigger rebuilding. a25696c [Shivaram Venkataraman] Merge pull request #76 from edwardt/addjira b8bbd93 [edwardt] Update README.md 615d930 [edwardt] Update README.md e522e69 [edwardt] Update README.md 03e6ced [edwardt] Update README.md 3007015 [root] don't check in gedit buffer file' c35c9a6 [root] Add where to enter bugs ad feeback 469eae3 [edwardt] Update README.md 61b4a43 [edwardt] Update Makefile (style uniformity) ce3337d [edwardt] Update README.md 7ff68fc [root] Merge branch 'master' of https://github.com/edwardt/SparkR-pkg 16353f5 [root] add links to devtools and install_github 513b9e5 [Shivaram Venkataraman] Merge pull request #72 from edwardt/master 31608a4 [edwardt] Update Makefile (style uniformity) 4ffe146 [root] Makefile: factor out SPARKR_VERSION to reduce potential copy&paste error; cp & rm called with -f in build/clean phase; .gitignore includes checkpoints and unit test log generated by run-tests.sh 715275f [Zongheng Yang] Merge pull request #68 from shivaram/master 90e2083 [Shivaram Venkataraman] Add return type to hasNext 8eb983d [Shivaram Venkataraman] Fix up comment 2206164 [Shivaram Venkataraman] Delete temporary files after they are read This change deletes temporary files used for communication between Rscript and the JVM once they have been completely read. 5881da7 [Zongheng Yang] Merge pull request #67 from shivaram/improve-shuffle 81251e2 [Shivaram Venkataraman] Address code review comments a5f573f [Shivaram Venkataraman] Use a better list append in shuffles This is helpful in scenarios where we have a large number of values in a bucket 388e64d [Shivaram Venkataraman] Merge pull request #55 from RevolutionAnalytics/master e1f95b6 [Zongheng Yang] Merge pull request #65 from concretevitamin/parallelize-fix fc1a71a [Zongheng Yang] Fix that collect(parallelize(sc,1:72,15)) drops elements. b8204c5 [Zongheng Yang] Minor: update a URL in README. 86f30c3 [Antonio Piccolboni] better fix for amplab-extras/SparkR-pkg#53 b3c318d [Antonio Piccolboni] delayed loading to have all namespaces available. f323e97 [Antonio Piccolboni] tentative fix for amplab-extras/SparkR-pkg#53 6f82269 [Zongheng Yang] Merge pull request #48 from shivaram/master 8f433e5 [Shivaram Venkataraman] Move up Hadoop in pom.xml and add back protobufs As Hadoop 1.0.4 doesn't use protobufs, we can't exclude protobufs from Spark always. This change tries to order the dependencies so that the shader first picks up Hadoop's protobufs over Mesos. bfe7e26 [Shivaram Venkataraman] Merge pull request #36 from RevolutionAnalytics/vectorize-examples 059ae41 [Antonio Piccolboni] and more formatting 9dbd531 [Antonio Piccolboni] more formatting per committer request 948738a [Antonio Piccolboni] converted tabs to spaces per project request 49f5f5a [Shivaram Venkataraman] Merge pull request #35 from shivaram/master 3eb5ad3 [Shivaram Venkataraman] on_failure -> after_failure in travis.yml 139bdee [Shivaram Venkataraman] Cache sbt, maven, ivy dependencies 4ebced2 [Shivaram Venkataraman] Merge pull request #34 from shivaram/master 8437061 [Shivaram Venkataraman] Exclude protobuf from Spark dependency in Maven This avoids pulling in multiple versions of protobuf from Mesos and Hadoop. 91aa527 [Antonio Piccolboni] vectorized version, 36s 10 slices 10^6 per slice. The older version takes 30 sec on 1/10th of data. f137a57 [Antonio Piccolboni] for rstudio users 1f7ffb0 [Antonio Piccolboni] implemented using matrices and vectorized calls wherever possible 46b23df [Antonio Piccolboni] replace require with library b15d7db [Antonio Piccolboni] faster parsing 8b7aeb3 [Antonio Piccolboni] 22x speed improvement, 3X mem impovement c5bce07 [Zongheng Yang] Merge pull request #30 from shivaram/string-tests 21fa2d8 [Shivaram Venkataraman] Fix bug where serialized was not changed for RRRD Reason: When an RRDD is created in getJRDD we have converted any possibly unserialized RDD to a serialized RDD. 9d1ea20 [Shivaram Venkataraman] Merge branch 'master' of github.com:amplab/SparkR-pkg into string-tests 7b9348c [Shivaram Venkataraman] Add tests for partition with string keys Add two tests one with a string array and one from a textFile to test both codepaths aacd726 [Shivaram Venkataraman] Update README with maven proxy instructions 803e62c [Shivaram Venkataraman] Merge pull request #28 from concretevitamin/master 7c093e6 [Zongheng Yang] Use inherits() to test an object's class. 061c591 [Shivaram Venkataraman] Merge pull request #26 from hafen/master 90f9fda [Ryan Hafen] Fix isRdd() to properly check for class 5b10cc7 [Zongheng Yang] Merge pull request #24 from shivaram/master 7014f83 [Shivaram Venkataraman] Remove unused transformers in maven's pom.xml b00cea5 [Shivaram Venkataraman] Add support for a Maven build 11ec9b2 [Shivaram Venkataraman] Merge pull request #12 from concretevitamin/pipelined 6b18a90 [Zongheng Yang] Merge branch 'master' into pipelined 57127b8 [Zongheng Yang] Merge pull request #23 from shivaram/master 1ac3940 [Zongheng Yang] Review feedback. a06fb34 [Zongheng Yang] Remove outdated comment. 0a1fc13 [Shivaram Venkataraman] Fixes for using SparkR with Hadoop2. 1. Exclude ASM, Netty from Hadoop similar to Spark. 2. Concat services files to ensure HDFS filesystems work. 3. Update README with an example 9a1db44 [Zongheng Yang] Merge pull request #22 from shivaram/master e462448 [Shivaram Venkataraman] Use `$` for calling `put` instead of .jrcall ed4559a [Shivaram Venkataraman] Add support for passing Spark environment vars This change creates a new `createSparkContext` method in RRDD as we can't pass Map through rJava. Also use SPARK_MEM in local mode to increase heap size and update the README with some examples. 10228fb [Shivaram Venkataraman] Merge pull request #20 from concretevitamin/digit-ex 1398d9f [Zongheng Yang] Add linear_solver_mnist to examples/. d484c2a [Zongheng Yang] Add tests for actions on PipelinedRDD. d9cb95c [Zongheng Yang] Add setCheckpointDir() to context.R; comment fix. f8bc8a9 [Zongheng Yang] Minor edits per Shivaram's comments. 8cd67f7 [Shivaram Venkataraman] Merge pull request #15 from shivaram/master d4468a9 [Shivaram Venkataraman] Remove trailing comma e2714b8 [Shivaram Venkataraman] Remove Apache Staging repo and update README 334eace [Zongheng Yang] Add a multi-transformation test to benchmark on pipelining. 5650ad7 [Zongheng Yang] Put serialized field inside env for both RDD and PipelinedRDD. 0b9e8bb [Zongheng Yang] First cut at PipelinedRDD. a4c431e [Zongheng Yang] Add `isCheckpointed` field and checkpoint(). dac0795 [Zongheng Yang] Minor inline comment style fix. bfb8e26 [Zongheng Yang] Add isCached field (inside an env) and unpersist(). 295bff6 [Zongheng Yang] Merge pull request #11 from shivaram/master 4cb209c [Shivaram Venkataraman] Search rLibDir in worker before libPaths This ensures we pick up the SparkR intended and not an older version installed on the same machine ef198ff [Zongheng Yang] Merge pull request #10 from shivaram/unit-tests e0557a8 [Shivaram Venkataraman] Update travis to install plyr 8b18bc1 [Shivaram Venkataraman] Merge branch 'master' of github.com:amplab/SparkR-pkg into unit-tests 4a9ca31 [Shivaram Venkataraman] Use smaller broadcast and plyr instead of Matrix Matrix package takes around 2s to load and slows down unit tests. 21c6a61 [Zongheng Yang] Merge pull request #8 from shivaram/master 08c2947 [Shivaram Venkataraman] Move dev install directory to front of libPaths bda42ee [Shivaram Venkataraman] Merge pull request #7 from JoshRosen/travis cc5f5c0 [Josh Rosen] Add Travis CI integration (using craigcitro/r-travis) b6c864b [Shivaram Venkataraman] Merge pull request #6 from concretevitamin/env-style-fix 4fcef22 [Zongheng Yang] Use one style ($) for accessing names in environments. 8a948c6 [Shivaram Venkataraman] Merge pull request #4 from shivaram/master 24978eb [Shivaram Venkataraman] Update README to use install_github 8899db4 [Shivaram Venkataraman] Update TODO.md 91792de [Shivaram Venkataraman] Update Spark requirements f34f4bf [Shivaram Venkataraman] Check tests for failures and output error msg cd750d3 [Shivaram Venkataraman] Update run-tests to use new path 1877b7c [Shivaram Venkataraman] Unset R_TESTS to make tests work with R CMD check Also silence Akka remoting logs and update Makefile to build on log4j changes e60e18a [Shivaram Venkataraman] Update README to remove Spark installation notes 4450189 [Shivaram Venkataraman] Add Spark 0.9 dependency from Apache Staging Also clean up assembly jar from inst on make clean 5eb2131 [Shivaram Venkataraman] Update repo path in README ec8210e [Shivaram Venkataraman] Remove broadcastId hack as it is public in Spark 9f0e080 [Shivaram Venkataraman] Merge branch 'install-github' 5c88fbd [Shivaram Venkataraman] Add helper script to run tests 77450a1 [Shivaram Venkataraman] Remove dependency on Spark Logging 6cb00d1 [Shivaram Venkataraman] Update README and add helper script install-dev.sh 28346ca [Shivaram Venkataraman] Only normalize if SPARK_HOME is not empty 0fd6571 [Shivaram Venkataraman] Normalize SPARK_HOME before passing it ff96d5c [Shivaram Venkataraman] Pass in SPARK_HOME and jar file path 34c4dce [Shivaram Venkataraman] Move src into pkg and update Makefile This enables the package to be installed using install_github using devtools and automates the build procedure. b25afed [Shivaram Venkataraman] Change package name to edu.berkeley.cs.amplab c691464 [Shivaram Venkataraman] Add Apache 2.0 License file 27a4a4b [Shivaram Venkataraman] Add notes on how to compile roxygen2 docs ca63844 [Shivaram Venkataraman] Add broadcast documentation Also generate documentation for sample, takeSample etc. e4dd976 [Shivaram Venkataraman] Update TODO.md e42d435 [Shivaram Venkataraman] Add support for broadcast variables 6b638e7 [Shivaram Venkataraman] Add the assembly jar to SparkContext bf24e32 [Shivaram Venkataraman] Merge branch 'master' of github.com:amplab/SparkR-pkg 43c05ce [Zongheng Yang] Fix a flaky/incorrect test for sampleRDD(). c6a9dfc [Zongheng Yang] Initial port of the kmeans example. 6885581 [Zongheng Yang] Implement element-level sampleRDD() and takeSample() with tests. d3a4987 [Zongheng Yang] Add a test for lapplyPartitionsWithIndex on pairwise RDD. c7899c1 [Zongheng Yang] Add lapplyPartitionsWithIndex, with a test and an alias function. a9a7436 [Shivaram Venkataraman] Add DFC example from Tselil, Benjamin and Jonah fbc5a95 [Zongheng Yang] Implement take() and takeSample(). c4a3409 [Shivaram Venkataraman] Use RDD instead of RRDD dfad3f5 [Zongheng Yang] Add test_utils.R: a unit test for convertJListToRList(). a45227d [Zongheng Yang] Update .gitignore. 238fe6e [Zongheng Yang] Add a unit test for textFile(). a88898b [Zongheng Yang] Rename test_rrd to test_rrdd 10c8baa [Shivaram Venkataraman] Make SparkR work as a standalone package. Changes include: 1. Adding a new `sbt` project that builds RRDD.scala 2. Change the onLoad functions to load the assembly jar for SparkR 3. Set rLibDir in RRDD.scala and worker.R to load things correctly 78adcd8 [Shivaram Venkataraman] Add a gitignore ca6108f [Shivaram Venkataraman] Merge branch 'SparkR-scalacode' of ../SparkR 999bd61 [Shivaram Venkataraman] Update collectPartition in R and use ClassTag c58f63e [Shivaram Venkataraman] Update collectPartition in R and use ClassTag 48265fd [Shivaram Venkataraman] Use new version of collectPartitions in take d4fe086 [Shivaram Venkataraman] Move collectPartitions to JavaRDDLike Also remove numPartitions in JavaRDD and update R code bfecd7b [Shivaram Venkataraman] Scala 2.10 changes 1. Update sparkR script 2. Use classTag instead of classManifest 092a4b3 [Shivaram Venkataraman] Add combineByKey, update TODO ac0d81d [Shivaram Venkataraman] Add more documentation d1dc3fa [Shivaram Venkataraman] Add more documentation c515e3a [Shivaram Venkataraman] Update TODO db56a34 [Shivaram Venkataraman] Add a test case for include package 41cea51 [Shivaram Venkataraman] Ensure all parent environments are serialized. Also add a test case with an inline function a978e84 [Shivaram Venkataraman] Add support to include packages in the worker 12bf8ce [Shivaram Venkataraman] Add support to include packages in the worker fb7e72c [Shivaram Venkataraman] Cleanup TODO 16ac314 [Shivaram Venkataraman] Add documentation for functions in context, sparkR 85b1d25 [Shivaram Venkataraman] Set license to Apache 88f1101 [Shivaram Venkataraman] Add unit test running instructions c40768e [Shivaram Venkataraman] Update TODO 0c7efbf [Shivaram Venkataraman] Refactor RRDD.scala and add comments to functions 5880d42 [Shivaram Venkataraman] Refactor RRDD.scala and add comments to functions 2dee36c [Shivaram Venkataraman] Remove empty test file a82219b [Shivaram Venkataraman] Update TODOs 5db00dc [Shivaram Venkataraman] Add reduceByKey, groupByKey and refactor shuffle Other changes include 1. Adding unit tests for basic RDD functions and shuffle 2. Add a word count example 3. Change the dependency serialization to handle double loading of SparkR package 4. Allow partitionBy to operate on any RDDs to create pair-wise RDD. f196479 [Shivaram Venkataraman] Add reduceByKey, groupByKey and refactor shuffle Other changes include 1. Adding unit tests for basic RDD functions and shuffle 2. Add a word count example 3. Change the dependency serialization to handle double loading of SparkR package 4. Allow partitionBy to operate on any RDDs to create pair-wise RDD. 987e36f [Shivaram Venkataraman] Add perf todo 0b03265 [Shivaram Venkataraman] Update TODO with testing, docs todo 685aaad [Zongheng Yang] First cut at refactoring worker.R. Remove pairwiseWorker.R. 95b9ddc [Zongheng Yang] First cut at refactoring worker.R. Remove pairwiseWorker.R. 4f00895 [Zongheng Yang] Remove the unnecessary `pairwise' flag in RRDD class. Reasons: 75d36d9 [Zongheng Yang] Working versions: partitionBy() and collectPartition() for RRDD. e3fbd9d [Zongheng Yang] Working versions: partitionBy() and collectPartition() for RRDD. 67a4335 [Zongheng Yang] Add unit test for parallelize() and collect() pairwise data. 100ae65 [Zongheng Yang] Properly parallelize() and collect() pairwise data. cd0a5e2 [Zongheng Yang] Properly parallelize() and collect() pairwise data. aea16c3 [Zongheng Yang] WIP: second cut at partitionBy. Running into R/Scala communication issues. 45eb943 [Zongheng Yang] WIP: second cut at partitionBy. Running into R/Scala communication issues. 11c893b [Zongheng Yang] WIP: need to figure out the logic of (whether or not) shipping a hash func 82c201a [Zongheng Yang] WIP: need to figure out the logic of (whether or not) shipping a hash func b3bfad2 [Zongheng Yang] Update TODO: take() done. 0e45293 [Zongheng Yang] Add ability to parallelize key-val collections in R. f60406a [Zongheng Yang] Add ability to parallelize key-val collections in R. 7d7fe3b [Zongheng Yang] Re-implement take(): take a partition at a time and append. a054e55 [Zongheng Yang] Fix take() tests(): mode difference. 9de0935 [Zongheng Yang] Implement take() for RRDD. 1e4427e [Zongheng Yang] Implement take() for RRDD. ec3cd67 [Shivaram Venkataraman] Use temp file in Spark to pipe output 417aaed [Shivaram Venkataraman] Use temp file in Spark to pipe output bb0a3c3 [Shivaram Venkataraman] Add conf directory to classpath 9594d8a [Shivaram Venkataraman] Clean up LR example 3b26b58 [Shivaram Venkataraman] Add a list of things to do. cabce68 [Shivaram Venkataraman] Fix warnings from package check fde3f9c [Shivaram Venkataraman] Flatten by default and disable recursive unlist ab2e061 [Shivaram Venkataraman] Create LIB_DIR before installing SparkR package 555220a [Shivaram Venkataraman] Add readme and update Makefile 1319cda [Shivaram Venkataraman] Make standalone programs run with sparkR ae19fa8 [Shivaram Venkataraman] Add support for cache and use `tempfile` 4e89ca4 [Shivaram Venkataraman] Add support for apply, reduce, count Also serialize closures using `save` and add two examples 25a0bea [Shivaram Venkataraman] Add support for apply, reduce, count Also serialize closures using `save` and add two examples f50223f [Zongheng Yang] Make parallelize() and collect() use lists. Add a few more tests for them. fc7693f [Zongheng Yang] Refactor and enhance the previously added unit test a little bit. 6de9b81 [Zongheng Yang] Add a simple unit test for parallelize(). 8b95155 [Zongheng Yang] Add testthat skeleton infrastructure ef305bf [Zongheng Yang] parallelize() followed by collect() now work for vectors/lists of strings and numerics (should work for other primitives as well). dc16af4 [Zongheng Yang] Comment: toArray() allocates memory for a copy f50121e [Zongheng Yang] Make parallelize() return JavaRDD[Array[Byte]]. Add RRDD.scala with a helper function in the singleton object. 46eb063 [Zongheng Yang] Make parallelize() return JavaRDD[Array[Byte]]. Add RRDD.scala with a helper function in the singleton object. 6b4938a [Zongheng Yang] parallelize(): a raw can be parallelized by JavaSparkContext and get back JavaRDD 978aa0f [Zongheng Yang] Add parallelize() skeleton: only return serialized slices now 84c1fd2 [Zongheng Yang] Use .jsimplify() to get around generic List's get() type erasure problem f16b891 [Zongheng Yang] Convert a few reflectionc alls to .jcall 1284c13 [Zongheng Yang] WIP on collect(): JavaListToRList() failed with errors. 4c2e516 [Zongheng Yang] Add simple prototype of S4 class RRDD. Make TextFile() returns an RRDD. 82aa17a [Zongheng Yang] Add textFile() 83ce63f [Zongheng Yang] Create a JavaSparkContext and save it in .sparkEnv using sparkR.init() 01cdf0e [Zongheng Yang] Add Makefile for SparkR fc9cae2 [Shivaram Venkataraman] Add skeleton R package --- .gitignore | 2 + .rat-excludes | 2 + R/.gitignore | 6 + R/DOCUMENTATION.md | 12 + R/README.md | 67 + R/WINDOWS.md | 13 + R/create-docs.sh | 46 + R/install-dev.bat | 27 + R/install-dev.sh | 36 + R/log4j.properties | 28 + R/pkg/DESCRIPTION | 35 + R/pkg/NAMESPACE | 182 ++ R/pkg/R/DataFrame.R | 1270 ++++++++++++++ R/pkg/R/RDD.R | 1539 +++++++++++++++++ R/pkg/R/SQLContext.R | 520 ++++++ R/pkg/R/SQLTypes.R | 64 + R/pkg/R/backend.R | 115 ++ R/pkg/R/broadcast.R | 86 + R/pkg/R/client.R | 57 + R/pkg/R/column.R | 199 +++ R/pkg/R/context.R | 225 +++ R/pkg/R/deserialize.R | 184 ++ R/pkg/R/generics.R | 543 ++++++ R/pkg/R/group.R | 132 ++ R/pkg/R/jobj.R | 101 ++ R/pkg/R/pairRDD.R | 789 +++++++++ R/pkg/R/serialize.R | 195 +++ R/pkg/R/sparkR.R | 266 +++ R/pkg/R/utils.R | 467 +++++ R/pkg/R/zzz.R | 21 + R/pkg/inst/profile/general.R | 22 + R/pkg/inst/profile/shell.R | 31 + R/pkg/inst/tests/test_binaryFile.R | 90 + R/pkg/inst/tests/test_binary_function.R | 68 + R/pkg/inst/tests/test_broadcast.R | 48 + R/pkg/inst/tests/test_context.R | 50 + R/pkg/inst/tests/test_includePackage.R | 57 + R/pkg/inst/tests/test_parallelize_collect.R | 109 ++ R/pkg/inst/tests/test_rdd.R | 644 +++++++ R/pkg/inst/tests/test_shuffle.R | 209 +++ R/pkg/inst/tests/test_sparkSQL.R | 695 ++++++++ R/pkg/inst/tests/test_take.R | 67 + R/pkg/inst/tests/test_textFile.R | 162 ++ R/pkg/inst/tests/test_utils.R | 137 ++ R/pkg/inst/worker/daemon.R | 52 + R/pkg/inst/worker/worker.R | 128 ++ R/pkg/src/Makefile | 27 + R/pkg/src/Makefile.win | 27 + R/pkg/src/string_hash_code.c | 49 + R/pkg/tests/run-all.R | 21 + R/run-tests.sh | 39 + bin/sparkR | 39 + bin/sparkR.cmd | 23 + bin/sparkR2.cmd | 26 + core/pom.xml | 51 + .../org/apache/spark/api/r/RBackend.scala | 145 ++ .../apache/spark/api/r/RBackendHandler.scala | 223 +++ .../scala/org/apache/spark/api/r/RRDD.scala | 450 +++++ .../scala/org/apache/spark/api/r/SerDe.scala | 340 ++++ .../org/apache/spark/deploy/RRunner.scala | 92 + .../org/apache/spark/deploy/SparkSubmit.scala | 73 +- .../spark/deploy/SparkSubmitArguments.scala | 8 +- dev/run-tests | 15 + dev/run-tests-codes.sh | 1 + dev/run-tests-jenkins | 2 + docs/README.md | 12 +- docs/_layouts/global.html | 1 + docs/_plugins/copy_api_dirs.rb | 15 +- examples/src/main/r/kmeans.R | 93 + examples/src/main/r/linear_solver_mnist.R | 107 ++ examples/src/main/r/logistic_regression.R | 62 + examples/src/main/r/pi.R | 46 + examples/src/main/r/wordcount.R | 42 + .../spark/launcher/CommandBuilderUtils.java | 8 +- .../launcher/SparkSubmitCommandBuilder.java | 87 +- .../launcher/CommandBuilderUtilsSuite.java | 6 +- pom.xml | 3 + .../org/apache/spark/sql/GroupedData.scala | 2 +- .../org/apache/spark/sql/api/r/SQLUtils.scala | 127 ++ .../spark/deploy/yarn/ApplicationMaster.scala | 3 + .../yarn/ApplicationMasterArguments.scala | 11 + .../org/apache/spark/deploy/yarn/Client.scala | 13 +- .../spark/deploy/yarn/ClientArguments.scala | 11 + 83 files changed, 12043 insertions(+), 55 deletions(-) create mode 100644 R/.gitignore create mode 100644 R/DOCUMENTATION.md create mode 100644 R/README.md create mode 100644 R/WINDOWS.md create mode 100755 R/create-docs.sh create mode 100644 R/install-dev.bat create mode 100755 R/install-dev.sh create mode 100644 R/log4j.properties create mode 100644 R/pkg/DESCRIPTION create mode 100644 R/pkg/NAMESPACE create mode 100644 R/pkg/R/DataFrame.R create mode 100644 R/pkg/R/RDD.R create mode 100644 R/pkg/R/SQLContext.R create mode 100644 R/pkg/R/SQLTypes.R create mode 100644 R/pkg/R/backend.R create mode 100644 R/pkg/R/broadcast.R create mode 100644 R/pkg/R/client.R create mode 100644 R/pkg/R/column.R create mode 100644 R/pkg/R/context.R create mode 100644 R/pkg/R/deserialize.R create mode 100644 R/pkg/R/generics.R create mode 100644 R/pkg/R/group.R create mode 100644 R/pkg/R/jobj.R create mode 100644 R/pkg/R/pairRDD.R create mode 100644 R/pkg/R/serialize.R create mode 100644 R/pkg/R/sparkR.R create mode 100644 R/pkg/R/utils.R create mode 100644 R/pkg/R/zzz.R create mode 100644 R/pkg/inst/profile/general.R create mode 100644 R/pkg/inst/profile/shell.R create mode 100644 R/pkg/inst/tests/test_binaryFile.R create mode 100644 R/pkg/inst/tests/test_binary_function.R create mode 100644 R/pkg/inst/tests/test_broadcast.R create mode 100644 R/pkg/inst/tests/test_context.R create mode 100644 R/pkg/inst/tests/test_includePackage.R create mode 100644 R/pkg/inst/tests/test_parallelize_collect.R create mode 100644 R/pkg/inst/tests/test_rdd.R create mode 100644 R/pkg/inst/tests/test_shuffle.R create mode 100644 R/pkg/inst/tests/test_sparkSQL.R create mode 100644 R/pkg/inst/tests/test_take.R create mode 100644 R/pkg/inst/tests/test_textFile.R create mode 100644 R/pkg/inst/tests/test_utils.R create mode 100644 R/pkg/inst/worker/daemon.R create mode 100644 R/pkg/inst/worker/worker.R create mode 100644 R/pkg/src/Makefile create mode 100644 R/pkg/src/Makefile.win create mode 100644 R/pkg/src/string_hash_code.c create mode 100644 R/pkg/tests/run-all.R create mode 100755 R/run-tests.sh create mode 100755 bin/sparkR create mode 100644 bin/sparkR.cmd create mode 100644 bin/sparkR2.cmd create mode 100644 core/src/main/scala/org/apache/spark/api/r/RBackend.scala create mode 100644 core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala create mode 100644 core/src/main/scala/org/apache/spark/api/r/RRDD.scala create mode 100644 core/src/main/scala/org/apache/spark/api/r/SerDe.scala create mode 100644 core/src/main/scala/org/apache/spark/deploy/RRunner.scala create mode 100644 examples/src/main/r/kmeans.R create mode 100644 examples/src/main/r/linear_solver_mnist.R create mode 100644 examples/src/main/r/logistic_regression.R create mode 100644 examples/src/main/r/pi.R create mode 100644 examples/src/main/r/wordcount.R create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala diff --git a/.gitignore b/.gitignore index d162fa9cca994..d54d21b802be8 100644 --- a/.gitignore +++ b/.gitignore @@ -63,6 +63,8 @@ ec2/lib/ rat-results.txt scalastyle.txt scalastyle-output.xml +R-unit-tests.log +R/unit-tests.out # For Hive metastore_db/ diff --git a/.rat-excludes b/.rat-excludes index 8c61e67a0c7d1..8aca5a7f7a967 100644 --- a/.rat-excludes +++ b/.rat-excludes @@ -67,3 +67,5 @@ logs .*scalastyle-output.xml .*dependency-reduced-pom.xml known_translations +DESCRIPTION +NAMESPACE diff --git a/R/.gitignore b/R/.gitignore new file mode 100644 index 0000000000000..9a5889ba28b2a --- /dev/null +++ b/R/.gitignore @@ -0,0 +1,6 @@ +*.o +*.so +*.Rd +lib +pkg/man +pkg/html diff --git a/R/DOCUMENTATION.md b/R/DOCUMENTATION.md new file mode 100644 index 0000000000000..931d01549b265 --- /dev/null +++ b/R/DOCUMENTATION.md @@ -0,0 +1,12 @@ +# SparkR Documentation + +SparkR documentation is generated using in-source comments annotated using using +`roxygen2`. After making changes to the documentation, to generate man pages, +you can run the following from an R console in the SparkR home directory + + library(devtools) + devtools::document(pkg="./pkg", roclets=c("rd")) + +You can verify if your changes are good by running + + R CMD check pkg/ diff --git a/R/README.md b/R/README.md new file mode 100644 index 0000000000000..a6970e39b55f3 --- /dev/null +++ b/R/README.md @@ -0,0 +1,67 @@ +# R on Spark + +SparkR is an R package that provides a light-weight frontend to use Spark from R. + +### SparkR development + +#### Build Spark + +Build Spark with [Maven](http://spark.apache.org/docs/latest/building-spark.html#building-with-buildmvn) and include the `-PsparkR` profile to build the R package. For example to use the default Hadoop versions you can run +``` + build/mvn -DskipTests -Psparkr package +``` + +#### Running sparkR + +You can start using SparkR by launching the SparkR shell with + + ./bin/sparkR + +The `sparkR` script automatically creates a SparkContext with Spark by default in +local mode. To specify the Spark master of a cluster for the automatically created +SparkContext, you can run + + ./bin/sparkR --master "local[2]" + +To set other options like driver memory, executor memory etc. you can pass in the [spark-submit](http://spark.apache.org/docs/latest/submitting-applications.html) arguments to `./bin/sparkR` + +#### Using SparkR from RStudio + +If you wish to use SparkR from RStudio or other R frontends you will need to set some environment variables which point SparkR to your Spark installation. For example +``` +# Set this to where Spark is installed +Sys.setenv(SPARK_HOME="/Users/shivaram/spark") +# This line loads SparkR from the installed directory +.libPaths(c(file.path(Sys.getenv("SPARK_HOME"), "R", "lib"), .libPaths())) +library(SparkR) +sc <- sparkR.init(master="local") +``` + +#### Making changes to SparkR + +The [instructions](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark) for making contributions to Spark also apply to SparkR. +If you only make R file changes (i.e. no Scala changes) then you can just re-install the R package using `R/install-dev.sh` and test your changes. +Once you have made your changes, please include unit tests for them and run existing unit tests using the `run-tests.sh` script as described below. + +#### Generating documentation + +The SparkR documentation (Rd files and HTML files) are not a part of the source repository. To generate them you can run the script `R/create-docs.sh`. This script uses `devtools` and `knitr` to generate the docs and these packages need to be installed on the machine before using the script. + +### Examples, Unit tests + +SparkR comes with several sample programs in the `examples/src/main/r` directory. +To run one of them, use `./bin/sparkR `. For example: + + ./bin/sparkR examples/src/main/r/pi.R local[2] + +You can also run the unit-tests for SparkR by running (you need to install the [testthat](http://cran.r-project.org/web/packages/testthat/index.html) package first): + + R -e 'install.packages("testthat", repos="http://cran.us.r-project.org")' + ./R/run-tests.sh + +### Running on YARN +The `./bin/spark-submit` and `./bin/sparkR` can also be used to submit jobs to YARN clusters. You will need to set YARN conf dir before doing so. For example on CDH you can run +``` +export YARN_CONF_DIR=/etc/hadoop/conf +./bin/spark-submit --master yarn examples/src/main/r/pi.R 4 +``` diff --git a/R/WINDOWS.md b/R/WINDOWS.md new file mode 100644 index 0000000000000..3f889c0ca3d1e --- /dev/null +++ b/R/WINDOWS.md @@ -0,0 +1,13 @@ +## Building SparkR on Windows + +To build SparkR on Windows, the following steps are required + +1. Install R (>= 3.1) and [Rtools](http://cran.r-project.org/bin/windows/Rtools/). Make sure to +include Rtools and R in `PATH`. +2. Install +[JDK7](http://www.oracle.com/technetwork/java/javase/downloads/jdk7-downloads-1880260.html) and set +`JAVA_HOME` in the system environment variables. +3. Download and install [Maven](http://maven.apache.org/download.html). Also include the `bin` +directory in Maven in `PATH`. +4. Set `MAVEN_OPTS` as described in [Building Spark](http://spark.apache.org/docs/latest/building-spark.html). +5. Open a command shell (`cmd`) in the Spark directory and run `mvn -DskipTests -Psparkr package` diff --git a/R/create-docs.sh b/R/create-docs.sh new file mode 100755 index 0000000000000..4194172a2e115 --- /dev/null +++ b/R/create-docs.sh @@ -0,0 +1,46 @@ +#!/bin/bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Script to create API docs for SparkR +# This requires `devtools` and `knitr` to be installed on the machine. + +# After running this script the html docs can be found in +# $SPARK_HOME/R/pkg/html + +# Figure out where the script is +export FWDIR="$(cd "`dirname "$0"`"; pwd)" +pushd $FWDIR + +# Generate Rd file +Rscript -e 'library(devtools); devtools::document(pkg="./pkg", roclets=c("rd"))' + +# Install the package +./install-dev.sh + +# Now create HTML files + +# knit_rd puts html in current working directory +mkdir -p pkg/html +pushd pkg/html + +Rscript -e 'library(SparkR, lib.loc="../../lib"); library(knitr); knit_rd("SparkR")' + +popd + +popd diff --git a/R/install-dev.bat b/R/install-dev.bat new file mode 100644 index 0000000000000..008a5c668bc45 --- /dev/null +++ b/R/install-dev.bat @@ -0,0 +1,27 @@ +@echo off + +rem +rem Licensed to the Apache Software Foundation (ASF) under one or more +rem contributor license agreements. See the NOTICE file distributed with +rem this work for additional information regarding copyright ownership. +rem The ASF licenses this file to You under the Apache License, Version 2.0 +rem (the "License"); you may not use this file except in compliance with +rem the License. You may obtain a copy of the License at +rem +rem http://www.apache.org/licenses/LICENSE-2.0 +rem +rem Unless required by applicable law or agreed to in writing, software +rem distributed under the License is distributed on an "AS IS" BASIS, +rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +rem See the License for the specific language governing permissions and +rem limitations under the License. +rem + +rem Install development version of SparkR +rem + +set SPARK_HOME=%~dp0.. + +MKDIR %SPARK_HOME%\R\lib + +R.exe CMD INSTALL --library="%SPARK_HOME%\R\lib" %SPARK_HOME%\R\pkg\ diff --git a/R/install-dev.sh b/R/install-dev.sh new file mode 100755 index 0000000000000..55ed6f4be1a4a --- /dev/null +++ b/R/install-dev.sh @@ -0,0 +1,36 @@ +#!/bin/bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# This scripts packages the SparkR source files (R and C files) and +# creates a package that can be loaded in R. The package is by default installed to +# $FWDIR/lib and the package can be loaded by using the following command in R: +# +# library(SparkR, lib.loc="$FWDIR/lib") +# +# NOTE(shivaram): Right now we use $SPARK_HOME/R/lib to be the installation directory +# to load the SparkR package on the worker nodes. + + +FWDIR="$(cd `dirname $0`; pwd)" +LIB_DIR="$FWDIR/lib" + +mkdir -p $LIB_DIR + +# Install R +R CMD INSTALL --library=$LIB_DIR $FWDIR/pkg/ diff --git a/R/log4j.properties b/R/log4j.properties new file mode 100644 index 0000000000000..701adb2a3da1d --- /dev/null +++ b/R/log4j.properties @@ -0,0 +1,28 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Set everything to be logged to the file target/unit-tests.log +log4j.rootCategory=INFO, file +log4j.appender.file=org.apache.log4j.FileAppender +log4j.appender.file.append=true +log4j.appender.file.file=R-unit-tests.log +log4j.appender.file.layout=org.apache.log4j.PatternLayout +log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n + +# Ignore messages below warning level from Jetty, because it's a bit verbose +log4j.logger.org.eclipse.jetty=WARN +org.eclipse.jetty.LEVEL=WARN diff --git a/R/pkg/DESCRIPTION b/R/pkg/DESCRIPTION new file mode 100644 index 0000000000000..1842b97d43651 --- /dev/null +++ b/R/pkg/DESCRIPTION @@ -0,0 +1,35 @@ +Package: SparkR +Type: Package +Title: R frontend for Spark +Version: 1.4.0 +Date: 2013-09-09 +Author: The Apache Software Foundation +Maintainer: Shivaram Venkataraman +Imports: + methods +Depends: + R (>= 3.0), + methods, +Suggests: + testthat +Description: R frontend for Spark +License: Apache License (== 2.0) +Collate: + 'generics.R' + 'jobj.R' + 'SQLTypes.R' + 'RDD.R' + 'pairRDD.R' + 'column.R' + 'group.R' + 'DataFrame.R' + 'SQLContext.R' + 'broadcast.R' + 'context.R' + 'deserialize.R' + 'serialize.R' + 'sparkR.R' + 'backend.R' + 'client.R' + 'utils.R' + 'zzz.R' diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE new file mode 100644 index 0000000000000..a354cdce74afa --- /dev/null +++ b/R/pkg/NAMESPACE @@ -0,0 +1,182 @@ +#exportPattern("^[[:alpha:]]+") +exportClasses("RDD") +exportClasses("Broadcast") +exportMethods( + "aggregateByKey", + "aggregateRDD", + "cache", + "checkpoint", + "coalesce", + "cogroup", + "collect", + "collectAsMap", + "collectPartition", + "combineByKey", + "count", + "countByKey", + "countByValue", + "distinct", + "Filter", + "filterRDD", + "first", + "flatMap", + "flatMapValues", + "fold", + "foldByKey", + "foreach", + "foreachPartition", + "fullOuterJoin", + "glom", + "groupByKey", + "join", + "keyBy", + "keys", + "length", + "lapply", + "lapplyPartition", + "lapplyPartitionsWithIndex", + "leftOuterJoin", + "lookup", + "map", + "mapPartitions", + "mapPartitionsWithIndex", + "mapValues", + "maximum", + "minimum", + "numPartitions", + "partitionBy", + "persist", + "pipeRDD", + "reduce", + "reduceByKey", + "reduceByKeyLocally", + "repartition", + "rightOuterJoin", + "sampleRDD", + "saveAsTextFile", + "saveAsObjectFile", + "sortBy", + "sortByKey", + "sumRDD", + "take", + "takeOrdered", + "takeSample", + "top", + "unionRDD", + "unpersist", + "value", + "values", + "zipRDD", + "zipWithIndex", + "zipWithUniqueId" + ) + +# S3 methods exported +export( + "textFile", + "objectFile", + "parallelize", + "hashCode", + "includePackage", + "broadcast", + "setBroadcastValue", + "setCheckpointDir" + ) +export("sparkR.init") +export("sparkR.stop") +export("print.jobj") +useDynLib(SparkR, stringHashCode) +importFrom(methods, setGeneric, setMethod, setOldClass) + +# SparkRSQL + +exportClasses("DataFrame") + +exportMethods("columns", + "distinct", + "dtypes", + "explain", + "filter", + "groupBy", + "head", + "insertInto", + "intersect", + "isLocal", + "limit", + "orderBy", + "names", + "printSchema", + "registerTempTable", + "repartition", + "sampleDF", + "saveAsParquetFile", + "saveAsTable", + "saveDF", + "schema", + "select", + "selectExpr", + "show", + "showDF", + "sortDF", + "subtract", + "toJSON", + "toRDD", + "unionAll", + "where", + "withColumn", + "withColumnRenamed") + +exportClasses("Column") + +exportMethods("abs", + "alias", + "approxCountDistinct", + "asc", + "avg", + "cast", + "contains", + "countDistinct", + "desc", + "endsWith", + "getField", + "getItem", + "isNotNull", + "isNull", + "last", + "like", + "lower", + "max", + "mean", + "min", + "rlike", + "sqrt", + "startsWith", + "substr", + "sum", + "sumDistinct", + "upper") + +exportClasses("GroupedData") +exportMethods("agg") + +export("sparkRSQL.init", + "sparkRHive.init") + +export("cacheTable", + "clearCache", + "createDataFrame", + "createExternalTable", + "dropTempTable", + "jsonFile", + "jsonRDD", + "loadDF", + "parquetFile", + "sql", + "table", + "tableNames", + "tables", + "toDF", + "uncacheTable") + +export("print.structType", + "print.structField") diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R new file mode 100644 index 0000000000000..feafd56909a67 --- /dev/null +++ b/R/pkg/R/DataFrame.R @@ -0,0 +1,1270 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# DataFrame.R - DataFrame class and methods implemented in S4 OO classes + +#' @include jobj.R SQLTypes.R RDD.R pairRDD.R column.R group.R +NULL + +setOldClass("jobj") + +#' @title S4 class that represents a DataFrame +#' @description DataFrames can be created using functions like +#' \code{jsonFile}, \code{table} etc. +#' @rdname DataFrame +#' @seealso jsonFile, table +#' +#' @param env An R environment that stores bookkeeping states of the DataFrame +#' @param sdf A Java object reference to the backing Scala DataFrame +#' @export +setClass("DataFrame", + slots = list(env = "environment", + sdf = "jobj")) + +setMethod("initialize", "DataFrame", function(.Object, sdf, isCached) { + .Object@env <- new.env() + .Object@env$isCached <- isCached + + .Object@sdf <- sdf + .Object +}) + +#' @rdname DataFrame +#' @export +dataFrame <- function(sdf, isCached = FALSE) { + new("DataFrame", sdf, isCached) +} + +############################ DataFrame Methods ############################################## + +#' Print Schema of a DataFrame +#' +#' Prints out the schema in tree format +#' +#' @param x A SparkSQL DataFrame +#' +#' @rdname printSchema +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' printSchema(df) +#'} +setMethod("printSchema", + signature(x = "DataFrame"), + function(x) { + schemaString <- callJMethod(schema(x)$jobj, "treeString") + cat(schemaString) + }) + +#' Get schema object +#' +#' Returns the schema of this DataFrame as a structType object. +#' +#' @param x A SparkSQL DataFrame +#' +#' @rdname schema +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' dfSchema <- schema(df) +#'} +setMethod("schema", + signature(x = "DataFrame"), + function(x) { + structType(callJMethod(x@sdf, "schema")) + }) + +#' Explain +#' +#' Print the logical and physical Catalyst plans to the console for debugging. +#' +#' @param x A SparkSQL DataFrame +#' @param extended Logical. If extended is False, explain() only prints the physical plan. +#' @rdname explain +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' explain(df, TRUE) +#'} +setMethod("explain", + signature(x = "DataFrame"), + function(x, extended = FALSE) { + queryExec <- callJMethod(x@sdf, "queryExecution") + if (extended) { + cat(callJMethod(queryExec, "toString")) + } else { + execPlan <- callJMethod(queryExec, "executedPlan") + cat(callJMethod(execPlan, "toString")) + } + }) + +#' isLocal +#' +#' Returns True if the `collect` and `take` methods can be run locally +#' (without any Spark executors). +#' +#' @param x A SparkSQL DataFrame +#' +#' @rdname isLocal +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' isLocal(df) +#'} +setMethod("isLocal", + signature(x = "DataFrame"), + function(x) { + callJMethod(x@sdf, "isLocal") + }) + +#' ShowDF +#' +#' Print the first numRows rows of a DataFrame +#' +#' @param x A SparkSQL DataFrame +#' @param numRows The number of rows to print. Defaults to 20. +#' +#' @rdname showDF +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' showDF(df) +#'} +setMethod("showDF", + signature(x = "DataFrame"), + function(x, numRows = 20) { + cat(callJMethod(x@sdf, "showString", numToInt(numRows)), "\n") + }) + +#' show +#' +#' Print the DataFrame column names and types +#' +#' @param x A SparkSQL DataFrame +#' +#' @rdname show +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' show(df) +#'} +setMethod("show", "DataFrame", + function(object) { + cols <- lapply(dtypes(object), function(l) { + paste(l, collapse = ":") + }) + s <- paste(cols, collapse = ", ") + cat(paste("DataFrame[", s, "]\n", sep = "")) + }) + +#' DataTypes +#' +#' Return all column names and their data types as a list +#' +#' @param x A SparkSQL DataFrame +#' +#' @rdname dtypes +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' dtypes(df) +#'} +setMethod("dtypes", + signature(x = "DataFrame"), + function(x) { + lapply(schema(x)$fields(), function(f) { + c(f$name(), f$dataType.simpleString()) + }) + }) + +#' Column names +#' +#' Return all column names as a list +#' +#' @param x A SparkSQL DataFrame +#' +#' @rdname columns +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' columns(df) +#'} +setMethod("columns", + signature(x = "DataFrame"), + function(x) { + sapply(schema(x)$fields(), function(f) { + f$name() + }) + }) + +#' @rdname columns +#' @export +setMethod("names", + signature(x = "DataFrame"), + function(x) { + columns(x) + }) + +#' Register Temporary Table +#' +#' Registers a DataFrame as a Temporary Table in the SQLContext +#' +#' @param x A SparkSQL DataFrame +#' @param tableName A character vector containing the name of the table +#' +#' @rdname registerTempTable +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' registerTempTable(df, "json_df") +#' new_df <- sql(sqlCtx, "SELECT * FROM json_df") +#'} +setMethod("registerTempTable", + signature(x = "DataFrame", tableName = "character"), + function(x, tableName) { + callJMethod(x@sdf, "registerTempTable", tableName) + }) + +#' insertInto +#' +#' Insert the contents of a DataFrame into a table registered in the current SQL Context. +#' +#' @param x A SparkSQL DataFrame +#' @param tableName A character vector containing the name of the table +#' @param overwrite A logical argument indicating whether or not to overwrite +#' the existing rows in the table. +#' +#' @rdname insertInto +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' df <- loadDF(sqlCtx, path, "parquet") +#' df2 <- loadDF(sqlCtx, path2, "parquet") +#' registerTempTable(df, "table1") +#' insertInto(df2, "table1", overwrite = TRUE) +#'} +setMethod("insertInto", + signature(x = "DataFrame", tableName = "character"), + function(x, tableName, overwrite = FALSE) { + callJMethod(x@sdf, "insertInto", tableName, overwrite) + }) + +#' Cache +#' +#' Persist with the default storage level (MEMORY_ONLY). +#' +#' @param x A SparkSQL DataFrame +#' +#' @rdname cache-methods +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' cache(df) +#'} +setMethod("cache", + signature(x = "DataFrame"), + function(x) { + cached <- callJMethod(x@sdf, "cache") + x@env$isCached <- TRUE + x + }) + +#' Persist +#' +#' Persist this DataFrame with the specified storage level. For details of the +#' supported storage levels, refer to +#' http://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence. +#' +#' @param x The DataFrame to persist +#' @rdname persist +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' persist(df, "MEMORY_AND_DISK") +#'} +setMethod("persist", + signature(x = "DataFrame", newLevel = "character"), + function(x, newLevel) { + callJMethod(x@sdf, "persist", getStorageLevel(newLevel)) + x@env$isCached <- TRUE + x + }) + +#' Unpersist +#' +#' Mark this DataFrame as non-persistent, and remove all blocks for it from memory and +#' disk. +#' +#' @param x The DataFrame to unpersist +#' @param blocking Whether to block until all blocks are deleted +#' @rdname unpersist-methods +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' persist(df, "MEMORY_AND_DISK") +#' unpersist(df) +#'} +setMethod("unpersist", + signature(x = "DataFrame"), + function(x, blocking = TRUE) { + callJMethod(x@sdf, "unpersist", blocking) + x@env$isCached <- FALSE + x + }) + +#' Repartition +#' +#' Return a new DataFrame that has exactly numPartitions partitions. +#' +#' @param x A SparkSQL DataFrame +#' @param numPartitions The number of partitions to use. +#' @rdname repartition +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' newDF <- repartition(df, 2L) +#'} +setMethod("repartition", + signature(x = "DataFrame", numPartitions = "numeric"), + function(x, numPartitions) { + sdf <- callJMethod(x@sdf, "repartition", numToInt(numPartitions)) + dataFrame(sdf) + }) + +#' toJSON +#' +#' Convert the rows of a DataFrame into JSON objects and return an RDD where +#' each element contains a JSON string. +#' +#' @param x A SparkSQL DataFrame +#' @return A StringRRDD of JSON objects +#' @rdname tojson +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' newRDD <- toJSON(df) +#'} +setMethod("toJSON", + signature(x = "DataFrame"), + function(x) { + rdd <- callJMethod(x@sdf, "toJSON") + jrdd <- callJMethod(rdd, "toJavaRDD") + RDD(jrdd, serializedMode = "string") + }) + +#' saveAsParquetFile +#' +#' Save the contents of a DataFrame as a Parquet file, preserving the schema. Files written out +#' with this method can be read back in as a DataFrame using parquetFile(). +#' +#' @param x A SparkSQL DataFrame +#' @param path The directory where the file is saved +#' @rdname saveAsParquetFile +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' saveAsParquetFile(df, "/tmp/sparkr-tmp/") +#'} +setMethod("saveAsParquetFile", + signature(x = "DataFrame", path = "character"), + function(x, path) { + invisible(callJMethod(x@sdf, "saveAsParquetFile", path)) + }) + +#' Distinct +#' +#' Return a new DataFrame containing the distinct rows in this DataFrame. +#' +#' @param x A SparkSQL DataFrame +#' @rdname distinct +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' distinctDF <- distinct(df) +#'} +setMethod("distinct", + signature(x = "DataFrame"), + function(x) { + sdf <- callJMethod(x@sdf, "distinct") + dataFrame(sdf) + }) + +#' SampleDF +#' +#' Return a sampled subset of this DataFrame using a random seed. +#' +#' @param x A SparkSQL DataFrame +#' @param withReplacement Sampling with replacement or not +#' @param fraction The (rough) sample target fraction +#' @rdname sampleDF +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' collect(sampleDF(df, FALSE, 0.5)) +#' collect(sampleDF(df, TRUE, 0.5)) +#'} +setMethod("sampleDF", + # TODO : Figure out how to send integer as java.lang.Long to JVM so + # we can send seed as an argument through callJMethod + signature(x = "DataFrame", withReplacement = "logical", + fraction = "numeric"), + function(x, withReplacement, fraction) { + if (fraction < 0.0) stop(cat("Negative fraction value:", fraction)) + sdf <- callJMethod(x@sdf, "sample", withReplacement, fraction) + dataFrame(sdf) + }) + +#' Count +#' +#' Returns the number of rows in a DataFrame +#' +#' @param x A SparkSQL DataFrame +#' +#' @rdname count +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' count(df) +#' } +setMethod("count", + signature(x = "DataFrame"), + function(x) { + callJMethod(x@sdf, "count") + }) + +#' Collects all the elements of a Spark DataFrame and coerces them into an R data.frame. +#' +#' @param x A SparkSQL DataFrame +#' @param stringsAsFactors (Optional) A logical indicating whether or not string columns +#' should be converted to factors. FALSE by default. + +#' @rdname collect-methods +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' collected <- collect(df) +#' firstName <- collected[[1]]$name +#' } +setMethod("collect", + signature(x = "DataFrame"), + function(x, stringsAsFactors = FALSE) { + # listCols is a list of raw vectors, one per column + listCols <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "dfToCols", x@sdf) + cols <- lapply(listCols, function(col) { + objRaw <- rawConnection(col) + numRows <- readInt(objRaw) + col <- readCol(objRaw, numRows) + close(objRaw) + col + }) + names(cols) <- columns(x) + do.call(cbind.data.frame, list(cols, stringsAsFactors = stringsAsFactors)) + }) + +#' Limit +#' +#' Limit the resulting DataFrame to the number of rows specified. +#' +#' @param x A SparkSQL DataFrame +#' @param num The number of rows to return +#' @return A new DataFrame containing the number of rows specified. +#' +#' @rdname limit +#' @export +#' @examples +#' \dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' limitedDF <- limit(df, 10) +#' } +setMethod("limit", + signature(x = "DataFrame", num = "numeric"), + function(x, num) { + res <- callJMethod(x@sdf, "limit", as.integer(num)) + dataFrame(res) + }) + +# Take the first NUM rows of a DataFrame and return a the results as a data.frame + +#' @rdname take +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' take(df, 2) +#' } +setMethod("take", + signature(x = "DataFrame", num = "numeric"), + function(x, num) { + limited <- limit(x, num) + collect(limited) + }) + +#' Head +#' +#' Return the first NUM rows of a DataFrame as a data.frame. If NUM is NULL, +#' then head() returns the first 6 rows in keeping with the current data.frame +#' convention in R. +#' +#' @param x A SparkSQL DataFrame +#' @param num The number of rows to return. Default is 6. +#' @return A data.frame +#' +#' @rdname head +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' head(df) +#' } +setMethod("head", + signature(x = "DataFrame"), + function(x, num = 6L) { + # Default num is 6L in keeping with R's data.frame convention + take(x, num) + }) + +#' Return the first row of a DataFrame +#' +#' @param x A SparkSQL DataFrame +#' +#' @rdname first +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' first(df) +#' } +setMethod("first", + signature(x = "DataFrame"), + function(x) { + take(x, 1) + }) + +#' toRDD() +#' +#' Converts a Spark DataFrame to an RDD while preserving column names. +#' +#' @param x A Spark DataFrame +#' +#' @rdname DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' rdd <- toRDD(df) +#' } +setMethod("toRDD", + signature(x = "DataFrame"), + function(x) { + jrdd <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "dfToRowRDD", x@sdf) + colNames <- callJMethod(x@sdf, "columns") + rdd <- RDD(jrdd, serializedMode = "row") + lapply(rdd, function(row) { + names(row) <- colNames + row + }) + }) + +#' GroupBy +#' +#' Groups the DataFrame using the specified columns, so we can run aggregation on them. +#' +#' @param x a DataFrame +#' @return a GroupedData +#' @seealso GroupedData +#' @rdname DataFrame +#' @export +#' @examples +#' \dontrun{ +#' # Compute the average for all numeric columns grouped by department. +#' avg(groupBy(df, "department")) +#' +#' # Compute the max age and average salary, grouped by department and gender. +#' agg(groupBy(df, "department", "gender"), salary="avg", "age" -> "max") +#' } +setMethod("groupBy", + signature(x = "DataFrame"), + function(x, ...) { + cols <- list(...) + if (length(cols) >= 1 && class(cols[[1]]) == "character") { + sgd <- callJMethod(x@sdf, "groupBy", cols[[1]], listToSeq(cols[-1])) + } else { + jcol <- lapply(cols, function(c) { c@jc }) + sgd <- callJMethod(x@sdf, "groupBy", listToSeq(jcol)) + } + groupedData(sgd) + }) + +#' Agg +#' +#' Compute aggregates by specifying a list of columns +#' +#' @rdname DataFrame +#' @export +setMethod("agg", + signature(x = "DataFrame"), + function(x, ...) { + agg(groupBy(x), ...) + }) + + +############################## RDD Map Functions ################################## +# All of the following functions mirror the existing RDD map functions, # +# but allow for use with DataFrames by first converting to an RRDD before calling # +# the requested map function. # +################################################################################### + +#' @rdname lapply +setMethod("lapply", + signature(X = "DataFrame", FUN = "function"), + function(X, FUN) { + rdd <- toRDD(X) + lapply(rdd, FUN) + }) + +#' @rdname lapply +setMethod("map", + signature(X = "DataFrame", FUN = "function"), + function(X, FUN) { + lapply(X, FUN) + }) + +#' @rdname flatMap +setMethod("flatMap", + signature(X = "DataFrame", FUN = "function"), + function(X, FUN) { + rdd <- toRDD(X) + flatMap(rdd, FUN) + }) + +#' @rdname lapplyPartition +setMethod("lapplyPartition", + signature(X = "DataFrame", FUN = "function"), + function(X, FUN) { + rdd <- toRDD(X) + lapplyPartition(rdd, FUN) + }) + +#' @rdname lapplyPartition +setMethod("mapPartitions", + signature(X = "DataFrame", FUN = "function"), + function(X, FUN) { + lapplyPartition(X, FUN) + }) + +#' @rdname foreach +setMethod("foreach", + signature(x = "DataFrame", func = "function"), + function(x, func) { + rdd <- toRDD(x) + foreach(rdd, func) + }) + +#' @rdname foreach +setMethod("foreachPartition", + signature(x = "DataFrame", func = "function"), + function(x, func) { + rdd <- toRDD(x) + foreachPartition(rdd, func) + }) + + +############################## SELECT ################################## + +getColumn <- function(x, c) { + column(callJMethod(x@sdf, "col", c)) +} + +#' @rdname select +setMethod("$", signature(x = "DataFrame"), + function(x, name) { + getColumn(x, name) + }) + +setMethod("$<-", signature(x = "DataFrame"), + function(x, name, value) { + stopifnot(class(value) == "Column") + cols <- columns(x) + if (name %in% cols) { + cols <- lapply(cols, function(c) { + if (c == name) { + alias(value, name) + } else { + col(c) + } + }) + nx <- select(x, cols) + } else { + nx <- withColumn(x, name, value) + } + x@sdf <- nx@sdf + x + }) + +#' @rdname select +setMethod("[[", signature(x = "DataFrame"), + function(x, i) { + if (is.numeric(i)) { + cols <- columns(x) + i <- cols[[i]] + } + getColumn(x, i) + }) + +#' @rdname select +setMethod("[", signature(x = "DataFrame", i = "missing"), + function(x, i, j, ...) { + if (is.numeric(j)) { + cols <- columns(x) + j <- cols[j] + } + if (length(j) > 1) { + j <- as.list(j) + } + select(x, j) + }) + +#' Select +#' +#' Selects a set of columns with names or Column expressions. +#' @param x A DataFrame +#' @param col A list of columns or single Column or name +#' @return A new DataFrame with selected columns +#' @export +#' @rdname select +#' @examples +#' \dontrun{ +#' select(df, "*") +#' select(df, "col1", "col2") +#' select(df, df$name, df$age + 1) +#' select(df, c("col1", "col2")) +#' select(df, list(df$name, df$age + 1)) +#' # Columns can also be selected using `[[` and `[` +#' df[[2]] == df[["age"]] +#' df[,2] == df[,"age"] +#' # Similar to R data frames columns can also be selected using `$` +#' df$age +#' } +setMethod("select", signature(x = "DataFrame", col = "character"), + function(x, col, ...) { + sdf <- callJMethod(x@sdf, "select", col, toSeq(...)) + dataFrame(sdf) + }) + +#' @rdname select +#' @export +setMethod("select", signature(x = "DataFrame", col = "Column"), + function(x, col, ...) { + jcols <- lapply(list(col, ...), function(c) { + c@jc + }) + sdf <- callJMethod(x@sdf, "select", listToSeq(jcols)) + dataFrame(sdf) + }) + +#' @rdname select +#' @export +setMethod("select", + signature(x = "DataFrame", col = "list"), + function(x, col) { + cols <- lapply(col, function(c) { + if (class(c)== "Column") { + c@jc + } else { + col(c)@jc + } + }) + sdf <- callJMethod(x@sdf, "select", listToSeq(cols)) + dataFrame(sdf) + }) + +#' SelectExpr +#' +#' Select from a DataFrame using a set of SQL expressions. +#' +#' @param x A DataFrame to be selected from. +#' @param expr A string containing a SQL expression +#' @param ... Additional expressions +#' @return A DataFrame +#' @rdname selectExpr +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' selectExpr(df, "col1", "(col2 * 5) as newCol") +#' } +setMethod("selectExpr", + signature(x = "DataFrame", expr = "character"), + function(x, expr, ...) { + exprList <- list(expr, ...) + sdf <- callJMethod(x@sdf, "selectExpr", listToSeq(exprList)) + dataFrame(sdf) + }) + +#' WithColumn +#' +#' Return a new DataFrame with the specified column added. +#' +#' @param x A DataFrame +#' @param colName A string containing the name of the new column. +#' @param col A Column expression. +#' @return A DataFrame with the new column added. +#' @rdname withColumn +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' newDF <- withColumn(df, "newCol", df$col1 * 5) +#' } +setMethod("withColumn", + signature(x = "DataFrame", colName = "character", col = "Column"), + function(x, colName, col) { + select(x, x$"*", alias(col, colName)) + }) + +#' WithColumnRenamed +#' +#' Rename an existing column in a DataFrame. +#' +#' @param x A DataFrame +#' @param existingCol The name of the column you want to change. +#' @param newCol The new column name. +#' @return A DataFrame with the column name changed. +#' @rdname withColumnRenamed +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' newDF <- withColumnRenamed(df, "col1", "newCol1") +#' } +setMethod("withColumnRenamed", + signature(x = "DataFrame", existingCol = "character", newCol = "character"), + function(x, existingCol, newCol) { + cols <- lapply(columns(x), function(c) { + if (c == existingCol) { + alias(col(c), newCol) + } else { + col(c) + } + }) + select(x, cols) + }) + +setClassUnion("characterOrColumn", c("character", "Column")) + +#' SortDF +#' +#' Sort a DataFrame by the specified column(s). +#' +#' @param x A DataFrame to be sorted. +#' @param col Either a Column object or character vector indicating the field to sort on +#' @param ... Additional sorting fields +#' @return A DataFrame where all elements are sorted. +#' @rdname sortDF +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' sortDF(df, df$col1) +#' sortDF(df, "col1") +#' sortDF(df, asc(df$col1), desc(abs(df$col2))) +#' } +setMethod("sortDF", + signature(x = "DataFrame", col = "characterOrColumn"), + function(x, col, ...) { + if (class(col) == "character") { + sdf <- callJMethod(x@sdf, "sort", col, toSeq(...)) + } else if (class(col) == "Column") { + jcols <- lapply(list(col, ...), function(c) { + c@jc + }) + sdf <- callJMethod(x@sdf, "sort", listToSeq(jcols)) + } + dataFrame(sdf) + }) + +#' @rdname sortDF +#' @export +setMethod("orderBy", + signature(x = "DataFrame", col = "characterOrColumn"), + function(x, col) { + sortDF(x, col) + }) + +#' Filter +#' +#' Filter the rows of a DataFrame according to a given condition. +#' +#' @param x A DataFrame to be sorted. +#' @param condition The condition to sort on. This may either be a Column expression +#' or a string containing a SQL statement +#' @return A DataFrame containing only the rows that meet the condition. +#' @rdname filter +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' filter(df, "col1 > 0") +#' filter(df, df$col2 != "abcdefg") +#' } +setMethod("filter", + signature(x = "DataFrame", condition = "characterOrColumn"), + function(x, condition) { + if (class(condition) == "Column") { + condition <- condition@jc + } + sdf <- callJMethod(x@sdf, "filter", condition) + dataFrame(sdf) + }) + +#' @rdname filter +#' @export +setMethod("where", + signature(x = "DataFrame", condition = "characterOrColumn"), + function(x, condition) { + filter(x, condition) + }) + +#' Join +#' +#' Join two DataFrames based on the given join expression. +#' +#' @param x A Spark DataFrame +#' @param y A Spark DataFrame +#' @param joinExpr (Optional) The expression used to perform the join. joinExpr must be a +#' Column expression. If joinExpr is omitted, join() wil perform a Cartesian join +#' @param joinType The type of join to perform. The following join types are available: +#' 'inner', 'outer', 'left_outer', 'right_outer', 'semijoin'. The default joinType is "inner". +#' @return A DataFrame containing the result of the join operation. +#' @rdname join +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' df1 <- jsonFile(sqlCtx, path) +#' df2 <- jsonFile(sqlCtx, path2) +#' join(df1, df2) # Performs a Cartesian +#' join(df1, df2, df1$col1 == df2$col2) # Performs an inner join based on expression +#' join(df1, df2, df1$col1 == df2$col2, "right_outer") +#' } +setMethod("join", + signature(x = "DataFrame", y = "DataFrame"), + function(x, y, joinExpr = NULL, joinType = NULL) { + if (is.null(joinExpr)) { + sdf <- callJMethod(x@sdf, "join", y@sdf) + } else { + if (class(joinExpr) != "Column") stop("joinExpr must be a Column") + if (is.null(joinType)) { + sdf <- callJMethod(x@sdf, "join", y@sdf, joinExpr@jc) + } else { + if (joinType %in% c("inner", "outer", "left_outer", "right_outer", "semijoin")) { + sdf <- callJMethod(x@sdf, "join", y@sdf, joinExpr@jc, joinType) + } else { + stop("joinType must be one of the following types: ", + "'inner', 'outer', 'left_outer', 'right_outer', 'semijoin'") + } + } + } + dataFrame(sdf) + }) + +#' UnionAll +#' +#' Return a new DataFrame containing the union of rows in this DataFrame +#' and another DataFrame. This is equivalent to `UNION ALL` in SQL. +#' +#' @param x A Spark DataFrame +#' @param y A Spark DataFrame +#' @return A DataFrame containing the result of the union. +#' @rdname unionAll +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' df1 <- jsonFile(sqlCtx, path) +#' df2 <- jsonFile(sqlCtx, path2) +#' unioned <- unionAll(df, df2) +#' } +setMethod("unionAll", + signature(x = "DataFrame", y = "DataFrame"), + function(x, y) { + unioned <- callJMethod(x@sdf, "unionAll", y@sdf) + dataFrame(unioned) + }) + +#' Intersect +#' +#' Return a new DataFrame containing rows only in both this DataFrame +#' and another DataFrame. This is equivalent to `INTERSECT` in SQL. +#' +#' @param x A Spark DataFrame +#' @param y A Spark DataFrame +#' @return A DataFrame containing the result of the intersect. +#' @rdname intersect +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' df1 <- jsonFile(sqlCtx, path) +#' df2 <- jsonFile(sqlCtx, path2) +#' intersectDF <- intersect(df, df2) +#' } +setMethod("intersect", + signature(x = "DataFrame", y = "DataFrame"), + function(x, y) { + intersected <- callJMethod(x@sdf, "intersect", y@sdf) + dataFrame(intersected) + }) + +#' Subtract +#' +#' Return a new DataFrame containing rows in this DataFrame +#' but not in another DataFrame. This is equivalent to `EXCEPT` in SQL. +#' +#' @param x A Spark DataFrame +#' @param y A Spark DataFrame +#' @return A DataFrame containing the result of the subtract operation. +#' @rdname subtract +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' df1 <- jsonFile(sqlCtx, path) +#' df2 <- jsonFile(sqlCtx, path2) +#' subtractDF <- subtract(df, df2) +#' } +setMethod("subtract", + signature(x = "DataFrame", y = "DataFrame"), + function(x, y) { + subtracted <- callJMethod(x@sdf, "except", y@sdf) + dataFrame(subtracted) + }) + +#' Save the contents of the DataFrame to a data source +#' +#' The data source is specified by the `source` and a set of options (...). +#' If `source` is not specified, the default data source configured by +#' spark.sql.sources.default will be used. +#' +#' Additionally, mode is used to specify the behavior of the save operation when +#' data already exists in the data source. There are four modes: +#' append: Contents of this DataFrame are expected to be appended to existing data. +#' overwrite: Existing data is expected to be overwritten by the contents of +# this DataFrame. +#' error: An exception is expected to be thrown. +#' ignore: The save operation is expected to not save the contents of the DataFrame +# and to not change the existing data. +#' +#' @param df A SparkSQL DataFrame +#' @param path A name for the table +#' @param source A name for external data source +#' @param mode One of 'append', 'overwrite', 'error', 'ignore' +#' +#' @rdname saveAsTable +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' saveAsTable(df, "myfile") +#' } +setMethod("saveDF", + signature(df = "DataFrame", path = 'character', source = 'character', + mode = 'character'), + function(df, path = NULL, source = NULL, mode = "append", ...){ + if (is.null(source)) { + sqlCtx <- get(".sparkRSQLsc", envir = .sparkREnv) + source <- callJMethod(sqlCtx, "getConf", "spark.sql.sources.default", + "org.apache.spark.sql.parquet") + } + allModes <- c("append", "overwrite", "error", "ignore") + if (!(mode %in% allModes)) { + stop('mode should be one of "append", "overwrite", "error", "ignore"') + } + jmode <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "saveMode", mode) + options <- varargsToEnv(...) + if (!is.null(path)) { + options[['path']] = path + } + callJMethod(df@sdf, "save", source, jmode, options) + }) + + +#' saveAsTable +#' +#' Save the contents of the DataFrame to a data source as a table +#' +#' The data source is specified by the `source` and a set of options (...). +#' If `source` is not specified, the default data source configured by +#' spark.sql.sources.default will be used. +#' +#' Additionally, mode is used to specify the behavior of the save operation when +#' data already exists in the data source. There are four modes: +#' append: Contents of this DataFrame are expected to be appended to existing data. +#' overwrite: Existing data is expected to be overwritten by the contents of +# this DataFrame. +#' error: An exception is expected to be thrown. +#' ignore: The save operation is expected to not save the contents of the DataFrame +# and to not change the existing data. +#' +#' @param df A SparkSQL DataFrame +#' @param tableName A name for the table +#' @param source A name for external data source +#' @param mode One of 'append', 'overwrite', 'error', 'ignore' +#' +#' @rdname saveAsTable +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' saveAsTable(df, "myfile") +#' } +setMethod("saveAsTable", + signature(df = "DataFrame", tableName = 'character', source = 'character', + mode = 'character'), + function(df, tableName, source = NULL, mode="append", ...){ + if (is.null(source)) { + sqlCtx <- get(".sparkRSQLsc", envir = .sparkREnv) + source <- callJMethod(sqlCtx, "getConf", "spark.sql.sources.default", + "org.apache.spark.sql.parquet") + } + allModes <- c("append", "overwrite", "error", "ignore") + if (!(mode %in% allModes)) { + stop('mode should be one of "append", "overwrite", "error", "ignore"') + } + jmode <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "saveMode", mode) + options <- varargsToEnv(...) + callJMethod(df@sdf, "saveAsTable", tableName, source, jmode, options) + }) + diff --git a/R/pkg/R/RDD.R b/R/pkg/R/RDD.R new file mode 100644 index 0000000000000..604ad03c407b9 --- /dev/null +++ b/R/pkg/R/RDD.R @@ -0,0 +1,1539 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# RDD in R implemented in S4 OO system. + +setOldClass("jobj") + +#' @title S4 class that represents an RDD +#' @description RDD can be created using functions like +#' \code{parallelize}, \code{textFile} etc. +#' @rdname RDD +#' @seealso parallelize, textFile +#' +#' @slot env An R environment that stores bookkeeping states of the RDD +#' @slot jrdd Java object reference to the backing JavaRDD +#' to an RDD +#' @export +setClass("RDD", + slots = list(env = "environment", + jrdd = "jobj")) + +setClass("PipelinedRDD", + slots = list(prev = "RDD", + func = "function", + prev_jrdd = "jobj"), + contains = "RDD") + +setMethod("initialize", "RDD", function(.Object, jrdd, serializedMode, + isCached, isCheckpointed) { + # Check that RDD constructor is using the correct version of serializedMode + stopifnot(class(serializedMode) == "character") + stopifnot(serializedMode %in% c("byte", "string", "row")) + # RDD has three serialization types: + # byte: The RDD stores data serialized in R. + # string: The RDD stores data as strings. + # row: The RDD stores the serialized rows of a DataFrame. + + # We use an environment to store mutable states inside an RDD object. + # Note that R's call-by-value semantics makes modifying slots inside an + # object (passed as an argument into a function, such as cache()) difficult: + # i.e. one needs to make a copy of the RDD object and sets the new slot value + # there. + + # The slots are inheritable from superclass. Here, both `env' and `jrdd' are + # inherited from RDD, but only the former is used. + .Object@env <- new.env() + .Object@env$isCached <- isCached + .Object@env$isCheckpointed <- isCheckpointed + .Object@env$serializedMode <- serializedMode + + .Object@jrdd <- jrdd + .Object +}) + +setMethod("initialize", "PipelinedRDD", function(.Object, prev, func, jrdd_val) { + .Object@env <- new.env() + .Object@env$isCached <- FALSE + .Object@env$isCheckpointed <- FALSE + .Object@env$jrdd_val <- jrdd_val + if (!is.null(jrdd_val)) { + # This tracks the serialization mode for jrdd_val + .Object@env$serializedMode <- prev@env$serializedMode + } + + .Object@prev <- prev + + isPipelinable <- function(rdd) { + e <- rdd@env + !(e$isCached || e$isCheckpointed) + } + + if (!inherits(prev, "PipelinedRDD") || !isPipelinable(prev)) { + # This transformation is the first in its stage: + .Object@func <- func + .Object@prev_jrdd <- getJRDD(prev) + .Object@env$prev_serializedMode <- prev@env$serializedMode + # NOTE: We use prev_serializedMode to track the serialization mode of prev_JRDD + # prev_serializedMode is used during the delayed computation of JRDD in getJRDD + } else { + pipelinedFunc <- function(split, iterator) { + func(split, prev@func(split, iterator)) + } + .Object@func <- pipelinedFunc + .Object@prev_jrdd <- prev@prev_jrdd # maintain the pipeline + # Get the serialization mode of the parent RDD + .Object@env$prev_serializedMode <- prev@env$prev_serializedMode + } + + .Object +}) + +#' @rdname RDD +#' @export +#' +#' @param jrdd Java object reference to the backing JavaRDD +#' @param serializedMode Use "byte" if the RDD stores data serialized in R, "string" if the RDD +#' stores strings, and "row" if the RDD stores the rows of a DataFrame +#' @param isCached TRUE if the RDD is cached +#' @param isCheckpointed TRUE if the RDD has been checkpointed +RDD <- function(jrdd, serializedMode = "byte", isCached = FALSE, + isCheckpointed = FALSE) { + new("RDD", jrdd, serializedMode, isCached, isCheckpointed) +} + +PipelinedRDD <- function(prev, func) { + new("PipelinedRDD", prev, func, NULL) +} + +# Return the serialization mode for an RDD. +setGeneric("getSerializedMode", function(rdd, ...) { standardGeneric("getSerializedMode") }) +# For normal RDDs we can directly read the serializedMode +setMethod("getSerializedMode", signature(rdd = "RDD"), function(rdd) rdd@env$serializedMode ) +# For pipelined RDDs if jrdd_val is set then serializedMode should exist +# if not we return the defaultSerialization mode of "byte" as we don't know the serialization +# mode at this point in time. +setMethod("getSerializedMode", signature(rdd = "PipelinedRDD"), + function(rdd) { + if (!is.null(rdd@env$jrdd_val)) { + return(rdd@env$serializedMode) + } else { + return("byte") + } + }) + +# The jrdd accessor function. +setMethod("getJRDD", signature(rdd = "RDD"), function(rdd) rdd@jrdd ) +setMethod("getJRDD", signature(rdd = "PipelinedRDD"), + function(rdd, serializedMode = "byte") { + if (!is.null(rdd@env$jrdd_val)) { + return(rdd@env$jrdd_val) + } + + computeFunc <- function(split, part) { + rdd@func(split, part) + } + + packageNamesArr <- serialize(.sparkREnv[[".packages"]], + connection = NULL) + + broadcastArr <- lapply(ls(.broadcastNames), + function(name) { get(name, .broadcastNames) }) + + serializedFuncArr <- serialize(computeFunc, connection = NULL) + + prev_jrdd <- rdd@prev_jrdd + + if (serializedMode == "string") { + rddRef <- newJObject("org.apache.spark.api.r.StringRRDD", + callJMethod(prev_jrdd, "rdd"), + serializedFuncArr, + rdd@env$prev_serializedMode, + packageNamesArr, + as.character(.sparkREnv[["libname"]]), + broadcastArr, + callJMethod(prev_jrdd, "classTag")) + } else { + rddRef <- newJObject("org.apache.spark.api.r.RRDD", + callJMethod(prev_jrdd, "rdd"), + serializedFuncArr, + rdd@env$prev_serializedMode, + serializedMode, + packageNamesArr, + as.character(.sparkREnv[["libname"]]), + broadcastArr, + callJMethod(prev_jrdd, "classTag")) + } + # Save the serialization flag after we create a RRDD + rdd@env$serializedMode <- serializedMode + rdd@env$jrdd_val <- callJMethod(rddRef, "asJavaRDD") # rddRef$asJavaRDD() + rdd@env$jrdd_val + }) + +setValidity("RDD", + function(object) { + jrdd <- getJRDD(object) + cls <- callJMethod(jrdd, "getClass") + className <- callJMethod(cls, "getName") + if (grep("spark.api.java.*RDD*", className) == 1) { + TRUE + } else { + paste("Invalid RDD class ", className) + } + }) + + +############ Actions and Transformations ############ + +#' Persist an RDD +#' +#' Persist this RDD with the default storage level (MEMORY_ONLY). +#' +#' @param x The RDD to cache +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10, 2L) +#' cache(rdd) +#'} +#' @rdname cache-methods +#' @aliases cache,RDD-method +setMethod("cache", + signature(x = "RDD"), + function(x) { + callJMethod(getJRDD(x), "cache") + x@env$isCached <- TRUE + x + }) + +#' Persist an RDD +#' +#' Persist this RDD with the specified storage level. For details of the +#' supported storage levels, refer to +#' http://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence. +#' +#' @param x The RDD to persist +#' @param newLevel The new storage level to be assigned +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10, 2L) +#' persist(rdd, "MEMORY_AND_DISK") +#'} +#' @rdname persist +#' @aliases persist,RDD-method +setMethod("persist", + signature(x = "RDD", newLevel = "character"), + function(x, newLevel) { + callJMethod(getJRDD(x), "persist", getStorageLevel(newLevel)) + x@env$isCached <- TRUE + x + }) + +#' Unpersist an RDD +#' +#' Mark the RDD as non-persistent, and remove all blocks for it from memory and +#' disk. +#' +#' @param x The RDD to unpersist +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10, 2L) +#' cache(rdd) # rdd@@env$isCached == TRUE +#' unpersist(rdd) # rdd@@env$isCached == FALSE +#'} +#' @rdname unpersist-methods +#' @aliases unpersist,RDD-method +setMethod("unpersist", + signature(x = "RDD"), + function(x) { + callJMethod(getJRDD(x), "unpersist") + x@env$isCached <- FALSE + x + }) + +#' Checkpoint an RDD +#' +#' Mark this RDD for checkpointing. It will be saved to a file inside the +#' checkpoint directory set with setCheckpointDir() and all references to its +#' parent RDDs will be removed. This function must be called before any job has +#' been executed on this RDD. It is strongly recommended that this RDD is +#' persisted in memory, otherwise saving it on a file will require recomputation. +#' +#' @param x The RDD to checkpoint +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' setCheckpointDir(sc, "checkpoints") +#' rdd <- parallelize(sc, 1:10, 2L) +#' checkpoint(rdd) +#'} +#' @rdname checkpoint-methods +#' @aliases checkpoint,RDD-method +setMethod("checkpoint", + signature(x = "RDD"), + function(x) { + jrdd <- getJRDD(x) + callJMethod(jrdd, "checkpoint") + x@env$isCheckpointed <- TRUE + x + }) + +#' Gets the number of partitions of an RDD +#' +#' @param x A RDD. +#' @return the number of partitions of rdd as an integer. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10, 2L) +#' numPartitions(rdd) # 2L +#'} +#' @rdname numPartitions +#' @aliases numPartitions,RDD-method +setMethod("numPartitions", + signature(x = "RDD"), + function(x) { + jrdd <- getJRDD(x) + partitions <- callJMethod(jrdd, "splits") + callJMethod(partitions, "size") + }) + +#' Collect elements of an RDD +#' +#' @description +#' \code{collect} returns a list that contains all of the elements in this RDD. +#' +#' @param x The RDD to collect +#' @param ... Other optional arguments to collect +#' @param flatten FALSE if the list should not flattened +#' @return a list containing elements in the RDD +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10, 2L) +#' collect(rdd) # list from 1 to 10 +#' collectPartition(rdd, 0L) # list from 1 to 5 +#'} +#' @rdname collect-methods +#' @aliases collect,RDD-method +setMethod("collect", + signature(x = "RDD"), + function(x, flatten = TRUE) { + # Assumes a pairwise RDD is backed by a JavaPairRDD. + collected <- callJMethod(getJRDD(x), "collect") + convertJListToRList(collected, flatten, + serializedMode = getSerializedMode(x)) + }) + + +#' @description +#' \code{collectPartition} returns a list that contains all of the elements +#' in the specified partition of the RDD. +#' @param partitionId the partition to collect (starts from 0) +#' @rdname collect-methods +#' @aliases collectPartition,integer,RDD-method +setMethod("collectPartition", + signature(x = "RDD", partitionId = "integer"), + function(x, partitionId) { + jPartitionsList <- callJMethod(getJRDD(x), + "collectPartitions", + as.list(as.integer(partitionId))) + + jList <- jPartitionsList[[1]] + convertJListToRList(jList, flatten = TRUE, + serializedMode = getSerializedMode(x)) + }) + +#' @description +#' \code{collectAsMap} returns a named list as a map that contains all of the elements +#' in a key-value pair RDD. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(list(1, 2), list(3, 4)), 2L) +#' collectAsMap(rdd) # list(`1` = 2, `3` = 4) +#'} +#' @rdname collect-methods +#' @aliases collectAsMap,RDD-method +setMethod("collectAsMap", + signature(x = "RDD"), + function(x) { + pairList <- collect(x) + map <- new.env() + lapply(pairList, function(i) { assign(as.character(i[[1]]), i[[2]], envir = map) }) + as.list(map) + }) + +#' Return the number of elements in the RDD. +#' +#' @param x The RDD to count +#' @return number of elements in the RDD. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' count(rdd) # 10 +#' length(rdd) # Same as count +#'} +#' @rdname count +#' @aliases count,RDD-method +setMethod("count", + signature(x = "RDD"), + function(x) { + countPartition <- function(part) { + as.integer(length(part)) + } + valsRDD <- lapplyPartition(x, countPartition) + vals <- collect(valsRDD) + sum(as.integer(vals)) + }) + +#' Return the number of elements in the RDD +#' @export +#' @rdname count +setMethod("length", + signature(x = "RDD"), + function(x) { + count(x) + }) + +#' Return the count of each unique value in this RDD as a list of +#' (value, count) pairs. +#' +#' Same as countByValue in Spark. +#' +#' @param x The RDD to count +#' @return list of (value, count) pairs, where count is number of each unique +#' value in rdd. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, c(1,2,3,2,1)) +#' countByValue(rdd) # (1,2L), (2,2L), (3,1L) +#'} +#' @rdname countByValue +#' @aliases countByValue,RDD-method +setMethod("countByValue", + signature(x = "RDD"), + function(x) { + ones <- lapply(x, function(item) { list(item, 1L) }) + collect(reduceByKey(ones, `+`, numPartitions(x))) + }) + +#' Apply a function to all elements +#' +#' This function creates a new RDD by applying the given transformation to all +#' elements of the given RDD +#' +#' @param X The RDD to apply the transformation. +#' @param FUN the transformation to apply on each element +#' @return a new RDD created by the transformation. +#' @rdname lapply +#' @aliases lapply +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' multiplyByTwo <- lapply(rdd, function(x) { x * 2 }) +#' collect(multiplyByTwo) # 2,4,6... +#'} +setMethod("lapply", + signature(X = "RDD", FUN = "function"), + function(X, FUN) { + func <- function(split, iterator) { + lapply(iterator, FUN) + } + lapplyPartitionsWithIndex(X, func) + }) + +#' @rdname lapply +#' @aliases map,RDD,function-method +setMethod("map", + signature(X = "RDD", FUN = "function"), + function(X, FUN) { + lapply(X, FUN) + }) + +#' Flatten results after apply a function to all elements +#' +#' This function return a new RDD by first applying a function to all +#' elements of this RDD, and then flattening the results. +#' +#' @param X The RDD to apply the transformation. +#' @param FUN the transformation to apply on each element +#' @return a new RDD created by the transformation. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' multiplyByTwo <- flatMap(rdd, function(x) { list(x*2, x*10) }) +#' collect(multiplyByTwo) # 2,20,4,40,6,60... +#'} +#' @rdname flatMap +#' @aliases flatMap,RDD,function-method +setMethod("flatMap", + signature(X = "RDD", FUN = "function"), + function(X, FUN) { + partitionFunc <- function(part) { + unlist( + lapply(part, FUN), + recursive = F + ) + } + lapplyPartition(X, partitionFunc) + }) + +#' Apply a function to each partition of an RDD +#' +#' Return a new RDD by applying a function to each partition of this RDD. +#' +#' @param X The RDD to apply the transformation. +#' @param FUN the transformation to apply on each partition. +#' @return a new RDD created by the transformation. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' partitionSum <- lapplyPartition(rdd, function(part) { Reduce("+", part) }) +#' collect(partitionSum) # 15, 40 +#'} +#' @rdname lapplyPartition +#' @aliases lapplyPartition,RDD,function-method +setMethod("lapplyPartition", + signature(X = "RDD", FUN = "function"), + function(X, FUN) { + lapplyPartitionsWithIndex(X, function(s, part) { FUN(part) }) + }) + +#' mapPartitions is the same as lapplyPartition. +#' +#' @rdname lapplyPartition +#' @aliases mapPartitions,RDD,function-method +setMethod("mapPartitions", + signature(X = "RDD", FUN = "function"), + function(X, FUN) { + lapplyPartition(X, FUN) + }) + +#' Return a new RDD by applying a function to each partition of this RDD, while +#' tracking the index of the original partition. +#' +#' @param X The RDD to apply the transformation. +#' @param FUN the transformation to apply on each partition; takes the partition +#' index and a list of elements in the particular partition. +#' @return a new RDD created by the transformation. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10, 5L) +#' prod <- lapplyPartitionsWithIndex(rdd, function(split, part) { +#' split * Reduce("+", part) }) +#' collect(prod, flatten = FALSE) # 0, 7, 22, 45, 76 +#'} +#' @rdname lapplyPartitionsWithIndex +#' @aliases lapplyPartitionsWithIndex,RDD,function-method +setMethod("lapplyPartitionsWithIndex", + signature(X = "RDD", FUN = "function"), + function(X, FUN) { + FUN <- cleanClosure(FUN) + closureCapturingFunc <- function(split, part) { + FUN(split, part) + } + PipelinedRDD(X, closureCapturingFunc) + }) + +#' @rdname lapplyPartitionsWithIndex +#' @aliases mapPartitionsWithIndex,RDD,function-method +setMethod("mapPartitionsWithIndex", + signature(X = "RDD", FUN = "function"), + function(X, FUN) { + lapplyPartitionsWithIndex(X, FUN) + }) + +#' This function returns a new RDD containing only the elements that satisfy +#' a predicate (i.e. returning TRUE in a given logical function). +#' The same as `filter()' in Spark. +#' +#' @param x The RDD to be filtered. +#' @param f A unary predicate function. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' unlist(collect(filterRDD(rdd, function (x) { x < 3 }))) # c(1, 2) +#'} +#' @rdname filterRDD +#' @aliases filterRDD,RDD,function-method +setMethod("filterRDD", + signature(x = "RDD", f = "function"), + function(x, f) { + filter.func <- function(part) { + Filter(f, part) + } + lapplyPartition(x, filter.func) + }) + +#' @rdname filterRDD +#' @aliases Filter +setMethod("Filter", + signature(f = "function", x = "RDD"), + function(f, x) { + filterRDD(x, f) + }) + +#' Reduce across elements of an RDD. +#' +#' This function reduces the elements of this RDD using the +#' specified commutative and associative binary operator. +#' +#' @param x The RDD to reduce +#' @param func Commutative and associative function to apply on elements +#' of the RDD. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' reduce(rdd, "+") # 55 +#'} +#' @rdname reduce +#' @aliases reduce,RDD,ANY-method +setMethod("reduce", + signature(x = "RDD", func = "ANY"), + function(x, func) { + + reducePartition <- function(part) { + Reduce(func, part) + } + + partitionList <- collect(lapplyPartition(x, reducePartition), + flatten = FALSE) + Reduce(func, partitionList) + }) + +#' Get the maximum element of an RDD. +#' +#' @param x The RDD to get the maximum element from +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' maximum(rdd) # 10 +#'} +#' @rdname maximum +#' @aliases maximum,RDD +setMethod("maximum", + signature(x = "RDD"), + function(x) { + reduce(x, max) + }) + +#' Get the minimum element of an RDD. +#' +#' @param x The RDD to get the minimum element from +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' minimum(rdd) # 1 +#'} +#' @rdname minimum +#' @aliases minimum,RDD +setMethod("minimum", + signature(x = "RDD"), + function(x) { + reduce(x, min) + }) + +#' Add up the elements in an RDD. +#' +#' @param x The RDD to add up the elements in +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' sumRDD(rdd) # 55 +#'} +#' @rdname sumRDD +#' @aliases sumRDD,RDD +setMethod("sumRDD", + signature(x = "RDD"), + function(x) { + reduce(x, "+") + }) + +#' Applies a function to all elements in an RDD, and force evaluation. +#' +#' @param x The RDD to apply the function +#' @param func The function to be applied. +#' @return invisible NULL. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' foreach(rdd, function(x) { save(x, file=...) }) +#'} +#' @rdname foreach +#' @aliases foreach,RDD,function-method +setMethod("foreach", + signature(x = "RDD", func = "function"), + function(x, func) { + partition.func <- function(x) { + lapply(x, func) + NULL + } + invisible(collect(mapPartitions(x, partition.func))) + }) + +#' Applies a function to each partition in an RDD, and force evaluation. +#' +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' foreachPartition(rdd, function(part) { save(part, file=...); NULL }) +#'} +#' @rdname foreach +#' @aliases foreachPartition,RDD,function-method +setMethod("foreachPartition", + signature(x = "RDD", func = "function"), + function(x, func) { + invisible(collect(mapPartitions(x, func))) + }) + +#' Take elements from an RDD. +#' +#' This function takes the first NUM elements in the RDD and +#' returns them in a list. +#' +#' @param x The RDD to take elements from +#' @param num Number of elements to take +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' take(rdd, 2L) # list(1, 2) +#'} +#' @rdname take +#' @aliases take,RDD,numeric-method +setMethod("take", + signature(x = "RDD", num = "numeric"), + function(x, num) { + resList <- list() + index <- -1 + jrdd <- getJRDD(x) + numPartitions <- numPartitions(x) + + # TODO(shivaram): Collect more than one partition based on size + # estimates similar to the scala version of `take`. + while (TRUE) { + index <- index + 1 + + if (length(resList) >= num || index >= numPartitions) + break + + # a JList of byte arrays + partitionArr <- callJMethod(jrdd, "collectPartitions", as.list(as.integer(index))) + partition <- partitionArr[[1]] + + size <- num - length(resList) + # elems is capped to have at most `size` elements + elems <- convertJListToRList(partition, + flatten = TRUE, + logicalUpperBound = size, + serializedMode = getSerializedMode(x)) + # TODO: Check if this append is O(n^2)? + resList <- append(resList, elems) + } + resList + }) + +#' First +#' +#' Return the first element of an RDD +#' +#' @rdname first +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' first(rdd) +#' } +setMethod("first", + signature(x = "RDD"), + function(x) { + take(x, 1)[[1]] + }) + +#' Removes the duplicates from RDD. +#' +#' This function returns a new RDD containing the distinct elements in the +#' given RDD. The same as `distinct()' in Spark. +#' +#' @param x The RDD to remove duplicates from. +#' @param numPartitions Number of partitions to create. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, c(1,2,2,3,3,3)) +#' sort(unlist(collect(distinct(rdd)))) # c(1, 2, 3) +#'} +#' @rdname distinct +#' @aliases distinct,RDD-method +setMethod("distinct", + signature(x = "RDD"), + function(x, numPartitions = SparkR::numPartitions(x)) { + identical.mapped <- lapply(x, function(x) { list(x, NULL) }) + reduced <- reduceByKey(identical.mapped, + function(x, y) { x }, + numPartitions) + resRDD <- lapply(reduced, function(x) { x[[1]] }) + resRDD + }) + +#' Return an RDD that is a sampled subset of the given RDD. +#' +#' The same as `sample()' in Spark. (We rename it due to signature +#' inconsistencies with the `sample()' function in R's base package.) +#' +#' @param x The RDD to sample elements from +#' @param withReplacement Sampling with replacement or not +#' @param fraction The (rough) sample target fraction +#' @param seed Randomness seed value +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) # ensure each num is in its own split +#' collect(sampleRDD(rdd, FALSE, 0.5, 1618L)) # ~5 distinct elements +#' collect(sampleRDD(rdd, TRUE, 0.5, 9L)) # ~5 elements possibly with duplicates +#'} +#' @rdname sampleRDD +#' @aliases sampleRDD,RDD +setMethod("sampleRDD", + signature(x = "RDD", withReplacement = "logical", + fraction = "numeric", seed = "integer"), + function(x, withReplacement, fraction, seed) { + + # The sampler: takes a partition and returns its sampled version. + samplingFunc <- function(split, part) { + set.seed(seed) + res <- vector("list", length(part)) + len <- 0 + + # Discards some random values to ensure each partition has a + # different random seed. + runif(split) + + for (elem in part) { + if (withReplacement) { + count <- rpois(1, fraction) + if (count > 0) { + res[(len + 1):(len + count)] <- rep(list(elem), count) + len <- len + count + } + } else { + if (runif(1) < fraction) { + len <- len + 1 + res[[len]] <- elem + } + } + } + + # TODO(zongheng): look into the performance of the current + # implementation. Look into some iterator package? Note that + # Scala avoids many calls to creating an empty list and PySpark + # similarly achieves this using `yield'. + if (len > 0) + res[1:len] + else + list() + } + + lapplyPartitionsWithIndex(x, samplingFunc) + }) + +#' Return a list of the elements that are a sampled subset of the given RDD. +#' +#' @param x The RDD to sample elements from +#' @param withReplacement Sampling with replacement or not +#' @param num Number of elements to return +#' @param seed Randomness seed value +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:100) +#' # exactly 5 elements sampled, which may not be distinct +#' takeSample(rdd, TRUE, 5L, 1618L) +#' # exactly 5 distinct elements sampled +#' takeSample(rdd, FALSE, 5L, 16181618L) +#'} +#' @rdname takeSample +#' @aliases takeSample,RDD +setMethod("takeSample", signature(x = "RDD", withReplacement = "logical", + num = "integer", seed = "integer"), + function(x, withReplacement, num, seed) { + # This function is ported from RDD.scala. + fraction <- 0.0 + total <- 0 + multiplier <- 3.0 + initialCount <- count(x) + maxSelected <- 0 + MAXINT <- .Machine$integer.max + + if (num < 0) + stop(paste("Negative number of elements requested")) + + if (initialCount > MAXINT - 1) { + maxSelected <- MAXINT - 1 + } else { + maxSelected <- initialCount + } + + if (num > initialCount && !withReplacement) { + total <- maxSelected + fraction <- multiplier * (maxSelected + 1) / initialCount + } else { + total <- num + fraction <- multiplier * (num + 1) / initialCount + } + + set.seed(seed) + samples <- collect(sampleRDD(x, withReplacement, fraction, + as.integer(ceiling(runif(1, + -MAXINT, + MAXINT))))) + # If the first sample didn't turn out large enough, keep trying to + # take samples; this shouldn't happen often because we use a big + # multiplier for thei initial size + while (length(samples) < total) + samples <- collect(sampleRDD(x, withReplacement, fraction, + as.integer(ceiling(runif(1, + -MAXINT, + MAXINT))))) + + # TODO(zongheng): investigate if this call is an in-place shuffle? + sample(samples)[1:total] + }) + +#' Creates tuples of the elements in this RDD by applying a function. +#' +#' @param x The RDD. +#' @param func The function to be applied. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1, 2, 3)) +#' collect(keyBy(rdd, function(x) { x*x })) # list(list(1, 1), list(4, 2), list(9, 3)) +#'} +#' @rdname keyBy +#' @aliases keyBy,RDD +setMethod("keyBy", + signature(x = "RDD", func = "function"), + function(x, func) { + apply.func <- function(x) { + list(func(x), x) + } + lapply(x, apply.func) + }) + +#' Return a new RDD that has exactly numPartitions partitions. +#' Can increase or decrease the level of parallelism in this RDD. Internally, +#' this uses a shuffle to redistribute data. +#' If you are decreasing the number of partitions in this RDD, consider using +#' coalesce, which can avoid performing a shuffle. +#' +#' @param x The RDD. +#' @param numPartitions Number of partitions to create. +#' @seealso coalesce +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1, 2, 3, 4, 5, 6, 7), 4L) +#' numPartitions(rdd) # 4 +#' numPartitions(repartition(rdd, 2L)) # 2 +#'} +#' @rdname repartition +#' @aliases repartition,RDD +setMethod("repartition", + signature(x = "RDD", numPartitions = "numeric"), + function(x, numPartitions) { + coalesce(x, numToInt(numPartitions), TRUE) + }) + +#' Return a new RDD that is reduced into numPartitions partitions. +#' +#' @param x The RDD. +#' @param numPartitions Number of partitions to create. +#' @seealso repartition +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1, 2, 3, 4, 5), 3L) +#' numPartitions(rdd) # 3 +#' numPartitions(coalesce(rdd, 1L)) # 1 +#'} +#' @rdname coalesce +#' @aliases coalesce,RDD +setMethod("coalesce", + signature(x = "RDD", numPartitions = "numeric"), + function(x, numPartitions, shuffle = FALSE) { + numPartitions <- numToInt(numPartitions) + if (shuffle || numPartitions > SparkR::numPartitions(x)) { + func <- function(s, part) { + set.seed(s) # split as seed + start <- as.integer(sample(numPartitions, 1) - 1) + lapply(seq_along(part), + function(i) { + pos <- (start + i) %% numPartitions + list(pos, part[[i]]) + }) + } + shuffled <- lapplyPartitionsWithIndex(x, func) + repartitioned <- partitionBy(shuffled, numPartitions) + values(repartitioned) + } else { + jrdd <- callJMethod(getJRDD(x), "coalesce", numPartitions, shuffle) + RDD(jrdd) + } + }) + +#' Save this RDD as a SequenceFile of serialized objects. +#' +#' @param x The RDD to save +#' @param path The directory where the file is saved +#' @seealso objectFile +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:3) +#' saveAsObjectFile(rdd, "/tmp/sparkR-tmp") +#'} +#' @rdname saveAsObjectFile +#' @aliases saveAsObjectFile,RDD +setMethod("saveAsObjectFile", + signature(x = "RDD", path = "character"), + function(x, path) { + # If serializedMode == "string" we need to serialize the data before saving it since + # objectFile() assumes serializedMode == "byte". + if (getSerializedMode(x) != "byte") { + x <- serializeToBytes(x) + } + # Return nothing + invisible(callJMethod(getJRDD(x), "saveAsObjectFile", path)) + }) + +#' Save this RDD as a text file, using string representations of elements. +#' +#' @param x The RDD to save +#' @param path The directory where the splits of the text file are saved +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:3) +#' saveAsTextFile(rdd, "/tmp/sparkR-tmp") +#'} +#' @rdname saveAsTextFile +#' @aliases saveAsTextFile,RDD +setMethod("saveAsTextFile", + signature(x = "RDD", path = "character"), + function(x, path) { + func <- function(str) { + toString(str) + } + stringRdd <- lapply(x, func) + # Return nothing + invisible( + callJMethod(getJRDD(stringRdd, serializedMode = "string"), "saveAsTextFile", path)) + }) + +#' Sort an RDD by the given key function. +#' +#' @param x An RDD to be sorted. +#' @param func A function used to compute the sort key for each element. +#' @param ascending A flag to indicate whether the sorting is ascending or descending. +#' @param numPartitions Number of partitions to create. +#' @return An RDD where all elements are sorted. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(3, 2, 1)) +#' collect(sortBy(rdd, function(x) { x })) # list (1, 2, 3) +#'} +#' @rdname sortBy +#' @aliases sortBy,RDD,RDD-method +setMethod("sortBy", + signature(x = "RDD", func = "function"), + function(x, func, ascending = TRUE, numPartitions = SparkR::numPartitions(x)) { + values(sortByKey(keyBy(x, func), ascending, numPartitions)) + }) + +# Helper function to get first N elements from an RDD in the specified order. +# Param: +# x An RDD. +# num Number of elements to return. +# ascending A flag to indicate whether the sorting is ascending or descending. +# Return: +# A list of the first N elements from the RDD in the specified order. +# +takeOrderedElem <- function(x, num, ascending = TRUE) { + if (num <= 0L) { + return(list()) + } + + partitionFunc <- function(part) { + if (num < length(part)) { + # R limitation: order works only on primitive types! + ord <- order(unlist(part, recursive = FALSE), decreasing = !ascending) + list(part[ord[1:num]]) + } else { + list(part) + } + } + + reduceFunc <- function(elems, part) { + newElems <- append(elems, part) + # R limitation: order works only on primitive types! + ord <- order(unlist(newElems, recursive = FALSE), decreasing = !ascending) + newElems[ord[1:num]] + } + + newRdd <- mapPartitions(x, partitionFunc) + reduce(newRdd, reduceFunc) +} + +#' Returns the first N elements from an RDD in ascending order. +#' +#' @param x An RDD. +#' @param num Number of elements to return. +#' @return The first N elements from the RDD in ascending order. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(10, 1, 2, 9, 3, 4, 5, 6, 7)) +#' takeOrdered(rdd, 6L) # list(1, 2, 3, 4, 5, 6) +#'} +#' @rdname takeOrdered +#' @aliases takeOrdered,RDD,RDD-method +setMethod("takeOrdered", + signature(x = "RDD", num = "integer"), + function(x, num) { + takeOrderedElem(x, num) + }) + +#' Returns the top N elements from an RDD. +#' +#' @param x An RDD. +#' @param num Number of elements to return. +#' @return The top N elements from the RDD. +#' @rdname top +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(10, 1, 2, 9, 3, 4, 5, 6, 7)) +#' top(rdd, 6L) # list(10, 9, 7, 6, 5, 4) +#'} +#' @rdname top +#' @aliases top,RDD,RDD-method +setMethod("top", + signature(x = "RDD", num = "integer"), + function(x, num) { + takeOrderedElem(x, num, FALSE) + }) + +#' Fold an RDD using a given associative function and a neutral "zero value". +#' +#' Aggregate the elements of each partition, and then the results for all the +#' partitions, using a given associative function and a neutral "zero value". +#' +#' @param x An RDD. +#' @param zeroValue A neutral "zero value". +#' @param op An associative function for the folding operation. +#' @return The folding result. +#' @rdname fold +#' @seealso reduce +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1, 2, 3, 4, 5)) +#' fold(rdd, 0, "+") # 15 +#'} +#' @rdname fold +#' @aliases fold,RDD,RDD-method +setMethod("fold", + signature(x = "RDD", zeroValue = "ANY", op = "ANY"), + function(x, zeroValue, op) { + aggregateRDD(x, zeroValue, op, op) + }) + +#' Aggregate an RDD using the given combine functions and a neutral "zero value". +#' +#' Aggregate the elements of each partition, and then the results for all the +#' partitions, using given combine functions and a neutral "zero value". +#' +#' @param x An RDD. +#' @param zeroValue A neutral "zero value". +#' @param seqOp A function to aggregate the RDD elements. It may return a different +#' result type from the type of the RDD elements. +#' @param combOp A function to aggregate results of seqOp. +#' @return The aggregation result. +#' @rdname aggregateRDD +#' @seealso reduce +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1, 2, 3, 4)) +#' zeroValue <- list(0, 0) +#' seqOp <- function(x, y) { list(x[[1]] + y, x[[2]] + 1) } +#' combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) } +#' aggregateRDD(rdd, zeroValue, seqOp, combOp) # list(10, 4) +#'} +#' @rdname aggregateRDD +#' @aliases aggregateRDD,RDD,RDD-method +setMethod("aggregateRDD", + signature(x = "RDD", zeroValue = "ANY", seqOp = "ANY", combOp = "ANY"), + function(x, zeroValue, seqOp, combOp) { + partitionFunc <- function(part) { + Reduce(seqOp, part, zeroValue) + } + + partitionList <- collect(lapplyPartition(x, partitionFunc), + flatten = FALSE) + Reduce(combOp, partitionList, zeroValue) + }) + +#' Pipes elements to a forked external process. +#' +#' The same as 'pipe()' in Spark. +#' +#' @param x The RDD whose elements are piped to the forked external process. +#' @param command The command to fork an external process. +#' @param env A named list to set environment variables of the external process. +#' @return A new RDD created by piping all elements to a forked external process. +#' @rdname pipeRDD +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' collect(pipeRDD(rdd, "more") +#' Output: c("1", "2", ..., "10") +#'} +#' @rdname pipeRDD +#' @aliases pipeRDD,RDD,character-method +setMethod("pipeRDD", + signature(x = "RDD", command = "character"), + function(x, command, env = list()) { + func <- function(part) { + trim.trailing.func <- function(x) { + sub("[\r\n]*$", "", toString(x)) + } + input <- unlist(lapply(part, trim.trailing.func)) + res <- system2(command, stdout = TRUE, input = input, env = env) + lapply(res, trim.trailing.func) + } + lapplyPartition(x, func) + }) + +# TODO: Consider caching the name in the RDD's environment +#' Return an RDD's name. +#' +#' @param x The RDD whose name is returned. +#' @rdname name +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1,2,3)) +#' name(rdd) # NULL (if not set before) +#'} +#' @rdname name +#' @aliases name,RDD +setMethod("name", + signature(x = "RDD"), + function(x) { + callJMethod(getJRDD(x), "name") + }) + +#' Set an RDD's name. +#' +#' @param x The RDD whose name is to be set. +#' @param name The RDD name to be set. +#' @return a new RDD renamed. +#' @rdname setName +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1,2,3)) +#' setName(rdd, "myRDD") +#' name(rdd) # "myRDD" +#'} +#' @rdname setName +#' @aliases setName,RDD +setMethod("setName", + signature(x = "RDD", name = "character"), + function(x, name) { + callJMethod(getJRDD(x), "setName", name) + x + }) + +#' Zip an RDD with generated unique Long IDs. +#' +#' Items in the kth partition will get ids k, n+k, 2*n+k, ..., where +#' n is the number of partitions. So there may exist gaps, but this +#' method won't trigger a spark job, which is different from +#' zipWithIndex. +#' +#' @param x An RDD to be zipped. +#' @return An RDD with zipped items. +#' @seealso zipWithIndex +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L) +#' collect(zipWithUniqueId(rdd)) +#' # list(list("a", 0), list("b", 3), list("c", 1), list("d", 4), list("e", 2)) +#'} +#' @rdname zipWithUniqueId +#' @aliases zipWithUniqueId,RDD +setMethod("zipWithUniqueId", + signature(x = "RDD"), + function(x) { + n <- numPartitions(x) + + partitionFunc <- function(split, part) { + mapply( + function(item, index) { + list(item, (index - 1) * n + split) + }, + part, + seq_along(part), + SIMPLIFY = FALSE) + } + + lapplyPartitionsWithIndex(x, partitionFunc) + }) + +#' Zip an RDD with its element indices. +#' +#' The ordering is first based on the partition index and then the +#' ordering of items within each partition. So the first item in +#' the first partition gets index 0, and the last item in the last +#' partition receives the largest index. +#' +#' This method needs to trigger a Spark job when this RDD contains +#' more than one partition. +#' +#' @param x An RDD to be zipped. +#' @return An RDD with zipped items. +#' @seealso zipWithUniqueId +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L) +#' collect(zipWithIndex(rdd)) +#' # list(list("a", 0), list("b", 1), list("c", 2), list("d", 3), list("e", 4)) +#'} +#' @rdname zipWithIndex +#' @aliases zipWithIndex,RDD +setMethod("zipWithIndex", + signature(x = "RDD"), + function(x) { + n <- numPartitions(x) + if (n > 1) { + nums <- collect(lapplyPartition(x, + function(part) { + list(length(part)) + })) + startIndices <- Reduce("+", nums, accumulate = TRUE) + } + + partitionFunc <- function(split, part) { + if (split == 0) { + startIndex <- 0 + } else { + startIndex <- startIndices[[split]] + } + + mapply( + function(item, index) { + list(item, index - 1 + startIndex) + }, + part, + seq_along(part), + SIMPLIFY = FALSE) + } + + lapplyPartitionsWithIndex(x, partitionFunc) + }) + +#' Coalesce all elements within each partition of an RDD into a list. +#' +#' @param x An RDD. +#' @return An RDD created by coalescing all elements within +#' each partition into a list. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, as.list(1:4), 2L) +#' collect(glom(rdd)) +#' # list(list(1, 2), list(3, 4)) +#'} +#' @rdname glom +#' @aliases glom,RDD +setMethod("glom", + signature(x = "RDD"), + function(x) { + partitionFunc <- function(part) { + list(part) + } + + lapplyPartition(x, partitionFunc) + }) + +############ Binary Functions ############# + +#' Return the union RDD of two RDDs. +#' The same as union() in Spark. +#' +#' @param x An RDD. +#' @param y An RDD. +#' @return a new RDD created by performing the simple union (witout removing +#' duplicates) of two input RDDs. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:3) +#' unionRDD(rdd, rdd) # 1, 2, 3, 1, 2, 3 +#'} +#' @rdname unionRDD +#' @aliases unionRDD,RDD,RDD-method +setMethod("unionRDD", + signature(x = "RDD", y = "RDD"), + function(x, y) { + if (getSerializedMode(x) == getSerializedMode(y)) { + jrdd <- callJMethod(getJRDD(x), "union", getJRDD(y)) + union.rdd <- RDD(jrdd, getSerializedMode(x)) + } else { + # One of the RDDs is not serialized, we need to serialize it first. + if (getSerializedMode(x) != "byte") x <- serializeToBytes(x) + if (getSerializedMode(y) != "byte") y <- serializeToBytes(y) + jrdd <- callJMethod(getJRDD(x), "union", getJRDD(y)) + union.rdd <- RDD(jrdd, "byte") + } + union.rdd + }) + +#' Zip an RDD with another RDD. +#' +#' Zips this RDD with another one, returning key-value pairs with the +#' first element in each RDD second element in each RDD, etc. Assumes +#' that the two RDDs have the same number of partitions and the same +#' number of elements in each partition (e.g. one was made through +#' a map on the other). +#' +#' @param x An RDD to be zipped. +#' @param other Another RDD to be zipped. +#' @return An RDD zipped from the two RDDs. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, 0:4) +#' rdd2 <- parallelize(sc, 1000:1004) +#' collect(zipRDD(rdd1, rdd2)) +#' # list(list(0, 1000), list(1, 1001), list(2, 1002), list(3, 1003), list(4, 1004)) +#'} +#' @rdname zipRDD +#' @aliases zipRDD,RDD +setMethod("zipRDD", + signature(x = "RDD", other = "RDD"), + function(x, other) { + n1 <- numPartitions(x) + n2 <- numPartitions(other) + if (n1 != n2) { + stop("Can only zip RDDs which have the same number of partitions.") + } + + if (getSerializedMode(x) != getSerializedMode(other) || + getSerializedMode(x) == "byte") { + # Append the number of elements in each partition to that partition so that we can later + # check if corresponding partitions of both RDDs have the same number of elements. + # + # Note that this appending also serves the purpose of reserialization, because even if + # any RDD is serialized, we need to reserialize it to make sure its partitions are encoded + # as a single byte array. For example, partitions of an RDD generated from partitionBy() + # may be encoded as multiple byte arrays. + appendLength <- function(part) { + part[[length(part) + 1]] <- length(part) + 1 + part + } + x <- lapplyPartition(x, appendLength) + other <- lapplyPartition(other, appendLength) + } + + zippedJRDD <- callJMethod(getJRDD(x), "zip", getJRDD(other)) + # The zippedRDD's elements are of scala Tuple2 type. The serialized + # flag Here is used for the elements inside the tuples. + serializerMode <- getSerializedMode(x) + zippedRDD <- RDD(zippedJRDD, serializerMode) + + partitionFunc <- function(split, part) { + len <- length(part) + if (len > 0) { + if (serializerMode == "byte") { + lengthOfValues <- part[[len]] + lengthOfKeys <- part[[len - lengthOfValues]] + stopifnot(len == lengthOfKeys + lengthOfValues) + + # check if corresponding partitions of both RDDs have the same number of elements. + if (lengthOfKeys != lengthOfValues) { + stop("Can only zip RDDs with same number of elements in each pair of corresponding partitions.") + } + + if (lengthOfKeys > 1) { + keys <- part[1 : (lengthOfKeys - 1)] + values <- part[(lengthOfKeys + 1) : (len - 1)] + } else { + keys <- list() + values <- list() + } + } else { + # Keys, values must have same length here, because this has + # been validated inside the JavaRDD.zip() function. + keys <- part[c(TRUE, FALSE)] + values <- part[c(FALSE, TRUE)] + } + mapply( + function(k, v) { + list(k, v) + }, + keys, + values, + SIMPLIFY = FALSE, + USE.NAMES = FALSE) + } else { + part + } + } + + PipelinedRDD(zippedRDD, partitionFunc) + }) diff --git a/R/pkg/R/SQLContext.R b/R/pkg/R/SQLContext.R new file mode 100644 index 0000000000000..930ada22f4c38 --- /dev/null +++ b/R/pkg/R/SQLContext.R @@ -0,0 +1,520 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# SQLcontext.R: SQLContext-driven functions + +#' infer the SQL type +infer_type <- function(x) { + if (is.null(x)) { + stop("can not infer type from NULL") + } + + # class of POSIXlt is c("POSIXlt" "POSIXt") + type <- switch(class(x)[[1]], + integer = "integer", + character = "string", + logical = "boolean", + double = "double", + numeric = "double", + raw = "binary", + list = "array", + environment = "map", + Date = "date", + POSIXlt = "timestamp", + POSIXct = "timestamp", + stop(paste("Unsupported type for DataFrame:", class(x)))) + + if (type == "map") { + stopifnot(length(x) > 0) + key <- ls(x)[[1]] + list(type = "map", + keyType = "string", + valueType = infer_type(get(key, x)), + valueContainsNull = TRUE) + } else if (type == "array") { + stopifnot(length(x) > 0) + names <- names(x) + if (is.null(names)) { + list(type = "array", elementType = infer_type(x[[1]]), containsNull = TRUE) + } else { + # StructType + types <- lapply(x, infer_type) + fields <- lapply(1:length(x), function(i) { + list(name = names[[i]], type = types[[i]], nullable = TRUE) + }) + list(type = "struct", fields = fields) + } + } else if (length(x) > 1) { + list(type = "array", elementType = type, containsNull = TRUE) + } else { + type + } +} + +#' dump the schema into JSON string +tojson <- function(x) { + if (is.list(x)) { + names <- names(x) + if (!is.null(names)) { + items <- lapply(names, function(n) { + safe_n <- gsub('"', '\\"', n) + paste(tojson(safe_n), ':', tojson(x[[n]]), sep = '') + }) + d <- paste(items, collapse = ', ') + paste('{', d, '}', sep = '') + } else { + l <- paste(lapply(x, tojson), collapse = ', ') + paste('[', l, ']', sep = '') + } + } else if (is.character(x)) { + paste('"', x, '"', sep = '') + } else if (is.logical(x)) { + if (x) "true" else "false" + } else { + stop(paste("unexpected type:", class(x))) + } +} + +#' Create a DataFrame from an RDD +#' +#' Converts an RDD to a DataFrame by infer the types. +#' +#' @param sqlCtx A SQLContext +#' @param data An RDD or list or data.frame +#' @param schema a list of column names or named list (StructType), optional +#' @return an DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' rdd <- lapply(parallelize(sc, 1:10), function(x) list(a=x, b=as.character(x))) +#' df <- createDataFrame(sqlCtx, rdd) +#' } + +# TODO(davies): support sampling and infer type from NA +createDataFrame <- function(sqlCtx, data, schema = NULL, samplingRatio = 1.0) { + if (is.data.frame(data)) { + # get the names of columns, they will be put into RDD + schema <- names(data) + n <- nrow(data) + m <- ncol(data) + # get rid of factor type + dropFactor <- function(x) { + if (is.factor(x)) { + as.character(x) + } else { + x + } + } + data <- lapply(1:n, function(i) { + lapply(1:m, function(j) { dropFactor(data[i,j]) }) + }) + } + if (is.list(data)) { + sc <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "getJavaSparkContext", sqlCtx) + rdd <- parallelize(sc, data) + } else if (inherits(data, "RDD")) { + rdd <- data + } else { + stop(paste("unexpected type:", class(data))) + } + + if (is.null(schema) || is.null(names(schema))) { + row <- first(rdd) + names <- if (is.null(schema)) { + names(row) + } else { + as.list(schema) + } + if (is.null(names)) { + names <- lapply(1:length(row), function(x) { + paste("_", as.character(x), sep = "") + }) + } + + # SPAKR-SQL does not support '.' in column name, so replace it with '_' + # TODO(davies): remove this once SPARK-2775 is fixed + names <- lapply(names, function(n) { + nn <- gsub("[.]", "_", n) + if (nn != n) { + warning(paste("Use", nn, "instead of", n, " as column name")) + } + nn + }) + + types <- lapply(row, infer_type) + fields <- lapply(1:length(row), function(i) { + list(name = names[[i]], type = types[[i]], nullable = TRUE) + }) + schema <- list(type = "struct", fields = fields) + } + + stopifnot(class(schema) == "list") + stopifnot(schema$type == "struct") + stopifnot(class(schema$fields) == "list") + schemaString <- tojson(schema) + + jrdd <- getJRDD(lapply(rdd, function(x) x), "row") + srdd <- callJMethod(jrdd, "rdd") + sdf <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "createDF", + srdd, schemaString, sqlCtx) + dataFrame(sdf) +} + +#' toDF +#' +#' Converts an RDD to a DataFrame by infer the types. +#' +#' @param x An RDD +#' +#' @rdname DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' rdd <- lapply(parallelize(sc, 1:10), function(x) list(a=x, b=as.character(x))) +#' df <- toDF(rdd) +#' } + +setGeneric("toDF", function(x, ...) { standardGeneric("toDF") }) + +setMethod("toDF", signature(x = "RDD"), + function(x, ...) { + sqlCtx <- if (exists(".sparkRHivesc", envir = .sparkREnv)) { + get(".sparkRHivesc", envir = .sparkREnv) + } else if (exists(".sparkRSQLsc", envir = .sparkREnv)) { + get(".sparkRSQLsc", envir = .sparkREnv) + } else { + stop("no SQL context available") + } + createDataFrame(sqlCtx, x, ...) + }) + +#' Create a DataFrame from a JSON file. +#' +#' Loads a JSON file (one object per line), returning the result as a DataFrame +#' It goes through the entire dataset once to determine the schema. +#' +#' @param sqlCtx SQLContext to use +#' @param path Path of file to read. A vector of multiple paths is allowed. +#' @return DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' } + +jsonFile <- function(sqlCtx, path) { + # Allow the user to have a more flexible definiton of the text file path + path <- normalizePath(path) + # Convert a string vector of paths to a string containing comma separated paths + path <- paste(path, collapse = ",") + sdf <- callJMethod(sqlCtx, "jsonFile", path) + dataFrame(sdf) +} + + +#' JSON RDD +#' +#' Loads an RDD storing one JSON object per string as a DataFrame. +#' +#' @param sqlCtx SQLContext to use +#' @param rdd An RDD of JSON string +#' @param schema A StructType object to use as schema +#' @param samplingRatio The ratio of simpling used to infer the schema +#' @return A DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' rdd <- texFile(sc, "path/to/json") +#' df <- jsonRDD(sqlCtx, rdd) +#' } + +# TODO: support schema +jsonRDD <- function(sqlCtx, rdd, schema = NULL, samplingRatio = 1.0) { + rdd <- serializeToString(rdd) + if (is.null(schema)) { + sdf <- callJMethod(sqlCtx, "jsonRDD", callJMethod(getJRDD(rdd), "rdd"), samplingRatio) + dataFrame(sdf) + } else { + stop("not implemented") + } +} + + +#' Create a DataFrame from a Parquet file. +#' +#' Loads a Parquet file, returning the result as a DataFrame. +#' +#' @param sqlCtx SQLContext to use +#' @param ... Path(s) of parquet file(s) to read. +#' @return DataFrame +#' @export + +# TODO: Implement saveasParquetFile and write examples for both +parquetFile <- function(sqlCtx, ...) { + # Allow the user to have a more flexible definiton of the text file path + paths <- lapply(list(...), normalizePath) + sdf <- callJMethod(sqlCtx, "parquetFile", paths) + dataFrame(sdf) +} + +#' SQL Query +#' +#' Executes a SQL query using Spark, returning the result as a DataFrame. +#' +#' @param sqlCtx SQLContext to use +#' @param sqlQuery A character vector containing the SQL query +#' @return DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' registerTempTable(df, "table") +#' new_df <- sql(sqlCtx, "SELECT * FROM table") +#' } + +sql <- function(sqlCtx, sqlQuery) { + sdf <- callJMethod(sqlCtx, "sql", sqlQuery) + dataFrame(sdf) +} + + +#' Create a DataFrame from a SparkSQL Table +#' +#' Returns the specified Table as a DataFrame. The Table must have already been registered +#' in the SQLContext. +#' +#' @param sqlCtx SQLContext to use +#' @param tableName The SparkSQL Table to convert to a DataFrame. +#' @return DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' registerTempTable(df, "table") +#' new_df <- table(sqlCtx, "table") +#' } + +table <- function(sqlCtx, tableName) { + sdf <- callJMethod(sqlCtx, "table", tableName) + dataFrame(sdf) +} + + +#' Tables +#' +#' Returns a DataFrame containing names of tables in the given database. +#' +#' @param sqlCtx SQLContext to use +#' @param databaseName name of the database +#' @return a DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' tables(sqlCtx, "hive") +#' } + +tables <- function(sqlCtx, databaseName = NULL) { + jdf <- if (is.null(databaseName)) { + callJMethod(sqlCtx, "tables") + } else { + callJMethod(sqlCtx, "tables", databaseName) + } + dataFrame(jdf) +} + + +#' Table Names +#' +#' Returns the names of tables in the given database as an array. +#' +#' @param sqlCtx SQLContext to use +#' @param databaseName name of the database +#' @return a list of table names +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' tableNames(sqlCtx, "hive") +#' } + +tableNames <- function(sqlCtx, databaseName = NULL) { + if (is.null(databaseName)) { + callJMethod(sqlCtx, "tableNames") + } else { + callJMethod(sqlCtx, "tableNames", databaseName) + } +} + + +#' Cache Table +#' +#' Caches the specified table in-memory. +#' +#' @param sqlCtx SQLContext to use +#' @param tableName The name of the table being cached +#' @return DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' registerTempTable(df, "table") +#' cacheTable(sqlCtx, "table") +#' } + +cacheTable <- function(sqlCtx, tableName) { + callJMethod(sqlCtx, "cacheTable", tableName) +} + +#' Uncache Table +#' +#' Removes the specified table from the in-memory cache. +#' +#' @param sqlCtx SQLContext to use +#' @param tableName The name of the table being uncached +#' @return DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlCtx, path) +#' registerTempTable(df, "table") +#' uncacheTable(sqlCtx, "table") +#' } + +uncacheTable <- function(sqlCtx, tableName) { + callJMethod(sqlCtx, "uncacheTable", tableName) +} + +#' Clear Cache +#' +#' Removes all cached tables from the in-memory cache. +#' +#' @param sqlCtx SQLContext to use +#' @examples +#' \dontrun{ +#' clearCache(sqlCtx) +#' } + +clearCache <- function(sqlCtx) { + callJMethod(sqlCtx, "clearCache") +} + +#' Drop Temporary Table +#' +#' Drops the temporary table with the given table name in the catalog. +#' If the table has been cached/persisted before, it's also unpersisted. +#' +#' @param sqlCtx SQLContext to use +#' @param tableName The name of the SparkSQL table to be dropped. +#' @examples +#' \dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' df <- loadDF(sqlCtx, path, "parquet") +#' registerTempTable(df, "table") +#' dropTempTable(sqlCtx, "table") +#' } + +dropTempTable <- function(sqlCtx, tableName) { + if (class(tableName) != "character") { + stop("tableName must be a string.") + } + callJMethod(sqlCtx, "dropTempTable", tableName) +} + +#' Load an DataFrame +#' +#' Returns the dataset in a data source as a DataFrame +#' +#' The data source is specified by the `source` and a set of options(...). +#' If `source` is not specified, the default data source configured by +#' "spark.sql.sources.default" will be used. +#' +#' @param sqlCtx SQLContext to use +#' @param path The path of files to load +#' @param source the name of external data source +#' @return DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' df <- load(sqlCtx, "path/to/file.json", source = "json") +#' } + +loadDF <- function(sqlCtx, path = NULL, source = NULL, ...) { + options <- varargsToEnv(...) + if (!is.null(path)) { + options[['path']] <- path + } + sdf <- callJMethod(sqlCtx, "load", source, options) + dataFrame(sdf) +} + +#' Create an external table +#' +#' Creates an external table based on the dataset in a data source, +#' Returns the DataFrame associated with the external table. +#' +#' The data source is specified by the `source` and a set of options(...). +#' If `source` is not specified, the default data source configured by +#' "spark.sql.sources.default" will be used. +#' +#' @param sqlCtx SQLContext to use +#' @param tableName A name of the table +#' @param path The path of files to load +#' @param source the name of external data source +#' @return DataFrame +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' df <- sparkRSQL.createExternalTable(sqlCtx, "myjson", path="path/to/json", source="json") +#' } + +createExternalTable <- function(sqlCtx, tableName, path = NULL, source = NULL, ...) { + options <- varargsToEnv(...) + if (!is.null(path)) { + options[['path']] <- path + } + sdf <- callJMethod(sqlCtx, "createExternalTable", tableName, source, options) + dataFrame(sdf) +} diff --git a/R/pkg/R/SQLTypes.R b/R/pkg/R/SQLTypes.R new file mode 100644 index 0000000000000..962fba5b3cf03 --- /dev/null +++ b/R/pkg/R/SQLTypes.R @@ -0,0 +1,64 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Utility functions for handling SparkSQL DataTypes. + +# Handler for StructType +structType <- function(st) { + obj <- structure(new.env(parent = emptyenv()), class = "structType") + obj$jobj <- st + obj$fields <- function() { lapply(callJMethod(st, "fields"), structField) } + obj +} + +#' Print a Spark StructType. +#' +#' This function prints the contents of a StructType returned from the +#' SparkR JVM backend. +#' +#' @param x A StructType object +#' @param ... further arguments passed to or from other methods +print.structType <- function(x, ...) { + fieldsList <- lapply(x$fields(), function(i) { i$print() }) + print(fieldsList) +} + +# Handler for StructField +structField <- function(sf) { + obj <- structure(new.env(parent = emptyenv()), class = "structField") + obj$jobj <- sf + obj$name <- function() { callJMethod(sf, "name") } + obj$dataType <- function() { callJMethod(sf, "dataType") } + obj$dataType.toString <- function() { callJMethod(obj$dataType(), "toString") } + obj$dataType.simpleString <- function() { callJMethod(obj$dataType(), "simpleString") } + obj$nullable <- function() { callJMethod(sf, "nullable") } + obj$print <- function() { paste("StructField(", + paste(obj$name(), obj$dataType.toString(), obj$nullable(), sep = ", "), + ")", sep = "") } + obj +} + +#' Print a Spark StructField. +#' +#' This function prints the contents of a StructField returned from the +#' SparkR JVM backend. +#' +#' @param x A StructField object +#' @param ... further arguments passed to or from other methods +print.structField <- function(x, ...) { + cat(x$print()) +} diff --git a/R/pkg/R/backend.R b/R/pkg/R/backend.R new file mode 100644 index 0000000000000..2fb6fae55f28c --- /dev/null +++ b/R/pkg/R/backend.R @@ -0,0 +1,115 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Methods to call into SparkRBackend. + + +# Returns TRUE if object is an instance of given class +isInstanceOf <- function(jobj, className) { + stopifnot(class(jobj) == "jobj") + cls <- callJStatic("java.lang.Class", "forName", className) + callJMethod(cls, "isInstance", jobj) +} + +# Call a Java method named methodName on the object +# specified by objId. objId should be a "jobj" returned +# from the SparkRBackend. +callJMethod <- function(objId, methodName, ...) { + stopifnot(class(objId) == "jobj") + if (!isValidJobj(objId)) { + stop("Invalid jobj ", objId$id, + ". If SparkR was restarted, Spark operations need to be re-executed.") + } + invokeJava(isStatic = FALSE, objId$id, methodName, ...) +} + +# Call a static method on a specified className +callJStatic <- function(className, methodName, ...) { + invokeJava(isStatic = TRUE, className, methodName, ...) +} + +# Create a new object of the specified class name +newJObject <- function(className, ...) { + invokeJava(isStatic = TRUE, className, methodName = "", ...) +} + +# Remove an object from the SparkR backend. This is done +# automatically when a jobj is garbage collected. +removeJObject <- function(objId) { + invokeJava(isStatic = TRUE, "SparkRHandler", "rm", objId) +} + +isRemoveMethod <- function(isStatic, objId, methodName) { + isStatic == TRUE && objId == "SparkRHandler" && methodName == "rm" +} + +# Invoke a Java method on the SparkR backend. Users +# should typically use one of the higher level methods like +# callJMethod, callJStatic etc. instead of using this. +# +# isStatic - TRUE if the method to be called is static +# objId - String that refers to the object on which method is invoked +# Should be a jobj id for non-static methods and the classname +# for static methods +# methodName - name of method to be invoked +invokeJava <- function(isStatic, objId, methodName, ...) { + if (!exists(".sparkRCon", .sparkREnv)) { + stop("No connection to backend found. Please re-run sparkR.init") + } + + # If this isn't a removeJObject call + if (!isRemoveMethod(isStatic, objId, methodName)) { + objsToRemove <- ls(.toRemoveJobjs) + if (length(objsToRemove) > 0) { + sapply(objsToRemove, + function(e) { + removeJObject(e) + }) + rm(list = objsToRemove, envir = .toRemoveJobjs) + } + } + + + rc <- rawConnection(raw(0), "r+") + + writeBoolean(rc, isStatic) + writeString(rc, objId) + writeString(rc, methodName) + + args <- list(...) + writeInt(rc, length(args)) + writeArgs(rc, args) + + # Construct the whole request message to send it once, + # avoiding write-write-read pattern in case of Nagle's algorithm. + # Refer to http://en.wikipedia.org/wiki/Nagle%27s_algorithm for the details. + bytesToSend <- rawConnectionValue(rc) + close(rc) + rc <- rawConnection(raw(0), "r+") + writeInt(rc, length(bytesToSend)) + writeBin(bytesToSend, rc) + requestMessage <- rawConnectionValue(rc) + close(rc) + + conn <- get(".sparkRCon", .sparkREnv) + writeBin(requestMessage, conn) + + # TODO: check the status code to output error information + returnStatus <- readInt(conn) + stopifnot(returnStatus == 0) + readObject(conn) +} diff --git a/R/pkg/R/broadcast.R b/R/pkg/R/broadcast.R new file mode 100644 index 0000000000000..583fa2e7fdcfd --- /dev/null +++ b/R/pkg/R/broadcast.R @@ -0,0 +1,86 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# S4 class representing Broadcast variables + +# Hidden environment that holds values for broadcast variables +# This will not be serialized / shipped by default +.broadcastNames <- new.env() +.broadcastValues <- new.env() +.broadcastIdToName <- new.env() + +#' @title S4 class that represents a Broadcast variable +#' @description Broadcast variables can be created using the broadcast +#' function from a \code{SparkContext}. +#' @rdname broadcast-class +#' @seealso broadcast +#' +#' @param id Id of the backing Spark broadcast variable +#' @export +setClass("Broadcast", slots = list(id = "character")) + +#' @rdname broadcast-class +#' @param value Value of the broadcast variable +#' @param jBroadcastRef reference to the backing Java broadcast object +#' @param objName name of broadcasted object +#' @export +Broadcast <- function(id, value, jBroadcastRef, objName) { + .broadcastValues[[id]] <- value + .broadcastNames[[as.character(objName)]] <- jBroadcastRef + .broadcastIdToName[[id]] <- as.character(objName) + new("Broadcast", id = id) +} + +#' @description +#' \code{value} can be used to get the value of a broadcast variable inside +#' a distributed function. +#' +#' @param bcast The broadcast variable to get +#' @rdname broadcast +#' @aliases value,Broadcast-method +setMethod("value", + signature(bcast = "Broadcast"), + function(bcast) { + if (exists(bcast@id, envir = .broadcastValues)) { + get(bcast@id, envir = .broadcastValues) + } else { + NULL + } + }) + +#' Internal function to set values of a broadcast variable. +#' +#' This function is used internally by Spark to set the value of a broadcast +#' variable on workers. Not intended for use outside the package. +#' +#' @rdname broadcast-internal +#' @seealso broadcast, value + +#' @param bcastId The id of broadcast variable to set +#' @param value The value to be set +#' @export +setBroadcastValue <- function(bcastId, value) { + bcastIdStr <- as.character(bcastId) + .broadcastValues[[bcastIdStr]] <- value +} + +#' Helper function to clear the list of broadcast variables we know about +#' Should be called when the SparkR JVM backend is shutdown +clearBroadcastVariables <- function() { + bcasts <- ls(.broadcastNames) + rm(list = bcasts, envir = .broadcastNames) +} diff --git a/R/pkg/R/client.R b/R/pkg/R/client.R new file mode 100644 index 0000000000000..1281c41213e32 --- /dev/null +++ b/R/pkg/R/client.R @@ -0,0 +1,57 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Client code to connect to SparkRBackend + +# Creates a SparkR client connection object +# if one doesn't already exist +connectBackend <- function(hostname, port, timeout = 6000) { + if (exists(".sparkRcon", envir = .sparkREnv)) { + if (isOpen(.sparkREnv[[".sparkRCon"]])) { + cat("SparkRBackend client connection already exists\n") + return(get(".sparkRcon", envir = .sparkREnv)) + } + } + + con <- socketConnection(host = hostname, port = port, server = FALSE, + blocking = TRUE, open = "wb", timeout = timeout) + + assign(".sparkRCon", con, envir = .sparkREnv) + con +} + +launchBackend <- function(args, sparkHome, jars, sparkSubmitOpts) { + if (.Platform$OS.type == "unix") { + sparkSubmitBinName = "spark-submit" + } else { + sparkSubmitBinName = "spark-submit.cmd" + } + + if (sparkHome != "") { + sparkSubmitBin <- file.path(sparkHome, "bin", sparkSubmitBinName) + } else { + sparkSubmitBin <- sparkSubmitBinName + } + + if (jars != "") { + jars <- paste("--jars", jars) + } + + combinedArgs <- paste(jars, sparkSubmitOpts, args, sep = " ") + cat("Launching java with spark-submit command", sparkSubmitBin, combinedArgs, "\n") + invisible(system2(sparkSubmitBin, combinedArgs, wait = F)) +} diff --git a/R/pkg/R/column.R b/R/pkg/R/column.R new file mode 100644 index 0000000000000..e196305186b9a --- /dev/null +++ b/R/pkg/R/column.R @@ -0,0 +1,199 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Column Class + +#' @include generics.R jobj.R +NULL + +setOldClass("jobj") + +#' @title S4 class that represents a DataFrame column +#' @description The column class supports unary, binary operations on DataFrame columns + +#' @rdname column +#' +#' @param jc reference to JVM DataFrame column +#' @export +setClass("Column", + slots = list(jc = "jobj")) + +setMethod("initialize", "Column", function(.Object, jc) { + .Object@jc <- jc + .Object +}) + +column <- function(jc) { + new("Column", jc) +} + +col <- function(x) { + column(callJStatic("org.apache.spark.sql.functions", "col", x)) +} + +#' @rdname show +setMethod("show", "Column", + function(object) { + cat("Column", callJMethod(object@jc, "toString"), "\n") + }) + +operators <- list( + "+" = "plus", "-" = "minus", "*" = "multiply", "/" = "divide", "%%" = "mod", + "==" = "equalTo", ">" = "gt", "<" = "lt", "!=" = "notEqual", "<=" = "leq", ">=" = "geq", + # we can not override `&&` and `||`, so use `&` and `|` instead + "&" = "and", "|" = "or" #, "!" = "unary_$bang" +) +column_functions1 <- c("asc", "desc", "isNull", "isNotNull") +column_functions2 <- c("like", "rlike", "startsWith", "endsWith", "getField", "getItem", "contains") +functions <- c("min", "max", "sum", "avg", "mean", "count", "abs", "sqrt", + "first", "last", "lower", "upper", "sumDistinct") + +createOperator <- function(op) { + setMethod(op, + signature(e1 = "Column"), + function(e1, e2) { + jc <- if (missing(e2)) { + if (op == "-") { + callJMethod(e1@jc, "unary_$minus") + } else { + callJMethod(e1@jc, operators[[op]]) + } + } else { + if (class(e2) == "Column") { + e2 <- e2@jc + } + callJMethod(e1@jc, operators[[op]], e2) + } + column(jc) + }) +} + +createColumnFunction1 <- function(name) { + setMethod(name, + signature(x = "Column"), + function(x) { + column(callJMethod(x@jc, name)) + }) +} + +createColumnFunction2 <- function(name) { + setMethod(name, + signature(x = "Column"), + function(x, data) { + if (class(data) == "Column") { + data <- data@jc + } + jc <- callJMethod(x@jc, name, data) + column(jc) + }) +} + +createStaticFunction <- function(name) { + setMethod(name, + signature(x = "Column"), + function(x) { + jc <- callJStatic("org.apache.spark.sql.functions", name, x@jc) + column(jc) + }) +} + +createMethods <- function() { + for (op in names(operators)) { + createOperator(op) + } + for (name in column_functions1) { + createColumnFunction1(name) + } + for (name in column_functions2) { + createColumnFunction2(name) + } + for (x in functions) { + createStaticFunction(x) + } +} + +createMethods() + +#' alias +#' +#' Set a new name for a column +setMethod("alias", + signature(object = "Column"), + function(object, data) { + if (is.character(data)) { + column(callJMethod(object@jc, "as", data)) + } else { + stop("data should be character") + } + }) + +#' An expression that returns a substring. +#' +#' @param start starting position +#' @param stop ending position +setMethod("substr", signature(x = "Column"), + function(x, start, stop) { + jc <- callJMethod(x@jc, "substr", as.integer(start - 1), as.integer(stop - start + 1)) + column(jc) + }) + +#' Casts the column to a different data type. +#' @examples +#' \dontrun{ +#' cast(df$age, "string") +#' cast(df$name, list(type="array", elementType="byte", containsNull = TRUE)) +#' } +setMethod("cast", + signature(x = "Column"), + function(x, dataType) { + if (is.character(dataType)) { + column(callJMethod(x@jc, "cast", dataType)) + } else if (is.list(dataType)) { + json <- tojson(dataType) + jdataType <- callJStatic("org.apache.spark.sql.types.DataType", "fromJson", json) + column(callJMethod(x@jc, "cast", jdataType)) + } else { + stop("dataType should be character or list") + } + }) + +#' Approx Count Distinct +#' +#' Returns the approximate number of distinct items in a group. +#' +setMethod("approxCountDistinct", + signature(x = "Column"), + function(x, rsd = 0.95) { + jc <- callJStatic("org.apache.spark.sql.functions", "approxCountDistinct", x@jc, rsd) + column(jc) + }) + +#' Count Distinct +#' +#' returns the number of distinct items in a group. +#' +setMethod("countDistinct", + signature(x = "Column"), + function(x, ...) { + jcol <- lapply(list(...), function (x) { + x@jc + }) + jc <- callJStatic("org.apache.spark.sql.functions", "countDistinct", x@jc, + listToSeq(jcol)) + column(jc) + }) + diff --git a/R/pkg/R/context.R b/R/pkg/R/context.R new file mode 100644 index 0000000000000..2fc0bb294bcce --- /dev/null +++ b/R/pkg/R/context.R @@ -0,0 +1,225 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# context.R: SparkContext driven functions + +getMinSplits <- function(sc, minSplits) { + if (is.null(minSplits)) { + defaultParallelism <- callJMethod(sc, "defaultParallelism") + minSplits <- min(defaultParallelism, 2) + } + as.integer(minSplits) +} + +#' Create an RDD from a text file. +#' +#' This function reads a text file from HDFS, a local file system (available on all +#' nodes), or any Hadoop-supported file system URI, and creates an +#' RDD of strings from it. +#' +#' @param sc SparkContext to use +#' @param path Path of file to read. A vector of multiple paths is allowed. +#' @param minSplits Minimum number of splits to be created. If NULL, the default +#' value is chosen based on available parallelism. +#' @return RDD where each item is of type \code{character} +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' lines <- textFile(sc, "myfile.txt") +#'} +textFile <- function(sc, path, minSplits = NULL) { + # Allow the user to have a more flexible definiton of the text file path + path <- suppressWarnings(normalizePath(path)) + #' Convert a string vector of paths to a string containing comma separated paths + path <- paste(path, collapse = ",") + + jrdd <- callJMethod(sc, "textFile", path, getMinSplits(sc, minSplits)) + # jrdd is of type JavaRDD[String] + RDD(jrdd, "string") +} + +#' Load an RDD saved as a SequenceFile containing serialized objects. +#' +#' The file to be loaded should be one that was previously generated by calling +#' saveAsObjectFile() of the RDD class. +#' +#' @param sc SparkContext to use +#' @param path Path of file to read. A vector of multiple paths is allowed. +#' @param minSplits Minimum number of splits to be created. If NULL, the default +#' value is chosen based on available parallelism. +#' @return RDD containing serialized R objects. +#' @seealso saveAsObjectFile +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- objectFile(sc, "myfile") +#'} +objectFile <- function(sc, path, minSplits = NULL) { + # Allow the user to have a more flexible definiton of the text file path + path <- suppressWarnings(normalizePath(path)) + #' Convert a string vector of paths to a string containing comma separated paths + path <- paste(path, collapse = ",") + + jrdd <- callJMethod(sc, "objectFile", path, getMinSplits(sc, minSplits)) + # Assume the RDD contains serialized R objects. + RDD(jrdd, "byte") +} + +#' Create an RDD from a homogeneous list or vector. +#' +#' This function creates an RDD from a local homogeneous list in R. The elements +#' in the list are split into \code{numSlices} slices and distributed to nodes +#' in the cluster. +#' +#' @param sc SparkContext to use +#' @param coll collection to parallelize +#' @param numSlices number of partitions to create in the RDD +#' @return an RDD created from this collection +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10, 2) +#' # The RDD should contain 10 elements +#' length(rdd) +#'} +parallelize <- function(sc, coll, numSlices = 1) { + # TODO: bound/safeguard numSlices + # TODO: unit tests for if the split works for all primitives + # TODO: support matrix, data frame, etc + if ((!is.list(coll) && !is.vector(coll)) || is.data.frame(coll)) { + if (is.data.frame(coll)) { + message(paste("context.R: A data frame is parallelized by columns.")) + } else { + if (is.matrix(coll)) { + message(paste("context.R: A matrix is parallelized by elements.")) + } else { + message(paste("context.R: parallelize() currently only supports lists and vectors.", + "Calling as.list() to coerce coll into a list.")) + } + } + coll <- as.list(coll) + } + + if (numSlices > length(coll)) + numSlices <- length(coll) + + sliceLen <- ceiling(length(coll) / numSlices) + slices <- split(coll, rep(1:(numSlices + 1), each = sliceLen)[1:length(coll)]) + + # Serialize each slice: obtain a list of raws, or a list of lists (slices) of + # 2-tuples of raws + serializedSlices <- lapply(slices, serialize, connection = NULL) + + jrdd <- callJStatic("org.apache.spark.api.r.RRDD", + "createRDDFromArray", sc, serializedSlices) + + RDD(jrdd, "byte") +} + +#' Include this specified package on all workers +#' +#' This function can be used to include a package on all workers before the +#' user's code is executed. This is useful in scenarios where other R package +#' functions are used in a function passed to functions like \code{lapply}. +#' NOTE: The package is assumed to be installed on every node in the Spark +#' cluster. +#' +#' @param sc SparkContext to use +#' @param pkg Package name +#' +#' @export +#' @examples +#'\dontrun{ +#' library(Matrix) +#' +#' sc <- sparkR.init() +#' # Include the matrix library we will be using +#' includePackage(sc, Matrix) +#' +#' generateSparse <- function(x) { +#' sparseMatrix(i=c(1, 2, 3), j=c(1, 2, 3), x=c(1, 2, 3)) +#' } +#' +#' rdd <- lapplyPartition(parallelize(sc, 1:2, 2L), generateSparse) +#' collect(rdd) +#'} +includePackage <- function(sc, pkg) { + pkg <- as.character(substitute(pkg)) + if (exists(".packages", .sparkREnv)) { + packages <- .sparkREnv$.packages + } else { + packages <- list() + } + packages <- c(packages, pkg) + .sparkREnv$.packages <- packages +} + +#' @title Broadcast a variable to all workers +#' +#' @description +#' Broadcast a read-only variable to the cluster, returning a \code{Broadcast} +#' object for reading it in distributed functions. +#' +#' @param sc Spark Context to use +#' @param object Object to be broadcast +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:2, 2L) +#' +#' # Large Matrix object that we want to broadcast +#' randomMat <- matrix(nrow=100, ncol=10, data=rnorm(1000)) +#' randomMatBr <- broadcast(sc, randomMat) +#' +#' # Use the broadcast variable inside the function +#' useBroadcast <- function(x) { +#' sum(value(randomMatBr) * x) +#' } +#' sumRDD <- lapply(rdd, useBroadcast) +#'} +broadcast <- function(sc, object) { + objName <- as.character(substitute(object)) + serializedObj <- serialize(object, connection = NULL) + + jBroadcast <- callJMethod(sc, "broadcast", serializedObj) + id <- as.character(callJMethod(jBroadcast, "id")) + + Broadcast(id, object, jBroadcast, objName) +} + +#' @title Set the checkpoint directory +#' +#' Set the directory under which RDDs are going to be checkpointed. The +#' directory must be a HDFS path if running on a cluster. +#' +#' @param sc Spark Context to use +#' @param dirName Directory path +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' setCheckpointDir(sc, "~/checkpoints") +#' rdd <- parallelize(sc, 1:2, 2L) +#' checkpoint(rdd) +#'} +setCheckpointDir <- function(sc, dirName) { + invisible(callJMethod(sc, "setCheckpointDir", suppressWarnings(normalizePath(dirName)))) +} diff --git a/R/pkg/R/deserialize.R b/R/pkg/R/deserialize.R new file mode 100644 index 0000000000000..257b435607ce8 --- /dev/null +++ b/R/pkg/R/deserialize.R @@ -0,0 +1,184 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Utility functions to deserialize objects from Java. + +# Type mapping from Java to R +# +# void -> NULL +# Int -> integer +# String -> character +# Boolean -> logical +# Double -> double +# Long -> double +# Array[Byte] -> raw +# Date -> Date +# Time -> POSIXct +# +# Array[T] -> list() +# Object -> jobj + +readObject <- function(con) { + # Read type first + type <- readType(con) + readTypedObject(con, type) +} + +readTypedObject <- function(con, type) { + switch (type, + "i" = readInt(con), + "c" = readString(con), + "b" = readBoolean(con), + "d" = readDouble(con), + "r" = readRaw(con), + "D" = readDate(con), + "t" = readTime(con), + "l" = readList(con), + "n" = NULL, + "j" = getJobj(readString(con)), + stop(paste("Unsupported type for deserialization", type))) +} + +readString <- function(con) { + stringLen <- readInt(con) + string <- readBin(con, raw(), stringLen, endian = "big") + rawToChar(string) +} + +readInt <- function(con) { + readBin(con, integer(), n = 1, endian = "big") +} + +readDouble <- function(con) { + readBin(con, double(), n = 1, endian = "big") +} + +readBoolean <- function(con) { + as.logical(readInt(con)) +} + +readType <- function(con) { + rawToChar(readBin(con, "raw", n = 1L)) +} + +readDate <- function(con) { + as.Date(readString(con)) +} + +readTime <- function(con) { + t <- readDouble(con) + as.POSIXct(t, origin = "1970-01-01") +} + +# We only support lists where all elements are of same type +readList <- function(con) { + type <- readType(con) + len <- readInt(con) + if (len > 0) { + l <- vector("list", len) + for (i in 1:len) { + l[[i]] <- readTypedObject(con, type) + } + l + } else { + list() + } +} + +readRaw <- function(con) { + dataLen <- readInt(con) + data <- readBin(con, raw(), as.integer(dataLen), endian = "big") +} + +readRawLen <- function(con, dataLen) { + data <- readBin(con, raw(), as.integer(dataLen), endian = "big") +} + +readDeserialize <- function(con) { + # We have two cases that are possible - In one, the entire partition is + # encoded as a byte array, so we have only one value to read. If so just + # return firstData + dataLen <- readInt(con) + firstData <- unserialize( + readBin(con, raw(), as.integer(dataLen), endian = "big")) + + # Else, read things into a list + dataLen <- readInt(con) + if (length(dataLen) > 0 && dataLen > 0) { + data <- list(firstData) + while (length(dataLen) > 0 && dataLen > 0) { + data[[length(data) + 1L]] <- unserialize( + readBin(con, raw(), as.integer(dataLen), endian = "big")) + dataLen <- readInt(con) + } + unlist(data, recursive = FALSE) + } else { + firstData + } +} + +readDeserializeRows <- function(inputCon) { + # readDeserializeRows will deserialize a DataOutputStream composed of + # a list of lists. Since the DOS is one continuous stream and + # the number of rows varies, we put the readRow function in a while loop + # that termintates when the next row is empty. + data <- list() + while(TRUE) { + row <- readRow(inputCon) + if (length(row) == 0) { + break + } + data[[length(data) + 1L]] <- row + } + data # this is a list of named lists now +} + +readRowList <- function(obj) { + # readRowList is meant for use inside an lapply. As a result, it is + # necessary to open a standalone connection for the row and consume + # the numCols bytes inside the read function in order to correctly + # deserialize the row. + rawObj <- rawConnection(obj, "r+") + on.exit(close(rawObj)) + readRow(rawObj) +} + +readRow <- function(inputCon) { + numCols <- readInt(inputCon) + if (length(numCols) > 0 && numCols > 0) { + lapply(1:numCols, function(x) { + obj <- readObject(inputCon) + if (is.null(obj)) { + NA + } else { + obj + } + }) # each row is a list now + } else { + list() + } +} + +# Take a single column as Array[Byte] and deserialize it into an atomic vector +readCol <- function(inputCon, numRows) { + # sapply can not work with POSIXlt + do.call(c, lapply(1:numRows, function(x) { + value <- readObject(inputCon) + # Replace NULL with NA so we can coerce to vectors + if (is.null(value)) NA else value + })) +} diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R new file mode 100644 index 0000000000000..5fb1ccaa84ee2 --- /dev/null +++ b/R/pkg/R/generics.R @@ -0,0 +1,543 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +############ RDD Actions and Transformations ############ + +#' @rdname aggregateRDD +#' @seealso reduce +#' @export +setGeneric("aggregateRDD", function(x, zeroValue, seqOp, combOp) { standardGeneric("aggregateRDD") }) + +#' @rdname cache-methods +#' @export +setGeneric("cache", function(x) { standardGeneric("cache") }) + +#' @rdname coalesce +#' @seealso repartition +#' @export +setGeneric("coalesce", function(x, numPartitions, ...) { standardGeneric("coalesce") }) + +#' @rdname checkpoint-methods +#' @export +setGeneric("checkpoint", function(x) { standardGeneric("checkpoint") }) + +#' @rdname collect-methods +#' @export +setGeneric("collect", function(x, ...) { standardGeneric("collect") }) + +#' @rdname collect-methods +#' @export +setGeneric("collectAsMap", function(x) { standardGeneric("collectAsMap") }) + +#' @rdname collect-methods +#' @export +setGeneric("collectPartition", + function(x, partitionId) { + standardGeneric("collectPartition") + }) + +#' @rdname count +#' @export +setGeneric("count", function(x) { standardGeneric("count") }) + +#' @rdname countByValue +#' @export +setGeneric("countByValue", function(x) { standardGeneric("countByValue") }) + +#' @rdname distinct +#' @export +setGeneric("distinct", function(x, numPartitions = 1L) { standardGeneric("distinct") }) + +#' @rdname filterRDD +#' @export +setGeneric("filterRDD", function(x, f) { standardGeneric("filterRDD") }) + +#' @rdname first +#' @export +setGeneric("first", function(x) { standardGeneric("first") }) + +#' @rdname flatMap +#' @export +setGeneric("flatMap", function(X, FUN) { standardGeneric("flatMap") }) + +#' @rdname fold +#' @seealso reduce +#' @export +setGeneric("fold", function(x, zeroValue, op) { standardGeneric("fold") }) + +#' @rdname foreach +#' @export +setGeneric("foreach", function(x, func) { standardGeneric("foreach") }) + +#' @rdname foreach +#' @export +setGeneric("foreachPartition", function(x, func) { standardGeneric("foreachPartition") }) + +# The jrdd accessor function. +setGeneric("getJRDD", function(rdd, ...) { standardGeneric("getJRDD") }) + +#' @rdname glom +#' @export +setGeneric("glom", function(x) { standardGeneric("glom") }) + +#' @rdname keyBy +#' @export +setGeneric("keyBy", function(x, func) { standardGeneric("keyBy") }) + +#' @rdname lapplyPartition +#' @export +setGeneric("lapplyPartition", function(X, FUN) { standardGeneric("lapplyPartition") }) + +#' @rdname lapplyPartitionsWithIndex +#' @export +setGeneric("lapplyPartitionsWithIndex", + function(X, FUN) { + standardGeneric("lapplyPartitionsWithIndex") + }) + +#' @rdname lapply +#' @export +setGeneric("map", function(X, FUN) { standardGeneric("map") }) + +#' @rdname lapplyPartition +#' @export +setGeneric("mapPartitions", function(X, FUN) { standardGeneric("mapPartitions") }) + +#' @rdname lapplyPartitionsWithIndex +#' @export +setGeneric("mapPartitionsWithIndex", + function(X, FUN) { standardGeneric("mapPartitionsWithIndex") }) + +#' @rdname maximum +#' @export +setGeneric("maximum", function(x) { standardGeneric("maximum") }) + +#' @rdname minimum +#' @export +setGeneric("minimum", function(x) { standardGeneric("minimum") }) + +#' @rdname sumRDD +#' @export +setGeneric("sumRDD", function(x) { standardGeneric("sumRDD") }) + +#' @rdname name +#' @export +setGeneric("name", function(x) { standardGeneric("name") }) + +#' @rdname numPartitions +#' @export +setGeneric("numPartitions", function(x) { standardGeneric("numPartitions") }) + +#' @rdname persist +#' @export +setGeneric("persist", function(x, newLevel) { standardGeneric("persist") }) + +#' @rdname pipeRDD +#' @export +setGeneric("pipeRDD", function(x, command, env = list()) { standardGeneric("pipeRDD")}) + +#' @rdname reduce +#' @export +setGeneric("reduce", function(x, func) { standardGeneric("reduce") }) + +#' @rdname repartition +#' @seealso coalesce +#' @export +setGeneric("repartition", function(x, numPartitions) { standardGeneric("repartition") }) + +#' @rdname sampleRDD +#' @export +setGeneric("sampleRDD", + function(x, withReplacement, fraction, seed) { + standardGeneric("sampleRDD") + }) + +#' @rdname saveAsObjectFile +#' @seealso objectFile +#' @export +setGeneric("saveAsObjectFile", function(x, path) { standardGeneric("saveAsObjectFile") }) + +#' @rdname saveAsTextFile +#' @export +setGeneric("saveAsTextFile", function(x, path) { standardGeneric("saveAsTextFile") }) + +#' @rdname setName +#' @export +setGeneric("setName", function(x, name) { standardGeneric("setName") }) + +#' @rdname sortBy +#' @export +setGeneric("sortBy", + function(x, func, ascending = TRUE, numPartitions = 1L) { + standardGeneric("sortBy") + }) + +#' @rdname take +#' @export +setGeneric("take", function(x, num) { standardGeneric("take") }) + +#' @rdname takeOrdered +#' @export +setGeneric("takeOrdered", function(x, num) { standardGeneric("takeOrdered") }) + +#' @rdname takeSample +#' @export +setGeneric("takeSample", + function(x, withReplacement, num, seed) { + standardGeneric("takeSample") + }) + +#' @rdname top +#' @export +setGeneric("top", function(x, num) { standardGeneric("top") }) + +#' @rdname unionRDD +#' @export +setGeneric("unionRDD", function(x, y) { standardGeneric("unionRDD") }) + +#' @rdname unpersist-methods +#' @export +setGeneric("unpersist", function(x, ...) { standardGeneric("unpersist") }) + +#' @rdname zipRDD +#' @export +setGeneric("zipRDD", function(x, other) { standardGeneric("zipRDD") }) + +#' @rdname zipWithIndex +#' @seealso zipWithUniqueId +#' @export +setGeneric("zipWithIndex", function(x) { standardGeneric("zipWithIndex") }) + +#' @rdname zipWithUniqueId +#' @seealso zipWithIndex +#' @export +setGeneric("zipWithUniqueId", function(x) { standardGeneric("zipWithUniqueId") }) + + +############ Binary Functions ############# + +#' @rdname countByKey +#' @export +setGeneric("countByKey", function(x) { standardGeneric("countByKey") }) + +#' @rdname flatMapValues +#' @export +setGeneric("flatMapValues", function(X, FUN) { standardGeneric("flatMapValues") }) + +#' @rdname keys +#' @export +setGeneric("keys", function(x) { standardGeneric("keys") }) + +#' @rdname lookup +#' @export +setGeneric("lookup", function(x, key) { standardGeneric("lookup") }) + +#' @rdname mapValues +#' @export +setGeneric("mapValues", function(X, FUN) { standardGeneric("mapValues") }) + +#' @rdname values +#' @export +setGeneric("values", function(x) { standardGeneric("values") }) + + + +############ Shuffle Functions ############ + +#' @rdname aggregateByKey +#' @seealso foldByKey, combineByKey +#' @export +setGeneric("aggregateByKey", + function(x, zeroValue, seqOp, combOp, numPartitions) { + standardGeneric("aggregateByKey") + }) + +#' @rdname cogroup +#' @export +setGeneric("cogroup", + function(..., numPartitions) { + standardGeneric("cogroup") + }, + signature = "...") + +#' @rdname combineByKey +#' @seealso groupByKey, reduceByKey +#' @export +setGeneric("combineByKey", + function(x, createCombiner, mergeValue, mergeCombiners, numPartitions) { + standardGeneric("combineByKey") + }) + +#' @rdname foldByKey +#' @seealso aggregateByKey, combineByKey +#' @export +setGeneric("foldByKey", + function(x, zeroValue, func, numPartitions) { + standardGeneric("foldByKey") + }) + +#' @rdname join-methods +#' @export +setGeneric("fullOuterJoin", function(x, y, numPartitions) { standardGeneric("fullOuterJoin") }) + +#' @rdname groupByKey +#' @seealso reduceByKey +#' @export +setGeneric("groupByKey", function(x, numPartitions) { standardGeneric("groupByKey") }) + +#' @rdname join-methods +#' @export +setGeneric("join", function(x, y, ...) { standardGeneric("join") }) + +#' @rdname join-methods +#' @export +setGeneric("leftOuterJoin", function(x, y, numPartitions) { standardGeneric("leftOuterJoin") }) + +#' @rdname partitionBy +#' @export +setGeneric("partitionBy", function(x, numPartitions, ...) { standardGeneric("partitionBy") }) + +#' @rdname reduceByKey +#' @seealso groupByKey +#' @export +setGeneric("reduceByKey", function(x, combineFunc, numPartitions) { standardGeneric("reduceByKey")}) + +#' @rdname reduceByKeyLocally +#' @seealso reduceByKey +#' @export +setGeneric("reduceByKeyLocally", + function(x, combineFunc) { + standardGeneric("reduceByKeyLocally") + }) + +#' @rdname join-methods +#' @export +setGeneric("rightOuterJoin", function(x, y, numPartitions) { standardGeneric("rightOuterJoin") }) + +#' @rdname sortByKey +#' @export +setGeneric("sortByKey", function(x, ascending = TRUE, numPartitions = 1L) { + standardGeneric("sortByKey") +}) + + +################### Broadcast Variable Methods ################# + +#' @rdname broadcast +#' @export +setGeneric("value", function(bcast) { standardGeneric("value") }) + + + +#################### DataFrame Methods ######################## + +#' @rdname schema +#' @export +setGeneric("columns", function(x) {standardGeneric("columns") }) + +#' @rdname schema +#' @export +setGeneric("dtypes", function(x) { standardGeneric("dtypes") }) + +#' @rdname explain +#' @export +setGeneric("explain", function(x, ...) { standardGeneric("explain") }) + +#' @rdname filter +#' @export +setGeneric("filter", function(x, condition) { standardGeneric("filter") }) + +#' @rdname DataFrame +#' @export +setGeneric("groupBy", function(x, ...) { standardGeneric("groupBy") }) + +#' @rdname insertInto +#' @export +setGeneric("insertInto", function(x, tableName, ...) { standardGeneric("insertInto") }) + +#' @rdname intersect +#' @export +setGeneric("intersect", function(x, y) { standardGeneric("intersect") }) + +#' @rdname isLocal +#' @export +setGeneric("isLocal", function(x) { standardGeneric("isLocal") }) + +#' @rdname limit +#' @export +setGeneric("limit", function(x, num) {standardGeneric("limit") }) + +#' @rdname sortDF +#' @export +setGeneric("orderBy", function(x, col) { standardGeneric("orderBy") }) + +#' @rdname schema +#' @export +setGeneric("printSchema", function(x) { standardGeneric("printSchema") }) + +#' @rdname registerTempTable +#' @export +setGeneric("registerTempTable", function(x, tableName) { standardGeneric("registerTempTable") }) + +#' @rdname sampleDF +#' @export +setGeneric("sampleDF", + function(x, withReplacement, fraction, seed) { + standardGeneric("sampleDF") + }) + +#' @rdname saveAsParquetFile +#' @export +setGeneric("saveAsParquetFile", function(x, path) { standardGeneric("saveAsParquetFile") }) + +#' @rdname saveAsTable +#' @export +setGeneric("saveAsTable", function(df, tableName, source, mode, ...) { + standardGeneric("saveAsTable") +}) + +#' @rdname saveAsTable +#' @export +setGeneric("saveDF", function(df, path, source, mode, ...) { standardGeneric("saveDF") }) + +#' @rdname schema +#' @export +setGeneric("schema", function(x) { standardGeneric("schema") }) + +#' @rdname select +#' @export +setGeneric("select", function(x, col, ...) { standardGeneric("select") } ) + +#' @rdname select +#' @export +setGeneric("selectExpr", function(x, expr, ...) { standardGeneric("selectExpr") }) + +#' @rdname showDF +#' @export +setGeneric("showDF", function(x,...) { standardGeneric("showDF") }) + +#' @rdname sortDF +#' @export +setGeneric("sortDF", function(x, col, ...) { standardGeneric("sortDF") }) + +#' @rdname subtract +#' @export +setGeneric("subtract", function(x, y) { standardGeneric("subtract") }) + +#' @rdname tojson +#' @export +setGeneric("toJSON", function(x) { standardGeneric("toJSON") }) + +#' @rdname DataFrame +#' @export +setGeneric("toRDD", function(x) { standardGeneric("toRDD") }) + +#' @rdname unionAll +#' @export +setGeneric("unionAll", function(x, y) { standardGeneric("unionAll") }) + +#' @rdname filter +#' @export +setGeneric("where", function(x, condition) { standardGeneric("where") }) + +#' @rdname withColumn +#' @export +setGeneric("withColumn", function(x, colName, col) { standardGeneric("withColumn") }) + +#' @rdname withColumnRenamed +#' @export +setGeneric("withColumnRenamed", function(x, existingCol, newCol) { + standardGeneric("withColumnRenamed") }) + + +###################### Column Methods ########################## + +#' @rdname column +#' @export +setGeneric("approxCountDistinct", function(x, ...) { standardGeneric("approxCountDistinct") }) + +#' @rdname column +#' @export +setGeneric("asc", function(x) { standardGeneric("asc") }) + +#' @rdname column +#' @export +setGeneric("avg", function(x, ...) { standardGeneric("avg") }) + +#' @rdname column +#' @export +setGeneric("cast", function(x, dataType) { standardGeneric("cast") }) + +#' @rdname column +#' @export +setGeneric("contains", function(x, ...) { standardGeneric("contains") }) +#' @rdname column +#' @export +setGeneric("countDistinct", function(x, ...) { standardGeneric("countDistinct") }) + +#' @rdname column +#' @export +setGeneric("desc", function(x) { standardGeneric("desc") }) + +#' @rdname column +#' @export +setGeneric("endsWith", function(x, ...) { standardGeneric("endsWith") }) + +#' @rdname column +#' @export +setGeneric("getField", function(x, ...) { standardGeneric("getField") }) + +#' @rdname column +#' @export +setGeneric("getItem", function(x, ...) { standardGeneric("getItem") }) + +#' @rdname column +#' @export +setGeneric("isNull", function(x) { standardGeneric("isNull") }) + +#' @rdname column +#' @export +setGeneric("isNotNull", function(x) { standardGeneric("isNotNull") }) + +#' @rdname column +#' @export +setGeneric("last", function(x) { standardGeneric("last") }) + +#' @rdname column +#' @export +setGeneric("like", function(x, ...) { standardGeneric("like") }) + +#' @rdname column +#' @export +setGeneric("lower", function(x) { standardGeneric("lower") }) + +#' @rdname column +#' @export +setGeneric("rlike", function(x, ...) { standardGeneric("rlike") }) + +#' @rdname column +#' @export +setGeneric("startsWith", function(x, ...) { standardGeneric("startsWith") }) + +#' @rdname column +#' @export +setGeneric("sumDistinct", function(x) { standardGeneric("sumDistinct") }) + +#' @rdname column +#' @export +setGeneric("upper", function(x) { standardGeneric("upper") }) + diff --git a/R/pkg/R/group.R b/R/pkg/R/group.R new file mode 100644 index 0000000000000..09fc0a7abe48a --- /dev/null +++ b/R/pkg/R/group.R @@ -0,0 +1,132 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# group.R - GroupedData class and methods implemented in S4 OO classes + +setOldClass("jobj") + +#' @title S4 class that represents a GroupedData +#' @description GroupedDatas can be created using groupBy() on a DataFrame +#' @rdname GroupedData +#' @seealso groupBy +#' +#' @param sgd A Java object reference to the backing Scala GroupedData +#' @export +setClass("GroupedData", + slots = list(sgd = "jobj")) + +setMethod("initialize", "GroupedData", function(.Object, sgd) { + .Object@sgd <- sgd + .Object +}) + +#' @rdname DataFrame +groupedData <- function(sgd) { + new("GroupedData", sgd) +} + + +#' @rdname show +setMethod("show", "GroupedData", + function(object) { + cat("GroupedData\n") + }) + +#' Count +#' +#' Count the number of rows for each group. +#' The resulting DataFrame will also contain the grouping columns. +#' +#' @param x a GroupedData +#' @return a DataFrame +#' @export +#' @examples +#' \dontrun{ +#' count(groupBy(df, "name")) +#' } +setMethod("count", + signature(x = "GroupedData"), + function(x) { + dataFrame(callJMethod(x@sgd, "count")) + }) + +#' Agg +#' +#' Aggregates on the entire DataFrame without groups. +#' The resulting DataFrame will also contain the grouping columns. +#' +#' df2 <- agg(df, = ) +#' df2 <- agg(df, newColName = aggFunction(column)) +#' +#' @param x a GroupedData +#' @return a DataFrame +#' @rdname agg +#' @examples +#' \dontrun{ +#' df2 <- agg(df, age = "sum") # new column name will be created as 'SUM(age#0)' +#' df2 <- agg(df, ageSum = sum(df$age)) # Creates a new column named ageSum +#' } +setGeneric("agg", function (x, ...) { standardGeneric("agg") }) + +setMethod("agg", + signature(x = "GroupedData"), + function(x, ...) { + cols = list(...) + stopifnot(length(cols) > 0) + if (is.character(cols[[1]])) { + cols <- varargsToEnv(...) + sdf <- callJMethod(x@sgd, "agg", cols) + } else if (class(cols[[1]]) == "Column") { + ns <- names(cols) + if (!is.null(ns)) { + for (n in ns) { + if (n != "") { + cols[[n]] = alias(cols[[n]], n) + } + } + } + jcols <- lapply(cols, function(c) { c@jc }) + # the GroupedData.agg(col, cols*) API does not contain grouping Column + sdf <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "aggWithGrouping", + x@sgd, listToSeq(jcols)) + } else { + stop("agg can only support Column or character") + } + dataFrame(sdf) + }) + + +# sum/mean/avg/min/max +methods <- c("sum", "mean", "avg", "min", "max") + +createMethod <- function(name) { + setMethod(name, + signature(x = "GroupedData"), + function(x, ...) { + sdf <- callJMethod(x@sgd, name, toSeq(...)) + dataFrame(sdf) + }) +} + +createMethods <- function() { + for (name in methods) { + createMethod(name) + } +} + +createMethods() + diff --git a/R/pkg/R/jobj.R b/R/pkg/R/jobj.R new file mode 100644 index 0000000000000..4180f146b7fbc --- /dev/null +++ b/R/pkg/R/jobj.R @@ -0,0 +1,101 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# References to objects that exist on the JVM backend +# are maintained using the jobj. + +# Maintain a reference count of Java object references +# This allows us to GC the java object when it is safe +.validJobjs <- new.env(parent = emptyenv()) + +# List of object ids to be removed +.toRemoveJobjs <- new.env(parent = emptyenv()) + +# Check if jobj was created with the current SparkContext +isValidJobj <- function(jobj) { + if (exists(".scStartTime", envir = .sparkREnv)) { + jobj$appId == get(".scStartTime", envir = .sparkREnv) + } else { + FALSE + } +} + +getJobj <- function(objId) { + newObj <- jobj(objId) + if (exists(objId, .validJobjs)) { + .validJobjs[[objId]] <- .validJobjs[[objId]] + 1 + } else { + .validJobjs[[objId]] <- 1 + } + newObj +} + +# Handler for a java object that exists on the backend. +jobj <- function(objId) { + if (!is.character(objId)) { + stop("object id must be a character") + } + # NOTE: We need a new env for a jobj as we can only register + # finalizers for environments or external references pointers. + obj <- structure(new.env(parent = emptyenv()), class = "jobj") + obj$id <- objId + obj$appId <- get(".scStartTime", envir = .sparkREnv) + + # Register a finalizer to remove the Java object when this reference + # is garbage collected in R + reg.finalizer(obj, cleanup.jobj) + obj +} + +#' Print a JVM object reference. +#' +#' This function prints the type and id for an object stored +#' in the SparkR JVM backend. +#' +#' @param x The JVM object reference +#' @param ... further arguments passed to or from other methods +print.jobj <- function(x, ...) { + cls <- callJMethod(x, "getClass") + name <- callJMethod(cls, "getName") + cat("Java ref type", name, "id", x$id, "\n", sep = " ") +} + +cleanup.jobj <- function(jobj) { + if (isValidJobj(jobj)) { + objId <- jobj$id + # If we don't know anything about this jobj, ignore it + if (exists(objId, envir = .validJobjs)) { + .validJobjs[[objId]] <- .validJobjs[[objId]] - 1 + + if (.validJobjs[[objId]] == 0) { + rm(list = objId, envir = .validJobjs) + # NOTE: We cannot call removeJObject here as the finalizer may be run + # in the middle of another RPC. Thus we queue up this object Id to be removed + # and then run all the removeJObject when the next RPC is called. + .toRemoveJobjs[[objId]] <- 1 + } + } + } +} + +clearJobjs <- function() { + valid <- ls(.validJobjs) + rm(list = valid, envir = .validJobjs) + + removeList <- ls(.toRemoveJobjs) + rm(list = removeList, envir = .toRemoveJobjs) +} diff --git a/R/pkg/R/pairRDD.R b/R/pkg/R/pairRDD.R new file mode 100644 index 0000000000000..c2396c32a7548 --- /dev/null +++ b/R/pkg/R/pairRDD.R @@ -0,0 +1,789 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Operations supported on RDDs contains pairs (i.e key, value) + +############ Actions and Transformations ############ + +#' Look up elements of a key in an RDD +#' +#' @description +#' \code{lookup} returns a list of values in this RDD for key key. +#' +#' @param x The RDD to collect +#' @param key The key to look up for +#' @return a list of values in this RDD for key key +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' pairs <- list(c(1, 1), c(2, 2), c(1, 3)) +#' rdd <- parallelize(sc, pairs) +#' lookup(rdd, 1) # list(1, 3) +#'} +#' @rdname lookup +#' @aliases lookup,RDD-method +setMethod("lookup", + signature(x = "RDD", key = "ANY"), + function(x, key) { + partitionFunc <- function(part) { + filtered <- part[unlist(lapply(part, function(i) { identical(key, i[[1]]) }))] + lapply(filtered, function(i) { i[[2]] }) + } + valsRDD <- lapplyPartition(x, partitionFunc) + collect(valsRDD) + }) + +#' Count the number of elements for each key, and return the result to the +#' master as lists of (key, count) pairs. +#' +#' Same as countByKey in Spark. +#' +#' @param x The RDD to count keys. +#' @return list of (key, count) pairs, where count is number of each key in rdd. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(c("a", 1), c("b", 1), c("a", 1))) +#' countByKey(rdd) # ("a", 2L), ("b", 1L) +#'} +#' @rdname countByKey +#' @aliases countByKey,RDD-method +setMethod("countByKey", + signature(x = "RDD"), + function(x) { + keys <- lapply(x, function(item) { item[[1]] }) + countByValue(keys) + }) + +#' Return an RDD with the keys of each tuple. +#' +#' @param x The RDD from which the keys of each tuple is returned. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(list(1, 2), list(3, 4))) +#' collect(keys(rdd)) # list(1, 3) +#'} +#' @rdname keys +#' @aliases keys,RDD +setMethod("keys", + signature(x = "RDD"), + function(x) { + func <- function(k) { + k[[1]] + } + lapply(x, func) + }) + +#' Return an RDD with the values of each tuple. +#' +#' @param x The RDD from which the values of each tuple is returned. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(list(1, 2), list(3, 4))) +#' collect(values(rdd)) # list(2, 4) +#'} +#' @rdname values +#' @aliases values,RDD +setMethod("values", + signature(x = "RDD"), + function(x) { + func <- function(v) { + v[[2]] + } + lapply(x, func) + }) + +#' Applies a function to all values of the elements, without modifying the keys. +#' +#' The same as `mapValues()' in Spark. +#' +#' @param X The RDD to apply the transformation. +#' @param FUN the transformation to apply on the value of each element. +#' @return a new RDD created by the transformation. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' makePairs <- lapply(rdd, function(x) { list(x, x) }) +#' collect(mapValues(makePairs, function(x) { x * 2) }) +#' Output: list(list(1,2), list(2,4), list(3,6), ...) +#'} +#' @rdname mapValues +#' @aliases mapValues,RDD,function-method +setMethod("mapValues", + signature(X = "RDD", FUN = "function"), + function(X, FUN) { + func <- function(x) { + list(x[[1]], FUN(x[[2]])) + } + lapply(X, func) + }) + +#' Pass each value in the key-value pair RDD through a flatMap function without +#' changing the keys; this also retains the original RDD's partitioning. +#' +#' The same as 'flatMapValues()' in Spark. +#' +#' @param X The RDD to apply the transformation. +#' @param FUN the transformation to apply on the value of each element. +#' @return a new RDD created by the transformation. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(list(1, c(1,2)), list(2, c(3,4)))) +#' collect(flatMapValues(rdd, function(x) { x })) +#' Output: list(list(1,1), list(1,2), list(2,3), list(2,4)) +#'} +#' @rdname flatMapValues +#' @aliases flatMapValues,RDD,function-method +setMethod("flatMapValues", + signature(X = "RDD", FUN = "function"), + function(X, FUN) { + flatMapFunc <- function(x) { + lapply(FUN(x[[2]]), function(v) { list(x[[1]], v) }) + } + flatMap(X, flatMapFunc) + }) + +############ Shuffle Functions ############ + +#' Partition an RDD by key +#' +#' This function operates on RDDs where every element is of the form list(K, V) or c(K, V). +#' For each element of this RDD, the partitioner is used to compute a hash +#' function and the RDD is partitioned using this hash value. +#' +#' @param x The RDD to partition. Should be an RDD where each element is +#' list(K, V) or c(K, V). +#' @param numPartitions Number of partitions to create. +#' @param ... Other optional arguments to partitionBy. +#' +#' @param partitionFunc The partition function to use. Uses a default hashCode +#' function if not provided +#' @return An RDD partitioned using the specified partitioner. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' pairs <- list(list(1, 2), list(1.1, 3), list(1, 4)) +#' rdd <- parallelize(sc, pairs) +#' parts <- partitionBy(rdd, 2L) +#' collectPartition(parts, 0L) # First partition should contain list(1, 2) and list(1, 4) +#'} +#' @rdname partitionBy +#' @aliases partitionBy,RDD,integer-method +setMethod("partitionBy", + signature(x = "RDD", numPartitions = "integer"), + function(x, numPartitions, partitionFunc = hashCode) { + + #if (missing(partitionFunc)) { + # partitionFunc <- hashCode + #} + + partitionFunc <- cleanClosure(partitionFunc) + serializedHashFuncBytes <- serialize(partitionFunc, connection = NULL) + + packageNamesArr <- serialize(.sparkREnv$.packages, + connection = NULL) + broadcastArr <- lapply(ls(.broadcastNames), function(name) { + get(name, .broadcastNames) }) + jrdd <- getJRDD(x) + + # We create a PairwiseRRDD that extends RDD[(Array[Byte], + # Array[Byte])], where the key is the hashed split, the value is + # the content (key-val pairs). + pairwiseRRDD <- newJObject("org.apache.spark.api.r.PairwiseRRDD", + callJMethod(jrdd, "rdd"), + as.integer(numPartitions), + serializedHashFuncBytes, + getSerializedMode(x), + packageNamesArr, + as.character(.sparkREnv$libname), + broadcastArr, + callJMethod(jrdd, "classTag")) + + # Create a corresponding partitioner. + rPartitioner <- newJObject("org.apache.spark.HashPartitioner", + as.integer(numPartitions)) + + # Call partitionBy on the obtained PairwiseRDD. + javaPairRDD <- callJMethod(pairwiseRRDD, "asJavaPairRDD") + javaPairRDD <- callJMethod(javaPairRDD, "partitionBy", rPartitioner) + + # Call .values() on the result to get back the final result, the + # shuffled acutal content key-val pairs. + r <- callJMethod(javaPairRDD, "values") + + RDD(r, serializedMode = "byte") + }) + +#' Group values by key +#' +#' This function operates on RDDs where every element is of the form list(K, V) or c(K, V). +#' and group values for each key in the RDD into a single sequence. +#' +#' @param x The RDD to group. Should be an RDD where each element is +#' list(K, V) or c(K, V). +#' @param numPartitions Number of partitions to create. +#' @return An RDD where each element is list(K, list(V)) +#' @seealso reduceByKey +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' pairs <- list(list(1, 2), list(1.1, 3), list(1, 4)) +#' rdd <- parallelize(sc, pairs) +#' parts <- groupByKey(rdd, 2L) +#' grouped <- collect(parts) +#' grouped[[1]] # Should be a list(1, list(2, 4)) +#'} +#' @rdname groupByKey +#' @aliases groupByKey,RDD,integer-method +setMethod("groupByKey", + signature(x = "RDD", numPartitions = "integer"), + function(x, numPartitions) { + shuffled <- partitionBy(x, numPartitions) + groupVals <- function(part) { + vals <- new.env() + keys <- new.env() + pred <- function(item) exists(item$hash, keys) + appendList <- function(acc, i) { + addItemToAccumulator(acc, i) + acc + } + makeList <- function(i) { + acc <- initAccumulator() + addItemToAccumulator(acc, i) + acc + } + # Each item in the partition is list of (K, V) + lapply(part, + function(item) { + item$hash <- as.character(hashCode(item[[1]])) + updateOrCreatePair(item, keys, vals, pred, + appendList, makeList) + }) + # extract out data field + vals <- eapply(vals, + function(i) { + length(i$data) <- i$counter + i$data + }) + # Every key in the environment contains a list + # Convert that to list(K, Seq[V]) + convertEnvsToList(keys, vals) + } + lapplyPartition(shuffled, groupVals) + }) + +#' Merge values by key +#' +#' This function operates on RDDs where every element is of the form list(K, V) or c(K, V). +#' and merges the values for each key using an associative reduce function. +#' +#' @param x The RDD to reduce by key. Should be an RDD where each element is +#' list(K, V) or c(K, V). +#' @param combineFunc The associative reduce function to use. +#' @param numPartitions Number of partitions to create. +#' @return An RDD where each element is list(K, V') where V' is the merged +#' value +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' pairs <- list(list(1, 2), list(1.1, 3), list(1, 4)) +#' rdd <- parallelize(sc, pairs) +#' parts <- reduceByKey(rdd, "+", 2L) +#' reduced <- collect(parts) +#' reduced[[1]] # Should be a list(1, 6) +#'} +#' @rdname reduceByKey +#' @aliases reduceByKey,RDD,integer-method +setMethod("reduceByKey", + signature(x = "RDD", combineFunc = "ANY", numPartitions = "integer"), + function(x, combineFunc, numPartitions) { + reduceVals <- function(part) { + vals <- new.env() + keys <- new.env() + pred <- function(item) exists(item$hash, keys) + lapply(part, + function(item) { + item$hash <- as.character(hashCode(item[[1]])) + updateOrCreatePair(item, keys, vals, pred, combineFunc, identity) + }) + convertEnvsToList(keys, vals) + } + locallyReduced <- lapplyPartition(x, reduceVals) + shuffled <- partitionBy(locallyReduced, numPartitions) + lapplyPartition(shuffled, reduceVals) + }) + +#' Merge values by key locally +#' +#' This function operates on RDDs where every element is of the form list(K, V) or c(K, V). +#' and merges the values for each key using an associative reduce function, but return the +#' results immediately to the driver as an R list. +#' +#' @param x The RDD to reduce by key. Should be an RDD where each element is +#' list(K, V) or c(K, V). +#' @param combineFunc The associative reduce function to use. +#' @return A list of elements of type list(K, V') where V' is the merged value for each key +#' @seealso reduceByKey +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' pairs <- list(list(1, 2), list(1.1, 3), list(1, 4)) +#' rdd <- parallelize(sc, pairs) +#' reduced <- reduceByKeyLocally(rdd, "+") +#' reduced # list(list(1, 6), list(1.1, 3)) +#'} +#' @rdname reduceByKeyLocally +#' @aliases reduceByKeyLocally,RDD,integer-method +setMethod("reduceByKeyLocally", + signature(x = "RDD", combineFunc = "ANY"), + function(x, combineFunc) { + reducePart <- function(part) { + vals <- new.env() + keys <- new.env() + pred <- function(item) exists(item$hash, keys) + lapply(part, + function(item) { + item$hash <- as.character(hashCode(item[[1]])) + updateOrCreatePair(item, keys, vals, pred, combineFunc, identity) + }) + list(list(keys, vals)) # return hash to avoid re-compute in merge + } + mergeParts <- function(accum, x) { + pred <- function(item) { + exists(item$hash, accum[[1]]) + } + lapply(ls(x[[1]]), + function(name) { + item <- list(x[[1]][[name]], x[[2]][[name]]) + item$hash <- name + updateOrCreatePair(item, accum[[1]], accum[[2]], pred, combineFunc, identity) + }) + accum + } + reduced <- mapPartitions(x, reducePart) + merged <- reduce(reduced, mergeParts) + convertEnvsToList(merged[[1]], merged[[2]]) + }) + +#' Combine values by key +#' +#' Generic function to combine the elements for each key using a custom set of +#' aggregation functions. Turns an RDD[(K, V)] into a result of type RDD[(K, C)], +#' for a "combined type" C. Note that V and C can be different -- for example, one +#' might group an RDD of type (Int, Int) into an RDD of type (Int, Seq[Int]). + +#' Users provide three functions: +#' \itemize{ +#' \item createCombiner, which turns a V into a C (e.g., creates a one-element list) +#' \item mergeValue, to merge a V into a C (e.g., adds it to the end of a list) - +#' \item mergeCombiners, to combine two C's into a single one (e.g., concatentates +#' two lists). +#' } +#' +#' @param x The RDD to combine. Should be an RDD where each element is +#' list(K, V) or c(K, V). +#' @param createCombiner Create a combiner (C) given a value (V) +#' @param mergeValue Merge the given value (V) with an existing combiner (C) +#' @param mergeCombiners Merge two combiners and return a new combiner +#' @param numPartitions Number of partitions to create. +#' @return An RDD where each element is list(K, C) where C is the combined type +#' +#' @seealso groupByKey, reduceByKey +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' pairs <- list(list(1, 2), list(1.1, 3), list(1, 4)) +#' rdd <- parallelize(sc, pairs) +#' parts <- combineByKey(rdd, function(x) { x }, "+", "+", 2L) +#' combined <- collect(parts) +#' combined[[1]] # Should be a list(1, 6) +#'} +#' @rdname combineByKey +#' @aliases combineByKey,RDD,ANY,ANY,ANY,integer-method +setMethod("combineByKey", + signature(x = "RDD", createCombiner = "ANY", mergeValue = "ANY", + mergeCombiners = "ANY", numPartitions = "integer"), + function(x, createCombiner, mergeValue, mergeCombiners, numPartitions) { + combineLocally <- function(part) { + combiners <- new.env() + keys <- new.env() + pred <- function(item) exists(item$hash, keys) + lapply(part, + function(item) { + item$hash <- as.character(item[[1]]) + updateOrCreatePair(item, keys, combiners, pred, mergeValue, createCombiner) + }) + convertEnvsToList(keys, combiners) + } + locallyCombined <- lapplyPartition(x, combineLocally) + shuffled <- partitionBy(locallyCombined, numPartitions) + mergeAfterShuffle <- function(part) { + combiners <- new.env() + keys <- new.env() + pred <- function(item) exists(item$hash, keys) + lapply(part, + function(item) { + item$hash <- as.character(item[[1]]) + updateOrCreatePair(item, keys, combiners, pred, mergeCombiners, identity) + }) + convertEnvsToList(keys, combiners) + } + lapplyPartition(shuffled, mergeAfterShuffle) + }) + +#' Aggregate a pair RDD by each key. +#' +#' Aggregate the values of each key in an RDD, using given combine functions +#' and a neutral "zero value". This function can return a different result type, +#' U, than the type of the values in this RDD, V. Thus, we need one operation +#' for merging a V into a U and one operation for merging two U's, The former +#' operation is used for merging values within a partition, and the latter is +#' used for merging values between partitions. To avoid memory allocation, both +#' of these functions are allowed to modify and return their first argument +#' instead of creating a new U. +#' +#' @param x An RDD. +#' @param zeroValue A neutral "zero value". +#' @param seqOp A function to aggregate the values of each key. It may return +#' a different result type from the type of the values. +#' @param combOp A function to aggregate results of seqOp. +#' @return An RDD containing the aggregation result. +#' @seealso foldByKey, combineByKey +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(list(1, 1), list(1, 2), list(2, 3), list(2, 4))) +#' zeroValue <- list(0, 0) +#' seqOp <- function(x, y) { list(x[[1]] + y, x[[2]] + 1) } +#' combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) } +#' aggregateByKey(rdd, zeroValue, seqOp, combOp, 2L) +#' # list(list(1, list(3, 2)), list(2, list(7, 2))) +#'} +#' @rdname aggregateByKey +#' @aliases aggregateByKey,RDD,ANY,ANY,ANY,integer-method +setMethod("aggregateByKey", + signature(x = "RDD", zeroValue = "ANY", seqOp = "ANY", + combOp = "ANY", numPartitions = "integer"), + function(x, zeroValue, seqOp, combOp, numPartitions) { + createCombiner <- function(v) { + do.call(seqOp, list(zeroValue, v)) + } + + combineByKey(x, createCombiner, seqOp, combOp, numPartitions) + }) + +#' Fold a pair RDD by each key. +#' +#' Aggregate the values of each key in an RDD, using an associative function "func" +#' and a neutral "zero value" which may be added to the result an arbitrary +#' number of times, and must not change the result (e.g., 0 for addition, or +#' 1 for multiplication.). +#' +#' @param x An RDD. +#' @param zeroValue A neutral "zero value". +#' @param func An associative function for folding values of each key. +#' @return An RDD containing the aggregation result. +#' @seealso aggregateByKey, combineByKey +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(list(1, 1), list(1, 2), list(2, 3), list(2, 4))) +#' foldByKey(rdd, 0, "+", 2L) # list(list(1, 3), list(2, 7)) +#'} +#' @rdname foldByKey +#' @aliases foldByKey,RDD,ANY,ANY,integer-method +setMethod("foldByKey", + signature(x = "RDD", zeroValue = "ANY", + func = "ANY", numPartitions = "integer"), + function(x, zeroValue, func, numPartitions) { + aggregateByKey(x, zeroValue, func, func, numPartitions) + }) + +############ Binary Functions ############# + +#' Join two RDDs +#' +#' @description +#' \code{join} This function joins two RDDs where every element is of the form list(K, V). +#' The key types of the two RDDs should be the same. +#' +#' @param x An RDD to be joined. Should be an RDD where each element is +#' list(K, V). +#' @param y An RDD to be joined. Should be an RDD where each element is +#' list(K, V). +#' @param numPartitions Number of partitions to create. +#' @return a new RDD containing all pairs of elements with matching keys in +#' two input RDDs. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, list(list(1, 1), list(2, 4))) +#' rdd2 <- parallelize(sc, list(list(1, 2), list(1, 3))) +#' join(rdd1, rdd2, 2L) # list(list(1, list(1, 2)), list(1, list(1, 3)) +#'} +#' @rdname join-methods +#' @aliases join,RDD,RDD-method +setMethod("join", + signature(x = "RDD", y = "RDD"), + function(x, y, numPartitions) { + xTagged <- lapply(x, function(i) { list(i[[1]], list(1L, i[[2]])) }) + yTagged <- lapply(y, function(i) { list(i[[1]], list(2L, i[[2]])) }) + + doJoin <- function(v) { + joinTaggedList(v, list(FALSE, FALSE)) + } + + joined <- flatMapValues(groupByKey(unionRDD(xTagged, yTagged), numToInt(numPartitions)), + doJoin) + }) + +#' Left outer join two RDDs +#' +#' @description +#' \code{leftouterjoin} This function left-outer-joins two RDDs where every element is of the form list(K, V). +#' The key types of the two RDDs should be the same. +#' +#' @param x An RDD to be joined. Should be an RDD where each element is +#' list(K, V). +#' @param y An RDD to be joined. Should be an RDD where each element is +#' list(K, V). +#' @param numPartitions Number of partitions to create. +#' @return For each element (k, v) in x, the resulting RDD will either contain +#' all pairs (k, (v, w)) for (k, w) in rdd2, or the pair (k, (v, NULL)) +#' if no elements in rdd2 have key k. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, list(list(1, 1), list(2, 4))) +#' rdd2 <- parallelize(sc, list(list(1, 2), list(1, 3))) +#' leftOuterJoin(rdd1, rdd2, 2L) +#' # list(list(1, list(1, 2)), list(1, list(1, 3)), list(2, list(4, NULL))) +#'} +#' @rdname join-methods +#' @aliases leftOuterJoin,RDD,RDD-method +setMethod("leftOuterJoin", + signature(x = "RDD", y = "RDD", numPartitions = "integer"), + function(x, y, numPartitions) { + xTagged <- lapply(x, function(i) { list(i[[1]], list(1L, i[[2]])) }) + yTagged <- lapply(y, function(i) { list(i[[1]], list(2L, i[[2]])) }) + + doJoin <- function(v) { + joinTaggedList(v, list(FALSE, TRUE)) + } + + joined <- flatMapValues(groupByKey(unionRDD(xTagged, yTagged), numPartitions), doJoin) + }) + +#' Right outer join two RDDs +#' +#' @description +#' \code{rightouterjoin} This function right-outer-joins two RDDs where every element is of the form list(K, V). +#' The key types of the two RDDs should be the same. +#' +#' @param x An RDD to be joined. Should be an RDD where each element is +#' list(K, V). +#' @param y An RDD to be joined. Should be an RDD where each element is +#' list(K, V). +#' @param numPartitions Number of partitions to create. +#' @return For each element (k, w) in y, the resulting RDD will either contain +#' all pairs (k, (v, w)) for (k, v) in x, or the pair (k, (NULL, w)) +#' if no elements in x have key k. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, list(list(1, 2), list(1, 3))) +#' rdd2 <- parallelize(sc, list(list(1, 1), list(2, 4))) +#' rightOuterJoin(rdd1, rdd2, 2L) +#' # list(list(1, list(2, 1)), list(1, list(3, 1)), list(2, list(NULL, 4))) +#'} +#' @rdname join-methods +#' @aliases rightOuterJoin,RDD,RDD-method +setMethod("rightOuterJoin", + signature(x = "RDD", y = "RDD", numPartitions = "integer"), + function(x, y, numPartitions) { + xTagged <- lapply(x, function(i) { list(i[[1]], list(1L, i[[2]])) }) + yTagged <- lapply(y, function(i) { list(i[[1]], list(2L, i[[2]])) }) + + doJoin <- function(v) { + joinTaggedList(v, list(TRUE, FALSE)) + } + + joined <- flatMapValues(groupByKey(unionRDD(xTagged, yTagged), numPartitions), doJoin) + }) + +#' Full outer join two RDDs +#' +#' @description +#' \code{fullouterjoin} This function full-outer-joins two RDDs where every element is of the form list(K, V). +#' The key types of the two RDDs should be the same. +#' +#' @param x An RDD to be joined. Should be an RDD where each element is +#' list(K, V). +#' @param y An RDD to be joined. Should be an RDD where each element is +#' list(K, V). +#' @param numPartitions Number of partitions to create. +#' @return For each element (k, v) in x and (k, w) in y, the resulting RDD +#' will contain all pairs (k, (v, w)) for both (k, v) in x and +#' (k, w) in y, or the pair (k, (NULL, w))/(k, (v, NULL)) if no elements +#' in x/y have key k. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, list(list(1, 2), list(1, 3), list(3, 3))) +#' rdd2 <- parallelize(sc, list(list(1, 1), list(2, 4))) +#' fullOuterJoin(rdd1, rdd2, 2L) # list(list(1, list(2, 1)), +#' # list(1, list(3, 1)), +#' # list(2, list(NULL, 4))) +#' # list(3, list(3, NULL)), +#'} +#' @rdname join-methods +#' @aliases fullOuterJoin,RDD,RDD-method +setMethod("fullOuterJoin", + signature(x = "RDD", y = "RDD", numPartitions = "integer"), + function(x, y, numPartitions) { + xTagged <- lapply(x, function(i) { list(i[[1]], list(1L, i[[2]])) }) + yTagged <- lapply(y, function(i) { list(i[[1]], list(2L, i[[2]])) }) + + doJoin <- function(v) { + joinTaggedList(v, list(TRUE, TRUE)) + } + + joined <- flatMapValues(groupByKey(unionRDD(xTagged, yTagged), numPartitions), doJoin) + }) + +#' For each key k in several RDDs, return a resulting RDD that +#' whose values are a list of values for the key in all RDDs. +#' +#' @param ... Several RDDs. +#' @param numPartitions Number of partitions to create. +#' @return a new RDD containing all pairs of elements with values in a list +#' in all RDDs. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, list(list(1, 1), list(2, 4))) +#' rdd2 <- parallelize(sc, list(list(1, 2), list(1, 3))) +#' cogroup(rdd1, rdd2, numPartitions = 2L) +#' # list(list(1, list(1, list(2, 3))), list(2, list(list(4), list())) +#'} +#' @rdname cogroup +#' @aliases cogroup,RDD-method +setMethod("cogroup", + "RDD", + function(..., numPartitions) { + rdds <- list(...) + rddsLen <- length(rdds) + for (i in 1:rddsLen) { + rdds[[i]] <- lapply(rdds[[i]], + function(x) { list(x[[1]], list(i, x[[2]])) }) + # TODO(hao): As issue [SparkR-142] mentions, the right value of i + # will not be captured into UDF if getJRDD is not invoked. + # It should be resolved together with that issue. + getJRDD(rdds[[i]]) # Capture the closure. + } + union.rdd <- Reduce(unionRDD, rdds) + group.func <- function(vlist) { + res <- list() + length(res) <- rddsLen + for (x in vlist) { + i <- x[[1]] + acc <- res[[i]] + # Create an accumulator. + if (is.null(acc)) { + acc <- initAccumulator() + } + addItemToAccumulator(acc, x[[2]]) + res[[i]] <- acc + } + lapply(res, function(acc) { + if (is.null(acc)) { + list() + } else { + acc$data + } + }) + } + cogroup.rdd <- mapValues(groupByKey(union.rdd, numPartitions), + group.func) + }) + +#' Sort a (k, v) pair RDD by k. +#' +#' @param x A (k, v) pair RDD to be sorted. +#' @param ascending A flag to indicate whether the sorting is ascending or descending. +#' @param numPartitions Number of partitions to create. +#' @return An RDD where all (k, v) pair elements are sorted. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(list(3, 1), list(2, 2), list(1, 3))) +#' collect(sortByKey(rdd)) # list (list(1, 3), list(2, 2), list(3, 1)) +#'} +#' @rdname sortByKey +#' @aliases sortByKey,RDD,RDD-method +setMethod("sortByKey", + signature(x = "RDD"), + function(x, ascending = TRUE, numPartitions = SparkR::numPartitions(x)) { + rangeBounds <- list() + + if (numPartitions > 1) { + rddSize <- count(x) + # constant from Spark's RangePartitioner + maxSampleSize <- numPartitions * 20 + fraction <- min(maxSampleSize / max(rddSize, 1), 1.0) + + samples <- collect(keys(sampleRDD(x, FALSE, fraction, 1L))) + + # Note: the built-in R sort() function only works on atomic vectors + samples <- sort(unlist(samples, recursive = FALSE), decreasing = !ascending) + + if (length(samples) > 0) { + rangeBounds <- lapply(seq_len(numPartitions - 1), + function(i) { + j <- ceiling(length(samples) * i / numPartitions) + samples[j] + }) + } + } + + rangePartitionFunc <- function(key) { + partition <- 0 + + # TODO: Use binary search instead of linear search, similar with Spark + while (partition < length(rangeBounds) && key > rangeBounds[[partition + 1]]) { + partition <- partition + 1 + } + + if (ascending) { + partition + } else { + numPartitions - partition - 1 + } + } + + partitionFunc <- function(part) { + sortKeyValueList(part, decreasing = !ascending) + } + + newRDD <- partitionBy(x, numPartitions, rangePartitionFunc) + lapplyPartition(newRDD, partitionFunc) + }) + diff --git a/R/pkg/R/serialize.R b/R/pkg/R/serialize.R new file mode 100644 index 0000000000000..8a9c0c652ce24 --- /dev/null +++ b/R/pkg/R/serialize.R @@ -0,0 +1,195 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Utility functions to serialize R objects so they can be read in Java. + +# Type mapping from R to Java +# +# NULL -> Void +# integer -> Int +# character -> String +# logical -> Boolean +# double, numeric -> Double +# raw -> Array[Byte] +# Date -> Date +# POSIXct,POSIXlt -> Time +# +# list[T] -> Array[T], where T is one of above mentioned types +# environment -> Map[String, T], where T is a native type +# jobj -> Object, where jobj is an object created in the backend + +writeObject <- function(con, object, writeType = TRUE) { + # NOTE: In R vectors have same type as objects. So we don't support + # passing in vectors as arrays and instead require arrays to be passed + # as lists. + type <- class(object)[[1]] # class of POSIXlt is c("POSIXlt", "POSIXt") + if (writeType) { + writeType(con, type) + } + switch(type, + NULL = writeVoid(con), + integer = writeInt(con, object), + character = writeString(con, object), + logical = writeBoolean(con, object), + double = writeDouble(con, object), + numeric = writeDouble(con, object), + raw = writeRaw(con, object), + list = writeList(con, object), + jobj = writeJobj(con, object), + environment = writeEnv(con, object), + Date = writeDate(con, object), + POSIXlt = writeTime(con, object), + POSIXct = writeTime(con, object), + stop(paste("Unsupported type for serialization", type))) +} + +writeVoid <- function(con) { + # no value for NULL +} + +writeJobj <- function(con, value) { + if (!isValidJobj(value)) { + stop("invalid jobj ", value$id) + } + writeString(con, value$id) +} + +writeString <- function(con, value) { + writeInt(con, as.integer(nchar(value) + 1)) + writeBin(value, con, endian = "big") +} + +writeInt <- function(con, value) { + writeBin(as.integer(value), con, endian = "big") +} + +writeDouble <- function(con, value) { + writeBin(value, con, endian = "big") +} + +writeBoolean <- function(con, value) { + # TRUE becomes 1, FALSE becomes 0 + writeInt(con, as.integer(value)) +} + +writeRawSerialize <- function(outputCon, batch) { + outputSer <- serialize(batch, ascii = FALSE, connection = NULL) + writeRaw(outputCon, outputSer) +} + +writeRowSerialize <- function(outputCon, rows) { + invisible(lapply(rows, function(r) { + bytes <- serializeRow(r) + writeRaw(outputCon, bytes) + })) +} + +serializeRow <- function(row) { + rawObj <- rawConnection(raw(0), "wb") + on.exit(close(rawObj)) + writeRow(rawObj, row) + rawConnectionValue(rawObj) +} + +writeRow <- function(con, row) { + numCols <- length(row) + writeInt(con, numCols) + for (i in 1:numCols) { + writeObject(con, row[[i]]) + } +} + +writeRaw <- function(con, batch) { + writeInt(con, length(batch)) + writeBin(batch, con, endian = "big") +} + +writeType <- function(con, class) { + type <- switch(class, + NULL = "n", + integer = "i", + character = "c", + logical = "b", + double = "d", + numeric = "d", + raw = "r", + list = "l", + jobj = "j", + environment = "e", + Date = "D", + POSIXlt = 't', + POSIXct = 't', + stop(paste("Unsupported type for serialization", class))) + writeBin(charToRaw(type), con) +} + +# Used to pass arrays where all the elements are of the same type +writeList <- function(con, arr) { + # All elements should be of same type + elemType <- unique(sapply(arr, function(elem) { class(elem) })) + stopifnot(length(elemType) <= 1) + + # TODO: Empty lists are given type "character" right now. + # This may not work if the Java side expects array of any other type. + if (length(elemType) == 0) { + elemType <- class("somestring") + } + + writeType(con, elemType) + writeInt(con, length(arr)) + + if (length(arr) > 0) { + for (a in arr) { + writeObject(con, a, FALSE) + } + } +} + +# Used to pass in hash maps required on Java side. +writeEnv <- function(con, env) { + len <- length(env) + + writeInt(con, len) + if (len > 0) { + writeList(con, as.list(ls(env))) + vals <- lapply(ls(env), function(x) { env[[x]] }) + writeList(con, as.list(vals)) + } +} + +writeDate <- function(con, date) { + writeString(con, as.character(date)) +} + +writeTime <- function(con, time) { + writeDouble(con, as.double(time)) +} + +# Used to serialize in a list of objects where each +# object can be of a different type. Serialization format is +# for each object +writeArgs <- function(con, args) { + if (length(args) > 0) { + for (a in args) { + writeObject(con, a) + } + } +} + +writeStrings <- function(con, stringList) { + writeLines(unlist(stringList), con) +} diff --git a/R/pkg/R/sparkR.R b/R/pkg/R/sparkR.R new file mode 100644 index 0000000000000..bc82df01f0fff --- /dev/null +++ b/R/pkg/R/sparkR.R @@ -0,0 +1,266 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +.sparkREnv <- new.env() + +sparkR.onLoad <- function(libname, pkgname) { + .sparkREnv$libname <- libname +} + +# Utility function that returns TRUE if we have an active connection to the +# backend and FALSE otherwise +connExists <- function(env) { + tryCatch({ + exists(".sparkRCon", envir = env) && isOpen(env[[".sparkRCon"]]) + }, error = function(err) { + return(FALSE) + }) +} + +#' Stop the Spark context. +#' +#' Also terminates the backend this R session is connected to +sparkR.stop <- function() { + env <- .sparkREnv + if (exists(".sparkRCon", envir = env)) { + # cat("Stopping SparkR\n") + if (exists(".sparkRjsc", envir = env)) { + sc <- get(".sparkRjsc", envir = env) + callJMethod(sc, "stop") + rm(".sparkRjsc", envir = env) + } + + if (exists(".backendLaunched", envir = env)) { + callJStatic("SparkRHandler", "stopBackend") + } + + # Also close the connection and remove it from our env + conn <- get(".sparkRCon", envir = env) + close(conn) + + rm(".sparkRCon", envir = env) + rm(".scStartTime", envir = env) + } + + if (exists(".monitorConn", envir = env)) { + conn <- get(".monitorConn", envir = env) + close(conn) + rm(".monitorConn", envir = env) + } + + # Clear all broadcast variables we have + # as the jobj will not be valid if we restart the JVM + clearBroadcastVariables() + + # Clear jobj maps + clearJobjs() +} + +#' Initialize a new Spark Context. +#' +#' This function initializes a new SparkContext. +#' +#' @param master The Spark master URL. +#' @param appName Application name to register with cluster manager +#' @param sparkHome Spark Home directory +#' @param sparkEnvir Named list of environment variables to set on worker nodes. +#' @param sparkExecutorEnv Named list of environment variables to be used when launching executors. +#' @param sparkJars Character string vector of jar files to pass to the worker nodes. +#' @param sparkRLibDir The path where R is installed on the worker nodes. +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init("local[2]", "SparkR", "/home/spark") +#' sc <- sparkR.init("local[2]", "SparkR", "/home/spark", +#' list(spark.executor.memory="1g")) +#' sc <- sparkR.init("yarn-client", "SparkR", "/home/spark", +#' list(spark.executor.memory="1g"), +#' list(LD_LIBRARY_PATH="/directory of JVM libraries (libjvm.so) on workers/"), +#' c("jarfile1.jar","jarfile2.jar")) +#'} + +sparkR.init <- function( + master = "", + appName = "SparkR", + sparkHome = Sys.getenv("SPARK_HOME"), + sparkEnvir = list(), + sparkExecutorEnv = list(), + sparkJars = "", + sparkRLibDir = "") { + + if (exists(".sparkRjsc", envir = .sparkREnv)) { + cat("Re-using existing Spark Context. Please stop SparkR with sparkR.stop() or restart R to create a new Spark Context\n") + return(get(".sparkRjsc", envir = .sparkREnv)) + } + + sparkMem <- Sys.getenv("SPARK_MEM", "512m") + jars <- suppressWarnings(normalizePath(as.character(sparkJars))) + + # Classpath separator is ";" on Windows + # URI needs four /// as from http://stackoverflow.com/a/18522792 + if (.Platform$OS.type == "unix") { + collapseChar <- ":" + uriSep <- "//" + } else { + collapseChar <- ";" + uriSep <- "////" + } + + existingPort <- Sys.getenv("EXISTING_SPARKR_BACKEND_PORT", "") + if (existingPort != "") { + backendPort <- existingPort + } else { + path <- tempfile(pattern = "backend_port") + launchBackend( + args = path, + sparkHome = sparkHome, + jars = jars, + sparkSubmitOpts = Sys.getenv("SPARKR_SUBMIT_ARGS", "sparkr-shell")) + # wait atmost 100 seconds for JVM to launch + wait <- 0.1 + for (i in 1:25) { + Sys.sleep(wait) + if (file.exists(path)) { + break + } + wait <- wait * 1.25 + } + if (!file.exists(path)) { + stop("JVM is not ready after 10 seconds") + } + f <- file(path, open='rb') + backendPort <- readInt(f) + monitorPort <- readInt(f) + close(f) + file.remove(path) + if (length(backendPort) == 0 || backendPort == 0 || + length(monitorPort) == 0 || monitorPort == 0) { + stop("JVM failed to launch") + } + assign(".monitorConn", socketConnection(port = monitorPort), envir = .sparkREnv) + assign(".backendLaunched", 1, envir = .sparkREnv) + } + + .sparkREnv$backendPort <- backendPort + tryCatch({ + connectBackend("localhost", backendPort) + }, error = function(err) { + stop("Failed to connect JVM\n") + }) + + if (nchar(sparkHome) != 0) { + sparkHome <- normalizePath(sparkHome) + } + + if (nchar(sparkRLibDir) != 0) { + .sparkREnv$libname <- sparkRLibDir + } + + sparkEnvirMap <- new.env() + for (varname in names(sparkEnvir)) { + sparkEnvirMap[[varname]] <- sparkEnvir[[varname]] + } + + sparkExecutorEnvMap <- new.env() + if (!any(names(sparkExecutorEnv) == "LD_LIBRARY_PATH")) { + sparkExecutorEnvMap[["LD_LIBRARY_PATH"]] <- paste0("$LD_LIBRARY_PATH:",Sys.getenv("LD_LIBRARY_PATH")) + } + for (varname in names(sparkExecutorEnv)) { + sparkExecutorEnvMap[[varname]] <- sparkExecutorEnv[[varname]] + } + + nonEmptyJars <- Filter(function(x) { x != "" }, jars) + localJarPaths <- sapply(nonEmptyJars, function(j) { utils::URLencode(paste("file:", uriSep, j, sep = "")) }) + + # Set the start time to identify jobjs + # Seconds resolution is good enough for this purpose, so use ints + assign(".scStartTime", as.integer(Sys.time()), envir = .sparkREnv) + + assign( + ".sparkRjsc", + callJStatic( + "org.apache.spark.api.r.RRDD", + "createSparkContext", + master, + appName, + as.character(sparkHome), + as.list(localJarPaths), + sparkEnvirMap, + sparkExecutorEnvMap), + envir = .sparkREnv + ) + + sc <- get(".sparkRjsc", envir = .sparkREnv) + + # Register a finalizer to sleep 1 seconds on R exit to make RStudio happy + reg.finalizer(.sparkREnv, function(x) { Sys.sleep(1) }, onexit = TRUE) + + sc +} + +#' Initialize a new SQLContext. +#' +#' This function creates a SparkContext from an existing JavaSparkContext and +#' then uses it to initialize a new SQLContext +#' +#' @param jsc The existing JavaSparkContext created with SparkR.init() +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#'} + +sparkRSQL.init <- function(jsc) { + if (exists(".sparkRSQLsc", envir = .sparkREnv)) { + return(get(".sparkRSQLsc", envir = .sparkREnv)) + } + + sqlCtx <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", + "createSQLContext", + jsc) + assign(".sparkRSQLsc", sqlCtx, envir = .sparkREnv) + sqlCtx +} + +#' Initialize a new HiveContext. +#' +#' This function creates a HiveContext from an existing JavaSparkContext +#' +#' @param jsc The existing JavaSparkContext created with SparkR.init() +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRHive.init(sc) +#'} + +sparkRHive.init <- function(jsc) { + if (exists(".sparkRHivesc", envir = .sparkREnv)) { + return(get(".sparkRHivesc", envir = .sparkREnv)) + } + + ssc <- callJMethod(jsc, "sc") + hiveCtx <- tryCatch({ + newJObject("org.apache.spark.sql.hive.HiveContext", ssc) + }, error = function(err) { + stop("Spark SQL is not built with Hive support") + }) + + assign(".sparkRHivesc", hiveCtx, envir = .sparkREnv) + hiveCtx +} diff --git a/R/pkg/R/utils.R b/R/pkg/R/utils.R new file mode 100644 index 0000000000000..c337fb0751e72 --- /dev/null +++ b/R/pkg/R/utils.R @@ -0,0 +1,467 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Utilities and Helpers + +# Given a JList, returns an R list containing the same elements, the number +# of which is optionally upper bounded by `logicalUpperBound` (by default, +# return all elements). Takes care of deserializations and type conversions. +convertJListToRList <- function(jList, flatten, logicalUpperBound = NULL, + serializedMode = "byte") { + arrSize <- callJMethod(jList, "size") + + # Datasets with serializedMode == "string" (such as an RDD directly generated by textFile()): + # each partition is not dense-packed into one Array[Byte], and `arrSize` + # here corresponds to number of logical elements. Thus we can prune here. + if (serializedMode == "string" && !is.null(logicalUpperBound)) { + arrSize <- min(arrSize, logicalUpperBound) + } + + results <- if (arrSize > 0) { + lapply(0:(arrSize - 1), + function(index) { + obj <- callJMethod(jList, "get", as.integer(index)) + + # Assume it is either an R object or a Java obj ref. + if (inherits(obj, "jobj")) { + if (isInstanceOf(obj, "scala.Tuple2")) { + # JavaPairRDD[Array[Byte], Array[Byte]]. + + keyBytes = callJMethod(obj, "_1") + valBytes = callJMethod(obj, "_2") + res <- list(unserialize(keyBytes), + unserialize(valBytes)) + } else { + stop(paste("utils.R: convertJListToRList only supports", + "RDD[Array[Byte]] and", + "JavaPairRDD[Array[Byte], Array[Byte]] for now")) + } + } else { + if (inherits(obj, "raw")) { + if (serializedMode == "byte") { + # RDD[Array[Byte]]. `obj` is a whole partition. + res <- unserialize(obj) + # For serialized datasets, `obj` (and `rRaw`) here corresponds to + # one whole partition dense-packed together. We deserialize the + # whole partition first, then cap the number of elements to be returned. + } else if (serializedMode == "row") { + res <- readRowList(obj) + # For DataFrames that have been converted to RRDDs, we call readRowList + # which will read in each row of the RRDD as a list and deserialize + # each element. + flatten <<- FALSE + # Use global assignment to change the flatten flag. This means + # we don't have to worry about the default argument in other functions + # e.g. collect + } + # TODO: is it possible to distinguish element boundary so that we can + # unserialize only what we need? + if (!is.null(logicalUpperBound)) { + res <- head(res, n = logicalUpperBound) + } + } else { + # obj is of a primitive Java type, is simplified to R's + # corresponding type. + res <- list(obj) + } + } + res + }) + } else { + list() + } + + if (flatten) { + as.list(unlist(results, recursive = FALSE)) + } else { + as.list(results) + } +} + +# Returns TRUE if `name` refers to an RDD in the given environment `env` +isRDD <- function(name, env) { + obj <- get(name, envir = env) + inherits(obj, "RDD") +} + +#' Compute the hashCode of an object +#' +#' Java-style function to compute the hashCode for the given object. Returns +#' an integer value. +#' +#' @details +#' This only works for integer, numeric and character types right now. +#' +#' @param key the object to be hashed +#' @return the hash code as an integer +#' @export +#' @examples +#' hashCode(1L) # 1 +#' hashCode(1.0) # 1072693248 +#' hashCode("1") # 49 +hashCode <- function(key) { + if (class(key) == "integer") { + as.integer(key[[1]]) + } else if (class(key) == "numeric") { + # Convert the double to long and then calculate the hash code + rawVec <- writeBin(key[[1]], con = raw()) + intBits <- packBits(rawToBits(rawVec), "integer") + as.integer(bitwXor(intBits[2], intBits[1])) + } else if (class(key) == "character") { + .Call("stringHashCode", key) + } else { + warning(paste("Could not hash object, returning 0", sep = "")) + as.integer(0) + } +} + +# Create a new RDD with serializedMode == "byte". +# Return itself if already in "byte" format. +serializeToBytes <- function(rdd) { + if (!inherits(rdd, "RDD")) { + stop("Argument 'rdd' is not an RDD type.") + } + if (getSerializedMode(rdd) != "byte") { + ser.rdd <- lapply(rdd, function(x) { x }) + return(ser.rdd) + } else { + return(rdd) + } +} + +# Create a new RDD with serializedMode == "string". +# Return itself if already in "string" format. +serializeToString <- function(rdd) { + if (!inherits(rdd, "RDD")) { + stop("Argument 'rdd' is not an RDD type.") + } + if (getSerializedMode(rdd) != "string") { + ser.rdd <- lapply(rdd, function(x) { toString(x) }) + # force it to create jrdd using "string" + getJRDD(ser.rdd, serializedMode = "string") + return(ser.rdd) + } else { + return(rdd) + } +} + +# Fast append to list by using an accumulator. +# http://stackoverflow.com/questions/17046336/here-we-go-again-append-an-element-to-a-list-in-r +# +# The accumulator should has three fields size, counter and data. +# This function amortizes the allocation cost by doubling +# the size of the list every time it fills up. +addItemToAccumulator <- function(acc, item) { + if(acc$counter == acc$size) { + acc$size <- acc$size * 2 + length(acc$data) <- acc$size + } + acc$counter <- acc$counter + 1 + acc$data[[acc$counter]] <- item +} + +initAccumulator <- function() { + acc <- new.env() + acc$counter <- 0 + acc$data <- list(NULL) + acc$size <- 1 + acc +} + +# Utility function to sort a list of key value pairs +# Used in unit tests +sortKeyValueList <- function(kv_list, decreasing = FALSE) { + keys <- sapply(kv_list, function(x) x[[1]]) + kv_list[order(keys, decreasing = decreasing)] +} + +# Utility function to generate compact R lists from grouped rdd +# Used in Join-family functions +# param: +# tagged_list R list generated via groupByKey with tags(1L, 2L, ...) +# cnull Boolean list where each element determines whether the corresponding list should +# be converted to list(NULL) +genCompactLists <- function(tagged_list, cnull) { + len <- length(tagged_list) + lists <- list(vector("list", len), vector("list", len)) + index <- list(1, 1) + + for (x in tagged_list) { + tag <- x[[1]] + idx <- index[[tag]] + lists[[tag]][[idx]] <- x[[2]] + index[[tag]] <- idx + 1 + } + + len <- lapply(index, function(x) x - 1) + for (i in (1:2)) { + if (cnull[[i]] && len[[i]] == 0) { + lists[[i]] <- list(NULL) + } else { + length(lists[[i]]) <- len[[i]] + } + } + + lists +} + +# Utility function to merge compact R lists +# Used in Join-family functions +# param: +# left/right Two compact lists ready for Cartesian product +mergeCompactLists <- function(left, right) { + result <- list() + length(result) <- length(left) * length(right) + index <- 1 + for (i in left) { + for (j in right) { + result[[index]] <- list(i, j) + index <- index + 1 + } + } + result +} + +# Utility function to wrapper above two operations +# Used in Join-family functions +# param (same as genCompactLists): +# tagged_list R list generated via groupByKey with tags(1L, 2L, ...) +# cnull Boolean list where each element determines whether the corresponding list should +# be converted to list(NULL) +joinTaggedList <- function(tagged_list, cnull) { + lists <- genCompactLists(tagged_list, cnull) + mergeCompactLists(lists[[1]], lists[[2]]) +} + +# Utility function to reduce a key-value list with predicate +# Used in *ByKey functions +# param +# pair key-value pair +# keys/vals env of key/value with hashes +# updateOrCreatePred predicate function +# updateFn update or merge function for existing pair, similar with `mergeVal` @combineByKey +# createFn create function for new pair, similar with `createCombiner` @combinebykey +updateOrCreatePair <- function(pair, keys, vals, updateOrCreatePred, updateFn, createFn) { + # assume hashVal bind to `$hash`, key/val with index 1/2 + hashVal <- pair$hash + key <- pair[[1]] + val <- pair[[2]] + if (updateOrCreatePred(pair)) { + assign(hashVal, do.call(updateFn, list(get(hashVal, envir = vals), val)), envir = vals) + } else { + assign(hashVal, do.call(createFn, list(val)), envir = vals) + assign(hashVal, key, envir = keys) + } +} + +# Utility function to convert key&values envs into key-val list +convertEnvsToList <- function(keys, vals) { + lapply(ls(keys), + function(name) { + list(keys[[name]], vals[[name]]) + }) +} + +# Utility function to capture the varargs into environment object +varargsToEnv <- function(...) { + pairs <- as.list(substitute(list(...)))[-1L] + env <- new.env() + for (name in names(pairs)) { + env[[name]] <- pairs[[name]] + } + env +} + +getStorageLevel <- function(newLevel = c("DISK_ONLY", + "DISK_ONLY_2", + "MEMORY_AND_DISK", + "MEMORY_AND_DISK_2", + "MEMORY_AND_DISK_SER", + "MEMORY_AND_DISK_SER_2", + "MEMORY_ONLY", + "MEMORY_ONLY_2", + "MEMORY_ONLY_SER", + "MEMORY_ONLY_SER_2", + "OFF_HEAP")) { + match.arg(newLevel) + storageLevel <- switch(newLevel, + "DISK_ONLY" = callJStatic("org.apache.spark.storage.StorageLevel", "DISK_ONLY"), + "DISK_ONLY_2" = callJStatic("org.apache.spark.storage.StorageLevel", "DISK_ONLY_2"), + "MEMORY_AND_DISK" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_AND_DISK"), + "MEMORY_AND_DISK_2" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_AND_DISK_2"), + "MEMORY_AND_DISK_SER" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_AND_DISK_SER"), + "MEMORY_AND_DISK_SER_2" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_AND_DISK_SER_2"), + "MEMORY_ONLY" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_ONLY"), + "MEMORY_ONLY_2" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_ONLY_2"), + "MEMORY_ONLY_SER" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_ONLY_SER"), + "MEMORY_ONLY_SER_2" = callJStatic("org.apache.spark.storage.StorageLevel", "MEMORY_ONLY_SER_2"), + "OFF_HEAP" = callJStatic("org.apache.spark.storage.StorageLevel", "OFF_HEAP")) +} + +# Utility function for functions where an argument needs to be integer but we want to allow +# the user to type (for example) `5` instead of `5L` to avoid a confusing error message. +numToInt <- function(num) { + if (as.integer(num) != num) { + warning(paste("Coercing", as.list(sys.call())[[2]], "to integer.")) + } + as.integer(num) +} + +# create a Seq in JVM +toSeq <- function(...) { + callJStatic("org.apache.spark.sql.api.r.SQLUtils", "toSeq", list(...)) +} + +# create a Seq in JVM from a list +listToSeq <- function(l) { + callJStatic("org.apache.spark.sql.api.r.SQLUtils", "toSeq", l) +} + +# Utility function to recursively traverse the Abstract Syntax Tree (AST) of a +# user defined function (UDF), and to examine variables in the UDF to decide +# if their values should be included in the new function environment. +# param +# node The current AST node in the traversal. +# oldEnv The original function environment. +# defVars An Accumulator of variables names defined in the function's calling environment, +# including function argument and local variable names. +# checkedFunc An environment of function objects examined during cleanClosure. It can +# be considered as a "name"-to-"list of functions" mapping. +# newEnv A new function environment to store necessary function dependencies, an output argument. +processClosure <- function(node, oldEnv, defVars, checkedFuncs, newEnv) { + nodeLen <- length(node) + + if (nodeLen > 1 && typeof(node) == "language") { + # Recursive case: current AST node is an internal node, check for its children. + if (length(node[[1]]) > 1) { + for (i in 1:nodeLen) { + processClosure(node[[i]], oldEnv, defVars, checkedFuncs, newEnv) + } + } else { # if node[[1]] is length of 1, check for some R special functions. + nodeChar <- as.character(node[[1]]) + if (nodeChar == "{" || nodeChar == "(") { # Skip start symbol. + for (i in 2:nodeLen) { + processClosure(node[[i]], oldEnv, defVars, checkedFuncs, newEnv) + } + } else if (nodeChar == "<-" || nodeChar == "=" || + nodeChar == "<<-") { # Assignment Ops. + defVar <- node[[2]] + if (length(defVar) == 1 && typeof(defVar) == "symbol") { + # Add the defined variable name into defVars. + addItemToAccumulator(defVars, as.character(defVar)) + } else { + processClosure(node[[2]], oldEnv, defVars, checkedFuncs, newEnv) + } + for (i in 3:nodeLen) { + processClosure(node[[i]], oldEnv, defVars, checkedFuncs, newEnv) + } + } else if (nodeChar == "function") { # Function definition. + # Add parameter names. + newArgs <- names(node[[2]]) + lapply(newArgs, function(arg) { addItemToAccumulator(defVars, arg) }) + for (i in 3:nodeLen) { + processClosure(node[[i]], oldEnv, defVars, checkedFuncs, newEnv) + } + } else if (nodeChar == "$") { # Skip the field. + processClosure(node[[2]], oldEnv, defVars, checkedFuncs, newEnv) + } else if (nodeChar == "::" || nodeChar == ":::") { + processClosure(node[[3]], oldEnv, defVars, checkedFuncs, newEnv) + } else { + for (i in 1:nodeLen) { + processClosure(node[[i]], oldEnv, defVars, checkedFuncs, newEnv) + } + } + } + } else if (nodeLen == 1 && + (typeof(node) == "symbol" || typeof(node) == "language")) { + # Base case: current AST node is a leaf node and a symbol or a function call. + nodeChar <- as.character(node) + if (!nodeChar %in% defVars$data) { # Not a function parameter or local variable. + func.env <- oldEnv + topEnv <- parent.env(.GlobalEnv) + # Search in function environment, and function's enclosing environments + # up to global environment. There is no need to look into package environments + # above the global or namespace environment that is not SparkR below the global, + # as they are assumed to be loaded on workers. + while (!identical(func.env, topEnv)) { + # Namespaces other than "SparkR" will not be searched. + if (!isNamespace(func.env) || + (getNamespaceName(func.env) == "SparkR" && + !(nodeChar %in% getNamespaceExports("SparkR")))) { # Only include SparkR internals. + # Set parameter 'inherits' to FALSE since we do not need to search in + # attached package environments. + if (tryCatch(exists(nodeChar, envir = func.env, inherits = FALSE), + error = function(e) { FALSE })) { + obj <- get(nodeChar, envir = func.env, inherits = FALSE) + if (is.function(obj)) { # If the node is a function call. + funcList <- mget(nodeChar, envir = checkedFuncs, inherits = F, + ifnotfound = list(list(NULL)))[[1]] + found <- sapply(funcList, function(func) { + ifelse(identical(func, obj), TRUE, FALSE) + }) + if (sum(found) > 0) { # If function has been examined, ignore. + break + } + # Function has not been examined, record it and recursively clean its closure. + assign(nodeChar, + if (is.null(funcList[[1]])) { + list(obj) + } else { + append(funcList, obj) + }, + envir = checkedFuncs) + obj <- cleanClosure(obj, checkedFuncs) + } + assign(nodeChar, obj, envir = newEnv) + break + } + } + + # Continue to search in enclosure. + func.env <- parent.env(func.env) + } + } + } +} + +# Utility function to get user defined function (UDF) dependencies (closure). +# More specifically, this function captures the values of free variables defined +# outside a UDF, and stores them in the function's environment. +# param +# func A function whose closure needs to be captured. +# checkedFunc An environment of function objects examined during cleanClosure. It can be +# considered as a "name"-to-"list of functions" mapping. +# return value +# a new version of func that has an correct environment (closure). +cleanClosure <- function(func, checkedFuncs = new.env()) { + if (is.function(func)) { + newEnv <- new.env(parent = .GlobalEnv) + func.body <- body(func) + oldEnv <- environment(func) + # defVars is an Accumulator of variables names defined in the function's calling + # environment. First, function's arguments are added to defVars. + defVars <- initAccumulator() + argNames <- names(as.list(args(func))) + for (i in 1:(length(argNames) - 1)) { # Remove the ending NULL in pairlist. + addItemToAccumulator(defVars, argNames[i]) + } + # Recursively examine variables in the function body. + processClosure(func.body, oldEnv, defVars, checkedFuncs, newEnv) + environment(func) <- newEnv + } + func +} diff --git a/R/pkg/R/zzz.R b/R/pkg/R/zzz.R new file mode 100644 index 0000000000000..80d796d467943 --- /dev/null +++ b/R/pkg/R/zzz.R @@ -0,0 +1,21 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +.onLoad <- function(libname, pkgname) { + sparkR.onLoad(libname, pkgname) +} + diff --git a/R/pkg/inst/profile/general.R b/R/pkg/inst/profile/general.R new file mode 100644 index 0000000000000..8fe711b622086 --- /dev/null +++ b/R/pkg/inst/profile/general.R @@ -0,0 +1,22 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +.First <- function() { + home <- Sys.getenv("SPARK_HOME") + .libPaths(c(file.path(home, "R", "lib"), .libPaths())) + Sys.setenv(NOAWT=1) +} diff --git a/R/pkg/inst/profile/shell.R b/R/pkg/inst/profile/shell.R new file mode 100644 index 0000000000000..7a7f2031152a0 --- /dev/null +++ b/R/pkg/inst/profile/shell.R @@ -0,0 +1,31 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +.First <- function() { + home <- Sys.getenv("SPARK_HOME") + .libPaths(c(file.path(home, "R", "lib"), .libPaths())) + Sys.setenv(NOAWT=1) + + library(utils) + library(SparkR) + sc <- sparkR.init(Sys.getenv("MASTER", unset = "")) + assign("sc", sc, envir=.GlobalEnv) + sqlCtx <- sparkRSQL.init(sc) + assign("sqlCtx", sqlCtx, envir=.GlobalEnv) + cat("\n Welcome to SparkR!") + cat("\n Spark context is available as sc, SQL context is available as sqlCtx\n") +} diff --git a/R/pkg/inst/tests/test_binaryFile.R b/R/pkg/inst/tests/test_binaryFile.R new file mode 100644 index 0000000000000..4bb5f58d83dc9 --- /dev/null +++ b/R/pkg/inst/tests/test_binaryFile.R @@ -0,0 +1,90 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("functions on binary files") + +# JavaSparkContext handle +sc <- sparkR.init() + +mockFile = c("Spark is pretty.", "Spark is awesome.") + +test_that("saveAsObjectFile()/objectFile() following textFile() works", { + fileName1 <- tempfile(pattern="spark-test", fileext=".tmp") + fileName2 <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName1) + + rdd <- textFile(sc, fileName1) + saveAsObjectFile(rdd, fileName2) + rdd <- objectFile(sc, fileName2) + expect_equal(collect(rdd), as.list(mockFile)) + + unlink(fileName1) + unlink(fileName2, recursive = TRUE) +}) + +test_that("saveAsObjectFile()/objectFile() works on a parallelized list", { + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + + l <- list(1, 2, 3) + rdd <- parallelize(sc, l) + saveAsObjectFile(rdd, fileName) + rdd <- objectFile(sc, fileName) + expect_equal(collect(rdd), l) + + unlink(fileName, recursive = TRUE) +}) + +test_that("saveAsObjectFile()/objectFile() following RDD transformations works", { + fileName1 <- tempfile(pattern="spark-test", fileext=".tmp") + fileName2 <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName1) + + rdd <- textFile(sc, fileName1) + + words <- flatMap(rdd, function(line) { strsplit(line, " ")[[1]] }) + wordCount <- lapply(words, function(word) { list(word, 1L) }) + + counts <- reduceByKey(wordCount, "+", 2L) + + saveAsObjectFile(counts, fileName2) + counts <- objectFile(sc, fileName2) + + output <- collect(counts) + expected <- list(list("awesome.", 1), list("Spark", 2), list("pretty.", 1), + list("is", 2)) + expect_equal(sortKeyValueList(output), sortKeyValueList(expected)) + + unlink(fileName1) + unlink(fileName2, recursive = TRUE) +}) + +test_that("saveAsObjectFile()/objectFile() works with multiple paths", { + fileName1 <- tempfile(pattern="spark-test", fileext=".tmp") + fileName2 <- tempfile(pattern="spark-test", fileext=".tmp") + + rdd1 <- parallelize(sc, "Spark is pretty.") + saveAsObjectFile(rdd1, fileName1) + rdd2 <- parallelize(sc, "Spark is awesome.") + saveAsObjectFile(rdd2, fileName2) + + rdd <- objectFile(sc, c(fileName1, fileName2)) + expect_true(count(rdd) == 2) + + unlink(fileName1, recursive = TRUE) + unlink(fileName2, recursive = TRUE) +}) + diff --git a/R/pkg/inst/tests/test_binary_function.R b/R/pkg/inst/tests/test_binary_function.R new file mode 100644 index 0000000000000..c15553ba28517 --- /dev/null +++ b/R/pkg/inst/tests/test_binary_function.R @@ -0,0 +1,68 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("binary functions") + +# JavaSparkContext handle +sc <- sparkR.init() + +# Data +nums <- 1:10 +rdd <- parallelize(sc, nums, 2L) + +# File content +mockFile <- c("Spark is pretty.", "Spark is awesome.") + +test_that("union on two RDDs", { + actual <- collect(unionRDD(rdd, rdd)) + expect_equal(actual, as.list(rep(nums, 2))) + + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName) + + text.rdd <- textFile(sc, fileName) + union.rdd <- unionRDD(rdd, text.rdd) + actual <- collect(union.rdd) + expect_equal(actual, c(as.list(nums), mockFile)) + expect_true(getSerializedMode(union.rdd) == "byte") + + rdd<- map(text.rdd, function(x) {x}) + union.rdd <- unionRDD(rdd, text.rdd) + actual <- collect(union.rdd) + expect_equal(actual, as.list(c(mockFile, mockFile))) + expect_true(getSerializedMode(union.rdd) == "byte") + + unlink(fileName) +}) + +test_that("cogroup on two RDDs", { + rdd1 <- parallelize(sc, list(list(1, 1), list(2, 4))) + rdd2 <- parallelize(sc, list(list(1, 2), list(1, 3))) + cogroup.rdd <- cogroup(rdd1, rdd2, numPartitions = 2L) + actual <- collect(cogroup.rdd) + expect_equal(actual, + list(list(1, list(list(1), list(2, 3))), list(2, list(list(4), list())))) + + rdd1 <- parallelize(sc, list(list("a", 1), list("a", 4))) + rdd2 <- parallelize(sc, list(list("b", 2), list("a", 3))) + cogroup.rdd <- cogroup(rdd1, rdd2, numPartitions = 2L) + actual <- collect(cogroup.rdd) + + expected <- list(list("b", list(list(), list(2))), list("a", list(list(1, 4), list(3)))) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(expected)) +}) diff --git a/R/pkg/inst/tests/test_broadcast.R b/R/pkg/inst/tests/test_broadcast.R new file mode 100644 index 0000000000000..fee91a427d6d5 --- /dev/null +++ b/R/pkg/inst/tests/test_broadcast.R @@ -0,0 +1,48 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("broadcast variables") + +# JavaSparkContext handle +sc <- sparkR.init() + +# Partitioned data +nums <- 1:2 +rrdd <- parallelize(sc, nums, 2L) + +test_that("using broadcast variable", { + randomMat <- matrix(nrow=10, ncol=10, data=rnorm(100)) + randomMatBr <- broadcast(sc, randomMat) + + useBroadcast <- function(x) { + sum(value(randomMatBr) * x) + } + actual <- collect(lapply(rrdd, useBroadcast)) + expected <- list(sum(randomMat) * 1, sum(randomMat) * 2) + expect_equal(actual, expected) +}) + +test_that("without using broadcast variable", { + randomMat <- matrix(nrow=10, ncol=10, data=rnorm(100)) + + useBroadcast <- function(x) { + sum(randomMat * x) + } + actual <- collect(lapply(rrdd, useBroadcast)) + expected <- list(sum(randomMat) * 1, sum(randomMat) * 2) + expect_equal(actual, expected) +}) diff --git a/R/pkg/inst/tests/test_context.R b/R/pkg/inst/tests/test_context.R new file mode 100644 index 0000000000000..e4aab37436a74 --- /dev/null +++ b/R/pkg/inst/tests/test_context.R @@ -0,0 +1,50 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("test functions in sparkR.R") + +test_that("repeatedly starting and stopping SparkR", { + for (i in 1:4) { + sc <- sparkR.init() + rdd <- parallelize(sc, 1:20, 2L) + expect_equal(count(rdd), 20) + sparkR.stop() + } +}) + +test_that("rdd GC across sparkR.stop", { + sparkR.stop() + sc <- sparkR.init() # sc should get id 0 + rdd1 <- parallelize(sc, 1:20, 2L) # rdd1 should get id 1 + rdd2 <- parallelize(sc, 1:10, 2L) # rdd2 should get id 2 + sparkR.stop() + + sc <- sparkR.init() # sc should get id 0 again + + # GC rdd1 before creating rdd3 and rdd2 after + rm(rdd1) + gc() + + rdd3 <- parallelize(sc, 1:20, 2L) # rdd3 should get id 1 now + rdd4 <- parallelize(sc, 1:10, 2L) # rdd4 should get id 2 now + + rm(rdd2) + gc() + + count(rdd3) + count(rdd4) +}) diff --git a/R/pkg/inst/tests/test_includePackage.R b/R/pkg/inst/tests/test_includePackage.R new file mode 100644 index 0000000000000..8152b448d0870 --- /dev/null +++ b/R/pkg/inst/tests/test_includePackage.R @@ -0,0 +1,57 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("include R packages") + +# JavaSparkContext handle +sc <- sparkR.init() + +# Partitioned data +nums <- 1:2 +rdd <- parallelize(sc, nums, 2L) + +test_that("include inside function", { + # Only run the test if plyr is installed. + if ("plyr" %in% rownames(installed.packages())) { + suppressPackageStartupMessages(library(plyr)) + generateData <- function(x) { + suppressPackageStartupMessages(library(plyr)) + attach(airquality) + result <- transform(Ozone, logOzone = log(Ozone)) + result + } + + data <- lapplyPartition(rdd, generateData) + actual <- collect(data) + } +}) + +test_that("use include package", { + # Only run the test if plyr is installed. + if ("plyr" %in% rownames(installed.packages())) { + suppressPackageStartupMessages(library(plyr)) + generateData <- function(x) { + attach(airquality) + result <- transform(Ozone, logOzone = log(Ozone)) + result + } + + includePackage(sc, plyr) + data <- lapplyPartition(rdd, generateData) + actual <- collect(data) + } +}) diff --git a/R/pkg/inst/tests/test_parallelize_collect.R b/R/pkg/inst/tests/test_parallelize_collect.R new file mode 100644 index 0000000000000..fff028657db37 --- /dev/null +++ b/R/pkg/inst/tests/test_parallelize_collect.R @@ -0,0 +1,109 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("parallelize() and collect()") + +# Mock data +numVector <- c(-10:97) +numList <- list(sqrt(1), sqrt(2), sqrt(3), 4 ** 10) +strVector <- c("Dexter Morgan: I suppose I should be upset, even feel", + "violated, but I'm not. No, in fact, I think this is a friendly", + "message, like \"Hey, wanna play?\" and yes, I want to play. ", + "I really, really do.") +strList <- list("Dexter Morgan: Blood. Sometimes it sets my teeth on edge, ", + "other times it helps me control the chaos.", + "Dexter Morgan: Harry and Dorris Morgan did a wonderful job ", + "raising me. But they're both dead now. I didn't kill them. Honest.") + +numPairs <- list(list(1, 1), list(1, 2), list(2, 2), list(2, 3)) +strPairs <- list(list(strList, strList), list(strList, strList)) + +# JavaSparkContext handle +jsc <- sparkR.init() + +# Tests + +test_that("parallelize() on simple vectors and lists returns an RDD", { + numVectorRDD <- parallelize(jsc, numVector, 1) + numVectorRDD2 <- parallelize(jsc, numVector, 10) + numListRDD <- parallelize(jsc, numList, 1) + numListRDD2 <- parallelize(jsc, numList, 4) + strVectorRDD <- parallelize(jsc, strVector, 2) + strVectorRDD2 <- parallelize(jsc, strVector, 3) + strListRDD <- parallelize(jsc, strList, 4) + strListRDD2 <- parallelize(jsc, strList, 1) + + rdds <- c(numVectorRDD, + numVectorRDD2, + numListRDD, + numListRDD2, + strVectorRDD, + strVectorRDD2, + strListRDD, + strListRDD2) + + for (rdd in rdds) { + expect_true(inherits(rdd, "RDD")) + expect_true(.hasSlot(rdd, "jrdd") + && inherits(rdd@jrdd, "jobj") + && isInstanceOf(rdd@jrdd, "org.apache.spark.api.java.JavaRDD")) + } +}) + +test_that("collect(), following a parallelize(), gives back the original collections", { + numVectorRDD <- parallelize(jsc, numVector, 10) + expect_equal(collect(numVectorRDD), as.list(numVector)) + + numListRDD <- parallelize(jsc, numList, 1) + numListRDD2 <- parallelize(jsc, numList, 4) + expect_equal(collect(numListRDD), as.list(numList)) + expect_equal(collect(numListRDD2), as.list(numList)) + + strVectorRDD <- parallelize(jsc, strVector, 2) + strVectorRDD2 <- parallelize(jsc, strVector, 3) + expect_equal(collect(strVectorRDD), as.list(strVector)) + expect_equal(collect(strVectorRDD2), as.list(strVector)) + + strListRDD <- parallelize(jsc, strList, 4) + strListRDD2 <- parallelize(jsc, strList, 1) + expect_equal(collect(strListRDD), as.list(strList)) + expect_equal(collect(strListRDD2), as.list(strList)) +}) + +test_that("regression: collect() following a parallelize() does not drop elements", { + # 10 %/% 6 = 1, ceiling(10 / 6) = 2 + collLen <- 10 + numPart <- 6 + expected <- runif(collLen) + actual <- collect(parallelize(jsc, expected, numPart)) + expect_equal(actual, as.list(expected)) +}) + +test_that("parallelize() and collect() work for lists of pairs (pairwise data)", { + # use the pairwise logical to indicate pairwise data + numPairsRDDD1 <- parallelize(jsc, numPairs, 1) + numPairsRDDD2 <- parallelize(jsc, numPairs, 2) + numPairsRDDD3 <- parallelize(jsc, numPairs, 3) + expect_equal(collect(numPairsRDDD1), numPairs) + expect_equal(collect(numPairsRDDD2), numPairs) + expect_equal(collect(numPairsRDDD3), numPairs) + # can also leave out the parameter name, if the params are supplied in order + strPairsRDDD1 <- parallelize(jsc, strPairs, 1) + strPairsRDDD2 <- parallelize(jsc, strPairs, 2) + expect_equal(collect(strPairsRDDD1), strPairs) + expect_equal(collect(strPairsRDDD2), strPairs) +}) diff --git a/R/pkg/inst/tests/test_rdd.R b/R/pkg/inst/tests/test_rdd.R new file mode 100644 index 0000000000000..f75e0817b9406 --- /dev/null +++ b/R/pkg/inst/tests/test_rdd.R @@ -0,0 +1,644 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("basic RDD functions") + +# JavaSparkContext handle +sc <- sparkR.init() + +# Data +nums <- 1:10 +rdd <- parallelize(sc, nums, 2L) + +intPairs <- list(list(1L, -1), list(2L, 100), list(2L, 1), list(1L, 200)) +intRdd <- parallelize(sc, intPairs, 2L) + +test_that("get number of partitions in RDD", { + expect_equal(numPartitions(rdd), 2) + expect_equal(numPartitions(intRdd), 2) +}) + +test_that("first on RDD", { + expect_true(first(rdd) == 1) + newrdd <- lapply(rdd, function(x) x + 1) + expect_true(first(newrdd) == 2) +}) + +test_that("count and length on RDD", { + expect_equal(count(rdd), 10) + expect_equal(length(rdd), 10) +}) + +test_that("count by values and keys", { + mods <- lapply(rdd, function(x) { x %% 3 }) + actual <- countByValue(mods) + expected <- list(list(0, 3L), list(1, 4L), list(2, 3L)) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) + + actual <- countByKey(intRdd) + expected <- list(list(2L, 2L), list(1L, 2L)) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) +}) + +test_that("lapply on RDD", { + multiples <- lapply(rdd, function(x) { 2 * x }) + actual <- collect(multiples) + expect_equal(actual, as.list(nums * 2)) +}) + +test_that("lapplyPartition on RDD", { + sums <- lapplyPartition(rdd, function(part) { sum(unlist(part)) }) + actual <- collect(sums) + expect_equal(actual, list(15, 40)) +}) + +test_that("mapPartitions on RDD", { + sums <- mapPartitions(rdd, function(part) { sum(unlist(part)) }) + actual <- collect(sums) + expect_equal(actual, list(15, 40)) +}) + +test_that("flatMap() on RDDs", { + flat <- flatMap(intRdd, function(x) { list(x, x) }) + actual <- collect(flat) + expect_equal(actual, rep(intPairs, each=2)) +}) + +test_that("filterRDD on RDD", { + filtered.rdd <- filterRDD(rdd, function(x) { x %% 2 == 0 }) + actual <- collect(filtered.rdd) + expect_equal(actual, list(2, 4, 6, 8, 10)) + + filtered.rdd <- Filter(function(x) { x[[2]] < 0 }, intRdd) + actual <- collect(filtered.rdd) + expect_equal(actual, list(list(1L, -1))) + + # Filter out all elements. + filtered.rdd <- filterRDD(rdd, function(x) { x > 10 }) + actual <- collect(filtered.rdd) + expect_equal(actual, list()) +}) + +test_that("lookup on RDD", { + vals <- lookup(intRdd, 1L) + expect_equal(vals, list(-1, 200)) + + vals <- lookup(intRdd, 3L) + expect_equal(vals, list()) +}) + +test_that("several transformations on RDD (a benchmark on PipelinedRDD)", { + rdd2 <- rdd + for (i in 1:12) + rdd2 <- lapplyPartitionsWithIndex( + rdd2, function(split, part) { + part <- as.list(unlist(part) * split + i) + }) + rdd2 <- lapply(rdd2, function(x) x + x) + actual <- collect(rdd2) + expected <- list(24, 24, 24, 24, 24, + 168, 170, 172, 174, 176) + expect_equal(actual, expected) +}) + +test_that("PipelinedRDD support actions: cache(), persist(), unpersist(), checkpoint()", { + # RDD + rdd2 <- rdd + # PipelinedRDD + rdd2 <- lapplyPartitionsWithIndex( + rdd2, + function(split, part) { + part <- as.list(unlist(part) * split) + }) + + cache(rdd2) + expect_true(rdd2@env$isCached) + rdd2 <- lapply(rdd2, function(x) x) + expect_false(rdd2@env$isCached) + + unpersist(rdd2) + expect_false(rdd2@env$isCached) + + persist(rdd2, "MEMORY_AND_DISK") + expect_true(rdd2@env$isCached) + rdd2 <- lapply(rdd2, function(x) x) + expect_false(rdd2@env$isCached) + + unpersist(rdd2) + expect_false(rdd2@env$isCached) + + setCheckpointDir(sc, "checkpoints") + checkpoint(rdd2) + expect_true(rdd2@env$isCheckpointed) + + rdd2 <- lapply(rdd2, function(x) x) + expect_false(rdd2@env$isCached) + expect_false(rdd2@env$isCheckpointed) + + # make sure the data is collectable + collect(rdd2) + + unlink("checkpoints") +}) + +test_that("reduce on RDD", { + sum <- reduce(rdd, "+") + expect_equal(sum, 55) + + # Also test with an inline function + sumInline <- reduce(rdd, function(x, y) { x + y }) + expect_equal(sumInline, 55) +}) + +test_that("lapply with dependency", { + fa <- 5 + multiples <- lapply(rdd, function(x) { fa * x }) + actual <- collect(multiples) + + expect_equal(actual, as.list(nums * 5)) +}) + +test_that("lapplyPartitionsWithIndex on RDDs", { + func <- function(splitIndex, part) { list(splitIndex, Reduce("+", part)) } + actual <- collect(lapplyPartitionsWithIndex(rdd, func), flatten = FALSE) + expect_equal(actual, list(list(0, 15), list(1, 40))) + + pairsRDD <- parallelize(sc, list(list(1, 2), list(3, 4), list(4, 8)), 1L) + partitionByParity <- function(key) { if (key %% 2 == 1) 0 else 1 } + mkTup <- function(splitIndex, part) { list(splitIndex, part) } + actual <- collect(lapplyPartitionsWithIndex( + partitionBy(pairsRDD, 2L, partitionByParity), + mkTup), + FALSE) + expect_equal(actual, list(list(0, list(list(1, 2), list(3, 4))), + list(1, list(list(4, 8))))) +}) + +test_that("sampleRDD() on RDDs", { + expect_equal(unlist(collect(sampleRDD(rdd, FALSE, 1.0, 2014L))), nums) +}) + +test_that("takeSample() on RDDs", { + # ported from RDDSuite.scala, modified seeds + data <- parallelize(sc, 1:100, 2L) + for (seed in 4:5) { + s <- takeSample(data, FALSE, 20L, seed) + expect_equal(length(s), 20L) + expect_equal(length(unique(s)), 20L) + for (elem in s) { + expect_true(elem >= 1 && elem <= 100) + } + } + for (seed in 4:5) { + s <- takeSample(data, FALSE, 200L, seed) + expect_equal(length(s), 100L) + expect_equal(length(unique(s)), 100L) + for (elem in s) { + expect_true(elem >= 1 && elem <= 100) + } + } + for (seed in 4:5) { + s <- takeSample(data, TRUE, 20L, seed) + expect_equal(length(s), 20L) + for (elem in s) { + expect_true(elem >= 1 && elem <= 100) + } + } + for (seed in 4:5) { + s <- takeSample(data, TRUE, 100L, seed) + expect_equal(length(s), 100L) + # Chance of getting all distinct elements is astronomically low, so test we + # got < 100 + expect_true(length(unique(s)) < 100L) + } + for (seed in 4:5) { + s <- takeSample(data, TRUE, 200L, seed) + expect_equal(length(s), 200L) + # Chance of getting all distinct elements is still quite low, so test we + # got < 100 + expect_true(length(unique(s)) < 100L) + } +}) + +test_that("mapValues() on pairwise RDDs", { + multiples <- mapValues(intRdd, function(x) { x * 2 }) + actual <- collect(multiples) + expected <- lapply(intPairs, function(x) { + list(x[[1]], x[[2]] * 2) + }) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) +}) + +test_that("flatMapValues() on pairwise RDDs", { + l <- parallelize(sc, list(list(1, c(1,2)), list(2, c(3,4)))) + actual <- collect(flatMapValues(l, function(x) { x })) + expect_equal(actual, list(list(1,1), list(1,2), list(2,3), list(2,4))) + + # Generate x to x+1 for every value + actual <- collect(flatMapValues(intRdd, function(x) { x:(x + 1) })) + expect_equal(actual, + list(list(1L, -1), list(1L, 0), list(2L, 100), list(2L, 101), + list(2L, 1), list(2L, 2), list(1L, 200), list(1L, 201))) +}) + +test_that("reduceByKeyLocally() on PairwiseRDDs", { + pairs <- parallelize(sc, list(list(1, 2), list(1.1, 3), list(1, 4)), 2L) + actual <- reduceByKeyLocally(pairs, "+") + expect_equal(sortKeyValueList(actual), + sortKeyValueList(list(list(1, 6), list(1.1, 3)))) + + pairs <- parallelize(sc, list(list("abc", 1.2), list(1.1, 0), list("abc", 1.3), + list("bb", 5)), 4L) + actual <- reduceByKeyLocally(pairs, "+") + expect_equal(sortKeyValueList(actual), + sortKeyValueList(list(list("abc", 2.5), list(1.1, 0), list("bb", 5)))) +}) + +test_that("distinct() on RDDs", { + nums.rep2 <- rep(1:10, 2) + rdd.rep2 <- parallelize(sc, nums.rep2, 2L) + uniques <- distinct(rdd.rep2) + actual <- sort(unlist(collect(uniques))) + expect_equal(actual, nums) +}) + +test_that("maximum() on RDDs", { + max <- maximum(rdd) + expect_equal(max, 10) +}) + +test_that("minimum() on RDDs", { + min <- minimum(rdd) + expect_equal(min, 1) +}) + +test_that("sumRDD() on RDDs", { + sum <- sumRDD(rdd) + expect_equal(sum, 55) +}) + +test_that("keyBy on RDDs", { + func <- function(x) { x*x } + keys <- keyBy(rdd, func) + actual <- collect(keys) + expect_equal(actual, lapply(nums, function(x) { list(func(x), x) })) +}) + +test_that("repartition/coalesce on RDDs", { + rdd <- parallelize(sc, 1:20, 4L) # each partition contains 5 elements + + # repartition + r1 <- repartition(rdd, 2) + expect_equal(numPartitions(r1), 2L) + count <- length(collectPartition(r1, 0L)) + expect_true(count >= 8 && count <= 12) + + r2 <- repartition(rdd, 6) + expect_equal(numPartitions(r2), 6L) + count <- length(collectPartition(r2, 0L)) + expect_true(count >=0 && count <= 4) + + # coalesce + r3 <- coalesce(rdd, 1) + expect_equal(numPartitions(r3), 1L) + count <- length(collectPartition(r3, 0L)) + expect_equal(count, 20) +}) + +test_that("sortBy() on RDDs", { + sortedRdd <- sortBy(rdd, function(x) { x * x }, ascending = FALSE) + actual <- collect(sortedRdd) + expect_equal(actual, as.list(sort(nums, decreasing = TRUE))) + + rdd2 <- parallelize(sc, sort(nums, decreasing = TRUE), 2L) + sortedRdd2 <- sortBy(rdd2, function(x) { x * x }) + actual <- collect(sortedRdd2) + expect_equal(actual, as.list(nums)) +}) + +test_that("takeOrdered() on RDDs", { + l <- list(10, 1, 2, 9, 3, 4, 5, 6, 7) + rdd <- parallelize(sc, l) + actual <- takeOrdered(rdd, 6L) + expect_equal(actual, as.list(sort(unlist(l)))[1:6]) + + l <- list("e", "d", "c", "d", "a") + rdd <- parallelize(sc, l) + actual <- takeOrdered(rdd, 3L) + expect_equal(actual, as.list(sort(unlist(l)))[1:3]) +}) + +test_that("top() on RDDs", { + l <- list(10, 1, 2, 9, 3, 4, 5, 6, 7) + rdd <- parallelize(sc, l) + actual <- top(rdd, 6L) + expect_equal(actual, as.list(sort(unlist(l), decreasing = TRUE))[1:6]) + + l <- list("e", "d", "c", "d", "a") + rdd <- parallelize(sc, l) + actual <- top(rdd, 3L) + expect_equal(actual, as.list(sort(unlist(l), decreasing = TRUE))[1:3]) +}) + +test_that("fold() on RDDs", { + actual <- fold(rdd, 0, "+") + expect_equal(actual, Reduce("+", nums, 0)) + + rdd <- parallelize(sc, list()) + actual <- fold(rdd, 0, "+") + expect_equal(actual, 0) +}) + +test_that("aggregateRDD() on RDDs", { + rdd <- parallelize(sc, list(1, 2, 3, 4)) + zeroValue <- list(0, 0) + seqOp <- function(x, y) { list(x[[1]] + y, x[[2]] + 1) } + combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) } + actual <- aggregateRDD(rdd, zeroValue, seqOp, combOp) + expect_equal(actual, list(10, 4)) + + rdd <- parallelize(sc, list()) + actual <- aggregateRDD(rdd, zeroValue, seqOp, combOp) + expect_equal(actual, list(0, 0)) +}) + +test_that("zipWithUniqueId() on RDDs", { + rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L) + actual <- collect(zipWithUniqueId(rdd)) + expected <- list(list("a", 0), list("b", 3), list("c", 1), + list("d", 4), list("e", 2)) + expect_equal(actual, expected) + + rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 1L) + actual <- collect(zipWithUniqueId(rdd)) + expected <- list(list("a", 0), list("b", 1), list("c", 2), + list("d", 3), list("e", 4)) + expect_equal(actual, expected) +}) + +test_that("zipWithIndex() on RDDs", { + rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L) + actual <- collect(zipWithIndex(rdd)) + expected <- list(list("a", 0), list("b", 1), list("c", 2), + list("d", 3), list("e", 4)) + expect_equal(actual, expected) + + rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 1L) + actual <- collect(zipWithIndex(rdd)) + expected <- list(list("a", 0), list("b", 1), list("c", 2), + list("d", 3), list("e", 4)) + expect_equal(actual, expected) +}) + +test_that("glom() on RDD", { + rdd <- parallelize(sc, as.list(1:4), 2L) + actual <- collect(glom(rdd)) + expect_equal(actual, list(list(1, 2), list(3, 4))) +}) + +test_that("keys() on RDDs", { + keys <- keys(intRdd) + actual <- collect(keys) + expect_equal(actual, lapply(intPairs, function(x) { x[[1]] })) +}) + +test_that("values() on RDDs", { + values <- values(intRdd) + actual <- collect(values) + expect_equal(actual, lapply(intPairs, function(x) { x[[2]] })) +}) + +test_that("pipeRDD() on RDDs", { + actual <- collect(pipeRDD(rdd, "more")) + expected <- as.list(as.character(1:10)) + expect_equal(actual, expected) + + trailed.rdd <- parallelize(sc, c("1", "", "2\n", "3\n\r\n")) + actual <- collect(pipeRDD(trailed.rdd, "sort")) + expected <- list("", "1", "2", "3") + expect_equal(actual, expected) + + rev.nums <- 9:0 + rev.rdd <- parallelize(sc, rev.nums, 2L) + actual <- collect(pipeRDD(rev.rdd, "sort")) + expected <- as.list(as.character(c(5:9, 0:4))) + expect_equal(actual, expected) +}) + +test_that("zipRDD() on RDDs", { + rdd1 <- parallelize(sc, 0:4, 2) + rdd2 <- parallelize(sc, 1000:1004, 2) + actual <- collect(zipRDD(rdd1, rdd2)) + expect_equal(actual, + list(list(0, 1000), list(1, 1001), list(2, 1002), list(3, 1003), list(4, 1004))) + + mockFile = c("Spark is pretty.", "Spark is awesome.") + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName) + + rdd <- textFile(sc, fileName, 1) + actual <- collect(zipRDD(rdd, rdd)) + expected <- lapply(mockFile, function(x) { list(x ,x) }) + expect_equal(actual, expected) + + rdd1 <- parallelize(sc, 0:1, 1) + actual <- collect(zipRDD(rdd1, rdd)) + expected <- lapply(0:1, function(x) { list(x, mockFile[x + 1]) }) + expect_equal(actual, expected) + + rdd1 <- map(rdd, function(x) { x }) + actual <- collect(zipRDD(rdd, rdd1)) + expected <- lapply(mockFile, function(x) { list(x, x) }) + expect_equal(actual, expected) + + unlink(fileName) +}) + +test_that("join() on pairwise RDDs", { + rdd1 <- parallelize(sc, list(list(1,1), list(2,4))) + rdd2 <- parallelize(sc, list(list(1,2), list(1,3))) + actual <- collect(join(rdd1, rdd2, 2L)) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(list(list(1, list(1, 2)), list(1, list(1, 3))))) + + rdd1 <- parallelize(sc, list(list("a",1), list("b",4))) + rdd2 <- parallelize(sc, list(list("a",2), list("a",3))) + actual <- collect(join(rdd1, rdd2, 2L)) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(list(list("a", list(1, 2)), list("a", list(1, 3))))) + + rdd1 <- parallelize(sc, list(list(1,1), list(2,2))) + rdd2 <- parallelize(sc, list(list(3,3), list(4,4))) + actual <- collect(join(rdd1, rdd2, 2L)) + expect_equal(actual, list()) + + rdd1 <- parallelize(sc, list(list("a",1), list("b",2))) + rdd2 <- parallelize(sc, list(list("c",3), list("d",4))) + actual <- collect(join(rdd1, rdd2, 2L)) + expect_equal(actual, list()) +}) + +test_that("leftOuterJoin() on pairwise RDDs", { + rdd1 <- parallelize(sc, list(list(1,1), list(2,4))) + rdd2 <- parallelize(sc, list(list(1,2), list(1,3))) + actual <- collect(leftOuterJoin(rdd1, rdd2, 2L)) + expected <- list(list(1, list(1, 2)), list(1, list(1, 3)), list(2, list(4, NULL))) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(expected)) + + rdd1 <- parallelize(sc, list(list("a",1), list("b",4))) + rdd2 <- parallelize(sc, list(list("a",2), list("a",3))) + actual <- collect(leftOuterJoin(rdd1, rdd2, 2L)) + expected <- list(list("b", list(4, NULL)), list("a", list(1, 2)), list("a", list(1, 3))) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(expected)) + + rdd1 <- parallelize(sc, list(list(1,1), list(2,2))) + rdd2 <- parallelize(sc, list(list(3,3), list(4,4))) + actual <- collect(leftOuterJoin(rdd1, rdd2, 2L)) + expected <- list(list(1, list(1, NULL)), list(2, list(2, NULL))) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(expected)) + + rdd1 <- parallelize(sc, list(list("a",1), list("b",2))) + rdd2 <- parallelize(sc, list(list("c",3), list("d",4))) + actual <- collect(leftOuterJoin(rdd1, rdd2, 2L)) + expected <- list(list("b", list(2, NULL)), list("a", list(1, NULL))) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(expected)) +}) + +test_that("rightOuterJoin() on pairwise RDDs", { + rdd1 <- parallelize(sc, list(list(1,2), list(1,3))) + rdd2 <- parallelize(sc, list(list(1,1), list(2,4))) + actual <- collect(rightOuterJoin(rdd1, rdd2, 2L)) + expected <- list(list(1, list(2, 1)), list(1, list(3, 1)), list(2, list(NULL, 4))) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) + + rdd1 <- parallelize(sc, list(list("a",2), list("a",3))) + rdd2 <- parallelize(sc, list(list("a",1), list("b",4))) + actual <- collect(rightOuterJoin(rdd1, rdd2, 2L)) + expected <- list(list("b", list(NULL, 4)), list("a", list(2, 1)), list("a", list(3, 1))) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(expected)) + + rdd1 <- parallelize(sc, list(list(1,1), list(2,2))) + rdd2 <- parallelize(sc, list(list(3,3), list(4,4))) + actual <- collect(rightOuterJoin(rdd1, rdd2, 2L)) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(list(list(3, list(NULL, 3)), list(4, list(NULL, 4))))) + + rdd1 <- parallelize(sc, list(list("a",1), list("b",2))) + rdd2 <- parallelize(sc, list(list("c",3), list("d",4))) + actual <- collect(rightOuterJoin(rdd1, rdd2, 2L)) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(list(list("d", list(NULL, 4)), list("c", list(NULL, 3))))) +}) + +test_that("fullOuterJoin() on pairwise RDDs", { + rdd1 <- parallelize(sc, list(list(1,2), list(1,3), list(3,3))) + rdd2 <- parallelize(sc, list(list(1,1), list(2,4))) + actual <- collect(fullOuterJoin(rdd1, rdd2, 2L)) + expected <- list(list(1, list(2, 1)), list(1, list(3, 1)), list(2, list(NULL, 4)), list(3, list(3, NULL))) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) + + rdd1 <- parallelize(sc, list(list("a",2), list("a",3), list("c", 1))) + rdd2 <- parallelize(sc, list(list("a",1), list("b",4))) + actual <- collect(fullOuterJoin(rdd1, rdd2, 2L)) + expected <- list(list("b", list(NULL, 4)), list("a", list(2, 1)), list("a", list(3, 1)), list("c", list(1, NULL))) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(expected)) + + rdd1 <- parallelize(sc, list(list(1,1), list(2,2))) + rdd2 <- parallelize(sc, list(list(3,3), list(4,4))) + actual <- collect(fullOuterJoin(rdd1, rdd2, 2L)) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(list(list(1, list(1, NULL)), list(2, list(2, NULL)), list(3, list(NULL, 3)), list(4, list(NULL, 4))))) + + rdd1 <- parallelize(sc, list(list("a",1), list("b",2))) + rdd2 <- parallelize(sc, list(list("c",3), list("d",4))) + actual <- collect(fullOuterJoin(rdd1, rdd2, 2L)) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(list(list("a", list(1, NULL)), list("b", list(2, NULL)), list("d", list(NULL, 4)), list("c", list(NULL, 3))))) +}) + +test_that("sortByKey() on pairwise RDDs", { + numPairsRdd <- map(rdd, function(x) { list (x, x) }) + sortedRdd <- sortByKey(numPairsRdd, ascending = FALSE) + actual <- collect(sortedRdd) + numPairs <- lapply(nums, function(x) { list (x, x) }) + expect_equal(actual, sortKeyValueList(numPairs, decreasing = TRUE)) + + rdd2 <- parallelize(sc, sort(nums, decreasing = TRUE), 2L) + numPairsRdd2 <- map(rdd2, function(x) { list (x, x) }) + sortedRdd2 <- sortByKey(numPairsRdd2) + actual <- collect(sortedRdd2) + expect_equal(actual, numPairs) + + # sort by string keys + l <- list(list("a", 1), list("b", 2), list("1", 3), list("d", 4), list("2", 5)) + rdd3 <- parallelize(sc, l, 2L) + sortedRdd3 <- sortByKey(rdd3) + actual <- collect(sortedRdd3) + expect_equal(actual, list(list("1", 3), list("2", 5), list("a", 1), list("b", 2), list("d", 4))) + + # test on the boundary cases + + # boundary case 1: the RDD to be sorted has only 1 partition + rdd4 <- parallelize(sc, l, 1L) + sortedRdd4 <- sortByKey(rdd4) + actual <- collect(sortedRdd4) + expect_equal(actual, list(list("1", 3), list("2", 5), list("a", 1), list("b", 2), list("d", 4))) + + # boundary case 2: the sorted RDD has only 1 partition + rdd5 <- parallelize(sc, l, 2L) + sortedRdd5 <- sortByKey(rdd5, numPartitions = 1L) + actual <- collect(sortedRdd5) + expect_equal(actual, list(list("1", 3), list("2", 5), list("a", 1), list("b", 2), list("d", 4))) + + # boundary case 3: the RDD to be sorted has only 1 element + l2 <- list(list("a", 1)) + rdd6 <- parallelize(sc, l2, 2L) + sortedRdd6 <- sortByKey(rdd6) + actual <- collect(sortedRdd6) + expect_equal(actual, l2) + + # boundary case 4: the RDD to be sorted has 0 element + l3 <- list() + rdd7 <- parallelize(sc, l3, 2L) + sortedRdd7 <- sortByKey(rdd7) + actual <- collect(sortedRdd7) + expect_equal(actual, l3) +}) + +test_that("collectAsMap() on a pairwise RDD", { + rdd <- parallelize(sc, list(list(1, 2), list(3, 4))) + vals <- collectAsMap(rdd) + expect_equal(vals, list(`1` = 2, `3` = 4)) + + rdd <- parallelize(sc, list(list("a", 1), list("b", 2))) + vals <- collectAsMap(rdd) + expect_equal(vals, list(a = 1, b = 2)) + + rdd <- parallelize(sc, list(list(1.1, 2.2), list(1.2, 2.4))) + vals <- collectAsMap(rdd) + expect_equal(vals, list(`1.1` = 2.2, `1.2` = 2.4)) + + rdd <- parallelize(sc, list(list(1, "a"), list(2, "b"))) + vals <- collectAsMap(rdd) + expect_equal(vals, list(`1` = "a", `2` = "b")) +}) diff --git a/R/pkg/inst/tests/test_shuffle.R b/R/pkg/inst/tests/test_shuffle.R new file mode 100644 index 0000000000000..d1da8232aea81 --- /dev/null +++ b/R/pkg/inst/tests/test_shuffle.R @@ -0,0 +1,209 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("partitionBy, groupByKey, reduceByKey etc.") + +# JavaSparkContext handle +sc <- sparkR.init() + +# Data +intPairs <- list(list(1L, -1), list(2L, 100), list(2L, 1), list(1L, 200)) +intRdd <- parallelize(sc, intPairs, 2L) + +doublePairs <- list(list(1.5, -1), list(2.5, 100), list(2.5, 1), list(1.5, 200)) +doubleRdd <- parallelize(sc, doublePairs, 2L) + +numPairs <- list(list(1L, 100), list(2L, 200), list(4L, -1), list(3L, 1), + list(3L, 0)) +numPairsRdd <- parallelize(sc, numPairs, length(numPairs)) + +strList <- list("Dexter Morgan: Blood. Sometimes it sets my teeth on edge and ", + "Dexter Morgan: Harry and Dorris Morgan did a wonderful job ") +strListRDD <- parallelize(sc, strList, 4) + +test_that("groupByKey for integers", { + grouped <- groupByKey(intRdd, 2L) + + actual <- collect(grouped) + + expected <- list(list(2L, list(100, 1)), list(1L, list(-1, 200))) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) +}) + +test_that("groupByKey for doubles", { + grouped <- groupByKey(doubleRdd, 2L) + + actual <- collect(grouped) + + expected <- list(list(1.5, list(-1, 200)), list(2.5, list(100, 1))) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) +}) + +test_that("reduceByKey for ints", { + reduced <- reduceByKey(intRdd, "+", 2L) + + actual <- collect(reduced) + + expected <- list(list(2L, 101), list(1L, 199)) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) +}) + +test_that("reduceByKey for doubles", { + reduced <- reduceByKey(doubleRdd, "+", 2L) + actual <- collect(reduced) + + expected <- list(list(1.5, 199), list(2.5, 101)) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) +}) + +test_that("combineByKey for ints", { + reduced <- combineByKey(intRdd, function(x) { x }, "+", "+", 2L) + + actual <- collect(reduced) + + expected <- list(list(2L, 101), list(1L, 199)) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) +}) + +test_that("combineByKey for doubles", { + reduced <- combineByKey(doubleRdd, function(x) { x }, "+", "+", 2L) + actual <- collect(reduced) + + expected <- list(list(1.5, 199), list(2.5, 101)) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) +}) + +test_that("aggregateByKey", { + # test aggregateByKey for int keys + rdd <- parallelize(sc, list(list(1, 1), list(1, 2), list(2, 3), list(2, 4))) + + zeroValue <- list(0, 0) + seqOp <- function(x, y) { list(x[[1]] + y, x[[2]] + 1) } + combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) } + aggregatedRDD <- aggregateByKey(rdd, zeroValue, seqOp, combOp, 2L) + + actual <- collect(aggregatedRDD) + + expected <- list(list(1, list(3, 2)), list(2, list(7, 2))) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) + + # test aggregateByKey for string keys + rdd <- parallelize(sc, list(list("a", 1), list("a", 2), list("b", 3), list("b", 4))) + + zeroValue <- list(0, 0) + seqOp <- function(x, y) { list(x[[1]] + y, x[[2]] + 1) } + combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) } + aggregatedRDD <- aggregateByKey(rdd, zeroValue, seqOp, combOp, 2L) + + actual <- collect(aggregatedRDD) + + expected <- list(list("a", list(3, 2)), list("b", list(7, 2))) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) +}) + +test_that("foldByKey", { + # test foldByKey for int keys + folded <- foldByKey(intRdd, 0, "+", 2L) + + actual <- collect(folded) + + expected <- list(list(2L, 101), list(1L, 199)) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) + + # test foldByKey for double keys + folded <- foldByKey(doubleRdd, 0, "+", 2L) + + actual <- collect(folded) + + expected <- list(list(1.5, 199), list(2.5, 101)) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) + + # test foldByKey for string keys + stringKeyPairs <- list(list("a", -1), list("b", 100), list("b", 1), list("a", 200)) + + stringKeyRDD <- parallelize(sc, stringKeyPairs) + folded <- foldByKey(stringKeyRDD, 0, "+", 2L) + + actual <- collect(folded) + + expected <- list(list("b", 101), list("a", 199)) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) + + # test foldByKey for empty pair RDD + rdd <- parallelize(sc, list()) + folded <- foldByKey(rdd, 0, "+", 2L) + actual <- collect(folded) + expected <- list() + expect_equal(actual, expected) + + # test foldByKey for RDD with only 1 pair + rdd <- parallelize(sc, list(list(1, 1))) + folded <- foldByKey(rdd, 0, "+", 2L) + actual <- collect(folded) + expected <- list(list(1, 1)) + expect_equal(actual, expected) +}) + +test_that("partitionBy() partitions data correctly", { + # Partition by magnitude + partitionByMagnitude <- function(key) { if (key >= 3) 1 else 0 } + + resultRDD <- partitionBy(numPairsRdd, 2L, partitionByMagnitude) + + expected_first <- list(list(1, 100), list(2, 200)) # key < 3 + expected_second <- list(list(4, -1), list(3, 1), list(3, 0)) # key >= 3 + actual_first <- collectPartition(resultRDD, 0L) + actual_second <- collectPartition(resultRDD, 1L) + + expect_equal(sortKeyValueList(actual_first), sortKeyValueList(expected_first)) + expect_equal(sortKeyValueList(actual_second), sortKeyValueList(expected_second)) +}) + +test_that("partitionBy works with dependencies", { + kOne <- 1 + partitionByParity <- function(key) { if (key %% 2 == kOne) 7 else 4 } + + # Partition by parity + resultRDD <- partitionBy(numPairsRdd, numPartitions = 2L, partitionByParity) + + # keys even; 100 %% 2 == 0 + expected_first <- list(list(2, 200), list(4, -1)) + # keys odd; 3 %% 2 == 1 + expected_second <- list(list(1, 100), list(3, 1), list(3, 0)) + actual_first <- collectPartition(resultRDD, 0L) + actual_second <- collectPartition(resultRDD, 1L) + + expect_equal(sortKeyValueList(actual_first), sortKeyValueList(expected_first)) + expect_equal(sortKeyValueList(actual_second), sortKeyValueList(expected_second)) +}) + +test_that("test partitionBy with string keys", { + words <- flatMap(strListRDD, function(line) { strsplit(line, " ")[[1]] }) + wordCount <- lapply(words, function(word) { list(word, 1L) }) + + resultRDD <- partitionBy(wordCount, 2L) + expected_first <- list(list("Dexter", 1), list("Dexter", 1)) + expected_second <- list(list("and", 1), list("and", 1)) + + actual_first <- Filter(function(item) { item[[1]] == "Dexter" }, + collectPartition(resultRDD, 0L)) + actual_second <- Filter(function(item) { item[[1]] == "and" }, + collectPartition(resultRDD, 1L)) + + expect_equal(sortKeyValueList(actual_first), sortKeyValueList(expected_first)) + expect_equal(sortKeyValueList(actual_second), sortKeyValueList(expected_second)) +}) diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R new file mode 100644 index 0000000000000..cf5cf6d1692af --- /dev/null +++ b/R/pkg/inst/tests/test_sparkSQL.R @@ -0,0 +1,695 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +library(testthat) + +context("SparkSQL functions") + +# Tests for SparkSQL functions in SparkR + +sc <- sparkR.init() + +sqlCtx <- sparkRSQL.init(sc) + +mockLines <- c("{\"name\":\"Michael\"}", + "{\"name\":\"Andy\", \"age\":30}", + "{\"name\":\"Justin\", \"age\":19}") +jsonPath <- tempfile(pattern="sparkr-test", fileext=".tmp") +parquetPath <- tempfile(pattern="sparkr-test", fileext=".parquet") +writeLines(mockLines, jsonPath) + +test_that("infer types", { + expect_equal(infer_type(1L), "integer") + expect_equal(infer_type(1.0), "double") + expect_equal(infer_type("abc"), "string") + expect_equal(infer_type(TRUE), "boolean") + expect_equal(infer_type(as.Date("2015-03-11")), "date") + expect_equal(infer_type(as.POSIXlt("2015-03-11 12:13:04.043")), "timestamp") + expect_equal(infer_type(c(1L, 2L)), + list(type = 'array', elementType = "integer", containsNull = TRUE)) + expect_equal(infer_type(list(1L, 2L)), + list(type = 'array', elementType = "integer", containsNull = TRUE)) + expect_equal(infer_type(list(a = 1L, b = "2")), + list(type = "struct", + fields = list(list(name = "a", type = "integer", nullable = TRUE), + list(name = "b", type = "string", nullable = TRUE)))) + e <- new.env() + assign("a", 1L, envir = e) + expect_equal(infer_type(e), + list(type = "map", keyType = "string", valueType = "integer", + valueContainsNull = TRUE)) +}) + +test_that("create DataFrame from RDD", { + rdd <- lapply(parallelize(sc, 1:10), function(x) { list(x, as.character(x)) }) + df <- createDataFrame(sqlCtx, rdd, list("a", "b")) + expect_true(inherits(df, "DataFrame")) + expect_true(count(df) == 10) + expect_equal(columns(df), c("a", "b")) + expect_equal(dtypes(df), list(c("a", "int"), c("b", "string"))) + + df <- createDataFrame(sqlCtx, rdd) + expect_true(inherits(df, "DataFrame")) + expect_equal(columns(df), c("_1", "_2")) + + fields <- list(list(name = "a", type = "integer", nullable = TRUE), + list(name = "b", type = "string", nullable = TRUE)) + schema <- list(type = "struct", fields = fields) + df <- createDataFrame(sqlCtx, rdd, schema) + expect_true(inherits(df, "DataFrame")) + expect_equal(columns(df), c("a", "b")) + expect_equal(dtypes(df), list(c("a", "int"), c("b", "string"))) + + rdd <- lapply(parallelize(sc, 1:10), function(x) { list(a = x, b = as.character(x)) }) + df <- createDataFrame(sqlCtx, rdd) + expect_true(inherits(df, "DataFrame")) + expect_true(count(df) == 10) + expect_equal(columns(df), c("a", "b")) + expect_equal(dtypes(df), list(c("a", "int"), c("b", "string"))) +}) + +test_that("toDF", { + rdd <- lapply(parallelize(sc, 1:10), function(x) { list(x, as.character(x)) }) + df <- toDF(rdd, list("a", "b")) + expect_true(inherits(df, "DataFrame")) + expect_true(count(df) == 10) + expect_equal(columns(df), c("a", "b")) + expect_equal(dtypes(df), list(c("a", "int"), c("b", "string"))) + + df <- toDF(rdd) + expect_true(inherits(df, "DataFrame")) + expect_equal(columns(df), c("_1", "_2")) + + fields <- list(list(name = "a", type = "integer", nullable = TRUE), + list(name = "b", type = "string", nullable = TRUE)) + schema <- list(type = "struct", fields = fields) + df <- toDF(rdd, schema) + expect_true(inherits(df, "DataFrame")) + expect_equal(columns(df), c("a", "b")) + expect_equal(dtypes(df), list(c("a", "int"), c("b", "string"))) + + rdd <- lapply(parallelize(sc, 1:10), function(x) { list(a = x, b = as.character(x)) }) + df <- toDF(rdd) + expect_true(inherits(df, "DataFrame")) + expect_true(count(df) == 10) + expect_equal(columns(df), c("a", "b")) + expect_equal(dtypes(df), list(c("a", "int"), c("b", "string"))) +}) + +test_that("create DataFrame from list or data.frame", { + l <- list(list(1, 2), list(3, 4)) + df <- createDataFrame(sqlCtx, l, c("a", "b")) + expect_equal(columns(df), c("a", "b")) + + l <- list(list(a=1, b=2), list(a=3, b=4)) + df <- createDataFrame(sqlCtx, l) + expect_equal(columns(df), c("a", "b")) + + a <- 1:3 + b <- c("a", "b", "c") + ldf <- data.frame(a, b) + df <- createDataFrame(sqlCtx, ldf) + expect_equal(columns(df), c("a", "b")) + expect_equal(dtypes(df), list(c("a", "int"), c("b", "string"))) + expect_equal(count(df), 3) + ldf2 <- collect(df) + expect_equal(ldf$a, ldf2$a) +}) + +test_that("create DataFrame with different data types", { + l <- list(a = 1L, b = 2, c = TRUE, d = "ss", e = as.Date("2012-12-13"), + f = as.POSIXct("2015-03-15 12:13:14.056")) + df <- createDataFrame(sqlCtx, list(l)) + expect_equal(dtypes(df), list(c("a", "int"), c("b", "double"), c("c", "boolean"), + c("d", "string"), c("e", "date"), c("f", "timestamp"))) + expect_equal(count(df), 1) + expect_equal(collect(df), data.frame(l, stringsAsFactors = FALSE)) +}) + +# TODO: enable this test after fix serialization for nested object +#test_that("create DataFrame with nested array and struct", { +# e <- new.env() +# assign("n", 3L, envir = e) +# l <- list(1:10, list("a", "b"), e, list(a="aa", b=3L)) +# df <- createDataFrame(sqlCtx, list(l), c("a", "b", "c", "d")) +# expect_equal(dtypes(df), list(c("a", "array"), c("b", "array"), +# c("c", "map"), c("d", "struct"))) +# expect_equal(count(df), 1) +# ldf <- collect(df) +# expect_equal(ldf[1,], l[[1]]) +#}) + +test_that("jsonFile() on a local file returns a DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + expect_true(inherits(df, "DataFrame")) + expect_true(count(df) == 3) +}) + +test_that("jsonRDD() on a RDD with json string", { + rdd <- parallelize(sc, mockLines) + expect_true(count(rdd) == 3) + df <- jsonRDD(sqlCtx, rdd) + expect_true(inherits(df, "DataFrame")) + expect_true(count(df) == 3) + + rdd2 <- flatMap(rdd, function(x) c(x, x)) + df <- jsonRDD(sqlCtx, rdd2) + expect_true(inherits(df, "DataFrame")) + expect_true(count(df) == 6) +}) + +test_that("test cache, uncache and clearCache", { + df <- jsonFile(sqlCtx, jsonPath) + registerTempTable(df, "table1") + cacheTable(sqlCtx, "table1") + uncacheTable(sqlCtx, "table1") + clearCache(sqlCtx) + dropTempTable(sqlCtx, "table1") +}) + +test_that("test tableNames and tables", { + df <- jsonFile(sqlCtx, jsonPath) + registerTempTable(df, "table1") + expect_true(length(tableNames(sqlCtx)) == 1) + df <- tables(sqlCtx) + expect_true(count(df) == 1) + dropTempTable(sqlCtx, "table1") +}) + +test_that("registerTempTable() results in a queryable table and sql() results in a new DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + registerTempTable(df, "table1") + newdf <- sql(sqlCtx, "SELECT * FROM table1 where name = 'Michael'") + expect_true(inherits(newdf, "DataFrame")) + expect_true(count(newdf) == 1) + dropTempTable(sqlCtx, "table1") +}) + +test_that("insertInto() on a registered table", { + df <- loadDF(sqlCtx, jsonPath, "json") + saveDF(df, parquetPath, "parquet", "overwrite") + dfParquet <- loadDF(sqlCtx, parquetPath, "parquet") + + lines <- c("{\"name\":\"Bob\", \"age\":24}", + "{\"name\":\"James\", \"age\":35}") + jsonPath2 <- tempfile(pattern="jsonPath2", fileext=".tmp") + parquetPath2 <- tempfile(pattern = "parquetPath2", fileext = ".parquet") + writeLines(lines, jsonPath2) + df2 <- loadDF(sqlCtx, jsonPath2, "json") + saveDF(df2, parquetPath2, "parquet", "overwrite") + dfParquet2 <- loadDF(sqlCtx, parquetPath2, "parquet") + + registerTempTable(dfParquet, "table1") + insertInto(dfParquet2, "table1") + expect_true(count(sql(sqlCtx, "select * from table1")) == 5) + expect_true(first(sql(sqlCtx, "select * from table1 order by age"))$name == "Michael") + dropTempTable(sqlCtx, "table1") + + registerTempTable(dfParquet, "table1") + insertInto(dfParquet2, "table1", overwrite = TRUE) + expect_true(count(sql(sqlCtx, "select * from table1")) == 2) + expect_true(first(sql(sqlCtx, "select * from table1 order by age"))$name == "Bob") + dropTempTable(sqlCtx, "table1") +}) + +test_that("table() returns a new DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + registerTempTable(df, "table1") + tabledf <- table(sqlCtx, "table1") + expect_true(inherits(tabledf, "DataFrame")) + expect_true(count(tabledf) == 3) + dropTempTable(sqlCtx, "table1") +}) + +test_that("toRDD() returns an RRDD", { + df <- jsonFile(sqlCtx, jsonPath) + testRDD <- toRDD(df) + expect_true(inherits(testRDD, "RDD")) + expect_true(count(testRDD) == 3) +}) + +test_that("union on two RDDs created from DataFrames returns an RRDD", { + df <- jsonFile(sqlCtx, jsonPath) + RDD1 <- toRDD(df) + RDD2 <- toRDD(df) + unioned <- unionRDD(RDD1, RDD2) + expect_true(inherits(unioned, "RDD")) + expect_true(SparkR:::getSerializedMode(unioned) == "byte") + expect_true(collect(unioned)[[2]]$name == "Andy") +}) + +test_that("union on mixed serialization types correctly returns a byte RRDD", { + # Byte RDD + nums <- 1:10 + rdd <- parallelize(sc, nums, 2L) + + # String RDD + textLines <- c("Michael", + "Andy, 30", + "Justin, 19") + textPath <- tempfile(pattern="sparkr-textLines", fileext=".tmp") + writeLines(textLines, textPath) + textRDD <- textFile(sc, textPath) + + df <- jsonFile(sqlCtx, jsonPath) + dfRDD <- toRDD(df) + + unionByte <- unionRDD(rdd, dfRDD) + expect_true(inherits(unionByte, "RDD")) + expect_true(SparkR:::getSerializedMode(unionByte) == "byte") + expect_true(collect(unionByte)[[1]] == 1) + expect_true(collect(unionByte)[[12]]$name == "Andy") + + unionString <- unionRDD(textRDD, dfRDD) + expect_true(inherits(unionString, "RDD")) + expect_true(SparkR:::getSerializedMode(unionString) == "byte") + expect_true(collect(unionString)[[1]] == "Michael") + expect_true(collect(unionString)[[5]]$name == "Andy") +}) + +test_that("objectFile() works with row serialization", { + objectPath <- tempfile(pattern="spark-test", fileext=".tmp") + df <- jsonFile(sqlCtx, jsonPath) + dfRDD <- toRDD(df) + saveAsObjectFile(coalesce(dfRDD, 1L), objectPath) + objectIn <- objectFile(sc, objectPath) + + expect_true(inherits(objectIn, "RDD")) + expect_equal(SparkR:::getSerializedMode(objectIn), "byte") + expect_equal(collect(objectIn)[[2]]$age, 30) +}) + +test_that("lapply() on a DataFrame returns an RDD with the correct columns", { + df <- jsonFile(sqlCtx, jsonPath) + testRDD <- lapply(df, function(row) { + row$newCol <- row$age + 5 + row + }) + expect_true(inherits(testRDD, "RDD")) + collected <- collect(testRDD) + expect_true(collected[[1]]$name == "Michael") + expect_true(collected[[2]]$newCol == "35") +}) + +test_that("collect() returns a data.frame", { + df <- jsonFile(sqlCtx, jsonPath) + rdf <- collect(df) + expect_true(is.data.frame(rdf)) + expect_true(names(rdf)[1] == "age") + expect_true(nrow(rdf) == 3) + expect_true(ncol(rdf) == 2) +}) + +test_that("limit() returns DataFrame with the correct number of rows", { + df <- jsonFile(sqlCtx, jsonPath) + dfLimited <- limit(df, 2) + expect_true(inherits(dfLimited, "DataFrame")) + expect_true(count(dfLimited) == 2) +}) + +test_that("collect() and take() on a DataFrame return the same number of rows and columns", { + df <- jsonFile(sqlCtx, jsonPath) + expect_true(nrow(collect(df)) == nrow(take(df, 10))) + expect_true(ncol(collect(df)) == ncol(take(df, 10))) +}) + +test_that("multiple pipeline transformations starting with a DataFrame result in an RDD with the correct values", { + df <- jsonFile(sqlCtx, jsonPath) + first <- lapply(df, function(row) { + row$age <- row$age + 5 + row + }) + second <- lapply(first, function(row) { + row$testCol <- if (row$age == 35 && !is.na(row$age)) TRUE else FALSE + row + }) + expect_true(inherits(second, "RDD")) + expect_true(count(second) == 3) + expect_true(collect(second)[[2]]$age == 35) + expect_true(collect(second)[[2]]$testCol) + expect_false(collect(second)[[3]]$testCol) +}) + +test_that("cache(), persist(), and unpersist() on a DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + expect_false(df@env$isCached) + cache(df) + expect_true(df@env$isCached) + + unpersist(df) + expect_false(df@env$isCached) + + persist(df, "MEMORY_AND_DISK") + expect_true(df@env$isCached) + + unpersist(df) + expect_false(df@env$isCached) + + # make sure the data is collectable + expect_true(is.data.frame(collect(df))) +}) + +test_that("schema(), dtypes(), columns(), names() return the correct values/format", { + df <- jsonFile(sqlCtx, jsonPath) + testSchema <- schema(df) + expect_true(length(testSchema$fields()) == 2) + expect_true(testSchema$fields()[[1]]$dataType.toString() == "LongType") + expect_true(testSchema$fields()[[2]]$dataType.simpleString() == "string") + expect_true(testSchema$fields()[[1]]$name() == "age") + + testTypes <- dtypes(df) + expect_true(length(testTypes[[1]]) == 2) + expect_true(testTypes[[1]][1] == "age") + + testCols <- columns(df) + expect_true(length(testCols) == 2) + expect_true(testCols[2] == "name") + + testNames <- names(df) + expect_true(length(testNames) == 2) + expect_true(testNames[2] == "name") +}) + +test_that("head() and first() return the correct data", { + df <- jsonFile(sqlCtx, jsonPath) + testHead <- head(df) + expect_true(nrow(testHead) == 3) + expect_true(ncol(testHead) == 2) + + testHead2 <- head(df, 2) + expect_true(nrow(testHead2) == 2) + expect_true(ncol(testHead2) == 2) + + testFirst <- first(df) + expect_true(nrow(testFirst) == 1) +}) + +test_that("distinct() on DataFrames", { + lines <- c("{\"name\":\"Michael\"}", + "{\"name\":\"Andy\", \"age\":30}", + "{\"name\":\"Justin\", \"age\":19}", + "{\"name\":\"Justin\", \"age\":19}") + jsonPathWithDup <- tempfile(pattern="sparkr-test", fileext=".tmp") + writeLines(lines, jsonPathWithDup) + + df <- jsonFile(sqlCtx, jsonPathWithDup) + uniques <- distinct(df) + expect_true(inherits(uniques, "DataFrame")) + expect_true(count(uniques) == 3) +}) + +test_that("sampleDF on a DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + sampled <- sampleDF(df, FALSE, 1.0) + expect_equal(nrow(collect(sampled)), count(df)) + expect_true(inherits(sampled, "DataFrame")) + sampled2 <- sampleDF(df, FALSE, 0.1) + expect_true(count(sampled2) < 3) +}) + +test_that("select operators", { + df <- select(jsonFile(sqlCtx, jsonPath), "name", "age") + expect_true(inherits(df$name, "Column")) + expect_true(inherits(df[[2]], "Column")) + expect_true(inherits(df[["age"]], "Column")) + + expect_true(inherits(df[,1], "DataFrame")) + expect_equal(columns(df[,1]), c("name")) + expect_equal(columns(df[,"age"]), c("age")) + df2 <- df[,c("age", "name")] + expect_true(inherits(df2, "DataFrame")) + expect_equal(columns(df2), c("age", "name")) + + df$age2 <- df$age + expect_equal(columns(df), c("name", "age", "age2")) + expect_equal(count(where(df, df$age2 == df$age)), 2) + df$age2 <- df$age * 2 + expect_equal(columns(df), c("name", "age", "age2")) + expect_equal(count(where(df, df$age2 == df$age * 2)), 2) +}) + +test_that("select with column", { + df <- jsonFile(sqlCtx, jsonPath) + df1 <- select(df, "name") + expect_true(columns(df1) == c("name")) + expect_true(count(df1) == 3) + + df2 <- select(df, df$age) + expect_true(columns(df2) == c("age")) + expect_true(count(df2) == 3) +}) + +test_that("selectExpr() on a DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + selected <- selectExpr(df, "age * 2") + expect_true(names(selected) == "(age * 2)") + expect_equal(collect(selected), collect(select(df, df$age * 2L))) + + selected2 <- selectExpr(df, "name as newName", "abs(age) as age") + expect_equal(names(selected2), c("newName", "age")) + expect_true(count(selected2) == 3) +}) + +test_that("column calculation", { + df <- jsonFile(sqlCtx, jsonPath) + d <- collect(select(df, alias(df$age + 1, "age2"))) + expect_true(names(d) == c("age2")) + df2 <- select(df, lower(df$name), abs(df$age)) + expect_true(inherits(df2, "DataFrame")) + expect_true(count(df2) == 3) +}) + +test_that("load() from json file", { + df <- loadDF(sqlCtx, jsonPath, "json") + expect_true(inherits(df, "DataFrame")) + expect_true(count(df) == 3) +}) + +test_that("save() as parquet file", { + df <- loadDF(sqlCtx, jsonPath, "json") + saveDF(df, parquetPath, "parquet", mode="overwrite") + df2 <- loadDF(sqlCtx, parquetPath, "parquet") + expect_true(inherits(df2, "DataFrame")) + expect_true(count(df2) == 3) +}) + +test_that("test HiveContext", { + hiveCtx <- tryCatch({ + newJObject("org.apache.spark.sql.hive.test.TestHiveContext", ssc) + }, error = function(err) { + skip("Hive is not build with SparkSQL, skipped") + }) + df <- createExternalTable(hiveCtx, "json", jsonPath, "json") + expect_true(inherits(df, "DataFrame")) + expect_true(count(df) == 3) + df2 <- sql(hiveCtx, "select * from json") + expect_true(inherits(df2, "DataFrame")) + expect_true(count(df2) == 3) + + jsonPath2 <- tempfile(pattern="sparkr-test", fileext=".tmp") + saveAsTable(df, "json", "json", "append", path = jsonPath2) + df3 <- sql(hiveCtx, "select * from json") + expect_true(inherits(df3, "DataFrame")) + expect_true(count(df3) == 6) +}) + +test_that("column operators", { + c <- SparkR:::col("a") + c2 <- (- c + 1 - 2) * 3 / 4.0 + c3 <- (c + c2 - c2) * c2 %% c2 + c4 <- (c > c2) & (c2 <= c3) | (c == c2) & (c2 != c3) +}) + +test_that("column functions", { + c <- SparkR:::col("a") + c2 <- min(c) + max(c) + sum(c) + avg(c) + count(c) + abs(c) + sqrt(c) + c3 <- lower(c) + upper(c) + first(c) + last(c) + c4 <- approxCountDistinct(c) + countDistinct(c) + cast(c, "string") +}) + +test_that("string operators", { + df <- jsonFile(sqlCtx, jsonPath) + expect_equal(count(where(df, like(df$name, "A%"))), 1) + expect_equal(count(where(df, startsWith(df$name, "A"))), 1) + expect_equal(first(select(df, substr(df$name, 1, 2)))[[1]], "Mi") + expect_equal(collect(select(df, cast(df$age, "string")))[[2, 1]], "30") +}) + +test_that("group by", { + df <- jsonFile(sqlCtx, jsonPath) + df1 <- agg(df, name = "max", age = "sum") + expect_true(1 == count(df1)) + df1 <- agg(df, age2 = max(df$age)) + expect_true(1 == count(df1)) + expect_equal(columns(df1), c("age2")) + + gd <- groupBy(df, "name") + expect_true(inherits(gd, "GroupedData")) + df2 <- count(gd) + expect_true(inherits(df2, "DataFrame")) + expect_true(3 == count(df2)) + + df3 <- agg(gd, age = "sum") + expect_true(inherits(df3, "DataFrame")) + expect_true(3 == count(df3)) + + df3 <- agg(gd, age = sum(df$age)) + expect_true(inherits(df3, "DataFrame")) + expect_true(3 == count(df3)) + expect_equal(columns(df3), c("name", "age")) + + df4 <- sum(gd, "age") + expect_true(inherits(df4, "DataFrame")) + expect_true(3 == count(df4)) + expect_true(3 == count(mean(gd, "age"))) + expect_true(3 == count(max(gd, "age"))) +}) + +test_that("sortDF() and orderBy() on a DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + sorted <- sortDF(df, df$age) + expect_true(collect(sorted)[1,2] == "Michael") + + sorted2 <- sortDF(df, "name") + expect_true(collect(sorted2)[2,"age"] == 19) + + sorted3 <- orderBy(df, asc(df$age)) + expect_true(is.na(first(sorted3)$age)) + expect_true(collect(sorted3)[2, "age"] == 19) + + sorted4 <- orderBy(df, desc(df$name)) + expect_true(first(sorted4)$name == "Michael") + expect_true(collect(sorted4)[3,"name"] == "Andy") +}) + +test_that("filter() on a DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + filtered <- filter(df, "age > 20") + expect_true(count(filtered) == 1) + expect_true(collect(filtered)$name == "Andy") + filtered2 <- where(df, df$name != "Michael") + expect_true(count(filtered2) == 2) + expect_true(collect(filtered2)$age[2] == 19) +}) + +test_that("join() on a DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + + mockLines2 <- c("{\"name\":\"Michael\", \"test\": \"yes\"}", + "{\"name\":\"Andy\", \"test\": \"no\"}", + "{\"name\":\"Justin\", \"test\": \"yes\"}", + "{\"name\":\"Bob\", \"test\": \"yes\"}") + jsonPath2 <- tempfile(pattern="sparkr-test", fileext=".tmp") + writeLines(mockLines2, jsonPath2) + df2 <- jsonFile(sqlCtx, jsonPath2) + + joined <- join(df, df2) + expect_equal(names(joined), c("age", "name", "name", "test")) + expect_true(count(joined) == 12) + + joined2 <- join(df, df2, df$name == df2$name) + expect_equal(names(joined2), c("age", "name", "name", "test")) + expect_true(count(joined2) == 3) + + joined3 <- join(df, df2, df$name == df2$name, "right_outer") + expect_equal(names(joined3), c("age", "name", "name", "test")) + expect_true(count(joined3) == 4) + expect_true(is.na(collect(orderBy(joined3, joined3$age))$age[2])) + + joined4 <- select(join(df, df2, df$name == df2$name, "outer"), + alias(df$age + 5, "newAge"), df$name, df2$test) + expect_equal(names(joined4), c("newAge", "name", "test")) + expect_true(count(joined4) == 4) + expect_equal(collect(orderBy(joined4, joined4$name))$newAge[3], 24) +}) + +test_that("toJSON() returns an RDD of the correct values", { + df <- jsonFile(sqlCtx, jsonPath) + testRDD <- toJSON(df) + expect_true(inherits(testRDD, "RDD")) + expect_true(SparkR:::getSerializedMode(testRDD) == "string") + expect_equal(collect(testRDD)[[1]], mockLines[1]) +}) + +test_that("showDF()", { + df <- jsonFile(sqlCtx, jsonPath) + expect_output(showDF(df), "age name \nnull Michael\n30 Andy \n19 Justin ") +}) + +test_that("isLocal()", { + df <- jsonFile(sqlCtx, jsonPath) + expect_false(isLocal(df)) +}) + +test_that("unionAll(), subtract(), and intersect() on a DataFrame", { + df <- jsonFile(sqlCtx, jsonPath) + + lines <- c("{\"name\":\"Bob\", \"age\":24}", + "{\"name\":\"Andy\", \"age\":30}", + "{\"name\":\"James\", \"age\":35}") + jsonPath2 <- tempfile(pattern="sparkr-test", fileext=".tmp") + writeLines(lines, jsonPath2) + df2 <- loadDF(sqlCtx, jsonPath2, "json") + + unioned <- sortDF(unionAll(df, df2), df$age) + expect_true(inherits(unioned, "DataFrame")) + expect_true(count(unioned) == 6) + expect_true(first(unioned)$name == "Michael") + + subtracted <- sortDF(subtract(df, df2), desc(df$age)) + expect_true(inherits(unioned, "DataFrame")) + expect_true(count(subtracted) == 2) + expect_true(first(subtracted)$name == "Justin") + + intersected <- sortDF(intersect(df, df2), df$age) + expect_true(inherits(unioned, "DataFrame")) + expect_true(count(intersected) == 1) + expect_true(first(intersected)$name == "Andy") +}) + +test_that("withColumn() and withColumnRenamed()", { + df <- jsonFile(sqlCtx, jsonPath) + newDF <- withColumn(df, "newAge", df$age + 2) + expect_true(length(columns(newDF)) == 3) + expect_true(columns(newDF)[3] == "newAge") + expect_true(first(filter(newDF, df$name != "Michael"))$newAge == 32) + + newDF2 <- withColumnRenamed(df, "age", "newerAge") + expect_true(length(columns(newDF2)) == 2) + expect_true(columns(newDF2)[1] == "newerAge") +}) + +test_that("saveDF() on DataFrame and works with parquetFile", { + df <- jsonFile(sqlCtx, jsonPath) + saveDF(df, parquetPath, "parquet", mode="overwrite") + parquetDF <- parquetFile(sqlCtx, parquetPath) + expect_true(inherits(parquetDF, "DataFrame")) + expect_equal(count(df), count(parquetDF)) +}) + +test_that("parquetFile works with multiple input paths", { + df <- jsonFile(sqlCtx, jsonPath) + saveDF(df, parquetPath, "parquet", mode="overwrite") + parquetPath2 <- tempfile(pattern = "parquetPath2", fileext = ".parquet") + saveDF(df, parquetPath2, "parquet", mode="overwrite") + parquetDF <- parquetFile(sqlCtx, parquetPath, parquetPath2) + expect_true(inherits(parquetDF, "DataFrame")) + expect_true(count(parquetDF) == count(df)*2) +}) + +unlink(parquetPath) +unlink(jsonPath) diff --git a/R/pkg/inst/tests/test_take.R b/R/pkg/inst/tests/test_take.R new file mode 100644 index 0000000000000..7f4c7c315d787 --- /dev/null +++ b/R/pkg/inst/tests/test_take.R @@ -0,0 +1,67 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("tests RDD function take()") + +# Mock data +numVector <- c(-10:97) +numList <- list(sqrt(1), sqrt(2), sqrt(3), 4 ** 10) +strVector <- c("Dexter Morgan: I suppose I should be upset, even feel", + "violated, but I'm not. No, in fact, I think this is a friendly", + "message, like \"Hey, wanna play?\" and yes, I want to play. ", + "I really, really do.") +strList <- list("Dexter Morgan: Blood. Sometimes it sets my teeth on edge, ", + "other times it helps me control the chaos.", + "Dexter Morgan: Harry and Dorris Morgan did a wonderful job ", + "raising me. But they're both dead now. I didn't kill them. Honest.") + +# JavaSparkContext handle +jsc <- sparkR.init() + +test_that("take() gives back the original elements in correct count and order", { + numVectorRDD <- parallelize(jsc, numVector, 10) + # case: number of elements to take is less than the size of the first partition + expect_equal(take(numVectorRDD, 1), as.list(head(numVector, n = 1))) + # case: number of elements to take is the same as the size of the first partition + expect_equal(take(numVectorRDD, 11), as.list(head(numVector, n = 11))) + # case: number of elements to take is greater than all elements + expect_equal(take(numVectorRDD, length(numVector)), as.list(numVector)) + expect_equal(take(numVectorRDD, length(numVector) + 1), as.list(numVector)) + + numListRDD <- parallelize(jsc, numList, 1) + numListRDD2 <- parallelize(jsc, numList, 4) + expect_equal(take(numListRDD, 3), take(numListRDD2, 3)) + expect_equal(take(numListRDD, 5), take(numListRDD2, 5)) + expect_equal(take(numListRDD, 1), as.list(head(numList, n = 1))) + expect_equal(take(numListRDD2, 999), numList) + + strVectorRDD <- parallelize(jsc, strVector, 2) + strVectorRDD2 <- parallelize(jsc, strVector, 3) + expect_equal(take(strVectorRDD, 4), as.list(strVector)) + expect_equal(take(strVectorRDD2, 2), as.list(head(strVector, n = 2))) + + strListRDD <- parallelize(jsc, strList, 4) + strListRDD2 <- parallelize(jsc, strList, 1) + expect_equal(take(strListRDD, 3), as.list(head(strList, n = 3))) + expect_equal(take(strListRDD2, 1), as.list(head(strList, n = 1))) + + expect_true(length(take(strListRDD, 0)) == 0) + expect_true(length(take(strVectorRDD, 0)) == 0) + expect_true(length(take(numListRDD, 0)) == 0) + expect_true(length(take(numVectorRDD, 0)) == 0) +}) + diff --git a/R/pkg/inst/tests/test_textFile.R b/R/pkg/inst/tests/test_textFile.R new file mode 100644 index 0000000000000..7bb3e8003131d --- /dev/null +++ b/R/pkg/inst/tests/test_textFile.R @@ -0,0 +1,162 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("the textFile() function") + +# JavaSparkContext handle +sc <- sparkR.init() + +mockFile = c("Spark is pretty.", "Spark is awesome.") + +test_that("textFile() on a local file returns an RDD", { + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName) + + rdd <- textFile(sc, fileName) + expect_true(inherits(rdd, "RDD")) + expect_true(count(rdd) > 0) + expect_true(count(rdd) == 2) + + unlink(fileName) +}) + +test_that("textFile() followed by a collect() returns the same content", { + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName) + + rdd <- textFile(sc, fileName) + expect_equal(collect(rdd), as.list(mockFile)) + + unlink(fileName) +}) + +test_that("textFile() word count works as expected", { + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName) + + rdd <- textFile(sc, fileName) + + words <- flatMap(rdd, function(line) { strsplit(line, " ")[[1]] }) + wordCount <- lapply(words, function(word) { list(word, 1L) }) + + counts <- reduceByKey(wordCount, "+", 2L) + output <- collect(counts) + expected <- list(list("pretty.", 1), list("is", 2), list("awesome.", 1), + list("Spark", 2)) + expect_equal(sortKeyValueList(output), sortKeyValueList(expected)) + + unlink(fileName) +}) + +test_that("several transformations on RDD created by textFile()", { + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName) + + rdd <- textFile(sc, fileName) # RDD + for (i in 1:10) { + # PipelinedRDD initially created from RDD + rdd <- lapply(rdd, function(x) paste(x, x)) + } + collect(rdd) + + unlink(fileName) +}) + +test_that("textFile() followed by a saveAsTextFile() returns the same content", { + fileName1 <- tempfile(pattern="spark-test", fileext=".tmp") + fileName2 <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName1) + + rdd <- textFile(sc, fileName1) + saveAsTextFile(rdd, fileName2) + rdd <- textFile(sc, fileName2) + expect_equal(collect(rdd), as.list(mockFile)) + + unlink(fileName1) + unlink(fileName2) +}) + +test_that("saveAsTextFile() on a parallelized list works as expected", { + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + l <- list(1, 2, 3) + rdd <- parallelize(sc, l) + saveAsTextFile(rdd, fileName) + rdd <- textFile(sc, fileName) + expect_equal(collect(rdd), lapply(l, function(x) {toString(x)})) + + unlink(fileName) +}) + +test_that("textFile() and saveAsTextFile() word count works as expected", { + fileName1 <- tempfile(pattern="spark-test", fileext=".tmp") + fileName2 <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName1) + + rdd <- textFile(sc, fileName1) + + words <- flatMap(rdd, function(line) { strsplit(line, " ")[[1]] }) + wordCount <- lapply(words, function(word) { list(word, 1L) }) + + counts <- reduceByKey(wordCount, "+", 2L) + + saveAsTextFile(counts, fileName2) + rdd <- textFile(sc, fileName2) + + output <- collect(rdd) + expected <- list(list("awesome.", 1), list("Spark", 2), + list("pretty.", 1), list("is", 2)) + expectedStr <- lapply(expected, function(x) { toString(x) }) + expect_equal(sortKeyValueList(output), sortKeyValueList(expectedStr)) + + unlink(fileName1) + unlink(fileName2) +}) + +test_that("textFile() on multiple paths", { + fileName1 <- tempfile(pattern="spark-test", fileext=".tmp") + fileName2 <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines("Spark is pretty.", fileName1) + writeLines("Spark is awesome.", fileName2) + + rdd <- textFile(sc, c(fileName1, fileName2)) + expect_true(count(rdd) == 2) + + unlink(fileName1) + unlink(fileName2) +}) + +test_that("Pipelined operations on RDDs created using textFile", { + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName) + + rdd <- textFile(sc, fileName) + + lengths <- lapply(rdd, function(x) { length(x) }) + expect_equal(collect(lengths), list(1, 1)) + + lengthsPipelined <- lapply(lengths, function(x) { x + 10 }) + expect_equal(collect(lengthsPipelined), list(11, 11)) + + lengths30 <- lapply(lengthsPipelined, function(x) { x + 20 }) + expect_equal(collect(lengths30), list(31, 31)) + + lengths20 <- lapply(lengths, function(x) { x + 20 }) + expect_equal(collect(lengths20), list(21, 21)) + + unlink(fileName) +}) + diff --git a/R/pkg/inst/tests/test_utils.R b/R/pkg/inst/tests/test_utils.R new file mode 100644 index 0000000000000..9c5bb427932b4 --- /dev/null +++ b/R/pkg/inst/tests/test_utils.R @@ -0,0 +1,137 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +context("functions in utils.R") + +# JavaSparkContext handle +sc <- sparkR.init() + +test_that("convertJListToRList() gives back (deserializes) the original JLists + of strings and integers", { + # It's hard to manually create a Java List using rJava, since it does not + # support generics well. Instead, we rely on collect() returning a + # JList. + nums <- as.list(1:10) + rdd <- parallelize(sc, nums, 1L) + jList <- callJMethod(rdd@jrdd, "collect") + rList <- convertJListToRList(jList, flatten = TRUE) + expect_equal(rList, nums) + + strs <- as.list("hello", "spark") + rdd <- parallelize(sc, strs, 2L) + jList <- callJMethod(rdd@jrdd, "collect") + rList <- convertJListToRList(jList, flatten = TRUE) + expect_equal(rList, strs) +}) + +test_that("serializeToBytes on RDD", { + # File content + mockFile <- c("Spark is pretty.", "Spark is awesome.") + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName) + + text.rdd <- textFile(sc, fileName) + expect_true(getSerializedMode(text.rdd) == "string") + ser.rdd <- serializeToBytes(text.rdd) + expect_equal(collect(ser.rdd), as.list(mockFile)) + expect_true(getSerializedMode(ser.rdd) == "byte") + + unlink(fileName) +}) + +test_that("cleanClosure on R functions", { + y <- c(1, 2, 3) + g <- function(x) { x + 1 } + f <- function(x) { g(x) + y } + newF <- cleanClosure(f) + env <- environment(newF) + expect_equal(length(ls(env)), 2) # y, g + actual <- get("y", envir = env, inherits = FALSE) + expect_equal(actual, y) + actual <- get("g", envir = env, inherits = FALSE) + expect_equal(actual, g) + + # Test for nested enclosures and package variables. + env2 <- new.env() + funcEnv <- new.env(parent = env2) + f <- function(x) { log(g(x) + y) } + environment(f) <- funcEnv # enclosing relationship: f -> funcEnv -> env2 -> .GlobalEnv + newF <- cleanClosure(f) + env <- environment(newF) + expect_equal(length(ls(env)), 2) # "min" should not be included + actual <- get("y", envir = env, inherits = FALSE) + expect_equal(actual, y) + actual <- get("g", envir = env, inherits = FALSE) + expect_equal(actual, g) + + base <- c(1, 2, 3) + l <- list(field = matrix(1)) + field <- matrix(2) + defUse <- 3 + g <- function(x) { x + y } + f <- function(x) { + defUse <- base::as.integer(x) + 1 # Test for access operators `::`. + lapply(x, g) + 1 # Test for capturing function call "g"'s closure as a argument of lapply. + l$field[1,1] <- 3 # Test for access operators `$`. + res <- defUse + l$field[1,] # Test for def-use chain of "defUse", and "" symbol. + f(res) # Test for recursive calls. + } + newF <- cleanClosure(f) + env <- environment(newF) + expect_equal(length(ls(env)), 3) # Only "g", "l" and "f". No "base", "field" or "defUse". + expect_true("g" %in% ls(env)) + expect_true("l" %in% ls(env)) + expect_true("f" %in% ls(env)) + expect_equal(get("l", envir = env, inherits = FALSE), l) + # "y" should be in the environemnt of g. + newG <- get("g", envir = env, inherits = FALSE) + env <- environment(newG) + expect_equal(length(ls(env)), 1) + actual <- get("y", envir = env, inherits = FALSE) + expect_equal(actual, y) + + # Test for function (and variable) definitions. + f <- function(x) { + g <- function(y) { y * 2 } + g(x) + } + newF <- cleanClosure(f) + env <- environment(newF) + expect_equal(length(ls(env)), 0) # "y" and "g" should not be included. + + # Test for overriding variables in base namespace (Issue: SparkR-196). + nums <- as.list(1:10) + rdd <- parallelize(sc, nums, 2L) + t = 4 # Override base::t in .GlobalEnv. + f <- function(x) { x > t } + newF <- cleanClosure(f) + env <- environment(newF) + expect_equal(ls(env), "t") + expect_equal(get("t", envir = env, inherits = FALSE), t) + actual <- collect(lapply(rdd, f)) + expected <- as.list(c(rep(FALSE, 4), rep(TRUE, 6))) + expect_equal(actual, expected) + + # Test for broadcast variables. + a <- matrix(nrow=10, ncol=10, data=rnorm(100)) + aBroadcast <- broadcast(sc, a) + normMultiply <- function(x) { norm(aBroadcast$value) * x } + newnormMultiply <- SparkR:::cleanClosure(normMultiply) + env <- environment(newnormMultiply) + expect_equal(ls(env), "aBroadcast") + expect_equal(get("aBroadcast", envir = env, inherits = FALSE), aBroadcast) +}) diff --git a/R/pkg/inst/worker/daemon.R b/R/pkg/inst/worker/daemon.R new file mode 100644 index 0000000000000..3584b418a71a9 --- /dev/null +++ b/R/pkg/inst/worker/daemon.R @@ -0,0 +1,52 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Worker daemon + +rLibDir <- Sys.getenv("SPARKR_RLIBDIR") +script <- paste(rLibDir, "SparkR/worker/worker.R", sep = "/") + +# preload SparkR package, speedup worker +.libPaths(c(rLibDir, .libPaths())) +suppressPackageStartupMessages(library(SparkR)) + +port <- as.integer(Sys.getenv("SPARKR_WORKER_PORT")) +inputCon <- socketConnection(port = port, open = "rb", blocking = TRUE, timeout = 3600) + +while (TRUE) { + ready <- socketSelect(list(inputCon)) + if (ready) { + port <- SparkR:::readInt(inputCon) + # There is a small chance that it could be interrupted by signal, retry one time + if (length(port) == 0) { + port <- SparkR:::readInt(inputCon) + if (length(port) == 0) { + cat("quitting daemon\n") + quit(save = "no") + } + } + p <- parallel:::mcfork() + if (inherits(p, "masterProcess")) { + close(inputCon) + Sys.setenv(SPARKR_WORKER_PORT = port) + source(script) + # Set SIGUSR1 so that child can exit + tools::pskill(Sys.getpid(), tools::SIGUSR1) + parallel:::mcexit(0L) + } + } +} diff --git a/R/pkg/inst/worker/worker.R b/R/pkg/inst/worker/worker.R new file mode 100644 index 0000000000000..c6542928e8ddd --- /dev/null +++ b/R/pkg/inst/worker/worker.R @@ -0,0 +1,128 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Worker class + +rLibDir <- Sys.getenv("SPARKR_RLIBDIR") +# Set libPaths to include SparkR package as loadNamespace needs this +# TODO: Figure out if we can avoid this by not loading any objects that require +# SparkR namespace +.libPaths(c(rLibDir, .libPaths())) +suppressPackageStartupMessages(library(SparkR)) + +port <- as.integer(Sys.getenv("SPARKR_WORKER_PORT")) +inputCon <- socketConnection(port = port, blocking = TRUE, open = "rb") +outputCon <- socketConnection(port = port, blocking = TRUE, open = "wb") + +# read the index of the current partition inside the RDD +partition <- SparkR:::readInt(inputCon) + +deserializer <- SparkR:::readString(inputCon) +serializer <- SparkR:::readString(inputCon) + +# Include packages as required +packageNames <- unserialize(SparkR:::readRaw(inputCon)) +for (pkg in packageNames) { + suppressPackageStartupMessages(require(as.character(pkg), character.only=TRUE)) +} + +# read function dependencies +funcLen <- SparkR:::readInt(inputCon) +computeFunc <- unserialize(SparkR:::readRawLen(inputCon, funcLen)) +env <- environment(computeFunc) +parent.env(env) <- .GlobalEnv # Attach under global environment. + +# Read and set broadcast variables +numBroadcastVars <- SparkR:::readInt(inputCon) +if (numBroadcastVars > 0) { + for (bcast in seq(1:numBroadcastVars)) { + bcastId <- SparkR:::readInt(inputCon) + value <- unserialize(SparkR:::readRaw(inputCon)) + setBroadcastValue(bcastId, value) + } +} + +# If -1: read as normal RDD; if >= 0, treat as pairwise RDD and treat the int +# as number of partitions to create. +numPartitions <- SparkR:::readInt(inputCon) + +isEmpty <- SparkR:::readInt(inputCon) + +if (isEmpty != 0) { + + if (numPartitions == -1) { + if (deserializer == "byte") { + # Now read as many characters as described in funcLen + data <- SparkR:::readDeserialize(inputCon) + } else if (deserializer == "string") { + data <- as.list(readLines(inputCon)) + } else if (deserializer == "row") { + data <- SparkR:::readDeserializeRows(inputCon) + } + output <- computeFunc(partition, data) + if (serializer == "byte") { + SparkR:::writeRawSerialize(outputCon, output) + } else if (serializer == "row") { + SparkR:::writeRowSerialize(outputCon, output) + } else { + SparkR:::writeStrings(outputCon, output) + } + } else { + if (deserializer == "byte") { + # Now read as many characters as described in funcLen + data <- SparkR:::readDeserialize(inputCon) + } else if (deserializer == "string") { + data <- readLines(inputCon) + } else if (deserializer == "row") { + data <- SparkR:::readDeserializeRows(inputCon) + } + + res <- new.env() + + # Step 1: hash the data to an environment + hashTupleToEnvir <- function(tuple) { + # NOTE: execFunction is the hash function here + hashVal <- computeFunc(tuple[[1]]) + bucket <- as.character(hashVal %% numPartitions) + acc <- res[[bucket]] + # Create a new accumulator + if (is.null(acc)) { + acc <- SparkR:::initAccumulator() + } + SparkR:::addItemToAccumulator(acc, tuple) + res[[bucket]] <- acc + } + invisible(lapply(data, hashTupleToEnvir)) + + # Step 2: write out all of the environment as key-value pairs. + for (name in ls(res)) { + SparkR:::writeInt(outputCon, 2L) + SparkR:::writeInt(outputCon, as.integer(name)) + # Truncate the accumulator list to the number of elements we have + length(res[[name]]$data) <- res[[name]]$counter + SparkR:::writeRawSerialize(outputCon, res[[name]]$data) + } + } +} + +# End of output +if (serializer %in% c("byte", "row")) { + SparkR:::writeInt(outputCon, 0L) +} + +close(outputCon) +close(inputCon) diff --git a/R/pkg/src/Makefile b/R/pkg/src/Makefile new file mode 100644 index 0000000000000..a55a56fe80e10 --- /dev/null +++ b/R/pkg/src/Makefile @@ -0,0 +1,27 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +all: sharelib + +sharelib: string_hash_code.c + R CMD SHLIB -o SparkR.so string_hash_code.c + +clean: + rm -f *.o + rm -f *.so + +.PHONY: all clean diff --git a/R/pkg/src/Makefile.win b/R/pkg/src/Makefile.win new file mode 100644 index 0000000000000..aa486d8228371 --- /dev/null +++ b/R/pkg/src/Makefile.win @@ -0,0 +1,27 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +all: sharelib + +sharelib: string_hash_code.c + R CMD SHLIB -o SparkR.dll string_hash_code.c + +clean: + rm -f *.o + rm -f *.dll + +.PHONY: all clean diff --git a/R/pkg/src/string_hash_code.c b/R/pkg/src/string_hash_code.c new file mode 100644 index 0000000000000..e3274b9a0c547 --- /dev/null +++ b/R/pkg/src/string_hash_code.c @@ -0,0 +1,49 @@ +/* + Licensed to the Apache Software Foundation (ASF) under one or more + contributor license agreements. See the NOTICE file distributed with + this work for additional information regarding copyright ownership. + The ASF licenses this file to You under the Apache License, Version 2.0 + (the "License"); you may not use this file except in compliance with + the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +*/ + +/* + * A C function for R extension which implements the Java String hash algorithm. + * Refer to http://en.wikipedia.org/wiki/Java_hashCode%28%29#The_java.lang.String_hash_function + * + */ + +#include +#include + +/* for compatibility with R before 3.1 */ +#ifndef IS_SCALAR +#define IS_SCALAR(x, type) (TYPEOF(x) == (type) && XLENGTH(x) == 1) +#endif + +SEXP stringHashCode(SEXP string) { + const char* str; + R_xlen_t len, i; + int hashCode = 0; + + if (!IS_SCALAR(string, STRSXP)) { + error("invalid input"); + } + + str = CHAR(asChar(string)); + len = XLENGTH(asChar(string)); + + for (i = 0; i < len; i++) { + hashCode = (hashCode << 5) - hashCode + *str++; + } + + return ScalarInteger(hashCode); +} diff --git a/R/pkg/tests/run-all.R b/R/pkg/tests/run-all.R new file mode 100644 index 0000000000000..4f8a1ed2d83ef --- /dev/null +++ b/R/pkg/tests/run-all.R @@ -0,0 +1,21 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +library(testthat) +library(SparkR) + +test_package("SparkR") diff --git a/R/run-tests.sh b/R/run-tests.sh new file mode 100755 index 0000000000000..e82ad0ba2cd06 --- /dev/null +++ b/R/run-tests.sh @@ -0,0 +1,39 @@ +#!/bin/bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +FWDIR="$(cd `dirname $0`; pwd)" + +FAILED=0 +LOGFILE=$FWDIR/unit-tests.out +rm -f $LOGFILE + +SPARK_TESTING=1 $FWDIR/../bin/sparkR --driver-java-options "-Dlog4j.configuration=file:$FWDIR/log4j.properties" $FWDIR/pkg/tests/run-all.R 2>&1 | tee -a $LOGFILE +FAILED=$((PIPESTATUS[0]||$FAILED)) + +if [[ $FAILED != 0 ]]; then + cat $LOGFILE + echo -en "\033[31m" # Red + echo "Had test failures; see logs." + echo -en "\033[0m" # No color + exit -1 +else + echo -en "\033[32m" # Green + echo "Tests passed." + echo -en "\033[0m" # No color +fi diff --git a/bin/sparkR b/bin/sparkR new file mode 100755 index 0000000000000..8c918e2b09aef --- /dev/null +++ b/bin/sparkR @@ -0,0 +1,39 @@ +#!/bin/bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Figure out where Spark is installed +export SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)" + +source "$SPARK_HOME"/bin/load-spark-env.sh + +function usage() { + if [ -n "$1" ]; then + echo $1 + fi + echo "Usage: ./bin/sparkR [options]" 1>&2 + "$SPARK_HOME"/bin/spark-submit --help 2>&1 | grep -v Usage 1>&2 + exit $2 +} +export -f usage + +if [[ "$@" = *--help ]] || [[ "$@" = *-h ]]; then + usage +fi + +exec "$SPARK_HOME"/bin/spark-submit sparkr-shell-main "$@" diff --git a/bin/sparkR.cmd b/bin/sparkR.cmd new file mode 100644 index 0000000000000..d7b60183ca8e0 --- /dev/null +++ b/bin/sparkR.cmd @@ -0,0 +1,23 @@ +@echo off + +rem +rem Licensed to the Apache Software Foundation (ASF) under one or more +rem contributor license agreements. See the NOTICE file distributed with +rem this work for additional information regarding copyright ownership. +rem The ASF licenses this file to You under the Apache License, Version 2.0 +rem (the "License"); you may not use this file except in compliance with +rem the License. You may obtain a copy of the License at +rem +rem http://www.apache.org/licenses/LICENSE-2.0 +rem +rem Unless required by applicable law or agreed to in writing, software +rem distributed under the License is distributed on an "AS IS" BASIS, +rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +rem See the License for the specific language governing permissions and +rem limitations under the License. +rem + +rem This is the entry point for running SparkR. To avoid polluting the +rem environment, it just launches a new cmd to do the real work. + +cmd /V /E /C %~dp0sparkR2.cmd %* diff --git a/bin/sparkR2.cmd b/bin/sparkR2.cmd new file mode 100644 index 0000000000000..e47f22c7300bb --- /dev/null +++ b/bin/sparkR2.cmd @@ -0,0 +1,26 @@ +@echo off + +rem +rem Licensed to the Apache Software Foundation (ASF) under one or more +rem contributor license agreements. See the NOTICE file distributed with +rem this work for additional information regarding copyright ownership. +rem The ASF licenses this file to You under the Apache License, Version 2.0 +rem (the "License"); you may not use this file except in compliance with +rem the License. You may obtain a copy of the License at +rem +rem http://www.apache.org/licenses/LICENSE-2.0 +rem +rem Unless required by applicable law or agreed to in writing, software +rem distributed under the License is distributed on an "AS IS" BASIS, +rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +rem See the License for the specific language governing permissions and +rem limitations under the License. +rem + +rem Figure out where the Spark framework is installed +set SPARK_HOME=%~dp0.. + +call %SPARK_HOME%\bin\load-spark-env.cmd + + +call %SPARK_HOME%\bin\spark-submit2.cmd sparkr-shell-main %* diff --git a/core/pom.xml b/core/pom.xml index 6cd1965ec37c2..e80829b7a7f3d 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -442,4 +442,55 @@ + + + Windows + + + Windows + + + + \ + .bat + + + + unix + + + unix + + + + / + .sh + + + + sparkr + + + + org.codehaus.mojo + exec-maven-plugin + 1.3.2 + + + sparkr-pkg + compile + + exec + + + + + ..${path.separator}R${path.separator}install-dev${script.extension} + + + + + + + diff --git a/core/src/main/scala/org/apache/spark/api/r/RBackend.scala b/core/src/main/scala/org/apache/spark/api/r/RBackend.scala new file mode 100644 index 0000000000000..3a2c94bd9d875 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/api/r/RBackend.scala @@ -0,0 +1,145 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.api.r + +import java.io.{DataOutputStream, File, FileOutputStream, IOException} +import java.net.{InetSocketAddress, ServerSocket} +import java.util.concurrent.TimeUnit + +import io.netty.bootstrap.ServerBootstrap +import io.netty.channel.{ChannelFuture, ChannelInitializer, EventLoopGroup} +import io.netty.channel.nio.NioEventLoopGroup +import io.netty.channel.socket.SocketChannel +import io.netty.channel.socket.nio.NioServerSocketChannel +import io.netty.handler.codec.LengthFieldBasedFrameDecoder +import io.netty.handler.codec.bytes.{ByteArrayDecoder, ByteArrayEncoder} + +import org.apache.spark.Logging + +/** + * Netty-based backend server that is used to communicate between R and Java. + */ +private[spark] class RBackend { + + private[this] var channelFuture: ChannelFuture = null + private[this] var bootstrap: ServerBootstrap = null + private[this] var bossGroup: EventLoopGroup = null + + def init(): Int = { + bossGroup = new NioEventLoopGroup(2) + val workerGroup = bossGroup + val handler = new RBackendHandler(this) + + bootstrap = new ServerBootstrap() + .group(bossGroup, workerGroup) + .channel(classOf[NioServerSocketChannel]) + + bootstrap.childHandler(new ChannelInitializer[SocketChannel]() { + def initChannel(ch: SocketChannel): Unit = { + ch.pipeline() + .addLast("encoder", new ByteArrayEncoder()) + .addLast("frameDecoder", + // maxFrameLength = 2G + // lengthFieldOffset = 0 + // lengthFieldLength = 4 + // lengthAdjustment = 0 + // initialBytesToStrip = 4, i.e. strip out the length field itself + new LengthFieldBasedFrameDecoder(Integer.MAX_VALUE, 0, 4, 0, 4)) + .addLast("decoder", new ByteArrayDecoder()) + .addLast("handler", handler) + } + }) + + channelFuture = bootstrap.bind(new InetSocketAddress(0)) + channelFuture.syncUninterruptibly() + channelFuture.channel().localAddress().asInstanceOf[InetSocketAddress].getPort() + } + + def run(): Unit = { + channelFuture.channel.closeFuture().syncUninterruptibly() + } + + def close(): Unit = { + if (channelFuture != null) { + // close is a local operation and should finish within milliseconds; timeout just to be safe + channelFuture.channel().close().awaitUninterruptibly(10, TimeUnit.SECONDS) + channelFuture = null + } + if (bootstrap != null && bootstrap.group() != null) { + bootstrap.group().shutdownGracefully() + } + if (bootstrap != null && bootstrap.childGroup() != null) { + bootstrap.childGroup().shutdownGracefully() + } + bootstrap = null + } + +} + +private[spark] object RBackend extends Logging { + def main(args: Array[String]): Unit = { + if (args.length < 1) { + System.err.println("Usage: RBackend ") + System.exit(-1) + } + val sparkRBackend = new RBackend() + try { + // bind to random port + val boundPort = sparkRBackend.init() + val serverSocket = new ServerSocket(0, 1) + val listenPort = serverSocket.getLocalPort() + + // tell the R process via temporary file + val path = args(0) + val f = new File(path + ".tmp") + val dos = new DataOutputStream(new FileOutputStream(f)) + dos.writeInt(boundPort) + dos.writeInt(listenPort) + dos.close() + f.renameTo(new File(path)) + + // wait for the end of stdin, then exit + new Thread("wait for socket to close") { + setDaemon(true) + override def run(): Unit = { + // any un-catched exception will also shutdown JVM + val buf = new Array[Byte](1024) + // shutdown JVM if R does not connect back in 10 seconds + serverSocket.setSoTimeout(10000) + try { + val inSocket = serverSocket.accept() + serverSocket.close() + // wait for the end of socket, closed if R process die + inSocket.getInputStream().read(buf) + } finally { + sparkRBackend.close() + System.exit(0) + } + } + }.start() + + sparkRBackend.run() + } catch { + case e: IOException => + logError("Server shutting down: failed with exception ", e) + sparkRBackend.close() + System.exit(1) + } + System.exit(0) + } +} diff --git a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala new file mode 100644 index 0000000000000..0075d963711f1 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala @@ -0,0 +1,223 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.api.r + +import java.io.{ByteArrayInputStream, ByteArrayOutputStream, DataInputStream, DataOutputStream} + +import scala.collection.mutable.HashMap + +import io.netty.channel.ChannelHandler.Sharable +import io.netty.channel.{ChannelHandlerContext, SimpleChannelInboundHandler} + +import org.apache.spark.Logging +import org.apache.spark.api.r.SerDe._ + +/** + * Handler for RBackend + * TODO: This is marked as sharable to get a handle to RBackend. Is it safe to re-use + * this across connections ? + */ +@Sharable +private[r] class RBackendHandler(server: RBackend) + extends SimpleChannelInboundHandler[Array[Byte]] with Logging { + + override def channelRead0(ctx: ChannelHandlerContext, msg: Array[Byte]): Unit = { + val bis = new ByteArrayInputStream(msg) + val dis = new DataInputStream(bis) + + val bos = new ByteArrayOutputStream() + val dos = new DataOutputStream(bos) + + // First bit is isStatic + val isStatic = readBoolean(dis) + val objId = readString(dis) + val methodName = readString(dis) + val numArgs = readInt(dis) + + if (objId == "SparkRHandler") { + methodName match { + case "stopBackend" => + writeInt(dos, 0) + writeType(dos, "void") + server.close() + case "rm" => + try { + val t = readObjectType(dis) + assert(t == 'c') + val objToRemove = readString(dis) + JVMObjectTracker.remove(objToRemove) + writeInt(dos, 0) + writeObject(dos, null) + } catch { + case e: Exception => + logError(s"Removing $objId failed", e) + writeInt(dos, -1) + } + case _ => dos.writeInt(-1) + } + } else { + handleMethodCall(isStatic, objId, methodName, numArgs, dis, dos) + } + + val reply = bos.toByteArray + ctx.write(reply) + } + + override def channelReadComplete(ctx: ChannelHandlerContext): Unit = { + ctx.flush() + } + + override def exceptionCaught(ctx: ChannelHandlerContext, cause: Throwable): Unit = { + // Close the connection when an exception is raised. + cause.printStackTrace() + ctx.close() + } + + def handleMethodCall( + isStatic: Boolean, + objId: String, + methodName: String, + numArgs: Int, + dis: DataInputStream, + dos: DataOutputStream): Unit = { + var obj: Object = null + try { + val cls = if (isStatic) { + Class.forName(objId) + } else { + JVMObjectTracker.get(objId) match { + case None => throw new IllegalArgumentException("Object not found " + objId) + case Some(o) => + obj = o + o.getClass + } + } + + val args = readArgs(numArgs, dis) + + val methods = cls.getMethods + val selectedMethods = methods.filter(m => m.getName == methodName) + if (selectedMethods.length > 0) { + val methods = selectedMethods.filter { x => + matchMethod(numArgs, args, x.getParameterTypes) + } + if (methods.isEmpty) { + logWarning(s"cannot find matching method ${cls}.$methodName. " + + s"Candidates are:") + selectedMethods.foreach { method => + logWarning(s"$methodName(${method.getParameterTypes.mkString(",")})") + } + throw new Exception(s"No matched method found for $cls.$methodName") + } + val ret = methods.head.invoke(obj, args:_*) + + // Write status bit + writeInt(dos, 0) + writeObject(dos, ret.asInstanceOf[AnyRef]) + } else if (methodName == "") { + // methodName should be "" for constructor + val ctor = cls.getConstructors.filter { x => + matchMethod(numArgs, args, x.getParameterTypes) + }.head + + val obj = ctor.newInstance(args:_*) + + writeInt(dos, 0) + writeObject(dos, obj.asInstanceOf[AnyRef]) + } else { + throw new IllegalArgumentException("invalid method " + methodName + " for object " + objId) + } + } catch { + case e: Exception => + logError(s"$methodName on $objId failed", e) + writeInt(dos, -1) + } + } + + // Read a number of arguments from the data input stream + def readArgs(numArgs: Int, dis: DataInputStream): Array[java.lang.Object] = { + (0 until numArgs).map { arg => + readObject(dis) + }.toArray + } + + // Checks if the arguments passed in args matches the parameter types. + // NOTE: Currently we do exact match. We may add type conversions later. + def matchMethod( + numArgs: Int, + args: Array[java.lang.Object], + parameterTypes: Array[Class[_]]): Boolean = { + if (parameterTypes.length != numArgs) { + return false + } + + for (i <- 0 to numArgs - 1) { + val parameterType = parameterTypes(i) + var parameterWrapperType = parameterType + + // Convert native parameters to Object types as args is Array[Object] here + if (parameterType.isPrimitive) { + parameterWrapperType = parameterType match { + case java.lang.Integer.TYPE => classOf[java.lang.Integer] + case java.lang.Double.TYPE => classOf[java.lang.Double] + case java.lang.Boolean.TYPE => classOf[java.lang.Boolean] + case _ => parameterType + } + } + if (!parameterWrapperType.isInstance(args(i))) { + return false + } + } + true + } +} + +/** + * Helper singleton that tracks JVM objects returned to R. + * This is useful for referencing these objects in RPC calls. + */ +private[r] object JVMObjectTracker { + + // TODO: This map should be thread-safe if we want to support multiple + // connections at the same time + private[this] val objMap = new HashMap[String, Object] + + // TODO: We support only one connection now, so an integer is fine. + // Investigate using use atomic integer in the future. + private[this] var objCounter: Int = 0 + + def getObject(id: String): Object = { + objMap(id) + } + + def get(id: String): Option[Object] = { + objMap.get(id) + } + + def put(obj: Object): String = { + val objId = objCounter.toString + objCounter = objCounter + 1 + objMap.put(objId, obj) + objId + } + + def remove(id: String): Option[Object] = { + objMap.remove(id) + } + +} diff --git a/core/src/main/scala/org/apache/spark/api/r/RRDD.scala b/core/src/main/scala/org/apache/spark/api/r/RRDD.scala new file mode 100644 index 0000000000000..5fa4d483b8342 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/api/r/RRDD.scala @@ -0,0 +1,450 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.api.r + +import java.io._ +import java.net.ServerSocket +import java.util.{Map => JMap} + +import scala.collection.JavaConversions._ +import scala.io.Source +import scala.reflect.ClassTag +import scala.util.Try + +import org.apache.spark._ +import org.apache.spark.api.java.{JavaPairRDD, JavaRDD, JavaSparkContext} +import org.apache.spark.broadcast.Broadcast +import org.apache.spark.rdd.RDD +import org.apache.spark.util.Utils + +private abstract class BaseRRDD[T: ClassTag, U: ClassTag]( + parent: RDD[T], + numPartitions: Int, + func: Array[Byte], + deserializer: String, + serializer: String, + packageNames: Array[Byte], + rLibDir: String, + broadcastVars: Array[Broadcast[Object]]) + extends RDD[U](parent) with Logging { + override def getPartitions: Array[Partition] = parent.partitions + + override def compute(partition: Partition, context: TaskContext): Iterator[U] = { + + // The parent may be also an RRDD, so we should launch it first. + val parentIterator = firstParent[T].iterator(partition, context) + + // we expect two connections + val serverSocket = new ServerSocket(0, 2) + val listenPort = serverSocket.getLocalPort() + + // The stdout/stderr is shared by multiple tasks, because we use one daemon + // to launch child process as worker. + val errThread = RRDD.createRWorker(rLibDir, listenPort) + + // We use two sockets to separate input and output, then it's easy to manage + // the lifecycle of them to avoid deadlock. + // TODO: optimize it to use one socket + + // the socket used to send out the input of task + serverSocket.setSoTimeout(10000) + val inSocket = serverSocket.accept() + startStdinThread(inSocket.getOutputStream(), parentIterator, partition.index) + + // the socket used to receive the output of task + val outSocket = serverSocket.accept() + val inputStream = new BufferedInputStream(outSocket.getInputStream) + val dataStream = openDataStream(inputStream) + serverSocket.close() + + try { + + return new Iterator[U] { + def next(): U = { + val obj = _nextObj + if (hasNext) { + _nextObj = read() + } + obj + } + + var _nextObj = read() + + def hasNext(): Boolean = { + val hasMore = (_nextObj != null) + if (!hasMore) { + dataStream.close() + } + hasMore + } + } + } catch { + case e: Exception => + throw new SparkException("R computation failed with\n " + errThread.getLines()) + } + } + + /** + * Start a thread to write RDD data to the R process. + */ + private def startStdinThread[T]( + output: OutputStream, + iter: Iterator[T], + partition: Int): Unit = { + + val env = SparkEnv.get + val bufferSize = System.getProperty("spark.buffer.size", "65536").toInt + val stream = new BufferedOutputStream(output, bufferSize) + + new Thread("writer for R") { + override def run(): Unit = { + try { + SparkEnv.set(env) + val dataOut = new DataOutputStream(stream) + dataOut.writeInt(partition) + + SerDe.writeString(dataOut, deserializer) + SerDe.writeString(dataOut, serializer) + + dataOut.writeInt(packageNames.length) + dataOut.write(packageNames) + + dataOut.writeInt(func.length) + dataOut.write(func) + + dataOut.writeInt(broadcastVars.length) + broadcastVars.foreach { broadcast => + // TODO(shivaram): Read a Long in R to avoid this cast + dataOut.writeInt(broadcast.id.toInt) + // TODO: Pass a byte array from R to avoid this cast ? + val broadcastByteArr = broadcast.value.asInstanceOf[Array[Byte]] + dataOut.writeInt(broadcastByteArr.length) + dataOut.write(broadcastByteArr) + } + + dataOut.writeInt(numPartitions) + + if (!iter.hasNext) { + dataOut.writeInt(0) + } else { + dataOut.writeInt(1) + } + + val printOut = new PrintStream(stream) + + def writeElem(elem: Any): Unit = { + if (deserializer == SerializationFormats.BYTE) { + val elemArr = elem.asInstanceOf[Array[Byte]] + dataOut.writeInt(elemArr.length) + dataOut.write(elemArr) + } else if (deserializer == SerializationFormats.ROW) { + dataOut.write(elem.asInstanceOf[Array[Byte]]) + } else if (deserializer == SerializationFormats.STRING) { + printOut.println(elem) + } + } + + for (elem <- iter) { + elem match { + case (key, value) => + writeElem(key) + writeElem(value) + case _ => + writeElem(elem) + } + } + stream.flush() + } catch { + // TODO: We should propogate this error to the task thread + case e: Exception => + logError("R Writer thread got an exception", e) + } finally { + Try(output.close()) + } + } + }.start() + } + + protected def openDataStream(input: InputStream): Closeable + + protected def read(): U +} + +/** + * Form an RDD[(Int, Array[Byte])] from key-value pairs returned from R. + * This is used by SparkR's shuffle operations. + */ +private class PairwiseRRDD[T: ClassTag]( + parent: RDD[T], + numPartitions: Int, + hashFunc: Array[Byte], + deserializer: String, + packageNames: Array[Byte], + rLibDir: String, + broadcastVars: Array[Object]) + extends BaseRRDD[T, (Int, Array[Byte])]( + parent, numPartitions, hashFunc, deserializer, + SerializationFormats.BYTE, packageNames, rLibDir, + broadcastVars.map(x => x.asInstanceOf[Broadcast[Object]])) { + + private var dataStream: DataInputStream = _ + + override protected def openDataStream(input: InputStream): Closeable = { + dataStream = new DataInputStream(input) + dataStream + } + + override protected def read(): (Int, Array[Byte]) = { + try { + val length = dataStream.readInt() + + length match { + case length if length == 2 => + val hashedKey = dataStream.readInt() + val contentPairsLength = dataStream.readInt() + val contentPairs = new Array[Byte](contentPairsLength) + dataStream.readFully(contentPairs) + (hashedKey, contentPairs) + case _ => null // End of input + } + } catch { + case eof: EOFException => { + throw new SparkException("R worker exited unexpectedly (crashed)", eof) + } + } + } + + lazy val asJavaPairRDD : JavaPairRDD[Int, Array[Byte]] = JavaPairRDD.fromRDD(this) +} + +/** + * An RDD that stores serialized R objects as Array[Byte]. + */ +private class RRDD[T: ClassTag]( + parent: RDD[T], + func: Array[Byte], + deserializer: String, + serializer: String, + packageNames: Array[Byte], + rLibDir: String, + broadcastVars: Array[Object]) + extends BaseRRDD[T, Array[Byte]]( + parent, -1, func, deserializer, serializer, packageNames, rLibDir, + broadcastVars.map(x => x.asInstanceOf[Broadcast[Object]])) { + + private var dataStream: DataInputStream = _ + + override protected def openDataStream(input: InputStream): Closeable = { + dataStream = new DataInputStream(input) + dataStream + } + + override protected def read(): Array[Byte] = { + try { + val length = dataStream.readInt() + + length match { + case length if length > 0 => + val obj = new Array[Byte](length) + dataStream.readFully(obj, 0, length) + obj + case _ => null + } + } catch { + case eof: EOFException => { + throw new SparkException("R worker exited unexpectedly (crashed)", eof) + } + } + } + + lazy val asJavaRDD : JavaRDD[Array[Byte]] = JavaRDD.fromRDD(this) +} + +/** + * An RDD that stores R objects as Array[String]. + */ +private class StringRRDD[T: ClassTag]( + parent: RDD[T], + func: Array[Byte], + deserializer: String, + packageNames: Array[Byte], + rLibDir: String, + broadcastVars: Array[Object]) + extends BaseRRDD[T, String]( + parent, -1, func, deserializer, SerializationFormats.STRING, packageNames, rLibDir, + broadcastVars.map(x => x.asInstanceOf[Broadcast[Object]])) { + + private var dataStream: BufferedReader = _ + + override protected def openDataStream(input: InputStream): Closeable = { + dataStream = new BufferedReader(new InputStreamReader(input)) + dataStream + } + + override protected def read(): String = { + try { + dataStream.readLine() + } catch { + case e: IOException => { + throw new SparkException("R worker exited unexpectedly (crashed)", e) + } + } + } + + lazy val asJavaRDD : JavaRDD[String] = JavaRDD.fromRDD(this) +} + +private[r] class BufferedStreamThread( + in: InputStream, + name: String, + errBufferSize: Int) extends Thread(name) with Logging { + val lines = new Array[String](errBufferSize) + var lineIdx = 0 + override def run() { + for (line <- Source.fromInputStream(in).getLines) { + synchronized { + lines(lineIdx) = line + lineIdx = (lineIdx + 1) % errBufferSize + } + logInfo(line) + } + } + + def getLines(): String = synchronized { + (0 until errBufferSize).filter { x => + lines((x + lineIdx) % errBufferSize) != null + }.map { x => + lines((x + lineIdx) % errBufferSize) + }.mkString("\n") + } +} + +private[r] object RRDD { + // Because forking processes from Java is expensive, we prefer to launch + // a single R daemon (daemon.R) and tell it to fork new workers for our tasks. + // This daemon currently only works on UNIX-based systems now, so we should + // also fall back to launching workers (worker.R) directly. + private[this] var errThread: BufferedStreamThread = _ + private[this] var daemonChannel: DataOutputStream = _ + + def createSparkContext( + master: String, + appName: String, + sparkHome: String, + jars: Array[String], + sparkEnvirMap: JMap[Object, Object], + sparkExecutorEnvMap: JMap[Object, Object]): JavaSparkContext = { + + val sparkConf = new SparkConf().setAppName(appName) + .setSparkHome(sparkHome) + .setJars(jars) + + // Override `master` if we have a user-specified value + if (master != "") { + sparkConf.setMaster(master) + } else { + // If conf has no master set it to "local" to maintain + // backwards compatibility + sparkConf.setIfMissing("spark.master", "local") + } + + for ((name, value) <- sparkEnvirMap) { + sparkConf.set(name.asInstanceOf[String], value.asInstanceOf[String]) + } + for ((name, value) <- sparkExecutorEnvMap) { + sparkConf.setExecutorEnv(name.asInstanceOf[String], value.asInstanceOf[String]) + } + + new JavaSparkContext(sparkConf) + } + + /** + * Start a thread to print the process's stderr to ours + */ + private def startStdoutThread(proc: Process): BufferedStreamThread = { + val BUFFER_SIZE = 100 + val thread = new BufferedStreamThread(proc.getInputStream, "stdout reader for R", BUFFER_SIZE) + thread.setDaemon(true) + thread.start() + thread + } + + private def createRProcess(rLibDir: String, port: Int, script: String): BufferedStreamThread = { + val rCommand = "Rscript" + val rOptions = "--vanilla" + val rExecScript = rLibDir + "/SparkR/worker/" + script + val pb = new ProcessBuilder(List(rCommand, rOptions, rExecScript)) + // Unset the R_TESTS environment variable for workers. + // This is set by R CMD check as startup.Rs + // (http://svn.r-project.org/R/trunk/src/library/tools/R/testing.R) + // and confuses worker script which tries to load a non-existent file + pb.environment().put("R_TESTS", "") + pb.environment().put("SPARKR_RLIBDIR", rLibDir) + pb.environment().put("SPARKR_WORKER_PORT", port.toString) + pb.redirectErrorStream(true) // redirect stderr into stdout + val proc = pb.start() + val errThread = startStdoutThread(proc) + errThread + } + + /** + * ProcessBuilder used to launch worker R processes. + */ + def createRWorker(rLibDir: String, port: Int): BufferedStreamThread = { + val useDaemon = SparkEnv.get.conf.getBoolean("spark.sparkr.use.daemon", true) + if (!Utils.isWindows && useDaemon) { + synchronized { + if (daemonChannel == null) { + // we expect one connections + val serverSocket = new ServerSocket(0, 1) + val daemonPort = serverSocket.getLocalPort + errThread = createRProcess(rLibDir, daemonPort, "daemon.R") + // the socket used to send out the input of task + serverSocket.setSoTimeout(10000) + val sock = serverSocket.accept() + daemonChannel = new DataOutputStream(new BufferedOutputStream(sock.getOutputStream)) + serverSocket.close() + } + try { + daemonChannel.writeInt(port) + daemonChannel.flush() + } catch { + case e: IOException => + // daemon process died + daemonChannel.close() + daemonChannel = null + errThread = null + // fail the current task, retry by scheduler + throw e + } + errThread + } + } else { + createRProcess(rLibDir, port, "worker.R") + } + } + + /** + * Create an RRDD given a sequence of byte arrays. Used to create RRDD when `parallelize` is + * called from R. + */ + def createRDDFromArray(jsc: JavaSparkContext, arr: Array[Array[Byte]]): JavaRDD[Array[Byte]] = { + JavaRDD.fromRDD(jsc.sc.parallelize(arr, arr.length)) + } + +} diff --git a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala new file mode 100644 index 0000000000000..ccb2a371f4e48 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala @@ -0,0 +1,340 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.api.r + +import java.io.{DataInputStream, DataOutputStream} +import java.sql.{Date, Time} + +import scala.collection.JavaConversions._ + +/** + * Utility functions to serialize, deserialize objects to / from R + */ +private[spark] object SerDe { + + // Type mapping from R to Java + // + // NULL -> void + // integer -> Int + // character -> String + // logical -> Boolean + // double, numeric -> Double + // raw -> Array[Byte] + // Date -> Date + // POSIXlt/POSIXct -> Time + // + // list[T] -> Array[T], where T is one of above mentioned types + // environment -> Map[String, T], where T is a native type + // jobj -> Object, where jobj is an object created in the backend + + def readObjectType(dis: DataInputStream): Char = { + dis.readByte().toChar + } + + def readObject(dis: DataInputStream): Object = { + val dataType = readObjectType(dis) + readTypedObject(dis, dataType) + } + + def readTypedObject( + dis: DataInputStream, + dataType: Char): Object = { + dataType match { + case 'n' => null + case 'i' => new java.lang.Integer(readInt(dis)) + case 'd' => new java.lang.Double(readDouble(dis)) + case 'b' => new java.lang.Boolean(readBoolean(dis)) + case 'c' => readString(dis) + case 'e' => readMap(dis) + case 'r' => readBytes(dis) + case 'l' => readList(dis) + case 'D' => readDate(dis) + case 't' => readTime(dis) + case 'j' => JVMObjectTracker.getObject(readString(dis)) + case _ => throw new IllegalArgumentException(s"Invalid type $dataType") + } + } + + def readBytes(in: DataInputStream): Array[Byte] = { + val len = readInt(in) + val out = new Array[Byte](len) + val bytesRead = in.readFully(out) + out + } + + def readInt(in: DataInputStream): Int = { + in.readInt() + } + + def readDouble(in: DataInputStream): Double = { + in.readDouble() + } + + def readString(in: DataInputStream): String = { + val len = in.readInt() + val asciiBytes = new Array[Byte](len) + in.readFully(asciiBytes) + assert(asciiBytes(len - 1) == 0) + val str = new String(asciiBytes.dropRight(1).map(_.toChar)) + str + } + + def readBoolean(in: DataInputStream): Boolean = { + val intVal = in.readInt() + if (intVal == 0) false else true + } + + def readDate(in: DataInputStream): Date = { + Date.valueOf(readString(in)) + } + + def readTime(in: DataInputStream): Time = { + val t = in.readDouble() + new Time((t * 1000L).toLong) + } + + def readBytesArr(in: DataInputStream): Array[Array[Byte]] = { + val len = readInt(in) + (0 until len).map(_ => readBytes(in)).toArray + } + + def readIntArr(in: DataInputStream): Array[Int] = { + val len = readInt(in) + (0 until len).map(_ => readInt(in)).toArray + } + + def readDoubleArr(in: DataInputStream): Array[Double] = { + val len = readInt(in) + (0 until len).map(_ => readDouble(in)).toArray + } + + def readBooleanArr(in: DataInputStream): Array[Boolean] = { + val len = readInt(in) + (0 until len).map(_ => readBoolean(in)).toArray + } + + def readStringArr(in: DataInputStream): Array[String] = { + val len = readInt(in) + (0 until len).map(_ => readString(in)).toArray + } + + def readList(dis: DataInputStream): Array[_] = { + val arrType = readObjectType(dis) + arrType match { + case 'i' => readIntArr(dis) + case 'c' => readStringArr(dis) + case 'd' => readDoubleArr(dis) + case 'b' => readBooleanArr(dis) + case 'j' => readStringArr(dis).map(x => JVMObjectTracker.getObject(x)) + case 'r' => readBytesArr(dis) + case _ => throw new IllegalArgumentException(s"Invalid array type $arrType") + } + } + + def readMap(in: DataInputStream): java.util.Map[Object, Object] = { + val len = readInt(in) + if (len > 0) { + val keysType = readObjectType(in) + val keysLen = readInt(in) + val keys = (0 until keysLen).map(_ => readTypedObject(in, keysType)) + + val valuesType = readObjectType(in) + val valuesLen = readInt(in) + val values = (0 until valuesLen).map(_ => readTypedObject(in, valuesType)) + mapAsJavaMap(keys.zip(values).toMap) + } else { + new java.util.HashMap[Object, Object]() + } + } + + // Methods to write out data from Java to R + // + // Type mapping from Java to R + // + // void -> NULL + // Int -> integer + // String -> character + // Boolean -> logical + // Double -> double + // Long -> double + // Array[Byte] -> raw + // Date -> Date + // Time -> POSIXct + // + // Array[T] -> list() + // Object -> jobj + + def writeType(dos: DataOutputStream, typeStr: String): Unit = { + typeStr match { + case "void" => dos.writeByte('n') + case "character" => dos.writeByte('c') + case "double" => dos.writeByte('d') + case "integer" => dos.writeByte('i') + case "logical" => dos.writeByte('b') + case "date" => dos.writeByte('D') + case "time" => dos.writeByte('t') + case "raw" => dos.writeByte('r') + case "list" => dos.writeByte('l') + case "jobj" => dos.writeByte('j') + case _ => throw new IllegalArgumentException(s"Invalid type $typeStr") + } + } + + def writeObject(dos: DataOutputStream, value: Object): Unit = { + if (value == null) { + writeType(dos, "void") + } else { + value.getClass.getName match { + case "java.lang.String" => + writeType(dos, "character") + writeString(dos, value.asInstanceOf[String]) + case "long" | "java.lang.Long" => + writeType(dos, "double") + writeDouble(dos, value.asInstanceOf[Long].toDouble) + case "double" | "java.lang.Double" => + writeType(dos, "double") + writeDouble(dos, value.asInstanceOf[Double]) + case "int" | "java.lang.Integer" => + writeType(dos, "integer") + writeInt(dos, value.asInstanceOf[Int]) + case "boolean" | "java.lang.Boolean" => + writeType(dos, "logical") + writeBoolean(dos, value.asInstanceOf[Boolean]) + case "java.sql.Date" => + writeType(dos, "date") + writeDate(dos, value.asInstanceOf[Date]) + case "java.sql.Time" => + writeType(dos, "time") + writeTime(dos, value.asInstanceOf[Time]) + case "[B" => + writeType(dos, "raw") + writeBytes(dos, value.asInstanceOf[Array[Byte]]) + // TODO: Types not handled right now include + // byte, char, short, float + + // Handle arrays + case "[Ljava.lang.String;" => + writeType(dos, "list") + writeStringArr(dos, value.asInstanceOf[Array[String]]) + case "[I" => + writeType(dos, "list") + writeIntArr(dos, value.asInstanceOf[Array[Int]]) + case "[J" => + writeType(dos, "list") + writeDoubleArr(dos, value.asInstanceOf[Array[Long]].map(_.toDouble)) + case "[D" => + writeType(dos, "list") + writeDoubleArr(dos, value.asInstanceOf[Array[Double]]) + case "[Z" => + writeType(dos, "list") + writeBooleanArr(dos, value.asInstanceOf[Array[Boolean]]) + case "[[B" => + writeType(dos, "list") + writeBytesArr(dos, value.asInstanceOf[Array[Array[Byte]]]) + case otherName => + // Handle array of objects + if (otherName.startsWith("[L")) { + val objArr = value.asInstanceOf[Array[Object]] + writeType(dos, "list") + writeType(dos, "jobj") + dos.writeInt(objArr.length) + objArr.foreach(o => writeJObj(dos, o)) + } else { + writeType(dos, "jobj") + writeJObj(dos, value) + } + } + } + } + + def writeInt(out: DataOutputStream, value: Int): Unit = { + out.writeInt(value) + } + + def writeDouble(out: DataOutputStream, value: Double): Unit = { + out.writeDouble(value) + } + + def writeBoolean(out: DataOutputStream, value: Boolean): Unit = { + val intValue = if (value) 1 else 0 + out.writeInt(intValue) + } + + def writeDate(out: DataOutputStream, value: Date): Unit = { + writeString(out, value.toString) + } + + def writeTime(out: DataOutputStream, value: Time): Unit = { + out.writeDouble(value.getTime.toDouble / 1000.0) + } + + + // NOTE: Only works for ASCII right now + def writeString(out: DataOutputStream, value: String): Unit = { + val len = value.length + out.writeInt(len + 1) // For the \0 + out.writeBytes(value) + out.writeByte(0) + } + + def writeBytes(out: DataOutputStream, value: Array[Byte]): Unit = { + out.writeInt(value.length) + out.write(value) + } + + def writeJObj(out: DataOutputStream, value: Object): Unit = { + val objId = JVMObjectTracker.put(value) + writeString(out, objId) + } + + def writeIntArr(out: DataOutputStream, value: Array[Int]): Unit = { + writeType(out, "integer") + out.writeInt(value.length) + value.foreach(v => out.writeInt(v)) + } + + def writeDoubleArr(out: DataOutputStream, value: Array[Double]): Unit = { + writeType(out, "double") + out.writeInt(value.length) + value.foreach(v => out.writeDouble(v)) + } + + def writeBooleanArr(out: DataOutputStream, value: Array[Boolean]): Unit = { + writeType(out, "logical") + out.writeInt(value.length) + value.foreach(v => writeBoolean(out, v)) + } + + def writeStringArr(out: DataOutputStream, value: Array[String]): Unit = { + writeType(out, "character") + out.writeInt(value.length) + value.foreach(v => writeString(out, v)) + } + + def writeBytesArr(out: DataOutputStream, value: Array[Array[Byte]]): Unit = { + writeType(out, "raw") + out.writeInt(value.length) + value.foreach(v => writeBytes(out, v)) + } +} + +private[r] object SerializationFormats { + val BYTE = "byte" + val STRING = "string" + val ROW = "row" +} diff --git a/core/src/main/scala/org/apache/spark/deploy/RRunner.scala b/core/src/main/scala/org/apache/spark/deploy/RRunner.scala new file mode 100644 index 0000000000000..e99779f299785 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/RRunner.scala @@ -0,0 +1,92 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy + +import java.io._ +import java.util.concurrent.{Semaphore, TimeUnit} + +import scala.collection.JavaConversions._ + +import org.apache.hadoop.fs.Path + +import org.apache.spark.api.r.RBackend +import org.apache.spark.util.RedirectThread + +/** + * Main class used to launch SparkR applications using spark-submit. It executes R as a + * subprocess and then has it connect back to the JVM to access system properties etc. + */ +object RRunner { + def main(args: Array[String]): Unit = { + val rFile = PythonRunner.formatPath(args(0)) + + val otherArgs = args.slice(1, args.length) + + // Time to wait for SparkR backend to initialize in seconds + val backendTimeout = sys.env.getOrElse("SPARKR_BACKEND_TIMEOUT", "120").toInt + val rCommand = "Rscript" + + // Check if the file path exists. + // If not, change directory to current working directory for YARN cluster mode + val rF = new File(rFile) + val rFileNormalized = if (!rF.exists()) { + new Path(rFile).getName + } else { + rFile + } + + // Launch a SparkR backend server for the R process to connect to; this will let it see our + // Java system properties etc. + val sparkRBackend = new RBackend() + @volatile var sparkRBackendPort = 0 + val initialized = new Semaphore(0) + val sparkRBackendThread = new Thread("SparkR backend") { + override def run() { + sparkRBackendPort = sparkRBackend.init() + initialized.release() + sparkRBackend.run() + } + } + + sparkRBackendThread.start() + // Wait for RBackend initialization to finish + if (initialized.tryAcquire(backendTimeout, TimeUnit.SECONDS)) { + // Launch R + val returnCode = try { + val builder = new ProcessBuilder(Seq(rCommand, rFileNormalized) ++ otherArgs) + val env = builder.environment() + env.put("EXISTING_SPARKR_BACKEND_PORT", sparkRBackendPort.toString) + val sparkHome = System.getenv("SPARK_HOME") + env.put("R_PROFILE_USER", + Seq(sparkHome, "R", "lib", "SparkR", "profile", "general.R").mkString(File.separator)) + builder.redirectErrorStream(true) // Ugly but needed for stdout and stderr to synchronize + val process = builder.start() + + new RedirectThread(process.getInputStream, System.out, "redirect R output").start() + + process.waitFor() + } finally { + sparkRBackend.close() + } + System.exit(returnCode) + } else { + System.err.println("SparkR backend did not initialize in " + backendTimeout + " seconds") + System.exit(-1) + } + } +} diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala index 660307d19eab4..60bc243ebf40a 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -77,6 +77,7 @@ object SparkSubmit { // Special primary resource names that represent shells rather than application jars. private val SPARK_SHELL = "spark-shell" private val PYSPARK_SHELL = "pyspark-shell" + private val SPARKR_SHELL = "sparkr-shell" private val CLASS_NOT_FOUND_EXIT_STATUS = 101 @@ -284,6 +285,13 @@ object SparkSubmit { } } + // Require all R files to be local + if (args.isR && !isYarnCluster) { + if (Utils.nonLocalPaths(args.primaryResource).nonEmpty) { + printErrorAndExit(s"Only local R files are supported: $args.primaryResource") + } + } + // The following modes are not supported or applicable (clusterManager, deployMode) match { case (MESOS, CLUSTER) => @@ -291,6 +299,9 @@ object SparkSubmit { case (STANDALONE, CLUSTER) if args.isPython => printErrorAndExit("Cluster deploy mode is currently not supported for python " + "applications on standalone clusters.") + case (STANDALONE, CLUSTER) if args.isR => + printErrorAndExit("Cluster deploy mode is currently not supported for R " + + "applications on standalone clusters.") case (_, CLUSTER) if isShell(args.primaryResource) => printErrorAndExit("Cluster deploy mode is not applicable to Spark shells.") case (_, CLUSTER) if isSqlShell(args.mainClass) => @@ -317,11 +328,32 @@ object SparkSubmit { } } - // In yarn-cluster mode for a python app, add primary resource and pyFiles to files - // that can be distributed with the job - if (args.isPython && isYarnCluster) { - args.files = mergeFileLists(args.files, args.primaryResource) - args.files = mergeFileLists(args.files, args.pyFiles) + // If we're running a R app, set the main class to our specific R runner + if (args.isR && deployMode == CLIENT) { + if (args.primaryResource == SPARKR_SHELL) { + args.mainClass = "org.apache.spark.api.r.RBackend" + } else { + // If a R file is provided, add it to the child arguments and list of files to deploy. + // Usage: RRunner
[app arguments] + args.mainClass = "org.apache.spark.deploy.RRunner" + args.childArgs = ArrayBuffer(args.primaryResource) ++ args.childArgs + args.files = mergeFileLists(args.files, args.primaryResource) + } + } + + if (isYarnCluster) { + // In yarn-cluster mode for a python app, add primary resource and pyFiles to files + // that can be distributed with the job + if (args.isPython) { + args.files = mergeFileLists(args.files, args.primaryResource) + args.files = mergeFileLists(args.files, args.pyFiles) + } + + // In yarn-cluster mode for a R app, add primary resource to files + // that can be distributed with the job + if (args.isR) { + args.files = mergeFileLists(args.files, args.primaryResource) + } } // Special flag to avoid deprecation warnings at the client @@ -405,8 +437,8 @@ object SparkSubmit { // Add the application jar automatically so the user doesn't have to call sc.addJar // For YARN cluster mode, the jar is already distributed on each node as "app.jar" - // For python files, the primary resource is already distributed as a regular file - if (!isYarnCluster && !args.isPython) { + // For python and R files, the primary resource is already distributed as a regular file + if (!isYarnCluster && !args.isPython && !args.isR) { var jars = sysProps.get("spark.jars").map(x => x.split(",").toSeq).getOrElse(Seq.empty) if (isUserJar(args.primaryResource)) { jars = jars ++ Seq(args.primaryResource) @@ -447,6 +479,10 @@ object SparkSubmit { childArgs += ("--py-files", pyFilesNames) } childArgs += ("--class", "org.apache.spark.deploy.PythonRunner") + } else if (args.isR) { + val mainFile = new Path(args.primaryResource).getName + childArgs += ("--primary-r-file", mainFile) + childArgs += ("--class", "org.apache.spark.deploy.RRunner") } else { if (args.primaryResource != SPARK_INTERNAL) { childArgs += ("--jar", args.primaryResource) @@ -591,15 +627,15 @@ object SparkSubmit { /** * Return whether the given primary resource represents a user jar. */ - private def isUserJar(primaryResource: String): Boolean = { - !isShell(primaryResource) && !isPython(primaryResource) && !isInternal(primaryResource) + private[deploy] def isUserJar(res: String): Boolean = { + !isShell(res) && !isPython(res) && !isInternal(res) && !isR(res) } /** * Return whether the given primary resource represents a shell. */ - private[deploy] def isShell(primaryResource: String): Boolean = { - primaryResource == SPARK_SHELL || primaryResource == PYSPARK_SHELL + private[deploy] def isShell(res: String): Boolean = { + (res == SPARK_SHELL || res == PYSPARK_SHELL || res == SPARKR_SHELL) } /** @@ -619,12 +655,19 @@ object SparkSubmit { /** * Return whether the given primary resource requires running python. */ - private[deploy] def isPython(primaryResource: String): Boolean = { - primaryResource.endsWith(".py") || primaryResource == PYSPARK_SHELL + private[deploy] def isPython(res: String): Boolean = { + res != null && res.endsWith(".py") || res == PYSPARK_SHELL + } + + /** + * Return whether the given primary resource requires running R. + */ + private[deploy] def isR(res: String): Boolean = { + res != null && res.endsWith(".R") || res == SPARKR_SHELL } - private[deploy] def isInternal(primaryResource: String): Boolean = { - primaryResource == SPARK_INTERNAL + private[deploy] def isInternal(res: String): Boolean = { + res == SPARK_INTERNAL } /** diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala index 6eb73c43470a5..03ecf3fd99ec5 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala @@ -59,6 +59,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S var verbose: Boolean = false var isPython: Boolean = false var pyFiles: String = null + var isR: Boolean = false var action: SparkSubmitAction = null val sparkProperties: HashMap[String, String] = new HashMap[String, String]() var proxyUser: String = null @@ -158,7 +159,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S .getOrElse(sparkProperties.get("spark.executor.instances").orNull) // Try to set main class from JAR if no --class argument is given - if (mainClass == null && !isPython && primaryResource != null) { + if (mainClass == null && !isPython && !isR && primaryResource != null) { val uri = new URI(primaryResource) val uriScheme = uri.getScheme() @@ -211,9 +212,9 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S printUsageAndExit(-1) } if (primaryResource == null) { - SparkSubmit.printErrorAndExit("Must specify a primary resource (JAR or Python file)") + SparkSubmit.printErrorAndExit("Must specify a primary resource (JAR or Python or R file)") } - if (mainClass == null && !isPython) { + if (mainClass == null && SparkSubmit.isUserJar(primaryResource)) { SparkSubmit.printErrorAndExit("No main class set in JAR; please specify one with --class") } if (pyFiles != null && !isPython) { @@ -414,6 +415,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S opt } isPython = SparkSubmit.isPython(opt) + isR = SparkSubmit.isR(opt) false } diff --git a/dev/run-tests b/dev/run-tests index 561d7fc9e7b1f..1b6cf78b5da01 100755 --- a/dev/run-tests +++ b/dev/run-tests @@ -236,3 +236,18 @@ echo "=========================================================================" CURRENT_BLOCK=$BLOCK_PYSPARK_UNIT_TESTS ./python/run-tests + +echo "" +echo "=========================================================================" +echo "Running SparkR tests" +echo "=========================================================================" + +CURRENT_BLOCK=$BLOCK_SPARKR_UNIT_TESTS + +if [ $(command -v R) ]; then + ./R/install-dev.sh + ./R/run-tests.sh +else + echo "Ignoring SparkR tests as R was not found in PATH" +fi + diff --git a/dev/run-tests-codes.sh b/dev/run-tests-codes.sh index 8ab6db6925d6e..154e01255b2ef 100644 --- a/dev/run-tests-codes.sh +++ b/dev/run-tests-codes.sh @@ -25,3 +25,4 @@ readonly BLOCK_BUILD=14 readonly BLOCK_MIMA=15 readonly BLOCK_SPARK_UNIT_TESTS=16 readonly BLOCK_PYSPARK_UNIT_TESTS=17 +readonly BLOCK_SPARKR_UNIT_TESTS=18 diff --git a/dev/run-tests-jenkins b/dev/run-tests-jenkins index f10aa6b59e1af..f6372835a6dbf 100755 --- a/dev/run-tests-jenkins +++ b/dev/run-tests-jenkins @@ -210,6 +210,8 @@ done failing_test="Spark unit tests" elif [ "$test_result" -eq "$BLOCK_PYSPARK_UNIT_TESTS" ]; then failing_test="PySpark unit tests" + elif [ "$test_result" -eq "$BLOCK_SPARKR_UNIT_TESTS" ]; then + failing_test="SparkR unit tests" else failing_test="some tests" fi diff --git a/docs/README.md b/docs/README.md index 3773ea25c8b67..5852f972a051d 100644 --- a/docs/README.md +++ b/docs/README.md @@ -58,13 +58,19 @@ phase, use the following sytax: We use Sphinx to generate Python API docs, so you will need to install it by running `sudo pip install sphinx`. -## API Docs (Scaladoc and Sphinx) +## knitr, devtools + +SparkR documentation is written using `roxygen2` and we use `knitr`, `devtools` to generate +documentation. To install these packages you can run `install.packages(c("knitr", "devtools"))` from a +R console. + +## API Docs (Scaladoc, Sphinx, roxygen2) You can build just the Spark scaladoc by running `build/sbt unidoc` from the SPARK_PROJECT_ROOT directory. Similarly, you can build just the PySpark docs by running `make html` from the SPARK_PROJECT_ROOT/python/docs directory. Documentation is only generated for classes that are listed as -public in `__init__.py`. +public in `__init__.py`. The SparkR docs can be built by running SPARK_PROJECT_ROOT/R/create-docs.sh. When you run `jekyll` in the `docs` directory, it will also copy over the scaladoc for the various Spark subprojects into the `docs` directory (and then also into the `_site` directory). We use a @@ -72,5 +78,5 @@ jekyll plugin to run `build/sbt unidoc` before building the site so if you haven may take some time as it generates all of the scaladoc. The jekyll plugin also generates the PySpark docs [Sphinx](http://sphinx-doc.org/). -NOTE: To skip the step of building and copying over the Scala and Python API docs, run `SKIP_API=1 +NOTE: To skip the step of building and copying over the Scala, Python, R API docs, run `SKIP_API=1 jekyll`. diff --git a/docs/_layouts/global.html b/docs/_layouts/global.html index 2e88b3093652d..b92c75f90b11c 100755 --- a/docs/_layouts/global.html +++ b/docs/_layouts/global.html @@ -84,6 +84,7 @@
  • Scala
  • Java
  • Python
    • +
  • R
    • diff --git a/docs/_plugins/copy_api_dirs.rb b/docs/_plugins/copy_api_dirs.rb index 3c626a0b7f54b..0ea3f8eab461b 100644 --- a/docs/_plugins/copy_api_dirs.rb +++ b/docs/_plugins/copy_api_dirs.rb @@ -78,5 +78,18 @@ puts "cp -r python/docs/_build/html/. docs/api/python" cp_r("python/docs/_build/html/.", "docs/api/python") - cd("..") + # Build SparkR API docs + puts "Moving to R directory and building roxygen docs." + cd("R") + puts `./create-docs.sh` + + puts "Moving back into home dir." + cd("../") + + puts "Making directory api/R" + mkdir_p "docs/api/R" + + puts "cp -r R/pkg/html/. docs/api/R" + cp_r("R/pkg/html/.", "docs/api/R") + end diff --git a/examples/src/main/r/kmeans.R b/examples/src/main/r/kmeans.R new file mode 100644 index 0000000000000..6e6b5cb93789c --- /dev/null +++ b/examples/src/main/r/kmeans.R @@ -0,0 +1,93 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +library(SparkR) + +# Logistic regression in Spark. +# Note: unlike the example in Scala, a point here is represented as a vector of +# doubles. + +parseVectors <- function(lines) { + lines <- strsplit(as.character(lines) , " ", fixed = TRUE) + list(matrix(as.numeric(unlist(lines)), ncol = length(lines[[1]]))) +} + +dist.fun <- function(P, C) { + apply( + C, + 1, + function(x) { + colSums((t(P) - x)^2) + } + ) +} + +closestPoint <- function(P, C) { + max.col(-dist.fun(P, C)) +} +# Main program + +args <- commandArgs(trailing = TRUE) + +if (length(args) != 3) { + print("Usage: kmeans ") + q("no") +} + +sc <- sparkR.init(appName = "RKMeans") +K <- as.integer(args[[2]]) +convergeDist <- as.double(args[[3]]) + +lines <- textFile(sc, args[[1]]) +points <- cache(lapplyPartition(lines, parseVectors)) +# kPoints <- take(points, K) +kPoints <- do.call(rbind, takeSample(points, FALSE, K, 16189L)) +tempDist <- 1.0 + +while (tempDist > convergeDist) { + closest <- lapplyPartition( + lapply(points, + function(p) { + cp <- closestPoint(p, kPoints); + mapply(list, unique(cp), split.data.frame(cbind(1, p), cp), SIMPLIFY=FALSE) + }), + function(x) {do.call(c, x) + }) + + pointStats <- reduceByKey(closest, + function(p1, p2) { + t(colSums(rbind(p1, p2))) + }, + 2L) + + newPoints <- do.call( + rbind, + collect(lapply(pointStats, + function(tup) { + point.sum <- tup[[2]][, -1] + point.count <- tup[[2]][, 1] + point.sum/point.count + }))) + + D <- dist.fun(kPoints, newPoints) + tempDist <- sum(D[cbind(1:3, max.col(-D))]) + kPoints <- newPoints + cat("Finished iteration (delta = ", tempDist, ")\n") +} + +cat("Final centers:\n") +writeLines(unlist(lapply(kPoints, paste, collapse = " "))) diff --git a/examples/src/main/r/linear_solver_mnist.R b/examples/src/main/r/linear_solver_mnist.R new file mode 100644 index 0000000000000..c864a4232d010 --- /dev/null +++ b/examples/src/main/r/linear_solver_mnist.R @@ -0,0 +1,107 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Instructions: https://github.com/amplab-extras/SparkR-pkg/wiki/SparkR-Example:-Digit-Recognition-on-EC2 + +library(SparkR) +library(Matrix) + +args <- commandArgs(trailing = TRUE) + +# number of random features; default to 1100 +D <- ifelse(length(args) > 0, as.integer(args[[1]]), 1100) +# number of partitions for training dataset +trainParts <- 12 +# dimension of digits +d <- 784 +# number of test examples +NTrain <- 60000 +# number of training examples +NTest <- 10000 +# scale of features +gamma <- 4e-4 + +sc <- sparkR.init(appName = "SparkR-LinearSolver") + +# You can also use HDFS path to speed things up: +# hdfs:///train-mnist-dense-with-labels.data +file <- textFile(sc, "/data/train-mnist-dense-with-labels.data", trainParts) + +W <- gamma * matrix(nrow=D, ncol=d, data=rnorm(D*d)) +b <- 2 * pi * matrix(nrow=D, ncol=1, data=runif(D)) +broadcastW <- broadcast(sc, W) +broadcastB <- broadcast(sc, b) + +includePackage(sc, Matrix) +numericLines <- lapplyPartitionsWithIndex(file, + function(split, part) { + matList <- sapply(part, function(line) { + as.numeric(strsplit(line, ",", fixed=TRUE)[[1]]) + }, simplify=FALSE) + mat <- Matrix(ncol=d+1, data=unlist(matList, F, F), + sparse=T, byrow=T) + mat + }) + +featureLabels <- cache(lapplyPartition( + numericLines, + function(part) { + label <- part[,1] + mat <- part[,-1] + ones <- rep(1, nrow(mat)) + features <- cos( + mat %*% t(value(broadcastW)) + (matrix(ncol=1, data=ones) %*% t(value(broadcastB)))) + onesMat <- Matrix(ones) + featuresPlus <- cBind(features, onesMat) + labels <- matrix(nrow=nrow(mat), ncol=10, data=-1) + for (i in 1:nrow(mat)) { + labels[i, label[i]] <- 1 + } + list(label=labels, features=featuresPlus) + })) + +FTF <- Reduce("+", collect(lapplyPartition(featureLabels, + function(part) { + t(part$features) %*% part$features + }), flatten=F)) + +FTY <- Reduce("+", collect(lapplyPartition(featureLabels, + function(part) { + t(part$features) %*% part$label + }), flatten=F)) + +# solve for the coefficient matrix +C <- solve(FTF, FTY) + +test <- Matrix(as.matrix(read.csv("/data/test-mnist-dense-with-labels.data", + header=F), sparse=T)) +testData <- test[,-1] +testLabels <- matrix(ncol=1, test[,1]) + +err <- 0 + +# contstruct the feature maps for all examples from this digit +featuresTest <- cos(testData %*% t(value(broadcastW)) + + (matrix(ncol=1, data=rep(1, NTest)) %*% t(value(broadcastB)))) +featuresTest <- cBind(featuresTest, Matrix(rep(1, NTest))) + +# extract the one vs. all assignment +results <- featuresTest %*% C +labelsGot <- apply(results, 1, which.max) +err <- sum(testLabels != labelsGot) / nrow(testLabels) + +cat("\nFinished running. The error rate is: ", err, ".\n") diff --git a/examples/src/main/r/logistic_regression.R b/examples/src/main/r/logistic_regression.R new file mode 100644 index 0000000000000..2a86aa98160d3 --- /dev/null +++ b/examples/src/main/r/logistic_regression.R @@ -0,0 +1,62 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +library(SparkR) + +args <- commandArgs(trailing = TRUE) + +if (length(args) != 3) { + print("Usage: logistic_regression ") + q("no") +} + +# Initialize Spark context +sc <- sparkR.init(appName = "LogisticRegressionR") +iterations <- as.integer(args[[2]]) +D <- as.integer(args[[3]]) + +readPartition <- function(part){ + part = strsplit(part, " ", fixed = T) + list(matrix(as.numeric(unlist(part)), ncol = length(part[[1]]))) +} + +# Read data points and convert each partition to a matrix +points <- cache(lapplyPartition(textFile(sc, args[[1]]), readPartition)) + +# Initialize w to a random value +w <- runif(n=D, min = -1, max = 1) +cat("Initial w: ", w, "\n") + +# Compute logistic regression gradient for a matrix of data points +gradient <- function(partition) { + partition = partition[[1]] + Y <- partition[, 1] # point labels (first column of input file) + X <- partition[, -1] # point coordinates + + # For each point (x, y), compute gradient function + dot <- X %*% w + logit <- 1 / (1 + exp(-Y * dot)) + grad <- t(X) %*% ((logit - 1) * Y) + list(grad) +} + +for (i in 1:iterations) { + cat("On iteration ", i, "\n") + w <- w - reduce(lapplyPartition(points, gradient), "+") +} + +cat("Final w: ", w, "\n") diff --git a/examples/src/main/r/pi.R b/examples/src/main/r/pi.R new file mode 100644 index 0000000000000..aa7a833e147a0 --- /dev/null +++ b/examples/src/main/r/pi.R @@ -0,0 +1,46 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +library(SparkR) + +args <- commandArgs(trailing = TRUE) + +sc <- sparkR.init(appName = "PiR") + +slices <- ifelse(length(args) > 1, as.integer(args[[2]]), 2) + +n <- 100000 * slices + +piFunc <- function(elem) { + rands <- runif(n = 2, min = -1, max = 1) + val <- ifelse((rands[1]^2 + rands[2]^2) < 1, 1.0, 0.0) + val +} + + +piFuncVec <- function(elems) { + message(length(elems)) + rands1 <- runif(n = length(elems), min = -1, max = 1) + rands2 <- runif(n = length(elems), min = -1, max = 1) + val <- ifelse((rands1^2 + rands2^2) < 1, 1.0, 0.0) + sum(val) +} + +rdd <- parallelize(sc, 1:n, slices) +count <- reduce(lapplyPartition(rdd, piFuncVec), sum) +cat("Pi is roughly", 4.0 * count / n, "\n") +cat("Num elements in RDD ", count(rdd), "\n") diff --git a/examples/src/main/r/wordcount.R b/examples/src/main/r/wordcount.R new file mode 100644 index 0000000000000..b734cb0ecf55b --- /dev/null +++ b/examples/src/main/r/wordcount.R @@ -0,0 +1,42 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +library(SparkR) + +args <- commandArgs(trailing = TRUE) + +if (length(args) != 1) { + print("Usage: wordcount ") + q("no") +} + +# Initialize Spark context +sc <- sparkR.init(appName = "RwordCount") +lines <- textFile(sc, args[[1]]) + +words <- flatMap(lines, + function(line) { + strsplit(line, " ")[[1]] + }) +wordCount <- lapply(words, function(word) { list(word, 1L) }) + +counts <- reduceByKey(wordCount, "+", 2L) +output <- collect(counts) + +for (wordcount in output) { + cat(wordcount[[1]], ": ", wordcount[[2]], "\n") +} diff --git a/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java b/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java index 9b04732afee14..f4ebc25bdd32b 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java +++ b/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java @@ -274,14 +274,14 @@ static String quoteForBatchScript(String arg) { } /** - * Quotes a string so that it can be used in a command string and be parsed back into a single - * argument by python's "shlex.split()" function. - * + * Quotes a string so that it can be used in a command string. * Basically, just add simple escapes. E.g.: * original single argument : ab "cd" ef * after: "ab \"cd\" ef" + * + * This can be parsed back into a single argument by python's "shlex.split()" function. */ - static String quoteForPython(String s) { + static String quoteForCommandString(String s) { StringBuilder quoted = new StringBuilder().append('"'); for (int i = 0; i < s.length(); i++) { int cp = s.codePointAt(i); diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java index 91dcf70f105db..a73c9c87e3126 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java +++ b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java @@ -17,14 +17,9 @@ package org.apache.spark.launcher; +import java.io.File; import java.io.IOException; -import java.util.ArrayList; -import java.util.Arrays; -import java.util.Collections; -import java.util.HashMap; -import java.util.List; -import java.util.Map; -import java.util.Properties; +import java.util.*; import static org.apache.spark.launcher.CommandBuilderUtils.*; @@ -53,6 +48,20 @@ class SparkSubmitCommandBuilder extends AbstractCommandBuilder { */ static final String PYSPARK_SHELL_RESOURCE = "pyspark-shell"; + /** + * Name of the app resource used to identify the SparkR shell. The command line parser expects + * the resource name to be the very first argument to spark-submit in this case. + * + * NOTE: this cannot be "sparkr-shell" since that identifies the SparkR shell to SparkSubmit + * (see sparkR.R), and can cause this code to enter into an infinite loop. + */ + static final String SPARKR_SHELL = "sparkr-shell-main"; + + /** + * This is the actual resource name that identifies the SparkR shell to SparkSubmit. + */ + static final String SPARKR_SHELL_RESOURCE = "sparkr-shell"; + /** * This map must match the class names for available special classes, since this modifies the way * command line parsing works. This maps the class name to the resource to use when calling @@ -87,6 +96,10 @@ class SparkSubmitCommandBuilder extends AbstractCommandBuilder { this.allowsMixedArguments = true; appResource = PYSPARK_SHELL_RESOURCE; submitArgs = args.subList(1, args.size()); + } else if (args.size() > 0 && args.get(0).equals(SPARKR_SHELL)) { + this.allowsMixedArguments = true; + appResource = SPARKR_SHELL_RESOURCE; + submitArgs = args.subList(1, args.size()); } else { this.allowsMixedArguments = false; } @@ -98,6 +111,8 @@ class SparkSubmitCommandBuilder extends AbstractCommandBuilder { public List buildCommand(Map env) throws IOException { if (PYSPARK_SHELL_RESOURCE.equals(appResource)) { return buildPySparkShellCommand(env); + } else if (SPARKR_SHELL_RESOURCE.equals(appResource)) { + return buildSparkRCommand(env); } else { return buildSparkSubmitCommand(env); } @@ -213,36 +228,62 @@ private List buildPySparkShellCommand(Map env) throws IO return buildCommand(env); } - // When launching the pyspark shell, the spark-submit arguments should be stored in the - // PYSPARK_SUBMIT_ARGS env variable. The executable is the PYSPARK_DRIVER_PYTHON env variable - // set by the pyspark script, followed by PYSPARK_DRIVER_PYTHON_OPTS. checkArgument(appArgs.isEmpty(), "pyspark does not support any application options."); + // When launching the pyspark shell, the spark-submit arguments should be stored in the + // PYSPARK_SUBMIT_ARGS env variable. + constructEnvVarArgs(env, "PYSPARK_SUBMIT_ARGS"); + + // The executable is the PYSPARK_DRIVER_PYTHON env variable set by the pyspark script, + // followed by PYSPARK_DRIVER_PYTHON_OPTS. + List pyargs = new ArrayList(); + pyargs.add(firstNonEmpty(System.getenv("PYSPARK_DRIVER_PYTHON"), "python")); + String pyOpts = System.getenv("PYSPARK_DRIVER_PYTHON_OPTS"); + if (!isEmpty(pyOpts)) { + pyargs.addAll(parseOptionString(pyOpts)); + } + + return pyargs; + } + + private List buildSparkRCommand(Map env) throws IOException { + if (!appArgs.isEmpty() && appArgs.get(0).endsWith(".R")) { + appResource = appArgs.get(0); + appArgs.remove(0); + return buildCommand(env); + } + // When launching the SparkR shell, store the spark-submit arguments in the SPARKR_SUBMIT_ARGS + // env variable. + constructEnvVarArgs(env, "SPARKR_SUBMIT_ARGS"); + + // Set shell.R as R_PROFILE_USER to load the SparkR package when the shell comes up. + String sparkHome = System.getenv("SPARK_HOME"); + env.put("R_PROFILE_USER", + join(File.separator, sparkHome, "R", "lib", "SparkR", "profile", "shell.R")); + + List args = new ArrayList(); + args.add(firstNonEmpty(System.getenv("SPARKR_DRIVER_R"), "R")); + return args; + } + + private void constructEnvVarArgs( + Map env, + String submitArgsEnvVariable) throws IOException { Properties props = loadPropertiesFile(); mergeEnvPathList(env, getLibPathEnvName(), firstNonEmptyValue(SparkLauncher.DRIVER_EXTRA_LIBRARY_PATH, conf, props)); - // Store spark-submit arguments in an environment variable, since there's no way to pass - // them to shell.py on the comand line. StringBuilder submitArgs = new StringBuilder(); for (String arg : buildSparkSubmitArgs()) { if (submitArgs.length() > 0) { submitArgs.append(" "); } - submitArgs.append(quoteForPython(arg)); + submitArgs.append(quoteForCommandString(arg)); } - env.put("PYSPARK_SUBMIT_ARGS", submitArgs.toString()); - - List pyargs = new ArrayList(); - pyargs.add(firstNonEmpty(System.getenv("PYSPARK_DRIVER_PYTHON"), "python")); - String pyOpts = System.getenv("PYSPARK_DRIVER_PYTHON_OPTS"); - if (!isEmpty(pyOpts)) { - pyargs.addAll(parseOptionString(pyOpts)); - } - - return pyargs; + env.put(submitArgsEnvVariable, submitArgs.toString()); } + private boolean isClientMode(Properties userProps) { String userMaster = firstNonEmpty(master, (String) userProps.get(SparkLauncher.SPARK_MASTER)); // Default master is "local[*]", so assume client mode in that case. diff --git a/launcher/src/test/java/org/apache/spark/launcher/CommandBuilderUtilsSuite.java b/launcher/src/test/java/org/apache/spark/launcher/CommandBuilderUtilsSuite.java index dba0203867372..1ae42eed8a3af 100644 --- a/launcher/src/test/java/org/apache/spark/launcher/CommandBuilderUtilsSuite.java +++ b/launcher/src/test/java/org/apache/spark/launcher/CommandBuilderUtilsSuite.java @@ -79,9 +79,9 @@ public void testWindowsBatchQuoting() { @Test public void testPythonArgQuoting() { - assertEquals("\"abc\"", quoteForPython("abc")); - assertEquals("\"a b c\"", quoteForPython("a b c")); - assertEquals("\"a \\\"b\\\" c\"", quoteForPython("a \"b\" c")); + assertEquals("\"abc\"", quoteForCommandString("abc")); + assertEquals("\"a b c\"", quoteForCommandString("a b c")); + assertEquals("\"a \\\"b\\\" c\"", quoteForCommandString("a \"b\" c")); } private void testOpt(String opts, List expected) { diff --git a/pom.xml b/pom.xml index 42bd926a2fcb8..70e297c4f082a 100644 --- a/pom.xml +++ b/pom.xml @@ -1749,5 +1749,8 @@ parquet-provided + + sparkr + diff --git a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala index a5e6b638d2150..53ad67372e024 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala @@ -34,7 +34,7 @@ import org.apache.spark.sql.types.NumericType @Experimental class GroupedData protected[sql](df: DataFrame, groupingExprs: Seq[Expression]) { - private[this] implicit def toDF(aggExprs: Seq[NamedExpression]): DataFrame = { + private[sql] implicit def toDF(aggExprs: Seq[NamedExpression]): DataFrame = { val namedGroupingExprs = groupingExprs.map { case expr: NamedExpression => expr case expr: Expression => Alias(expr, expr.prettyString)() diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala new file mode 100644 index 0000000000000..d1ea7cc3e9162 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala @@ -0,0 +1,127 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.api.r + +import java.io.{ByteArrayInputStream, ByteArrayOutputStream, DataInputStream, DataOutputStream} + +import org.apache.spark.api.java.{JavaRDD, JavaSparkContext} +import org.apache.spark.api.r.SerDe +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.expressions.{Alias, Expression, NamedExpression} +import org.apache.spark.sql.types.{DataType, StructType} +import org.apache.spark.sql.{Column, DataFrame, GroupedData, Row, SQLContext, SaveMode} + +private[r] object SQLUtils { + def createSQLContext(jsc: JavaSparkContext): SQLContext = { + new SQLContext(jsc) + } + + def getJavaSparkContext(sqlCtx: SQLContext): JavaSparkContext = { + new JavaSparkContext(sqlCtx.sparkContext) + } + + def toSeq[T](arr: Array[T]): Seq[T] = { + arr.toSeq + } + + def createDF(rdd: RDD[Array[Byte]], schemaString: String, sqlContext: SQLContext): DataFrame = { + val schema = DataType.fromJson(schemaString).asInstanceOf[StructType] + val num = schema.fields.size + val rowRDD = rdd.map(bytesToRow) + sqlContext.createDataFrame(rowRDD, schema) + } + + // A helper to include grouping columns in Agg() + def aggWithGrouping(gd: GroupedData, exprs: Column*): DataFrame = { + val aggExprs = exprs.map { col => + col.expr match { + case expr: NamedExpression => expr + case expr: Expression => Alias(expr, expr.simpleString)() + } + } + gd.toDF(aggExprs) + } + + def dfToRowRDD(df: DataFrame): JavaRDD[Array[Byte]] = { + df.map(r => rowToRBytes(r)) + } + + private[this] def bytesToRow(bytes: Array[Byte]): Row = { + val bis = new ByteArrayInputStream(bytes) + val dis = new DataInputStream(bis) + val num = SerDe.readInt(dis) + Row.fromSeq((0 until num).map { i => + SerDe.readObject(dis) + }.toSeq) + } + + private[this] def rowToRBytes(row: Row): Array[Byte] = { + val bos = new ByteArrayOutputStream() + val dos = new DataOutputStream(bos) + + SerDe.writeInt(dos, row.length) + (0 until row.length).map { idx => + val obj: Object = row(idx).asInstanceOf[Object] + SerDe.writeObject(dos, obj) + } + bos.toByteArray() + } + + def dfToCols(df: DataFrame): Array[Array[Byte]] = { + // localDF is Array[Row] + val localDF = df.collect() + val numCols = df.columns.length + // dfCols is Array[Array[Any]] + val dfCols = convertRowsToColumns(localDF, numCols) + + dfCols.map { col => + colToRBytes(col) + } + } + + def convertRowsToColumns(localDF: Array[Row], numCols: Int): Array[Array[Any]] = { + (0 until numCols).map { colIdx => + localDF.map { row => + row(colIdx) + } + }.toArray + } + + def colToRBytes(col: Array[Any]): Array[Byte] = { + val numRows = col.length + val bos = new ByteArrayOutputStream() + val dos = new DataOutputStream(bos) + + SerDe.writeInt(dos, numRows) + + col.map { item => + val obj: Object = item.asInstanceOf[Object] + SerDe.writeObject(dos, obj) + } + bos.toByteArray() + } + + def saveMode(mode: String): SaveMode = { + mode match { + case "append" => SaveMode.Append + case "overwrite" => SaveMode.Overwrite + case "error" => SaveMode.ErrorIfExists + case "ignore" => SaveMode.Ignore + } + } +} diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala index 24a1e02795218..32bc4e5663062 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -469,6 +469,9 @@ private[spark] class ApplicationMaster( System.setProperty("spark.submit.pyFiles", PythonRunner.formatPaths(args.pyFiles).mkString(",")) } + if (args.primaryRFile != null && args.primaryRFile.endsWith(".R")) { + // TODO(davies): add R dependencies here + } val mainMethod = userClassLoader.loadClass(args.userClass) .getMethod("main", classOf[Array[String]]) diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala index e1a992af3aae7..ae6dc1094d724 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala @@ -25,6 +25,7 @@ class ApplicationMasterArguments(val args: Array[String]) { var userJar: String = null var userClass: String = null var primaryPyFile: String = null + var primaryRFile: String = null var pyFiles: String = null var userArgs: Seq[String] = Seq[String]() var executorMemory = 1024 @@ -54,6 +55,10 @@ class ApplicationMasterArguments(val args: Array[String]) { primaryPyFile = value args = tail + case ("--primary-r-file") :: value :: tail => + primaryRFile = value + args = tail + case ("--py-files") :: value :: tail => pyFiles = value args = tail @@ -79,6 +84,11 @@ class ApplicationMasterArguments(val args: Array[String]) { } } + if (primaryPyFile != null && primaryRFile != null) { + System.err.println("Cannot have primary-py-file and primary-r-file at the same time") + System.exit(-1) + } + userArgs = userArgsBuffer.readOnly } @@ -92,6 +102,7 @@ class ApplicationMasterArguments(val args: Array[String]) { | --jar JAR_PATH Path to your application's JAR file | --class CLASS_NAME Name of your application's main class | --primary-py-file A main Python file + | --primary-r-file A main R file | --py-files PY_FILES Comma-separated list of .zip, .egg, or .py files to | place on the PYTHONPATH for Python apps. | --args ARGS Arguments to be passed to your application's main class. diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 7219852c0a752..c1effd3c8a718 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -491,6 +491,12 @@ private[spark] class Client( } else { Nil } + val primaryRFile = + if (args.primaryRFile != null) { + Seq("--primary-r-file", args.primaryRFile) + } else { + Nil + } val amClass = if (isClusterMode) { Class.forName("org.apache.spark.deploy.yarn.ApplicationMaster").getName @@ -500,12 +506,15 @@ private[spark] class Client( if (args.primaryPyFile != null && args.primaryPyFile.endsWith(".py")) { args.userArgs = ArrayBuffer(args.primaryPyFile, args.pyFiles) ++ args.userArgs } + if (args.primaryRFile != null && args.primaryRFile.endsWith(".R")) { + args.userArgs = ArrayBuffer(args.primaryRFile) ++ args.userArgs + } val userArgs = args.userArgs.flatMap { arg => Seq("--arg", YarnSparkHadoopUtil.escapeForShell(arg)) } val amArgs = - Seq(amClass) ++ userClass ++ userJar ++ primaryPyFile ++ pyFiles ++ userArgs ++ - Seq( + Seq(amClass) ++ userClass ++ userJar ++ primaryPyFile ++ pyFiles ++ primaryRFile ++ + userArgs ++ Seq( "--executor-memory", args.executorMemory.toString + "m", "--executor-cores", args.executorCores.toString, "--num-executors ", args.numExecutors.toString) diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala index 3bc7eb1abf341..da6798cb1b279 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala @@ -32,6 +32,7 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) var userClass: String = null var pyFiles: String = null var primaryPyFile: String = null + var primaryRFile: String = null var userArgs: ArrayBuffer[String] = new ArrayBuffer[String]() var executorMemory = 1024 // MB var executorCores = 1 @@ -150,6 +151,10 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) primaryPyFile = value args = tail + case ("--primary-r-file") :: value :: tail => + primaryRFile = value + args = tail + case ("--args" | "--arg") :: value :: tail => if (args(0) == "--args") { println("--args is deprecated. Use --arg instead.") @@ -228,6 +233,11 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) throw new IllegalArgumentException(getUsageMessage(args)) } } + + if (primaryPyFile != null && primaryRFile != null) { + throw new IllegalArgumentException("Cannot have primary-py-file and primary-r-file" + + " at the same time") + } } private def getUsageMessage(unknownParam: List[String] = null): String = { @@ -240,6 +250,7 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) | mode) | --class CLASS_NAME Name of your application's main class (required) | --primary-py-file A main Python file + | --primary-r-file A main R file | --arg ARG Argument to be passed to your application's main class. | Multiple invocations are possible, each will be passed in order. | --num-executors NUM Number of executors to start (Default: 2) From b9c51c04932efeeda790752276078314db440634 Mon Sep 17 00:00:00 2001 From: Peter Parente Date: Thu, 9 Apr 2015 06:37:20 -0400 Subject: [PATCH 204/591] [SPARK-6343] Doc driver-worker network reqs Attempt at making the driver-worker networking requirement more explicit and up-front in the documentation (see https://issues.apache.org/jira/browse/SPARK-6343). Update cluster overview diagram to show connections from workers to driver. Add a bullet below about how driver listens / accepts connections from workers. Author: Peter Parente Closes #5382 from parente/SPARK-6343 and squashes the following commits: 0b2fb9d [Peter Parente] [SPARK-6343] Doc driver-worker network reqs --- docs/cluster-overview.md | 6 +++++- docs/img/cluster-overview.png | Bin 28011 -> 33565 bytes docs/img/cluster-overview.pptx | Bin 51771 -> 28133 bytes 3 files changed, 5 insertions(+), 1 deletion(-) diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md index 6a75d5c457f02..7079de546e2f5 100644 --- a/docs/cluster-overview.md +++ b/docs/cluster-overview.md @@ -33,7 +33,11 @@ There are several useful things to note about this architecture: 2. Spark is agnostic to the underlying cluster manager. As long as it can acquire executor processes, and these communicate with each other, it is relatively easy to run it even on a cluster manager that also supports other applications (e.g. Mesos/YARN). -3. Because the driver schedules tasks on the cluster, it should be run close to the worker +3. The driver program must listen for and accept incoming connections from its executors throughout + its lifetime (e.g., see [spark.driver.port and spark.fileserver.port in the network config + section](configuration.html#networking)). As such, the driver program must be network + addressable from the worker nodes. +4. Because the driver schedules tasks on the cluster, it should be run close to the worker nodes, preferably on the same local area network. 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z%<=Ek@hddx&3^^W{+A~D*ZQCWh(7*V>P!B%f7HjX*1>e^-yq-pFJ$Hf){XvIX3qZt zSrBjr|BTG_Um#0R{u|^=O6vbZJuBS)H^_f&c3=7UIYo2zdDiZCtvb z@8IsAW58D|12iCjXg?i!K+*l*@qY6cmv}#UimPzXm1%}j0P&E3^v|zw zOTZ8O4)@yy_p^8xr|BvjW{$&7IWX^i0gOJs!nJVzJ=~?GUWMC1wFut;q|^O_bd^A8 z_;=D>n&(wGBNly)Za}(TK)PSg#e&e^!*N_*{8#ZdqZjO~{Q)Nou;Kh2 z+@-s}3U`&?@(0{=fb8e*;4Z!8Rk*7Je?Q=603M;ggS#B&zQg@BH`Na~XT!f_s=6xE z)rI&UAYIwtWxBd7e-+{C3d0Ws&Z6%KzpOW0ZGJU({i8W?$$td+HIsc6;A(#O2f+S+ z0Dj39Uv2(thVw^bU}jkMlNSGz@4VXj*EG-1t#d1XYW-W%=W6q-A@~mf7| ZX+W;3fP{R}k{0+&1KdWy#Vio+e*mTV$`1el From 53f6bb1dfec74cbe8be9ae6a670d82eb6759cc8c Mon Sep 17 00:00:00 2001 From: raschild Date: Thu, 9 Apr 2015 07:04:18 -0400 Subject: [PATCH 205/591] SPARK-4924 addendum. Minor assembly directory fix in load-spark-env-sh Set the current dir path $FWDIR and same at $ASSEMBLY_DIR1, $ASSEMBLY_DIR2 otherwise $SPARK_HOME cannot be visible from spark-env.sh -- no SPARK_HOME variable is assigned there. I am using the Spark-1.3.0 source code package and I come across with this when trying to start the master: sbin/start-master.sh Author: raschild Closes #5261 from raschild/patch-1 and squashes the following commits: b9babcd [raschild] Update load-spark-env.sh --- bin/load-spark-env.sh | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/bin/load-spark-env.sh b/bin/load-spark-env.sh index 2d7070c25d328..95779e9ddbb18 100644 --- a/bin/load-spark-env.sh +++ b/bin/load-spark-env.sh @@ -20,6 +20,7 @@ # This script loads spark-env.sh if it exists, and ensures it is only loaded once. # spark-env.sh is loaded from SPARK_CONF_DIR if set, or within the current directory's # conf/ subdirectory. +FWDIR="$(cd "`dirname "$0"`"/..; pwd)" if [ -z "$SPARK_ENV_LOADED" ]; then export SPARK_ENV_LOADED=1 @@ -41,8 +42,8 @@ fi if [ -z "$SPARK_SCALA_VERSION" ]; then - ASSEMBLY_DIR2="$SPARK_HOME/assembly/target/scala-2.11" - ASSEMBLY_DIR1="$SPARK_HOME/assembly/target/scala-2.10" + ASSEMBLY_DIR2="$FWDIR/assembly/target/scala-2.11" + ASSEMBLY_DIR1="$FWDIR/assembly/target/scala-2.10" if [[ -d "$ASSEMBLY_DIR2" && -d "$ASSEMBLY_DIR1" ]]; then echo -e "Presence of build for both scala versions(SCALA 2.10 and SCALA 2.11) detected." 1>&2 From 470d7453a56c56a41b2851551fe1830065f88b2c Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Thu, 9 Apr 2015 07:07:50 -0400 Subject: [PATCH 206/591] [minor] [examples] Avoid packaging duplicate classes. Add exclusions and explicit dependencies so that the examples assembly does not duplicate classes already packaged in the main assembly. Also avoid relocating the commons-math3 package since it's already a dependency of spark-core, and thus is already available in the main assembly. Author: Marcelo Vanzin Closes #5379 from vanzin/examples-deps and squashes the following commits: 12c258e [Marcelo Vanzin] [minor] [examples] Avoid re-packaging unneeded classes. --- examples/pom.xml | 52 ++++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 46 insertions(+), 6 deletions(-) diff --git a/examples/pom.xml b/examples/pom.xml index 7e93f0eec0b91..afd7c6d52f0dd 100644 --- a/examples/pom.xml +++ b/examples/pom.xml @@ -90,6 +90,12 @@ org.apache.spark spark-streaming-zeromq_${scala.binary.version} ${project.version} + + + org.spark-project.protobuf + protobuf-java + + org.apache.hbase @@ -234,6 +240,7 @@ org.apache.commons commons-math3 + provided com.twitter @@ -262,6 +269,22 @@ com.ning compress-lzf + + commons-cli + commons-cli + + + commons-codec + commons-codec + + + commons-lang + commons-lang + + + commons-logging + commons-logging + io.netty netty @@ -270,10 +293,22 @@ jline jline + + net.jpountz.lz4 + lz4 + org.apache.cassandra.deps avro + + org.apache.commons + commons-math3 + + + org.apache.thrift + libthrift + @@ -281,6 +316,17 @@ scopt_${scala.binary.version} 3.2.0 + + + + org.scala-lang + scala-library + provided + + @@ -322,12 +368,6 @@ - - - org.apache.commons.math3 - org.spark-project.commons.math3 - - From 7d92db342e01fa694d3522fb8d2254d6297a4203 Mon Sep 17 00:00:00 2001 From: WangTaoTheTonic Date: Thu, 9 Apr 2015 17:44:08 -0400 Subject: [PATCH 207/591] [SPARK-6758]block the right jetty package in log https://issues.apache.org/jira/browse/SPARK-6758 I am not sure if it is ok to block them in test resources too (as we shade jetty in assembly?). Author: WangTaoTheTonic Closes #5406 from WangTaoTheTonic/SPARK-6758 and squashes the following commits: e09605b [WangTaoTheTonic] block the right jetty package --- bagel/src/test/resources/log4j.properties | 2 +- conf/log4j.properties.template | 4 ++-- .../main/resources/org/apache/spark/log4j-defaults.properties | 4 ++-- core/src/test/resources/log4j.properties | 4 ++-- external/flume-sink/src/test/resources/log4j.properties | 2 +- external/flume/src/test/resources/log4j.properties | 2 +- external/kafka/src/test/resources/log4j.properties | 2 +- external/mqtt/src/test/resources/log4j.properties | 2 +- external/twitter/src/test/resources/log4j.properties | 2 +- external/zeromq/src/test/resources/log4j.properties | 2 +- extras/java8-tests/src/test/resources/log4j.properties | 4 ++-- extras/kinesis-asl/src/main/resources/log4j.properties | 4 ++-- extras/kinesis-asl/src/test/resources/log4j.properties | 2 +- graphx/src/test/resources/log4j.properties | 4 ++-- launcher/src/test/resources/log4j.properties | 4 ++-- mllib/src/test/resources/log4j.properties | 2 +- repl/src/test/resources/log4j.properties | 2 +- sql/catalyst/src/test/resources/log4j.properties | 4 ++-- streaming/src/test/resources/log4j.properties | 2 +- yarn/src/test/resources/log4j.properties | 2 +- 20 files changed, 28 insertions(+), 28 deletions(-) diff --git a/bagel/src/test/resources/log4j.properties b/bagel/src/test/resources/log4j.properties index 853ef0ed2986f..edbecdae92096 100644 --- a/bagel/src/test/resources/log4j.properties +++ b/bagel/src/test/resources/log4j.properties @@ -24,4 +24,4 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/conf/log4j.properties.template b/conf/log4j.properties.template index 89eec7d4b7f61..3a2a88219818f 100644 --- a/conf/log4j.properties.template +++ b/conf/log4j.properties.template @@ -6,7 +6,7 @@ log4j.appender.console.layout=org.apache.log4j.PatternLayout log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n # Settings to quiet third party logs that are too verbose -log4j.logger.org.eclipse.jetty=WARN -log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR +log4j.logger.org.spark-project.jetty=WARN +log4j.logger.org.spark-project.jetty.util.component.AbstractLifeCycle=ERROR log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO diff --git a/core/src/main/resources/org/apache/spark/log4j-defaults.properties b/core/src/main/resources/org/apache/spark/log4j-defaults.properties index 89eec7d4b7f61..3a2a88219818f 100644 --- a/core/src/main/resources/org/apache/spark/log4j-defaults.properties +++ b/core/src/main/resources/org/apache/spark/log4j-defaults.properties @@ -6,7 +6,7 @@ log4j.appender.console.layout=org.apache.log4j.PatternLayout log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n # Settings to quiet third party logs that are too verbose -log4j.logger.org.eclipse.jetty=WARN -log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR +log4j.logger.org.spark-project.jetty=WARN +log4j.logger.org.spark-project.jetty.util.component.AbstractLifeCycle=ERROR log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO diff --git a/core/src/test/resources/log4j.properties b/core/src/test/resources/log4j.properties index 287c8e3563503..eb3b1999eb996 100644 --- a/core/src/test/resources/log4j.properties +++ b/core/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN -org.eclipse.jetty.LEVEL=WARN +log4j.logger.org.spark-project.jetty=WARN +org.spark-project.jetty.LEVEL=WARN diff --git a/external/flume-sink/src/test/resources/log4j.properties b/external/flume-sink/src/test/resources/log4j.properties index 2a58e99817224..42df8792f147f 100644 --- a/external/flume-sink/src/test/resources/log4j.properties +++ b/external/flume-sink/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/external/flume/src/test/resources/log4j.properties b/external/flume/src/test/resources/log4j.properties index 9697237bfa1a3..75e3b53a093f6 100644 --- a/external/flume/src/test/resources/log4j.properties +++ b/external/flume/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/external/kafka/src/test/resources/log4j.properties b/external/kafka/src/test/resources/log4j.properties index 9697237bfa1a3..75e3b53a093f6 100644 --- a/external/kafka/src/test/resources/log4j.properties +++ b/external/kafka/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/external/mqtt/src/test/resources/log4j.properties b/external/mqtt/src/test/resources/log4j.properties index 9697237bfa1a3..75e3b53a093f6 100644 --- a/external/mqtt/src/test/resources/log4j.properties +++ b/external/mqtt/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/external/twitter/src/test/resources/log4j.properties b/external/twitter/src/test/resources/log4j.properties index 64bfc5745088f..9a3569789d2e0 100644 --- a/external/twitter/src/test/resources/log4j.properties +++ b/external/twitter/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/external/zeromq/src/test/resources/log4j.properties b/external/zeromq/src/test/resources/log4j.properties index 9697237bfa1a3..75e3b53a093f6 100644 --- a/external/zeromq/src/test/resources/log4j.properties +++ b/external/zeromq/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/extras/java8-tests/src/test/resources/log4j.properties b/extras/java8-tests/src/test/resources/log4j.properties index 287c8e3563503..eb3b1999eb996 100644 --- a/extras/java8-tests/src/test/resources/log4j.properties +++ b/extras/java8-tests/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN -org.eclipse.jetty.LEVEL=WARN +log4j.logger.org.spark-project.jetty=WARN +org.spark-project.jetty.LEVEL=WARN diff --git a/extras/kinesis-asl/src/main/resources/log4j.properties b/extras/kinesis-asl/src/main/resources/log4j.properties index 97348fb5b6123..6cdc9286c5d76 100644 --- a/extras/kinesis-asl/src/main/resources/log4j.properties +++ b/extras/kinesis-asl/src/main/resources/log4j.properties @@ -31,7 +31,7 @@ log4j.appender.console.layout=org.apache.log4j.PatternLayout log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n # Settings to quiet third party logs that are too verbose -log4j.logger.org.eclipse.jetty=WARN -log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR +log4j.logger.org.spark-project.jetty=WARN +log4j.logger.org.spark-project.jetty.util.component.AbstractLifeCycle=ERROR log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO \ No newline at end of file diff --git a/extras/kinesis-asl/src/test/resources/log4j.properties b/extras/kinesis-asl/src/test/resources/log4j.properties index 853ef0ed2986f..edbecdae92096 100644 --- a/extras/kinesis-asl/src/test/resources/log4j.properties +++ b/extras/kinesis-asl/src/test/resources/log4j.properties @@ -24,4 +24,4 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/graphx/src/test/resources/log4j.properties b/graphx/src/test/resources/log4j.properties index 287c8e3563503..eb3b1999eb996 100644 --- a/graphx/src/test/resources/log4j.properties +++ b/graphx/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN -org.eclipse.jetty.LEVEL=WARN +log4j.logger.org.spark-project.jetty=WARN +org.spark-project.jetty.LEVEL=WARN diff --git a/launcher/src/test/resources/log4j.properties b/launcher/src/test/resources/log4j.properties index 00c20ad69cd4d..67a6a98217118 100644 --- a/launcher/src/test/resources/log4j.properties +++ b/launcher/src/test/resources/log4j.properties @@ -27,5 +27,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN -org.eclipse.jetty.LEVEL=WARN +log4j.logger.org.spark-project.jetty=WARN +org.spark-project.jetty.LEVEL=WARN diff --git a/mllib/src/test/resources/log4j.properties b/mllib/src/test/resources/log4j.properties index 9697237bfa1a3..75e3b53a093f6 100644 --- a/mllib/src/test/resources/log4j.properties +++ b/mllib/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/repl/src/test/resources/log4j.properties b/repl/src/test/resources/log4j.properties index e7e4a4113174a..e2ee9c963a4da 100644 --- a/repl/src/test/resources/log4j.properties +++ b/repl/src/test/resources/log4j.properties @@ -24,4 +24,4 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/sql/catalyst/src/test/resources/log4j.properties b/sql/catalyst/src/test/resources/log4j.properties index 287c8e3563503..eb3b1999eb996 100644 --- a/sql/catalyst/src/test/resources/log4j.properties +++ b/sql/catalyst/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN -org.eclipse.jetty.LEVEL=WARN +log4j.logger.org.spark-project.jetty=WARN +org.spark-project.jetty.LEVEL=WARN diff --git a/streaming/src/test/resources/log4j.properties b/streaming/src/test/resources/log4j.properties index 9697237bfa1a3..75e3b53a093f6 100644 --- a/streaming/src/test/resources/log4j.properties +++ b/streaming/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN diff --git a/yarn/src/test/resources/log4j.properties b/yarn/src/test/resources/log4j.properties index aab41fa49430f..6b8a5dbf6373e 100644 --- a/yarn/src/test/resources/log4j.properties +++ b/yarn/src/test/resources/log4j.properties @@ -24,5 +24,5 @@ log4j.appender.file.layout=org.apache.log4j.PatternLayout log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %t %p %c{1}: %m%n # Ignore messages below warning level from Jetty, because it's a bit verbose -log4j.logger.org.eclipse.jetty=WARN +log4j.logger.org.spark-project.jetty=WARN log4j.logger.org.apache.hadoop=WARN From a0411aebee7c134f0426f0c2b2cb4c1c7856a291 Mon Sep 17 00:00:00 2001 From: Yanbo Liang Date: Thu, 9 Apr 2015 15:10:10 -0700 Subject: [PATCH 208/591] [SPARK-6264] [MLLIB] Support FPGrowth algorithm in Python API Support FPGrowth algorithm in Python API. Should we remove "Experimental" which were marked for FPGrowth and FPGrowthModel in Scala? jkbradley Author: Yanbo Liang Closes #5213 from yanboliang/spark-6264 and squashes the following commits: ed62ead [Yanbo Liang] trigger jenkins 8ce0359 [Yanbo Liang] fix docstring style 544c725 [Yanbo Liang] address comments a2d7cf7 [Yanbo Liang] add doc for FPGrowth.train() dcf7d73 [Yanbo Liang] add python doc b18fd07 [Yanbo Liang] trigger jenkins 2c951b8 [Yanbo Liang] fix typos 7f62c8f [Yanbo Liang] add fpm to __init__.py b96206a [Yanbo Liang] Support FPGrowth algorithm in Python API --- .../api/python/FPGrowthModelWrapper.scala | 33 ++++++++ .../mllib/api/python/PythonMLLibAPI.scala | 23 +++++- python/docs/pyspark.mllib.rst | 7 ++ python/pyspark/mllib/__init__.py | 2 +- python/pyspark/mllib/fpm.py | 81 +++++++++++++++++++ python/run-tests | 1 + 6 files changed, 143 insertions(+), 4 deletions(-) create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/api/python/FPGrowthModelWrapper.scala create mode 100644 python/pyspark/mllib/fpm.py diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/FPGrowthModelWrapper.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/FPGrowthModelWrapper.scala new file mode 100644 index 0000000000000..ee933f4cfcafd --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/FPGrowthModelWrapper.scala @@ -0,0 +1,33 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.api.python + +import org.apache.spark.api.java.JavaRDD +import org.apache.spark.mllib.fpm.{FPGrowth, FPGrowthModel} +import org.apache.spark.rdd.RDD + +/** + * A Wrapper of FPGrowthModel to provide helper method for Python + */ +private[python] class FPGrowthModelWrapper(model: FPGrowthModel[Any]) + extends FPGrowthModel(model.freqItemsets) { + + def getFreqItemsets: RDD[Array[Any]] = { + SerDe.fromTuple2RDD(model.freqItemsets.map(x => (x.javaItems, x.freq))) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index 6c386cacfb7ca..1faa3def0e042 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -34,6 +34,7 @@ import org.apache.spark.api.python.SerDeUtil import org.apache.spark.mllib.classification._ import org.apache.spark.mllib.clustering._ import org.apache.spark.mllib.feature._ +import org.apache.spark.mllib.fpm.{FPGrowth, FPGrowthModel} import org.apache.spark.mllib.linalg._ import org.apache.spark.mllib.optimization._ import org.apache.spark.mllib.random.{RandomRDDs => RG} @@ -358,9 +359,7 @@ private[python] class PythonMLLibAPI extends Serializable { val model = new GaussianMixtureModel(weight, gaussians) model.predictSoft(data) } - - - + /** * Java stub for Python mllib ALS.train(). This stub returns a handle * to the Java object instead of the content of the Java object. Extra care @@ -420,6 +419,24 @@ private[python] class PythonMLLibAPI extends Serializable { new MatrixFactorizationModelWrapper(model) } + /** + * Java stub for Python mllib FPGrowth.train(). This stub returns a handle + * to the Java object instead of the content of the Java object. Extra care + * needs to be taken in the Python code to ensure it gets freed on exit; see + * the Py4J documentation. + */ + def trainFPGrowthModel( + data: JavaRDD[java.lang.Iterable[Any]], + minSupport: Double, + numPartitions: Int): FPGrowthModel[Any] = { + val fpg = new FPGrowth() + .setMinSupport(minSupport) + .setNumPartitions(numPartitions) + + val model = fpg.run(data.rdd.map(_.asScala.toArray)) + new FPGrowthModelWrapper(model) + } + /** * Java stub for Normalizer.transform() */ diff --git a/python/docs/pyspark.mllib.rst b/python/docs/pyspark.mllib.rst index 15101470afc07..26ece4c2c389a 100644 --- a/python/docs/pyspark.mllib.rst +++ b/python/docs/pyspark.mllib.rst @@ -31,6 +31,13 @@ pyspark.mllib.feature module :undoc-members: :show-inheritance: +pyspark.mllib.fpm module +------------------------ + +.. automodule:: pyspark.mllib.fpm + :members: + :undoc-members: + pyspark.mllib.linalg module --------------------------- diff --git a/python/pyspark/mllib/__init__.py b/python/pyspark/mllib/__init__.py index 6449800d9c120..f2ef573fe9f6f 100644 --- a/python/pyspark/mllib/__init__.py +++ b/python/pyspark/mllib/__init__.py @@ -25,7 +25,7 @@ if numpy.version.version < '1.4': raise Exception("MLlib requires NumPy 1.4+") -__all__ = ['classification', 'clustering', 'feature', 'linalg', 'random', +__all__ = ['classification', 'clustering', 'feature', 'fpm', 'linalg', 'random', 'recommendation', 'regression', 'stat', 'tree', 'util'] import sys diff --git a/python/pyspark/mllib/fpm.py b/python/pyspark/mllib/fpm.py new file mode 100644 index 0000000000000..3aa6d79d7093c --- /dev/null +++ b/python/pyspark/mllib/fpm.py @@ -0,0 +1,81 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from pyspark import SparkContext +from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, inherit_doc + +__all__ = ['FPGrowth', 'FPGrowthModel'] + + +@inherit_doc +class FPGrowthModel(JavaModelWrapper): + + """ + .. note:: Experimental + + A FP-Growth model for mining frequent itemsets + using the Parallel FP-Growth algorithm. + + >>> data = [["a", "b", "c"], ["a", "b", "d", "e"], ["a", "c", "e"], ["a", "c", "f"]] + >>> rdd = sc.parallelize(data, 2) + >>> model = FPGrowth.train(rdd, 0.6, 2) + >>> sorted(model.freqItemsets().collect()) + [([u'a'], 4), ([u'c'], 3), ([u'c', u'a'], 3)] + """ + + def freqItemsets(self): + """ + Get the frequent itemsets of this model + """ + return self.call("getFreqItemsets") + + +class FPGrowth(object): + """ + .. note:: Experimental + + A Parallel FP-growth algorithm to mine frequent itemsets. + """ + + @classmethod + def train(cls, data, minSupport=0.3, numPartitions=-1): + """ + Computes an FP-Growth model that contains frequent itemsets. + :param data: The input data set, each element + contains a transaction. + :param minSupport: The minimal support level + (default: `0.3`). + :param numPartitions: The number of partitions used by parallel + FP-growth (default: same as input data). + """ + model = callMLlibFunc("trainFPGrowthModel", data, float(minSupport), int(numPartitions)) + return FPGrowthModel(model) + + +def _test(): + import doctest + import pyspark.mllib.fpm + globs = pyspark.mllib.fpm.__dict__.copy() + globs['sc'] = SparkContext('local[4]', 'PythonTest') + (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) + globs['sc'].stop() + if failure_count: + exit(-1) + + +if __name__ == "__main__": + _test() diff --git a/python/run-tests b/python/run-tests index b7630c356cfae..f569a56fb7a9a 100755 --- a/python/run-tests +++ b/python/run-tests @@ -77,6 +77,7 @@ function run_mllib_tests() { run_test "pyspark/mllib/clustering.py" run_test "pyspark/mllib/evaluation.py" run_test "pyspark/mllib/feature.py" + run_test "pyspark/mllib/fpm.py" run_test "pyspark/mllib/linalg.py" run_test "pyspark/mllib/rand.py" run_test "pyspark/mllib/recommendation.py" From 9c67049b4ef416a80803ccb958bbac1dd02cc380 Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Thu, 9 Apr 2015 15:37:45 -0700 Subject: [PATCH 209/591] [Spark-6693][MLlib]add tostring with max lines and width for matrix jira: https://issues.apache.org/jira/browse/SPARK-6693 It's kind of annoying when debugging and found you cannot print out the matrix as you want. original toString of Matrix only print like following, 0.17810102596909183 0.5616906241468385 ... (10 total) 0.9692861997823815 0.015558159784155756 ... 0.8513015122819192 0.031523763918528847 ... 0.5396875653953941 0.3267864552779176 ... The def toString(maxLines : Int, maxWidth : Int) is useful when debuging, logging and saving matrix to files. Author: Yuhao Yang Closes #5344 from hhbyyh/addToString and squashes the following commits: 19a6836 [Yuhao Yang] remove extra line 6314b21 [Yuhao Yang] add exclude 736c324 [Yuhao Yang] add ut and exclude 420da39 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into addToString c22f352 [Yuhao Yang] style change 64a9e0f [Yuhao Yang] add specific to string to matrix --- .../org/apache/spark/mllib/linalg/Matrices.scala | 3 +++ .../spark/mllib/linalg/MatricesSuite.scala | 16 ++++++++++++++++ project/MimaExcludes.scala | 4 ++++ 3 files changed, 23 insertions(+) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala index d1a174063caba..3fa5e068d16d4 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala @@ -87,6 +87,9 @@ sealed trait Matrix extends Serializable { /** A human readable representation of the matrix */ override def toString: String = toBreeze.toString() + /** A human readable representation of the matrix with maximum lines and width */ + def toString(maxLines: Int, maxLineWidth: Int): String = toBreeze.toString(maxLines, maxLineWidth) + /** Map the values of this matrix using a function. Generates a new matrix. Performs the * function on only the backing array. For example, an operation such as addition or * subtraction will only be performed on the non-zero values in a `SparseMatrix`. */ diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala index 0d2cec58e2c03..86119ec38101e 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala @@ -439,4 +439,20 @@ class MatricesSuite extends FunSuite { assert(mUDT.typeName == "matrix") assert(mUDT.simpleString == "matrix") } + + test("toString") { + val empty = Matrices.ones(0, 0) + empty.toString(0, 0) + + val mat = Matrices.rand(5, 10, new Random()) + mat.toString(-1, -5) + mat.toString(0, 0) + mat.toString(Int.MinValue, Int.MinValue) + mat.toString(Int.MaxValue, Int.MaxValue) + var lines = mat.toString(6, 50).lines.toArray + assert(lines.size == 5 && lines.forall(_.size <= 50)) + + lines = mat.toString(5, 100).lines.toArray + assert(lines.size == 5 && lines.forall(_.size <= 100)) + } } diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala index c2d828f982fe0..1564babefa62f 100644 --- a/project/MimaExcludes.scala +++ b/project/MimaExcludes.scala @@ -64,6 +64,10 @@ object MimaExcludes { // SPARK-6492 Fix deadlock in SparkContext.stop() ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.SparkContext.org$" + "apache$spark$SparkContext$$SPARK_CONTEXT_CONSTRUCTOR_LOCK") + )++ Seq( + // SPARK-6693 add tostring with max lines and width for matrix + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.linalg.Matrix.toString") ) case v if v.startsWith("1.3") => From b5c51c8df480f1a82a82e4d597d8eea631bffb4e Mon Sep 17 00:00:00 2001 From: Davies Liu Date: Thu, 9 Apr 2015 17:07:23 -0700 Subject: [PATCH 210/591] [SPARK-3074] [PySpark] support groupByKey() with single huge key This patch change groupByKey() to use external sort based approach, so it can support single huge key. For example, it can group by a dataset including one hot key with 40 millions values (strings), using 500M memory for Python worker, finished in about 2 minutes. (it will need 6G memory in hash based approach). During groupByKey(), it will do in-memory groupBy first. If the dataset can not fit in memory, then data will be partitioned by hash. If one partition still can not fit in memory, it will switch to sort based groupBy(). Author: Davies Liu Author: Davies Liu Closes #1977 from davies/groupby and squashes the following commits: af3713a [Davies Liu] make sure it's iterator 67772dd [Davies Liu] fix tests e78c15c [Davies Liu] address comments 0b0fde8 [Davies Liu] address comments 0dcf320 [Davies Liu] address comments, rollback changes in ResultIterable e3b8eab [Davies Liu] fix narrow dependency 2a1857a [Davies Liu] typo d2f053b [Davies Liu] add repr for FlattedValuesSerializer c6a2f8d [Davies Liu] address comments 9e2df24 [Davies Liu] Merge branch 'master' of github.com:apache/spark into groupby 2b9c261 [Davies Liu] fix typo in comments 70aadcd [Davies Liu] Merge branch 'master' of github.com:apache/spark into groupby a14b4bd [Davies Liu] Merge branch 'master' of github.com:apache/spark into groupby ab5515b [Davies Liu] Merge branch 'master' into groupby 651f891 [Davies Liu] simplify GroupByKey 1578f2e [Davies Liu] Merge branch 'master' of github.com:apache/spark into groupby 1f69f93 [Davies Liu] fix tests 0d3395f [Davies Liu] Merge branch 'master' of github.com:apache/spark into groupby 341f1e0 [Davies Liu] add comments, refactor 47918b8 [Davies Liu] remove unused code 6540948 [Davies Liu] address comments: 17f4ec6 [Davies Liu] Merge branch 'master' of github.com:apache/spark into groupby 4d4bc86 [Davies Liu] bugfix 8ef965e [Davies Liu] Merge branch 'master' into groupby fbc504a [Davies Liu] Merge branch 'master' into groupby 779ed03 [Davies Liu] fix merge conflict 2c1d05b [Davies Liu] refactor, minor turning b48cda5 [Davies Liu] Merge branch 'master' into groupby 85138e6 [Davies Liu] Merge branch 'master' into groupby acd8e1b [Davies Liu] fix memory when groupByKey().count() 905b233 [Davies Liu] Merge branch 'sort' into groupby 1f075ed [Davies Liu] Merge branch 'master' into sort 4b07d39 [Davies Liu] compress the data while spilling 0a081c6 [Davies Liu] Merge branch 'master' into groupby f157fe7 [Davies Liu] Merge branch 'sort' into groupby eb53ca6 [Davies Liu] Merge branch 'master' into sort b2dc3bf [Davies Liu] Merge branch 'sort' into groupby 644abaf [Davies Liu] add license in LICENSE 19f7873 [Davies Liu] improve tests 11ba318 [Davies Liu] typo 085aef8 [Davies Liu] Merge branch 'master' into groupby 3ee58e5 [Davies Liu] switch to sort based groupBy, based on size of data 1ea0669 [Davies Liu] choose sort based groupByKey() automatically b40bae7 [Davies Liu] bugfix efa23df [Davies Liu] refactor, add spark.shuffle.sort=False 250be4e [Davies Liu] flatten the combined values when dumping into disks d05060d [Davies Liu] group the same key before shuffle, reduce the comparison during sorting 083d842 [Davies Liu] sorted based groupByKey() 55602ee [Davies Liu] use external sort in sortBy() and sortByKey() --- python/pyspark/join.py | 13 +- python/pyspark/rdd.py | 48 ++- python/pyspark/resultiterable.py | 7 +- python/pyspark/serializers.py | 25 +- python/pyspark/shuffle.py | 531 ++++++++++++++++++++++++------- python/pyspark/tests.py | 50 ++- 6 files changed, 531 insertions(+), 143 deletions(-) diff --git a/python/pyspark/join.py b/python/pyspark/join.py index efc1ef9396412..c3491defb2b29 100644 --- a/python/pyspark/join.py +++ b/python/pyspark/join.py @@ -48,7 +48,7 @@ def dispatch(seq): vbuf.append(v) elif n == 2: wbuf.append(v) - return [(v, w) for v in vbuf for w in wbuf] + return ((v, w) for v in vbuf for w in wbuf) return _do_python_join(rdd, other, numPartitions, dispatch) @@ -62,7 +62,7 @@ def dispatch(seq): wbuf.append(v) if not vbuf: vbuf.append(None) - return [(v, w) for v in vbuf for w in wbuf] + return ((v, w) for v in vbuf for w in wbuf) return _do_python_join(rdd, other, numPartitions, dispatch) @@ -76,7 +76,7 @@ def dispatch(seq): wbuf.append(v) if not wbuf: wbuf.append(None) - return [(v, w) for v in vbuf for w in wbuf] + return ((v, w) for v in vbuf for w in wbuf) return _do_python_join(rdd, other, numPartitions, dispatch) @@ -104,8 +104,9 @@ def make_mapper(i): rdd_len = len(vrdds) def dispatch(seq): - bufs = [[] for i in range(rdd_len)] - for (n, v) in seq: + bufs = [[] for _ in range(rdd_len)] + for n, v in seq: bufs[n].append(v) - return tuple(map(ResultIterable, bufs)) + return tuple(ResultIterable(vs) for vs in bufs) + return union_vrdds.groupByKey(numPartitions).mapValues(dispatch) diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py index 2d05611321ed6..1b18789040360 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.py @@ -41,7 +41,7 @@ from pyspark.storagelevel import StorageLevel from pyspark.resultiterable import ResultIterable from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger, \ - get_used_memory, ExternalSorter + get_used_memory, ExternalSorter, ExternalGroupBy from pyspark.traceback_utils import SCCallSiteSync from py4j.java_collections import ListConverter, MapConverter @@ -573,8 +573,8 @@ def sortByKey(self, ascending=True, numPartitions=None, keyfunc=lambda x: x): if numPartitions is None: numPartitions = self._defaultReducePartitions() - spill = (self.ctx._conf.get("spark.shuffle.spill", 'True').lower() == 'true') - memory = _parse_memory(self.ctx._conf.get("spark.python.worker.memory", "512m")) + spill = self._can_spill() + memory = self._memory_limit() serializer = self._jrdd_deserializer def sortPartition(iterator): @@ -1699,10 +1699,8 @@ def combineByKey(self, createCombiner, mergeValue, mergeCombiners, numPartitions = self._defaultReducePartitions() serializer = self.ctx.serializer - spill = (self.ctx._conf.get("spark.shuffle.spill", 'True').lower() - == 'true') - memory = _parse_memory(self.ctx._conf.get( - "spark.python.worker.memory", "512m")) + spill = self._can_spill() + memory = self._memory_limit() agg = Aggregator(createCombiner, mergeValue, mergeCombiners) def combineLocally(iterator): @@ -1755,21 +1753,28 @@ def createZero(): return self.combineByKey(lambda v: func(createZero(), v), func, func, numPartitions) + def _can_spill(self): + return self.ctx._conf.get("spark.shuffle.spill", "True").lower() == "true" + + def _memory_limit(self): + return _parse_memory(self.ctx._conf.get("spark.python.worker.memory", "512m")) + # TODO: support variant with custom partitioner def groupByKey(self, numPartitions=None): """ Group the values for each key in the RDD into a single sequence. - Hash-partitions the resulting RDD with into numPartitions partitions. + Hash-partitions the resulting RDD with numPartitions partitions. Note: If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using reduceByKey or aggregateByKey will provide much better performance. >>> x = sc.parallelize([("a", 1), ("b", 1), ("a", 1)]) - >>> map((lambda (x,y): (x, list(y))), sorted(x.groupByKey().collect())) + >>> sorted(x.groupByKey().mapValues(len).collect()) + [('a', 2), ('b', 1)] + >>> sorted(x.groupByKey().mapValues(list).collect()) [('a', [1, 1]), ('b', [1])] """ - def createCombiner(x): return [x] @@ -1781,8 +1786,27 @@ def mergeCombiners(a, b): a.extend(b) return a - return self.combineByKey(createCombiner, mergeValue, mergeCombiners, - numPartitions).mapValues(lambda x: ResultIterable(x)) + spill = self._can_spill() + memory = self._memory_limit() + serializer = self._jrdd_deserializer + agg = Aggregator(createCombiner, mergeValue, mergeCombiners) + + def combine(iterator): + merger = ExternalMerger(agg, memory * 0.9, serializer) \ + if spill else InMemoryMerger(agg) + merger.mergeValues(iterator) + return merger.iteritems() + + locally_combined = self.mapPartitions(combine, preservesPartitioning=True) + shuffled = locally_combined.partitionBy(numPartitions) + + def groupByKey(it): + merger = ExternalGroupBy(agg, memory, serializer)\ + if spill else InMemoryMerger(agg) + merger.mergeCombiners(it) + return merger.iteritems() + + return shuffled.mapPartitions(groupByKey, True).mapValues(ResultIterable) def flatMapValues(self, f): """ diff --git a/python/pyspark/resultiterable.py b/python/pyspark/resultiterable.py index ef04c82866e6c..1ab5ce14c3531 100644 --- a/python/pyspark/resultiterable.py +++ b/python/pyspark/resultiterable.py @@ -15,15 +15,16 @@ # limitations under the License. # -__all__ = ["ResultIterable"] - import collections +__all__ = ["ResultIterable"] + class ResultIterable(collections.Iterable): """ - A special result iterable. This is used because the standard iterator can not be pickled + A special result iterable. This is used because the standard + iterator can not be pickled """ def __init__(self, data): diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py index 0ffb41d02f6f6..4afa82f4b2973 100644 --- a/python/pyspark/serializers.py +++ b/python/pyspark/serializers.py @@ -220,6 +220,29 @@ def __repr__(self): return "BatchedSerializer(%s, %d)" % (str(self.serializer), self.batchSize) +class FlattenedValuesSerializer(BatchedSerializer): + + """ + Serializes a stream of list of pairs, split the list of values + which contain more than a certain number of objects to make them + have similar sizes. + """ + def __init__(self, serializer, batchSize=10): + BatchedSerializer.__init__(self, serializer, batchSize) + + def _batched(self, iterator): + n = self.batchSize + for key, values in iterator: + for i in xrange(0, len(values), n): + yield key, values[i:i + n] + + def load_stream(self, stream): + return self.serializer.load_stream(stream) + + def __repr__(self): + return "FlattenedValuesSerializer(%d)" % self.batchSize + + class AutoBatchedSerializer(BatchedSerializer): """ Choose the size of batch automatically based on the size of object @@ -251,7 +274,7 @@ def __eq__(self, other): return (isinstance(other, AutoBatchedSerializer) and other.serializer == self.serializer and other.bestSize == self.bestSize) - def __str__(self): + def __repr__(self): return "AutoBatchedSerializer(%s)" % str(self.serializer) diff --git a/python/pyspark/shuffle.py b/python/pyspark/shuffle.py index 10a7ccd502000..8a6fc627eb383 100644 --- a/python/pyspark/shuffle.py +++ b/python/pyspark/shuffle.py @@ -16,28 +16,35 @@ # import os -import sys import platform import shutil import warnings import gc import itertools +import operator import random import pyspark.heapq3 as heapq -from pyspark.serializers import AutoBatchedSerializer, PickleSerializer +from pyspark.serializers import BatchedSerializer, PickleSerializer, FlattenedValuesSerializer, \ + CompressedSerializer, AutoBatchedSerializer + try: import psutil + process = None + def get_used_memory(): """ Return the used memory in MB """ - process = psutil.Process(os.getpid()) + global process + if process is None or process._pid != os.getpid(): + process = psutil.Process(os.getpid()) if hasattr(process, "memory_info"): info = process.memory_info() else: info = process.get_memory_info() return info.rss >> 20 + except ImportError: def get_used_memory(): @@ -46,6 +53,7 @@ def get_used_memory(): for line in open('/proc/self/status'): if line.startswith('VmRSS:'): return int(line.split()[1]) >> 10 + else: warnings.warn("Please install psutil to have better " "support with spilling") @@ -54,6 +62,7 @@ def get_used_memory(): rss = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss return rss >> 20 # TODO: support windows + return 0 @@ -148,10 +157,16 @@ def mergeCombiners(self, iterator): d[k] = comb(d[k], v) if k in d else v def iteritems(self): - """ Return the merged items ad iterator """ + """ Return the merged items as iterator """ return self.data.iteritems() +def _compressed_serializer(self, serializer=None): + # always use PickleSerializer to simplify implementation + ser = PickleSerializer() + return AutoBatchedSerializer(CompressedSerializer(ser)) + + class ExternalMerger(Merger): """ @@ -173,7 +188,7 @@ class ExternalMerger(Merger): dict. Repeat this again until combine all the items. - Before return any items, it will load each partition and - combine them seperately. Yield them before loading next + combine them separately. Yield them before loading next partition. - During loading a partition, if the memory goes over limit, @@ -182,7 +197,7 @@ class ExternalMerger(Merger): `data` and `pdata` are used to hold the merged items in memory. At first, all the data are merged into `data`. Once the used - memory goes over limit, the items in `data` are dumped indo + memory goes over limit, the items in `data` are dumped into disks, `data` will be cleared, all rest of items will be merged into `pdata` and then dumped into disks. Before returning, all the items in `pdata` will be dumped into disks. @@ -193,16 +208,16 @@ class ExternalMerger(Merger): >>> agg = SimpleAggregator(lambda x, y: x + y) >>> merger = ExternalMerger(agg, 10) >>> N = 10000 - >>> merger.mergeValues(zip(xrange(N), xrange(N)) * 10) + >>> merger.mergeValues(zip(xrange(N), xrange(N))) >>> assert merger.spills > 0 >>> sum(v for k,v in merger.iteritems()) - 499950000 + 49995000 >>> merger = ExternalMerger(agg, 10) - >>> merger.mergeCombiners(zip(xrange(N), xrange(N)) * 10) + >>> merger.mergeCombiners(zip(xrange(N), xrange(N))) >>> assert merger.spills > 0 >>> sum(v for k,v in merger.iteritems()) - 499950000 + 49995000 """ # the max total partitions created recursively @@ -212,8 +227,7 @@ def __init__(self, aggregator, memory_limit=512, serializer=None, localdirs=None, scale=1, partitions=59, batch=1000): Merger.__init__(self, aggregator) self.memory_limit = memory_limit - # default serializer is only used for tests - self.serializer = serializer or AutoBatchedSerializer(PickleSerializer()) + self.serializer = _compressed_serializer(serializer) self.localdirs = localdirs or _get_local_dirs(str(id(self))) # number of partitions when spill data into disks self.partitions = partitions @@ -221,7 +235,7 @@ def __init__(self, aggregator, memory_limit=512, serializer=None, self.batch = batch # scale is used to scale down the hash of key for recursive hash map self.scale = scale - # unpartitioned merged data + # un-partitioned merged data self.data = {} # partitioned merged data, list of dicts self.pdata = [] @@ -244,72 +258,63 @@ def _next_limit(self): def mergeValues(self, iterator): """ Combine the items by creator and combiner """ - iterator = iter(iterator) # speedup attribute lookup creator, comb = self.agg.createCombiner, self.agg.mergeValue - d, c, batch = self.data, 0, self.batch + c, data, pdata, hfun, batch = 0, self.data, self.pdata, self._partition, self.batch + limit = self.memory_limit for k, v in iterator: + d = pdata[hfun(k)] if pdata else data d[k] = comb(d[k], v) if k in d else creator(v) c += 1 - if c % batch == 0 and get_used_memory() > self.memory_limit: - self._spill() - self._partitioned_mergeValues(iterator, self._next_limit()) - break + if c >= batch: + if get_used_memory() >= limit: + self._spill() + limit = self._next_limit() + batch /= 2 + c = 0 + else: + batch *= 1.5 + + if get_used_memory() >= limit: + self._spill() def _partition(self, key): """ Return the partition for key """ return hash((key, self._seed)) % self.partitions - def _partitioned_mergeValues(self, iterator, limit=0): - """ Partition the items by key, then combine them """ - # speedup attribute lookup - creator, comb = self.agg.createCombiner, self.agg.mergeValue - c, pdata, hfun, batch = 0, self.pdata, self._partition, self.batch - - for k, v in iterator: - d = pdata[hfun(k)] - d[k] = comb(d[k], v) if k in d else creator(v) - if not limit: - continue - - c += 1 - if c % batch == 0 and get_used_memory() > limit: - self._spill() - limit = self._next_limit() + def _object_size(self, obj): + """ How much of memory for this obj, assume that all the objects + consume similar bytes of memory + """ + return 1 - def mergeCombiners(self, iterator, check=True): + def mergeCombiners(self, iterator, limit=None): """ Merge (K,V) pair by mergeCombiner """ - iterator = iter(iterator) + if limit is None: + limit = self.memory_limit # speedup attribute lookup - d, comb, batch = self.data, self.agg.mergeCombiners, self.batch - c = 0 - for k, v in iterator: - d[k] = comb(d[k], v) if k in d else v - if not check: - continue - - c += 1 - if c % batch == 0 and get_used_memory() > self.memory_limit: - self._spill() - self._partitioned_mergeCombiners(iterator, self._next_limit()) - break - - def _partitioned_mergeCombiners(self, iterator, limit=0): - """ Partition the items by key, then merge them """ - comb, pdata = self.agg.mergeCombiners, self.pdata - c, hfun = 0, self._partition + comb, hfun, objsize = self.agg.mergeCombiners, self._partition, self._object_size + c, data, pdata, batch = 0, self.data, self.pdata, self.batch for k, v in iterator: - d = pdata[hfun(k)] + d = pdata[hfun(k)] if pdata else data d[k] = comb(d[k], v) if k in d else v if not limit: continue - c += 1 - if c % self.batch == 0 and get_used_memory() > limit: - self._spill() - limit = self._next_limit() + c += objsize(v) + if c > batch: + if get_used_memory() > limit: + self._spill() + limit = self._next_limit() + batch /= 2 + c = 0 + else: + batch *= 1.5 + + if limit and get_used_memory() >= limit: + self._spill() def _spill(self): """ @@ -335,7 +340,7 @@ def _spill(self): for k, v in self.data.iteritems(): h = self._partition(k) - # put one item in batch, make it compatitable with load_stream + # put one item in batch, make it compatible with load_stream # it will increase the memory if dump them in batch self.serializer.dump_stream([(k, v)], streams[h]) @@ -344,7 +349,7 @@ def _spill(self): s.close() self.data.clear() - self.pdata = [{} for i in range(self.partitions)] + self.pdata.extend([{} for i in range(self.partitions)]) else: for i in range(self.partitions): @@ -370,29 +375,12 @@ def _external_items(self): assert not self.data if any(self.pdata): self._spill() - hard_limit = self._next_limit() + # disable partitioning and spilling when merge combiners from disk + self.pdata = [] try: for i in range(self.partitions): - self.data = {} - for j in range(self.spills): - path = self._get_spill_dir(j) - p = os.path.join(path, str(i)) - # do not check memory during merging - self.mergeCombiners(self.serializer.load_stream(open(p)), - False) - - # limit the total partitions - if (self.scale * self.partitions < self.MAX_TOTAL_PARTITIONS - and j < self.spills - 1 - and get_used_memory() > hard_limit): - self.data.clear() # will read from disk again - gc.collect() # release the memory as much as possible - for v in self._recursive_merged_items(i): - yield v - return - - for v in self.data.iteritems(): + for v in self._merged_items(i): yield v self.data.clear() @@ -400,53 +388,56 @@ def _external_items(self): for j in range(self.spills): path = self._get_spill_dir(j) os.remove(os.path.join(path, str(i))) - finally: self._cleanup() - def _cleanup(self): - """ Clean up all the files in disks """ - for d in self.localdirs: - shutil.rmtree(d, True) + def _merged_items(self, index): + self.data = {} + limit = self._next_limit() + for j in range(self.spills): + path = self._get_spill_dir(j) + p = os.path.join(path, str(index)) + # do not check memory during merging + self.mergeCombiners(self.serializer.load_stream(open(p)), 0) + + # limit the total partitions + if (self.scale * self.partitions < self.MAX_TOTAL_PARTITIONS + and j < self.spills - 1 + and get_used_memory() > limit): + self.data.clear() # will read from disk again + gc.collect() # release the memory as much as possible + return self._recursive_merged_items(index) - def _recursive_merged_items(self, start): + return self.data.iteritems() + + def _recursive_merged_items(self, index): """ merge the partitioned items and return the as iterator If one partition can not be fit in memory, then them will be partitioned and merged recursively. """ - # make sure all the data are dumps into disks. - assert not self.data - if any(self.pdata): - self._spill() - assert self.spills > 0 - - for i in range(start, self.partitions): - subdirs = [os.path.join(d, "parts", str(i)) - for d in self.localdirs] - m = ExternalMerger(self.agg, self.memory_limit, self.serializer, - subdirs, self.scale * self.partitions, self.partitions) - m.pdata = [{} for _ in range(self.partitions)] - limit = self._next_limit() - - for j in range(self.spills): - path = self._get_spill_dir(j) - p = os.path.join(path, str(i)) - m._partitioned_mergeCombiners( - self.serializer.load_stream(open(p))) - - if get_used_memory() > limit: - m._spill() - limit = self._next_limit() + subdirs = [os.path.join(d, "parts", str(index)) for d in self.localdirs] + m = ExternalMerger(self.agg, self.memory_limit, self.serializer, subdirs, + self.scale * self.partitions, self.partitions, self.batch) + m.pdata = [{} for _ in range(self.partitions)] + limit = self._next_limit() + + for j in range(self.spills): + path = self._get_spill_dir(j) + p = os.path.join(path, str(index)) + m.mergeCombiners(self.serializer.load_stream(open(p)), 0) + + if get_used_memory() > limit: + m._spill() + limit = self._next_limit() - for v in m._external_items(): - yield v + return m._external_items() - # remove the merged partition - for j in range(self.spills): - path = self._get_spill_dir(j) - os.remove(os.path.join(path, str(i))) + def _cleanup(self): + """ Clean up all the files in disks """ + for d in self.localdirs: + shutil.rmtree(d, True) class ExternalSorter(object): @@ -457,6 +448,7 @@ class ExternalSorter(object): The spilling will only happen when the used memory goes above the limit. + >>> sorter = ExternalSorter(1) # 1M >>> import random >>> l = range(1024) @@ -469,7 +461,7 @@ class ExternalSorter(object): def __init__(self, memory_limit, serializer=None): self.memory_limit = memory_limit self.local_dirs = _get_local_dirs("sort") - self.serializer = serializer or AutoBatchedSerializer(PickleSerializer()) + self.serializer = _compressed_serializer(serializer) def _get_path(self, n): """ Choose one directory for spill by number n """ @@ -515,6 +507,7 @@ def sorted(self, iterator, key=None, reverse=False): limit = self._next_limit() MemoryBytesSpilled += (used_memory - get_used_memory()) << 20 DiskBytesSpilled += os.path.getsize(path) + os.unlink(path) # data will be deleted after close elif not chunks: batch = min(batch * 2, 10000) @@ -529,6 +522,310 @@ def sorted(self, iterator, key=None, reverse=False): return heapq.merge(chunks, key=key, reverse=reverse) +class ExternalList(object): + """ + ExternalList can have many items which cannot be hold in memory in + the same time. + + >>> l = ExternalList(range(100)) + >>> len(l) + 100 + >>> l.append(10) + >>> len(l) + 101 + >>> for i in range(20240): + ... l.append(i) + >>> len(l) + 20341 + >>> import pickle + >>> l2 = pickle.loads(pickle.dumps(l)) + >>> len(l2) + 20341 + >>> list(l2)[100] + 10 + """ + LIMIT = 10240 + + def __init__(self, values): + self.values = values + self.count = len(values) + self._file = None + self._ser = None + + def __getstate__(self): + if self._file is not None: + self._file.flush() + f = os.fdopen(os.dup(self._file.fileno())) + f.seek(0) + serialized = f.read() + else: + serialized = '' + return self.values, self.count, serialized + + def __setstate__(self, item): + self.values, self.count, serialized = item + if serialized: + self._open_file() + self._file.write(serialized) + else: + self._file = None + self._ser = None + + def __iter__(self): + if self._file is not None: + self._file.flush() + # read all items from disks first + with os.fdopen(os.dup(self._file.fileno()), 'r') as f: + f.seek(0) + for v in self._ser.load_stream(f): + yield v + + for v in self.values: + yield v + + def __len__(self): + return self.count + + def append(self, value): + self.values.append(value) + self.count += 1 + # dump them into disk if the key is huge + if len(self.values) >= self.LIMIT: + self._spill() + + def _open_file(self): + dirs = _get_local_dirs("objects") + d = dirs[id(self) % len(dirs)] + if not os.path.exists(d): + os.makedirs(d) + p = os.path.join(d, str(id)) + self._file = open(p, "w+", 65536) + self._ser = BatchedSerializer(CompressedSerializer(PickleSerializer()), 1024) + os.unlink(p) + + def _spill(self): + """ dump the values into disk """ + global MemoryBytesSpilled, DiskBytesSpilled + if self._file is None: + self._open_file() + + used_memory = get_used_memory() + pos = self._file.tell() + self._ser.dump_stream(self.values, self._file) + self.values = [] + gc.collect() + DiskBytesSpilled += self._file.tell() - pos + MemoryBytesSpilled += (used_memory - get_used_memory()) << 20 + + +class ExternalListOfList(ExternalList): + """ + An external list for list. + + >>> l = ExternalListOfList([[i, i] for i in range(100)]) + >>> len(l) + 200 + >>> l.append(range(10)) + >>> len(l) + 210 + >>> len(list(l)) + 210 + """ + + def __init__(self, values): + ExternalList.__init__(self, values) + self.count = sum(len(i) for i in values) + + def append(self, value): + ExternalList.append(self, value) + # already counted 1 in ExternalList.append + self.count += len(value) - 1 + + def __iter__(self): + for values in ExternalList.__iter__(self): + for v in values: + yield v + + +class GroupByKey(object): + """ + Group a sorted iterator as [(k1, it1), (k2, it2), ...] + + >>> k = [i/3 for i in range(6)] + >>> v = [[i] for i in range(6)] + >>> g = GroupByKey(iter(zip(k, v))) + >>> [(k, list(it)) for k, it in g] + [(0, [0, 1, 2]), (1, [3, 4, 5])] + """ + + def __init__(self, iterator): + self.iterator = iter(iterator) + self.next_item = None + + def __iter__(self): + return self + + def next(self): + key, value = self.next_item if self.next_item else next(self.iterator) + values = ExternalListOfList([value]) + try: + while True: + k, v = next(self.iterator) + if k != key: + self.next_item = (k, v) + break + values.append(v) + except StopIteration: + self.next_item = None + return key, values + + +class ExternalGroupBy(ExternalMerger): + + """ + Group by the items by key. If any partition of them can not been + hold in memory, it will do sort based group by. + + This class works as follows: + + - It repeatedly group the items by key and save them in one dict in + memory. + + - When the used memory goes above memory limit, it will split + the combined data into partitions by hash code, dump them + into disk, one file per partition. If the number of keys + in one partitions is smaller than 1000, it will sort them + by key before dumping into disk. + + - Then it goes through the rest of the iterator, group items + by key into different dict by hash. Until the used memory goes over + memory limit, it dump all the dicts into disks, one file per + dict. Repeat this again until combine all the items. It + also will try to sort the items by key in each partition + before dumping into disks. + + - It will yield the grouped items partitions by partitions. + If the data in one partitions can be hold in memory, then it + will load and combine them in memory and yield. + + - If the dataset in one partition cannot be hold in memory, + it will sort them first. If all the files are already sorted, + it merge them by heap.merge(), so it will do external sort + for all the files. + + - After sorting, `GroupByKey` class will put all the continuous + items with the same key as a group, yield the values as + an iterator. + """ + SORT_KEY_LIMIT = 1000 + + def flattened_serializer(self): + assert isinstance(self.serializer, BatchedSerializer) + ser = self.serializer + return FlattenedValuesSerializer(ser, 20) + + def _object_size(self, obj): + return len(obj) + + def _spill(self): + """ + dump already partitioned data into disks. + """ + global MemoryBytesSpilled, DiskBytesSpilled + path = self._get_spill_dir(self.spills) + if not os.path.exists(path): + os.makedirs(path) + + used_memory = get_used_memory() + if not self.pdata: + # The data has not been partitioned, it will iterator the + # data once, write them into different files, has no + # additional memory. It only called when the memory goes + # above limit at the first time. + + # open all the files for writing + streams = [open(os.path.join(path, str(i)), 'w') + for i in range(self.partitions)] + + # If the number of keys is small, then the overhead of sort is small + # sort them before dumping into disks + self._sorted = len(self.data) < self.SORT_KEY_LIMIT + if self._sorted: + self.serializer = self.flattened_serializer() + for k in sorted(self.data.keys()): + h = self._partition(k) + self.serializer.dump_stream([(k, self.data[k])], streams[h]) + else: + for k, v in self.data.iteritems(): + h = self._partition(k) + self.serializer.dump_stream([(k, v)], streams[h]) + + for s in streams: + DiskBytesSpilled += s.tell() + s.close() + + self.data.clear() + # self.pdata is cached in `mergeValues` and `mergeCombiners` + self.pdata.extend([{} for i in range(self.partitions)]) + + else: + for i in range(self.partitions): + p = os.path.join(path, str(i)) + with open(p, "w") as f: + # dump items in batch + if self._sorted: + # sort by key only (stable) + sorted_items = sorted(self.pdata[i].iteritems(), key=operator.itemgetter(0)) + self.serializer.dump_stream(sorted_items, f) + else: + self.serializer.dump_stream(self.pdata[i].iteritems(), f) + self.pdata[i].clear() + DiskBytesSpilled += os.path.getsize(p) + + self.spills += 1 + gc.collect() # release the memory as much as possible + MemoryBytesSpilled += (used_memory - get_used_memory()) << 20 + + def _merged_items(self, index): + size = sum(os.path.getsize(os.path.join(self._get_spill_dir(j), str(index))) + for j in range(self.spills)) + # if the memory can not hold all the partition, + # then use sort based merge. Because of compression, + # the data on disks will be much smaller than needed memory + if (size >> 20) >= self.memory_limit / 10: + return self._merge_sorted_items(index) + + self.data = {} + for j in range(self.spills): + path = self._get_spill_dir(j) + p = os.path.join(path, str(index)) + # do not check memory during merging + self.mergeCombiners(self.serializer.load_stream(open(p)), 0) + return self.data.iteritems() + + def _merge_sorted_items(self, index): + """ load a partition from disk, then sort and group by key """ + def load_partition(j): + path = self._get_spill_dir(j) + p = os.path.join(path, str(index)) + return self.serializer.load_stream(open(p, 'r', 65536)) + + disk_items = [load_partition(j) for j in range(self.spills)] + + if self._sorted: + # all the partitions are already sorted + sorted_items = heapq.merge(disk_items, key=operator.itemgetter(0)) + + else: + # Flatten the combined values, so it will not consume huge + # memory during merging sort. + ser = self.flattened_serializer() + sorter = ExternalSorter(self.memory_limit, ser) + sorted_items = sorter.sorted(itertools.chain(*disk_items), + key=operator.itemgetter(0)) + return ((k, vs) for k, vs in GroupByKey(sorted_items)) + + if __name__ == "__main__": import doctest doctest.testmod() diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py index dd8d3b1c53733..0bd5d20f7877f 100644 --- a/python/pyspark/tests.py +++ b/python/pyspark/tests.py @@ -31,6 +31,7 @@ import time import zipfile import random +import itertools import threading import hashlib @@ -76,7 +77,7 @@ class MergerTests(unittest.TestCase): def setUp(self): - self.N = 1 << 14 + self.N = 1 << 12 self.l = [i for i in xrange(self.N)] self.data = zip(self.l, self.l) self.agg = Aggregator(lambda x: [x], @@ -108,7 +109,7 @@ def test_small_dataset(self): sum(xrange(self.N))) def test_medium_dataset(self): - m = ExternalMerger(self.agg, 10) + m = ExternalMerger(self.agg, 30) m.mergeValues(self.data) self.assertTrue(m.spills >= 1) self.assertEqual(sum(sum(v) for k, v in m.iteritems()), @@ -124,10 +125,36 @@ def test_huge_dataset(self): m = ExternalMerger(self.agg, 10, partitions=3) m.mergeCombiners(map(lambda (k, v): (k, [str(v)]), self.data * 10)) self.assertTrue(m.spills >= 1) - self.assertEqual(sum(len(v) for k, v in m._recursive_merged_items(0)), + self.assertEqual(sum(len(v) for k, v in m.iteritems()), self.N * 10) m._cleanup() + def test_group_by_key(self): + + def gen_data(N, step): + for i in range(1, N + 1, step): + for j in range(i): + yield (i, [j]) + + def gen_gs(N, step=1): + return shuffle.GroupByKey(gen_data(N, step)) + + self.assertEqual(1, len(list(gen_gs(1)))) + self.assertEqual(2, len(list(gen_gs(2)))) + self.assertEqual(100, len(list(gen_gs(100)))) + self.assertEqual(range(1, 101), [k for k, _ in gen_gs(100)]) + self.assertTrue(all(range(k) == list(vs) for k, vs in gen_gs(100))) + + for k, vs in gen_gs(50002, 10000): + self.assertEqual(k, len(vs)) + self.assertEqual(range(k), list(vs)) + + ser = PickleSerializer() + l = ser.loads(ser.dumps(list(gen_gs(50002, 30000)))) + for k, vs in l: + self.assertEqual(k, len(vs)) + self.assertEqual(range(k), list(vs)) + class SorterTests(unittest.TestCase): def test_in_memory_sort(self): @@ -702,6 +729,21 @@ def test_distinct(self): self.assertEquals(result.getNumPartitions(), 5) self.assertEquals(result.count(), 3) + def test_external_group_by_key(self): + self.sc._conf.set("spark.python.worker.memory", "5m") + N = 200001 + kv = self.sc.parallelize(range(N)).map(lambda x: (x % 3, x)) + gkv = kv.groupByKey().cache() + self.assertEqual(3, gkv.count()) + filtered = gkv.filter(lambda (k, vs): k == 1) + self.assertEqual(1, filtered.count()) + self.assertEqual([(1, N/3)], filtered.mapValues(len).collect()) + self.assertEqual([(N/3, N/3)], + filtered.values().map(lambda x: (len(x), len(list(x)))).collect()) + result = filtered.collect()[0][1] + self.assertEqual(N/3, len(result)) + self.assertTrue(isinstance(result.data, shuffle.ExternalList)) + def test_sort_on_empty_rdd(self): self.assertEqual([], self.sc.parallelize(zip([], [])).sortByKey().collect()) @@ -752,9 +794,9 @@ def test_narrow_dependency_in_join(self): self.assertEqual(rdd.getNumPartitions() + 2, parted.union(rdd).getNumPartitions()) self.assertEqual(rdd.getNumPartitions() + 2, rdd.union(parted).getNumPartitions()) - self.sc.setJobGroup("test1", "test", True) tracker = self.sc.statusTracker() + self.sc.setJobGroup("test1", "test", True) d = sorted(parted.join(parted).collect()) self.assertEqual(10, len(d)) self.assertEqual((0, (0, 0)), d[0]) From e2360810f50de77f79d372cc9b46db117d451cfc Mon Sep 17 00:00:00 2001 From: MechCoder Date: Thu, 9 Apr 2015 23:10:13 -0700 Subject: [PATCH 211/591] [SPARK-6577] [MLlib] [PySpark] SparseMatrix should be supported in PySpark Supporting of SparseMatrix in PySpark. Author: MechCoder Closes #5355 from MechCoder/spark-6577 and squashes the following commits: 7492190 [MechCoder] More readable code for densifying ea2c54b [MechCoder] Check bounds for indexing 454ef2c [MechCoder] Made the following changes 1. Used convert_to_array for array conversion. 2. Used F order for toArray 3. Minor improvements in speed. db76caf [MechCoder] Add support for CSR matrix 29653e7 [MechCoder] Renamed indices to rowIndices and indptr to colPtrs b6384fe [MechCoder] [SPARK-6577] SparseMatrix should be supported in PySpark --- python/pyspark/mllib/linalg.py | 110 +++++++++++++++++++++++++++++++-- python/pyspark/mllib/tests.py | 52 +++++++++++++++- 2 files changed, 154 insertions(+), 8 deletions(-) diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py index 51c1490b1618d..a80320c52d1d0 100644 --- a/python/pyspark/mllib/linalg.py +++ b/python/pyspark/mllib/linalg.py @@ -640,6 +640,15 @@ def toArray(self): """ raise NotImplementedError + @staticmethod + def _convert_to_array(array_like, dtype): + """ + Convert Matrix attributes which are array-like or buffer to array. + """ + if isinstance(array_like, basestring): + return np.frombuffer(array_like, dtype=dtype) + return np.asarray(array_like, dtype=dtype) + class DenseMatrix(Matrix): """ @@ -647,13 +656,8 @@ class DenseMatrix(Matrix): """ def __init__(self, numRows, numCols, values): Matrix.__init__(self, numRows, numCols) - if isinstance(values, basestring): - values = np.frombuffer(values, dtype=np.float64) - elif not isinstance(values, np.ndarray): - values = np.array(values, dtype=np.float64) + values = self._convert_to_array(values, np.float64) assert len(values) == numRows * numCols - if values.dtype != np.float64: - values.astype(np.float64) self.values = values def __reduce__(self): @@ -670,6 +674,17 @@ def toArray(self): """ return self.values.reshape((self.numRows, self.numCols), order='F') + def toSparse(self): + """Convert to SparseMatrix""" + indices = np.nonzero(self.values)[0] + colCounts = np.bincount(indices / self.numRows) + colPtrs = np.cumsum(np.hstack( + (0, colCounts, np.zeros(self.numCols - colCounts.size)))) + values = self.values[indices] + rowIndices = indices % self.numRows + + return SparseMatrix(self.numRows, self.numCols, colPtrs, rowIndices, values) + def __getitem__(self, indices): i, j = indices if i < 0 or i >= self.numRows: @@ -687,6 +702,82 @@ def __eq__(self, other): all(self.values == other.values)) +class SparseMatrix(Matrix): + """Sparse Matrix stored in CSC format.""" + def __init__(self, numRows, numCols, colPtrs, rowIndices, values, + isTransposed=False): + Matrix.__init__(self, numRows, numCols) + self.isTransposed = isTransposed + self.colPtrs = self._convert_to_array(colPtrs, np.int32) + self.rowIndices = self._convert_to_array(rowIndices, np.int32) + self.values = self._convert_to_array(values, np.float64) + + if self.isTransposed: + if self.colPtrs.size != numRows + 1: + raise ValueError("Expected colPtrs of size %d, got %d." + % (numRows + 1, self.colPtrs.size)) + else: + if self.colPtrs.size != numCols + 1: + raise ValueError("Expected colPtrs of size %d, got %d." + % (numCols + 1, self.colPtrs.size)) + if self.rowIndices.size != self.values.size: + raise ValueError("Expected rowIndices of length %d, got %d." + % (self.rowIndices.size, self.values.size)) + + def __reduce__(self): + return SparseMatrix, ( + self.numRows, self.numCols, self.colPtrs.tostring(), + self.rowIndices.tostring(), self.values.tostring(), + self.isTransposed) + + def __getitem__(self, indices): + i, j = indices + if i < 0 or i >= self.numRows: + raise ValueError("Row index %d is out of range [0, %d)" + % (i, self.numRows)) + if j < 0 or j >= self.numCols: + raise ValueError("Column index %d is out of range [0, %d)" + % (j, self.numCols)) + + # If a CSR matrix is given, then the row index should be searched + # for in ColPtrs, and the column index should be searched for in the + # corresponding slice obtained from rowIndices. + if self.isTransposed: + j, i = i, j + + colStart = self.colPtrs[j] + colEnd = self.colPtrs[j + 1] + nz = self.rowIndices[colStart: colEnd] + ind = np.searchsorted(nz, i) + colStart + if ind < colEnd and self.rowIndices[ind] == i: + return self.values[ind] + else: + return 0.0 + + def toArray(self): + """ + Return an numpy.ndarray + """ + A = np.zeros((self.numRows, self.numCols), dtype=np.float64, order='F') + for k in xrange(self.colPtrs.size - 1): + startptr = self.colPtrs[k] + endptr = self.colPtrs[k + 1] + if self.isTransposed: + A[k, self.rowIndices[startptr:endptr]] = self.values[startptr:endptr] + else: + A[self.rowIndices[startptr:endptr], k] = self.values[startptr:endptr] + return A + + def toDense(self): + densevals = np.reshape( + self.toArray(), (self.numRows * self.numCols), order='F') + return DenseMatrix(self.numRows, self.numCols, densevals) + + # TODO: More efficient implementation: + def __eq__(self, other): + return np.all(self.toArray == other.toArray) + + class Matrices(object): @staticmethod def dense(numRows, numCols, values): @@ -695,6 +786,13 @@ def dense(numRows, numCols, values): """ return DenseMatrix(numRows, numCols, values) + @staticmethod + def sparse(numRows, numCols, colPtrs, rowIndices, values): + """ + Create a SparseMatrix + """ + return SparseMatrix(numRows, numCols, colPtrs, rowIndices, values) + def _test(): import doctest diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 61ef398487c0c..3b40158c12b74 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -24,7 +24,7 @@ import tempfile import array as pyarray -from numpy import array, array_equal +from numpy import array, array_equal, zeros from py4j.protocol import Py4JJavaError if sys.version_info[:2] <= (2, 6): @@ -38,7 +38,7 @@ from pyspark.mllib.common import _to_java_object_rdd from pyspark.mllib.linalg import Vector, SparseVector, DenseVector, VectorUDT, _convert_to_vector,\ - DenseMatrix, Vectors, Matrices + DenseMatrix, SparseMatrix, Vectors, Matrices from pyspark.mllib.regression import LabeledPoint from pyspark.mllib.random import RandomRDDs from pyspark.mllib.stat import Statistics @@ -144,6 +144,54 @@ def test_matrix_indexing(self): for j in range(2): self.assertEquals(mat[i, j], expected[i][j]) + def test_sparse_matrix(self): + # Test sparse matrix creation. + sm1 = SparseMatrix( + 3, 4, [0, 2, 2, 4, 4], [1, 2, 1, 2], [1.0, 2.0, 4.0, 5.0]) + self.assertEquals(sm1.numRows, 3) + self.assertEquals(sm1.numCols, 4) + self.assertEquals(sm1.colPtrs.tolist(), [0, 2, 2, 4, 4]) + self.assertEquals(sm1.rowIndices.tolist(), [1, 2, 1, 2]) + self.assertEquals(sm1.values.tolist(), [1.0, 2.0, 4.0, 5.0]) + + # Test indexing + expected = [ + [0, 0, 0, 0], + [1, 0, 4, 0], + [2, 0, 5, 0]] + + for i in range(3): + for j in range(4): + self.assertEquals(expected[i][j], sm1[i, j]) + self.assertTrue(array_equal(sm1.toArray(), expected)) + + # Test conversion to dense and sparse. + smnew = sm1.toDense().toSparse() + self.assertEquals(sm1.numRows, smnew.numRows) + self.assertEquals(sm1.numCols, smnew.numCols) + self.assertTrue(array_equal(sm1.colPtrs, smnew.colPtrs)) + self.assertTrue(array_equal(sm1.rowIndices, smnew.rowIndices)) + self.assertTrue(array_equal(sm1.values, smnew.values)) + + sm1t = SparseMatrix( + 3, 4, [0, 2, 3, 5], [0, 1, 2, 0, 2], [3.0, 2.0, 4.0, 9.0, 8.0], + isTransposed=True) + self.assertEquals(sm1t.numRows, 3) + self.assertEquals(sm1t.numCols, 4) + self.assertEquals(sm1t.colPtrs.tolist(), [0, 2, 3, 5]) + self.assertEquals(sm1t.rowIndices.tolist(), [0, 1, 2, 0, 2]) + self.assertEquals(sm1t.values.tolist(), [3.0, 2.0, 4.0, 9.0, 8.0]) + + expected = [ + [3, 2, 0, 0], + [0, 0, 4, 0], + [9, 0, 8, 0]] + + for i in range(3): + for j in range(4): + self.assertEquals(expected[i][j], sm1t[i, j]) + self.assertTrue(array_equal(sm1t.toArray(), expected)) + class ListTests(PySparkTestCase): From 3290d2d13bb4bd875aec14425c8e3766f9cc644b Mon Sep 17 00:00:00 2001 From: jerryshao Date: Thu, 9 Apr 2015 23:14:24 -0700 Subject: [PATCH 212/591] [SPARK-6211][Streaming] Add Python Kafka API unit test Refactor the Kafka unit test and add Python API support. CC tdas davies please help to review, thanks a lot. Author: jerryshao Author: Saisai Shao Closes #4961 from jerryshao/SPARK-6211 and squashes the following commits: ee4b919 [jerryshao] Fixed newly merged issue 82c756e [jerryshao] Address the comments 92912d1 [jerryshao] Address the commits 0708bb1 [jerryshao] Fix rebase issue 40b47a3 [Saisai Shao] Style fix f889657 [Saisai Shao] Update the code according 8a2f3e2 [jerryshao] Address the issues 0f1b7ce [jerryshao] Still fix the bug 61a04f0 [jerryshao] Fix bugs and address the issues 64d9877 [jerryshao] Fix rebase bugs 8ad442f [jerryshao] Add kafka-assembly in run-tests 6020b00 [jerryshao] Add more debug info in Shell 8102d6e [jerryshao] Fix bug in Jenkins test fde1213 [jerryshao] Code style changes 5536f95 [jerryshao] Refactor the Kafka unit test and add Python Kafka unittest support --- dev/run-tests | 2 +- .../streaming/kafka/KafkaTestUtils.scala | 261 ++++++++++++++++++ .../kafka/JavaDirectKafkaStreamSuite.java | 28 +- .../streaming/kafka/JavaKafkaRDDSuite.java | 28 +- .../streaming/kafka/JavaKafkaStreamSuite.java | 34 ++- .../kafka/DirectKafkaStreamSuite.scala | 56 ++-- .../streaming/kafka/KafkaClusterSuite.scala | 29 +- .../spark/streaming/kafka/KafkaRDDSuite.scala | 40 +-- .../streaming/kafka/KafkaStreamSuite.scala | 211 ++------------ .../kafka/ReliableKafkaStreamSuite.scala | 62 +++-- python/pyspark/streaming/tests.py | 43 ++- python/run-tests | 19 +- 12 files changed, 502 insertions(+), 311 deletions(-) create mode 100644 external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala diff --git a/dev/run-tests b/dev/run-tests index 1b6cf78b5da01..bb21ab6c9aa04 100755 --- a/dev/run-tests +++ b/dev/run-tests @@ -173,7 +173,7 @@ CURRENT_BLOCK=$BLOCK_BUILD build/mvn $HIVE_BUILD_ARGS clean package -DskipTests else echo -e "q\n" \ - | build/sbt $HIVE_BUILD_ARGS package assembly/assembly \ + | build/sbt $HIVE_BUILD_ARGS package assembly/assembly streaming-kafka-assembly/assembly \ | grep -v -e "info.*Resolving" -e "warn.*Merging" -e "info.*Including" fi } diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala new file mode 100644 index 0000000000000..13e9475065979 --- /dev/null +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala @@ -0,0 +1,261 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.kafka + +import java.io.File +import java.lang.{Integer => JInt} +import java.net.InetSocketAddress +import java.util.{Map => JMap} +import java.util.Properties +import java.util.concurrent.TimeoutException + +import scala.annotation.tailrec +import scala.language.postfixOps +import scala.util.control.NonFatal + +import kafka.admin.AdminUtils +import kafka.producer.{KeyedMessage, Producer, ProducerConfig} +import kafka.serializer.StringEncoder +import kafka.server.{KafkaConfig, KafkaServer} +import kafka.utils.ZKStringSerializer +import org.apache.zookeeper.server.{NIOServerCnxnFactory, ZooKeeperServer} +import org.I0Itec.zkclient.ZkClient + +import org.apache.spark.{Logging, SparkConf} +import org.apache.spark.streaming.Time +import org.apache.spark.util.Utils + +/** + * This is a helper class for Kafka test suites. This has the functionality to set up + * and tear down local Kafka servers, and to push data using Kafka producers. + * + * The reason to put Kafka test utility class in src is to test Python related Kafka APIs. + */ +private class KafkaTestUtils extends Logging { + + // Zookeeper related configurations + private val zkHost = "localhost" + private var zkPort: Int = 0 + private val zkConnectionTimeout = 6000 + private val zkSessionTimeout = 6000 + + private var zookeeper: EmbeddedZookeeper = _ + + private var zkClient: ZkClient = _ + + // Kafka broker related configurations + private val brokerHost = "localhost" + private var brokerPort = 9092 + private var brokerConf: KafkaConfig = _ + + // Kafka broker server + private var server: KafkaServer = _ + + // Kafka producer + private var producer: Producer[String, String] = _ + + // Flag to test whether the system is correctly started + private var zkReady = false + private var brokerReady = false + + def zkAddress: String = { + assert(zkReady, "Zookeeper not setup yet or already torn down, cannot get zookeeper address") + s"$zkHost:$zkPort" + } + + def brokerAddress: String = { + assert(brokerReady, "Kafka not setup yet or already torn down, cannot get broker address") + s"$brokerHost:$brokerPort" + } + + def zookeeperClient: ZkClient = { + assert(zkReady, "Zookeeper not setup yet or already torn down, cannot get zookeeper client") + Option(zkClient).getOrElse( + throw new IllegalStateException("Zookeeper client is not yet initialized")) + } + + // Set up the Embedded Zookeeper server and get the proper Zookeeper port + private def setupEmbeddedZookeeper(): Unit = { + // Zookeeper server startup + zookeeper = new EmbeddedZookeeper(s"$zkHost:$zkPort") + // Get the actual zookeeper binding port + zkPort = zookeeper.actualPort + zkClient = new ZkClient(s"$zkHost:$zkPort", zkSessionTimeout, zkConnectionTimeout, + ZKStringSerializer) + zkReady = true + } + + // Set up the Embedded Kafka server + private def setupEmbeddedKafkaServer(): Unit = { + assert(zkReady, "Zookeeper should be set up beforehand") + + // Kafka broker startup + Utils.startServiceOnPort(brokerPort, port => { + brokerPort = port + brokerConf = new KafkaConfig(brokerConfiguration) + server = new KafkaServer(brokerConf) + server.startup() + (server, port) + }, new SparkConf(), "KafkaBroker") + + brokerReady = true + } + + /** setup the whole embedded servers, including Zookeeper and Kafka brokers */ + def setup(): Unit = { + setupEmbeddedZookeeper() + setupEmbeddedKafkaServer() + } + + /** Teardown the whole servers, including Kafka broker and Zookeeper */ + def teardown(): Unit = { + brokerReady = false + zkReady = false + + if (producer != null) { + producer.close() + producer = null + } + + if (server != null) { + server.shutdown() + server = null + } + + brokerConf.logDirs.foreach { f => Utils.deleteRecursively(new File(f)) } + + if (zkClient != null) { + zkClient.close() + zkClient = null + } + + if (zookeeper != null) { + zookeeper.shutdown() + zookeeper = null + } + } + + /** Create a Kafka topic and wait until it propagated to the whole cluster */ + def createTopic(topic: String): Unit = { + AdminUtils.createTopic(zkClient, topic, 1, 1) + // wait until metadata is propagated + waitUntilMetadataIsPropagated(topic, 0) + } + + /** Java-friendly function for sending messages to the Kafka broker */ + def sendMessages(topic: String, messageToFreq: JMap[String, JInt]): Unit = { + import scala.collection.JavaConversions._ + sendMessages(topic, Map(messageToFreq.mapValues(_.intValue()).toSeq: _*)) + } + + /** Send the messages to the Kafka broker */ + def sendMessages(topic: String, messageToFreq: Map[String, Int]): Unit = { + val messages = messageToFreq.flatMap { case (s, freq) => Seq.fill(freq)(s) }.toArray + sendMessages(topic, messages) + } + + /** Send the array of messages to the Kafka broker */ + def sendMessages(topic: String, messages: Array[String]): Unit = { + producer = new Producer[String, String](new ProducerConfig(producerConfiguration)) + producer.send(messages.map { new KeyedMessage[String, String](topic, _ ) }: _*) + producer.close() + producer = null + } + + private def brokerConfiguration: Properties = { + val props = new Properties() + props.put("broker.id", "0") + props.put("host.name", "localhost") + props.put("port", brokerPort.toString) + props.put("log.dir", Utils.createTempDir().getAbsolutePath) + props.put("zookeeper.connect", zkAddress) + props.put("log.flush.interval.messages", "1") + props.put("replica.socket.timeout.ms", "1500") + props + } + + private def producerConfiguration: Properties = { + val props = new Properties() + props.put("metadata.broker.list", brokerAddress) + props.put("serializer.class", classOf[StringEncoder].getName) + props + } + + // A simplified version of scalatest eventually, rewritten here to avoid adding extra test + // dependency + def eventually[T](timeout: Time, interval: Time)(func: => T): T = { + def makeAttempt(): Either[Throwable, T] = { + try { + Right(func) + } catch { + case e if NonFatal(e) => Left(e) + } + } + + val startTime = System.currentTimeMillis() + @tailrec + def tryAgain(attempt: Int): T = { + makeAttempt() match { + case Right(result) => result + case Left(e) => + val duration = System.currentTimeMillis() - startTime + if (duration < timeout.milliseconds) { + Thread.sleep(interval.milliseconds) + } else { + throw new TimeoutException(e.getMessage) + } + + tryAgain(attempt + 1) + } + } + + tryAgain(1) + } + + private def waitUntilMetadataIsPropagated(topic: String, partition: Int): Unit = { + eventually(Time(10000), Time(100)) { + assert( + server.apis.metadataCache.containsTopicAndPartition(topic, partition), + s"Partition [$topic, $partition] metadata not propagated after timeout" + ) + } + } + + private class EmbeddedZookeeper(val zkConnect: String) { + val snapshotDir = Utils.createTempDir() + val logDir = Utils.createTempDir() + + val zookeeper = new ZooKeeperServer(snapshotDir, logDir, 500) + val (ip, port) = { + val splits = zkConnect.split(":") + (splits(0), splits(1).toInt) + } + val factory = new NIOServerCnxnFactory() + factory.configure(new InetSocketAddress(ip, port), 16) + factory.startup(zookeeper) + + val actualPort = factory.getLocalPort + + def shutdown() { + factory.shutdown() + Utils.deleteRecursively(snapshotDir) + Utils.deleteRecursively(logDir) + } + } +} + diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java index d6ca6d58b5665..4c1d6a03eb2b8 100644 --- a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java +++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java @@ -41,24 +41,28 @@ public class JavaDirectKafkaStreamSuite implements Serializable { private transient JavaStreamingContext ssc = null; - private transient KafkaStreamSuiteBase suiteBase = null; + private transient KafkaTestUtils kafkaTestUtils = null; @Before public void setUp() { - suiteBase = new KafkaStreamSuiteBase() { }; - suiteBase.setupKafka(); - System.clearProperty("spark.driver.port"); - SparkConf sparkConf = new SparkConf() - .setMaster("local[4]").setAppName(this.getClass().getSimpleName()); - ssc = new JavaStreamingContext(sparkConf, Durations.milliseconds(200)); + kafkaTestUtils = new KafkaTestUtils(); + kafkaTestUtils.setup(); + SparkConf sparkConf = new SparkConf() + .setMaster("local[4]").setAppName(this.getClass().getSimpleName()); + ssc = new JavaStreamingContext(sparkConf, Durations.milliseconds(200)); } @After public void tearDown() { + if (ssc != null) { ssc.stop(); ssc = null; - System.clearProperty("spark.driver.port"); - suiteBase.tearDownKafka(); + } + + if (kafkaTestUtils != null) { + kafkaTestUtils.teardown(); + kafkaTestUtils = null; + } } @Test @@ -74,7 +78,7 @@ public void testKafkaStream() throws InterruptedException { sent.addAll(Arrays.asList(topic2data)); HashMap kafkaParams = new HashMap(); - kafkaParams.put("metadata.broker.list", suiteBase.brokerAddress()); + kafkaParams.put("metadata.broker.list", kafkaTestUtils.brokerAddress()); kafkaParams.put("auto.offset.reset", "smallest"); JavaDStream stream1 = KafkaUtils.createDirectStream( @@ -147,8 +151,8 @@ private HashMap topicOffsetToMap(String topic, Long off private String[] createTopicAndSendData(String topic) { String[] data = { topic + "-1", topic + "-2", topic + "-3"}; - suiteBase.createTopic(topic); - suiteBase.sendMessages(topic, data); + kafkaTestUtils.createTopic(topic); + kafkaTestUtils.sendMessages(topic, data); return data; } } diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java index 4477b81827c70..a9dc6e50613ca 100644 --- a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java +++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java @@ -37,13 +37,12 @@ public class JavaKafkaRDDSuite implements Serializable { private transient JavaSparkContext sc = null; - private transient KafkaStreamSuiteBase suiteBase = null; + private transient KafkaTestUtils kafkaTestUtils = null; @Before public void setUp() { - suiteBase = new KafkaStreamSuiteBase() { }; - suiteBase.setupKafka(); - System.clearProperty("spark.driver.port"); + kafkaTestUtils = new KafkaTestUtils(); + kafkaTestUtils.setup(); SparkConf sparkConf = new SparkConf() .setMaster("local[4]").setAppName(this.getClass().getSimpleName()); sc = new JavaSparkContext(sparkConf); @@ -51,10 +50,15 @@ public void setUp() { @After public void tearDown() { - sc.stop(); - sc = null; - System.clearProperty("spark.driver.port"); - suiteBase.tearDownKafka(); + if (sc != null) { + sc.stop(); + sc = null; + } + + if (kafkaTestUtils != null) { + kafkaTestUtils.teardown(); + kafkaTestUtils = null; + } } @Test @@ -66,7 +70,7 @@ public void testKafkaRDD() throws InterruptedException { String[] topic2data = createTopicAndSendData(topic2); HashMap kafkaParams = new HashMap(); - kafkaParams.put("metadata.broker.list", suiteBase.brokerAddress()); + kafkaParams.put("metadata.broker.list", kafkaTestUtils.brokerAddress()); OffsetRange[] offsetRanges = { OffsetRange.create(topic1, 0, 0, 1), @@ -75,7 +79,7 @@ public void testKafkaRDD() throws InterruptedException { HashMap emptyLeaders = new HashMap(); HashMap leaders = new HashMap(); - String[] hostAndPort = suiteBase.brokerAddress().split(":"); + String[] hostAndPort = kafkaTestUtils.brokerAddress().split(":"); Broker broker = Broker.create(hostAndPort[0], Integer.parseInt(hostAndPort[1])); leaders.put(new TopicAndPartition(topic1, 0), broker); leaders.put(new TopicAndPartition(topic2, 0), broker); @@ -144,8 +148,8 @@ public String call(MessageAndMetadata msgAndMd) throws Exception private String[] createTopicAndSendData(String topic) { String[] data = { topic + "-1", topic + "-2", topic + "-3"}; - suiteBase.createTopic(topic); - suiteBase.sendMessages(topic, data); + kafkaTestUtils.createTopic(topic); + kafkaTestUtils.sendMessages(topic, data); return data; } } diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java index bad0a93eb2e84..540f4ceabab47 100644 --- a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java +++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java @@ -22,9 +22,7 @@ import java.util.List; import java.util.Random; -import scala.Predef; import scala.Tuple2; -import scala.collection.JavaConverters; import kafka.serializer.StringDecoder; import org.junit.After; @@ -44,13 +42,12 @@ public class JavaKafkaStreamSuite implements Serializable { private transient JavaStreamingContext ssc = null; private transient Random random = new Random(); - private transient KafkaStreamSuiteBase suiteBase = null; + private transient KafkaTestUtils kafkaTestUtils = null; @Before public void setUp() { - suiteBase = new KafkaStreamSuiteBase() { }; - suiteBase.setupKafka(); - System.clearProperty("spark.driver.port"); + kafkaTestUtils = new KafkaTestUtils(); + kafkaTestUtils.setup(); SparkConf sparkConf = new SparkConf() .setMaster("local[4]").setAppName(this.getClass().getSimpleName()); ssc = new JavaStreamingContext(sparkConf, new Duration(500)); @@ -58,10 +55,15 @@ public void setUp() { @After public void tearDown() { - ssc.stop(); - ssc = null; - System.clearProperty("spark.driver.port"); - suiteBase.tearDownKafka(); + if (ssc != null) { + ssc.stop(); + ssc = null; + } + + if (kafkaTestUtils != null) { + kafkaTestUtils.teardown(); + kafkaTestUtils = null; + } } @Test @@ -75,15 +77,11 @@ public void testKafkaStream() throws InterruptedException { sent.put("b", 3); sent.put("c", 10); - suiteBase.createTopic(topic); - HashMap tmp = new HashMap(sent); - suiteBase.sendMessages(topic, - JavaConverters.mapAsScalaMapConverter(tmp).asScala().toMap( - Predef.>conforms()) - ); + kafkaTestUtils.createTopic(topic); + kafkaTestUtils.sendMessages(topic, sent); HashMap kafkaParams = new HashMap(); - kafkaParams.put("zookeeper.connect", suiteBase.zkAddress()); + kafkaParams.put("zookeeper.connect", kafkaTestUtils.zkAddress()); kafkaParams.put("group.id", "test-consumer-" + random.nextInt(10000)); kafkaParams.put("auto.offset.reset", "smallest"); @@ -126,6 +124,7 @@ public Void call(JavaPairRDD rdd) throws Exception { ); ssc.start(); + long startTime = System.currentTimeMillis(); boolean sizeMatches = false; while (!sizeMatches && System.currentTimeMillis() - startTime < 20000) { @@ -136,6 +135,5 @@ public Void call(JavaPairRDD rdd) throws Exception { for (String k : sent.keySet()) { Assert.assertEquals(sent.get(k).intValue(), result.get(k).intValue()); } - ssc.stop(); } } diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala index 17ca9d145d665..415730f5559c5 100644 --- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala +++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala @@ -27,31 +27,41 @@ import scala.language.postfixOps import kafka.common.TopicAndPartition import kafka.message.MessageAndMetadata import kafka.serializer.StringDecoder -import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll} +import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll, FunSuite} import org.scalatest.concurrent.Eventually -import org.apache.spark.{SparkConf, SparkContext} +import org.apache.spark.{Logging, SparkConf, SparkContext} import org.apache.spark.rdd.RDD import org.apache.spark.streaming.{Milliseconds, StreamingContext, Time} import org.apache.spark.streaming.dstream.DStream import org.apache.spark.util.Utils -class DirectKafkaStreamSuite extends KafkaStreamSuiteBase - with BeforeAndAfter with BeforeAndAfterAll with Eventually { +class DirectKafkaStreamSuite + extends FunSuite + with BeforeAndAfter + with BeforeAndAfterAll + with Eventually + with Logging { val sparkConf = new SparkConf() .setMaster("local[4]") .setAppName(this.getClass.getSimpleName) - var sc: SparkContext = _ - var ssc: StreamingContext = _ - var testDir: File = _ + private var sc: SparkContext = _ + private var ssc: StreamingContext = _ + private var testDir: File = _ + + private var kafkaTestUtils: KafkaTestUtils = _ override def beforeAll { - setupKafka() + kafkaTestUtils = new KafkaTestUtils + kafkaTestUtils.setup() } override def afterAll { - tearDownKafka() + if (kafkaTestUtils != null) { + kafkaTestUtils.teardown() + kafkaTestUtils = null + } } after { @@ -72,12 +82,12 @@ class DirectKafkaStreamSuite extends KafkaStreamSuiteBase val topics = Set("basic1", "basic2", "basic3") val data = Map("a" -> 7, "b" -> 9) topics.foreach { t => - createTopic(t) - sendMessages(t, data) + kafkaTestUtils.createTopic(t) + kafkaTestUtils.sendMessages(t, data) } val totalSent = data.values.sum * topics.size val kafkaParams = Map( - "metadata.broker.list" -> s"$brokerAddress", + "metadata.broker.list" -> kafkaTestUtils.brokerAddress, "auto.offset.reset" -> "smallest" ) @@ -121,9 +131,9 @@ class DirectKafkaStreamSuite extends KafkaStreamSuiteBase val topic = "largest" val topicPartition = TopicAndPartition(topic, 0) val data = Map("a" -> 10) - createTopic(topic) + kafkaTestUtils.createTopic(topic) val kafkaParams = Map( - "metadata.broker.list" -> s"$brokerAddress", + "metadata.broker.list" -> kafkaTestUtils.brokerAddress, "auto.offset.reset" -> "largest" ) val kc = new KafkaCluster(kafkaParams) @@ -132,7 +142,7 @@ class DirectKafkaStreamSuite extends KafkaStreamSuiteBase } // Send some initial messages before starting context - sendMessages(topic, data) + kafkaTestUtils.sendMessages(topic, data) eventually(timeout(10 seconds), interval(20 milliseconds)) { assert(getLatestOffset() > 3) } @@ -154,7 +164,7 @@ class DirectKafkaStreamSuite extends KafkaStreamSuiteBase stream.map { _._2 }.foreachRDD { rdd => collectedData ++= rdd.collect() } ssc.start() val newData = Map("b" -> 10) - sendMessages(topic, newData) + kafkaTestUtils.sendMessages(topic, newData) eventually(timeout(10 seconds), interval(50 milliseconds)) { collectedData.contains("b") } @@ -166,9 +176,9 @@ class DirectKafkaStreamSuite extends KafkaStreamSuiteBase val topic = "offset" val topicPartition = TopicAndPartition(topic, 0) val data = Map("a" -> 10) - createTopic(topic) + kafkaTestUtils.createTopic(topic) val kafkaParams = Map( - "metadata.broker.list" -> s"$brokerAddress", + "metadata.broker.list" -> kafkaTestUtils.brokerAddress, "auto.offset.reset" -> "largest" ) val kc = new KafkaCluster(kafkaParams) @@ -177,7 +187,7 @@ class DirectKafkaStreamSuite extends KafkaStreamSuiteBase } // Send some initial messages before starting context - sendMessages(topic, data) + kafkaTestUtils.sendMessages(topic, data) eventually(timeout(10 seconds), interval(20 milliseconds)) { assert(getLatestOffset() >= 10) } @@ -200,7 +210,7 @@ class DirectKafkaStreamSuite extends KafkaStreamSuiteBase stream.foreachRDD { rdd => collectedData ++= rdd.collect() } ssc.start() val newData = Map("b" -> 10) - sendMessages(topic, newData) + kafkaTestUtils.sendMessages(topic, newData) eventually(timeout(10 seconds), interval(50 milliseconds)) { collectedData.contains("b") } @@ -210,18 +220,18 @@ class DirectKafkaStreamSuite extends KafkaStreamSuiteBase // Test to verify the offset ranges can be recovered from the checkpoints test("offset recovery") { val topic = "recovery" - createTopic(topic) + kafkaTestUtils.createTopic(topic) testDir = Utils.createTempDir() val kafkaParams = Map( - "metadata.broker.list" -> s"$brokerAddress", + "metadata.broker.list" -> kafkaTestUtils.brokerAddress, "auto.offset.reset" -> "smallest" ) // Send data to Kafka and wait for it to be received def sendDataAndWaitForReceive(data: Seq[Int]) { val strings = data.map { _.toString} - sendMessages(topic, strings.map { _ -> 1}.toMap) + kafkaTestUtils.sendMessages(topic, strings.map { _ -> 1}.toMap) eventually(timeout(10 seconds), interval(50 milliseconds)) { assert(strings.forall { DirectKafkaStreamSuite.collectedData.contains }) } diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaClusterSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaClusterSuite.scala index fc9275b7207be..2b33d2a220b2b 100644 --- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaClusterSuite.scala +++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaClusterSuite.scala @@ -20,28 +20,35 @@ package org.apache.spark.streaming.kafka import scala.util.Random import kafka.common.TopicAndPartition -import org.scalatest.BeforeAndAfterAll +import org.scalatest.{BeforeAndAfterAll, FunSuite} -class KafkaClusterSuite extends KafkaStreamSuiteBase with BeforeAndAfterAll { - val topic = "kcsuitetopic" + Random.nextInt(10000) - val topicAndPartition = TopicAndPartition(topic, 0) - var kc: KafkaCluster = null +class KafkaClusterSuite extends FunSuite with BeforeAndAfterAll { + private val topic = "kcsuitetopic" + Random.nextInt(10000) + private val topicAndPartition = TopicAndPartition(topic, 0) + private var kc: KafkaCluster = null + + private var kafkaTestUtils: KafkaTestUtils = _ override def beforeAll() { - setupKafka() - createTopic(topic) - sendMessages(topic, Map("a" -> 1)) - kc = new KafkaCluster(Map("metadata.broker.list" -> s"$brokerAddress")) + kafkaTestUtils = new KafkaTestUtils + kafkaTestUtils.setup() + + kafkaTestUtils.createTopic(topic) + kafkaTestUtils.sendMessages(topic, Map("a" -> 1)) + kc = new KafkaCluster(Map("metadata.broker.list" -> kafkaTestUtils.brokerAddress)) } override def afterAll() { - tearDownKafka() + if (kafkaTestUtils != null) { + kafkaTestUtils.teardown() + kafkaTestUtils = null + } } test("metadata apis") { val leader = kc.findLeaders(Set(topicAndPartition)).right.get(topicAndPartition) val leaderAddress = s"${leader._1}:${leader._2}" - assert(leaderAddress === brokerAddress, "didn't get leader") + assert(leaderAddress === kafkaTestUtils.brokerAddress, "didn't get leader") val parts = kc.getPartitions(Set(topic)).right.get assert(parts(topicAndPartition), "didn't get partitions") diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaRDDSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaRDDSuite.scala index a223da70b043f..7d26ce50875b3 100644 --- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaRDDSuite.scala +++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaRDDSuite.scala @@ -22,18 +22,22 @@ import scala.util.Random import kafka.serializer.StringDecoder import kafka.common.TopicAndPartition import kafka.message.MessageAndMetadata -import org.scalatest.BeforeAndAfterAll +import org.scalatest.{BeforeAndAfterAll, FunSuite} import org.apache.spark._ -import org.apache.spark.SparkContext._ -class KafkaRDDSuite extends KafkaStreamSuiteBase with BeforeAndAfterAll { - val sparkConf = new SparkConf().setMaster("local[4]").setAppName(this.getClass.getSimpleName) - var sc: SparkContext = _ +class KafkaRDDSuite extends FunSuite with BeforeAndAfterAll { + + private var kafkaTestUtils: KafkaTestUtils = _ + + private val sparkConf = new SparkConf().setMaster("local[4]") + .setAppName(this.getClass.getSimpleName) + private var sc: SparkContext = _ + override def beforeAll { sc = new SparkContext(sparkConf) - - setupKafka() + kafkaTestUtils = new KafkaTestUtils + kafkaTestUtils.setup() } override def afterAll { @@ -41,17 +45,21 @@ class KafkaRDDSuite extends KafkaStreamSuiteBase with BeforeAndAfterAll { sc.stop sc = null } - tearDownKafka() + + if (kafkaTestUtils != null) { + kafkaTestUtils.teardown() + kafkaTestUtils = null + } } test("basic usage") { val topic = "topicbasic" - createTopic(topic) + kafkaTestUtils.createTopic(topic) val messages = Set("the", "quick", "brown", "fox") - sendMessages(topic, messages.toArray) + kafkaTestUtils.sendMessages(topic, messages.toArray) - val kafkaParams = Map("metadata.broker.list" -> brokerAddress, + val kafkaParams = Map("metadata.broker.list" -> kafkaTestUtils.brokerAddress, "group.id" -> s"test-consumer-${Random.nextInt(10000)}") val offsetRanges = Array(OffsetRange(topic, 0, 0, messages.size)) @@ -67,15 +75,15 @@ class KafkaRDDSuite extends KafkaStreamSuiteBase with BeforeAndAfterAll { // the idea is to find e.g. off-by-one errors between what kafka has available and the rdd val topic = "topic1" val sent = Map("a" -> 5, "b" -> 3, "c" -> 10) - createTopic(topic) + kafkaTestUtils.createTopic(topic) - val kafkaParams = Map("metadata.broker.list" -> brokerAddress, + val kafkaParams = Map("metadata.broker.list" -> kafkaTestUtils.brokerAddress, "group.id" -> s"test-consumer-${Random.nextInt(10000)}") val kc = new KafkaCluster(kafkaParams) // this is the "lots of messages" case - sendMessages(topic, sent) + kafkaTestUtils.sendMessages(topic, sent) // rdd defined from leaders after sending messages, should get the number sent val rdd = getRdd(kc, Set(topic)) @@ -92,14 +100,14 @@ class KafkaRDDSuite extends KafkaStreamSuiteBase with BeforeAndAfterAll { // shouldn't get anything, since message is sent after rdd was defined val sentOnlyOne = Map("d" -> 1) - sendMessages(topic, sentOnlyOne) + kafkaTestUtils.sendMessages(topic, sentOnlyOne) assert(rdd2.isDefined) assert(rdd2.get.count === 0, "got messages when there shouldn't be any") // this is the "exactly 1 message" case, namely the single message from sentOnlyOne above val rdd3 = getRdd(kc, Set(topic)) // send lots of messages after rdd was defined, they shouldn't show up - sendMessages(topic, Map("extra" -> 22)) + kafkaTestUtils.sendMessages(topic, Map("extra" -> 22)) assert(rdd3.isDefined) assert(rdd3.get.count === sentOnlyOne.values.sum, "didn't get exactly one message") diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaStreamSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaStreamSuite.scala index e4966eebb9b34..24699dfc33adb 100644 --- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaStreamSuite.scala +++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaStreamSuite.scala @@ -17,209 +17,38 @@ package org.apache.spark.streaming.kafka -import java.io.File -import java.net.InetSocketAddress -import java.util.Properties - import scala.collection.mutable import scala.concurrent.duration._ import scala.language.postfixOps import scala.util.Random -import kafka.admin.AdminUtils -import kafka.common.{KafkaException, TopicAndPartition} -import kafka.producer.{KeyedMessage, Producer, ProducerConfig} -import kafka.serializer.{StringDecoder, StringEncoder} -import kafka.server.{KafkaConfig, KafkaServer} -import kafka.utils.ZKStringSerializer -import org.I0Itec.zkclient.ZkClient -import org.apache.zookeeper.server.{NIOServerCnxnFactory, ZooKeeperServer} -import org.scalatest.{BeforeAndAfter, FunSuite} +import kafka.serializer.StringDecoder +import org.scalatest.{BeforeAndAfterAll, FunSuite} import org.scalatest.concurrent.Eventually -import org.apache.spark.{Logging, SparkConf} +import org.apache.spark.SparkConf import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.{Milliseconds, StreamingContext} -import org.apache.spark.util.Utils - -/** - * This is an abstract base class for Kafka testsuites. This has the functionality to set up - * and tear down local Kafka servers, and to push data using Kafka producers. - */ -abstract class KafkaStreamSuiteBase extends FunSuite with Eventually with Logging { - - private val zkHost = "localhost" - private var zkPort: Int = 0 - private val zkConnectionTimeout = 6000 - private val zkSessionTimeout = 6000 - private var zookeeper: EmbeddedZookeeper = _ - private val brokerHost = "localhost" - private var brokerPort = 9092 - private var brokerConf: KafkaConfig = _ - private var server: KafkaServer = _ - private var producer: Producer[String, String] = _ - private var zkReady = false - private var brokerReady = false - - protected var zkClient: ZkClient = _ - - def zkAddress: String = { - assert(zkReady, "Zookeeper not setup yet or already torn down, cannot get zookeeper address") - s"$zkHost:$zkPort" - } - def brokerAddress: String = { - assert(brokerReady, "Kafka not setup yet or already torn down, cannot get broker address") - s"$brokerHost:$brokerPort" - } - - def setupKafka() { - // Zookeeper server startup - zookeeper = new EmbeddedZookeeper(s"$zkHost:$zkPort") - // Get the actual zookeeper binding port - zkPort = zookeeper.actualPort - zkReady = true - logInfo("==================== Zookeeper Started ====================") +class KafkaStreamSuite extends FunSuite with Eventually with BeforeAndAfterAll { + private var ssc: StreamingContext = _ + private var kafkaTestUtils: KafkaTestUtils = _ - zkClient = new ZkClient(zkAddress, zkSessionTimeout, zkConnectionTimeout, ZKStringSerializer) - logInfo("==================== Zookeeper Client Created ====================") - - // Kafka broker startup - var bindSuccess: Boolean = false - while(!bindSuccess) { - try { - val brokerProps = getBrokerConfig() - brokerConf = new KafkaConfig(brokerProps) - server = new KafkaServer(brokerConf) - server.startup() - logInfo("==================== Kafka Broker Started ====================") - bindSuccess = true - } catch { - case e: KafkaException => - if (e.getMessage != null && e.getMessage.contains("Socket server failed to bind to")) { - brokerPort += 1 - } - case e: Exception => throw new Exception("Kafka server create failed", e) - } - } - - Thread.sleep(2000) - logInfo("==================== Kafka + Zookeeper Ready ====================") - brokerReady = true + override def beforeAll(): Unit = { + kafkaTestUtils = new KafkaTestUtils + kafkaTestUtils.setup() } - def tearDownKafka() { - brokerReady = false - zkReady = false - if (producer != null) { - producer.close() - producer = null - } - - if (server != null) { - server.shutdown() - server = null - } - - brokerConf.logDirs.foreach { f => Utils.deleteRecursively(new File(f)) } - - if (zkClient != null) { - zkClient.close() - zkClient = null - } - - if (zookeeper != null) { - zookeeper.shutdown() - zookeeper = null - } - } - - def createTopic(topic: String) { - AdminUtils.createTopic(zkClient, topic, 1, 1) - // wait until metadata is propagated - waitUntilMetadataIsPropagated(topic, 0) - logInfo(s"==================== Topic $topic Created ====================") - } - - def sendMessages(topic: String, messageToFreq: Map[String, Int]) { - val messages = messageToFreq.flatMap { case (s, freq) => Seq.fill(freq)(s) }.toArray - sendMessages(topic, messages) - } - - def sendMessages(topic: String, messages: Array[String]) { - producer = new Producer[String, String](new ProducerConfig(getProducerConfig())) - producer.send(messages.map { new KeyedMessage[String, String](topic, _ ) }: _*) - producer.close() - logInfo(s"==================== Sent Messages: ${messages.mkString(", ")} ====================") - } - - private def getBrokerConfig(): Properties = { - val props = new Properties() - props.put("broker.id", "0") - props.put("host.name", "localhost") - props.put("port", brokerPort.toString) - props.put("log.dir", Utils.createTempDir().getAbsolutePath) - props.put("zookeeper.connect", zkAddress) - props.put("log.flush.interval.messages", "1") - props.put("replica.socket.timeout.ms", "1500") - props - } - - private def getProducerConfig(): Properties = { - val brokerAddr = brokerConf.hostName + ":" + brokerConf.port - val props = new Properties() - props.put("metadata.broker.list", brokerAddr) - props.put("serializer.class", classOf[StringEncoder].getName) - props - } - - private def waitUntilMetadataIsPropagated(topic: String, partition: Int) { - eventually(timeout(10000 milliseconds), interval(100 milliseconds)) { - assert( - server.apis.metadataCache.containsTopicAndPartition(topic, partition), - s"Partition [$topic, $partition] metadata not propagated after timeout" - ) - } - } - - class EmbeddedZookeeper(val zkConnect: String) { - val random = new Random() - val snapshotDir = Utils.createTempDir() - val logDir = Utils.createTempDir() - - val zookeeper = new ZooKeeperServer(snapshotDir, logDir, 500) - val (ip, port) = { - val splits = zkConnect.split(":") - (splits(0), splits(1).toInt) - } - val factory = new NIOServerCnxnFactory() - factory.configure(new InetSocketAddress(ip, port), 16) - factory.startup(zookeeper) - - val actualPort = factory.getLocalPort - - def shutdown() { - factory.shutdown() - Utils.deleteRecursively(snapshotDir) - Utils.deleteRecursively(logDir) - } - } -} - - -class KafkaStreamSuite extends KafkaStreamSuiteBase with BeforeAndAfter { - var ssc: StreamingContext = _ - - before { - setupKafka() - } - - after { + override def afterAll(): Unit = { if (ssc != null) { ssc.stop() ssc = null } - tearDownKafka() + + if (kafkaTestUtils != null) { + kafkaTestUtils.teardown() + kafkaTestUtils = null + } } test("Kafka input stream") { @@ -227,10 +56,10 @@ class KafkaStreamSuite extends KafkaStreamSuiteBase with BeforeAndAfter { ssc = new StreamingContext(sparkConf, Milliseconds(500)) val topic = "topic1" val sent = Map("a" -> 5, "b" -> 3, "c" -> 10) - createTopic(topic) - sendMessages(topic, sent) + kafkaTestUtils.createTopic(topic) + kafkaTestUtils.sendMessages(topic, sent) - val kafkaParams = Map("zookeeper.connect" -> zkAddress, + val kafkaParams = Map("zookeeper.connect" -> kafkaTestUtils.zkAddress, "group.id" -> s"test-consumer-${Random.nextInt(10000)}", "auto.offset.reset" -> "smallest") @@ -244,14 +73,14 @@ class KafkaStreamSuite extends KafkaStreamSuiteBase with BeforeAndAfter { result.put(kv._1, count) } } + ssc.start() + eventually(timeout(10000 milliseconds), interval(100 milliseconds)) { assert(sent.size === result.size) sent.keys.foreach { k => assert(sent(k) === result(k).toInt) } } - ssc.stop() } } - diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/ReliableKafkaStreamSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/ReliableKafkaStreamSuite.scala index 3cd960d1fd1d4..38548dd73b82c 100644 --- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/ReliableKafkaStreamSuite.scala +++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/ReliableKafkaStreamSuite.scala @@ -17,7 +17,6 @@ package org.apache.spark.streaming.kafka - import java.io.File import scala.collection.mutable @@ -27,7 +26,7 @@ import scala.util.Random import kafka.serializer.StringDecoder import kafka.utils.{ZKGroupTopicDirs, ZkUtils} -import org.scalatest.BeforeAndAfter +import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll, FunSuite} import org.scalatest.concurrent.Eventually import org.apache.spark.SparkConf @@ -35,47 +34,61 @@ import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.{Milliseconds, StreamingContext} import org.apache.spark.util.Utils -class ReliableKafkaStreamSuite extends KafkaStreamSuiteBase with BeforeAndAfter with Eventually { +class ReliableKafkaStreamSuite extends FunSuite + with BeforeAndAfterAll with BeforeAndAfter with Eventually { - val sparkConf = new SparkConf() + private val sparkConf = new SparkConf() .setMaster("local[4]") .setAppName(this.getClass.getSimpleName) .set("spark.streaming.receiver.writeAheadLog.enable", "true") - val data = Map("a" -> 10, "b" -> 10, "c" -> 10) + private val data = Map("a" -> 10, "b" -> 10, "c" -> 10) + private var kafkaTestUtils: KafkaTestUtils = _ - var groupId: String = _ - var kafkaParams: Map[String, String] = _ - var ssc: StreamingContext = _ - var tempDirectory: File = null + private var groupId: String = _ + private var kafkaParams: Map[String, String] = _ + private var ssc: StreamingContext = _ + private var tempDirectory: File = null + + override def beforeAll() : Unit = { + kafkaTestUtils = new KafkaTestUtils + kafkaTestUtils.setup() - before { - setupKafka() groupId = s"test-consumer-${Random.nextInt(10000)}" kafkaParams = Map( - "zookeeper.connect" -> zkAddress, + "zookeeper.connect" -> kafkaTestUtils.zkAddress, "group.id" -> groupId, "auto.offset.reset" -> "smallest" ) - ssc = new StreamingContext(sparkConf, Milliseconds(500)) tempDirectory = Utils.createTempDir() + } + + override def afterAll(): Unit = { + Utils.deleteRecursively(tempDirectory) + + if (kafkaTestUtils != null) { + kafkaTestUtils.teardown() + kafkaTestUtils = null + } + } + + before { + ssc = new StreamingContext(sparkConf, Milliseconds(500)) ssc.checkpoint(tempDirectory.getAbsolutePath) } after { if (ssc != null) { ssc.stop() + ssc = null } - Utils.deleteRecursively(tempDirectory) - tearDownKafka() } - test("Reliable Kafka input stream with single topic") { - var topic = "test-topic" - createTopic(topic) - sendMessages(topic, data) + val topic = "test-topic" + kafkaTestUtils.createTopic(topic) + kafkaTestUtils.sendMessages(topic, data) // Verify whether the offset of this group/topic/partition is 0 before starting. assert(getCommitOffset(groupId, topic, 0) === None) @@ -91,6 +104,7 @@ class ReliableKafkaStreamSuite extends KafkaStreamSuiteBase with BeforeAndAfter } } ssc.start() + eventually(timeout(20000 milliseconds), interval(200 milliseconds)) { // A basic process verification for ReliableKafkaReceiver. // Verify whether received message number is equal to the sent message number. @@ -100,14 +114,13 @@ class ReliableKafkaStreamSuite extends KafkaStreamSuiteBase with BeforeAndAfter // Verify the offset number whether it is equal to the total message number. assert(getCommitOffset(groupId, topic, 0) === Some(29L)) } - ssc.stop() } test("Reliable Kafka input stream with multiple topics") { val topics = Map("topic1" -> 1, "topic2" -> 1, "topic3" -> 1) topics.foreach { case (t, _) => - createTopic(t) - sendMessages(t, data) + kafkaTestUtils.createTopic(t) + kafkaTestUtils.sendMessages(t, data) } // Before started, verify all the group/topic/partition offsets are 0. @@ -118,19 +131,18 @@ class ReliableKafkaStreamSuite extends KafkaStreamSuiteBase with BeforeAndAfter ssc, kafkaParams, topics, StorageLevel.MEMORY_ONLY) stream.foreachRDD(_ => Unit) ssc.start() + eventually(timeout(20000 milliseconds), interval(100 milliseconds)) { // Verify the offset for each group/topic to see whether they are equal to the expected one. topics.foreach { case (t, _) => assert(getCommitOffset(groupId, t, 0) === Some(29L)) } } - ssc.stop() } /** Getting partition offset from Zookeeper. */ private def getCommitOffset(groupId: String, topic: String, partition: Int): Option[Long] = { - assert(zkClient != null, "Zookeeper client is not initialized") val topicDirs = new ZKGroupTopicDirs(groupId, topic) val zkPath = s"${topicDirs.consumerOffsetDir}/$partition" - ZkUtils.readDataMaybeNull(zkClient, zkPath)._1.map(_.toLong) + ZkUtils.readDataMaybeNull(kafkaTestUtils.zookeeperClient, zkPath)._1.map(_.toLong) } } diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py index 608f8e26473a6..9b4635e49020b 100644 --- a/python/pyspark/streaming/tests.py +++ b/python/pyspark/streaming/tests.py @@ -23,13 +23,16 @@ import tempfile import struct +from py4j.java_collections import MapConverter + from pyspark.context import SparkConf, SparkContext, RDD from pyspark.streaming.context import StreamingContext +from pyspark.streaming.kafka import KafkaUtils class PySparkStreamingTestCase(unittest.TestCase): - timeout = 10 # seconds + timeout = 20 # seconds duration = 1 def setUp(self): @@ -556,5 +559,43 @@ def check_output(n): check_output(3) +class KafkaStreamTests(PySparkStreamingTestCase): + + def setUp(self): + super(KafkaStreamTests, self).setUp() + + kafkaTestUtilsClz = self.ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader()\ + .loadClass("org.apache.spark.streaming.kafka.KafkaTestUtils") + self._kafkaTestUtils = kafkaTestUtilsClz.newInstance() + self._kafkaTestUtils.setup() + + def tearDown(self): + if self._kafkaTestUtils is not None: + self._kafkaTestUtils.teardown() + self._kafkaTestUtils = None + + super(KafkaStreamTests, self).tearDown() + + def test_kafka_stream(self): + """Test the Python Kafka stream API.""" + topic = "topic1" + sendData = {"a": 3, "b": 5, "c": 10} + jSendData = MapConverter().convert(sendData, + self.ssc.sparkContext._gateway._gateway_client) + + self._kafkaTestUtils.createTopic(topic) + self._kafkaTestUtils.sendMessages(topic, jSendData) + + stream = KafkaUtils.createStream(self.ssc, self._kafkaTestUtils.zkAddress(), + "test-streaming-consumer", {topic: 1}, + {"auto.offset.reset": "smallest"}) + + result = {} + for i in chain.from_iterable(self._collect(stream.map(lambda x: x[1]), + sum(sendData.values()))): + result[i] = result.get(i, 0) + 1 + + self.assertEqual(sendData, result) + if __name__ == "__main__": unittest.main() diff --git a/python/run-tests b/python/run-tests index f569a56fb7a9a..f3a07d8aba562 100755 --- a/python/run-tests +++ b/python/run-tests @@ -21,6 +21,8 @@ # Figure out where the Spark framework is installed FWDIR="$(cd "`dirname "$0"`"; cd ../; pwd)" +. "$FWDIR"/bin/load-spark-env.sh + # CD into the python directory to find things on the right path cd "$FWDIR/python" @@ -57,7 +59,7 @@ function run_core_tests() { PYSPARK_DOC_TEST=1 run_test "pyspark/broadcast.py" PYSPARK_DOC_TEST=1 run_test "pyspark/accumulators.py" run_test "pyspark/serializers.py" - run_test "pyspark/profiler.py" + run_test "pyspark/profiler.py" run_test "pyspark/shuffle.py" run_test "pyspark/tests.py" } @@ -97,6 +99,21 @@ function run_ml_tests() { function run_streaming_tests() { echo "Run streaming tests ..." + + KAFKA_ASSEMBLY_DIR="$FWDIR"/external/kafka-assembly + JAR_PATH="${KAFKA_ASSEMBLY_DIR}/target/scala-${SPARK_SCALA_VERSION}" + for f in "${JAR_PATH}"/spark-streaming-kafka-assembly-*.jar; do + if [[ ! -e "$f" ]]; then + echo "Failed to find Spark Streaming Kafka assembly jar in $KAFKA_ASSEMBLY_DIR" 1>&2 + echo "You need to build Spark with " \ + "'build/sbt assembly/assembly streaming-kafka-assembly/assembly' or" \ + "'build/mvn package' before running this program" 1>&2 + exit 1 + fi + KAFKA_ASSEMBLY_JAR="$f" + done + + export PYSPARK_SUBMIT_ARGS="--jars ${KAFKA_ASSEMBLY_JAR} pyspark-shell" run_test "pyspark/streaming/util.py" run_test "pyspark/streaming/tests.py" } From 18ca089bed41ce3e87deeb14206317863518c12c Mon Sep 17 00:00:00 2001 From: zsxwing Date: Fri, 10 Apr 2015 01:51:42 -0700 Subject: [PATCH 213/591] [SPARK-6766][Streaming] Fix issue about StreamingListenerBatchSubmitted and StreamingListenerBatchStarted This PR includes: 1. Send `StreamingListenerBatchSubmitted` when `JobSet` is submitted 1. Fix `StreamingListenerBatchStarted.batchInfo.processingStartTime` 1. Fix a type: `completedaBatchInfos` -> `completedBatchInfos` Author: zsxwing Closes #5414 from zsxwing/SPARK-6766 and squashes the following commits: 2f85060 [zsxwing] Update tests ca0955b [zsxwing] Combine unit tests 79b4fed [zsxwing] Add StreamingJobProgressListenerSuite to test StreamingJobProgressListener fc3a2a1 [zsxwing] Add unit tests for SPARK-6766 74aed99 [zsxwing] Refactor as per TD's suggestion 493f978 [zsxwing] Send StreamingListenerBatchSubmitted when JobSet is submitted; fix StreamingListenerBatchStarted.batchInfo.processingStartTime; fix a typo --- .../streaming/scheduler/JobScheduler.scala | 8 +- .../ui/StreamingJobProgressListener.scala | 16 +-- .../streaming/StreamingListenerSuite.scala | 55 ++++++-- .../StreamingJobProgressListenerSuite.scala | 119 ++++++++++++++++++ 4 files changed, 180 insertions(+), 18 deletions(-) create mode 100644 streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala index d6a93acbe711b..95f1857b4c377 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala @@ -105,6 +105,7 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { if (jobSet.jobs.isEmpty) { logInfo("No jobs added for time " + jobSet.time) } else { + listenerBus.post(StreamingListenerBatchSubmitted(jobSet.toBatchInfo)) jobSets.put(jobSet.time, jobSet) jobSet.jobs.foreach(job => jobExecutor.execute(new JobHandler(job))) logInfo("Added jobs for time " + jobSet.time) @@ -134,10 +135,13 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { private def handleJobStart(job: Job) { val jobSet = jobSets.get(job.time) - if (!jobSet.hasStarted) { + val isFirstJobOfJobSet = !jobSet.hasStarted + jobSet.handleJobStart(job) + if (isFirstJobOfJobSet) { + // "StreamingListenerBatchStarted" should be posted after calling "handleJobStart" to get the + // correct "jobSet.processingStartTime". listenerBus.post(StreamingListenerBatchStarted(jobSet.toBatchInfo)) } - jobSet.handleJobStart(job) logInfo("Starting job " + job.id + " from job set of time " + jobSet.time) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala index e4bd067cacb77..84f80e638f638 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala @@ -33,7 +33,7 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) private val waitingBatchInfos = new HashMap[Time, BatchInfo] private val runningBatchInfos = new HashMap[Time, BatchInfo] - private val completedaBatchInfos = new Queue[BatchInfo] + private val completedBatchInfos = new Queue[BatchInfo] private val batchInfoLimit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 100) private var totalCompletedBatches = 0L private var totalReceivedRecords = 0L @@ -62,7 +62,7 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) override def onBatchSubmitted(batchSubmitted: StreamingListenerBatchSubmitted): Unit = { synchronized { - runningBatchInfos(batchSubmitted.batchInfo.batchTime) = batchSubmitted.batchInfo + waitingBatchInfos(batchSubmitted.batchInfo.batchTime) = batchSubmitted.batchInfo } } @@ -79,8 +79,8 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) synchronized { waitingBatchInfos.remove(batchCompleted.batchInfo.batchTime) runningBatchInfos.remove(batchCompleted.batchInfo.batchTime) - completedaBatchInfos.enqueue(batchCompleted.batchInfo) - if (completedaBatchInfos.size > batchInfoLimit) completedaBatchInfos.dequeue() + completedBatchInfos.enqueue(batchCompleted.batchInfo) + if (completedBatchInfos.size > batchInfoLimit) completedBatchInfos.dequeue() totalCompletedBatches += 1L batchCompleted.batchInfo.receivedBlockInfo.foreach { case (_, infos) => @@ -118,7 +118,7 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) } def retainedCompletedBatches: Seq[BatchInfo] = synchronized { - completedaBatchInfos.toSeq + completedBatchInfos.toSeq } def processingDelayDistribution: Option[Distribution] = synchronized { @@ -165,7 +165,7 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) } def lastCompletedBatch: Option[BatchInfo] = { - completedaBatchInfos.sortBy(_.batchTime)(Time.ordering).lastOption + completedBatchInfos.sortBy(_.batchTime)(Time.ordering).lastOption } def lastReceivedBatch: Option[BatchInfo] = { @@ -174,10 +174,10 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) private def retainedBatches: Seq[BatchInfo] = synchronized { (waitingBatchInfos.values.toSeq ++ - runningBatchInfos.values.toSeq ++ completedaBatchInfos).sortBy(_.batchTime)(Time.ordering) + runningBatchInfos.values.toSeq ++ completedBatchInfos).sortBy(_.batchTime)(Time.ordering) } private def extractDistribution(getMetric: BatchInfo => Option[Long]): Option[Distribution] = { - Distribution(completedaBatchInfos.flatMap(getMetric(_)).map(_.toDouble)) + Distribution(completedBatchInfos.flatMap(getMetric(_)).map(_.toDouble)) } } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala index 852e8bb71d4f6..7210439509541 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala @@ -46,10 +46,38 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers { val collector = new BatchInfoCollector ssc.addStreamingListener(collector) runStreams(ssc, input.size, input.size) - val batchInfos = collector.batchInfos - batchInfos should have size 4 - batchInfos.foreach(info => { + // SPARK-6766: batch info should be submitted + val batchInfosSubmitted = collector.batchInfosSubmitted + batchInfosSubmitted should have size 4 + + batchInfosSubmitted.foreach(info => { + info.schedulingDelay should be (None) + info.processingDelay should be (None) + info.totalDelay should be (None) + }) + + isInIncreasingOrder(batchInfosSubmitted.map(_.submissionTime)) should be (true) + + // SPARK-6766: processingStartTime of batch info should not be None when starting + val batchInfosStarted = collector.batchInfosStarted + batchInfosStarted should have size 4 + + batchInfosStarted.foreach(info => { + info.schedulingDelay should not be None + info.schedulingDelay.get should be >= 0L + info.processingDelay should be (None) + info.totalDelay should be (None) + }) + + isInIncreasingOrder(batchInfosStarted.map(_.submissionTime)) should be (true) + isInIncreasingOrder(batchInfosStarted.map(_.processingStartTime.get)) should be (true) + + // test onBatchCompleted + val batchInfosCompleted = collector.batchInfosCompleted + batchInfosCompleted should have size 4 + + batchInfosCompleted.foreach(info => { info.schedulingDelay should not be None info.processingDelay should not be None info.totalDelay should not be None @@ -58,9 +86,9 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers { info.totalDelay.get should be >= 0L }) - isInIncreasingOrder(batchInfos.map(_.submissionTime)) should be (true) - isInIncreasingOrder(batchInfos.map(_.processingStartTime.get)) should be (true) - isInIncreasingOrder(batchInfos.map(_.processingEndTime.get)) should be (true) + isInIncreasingOrder(batchInfosCompleted.map(_.submissionTime)) should be (true) + isInIncreasingOrder(batchInfosCompleted.map(_.processingStartTime.get)) should be (true) + isInIncreasingOrder(batchInfosCompleted.map(_.processingEndTime.get)) should be (true) } test("receiver info reporting") { @@ -99,9 +127,20 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers { /** Listener that collects information on processed batches */ class BatchInfoCollector extends StreamingListener { - val batchInfos = new ArrayBuffer[BatchInfo] + val batchInfosCompleted = new ArrayBuffer[BatchInfo] + val batchInfosStarted = new ArrayBuffer[BatchInfo] + val batchInfosSubmitted = new ArrayBuffer[BatchInfo] + + override def onBatchSubmitted(batchSubmitted: StreamingListenerBatchSubmitted) { + batchInfosSubmitted += batchSubmitted.batchInfo + } + + override def onBatchStarted(batchStarted: StreamingListenerBatchStarted) { + batchInfosStarted += batchStarted.batchInfo + } + override def onBatchCompleted(batchCompleted: StreamingListenerBatchCompleted) { - batchInfos += batchCompleted.batchInfo + batchInfosCompleted += batchCompleted.batchInfo } } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala new file mode 100644 index 0000000000000..2b9d164500b72 --- /dev/null +++ b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala @@ -0,0 +1,119 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.ui + +import org.scalatest.Matchers + +import org.apache.spark.streaming.dstream.DStream +import org.apache.spark.streaming.scheduler._ +import org.apache.spark.streaming.{Time, Milliseconds, TestSuiteBase} + +class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { + + val input = (1 to 4).map(Seq(_)).toSeq + val operation = (d: DStream[Int]) => d.map(x => x) + + override def batchDuration = Milliseconds(100) + + test("onBatchSubmitted, onBatchStarted, onBatchCompleted, " + + "onReceiverStarted, onReceiverError, onReceiverStopped") { + val ssc = setupStreams(input, operation) + val listener = new StreamingJobProgressListener(ssc) + + val receivedBlockInfo = Map( + 0 -> Array(ReceivedBlockInfo(0, 100, null), ReceivedBlockInfo(0, 200, null)), + 1 -> Array(ReceivedBlockInfo(1, 300, null)) + ) + + // onBatchSubmitted + val batchInfoSubmitted = BatchInfo(Time(1000), receivedBlockInfo, 1000, None, None) + listener.onBatchSubmitted(StreamingListenerBatchSubmitted(batchInfoSubmitted)) + listener.waitingBatches should be (List(batchInfoSubmitted)) + listener.runningBatches should be (Nil) + listener.retainedCompletedBatches should be (Nil) + listener.lastCompletedBatch should be (None) + listener.numUnprocessedBatches should be (1) + listener.numTotalCompletedBatches should be (0) + listener.numTotalProcessedRecords should be (0) + listener.numTotalReceivedRecords should be (0) + + // onBatchStarted + val batchInfoStarted = BatchInfo(Time(1000), receivedBlockInfo, 1000, Some(2000), None) + listener.onBatchStarted(StreamingListenerBatchStarted(batchInfoStarted)) + listener.waitingBatches should be (Nil) + listener.runningBatches should be (List(batchInfoStarted)) + listener.retainedCompletedBatches should be (Nil) + listener.lastCompletedBatch should be (None) + listener.numUnprocessedBatches should be (1) + listener.numTotalCompletedBatches should be (0) + listener.numTotalProcessedRecords should be (0) + listener.numTotalReceivedRecords should be (600) + + // onBatchCompleted + val batchInfoCompleted = BatchInfo(Time(1000), receivedBlockInfo, 1000, Some(2000), None) + listener.onBatchCompleted(StreamingListenerBatchCompleted(batchInfoCompleted)) + listener.waitingBatches should be (Nil) + listener.runningBatches should be (Nil) + listener.retainedCompletedBatches should be (List(batchInfoCompleted)) + listener.lastCompletedBatch should be (Some(batchInfoCompleted)) + listener.numUnprocessedBatches should be (0) + listener.numTotalCompletedBatches should be (1) + listener.numTotalProcessedRecords should be (600) + listener.numTotalReceivedRecords should be (600) + + // onReceiverStarted + val receiverInfoStarted = ReceiverInfo(0, "test", null, true, "localhost") + listener.onReceiverStarted(StreamingListenerReceiverStarted(receiverInfoStarted)) + listener.receiverInfo(0) should be (Some(receiverInfoStarted)) + listener.receiverInfo(1) should be (None) + + // onReceiverError + val receiverInfoError = ReceiverInfo(1, "test", null, true, "localhost") + listener.onReceiverError(StreamingListenerReceiverError(receiverInfoError)) + listener.receiverInfo(0) should be (Some(receiverInfoStarted)) + listener.receiverInfo(1) should be (Some(receiverInfoError)) + listener.receiverInfo(2) should be (None) + + // onReceiverStopped + val receiverInfoStopped = ReceiverInfo(2, "test", null, true, "localhost") + listener.onReceiverStopped(StreamingListenerReceiverStopped(receiverInfoStopped)) + listener.receiverInfo(0) should be (Some(receiverInfoStarted)) + listener.receiverInfo(1) should be (Some(receiverInfoError)) + listener.receiverInfo(2) should be (Some(receiverInfoStopped)) + listener.receiverInfo(3) should be (None) + } + + test("Remove the old completed batches when exceeding the limit") { + val ssc = setupStreams(input, operation) + val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 100) + val listener = new StreamingJobProgressListener(ssc) + + val receivedBlockInfo = Map( + 0 -> Array(ReceivedBlockInfo(0, 100, null), ReceivedBlockInfo(0, 200, null)), + 1 -> Array(ReceivedBlockInfo(1, 300, null)) + ) + val batchInfoCompleted = BatchInfo(Time(1000), receivedBlockInfo, 1000, Some(2000), None) + + for(_ <- 0 until (limit + 10)) { + listener.onBatchCompleted(StreamingListenerBatchCompleted(batchInfoCompleted)) + } + + listener.retainedCompletedBatches.size should be (limit) + listener.numTotalCompletedBatches should be(limit + 10) + } +} From 9f5ed99d644949443d19c4895de6e0ece4be24d0 Mon Sep 17 00:00:00 2001 From: "June.He" Date: Fri, 10 Apr 2015 20:02:35 +0100 Subject: [PATCH 214/591] [SPARK-6773][Tests]Fix RAT checks still passed issue when download rat jar failed check -license will passed in next time when rat jar download failed. Add 2 step to fix this: 1. Clean the rat.jar if download failed. 2. Add a check logic after run rat checking. Author: June.He Closes #5421 from sisihj/rat-issue and squashes the following commits: 4958302 [June.He] delete redundant check and add quoted 66f7088 [June.He] Fix RAT checks still passed issue when download rat jar failed 7311e83 [June.He] Fix RAT checks still passed issue when download rat jar failed --- dev/check-license | 45 ++++++++++++++++++++++++--------------------- 1 file changed, 24 insertions(+), 21 deletions(-) diff --git a/dev/check-license b/dev/check-license index 39943f882b6ca..10740cfdc5242 100755 --- a/dev/check-license +++ b/dev/check-license @@ -24,29 +24,27 @@ acquire_rat_jar () { JAR="$rat_jar" - if [[ ! -f "$rat_jar" ]]; then - # Download rat launch jar if it hasn't been downloaded yet - if [ ! -f "$JAR" ]; then - # Download - printf "Attempting to fetch rat\n" - JAR_DL="${JAR}.part" - if [ $(command -v curl) ]; then - curl -L --silent "${URL}" > "$JAR_DL" && mv "$JAR_DL" "$JAR" - elif [ $(command -v wget) ]; then - wget --quiet ${URL} -O "$JAR_DL" && mv "$JAR_DL" "$JAR" - else - printf "You do not have curl or wget installed, please install rat manually.\n" - exit -1 - fi - fi - - unzip -tq $JAR &> /dev/null - if [ $? -ne 0 ]; then - # We failed to download - printf "Our attempt to download rat locally to ${JAR} failed. Please install rat manually.\n" + # Download rat launch jar if it hasn't been downloaded yet + if [ ! -f "$JAR" ]; then + # Download + printf "Attempting to fetch rat\n" + JAR_DL="${JAR}.part" + if [ $(command -v curl) ]; then + curl -L --silent "${URL}" > "$JAR_DL" && mv "$JAR_DL" "$JAR" + elif [ $(command -v wget) ]; then + wget --quiet ${URL} -O "$JAR_DL" && mv "$JAR_DL" "$JAR" + else + printf "You do not have curl or wget installed, please install rat manually.\n" exit -1 fi - printf "Launching rat from ${JAR}\n" + fi + + unzip -tq "$JAR" &> /dev/null + if [ $? -ne 0 ]; then + # We failed to download + rm "$JAR" + printf "Our attempt to download rat locally to ${JAR} failed. Please install rat manually.\n" + exit -1 fi } @@ -71,6 +69,11 @@ mkdir -p "$FWDIR"/lib $java_cmd -jar "$rat_jar" -E "$FWDIR"/.rat-excludes -d "$FWDIR" > rat-results.txt +if [ $? -ne 0 ]; then + echo "RAT exited abnormally" + exit 1 +fi + ERRORS="$(cat rat-results.txt | grep -e "??")" if test ! -z "$ERRORS"; then From b9baa4cd9f6e9fc58161f79744b6b7729894d920 Mon Sep 17 00:00:00 2001 From: Volodymyr Lyubinets Date: Fri, 10 Apr 2015 12:09:54 -0700 Subject: [PATCH 215/591] [SQL] [SPARK-6794] Use kryo-based SparkSqlSerializer for GeneralHashedRelation Benchmarking results: http://pastie.org/private/1dneo1mta5zpsw6gmsoeq Author: Volodymyr Lyubinets Closes #5433 from vlyubin/joins and squashes the following commits: d70c829 [Volodymyr Lyubinets] Addressed review feedback 527eac6 [Volodymyr Lyubinets] Use kryo-based SparkSqlSerializer for GeneralHashedRelation --- .../sql/execution/SparkSqlSerializer.scala | 3 +- .../sql/execution/joins/HashedRelation.scala | 47 +++++++++++++++++-- 2 files changed, 44 insertions(+), 6 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala index 967bd76b302d8..347e2f4a1a1af 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala @@ -26,14 +26,13 @@ import scala.reflect.ClassTag import com.clearspring.analytics.stream.cardinality.HyperLogLog import com.esotericsoftware.kryo.io.{Input, Output} import com.esotericsoftware.kryo.{Serializer, Kryo} -import com.twitter.chill.{AllScalaRegistrar, ResourcePool} +import com.twitter.chill.ResourcePool import org.apache.spark.{SparkEnv, SparkConf} import org.apache.spark.serializer.{SerializerInstance, KryoSerializer} import org.apache.spark.sql.catalyst.expressions.GenericRow import org.apache.spark.util.collection.OpenHashSet import org.apache.spark.util.MutablePair -import org.apache.spark.util.Utils import org.apache.spark.sql.catalyst.expressions.codegen.{IntegerHashSet, LongHashSet} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala index 2fa1cf5add3b5..ab84c123e0c0b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala @@ -17,9 +17,11 @@ package org.apache.spark.sql.execution.joins +import java.io.{ObjectInput, ObjectOutput, Externalizable} import java.util.{HashMap => JavaHashMap} import org.apache.spark.sql.catalyst.expressions.{Projection, Row} +import org.apache.spark.sql.execution.SparkSqlSerializer import org.apache.spark.util.collection.CompactBuffer @@ -29,16 +31,43 @@ import org.apache.spark.util.collection.CompactBuffer */ private[joins] sealed trait HashedRelation { def get(key: Row): CompactBuffer[Row] + + // This is a helper method to implement Externalizable, and is used by + // GeneralHashedRelation and UniqueKeyHashedRelation + protected def writeBytes(out: ObjectOutput, serialized: Array[Byte]): Unit = { + out.writeInt(serialized.length) // Write the length of serialized bytes first + out.write(serialized) + } + + // This is a helper method to implement Externalizable, and is used by + // GeneralHashedRelation and UniqueKeyHashedRelation + protected def readBytes(in: ObjectInput): Array[Byte] = { + val serializedSize = in.readInt() // Read the length of serialized bytes first + val bytes = new Array[Byte](serializedSize) + in.readFully(bytes) + bytes + } } /** * A general [[HashedRelation]] backed by a hash map that maps the key into a sequence of values. */ -private[joins] final class GeneralHashedRelation(hashTable: JavaHashMap[Row, CompactBuffer[Row]]) - extends HashedRelation with Serializable { +private[joins] final class GeneralHashedRelation( + private var hashTable: JavaHashMap[Row, CompactBuffer[Row]]) + extends HashedRelation with Externalizable { + + def this() = this(null) // Needed for serialization override def get(key: Row): CompactBuffer[Row] = hashTable.get(key) + + override def writeExternal(out: ObjectOutput): Unit = { + writeBytes(out, SparkSqlSerializer.serialize(hashTable)) + } + + override def readExternal(in: ObjectInput): Unit = { + hashTable = SparkSqlSerializer.deserialize(readBytes(in)) + } } @@ -46,8 +75,10 @@ private[joins] final class GeneralHashedRelation(hashTable: JavaHashMap[Row, Com * A specialized [[HashedRelation]] that maps key into a single value. This implementation * assumes the key is unique. */ -private[joins] final class UniqueKeyHashedRelation(hashTable: JavaHashMap[Row, Row]) - extends HashedRelation with Serializable { +private[joins] final class UniqueKeyHashedRelation(private var hashTable: JavaHashMap[Row, Row]) + extends HashedRelation with Externalizable { + + def this() = this(null) // Needed for serialization override def get(key: Row): CompactBuffer[Row] = { val v = hashTable.get(key) @@ -55,6 +86,14 @@ private[joins] final class UniqueKeyHashedRelation(hashTable: JavaHashMap[Row, R } def getValue(key: Row): Row = hashTable.get(key) + + override def writeExternal(out: ObjectOutput): Unit = { + writeBytes(out, SparkSqlSerializer.serialize(hashTable)) + } + + override def readExternal(in: ObjectInput): Unit = { + hashTable = SparkSqlSerializer.deserialize(readBytes(in)) + } } From 0375134f42197f2e29aa865a513cda381f0a1445 Mon Sep 17 00:00:00 2001 From: Milan Straka Date: Fri, 10 Apr 2015 13:50:32 -0700 Subject: [PATCH 216/591] [SPARK-5969][PySpark] Fix descending pyspark.rdd.sortByKey. The samples should always be sorted in ascending order, because bisect.bisect_left is used on it. The reverse order of the result is already achieved in rangePartitioner by reversing the found index. The current implementation also work, but always uses only two partitions -- the first one and the last one (because the bisect_left return returns either "beginning" or "end" for a descending sequence). Author: Milan Straka This patch had conflicts when merged, resolved by Committer: Josh Rosen Closes #4761 from foxik/fix-descending-sort and squashes the following commits: 95896b5 [Milan Straka] Add regression test for SPARK-5969. 5757490 [Milan Straka] Fix descending pyspark.rdd.sortByKey. --- python/pyspark/rdd.py | 2 +- python/pyspark/tests.py | 11 +++++++++++ 2 files changed, 12 insertions(+), 1 deletion(-) diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py index 1b18789040360..c8e54ed5c6b2c 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.py @@ -595,7 +595,7 @@ def sortPartition(iterator): maxSampleSize = numPartitions * 20.0 # constant from Spark's RangePartitioner fraction = min(maxSampleSize / max(rddSize, 1), 1.0) samples = self.sample(False, fraction, 1).map(lambda (k, v): k).collect() - samples = sorted(samples, reverse=(not ascending), key=keyfunc) + samples = sorted(samples, key=keyfunc) # we have numPartitions many parts but one of the them has # an implicit boundary diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py index 0bd5d20f7877f..0e3721b55adf7 100644 --- a/python/pyspark/tests.py +++ b/python/pyspark/tests.py @@ -829,6 +829,17 @@ def test_take_on_jrdd(self): rdd = self.sc.parallelize(range(1 << 20)).map(lambda x: str(x)) rdd._jrdd.first() + def test_sortByKey_uses_all_partitions_not_only_first_and_last(self): + # Regression test for SPARK-5969 + seq = [(i * 59 % 101, i) for i in range(101)] # unsorted sequence + rdd = self.sc.parallelize(seq) + for ascending in [True, False]: + sort = rdd.sortByKey(ascending=ascending, numPartitions=5) + self.assertEqual(sort.collect(), sorted(seq, reverse=not ascending)) + sizes = sort.glom().map(len).collect() + for size in sizes: + self.assertGreater(size, 0) + class ProfilerTests(PySparkTestCase): From 4740d6a158cb4d35408a95265c5b950b9e9628a3 Mon Sep 17 00:00:00 2001 From: Davies Liu Date: Fri, 10 Apr 2015 14:04:53 -0700 Subject: [PATCH 217/591] [SPARK-6216] [PySpark] check the python version in worker Author: Davies Liu Closes #5404 from davies/check_version and squashes the following commits: e559248 [Davies Liu] add tests ec33b5f [Davies Liu] check the python version in worker --- python/pyspark/rdd.py | 2 +- python/pyspark/tests.py | 16 ++++++++++++++++ python/pyspark/worker.py | 6 +++++- 3 files changed, 22 insertions(+), 2 deletions(-) diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py index c8e54ed5c6b2c..c9ac95d117574 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.py @@ -2233,7 +2233,7 @@ def toLocalIterator(self): def _prepare_for_python_RDD(sc, command, obj=None): # the serialized command will be compressed by broadcast ser = CloudPickleSerializer() - pickled_command = ser.dumps(command) + pickled_command = ser.dumps((command, sys.version_info[:2])) if len(pickled_command) > (1 << 20): # 1M broadcast = sc.broadcast(pickled_command) pickled_command = ser.dumps(broadcast) diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py index 0e3721b55adf7..b938b9ce12395 100644 --- a/python/pyspark/tests.py +++ b/python/pyspark/tests.py @@ -35,6 +35,8 @@ import threading import hashlib +from py4j.protocol import Py4JJavaError + if sys.version_info[:2] <= (2, 6): try: import unittest2 as unittest @@ -1494,6 +1496,20 @@ def count(): self.assertTrue(not t.isAlive()) self.assertEqual(100000, rdd.count()) + def test_with_different_versions_of_python(self): + rdd = self.sc.parallelize(range(10)) + rdd.count() + version = sys.version_info + sys.version_info = (2, 0, 0) + log4j = self.sc._jvm.org.apache.log4j + old_level = log4j.LogManager.getRootLogger().getLevel() + log4j.LogManager.getRootLogger().setLevel(log4j.Level.FATAL) + try: + self.assertRaises(Py4JJavaError, lambda: rdd.count()) + finally: + sys.version_info = version + log4j.LogManager.getRootLogger().setLevel(old_level) + class SparkSubmitTests(unittest.TestCase): diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py index 8a93c320ec5d3..452d6fabdcc17 100644 --- a/python/pyspark/worker.py +++ b/python/pyspark/worker.py @@ -88,7 +88,11 @@ def main(infile, outfile): command = pickleSer._read_with_length(infile) if isinstance(command, Broadcast): command = pickleSer.loads(command.value) - (func, profiler, deserializer, serializer) = command + (func, profiler, deserializer, serializer), version = command + if version != sys.version_info[:2]: + raise Exception(("Python in worker has different version %s than that in " + + "driver %s, PySpark cannot run with different minor versions") % + (sys.version_info[:2], version)) init_time = time.time() def process(): From 68ecdb7f99ae30f7c04c33a47ab7f59a3836f2a4 Mon Sep 17 00:00:00 2001 From: Davies Liu Date: Fri, 10 Apr 2015 15:35:45 -0700 Subject: [PATCH 218/591] [SPARK-6850] [SparkR] use one partition when we need to compare the whole result Author: Davies Liu Closes #5460 from davies/r_test and squashes the following commits: 0a593ce [Davies Liu] use one partition when we need to compare the whole result --- R/pkg/inst/tests/test_binaryFile.R | 4 ++-- R/pkg/inst/tests/test_textFile.R | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/R/pkg/inst/tests/test_binaryFile.R b/R/pkg/inst/tests/test_binaryFile.R index 4bb5f58d83dc9..ca4218f3819f8 100644 --- a/R/pkg/inst/tests/test_binaryFile.R +++ b/R/pkg/inst/tests/test_binaryFile.R @@ -27,7 +27,7 @@ test_that("saveAsObjectFile()/objectFile() following textFile() works", { fileName2 <- tempfile(pattern="spark-test", fileext=".tmp") writeLines(mockFile, fileName1) - rdd <- textFile(sc, fileName1) + rdd <- textFile(sc, fileName1, 1) saveAsObjectFile(rdd, fileName2) rdd <- objectFile(sc, fileName2) expect_equal(collect(rdd), as.list(mockFile)) @@ -40,7 +40,7 @@ test_that("saveAsObjectFile()/objectFile() works on a parallelized list", { fileName <- tempfile(pattern="spark-test", fileext=".tmp") l <- list(1, 2, 3) - rdd <- parallelize(sc, l) + rdd <- parallelize(sc, l, 1) saveAsObjectFile(rdd, fileName) rdd <- objectFile(sc, fileName) expect_equal(collect(rdd), l) diff --git a/R/pkg/inst/tests/test_textFile.R b/R/pkg/inst/tests/test_textFile.R index 7bb3e8003131d..6b87b4b3e0b08 100644 --- a/R/pkg/inst/tests/test_textFile.R +++ b/R/pkg/inst/tests/test_textFile.R @@ -81,7 +81,7 @@ test_that("textFile() followed by a saveAsTextFile() returns the same content", fileName2 <- tempfile(pattern="spark-test", fileext=".tmp") writeLines(mockFile, fileName1) - rdd <- textFile(sc, fileName1) + rdd <- textFile(sc, fileName1, 1L) saveAsTextFile(rdd, fileName2) rdd <- textFile(sc, fileName2) expect_equal(collect(rdd), as.list(mockFile)) @@ -93,7 +93,7 @@ test_that("textFile() followed by a saveAsTextFile() returns the same content", test_that("saveAsTextFile() on a parallelized list works as expected", { fileName <- tempfile(pattern="spark-test", fileext=".tmp") l <- list(1, 2, 3) - rdd <- parallelize(sc, l) + rdd <- parallelize(sc, l, 1L) saveAsTextFile(rdd, fileName) rdd <- textFile(sc, fileName) expect_equal(collect(rdd), lapply(l, function(x) {toString(x)})) From 23d5f8864f7d665a74b1d38118700139854dbb1c Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Fri, 10 Apr 2015 16:05:14 -0700 Subject: [PATCH 219/591] [SPARK-6851][SQL] Create new instance for each converted parquet relation Otherwise we end up rewriting predicates to be trivially equal (i.e. `a#1 = a#2` -> `a#3 = a#3`), at which point the query is no longer valid. Author: Michael Armbrust Closes #5458 from marmbrus/selfJoinParquet and squashes the following commits: 22df77c [Michael Armbrust] [SPARK-6851][SQL] Create new instance for each converted parquet relation --- .../spark/sql/hive/HiveMetastoreCatalog.scala | 4 +- .../sql/hive/execution/SQLQuerySuite.scala | 78 ++++++++++++++++++- 2 files changed, 80 insertions(+), 2 deletions(-) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index 315fab673da5c..3ed5c5b031736 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -279,7 +279,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with } } - if (metastoreRelation.hiveQlTable.isPartitioned) { + val result = if (metastoreRelation.hiveQlTable.isPartitioned) { val partitionSchema = StructType.fromAttributes(metastoreRelation.partitionKeys) val partitionColumnDataTypes = partitionSchema.map(_.dataType) val partitions = metastoreRelation.hiveQlPartitions.map { p => @@ -314,6 +314,8 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with parquetRelation } + + result.newInstance() } override def getTables(databaseName: Option[String]): Seq[(String, Boolean)] = synchronized { diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala index 7811bd2e9effb..4c369c0634bda 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala @@ -34,6 +34,17 @@ case class Nested3(f3: Int) case class NestedArray2(b: Seq[Int]) case class NestedArray1(a: NestedArray2) +case class Order( + id: Int, + make: String, + `type`: String, + price: Int, + pdate: String, + customer: String, + city: String, + state: String, + month: Int) + /** * A collection of hive query tests where we generate the answers ourselves instead of depending on * Hive to generate them (in contrast to HiveQuerySuite). Often this is because the query is @@ -41,6 +52,72 @@ case class NestedArray1(a: NestedArray2) */ class SQLQuerySuite extends QueryTest { + test("SPARK-6851: Self-joined converted parquet tables") { + val orders = Seq( + Order(1, "Atlas", "MTB", 234, "2015-01-07", "John D", "Pacifica", "CA", 20151), + Order(3, "Swift", "MTB", 285, "2015-01-17", "John S", "Redwood City", "CA", 20151), + Order(4, "Atlas", "Hybrid", 303, "2015-01-23", "Jones S", "San Mateo", "CA", 20151), + Order(7, "Next", "MTB", 356, "2015-01-04", "Jane D", "Daly City", "CA", 20151), + Order(10, "Next", "YFlikr", 187, "2015-01-09", "John D", "Fremont", "CA", 20151), + Order(11, "Swift", "YFlikr", 187, "2015-01-23", "John D", "Hayward", "CA", 20151), + Order(2, "Next", "Hybrid", 324, "2015-02-03", "Jane D", "Daly City", "CA", 20152), + Order(5, "Next", "Street", 187, "2015-02-08", "John D", "Fremont", "CA", 20152), + Order(6, "Atlas", "Street", 154, "2015-02-09", "John D", "Pacifica", "CA", 20152), + Order(8, "Swift", "Hybrid", 485, "2015-02-19", "John S", "Redwood City", "CA", 20152), + Order(9, "Atlas", "Split", 303, "2015-02-28", "Jones S", "San Mateo", "CA", 20152)) + + val orderUpdates = Seq( + Order(1, "Atlas", "MTB", 434, "2015-01-07", "John D", "Pacifica", "CA", 20151), + Order(11, "Swift", "YFlikr", 137, "2015-01-23", "John D", "Hayward", "CA", 20151)) + + orders.toDF.registerTempTable("orders1") + orderUpdates.toDF.registerTempTable("orderupdates1") + + sql( + """CREATE TABLE orders( + | id INT, + | make String, + | type String, + | price INT, + | pdate String, + | customer String, + | city String) + |PARTITIONED BY (state STRING, month INT) + |STORED AS PARQUET + """.stripMargin) + + sql( + """CREATE TABLE orderupdates( + | id INT, + | make String, + | type String, + | price INT, + | pdate String, + | customer String, + | city String) + |PARTITIONED BY (state STRING, month INT) + |STORED AS PARQUET + """.stripMargin) + + sql("set hive.exec.dynamic.partition.mode=nonstrict") + sql("INSERT INTO TABLE orders PARTITION(state, month) SELECT * FROM orders1") + sql("INSERT INTO TABLE orderupdates PARTITION(state, month) SELECT * FROM orderupdates1") + + checkAnswer( + sql( + """ + |select orders.state, orders.month + |from orders + |join ( + | select distinct orders.state,orders.month + | from orders + | join orderupdates + | on orderupdates.id = orders.id) ao + | on ao.state = orders.state and ao.month = orders.month + """.stripMargin), + (1 to 6).map(_ => Row("CA", 20151))) + } + test("SPARK-5371: union with null and sum") { val df = Seq((1, 1)).toDF("c1", "c2") df.registerTempTable("table1") @@ -478,5 +555,4 @@ class SQLQuerySuite extends QueryTest { sql("select d from dn union all select d * 2 from dn") .queryExecution.analyzed } - } From 67d06880e47e0324409cf7e5b21db1dcb0107b82 Mon Sep 17 00:00:00 2001 From: Volodymyr Lyubinets Date: Fri, 10 Apr 2015 16:27:56 -0700 Subject: [PATCH 220/591] [SQL] [SPARK-6620] Speed up toDF() and rdd() functions by constructing converters in ScalaReflection cc marmbrus Author: Volodymyr Lyubinets Closes #5279 from vlyubin/speedup and squashes the following commits: e75a387 [Volodymyr Lyubinets] Changes to ScalaUDF 11a20ec [Volodymyr Lyubinets] Avoid creating a tuple c327bc9 [Volodymyr Lyubinets] Moved the only remaining function from DataTypeConversions to DateUtils dec6802 [Volodymyr Lyubinets] Addresed review feedback 74301fa [Volodymyr Lyubinets] Addressed review comments afa3aa5 [Volodymyr Lyubinets] Minor refactoring, added license, removed debug output 881dc60 [Volodymyr Lyubinets] Moved to a separate module; addressed review comments; one extra place of usage; changed behaviour for Java 8cad6e2 [Volodymyr Lyubinets] Addressed review commments 41b2aa9 [Volodymyr Lyubinets] Creating converters for ScalaReflection stuff, and more --- .../spark/ml/feature/TokenizerSuite.scala | 17 +- .../sql/catalyst/CatalystTypeConverters.scala | 295 +++++++ .../spark/sql/catalyst/ScalaReflection.scala | 55 -- .../sql/catalyst/expressions/ScalaUdf.scala | 819 ++++++++++++------ .../plans/logical/LocalRelation.scala | 7 +- .../spark/sql/types/DataTypeConversions.scala | 77 -- .../apache/spark/sql/types/DateUtils.scala | 29 + .../sql/catalyst/ScalaReflectionSuite.scala | 4 +- .../org/apache/spark/sql/DataFrame.scala | 11 +- .../org/apache/spark/sql/SQLContext.scala | 9 +- .../spark/sql/execution/ExistingRDD.scala | 14 +- .../spark/sql/execution/LocalTableScan.scala | 16 +- .../spark/sql/execution/SparkPlan.scala | 11 +- .../spark/sql/execution/basicOperators.scala | 9 +- .../org/apache/spark/sql/json/JsonRDD.scala | 4 +- .../apache/spark/sql/JavaDataFrameSuite.java | 10 +- .../org/apache/spark/sql/json/JsonSuite.scala | 3 +- 17 files changed, 929 insertions(+), 461 deletions(-) create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala delete mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeConversions.scala diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/TokenizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/TokenizerSuite.scala index bf862b912d326..d186ead8f542f 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/TokenizerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/TokenizerSuite.scala @@ -25,10 +25,7 @@ import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.sql.{DataFrame, Row, SQLContext} @BeanInfo -case class TokenizerTestData(rawText: String, wantedTokens: Seq[String]) { - /** Constructor used in [[org.apache.spark.ml.feature.JavaTokenizerSuite]] */ - def this(rawText: String, wantedTokens: Array[String]) = this(rawText, wantedTokens.toSeq) -} +case class TokenizerTestData(rawText: String, wantedTokens: Array[String]) class RegexTokenizerSuite extends FunSuite with MLlibTestSparkContext { import org.apache.spark.ml.feature.RegexTokenizerSuite._ @@ -46,14 +43,14 @@ class RegexTokenizerSuite extends FunSuite with MLlibTestSparkContext { .setOutputCol("tokens") val dataset0 = sqlContext.createDataFrame(Seq( - TokenizerTestData("Test for tokenization.", Seq("Test", "for", "tokenization", ".")), - TokenizerTestData("Te,st. punct", Seq("Te", ",", "st", ".", "punct")) + TokenizerTestData("Test for tokenization.", Array("Test", "for", "tokenization", ".")), + TokenizerTestData("Te,st. punct", Array("Te", ",", "st", ".", "punct")) )) testRegexTokenizer(tokenizer, dataset0) val dataset1 = sqlContext.createDataFrame(Seq( - TokenizerTestData("Test for tokenization.", Seq("Test", "for", "tokenization")), - TokenizerTestData("Te,st. punct", Seq("punct")) + TokenizerTestData("Test for tokenization.", Array("Test", "for", "tokenization")), + TokenizerTestData("Te,st. punct", Array("punct")) )) tokenizer.setMinTokenLength(3) @@ -64,8 +61,8 @@ class RegexTokenizerSuite extends FunSuite with MLlibTestSparkContext { .setGaps(true) .setMinTokenLength(0) val dataset2 = sqlContext.createDataFrame(Seq( - TokenizerTestData("Test for tokenization.", Seq("Test", "for", "tokenization.")), - TokenizerTestData("Te,st. punct", Seq("Te,st.", "", "punct")) + TokenizerTestData("Test for tokenization.", Array("Test", "for", "tokenization.")), + TokenizerTestData("Te,st. punct", Array("Te,st.", "", "punct")) )) testRegexTokenizer(tokenizer, dataset2) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala new file mode 100644 index 0000000000000..91976fef6dc0d --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala @@ -0,0 +1,295 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst + +import java.util.{Map => JavaMap} + +import scala.collection.mutable.HashMap + +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.types._ + +/** + * Functions to convert Scala types to Catalyst types and vice versa. + */ +object CatalystTypeConverters { + // The Predef.Map is scala.collection.immutable.Map. + // Since the map values can be mutable, we explicitly import scala.collection.Map at here. + import scala.collection.Map + + /** + * Converts Scala objects to catalyst rows / types. This method is slow, and for batch + * conversion you should be using converter produced by createToCatalystConverter. + * Note: This is always called after schemaFor has been called. + * This ordering is important for UDT registration. + */ + def convertToCatalyst(a: Any, dataType: DataType): Any = (a, dataType) match { + // Check UDT first since UDTs can override other types + case (obj, udt: UserDefinedType[_]) => + udt.serialize(obj) + + case (o: Option[_], _) => + o.map(convertToCatalyst(_, dataType)).orNull + + case (s: Seq[_], arrayType: ArrayType) => + s.map(convertToCatalyst(_, arrayType.elementType)) + + case (s: Array[_], arrayType: ArrayType) => + s.toSeq.map(convertToCatalyst(_, arrayType.elementType)) + + case (m: Map[_, _], mapType: MapType) => + m.map { case (k, v) => + convertToCatalyst(k, mapType.keyType) -> convertToCatalyst(v, mapType.valueType) + } + + case (jmap: JavaMap[_, _], mapType: MapType) => + val iter = jmap.entrySet.iterator + var listOfEntries: List[(Any, Any)] = List() + while (iter.hasNext) { + val entry = iter.next() + listOfEntries :+= (convertToCatalyst(entry.getKey, mapType.keyType), + convertToCatalyst(entry.getValue, mapType.valueType)) + } + listOfEntries.toMap + + case (p: Product, structType: StructType) => + val ar = new Array[Any](structType.size) + val iter = p.productIterator + var idx = 0 + while (idx < structType.size) { + ar(idx) = convertToCatalyst(iter.next(), structType.fields(idx).dataType) + idx += 1 + } + new GenericRowWithSchema(ar, structType) + + case (d: BigDecimal, _) => + Decimal(d) + + case (d: java.math.BigDecimal, _) => + Decimal(d) + + case (d: java.sql.Date, _) => + DateUtils.fromJavaDate(d) + + case (r: Row, structType: StructType) => + val converters = structType.fields.map { + f => (item: Any) => convertToCatalyst(item, f.dataType) + } + convertRowWithConverters(r, structType, converters) + + case (other, _) => + other + } + + /** + * Creates a converter function that will convert Scala objects to the specified catalyst type. + * Typical use case would be converting a collection of rows that have the same schema. You will + * call this function once to get a converter, and apply it to every row. + */ + private[sql] def createToCatalystConverter(dataType: DataType): Any => Any = { + def extractOption(item: Any): Any = item match { + case opt: Option[_] => opt.orNull + case other => other + } + + dataType match { + // Check UDT first since UDTs can override other types + case udt: UserDefinedType[_] => + (item) => extractOption(item) match { + case null => null + case other => udt.serialize(other) + } + + case arrayType: ArrayType => + val elementConverter = createToCatalystConverter(arrayType.elementType) + (item: Any) => { + extractOption(item) match { + case a: Array[_] => a.toSeq.map(elementConverter) + case s: Seq[_] => s.map(elementConverter) + case null => null + } + } + + case mapType: MapType => + val keyConverter = createToCatalystConverter(mapType.keyType) + val valueConverter = createToCatalystConverter(mapType.valueType) + (item: Any) => { + extractOption(item) match { + case m: Map[_, _] => + m.map { case (k, v) => + keyConverter(k) -> valueConverter(v) + } + + case jmap: JavaMap[_, _] => + val iter = jmap.entrySet.iterator + val convertedMap: HashMap[Any, Any] = HashMap() + while (iter.hasNext) { + val entry = iter.next() + convertedMap(keyConverter(entry.getKey)) = valueConverter(entry.getValue) + } + convertedMap + + case null => null + } + } + + case structType: StructType => + val converters = structType.fields.map(f => createToCatalystConverter(f.dataType)) + (item: Any) => { + extractOption(item) match { + case r: Row => + convertRowWithConverters(r, structType, converters) + + case p: Product => + val ar = new Array[Any](structType.size) + val iter = p.productIterator + var idx = 0 + while (idx < structType.size) { + ar(idx) = converters(idx)(iter.next()) + idx += 1 + } + new GenericRowWithSchema(ar, structType) + + case null => + null + } + } + + case dateType: DateType => (item: Any) => extractOption(item) match { + case d: java.sql.Date => DateUtils.fromJavaDate(d) + case other => other + } + + case _ => + (item: Any) => extractOption(item) match { + case d: BigDecimal => Decimal(d) + case d: java.math.BigDecimal => Decimal(d) + case other => other + } + } + } + + /** + * Converts Catalyst types used internally in rows to standard Scala types + * This method is slow, and for batch conversion you should be using converter + * produced by createToScalaConverter. + */ + def convertToScala(a: Any, dataType: DataType): Any = (a, dataType) match { + // Check UDT first since UDTs can override other types + case (d, udt: UserDefinedType[_]) => + udt.deserialize(d) + + case (s: Seq[_], arrayType: ArrayType) => + s.map(convertToScala(_, arrayType.elementType)) + + case (m: Map[_, _], mapType: MapType) => + m.map { case (k, v) => + convertToScala(k, mapType.keyType) -> convertToScala(v, mapType.valueType) + } + + case (r: Row, s: StructType) => + convertRowToScala(r, s) + + case (d: Decimal, _: DecimalType) => + d.toJavaBigDecimal + + case (i: Int, DateType) => + DateUtils.toJavaDate(i) + + case (other, _) => + other + } + + /** + * Creates a converter function that will convert Catalyst types to Scala type. + * Typical use case would be converting a collection of rows that have the same schema. You will + * call this function once to get a converter, and apply it to every row. + */ + private[sql] def createToScalaConverter(dataType: DataType): Any => Any = dataType match { + // Check UDT first since UDTs can override other types + case udt: UserDefinedType[_] => + (item: Any) => if (item == null) null else udt.deserialize(item) + + case arrayType: ArrayType => + val elementConverter = createToScalaConverter(arrayType.elementType) + (item: Any) => if (item == null) null else item.asInstanceOf[Seq[_]].map(elementConverter) + + case mapType: MapType => + val keyConverter = createToScalaConverter(mapType.keyType) + val valueConverter = createToScalaConverter(mapType.valueType) + (item: Any) => if (item == null) { + null + } else { + item.asInstanceOf[Map[_, _]].map { case (k, v) => + keyConverter(k) -> valueConverter(v) + } + } + + case s: StructType => + val converters = s.fields.map(f => createToScalaConverter(f.dataType)) + (item: Any) => { + if (item == null) { + null + } else { + convertRowWithConverters(item.asInstanceOf[Row], s, converters) + } + } + + case _: DecimalType => + (item: Any) => item match { + case d: Decimal => d.toJavaBigDecimal + case other => other + } + + case DateType => + (item: Any) => item match { + case i: Int => DateUtils.toJavaDate(i) + case other => other + } + + case other => + (item: Any) => item + } + + def convertRowToScala(r: Row, schema: StructType): Row = { + val ar = new Array[Any](r.size) + var idx = 0 + while (idx < r.size) { + ar(idx) = convertToScala(r(idx), schema.fields(idx).dataType) + idx += 1 + } + new GenericRowWithSchema(ar, schema) + } + + /** + * Converts a row by applying the provided set of converter functions. It is used for both + * toScala and toCatalyst conversions. + */ + private[sql] def convertRowWithConverters( + row: Row, + schema: StructType, + converters: Array[Any => Any]): Row = { + val ar = new Array[Any](row.size) + var idx = 0 + while (idx < row.size) { + ar(idx) = converters(idx)(row(idx)) + idx += 1 + } + new GenericRowWithSchema(ar, schema) + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala index 8bfd0471d9c7a..01d5c1512201a 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala @@ -46,61 +46,6 @@ trait ScalaReflection { case class Schema(dataType: DataType, nullable: Boolean) - /** - * Converts Scala objects to catalyst rows / types. - * Note: This is always called after schemaFor has been called. - * This ordering is important for UDT registration. - */ - def convertToCatalyst(a: Any, dataType: DataType): Any = (a, dataType) match { - // Check UDT first since UDTs can override other types - case (obj, udt: UserDefinedType[_]) => udt.serialize(obj) - case (o: Option[_], _) => o.map(convertToCatalyst(_, dataType)).orNull - case (s: Seq[_], arrayType: ArrayType) => s.map(convertToCatalyst(_, arrayType.elementType)) - case (s: Array[_], arrayType: ArrayType) => if (arrayType.elementType.isPrimitive) { - s.toSeq - } else { - s.toSeq.map(convertToCatalyst(_, arrayType.elementType)) - } - case (m: Map[_, _], mapType: MapType) => m.map { case (k, v) => - convertToCatalyst(k, mapType.keyType) -> convertToCatalyst(v, mapType.valueType) - } - case (p: Product, structType: StructType) => - new GenericRow( - p.productIterator.toSeq.zip(structType.fields).map { case (elem, field) => - convertToCatalyst(elem, field.dataType) - }.toArray) - case (d: BigDecimal, _) => Decimal(d) - case (d: java.math.BigDecimal, _) => Decimal(d) - case (d: java.sql.Date, _) => DateUtils.fromJavaDate(d) - case (r: Row, structType: StructType) => - new GenericRow( - r.toSeq.zip(structType.fields).map { case (elem, field) => - convertToCatalyst(elem, field.dataType) - }.toArray) - case (other, _) => other - } - - /** Converts Catalyst types used internally in rows to standard Scala types */ - def convertToScala(a: Any, dataType: DataType): Any = (a, dataType) match { - // Check UDT first since UDTs can override other types - case (d, udt: UserDefinedType[_]) => udt.deserialize(d) - case (s: Seq[_], arrayType: ArrayType) => s.map(convertToScala(_, arrayType.elementType)) - case (m: Map[_, _], mapType: MapType) => m.map { case (k, v) => - convertToScala(k, mapType.keyType) -> convertToScala(v, mapType.valueType) - } - case (r: Row, s: StructType) => convertRowToScala(r, s) - case (d: Decimal, _: DecimalType) => d.toJavaBigDecimal - case (i: Int, DateType) => DateUtils.toJavaDate(i) - case (other, _) => other - } - - def convertRowToScala(r: Row, schema: StructType): Row = { - // TODO: This is very slow!!! - new GenericRowWithSchema( - r.toSeq.zip(schema.fields.map(_.dataType)) - .map(r_dt => convertToScala(r_dt._1, r_dt._2)).toArray, schema) - } - /** Returns a Sequence of attributes for the given case class type. */ def attributesFor[T: TypeTag]: Seq[Attribute] = schemaFor[T] match { case Schema(s: StructType, _) => diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala index 389dc4f745723..9a77ca624ebe2 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ScalaUdf.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql.catalyst.expressions -import org.apache.spark.sql.catalyst.ScalaReflection +import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.types.DataType /** @@ -39,12 +39,14 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi (1 to 22).map { x => val anys = (1 to x).map(x => "Any").reduce(_ + ", " + _) - val childs = (0 to x - 1).map(x => s"val child$x = children($x)").reduce(_ + "\n " + _) - val evals = (0 to x - 1).map(x => s"ScalaReflection.convertToScala(child$x.eval(input), child$x.dataType)").reduce(_ + ",\n " + _) + val childs = (0 to x - 1).map(x => s"val child$x = children($x)").reduce(_ + "\n " + _) + lazy val converters = (0 to x - 1).map(x => s"lazy val converter$x = CatalystTypeConverters.createToScalaConverter(child$x.dataType)").reduce(_ + "\n " + _) + val evals = (0 to x - 1).map(x => s"converter$x(child$x.eval(input))").reduce(_ + ",\n " + _) - s""" case $x => + s"""case $x => val func = function.asInstanceOf[($anys) => Any] $childs + $converters (input: Row) => { func( $evals) @@ -60,51 +62,61 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi (input: Row) => { func() } - + case 1 => val func = function.asInstanceOf[(Any) => Any] val child0 = children(0) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType)) + converter0(child0.eval(input))) } - + case 2 => val func = function.asInstanceOf[(Any, Any) => Any] val child0 = children(0) val child1 = children(1) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input))) } - + case 3 => val func = function.asInstanceOf[(Any, Any, Any) => Any] val child0 = children(0) val child1 = children(1) val child2 = children(2) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType), - ScalaReflection.convertToScala(child2.eval(input), child2.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input))) } - + case 4 => val func = function.asInstanceOf[(Any, Any, Any, Any) => Any] val child0 = children(0) val child1 = children(1) val child2 = children(2) val child3 = children(3) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType), - ScalaReflection.convertToScala(child2.eval(input), child2.dataType), - ScalaReflection.convertToScala(child3.eval(input), child3.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input))) } - + case 5 => val func = function.asInstanceOf[(Any, Any, Any, Any, Any) => Any] val child0 = children(0) @@ -112,15 +124,20 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val child2 = children(2) val child3 = children(3) val child4 = children(4) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType), - ScalaReflection.convertToScala(child2.eval(input), child2.dataType), - ScalaReflection.convertToScala(child3.eval(input), child3.dataType), - ScalaReflection.convertToScala(child4.eval(input), child4.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input))) } - + case 6 => val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any) => Any] val child0 = children(0) @@ -129,16 +146,22 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val child3 = children(3) val child4 = children(4) val child5 = children(5) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType), - ScalaReflection.convertToScala(child2.eval(input), child2.dataType), - ScalaReflection.convertToScala(child3.eval(input), child3.dataType), - ScalaReflection.convertToScala(child4.eval(input), child4.dataType), - ScalaReflection.convertToScala(child5.eval(input), child5.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input))) } - + case 7 => val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any) => Any] val child0 = children(0) @@ -148,17 +171,24 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val child4 = children(4) val child5 = children(5) val child6 = children(6) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType), - ScalaReflection.convertToScala(child2.eval(input), child2.dataType), - ScalaReflection.convertToScala(child3.eval(input), child3.dataType), - ScalaReflection.convertToScala(child4.eval(input), child4.dataType), - ScalaReflection.convertToScala(child5.eval(input), child5.dataType), - ScalaReflection.convertToScala(child6.eval(input), child6.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input))) } - + case 8 => val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any) => Any] val child0 = children(0) @@ -169,18 +199,26 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val child5 = children(5) val child6 = children(6) val child7 = children(7) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType), - ScalaReflection.convertToScala(child2.eval(input), child2.dataType), - ScalaReflection.convertToScala(child3.eval(input), child3.dataType), - ScalaReflection.convertToScala(child4.eval(input), child4.dataType), - ScalaReflection.convertToScala(child5.eval(input), child5.dataType), - ScalaReflection.convertToScala(child6.eval(input), child6.dataType), - ScalaReflection.convertToScala(child7.eval(input), child7.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input))) } - + case 9 => val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] val child0 = children(0) @@ -192,19 +230,28 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val child6 = children(6) val child7 = children(7) val child8 = children(8) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType), - ScalaReflection.convertToScala(child2.eval(input), child2.dataType), - ScalaReflection.convertToScala(child3.eval(input), child3.dataType), - ScalaReflection.convertToScala(child4.eval(input), child4.dataType), - ScalaReflection.convertToScala(child5.eval(input), child5.dataType), - ScalaReflection.convertToScala(child6.eval(input), child6.dataType), - ScalaReflection.convertToScala(child7.eval(input), child7.dataType), - ScalaReflection.convertToScala(child8.eval(input), child8.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input))) } - + case 10 => val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] val child0 = children(0) @@ -217,20 +264,30 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val child7 = children(7) val child8 = children(8) val child9 = children(9) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType), - ScalaReflection.convertToScala(child2.eval(input), child2.dataType), - ScalaReflection.convertToScala(child3.eval(input), child3.dataType), - ScalaReflection.convertToScala(child4.eval(input), child4.dataType), - ScalaReflection.convertToScala(child5.eval(input), child5.dataType), - ScalaReflection.convertToScala(child6.eval(input), child6.dataType), - ScalaReflection.convertToScala(child7.eval(input), child7.dataType), - ScalaReflection.convertToScala(child8.eval(input), child8.dataType), - ScalaReflection.convertToScala(child9.eval(input), child9.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input))) } - + case 11 => val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] val child0 = children(0) @@ -244,21 +301,32 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val child8 = children(8) val child9 = children(9) val child10 = children(10) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType), - ScalaReflection.convertToScala(child2.eval(input), child2.dataType), - ScalaReflection.convertToScala(child3.eval(input), child3.dataType), - ScalaReflection.convertToScala(child4.eval(input), child4.dataType), - ScalaReflection.convertToScala(child5.eval(input), child5.dataType), - ScalaReflection.convertToScala(child6.eval(input), child6.dataType), - ScalaReflection.convertToScala(child7.eval(input), child7.dataType), - ScalaReflection.convertToScala(child8.eval(input), child8.dataType), - ScalaReflection.convertToScala(child9.eval(input), child9.dataType), - ScalaReflection.convertToScala(child10.eval(input), child10.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input))) } - + case 12 => val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] val child0 = children(0) @@ -273,22 +341,34 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val child9 = children(9) val child10 = children(10) val child11 = children(11) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType), - ScalaReflection.convertToScala(child2.eval(input), child2.dataType), - ScalaReflection.convertToScala(child3.eval(input), child3.dataType), - ScalaReflection.convertToScala(child4.eval(input), child4.dataType), - ScalaReflection.convertToScala(child5.eval(input), child5.dataType), - ScalaReflection.convertToScala(child6.eval(input), child6.dataType), - ScalaReflection.convertToScala(child7.eval(input), child7.dataType), - ScalaReflection.convertToScala(child8.eval(input), child8.dataType), - ScalaReflection.convertToScala(child9.eval(input), child9.dataType), - ScalaReflection.convertToScala(child10.eval(input), child10.dataType), - ScalaReflection.convertToScala(child11.eval(input), child11.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input))) } - + case 13 => val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] val child0 = children(0) @@ -304,23 +384,36 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val child10 = children(10) val child11 = children(11) val child12 = children(12) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType), - ScalaReflection.convertToScala(child2.eval(input), child2.dataType), - ScalaReflection.convertToScala(child3.eval(input), child3.dataType), - ScalaReflection.convertToScala(child4.eval(input), child4.dataType), - ScalaReflection.convertToScala(child5.eval(input), child5.dataType), - ScalaReflection.convertToScala(child6.eval(input), child6.dataType), - ScalaReflection.convertToScala(child7.eval(input), child7.dataType), - ScalaReflection.convertToScala(child8.eval(input), child8.dataType), - ScalaReflection.convertToScala(child9.eval(input), child9.dataType), - ScalaReflection.convertToScala(child10.eval(input), child10.dataType), - ScalaReflection.convertToScala(child11.eval(input), child11.dataType), - ScalaReflection.convertToScala(child12.eval(input), child12.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input)), + converter12(child12.eval(input))) } - + case 14 => val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] val child0 = children(0) @@ -337,24 +430,38 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val child11 = children(11) val child12 = children(12) val child13 = children(13) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) + lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType), - ScalaReflection.convertToScala(child2.eval(input), child2.dataType), - ScalaReflection.convertToScala(child3.eval(input), child3.dataType), - ScalaReflection.convertToScala(child4.eval(input), child4.dataType), - ScalaReflection.convertToScala(child5.eval(input), child5.dataType), - ScalaReflection.convertToScala(child6.eval(input), child6.dataType), - ScalaReflection.convertToScala(child7.eval(input), child7.dataType), - ScalaReflection.convertToScala(child8.eval(input), child8.dataType), - ScalaReflection.convertToScala(child9.eval(input), child9.dataType), - ScalaReflection.convertToScala(child10.eval(input), child10.dataType), - ScalaReflection.convertToScala(child11.eval(input), child11.dataType), - ScalaReflection.convertToScala(child12.eval(input), child12.dataType), - ScalaReflection.convertToScala(child13.eval(input), child13.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input)), + converter12(child12.eval(input)), + converter13(child13.eval(input))) } - + case 15 => val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] val child0 = children(0) @@ -372,25 +479,40 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val child12 = children(12) val child13 = children(13) val child14 = children(14) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) + lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) + lazy val converter14 = CatalystTypeConverters.createToScalaConverter(child14.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType), - ScalaReflection.convertToScala(child2.eval(input), child2.dataType), - ScalaReflection.convertToScala(child3.eval(input), child3.dataType), - ScalaReflection.convertToScala(child4.eval(input), child4.dataType), - ScalaReflection.convertToScala(child5.eval(input), child5.dataType), - ScalaReflection.convertToScala(child6.eval(input), child6.dataType), - ScalaReflection.convertToScala(child7.eval(input), child7.dataType), - ScalaReflection.convertToScala(child8.eval(input), child8.dataType), - ScalaReflection.convertToScala(child9.eval(input), child9.dataType), - ScalaReflection.convertToScala(child10.eval(input), child10.dataType), - ScalaReflection.convertToScala(child11.eval(input), child11.dataType), - ScalaReflection.convertToScala(child12.eval(input), child12.dataType), - ScalaReflection.convertToScala(child13.eval(input), child13.dataType), - ScalaReflection.convertToScala(child14.eval(input), child14.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input)), + converter12(child12.eval(input)), + converter13(child13.eval(input)), + converter14(child14.eval(input))) } - + case 16 => val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] val child0 = children(0) @@ -409,26 +531,42 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val child13 = children(13) val child14 = children(14) val child15 = children(15) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) + lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) + lazy val converter14 = CatalystTypeConverters.createToScalaConverter(child14.dataType) + lazy val converter15 = CatalystTypeConverters.createToScalaConverter(child15.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType), - ScalaReflection.convertToScala(child2.eval(input), child2.dataType), - ScalaReflection.convertToScala(child3.eval(input), child3.dataType), - ScalaReflection.convertToScala(child4.eval(input), child4.dataType), - ScalaReflection.convertToScala(child5.eval(input), child5.dataType), - ScalaReflection.convertToScala(child6.eval(input), child6.dataType), - ScalaReflection.convertToScala(child7.eval(input), child7.dataType), - ScalaReflection.convertToScala(child8.eval(input), child8.dataType), - ScalaReflection.convertToScala(child9.eval(input), child9.dataType), - ScalaReflection.convertToScala(child10.eval(input), child10.dataType), - ScalaReflection.convertToScala(child11.eval(input), child11.dataType), - ScalaReflection.convertToScala(child12.eval(input), child12.dataType), - ScalaReflection.convertToScala(child13.eval(input), child13.dataType), - ScalaReflection.convertToScala(child14.eval(input), child14.dataType), - ScalaReflection.convertToScala(child15.eval(input), child15.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input)), + converter12(child12.eval(input)), + converter13(child13.eval(input)), + converter14(child14.eval(input)), + converter15(child15.eval(input))) } - + case 17 => val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] val child0 = children(0) @@ -448,27 +586,44 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val child14 = children(14) val child15 = children(15) val child16 = children(16) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) + lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) + lazy val converter14 = CatalystTypeConverters.createToScalaConverter(child14.dataType) + lazy val converter15 = CatalystTypeConverters.createToScalaConverter(child15.dataType) + lazy val converter16 = CatalystTypeConverters.createToScalaConverter(child16.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType), - ScalaReflection.convertToScala(child2.eval(input), child2.dataType), - ScalaReflection.convertToScala(child3.eval(input), child3.dataType), - ScalaReflection.convertToScala(child4.eval(input), child4.dataType), - ScalaReflection.convertToScala(child5.eval(input), child5.dataType), - ScalaReflection.convertToScala(child6.eval(input), child6.dataType), - ScalaReflection.convertToScala(child7.eval(input), child7.dataType), - ScalaReflection.convertToScala(child8.eval(input), child8.dataType), - ScalaReflection.convertToScala(child9.eval(input), child9.dataType), - ScalaReflection.convertToScala(child10.eval(input), child10.dataType), - ScalaReflection.convertToScala(child11.eval(input), child11.dataType), - ScalaReflection.convertToScala(child12.eval(input), child12.dataType), - ScalaReflection.convertToScala(child13.eval(input), child13.dataType), - ScalaReflection.convertToScala(child14.eval(input), child14.dataType), - ScalaReflection.convertToScala(child15.eval(input), child15.dataType), - ScalaReflection.convertToScala(child16.eval(input), child16.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input)), + converter12(child12.eval(input)), + converter13(child13.eval(input)), + converter14(child14.eval(input)), + converter15(child15.eval(input)), + converter16(child16.eval(input))) } - + case 18 => val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] val child0 = children(0) @@ -489,28 +644,46 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val child15 = children(15) val child16 = children(16) val child17 = children(17) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) + lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) + lazy val converter14 = CatalystTypeConverters.createToScalaConverter(child14.dataType) + lazy val converter15 = CatalystTypeConverters.createToScalaConverter(child15.dataType) + lazy val converter16 = CatalystTypeConverters.createToScalaConverter(child16.dataType) + lazy val converter17 = CatalystTypeConverters.createToScalaConverter(child17.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType), - ScalaReflection.convertToScala(child2.eval(input), child2.dataType), - ScalaReflection.convertToScala(child3.eval(input), child3.dataType), - ScalaReflection.convertToScala(child4.eval(input), child4.dataType), - ScalaReflection.convertToScala(child5.eval(input), child5.dataType), - ScalaReflection.convertToScala(child6.eval(input), child6.dataType), - ScalaReflection.convertToScala(child7.eval(input), child7.dataType), - ScalaReflection.convertToScala(child8.eval(input), child8.dataType), - ScalaReflection.convertToScala(child9.eval(input), child9.dataType), - ScalaReflection.convertToScala(child10.eval(input), child10.dataType), - ScalaReflection.convertToScala(child11.eval(input), child11.dataType), - ScalaReflection.convertToScala(child12.eval(input), child12.dataType), - ScalaReflection.convertToScala(child13.eval(input), child13.dataType), - ScalaReflection.convertToScala(child14.eval(input), child14.dataType), - ScalaReflection.convertToScala(child15.eval(input), child15.dataType), - ScalaReflection.convertToScala(child16.eval(input), child16.dataType), - ScalaReflection.convertToScala(child17.eval(input), child17.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input)), + converter12(child12.eval(input)), + converter13(child13.eval(input)), + converter14(child14.eval(input)), + converter15(child15.eval(input)), + converter16(child16.eval(input)), + converter17(child17.eval(input))) } - + case 19 => val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] val child0 = children(0) @@ -532,29 +705,48 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val child16 = children(16) val child17 = children(17) val child18 = children(18) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) + lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) + lazy val converter14 = CatalystTypeConverters.createToScalaConverter(child14.dataType) + lazy val converter15 = CatalystTypeConverters.createToScalaConverter(child15.dataType) + lazy val converter16 = CatalystTypeConverters.createToScalaConverter(child16.dataType) + lazy val converter17 = CatalystTypeConverters.createToScalaConverter(child17.dataType) + lazy val converter18 = CatalystTypeConverters.createToScalaConverter(child18.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType), - ScalaReflection.convertToScala(child2.eval(input), child2.dataType), - ScalaReflection.convertToScala(child3.eval(input), child3.dataType), - ScalaReflection.convertToScala(child4.eval(input), child4.dataType), - ScalaReflection.convertToScala(child5.eval(input), child5.dataType), - ScalaReflection.convertToScala(child6.eval(input), child6.dataType), - ScalaReflection.convertToScala(child7.eval(input), child7.dataType), - ScalaReflection.convertToScala(child8.eval(input), child8.dataType), - ScalaReflection.convertToScala(child9.eval(input), child9.dataType), - ScalaReflection.convertToScala(child10.eval(input), child10.dataType), - ScalaReflection.convertToScala(child11.eval(input), child11.dataType), - ScalaReflection.convertToScala(child12.eval(input), child12.dataType), - ScalaReflection.convertToScala(child13.eval(input), child13.dataType), - ScalaReflection.convertToScala(child14.eval(input), child14.dataType), - ScalaReflection.convertToScala(child15.eval(input), child15.dataType), - ScalaReflection.convertToScala(child16.eval(input), child16.dataType), - ScalaReflection.convertToScala(child17.eval(input), child17.dataType), - ScalaReflection.convertToScala(child18.eval(input), child18.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input)), + converter12(child12.eval(input)), + converter13(child13.eval(input)), + converter14(child14.eval(input)), + converter15(child15.eval(input)), + converter16(child16.eval(input)), + converter17(child17.eval(input)), + converter18(child18.eval(input))) } - + case 20 => val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] val child0 = children(0) @@ -577,30 +769,50 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val child17 = children(17) val child18 = children(18) val child19 = children(19) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) + lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) + lazy val converter14 = CatalystTypeConverters.createToScalaConverter(child14.dataType) + lazy val converter15 = CatalystTypeConverters.createToScalaConverter(child15.dataType) + lazy val converter16 = CatalystTypeConverters.createToScalaConverter(child16.dataType) + lazy val converter17 = CatalystTypeConverters.createToScalaConverter(child17.dataType) + lazy val converter18 = CatalystTypeConverters.createToScalaConverter(child18.dataType) + lazy val converter19 = CatalystTypeConverters.createToScalaConverter(child19.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType), - ScalaReflection.convertToScala(child2.eval(input), child2.dataType), - ScalaReflection.convertToScala(child3.eval(input), child3.dataType), - ScalaReflection.convertToScala(child4.eval(input), child4.dataType), - ScalaReflection.convertToScala(child5.eval(input), child5.dataType), - ScalaReflection.convertToScala(child6.eval(input), child6.dataType), - ScalaReflection.convertToScala(child7.eval(input), child7.dataType), - ScalaReflection.convertToScala(child8.eval(input), child8.dataType), - ScalaReflection.convertToScala(child9.eval(input), child9.dataType), - ScalaReflection.convertToScala(child10.eval(input), child10.dataType), - ScalaReflection.convertToScala(child11.eval(input), child11.dataType), - ScalaReflection.convertToScala(child12.eval(input), child12.dataType), - ScalaReflection.convertToScala(child13.eval(input), child13.dataType), - ScalaReflection.convertToScala(child14.eval(input), child14.dataType), - ScalaReflection.convertToScala(child15.eval(input), child15.dataType), - ScalaReflection.convertToScala(child16.eval(input), child16.dataType), - ScalaReflection.convertToScala(child17.eval(input), child17.dataType), - ScalaReflection.convertToScala(child18.eval(input), child18.dataType), - ScalaReflection.convertToScala(child19.eval(input), child19.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input)), + converter12(child12.eval(input)), + converter13(child13.eval(input)), + converter14(child14.eval(input)), + converter15(child15.eval(input)), + converter16(child16.eval(input)), + converter17(child17.eval(input)), + converter18(child18.eval(input)), + converter19(child19.eval(input))) } - + case 21 => val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] val child0 = children(0) @@ -624,31 +836,52 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val child18 = children(18) val child19 = children(19) val child20 = children(20) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) + lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) + lazy val converter14 = CatalystTypeConverters.createToScalaConverter(child14.dataType) + lazy val converter15 = CatalystTypeConverters.createToScalaConverter(child15.dataType) + lazy val converter16 = CatalystTypeConverters.createToScalaConverter(child16.dataType) + lazy val converter17 = CatalystTypeConverters.createToScalaConverter(child17.dataType) + lazy val converter18 = CatalystTypeConverters.createToScalaConverter(child18.dataType) + lazy val converter19 = CatalystTypeConverters.createToScalaConverter(child19.dataType) + lazy val converter20 = CatalystTypeConverters.createToScalaConverter(child20.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType), - ScalaReflection.convertToScala(child2.eval(input), child2.dataType), - ScalaReflection.convertToScala(child3.eval(input), child3.dataType), - ScalaReflection.convertToScala(child4.eval(input), child4.dataType), - ScalaReflection.convertToScala(child5.eval(input), child5.dataType), - ScalaReflection.convertToScala(child6.eval(input), child6.dataType), - ScalaReflection.convertToScala(child7.eval(input), child7.dataType), - ScalaReflection.convertToScala(child8.eval(input), child8.dataType), - ScalaReflection.convertToScala(child9.eval(input), child9.dataType), - ScalaReflection.convertToScala(child10.eval(input), child10.dataType), - ScalaReflection.convertToScala(child11.eval(input), child11.dataType), - ScalaReflection.convertToScala(child12.eval(input), child12.dataType), - ScalaReflection.convertToScala(child13.eval(input), child13.dataType), - ScalaReflection.convertToScala(child14.eval(input), child14.dataType), - ScalaReflection.convertToScala(child15.eval(input), child15.dataType), - ScalaReflection.convertToScala(child16.eval(input), child16.dataType), - ScalaReflection.convertToScala(child17.eval(input), child17.dataType), - ScalaReflection.convertToScala(child18.eval(input), child18.dataType), - ScalaReflection.convertToScala(child19.eval(input), child19.dataType), - ScalaReflection.convertToScala(child20.eval(input), child20.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input)), + converter12(child12.eval(input)), + converter13(child13.eval(input)), + converter14(child14.eval(input)), + converter15(child15.eval(input)), + converter16(child16.eval(input)), + converter17(child17.eval(input)), + converter18(child18.eval(input)), + converter19(child19.eval(input)), + converter20(child20.eval(input))) } - + case 22 => val func = function.asInstanceOf[(Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) => Any] val child0 = children(0) @@ -673,35 +906,57 @@ case class ScalaUdf(function: AnyRef, dataType: DataType, children: Seq[Expressi val child19 = children(19) val child20 = children(20) val child21 = children(21) + lazy val converter0 = CatalystTypeConverters.createToScalaConverter(child0.dataType) + lazy val converter1 = CatalystTypeConverters.createToScalaConverter(child1.dataType) + lazy val converter2 = CatalystTypeConverters.createToScalaConverter(child2.dataType) + lazy val converter3 = CatalystTypeConverters.createToScalaConverter(child3.dataType) + lazy val converter4 = CatalystTypeConverters.createToScalaConverter(child4.dataType) + lazy val converter5 = CatalystTypeConverters.createToScalaConverter(child5.dataType) + lazy val converter6 = CatalystTypeConverters.createToScalaConverter(child6.dataType) + lazy val converter7 = CatalystTypeConverters.createToScalaConverter(child7.dataType) + lazy val converter8 = CatalystTypeConverters.createToScalaConverter(child8.dataType) + lazy val converter9 = CatalystTypeConverters.createToScalaConverter(child9.dataType) + lazy val converter10 = CatalystTypeConverters.createToScalaConverter(child10.dataType) + lazy val converter11 = CatalystTypeConverters.createToScalaConverter(child11.dataType) + lazy val converter12 = CatalystTypeConverters.createToScalaConverter(child12.dataType) + lazy val converter13 = CatalystTypeConverters.createToScalaConverter(child13.dataType) + lazy val converter14 = CatalystTypeConverters.createToScalaConverter(child14.dataType) + lazy val converter15 = CatalystTypeConverters.createToScalaConverter(child15.dataType) + lazy val converter16 = CatalystTypeConverters.createToScalaConverter(child16.dataType) + lazy val converter17 = CatalystTypeConverters.createToScalaConverter(child17.dataType) + lazy val converter18 = CatalystTypeConverters.createToScalaConverter(child18.dataType) + lazy val converter19 = CatalystTypeConverters.createToScalaConverter(child19.dataType) + lazy val converter20 = CatalystTypeConverters.createToScalaConverter(child20.dataType) + lazy val converter21 = CatalystTypeConverters.createToScalaConverter(child21.dataType) (input: Row) => { func( - ScalaReflection.convertToScala(child0.eval(input), child0.dataType), - ScalaReflection.convertToScala(child1.eval(input), child1.dataType), - ScalaReflection.convertToScala(child2.eval(input), child2.dataType), - ScalaReflection.convertToScala(child3.eval(input), child3.dataType), - ScalaReflection.convertToScala(child4.eval(input), child4.dataType), - ScalaReflection.convertToScala(child5.eval(input), child5.dataType), - ScalaReflection.convertToScala(child6.eval(input), child6.dataType), - ScalaReflection.convertToScala(child7.eval(input), child7.dataType), - ScalaReflection.convertToScala(child8.eval(input), child8.dataType), - ScalaReflection.convertToScala(child9.eval(input), child9.dataType), - ScalaReflection.convertToScala(child10.eval(input), child10.dataType), - ScalaReflection.convertToScala(child11.eval(input), child11.dataType), - ScalaReflection.convertToScala(child12.eval(input), child12.dataType), - ScalaReflection.convertToScala(child13.eval(input), child13.dataType), - ScalaReflection.convertToScala(child14.eval(input), child14.dataType), - ScalaReflection.convertToScala(child15.eval(input), child15.dataType), - ScalaReflection.convertToScala(child16.eval(input), child16.dataType), - ScalaReflection.convertToScala(child17.eval(input), child17.dataType), - ScalaReflection.convertToScala(child18.eval(input), child18.dataType), - ScalaReflection.convertToScala(child19.eval(input), child19.dataType), - ScalaReflection.convertToScala(child20.eval(input), child20.dataType), - ScalaReflection.convertToScala(child21.eval(input), child21.dataType)) + converter0(child0.eval(input)), + converter1(child1.eval(input)), + converter2(child2.eval(input)), + converter3(child3.eval(input)), + converter4(child4.eval(input)), + converter5(child5.eval(input)), + converter6(child6.eval(input)), + converter7(child7.eval(input)), + converter8(child8.eval(input)), + converter9(child9.eval(input)), + converter10(child10.eval(input)), + converter11(child11.eval(input)), + converter12(child12.eval(input)), + converter13(child13.eval(input)), + converter14(child14.eval(input)), + converter15(child15.eval(input)), + converter16(child16.eval(input)), + converter17(child17.eval(input)), + converter18(child18.eval(input)), + converter19(child19.eval(input)), + converter20(child20.eval(input)), + converter21(child21.eval(input))) } } - + // scalastyle:on - - override def eval(input: Row): Any = ScalaReflection.convertToCatalyst(f(input), dataType) + + override def eval(input: Row): Any = CatalystTypeConverters.convertToCatalyst(f(input), dataType) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LocalRelation.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LocalRelation.scala index bb79dc340553b..e3e070f0ff307 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LocalRelation.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LocalRelation.scala @@ -18,9 +18,9 @@ package org.apache.spark.sql.catalyst.plans.logical import org.apache.spark.sql.Row -import org.apache.spark.sql.catalyst.analysis +import org.apache.spark.sql.catalyst.{CatalystTypeConverters, analysis} import org.apache.spark.sql.catalyst.expressions.Attribute -import org.apache.spark.sql.types.{DataTypeConversions, StructType, StructField} +import org.apache.spark.sql.types.{StructType, StructField} object LocalRelation { def apply(output: Attribute*): LocalRelation = new LocalRelation(output) @@ -31,7 +31,8 @@ object LocalRelation { def fromProduct(output: Seq[Attribute], data: Seq[Product]): LocalRelation = { val schema = StructType.fromAttributes(output) - LocalRelation(output, data.map(row => DataTypeConversions.productToRow(row, schema))) + val converter = CatalystTypeConverters.createToCatalystConverter(schema) + LocalRelation(output, data.map(converter(_).asInstanceOf[Row])) } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeConversions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeConversions.scala deleted file mode 100644 index a9d63e784963d..0000000000000 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeConversions.scala +++ /dev/null @@ -1,77 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.types - -import java.text.SimpleDateFormat - -import org.apache.spark.sql.Row -import org.apache.spark.sql.catalyst.ScalaReflection -import org.apache.spark.sql.catalyst.expressions.GenericMutableRow - - -private[sql] object DataTypeConversions { - - def productToRow(product: Product, schema: StructType): Row = { - val mutableRow = new GenericMutableRow(product.productArity) - val schemaFields = schema.fields.toArray - - var i = 0 - while (i < mutableRow.length) { - mutableRow(i) = - ScalaReflection.convertToCatalyst(product.productElement(i), schemaFields(i).dataType) - i += 1 - } - - mutableRow - } - - def stringToTime(s: String): java.util.Date = { - if (!s.contains('T')) { - // JDBC escape string - if (s.contains(' ')) { - java.sql.Timestamp.valueOf(s) - } else { - java.sql.Date.valueOf(s) - } - } else if (s.endsWith("Z")) { - // this is zero timezone of ISO8601 - stringToTime(s.substring(0, s.length - 1) + "GMT-00:00") - } else if (s.indexOf("GMT") == -1) { - // timezone with ISO8601 - val inset = "+00.00".length - val s0 = s.substring(0, s.length - inset) - val s1 = s.substring(s.length - inset, s.length) - if (s0.substring(s0.lastIndexOf(':')).contains('.')) { - stringToTime(s0 + "GMT" + s1) - } else { - stringToTime(s0 + ".0GMT" + s1) - } - } else { - // ISO8601 with GMT insert - val ISO8601GMT: SimpleDateFormat = new SimpleDateFormat( "yyyy-MM-dd'T'HH:mm:ss.SSSz" ) - ISO8601GMT.parse(s) - } - } - - /** Converts Java objects to catalyst rows / types */ - def convertJavaToCatalyst(a: Any, dataType: DataType): Any = (a, dataType) match { - case (obj, udt: UserDefinedType[_]) => ScalaReflection.convertToCatalyst(obj, udt) // Scala type - case (d: java.math.BigDecimal, _) => Decimal(d) - case (other, _) => other - } -} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateUtils.scala index 8a1a3b81b3d2c..504fb05842505 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateUtils.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateUtils.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.types import java.sql.Date +import java.text.SimpleDateFormat import java.util.{Calendar, TimeZone} import org.apache.spark.sql.catalyst.expressions.Cast @@ -57,4 +58,32 @@ object DateUtils { } def toString(days: Int): String = Cast.threadLocalDateFormat.get.format(toJavaDate(days)) + + def stringToTime(s: String): java.util.Date = { + if (!s.contains('T')) { + // JDBC escape string + if (s.contains(' ')) { + java.sql.Timestamp.valueOf(s) + } else { + java.sql.Date.valueOf(s) + } + } else if (s.endsWith("Z")) { + // this is zero timezone of ISO8601 + stringToTime(s.substring(0, s.length - 1) + "GMT-00:00") + } else if (s.indexOf("GMT") == -1) { + // timezone with ISO8601 + val inset = "+00.00".length + val s0 = s.substring(0, s.length - inset) + val s1 = s.substring(s.length - inset, s.length) + if (s0.substring(s0.lastIndexOf(':')).contains('.')) { + stringToTime(s0 + "GMT" + s1) + } else { + stringToTime(s0 + ".0GMT" + s1) + } + } else { + // ISO8601 with GMT insert + val ISO8601GMT: SimpleDateFormat = new SimpleDateFormat( "yyyy-MM-dd'T'HH:mm:ss.SSSz" ) + ISO8601GMT.parse(s) + } + } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala index eee00e3f7ea76..bbc0b661a0c0c 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/ScalaReflectionSuite.scala @@ -260,7 +260,7 @@ class ScalaReflectionSuite extends FunSuite { val data = PrimitiveData(1, 1, 1, 1, 1, 1, true) val convertedData = Row(1, 1.toLong, 1.toDouble, 1.toFloat, 1.toShort, 1.toByte, true) val dataType = schemaFor[PrimitiveData].dataType - assert(convertToCatalyst(data, dataType) === convertedData) + assert(CatalystTypeConverters.convertToCatalyst(data, dataType) === convertedData) } test("convert Option[Product] to catalyst") { @@ -270,7 +270,7 @@ class ScalaReflectionSuite extends FunSuite { val dataType = schemaFor[OptionalData].dataType val convertedData = Row(2, 2.toLong, 2.toDouble, 2.toFloat, 2.toShort, 2.toByte, true, Row(1, 1, 1, 1, 1, 1, true)) - assert(convertToCatalyst(data, dataType) === convertedData) + assert(CatalystTypeConverters.convertToCatalyst(data, dataType) === convertedData) } test("infer schema from case class with multiple constructors") { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 5c6016a4a2ce2..9b9adf855077a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -33,7 +33,7 @@ import org.apache.spark.api.java.JavaRDD import org.apache.spark.api.python.SerDeUtil import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel -import org.apache.spark.sql.catalyst.{ScalaReflection, SqlParser} +import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, SqlParser} import org.apache.spark.sql.catalyst.analysis.{UnresolvedRelation, ResolvedStar} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.{JoinType, Inner} @@ -713,7 +713,7 @@ class DataFrame private[sql]( val schema = ScalaReflection.schemaFor[A].dataType.asInstanceOf[StructType] val attributes = schema.toAttributes val rowFunction = - f.andThen(_.map(ScalaReflection.convertToCatalyst(_, schema).asInstanceOf[Row])) + f.andThen(_.map(CatalystTypeConverters.convertToCatalyst(_, schema).asInstanceOf[Row])) val generator = UserDefinedGenerator(attributes, rowFunction, input.map(_.expr)) Generate(generator, join = true, outer = false, None, logicalPlan) @@ -734,7 +734,7 @@ class DataFrame private[sql]( val dataType = ScalaReflection.schemaFor[B].dataType val attributes = AttributeReference(outputColumn, dataType)() :: Nil def rowFunction(row: Row): TraversableOnce[Row] = { - f(row(0).asInstanceOf[A]).map(o => Row(ScalaReflection.convertToCatalyst(o, dataType))) + f(row(0).asInstanceOf[A]).map(o => Row(CatalystTypeConverters.convertToCatalyst(o, dataType))) } val generator = UserDefinedGenerator(attributes, rowFunction, apply(inputColumn).expr :: Nil) @@ -961,7 +961,10 @@ class DataFrame private[sql]( lazy val rdd: RDD[Row] = { // use a local variable to make sure the map closure doesn't capture the whole DataFrame val schema = this.schema - queryExecution.executedPlan.execute().map(ScalaReflection.convertRowToScala(_, schema)) + queryExecution.executedPlan.execute().mapPartitions { rows => + val converter = CatalystTypeConverters.createToScalaConverter(schema) + rows.map(converter(_).asInstanceOf[Row]) + } } /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala index 39dd14e796f06..c25ef58e6f62a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala @@ -31,9 +31,9 @@ import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.analysis._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.optimizer.{DefaultOptimizer, Optimizer} -import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan, OneRowRelation} +import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan} import org.apache.spark.sql.catalyst.rules.RuleExecutor -import org.apache.spark.sql.catalyst.{ScalaReflection, expressions} +import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, expressions} import org.apache.spark.sql.execution.{Filter, _} import org.apache.spark.sql.jdbc.{JDBCPartition, JDBCPartitioningInfo, JDBCRelation} import org.apache.spark.sql.json._ @@ -404,7 +404,8 @@ class SQLContext(@transient val sparkContext: SparkContext) // TODO: use MutableProjection when rowRDD is another DataFrame and the applied // schema differs from the existing schema on any field data type. val catalystRows = if (needsConversion) { - rowRDD.map(ScalaReflection.convertToCatalyst(_, schema).asInstanceOf[Row]) + val converter = CatalystTypeConverters.createToCatalystConverter(schema) + rowRDD.map(converter(_).asInstanceOf[Row]) } else { rowRDD } @@ -459,7 +460,7 @@ class SQLContext(@transient val sparkContext: SparkContext) iter.map { row => new GenericRow( extractors.zip(attributeSeq).map { case (e, attr) => - DataTypeConversions.convertJavaToCatalyst(e.invoke(row), attr.dataType) + CatalystTypeConverters.convertToCatalyst(e.invoke(row), attr.dataType) }.toArray[Any] ) : Row } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala index d8955725e59b1..656bdd7212f56 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala @@ -20,14 +20,12 @@ package org.apache.spark.sql.execution import org.apache.spark.annotation.DeveloperApi import org.apache.spark.rdd.RDD import org.apache.spark.sql.{Row, SQLContext} -import org.apache.spark.sql.catalyst.ScalaReflection +import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation -import org.apache.spark.sql.catalyst.expressions.{Attribute, GenericMutableRow} +import org.apache.spark.sql.catalyst.expressions.{SpecificMutableRow, Attribute} import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Statistics} import org.apache.spark.sql.types.StructType -import scala.collection.immutable - /** * :: DeveloperApi :: */ @@ -39,13 +37,15 @@ object RDDConversions { Iterator.empty } else { val bufferedIterator = iterator.buffered - val mutableRow = new GenericMutableRow(bufferedIterator.head.productArity) + val mutableRow = new SpecificMutableRow(schema.fields.map(_.dataType)) val schemaFields = schema.fields.toArray + val converters = schemaFields.map { + f => CatalystTypeConverters.createToCatalystConverter(f.dataType) + } bufferedIterator.map { r => var i = 0 while (i < mutableRow.length) { - mutableRow(i) = - ScalaReflection.convertToCatalyst(r.productElement(i), schemaFields(i).dataType) + mutableRow(i) = converters(i)(r.productElement(i)) i += 1 } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala index 5bd699a2fa949..8a8c3a404323a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.execution import org.apache.spark.rdd.RDD import org.apache.spark.sql.Row -import org.apache.spark.sql.catalyst.ScalaReflection +import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.catalyst.expressions.Attribute @@ -32,9 +32,15 @@ case class LocalTableScan(output: Seq[Attribute], rows: Seq[Row]) extends LeafNo override def execute(): RDD[Row] = rdd - override def executeCollect(): Array[Row] = - rows.map(ScalaReflection.convertRowToScala(_, schema)).toArray - override def executeTake(limit: Int): Array[Row] = - rows.map(ScalaReflection.convertRowToScala(_, schema)).take(limit).toArray + override def executeCollect(): Array[Row] = { + val converter = CatalystTypeConverters.createToScalaConverter(schema) + rows.map(converter(_).asInstanceOf[Row]).toArray + } + + + override def executeTake(limit: Int): Array[Row] = { + val converter = CatalystTypeConverters.createToScalaConverter(schema) + rows.map(converter(_).asInstanceOf[Row]).take(limit).toArray + } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala index d239637cd4b4e..fabcf6b4a0570 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala @@ -21,7 +21,7 @@ import org.apache.spark.annotation.DeveloperApi import org.apache.spark.Logging import org.apache.spark.rdd.RDD import org.apache.spark.sql.SQLContext -import org.apache.spark.sql.catalyst.{ScalaReflection, trees} +import org.apache.spark.sql.catalyst.{CatalystTypeConverters, trees} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.expressions.codegen._ import org.apache.spark.sql.catalyst.plans.QueryPlan @@ -80,8 +80,12 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ /** * Runs this query returning the result as an array. */ + def executeCollect(): Array[Row] = { - execute().map(ScalaReflection.convertRowToScala(_, schema)).collect() + execute().mapPartitions { iter => + val converter = CatalystTypeConverters.createToScalaConverter(schema) + iter.map(converter(_).asInstanceOf[Row]) + }.collect() } /** @@ -125,7 +129,8 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ partsScanned += numPartsToTry } - buf.toArray.map(ScalaReflection.convertRowToScala(_, this.schema)) + val converter = CatalystTypeConverters.createToScalaConverter(schema) + buf.toArray.map(converter(_).asInstanceOf[Row]) } protected def newProjection( diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala index 1f5251a20376f..6eec520abff53 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala @@ -21,7 +21,7 @@ import org.apache.spark.{SparkEnv, HashPartitioner, SparkConf} import org.apache.spark.annotation.DeveloperApi import org.apache.spark.rdd.{RDD, ShuffledRDD} import org.apache.spark.shuffle.sort.SortShuffleManager -import org.apache.spark.sql.catalyst.ScalaReflection +import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.catalyst.errors._ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.physical._ @@ -139,9 +139,10 @@ case class TakeOrdered(limit: Int, sortOrder: Seq[SortOrder], child: SparkPlan) private def collectData(): Array[Row] = child.execute().map(_.copy()).takeOrdered(limit)(ord) - // TODO: Is this copying for no reason? - override def executeCollect(): Array[Row] = - collectData().map(ScalaReflection.convertRowToScala(_, this.schema)) + override def executeCollect(): Array[Row] = { + val converter = CatalystTypeConverters.createToScalaConverter(schema) + collectData().map(converter(_).asInstanceOf[Row]) + } // TODO: Terminal split should be implemented differently from non-terminal split. // TODO: Pick num splits based on |limit|. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala index 0b770f2251943..b1e8521383756 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala @@ -391,7 +391,7 @@ private[sql] object JsonRDD extends Logging { value match { // only support string as date case value: java.lang.String => - DateUtils.millisToDays(DataTypeConversions.stringToTime(value).getTime) + DateUtils.millisToDays(DateUtils.stringToTime(value).getTime) case value: java.sql.Date => DateUtils.fromJavaDate(value) } } @@ -400,7 +400,7 @@ private[sql] object JsonRDD extends Logging { value match { case value: java.lang.Integer => new Timestamp(value.asInstanceOf[Int].toLong) case value: java.lang.Long => new Timestamp(value) - case value: java.lang.String => toTimestamp(DataTypeConversions.stringToTime(value).getTime) + case value: java.lang.String => toTimestamp(DateUtils.stringToTime(value).getTime) } } diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java index 1ff2d5a190521..6d0fbe83c2f36 100644 --- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java @@ -20,6 +20,8 @@ import java.io.Serializable; import java.util.Arrays; +import scala.collection.Seq; + import org.junit.After; import org.junit.Assert; import org.junit.Before; @@ -127,6 +129,12 @@ public void testCreateDataFrameFromJavaBeans() { schema.apply("b")); Row first = df.select("a", "b").first(); Assert.assertEquals(bean.getA(), first.getDouble(0), 0.0); - Assert.assertArrayEquals(bean.getB(), first.getAs(1)); + // Now Java lists and maps are converetd to Scala Seq's and Map's. Once we get a Seq below, + // verify that it has the expected length, and contains expected elements. + Seq result = first.getAs(1); + Assert.assertEquals(bean.getB().length, result.length()); + for (int i = 0; i < result.length(); i++) { + Assert.assertEquals(bean.getB()[i], result.apply(i)); + } } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala index 1fe0b76c00be3..fd0e2746dc045 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala @@ -895,8 +895,7 @@ class JsonSuite extends QueryTest { ) } - test("SPARK-4228 DataFrame to JSON") - { + test("SPARK-4228 DataFrame to JSON") { val schema1 = StructType( StructField("f1", IntegerType, false) :: StructField("f2", StringType, false) :: From 95a07591b3e23782a7021ef2bbf07a67a1e4a83a Mon Sep 17 00:00:00 2001 From: Liang-Chi Hsieh Date: Sat, 11 Apr 2015 13:07:41 +0100 Subject: [PATCH 221/591] [Minor][Core] Fix typo Author: Liang-Chi Hsieh Closes #5466 from viirya/fix_ShuffleMapTask_typo and squashes the following commits: 2789fd5 [Liang-Chi Hsieh] fix typo. --- .../main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala index fd0d484b45460..6c7d00069acb2 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala @@ -33,7 +33,7 @@ import org.apache.spark.shuffle.ShuffleWriter * See [[org.apache.spark.scheduler.Task]] for more information. * * @param stageId id of the stage this task belongs to - * @param taskBinary broadcast version of of the RDD and the ShuffleDependency. Once deserialized, + * @param taskBinary broadcast version of the RDD and the ShuffleDependency. Once deserialized, * the type should be (RDD[_], ShuffleDependency[_, _, _]). * @param partition partition of the RDD this task is associated with * @param locs preferred task execution locations for locality scheduling From 694aef0d71d2683eaf63cbd1d8e95c2da423b72e Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Sat, 11 Apr 2015 13:10:01 +0100 Subject: [PATCH 222/591] [hotfix] [build] Make sure JAVA_HOME is set for tests. This is needed at least for YARN integration tests, since `$JAVA_HOME` is used to launch the executors. Author: Marcelo Vanzin Closes #5441 from vanzin/yarn-test-test and squashes the following commits: 3eeec30 [Marcelo Vanzin] Use JAVA_HOME when available, java.home otherwise. d71f1bb [Marcelo Vanzin] And sbt too. 6bda399 [Marcelo Vanzin] WIP: Testing to see whether this fixes the yarn test issue. --- pom.xml | 14 ++++++++++++++ project/SparkBuild.scala | 10 +++++++--- 2 files changed, 21 insertions(+), 3 deletions(-) diff --git a/pom.xml b/pom.xml index 70e297c4f082a..d8881c213bf07 100644 --- a/pom.xml +++ b/pom.xml @@ -159,6 +159,8 @@ 1.1.1.6 1.1.2 + ${java.home} + ${test_classpath} + ${test.java.home} true @@ -1224,6 +1227,7 @@ launched by the tests have access to the correct test-time classpath. --> ${test_classpath} + ${test.java.home} true @@ -1716,6 +1720,16 @@ + + test-java-home + + env.JAVA_HOME + + + ${env.JAVA_HOME} + + + scala-2.11 diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index d3faa551a4b14..5f51f4b58f97a 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -119,7 +119,9 @@ object SparkBuild extends PomBuild { lazy val publishLocalBoth = TaskKey[Unit]("publish-local", "publish local for m2 and ivy") lazy val sharedSettings = graphSettings ++ genjavadocSettings ++ Seq ( - javaHome := Properties.envOrNone("JAVA_HOME").map(file), + javaHome := sys.env.get("JAVA_HOME") + .orElse(sys.props.get("java.home").map { p => new File(p).getParentFile().getAbsolutePath() }) + .map(file), incOptions := incOptions.value.withNameHashing(true), retrieveManaged := true, retrievePattern := "[type]s/[artifact](-[revision])(-[classifier]).[ext]", @@ -426,8 +428,10 @@ object TestSettings { fork := true, // Setting SPARK_DIST_CLASSPATH is a simple way to make sure any child processes // launched by the tests have access to the correct test-time classpath. - envVars in Test += ("SPARK_DIST_CLASSPATH" -> - (fullClasspath in Test).value.files.map(_.getAbsolutePath).mkString(":").stripSuffix(":")), + envVars in Test ++= Map( + "SPARK_DIST_CLASSPATH" -> + (fullClasspath in Test).value.files.map(_.getAbsolutePath).mkString(":").stripSuffix(":"), + "JAVA_HOME" -> sys.env.get("JAVA_HOME").getOrElse(sys.props("java.home"))), javaOptions in Test += "-Dspark.test.home=" + sparkHome, javaOptions in Test += "-Dspark.testing=1", javaOptions in Test += "-Dspark.port.maxRetries=100", From 3ceb810aa8e69bc4abb69cbe713a624cb351cb35 Mon Sep 17 00:00:00 2001 From: Cheng Hao Date: Sat, 11 Apr 2015 22:11:03 +0800 Subject: [PATCH 223/591] [SPARK-6835] [SQL] Fix bug of Hive UDTF in Lateral View (ClassNotFound) ```SQL select key, v from src lateral view stack(3, 1+1, 2+2, 3) d as v; ``` Will cause exception ``` java.lang.ClassNotFoundException: stack at java.net.URLClassLoader$1.run(URLClassLoader.java:366) at java.net.URLClassLoader$1.run(URLClassLoader.java:355) at java.security.AccessController.doPrivileged(Native Method) at java.net.URLClassLoader.findClass(URLClassLoader.java:354) at java.lang.ClassLoader.loadClass(ClassLoader.java:425) at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:308) at java.lang.ClassLoader.loadClass(ClassLoader.java:358) at org.apache.spark.sql.hive.HiveFunctionWrapper.createFunction(Shim13.scala:148) at org.apache.spark.sql.hive.HiveGenericUdtf.function$lzycompute(hiveUdfs.scala:274) at org.apache.spark.sql.hive.HiveGenericUdtf.function(hiveUdfs.scala:274) at org.apache.spark.sql.hive.HiveGenericUdtf.outputInspector$lzycompute(hiveUdfs.scala:280) at org.apache.spark.sql.hive.HiveGenericUdtf.outputInspector(hiveUdfs.scala:280) at org.apache.spark.sql.hive.HiveGenericUdtf.outputDataTypes$lzycompute(hiveUdfs.scala:285) at org.apache.spark.sql.hive.HiveGenericUdtf.outputDataTypes(hiveUdfs.scala:285) at org.apache.spark.sql.hive.HiveGenericUdtf.makeOutput(hiveUdfs.scala:291) at org.apache.spark.sql.catalyst.expressions.Generator.output(generators.scala:60) at org.apache.spark.sql.catalyst.plans.logical.Generate$$anonfun$2.apply(basicOperators.scala:60) at org.apache.spark.sql.catalyst.plans.logical.Generate$$anonfun$2.apply(basicOperators.scala:60) at scala.Option.map(Option.scala:145) at org.apache.spark.sql.catalyst.plans.logical.Generate.generatorOutput(basicOperators.scala:60) at org.apache.spark.sql.catalyst.plans.logical.Generate.output(basicOperators.scala:70) at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveChildren$1.apply(LogicalPlan.scala:117) at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveChildren$1.apply(LogicalPlan.scala:117) ``` Author: Cheng Hao Closes #5444 from chenghao-intel/hive_udtf and squashes the following commits: 065a98c [Cheng Hao] fix bug of Hive UDTF in Lateral View (ClassNotFound) --- .../main/scala/org/apache/spark/sql/hive/HiveQl.scala | 9 ++++++++- .../apache/spark/sql/hive/execution/SQLQuerySuite.scala | 6 ++++++ 2 files changed, 14 insertions(+), 1 deletion(-) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala index 077e64133faad..0bdaf5f7ef8ef 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala @@ -20,6 +20,8 @@ package org.apache.spark.sql.hive import java.sql.Date +import org.apache.hadoop.hive.ql.exec.{FunctionRegistry, FunctionInfo} + import scala.collection.mutable.ArrayBuffer import org.apache.hadoop.hive.conf.HiveConf @@ -1284,8 +1286,13 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C Explode(attributes, nodeToExpr(child)) case Token("TOK_FUNCTION", Token(functionName, Nil) :: children) => + val functionInfo: FunctionInfo = + Option(FunctionRegistry.getFunctionInfo(functionName.toLowerCase)).getOrElse( + sys.error(s"Couldn't find function $functionName")) + val functionClassName = functionInfo.getFunctionClass.getName + HiveGenericUdtf( - new HiveFunctionWrapper(functionName), + new HiveFunctionWrapper(functionClassName), attributes, children.map(nodeToExpr)) diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala index 4c369c0634bda..47b4cb9ca61ff 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala @@ -51,6 +51,12 @@ case class Order( * valid, but Hive currently cannot execute it. */ class SQLQuerySuite extends QueryTest { + test("SPARK-6835: udtf in lateral view") { + val df = Seq((1, 1)).toDF("c1", "c2") + df.registerTempTable("table1") + val query = sql("SELECT c1, v FROM table1 LATERAL VIEW stack(3, 1, c1 + 1, c1 + 2) d AS v") + checkAnswer(query, Row(1, 1) :: Row(1, 2) :: Row(1, 3) :: Nil) + } test("SPARK-6851: Self-joined converted parquet tables") { val orders = Seq( From 198cf2a3fa9babb5e8b7b44da0471b63b9f3ec04 Mon Sep 17 00:00:00 2001 From: Liang-Chi Hsieh Date: Sat, 11 Apr 2015 14:50:50 -0700 Subject: [PATCH 224/591] [SPARK-6858][SQL] Register Java HashMap for SparkSqlSerializer Since now kyro serializer is used for `GeneralHashedRelation` whether kyro is enabled or not, it is better to register Java `HashMap` in `SparkSqlSerializer`. Author: Liang-Chi Hsieh Closes #5465 from viirya/register_hashmap and squashes the following commits: 9062601 [Liang-Chi Hsieh] Register Java HashMap for SparkSqlSerializer. --- .../org/apache/spark/sql/execution/SparkSqlSerializer.scala | 2 ++ 1 file changed, 2 insertions(+) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala index 347e2f4a1a1af..914f387dec78f 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.execution import java.nio.ByteBuffer +import java.util.{HashMap => JavaHashMap} import org.apache.spark.sql.types.Decimal @@ -54,6 +55,7 @@ private[sql] class SparkSqlSerializer(conf: SparkConf) extends KryoSerializer(co kryo.register(classOf[org.apache.spark.util.collection.OpenHashSet[_]], new OpenHashSetSerializer) kryo.register(classOf[Decimal]) + kryo.register(classOf[JavaHashMap[_, _]]) kryo.setReferences(false) kryo From 5f7b7cdab41002ad291f0a1ba7777cfff21d0019 Mon Sep 17 00:00:00 2001 From: "Santiago M. Mola" Date: Sat, 11 Apr 2015 14:52:49 -0700 Subject: [PATCH 225/591] [SPARK-6611][SQL] Add support for INTEGER as synonym of INT. https://issues.apache.org/jira/browse/SPARK-6611 Author: Santiago M. Mola Closes #5271 from smola/features/integer-parse and squashes the following commits: f5c1c64 [Santiago M. Mola] [SPARK-6611] Add support for INTEGER as synonym of INT. --- .../main/scala/org/apache/spark/sql/types/DataTypeParser.scala | 2 +- .../scala/org/apache/spark/sql/types/DataTypeParserSuite.scala | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala index 34270d0ca7cd7..5163f05879e42 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala @@ -40,7 +40,7 @@ private[sql] trait DataTypeParser extends StandardTokenParsers { protected lazy val primitiveType: Parser[DataType] = "(?i)string".r ^^^ StringType | "(?i)float".r ^^^ FloatType | - "(?i)int".r ^^^ IntegerType | + "(?i)(?:int|integer)".r ^^^ IntegerType | "(?i)tinyint".r ^^^ ByteType | "(?i)smallint".r ^^^ ShortType | "(?i)double".r ^^^ DoubleType | diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala index 1ba21b64603ac..169125264a803 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala @@ -34,10 +34,12 @@ class DataTypeParserSuite extends FunSuite { } checkDataType("int", IntegerType) + checkDataType("integer", IntegerType) checkDataType("BooLean", BooleanType) checkDataType("tinYint", ByteType) checkDataType("smallINT", ShortType) checkDataType("INT", IntegerType) + checkDataType("INTEGER", IntegerType) checkDataType("bigint", LongType) checkDataType("float", FloatType) checkDataType("dOUBle", DoubleType) From 6437e7cc3bd405ebd4ad7d8f9c7a5e703652ad36 Mon Sep 17 00:00:00 2001 From: "Santiago M. Mola" Date: Sat, 11 Apr 2015 15:42:03 -0700 Subject: [PATCH 226/591] [SPARK-6863] Fix formatting on SQL programming guide. https://issues.apache.org/jira/browse/SPARK-6863 Author: Santiago M. Mola Closes #5472 from smola/fix/sql-docs and squashes the following commits: 42503d4 [Santiago M. Mola] [SPARK-6863] Fix formatting on SQL programming guide. --- docs/sql-programming-guide.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index 663f656883721..332618edf0c55 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -1784,6 +1784,7 @@ in Hive deployments. **Esoteric Hive Features** + * `UNION` type * Unique join * Column statistics collecting: Spark SQL does not piggyback scans to collect column statistics at From 7dbd37160ff57f80cc7abdcaef95f8c6df20a0f0 Mon Sep 17 00:00:00 2001 From: "Guancheng (G.C.) Chen" Date: Sat, 11 Apr 2015 15:43:12 -0700 Subject: [PATCH 227/591] [Minor][SQL] Fix typo in sql In this PR, "analyser" is changed to "analyzer" to keep a consistent naming. Some other typos are also fixed. Author: Guancheng (G.C.) Chen Closes #5474 from gchen/sql-typo and squashes the following commits: 70e6e76 [Guancheng (G.C.) Chen] Merge branch 'sql-typo' of github.com:gchen/spark into sql-typo fb7a6e2 [Guancheng (G.C.) Chen] fix typo in sql 37e3da1 [Guancheng (G.C.) Chen] fix type in sql --- .../org/apache/spark/sql/catalyst/analysis/Analyzer.scala | 2 +- .../org/apache/spark/sql/catalyst/analysis/Catalog.scala | 4 ++-- .../apache/spark/sql/catalyst/analysis/FunctionRegistry.scala | 4 ++-- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala index b3aba4f68ddf9..524c73c31bbe1 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala @@ -27,7 +27,7 @@ import org.apache.spark.sql.types._ /** * A trivial [[Analyzer]] with an [[EmptyCatalog]] and [[EmptyFunctionRegistry]]. Used for testing - * when all relations are already filled in and the analyser needs only to resolve attribute + * when all relations are already filled in and the analyzer needs only to resolve attribute * references. */ object SimpleAnalyzer extends Analyzer(EmptyCatalog, EmptyFunctionRegistry, true) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala index 5eb7dff0cede8..b2f8157a1a61f 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala @@ -22,7 +22,7 @@ import scala.collection.mutable import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Subquery} /** - * Thrown by a catalog when a table cannot be found. The analzyer will rethrow the exception + * Thrown by a catalog when a table cannot be found. The analyzer will rethrow the exception * as an AnalysisException with the correct position information. */ class NoSuchTableException extends Exception @@ -201,7 +201,7 @@ trait OverrideCatalog extends Catalog { /** * A trivial catalog that returns an error when a relation is requested. Used for testing when all - * relations are already filled in and the analyser needs only to resolve attribute references. + * relations are already filled in and the analyzer needs only to resolve attribute references. */ object EmptyCatalog extends Catalog { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala index c43ea55899695..16ca5bcd57a72 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala @@ -57,8 +57,8 @@ class SimpleFunctionRegistry(val caseSensitive: Boolean) extends FunctionRegistr } /** - * A trivial catalog that returns an error when a function is requested. Used for testing when all - * functions are already filled in and the analyser needs only to resolve attribute references. + * A trivial catalog that returns an error when a function is requested. Used for testing when all + * functions are already filled in and the analyzer needs only to resolve attribute references. */ object EmptyFunctionRegistry extends FunctionRegistry { override def registerFunction(name: String, builder: FunctionBuilder): Unit = { From 2f53588738e95a2191f9844818e47f0d2ebbfd54 Mon Sep 17 00:00:00 2001 From: haiyang Date: Sat, 11 Apr 2015 18:30:17 -0700 Subject: [PATCH 228/591] [SPARK-6199] [SQL] Support CTE in HiveContext and SQLContext Author: haiyang Closes #4929 from haiyangsea/cte and squashes the following commits: 220b67d [haiyang] add golden files for cte test d3c7681 [haiyang] Merge branch 'master' into cte-repair 0ba2070 [haiyang] modify code style 9ce6b58 [haiyang] fix conflict ff74741 [haiyang] add comment for With plan 0d56af4 [haiyang] code indention 776a440 [haiyang] add comments for resolve relation strategy 2fccd7e [haiyang] add comments for resolve relation strategy 241bbe2 [haiyang] fix cte problem of view e9e1237 [haiyang] fix test case problem 614182f [haiyang] add test cases for CTE feature 32e415b [haiyang] add comment 1cc8c15 [haiyang] support with 03f1097 [haiyang] support with e960099 [haiyang] support with 9aaa874 [haiyang] support with 0566978 [haiyang] support with a99ecd2 [haiyang] support with c3fa4c2 [haiyang] support with 3b6077f [haiyang] support with 5f8abe3 [haiyang] support with 4572b05 [haiyang] support with f801f54 [haiyang] support with --- .../apache/spark/sql/catalyst/SqlParser.scala | 7 +++++ .../sql/catalyst/analysis/Analyzer.scala | 31 ++++++++++++++----- .../plans/logical/basicOperators.scala | 12 +++++++ .../org/apache/spark/sql/SQLQuerySuite.scala | 14 +++++++++ .../org/apache/spark/sql/hive/HiveQl.scala | 27 ++++++++++++---- ...ture #1-0-eedabbfe6ba8799f7b7782fb47a82768 | 3 ++ ...ture #2-0-aa03d104251f97e36bc52279cb9931c9 | 4 +++ ...ture #3-0-b5d4bf3c0ee92b2fda0ca24f422383f2 | 1 + .../sql/hive/execution/HiveQuerySuite.scala | 15 +++++++++ 9 files changed, 100 insertions(+), 14 deletions(-) create mode 100644 sql/hive/src/test/resources/golden/CTE feature #1-0-eedabbfe6ba8799f7b7782fb47a82768 create mode 100644 sql/hive/src/test/resources/golden/CTE feature #2-0-aa03d104251f97e36bc52279cb9931c9 create mode 100644 sql/hive/src/test/resources/golden/CTE feature #3-0-b5d4bf3c0ee92b2fda0ca24f422383f2 diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala index 89f4a19add1c6..ee04cb579deb6 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala @@ -111,6 +111,7 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser { protected val UPPER = Keyword("UPPER") protected val WHEN = Keyword("WHEN") protected val WHERE = Keyword("WHERE") + protected val WITH = Keyword("WITH") protected def assignAliases(exprs: Seq[Expression]): Seq[NamedExpression] = { exprs.zipWithIndex.map { @@ -127,6 +128,7 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser { | UNION ~ DISTINCT.? ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Distinct(Union(q1, q2)) } ) | insert + | cte ) protected lazy val select: Parser[LogicalPlan] = @@ -156,6 +158,11 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser { case o ~ r ~ s => InsertIntoTable(r, Map.empty[String, Option[String]], s, o) } + protected lazy val cte: Parser[LogicalPlan] = + WITH ~> rep1sep(ident ~ ( AS ~ "(" ~> start <~ ")"), ",") ~ start ^^ { + case r ~ s => With(s, r.map({case n ~ s => (n, Subquery(n, s))}).toMap) + } + protected lazy val projection: Parser[Expression] = expression ~ (AS.? ~> ident.?) ^^ { case e ~ a => a.fold(e)(Alias(e, _)()) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala index 524c73c31bbe1..b83f18abdd239 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala @@ -169,21 +169,36 @@ class Analyzer( * Replaces [[UnresolvedRelation]]s with concrete relations from the catalog. */ object ResolveRelations extends Rule[LogicalPlan] { - def getTable(u: UnresolvedRelation): LogicalPlan = { + def getTable(u: UnresolvedRelation, cteRelations: Map[String, LogicalPlan]) = { try { - catalog.lookupRelation(u.tableIdentifier, u.alias) + // In hive, if there is same table name in database and CTE definition, + // hive will use the table in database, not the CTE one. + // Taking into account the reasonableness and the implementation complexity, + // here use the CTE definition first, check table name only and ignore database name + cteRelations.get(u.tableIdentifier.last) + .map(relation => u.alias.map(Subquery(_, relation)).getOrElse(relation)) + .getOrElse(catalog.lookupRelation(u.tableIdentifier, u.alias)) } catch { case _: NoSuchTableException => u.failAnalysis(s"no such table ${u.tableName}") } } - def apply(plan: LogicalPlan): LogicalPlan = plan transform { - case i @ InsertIntoTable(u: UnresolvedRelation, _, _, _) => - i.copy( - table = EliminateSubQueries(getTable(u))) - case u: UnresolvedRelation => - getTable(u) + def apply(plan: LogicalPlan): LogicalPlan = { + val (realPlan, cteRelations) = plan match { + // TODO allow subquery to define CTE + // Add cte table to a temp relation map,drop `with` plan and keep its child + case With(child, relations) => (child, relations) + case other => (other, Map.empty[String, LogicalPlan]) + } + + realPlan transform { + case i@InsertIntoTable(u: UnresolvedRelation, _, _, _) => + i.copy( + table = EliminateSubQueries(getTable(u, cteRelations))) + case u: UnresolvedRelation => + getTable(u, cteRelations) + } } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala index 8633e06093cf3..3bd5aa5964221 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala @@ -147,6 +147,18 @@ case class CreateTableAsSelect[T]( override lazy val resolved: Boolean = databaseName != None && childrenResolved } +/** + * A container for holding named common table expressions (CTEs) and a query plan. + * This operator will be removed during analysis and the relations will be substituted into child. + * @param child The final query of this CTE. + * @param cteRelations Queries that this CTE defined, + * key is the alias of the CTE definition, + * value is the CTE definition. + */ +case class With(child: LogicalPlan, cteRelations: Map[String, Subquery]) extends UnaryNode { + override def output = child.output +} + case class WriteToFile( path: String, child: LogicalPlan) extends UnaryNode { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala index 1392b4819131b..fb8fc6dbd1e1e 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala @@ -407,6 +407,20 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { mapData.collect().take(1).map(Row.fromTuple).toSeq) } + test("CTE feature") { + checkAnswer( + sql("with q1 as (select * from testData limit 10) select * from q1"), + testData.take(10).toSeq) + + checkAnswer( + sql(""" + |with q1 as (select * from testData where key= '5'), + |q2 as (select * from testData where key = '4') + |select * from q1 union all select * from q2""".stripMargin), + Row(5, "5") :: Row(4, "4") :: Nil) + + } + test("date row") { checkAnswer(sql( """select cast("2015-01-28" as date) from testData limit 1"""), diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala index 0bdaf5f7ef8ef..2fb2e7c4a5370 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala @@ -576,11 +576,23 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C case Token("TOK_QUERY", queryArgs) if Seq("TOK_FROM", "TOK_INSERT").contains(queryArgs.head.getText) => - val (fromClause: Option[ASTNode], insertClauses) = queryArgs match { - case Token("TOK_FROM", args: Seq[ASTNode]) :: insertClauses => - (Some(args.head), insertClauses) - case Token("TOK_INSERT", _) :: Nil => (None, queryArgs) - } + val (fromClause: Option[ASTNode], insertClauses, cteRelations) = + queryArgs match { + case Token("TOK_FROM", args: Seq[ASTNode]) :: insertClauses => + // check if has CTE + insertClauses.last match { + case Token("TOK_CTE", cteClauses) => + val cteRelations = cteClauses.map(node => { + val relation = nodeToRelation(node).asInstanceOf[Subquery] + (relation.alias, relation) + }).toMap + (Some(args.head), insertClauses.init, Some(cteRelations)) + + case _ => (Some(args.head), insertClauses, None) + } + + case Token("TOK_INSERT", _) :: Nil => (None, queryArgs, None) + } // Return one query for each insert clause. val queries = insertClauses.map { case Token("TOK_INSERT", singleInsert) => @@ -794,7 +806,10 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C } // If there are multiple INSERTS just UNION them together into on query. - queries.reduceLeft(Union) + val query = queries.reduceLeft(Union) + + // return With plan if there is CTE + cteRelations.map(With(query, _)).getOrElse(query) case Token("TOK_UNION", left :: right :: Nil) => Union(nodeToPlan(left), nodeToPlan(right)) diff --git a/sql/hive/src/test/resources/golden/CTE feature #1-0-eedabbfe6ba8799f7b7782fb47a82768 b/sql/hive/src/test/resources/golden/CTE feature #1-0-eedabbfe6ba8799f7b7782fb47a82768 new file mode 100644 index 0000000000000..f6ba75da254ca --- /dev/null +++ b/sql/hive/src/test/resources/golden/CTE feature #1-0-eedabbfe6ba8799f7b7782fb47a82768 @@ -0,0 +1,3 @@ +5 +5 +5 diff --git a/sql/hive/src/test/resources/golden/CTE feature #2-0-aa03d104251f97e36bc52279cb9931c9 b/sql/hive/src/test/resources/golden/CTE feature #2-0-aa03d104251f97e36bc52279cb9931c9 new file mode 100644 index 0000000000000..ca7b591095e28 --- /dev/null +++ b/sql/hive/src/test/resources/golden/CTE feature #2-0-aa03d104251f97e36bc52279cb9931c9 @@ -0,0 +1,4 @@ +val_4 +val_5 +val_5 +val_5 diff --git a/sql/hive/src/test/resources/golden/CTE feature #3-0-b5d4bf3c0ee92b2fda0ca24f422383f2 b/sql/hive/src/test/resources/golden/CTE feature #3-0-b5d4bf3c0ee92b2fda0ca24f422383f2 new file mode 100644 index 0000000000000..b8626c4cff284 --- /dev/null +++ b/sql/hive/src/test/resources/golden/CTE feature #3-0-b5d4bf3c0ee92b2fda0ca24f422383f2 @@ -0,0 +1 @@ +4 diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala index af781a502e9f3..1222fbabd8b33 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala @@ -542,6 +542,21 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { createQueryTest("select null from table", "SELECT null FROM src LIMIT 1") + createQueryTest("CTE feature #1", + "with q1 as (select key from src) select * from q1 where key = 5") + + createQueryTest("CTE feature #2", + """with q1 as (select * from src where key= 5), + |q2 as (select * from src s2 where key = 4) + |select value from q1 union all select value from q2 + """.stripMargin) + + createQueryTest("CTE feature #3", + """with q1 as (select key from src) + |from q1 + |select * where key = 4 + """.stripMargin) + test("predicates contains an empty AttributeSet() references") { sql( """ From 1f39a61118184e136f38381a9f3ba0b2d5d589d9 Mon Sep 17 00:00:00 2001 From: lazymam500 Date: Sat, 11 Apr 2015 18:33:14 -0700 Subject: [PATCH 229/591] [Spark-5068][SQL]Fix bug query data when path doesn't exist for HiveContext This PR follow up PR #3907 & #3891 & #4356. According to marmbrus liancheng 's comments, I try to use fs.globStatus to retrieve all FileStatus objects under path(s), and then do the filtering locally. [1]. get pathPattern by path, and put it into pathPatternSet. (hdfs://cluster/user/demo/2016/08/12 -> hdfs://cluster/user/demo/*/*/*) [2]. retrieve all FileStatus objects ,and cache them by undating existPathSet. [3]. do the filtering locally [4]. if we have new pathPattern,do 1,2 step again. (external table maybe have more than one partition pathPattern) chenghao-intel jeanlyn Author: lazymam500 Author: lazyman Closes #5059 from lazyman500/SPARK-5068 and squashes the following commits: 5bfcbfd [lazyman] move spark.sql.hive.verifyPartitionPath to SQLConf,fix scala style e1d6386 [lazymam500] fix scala style f23133f [lazymam500] bug fix 47e0023 [lazymam500] fix scala style,add config flag,break the chaining 04c443c [lazyman] SPARK-5068: fix bug when partition path doesn't exists #2 41f60ce [lazymam500] Merge pull request #1 from apache/master --- .../scala/org/apache/spark/sql/SQLConf.scala | 6 ++ .../apache/spark/sql/hive/TableReader.scala | 41 +++++++++++- .../spark/sql/hive/QueryPartitionSuite.scala | 64 +++++++++++++++++++ 3 files changed, 110 insertions(+), 1 deletion(-) create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala index 4815620c6fe57..ee641bdfeb2d7 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala @@ -39,6 +39,8 @@ private[spark] object SQLConf { val PARQUET_FILTER_PUSHDOWN_ENABLED = "spark.sql.parquet.filterPushdown" val PARQUET_USE_DATA_SOURCE_API = "spark.sql.parquet.useDataSourceApi" + val HIVE_VERIFY_PARTITIONPATH = "spark.sql.hive.verifyPartitionPath" + val COLUMN_NAME_OF_CORRUPT_RECORD = "spark.sql.columnNameOfCorruptRecord" val BROADCAST_TIMEOUT = "spark.sql.broadcastTimeout" @@ -119,6 +121,10 @@ private[sql] class SQLConf extends Serializable { private[spark] def parquetUseDataSourceApi = getConf(PARQUET_USE_DATA_SOURCE_API, "true").toBoolean + /** When true uses verifyPartitionPath to prune the path which is not exists. */ + private[spark] def verifyPartitionPath = + getConf(HIVE_VERIFY_PARTITIONPATH, "true").toBoolean + /** When true the planner will use the external sort, which may spill to disk. */ private[spark] def externalSortEnabled: Boolean = getConf(EXTERNAL_SORT, "false").toBoolean diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala index 3563472c7ae81..d35291543c9f9 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala @@ -142,7 +142,46 @@ class HadoopTableReader( partitionToDeserializer: Map[HivePartition, Class[_ <: Deserializer]], filterOpt: Option[PathFilter]): RDD[Row] = { - val hivePartitionRDDs = partitionToDeserializer.map { case (partition, partDeserializer) => + + // SPARK-5068:get FileStatus and do the filtering locally when the path is not exists + def verifyPartitionPath( + partitionToDeserializer: Map[HivePartition, Class[_ <: Deserializer]]): + Map[HivePartition, Class[_ <: Deserializer]] = { + if (!sc.conf.verifyPartitionPath) { + partitionToDeserializer + } else { + var existPathSet = collection.mutable.Set[String]() + var pathPatternSet = collection.mutable.Set[String]() + partitionToDeserializer.filter { + case (partition, partDeserializer) => + def updateExistPathSetByPathPattern(pathPatternStr: String) { + val pathPattern = new Path(pathPatternStr) + val fs = pathPattern.getFileSystem(sc.hiveconf) + val matches = fs.globStatus(pathPattern) + matches.foreach(fileStatus => existPathSet += fileStatus.getPath.toString) + } + // convert /demo/data/year/month/day to /demo/data/*/*/*/ + def getPathPatternByPath(parNum: Int, tempPath: Path): String = { + var path = tempPath + for (i <- (1 to parNum)) path = path.getParent + val tails = (1 to parNum).map(_ => "*").mkString("/", "/", "/") + path.toString + tails + } + + val partPath = HiveShim.getDataLocationPath(partition) + val partNum = Utilities.getPartitionDesc(partition).getPartSpec.size(); + var pathPatternStr = getPathPatternByPath(partNum, partPath) + if (!pathPatternSet.contains(pathPatternStr)) { + pathPatternSet += pathPatternStr + updateExistPathSetByPathPattern(pathPatternStr) + } + existPathSet.contains(partPath.toString) + } + } + } + + val hivePartitionRDDs = verifyPartitionPath(partitionToDeserializer) + .map { case (partition, partDeserializer) => val partDesc = Utilities.getPartitionDesc(partition) val partPath = HiveShim.getDataLocationPath(partition) val inputPathStr = applyFilterIfNeeded(partPath, filterOpt) diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala new file mode 100644 index 0000000000000..83f97128c5e83 --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala @@ -0,0 +1,64 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive + +import java.io.File + +import com.google.common.io.Files +import org.apache.spark.sql.{QueryTest, _} +import org.apache.spark.sql.hive.test.TestHive +import org.apache.spark.util.Utils +/* Implicits */ +import org.apache.spark.sql.hive.test.TestHive._ + + + +class QueryPartitionSuite extends QueryTest { + import org.apache.spark.sql.hive.test.TestHive.implicits._ + + test("SPARK-5068: query data when path doesn't exists"){ + val testData = TestHive.sparkContext.parallelize( + (1 to 10).map(i => TestData(i, i.toString))).toDF() + testData.registerTempTable("testData") + + val tmpDir = Files.createTempDir() + //create the table for test + sql(s"CREATE TABLE table_with_partition(key int,value string) PARTITIONED by (ds string) location '${tmpDir.toURI.toString}' ") + sql("INSERT OVERWRITE TABLE table_with_partition partition (ds='1') SELECT key,value FROM testData") + sql("INSERT OVERWRITE TABLE table_with_partition partition (ds='2') SELECT key,value FROM testData") + sql("INSERT OVERWRITE TABLE table_with_partition partition (ds='3') SELECT key,value FROM testData") + sql("INSERT OVERWRITE TABLE table_with_partition partition (ds='4') SELECT key,value FROM testData") + + //test for the exist path + checkAnswer(sql("select key,value from table_with_partition"), + testData.toSchemaRDD.collect ++ testData.toSchemaRDD.collect + ++ testData.toSchemaRDD.collect ++ testData.toSchemaRDD.collect) + + //delect the path of one partition + val folders = tmpDir.listFiles.filter(_.isDirectory) + Utils.deleteRecursively(folders(0)) + + //test for affter delete the path + checkAnswer(sql("select key,value from table_with_partition"), + testData.toSchemaRDD.collect ++ testData.toSchemaRDD.collect + ++ testData.toSchemaRDD.collect) + + sql("DROP TABLE table_with_partition") + sql("DROP TABLE createAndInsertTest") + } +} From 48cc840021c43fcb4c5bb365d2c80512678cf120 Mon Sep 17 00:00:00 2001 From: DoingDone9 <799203320@qq.com> Date: Sat, 11 Apr 2015 18:34:17 -0700 Subject: [PATCH 230/591] [SPARK-6179][SQL] Add token for "SHOW PRINCIPALS role_name" and "SHOW TRANSACTIONS" and "SHOW COMPACTIONS" [SHOW PRINCIPALS role_name] Lists all roles and users who belong to this role. Only the admin role has privilege for this. [SHOW COMPACTIONS] It returns a list of all tables and partitions currently being compacted or scheduled for compaction when Hive transactions are being used. [SHOW TRANSACTIONS] It is for use by administrators when Hive transactions are being used. It returns a list of all currently open and aborted transactions in the system. Author: DoingDone9 <799203320@qq.com> Author: Zhongshuai Pei <799203320@qq.com> Author: Xu Tingjun Closes #4902 from DoingDone9/SHOW_PRINCIPALS and squashes the following commits: 4add42f [Zhongshuai Pei] for test 311f806 [Zhongshuai Pei] for test 0c7550a [DoingDone9] Update HiveQl.scala c8aeb1c [Xu Tingjun] aa 802261c [DoingDone9] Merge pull request #7 from apache/master d00303b [DoingDone9] Merge pull request #6 from apache/master 98b134f [DoingDone9] Merge pull request #5 from apache/master 161cae3 [DoingDone9] Merge pull request #4 from apache/master c87e8b6 [DoingDone9] Merge pull request #3 from apache/master cb1852d [DoingDone9] Merge pull request #2 from apache/master c3f046f [DoingDone9] Merge pull request #1 from apache/master --- sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala | 3 +++ 1 file changed, 3 insertions(+) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala index 2fb2e7c4a5370..b2ae74efeb097 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala @@ -113,13 +113,16 @@ private[hive] object HiveQl { "TOK_REVOKE", + "TOK_SHOW_COMPACTIONS", "TOK_SHOW_CREATETABLE", "TOK_SHOW_GRANT", "TOK_SHOW_ROLE_GRANT", + "TOK_SHOW_ROLE_PRINCIPALS", "TOK_SHOW_ROLES", "TOK_SHOW_SET_ROLE", "TOK_SHOW_TABLESTATUS", "TOK_SHOW_TBLPROPERTIES", + "TOK_SHOW_TRANSACTIONS", "TOK_SHOWCOLUMNS", "TOK_SHOWDATABASES", "TOK_SHOWFUNCTIONS", From 352a5da421d61379f2a8bcd7548ccc5d2647120a Mon Sep 17 00:00:00 2001 From: Takeshi YAMAMURO Date: Sat, 11 Apr 2015 18:41:12 -0700 Subject: [PATCH 231/591] [SPARK-6379][SQL] Support a functon to call user-defined functions registered in SQLContext This is useful for using pre-defined UDFs in SQLContext; val df = Seq(("id1", 1), ("id2", 4), ("id3", 5)).toDF("id", "value") val sqlctx = df.sqlContext sqlctx.udf.register("simpleUdf", (v: Int) => v * v) df.select($"id", sqlctx.callUdf("simpleUdf", $"value")) Author: Takeshi YAMAMURO Closes #5061 from maropu/SupportUDFConversionInSparkContext and squashes the following commits: f858aff [Takeshi YAMAMURO] Move the function into functions.scala afd0380 [Takeshi YAMAMURO] Add a return type of callUDF 599b76c [Takeshi YAMAMURO] Remove the implicit conversion and add SqlContext#callUdf 8b56f10 [Takeshi YAMAMURO] Support an implicit conversion from udf"name" to an UDF defined in SQLContext --- .../org/apache/spark/sql/functions.scala | 21 ++++++++++++++++++- .../org/apache/spark/sql/DataFrameSuite.scala | 9 ++++++++ 2 files changed, 29 insertions(+), 1 deletion(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala index 111e751588a8b..ff91e1d74bc2c 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala @@ -22,7 +22,7 @@ import scala.reflect.runtime.universe.{TypeTag, typeTag} import org.apache.spark.annotation.Experimental import org.apache.spark.sql.catalyst.ScalaReflection -import org.apache.spark.sql.catalyst.analysis.Star +import org.apache.spark.sql.catalyst.analysis.{UnresolvedFunction, Star} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.types._ @@ -605,4 +605,23 @@ object functions { } // scalastyle:on + + /** + * Call an user-defined function. + * Example: + * {{{ + * import org.apache.spark.sql._ + * + * val df = Seq(("id1", 1), ("id2", 4), ("id3", 5)).toDF("id", "value") + * val sqlContext = df.sqlContext + * sqlContext.udf.register("simpleUdf", (v: Int) => v * v) + * df.select($"id", callUdf("simpleUdf", $"value")) + * }}} + * + * @group udf_funcs + */ + def callUdf(udfName: String, cols: Column*): Column = { + UnresolvedFunction(udfName, cols.map(_.expr)) + } + } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index f5df8c6a59f10..b26e22f6229fe 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -440,6 +440,15 @@ class DataFrameSuite extends QueryTest { ) } + test("call udf in SQLContext") { + val df = Seq(("id1", 1), ("id2", 4), ("id3", 5)).toDF("id", "value") + val sqlctx = df.sqlContext + sqlctx.udf.register("simpleUdf", (v: Int) => v * v) + checkAnswer( + df.select($"id", callUdf("simpleUdf", $"value")), + Row("id1", 1) :: Row("id2", 16) :: Row("id3", 25) :: Nil) + } + test("withColumn") { val df = testData.toDF().withColumn("newCol", col("key") + 1) checkAnswer( From d2383fb5ffafd6b3a56b1ee6e0e035594473e2c8 Mon Sep 17 00:00:00 2001 From: Yin Huai Date: Sat, 11 Apr 2015 18:44:54 -0700 Subject: [PATCH 232/591] [SQL] Handle special characters in the authority of a Path's URI. Author: Yin Huai Closes #5381 from yhuai/parquetPath2 and squashes the following commits: fe296b4 [Yin Huai] Create new Path to take care special characters in the authority of a Path's URI. --- .../apache/spark/sql/parquet/newParquet.scala | 30 +++++++++++++++++-- 1 file changed, 27 insertions(+), 3 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala index 0dce3623a66df..20fdf5e58ef82 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala @@ -432,7 +432,10 @@ private[sql] case class ParquetRelation2( // FileInputFormat cannot handle empty lists. if (selectedFiles.nonEmpty) { - FileInputFormat.setInputPaths(job, selectedFiles.map(_.getPath): _*) + // In order to encode the authority of a Path containning special characters such as /, + // we need to use the string retruned by the URI of the path to create a new Path. + val selectedPaths = selectedFiles.map(status => new Path(status.getPath.toUri.toString)) + FileInputFormat.setInputPaths(job, selectedPaths: _*) } // Try to push down filters when filter push-down is enabled. @@ -484,10 +487,31 @@ private[sql] case class ParquetRelation2( val cacheMetadata = useCache @transient - val cachedStatus = selectedFiles + val cachedStatus = selectedFiles.map { st => + // In order to encode the authority of a Path containning special characters such as /, + // we need to use the string retruned by the URI of the path to create a new Path. + val newPath = new Path(st.getPath.toUri.toString) + + new FileStatus( + st.getLen, + st.isDir, + st.getReplication, + st.getBlockSize, + st.getModificationTime, + st.getAccessTime, + st.getPermission, + st.getOwner, + st.getGroup, + newPath) + } @transient - val cachedFooters = selectedFooters + val cachedFooters = selectedFooters.map { f => + // In order to encode the authority of a Path containning special characters such as /, + // we need to use the string retruned by the URI of the path to create a new Path. + new Footer(new Path(f.getFile.toUri.toString), f.getParquetMetadata) + } + // Overridden so we can inject our own cached files statuses. override def getPartitions: Array[SparkPartition] = { From 6d4e854ffbd7dee9a3cd7b44a00fd9c0e551f5b8 Mon Sep 17 00:00:00 2001 From: Yin Huai Date: Sat, 11 Apr 2015 19:26:15 -0700 Subject: [PATCH 233/591] [SPARK-6367][SQL] Use the proper data type for those expressions that are hijacking existing data types. This PR adds internal UDTs for expressions that are hijacking existing data types. The following UDTs are added: * `HyperLogLogUDT` (`BinaryType` as the SQL type) for `ApproxCountDistinctPartition` * `OpenHashSetUDT` (`ArrayType` as the SQL type) for `CollectHashSet`, `NewSet`, `AddItemToSet`, and `CombineSets`. I am also adding more unit tests for aggregation with code gen enabled. JIRA: https://issues.apache.org/jira/browse/SPARK-6367 Author: Yin Huai Closes #5094 from yhuai/expressionType and squashes the following commits: 8bcd11a [Yin Huai] Return types. 61a1d66 [Yin Huai] Merge remote-tracking branch 'upstream/master' into expressionType e8b4599 [Yin Huai] Merge remote-tracking branch 'upstream/master' into expressionType 2753156 [Yin Huai] Ignore aggregations having sum functions for now. b5eb259 [Yin Huai] Case object for HyperLogLog type. 00ebdbd [Yin Huai] deserialize/serialize. 54b87ae [Yin Huai] Add UDTs for expressions that return HyperLogLog and OpenHashSet. --- .../sql/catalyst/expressions/aggregates.scala | 24 +++++++++++-- .../expressions/codegen/CodeGenerator.scala | 4 +-- .../spark/sql/catalyst/expressions/sets.scala | 35 ++++++++++++++++--- .../sql/execution/GeneratedAggregate.scala | 12 ++++--- .../org/apache/spark/sql/SQLQuerySuite.scala | 12 ++++--- .../spark/sql/UserDefinedTypeSuite.scala | 24 ++++++++++++- 6 files changed, 91 insertions(+), 20 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala index 406de38d1c483..14a855054b94d 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala @@ -189,9 +189,10 @@ case class CollectHashSet(expressions: Seq[Expression]) extends AggregateExpress override def children: Seq[Expression] = expressions override def nullable: Boolean = false - override def dataType: ArrayType = ArrayType(expressions.head.dataType) + override def dataType: OpenHashSetUDT = new OpenHashSetUDT(expressions.head.dataType) override def toString: String = s"AddToHashSet(${expressions.mkString(",")})" - override def newInstance(): CollectHashSetFunction = new CollectHashSetFunction(expressions, this) + override def newInstance(): CollectHashSetFunction = + new CollectHashSetFunction(expressions, this) } case class CollectHashSetFunction( @@ -250,11 +251,28 @@ case class CombineSetsAndCountFunction( override def eval(input: Row): Any = seen.size.toLong } +/** The data type of ApproxCountDistinctPartition since its output is a HyperLogLog object. */ +private[sql] case object HyperLogLogUDT extends UserDefinedType[HyperLogLog] { + + override def sqlType: DataType = BinaryType + + /** Since we are using HyperLogLog internally, usually it will not be called. */ + override def serialize(obj: Any): Array[Byte] = + obj.asInstanceOf[HyperLogLog].getBytes + + + /** Since we are using HyperLogLog internally, usually it will not be called. */ + override def deserialize(datum: Any): HyperLogLog = + HyperLogLog.Builder.build(datum.asInstanceOf[Array[Byte]]) + + override def userClass: Class[HyperLogLog] = classOf[HyperLogLog] +} + case class ApproxCountDistinctPartition(child: Expression, relativeSD: Double) extends AggregateExpression with trees.UnaryNode[Expression] { override def nullable: Boolean = false - override def dataType: DataType = child.dataType + override def dataType: DataType = HyperLogLogUDT override def toString: String = s"APPROXIMATE COUNT(DISTINCT $child)" override def newInstance(): ApproxCountDistinctPartitionFunction = { new ApproxCountDistinctPartitionFunction(child, this, relativeSD) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala index d1abf3c0b64a5..aac56e1568332 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala @@ -464,7 +464,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin val itemEval = expressionEvaluator(item) val setEval = expressionEvaluator(set) - val ArrayType(elementType, _) = set.dataType + val elementType = set.dataType.asInstanceOf[OpenHashSetUDT].elementType itemEval.code ++ setEval.code ++ q""" @@ -482,7 +482,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin val leftEval = expressionEvaluator(left) val rightEval = expressionEvaluator(right) - val ArrayType(elementType, _) = left.dataType + val elementType = left.dataType.asInstanceOf[OpenHashSetUDT].elementType leftEval.code ++ rightEval.code ++ q""" diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala index 35faa00782e80..4c44182278207 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/sets.scala @@ -20,6 +20,33 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.types._ import org.apache.spark.util.collection.OpenHashSet +/** The data type for expressions returning an OpenHashSet as the result. */ +private[sql] class OpenHashSetUDT( + val elementType: DataType) extends UserDefinedType[OpenHashSet[Any]] { + + override def sqlType: DataType = ArrayType(elementType) + + /** Since we are using OpenHashSet internally, usually it will not be called. */ + override def serialize(obj: Any): Seq[Any] = { + obj.asInstanceOf[OpenHashSet[Any]].iterator.toSeq + } + + /** Since we are using OpenHashSet internally, usually it will not be called. */ + override def deserialize(datum: Any): OpenHashSet[Any] = { + val iterator = datum.asInstanceOf[Seq[Any]].iterator + val set = new OpenHashSet[Any] + while(iterator.hasNext) { + set.add(iterator.next()) + } + + set + } + + override def userClass: Class[OpenHashSet[Any]] = classOf[OpenHashSet[Any]] + + private[spark] override def asNullable: OpenHashSetUDT = this +} + /** * Creates a new set of the specified type */ @@ -28,9 +55,7 @@ case class NewSet(elementType: DataType) extends LeafExpression { override def nullable: Boolean = false - // We are currently only using these Expressions internally for aggregation. However, if we ever - // expose these to users we'll want to create a proper type instead of hijacking ArrayType. - override def dataType: DataType = ArrayType(elementType) + override def dataType: OpenHashSetUDT = new OpenHashSetUDT(elementType) override def eval(input: Row): Any = { new OpenHashSet[Any]() @@ -50,7 +75,7 @@ case class AddItemToSet(item: Expression, set: Expression) extends Expression { override def nullable: Boolean = set.nullable - override def dataType: DataType = set.dataType + override def dataType: OpenHashSetUDT = set.dataType.asInstanceOf[OpenHashSetUDT] override def eval(input: Row): Any = { val itemEval = item.eval(input) @@ -80,7 +105,7 @@ case class CombineSets(left: Expression, right: Expression) extends BinaryExpres override def nullable: Boolean = left.nullable || right.nullable - override def dataType: DataType = left.dataType + override def dataType: OpenHashSetUDT = left.dataType.asInstanceOf[OpenHashSetUDT] override def symbol: String = "++=" diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala index 861a2c21ad9a0..3c58e93b45e9d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala @@ -68,6 +68,8 @@ case class GeneratedAggregate( a.collect { case agg: AggregateExpression => agg} } + // If you add any new function support, please add tests in org.apache.spark.sql.SQLQuerySuite + // (in test "aggregation with codegen"). val computeFunctions = aggregatesToCompute.map { case c @ Count(expr) => // If we're evaluating UnscaledValue(x), we can do Count on x directly, since its @@ -208,7 +210,8 @@ case class GeneratedAggregate( currentMax) case CollectHashSet(Seq(expr)) => - val set = AttributeReference("hashSet", ArrayType(expr.dataType), nullable = false)() + val set = + AttributeReference("hashSet", new OpenHashSetUDT(expr.dataType), nullable = false)() val initialValue = NewSet(expr.dataType) val addToSet = AddItemToSet(expr, set) @@ -219,9 +222,10 @@ case class GeneratedAggregate( set) case CombineSetsAndCount(inputSet) => - val ArrayType(inputType, _) = inputSet.dataType - val set = AttributeReference("hashSet", inputSet.dataType, nullable = false)() - val initialValue = NewSet(inputType) + val elementType = inputSet.dataType.asInstanceOf[OpenHashSetUDT].elementType + val set = + AttributeReference("hashSet", new OpenHashSetUDT(elementType), nullable = false)() + val initialValue = NewSet(elementType) val collectSets = CombineSets(set, inputSet) AggregateEvaluation( diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala index fb8fc6dbd1e1e..5e453e05e2ac7 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala @@ -17,6 +17,7 @@ package org.apache.spark.sql +import org.apache.spark.sql.execution.GeneratedAggregate import org.apache.spark.sql.test.TestSQLContext import org.scalatest.BeforeAndAfterAll @@ -151,10 +152,10 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { "SELECT count(distinct key) FROM testData3x", Row(100) :: Nil) // SUM - testCodeGen( - "SELECT value, sum(key) FROM testData3x GROUP BY value", - (1 to 100).map(i => Row(i.toString, 3 * i))) - testCodeGen( + testCodeGen( + "SELECT value, sum(key) FROM testData3x GROUP BY value", + (1 to 100).map(i => Row(i.toString, 3 * i))) + testCodeGen( "SELECT sum(key), SUM(CAST(key as Double)) FROM testData3x", Row(5050 * 3, 5050 * 3.0) :: Nil) // AVERAGE @@ -192,10 +193,11 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { testCodeGen( "SELECT sum('a'), avg('a'), count(null) FROM testData", Row(0, null, 0) :: Nil) - + dropTempTable("testData3x") setConf(SQLConf.CODEGEN_ENABLED, originalValue.toString) } + test("Add Parser of SQL COALESCE()") { checkAnswer( sql("""SELECT COALESCE(1, 2)"""), diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala index 902da5c3ba6d6..2672e20deadc5 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala @@ -23,13 +23,16 @@ import org.apache.spark.util.Utils import scala.beans.{BeanInfo, BeanProperty} +import com.clearspring.analytics.stream.cardinality.HyperLogLog + import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.expressions.{OpenHashSetUDT, HyperLogLogUDT} import org.apache.spark.sql.functions._ import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.test.TestSQLContext.{sparkContext, sql} import org.apache.spark.sql.test.TestSQLContext.implicits._ import org.apache.spark.sql.types._ - +import org.apache.spark.util.collection.OpenHashSet @SQLUserDefinedType(udt = classOf[MyDenseVectorUDT]) private[sql] class MyDenseVector(val data: Array[Double]) extends Serializable { @@ -119,4 +122,23 @@ class UserDefinedTypeSuite extends QueryTest { df.limit(1).groupBy('int).agg(first('vec)).collect()(0).getAs[MyDenseVector](0) df.orderBy('int).limit(1).groupBy('int).agg(first('vec)).collect()(0).getAs[MyDenseVector](0) } + + test("HyperLogLogUDT") { + val hyperLogLogUDT = HyperLogLogUDT + val hyperLogLog = new HyperLogLog(0.4) + (1 to 10).foreach(i => hyperLogLog.offer(Row(i))) + + val actual = hyperLogLogUDT.deserialize(hyperLogLogUDT.serialize(hyperLogLog)) + assert(actual.cardinality() === hyperLogLog.cardinality()) + assert(java.util.Arrays.equals(actual.getBytes, hyperLogLog.getBytes)) + } + + test("OpenHashSetUDT") { + val openHashSetUDT = new OpenHashSetUDT(IntegerType) + val set = new OpenHashSet[Int] + (1 to 10).foreach(i => set.add(i)) + + val actual = openHashSetUDT.deserialize(openHashSetUDT.serialize(set)) + assert(actual.iterator.toSet === set.iterator.toSet) + } } From 5c2844c51aca6a0da9251a3fd346a6f872cf17f8 Mon Sep 17 00:00:00 2001 From: Wenchen Fan Date: Sat, 11 Apr 2015 19:35:56 -0700 Subject: [PATCH 234/591] [SQL][minor] move `resolveGetField` into a object The method `resolveGetField` isn't belong to `LogicalPlan` logically and didn't access any members of it. Author: Wenchen Fan Closes #5435 from cloud-fan/tmp and squashes the following commits: 9a66c83 [Wenchen Fan] code clean up --- .../sql/catalyst/analysis/Analyzer.scala | 2 +- .../catalyst/expressions/complexTypes.scala | 37 +++++++++++++++++++ .../catalyst/plans/logical/LogicalPlan.scala | 35 +----------------- 3 files changed, 39 insertions(+), 35 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala index b83f18abdd239..fd1ceb1f77931 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala @@ -308,7 +308,7 @@ class Analyzer( logDebug(s"Resolving $u to $result") result case UnresolvedGetField(child, fieldName) if child.resolved => - q.resolveGetField(child, fieldName, resolver) + GetField(child, fieldName, resolver) } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala index 3b2b9211268a9..fc1f69655963d 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala @@ -19,6 +19,8 @@ package org.apache.spark.sql.catalyst.expressions import scala.collection.Map +import org.apache.spark.sql.AnalysisException +import org.apache.spark.sql.catalyst.analysis.Resolver import org.apache.spark.sql.types._ /** @@ -81,6 +83,41 @@ trait GetField extends UnaryExpression { def field: StructField } +object GetField { + /** + * Returns the resolved `GetField`, and report error if no desired field or over one + * desired fields are found. + */ + def apply( + expr: Expression, + fieldName: String, + resolver: Resolver): GetField = { + def findField(fields: Array[StructField]): Int = { + val checkField = (f: StructField) => resolver(f.name, fieldName) + val ordinal = fields.indexWhere(checkField) + if (ordinal == -1) { + throw new AnalysisException( + s"No such struct field $fieldName in ${fields.map(_.name).mkString(", ")}") + } else if (fields.indexWhere(checkField, ordinal + 1) != -1) { + throw new AnalysisException( + s"Ambiguous reference to fields ${fields.filter(checkField).mkString(", ")}") + } else { + ordinal + } + } + expr.dataType match { + case StructType(fields) => + val ordinal = findField(fields) + StructGetField(expr, fields(ordinal), ordinal) + case ArrayType(StructType(fields), containsNull) => + val ordinal = findField(fields) + ArrayGetField(expr, fields(ordinal), ordinal, containsNull) + case otherType => + throw new AnalysisException(s"GetField is not valid on fields of type $otherType") + } + } +} + /** * Returns the value of fields in the Struct `child`. */ diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala index d8f5858f5033e..579a0fb8d3f93 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala @@ -210,7 +210,7 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { // For example, consider "a.b.c", where "a" is resolved to an existing attribute. // Then this will add GetField("c", GetField("b", a)), and alias // the final expression as "c". - val fieldExprs = nestedFields.foldLeft(a: Expression)(resolveGetField(_, _, resolver)) + val fieldExprs = nestedFields.foldLeft(a: Expression)(GetField(_, _, resolver)) val aliasName = nestedFields.last Some(Alias(fieldExprs, aliasName)()) } catch { @@ -229,39 +229,6 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { s"Reference '$name' is ambiguous, could be: $referenceNames.") } } - - /** - * Returns the resolved `GetField`, and report error if no desired field or over one - * desired fields are found. - */ - def resolveGetField( - expr: Expression, - fieldName: String, - resolver: Resolver): Expression = { - def findField(fields: Array[StructField]): Int = { - val checkField = (f: StructField) => resolver(f.name, fieldName) - val ordinal = fields.indexWhere(checkField) - if (ordinal == -1) { - throw new AnalysisException( - s"No such struct field $fieldName in ${fields.map(_.name).mkString(", ")}") - } else if (fields.indexWhere(checkField, ordinal + 1) != -1) { - throw new AnalysisException( - s"Ambiguous reference to fields ${fields.filter(checkField).mkString(", ")}") - } else { - ordinal - } - } - expr.dataType match { - case StructType(fields) => - val ordinal = findField(fields) - StructGetField(expr, fields(ordinal), ordinal) - case ArrayType(StructType(fields), containsNull) => - val ordinal = findField(fields) - ArrayGetField(expr, fields(ordinal), ordinal, containsNull) - case otherType => - throw new AnalysisException(s"GetField is not valid on fields of type $otherType") - } - } } /** From dea5dacc5d701c3dfe433360b2e17e50edec31e8 Mon Sep 17 00:00:00 2001 From: Josh Rosen Date: Sat, 11 Apr 2015 20:12:40 -0700 Subject: [PATCH 235/591] [HOTFIX] Add explicit return types to fix lint errors --- .../scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala | 2 +- .../spark/sql/catalyst/plans/logical/basicOperators.scala | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala index fd1ceb1f77931..50702ac6832ec 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala @@ -169,7 +169,7 @@ class Analyzer( * Replaces [[UnresolvedRelation]]s with concrete relations from the catalog. */ object ResolveRelations extends Rule[LogicalPlan] { - def getTable(u: UnresolvedRelation, cteRelations: Map[String, LogicalPlan]) = { + def getTable(u: UnresolvedRelation, cteRelations: Map[String, LogicalPlan]): LogicalPlan = { try { // In hive, if there is same table name in database and CTE definition, // hive will use the table in database, not the CTE one. diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala index 3bd5aa5964221..5d31a6eecfce2 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala @@ -156,7 +156,7 @@ case class CreateTableAsSelect[T]( * value is the CTE definition. */ case class With(child: LogicalPlan, cteRelations: Map[String, Subquery]) extends UnaryNode { - override def output = child.output + override def output: Seq[Attribute] = child.output } case class WriteToFile( From 1205f7ea6165089985edf46ea4d2d53975d0f1f4 Mon Sep 17 00:00:00 2001 From: Michael Malak Date: Sat, 11 Apr 2015 21:01:23 -0700 Subject: [PATCH 236/591] SPARK-6710 GraphX Fixed Wrong initial bias in GraphX SVDPlusPlus Author: Michael Malak Closes #5464 from michaelmalak/master and squashes the following commits: 9d942ba [Michael Malak] SPARK-6710 GraphX Fixed Wrong initial bias in GraphX SVDPlusPlus --- .../main/scala/org/apache/spark/graphx/lib/SVDPlusPlus.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/SVDPlusPlus.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/SVDPlusPlus.scala index 1a7178b82e3af..3b0e1628d86b5 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/lib/SVDPlusPlus.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/SVDPlusPlus.scala @@ -93,7 +93,7 @@ object SVDPlusPlus { val gJoinT0 = g.outerJoinVertices(t0) { (vid: VertexId, vd: (Array[Double], Array[Double], Double, Double), msg: Option[(Long, Double)]) => - (vd._1, vd._2, msg.get._2 / msg.get._1, 1.0 / scala.math.sqrt(msg.get._1)) + (vd._1, vd._2, msg.get._2 / msg.get._1 - u, 1.0 / scala.math.sqrt(msg.get._1)) }.cache() materialize(gJoinT0) g.unpersist() From 0cc8fcb4cd20cb90a1fac50b1a3ffed833ce5eac Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Sat, 11 Apr 2015 22:12:56 -0700 Subject: [PATCH 237/591] MAINTENANCE: Automated closing of pull requests. This commit exists to close the following pull requests on Github: Closes #4994 (close requested by 'marmbrus') Closes #4995 (close requested by 'marmbrus') Closes #4491 (close requested by 'srowen') Closes #3597 (close requested by 'srowen') Closes #4693 (close requested by 'marmbrus') Closes #3855 (close requested by 'marmbrus') Closes #4398 (close requested by 'marmbrus') Closes #4246 (close requested by 'marmbrus') Closes #5153 (close requested by 'srowen') Closes #3626 (close requested by 'srowen') Closes #5166 (close requested by 'marmbrus') Closes #5040 (close requested by 'marmbrus') Closes #5044 (close requested by 'marmbrus') Closes #5440 (close requested by 'JoshRosen') Closes #4039 (close requested by 'marmbrus') Closes #1237 (close requested by 'srowen') Closes #216 (close requested by 'mengxr') Closes #5092 (close requested by 'srowen') Closes #5100 (close requested by 'marmbrus') Closes #4469 (close requested by 'marmbrus') Closes #5246 (close requested by 'srowen') Closes #5013 (close requested by 'marmbrus') From 5d8f7b9e87e8066d54717a1a78b06e8531d8b0d4 Mon Sep 17 00:00:00 2001 From: Davies Liu Date: Sat, 11 Apr 2015 22:33:23 -0700 Subject: [PATCH 238/591] [SPARK-6677] [SQL] [PySpark] fix cached classes It's possible to have two DataType object with same id (memory address) at different time, we should check the cached classes to verify that it's generated by given datatype. This PR also change `__FIELDS__` and `__DATATYPE__` to lower case to match Python code style. Author: Davies Liu Closes #5445 from davies/fix_type_cache and squashes the following commits: 63b3238 [Davies Liu] typo 47bdede [Davies Liu] fix cached classes --- python/pyspark/sql/types.py | 39 +++++++++++++++++++------------------ 1 file changed, 20 insertions(+), 19 deletions(-) diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py index 7e0124b13671b..ef76d84c00481 100644 --- a/python/pyspark/sql/types.py +++ b/python/pyspark/sql/types.py @@ -567,8 +567,8 @@ def _infer_schema(row): elif isinstance(row, (tuple, list)): if hasattr(row, "_fields"): # namedtuple items = zip(row._fields, tuple(row)) - elif hasattr(row, "__FIELDS__"): # Row - items = zip(row.__FIELDS__, tuple(row)) + elif hasattr(row, "__fields__"): # Row + items = zip(row.__fields__, tuple(row)) else: names = ['_%d' % i for i in range(1, len(row) + 1)] items = zip(names, row) @@ -647,7 +647,7 @@ def converter(obj): if isinstance(obj, dict): return tuple(c(obj.get(n)) for n, c in zip(names, converters)) elif isinstance(obj, tuple): - if hasattr(obj, "_fields") or hasattr(obj, "__FIELDS__"): + if hasattr(obj, "_fields") or hasattr(obj, "__fields__"): return tuple(c(v) for c, v in zip(converters, obj)) elif all(isinstance(x, tuple) and len(x) == 2 for x in obj): # k-v pairs d = dict(obj) @@ -997,12 +997,13 @@ def _restore_object(dataType, obj): # same object in most cases. k = id(dataType) cls = _cached_cls.get(k) - if cls is None: + if cls is None or cls.__datatype is not dataType: # use dataType as key to avoid create multiple class cls = _cached_cls.get(dataType) if cls is None: cls = _create_cls(dataType) _cached_cls[dataType] = cls + cls.__datatype = dataType _cached_cls[k] = cls return cls(obj) @@ -1119,8 +1120,8 @@ def Dict(d): class Row(tuple): """ Row in DataFrame """ - __DATATYPE__ = dataType - __FIELDS__ = tuple(f.name for f in dataType.fields) + __datatype = dataType + __fields__ = tuple(f.name for f in dataType.fields) __slots__ = () # create property for fast access @@ -1128,22 +1129,22 @@ class Row(tuple): def asDict(self): """ Return as a dict """ - return dict((n, getattr(self, n)) for n in self.__FIELDS__) + return dict((n, getattr(self, n)) for n in self.__fields__) def __repr__(self): # call collect __repr__ for nested objects return ("Row(%s)" % ", ".join("%s=%r" % (n, getattr(self, n)) - for n in self.__FIELDS__)) + for n in self.__fields__)) def __reduce__(self): - return (_restore_object, (self.__DATATYPE__, tuple(self))) + return (_restore_object, (self.__datatype, tuple(self))) return Row def _create_row(fields, values): row = Row(*values) - row.__FIELDS__ = fields + row.__fields__ = fields return row @@ -1183,7 +1184,7 @@ def __new__(self, *args, **kwargs): # create row objects names = sorted(kwargs.keys()) row = tuple.__new__(self, [kwargs[n] for n in names]) - row.__FIELDS__ = names + row.__fields__ = names return row else: @@ -1193,11 +1194,11 @@ def asDict(self): """ Return as an dict """ - if not hasattr(self, "__FIELDS__"): + if not hasattr(self, "__fields__"): raise TypeError("Cannot convert a Row class into dict") - return dict(zip(self.__FIELDS__, self)) + return dict(zip(self.__fields__, self)) - # let obect acs like class + # let object acts like class def __call__(self, *args): """create new Row object""" return _create_row(self, args) @@ -1208,21 +1209,21 @@ def __getattr__(self, item): try: # it will be slow when it has many fields, # but this will not be used in normal cases - idx = self.__FIELDS__.index(item) + idx = self.__fields__.index(item) return self[idx] except IndexError: raise AttributeError(item) def __reduce__(self): - if hasattr(self, "__FIELDS__"): - return (_create_row, (self.__FIELDS__, tuple(self))) + if hasattr(self, "__fields__"): + return (_create_row, (self.__fields__, tuple(self))) else: return tuple.__reduce__(self) def __repr__(self): - if hasattr(self, "__FIELDS__"): + if hasattr(self, "__fields__"): return "Row(%s)" % ", ".join("%s=%r" % (k, v) - for k, v in zip(self.__FIELDS__, self)) + for k, v in zip(self.__fields__, tuple(self))) else: return "" % ", ".join(self) From e9445b187e8f5c3703771b775e60164166309570 Mon Sep 17 00:00:00 2001 From: "Guancheng (G.C.) Chen" Date: Sun, 12 Apr 2015 11:36:41 +0100 Subject: [PATCH 239/591] [SPARK-6866][Build] Remove duplicated dependency in launcher/pom.xml JIRA: https://issues.apache.org/jira/browse/SPARK-6866 Remove duplicated dependency of scalatest in launcher/pom.xml since it already inherited the dependency from the parent pom.xml. Author: Guancheng (G.C.) Chen Closes #5476 from gchen/SPARK-6866 and squashes the following commits: 1ab484b [Guancheng (G.C.) Chen] remove duplicated dependency in launcher/pom.xml --- launcher/pom.xml | 5 ----- 1 file changed, 5 deletions(-) diff --git a/launcher/pom.xml b/launcher/pom.xml index 0fe2814135d88..182e5f60218db 100644 --- a/launcher/pom.xml +++ b/launcher/pom.xml @@ -52,11 +52,6 @@ mockito-all test - - org.scalatest - scalatest_${scala.binary.version} - test - org.slf4j slf4j-api From ddc17431a4108ab6efe0cd329d69e1f2fca5ac12 Mon Sep 17 00:00:00 2001 From: lisurprise Date: Sun, 12 Apr 2015 13:41:44 +0100 Subject: [PATCH 240/591] [SPARK-6843][core]Add volatile for the "state" Fix potential visibility problem for the "state" of Executor The field of "state" is shared and modified by multiple threads. i.e: ```scala Within ExecutorRunner.scala (1) workerThread = new Thread("ExecutorRunner for " + fullId) { override def run() { fetchAndRunExecutor() } } workerThread.start() // Shutdown hook that kills actors on shutdown. (2)shutdownHook = new Thread() { override def run() { killProcess(Some("Worker shutting down")) } } (3)and also the "Actor thread" for worker. ``` I think we should at lease add volatile to ensure the visibility among threads otherwise the worker might send an out-of-date status to the master. https://issues.apache.org/jira/browse/SPARK-6843 Author: lisurprise Closes #5448 from zhichao-li/state and squashes the following commits: a2386e7 [lisurprise] add volatile for state field --- .../scala/org/apache/spark/deploy/worker/ExecutorRunner.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala index 83e24a7a1f80c..7d5acabb95a48 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala @@ -50,7 +50,7 @@ private[deploy] class ExecutorRunner( val workerUrl: String, conf: SparkConf, val appLocalDirs: Seq[String], - var state: ExecutorState.Value) + @volatile var state: ExecutorState.Value) extends Logging { private val fullId = appId + "/" + execId From 6ac8eea2fc6b782015236e4e7106e59d0d9e1b38 Mon Sep 17 00:00:00 2001 From: cody koeninger Date: Sun, 12 Apr 2015 17:37:30 +0100 Subject: [PATCH 241/591] [SPARK-6431][Streaming][Kafka] Error message for partition metadata requ... ...ests The original reported problem was misdiagnosed; the topic just didn't exist yet. Agreed upon solution was to improve error handling / message Author: cody koeninger Closes #5454 from koeninger/spark-6431-master and squashes the following commits: 44300f8 [cody koeninger] [SPARK-6431][Streaming][Kafka] Error message for partition metadata requests --- .../spark/streaming/kafka/KafkaCluster.scala | 14 +++++++++++--- .../spark/streaming/kafka/KafkaClusterSuite.scala | 3 +++ 2 files changed, 14 insertions(+), 3 deletions(-) diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala index 2f7e0ab39fefd..bd767031c1849 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala @@ -123,9 +123,17 @@ class KafkaCluster(val kafkaParams: Map[String, String]) extends Serializable { val errs = new Err withBrokers(Random.shuffle(config.seedBrokers), errs) { consumer => val resp: TopicMetadataResponse = consumer.send(req) - // error codes here indicate missing / just created topic, - // repeating on a different broker wont be useful - return Right(resp.topicsMetadata.toSet) + val respErrs = resp.topicsMetadata.filter(m => m.errorCode != ErrorMapping.NoError) + + if (respErrs.isEmpty) { + return Right(resp.topicsMetadata.toSet) + } else { + respErrs.foreach { m => + val cause = ErrorMapping.exceptionFor(m.errorCode) + val msg = s"Error getting partition metadata for '${m.topic}'. Does the topic exist?" + errs.append(new SparkException(msg, cause)) + } + } } Left(errs) } diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaClusterSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaClusterSuite.scala index 2b33d2a220b2b..7fb841b79cb65 100644 --- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaClusterSuite.scala +++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaClusterSuite.scala @@ -52,6 +52,9 @@ class KafkaClusterSuite extends FunSuite with BeforeAndAfterAll { val parts = kc.getPartitions(Set(topic)).right.get assert(parts(topicAndPartition), "didn't get partitions") + + val err = kc.getPartitions(Set(topic + "BAD")) + assert(err.isLeft, "getPartitions for a nonexistant topic should be an error") } test("leader offset apis") { From 04bcd67cfc50f847559a9ff59a31aa93028b3628 Mon Sep 17 00:00:00 2001 From: Daoyuan Wang Date: Sun, 12 Apr 2015 18:58:53 +0100 Subject: [PATCH 242/591] [MINOR] a typo: coalesce Author: Daoyuan Wang Closes #5482 from adrian-wang/typo and squashes the following commits: e65ef6f [Daoyuan Wang] typo --- .../org/apache/spark/sql/execution/GeneratedAggregate.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala index 3c58e93b45e9d..95176e425132d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala @@ -97,7 +97,7 @@ case class GeneratedAggregate( val currentSum = AttributeReference("currentSum", calcType, nullable = true)() val initialValue = Literal.create(null, calcType) - // Coalasce avoids double calculation... + // Coalesce avoids double calculation... // but really, common sub expression elimination would be better.... val zero = Cast(Literal(0), calcType) val updateFunction = Coalesce( From a1fe59dae50f551d02dd18676308eca054ff6b07 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Sun, 12 Apr 2015 20:50:49 -0700 Subject: [PATCH 243/591] [SPARK-6765] Fix test code style for core. Author: Reynold Xin Closes #5484 from rxin/test-style-core and squashes the following commits: e0b0100 [Reynold Xin] [SPARK-6765] Fix test code style for core. --- .../org/apache/spark/AccumulatorSuite.scala | 30 ++--- .../org/apache/spark/CacheManagerSuite.scala | 7 +- .../org/apache/spark/CheckpointSuite.scala | 15 ++- .../apache/spark/ContextCleanerSuite.scala | 4 +- .../scala/org/apache/spark/FileSuite.scala | 26 +++-- .../apache/spark/ImplicitOrderingSuite.scala | 6 +- .../apache/spark/JobCancellationSuite.scala | 2 +- .../org/apache/spark/LocalSparkContext.scala | 4 +- .../org/apache/spark/PartitioningSuite.scala | 30 ++--- .../org/apache/spark/SSLOptionsSuite.scala | 15 ++- .../org/apache/spark/SSLSampleConfigs.scala | 7 +- .../scala/org/apache/spark/ShuffleSuite.scala | 8 +- .../org/apache/spark/SparkContextSuite.scala | 6 +- .../org/apache/spark/StatusTrackerSuite.scala | 5 +- .../spark/broadcast/BroadcastSuite.scala | 2 +- .../spark/deploy/JsonProtocolSuite.scala | 6 +- .../spark/deploy/LogUrlsStandaloneSuite.scala | 2 +- .../deploy/history/HistoryServerSuite.scala | 4 +- .../rest/StandaloneRestSubmitSuite.scala | 4 +- .../deploy/rest/SubmitRestProtocolSuite.scala | 3 +- .../deploy/worker/ExecutorRunnerTest.scala | 3 +- .../deploy/worker/WorkerArgumentsTest.scala | 4 +- .../spark/deploy/worker/WorkerSuite.scala | 6 +- .../metrics/InputOutputMetricsSuite.scala | 2 +- .../spark/metrics/MetricsConfigSuite.scala | 15 ++- .../org/apache/spark/rdd/JdbcRDDSuite.scala | 4 +- .../spark/rdd/PairRDDFunctionsSuite.scala | 41 +++---- .../rdd/ParallelCollectionSplitSuite.scala | 20 ++-- .../spark/rdd/PartitionPruningRDDSuite.scala | 7 +- .../rdd/PartitionwiseSampledRDDSuite.scala | 2 +- .../scala/org/apache/spark/rdd/RDDSuite.scala | 22 ++-- .../org/apache/spark/rdd/RDDSuiteUtils.scala | 4 +- .../org/apache/spark/rpc/RpcEnvSuite.scala | 40 +++---- .../spark/scheduler/DAGSchedulerSuite.scala | 103 ++++++++++-------- .../scheduler/EventLoggingListenerSuite.scala | 4 +- .../scheduler/NotSerializableFakeTask.scala | 4 +- .../spark/scheduler/SparkListenerSuite.scala | 21 ++-- .../scheduler/TaskSchedulerImplSuite.scala | 10 +- .../spark/scheduler/TaskSetManagerSuite.scala | 26 +++-- .../mesos/MesosSchedulerBackendSuite.scala | 14 ++- .../serializer/KryoSerializerSuite.scala | 15 ++- .../ProactiveClosureSerializationSuite.scala | 8 +- .../spark/serializer/TestSerializer.scala | 5 +- .../hash/HashShuffleManagerSuite.scala | 4 +- .../spark/storage/BlockManagerSuite.scala | 32 +++--- .../apache/spark/storage/LocalDirsSuite.scala | 2 +- .../org/apache/spark/ui/UISeleniumSuite.scala | 2 +- .../ui/jobs/JobProgressListenerSuite.scala | 4 +- .../spark/ui/storage/StorageTabSuite.scala | 3 +- .../spark/util/ClosureCleanerSuite.scala | 6 +- .../apache/spark/util/FileAppenderSuite.scala | 13 ++- .../apache/spark/util/NextIteratorSuite.scala | 2 +- .../spark/util/SizeEstimatorSuite.scala | 6 +- .../spark/util/TimeStampedHashMapSuite.scala | 4 +- .../org/apache/spark/util/UtilsSuite.scala | 5 +- .../org/apache/spark/util/VectorSuite.scala | 2 +- .../ExternalAppendOnlyMapSuite.scala | 2 +- .../util/collection/ExternalSorterSuite.scala | 35 +++--- .../util/random/XORShiftRandomSuite.scala | 2 +- 59 files changed, 386 insertions(+), 304 deletions(-) diff --git a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala index bd0f8bdefa171..75399461f2a5f 100644 --- a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala +++ b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala @@ -27,19 +27,20 @@ import org.scalatest.Matchers class AccumulatorSuite extends FunSuite with Matchers with LocalSparkContext { - implicit def setAccum[A] = new AccumulableParam[mutable.Set[A], A] { - def addInPlace(t1: mutable.Set[A], t2: mutable.Set[A]) : mutable.Set[A] = { - t1 ++= t2 - t1 - } - def addAccumulator(t1: mutable.Set[A], t2: A) : mutable.Set[A] = { - t1 += t2 - t1 - } - def zero(t: mutable.Set[A]) : mutable.Set[A] = { - new mutable.HashSet[A]() + implicit def setAccum[A]: AccumulableParam[mutable.Set[A], A] = + new AccumulableParam[mutable.Set[A], A] { + def addInPlace(t1: mutable.Set[A], t2: mutable.Set[A]) : mutable.Set[A] = { + t1 ++= t2 + t1 + } + def addAccumulator(t1: mutable.Set[A], t2: A) : mutable.Set[A] = { + t1 += t2 + t1 + } + def zero(t: mutable.Set[A]) : mutable.Set[A] = { + new mutable.HashSet[A]() + } } - } test ("basic accumulation"){ sc = new SparkContext("local", "test") @@ -49,11 +50,10 @@ class AccumulatorSuite extends FunSuite with Matchers with LocalSparkContext { d.foreach{x => acc += x} acc.value should be (210) - - val longAcc = sc.accumulator(0l) + val longAcc = sc.accumulator(0L) val maxInt = Integer.MAX_VALUE.toLong d.foreach{x => longAcc += maxInt + x} - longAcc.value should be (210l + maxInt * 20) + longAcc.value should be (210L + maxInt * 20) } test ("value not assignable from tasks") { diff --git a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala index 4b25c200a695a..70529d9216591 100644 --- a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala @@ -45,16 +45,17 @@ class CacheManagerSuite extends FunSuite with LocalSparkContext with BeforeAndAf rdd = new RDD[Int](sc, Nil) { override def getPartitions: Array[Partition] = Array(split) override val getDependencies = List[Dependency[_]]() - override def compute(split: Partition, context: TaskContext) = Array(1, 2, 3, 4).iterator + override def compute(split: Partition, context: TaskContext): Iterator[Int] = + Array(1, 2, 3, 4).iterator } rdd2 = new RDD[Int](sc, List(new OneToOneDependency(rdd))) { override def getPartitions: Array[Partition] = firstParent[Int].partitions - override def compute(split: Partition, context: TaskContext) = + override def compute(split: Partition, context: TaskContext): Iterator[Int] = firstParent[Int].iterator(split, context) }.cache() rdd3 = new RDD[Int](sc, List(new OneToOneDependency(rdd2))) { override def getPartitions: Array[Partition] = firstParent[Int].partitions - override def compute(split: Partition, context: TaskContext) = + override def compute(split: Partition, context: TaskContext): Iterator[Int] = firstParent[Int].iterator(split, context) }.cache() } diff --git a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala index 32abc65385267..e1faddeabec79 100644 --- a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala +++ b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala @@ -75,7 +75,8 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { assert(sc.checkpointFile[Int](parCollection.getCheckpointFile.get).collect() === result) assert(parCollection.dependencies != Nil) assert(parCollection.partitions.length === numPartitions) - assert(parCollection.partitions.toList === parCollection.checkpointData.get.getPartitions.toList) + assert(parCollection.partitions.toList === + parCollection.checkpointData.get.getPartitions.toList) assert(parCollection.collect() === result) } @@ -102,13 +103,13 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { } test("UnionRDD") { - def otherRDD = sc.makeRDD(1 to 10, 1) + def otherRDD: RDD[Int] = sc.makeRDD(1 to 10, 1) testRDD(_.union(otherRDD)) testRDDPartitions(_.union(otherRDD)) } test("CartesianRDD") { - def otherRDD = sc.makeRDD(1 to 10, 1) + def otherRDD: RDD[Int] = sc.makeRDD(1 to 10, 1) testRDD(new CartesianRDD(sc, _, otherRDD)) testRDDPartitions(new CartesianRDD(sc, _, otherRDD)) @@ -223,7 +224,8 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { val partitionAfterCheckpoint = serializeDeserialize( unionRDD.partitions.head.asInstanceOf[PartitionerAwareUnionRDDPartition]) assert( - partitionBeforeCheckpoint.parents.head.getClass != partitionAfterCheckpoint.parents.head.getClass, + partitionBeforeCheckpoint.parents.head.getClass != + partitionAfterCheckpoint.parents.head.getClass, "PartitionerAwareUnionRDDPartition.parents not updated after parent RDD is checkpointed" ) } @@ -358,7 +360,7 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { * Generate an pair RDD (with partitioner) such that both the RDD and its partitions * have large size. */ - def generateFatPairRDD() = { + def generateFatPairRDD(): RDD[(Int, Int)] = { new FatPairRDD(sc.makeRDD(1 to 100, 4), partitioner).mapValues(x => x) } @@ -445,7 +447,8 @@ class FatPairRDD(parent: RDD[Int], _partitioner: Partitioner) extends RDD[(Int, object CheckpointSuite { // This is a custom cogroup function that does not use mapValues like // the PairRDDFunctions.cogroup() - def cogroup[K, V](first: RDD[(K, V)], second: RDD[(K, V)], part: Partitioner) = { + def cogroup[K, V](first: RDD[(K, V)], second: RDD[(K, V)], part: Partitioner) + : RDD[(K, Array[Iterable[V]])] = { new CoGroupedRDD[K]( Seq(first.asInstanceOf[RDD[(K, _)]], second.asInstanceOf[RDD[(K, _)]]), part diff --git a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala index cdfaacee7da40..1de169d964d23 100644 --- a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala +++ b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala @@ -64,7 +64,7 @@ abstract class ContextCleanerSuiteBase(val shuffleManager: Class[_] = classOf[Ha } } - //------ Helper functions ------ + // ------ Helper functions ------ protected def newRDD() = sc.makeRDD(1 to 10) protected def newPairRDD() = newRDD().map(_ -> 1) @@ -370,7 +370,7 @@ class CleanerTester( val cleanerListener = new CleanerListener { def rddCleaned(rddId: Int): Unit = { toBeCleanedRDDIds -= rddId - logInfo("RDD "+ rddId + " cleaned") + logInfo("RDD " + rddId + " cleaned") } def shuffleCleaned(shuffleId: Int): Unit = { diff --git a/core/src/test/scala/org/apache/spark/FileSuite.scala b/core/src/test/scala/org/apache/spark/FileSuite.scala index 7acd27c735727..c8f08eed47c76 100644 --- a/core/src/test/scala/org/apache/spark/FileSuite.scala +++ b/core/src/test/scala/org/apache/spark/FileSuite.scala @@ -222,7 +222,7 @@ class FileSuite extends FunSuite with LocalSparkContext { val nums = sc.makeRDD(1 to 3).map(x => (new IntWritable(x), new Text("a" * x))) nums.saveAsSequenceFile(outputDir) val output = - sc.newAPIHadoopFile[IntWritable, Text, SequenceFileInputFormat[IntWritable, Text]](outputDir) + sc.newAPIHadoopFile[IntWritable, Text, SequenceFileInputFormat[IntWritable, Text]](outputDir) assert(output.map(_.toString).collect().toList === List("(1,a)", "(2,aa)", "(3,aaa)")) } @@ -451,7 +451,8 @@ class FileSuite extends FunSuite with LocalSparkContext { test ("prevent user from overwriting the empty directory (new Hadoop API)") { sc = new SparkContext("local", "test") - val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) + val randomRDD = sc.parallelize( + Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) intercept[FileAlreadyExistsException] { randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempDir.getPath) } @@ -459,8 +460,10 @@ class FileSuite extends FunSuite with LocalSparkContext { test ("prevent user from overwriting the non-empty directory (new Hadoop API)") { sc = new SparkContext("local", "test") - val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) - randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempDir.getPath + "/output") + val randomRDD = sc.parallelize( + Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) + randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]]( + tempDir.getPath + "/output") assert(new File(tempDir.getPath + "/output/part-r-00000").exists() === true) intercept[FileAlreadyExistsException] { randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempDir.getPath) @@ -471,16 +474,20 @@ class FileSuite extends FunSuite with LocalSparkContext { val sf = new SparkConf() sf.setAppName("test").setMaster("local").set("spark.hadoop.validateOutputSpecs", "false") sc = new SparkContext(sf) - val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) - randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempDir.getPath + "/output") + val randomRDD = sc.parallelize( + Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) + randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]]( + tempDir.getPath + "/output") assert(new File(tempDir.getPath + "/output/part-r-00000").exists() === true) - randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]](tempDir.getPath + "/output") + randomRDD.saveAsNewAPIHadoopFile[NewTextOutputFormat[String, String]]( + tempDir.getPath + "/output") assert(new File(tempDir.getPath + "/output/part-r-00000").exists() === true) } test ("save Hadoop Dataset through old Hadoop API") { sc = new SparkContext("local", "test") - val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) + val randomRDD = sc.parallelize( + Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) val job = new JobConf() job.setOutputKeyClass(classOf[String]) job.setOutputValueClass(classOf[String]) @@ -492,7 +499,8 @@ class FileSuite extends FunSuite with LocalSparkContext { test ("save Hadoop Dataset through new Hadoop API") { sc = new SparkContext("local", "test") - val randomRDD = sc.parallelize(Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) + val randomRDD = sc.parallelize( + Array(("key1", "a"), ("key2", "a"), ("key3", "b"), ("key4", "c")), 1) val job = new Job(sc.hadoopConfiguration) job.setOutputKeyClass(classOf[String]) job.setOutputValueClass(classOf[String]) diff --git a/core/src/test/scala/org/apache/spark/ImplicitOrderingSuite.scala b/core/src/test/scala/org/apache/spark/ImplicitOrderingSuite.scala index d895230ecf330..51348c039b5c9 100644 --- a/core/src/test/scala/org/apache/spark/ImplicitOrderingSuite.scala +++ b/core/src/test/scala/org/apache/spark/ImplicitOrderingSuite.scala @@ -51,7 +51,7 @@ private object ImplicitOrderingSuite { override def compare(o: OrderedClass): Int = ??? } - def basicMapExpectations(rdd: RDD[Int]) = { + def basicMapExpectations(rdd: RDD[Int]): List[(Boolean, String)] = { List((rdd.map(x => (x, x)).keyOrdering.isDefined, "rdd.map(x => (x, x)).keyOrdering.isDefined"), (rdd.map(x => (1, x)).keyOrdering.isDefined, @@ -68,7 +68,7 @@ private object ImplicitOrderingSuite { "rdd.map(x => (new OrderedClass, x)).keyOrdering.isDefined")) } - def otherRDDMethodExpectations(rdd: RDD[Int]) = { + def otherRDDMethodExpectations(rdd: RDD[Int]): List[(Boolean, String)] = { List((rdd.groupBy(x => x).keyOrdering.isDefined, "rdd.groupBy(x => x).keyOrdering.isDefined"), (rdd.groupBy(x => new NonOrderedClass).keyOrdering.isEmpty, @@ -82,4 +82,4 @@ private object ImplicitOrderingSuite { (rdd.groupBy((x: Int) => x, new HashPartitioner(5)).keyOrdering.isDefined, "rdd.groupBy((x: Int) => x, new HashPartitioner(5)).keyOrdering.isDefined")) } -} \ No newline at end of file +} diff --git a/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala b/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala index 21487bc24d58a..4d3e09793faff 100644 --- a/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala +++ b/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala @@ -188,7 +188,7 @@ class JobCancellationSuite extends FunSuite with Matchers with BeforeAndAfter val rdd = sc.parallelize(1 to 10, 2).map { i => JobCancellationSuite.twoJobsSharingStageSemaphore.acquire() (i, i) - }.reduceByKey(_+_) + }.reduceByKey(_ + _) val f1 = rdd.collectAsync() val f2 = rdd.countAsync() diff --git a/core/src/test/scala/org/apache/spark/LocalSparkContext.scala b/core/src/test/scala/org/apache/spark/LocalSparkContext.scala index 53e367a61715b..8bf2e55defd02 100644 --- a/core/src/test/scala/org/apache/spark/LocalSparkContext.scala +++ b/core/src/test/scala/org/apache/spark/LocalSparkContext.scala @@ -37,7 +37,7 @@ trait LocalSparkContext extends BeforeAndAfterEach with BeforeAndAfterAll { self super.afterEach() } - def resetSparkContext() = { + def resetSparkContext(): Unit = { LocalSparkContext.stop(sc) sc = null } @@ -54,7 +54,7 @@ object LocalSparkContext { } /** Runs `f` by passing in `sc` and ensures that `sc` is stopped. */ - def withSpark[T](sc: SparkContext)(f: SparkContext => T) = { + def withSpark[T](sc: SparkContext)(f: SparkContext => T): T = { try { f(sc) } finally { diff --git a/core/src/test/scala/org/apache/spark/PartitioningSuite.scala b/core/src/test/scala/org/apache/spark/PartitioningSuite.scala index b7532314ada01..47e3bf6e1ac41 100644 --- a/core/src/test/scala/org/apache/spark/PartitioningSuite.scala +++ b/core/src/test/scala/org/apache/spark/PartitioningSuite.scala @@ -92,7 +92,7 @@ class PartitioningSuite extends FunSuite with SharedSparkContext with PrivateMet test("RangePartitioner for keys that are not Comparable (but with Ordering)") { // Row does not extend Comparable, but has an implicit Ordering defined. implicit object RowOrdering extends Ordering[Row] { - override def compare(x: Row, y: Row) = x.value - y.value + override def compare(x: Row, y: Row): Int = x.value - y.value } val rdd = sc.parallelize(1 to 4500).map(x => (Row(x), Row(x))) @@ -212,20 +212,24 @@ class PartitioningSuite extends FunSuite with SharedSparkContext with PrivateMet val arrPairs: RDD[(Array[Int], Int)] = sc.parallelize(Array(1, 2, 3, 4), 2).map(x => (Array(x), x)) - assert(intercept[SparkException]{ arrs.distinct() }.getMessage.contains("array")) + def verify(testFun: => Unit): Unit = { + intercept[SparkException](testFun).getMessage.contains("array") + } + + verify(arrs.distinct()) // We can't catch all usages of arrays, since they might occur inside other collections: // assert(fails { arrPairs.distinct() }) - assert(intercept[SparkException]{ arrPairs.partitionBy(new HashPartitioner(2)) }.getMessage.contains("array")) - assert(intercept[SparkException]{ arrPairs.join(arrPairs) }.getMessage.contains("array")) - assert(intercept[SparkException]{ arrPairs.leftOuterJoin(arrPairs) }.getMessage.contains("array")) - assert(intercept[SparkException]{ arrPairs.rightOuterJoin(arrPairs) }.getMessage.contains("array")) - assert(intercept[SparkException]{ arrPairs.fullOuterJoin(arrPairs) }.getMessage.contains("array")) - assert(intercept[SparkException]{ arrPairs.groupByKey() }.getMessage.contains("array")) - assert(intercept[SparkException]{ arrPairs.countByKey() }.getMessage.contains("array")) - assert(intercept[SparkException]{ arrPairs.countByKeyApprox(1) }.getMessage.contains("array")) - assert(intercept[SparkException]{ arrPairs.cogroup(arrPairs) }.getMessage.contains("array")) - assert(intercept[SparkException]{ arrPairs.reduceByKeyLocally(_ + _) }.getMessage.contains("array")) - assert(intercept[SparkException]{ arrPairs.reduceByKey(_ + _) }.getMessage.contains("array")) + verify(arrPairs.partitionBy(new HashPartitioner(2))) + verify(arrPairs.join(arrPairs)) + verify(arrPairs.leftOuterJoin(arrPairs)) + verify(arrPairs.rightOuterJoin(arrPairs)) + verify(arrPairs.fullOuterJoin(arrPairs)) + verify(arrPairs.groupByKey()) + verify(arrPairs.countByKey()) + verify(arrPairs.countByKeyApprox(1)) + verify(arrPairs.cogroup(arrPairs)) + verify(arrPairs.reduceByKeyLocally(_ + _)) + verify(arrPairs.reduceByKey(_ + _)) } test("zero-length partitions should be correctly handled") { diff --git a/core/src/test/scala/org/apache/spark/SSLOptionsSuite.scala b/core/src/test/scala/org/apache/spark/SSLOptionsSuite.scala index 444a33371bd71..93f46ef11c0e2 100644 --- a/core/src/test/scala/org/apache/spark/SSLOptionsSuite.scala +++ b/core/src/test/scala/org/apache/spark/SSLOptionsSuite.scala @@ -36,7 +36,8 @@ class SSLOptionsSuite extends FunSuite with BeforeAndAfterAll { conf.set("spark.ssl.keyPassword", "password") conf.set("spark.ssl.trustStore", trustStorePath) conf.set("spark.ssl.trustStorePassword", "password") - conf.set("spark.ssl.enabledAlgorithms", "TLS_RSA_WITH_AES_128_CBC_SHA, TLS_RSA_WITH_AES_256_CBC_SHA") + conf.set("spark.ssl.enabledAlgorithms", + "TLS_RSA_WITH_AES_128_CBC_SHA, TLS_RSA_WITH_AES_256_CBC_SHA") conf.set("spark.ssl.protocol", "SSLv3") val opts = SSLOptions.parse(conf, "spark.ssl") @@ -52,7 +53,8 @@ class SSLOptionsSuite extends FunSuite with BeforeAndAfterAll { assert(opts.keyStorePassword === Some("password")) assert(opts.keyPassword === Some("password")) assert(opts.protocol === Some("SSLv3")) - assert(opts.enabledAlgorithms === Set("TLS_RSA_WITH_AES_128_CBC_SHA", "TLS_RSA_WITH_AES_256_CBC_SHA")) + assert(opts.enabledAlgorithms === + Set("TLS_RSA_WITH_AES_128_CBC_SHA", "TLS_RSA_WITH_AES_256_CBC_SHA")) } test("test resolving property with defaults specified ") { @@ -66,7 +68,8 @@ class SSLOptionsSuite extends FunSuite with BeforeAndAfterAll { conf.set("spark.ssl.keyPassword", "password") conf.set("spark.ssl.trustStore", trustStorePath) conf.set("spark.ssl.trustStorePassword", "password") - conf.set("spark.ssl.enabledAlgorithms", "TLS_RSA_WITH_AES_128_CBC_SHA, TLS_RSA_WITH_AES_256_CBC_SHA") + conf.set("spark.ssl.enabledAlgorithms", + "TLS_RSA_WITH_AES_128_CBC_SHA, TLS_RSA_WITH_AES_256_CBC_SHA") conf.set("spark.ssl.protocol", "SSLv3") val defaultOpts = SSLOptions.parse(conf, "spark.ssl", defaults = None) @@ -83,7 +86,8 @@ class SSLOptionsSuite extends FunSuite with BeforeAndAfterAll { assert(opts.keyStorePassword === Some("password")) assert(opts.keyPassword === Some("password")) assert(opts.protocol === Some("SSLv3")) - assert(opts.enabledAlgorithms === Set("TLS_RSA_WITH_AES_128_CBC_SHA", "TLS_RSA_WITH_AES_256_CBC_SHA")) + assert(opts.enabledAlgorithms === + Set("TLS_RSA_WITH_AES_128_CBC_SHA", "TLS_RSA_WITH_AES_256_CBC_SHA")) } test("test whether defaults can be overridden ") { @@ -99,7 +103,8 @@ class SSLOptionsSuite extends FunSuite with BeforeAndAfterAll { conf.set("spark.ssl.keyPassword", "password") conf.set("spark.ssl.trustStore", trustStorePath) conf.set("spark.ssl.trustStorePassword", "password") - conf.set("spark.ssl.enabledAlgorithms", "TLS_RSA_WITH_AES_128_CBC_SHA, TLS_RSA_WITH_AES_256_CBC_SHA") + conf.set("spark.ssl.enabledAlgorithms", + "TLS_RSA_WITH_AES_128_CBC_SHA, TLS_RSA_WITH_AES_256_CBC_SHA") conf.set("spark.ui.ssl.enabledAlgorithms", "ABC, DEF") conf.set("spark.ssl.protocol", "SSLv3") diff --git a/core/src/test/scala/org/apache/spark/SSLSampleConfigs.scala b/core/src/test/scala/org/apache/spark/SSLSampleConfigs.scala index ace8123a8961f..308b9ea17708d 100644 --- a/core/src/test/scala/org/apache/spark/SSLSampleConfigs.scala +++ b/core/src/test/scala/org/apache/spark/SSLSampleConfigs.scala @@ -21,10 +21,11 @@ import java.io.File object SSLSampleConfigs { val keyStorePath = new File(this.getClass.getResource("/keystore").toURI).getAbsolutePath - val untrustedKeyStorePath = new File(this.getClass.getResource("/untrusted-keystore").toURI).getAbsolutePath + val untrustedKeyStorePath = new File( + this.getClass.getResource("/untrusted-keystore").toURI).getAbsolutePath val trustStorePath = new File(this.getClass.getResource("/truststore").toURI).getAbsolutePath - def sparkSSLConfig() = { + def sparkSSLConfig(): SparkConf = { val conf = new SparkConf(loadDefaults = false) conf.set("spark.ssl.enabled", "true") conf.set("spark.ssl.keyStore", keyStorePath) @@ -38,7 +39,7 @@ object SSLSampleConfigs { conf } - def sparkSSLConfigUntrusted() = { + def sparkSSLConfigUntrusted(): SparkConf = { val conf = new SparkConf(loadDefaults = false) conf.set("spark.ssl.enabled", "true") conf.set("spark.ssl.keyStore", untrustedKeyStorePath) diff --git a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala index 30b6184c77839..d7180516029d5 100644 --- a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala +++ b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala @@ -142,7 +142,7 @@ abstract class ShuffleSuite extends FunSuite with Matchers with LocalSparkContex test("shuffle on mutable pairs") { // Use a local cluster with 2 processes to make sure there are both local and remote blocks sc = new SparkContext("local-cluster[2,1,512]", "test", conf) - def p[T1, T2](_1: T1, _2: T2) = MutablePair(_1, _2) + def p[T1, T2](_1: T1, _2: T2): MutablePair[T1, T2] = MutablePair(_1, _2) val data = Array(p(1, 1), p(1, 2), p(1, 3), p(2, 1)) val pairs: RDD[MutablePair[Int, Int]] = sc.parallelize(data, 2) val results = new ShuffledRDD[Int, Int, Int](pairs, @@ -155,7 +155,7 @@ abstract class ShuffleSuite extends FunSuite with Matchers with LocalSparkContex // This is not in SortingSuite because of the local cluster setup. // Use a local cluster with 2 processes to make sure there are both local and remote blocks sc = new SparkContext("local-cluster[2,1,512]", "test", conf) - def p[T1, T2](_1: T1, _2: T2) = MutablePair(_1, _2) + def p[T1, T2](_1: T1, _2: T2): MutablePair[T1, T2] = MutablePair(_1, _2) val data = Array(p(1, 11), p(3, 33), p(100, 100), p(2, 22)) val pairs: RDD[MutablePair[Int, Int]] = sc.parallelize(data, 2) val results = new OrderedRDDFunctions[Int, Int, MutablePair[Int, Int]](pairs) @@ -169,7 +169,7 @@ abstract class ShuffleSuite extends FunSuite with Matchers with LocalSparkContex test("cogroup using mutable pairs") { // Use a local cluster with 2 processes to make sure there are both local and remote blocks sc = new SparkContext("local-cluster[2,1,512]", "test", conf) - def p[T1, T2](_1: T1, _2: T2) = MutablePair(_1, _2) + def p[T1, T2](_1: T1, _2: T2): MutablePair[T1, T2] = MutablePair(_1, _2) val data1 = Seq(p(1, 1), p(1, 2), p(1, 3), p(2, 1)) val data2 = Seq(p(1, "11"), p(1, "12"), p(2, "22"), p(3, "3")) val pairs1: RDD[MutablePair[Int, Int]] = sc.parallelize(data1, 2) @@ -196,7 +196,7 @@ abstract class ShuffleSuite extends FunSuite with Matchers with LocalSparkContex test("subtract mutable pairs") { // Use a local cluster with 2 processes to make sure there are both local and remote blocks sc = new SparkContext("local-cluster[2,1,512]", "test", conf) - def p[T1, T2](_1: T1, _2: T2) = MutablePair(_1, _2) + def p[T1, T2](_1: T1, _2: T2): MutablePair[T1, T2] = MutablePair(_1, _2) val data1 = Seq(p(1, 1), p(1, 2), p(1, 3), p(2, 1), p(3, 33)) val data2 = Seq(p(1, "11"), p(1, "12"), p(2, "22")) val pairs1: RDD[MutablePair[Int, Int]] = sc.parallelize(data1, 2) diff --git a/core/src/test/scala/org/apache/spark/SparkContextSuite.scala b/core/src/test/scala/org/apache/spark/SparkContextSuite.scala index c7301a30d8b11..94be1c6d6397c 100644 --- a/core/src/test/scala/org/apache/spark/SparkContextSuite.scala +++ b/core/src/test/scala/org/apache/spark/SparkContextSuite.scala @@ -114,11 +114,13 @@ class SparkContextSuite extends FunSuite with LocalSparkContext { if (length1 != gotten1.length()) { throw new SparkException( - s"file has different length $length1 than added file ${gotten1.length()} : " + absolutePath1) + s"file has different length $length1 than added file ${gotten1.length()} : " + + absolutePath1) } if (length2 != gotten2.length()) { throw new SparkException( - s"file has different length $length2 than added file ${gotten2.length()} : " + absolutePath2) + s"file has different length $length2 than added file ${gotten2.length()} : " + + absolutePath2) } if (absolutePath1 == gotten1.getAbsolutePath) { diff --git a/core/src/test/scala/org/apache/spark/StatusTrackerSuite.scala b/core/src/test/scala/org/apache/spark/StatusTrackerSuite.scala index 41d6ea29d5b06..084eb237d70d1 100644 --- a/core/src/test/scala/org/apache/spark/StatusTrackerSuite.scala +++ b/core/src/test/scala/org/apache/spark/StatusTrackerSuite.scala @@ -82,7 +82,8 @@ class StatusTrackerSuite extends FunSuite with Matchers with LocalSparkContext { secondJobFuture.jobIds.head } eventually(timeout(10 seconds)) { - sc.statusTracker.getJobIdsForGroup("my-job-group").toSet should be (Set(firstJobId, secondJobId)) + sc.statusTracker.getJobIdsForGroup("my-job-group").toSet should be ( + Set(firstJobId, secondJobId)) } } -} \ No newline at end of file +} diff --git a/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala b/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala index af3272692d7a1..c8fdfa693912e 100644 --- a/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala +++ b/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala @@ -33,7 +33,7 @@ class DummyBroadcastClass(rdd: RDD[Int]) extends Serializable { val broadcast = rdd.context.broadcast(list) val bid = broadcast.id - def doSomething() = { + def doSomething(): Set[(Int, Boolean)] = { rdd.map { x => val bm = SparkEnv.get.blockManager // Check if broadcast block was fetched diff --git a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala index 68b5776fc6515..2071701b313db 100644 --- a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala @@ -100,13 +100,13 @@ class JsonProtocolSuite extends FunSuite { appInfo } - def createDriverCommand() = new Command( + def createDriverCommand(): Command = new Command( "org.apache.spark.FakeClass", Seq("some arg --and-some options -g foo"), Map(("K1", "V1"), ("K2", "V2")), Seq("cp1", "cp2"), Seq("lp1", "lp2"), Seq("-Dfoo") ) - def createDriverDesc() = new DriverDescription("hdfs://some-dir/some.jar", 100, 3, - false, createDriverCommand()) + def createDriverDesc(): DriverDescription = + new DriverDescription("hdfs://some-dir/some.jar", 100, 3, false, createDriverCommand()) def createDriverInfo(): DriverInfo = new DriverInfo(3, "driver-3", createDriverDesc(), new Date()) diff --git a/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala b/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala index 54dd7c9c45c61..9cdb42814ca32 100644 --- a/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala @@ -56,7 +56,7 @@ class LogUrlsStandaloneSuite extends FunSuite with LocalSparkContext { test("verify that log urls reflect SPARK_PUBLIC_DNS (SPARK-6175)") { val SPARK_PUBLIC_DNS = "public_dns" class MySparkConf extends SparkConf(false) { - override def getenv(name: String) = { + override def getenv(name: String): String = { if (name == "SPARK_PUBLIC_DNS") SPARK_PUBLIC_DNS else super.getenv(name) } diff --git a/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala b/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala index 3a9963a5ce7b7..20de46fdab909 100644 --- a/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala @@ -42,10 +42,10 @@ class HistoryServerSuite extends FunSuite with Matchers with MockitoSugar { when(historyServer.getProviderConfig()).thenReturn(Map[String, String]()) val page = new HistoryPage(historyServer) - //when + // when val response = page.render(request) - //then + // then val links = response \\ "a" val justHrefs = for { l <- links diff --git a/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala index 2fa90e3bd1c63..8e09976636386 100644 --- a/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala @@ -508,7 +508,7 @@ private class DummyMaster( exception: Option[Exception] = None) extends Actor { - override def receive = { + override def receive: PartialFunction[Any, Unit] = { case RequestSubmitDriver(driverDesc) => sender ! SubmitDriverResponse(success = true, Some(submitId), submitMessage) case RequestKillDriver(driverId) => @@ -531,7 +531,7 @@ private class SmarterMaster extends Actor { private var counter: Int = 0 private val submittedDrivers = new mutable.HashMap[String, DriverState] - override def receive = { + override def receive: PartialFunction[Any, Unit] = { case RequestSubmitDriver(driverDesc) => val driverId = s"driver-$counter" submittedDrivers(driverId) = RUNNING diff --git a/core/src/test/scala/org/apache/spark/deploy/rest/SubmitRestProtocolSuite.scala b/core/src/test/scala/org/apache/spark/deploy/rest/SubmitRestProtocolSuite.scala index 1d64ec201e647..61071ee17256c 100644 --- a/core/src/test/scala/org/apache/spark/deploy/rest/SubmitRestProtocolSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/rest/SubmitRestProtocolSuite.scala @@ -129,7 +129,8 @@ class SubmitRestProtocolSuite extends FunSuite { assert(newMessage.sparkProperties("spark.files") === "fireball.png") assert(newMessage.sparkProperties("spark.driver.memory") === "512m") assert(newMessage.sparkProperties("spark.driver.cores") === "180") - assert(newMessage.sparkProperties("spark.driver.extraJavaOptions") === " -Dslices=5 -Dcolor=mostly_red") + assert(newMessage.sparkProperties("spark.driver.extraJavaOptions") === + " -Dslices=5 -Dcolor=mostly_red") assert(newMessage.sparkProperties("spark.driver.extraClassPath") === "food-coloring.jar") assert(newMessage.sparkProperties("spark.driver.extraLibraryPath") === "pickle.jar") assert(newMessage.sparkProperties("spark.driver.supervise") === "false") diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala b/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala index 6fca6321e5a1b..a8b9df227c996 100644 --- a/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala +++ b/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala @@ -35,7 +35,8 @@ class ExecutorRunnerTest extends FunSuite { val er = new ExecutorRunner(appId, 1, appDesc, 8, 500, null, "blah", "worker321", 123, "publicAddr", new File(sparkHome), new File("ooga"), "blah", new SparkConf, Seq("localDir"), ExecutorState.RUNNING) - val builder = CommandUtils.buildProcessBuilder(appDesc.command, 512, sparkHome, er.substituteVariables) + val builder = CommandUtils.buildProcessBuilder( + appDesc.command, 512, sparkHome, er.substituteVariables) assert(builder.command().last === appId) } } diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerArgumentsTest.scala b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerArgumentsTest.scala index 372d7aa453008..7cc2104281464 100644 --- a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerArgumentsTest.scala +++ b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerArgumentsTest.scala @@ -37,7 +37,7 @@ class WorkerArgumentsTest extends FunSuite { val args = Array("spark://localhost:0000 ") class MySparkConf extends SparkConf(false) { - override def getenv(name: String) = { + override def getenv(name: String): String = { if (name == "SPARK_WORKER_MEMORY") "50000" else super.getenv(name) } @@ -56,7 +56,7 @@ class WorkerArgumentsTest extends FunSuite { val args = Array("spark://localhost:0000 ") class MySparkConf extends SparkConf(false) { - override def getenv(name: String) = { + override def getenv(name: String): String = { if (name == "SPARK_WORKER_MEMORY") "5G" else super.getenv(name) } diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerSuite.scala b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerSuite.scala index 84e2fd7ad936d..450fba21f4b5c 100644 --- a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerSuite.scala @@ -24,8 +24,10 @@ import org.scalatest.{Matchers, FunSuite} class WorkerSuite extends FunSuite with Matchers { - def cmd(javaOpts: String*) = Command("", Seq.empty, Map.empty, Seq.empty, Seq.empty, Seq(javaOpts:_*)) - def conf(opts: (String, String)*) = new SparkConf(loadDefaults = false).setAll(opts) + def cmd(javaOpts: String*): Command = { + Command("", Seq.empty, Map.empty, Seq.empty, Seq.empty, Seq(javaOpts:_*)) + } + def conf(opts: (String, String)*): SparkConf = new SparkConf(loadDefaults = false).setAll(opts) test("test isUseLocalNodeSSLConfig") { Worker.isUseLocalNodeSSLConfig(cmd("-Dasdf=dfgh")) shouldBe false diff --git a/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala b/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala index 78fa98a3b9065..190b08d950a02 100644 --- a/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala +++ b/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala @@ -238,7 +238,7 @@ class InputOutputMetricsSuite extends FunSuite with SharedSparkContext sc.textFile(tmpFilePath, 4) .map(key => (key, 1)) - .reduceByKey(_+_) + .reduceByKey(_ + _) .saveAsTextFile("file://" + tmpFile.getAbsolutePath) sc.listenerBus.waitUntilEmpty(500) diff --git a/core/src/test/scala/org/apache/spark/metrics/MetricsConfigSuite.scala b/core/src/test/scala/org/apache/spark/metrics/MetricsConfigSuite.scala index 37e528435aa5d..100ac77dec1f7 100644 --- a/core/src/test/scala/org/apache/spark/metrics/MetricsConfigSuite.scala +++ b/core/src/test/scala/org/apache/spark/metrics/MetricsConfigSuite.scala @@ -35,7 +35,8 @@ class MetricsConfigSuite extends FunSuite with BeforeAndAfter { val property = conf.getInstance("random") assert(property.size() === 2) - assert(property.getProperty("sink.servlet.class") === "org.apache.spark.metrics.sink.MetricsServlet") + assert(property.getProperty("sink.servlet.class") === + "org.apache.spark.metrics.sink.MetricsServlet") assert(property.getProperty("sink.servlet.path") === "/metrics/json") } @@ -47,16 +48,20 @@ class MetricsConfigSuite extends FunSuite with BeforeAndAfter { assert(masterProp.size() === 5) assert(masterProp.getProperty("sink.console.period") === "20") assert(masterProp.getProperty("sink.console.unit") === "minutes") - assert(masterProp.getProperty("source.jvm.class") === "org.apache.spark.metrics.source.JvmSource") - assert(masterProp.getProperty("sink.servlet.class") === "org.apache.spark.metrics.sink.MetricsServlet") + assert(masterProp.getProperty("source.jvm.class") === + "org.apache.spark.metrics.source.JvmSource") + assert(masterProp.getProperty("sink.servlet.class") === + "org.apache.spark.metrics.sink.MetricsServlet") assert(masterProp.getProperty("sink.servlet.path") === "/metrics/master/json") val workerProp = conf.getInstance("worker") assert(workerProp.size() === 5) assert(workerProp.getProperty("sink.console.period") === "10") assert(workerProp.getProperty("sink.console.unit") === "seconds") - assert(workerProp.getProperty("source.jvm.class") === "org.apache.spark.metrics.source.JvmSource") - assert(workerProp.getProperty("sink.servlet.class") === "org.apache.spark.metrics.sink.MetricsServlet") + assert(workerProp.getProperty("source.jvm.class") === + "org.apache.spark.metrics.source.JvmSource") + assert(workerProp.getProperty("sink.servlet.class") === + "org.apache.spark.metrics.sink.MetricsServlet") assert(workerProp.getProperty("sink.servlet.path") === "/metrics/json") } diff --git a/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala index 0dc59888f7304..be8467354b222 100644 --- a/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala @@ -80,7 +80,7 @@ class JdbcRDDSuite extends FunSuite with BeforeAndAfter with LocalSparkContext { (r: ResultSet) => { r.getInt(1) } ).cache() assert(rdd.count === 100) - assert(rdd.reduce(_+_) === 10100) + assert(rdd.reduce(_ + _) === 10100) } test("large id overflow") { @@ -92,7 +92,7 @@ class JdbcRDDSuite extends FunSuite with BeforeAndAfter with LocalSparkContext { 1131544775L, 567279358897692673L, 20, (r: ResultSet) => { r.getInt(1) } ).cache() assert(rdd.count === 100) - assert(rdd.reduce(_+_) === 5050) + assert(rdd.reduce(_ + _) === 5050) } after { diff --git a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala index 108f70af43f37..ca0d953d306d8 100644 --- a/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/PairRDDFunctionsSuite.scala @@ -168,13 +168,13 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { test("reduceByKey") { val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) - val sums = pairs.reduceByKey(_+_).collect() + val sums = pairs.reduceByKey(_ + _).collect() assert(sums.toSet === Set((1, 7), (2, 1))) } test("reduceByKey with collectAsMap") { val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) - val sums = pairs.reduceByKey(_+_).collectAsMap() + val sums = pairs.reduceByKey(_ + _).collectAsMap() assert(sums.size === 2) assert(sums(1) === 7) assert(sums(2) === 1) @@ -182,7 +182,7 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { test("reduceByKey with many output partitons") { val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) - val sums = pairs.reduceByKey(_+_, 10).collect() + val sums = pairs.reduceByKey(_ + _, 10).collect() assert(sums.toSet === Set((1, 7), (2, 1))) } @@ -192,7 +192,7 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { def getPartition(key: Any) = key.asInstanceOf[Int] } val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 1), (0, 1))).partitionBy(p) - val sums = pairs.reduceByKey(_+_) + val sums = pairs.reduceByKey(_ + _) assert(sums.collect().toSet === Set((1, 4), (0, 1))) assert(sums.partitioner === Some(p)) // count the dependencies to make sure there is only 1 ShuffledRDD @@ -208,7 +208,7 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { } test("countApproxDistinctByKey") { - def error(est: Long, size: Long) = math.abs(est - size) / size.toDouble + def error(est: Long, size: Long): Double = math.abs(est - size) / size.toDouble /* Since HyperLogLog unique counting is approximate, and the relative standard deviation is * only a statistical bound, the tests can fail for large values of relativeSD. We will be using @@ -465,7 +465,7 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { test("foldByKey") { val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) - val sums = pairs.foldByKey(0)(_+_).collect() + val sums = pairs.foldByKey(0)(_ + _).collect() assert(sums.toSet === Set((1, 7), (2, 1))) } @@ -505,7 +505,8 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { conf.setOutputCommitter(classOf[FakeOutputCommitter]) FakeOutputCommitter.ran = false - pairs.saveAsHadoopFile("ignored", pairs.keyClass, pairs.valueClass, classOf[FakeOutputFormat], conf) + pairs.saveAsHadoopFile( + "ignored", pairs.keyClass, pairs.valueClass, classOf[FakeOutputFormat], conf) assert(FakeOutputCommitter.ran, "OutputCommitter was never called") } @@ -552,7 +553,7 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { } private object StratifiedAuxiliary { - def stratifier (fractionPositive: Double) = { + def stratifier (fractionPositive: Double): (Int) => String = { (x: Int) => if (x % 10 < (10 * fractionPositive).toInt) "1" else "0" } @@ -572,7 +573,7 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { def testSampleExact(stratifiedData: RDD[(String, Int)], samplingRate: Double, seed: Long, - n: Long) = { + n: Long): Unit = { testBernoulli(stratifiedData, true, samplingRate, seed, n) testPoisson(stratifiedData, true, samplingRate, seed, n) } @@ -580,7 +581,7 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { def testSample(stratifiedData: RDD[(String, Int)], samplingRate: Double, seed: Long, - n: Long) = { + n: Long): Unit = { testBernoulli(stratifiedData, false, samplingRate, seed, n) testPoisson(stratifiedData, false, samplingRate, seed, n) } @@ -590,7 +591,7 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { exact: Boolean, samplingRate: Double, seed: Long, - n: Long) = { + n: Long): Unit = { val expectedSampleSize = stratifiedData.countByKey() .mapValues(count => math.ceil(count * samplingRate).toInt) val fractions = Map("1" -> samplingRate, "0" -> samplingRate) @@ -612,7 +613,7 @@ class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { exact: Boolean, samplingRate: Double, seed: Long, - n: Long) = { + n: Long): Unit = { val expectedSampleSize = stratifiedData.countByKey().mapValues(count => math.ceil(count * samplingRate).toInt) val fractions = Map("1" -> samplingRate, "0" -> samplingRate) @@ -701,27 +702,27 @@ class FakeOutputFormat() extends OutputFormat[Integer, Integer]() { */ class NewFakeWriter extends NewRecordWriter[Integer, Integer] { - def close(p1: NewTaskAttempContext) = () + def close(p1: NewTaskAttempContext): Unit = () - def write(p1: Integer, p2: Integer) = () + def write(p1: Integer, p2: Integer): Unit = () } class NewFakeCommitter extends NewOutputCommitter { - def setupJob(p1: NewJobContext) = () + def setupJob(p1: NewJobContext): Unit = () def needsTaskCommit(p1: NewTaskAttempContext): Boolean = false - def setupTask(p1: NewTaskAttempContext) = () + def setupTask(p1: NewTaskAttempContext): Unit = () - def commitTask(p1: NewTaskAttempContext) = () + def commitTask(p1: NewTaskAttempContext): Unit = () - def abortTask(p1: NewTaskAttempContext) = () + def abortTask(p1: NewTaskAttempContext): Unit = () } class NewFakeFormat() extends NewOutputFormat[Integer, Integer]() { - def checkOutputSpecs(p1: NewJobContext) = () + def checkOutputSpecs(p1: NewJobContext): Unit = () def getRecordWriter(p1: NewTaskAttempContext): NewRecordWriter[Integer, Integer] = { new NewFakeWriter() @@ -735,7 +736,7 @@ class NewFakeFormat() extends NewOutputFormat[Integer, Integer]() { class ConfigTestFormat() extends NewFakeFormat() with Configurable { var setConfCalled = false - def setConf(p1: Configuration) = { + def setConf(p1: Configuration): Unit = { setConfCalled = true () } diff --git a/core/src/test/scala/org/apache/spark/rdd/ParallelCollectionSplitSuite.scala b/core/src/test/scala/org/apache/spark/rdd/ParallelCollectionSplitSuite.scala index cd193ae4f5238..1880364581c1a 100644 --- a/core/src/test/scala/org/apache/spark/rdd/ParallelCollectionSplitSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/ParallelCollectionSplitSuite.scala @@ -100,7 +100,7 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers { val data = 1 until 100 val slices = ParallelCollectionRDD.slice(data, 3) assert(slices.size === 3) - assert(slices.map(_.size).reduceLeft(_+_) === 99) + assert(slices.map(_.size).reduceLeft(_ + _) === 99) assert(slices.forall(_.isInstanceOf[Range])) } @@ -108,7 +108,7 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers { val data = 1 to 100 val slices = ParallelCollectionRDD.slice(data, 3) assert(slices.size === 3) - assert(slices.map(_.size).reduceLeft(_+_) === 100) + assert(slices.map(_.size).reduceLeft(_ + _) === 100) assert(slices.forall(_.isInstanceOf[Range])) } @@ -139,7 +139,7 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers { assert(slices(i).isInstanceOf[Range]) val range = slices(i).asInstanceOf[Range] assert(range.start === i * (N / 40), "slice " + i + " start") - assert(range.end === (i+1) * (N / 40), "slice " + i + " end") + assert(range.end === (i + 1) * (N / 40), "slice " + i + " end") assert(range.step === 1, "slice " + i + " step") } } @@ -156,7 +156,7 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers { val slices = ParallelCollectionRDD.slice(d, n) ("n slices" |: slices.size == n) && ("concat to d" |: Seq.concat(slices: _*).mkString(",") == d.mkString(",")) && - ("equal sizes" |: slices.map(_.size).forall(x => x==d.size/n || x==d.size/n+1)) + ("equal sizes" |: slices.map(_.size).forall(x => x == d.size / n || x == d.size /n + 1)) } check(prop) } @@ -174,7 +174,7 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers { ("n slices" |: slices.size == n) && ("all ranges" |: slices.forall(_.isInstanceOf[Range])) && ("concat to d" |: Seq.concat(slices: _*).mkString(",") == d.mkString(",")) && - ("equal sizes" |: slices.map(_.size).forall(x => x==d.size/n || x==d.size/n+1)) + ("equal sizes" |: slices.map(_.size).forall(x => x == d.size / n || x == d.size / n + 1)) } check(prop) } @@ -192,7 +192,7 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers { ("n slices" |: slices.size == n) && ("all ranges" |: slices.forall(_.isInstanceOf[Range])) && ("concat to d" |: Seq.concat(slices: _*).mkString(",") == d.mkString(",")) && - ("equal sizes" |: slices.map(_.size).forall(x => x==d.size/n || x==d.size/n+1)) + ("equal sizes" |: slices.map(_.size).forall(x => x == d.size / n || x == d.size / n + 1)) } check(prop) } @@ -201,7 +201,7 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers { val data = 1L until 100L val slices = ParallelCollectionRDD.slice(data, 3) assert(slices.size === 3) - assert(slices.map(_.size).reduceLeft(_+_) === 99) + assert(slices.map(_.size).reduceLeft(_ + _) === 99) assert(slices.forall(_.isInstanceOf[NumericRange[_]])) } @@ -209,7 +209,7 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers { val data = 1L to 100L val slices = ParallelCollectionRDD.slice(data, 3) assert(slices.size === 3) - assert(slices.map(_.size).reduceLeft(_+_) === 100) + assert(slices.map(_.size).reduceLeft(_ + _) === 100) assert(slices.forall(_.isInstanceOf[NumericRange[_]])) } @@ -217,7 +217,7 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers { val data = 1.0 until 100.0 by 1.0 val slices = ParallelCollectionRDD.slice(data, 3) assert(slices.size === 3) - assert(slices.map(_.size).reduceLeft(_+_) === 99) + assert(slices.map(_.size).reduceLeft(_ + _) === 99) assert(slices.forall(_.isInstanceOf[NumericRange[_]])) } @@ -225,7 +225,7 @@ class ParallelCollectionSplitSuite extends FunSuite with Checkers { val data = 1.0 to 100.0 by 1.0 val slices = ParallelCollectionRDD.slice(data, 3) assert(slices.size === 3) - assert(slices.map(_.size).reduceLeft(_+_) === 100) + assert(slices.map(_.size).reduceLeft(_ + _) === 100) assert(slices.forall(_.isInstanceOf[NumericRange[_]])) } diff --git a/core/src/test/scala/org/apache/spark/rdd/PartitionPruningRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PartitionPruningRDDSuite.scala index 8408d7e785c65..465068c6cbb16 100644 --- a/core/src/test/scala/org/apache/spark/rdd/PartitionPruningRDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/PartitionPruningRDDSuite.scala @@ -23,7 +23,6 @@ import org.apache.spark.{Partition, SharedSparkContext, TaskContext} class PartitionPruningRDDSuite extends FunSuite with SharedSparkContext { - test("Pruned Partitions inherit locality prefs correctly") { val rdd = new RDD[Int](sc, Nil) { @@ -74,8 +73,6 @@ class PartitionPruningRDDSuite extends FunSuite with SharedSparkContext { } class TestPartition(i: Int, value: Int) extends Partition with Serializable { - def index = i - - def testValue = this.value - + def index: Int = i + def testValue: Int = this.value } diff --git a/core/src/test/scala/org/apache/spark/rdd/PartitionwiseSampledRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PartitionwiseSampledRDDSuite.scala index a0483886f8db3..0d1369c19c69e 100644 --- a/core/src/test/scala/org/apache/spark/rdd/PartitionwiseSampledRDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/PartitionwiseSampledRDDSuite.scala @@ -35,7 +35,7 @@ class MockSampler extends RandomSampler[Long, Long] { Iterator(s) } - override def clone = new MockSampler + override def clone: MockSampler = new MockSampler } class PartitionwiseSampledRDDSuite extends FunSuite with SharedSparkContext { diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala index bede1ffb3e2d0..df42faab64505 100644 --- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala @@ -82,7 +82,7 @@ class RDDSuite extends FunSuite with SharedSparkContext { test("countApproxDistinct") { - def error(est: Long, size: Long) = math.abs(est - size) / size.toDouble + def error(est: Long, size: Long): Double = math.abs(est - size) / size.toDouble val size = 1000 val uniformDistro = for (i <- 1 to 5000) yield i % size @@ -100,7 +100,7 @@ class RDDSuite extends FunSuite with SharedSparkContext { } test("partitioner aware union") { - def makeRDDWithPartitioner(seq: Seq[Int]) = { + def makeRDDWithPartitioner(seq: Seq[Int]): RDD[Int] = { sc.makeRDD(seq, 1) .map(x => (x, null)) .partitionBy(new HashPartitioner(2)) @@ -159,8 +159,8 @@ class RDDSuite extends FunSuite with SharedSparkContext { test("treeAggregate") { val rdd = sc.makeRDD(-1000 until 1000, 10) - def seqOp = (c: Long, x: Int) => c + x - def combOp = (c1: Long, c2: Long) => c1 + c2 + def seqOp: (Long, Int) => Long = (c: Long, x: Int) => c + x + def combOp: (Long, Long) => Long = (c1: Long, c2: Long) => c1 + c2 for (depth <- 1 until 10) { val sum = rdd.treeAggregate(0L)(seqOp, combOp, depth) assert(sum === -1000L) @@ -204,7 +204,7 @@ class RDDSuite extends FunSuite with SharedSparkContext { assert(empty.collect().size === 0) val thrown = intercept[UnsupportedOperationException]{ - empty.reduce(_+_) + empty.reduce(_ + _) } assert(thrown.getMessage.contains("empty")) @@ -321,7 +321,7 @@ class RDDSuite extends FunSuite with SharedSparkContext { assert(list3.sorted === Array("a","b","c"), "Locality preferences are dropped") // RDD with locality preferences spread (non-randomly) over 6 machines, m0 through m5 - val data = sc.makeRDD((1 to 9).map(i => (i, (i to (i+2)).map{ j => "m" + (j%6)}))) + val data = sc.makeRDD((1 to 9).map(i => (i, (i to (i + 2)).map{ j => "m" + (j%6)}))) val coalesced1 = data.coalesce(3) assert(coalesced1.collect().toList.sorted === (1 to 9).toList, "Data got *lost* in coalescing") @@ -921,15 +921,17 @@ class RDDSuite extends FunSuite with SharedSparkContext { test("task serialization exception should not hang scheduler") { class BadSerializable extends Serializable { @throws(classOf[IOException]) - private def writeObject(out: ObjectOutputStream): Unit = throw new KryoException("Bad serialization") + private def writeObject(out: ObjectOutputStream): Unit = + throw new KryoException("Bad serialization") @throws(classOf[IOException]) private def readObject(in: ObjectInputStream): Unit = {} } - // Note that in the original bug, SPARK-4349, that this verifies, the job would only hang if there were - // more threads in the Spark Context than there were number of objects in this sequence. + // Note that in the original bug, SPARK-4349, that this verifies, the job would only hang if + // there were more threads in the Spark Context than there were number of objects in this + // sequence. intercept[Throwable] { - sc.parallelize(Seq(new BadSerializable, new BadSerializable)).collect + sc.parallelize(Seq(new BadSerializable, new BadSerializable)).collect() } // Check that the context has not crashed sc.parallelize(1 to 100).map(x => x*2).collect diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuiteUtils.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuiteUtils.scala index 4762fc17855ce..fe695d85e29dd 100644 --- a/core/src/test/scala/org/apache/spark/rdd/RDDSuiteUtils.scala +++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuiteUtils.scala @@ -21,11 +21,11 @@ object RDDSuiteUtils { case class Person(first: String, last: String, age: Int) object AgeOrdering extends Ordering[Person] { - def compare(a:Person, b:Person) = a.age compare b.age + def compare(a:Person, b:Person): Int = a.age.compare(b.age) } object NameOrdering extends Ordering[Person] { - def compare(a:Person, b:Person) = + def compare(a:Person, b:Person): Int = implicitly[Ordering[Tuple2[String,String]]].compare((a.last, a.first), (b.last, b.first)) } } diff --git a/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala b/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala index 5a734ec5ba5ec..ada07ef11cd7a 100644 --- a/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala +++ b/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala @@ -70,7 +70,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { env.setupEndpoint("send-remotely", new RpcEndpoint { override val rpcEnv = env - override def receive = { + override def receive: PartialFunction[Any, Unit] = { case msg: String => message = msg } }) @@ -109,7 +109,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { val rpcEndpointRef = env.setupEndpoint("ask-locally", new RpcEndpoint { override val rpcEnv = env - override def receiveAndReply(context: RpcCallContext) = { + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case msg: String => { context.reply(msg) } @@ -123,7 +123,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { env.setupEndpoint("ask-remotely", new RpcEndpoint { override val rpcEnv = env - override def receiveAndReply(context: RpcCallContext) = { + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case msg: String => { context.reply(msg) } @@ -146,7 +146,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { env.setupEndpoint("ask-timeout", new RpcEndpoint { override val rpcEnv = env - override def receiveAndReply(context: RpcCallContext) = { + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case msg: String => { Thread.sleep(100) context.reply(msg) @@ -182,7 +182,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { calledMethods += "start" } - override def receive = { + override def receive: PartialFunction[Any, Unit] = { case msg: String => } @@ -206,7 +206,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { throw new RuntimeException("Oops!") } - override def receive = { + override def receive: PartialFunction[Any, Unit] = { case m => } @@ -225,7 +225,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { val endpointRef = env.setupEndpoint("onError-onStop", new RpcEndpoint { override val rpcEnv = env - override def receive = { + override def receive: PartialFunction[Any, Unit] = { case m => } @@ -250,8 +250,8 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { val endpointRef = env.setupEndpoint("onError-receive", new RpcEndpoint { override val rpcEnv = env - override def receive = { - case m => throw new RuntimeException("Oops!") + override def receive: PartialFunction[Any, Unit] = { + case m => throw new RuntimeException("Oops!") } override def onError(cause: Throwable): Unit = { @@ -277,7 +277,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { callSelfSuccessfully = true } - override def receive = { + override def receive: PartialFunction[Any, Unit] = { case m => } }) @@ -294,7 +294,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { val endpointRef = env.setupEndpoint("self-receive", new RpcEndpoint { override val rpcEnv = env - override def receive = { + override def receive: PartialFunction[Any, Unit] = { case m => { self callSelfSuccessfully = true @@ -316,7 +316,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { val endpointRef = env.setupEndpoint("self-onStop", new RpcEndpoint { override val rpcEnv = env - override def receive = { + override def receive: PartialFunction[Any, Unit] = { case m => } @@ -343,7 +343,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { val endpointRef = env.setupEndpoint(s"receive-in-sequence-$i", new ThreadSafeRpcEndpoint { override val rpcEnv = env - override def receive = { + override def receive: PartialFunction[Any, Unit] = { case m => result += 1 } @@ -372,7 +372,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { val endpointRef = env.setupEndpoint("stop-reentrant", new RpcEndpoint { override val rpcEnv = env - override def receive = { + override def receive: PartialFunction[Any, Unit] = { case m => } @@ -394,7 +394,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { val endpointRef = env.setupEndpoint("sendWithReply", new RpcEndpoint { override val rpcEnv = env - override def receiveAndReply(context: RpcCallContext) = { + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case m => context.reply("ack") } }) @@ -410,7 +410,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { env.setupEndpoint("sendWithReply-remotely", new RpcEndpoint { override val rpcEnv = env - override def receiveAndReply(context: RpcCallContext) = { + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case m => context.reply("ack") } }) @@ -432,7 +432,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { val endpointRef = env.setupEndpoint("sendWithReply-error", new RpcEndpoint { override val rpcEnv = env - override def receiveAndReply(context: RpcCallContext) = { + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case m => context.sendFailure(new SparkException("Oops")) } }) @@ -450,7 +450,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { env.setupEndpoint("sendWithReply-remotely-error", new RpcEndpoint { override val rpcEnv = env - override def receiveAndReply(context: RpcCallContext) = { + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case msg: String => context.sendFailure(new SparkException("Oops")) } }) @@ -476,7 +476,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { env.setupEndpoint("network-events", new ThreadSafeRpcEndpoint { override val rpcEnv = env - override def receive = { + override def receive: PartialFunction[Any, Unit] = { case "hello" => case m => events += "receive" -> m } @@ -519,7 +519,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { env.setupEndpoint("sendWithReply-unserializable-error", new RpcEndpoint { override val rpcEnv = env - override def receiveAndReply(context: RpcCallContext) = { + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case msg: String => context.sendFailure(new UnserializableException) } }) diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala index eb759f0807a17..3c52a8c4460c6 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala @@ -57,20 +57,18 @@ class MyRDD( locations: Seq[Seq[String]] = Nil) extends RDD[(Int, Int)](sc, dependencies) with Serializable { override def compute(split: Partition, context: TaskContext): Iterator[(Int, Int)] = throw new RuntimeException("should not be reached") - override def getPartitions = (0 until numPartitions).map(i => new Partition { - override def index = i + override def getPartitions: Array[Partition] = (0 until numPartitions).map(i => new Partition { + override def index: Int = i }).toArray override def getPreferredLocations(split: Partition): Seq[String] = - if (locations.isDefinedAt(split.index)) - locations(split.index) - else - Nil + if (locations.isDefinedAt(split.index)) locations(split.index) else Nil override def toString: String = "DAGSchedulerSuiteRDD " + id } class DAGSchedulerSuiteDummyException extends Exception -class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSparkContext with Timeouts { +class DAGSchedulerSuite + extends FunSuiteLike with BeforeAndAfter with LocalSparkContext with Timeouts { val conf = new SparkConf /** Set of TaskSets the DAGScheduler has requested executed. */ @@ -209,7 +207,8 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar assert(taskSet.tasks.size >= results.size) for ((result, i) <- results.zipWithIndex) { if (i < taskSet.tasks.size) { - runEvent(CompletionEvent(taskSet.tasks(i), result._1, result._2, null, createFakeTaskInfo(), null)) + runEvent(CompletionEvent( + taskSet.tasks(i), result._1, result._2, null, createFakeTaskInfo(), null)) } } } @@ -269,21 +268,23 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar submit(new MyRDD(sc, 1, Nil), Array(0)) complete(taskSets(0), List((Success, 42))) assert(results === Map(0 -> 42)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("local job") { val rdd = new PairOfIntsRDD(sc, Nil) { override def compute(split: Partition, context: TaskContext): Iterator[(Int, Int)] = Array(42 -> 0).iterator - override def getPartitions = Array( new Partition { override def index = 0 } ) - override def getPreferredLocations(split: Partition) = Nil - override def toString = "DAGSchedulerSuite Local RDD" + override def getPartitions: Array[Partition] = + Array( new Partition { override def index: Int = 0 } ) + override def getPreferredLocations(split: Partition): List[String] = Nil + override def toString: String = "DAGSchedulerSuite Local RDD" } val jobId = scheduler.nextJobId.getAndIncrement() - runEvent(JobSubmitted(jobId, rdd, jobComputeFunc, Array(0), true, CallSite("", ""), jobListener)) + runEvent( + JobSubmitted(jobId, rdd, jobComputeFunc, Array(0), true, CallSite("", ""), jobListener)) assert(results === Map(0 -> 42)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("local job oom") { @@ -295,9 +296,10 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar override def toString = "DAGSchedulerSuite Local RDD" } val jobId = scheduler.nextJobId.getAndIncrement() - runEvent(JobSubmitted(jobId, rdd, jobComputeFunc, Array(0), true, CallSite("", ""), jobListener)) + runEvent( + JobSubmitted(jobId, rdd, jobComputeFunc, Array(0), true, CallSite("", ""), jobListener)) assert(results.size == 0) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("run trivial job w/ dependency") { @@ -306,7 +308,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar submit(finalRdd, Array(0)) complete(taskSets(0), Seq((Success, 42))) assert(results === Map(0 -> 42)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("cache location preferences w/ dependency") { @@ -319,7 +321,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar assertLocations(taskSet, Seq(Seq("hostA", "hostB"))) complete(taskSet, Seq((Success, 42))) assert(results === Map(0 -> 42)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("regression test for getCacheLocs") { @@ -335,7 +337,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar } test("avoid exponential blowup when getting preferred locs list") { - // Build up a complex dependency graph with repeated zip operations, without preferred locations. + // Build up a complex dependency graph with repeated zip operations, without preferred locations var rdd: RDD[_] = new MyRDD(sc, 1, Nil) (1 to 30).foreach(_ => rdd = rdd.zip(rdd)) // getPreferredLocs runs quickly, indicating that exponential graph traversal is avoided. @@ -357,7 +359,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) assert(sparkListener.failedStages.contains(0)) assert(sparkListener.failedStages.size === 1) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("trivial job failure") { @@ -367,7 +369,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) assert(sparkListener.failedStages.contains(0)) assert(sparkListener.failedStages.size === 1) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("trivial job cancellation") { @@ -378,7 +380,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) assert(sparkListener.failedStages.contains(0)) assert(sparkListener.failedStages.size === 1) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("job cancellation no-kill backend") { @@ -387,18 +389,20 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar val noKillTaskScheduler = new TaskScheduler() { override def rootPool: Pool = null override def schedulingMode: SchedulingMode = SchedulingMode.NONE - override def start() = {} - override def stop() = {} - override def submitTasks(taskSet: TaskSet) = { + override def start(): Unit = {} + override def stop(): Unit = {} + override def submitTasks(taskSet: TaskSet): Unit = { taskSets += taskSet } override def cancelTasks(stageId: Int, interruptThread: Boolean) { throw new UnsupportedOperationException } - override def setDAGScheduler(dagScheduler: DAGScheduler) = {} - override def defaultParallelism() = 2 - override def executorHeartbeatReceived(execId: String, taskMetrics: Array[(Long, TaskMetrics)], - blockManagerId: BlockManagerId): Boolean = true + override def setDAGScheduler(dagScheduler: DAGScheduler): Unit = {} + override def defaultParallelism(): Int = 2 + override def executorHeartbeatReceived( + execId: String, + taskMetrics: Array[(Long, TaskMetrics)], + blockManagerId: BlockManagerId): Boolean = true override def executorLost(executorId: String, reason: ExecutorLossReason): Unit = {} } val noKillScheduler = new DAGScheduler( @@ -422,7 +426,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar // When the task set completes normally, state should be correctly updated. complete(taskSets(0), Seq((Success, 42))) assert(results === Map(0 -> 42)) - assertDataStructuresEmpty + assertDataStructuresEmpty() assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) assert(sparkListener.failedStages.isEmpty) @@ -442,7 +446,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar Array(makeBlockManagerId("hostA"), makeBlockManagerId("hostB"))) complete(taskSets(1), Seq((Success, 42))) assert(results === Map(0 -> 42)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("run trivial shuffle with fetch failure") { @@ -465,10 +469,11 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar // have the 2nd attempt pass complete(taskSets(2), Seq((Success, makeMapStatus("hostA", 1)))) // we can see both result blocks now - assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1.host) === Array("hostA", "hostB")) + assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1.host) === + Array("hostA", "hostB")) complete(taskSets(3), Seq((Success, 43))) assert(results === Map(0 -> 42, 1 -> 43)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("trivial shuffle with multiple fetch failures") { @@ -521,19 +526,23 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar assert(newEpoch > oldEpoch) val taskSet = taskSets(0) // should be ignored for being too old - runEvent(CompletionEvent(taskSet.tasks(0), Success, makeMapStatus("hostA", 1), null, createFakeTaskInfo(), null)) + runEvent(CompletionEvent( + taskSet.tasks(0), Success, makeMapStatus("hostA", 1), null, createFakeTaskInfo(), null)) // should work because it's a non-failed host - runEvent(CompletionEvent(taskSet.tasks(0), Success, makeMapStatus("hostB", 1), null, createFakeTaskInfo(), null)) + runEvent(CompletionEvent( + taskSet.tasks(0), Success, makeMapStatus("hostB", 1), null, createFakeTaskInfo(), null)) // should be ignored for being too old - runEvent(CompletionEvent(taskSet.tasks(0), Success, makeMapStatus("hostA", 1), null, createFakeTaskInfo(), null)) + runEvent(CompletionEvent( + taskSet.tasks(0), Success, makeMapStatus("hostA", 1), null, createFakeTaskInfo(), null)) // should work because it's a new epoch taskSet.tasks(1).epoch = newEpoch - runEvent(CompletionEvent(taskSet.tasks(1), Success, makeMapStatus("hostA", 1), null, createFakeTaskInfo(), null)) + runEvent(CompletionEvent( + taskSet.tasks(1), Success, makeMapStatus("hostA", 1), null, createFakeTaskInfo(), null)) assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1) === Array(makeBlockManagerId("hostB"), makeBlockManagerId("hostA"))) complete(taskSets(1), Seq((Success, 42), (Success, 43))) assert(results === Map(0 -> 42, 1 -> 43)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("run shuffle with map stage failure") { @@ -552,7 +561,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) assert(sparkListener.failedStages.toSet === Set(0)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } /** @@ -586,7 +595,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar class FailureRecordingJobListener() extends JobListener { var failureMessage: String = _ override def taskSucceeded(index: Int, result: Any) {} - override def jobFailed(exception: Exception) = { failureMessage = exception.getMessage } + override def jobFailed(exception: Exception): Unit = { failureMessage = exception.getMessage } } val listener1 = new FailureRecordingJobListener() val listener2 = new FailureRecordingJobListener() @@ -606,7 +615,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar assert(listener1.failureMessage === s"Job aborted due to stage failure: $stageFailureMessage") assert(listener2.failureMessage === s"Job aborted due to stage failure: $stageFailureMessage") - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("run trivial shuffle with out-of-band failure and retry") { @@ -629,7 +638,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar Array(makeBlockManagerId("hostC"), makeBlockManagerId("hostB"))) complete(taskSets(2), Seq((Success, 42))) assert(results === Map(0 -> 42)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("recursive shuffle failures") { @@ -658,7 +667,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar complete(taskSets(4), Seq((Success, makeMapStatus("hostA", 1)))) complete(taskSets(5), Seq((Success, 42))) assert(results === Map(0 -> 42)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("cached post-shuffle") { @@ -690,7 +699,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar complete(taskSets(3), Seq((Success, makeMapStatus("hostD", 1)))) complete(taskSets(4), Seq((Success, 42))) assert(results === Map(0 -> 42)) - assertDataStructuresEmpty + assertDataStructuresEmpty() } test("misbehaved accumulator should not crash DAGScheduler and SparkContext") { @@ -742,7 +751,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar } test("accumulator not calculated for resubmitted result stage") { - //just for register + // just for register val accum = new Accumulator[Int](0, AccumulatorParam.IntAccumulatorParam) val finalRdd = new MyRDD(sc, 1, Nil) submit(finalRdd, Array(0)) @@ -754,7 +763,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar assert(accVal === 1) - assertDataStructuresEmpty + assertDataStructuresEmpty() } /** @@ -774,7 +783,7 @@ class DAGSchedulerSuite extends FunSuiteLike with BeforeAndAfter with LocalSpar private def makeBlockManagerId(host: String): BlockManagerId = BlockManagerId("exec-" + host, host, 12345) - private def assertDataStructuresEmpty = { + private def assertDataStructuresEmpty(): Unit = { assert(scheduler.activeJobs.isEmpty) assert(scheduler.failedStages.isEmpty) assert(scheduler.jobIdToActiveJob.isEmpty) diff --git a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala index 30ee63e78d9d8..6d25edb7d20dc 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala @@ -268,7 +268,7 @@ class EventLoggingListenerSuite extends FunSuite with LocalSparkContext with Bef object EventLoggingListenerSuite { /** Get a SparkConf with event logging enabled. */ - def getLoggingConf(logDir: Path, compressionCodec: Option[String] = None) = { + def getLoggingConf(logDir: Path, compressionCodec: Option[String] = None): SparkConf = { val conf = new SparkConf conf.set("spark.eventLog.enabled", "true") conf.set("spark.eventLog.testing", "true") @@ -280,5 +280,5 @@ object EventLoggingListenerSuite { conf } - def getUniqueApplicationId = "test-" + System.currentTimeMillis + def getUniqueApplicationId: String = "test-" + System.currentTimeMillis } diff --git a/core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala b/core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala index 6b75c98839e03..9b92f8de56759 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala @@ -24,7 +24,9 @@ import org.apache.spark.TaskContext /** * A Task implementation that fails to serialize. */ -private[spark] class NotSerializableFakeTask(myId: Int, stageId: Int) extends Task[Array[Byte]](stageId, 0) { +private[spark] class NotSerializableFakeTask(myId: Int, stageId: Int) + extends Task[Array[Byte]](stageId, 0) { + override def runTask(context: TaskContext): Array[Byte] = Array.empty[Byte] override def preferredLocations: Seq[TaskLocation] = Seq[TaskLocation]() diff --git a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala index 627c9a4ddfffc..825c616c0c3e0 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala @@ -85,7 +85,7 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers val stopperReturned = new Semaphore(0) class BlockingListener extends SparkListener { - override def onJobEnd(jobEnd: SparkListenerJobEnd) = { + override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = { listenerStarted.release() listenerWait.acquire() drained = true @@ -206,8 +206,9 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers sc.addSparkListener(new StatsReportListener) // just to make sure some of the tasks take a noticeable amount of time val w = { i: Int => - if (i == 0) + if (i == 0) { Thread.sleep(100) + } i } @@ -247,12 +248,12 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers */ taskInfoMetrics.foreach { case (taskInfo, taskMetrics) => - taskMetrics.resultSize should be > (0l) + taskMetrics.resultSize should be > (0L) if (stageInfo.rddInfos.exists(info => info.name == d2.name || info.name == d3.name)) { taskMetrics.inputMetrics should not be ('defined) taskMetrics.outputMetrics should not be ('defined) taskMetrics.shuffleWriteMetrics should be ('defined) - taskMetrics.shuffleWriteMetrics.get.shuffleBytesWritten should be > (0l) + taskMetrics.shuffleWriteMetrics.get.shuffleBytesWritten should be > (0L) } if (stageInfo.rddInfos.exists(_.name == d4.name)) { taskMetrics.shuffleReadMetrics should be ('defined) @@ -260,7 +261,7 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers sm.totalBlocksFetched should be (128) sm.localBlocksFetched should be (128) sm.remoteBlocksFetched should be (0) - sm.remoteBytesRead should be (0l) + sm.remoteBytesRead should be (0L) } } } @@ -406,12 +407,12 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers val startedGettingResultTasks = new mutable.HashSet[Int]() val endedTasks = new mutable.HashSet[Int]() - override def onTaskStart(taskStart: SparkListenerTaskStart) = synchronized { + override def onTaskStart(taskStart: SparkListenerTaskStart): Unit = synchronized { startedTasks += taskStart.taskInfo.index notify() } - override def onTaskEnd(taskEnd: SparkListenerTaskEnd) = synchronized { + override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = synchronized { endedTasks += taskEnd.taskInfo.index notify() } @@ -425,7 +426,7 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers * A simple listener that throws an exception on job end. */ private class BadListener extends SparkListener { - override def onJobEnd(jobEnd: SparkListenerJobEnd) = { throw new Exception } + override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = { throw new Exception } } } @@ -438,10 +439,10 @@ class SparkListenerSuite extends FunSuite with LocalSparkContext with Matchers */ private class BasicJobCounter extends SparkListener { var count = 0 - override def onJobEnd(job: SparkListenerJobEnd) = count += 1 + override def onJobEnd(job: SparkListenerJobEnd): Unit = count += 1 } private class ListenerThatAcceptsSparkConf(conf: SparkConf) extends SparkListener { var count = 0 - override def onJobEnd(job: SparkListenerJobEnd) = count += 1 + override def onJobEnd(job: SparkListenerJobEnd): Unit = count += 1 } diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala index add13f5b21765..ffa4381969b68 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala @@ -17,8 +17,6 @@ package org.apache.spark.scheduler -import java.util.Properties - import org.scalatest.FunSuite import org.apache.spark._ @@ -27,7 +25,7 @@ class FakeSchedulerBackend extends SchedulerBackend { def start() {} def stop() {} def reviveOffers() {} - def defaultParallelism() = 1 + def defaultParallelism(): Int = 1 } class TaskSchedulerImplSuite extends FunSuite with LocalSparkContext with Logging { @@ -115,7 +113,8 @@ class TaskSchedulerImplSuite extends FunSuite with LocalSparkContext with Loggin } val numFreeCores = 1 taskScheduler.setDAGScheduler(dagScheduler) - var taskSet = new TaskSet(Array(new NotSerializableFakeTask(1, 0), new NotSerializableFakeTask(0, 1)), 0, 0, 0, null) + val taskSet = new TaskSet( + Array(new NotSerializableFakeTask(1, 0), new NotSerializableFakeTask(0, 1)), 0, 0, 0, null) val multiCoreWorkerOffers = Seq(new WorkerOffer("executor0", "host0", taskCpus), new WorkerOffer("executor1", "host1", numFreeCores)) taskScheduler.submitTasks(taskSet) @@ -123,7 +122,8 @@ class TaskSchedulerImplSuite extends FunSuite with LocalSparkContext with Loggin assert(0 === taskDescriptions.length) // Now check that we can still submit tasks - // Even if one of the tasks has not-serializable tasks, the other task set should still be processed without error + // Even if one of the tasks has not-serializable tasks, the other task set should + // still be processed without error taskScheduler.submitTasks(taskSet) taskScheduler.submitTasks(FakeTask.createTaskSet(1)) taskDescriptions = taskScheduler.resourceOffers(multiCoreWorkerOffers).flatten diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala index 12330d8f63c40..716d12c0762cf 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala @@ -67,7 +67,7 @@ object FakeRackUtil { hostToRack(host) = rack } - def getRackForHost(host: String) = { + def getRackForHost(host: String): Option[String] = { hostToRack.get(host) } } @@ -327,8 +327,8 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { // First offer host1: first task should be chosen assert(manager.resourceOffer("exec1", "host1", ANY).get.index === 0) - // After this, nothing should get chosen, because we have separated tasks with unavailable preference - // from the noPrefPendingTasks + // After this, nothing should get chosen, because we have separated tasks with unavailable + // preference from the noPrefPendingTasks assert(manager.resourceOffer("exec1", "host1", ANY) === None) // Now mark host2 as dead @@ -499,7 +499,8 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { sched.addExecutor("execC", "host2") manager.executorAdded() // Valid locality should contain PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL and ANY - assert(manager.myLocalityLevels.sameElements(Array(PROCESS_LOCAL, NODE_LOCAL, NO_PREF, RACK_LOCAL, ANY))) + assert(manager.myLocalityLevels.sameElements( + Array(PROCESS_LOCAL, NODE_LOCAL, NO_PREF, RACK_LOCAL, ANY))) // test if the valid locality is recomputed when the executor is lost sched.removeExecutor("execC") manager.executorLost("execC", "host2") @@ -569,7 +570,8 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { sc = new SparkContext("local", "test") val sched = new FakeTaskScheduler(sc, ("exec1", "host1")) - val taskSet = new TaskSet(Array(new NotSerializableFakeTask(1, 0), new NotSerializableFakeTask(0, 1)), 0, 0, 0, null) + val taskSet = new TaskSet( + Array(new NotSerializableFakeTask(1, 0), new NotSerializableFakeTask(0, 1)), 0, 0, 0, null) val manager = new TaskSetManager(sched, taskSet, MAX_TASK_FAILURES) intercept[TaskNotSerializableException] { @@ -582,7 +584,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { val conf = new SparkConf().set("spark.driver.maxResultSize", "2m") sc = new SparkContext("local", "test", conf) - def genBytes(size: Int) = { (x: Int) => + def genBytes(size: Int): (Int) => Array[Byte] = { (x: Int) => val bytes = Array.ofDim[Byte](size) scala.util.Random.nextBytes(bytes) bytes @@ -605,7 +607,8 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { test("speculative and noPref task should be scheduled after node-local") { sc = new SparkContext("local", "test") - val sched = new FakeTaskScheduler(sc, ("execA", "host1"), ("execB", "host2"), ("execC", "host3")) + val sched = new FakeTaskScheduler( + sc, ("execA", "host1"), ("execB", "host2"), ("execC", "host3")) val taskSet = FakeTask.createTaskSet(4, Seq(TaskLocation("host1", "execA")), Seq(TaskLocation("host2"), TaskLocation("host1")), @@ -629,9 +632,11 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { assert(manager.resourceOffer("execB", "host2", ANY).get.index === 3) } - test("node-local tasks should be scheduled right away when there are only node-local and no-preference tasks") { + test("node-local tasks should be scheduled right away " + + "when there are only node-local and no-preference tasks") { sc = new SparkContext("local", "test") - val sched = new FakeTaskScheduler(sc, ("execA", "host1"), ("execB", "host2"), ("execC", "host3")) + val sched = new FakeTaskScheduler( + sc, ("execA", "host1"), ("execB", "host2"), ("execC", "host3")) val taskSet = FakeTask.createTaskSet(4, Seq(TaskLocation("host1")), Seq(TaskLocation("host2")), @@ -650,7 +655,8 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { assert(manager.resourceOffer("execA", "host3", NO_PREF).get.index === 2) } - test("SPARK-4939: node-local tasks should be scheduled right after process-local tasks finished") { + test("SPARK-4939: node-local tasks should be scheduled right after process-local tasks finished") + { sc = new SparkContext("local", "test") val sched = new FakeTaskScheduler(sc, ("execA", "host1"), ("execB", "host2")) val taskSet = FakeTask.createTaskSet(4, diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala index f1a4380d349b3..a311512e82c5e 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala @@ -63,16 +63,18 @@ class MesosSchedulerBackendSuite extends FunSuite with LocalSparkContext with Mo // uri is null. val executorInfo = mesosSchedulerBackend.createExecutorInfo("test-id") - assert(executorInfo.getCommand.getValue === s" /mesos-home/bin/spark-class ${classOf[MesosExecutorBackend].getName}") + assert(executorInfo.getCommand.getValue === + s" /mesos-home/bin/spark-class ${classOf[MesosExecutorBackend].getName}") // uri exists. conf.set("spark.executor.uri", "hdfs:///test-app-1.0.0.tgz") val executorInfo1 = mesosSchedulerBackend.createExecutorInfo("test-id") - assert(executorInfo1.getCommand.getValue === s"cd test-app-1*; ./bin/spark-class ${classOf[MesosExecutorBackend].getName}") + assert(executorInfo1.getCommand.getValue === + s"cd test-app-1*; ./bin/spark-class ${classOf[MesosExecutorBackend].getName}") } test("mesos resource offers result in launching tasks") { - def createOffer(id: Int, mem: Int, cpu: Int) = { + def createOffer(id: Int, mem: Int, cpu: Int): Offer = { val builder = Offer.newBuilder() builder.addResourcesBuilder() .setName("mem") @@ -82,8 +84,10 @@ class MesosSchedulerBackendSuite extends FunSuite with LocalSparkContext with Mo .setName("cpus") .setType(Value.Type.SCALAR) .setScalar(Scalar.newBuilder().setValue(cpu)) - builder.setId(OfferID.newBuilder().setValue(s"o${id.toString}").build()).setFrameworkId(FrameworkID.newBuilder().setValue("f1")) - .setSlaveId(SlaveID.newBuilder().setValue(s"s${id.toString}")).setHostname(s"host${id.toString}").build() + builder.setId(OfferID.newBuilder().setValue(s"o${id.toString}").build()) + .setFrameworkId(FrameworkID.newBuilder().setValue("f1")) + .setSlaveId(SlaveID.newBuilder().setValue(s"s${id.toString}")) + .setHostname(s"host${id.toString}").build() } val driver = mock[SchedulerDriver] diff --git a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala index 6198df84fab3d..b070a54aa989b 100644 --- a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala +++ b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala @@ -106,7 +106,9 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext { check(mutable.HashMap(1 -> "one", 2 -> "two")) check(mutable.HashMap("one" -> 1, "two" -> 2)) check(List(Some(mutable.HashMap(1->1, 2->2)), None, Some(mutable.HashMap(3->4)))) - check(List(mutable.HashMap("one" -> 1, "two" -> 2),mutable.HashMap(1->"one",2->"two",3->"three"))) + check(List( + mutable.HashMap("one" -> 1, "two" -> 2), + mutable.HashMap(1->"one",2->"two",3->"three"))) } test("ranges") { @@ -169,7 +171,10 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext { test("kryo with collect") { val control = 1 :: 2 :: Nil - val result = sc.parallelize(control, 2).map(new ClassWithoutNoArgConstructor(_)).collect().map(_.x) + val result = sc.parallelize(control, 2) + .map(new ClassWithoutNoArgConstructor(_)) + .collect() + .map(_.x) assert(control === result.toSeq) } @@ -237,7 +242,7 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext { // Set a special, broken ClassLoader and make sure we get an exception on deserialization ser.setDefaultClassLoader(new ClassLoader() { - override def loadClass(name: String) = throw new UnsupportedOperationException + override def loadClass(name: String): Class[_] = throw new UnsupportedOperationException }) intercept[UnsupportedOperationException] { ser.newInstance().deserialize[ClassLoaderTestingObject](bytes) @@ -287,14 +292,14 @@ object KryoTest { class ClassWithNoArgConstructor { var x: Int = 0 - override def equals(other: Any) = other match { + override def equals(other: Any): Boolean = other match { case c: ClassWithNoArgConstructor => x == c.x case _ => false } } class ClassWithoutNoArgConstructor(val x: Int) { - override def equals(other: Any) = other match { + override def equals(other: Any): Boolean = other match { case c: ClassWithoutNoArgConstructor => x == c.x case _ => false } diff --git a/core/src/test/scala/org/apache/spark/serializer/ProactiveClosureSerializationSuite.scala b/core/src/test/scala/org/apache/spark/serializer/ProactiveClosureSerializationSuite.scala index d037e2c19a64d..433fd6bb4a11d 100644 --- a/core/src/test/scala/org/apache/spark/serializer/ProactiveClosureSerializationSuite.scala +++ b/core/src/test/scala/org/apache/spark/serializer/ProactiveClosureSerializationSuite.scala @@ -24,14 +24,16 @@ import org.apache.spark.rdd.RDD /* A trivial (but unserializable) container for trivial functions */ class UnserializableClass { - def op[T](x: T) = x.toString + def op[T](x: T): String = x.toString - def pred[T](x: T) = x.toString.length % 2 == 0 + def pred[T](x: T): Boolean = x.toString.length % 2 == 0 } class ProactiveClosureSerializationSuite extends FunSuite with SharedSparkContext { - def fixture = (sc.parallelize(0 until 1000).map(_.toString), new UnserializableClass) + def fixture: (RDD[String], UnserializableClass) = { + (sc.parallelize(0 until 1000).map(_.toString), new UnserializableClass) + } test("throws expected serialization exceptions on actions") { val (data, uc) = fixture diff --git a/core/src/test/scala/org/apache/spark/serializer/TestSerializer.scala b/core/src/test/scala/org/apache/spark/serializer/TestSerializer.scala index 0ade1bab18d7e..963264cef3a71 100644 --- a/core/src/test/scala/org/apache/spark/serializer/TestSerializer.scala +++ b/core/src/test/scala/org/apache/spark/serializer/TestSerializer.scala @@ -27,7 +27,7 @@ import scala.reflect.ClassTag * A serializer implementation that always return a single element in a deserialization stream. */ class TestSerializer extends Serializer { - override def newInstance() = new TestSerializerInstance + override def newInstance(): TestSerializerInstance = new TestSerializerInstance } @@ -36,7 +36,8 @@ class TestSerializerInstance extends SerializerInstance { override def serializeStream(s: OutputStream): SerializationStream = ??? - override def deserializeStream(s: InputStream) = new TestDeserializationStream + override def deserializeStream(s: InputStream): TestDeserializationStream = + new TestDeserializationStream override def deserialize[T: ClassTag](bytes: ByteBuffer): T = ??? diff --git a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala index b834dc0e735eb..7d76435cd75e7 100644 --- a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala @@ -85,8 +85,8 @@ class HashShuffleManagerSuite extends FunSuite with LocalSparkContext { // Now comes the test : // Write to shuffle 3; and close it, but before registering it, check if the file lengths for // previous task (forof shuffle1) is the same as 'segments'. Earlier, we were inferring length - // of block based on remaining data in file : which could mess things up when there is concurrent read - // and writes happening to the same shuffle group. + // of block based on remaining data in file : which could mess things up when there is + // concurrent read and writes happening to the same shuffle group. val shuffle3 = shuffleBlockManager.forMapTask(1, 3, 1, new JavaSerializer(testConf), new ShuffleWriteMetrics) diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala index 6dc5bc4cb08c4..545722b050ee8 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala @@ -60,7 +60,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach // Implicitly convert strings to BlockIds for test clarity. implicit def StringToBlockId(value: String): BlockId = new TestBlockId(value) - def rdd(rddId: Int, splitId: Int) = RDDBlockId(rddId, splitId) + def rdd(rddId: Int, splitId: Int): RDDBlockId = RDDBlockId(rddId, splitId) private def makeBlockManager( maxMem: Long, @@ -107,8 +107,10 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach test("StorageLevel object caching") { val level1 = StorageLevel(false, false, false, false, 3) - val level2 = StorageLevel(false, false, false, false, 3) // this should return the same object as level1 - val level3 = StorageLevel(false, false, false, false, 2) // this should return a different object + // this should return the same object as level1 + val level2 = StorageLevel(false, false, false, false, 3) + // this should return a different object + val level3 = StorageLevel(false, false, false, false, 2) assert(level2 === level1, "level2 is not same as level1") assert(level2.eq(level1), "level2 is not the same object as level1") assert(level3 != level1, "level3 is same as level1") @@ -802,7 +804,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach // Create a non-trivial (not all zeros) byte array var counter = 0.toByte - def incr = {counter = (counter + 1).toByte; counter;} + def incr: Byte = {counter = (counter + 1).toByte; counter;} val bytes = Array.fill[Byte](1000)(incr) val byteBuffer = ByteBuffer.wrap(bytes) @@ -956,8 +958,10 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach store.putIterator("list3", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true) // getLocations and getBlockStatus should yield the same locations - assert(store.master.getMatchingBlockIds(_.toString.contains("list"), askSlaves = false).size === 3) - assert(store.master.getMatchingBlockIds(_.toString.contains("list1"), askSlaves = false).size === 1) + assert(store.master.getMatchingBlockIds(_.toString.contains("list"), askSlaves = false).size + === 3) + assert(store.master.getMatchingBlockIds(_.toString.contains("list1"), askSlaves = false).size + === 1) // insert some more blocks store.putIterator("newlist1", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = true) @@ -965,8 +969,10 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach store.putIterator("newlist3", list.iterator, StorageLevel.MEMORY_AND_DISK, tellMaster = false) // getLocations and getBlockStatus should yield the same locations - assert(store.master.getMatchingBlockIds(_.toString.contains("newlist"), askSlaves = false).size === 1) - assert(store.master.getMatchingBlockIds(_.toString.contains("newlist"), askSlaves = true).size === 3) + assert(store.master.getMatchingBlockIds(_.toString.contains("newlist"), askSlaves = false).size + === 1) + assert(store.master.getMatchingBlockIds(_.toString.contains("newlist"), askSlaves = true).size + === 3) val blockIds = Seq(RDDBlockId(1, 0), RDDBlockId(1, 1), RDDBlockId(2, 0)) blockIds.foreach { blockId => @@ -1090,8 +1096,8 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach val memoryStore = store.memoryStore val smallList = List.fill(40)(new Array[Byte](100)) val bigList = List.fill(40)(new Array[Byte](1000)) - def smallIterator = smallList.iterator.asInstanceOf[Iterator[Any]] - def bigIterator = bigList.iterator.asInstanceOf[Iterator[Any]] + def smallIterator: Iterator[Any] = smallList.iterator.asInstanceOf[Iterator[Any]] + def bigIterator: Iterator[Any] = bigList.iterator.asInstanceOf[Iterator[Any]] assert(memoryStore.currentUnrollMemoryForThisThread === 0) // Unroll with plenty of space. This should succeed and cache both blocks. @@ -1144,8 +1150,8 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach val diskStore = store.diskStore val smallList = List.fill(40)(new Array[Byte](100)) val bigList = List.fill(40)(new Array[Byte](1000)) - def smallIterator = smallList.iterator.asInstanceOf[Iterator[Any]] - def bigIterator = bigList.iterator.asInstanceOf[Iterator[Any]] + def smallIterator: Iterator[Any] = smallList.iterator.asInstanceOf[Iterator[Any]] + def bigIterator: Iterator[Any] = bigList.iterator.asInstanceOf[Iterator[Any]] assert(memoryStore.currentUnrollMemoryForThisThread === 0) store.putIterator("b1", smallIterator, memAndDisk) @@ -1187,7 +1193,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach val memOnly = StorageLevel.MEMORY_ONLY val memoryStore = store.memoryStore val smallList = List.fill(40)(new Array[Byte](100)) - def smallIterator = smallList.iterator.asInstanceOf[Iterator[Any]] + def smallIterator: Iterator[Any] = smallList.iterator.asInstanceOf[Iterator[Any]] assert(memoryStore.currentUnrollMemoryForThisThread === 0) // All unroll memory used is released because unrollSafely returned an array diff --git a/core/src/test/scala/org/apache/spark/storage/LocalDirsSuite.scala b/core/src/test/scala/org/apache/spark/storage/LocalDirsSuite.scala index 82a82e23eecf2..b47157f8331cc 100644 --- a/core/src/test/scala/org/apache/spark/storage/LocalDirsSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/LocalDirsSuite.scala @@ -47,7 +47,7 @@ class LocalDirsSuite extends FunSuite with BeforeAndAfter { assert(!new File("/NONEXISTENT_DIR").exists()) // SPARK_LOCAL_DIRS is a valid directory: class MySparkConf extends SparkConf(false) { - override def getenv(name: String) = { + override def getenv(name: String): String = { if (name == "SPARK_LOCAL_DIRS") System.getProperty("java.io.tmpdir") else super.getenv(name) } diff --git a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala index 0d155982a8c54..1cb594633f331 100644 --- a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala +++ b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala @@ -137,7 +137,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before new SparkContext(conf) } - def hasKillLink = find(className("kill-link")).isDefined + def hasKillLink: Boolean = find(className("kill-link")).isDefined def runSlowJob(sc: SparkContext) { sc.parallelize(1 to 10).map{x => Thread.sleep(10000); x}.countAsync() } diff --git a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala index c0c28cb60e21d..21d8267114133 100644 --- a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala @@ -269,7 +269,7 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc val taskType = Utils.getFormattedClassName(new ShuffleMapTask(0)) val execId = "exe-1" - def makeTaskMetrics(base: Int) = { + def makeTaskMetrics(base: Int): TaskMetrics = { val taskMetrics = new TaskMetrics() val shuffleReadMetrics = new ShuffleReadMetrics() val shuffleWriteMetrics = new ShuffleWriteMetrics() @@ -291,7 +291,7 @@ class JobProgressListenerSuite extends FunSuite with LocalSparkContext with Matc taskMetrics } - def makeTaskInfo(taskId: Long, finishTime: Int = 0) = { + def makeTaskInfo(taskId: Long, finishTime: Int = 0): TaskInfo = { val taskInfo = new TaskInfo(taskId, 0, 1, 0L, execId, "host1", TaskLocality.NODE_LOCAL, false) taskInfo.finishTime = finishTime diff --git a/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala b/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala index e1bc1379b5d80..3744e479d2f05 100644 --- a/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala +++ b/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala @@ -107,7 +107,8 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter { val myRddInfo0 = rddInfo0 val myRddInfo1 = rddInfo1 val myRddInfo2 = rddInfo2 - val stageInfo0 = new StageInfo(0, 0, "0", 100, Seq(myRddInfo0, myRddInfo1, myRddInfo2), "details") + val stageInfo0 = new StageInfo( + 0, 0, "0", 100, Seq(myRddInfo0, myRddInfo1, myRddInfo2), "details") bus.postToAll(SparkListenerBlockManagerAdded(1L, bm1, 1000L)) bus.postToAll(SparkListenerStageSubmitted(stageInfo0)) assert(storageListener._rddInfoMap.size === 3) diff --git a/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala index 054ef54e746a5..c47162779bbba 100644 --- a/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala @@ -83,7 +83,7 @@ object TestObject { class TestClass extends Serializable { var x = 5 - def getX = x + def getX: Int = x def run(): Int = { var nonSer = new NonSerializable @@ -95,7 +95,7 @@ class TestClass extends Serializable { } class TestClassWithoutDefaultConstructor(x: Int) extends Serializable { - def getX = x + def getX: Int = x def run(): Int = { var nonSer = new NonSerializable @@ -164,7 +164,7 @@ object TestObjectWithNesting { } class TestClassWithNesting(val y: Int) extends Serializable { - def getY = y + def getY: Int = y def run(): Int = { var nonSer = new NonSerializable diff --git a/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala b/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala index 43b6a405cb68c..c05317534cddf 100644 --- a/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/FileAppenderSuite.scala @@ -109,7 +109,8 @@ class FileAppenderSuite extends FunSuite with BeforeAndAfter with Logging { // verify whether the earliest file has been deleted val rolledOverFiles = allGeneratedFiles.filter { _ != testFile.toString }.toArray.sorted - logInfo(s"All rolled over files generated:${rolledOverFiles.size}\n" + rolledOverFiles.mkString("\n")) + logInfo(s"All rolled over files generated:${rolledOverFiles.size}\n" + + rolledOverFiles.mkString("\n")) assert(rolledOverFiles.size > 2) val earliestRolledOverFile = rolledOverFiles.head val existingRolledOverFiles = RollingFileAppender.getSortedRolledOverFiles( @@ -135,7 +136,7 @@ class FileAppenderSuite extends FunSuite with BeforeAndAfter with Logging { val testOutputStream = new PipedOutputStream() val testInputStream = new PipedInputStream(testOutputStream) val appender = FileAppender(testInputStream, testFile, conf) - //assert(appender.getClass === classTag[ExpectedAppender].getClass) + // assert(appender.getClass === classTag[ExpectedAppender].getClass) assert(appender.getClass.getSimpleName === classTag[ExpectedAppender].runtimeClass.getSimpleName) if (appender.isInstanceOf[RollingFileAppender]) { @@ -153,9 +154,11 @@ class FileAppenderSuite extends FunSuite with BeforeAndAfter with Logging { import RollingFileAppender._ - def rollingStrategy(strategy: String) = Seq(STRATEGY_PROPERTY -> strategy) - def rollingSize(size: String) = Seq(SIZE_PROPERTY -> size) - def rollingInterval(interval: String) = Seq(INTERVAL_PROPERTY -> interval) + def rollingStrategy(strategy: String): Seq[(String, String)] = + Seq(STRATEGY_PROPERTY -> strategy) + def rollingSize(size: String): Seq[(String, String)] = Seq(SIZE_PROPERTY -> size) + def rollingInterval(interval: String): Seq[(String, String)] = + Seq(INTERVAL_PROPERTY -> interval) val msInDay = 24 * 60 * 60 * 1000L val msInHour = 60 * 60 * 1000L diff --git a/core/src/test/scala/org/apache/spark/util/NextIteratorSuite.scala b/core/src/test/scala/org/apache/spark/util/NextIteratorSuite.scala index 72e81f3f1a884..403dcb03bd6e5 100644 --- a/core/src/test/scala/org/apache/spark/util/NextIteratorSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/NextIteratorSuite.scala @@ -71,7 +71,7 @@ class NextIteratorSuite extends FunSuite with Matchers { class StubIterator(ints: Buffer[Int]) extends NextIterator[Int] { var closeCalled = 0 - override def getNext() = { + override def getNext(): Int = { if (ints.size == 0) { finished = true 0 diff --git a/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala b/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala index 7424c2e91d4f2..67a9f75ff2187 100644 --- a/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala @@ -98,8 +98,10 @@ class SizeEstimatorSuite // If an array contains the *same* element many times, we should only count it once. val d1 = new DummyClass1 - assertResult(72)(SizeEstimator.estimate(Array.fill(10)(d1))) // 10 pointers plus 8-byte object - assertResult(432)(SizeEstimator.estimate(Array.fill(100)(d1))) // 100 pointers plus 8-byte object + // 10 pointers plus 8-byte object + assertResult(72)(SizeEstimator.estimate(Array.fill(10)(d1))) + // 100 pointers plus 8-byte object + assertResult(432)(SizeEstimator.estimate(Array.fill(100)(d1))) // Same thing with huge array containing the same element many times. Note that this won't // return exactly 4032 because it can't tell that *all* the elements will equal the first diff --git a/core/src/test/scala/org/apache/spark/util/TimeStampedHashMapSuite.scala b/core/src/test/scala/org/apache/spark/util/TimeStampedHashMapSuite.scala index c1c605cdb487b..8b72fe665c214 100644 --- a/core/src/test/scala/org/apache/spark/util/TimeStampedHashMapSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/TimeStampedHashMapSuite.scala @@ -63,7 +63,7 @@ class TimeStampedHashMapSuite extends FunSuite { assert(map1.getTimestamp("k1").get < threshTime1) assert(map1.getTimestamp("k2").isDefined) assert(map1.getTimestamp("k2").get >= threshTime1) - map1.clearOldValues(threshTime1) //should only clear k1 + map1.clearOldValues(threshTime1) // should only clear k1 assert(map1.get("k1") === None) assert(map1.get("k2").isDefined) } @@ -93,7 +93,7 @@ class TimeStampedHashMapSuite extends FunSuite { assert(map1.getTimestamp("k1").get < threshTime1) assert(map1.getTimestamp("k2").isDefined) assert(map1.getTimestamp("k2").get >= threshTime1) - map1.clearOldValues(threshTime1) //should only clear k1 + map1.clearOldValues(threshTime1) // should only clear k1 assert(map1.get("k1") === None) assert(map1.get("k2").isDefined) } diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala index 5d93086082189..449fb87f111c4 100644 --- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala @@ -106,7 +106,7 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { val second = 1000 val minute = second * 60 val hour = minute * 60 - def str = Utils.msDurationToString(_) + def str: (Long) => String = Utils.msDurationToString(_) val sep = new DecimalFormatSymbols(Locale.getDefault()).getDecimalSeparator() @@ -199,7 +199,8 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { test("doesDirectoryContainFilesNewerThan") { // create some temporary directories and files val parent: File = Utils.createTempDir() - val child1: File = Utils.createTempDir(parent.getCanonicalPath) // The parent directory has two child directories + // The parent directory has two child directories + val child1: File = Utils.createTempDir(parent.getCanonicalPath) val child2: File = Utils.createTempDir(parent.getCanonicalPath) val child3: File = Utils.createTempDir(child1.getCanonicalPath) // set the last modified time of child1 to 30 secs old diff --git a/core/src/test/scala/org/apache/spark/util/VectorSuite.scala b/core/src/test/scala/org/apache/spark/util/VectorSuite.scala index 794a55d61750b..ce2968728a996 100644 --- a/core/src/test/scala/org/apache/spark/util/VectorSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/VectorSuite.scala @@ -27,7 +27,7 @@ import org.scalatest.FunSuite @deprecated("suppress compile time deprecation warning", "1.0.0") class VectorSuite extends FunSuite { - def verifyVector(vector: Vector, expectedLength: Int) = { + def verifyVector(vector: Vector, expectedLength: Int): Unit = { assert(vector.length == expectedLength) assert(vector.elements.min > 0.0) assert(vector.elements.max < 1.0) diff --git a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala index 48f79ea651018..dff8f3ddc816f 100644 --- a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala @@ -185,7 +185,7 @@ class ExternalAppendOnlyMapSuite extends FunSuite with LocalSparkContext { // reduceByKey val rdd = sc.parallelize(1 to 10).map(i => (i%2, 1)) - val result1 = rdd.reduceByKey(_+_).collect() + val result1 = rdd.reduceByKey(_ + _).collect() assert(result1.toSet === Set[(Int, Int)]((0, 5), (1, 5))) // groupByKey diff --git a/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala index 72d96798b1141..9ff067f86af44 100644 --- a/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala @@ -553,10 +553,10 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe conf.set("spark.shuffle.memoryFraction", "0.001") sc = new SparkContext("local-cluster[1,1,512]", "test", conf) - def createCombiner(i: String) = ArrayBuffer[String](i) - def mergeValue(buffer: ArrayBuffer[String], i: String) = buffer += i - def mergeCombiners(buffer1: ArrayBuffer[String], buffer2: ArrayBuffer[String]) = - buffer1 ++= buffer2 + def createCombiner(i: String): ArrayBuffer[String] = ArrayBuffer[String](i) + def mergeValue(buffer: ArrayBuffer[String], i: String): ArrayBuffer[String] = buffer += i + def mergeCombiners(buffer1: ArrayBuffer[String], buffer2: ArrayBuffer[String]) + : ArrayBuffer[String] = buffer1 ++= buffer2 val agg = new Aggregator[String, String, ArrayBuffer[String]]( createCombiner _, mergeValue _, mergeCombiners _) @@ -633,14 +633,17 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe conf.set("spark.shuffle.memoryFraction", "0.001") sc = new SparkContext("local-cluster[1,1,512]", "test", conf) - def createCombiner(i: Int) = ArrayBuffer[Int](i) - def mergeValue(buffer: ArrayBuffer[Int], i: Int) = buffer += i - def mergeCombiners(buf1: ArrayBuffer[Int], buf2: ArrayBuffer[Int]) = buf1 ++= buf2 + def createCombiner(i: Int): ArrayBuffer[Int] = ArrayBuffer[Int](i) + def mergeValue(buffer: ArrayBuffer[Int], i: Int): ArrayBuffer[Int] = buffer += i + def mergeCombiners(buf1: ArrayBuffer[Int], buf2: ArrayBuffer[Int]): ArrayBuffer[Int] = { + buf1 ++= buf2 + } val agg = new Aggregator[Int, Int, ArrayBuffer[Int]](createCombiner, mergeValue, mergeCombiners) val sorter = new ExternalSorter[Int, Int, ArrayBuffer[Int]](Some(agg), None, None, None) - sorter.insertAll((1 to 100000).iterator.map(i => (i, i)) ++ Iterator((Int.MaxValue, Int.MaxValue))) + sorter.insertAll( + (1 to 100000).iterator.map(i => (i, i)) ++ Iterator((Int.MaxValue, Int.MaxValue))) val it = sorter.iterator while (it.hasNext) { @@ -654,9 +657,10 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe conf.set("spark.shuffle.memoryFraction", "0.001") sc = new SparkContext("local-cluster[1,1,512]", "test", conf) - def createCombiner(i: String) = ArrayBuffer[String](i) - def mergeValue(buffer: ArrayBuffer[String], i: String) = buffer += i - def mergeCombiners(buf1: ArrayBuffer[String], buf2: ArrayBuffer[String]) = buf1 ++= buf2 + def createCombiner(i: String): ArrayBuffer[String] = ArrayBuffer[String](i) + def mergeValue(buffer: ArrayBuffer[String], i: String): ArrayBuffer[String] = buffer += i + def mergeCombiners(buf1: ArrayBuffer[String], buf2: ArrayBuffer[String]): ArrayBuffer[String] = + buf1 ++= buf2 val agg = new Aggregator[String, String, ArrayBuffer[String]]( createCombiner, mergeValue, mergeCombiners) @@ -720,7 +724,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe // Using wrongOrdering to show integer overflow introduced exception. val rand = new Random(100L) val wrongOrdering = new Ordering[String] { - override def compare(a: String, b: String) = { + override def compare(a: String, b: String): Int = { val h1 = if (a == null) 0 else a.hashCode() val h2 = if (b == null) 0 else b.hashCode() h1 - h2 @@ -742,9 +746,10 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe // Using aggregation and external spill to make sure ExternalSorter using // partitionKeyComparator. - def createCombiner(i: String) = ArrayBuffer(i) - def mergeValue(c: ArrayBuffer[String], i: String) = c += i - def mergeCombiners(c1: ArrayBuffer[String], c2: ArrayBuffer[String]) = c1 ++= c2 + def createCombiner(i: String): ArrayBuffer[String] = ArrayBuffer(i) + def mergeValue(c: ArrayBuffer[String], i: String): ArrayBuffer[String] = c += i + def mergeCombiners(c1: ArrayBuffer[String], c2: ArrayBuffer[String]): ArrayBuffer[String] = + c1 ++= c2 val agg = new Aggregator[String, String, ArrayBuffer[String]]( createCombiner, mergeValue, mergeCombiners) diff --git a/core/src/test/scala/org/apache/spark/util/random/XORShiftRandomSuite.scala b/core/src/test/scala/org/apache/spark/util/random/XORShiftRandomSuite.scala index ef7178bcdf5c2..03f5f2d1b8528 100644 --- a/core/src/test/scala/org/apache/spark/util/random/XORShiftRandomSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/random/XORShiftRandomSuite.scala @@ -28,7 +28,7 @@ import scala.language.reflectiveCalls class XORShiftRandomSuite extends FunSuite with Matchers { - def fixture = new { + def fixture: Object {val seed: Long; val hundMil: Int; val xorRand: XORShiftRandom} = new { val seed = 1L val xorRand = new XORShiftRandom(seed) val hundMil = 1e8.toInt From fc17661475443d9f0a8d28e3439feeb7a7bca67b Mon Sep 17 00:00:00 2001 From: lewuathe Date: Sun, 12 Apr 2015 22:17:16 -0700 Subject: [PATCH 244/591] [SPARK-6643][MLLIB] Implement StandardScalerModel missing methods This is the sub-task of SPARK-6254. Wrap missing method for `StandardScalerModel`. Author: lewuathe Closes #5310 from Lewuathe/SPARK-6643 and squashes the following commits: fafd690 [lewuathe] Fix for lint-python bd31a64 [lewuathe] Merge branch 'master' into SPARK-6643 578f5ee [lewuathe] Remove unnecessary class a38f155 [lewuathe] Merge master 66bb2ab [lewuathe] Fix typos 82683a0 [lewuathe] [SPARK-6643] Implement StandardScalerModel missing methods --- .../mllib/api/python/PythonMLLibAPI.scala | 4 ++-- python/pyspark/mllib/feature.py | 16 +++++++++++++ python/pyspark/mllib/tests.py | 24 +++++++++++++++++++ 3 files changed, 42 insertions(+), 2 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index 1faa3def0e042..ab15f0f36a14b 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -450,9 +450,9 @@ private[python] class PythonMLLibAPI extends Serializable { def normalizeVector(p: Double, rdd: JavaRDD[Vector]): JavaRDD[Vector] = { new Normalizer(p).transform(rdd) } - + /** - * Java stub for IDF.fit(). This stub returns a + * Java stub for StandardScaler.fit(). This stub returns a * handle to the Java object instead of the content of the Java object. * Extra care needs to be taken in the Python code to ensure it gets freed on * exit; see the Py4J documentation. diff --git a/python/pyspark/mllib/feature.py b/python/pyspark/mllib/feature.py index 3cda1205e1391..8be819aceec24 100644 --- a/python/pyspark/mllib/feature.py +++ b/python/pyspark/mllib/feature.py @@ -132,6 +132,22 @@ def transform(self, vector): """ return JavaVectorTransformer.transform(self, vector) + def setWithMean(self, withMean): + """ + Setter of the boolean which decides + whether it uses mean or not + """ + self.call("setWithMean", withMean) + return self + + def setWithStd(self, withStd): + """ + Setter of the boolean which decides + whether it uses std or not + """ + self.call("setWithStd", withStd) + return self + class StandardScaler(object): """ diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 3b40158c12b74..8eaddcf8b9b5e 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -44,6 +44,7 @@ from pyspark.mllib.stat import Statistics from pyspark.mllib.feature import Word2Vec from pyspark.mllib.feature import IDF +from pyspark.mllib.feature import StandardScaler from pyspark.serializers import PickleSerializer from pyspark.sql import SQLContext from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase @@ -745,6 +746,29 @@ def test_word2vec_get_vectors(self): model = Word2Vec().fit(self.sc.parallelize(data)) self.assertEquals(len(model.getVectors()), 3) + +class StandardScalerTests(PySparkTestCase): + def test_model_setters(self): + data = [ + [1.0, 2.0, 3.0], + [2.0, 3.0, 4.0], + [3.0, 4.0, 5.0] + ] + model = StandardScaler().fit(self.sc.parallelize(data)) + self.assertIsNotNone(model.setWithMean(True)) + self.assertIsNotNone(model.setWithStd(True)) + self.assertEqual(model.transform([1.0, 2.0, 3.0]), DenseVector([-1.0, -1.0, -1.0])) + + def test_model_transform(self): + data = [ + [1.0, 2.0, 3.0], + [2.0, 3.0, 4.0], + [3.0, 4.0, 5.0] + ] + model = StandardScaler().fit(self.sc.parallelize(data)) + self.assertEqual(model.transform([1.0, 2.0, 3.0]), DenseVector([1.0, 2.0, 3.0])) + + if __name__ == "__main__": if not _have_scipy: print "NOTE: Skipping SciPy tests as it does not seem to be installed" From d3792f54974e16cbe8f10b3091d248e0bdd48986 Mon Sep 17 00:00:00 2001 From: "Joseph K. Bradley" Date: Sun, 12 Apr 2015 22:38:27 -0700 Subject: [PATCH 245/591] [SPARK-4081] [mllib] VectorIndexer **Ready for review!** Since the original PR, I moved the code to the spark.ml API and renamed this to VectorIndexer. This introduces a VectorIndexer class which does the following: * VectorIndexer.fit(): collect statistics about how many values each feature in a dataset (RDD[Vector]) can take (limited by maxCategories) * Feature which exceed maxCategories are declared continuous, and the Model will treat them as such. * VectorIndexerModel.transform(): Convert categorical feature values to corresponding 0-based indices Design notes: * This maintains sparsity in vectors by ensuring that categorical feature value 0.0 gets index 0. * This does not yet support transforming data with new (unknown) categorical feature values. That can be added later. * This is necessary for DecisionTree and tree ensembles. Reviewers: Please check my use of metadata and my unit tests for it; I'm not sure if I covered everything in the tests. Other notes: * This also adds a public toMetadata method to AttributeGroup (for simpler construction of metadata). CC: mengxr Author: Joseph K. Bradley Closes #3000 from jkbradley/indexer and squashes the following commits: 5956d91 [Joseph K. Bradley] minor cleanups f5c57a8 [Joseph K. Bradley] added Java test suite 643b444 [Joseph K. Bradley] removed FeatureTests 02236c3 [Joseph K. Bradley] Updated VectorIndexer, ready for PR 286d221 [Joseph K. Bradley] Reworked DatasetIndexer for spark.ml API, and renamed it to VectorIndexer 12e6cf2 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into indexer 6d8f3f1 [Joseph K. Bradley] Added partly done DatasetIndexer to spark.ml 6a2f553 [Joseph K. Bradley] Updated TODO for allowUnknownCategories 3f041f8 [Joseph K. Bradley] Final cleanups for DatasetIndexer 038b9e3 [Joseph K. Bradley] DatasetIndexer now maintains sparsity in SparseVector 3a4a0bd [Joseph K. Bradley] Added another test for DatasetIndexer 2006923 [Joseph K. Bradley] DatasetIndexer now passes tests f409987 [Joseph K. Bradley] partly done with DatasetIndexerSuite 5e7c874 [Joseph K. Bradley] working on DatasetIndexer --- .../scala/org/apache/spark/ml/Pipeline.scala | 3 + .../spark/ml/attribute/AttributeGroup.scala | 21 +- .../spark/ml/feature/VectorIndexer.scala | 393 ++++++++++++++++++ .../org/apache/spark/ml/param/params.scala | 20 +- .../ml/feature/JavaVectorIndexerSuite.java | 70 ++++ .../ml/attribute/AttributeGroupSuite.scala | 8 +- .../spark/ml/feature/NormalizerSuite.scala | 7 +- .../spark/ml/feature/VectorIndexerSuite.scala | 255 ++++++++++++ .../apache/spark/ml/util/TestingUtils.scala | 60 +++ 9 files changed, 818 insertions(+), 19 deletions(-) create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala create mode 100644 mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala create mode 100644 mllib/src/test/scala/org/apache/spark/ml/util/TestingUtils.scala diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala index c4a36103303a2..a455341a1f723 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala @@ -47,6 +47,9 @@ abstract class PipelineStage extends Serializable with Logging { /** * Derives the output schema from the input schema and parameters, optionally with logging. + * + * This should be optimistic. If it is unclear whether the schema will be valid, then it should + * be assumed valid until proven otherwise. */ protected def transformSchema( schema: StructType, diff --git a/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala b/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala index 970e6ad5514d1..aa27a668f1695 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala @@ -106,7 +106,7 @@ class AttributeGroup private ( def getAttr(attrIndex: Int): Attribute = this(attrIndex) /** Converts to metadata without name. */ - private[attribute] def toMetadata: Metadata = { + private[attribute] def toMetadataImpl: Metadata = { import AttributeKeys._ val bldr = new MetadataBuilder() if (attributes.isDefined) { @@ -142,17 +142,24 @@ class AttributeGroup private ( bldr.build() } - /** Converts to a StructField with some existing metadata. */ - def toStructField(existingMetadata: Metadata): StructField = { - val newMetadata = new MetadataBuilder() + /** Converts to ML metadata with some existing metadata. */ + def toMetadata(existingMetadata: Metadata): Metadata = { + new MetadataBuilder() .withMetadata(existingMetadata) - .putMetadata(AttributeKeys.ML_ATTR, toMetadata) + .putMetadata(AttributeKeys.ML_ATTR, toMetadataImpl) .build() - StructField(name, new VectorUDT, nullable = false, newMetadata) + } + + /** Converts to ML metadata */ + def toMetadata: Metadata = toMetadata(Metadata.empty) + + /** Converts to a StructField with some existing metadata. */ + def toStructField(existingMetadata: Metadata): StructField = { + StructField(name, new VectorUDT, nullable = false, toMetadata(existingMetadata)) } /** Converts to a StructField. */ - def toStructField(): StructField = toStructField(Metadata.empty) + def toStructField: StructField = toStructField(Metadata.empty) override def equals(other: Any): Boolean = { other match { diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala new file mode 100644 index 0000000000000..8760960e19272 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala @@ -0,0 +1,393 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.{Estimator, Model} +import org.apache.spark.ml.attribute.{BinaryAttribute, NumericAttribute, NominalAttribute, + Attribute, AttributeGroup} +import org.apache.spark.ml.param.{HasInputCol, HasOutputCol, IntParam, ParamMap, Params} +import org.apache.spark.mllib.linalg.{SparseVector, DenseVector, Vector, VectorUDT} +import org.apache.spark.sql.{Row, DataFrame} +import org.apache.spark.sql.functions.callUDF +import org.apache.spark.sql.types.{StructField, StructType} +import org.apache.spark.util.collection.OpenHashSet + + +/** Private trait for params for VectorIndexer and VectorIndexerModel */ +private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOutputCol { + + /** + * Threshold for the number of values a categorical feature can take. + * If a feature is found to have > maxCategories values, then it is declared continuous. + * + * (default = 20) + */ + val maxCategories = new IntParam(this, "maxCategories", + "Threshold for the number of values a categorical feature can take." + + " If a feature is found to have > maxCategories values, then it is declared continuous.", + Some(20)) + + /** @group getParam */ + def getMaxCategories: Int = get(maxCategories) +} + +/** + * :: AlphaComponent :: + * + * Class for indexing categorical feature columns in a dataset of [[Vector]]. + * + * This has 2 usage modes: + * - Automatically identify categorical features (default behavior) + * - This helps process a dataset of unknown vectors into a dataset with some continuous + * features and some categorical features. The choice between continuous and categorical + * is based upon a maxCategories parameter. + * - Set maxCategories to the maximum number of categorical any categorical feature should have. + * - E.g.: Feature 0 has unique values {-1.0, 0.0}, and feature 1 values {1.0, 3.0, 5.0}. + * If maxCategories = 2, then feature 0 will be declared categorical and use indices {0, 1}, + * and feature 1 will be declared continuous. + * - Index all features, if all features are categorical + * - If maxCategories is set to be very large, then this will build an index of unique + * values for all features. + * - Warning: This can cause problems if features are continuous since this will collect ALL + * unique values to the driver. + * - E.g.: Feature 0 has unique values {-1.0, 0.0}, and feature 1 values {1.0, 3.0, 5.0}. + * If maxCategories >= 3, then both features will be declared categorical. + * + * This returns a model which can transform categorical features to use 0-based indices. + * + * Index stability: + * - This is not guaranteed to choose the same category index across multiple runs. + * - If a categorical feature includes value 0, then this is guaranteed to map value 0 to index 0. + * This maintains vector sparsity. + * - More stability may be added in the future. + * + * TODO: Future extensions: The following functionality is planned for the future: + * - Preserve metadata in transform; if a feature's metadata is already present, do not recompute. + * - Specify certain features to not index, either via a parameter or via existing metadata. + * - Add warning if a categorical feature has only 1 category. + * - Add option for allowing unknown categories. + */ +@AlphaComponent +class VectorIndexer extends Estimator[VectorIndexerModel] with VectorIndexerParams { + + /** @group setParam */ + def setMaxCategories(value: Int): this.type = { + require(value > 1, + s"DatasetIndexer given maxCategories = value, but requires maxCategories > 1.") + set(maxCategories, value) + } + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + override def fit(dataset: DataFrame, paramMap: ParamMap): VectorIndexerModel = { + transformSchema(dataset.schema, paramMap, logging = true) + val map = this.paramMap ++ paramMap + val firstRow = dataset.select(map(inputCol)).take(1) + require(firstRow.length == 1, s"VectorIndexer cannot be fit on an empty dataset.") + val numFeatures = firstRow(0).getAs[Vector](0).size + val vectorDataset = dataset.select(map(inputCol)).map { case Row(v: Vector) => v } + val maxCats = map(maxCategories) + val categoryStats: VectorIndexer.CategoryStats = vectorDataset.mapPartitions { iter => + val localCatStats = new VectorIndexer.CategoryStats(numFeatures, maxCats) + iter.foreach(localCatStats.addVector) + Iterator(localCatStats) + }.reduce((stats1, stats2) => stats1.merge(stats2)) + val model = new VectorIndexerModel(this, map, numFeatures, categoryStats.getCategoryMaps) + Params.inheritValues(map, this, model) + model + } + + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + // We do not transfer feature metadata since we do not know what types of features we will + // produce in transform(). + val map = this.paramMap ++ paramMap + val dataType = new VectorUDT + require(map.contains(inputCol), s"VectorIndexer requires input column parameter: $inputCol") + require(map.contains(outputCol), s"VectorIndexer requires output column parameter: $outputCol") + checkInputColumn(schema, map(inputCol), dataType) + addOutputColumn(schema, map(outputCol), dataType) + } +} + +private object VectorIndexer { + + /** + * Helper class for tracking unique values for each feature. + * + * TODO: Track which features are known to be continuous already; do not update counts for them. + * + * @param numFeatures This class fails if it encounters a Vector whose length is not numFeatures. + * @param maxCategories This class caps the number of unique values collected at maxCategories. + */ + class CategoryStats(private val numFeatures: Int, private val maxCategories: Int) + extends Serializable { + + /** featureValueSets[feature index] = set of unique values */ + private val featureValueSets = + Array.fill[OpenHashSet[Double]](numFeatures)(new OpenHashSet[Double]()) + + /** Merge with another instance, modifying this instance. */ + def merge(other: CategoryStats): CategoryStats = { + featureValueSets.zip(other.featureValueSets).foreach { case (thisValSet, otherValSet) => + otherValSet.iterator.foreach { x => + // Once we have found > maxCategories values, we know the feature is continuous + // and do not need to collect more values for it. + if (thisValSet.size <= maxCategories) thisValSet.add(x) + } + } + this + } + + /** Add a new vector to this index, updating sets of unique feature values */ + def addVector(v: Vector): Unit = { + require(v.size == numFeatures, s"VectorIndexer expected $numFeatures features but" + + s" found vector of size ${v.size}.") + v match { + case dv: DenseVector => addDenseVector(dv) + case sv: SparseVector => addSparseVector(sv) + } + } + + /** + * Based on stats collected, decide which features are categorical, + * and choose indices for categories. + * + * Sparsity: This tries to maintain sparsity by treating value 0.0 specially. + * If a categorical feature takes value 0.0, then value 0.0 is given index 0. + * + * @return Feature value index. Keys are categorical feature indices (column indices). + * Values are mappings from original features values to 0-based category indices. + */ + def getCategoryMaps: Map[Int, Map[Double, Int]] = { + // Filter out features which are declared continuous. + featureValueSets.zipWithIndex.filter(_._1.size <= maxCategories).map { + case (featureValues: OpenHashSet[Double], featureIndex: Int) => + var sortedFeatureValues = featureValues.iterator.filter(_ != 0.0).toArray.sorted + val zeroExists = sortedFeatureValues.length + 1 == featureValues.size + if (zeroExists) { + sortedFeatureValues = 0.0 +: sortedFeatureValues + } + val categoryMap: Map[Double, Int] = sortedFeatureValues.zipWithIndex.toMap + (featureIndex, categoryMap) + }.toMap + } + + private def addDenseVector(dv: DenseVector): Unit = { + var i = 0 + while (i < dv.size) { + if (featureValueSets(i).size <= maxCategories) { + featureValueSets(i).add(dv(i)) + } + i += 1 + } + } + + private def addSparseVector(sv: SparseVector): Unit = { + // TODO: This might be able to handle 0's more efficiently. + var vecIndex = 0 // index into vector + var k = 0 // index into non-zero elements + while (vecIndex < sv.size) { + val featureValue = if (k < sv.indices.length && vecIndex == sv.indices(k)) { + k += 1 + sv.values(k - 1) + } else { + 0.0 + } + if (featureValueSets(vecIndex).size <= maxCategories) { + featureValueSets(vecIndex).add(featureValue) + } + vecIndex += 1 + } + } + } +} + +/** + * :: AlphaComponent :: + * + * Transform categorical features to use 0-based indices instead of their original values. + * - Categorical features are mapped to indices. + * - Continuous features (columns) are left unchanged. + * This also appends metadata to the output column, marking features as Numeric (continuous), + * Nominal (categorical), or Binary (either continuous or categorical). + * + * This maintains vector sparsity. + * + * @param numFeatures Number of features, i.e., length of Vectors which this transforms + * @param categoryMaps Feature value index. Keys are categorical feature indices (column indices). + * Values are maps from original features values to 0-based category indices. + * If a feature is not in this map, it is treated as continuous. + */ +@AlphaComponent +class VectorIndexerModel private[ml] ( + override val parent: VectorIndexer, + override val fittingParamMap: ParamMap, + val numFeatures: Int, + val categoryMaps: Map[Int, Map[Double, Int]]) + extends Model[VectorIndexerModel] with VectorIndexerParams { + + /** + * Pre-computed feature attributes, with some missing info. + * In transform(), set attribute name and other info, if available. + */ + private val partialFeatureAttributes: Array[Attribute] = { + val attrs = new Array[Attribute](numFeatures) + var categoricalFeatureCount = 0 // validity check for numFeatures, categoryMaps + var featureIndex = 0 + while (featureIndex < numFeatures) { + if (categoryMaps.contains(featureIndex)) { + // categorical feature + val featureValues: Array[String] = + categoryMaps(featureIndex).toArray.sortBy(_._1).map(_._1).map(_.toString) + if (featureValues.length == 2) { + attrs(featureIndex) = new BinaryAttribute(index = Some(featureIndex), + values = Some(featureValues)) + } else { + attrs(featureIndex) = new NominalAttribute(index = Some(featureIndex), + isOrdinal = Some(false), values = Some(featureValues)) + } + categoricalFeatureCount += 1 + } else { + // continuous feature + attrs(featureIndex) = new NumericAttribute(index = Some(featureIndex)) + } + featureIndex += 1 + } + require(categoricalFeatureCount == categoryMaps.size, "VectorIndexerModel given categoryMaps" + + s" with keys outside expected range [0,...,numFeatures), where numFeatures=$numFeatures") + attrs + } + + // TODO: Check more carefully about whether this whole class will be included in a closure. + + private val transformFunc: Vector => Vector = { + val sortedCategoricalFeatureIndices = categoryMaps.keys.toArray.sorted + val localVectorMap = categoryMaps + val f: Vector => Vector = { + case dv: DenseVector => + val tmpv = dv.copy + localVectorMap.foreach { case (featureIndex: Int, categoryMap: Map[Double, Int]) => + tmpv.values(featureIndex) = categoryMap(tmpv(featureIndex)) + } + tmpv + case sv: SparseVector => + // We use the fact that categorical value 0 is always mapped to index 0. + val tmpv = sv.copy + var catFeatureIdx = 0 // index into sortedCategoricalFeatureIndices + var k = 0 // index into non-zero elements of sparse vector + while (catFeatureIdx < sortedCategoricalFeatureIndices.length && k < tmpv.indices.length) { + val featureIndex = sortedCategoricalFeatureIndices(catFeatureIdx) + if (featureIndex < tmpv.indices(k)) { + catFeatureIdx += 1 + } else if (featureIndex > tmpv.indices(k)) { + k += 1 + } else { + tmpv.values(k) = localVectorMap(featureIndex)(tmpv.values(k)) + catFeatureIdx += 1 + k += 1 + } + } + tmpv + } + f + } + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + transformSchema(dataset.schema, paramMap, logging = true) + val map = this.paramMap ++ paramMap + val newField = prepOutputField(dataset.schema, map) + val newCol = callUDF(transformFunc, new VectorUDT, dataset(map(inputCol))) + // For now, just check the first row of inputCol for vector length. + val firstRow = dataset.select(map(inputCol)).take(1) + if (firstRow.length != 0) { + val actualNumFeatures = firstRow(0).getAs[Vector](0).size + require(numFeatures == actualNumFeatures, "VectorIndexerModel expected vector of length" + + s" $numFeatures but found length $actualNumFeatures") + } + dataset.withColumn(map(outputCol), newCol.as(map(outputCol), newField.metadata)) + } + + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + val map = this.paramMap ++ paramMap + val dataType = new VectorUDT + require(map.contains(inputCol), + s"VectorIndexerModel requires input column parameter: $inputCol") + require(map.contains(outputCol), + s"VectorIndexerModel requires output column parameter: $outputCol") + checkInputColumn(schema, map(inputCol), dataType) + + val origAttrGroup = AttributeGroup.fromStructField(schema(map(inputCol))) + val origNumFeatures: Option[Int] = if (origAttrGroup.attributes.nonEmpty) { + Some(origAttrGroup.attributes.get.length) + } else { + origAttrGroup.numAttributes + } + require(origNumFeatures.forall(_ == numFeatures), "VectorIndexerModel expected" + + s" $numFeatures features, but input column ${map(inputCol)} had metadata specifying" + + s" ${origAttrGroup.numAttributes.get} features.") + + val newField = prepOutputField(schema, map) + val outputFields = schema.fields :+ newField + StructType(outputFields) + } + + /** + * Prepare the output column field, including per-feature metadata. + * @param schema Input schema + * @param map Parameter map (with this class' embedded parameter map folded in) + * @return Output column field + */ + private def prepOutputField(schema: StructType, map: ParamMap): StructField = { + val origAttrGroup = AttributeGroup.fromStructField(schema(map(inputCol))) + val featureAttributes: Array[Attribute] = if (origAttrGroup.attributes.nonEmpty) { + // Convert original attributes to modified attributes + val origAttrs: Array[Attribute] = origAttrGroup.attributes.get + origAttrs.zip(partialFeatureAttributes).map { + case (origAttr: Attribute, featAttr: BinaryAttribute) => + if (origAttr.name.nonEmpty) { + featAttr.withName(origAttr.name.get) + } else { + featAttr + } + case (origAttr: Attribute, featAttr: NominalAttribute) => + if (origAttr.name.nonEmpty) { + featAttr.withName(origAttr.name.get) + } else { + featAttr + } + case (origAttr: Attribute, featAttr: NumericAttribute) => + origAttr.withIndex(featAttr.index.get) + } + } else { + partialFeatureAttributes + } + val newAttributeGroup = new AttributeGroup(map(outputCol), featureAttributes) + newAttributeGroup.toStructField(schema(map(inputCol)).metadata) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala index 17ece897a6c55..7d5178d0abb2d 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala @@ -198,23 +198,31 @@ trait Params extends Identifiable with Serializable { /** * Check whether the given schema contains an input column. - * @param colName Parameter name for the input column. - * @param dataType SQL DataType of the input column. + * @param colName Input column name + * @param dataType Input column DataType */ protected def checkInputColumn(schema: StructType, colName: String, dataType: DataType): Unit = { val actualDataType = schema(colName).dataType - require(actualDataType.equals(dataType), - s"Input column $colName must be of type $dataType" + - s" but was actually $actualDataType. Column param description: ${getParam(colName)}") + require(actualDataType.equals(dataType), s"Input column $colName must be of type $dataType" + + s" but was actually $actualDataType. Column param description: ${getParam(colName)}") } + /** + * Add an output column to the given schema. + * This fails if the given output column already exists. + * @param schema Initial schema (not modified) + * @param colName Output column name. If this column name is an empy String "", this method + * returns the initial schema, unchanged. This allows users to disable output + * columns. + * @param dataType Output column DataType + */ protected def addOutputColumn( schema: StructType, colName: String, dataType: DataType): StructType = { if (colName.length == 0) return schema val fieldNames = schema.fieldNames - require(!fieldNames.contains(colName), s"Prediction column $colName already exists.") + require(!fieldNames.contains(colName), s"Output column $colName already exists.") val outputFields = schema.fields ++ Seq(StructField(colName, dataType, nullable = false)) StructType(outputFields) } diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java new file mode 100644 index 0000000000000..161100134c92d --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java @@ -0,0 +1,70 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature; + +import java.io.Serializable; +import java.util.List; + +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import com.google.common.collect.Lists; + +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.ml.feature.VectorIndexerSuite.FeatureData; +import org.apache.spark.mllib.linalg.Vectors; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.SQLContext; + + +public class JavaVectorIndexerSuite implements Serializable { + private transient JavaSparkContext sc; + + @Before + public void setUp() { + sc = new JavaSparkContext("local", "JavaVectorIndexerSuite"); + } + + @After + public void tearDown() { + sc.stop(); + sc = null; + } + + @Test + public void vectorIndexerAPI() { + // The tests are to check Java compatibility. + List points = Lists.newArrayList( + new FeatureData(Vectors.dense(0.0, -2.0)), + new FeatureData(Vectors.dense(1.0, 3.0)), + new FeatureData(Vectors.dense(1.0, 4.0)) + ); + SQLContext sqlContext = new SQLContext(sc); + DataFrame data = sqlContext.createDataFrame(sc.parallelize(points, 2), FeatureData.class); + VectorIndexer indexer = new VectorIndexer() + .setInputCol("features") + .setOutputCol("indexed") + .setMaxCategories(2); + VectorIndexerModel model = indexer.fit(data); + Assert.assertEquals(model.numFeatures(), 2); + Assert.assertEquals(model.categoryMaps().size(), 1); + DataFrame indexedData = model.transform(data); + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeGroupSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeGroupSuite.scala index 3fb6e2ec46468..0dcfe5a2002dc 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeGroupSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeGroupSuite.scala @@ -43,8 +43,8 @@ class AttributeGroupSuite extends FunSuite { intercept[NoSuchElementException] { group("abc") } - assert(group === AttributeGroup.fromMetadata(group.toMetadata, group.name)) - assert(group === AttributeGroup.fromStructField(group.toStructField())) + assert(group === AttributeGroup.fromMetadata(group.toMetadataImpl, group.name)) + assert(group === AttributeGroup.fromStructField(group.toStructField)) } test("attribute group without attributes") { @@ -53,8 +53,8 @@ class AttributeGroupSuite extends FunSuite { assert(group0.numAttributes === Some(10)) assert(group0.size === 10) assert(group0.attributes.isEmpty) - assert(group0 === AttributeGroup.fromMetadata(group0.toMetadata, group0.name)) - assert(group0 === AttributeGroup.fromStructField(group0.toStructField())) + assert(group0 === AttributeGroup.fromMetadata(group0.toMetadataImpl, group0.name)) + assert(group0 === AttributeGroup.fromStructField(group0.toStructField)) val group1 = new AttributeGroup("item") assert(group1.name === "item") diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala index a18c335952b96..9d09f24709e23 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/NormalizerSuite.scala @@ -24,7 +24,6 @@ import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.sql.{DataFrame, Row, SQLContext} -private case class DataSet(features: Vector) class NormalizerSuite extends FunSuite with MLlibTestSparkContext { @@ -63,7 +62,7 @@ class NormalizerSuite extends FunSuite with MLlibTestSparkContext { ) val sqlContext = new SQLContext(sc) - dataFrame = sqlContext.createDataFrame(sc.parallelize(data, 2).map(DataSet)) + dataFrame = sqlContext.createDataFrame(sc.parallelize(data, 2).map(NormalizerSuite.FeatureData)) normalizer = new Normalizer() .setInputCol("features") .setOutputCol("normalized_features") @@ -107,3 +106,7 @@ class NormalizerSuite extends FunSuite with MLlibTestSparkContext { assertValues(result, l1Normalized) } } + +private object NormalizerSuite { + case class FeatureData(features: Vector) +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala new file mode 100644 index 0000000000000..61c46c85a78b5 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala @@ -0,0 +1,255 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import scala.beans.{BeanInfo, BeanProperty} + +import org.scalatest.FunSuite + +import org.apache.spark.SparkException +import org.apache.spark.ml.attribute._ +import org.apache.spark.ml.util.TestingUtils +import org.apache.spark.mllib.linalg.{SparseVector, Vector, Vectors} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.{DataFrame, SQLContext} + + +class VectorIndexerSuite extends FunSuite with MLlibTestSparkContext { + + import VectorIndexerSuite.FeatureData + + @transient var sqlContext: SQLContext = _ + + // identical, of length 3 + @transient var densePoints1: DataFrame = _ + @transient var sparsePoints1: DataFrame = _ + @transient var point1maxes: Array[Double] = _ + + // identical, of length 2 + @transient var densePoints2: DataFrame = _ + @transient var sparsePoints2: DataFrame = _ + + // different lengths + @transient var badPoints: DataFrame = _ + + override def beforeAll(): Unit = { + super.beforeAll() + + val densePoints1Seq = Seq( + Vectors.dense(1.0, 2.0, 0.0), + Vectors.dense(0.0, 1.0, 2.0), + Vectors.dense(0.0, 0.0, -1.0), + Vectors.dense(1.0, 3.0, 2.0)) + val sparsePoints1Seq = Seq( + Vectors.sparse(3, Array(0, 1), Array(1.0, 2.0)), + Vectors.sparse(3, Array(1, 2), Array(1.0, 2.0)), + Vectors.sparse(3, Array(2), Array(-1.0)), + Vectors.sparse(3, Array(0, 1, 2), Array(1.0, 3.0, 2.0))) + point1maxes = Array(1.0, 3.0, 2.0) + + val densePoints2Seq = Seq( + Vectors.dense(1.0, 1.0, 0.0, 1.0), + Vectors.dense(0.0, 1.0, 1.0, 1.0), + Vectors.dense(-1.0, 1.0, 2.0, 0.0)) + val sparsePoints2Seq = Seq( + Vectors.sparse(4, Array(0, 1, 3), Array(1.0, 1.0, 1.0)), + Vectors.sparse(4, Array(1, 2, 3), Array(1.0, 1.0, 1.0)), + Vectors.sparse(4, Array(0, 1, 2), Array(-1.0, 1.0, 2.0))) + + val badPointsSeq = Seq( + Vectors.sparse(2, Array(0, 1), Array(1.0, 1.0)), + Vectors.sparse(3, Array(2), Array(-1.0))) + + // Sanity checks for assumptions made in tests + assert(densePoints1Seq.head.size == sparsePoints1Seq.head.size) + assert(densePoints2Seq.head.size == sparsePoints2Seq.head.size) + assert(densePoints1Seq.head.size != densePoints2Seq.head.size) + def checkPair(dvSeq: Seq[Vector], svSeq: Seq[Vector]): Unit = { + assert(dvSeq.zip(svSeq).forall { case (dv, sv) => dv.toArray === sv.toArray }, + "typo in unit test") + } + checkPair(densePoints1Seq, sparsePoints1Seq) + checkPair(densePoints2Seq, sparsePoints2Seq) + + sqlContext = new SQLContext(sc) + densePoints1 = sqlContext.createDataFrame(sc.parallelize(densePoints1Seq, 2).map(FeatureData)) + sparsePoints1 = sqlContext.createDataFrame(sc.parallelize(sparsePoints1Seq, 2).map(FeatureData)) + densePoints2 = sqlContext.createDataFrame(sc.parallelize(densePoints2Seq, 2).map(FeatureData)) + sparsePoints2 = sqlContext.createDataFrame(sc.parallelize(sparsePoints2Seq, 2).map(FeatureData)) + badPoints = sqlContext.createDataFrame(sc.parallelize(badPointsSeq, 2).map(FeatureData)) + } + + private def getIndexer: VectorIndexer = + new VectorIndexer().setInputCol("features").setOutputCol("indexed") + + test("Cannot fit an empty DataFrame") { + val rdd = sqlContext.createDataFrame(sc.parallelize(Array.empty[Vector], 2).map(FeatureData)) + val vectorIndexer = getIndexer + intercept[IllegalArgumentException] { + vectorIndexer.fit(rdd) + } + } + + test("Throws error when given RDDs with different size vectors") { + val vectorIndexer = getIndexer + val model = vectorIndexer.fit(densePoints1) // vectors of length 3 + model.transform(densePoints1) // should work + model.transform(sparsePoints1) // should work + intercept[IllegalArgumentException] { + model.transform(densePoints2) + println("Did not throw error when fit, transform were called on vectors of different lengths") + } + intercept[SparkException] { + vectorIndexer.fit(badPoints) + println("Did not throw error when fitting vectors of different lengths in same RDD.") + } + } + + test("Same result with dense and sparse vectors") { + def testDenseSparse(densePoints: DataFrame, sparsePoints: DataFrame): Unit = { + val denseVectorIndexer = getIndexer.setMaxCategories(2) + val sparseVectorIndexer = getIndexer.setMaxCategories(2) + val denseModel = denseVectorIndexer.fit(densePoints) + val sparseModel = sparseVectorIndexer.fit(sparsePoints) + val denseMap = denseModel.categoryMaps + val sparseMap = sparseModel.categoryMaps + assert(denseMap.keys.toSet == sparseMap.keys.toSet, + "Categorical features chosen from dense vs. sparse vectors did not match.") + assert(denseMap == sparseMap, + "Categorical feature value indexes chosen from dense vs. sparse vectors did not match.") + } + testDenseSparse(densePoints1, sparsePoints1) + testDenseSparse(densePoints2, sparsePoints2) + } + + test("Builds valid categorical feature value index, transform correctly, check metadata") { + def checkCategoryMaps( + data: DataFrame, + maxCategories: Int, + categoricalFeatures: Set[Int]): Unit = { + val collectedData = data.collect().map(_.getAs[Vector](0)) + val errMsg = s"checkCategoryMaps failed for input with maxCategories=$maxCategories," + + s" categoricalFeatures=${categoricalFeatures.mkString(", ")}" + try { + val vectorIndexer = getIndexer.setMaxCategories(maxCategories) + val model = vectorIndexer.fit(data) + val categoryMaps = model.categoryMaps + assert(categoryMaps.keys.toSet === categoricalFeatures) // Chose correct categorical features + val transformed = model.transform(data).select("indexed") + val indexedRDD: RDD[Vector] = transformed.map(_.getAs[Vector](0)) + val featureAttrs = AttributeGroup.fromStructField(transformed.schema("indexed")) + assert(featureAttrs.name === "indexed") + assert(featureAttrs.attributes.get.length === model.numFeatures) + categoricalFeatures.foreach { feature: Int => + val origValueSet = collectedData.map(_(feature)).toSet + val targetValueIndexSet = Range(0, origValueSet.size).toSet + val catMap = categoryMaps(feature) + assert(catMap.keys.toSet === origValueSet) // Correct categories + assert(catMap.values.toSet === targetValueIndexSet) // Correct category indices + if (origValueSet.contains(0.0)) { + assert(catMap(0.0) === 0) // value 0 gets index 0 + } + // Check transformed data + assert(indexedRDD.map(_(feature)).collect().toSet === targetValueIndexSet) + // Check metadata + val featureAttr = featureAttrs(feature) + assert(featureAttr.index.get === feature) + featureAttr match { + case attr: BinaryAttribute => + assert(attr.values.get === origValueSet.toArray.sorted.map(_.toString)) + case attr: NominalAttribute => + assert(attr.values.get === origValueSet.toArray.sorted.map(_.toString)) + assert(attr.isOrdinal.get === false) + case _ => + throw new RuntimeException(errMsg + s". Categorical feature $feature failed" + + s" metadata check. Found feature attribute: $featureAttr.") + } + } + // Check numerical feature metadata. + Range(0, model.numFeatures).filter(feature => !categoricalFeatures.contains(feature)) + .foreach { feature: Int => + val featureAttr = featureAttrs(feature) + featureAttr match { + case attr: NumericAttribute => + assert(featureAttr.index.get === feature) + case _ => + throw new RuntimeException(errMsg + s". Numerical feature $feature failed" + + s" metadata check. Found feature attribute: $featureAttr.") + } + } + } catch { + case e: org.scalatest.exceptions.TestFailedException => + println(errMsg) + throw e + } + } + checkCategoryMaps(densePoints1, maxCategories = 2, categoricalFeatures = Set(0)) + checkCategoryMaps(densePoints1, maxCategories = 3, categoricalFeatures = Set(0, 2)) + checkCategoryMaps(densePoints2, maxCategories = 2, categoricalFeatures = Set(1, 3)) + } + + test("Maintain sparsity for sparse vectors") { + def checkSparsity(data: DataFrame, maxCategories: Int): Unit = { + val points = data.collect().map(_.getAs[Vector](0)) + val vectorIndexer = getIndexer.setMaxCategories(maxCategories) + val model = vectorIndexer.fit(data) + val indexedPoints = model.transform(data).select("indexed").map(_.getAs[Vector](0)).collect() + points.zip(indexedPoints).foreach { + case (orig: SparseVector, indexed: SparseVector) => + assert(orig.indices.length == indexed.indices.length) + case _ => throw new UnknownError("Unit test has a bug in it.") // should never happen + } + } + checkSparsity(sparsePoints1, maxCategories = 2) + checkSparsity(sparsePoints2, maxCategories = 2) + } + + test("Preserve metadata") { + // For continuous features, preserve name and stats. + val featureAttributes: Array[Attribute] = point1maxes.zipWithIndex.map { case (maxVal, i) => + NumericAttribute.defaultAttr.withName(i.toString).withMax(maxVal) + } + val attrGroup = new AttributeGroup("features", featureAttributes) + val densePoints1WithMeta = + densePoints1.select(densePoints1("features").as("features", attrGroup.toMetadata)) + val vectorIndexer = getIndexer.setMaxCategories(2) + val model = vectorIndexer.fit(densePoints1WithMeta) + // Check that ML metadata are preserved. + val indexedPoints = model.transform(densePoints1WithMeta) + val transAttributes: Array[Attribute] = + AttributeGroup.fromStructField(indexedPoints.schema("indexed")).attributes.get + featureAttributes.zip(transAttributes).foreach { case (orig, trans) => + assert(orig.name === trans.name) + (orig, trans) match { + case (orig: NumericAttribute, trans: NumericAttribute) => + assert(orig.max.nonEmpty && orig.max === trans.max) + case _ => + // do nothing + // TODO: Once input features marked as categorical are handled correctly, check that here. + } + } + // Check that non-ML metadata are preserved. + TestingUtils.testPreserveMetadata(densePoints1WithMeta, model, "features", "indexed") + } +} + +private[feature] object VectorIndexerSuite { + @BeanInfo + case class FeatureData(@BeanProperty features: Vector) +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/util/TestingUtils.scala b/mllib/src/test/scala/org/apache/spark/ml/util/TestingUtils.scala new file mode 100644 index 0000000000000..c44cb61b34171 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/util/TestingUtils.scala @@ -0,0 +1,60 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.util + +import org.apache.spark.ml.Transformer +import org.apache.spark.sql.DataFrame +import org.apache.spark.sql.types.MetadataBuilder +import org.scalatest.FunSuite + +private[ml] object TestingUtils extends FunSuite { + + /** + * Test whether unrelated metadata are preserved for this transformer. + * This attaches extra metadata to a column, transforms the column, and check to ensure the + * extra metadata have not changed. + * @param data Input dataset + * @param transformer Transformer to test + * @param inputCol Unique input column for Transformer. This must be the ONLY input column. + * @param outputCol Output column to test for metadata presence. + */ + def testPreserveMetadata( + data: DataFrame, + transformer: Transformer, + inputCol: String, + outputCol: String): Unit = { + // Create some fake metadata + val origMetadata = data.schema(inputCol).metadata + val metaKey = "__testPreserveMetadata__fake_key" + val metaValue = 12345 + assert(!origMetadata.contains(metaKey), + s"Unit test with testPreserveMetadata will fail since metadata key was present: $metaKey") + val newMetadata = + new MetadataBuilder().withMetadata(origMetadata).putLong(metaKey, metaValue).build() + // Add metadata to the inputCol + val withMetadata = data.select(data(inputCol).as(inputCol, newMetadata)) + // Transform, and ensure extra metadata was not affected + val transformed = transformer.transform(withMetadata) + val transMetadata = transformed.schema(outputCol).metadata + assert(transMetadata.contains(metaKey), + "Unit test with testPreserveMetadata failed; extra metadata key was not present.") + assert(transMetadata.getLong(metaKey) === metaValue, + "Unit test with testPreserveMetadata failed; extra metadata value was wrong." + + s" Expected $metaValue but found ${transMetadata.getLong(metaKey)}") + } +} From 685ddcf5253c0ecb39853802431e22b0c7b61dee Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Sun, 12 Apr 2015 22:41:05 -0700 Subject: [PATCH 246/591] [SPARK-5886][ML] Add StringIndexer as a feature transformer This PR adds string indexer, which takes a column of string labels and outputs a double column with labels indexed by their frequency. TODOs: - [x] store feature to index map in output metadata Author: Xiangrui Meng Closes #4735 from mengxr/SPARK-5886 and squashes the following commits: d82575f [Xiangrui Meng] fix test 700e70f [Xiangrui Meng] rename LabelIndexer to StringIndexer 16a6f8c [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-5886 457166e [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-5886 f8b30f4 [Xiangrui Meng] update label indexer to output metadata e81ec28 [Xiangrui Meng] Merge branch 'openhashmap-contains' into SPARK-5886-2 d6e6f1f [Xiangrui Meng] add contains to primitivekeyopenhashmap 748a69b [Xiangrui Meng] add contains to OpenHashMap def3c5c [Xiangrui Meng] add LabelIndexer --- .../spark/ml/feature/StringIndexer.scala | 126 ++++++++++++++++++ .../spark/ml/feature/StringIndexerSuite.scala | 52 ++++++++ 2 files changed, 178 insertions(+) create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala new file mode 100644 index 0000000000000..61e6742e880d8 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala @@ -0,0 +1,126 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.apache.spark.SparkException +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.{Estimator, Model} +import org.apache.spark.ml.attribute.NominalAttribute +import org.apache.spark.ml.param._ +import org.apache.spark.sql.DataFrame +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types.{StringType, StructType} +import org.apache.spark.util.collection.OpenHashMap + +/** + * Base trait for [[StringIndexer]] and [[StringIndexerModel]]. + */ +private[feature] trait StringIndexerBase extends Params with HasInputCol with HasOutputCol { + + /** Validates and transforms the input schema. */ + protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = { + val map = this.paramMap ++ paramMap + checkInputColumn(schema, map(inputCol), StringType) + val inputFields = schema.fields + val outputColName = map(outputCol) + require(inputFields.forall(_.name != outputColName), + s"Output column $outputColName already exists.") + val attr = NominalAttribute.defaultAttr.withName(map(outputCol)) + val outputFields = inputFields :+ attr.toStructField() + StructType(outputFields) + } +} + +/** + * :: AlphaComponent :: + * A label indexer that maps a string column of labels to an ML column of label indices. + * The indices are in [0, numLabels), ordered by label frequencies. + * So the most frequent label gets index 0. + */ +@AlphaComponent +class StringIndexer extends Estimator[StringIndexerModel] with StringIndexerBase { + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + // TODO: handle unseen labels + + override def fit(dataset: DataFrame, paramMap: ParamMap): StringIndexerModel = { + val map = this.paramMap ++ paramMap + val counts = dataset.select(map(inputCol)).map(_.getString(0)).countByValue() + val labels = counts.toSeq.sortBy(-_._2).map(_._1).toArray + val model = new StringIndexerModel(this, map, labels) + Params.inheritValues(map, this, model) + model + } + + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + validateAndTransformSchema(schema, paramMap) + } +} + +/** + * :: AlphaComponent :: + * Model fitted by [[StringIndexer]]. + */ +@AlphaComponent +class StringIndexerModel private[ml] ( + override val parent: StringIndexer, + override val fittingParamMap: ParamMap, + labels: Array[String]) extends Model[StringIndexerModel] with StringIndexerBase { + + private val labelToIndex: OpenHashMap[String, Double] = { + val n = labels.length + val map = new OpenHashMap[String, Double](n) + var i = 0 + while (i < n) { + map.update(labels(i), i) + i += 1 + } + map + } + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + val map = this.paramMap ++ paramMap + val indexer = udf { label: String => + if (labelToIndex.contains(label)) { + labelToIndex(label) + } else { + // TODO: handle unseen labels + throw new SparkException(s"Unseen label: $label.") + } + } + val outputColName = map(outputCol) + val metadata = NominalAttribute.defaultAttr + .withName(outputColName).withValues(labels).toStructField().metadata + dataset.select(col("*"), indexer(dataset(map(inputCol))).as(outputColName, metadata)) + } + + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + validateAndTransformSchema(schema, paramMap) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala new file mode 100644 index 0000000000000..00b5d094d82f1 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala @@ -0,0 +1,52 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.scalatest.FunSuite + +import org.apache.spark.ml.attribute.{Attribute, NominalAttribute} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.sql.SQLContext + +class StringIndexerSuite extends FunSuite with MLlibTestSparkContext { + private var sqlContext: SQLContext = _ + + override def beforeAll(): Unit = { + super.beforeAll() + sqlContext = new SQLContext(sc) + } + + test("StringIndexer") { + val data = sc.parallelize(Seq((0, "a"), (1, "b"), (2, "c"), (3, "a"), (4, "a"), (5, "c")), 2) + val df = sqlContext.createDataFrame(data).toDF("id", "label") + val indexer = new StringIndexer() + .setInputCol("label") + .setOutputCol("labelIndex") + .fit(df) + val transformed = indexer.transform(df) + val attr = Attribute.fromStructField(transformed.schema("labelIndex")) + .asInstanceOf[NominalAttribute] + assert(attr.values.get === Array("a", "c", "b")) + val output = transformed.select("id", "labelIndex").map { r => + (r.getInt(0), r.getDouble(1)) + }.collect().toSet + // a -> 0, b -> 2, c -> 1 + val expected = Set((0, 0.0), (1, 2.0), (2, 1.0), (3, 0.0), (4, 0.0), (5, 1.0)) + assert(output === expected) + } +} From 929404498506c34180e2eaaa1a4d4a3c4ed51daa Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Sun, 12 Apr 2015 22:42:01 -0700 Subject: [PATCH 247/591] [SPARK-5885][MLLIB] Add VectorAssembler as a feature transformer VectorAssembler merges multiple columns into a vector column. This PR contains content from #5195. ~~carry ML attributes~~ (moved to a follow-up PR) Author: Xiangrui Meng Closes #5196 from mengxr/SPARK-5885 and squashes the following commits: a52b101 [Xiangrui Meng] recognize more types 35daac2 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-5885 bb5e64b [Xiangrui Meng] add TODO for null 976a3d6 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-5885 0859311 [Xiangrui Meng] Revert "add CreateStruct" 29fb6ac [Xiangrui Meng] use CreateStruct adb71c4 [Xiangrui Meng] Merge branch 'SPARK-6542' into SPARK-5885 85f3106 [Xiangrui Meng] add CreateStruct 4ff16ce [Xiangrui Meng] add VectorAssembler --- .../org/apache/spark/ml/Identifiable.scala | 2 +- .../spark/ml/feature/VectorAssembler.scala | 111 ++++++++++++++++++ .../apache/spark/ml/param/sharedParams.scala | 10 ++ .../ml/feature/VectorAssemblerSuite.scala | 63 ++++++++++ 4 files changed, 185 insertions(+), 1 deletion(-) create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala diff --git a/mllib/src/main/scala/org/apache/spark/ml/Identifiable.scala b/mllib/src/main/scala/org/apache/spark/ml/Identifiable.scala index cd84b05bfb496..a50090671ae48 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Identifiable.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Identifiable.scala @@ -29,5 +29,5 @@ private[ml] trait Identifiable extends Serializable { * random hex chars. */ private[ml] val uid: String = - this.getClass.getSimpleName + "-" + UUID.randomUUID().toString.take(8) + this.getClass.getSimpleName + "_" + UUID.randomUUID().toString.take(8) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala new file mode 100644 index 0000000000000..d1b8f7e6e9295 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala @@ -0,0 +1,111 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import scala.collection.mutable.ArrayBuilder + +import org.apache.spark.SparkException +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.Transformer +import org.apache.spark.ml.param.{HasInputCols, HasOutputCol, ParamMap} +import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors} +import org.apache.spark.sql.{Column, DataFrame, Row} +import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute +import org.apache.spark.sql.catalyst.expressions.{Alias, Cast, CreateStruct} +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types._ + +/** + * :: AlphaComponent :: + * A feature transformer than merge multiple columns into a vector column. + */ +@AlphaComponent +class VectorAssembler extends Transformer with HasInputCols with HasOutputCol { + + /** @group setParam */ + def setInputCols(value: Array[String]): this.type = set(inputCols, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + val map = this.paramMap ++ paramMap + val assembleFunc = udf { r: Row => + VectorAssembler.assemble(r.toSeq: _*) + } + val schema = dataset.schema + val inputColNames = map(inputCols) + val args = inputColNames.map { c => + schema(c).dataType match { + case DoubleType => UnresolvedAttribute(c) + case t if t.isInstanceOf[VectorUDT] => UnresolvedAttribute(c) + case _: NativeType => Alias(Cast(UnresolvedAttribute(c), DoubleType), s"${c}_double_$uid")() + } + } + dataset.select(col("*"), assembleFunc(new Column(CreateStruct(args))).as(map(outputCol))) + } + + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + val map = this.paramMap ++ paramMap + val inputColNames = map(inputCols) + val outputColName = map(outputCol) + val inputDataTypes = inputColNames.map(name => schema(name).dataType) + inputDataTypes.foreach { + case _: NativeType => + case t if t.isInstanceOf[VectorUDT] => + case other => + throw new IllegalArgumentException(s"Data type $other is not supported.") + } + if (schema.fieldNames.contains(outputColName)) { + throw new IllegalArgumentException(s"Output column $outputColName already exists.") + } + StructType(schema.fields :+ new StructField(outputColName, new VectorUDT, false)) + } +} + +@AlphaComponent +object VectorAssembler { + + private[feature] def assemble(vv: Any*): Vector = { + val indices = ArrayBuilder.make[Int] + val values = ArrayBuilder.make[Double] + var cur = 0 + vv.foreach { + case v: Double => + if (v != 0.0) { + indices += cur + values += v + } + cur += 1 + case vec: Vector => + vec.foreachActive { case (i, v) => + if (v != 0.0) { + indices += cur + i + values += v + } + } + cur += vec.size + case null => + // TODO: output Double.NaN? + throw new SparkException("Values to assemble cannot be null.") + case o => + throw new SparkException(s"$o of type ${o.getClass.getName} is not supported.") + } + Vectors.sparse(cur, indices.result(), values.result()) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala index 0739fdbfcbaae..07e6eb417763d 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala @@ -140,6 +140,16 @@ private[ml] trait HasInputCol extends Params { def getInputCol: String = get(inputCol) } +private[ml] trait HasInputCols extends Params { + /** + * Param for input column names. + */ + val inputCols: Param[Array[String]] = new Param(this, "inputCols", "input column names") + + /** @group getParam */ + def getInputCols: Array[String] = get(inputCols) +} + private[ml] trait HasOutputCol extends Params { /** * param for output column name diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala new file mode 100644 index 0000000000000..57d0278e03639 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala @@ -0,0 +1,63 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.scalatest.FunSuite + +import org.apache.spark.SparkException +import org.apache.spark.mllib.linalg.{Vector, Vectors} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.sql.{Row, SQLContext} + +class VectorAssemblerSuite extends FunSuite with MLlibTestSparkContext { + + @transient var sqlContext: SQLContext = _ + + override def beforeAll(): Unit = { + super.beforeAll() + sqlContext = new SQLContext(sc) + } + + test("assemble") { + import org.apache.spark.ml.feature.VectorAssembler.assemble + assert(assemble(0.0) === Vectors.sparse(1, Array.empty, Array.empty)) + assert(assemble(0.0, 1.0) === Vectors.sparse(2, Array(1), Array(1.0))) + val dv = Vectors.dense(2.0, 0.0) + assert(assemble(0.0, dv, 1.0) === Vectors.sparse(4, Array(1, 3), Array(2.0, 1.0))) + val sv = Vectors.sparse(2, Array(0, 1), Array(3.0, 4.0)) + assert(assemble(0.0, dv, 1.0, sv) === + Vectors.sparse(6, Array(1, 3, 4, 5), Array(2.0, 1.0, 3.0, 4.0))) + for (v <- Seq(1, "a", null)) { + intercept[SparkException](assemble(v)) + intercept[SparkException](assemble(1.0, v)) + } + } + + test("VectorAssembler") { + val df = sqlContext.createDataFrame(Seq( + (0, 0.0, Vectors.dense(1.0, 2.0), "a", Vectors.sparse(2, Array(1), Array(3.0)), 10L) + )).toDF("id", "x", "y", "name", "z", "n") + val assembler = new VectorAssembler() + .setInputCols(Array("x", "y", "z", "n")) + .setOutputCol("features") + assembler.transform(df).select("features").collect().foreach { + case Row(v: Vector) => + assert(v === Vectors.sparse(6, Array(1, 2, 4, 5), Array(1.0, 2.0, 3.0, 10.0))) + } + } +} From 68d1faa3c04e9412bbc2b60421dc12bd19c396b2 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Sun, 12 Apr 2015 22:56:12 -0700 Subject: [PATCH 248/591] [SPARK-6562][SQL] DataFrame.replace Supports replacing values with other values in DataFrames. Python support should be in a separate pull request. Author: Reynold Xin Closes #5282 from rxin/df-na-replace and squashes the following commits: 4b72434 [Reynold Xin] Removed println. c8d9946 [Reynold Xin] col -> cols fbb3c21 [Reynold Xin] [SPARK-6562][SQL] DataFrame.replace --- .../spark/sql/DataFrameNaFunctions.scala | 144 ++++++++++++++++++ .../spark/sql/DataFrameNaFunctionsSuite.scala | 34 +++++ 2 files changed, 178 insertions(+) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala index bf3c3fe876873..481ed4924857e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala @@ -192,6 +192,127 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) { */ def fill(valueMap: Map[String, Any]): DataFrame = fill0(valueMap.toSeq) + /** + * Replaces values matching keys in `replacement` map with the corresponding values. + * Key and value of `replacement` map must have the same type, and can only be doubles or strings. + * If `col` is "*", then the replacement is applied on all string columns or numeric columns. + * + * {{{ + * import com.google.common.collect.ImmutableMap; + * + * // Replaces all occurrences of 1.0 with 2.0 in column "height". + * df.replace("height", ImmutableMap.of(1.0, 2.0)); + * + * // Replaces all occurrences of "UNKNOWN" with "unnamed" in column "name". + * df.replace("name", ImmutableMap.of("UNKNOWN", "unnamed")); + * + * // Replaces all occurrences of "UNKNOWN" with "unnamed" in all string columns. + * df.replace("*", ImmutableMap.of("UNKNOWN", "unnamed")); + * }}} + * + * @param col name of the column to apply the value replacement + * @param replacement value replacement map, as explained above + */ + def replace[T](col: String, replacement: java.util.Map[T, T]): DataFrame = { + replace[T](col, replacement.toMap : Map[T, T]) + } + + /** + * Replaces values matching keys in `replacement` map with the corresponding values. + * Key and value of `replacement` map must have the same type, and can only be doubles or strings. + * + * {{{ + * import com.google.common.collect.ImmutableMap; + * + * // Replaces all occurrences of 1.0 with 2.0 in column "height" and "weight". + * df.replace(new String[] {"height", "weight"}, ImmutableMap.of(1.0, 2.0)); + * + * // Replaces all occurrences of "UNKNOWN" with "unnamed" in column "firstname" and "lastname". + * df.replace(new String[] {"firstname", "lastname"}, ImmutableMap.of("UNKNOWN", "unnamed")); + * }}} + * + * @param cols list of columns to apply the value replacement + * @param replacement value replacement map, as explained above + */ + def replace[T](cols: Array[String], replacement: java.util.Map[T, T]): DataFrame = { + replace(cols.toSeq, replacement.toMap) + } + + /** + * (Scala-specific) Replaces values matching keys in `replacement` map. + * Key and value of `replacement` map must have the same type, and can only be doubles or strings. + * If `col` is "*", then the replacement is applied on all string columns or numeric columns. + * + * {{{ + * // Replaces all occurrences of 1.0 with 2.0 in column "height". + * df.replace("height", Map(1.0 -> 2.0)) + * + * // Replaces all occurrences of "UNKNOWN" with "unnamed" in column "name". + * df.replace("name", Map("UNKNOWN" -> "unnamed") + * + * // Replaces all occurrences of "UNKNOWN" with "unnamed" in all string columns. + * df.replace("*", Map("UNKNOWN" -> "unnamed") + * }}} + * + * @param col name of the column to apply the value replacement + * @param replacement value replacement map, as explained above + */ + def replace[T](col: String, replacement: Map[T, T]): DataFrame = { + if (col == "*") { + replace0(df.columns, replacement) + } else { + replace0(Seq(col), replacement) + } + } + + /** + * (Scala-specific) Replaces values matching keys in `replacement` map. + * Key and value of `replacement` map must have the same type, and can only be doubles or strings. + * + * {{{ + * // Replaces all occurrences of 1.0 with 2.0 in column "height" and "weight". + * df.replace("height" :: "weight" :: Nil, Map(1.0 -> 2.0)); + * + * // Replaces all occurrences of "UNKNOWN" with "unnamed" in column "firstname" and "lastname". + * df.replace("firstname" :: "lastname" :: Nil, Map("UNKNOWN" -> "unnamed"); + * }}} + * + * @param cols list of columns to apply the value replacement + * @param replacement value replacement map, as explained above + */ + def replace[T](cols: Seq[String], replacement: Map[T, T]): DataFrame = replace0(cols, replacement) + + private def replace0[T](cols: Seq[String], replacement: Map[T, T]): DataFrame = { + if (replacement.isEmpty || cols.isEmpty) { + return df + } + + // replacementMap is either Map[String, String] or Map[Double, Double] + val replacementMap: Map[_, _] = replacement.head._2 match { + case v: String => replacement + case _ => replacement.map { case (k, v) => (convertToDouble(k), convertToDouble(v)) } + } + + // targetColumnType is either DoubleType or StringType + val targetColumnType = replacement.head._1 match { + case _: jl.Double | _: jl.Float | _: jl.Integer | _: jl.Long => DoubleType + case _: String => StringType + } + + val columnEquals = df.sqlContext.analyzer.resolver + val projections = df.schema.fields.map { f => + val shouldReplace = cols.exists(colName => columnEquals(colName, f.name)) + if (f.dataType.isInstanceOf[NumericType] && targetColumnType == DoubleType && shouldReplace) { + replaceCol(f, replacementMap) + } else if (f.dataType == targetColumnType && shouldReplace) { + replaceCol(f, replacementMap) + } else { + df.col(f.name) + } + } + df.select(projections : _*) + } + private def fill0(values: Seq[(String, Any)]): DataFrame = { // Error handling values.foreach { case (colName, replaceValue) => @@ -228,4 +349,27 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) { private def fillCol[T](col: StructField, replacement: T): Column = { coalesce(df.col(col.name), lit(replacement).cast(col.dataType)).as(col.name) } + + /** + * Returns a [[Column]] expression that replaces value matching key in `replacementMap` with + * value in `replacementMap`, using [[CaseWhen]]. + * + * TODO: This can be optimized to use broadcast join when replacementMap is large. + */ + private def replaceCol(col: StructField, replacementMap: Map[_, _]): Column = { + val branches: Seq[Expression] = replacementMap.flatMap { case (source, target) => + df.col(col.name).equalTo(lit(source).cast(col.dataType)).expr :: + lit(target).cast(col.dataType).expr :: Nil + }.toSeq + new Column(CaseWhen(branches ++ Seq(df.col(col.name).expr))).as(col.name) + } + + private def convertToDouble(v: Any): Double = v match { + case v: Float => v.toDouble + case v: Double => v + case v: Long => v.toDouble + case v: Int => v.toDouble + case v => throw new IllegalArgumentException( + s"Unsupported value type ${v.getClass.getName} ($v).") + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala index 0896f175c056f..41b4f02e6a294 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala @@ -154,4 +154,38 @@ class DataFrameNaFunctionsSuite extends QueryTest { ))), Row("test", null, 1, 2.2)) } + + test("replace") { + val input = createDF() + + // Replace two numeric columns: age and height + val out = input.na.replace(Seq("age", "height"), Map( + 16 -> 61, + 60 -> 6, + 164.3 -> 461.3 // Alice is really tall + )) + + checkAnswer( + out, + Row("Bob", 61, 176.5) :: + Row("Alice", null, 461.3) :: + Row("David", 6, null) :: + Row("Amy", null, null) :: + Row(null, null, null) :: Nil) + + // Replace only the age column + val out1 = input.na.replace("age", Map( + 16 -> 61, + 60 -> 6, + 164.3 -> 461.3 // Alice is really tall + )) + + checkAnswer( + out1, + Row("Bob", 61, 176.5) :: + Row("Alice", null, 164.3) :: + Row("David", 6, null) :: + Row("Amy", null, null) :: + Row(null, null, null) :: Nil) + } } From 950645d597dbc5a8c5010bcb1a9b51c6abad86ea Mon Sep 17 00:00:00 2001 From: Dean Chen Date: Mon, 13 Apr 2015 12:08:55 +0100 Subject: [PATCH 249/591] [SPARK-6868][YARN] Fix broken container log link on executor page when HTTPS_ONLY. Correct http schema in YARN container log link in Spark UI when container logs when YARN is configured to be HTTPS_ONLY. Uses the same logic as the YARN jobtracker webapp. Entry point is [JobBlock](https://github.com/apache/hadoop/blob/e1109fb65608a668cd53dc324dadc6f63a74eeb9/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-app/src/main/java/org/apache/hadoop/mapreduce/v2/app/webapp/JobBlock.java#L108) and logic is in [MRWebAppUtil](https://github.com/apache/hadoop/blob/e1109fb65608a668cd53dc324dadc6f63a74eeb9/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-common/src/main/java/org/apache/hadoop/mapreduce/v2/util/MRWebAppUtil.java#L75). I chose to migrate the logic over instead of importing MRWebAppUtil(but can update the PR to do so) since the class is designated as private and the logic was straightforward. Author: Dean Chen Closes #5477 from deanchen/container-url and squashes the following commits: 91d3090 [Dean Chen] Correct http schema in YARN container log link in Spark UI when container logs when YARN is configured to be HTTPS_ONLY. --- .../apache/spark/deploy/yarn/ExecutorRunnable.scala | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala index 1ce10d906ab23..b06069c07f451 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala @@ -290,10 +290,19 @@ class ExecutorRunnable( YarnSparkHadoopUtil.setEnvFromInputString(env, userEnvs) } + // lookup appropriate http scheme for container log urls + val yarnHttpPolicy = yarnConf.get( + YarnConfiguration.YARN_HTTP_POLICY_KEY, + YarnConfiguration.YARN_HTTP_POLICY_DEFAULT + ) + val httpScheme = if (yarnHttpPolicy == "HTTPS_ONLY") "https://" else "http://" + // Add log urls sys.env.get("SPARK_USER").foreach { user => - val baseUrl = "http://%s/node/containerlogs/%s/%s" - .format(container.getNodeHttpAddress, ConverterUtils.toString(container.getId), user) + val containerId = ConverterUtils.toString(container.getId) + val address = container.getNodeHttpAddress + val baseUrl = s"$httpScheme$address/node/containerlogs/$containerId/$user" + env("SPARK_LOG_URL_STDERR") = s"$baseUrl/stderr?start=0" env("SPARK_LOG_URL_STDOUT") = s"$baseUrl/stdout?start=0" } From cadd7d72c52ccc8d2def405a77dcf807fb5c17c2 Mon Sep 17 00:00:00 2001 From: lisurprise Date: Mon, 13 Apr 2015 12:18:05 +0100 Subject: [PATCH 250/591] [SPARK-6762]Fix potential resource leaks in CheckPoint CheckpointWriter and CheckpointReader The close action should be placed within finally block to avoid the potential resource leaks Author: lisurprise Closes #5407 from zhichao-li/master and squashes the following commits: 065999f [lisurprise] add guard for null ef862d6 [lisurprise] remove fs.close a754adc [lisurprise] refactor with tryWithSafeFinally 824adb3 [lisurprise] close before validation c877da7 [lisurprise] Fix potential resource leaks --- .../apache/spark/streaming/Checkpoint.scala | 47 ++++++++++++------- .../spark/streaming/util/RawTextSender.scala | 3 +- 2 files changed, 31 insertions(+), 19 deletions(-) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala index 28703ef8129b3..0a50485118588 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala @@ -26,7 +26,7 @@ import org.apache.hadoop.conf.Configuration import org.apache.spark.{SparkException, SparkConf, Logging} import org.apache.spark.io.CompressionCodec -import org.apache.spark.util.MetadataCleaner +import org.apache.spark.util.{MetadataCleaner, Utils} import org.apache.spark.streaming.scheduler.JobGenerator @@ -139,8 +139,11 @@ class CheckpointWriter( // Write checkpoint to temp file fs.delete(tempFile, true) // just in case it exists val fos = fs.create(tempFile) - fos.write(bytes) - fos.close() + Utils.tryWithSafeFinally { + fos.write(bytes) + } { + fos.close() + } // If the checkpoint file exists, back it up // If the backup exists as well, just delete it, otherwise rename will fail @@ -187,9 +190,11 @@ class CheckpointWriter( val bos = new ByteArrayOutputStream() val zos = compressionCodec.compressedOutputStream(bos) val oos = new ObjectOutputStream(zos) - oos.writeObject(checkpoint) - oos.close() - bos.close() + Utils.tryWithSafeFinally { + oos.writeObject(checkpoint) + } { + oos.close() + } try { executor.execute(new CheckpointWriteHandler( checkpoint.checkpointTime, bos.toByteArray, clearCheckpointDataLater)) @@ -248,18 +253,24 @@ object CheckpointReader extends Logging { checkpointFiles.foreach(file => { logInfo("Attempting to load checkpoint from file " + file) try { - val fis = fs.open(file) - // ObjectInputStream uses the last defined user-defined class loader in the stack - // to find classes, which maybe the wrong class loader. Hence, a inherited version - // of ObjectInputStream is used to explicitly use the current thread's default class - // loader to find and load classes. This is a well know Java issue and has popped up - // in other places (e.g., http://jira.codehaus.org/browse/GROOVY-1627) - val zis = compressionCodec.compressedInputStream(fis) - val ois = new ObjectInputStreamWithLoader(zis, - Thread.currentThread().getContextClassLoader) - val cp = ois.readObject.asInstanceOf[Checkpoint] - ois.close() - fs.close() + var ois: ObjectInputStreamWithLoader = null + var cp: Checkpoint = null + Utils.tryWithSafeFinally { + val fis = fs.open(file) + // ObjectInputStream uses the last defined user-defined class loader in the stack + // to find classes, which maybe the wrong class loader. Hence, a inherited version + // of ObjectInputStream is used to explicitly use the current thread's default class + // loader to find and load classes. This is a well know Java issue and has popped up + // in other places (e.g., http://jira.codehaus.org/browse/GROOVY-1627) + val zis = compressionCodec.compressedInputStream(fis) + ois = new ObjectInputStreamWithLoader(zis, + Thread.currentThread().getContextClassLoader) + cp = ois.readObject.asInstanceOf[Checkpoint] + } { + if (ois != null) { + ois.close() + } + } cp.validate() logInfo("Checkpoint successfully loaded from file " + file) logInfo("Checkpoint was generated at time " + cp.checkpointTime) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextSender.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextSender.scala index a7850812bd612..ca2f319f174a2 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextSender.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextSender.scala @@ -72,7 +72,8 @@ object RawTextSender extends Logging { } catch { case e: IOException => logError("Client disconnected") - socket.close() + } finally { + socket.close() } } } From 14ce3ea2c9546c58203af85aceb76b1bfc1f650a Mon Sep 17 00:00:00 2001 From: zsxwing Date: Mon, 13 Apr 2015 12:21:29 +0100 Subject: [PATCH 251/591] [SPARK-6860][Streaming][WebUI] Fix the possible inconsistency of StreamingPage Because `StreamingPage.render` doesn't hold the `listener` lock when generating the content, the different parts of content may have some inconsistent values if `listener` updates its status at the same time. And it will confuse people. This PR added `listener.synchronized` to make sure we have a consistent view of StreamingJobProgressListener when creating the content. Author: zsxwing Closes #5470 from zsxwing/SPARK-6860 and squashes the following commits: cec6f92 [zsxwing] Add missing 'synchronized' in StreamingJobProgressListener 7182498 [zsxwing] Add synchronized to make sure we have a consistent view of StreamingJobProgressListener when creating the content --- .../streaming/ui/StreamingJobProgressListener.scala | 10 +++++----- .../org/apache/spark/streaming/ui/StreamingPage.scala | 3 ++- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala index 84f80e638f638..be1e8686cf9fa 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala @@ -149,7 +149,7 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) }.toMap } - def lastReceivedBatchRecords: Map[Int, Long] = { + def lastReceivedBatchRecords: Map[Int, Long] = synchronized { val lastReceivedBlockInfoOption = lastReceivedBatch.map(_.receivedBlockInfo) lastReceivedBlockInfoOption.map { lastReceivedBlockInfo => (0 until numReceivers).map { receiverId => @@ -160,19 +160,19 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) } } - def receiverInfo(receiverId: Int): Option[ReceiverInfo] = { + def receiverInfo(receiverId: Int): Option[ReceiverInfo] = synchronized { receiverInfos.get(receiverId) } - def lastCompletedBatch: Option[BatchInfo] = { + def lastCompletedBatch: Option[BatchInfo] = synchronized { completedBatchInfos.sortBy(_.batchTime)(Time.ordering).lastOption } - def lastReceivedBatch: Option[BatchInfo] = { + def lastReceivedBatch: Option[BatchInfo] = synchronized { retainedBatches.lastOption } - private def retainedBatches: Seq[BatchInfo] = synchronized { + private def retainedBatches: Seq[BatchInfo] = { (waitingBatchInfos.values.toSeq ++ runningBatchInfos.values.toSeq ++ completedBatchInfos).sortBy(_.batchTime)(Time.ordering) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala index bfe8086fcf8fe..b6dcb62bfeec8 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala @@ -37,11 +37,12 @@ private[ui] class StreamingPage(parent: StreamingTab) /** Render the page */ def render(request: HttpServletRequest): Seq[Node] = { - val content = + val content = listener.synchronized { generateBasicStats() ++

    ++

    Statistics over last {listener.retainedCompletedBatches.size} processed batches

    ++ generateReceiverStats() ++ generateBatchStatsTable() + } UIUtils.headerSparkPage("Streaming", content, parent, Some(5000)) } From 9d117cee0be2c73a25702d98f78211055d50babe Mon Sep 17 00:00:00 2001 From: nyaapa Date: Mon, 13 Apr 2015 12:55:25 +0100 Subject: [PATCH 252/591] [SPARK-6440][CORE]Handle IPv6 addresses properly when constructing URI Author: nyaapa Closes #5424 from nyaapa/master and squashes the following commits: 6b717aa [nyaapa] [SPARK-6440][CORE] Remove Utils.localIpAddressHostname, Utils.localIpAddressURI and Utils.getAddressHostName; make Utils.localIpAddress private; rename Utils.localHostURI into Utils.localHostNameForURI; use Utils.localHostName in org.apache.spark.streaming.kinesis.KinesisReceiver and org.apache.spark.sql.hive.thriftserver.SparkSQLEnv 2098081 [nyaapa] [SPARK-6440][CORE] style fixes and use getHostAddress instead of getHostName 84763d7 [nyaapa] [SPARK-6440][CORE]Handle IPv6 addresses properly when constructing URI --- .../scala/org/apache/spark/HttpServer.scala | 2 +- .../spark/deploy/LocalSparkCluster.scala | 2 +- .../spark/deploy/client/TestClient.scala | 2 +- .../scala/org/apache/spark/ui/WebUI.scala | 2 +- .../scala/org/apache/spark/util/Utils.scala | 34 ++++++++++++------- .../streaming/kinesis/KinesisReceiver.scala | 3 +- .../sql/hive/thriftserver/SparkSQLEnv.scala | 3 +- 7 files changed, 29 insertions(+), 19 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/HttpServer.scala b/core/src/main/scala/org/apache/spark/HttpServer.scala index 09a9ccc226721..8de3a6c04df34 100644 --- a/core/src/main/scala/org/apache/spark/HttpServer.scala +++ b/core/src/main/scala/org/apache/spark/HttpServer.scala @@ -160,7 +160,7 @@ private[spark] class HttpServer( throw new ServerStateException("Server is not started") } else { val scheme = if (securityManager.fileServerSSLOptions.enabled) "https" else "http" - s"$scheme://${Utils.localIpAddress}:$port" + s"$scheme://${Utils.localHostNameForURI()}:$port" } } } diff --git a/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala b/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala index 3ab425aab84c8..f0e77c2ba982b 100644 --- a/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala +++ b/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala @@ -53,7 +53,7 @@ class LocalSparkCluster( /* Start the Master */ val (masterSystem, masterPort, _, _) = Master.startSystemAndActor(localHostname, 0, 0, _conf) masterActorSystems += masterSystem - val masterUrl = "spark://" + localHostname + ":" + masterPort + val masterUrl = "spark://" + Utils.localHostNameForURI() + ":" + masterPort val masters = Array(masterUrl) /* Start the Workers */ diff --git a/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala index c1c4812f17fbe..40835b9550586 100644 --- a/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala +++ b/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala @@ -46,7 +46,7 @@ private[spark] object TestClient { def main(args: Array[String]) { val url = args(0) val conf = new SparkConf - val (actorSystem, _) = AkkaUtils.createActorSystem("spark", Utils.localIpAddress, 0, + val (actorSystem, _) = AkkaUtils.createActorSystem("spark", Utils.localHostName(), 0, conf = conf, securityManager = new SecurityManager(conf)) val desc = new ApplicationDescription("TestClient", Some(1), 512, Command("spark.deploy.client.TestExecutor", Seq(), Map(), Seq(), Seq(), Seq()), "ignored") diff --git a/core/src/main/scala/org/apache/spark/ui/WebUI.scala b/core/src/main/scala/org/apache/spark/ui/WebUI.scala index ea548f23120d9..f9860d1a5ce76 100644 --- a/core/src/main/scala/org/apache/spark/ui/WebUI.scala +++ b/core/src/main/scala/org/apache/spark/ui/WebUI.scala @@ -48,7 +48,7 @@ private[spark] abstract class WebUI( protected val handlers = ArrayBuffer[ServletContextHandler]() protected val pageToHandlers = new HashMap[WebUIPage, ArrayBuffer[ServletContextHandler]] protected var serverInfo: Option[ServerInfo] = None - protected val localHostName = Utils.localHostName() + protected val localHostName = Utils.localHostNameForURI() protected val publicHostName = Option(conf.getenv("SPARK_PUBLIC_DNS")).getOrElse(localHostName) private val className = Utils.getFormattedClassName(this) diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index 0fdfaf300e95d..a541d660cd5c6 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -34,6 +34,7 @@ import scala.util.Try import scala.util.control.{ControlThrowable, NonFatal} import com.google.common.io.{ByteStreams, Files} +import com.google.common.net.InetAddresses import com.google.common.util.concurrent.ThreadFactoryBuilder import org.apache.commons.lang3.SystemUtils import org.apache.hadoop.conf.Configuration @@ -789,13 +790,12 @@ private[spark] object Utils extends Logging { * Get the local host's IP address in dotted-quad format (e.g. 1.2.3.4). * Note, this is typically not used from within core spark. */ - lazy val localIpAddress: String = findLocalIpAddress() - lazy val localIpAddressHostname: String = getAddressHostName(localIpAddress) + private lazy val localIpAddress: InetAddress = findLocalInetAddress() - private def findLocalIpAddress(): String = { + private def findLocalInetAddress(): InetAddress = { val defaultIpOverride = System.getenv("SPARK_LOCAL_IP") if (defaultIpOverride != null) { - defaultIpOverride + InetAddress.getByName(defaultIpOverride) } else { val address = InetAddress.getLocalHost if (address.isLoopbackAddress) { @@ -806,15 +806,20 @@ private[spark] object Utils extends Logging { // It's more proper to pick ip address following system output order. val activeNetworkIFs = NetworkInterface.getNetworkInterfaces.toList val reOrderedNetworkIFs = if (isWindows) activeNetworkIFs else activeNetworkIFs.reverse + for (ni <- reOrderedNetworkIFs) { - for (addr <- ni.getInetAddresses if !addr.isLinkLocalAddress && - !addr.isLoopbackAddress && addr.isInstanceOf[Inet4Address]) { + val addresses = ni.getInetAddresses.toList + .filterNot(addr => addr.isLinkLocalAddress || addr.isLoopbackAddress) + if (addresses.nonEmpty) { + val addr = addresses.find(_.isInstanceOf[Inet4Address]).getOrElse(addresses.head) + // because of Inet6Address.toHostName may add interface at the end if it knows about it + val strippedAddress = InetAddress.getByAddress(addr.getAddress) // We've found an address that looks reasonable! logWarning("Your hostname, " + InetAddress.getLocalHost.getHostName + " resolves to" + - " a loopback address: " + address.getHostAddress + "; using " + addr.getHostAddress + - " instead (on interface " + ni.getName + ")") + " a loopback address: " + address.getHostAddress + "; using " + + strippedAddress.getHostAddress + " instead (on interface " + ni.getName + ")") logWarning("Set SPARK_LOCAL_IP if you need to bind to another address") - return addr.getHostAddress + return strippedAddress } } logWarning("Your hostname, " + InetAddress.getLocalHost.getHostName + " resolves to" + @@ -822,7 +827,7 @@ private[spark] object Utils extends Logging { " external IP address!") logWarning("Set SPARK_LOCAL_IP if you need to bind to another address") } - address.getHostAddress + address } } @@ -842,11 +847,14 @@ private[spark] object Utils extends Logging { * Get the local machine's hostname. */ def localHostName(): String = { - customHostname.getOrElse(localIpAddressHostname) + customHostname.getOrElse(localIpAddress.getHostAddress) } - def getAddressHostName(address: String): String = { - InetAddress.getByName(address).getHostName + /** + * Get the local machine's URI. + */ + def localHostNameForURI(): String = { + customHostname.getOrElse(InetAddresses.toUriString(localIpAddress)) } def checkHost(host: String, message: String = "") { diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala index 1bd1f324298e7..a7fe4476cacb8 100644 --- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala +++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala @@ -23,6 +23,7 @@ import org.apache.spark.Logging import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.Duration import org.apache.spark.streaming.receiver.Receiver +import org.apache.spark.util.Utils import com.amazonaws.auth.AWSCredentialsProvider import com.amazonaws.auth.DefaultAWSCredentialsProviderChain @@ -118,7 +119,7 @@ private[kinesis] class KinesisReceiver( * method. */ override def onStart() { - workerId = InetAddress.getLocalHost.getHostAddress() + ":" + UUID.randomUUID() + workerId = Utils.localHostName() + ":" + UUID.randomUUID() credentialsProvider = new DefaultAWSCredentialsProviderChain() kinesisClientLibConfiguration = new KinesisClientLibConfiguration(appName, streamName, credentialsProvider, workerId).withKinesisEndpoint(endpointUrl) diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala index 158c225159720..97b46a01ba5b4 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala @@ -22,6 +22,7 @@ import scala.collection.JavaConversions._ import org.apache.spark.scheduler.StatsReportListener import org.apache.spark.sql.hive.{HiveShim, HiveContext} import org.apache.spark.{Logging, SparkConf, SparkContext} +import org.apache.spark.util.Utils /** A singleton object for the master program. The slaves should not access this. */ private[hive] object SparkSQLEnv extends Logging { @@ -37,7 +38,7 @@ private[hive] object SparkSQLEnv extends Logging { val maybeKryoReferenceTracking = sparkConf.getOption("spark.kryo.referenceTracking") sparkConf - .setAppName(s"SparkSQL::${java.net.InetAddress.getLocalHost.getHostName}") + .setAppName(s"SparkSQL::${Utils.localHostName()}") .set("spark.sql.hive.version", HiveShim.version) .set( "spark.serializer", From 240ea03faea005306e73c10253716b95487325ff Mon Sep 17 00:00:00 2001 From: Pradeep Chanumolu Date: Mon, 13 Apr 2015 13:02:55 +0100 Subject: [PATCH 253/591] [SPARK-6671] Add status command for spark daemons SPARK-6671 Currently using the spark-daemon.sh script we can start and stop the spark demons. But we cannot get the status of the daemons. It will be nice to include the status command in the spark-daemon.sh script, through which we can know if the spark demon is alive or not. Author: Pradeep Chanumolu Closes #5327 from pchanumolu/master and squashes the following commits: d3a1f05 [Pradeep Chanumolu] Make status command check consistent with Stop command 5062926 [Pradeep Chanumolu] Fix indentation in spark-daemon.sh 3e66bc8 [Pradeep Chanumolu] SPARK-6671 : Add status command to spark daemons 1ac3918 [Pradeep Chanumolu] Add status command to spark-daemon --- sbin/spark-daemon.sh | 19 ++++++++++++++++++- 1 file changed, 18 insertions(+), 1 deletion(-) diff --git a/sbin/spark-daemon.sh b/sbin/spark-daemon.sh index 92e76a3fe6ca2..d8e0facb81169 100755 --- a/sbin/spark-daemon.sh +++ b/sbin/spark-daemon.sh @@ -29,7 +29,7 @@ # SPARK_NICENESS The scheduling priority for daemons. Defaults to 0. ## -usage="Usage: spark-daemon.sh [--config ] (start|stop) " +usage="Usage: spark-daemon.sh [--config ] (start|stop|status) " # if no args specified, show usage if [ $# -le 1 ]; then @@ -195,6 +195,23 @@ case $option in fi ;; + (status) + + if [ -f $pid ]; then + TARGET_ID="$(cat "$pid")" + if [[ $(ps -p "$TARGET_ID" -o comm=) =~ "java" ]]; then + echo $command is running. + exit 0 + else + echo $pid file is present but $command not running + exit 1 + fi + else + echo $command not running. + exit 2 + fi + ;; + (*) echo $usage exit 1 From 202ebf06e0f2d5df8b712e604fd95fa58e34ea20 Mon Sep 17 00:00:00 2001 From: linweizhong Date: Mon, 13 Apr 2015 13:06:54 +0100 Subject: [PATCH 254/591] [SPARK-6870][Yarn] Catch InterruptedException when yarn application state monitor thread been interrupted On PR #5305 we interrupt the monitor thread but forget to catch the InterruptedException, then in the log will print the stack info, so we need to catch it. Author: linweizhong Closes #5479 from Sephiroth-Lin/SPARK-6870 and squashes the following commits: f775f93 [linweizhong] Update, don't need to call Thread.currentThread() on monitor thread 0e2ef1f [linweizhong] Update 0d8958a [linweizhong] Update 3513fdb [linweizhong] Catch InterruptedException --- .../cluster/YarnClientSchedulerBackend.scala | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala index 407dc1ac4d37d..99c05329b4d73 100644 --- a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala +++ b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala @@ -128,10 +128,13 @@ private[spark] class YarnClientSchedulerBackend( assert(client != null && appId != null, "Application has not been submitted yet!") val t = new Thread { override def run() { - val (state, _) = client.monitorApplication(appId, logApplicationReport = false) - logError(s"Yarn application has already exited with state $state!") - sc.stop() - Thread.currentThread().interrupt() + try { + val (state, _) = client.monitorApplication(appId, logApplicationReport = false) + logError(s"Yarn application has already exited with state $state!") + sc.stop() + } catch { + case e: InterruptedException => logInfo("Interrupting monitor thread") + } } } t.setName("Yarn application state monitor") From b29663eeea440b1d1a288d41b5ddf67e77c5bd54 Mon Sep 17 00:00:00 2001 From: Pei-Lun Lee Date: Mon, 13 Apr 2015 21:52:00 +0800 Subject: [PATCH 255/591] [SPARK-6352] [SQL] Add DirectParquetOutputCommitter Add a DirectParquetOutputCommitter class that skips _temporary directory when saving to s3. Add new config value "spark.sql.parquet.useDirectParquetOutputCommitter" (default false) to choose between the default output committer. Author: Pei-Lun Lee Closes #5042 from ypcat/spark-6352 and squashes the following commits: e17bf47 [Pei-Lun Lee] Merge branch 'master' of https://github.com/apache/spark into spark-6352 9ae7545 [Pei-Lun Lee] [SPARL-6352] [SQL] Change to allow custom parquet output committer. 0d540b9 [Pei-Lun Lee] [SPARK-6352] [SQL] add license c42468c [Pei-Lun Lee] [SPARK-6352] [SQL] add test case 0fc03ca [Pei-Lun Lee] [SPARK-6532] [SQL] hide class DirectParquetOutputCommitter 769bd67 [Pei-Lun Lee] DirectParquetOutputCommitter f75e261 [Pei-Lun Lee] DirectParquetOutputCommitter --- .../DirectParquetOutputCommitter.scala | 66 +++++++++++++++++++ .../sql/parquet/ParquetTableOperations.scala | 22 +++++++ .../spark/sql/parquet/ParquetIOSuite.scala | 21 ++++++ 3 files changed, 109 insertions(+) create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala new file mode 100644 index 0000000000000..25a66cb488103 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala @@ -0,0 +1,66 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.parquet + +import org.apache.hadoop.fs.Path +import org.apache.hadoop.mapreduce.{JobContext, TaskAttemptContext} +import org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter + +import parquet.Log +import parquet.hadoop.util.ContextUtil +import parquet.hadoop.{ParquetFileReader, ParquetFileWriter, ParquetOutputCommitter} + +private[parquet] class DirectParquetOutputCommitter(outputPath: Path, context: TaskAttemptContext) + extends ParquetOutputCommitter(outputPath, context) { + val LOG = Log.getLog(classOf[ParquetOutputCommitter]) + + override def getWorkPath(): Path = outputPath + override def abortTask(taskContext: TaskAttemptContext): Unit = {} + override def commitTask(taskContext: TaskAttemptContext): Unit = {} + override def needsTaskCommit(taskContext: TaskAttemptContext): Boolean = true + override def setupJob(jobContext: JobContext): Unit = {} + override def setupTask(taskContext: TaskAttemptContext): Unit = {} + + override def commitJob(jobContext: JobContext) { + try { + val configuration = ContextUtil.getConfiguration(jobContext) + val fileSystem = outputPath.getFileSystem(configuration) + val outputStatus = fileSystem.getFileStatus(outputPath) + val footers = ParquetFileReader.readAllFootersInParallel(configuration, outputStatus) + try { + ParquetFileWriter.writeMetadataFile(configuration, outputPath, footers) + if (configuration.getBoolean("mapreduce.fileoutputcommitter.marksuccessfuljobs", true)) { + val successPath = new Path(outputPath, FileOutputCommitter.SUCCEEDED_FILE_NAME) + fileSystem.create(successPath).close() + } + } catch { + case e: Exception => { + LOG.warn("could not write summary file for " + outputPath, e) + val metadataPath = new Path(outputPath, ParquetFileWriter.PARQUET_METADATA_FILE) + if (fileSystem.exists(metadataPath)) { + fileSystem.delete(metadataPath, true) + } + } + } + } catch { + case e: Exception => LOG.warn("could not write summary file for " + outputPath, e) + } + } + +} + diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala index 1c868da23e060..3724bda829d30 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala @@ -379,6 +379,8 @@ private[sql] case class InsertIntoParquetTable( */ private[parquet] class AppendingParquetOutputFormat(offset: Int) extends parquet.hadoop.ParquetOutputFormat[Row] { + var committer: OutputCommitter = null + // override to accept existing directories as valid output directory override def checkOutputSpecs(job: JobContext): Unit = {} @@ -403,6 +405,26 @@ private[parquet] class AppendingParquetOutputFormat(offset: Int) private def getTaskAttemptID(context: TaskAttemptContext): TaskAttemptID = { context.getClass.getMethod("getTaskAttemptID").invoke(context).asInstanceOf[TaskAttemptID] } + + // override to create output committer from configuration + override def getOutputCommitter(context: TaskAttemptContext): OutputCommitter = { + if (committer == null) { + val output = getOutputPath(context) + val cls = context.getConfiguration.getClass("spark.sql.parquet.output.committer.class", + classOf[ParquetOutputCommitter], classOf[ParquetOutputCommitter]) + val ctor = cls.getDeclaredConstructor(classOf[Path], classOf[TaskAttemptContext]) + committer = ctor.newInstance(output, context).asInstanceOf[ParquetOutputCommitter] + } + committer + } + + // FileOutputFormat.getOutputPath takes JobConf in hadoop-1 but JobContext in hadoop-2 + private def getOutputPath(context: TaskAttemptContext): Path = { + context.getConfiguration().get("mapred.output.dir") match { + case null => null + case name => new Path(name) + } + } } /** diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala index 97c0f439acf13..4d0bf7cf99cdf 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala @@ -381,6 +381,27 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest { } } } + + test("SPARK-6352 DirectParquetOutputCommitter") { + // Write to a parquet file and let it fail. + // _temporary should be missing if direct output committer works. + try { + configuration.set("spark.sql.parquet.output.committer.class", + "org.apache.spark.sql.parquet.DirectParquetOutputCommitter") + sqlContext.udf.register("div0", (x: Int) => x / 0) + withTempPath { dir => + intercept[org.apache.spark.SparkException] { + sqlContext.sql("select div0(1)").saveAsParquetFile(dir.getCanonicalPath) + } + val path = new Path(dir.getCanonicalPath, "_temporary") + val fs = path.getFileSystem(configuration) + assert(!fs.exists(path)) + } + } + finally { + configuration.unset("spark.sql.parquet.output.committer.class") + } + } } class ParquetDataSourceOnIOSuite extends ParquetIOSuiteBase with BeforeAndAfterAll { From 77620be76e82b6cdaae406cd752d3272656f5fe0 Mon Sep 17 00:00:00 2001 From: Doug Balog Date: Mon, 13 Apr 2015 09:49:58 -0500 Subject: [PATCH 256/591] [SPARK-6207] [YARN] [SQL] Adds delegation tokens for metastore to conf. Adds hive2-metastore delegation token to conf when running in secure mode. Without this change, running on YARN in cluster mode fails with a GSS exception. This is a rough patch that adds a dependency to spark/yarn on hive-exec. I'm looking for suggestions on how to make this patch better. This contribution is my original work and that I licenses the work to the Apache Spark project under the project's open source licenses. Author: Doug Balog Author: Doug Balog Closes #5031 from dougb/SPARK-6207 and squashes the following commits: 3e9ac16 [Doug Balog] [SPARK-6207] Fixes minor code spacing issues. e260765 [Doug Balog] [SPARK-6207] Second pass at adding Hive delegation token to conf. - Use reflection instead of adding dependency on hive. - Tested on Hive 0.13 and Hadoop 2.4.1 1ab1729 [Doug Balog] Merge branch 'master' of git://github.com/apache/spark into SPARK-6207 bf356d2 [Doug Balog] [SPARK-6207] [YARN] [SQL] Adds delegation tokens for metastore to conf. Adds hive2-metastore delagations token to conf when running in securemode. Without this change, runing on YARN in cluster mode fails with a GSS exception. --- .../org/apache/spark/deploy/yarn/Client.scala | 63 +++++++++++++++++++ 1 file changed, 63 insertions(+) diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index c1effd3c8a718..1091ff54b0463 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -22,17 +22,21 @@ import java.nio.ByteBuffer import scala.collection.JavaConversions._ import scala.collection.mutable.{ArrayBuffer, HashMap, ListBuffer, Map} +import scala.reflect.runtime.universe import scala.util.{Try, Success, Failure} import com.google.common.base.Objects import org.apache.hadoop.io.DataOutputBuffer import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.hdfs.security.token.delegation.DelegationTokenIdentifier import org.apache.hadoop.fs._ import org.apache.hadoop.fs.permission.FsPermission +import org.apache.hadoop.io.Text import org.apache.hadoop.mapred.Master import org.apache.hadoop.mapreduce.MRJobConfig import org.apache.hadoop.security.{Credentials, UserGroupInformation} +import org.apache.hadoop.security.token.Token import org.apache.hadoop.util.StringUtils import org.apache.hadoop.yarn.api._ import org.apache.hadoop.yarn.api.ApplicationConstants.Environment @@ -220,6 +224,7 @@ private[spark] class Client( val dst = new Path(fs.getHomeDirectory(), appStagingDir) val nns = getNameNodesToAccess(sparkConf) + dst obtainTokensForNamenodes(nns, hadoopConf, credentials) + obtainTokenForHiveMetastore(hadoopConf, credentials) val replication = sparkConf.getInt("spark.yarn.submit.file.replication", fs.getDefaultReplication(dst)).toShort @@ -936,6 +941,64 @@ object Client extends Logging { } } + /** + * Obtains token for the Hive metastore and adds them to the credentials. + */ + private def obtainTokenForHiveMetastore(conf: Configuration, credentials: Credentials) { + if (UserGroupInformation.isSecurityEnabled) { + val mirror = universe.runtimeMirror(getClass.getClassLoader) + + try { + val hiveClass = mirror.classLoader.loadClass("org.apache.hadoop.hive.ql.metadata.Hive") + val hive = hiveClass.getMethod("get").invoke(null) + + val hiveConf = hiveClass.getMethod("getConf").invoke(hive) + val hiveConfClass = mirror.classLoader.loadClass("org.apache.hadoop.hive.conf.HiveConf") + + val hiveConfGet = (param:String) => Option(hiveConfClass + .getMethod("get", classOf[java.lang.String]) + .invoke(hiveConf, param)) + + val metastore_uri = hiveConfGet("hive.metastore.uris") + + // Check for local metastore + if (metastore_uri != None && metastore_uri.get.toString.size > 0) { + val metastore_kerberos_principal_conf_var = mirror.classLoader + .loadClass("org.apache.hadoop.hive.conf.HiveConf$ConfVars") + .getField("METASTORE_KERBEROS_PRINCIPAL").get("varname").toString + + val principal = hiveConfGet(metastore_kerberos_principal_conf_var) + + val username = Option(UserGroupInformation.getCurrentUser().getUserName) + if (principal != None && username != None) { + val tokenStr = hiveClass.getMethod("getDelegationToken", + classOf[java.lang.String], classOf[java.lang.String]) + .invoke(hive, username.get, principal.get).asInstanceOf[java.lang.String] + + val hive2Token = new Token[DelegationTokenIdentifier]() + hive2Token.decodeFromUrlString(tokenStr) + credentials.addToken(new Text("hive.server2.delegation.token"),hive2Token) + logDebug("Added hive.Server2.delegation.token to conf.") + hiveClass.getMethod("closeCurrent").invoke(null) + } else { + logError("Username or principal == NULL") + logError(s"""username=${username.getOrElse("(NULL)")}""") + logError(s"""principal=${principal.getOrElse("(NULL)")}""") + throw new IllegalArgumentException("username and/or principal is equal to null!") + } + } else { + logDebug("HiveMetaStore configured in localmode") + } + } catch { + case e:java.lang.NoSuchMethodException => { logInfo("Hive Method not found " + e); return } + case e:java.lang.ClassNotFoundException => { logInfo("Hive Class not found " + e); return } + case e:Exception => { logError("Unexpected Exception " + e) + throw new RuntimeException("Unexpected exception", e) + } + } + } + } + /** * Return whether the two file systems are the same. */ From c5b0b296b842926b5c07531a5affe8984bc799c5 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Mon, 13 Apr 2015 09:29:04 -0700 Subject: [PATCH 257/591] [SPARK-6765] Enable scalastyle on test code. Turn scalastyle on for all test code. Most of the violations have been resolved in my previous pull requests: Core: https://github.com/apache/spark/pull/5484 SQL: https://github.com/apache/spark/pull/5412 MLlib: https://github.com/apache/spark/pull/5411 GraphX: https://github.com/apache/spark/pull/5410 Streaming: https://github.com/apache/spark/pull/5409 Author: Reynold Xin Closes #5486 from rxin/test-style-enable and squashes the following commits: 01683de [Reynold Xin] Fixed new code. a4ab46e [Reynold Xin] Fixed tests. 20adbc8 [Reynold Xin] Missed one violation. 5e36521 [Reynold Xin] [SPARK-6765] Enable scalastyle on test code. --- .../org/apache/spark/deploy/ClientSuite.scala | 1 - dev/scalastyle | 5 ++-- .../spark/ml/feature/VectorIndexerSuite.scala | 3 +- .../sql/hive/thriftserver/CliSuite.scala | 15 +++++----- .../HiveThriftServer2Suites.scala | 7 +++-- .../spark/sql/hive/QueryPartitionSuite.scala | 30 ++++++++++--------- .../StreamingJobProgressListenerSuite.scala | 4 +-- .../spark/deploy/yarn/ClientSuite.scala | 19 ++++++++---- .../deploy/yarn/YarnAllocatorSuite.scala | 6 ++-- .../yarn/YarnSparkHadoopUtilSuite.scala | 2 +- 10 files changed, 52 insertions(+), 40 deletions(-) diff --git a/core/src/test/scala/org/apache/spark/deploy/ClientSuite.scala b/core/src/test/scala/org/apache/spark/deploy/ClientSuite.scala index 518073dcbb64e..745f9eeee7536 100644 --- a/core/src/test/scala/org/apache/spark/deploy/ClientSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/ClientSuite.scala @@ -46,5 +46,4 @@ class ClientSuite extends FunSuite with Matchers { // Invalid syntax. ClientArguments.isValidJarUrl("hdfs:") should be (false) } - } diff --git a/dev/scalastyle b/dev/scalastyle index 86919227ed1ab..4e03f89ed5d5d 100755 --- a/dev/scalastyle +++ b/dev/scalastyle @@ -18,9 +18,10 @@ # echo -e "q\n" | build/sbt -Phive -Phive-thriftserver scalastyle > scalastyle.txt +echo -e "q\n" | build/sbt -Phive -Phive-thriftserver test:scalastyle >> scalastyle.txt # Check style with YARN built too -echo -e "q\n" | build/sbt -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 scalastyle \ - >> scalastyle.txt +echo -e "q\n" | build/sbt -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 scalastyle >> scalastyle.txt +echo -e "q\n" | build/sbt -Pyarn -Phadoop-2.2 -Dhadoop.version=2.2.0 test:scalastyle >> scalastyle.txt ERRORS=$(cat scalastyle.txt | awk '{if($1~/error/)print}') rm scalastyle.txt diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala index 61c46c85a78b5..81ef831c42e55 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala @@ -150,7 +150,8 @@ class VectorIndexerSuite extends FunSuite with MLlibTestSparkContext { val vectorIndexer = getIndexer.setMaxCategories(maxCategories) val model = vectorIndexer.fit(data) val categoryMaps = model.categoryMaps - assert(categoryMaps.keys.toSet === categoricalFeatures) // Chose correct categorical features + // Chose correct categorical features + assert(categoryMaps.keys.toSet === categoricalFeatures) val transformed = model.transform(data).select("indexed") val indexedRDD: RDD[Vector] = transformed.map(_.getAs[Vector](0)) val featureAttrs = AttributeGroup.fromStructField(transformed.schema("indexed")) diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala index 75738fa22b572..6d1d7c3a4e698 100644 --- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala +++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala @@ -1,13 +1,12 @@ /* - * Licensed to the Apache Software Foundation (ASF) under one - * or more contributor license agreements. See the NOTICE file - * distributed with this work for additional information - * regarding copyright ownership. The ASF licenses this file - * to you under the Apache License, Version 2.0 (the - * "License"); you may not use this file except in compliance - * with the License. You may obtain a copy of the License at + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at * - * http://www.apache.org/licenses/LICENSE-2.0 + * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala index bf20acecb1f32..4cf95e7bdfb2b 100644 --- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala +++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.hive.thriftserver import java.io.File +import java.net.URL import java.sql.{Date, DriverManager, Statement} import scala.collection.mutable.ArrayBuffer @@ -41,7 +42,7 @@ import org.apache.spark.sql.hive.HiveShim import org.apache.spark.util.Utils object TestData { - def getTestDataFilePath(name: String) = { + def getTestDataFilePath(name: String): URL = { Thread.currentThread().getContextClassLoader.getResource(s"data/files/$name") } @@ -50,7 +51,7 @@ object TestData { } class HiveThriftBinaryServerSuite extends HiveThriftJdbcTest { - override def mode = ServerMode.binary + override def mode: ServerMode.Value = ServerMode.binary private def withCLIServiceClient(f: ThriftCLIServiceClient => Unit): Unit = { // Transport creation logics below mimics HiveConnection.createBinaryTransport @@ -337,7 +338,7 @@ class HiveThriftBinaryServerSuite extends HiveThriftJdbcTest { } class HiveThriftHttpServerSuite extends HiveThriftJdbcTest { - override def mode = ServerMode.http + override def mode: ServerMode.Value = ServerMode.http test("JDBC query execution") { withJdbcStatement { statement => diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala index 83f97128c5e83..a787fa5546e76 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala @@ -17,15 +17,12 @@ package org.apache.spark.sql.hive -import java.io.File - import com.google.common.io.Files + import org.apache.spark.sql.{QueryTest, _} import org.apache.spark.sql.hive.test.TestHive -import org.apache.spark.util.Utils -/* Implicits */ import org.apache.spark.sql.hive.test.TestHive._ - +import org.apache.spark.util.Utils class QueryPartitionSuite extends QueryTest { @@ -37,23 +34,28 @@ class QueryPartitionSuite extends QueryTest { testData.registerTempTable("testData") val tmpDir = Files.createTempDir() - //create the table for test - sql(s"CREATE TABLE table_with_partition(key int,value string) PARTITIONED by (ds string) location '${tmpDir.toURI.toString}' ") - sql("INSERT OVERWRITE TABLE table_with_partition partition (ds='1') SELECT key,value FROM testData") - sql("INSERT OVERWRITE TABLE table_with_partition partition (ds='2') SELECT key,value FROM testData") - sql("INSERT OVERWRITE TABLE table_with_partition partition (ds='3') SELECT key,value FROM testData") - sql("INSERT OVERWRITE TABLE table_with_partition partition (ds='4') SELECT key,value FROM testData") + // create the table for test + sql(s"CREATE TABLE table_with_partition(key int,value string) " + + s"PARTITIONED by (ds string) location '${tmpDir.toURI.toString}' ") + sql("INSERT OVERWRITE TABLE table_with_partition partition (ds='1') " + + "SELECT key,value FROM testData") + sql("INSERT OVERWRITE TABLE table_with_partition partition (ds='2') " + + "SELECT key,value FROM testData") + sql("INSERT OVERWRITE TABLE table_with_partition partition (ds='3') " + + "SELECT key,value FROM testData") + sql("INSERT OVERWRITE TABLE table_with_partition partition (ds='4') " + + "SELECT key,value FROM testData") - //test for the exist path + // test for the exist path checkAnswer(sql("select key,value from table_with_partition"), testData.toSchemaRDD.collect ++ testData.toSchemaRDD.collect ++ testData.toSchemaRDD.collect ++ testData.toSchemaRDD.collect) - //delect the path of one partition + // delete the path of one partition val folders = tmpDir.listFiles.filter(_.isDirectory) Utils.deleteRecursively(folders(0)) - //test for affter delete the path + // test for after delete the path checkAnswer(sql("select key,value from table_with_partition"), testData.toSchemaRDD.collect ++ testData.toSchemaRDD.collect ++ testData.toSchemaRDD.collect) diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala index 2b9d164500b72..94b1985116feb 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala @@ -21,14 +21,14 @@ import org.scalatest.Matchers import org.apache.spark.streaming.dstream.DStream import org.apache.spark.streaming.scheduler._ -import org.apache.spark.streaming.{Time, Milliseconds, TestSuiteBase} +import org.apache.spark.streaming.{Duration, Time, Milliseconds, TestSuiteBase} class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { val input = (1 to 4).map(Seq(_)).toSeq val operation = (d: DStream[Int]) => d.map(x => x) - override def batchDuration = Milliseconds(100) + override def batchDuration: Duration = Milliseconds(100) test("onBatchSubmitted, onBatchStarted, onBatchCompleted, " + "onReceiverStarted, onReceiverError, onReceiverStopped") { diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala index 92f04b4b859b3..c1b94ac9c5bdd 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala @@ -232,19 +232,26 @@ class ClientSuite extends FunSuite with Matchers with BeforeAndAfterAll { testCode(conf) } - def newEnv = MutableHashMap[String, String]() + def newEnv: MutableHashMap[String, String] = MutableHashMap[String, String]() - def classpath(env: MutableHashMap[String, String]) = env(Environment.CLASSPATH.name).split(":|;|") + def classpath(env: MutableHashMap[String, String]): Array[String] = + env(Environment.CLASSPATH.name).split(":|;|") - def flatten(a: Option[Seq[String]], b: Option[Seq[String]]) = (a ++ b).flatten.toArray + def flatten(a: Option[Seq[String]], b: Option[Seq[String]]): Array[String] = + (a ++ b).flatten.toArray - def getFieldValue[A, B](clazz: Class[_], field: String, defaults: => B)(mapTo: A => B): B = - Try(clazz.getField(field)).map(_.get(null).asInstanceOf[A]).toOption.map(mapTo).getOrElse(defaults) + def getFieldValue[A, B](clazz: Class[_], field: String, defaults: => B)(mapTo: A => B): B = { + Try(clazz.getField(field)) + .map(_.get(null).asInstanceOf[A]) + .toOption + .map(mapTo) + .getOrElse(defaults) + } def getFieldValue2[A: ClassTag, A1: ClassTag, B]( clazz: Class[_], field: String, - defaults: => B)(mapTo: A => B)(mapTo1: A1 => B) : B = { + defaults: => B)(mapTo: A => B)(mapTo1: A1 => B): B = { Try(clazz.getField(field)).map(_.get(null)).map { case v: A => mapTo(v) case v1: A1 => mapTo1(v1) diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala index c09b01bafce37..455f1019d86dd 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala @@ -79,7 +79,7 @@ class YarnAllocatorSuite extends FunSuite with Matchers with BeforeAndAfterEach } class MockSplitInfo(host: String) extends SplitInfo(null, host, null, 1, null) { - override def equals(other: Any) = false + override def equals(other: Any): Boolean = false } def createAllocator(maxExecutors: Int = 5): YarnAllocator = { @@ -118,7 +118,9 @@ class YarnAllocatorSuite extends FunSuite with Matchers with BeforeAndAfterEach handler.getNumExecutorsRunning should be (1) handler.allocatedContainerToHostMap.get(container.getId).get should be ("host1") handler.allocatedHostToContainersMap.get("host1").get should contain (container.getId) - rmClient.getMatchingRequests(container.getPriority, "host1", containerResource).size should be (0) + + val size = rmClient.getMatchingRequests(container.getPriority, "host1", containerResource).size + size should be (0) } test("some containers allocated") { diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala index 4194f36499e66..9395316b71ff4 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala @@ -46,7 +46,7 @@ class YarnSparkHadoopUtilSuite extends FunSuite with Matchers with Logging { logWarning("Cannot execute bash, skipping bash tests.") } - def bashTest(name: String)(fn: => Unit) = + def bashTest(name: String)(fn: => Unit): Unit = if (hasBash) test(name)(fn) else ignore(name)(fn) bashTest("shell script escaping") { From 6cc5b3ed3c0c729f97956fa017d8eb7d6b43f90f Mon Sep 17 00:00:00 2001 From: Cheolsoo Park Date: Mon, 13 Apr 2015 13:45:10 -0500 Subject: [PATCH 258/591] [SPARK-6662][YARN] Allow variable substitution in spark.yarn.historyServer.address In Spark on YARN, explicit hostname and port number need to be set for "spark.yarn.historyServer.address" in SparkConf to make the HISTORY link. If the history server address is known and static, this is usually not a problem. But in cloud, that is usually not true. Particularly in EMR, the history server always runs on the same node as with RM. So I could simply set it to ${yarn.resourcemanager.hostname}:18080 if variable substitution is allowed. In fact, Hadoop configuration already implements variable substitution, so if this property is read via YarnConf, this can be easily achievable. Author: Cheolsoo Park Closes #5321 from piaozhexiu/SPARK-6662 and squashes the following commits: e37de75 [Cheolsoo Park] Preserve the space between the Hadoop and Spark imports 79757c6 [Cheolsoo Park] Incorporate review comments 10e2917 [Cheolsoo Park] Add helper function that substitutes hadoop vars to SparkHadoopUtil 589b52c [Cheolsoo Park] Revert "Allow variable substitution for spark.yarn. properties" ff9c35d [Cheolsoo Park] Allow variable substitution for spark.yarn. properties --- .../apache/spark/deploy/SparkHadoopUtil.scala | 38 +++++++++++++++++-- docs/running-on-yarn.md | 3 +- .../spark/deploy/yarn/ApplicationMaster.scala | 1 + 3 files changed, 37 insertions(+), 5 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala index c2568eb4b60ac..cfaebf9ea5050 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala @@ -24,11 +24,10 @@ import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FileStatus, FileSystem, Path} import org.apache.hadoop.fs.FileSystem.Statistics import org.apache.hadoop.mapred.JobConf -import org.apache.hadoop.mapreduce.{JobContext, TaskAttemptContext} -import org.apache.hadoop.security.Credentials -import org.apache.hadoop.security.UserGroupInformation +import org.apache.hadoop.mapreduce.JobContext +import org.apache.hadoop.security.{Credentials, UserGroupInformation} -import org.apache.spark.{Logging, SparkContext, SparkConf, SparkException} +import org.apache.spark.{Logging, SparkConf, SparkException} import org.apache.spark.annotation.DeveloperApi import org.apache.spark.util.Utils @@ -201,6 +200,37 @@ class SparkHadoopUtil extends Logging { val baseStatus = fs.getFileStatus(basePath) if (baseStatus.isDir) recurse(basePath) else Array(baseStatus) } + + private val HADOOP_CONF_PATTERN = "(\\$\\{hadoopconf-[^\\}\\$\\s]+\\})".r.unanchored + + /** + * Substitute variables by looking them up in Hadoop configs. Only variables that match the + * ${hadoopconf- .. } pattern are substituted. + */ + def substituteHadoopVariables(text: String, hadoopConf: Configuration): String = { + text match { + case HADOOP_CONF_PATTERN(matched) => { + logDebug(text + " matched " + HADOOP_CONF_PATTERN) + val key = matched.substring(13, matched.length() - 1) // remove ${hadoopconf- .. } + val eval = Option[String](hadoopConf.get(key)) + .map { value => + logDebug("Substituted " + matched + " with " + value) + text.replace(matched, value) + } + if (eval.isEmpty) { + // The variable was not found in Hadoop configs, so return text as is. + text + } else { + // Continue to substitute more variables. + substituteHadoopVariables(eval.get, hadoopConf) + } + } + case _ => { + logDebug(text + " didn't match " + HADOOP_CONF_PATTERN) + text + } + } + } } object SparkHadoopUtil { diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md index b7e68d4f71714..ed5bb263a5809 100644 --- a/docs/running-on-yarn.md +++ b/docs/running-on-yarn.md @@ -87,7 +87,8 @@ Most of the configs are the same for Spark on YARN as for other deployment modes spark.yarn.historyServer.address (none) - The address of the Spark history server (i.e. host.com:18080). The address should not contain a scheme (http://). Defaults to not being set since the history server is an optional service. This address is given to the YARN ResourceManager when the Spark application finishes to link the application from the ResourceManager UI to the Spark history server UI. + The address of the Spark history server (i.e. host.com:18080). The address should not contain a scheme (http://). Defaults to not being set since the history server is an optional service. This address is given to the YARN ResourceManager when the Spark application finishes to link the application from the ResourceManager UI to the Spark history server UI. + For this property, YARN properties can be used as variables, and these are substituted by Spark at runtime. For eg, if the Spark history server runs on the same node as the YARN ResourceManager, it can be set to `${hadoopconf-yarn.resourcemanager.hostname}:18080`. diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala index 32bc4e5663062..26259cee77151 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -223,6 +223,7 @@ private[spark] class ApplicationMaster( val appId = client.getAttemptId().getApplicationId().toString() val historyAddress = sparkConf.getOption("spark.yarn.historyServer.address") + .map { text => SparkHadoopUtil.get.substituteHadoopVariables(text, yarnConf) } .map { address => s"${address}${HistoryServer.UI_PATH_PREFIX}/${appId}" } .getOrElse("") From 1e340c3ae4d5361d048a3d6990f144cfc923666f Mon Sep 17 00:00:00 2001 From: Xusen Yin Date: Mon, 13 Apr 2015 11:53:17 -0700 Subject: [PATCH 259/591] [SPARK-5988][MLlib] add save/load for PowerIterationClusteringModel See JIRA issue [SPARK-5988](https://issues.apache.org/jira/browse/SPARK-5988). Author: Xusen Yin Closes #5450 from yinxusen/SPARK-5988 and squashes the following commits: cb1ecfa [Xusen Yin] change Assignment into case class b1dd24c [Xusen Yin] add test suite 63c3923 [Xusen Yin] add save load for power iteration clustering --- .../clustering/PowerIterationClustering.scala | 68 +++++++++++++++++-- .../PowerIterationClusteringSuite.scala | 34 ++++++++++ 2 files changed, 97 insertions(+), 5 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala index 180023922a9b0..aa53e88d59856 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala @@ -17,15 +17,20 @@ package org.apache.spark.mllib.clustering -import org.apache.spark.{Logging, SparkException} +import org.json4s.JsonDSL._ +import org.json4s._ +import org.json4s.jackson.JsonMethods._ + import org.apache.spark.annotation.Experimental import org.apache.spark.api.java.JavaRDD import org.apache.spark.graphx._ import org.apache.spark.graphx.impl.GraphImpl import org.apache.spark.mllib.linalg.Vectors -import org.apache.spark.mllib.util.MLUtils +import org.apache.spark.mllib.util.{Loader, MLUtils, Saveable} import org.apache.spark.rdd.RDD +import org.apache.spark.sql.{Row, SQLContext} import org.apache.spark.util.random.XORShiftRandom +import org.apache.spark.{Logging, SparkContext, SparkException} /** * :: Experimental :: @@ -38,7 +43,60 @@ import org.apache.spark.util.random.XORShiftRandom @Experimental class PowerIterationClusteringModel( val k: Int, - val assignments: RDD[PowerIterationClustering.Assignment]) extends Serializable + val assignments: RDD[PowerIterationClustering.Assignment]) extends Saveable with Serializable { + + override def save(sc: SparkContext, path: String): Unit = { + PowerIterationClusteringModel.SaveLoadV1_0.save(sc, this, path) + } + + override protected def formatVersion: String = "1.0" +} + +object PowerIterationClusteringModel extends Loader[PowerIterationClusteringModel] { + override def load(sc: SparkContext, path: String): PowerIterationClusteringModel = { + PowerIterationClusteringModel.SaveLoadV1_0.load(sc, path) + } + + private[clustering] + object SaveLoadV1_0 { + + private val thisFormatVersion = "1.0" + + private[clustering] + val thisClassName = "org.apache.spark.mllib.clustering.PowerIterationClusteringModel" + + def save(sc: SparkContext, model: PowerIterationClusteringModel, path: String): Unit = { + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + + val metadata = compact(render( + ("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~ ("k" -> model.k))) + sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path)) + + val dataRDD = model.assignments.toDF() + dataRDD.saveAsParquetFile(Loader.dataPath(path)) + } + + def load(sc: SparkContext, path: String): PowerIterationClusteringModel = { + implicit val formats = DefaultFormats + val sqlContext = new SQLContext(sc) + + val (className, formatVersion, metadata) = Loader.loadMetadata(sc, path) + assert(className == thisClassName) + assert(formatVersion == thisFormatVersion) + + val k = (metadata \ "k").extract[Int] + val assignments = sqlContext.parquetFile(Loader.dataPath(path)) + Loader.checkSchema[PowerIterationClustering.Assignment](assignments.schema) + + val assignmentsRDD = assignments.map { + case Row(id: Long, cluster: Int) => PowerIterationClustering.Assignment(id, cluster) + } + + new PowerIterationClusteringModel(k, assignmentsRDD) + } + } +} /** * :: Experimental :: @@ -135,7 +193,7 @@ class PowerIterationClustering private[clustering] ( val v = powerIter(w, maxIterations) val assignments = kMeans(v, k).mapPartitions({ iter => iter.map { case (id, cluster) => - new Assignment(id, cluster) + Assignment(id, cluster) } }, preservesPartitioning = true) new PowerIterationClusteringModel(k, assignments) @@ -152,7 +210,7 @@ object PowerIterationClustering extends Logging { * @param cluster assigned cluster id */ @Experimental - class Assignment(val id: Long, val cluster: Int) extends Serializable + case class Assignment(id: Long, cluster: Int) /** * Normalizes the affinity matrix (A) by row sums and returns the normalized affinity matrix (W). diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala index 6315c03a700f1..6d6fe6fe46bab 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/PowerIterationClusteringSuite.scala @@ -18,12 +18,15 @@ package org.apache.spark.mllib.clustering import scala.collection.mutable +import scala.util.Random import org.scalatest.FunSuite +import org.apache.spark.SparkContext import org.apache.spark.graphx.{Edge, Graph} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.util.Utils class PowerIterationClusteringSuite extends FunSuite with MLlibTestSparkContext { @@ -110,4 +113,35 @@ class PowerIterationClusteringSuite extends FunSuite with MLlibTestSparkContext assert(x ~== u1(i.toInt) absTol 1e-14) } } + + test("model save/load") { + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + val model = PowerIterationClusteringSuite.createModel(sc, 3, 10) + try { + model.save(sc, path) + val sameModel = PowerIterationClusteringModel.load(sc, path) + PowerIterationClusteringSuite.checkEqual(model, sameModel) + } finally { + Utils.deleteRecursively(tempDir) + } + } +} + +object PowerIterationClusteringSuite extends FunSuite { + def createModel(sc: SparkContext, k: Int, nPoints: Int): PowerIterationClusteringModel = { + val assignments = sc.parallelize( + (0 until nPoints).map(p => PowerIterationClustering.Assignment(p, Random.nextInt(k)))) + new PowerIterationClusteringModel(k, assignments) + } + + def checkEqual(a: PowerIterationClusteringModel, b: PowerIterationClusteringModel): Unit = { + assert(a.k === b.k) + + val aAssignments = a.assignments.map(x => (x.id, x.cluster)) + val bAssignments = b.assignments.map(x => (x.id, x.cluster)) + val unequalElements = aAssignments.join(bAssignments).filter { + case (id, (c1, c2)) => c1 != c2 }.count() + assert(unequalElements === 0L) + } } From 85ee0cabe87a27b6947c2d3e8525f04c77f80f6f Mon Sep 17 00:00:00 2001 From: Daoyuan Wang Date: Mon, 13 Apr 2015 14:29:07 -0700 Subject: [PATCH 260/591] [SPARK-6130] [SQL] support if not exists for insert overwrite into partition in hiveQl MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Standard syntax: INSERT OVERWRITE TABLE tablename1 [PARTITION (partcol1=val1, partcol2=val2 ...) [IF NOT EXISTS]] select_statement1 FROM from_statement; INSERT INTO TABLE tablename1 [PARTITION (partcol1=val1, partcol2=val2 ...)] select_statement1 FROM from_statement;   Hive extension (multiple inserts): FROM from_statement INSERT OVERWRITE TABLE tablename1 [PARTITION (partcol1=val1, partcol2=val2 ...) [IF NOT EXISTS]] select_statement1 [INSERT OVERWRITE TABLE tablename2 [PARTITION ... [IF NOT EXISTS]] select_statement2] [INSERT INTO TABLE tablename2 [PARTITION ...] select_statement2] ...; FROM from_statement INSERT INTO TABLE tablename1 [PARTITION (partcol1=val1, partcol2=val2 ...)] select_statement1 [INSERT INTO TABLE tablename2 [PARTITION ...] select_statement2] [INSERT OVERWRITE TABLE tablename2 [PARTITION ... [IF NOT EXISTS]] select_statement2] ...;   Hive extension (dynamic partition inserts): INSERT OVERWRITE TABLE tablename PARTITION (partcol1[=val1], partcol2[=val2] ...) select_statement FROM from_statement; INSERT INTO TABLE tablename PARTITION (partcol1[=val1], partcol2[=val2] ...) select_statement FROM from_statement; Author: Daoyuan Wang Closes #4865 from adrian-wang/insertoverwrite and squashes the following commits: 2fce94f [Daoyuan Wang] add assert 10ea6f3 [Daoyuan Wang] add name for boolean parameter 0bbe9b9 [Daoyuan Wang] fix failure 4391154 [Daoyuan Wang] support if not exists for insert overwrite into partition in hiveQl --- .../apache/spark/sql/catalyst/SqlParser.scala | 2 +- .../sql/catalyst/analysis/Analyzer.scala | 2 +- .../spark/sql/catalyst/dsl/package.scala | 2 +- .../plans/logical/basicOperators.scala | 4 ++- .../org/apache/spark/sql/DataFrame.scala | 2 +- .../spark/sql/execution/SparkStrategies.scala | 3 +- .../sql/sources/DataSourceStrategy.scala | 2 +- .../org/apache/spark/sql/sources/rules.scala | 7 +++-- .../execution/HiveCompatibilitySuite.scala | 1 + .../spark/sql/hive/HiveMetastoreCatalog.scala | 19 ++++++------ .../org/apache/spark/sql/hive/HiveQl.scala | 22 ++++++++++++- .../spark/sql/hive/HiveStrategies.scala | 10 +++--- .../hive/execution/CreateTableAsSelect.scala | 2 +- .../hive/execution/InsertIntoHiveTable.scala | 31 +++++++++++++------ ...titions-0-d5edc0daa94b33915df794df3b710774 | 0 ...titions-1-9eb9372f4855928fae16f5fa554b3a62 | 0 ...itions-10-ec2cef3d37146c450c60202a572f5cab | 0 ...itions-11-8854d6001200fc11529b2e2da755e5a2 | 0 ...itions-12-71ff68fda0aa7a36cb50d8fab0d70d25 | 0 ...titions-13-7e4e7d7003fc6ef17bc19c3461ad899 | 0 ...itions-14-ec2cef3d37146c450c60202a572f5cab | 0 ...itions-15-a3b2e230efde74e970ae8a3b55f383fc | 0 ...titions-2-8396c17a66e3d9a374d4361873b9bfe3 | 0 ...titions-3-3876bb356dd8af7e78d061093d555457 | 0 ...rtitions-4-528e23afb272c2e69004c86ddaa70ee | 0 ...titions-5-de5d56456c28d63775554e56355911d2 | 0 ...titions-6-3efdc331b3b4bdac3e60c757600fff53 | 5 +++ ...titions-7-92f6af82704504968de078c133f222f8 | 0 ...titions-8-316cad7c63ddd4fb043be2affa5b0a67 | 0 ...titions-9-3efdc331b3b4bdac3e60c757600fff53 | 5 +++ 30 files changed, 84 insertions(+), 35 deletions(-) create mode 100644 sql/hive/src/test/resources/golden/insert1_overwrite_partitions-0-d5edc0daa94b33915df794df3b710774 create mode 100644 sql/hive/src/test/resources/golden/insert1_overwrite_partitions-1-9eb9372f4855928fae16f5fa554b3a62 create mode 100644 sql/hive/src/test/resources/golden/insert1_overwrite_partitions-10-ec2cef3d37146c450c60202a572f5cab create mode 100644 sql/hive/src/test/resources/golden/insert1_overwrite_partitions-11-8854d6001200fc11529b2e2da755e5a2 create mode 100644 sql/hive/src/test/resources/golden/insert1_overwrite_partitions-12-71ff68fda0aa7a36cb50d8fab0d70d25 create mode 100644 sql/hive/src/test/resources/golden/insert1_overwrite_partitions-13-7e4e7d7003fc6ef17bc19c3461ad899 create mode 100644 sql/hive/src/test/resources/golden/insert1_overwrite_partitions-14-ec2cef3d37146c450c60202a572f5cab create mode 100644 sql/hive/src/test/resources/golden/insert1_overwrite_partitions-15-a3b2e230efde74e970ae8a3b55f383fc create mode 100644 sql/hive/src/test/resources/golden/insert1_overwrite_partitions-2-8396c17a66e3d9a374d4361873b9bfe3 create mode 100644 sql/hive/src/test/resources/golden/insert1_overwrite_partitions-3-3876bb356dd8af7e78d061093d555457 create mode 100644 sql/hive/src/test/resources/golden/insert1_overwrite_partitions-4-528e23afb272c2e69004c86ddaa70ee create mode 100644 sql/hive/src/test/resources/golden/insert1_overwrite_partitions-5-de5d56456c28d63775554e56355911d2 create mode 100644 sql/hive/src/test/resources/golden/insert1_overwrite_partitions-6-3efdc331b3b4bdac3e60c757600fff53 create mode 100644 sql/hive/src/test/resources/golden/insert1_overwrite_partitions-7-92f6af82704504968de078c133f222f8 create mode 100644 sql/hive/src/test/resources/golden/insert1_overwrite_partitions-8-316cad7c63ddd4fb043be2affa5b0a67 create mode 100644 sql/hive/src/test/resources/golden/insert1_overwrite_partitions-9-3efdc331b3b4bdac3e60c757600fff53 diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala index ee04cb579deb6..bc8d3751f6616 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala @@ -155,7 +155,7 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser { protected lazy val insert: Parser[LogicalPlan] = INSERT ~> (OVERWRITE ^^^ true | INTO ^^^ false) ~ (TABLE ~> relation) ~ select ^^ { - case o ~ r ~ s => InsertIntoTable(r, Map.empty[String, Option[String]], s, o) + case o ~ r ~ s => InsertIntoTable(r, Map.empty[String, Option[String]], s, o, false) } protected lazy val cte: Parser[LogicalPlan] = diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala index 50702ac6832ec..8b68b0df35f48 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala @@ -193,7 +193,7 @@ class Analyzer( } realPlan transform { - case i@InsertIntoTable(u: UnresolvedRelation, _, _, _) => + case i@InsertIntoTable(u: UnresolvedRelation, _, _, _, _) => i.copy( table = EliminateSubQueries(getTable(u, cteRelations))) case u: UnresolvedRelation => diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala index 145f062dd6817..21c15ad14fd19 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala @@ -293,7 +293,7 @@ package object dsl { def insertInto(tableName: String, overwrite: Boolean = false): LogicalPlan = InsertIntoTable( - analysis.UnresolvedRelation(Seq(tableName)), Map.empty, logicalPlan, overwrite) + analysis.UnresolvedRelation(Seq(tableName)), Map.empty, logicalPlan, overwrite, false) def analyze: LogicalPlan = EliminateSubQueries(analysis.SimpleAnalyzer(logicalPlan)) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala index 5d31a6eecfce2..17522976dc2c9 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala @@ -125,12 +125,14 @@ case class InsertIntoTable( table: LogicalPlan, partition: Map[String, Option[String]], child: LogicalPlan, - overwrite: Boolean) + overwrite: Boolean, + ifNotExists: Boolean) extends LogicalPlan { override def children: Seq[LogicalPlan] = child :: Nil override def output: Seq[Attribute] = child.output + assert(overwrite || !ifNotExists) override lazy val resolved: Boolean = childrenResolved && child.output.zip(table.output).forall { case (childAttr, tableAttr) => DataType.equalsIgnoreCompatibleNullability(childAttr.dataType, tableAttr.dataType) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 9b9adf855077a..94ae2d65fd0e4 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -1209,7 +1209,7 @@ class DataFrame private[sql]( @Experimental def insertInto(tableName: String, overwrite: Boolean): Unit = { sqlContext.executePlan(InsertIntoTable(UnresolvedRelation(Seq(tableName)), - Map.empty, logicalPlan, overwrite)).toRdd + Map.empty, logicalPlan, overwrite, ifNotExists = false)).toRdd } /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala index 23f7e5609414b..5268b7334051a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala @@ -211,7 +211,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { ParquetRelation.create(path, child, sparkContext.hadoopConfiguration, sqlContext) // Note: overwrite=false because otherwise the metadata we just created will be deleted InsertIntoParquetTable(relation, planLater(child), overwrite = false) :: Nil - case logical.InsertIntoTable(table: ParquetRelation, partition, child, overwrite) => + case logical.InsertIntoTable( + table: ParquetRelation, partition, child, overwrite, ifNotExists) => InsertIntoParquetTable(table, planLater(child), overwrite) :: Nil case PhysicalOperation(projectList, filters: Seq[Expression], relation: ParquetRelation) => val prunePushedDownFilters = diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala index e13759b7feb7b..34d048e426d10 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala @@ -56,7 +56,7 @@ private[sql] object DataSourceStrategy extends Strategy { execution.PhysicalRDD(l.output, t.buildScan()) :: Nil case i @ logical.InsertIntoTable( - l @ LogicalRelation(t: InsertableRelation), part, query, overwrite) if part.isEmpty => + l @ LogicalRelation(t: InsertableRelation), part, query, overwrite, false) if part.isEmpty => execution.ExecutedCommand(InsertIntoDataSource(l, query, overwrite)) :: Nil case _ => Nil diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/rules.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/rules.scala index 5a78001117d1b..6ed68d179edc9 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/rules.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/rules.scala @@ -37,7 +37,7 @@ private[sql] object PreInsertCastAndRename extends Rule[LogicalPlan] { // We are inserting into an InsertableRelation. case i @ InsertIntoTable( - l @ LogicalRelation(r: InsertableRelation), partition, child, overwrite) => { + l @ LogicalRelation(r: InsertableRelation), partition, child, overwrite, ifNotExists) => { // First, make sure the data to be inserted have the same number of fields with the // schema of the relation. if (l.output.size != child.output.size) { @@ -84,7 +84,7 @@ private[sql] case class PreWriteCheck(catalog: Catalog) extends (LogicalPlan => def apply(plan: LogicalPlan): Unit = { plan.foreach { case i @ logical.InsertIntoTable( - l @ LogicalRelation(t: InsertableRelation), partition, query, overwrite) => + l @ LogicalRelation(t: InsertableRelation), partition, query, overwrite, ifNotExists) => // Right now, we do not support insert into a data source table with partition specs. if (partition.nonEmpty) { failAnalysis(s"Insert into a partition is not allowed because $l is not partitioned.") @@ -102,7 +102,8 @@ private[sql] case class PreWriteCheck(catalog: Catalog) extends (LogicalPlan => } case i @ logical.InsertIntoTable( - l: LogicalRelation, partition, query, overwrite) if !l.isInstanceOf[InsertableRelation] => + l: LogicalRelation, partition, query, overwrite, ifNotExists) + if !l.isInstanceOf[InsertableRelation] => // The relation in l is not an InsertableRelation. failAnalysis(s"$l does not allow insertion.") diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala index 2ae9d018e1b1b..81ee48ef4152f 100644 --- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala +++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala @@ -532,6 +532,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter { "inputddl7", "inputddl8", "insert1", + "insert1_overwrite_partitions", "insert2_overwrite_partitions", "insert_compressed", "join0", diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index 3ed5c5b031736..f1c0bd92aa23d 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -527,7 +527,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with // Collects all `MetastoreRelation`s which should be replaced val toBeReplaced = plan.collect { // Write path - case InsertIntoTable(relation: MetastoreRelation, _, _, _) + case InsertIntoTable(relation: MetastoreRelation, _, _, _, _) // Inserting into partitioned table is not supported in Parquet data source (yet). if !relation.hiveQlTable.isPartitioned && hive.convertMetastoreParquet && @@ -538,7 +538,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with (relation, parquetRelation, attributedRewrites) // Write path - case InsertIntoHiveTable(relation: MetastoreRelation, _, _, _) + case InsertIntoHiveTable(relation: MetastoreRelation, _, _, _, _) // Inserting into partitioned table is not supported in Parquet data source (yet). if !relation.hiveQlTable.isPartitioned && hive.convertMetastoreParquet && @@ -569,15 +569,15 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with val alias = r.alias.getOrElse(r.tableName) Subquery(alias, parquetRelation) - case InsertIntoTable(r: MetastoreRelation, partition, child, overwrite) + case InsertIntoTable(r: MetastoreRelation, partition, child, overwrite, ifNotExists) if relationMap.contains(r) => val parquetRelation = relationMap(r) - InsertIntoTable(parquetRelation, partition, child, overwrite) + InsertIntoTable(parquetRelation, partition, child, overwrite, ifNotExists) - case InsertIntoHiveTable(r: MetastoreRelation, partition, child, overwrite) + case InsertIntoHiveTable(r: MetastoreRelation, partition, child, overwrite, ifNotExists) if relationMap.contains(r) => val parquetRelation = relationMap(r) - InsertIntoTable(parquetRelation, partition, child, overwrite) + InsertIntoTable(parquetRelation, partition, child, overwrite, ifNotExists) case other => other.transformExpressions { case a: Attribute if a.resolved => attributedRewrites.getOrElse(a, a) @@ -698,7 +698,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with // Wait until children are resolved. case p: LogicalPlan if !p.childrenResolved => p - case p @ InsertIntoTable(table: MetastoreRelation, _, child, _) => + case p @ InsertIntoTable(table: MetastoreRelation, _, child, _, _) => castChildOutput(p, table, child) } @@ -715,7 +715,7 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with .forall { case (left, right) => left.sameType(right) }) { // If both types ignoring nullability of ArrayType, MapType, StructType are the same, // use InsertIntoHiveTable instead of InsertIntoTable. - InsertIntoHiveTable(p.table, p.partition, p.child, p.overwrite) + InsertIntoHiveTable(p.table, p.partition, p.child, p.overwrite, p.ifNotExists) } else { // Only do the casting when child output data types differ from table output data types. val castedChildOutput = child.output.zip(table.output).map { @@ -753,7 +753,8 @@ private[hive] case class InsertIntoHiveTable( table: LogicalPlan, partition: Map[String, Option[String]], child: LogicalPlan, - overwrite: Boolean) + overwrite: Boolean, + ifNotExists: Boolean) extends LogicalPlan { override def children: Seq[LogicalPlan] = child :: Nil diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala index b2ae74efeb097..53a204b8c2932 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala @@ -1002,7 +1002,27 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C cleanIdentifier(key.toLowerCase) -> None }.toMap).getOrElse(Map.empty) - InsertIntoTable(UnresolvedRelation(tableIdent, None), partitionKeys, query, overwrite) + InsertIntoTable(UnresolvedRelation(tableIdent, None), partitionKeys, query, overwrite, false) + + case Token(destinationToken(), + Token("TOK_TAB", + tableArgs) :: + Token("TOK_IFNOTEXISTS", + ifNotExists) :: Nil) => + val Some(tableNameParts) :: partitionClause :: Nil = + getClauses(Seq("TOK_TABNAME", "TOK_PARTSPEC"), tableArgs) + + val tableIdent = extractTableIdent(tableNameParts) + + val partitionKeys = partitionClause.map(_.getChildren.map { + // Parse partitions. We also make keys case insensitive. + case Token("TOK_PARTVAL", Token(key, Nil) :: Token(value, Nil) :: Nil) => + cleanIdentifier(key.toLowerCase) -> Some(PlanUtils.stripQuotes(value)) + case Token("TOK_PARTVAL", Token(key, Nil) :: Nil) => + cleanIdentifier(key.toLowerCase) -> None + }.toMap).getOrElse(Map.empty) + + InsertIntoTable(UnresolvedRelation(tableIdent, None), partitionKeys, query, overwrite, true) case a: ASTNode => throw new NotImplementedError(s"No parse rules for:\n ${dumpTree(a).toString} ") diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala index 5f7e897295117..1ccb0c279c60e 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala @@ -184,12 +184,14 @@ private[hive] trait HiveStrategies { object DataSinks extends Strategy { def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match { - case logical.InsertIntoTable(table: MetastoreRelation, partition, child, overwrite) => + case logical.InsertIntoTable( + table: MetastoreRelation, partition, child, overwrite, ifNotExists) => execution.InsertIntoHiveTable( - table, partition, planLater(child), overwrite) :: Nil - case hive.InsertIntoHiveTable(table: MetastoreRelation, partition, child, overwrite) => + table, partition, planLater(child), overwrite, ifNotExists) :: Nil + case hive.InsertIntoHiveTable( + table: MetastoreRelation, partition, child, overwrite, ifNotExists) => execution.InsertIntoHiveTable( - table, partition, planLater(child), overwrite) :: Nil + table, partition, planLater(child), overwrite, ifNotExists) :: Nil case _ => Nil } } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala index fade9e5852eaa..76a1965f3cb25 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala @@ -67,7 +67,7 @@ case class CreateTableAsSelect( new org.apache.hadoop.hive.metastore.api.AlreadyExistsException(s"$database.$tableName") } } else { - hiveContext.executePlan(InsertIntoTable(metastoreRelation, Map(), query, true)).toRdd + hiveContext.executePlan(InsertIntoTable(metastoreRelation, Map(), query, true, false)).toRdd } Seq.empty[Row] diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala index 6c96747439683..89995a91b1a92 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala @@ -45,7 +45,8 @@ case class InsertIntoHiveTable( table: MetastoreRelation, partition: Map[String, Option[String]], child: SparkPlan, - overwrite: Boolean) extends UnaryNode with HiveInspectors { + overwrite: Boolean, + ifNotExists: Boolean) extends UnaryNode with HiveInspectors { @transient val sc: HiveContext = sqlContext.asInstanceOf[HiveContext] @transient lazy val outputClass = newSerializer(table.tableDesc).getSerializedClass @@ -219,15 +220,25 @@ case class InsertIntoHiveTable( isSkewedStoreAsSubdir) } } else { - catalog.synchronized { - catalog.client.loadPartition( - outputPath, - qualifiedTableName, - orderedPartitionSpec, - overwrite, - holdDDLTime, - inheritTableSpecs, - isSkewedStoreAsSubdir) + // scalastyle:off + // ifNotExists is only valid with static partition, refer to + // https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DML#LanguageManualDML-InsertingdataintoHiveTablesfromqueries + // scalastyle:on + val oldPart = catalog.synchronized { + catalog.client.getPartition( + catalog.client.getTable(qualifiedTableName), partitionSpec, false) + } + if (oldPart == null || !ifNotExists) { + catalog.synchronized { + catalog.client.loadPartition( + outputPath, + qualifiedTableName, + orderedPartitionSpec, + overwrite, + holdDDLTime, + inheritTableSpecs, + isSkewedStoreAsSubdir) + } } } } else { diff --git a/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-0-d5edc0daa94b33915df794df3b710774 b/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-0-d5edc0daa94b33915df794df3b710774 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-1-9eb9372f4855928fae16f5fa554b3a62 b/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-1-9eb9372f4855928fae16f5fa554b3a62 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-10-ec2cef3d37146c450c60202a572f5cab b/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-10-ec2cef3d37146c450c60202a572f5cab new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-11-8854d6001200fc11529b2e2da755e5a2 b/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-11-8854d6001200fc11529b2e2da755e5a2 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-12-71ff68fda0aa7a36cb50d8fab0d70d25 b/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-12-71ff68fda0aa7a36cb50d8fab0d70d25 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-13-7e4e7d7003fc6ef17bc19c3461ad899 b/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-13-7e4e7d7003fc6ef17bc19c3461ad899 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-14-ec2cef3d37146c450c60202a572f5cab b/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-14-ec2cef3d37146c450c60202a572f5cab new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-15-a3b2e230efde74e970ae8a3b55f383fc b/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-15-a3b2e230efde74e970ae8a3b55f383fc new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-2-8396c17a66e3d9a374d4361873b9bfe3 b/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-2-8396c17a66e3d9a374d4361873b9bfe3 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-3-3876bb356dd8af7e78d061093d555457 b/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-3-3876bb356dd8af7e78d061093d555457 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-4-528e23afb272c2e69004c86ddaa70ee b/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-4-528e23afb272c2e69004c86ddaa70ee new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-5-de5d56456c28d63775554e56355911d2 b/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-5-de5d56456c28d63775554e56355911d2 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-6-3efdc331b3b4bdac3e60c757600fff53 b/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-6-3efdc331b3b4bdac3e60c757600fff53 new file mode 100644 index 0000000000000..185a91c110d6f --- /dev/null +++ b/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-6-3efdc331b3b4bdac3e60c757600fff53 @@ -0,0 +1,5 @@ +98 val_98 +98 val_98 +97 val_97 +97 val_97 +96 val_96 diff --git a/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-7-92f6af82704504968de078c133f222f8 b/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-7-92f6af82704504968de078c133f222f8 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-8-316cad7c63ddd4fb043be2affa5b0a67 b/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-8-316cad7c63ddd4fb043be2affa5b0a67 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-9-3efdc331b3b4bdac3e60c757600fff53 b/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-9-3efdc331b3b4bdac3e60c757600fff53 new file mode 100644 index 0000000000000..185a91c110d6f --- /dev/null +++ b/sql/hive/src/test/resources/golden/insert1_overwrite_partitions-9-3efdc331b3b4bdac3e60c757600fff53 @@ -0,0 +1,5 @@ +98 val_98 +98 val_98 +97 val_97 +97 val_97 +96 val_96 From 3a205bbd9e352668a020c3146391e1e4441467af Mon Sep 17 00:00:00 2001 From: Yash Datta Date: Mon, 13 Apr 2015 14:43:07 -0700 Subject: [PATCH 261/591] [SQL][SPARK-6742]: Don't push down predicates which reference partition column(s) cc liancheng Author: Yash Datta Closes #5390 from saucam/fpush and squashes the following commits: 3f026d6 [Yash Datta] SPARK-6742: Fix scalastyle ce3d702 [Yash Datta] SPARK-6742: Add test case, fix scalastyle 8592acc [Yash Datta] SPARK-6742: Don't push down predicates which reference partition column(s) --- .../spark/sql/execution/SparkStrategies.scala | 11 ++++++++- .../sql/parquet/ParquetFilterSuite.scala | 24 ++++++++++++++++++- 2 files changed, 33 insertions(+), 2 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala index 5268b7334051a..f0d92ffffcda3 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala @@ -215,6 +215,11 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { table: ParquetRelation, partition, child, overwrite, ifNotExists) => InsertIntoParquetTable(table, planLater(child), overwrite) :: Nil case PhysicalOperation(projectList, filters: Seq[Expression], relation: ParquetRelation) => + val partitionColNames = relation.partitioningAttributes.map(_.name).toSet + val filtersToPush = filters.filter { pred => + val referencedColNames = pred.references.map(_.name).toSet + referencedColNames.intersect(partitionColNames).isEmpty + } val prunePushedDownFilters = if (sqlContext.conf.parquetFilterPushDown) { (predicates: Seq[Expression]) => { @@ -226,6 +231,10 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { // "A AND B" in the higher-level filter, not just "B". predicates.map(p => p -> ParquetFilters.createFilter(p)).collect { case (predicate, None) => predicate + // Filter needs to be applied above when it contains partitioning + // columns + case (predicate, _) if(!predicate.references.map(_.name).toSet + .intersect (partitionColNames).isEmpty) => predicate } } } else { @@ -238,7 +247,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { ParquetTableScan( _, relation, - if (sqlContext.conf.parquetFilterPushDown) filters else Nil)) :: Nil + if (sqlContext.conf.parquetFilterPushDown) filtersToPush else Nil)) :: Nil case _ => Nil } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala index 6a2c2a7c4080a..10d0ede4dc0dc 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala @@ -22,7 +22,7 @@ import parquet.filter2.predicate.Operators._ import parquet.filter2.predicate.{FilterPredicate, Operators} import org.apache.spark.sql.catalyst.dsl.expressions._ -import org.apache.spark.sql.catalyst.expressions.{Attribute, Cast, Literal, Predicate, Row} +import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.planning.PhysicalOperation import org.apache.spark.sql.sources.LogicalRelation import org.apache.spark.sql.test.TestSQLContext @@ -350,4 +350,26 @@ class ParquetDataSourceOffFilterSuite extends ParquetFilterSuiteBase with Before override protected def afterAll(): Unit = { sqlContext.setConf(SQLConf.PARQUET_USE_DATA_SOURCE_API, originalConf.toString) } + + test("SPARK-6742: don't push down predicates which reference partition columns") { + import sqlContext.implicits._ + + withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED -> "true") { + withTempPath { dir => + val path = s"${dir.getCanonicalPath}/part=1" + (1 to 3).map(i => (i, i.toString)).toDF("a", "b").saveAsParquetFile(path) + + // If the "part = 1" filter gets pushed down, this query will throw an exception since + // "part" is not a valid column in the actual Parquet file + val df = DataFrame(sqlContext, org.apache.spark.sql.parquet.ParquetRelation( + path, + Some(sqlContext.sparkContext.hadoopConfiguration), sqlContext, + Seq(AttributeReference("part", IntegerType, false)()) )) + + checkAnswer( + df.filter("a = 1 or part = 1"), + (1 to 3).map(i => Row(1, i, i.toString))) + } + } + } } From 2a55cb41bf7da1786be2c76b8af398da8fedb44b Mon Sep 17 00:00:00 2001 From: MechCoder Date: Mon, 13 Apr 2015 15:36:33 -0700 Subject: [PATCH 262/591] [SPARK-5972] [MLlib] Cache residuals and gradient in GBT during training and validation The previous PR https://github.com/apache/spark/pull/4906 helped to extract the learning curve giving the error for each iteration. This continues the work refactoring some code and extending the same logic during training and validation. Author: MechCoder Closes #5330 from MechCoder/spark-5972 and squashes the following commits: 0b5d659 [MechCoder] minor 32d409d [MechCoder] EvaluateeachIteration and training cache should follow different paths d542bb0 [MechCoder] Remove unused imports and docs 58f4932 [MechCoder] Remove unpersist 70d3b4c [MechCoder] Broadcast for each tree 5869533 [MechCoder] Access broadcasted values locally and other minor changes 923dbf6 [MechCoder] [SPARK-5972] Cache residuals and gradient in GBT during training and validation --- .../mllib/tree/GradientBoostedTrees.scala | 42 ++++++---- .../spark/mllib/tree/loss/AbsoluteError.scala | 10 +-- .../spark/mllib/tree/loss/LogLoss.scala | 11 +-- .../apache/spark/mllib/tree/loss/Loss.scala | 8 +- .../spark/mllib/tree/loss/SquaredError.scala | 10 +-- .../mllib/tree/model/treeEnsembleModels.scala | 77 +++++++++++++++---- 6 files changed, 105 insertions(+), 53 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala index a9c93e181e3ce..c02c79f094b66 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala @@ -157,7 +157,6 @@ object GradientBoostedTrees extends Logging { validationInput: RDD[LabeledPoint], boostingStrategy: BoostingStrategy, validate: Boolean): GradientBoostedTreesModel = { - val timer = new TimeTracker() timer.start("total") timer.start("init") @@ -192,20 +191,29 @@ object GradientBoostedTrees extends Logging { // Initialize tree timer.start("building tree 0") val firstTreeModel = new DecisionTree(treeStrategy).run(data) + val firstTreeWeight = 1.0 baseLearners(0) = firstTreeModel - baseLearnerWeights(0) = 1.0 - val startingModel = new GradientBoostedTreesModel(Regression, Array(firstTreeModel), Array(1.0)) - logDebug("error of gbt = " + loss.computeError(startingModel, input)) + baseLearnerWeights(0) = firstTreeWeight + val startingModel = new GradientBoostedTreesModel( + Regression, Array(firstTreeModel), baseLearnerWeights.slice(0, 1)) + + var predError: RDD[(Double, Double)] = GradientBoostedTreesModel. + computeInitialPredictionAndError(input, firstTreeWeight, firstTreeModel, loss) + logDebug("error of gbt = " + predError.values.mean()) // Note: A model of type regression is used since we require raw prediction timer.stop("building tree 0") - var bestValidateError = if (validate) loss.computeError(startingModel, validationInput) else 0.0 + var validatePredError: RDD[(Double, Double)] = GradientBoostedTreesModel. + computeInitialPredictionAndError(validationInput, firstTreeWeight, firstTreeModel, loss) + var bestValidateError = if (validate) validatePredError.values.mean() else 0.0 var bestM = 1 - // psuedo-residual for second iteration - data = input.map(point => LabeledPoint(loss.gradient(startingModel, point), - point.features)) + // pseudo-residual for second iteration + data = predError.zip(input).map { case ((pred, _), point) => + LabeledPoint(-loss.gradient(pred, point.label), point.features) + } + var m = 1 while (m < numIterations) { timer.start(s"building tree $m") @@ -222,15 +230,22 @@ object GradientBoostedTrees extends Logging { baseLearnerWeights(m) = learningRate // Note: A model of type regression is used since we require raw prediction val partialModel = new GradientBoostedTreesModel( - Regression, baseLearners.slice(0, m + 1), baseLearnerWeights.slice(0, m + 1)) - logDebug("error of gbt = " + loss.computeError(partialModel, input)) + Regression, baseLearners.slice(0, m + 1), + baseLearnerWeights.slice(0, m + 1)) + + predError = GradientBoostedTreesModel.updatePredictionError( + input, predError, baseLearnerWeights(m), baseLearners(m), loss) + logDebug("error of gbt = " + predError.values.mean()) if (validate) { // Stop training early if // 1. Reduction in error is less than the validationTol or // 2. If the error increases, that is if the model is overfit. // We want the model returned corresponding to the best validation error. - val currentValidateError = loss.computeError(partialModel, validationInput) + + validatePredError = GradientBoostedTreesModel.updatePredictionError( + validationInput, validatePredError, baseLearnerWeights(m), baseLearners(m), loss) + val currentValidateError = validatePredError.values.mean() if (bestValidateError - currentValidateError < validationTol) { return new GradientBoostedTreesModel( boostingStrategy.treeStrategy.algo, @@ -242,8 +257,9 @@ object GradientBoostedTrees extends Logging { } } // Update data with pseudo-residuals - data = input.map(point => LabeledPoint(-loss.gradient(partialModel, point), - point.features)) + data = predError.zip(input).map { case ((pred, _), point) => + LabeledPoint(-loss.gradient(pred, point.label), point.features) + } m += 1 } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala index 793dd664c5d5a..6f570b4e09c79 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala @@ -37,14 +37,12 @@ object AbsoluteError extends Loss { * Method to calculate the gradients for the gradient boosting calculation for least * absolute error calculation. * The gradient with respect to F(x) is: sign(F(x) - y) - * @param model Ensemble model - * @param point Instance of the training dataset + * @param prediction Predicted label. + * @param label True label. * @return Loss gradient */ - override def gradient( - model: TreeEnsembleModel, - point: LabeledPoint): Double = { - if ((point.label - model.predict(point.features)) < 0) 1.0 else -1.0 + override def gradient(prediction: Double, label: Double): Double = { + if (label - prediction < 0) 1.0 else -1.0 } override def computeError(prediction: Double, label: Double): Double = { diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala index 51b1aed167b66..24ee9f3d51293 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala @@ -39,15 +39,12 @@ object LogLoss extends Loss { * Method to calculate the loss gradients for the gradient boosting calculation for binary * classification * The gradient with respect to F(x) is: - 4 y / (1 + exp(2 y F(x))) - * @param model Ensemble model - * @param point Instance of the training dataset + * @param prediction Predicted label. + * @param label True label. * @return Loss gradient */ - override def gradient( - model: TreeEnsembleModel, - point: LabeledPoint): Double = { - val prediction = model.predict(point.features) - - 4.0 * point.label / (1.0 + math.exp(2.0 * point.label * prediction)) + override def gradient(prediction: Double, label: Double): Double = { + - 4.0 * label / (1.0 + math.exp(2.0 * label * prediction)) } override def computeError(prediction: Double, label: Double): Double = { diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala index 357869ff6b333..d3b82b752fa0d 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala @@ -31,13 +31,11 @@ trait Loss extends Serializable { /** * Method to calculate the gradients for the gradient boosting calculation. - * @param model Model of the weak learner. - * @param point Instance of the training dataset. + * @param prediction Predicted feature + * @param label true label. * @return Loss gradient. */ - def gradient( - model: TreeEnsembleModel, - point: LabeledPoint): Double + def gradient(prediction: Double, label: Double): Double /** * Method to calculate error of the base learner for the gradient boosting calculation. diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala index b990707ca4525..58857ae15e93e 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala @@ -37,14 +37,12 @@ object SquaredError extends Loss { * Method to calculate the gradients for the gradient boosting calculation for least * squares error calculation. * The gradient with respect to F(x) is: - 2 (y - F(x)) - * @param model Ensemble model - * @param point Instance of the training dataset + * @param prediction Predicted label. + * @param label True label. * @return Loss gradient */ - override def gradient( - model: TreeEnsembleModel, - point: LabeledPoint): Double = { - 2.0 * (model.predict(point.features) - point.label) + override def gradient(prediction: Double, label: Double): Double = { + 2.0 * (prediction - label) } override def computeError(prediction: Double, label: Double): Double = { diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala index 1950254b2aa6d..fef3d2acb202a 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala @@ -130,35 +130,28 @@ class GradientBoostedTreesModel( val numIterations = trees.length val evaluationArray = Array.fill(numIterations)(0.0) + val localTreeWeights = treeWeights + + var predictionAndError = GradientBoostedTreesModel.computeInitialPredictionAndError( + remappedData, localTreeWeights(0), trees(0), loss) - var predictionAndError: RDD[(Double, Double)] = remappedData.map { i => - val pred = treeWeights(0) * trees(0).predict(i.features) - val error = loss.computeError(pred, i.label) - (pred, error) - } evaluationArray(0) = predictionAndError.values.mean() - // Avoid the model being copied across numIterations. val broadcastTrees = sc.broadcast(trees) - val broadcastWeights = sc.broadcast(treeWeights) - (1 until numIterations).map { nTree => predictionAndError = remappedData.zip(predictionAndError).mapPartitions { iter => val currentTree = broadcastTrees.value(nTree) - val currentTreeWeight = broadcastWeights.value(nTree) - iter.map { - case (point, (pred, error)) => { - val newPred = pred + currentTree.predict(point.features) * currentTreeWeight - val newError = loss.computeError(newPred, point.label) - (newPred, newError) - } + val currentTreeWeight = localTreeWeights(nTree) + iter.map { case (point, (pred, error)) => + val newPred = pred + currentTree.predict(point.features) * currentTreeWeight + val newError = loss.computeError(newPred, point.label) + (newPred, newError) } } evaluationArray(nTree) = predictionAndError.values.mean() } broadcastTrees.unpersist() - broadcastWeights.unpersist() evaluationArray } @@ -166,6 +159,58 @@ class GradientBoostedTreesModel( object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] { + /** + * Compute the initial predictions and errors for a dataset for the first + * iteration of gradient boosting. + * @param data: training data. + * @param initTreeWeight: learning rate assigned to the first tree. + * @param initTree: first DecisionTreeModel. + * @param loss: evaluation metric. + * @return a RDD with each element being a zip of the prediction and error + * corresponding to every sample. + */ + def computeInitialPredictionAndError( + data: RDD[LabeledPoint], + initTreeWeight: Double, + initTree: DecisionTreeModel, + loss: Loss): RDD[(Double, Double)] = { + data.map { lp => + val pred = initTreeWeight * initTree.predict(lp.features) + val error = loss.computeError(pred, lp.label) + (pred, error) + } + } + + /** + * Update a zipped predictionError RDD + * (as obtained with computeInitialPredictionAndError) + * @param data: training data. + * @param predictionAndError: predictionError RDD + * @param treeWeight: Learning rate. + * @param tree: Tree using which the prediction and error should be updated. + * @param loss: evaluation metric. + * @return a RDD with each element being a zip of the prediction and error + * corresponding to each sample. + */ + def updatePredictionError( + data: RDD[LabeledPoint], + predictionAndError: RDD[(Double, Double)], + treeWeight: Double, + tree: DecisionTreeModel, + loss: Loss): RDD[(Double, Double)] = { + + val newPredError = data.zip(predictionAndError).mapPartitions { iter => + iter.map { + case (lp, (pred, error)) => { + val newPred = pred + tree.predict(lp.features) * treeWeight + val newError = loss.computeError(newPred, lp.label) + (newPred, newError) + } + } + } + newPredError + } + override def load(sc: SparkContext, path: String): GradientBoostedTreesModel = { val (loadedClassName, version, jsonMetadata) = Loader.loadMetadata(sc, path) val classNameV1_0 = SaveLoadV1_0.thisClassName From e63a86abe2794332cdad71d87b72a7c56327a43d Mon Sep 17 00:00:00 2001 From: Daoyuan Wang Date: Mon, 13 Apr 2015 16:00:58 -0700 Subject: [PATCH 263/591] [SPARK-6872] [SQL] add copy in external sort We need add copy before call externalsort. Author: Daoyuan Wang Closes #5481 from adrian-wang/extsort and squashes the following commits: 9611586 [Daoyuan Wang] fix bug in external sort --- .../scala/org/apache/spark/sql/execution/basicOperators.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala index 6eec520abff53..f8221f41bc6c3 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala @@ -194,7 +194,7 @@ case class ExternalSort( child.execute().mapPartitions( { iterator => val ordering = newOrdering(sortOrder, child.output) val sorter = new ExternalSorter[Row, Null, Row](ordering = Some(ordering)) - sorter.insertAll(iterator.map(r => (r, null))) + sorter.insertAll(iterator.map(r => (r.copy, null))) val baseIterator = sorter.iterator.map(_._1) // TODO(marmbrus): The complex type signature below thwarts inference for no reason. CompletionIterator[Row, Iterator[Row]](baseIterator, sorter.stop()) From c5602bdc310cc8f82dc304500bebe40217cba785 Mon Sep 17 00:00:00 2001 From: Cheng Hao Date: Mon, 13 Apr 2015 16:02:18 -0700 Subject: [PATCH 264/591] [SPARK-5941] [SQL] Unit Test loads the table `src` twice for leftsemijoin.q In `leftsemijoin.q`, there is a data loading command for table `sales` already, but in `TestHive`, it also created the table `sales`, which causes duplicated records inserted into the `sales`. Author: Cheng Hao Closes #4506 from chenghao-intel/df_table and squashes the following commits: 0be05f7 [Cheng Hao] Remove the table `sales` creating from TestHive --- .../sql/columnar/InMemoryColumnarQuerySuite.scala | 2 +- .../org/apache/spark/sql/hive/test/TestHive.scala | 6 ------ ...ftsemijoin-10-89737a8857b5b61cc909e0c797f86aea | 2 -- .../leftsemijoin-8-73cad58a10a1483ccb15e94a857013 | 2 -- .../spark/sql/hive/execution/HiveSerDeSuite.scala | 15 +++++++++++---- 5 files changed, 12 insertions(+), 15 deletions(-) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala index 27dfabca90217..479210d1c9c43 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala @@ -42,7 +42,7 @@ class InMemoryColumnarQuerySuite extends QueryTest { .toDF().registerTempTable("sizeTst") cacheTable("sizeTst") assert( - table("sizeTst").queryExecution.logical.statistics.sizeInBytes > + table("sizeTst").queryExecution.analyzed.statistics.sizeInBytes > conf.autoBroadcastJoinThreshold) } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala index a3497eadd67f6..6570fa1043900 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala @@ -262,12 +262,6 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { |WITH SERDEPROPERTIES ('field.delim'='\\t') """.stripMargin.cmd, "INSERT OVERWRITE TABLE serdeins SELECT * FROM src".cmd), - TestTable("sales", - s"""CREATE TABLE IF NOT EXISTS sales (key STRING, value INT) - |ROW FORMAT SERDE '${classOf[RegexSerDe].getCanonicalName}' - |WITH SERDEPROPERTIES ("input.regex" = "([^ ]*)\t([^ ]*)") - """.stripMargin.cmd, - s"LOAD DATA LOCAL INPATH '${getHiveFile("data/files/sales.txt")}' INTO TABLE sales".cmd), TestTable("episodes", s"""CREATE TABLE episodes (title STRING, air_date STRING, doctor INT) |ROW FORMAT SERDE '${classOf[AvroSerDe].getCanonicalName}' diff --git a/sql/hive/src/test/resources/golden/leftsemijoin-10-89737a8857b5b61cc909e0c797f86aea b/sql/hive/src/test/resources/golden/leftsemijoin-10-89737a8857b5b61cc909e0c797f86aea index 25ce912507d55..a1963ba81e0da 100644 --- a/sql/hive/src/test/resources/golden/leftsemijoin-10-89737a8857b5b61cc909e0c797f86aea +++ b/sql/hive/src/test/resources/golden/leftsemijoin-10-89737a8857b5b61cc909e0c797f86aea @@ -1,4 +1,2 @@ Hank 2 -Hank 2 -Joe 2 Joe 2 diff --git a/sql/hive/src/test/resources/golden/leftsemijoin-8-73cad58a10a1483ccb15e94a857013 b/sql/hive/src/test/resources/golden/leftsemijoin-8-73cad58a10a1483ccb15e94a857013 index 25ce912507d55..a1963ba81e0da 100644 --- a/sql/hive/src/test/resources/golden/leftsemijoin-8-73cad58a10a1483ccb15e94a857013 +++ b/sql/hive/src/test/resources/golden/leftsemijoin-8-73cad58a10a1483ccb15e94a857013 @@ -1,4 +1,2 @@ Hank 2 -Hank 2 -Joe 2 Joe 2 diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala index d05e11fcf281b..5586a793618bd 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveSerDeSuite.scala @@ -25,18 +25,25 @@ import org.apache.spark.sql.hive.test.TestHive * A set of tests that validates support for Hive SerDe. */ class HiveSerDeSuite extends HiveComparisonTest with BeforeAndAfterAll { - override def beforeAll(): Unit = { + import TestHive._ + import org.apache.hadoop.hive.serde2.RegexSerDe + super.beforeAll() TestHive.cacheTables = false - super.beforeAll() + sql(s"""CREATE TABLE IF NOT EXISTS sales (key STRING, value INT) + |ROW FORMAT SERDE '${classOf[RegexSerDe].getCanonicalName}' + |WITH SERDEPROPERTIES ("input.regex" = "([^ ]*)\t([^ ]*)") + """.stripMargin) + sql(s"LOAD DATA LOCAL INPATH '${getHiveFile("data/files/sales.txt")}' INTO TABLE sales") } + // table sales is not a cache table, and will be clear after reset + createQueryTest("Read with RegexSerDe", "SELECT * FROM sales", false) + createQueryTest( "Read and write with LazySimpleSerDe (tab separated)", "SELECT * from serdeins") - createQueryTest("Read with RegexSerDe", "SELECT * FROM sales") - createQueryTest("Read with AvroSerDe", "SELECT * FROM episodes") createQueryTest("Read Partitioned with AvroSerDe", "SELECT * FROM episodes_part") From c4ab255e94366ba9b9023d5431f9d2412e0d6dc7 Mon Sep 17 00:00:00 2001 From: Ilya Ganelin Date: Mon, 13 Apr 2015 16:28:07 -0700 Subject: [PATCH 265/591] [SPARK-5931][CORE] Use consistent naming for time properties I've added new utility methods to do the conversion from times specified as e.g. 120s, 240ms, 360us to convert to a consistent internal representation. I've updated usage of these constants throughout the code to be consistent. I believe I've captured all usages of time-based properties throughout the code. I've also updated variable names in a number of places to reflect their units for clarity and updated documentation where appropriate. Author: Ilya Ganelin Author: Ilya Ganelin Closes #5236 from ilganeli/SPARK-5931 and squashes the following commits: 4526c81 [Ilya Ganelin] Update configuration.md de3bff9 [Ilya Ganelin] Fixing style errors f5fafcd [Ilya Ganelin] Doc updates 951ca2d [Ilya Ganelin] Made the most recent round of changes bc04e05 [Ilya Ganelin] Minor fixes and doc updates 25d3f52 [Ilya Ganelin] Minor nit fixes 642a06d [Ilya Ganelin] Fixed logic for invalid suffixes and addid matching test 8927e66 [Ilya Ganelin] Fixed handling of -1 69fedcc [Ilya Ganelin] Added test for zero dc7bd08 [Ilya Ganelin] Fixed error in exception handling 7d19cdd [Ilya Ganelin] Added fix for possible NPE 6f651a8 [Ilya Ganelin] Now using regexes to simplify code in parseTimeString. Introduces getTimeAsSec and getTimeAsMs methods in SparkConf. Updated documentation cbd2ca6 [Ilya Ganelin] Formatting error 1a1122c [Ilya Ganelin] Formatting fixes and added m for use as minute formatter 4e48679 [Ilya Ganelin] Fixed priority order and mixed up conversions in a couple spots d4efd26 [Ilya Ganelin] Added time conversion for yarn.scheduler.heartbeat.interval-ms cbf41db [Ilya Ganelin] Got rid of thrown exceptions 1465390 [Ilya Ganelin] Nit 28187bf [Ilya Ganelin] Convert straight to seconds ff40bfe [Ilya Ganelin] Updated tests to fix small bugs 19c31af [Ilya Ganelin] Added cleaner computation of time conversions in tests 6387772 [Ilya Ganelin] Updated suffix handling to handle overlap of units more gracefully 5193d5f [Ilya Ganelin] Resolved merge conflicts 76cfa27 [Ilya Ganelin] [SPARK-5931] Minor nit fixes' bf779b0 [Ilya Ganelin] Special handling of overlapping usffixes for java dd0a680 [Ilya Ganelin] Updated scala code to call into java b2fc965 [Ilya Ganelin] replaced get or default since it's not present in this version of java 39164f9 [Ilya Ganelin] [SPARK-5931] Updated Java conversion to be similar to scala conversion. Updated conversions to clean up code a little using TimeUnit.convert. Added Unit tests 3b126e1 [Ilya Ganelin] Fixed conversion to US from seconds 1858197 [Ilya Ganelin] Fixed bug where all time was being converted to us instead of the appropriate units bac9edf [Ilya Ganelin] More whitespace 8613631 [Ilya Ganelin] Whitespace 1c0c07c [Ilya Ganelin] Updated Java code to add day, minutes, and hours 647b5ac [Ilya Ganelin] Udpated time conversion to use map iterator instead of if fall through 70ac213 [Ilya Ganelin] Fixed remaining usages to be consistent. Updated Java-side time conversion 68f4e93 [Ilya Ganelin] Updated more files to clean up usage of default time strings 3a12dd8 [Ilya Ganelin] Updated host revceiver 5232a36 [Ilya Ganelin] [SPARK-5931] Changed default behavior of time string conversion. 499bdf0 [Ilya Ganelin] Merge branch 'SPARK-5931' of github.com:ilganeli/spark into SPARK-5931 9e2547c [Ilya Ganelin] Reverting doc changes 8f741e1 [Ilya Ganelin] Update JavaUtils.java 34f87c2 [Ilya Ganelin] Update Utils.scala 9a29d8d [Ilya Ganelin] Fixed misuse of time in streaming context test 42477aa [Ilya Ganelin] Updated configuration doc with note on specifying time properties cde9bff [Ilya Ganelin] Updated spark.streaming.blockInterval c6a0095 [Ilya Ganelin] Updated spark.core.connection.auth.wait.timeout 5181597 [Ilya Ganelin] Updated spark.dynamicAllocation.schedulerBacklogTimeout 2fcc91c [Ilya Ganelin] Updated spark.dynamicAllocation.executorIdleTimeout 6d1518e [Ilya Ganelin] Upated spark.speculation.interval 3f1cfc8 [Ilya Ganelin] Updated spark.scheduler.revive.interval 3352d34 [Ilya Ganelin] Updated spark.scheduler.maxRegisteredResourcesWaitingTime 272c215 [Ilya Ganelin] Updated spark.locality.wait 7320c87 [Ilya Ganelin] updated spark.akka.heartbeat.interval 064ebd6 [Ilya Ganelin] Updated usage of spark.cleaner.ttl 21ef3dd [Ilya Ganelin] updated spark.shuffle.sasl.timeout c9f5cad [Ilya Ganelin] Updated spark.shuffle.io.retryWait 4933fda [Ilya Ganelin] Updated usage of spark.storage.blockManagerSlaveTimeout 7db6d2a [Ilya Ganelin] Updated usage of spark.akka.timeout 404f8c3 [Ilya Ganelin] Updated usage of spark.core.connection.ack.wait.timeout 59bf9e1 [Ilya Ganelin] [SPARK-5931] Updated Utils and JavaUtils classes to add helper methods to handle time strings. Updated time strings in a few places to properly parse time --- .../spark/ExecutorAllocationManager.scala | 34 ++++---- .../org/apache/spark/HeartbeatReceiver.scala | 15 ++-- .../scala/org/apache/spark/SparkConf.scala | 36 ++++++++ .../org/apache/spark/executor/Executor.scala | 6 +- .../spark/network/nio/ConnectionManager.scala | 3 +- .../spark/scheduler/TaskSchedulerImpl.scala | 10 +-- .../spark/scheduler/TaskSetManager.scala | 21 +++-- .../CoarseGrainedSchedulerBackend.scala | 13 +-- .../org/apache/spark/util/AkkaUtils.scala | 14 +-- .../apache/spark/util/MetadataCleaner.scala | 2 +- .../scala/org/apache/spark/util/Utils.scala | 26 +++++- .../ExecutorAllocationManagerSuite.scala | 7 +- .../network/nio/ConnectionManagerSuite.scala | 8 +- .../spark/scheduler/TaskSetManagerSuite.scala | 25 +++--- .../BlockManagerReplicationSuite.scala | 2 +- .../org/apache/spark/util/UtilsSuite.scala | 44 ++++++++++ docs/configuration.md | 86 ++++++++++--------- docs/running-on-yarn.md | 4 +- .../apache/spark/network/util/JavaUtils.java | 66 ++++++++++++++ .../spark/network/util/TransportConf.java | 15 +++- .../streaming/receiver/BlockGenerator.scala | 8 +- .../streaming/scheduler/JobGenerator.scala | 12 ++- .../spark/streaming/ReceiverSuite.scala | 14 +-- .../streaming/StreamingContextSuite.scala | 22 ++--- .../spark/deploy/yarn/ApplicationMaster.scala | 9 +- 25 files changed, 345 insertions(+), 157 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala index 9385f557c4614..4e7bf51fc0622 100644 --- a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala +++ b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala @@ -80,16 +80,16 @@ private[spark] class ExecutorAllocationManager( Integer.MAX_VALUE) // How long there must be backlogged tasks for before an addition is triggered (seconds) - private val schedulerBacklogTimeout = conf.getLong( - "spark.dynamicAllocation.schedulerBacklogTimeout", 5) + private val schedulerBacklogTimeoutS = conf.getTimeAsSeconds( + "spark.dynamicAllocation.schedulerBacklogTimeout", "5s") - // Same as above, but used only after `schedulerBacklogTimeout` is exceeded - private val sustainedSchedulerBacklogTimeout = conf.getLong( - "spark.dynamicAllocation.sustainedSchedulerBacklogTimeout", schedulerBacklogTimeout) + // Same as above, but used only after `schedulerBacklogTimeoutS` is exceeded + private val sustainedSchedulerBacklogTimeoutS = conf.getTimeAsSeconds( + "spark.dynamicAllocation.sustainedSchedulerBacklogTimeout", s"${schedulerBacklogTimeoutS}s") // How long an executor must be idle for before it is removed (seconds) - private val executorIdleTimeout = conf.getLong( - "spark.dynamicAllocation.executorIdleTimeout", 600) + private val executorIdleTimeoutS = conf.getTimeAsSeconds( + "spark.dynamicAllocation.executorIdleTimeout", "600s") // During testing, the methods to actually kill and add executors are mocked out private val testing = conf.getBoolean("spark.dynamicAllocation.testing", false) @@ -150,14 +150,14 @@ private[spark] class ExecutorAllocationManager( throw new SparkException(s"spark.dynamicAllocation.minExecutors ($minNumExecutors) must " + s"be less than or equal to spark.dynamicAllocation.maxExecutors ($maxNumExecutors)!") } - if (schedulerBacklogTimeout <= 0) { + if (schedulerBacklogTimeoutS <= 0) { throw new SparkException("spark.dynamicAllocation.schedulerBacklogTimeout must be > 0!") } - if (sustainedSchedulerBacklogTimeout <= 0) { + if (sustainedSchedulerBacklogTimeoutS <= 0) { throw new SparkException( "spark.dynamicAllocation.sustainedSchedulerBacklogTimeout must be > 0!") } - if (executorIdleTimeout <= 0) { + if (executorIdleTimeoutS <= 0) { throw new SparkException("spark.dynamicAllocation.executorIdleTimeout must be > 0!") } // Require external shuffle service for dynamic allocation @@ -262,8 +262,8 @@ private[spark] class ExecutorAllocationManager( } else if (addTime != NOT_SET && now >= addTime) { val delta = addExecutors(maxNeeded) logDebug(s"Starting timer to add more executors (to " + - s"expire in $sustainedSchedulerBacklogTimeout seconds)") - addTime += sustainedSchedulerBacklogTimeout * 1000 + s"expire in $sustainedSchedulerBacklogTimeoutS seconds)") + addTime += sustainedSchedulerBacklogTimeoutS * 1000 delta } else { 0 @@ -351,7 +351,7 @@ private[spark] class ExecutorAllocationManager( val removeRequestAcknowledged = testing || client.killExecutor(executorId) if (removeRequestAcknowledged) { logInfo(s"Removing executor $executorId because it has been idle for " + - s"$executorIdleTimeout seconds (new desired total will be ${numExistingExecutors - 1})") + s"$executorIdleTimeoutS seconds (new desired total will be ${numExistingExecutors - 1})") executorsPendingToRemove.add(executorId) true } else { @@ -407,8 +407,8 @@ private[spark] class ExecutorAllocationManager( private def onSchedulerBacklogged(): Unit = synchronized { if (addTime == NOT_SET) { logDebug(s"Starting timer to add executors because pending tasks " + - s"are building up (to expire in $schedulerBacklogTimeout seconds)") - addTime = clock.getTimeMillis + schedulerBacklogTimeout * 1000 + s"are building up (to expire in $schedulerBacklogTimeoutS seconds)") + addTime = clock.getTimeMillis + schedulerBacklogTimeoutS * 1000 } } @@ -431,8 +431,8 @@ private[spark] class ExecutorAllocationManager( if (executorIds.contains(executorId)) { if (!removeTimes.contains(executorId) && !executorsPendingToRemove.contains(executorId)) { logDebug(s"Starting idle timer for $executorId because there are no more tasks " + - s"scheduled to run on the executor (to expire in $executorIdleTimeout seconds)") - removeTimes(executorId) = clock.getTimeMillis + executorIdleTimeout * 1000 + s"scheduled to run on the executor (to expire in $executorIdleTimeoutS seconds)") + removeTimes(executorId) = clock.getTimeMillis + executorIdleTimeoutS * 1000 } } else { logWarning(s"Attempted to mark unknown executor $executorId idle") diff --git a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala index 5871b8c869f03..e3bd16f1cbf24 100644 --- a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala +++ b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala @@ -62,14 +62,17 @@ private[spark] class HeartbeatReceiver(sc: SparkContext) // "spark.network.timeout" uses "seconds", while `spark.storage.blockManagerSlaveTimeoutMs` uses // "milliseconds" - private val executorTimeoutMs = sc.conf.getOption("spark.network.timeout").map(_.toLong * 1000). - getOrElse(sc.conf.getLong("spark.storage.blockManagerSlaveTimeoutMs", 120000)) - + private val slaveTimeoutMs = + sc.conf.getTimeAsMs("spark.storage.blockManagerSlaveTimeoutMs", "120s") + private val executorTimeoutMs = + sc.conf.getTimeAsSeconds("spark.network.timeout", s"${slaveTimeoutMs}ms") * 1000 + // "spark.network.timeoutInterval" uses "seconds", while // "spark.storage.blockManagerTimeoutIntervalMs" uses "milliseconds" - private val checkTimeoutIntervalMs = - sc.conf.getOption("spark.network.timeoutInterval").map(_.toLong * 1000). - getOrElse(sc.conf.getLong("spark.storage.blockManagerTimeoutIntervalMs", 60000)) + private val timeoutIntervalMs = + sc.conf.getTimeAsMs("spark.storage.blockManagerTimeoutIntervalMs", "60s") + private val checkTimeoutIntervalMs = + sc.conf.getTimeAsSeconds("spark.network.timeoutInterval", s"${timeoutIntervalMs}ms") * 1000 private var timeoutCheckingTask: ScheduledFuture[_] = null diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala index 0c123c96b8d7b..390e631647bd6 100644 --- a/core/src/main/scala/org/apache/spark/SparkConf.scala +++ b/core/src/main/scala/org/apache/spark/SparkConf.scala @@ -174,6 +174,42 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { getOption(key).getOrElse(defaultValue) } + /** + * Get a time parameter as seconds; throws a NoSuchElementException if it's not set. If no + * suffix is provided then seconds are assumed. + * @throws NoSuchElementException + */ + def getTimeAsSeconds(key: String): Long = { + Utils.timeStringAsSeconds(get(key)) + } + + /** + * Get a time parameter as seconds, falling back to a default if not set. If no + * suffix is provided then seconds are assumed. + * + */ + def getTimeAsSeconds(key: String, defaultValue: String): Long = { + Utils.timeStringAsSeconds(get(key, defaultValue)) + } + + /** + * Get a time parameter as milliseconds; throws a NoSuchElementException if it's not set. If no + * suffix is provided then milliseconds are assumed. + * @throws NoSuchElementException + */ + def getTimeAsMs(key: String): Long = { + Utils.timeStringAsMs(get(key)) + } + + /** + * Get a time parameter as milliseconds, falling back to a default if not set. If no + * suffix is provided then milliseconds are assumed. + */ + def getTimeAsMs(key: String, defaultValue: String): Long = { + Utils.timeStringAsMs(get(key, defaultValue)) + } + + /** Get a parameter as an Option */ def getOption(key: String): Option[String] = { Option(settings.get(key)) diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala index 14f99a464b6e9..516f619529c48 100644 --- a/core/src/main/scala/org/apache/spark/executor/Executor.scala +++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala @@ -436,14 +436,14 @@ private[spark] class Executor( * This thread stops running when the executor is stopped. */ private def startDriverHeartbeater(): Unit = { - val interval = conf.getInt("spark.executor.heartbeatInterval", 10000) + val intervalMs = conf.getTimeAsMs("spark.executor.heartbeatInterval", "10s") val thread = new Thread() { override def run() { // Sleep a random interval so the heartbeats don't end up in sync - Thread.sleep(interval + (math.random * interval).asInstanceOf[Int]) + Thread.sleep(intervalMs + (math.random * intervalMs).asInstanceOf[Int]) while (!isStopped) { reportHeartBeat() - Thread.sleep(interval) + Thread.sleep(intervalMs) } } } diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala index 741fe3e1ea750..8e3c30fc3d781 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala @@ -82,7 +82,8 @@ private[nio] class ConnectionManager( new HashedWheelTimer(Utils.namedThreadFactory("AckTimeoutMonitor")) private val ackTimeout = - conf.getInt("spark.core.connection.ack.wait.timeout", conf.getInt("spark.network.timeout", 120)) + conf.getTimeAsSeconds("spark.core.connection.ack.wait.timeout", + conf.get("spark.network.timeout", "120s")) // Get the thread counts from the Spark Configuration. // diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala index 076b36e86c0ce..2362cc7240039 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala @@ -62,10 +62,10 @@ private[spark] class TaskSchedulerImpl( val conf = sc.conf // How often to check for speculative tasks - val SPECULATION_INTERVAL = conf.getLong("spark.speculation.interval", 100) + val SPECULATION_INTERVAL_MS = conf.getTimeAsMs("spark.speculation.interval", "100ms") // Threshold above which we warn user initial TaskSet may be starved - val STARVATION_TIMEOUT = conf.getLong("spark.starvation.timeout", 15000) + val STARVATION_TIMEOUT_MS = conf.getTimeAsMs("spark.starvation.timeout", "15s") // CPUs to request per task val CPUS_PER_TASK = conf.getInt("spark.task.cpus", 1) @@ -143,8 +143,8 @@ private[spark] class TaskSchedulerImpl( if (!isLocal && conf.getBoolean("spark.speculation", false)) { logInfo("Starting speculative execution thread") import sc.env.actorSystem.dispatcher - sc.env.actorSystem.scheduler.schedule(SPECULATION_INTERVAL milliseconds, - SPECULATION_INTERVAL milliseconds) { + sc.env.actorSystem.scheduler.schedule(SPECULATION_INTERVAL_MS milliseconds, + SPECULATION_INTERVAL_MS milliseconds) { Utils.tryOrStopSparkContext(sc) { checkSpeculatableTasks() } } } @@ -173,7 +173,7 @@ private[spark] class TaskSchedulerImpl( this.cancel() } } - }, STARVATION_TIMEOUT, STARVATION_TIMEOUT) + }, STARVATION_TIMEOUT_MS, STARVATION_TIMEOUT_MS) } hasReceivedTask = true } diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala index d509881c74fef..7dc325283d961 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala @@ -848,15 +848,18 @@ private[spark] class TaskSetManager( } private def getLocalityWait(level: TaskLocality.TaskLocality): Long = { - val defaultWait = conf.get("spark.locality.wait", "3000") - level match { - case TaskLocality.PROCESS_LOCAL => - conf.get("spark.locality.wait.process", defaultWait).toLong - case TaskLocality.NODE_LOCAL => - conf.get("spark.locality.wait.node", defaultWait).toLong - case TaskLocality.RACK_LOCAL => - conf.get("spark.locality.wait.rack", defaultWait).toLong - case _ => 0L + val defaultWait = conf.get("spark.locality.wait", "3s") + val localityWaitKey = level match { + case TaskLocality.PROCESS_LOCAL => "spark.locality.wait.process" + case TaskLocality.NODE_LOCAL => "spark.locality.wait.node" + case TaskLocality.RACK_LOCAL => "spark.locality.wait.rack" + case _ => null + } + + if (localityWaitKey != null) { + conf.getTimeAsMs(localityWaitKey, defaultWait) + } else { + 0L } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala index 4c49da87af9dc..63987dfb32695 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala @@ -52,8 +52,8 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp math.min(1, conf.getDouble("spark.scheduler.minRegisteredResourcesRatio", 0)) // Submit tasks after maxRegisteredWaitingTime milliseconds // if minRegisteredRatio has not yet been reached - val maxRegisteredWaitingTime = - conf.getInt("spark.scheduler.maxRegisteredResourcesWaitingTime", 30000) + val maxRegisteredWaitingTimeMs = + conf.getTimeAsMs("spark.scheduler.maxRegisteredResourcesWaitingTime", "30s") val createTime = System.currentTimeMillis() private val executorDataMap = new HashMap[String, ExecutorData] @@ -77,12 +77,13 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp override def onStart() { // Periodically revive offers to allow delay scheduling to work - val reviveInterval = conf.getLong("spark.scheduler.revive.interval", 1000) + val reviveIntervalMs = conf.getTimeAsMs("spark.scheduler.revive.interval", "1s") + reviveThread.scheduleAtFixedRate(new Runnable { override def run(): Unit = Utils.tryLogNonFatalError { Option(self).foreach(_.send(ReviveOffers)) } - }, 0, reviveInterval, TimeUnit.MILLISECONDS) + }, 0, reviveIntervalMs, TimeUnit.MILLISECONDS) } override def receive: PartialFunction[Any, Unit] = { @@ -301,9 +302,9 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp s"reached minRegisteredResourcesRatio: $minRegisteredRatio") return true } - if ((System.currentTimeMillis() - createTime) >= maxRegisteredWaitingTime) { + if ((System.currentTimeMillis() - createTime) >= maxRegisteredWaitingTimeMs) { logInfo("SchedulerBackend is ready for scheduling beginning after waiting " + - s"maxRegisteredResourcesWaitingTime: $maxRegisteredWaitingTime(ms)") + s"maxRegisteredResourcesWaitingTime: $maxRegisteredWaitingTimeMs(ms)") return true } false diff --git a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala index 6c2c5261306e7..8e8cc7cc6389e 100644 --- a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala +++ b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala @@ -20,7 +20,6 @@ package org.apache.spark.util import scala.collection.JavaConversions.mapAsJavaMap import scala.concurrent.Await import scala.concurrent.duration.{Duration, FiniteDuration} -import scala.util.Try import akka.actor.{ActorRef, ActorSystem, ExtendedActorSystem} import akka.pattern.ask @@ -66,7 +65,8 @@ private[spark] object AkkaUtils extends Logging { val akkaThreads = conf.getInt("spark.akka.threads", 4) val akkaBatchSize = conf.getInt("spark.akka.batchSize", 15) - val akkaTimeout = conf.getInt("spark.akka.timeout", conf.getInt("spark.network.timeout", 120)) + val akkaTimeoutS = conf.getTimeAsSeconds("spark.akka.timeout", + conf.get("spark.network.timeout", "120s")) val akkaFrameSize = maxFrameSizeBytes(conf) val akkaLogLifecycleEvents = conf.getBoolean("spark.akka.logLifecycleEvents", false) val lifecycleEvents = if (akkaLogLifecycleEvents) "on" else "off" @@ -78,8 +78,8 @@ private[spark] object AkkaUtils extends Logging { val logAkkaConfig = if (conf.getBoolean("spark.akka.logAkkaConfig", false)) "on" else "off" - val akkaHeartBeatPauses = conf.getInt("spark.akka.heartbeat.pauses", 6000) - val akkaHeartBeatInterval = conf.getInt("spark.akka.heartbeat.interval", 1000) + val akkaHeartBeatPausesS = conf.getTimeAsSeconds("spark.akka.heartbeat.pauses", "6000s") + val akkaHeartBeatIntervalS = conf.getTimeAsSeconds("spark.akka.heartbeat.interval", "1000s") val secretKey = securityManager.getSecretKey() val isAuthOn = securityManager.isAuthenticationEnabled() @@ -102,14 +102,14 @@ private[spark] object AkkaUtils extends Logging { |akka.jvm-exit-on-fatal-error = off |akka.remote.require-cookie = "$requireCookie" |akka.remote.secure-cookie = "$secureCookie" - |akka.remote.transport-failure-detector.heartbeat-interval = $akkaHeartBeatInterval s - |akka.remote.transport-failure-detector.acceptable-heartbeat-pause = $akkaHeartBeatPauses s + |akka.remote.transport-failure-detector.heartbeat-interval = $akkaHeartBeatIntervalS s + |akka.remote.transport-failure-detector.acceptable-heartbeat-pause = $akkaHeartBeatPausesS s |akka.actor.provider = "akka.remote.RemoteActorRefProvider" |akka.remote.netty.tcp.transport-class = "akka.remote.transport.netty.NettyTransport" |akka.remote.netty.tcp.hostname = "$host" |akka.remote.netty.tcp.port = $port |akka.remote.netty.tcp.tcp-nodelay = on - |akka.remote.netty.tcp.connection-timeout = $akkaTimeout s + |akka.remote.netty.tcp.connection-timeout = $akkaTimeoutS s |akka.remote.netty.tcp.maximum-frame-size = ${akkaFrameSize}B |akka.remote.netty.tcp.execution-pool-size = $akkaThreads |akka.actor.default-dispatcher.throughput = $akkaBatchSize diff --git a/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala b/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala index 375ed430bde45..2bbfc988a99a8 100644 --- a/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala +++ b/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala @@ -76,7 +76,7 @@ private[spark] object MetadataCleanerType extends Enumeration { // initialization of StreamingContext. It's okay for users trying to configure stuff themselves. private[spark] object MetadataCleaner { def getDelaySeconds(conf: SparkConf): Int = { - conf.getInt("spark.cleaner.ttl", -1) + conf.getTimeAsSeconds("spark.cleaner.ttl", "-1").toInt } def getDelaySeconds( diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index a541d660cd5c6..1029b0f9fce1e 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -22,7 +22,7 @@ import java.lang.management.ManagementFactory import java.net._ import java.nio.ByteBuffer import java.util.{Properties, Locale, Random, UUID} -import java.util.concurrent.{ThreadFactory, ConcurrentHashMap, Executors, ThreadPoolExecutor} +import java.util.concurrent._ import javax.net.ssl.HttpsURLConnection import scala.collection.JavaConversions._ @@ -47,6 +47,7 @@ import tachyon.client.{TachyonFS, TachyonFile} import org.apache.spark._ import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.network.util.JavaUtils import org.apache.spark.serializer.{DeserializationStream, SerializationStream, SerializerInstance} /** CallSite represents a place in user code. It can have a short and a long form. */ @@ -612,9 +613,10 @@ private[spark] object Utils extends Logging { } Utils.setupSecureURLConnection(uc, securityMgr) - val timeout = conf.getInt("spark.files.fetchTimeout", 60) * 1000 - uc.setConnectTimeout(timeout) - uc.setReadTimeout(timeout) + val timeoutMs = + conf.getTimeAsSeconds("spark.files.fetchTimeout", "60s").toInt * 1000 + uc.setConnectTimeout(timeoutMs) + uc.setReadTimeout(timeoutMs) uc.connect() val in = uc.getInputStream() downloadFile(url, in, targetFile, fileOverwrite) @@ -1018,6 +1020,22 @@ private[spark] object Utils extends Logging { ) } + /** + * Convert a time parameter such as (50s, 100ms, or 250us) to microseconds for internal use. If + * no suffix is provided, the passed number is assumed to be in ms. + */ + def timeStringAsMs(str: String): Long = { + JavaUtils.timeStringAsMs(str) + } + + /** + * Convert a time parameter such as (50s, 100ms, or 250us) to microseconds for internal use. If + * no suffix is provided, the passed number is assumed to be in seconds. + */ + def timeStringAsSeconds(str: String): Long = { + JavaUtils.timeStringAsSec(str) + } + /** * Convert a Java memory parameter passed to -Xmx (such as 300m or 1g) to a number of megabytes. */ diff --git a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala index 3ded1e4af8742..6b3049b28cd5e 100644 --- a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala @@ -684,10 +684,11 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext wit .set("spark.dynamicAllocation.enabled", "true") .set("spark.dynamicAllocation.minExecutors", minExecutors.toString) .set("spark.dynamicAllocation.maxExecutors", maxExecutors.toString) - .set("spark.dynamicAllocation.schedulerBacklogTimeout", schedulerBacklogTimeout.toString) + .set("spark.dynamicAllocation.schedulerBacklogTimeout", + s"${schedulerBacklogTimeout.toString}s") .set("spark.dynamicAllocation.sustainedSchedulerBacklogTimeout", - sustainedSchedulerBacklogTimeout.toString) - .set("spark.dynamicAllocation.executorIdleTimeout", executorIdleTimeout.toString) + s"${sustainedSchedulerBacklogTimeout.toString}s") + .set("spark.dynamicAllocation.executorIdleTimeout", s"${executorIdleTimeout.toString}s") .set("spark.dynamicAllocation.testing", "true") val sc = new SparkContext(conf) contexts += sc diff --git a/core/src/test/scala/org/apache/spark/network/nio/ConnectionManagerSuite.scala b/core/src/test/scala/org/apache/spark/network/nio/ConnectionManagerSuite.scala index 716f875d30b8a..02424c59d6831 100644 --- a/core/src/test/scala/org/apache/spark/network/nio/ConnectionManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/network/nio/ConnectionManagerSuite.scala @@ -260,8 +260,8 @@ class ConnectionManagerSuite extends FunSuite { test("sendMessageReliably timeout") { val clientConf = new SparkConf clientConf.set("spark.authenticate", "false") - val ackTimeout = 30 - clientConf.set("spark.core.connection.ack.wait.timeout", s"${ackTimeout}") + val ackTimeoutS = 30 + clientConf.set("spark.core.connection.ack.wait.timeout", s"${ackTimeoutS}s") val clientSecurityManager = new SecurityManager(clientConf) val manager = new ConnectionManager(0, clientConf, clientSecurityManager) @@ -272,7 +272,7 @@ class ConnectionManagerSuite extends FunSuite { val managerServer = new ConnectionManager(0, serverConf, serverSecurityManager) managerServer.onReceiveMessage((msg: Message, id: ConnectionManagerId) => { // sleep 60 sec > ack timeout for simulating server slow down or hang up - Thread.sleep(ackTimeout * 3 * 1000) + Thread.sleep(ackTimeoutS * 3 * 1000) None }) @@ -287,7 +287,7 @@ class ConnectionManagerSuite extends FunSuite { // Otherwise TimeoutExcepton is thrown from Await.result. // We expect TimeoutException is not thrown. intercept[IOException] { - Await.result(future, (ackTimeout * 2) second) + Await.result(future, (ackTimeoutS * 2) second) } manager.stop() diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala index 716d12c0762cf..6198cea46ddf8 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala @@ -17,7 +17,6 @@ package org.apache.spark.scheduler -import java.io.{ObjectInputStream, ObjectOutputStream, IOException} import java.util.Random import scala.collection.mutable.ArrayBuffer @@ -27,7 +26,7 @@ import org.scalatest.FunSuite import org.apache.spark._ import org.apache.spark.executor.TaskMetrics -import org.apache.spark.util.ManualClock +import org.apache.spark.util.{ManualClock, Utils} class FakeDAGScheduler(sc: SparkContext, taskScheduler: FakeTaskScheduler) extends DAGScheduler(sc) { @@ -152,7 +151,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { private val conf = new SparkConf - val LOCALITY_WAIT = conf.getLong("spark.locality.wait", 3000) + val LOCALITY_WAIT_MS = conf.getTimeAsMs("spark.locality.wait", "3s") val MAX_TASK_FAILURES = 4 override def beforeEach() { @@ -240,7 +239,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { assert(manager.resourceOffer("exec1", "host1", ANY).get.index === 0) assert(manager.resourceOffer("exec1", "host1", PROCESS_LOCAL) == None) - clock.advance(LOCALITY_WAIT) + clock.advance(LOCALITY_WAIT_MS) // Offer host1, exec1 again, at NODE_LOCAL level: the node local (task 2) should // get chosen before the noPref task assert(manager.resourceOffer("exec1", "host1", NODE_LOCAL).get.index == 2) @@ -251,7 +250,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { // Offer host2, exec3 again, at NODE_LOCAL level: we should get noPref task // after failing to find a node_Local task assert(manager.resourceOffer("exec2", "host2", NODE_LOCAL) == None) - clock.advance(LOCALITY_WAIT) + clock.advance(LOCALITY_WAIT_MS) assert(manager.resourceOffer("exec2", "host2", NO_PREF).get.index == 3) } @@ -292,7 +291,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { // Offer host1 again: nothing should get chosen assert(manager.resourceOffer("exec1", "host1", ANY) === None) - clock.advance(LOCALITY_WAIT) + clock.advance(LOCALITY_WAIT_MS) // Offer host1 again: second task (on host2) should get chosen assert(manager.resourceOffer("exec1", "host1", ANY).get.index === 1) @@ -306,7 +305,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { // Now that we've launched a local task, we should no longer launch the task for host3 assert(manager.resourceOffer("exec2", "host2", ANY) === None) - clock.advance(LOCALITY_WAIT) + clock.advance(LOCALITY_WAIT_MS) // After another delay, we can go ahead and launch that task non-locally assert(manager.resourceOffer("exec2", "host2", ANY).get.index === 3) @@ -338,7 +337,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { // nothing should be chosen assert(manager.resourceOffer("exec1", "host1", ANY) === None) - clock.advance(LOCALITY_WAIT * 2) + clock.advance(LOCALITY_WAIT_MS * 2) // task 1 and 2 would be scheduled as nonLocal task assert(manager.resourceOffer("exec1", "host1", ANY).get.index === 1) @@ -528,7 +527,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { assert(manager.myLocalityLevels.sameElements(Array(PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL, ANY))) // Set allowed locality to ANY - clock.advance(LOCALITY_WAIT * 3) + clock.advance(LOCALITY_WAIT_MS * 3) // Offer host3 // No task is scheduled if we restrict locality to RACK_LOCAL assert(manager.resourceOffer("execC", "host3", RACK_LOCAL) === None) @@ -622,12 +621,12 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { assert(manager.resourceOffer("execA", "host1", NO_PREF).get.index == 1) manager.speculatableTasks += 1 - clock.advance(LOCALITY_WAIT) + clock.advance(LOCALITY_WAIT_MS) // schedule the nonPref task assert(manager.resourceOffer("execA", "host1", NO_PREF).get.index === 2) // schedule the speculative task assert(manager.resourceOffer("execB", "host2", NO_PREF).get.index === 1) - clock.advance(LOCALITY_WAIT * 3) + clock.advance(LOCALITY_WAIT_MS * 3) // schedule non-local tasks assert(manager.resourceOffer("execB", "host2", ANY).get.index === 3) } @@ -716,13 +715,13 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { // Valid locality should contain PROCESS_LOCAL, NODE_LOCAL and ANY assert(manager.myLocalityLevels.sameElements(Array(PROCESS_LOCAL, NODE_LOCAL, ANY))) assert(manager.resourceOffer("execA", "host1", ANY) !== None) - clock.advance(LOCALITY_WAIT * 4) + clock.advance(LOCALITY_WAIT_MS * 4) assert(manager.resourceOffer("execB.2", "host2", ANY) !== None) sched.removeExecutor("execA") sched.removeExecutor("execB.2") manager.executorLost("execA", "host1") manager.executorLost("execB.2", "host2") - clock.advance(LOCALITY_WAIT * 4) + clock.advance(LOCALITY_WAIT_MS * 4) sched.addExecutor("execC", "host3") manager.executorAdded() // Prior to the fix, this line resulted in an ArrayIndexOutOfBoundsException: diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala index b4de90b65d545..ffa5162a31841 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala @@ -76,7 +76,7 @@ class BlockManagerReplicationSuite extends FunSuite with Matchers with BeforeAnd conf.set("spark.storage.unrollMemoryThreshold", "512") // to make a replication attempt to inactive store fail fast - conf.set("spark.core.connection.ack.wait.timeout", "1") + conf.set("spark.core.connection.ack.wait.timeout", "1s") // to make cached peers refresh frequently conf.set("spark.storage.cachedPeersTtl", "10") diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala index 449fb87f111c4..fb97e650ff95c 100644 --- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala @@ -23,6 +23,7 @@ import java.io.{File, ByteArrayOutputStream, ByteArrayInputStream, FileOutputStr import java.net.{BindException, ServerSocket, URI} import java.nio.{ByteBuffer, ByteOrder} import java.text.DecimalFormatSymbols +import java.util.concurrent.TimeUnit import java.util.Locale import com.google.common.base.Charsets.UTF_8 @@ -35,7 +36,50 @@ import org.apache.hadoop.fs.Path import org.apache.spark.SparkConf class UtilsSuite extends FunSuite with ResetSystemProperties { + + test("timeConversion") { + // Test -1 + assert(Utils.timeStringAsSeconds("-1") === -1) + + // Test zero + assert(Utils.timeStringAsSeconds("0") === 0) + + assert(Utils.timeStringAsSeconds("1") === 1) + assert(Utils.timeStringAsSeconds("1s") === 1) + assert(Utils.timeStringAsSeconds("1000ms") === 1) + assert(Utils.timeStringAsSeconds("1000000us") === 1) + assert(Utils.timeStringAsSeconds("1m") === TimeUnit.MINUTES.toSeconds(1)) + assert(Utils.timeStringAsSeconds("1min") === TimeUnit.MINUTES.toSeconds(1)) + assert(Utils.timeStringAsSeconds("1h") === TimeUnit.HOURS.toSeconds(1)) + assert(Utils.timeStringAsSeconds("1d") === TimeUnit.DAYS.toSeconds(1)) + + assert(Utils.timeStringAsMs("1") === 1) + assert(Utils.timeStringAsMs("1ms") === 1) + assert(Utils.timeStringAsMs("1000us") === 1) + assert(Utils.timeStringAsMs("1s") === TimeUnit.SECONDS.toMillis(1)) + assert(Utils.timeStringAsMs("1m") === TimeUnit.MINUTES.toMillis(1)) + assert(Utils.timeStringAsMs("1min") === TimeUnit.MINUTES.toMillis(1)) + assert(Utils.timeStringAsMs("1h") === TimeUnit.HOURS.toMillis(1)) + assert(Utils.timeStringAsMs("1d") === TimeUnit.DAYS.toMillis(1)) + + // Test invalid strings + intercept[NumberFormatException] { + Utils.timeStringAsMs("This breaks 600s") + } + + intercept[NumberFormatException] { + Utils.timeStringAsMs("This breaks 600ds") + } + intercept[NumberFormatException] { + Utils.timeStringAsMs("600s This breaks") + } + + intercept[NumberFormatException] { + Utils.timeStringAsMs("This 123s breaks") + } + } + test("bytesToString") { assert(Utils.bytesToString(10) === "10.0 B") assert(Utils.bytesToString(1500) === "1500.0 B") diff --git a/docs/configuration.md b/docs/configuration.md index 7fe11475212b3..7169ec295ef7f 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -35,9 +35,19 @@ val conf = new SparkConf() val sc = new SparkContext(conf) {% endhighlight %} -Note that we can have more than 1 thread in local mode, and in cases like spark streaming, we may actually -require one to prevent any sort of starvation issues. +Note that we can have more than 1 thread in local mode, and in cases like Spark Streaming, we may +actually require one to prevent any sort of starvation issues. +Properties that specify some time duration should be configured with a unit of time. +The following format is accepted: + + 25ms (milliseconds) + 5s (seconds) + 10m or 10min (minutes) + 3h (hours) + 5d (days) + 1y (years) + ## Dynamically Loading Spark Properties In some cases, you may want to avoid hard-coding certain configurations in a `SparkConf`. For instance, if you'd like to run the same application with different masters or different @@ -429,10 +439,10 @@ Apart from these, the following properties are also available, and may be useful spark.shuffle.io.retryWait - 5 + 5s - (Netty only) Seconds to wait between retries of fetches. The maximum delay caused by retrying - is simply maxRetries * retryWait, by default 15 seconds. + (Netty only) How long to wait between retries of fetches. The maximum delay caused by retrying + is 15 seconds by default, calculated as maxRetries * retryWait. @@ -732,17 +742,17 @@ Apart from these, the following properties are also available, and may be useful spark.executor.heartbeatInterval - 10000 - Interval (milliseconds) between each executor's heartbeats to the driver. Heartbeats let + 10s + Interval between each executor's heartbeats to the driver. Heartbeats let the driver know that the executor is still alive and update it with metrics for in-progress tasks. spark.files.fetchTimeout - 60 + 60s Communication timeout to use when fetching files added through SparkContext.addFile() from - the driver, in seconds. + the driver. @@ -853,11 +863,11 @@ Apart from these, the following properties are also available, and may be useful spark.akka.heartbeat.interval - 1000 + 1000s This is set to a larger value to disable the transport failure detector that comes built in to Akka. It can be enabled again, if you plan to use this feature (Not recommended). A larger - interval value in seconds reduces network overhead and a smaller value ( ~ 1 s) might be more + interval value reduces network overhead and a smaller value ( ~ 1 s) might be more informative for Akka's failure detector. Tune this in combination of `spark.akka.heartbeat.pauses` if you need to. A likely positive use case for using failure detector would be: a sensistive failure detector can help evict rogue executors quickly. However this is usually not the case @@ -868,11 +878,11 @@ Apart from these, the following properties are also available, and may be useful spark.akka.heartbeat.pauses - 6000 + 6000s This is set to a larger value to disable the transport failure detector that comes built in to Akka. It can be enabled again, if you plan to use this feature (Not recommended). Acceptable heart - beat pause in seconds for Akka. This can be used to control sensitivity to GC pauses. Tune + beat pause for Akka. This can be used to control sensitivity to GC pauses. Tune this along with `spark.akka.heartbeat.interval` if you need to. @@ -886,9 +896,9 @@ Apart from these, the following properties are also available, and may be useful spark.akka.timeout - 100 + 100s - Communication timeout between Spark nodes, in seconds. + Communication timeout between Spark nodes. @@ -938,10 +948,10 @@ Apart from these, the following properties are also available, and may be useful spark.network.timeout - 120 + 120s - Default timeout for all network interactions, in seconds. This config will be used in - place of spark.core.connection.ack.wait.timeout, spark.akka.timeout, + Default timeout for all network interactions. This config will be used in place of + spark.core.connection.ack.wait.timeout, spark.akka.timeout, spark.storage.blockManagerSlaveTimeoutMs or spark.shuffle.io.connectionTimeout, if they are not configured. @@ -989,9 +999,9 @@ Apart from these, the following properties are also available, and may be useful spark.locality.wait - 3000 + 3s - Number of milliseconds to wait to launch a data-local task before giving up and launching it + How long to wait to launch a data-local task before giving up and launching it on a less-local node. The same wait will be used to step through multiple locality levels (process-local, node-local, rack-local and then any). It is also possible to customize the waiting time for each level by setting spark.locality.wait.node, etc. @@ -1024,10 +1034,9 @@ Apart from these, the following properties are also available, and may be useful spark.scheduler.maxRegisteredResourcesWaitingTime - 30000 + 30s - Maximum amount of time to wait for resources to register before scheduling begins - (in milliseconds). + Maximum amount of time to wait for resources to register before scheduling begins. @@ -1054,10 +1063,9 @@ Apart from these, the following properties are also available, and may be useful spark.scheduler.revive.interval - 1000 + 1s - The interval length for the scheduler to revive the worker resource offers to run tasks - (in milliseconds). + The interval length for the scheduler to revive the worker resource offers to run tasks. @@ -1070,9 +1078,9 @@ Apart from these, the following properties are also available, and may be useful spark.speculation.interval - 100 + 100ms - How often Spark will check for tasks to speculate, in milliseconds. + How often Spark will check for tasks to speculate. @@ -1127,10 +1135,10 @@ Apart from these, the following properties are also available, and may be useful spark.dynamicAllocation.executorIdleTimeout - 600 + 600s - If dynamic allocation is enabled and an executor has been idle for more than this duration - (in seconds), the executor will be removed. For more detail, see this + If dynamic allocation is enabled and an executor has been idle for more than this duration, + the executor will be removed. For more detail, see this     description. @@ -1157,10 +1165,10 @@ Apart from these, the following properties are also available, and may be useful spark.dynamicAllocation.schedulerBacklogTimeout - 5 + 5s If dynamic allocation is enabled and there have been pending tasks backlogged for more than - this duration (in seconds), new executors will be requested. For more detail, see this + this duration, new executors will be requested. For more detail, see this description. @@ -1215,18 +1223,18 @@ Apart from these, the following properties are also available, and may be useful spark.core.connection.ack.wait.timeout - 60 + 60s - Number of seconds for the connection to wait for ack to occur before timing + How long for the connection to wait for ack to occur before timing out and giving up. To avoid unwilling timeout caused by long pause like GC, you can set larger value. spark.core.connection.auth.wait.timeout - 30 + 30s - Number of seconds for the connection to wait for authentication to occur before timing + How long for the connection to wait for authentication to occur before timing out and giving up. @@ -1347,9 +1355,9 @@ Apart from these, the following properties are also available, and may be useful Property NameDefaultMeaning spark.streaming.blockInterval - 200 + 200ms - Interval (milliseconds) at which data received by Spark Streaming receivers is chunked + Interval at which data received by Spark Streaming receivers is chunked into blocks of data before storing them in Spark. Minimum recommended - 50 ms. See the performance tuning section in the Spark Streaming programing guide for more details. diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md index ed5bb263a5809..853c9f26b0ec9 100644 --- a/docs/running-on-yarn.md +++ b/docs/running-on-yarn.md @@ -48,9 +48,9 @@ Most of the configs are the same for Spark on YARN as for other deployment modes spark.yarn.am.waitTime - 100000 + 100s - In yarn-cluster mode, time in milliseconds for the application master to wait for the + In yarn-cluster mode, time for the application master to wait for the SparkContext to be initialized. In yarn-client mode, time for the application master to wait for the driver to connect to it. diff --git a/network/common/src/main/java/org/apache/spark/network/util/JavaUtils.java b/network/common/src/main/java/org/apache/spark/network/util/JavaUtils.java index 73da9b7346f4d..b6fbace509a0e 100644 --- a/network/common/src/main/java/org/apache/spark/network/util/JavaUtils.java +++ b/network/common/src/main/java/org/apache/spark/network/util/JavaUtils.java @@ -21,9 +21,13 @@ import java.io.File; import java.io.IOException; import java.nio.ByteBuffer; +import java.util.concurrent.TimeUnit; +import java.util.regex.Matcher; +import java.util.regex.Pattern; import com.google.common.base.Charsets; import com.google.common.base.Preconditions; +import com.google.common.collect.ImmutableMap; import io.netty.buffer.Unpooled; import org.slf4j.Logger; import org.slf4j.LoggerFactory; @@ -121,4 +125,66 @@ private static boolean isSymlink(File file) throws IOException { } return !fileInCanonicalDir.getCanonicalFile().equals(fileInCanonicalDir.getAbsoluteFile()); } + + private static ImmutableMap timeSuffixes = + ImmutableMap.builder() + .put("us", TimeUnit.MICROSECONDS) + .put("ms", TimeUnit.MILLISECONDS) + .put("s", TimeUnit.SECONDS) + .put("m", TimeUnit.MINUTES) + .put("min", TimeUnit.MINUTES) + .put("h", TimeUnit.HOURS) + .put("d", TimeUnit.DAYS) + .build(); + + /** + * Convert a passed time string (e.g. 50s, 100ms, or 250us) to a time count for + * internal use. If no suffix is provided a direct conversion is attempted. + */ + private static long parseTimeString(String str, TimeUnit unit) { + String lower = str.toLowerCase().trim(); + + try { + String suffix; + long val; + Matcher m = Pattern.compile("(-?[0-9]+)([a-z]+)?").matcher(lower); + if (m.matches()) { + val = Long.parseLong(m.group(1)); + suffix = m.group(2); + } else { + throw new NumberFormatException("Failed to parse time string: " + str); + } + + // Check for invalid suffixes + if (suffix != null && !timeSuffixes.containsKey(suffix)) { + throw new NumberFormatException("Invalid suffix: \"" + suffix + "\""); + } + + // If suffix is valid use that, otherwise none was provided and use the default passed + return unit.convert(val, suffix != null ? timeSuffixes.get(suffix) : unit); + } catch (NumberFormatException e) { + String timeError = "Time must be specified as seconds (s), " + + "milliseconds (ms), microseconds (us), minutes (m or min) hour (h), or day (d). " + + "E.g. 50s, 100ms, or 250us."; + + throw new NumberFormatException(timeError + "\n" + e.getMessage()); + } + } + + /** + * Convert a time parameter such as (50s, 100ms, or 250us) to milliseconds for internal use. If + * no suffix is provided, the passed number is assumed to be in ms. + */ + public static long timeStringAsMs(String str) { + return parseTimeString(str, TimeUnit.MILLISECONDS); + } + + /** + * Convert a time parameter such as (50s, 100ms, or 250us) to seconds for internal use. If + * no suffix is provided, the passed number is assumed to be in seconds. + */ + public static long timeStringAsSec(String str) { + return parseTimeString(str, TimeUnit.SECONDS); + } + } diff --git a/network/common/src/main/java/org/apache/spark/network/util/TransportConf.java b/network/common/src/main/java/org/apache/spark/network/util/TransportConf.java index 2eaf3b71d9a49..0aef7f1987315 100644 --- a/network/common/src/main/java/org/apache/spark/network/util/TransportConf.java +++ b/network/common/src/main/java/org/apache/spark/network/util/TransportConf.java @@ -37,8 +37,11 @@ public boolean preferDirectBufs() { /** Connect timeout in milliseconds. Default 120 secs. */ public int connectionTimeoutMs() { - int defaultTimeout = conf.getInt("spark.network.timeout", 120); - return conf.getInt("spark.shuffle.io.connectionTimeout", defaultTimeout) * 1000; + long defaultNetworkTimeoutS = JavaUtils.timeStringAsSec( + conf.get("spark.network.timeout", "120s")); + long defaultTimeoutMs = JavaUtils.timeStringAsSec( + conf.get("spark.shuffle.io.connectionTimeout", defaultNetworkTimeoutS + "s")) * 1000; + return (int) defaultTimeoutMs; } /** Number of concurrent connections between two nodes for fetching data. */ @@ -68,7 +71,9 @@ public int numConnectionsPerPeer() { public int sendBuf() { return conf.getInt("spark.shuffle.io.sendBuffer", -1); } /** Timeout for a single round trip of SASL token exchange, in milliseconds. */ - public int saslRTTimeoutMs() { return conf.getInt("spark.shuffle.sasl.timeout", 30) * 1000; } + public int saslRTTimeoutMs() { + return (int) JavaUtils.timeStringAsSec(conf.get("spark.shuffle.sasl.timeout", "30s")) * 1000; + } /** * Max number of times we will try IO exceptions (such as connection timeouts) per request. @@ -80,7 +85,9 @@ public int numConnectionsPerPeer() { * Time (in milliseconds) that we will wait in order to perform a retry after an IOException. * Only relevant if maxIORetries > 0. */ - public int ioRetryWaitTimeMs() { return conf.getInt("spark.shuffle.io.retryWait", 5) * 1000; } + public int ioRetryWaitTimeMs() { + return (int) JavaUtils.timeStringAsSec(conf.get("spark.shuffle.io.retryWait", "5s")) * 1000; + } /** * Minimum size of a block that we should start using memory map rather than reading in through diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala index 42514d8b47dcf..f4963a78e1d18 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala @@ -24,7 +24,7 @@ import scala.collection.mutable.ArrayBuffer import org.apache.spark.{Logging, SparkConf} import org.apache.spark.storage.StreamBlockId import org.apache.spark.streaming.util.RecurringTimer -import org.apache.spark.util.SystemClock +import org.apache.spark.util.{SystemClock, Utils} /** Listener object for BlockGenerator events */ private[streaming] trait BlockGeneratorListener { @@ -79,9 +79,9 @@ private[streaming] class BlockGenerator( private case class Block(id: StreamBlockId, buffer: ArrayBuffer[Any]) private val clock = new SystemClock() - private val blockInterval = conf.getLong("spark.streaming.blockInterval", 200) + private val blockIntervalMs = conf.getTimeAsMs("spark.streaming.blockInterval", "200ms") private val blockIntervalTimer = - new RecurringTimer(clock, blockInterval, updateCurrentBuffer, "BlockGenerator") + new RecurringTimer(clock, blockIntervalMs, updateCurrentBuffer, "BlockGenerator") private val blockQueueSize = conf.getInt("spark.streaming.blockQueueSize", 10) private val blocksForPushing = new ArrayBlockingQueue[Block](blockQueueSize) private val blockPushingThread = new Thread() { override def run() { keepPushingBlocks() } } @@ -132,7 +132,7 @@ private[streaming] class BlockGenerator( val newBlockBuffer = currentBuffer currentBuffer = new ArrayBuffer[Any] if (newBlockBuffer.size > 0) { - val blockId = StreamBlockId(receiverId, time - blockInterval) + val blockId = StreamBlockId(receiverId, time - blockIntervalMs) val newBlock = new Block(blockId, newBlockBuffer) listener.onGenerateBlock(blockId) blocksForPushing.put(newBlock) // put is blocking when queue is full diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala index 4946806d2ee95..58e56638a2dca 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala @@ -24,7 +24,7 @@ import akka.actor.{ActorRef, Props, Actor} import org.apache.spark.{SparkEnv, Logging} import org.apache.spark.streaming.{Checkpoint, CheckpointWriter, Time} import org.apache.spark.streaming.util.RecurringTimer -import org.apache.spark.util.{Clock, ManualClock} +import org.apache.spark.util.{Clock, ManualClock, Utils} /** Event classes for JobGenerator */ private[scheduler] sealed trait JobGeneratorEvent @@ -104,17 +104,15 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { if (processReceivedData) { logInfo("Stopping JobGenerator gracefully") val timeWhenStopStarted = System.currentTimeMillis() - val stopTimeout = conf.getLong( - "spark.streaming.gracefulStopTimeout", - 10 * ssc.graph.batchDuration.milliseconds - ) + val stopTimeoutMs = conf.getTimeAsMs( + "spark.streaming.gracefulStopTimeout", s"${10 * ssc.graph.batchDuration.milliseconds}ms") val pollTime = 100 // To prevent graceful stop to get stuck permanently def hasTimedOut: Boolean = { - val timedOut = (System.currentTimeMillis() - timeWhenStopStarted) > stopTimeout + val timedOut = (System.currentTimeMillis() - timeWhenStopStarted) > stopTimeoutMs if (timedOut) { - logWarning("Timed out while stopping the job generator (timeout = " + stopTimeout + ")") + logWarning("Timed out while stopping the job generator (timeout = " + stopTimeoutMs + ")") } timedOut } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala index 10c35cba8dc53..91261a9db7360 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala @@ -131,11 +131,11 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable { test("block generator") { val blockGeneratorListener = new FakeBlockGeneratorListener - val blockInterval = 200 - val conf = new SparkConf().set("spark.streaming.blockInterval", blockInterval.toString) + val blockIntervalMs = 200 + val conf = new SparkConf().set("spark.streaming.blockInterval", s"${blockIntervalMs}ms") val blockGenerator = new BlockGenerator(blockGeneratorListener, 1, conf) val expectedBlocks = 5 - val waitTime = expectedBlocks * blockInterval + (blockInterval / 2) + val waitTime = expectedBlocks * blockIntervalMs + (blockIntervalMs / 2) val generatedData = new ArrayBuffer[Int] // Generate blocks @@ -157,15 +157,15 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable { test("block generator throttling") { val blockGeneratorListener = new FakeBlockGeneratorListener - val blockInterval = 100 + val blockIntervalMs = 100 val maxRate = 100 - val conf = new SparkConf().set("spark.streaming.blockInterval", blockInterval.toString). + val conf = new SparkConf().set("spark.streaming.blockInterval", s"${blockIntervalMs}ms"). set("spark.streaming.receiver.maxRate", maxRate.toString) val blockGenerator = new BlockGenerator(blockGeneratorListener, 1, conf) val expectedBlocks = 20 - val waitTime = expectedBlocks * blockInterval + val waitTime = expectedBlocks * blockIntervalMs val expectedMessages = maxRate * waitTime / 1000 - val expectedMessagesPerBlock = maxRate * blockInterval / 1000 + val expectedMessagesPerBlock = maxRate * blockIntervalMs / 1000 val generatedData = new ArrayBuffer[Int] // Generate blocks diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala index d1bbf39dc7897..58353a5f97c8a 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala @@ -73,9 +73,9 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w test("from conf with settings") { val myConf = SparkContext.updatedConf(new SparkConf(false), master, appName) - myConf.set("spark.cleaner.ttl", "10") + myConf.set("spark.cleaner.ttl", "10s") ssc = new StreamingContext(myConf, batchDuration) - assert(ssc.conf.getInt("spark.cleaner.ttl", -1) === 10) + assert(ssc.conf.getTimeAsSeconds("spark.cleaner.ttl", "-1") === 10) } test("from existing SparkContext") { @@ -85,24 +85,26 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w test("from existing SparkContext with settings") { val myConf = SparkContext.updatedConf(new SparkConf(false), master, appName) - myConf.set("spark.cleaner.ttl", "10") + myConf.set("spark.cleaner.ttl", "10s") ssc = new StreamingContext(myConf, batchDuration) - assert(ssc.conf.getInt("spark.cleaner.ttl", -1) === 10) + assert(ssc.conf.getTimeAsSeconds("spark.cleaner.ttl", "-1") === 10) } test("from checkpoint") { val myConf = SparkContext.updatedConf(new SparkConf(false), master, appName) - myConf.set("spark.cleaner.ttl", "10") + myConf.set("spark.cleaner.ttl", "10s") val ssc1 = new StreamingContext(myConf, batchDuration) addInputStream(ssc1).register() ssc1.start() val cp = new Checkpoint(ssc1, Time(1000)) - assert(cp.sparkConfPairs.toMap.getOrElse("spark.cleaner.ttl", "-1") === "10") + assert( + Utils.timeStringAsSeconds(cp.sparkConfPairs + .toMap.getOrElse("spark.cleaner.ttl", "-1")) === 10) ssc1.stop() val newCp = Utils.deserialize[Checkpoint](Utils.serialize(cp)) - assert(newCp.createSparkConf().getInt("spark.cleaner.ttl", -1) === 10) + assert(newCp.createSparkConf().getTimeAsSeconds("spark.cleaner.ttl", "-1") === 10) ssc = new StreamingContext(null, newCp, null) - assert(ssc.conf.getInt("spark.cleaner.ttl", -1) === 10) + assert(ssc.conf.getTimeAsSeconds("spark.cleaner.ttl", "-1") === 10) } test("start and stop state check") { @@ -176,7 +178,7 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w test("stop gracefully") { val conf = new SparkConf().setMaster(master).setAppName(appName) - conf.set("spark.cleaner.ttl", "3600") + conf.set("spark.cleaner.ttl", "3600s") sc = new SparkContext(conf) for (i <- 1 to 4) { logInfo("==================================\n\n\n") @@ -207,7 +209,7 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w test("stop slow receiver gracefully") { val conf = new SparkConf().setMaster(master).setAppName(appName) - conf.set("spark.streaming.gracefulStopTimeout", "20000") + conf.set("spark.streaming.gracefulStopTimeout", "20000s") sc = new SparkContext(conf) logInfo("==================================\n\n\n") ssc = new StreamingContext(sc, Milliseconds(100)) diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala index 26259cee77151..c357b7ae9d4da 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -296,7 +296,7 @@ private[spark] class ApplicationMaster( // we want to be reasonably responsive without causing too many requests to RM. val schedulerInterval = - sparkConf.getLong("spark.yarn.scheduler.heartbeat.interval-ms", 5000) + sparkConf.getTimeAsMs("spark.yarn.scheduler.heartbeat.interval-ms", "5s") // must be <= expiryInterval / 2. val interval = math.max(0, math.min(expiryInterval / 2, schedulerInterval)) @@ -379,7 +379,8 @@ private[spark] class ApplicationMaster( logWarning( "spark.yarn.applicationMaster.waitTries is deprecated, use spark.yarn.am.waitTime") } - val totalWaitTime = sparkConf.getLong("spark.yarn.am.waitTime", waitTries.getOrElse(100000L)) + val totalWaitTime = sparkConf.getTimeAsMs("spark.yarn.am.waitTime", + s"${waitTries.getOrElse(100000L)}ms") val deadline = System.currentTimeMillis() + totalWaitTime while (sparkContextRef.get() == null && System.currentTimeMillis < deadline && !finished) { @@ -404,8 +405,8 @@ private[spark] class ApplicationMaster( // Spark driver should already be up since it launched us, but we don't want to // wait forever, so wait 100 seconds max to match the cluster mode setting. - val totalWaitTime = sparkConf.getLong("spark.yarn.am.waitTime", 100000L) - val deadline = System.currentTimeMillis + totalWaitTime + val totalWaitTimeMs = sparkConf.getTimeAsMs("spark.yarn.am.waitTime", "100s") + val deadline = System.currentTimeMillis + totalWaitTimeMs while (!driverUp && !finished && System.currentTimeMillis < deadline) { try { From d7f2c198678d1f5ffc1c6da3a6fb189b0a4ef070 Mon Sep 17 00:00:00 2001 From: hlin09 Date: Mon, 13 Apr 2015 16:53:50 -0700 Subject: [PATCH 266/591] [SPARK-6881][SparkR] Changes the checkpoint directory name. Author: hlin09 Closes #5493 from hlin09/fixCheckpointDir and squashes the following commits: e67fc40 [hlin09] Change to temp dir. 1f7ed9e [hlin09] Change the checkpoint dir name. --- R/pkg/R/RDD.R | 2 +- R/pkg/R/context.R | 2 +- R/pkg/inst/tests/test_rdd.R | 5 +++-- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/R/pkg/R/RDD.R b/R/pkg/R/RDD.R index 604ad03c407b9..d6a75007a6ad7 100644 --- a/R/pkg/R/RDD.R +++ b/R/pkg/R/RDD.R @@ -279,7 +279,7 @@ setMethod("unpersist", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' setCheckpointDir(sc, "checkpoints") +#' setCheckpointDir(sc, "checkpoint") #' rdd <- parallelize(sc, 1:10, 2L) #' checkpoint(rdd) #'} diff --git a/R/pkg/R/context.R b/R/pkg/R/context.R index 2fc0bb294bcce..ebbb8fba1052d 100644 --- a/R/pkg/R/context.R +++ b/R/pkg/R/context.R @@ -216,7 +216,7 @@ broadcast <- function(sc, object) { #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' setCheckpointDir(sc, "~/checkpoints") +#' setCheckpointDir(sc, "~/checkpoint") #' rdd <- parallelize(sc, 1:2, 2L) #' checkpoint(rdd) #'} diff --git a/R/pkg/inst/tests/test_rdd.R b/R/pkg/inst/tests/test_rdd.R index f75e0817b9406..b76e4db03e715 100644 --- a/R/pkg/inst/tests/test_rdd.R +++ b/R/pkg/inst/tests/test_rdd.R @@ -141,7 +141,8 @@ test_that("PipelinedRDD support actions: cache(), persist(), unpersist(), checkp unpersist(rdd2) expect_false(rdd2@env$isCached) - setCheckpointDir(sc, "checkpoints") + tempDir <- tempfile(pattern = "checkpoint") + setCheckpointDir(sc, tempDir) checkpoint(rdd2) expect_true(rdd2@env$isCheckpointed) @@ -152,7 +153,7 @@ test_that("PipelinedRDD support actions: cache(), persist(), unpersist(), checkp # make sure the data is collectable collect(rdd2) - unlink("checkpoints") + unlink(tempDir) }) test_that("reduce on RDD", { From 5b8b324f33e857b95de65031334846a7ca26fa60 Mon Sep 17 00:00:00 2001 From: Liang-Chi Hsieh Date: Mon, 13 Apr 2015 18:15:29 -0700 Subject: [PATCH 267/591] [SPARK-6303][SQL] Remove unnecessary Average in GeneratedAggregate Because `Average` is a `PartialAggregate`, we never get a `Average` node when reaching `HashAggregation` to prepare `GeneratedAggregate`. That is why in SQLQuerySuite there is already a test for `avg` with codegen. And it works. But we can find a case in `GeneratedAggregate` to deal with `Average`. Based on the above, we actually never execute this case. So we can remove this case from `GeneratedAggregate`. Author: Liang-Chi Hsieh Closes #4996 from viirya/add_average_codegened and squashes the following commits: 621c12f [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into add_average_codegened 368cfbc [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into add_average_codegened 74926d1 [Liang-Chi Hsieh] Add Average in canBeCodeGened lists. --- .../sql/execution/GeneratedAggregate.scala | 45 ------------------- 1 file changed, 45 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala index 95176e425132d..b510cf033c4a4 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala @@ -153,51 +153,6 @@ case class GeneratedAggregate( AggregateEvaluation(currentSum :: Nil, initialValue :: Nil, updateFunction :: Nil, result) - case a @ Average(expr) => - val calcType = - expr.dataType match { - case DecimalType.Fixed(_, _) => - DecimalType.Unlimited - case _ => - expr.dataType - } - - val currentCount = AttributeReference("currentCount", LongType, nullable = false)() - val currentSum = AttributeReference("currentSum", calcType, nullable = false)() - val initialCount = Literal(0L) - val initialSum = Cast(Literal(0L), calcType) - - // If we're evaluating UnscaledValue(x), we can do Count on x directly, since its - // UnscaledValue will be null if and only if x is null; helps with Average on decimals - val toCount = expr match { - case UnscaledValue(e) => e - case _ => expr - } - - val updateCount = If(IsNotNull(toCount), Add(currentCount, Literal(1L)), currentCount) - val updateSum = Coalesce(Add(Cast(expr, calcType), currentSum) :: currentSum :: Nil) - - val result = - expr.dataType match { - case DecimalType.Fixed(_, _) => - If(EqualTo(currentCount, Literal(0L)), - Literal.create(null, a.dataType), - Cast(Divide( - Cast(currentSum, DecimalType.Unlimited), - Cast(currentCount, DecimalType.Unlimited)), a.dataType)) - case _ => - If(EqualTo(currentCount, Literal(0L)), - Literal.create(null, a.dataType), - Divide(Cast(currentSum, a.dataType), Cast(currentCount, a.dataType))) - } - - AggregateEvaluation( - currentCount :: currentSum :: Nil, - initialCount :: initialSum :: Nil, - updateCount :: updateSum :: Nil, - result - ) - case m @ Max(expr) => val currentMax = AttributeReference("currentMax", expr.dataType, nullable = true)() val initialValue = Literal.create(null, expr.dataType) From 4898dfa464be55772e3f9db10c48adcb3cfc9a3d Mon Sep 17 00:00:00 2001 From: Liang-Chi Hsieh Date: Mon, 13 Apr 2015 18:16:33 -0700 Subject: [PATCH 268/591] [SPARK-6877][SQL] Add code generation support for Min Currently `min` is not supported in code generation. This pr adds the support for it. Author: Liang-Chi Hsieh Closes #5487 from viirya/add_min_codegen and squashes the following commits: 0ddec23 [Liang-Chi Hsieh] Add code generation support for Min. --- .../sql/catalyst/expressions/arithmetic.scala | 45 +++++++++++++++++++ .../expressions/codegen/CodeGenerator.scala | 24 ++++++++++ .../ExpressionEvaluationSuite.scala | 10 +++++ .../sql/execution/GeneratedAggregate.scala | 13 ++++++ .../spark/sql/execution/SparkStrategies.scala | 2 +- .../org/apache/spark/sql/SQLQuerySuite.scala | 14 ++++-- 6 files changed, 104 insertions(+), 4 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala index 1f6526ef66c56..566b34f7c3a6a 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala @@ -369,6 +369,51 @@ case class MaxOf(left: Expression, right: Expression) extends Expression { override def toString: String = s"MaxOf($left, $right)" } +case class MinOf(left: Expression, right: Expression) extends Expression { + type EvaluatedType = Any + + override def foldable: Boolean = left.foldable && right.foldable + + override def nullable: Boolean = left.nullable && right.nullable + + override def children: Seq[Expression] = left :: right :: Nil + + override lazy val resolved = + left.resolved && right.resolved && + left.dataType == right.dataType + + override def dataType: DataType = { + if (!resolved) { + throw new UnresolvedException(this, + s"datatype. Can not resolve due to differing types ${left.dataType}, ${right.dataType}") + } + left.dataType + } + + lazy val ordering = left.dataType match { + case i: NativeType => i.ordering.asInstanceOf[Ordering[Any]] + case other => sys.error(s"Type $other does not support ordered operations") + } + + override def eval(input: Row): Any = { + val evalE1 = left.eval(input) + val evalE2 = right.eval(input) + if (evalE1 == null) { + evalE2 + } else if (evalE2 == null) { + evalE1 + } else { + if (ordering.compare(evalE1, evalE2) < 0) { + evalE1 + } else { + evalE2 + } + } + } + + override def toString: String = s"MinOf($left, $right)" +} + /** * A function that get the absolute value of the numeric value. */ diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala index aac56e1568332..d141354a0f427 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala @@ -524,6 +524,30 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin } """.children + case MinOf(e1, e2) => + val eval1 = expressionEvaluator(e1) + val eval2 = expressionEvaluator(e2) + + eval1.code ++ eval2.code ++ + q""" + var $nullTerm = false + var $primitiveTerm: ${termForType(e1.dataType)} = ${defaultPrimitive(e1.dataType)} + + if (${eval1.nullTerm}) { + $nullTerm = ${eval2.nullTerm} + $primitiveTerm = ${eval2.primitiveTerm} + } else if (${eval2.nullTerm}) { + $nullTerm = ${eval1.nullTerm} + $primitiveTerm = ${eval1.primitiveTerm} + } else { + if (${eval1.primitiveTerm} < ${eval2.primitiveTerm}) { + $primitiveTerm = ${eval1.primitiveTerm} + } else { + $primitiveTerm = ${eval2.primitiveTerm} + } + } + """.children + case UnscaledValue(child) => val childEval = expressionEvaluator(child) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala index d2b1090a0cdd5..d4362a91d992c 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala @@ -233,6 +233,16 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(MaxOf(2, Literal.create(null, IntegerType)), 2) } + test("MinOf") { + checkEvaluation(MinOf(1, 2), 1) + checkEvaluation(MinOf(2, 1), 1) + checkEvaluation(MinOf(1L, 2L), 1L) + checkEvaluation(MinOf(2L, 1L), 1L) + + checkEvaluation(MinOf(Literal.create(null, IntegerType), 1), 1) + checkEvaluation(MinOf(1, Literal.create(null, IntegerType)), 1) + } + test("LIKE literal Regular Expression") { checkEvaluation(Literal.create(null, StringType).like("a"), null) checkEvaluation(Literal.create("a", StringType).like(Literal.create(null, StringType)), null) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala index b510cf033c4a4..b1ef6556de1e9 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala @@ -164,6 +164,17 @@ case class GeneratedAggregate( updateMax :: Nil, currentMax) + case m @ Min(expr) => + val currentMin = AttributeReference("currentMin", expr.dataType, nullable = true)() + val initialValue = Literal.create(null, expr.dataType) + val updateMin = MinOf(currentMin, expr) + + AggregateEvaluation( + currentMin :: Nil, + initialValue :: Nil, + updateMin :: Nil, + currentMin) + case CollectHashSet(Seq(expr)) => val set = AttributeReference("hashSet", new OpenHashSetUDT(expr.dataType), nullable = false)() @@ -188,6 +199,8 @@ case class GeneratedAggregate( initialValue :: Nil, collectSets :: Nil, CountSet(set)) + + case o => sys.error(s"$o can't be codegened.") } val computationSchema = computeFunctions.flatMap(_.schema) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala index f0d92ffffcda3..5b99e40c2f491 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala @@ -155,7 +155,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { } def canBeCodeGened(aggs: Seq[AggregateExpression]): Boolean = !aggs.exists { - case _: CombineSum | _: Sum | _: Count | _: Max | _: CombineSetsAndCount => false + case _: CombineSum | _: Sum | _: Count | _: Max | _: Min | _: CombineSetsAndCount => false // The generated set implementation is pretty limited ATM. case CollectHashSet(exprs) if exprs.size == 1 && Seq(IntegerType, LongType).contains(exprs.head.dataType) => false diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala index 5e453e05e2ac7..73fb791c3ead7 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala @@ -172,6 +172,13 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { testCodeGen( "SELECT max(key) FROM testData3x", Row(100) :: Nil) + // MIN + testCodeGen( + "SELECT value, min(key) FROM testData3x GROUP BY value", + (1 to 100).map(i => Row(i.toString, i))) + testCodeGen( + "SELECT min(key) FROM testData3x", + Row(1) :: Nil) // Some combinations. testCodeGen( """ @@ -179,16 +186,17 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { | value, | sum(key), | max(key), + | min(key), | avg(key), | count(key), | count(distinct key) |FROM testData3x |GROUP BY value """.stripMargin, - (1 to 100).map(i => Row(i.toString, i*3, i, i, 3, 1))) + (1 to 100).map(i => Row(i.toString, i*3, i, i, i, 3, 1))) testCodeGen( - "SELECT max(key), avg(key), count(key), count(distinct key) FROM testData3x", - Row(100, 50.5, 300, 100) :: Nil) + "SELECT max(key), min(key), avg(key), count(key), count(distinct key) FROM testData3x", + Row(100, 1, 50.5, 300, 100) :: Nil) // Aggregate with Code generation handling all null values testCodeGen( "SELECT sum('a'), avg('a'), count(null) FROM testData", From 435b8779df01a7477addecb1023605957bca4e9b Mon Sep 17 00:00:00 2001 From: Nathan Kronenfeld Date: Mon, 13 Apr 2015 18:21:16 -0700 Subject: [PATCH 269/591] [Spark-4848] Allow different Worker configurations in standalone cluster This refixes #3699 with the latest code. This fixes SPARK-4848 I've changed the stand-alone cluster scripts to allow different workers to have different numbers of instances, with both port and web-ui port following allong appropriately. I did this by moving the loop over instances from start-slaves and stop-slaves (on the master) to start-slave and stop-slave (on the worker). Wile I was at it, I changed SPARK_WORKER_PORT to work the same way as SPARK_WORKER_WEBUI_PORT, since the new methods work fine for both. Author: Nathan Kronenfeld Closes #5140 from nkronenfeld/feature/spark-4848 and squashes the following commits: cf5f47e [Nathan Kronenfeld] Merge remote branch 'upstream/master' into feature/spark-4848 044ca6f [Nathan Kronenfeld] Documentation and formatting as requested by by andrewor14 d739640 [Nathan Kronenfeld] Move looping through instances from the master to the workers, so that each worker respects its own number of instances and web-ui port --- sbin/start-slave.sh | 59 +++++++++++++++++++++++++++++++++++++++++--- sbin/start-slaves.sh | 11 +-------- sbin/stop-slave.sh | 43 ++++++++++++++++++++++++++++++++ sbin/stop-slaves.sh | 12 +++------ 4 files changed, 103 insertions(+), 22 deletions(-) create mode 100755 sbin/stop-slave.sh diff --git a/sbin/start-slave.sh b/sbin/start-slave.sh index 5a6de11afdd3d..4c919ff76a8f5 100755 --- a/sbin/start-slave.sh +++ b/sbin/start-slave.sh @@ -18,15 +18,68 @@ # # Starts a slave on the machine this script is executed on. +# +# Environment Variables +# +# SPARK_WORKER_INSTANCES The number of worker instances to run on this +# slave. Default is 1. +# SPARK_WORKER_PORT The base port number for the first worker. If set, +# subsequent workers will increment this number. If +# unset, Spark will find a valid port number, but +# with no guarantee of a predictable pattern. +# SPARK_WORKER_WEBUI_PORT The base port for the web interface of the first +# worker. Subsequent workers will increment this +# number. Default is 8081. -usage="Usage: start-slave.sh where is like spark://localhost:7077" +usage="Usage: start-slave.sh where is like spark://localhost:7077" -if [ $# -lt 2 ]; then +if [ $# -lt 1 ]; then echo $usage + echo Called as start-slave.sh $* exit 1 fi sbin="`dirname "$0"`" sbin="`cd "$sbin"; pwd`" -"$sbin"/spark-daemon.sh start org.apache.spark.deploy.worker.Worker "$@" +. "$sbin/spark-config.sh" + +. "$SPARK_PREFIX/bin/load-spark-env.sh" + +# First argument should be the master; we need to store it aside because we may +# need to insert arguments between it and the other arguments +MASTER=$1 +shift + +# Determine desired worker port +if [ "$SPARK_WORKER_WEBUI_PORT" = "" ]; then + SPARK_WORKER_WEBUI_PORT=8081 +fi + +# Start up the appropriate number of workers on this machine. +# quick local function to start a worker +function start_instance { + WORKER_NUM=$1 + shift + + if [ "$SPARK_WORKER_PORT" = "" ]; then + PORT_FLAG= + PORT_NUM= + else + PORT_FLAG="--port" + PORT_NUM=$(( $SPARK_WORKER_PORT + $WORKER_NUM - 1 )) + fi + WEBUI_PORT=$(( $SPARK_WORKER_WEBUI_PORT + $WORKER_NUM - 1 )) + + "$sbin"/spark-daemon.sh start org.apache.spark.deploy.worker.Worker $WORKER_NUM \ + --webui-port "$WEBUI_PORT" $PORT_FLAG $PORT_NUM $MASTER "$@" +} + +if [ "$SPARK_WORKER_INSTANCES" = "" ]; then + start_instance 1 "$@" +else + for ((i=0; i<$SPARK_WORKER_INSTANCES; i++)); do + start_instance $(( 1 + $i )) "$@" + done +fi + diff --git a/sbin/start-slaves.sh b/sbin/start-slaves.sh index 4356c03657109..24d6268815ed3 100755 --- a/sbin/start-slaves.sh +++ b/sbin/start-slaves.sh @@ -59,13 +59,4 @@ if [ "$START_TACHYON" == "true" ]; then fi # Launch the slaves -if [ "$SPARK_WORKER_INSTANCES" = "" ]; then - exec "$sbin/slaves.sh" cd "$SPARK_HOME" \; "$sbin/start-slave.sh" 1 "spark://$SPARK_MASTER_IP:$SPARK_MASTER_PORT" -else - if [ "$SPARK_WORKER_WEBUI_PORT" = "" ]; then - SPARK_WORKER_WEBUI_PORT=8081 - fi - for ((i=0; i<$SPARK_WORKER_INSTANCES; i++)); do - "$sbin/slaves.sh" cd "$SPARK_HOME" \; "$sbin/start-slave.sh" $(( $i + 1 )) --webui-port $(( $SPARK_WORKER_WEBUI_PORT + $i )) "spark://$SPARK_MASTER_IP:$SPARK_MASTER_PORT" - done -fi +"$sbin/slaves.sh" cd "$SPARK_HOME" \; "$sbin/start-slave.sh" "spark://$SPARK_MASTER_IP:$SPARK_MASTER_PORT" diff --git a/sbin/stop-slave.sh b/sbin/stop-slave.sh new file mode 100755 index 0000000000000..3d1da5b254f2a --- /dev/null +++ b/sbin/stop-slave.sh @@ -0,0 +1,43 @@ +#!/usr/bin/env bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# A shell script to stop all workers on a single slave +# +# Environment variables +# +# SPARK_WORKER_INSTANCES The number of worker instances that should be +# running on this slave. Default is 1. + +# Usage: stop-slave.sh +# Stops all slaves on this worker machine + +sbin="`dirname "$0"`" +sbin="`cd "$sbin"; pwd`" + +. "$sbin/spark-config.sh" + +. "$SPARK_PREFIX/bin/load-spark-env.sh" + +if [ "$SPARK_WORKER_INSTANCES" = "" ]; then + "$sbin"/spark-daemon.sh stop org.apache.spark.deploy.worker.Worker 1 +else + for ((i=0; i<$SPARK_WORKER_INSTANCES; i++)); do + "$sbin"/spark-daemon.sh stop org.apache.spark.deploy.worker.Worker $(( $i + 1 )) + done +fi diff --git a/sbin/stop-slaves.sh b/sbin/stop-slaves.sh index 7c2201100ef97..54c9bd46803a9 100755 --- a/sbin/stop-slaves.sh +++ b/sbin/stop-slaves.sh @@ -17,8 +17,8 @@ # limitations under the License. # -sbin=`dirname "$0"` -sbin=`cd "$sbin"; pwd` +sbin="`dirname "$0"`" +sbin="`cd "$sbin"; pwd`" . "$sbin/spark-config.sh" @@ -29,10 +29,4 @@ if [ -e "$sbin"/../tachyon/bin/tachyon ]; then "$sbin/slaves.sh" cd "$SPARK_HOME" \; "$sbin"/../tachyon/bin/tachyon killAll tachyon.worker.Worker fi -if [ "$SPARK_WORKER_INSTANCES" = "" ]; then - "$sbin"/spark-daemons.sh stop org.apache.spark.deploy.worker.Worker 1 -else - for ((i=0; i<$SPARK_WORKER_INSTANCES; i++)); do - "$sbin"/spark-daemons.sh stop org.apache.spark.deploy.worker.Worker $(( $i + 1 )) - done -fi +"$sbin/slaves.sh" cd "$SPARK_HOME" \; "$sbin"/stop-slave.sh From 3782e1f2bec07b5ffbc8503e40591e96fce67256 Mon Sep 17 00:00:00 2001 From: Fei Wang Date: Mon, 13 Apr 2015 18:23:35 -0700 Subject: [PATCH 270/591] [SQL] [Minor] Fix for SqlApp.scala SqlApp.scala is out of date. Author: Fei Wang Closes #5485 from scwf/patch-1 and squashes the following commits: 6f731c2 [Fei Wang] SqlApp.scala compile error --- dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala b/dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala index d888de929fdda..cc86ef45858c9 100644 --- a/dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala +++ b/dev/audit-release/sbt_app_sql/src/main/scala/SqlApp.scala @@ -36,8 +36,10 @@ object SparkSqlExample { val sc = new SparkContext(conf) val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ import sqlContext._ - val people = sc.makeRDD(1 to 100, 10).map(x => Person(s"Name$x", x)) + + val people = sc.makeRDD(1 to 100, 10).map(x => Person(s"Name$x", x)).toDF() people.registerTempTable("people") val teenagers = sql("SELECT name FROM people WHERE age >= 13 AND age <= 19") val teenagerNames = teenagers.map(t => "Name: " + t(0)).collect() From b45059d0d7809a986ba07a447deb71f11ec6afe4 Mon Sep 17 00:00:00 2001 From: Daoyuan Wang Date: Mon, 13 Apr 2015 18:26:00 -0700 Subject: [PATCH 271/591] [SPARK-5794] [SQL] fix add jar Author: Daoyuan Wang Closes #4586 from adrian-wang/addjar and squashes the following commits: efdd602 [Daoyuan Wang] move jar to another place 6c707e8 [Daoyuan Wang] restrict hive version for test 32c4fb8 [Daoyuan Wang] fix style and add a test 9957d87 [Daoyuan Wang] use sessionstate classloader in makeRDDforTable 0810e71 [Daoyuan Wang] remove variable substitution 1898309 [Daoyuan Wang] fix classnotfound 95a40da [Daoyuan Wang] support env argus in add jar, and set add jar ret to 0 --- .../hive/thriftserver/SparkSQLCLIDriver.scala | 5 +++-- .../apache/spark/sql/hive/TableReader.scala | 5 ++++- .../spark/sql/hive/execution/commands.scala | 2 +- .../resources/hive-hcatalog-core-0.13.1.jar | Bin 0 -> 468533 bytes .../sql/hive/execution/HiveQuerySuite.scala | 15 +++++++++++++++ 5 files changed, 23 insertions(+), 4 deletions(-) create mode 100644 sql/hive/src/test/resources/hive-hcatalog-core-0.13.1.jar diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala index 6272cdedb3e48..62c061bef690a 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala @@ -33,7 +33,7 @@ import org.apache.hadoop.hive.common.{HiveInterruptCallback, HiveInterruptUtils, import org.apache.hadoop.hive.conf.HiveConf import org.apache.hadoop.hive.ql.Driver import org.apache.hadoop.hive.ql.exec.Utilities -import org.apache.hadoop.hive.ql.processors.{SetProcessor, CommandProcessor, CommandProcessorFactory} +import org.apache.hadoop.hive.ql.processors.{AddResourceProcessor, SetProcessor, CommandProcessor, CommandProcessorFactory} import org.apache.hadoop.hive.ql.session.SessionState import org.apache.hadoop.hive.shims.ShimLoader import org.apache.thrift.transport.TSocket @@ -264,7 +264,8 @@ private[hive] class SparkSQLCLIDriver extends CliDriver with Logging { val proc: CommandProcessor = HiveShim.getCommandProcessor(Array(tokens(0)), hconf) if (proc != null) { - if (proc.isInstanceOf[Driver] || proc.isInstanceOf[SetProcessor]) { + if (proc.isInstanceOf[Driver] || proc.isInstanceOf[SetProcessor] || + proc.isInstanceOf[AddResourceProcessor]) { val driver = new SparkSQLDriver driver.init() diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala index d35291543c9f9..e556c74ffb015 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala @@ -35,6 +35,7 @@ import org.apache.spark.broadcast.Broadcast import org.apache.spark.rdd.{EmptyRDD, HadoopRDD, RDD, UnionRDD} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.types.DateUtils +import org.apache.spark.util.Utils /** * A trait for subclasses that handle table scans. @@ -76,7 +77,9 @@ class HadoopTableReader( override def makeRDDForTable(hiveTable: HiveTable): RDD[Row] = makeRDDForTable( hiveTable, - relation.tableDesc.getDeserializerClass.asInstanceOf[Class[Deserializer]], + Class.forName( + relation.tableDesc.getSerdeClassName, true, sc.sessionState.getConf.getClassLoader) + .asInstanceOf[Class[Deserializer]], filterOpt = None) /** diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala index 99dc58646ddd6..902a12785e3e9 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala @@ -80,7 +80,7 @@ case class AddJar(path: String) extends RunnableCommand { val hiveContext = sqlContext.asInstanceOf[HiveContext] hiveContext.runSqlHive(s"ADD JAR $path") hiveContext.sparkContext.addJar(path) - Seq.empty[Row] + Seq(Row(0)) } } diff --git a/sql/hive/src/test/resources/hive-hcatalog-core-0.13.1.jar b/sql/hive/src/test/resources/hive-hcatalog-core-0.13.1.jar new file mode 100644 index 0000000000000000000000000000000000000000..37af9aafad8a41d3007b63a610b1af37c324544a GIT binary patch literal 468533 zcmb5V1DIt`l0IDSvTfV8ZQHkO+qP{RUG74cZQHKuGP>AR|DN5M{l1yq>EG^s&U5kH zc;iGy<{KFqc~VgZ6bu^ZZ!ex2Q?dW}^7l8W-=A_~szP*9^5P7Ne;0!U;{GkhtKaH6 z|NHN+z(7E#|9>$#A$cirF%?yMIq}ehDXRfS#E@%{?*rWdA##PlTgW0((1C)&bw-KH z4HdLlTJrO4^%oCcjHT;Xnpg-r#q6!mlR1?yY;T~YwH#VwIvOyd{O_jcY)|(_c59+S 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zAQ^~7`l|Qc{lf4fg4bpw4&D3T94>=$`bolET|cWdF$NhqILHVPHC_gwL|Q%R822ak z(o_>-fG&sYVr2j_zN*jN6%!fXV->7Jw+Z&d$zAfsg7C$wgS*zX1*8A#KiIgW&q|VExFVLHC72QywuvA zuWor2ky_(pOo#FLDj?Ez?uPnf{j;(65d}@4i2JhJLRP5&2-v8$nqH-SAUegv-{;rJ zM0-q3DnwM@!ZTMAu=w)@KW@5P6Bb}G>#YOohTsl3eqg^+`9t$(DDrn9?UM-Z^)1$M zEznQdfd|#qUllIKzRLnM95A%|vfIXQ>#H4jFayW-CeDu0b9Z?M{km?q+v<1g?oRAJ z_Sbl`@$_Bo=9QJYoAqb$Y9bJF7`^Kj_7_Uryx2FESG=ne;X2Vs`!2ij<`CDt-Jd<* z<#{vei`fZB=2EpSCN`uN3sTuD4<08Txq$d*9=SBI+hQFax| Date: Mon, 13 Apr 2015 20:43:24 -0700 Subject: [PATCH 272/591] [Minor][SparkR] Minor refactor and removes redundancy related to cleanClosure. 1. Only use `cleanClosure` in creation of RRDDs. Normally, user and developer do not need to call `cleanClosure` in their function definition. 2. Removes redundant code (e.g. unnecessary wrapper functions) related to `cleanClosure`. Author: hlin09 Closes #5495 from hlin09/cleanClosureFix and squashes the following commits: 74ec303 [hlin09] Minor refactor and removes redundancy. --- R/pkg/R/RDD.R | 16 ++++------------ R/pkg/R/pairRDD.R | 4 ---- 2 files changed, 4 insertions(+), 16 deletions(-) diff --git a/R/pkg/R/RDD.R b/R/pkg/R/RDD.R index d6a75007a6ad7..820027ef67e3b 100644 --- a/R/pkg/R/RDD.R +++ b/R/pkg/R/RDD.R @@ -85,7 +85,7 @@ setMethod("initialize", "PipelinedRDD", function(.Object, prev, func, jrdd_val) if (!inherits(prev, "PipelinedRDD") || !isPipelinable(prev)) { # This transformation is the first in its stage: - .Object@func <- func + .Object@func <- cleanClosure(func) .Object@prev_jrdd <- getJRDD(prev) .Object@env$prev_serializedMode <- prev@env$serializedMode # NOTE: We use prev_serializedMode to track the serialization mode of prev_JRDD @@ -94,7 +94,7 @@ setMethod("initialize", "PipelinedRDD", function(.Object, prev, func, jrdd_val) pipelinedFunc <- function(split, iterator) { func(split, prev@func(split, iterator)) } - .Object@func <- pipelinedFunc + .Object@func <- cleanClosure(pipelinedFunc) .Object@prev_jrdd <- prev@prev_jrdd # maintain the pipeline # Get the serialization mode of the parent RDD .Object@env$prev_serializedMode <- prev@env$prev_serializedMode @@ -144,17 +144,13 @@ setMethod("getJRDD", signature(rdd = "PipelinedRDD"), return(rdd@env$jrdd_val) } - computeFunc <- function(split, part) { - rdd@func(split, part) - } - packageNamesArr <- serialize(.sparkREnv[[".packages"]], connection = NULL) broadcastArr <- lapply(ls(.broadcastNames), function(name) { get(name, .broadcastNames) }) - serializedFuncArr <- serialize(computeFunc, connection = NULL) + serializedFuncArr <- serialize(rdd@func, connection = NULL) prev_jrdd <- rdd@prev_jrdd @@ -551,11 +547,7 @@ setMethod("mapPartitions", setMethod("lapplyPartitionsWithIndex", signature(X = "RDD", FUN = "function"), function(X, FUN) { - FUN <- cleanClosure(FUN) - closureCapturingFunc <- function(split, part) { - FUN(split, part) - } - PipelinedRDD(X, closureCapturingFunc) + PipelinedRDD(X, FUN) }) #' @rdname lapplyPartitionsWithIndex diff --git a/R/pkg/R/pairRDD.R b/R/pkg/R/pairRDD.R index c2396c32a7548..739d399f0820f 100644 --- a/R/pkg/R/pairRDD.R +++ b/R/pkg/R/pairRDD.R @@ -694,10 +694,6 @@ setMethod("cogroup", for (i in 1:rddsLen) { rdds[[i]] <- lapply(rdds[[i]], function(x) { list(x[[1]], list(i, x[[2]])) }) - # TODO(hao): As issue [SparkR-142] mentions, the right value of i - # will not be captured into UDF if getJRDD is not invoked. - # It should be resolved together with that issue. - getJRDD(rdds[[i]]) # Capture the closure. } union.rdd <- Reduce(unionRDD, rdds) group.func <- function(vlist) { From 971b95b0c9002bd541bcbe0da54a9967ba22588f Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Mon, 13 Apr 2015 21:18:05 -0700 Subject: [PATCH 273/591] [SPARK-5957][ML] better handling of parameters The design doc was posted on the JIRA page. Python changes will be in a follow-up PR. jkbradley 1. Use codegen for shared params. 1. Move shared params to package `ml.param.shared`. 1. Set default values in `Params` instead of in `Param`. 1. Add a few methods to `Params` and `ParamMap`. 1. Move schema handling to `SchemaUtils` from `Params`. - [x] check visibility of the methods added Author: Xiangrui Meng Closes #5431 from mengxr/SPARK-5957 and squashes the following commits: d19236d [Xiangrui Meng] fix test 26ae2d7 [Xiangrui Meng] re-gen code and mark clear protected 38b78c7 [Xiangrui Meng] update Param.toString and remove Params.explain() 409e2d5 [Xiangrui Meng] address comments 2d637bd [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-5957 eec2264 [Xiangrui Meng] make get* public in Params 4090d95 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-5957 4fee9e7 [Xiangrui Meng] re-gen shared params 2737c2d [Xiangrui Meng] rename SharedParamCodeGen to SharedParamsCodeGen e938f81 [Xiangrui Meng] update code to set default parameter values 28ed322 [Xiangrui Meng] merge master 55be1f3 [Xiangrui Meng] merge master d63b5cc [Xiangrui Meng] fix examples 29b004c [Xiangrui Meng] update ParamsSuite 94fd98e [Xiangrui Meng] fix explain params 48d0e84 [Xiangrui Meng] add remove and update explainParams 4ac6348 [Xiangrui Meng] move schema utils to SchemaUtils add a few methods to Params 0d9594e [Xiangrui Meng] add getOrElse to ParamMap eeeffe8 [Xiangrui Meng] map ++ paramMap => extractValues 0d3fc5b [Xiangrui Meng] setDefault after param a9dbf59 [Xiangrui Meng] minor updates d9302b8 [Xiangrui Meng] generate default values 1c72579 [Xiangrui Meng] pass test compile abb7a3b [Xiangrui Meng] update default values handling dcab97a [Xiangrui Meng] add codegen for shared params --- .../examples/ml/JavaDeveloperApiExample.java | 4 +- .../examples/ml/DeveloperApiExample.scala | 6 +- .../scala/org/apache/spark/ml/Estimator.scala | 2 +- .../scala/org/apache/spark/ml/Pipeline.scala | 10 +- .../org/apache/spark/ml/Transformer.scala | 5 +- .../spark/ml/classification/Classifier.scala | 17 +- .../classification/LogisticRegression.scala | 18 +- .../ProbabilisticClassifier.scala | 11 +- .../BinaryClassificationEvaluator.scala | 15 +- .../apache/spark/ml/feature/HashingTF.scala | 6 +- .../apache/spark/ml/feature/Normalizer.scala | 7 +- .../spark/ml/feature/StandardScaler.scala | 9 +- .../spark/ml/feature/StringIndexer.scala | 10 +- .../apache/spark/ml/feature/Tokenizer.scala | 16 +- .../spark/ml/feature/VectorAssembler.scala | 7 +- .../spark/ml/feature/VectorIndexer.scala | 25 +- .../spark/ml/impl/estimator/Predictor.scala | 16 +- .../org/apache/spark/ml/param/params.scala | 236 ++++++++++------ .../ml/param/shared/SharedParamsCodeGen.scala | 169 ++++++++++++ .../spark/ml/param/shared/sharedParams.scala | 259 ++++++++++++++++++ .../apache/spark/ml/param/sharedParams.scala | 173 ------------ .../apache/spark/ml/recommendation/ALS.scala | 49 ++-- .../ml/regression/LinearRegression.scala | 8 +- .../spark/ml/tuning/CrossValidator.scala | 18 +- .../apache/spark/ml/util/SchemaUtils.scala | 61 +++++ .../apache/spark/ml/param/ParamsSuite.scala | 47 +++- .../apache/spark/ml/param/TestParams.scala | 12 +- 27 files changed, 820 insertions(+), 396 deletions(-) create mode 100644 mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala create mode 100644 mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala delete mode 100644 mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala create mode 100644 mllib/src/main/scala/org/apache/spark/ml/util/SchemaUtils.scala diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java index 19d0eb216848e..eaf00d09f550d 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java @@ -116,7 +116,7 @@ class MyJavaLogisticRegression */ IntParam maxIter = new IntParam(this, "maxIter", "max number of iterations"); - int getMaxIter() { return (Integer) get(maxIter); } + int getMaxIter() { return (Integer) getOrDefault(maxIter); } public MyJavaLogisticRegression() { setMaxIter(100); @@ -211,7 +211,7 @@ public Vector predictRaw(Vector features) { public MyJavaLogisticRegressionModel copy() { MyJavaLogisticRegressionModel m = new MyJavaLogisticRegressionModel(parent_, fittingParamMap_, weights_); - Params$.MODULE$.inheritValues(this.paramMap(), this, m); + Params$.MODULE$.inheritValues(this.extractParamMap(), this, m); return m; } } diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala index df26798e41b7b..2245fa429fda3 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala @@ -99,7 +99,7 @@ private trait MyLogisticRegressionParams extends ClassifierParams { * class since the maxIter parameter is only used during training (not in the Model). */ val maxIter: IntParam = new IntParam(this, "maxIter", "max number of iterations") - def getMaxIter: Int = get(maxIter) + def getMaxIter: Int = getOrDefault(maxIter) } /** @@ -174,11 +174,11 @@ private class MyLogisticRegressionModel( * Create a copy of the model. * The copy is shallow, except for the embedded paramMap, which gets a deep copy. * - * This is used for the defaul implementation of [[transform()]]. + * This is used for the default implementation of [[transform()]]. */ override protected def copy(): MyLogisticRegressionModel = { val m = new MyLogisticRegressionModel(parent, fittingParamMap, weights) - Params.inheritValues(this.paramMap, this, m) + Params.inheritValues(extractParamMap(), this, m) m } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala b/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala index eff7ef925dfbd..d6b3503ebdd9a 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala @@ -40,7 +40,7 @@ abstract class Estimator[M <: Model[M]] extends PipelineStage with Params { */ @varargs def fit(dataset: DataFrame, paramPairs: ParamPair[_]*): M = { - val map = new ParamMap().put(paramPairs: _*) + val map = ParamMap(paramPairs: _*) fit(dataset, map) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala index a455341a1f723..8eddf79cdfe28 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala @@ -84,7 +84,7 @@ class Pipeline extends Estimator[PipelineModel] { /** param for pipeline stages */ val stages: Param[Array[PipelineStage]] = new Param(this, "stages", "stages of the pipeline") def setStages(value: Array[PipelineStage]): this.type = { set(stages, value); this } - def getStages: Array[PipelineStage] = get(stages) + def getStages: Array[PipelineStage] = getOrDefault(stages) /** * Fits the pipeline to the input dataset with additional parameters. If a stage is an @@ -101,7 +101,7 @@ class Pipeline extends Estimator[PipelineModel] { */ override def fit(dataset: DataFrame, paramMap: ParamMap): PipelineModel = { transformSchema(dataset.schema, paramMap, logging = true) - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val theStages = map(stages) // Search for the last estimator. var indexOfLastEstimator = -1 @@ -138,7 +138,7 @@ class Pipeline extends Estimator[PipelineModel] { } override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val theStages = map(stages) require(theStages.toSet.size == theStages.size, "Cannot have duplicate components in a pipeline.") @@ -177,14 +177,14 @@ class PipelineModel private[ml] ( override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { // Precedence of ParamMaps: paramMap > this.paramMap > fittingParamMap - val map = (fittingParamMap ++ this.paramMap) ++ paramMap + val map = fittingParamMap ++ extractParamMap(paramMap) transformSchema(dataset.schema, map, logging = true) stages.foldLeft(dataset)((cur, transformer) => transformer.transform(cur, map)) } override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { // Precedence of ParamMaps: paramMap > this.paramMap > fittingParamMap - val map = (fittingParamMap ++ this.paramMap) ++ paramMap + val map = fittingParamMap ++ extractParamMap(paramMap) stages.foldLeft(schema)((cur, transformer) => transformer.transformSchema(cur, map)) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala index 9a5848684b179..7fb87fe452ee6 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala @@ -22,6 +22,7 @@ import scala.annotation.varargs import org.apache.spark.Logging import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared._ import org.apache.spark.sql.DataFrame import org.apache.spark.sql.functions._ import org.apache.spark.sql.types._ @@ -86,7 +87,7 @@ private[ml] abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, O protected def validateInputType(inputType: DataType): Unit = {} override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val inputType = schema(map(inputCol)).dataType validateInputType(inputType) if (schema.fieldNames.contains(map(outputCol))) { @@ -99,7 +100,7 @@ private[ml] abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, O override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { transformSchema(dataset.schema, paramMap, logging = true) - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) dataset.withColumn(map(outputCol), callUDF(this.createTransformFunc(map), outputDataType, dataset(map(inputCol)))) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala index c5fc89f935432..29339c98f51cf 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala @@ -17,12 +17,14 @@ package org.apache.spark.ml.classification -import org.apache.spark.annotation.{DeveloperApi, AlphaComponent} +import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor, PredictorParams} -import org.apache.spark.ml.param.{Params, ParamMap, HasRawPredictionCol} +import org.apache.spark.ml.param.{ParamMap, Params} +import org.apache.spark.ml.param.shared.HasRawPredictionCol +import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.mllib.linalg.{Vector, VectorUDT} -import org.apache.spark.sql.functions._ import org.apache.spark.sql.DataFrame +import org.apache.spark.sql.functions._ import org.apache.spark.sql.types.{DataType, DoubleType, StructType} @@ -42,8 +44,8 @@ private[spark] trait ClassifierParams extends PredictorParams fitting: Boolean, featuresDataType: DataType): StructType = { val parentSchema = super.validateAndTransformSchema(schema, paramMap, fitting, featuresDataType) - val map = this.paramMap ++ paramMap - addOutputColumn(parentSchema, map(rawPredictionCol), new VectorUDT) + val map = extractParamMap(paramMap) + SchemaUtils.appendColumn(parentSchema, map(rawPredictionCol), new VectorUDT) } } @@ -67,8 +69,7 @@ private[spark] abstract class Classifier[ with ClassifierParams { /** @group setParam */ - def setRawPredictionCol(value: String): E = - set(rawPredictionCol, value).asInstanceOf[E] + def setRawPredictionCol(value: String): E = set(rawPredictionCol, value).asInstanceOf[E] // TODO: defaultEvaluator (follow-up PR) } @@ -109,7 +110,7 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur // Check schema transformSchema(dataset.schema, paramMap, logging = true) - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) // Prepare model val tmpModel = if (paramMap.size != 0) { diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala index 34625745dd0a8..cc8b0721cf2b6 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala @@ -19,11 +19,11 @@ package org.apache.spark.ml.classification import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared._ import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS import org.apache.spark.mllib.linalg.{VectorUDT, BLAS, Vector, Vectors} import org.apache.spark.sql.DataFrame import org.apache.spark.sql.functions._ -import org.apache.spark.sql.types.DoubleType import org.apache.spark.storage.StorageLevel @@ -31,8 +31,10 @@ import org.apache.spark.storage.StorageLevel * Params for logistic regression. */ private[classification] trait LogisticRegressionParams extends ProbabilisticClassifierParams - with HasRegParam with HasMaxIter with HasFitIntercept with HasThreshold + with HasRegParam with HasMaxIter with HasFitIntercept with HasThreshold { + setDefault(regParam -> 0.1, maxIter -> 100, threshold -> 0.5) +} /** * :: AlphaComponent :: @@ -45,10 +47,6 @@ class LogisticRegression extends ProbabilisticClassifier[Vector, LogisticRegression, LogisticRegressionModel] with LogisticRegressionParams { - setRegParam(0.1) - setMaxIter(100) - setThreshold(0.5) - /** @group setParam */ def setRegParam(value: Double): this.type = set(regParam, value) @@ -100,8 +98,6 @@ class LogisticRegressionModel private[ml] ( extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel] with LogisticRegressionParams { - setThreshold(0.5) - /** @group setParam */ def setThreshold(value: Double): this.type = set(threshold, value) @@ -123,7 +119,7 @@ class LogisticRegressionModel private[ml] ( // Check schema transformSchema(dataset.schema, paramMap, logging = true) - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) // Output selected columns only. // This is a bit complicated since it tries to avoid repeated computation. @@ -184,7 +180,7 @@ class LogisticRegressionModel private[ml] ( * The behavior of this can be adjusted using [[threshold]]. */ override protected def predict(features: Vector): Double = { - if (score(features) > paramMap(threshold)) 1 else 0 + if (score(features) > getThreshold) 1 else 0 } override protected def predictProbabilities(features: Vector): Vector = { @@ -199,7 +195,7 @@ class LogisticRegressionModel private[ml] ( override protected def copy(): LogisticRegressionModel = { val m = new LogisticRegressionModel(parent, fittingParamMap, weights, intercept) - Params.inheritValues(this.paramMap, this, m) + Params.inheritValues(this.extractParamMap(), this, m) m } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala index bd8caac855981..10404548ccfde 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala @@ -18,13 +18,14 @@ package org.apache.spark.ml.classification import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} -import org.apache.spark.ml.param.{HasProbabilityCol, ParamMap, Params} +import org.apache.spark.ml.param.{ParamMap, Params} +import org.apache.spark.ml.param.shared._ +import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.sql.DataFrame import org.apache.spark.sql.functions._ import org.apache.spark.sql.types.{DataType, StructType} - /** * Params for probabilistic classification. */ @@ -37,8 +38,8 @@ private[classification] trait ProbabilisticClassifierParams fitting: Boolean, featuresDataType: DataType): StructType = { val parentSchema = super.validateAndTransformSchema(schema, paramMap, fitting, featuresDataType) - val map = this.paramMap ++ paramMap - addOutputColumn(parentSchema, map(probabilityCol), new VectorUDT) + val map = extractParamMap(paramMap) + SchemaUtils.appendColumn(parentSchema, map(probabilityCol), new VectorUDT) } } @@ -102,7 +103,7 @@ private[spark] abstract class ProbabilisticClassificationModel[ // Check schema transformSchema(dataset.schema, paramMap, logging = true) - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) // Prepare model val tmpModel = if (paramMap.size != 0) { diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala index 2360f4479f1c2..c865eb9fe092d 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala @@ -20,12 +20,13 @@ package org.apache.spark.ml.evaluation import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.Evaluator import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared._ +import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.sql.{DataFrame, Row} import org.apache.spark.sql.types.DoubleType - /** * :: AlphaComponent :: * @@ -40,10 +41,10 @@ class BinaryClassificationEvaluator extends Evaluator with Params * @group param */ val metricName: Param[String] = new Param(this, "metricName", - "metric name in evaluation (areaUnderROC|areaUnderPR)", Some("areaUnderROC")) + "metric name in evaluation (areaUnderROC|areaUnderPR)") /** @group getParam */ - def getMetricName: String = get(metricName) + def getMetricName: String = getOrDefault(metricName) /** @group setParam */ def setMetricName(value: String): this.type = set(metricName, value) @@ -54,12 +55,14 @@ class BinaryClassificationEvaluator extends Evaluator with Params /** @group setParam */ def setLabelCol(value: String): this.type = set(labelCol, value) + setDefault(metricName -> "areaUnderROC") + override def evaluate(dataset: DataFrame, paramMap: ParamMap): Double = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val schema = dataset.schema - checkInputColumn(schema, map(rawPredictionCol), new VectorUDT) - checkInputColumn(schema, map(labelCol), DoubleType) + SchemaUtils.checkColumnType(schema, map(rawPredictionCol), new VectorUDT) + SchemaUtils.checkColumnType(schema, map(labelCol), DoubleType) // TODO: When dataset metadata has been implemented, check rawPredictionCol vector length = 2. val scoreAndLabels = dataset.select(map(rawPredictionCol), map(labelCol)) diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala index fc4e12773c46d..b20f2fc49a8f6 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala @@ -35,14 +35,16 @@ class HashingTF extends UnaryTransformer[Iterable[_], Vector, HashingTF] { * number of features * @group param */ - val numFeatures = new IntParam(this, "numFeatures", "number of features", Some(1 << 18)) + val numFeatures = new IntParam(this, "numFeatures", "number of features") /** @group getParam */ - def getNumFeatures: Int = get(numFeatures) + def getNumFeatures: Int = getOrDefault(numFeatures) /** @group setParam */ def setNumFeatures(value: Int): this.type = set(numFeatures, value) + setDefault(numFeatures -> (1 << 18)) + override protected def createTransformFunc(paramMap: ParamMap): Iterable[_] => Vector = { val hashingTF = new feature.HashingTF(paramMap(numFeatures)) hashingTF.transform diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala index 05f91dc9105fe..decaeb0da6246 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala @@ -35,14 +35,16 @@ class Normalizer extends UnaryTransformer[Vector, Vector, Normalizer] { * Normalization in L^p^ space, p = 2 by default. * @group param */ - val p = new DoubleParam(this, "p", "the p norm value", Some(2)) + val p = new DoubleParam(this, "p", "the p norm value") /** @group getParam */ - def getP: Double = get(p) + def getP: Double = getOrDefault(p) /** @group setParam */ def setP(value: Double): this.type = set(p, value) + setDefault(p -> 2.0) + override protected def createTransformFunc(paramMap: ParamMap): Vector => Vector = { val normalizer = new feature.Normalizer(paramMap(p)) normalizer.transform @@ -50,4 +52,3 @@ class Normalizer extends UnaryTransformer[Vector, Vector, Normalizer] { override protected def outputDataType: DataType = new VectorUDT() } - diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala index 1142aa4f8e73d..1b102619b3524 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala @@ -20,6 +20,7 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml._ import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared._ import org.apache.spark.mllib.feature import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.sql._ @@ -47,7 +48,7 @@ class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerP override def fit(dataset: DataFrame, paramMap: ParamMap): StandardScalerModel = { transformSchema(dataset.schema, paramMap, logging = true) - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val input = dataset.select(map(inputCol)).map { case Row(v: Vector) => v } val scaler = new feature.StandardScaler().fit(input) val model = new StandardScalerModel(this, map, scaler) @@ -56,7 +57,7 @@ class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerP } override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val inputType = schema(map(inputCol)).dataType require(inputType.isInstanceOf[VectorUDT], s"Input column ${map(inputCol)} must be a vector column") @@ -86,13 +87,13 @@ class StandardScalerModel private[ml] ( override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { transformSchema(dataset.schema, paramMap, logging = true) - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val scale = udf((v: Vector) => { scaler.transform(v) } : Vector) dataset.withColumn(map(outputCol), scale(col(map(inputCol)))) } override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val inputType = schema(map(inputCol)).dataType require(inputType.isInstanceOf[VectorUDT], s"Input column ${map(inputCol)} must be a vector column") diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala index 61e6742e880d8..4d960df357fe9 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala @@ -22,6 +22,8 @@ import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.{Estimator, Model} import org.apache.spark.ml.attribute.NominalAttribute import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared._ +import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.sql.DataFrame import org.apache.spark.sql.functions._ import org.apache.spark.sql.types.{StringType, StructType} @@ -34,8 +36,8 @@ private[feature] trait StringIndexerBase extends Params with HasInputCol with Ha /** Validates and transforms the input schema. */ protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = this.paramMap ++ paramMap - checkInputColumn(schema, map(inputCol), StringType) + val map = extractParamMap(paramMap) + SchemaUtils.checkColumnType(schema, map(inputCol), StringType) val inputFields = schema.fields val outputColName = map(outputCol) require(inputFields.forall(_.name != outputColName), @@ -64,7 +66,7 @@ class StringIndexer extends Estimator[StringIndexerModel] with StringIndexerBase // TODO: handle unseen labels override def fit(dataset: DataFrame, paramMap: ParamMap): StringIndexerModel = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val counts = dataset.select(map(inputCol)).map(_.getString(0)).countByValue() val labels = counts.toSeq.sortBy(-_._2).map(_._1).toArray val model = new StringIndexerModel(this, map, labels) @@ -105,7 +107,7 @@ class StringIndexerModel private[ml] ( def setOutputCol(value: String): this.type = set(outputCol, value) override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val indexer = udf { label: String => if (labelToIndex.contains(label)) { labelToIndex(label) diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala index 68401e36950bd..376a004858b4c 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala @@ -56,39 +56,39 @@ class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenize * param for minimum token length, default is one to avoid returning empty strings * @group param */ - val minTokenLength: IntParam = new IntParam(this, "minLength", "minimum token length", Some(1)) + val minTokenLength: IntParam = new IntParam(this, "minLength", "minimum token length") /** @group setParam */ def setMinTokenLength(value: Int): this.type = set(minTokenLength, value) /** @group getParam */ - def getMinTokenLength: Int = get(minTokenLength) + def getMinTokenLength: Int = getOrDefault(minTokenLength) /** * param sets regex as splitting on gaps (true) or matching tokens (false) * @group param */ - val gaps: BooleanParam = new BooleanParam( - this, "gaps", "Set regex to match gaps or tokens", Some(false)) + val gaps: BooleanParam = new BooleanParam(this, "gaps", "Set regex to match gaps or tokens") /** @group setParam */ def setGaps(value: Boolean): this.type = set(gaps, value) /** @group getParam */ - def getGaps: Boolean = get(gaps) + def getGaps: Boolean = getOrDefault(gaps) /** * param sets regex pattern used by tokenizer * @group param */ - val pattern: Param[String] = new Param( - this, "pattern", "regex pattern used for tokenizing", Some("\\p{L}+|[^\\p{L}\\s]+")) + val pattern: Param[String] = new Param(this, "pattern", "regex pattern used for tokenizing") /** @group setParam */ def setPattern(value: String): this.type = set(pattern, value) /** @group getParam */ - def getPattern: String = get(pattern) + def getPattern: String = getOrDefault(pattern) + + setDefault(minTokenLength -> 1, gaps -> false, pattern -> "\\p{L}+|[^\\p{L}\\s]+") override protected def createTransformFunc(paramMap: ParamMap): String => Seq[String] = { str => val re = paramMap(pattern).r diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala index d1b8f7e6e9295..e567e069e7c0b 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala @@ -22,7 +22,8 @@ import scala.collection.mutable.ArrayBuilder import org.apache.spark.SparkException import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.Transformer -import org.apache.spark.ml.param.{HasInputCols, HasOutputCol, ParamMap} +import org.apache.spark.ml.param.ParamMap +import org.apache.spark.ml.param.shared._ import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors} import org.apache.spark.sql.{Column, DataFrame, Row} import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute @@ -44,7 +45,7 @@ class VectorAssembler extends Transformer with HasInputCols with HasOutputCol { def setOutputCol(value: String): this.type = set(outputCol, value) override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val assembleFunc = udf { r: Row => VectorAssembler.assemble(r.toSeq: _*) } @@ -61,7 +62,7 @@ class VectorAssembler extends Transformer with HasInputCols with HasOutputCol { } override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val inputColNames = map(inputCols) val outputColName = map(outputCol) val inputDataTypes = inputColNames.map(name => schema(name).dataType) diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala index 8760960e19272..452faa06e2021 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala @@ -18,10 +18,12 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.ml.{Estimator, Model} import org.apache.spark.ml.attribute.{BinaryAttribute, NumericAttribute, NominalAttribute, Attribute, AttributeGroup} -import org.apache.spark.ml.param.{HasInputCol, HasOutputCol, IntParam, ParamMap, Params} +import org.apache.spark.ml.param.{IntParam, ParamMap, Params} +import org.apache.spark.ml.param.shared._ import org.apache.spark.mllib.linalg.{SparseVector, DenseVector, Vector, VectorUDT} import org.apache.spark.sql.{Row, DataFrame} import org.apache.spark.sql.functions.callUDF @@ -40,11 +42,12 @@ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOu */ val maxCategories = new IntParam(this, "maxCategories", "Threshold for the number of values a categorical feature can take." + - " If a feature is found to have > maxCategories values, then it is declared continuous.", - Some(20)) + " If a feature is found to have > maxCategories values, then it is declared continuous.") /** @group getParam */ - def getMaxCategories: Int = get(maxCategories) + def getMaxCategories: Int = getOrDefault(maxCategories) + + setDefault(maxCategories -> 20) } /** @@ -101,7 +104,7 @@ class VectorIndexer extends Estimator[VectorIndexerModel] with VectorIndexerPara override def fit(dataset: DataFrame, paramMap: ParamMap): VectorIndexerModel = { transformSchema(dataset.schema, paramMap, logging = true) - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val firstRow = dataset.select(map(inputCol)).take(1) require(firstRow.length == 1, s"VectorIndexer cannot be fit on an empty dataset.") val numFeatures = firstRow(0).getAs[Vector](0).size @@ -120,12 +123,12 @@ class VectorIndexer extends Estimator[VectorIndexerModel] with VectorIndexerPara override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { // We do not transfer feature metadata since we do not know what types of features we will // produce in transform(). - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val dataType = new VectorUDT require(map.contains(inputCol), s"VectorIndexer requires input column parameter: $inputCol") require(map.contains(outputCol), s"VectorIndexer requires output column parameter: $outputCol") - checkInputColumn(schema, map(inputCol), dataType) - addOutputColumn(schema, map(outputCol), dataType) + SchemaUtils.checkColumnType(schema, map(inputCol), dataType) + SchemaUtils.appendColumn(schema, map(outputCol), dataType) } } @@ -320,7 +323,7 @@ class VectorIndexerModel private[ml] ( override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { transformSchema(dataset.schema, paramMap, logging = true) - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val newField = prepOutputField(dataset.schema, map) val newCol = callUDF(transformFunc, new VectorUDT, dataset(map(inputCol))) // For now, just check the first row of inputCol for vector length. @@ -334,13 +337,13 @@ class VectorIndexerModel private[ml] ( } override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val dataType = new VectorUDT require(map.contains(inputCol), s"VectorIndexerModel requires input column parameter: $inputCol") require(map.contains(outputCol), s"VectorIndexerModel requires output column parameter: $outputCol") - checkInputColumn(schema, map(inputCol), dataType) + SchemaUtils.checkColumnType(schema, map(inputCol), dataType) val origAttrGroup = AttributeGroup.fromStructField(schema(map(inputCol))) val origNumFeatures: Option[Int] = if (origAttrGroup.attributes.nonEmpty) { diff --git a/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala b/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala index dfb89cc8d4af3..195333a5cc47f 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala @@ -18,8 +18,10 @@ package org.apache.spark.ml.impl.estimator import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} +import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.ml.{Estimator, Model} import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared._ import org.apache.spark.mllib.linalg.{VectorUDT, Vector} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.rdd.RDD @@ -53,14 +55,14 @@ private[spark] trait PredictorParams extends Params paramMap: ParamMap, fitting: Boolean, featuresDataType: DataType): StructType = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) // TODO: Support casting Array[Double] and Array[Float] to Vector when FeaturesType = Vector - checkInputColumn(schema, map(featuresCol), featuresDataType) + SchemaUtils.checkColumnType(schema, map(featuresCol), featuresDataType) if (fitting) { // TODO: Allow other numeric types - checkInputColumn(schema, map(labelCol), DoubleType) + SchemaUtils.checkColumnType(schema, map(labelCol), DoubleType) } - addOutputColumn(schema, map(predictionCol), DoubleType) + SchemaUtils.appendColumn(schema, map(predictionCol), DoubleType) } } @@ -98,7 +100,7 @@ private[spark] abstract class Predictor[ // This handles a few items such as schema validation. // Developers only need to implement train(). transformSchema(dataset.schema, paramMap, logging = true) - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val model = train(dataset, map) Params.inheritValues(map, this, model) // copy params to model model @@ -141,7 +143,7 @@ private[spark] abstract class Predictor[ * and put it in an RDD with strong types. */ protected def extractLabeledPoints(dataset: DataFrame, paramMap: ParamMap): RDD[LabeledPoint] = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) dataset.select(map(labelCol), map(featuresCol)) .map { case Row(label: Double, features: Vector) => LabeledPoint(label, features) @@ -201,7 +203,7 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel // Check schema transformSchema(dataset.schema, paramMap, logging = true) - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) // Prepare model val tmpModel = if (paramMap.size != 0) { diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala index 7d5178d0abb2d..849c60433c777 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala @@ -17,15 +17,14 @@ package org.apache.spark.ml.param +import java.lang.reflect.Modifier +import java.util.NoSuchElementException + import scala.annotation.varargs import scala.collection.mutable -import java.lang.reflect.Modifier - import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} import org.apache.spark.ml.Identifiable -import org.apache.spark.sql.types.{DataType, StructField, StructType} - /** * :: AlphaComponent :: @@ -38,12 +37,7 @@ import org.apache.spark.sql.types.{DataType, StructField, StructType} * @tparam T param value type */ @AlphaComponent -class Param[T] ( - val parent: Params, - val name: String, - val doc: String, - val defaultValue: Option[T] = None) - extends Serializable { +class Param[T] (val parent: Params, val name: String, val doc: String) extends Serializable { /** * Creates a param pair with the given value (for Java). @@ -55,58 +49,55 @@ class Param[T] ( */ def ->(value: T): ParamPair[T] = ParamPair(this, value) + /** + * Converts this param's name, doc, and optionally its default value and the user-supplied + * value in its parent to string. + */ override def toString: String = { - if (defaultValue.isDefined) { - s"$name: $doc (default: ${defaultValue.get})" + val valueStr = if (parent.isDefined(this)) { + val defaultValueStr = parent.getDefault(this).map("default: " + _) + val currentValueStr = parent.get(this).map("current: " + _) + (defaultValueStr ++ currentValueStr).mkString("(", ", ", ")") } else { - s"$name: $doc" + "(undefined)" } + s"$name: $doc $valueStr" } } // specialize primitive-typed params because Java doesn't recognize scala.Double, scala.Int, ... /** Specialized version of [[Param[Double]]] for Java. */ -class DoubleParam(parent: Params, name: String, doc: String, defaultValue: Option[Double]) - extends Param[Double](parent, name, doc, defaultValue) { - - def this(parent: Params, name: String, doc: String) = this(parent, name, doc, None) +class DoubleParam(parent: Params, name: String, doc: String) + extends Param[Double](parent, name, doc) { override def w(value: Double): ParamPair[Double] = super.w(value) } /** Specialized version of [[Param[Int]]] for Java. */ -class IntParam(parent: Params, name: String, doc: String, defaultValue: Option[Int]) - extends Param[Int](parent, name, doc, defaultValue) { - - def this(parent: Params, name: String, doc: String) = this(parent, name, doc, None) +class IntParam(parent: Params, name: String, doc: String) + extends Param[Int](parent, name, doc) { override def w(value: Int): ParamPair[Int] = super.w(value) } /** Specialized version of [[Param[Float]]] for Java. */ -class FloatParam(parent: Params, name: String, doc: String, defaultValue: Option[Float]) - extends Param[Float](parent, name, doc, defaultValue) { - - def this(parent: Params, name: String, doc: String) = this(parent, name, doc, None) +class FloatParam(parent: Params, name: String, doc: String) + extends Param[Float](parent, name, doc) { override def w(value: Float): ParamPair[Float] = super.w(value) } /** Specialized version of [[Param[Long]]] for Java. */ -class LongParam(parent: Params, name: String, doc: String, defaultValue: Option[Long]) - extends Param[Long](parent, name, doc, defaultValue) { - - def this(parent: Params, name: String, doc: String) = this(parent, name, doc, None) +class LongParam(parent: Params, name: String, doc: String) + extends Param[Long](parent, name, doc) { override def w(value: Long): ParamPair[Long] = super.w(value) } /** Specialized version of [[Param[Boolean]]] for Java. */ -class BooleanParam(parent: Params, name: String, doc: String, defaultValue: Option[Boolean]) - extends Param[Boolean](parent, name, doc, defaultValue) { - - def this(parent: Params, name: String, doc: String) = this(parent, name, doc, None) +class BooleanParam(parent: Params, name: String, doc: String) + extends Param[Boolean](parent, name, doc) { override def w(value: Boolean): ParamPair[Boolean] = super.w(value) } @@ -124,8 +115,11 @@ case class ParamPair[T](param: Param[T], value: T) @AlphaComponent trait Params extends Identifiable with Serializable { - /** Returns all params. */ - def params: Array[Param[_]] = { + /** + * Returns all params sorted by their names. The default implementation uses Java reflection to + * list all public methods that have no arguments and return [[Param]]. + */ + lazy val params: Array[Param[_]] = { val methods = this.getClass.getMethods methods.filter { m => Modifier.isPublic(m.getModifiers) && @@ -153,25 +147,29 @@ trait Params extends Identifiable with Serializable { def explainParams(): String = params.mkString("\n") /** Checks whether a param is explicitly set. */ - def isSet(param: Param[_]): Boolean = { - require(param.parent.eq(this)) + final def isSet(param: Param[_]): Boolean = { + shouldOwn(param) paramMap.contains(param) } + /** Checks whether a param is explicitly set or has a default value. */ + final def isDefined(param: Param[_]): Boolean = { + shouldOwn(param) + defaultParamMap.contains(param) || paramMap.contains(param) + } + /** Gets a param by its name. */ - private[ml] def getParam(paramName: String): Param[Any] = { - val m = this.getClass.getMethod(paramName) - assert(Modifier.isPublic(m.getModifiers) && - classOf[Param[_]].isAssignableFrom(m.getReturnType) && - m.getParameterTypes.isEmpty) - m.invoke(this).asInstanceOf[Param[Any]] + def getParam(paramName: String): Param[Any] = { + params.find(_.name == paramName).getOrElse { + throw new NoSuchElementException(s"Param $paramName does not exist.") + }.asInstanceOf[Param[Any]] } /** * Sets a parameter in the embedded param map. */ - protected def set[T](param: Param[T], value: T): this.type = { - require(param.parent.eq(this)) + protected final def set[T](param: Param[T], value: T): this.type = { + shouldOwn(param) paramMap.put(param.asInstanceOf[Param[Any]], value) this } @@ -179,52 +177,102 @@ trait Params extends Identifiable with Serializable { /** * Sets a parameter (by name) in the embedded param map. */ - private[ml] def set(param: String, value: Any): this.type = { + protected final def set(param: String, value: Any): this.type = { set(getParam(param), value) } /** - * Gets the value of a parameter in the embedded param map. + * Optionally returns the user-supplied value of a param. + */ + final def get[T](param: Param[T]): Option[T] = { + shouldOwn(param) + paramMap.get(param) + } + + /** + * Clears the user-supplied value for the input param. + */ + protected final def clear(param: Param[_]): this.type = { + shouldOwn(param) + paramMap.remove(param) + this + } + + /** + * Gets the value of a param in the embedded param map or its default value. Throws an exception + * if neither is set. + */ + final def getOrDefault[T](param: Param[T]): T = { + shouldOwn(param) + get(param).orElse(getDefault(param)).get + } + + /** + * Sets a default value for a param. + * @param param param to set the default value. Make sure that this param is initialized before + * this method gets called. + * @param value the default value */ - protected def get[T](param: Param[T]): T = { - require(param.parent.eq(this)) - paramMap(param) + protected final def setDefault[T](param: Param[T], value: T): this.type = { + shouldOwn(param) + defaultParamMap.put(param, value) + this } /** - * Internal param map. + * Sets default values for a list of params. + * @param paramPairs a list of param pairs that specify params and their default values to set + * respectively. Make sure that the params are initialized before this method + * gets called. */ - protected val paramMap: ParamMap = ParamMap.empty + protected final def setDefault(paramPairs: ParamPair[_]*): this.type = { + paramPairs.foreach { p => + setDefault(p.param.asInstanceOf[Param[Any]], p.value) + } + this + } /** - * Check whether the given schema contains an input column. - * @param colName Input column name - * @param dataType Input column DataType + * Gets the default value of a parameter. */ - protected def checkInputColumn(schema: StructType, colName: String, dataType: DataType): Unit = { - val actualDataType = schema(colName).dataType - require(actualDataType.equals(dataType), s"Input column $colName must be of type $dataType" + - s" but was actually $actualDataType. Column param description: ${getParam(colName)}") + final def getDefault[T](param: Param[T]): Option[T] = { + shouldOwn(param) + defaultParamMap.get(param) } /** - * Add an output column to the given schema. - * This fails if the given output column already exists. - * @param schema Initial schema (not modified) - * @param colName Output column name. If this column name is an empy String "", this method - * returns the initial schema, unchanged. This allows users to disable output - * columns. - * @param dataType Output column DataType - */ - protected def addOutputColumn( - schema: StructType, - colName: String, - dataType: DataType): StructType = { - if (colName.length == 0) return schema - val fieldNames = schema.fieldNames - require(!fieldNames.contains(colName), s"Output column $colName already exists.") - val outputFields = schema.fields ++ Seq(StructField(colName, dataType, nullable = false)) - StructType(outputFields) + * Tests whether the input param has a default value set. + */ + final def hasDefault[T](param: Param[T]): Boolean = { + shouldOwn(param) + defaultParamMap.contains(param) + } + + /** + * Extracts the embedded default param values and user-supplied values, and then merges them with + * extra values from input into a flat param map, where the latter value is used if there exist + * conflicts, i.e., with ordering: default param values < user-supplied values < extraParamMap. + */ + protected final def extractParamMap(extraParamMap: ParamMap): ParamMap = { + defaultParamMap ++ paramMap ++ extraParamMap + } + + /** + * [[extractParamMap]] with no extra values. + */ + protected final def extractParamMap(): ParamMap = { + extractParamMap(ParamMap.empty) + } + + /** Internal param map for user-supplied values. */ + private val paramMap: ParamMap = ParamMap.empty + + /** Internal param map for default values. */ + private val defaultParamMap: ParamMap = ParamMap.empty + + /** Validates that the input param belongs to this instance. */ + private def shouldOwn(param: Param[_]): Unit = { + require(param.parent.eq(this), s"Param $param does not belong to $this.") } } @@ -261,12 +309,13 @@ private[spark] object Params { * A param to value map. */ @AlphaComponent -class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) extends Serializable { +final class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) + extends Serializable { /** * Creates an empty param map. */ - def this() = this(mutable.Map.empty[Param[Any], Any]) + def this() = this(mutable.Map.empty) /** * Puts a (param, value) pair (overwrites if the input param exists). @@ -288,12 +337,17 @@ class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) exten } /** - * Optionally returns the value associated with a param or its default. + * Optionally returns the value associated with a param. */ def get[T](param: Param[T]): Option[T] = { - map.get(param.asInstanceOf[Param[Any]]) - .orElse(param.defaultValue) - .asInstanceOf[Option[T]] + map.get(param.asInstanceOf[Param[Any]]).asInstanceOf[Option[T]] + } + + /** + * Returns the value associated with a param or a default value. + */ + def getOrElse[T](param: Param[T], default: T): T = { + get(param).getOrElse(default) } /** @@ -301,10 +355,7 @@ class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) exten * Raises a NoSuchElementException if there is no value associated with the input param. */ def apply[T](param: Param[T]): T = { - val value = get(param) - if (value.isDefined) { - value.get - } else { + get(param).getOrElse { throw new NoSuchElementException(s"Cannot find param ${param.name}.") } } @@ -316,6 +367,13 @@ class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) exten map.contains(param.asInstanceOf[Param[Any]]) } + /** + * Removes a key from this map and returns its value associated previously as an option. + */ + def remove[T](param: Param[T]): Option[T] = { + map.remove(param.asInstanceOf[Param[Any]]).asInstanceOf[Option[T]] + } + /** * Filters this param map for the given parent. */ @@ -325,7 +383,7 @@ class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) exten } /** - * Make a copy of this param map. + * Creates a copy of this param map. */ def copy: ParamMap = new ParamMap(map.clone()) @@ -337,7 +395,7 @@ class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) exten /** * Returns a new param map that contains parameters in this map and the given map, - * where the latter overwrites this if there exists conflicts. + * where the latter overwrites this if there exist conflicts. */ def ++(other: ParamMap): ParamMap = { // TODO: Provide a better method name for Java users. @@ -363,7 +421,7 @@ class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) exten } /** - * Number of param pairs in this set. + * Number of param pairs in this map. */ def size: Int = map.size } diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala new file mode 100644 index 0000000000000..95d7e64790c79 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala @@ -0,0 +1,169 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.param.shared + +import java.io.PrintWriter + +import scala.reflect.ClassTag + +/** + * Code generator for shared params (sharedParams.scala). Run under the Spark folder with + * {{{ + * build/sbt "mllib/runMain org.apache.spark.ml.param.shared.SharedParamsCodeGen" + * }}} + */ +private[shared] object SharedParamsCodeGen { + + def main(args: Array[String]): Unit = { + val params = Seq( + ParamDesc[Double]("regParam", "regularization parameter"), + ParamDesc[Int]("maxIter", "max number of iterations"), + ParamDesc[String]("featuresCol", "features column name", Some("\"features\"")), + ParamDesc[String]("labelCol", "label column name", Some("\"label\"")), + ParamDesc[String]("predictionCol", "prediction column name", Some("\"prediction\"")), + ParamDesc[String]("rawPredictionCol", "raw prediction (a.k.a. confidence) column name", + Some("\"rawPrediction\"")), + ParamDesc[String]("probabilityCol", + "column name for predicted class conditional probabilities", Some("\"probability\"")), + ParamDesc[Double]("threshold", "threshold in binary classification prediction"), + ParamDesc[String]("inputCol", "input column name"), + ParamDesc[Array[String]]("inputCols", "input column names"), + ParamDesc[String]("outputCol", "output column name"), + ParamDesc[Int]("checkpointInterval", "checkpoint interval"), + ParamDesc[Boolean]("fitIntercept", "whether to fit an intercept term", Some("true"))) + + val code = genSharedParams(params) + val file = "src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala" + val writer = new PrintWriter(file) + writer.write(code) + writer.close() + } + + /** Description of a param. */ + private case class ParamDesc[T: ClassTag]( + name: String, + doc: String, + defaultValueStr: Option[String] = None) { + + require(name.matches("[a-z][a-zA-Z0-9]*"), s"Param name $name is invalid.") + require(doc.nonEmpty) // TODO: more rigorous on doc + + def paramTypeName: String = { + val c = implicitly[ClassTag[T]].runtimeClass + c match { + case _ if c == classOf[Int] => "IntParam" + case _ if c == classOf[Long] => "LongParam" + case _ if c == classOf[Float] => "FloatParam" + case _ if c == classOf[Double] => "DoubleParam" + case _ if c == classOf[Boolean] => "BooleanParam" + case _ => s"Param[${getTypeString(c)}]" + } + } + + def valueTypeName: String = { + val c = implicitly[ClassTag[T]].runtimeClass + getTypeString(c) + } + + private def getTypeString(c: Class[_]): String = { + c match { + case _ if c == classOf[Int] => "Int" + case _ if c == classOf[Long] => "Long" + case _ if c == classOf[Float] => "Float" + case _ if c == classOf[Double] => "Double" + case _ if c == classOf[Boolean] => "Boolean" + case _ if c == classOf[String] => "String" + case _ if c.isArray => s"Array[${getTypeString(c.getComponentType)}]" + } + } + } + + /** Generates the HasParam trait code for the input param. */ + private def genHasParamTrait(param: ParamDesc[_]): String = { + val name = param.name + val Name = name(0).toUpper +: name.substring(1) + val Param = param.paramTypeName + val T = param.valueTypeName + val doc = param.doc + val defaultValue = param.defaultValueStr + val defaultValueDoc = defaultValue.map { v => + s" (default: $v)" + }.getOrElse("") + val setDefault = defaultValue.map { v => + s""" + | setDefault($name, $v) + |""".stripMargin + }.getOrElse("") + + s""" + |/** + | * :: DeveloperApi :: + | * Trait for shared param $name$defaultValueDoc. + | */ + |@DeveloperApi + |trait Has$Name extends Params { + | + | /** + | * Param for $doc. + | * @group param + | */ + | final val $name: $Param = new $Param(this, "$name", "$doc") + |$setDefault + | /** @group getParam */ + | final def get$Name: $T = getOrDefault($name) + |} + |""".stripMargin + } + + /** Generates Scala source code for the input params with header. */ + private def genSharedParams(params: Seq[ParamDesc[_]]): String = { + val header = + """/* + | * Licensed to the Apache Software Foundation (ASF) under one or more + | * contributor license agreements. See the NOTICE file distributed with + | * this work for additional information regarding copyright ownership. + | * The ASF licenses this file to You under the Apache License, Version 2.0 + | * (the "License"); you may not use this file except in compliance with + | * the License. You may obtain a copy of the License at + | * + | * http://www.apache.org/licenses/LICENSE-2.0 + | * + | * Unless required by applicable law or agreed to in writing, software + | * distributed under the License is distributed on an "AS IS" BASIS, + | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + | * See the License for the specific language governing permissions and + | * limitations under the License. + | */ + | + |package org.apache.spark.ml.param.shared + | + |import org.apache.spark.annotation.DeveloperApi + |import org.apache.spark.ml.param._ + | + |// DO NOT MODIFY THIS FILE! It was generated by SharedParamsCodeGen. + | + |// scalastyle:off + |""".stripMargin + + val footer = "// scalastyle:on\n" + + val traits = params.map(genHasParamTrait).mkString + + header + traits + footer + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala new file mode 100644 index 0000000000000..72b08bf276483 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala @@ -0,0 +1,259 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.param.shared + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.ml.param._ + +// DO NOT MODIFY THIS FILE! It was generated by SharedParamsCodeGen. + +// scalastyle:off + +/** + * :: DeveloperApi :: + * Trait for shared param regParam. + */ +@DeveloperApi +trait HasRegParam extends Params { + + /** + * Param for regularization parameter. + * @group param + */ + final val regParam: DoubleParam = new DoubleParam(this, "regParam", "regularization parameter") + + /** @group getParam */ + final def getRegParam: Double = getOrDefault(regParam) +} + +/** + * :: DeveloperApi :: + * Trait for shared param maxIter. + */ +@DeveloperApi +trait HasMaxIter extends Params { + + /** + * Param for max number of iterations. + * @group param + */ + final val maxIter: IntParam = new IntParam(this, "maxIter", "max number of iterations") + + /** @group getParam */ + final def getMaxIter: Int = getOrDefault(maxIter) +} + +/** + * :: DeveloperApi :: + * Trait for shared param featuresCol (default: "features"). + */ +@DeveloperApi +trait HasFeaturesCol extends Params { + + /** + * Param for features column name. + * @group param + */ + final val featuresCol: Param[String] = new Param[String](this, "featuresCol", "features column name") + + setDefault(featuresCol, "features") + + /** @group getParam */ + final def getFeaturesCol: String = getOrDefault(featuresCol) +} + +/** + * :: DeveloperApi :: + * Trait for shared param labelCol (default: "label"). + */ +@DeveloperApi +trait HasLabelCol extends Params { + + /** + * Param for label column name. + * @group param + */ + final val labelCol: Param[String] = new Param[String](this, "labelCol", "label column name") + + setDefault(labelCol, "label") + + /** @group getParam */ + final def getLabelCol: String = getOrDefault(labelCol) +} + +/** + * :: DeveloperApi :: + * Trait for shared param predictionCol (default: "prediction"). + */ +@DeveloperApi +trait HasPredictionCol extends Params { + + /** + * Param for prediction column name. + * @group param + */ + final val predictionCol: Param[String] = new Param[String](this, "predictionCol", "prediction column name") + + setDefault(predictionCol, "prediction") + + /** @group getParam */ + final def getPredictionCol: String = getOrDefault(predictionCol) +} + +/** + * :: DeveloperApi :: + * Trait for shared param rawPredictionCol (default: "rawPrediction"). + */ +@DeveloperApi +trait HasRawPredictionCol extends Params { + + /** + * Param for raw prediction (a.k.a. confidence) column name. + * @group param + */ + final val rawPredictionCol: Param[String] = new Param[String](this, "rawPredictionCol", "raw prediction (a.k.a. confidence) column name") + + setDefault(rawPredictionCol, "rawPrediction") + + /** @group getParam */ + final def getRawPredictionCol: String = getOrDefault(rawPredictionCol) +} + +/** + * :: DeveloperApi :: + * Trait for shared param probabilityCol (default: "probability"). + */ +@DeveloperApi +trait HasProbabilityCol extends Params { + + /** + * Param for column name for predicted class conditional probabilities. + * @group param + */ + final val probabilityCol: Param[String] = new Param[String](this, "probabilityCol", "column name for predicted class conditional probabilities") + + setDefault(probabilityCol, "probability") + + /** @group getParam */ + final def getProbabilityCol: String = getOrDefault(probabilityCol) +} + +/** + * :: DeveloperApi :: + * Trait for shared param threshold. + */ +@DeveloperApi +trait HasThreshold extends Params { + + /** + * Param for threshold in binary classification prediction. + * @group param + */ + final val threshold: DoubleParam = new DoubleParam(this, "threshold", "threshold in binary classification prediction") + + /** @group getParam */ + final def getThreshold: Double = getOrDefault(threshold) +} + +/** + * :: DeveloperApi :: + * Trait for shared param inputCol. + */ +@DeveloperApi +trait HasInputCol extends Params { + + /** + * Param for input column name. + * @group param + */ + final val inputCol: Param[String] = new Param[String](this, "inputCol", "input column name") + + /** @group getParam */ + final def getInputCol: String = getOrDefault(inputCol) +} + +/** + * :: DeveloperApi :: + * Trait for shared param inputCols. + */ +@DeveloperApi +trait HasInputCols extends Params { + + /** + * Param for input column names. + * @group param + */ + final val inputCols: Param[Array[String]] = new Param[Array[String]](this, "inputCols", "input column names") + + /** @group getParam */ + final def getInputCols: Array[String] = getOrDefault(inputCols) +} + +/** + * :: DeveloperApi :: + * Trait for shared param outputCol. + */ +@DeveloperApi +trait HasOutputCol extends Params { + + /** + * Param for output column name. + * @group param + */ + final val outputCol: Param[String] = new Param[String](this, "outputCol", "output column name") + + /** @group getParam */ + final def getOutputCol: String = getOrDefault(outputCol) +} + +/** + * :: DeveloperApi :: + * Trait for shared param checkpointInterval. + */ +@DeveloperApi +trait HasCheckpointInterval extends Params { + + /** + * Param for checkpoint interval. + * @group param + */ + final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "checkpoint interval") + + /** @group getParam */ + final def getCheckpointInterval: Int = getOrDefault(checkpointInterval) +} + +/** + * :: DeveloperApi :: + * Trait for shared param fitIntercept (default: true). + */ +@DeveloperApi +trait HasFitIntercept extends Params { + + /** + * Param for whether to fit an intercept term. + * @group param + */ + final val fitIntercept: BooleanParam = new BooleanParam(this, "fitIntercept", "whether to fit an intercept term") + + setDefault(fitIntercept, true) + + /** @group getParam */ + final def getFitIntercept: Boolean = getOrDefault(fitIntercept) +} +// scalastyle:on diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala deleted file mode 100644 index 07e6eb417763d..0000000000000 --- a/mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala +++ /dev/null @@ -1,173 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.ml.param - -/* NOTE TO DEVELOPERS: - * If you mix these parameter traits into your algorithm, please add a setter method as well - * so that users may use a builder pattern: - * val myLearner = new MyLearner().setParam1(x).setParam2(y)... - */ - -private[ml] trait HasRegParam extends Params { - /** - * param for regularization parameter - * @group param - */ - val regParam: DoubleParam = new DoubleParam(this, "regParam", "regularization parameter") - - /** @group getParam */ - def getRegParam: Double = get(regParam) -} - -private[ml] trait HasMaxIter extends Params { - /** - * param for max number of iterations - * @group param - */ - val maxIter: IntParam = new IntParam(this, "maxIter", "max number of iterations") - - /** @group getParam */ - def getMaxIter: Int = get(maxIter) -} - -private[ml] trait HasFeaturesCol extends Params { - /** - * param for features column name - * @group param - */ - val featuresCol: Param[String] = - new Param(this, "featuresCol", "features column name", Some("features")) - - /** @group getParam */ - def getFeaturesCol: String = get(featuresCol) -} - -private[ml] trait HasLabelCol extends Params { - /** - * param for label column name - * @group param - */ - val labelCol: Param[String] = new Param(this, "labelCol", "label column name", Some("label")) - - /** @group getParam */ - def getLabelCol: String = get(labelCol) -} - -private[ml] trait HasPredictionCol extends Params { - /** - * param for prediction column name - * @group param - */ - val predictionCol: Param[String] = - new Param(this, "predictionCol", "prediction column name", Some("prediction")) - - /** @group getParam */ - def getPredictionCol: String = get(predictionCol) -} - -private[ml] trait HasRawPredictionCol extends Params { - /** - * param for raw prediction column name - * @group param - */ - val rawPredictionCol: Param[String] = - new Param(this, "rawPredictionCol", "raw prediction (a.k.a. confidence) column name", - Some("rawPrediction")) - - /** @group getParam */ - def getRawPredictionCol: String = get(rawPredictionCol) -} - -private[ml] trait HasProbabilityCol extends Params { - /** - * param for predicted class conditional probabilities column name - * @group param - */ - val probabilityCol: Param[String] = - new Param(this, "probabilityCol", "column name for predicted class conditional probabilities", - Some("probability")) - - /** @group getParam */ - def getProbabilityCol: String = get(probabilityCol) -} - -private[ml] trait HasFitIntercept extends Params { - /** - * param for fitting the intercept term, defaults to true - * @group param - */ - val fitIntercept: BooleanParam = - new BooleanParam(this, "fitIntercept", "indicates whether to fit an intercept term", Some(true)) - - /** @group getParam */ - def getFitIntercept: Boolean = get(fitIntercept) -} - -private[ml] trait HasThreshold extends Params { - /** - * param for threshold in (binary) prediction - * @group param - */ - val threshold: DoubleParam = new DoubleParam(this, "threshold", "threshold in prediction") - - /** @group getParam */ - def getThreshold: Double = get(threshold) -} - -private[ml] trait HasInputCol extends Params { - /** - * param for input column name - * @group param - */ - val inputCol: Param[String] = new Param(this, "inputCol", "input column name") - - /** @group getParam */ - def getInputCol: String = get(inputCol) -} - -private[ml] trait HasInputCols extends Params { - /** - * Param for input column names. - */ - val inputCols: Param[Array[String]] = new Param(this, "inputCols", "input column names") - - /** @group getParam */ - def getInputCols: Array[String] = get(inputCols) -} - -private[ml] trait HasOutputCol extends Params { - /** - * param for output column name - * @group param - */ - val outputCol: Param[String] = new Param(this, "outputCol", "output column name") - - /** @group getParam */ - def getOutputCol: String = get(outputCol) -} - -private[ml] trait HasCheckpointInterval extends Params { - /** - * param for checkpoint interval - * @group param - */ - val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "checkpoint interval") - - /** @group getParam */ - def getCheckpointInterval: Int = get(checkpointInterval) -} diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala index 52c9e95d6012f..bd793beba35b6 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala @@ -34,6 +34,7 @@ import org.apache.spark.{Logging, Partitioner} import org.apache.spark.annotation.DeveloperApi import org.apache.spark.ml.{Estimator, Model} import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared._ import org.apache.spark.mllib.optimization.NNLS import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame @@ -54,86 +55,88 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR * Param for rank of the matrix factorization. * @group param */ - val rank = new IntParam(this, "rank", "rank of the factorization", Some(10)) + val rank = new IntParam(this, "rank", "rank of the factorization") /** @group getParam */ - def getRank: Int = get(rank) + def getRank: Int = getOrDefault(rank) /** * Param for number of user blocks. * @group param */ - val numUserBlocks = new IntParam(this, "numUserBlocks", "number of user blocks", Some(10)) + val numUserBlocks = new IntParam(this, "numUserBlocks", "number of user blocks") /** @group getParam */ - def getNumUserBlocks: Int = get(numUserBlocks) + def getNumUserBlocks: Int = getOrDefault(numUserBlocks) /** * Param for number of item blocks. * @group param */ val numItemBlocks = - new IntParam(this, "numItemBlocks", "number of item blocks", Some(10)) + new IntParam(this, "numItemBlocks", "number of item blocks") /** @group getParam */ - def getNumItemBlocks: Int = get(numItemBlocks) + def getNumItemBlocks: Int = getOrDefault(numItemBlocks) /** * Param to decide whether to use implicit preference. * @group param */ - val implicitPrefs = - new BooleanParam(this, "implicitPrefs", "whether to use implicit preference", Some(false)) + val implicitPrefs = new BooleanParam(this, "implicitPrefs", "whether to use implicit preference") /** @group getParam */ - def getImplicitPrefs: Boolean = get(implicitPrefs) + def getImplicitPrefs: Boolean = getOrDefault(implicitPrefs) /** * Param for the alpha parameter in the implicit preference formulation. * @group param */ - val alpha = new DoubleParam(this, "alpha", "alpha for implicit preference", Some(1.0)) + val alpha = new DoubleParam(this, "alpha", "alpha for implicit preference") /** @group getParam */ - def getAlpha: Double = get(alpha) + def getAlpha: Double = getOrDefault(alpha) /** * Param for the column name for user ids. * @group param */ - val userCol = new Param[String](this, "userCol", "column name for user ids", Some("user")) + val userCol = new Param[String](this, "userCol", "column name for user ids") /** @group getParam */ - def getUserCol: String = get(userCol) + def getUserCol: String = getOrDefault(userCol) /** * Param for the column name for item ids. * @group param */ - val itemCol = - new Param[String](this, "itemCol", "column name for item ids", Some("item")) + val itemCol = new Param[String](this, "itemCol", "column name for item ids") /** @group getParam */ - def getItemCol: String = get(itemCol) + def getItemCol: String = getOrDefault(itemCol) /** * Param for the column name for ratings. * @group param */ - val ratingCol = new Param[String](this, "ratingCol", "column name for ratings", Some("rating")) + val ratingCol = new Param[String](this, "ratingCol", "column name for ratings") /** @group getParam */ - def getRatingCol: String = get(ratingCol) + def getRatingCol: String = getOrDefault(ratingCol) /** * Param for whether to apply nonnegativity constraints. * @group param */ val nonnegative = new BooleanParam( - this, "nonnegative", "whether to use nonnegative constraint for least squares", Some(false)) + this, "nonnegative", "whether to use nonnegative constraint for least squares") /** @group getParam */ - val getNonnegative: Boolean = get(nonnegative) + def getNonnegative: Boolean = getOrDefault(nonnegative) + + setDefault(rank -> 10, maxIter -> 10, regParam -> 0.1, numUserBlocks -> 10, numItemBlocks -> 10, + implicitPrefs -> false, alpha -> 1.0, userCol -> "user", itemCol -> "item", + ratingCol -> "rating", nonnegative -> false) /** * Validates and transforms the input schema. @@ -142,7 +145,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR * @return output schema */ protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) assert(schema(map(userCol)).dataType == IntegerType) assert(schema(map(itemCol)).dataType== IntegerType) val ratingType = schema(map(ratingCol)).dataType @@ -171,7 +174,7 @@ class ALSModel private[ml] ( override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { import dataset.sqlContext.implicits._ - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val users = userFactors.toDF("id", "features") val items = itemFactors.toDF("id", "features") @@ -283,7 +286,7 @@ class ALS extends Estimator[ALSModel] with ALSParams { setCheckpointInterval(10) override def fit(dataset: DataFrame, paramMap: ParamMap): ALSModel = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val ratings = dataset .select(col(map(userCol)), col(map(itemCol)), col(map(ratingCol)).cast(FloatType)) .map { row => diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala index 65f6627a0c351..26ca7459c4fdf 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala @@ -18,7 +18,8 @@ package org.apache.spark.ml.regression import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.param.{Params, ParamMap, HasMaxIter, HasRegParam} +import org.apache.spark.ml.param.{Params, ParamMap} +import org.apache.spark.ml.param.shared._ import org.apache.spark.mllib.linalg.{BLAS, Vector} import org.apache.spark.mllib.regression.LinearRegressionWithSGD import org.apache.spark.sql.DataFrame @@ -41,8 +42,7 @@ private[regression] trait LinearRegressionParams extends RegressorParams class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegressionModel] with LinearRegressionParams { - setRegParam(0.1) - setMaxIter(100) + setDefault(regParam -> 0.1, maxIter -> 100) /** @group setParam */ def setRegParam(value: Double): this.type = set(regParam, value) @@ -93,7 +93,7 @@ class LinearRegressionModel private[ml] ( override protected def copy(): LinearRegressionModel = { val m = new LinearRegressionModel(parent, fittingParamMap, weights, intercept) - Params.inheritValues(this.paramMap, this, m) + Params.inheritValues(extractParamMap(), this, m) m } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala index 2eb1dac56f1e9..4bb4ed813c006 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala @@ -31,6 +31,7 @@ import org.apache.spark.sql.types.StructType * Params for [[CrossValidator]] and [[CrossValidatorModel]]. */ private[ml] trait CrossValidatorParams extends Params { + /** * param for the estimator to be cross-validated * @group param @@ -38,7 +39,7 @@ private[ml] trait CrossValidatorParams extends Params { val estimator: Param[Estimator[_]] = new Param(this, "estimator", "estimator for selection") /** @group getParam */ - def getEstimator: Estimator[_] = get(estimator) + def getEstimator: Estimator[_] = getOrDefault(estimator) /** * param for estimator param maps @@ -48,7 +49,7 @@ private[ml] trait CrossValidatorParams extends Params { new Param(this, "estimatorParamMaps", "param maps for the estimator") /** @group getParam */ - def getEstimatorParamMaps: Array[ParamMap] = get(estimatorParamMaps) + def getEstimatorParamMaps: Array[ParamMap] = getOrDefault(estimatorParamMaps) /** * param for the evaluator for selection @@ -57,17 +58,18 @@ private[ml] trait CrossValidatorParams extends Params { val evaluator: Param[Evaluator] = new Param(this, "evaluator", "evaluator for selection") /** @group getParam */ - def getEvaluator: Evaluator = get(evaluator) + def getEvaluator: Evaluator = getOrDefault(evaluator) /** * param for number of folds for cross validation * @group param */ - val numFolds: IntParam = - new IntParam(this, "numFolds", "number of folds for cross validation", Some(3)) + val numFolds: IntParam = new IntParam(this, "numFolds", "number of folds for cross validation") /** @group getParam */ - def getNumFolds: Int = get(numFolds) + def getNumFolds: Int = getOrDefault(numFolds) + + setDefault(numFolds -> 3) } /** @@ -92,7 +94,7 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP def setNumFolds(value: Int): this.type = set(numFolds, value) override def fit(dataset: DataFrame, paramMap: ParamMap): CrossValidatorModel = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) val schema = dataset.schema transformSchema(dataset.schema, paramMap, logging = true) val sqlCtx = dataset.sqlContext @@ -130,7 +132,7 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP } override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = this.paramMap ++ paramMap + val map = extractParamMap(paramMap) map(estimator).transformSchema(schema, paramMap) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/SchemaUtils.scala b/mllib/src/main/scala/org/apache/spark/ml/util/SchemaUtils.scala new file mode 100644 index 0000000000000..0383bf0b382b7 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/util/SchemaUtils.scala @@ -0,0 +1,61 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.util + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.types.{DataType, StructField, StructType} + +/** + * :: DeveloperApi :: + * Utils for handling schemas. + */ +@DeveloperApi +object SchemaUtils { + + // TODO: Move the utility methods to SQL. + + /** + * Check whether the given schema contains a column of the required data type. + * @param colName column name + * @param dataType required column data type + */ + def checkColumnType(schema: StructType, colName: String, dataType: DataType): Unit = { + val actualDataType = schema(colName).dataType + require(actualDataType.equals(dataType), + s"Column $colName must be of type $dataType but was actually $actualDataType.") + } + + /** + * Appends a new column to the input schema. This fails if the given output column already exists. + * @param schema input schema + * @param colName new column name. If this column name is an empty string "", this method returns + * the input schema unchanged. This allows users to disable output columns. + * @param dataType new column data type + * @return new schema with the input column appended + */ + def appendColumn( + schema: StructType, + colName: String, + dataType: DataType): StructType = { + if (colName.isEmpty) return schema + val fieldNames = schema.fieldNames + require(!fieldNames.contains(colName), s"Column $colName already exists.") + val outputFields = schema.fields :+ StructField(colName, dataType, nullable = false) + StructType(outputFields) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala index 1ce2987612378..88ea679eeaad5 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala @@ -21,19 +21,25 @@ import org.scalatest.FunSuite class ParamsSuite extends FunSuite { - val solver = new TestParams() - import solver.{inputCol, maxIter} - test("param") { + val solver = new TestParams() + import solver.{maxIter, inputCol} + assert(maxIter.name === "maxIter") assert(maxIter.doc === "max number of iterations") - assert(maxIter.defaultValue.get === 100) assert(maxIter.parent.eq(solver)) - assert(maxIter.toString === "maxIter: max number of iterations (default: 100)") - assert(inputCol.defaultValue === None) + assert(maxIter.toString === "maxIter: max number of iterations (default: 10)") + + solver.setMaxIter(5) + assert(maxIter.toString === "maxIter: max number of iterations (default: 10, current: 5)") + + assert(inputCol.toString === "inputCol: input column name (undefined)") } test("param pair") { + val solver = new TestParams() + import solver.maxIter + val pair0 = maxIter -> 5 val pair1 = maxIter.w(5) val pair2 = ParamPair(maxIter, 5) @@ -44,10 +50,12 @@ class ParamsSuite extends FunSuite { } test("param map") { + val solver = new TestParams() + import solver.{maxIter, inputCol} + val map0 = ParamMap.empty assert(!map0.contains(maxIter)) - assert(map0(maxIter) === maxIter.defaultValue.get) map0.put(maxIter, 10) assert(map0.contains(maxIter)) assert(map0(maxIter) === 10) @@ -78,23 +86,39 @@ class ParamsSuite extends FunSuite { } test("params") { + val solver = new TestParams() + import solver.{maxIter, inputCol} + val params = solver.params - assert(params.size === 2) + assert(params.length === 2) assert(params(0).eq(inputCol), "params must be ordered by name") assert(params(1).eq(maxIter)) + + assert(!solver.isSet(maxIter)) + assert(solver.isDefined(maxIter)) + assert(solver.getMaxIter === 10) + solver.setMaxIter(100) + assert(solver.isSet(maxIter)) + assert(solver.getMaxIter === 100) + assert(!solver.isSet(inputCol)) + assert(!solver.isDefined(inputCol)) + intercept[NoSuchElementException](solver.getInputCol) + assert(solver.explainParams() === Seq(inputCol, maxIter).mkString("\n")) + assert(solver.getParam("inputCol").eq(inputCol)) assert(solver.getParam("maxIter").eq(maxIter)) - intercept[NoSuchMethodException] { + intercept[NoSuchElementException] { solver.getParam("abc") } - assert(!solver.isSet(inputCol)) + intercept[IllegalArgumentException] { solver.validate() } solver.validate(ParamMap(inputCol -> "input")) solver.setInputCol("input") assert(solver.isSet(inputCol)) + assert(solver.isDefined(inputCol)) assert(solver.getInputCol === "input") solver.validate() intercept[IllegalArgumentException] { @@ -104,5 +128,8 @@ class ParamsSuite extends FunSuite { intercept[IllegalArgumentException] { solver.validate() } + + solver.clearMaxIter() + assert(!solver.isSet(maxIter)) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala index ce52f2f230085..8f9ab687c05cb 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala @@ -20,17 +20,21 @@ package org.apache.spark.ml.param /** A subclass of Params for testing. */ class TestParams extends Params { - val maxIter = new IntParam(this, "maxIter", "max number of iterations", Some(100)) + val maxIter = new IntParam(this, "maxIter", "max number of iterations") def setMaxIter(value: Int): this.type = { set(maxIter, value); this } - def getMaxIter: Int = get(maxIter) + def getMaxIter: Int = getOrDefault(maxIter) val inputCol = new Param[String](this, "inputCol", "input column name") def setInputCol(value: String): this.type = { set(inputCol, value); this } - def getInputCol: String = get(inputCol) + def getInputCol: String = getOrDefault(inputCol) + + setDefault(maxIter -> 10) override def validate(paramMap: ParamMap): Unit = { - val m = this.paramMap ++ paramMap + val m = extractParamMap(paramMap) require(m(maxIter) >= 0) require(m.contains(inputCol)) } + + def clearMaxIter(): this.type = clear(maxIter) } From 77eeb10fd1f5beb558d6278d9443d267fc6c2f62 Mon Sep 17 00:00:00 2001 From: Brennon York Date: Mon, 13 Apr 2015 22:31:44 -0700 Subject: [PATCH 274/591] [WIP][HOTFIX][SPARK-4123]: Fix bug in PR dependency (all deps. removed issue) We're seeing a bug sporadically in the new PR dependency comparison test whereby it notes that *all* dependencies are removed. This happens when the current PR is built, but the final, sorted, dependency file is left blank. I believe this is an error either in the way the `git checkout` calls have been or an error within the `mvn` build for that PR (again, likely related to the `git checkout`). As such I've set the checkouts to now force (with `-f` flag) which is more in line with what Jenkins currently does on the initial checkout. Setting this as a WIP for now to trigger the build process myriad times to see if the issue still arises. Author: Brennon York Closes #5443 from brennonyork/HOTFIX2-SPARK-4123 and squashes the following commits: f2186be [Brennon York] added output for the various git commit refs 3f073d6 [Brennon York] removed the git checkouts piping to dev null 07765a6 [Brennon York] updated the diff logic to reference the filenames rather than hardlink e3f63c7 [Brennon York] added '-f' to the checkout flags for git 710c8d1 [Brennon York] added 30 minutes to the test benchmark --- dev/run-tests-jenkins | 4 ++++ dev/tests/pr_new_dependencies.sh | 8 ++++---- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/dev/run-tests-jenkins b/dev/run-tests-jenkins index f6372835a6dbf..3c1c91a111357 100755 --- a/dev/run-tests-jenkins +++ b/dev/run-tests-jenkins @@ -161,6 +161,10 @@ pr_message="" # Ensure we save off the current HEAD to revert to current_pr_head="`git rev-parse HEAD`" +echo "HEAD: `git rev-parse HEAD`" +echo "GHPRB: $ghprbActualCommit" +echo "SHA1: $sha1" + # Run pull request tests for t in "${PR_TESTS[@]}"; do this_test="${FWDIR}/dev/tests/${t}.sh" diff --git a/dev/tests/pr_new_dependencies.sh b/dev/tests/pr_new_dependencies.sh index 370c7cc737bbd..fdfb3c62aff58 100755 --- a/dev/tests/pr_new_dependencies.sh +++ b/dev/tests/pr_new_dependencies.sh @@ -39,12 +39,12 @@ CURR_CP_FILE="my-classpath.txt" MASTER_CP_FILE="master-classpath.txt" # First switch over to the master branch -git checkout master &>/dev/null +git checkout -f master # Find and copy all pom.xml files into a *.gate file that we can check # against through various `git` changes find -name "pom.xml" -exec cp {} {}.gate \; # Switch back to the current PR -git checkout "${current_pr_head}" &>/dev/null +git checkout -f "${current_pr_head}" # Check if any *.pom files from the current branch are different from the master difference_q="" @@ -71,7 +71,7 @@ else sort > ${CURR_CP_FILE} # Checkout the master branch to compare against - git checkout master &>/dev/null + git checkout -f master ${MVN_BIN} clean package dependency:build-classpath -DskipTests 2>/dev/null | \ sed -n -e '/Building Spark Project Assembly/,$p' | \ @@ -84,7 +84,7 @@ else rev | \ sort > ${MASTER_CP_FILE} - DIFF_RESULTS="`diff my-classpath.txt master-classpath.txt`" + DIFF_RESULTS="`diff ${CURR_CP_FILE} ${MASTER_CP_FILE}`" if [ -z "${DIFF_RESULTS}" ]; then echo " * This patch does not change any dependencies." From 628a72f70ed06b8d7aee81cfb16070eb2c87b9cd Mon Sep 17 00:00:00 2001 From: Punyashloka Biswal Date: Tue, 14 Apr 2015 11:43:06 +0100 Subject: [PATCH 275/591] [SPARK-6731] Bump version of apache commons-math3 Version 3.1.1 is two years old and the newer version includes approximate percentile statistics (among other things). Author: Punyashloka Biswal Closes #5380 from punya/patch-1 and squashes the following commits: 226622b [Punyashloka Biswal] Bump version of apache commons-math3 --- pom.xml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pom.xml b/pom.xml index d8881c213bf07..0b8d664dbfe1b 100644 --- a/pom.xml +++ b/pom.xml @@ -147,7 +147,7 @@ 1.8.3 1.1.0 4.2.6 - 3.1.1 + 3.4.1 ${project.build.directory}/spark-test-classpath.txt 2.10.4 2.10 From 51b306b930cfe03ad21af72a3a6ef31e6e626235 Mon Sep 17 00:00:00 2001 From: Erik van Oosten Date: Tue, 14 Apr 2015 12:39:56 +0100 Subject: [PATCH 276/591] SPARK-6878 [CORE] Fix for sum on empty RDD fails with exception Author: Erik van Oosten Closes #5489 from erikvanoosten/master and squashes the following commits: 1c91954 [Erik van Oosten] Rewrote double range matcher to an exact equality assert (SPARK-6878) f1708c9 [Erik van Oosten] Fix for sum on empty RDD fails with exception (SPARK-6878) --- .../scala/org/apache/spark/rdd/DoubleRDDFunctions.scala | 2 +- .../test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala | 6 ++++++ 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala index 29ca3e9c4bd04..843a893235e56 100644 --- a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala @@ -31,7 +31,7 @@ import org.apache.spark.util.StatCounter class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { /** Add up the elements in this RDD. */ def sum(): Double = { - self.reduce(_ + _) + self.fold(0.0)(_ + _) } /** diff --git a/core/src/test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala index 97079382c716f..01039b9449daf 100644 --- a/core/src/test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/DoubleRDDSuite.scala @@ -22,6 +22,12 @@ import org.scalatest.FunSuite import org.apache.spark._ class DoubleRDDSuite extends FunSuite with SharedSparkContext { + test("sum") { + assert(sc.parallelize(Seq.empty[Double]).sum() === 0.0) + assert(sc.parallelize(Seq(1.0)).sum() === 1.0) + assert(sc.parallelize(Seq(1.0, 2.0)).sum() === 3.0) + } + // Verify tests on the histogram functionality. We test with both evenly // and non-evenly spaced buckets as the bucket lookup function changes. test("WorksOnEmpty") { From 320bca4508e890b874c2eb7abb76a30ef14c932f Mon Sep 17 00:00:00 2001 From: Timothy Chen Date: Tue, 14 Apr 2015 11:48:12 -0700 Subject: [PATCH 277/591] [SPARK-6081] Support fetching http/https uris in driver runner. Currently if passed uris such as http/https, it won't able to fetch them as it only calls HadoopFs get. This fix utilizes the existing util method to fetch remote uris as well. Author: Timothy Chen Closes #4832 from tnachen/driver_remote and squashes the following commits: aa52cd6 [Timothy Chen] Support fetching remote uris in driver runner. --- .../spark/deploy/worker/DriverRunner.scala | 21 ++++++++++++------- .../apache/spark/deploy/worker/Worker.scala | 3 ++- .../spark/deploy/JsonProtocolSuite.scala | 7 ++++--- .../deploy/worker/DriverRunnerTest.scala | 7 ++++--- 4 files changed, 23 insertions(+), 15 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala index e0948e16ef354..ef7a703bffe67 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala @@ -24,14 +24,14 @@ import scala.collection.JavaConversions._ import akka.actor.ActorRef import com.google.common.base.Charsets.UTF_8 import com.google.common.io.Files -import org.apache.hadoop.fs.{FileUtil, Path} +import org.apache.hadoop.fs.Path -import org.apache.spark.{Logging, SparkConf} +import org.apache.spark.{Logging, SparkConf, SecurityManager} import org.apache.spark.deploy.{DriverDescription, SparkHadoopUtil} import org.apache.spark.deploy.DeployMessages.DriverStateChanged import org.apache.spark.deploy.master.DriverState import org.apache.spark.deploy.master.DriverState.DriverState -import org.apache.spark.util.{Clock, SystemClock} +import org.apache.spark.util.{Utils, Clock, SystemClock} /** * Manages the execution of one driver, including automatically restarting the driver on failure. @@ -44,7 +44,8 @@ private[deploy] class DriverRunner( val sparkHome: File, val driverDesc: DriverDescription, val worker: ActorRef, - val workerUrl: String) + val workerUrl: String, + val securityManager: SecurityManager) extends Logging { @volatile private var process: Option[Process] = None @@ -136,12 +137,9 @@ private[deploy] class DriverRunner( * Will throw an exception if there are errors downloading the jar. */ private def downloadUserJar(driverDir: File): String = { - val jarPath = new Path(driverDesc.jarUrl) val hadoopConf = SparkHadoopUtil.get.newConfiguration(conf) - val jarFileSystem = jarPath.getFileSystem(hadoopConf) - val destPath = new File(driverDir.getAbsolutePath, jarPath.getName) val jarFileName = jarPath.getName val localJarFile = new File(driverDir, jarFileName) @@ -149,7 +147,14 @@ private[deploy] class DriverRunner( if (!localJarFile.exists()) { // May already exist if running multiple workers on one node logInfo(s"Copying user jar $jarPath to $destPath") - FileUtil.copy(jarFileSystem, jarPath, destPath, false, hadoopConf) + Utils.fetchFile( + driverDesc.jarUrl, + driverDir, + conf, + securityManager, + hadoopConf, + System.currentTimeMillis(), + useCache = false) } if (!localJarFile.exists()) { // Verify copy succeeded diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala index c4c24a7866aa3..3ee2eb69e8a4e 100755 --- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala @@ -436,7 +436,8 @@ private[worker] class Worker( sparkHome, driverDesc.copy(command = Worker.maybeUpdateSSLSettings(driverDesc.command, conf)), self, - akkaUrl) + akkaUrl, + securityMgr) drivers(driverId) = driver driver.start() diff --git a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala index 2071701b313db..b58d62567afe1 100644 --- a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala @@ -28,7 +28,7 @@ import org.scalatest.FunSuite import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, WorkerStateResponse} import org.apache.spark.deploy.master.{ApplicationInfo, DriverInfo, RecoveryState, WorkerInfo} import org.apache.spark.deploy.worker.{DriverRunner, ExecutorRunner} -import org.apache.spark.SparkConf +import org.apache.spark.{SecurityManager, SparkConf} class JsonProtocolSuite extends FunSuite { @@ -124,8 +124,9 @@ class JsonProtocolSuite extends FunSuite { } def createDriverRunner(): DriverRunner = { - new DriverRunner(new SparkConf(), "driverId", new File("workDir"), new File("sparkHome"), - createDriverDesc(), null, "akka://worker") + val conf = new SparkConf() + new DriverRunner(conf, "driverId", new File("workDir"), new File("sparkHome"), + createDriverDesc(), null, "akka://worker", new SecurityManager(conf)) } def assertValidJson(json: JValue) { diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/DriverRunnerTest.scala b/core/src/test/scala/org/apache/spark/deploy/worker/DriverRunnerTest.scala index aa6e4874cecde..2159fd8c16c6f 100644 --- a/core/src/test/scala/org/apache/spark/deploy/worker/DriverRunnerTest.scala +++ b/core/src/test/scala/org/apache/spark/deploy/worker/DriverRunnerTest.scala @@ -25,7 +25,7 @@ import org.mockito.invocation.InvocationOnMock import org.mockito.stubbing.Answer import org.scalatest.FunSuite -import org.apache.spark.SparkConf +import org.apache.spark.{SecurityManager, SparkConf} import org.apache.spark.deploy.{Command, DriverDescription} import org.apache.spark.util.Clock @@ -33,8 +33,9 @@ class DriverRunnerTest extends FunSuite { private def createDriverRunner() = { val command = new Command("mainClass", Seq(), Map(), Seq(), Seq(), Seq()) val driverDescription = new DriverDescription("jarUrl", 512, 1, true, command) - new DriverRunner(new SparkConf(), "driverId", new File("workDir"), new File("sparkHome"), - driverDescription, null, "akka://1.2.3.4/worker/") + val conf = new SparkConf() + new DriverRunner(conf, "driverId", new File("workDir"), new File("sparkHome"), + driverDescription, null, "akka://1.2.3.4/worker/", new SecurityManager(conf)) } private def createProcessBuilderAndProcess(): (ProcessBuilderLike, Process) = { From f63b44a5c201d9678738a906462be9a6d7e3e8f8 Mon Sep 17 00:00:00 2001 From: WangTaoTheTonic Date: Tue, 14 Apr 2015 12:02:11 -0700 Subject: [PATCH 278/591] [SPARK-6894]spark.executor.extraLibraryOptions => spark.executor.extraLibraryPath https://issues.apache.org/jira/browse/SPARK-6894 cc vanzin Author: WangTaoTheTonic Closes #5506 from WangTaoTheTonic/SPARK-6894 and squashes the following commits: 4b7ced7 [WangTaoTheTonic] spark.executor.extraLibraryOptions => spark.executor.extraLibraryPath --- .../src/main/java/org/apache/spark/launcher/SparkLauncher.java | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java b/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java index b566507ee6061..d4cfeacb6ef18 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java +++ b/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java @@ -52,7 +52,7 @@ public class SparkLauncher { /** Configuration key for the executor VM options. */ public static final String EXECUTOR_EXTRA_JAVA_OPTIONS = "spark.executor.extraJavaOptions"; /** Configuration key for the executor native library path. */ - public static final String EXECUTOR_EXTRA_LIBRARY_PATH = "spark.executor.extraLibraryOptions"; + public static final String EXECUTOR_EXTRA_LIBRARY_PATH = "spark.executor.extraLibraryPath"; /** Configuration key for the number of executor CPU cores. */ public static final String EXECUTOR_CORES = "spark.executor.cores"; From dcf8a9f331c6193a62bbc9282bdc99663e23ca19 Mon Sep 17 00:00:00 2001 From: pankaj arora Date: Tue, 14 Apr 2015 12:06:46 -0700 Subject: [PATCH 279/591] [CORE] SPARK-6880: Fixed null check when all the dependent stages are cancelled due to previous stage failure Fixed null check when all the dependent stages are cancelled due to previous stage failure. This happens when one of the executor node goes down and all the dependent stages are cancelled. Author: pankaj arora Closes #5494 from pankajarora12/NEWBRANCH and squashes the following commits: 55ba5e3 [pankaj arora] [CORE] SPARK-6880: Fixed null check when all the dependent stages are cancelled due to previous stage failure 4575720 [pankaj arora] [CORE] SPARK-6880: Fixed null check when all the dependent stages are cancelled due to previous stage failure --- .../scala/org/apache/spark/scheduler/DAGScheduler.scala | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala index 508fe7b3303ca..4a32f8936fb0e 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -818,12 +818,7 @@ class DAGScheduler( } } - val properties = if (jobIdToActiveJob.contains(jobId)) { - jobIdToActiveJob(stage.jobId).properties - } else { - // this stage will be assigned to "default" pool - null - } + val properties = jobIdToActiveJob.get(stage.jobId).map(_.properties).orNull runningStages += stage // SparkListenerStageSubmitted should be posted before testing whether tasks are From 25998e4d73bcc95ac85d9af71adfdc726ec89568 Mon Sep 17 00:00:00 2001 From: GuoQiang Li Date: Tue, 14 Apr 2015 12:56:47 -0700 Subject: [PATCH 280/591] [SPARK-2033] Automatically cleanup checkpoint Author: GuoQiang Li Closes #855 from witgo/cleanup_checkpoint_date and squashes the following commits: 1649850 [GuoQiang Li] review commit c0087e0 [GuoQiang Li] Automatically cleanup checkpoint --- .../org/apache/spark/ContextCleaner.scala | 44 ++++++++++++----- .../apache/spark/rdd/RDDCheckpointData.scala | 27 ++++++++-- .../apache/spark/ContextCleanerSuite.scala | 49 ++++++++++++++++++- 3 files changed, 102 insertions(+), 18 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/ContextCleaner.scala b/core/src/main/scala/org/apache/spark/ContextCleaner.scala index 9b05c9623b704..715b259057569 100644 --- a/core/src/main/scala/org/apache/spark/ContextCleaner.scala +++ b/core/src/main/scala/org/apache/spark/ContextCleaner.scala @@ -22,7 +22,7 @@ import java.lang.ref.{ReferenceQueue, WeakReference} import scala.collection.mutable.{ArrayBuffer, SynchronizedBuffer} import org.apache.spark.broadcast.Broadcast -import org.apache.spark.rdd.RDD +import org.apache.spark.rdd.{RDDCheckpointData, RDD} import org.apache.spark.util.Utils /** @@ -33,6 +33,7 @@ private case class CleanRDD(rddId: Int) extends CleanupTask private case class CleanShuffle(shuffleId: Int) extends CleanupTask private case class CleanBroadcast(broadcastId: Long) extends CleanupTask private case class CleanAccum(accId: Long) extends CleanupTask +private case class CleanCheckpoint(rddId: Int) extends CleanupTask /** * A WeakReference associated with a CleanupTask. @@ -94,12 +95,12 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging { @volatile private var stopped = false /** Attach a listener object to get information of when objects are cleaned. */ - def attachListener(listener: CleanerListener) { + def attachListener(listener: CleanerListener): Unit = { listeners += listener } /** Start the cleaner. */ - def start() { + def start(): Unit = { cleaningThread.setDaemon(true) cleaningThread.setName("Spark Context Cleaner") cleaningThread.start() @@ -108,7 +109,7 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging { /** * Stop the cleaning thread and wait until the thread has finished running its current task. */ - def stop() { + def stop(): Unit = { stopped = true // Interrupt the cleaning thread, but wait until the current task has finished before // doing so. This guards against the race condition where a cleaning thread may @@ -121,7 +122,7 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging { } /** Register a RDD for cleanup when it is garbage collected. */ - def registerRDDForCleanup(rdd: RDD[_]) { + def registerRDDForCleanup(rdd: RDD[_]): Unit = { registerForCleanup(rdd, CleanRDD(rdd.id)) } @@ -130,17 +131,22 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging { } /** Register a ShuffleDependency for cleanup when it is garbage collected. */ - def registerShuffleForCleanup(shuffleDependency: ShuffleDependency[_, _, _]) { + def registerShuffleForCleanup(shuffleDependency: ShuffleDependency[_, _, _]): Unit = { registerForCleanup(shuffleDependency, CleanShuffle(shuffleDependency.shuffleId)) } /** Register a Broadcast for cleanup when it is garbage collected. */ - def registerBroadcastForCleanup[T](broadcast: Broadcast[T]) { + def registerBroadcastForCleanup[T](broadcast: Broadcast[T]): Unit = { registerForCleanup(broadcast, CleanBroadcast(broadcast.id)) } + /** Register a RDDCheckpointData for cleanup when it is garbage collected. */ + def registerRDDCheckpointDataForCleanup[T](rdd: RDD[_], parentId: Int): Unit = { + registerForCleanup(rdd, CleanCheckpoint(parentId)) + } + /** Register an object for cleanup. */ - private def registerForCleanup(objectForCleanup: AnyRef, task: CleanupTask) { + private def registerForCleanup(objectForCleanup: AnyRef, task: CleanupTask): Unit = { referenceBuffer += new CleanupTaskWeakReference(task, objectForCleanup, referenceQueue) } @@ -164,6 +170,8 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging { doCleanupBroadcast(broadcastId, blocking = blockOnCleanupTasks) case CleanAccum(accId) => doCleanupAccum(accId, blocking = blockOnCleanupTasks) + case CleanCheckpoint(rddId) => + doCleanCheckpoint(rddId) } } } @@ -175,7 +183,7 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging { } /** Perform RDD cleanup. */ - def doCleanupRDD(rddId: Int, blocking: Boolean) { + def doCleanupRDD(rddId: Int, blocking: Boolean): Unit = { try { logDebug("Cleaning RDD " + rddId) sc.unpersistRDD(rddId, blocking) @@ -187,7 +195,7 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging { } /** Perform shuffle cleanup, asynchronously. */ - def doCleanupShuffle(shuffleId: Int, blocking: Boolean) { + def doCleanupShuffle(shuffleId: Int, blocking: Boolean): Unit = { try { logDebug("Cleaning shuffle " + shuffleId) mapOutputTrackerMaster.unregisterShuffle(shuffleId) @@ -200,7 +208,7 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging { } /** Perform broadcast cleanup. */ - def doCleanupBroadcast(broadcastId: Long, blocking: Boolean) { + def doCleanupBroadcast(broadcastId: Long, blocking: Boolean): Unit = { try { logDebug(s"Cleaning broadcast $broadcastId") broadcastManager.unbroadcast(broadcastId, true, blocking) @@ -212,7 +220,7 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging { } /** Perform accumulator cleanup. */ - def doCleanupAccum(accId: Long, blocking: Boolean) { + def doCleanupAccum(accId: Long, blocking: Boolean): Unit = { try { logDebug("Cleaning accumulator " + accId) Accumulators.remove(accId) @@ -223,6 +231,18 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging { } } + /** Perform checkpoint cleanup. */ + def doCleanCheckpoint(rddId: Int): Unit = { + try { + logDebug("Cleaning rdd checkpoint data " + rddId) + RDDCheckpointData.clearRDDCheckpointData(sc, rddId) + logInfo("Cleaned rdd checkpoint data " + rddId) + } + catch { + case e: Exception => logError("Error cleaning rdd checkpoint data " + rddId, e) + } + } + private def blockManagerMaster = sc.env.blockManager.master private def broadcastManager = sc.env.broadcastManager private def mapOutputTrackerMaster = sc.env.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster] diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala index 6afd63d537d75..1722c27e55003 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala @@ -21,7 +21,7 @@ import scala.reflect.ClassTag import org.apache.hadoop.fs.Path -import org.apache.spark.{Logging, Partition, SerializableWritable, SparkException} +import org.apache.spark._ import org.apache.spark.scheduler.{ResultTask, ShuffleMapTask} /** @@ -83,7 +83,7 @@ private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T]) } // Create the output path for the checkpoint - val path = new Path(rdd.context.checkpointDir.get, "rdd-" + rdd.id) + val path = RDDCheckpointData.rddCheckpointDataPath(rdd.context, rdd.id).get val fs = path.getFileSystem(rdd.context.hadoopConfiguration) if (!fs.mkdirs(path)) { throw new SparkException("Failed to create checkpoint path " + path) @@ -92,8 +92,13 @@ private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T]) // Save to file, and reload it as an RDD val broadcastedConf = rdd.context.broadcast( new SerializableWritable(rdd.context.hadoopConfiguration)) - rdd.context.runJob(rdd, CheckpointRDD.writeToFile[T](path.toString, broadcastedConf) _) val newRDD = new CheckpointRDD[T](rdd.context, path.toString) + if (rdd.conf.getBoolean("spark.cleaner.referenceTracking.cleanCheckpoints", false)) { + rdd.context.cleaner.foreach { cleaner => + cleaner.registerRDDCheckpointDataForCleanup(newRDD, rdd.id) + } + } + rdd.context.runJob(rdd, CheckpointRDD.writeToFile[T](path.toString, broadcastedConf) _) if (newRDD.partitions.length != rdd.partitions.length) { throw new SparkException( "Checkpoint RDD " + newRDD + "(" + newRDD.partitions.length + ") has different " + @@ -130,5 +135,17 @@ private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T]) } } -// Used for synchronization -private[spark] object RDDCheckpointData +private[spark] object RDDCheckpointData { + def rddCheckpointDataPath(sc: SparkContext, rddId: Int): Option[Path] = { + sc.checkpointDir.map { dir => new Path(dir, "rdd-" + rddId) } + } + + def clearRDDCheckpointData(sc: SparkContext, rddId: Int): Unit = { + rddCheckpointDataPath(sc, rddId).foreach { path => + val fs = path.getFileSystem(sc.hadoopConfiguration) + if (fs.exists(path)) { + fs.delete(path, true) + } + } + } +} diff --git a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala index 1de169d964d23..097e7076e5391 100644 --- a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala +++ b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala @@ -28,7 +28,8 @@ import org.scalatest.concurrent.{PatienceConfiguration, Eventually} import org.scalatest.concurrent.Eventually._ import org.scalatest.time.SpanSugar._ -import org.apache.spark.rdd.RDD +import org.apache.spark.SparkContext._ +import org.apache.spark.rdd.{RDDCheckpointData, RDD} import org.apache.spark.storage._ import org.apache.spark.shuffle.hash.HashShuffleManager import org.apache.spark.shuffle.sort.SortShuffleManager @@ -205,6 +206,52 @@ class ContextCleanerSuite extends ContextCleanerSuiteBase { postGCTester.assertCleanup() } + test("automatically cleanup checkpoint") { + val checkpointDir = java.io.File.createTempFile("temp", "") + checkpointDir.deleteOnExit() + checkpointDir.delete() + var rdd = newPairRDD + sc.setCheckpointDir(checkpointDir.toString) + rdd.checkpoint() + rdd.cache() + rdd.collect() + var rddId = rdd.id + + // Confirm the checkpoint directory exists + assert(RDDCheckpointData.rddCheckpointDataPath(sc, rddId).isDefined) + val path = RDDCheckpointData.rddCheckpointDataPath(sc, rddId).get + val fs = path.getFileSystem(sc.hadoopConfiguration) + assert(fs.exists(path)) + + // the checkpoint is not cleaned by default (without the configuration set) + var postGCTester = new CleanerTester(sc, Seq(rddId), Nil, Nil) + rdd = null // Make RDD out of scope + runGC() + postGCTester.assertCleanup() + assert(fs.exists(RDDCheckpointData.rddCheckpointDataPath(sc, rddId).get)) + + sc.stop() + val conf = new SparkConf().setMaster("local[2]").setAppName("cleanupCheckpoint"). + set("spark.cleaner.referenceTracking.cleanCheckpoints", "true") + sc = new SparkContext(conf) + rdd = newPairRDD + sc.setCheckpointDir(checkpointDir.toString) + rdd.checkpoint() + rdd.cache() + rdd.collect() + rddId = rdd.id + + // Confirm the checkpoint directory exists + assert(fs.exists(RDDCheckpointData.rddCheckpointDataPath(sc, rddId).get)) + + // Test that GC causes checkpoint data cleanup after dereferencing the RDD + postGCTester = new CleanerTester(sc, Seq(rddId), Nil, Nil) + rdd = null // Make RDD out of scope + runGC() + postGCTester.assertCleanup() + assert(!fs.exists(RDDCheckpointData.rddCheckpointDataPath(sc, rddId).get)) + } + test("automatically cleanup RDD + shuffle + broadcast") { val numRdds = 100 val numBroadcasts = 4 // Broadcasts are more costly From 8f8dc45f6d4c8d7b740eaa3d2ea09d0b531af9dd Mon Sep 17 00:00:00 2001 From: CodingCat Date: Tue, 14 Apr 2015 13:32:06 -0700 Subject: [PATCH 281/591] SPARK-1706: Allow multiple executors per worker in Standalone mode resubmit of https://github.com/apache/spark/pull/636 for a totally different algorithm https://issues.apache.org/jira/browse/SPARK-1706 In current implementation, the user has to start multiple workers in a server for starting multiple executors in a server, which introduces additional overhead due to the more JVM processes... In this patch, I changed the scheduling logic in master to enable the user to start multiple executor processes within the same JVM process. 1. user configure spark.executor.maxCoreNumPerExecutor to suggest the maximum core he/she would like to allocate to each executor 2. Master assigns the executors to the workers with the major consideration on the memoryPerExecutor and the worker.freeMemory, and tries to allocate as many as possible cores to the executor ```min(min(memoryPerExecutor, worker.freeCore), maxLeftCoreToAssign)``` where ```maxLeftCoreToAssign = maxExecutorCanAssign * maxCoreNumPerExecutor``` --------------------------------------- Other small changes include change memoryPerSlave in ApplicationDescription to memoryPerExecutor, as "Slave" is overrided to represent both worker and executor in the documents... (we have some discussion on this before?) Author: CodingCat Closes #731 from CodingCat/SPARK-1706-2 and squashes the following commits: 6dee808 [CodingCat] change filter predicate fbeb7e5 [CodingCat] address the comments 940cb42 [CodingCat] avoid unnecessary allocation b8ca561 [CodingCat] revert a change 45967b4 [CodingCat] remove unused method 2eeff77 [CodingCat] stylistic fixes 12a1b32 [CodingCat] change the semantic of coresPerExecutor to exact core number f035423 [CodingCat] stylistic fix d9c1685 [CodingCat] remove unused var f595bd6 [CodingCat] recover some unintentional changes 63b3df9 [CodingCat] change the description of the parameter in the submit script 4cf61f1 [CodingCat] improve the code and docs ff011e2 [CodingCat] start multiple executors on the worker by rewriting startExeuctor logic 2c2bcc5 [CodingCat] fix wrong usage info 497ec2c [CodingCat] address andrew's comments 878402c [CodingCat] change the launching executor code f64a28d [CodingCat] typo fix 387f4ec [CodingCat] bug fix 35c462c [CodingCat] address Andrew's comments 0b64fea [CodingCat] fix compilation issue 19d3da7 [CodingCat] address the comments 5b81466 [CodingCat] remove outdated comments ec7d421 [CodingCat] test commit e5efabb [CodingCat] more java docs and consolidate canUse function a26096d [CodingCat] stylistic fix a5d629a [CodingCat] java doc b34ec0c [CodingCat] make master support multiple executors per worker --- .../spark/deploy/ApplicationDescription.scala | 9 +- .../apache/spark/deploy/JsonProtocol.scala | 4 +- .../org/apache/spark/deploy/SparkSubmit.scala | 2 + .../spark/deploy/SparkSubmitArguments.scala | 5 +- .../spark/deploy/master/ApplicationInfo.scala | 8 +- .../apache/spark/deploy/master/Master.scala | 117 +++++++++--------- .../deploy/master/ui/ApplicationPage.scala | 2 +- .../spark/deploy/master/ui/MasterPage.scala | 4 +- .../cluster/SparkDeploySchedulerBackend.scala | 7 +- docs/configuration.md | 11 ++ 10 files changed, 96 insertions(+), 73 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/deploy/ApplicationDescription.scala b/core/src/main/scala/org/apache/spark/deploy/ApplicationDescription.scala index b7ae9c1fc0a23..ae99432f5ce86 100644 --- a/core/src/main/scala/org/apache/spark/deploy/ApplicationDescription.scala +++ b/core/src/main/scala/org/apache/spark/deploy/ApplicationDescription.scala @@ -22,12 +22,13 @@ import java.net.URI private[spark] class ApplicationDescription( val name: String, val maxCores: Option[Int], - val memoryPerSlave: Int, + val memoryPerExecutorMB: Int, val command: Command, var appUiUrl: String, val eventLogDir: Option[URI] = None, // short name of compression codec used when writing event logs, if any (e.g. lzf) - val eventLogCodec: Option[String] = None) + val eventLogCodec: Option[String] = None, + val coresPerExecutor: Option[Int] = None) extends Serializable { val user = System.getProperty("user.name", "") @@ -35,13 +36,13 @@ private[spark] class ApplicationDescription( def copy( name: String = name, maxCores: Option[Int] = maxCores, - memoryPerSlave: Int = memoryPerSlave, + memoryPerExecutorMB: Int = memoryPerExecutorMB, command: Command = command, appUiUrl: String = appUiUrl, eventLogDir: Option[URI] = eventLogDir, eventLogCodec: Option[String] = eventLogCodec): ApplicationDescription = new ApplicationDescription( - name, maxCores, memoryPerSlave, command, appUiUrl, eventLogDir, eventLogCodec) + name, maxCores, memoryPerExecutorMB, command, appUiUrl, eventLogDir, eventLogCodec) override def toString: String = "ApplicationDescription(" + name + ")" } diff --git a/core/src/main/scala/org/apache/spark/deploy/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/deploy/JsonProtocol.scala index dfc5b97e6a6c8..2954f932b4f41 100644 --- a/core/src/main/scala/org/apache/spark/deploy/JsonProtocol.scala +++ b/core/src/main/scala/org/apache/spark/deploy/JsonProtocol.scala @@ -46,7 +46,7 @@ private[deploy] object JsonProtocol { ("name" -> obj.desc.name) ~ ("cores" -> obj.desc.maxCores) ~ ("user" -> obj.desc.user) ~ - ("memoryperslave" -> obj.desc.memoryPerSlave) ~ + ("memoryperslave" -> obj.desc.memoryPerExecutorMB) ~ ("submitdate" -> obj.submitDate.toString) ~ ("state" -> obj.state.toString) ~ ("duration" -> obj.duration) @@ -55,7 +55,7 @@ private[deploy] object JsonProtocol { def writeApplicationDescription(obj: ApplicationDescription): JObject = { ("name" -> obj.name) ~ ("cores" -> obj.maxCores) ~ - ("memoryperslave" -> obj.memoryPerSlave) ~ + ("memoryperslave" -> obj.memoryPerExecutorMB) ~ ("user" -> obj.user) ~ ("command" -> obj.command.toString) } diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala index 60bc243ebf40a..296a0764b8baf 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -406,6 +406,8 @@ object SparkSubmit { OptionAssigner(args.jars, YARN, CLUSTER, clOption = "--addJars"), // Other options + OptionAssigner(args.executorCores, STANDALONE, ALL_DEPLOY_MODES, + sysProp = "spark.executor.cores"), OptionAssigner(args.executorMemory, STANDALONE | MESOS | YARN, ALL_DEPLOY_MODES, sysProp = "spark.executor.memory"), OptionAssigner(args.totalExecutorCores, STANDALONE | MESOS, ALL_DEPLOY_MODES, diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala index 03ecf3fd99ec5..faa8780288ea3 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala @@ -482,10 +482,13 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S | Spark standalone and Mesos only: | --total-executor-cores NUM Total cores for all executors. | + | Spark standalone and YARN only: + | --executor-cores NUM Number of cores per executor. (Default: 1 in YARN mode, + | or all available cores on the worker in standalone mode) + | | YARN-only: | --driver-cores NUM Number of cores used by the driver, only in cluster mode | (Default: 1). - | --executor-cores NUM Number of cores per executor (Default: 1). | --queue QUEUE_NAME The YARN queue to submit to (Default: "default"). | --num-executors NUM Number of executors to launch (Default: 2). | --archives ARCHIVES Comma separated list of archives to be extracted into the diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala index bc5b293379f2b..f59d550d4f3b3 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala @@ -75,9 +75,11 @@ private[deploy] class ApplicationInfo( } } - private[master] def addExecutor(worker: WorkerInfo, cores: Int, useID: Option[Int] = None): - ExecutorDesc = { - val exec = new ExecutorDesc(newExecutorId(useID), this, worker, cores, desc.memoryPerSlave) + private[master] def addExecutor( + worker: WorkerInfo, + cores: Int, + useID: Option[Int] = None): ExecutorDesc = { + val exec = new ExecutorDesc(newExecutorId(useID), this, worker, cores, desc.memoryPerExecutorMB) executors(exec.id) = exec coresGranted += cores exec diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala index 9a5d5877da86d..c5a6b1beac9be 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala @@ -524,52 +524,28 @@ private[master] class Master( } /** - * Can an app use the given worker? True if the worker has enough memory and we haven't already - * launched an executor for the app on it (right now the standalone backend doesn't like having - * two executors on the same worker). - */ - private def canUse(app: ApplicationInfo, worker: WorkerInfo): Boolean = { - worker.memoryFree >= app.desc.memoryPerSlave && !worker.hasExecutor(app) - } - - /** - * Schedule the currently available resources among waiting apps. This method will be called - * every time a new app joins or resource availability changes. + * Schedule executors to be launched on the workers. + * + * There are two modes of launching executors. The first attempts to spread out an application's + * executors on as many workers as possible, while the second does the opposite (i.e. launch them + * on as few workers as possible). The former is usually better for data locality purposes and is + * the default. + * + * The number of cores assigned to each executor is configurable. When this is explicitly set, + * multiple executors from the same application may be launched on the same worker if the worker + * has enough cores and memory. Otherwise, each executor grabs all the cores available on the + * worker by default, in which case only one executor may be launched on each worker. */ - private def schedule() { - if (state != RecoveryState.ALIVE) { return } - - // First schedule drivers, they take strict precedence over applications - // Randomization helps balance drivers - val shuffledAliveWorkers = Random.shuffle(workers.toSeq.filter(_.state == WorkerState.ALIVE)) - val numWorkersAlive = shuffledAliveWorkers.size - var curPos = 0 - - for (driver <- waitingDrivers.toList) { // iterate over a copy of waitingDrivers - // We assign workers to each waiting driver in a round-robin fashion. For each driver, we - // start from the last worker that was assigned a driver, and continue onwards until we have - // explored all alive workers. - var launched = false - var numWorkersVisited = 0 - while (numWorkersVisited < numWorkersAlive && !launched) { - val worker = shuffledAliveWorkers(curPos) - numWorkersVisited += 1 - if (worker.memoryFree >= driver.desc.mem && worker.coresFree >= driver.desc.cores) { - launchDriver(worker, driver) - waitingDrivers -= driver - launched = true - } - curPos = (curPos + 1) % numWorkersAlive - } - } - + private def startExecutorsOnWorkers(): Unit = { // Right now this is a very simple FIFO scheduler. We keep trying to fit in the first app // in the queue, then the second app, etc. if (spreadOutApps) { - // Try to spread out each app among all the nodes, until it has all its cores + // Try to spread out each app among all the workers, until it has all its cores for (app <- waitingApps if app.coresLeft > 0) { val usableWorkers = workers.toArray.filter(_.state == WorkerState.ALIVE) - .filter(canUse(app, _)).sortBy(_.coresFree).reverse + .filter(worker => worker.memoryFree >= app.desc.memoryPerExecutorMB && + worker.coresFree >= app.desc.coresPerExecutor.getOrElse(1)) + .sortBy(_.coresFree).reverse val numUsable = usableWorkers.length val assigned = new Array[Int](numUsable) // Number of cores to give on each node var toAssign = math.min(app.coresLeft, usableWorkers.map(_.coresFree).sum) @@ -582,32 +558,61 @@ private[master] class Master( pos = (pos + 1) % numUsable } // Now that we've decided how many cores to give on each node, let's actually give them - for (pos <- 0 until numUsable) { - if (assigned(pos) > 0) { - val exec = app.addExecutor(usableWorkers(pos), assigned(pos)) - launchExecutor(usableWorkers(pos), exec) - app.state = ApplicationState.RUNNING - } + for (pos <- 0 until numUsable if assigned(pos) > 0) { + allocateWorkerResourceToExecutors(app, assigned(pos), usableWorkers(pos)) } } } else { - // Pack each app into as few nodes as possible until we've assigned all its cores + // Pack each app into as few workers as possible until we've assigned all its cores for (worker <- workers if worker.coresFree > 0 && worker.state == WorkerState.ALIVE) { for (app <- waitingApps if app.coresLeft > 0) { - if (canUse(app, worker)) { - val coresToUse = math.min(worker.coresFree, app.coresLeft) - if (coresToUse > 0) { - val exec = app.addExecutor(worker, coresToUse) - launchExecutor(worker, exec) - app.state = ApplicationState.RUNNING - } - } + allocateWorkerResourceToExecutors(app, app.coresLeft, worker) + } + } + } + } + + /** + * Allocate a worker's resources to one or more executors. + * @param app the info of the application which the executors belong to + * @param coresToAllocate cores on this worker to be allocated to this application + * @param worker the worker info + */ + private def allocateWorkerResourceToExecutors( + app: ApplicationInfo, + coresToAllocate: Int, + worker: WorkerInfo): Unit = { + val memoryPerExecutor = app.desc.memoryPerExecutorMB + val coresPerExecutor = app.desc.coresPerExecutor.getOrElse(coresToAllocate) + var coresLeft = coresToAllocate + while (coresLeft >= coresPerExecutor && worker.memoryFree >= memoryPerExecutor) { + val exec = app.addExecutor(worker, coresPerExecutor) + coresLeft -= coresPerExecutor + launchExecutor(worker, exec) + app.state = ApplicationState.RUNNING + } + } + + /** + * Schedule the currently available resources among waiting apps. This method will be called + * every time a new app joins or resource availability changes. + */ + private def schedule(): Unit = { + if (state != RecoveryState.ALIVE) { return } + // Drivers take strict precedence over executors + val shuffledWorkers = Random.shuffle(workers) // Randomization helps balance drivers + for (worker <- shuffledWorkers if worker.state == WorkerState.ALIVE) { + for (driver <- waitingDrivers) { + if (worker.memoryFree >= driver.desc.mem && worker.coresFree >= driver.desc.cores) { + launchDriver(worker, driver) + waitingDrivers -= driver } } } + startExecutorsOnWorkers() } - private def launchExecutor(worker: WorkerInfo, exec: ExecutorDesc) { + private def launchExecutor(worker: WorkerInfo, exec: ExecutorDesc): Unit = { logInfo("Launching executor " + exec.fullId + " on worker " + worker.id) worker.addExecutor(exec) worker.actor ! LaunchExecutor(masterUrl, diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala index 761aa8f7b1ef6..273f077bd8f57 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/ApplicationPage.scala @@ -94,7 +94,7 @@ private[ui] class ApplicationPage(parent: MasterWebUI) extends WebUIPage("app")

  • Executor Memory: - {Utils.megabytesToString(app.desc.memoryPerSlave)} + {Utils.megabytesToString(app.desc.memoryPerExecutorMB)}
  • Submit Date: {app.submitDate}
  • State: {app.state}
    • diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala index 45412a35e9a7d..399f07399a0aa 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala @@ -208,8 +208,8 @@ private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") { {app.coresGranted} - - {Utils.megabytesToString(app.desc.memoryPerSlave)} + + {Utils.megabytesToString(app.desc.memoryPerExecutorMB)} {UIUtils.formatDate(app.submitDate)} {app.desc.user} diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala index 7eb3fdc19b5b8..ed5b7c1088196 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala @@ -82,12 +82,11 @@ private[spark] class SparkDeploySchedulerBackend( val command = Command("org.apache.spark.executor.CoarseGrainedExecutorBackend", args, sc.executorEnvs, classPathEntries ++ testingClassPath, libraryPathEntries, javaOpts) val appUIAddress = sc.ui.map(_.appUIAddress).getOrElse("") - val appDesc = new ApplicationDescription(sc.appName, maxCores, sc.executorMemory, command, - appUIAddress, sc.eventLogDir, sc.eventLogCodec) - + val coresPerExecutor = conf.getOption("spark.executor.cores").map(_.toInt) + val appDesc = new ApplicationDescription(sc.appName, maxCores, sc.executorMemory, + command, appUIAddress, sc.eventLogDir, sc.eventLogCodec, coresPerExecutor) client = new AppClient(sc.env.actorSystem, masters, appDesc, this, conf) client.start() - waitForRegistration() } diff --git a/docs/configuration.md b/docs/configuration.md index 7169ec295ef7f..d9e9e67026cbb 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -723,6 +723,17 @@ Apart from these, the following properties are also available, and may be useful this duration will be cleared as well. + + spark.executor.cores + 1 in YARN mode, all the available cores on the worker in standalone mode. + + The number of cores to use on each executor. For YARN and standalone mode only. + + In standalone mode, setting this parameter allows an application to run multiple executors on + the same worker, provided that there are enough cores on that worker. Otherwise, only one + executor per application will run on each worker. + + spark.default.parallelism From b075e4b720221a8204cae93468065a6708348830 Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Tue, 14 Apr 2015 13:34:44 -0700 Subject: [PATCH 282/591] [SPARK-6700] [yarn] Re-enable flaky test. Test runs have been successful on jenkins. So let's re-enable the test and look out for any failures, and fix things appropriately. Author: Marcelo Vanzin Closes #5459 from vanzin/SPARK-6700 and squashes the following commits: 2ead85b [Marcelo Vanzin] WIP: re-enable flaky test to catch failure in jenkins. --- .../scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala index c06c0105670c0..76952e3341cc4 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala @@ -144,7 +144,7 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit } // Enable this once fix SPARK-6700 - ignore("run Python application in yarn-cluster mode") { + test("run Python application in yarn-cluster mode") { val primaryPyFile = new File(tempDir, "test.py") Files.write(TEST_PYFILE, primaryPyFile, UTF_8) val pyFile = new File(tempDir, "test2.py") From 6adb8bcbf0a1a7bfe2990de18c59c66cd7a0aeb8 Mon Sep 17 00:00:00 2001 From: Josh Rosen Date: Tue, 14 Apr 2015 13:40:07 -0700 Subject: [PATCH 283/591] [SPARK-6905] Upgrade to snappy-java 1.1.1.7 We should upgrade our snappy-java dependency to 1.1.1.7 in order to include a fix for a bug that results in worse compression in SnappyOutputStream (see https://github.com/xerial/snappy-java/issues/100). Author: Josh Rosen Closes #5512 from JoshRosen/snappy-1.1.1.7 and squashes the following commits: f1ac0f8 [Josh Rosen] Upgrade to snappy-java 1.1.1.7. --- pom.xml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pom.xml b/pom.xml index 0b8d664dbfe1b..261292d5b6cde 100644 --- a/pom.xml +++ b/pom.xml @@ -156,7 +156,7 @@ 3.6.3 1.8.8 2.4.4 - 1.1.1.6 + 1.1.1.7 1.1.2 ${java.home} From 65774370a1275e25cd8a3357e397d116767793a9 Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Tue, 14 Apr 2015 13:41:38 -0700 Subject: [PATCH 284/591] [SPARK-5808] [build] Package pyspark files in sbt assembly. This turned out to be more complicated than I wanted because the layout of python/ doesn't really follow the usual maven conventions. So some extra code is needed to copy just the right things. Author: Marcelo Vanzin Closes #5461 from vanzin/SPARK-5808 and squashes the following commits: 7153dac [Marcelo Vanzin] Only try to create resource dir if it doesn't already exist. ee90e84 [Marcelo Vanzin] [SPARK-5808] [build] Package pyspark files in sbt assembly. --- project/SparkBuild.scala | 60 +++++++++++++++++++++++++++++++++++++++- 1 file changed, 59 insertions(+), 1 deletion(-) diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index 5f51f4b58f97a..09b4976d10c26 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -15,7 +15,7 @@ * limitations under the License. */ -import java.io.File +import java.io._ import scala.util.Properties import scala.collection.JavaConversions._ @@ -166,6 +166,9 @@ object SparkBuild extends PomBuild { /* Enable Assembly for all assembly projects */ assemblyProjects.foreach(enable(Assembly.settings)) + /* Package pyspark artifacts in the main assembly. */ + enable(PySparkAssembly.settings)(assembly) + /* Enable unidoc only for the root spark project */ enable(Unidoc.settings)(spark) @@ -316,6 +319,7 @@ object Hive { } object Assembly { + import sbtassembly.AssemblyUtils._ import sbtassembly.Plugin._ import AssemblyKeys._ @@ -347,6 +351,60 @@ object Assembly { ) } +object PySparkAssembly { + import sbtassembly.Plugin._ + import AssemblyKeys._ + + lazy val settings = Seq( + unmanagedJars in Compile += { BuildCommons.sparkHome / "python/lib/py4j-0.8.2.1-src.zip" }, + // Use a resource generator to copy all .py files from python/pyspark into a managed directory + // to be included in the assembly. We can't just add "python/" to the assembly's resource dir + // list since that will copy unneeded / unwanted files. + resourceGenerators in Compile <+= resourceManaged in Compile map { outDir: File => + val dst = new File(outDir, "pyspark") + if (!dst.isDirectory()) { + require(dst.mkdirs()) + } + + val src = new File(BuildCommons.sparkHome, "python/pyspark") + copy(src, dst) + } + ) + + private def copy(src: File, dst: File): Seq[File] = { + src.listFiles().flatMap { f => + val child = new File(dst, f.getName()) + if (f.isDirectory()) { + child.mkdir() + copy(f, child) + } else if (f.getName().endsWith(".py")) { + var in: Option[FileInputStream] = None + var out: Option[FileOutputStream] = None + try { + in = Some(new FileInputStream(f)) + out = Some(new FileOutputStream(child)) + + val bytes = new Array[Byte](1024) + var read = 0 + while (read >= 0) { + read = in.get.read(bytes) + if (read > 0) { + out.get.write(bytes, 0, read) + } + } + + Some(child) + } finally { + in.foreach(_.close()) + out.foreach(_.close()) + } + } else { + None + } + } + } +} + object Unidoc { import BuildCommons._ From 4d4b24927417b2c17810e94d6d46c37491c68869 Mon Sep 17 00:00:00 2001 From: Kousuke Saruta Date: Tue, 14 Apr 2015 14:00:49 -0700 Subject: [PATCH 285/591] [SPARK-6769][YARN][TEST] Usage of the ListenerBus in YarnClusterSuite is wrong In YarnClusterSuite, a test case uses `SaveExecutorInfo` to handle ExecutorAddedEvent as follows. ``` private class SaveExecutorInfo extends SparkListener { val addedExecutorInfos = mutable.Map[String, ExecutorInfo]() override def onExecutorAdded(executor: SparkListenerExecutorAdded) { addedExecutorInfos(executor.executorId) = executor.executorInfo } } ... listener = new SaveExecutorInfo val sc = new SparkContext(new SparkConf() .setAppName("yarn \"test app\" 'with quotes' and \\back\\slashes and $dollarSigns")) sc.addSparkListener(listener) val status = new File(args(0)) var result = "failure" try { val data = sc.parallelize(1 to 4, 4).collect().toSet assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) data should be (Set(1, 2, 3, 4)) result = "success" } finally { sc.stop() Files.write(result, status, UTF_8) } ``` But, the usage is wrong because Executors will spawn during initializing SparkContext and SparkContext#addSparkListener should be invoked after the initialization, thus after Executors spawn, so SaveExecutorInfo cannot handle ExecutorAddedEvent. Following code refers the result of the handling ExecutorAddedEvent. Because of the reason above, we cannot reach the assertion. ``` // verify log urls are present listener.addedExecutorInfos.values.foreach { info => assert(info.logUrlMap.nonEmpty) } ``` Author: Kousuke Saruta Closes #5417 from sarutak/SPARK-6769 and squashes the following commits: 8adc8ba [Kousuke Saruta] Fixed compile error e258530 [Kousuke Saruta] Fixed style 591cf3e [Kousuke Saruta] Fixed style 48ec89a [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-6769 860c965 [Kousuke Saruta] Simplified code 207d325 [Kousuke Saruta] Added findListenersByClass method to ListenerBus 2408c84 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-6769 2d7e409 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-6769 3874adf [Kousuke Saruta] Fixed the usage of listener bus in LogUrlsStandaloneSuite 153a91b [Kousuke Saruta] Fixed the usage of listener bus in YarnClusterSuite --- .../org/apache/spark/util/ListenerBus.scala | 8 ++++++++ .../spark/deploy/LogUrlsStandaloneSuite.scala | 20 ++++++++++--------- .../spark/deploy/yarn/YarnClusterSuite.scala | 17 +++++++++------- 3 files changed, 29 insertions(+), 16 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/util/ListenerBus.scala b/core/src/main/scala/org/apache/spark/util/ListenerBus.scala index d60b8b9a31a9b..a725767d08cc2 100644 --- a/core/src/main/scala/org/apache/spark/util/ListenerBus.scala +++ b/core/src/main/scala/org/apache/spark/util/ListenerBus.scala @@ -19,9 +19,12 @@ package org.apache.spark.util import java.util.concurrent.CopyOnWriteArrayList +import scala.collection.JavaConversions._ +import scala.reflect.ClassTag import scala.util.control.NonFatal import org.apache.spark.Logging +import org.apache.spark.scheduler.SparkListener /** * An event bus which posts events to its listeners. @@ -64,4 +67,9 @@ private[spark] trait ListenerBus[L <: AnyRef, E] extends Logging { */ def onPostEvent(listener: L, event: E): Unit + private[spark] def findListenersByClass[T <: L : ClassTag](): Seq[T] = { + val c = implicitly[ClassTag[T]].runtimeClass + listeners.filter(_.getClass == c).map(_.asInstanceOf[T]).toSeq + } + } diff --git a/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala b/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala index 9cdb42814ca32..c93d16f8a1586 100644 --- a/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala @@ -19,6 +19,7 @@ package org.apache.spark.deploy import java.net.URL +import scala.collection.JavaConversions._ import scala.collection.mutable import scala.io.Source @@ -65,16 +66,17 @@ class LogUrlsStandaloneSuite extends FunSuite with LocalSparkContext { new MySparkConf().setAll(getAll) } } - val conf = new MySparkConf() + val conf = new MySparkConf().set( + "spark.extraListeners", classOf[SaveExecutorInfo].getName) sc = new SparkContext("local-cluster[2,1,512]", "test", conf) - val listener = new SaveExecutorInfo - sc.addSparkListener(listener) - // Trigger a job so that executors get added sc.parallelize(1 to 100, 4).map(_.toString).count() assert(sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) + val listeners = sc.listenerBus.findListenersByClass[SaveExecutorInfo] + assert(listeners.size === 1) + val listener = listeners(0) listener.addedExecutorInfos.values.foreach { info => assert(info.logUrlMap.nonEmpty) info.logUrlMap.values.foreach { logUrl => @@ -82,12 +84,12 @@ class LogUrlsStandaloneSuite extends FunSuite with LocalSparkContext { } } } +} - private class SaveExecutorInfo extends SparkListener { - val addedExecutorInfos = mutable.Map[String, ExecutorInfo]() +private[spark] class SaveExecutorInfo extends SparkListener { + val addedExecutorInfos = mutable.Map[String, ExecutorInfo]() - override def onExecutorAdded(executor: SparkListenerExecutorAdded) { - addedExecutorInfos(executor.executorId) = executor.executorInfo - } + override def onExecutorAdded(executor: SparkListenerExecutorAdded) { + addedExecutorInfos(executor.executorId) = executor.executorInfo } } diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala index 76952e3341cc4..a18c94d4ab4a8 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala @@ -33,7 +33,7 @@ import org.scalatest.{BeforeAndAfterAll, FunSuite, Matchers} import org.apache.spark.{Logging, SparkConf, SparkContext, SparkException, TestUtils} import org.apache.spark.scheduler.cluster.ExecutorInfo -import org.apache.spark.scheduler.{SparkListener, SparkListenerExecutorAdded} +import org.apache.spark.scheduler.{SparkListenerJobStart, SparkListener, SparkListenerExecutorAdded} import org.apache.spark.util.Utils /** @@ -282,10 +282,10 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit } -private class SaveExecutorInfo extends SparkListener { +private[spark] class SaveExecutorInfo extends SparkListener { val addedExecutorInfos = mutable.Map[String, ExecutorInfo]() - override def onExecutorAdded(executor : SparkListenerExecutorAdded) { + override def onExecutorAdded(executor: SparkListenerExecutorAdded) { addedExecutorInfos(executor.executorId) = executor.executorInfo } } @@ -293,7 +293,6 @@ private class SaveExecutorInfo extends SparkListener { private object YarnClusterDriver extends Logging with Matchers { val WAIT_TIMEOUT_MILLIS = 10000 - var listener: SaveExecutorInfo = null def main(args: Array[String]): Unit = { if (args.length != 1) { @@ -306,10 +305,9 @@ private object YarnClusterDriver extends Logging with Matchers { System.exit(1) } - listener = new SaveExecutorInfo val sc = new SparkContext(new SparkConf() + .set("spark.extraListeners", classOf[SaveExecutorInfo].getName) .setAppName("yarn \"test app\" 'with quotes' and \\back\\slashes and $dollarSigns")) - sc.addSparkListener(listener) val status = new File(args(0)) var result = "failure" try { @@ -323,7 +321,12 @@ private object YarnClusterDriver extends Logging with Matchers { } // verify log urls are present - listener.addedExecutorInfos.values.foreach { info => + val listeners = sc.listenerBus.findListenersByClass[SaveExecutorInfo] + assert(listeners.size === 1) + val listener = listeners(0) + val executorInfos = listener.addedExecutorInfos.values + assert(executorInfos.nonEmpty) + executorInfos.foreach { info => assert(info.logUrlMap.nonEmpty) } } From a76b921a923ac37d3c73ee18d24df4bb611daba3 Mon Sep 17 00:00:00 2001 From: Josh Rosen Date: Tue, 14 Apr 2015 14:07:25 -0700 Subject: [PATCH 286/591] Revert "[SPARK-6352] [SQL] Add DirectParquetOutputCommitter" This reverts commit b29663eeea440b1d1a288d41b5ddf67e77c5bd54. I'm reverting this because it broke test compilation for the Hadoop 1.x profiles. --- .../DirectParquetOutputCommitter.scala | 66 ------------------- .../sql/parquet/ParquetTableOperations.scala | 22 ------- .../spark/sql/parquet/ParquetIOSuite.scala | 21 ------ 3 files changed, 109 deletions(-) delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala deleted file mode 100644 index 25a66cb488103..0000000000000 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala +++ /dev/null @@ -1,66 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.parquet - -import org.apache.hadoop.fs.Path -import org.apache.hadoop.mapreduce.{JobContext, TaskAttemptContext} -import org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter - -import parquet.Log -import parquet.hadoop.util.ContextUtil -import parquet.hadoop.{ParquetFileReader, ParquetFileWriter, ParquetOutputCommitter} - -private[parquet] class DirectParquetOutputCommitter(outputPath: Path, context: TaskAttemptContext) - extends ParquetOutputCommitter(outputPath, context) { - val LOG = Log.getLog(classOf[ParquetOutputCommitter]) - - override def getWorkPath(): Path = outputPath - override def abortTask(taskContext: TaskAttemptContext): Unit = {} - override def commitTask(taskContext: TaskAttemptContext): Unit = {} - override def needsTaskCommit(taskContext: TaskAttemptContext): Boolean = true - override def setupJob(jobContext: JobContext): Unit = {} - override def setupTask(taskContext: TaskAttemptContext): Unit = {} - - override def commitJob(jobContext: JobContext) { - try { - val configuration = ContextUtil.getConfiguration(jobContext) - val fileSystem = outputPath.getFileSystem(configuration) - val outputStatus = fileSystem.getFileStatus(outputPath) - val footers = ParquetFileReader.readAllFootersInParallel(configuration, outputStatus) - try { - ParquetFileWriter.writeMetadataFile(configuration, outputPath, footers) - if (configuration.getBoolean("mapreduce.fileoutputcommitter.marksuccessfuljobs", true)) { - val successPath = new Path(outputPath, FileOutputCommitter.SUCCEEDED_FILE_NAME) - fileSystem.create(successPath).close() - } - } catch { - case e: Exception => { - LOG.warn("could not write summary file for " + outputPath, e) - val metadataPath = new Path(outputPath, ParquetFileWriter.PARQUET_METADATA_FILE) - if (fileSystem.exists(metadataPath)) { - fileSystem.delete(metadataPath, true) - } - } - } - } catch { - case e: Exception => LOG.warn("could not write summary file for " + outputPath, e) - } - } - -} - diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala index 3724bda829d30..1c868da23e060 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala @@ -379,8 +379,6 @@ private[sql] case class InsertIntoParquetTable( */ private[parquet] class AppendingParquetOutputFormat(offset: Int) extends parquet.hadoop.ParquetOutputFormat[Row] { - var committer: OutputCommitter = null - // override to accept existing directories as valid output directory override def checkOutputSpecs(job: JobContext): Unit = {} @@ -405,26 +403,6 @@ private[parquet] class AppendingParquetOutputFormat(offset: Int) private def getTaskAttemptID(context: TaskAttemptContext): TaskAttemptID = { context.getClass.getMethod("getTaskAttemptID").invoke(context).asInstanceOf[TaskAttemptID] } - - // override to create output committer from configuration - override def getOutputCommitter(context: TaskAttemptContext): OutputCommitter = { - if (committer == null) { - val output = getOutputPath(context) - val cls = context.getConfiguration.getClass("spark.sql.parquet.output.committer.class", - classOf[ParquetOutputCommitter], classOf[ParquetOutputCommitter]) - val ctor = cls.getDeclaredConstructor(classOf[Path], classOf[TaskAttemptContext]) - committer = ctor.newInstance(output, context).asInstanceOf[ParquetOutputCommitter] - } - committer - } - - // FileOutputFormat.getOutputPath takes JobConf in hadoop-1 but JobContext in hadoop-2 - private def getOutputPath(context: TaskAttemptContext): Path = { - context.getConfiguration().get("mapred.output.dir") match { - case null => null - case name => new Path(name) - } - } } /** diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala index 4d0bf7cf99cdf..97c0f439acf13 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala @@ -381,27 +381,6 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest { } } } - - test("SPARK-6352 DirectParquetOutputCommitter") { - // Write to a parquet file and let it fail. - // _temporary should be missing if direct output committer works. - try { - configuration.set("spark.sql.parquet.output.committer.class", - "org.apache.spark.sql.parquet.DirectParquetOutputCommitter") - sqlContext.udf.register("div0", (x: Int) => x / 0) - withTempPath { dir => - intercept[org.apache.spark.SparkException] { - sqlContext.sql("select div0(1)").saveAsParquetFile(dir.getCanonicalPath) - } - val path = new Path(dir.getCanonicalPath, "_temporary") - val fs = path.getFileSystem(configuration) - assert(!fs.exists(path)) - } - } - finally { - configuration.unset("spark.sql.parquet.output.committer.class") - } - } } class ParquetDataSourceOnIOSuite extends ParquetIOSuiteBase with BeforeAndAfterAll { From 6de282e2de3cb69f9b746d03fde581429248824a Mon Sep 17 00:00:00 2001 From: zsxwing Date: Tue, 14 Apr 2015 16:51:36 -0700 Subject: [PATCH 287/591] [SPARK-6796][Streaming][WebUI] Add "Active Batches" and "Completed Batches" lists to StreamingPage This PR adds two lists, `Active Batches` and `Completed Batches`. Here is the screenshot: ![batch_list](https://cloud.githubusercontent.com/assets/1000778/7060458/d8898572-deb3-11e4-938b-6f8602c71a9f.png) Due to [SPARK-6766](https://issues.apache.org/jira/browse/SPARK-6766), I need to merge #5414 in my local machine to get the above screenshot. Author: zsxwing Closes #5434 from zsxwing/SPARK-6796 and squashes the following commits: be50fc6 [zsxwing] Fix the code style 51b792e [zsxwing] Fix the unit test 6f3078e [zsxwing] Make 'startTime' readable f40e0a9 [zsxwing] Merge branch 'master' into SPARK-6796 2525336 [zsxwing] Rename 'Processed batches' and 'Waiting batches' and also add links a69c091 [zsxwing] Show the number of total completed batches too a12ad7b [zsxwing] Change 'records' to 'events' in the UI 86b5e7f [zsxwing] Make BatchTableBase abstract b248787 [zsxwing] Add tests to verify the new tables d18ab7d [zsxwing] Fix the code style 6ceffb3 [zsxwing] Add "Active Batches" and "Completed Batches" lists to StreamingPage --- .../spark/streaming/ui/AllBatchesTable.scala | 114 ++++++++++++++++++ .../spark/streaming/ui/StreamingPage.scala | 44 +++++-- .../spark/streaming/UISeleniumSuite.scala | 11 ++ 3 files changed, 159 insertions(+), 10 deletions(-) create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala new file mode 100644 index 0000000000000..df1c0a10704c3 --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala @@ -0,0 +1,114 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.ui + +import scala.xml.Node + +import org.apache.spark.streaming.scheduler.BatchInfo +import org.apache.spark.ui.UIUtils + +private[ui] abstract class BatchTableBase(tableId: String) { + + protected def columns: Seq[Node] = { + Batch Time + Input Size + Scheduling Delay + Processing Time + } + + protected def baseRow(batch: BatchInfo): Seq[Node] = { + val batchTime = batch.batchTime.milliseconds + val formattedBatchTime = UIUtils.formatDate(batch.batchTime.milliseconds) + val eventCount = batch.receivedBlockInfo.values.map { + receivers => receivers.map(_.numRecords).sum + }.sum + val schedulingDelay = batch.schedulingDelay + val formattedSchedulingDelay = schedulingDelay.map(UIUtils.formatDuration).getOrElse("-") + val processingTime = batch.processingDelay + val formattedProcessingTime = processingTime.map(UIUtils.formatDuration).getOrElse("-") + + {formattedBatchTime} + {eventCount.toString} events + + {formattedSchedulingDelay} + + + {formattedProcessingTime} + + } + + private def batchTable: Seq[Node] = { + + + {columns} + + + {renderRows} + +
    + } + + def toNodeSeq: Seq[Node] = { + batchTable + } + + /** + * Return HTML for all rows of this table. + */ + protected def renderRows: Seq[Node] +} + +private[ui] class ActiveBatchTable(runningBatches: Seq[BatchInfo], waitingBatches: Seq[BatchInfo]) + extends BatchTableBase("active-batches-table") { + + override protected def columns: Seq[Node] = super.columns ++ Status + + override protected def renderRows: Seq[Node] = { + // The "batchTime"s of "waitingBatches" must be greater than "runningBatches"'s, so display + // waiting batches before running batches + waitingBatches.flatMap(batch => {waitingBatchRow(batch)}) ++ + runningBatches.flatMap(batch => {runningBatchRow(batch)}) + } + + private def runningBatchRow(batch: BatchInfo): Seq[Node] = { + baseRow(batch) ++ processing + } + + private def waitingBatchRow(batch: BatchInfo): Seq[Node] = { + baseRow(batch) ++ queued + } +} + +private[ui] class CompletedBatchTable(batches: Seq[BatchInfo]) + extends BatchTableBase("completed-batches-table") { + + override protected def columns: Seq[Node] = super.columns ++ Total Delay + + override protected def renderRows: Seq[Node] = { + batches.flatMap(batch => {completedBatchRow(batch)}) + } + + private def completedBatchRow(batch: BatchInfo): Seq[Node] = { + val totalDelay = batch.totalDelay + val formattedTotalDelay = totalDelay.map(UIUtils.formatDuration).getOrElse("-") + baseRow(batch) ++ + + {formattedTotalDelay} + + } +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala index b6dcb62bfeec8..07fa285642eec 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala @@ -41,7 +41,8 @@ private[ui] class StreamingPage(parent: StreamingTab) generateBasicStats() ++

    ++

    Statistics over last {listener.retainedCompletedBatches.size} processed batches

    ++ generateReceiverStats() ++ - generateBatchStatsTable() + generateBatchStatsTable() ++ + generateBatchListTables() } UIUtils.headerSparkPage("Streaming", content, parent, Some(5000)) } @@ -49,9 +50,10 @@ private[ui] class StreamingPage(parent: StreamingTab) /** Generate basic stats of the streaming program */ private def generateBasicStats(): Seq[Node] = { val timeSinceStart = System.currentTimeMillis() - startTime + // scalastyle:off     + // scalastyle:on } /** Generate stats of data received by the receivers in the streaming program */ @@ -86,10 +89,10 @@ private[ui] class StreamingPage(parent: StreamingTab) "Receiver", "Status", "Location", - "Records in last batch\n[" + formatDate(Calendar.getInstance().getTime()) + "]", - "Minimum rate\n[records/sec]", - "Median rate\n[records/sec]", - "Maximum rate\n[records/sec]", + "Events in last batch\n[" + formatDate(Calendar.getInstance().getTime()) + "]", + "Minimum rate\n[events/sec]", + "Median rate\n[events/sec]", + "Maximum rate\n[events/sec]", "Last Error" ) val dataRows = (0 until listener.numReceivers).map { receiverId => @@ -190,5 +193,26 @@ private[ui] class StreamingPage(parent: StreamingTab) } UIUtils.listingTable(headers, generateDataRow, data, fixedWidth = true) } + + private def generateBatchListTables(): Seq[Node] = { + val runningBatches = listener.runningBatches.sortBy(_.batchTime.milliseconds).reverse + val waitingBatches = listener.waitingBatches.sortBy(_.batchTime.milliseconds).reverse + val completedBatches = listener.retainedCompletedBatches. + sortBy(_.batchTime.milliseconds).reverse + + val activeBatchesContent = { +

    Active Batches ({runningBatches.size + waitingBatches.size})

    ++ + new ActiveBatchTable(runningBatches, waitingBatches).toNodeSeq + } + + val completedBatchesContent = { +

    + Completed Batches (last {completedBatches.size} out of {listener.numTotalCompletedBatches}) +

    ++ + new CompletedBatchTable(completedBatches).toNodeSeq + } + + activeBatchesContent ++ completedBatchesContent + } } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala index 998426ebb82e5..205ddf6dbe9b0 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala @@ -75,6 +75,17 @@ class UISeleniumSuite val statisticText = findAll(cssSelector("li strong")).map(_.text).toSeq statisticText should contain("Network receivers:") statisticText should contain("Batch interval:") + + val h4Text = findAll(cssSelector("h4")).map(_.text).toSeq + h4Text should contain("Active Batches (0)") + h4Text should contain("Completed Batches (last 0 out of 0)") + + findAll(cssSelector("""#active-batches-table th""")).map(_.text).toSeq should be { + List("Batch Time", "Input Size", "Scheduling Delay", "Processing Time", "Status") + } + findAll(cssSelector("""#completed-batches-table th""")).map(_.text).toSeq should be { + List("Batch Time", "Input Size", "Scheduling Delay", "Processing Time", "Total Delay") + } } ssc.stop(false) From 9717389365772d218cd7c67f9a13c3440f3c6791 Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Tue, 14 Apr 2015 18:51:39 -0700 Subject: [PATCH 288/591] [SPARK-6890] [core] Fix launcher lib work with SPARK_PREPEND_CLASSES. The fix for SPARK-6406 broke the case where sub-processes are launched when SPARK_PREPEND_CLASSES is set, because the code now would only add the launcher's build directory to the sub-process's classpath instead of the complete assembly. This patch fixes the problem by having the launch scripts stash the assembly's location in an environment variable. This is not the prettiest solution, but it avoids having to plumb that location all the way through the Worker code that launches executors. The env variable is always set by the launch scripts, so users cannot override it. Author: Marcelo Vanzin Closes #5504 from vanzin/SPARK-6890 and squashes the following commits: 7aec921 [Marcelo Vanzin] Fix tests. ff87a60 [Marcelo Vanzin] Merge branch 'master' into SPARK-6890 31d3ce8 [Marcelo Vanzin] [SPARK-6890] [core] Fix launcher lib work with SPARK_PREPEND_CLASSES. --- bin/spark-class | 11 ++++- bin/spark-class2.cmd | 11 ++++- .../launcher/AbstractCommandBuilder.java | 44 +++++++++++++++++-- .../spark/launcher/CommandBuilderUtils.java | 1 + .../SparkSubmitCommandBuilderSuite.java | 15 ++++--- 5 files changed, 71 insertions(+), 11 deletions(-) diff --git a/bin/spark-class b/bin/spark-class index c03946d92e2e4..c49d97ce5cf25 100755 --- a/bin/spark-class +++ b/bin/spark-class @@ -82,13 +82,22 @@ if [ $(command -v "$JAR_CMD") ] ; then fi fi +LAUNCH_CLASSPATH="$SPARK_ASSEMBLY_JAR" + +# Add the launcher build dir to the classpath if requested. +if [ -n "$SPARK_PREPEND_CLASSES" ]; then + LAUNCH_CLASSPATH="$SPARK_HOME/launcher/target/scala-$SPARK_SCALA_VERSION/classes:$LAUNCH_CLASSPATH" +fi + +export _SPARK_ASSEMBLY="$SPARK_ASSEMBLY_JAR" + # The launcher library will print arguments separated by a NULL character, to allow arguments with # characters that would be otherwise interpreted by the shell. Read that in a while loop, populating # an array that will be used to exec the final command. CMD=() while IFS= read -d '' -r ARG; do CMD+=("$ARG") -done < <("$RUNNER" -cp "$SPARK_ASSEMBLY_JAR" org.apache.spark.launcher.Main "$@") +done < <("$RUNNER" -cp "$LAUNCH_CLASSPATH" org.apache.spark.launcher.Main "$@") if [ "${CMD[0]}" = "usage" ]; then "${CMD[@]}" diff --git a/bin/spark-class2.cmd b/bin/spark-class2.cmd index 4b3401d745f2a..3d068dd3a2739 100644 --- a/bin/spark-class2.cmd +++ b/bin/spark-class2.cmd @@ -46,13 +46,22 @@ if "%SPARK_ASSEMBLY_JAR%"=="0" ( exit /b 1 ) +set LAUNCH_CLASSPATH=%SPARK_ASSEMBLY_JAR% + +rem Add the launcher build dir to the classpath if requested. +if not "x%SPARK_PREPEND_CLASSES%"=="x" ( + set LAUNCH_CLASSPATH=%SPARK_HOME%\launcher\target\scala-%SPARK_SCALA_VERSION%\classes;%LAUNCH_CLASSPATH% +) + +set _SPARK_ASSEMBLY=%SPARK_ASSEMBLY_JAR% + rem Figure out where java is. set RUNNER=java if not "x%JAVA_HOME%"=="x" set RUNNER=%JAVA_HOME%\bin\java rem The launcher library prints the command to be executed in a single line suitable for being rem executed by the batch interpreter. So read all the output of the launcher into a variable. -for /f "tokens=*" %%i in ('cmd /C ""%RUNNER%" -cp %SPARK_ASSEMBLY_JAR% org.apache.spark.launcher.Main %*"') do ( +for /f "tokens=*" %%i in ('cmd /C ""%RUNNER%" -cp %LAUNCH_CLASSPATH% org.apache.spark.launcher.Main %*"') do ( set SPARK_CMD=%%i ) %SPARK_CMD% diff --git a/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java index d8279145d8e90..b8f02b961113d 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java +++ b/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java @@ -186,12 +186,24 @@ List buildClassPath(String appClassPath) throws IOException { addToClassPath(cp, String.format("%s/core/target/jars/*", sparkHome)); } - final String assembly = AbstractCommandBuilder.class.getProtectionDomain().getCodeSource(). - getLocation().getPath(); + // We can't rely on the ENV_SPARK_ASSEMBLY variable to be set. Certain situations, such as + // when running unit tests, or user code that embeds Spark and creates a SparkContext + // with a local or local-cluster master, will cause this code to be called from an + // environment where that env variable is not guaranteed to exist. + // + // For the testing case, we rely on the test code to set and propagate the test classpath + // appropriately. + // + // For the user code case, we fall back to looking for the Spark assembly under SPARK_HOME. + // That duplicates some of the code in the shell scripts that look for the assembly, though. + String assembly = getenv(ENV_SPARK_ASSEMBLY); + if (assembly == null && isEmpty(getenv("SPARK_TESTING"))) { + assembly = findAssembly(); + } addToClassPath(cp, assembly); - // Datanucleus jars must be included on the classpath. Datanucleus jars do not work if only - // included in the uber jar as plugin.xml metadata is lost. Both sbt and maven will populate + // Datanucleus jars must be included on the classpath. Datanucleus jars do not work if only + // included in the uber jar as plugin.xml metadata is lost. Both sbt and maven will populate // "lib_managed/jars/" with the datanucleus jars when Spark is built with Hive File libdir; if (new File(sparkHome, "RELEASE").isFile()) { @@ -299,6 +311,30 @@ String getenv(String key) { return firstNonEmpty(childEnv.get(key), System.getenv(key)); } + private String findAssembly() { + String sparkHome = getSparkHome(); + File libdir; + if (new File(sparkHome, "RELEASE").isFile()) { + libdir = new File(sparkHome, "lib"); + checkState(libdir.isDirectory(), "Library directory '%s' does not exist.", + libdir.getAbsolutePath()); + } else { + libdir = new File(sparkHome, String.format("assembly/target/scala-%s", getScalaVersion())); + } + + final Pattern re = Pattern.compile("spark-assembly.*hadoop.*\\.jar"); + FileFilter filter = new FileFilter() { + @Override + public boolean accept(File file) { + return file.isFile() && re.matcher(file.getName()).matches(); + } + }; + File[] assemblies = libdir.listFiles(filter); + checkState(assemblies != null && assemblies.length > 0, "No assemblies found in '%s'.", libdir); + checkState(assemblies.length == 1, "Multiple assemblies found in '%s'.", libdir); + return assemblies[0].getAbsolutePath(); + } + private String getConfDir() { String confDir = getenv("SPARK_CONF_DIR"); return confDir != null ? confDir : join(File.separator, getSparkHome(), "conf"); diff --git a/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java b/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java index f4ebc25bdd32b..8028e42ffb483 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java +++ b/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java @@ -30,6 +30,7 @@ class CommandBuilderUtils { static final String DEFAULT_MEM = "512m"; static final String DEFAULT_PROPERTIES_FILE = "spark-defaults.conf"; static final String ENV_SPARK_HOME = "SPARK_HOME"; + static final String ENV_SPARK_ASSEMBLY = "_SPARK_ASSEMBLY"; /** Returns whether the given string is null or empty. */ static boolean isEmpty(String s) { diff --git a/launcher/src/test/java/org/apache/spark/launcher/SparkSubmitCommandBuilderSuite.java b/launcher/src/test/java/org/apache/spark/launcher/SparkSubmitCommandBuilderSuite.java index 626116a9e7477..97043a76cc612 100644 --- a/launcher/src/test/java/org/apache/spark/launcher/SparkSubmitCommandBuilderSuite.java +++ b/launcher/src/test/java/org/apache/spark/launcher/SparkSubmitCommandBuilderSuite.java @@ -98,7 +98,7 @@ public void testShellCliParser() throws Exception { parser.NAME, "appName"); - List args = new SparkSubmitCommandBuilder(sparkSubmitArgs).buildSparkSubmitArgs(); + List args = newCommandBuilder(sparkSubmitArgs).buildSparkSubmitArgs(); List expected = Arrays.asList("spark-shell", "--app-arg", "bar", "--app-switch"); assertEquals(expected, args.subList(args.size() - expected.size(), args.size())); } @@ -110,7 +110,7 @@ public void testAlternateSyntaxParsing() throws Exception { parser.MASTER + "=foo", parser.DEPLOY_MODE + "=bar"); - List cmd = new SparkSubmitCommandBuilder(sparkSubmitArgs).buildSparkSubmitArgs(); + List cmd = newCommandBuilder(sparkSubmitArgs).buildSparkSubmitArgs(); assertEquals("org.my.Class", findArgValue(cmd, parser.CLASS)); assertEquals("foo", findArgValue(cmd, parser.MASTER)); assertEquals("bar", findArgValue(cmd, parser.DEPLOY_MODE)); @@ -153,7 +153,7 @@ private void testCmdBuilder(boolean isDriver) throws Exception { String deployMode = isDriver ? "client" : "cluster"; SparkSubmitCommandBuilder launcher = - new SparkSubmitCommandBuilder(Collections.emptyList()); + newCommandBuilder(Collections.emptyList()); launcher.childEnv.put(CommandBuilderUtils.ENV_SPARK_HOME, System.getProperty("spark.test.home")); launcher.master = "yarn"; @@ -273,10 +273,15 @@ private boolean findInStringList(String list, String sep, String needle) { return contains(needle, list.split(sep)); } - private List buildCommand(List args, Map env) throws Exception { + private SparkSubmitCommandBuilder newCommandBuilder(List args) { SparkSubmitCommandBuilder builder = new SparkSubmitCommandBuilder(args); builder.childEnv.put(CommandBuilderUtils.ENV_SPARK_HOME, System.getProperty("spark.test.home")); - return builder.buildCommand(env); + builder.childEnv.put(CommandBuilderUtils.ENV_SPARK_ASSEMBLY, "dummy"); + return builder; + } + + private List buildCommand(List args, Map env) throws Exception { + return newCommandBuilder(args).buildCommand(env); } } From 30a6e0dcc0bd298731c1387546779cddcc16bc72 Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Tue, 14 Apr 2015 18:52:48 -0700 Subject: [PATCH 289/591] [SPARK-5634] [core] Show correct message in HS when no incomplete apps f... ...ound. Author: Marcelo Vanzin Closes #5515 from vanzin/SPARK-5634 and squashes the following commits: f74ecf1 [Marcelo Vanzin] [SPARK-5634] [core] Show correct message in HS when no incomplete apps found. --- .../scala/org/apache/spark/deploy/history/HistoryPage.scala | 2 ++ 1 file changed, 2 insertions(+) diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala index 6e432d63c6b5a..3781b4e8c12bd 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala @@ -90,6 +90,8 @@ private[history] class HistoryPage(parent: HistoryServer) extends WebUIPage("") ++ appTable + } else if (requestedIncomplete) { +

    No incomplete applications found!

    } else {

    No completed applications found!

    ++
    {% highlight scala %} -val df = sqlContext.load("people.parquet") -df.select("name", "age").save("namesAndAges.parquet") +val df = sqlContext.load("examples/src/main/resources/users.parquet") +df.select("name", "favorite_color").save("namesAndFavColors.parquet") {% endhighlight %}
    @@ -691,8 +691,8 @@ df.select("name", "age").save("namesAndAges.parquet") {% highlight java %} -DataFrame df = sqlContext.load("people.parquet"); -df.select("name", "age").save("namesAndAges.parquet"); +DataFrame df = sqlContext.load("examples/src/main/resources/users.parquet"); +df.select("name", "favorite_color").save("namesAndFavColors.parquet"); {% endhighlight %} @@ -702,8 +702,8 @@ df.select("name", "age").save("namesAndAges.parquet"); {% highlight python %} -df = sqlContext.load("people.parquet") -df.select("name", "age").save("namesAndAges.parquet") +df = sqlContext.load("examples/src/main/resources/users.parquet") +df.select("name", "favorite_color").save("namesAndFavColors.parquet") {% endhighlight %} @@ -722,7 +722,7 @@ using this syntax.
    {% highlight scala %} -val df = sqlContext.load("people.json", "json") +val df = sqlContext.load("examples/src/main/resources/people.json", "json") df.select("name", "age").save("namesAndAges.parquet", "parquet") {% endhighlight %} @@ -732,7 +732,7 @@ df.select("name", "age").save("namesAndAges.parquet", "parquet") {% highlight java %} -DataFrame df = sqlContext.load("people.json", "json"); +DataFrame df = sqlContext.load("examples/src/main/resources/people.json", "json"); df.select("name", "age").save("namesAndAges.parquet", "parquet"); {% endhighlight %} @@ -743,7 +743,7 @@ df.select("name", "age").save("namesAndAges.parquet", "parquet"); {% highlight python %} -df = sqlContext.load("people.json", "json") +df = sqlContext.load("examples/src/main/resources/people.json", "json") df.select("name", "age").save("namesAndAges.parquet", "parquet") {% endhighlight %} From cca9905b93483614b330b09b36c6526b551e17dc Mon Sep 17 00:00:00 2001 From: KeheCAI Date: Sat, 25 Apr 2015 08:42:38 -0400 Subject: [PATCH 417/591] update the deprecated CountMinSketchMonoid function to TopPctCMS function http://twitter.github.io/algebird/index.html#com.twitter.algebird.legacy.CountMinSketchMonoid$ The CountMinSketchMonoid has been deprecated since 0.8.1. Newer code should use TopPctCMS.monoid(). ![image](https://cloud.githubusercontent.com/assets/1327396/7269619/d8b48b92-e8d5-11e4-8902-087f630e6308.png) Author: KeheCAI Closes #5629 from caikehe/master and squashes the following commits: e8aa06f [KeheCAI] update algebird-core to version 0.9.0 from 0.8.1 5653351 [KeheCAI] change scala code style 4c0dfd1 [KeheCAI] update the deprecated CountMinSketchMonoid function to TopPctCMS function --- examples/pom.xml | 2 +- .../apache/spark/examples/streaming/TwitterAlgebirdCMS.scala | 4 +++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/examples/pom.xml b/examples/pom.xml index df1717403b673..5b04b4f8d6ca0 100644 --- a/examples/pom.xml +++ b/examples/pom.xml @@ -245,7 +245,7 @@ com.twitter algebird-core_${scala.binary.version} - 0.8.1 + 0.9.0 org.scalacheck diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala index 62f49530edb12..c10de84a80ffe 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala @@ -18,6 +18,7 @@ package org.apache.spark.examples.streaming import com.twitter.algebird._ +import com.twitter.algebird.CMSHasherImplicits._ import org.apache.spark.SparkConf import org.apache.spark.SparkContext._ @@ -67,7 +68,8 @@ object TwitterAlgebirdCMS { val users = stream.map(status => status.getUser.getId) - val cms = new CountMinSketchMonoid(EPS, DELTA, SEED, PERC) + // val cms = new CountMinSketchMonoid(EPS, DELTA, SEED, PERC) + val cms = TopPctCMS.monoid[Long](EPS, DELTA, SEED, PERC) var globalCMS = cms.zero val mm = new MapMonoid[Long, Int]() var globalExact = Map[Long, Int]() From a61d65fc8b97c01be0fa756b52afdc91c46a8561 Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Sat, 25 Apr 2015 10:37:34 -0700 Subject: [PATCH 418/591] Revert "[SPARK-6752][Streaming] Allow StreamingContext to be recreated from checkpoint and existing SparkContext" This reverts commit 534f2a43625fbf1a3a65d09550a19875cd1dce43. --- .../spark/api/java/function/Function0.java | 27 --- .../apache/spark/streaming/Checkpoint.scala | 26 +-- .../spark/streaming/StreamingContext.scala | 85 ++-------- .../api/java/JavaStreamingContext.scala | 119 +------------ .../apache/spark/streaming/JavaAPISuite.java | 145 ++++------------ .../spark/streaming/CheckpointSuite.scala | 3 +- .../streaming/StreamingContextSuite.scala | 159 ------------------ 7 files changed, 61 insertions(+), 503 deletions(-) delete mode 100644 core/src/main/java/org/apache/spark/api/java/function/Function0.java diff --git a/core/src/main/java/org/apache/spark/api/java/function/Function0.java b/core/src/main/java/org/apache/spark/api/java/function/Function0.java deleted file mode 100644 index 38e410c5debe6..0000000000000 --- a/core/src/main/java/org/apache/spark/api/java/function/Function0.java +++ /dev/null @@ -1,27 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.api.java.function; - -import java.io.Serializable; - -/** - * A zero-argument function that returns an R. - */ -public interface Function0 extends Serializable { - public R call() throws Exception; -} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala index 7bfae253c3a0c..0a50485118588 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala @@ -77,8 +77,7 @@ object Checkpoint extends Logging { } /** Get checkpoint files present in the give directory, ordered by oldest-first */ - def getCheckpointFiles(checkpointDir: String, fsOption: Option[FileSystem] = None): Seq[Path] = { - + def getCheckpointFiles(checkpointDir: String, fs: FileSystem): Seq[Path] = { def sortFunc(path1: Path, path2: Path): Boolean = { val (time1, bk1) = path1.getName match { case REGEX(x, y) => (x.toLong, !y.isEmpty) } val (time2, bk2) = path2.getName match { case REGEX(x, y) => (x.toLong, !y.isEmpty) } @@ -86,7 +85,6 @@ object Checkpoint extends Logging { } val path = new Path(checkpointDir) - val fs = fsOption.getOrElse(path.getFileSystem(new Configuration())) if (fs.exists(path)) { val statuses = fs.listStatus(path) if (statuses != null) { @@ -162,7 +160,7 @@ class CheckpointWriter( } // Delete old checkpoint files - val allCheckpointFiles = Checkpoint.getCheckpointFiles(checkpointDir, Some(fs)) + val allCheckpointFiles = Checkpoint.getCheckpointFiles(checkpointDir, fs) if (allCheckpointFiles.size > 10) { allCheckpointFiles.take(allCheckpointFiles.size - 10).foreach(file => { logInfo("Deleting " + file) @@ -236,24 +234,15 @@ class CheckpointWriter( private[streaming] object CheckpointReader extends Logging { - /** - * Read checkpoint files present in the given checkpoint directory. If there are no checkpoint - * files, then return None, else try to return the latest valid checkpoint object. If no - * checkpoint files could be read correctly, then return None (if ignoreReadError = true), - * or throw exception (if ignoreReadError = false). - */ - def read( - checkpointDir: String, - conf: SparkConf, - hadoopConf: Configuration, - ignoreReadError: Boolean = false): Option[Checkpoint] = { + def read(checkpointDir: String, conf: SparkConf, hadoopConf: Configuration): Option[Checkpoint] = + { val checkpointPath = new Path(checkpointDir) // TODO(rxin): Why is this a def?! def fs: FileSystem = checkpointPath.getFileSystem(hadoopConf) // Try to find the checkpoint files - val checkpointFiles = Checkpoint.getCheckpointFiles(checkpointDir, Some(fs)).reverse + val checkpointFiles = Checkpoint.getCheckpointFiles(checkpointDir, fs).reverse if (checkpointFiles.isEmpty) { return None } @@ -293,10 +282,7 @@ object CheckpointReader extends Logging { }) // If none of checkpoint files could be read, then throw exception - if (!ignoreReadError) { - throw new SparkException(s"Failed to read checkpoint from directory $checkpointPath") - } - None + throw new SparkException("Failed to read checkpoint from directory " + checkpointPath) } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala index 90c8b47aebce0..f57f295874645 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala @@ -107,19 +107,6 @@ class StreamingContext private[streaming] ( */ def this(path: String) = this(path, new Configuration) - /** - * Recreate a StreamingContext from a checkpoint file using an existing SparkContext. - * @param path Path to the directory that was specified as the checkpoint directory - * @param sparkContext Existing SparkContext - */ - def this(path: String, sparkContext: SparkContext) = { - this( - sparkContext, - CheckpointReader.read(path, sparkContext.conf, sparkContext.hadoopConfiguration).get, - null) - } - - if (sc_ == null && cp_ == null) { throw new Exception("Spark Streaming cannot be initialized with " + "both SparkContext and checkpoint as null") @@ -128,12 +115,10 @@ class StreamingContext private[streaming] ( private[streaming] val isCheckpointPresent = (cp_ != null) private[streaming] val sc: SparkContext = { - if (sc_ != null) { - sc_ - } else if (isCheckpointPresent) { + if (isCheckpointPresent) { new SparkContext(cp_.createSparkConf()) } else { - throw new SparkException("Cannot create StreamingContext without a SparkContext") + sc_ } } @@ -144,7 +129,7 @@ class StreamingContext private[streaming] ( private[streaming] val conf = sc.conf - private[streaming] val env = sc.env + private[streaming] val env = SparkEnv.get private[streaming] val graph: DStreamGraph = { if (isCheckpointPresent) { @@ -189,9 +174,7 @@ class StreamingContext private[streaming] ( /** Register streaming source to metrics system */ private val streamingSource = new StreamingSource(this) - assert(env != null) - assert(env.metricsSystem != null) - env.metricsSystem.registerSource(streamingSource) + SparkEnv.get.metricsSystem.registerSource(streamingSource) /** Enumeration to identify current state of the StreamingContext */ private[streaming] object StreamingContextState extends Enumeration { @@ -638,59 +621,19 @@ object StreamingContext extends Logging { hadoopConf: Configuration = new Configuration(), createOnError: Boolean = false ): StreamingContext = { - val checkpointOption = CheckpointReader.read( - checkpointPath, new SparkConf(), hadoopConf, createOnError) + val checkpointOption = try { + CheckpointReader.read(checkpointPath, new SparkConf(), hadoopConf) + } catch { + case e: Exception => + if (createOnError) { + None + } else { + throw e + } + } checkpointOption.map(new StreamingContext(null, _, null)).getOrElse(creatingFunc()) } - - /** - * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. - * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be - * recreated from the checkpoint data. If the data does not exist, then the StreamingContext - * will be created by called the provided `creatingFunc` on the provided `sparkContext`. Note - * that the SparkConf configuration in the checkpoint data will not be restored as the - * SparkContext has already been created. - * - * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program - * @param creatingFunc Function to create a new StreamingContext using the given SparkContext - * @param sparkContext SparkContext using which the StreamingContext will be created - */ - def getOrCreate( - checkpointPath: String, - creatingFunc: SparkContext => StreamingContext, - sparkContext: SparkContext - ): StreamingContext = { - getOrCreate(checkpointPath, creatingFunc, sparkContext, createOnError = false) - } - - /** - * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. - * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be - * recreated from the checkpoint data. If the data does not exist, then the StreamingContext - * will be created by called the provided `creatingFunc` on the provided `sparkContext`. Note - * that the SparkConf configuration in the checkpoint data will not be restored as the - * SparkContext has already been created. - * - * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program - * @param creatingFunc Function to create a new StreamingContext using the given SparkContext - * @param sparkContext SparkContext using which the StreamingContext will be created - * @param createOnError Whether to create a new StreamingContext if there is an - * error in reading checkpoint data. By default, an exception will be - * thrown on error. - */ - def getOrCreate( - checkpointPath: String, - creatingFunc: SparkContext => StreamingContext, - sparkContext: SparkContext, - createOnError: Boolean - ): StreamingContext = { - val checkpointOption = CheckpointReader.read( - checkpointPath, sparkContext.conf, sparkContext.hadoopConfiguration, createOnError) - checkpointOption.map(new StreamingContext(sparkContext, _, null)) - .getOrElse(creatingFunc(sparkContext)) - } - /** * Find the JAR from which a given class was loaded, to make it easy for users to pass * their JARs to StreamingContext. diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala index 572d7d8e8753d..4095a7cc84946 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala @@ -32,14 +32,13 @@ import org.apache.spark.{SparkConf, SparkContext} import org.apache.spark.annotation.Experimental import org.apache.spark.api.java.{JavaPairRDD, JavaRDD, JavaSparkContext} import org.apache.spark.api.java.function.{Function => JFunction, Function2 => JFunction2} -import org.apache.spark.api.java.function.{Function0 => JFunction0} import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming._ import org.apache.spark.streaming.scheduler.StreamingListener -import org.apache.spark.streaming.dstream.DStream -import org.apache.spark.streaming.receiver.Receiver import org.apache.hadoop.conf.Configuration +import org.apache.spark.streaming.dstream.{PluggableInputDStream, ReceiverInputDStream, DStream} +import org.apache.spark.streaming.receiver.Receiver /** * A Java-friendly version of [[org.apache.spark.streaming.StreamingContext]] which is the main @@ -656,7 +655,6 @@ object JavaStreamingContext { * @param checkpointPath Checkpoint directory used in an earlier JavaStreamingContext program * @param factory JavaStreamingContextFactory object to create a new JavaStreamingContext */ - @deprecated("use getOrCreate without JavaStreamingContextFactor", "1.4.0") def getOrCreate( checkpointPath: String, factory: JavaStreamingContextFactory @@ -678,7 +676,6 @@ object JavaStreamingContext { * @param hadoopConf Hadoop configuration if necessary for reading from any HDFS compatible * file system */ - @deprecated("use getOrCreate without JavaStreamingContextFactory", "1.4.0") def getOrCreate( checkpointPath: String, hadoopConf: Configuration, @@ -703,7 +700,6 @@ object JavaStreamingContext { * @param createOnError Whether to create a new JavaStreamingContext if there is an * error in reading checkpoint data. */ - @deprecated("use getOrCreate without JavaStreamingContextFactory", "1.4.0") def getOrCreate( checkpointPath: String, hadoopConf: Configuration, @@ -716,117 +712,6 @@ object JavaStreamingContext { new JavaStreamingContext(ssc) } - /** - * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. - * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be - * recreated from the checkpoint data. If the data does not exist, then the provided factory - * will be used to create a JavaStreamingContext. - * - * @param checkpointPath Checkpoint directory used in an earlier JavaStreamingContext program - * @param creatingFunc Function to create a new JavaStreamingContext - */ - def getOrCreate( - checkpointPath: String, - creatingFunc: JFunction0[JavaStreamingContext] - ): JavaStreamingContext = { - val ssc = StreamingContext.getOrCreate(checkpointPath, () => { - creatingFunc.call().ssc - }) - new JavaStreamingContext(ssc) - } - - /** - * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. - * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be - * recreated from the checkpoint data. If the data does not exist, then the provided factory - * will be used to create a JavaStreamingContext. - * - * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program - * @param creatingFunc Function to create a new JavaStreamingContext - * @param hadoopConf Hadoop configuration if necessary for reading from any HDFS compatible - * file system - */ - def getOrCreate( - checkpointPath: String, - creatingFunc: JFunction0[JavaStreamingContext], - hadoopConf: Configuration - ): JavaStreamingContext = { - val ssc = StreamingContext.getOrCreate(checkpointPath, () => { - creatingFunc.call().ssc - }, hadoopConf) - new JavaStreamingContext(ssc) - } - - /** - * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. - * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be - * recreated from the checkpoint data. If the data does not exist, then the provided factory - * will be used to create a JavaStreamingContext. - * - * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program - * @param creatingFunc Function to create a new JavaStreamingContext - * @param hadoopConf Hadoop configuration if necessary for reading from any HDFS compatible - * file system - * @param createOnError Whether to create a new JavaStreamingContext if there is an - * error in reading checkpoint data. - */ - def getOrCreate( - checkpointPath: String, - creatingFunc: JFunction0[JavaStreamingContext], - hadoopConf: Configuration, - createOnError: Boolean - ): JavaStreamingContext = { - val ssc = StreamingContext.getOrCreate(checkpointPath, () => { - creatingFunc.call().ssc - }, hadoopConf, createOnError) - new JavaStreamingContext(ssc) - } - - /** - * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. - * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be - * recreated from the checkpoint data. If the data does not exist, then the provided factory - * will be used to create a JavaStreamingContext. - * - * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program - * @param creatingFunc Function to create a new JavaStreamingContext - * @param sparkContext SparkContext using which the StreamingContext will be created - */ - def getOrCreate( - checkpointPath: String, - creatingFunc: JFunction[JavaSparkContext, JavaStreamingContext], - sparkContext: JavaSparkContext - ): JavaStreamingContext = { - val ssc = StreamingContext.getOrCreate(checkpointPath, (sparkContext: SparkContext) => { - creatingFunc.call(new JavaSparkContext(sparkContext)).ssc - }, sparkContext.sc) - new JavaStreamingContext(ssc) - } - - /** - * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. - * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be - * recreated from the checkpoint data. If the data does not exist, then the provided factory - * will be used to create a JavaStreamingContext. - * - * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program - * @param creatingFunc Function to create a new JavaStreamingContext - * @param sparkContext SparkContext using which the StreamingContext will be created - * @param createOnError Whether to create a new JavaStreamingContext if there is an - * error in reading checkpoint data. - */ - def getOrCreate( - checkpointPath: String, - creatingFunc: JFunction[JavaSparkContext, JavaStreamingContext], - sparkContext: JavaSparkContext, - createOnError: Boolean - ): JavaStreamingContext = { - val ssc = StreamingContext.getOrCreate(checkpointPath, (sparkContext: SparkContext) => { - creatingFunc.call(new JavaSparkContext(sparkContext)).ssc - }, sparkContext.sc, createOnError) - new JavaStreamingContext(ssc) - } - /** * Find the JAR from which a given class was loaded, to make it easy for users to pass * their JARs to StreamingContext. diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java index cb2e8380b4933..90340753a4eed 100644 --- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java +++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java @@ -22,12 +22,10 @@ import java.nio.charset.Charset; import java.util.*; -import org.apache.commons.lang.mutable.MutableBoolean; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.lib.input.TextInputFormat; - import scala.Tuple2; import org.junit.Assert; @@ -47,7 +45,6 @@ import org.apache.spark.storage.StorageLevel; import org.apache.spark.streaming.api.java.*; import org.apache.spark.util.Utils; -import org.apache.spark.SparkConf; // The test suite itself is Serializable so that anonymous Function implementations can be // serialized, as an alternative to converting these anonymous classes to static inner classes; @@ -932,7 +929,7 @@ public void testPairMap() { // Maps pair -> pair of different type public Tuple2 call(Tuple2 in) throws Exception { return in.swap(); } - }); + }); JavaTestUtils.attachTestOutputStream(reversed); List>> result = JavaTestUtils.runStreams(ssc, 2, 2); @@ -990,12 +987,12 @@ public void testPairMap2() { // Maps pair -> single JavaDStream> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); JavaPairDStream pairStream = JavaPairDStream.fromJavaDStream(stream); JavaDStream reversed = pairStream.map( - new Function, Integer>() { - @Override - public Integer call(Tuple2 in) throws Exception { - return in._2(); - } - }); + new Function, Integer>() { + @Override + public Integer call(Tuple2 in) throws Exception { + return in._2(); + } + }); JavaTestUtils.attachTestOutputStream(reversed); List> result = JavaTestUtils.runStreams(ssc, 2, 2); @@ -1126,7 +1123,7 @@ public void testCombineByKey() { JavaPairDStream combined = pairStream.combineByKey( new Function() { - @Override + @Override public Integer call(Integer i) throws Exception { return i; } @@ -1147,14 +1144,14 @@ public void testCountByValue() { Arrays.asList("hello")); List>> expected = Arrays.asList( - Arrays.asList( - new Tuple2("hello", 1L), - new Tuple2("world", 1L)), - Arrays.asList( - new Tuple2("hello", 1L), - new Tuple2("moon", 1L)), - Arrays.asList( - new Tuple2("hello", 1L))); + Arrays.asList( + new Tuple2("hello", 1L), + new Tuple2("world", 1L)), + Arrays.asList( + new Tuple2("hello", 1L), + new Tuple2("moon", 1L)), + Arrays.asList( + new Tuple2("hello", 1L))); JavaDStream stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); JavaPairDStream counted = stream.countByValue(); @@ -1252,17 +1249,17 @@ public void testUpdateStateByKey() { JavaPairDStream updated = pairStream.updateStateByKey( new Function2, Optional, Optional>() { - @Override - public Optional call(List values, Optional state) { - int out = 0; - if (state.isPresent()) { - out = out + state.get(); - } - for (Integer v : values) { - out = out + v; - } - return Optional.of(out); + @Override + public Optional call(List values, Optional state) { + int out = 0; + if (state.isPresent()) { + out = out + state.get(); + } + for (Integer v: values) { + out = out + v; } + return Optional.of(out); + } }); JavaTestUtils.attachTestOutputStream(updated); List>> result = JavaTestUtils.runStreams(ssc, 3, 3); @@ -1295,17 +1292,17 @@ public void testUpdateStateByKeyWithInitial() { JavaPairDStream updated = pairStream.updateStateByKey( new Function2, Optional, Optional>() { - @Override - public Optional call(List values, Optional state) { - int out = 0; - if (state.isPresent()) { - out = out + state.get(); - } - for (Integer v : values) { - out = out + v; - } - return Optional.of(out); + @Override + public Optional call(List values, Optional state) { + int out = 0; + if (state.isPresent()) { + out = out + state.get(); + } + for (Integer v: values) { + out = out + v; } + return Optional.of(out); + } }, new HashPartitioner(1), initialRDD); JavaTestUtils.attachTestOutputStream(updated); List>> result = JavaTestUtils.runStreams(ssc, 3, 3); @@ -1331,7 +1328,7 @@ public void testReduceByKeyAndWindowWithInverse() { JavaPairDStream reduceWindowed = pairStream.reduceByKeyAndWindow(new IntegerSum(), new IntegerDifference(), - new Duration(2000), new Duration(1000)); + new Duration(2000), new Duration(1000)); JavaTestUtils.attachTestOutputStream(reduceWindowed); List>> result = JavaTestUtils.runStreams(ssc, 3, 3); @@ -1710,74 +1707,6 @@ public Integer call(String s) throws Exception { Utils.deleteRecursively(tempDir); } - @SuppressWarnings("unchecked") - @Test - public void testContextGetOrCreate() throws InterruptedException { - - final SparkConf conf = new SparkConf() - .setMaster("local[2]") - .setAppName("test") - .set("newContext", "true"); - - File emptyDir = Files.createTempDir(); - emptyDir.deleteOnExit(); - StreamingContextSuite contextSuite = new StreamingContextSuite(); - String corruptedCheckpointDir = contextSuite.createCorruptedCheckpoint(); - String checkpointDir = contextSuite.createValidCheckpoint(); - - // Function to create JavaStreamingContext without any output operations - // (used to detect the new context) - final MutableBoolean newContextCreated = new MutableBoolean(false); - Function0 creatingFunc = new Function0() { - public JavaStreamingContext call() { - newContextCreated.setValue(true); - return new JavaStreamingContext(conf, Seconds.apply(1)); - } - }; - - newContextCreated.setValue(false); - ssc = JavaStreamingContext.getOrCreate(emptyDir.getAbsolutePath(), creatingFunc); - Assert.assertTrue("new context not created", newContextCreated.isTrue()); - ssc.stop(); - - newContextCreated.setValue(false); - ssc = JavaStreamingContext.getOrCreate(corruptedCheckpointDir, creatingFunc, - new org.apache.hadoop.conf.Configuration(), true); - Assert.assertTrue("new context not created", newContextCreated.isTrue()); - ssc.stop(); - - newContextCreated.setValue(false); - ssc = JavaStreamingContext.getOrCreate(checkpointDir, creatingFunc, - new org.apache.hadoop.conf.Configuration()); - Assert.assertTrue("old context not recovered", newContextCreated.isFalse()); - ssc.stop(); - - // Function to create JavaStreamingContext using existing JavaSparkContext - // without any output operations (used to detect the new context) - Function creatingFunc2 = - new Function() { - public JavaStreamingContext call(JavaSparkContext context) { - newContextCreated.setValue(true); - return new JavaStreamingContext(context, Seconds.apply(1)); - } - }; - - JavaSparkContext sc = new JavaSparkContext(conf); - newContextCreated.setValue(false); - ssc = JavaStreamingContext.getOrCreate(emptyDir.getAbsolutePath(), creatingFunc2, sc); - Assert.assertTrue("new context not created", newContextCreated.isTrue()); - ssc.stop(false); - - newContextCreated.setValue(false); - ssc = JavaStreamingContext.getOrCreate(corruptedCheckpointDir, creatingFunc2, sc, true); - Assert.assertTrue("new context not created", newContextCreated.isTrue()); - ssc.stop(false); - - newContextCreated.setValue(false); - ssc = JavaStreamingContext.getOrCreate(checkpointDir, creatingFunc2, sc); - Assert.assertTrue("old context not recovered", newContextCreated.isFalse()); - ssc.stop(); - } /* TEST DISABLED: Pending a discussion about checkpoint() semantics with TD @SuppressWarnings("unchecked") diff --git a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala index 6b0a3f91d4d06..54c30440a6e8d 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala @@ -430,8 +430,9 @@ class CheckpointSuite extends TestSuiteBase { assert(recordedFiles(ssc) === Seq(1, 2, 3) && batchCounter.getNumStartedBatches === 3) } // Wait for a checkpoint to be written + val fs = new Path(checkpointDir).getFileSystem(ssc.sc.hadoopConfiguration) eventually(eventuallyTimeout) { - assert(Checkpoint.getCheckpointFiles(checkpointDir).size === 6) + assert(Checkpoint.getCheckpointFiles(checkpointDir, fs).size === 6) } ssc.stop() // Check that we shut down while the third batch was being processed diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala index 4f193322ad33e..58353a5f97c8a 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala @@ -17,10 +17,8 @@ package org.apache.spark.streaming -import java.io.File import java.util.concurrent.atomic.AtomicInteger -import org.apache.commons.io.FileUtils import org.scalatest.{Assertions, BeforeAndAfter, FunSuite} import org.scalatest.concurrent.Timeouts import org.scalatest.concurrent.Eventually._ @@ -332,139 +330,6 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w } } - test("getOrCreate") { - val conf = new SparkConf().setMaster(master).setAppName(appName) - - // Function to create StreamingContext that has a config to identify it to be new context - var newContextCreated = false - def creatingFunction(): StreamingContext = { - newContextCreated = true - new StreamingContext(conf, batchDuration) - } - - // Call ssc.stop after a body of code - def testGetOrCreate(body: => Unit): Unit = { - newContextCreated = false - try { - body - } finally { - if (ssc != null) { - ssc.stop() - } - ssc = null - } - } - - val emptyPath = Utils.createTempDir().getAbsolutePath() - - // getOrCreate should create new context with empty path - testGetOrCreate { - ssc = StreamingContext.getOrCreate(emptyPath, creatingFunction _) - assert(ssc != null, "no context created") - assert(newContextCreated, "new context not created") - } - - val corrutedCheckpointPath = createCorruptedCheckpoint() - - // getOrCreate should throw exception with fake checkpoint file and createOnError = false - intercept[Exception] { - ssc = StreamingContext.getOrCreate(corrutedCheckpointPath, creatingFunction _) - } - - // getOrCreate should throw exception with fake checkpoint file - intercept[Exception] { - ssc = StreamingContext.getOrCreate( - corrutedCheckpointPath, creatingFunction _, createOnError = false) - } - - // getOrCreate should create new context with fake checkpoint file and createOnError = true - testGetOrCreate { - ssc = StreamingContext.getOrCreate( - corrutedCheckpointPath, creatingFunction _, createOnError = true) - assert(ssc != null, "no context created") - assert(newContextCreated, "new context not created") - } - - val checkpointPath = createValidCheckpoint() - - // getOrCreate should recover context with checkpoint path, and recover old configuration - testGetOrCreate { - ssc = StreamingContext.getOrCreate(checkpointPath, creatingFunction _) - assert(ssc != null, "no context created") - assert(!newContextCreated, "old context not recovered") - assert(ssc.conf.get("someKey") === "someValue") - } - } - - test("getOrCreate with existing SparkContext") { - val conf = new SparkConf().setMaster(master).setAppName(appName) - sc = new SparkContext(conf) - - // Function to create StreamingContext that has a config to identify it to be new context - var newContextCreated = false - def creatingFunction(sparkContext: SparkContext): StreamingContext = { - newContextCreated = true - new StreamingContext(sparkContext, batchDuration) - } - - // Call ssc.stop(stopSparkContext = false) after a body of cody - def testGetOrCreate(body: => Unit): Unit = { - newContextCreated = false - try { - body - } finally { - if (ssc != null) { - ssc.stop(stopSparkContext = false) - } - ssc = null - } - } - - val emptyPath = Utils.createTempDir().getAbsolutePath() - - // getOrCreate should create new context with empty path - testGetOrCreate { - ssc = StreamingContext.getOrCreate(emptyPath, creatingFunction _, sc, createOnError = true) - assert(ssc != null, "no context created") - assert(newContextCreated, "new context not created") - assert(ssc.sparkContext === sc, "new StreamingContext does not use existing SparkContext") - } - - val corrutedCheckpointPath = createCorruptedCheckpoint() - - // getOrCreate should throw exception with fake checkpoint file and createOnError = false - intercept[Exception] { - ssc = StreamingContext.getOrCreate(corrutedCheckpointPath, creatingFunction _, sc) - } - - // getOrCreate should throw exception with fake checkpoint file - intercept[Exception] { - ssc = StreamingContext.getOrCreate( - corrutedCheckpointPath, creatingFunction _, sc, createOnError = false) - } - - // getOrCreate should create new context with fake checkpoint file and createOnError = true - testGetOrCreate { - ssc = StreamingContext.getOrCreate( - corrutedCheckpointPath, creatingFunction _, sc, createOnError = true) - assert(ssc != null, "no context created") - assert(newContextCreated, "new context not created") - assert(ssc.sparkContext === sc, "new StreamingContext does not use existing SparkContext") - } - - val checkpointPath = createValidCheckpoint() - - // StreamingContext.getOrCreate should recover context with checkpoint path - testGetOrCreate { - ssc = StreamingContext.getOrCreate(checkpointPath, creatingFunction _, sc) - assert(ssc != null, "no context created") - assert(!newContextCreated, "old context not recovered") - assert(ssc.sparkContext === sc, "new StreamingContext does not use existing SparkContext") - assert(!ssc.conf.contains("someKey"), - "recovered StreamingContext unexpectedly has old config") - } - } - test("DStream and generated RDD creation sites") { testPackage.test() } @@ -474,30 +339,6 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w val inputStream = new TestInputStream(s, input, 1) inputStream } - - def createValidCheckpoint(): String = { - val testDirectory = Utils.createTempDir().getAbsolutePath() - val checkpointDirectory = Utils.createTempDir().getAbsolutePath() - val conf = new SparkConf().setMaster(master).setAppName(appName) - conf.set("someKey", "someValue") - ssc = new StreamingContext(conf, batchDuration) - ssc.checkpoint(checkpointDirectory) - ssc.textFileStream(testDirectory).foreachRDD { rdd => rdd.count() } - ssc.start() - eventually(timeout(10000 millis)) { - assert(Checkpoint.getCheckpointFiles(checkpointDirectory).size > 1) - } - ssc.stop() - checkpointDirectory - } - - def createCorruptedCheckpoint(): String = { - val checkpointDirectory = Utils.createTempDir().getAbsolutePath() - val fakeCheckpointFile = Checkpoint.checkpointFile(checkpointDirectory, Time(1000)) - FileUtils.write(new File(fakeCheckpointFile.toString()), "blablabla") - assert(Checkpoint.getCheckpointFiles(checkpointDirectory).nonEmpty) - checkpointDirectory - } } class TestException(msg: String) extends Exception(msg) From a7160c4e3aae22600d05e257d0b4d2428754b8ea Mon Sep 17 00:00:00 2001 From: "Joseph K. Bradley" Date: Sat, 25 Apr 2015 12:27:19 -0700 Subject: [PATCH 419/591] [SPARK-6113] [ML] Tree ensembles for Pipelines API This is a continuation of [https://github.com/apache/spark/pull/5530] (which was for Decision Trees), but for ensembles: Random Forests and Gradient-Boosted Trees. Please refer to the JIRA [https://issues.apache.org/jira/browse/SPARK-6113], the design doc linked from the JIRA, and the previous PR linked above for design discussions. This PR follows the example set by the previous PR for Decision Trees. It includes a few cleanups to Decision Trees. Note: There is one issue which will be addressed in a separate PR: Ensembles' component Models have no parent or fittingParamMap. I plan to submit a separate PR which makes those values in Model be Options. It does not matter much which PR gets merged first. CC: mengxr manishamde codedeft chouqin Author: Joseph K. Bradley Closes #5626 from jkbradley/dt-api-ensembles and squashes the following commits: 729167a [Joseph K. Bradley] small cleanups based on code review bbae2a2 [Joseph K. Bradley] Updated per all comments in code review 855aa9a [Joseph K. Bradley] scala style fix ea3d901 [Joseph K. Bradley] Added GBT to spark.ml, with tests and examples c0f30c1 [Joseph K. Bradley] Added random forests and test suites to spark.ml. Not tested yet. Need to add example as well d045ebd [Joseph K. Bradley] some more updates, but far from done ee1a10b [Joseph K. Bradley] Added files from old PR and did some initial updates. --- .../examples/ml/DecisionTreeExample.scala | 139 ++++++---- .../apache/spark/examples/ml/GBTExample.scala | 238 +++++++++++++++++ .../examples/ml/RandomForestExample.scala | 248 +++++++++++++++++ .../mllib/GradientBoostedTreesRunner.scala | 1 + .../scala/org/apache/spark/ml/Model.scala | 2 + .../DecisionTreeClassifier.scala | 24 +- .../ml/classification/GBTClassifier.scala | 228 ++++++++++++++++ .../RandomForestClassifier.scala | 185 +++++++++++++ .../spark/ml/impl/tree/treeParams.scala | 249 +++++++++++++++--- .../ml/param/shared/SharedParamsCodeGen.scala | 4 +- .../spark/ml/param/shared/sharedParams.scala | 20 ++ .../ml/regression/DecisionTreeRegressor.scala | 14 +- .../spark/ml/regression/GBTRegressor.scala | 218 +++++++++++++++ .../ml/regression/RandomForestRegressor.scala | 167 ++++++++++++ .../scala/org/apache/spark/ml/tree/Node.scala | 6 +- .../org/apache/spark/ml/tree/Split.scala | 22 +- .../org/apache/spark/ml/tree/treeModels.scala | 46 +++- .../JavaDecisionTreeClassifierSuite.java | 10 +- .../JavaGBTClassifierSuite.java | 100 +++++++ .../JavaRandomForestClassifierSuite.java | 103 ++++++++ .../JavaDecisionTreeRegressorSuite.java | 26 +- .../ml/regression/JavaGBTRegressorSuite.java | 99 +++++++ .../JavaRandomForestRegressorSuite.java | 102 +++++++ .../DecisionTreeClassifierSuite.scala | 2 +- .../classification/GBTClassifierSuite.scala | 136 ++++++++++ .../RandomForestClassifierSuite.scala | 166 ++++++++++++ .../org/apache/spark/ml/impl/TreeTests.scala | 10 +- .../DecisionTreeRegressorSuite.scala | 2 +- .../ml/regression/GBTRegressorSuite.scala | 137 ++++++++++ .../RandomForestRegressorSuite.scala | 122 +++++++++ .../spark/mllib/tree/DecisionTreeSuite.scala | 6 +- 31 files changed, 2658 insertions(+), 174 deletions(-) create mode 100644 examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala create mode 100644 examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala create mode 100644 mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala create mode 100644 mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala create mode 100644 mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala create mode 100644 mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala create mode 100644 mllib/src/test/java/org/apache/spark/ml/classification/JavaGBTClassifierSuite.java create mode 100644 mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java create mode 100644 mllib/src/test/java/org/apache/spark/ml/regression/JavaGBTRegressorSuite.java create mode 100644 mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java create mode 100644 mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala create mode 100644 mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala create mode 100644 mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala create mode 100644 mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala index 2cd515c89d3d2..9002e99d82ad3 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala @@ -22,10 +22,9 @@ import scala.language.reflectiveCalls import scopt.OptionParser -import org.apache.spark.ml.tree.DecisionTreeModel import org.apache.spark.{SparkConf, SparkContext} import org.apache.spark.examples.mllib.AbstractParams -import org.apache.spark.ml.{Pipeline, PipelineStage} +import org.apache.spark.ml.{Pipeline, PipelineStage, Transformer} import org.apache.spark.ml.classification.{DecisionTreeClassificationModel, DecisionTreeClassifier} import org.apache.spark.ml.feature.{VectorIndexer, StringIndexer} import org.apache.spark.ml.regression.{DecisionTreeRegressionModel, DecisionTreeRegressor} @@ -64,8 +63,6 @@ object DecisionTreeExample { maxBins: Int = 32, minInstancesPerNode: Int = 1, minInfoGain: Double = 0.0, - numTrees: Int = 1, - featureSubsetStrategy: String = "auto", fracTest: Double = 0.2, cacheNodeIds: Boolean = false, checkpointDir: Option[String] = None, @@ -123,8 +120,8 @@ object DecisionTreeExample { .required() .action((x, c) => c.copy(input = x)) checkConfig { params => - if (params.fracTest < 0 || params.fracTest > 1) { - failure(s"fracTest ${params.fracTest} value incorrect; should be in [0,1].") + if (params.fracTest < 0 || params.fracTest >= 1) { + failure(s"fracTest ${params.fracTest} value incorrect; should be in [0,1).") } else { success } @@ -200,9 +197,18 @@ object DecisionTreeExample { throw new IllegalArgumentException("Algo ${params.algo} not supported.") } } - val dataframes = splits.map(_.toDF()).map(labelsToStrings).map(_.cache()) + val dataframes = splits.map(_.toDF()).map(labelsToStrings) + val training = dataframes(0).cache() + val test = dataframes(1).cache() - (dataframes(0), dataframes(1)) + val numTraining = training.count() + val numTest = test.count() + val numFeatures = training.select("features").first().getAs[Vector](0).size + println("Loaded data:") + println(s" numTraining = $numTraining, numTest = $numTest") + println(s" numFeatures = $numFeatures") + + (training, test) } def run(params: Params) { @@ -217,13 +223,6 @@ object DecisionTreeExample { val (training: DataFrame, test: DataFrame) = loadDatasets(sc, params.input, params.dataFormat, params.testInput, algo, params.fracTest) - val numTraining = training.count() - val numTest = test.count() - val numFeatures = training.select("features").first().getAs[Vector](0).size - println("Loaded data:") - println(s" numTraining = $numTraining, numTest = $numTest") - println(s" numFeatures = $numFeatures") - // Set up Pipeline val stages = new mutable.ArrayBuffer[PipelineStage]() // (1) For classification, re-index classes. @@ -241,7 +240,7 @@ object DecisionTreeExample { .setOutputCol("indexedFeatures") .setMaxCategories(10) stages += featuresIndexer - // (3) Learn DecisionTree + // (3) Learn Decision Tree val dt = algo match { case "classification" => new DecisionTreeClassifier() @@ -275,62 +274,86 @@ object DecisionTreeExample { println(s"Training time: $elapsedTime seconds") // Get the trained Decision Tree from the fitted PipelineModel - val treeModel: DecisionTreeModel = algo match { + algo match { case "classification" => - pipelineModel.getModel[DecisionTreeClassificationModel]( + val treeModel = pipelineModel.getModel[DecisionTreeClassificationModel]( dt.asInstanceOf[DecisionTreeClassifier]) + if (treeModel.numNodes < 20) { + println(treeModel.toDebugString) // Print full model. + } else { + println(treeModel) // Print model summary. + } case "regression" => - pipelineModel.getModel[DecisionTreeRegressionModel](dt.asInstanceOf[DecisionTreeRegressor]) - case _ => throw new IllegalArgumentException("Algo ${params.algo} not supported.") - } - if (treeModel.numNodes < 20) { - println(treeModel.toDebugString) // Print full model. - } else { - println(treeModel) // Print model summary. - } - - // Predict on training - val trainingFullPredictions = pipelineModel.transform(training).cache() - val trainingPredictions = trainingFullPredictions.select("prediction") - .map(_.getDouble(0)) - val trainingLabels = trainingFullPredictions.select(labelColName).map(_.getDouble(0)) - // Predict on test data - val testFullPredictions = pipelineModel.transform(test).cache() - val testPredictions = testFullPredictions.select("prediction") - .map(_.getDouble(0)) - val testLabels = testFullPredictions.select(labelColName).map(_.getDouble(0)) - - // For classification, print number of classes for reference. - if (algo == "classification") { - val numClasses = - MetadataUtils.getNumClasses(trainingFullPredictions.schema(labelColName)) match { - case Some(n) => n - case None => throw new RuntimeException( - "DecisionTreeExample had unknown failure when indexing labels for classification.") + val treeModel = pipelineModel.getModel[DecisionTreeRegressionModel]( + dt.asInstanceOf[DecisionTreeRegressor]) + if (treeModel.numNodes < 20) { + println(treeModel.toDebugString) // Print full model. + } else { + println(treeModel) // Print model summary. } - println(s"numClasses = $numClasses.") + case _ => throw new IllegalArgumentException("Algo ${params.algo} not supported.") } // Evaluate model on training, test data algo match { case "classification" => - val trainingAccuracy = - new MulticlassMetrics(trainingPredictions.zip(trainingLabels)).precision - println(s"Train accuracy = $trainingAccuracy") - val testAccuracy = - new MulticlassMetrics(testPredictions.zip(testLabels)).precision - println(s"Test accuracy = $testAccuracy") + println("Training data results:") + evaluateClassificationModel(pipelineModel, training, labelColName) + println("Test data results:") + evaluateClassificationModel(pipelineModel, test, labelColName) case "regression" => - val trainingRMSE = - new RegressionMetrics(trainingPredictions.zip(trainingLabels)).rootMeanSquaredError - println(s"Training root mean squared error (RMSE) = $trainingRMSE") - val testRMSE = - new RegressionMetrics(testPredictions.zip(testLabels)).rootMeanSquaredError - println(s"Test root mean squared error (RMSE) = $testRMSE") + println("Training data results:") + evaluateRegressionModel(pipelineModel, training, labelColName) + println("Test data results:") + evaluateRegressionModel(pipelineModel, test, labelColName) case _ => throw new IllegalArgumentException("Algo ${params.algo} not supported.") } sc.stop() } + + /** + * Evaluate the given ClassificationModel on data. Print the results. + * @param model Must fit ClassificationModel abstraction + * @param data DataFrame with "prediction" and labelColName columns + * @param labelColName Name of the labelCol parameter for the model + * + * TODO: Change model type to ClassificationModel once that API is public. SPARK-5995 + */ + private[ml] def evaluateClassificationModel( + model: Transformer, + data: DataFrame, + labelColName: String): Unit = { + val fullPredictions = model.transform(data).cache() + val predictions = fullPredictions.select("prediction").map(_.getDouble(0)) + val labels = fullPredictions.select(labelColName).map(_.getDouble(0)) + // Print number of classes for reference + val numClasses = MetadataUtils.getNumClasses(fullPredictions.schema(labelColName)) match { + case Some(n) => n + case None => throw new RuntimeException( + "Unknown failure when indexing labels for classification.") + } + val accuracy = new MulticlassMetrics(predictions.zip(labels)).precision + println(s" Accuracy ($numClasses classes): $accuracy") + } + + /** + * Evaluate the given RegressionModel on data. Print the results. + * @param model Must fit RegressionModel abstraction + * @param data DataFrame with "prediction" and labelColName columns + * @param labelColName Name of the labelCol parameter for the model + * + * TODO: Change model type to RegressionModel once that API is public. SPARK-5995 + */ + private[ml] def evaluateRegressionModel( + model: Transformer, + data: DataFrame, + labelColName: String): Unit = { + val fullPredictions = model.transform(data).cache() + val predictions = fullPredictions.select("prediction").map(_.getDouble(0)) + val labels = fullPredictions.select(labelColName).map(_.getDouble(0)) + val RMSE = new RegressionMetrics(predictions.zip(labels)).rootMeanSquaredError + println(s" Root mean squared error (RMSE): $RMSE") + } } diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala new file mode 100644 index 0000000000000..5fccb142d4c3d --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala @@ -0,0 +1,238 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.ml + +import scala.collection.mutable +import scala.language.reflectiveCalls + +import scopt.OptionParser + +import org.apache.spark.{SparkConf, SparkContext} +import org.apache.spark.examples.mllib.AbstractParams +import org.apache.spark.ml.{Pipeline, PipelineStage} +import org.apache.spark.ml.classification.{GBTClassificationModel, GBTClassifier} +import org.apache.spark.ml.feature.{StringIndexer, VectorIndexer} +import org.apache.spark.ml.regression.{GBTRegressionModel, GBTRegressor} +import org.apache.spark.sql.DataFrame + + +/** + * An example runner for decision trees. Run with + * {{{ + * ./bin/run-example ml.GBTExample [options] + * }}} + * Decision Trees and ensembles can take a large amount of memory. If the run-example command + * above fails, try running via spark-submit and specifying the amount of memory as at least 1g. + * For local mode, run + * {{{ + * ./bin/spark-submit --class org.apache.spark.examples.ml.GBTExample --driver-memory 1g + * [examples JAR path] [options] + * }}} + * If you use it as a template to create your own app, please use `spark-submit` to submit your app. + */ +object GBTExample { + + case class Params( + input: String = null, + testInput: String = "", + dataFormat: String = "libsvm", + algo: String = "classification", + maxDepth: Int = 5, + maxBins: Int = 32, + minInstancesPerNode: Int = 1, + minInfoGain: Double = 0.0, + maxIter: Int = 10, + fracTest: Double = 0.2, + cacheNodeIds: Boolean = false, + checkpointDir: Option[String] = None, + checkpointInterval: Int = 10) extends AbstractParams[Params] + + def main(args: Array[String]) { + val defaultParams = Params() + + val parser = new OptionParser[Params]("GBTExample") { + head("GBTExample: an example Gradient-Boosted Trees app.") + opt[String]("algo") + .text(s"algorithm (classification, regression), default: ${defaultParams.algo}") + .action((x, c) => c.copy(algo = x)) + opt[Int]("maxDepth") + .text(s"max depth of the tree, default: ${defaultParams.maxDepth}") + .action((x, c) => c.copy(maxDepth = x)) + opt[Int]("maxBins") + .text(s"max number of bins, default: ${defaultParams.maxBins}") + .action((x, c) => c.copy(maxBins = x)) + opt[Int]("minInstancesPerNode") + .text(s"min number of instances required at child nodes to create the parent split," + + s" default: ${defaultParams.minInstancesPerNode}") + .action((x, c) => c.copy(minInstancesPerNode = x)) + opt[Double]("minInfoGain") + .text(s"min info gain required to create a split, default: ${defaultParams.minInfoGain}") + .action((x, c) => c.copy(minInfoGain = x)) + opt[Int]("maxIter") + .text(s"number of trees in ensemble, default: ${defaultParams.maxIter}") + .action((x, c) => c.copy(maxIter = x)) + opt[Double]("fracTest") + .text(s"fraction of data to hold out for testing. If given option testInput, " + + s"this option is ignored. default: ${defaultParams.fracTest}") + .action((x, c) => c.copy(fracTest = x)) + opt[Boolean]("cacheNodeIds") + .text(s"whether to use node Id cache during training, " + + s"default: ${defaultParams.cacheNodeIds}") + .action((x, c) => c.copy(cacheNodeIds = x)) + opt[String]("checkpointDir") + .text(s"checkpoint directory where intermediate node Id caches will be stored, " + + s"default: ${ + defaultParams.checkpointDir match { + case Some(strVal) => strVal + case None => "None" + } + }") + .action((x, c) => c.copy(checkpointDir = Some(x))) + opt[Int]("checkpointInterval") + .text(s"how often to checkpoint the node Id cache, " + + s"default: ${defaultParams.checkpointInterval}") + .action((x, c) => c.copy(checkpointInterval = x)) + opt[String]("testInput") + .text(s"input path to test dataset. If given, option fracTest is ignored." + + s" default: ${defaultParams.testInput}") + .action((x, c) => c.copy(testInput = x)) + opt[String]("") + .text("data format: libsvm (default), dense (deprecated in Spark v1.1)") + .action((x, c) => c.copy(dataFormat = x)) + arg[String]("") + .text("input path to labeled examples") + .required() + .action((x, c) => c.copy(input = x)) + checkConfig { params => + if (params.fracTest < 0 || params.fracTest >= 1) { + failure(s"fracTest ${params.fracTest} value incorrect; should be in [0,1).") + } else { + success + } + } + } + + parser.parse(args, defaultParams).map { params => + run(params) + }.getOrElse { + sys.exit(1) + } + } + + def run(params: Params) { + val conf = new SparkConf().setAppName(s"GBTExample with $params") + val sc = new SparkContext(conf) + params.checkpointDir.foreach(sc.setCheckpointDir) + val algo = params.algo.toLowerCase + + println(s"GBTExample with parameters:\n$params") + + // Load training and test data and cache it. + val (training: DataFrame, test: DataFrame) = DecisionTreeExample.loadDatasets(sc, params.input, + params.dataFormat, params.testInput, algo, params.fracTest) + + // Set up Pipeline + val stages = new mutable.ArrayBuffer[PipelineStage]() + // (1) For classification, re-index classes. + val labelColName = if (algo == "classification") "indexedLabel" else "label" + if (algo == "classification") { + val labelIndexer = new StringIndexer() + .setInputCol("labelString") + .setOutputCol(labelColName) + stages += labelIndexer + } + // (2) Identify categorical features using VectorIndexer. + // Features with more than maxCategories values will be treated as continuous. + val featuresIndexer = new VectorIndexer() + .setInputCol("features") + .setOutputCol("indexedFeatures") + .setMaxCategories(10) + stages += featuresIndexer + // (3) Learn GBT + val dt = algo match { + case "classification" => + new GBTClassifier() + .setFeaturesCol("indexedFeatures") + .setLabelCol(labelColName) + .setMaxDepth(params.maxDepth) + .setMaxBins(params.maxBins) + .setMinInstancesPerNode(params.minInstancesPerNode) + .setMinInfoGain(params.minInfoGain) + .setCacheNodeIds(params.cacheNodeIds) + .setCheckpointInterval(params.checkpointInterval) + .setMaxIter(params.maxIter) + case "regression" => + new GBTRegressor() + .setFeaturesCol("indexedFeatures") + .setLabelCol(labelColName) + .setMaxDepth(params.maxDepth) + .setMaxBins(params.maxBins) + .setMinInstancesPerNode(params.minInstancesPerNode) + .setMinInfoGain(params.minInfoGain) + .setCacheNodeIds(params.cacheNodeIds) + .setCheckpointInterval(params.checkpointInterval) + .setMaxIter(params.maxIter) + case _ => throw new IllegalArgumentException("Algo ${params.algo} not supported.") + } + stages += dt + val pipeline = new Pipeline().setStages(stages.toArray) + + // Fit the Pipeline + val startTime = System.nanoTime() + val pipelineModel = pipeline.fit(training) + val elapsedTime = (System.nanoTime() - startTime) / 1e9 + println(s"Training time: $elapsedTime seconds") + + // Get the trained GBT from the fitted PipelineModel + algo match { + case "classification" => + val rfModel = pipelineModel.getModel[GBTClassificationModel](dt.asInstanceOf[GBTClassifier]) + if (rfModel.totalNumNodes < 30) { + println(rfModel.toDebugString) // Print full model. + } else { + println(rfModel) // Print model summary. + } + case "regression" => + val rfModel = pipelineModel.getModel[GBTRegressionModel](dt.asInstanceOf[GBTRegressor]) + if (rfModel.totalNumNodes < 30) { + println(rfModel.toDebugString) // Print full model. + } else { + println(rfModel) // Print model summary. + } + case _ => throw new IllegalArgumentException("Algo ${params.algo} not supported.") + } + + // Evaluate model on training, test data + algo match { + case "classification" => + println("Training data results:") + DecisionTreeExample.evaluateClassificationModel(pipelineModel, training, labelColName) + println("Test data results:") + DecisionTreeExample.evaluateClassificationModel(pipelineModel, test, labelColName) + case "regression" => + println("Training data results:") + DecisionTreeExample.evaluateRegressionModel(pipelineModel, training, labelColName) + println("Test data results:") + DecisionTreeExample.evaluateRegressionModel(pipelineModel, test, labelColName) + case _ => + throw new IllegalArgumentException("Algo ${params.algo} not supported.") + } + + sc.stop() + } +} diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala new file mode 100644 index 0000000000000..9b909324ec82a --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala @@ -0,0 +1,248 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.ml + +import scala.collection.mutable +import scala.language.reflectiveCalls + +import scopt.OptionParser + +import org.apache.spark.{SparkConf, SparkContext} +import org.apache.spark.examples.mllib.AbstractParams +import org.apache.spark.ml.{Pipeline, PipelineStage} +import org.apache.spark.ml.classification.{RandomForestClassificationModel, RandomForestClassifier} +import org.apache.spark.ml.feature.{StringIndexer, VectorIndexer} +import org.apache.spark.ml.regression.{RandomForestRegressionModel, RandomForestRegressor} +import org.apache.spark.sql.DataFrame + + +/** + * An example runner for decision trees. Run with + * {{{ + * ./bin/run-example ml.RandomForestExample [options] + * }}} + * Decision Trees and ensembles can take a large amount of memory. If the run-example command + * above fails, try running via spark-submit and specifying the amount of memory as at least 1g. + * For local mode, run + * {{{ + * ./bin/spark-submit --class org.apache.spark.examples.ml.RandomForestExample --driver-memory 1g + * [examples JAR path] [options] + * }}} + * If you use it as a template to create your own app, please use `spark-submit` to submit your app. + */ +object RandomForestExample { + + case class Params( + input: String = null, + testInput: String = "", + dataFormat: String = "libsvm", + algo: String = "classification", + maxDepth: Int = 5, + maxBins: Int = 32, + minInstancesPerNode: Int = 1, + minInfoGain: Double = 0.0, + numTrees: Int = 10, + featureSubsetStrategy: String = "auto", + fracTest: Double = 0.2, + cacheNodeIds: Boolean = false, + checkpointDir: Option[String] = None, + checkpointInterval: Int = 10) extends AbstractParams[Params] + + def main(args: Array[String]) { + val defaultParams = Params() + + val parser = new OptionParser[Params]("RandomForestExample") { + head("RandomForestExample: an example random forest app.") + opt[String]("algo") + .text(s"algorithm (classification, regression), default: ${defaultParams.algo}") + .action((x, c) => c.copy(algo = x)) + opt[Int]("maxDepth") + .text(s"max depth of the tree, default: ${defaultParams.maxDepth}") + .action((x, c) => c.copy(maxDepth = x)) + opt[Int]("maxBins") + .text(s"max number of bins, default: ${defaultParams.maxBins}") + .action((x, c) => c.copy(maxBins = x)) + opt[Int]("minInstancesPerNode") + .text(s"min number of instances required at child nodes to create the parent split," + + s" default: ${defaultParams.minInstancesPerNode}") + .action((x, c) => c.copy(minInstancesPerNode = x)) + opt[Double]("minInfoGain") + .text(s"min info gain required to create a split, default: ${defaultParams.minInfoGain}") + .action((x, c) => c.copy(minInfoGain = x)) + opt[Int]("numTrees") + .text(s"number of trees in ensemble, default: ${defaultParams.numTrees}") + .action((x, c) => c.copy(numTrees = x)) + opt[String]("featureSubsetStrategy") + .text(s"number of features to use per node (supported:" + + s" ${RandomForestClassifier.supportedFeatureSubsetStrategies.mkString(",")})," + + s" default: ${defaultParams.numTrees}") + .action((x, c) => c.copy(featureSubsetStrategy = x)) + opt[Double]("fracTest") + .text(s"fraction of data to hold out for testing. If given option testInput, " + + s"this option is ignored. default: ${defaultParams.fracTest}") + .action((x, c) => c.copy(fracTest = x)) + opt[Boolean]("cacheNodeIds") + .text(s"whether to use node Id cache during training, " + + s"default: ${defaultParams.cacheNodeIds}") + .action((x, c) => c.copy(cacheNodeIds = x)) + opt[String]("checkpointDir") + .text(s"checkpoint directory where intermediate node Id caches will be stored, " + + s"default: ${ + defaultParams.checkpointDir match { + case Some(strVal) => strVal + case None => "None" + } + }") + .action((x, c) => c.copy(checkpointDir = Some(x))) + opt[Int]("checkpointInterval") + .text(s"how often to checkpoint the node Id cache, " + + s"default: ${defaultParams.checkpointInterval}") + .action((x, c) => c.copy(checkpointInterval = x)) + opt[String]("testInput") + .text(s"input path to test dataset. If given, option fracTest is ignored." + + s" default: ${defaultParams.testInput}") + .action((x, c) => c.copy(testInput = x)) + opt[String]("") + .text("data format: libsvm (default), dense (deprecated in Spark v1.1)") + .action((x, c) => c.copy(dataFormat = x)) + arg[String]("") + .text("input path to labeled examples") + .required() + .action((x, c) => c.copy(input = x)) + checkConfig { params => + if (params.fracTest < 0 || params.fracTest >= 1) { + failure(s"fracTest ${params.fracTest} value incorrect; should be in [0,1).") + } else { + success + } + } + } + + parser.parse(args, defaultParams).map { params => + run(params) + }.getOrElse { + sys.exit(1) + } + } + + def run(params: Params) { + val conf = new SparkConf().setAppName(s"RandomForestExample with $params") + val sc = new SparkContext(conf) + params.checkpointDir.foreach(sc.setCheckpointDir) + val algo = params.algo.toLowerCase + + println(s"RandomForestExample with parameters:\n$params") + + // Load training and test data and cache it. + val (training: DataFrame, test: DataFrame) = DecisionTreeExample.loadDatasets(sc, params.input, + params.dataFormat, params.testInput, algo, params.fracTest) + + // Set up Pipeline + val stages = new mutable.ArrayBuffer[PipelineStage]() + // (1) For classification, re-index classes. + val labelColName = if (algo == "classification") "indexedLabel" else "label" + if (algo == "classification") { + val labelIndexer = new StringIndexer() + .setInputCol("labelString") + .setOutputCol(labelColName) + stages += labelIndexer + } + // (2) Identify categorical features using VectorIndexer. + // Features with more than maxCategories values will be treated as continuous. + val featuresIndexer = new VectorIndexer() + .setInputCol("features") + .setOutputCol("indexedFeatures") + .setMaxCategories(10) + stages += featuresIndexer + // (3) Learn Random Forest + val dt = algo match { + case "classification" => + new RandomForestClassifier() + .setFeaturesCol("indexedFeatures") + .setLabelCol(labelColName) + .setMaxDepth(params.maxDepth) + .setMaxBins(params.maxBins) + .setMinInstancesPerNode(params.minInstancesPerNode) + .setMinInfoGain(params.minInfoGain) + .setCacheNodeIds(params.cacheNodeIds) + .setCheckpointInterval(params.checkpointInterval) + .setFeatureSubsetStrategy(params.featureSubsetStrategy) + .setNumTrees(params.numTrees) + case "regression" => + new RandomForestRegressor() + .setFeaturesCol("indexedFeatures") + .setLabelCol(labelColName) + .setMaxDepth(params.maxDepth) + .setMaxBins(params.maxBins) + .setMinInstancesPerNode(params.minInstancesPerNode) + .setMinInfoGain(params.minInfoGain) + .setCacheNodeIds(params.cacheNodeIds) + .setCheckpointInterval(params.checkpointInterval) + .setFeatureSubsetStrategy(params.featureSubsetStrategy) + .setNumTrees(params.numTrees) + case _ => throw new IllegalArgumentException("Algo ${params.algo} not supported.") + } + stages += dt + val pipeline = new Pipeline().setStages(stages.toArray) + + // Fit the Pipeline + val startTime = System.nanoTime() + val pipelineModel = pipeline.fit(training) + val elapsedTime = (System.nanoTime() - startTime) / 1e9 + println(s"Training time: $elapsedTime seconds") + + // Get the trained Random Forest from the fitted PipelineModel + algo match { + case "classification" => + val rfModel = pipelineModel.getModel[RandomForestClassificationModel]( + dt.asInstanceOf[RandomForestClassifier]) + if (rfModel.totalNumNodes < 30) { + println(rfModel.toDebugString) // Print full model. + } else { + println(rfModel) // Print model summary. + } + case "regression" => + val rfModel = pipelineModel.getModel[RandomForestRegressionModel]( + dt.asInstanceOf[RandomForestRegressor]) + if (rfModel.totalNumNodes < 30) { + println(rfModel.toDebugString) // Print full model. + } else { + println(rfModel) // Print model summary. + } + case _ => throw new IllegalArgumentException("Algo ${params.algo} not supported.") + } + + // Evaluate model on training, test data + algo match { + case "classification" => + println("Training data results:") + DecisionTreeExample.evaluateClassificationModel(pipelineModel, training, labelColName) + println("Test data results:") + DecisionTreeExample.evaluateClassificationModel(pipelineModel, test, labelColName) + case "regression" => + println("Training data results:") + DecisionTreeExample.evaluateRegressionModel(pipelineModel, training, labelColName) + println("Test data results:") + DecisionTreeExample.evaluateRegressionModel(pipelineModel, test, labelColName) + case _ => + throw new IllegalArgumentException("Algo ${params.algo} not supported.") + } + + sc.stop() + } +} diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala index 431ead8c0c165..0763a7736305a 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/GradientBoostedTreesRunner.scala @@ -25,6 +25,7 @@ import org.apache.spark.mllib.tree.GradientBoostedTrees import org.apache.spark.mllib.tree.configuration.{BoostingStrategy, Algo} import org.apache.spark.util.Utils + /** * An example runner for Gradient Boosting using decision trees as weak learners. Run with * {{{ diff --git a/mllib/src/main/scala/org/apache/spark/ml/Model.scala b/mllib/src/main/scala/org/apache/spark/ml/Model.scala index cae5082b51196..a491bc7ee8295 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Model.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Model.scala @@ -30,11 +30,13 @@ import org.apache.spark.ml.param.ParamMap abstract class Model[M <: Model[M]] extends Transformer { /** * The parent estimator that produced this model. + * Note: For ensembles' component Models, this value can be null. */ val parent: Estimator[M] /** * Fitting parameters, such that parent.fit(..., fittingParamMap) could reproduce the model. + * Note: For ensembles' component Models, this value can be null. */ val fittingParamMap: ParamMap } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala index 3855e396b5534..ee2a8dc6db171 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala @@ -43,8 +43,7 @@ import org.apache.spark.sql.DataFrame @AlphaComponent final class DecisionTreeClassifier extends Predictor[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel] - with DecisionTreeParams - with TreeClassifierParams { + with DecisionTreeParams with TreeClassifierParams { // Override parameter setters from parent trait for Java API compatibility. @@ -59,11 +58,9 @@ final class DecisionTreeClassifier override def setMaxMemoryInMB(value: Int): this.type = super.setMaxMemoryInMB(value) - override def setCacheNodeIds(value: Boolean): this.type = - super.setCacheNodeIds(value) + override def setCacheNodeIds(value: Boolean): this.type = super.setCacheNodeIds(value) - override def setCheckpointInterval(value: Int): this.type = - super.setCheckpointInterval(value) + override def setCheckpointInterval(value: Int): this.type = super.setCheckpointInterval(value) override def setImpurity(value: String): this.type = super.setImpurity(value) @@ -75,8 +72,9 @@ final class DecisionTreeClassifier val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema(paramMap(labelCol))) match { case Some(n: Int) => n case None => throw new IllegalArgumentException("DecisionTreeClassifier was given input" + - s" with invalid label column, without the number of classes specified.") - // TODO: Automatically index labels. + s" with invalid label column ${paramMap(labelCol)}, without the number of classes" + + " specified. See StringIndexer.") + // TODO: Automatically index labels: SPARK-7126 } val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) val strategy = getOldStrategy(categoricalFeatures, numClasses) @@ -85,18 +83,16 @@ final class DecisionTreeClassifier } /** (private[ml]) Create a Strategy instance to use with the old API. */ - override private[ml] def getOldStrategy( + private[ml] def getOldStrategy( categoricalFeatures: Map[Int, Int], numClasses: Int): OldStrategy = { - val strategy = super.getOldStrategy(categoricalFeatures, numClasses) - strategy.algo = OldAlgo.Classification - strategy.setImpurity(getOldImpurity) - strategy + super.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, getOldImpurity, + subsamplingRate = 1.0) } } object DecisionTreeClassifier { - /** Accessor for supported impurities */ + /** Accessor for supported impurities: entropy, gini */ final val supportedImpurities: Array[String] = TreeClassifierParams.supportedImpurities } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala new file mode 100644 index 0000000000000..d2e052fbbbf22 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala @@ -0,0 +1,228 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.classification + +import com.github.fommil.netlib.BLAS.{getInstance => blas} + +import org.apache.spark.Logging +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} +import org.apache.spark.ml.impl.tree._ +import org.apache.spark.ml.param.{Param, Params, ParamMap} +import org.apache.spark.ml.regression.DecisionTreeRegressionModel +import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel} +import org.apache.spark.ml.util.MetadataUtils +import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.tree.{GradientBoostedTrees => OldGBT} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} +import org.apache.spark.mllib.tree.loss.{Loss => OldLoss, LogLoss => OldLogLoss} +import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel} +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.DataFrame + + +/** + * :: AlphaComponent :: + * + * [[http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)]] + * learning algorithm for classification. + * It supports binary labels, as well as both continuous and categorical features. + * Note: Multiclass labels are not currently supported. + */ +@AlphaComponent +final class GBTClassifier + extends Predictor[Vector, GBTClassifier, GBTClassificationModel] + with GBTParams with TreeClassifierParams with Logging { + + // Override parameter setters from parent trait for Java API compatibility. + + // Parameters from TreeClassifierParams: + + override def setMaxDepth(value: Int): this.type = super.setMaxDepth(value) + + override def setMaxBins(value: Int): this.type = super.setMaxBins(value) + + override def setMinInstancesPerNode(value: Int): this.type = + super.setMinInstancesPerNode(value) + + override def setMinInfoGain(value: Double): this.type = super.setMinInfoGain(value) + + override def setMaxMemoryInMB(value: Int): this.type = super.setMaxMemoryInMB(value) + + override def setCacheNodeIds(value: Boolean): this.type = super.setCacheNodeIds(value) + + override def setCheckpointInterval(value: Int): this.type = super.setCheckpointInterval(value) + + /** + * The impurity setting is ignored for GBT models. + * Individual trees are built using impurity "Variance." + */ + override def setImpurity(value: String): this.type = { + logWarning("GBTClassifier.setImpurity should NOT be used") + this + } + + // Parameters from TreeEnsembleParams: + + override def setSubsamplingRate(value: Double): this.type = super.setSubsamplingRate(value) + + override def setSeed(value: Long): this.type = { + logWarning("The 'seed' parameter is currently ignored by Gradient Boosting.") + super.setSeed(value) + } + + // Parameters from GBTParams: + + override def setMaxIter(value: Int): this.type = super.setMaxIter(value) + + override def setStepSize(value: Double): this.type = super.setStepSize(value) + + // Parameters for GBTClassifier: + + /** + * Loss function which GBT tries to minimize. (case-insensitive) + * Supported: "logistic" + * (default = logistic) + * @group param + */ + val lossType: Param[String] = new Param[String](this, "lossType", "Loss function which GBT" + + " tries to minimize (case-insensitive). Supported options:" + + s" ${GBTClassifier.supportedLossTypes.mkString(", ")}") + + setDefault(lossType -> "logistic") + + /** @group setParam */ + def setLossType(value: String): this.type = { + val lossStr = value.toLowerCase + require(GBTClassifier.supportedLossTypes.contains(lossStr), "GBTClassifier was given bad loss" + + s" type: $value. Supported options: ${GBTClassifier.supportedLossTypes.mkString(", ")}") + set(lossType, lossStr) + this + } + + /** @group getParam */ + def getLossType: String = getOrDefault(lossType) + + /** (private[ml]) Convert new loss to old loss. */ + override private[ml] def getOldLossType: OldLoss = { + getLossType match { + case "logistic" => OldLogLoss + case _ => + // Should never happen because of check in setter method. + throw new RuntimeException(s"GBTClassifier was given bad loss type: $getLossType") + } + } + + override protected def train( + dataset: DataFrame, + paramMap: ParamMap): GBTClassificationModel = { + val categoricalFeatures: Map[Int, Int] = + MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) + val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema(paramMap(labelCol))) match { + case Some(n: Int) => n + case None => throw new IllegalArgumentException("GBTClassifier was given input" + + s" with invalid label column ${paramMap(labelCol)}, without the number of classes" + + " specified. See StringIndexer.") + // TODO: Automatically index labels: SPARK-7126 + } + require(numClasses == 2, + s"GBTClassifier only supports binary classification but was given numClasses = $numClasses") + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + val boostingStrategy = super.getOldBoostingStrategy(categoricalFeatures, OldAlgo.Classification) + val oldGBT = new OldGBT(boostingStrategy) + val oldModel = oldGBT.run(oldDataset) + GBTClassificationModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + } +} + +object GBTClassifier { + // The losses below should be lowercase. + /** Accessor for supported loss settings: logistic */ + final val supportedLossTypes: Array[String] = Array("logistic").map(_.toLowerCase) +} + +/** + * :: AlphaComponent :: + * + * [[http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)]] + * model for classification. + * It supports binary labels, as well as both continuous and categorical features. + * Note: Multiclass labels are not currently supported. + * @param _trees Decision trees in the ensemble. + * @param _treeWeights Weights for the decision trees in the ensemble. + */ +@AlphaComponent +final class GBTClassificationModel( + override val parent: GBTClassifier, + override val fittingParamMap: ParamMap, + private val _trees: Array[DecisionTreeRegressionModel], + private val _treeWeights: Array[Double]) + extends PredictionModel[Vector, GBTClassificationModel] + with TreeEnsembleModel with Serializable { + + require(numTrees > 0, "GBTClassificationModel requires at least 1 tree.") + require(_trees.length == _treeWeights.length, "GBTClassificationModel given trees, treeWeights" + + s" of non-matching lengths (${_trees.length}, ${_treeWeights.length}, respectively).") + + override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]] + + override def treeWeights: Array[Double] = _treeWeights + + override protected def predict(features: Vector): Double = { + // TODO: Override transform() to broadcast model: SPARK-7127 + // TODO: When we add a generic Boosting class, handle transform there? SPARK-7129 + // Classifies by thresholding sum of weighted tree predictions + val treePredictions = _trees.map(_.rootNode.predict(features)) + val prediction = blas.ddot(numTrees, treePredictions, 1, _treeWeights, 1) + if (prediction > 0.0) 1.0 else 0.0 + } + + override protected def copy(): GBTClassificationModel = { + val m = new GBTClassificationModel(parent, fittingParamMap, _trees, _treeWeights) + Params.inheritValues(this.extractParamMap(), this, m) + m + } + + override def toString: String = { + s"GBTClassificationModel with $numTrees trees" + } + + /** (private[ml]) Convert to a model in the old API */ + private[ml] def toOld: OldGBTModel = { + new OldGBTModel(OldAlgo.Classification, _trees.map(_.toOld), _treeWeights) + } +} + +private[ml] object GBTClassificationModel { + + /** (private[ml]) Convert a model from the old API */ + def fromOld( + oldModel: OldGBTModel, + parent: GBTClassifier, + fittingParamMap: ParamMap, + categoricalFeatures: Map[Int, Int]): GBTClassificationModel = { + require(oldModel.algo == OldAlgo.Classification, "Cannot convert GradientBoostedTreesModel" + + s" with algo=${oldModel.algo} (old API) to GBTClassificationModel (new API).") + val newTrees = oldModel.trees.map { tree => + // parent, fittingParamMap for each tree is null since there are no good ways to set these. + DecisionTreeRegressionModel.fromOld(tree, null, null, categoricalFeatures) + } + new GBTClassificationModel(parent, fittingParamMap, newTrees, oldModel.treeWeights) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala new file mode 100644 index 0000000000000..cfd6508fce890 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala @@ -0,0 +1,185 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.classification + +import scala.collection.mutable + +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} +import org.apache.spark.ml.impl.tree._ +import org.apache.spark.ml.param.{Params, ParamMap} +import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel} +import org.apache.spark.ml.util.MetadataUtils +import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.tree.{RandomForest => OldRandomForest} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy} +import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel} +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.DataFrame + + +/** + * :: AlphaComponent :: + * + * [[http://en.wikipedia.org/wiki/Random_forest Random Forest]] learning algorithm for + * classification. + * It supports both binary and multiclass labels, as well as both continuous and categorical + * features. + */ +@AlphaComponent +final class RandomForestClassifier + extends Predictor[Vector, RandomForestClassifier, RandomForestClassificationModel] + with RandomForestParams with TreeClassifierParams { + + // Override parameter setters from parent trait for Java API compatibility. + + // Parameters from TreeClassifierParams: + + override def setMaxDepth(value: Int): this.type = super.setMaxDepth(value) + + override def setMaxBins(value: Int): this.type = super.setMaxBins(value) + + override def setMinInstancesPerNode(value: Int): this.type = + super.setMinInstancesPerNode(value) + + override def setMinInfoGain(value: Double): this.type = super.setMinInfoGain(value) + + override def setMaxMemoryInMB(value: Int): this.type = super.setMaxMemoryInMB(value) + + override def setCacheNodeIds(value: Boolean): this.type = super.setCacheNodeIds(value) + + override def setCheckpointInterval(value: Int): this.type = super.setCheckpointInterval(value) + + override def setImpurity(value: String): this.type = super.setImpurity(value) + + // Parameters from TreeEnsembleParams: + + override def setSubsamplingRate(value: Double): this.type = super.setSubsamplingRate(value) + + override def setSeed(value: Long): this.type = super.setSeed(value) + + // Parameters from RandomForestParams: + + override def setNumTrees(value: Int): this.type = super.setNumTrees(value) + + override def setFeatureSubsetStrategy(value: String): this.type = + super.setFeatureSubsetStrategy(value) + + override protected def train( + dataset: DataFrame, + paramMap: ParamMap): RandomForestClassificationModel = { + val categoricalFeatures: Map[Int, Int] = + MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) + val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema(paramMap(labelCol))) match { + case Some(n: Int) => n + case None => throw new IllegalArgumentException("RandomForestClassifier was given input" + + s" with invalid label column ${paramMap(labelCol)}, without the number of classes" + + " specified. See StringIndexer.") + // TODO: Automatically index labels: SPARK-7126 + } + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + val strategy = + super.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, getOldImpurity) + val oldModel = OldRandomForest.trainClassifier( + oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed.toInt) + RandomForestClassificationModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + } +} + +object RandomForestClassifier { + /** Accessor for supported impurity settings: entropy, gini */ + final val supportedImpurities: Array[String] = TreeClassifierParams.supportedImpurities + + /** Accessor for supported featureSubsetStrategy settings: auto, all, onethird, sqrt, log2 */ + final val supportedFeatureSubsetStrategies: Array[String] = + RandomForestParams.supportedFeatureSubsetStrategies +} + +/** + * :: AlphaComponent :: + * + * [[http://en.wikipedia.org/wiki/Random_forest Random Forest]] model for classification. + * It supports both binary and multiclass labels, as well as both continuous and categorical + * features. + * @param _trees Decision trees in the ensemble. + * Warning: These have null parents. + */ +@AlphaComponent +final class RandomForestClassificationModel private[ml] ( + override val parent: RandomForestClassifier, + override val fittingParamMap: ParamMap, + private val _trees: Array[DecisionTreeClassificationModel]) + extends PredictionModel[Vector, RandomForestClassificationModel] + with TreeEnsembleModel with Serializable { + + require(numTrees > 0, "RandomForestClassificationModel requires at least 1 tree.") + + override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]] + + // Note: We may add support for weights (based on tree performance) later on. + private lazy val _treeWeights: Array[Double] = Array.fill[Double](numTrees)(1.0) + + override def treeWeights: Array[Double] = _treeWeights + + override protected def predict(features: Vector): Double = { + // TODO: Override transform() to broadcast model. SPARK-7127 + // TODO: When we add a generic Bagging class, handle transform there: SPARK-7128 + // Classifies using majority votes. + // Ignore the weights since all are 1.0 for now. + val votes = mutable.Map.empty[Int, Double] + _trees.view.foreach { tree => + val prediction = tree.rootNode.predict(features).toInt + votes(prediction) = votes.getOrElse(prediction, 0.0) + 1.0 // 1.0 = weight + } + votes.maxBy(_._2)._1 + } + + override protected def copy(): RandomForestClassificationModel = { + val m = new RandomForestClassificationModel(parent, fittingParamMap, _trees) + Params.inheritValues(this.extractParamMap(), this, m) + m + } + + override def toString: String = { + s"RandomForestClassificationModel with $numTrees trees" + } + + /** (private[ml]) Convert to a model in the old API */ + private[ml] def toOld: OldRandomForestModel = { + new OldRandomForestModel(OldAlgo.Classification, _trees.map(_.toOld)) + } +} + +private[ml] object RandomForestClassificationModel { + + /** (private[ml]) Convert a model from the old API */ + def fromOld( + oldModel: OldRandomForestModel, + parent: RandomForestClassifier, + fittingParamMap: ParamMap, + categoricalFeatures: Map[Int, Int]): RandomForestClassificationModel = { + require(oldModel.algo == OldAlgo.Classification, "Cannot convert RandomForestModel" + + s" with algo=${oldModel.algo} (old API) to RandomForestClassificationModel (new API).") + val newTrees = oldModel.trees.map { tree => + // parent, fittingParamMap for each tree is null since there are no good ways to set these. + DecisionTreeClassificationModel.fromOld(tree, null, null, categoricalFeatures) + } + new RandomForestClassificationModel(parent, fittingParamMap, newTrees) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala b/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala index eb2609faef05a..ab6281b9b2e34 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala @@ -20,9 +20,12 @@ package org.apache.spark.ml.impl.tree import org.apache.spark.annotation.DeveloperApi import org.apache.spark.ml.impl.estimator.PredictorParams import org.apache.spark.ml.param._ -import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy} +import org.apache.spark.ml.param.shared.{HasSeed, HasMaxIter} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, + BoostingStrategy => OldBoostingStrategy, Strategy => OldStrategy} import org.apache.spark.mllib.tree.impurity.{Gini => OldGini, Entropy => OldEntropy, Impurity => OldImpurity, Variance => OldVariance} +import org.apache.spark.mllib.tree.loss.{Loss => OldLoss} /** @@ -117,79 +120,68 @@ private[ml] trait DecisionTreeParams extends PredictorParams { def setMaxDepth(value: Int): this.type = { require(value >= 0, s"maxDepth parameter must be >= 0. Given bad value: $value") set(maxDepth, value) - this } /** @group getParam */ - def getMaxDepth: Int = getOrDefault(maxDepth) + final def getMaxDepth: Int = getOrDefault(maxDepth) /** @group setParam */ def setMaxBins(value: Int): this.type = { require(value >= 2, s"maxBins parameter must be >= 2. Given bad value: $value") set(maxBins, value) - this } /** @group getParam */ - def getMaxBins: Int = getOrDefault(maxBins) + final def getMaxBins: Int = getOrDefault(maxBins) /** @group setParam */ def setMinInstancesPerNode(value: Int): this.type = { require(value >= 1, s"minInstancesPerNode parameter must be >= 1. Given bad value: $value") set(minInstancesPerNode, value) - this } /** @group getParam */ - def getMinInstancesPerNode: Int = getOrDefault(minInstancesPerNode) + final def getMinInstancesPerNode: Int = getOrDefault(minInstancesPerNode) /** @group setParam */ - def setMinInfoGain(value: Double): this.type = { - set(minInfoGain, value) - this - } + def setMinInfoGain(value: Double): this.type = set(minInfoGain, value) /** @group getParam */ - def getMinInfoGain: Double = getOrDefault(minInfoGain) + final def getMinInfoGain: Double = getOrDefault(minInfoGain) /** @group expertSetParam */ def setMaxMemoryInMB(value: Int): this.type = { require(value > 0, s"maxMemoryInMB parameter must be > 0. Given bad value: $value") set(maxMemoryInMB, value) - this } /** @group expertGetParam */ - def getMaxMemoryInMB: Int = getOrDefault(maxMemoryInMB) + final def getMaxMemoryInMB: Int = getOrDefault(maxMemoryInMB) /** @group expertSetParam */ - def setCacheNodeIds(value: Boolean): this.type = { - set(cacheNodeIds, value) - this - } + def setCacheNodeIds(value: Boolean): this.type = set(cacheNodeIds, value) /** @group expertGetParam */ - def getCacheNodeIds: Boolean = getOrDefault(cacheNodeIds) + final def getCacheNodeIds: Boolean = getOrDefault(cacheNodeIds) /** @group expertSetParam */ def setCheckpointInterval(value: Int): this.type = { require(value >= 1, s"checkpointInterval parameter must be >= 1. Given bad value: $value") set(checkpointInterval, value) - this } /** @group expertGetParam */ - def getCheckpointInterval: Int = getOrDefault(checkpointInterval) + final def getCheckpointInterval: Int = getOrDefault(checkpointInterval) - /** - * Create a Strategy instance to use with the old API. - * NOTE: The caller should set impurity and subsamplingRate (which is set to 1.0, - * the default for single trees). - */ + /** (private[ml]) Create a Strategy instance to use with the old API. */ private[ml] def getOldStrategy( categoricalFeatures: Map[Int, Int], - numClasses: Int): OldStrategy = { - val strategy = OldStrategy.defaultStategy(OldAlgo.Classification) + numClasses: Int, + oldAlgo: OldAlgo.Algo, + oldImpurity: OldImpurity, + subsamplingRate: Double): OldStrategy = { + val strategy = OldStrategy.defaultStategy(oldAlgo) + strategy.impurity = oldImpurity strategy.checkpointInterval = getCheckpointInterval strategy.maxBins = getMaxBins strategy.maxDepth = getMaxDepth @@ -199,13 +191,13 @@ private[ml] trait DecisionTreeParams extends PredictorParams { strategy.useNodeIdCache = getCacheNodeIds strategy.numClasses = numClasses strategy.categoricalFeaturesInfo = categoricalFeatures - strategy.subsamplingRate = 1.0 // default for individual trees + strategy.subsamplingRate = subsamplingRate strategy } } /** - * (private trait) Parameters for Decision Tree-based classification algorithms. + * Parameters for Decision Tree-based classification algorithms. */ private[ml] trait TreeClassifierParams extends Params { @@ -215,7 +207,7 @@ private[ml] trait TreeClassifierParams extends Params { * (default = gini) * @group param */ - val impurity: Param[String] = new Param[String](this, "impurity", "Criterion used for" + + final val impurity: Param[String] = new Param[String](this, "impurity", "Criterion used for" + " information gain calculation (case-insensitive). Supported options:" + s" ${TreeClassifierParams.supportedImpurities.mkString(", ")}") @@ -228,11 +220,10 @@ private[ml] trait TreeClassifierParams extends Params { s"Tree-based classifier was given unrecognized impurity: $value." + s" Supported options: ${TreeClassifierParams.supportedImpurities.mkString(", ")}") set(impurity, impurityStr) - this } /** @group getParam */ - def getImpurity: String = getOrDefault(impurity) + final def getImpurity: String = getOrDefault(impurity) /** Convert new impurity to old impurity. */ private[ml] def getOldImpurity: OldImpurity = { @@ -249,11 +240,11 @@ private[ml] trait TreeClassifierParams extends Params { private[ml] object TreeClassifierParams { // These options should be lowercase. - val supportedImpurities: Array[String] = Array("entropy", "gini").map(_.toLowerCase) + final val supportedImpurities: Array[String] = Array("entropy", "gini").map(_.toLowerCase) } /** - * (private trait) Parameters for Decision Tree-based regression algorithms. + * Parameters for Decision Tree-based regression algorithms. */ private[ml] trait TreeRegressorParams extends Params { @@ -263,7 +254,7 @@ private[ml] trait TreeRegressorParams extends Params { * (default = variance) * @group param */ - val impurity: Param[String] = new Param[String](this, "impurity", "Criterion used for" + + final val impurity: Param[String] = new Param[String](this, "impurity", "Criterion used for" + " information gain calculation (case-insensitive). Supported options:" + s" ${TreeRegressorParams.supportedImpurities.mkString(", ")}") @@ -276,11 +267,10 @@ private[ml] trait TreeRegressorParams extends Params { s"Tree-based regressor was given unrecognized impurity: $value." + s" Supported options: ${TreeRegressorParams.supportedImpurities.mkString(", ")}") set(impurity, impurityStr) - this } /** @group getParam */ - def getImpurity: String = getOrDefault(impurity) + final def getImpurity: String = getOrDefault(impurity) /** Convert new impurity to old impurity. */ private[ml] def getOldImpurity: OldImpurity = { @@ -296,5 +286,186 @@ private[ml] trait TreeRegressorParams extends Params { private[ml] object TreeRegressorParams { // These options should be lowercase. - val supportedImpurities: Array[String] = Array("variance").map(_.toLowerCase) + final val supportedImpurities: Array[String] = Array("variance").map(_.toLowerCase) +} + +/** + * :: DeveloperApi :: + * Parameters for Decision Tree-based ensemble algorithms. + * + * Note: Marked as private and DeveloperApi since this may be made public in the future. + */ +@DeveloperApi +private[ml] trait TreeEnsembleParams extends DecisionTreeParams with HasSeed { + + /** + * Fraction of the training data used for learning each decision tree. + * (default = 1.0) + * @group param + */ + final val subsamplingRate: DoubleParam = new DoubleParam(this, "subsamplingRate", + "Fraction of the training data used for learning each decision tree.") + + setDefault(subsamplingRate -> 1.0) + + /** @group setParam */ + def setSubsamplingRate(value: Double): this.type = { + require(value > 0.0 && value <= 1.0, + s"Subsampling rate must be in range (0,1]. Bad rate: $value") + set(subsamplingRate, value) + } + + /** @group getParam */ + final def getSubsamplingRate: Double = getOrDefault(subsamplingRate) + + /** @group setParam */ + def setSeed(value: Long): this.type = set(seed, value) + + /** + * Create a Strategy instance to use with the old API. + * NOTE: The caller should set impurity and seed. + */ + private[ml] def getOldStrategy( + categoricalFeatures: Map[Int, Int], + numClasses: Int, + oldAlgo: OldAlgo.Algo, + oldImpurity: OldImpurity): OldStrategy = { + super.getOldStrategy(categoricalFeatures, numClasses, oldAlgo, oldImpurity, getSubsamplingRate) + } +} + +/** + * :: DeveloperApi :: + * Parameters for Random Forest algorithms. + * + * Note: Marked as private and DeveloperApi since this may be made public in the future. + */ +@DeveloperApi +private[ml] trait RandomForestParams extends TreeEnsembleParams { + + /** + * Number of trees to train (>= 1). + * If 1, then no bootstrapping is used. If > 1, then bootstrapping is done. + * TODO: Change to always do bootstrapping (simpler). SPARK-7130 + * (default = 20) + * @group param + */ + final val numTrees: IntParam = new IntParam(this, "numTrees", "Number of trees to train (>= 1)") + + /** + * The number of features to consider for splits at each tree node. + * Supported options: + * - "auto": Choose automatically for task: + * If numTrees == 1, set to "all." + * If numTrees > 1 (forest), set to "sqrt" for classification and + * to "onethird" for regression. + * - "all": use all features + * - "onethird": use 1/3 of the features + * - "sqrt": use sqrt(number of features) + * - "log2": use log2(number of features) + * (default = "auto") + * + * These various settings are based on the following references: + * - log2: tested in Breiman (2001) + * - sqrt: recommended by Breiman manual for random forests + * - The defaults of sqrt (classification) and onethird (regression) match the R randomForest + * package. + * @see [[http://www.stat.berkeley.edu/~breiman/randomforest2001.pdf Breiman (2001)]] + * @see [[http://www.stat.berkeley.edu/~breiman/Using_random_forests_V3.1.pdf Breiman manual for + * random forests]] + * + * @group param + */ + final val featureSubsetStrategy: Param[String] = new Param[String](this, "featureSubsetStrategy", + "The number of features to consider for splits at each tree node." + + s" Supported options: ${RandomForestParams.supportedFeatureSubsetStrategies.mkString(", ")}") + + setDefault(numTrees -> 20, featureSubsetStrategy -> "auto") + + /** @group setParam */ + def setNumTrees(value: Int): this.type = { + require(value >= 1, s"Random Forest numTrees parameter cannot be $value; it must be >= 1.") + set(numTrees, value) + } + + /** @group getParam */ + final def getNumTrees: Int = getOrDefault(numTrees) + + /** @group setParam */ + def setFeatureSubsetStrategy(value: String): this.type = { + val strategyStr = value.toLowerCase + require(RandomForestParams.supportedFeatureSubsetStrategies.contains(strategyStr), + s"RandomForestParams was given unrecognized featureSubsetStrategy: $value. Supported" + + s" options: ${RandomForestParams.supportedFeatureSubsetStrategies.mkString(", ")}") + set(featureSubsetStrategy, strategyStr) + } + + /** @group getParam */ + final def getFeatureSubsetStrategy: String = getOrDefault(featureSubsetStrategy) +} + +private[ml] object RandomForestParams { + // These options should be lowercase. + final val supportedFeatureSubsetStrategies: Array[String] = + Array("auto", "all", "onethird", "sqrt", "log2").map(_.toLowerCase) +} + +/** + * :: DeveloperApi :: + * Parameters for Gradient-Boosted Tree algorithms. + * + * Note: Marked as private and DeveloperApi since this may be made public in the future. + */ +@DeveloperApi +private[ml] trait GBTParams extends TreeEnsembleParams with HasMaxIter { + + /** + * Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the contribution of each + * estimator. + * (default = 0.1) + * @group param + */ + final val stepSize: DoubleParam = new DoubleParam(this, "stepSize", "Step size (a.k.a." + + " learning rate) in interval (0, 1] for shrinking the contribution of each estimator") + + /* TODO: Add this doc when we add this param. SPARK-7132 + * Threshold for stopping early when runWithValidation is used. + * If the error rate on the validation input changes by less than the validationTol, + * then learning will stop early (before [[numIterations]]). + * This parameter is ignored when run is used. + * (default = 1e-5) + * @group param + */ + // final val validationTol: DoubleParam = new DoubleParam(this, "validationTol", "") + // validationTol -> 1e-5 + + setDefault(maxIter -> 20, stepSize -> 0.1) + + /** @group setParam */ + def setMaxIter(value: Int): this.type = { + require(value >= 1, s"Gradient Boosting maxIter parameter cannot be $value; it must be >= 1.") + set(maxIter, value) + } + + /** @group setParam */ + def setStepSize(value: Double): this.type = { + require(value > 0.0 && value <= 1.0, + s"GBT given invalid step size ($value). Value should be in (0,1].") + set(stepSize, value) + } + + /** @group getParam */ + final def getStepSize: Double = getOrDefault(stepSize) + + /** (private[ml]) Create a BoostingStrategy instance to use with the old API. */ + private[ml] def getOldBoostingStrategy( + categoricalFeatures: Map[Int, Int], + oldAlgo: OldAlgo.Algo): OldBoostingStrategy = { + val strategy = super.getOldStrategy(categoricalFeatures, numClasses = 2, oldAlgo, OldVariance) + // NOTE: The old API does not support "seed" so we ignore it. + new OldBoostingStrategy(strategy, getOldLossType, getMaxIter, getStepSize) + } + + /** Get old Gradient Boosting Loss type */ + private[ml] def getOldLossType: OldLoss } diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala index 95d7e64790c79..e88c48741e99f 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala @@ -45,7 +45,8 @@ private[shared] object SharedParamsCodeGen { ParamDesc[Array[String]]("inputCols", "input column names"), ParamDesc[String]("outputCol", "output column name"), ParamDesc[Int]("checkpointInterval", "checkpoint interval"), - ParamDesc[Boolean]("fitIntercept", "whether to fit an intercept term", Some("true"))) + ParamDesc[Boolean]("fitIntercept", "whether to fit an intercept term", Some("true")), + ParamDesc[Long]("seed", "random seed", Some("Utils.random.nextLong()"))) val code = genSharedParams(params) val file = "src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala" @@ -154,6 +155,7 @@ private[shared] object SharedParamsCodeGen { | |import org.apache.spark.annotation.DeveloperApi |import org.apache.spark.ml.param._ + |import org.apache.spark.util.Utils | |// DO NOT MODIFY THIS FILE! It was generated by SharedParamsCodeGen. | diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala index 72b08bf276483..a860b8834cff9 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala @@ -19,6 +19,7 @@ package org.apache.spark.ml.param.shared import org.apache.spark.annotation.DeveloperApi import org.apache.spark.ml.param._ +import org.apache.spark.util.Utils // DO NOT MODIFY THIS FILE! It was generated by SharedParamsCodeGen. @@ -256,4 +257,23 @@ trait HasFitIntercept extends Params { /** @group getParam */ final def getFitIntercept: Boolean = getOrDefault(fitIntercept) } + +/** + * :: DeveloperApi :: + * Trait for shared param seed (default: Utils.random.nextLong()). + */ +@DeveloperApi +trait HasSeed extends Params { + + /** + * Param for random seed. + * @group param + */ + final val seed: LongParam = new LongParam(this, "seed", "random seed") + + setDefault(seed, Utils.random.nextLong()) + + /** @group getParam */ + final def getSeed: Long = getOrDefault(seed) +} // scalastyle:on diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala index 49a8b77acf960..756725a64b0f3 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala @@ -42,8 +42,7 @@ import org.apache.spark.sql.DataFrame @AlphaComponent final class DecisionTreeRegressor extends Predictor[Vector, DecisionTreeRegressor, DecisionTreeRegressionModel] - with DecisionTreeParams - with TreeRegressorParams { + with DecisionTreeParams with TreeRegressorParams { // Override parameter setters from parent trait for Java API compatibility. @@ -60,8 +59,7 @@ final class DecisionTreeRegressor override def setCacheNodeIds(value: Boolean): this.type = super.setCacheNodeIds(value) - override def setCheckpointInterval(value: Int): this.type = - super.setCheckpointInterval(value) + override def setCheckpointInterval(value: Int): this.type = super.setCheckpointInterval(value) override def setImpurity(value: String): this.type = super.setImpurity(value) @@ -78,15 +76,13 @@ final class DecisionTreeRegressor /** (private[ml]) Create a Strategy instance to use with the old API. */ private[ml] def getOldStrategy(categoricalFeatures: Map[Int, Int]): OldStrategy = { - val strategy = super.getOldStrategy(categoricalFeatures, numClasses = 0) - strategy.algo = OldAlgo.Regression - strategy.setImpurity(getOldImpurity) - strategy + super.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression, getOldImpurity, + subsamplingRate = 1.0) } } object DecisionTreeRegressor { - /** Accessor for supported impurities */ + /** Accessor for supported impurities: variance */ final val supportedImpurities: Array[String] = TreeRegressorParams.supportedImpurities } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala new file mode 100644 index 0000000000000..c784cf39ed31a --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala @@ -0,0 +1,218 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.regression + +import com.github.fommil.netlib.BLAS.{getInstance => blas} + +import org.apache.spark.Logging +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} +import org.apache.spark.ml.impl.tree._ +import org.apache.spark.ml.param.{Params, ParamMap, Param} +import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel} +import org.apache.spark.ml.util.MetadataUtils +import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.tree.{GradientBoostedTrees => OldGBT} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} +import org.apache.spark.mllib.tree.loss.{AbsoluteError => OldAbsoluteError, Loss => OldLoss, + SquaredError => OldSquaredError} +import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel} +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.DataFrame + + +/** + * :: AlphaComponent :: + * + * [[http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)]] + * learning algorithm for regression. + * It supports both continuous and categorical features. + */ +@AlphaComponent +final class GBTRegressor + extends Predictor[Vector, GBTRegressor, GBTRegressionModel] + with GBTParams with TreeRegressorParams with Logging { + + // Override parameter setters from parent trait for Java API compatibility. + + // Parameters from TreeRegressorParams: + + override def setMaxDepth(value: Int): this.type = super.setMaxDepth(value) + + override def setMaxBins(value: Int): this.type = super.setMaxBins(value) + + override def setMinInstancesPerNode(value: Int): this.type = + super.setMinInstancesPerNode(value) + + override def setMinInfoGain(value: Double): this.type = super.setMinInfoGain(value) + + override def setMaxMemoryInMB(value: Int): this.type = super.setMaxMemoryInMB(value) + + override def setCacheNodeIds(value: Boolean): this.type = super.setCacheNodeIds(value) + + override def setCheckpointInterval(value: Int): this.type = super.setCheckpointInterval(value) + + /** + * The impurity setting is ignored for GBT models. + * Individual trees are built using impurity "Variance." + */ + override def setImpurity(value: String): this.type = { + logWarning("GBTRegressor.setImpurity should NOT be used") + this + } + + // Parameters from TreeEnsembleParams: + + override def setSubsamplingRate(value: Double): this.type = super.setSubsamplingRate(value) + + override def setSeed(value: Long): this.type = { + logWarning("The 'seed' parameter is currently ignored by Gradient Boosting.") + super.setSeed(value) + } + + // Parameters from GBTParams: + + override def setMaxIter(value: Int): this.type = super.setMaxIter(value) + + override def setStepSize(value: Double): this.type = super.setStepSize(value) + + // Parameters for GBTRegressor: + + /** + * Loss function which GBT tries to minimize. (case-insensitive) + * Supported: "squared" (L2) and "absolute" (L1) + * (default = squared) + * @group param + */ + val lossType: Param[String] = new Param[String](this, "lossType", "Loss function which GBT" + + " tries to minimize (case-insensitive). Supported options:" + + s" ${GBTRegressor.supportedLossTypes.mkString(", ")}") + + setDefault(lossType -> "squared") + + /** @group setParam */ + def setLossType(value: String): this.type = { + val lossStr = value.toLowerCase + require(GBTRegressor.supportedLossTypes.contains(lossStr), "GBTRegressor was given bad loss" + + s" type: $value. Supported options: ${GBTRegressor.supportedLossTypes.mkString(", ")}") + set(lossType, lossStr) + this + } + + /** @group getParam */ + def getLossType: String = getOrDefault(lossType) + + /** (private[ml]) Convert new loss to old loss. */ + override private[ml] def getOldLossType: OldLoss = { + getLossType match { + case "squared" => OldSquaredError + case "absolute" => OldAbsoluteError + case _ => + // Should never happen because of check in setter method. + throw new RuntimeException(s"GBTRegressorParams was given bad loss type: $getLossType") + } + } + + override protected def train( + dataset: DataFrame, + paramMap: ParamMap): GBTRegressionModel = { + val categoricalFeatures: Map[Int, Int] = + MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + val boostingStrategy = super.getOldBoostingStrategy(categoricalFeatures, OldAlgo.Regression) + val oldGBT = new OldGBT(boostingStrategy) + val oldModel = oldGBT.run(oldDataset) + GBTRegressionModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + } +} + +object GBTRegressor { + // The losses below should be lowercase. + /** Accessor for supported loss settings: squared (L2), absolute (L1) */ + final val supportedLossTypes: Array[String] = Array("squared", "absolute").map(_.toLowerCase) +} + +/** + * :: AlphaComponent :: + * + * [[http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)]] + * model for regression. + * It supports both continuous and categorical features. + * @param _trees Decision trees in the ensemble. + * @param _treeWeights Weights for the decision trees in the ensemble. + */ +@AlphaComponent +final class GBTRegressionModel( + override val parent: GBTRegressor, + override val fittingParamMap: ParamMap, + private val _trees: Array[DecisionTreeRegressionModel], + private val _treeWeights: Array[Double]) + extends PredictionModel[Vector, GBTRegressionModel] + with TreeEnsembleModel with Serializable { + + require(numTrees > 0, "GBTRegressionModel requires at least 1 tree.") + require(_trees.length == _treeWeights.length, "GBTRegressionModel given trees, treeWeights of" + + s" non-matching lengths (${_trees.length}, ${_treeWeights.length}, respectively).") + + override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]] + + override def treeWeights: Array[Double] = _treeWeights + + override protected def predict(features: Vector): Double = { + // TODO: Override transform() to broadcast model. SPARK-7127 + // TODO: When we add a generic Boosting class, handle transform there? SPARK-7129 + // Classifies by thresholding sum of weighted tree predictions + val treePredictions = _trees.map(_.rootNode.predict(features)) + val prediction = blas.ddot(numTrees, treePredictions, 1, _treeWeights, 1) + if (prediction > 0.0) 1.0 else 0.0 + } + + override protected def copy(): GBTRegressionModel = { + val m = new GBTRegressionModel(parent, fittingParamMap, _trees, _treeWeights) + Params.inheritValues(this.extractParamMap(), this, m) + m + } + + override def toString: String = { + s"GBTRegressionModel with $numTrees trees" + } + + /** (private[ml]) Convert to a model in the old API */ + private[ml] def toOld: OldGBTModel = { + new OldGBTModel(OldAlgo.Regression, _trees.map(_.toOld), _treeWeights) + } +} + +private[ml] object GBTRegressionModel { + + /** (private[ml]) Convert a model from the old API */ + def fromOld( + oldModel: OldGBTModel, + parent: GBTRegressor, + fittingParamMap: ParamMap, + categoricalFeatures: Map[Int, Int]): GBTRegressionModel = { + require(oldModel.algo == OldAlgo.Regression, "Cannot convert GradientBoostedTreesModel" + + s" with algo=${oldModel.algo} (old API) to GBTRegressionModel (new API).") + val newTrees = oldModel.trees.map { tree => + // parent, fittingParamMap for each tree is null since there are no good ways to set these. + DecisionTreeRegressionModel.fromOld(tree, null, null, categoricalFeatures) + } + new GBTRegressionModel(parent, fittingParamMap, newTrees, oldModel.treeWeights) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala new file mode 100644 index 0000000000000..2171ef3d32c26 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala @@ -0,0 +1,167 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.regression + +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} +import org.apache.spark.ml.impl.tree.{RandomForestParams, TreeRegressorParams} +import org.apache.spark.ml.param.{Params, ParamMap} +import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel} +import org.apache.spark.ml.util.MetadataUtils +import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.tree.{RandomForest => OldRandomForest} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy} +import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel} +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.DataFrame + + +/** + * :: AlphaComponent :: + * + * [[http://en.wikipedia.org/wiki/Random_forest Random Forest]] learning algorithm for regression. + * It supports both continuous and categorical features. + */ +@AlphaComponent +final class RandomForestRegressor + extends Predictor[Vector, RandomForestRegressor, RandomForestRegressionModel] + with RandomForestParams with TreeRegressorParams { + + // Override parameter setters from parent trait for Java API compatibility. + + // Parameters from TreeRegressorParams: + + override def setMaxDepth(value: Int): this.type = super.setMaxDepth(value) + + override def setMaxBins(value: Int): this.type = super.setMaxBins(value) + + override def setMinInstancesPerNode(value: Int): this.type = + super.setMinInstancesPerNode(value) + + override def setMinInfoGain(value: Double): this.type = super.setMinInfoGain(value) + + override def setMaxMemoryInMB(value: Int): this.type = super.setMaxMemoryInMB(value) + + override def setCacheNodeIds(value: Boolean): this.type = super.setCacheNodeIds(value) + + override def setCheckpointInterval(value: Int): this.type = super.setCheckpointInterval(value) + + override def setImpurity(value: String): this.type = super.setImpurity(value) + + // Parameters from TreeEnsembleParams: + + override def setSubsamplingRate(value: Double): this.type = super.setSubsamplingRate(value) + + override def setSeed(value: Long): this.type = super.setSeed(value) + + // Parameters from RandomForestParams: + + override def setNumTrees(value: Int): this.type = super.setNumTrees(value) + + override def setFeatureSubsetStrategy(value: String): this.type = + super.setFeatureSubsetStrategy(value) + + override protected def train( + dataset: DataFrame, + paramMap: ParamMap): RandomForestRegressionModel = { + val categoricalFeatures: Map[Int, Int] = + MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + val strategy = + super.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression, getOldImpurity) + val oldModel = OldRandomForest.trainRegressor( + oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed.toInt) + RandomForestRegressionModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + } +} + +object RandomForestRegressor { + /** Accessor for supported impurity settings: variance */ + final val supportedImpurities: Array[String] = TreeRegressorParams.supportedImpurities + + /** Accessor for supported featureSubsetStrategy settings: auto, all, onethird, sqrt, log2 */ + final val supportedFeatureSubsetStrategies: Array[String] = + RandomForestParams.supportedFeatureSubsetStrategies +} + +/** + * :: AlphaComponent :: + * + * [[http://en.wikipedia.org/wiki/Random_forest Random Forest]] model for regression. + * It supports both continuous and categorical features. + * @param _trees Decision trees in the ensemble. + */ +@AlphaComponent +final class RandomForestRegressionModel private[ml] ( + override val parent: RandomForestRegressor, + override val fittingParamMap: ParamMap, + private val _trees: Array[DecisionTreeRegressionModel]) + extends PredictionModel[Vector, RandomForestRegressionModel] + with TreeEnsembleModel with Serializable { + + require(numTrees > 0, "RandomForestRegressionModel requires at least 1 tree.") + + override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]] + + // Note: We may add support for weights (based on tree performance) later on. + private lazy val _treeWeights: Array[Double] = Array.fill[Double](numTrees)(1.0) + + override def treeWeights: Array[Double] = _treeWeights + + override protected def predict(features: Vector): Double = { + // TODO: Override transform() to broadcast model. SPARK-7127 + // TODO: When we add a generic Bagging class, handle transform there. SPARK-7128 + // Predict average of tree predictions. + // Ignore the weights since all are 1.0 for now. + _trees.map(_.rootNode.predict(features)).sum / numTrees + } + + override protected def copy(): RandomForestRegressionModel = { + val m = new RandomForestRegressionModel(parent, fittingParamMap, _trees) + Params.inheritValues(this.extractParamMap(), this, m) + m + } + + override def toString: String = { + s"RandomForestRegressionModel with $numTrees trees" + } + + /** (private[ml]) Convert to a model in the old API */ + private[ml] def toOld: OldRandomForestModel = { + new OldRandomForestModel(OldAlgo.Regression, _trees.map(_.toOld)) + } +} + +private[ml] object RandomForestRegressionModel { + + /** (private[ml]) Convert a model from the old API */ + def fromOld( + oldModel: OldRandomForestModel, + parent: RandomForestRegressor, + fittingParamMap: ParamMap, + categoricalFeatures: Map[Int, Int]): RandomForestRegressionModel = { + require(oldModel.algo == OldAlgo.Regression, "Cannot convert RandomForestModel" + + s" with algo=${oldModel.algo} (old API) to RandomForestRegressionModel (new API).") + val newTrees = oldModel.trees.map { tree => + // parent, fittingParamMap for each tree is null since there are no good ways to set these. + DecisionTreeRegressionModel.fromOld(tree, null, null, categoricalFeatures) + } + new RandomForestRegressionModel(parent, fittingParamMap, newTrees) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala index d6e2203d9f937..d2dec0c76cb12 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala @@ -28,9 +28,9 @@ import org.apache.spark.mllib.tree.model.{InformationGainStats => OldInformation sealed abstract class Node extends Serializable { // TODO: Add aggregate stats (once available). This will happen after we move the DecisionTree - // code into the new API and deprecate the old API. + // code into the new API and deprecate the old API. SPARK-3727 - /** Prediction this node makes (or would make, if it is an internal node) */ + /** Prediction a leaf node makes, or which an internal node would make if it were a leaf node */ def prediction: Double /** Impurity measure at this node (for training data) */ @@ -194,7 +194,7 @@ private object InternalNode { s"$featureStr > ${contSplit.threshold}" } case catSplit: CategoricalSplit => - val categoriesStr = catSplit.getLeftCategories.mkString("{", ",", "}") + val categoriesStr = catSplit.leftCategories.mkString("{", ",", "}") if (left) { s"$featureStr in $categoriesStr" } else { diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala index 708c769087dd0..90f1d052764d3 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala @@ -44,7 +44,7 @@ private[tree] object Split { oldSplit.featureType match { case OldFeatureType.Categorical => new CategoricalSplit(featureIndex = oldSplit.feature, - leftCategories = oldSplit.categories.toArray, categoricalFeatures(oldSplit.feature)) + _leftCategories = oldSplit.categories.toArray, categoricalFeatures(oldSplit.feature)) case OldFeatureType.Continuous => new ContinuousSplit(featureIndex = oldSplit.feature, threshold = oldSplit.threshold) } @@ -54,30 +54,30 @@ private[tree] object Split { /** * Split which tests a categorical feature. * @param featureIndex Index of the feature to test - * @param leftCategories If the feature value is in this set of categories, then the split goes - * left. Otherwise, it goes right. + * @param _leftCategories If the feature value is in this set of categories, then the split goes + * left. Otherwise, it goes right. * @param numCategories Number of categories for this feature. */ final class CategoricalSplit private[ml] ( override val featureIndex: Int, - leftCategories: Array[Double], + _leftCategories: Array[Double], private val numCategories: Int) extends Split { - require(leftCategories.forall(cat => 0 <= cat && cat < numCategories), "Invalid leftCategories" + - s" (should be in range [0, $numCategories)): ${leftCategories.mkString(",")}") + require(_leftCategories.forall(cat => 0 <= cat && cat < numCategories), "Invalid leftCategories" + + s" (should be in range [0, $numCategories)): ${_leftCategories.mkString(",")}") /** * If true, then "categories" is the set of categories for splitting to the left, and vice versa. */ - private val isLeft: Boolean = leftCategories.length <= numCategories / 2 + private val isLeft: Boolean = _leftCategories.length <= numCategories / 2 /** Set of categories determining the splitting rule, along with [[isLeft]]. */ private val categories: Set[Double] = { if (isLeft) { - leftCategories.toSet + _leftCategories.toSet } else { - setComplement(leftCategories.toSet) + setComplement(_leftCategories.toSet) } } @@ -107,13 +107,13 @@ final class CategoricalSplit private[ml] ( } /** Get sorted categories which split to the left */ - def getLeftCategories: Array[Double] = { + def leftCategories: Array[Double] = { val cats = if (isLeft) categories else setComplement(categories) cats.toArray.sorted } /** Get sorted categories which split to the right */ - def getRightCategories: Array[Double] = { + def rightCategories: Array[Double] = { val cats = if (isLeft) setComplement(categories) else categories cats.toArray.sorted } diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala index 8e3bc3849dcf0..1929f9d02156e 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala @@ -17,18 +17,13 @@ package org.apache.spark.ml.tree -import org.apache.spark.annotation.AlphaComponent - /** - * :: AlphaComponent :: - * * Abstraction for Decision Tree models. * - * TODO: Add support for predicting probabilities and raw predictions + * TODO: Add support for predicting probabilities and raw predictions SPARK-3727 */ -@AlphaComponent -trait DecisionTreeModel { +private[ml] trait DecisionTreeModel { /** Root of the decision tree */ def rootNode: Node @@ -58,3 +53,40 @@ trait DecisionTreeModel { header + rootNode.subtreeToString(2) } } + +/** + * Abstraction for models which are ensembles of decision trees + * + * TODO: Add support for predicting probabilities and raw predictions SPARK-3727 + */ +private[ml] trait TreeEnsembleModel { + + // Note: We use getTrees since subclasses of TreeEnsembleModel will store subclasses of + // DecisionTreeModel. + + /** Trees in this ensemble. Warning: These have null parent Estimators. */ + def trees: Array[DecisionTreeModel] + + /** Weights for each tree, zippable with [[trees]] */ + def treeWeights: Array[Double] + + /** Summary of the model */ + override def toString: String = { + // Implementing classes should generally override this method to be more descriptive. + s"TreeEnsembleModel with $numTrees trees" + } + + /** Full description of model */ + def toDebugString: String = { + val header = toString + "\n" + header + trees.zip(treeWeights).zipWithIndex.map { case ((tree, weight), treeIndex) => + s" Tree $treeIndex (weight $weight):\n" + tree.rootNode.subtreeToString(4) + }.fold("")(_ + _) + } + + /** Number of trees in ensemble */ + val numTrees: Int = trees.length + + /** Total number of nodes, summed over all trees in the ensemble. */ + lazy val totalNumNodes: Int = trees.map(_.numNodes).sum +} diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaDecisionTreeClassifierSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaDecisionTreeClassifierSuite.java index 43b8787f9dd7e..60f25e5cce437 100644 --- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaDecisionTreeClassifierSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaDecisionTreeClassifierSuite.java @@ -17,7 +17,6 @@ package org.apache.spark.ml.classification; -import java.io.File; import java.io.Serializable; import java.util.HashMap; import java.util.Map; @@ -32,7 +31,6 @@ import org.apache.spark.mllib.classification.LogisticRegressionSuite; import org.apache.spark.mllib.regression.LabeledPoint; import org.apache.spark.sql.DataFrame; -import org.apache.spark.util.Utils; public class JavaDecisionTreeClassifierSuite implements Serializable { @@ -57,7 +55,7 @@ public void runDT() { double B = -1.5; JavaRDD data = sc.parallelize( - LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache(); + LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache(); Map categoricalFeatures = new HashMap(); DataFrame dataFrame = TreeTests.setMetadata(data, categoricalFeatures, 2); @@ -71,8 +69,8 @@ public void runDT() { .setCacheNodeIds(false) .setCheckpointInterval(10) .setMaxDepth(2); // duplicate setMaxDepth to check builder pattern - for (int i = 0; i < DecisionTreeClassifier.supportedImpurities().length; ++i) { - dt.setImpurity(DecisionTreeClassifier.supportedImpurities()[i]); + for (String impurity: DecisionTreeClassifier.supportedImpurities()) { + dt.setImpurity(impurity); } DecisionTreeClassificationModel model = dt.fit(dataFrame); @@ -82,7 +80,7 @@ public void runDT() { model.toDebugString(); /* - // TODO: Add test once save/load are implemented. + // TODO: Add test once save/load are implemented. SPARK-6725 File tempDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "spark"); String path = tempDir.toURI().toString(); try { diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaGBTClassifierSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaGBTClassifierSuite.java new file mode 100644 index 0000000000000..3c69467fa119e --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaGBTClassifierSuite.java @@ -0,0 +1,100 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.classification; + +import java.io.Serializable; +import java.util.HashMap; +import java.util.Map; + +import org.junit.After; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.ml.impl.TreeTests; +import org.apache.spark.mllib.classification.LogisticRegressionSuite; +import org.apache.spark.mllib.regression.LabeledPoint; +import org.apache.spark.sql.DataFrame; + + +public class JavaGBTClassifierSuite implements Serializable { + + private transient JavaSparkContext sc; + + @Before + public void setUp() { + sc = new JavaSparkContext("local", "JavaGBTClassifierSuite"); + } + + @After + public void tearDown() { + sc.stop(); + sc = null; + } + + @Test + public void runDT() { + int nPoints = 20; + double A = 2.0; + double B = -1.5; + + JavaRDD data = sc.parallelize( + LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache(); + Map categoricalFeatures = new HashMap(); + DataFrame dataFrame = TreeTests.setMetadata(data, categoricalFeatures, 2); + + // This tests setters. Training with various options is tested in Scala. + GBTClassifier rf = new GBTClassifier() + .setMaxDepth(2) + .setMaxBins(10) + .setMinInstancesPerNode(5) + .setMinInfoGain(0.0) + .setMaxMemoryInMB(256) + .setCacheNodeIds(false) + .setCheckpointInterval(10) + .setSubsamplingRate(1.0) + .setSeed(1234) + .setMaxIter(3) + .setStepSize(0.1) + .setMaxDepth(2); // duplicate setMaxDepth to check builder pattern + for (String lossType: GBTClassifier.supportedLossTypes()) { + rf.setLossType(lossType); + } + GBTClassificationModel model = rf.fit(dataFrame); + + model.transform(dataFrame); + model.totalNumNodes(); + model.toDebugString(); + model.trees(); + model.treeWeights(); + + /* + // TODO: Add test once save/load are implemented. SPARK-6725 + File tempDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "spark"); + String path = tempDir.toURI().toString(); + try { + model3.save(sc.sc(), path); + GBTClassificationModel sameModel = GBTClassificationModel.load(sc.sc(), path); + TreeTests.checkEqual(model3, sameModel); + } finally { + Utils.deleteRecursively(tempDir); + } + */ + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java new file mode 100644 index 0000000000000..32d0b3856b7e2 --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java @@ -0,0 +1,103 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.classification; + +import java.io.Serializable; +import java.util.HashMap; +import java.util.Map; + +import org.junit.After; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.ml.impl.TreeTests; +import org.apache.spark.mllib.classification.LogisticRegressionSuite; +import org.apache.spark.mllib.regression.LabeledPoint; +import org.apache.spark.sql.DataFrame; + + +public class JavaRandomForestClassifierSuite implements Serializable { + + private transient JavaSparkContext sc; + + @Before + public void setUp() { + sc = new JavaSparkContext("local", "JavaRandomForestClassifierSuite"); + } + + @After + public void tearDown() { + sc.stop(); + sc = null; + } + + @Test + public void runDT() { + int nPoints = 20; + double A = 2.0; + double B = -1.5; + + JavaRDD data = sc.parallelize( + LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache(); + Map categoricalFeatures = new HashMap(); + DataFrame dataFrame = TreeTests.setMetadata(data, categoricalFeatures, 2); + + // This tests setters. Training with various options is tested in Scala. + RandomForestClassifier rf = new RandomForestClassifier() + .setMaxDepth(2) + .setMaxBins(10) + .setMinInstancesPerNode(5) + .setMinInfoGain(0.0) + .setMaxMemoryInMB(256) + .setCacheNodeIds(false) + .setCheckpointInterval(10) + .setSubsamplingRate(1.0) + .setSeed(1234) + .setNumTrees(3) + .setMaxDepth(2); // duplicate setMaxDepth to check builder pattern + for (String impurity: RandomForestClassifier.supportedImpurities()) { + rf.setImpurity(impurity); + } + for (String featureSubsetStrategy: RandomForestClassifier.supportedFeatureSubsetStrategies()) { + rf.setFeatureSubsetStrategy(featureSubsetStrategy); + } + RandomForestClassificationModel model = rf.fit(dataFrame); + + model.transform(dataFrame); + model.totalNumNodes(); + model.toDebugString(); + model.trees(); + model.treeWeights(); + + /* + // TODO: Add test once save/load are implemented. SPARK-6725 + File tempDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "spark"); + String path = tempDir.toURI().toString(); + try { + model3.save(sc.sc(), path); + RandomForestClassificationModel sameModel = + RandomForestClassificationModel.load(sc.sc(), path); + TreeTests.checkEqual(model3, sameModel); + } finally { + Utils.deleteRecursively(tempDir); + } + */ + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java index a3a339004f31c..71b041818d7ee 100644 --- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java @@ -17,7 +17,6 @@ package org.apache.spark.ml.regression; -import java.io.File; import java.io.Serializable; import java.util.HashMap; import java.util.Map; @@ -32,7 +31,6 @@ import org.apache.spark.mllib.classification.LogisticRegressionSuite; import org.apache.spark.mllib.regression.LabeledPoint; import org.apache.spark.sql.DataFrame; -import org.apache.spark.util.Utils; public class JavaDecisionTreeRegressorSuite implements Serializable { @@ -57,22 +55,22 @@ public void runDT() { double B = -1.5; JavaRDD data = sc.parallelize( - LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache(); + LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache(); Map categoricalFeatures = new HashMap(); DataFrame dataFrame = TreeTests.setMetadata(data, categoricalFeatures, 2); // This tests setters. Training with various options is tested in Scala. DecisionTreeRegressor dt = new DecisionTreeRegressor() - .setMaxDepth(2) - .setMaxBins(10) - .setMinInstancesPerNode(5) - .setMinInfoGain(0.0) - .setMaxMemoryInMB(256) - .setCacheNodeIds(false) - .setCheckpointInterval(10) - .setMaxDepth(2); // duplicate setMaxDepth to check builder pattern - for (int i = 0; i < DecisionTreeRegressor.supportedImpurities().length; ++i) { - dt.setImpurity(DecisionTreeRegressor.supportedImpurities()[i]); + .setMaxDepth(2) + .setMaxBins(10) + .setMinInstancesPerNode(5) + .setMinInfoGain(0.0) + .setMaxMemoryInMB(256) + .setCacheNodeIds(false) + .setCheckpointInterval(10) + .setMaxDepth(2); // duplicate setMaxDepth to check builder pattern + for (String impurity: DecisionTreeRegressor.supportedImpurities()) { + dt.setImpurity(impurity); } DecisionTreeRegressionModel model = dt.fit(dataFrame); @@ -82,7 +80,7 @@ public void runDT() { model.toDebugString(); /* - // TODO: Add test once save/load are implemented. + // TODO: Add test once save/load are implemented. SPARK-6725 File tempDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "spark"); String path = tempDir.toURI().toString(); try { diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaGBTRegressorSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaGBTRegressorSuite.java new file mode 100644 index 0000000000000..fc8c13db07e6f --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaGBTRegressorSuite.java @@ -0,0 +1,99 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.regression; + +import java.io.Serializable; +import java.util.HashMap; +import java.util.Map; + +import org.junit.After; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.ml.impl.TreeTests; +import org.apache.spark.mllib.classification.LogisticRegressionSuite; +import org.apache.spark.mllib.regression.LabeledPoint; +import org.apache.spark.sql.DataFrame; + + +public class JavaGBTRegressorSuite implements Serializable { + + private transient JavaSparkContext sc; + + @Before + public void setUp() { + sc = new JavaSparkContext("local", "JavaGBTRegressorSuite"); + } + + @After + public void tearDown() { + sc.stop(); + sc = null; + } + + @Test + public void runDT() { + int nPoints = 20; + double A = 2.0; + double B = -1.5; + + JavaRDD data = sc.parallelize( + LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache(); + Map categoricalFeatures = new HashMap(); + DataFrame dataFrame = TreeTests.setMetadata(data, categoricalFeatures, 0); + + GBTRegressor rf = new GBTRegressor() + .setMaxDepth(2) + .setMaxBins(10) + .setMinInstancesPerNode(5) + .setMinInfoGain(0.0) + .setMaxMemoryInMB(256) + .setCacheNodeIds(false) + .setCheckpointInterval(10) + .setSubsamplingRate(1.0) + .setSeed(1234) + .setMaxIter(3) + .setStepSize(0.1) + .setMaxDepth(2); // duplicate setMaxDepth to check builder pattern + for (String lossType: GBTRegressor.supportedLossTypes()) { + rf.setLossType(lossType); + } + GBTRegressionModel model = rf.fit(dataFrame); + + model.transform(dataFrame); + model.totalNumNodes(); + model.toDebugString(); + model.trees(); + model.treeWeights(); + + /* + // TODO: Add test once save/load are implemented. SPARK-6725 + File tempDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "spark"); + String path = tempDir.toURI().toString(); + try { + model2.save(sc.sc(), path); + GBTRegressionModel sameModel = GBTRegressionModel.load(sc.sc(), path); + TreeTests.checkEqual(model2, sameModel); + } finally { + Utils.deleteRecursively(tempDir); + } + */ + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java new file mode 100644 index 0000000000000..e306ebadfe7cf --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java @@ -0,0 +1,102 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.regression; + +import java.io.Serializable; +import java.util.HashMap; +import java.util.Map; + +import org.junit.After; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.mllib.classification.LogisticRegressionSuite; +import org.apache.spark.ml.impl.TreeTests; +import org.apache.spark.mllib.regression.LabeledPoint; +import org.apache.spark.sql.DataFrame; + + +public class JavaRandomForestRegressorSuite implements Serializable { + + private transient JavaSparkContext sc; + + @Before + public void setUp() { + sc = new JavaSparkContext("local", "JavaRandomForestRegressorSuite"); + } + + @After + public void tearDown() { + sc.stop(); + sc = null; + } + + @Test + public void runDT() { + int nPoints = 20; + double A = 2.0; + double B = -1.5; + + JavaRDD data = sc.parallelize( + LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache(); + Map categoricalFeatures = new HashMap(); + DataFrame dataFrame = TreeTests.setMetadata(data, categoricalFeatures, 0); + + // This tests setters. Training with various options is tested in Scala. + RandomForestRegressor rf = new RandomForestRegressor() + .setMaxDepth(2) + .setMaxBins(10) + .setMinInstancesPerNode(5) + .setMinInfoGain(0.0) + .setMaxMemoryInMB(256) + .setCacheNodeIds(false) + .setCheckpointInterval(10) + .setSubsamplingRate(1.0) + .setSeed(1234) + .setNumTrees(3) + .setMaxDepth(2); // duplicate setMaxDepth to check builder pattern + for (String impurity: RandomForestRegressor.supportedImpurities()) { + rf.setImpurity(impurity); + } + for (String featureSubsetStrategy: RandomForestRegressor.supportedFeatureSubsetStrategies()) { + rf.setFeatureSubsetStrategy(featureSubsetStrategy); + } + RandomForestRegressionModel model = rf.fit(dataFrame); + + model.transform(dataFrame); + model.totalNumNodes(); + model.toDebugString(); + model.trees(); + model.treeWeights(); + + /* + // TODO: Add test once save/load are implemented. SPARK-6725 + File tempDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "spark"); + String path = tempDir.toURI().toString(); + try { + model2.save(sc.sc(), path); + RandomForestRegressionModel sameModel = RandomForestRegressionModel.load(sc.sc(), path); + TreeTests.checkEqual(model2, sameModel); + } finally { + Utils.deleteRecursively(tempDir); + } + */ + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala index af88595df5245..9b31adecdcb1c 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala @@ -230,7 +230,7 @@ class DecisionTreeClassifierSuite extends FunSuite with MLlibTestSparkContext { // Tests of model save/load ///////////////////////////////////////////////////////////////////////////// - // TODO: Reinstate test once save/load are implemented + // TODO: Reinstate test once save/load are implemented SPARK-6725 /* test("model save/load") { val tempDir = Utils.createTempDir() diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala new file mode 100644 index 0000000000000..e6ccc2c93cba8 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala @@ -0,0 +1,136 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.classification + +import org.scalatest.FunSuite + +import org.apache.spark.ml.impl.TreeTests +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.tree.{EnsembleTestHelper, GradientBoostedTrees => OldGBT} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.DataFrame + + +/** + * Test suite for [[GBTClassifier]]. + */ +class GBTClassifierSuite extends FunSuite with MLlibTestSparkContext { + + import GBTClassifierSuite.compareAPIs + + // Combinations for estimators, learning rates and subsamplingRate + private val testCombinations = + Array((10, 1.0, 1.0), (10, 0.1, 1.0), (10, 0.5, 0.75), (10, 0.1, 0.75)) + + private var data: RDD[LabeledPoint] = _ + private var trainData: RDD[LabeledPoint] = _ + private var validationData: RDD[LabeledPoint] = _ + + override def beforeAll() { + super.beforeAll() + data = sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 10, 100), 2) + trainData = + sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 20, 120), 2) + validationData = + sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 20, 80), 2) + } + + test("Binary classification with continuous features: Log Loss") { + val categoricalFeatures = Map.empty[Int, Int] + testCombinations.foreach { + case (maxIter, learningRate, subsamplingRate) => + val gbt = new GBTClassifier() + .setMaxDepth(2) + .setSubsamplingRate(subsamplingRate) + .setLossType("logistic") + .setMaxIter(maxIter) + .setStepSize(learningRate) + compareAPIs(data, None, gbt, categoricalFeatures) + } + } + + // TODO: Reinstate test once runWithValidation is implemented SPARK-7132 + /* + test("runWithValidation stops early and performs better on a validation dataset") { + val categoricalFeatures = Map.empty[Int, Int] + // Set maxIter large enough so that it stops early. + val maxIter = 20 + GBTClassifier.supportedLossTypes.foreach { loss => + val gbt = new GBTClassifier() + .setMaxIter(maxIter) + .setMaxDepth(2) + .setLossType(loss) + .setValidationTol(0.0) + compareAPIs(trainData, None, gbt, categoricalFeatures) + compareAPIs(trainData, Some(validationData), gbt, categoricalFeatures) + } + } + */ + + ///////////////////////////////////////////////////////////////////////////// + // Tests of model save/load + ///////////////////////////////////////////////////////////////////////////// + + // TODO: Reinstate test once save/load are implemented SPARK-6725 + /* + test("model save/load") { + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + + val trees = Range(0, 3).map(_ => OldDecisionTreeSuite.createModel(OldAlgo.Regression)).toArray + val treeWeights = Array(0.1, 0.3, 1.1) + val oldModel = new OldGBTModel(OldAlgo.Classification, trees, treeWeights) + val newModel = GBTClassificationModel.fromOld(oldModel) + + // Save model, load it back, and compare. + try { + newModel.save(sc, path) + val sameNewModel = GBTClassificationModel.load(sc, path) + TreeTests.checkEqual(newModel, sameNewModel) + } finally { + Utils.deleteRecursively(tempDir) + } + } + */ +} + +private object GBTClassifierSuite { + + /** + * Train 2 models on the given dataset, one using the old API and one using the new API. + * Convert the old model to the new format, compare them, and fail if they are not exactly equal. + */ + def compareAPIs( + data: RDD[LabeledPoint], + validationData: Option[RDD[LabeledPoint]], + gbt: GBTClassifier, + categoricalFeatures: Map[Int, Int]): Unit = { + val oldBoostingStrategy = + gbt.getOldBoostingStrategy(categoricalFeatures, OldAlgo.Classification) + val oldGBT = new OldGBT(oldBoostingStrategy) + val oldModel = oldGBT.run(data) + val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 2) + val newModel = gbt.fit(newData) + // Use parent, fittingParamMap from newTree since these are not checked anyways. + val oldModelAsNew = GBTClassificationModel.fromOld(oldModel, newModel.parent, + newModel.fittingParamMap, categoricalFeatures) + TreeTests.checkEqual(oldModelAsNew, newModel) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala new file mode 100644 index 0000000000000..ed41a9664f94f --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala @@ -0,0 +1,166 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.classification + +import org.scalatest.FunSuite + +import org.apache.spark.ml.impl.TreeTests +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.tree.{EnsembleTestHelper, RandomForest => OldRandomForest} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.DataFrame + + +/** + * Test suite for [[RandomForestClassifier]]. + */ +class RandomForestClassifierSuite extends FunSuite with MLlibTestSparkContext { + + import RandomForestClassifierSuite.compareAPIs + + private var orderedLabeledPoints50_1000: RDD[LabeledPoint] = _ + private var orderedLabeledPoints5_20: RDD[LabeledPoint] = _ + + override def beforeAll() { + super.beforeAll() + orderedLabeledPoints50_1000 = + sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 50, 1000)) + orderedLabeledPoints5_20 = + sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 5, 20)) + } + + ///////////////////////////////////////////////////////////////////////////// + // Tests calling train() + ///////////////////////////////////////////////////////////////////////////// + + def binaryClassificationTestWithContinuousFeatures(rf: RandomForestClassifier) { + val categoricalFeatures = Map.empty[Int, Int] + val numClasses = 2 + val newRF = rf + .setImpurity("Gini") + .setMaxDepth(2) + .setNumTrees(1) + .setFeatureSubsetStrategy("auto") + .setSeed(123) + compareAPIs(orderedLabeledPoints50_1000, newRF, categoricalFeatures, numClasses) + } + + test("Binary classification with continuous features:" + + " comparing DecisionTree vs. RandomForest(numTrees = 1)") { + val rf = new RandomForestClassifier() + binaryClassificationTestWithContinuousFeatures(rf) + } + + test("Binary classification with continuous features and node Id cache:" + + " comparing DecisionTree vs. RandomForest(numTrees = 1)") { + val rf = new RandomForestClassifier() + .setCacheNodeIds(true) + binaryClassificationTestWithContinuousFeatures(rf) + } + + test("alternating categorical and continuous features with multiclass labels to test indexing") { + val arr = Array( + LabeledPoint(0.0, Vectors.dense(1.0, 0.0, 0.0, 3.0, 1.0)), + LabeledPoint(1.0, Vectors.dense(0.0, 1.0, 1.0, 1.0, 2.0)), + LabeledPoint(0.0, Vectors.dense(2.0, 0.0, 0.0, 6.0, 3.0)), + LabeledPoint(2.0, Vectors.dense(0.0, 2.0, 1.0, 3.0, 2.0)) + ) + val rdd = sc.parallelize(arr) + val categoricalFeatures = Map(0 -> 3, 2 -> 2, 4 -> 4) + val numClasses = 3 + + val rf = new RandomForestClassifier() + .setImpurity("Gini") + .setMaxDepth(5) + .setNumTrees(2) + .setFeatureSubsetStrategy("sqrt") + .setSeed(12345) + compareAPIs(rdd, rf, categoricalFeatures, numClasses) + } + + test("subsampling rate in RandomForest"){ + val rdd = orderedLabeledPoints5_20 + val categoricalFeatures = Map.empty[Int, Int] + val numClasses = 2 + + val rf1 = new RandomForestClassifier() + .setImpurity("Gini") + .setMaxDepth(2) + .setCacheNodeIds(true) + .setNumTrees(3) + .setFeatureSubsetStrategy("auto") + .setSeed(123) + compareAPIs(rdd, rf1, categoricalFeatures, numClasses) + + val rf2 = rf1.setSubsamplingRate(0.5) + compareAPIs(rdd, rf2, categoricalFeatures, numClasses) + } + + ///////////////////////////////////////////////////////////////////////////// + // Tests of model save/load + ///////////////////////////////////////////////////////////////////////////// + + // TODO: Reinstate test once save/load are implemented SPARK-6725 + /* + test("model save/load") { + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + + val trees = + Range(0, 3).map(_ => OldDecisionTreeSuite.createModel(OldAlgo.Classification)).toArray + val oldModel = new OldRandomForestModel(OldAlgo.Classification, trees) + val newModel = RandomForestClassificationModel.fromOld(oldModel) + + // Save model, load it back, and compare. + try { + newModel.save(sc, path) + val sameNewModel = RandomForestClassificationModel.load(sc, path) + TreeTests.checkEqual(newModel, sameNewModel) + } finally { + Utils.deleteRecursively(tempDir) + } + } + */ +} + +private object RandomForestClassifierSuite { + + /** + * Train 2 models on the given dataset, one using the old API and one using the new API. + * Convert the old model to the new format, compare them, and fail if they are not exactly equal. + */ + def compareAPIs( + data: RDD[LabeledPoint], + rf: RandomForestClassifier, + categoricalFeatures: Map[Int, Int], + numClasses: Int): Unit = { + val oldStrategy = + rf.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, rf.getOldImpurity) + val oldModel = OldRandomForest.trainClassifier( + data, oldStrategy, rf.getNumTrees, rf.getFeatureSubsetStrategy, rf.getSeed.toInt) + val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses) + val newModel = rf.fit(newData) + // Use parent, fittingParamMap from newTree since these are not checked anyways. + val oldModelAsNew = RandomForestClassificationModel.fromOld(oldModel, newModel.parent, + newModel.fittingParamMap, categoricalFeatures) + TreeTests.checkEqual(oldModelAsNew, newModel) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala b/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala index 2e57d4ce37f1d..1505ad872536b 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala @@ -23,8 +23,7 @@ import org.scalatest.FunSuite import org.apache.spark.api.java.JavaRDD import org.apache.spark.ml.attribute.{AttributeGroup, NominalAttribute, NumericAttribute} -import org.apache.spark.ml.impl.tree._ -import org.apache.spark.ml.tree.{DecisionTreeModel, InternalNode, LeafNode, Node} +import org.apache.spark.ml.tree._ import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.rdd.RDD import org.apache.spark.sql.{SQLContext, DataFrame} @@ -111,22 +110,19 @@ private[ml] object TreeTests extends FunSuite { } } - // TODO: Reinstate after adding ensembles /** * Check if the two models are exactly the same. * If the models are not equal, this throws an exception. */ - /* def checkEqual(a: TreeEnsembleModel, b: TreeEnsembleModel): Unit = { try { - a.getTrees.zip(b.getTrees).foreach { case (treeA, treeB) => + a.trees.zip(b.trees).foreach { case (treeA, treeB) => TreeTests.checkEqual(treeA, treeB) } - assert(a.getTreeWeights === b.getTreeWeights) + assert(a.treeWeights === b.treeWeights) } catch { case ex: Exception => throw new AssertionError( "checkEqual failed since the two tree ensembles were not identical") } } - */ } diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala index 0b40fe33fae9d..c87a171b4b229 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala @@ -66,7 +66,7 @@ class DecisionTreeRegressorSuite extends FunSuite with MLlibTestSparkContext { // Tests of model save/load ///////////////////////////////////////////////////////////////////////////// - // TODO: test("model save/load") + // TODO: test("model save/load") SPARK-6725 } private[ml] object DecisionTreeRegressorSuite extends FunSuite { diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala new file mode 100644 index 0000000000000..4aec36948ac92 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala @@ -0,0 +1,137 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.regression + +import org.scalatest.FunSuite + +import org.apache.spark.ml.impl.TreeTests +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.tree.{EnsembleTestHelper, GradientBoostedTrees => OldGBT} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.DataFrame + + +/** + * Test suite for [[GBTRegressor]]. + */ +class GBTRegressorSuite extends FunSuite with MLlibTestSparkContext { + + import GBTRegressorSuite.compareAPIs + + // Combinations for estimators, learning rates and subsamplingRate + private val testCombinations = + Array((10, 1.0, 1.0), (10, 0.1, 1.0), (10, 0.5, 0.75), (10, 0.1, 0.75)) + + private var data: RDD[LabeledPoint] = _ + private var trainData: RDD[LabeledPoint] = _ + private var validationData: RDD[LabeledPoint] = _ + + override def beforeAll() { + super.beforeAll() + data = sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 10, 100), 2) + trainData = + sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 20, 120), 2) + validationData = + sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 20, 80), 2) + } + + test("Regression with continuous features: SquaredError") { + val categoricalFeatures = Map.empty[Int, Int] + GBTRegressor.supportedLossTypes.foreach { loss => + testCombinations.foreach { + case (maxIter, learningRate, subsamplingRate) => + val gbt = new GBTRegressor() + .setMaxDepth(2) + .setSubsamplingRate(subsamplingRate) + .setLossType(loss) + .setMaxIter(maxIter) + .setStepSize(learningRate) + compareAPIs(data, None, gbt, categoricalFeatures) + } + } + } + + // TODO: Reinstate test once runWithValidation is implemented SPARK-7132 + /* + test("runWithValidation stops early and performs better on a validation dataset") { + val categoricalFeatures = Map.empty[Int, Int] + // Set maxIter large enough so that it stops early. + val maxIter = 20 + GBTRegressor.supportedLossTypes.foreach { loss => + val gbt = new GBTRegressor() + .setMaxIter(maxIter) + .setMaxDepth(2) + .setLossType(loss) + .setValidationTol(0.0) + compareAPIs(trainData, None, gbt, categoricalFeatures) + compareAPIs(trainData, Some(validationData), gbt, categoricalFeatures) + } + } + */ + + ///////////////////////////////////////////////////////////////////////////// + // Tests of model save/load + ///////////////////////////////////////////////////////////////////////////// + + // TODO: Reinstate test once save/load are implemented SPARK-6725 + /* + test("model save/load") { + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + + val trees = Range(0, 3).map(_ => OldDecisionTreeSuite.createModel(OldAlgo.Regression)).toArray + val treeWeights = Array(0.1, 0.3, 1.1) + val oldModel = new OldGBTModel(OldAlgo.Regression, trees, treeWeights) + val newModel = GBTRegressionModel.fromOld(oldModel) + + // Save model, load it back, and compare. + try { + newModel.save(sc, path) + val sameNewModel = GBTRegressionModel.load(sc, path) + TreeTests.checkEqual(newModel, sameNewModel) + } finally { + Utils.deleteRecursively(tempDir) + } + } + */ +} + +private object GBTRegressorSuite { + + /** + * Train 2 models on the given dataset, one using the old API and one using the new API. + * Convert the old model to the new format, compare them, and fail if they are not exactly equal. + */ + def compareAPIs( + data: RDD[LabeledPoint], + validationData: Option[RDD[LabeledPoint]], + gbt: GBTRegressor, + categoricalFeatures: Map[Int, Int]): Unit = { + val oldBoostingStrategy = gbt.getOldBoostingStrategy(categoricalFeatures, OldAlgo.Regression) + val oldGBT = new OldGBT(oldBoostingStrategy) + val oldModel = oldGBT.run(data) + val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0) + val newModel = gbt.fit(newData) + // Use parent, fittingParamMap from newTree since these are not checked anyways. + val oldModelAsNew = GBTRegressionModel.fromOld(oldModel, newModel.parent, + newModel.fittingParamMap, categoricalFeatures) + TreeTests.checkEqual(oldModelAsNew, newModel) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala new file mode 100644 index 0000000000000..c6dc1cc29b6ff --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala @@ -0,0 +1,122 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.regression + +import org.scalatest.FunSuite + +import org.apache.spark.ml.impl.TreeTests +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.tree.{EnsembleTestHelper, RandomForest => OldRandomForest} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.DataFrame + + +/** + * Test suite for [[RandomForestRegressor]]. + */ +class RandomForestRegressorSuite extends FunSuite with MLlibTestSparkContext { + + import RandomForestRegressorSuite.compareAPIs + + private var orderedLabeledPoints50_1000: RDD[LabeledPoint] = _ + + override def beforeAll() { + super.beforeAll() + orderedLabeledPoints50_1000 = + sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 50, 1000)) + } + + ///////////////////////////////////////////////////////////////////////////// + // Tests calling train() + ///////////////////////////////////////////////////////////////////////////// + + def regressionTestWithContinuousFeatures(rf: RandomForestRegressor) { + val categoricalFeaturesInfo = Map.empty[Int, Int] + val newRF = rf + .setImpurity("variance") + .setMaxDepth(2) + .setMaxBins(10) + .setNumTrees(1) + .setFeatureSubsetStrategy("auto") + .setSeed(123) + compareAPIs(orderedLabeledPoints50_1000, newRF, categoricalFeaturesInfo) + } + + test("Regression with continuous features:" + + " comparing DecisionTree vs. RandomForest(numTrees = 1)") { + val rf = new RandomForestRegressor() + regressionTestWithContinuousFeatures(rf) + } + + test("Regression with continuous features and node Id cache :" + + " comparing DecisionTree vs. RandomForest(numTrees = 1)") { + val rf = new RandomForestRegressor() + .setCacheNodeIds(true) + regressionTestWithContinuousFeatures(rf) + } + + ///////////////////////////////////////////////////////////////////////////// + // Tests of model save/load + ///////////////////////////////////////////////////////////////////////////// + + // TODO: Reinstate test once save/load are implemented SPARK-6725 + /* + test("model save/load") { + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + + val trees = Range(0, 3).map(_ => OldDecisionTreeSuite.createModel(OldAlgo.Regression)).toArray + val oldModel = new OldRandomForestModel(OldAlgo.Regression, trees) + val newModel = RandomForestRegressionModel.fromOld(oldModel) + + // Save model, load it back, and compare. + try { + newModel.save(sc, path) + val sameNewModel = RandomForestRegressionModel.load(sc, path) + TreeTests.checkEqual(newModel, sameNewModel) + } finally { + Utils.deleteRecursively(tempDir) + } + } + */ +} + +private object RandomForestRegressorSuite extends FunSuite { + + /** + * Train 2 models on the given dataset, one using the old API and one using the new API. + * Convert the old model to the new format, compare them, and fail if they are not exactly equal. + */ + def compareAPIs( + data: RDD[LabeledPoint], + rf: RandomForestRegressor, + categoricalFeatures: Map[Int, Int]): Unit = { + val oldStrategy = + rf.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression, rf.getOldImpurity) + val oldModel = OldRandomForest.trainRegressor( + data, oldStrategy, rf.getNumTrees, rf.getFeatureSubsetStrategy, rf.getSeed.toInt) + val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0) + val newModel = rf.fit(newData) + // Use parent, fittingParamMap from newTree since these are not checked anyways. + val oldModelAsNew = RandomForestRegressionModel.fromOld(oldModel, newModel.parent, + newModel.fittingParamMap, categoricalFeatures) + TreeTests.checkEqual(oldModelAsNew, newModel) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala index 249b8eae19b17..ce983eb27fa35 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala @@ -998,7 +998,7 @@ object DecisionTreeSuite extends FunSuite { node.split = Some(new Split(feature = 1, threshold = 0.0, Categorical, categories = List(0.0, 1.0))) } - // TODO: The information gain stats should be consistent with the same info stored in children. + // TODO: The information gain stats should be consistent with info in children: SPARK-7131 node.stats = Some(new InformationGainStats(gain = 0.1, impurity = 0.2, leftImpurity = 0.3, rightImpurity = 0.4, new Predict(1.0, 0.4), new Predict(0.0, 0.6))) node @@ -1006,9 +1006,9 @@ object DecisionTreeSuite extends FunSuite { /** * Create a tree model. This is deterministic and contains a variety of node and feature types. - * TODO: Update this to be a correct tree (with matching probabilities, impurities, etc.) + * TODO: Update to be a correct tree (with matching probabilities, impurities, etc.): SPARK-7131 */ - private[mllib] def createModel(algo: Algo): DecisionTreeModel = { + private[spark] def createModel(algo: Algo): DecisionTreeModel = { val topNode = createInternalNode(id = 1, Continuous) val (node2, node3) = (createLeafNode(id = 2), createInternalNode(id = 3, Categorical)) val (node6, node7) = (createLeafNode(id = 6), createLeafNode(id = 7)) From aa6966ff34dacc83c3ca675b5109b05e35015469 Mon Sep 17 00:00:00 2001 From: Yin Huai Date: Sat, 25 Apr 2015 13:43:39 -0700 Subject: [PATCH 420/591] [SQL] Update SQL readme to include instructions on generating golden answer files based on Hive 0.13.1. Author: Yin Huai Closes #5702 from yhuai/howToGenerateGoldenFiles and squashes the following commits: 9c4a7f8 [Yin Huai] Update readme to include instructions on generating golden answer files based on Hive 0.13.1. --- sql/README.md | 23 ++++++++++++++++++++++- 1 file changed, 22 insertions(+), 1 deletion(-) diff --git a/sql/README.md b/sql/README.md index 237620e3fa808..46aec7cef7984 100644 --- a/sql/README.md +++ b/sql/README.md @@ -12,7 +12,10 @@ Spark SQL is broken up into four subprojects: Other dependencies for developers --------------------------------- -In order to create new hive test cases , you will need to set several environmental variables. +In order to create new hive test cases (i.e. a test suite based on `HiveComparisonTest`), +you will need to setup your development environment based on the following instructions. + +If you are working with Hive 0.12.0, you will need to set several environmental variables as follows. ``` export HIVE_HOME="/hive/build/dist" @@ -20,6 +23,24 @@ export HIVE_DEV_HOME="/hive/" export HADOOP_HOME="/hadoop-1.0.4" ``` +If you are working with Hive 0.13.1, the following steps are needed: + +1. Download Hive's [0.13.1](https://hive.apache.org/downloads.html) and set `HIVE_HOME` with `export HIVE_HOME=""`. Please do not set `HIVE_DEV_HOME` (See [SPARK-4119](https://issues.apache.org/jira/browse/SPARK-4119)). +2. Set `HADOOP_HOME` with `export HADOOP_HOME=""` +3. Download all Hive 0.13.1a jars (Hive jars actually used by Spark) from [here](http://mvnrepository.com/artifact/org.spark-project.hive) and replace corresponding original 0.13.1 jars in `$HIVE_HOME/lib`. +4. Download [Kryo 2.21 jar](http://mvnrepository.com/artifact/com.esotericsoftware.kryo/kryo/2.21) (Note: 2.22 jar does not work) and [Javolution 5.5.1 jar](http://mvnrepository.com/artifact/javolution/javolution/5.5.1) to `$HIVE_HOME/lib`. +5. This step is optional. But, when generating golden answer files, if a Hive query fails and you find that Hive tries to talk to HDFS or you find weird runtime NPEs, set the following in your test suite... + +``` +val testTempDir = Utils.createTempDir() +// We have to use kryo to let Hive correctly serialize some plans. +sql("set hive.plan.serialization.format=kryo") +// Explicitly set fs to local fs. +sql(s"set fs.default.name=file://$testTempDir/") +// Ask Hive to run jobs in-process as a single map and reduce task. +sql("set mapred.job.tracker=local") +``` + Using the console ================= An interactive scala console can be invoked by running `build/sbt hive/console`. From a11c8683c76c67f45749a1b50a0912a731fd2487 Mon Sep 17 00:00:00 2001 From: Prashant Sharma Date: Sat, 25 Apr 2015 18:07:34 -0400 Subject: [PATCH 421/591] [SPARK-7092] Update spark scala version to 2.11.6 Author: Prashant Sharma Closes #5662 from ScrapCodes/SPARK-7092/scala-update-2.11.6 and squashes the following commits: 58cf4f9 [Prashant Sharma] [SPARK-7092] Update spark scala version to 2.11.6 --- pom.xml | 4 ++-- .../src/main/scala/org/apache/spark/repl/SparkIMain.scala | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pom.xml b/pom.xml index 4b0b0c85eff21..9fbce1d639d8b 100644 --- a/pom.xml +++ b/pom.xml @@ -1745,9 +1745,9 @@ scala-2.11 - 2.11.2 + 2.11.6 2.11 - 2.12 + 2.12.1 jline diff --git a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkIMain.scala b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkIMain.scala index 1bb62c84abddc..1cb910f376060 100644 --- a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkIMain.scala +++ b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkIMain.scala @@ -1129,7 +1129,7 @@ class SparkIMain(@BeanProperty val factory: ScriptEngineFactory, initialSettings def apply(line: String): Result = debugging(s"""parse("$line")""") { var isIncomplete = false - currentRun.reporting.withIncompleteHandler((_, _) => isIncomplete = true) { + currentRun.parsing.withIncompleteHandler((_, _) => isIncomplete = true) { reporter.reset() val trees = newUnitParser(line).parseStats() if (reporter.hasErrors) Error From f5473c2bbf66cc1144a90b4c29f3ce54ad7cc419 Mon Sep 17 00:00:00 2001 From: Nishkam Ravi Date: Sat, 25 Apr 2015 20:02:23 -0400 Subject: [PATCH 422/591] [SPARK-6014] [CORE] [HOTFIX] Add try-catch block around ShutDownHook Add a try/catch block around removeShutDownHook else IllegalStateException thrown in YARN cluster mode (see https://github.com/apache/spark/pull/4690) cc andrewor14, srowen Author: Nishkam Ravi Author: nishkamravi2 Author: nravi Closes #5672 from nishkamravi2/master_nravi and squashes the following commits: 0f1abd0 [nishkamravi2] Update Utils.scala 474e3bf [nishkamravi2] Update DiskBlockManager.scala 97c383e [nishkamravi2] Update Utils.scala 8691e0c [Nishkam Ravi] Add a try/catch block around Utils.removeShutdownHook 2be1e76 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 1c13b79 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi bad4349 [nishkamravi2] Update Main.java 36a6f87 [Nishkam Ravi] Minor changes and bug fixes b7f4ae7 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 4a45d6a [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 458af39 [Nishkam Ravi] Locate the jar using getLocation, obviates the need to pass assembly path as an argument d9658d6 [Nishkam Ravi] Changes for SPARK-6406 ccdc334 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 3faa7a4 [Nishkam Ravi] Launcher library changes (SPARK-6406) 345206a [Nishkam Ravi] spark-class merge Merge branch 'master_nravi' of https://github.com/nishkamravi2/spark into master_nravi ac58975 [Nishkam Ravi] spark-class changes 06bfeb0 [nishkamravi2] Update spark-class 35af990 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 32c3ab3 [nishkamravi2] Update AbstractCommandBuilder.java 4bd4489 [nishkamravi2] Update AbstractCommandBuilder.java 746f35b [Nishkam Ravi] "hadoop" string in the assembly name should not be mandatory (everywhere else in spark we mandate spark-assembly*hadoop*.jar) bfe96e0 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi ee902fa [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi d453197 [nishkamravi2] Update NewHadoopRDD.scala 6f41a1d [nishkamravi2] Update NewHadoopRDD.scala 0ce2c32 [nishkamravi2] Update HadoopRDD.scala f7e33c2 [Nishkam Ravi] Merge branch 'master_nravi' of https://github.com/nishkamravi2/spark into master_nravi ba1eb8b [Nishkam Ravi] Try-catch block around the two occurrences of removeShutDownHook. Deletion of semi-redundant occurrences of expensive operation inShutDown. 71d0e17 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 494d8c0 [nishkamravi2] Update DiskBlockManager.scala 3c5ddba [nishkamravi2] Update DiskBlockManager.scala f0d12de [Nishkam Ravi] Workaround for IllegalStateException caused by recent changes to BlockManager.stop 79ea8b4 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi b446edc [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 5c9a4cb [nishkamravi2] Update TaskSetManagerSuite.scala 535295a [nishkamravi2] Update TaskSetManager.scala 3e1b616 [Nishkam Ravi] Modify test for maxResultSize 9f6583e [Nishkam Ravi] Changes to maxResultSize code (improve error message and add condition to check if maxResultSize > 0) 5f8f9ed [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 636a9ff [nishkamravi2] Update YarnAllocator.scala 8f76c8b [Nishkam Ravi] Doc change for yarn memory overhead 35daa64 [Nishkam Ravi] Slight change in the doc for yarn memory overhead 5ac2ec1 [Nishkam Ravi] Remove out dac1047 [Nishkam Ravi] Additional documentation for yarn memory overhead issue 42c2c3d [Nishkam Ravi] Additional changes for yarn memory overhead issue 362da5e [Nishkam Ravi] Additional changes for yarn memory overhead c726bd9 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi f00fa31 [Nishkam Ravi] Improving logging for AM memoryOverhead 1cf2d1e [nishkamravi2] Update YarnAllocator.scala ebcde10 [Nishkam Ravi] Modify default YARN memory_overhead-- from an additive constant to a multiplier (redone to resolve merge conflicts) 2e69f11 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi efd688a [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark 2b630f9 [nravi] Accept memory input as "30g", "512M" instead of an int value, to be consistent with rest of Spark 3bf8fad [nravi] Merge branch 'master' of https://github.com/apache/spark 5423a03 [nravi] Merge branch 'master' of https://github.com/apache/spark eb663ca [nravi] Merge branch 'master' of https://github.com/apache/spark df2aeb1 [nravi] Improved fix for ConcurrentModificationIssue (Spark-1097, Hadoop-10456) 6b840f0 [nravi] Undo the fix for SPARK-1758 (the problem is fixed) 5108700 [nravi] Fix in Spark for the Concurrent thread modification issue (SPARK-1097, HADOOP-10456) 681b36f [nravi] Fix for SPARK-1758: failing test org.apache.spark.JavaAPISuite.wholeTextFiles --- .../scala/org/apache/spark/storage/DiskBlockManager.scala | 7 ++++++- core/src/main/scala/org/apache/spark/util/Utils.scala | 3 +-- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala index 7ea5e54f9e1fe..5764c16902c66 100644 --- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala @@ -148,7 +148,12 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon /** Cleanup local dirs and stop shuffle sender. */ private[spark] def stop() { // Remove the shutdown hook. It causes memory leaks if we leave it around. - Utils.removeShutdownHook(shutdownHook) + try { + Utils.removeShutdownHook(shutdownHook) + } catch { + case e: Exception => + logError(s"Exception while removing shutdown hook.", e) + } doStop() } diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index 667aa168e7ef3..c6c6df7cfa56e 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -2172,7 +2172,7 @@ private [util] class SparkShutdownHookManager { def runAll(): Unit = synchronized { shuttingDown = true while (!hooks.isEmpty()) { - Utils.logUncaughtExceptions(hooks.poll().run()) + Try(Utils.logUncaughtExceptions(hooks.poll().run())) } } @@ -2184,7 +2184,6 @@ private [util] class SparkShutdownHookManager { } def remove(ref: AnyRef): Boolean = synchronized { - checkState() hooks.remove(ref) } From 9a5bbe05fc1b1141e139d32661821fef47d7a13c Mon Sep 17 00:00:00 2001 From: Alain Date: Sun, 26 Apr 2015 07:14:24 -0400 Subject: [PATCH 423/591] [MINOR] [MLLIB] Refactor toString method in MLLIB MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 1. predict(predict.toString) has already output prefix “predict” thus it’s duplicated to print ", predict = " again 2. there are some extra spaces Author: Alain Closes #5687 from AiHe/tree-node-issue-2 and squashes the following commits: 9862b9a [Alain] Pass scala coding style checking 44ba947 [Alain] Minor][MLLIB] Format toString method in MLLIB bdc402f [Alain] [Minor][MLLIB] Fix a formatting bug in toString method in Node 426eee7 [Alain] [Minor][MLLIB] Fix a formatting bug in toString method in Node.scala --- .../main/scala/org/apache/spark/mllib/linalg/Vectors.scala | 2 +- .../org/apache/spark/mllib/regression/LabeledPoint.scala | 2 +- .../apache/spark/mllib/tree/model/InformationGainStats.scala | 4 ++-- .../main/scala/org/apache/spark/mllib/tree/model/Node.scala | 4 ++-- .../scala/org/apache/spark/mllib/tree/model/Predict.scala | 4 +--- .../main/scala/org/apache/spark/mllib/tree/model/Split.scala | 5 ++--- 6 files changed, 9 insertions(+), 12 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala index 4ef171f4f0419..166c00cff634d 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala @@ -526,7 +526,7 @@ class SparseVector( s" ${values.size} values.") override def toString: String = - "(%s,%s,%s)".format(size, indices.mkString("[", ",", "]"), values.mkString("[", ",", "]")) + s"($size,${indices.mkString("[", ",", "]")},${values.mkString("[", ",", "]")})" override def toArray: Array[Double] = { val data = new Array[Double](size) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala index 2067b36f246b3..d5fea822ad77b 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala @@ -32,7 +32,7 @@ import org.apache.spark.SparkException @BeanInfo case class LabeledPoint(label: Double, features: Vector) { override def toString: String = { - "(%s,%s)".format(label, features) + s"($label,$features)" } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala index f209fdafd3653..2d087c967f679 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala @@ -39,8 +39,8 @@ class InformationGainStats( val rightPredict: Predict) extends Serializable { override def toString: String = { - "gain = %f, impurity = %f, left impurity = %f, right impurity = %f" - .format(gain, impurity, leftImpurity, rightImpurity) + s"gain = $gain, impurity = $impurity, left impurity = $leftImpurity, " + + s"right impurity = $rightImpurity" } override def equals(o: Any): Boolean = o match { diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala index 86390a20cb5cc..431a839817eac 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala @@ -51,8 +51,8 @@ class Node ( var stats: Option[InformationGainStats]) extends Serializable with Logging { override def toString: String = { - "id = " + id + ", isLeaf = " + isLeaf + ", predict = " + predict + ", " + - "impurity = " + impurity + ", split = " + split + ", stats = " + stats + s"id = $id, isLeaf = $isLeaf, predict = $predict, impurity = $impurity, " + + s"split = $split, stats = $stats" } /** diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala index 25990af7c6cf7..5cbe7c280dbee 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala @@ -29,9 +29,7 @@ class Predict( val predict: Double, val prob: Double = 0.0) extends Serializable { - override def toString: String = { - "predict = %f, prob = %f".format(predict, prob) - } + override def toString: String = s"$predict (prob = $prob)" override def equals(other: Any): Boolean = { other match { diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala index fb35e70a8d077..be6c9b3de5479 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala @@ -39,8 +39,8 @@ case class Split( categories: List[Double]) { override def toString: String = { - "Feature = " + feature + ", threshold = " + threshold + ", featureType = " + featureType + - ", categories = " + categories + s"Feature = $feature, threshold = $threshold, featureType = $featureType, " + + s"categories = $categories" } } @@ -68,4 +68,3 @@ private[tree] class DummyHighSplit(feature: Int, featureType: FeatureType) */ private[tree] class DummyCategoricalSplit(feature: Int, featureType: FeatureType) extends Split(feature, Double.MaxValue, featureType, List()) - From ca55dc95b777d96b27d4e4c0457dd25145dcd6e9 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Sun, 26 Apr 2015 11:46:58 -0700 Subject: [PATCH 424/591] [SPARK-7152][SQL] Add a Column expression for partition ID. Author: Reynold Xin Closes #5705 from rxin/df-pid and squashes the following commits: 401018f [Reynold Xin] [SPARK-7152][SQL] Add a Column expression for partition ID. --- python/pyspark/sql/functions.py | 30 +++++++++----- .../expressions/SparkPartitionID.scala | 39 +++++++++++++++++++ .../sql/execution/expressions/package.scala | 23 +++++++++++ .../org/apache/spark/sql/functions.scala | 29 +++++++++----- .../spark/sql/ColumnExpressionSuite.scala | 8 ++++ 5 files changed, 110 insertions(+), 19 deletions(-) create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/package.scala diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py index bb47923f24b82..f48b7b5d10af7 100644 --- a/python/pyspark/sql/functions.py +++ b/python/pyspark/sql/functions.py @@ -75,6 +75,20 @@ def _(col): __all__.sort() +def approxCountDistinct(col, rsd=None): + """Returns a new :class:`Column` for approximate distinct count of ``col``. + + >>> df.agg(approxCountDistinct(df.age).alias('c')).collect() + [Row(c=2)] + """ + sc = SparkContext._active_spark_context + if rsd is None: + jc = sc._jvm.functions.approxCountDistinct(_to_java_column(col)) + else: + jc = sc._jvm.functions.approxCountDistinct(_to_java_column(col), rsd) + return Column(jc) + + def countDistinct(col, *cols): """Returns a new :class:`Column` for distinct count of ``col`` or ``cols``. @@ -89,18 +103,16 @@ def countDistinct(col, *cols): return Column(jc) -def approxCountDistinct(col, rsd=None): - """Returns a new :class:`Column` for approximate distinct count of ``col``. +def sparkPartitionId(): + """Returns a column for partition ID of the Spark task. - >>> df.agg(approxCountDistinct(df.age).alias('c')).collect() - [Row(c=2)] + Note that this is indeterministic because it depends on data partitioning and task scheduling. + + >>> df.repartition(1).select(sparkPartitionId().alias("pid")).collect() + [Row(pid=0), Row(pid=0)] """ sc = SparkContext._active_spark_context - if rsd is None: - jc = sc._jvm.functions.approxCountDistinct(_to_java_column(col)) - else: - jc = sc._jvm.functions.approxCountDistinct(_to_java_column(col), rsd) - return Column(jc) + return Column(sc._jvm.functions.sparkPartitionId()) class UserDefinedFunction(object): diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala new file mode 100644 index 0000000000000..fe7607c6ac340 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala @@ -0,0 +1,39 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.expressions + +import org.apache.spark.TaskContext +import org.apache.spark.sql.catalyst.expressions.{Row, Expression} +import org.apache.spark.sql.catalyst.trees +import org.apache.spark.sql.types.{IntegerType, DataType} + + +/** + * Expression that returns the current partition id of the Spark task. + */ +case object SparkPartitionID extends Expression with trees.LeafNode[Expression] { + self: Product => + + override type EvaluatedType = Int + + override def nullable: Boolean = false + + override def dataType: DataType = IntegerType + + override def eval(input: Row): Int = TaskContext.get().partitionId() +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/package.scala new file mode 100644 index 0000000000000..568b7ac2c5987 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/package.scala @@ -0,0 +1,23 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution + +/** + * Package containing expressions that are specific to Spark runtime. + */ +package object expressions diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala index ff91e1d74bc2c..9738fd4f93bad 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala @@ -276,6 +276,13 @@ object functions { // Non-aggregate functions ////////////////////////////////////////////////////////////////////////////////////////////// + /** + * Computes the absolute value. + * + * @group normal_funcs + */ + def abs(e: Column): Column = Abs(e.expr) + /** * Returns the first column that is not null. * {{{ @@ -287,6 +294,13 @@ object functions { @scala.annotation.varargs def coalesce(e: Column*): Column = Coalesce(e.map(_.expr)) + /** + * Converts a string exprsesion to lower case. + * + * @group normal_funcs + */ + def lower(e: Column): Column = Lower(e.expr) + /** * Unary minus, i.e. negate the expression. * {{{ @@ -317,18 +331,13 @@ object functions { def not(e: Column): Column = !e /** - * Converts a string expression to upper case. + * Partition ID of the Spark task. * - * @group normal_funcs - */ - def upper(e: Column): Column = Upper(e.expr) - - /** - * Converts a string exprsesion to lower case. + * Note that this is indeterministic because it depends on data partitioning and task scheduling. * * @group normal_funcs */ - def lower(e: Column): Column = Lower(e.expr) + def sparkPartitionId(): Column = execution.expressions.SparkPartitionID /** * Computes the square root of the specified float value. @@ -338,11 +347,11 @@ object functions { def sqrt(e: Column): Column = Sqrt(e.expr) /** - * Computes the absolutle value. + * Converts a string expression to upper case. * * @group normal_funcs */ - def abs(e: Column): Column = Abs(e.expr) + def upper(e: Column): Column = Upper(e.expr) ////////////////////////////////////////////////////////////////////////////////////////////// ////////////////////////////////////////////////////////////////////////////////////////////// diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala index bc8fae100db6a..904073b8cb2aa 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala @@ -310,6 +310,14 @@ class ColumnExpressionSuite extends QueryTest { ) } + test("sparkPartitionId") { + val df = TestSQLContext.sparkContext.parallelize(1 to 1, 1).map(i => (i, i)).toDF("a", "b") + checkAnswer( + df.select(sparkPartitionId()), + Row(0) + ) + } + test("lift alias out of cast") { compareExpressions( col("1234").as("name").cast("int").expr, From d188b8bad82836bf654e57f9dd4e1ddde1d530f4 Mon Sep 17 00:00:00 2001 From: wangfei Date: Sun, 26 Apr 2015 21:08:47 -0700 Subject: [PATCH 425/591] [SQL][Minor] rename DataTypeParser.apply to DataTypeParser.parse rename DataTypeParser.apply to DataTypeParser.parse to make it more clear and readable. /cc rxin Author: wangfei Closes #5710 from scwf/apply and squashes the following commits: c319977 [wangfei] rename apply to parse --- .../org/apache/spark/sql/catalyst/planning/patterns.scala | 2 +- .../scala/org/apache/spark/sql/types/DataTypeParser.scala | 2 +- .../org/apache/spark/sql/types/DataTypeParserSuite.scala | 4 ++-- sql/core/src/main/scala/org/apache/spark/sql/Column.scala | 2 +- .../org/apache/spark/sql/hive/HiveMetastoreCatalog.scala | 2 +- 5 files changed, 6 insertions(+), 6 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala index 9c8c643f7d17a..4574934d910db 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala @@ -92,7 +92,7 @@ object PhysicalOperation extends PredicateHelper { } def collectAliases(fields: Seq[Expression]): Map[Attribute, Expression] = fields.collect { - case a @ Alias(child, _) => a.toAttribute.asInstanceOf[Attribute] -> child + case a @ Alias(child, _) => a.toAttribute -> child }.toMap def substitute(aliases: Map[Attribute, Expression])(expr: Expression): Expression = { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala index 5163f05879e42..04f3379afb38d 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataTypeParser.scala @@ -108,7 +108,7 @@ private[sql] object DataTypeParser { override val lexical = new SqlLexical } - def apply(dataTypeString: String): DataType = dataTypeParser.toDataType(dataTypeString) + def parse(dataTypeString: String): DataType = dataTypeParser.toDataType(dataTypeString) } /** The exception thrown from the [[DataTypeParser]]. */ diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala index 169125264a803..3e7cf7cbb5e63 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeParserSuite.scala @@ -23,13 +23,13 @@ class DataTypeParserSuite extends FunSuite { def checkDataType(dataTypeString: String, expectedDataType: DataType): Unit = { test(s"parse ${dataTypeString.replace("\n", "")}") { - assert(DataTypeParser(dataTypeString) === expectedDataType) + assert(DataTypeParser.parse(dataTypeString) === expectedDataType) } } def unsupported(dataTypeString: String): Unit = { test(s"$dataTypeString is not supported") { - intercept[DataTypeException](DataTypeParser(dataTypeString)) + intercept[DataTypeException](DataTypeParser.parse(dataTypeString)) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala index edb229c059e6b..33f9d0b37d006 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala @@ -647,7 +647,7 @@ class Column(protected[sql] val expr: Expression) extends Logging { * * @group expr_ops */ - def cast(to: String): Column = cast(DataTypeParser(to)) + def cast(to: String): Column = cast(DataTypeParser.parse(to)) /** * Returns an ordering used in sorting. diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala index f1c0bd92aa23d..4d222cf88e5e8 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala @@ -871,7 +871,7 @@ private[hive] case class MetastoreRelation private[hive] object HiveMetastoreTypes { - def toDataType(metastoreType: String): DataType = DataTypeParser(metastoreType) + def toDataType(metastoreType: String): DataType = DataTypeParser.parse(metastoreType) def toMetastoreType(dt: DataType): String = dt match { case ArrayType(elementType, _) => s"array<${toMetastoreType(elementType)}>" From 82bb7fd41a2c7992e0aea69623c504bd439744f7 Mon Sep 17 00:00:00 2001 From: baishuo Date: Mon, 27 Apr 2015 14:08:05 +0800 Subject: [PATCH 426/591] [SPARK-6505] [SQL] Remove the reflection call in HiveFunctionWrapper MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit according liancheng‘s comment in https://issues.apache.org/jira/browse/SPARK-6505, this patch remove the reflection call in HiveFunctionWrapper, and implement the functions named "deserializeObjectByKryo" and "serializeObjectByKryo" according the functions with the save name in org.apache.hadoop.hive.ql.exec.Utilities.java Author: baishuo Closes #5660 from baishuo/SPARK-6505-20150423 and squashes the following commits: ae61ec4 [baishuo] modify code style 78d9fa3 [baishuo] modify code style 0b522a7 [baishuo] modify code style a5ff9c7 [baishuo] Remove the reflection call in HiveFunctionWrapper --- .../org/apache/spark/sql/hive/Shim13.scala | 44 +++++++++---------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/sql/hive/v0.13.1/src/main/scala/org/apache/spark/sql/hive/Shim13.scala b/sql/hive/v0.13.1/src/main/scala/org/apache/spark/sql/hive/Shim13.scala index d331c210e8939..dbc5e029e2047 100644 --- a/sql/hive/v0.13.1/src/main/scala/org/apache/spark/sql/hive/Shim13.scala +++ b/sql/hive/v0.13.1/src/main/scala/org/apache/spark/sql/hive/Shim13.scala @@ -19,11 +19,15 @@ package org.apache.spark.sql.hive import java.rmi.server.UID import java.util.{Properties, ArrayList => JArrayList} +import java.io.{OutputStream, InputStream} import scala.collection.JavaConversions._ import scala.language.implicitConversions +import scala.reflect.ClassTag import com.esotericsoftware.kryo.Kryo +import com.esotericsoftware.kryo.io.Input +import com.esotericsoftware.kryo.io.Output import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.apache.hadoop.hive.common.StatsSetupConst @@ -46,6 +50,7 @@ import org.apache.hadoop.{io => hadoopIo} import org.apache.spark.Logging import org.apache.spark.sql.types.{Decimal, DecimalType, UTF8String} +import org.apache.spark.util.Utils._ /** * This class provides the UDF creation and also the UDF instance serialization and @@ -61,39 +66,34 @@ private[hive] case class HiveFunctionWrapper(var functionClassName: String) // for Serialization def this() = this(null) - import org.apache.spark.util.Utils._ - @transient - private val methodDeSerialize = { - val method = classOf[Utilities].getDeclaredMethod( - "deserializeObjectByKryo", - classOf[Kryo], - classOf[java.io.InputStream], - classOf[Class[_]]) - method.setAccessible(true) - - method + def deserializeObjectByKryo[T: ClassTag]( + kryo: Kryo, + in: InputStream, + clazz: Class[_]): T = { + val inp = new Input(in) + val t: T = kryo.readObject(inp,clazz).asInstanceOf[T] + inp.close() + t } @transient - private val methodSerialize = { - val method = classOf[Utilities].getDeclaredMethod( - "serializeObjectByKryo", - classOf[Kryo], - classOf[Object], - classOf[java.io.OutputStream]) - method.setAccessible(true) - - method + def serializeObjectByKryo( + kryo: Kryo, + plan: Object, + out: OutputStream ) { + val output: Output = new Output(out) + kryo.writeObject(output, plan) + output.close() } def deserializePlan[UDFType](is: java.io.InputStream, clazz: Class[_]): UDFType = { - methodDeSerialize.invoke(null, Utilities.runtimeSerializationKryo.get(), is, clazz) + deserializeObjectByKryo(Utilities.runtimeSerializationKryo.get(), is, clazz) .asInstanceOf[UDFType] } def serializePlan(function: AnyRef, out: java.io.OutputStream): Unit = { - methodSerialize.invoke(null, Utilities.runtimeSerializationKryo.get(), function, out) + serializeObjectByKryo(Utilities.runtimeSerializationKryo.get(), function, out) } private var instance: AnyRef = null From 998aac21f0a0588a70f8cf123ae4080163c612fb Mon Sep 17 00:00:00 2001 From: Misha Chernetsov Date: Mon, 27 Apr 2015 11:27:56 -0700 Subject: [PATCH 427/591] [SPARK-4925] Publish Spark SQL hive-thriftserver maven artifact turned on hive-thriftserver profile in release script Author: Misha Chernetsov Closes #5429 from chernetsov/master and squashes the following commits: 9cc36af [Misha Chernetsov] [SPARK-4925] Publish Spark SQL hive-thriftserver maven artifact turned on hive-thriftserver profile in release script for scala 2.10 --- dev/create-release/create-release.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh index b5a67dd783b93..3dbb35f7054a2 100755 --- a/dev/create-release/create-release.sh +++ b/dev/create-release/create-release.sh @@ -119,7 +119,7 @@ if [[ ! "$@" =~ --skip-publish ]]; then rm -rf $SPARK_REPO build/mvn -DskipTests -Dhadoop.version=2.2.0 -Dyarn.version=2.2.0 \ - -Pyarn -Phive -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \ + -Pyarn -Phive -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \ clean install ./dev/change-version-to-2.11.sh From 7078f6028bf012235c664b02ec3541cbb0a248a7 Mon Sep 17 00:00:00 2001 From: Jeff Harrison Date: Mon, 27 Apr 2015 13:38:25 -0700 Subject: [PATCH 428/591] [SPARK-6856] [R] Make RDD information more useful in SparkR Author: Jeff Harrison Closes #5667 from His-name-is-Joof/joofspark and squashes the following commits: f8814a6 [Jeff Harrison] newline added after RDD show() output 4d9d972 [Jeff Harrison] Merge branch 'master' into joofspark 9d2295e [Jeff Harrison] parallelize with 1:10 878b830 [Jeff Harrison] Merge branch 'master' into joofspark c8c0b80 [Jeff Harrison] add test for RDD function show() 123be65 [Jeff Harrison] SPARK-6856 --- R/pkg/R/RDD.R | 5 +++++ R/pkg/inst/tests/test_rdd.R | 5 +++++ 2 files changed, 10 insertions(+) diff --git a/R/pkg/R/RDD.R b/R/pkg/R/RDD.R index 1662d6bb3b1ac..f90c26b253455 100644 --- a/R/pkg/R/RDD.R +++ b/R/pkg/R/RDD.R @@ -66,6 +66,11 @@ setMethod("initialize", "RDD", function(.Object, jrdd, serializedMode, .Object }) +setMethod("show", "RDD", + function(.Object) { + cat(paste(callJMethod(.Object@jrdd, "toString"), "\n", sep="")) + }) + setMethod("initialize", "PipelinedRDD", function(.Object, prev, func, jrdd_val) { .Object@env <- new.env() .Object@env$isCached <- FALSE diff --git a/R/pkg/inst/tests/test_rdd.R b/R/pkg/inst/tests/test_rdd.R index d55af93e3e50a..03207353c31c6 100644 --- a/R/pkg/inst/tests/test_rdd.R +++ b/R/pkg/inst/tests/test_rdd.R @@ -759,6 +759,11 @@ test_that("collectAsMap() on a pairwise RDD", { expect_equal(vals, list(`1` = "a", `2` = "b")) }) +test_that("show()", { + rdd <- parallelize(sc, list(1:10)) + expect_output(show(rdd), "ParallelCollectionRDD\\[\\d+\\] at parallelize at RRDD\\.scala:\\d+") +}) + test_that("sampleByKey() on pairwise RDDs", { rdd <- parallelize(sc, 1:2000) pairsRDD <- lapply(rdd, function(x) { if (x %% 2 == 0) list("a", x) else list("b", x) }) From ef82bddc11d1aea42e22d2f85613a869cbe9a990 Mon Sep 17 00:00:00 2001 From: tedyu Date: Mon, 27 Apr 2015 14:42:40 -0700 Subject: [PATCH 429/591] SPARK-7107 Add parameter for zookeeper.znode.parent to hbase_inputformat... ....py Author: tedyu Closes #5673 from tedyu/master and squashes the following commits: ab7c72b [tedyu] SPARK-7107 Adjust indentation to pass Python style tests 6e25939 [tedyu] Adjust line length to be shorter than 100 characters 18d172a [tedyu] SPARK-7107 Add parameter for zookeeper.znode.parent to hbase_inputformat.py --- examples/src/main/python/hbase_inputformat.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/examples/src/main/python/hbase_inputformat.py b/examples/src/main/python/hbase_inputformat.py index e17819d5feb76..5b82a14fba413 100644 --- a/examples/src/main/python/hbase_inputformat.py +++ b/examples/src/main/python/hbase_inputformat.py @@ -54,8 +54,9 @@ Run with example jar: ./bin/spark-submit --driver-class-path /path/to/example/jar \ - /path/to/examples/hbase_inputformat.py + /path/to/examples/hbase_inputformat.py
    [] Assumes you have some data in HBase already, running on , in
    + optionally, you can specify parent znode for your hbase cluster - """, file=sys.stderr) exit(-1) @@ -64,6 +65,9 @@ sc = SparkContext(appName="HBaseInputFormat") conf = {"hbase.zookeeper.quorum": host, "hbase.mapreduce.inputtable": table} + if len(sys.argv) > 3: + conf = {"hbase.zookeeper.quorum": host, "zookeeper.znode.parent": sys.argv[3], + "hbase.mapreduce.inputtable": table} keyConv = "org.apache.spark.examples.pythonconverters.ImmutableBytesWritableToStringConverter" valueConv = "org.apache.spark.examples.pythonconverters.HBaseResultToStringConverter" From ca9f4ebb8e510e521bf4df0331375ddb385fb9d2 Mon Sep 17 00:00:00 2001 From: hlin09 Date: Mon, 27 Apr 2015 15:04:37 -0700 Subject: [PATCH 430/591] [SPARK-6991] [SPARKR] Adds support for zipPartitions. Author: hlin09 Closes #5568 from hlin09/zipPartitions and squashes the following commits: 12c08a5 [hlin09] Fix comments d2d32db [hlin09] Merge branch 'master' into zipPartitions ec56d2f [hlin09] Fix test. 27655d3 [hlin09] Adds support for zipPartitions. --- R/pkg/NAMESPACE | 1 + R/pkg/R/RDD.R | 46 +++++++++++++++++++++++++ R/pkg/R/generics.R | 5 +++ R/pkg/inst/tests/test_binary_function.R | 33 ++++++++++++++++++ 4 files changed, 85 insertions(+) diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE index 80283643861ac..e077eace74375 100644 --- a/R/pkg/NAMESPACE +++ b/R/pkg/NAMESPACE @@ -71,6 +71,7 @@ exportMethods( "unpersist", "value", "values", + "zipPartitions", "zipRDD", "zipWithIndex", "zipWithUniqueId" diff --git a/R/pkg/R/RDD.R b/R/pkg/R/RDD.R index f90c26b253455..a3a0421a0746d 100644 --- a/R/pkg/R/RDD.R +++ b/R/pkg/R/RDD.R @@ -1595,3 +1595,49 @@ setMethod("intersection", keys(filterRDD(cogroup(rdd1, rdd2, numPartitions = numPartitions), filterFunction)) }) + +#' Zips an RDD's partitions with one (or more) RDD(s). +#' Same as zipPartitions in Spark. +#' +#' @param ... RDDs to be zipped. +#' @param func A function to transform zipped partitions. +#' @return A new RDD by applying a function to the zipped partitions. +#' Assumes that all the RDDs have the *same number of partitions*, but +#' does *not* require them to have the same number of elements in each partition. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, 1:2, 2L) # 1, 2 +#' rdd2 <- parallelize(sc, 1:4, 2L) # 1:2, 3:4 +#' rdd3 <- parallelize(sc, 1:6, 2L) # 1:3, 4:6 +#' collect(zipPartitions(rdd1, rdd2, rdd3, +#' func = function(x, y, z) { list(list(x, y, z))} )) +#' # list(list(1, c(1,2), c(1,2,3)), list(2, c(3,4), c(4,5,6))) +#'} +#' @rdname zipRDD +#' @aliases zipPartitions,RDD +setMethod("zipPartitions", + "RDD", + function(..., func) { + rrdds <- list(...) + if (length(rrdds) == 1) { + return(rrdds[[1]]) + } + nPart <- sapply(rrdds, numPartitions) + if (length(unique(nPart)) != 1) { + stop("Can only zipPartitions RDDs which have the same number of partitions.") + } + + rrdds <- lapply(rrdds, function(rdd) { + mapPartitionsWithIndex(rdd, function(partIndex, part) { + print(length(part)) + list(list(partIndex, part)) + }) + }) + union.rdd <- Reduce(unionRDD, rrdds) + zipped.rdd <- values(groupByKey(union.rdd, numPartitions = nPart[1])) + res <- mapPartitions(zipped.rdd, function(plist) { + do.call(func, plist[[1]]) + }) + res + }) diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R index 34dbe84051c50..e88729387ef95 100644 --- a/R/pkg/R/generics.R +++ b/R/pkg/R/generics.R @@ -217,6 +217,11 @@ setGeneric("unpersist", function(x, ...) { standardGeneric("unpersist") }) #' @export setGeneric("zipRDD", function(x, other) { standardGeneric("zipRDD") }) +#' @rdname zipRDD +#' @export +setGeneric("zipPartitions", function(..., func) { standardGeneric("zipPartitions") }, + signature = "...") + #' @rdname zipWithIndex #' @seealso zipWithUniqueId #' @export diff --git a/R/pkg/inst/tests/test_binary_function.R b/R/pkg/inst/tests/test_binary_function.R index c15553ba28517..6785a7bdae8cb 100644 --- a/R/pkg/inst/tests/test_binary_function.R +++ b/R/pkg/inst/tests/test_binary_function.R @@ -66,3 +66,36 @@ test_that("cogroup on two RDDs", { expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) }) + +test_that("zipPartitions() on RDDs", { + rdd1 <- parallelize(sc, 1:2, 2L) # 1, 2 + rdd2 <- parallelize(sc, 1:4, 2L) # 1:2, 3:4 + rdd3 <- parallelize(sc, 1:6, 2L) # 1:3, 4:6 + actual <- collect(zipPartitions(rdd1, rdd2, rdd3, + func = function(x, y, z) { list(list(x, y, z))} )) + expect_equal(actual, + list(list(1, c(1,2), c(1,2,3)), list(2, c(3,4), c(4,5,6)))) + + mockFile = c("Spark is pretty.", "Spark is awesome.") + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName) + + rdd <- textFile(sc, fileName, 1) + actual <- collect(zipPartitions(rdd, rdd, + func = function(x, y) { list(paste(x, y, sep = "\n")) })) + expected <- list(paste(mockFile, mockFile, sep = "\n")) + expect_equal(actual, expected) + + rdd1 <- parallelize(sc, 0:1, 1) + actual <- collect(zipPartitions(rdd1, rdd, + func = function(x, y) { list(x + nchar(y)) })) + expected <- list(0:1 + nchar(mockFile)) + expect_equal(actual, expected) + + rdd <- map(rdd, function(x) { x }) + actual <- collect(zipPartitions(rdd, rdd1, + func = function(x, y) { list(y + nchar(x)) })) + expect_equal(actual, expected) + + unlink(fileName) +}) From b9de9e040aff371c6acf9b3f3d1ff8b360c0cd56 Mon Sep 17 00:00:00 2001 From: Steven She Date: Mon, 27 Apr 2015 18:55:02 -0400 Subject: [PATCH 431/591] [SPARK-7103] Fix crash with SparkContext.union when RDD has no partitioner Added a check to the SparkContext.union method to check that a partitioner is defined on all RDDs when instantiating a PartitionerAwareUnionRDD. Author: Steven She Closes #5679 from stevencanopy/SPARK-7103 and squashes the following commits: 5a3d846 [Steven She] SPARK-7103: Fix crash with SparkContext.union when at least one RDD has no partitioner --- .../scala/org/apache/spark/SparkContext.scala | 2 +- .../spark/rdd/PartitionerAwareUnionRDD.scala | 1 + .../scala/org/apache/spark/rdd/RDDSuite.scala | 21 +++++++++++++++++++ 3 files changed, 23 insertions(+), 1 deletion(-) diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 86269eac52db0..ea4ddcc2e265d 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -1055,7 +1055,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli /** Build the union of a list of RDDs. */ def union[T: ClassTag](rdds: Seq[RDD[T]]): RDD[T] = { val partitioners = rdds.flatMap(_.partitioner).toSet - if (partitioners.size == 1) { + if (rdds.forall(_.partitioner.isDefined) && partitioners.size == 1) { new PartitionerAwareUnionRDD(this, rdds) } else { new UnionRDD(this, rdds) diff --git a/core/src/main/scala/org/apache/spark/rdd/PartitionerAwareUnionRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PartitionerAwareUnionRDD.scala index 92b0641d0fb6e..7598ff617b399 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PartitionerAwareUnionRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PartitionerAwareUnionRDD.scala @@ -60,6 +60,7 @@ class PartitionerAwareUnionRDD[T: ClassTag]( var rdds: Seq[RDD[T]] ) extends RDD[T](sc, rdds.map(x => new OneToOneDependency(x))) { require(rdds.length > 0) + require(rdds.forall(_.partitioner.isDefined)) require(rdds.flatMap(_.partitioner).toSet.size == 1, "Parent RDDs have different partitioners: " + rdds.flatMap(_.partitioner)) diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala index df42faab64505..ef8c36a28655b 100644 --- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala @@ -99,6 +99,27 @@ class RDDSuite extends FunSuite with SharedSparkContext { assert(sc.union(Seq(nums, nums)).collect().toList === List(1, 2, 3, 4, 1, 2, 3, 4)) } + test("SparkContext.union creates UnionRDD if at least one RDD has no partitioner") { + val rddWithPartitioner = sc.parallelize(Seq(1->true)).partitionBy(new HashPartitioner(1)) + val rddWithNoPartitioner = sc.parallelize(Seq(2->true)) + val unionRdd = sc.union(rddWithNoPartitioner, rddWithPartitioner) + assert(unionRdd.isInstanceOf[UnionRDD[_]]) + } + + test("SparkContext.union creates PartitionAwareUnionRDD if all RDDs have partitioners") { + val rddWithPartitioner = sc.parallelize(Seq(1->true)).partitionBy(new HashPartitioner(1)) + val unionRdd = sc.union(rddWithPartitioner, rddWithPartitioner) + assert(unionRdd.isInstanceOf[PartitionerAwareUnionRDD[_]]) + } + + test("PartitionAwareUnionRDD raises exception if at least one RDD has no partitioner") { + val rddWithPartitioner = sc.parallelize(Seq(1->true)).partitionBy(new HashPartitioner(1)) + val rddWithNoPartitioner = sc.parallelize(Seq(2->true)) + intercept[IllegalArgumentException] { + new PartitionerAwareUnionRDD(sc, Seq(rddWithNoPartitioner, rddWithPartitioner)) + } + } + test("partitioner aware union") { def makeRDDWithPartitioner(seq: Seq[Int]): RDD[Int] = { sc.makeRDD(seq, 1) From 8e1c00dbf4b60962908626dead744e5d73c8085e Mon Sep 17 00:00:00 2001 From: Hong Shen Date: Mon, 27 Apr 2015 18:57:31 -0400 Subject: [PATCH 432/591] [SPARK-6738] [CORE] Improve estimate the size of a large array Currently, SizeEstimator.visitArray is not correct in the follow case, ``` array size > 200, elem has the share object ``` when I add a debug log in SizeTracker.scala: ``` System.err.println(s"numUpdates:$numUpdates, size:$ts, bytesPerUpdate:$bytesPerUpdate, cost time:$b") ``` I get the following log: ``` numUpdates:1, size:262448, bytesPerUpdate:0.0, cost time:35 numUpdates:2, size:420698, bytesPerUpdate:158250.0, cost time:35 numUpdates:4, size:420754, bytesPerUpdate:28.0, cost time:32 numUpdates:7, size:420754, bytesPerUpdate:0.0, cost time:27 numUpdates:12, size:420754, bytesPerUpdate:0.0, cost time:28 numUpdates:20, size:420754, bytesPerUpdate:0.0, cost time:25 numUpdates:32, size:420754, bytesPerUpdate:0.0, cost time:21 numUpdates:52, size:420754, bytesPerUpdate:0.0, cost time:20 numUpdates:84, size:420754, bytesPerUpdate:0.0, cost time:20 numUpdates:135, size:420754, bytesPerUpdate:0.0, cost time:20 numUpdates:216, size:420754, bytesPerUpdate:0.0, cost time:11 numUpdates:346, size:420754, bytesPerUpdate:0.0, cost time:6 numUpdates:554, size:488911, bytesPerUpdate:327.67788461538464, cost time:8 numUpdates:887, size:2312259426, bytesPerUpdate:6942253.798798799, cost time:198 15/04/21 14:27:26 INFO collection.ExternalAppendOnlyMap: Thread 51 spilling in-memory map of 3.0 GB to disk (1 time so far) 15/04/21 14:27:26 INFO collection.ExternalAppendOnlyMap: /data11/yarnenv/local/usercache/spark/appcache/application_1426746631567_11745/spark-local-20150421142719-c001/30/temp_local_066af981-c2fc-4b70-a00e-110e23006fbc ``` But in fact the file size is only 162K: ``` $ ll -h /data11/yarnenv/local/usercache/spark/appcache/application_1426746631567_11745/spark-local-20150421142719-c001/30/temp_local_066af981-c2fc-4b70-a00e-110e23006fbc -rw-r----- 1 spark users 162K Apr 21 14:27 /data11/yarnenv/local/usercache/spark/appcache/application_1426746631567_11745/spark-local-20150421142719-c001/30/temp_local_066af981-c2fc-4b70-a00e-110e23006fbc ``` In order to test case, I change visitArray to: ``` var size = 0l for (i <- 0 until length) { val obj = JArray.get(array, i) size += SizeEstimator.estimate(obj, state.visited).toLong } state.size += size ``` I get the following log: ``` ... 14895 277016088 566.9046118590662 time:8470 23832 281840544 552.3308270676691 time:8031 38132 289891824 539.8294729775092 time:7897 61012 302803640 563.0265734265735 time:13044 97620 322904416 564.3276223776223 time:13554 15/04/14 11:46:43 INFO collection.ExternalAppendOnlyMap: Thread 51 spilling in-memory map of 314.5 MB to disk (1 time so far) 15/04/14 11:46:43 INFO collection.ExternalAppendOnlyMap: /data1/yarnenv/local/usercache/spark/appcache/application_1426746631567_8477/spark-local-20150414114020-2fcb/14/temp_local_5b6b98d5-5bfa-47e2-8216-059482ccbda0 ``` the file size is 85M. ``` $ ll -h /data1/yarnenv/local/usercache/spark/appcache/application_1426746631567_8477/spark- local-20150414114020-2fcb/14/ total 85M -rw-r----- 1 spark users 85M Apr 14 11:46 temp_local_5b6b98d5-5bfa-47e2-8216-059482ccbda0 ``` The following log is when I use this patch, ``` .... numUpdates:32, size:365484, bytesPerUpdate:0.0, cost time:7 numUpdates:52, size:365484, bytesPerUpdate:0.0, cost time:5 numUpdates:84, size:365484, bytesPerUpdate:0.0, cost time:5 numUpdates:135, size:372208, bytesPerUpdate:131.84313725490196, cost time:86 numUpdates:216, size:379020, bytesPerUpdate:84.09876543209876, cost time:21 numUpdates:346, size:1865208, bytesPerUpdate:11432.215384615385, cost time:23 numUpdates:554, size:2052380, bytesPerUpdate:899.8653846153846, cost time:16 numUpdates:887, size:2142820, bytesPerUpdate:271.59159159159157, cost time:15 .. numUpdates:14895, size:251675500, bytesPerUpdate:438.5263157894737, cost time:13 numUpdates:23832, size:257010268, bytesPerUpdate:596.9305135951662, cost time:14 numUpdates:38132, size:263922396, bytesPerUpdate:483.3655944055944, cost time:15 numUpdates:61012, size:268962596, bytesPerUpdate:220.28846153846155, cost time:24 numUpdates:97620, size:286980644, bytesPerUpdate:492.1888111888112, cost time:22 15/04/21 14:45:12 INFO collection.ExternalAppendOnlyMap: Thread 53 spilling in-memory map of 328.7 MB to disk (1 time so far) 15/04/21 14:45:12 INFO collection.ExternalAppendOnlyMap: /data4/yarnenv/local/usercache/spark/appcache/application_1426746631567_11758/spark-local-20150421144456-a2a5/2a/temp_local_9c109510-af16-4468-8f23-48cad04da88f ``` the file size is 88M. ``` $ ll -h /data4/yarnenv/local/usercache/spark/appcache/application_1426746631567_11758/spark-local-20150421144456-a2a5/2a/ total 88M -rw-r----- 1 spark users 88M Apr 21 14:45 temp_local_9c109510-af16-4468-8f23-48cad04da88f ``` Author: Hong Shen Closes #5608 from shenh062326/my_change5 and squashes the following commits: 5506bae [Hong Shen] Fix compile error c275dd3 [Hong Shen] Alter code style fe202a2 [Hong Shen] Change the code style and add documentation. a9fca84 [Hong Shen] Add test case for SizeEstimator 4877eee [Hong Shen] Improve estimate the size of a large array a2ea7ac [Hong Shen] Alter code style 4c28e36 [Hong Shen] Improve estimate the size of a large array --- .../org/apache/spark/util/SizeEstimator.scala | 45 ++++++++++++------- .../spark/util/SizeEstimatorSuite.scala | 18 ++++++++ 2 files changed, 48 insertions(+), 15 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala b/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala index 26ffbf9350388..4dd7ab9e0767b 100644 --- a/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala +++ b/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala @@ -179,7 +179,7 @@ private[spark] object SizeEstimator extends Logging { } // Estimate the size of arrays larger than ARRAY_SIZE_FOR_SAMPLING by sampling. - private val ARRAY_SIZE_FOR_SAMPLING = 200 + private val ARRAY_SIZE_FOR_SAMPLING = 400 private val ARRAY_SAMPLE_SIZE = 100 // should be lower than ARRAY_SIZE_FOR_SAMPLING private def visitArray(array: AnyRef, arrayClass: Class[_], state: SearchState) { @@ -204,25 +204,40 @@ private[spark] object SizeEstimator extends Logging { } } else { // Estimate the size of a large array by sampling elements without replacement. - var size = 0.0 + // To exclude the shared objects that the array elements may link, sample twice + // and use the min one to caculate array size. val rand = new Random(42) - val drawn = new OpenHashSet[Int](ARRAY_SAMPLE_SIZE) - var numElementsDrawn = 0 - while (numElementsDrawn < ARRAY_SAMPLE_SIZE) { - var index = 0 - do { - index = rand.nextInt(length) - } while (drawn.contains(index)) - drawn.add(index) - val elem = ScalaRunTime.array_apply(array, index).asInstanceOf[AnyRef] - size += SizeEstimator.estimate(elem, state.visited) - numElementsDrawn += 1 - } - state.size += ((length / (ARRAY_SAMPLE_SIZE * 1.0)) * size).toLong + val drawn = new OpenHashSet[Int](2 * ARRAY_SAMPLE_SIZE) + val s1 = sampleArray(array, state, rand, drawn, length) + val s2 = sampleArray(array, state, rand, drawn, length) + val size = math.min(s1, s2) + state.size += math.max(s1, s2) + + (size * ((length - ARRAY_SAMPLE_SIZE) / (ARRAY_SAMPLE_SIZE))).toLong } } } + private def sampleArray( + array: AnyRef, + state: SearchState, + rand: Random, + drawn: OpenHashSet[Int], + length: Int): Long = { + var size = 0L + for (i <- 0 until ARRAY_SAMPLE_SIZE) { + var index = 0 + do { + index = rand.nextInt(length) + } while (drawn.contains(index)) + drawn.add(index) + val obj = ScalaRunTime.array_apply(array, index).asInstanceOf[AnyRef] + if (obj != null) { + size += SizeEstimator.estimate(obj, state.visited).toLong + } + } + size + } + private def primitiveSize(cls: Class[_]): Long = { if (cls == classOf[Byte]) { BYTE_SIZE diff --git a/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala b/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala index 67a9f75ff2187..28915bd53354e 100644 --- a/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala @@ -17,6 +17,8 @@ package org.apache.spark.util +import scala.collection.mutable.ArrayBuffer + import org.scalatest.{BeforeAndAfterEach, BeforeAndAfterAll, FunSuite, PrivateMethodTester} class DummyClass1 {} @@ -96,6 +98,22 @@ class SizeEstimatorSuite // Past size 100, our samples 100 elements, but we should still get the right size. assertResult(28016)(SizeEstimator.estimate(Array.fill(1000)(new DummyClass3))) + + val arr = new Array[Char](100000) + assertResult(200016)(SizeEstimator.estimate(arr)) + assertResult(480032)(SizeEstimator.estimate(Array.fill(10000)(new DummyString(arr)))) + + val buf = new ArrayBuffer[DummyString]() + for (i <- 0 until 5000) { + buf.append(new DummyString(new Array[Char](10))) + } + assertResult(340016)(SizeEstimator.estimate(buf.toArray)) + + for (i <- 0 until 5000) { + buf.append(new DummyString(arr)) + } + assertResult(683912)(SizeEstimator.estimate(buf.toArray)) + // If an array contains the *same* element many times, we should only count it once. val d1 = new DummyClass1 // 10 pointers plus 8-byte object From 5d45e1f60059e2f2fc8ad64778b9ddcc8887c570 Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Mon, 27 Apr 2015 19:46:17 -0400 Subject: [PATCH 433/591] [SPARK-3090] [CORE] Stop SparkContext if user forgets to. Set up a shutdown hook to try to stop the Spark context in case the user forgets to do it. The main effect is that any open logs files are flushed and closed, which is particularly interesting for event logs. Author: Marcelo Vanzin Closes #5696 from vanzin/SPARK-3090 and squashes the following commits: 3b554b5 [Marcelo Vanzin] [SPARK-3090] [core] Stop SparkContext if user forgets to. --- .../scala/org/apache/spark/SparkContext.scala | 38 ++++++++++++------- .../scala/org/apache/spark/util/Utils.scala | 10 ++++- .../spark/deploy/yarn/ApplicationMaster.scala | 10 +---- 3 files changed, 35 insertions(+), 23 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index ea4ddcc2e265d..65b903a55d5bd 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -223,6 +223,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli private var _listenerBusStarted: Boolean = false private var _jars: Seq[String] = _ private var _files: Seq[String] = _ + private var _shutdownHookRef: AnyRef = _ /* ------------------------------------------------------------------------------------- * | Accessors and public fields. These provide access to the internal state of the | @@ -517,6 +518,14 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli _taskScheduler.postStartHook() _env.metricsSystem.registerSource(new DAGSchedulerSource(dagScheduler)) _env.metricsSystem.registerSource(new BlockManagerSource(_env.blockManager)) + + // Make sure the context is stopped if the user forgets about it. This avoids leaving + // unfinished event logs around after the JVM exits cleanly. It doesn't help if the JVM + // is killed, though. + _shutdownHookRef = Utils.addShutdownHook(Utils.SPARK_CONTEXT_SHUTDOWN_PRIORITY) { () => + logInfo("Invoking stop() from shutdown hook") + stop() + } } catch { case NonFatal(e) => logError("Error initializing SparkContext.", e) @@ -1481,6 +1490,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli logInfo("SparkContext already stopped.") return } + if (_shutdownHookRef != null) { + Utils.removeShutdownHook(_shutdownHookRef) + } postApplicationEnd() _ui.foreach(_.stop()) @@ -1891,7 +1903,7 @@ object SparkContext extends Logging { * * Access to this field is guarded by SPARK_CONTEXT_CONSTRUCTOR_LOCK. */ - private val activeContext: AtomicReference[SparkContext] = + private val activeContext: AtomicReference[SparkContext] = new AtomicReference[SparkContext](null) /** @@ -1944,11 +1956,11 @@ object SparkContext extends Logging { } /** - * This function may be used to get or instantiate a SparkContext and register it as a - * singleton object. Because we can only have one active SparkContext per JVM, - * this is useful when applications may wish to share a SparkContext. + * This function may be used to get or instantiate a SparkContext and register it as a + * singleton object. Because we can only have one active SparkContext per JVM, + * this is useful when applications may wish to share a SparkContext. * - * Note: This function cannot be used to create multiple SparkContext instances + * Note: This function cannot be used to create multiple SparkContext instances * even if multiple contexts are allowed. */ def getOrCreate(config: SparkConf): SparkContext = { @@ -1961,17 +1973,17 @@ object SparkContext extends Logging { activeContext.get() } } - + /** - * This function may be used to get or instantiate a SparkContext and register it as a - * singleton object. Because we can only have one active SparkContext per JVM, + * This function may be used to get or instantiate a SparkContext and register it as a + * singleton object. Because we can only have one active SparkContext per JVM, * this is useful when applications may wish to share a SparkContext. - * + * * This method allows not passing a SparkConf (useful if just retrieving). - * - * Note: This function cannot be used to create multiple SparkContext instances - * even if multiple contexts are allowed. - */ + * + * Note: This function cannot be used to create multiple SparkContext instances + * even if multiple contexts are allowed. + */ def getOrCreate(): SparkContext = { getOrCreate(new SparkConf()) } diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index c6c6df7cfa56e..342bc9a06db47 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -67,6 +67,12 @@ private[spark] object Utils extends Logging { val DEFAULT_SHUTDOWN_PRIORITY = 100 + /** + * The shutdown priority of the SparkContext instance. This is lower than the default + * priority, so that by default hooks are run before the context is shut down. + */ + val SPARK_CONTEXT_SHUTDOWN_PRIORITY = 50 + private val MAX_DIR_CREATION_ATTEMPTS: Int = 10 @volatile private var localRootDirs: Array[String] = null @@ -2116,7 +2122,7 @@ private[spark] object Utils extends Logging { * @return A handle that can be used to unregister the shutdown hook. */ def addShutdownHook(hook: () => Unit): AnyRef = { - addShutdownHook(DEFAULT_SHUTDOWN_PRIORITY, hook) + addShutdownHook(DEFAULT_SHUTDOWN_PRIORITY)(hook) } /** @@ -2126,7 +2132,7 @@ private[spark] object Utils extends Logging { * @param hook The code to run during shutdown. * @return A handle that can be used to unregister the shutdown hook. */ - def addShutdownHook(priority: Int, hook: () => Unit): AnyRef = { + def addShutdownHook(priority: Int)(hook: () => Unit): AnyRef = { shutdownHooks.add(priority, hook) } diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala index 93ae45133ce24..70cb57ffd8c69 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -95,14 +95,8 @@ private[spark] class ApplicationMaster( val fs = FileSystem.get(yarnConf) - Utils.addShutdownHook { () => - // If the SparkContext is still registered, shut it down as a best case effort in case - // users do not call sc.stop or do System.exit(). - val sc = sparkContextRef.get() - if (sc != null) { - logInfo("Invoking sc stop from shutdown hook") - sc.stop() - } + // This shutdown hook should run *after* the SparkContext is shut down. + Utils.addShutdownHook(Utils.SPARK_CONTEXT_SHUTDOWN_PRIORITY - 1) { () => val maxAppAttempts = client.getMaxRegAttempts(sparkConf, yarnConf) val isLastAttempt = client.getAttemptId().getAttemptId() >= maxAppAttempts From ab5adb7a973eec9d95c7575c864cba9f8d83a0fd Mon Sep 17 00:00:00 2001 From: Sean Owen Date: Mon, 27 Apr 2015 19:50:55 -0400 Subject: [PATCH 434/591] [SPARK-7145] [CORE] commons-lang (2.x) classes used instead of commons-lang3 (3.x); commons-io used without dependency Remove use of commons-lang in favor of commons-lang3 classes; remove commons-io use in favor of Guava Author: Sean Owen Closes #5703 from srowen/SPARK-7145 and squashes the following commits: 21fbe03 [Sean Owen] Remove use of commons-lang in favor of commons-lang3 classes; remove commons-io use in favor of Guava --- .../test/scala/org/apache/spark/FileServerSuite.scala | 7 +++---- .../apache/spark/metrics/InputOutputMetricsSuite.scala | 4 ++-- .../netty/NettyBlockTransferSecuritySuite.scala | 10 +++++++--- external/flume-sink/pom.xml | 4 ++++ .../flume/sink/SparkAvroCallbackHandler.scala | 4 ++-- .../main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala | 6 +++++- .../sql/hive/thriftserver/AbstractSparkSQLDriver.scala | 4 ++-- .../sql/hive/thriftserver/SparkSQLCLIDriver.scala | 8 +++----- .../apache/spark/sql/hive/execution/UDFListString.java | 6 +++--- .../spark/sql/hive/MetastoreDataSourcesSuite.scala | 9 ++++----- 10 files changed, 35 insertions(+), 27 deletions(-) diff --git a/core/src/test/scala/org/apache/spark/FileServerSuite.scala b/core/src/test/scala/org/apache/spark/FileServerSuite.scala index a69e9b761f9a7..c0439f934813e 100644 --- a/core/src/test/scala/org/apache/spark/FileServerSuite.scala +++ b/core/src/test/scala/org/apache/spark/FileServerSuite.scala @@ -22,8 +22,7 @@ import java.net.URI import java.util.jar.{JarEntry, JarOutputStream} import javax.net.ssl.SSLException -import com.google.common.io.ByteStreams -import org.apache.commons.io.{FileUtils, IOUtils} +import com.google.common.io.{ByteStreams, Files} import org.apache.commons.lang3.RandomUtils import org.scalatest.FunSuite @@ -239,7 +238,7 @@ class FileServerSuite extends FunSuite with LocalSparkContext { def fileTransferTest(server: HttpFileServer, sm: SecurityManager = null): Unit = { val randomContent = RandomUtils.nextBytes(100) val file = File.createTempFile("FileServerSuite", "sslTests", tmpDir) - FileUtils.writeByteArrayToFile(file, randomContent) + Files.write(randomContent, file) server.addFile(file) val uri = new URI(server.serverUri + "/files/" + file.getName) @@ -254,7 +253,7 @@ class FileServerSuite extends FunSuite with LocalSparkContext { Utils.setupSecureURLConnection(connection, sm) } - val buf = IOUtils.toByteArray(connection.getInputStream) + val buf = ByteStreams.toByteArray(connection.getInputStream) assert(buf === randomContent) } diff --git a/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala b/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala index 190b08d950a02..ef3e213f1fcce 100644 --- a/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala +++ b/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala @@ -21,7 +21,7 @@ import java.io.{File, FileWriter, PrintWriter} import scala.collection.mutable.ArrayBuffer -import org.apache.commons.lang.math.RandomUtils +import org.apache.commons.lang3.RandomUtils import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FileSystem, Path} import org.apache.hadoop.io.{LongWritable, Text} @@ -60,7 +60,7 @@ class InputOutputMetricsSuite extends FunSuite with SharedSparkContext tmpFile = new File(testTempDir, getClass.getSimpleName + ".txt") val pw = new PrintWriter(new FileWriter(tmpFile)) for (x <- 1 to numRecords) { - pw.println(RandomUtils.nextInt(numBuckets)) + pw.println(RandomUtils.nextInt(0, numBuckets)) } pw.close() diff --git a/core/src/test/scala/org/apache/spark/network/netty/NettyBlockTransferSecuritySuite.scala b/core/src/test/scala/org/apache/spark/network/netty/NettyBlockTransferSecuritySuite.scala index 94bfa67451892..46d2e5173acae 100644 --- a/core/src/test/scala/org/apache/spark/network/netty/NettyBlockTransferSecuritySuite.scala +++ b/core/src/test/scala/org/apache/spark/network/netty/NettyBlockTransferSecuritySuite.scala @@ -17,14 +17,16 @@ package org.apache.spark.network.netty +import java.io.InputStreamReader import java.nio._ +import java.nio.charset.Charset import java.util.concurrent.TimeUnit import scala.concurrent.duration._ import scala.concurrent.{Await, Promise} import scala.util.{Failure, Success, Try} -import org.apache.commons.io.IOUtils +import com.google.common.io.CharStreams import org.apache.spark.network.buffer.{ManagedBuffer, NioManagedBuffer} import org.apache.spark.network.shuffle.BlockFetchingListener import org.apache.spark.network.{BlockDataManager, BlockTransferService} @@ -32,7 +34,7 @@ import org.apache.spark.storage.{BlockId, ShuffleBlockId} import org.apache.spark.{SecurityManager, SparkConf} import org.mockito.Mockito._ import org.scalatest.mock.MockitoSugar -import org.scalatest.{BeforeAndAfterAll, BeforeAndAfterEach, FunSuite, ShouldMatchers} +import org.scalatest.{FunSuite, ShouldMatchers} class NettyBlockTransferSecuritySuite extends FunSuite with MockitoSugar with ShouldMatchers { test("security default off") { @@ -113,7 +115,9 @@ class NettyBlockTransferSecuritySuite extends FunSuite with MockitoSugar with Sh val result = fetchBlock(exec0, exec1, "1", blockId) match { case Success(buf) => - IOUtils.toString(buf.createInputStream()) should equal(blockString) + val actualString = CharStreams.toString( + new InputStreamReader(buf.createInputStream(), Charset.forName("UTF-8"))) + actualString should equal(blockString) buf.release() Success() case Failure(t) => diff --git a/external/flume-sink/pom.xml b/external/flume-sink/pom.xml index 67907bbfb6d1b..1f3e619d97a24 100644 --- a/external/flume-sink/pom.xml +++ b/external/flume-sink/pom.xml @@ -35,6 +35,10 @@ http://spark.apache.org/ + + org.apache.commons + commons-lang3 + org.apache.flume flume-ng-sdk diff --git a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkAvroCallbackHandler.scala b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkAvroCallbackHandler.scala index 4373be443e67d..fd01807fc3ac4 100644 --- a/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkAvroCallbackHandler.scala +++ b/external/flume-sink/src/main/scala/org/apache/spark/streaming/flume/sink/SparkAvroCallbackHandler.scala @@ -21,9 +21,9 @@ import java.util.concurrent.atomic.AtomicLong import scala.collection.mutable -import org.apache.flume.Channel -import org.apache.commons.lang.RandomStringUtils import com.google.common.util.concurrent.ThreadFactoryBuilder +import org.apache.flume.Channel +import org.apache.commons.lang3.RandomStringUtils /** * Class that implements the SparkFlumeProtocol, that is used by the Avro Netty Server to process diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala index f326510042122..f3b5455574d1a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala @@ -20,7 +20,8 @@ package org.apache.spark.sql.jdbc import java.sql.{Connection, DriverManager, ResultSet, ResultSetMetaData, SQLException} import java.util.Properties -import org.apache.commons.lang.StringEscapeUtils.escapeSql +import org.apache.commons.lang3.StringUtils + import org.apache.spark.{Logging, Partition, SparkContext, TaskContext} import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.expressions.{Row, SpecificMutableRow} @@ -239,6 +240,9 @@ private[sql] class JDBCRDD( case _ => value } + private def escapeSql(value: String): String = + if (value == null) null else StringUtils.replace(value, "'", "''") + /** * Turns a single Filter into a String representing a SQL expression. * Returns null for an unhandled filter. diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/AbstractSparkSQLDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/AbstractSparkSQLDriver.scala index 59f3a75768082..48ac9062af96a 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/AbstractSparkSQLDriver.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/AbstractSparkSQLDriver.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.hive.thriftserver import scala.collection.JavaConversions._ -import org.apache.commons.lang.exception.ExceptionUtils +import org.apache.commons.lang3.exception.ExceptionUtils import org.apache.hadoop.hive.metastore.api.{FieldSchema, Schema} import org.apache.hadoop.hive.ql.Driver import org.apache.hadoop.hive.ql.processors.CommandProcessorResponse @@ -61,7 +61,7 @@ private[hive] abstract class AbstractSparkSQLDriver( } catch { case cause: Throwable => logError(s"Failed in [$command]", cause) - new CommandProcessorResponse(1, ExceptionUtils.getFullStackTrace(cause), null) + new CommandProcessorResponse(1, ExceptionUtils.getStackTrace(cause), null) } } diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala index 7e307bb4ad1e8..b7b6925aa87f7 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala @@ -24,18 +24,16 @@ import java.util.{ArrayList => JArrayList} import jline.{ConsoleReader, History} -import org.apache.commons.lang.StringUtils +import org.apache.commons.lang3.StringUtils import org.apache.commons.logging.LogFactory import org.apache.hadoop.conf.Configuration import org.apache.hadoop.hive.cli.{CliDriver, CliSessionState, OptionsProcessor} -import org.apache.hadoop.hive.common.LogUtils.LogInitializationException -import org.apache.hadoop.hive.common.{HiveInterruptCallback, HiveInterruptUtils, LogUtils} +import org.apache.hadoop.hive.common.{HiveInterruptCallback, HiveInterruptUtils} import org.apache.hadoop.hive.conf.HiveConf import org.apache.hadoop.hive.ql.Driver import org.apache.hadoop.hive.ql.exec.Utilities -import org.apache.hadoop.hive.ql.processors.{AddResourceProcessor, SetProcessor, CommandProcessor, CommandProcessorFactory} +import org.apache.hadoop.hive.ql.processors.{AddResourceProcessor, SetProcessor, CommandProcessor} import org.apache.hadoop.hive.ql.session.SessionState -import org.apache.hadoop.hive.shims.ShimLoader import org.apache.thrift.transport.TSocket import org.apache.spark.Logging diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFListString.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFListString.java index efd34df293c88..f33210ebdae1b 100644 --- a/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFListString.java +++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFListString.java @@ -17,10 +17,10 @@ package org.apache.spark.sql.hive.execution; -import org.apache.hadoop.hive.ql.exec.UDF; - import java.util.List; -import org.apache.commons.lang.StringUtils; + +import org.apache.commons.lang3.StringUtils; +import org.apache.hadoop.hive.ql.exec.UDF; public class UDFListString extends UDF { diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala index e09c702c8969e..0538aa203c5a0 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala @@ -23,7 +23,6 @@ import scala.collection.mutable.ArrayBuffer import org.scalatest.BeforeAndAfterEach -import org.apache.commons.io.FileUtils import org.apache.hadoop.fs.Path import org.apache.hadoop.hive.metastore.TableType import org.apache.hadoop.hive.ql.metadata.Table @@ -174,7 +173,7 @@ class MetastoreDataSourcesSuite extends QueryTest with BeforeAndAfterEach { sql("SELECT * FROM jsonTable"), Row("a", "b")) - FileUtils.deleteDirectory(tempDir) + Utils.deleteRecursively(tempDir) sparkContext.parallelize(("a1", "b1", "c1") :: Nil).toDF() .toJSON.saveAsTextFile(tempDir.getCanonicalPath) @@ -190,7 +189,7 @@ class MetastoreDataSourcesSuite extends QueryTest with BeforeAndAfterEach { checkAnswer( sql("SELECT * FROM jsonTable"), Row("a1", "b1", "c1")) - FileUtils.deleteDirectory(tempDir) + Utils.deleteRecursively(tempDir) } test("drop, change, recreate") { @@ -212,7 +211,7 @@ class MetastoreDataSourcesSuite extends QueryTest with BeforeAndAfterEach { sql("SELECT * FROM jsonTable"), Row("a", "b")) - FileUtils.deleteDirectory(tempDir) + Utils.deleteRecursively(tempDir) sparkContext.parallelize(("a", "b", "c") :: Nil).toDF() .toJSON.saveAsTextFile(tempDir.getCanonicalPath) @@ -231,7 +230,7 @@ class MetastoreDataSourcesSuite extends QueryTest with BeforeAndAfterEach { checkAnswer( sql("SELECT * FROM jsonTable"), Row("a", "b", "c")) - FileUtils.deleteDirectory(tempDir) + Utils.deleteRecursively(tempDir) } test("invalidate cache and reload") { From 62888a4ded91b3c2cbb05936c374c7ebfc10799e Mon Sep 17 00:00:00 2001 From: GuoQiang Li Date: Mon, 27 Apr 2015 19:52:41 -0400 Subject: [PATCH 435/591] [SPARK-7162] [YARN] Launcher error in yarn-client jira: https://issues.apache.org/jira/browse/SPARK-7162 Author: GuoQiang Li Closes #5716 from witgo/SPARK-7162 and squashes the following commits: b64564c [GuoQiang Li] Launcher error in yarn-client --- yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 019afbd1a1743..741239c953794 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -354,7 +354,7 @@ private[spark] class Client( val dir = new File(path) if (dir.isDirectory()) { dir.listFiles().foreach { file => - if (!hadoopConfFiles.contains(file.getName())) { + if (file.isFile && !hadoopConfFiles.contains(file.getName())) { hadoopConfFiles(file.getName()) = file } } From 4d9e560b5470029143926827b1cb9d72a0bfbeff Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Mon, 27 Apr 2015 19:02:51 -0700 Subject: [PATCH 436/591] [SPARK-7090] [MLLIB] Introduce LDAOptimizer to LDA to further improve extensibility jira: https://issues.apache.org/jira/browse/SPARK-7090 LDA was implemented with extensibility in mind. And with the development of OnlineLDA and Gibbs Sampling, we are collecting more detailed requirements from different algorithms. As Joseph Bradley jkbradley proposed in https://github.com/apache/spark/pull/4807 and with some further discussion, we'd like to adjust the code structure a little to present the common interface and extension point clearly. Basically class LDA would be a common entrance for LDA computing. And each LDA object will refer to a LDAOptimizer for the concrete algorithm implementation. Users can customize LDAOptimizer with specific parameters and assign it to LDA. Concrete changes: 1. Add a trait `LDAOptimizer`, which defines the common iterface for concrete implementations. Each subClass is a wrapper for a specific LDA algorithm. 2. Move EMOptimizer to file LDAOptimizer and inherits from LDAOptimizer, rename to EMLDAOptimizer. (in case a more generic EMOptimizer comes in the future) -adjust the constructor of EMOptimizer, since all the parameters should be passed in through initialState method. This can avoid unwanted confusion or overwrite. -move the code from LDA.initalState to initalState of EMLDAOptimizer 3. Add property ldaOptimizer to LDA and its getter/setter, and EMLDAOptimizer is the default Optimizer. 4. Change the return type of LDA.run from DistributedLDAModel to LDAModel. Further work: add OnlineLDAOptimizer and other possible Optimizers once ready. Author: Yuhao Yang Closes #5661 from hhbyyh/ldaRefactor and squashes the following commits: 0e2e006 [Yuhao Yang] respond to review comments 08a45da [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaRefactor e756ce4 [Yuhao Yang] solve mima exception d74fd8f [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaRefactor 0bb8400 [Yuhao Yang] refactor LDA with Optimizer ec2f857 [Yuhao Yang] protoptype for discussion --- .../spark/examples/mllib/JavaLDAExample.java | 2 +- .../spark/examples/mllib/LDAExample.scala | 4 +- .../apache/spark/mllib/clustering/LDA.scala | 181 +++------------ .../spark/mllib/clustering/LDAModel.scala | 2 +- .../spark/mllib/clustering/LDAOptimizer.scala | 210 ++++++++++++++++++ .../spark/mllib/clustering/JavaLDASuite.java | 2 +- .../spark/mllib/clustering/LDASuite.scala | 2 +- project/MimaExcludes.scala | 4 + 8 files changed, 256 insertions(+), 151 deletions(-) create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLDAExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLDAExample.java index 36207ae38d9a9..fd53c81cc4974 100644 --- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLDAExample.java +++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLDAExample.java @@ -58,7 +58,7 @@ public Tuple2 call(Tuple2 doc_id) { corpus.cache(); // Cluster the documents into three topics using LDA - DistributedLDAModel ldaModel = new LDA().setK(3).run(corpus); + DistributedLDAModel ldaModel = (DistributedLDAModel)new LDA().setK(3).run(corpus); // Output topics. Each is a distribution over words (matching word count vectors) System.out.println("Learned topics (as distributions over vocab of " + ldaModel.vocabSize() diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LDAExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LDAExample.scala index 08a93595a2e17..a1850390c0a86 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/LDAExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LDAExample.scala @@ -26,7 +26,7 @@ import scopt.OptionParser import org.apache.log4j.{Level, Logger} import org.apache.spark.{SparkContext, SparkConf} -import org.apache.spark.mllib.clustering.LDA +import org.apache.spark.mllib.clustering.{DistributedLDAModel, LDA} import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.rdd.RDD @@ -137,7 +137,7 @@ object LDAExample { sc.setCheckpointDir(params.checkpointDir.get) } val startTime = System.nanoTime() - val ldaModel = lda.run(corpus) + val ldaModel = lda.run(corpus).asInstanceOf[DistributedLDAModel] val elapsed = (System.nanoTime() - startTime) / 1e9 println(s"Finished training LDA model. Summary:") diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala index d006b39acb213..37bf88b73b911 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala @@ -17,16 +17,11 @@ package org.apache.spark.mllib.clustering -import java.util.Random - -import breeze.linalg.{DenseVector => BDV, normalize} - +import breeze.linalg.{DenseVector => BDV} import org.apache.spark.Logging import org.apache.spark.annotation.Experimental import org.apache.spark.api.java.JavaPairRDD import org.apache.spark.graphx._ -import org.apache.spark.graphx.impl.GraphImpl -import org.apache.spark.mllib.impl.PeriodicGraphCheckpointer import org.apache.spark.mllib.linalg.Vector import org.apache.spark.rdd.RDD import org.apache.spark.util.Utils @@ -42,16 +37,9 @@ import org.apache.spark.util.Utils * - "token": instance of a term appearing in a document * - "topic": multinomial distribution over words representing some concept * - * Currently, the underlying implementation uses Expectation-Maximization (EM), implemented - * according to the Asuncion et al. (2009) paper referenced below. - * * References: * - Original LDA paper (journal version): * Blei, Ng, and Jordan. "Latent Dirichlet Allocation." JMLR, 2003. - * - This class implements their "smoothed" LDA model. - * - Paper which clearly explains several algorithms, including EM: - * Asuncion, Welling, Smyth, and Teh. - * "On Smoothing and Inference for Topic Models." UAI, 2009. * * @see [[http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation Latent Dirichlet allocation * (Wikipedia)]] @@ -63,10 +51,11 @@ class LDA private ( private var docConcentration: Double, private var topicConcentration: Double, private var seed: Long, - private var checkpointInterval: Int) extends Logging { + private var checkpointInterval: Int, + private var ldaOptimizer: LDAOptimizer) extends Logging { def this() = this(k = 10, maxIterations = 20, docConcentration = -1, topicConcentration = -1, - seed = Utils.random.nextLong(), checkpointInterval = 10) + seed = Utils.random.nextLong(), checkpointInterval = 10, ldaOptimizer = new EMLDAOptimizer) /** * Number of topics to infer. I.e., the number of soft cluster centers. @@ -220,6 +209,32 @@ class LDA private ( this } + + /** LDAOptimizer used to perform the actual calculation */ + def getOptimizer: LDAOptimizer = ldaOptimizer + + /** + * LDAOptimizer used to perform the actual calculation (default = EMLDAOptimizer) + */ + def setOptimizer(optimizer: LDAOptimizer): this.type = { + this.ldaOptimizer = optimizer + this + } + + /** + * Set the LDAOptimizer used to perform the actual calculation by algorithm name. + * Currently "em" is supported. + */ + def setOptimizer(optimizerName: String): this.type = { + this.ldaOptimizer = + optimizerName.toLowerCase match { + case "em" => new EMLDAOptimizer + case other => + throw new IllegalArgumentException(s"Only em is supported but got $other.") + } + this + } + /** * Learn an LDA model using the given dataset. * @@ -229,9 +244,9 @@ class LDA private ( * Document IDs must be unique and >= 0. * @return Inferred LDA model */ - def run(documents: RDD[(Long, Vector)]): DistributedLDAModel = { - val state = LDA.initialState(documents, k, getDocConcentration, getTopicConcentration, seed, - checkpointInterval) + def run(documents: RDD[(Long, Vector)]): LDAModel = { + val state = ldaOptimizer.initialState(documents, k, getDocConcentration, getTopicConcentration, + seed, checkpointInterval) var iter = 0 val iterationTimes = Array.fill[Double](maxIterations)(0) while (iter < maxIterations) { @@ -241,12 +256,11 @@ class LDA private ( iterationTimes(iter) = elapsedSeconds iter += 1 } - state.graphCheckpointer.deleteAllCheckpoints() - new DistributedLDAModel(state, iterationTimes) + state.getLDAModel(iterationTimes) } /** Java-friendly version of [[run()]] */ - def run(documents: JavaPairRDD[java.lang.Long, Vector]): DistributedLDAModel = { + def run(documents: JavaPairRDD[java.lang.Long, Vector]): LDAModel = { run(documents.rdd.asInstanceOf[RDD[(Long, Vector)]]) } } @@ -320,88 +334,10 @@ private[clustering] object LDA { private[clustering] def isTermVertex(v: (VertexId, _)): Boolean = v._1 < 0 - /** - * Optimizer for EM algorithm which stores data + parameter graph, plus algorithm parameters. - * - * @param graph EM graph, storing current parameter estimates in vertex descriptors and - * data (token counts) in edge descriptors. - * @param k Number of topics - * @param vocabSize Number of unique terms - * @param docConcentration "alpha" - * @param topicConcentration "beta" or "eta" - */ - private[clustering] class EMOptimizer( - var graph: Graph[TopicCounts, TokenCount], - val k: Int, - val vocabSize: Int, - val docConcentration: Double, - val topicConcentration: Double, - checkpointInterval: Int) { - - private[LDA] val graphCheckpointer = new PeriodicGraphCheckpointer[TopicCounts, TokenCount]( - graph, checkpointInterval) - - def next(): EMOptimizer = { - val eta = topicConcentration - val W = vocabSize - val alpha = docConcentration - - val N_k = globalTopicTotals - val sendMsg: EdgeContext[TopicCounts, TokenCount, (Boolean, TopicCounts)] => Unit = - (edgeContext) => { - // Compute N_{wj} gamma_{wjk} - val N_wj = edgeContext.attr - // E-STEP: Compute gamma_{wjk} (smoothed topic distributions), scaled by token count - // N_{wj}. - val scaledTopicDistribution: TopicCounts = - computePTopic(edgeContext.srcAttr, edgeContext.dstAttr, N_k, W, eta, alpha) *= N_wj - edgeContext.sendToDst((false, scaledTopicDistribution)) - edgeContext.sendToSrc((false, scaledTopicDistribution)) - } - // This is a hack to detect whether we could modify the values in-place. - // TODO: Add zero/seqOp/combOp option to aggregateMessages. (SPARK-5438) - val mergeMsg: ((Boolean, TopicCounts), (Boolean, TopicCounts)) => (Boolean, TopicCounts) = - (m0, m1) => { - val sum = - if (m0._1) { - m0._2 += m1._2 - } else if (m1._1) { - m1._2 += m0._2 - } else { - m0._2 + m1._2 - } - (true, sum) - } - // M-STEP: Aggregation computes new N_{kj}, N_{wk} counts. - val docTopicDistributions: VertexRDD[TopicCounts] = - graph.aggregateMessages[(Boolean, TopicCounts)](sendMsg, mergeMsg) - .mapValues(_._2) - // Update the vertex descriptors with the new counts. - val newGraph = GraphImpl.fromExistingRDDs(docTopicDistributions, graph.edges) - graph = newGraph - graphCheckpointer.updateGraph(newGraph) - globalTopicTotals = computeGlobalTopicTotals() - this - } - - /** - * Aggregate distributions over topics from all term vertices. - * - * Note: This executes an action on the graph RDDs. - */ - var globalTopicTotals: TopicCounts = computeGlobalTopicTotals() - - private def computeGlobalTopicTotals(): TopicCounts = { - val numTopics = k - graph.vertices.filter(isTermVertex).values.fold(BDV.zeros[Double](numTopics))(_ += _) - } - - } - /** * Compute gamma_{wjk}, a distribution over topics k. */ - private def computePTopic( + private[clustering] def computePTopic( docTopicCounts: TopicCounts, termTopicCounts: TopicCounts, totalTopicCounts: TopicCounts, @@ -427,49 +363,4 @@ private[clustering] object LDA { // normalize BDV(gamma_wj) /= sum } - - /** - * Compute bipartite term/doc graph. - */ - private def initialState( - docs: RDD[(Long, Vector)], - k: Int, - docConcentration: Double, - topicConcentration: Double, - randomSeed: Long, - checkpointInterval: Int): EMOptimizer = { - // For each document, create an edge (Document -> Term) for each unique term in the document. - val edges: RDD[Edge[TokenCount]] = docs.flatMap { case (docID: Long, termCounts: Vector) => - // Add edges for terms with non-zero counts. - termCounts.toBreeze.activeIterator.filter(_._2 != 0.0).map { case (term, cnt) => - Edge(docID, term2index(term), cnt) - } - } - - val vocabSize = docs.take(1).head._2.size - - // Create vertices. - // Initially, we use random soft assignments of tokens to topics (random gamma). - def createVertices(): RDD[(VertexId, TopicCounts)] = { - val verticesTMP: RDD[(VertexId, TopicCounts)] = - edges.mapPartitionsWithIndex { case (partIndex, partEdges) => - val random = new Random(partIndex + randomSeed) - partEdges.flatMap { edge => - val gamma = normalize(BDV.fill[Double](k)(random.nextDouble()), 1.0) - val sum = gamma * edge.attr - Seq((edge.srcId, sum), (edge.dstId, sum)) - } - } - verticesTMP.reduceByKey(_ + _) - } - - val docTermVertices = createVertices() - - // Partition such that edges are grouped by document - val graph = Graph(docTermVertices, edges) - .partitionBy(PartitionStrategy.EdgePartition1D) - - new EMOptimizer(graph, k, vocabSize, docConcentration, topicConcentration, checkpointInterval) - } - } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala index 0a3f21ecee0dc..6cf26445f20a0 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala @@ -203,7 +203,7 @@ class DistributedLDAModel private ( import LDA._ - private[clustering] def this(state: LDA.EMOptimizer, iterationTimes: Array[Double]) = { + private[clustering] def this(state: EMLDAOptimizer, iterationTimes: Array[Double]) = { this(state.graph, state.globalTopicTotals, state.k, state.vocabSize, state.docConcentration, state.topicConcentration, iterationTimes) } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala new file mode 100644 index 0000000000000..ffd72a294c6c6 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala @@ -0,0 +1,210 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.clustering + +import java.util.Random + +import breeze.linalg.{DenseVector => BDV, normalize} + +import org.apache.spark.annotation.Experimental +import org.apache.spark.graphx._ +import org.apache.spark.graphx.impl.GraphImpl +import org.apache.spark.mllib.impl.PeriodicGraphCheckpointer +import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.rdd.RDD + +/** + * :: Experimental :: + * + * An LDAOptimizer specifies which optimization/learning/inference algorithm to use, and it can + * hold optimizer-specific parameters for users to set. + */ +@Experimental +trait LDAOptimizer{ + + /* + DEVELOPERS NOTE: + + An LDAOptimizer contains an algorithm for LDA and performs the actual computation, which + stores internal data structure (Graph or Matrix) and other parameters for the algorithm. + The interface is isolated to improve the extensibility of LDA. + */ + + /** + * Initializer for the optimizer. LDA passes the common parameters to the optimizer and + * the internal structure can be initialized properly. + */ + private[clustering] def initialState( + docs: RDD[(Long, Vector)], + k: Int, + docConcentration: Double, + topicConcentration: Double, + randomSeed: Long, + checkpointInterval: Int): LDAOptimizer + + private[clustering] def next(): LDAOptimizer + + private[clustering] def getLDAModel(iterationTimes: Array[Double]): LDAModel +} + +/** + * :: Experimental :: + * + * Optimizer for EM algorithm which stores data + parameter graph, plus algorithm parameters. + * + * Currently, the underlying implementation uses Expectation-Maximization (EM), implemented + * according to the Asuncion et al. (2009) paper referenced below. + * + * References: + * - Original LDA paper (journal version): + * Blei, Ng, and Jordan. "Latent Dirichlet Allocation." JMLR, 2003. + * - This class implements their "smoothed" LDA model. + * - Paper which clearly explains several algorithms, including EM: + * Asuncion, Welling, Smyth, and Teh. + * "On Smoothing and Inference for Topic Models." UAI, 2009. + * + */ +@Experimental +class EMLDAOptimizer extends LDAOptimizer{ + + import LDA._ + + /** + * Following fields will only be initialized through initialState method + */ + private[clustering] var graph: Graph[TopicCounts, TokenCount] = null + private[clustering] var k: Int = 0 + private[clustering] var vocabSize: Int = 0 + private[clustering] var docConcentration: Double = 0 + private[clustering] var topicConcentration: Double = 0 + private[clustering] var checkpointInterval: Int = 10 + private var graphCheckpointer: PeriodicGraphCheckpointer[TopicCounts, TokenCount] = null + + /** + * Compute bipartite term/doc graph. + */ + private[clustering] override def initialState( + docs: RDD[(Long, Vector)], + k: Int, + docConcentration: Double, + topicConcentration: Double, + randomSeed: Long, + checkpointInterval: Int): LDAOptimizer = { + // For each document, create an edge (Document -> Term) for each unique term in the document. + val edges: RDD[Edge[TokenCount]] = docs.flatMap { case (docID: Long, termCounts: Vector) => + // Add edges for terms with non-zero counts. + termCounts.toBreeze.activeIterator.filter(_._2 != 0.0).map { case (term, cnt) => + Edge(docID, term2index(term), cnt) + } + } + + val vocabSize = docs.take(1).head._2.size + + // Create vertices. + // Initially, we use random soft assignments of tokens to topics (random gamma). + def createVertices(): RDD[(VertexId, TopicCounts)] = { + val verticesTMP: RDD[(VertexId, TopicCounts)] = + edges.mapPartitionsWithIndex { case (partIndex, partEdges) => + val random = new Random(partIndex + randomSeed) + partEdges.flatMap { edge => + val gamma = normalize(BDV.fill[Double](k)(random.nextDouble()), 1.0) + val sum = gamma * edge.attr + Seq((edge.srcId, sum), (edge.dstId, sum)) + } + } + verticesTMP.reduceByKey(_ + _) + } + + val docTermVertices = createVertices() + + // Partition such that edges are grouped by document + this.graph = Graph(docTermVertices, edges).partitionBy(PartitionStrategy.EdgePartition1D) + this.k = k + this.vocabSize = vocabSize + this.docConcentration = docConcentration + this.topicConcentration = topicConcentration + this.checkpointInterval = checkpointInterval + this.graphCheckpointer = new + PeriodicGraphCheckpointer[TopicCounts, TokenCount](graph, checkpointInterval) + this.globalTopicTotals = computeGlobalTopicTotals() + this + } + + private[clustering] override def next(): EMLDAOptimizer = { + require(graph != null, "graph is null, EMLDAOptimizer not initialized.") + + val eta = topicConcentration + val W = vocabSize + val alpha = docConcentration + + val N_k = globalTopicTotals + val sendMsg: EdgeContext[TopicCounts, TokenCount, (Boolean, TopicCounts)] => Unit = + (edgeContext) => { + // Compute N_{wj} gamma_{wjk} + val N_wj = edgeContext.attr + // E-STEP: Compute gamma_{wjk} (smoothed topic distributions), scaled by token count + // N_{wj}. + val scaledTopicDistribution: TopicCounts = + computePTopic(edgeContext.srcAttr, edgeContext.dstAttr, N_k, W, eta, alpha) *= N_wj + edgeContext.sendToDst((false, scaledTopicDistribution)) + edgeContext.sendToSrc((false, scaledTopicDistribution)) + } + // This is a hack to detect whether we could modify the values in-place. + // TODO: Add zero/seqOp/combOp option to aggregateMessages. (SPARK-5438) + val mergeMsg: ((Boolean, TopicCounts), (Boolean, TopicCounts)) => (Boolean, TopicCounts) = + (m0, m1) => { + val sum = + if (m0._1) { + m0._2 += m1._2 + } else if (m1._1) { + m1._2 += m0._2 + } else { + m0._2 + m1._2 + } + (true, sum) + } + // M-STEP: Aggregation computes new N_{kj}, N_{wk} counts. + val docTopicDistributions: VertexRDD[TopicCounts] = + graph.aggregateMessages[(Boolean, TopicCounts)](sendMsg, mergeMsg) + .mapValues(_._2) + // Update the vertex descriptors with the new counts. + val newGraph = GraphImpl.fromExistingRDDs(docTopicDistributions, graph.edges) + graph = newGraph + graphCheckpointer.updateGraph(newGraph) + globalTopicTotals = computeGlobalTopicTotals() + this + } + + /** + * Aggregate distributions over topics from all term vertices. + * + * Note: This executes an action on the graph RDDs. + */ + private[clustering] var globalTopicTotals: TopicCounts = null + + private def computeGlobalTopicTotals(): TopicCounts = { + val numTopics = k + graph.vertices.filter(isTermVertex).values.fold(BDV.zeros[Double](numTopics))(_ += _) + } + + private[clustering] override def getLDAModel(iterationTimes: Array[Double]): LDAModel = { + require(graph != null, "graph is null, EMLDAOptimizer not initialized.") + this.graphCheckpointer.deleteAllCheckpoints() + new DistributedLDAModel(this, iterationTimes) + } +} diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java index dc10aa67c7c1f..fbe171b4b1ab1 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java @@ -88,7 +88,7 @@ public void distributedLDAModel() { .setMaxIterations(5) .setSeed(12345); - DistributedLDAModel model = lda.run(corpus); + DistributedLDAModel model = (DistributedLDAModel)lda.run(corpus); // Check: basic parameters LocalLDAModel localModel = model.toLocal(); diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala index cc747dabb9968..41ec794146c69 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala @@ -68,7 +68,7 @@ class LDASuite extends FunSuite with MLlibTestSparkContext { .setSeed(12345) val corpus = sc.parallelize(tinyCorpus, 2) - val model: DistributedLDAModel = lda.run(corpus) + val model: DistributedLDAModel = lda.run(corpus).asInstanceOf[DistributedLDAModel] // Check: basic parameters val localModel = model.toLocal diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala index 7ef363a2f07ad..967961c2bf5c3 100644 --- a/project/MimaExcludes.scala +++ b/project/MimaExcludes.scala @@ -72,6 +72,10 @@ object MimaExcludes { // SPARK-6703 Add getOrCreate method to SparkContext ProblemFilters.exclude[IncompatibleResultTypeProblem] ("org.apache.spark.SparkContext.org$apache$spark$SparkContext$$activeContext") + )++ Seq( + // SPARK-7090 Introduce LDAOptimizer to LDA to further improve extensibility + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.mllib.clustering.LDA$EMOptimizer") ) case v if v.startsWith("1.3") => From 874a2ca93d095a0dfa1acfdacf0e9d80388c4422 Mon Sep 17 00:00:00 2001 From: zsxwing Date: Mon, 27 Apr 2015 21:45:40 -0700 Subject: [PATCH 437/591] [SPARK-7174][Core] Move calling `TaskScheduler.executorHeartbeatReceived` to another thread `HeartbeatReceiver` will call `TaskScheduler.executorHeartbeatReceived`, which is a blocking operation because `TaskScheduler.executorHeartbeatReceived` will call ```Scala blockManagerMaster.driverEndpoint.askWithReply[Boolean]( BlockManagerHeartbeat(blockManagerId), 600 seconds) ``` finally. Even if it asks from a local Actor, it may block the current Akka thread. E.g., the reply may be dispatched to the same thread of the ask operation. So the reply cannot be processed. An extreme case is setting the thread number of Akka dispatch thread pool to 1. jstack log: ``` "sparkDriver-akka.actor.default-dispatcher-14" daemon prio=10 tid=0x00007f2a8c02d000 nid=0x725 waiting on condition [0x00007f2b1d6d0000] java.lang.Thread.State: TIMED_WAITING (parking) at sun.misc.Unsafe.park(Native Method) - parking to wait for <0x00000006197a0868> (a scala.concurrent.impl.Promise$CompletionLatch) at java.util.concurrent.locks.LockSupport.parkNanos(LockSupport.java:226) at java.util.concurrent.locks.AbstractQueuedSynchronizer.doAcquireSharedNanos(AbstractQueuedSynchronizer.java:1033) at java.util.concurrent.locks.AbstractQueuedSynchronizer.tryAcquireSharedNanos(AbstractQueuedSynchronizer.java:1326) at scala.concurrent.impl.Promise$DefaultPromise.tryAwait(Promise.scala:208) at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:218) at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223) at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107) at akka.dispatch.MonitorableThreadFactory$AkkaForkJoinWorkerThread$$anon$3.block(ThreadPoolBuilder.scala:169) at scala.concurrent.forkjoin.ForkJoinPool.managedBlock(ForkJoinPool.java:3640) at akka.dispatch.MonitorableThreadFactory$AkkaForkJoinWorkerThread.blockOn(ThreadPoolBuilder.scala:167) at scala.concurrent.Await$.result(package.scala:107) at org.apache.spark.rpc.RpcEndpointRef.askWithReply(RpcEnv.scala:355) at org.apache.spark.scheduler.DAGScheduler.executorHeartbeatReceived(DAGScheduler.scala:169) at org.apache.spark.scheduler.TaskSchedulerImpl.executorHeartbeatReceived(TaskSchedulerImpl.scala:367) at org.apache.spark.HeartbeatReceiver$$anonfun$receiveAndReply$1.applyOrElse(HeartbeatReceiver.scala:103) at org.apache.spark.rpc.akka.AkkaRpcEnv.org$apache$spark$rpc$akka$AkkaRpcEnv$$processMessage(AkkaRpcEnv.scala:182) at org.apache.spark.rpc.akka.AkkaRpcEnv$$anonfun$actorRef$lzycompute$1$1$$anon$1$$anonfun$receiveWithLogging$1$$anonfun$applyOrElse$4.apply$mcV$sp(AkkaRpcEnv.scala:128) at org.apache.spark.rpc.akka.AkkaRpcEnv.org$apache$spark$rpc$akka$AkkaRpcEnv$$safelyCall(AkkaRpcEnv.scala:203) at org.apache.spark.rpc.akka.AkkaRpcEnv$$anonfun$actorRef$lzycompute$1$1$$anon$1$$anonfun$receiveWithLogging$1.applyOrElse(AkkaRpcEnv.scala:127) at scala.runtime.AbstractPartialFunction$mcVL$sp.apply$mcVL$sp(AbstractPartialFunction.scala:33) at scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:33) at scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:25) at org.apache.spark.util.ActorLogReceive$$anon$1.apply(ActorLogReceive.scala:59) at org.apache.spark.util.ActorLogReceive$$anon$1.apply(ActorLogReceive.scala:42) at scala.PartialFunction$class.applyOrElse(PartialFunction.scala:118) at org.apache.spark.util.ActorLogReceive$$anon$1.applyOrElse(ActorLogReceive.scala:42) at akka.actor.Actor$class.aroundReceive(Actor.scala:465) at org.apache.spark.rpc.akka.AkkaRpcEnv$$anonfun$actorRef$lzycompute$1$1$$anon$1.aroundReceive(AkkaRpcEnv.scala:94) at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516) at akka.actor.ActorCell.invoke(ActorCell.scala:487) at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:238) at akka.dispatch.Mailbox.run(Mailbox.scala:220) at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:393) at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260) at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339) at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979) at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107) ``` This PR moved this blocking operation to a separated thread. Author: zsxwing Closes #5723 from zsxwing/SPARK-7174 and squashes the following commits: 98bfe48 [zsxwing] Use a single thread for checking timeout and reporting executorHeartbeatReceived 5b3b545 [zsxwing] Move calling `TaskScheduler.executorHeartbeatReceived` to another thread to avoid blocking the Akka thread pool --- .../org/apache/spark/HeartbeatReceiver.scala | 26 ++++++++++++------- 1 file changed, 17 insertions(+), 9 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala index 68d05d5b02537..f2b024ff6cb67 100644 --- a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala +++ b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala @@ -76,13 +76,15 @@ private[spark] class HeartbeatReceiver(sc: SparkContext) private var timeoutCheckingTask: ScheduledFuture[_] = null - private val timeoutCheckingThread = - ThreadUtils.newDaemonSingleThreadScheduledExecutor("heartbeat-timeout-checking-thread") + // "eventLoopThread" is used to run some pretty fast actions. The actions running in it should not + // block the thread for a long time. + private val eventLoopThread = + ThreadUtils.newDaemonSingleThreadScheduledExecutor("heartbeat-receiver-event-loop-thread") private val killExecutorThread = ThreadUtils.newDaemonSingleThreadExecutor("kill-executor-thread") override def onStart(): Unit = { - timeoutCheckingTask = timeoutCheckingThread.scheduleAtFixedRate(new Runnable { + timeoutCheckingTask = eventLoopThread.scheduleAtFixedRate(new Runnable { override def run(): Unit = Utils.tryLogNonFatalError { Option(self).foreach(_.send(ExpireDeadHosts)) } @@ -99,11 +101,15 @@ private[spark] class HeartbeatReceiver(sc: SparkContext) override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case heartbeat @ Heartbeat(executorId, taskMetrics, blockManagerId) => if (scheduler != null) { - val unknownExecutor = !scheduler.executorHeartbeatReceived( - executorId, taskMetrics, blockManagerId) - val response = HeartbeatResponse(reregisterBlockManager = unknownExecutor) executorLastSeen(executorId) = System.currentTimeMillis() - context.reply(response) + eventLoopThread.submit(new Runnable { + override def run(): Unit = Utils.tryLogNonFatalError { + val unknownExecutor = !scheduler.executorHeartbeatReceived( + executorId, taskMetrics, blockManagerId) + val response = HeartbeatResponse(reregisterBlockManager = unknownExecutor) + context.reply(response) + } + }) } else { // Because Executor will sleep several seconds before sending the first "Heartbeat", this // case rarely happens. However, if it really happens, log it and ask the executor to @@ -125,7 +131,9 @@ private[spark] class HeartbeatReceiver(sc: SparkContext) if (sc.supportDynamicAllocation) { // Asynchronously kill the executor to avoid blocking the current thread killExecutorThread.submit(new Runnable { - override def run(): Unit = sc.killExecutor(executorId) + override def run(): Unit = Utils.tryLogNonFatalError { + sc.killExecutor(executorId) + } }) } executorLastSeen.remove(executorId) @@ -137,7 +145,7 @@ private[spark] class HeartbeatReceiver(sc: SparkContext) if (timeoutCheckingTask != null) { timeoutCheckingTask.cancel(true) } - timeoutCheckingThread.shutdownNow() + eventLoopThread.shutdownNow() killExecutorThread.shutdownNow() } } From 29576e786072bd4218e10036ddfc8d367b1c1446 Mon Sep 17 00:00:00 2001 From: Burak Yavuz Date: Mon, 27 Apr 2015 23:10:14 -0700 Subject: [PATCH 438/591] [SPARK-6829] Added math functions for DataFrames Implemented almost all math functions found in scala.math (max, min and abs were already present). cc mengxr marmbrus Author: Burak Yavuz Closes #5616 from brkyvz/math-udfs and squashes the following commits: fb27153 [Burak Yavuz] reverted exception message 836a098 [Burak Yavuz] fixed test and addressed small comment e5f0d13 [Burak Yavuz] addressed code review v2.2 b26c5fb [Burak Yavuz] addressed review v2.1 2761f08 [Burak Yavuz] addressed review v2 6588a5b [Burak Yavuz] fixed merge conflicts b084e10 [Burak Yavuz] Addressed code review 029e739 [Burak Yavuz] fixed atan2 test 534cc11 [Burak Yavuz] added more tests, addressed comments fa68dbe [Burak Yavuz] added double specific test data 937d5a5 [Burak Yavuz] use doubles instead of ints 8e28fff [Burak Yavuz] Added apache header 7ec8f7f [Burak Yavuz] Added math functions for DataFrames --- .../catalyst/analysis/HiveTypeCoercion.scala | 19 + .../sql/catalyst/expressions/Expression.scala | 10 + .../expressions/mathfuncs/binary.scala | 93 +++ .../expressions/mathfuncs/unary.scala | 168 ++++++ .../ExpressionEvaluationSuite.scala | 165 +++++ .../org/apache/spark/sql/mathfunctions.scala | 562 ++++++++++++++++++ .../apache/spark/sql/JavaDataFrameSuite.java | 9 + .../spark/sql/ColumnExpressionSuite.scala | 1 - .../spark/sql/MathExpressionsSuite.scala | 233 ++++++++ 9 files changed, 1259 insertions(+), 1 deletion(-) create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/binary.scala create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/unary.scala create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/mathfunctions.scala create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala index 35c7f00d4e42a..73c9a1c7afdad 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala @@ -79,6 +79,7 @@ trait HiveTypeCoercion { CaseWhenCoercion :: Division :: PropagateTypes :: + ExpectedInputConversion :: Nil /** @@ -643,4 +644,22 @@ trait HiveTypeCoercion { } } + /** + * Casts types according to the expected input types for Expressions that have the trait + * `ExpectsInputTypes`. + */ + object ExpectedInputConversion extends Rule[LogicalPlan] { + + def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions { + // Skip nodes who's children have not been resolved yet. + case e if !e.childrenResolved => e + + case e: ExpectsInputTypes if e.children.map(_.dataType) != e.expectedChildTypes => + val newC = (e.children, e.children.map(_.dataType), e.expectedChildTypes).zipped.map { + case (child, actual, expected) => + if (actual == expected) child else Cast(child, expected) + } + e.withNewChildren(newC) + } + } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala index 4e3bbc06a5b4c..1d71c1b4b0c7c 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala @@ -109,3 +109,13 @@ case class GroupExpression(children: Seq[Expression]) extends Expression { override def foldable: Boolean = false override def dataType: DataType = throw new UnsupportedOperationException } + +/** + * Expressions that require a specific `DataType` as input should implement this trait + * so that the proper type conversions can be performed in the analyzer. + */ +trait ExpectsInputTypes { + + def expectedChildTypes: Seq[DataType] + +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/binary.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/binary.scala new file mode 100644 index 0000000000000..5b4d912a64f71 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/binary.scala @@ -0,0 +1,93 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions.mathfuncs + +import org.apache.spark.sql.catalyst.analysis.UnresolvedException +import org.apache.spark.sql.catalyst.expressions.{ExpectsInputTypes, BinaryExpression, Expression, Row} +import org.apache.spark.sql.types._ + +/** + * A binary expression specifically for math functions that take two `Double`s as input and returns + * a `Double`. + * @param f The math function. + * @param name The short name of the function + */ +abstract class BinaryMathExpression(f: (Double, Double) => Double, name: String) + extends BinaryExpression with Serializable with ExpectsInputTypes { self: Product => + type EvaluatedType = Any + override def symbol: String = null + override def expectedChildTypes: Seq[DataType] = Seq(DoubleType, DoubleType) + + override def nullable: Boolean = left.nullable || right.nullable + override def toString: String = s"$name($left, $right)" + + override lazy val resolved = + left.resolved && right.resolved && + left.dataType == right.dataType && + !DecimalType.isFixed(left.dataType) + + override def dataType: DataType = { + if (!resolved) { + throw new UnresolvedException(this, + s"datatype. Can not resolve due to differing types ${left.dataType}, ${right.dataType}") + } + left.dataType + } + + override def eval(input: Row): Any = { + val evalE1 = left.eval(input) + if (evalE1 == null) { + null + } else { + val evalE2 = right.eval(input) + if (evalE2 == null) { + null + } else { + val result = f(evalE1.asInstanceOf[Double], evalE2.asInstanceOf[Double]) + if (result.isNaN) null else result + } + } + } +} + +case class Pow(left: Expression, right: Expression) extends BinaryMathExpression(math.pow, "POWER") + +case class Hypot( + left: Expression, + right: Expression) extends BinaryMathExpression(math.hypot, "HYPOT") + +case class Atan2( + left: Expression, + right: Expression) extends BinaryMathExpression(math.atan2, "ATAN2") { + override def eval(input: Row): Any = { + val evalE1 = left.eval(input) + if (evalE1 == null) { + null + } else { + val evalE2 = right.eval(input) + if (evalE2 == null) { + null + } else { + // With codegen, the values returned by -0.0 and 0.0 are different. Handled with +0.0 + val result = math.atan2(evalE1.asInstanceOf[Double] + 0.0, + evalE2.asInstanceOf[Double] + 0.0) + if (result.isNaN) null else result + } + } + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/unary.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/unary.scala new file mode 100644 index 0000000000000..96cb77d487529 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/unary.scala @@ -0,0 +1,168 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions.mathfuncs + +import org.apache.spark.sql.catalyst.expressions.{ExpectsInputTypes, Expression, Row, UnaryExpression} +import org.apache.spark.sql.types._ + +/** + * A unary expression specifically for math functions. Math Functions expect a specific type of + * input format, therefore these functions extend `ExpectsInputTypes`. + * @param name The short name of the function + */ +abstract class MathematicalExpression(name: String) + extends UnaryExpression with Serializable with ExpectsInputTypes { + self: Product => + type EvaluatedType = Any + + override def dataType: DataType = DoubleType + override def foldable: Boolean = child.foldable + override def nullable: Boolean = true + override def toString: String = s"$name($child)" +} + +/** + * A unary expression specifically for math functions that take a `Double` as input and return + * a `Double`. + * @param f The math function. + * @param name The short name of the function + */ +abstract class MathematicalExpressionForDouble(f: Double => Double, name: String) + extends MathematicalExpression(name) { self: Product => + + override def expectedChildTypes: Seq[DataType] = Seq(DoubleType) + + override def eval(input: Row): Any = { + val evalE = child.eval(input) + if (evalE == null) { + null + } else { + val result = f(evalE.asInstanceOf[Double]) + if (result.isNaN) null else result + } + } +} + +/** + * A unary expression specifically for math functions that take an `Int` as input and return + * an `Int`. + * @param f The math function. + * @param name The short name of the function + */ +abstract class MathematicalExpressionForInt(f: Int => Int, name: String) + extends MathematicalExpression(name) { self: Product => + + override def dataType: DataType = IntegerType + override def expectedChildTypes: Seq[DataType] = Seq(IntegerType) + + override def eval(input: Row): Any = { + val evalE = child.eval(input) + if (evalE == null) null else f(evalE.asInstanceOf[Int]) + } +} + +/** + * A unary expression specifically for math functions that take a `Float` as input and return + * a `Float`. + * @param f The math function. + * @param name The short name of the function + */ +abstract class MathematicalExpressionForFloat(f: Float => Float, name: String) + extends MathematicalExpression(name) { self: Product => + + override def dataType: DataType = FloatType + override def expectedChildTypes: Seq[DataType] = Seq(FloatType) + + override def eval(input: Row): Any = { + val evalE = child.eval(input) + if (evalE == null) { + null + } else { + val result = f(evalE.asInstanceOf[Float]) + if (result.isNaN) null else result + } + } +} + +/** + * A unary expression specifically for math functions that take a `Long` as input and return + * a `Long`. + * @param f The math function. + * @param name The short name of the function + */ +abstract class MathematicalExpressionForLong(f: Long => Long, name: String) + extends MathematicalExpression(name) { self: Product => + + override def dataType: DataType = LongType + override def expectedChildTypes: Seq[DataType] = Seq(LongType) + + override def eval(input: Row): Any = { + val evalE = child.eval(input) + if (evalE == null) null else f(evalE.asInstanceOf[Long]) + } +} + +case class Sin(child: Expression) extends MathematicalExpressionForDouble(math.sin, "SIN") + +case class Asin(child: Expression) extends MathematicalExpressionForDouble(math.asin, "ASIN") + +case class Sinh(child: Expression) extends MathematicalExpressionForDouble(math.sinh, "SINH") + +case class Cos(child: Expression) extends MathematicalExpressionForDouble(math.cos, "COS") + +case class Acos(child: Expression) extends MathematicalExpressionForDouble(math.acos, "ACOS") + +case class Cosh(child: Expression) extends MathematicalExpressionForDouble(math.cosh, "COSH") + +case class Tan(child: Expression) extends MathematicalExpressionForDouble(math.tan, "TAN") + +case class Atan(child: Expression) extends MathematicalExpressionForDouble(math.atan, "ATAN") + +case class Tanh(child: Expression) extends MathematicalExpressionForDouble(math.tanh, "TANH") + +case class Ceil(child: Expression) extends MathematicalExpressionForDouble(math.ceil, "CEIL") + +case class Floor(child: Expression) extends MathematicalExpressionForDouble(math.floor, "FLOOR") + +case class Rint(child: Expression) extends MathematicalExpressionForDouble(math.rint, "ROUND") + +case class Cbrt(child: Expression) extends MathematicalExpressionForDouble(math.cbrt, "CBRT") + +case class Signum(child: Expression) extends MathematicalExpressionForDouble(math.signum, "SIGNUM") + +case class ISignum(child: Expression) extends MathematicalExpressionForInt(math.signum, "ISIGNUM") + +case class FSignum(child: Expression) extends MathematicalExpressionForFloat(math.signum, "FSIGNUM") + +case class LSignum(child: Expression) extends MathematicalExpressionForLong(math.signum, "LSIGNUM") + +case class ToDegrees(child: Expression) + extends MathematicalExpressionForDouble(math.toDegrees, "DEGREES") + +case class ToRadians(child: Expression) + extends MathematicalExpressionForDouble(math.toRadians, "RADIANS") + +case class Log(child: Expression) extends MathematicalExpressionForDouble(math.log, "LOG") + +case class Log10(child: Expression) extends MathematicalExpressionForDouble(math.log10, "LOG10") + +case class Log1p(child: Expression) extends MathematicalExpressionForDouble(math.log1p, "LOG1P") + +case class Exp(child: Expression) extends MathematicalExpressionForDouble(math.exp, "EXP") + +case class Expm1(child: Expression) extends MathematicalExpressionForDouble(math.expm1, "EXPM1") diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala index 76298f03c94ae..5390ce43c6639 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala @@ -28,6 +28,7 @@ import org.scalatest.Matchers._ import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.catalyst.analysis.UnresolvedGetField import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.catalyst.expressions.mathfuncs._ import org.apache.spark.sql.types._ @@ -1152,6 +1153,170 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { checkEvaluation(c1 ^ c2, 3, row) checkEvaluation(~c1, -2, row) } + + /** + * Used for testing math functions for DataFrames. + * @param c The DataFrame function + * @param f The functions in scala.math + * @param domain The set of values to run the function with + * @param expectNull Whether the given values should return null or not + * @tparam T Generic type for primitives + */ + def unaryMathFunctionEvaluation[@specialized(Int, Double, Float, Long) T]( + c: Expression => Expression, + f: T => T, + domain: Iterable[T] = (-20 to 20).map(_ * 0.1), + expectNull: Boolean = false): Unit = { + if (expectNull) { + domain.foreach { value => + checkEvaluation(c(Literal(value)), null, EmptyRow) + } + } else { + domain.foreach { value => + checkEvaluation(c(Literal(value)), f(value), EmptyRow) + } + } + checkEvaluation(c(Literal.create(null, DoubleType)), null, create_row(null)) + } + + test("sin") { + unaryMathFunctionEvaluation(Sin, math.sin) + } + + test("asin") { + unaryMathFunctionEvaluation(Asin, math.asin, (-10 to 10).map(_ * 0.1)) + unaryMathFunctionEvaluation(Asin, math.asin, (11 to 20).map(_ * 0.1), true) + } + + test("sinh") { + unaryMathFunctionEvaluation(Sinh, math.sinh) + } + + test("cos") { + unaryMathFunctionEvaluation(Cos, math.cos) + } + + test("acos") { + unaryMathFunctionEvaluation(Acos, math.acos, (-10 to 10).map(_ * 0.1)) + unaryMathFunctionEvaluation(Acos, math.acos, (11 to 20).map(_ * 0.1), true) + } + + test("cosh") { + unaryMathFunctionEvaluation(Cosh, math.cosh) + } + + test("tan") { + unaryMathFunctionEvaluation(Tan, math.tan) + } + + test("atan") { + unaryMathFunctionEvaluation(Atan, math.atan) + } + + test("tanh") { + unaryMathFunctionEvaluation(Tanh, math.tanh) + } + + test("toDeg") { + unaryMathFunctionEvaluation(ToDegrees, math.toDegrees) + } + + test("toRad") { + unaryMathFunctionEvaluation(ToRadians, math.toRadians) + } + + test("cbrt") { + unaryMathFunctionEvaluation(Cbrt, math.cbrt) + } + + test("ceil") { + unaryMathFunctionEvaluation(Ceil, math.ceil) + } + + test("floor") { + unaryMathFunctionEvaluation(Floor, math.floor) + } + + test("rint") { + unaryMathFunctionEvaluation(Rint, math.rint) + } + + test("exp") { + unaryMathFunctionEvaluation(Exp, math.exp) + } + + test("expm1") { + unaryMathFunctionEvaluation(Expm1, math.expm1) + } + + test("signum") { + unaryMathFunctionEvaluation[Double](Signum, math.signum) + } + + test("isignum") { + unaryMathFunctionEvaluation[Int](ISignum, math.signum, (-5 to 5)) + } + + test("fsignum") { + unaryMathFunctionEvaluation[Float](FSignum, math.signum, (-5 to 5).map(_.toFloat)) + } + + test("lsignum") { + unaryMathFunctionEvaluation[Long](LSignum, math.signum, (5 to 5).map(_.toLong)) + } + + test("log") { + unaryMathFunctionEvaluation(Log, math.log, (0 to 20).map(_ * 0.1)) + unaryMathFunctionEvaluation(Log, math.log, (-5 to -1).map(_ * 0.1), true) + } + + test("log10") { + unaryMathFunctionEvaluation(Log10, math.log10, (0 to 20).map(_ * 0.1)) + unaryMathFunctionEvaluation(Log10, math.log10, (-5 to -1).map(_ * 0.1), true) + } + + test("log1p") { + unaryMathFunctionEvaluation(Log1p, math.log1p, (-1 to 20).map(_ * 0.1)) + unaryMathFunctionEvaluation(Log1p, math.log1p, (-10 to -2).map(_ * 1.0), true) + } + + /** + * Used for testing math functions for DataFrames. + * @param c The DataFrame function + * @param f The functions in scala.math + * @param domain The set of values to run the function with + */ + def binaryMathFunctionEvaluation( + c: (Expression, Expression) => Expression, + f: (Double, Double) => Double, + domain: Iterable[(Double, Double)] = (-20 to 20).map(v => (v * 0.1, v * -0.1)), + expectNull: Boolean = false): Unit = { + if (expectNull) { + domain.foreach { case (v1, v2) => + checkEvaluation(c(v1, v2), null, create_row(null)) + } + } else { + domain.foreach { case (v1, v2) => + checkEvaluation(c(v1, v2), f(v1 + 0.0, v2 + 0.0), EmptyRow) + checkEvaluation(c(v2, v1), f(v2 + 0.0, v1 + 0.0), EmptyRow) + } + } + checkEvaluation(c(Literal.create(null, DoubleType), 1.0), null, create_row(null)) + checkEvaluation(c(1.0, Literal.create(null, DoubleType)), null, create_row(null)) + } + + test("pow") { + binaryMathFunctionEvaluation(Pow, math.pow, (-5 to 5).map(v => (v * 1.0, v * 1.0))) + binaryMathFunctionEvaluation(Pow, math.pow, Seq((-1.0, 0.9), (-2.2, 1.7), (-2.2, -1.7)), true) + } + + test("hypot") { + binaryMathFunctionEvaluation(Hypot, math.hypot) + } + + test("atan2") { + binaryMathFunctionEvaluation(Atan2, math.atan2) + } } // TODO: Make the tests work with codegen. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/mathfunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/mathfunctions.scala new file mode 100644 index 0000000000000..84f62bf47f955 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/mathfunctions.scala @@ -0,0 +1,562 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import scala.language.implicitConversions + +import org.apache.spark.annotation.Experimental +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.expressions.mathfuncs._ +import org.apache.spark.sql.functions.lit + +/** + * :: Experimental :: + * Mathematical Functions available for [[DataFrame]]. + * + * @groupname double_funcs Functions that require DoubleType as an input + * @groupname int_funcs Functions that require IntegerType as an input + * @groupname float_funcs Functions that require FloatType as an input + * @groupname long_funcs Functions that require LongType as an input + */ +@Experimental +// scalastyle:off +object mathfunctions { +// scalastyle:on + + private[this] implicit def toColumn(expr: Expression): Column = Column(expr) + + /** + * Computes the sine of the given value. + * + * @group double_funcs + */ + def sin(e: Column): Column = Sin(e.expr) + + /** + * Computes the sine of the given column. + * + * @group double_funcs + */ + def sin(columnName: String): Column = sin(Column(columnName)) + + /** + * Computes the sine inverse of the given value; the returned angle is in the range + * -pi/2 through pi/2. + * + * @group double_funcs + */ + def asin(e: Column): Column = Asin(e.expr) + + /** + * Computes the sine inverse of the given column; the returned angle is in the range + * -pi/2 through pi/2. + * + * @group double_funcs + */ + def asin(columnName: String): Column = asin(Column(columnName)) + + /** + * Computes the hyperbolic sine of the given value. + * + * @group double_funcs + */ + def sinh(e: Column): Column = Sinh(e.expr) + + /** + * Computes the hyperbolic sine of the given column. + * + * @group double_funcs + */ + def sinh(columnName: String): Column = sinh(Column(columnName)) + + /** + * Computes the cosine of the given value. + * + * @group double_funcs + */ + def cos(e: Column): Column = Cos(e.expr) + + /** + * Computes the cosine of the given column. + * + * @group double_funcs + */ + def cos(columnName: String): Column = cos(Column(columnName)) + + /** + * Computes the cosine inverse of the given value; the returned angle is in the range + * 0.0 through pi. + * + * @group double_funcs + */ + def acos(e: Column): Column = Acos(e.expr) + + /** + * Computes the cosine inverse of the given column; the returned angle is in the range + * 0.0 through pi. + * + * @group double_funcs + */ + def acos(columnName: String): Column = acos(Column(columnName)) + + /** + * Computes the hyperbolic cosine of the given value. + * + * @group double_funcs + */ + def cosh(e: Column): Column = Cosh(e.expr) + + /** + * Computes the hyperbolic cosine of the given column. + * + * @group double_funcs + */ + def cosh(columnName: String): Column = cosh(Column(columnName)) + + /** + * Computes the tangent of the given value. + * + * @group double_funcs + */ + def tan(e: Column): Column = Tan(e.expr) + + /** + * Computes the tangent of the given column. + * + * @group double_funcs + */ + def tan(columnName: String): Column = tan(Column(columnName)) + + /** + * Computes the tangent inverse of the given value. + * + * @group double_funcs + */ + def atan(e: Column): Column = Atan(e.expr) + + /** + * Computes the tangent inverse of the given column. + * + * @group double_funcs + */ + def atan(columnName: String): Column = atan(Column(columnName)) + + /** + * Computes the hyperbolic tangent of the given value. + * + * @group double_funcs + */ + def tanh(e: Column): Column = Tanh(e.expr) + + /** + * Computes the hyperbolic tangent of the given column. + * + * @group double_funcs + */ + def tanh(columnName: String): Column = tanh(Column(columnName)) + + /** + * Converts an angle measured in radians to an approximately equivalent angle measured in degrees. + * + * @group double_funcs + */ + def toDeg(e: Column): Column = ToDegrees(e.expr) + + /** + * Converts an angle measured in radians to an approximately equivalent angle measured in degrees. + * + * @group double_funcs + */ + def toDeg(columnName: String): Column = toDeg(Column(columnName)) + + /** + * Converts an angle measured in degrees to an approximately equivalent angle measured in radians. + * + * @group double_funcs + */ + def toRad(e: Column): Column = ToRadians(e.expr) + + /** + * Converts an angle measured in degrees to an approximately equivalent angle measured in radians. + * + * @group double_funcs + */ + def toRad(columnName: String): Column = toRad(Column(columnName)) + + /** + * Computes the ceiling of the given value. + * + * @group double_funcs + */ + def ceil(e: Column): Column = Ceil(e.expr) + + /** + * Computes the ceiling of the given column. + * + * @group double_funcs + */ + def ceil(columnName: String): Column = ceil(Column(columnName)) + + /** + * Computes the floor of the given value. + * + * @group double_funcs + */ + def floor(e: Column): Column = Floor(e.expr) + + /** + * Computes the floor of the given column. + * + * @group double_funcs + */ + def floor(columnName: String): Column = floor(Column(columnName)) + + /** + * Returns the double value that is closest in value to the argument and + * is equal to a mathematical integer. + * + * @group double_funcs + */ + def rint(e: Column): Column = Rint(e.expr) + + /** + * Returns the double value that is closest in value to the argument and + * is equal to a mathematical integer. + * + * @group double_funcs + */ + def rint(columnName: String): Column = rint(Column(columnName)) + + /** + * Computes the cube-root of the given value. + * + * @group double_funcs + */ + def cbrt(e: Column): Column = Cbrt(e.expr) + + /** + * Computes the cube-root of the given column. + * + * @group double_funcs + */ + def cbrt(columnName: String): Column = cbrt(Column(columnName)) + + /** + * Computes the signum of the given value. + * + * @group double_funcs + */ + def signum(e: Column): Column = Signum(e.expr) + + /** + * Computes the signum of the given column. + * + * @group double_funcs + */ + def signum(columnName: String): Column = signum(Column(columnName)) + + /** + * Computes the signum of the given value. For IntegerType. + * + * @group int_funcs + */ + def isignum(e: Column): Column = ISignum(e.expr) + + /** + * Computes the signum of the given column. For IntegerType. + * + * @group int_funcs + */ + def isignum(columnName: String): Column = isignum(Column(columnName)) + + /** + * Computes the signum of the given value. For FloatType. + * + * @group float_funcs + */ + def fsignum(e: Column): Column = FSignum(e.expr) + + /** + * Computes the signum of the given column. For FloatType. + * + * @group float_funcs + */ + def fsignum(columnName: String): Column = fsignum(Column(columnName)) + + /** + * Computes the signum of the given value. For LongType. + * + * @group long_funcs + */ + def lsignum(e: Column): Column = LSignum(e.expr) + + /** + * Computes the signum of the given column. For FloatType. + * + * @group long_funcs + */ + def lsignum(columnName: String): Column = lsignum(Column(columnName)) + + /** + * Computes the natural logarithm of the given value. + * + * @group double_funcs + */ + def log(e: Column): Column = Log(e.expr) + + /** + * Computes the natural logarithm of the given column. + * + * @group double_funcs + */ + def log(columnName: String): Column = log(Column(columnName)) + + /** + * Computes the logarithm of the given value in Base 10. + * + * @group double_funcs + */ + def log10(e: Column): Column = Log10(e.expr) + + /** + * Computes the logarithm of the given value in Base 10. + * + * @group double_funcs + */ + def log10(columnName: String): Column = log10(Column(columnName)) + + /** + * Computes the natural logarithm of the given value plus one. + * + * @group double_funcs + */ + def log1p(e: Column): Column = Log1p(e.expr) + + /** + * Computes the natural logarithm of the given column plus one. + * + * @group double_funcs + */ + def log1p(columnName: String): Column = log1p(Column(columnName)) + + /** + * Computes the exponential of the given value. + * + * @group double_funcs + */ + def exp(e: Column): Column = Exp(e.expr) + + /** + * Computes the exponential of the given column. + * + * @group double_funcs + */ + def exp(columnName: String): Column = exp(Column(columnName)) + + /** + * Computes the exponential of the given value minus one. + * + * @group double_funcs + */ + def expm1(e: Column): Column = Expm1(e.expr) + + /** + * Computes the exponential of the given column. + * + * @group double_funcs + */ + def expm1(columnName: String): Column = expm1(Column(columnName)) + + /** + * Returns the value of the first argument raised to the power of the second argument. + * + * @group double_funcs + */ + def pow(l: Column, r: Column): Column = Pow(l.expr, r.expr) + + /** + * Returns the value of the first argument raised to the power of the second argument. + * + * @group double_funcs + */ + def pow(l: Column, rightName: String): Column = pow(l, Column(rightName)) + + /** + * Returns the value of the first argument raised to the power of the second argument. + * + * @group double_funcs + */ + def pow(leftName: String, r: Column): Column = pow(Column(leftName), r) + + /** + * Returns the value of the first argument raised to the power of the second argument. + * + * @group double_funcs + */ + def pow(leftName: String, rightName: String): Column = pow(Column(leftName), Column(rightName)) + + /** + * Returns the value of the first argument raised to the power of the second argument. + * + * @group double_funcs + */ + def pow(l: Column, r: Double): Column = pow(l, lit(r).expr) + + /** + * Returns the value of the first argument raised to the power of the second argument. + * + * @group double_funcs + */ + def pow(leftName: String, r: Double): Column = pow(Column(leftName), r) + + /** + * Returns the value of the first argument raised to the power of the second argument. + * + * @group double_funcs + */ + def pow(l: Double, r: Column): Column = pow(lit(l).expr, r) + + /** + * Returns the value of the first argument raised to the power of the second argument. + * + * @group double_funcs + */ + def pow(l: Double, rightName: String): Column = pow(l, Column(rightName)) + + /** + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. + * + * @group double_funcs + */ + def hypot(l: Column, r: Column): Column = Hypot(l.expr, r.expr) + + /** + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. + * + * @group double_funcs + */ + def hypot(l: Column, rightName: String): Column = hypot(l, Column(rightName)) + + /** + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. + * + * @group double_funcs + */ + def hypot(leftName: String, r: Column): Column = hypot(Column(leftName), r) + + /** + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. + * + * @group double_funcs + */ + def hypot(leftName: String, rightName: String): Column = + hypot(Column(leftName), Column(rightName)) + + /** + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. + * + * @group double_funcs + */ + def hypot(l: Column, r: Double): Column = hypot(l, lit(r).expr) + + /** + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. + * + * @group double_funcs + */ + def hypot(leftName: String, r: Double): Column = hypot(Column(leftName), r) + + /** + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. + * + * @group double_funcs + */ + def hypot(l: Double, r: Column): Column = hypot(lit(l).expr, r) + + /** + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. + * + * @group double_funcs + */ + def hypot(l: Double, rightName: String): Column = hypot(l, Column(rightName)) + + /** + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). + * + * @group double_funcs + */ + def atan2(l: Column, r: Column): Column = Atan2(l.expr, r.expr) + + /** + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). + * + * @group double_funcs + */ + def atan2(l: Column, rightName: String): Column = atan2(l, Column(rightName)) + + /** + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). + * + * @group double_funcs + */ + def atan2(leftName: String, r: Column): Column = atan2(Column(leftName), r) + + /** + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). + * + * @group double_funcs + */ + def atan2(leftName: String, rightName: String): Column = + atan2(Column(leftName), Column(rightName)) + + /** + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). + * + * @group double_funcs + */ + def atan2(l: Column, r: Double): Column = atan2(l, lit(r).expr) + + /** + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). + * + * @group double_funcs + */ + def atan2(leftName: String, r: Double): Column = atan2(Column(leftName), r) + + /** + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). + * + * @group double_funcs + */ + def atan2(l: Double, r: Column): Column = atan2(lit(l).expr, r) + + /** + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). + * + * @group double_funcs + */ + def atan2(l: Double, rightName: String): Column = atan2(l, Column(rightName)) +} diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java index e02c84872c628..e5c9504d21042 100644 --- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java @@ -41,6 +41,7 @@ import java.util.Map; import static org.apache.spark.sql.functions.*; +import static org.apache.spark.sql.mathfunctions.*; public class JavaDataFrameSuite { private transient JavaSparkContext jsc; @@ -98,6 +99,14 @@ public void testVarargMethods() { df.groupBy().agg(countDistinct("key", "value")); df.groupBy().agg(countDistinct(col("key"), col("value"))); df.select(coalesce(col("key"))); + + // Varargs with mathfunctions + DataFrame df2 = context.table("testData2"); + df2.select(exp("a"), exp("b")); + df2.select(exp(log("a"))); + df2.select(pow("a", "a"), pow("b", 2.0)); + df2.select(pow(col("a"), col("b")), exp("b")); + df2.select(sin("a"), acos("b")); } @Ignore diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala index 904073b8cb2aa..680b5c636960d 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala @@ -22,7 +22,6 @@ import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.test.TestSQLContext.implicits._ import org.apache.spark.sql.types._ - class ColumnExpressionSuite extends QueryTest { import org.apache.spark.sql.TestData._ diff --git a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala new file mode 100644 index 0000000000000..561553cc925cb --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala @@ -0,0 +1,233 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import java.lang.{Double => JavaDouble} + +import org.apache.spark.sql.functions.lit +import org.apache.spark.sql.mathfunctions._ +import org.apache.spark.sql.test.TestSQLContext +import org.apache.spark.sql.test.TestSQLContext.implicits._ + +private[this] object MathExpressionsTestData { + + case class DoubleData(a: JavaDouble, b: JavaDouble) + val doubleData = TestSQLContext.sparkContext.parallelize( + (1 to 10).map(i => DoubleData(i * 0.2 - 1, i * -0.2 + 1))).toDF() + + val nnDoubleData = TestSQLContext.sparkContext.parallelize( + (1 to 10).map(i => DoubleData(i * 0.1, i * -0.1))).toDF() + + case class NullDoubles(a: JavaDouble) + val nullDoubles = + TestSQLContext.sparkContext.parallelize( + NullDoubles(1.0) :: + NullDoubles(2.0) :: + NullDoubles(3.0) :: + NullDoubles(null) :: Nil + ).toDF() +} + +class MathExpressionsSuite extends QueryTest { + + import MathExpressionsTestData._ + + def testOneToOneMathFunction[@specialized(Int, Long, Float, Double) T]( + c: Column => Column, + f: T => T): Unit = { + checkAnswer( + doubleData.select(c('a)), + (1 to 10).map(n => Row(f((n * 0.2 - 1).asInstanceOf[T]))) + ) + + checkAnswer( + doubleData.select(c('b)), + (1 to 10).map(n => Row(f((-n * 0.2 + 1).asInstanceOf[T]))) + ) + + checkAnswer( + doubleData.select(c(lit(null))), + (1 to 10).map(_ => Row(null)) + ) + } + + def testOneToOneNonNegativeMathFunction(c: Column => Column, f: Double => Double): Unit = { + checkAnswer( + nnDoubleData.select(c('a)), + (1 to 10).map(n => Row(f(n * 0.1))) + ) + + if (f(-1) === math.log1p(-1)) { + checkAnswer( + nnDoubleData.select(c('b)), + (1 to 9).map(n => Row(f(n * -0.1))) :+ Row(Double.NegativeInfinity) + ) + } else { + checkAnswer( + nnDoubleData.select(c('b)), + (1 to 10).map(n => Row(null)) + ) + } + + checkAnswer( + nnDoubleData.select(c(lit(null))), + (1 to 10).map(_ => Row(null)) + ) + } + + def testTwoToOneMathFunction( + c: (Column, Column) => Column, + d: (Column, Double) => Column, + f: (Double, Double) => Double): Unit = { + checkAnswer( + nnDoubleData.select(c('a, 'a)), + nnDoubleData.collect().toSeq.map(r => Row(f(r.getDouble(0), r.getDouble(0)))) + ) + + checkAnswer( + nnDoubleData.select(c('a, 'b)), + nnDoubleData.collect().toSeq.map(r => Row(f(r.getDouble(0), r.getDouble(1)))) + ) + + checkAnswer( + nnDoubleData.select(d('a, 2.0)), + nnDoubleData.collect().toSeq.map(r => Row(f(r.getDouble(0), 2.0))) + ) + + checkAnswer( + nnDoubleData.select(d('a, -0.5)), + nnDoubleData.collect().toSeq.map(r => Row(f(r.getDouble(0), -0.5))) + ) + + val nonNull = nullDoubles.collect().toSeq.filter(r => r.get(0) != null) + + checkAnswer( + nullDoubles.select(c('a, 'a)).orderBy('a.asc), + Row(null) +: nonNull.map(r => Row(f(r.getDouble(0), r.getDouble(0)))) + ) + } + + test("sin") { + testOneToOneMathFunction(sin, math.sin) + } + + test("asin") { + testOneToOneMathFunction(asin, math.asin) + } + + test("sinh") { + testOneToOneMathFunction(sinh, math.sinh) + } + + test("cos") { + testOneToOneMathFunction(cos, math.cos) + } + + test("acos") { + testOneToOneMathFunction(acos, math.acos) + } + + test("cosh") { + testOneToOneMathFunction(cosh, math.cosh) + } + + test("tan") { + testOneToOneMathFunction(tan, math.tan) + } + + test("atan") { + testOneToOneMathFunction(atan, math.atan) + } + + test("tanh") { + testOneToOneMathFunction(tanh, math.tanh) + } + + test("toDeg") { + testOneToOneMathFunction(toDeg, math.toDegrees) + } + + test("toRad") { + testOneToOneMathFunction(toRad, math.toRadians) + } + + test("cbrt") { + testOneToOneMathFunction(cbrt, math.cbrt) + } + + test("ceil") { + testOneToOneMathFunction(ceil, math.ceil) + } + + test("floor") { + testOneToOneMathFunction(floor, math.floor) + } + + test("rint") { + testOneToOneMathFunction(rint, math.rint) + } + + test("exp") { + testOneToOneMathFunction(exp, math.exp) + } + + test("expm1") { + testOneToOneMathFunction(expm1, math.expm1) + } + + test("signum") { + testOneToOneMathFunction[Double](signum, math.signum) + } + + test("isignum") { + testOneToOneMathFunction[Int](isignum, math.signum) + } + + test("fsignum") { + testOneToOneMathFunction[Float](fsignum, math.signum) + } + + test("lsignum") { + testOneToOneMathFunction[Long](lsignum, math.signum) + } + + test("pow") { + testTwoToOneMathFunction(pow, pow, math.pow) + } + + test("hypot") { + testTwoToOneMathFunction(hypot, hypot, math.hypot) + } + + test("atan2") { + testTwoToOneMathFunction(atan2, atan2, math.atan2) + } + + test("log") { + testOneToOneNonNegativeMathFunction(log, math.log) + } + + test("log10") { + testOneToOneNonNegativeMathFunction(log10, math.log10) + } + + test("log1p") { + testOneToOneNonNegativeMathFunction(log1p, math.log1p) + } + +} From 9e4e82b7bca1129bcd5e0274b9ae1b1be3fb93da Mon Sep 17 00:00:00 2001 From: jerryshao Date: Mon, 27 Apr 2015 23:48:02 -0700 Subject: [PATCH 439/591] [SPARK-5946] [STREAMING] Add Python API for direct Kafka stream Currently only added `createDirectStream` API, I'm not sure if `createRDD` is also needed, since some Java object needs to be wrapped in Python. Please help to review, thanks a lot. Author: jerryshao Author: Saisai Shao Closes #4723 from jerryshao/direct-kafka-python-api and squashes the following commits: a1fe97c [jerryshao] Fix rebase issue eebf333 [jerryshao] Address the comments da40f4e [jerryshao] Fix Python 2.6 Syntax error issue 5c0ee85 [jerryshao] Style fix 4aeac18 [jerryshao] Fix bug in example code 7146d86 [jerryshao] Add unit test bf3bdd6 [jerryshao] Add more APIs and address the comments f5b3801 [jerryshao] Small style fix 8641835 [Saisai Shao] Rebase and update the code 589c05b [Saisai Shao] Fix the style d6fcb6a [Saisai Shao] Address the comments dfda902 [Saisai Shao] Style fix 0f7d168 [Saisai Shao] Add the doc and fix some style issues 67e6880 [Saisai Shao] Fix test bug 917b0db [Saisai Shao] Add Python createRDD API for Kakfa direct stream c3fc11d [jerryshao] Modify the docs 2c00936 [Saisai Shao] address the comments 3360f44 [jerryshao] Fix code style e0e0f0d [jerryshao] Code clean and bug fix 338c41f [Saisai Shao] Add python API and example for direct kafka stream --- .../streaming/direct_kafka_wordcount.py | 55 ++++++ .../spark/streaming/kafka/KafkaUtils.scala | 92 +++++++++- python/pyspark/streaming/kafka.py | 167 +++++++++++++++++- python/pyspark/streaming/tests.py | 84 ++++++++- 4 files changed, 383 insertions(+), 15 deletions(-) create mode 100644 examples/src/main/python/streaming/direct_kafka_wordcount.py diff --git a/examples/src/main/python/streaming/direct_kafka_wordcount.py b/examples/src/main/python/streaming/direct_kafka_wordcount.py new file mode 100644 index 0000000000000..6ef188a220c51 --- /dev/null +++ b/examples/src/main/python/streaming/direct_kafka_wordcount.py @@ -0,0 +1,55 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +""" + Counts words in UTF8 encoded, '\n' delimited text directly received from Kafka in every 2 seconds. + Usage: direct_kafka_wordcount.py + + To run this on your local machine, you need to setup Kafka and create a producer first, see + http://kafka.apache.org/documentation.html#quickstart + + and then run the example + `$ bin/spark-submit --jars external/kafka-assembly/target/scala-*/\ + spark-streaming-kafka-assembly-*.jar \ + examples/src/main/python/streaming/direct_kafka_wordcount.py \ + localhost:9092 test` +""" + +import sys + +from pyspark import SparkContext +from pyspark.streaming import StreamingContext +from pyspark.streaming.kafka import KafkaUtils + +if __name__ == "__main__": + if len(sys.argv) != 3: + print >> sys.stderr, "Usage: direct_kafka_wordcount.py " + exit(-1) + + sc = SparkContext(appName="PythonStreamingDirectKafkaWordCount") + ssc = StreamingContext(sc, 2) + + brokers, topic = sys.argv[1:] + kvs = KafkaUtils.createDirectStream(ssc, [topic], {"metadata.broker.list": brokers}) + lines = kvs.map(lambda x: x[1]) + counts = lines.flatMap(lambda line: line.split(" ")) \ + .map(lambda word: (word, 1)) \ + .reduceByKey(lambda a, b: a+b) + counts.pprint() + + ssc.start() + ssc.awaitTermination() diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala index 5a9bd4214cf51..0721ddaf7055a 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala @@ -21,6 +21,7 @@ import java.lang.{Integer => JInt} import java.lang.{Long => JLong} import java.util.{Map => JMap} import java.util.{Set => JSet} +import java.util.{List => JList} import scala.reflect.ClassTag import scala.collection.JavaConversions._ @@ -234,7 +235,6 @@ object KafkaUtils { new KafkaRDD[K, V, KD, VD, R](sc, kafkaParams, offsetRanges, leaderMap, messageHandler) } - /** * Create a RDD from Kafka using offset ranges for each topic and partition. * @@ -558,4 +558,94 @@ private class KafkaUtilsPythonHelper { topics, storageLevel) } + + def createRDD( + jsc: JavaSparkContext, + kafkaParams: JMap[String, String], + offsetRanges: JList[OffsetRange], + leaders: JMap[TopicAndPartition, Broker]): JavaPairRDD[Array[Byte], Array[Byte]] = { + val messageHandler = new JFunction[MessageAndMetadata[Array[Byte], Array[Byte]], + (Array[Byte], Array[Byte])] { + def call(t1: MessageAndMetadata[Array[Byte], Array[Byte]]): (Array[Byte], Array[Byte]) = + (t1.key(), t1.message()) + } + + val jrdd = KafkaUtils.createRDD[ + Array[Byte], + Array[Byte], + DefaultDecoder, + DefaultDecoder, + (Array[Byte], Array[Byte])]( + jsc, + classOf[Array[Byte]], + classOf[Array[Byte]], + classOf[DefaultDecoder], + classOf[DefaultDecoder], + classOf[(Array[Byte], Array[Byte])], + kafkaParams, + offsetRanges.toArray(new Array[OffsetRange](offsetRanges.size())), + leaders, + messageHandler + ) + new JavaPairRDD(jrdd.rdd) + } + + def createDirectStream( + jssc: JavaStreamingContext, + kafkaParams: JMap[String, String], + topics: JSet[String], + fromOffsets: JMap[TopicAndPartition, JLong] + ): JavaPairInputDStream[Array[Byte], Array[Byte]] = { + + if (!fromOffsets.isEmpty) { + import scala.collection.JavaConversions._ + val topicsFromOffsets = fromOffsets.keySet().map(_.topic) + if (topicsFromOffsets != topics.toSet) { + throw new IllegalStateException(s"The specified topics: ${topics.toSet.mkString(" ")} " + + s"do not equal to the topic from offsets: ${topicsFromOffsets.mkString(" ")}") + } + } + + if (fromOffsets.isEmpty) { + KafkaUtils.createDirectStream[Array[Byte], Array[Byte], DefaultDecoder, DefaultDecoder]( + jssc, + classOf[Array[Byte]], + classOf[Array[Byte]], + classOf[DefaultDecoder], + classOf[DefaultDecoder], + kafkaParams, + topics) + } else { + val messageHandler = new JFunction[MessageAndMetadata[Array[Byte], Array[Byte]], + (Array[Byte], Array[Byte])] { + def call(t1: MessageAndMetadata[Array[Byte], Array[Byte]]): (Array[Byte], Array[Byte]) = + (t1.key(), t1.message()) + } + + val jstream = KafkaUtils.createDirectStream[ + Array[Byte], + Array[Byte], + DefaultDecoder, + DefaultDecoder, + (Array[Byte], Array[Byte])]( + jssc, + classOf[Array[Byte]], + classOf[Array[Byte]], + classOf[DefaultDecoder], + classOf[DefaultDecoder], + classOf[(Array[Byte], Array[Byte])], + kafkaParams, + fromOffsets, + messageHandler) + new JavaPairInputDStream(jstream.inputDStream) + } + } + + def createOffsetRange(topic: String, partition: JInt, fromOffset: JLong, untilOffset: JLong + ): OffsetRange = OffsetRange.create(topic, partition, fromOffset, untilOffset) + + def createTopicAndPartition(topic: String, partition: JInt): TopicAndPartition = + TopicAndPartition(topic, partition) + + def createBroker(host: String, port: JInt): Broker = Broker(host, port) } diff --git a/python/pyspark/streaming/kafka.py b/python/pyspark/streaming/kafka.py index 8d610d6569b4a..e278b29003f69 100644 --- a/python/pyspark/streaming/kafka.py +++ b/python/pyspark/streaming/kafka.py @@ -17,11 +17,12 @@ from py4j.java_gateway import Py4JJavaError +from pyspark.rdd import RDD from pyspark.storagelevel import StorageLevel from pyspark.serializers import PairDeserializer, NoOpSerializer from pyspark.streaming import DStream -__all__ = ['KafkaUtils', 'utf8_decoder'] +__all__ = ['Broker', 'KafkaUtils', 'OffsetRange', 'TopicAndPartition', 'utf8_decoder'] def utf8_decoder(s): @@ -67,7 +68,104 @@ def createStream(ssc, zkQuorum, groupId, topics, kafkaParams={}, except Py4JJavaError as e: # TODO: use --jar once it also work on driver if 'ClassNotFoundException' in str(e.java_exception): - print(""" + KafkaUtils._printErrorMsg(ssc.sparkContext) + raise e + ser = PairDeserializer(NoOpSerializer(), NoOpSerializer()) + stream = DStream(jstream, ssc, ser) + return stream.map(lambda k_v: (keyDecoder(k_v[0]), valueDecoder(k_v[1]))) + + @staticmethod + def createDirectStream(ssc, topics, kafkaParams, fromOffsets={}, + keyDecoder=utf8_decoder, valueDecoder=utf8_decoder): + """ + .. note:: Experimental + + Create an input stream that directly pulls messages from a Kafka Broker and specific offset. + + This is not a receiver based Kafka input stream, it directly pulls the message from Kafka + in each batch duration and processed without storing. + + This does not use Zookeeper to store offsets. The consumed offsets are tracked + by the stream itself. For interoperability with Kafka monitoring tools that depend on + Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application. + You can access the offsets used in each batch from the generated RDDs (see + + To recover from driver failures, you have to enable checkpointing in the StreamingContext. + The information on consumed offset can be recovered from the checkpoint. + See the programming guide for details (constraints, etc.). + + :param ssc: StreamingContext object. + :param topics: list of topic_name to consume. + :param kafkaParams: Additional params for Kafka. + :param fromOffsets: Per-topic/partition Kafka offsets defining the (inclusive) starting + point of the stream. + :param keyDecoder: A function used to decode key (default is utf8_decoder). + :param valueDecoder: A function used to decode value (default is utf8_decoder). + :return: A DStream object + """ + if not isinstance(topics, list): + raise TypeError("topics should be list") + if not isinstance(kafkaParams, dict): + raise TypeError("kafkaParams should be dict") + + try: + helperClass = ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader() \ + .loadClass("org.apache.spark.streaming.kafka.KafkaUtilsPythonHelper") + helper = helperClass.newInstance() + + jfromOffsets = dict([(k._jTopicAndPartition(helper), + v) for (k, v) in fromOffsets.items()]) + jstream = helper.createDirectStream(ssc._jssc, kafkaParams, set(topics), jfromOffsets) + except Py4JJavaError as e: + if 'ClassNotFoundException' in str(e.java_exception): + KafkaUtils._printErrorMsg(ssc.sparkContext) + raise e + + ser = PairDeserializer(NoOpSerializer(), NoOpSerializer()) + stream = DStream(jstream, ssc, ser) + return stream.map(lambda k_v: (keyDecoder(k_v[0]), valueDecoder(k_v[1]))) + + @staticmethod + def createRDD(sc, kafkaParams, offsetRanges, leaders={}, + keyDecoder=utf8_decoder, valueDecoder=utf8_decoder): + """ + .. note:: Experimental + + Create a RDD from Kafka using offset ranges for each topic and partition. + :param sc: SparkContext object + :param kafkaParams: Additional params for Kafka + :param offsetRanges: list of offsetRange to specify topic:partition:[start, end) to consume + :param leaders: Kafka brokers for each TopicAndPartition in offsetRanges. May be an empty + map, in which case leaders will be looked up on the driver. + :param keyDecoder: A function used to decode key (default is utf8_decoder) + :param valueDecoder: A function used to decode value (default is utf8_decoder) + :return: A RDD object + """ + if not isinstance(kafkaParams, dict): + raise TypeError("kafkaParams should be dict") + if not isinstance(offsetRanges, list): + raise TypeError("offsetRanges should be list") + + try: + helperClass = sc._jvm.java.lang.Thread.currentThread().getContextClassLoader() \ + .loadClass("org.apache.spark.streaming.kafka.KafkaUtilsPythonHelper") + helper = helperClass.newInstance() + joffsetRanges = [o._jOffsetRange(helper) for o in offsetRanges] + jleaders = dict([(k._jTopicAndPartition(helper), + v._jBroker(helper)) for (k, v) in leaders.items()]) + jrdd = helper.createRDD(sc._jsc, kafkaParams, joffsetRanges, jleaders) + except Py4JJavaError as e: + if 'ClassNotFoundException' in str(e.java_exception): + KafkaUtils._printErrorMsg(sc) + raise e + + ser = PairDeserializer(NoOpSerializer(), NoOpSerializer()) + rdd = RDD(jrdd, sc, ser) + return rdd.map(lambda k_v: (keyDecoder(k_v[0]), valueDecoder(k_v[1]))) + + @staticmethod + def _printErrorMsg(sc): + print(""" ________________________________________________________________________________________________ Spark Streaming's Kafka libraries not found in class path. Try one of the following. @@ -85,8 +183,63 @@ def createStream(ssc, zkQuorum, groupId, topics, kafkaParams={}, ________________________________________________________________________________________________ -""" % (ssc.sparkContext.version, ssc.sparkContext.version)) - raise e - ser = PairDeserializer(NoOpSerializer(), NoOpSerializer()) - stream = DStream(jstream, ssc, ser) - return stream.map(lambda k_v: (keyDecoder(k_v[0]), valueDecoder(k_v[1]))) +""" % (sc.version, sc.version)) + + +class OffsetRange(object): + """ + Represents a range of offsets from a single Kafka TopicAndPartition. + """ + + def __init__(self, topic, partition, fromOffset, untilOffset): + """ + Create a OffsetRange to represent range of offsets + :param topic: Kafka topic name. + :param partition: Kafka partition id. + :param fromOffset: Inclusive starting offset. + :param untilOffset: Exclusive ending offset. + """ + self._topic = topic + self._partition = partition + self._fromOffset = fromOffset + self._untilOffset = untilOffset + + def _jOffsetRange(self, helper): + return helper.createOffsetRange(self._topic, self._partition, self._fromOffset, + self._untilOffset) + + +class TopicAndPartition(object): + """ + Represents a specific top and partition for Kafka. + """ + + def __init__(self, topic, partition): + """ + Create a Python TopicAndPartition to map to the Java related object + :param topic: Kafka topic name. + :param partition: Kafka partition id. + """ + self._topic = topic + self._partition = partition + + def _jTopicAndPartition(self, helper): + return helper.createTopicAndPartition(self._topic, self._partition) + + +class Broker(object): + """ + Represent the host and port info for a Kafka broker. + """ + + def __init__(self, host, port): + """ + Create a Python Broker to map to the Java related object. + :param host: Broker's hostname. + :param port: Broker's port. + """ + self._host = host + self._port = port + + def _jBroker(self, helper): + return helper.createBroker(self._host, self._port) diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py index 5fa1e5ef081ab..7c06c203455d9 100644 --- a/python/pyspark/streaming/tests.py +++ b/python/pyspark/streaming/tests.py @@ -21,6 +21,7 @@ import time import operator import tempfile +import random import struct from functools import reduce @@ -35,7 +36,7 @@ from pyspark.context import SparkConf, SparkContext, RDD from pyspark.streaming.context import StreamingContext -from pyspark.streaming.kafka import KafkaUtils +from pyspark.streaming.kafka import Broker, KafkaUtils, OffsetRange, TopicAndPartition class PySparkStreamingTestCase(unittest.TestCase): @@ -590,9 +591,27 @@ def tearDown(self): super(KafkaStreamTests, self).tearDown() + def _randomTopic(self): + return "topic-%d" % random.randint(0, 10000) + + def _validateStreamResult(self, sendData, stream): + result = {} + for i in chain.from_iterable(self._collect(stream.map(lambda x: x[1]), + sum(sendData.values()))): + result[i] = result.get(i, 0) + 1 + + self.assertEqual(sendData, result) + + def _validateRddResult(self, sendData, rdd): + result = {} + for i in rdd.map(lambda x: x[1]).collect(): + result[i] = result.get(i, 0) + 1 + + self.assertEqual(sendData, result) + def test_kafka_stream(self): """Test the Python Kafka stream API.""" - topic = "topic1" + topic = self._randomTopic() sendData = {"a": 3, "b": 5, "c": 10} self._kafkaTestUtils.createTopic(topic) @@ -601,13 +620,64 @@ def test_kafka_stream(self): stream = KafkaUtils.createStream(self.ssc, self._kafkaTestUtils.zkAddress(), "test-streaming-consumer", {topic: 1}, {"auto.offset.reset": "smallest"}) + self._validateStreamResult(sendData, stream) - result = {} - for i in chain.from_iterable(self._collect(stream.map(lambda x: x[1]), - sum(sendData.values()))): - result[i] = result.get(i, 0) + 1 + def test_kafka_direct_stream(self): + """Test the Python direct Kafka stream API.""" + topic = self._randomTopic() + sendData = {"a": 1, "b": 2, "c": 3} + kafkaParams = {"metadata.broker.list": self._kafkaTestUtils.brokerAddress(), + "auto.offset.reset": "smallest"} - self.assertEqual(sendData, result) + self._kafkaTestUtils.createTopic(topic) + self._kafkaTestUtils.sendMessages(topic, sendData) + + stream = KafkaUtils.createDirectStream(self.ssc, [topic], kafkaParams) + self._validateStreamResult(sendData, stream) + + @unittest.skipIf(sys.version >= "3", "long type not support") + def test_kafka_direct_stream_from_offset(self): + """Test the Python direct Kafka stream API with start offset specified.""" + topic = self._randomTopic() + sendData = {"a": 1, "b": 2, "c": 3} + fromOffsets = {TopicAndPartition(topic, 0): long(0)} + kafkaParams = {"metadata.broker.list": self._kafkaTestUtils.brokerAddress()} + + self._kafkaTestUtils.createTopic(topic) + self._kafkaTestUtils.sendMessages(topic, sendData) + + stream = KafkaUtils.createDirectStream(self.ssc, [topic], kafkaParams, fromOffsets) + self._validateStreamResult(sendData, stream) + + @unittest.skipIf(sys.version >= "3", "long type not support") + def test_kafka_rdd(self): + """Test the Python direct Kafka RDD API.""" + topic = self._randomTopic() + sendData = {"a": 1, "b": 2} + offsetRanges = [OffsetRange(topic, 0, long(0), long(sum(sendData.values())))] + kafkaParams = {"metadata.broker.list": self._kafkaTestUtils.brokerAddress()} + + self._kafkaTestUtils.createTopic(topic) + self._kafkaTestUtils.sendMessages(topic, sendData) + + rdd = KafkaUtils.createRDD(self.sc, kafkaParams, offsetRanges) + self._validateRddResult(sendData, rdd) + + @unittest.skipIf(sys.version >= "3", "long type not support") + def test_kafka_rdd_with_leaders(self): + """Test the Python direct Kafka RDD API with leaders.""" + topic = self._randomTopic() + sendData = {"a": 1, "b": 2, "c": 3} + offsetRanges = [OffsetRange(topic, 0, long(0), long(sum(sendData.values())))] + kafkaParams = {"metadata.broker.list": self._kafkaTestUtils.brokerAddress()} + address = self._kafkaTestUtils.brokerAddress().split(":") + leaders = {TopicAndPartition(topic, 0): Broker(address[0], int(address[1]))} + + self._kafkaTestUtils.createTopic(topic) + self._kafkaTestUtils.sendMessages(topic, sendData) + + rdd = KafkaUtils.createRDD(self.sc, kafkaParams, offsetRanges, leaders) + self._validateRddResult(sendData, rdd) if __name__ == "__main__": unittest.main() From bf35edd9d4b8b11df9f47b6ff43831bc95f06322 Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Tue, 28 Apr 2015 00:38:14 -0700 Subject: [PATCH 440/591] [SPARK-7187] SerializationDebugger should not crash user code rxin Author: Andrew Or Closes #5734 from andrewor14/ser-deb and squashes the following commits: e8aad6c [Andrew Or] NonFatal 57d0ef4 [Andrew Or] try catch improveException --- .../spark/serializer/SerializationDebugger.scala | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/serializer/SerializationDebugger.scala b/core/src/main/scala/org/apache/spark/serializer/SerializationDebugger.scala index cecb992579655..5abfa467c0ec8 100644 --- a/core/src/main/scala/org/apache/spark/serializer/SerializationDebugger.scala +++ b/core/src/main/scala/org/apache/spark/serializer/SerializationDebugger.scala @@ -23,6 +23,7 @@ import java.security.AccessController import scala.annotation.tailrec import scala.collection.mutable +import scala.util.control.NonFatal import org.apache.spark.Logging @@ -35,8 +36,15 @@ private[serializer] object SerializationDebugger extends Logging { */ def improveException(obj: Any, e: NotSerializableException): NotSerializableException = { if (enableDebugging && reflect != null) { - new NotSerializableException( - e.getMessage + "\nSerialization stack:\n" + find(obj).map("\t- " + _).mkString("\n")) + try { + new NotSerializableException( + e.getMessage + "\nSerialization stack:\n" + find(obj).map("\t- " + _).mkString("\n")) + } catch { + case NonFatal(t) => + // Fall back to old exception + logWarning("Exception in serialization debugger", t) + e + } } else { e } From d94cd1a733d5715792e6c4eac87f0d5c81aebbe2 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Tue, 28 Apr 2015 00:39:08 -0700 Subject: [PATCH 441/591] [SPARK-7135][SQL] DataFrame expression for monotonically increasing IDs. Author: Reynold Xin Closes #5709 from rxin/inc-id and squashes the following commits: 7853611 [Reynold Xin] private sql. a9fda0d [Reynold Xin] Missed a few numbers. 343d896 [Reynold Xin] Self review feedback. a7136cb [Reynold Xin] [SPARK-7135][SQL] DataFrame expression for monotonically increasing IDs. --- python/pyspark/sql/functions.py | 22 +++++++- .../MonotonicallyIncreasingID.scala | 53 +++++++++++++++++++ .../expressions/SparkPartitionID.scala | 6 +-- .../org/apache/spark/sql/functions.scala | 16 ++++++ .../spark/sql/ColumnExpressionSuite.scala | 11 ++++ 5 files changed, 103 insertions(+), 5 deletions(-) create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py index f48b7b5d10af7..7b86655d9c82f 100644 --- a/python/pyspark/sql/functions.py +++ b/python/pyspark/sql/functions.py @@ -103,8 +103,28 @@ def countDistinct(col, *cols): return Column(jc) +def monotonicallyIncreasingId(): + """A column that generates monotonically increasing 64-bit integers. + + The generated ID is guaranteed to be monotonically increasing and unique, but not consecutive. + The current implementation puts the partition ID in the upper 31 bits, and the record number + within each partition in the lower 33 bits. The assumption is that the data frame has + less than 1 billion partitions, and each partition has less than 8 billion records. + + As an example, consider a [[DataFrame]] with two partitions, each with 3 records. + This expression would return the following IDs: + 0, 1, 2, 8589934592 (1L << 33), 8589934593, 8589934594. + + >>> df0 = sc.parallelize(range(2), 2).mapPartitions(lambda x: [(1,), (2,), (3,)]).toDF(['col1']) + >>> df0.select(monotonicallyIncreasingId().alias('id')).collect() + [Row(id=0), Row(id=1), Row(id=2), Row(id=8589934592), Row(id=8589934593), Row(id=8589934594)] + """ + sc = SparkContext._active_spark_context + return Column(sc._jvm.functions.monotonicallyIncreasingId()) + + def sparkPartitionId(): - """Returns a column for partition ID of the Spark task. + """A column for partition ID of the Spark task. Note that this is indeterministic because it depends on data partitioning and task scheduling. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala new file mode 100644 index 0000000000000..9ac732b55b188 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/MonotonicallyIncreasingID.scala @@ -0,0 +1,53 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.expressions + +import org.apache.spark.TaskContext +import org.apache.spark.sql.catalyst.expressions.{Row, LeafExpression} +import org.apache.spark.sql.types.{LongType, DataType} + +/** + * Returns monotonically increasing 64-bit integers. + * + * The generated ID is guaranteed to be monotonically increasing and unique, but not consecutive. + * The current implementation puts the partition ID in the upper 31 bits, and the lower 33 bits + * represent the record number within each partition. The assumption is that the data frame has + * less than 1 billion partitions, and each partition has less than 8 billion records. + * + * Since this expression is stateful, it cannot be a case object. + */ +private[sql] case class MonotonicallyIncreasingID() extends LeafExpression { + + /** + * Record ID within each partition. By being transient, count's value is reset to 0 every time + * we serialize and deserialize it. + */ + @transient private[this] var count: Long = 0L + + override type EvaluatedType = Long + + override def nullable: Boolean = false + + override def dataType: DataType = LongType + + override def eval(input: Row): Long = { + val currentCount = count + count += 1 + (TaskContext.get().partitionId().toLong << 33) + currentCount + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala index fe7607c6ac340..c2c6cbd491598 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/expressions/SparkPartitionID.scala @@ -18,16 +18,14 @@ package org.apache.spark.sql.execution.expressions import org.apache.spark.TaskContext -import org.apache.spark.sql.catalyst.expressions.{Row, Expression} -import org.apache.spark.sql.catalyst.trees +import org.apache.spark.sql.catalyst.expressions.{LeafExpression, Row} import org.apache.spark.sql.types.{IntegerType, DataType} /** * Expression that returns the current partition id of the Spark task. */ -case object SparkPartitionID extends Expression with trees.LeafNode[Expression] { - self: Product => +private[sql] case object SparkPartitionID extends LeafExpression { override type EvaluatedType = Int diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala index 9738fd4f93bad..aa31d04a0cbe4 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala @@ -301,6 +301,22 @@ object functions { */ def lower(e: Column): Column = Lower(e.expr) + /** + * A column expression that generates monotonically increasing 64-bit integers. + * + * The generated ID is guaranteed to be monotonically increasing and unique, but not consecutive. + * The current implementation puts the partition ID in the upper 31 bits, and the record number + * within each partition in the lower 33 bits. The assumption is that the data frame has + * less than 1 billion partitions, and each partition has less than 8 billion records. + * + * As an example, consider a [[DataFrame]] with two partitions, each with 3 records. + * This expression would return the following IDs: + * 0, 1, 2, 8589934592 (1L << 33), 8589934593, 8589934594. + * + * @group normal_funcs + */ + def monotonicallyIncreasingId(): Column = execution.expressions.MonotonicallyIncreasingID() + /** * Unary minus, i.e. negate the expression. * {{{ diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala index 680b5c636960d..2ba5fc21ff57c 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala @@ -309,6 +309,17 @@ class ColumnExpressionSuite extends QueryTest { ) } + test("monotonicallyIncreasingId") { + // Make sure we have 2 partitions, each with 2 records. + val df = TestSQLContext.sparkContext.parallelize(1 to 2, 2).mapPartitions { iter => + Iterator(Tuple1(1), Tuple1(2)) + }.toDF("a") + checkAnswer( + df.select(monotonicallyIncreasingId()), + Row(0L) :: Row(1L) :: Row((1L << 33) + 0L) :: Row((1L << 33) + 1L) :: Nil + ) + } + test("sparkPartitionId") { val df = TestSQLContext.sparkContext.parallelize(1 to 1, 1).map(i => (i, i)).toDF("a", "b") checkAnswer( From e13cd86567a43672297bb488088dd8f40ec799bf Mon Sep 17 00:00:00 2001 From: Pei-Lun Lee Date: Tue, 28 Apr 2015 16:50:18 +0800 Subject: [PATCH 442/591] [SPARK-6352] [SQL] Custom parquet output committer Add new config "spark.sql.parquet.output.committer.class" to allow custom parquet output committer and an output committer class specific to use on s3. Fix compilation error introduced by https://github.com/apache/spark/pull/5042. Respect ParquetOutputFormat.ENABLE_JOB_SUMMARY flag. Author: Pei-Lun Lee Closes #5525 from ypcat/spark-6352 and squashes the following commits: 54c6b15 [Pei-Lun Lee] error handling 472870e [Pei-Lun Lee] add back custom parquet output committer ddd0f69 [Pei-Lun Lee] Merge branch 'master' of https://github.com/apache/spark into spark-6352 9ece5c5 [Pei-Lun Lee] compatibility with hadoop 1.x 8413fcd [Pei-Lun Lee] Merge branch 'master' of https://github.com/apache/spark into spark-6352 fe65915 [Pei-Lun Lee] add support for parquet config parquet.enable.summary-metadata e17bf47 [Pei-Lun Lee] Merge branch 'master' of https://github.com/apache/spark into spark-6352 9ae7545 [Pei-Lun Lee] [SPARL-6352] [SQL] Change to allow custom parquet output committer. 0d540b9 [Pei-Lun Lee] [SPARK-6352] [SQL] add license c42468c [Pei-Lun Lee] [SPARK-6352] [SQL] add test case 0fc03ca [Pei-Lun Lee] [SPARK-6532] [SQL] hide class DirectParquetOutputCommitter 769bd67 [Pei-Lun Lee] DirectParquetOutputCommitter f75e261 [Pei-Lun Lee] DirectParquetOutputCommitter --- .../DirectParquetOutputCommitter.scala | 73 +++++++++++++++++++ .../sql/parquet/ParquetTableOperations.scala | 21 ++++++ .../spark/sql/parquet/ParquetIOSuite.scala | 22 ++++++ 3 files changed, 116 insertions(+) create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala new file mode 100644 index 0000000000000..f5ce2718bec4a --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala @@ -0,0 +1,73 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.parquet + +import org.apache.hadoop.fs.Path +import org.apache.hadoop.mapreduce.{JobContext, TaskAttemptContext} +import org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter + +import parquet.Log +import parquet.hadoop.util.ContextUtil +import parquet.hadoop.{ParquetFileReader, ParquetFileWriter, ParquetOutputCommitter, ParquetOutputFormat} + +private[parquet] class DirectParquetOutputCommitter(outputPath: Path, context: TaskAttemptContext) + extends ParquetOutputCommitter(outputPath, context) { + val LOG = Log.getLog(classOf[ParquetOutputCommitter]) + + override def getWorkPath(): Path = outputPath + override def abortTask(taskContext: TaskAttemptContext): Unit = {} + override def commitTask(taskContext: TaskAttemptContext): Unit = {} + override def needsTaskCommit(taskContext: TaskAttemptContext): Boolean = true + override def setupJob(jobContext: JobContext): Unit = {} + override def setupTask(taskContext: TaskAttemptContext): Unit = {} + + override def commitJob(jobContext: JobContext) { + val configuration = ContextUtil.getConfiguration(jobContext) + val fileSystem = outputPath.getFileSystem(configuration) + + if (configuration.getBoolean(ParquetOutputFormat.ENABLE_JOB_SUMMARY, true)) { + try { + val outputStatus = fileSystem.getFileStatus(outputPath) + val footers = ParquetFileReader.readAllFootersInParallel(configuration, outputStatus) + try { + ParquetFileWriter.writeMetadataFile(configuration, outputPath, footers) + } catch { + case e: Exception => { + LOG.warn("could not write summary file for " + outputPath, e) + val metadataPath = new Path(outputPath, ParquetFileWriter.PARQUET_METADATA_FILE) + if (fileSystem.exists(metadataPath)) { + fileSystem.delete(metadataPath, true) + } + } + } + } catch { + case e: Exception => LOG.warn("could not write summary file for " + outputPath, e) + } + } + + if (configuration.getBoolean("mapreduce.fileoutputcommitter.marksuccessfuljobs", true)) { + try { + val successPath = new Path(outputPath, FileOutputCommitter.SUCCEEDED_FILE_NAME) + fileSystem.create(successPath).close() + } catch { + case e: Exception => LOG.warn("could not write success file for " + outputPath, e) + } + } + } +} + diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala index a938b77578686..aded126ea0615 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala @@ -381,6 +381,7 @@ private[parquet] class AppendingParquetOutputFormat(offset: Int) extends parquet.hadoop.ParquetOutputFormat[Row] { // override to accept existing directories as valid output directory override def checkOutputSpecs(job: JobContext): Unit = {} + var committer: OutputCommitter = null // override to choose output filename so not overwrite existing ones override def getDefaultWorkFile(context: TaskAttemptContext, extension: String): Path = { @@ -403,6 +404,26 @@ private[parquet] class AppendingParquetOutputFormat(offset: Int) private def getTaskAttemptID(context: TaskAttemptContext): TaskAttemptID = { context.getClass.getMethod("getTaskAttemptID").invoke(context).asInstanceOf[TaskAttemptID] } + + // override to create output committer from configuration + override def getOutputCommitter(context: TaskAttemptContext): OutputCommitter = { + if (committer == null) { + val output = getOutputPath(context) + val cls = context.getConfiguration.getClass("spark.sql.parquet.output.committer.class", + classOf[ParquetOutputCommitter], classOf[ParquetOutputCommitter]) + val ctor = cls.getDeclaredConstructor(classOf[Path], classOf[TaskAttemptContext]) + committer = ctor.newInstance(output, context).asInstanceOf[ParquetOutputCommitter] + } + committer + } + + // FileOutputFormat.getOutputPath takes JobConf in hadoop-1 but JobContext in hadoop-2 + private def getOutputPath(context: TaskAttemptContext): Path = { + context.getConfiguration().get("mapred.output.dir") match { + case null => null + case name => new Path(name) + } + } } /** diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala index 97c0f439acf13..b504842053690 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala @@ -381,6 +381,28 @@ class ParquetIOSuiteBase extends QueryTest with ParquetTest { } } } + + test("SPARK-6352 DirectParquetOutputCommitter") { + // Write to a parquet file and let it fail. + // _temporary should be missing if direct output committer works. + try { + configuration.set("spark.sql.parquet.output.committer.class", + "org.apache.spark.sql.parquet.DirectParquetOutputCommitter") + sqlContext.udf.register("div0", (x: Int) => x / 0) + withTempPath { dir => + intercept[org.apache.spark.SparkException] { + sqlContext.sql("select div0(1)").saveAsParquetFile(dir.getCanonicalPath) + } + val path = new Path(dir.getCanonicalPath, "_temporary") + val fs = path.getFileSystem(configuration) + assert(!fs.exists(path)) + } + } + finally { + configuration.set("spark.sql.parquet.output.committer.class", + "parquet.hadoop.ParquetOutputCommitter") + } + } } class ParquetDataSourceOnIOSuite extends ParquetIOSuiteBase with BeforeAndAfterAll { From 7f3b3b7eb7d14767124a28ec0062c4d60d6c16fc Mon Sep 17 00:00:00 2001 From: Sean Owen Date: Tue, 28 Apr 2015 07:48:34 -0400 Subject: [PATCH 443/591] [SPARK-7168] [BUILD] Update plugin versions in Maven build and centralize versions Update Maven build plugin versions and centralize plugin version management Author: Sean Owen Closes #5720 from srowen/SPARK-7168 and squashes the following commits: 98a8947 [Sean Owen] Make install, deploy plugin versions explicit 4ecf3b2 [Sean Owen] Update Maven build plugin versions and centralize plugin version management --- assembly/pom.xml | 1 - core/pom.xml | 1 - network/common/pom.xml | 1 - pom.xml | 44 ++++++++++++++++++++++++++++++------------ sql/hive/pom.xml | 1 - 5 files changed, 32 insertions(+), 16 deletions(-) diff --git a/assembly/pom.xml b/assembly/pom.xml index 20593e710dedb..2b4d0a990bf22 100644 --- a/assembly/pom.xml +++ b/assembly/pom.xml @@ -194,7 +194,6 @@ org.apache.maven.plugins maven-assembly-plugin - 2.4 dist diff --git a/core/pom.xml b/core/pom.xml index 5e89d548cd47f..459ef66712c36 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -478,7 +478,6 @@ org.codehaus.mojo exec-maven-plugin - 1.3.2 sparkr-pkg diff --git a/network/common/pom.xml b/network/common/pom.xml index 22c738bde6d42..0c3147761cfc5 100644 --- a/network/common/pom.xml +++ b/network/common/pom.xml @@ -95,7 +95,6 @@ org.apache.maven.plugins maven-jar-plugin - 2.2 test-jar-on-test-compile diff --git a/pom.xml b/pom.xml index 9fbce1d639d8b..928f5d0f5efad 100644 --- a/pom.xml +++ b/pom.xml @@ -1082,7 +1082,7 @@ org.apache.maven.plugins maven-enforcer-plugin - 1.3.1 + 1.4 enforce-versions @@ -1105,7 +1105,7 @@ org.codehaus.mojo build-helper-maven-plugin - 1.8 + 1.9.1 net.alchim31.maven @@ -1176,7 +1176,7 @@ org.apache.maven.plugins maven-compiler-plugin - 3.1 + 3.3 ${java.version} ${java.version} @@ -1189,7 +1189,7 @@ org.apache.maven.plugins maven-surefire-plugin - 2.18 + 2.18.1 @@ -1260,17 +1260,17 @@ org.apache.maven.plugins maven-jar-plugin - 2.4 + 2.6 org.apache.maven.plugins maven-antrun-plugin - 1.7 + 1.8 org.apache.maven.plugins maven-source-plugin - 2.2.1 + 2.4 true @@ -1287,7 +1287,7 @@ org.apache.maven.plugins maven-clean-plugin - 2.5 + 2.6.1 @@ -1305,7 +1305,27 @@ org.apache.maven.plugins maven-javadoc-plugin - 2.10.1 + 2.10.3 + + + org.codehaus.mojo + exec-maven-plugin + 1.4.0 + + + org.apache.maven.plugins + maven-assembly-plugin + 2.5.3 + + + org.apache.maven.plugins + maven-install-plugin + 2.5.2 + + + org.apache.maven.plugins + maven-deploy-plugin + 2.8.2 @@ -1315,7 +1335,7 @@ org.apache.maven.plugins maven-dependency-plugin - 2.9 + 2.10 test-compile @@ -1334,7 +1354,7 @@ org.codehaus.gmavenplus gmavenplus-plugin - 1.2 + 1.5 process-test-classes @@ -1359,7 +1379,7 @@ org.apache.maven.plugins maven-shade-plugin - 2.2 + 2.3 false diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml index 21dce8d8a565a..e322340094e6f 100644 --- a/sql/hive/pom.xml +++ b/sql/hive/pom.xml @@ -183,7 +183,6 @@ org.apache.maven.plugins maven-dependency-plugin - 2.4 copy-dependencies From 75905c57cd57bc5b650ac5f486580ef8a229b260 Mon Sep 17 00:00:00 2001 From: Jim Carroll Date: Tue, 28 Apr 2015 07:51:02 -0400 Subject: [PATCH 444/591] [SPARK-7100] [MLLIB] Fix persisted RDD leak in GradientBoostTrees This fixes a leak of a persisted RDD where GradientBoostTrees can call persist but never unpersists. Jira: https://issues.apache.org/jira/browse/SPARK-7100 Discussion: http://apache-spark-developers-list.1001551.n3.nabble.com/GradientBoostTrees-leaks-a-persisted-RDD-td11750.html Author: Jim Carroll Closes #5669 from jimfcarroll/gb-unpersist-fix and squashes the following commits: 45f4b03 [Jim Carroll] [SPARK-7100][MLLib] Fix persisted RDD leak in GradientBoostTrees --- .../apache/spark/mllib/tree/GradientBoostedTrees.scala | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala index 0e31c7ed58df8..deac390130128 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala @@ -177,9 +177,10 @@ object GradientBoostedTrees extends Logging { treeStrategy.assertValid() // Cache input - if (input.getStorageLevel == StorageLevel.NONE) { + val persistedInput = if (input.getStorageLevel == StorageLevel.NONE) { input.persist(StorageLevel.MEMORY_AND_DISK) - } + true + } else false timer.stop("init") @@ -265,6 +266,9 @@ object GradientBoostedTrees extends Logging { logInfo("Internal timing for DecisionTree:") logInfo(s"$timer") + + if (persistedInput) input.unpersist() + if (validate) { new GradientBoostedTreesModel( boostingStrategy.treeStrategy.algo, From 268c419f1586110b90e68f98cd000a782d18828c Mon Sep 17 00:00:00 2001 From: Masayoshi TSUZUKI Date: Tue, 28 Apr 2015 07:55:21 -0400 Subject: [PATCH 445/591] [SPARK-6435] spark-shell --jars option does not add all jars to classpath Modified to accept double-quotated args properly in spark-shell.cmd. Author: Masayoshi TSUZUKI Closes #5227 from tsudukim/feature/SPARK-6435-2 and squashes the following commits: ac55787 [Masayoshi TSUZUKI] removed unnecessary argument. 60789a7 [Masayoshi TSUZUKI] Merge branch 'master' of https://github.com/apache/spark into feature/SPARK-6435-2 1fee420 [Masayoshi TSUZUKI] fixed test code for escaping '='. 0d4dc41 [Masayoshi TSUZUKI] - escaped comman and semicolon in CommandBuilderUtils.java - added random string to the temporary filename - double-quotation followed by `cmd /c` did not worked properly - no need to escape `=` by `^` - if double-quoted string ended with `\` like classpath, the last `\` is parsed as the escape charactor and the closing `"` didn't work properly 2a332e5 [Masayoshi TSUZUKI] Merge branch 'master' into feature/SPARK-6435-2 04f4291 [Masayoshi TSUZUKI] [SPARK-6435] spark-shell --jars option does not add all jars to classpath --- bin/spark-class2.cmd | 5 ++++- .../org/apache/spark/launcher/CommandBuilderUtils.java | 9 ++++----- .../src/main/java/org/apache/spark/launcher/Main.java | 6 +----- .../apache/spark/launcher/CommandBuilderUtilsSuite.java | 5 ++++- 4 files changed, 13 insertions(+), 12 deletions(-) diff --git a/bin/spark-class2.cmd b/bin/spark-class2.cmd index 3d068dd3a2739..db09fa27e51a6 100644 --- a/bin/spark-class2.cmd +++ b/bin/spark-class2.cmd @@ -61,7 +61,10 @@ if not "x%JAVA_HOME%"=="x" set RUNNER=%JAVA_HOME%\bin\java rem The launcher library prints the command to be executed in a single line suitable for being rem executed by the batch interpreter. So read all the output of the launcher into a variable. -for /f "tokens=*" %%i in ('cmd /C ""%RUNNER%" -cp %LAUNCH_CLASSPATH% org.apache.spark.launcher.Main %*"') do ( +set LAUNCHER_OUTPUT=%temp%\spark-class-launcher-output-%RANDOM%.txt +"%RUNNER%" -cp %LAUNCH_CLASSPATH% org.apache.spark.launcher.Main %* > %LAUNCHER_OUTPUT% +for /f "tokens=*" %%i in (%LAUNCHER_OUTPUT%) do ( set SPARK_CMD=%%i ) +del %LAUNCHER_OUTPUT% %SPARK_CMD% diff --git a/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java b/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java index 8028e42ffb483..261402856ac5e 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java +++ b/launcher/src/main/java/org/apache/spark/launcher/CommandBuilderUtils.java @@ -244,7 +244,7 @@ static String quoteForBatchScript(String arg) { boolean needsQuotes = false; for (int i = 0; i < arg.length(); i++) { int c = arg.codePointAt(i); - if (Character.isWhitespace(c) || c == '"' || c == '=') { + if (Character.isWhitespace(c) || c == '"' || c == '=' || c == ',' || c == ';') { needsQuotes = true; break; } @@ -261,15 +261,14 @@ static String quoteForBatchScript(String arg) { quoted.append('"'); break; - case '=': - quoted.append('^'); - break; - default: break; } quoted.appendCodePoint(cp); } + if (arg.codePointAt(arg.length() - 1) == '\\') { + quoted.append("\\"); + } quoted.append("\""); return quoted.toString(); } diff --git a/launcher/src/main/java/org/apache/spark/launcher/Main.java b/launcher/src/main/java/org/apache/spark/launcher/Main.java index 206acfb514d86..929b29a49ed70 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/Main.java +++ b/launcher/src/main/java/org/apache/spark/launcher/Main.java @@ -101,12 +101,9 @@ public static void main(String[] argsArray) throws Exception { * The method quotes all arguments so that spaces are handled as expected. Quotes within arguments * are "double quoted" (which is batch for escaping a quote). This page has more details about * quoting and other batch script fun stuff: http://ss64.com/nt/syntax-esc.html - * - * The command is executed using "cmd /c" and formatted in single line, since that's the - * easiest way to consume this from a batch script (see spark-class2.cmd). */ private static String prepareWindowsCommand(List cmd, Map childEnv) { - StringBuilder cmdline = new StringBuilder("cmd /c \""); + StringBuilder cmdline = new StringBuilder(); for (Map.Entry e : childEnv.entrySet()) { cmdline.append(String.format("set %s=%s", e.getKey(), e.getValue())); cmdline.append(" && "); @@ -115,7 +112,6 @@ private static String prepareWindowsCommand(List cmd, Map Date: Tue, 28 Apr 2015 09:46:08 -0700 Subject: [PATCH 446/591] [SPARK-5253] [ML] LinearRegression with L1/L2 (ElasticNet) using OWLQN Author: DB Tsai Author: DB Tsai Closes #4259 from dbtsai/lir and squashes the following commits: a81c201 [DB Tsai] add import org.apache.spark.util.Utils back 9fc48ed [DB Tsai] rebase 2178b63 [DB Tsai] add comments 9988ca8 [DB Tsai] addressed feedback and fixed a bug. TODO: documentation and build another synthetic dataset which can catch the bug fixed in this commit. fcbaefe [DB Tsai] Refactoring 4eb078d [DB Tsai] first commit --- .../ml/param/shared/SharedParamsCodeGen.scala | 4 +- .../spark/ml/param/shared/sharedParams.scala | 34 ++ .../ml/regression/LinearRegression.scala | 304 ++++++++++++++++-- .../apache/spark/mllib/linalg/Vectors.scala | 8 +- .../spark/mllib/optimization/Gradient.scala | 6 +- .../spark/mllib/optimization/LBFGS.scala | 15 +- .../mllib/util/LinearDataGenerator.scala | 43 ++- .../ml/regression/LinearRegressionSuite.scala | 158 +++++++-- 8 files changed, 508 insertions(+), 64 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala index e88c48741e99f..3f7e8f5a0b22c 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala @@ -46,7 +46,9 @@ private[shared] object SharedParamsCodeGen { ParamDesc[String]("outputCol", "output column name"), ParamDesc[Int]("checkpointInterval", "checkpoint interval"), ParamDesc[Boolean]("fitIntercept", "whether to fit an intercept term", Some("true")), - ParamDesc[Long]("seed", "random seed", Some("Utils.random.nextLong()"))) + ParamDesc[Long]("seed", "random seed", Some("Utils.random.nextLong()")), + ParamDesc[Double]("elasticNetParam", "the ElasticNet mixing parameter"), + ParamDesc[Double]("tol", "the convergence tolerance for iterative algorithms")) val code = genSharedParams(params) val file = "src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala" diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala index a860b8834cff9..7d2c76d6c62c8 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala @@ -276,4 +276,38 @@ trait HasSeed extends Params { /** @group getParam */ final def getSeed: Long = getOrDefault(seed) } + +/** + * :: DeveloperApi :: + * Trait for shared param elasticNetParam. + */ +@DeveloperApi +trait HasElasticNetParam extends Params { + + /** + * Param for the ElasticNet mixing parameter. + * @group param + */ + final val elasticNetParam: DoubleParam = new DoubleParam(this, "elasticNetParam", "the ElasticNet mixing parameter") + + /** @group getParam */ + final def getElasticNetParam: Double = getOrDefault(elasticNetParam) +} + +/** + * :: DeveloperApi :: + * Trait for shared param tol. + */ +@DeveloperApi +trait HasTol extends Params { + + /** + * Param for the convergence tolerance for iterative algorithms. + * @group param + */ + final val tol: DoubleParam = new DoubleParam(this, "tol", "the convergence tolerance for iterative algorithms") + + /** @group getParam */ + final def getTol: Double = getOrDefault(tol) +} // scalastyle:on diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala index 26ca7459c4fdf..f92c6816eb54c 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala @@ -17,21 +17,29 @@ package org.apache.spark.ml.regression +import scala.collection.mutable + +import breeze.linalg.{norm => brzNorm, DenseVector => BDV} +import breeze.optimize.{LBFGS => BreezeLBFGS, OWLQN => BreezeOWLQN} +import breeze.optimize.{CachedDiffFunction, DiffFunction} + import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.param.{Params, ParamMap} -import org.apache.spark.ml.param.shared._ -import org.apache.spark.mllib.linalg.{BLAS, Vector} -import org.apache.spark.mllib.regression.LinearRegressionWithSGD +import org.apache.spark.ml.param.shared.{HasElasticNetParam, HasMaxIter, HasRegParam, HasTol} +import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer +import org.apache.spark.mllib.linalg.{Vector, Vectors} +import org.apache.spark.mllib.linalg.BLAS._ +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame import org.apache.spark.storage.StorageLevel - +import org.apache.spark.util.StatCounter /** * Params for linear regression. */ private[regression] trait LinearRegressionParams extends RegressorParams - with HasRegParam with HasMaxIter - + with HasRegParam with HasElasticNetParam with HasMaxIter with HasTol /** * :: AlphaComponent :: @@ -42,34 +50,119 @@ private[regression] trait LinearRegressionParams extends RegressorParams class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegressionModel] with LinearRegressionParams { - setDefault(regParam -> 0.1, maxIter -> 100) - - /** @group setParam */ + /** + * Set the regularization parameter. + * Default is 0.0. + * @group setParam + */ def setRegParam(value: Double): this.type = set(regParam, value) + setDefault(regParam -> 0.0) + + /** + * Set the ElasticNet mixing parameter. + * For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. + * For 0 < alpha < 1, the penalty is a combination of L1 and L2. + * Default is 0.0 which is an L2 penalty. + * @group setParam + */ + def setElasticNetParam(value: Double): this.type = set(elasticNetParam, value) + setDefault(elasticNetParam -> 0.0) - /** @group setParam */ + /** + * Set the maximal number of iterations. + * Default is 100. + * @group setParam + */ def setMaxIter(value: Int): this.type = set(maxIter, value) + setDefault(maxIter -> 100) + + /** + * Set the convergence tolerance of iterations. + * Smaller value will lead to higher accuracy with the cost of more iterations. + * Default is 1E-6. + * @group setParam + */ + def setTol(value: Double): this.type = set(tol, value) + setDefault(tol -> 1E-6) override protected def train(dataset: DataFrame, paramMap: ParamMap): LinearRegressionModel = { - // Extract columns from data. If dataset is persisted, do not persist oldDataset. - val oldDataset = extractLabeledPoints(dataset, paramMap) + // Extract columns from data. If dataset is persisted, do not persist instances. + val instances = extractLabeledPoints(dataset, paramMap).map { + case LabeledPoint(label: Double, features: Vector) => (label, features) + } val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE if (handlePersistence) { - oldDataset.persist(StorageLevel.MEMORY_AND_DISK) + instances.persist(StorageLevel.MEMORY_AND_DISK) + } + + val (summarizer, statCounter) = instances.treeAggregate( + (new MultivariateOnlineSummarizer, new StatCounter))( { + case ((summarizer: MultivariateOnlineSummarizer, statCounter: StatCounter), + (label: Double, features: Vector)) => + (summarizer.add(features), statCounter.merge(label)) + }, { + case ((summarizer1: MultivariateOnlineSummarizer, statCounter1: StatCounter), + (summarizer2: MultivariateOnlineSummarizer, statCounter2: StatCounter)) => + (summarizer1.merge(summarizer2), statCounter1.merge(statCounter2)) + }) + + val numFeatures = summarizer.mean.size + val yMean = statCounter.mean + val yStd = math.sqrt(statCounter.variance) + + val featuresMean = summarizer.mean.toArray + val featuresStd = summarizer.variance.toArray.map(math.sqrt) + + // Since we implicitly do the feature scaling when we compute the cost function + // to improve the convergence, the effective regParam will be changed. + val effectiveRegParam = paramMap(regParam) / yStd + val effectiveL1RegParam = paramMap(elasticNetParam) * effectiveRegParam + val effectiveL2RegParam = (1.0 - paramMap(elasticNetParam)) * effectiveRegParam + + val costFun = new LeastSquaresCostFun(instances, yStd, yMean, + featuresStd, featuresMean, effectiveL2RegParam) + + val optimizer = if (paramMap(elasticNetParam) == 0.0 || effectiveRegParam == 0.0) { + new BreezeLBFGS[BDV[Double]](paramMap(maxIter), 10, paramMap(tol)) + } else { + new BreezeOWLQN[Int, BDV[Double]](paramMap(maxIter), 10, effectiveL1RegParam, paramMap(tol)) + } + + val initialWeights = Vectors.zeros(numFeatures) + val states = + optimizer.iterations(new CachedDiffFunction(costFun), initialWeights.toBreeze.toDenseVector) + + var state = states.next() + val lossHistory = mutable.ArrayBuilder.make[Double] + + while (states.hasNext) { + lossHistory += state.value + state = states.next() + } + lossHistory += state.value + + // TODO: Based on the sparsity of weights, we may convert the weights to the sparse vector. + // The weights are trained in the scaled space; we're converting them back to + // the original space. + val weights = { + val rawWeights = state.x.toArray.clone() + var i = 0 + while (i < rawWeights.length) { + rawWeights(i) *= { if (featuresStd(i) != 0.0) yStd / featuresStd(i) else 0.0 } + i += 1 + } + Vectors.dense(rawWeights) } - // Train model - val lr = new LinearRegressionWithSGD() - lr.optimizer - .setRegParam(paramMap(regParam)) - .setNumIterations(paramMap(maxIter)) - val model = lr.run(oldDataset) - val lrm = new LinearRegressionModel(this, paramMap, model.weights, model.intercept) + // The intercept in R's GLMNET is computed using closed form after the coefficients are + // converged. See the following discussion for detail. + // http://stats.stackexchange.com/questions/13617/how-is-the-intercept-computed-in-glmnet + val intercept = yMean - dot(weights, Vectors.dense(featuresMean)) if (handlePersistence) { - oldDataset.unpersist() + instances.unpersist() } - lrm + new LinearRegressionModel(this, paramMap, weights, intercept) } } @@ -88,7 +181,7 @@ class LinearRegressionModel private[ml] ( with LinearRegressionParams { override protected def predict(features: Vector): Double = { - BLAS.dot(features, weights) + intercept + dot(features, weights) + intercept } override protected def copy(): LinearRegressionModel = { @@ -97,3 +190,168 @@ class LinearRegressionModel private[ml] ( m } } + +/** + * LeastSquaresAggregator computes the gradient and loss for a Least-squared loss function, + * as used in linear regression for samples in sparse or dense vector in a online fashion. + * + * Two LeastSquaresAggregator can be merged together to have a summary of loss and gradient of + * the corresponding joint dataset. + * + + * * Compute gradient and loss for a Least-squared loss function, as used in linear regression. + * This is correct for the averaged least squares loss function (mean squared error) + * L = 1/2n ||A weights-y||^2 + * See also the documentation for the precise formulation. + * + * @param weights weights/coefficients corresponding to features + * + * @param updater Updater to be used to update weights after every iteration. + */ +private class LeastSquaresAggregator( + weights: Vector, + labelStd: Double, + labelMean: Double, + featuresStd: Array[Double], + featuresMean: Array[Double]) extends Serializable { + + private var totalCnt: Long = 0 + private var lossSum = 0.0 + private var diffSum = 0.0 + + private val (effectiveWeightsArray: Array[Double], offset: Double, dim: Int) = { + val weightsArray = weights.toArray.clone() + var sum = 0.0 + var i = 0 + while (i < weightsArray.length) { + if (featuresStd(i) != 0.0) { + weightsArray(i) /= featuresStd(i) + sum += weightsArray(i) * featuresMean(i) + } else { + weightsArray(i) = 0.0 + } + i += 1 + } + (weightsArray, -sum + labelMean / labelStd, weightsArray.length) + } + private val effectiveWeightsVector = Vectors.dense(effectiveWeightsArray) + + private val gradientSumArray: Array[Double] = Array.ofDim[Double](dim) + + /** + * Add a new training data to this LeastSquaresAggregator, and update the loss and gradient + * of the objective function. + * + * @param label The label for this data point. + * @param data The features for one data point in dense/sparse vector format to be added + * into this aggregator. + * @return This LeastSquaresAggregator object. + */ + def add(label: Double, data: Vector): this.type = { + require(dim == data.size, s"Dimensions mismatch when adding new sample." + + s" Expecting $dim but got ${data.size}.") + + val diff = dot(data, effectiveWeightsVector) - label / labelStd + offset + + if (diff != 0) { + val localGradientSumArray = gradientSumArray + data.foreachActive { (index, value) => + if (featuresStd(index) != 0.0 && value != 0.0) { + localGradientSumArray(index) += diff * value / featuresStd(index) + } + } + lossSum += diff * diff / 2.0 + diffSum += diff + } + + totalCnt += 1 + this + } + + /** + * Merge another LeastSquaresAggregator, and update the loss and gradient + * of the objective function. + * (Note that it's in place merging; as a result, `this` object will be modified.) + * + * @param other The other LeastSquaresAggregator to be merged. + * @return This LeastSquaresAggregator object. + */ + def merge(other: LeastSquaresAggregator): this.type = { + require(dim == other.dim, s"Dimensions mismatch when merging with another " + + s"LeastSquaresAggregator. Expecting $dim but got ${other.dim}.") + + if (other.totalCnt != 0) { + totalCnt += other.totalCnt + lossSum += other.lossSum + diffSum += other.diffSum + + var i = 0 + val localThisGradientSumArray = this.gradientSumArray + val localOtherGradientSumArray = other.gradientSumArray + while (i < dim) { + localThisGradientSumArray(i) += localOtherGradientSumArray(i) + i += 1 + } + } + this + } + + def count: Long = totalCnt + + def loss: Double = lossSum / totalCnt + + def gradient: Vector = { + val result = Vectors.dense(gradientSumArray.clone()) + + val correction = { + val temp = effectiveWeightsArray.clone() + var i = 0 + while (i < temp.length) { + temp(i) *= featuresMean(i) + i += 1 + } + Vectors.dense(temp) + } + + axpy(-diffSum, correction, result) + scal(1.0 / totalCnt, result) + result + } +} + +/** + * LeastSquaresCostFun implements Breeze's DiffFunction[T] for Least Squares cost. + * It returns the loss and gradient with L2 regularization at a particular point (weights). + * It's used in Breeze's convex optimization routines. + */ +private class LeastSquaresCostFun( + data: RDD[(Double, Vector)], + labelStd: Double, + labelMean: Double, + featuresStd: Array[Double], + featuresMean: Array[Double], + effectiveL2regParam: Double) extends DiffFunction[BDV[Double]] { + + override def calculate(weights: BDV[Double]): (Double, BDV[Double]) = { + val w = Vectors.fromBreeze(weights) + + val leastSquaresAggregator = data.treeAggregate(new LeastSquaresAggregator(w, labelStd, + labelMean, featuresStd, featuresMean))( + seqOp = (c, v) => (c, v) match { + case (aggregator, (label, features)) => aggregator.add(label, features) + }, + combOp = (c1, c2) => (c1, c2) match { + case (aggregator1, aggregator2) => aggregator1.merge(aggregator2) + }) + + // regVal is the sum of weight squares for L2 regularization + val norm = brzNorm(weights, 2.0) + val regVal = 0.5 * effectiveL2regParam * norm * norm + + val loss = leastSquaresAggregator.loss + regVal + val gradient = leastSquaresAggregator.gradient + axpy(effectiveL2regParam, w, gradient) + + (loss, gradient.toBreeze.asInstanceOf[BDV[Double]]) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala index 166c00cff634d..af0cfe22ca10d 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala @@ -85,7 +85,7 @@ sealed trait Vector extends Serializable { /** * Converts the instance to a breeze vector. */ - private[mllib] def toBreeze: BV[Double] + private[spark] def toBreeze: BV[Double] /** * Gets the value of the ith element. @@ -284,7 +284,7 @@ object Vectors { /** * Creates a vector instance from a breeze vector. */ - private[mllib] def fromBreeze(breezeVector: BV[Double]): Vector = { + private[spark] def fromBreeze(breezeVector: BV[Double]): Vector = { breezeVector match { case v: BDV[Double] => if (v.offset == 0 && v.stride == 1 && v.length == v.data.length) { @@ -483,7 +483,7 @@ class DenseVector(val values: Array[Double]) extends Vector { override def toArray: Array[Double] = values - private[mllib] override def toBreeze: BV[Double] = new BDV[Double](values) + private[spark] override def toBreeze: BV[Double] = new BDV[Double](values) override def apply(i: Int): Double = values(i) @@ -543,7 +543,7 @@ class SparseVector( new SparseVector(size, indices.clone(), values.clone()) } - private[mllib] override def toBreeze: BV[Double] = new BSV[Double](indices, values, size) + private[spark] override def toBreeze: BV[Double] = new BSV[Double](indices, values, size) private[spark] override def foreachActive(f: (Int, Double) => Unit) = { var i = 0 diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala index 8bfa0d2b64995..240baeb5a158b 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala @@ -37,7 +37,11 @@ abstract class Gradient extends Serializable { * * @return (gradient: Vector, loss: Double) */ - def compute(data: Vector, label: Double, weights: Vector): (Vector, Double) + def compute(data: Vector, label: Double, weights: Vector): (Vector, Double) = { + val gradient = Vectors.zeros(weights.size) + val loss = compute(data, label, weights, gradient) + (gradient, loss) + } /** * Compute the gradient and loss given the features of a single data point, diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala index ef6eccd90711a..efedc112d380e 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala @@ -17,6 +17,7 @@ package org.apache.spark.mllib.optimization +import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import breeze.linalg.{DenseVector => BDV} @@ -164,7 +165,7 @@ object LBFGS extends Logging { regParam: Double, initialWeights: Vector): (Vector, Array[Double]) = { - val lossHistory = new ArrayBuffer[Double](maxNumIterations) + val lossHistory = mutable.ArrayBuilder.make[Double] val numExamples = data.count() @@ -181,17 +182,19 @@ object LBFGS extends Logging { * and regVal is the regularization value computed in the previous iteration as well. */ var state = states.next() - while(states.hasNext) { - lossHistory.append(state.value) + while (states.hasNext) { + lossHistory += state.value state = states.next() } - lossHistory.append(state.value) + lossHistory += state.value val weights = Vectors.fromBreeze(state.x) + val lossHistoryArray = lossHistory.result() + logInfo("LBFGS.runLBFGS finished. Last 10 losses %s".format( - lossHistory.takeRight(10).mkString(", "))) + lossHistoryArray.takeRight(10).mkString(", "))) - (weights, lossHistory.toArray) + (weights, lossHistoryArray) } /** diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala index c9d33787b0bb5..d7bb943e84f53 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala @@ -56,6 +56,10 @@ object LinearDataGenerator { } /** + * For compatibility, the generated data without specifying the mean and variance + * will have zero mean and variance of (1.0/3.0) since the original output range is + * [-1, 1] with uniform distribution, and the variance of uniform distribution + * is (b - a)^2^ / 12 which will be (1.0/3.0) * * @param intercept Data intercept * @param weights Weights to be applied. @@ -70,10 +74,47 @@ object LinearDataGenerator { nPoints: Int, seed: Int, eps: Double = 0.1): Seq[LabeledPoint] = { + generateLinearInput(intercept, weights, + Array.fill[Double](weights.size)(0.0), + Array.fill[Double](weights.size)(1.0 / 3.0), + nPoints, seed, eps)} + + /** + * + * @param intercept Data intercept + * @param weights Weights to be applied. + * @param xMean the mean of the generated features. Lots of time, if the features are not properly + * standardized, the algorithm with poor implementation will have difficulty + * to converge. + * @param xVariance the variance of the generated features. + * @param nPoints Number of points in sample. + * @param seed Random seed + * @param eps Epsilon scaling factor. + * @return Seq of input. + */ + def generateLinearInput( + intercept: Double, + weights: Array[Double], + xMean: Array[Double], + xVariance: Array[Double], + nPoints: Int, + seed: Int, + eps: Double): Seq[LabeledPoint] = { val rnd = new Random(seed) val x = Array.fill[Array[Double]](nPoints)( - Array.fill[Double](weights.length)(2 * rnd.nextDouble - 1.0)) + Array.fill[Double](weights.length)(rnd.nextDouble)) + + x.map(vector => { + // This doesn't work if `vector` is a sparse vector. + val vectorArray = vector.toArray + var i = 0 + while (i < vectorArray.size) { + vectorArray(i) = (vectorArray(i) - 0.5) * math.sqrt(12.0 * xVariance(i)) + xMean(i) + i += 1 + } + }) + val y = x.map { xi => blas.ddot(weights.length, xi, 1, weights, 1) + intercept + eps * rnd.nextGaussian() } diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala index bbb44c3e2dfc2..80323ef5201a6 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala @@ -19,47 +19,149 @@ package org.apache.spark.ml.regression import org.scalatest.FunSuite -import org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInput -import org.apache.spark.mllib.util.MLlibTestSparkContext -import org.apache.spark.sql.{DataFrame, SQLContext} +import org.apache.spark.mllib.linalg.DenseVector +import org.apache.spark.mllib.util.{LinearDataGenerator, MLlibTestSparkContext} +import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.sql.{Row, SQLContext, DataFrame} class LinearRegressionSuite extends FunSuite with MLlibTestSparkContext { @transient var sqlContext: SQLContext = _ @transient var dataset: DataFrame = _ + /** + * In `LinearRegressionSuite`, we will make sure that the model trained by SparkML + * is the same as the one trained by R's glmnet package. The following instruction + * describes how to reproduce the data in R. + * + * import org.apache.spark.mllib.util.LinearDataGenerator + * val data = + * sc.parallelize(LinearDataGenerator.generateLinearInput(6.3, Array(4.7, 7.2), 10000, 42), 2) + * data.map(x=> x.label + ", " + x.features(0) + ", " + x.features(1)).saveAsTextFile("path") + */ override def beforeAll(): Unit = { super.beforeAll() sqlContext = new SQLContext(sc) dataset = sqlContext.createDataFrame( - sc.parallelize(generateLogisticInput(1.0, 1.0, nPoints = 100, seed = 42), 2)) + sc.parallelize(LinearDataGenerator.generateLinearInput( + 6.3, Array(4.7, 7.2), Array(0.9, -1.3), Array(0.7, 1.2), 10000, 42, 0.1), 2)) } - test("linear regression: default params") { - val lr = new LinearRegression - assert(lr.getLabelCol == "label") - val model = lr.fit(dataset) - model.transform(dataset) - .select("label", "prediction") - .collect() - // Check defaults - assert(model.getFeaturesCol == "features") - assert(model.getPredictionCol == "prediction") + test("linear regression with intercept without regularization") { + val trainer = new LinearRegression + val model = trainer.fit(dataset) + + /** + * Using the following R code to load the data and train the model using glmnet package. + * + * library("glmnet") + * data <- read.csv("path", header=FALSE, stringsAsFactors=FALSE) + * features <- as.matrix(data.frame(as.numeric(data$V2), as.numeric(data$V3))) + * label <- as.numeric(data$V1) + * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0, lambda = 0)) + * > weights + * 3 x 1 sparse Matrix of class "dgCMatrix" + * s0 + * (Intercept) 6.300528 + * as.numeric.data.V2. 4.701024 + * as.numeric.data.V3. 7.198257 + */ + val interceptR = 6.298698 + val weightsR = Array(4.700706, 7.199082) + + assert(model.intercept ~== interceptR relTol 1E-3) + assert(model.weights(0) ~== weightsR(0) relTol 1E-3) + assert(model.weights(1) ~== weightsR(1) relTol 1E-3) + + model.transform(dataset).select("features", "prediction").collect().foreach { + case Row(features: DenseVector, prediction1: Double) => + val prediction2 = + features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept + assert(prediction1 ~== prediction2 relTol 1E-5) + } + } + + test("linear regression with intercept with L1 regularization") { + val trainer = (new LinearRegression).setElasticNetParam(1.0).setRegParam(0.57) + val model = trainer.fit(dataset) + + /** + * weights <- coef(glmnet(features, label, family="gaussian", alpha = 1.0, lambda = 0.57)) + * > weights + * 3 x 1 sparse Matrix of class "dgCMatrix" + * s0 + * (Intercept) 6.311546 + * as.numeric.data.V2. 2.123522 + * as.numeric.data.V3. 4.605651 + */ + val interceptR = 6.243000 + val weightsR = Array(4.024821, 6.679841) + + assert(model.intercept ~== interceptR relTol 1E-3) + assert(model.weights(0) ~== weightsR(0) relTol 1E-3) + assert(model.weights(1) ~== weightsR(1) relTol 1E-3) + + model.transform(dataset).select("features", "prediction").collect().foreach { + case Row(features: DenseVector, prediction1: Double) => + val prediction2 = + features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept + assert(prediction1 ~== prediction2 relTol 1E-5) + } } - test("linear regression with setters") { - // Set params, train, and check as many as we can. - val lr = new LinearRegression() - .setMaxIter(10) - .setRegParam(1.0) - val model = lr.fit(dataset) - assert(model.fittingParamMap.get(lr.maxIter).get === 10) - assert(model.fittingParamMap.get(lr.regParam).get === 1.0) - - // Call fit() with new params, and check as many as we can. - val model2 = lr.fit(dataset, lr.maxIter -> 5, lr.regParam -> 0.1, lr.predictionCol -> "thePred") - assert(model2.fittingParamMap.get(lr.maxIter).get === 5) - assert(model2.fittingParamMap.get(lr.regParam).get === 0.1) - assert(model2.getPredictionCol == "thePred") + test("linear regression with intercept with L2 regularization") { + val trainer = (new LinearRegression).setElasticNetParam(0.0).setRegParam(2.3) + val model = trainer.fit(dataset) + + /** + * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3)) + * > weights + * 3 x 1 sparse Matrix of class "dgCMatrix" + * s0 + * (Intercept) 6.328062 + * as.numeric.data.V2. 3.222034 + * as.numeric.data.V3. 4.926260 + */ + val interceptR = 5.269376 + val weightsR = Array(3.736216, 5.712356) + + assert(model.intercept ~== interceptR relTol 1E-3) + assert(model.weights(0) ~== weightsR(0) relTol 1E-3) + assert(model.weights(1) ~== weightsR(1) relTol 1E-3) + + model.transform(dataset).select("features", "prediction").collect().foreach { + case Row(features: DenseVector, prediction1: Double) => + val prediction2 = + features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept + assert(prediction1 ~== prediction2 relTol 1E-5) + } + } + + test("linear regression with intercept with ElasticNet regularization") { + val trainer = (new LinearRegression).setElasticNetParam(0.3).setRegParam(1.6) + val model = trainer.fit(dataset) + + /** + * weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.3, lambda = 1.6)) + * > weights + * 3 x 1 sparse Matrix of class "dgCMatrix" + * s0 + * (Intercept) 6.324108 + * as.numeric.data.V2. 3.168435 + * as.numeric.data.V3. 5.200403 + */ + val interceptR = 5.696056 + val weightsR = Array(3.670489, 6.001122) + + assert(model.intercept ~== interceptR relTol 1E-3) + assert(model.weights(0) ~== weightsR(0) relTol 1E-3) + assert(model.weights(1) ~== weightsR(1) relTol 1E-3) + + model.transform(dataset).select("features", "prediction").collect().foreach { + case Row(features: DenseVector, prediction1: Double) => + val prediction2 = + features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept + assert(prediction1 ~== prediction2 relTol 1E-5) + } } } From b14cd2364932e504695bcc49486ffb4518fdf33d Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Tue, 28 Apr 2015 09:59:36 -0700 Subject: [PATCH 447/591] [SPARK-7140] [MLLIB] only scan the first 16 entries in Vector.hashCode The Python SerDe calls `Object.hashCode`, which is very expensive for Vectors. It is not necessary to scan the whole vector, especially for large ones. In this PR, we only scan the first 16 nonzeros. srowen Author: Xiangrui Meng Closes #5697 from mengxr/SPARK-7140 and squashes the following commits: 2abc86d [Xiangrui Meng] typo 8fb7d74 [Xiangrui Meng] update impl 1ebad60 [Xiangrui Meng] only scan the first 16 nonzeros in Vector.hashCode --- .../apache/spark/mllib/linalg/Vectors.scala | 88 ++++++++++++++----- 1 file changed, 67 insertions(+), 21 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala index af0cfe22ca10d..34833e90d4af0 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala @@ -52,7 +52,7 @@ sealed trait Vector extends Serializable { override def equals(other: Any): Boolean = { other match { - case v2: Vector => { + case v2: Vector => if (this.size != v2.size) return false (this, v2) match { case (s1: SparseVector, s2: SparseVector) => @@ -63,20 +63,28 @@ sealed trait Vector extends Serializable { Vectors.equals(0 until d1.size, d1.values, s1.indices, s1.values) case (_, _) => util.Arrays.equals(this.toArray, v2.toArray) } - } case _ => false } } + /** + * Returns a hash code value for the vector. The hash code is based on its size and its nonzeros + * in the first 16 entries, using a hash algorithm similar to [[java.util.Arrays.hashCode]]. + */ override def hashCode(): Int = { - var result: Int = size + 31 - this.foreachActive { case (index, value) => - // ignore explict 0 for comparison between sparse and dense - if (value != 0) { - result = 31 * result + index - // refer to {@link java.util.Arrays.equals} for hash algorithm - val bits = java.lang.Double.doubleToLongBits(value) - result = 31 * result + (bits ^ (bits >>> 32)).toInt + // This is a reference implementation. It calls return in foreachActive, which is slow. + // Subclasses should override it with optimized implementation. + var result: Int = 31 + size + this.foreachActive { (index, value) => + if (index < 16) { + // ignore explicit 0 for comparison between sparse and dense + if (value != 0) { + result = 31 * result + index + val bits = java.lang.Double.doubleToLongBits(value) + result = 31 * result + (bits ^ (bits >>> 32)).toInt + } + } else { + return result } } result @@ -317,7 +325,7 @@ object Vectors { case SparseVector(n, ids, vs) => vs case v => throw new IllegalArgumentException("Do not support vector type " + v.getClass) } - val size = values.size + val size = values.length if (p == 1) { var sum = 0.0 @@ -371,8 +379,8 @@ object Vectors { val v1Indices = v1.indices val v2Values = v2.values val v2Indices = v2.indices - val nnzv1 = v1Indices.size - val nnzv2 = v2Indices.size + val nnzv1 = v1Indices.length + val nnzv2 = v2Indices.length var kv1 = 0 var kv2 = 0 @@ -401,7 +409,7 @@ object Vectors { case (DenseVector(vv1), DenseVector(vv2)) => var kv = 0 - val sz = vv1.size + val sz = vv1.length while (kv < sz) { val score = vv1(kv) - vv2(kv) squaredDistance += score * score @@ -422,7 +430,7 @@ object Vectors { var kv2 = 0 val indices = v1.indices var squaredDistance = 0.0 - val nnzv1 = indices.size + val nnzv1 = indices.length val nnzv2 = v2.size var iv1 = if (nnzv1 > 0) indices(kv1) else -1 @@ -451,8 +459,8 @@ object Vectors { v1Values: Array[Double], v2Indices: IndexedSeq[Int], v2Values: Array[Double]): Boolean = { - val v1Size = v1Values.size - val v2Size = v2Values.size + val v1Size = v1Values.length + val v2Size = v2Values.length var k1 = 0 var k2 = 0 var allEqual = true @@ -493,7 +501,7 @@ class DenseVector(val values: Array[Double]) extends Vector { private[spark] override def foreachActive(f: (Int, Double) => Unit) = { var i = 0 - val localValuesSize = values.size + val localValuesSize = values.length val localValues = values while (i < localValuesSize) { @@ -501,6 +509,22 @@ class DenseVector(val values: Array[Double]) extends Vector { i += 1 } } + + override def hashCode(): Int = { + var result: Int = 31 + size + var i = 0 + val end = math.min(values.length, 16) + while (i < end) { + val v = values(i) + if (v != 0.0) { + result = 31 * result + i + val bits = java.lang.Double.doubleToLongBits(values(i)) + result = 31 * result + (bits ^ (bits >>> 32)).toInt + } + i += 1 + } + result + } } object DenseVector { @@ -522,8 +546,8 @@ class SparseVector( val values: Array[Double]) extends Vector { require(indices.length == values.length, "Sparse vectors require that the dimension of the" + - s" indices match the dimension of the values. You provided ${indices.size} indices and " + - s" ${values.size} values.") + s" indices match the dimension of the values. You provided ${indices.length} indices and " + + s" ${values.length} values.") override def toString: String = s"($size,${indices.mkString("[", ",", "]")},${values.mkString("[", ",", "]")})" @@ -547,7 +571,7 @@ class SparseVector( private[spark] override def foreachActive(f: (Int, Double) => Unit) = { var i = 0 - val localValuesSize = values.size + val localValuesSize = values.length val localIndices = indices val localValues = values @@ -556,6 +580,28 @@ class SparseVector( i += 1 } } + + override def hashCode(): Int = { + var result: Int = 31 + size + val end = values.length + var continue = true + var k = 0 + while ((k < end) & continue) { + val i = indices(k) + if (i < 16) { + val v = values(k) + if (v != 0.0) { + result = 31 * result + i + val bits = java.lang.Double.doubleToLongBits(v) + result = 31 * result + (bits ^ (bits >>> 32)).toInt + } + } else { + continue = false + } + k += 1 + } + result + } } object SparseVector { From 52ccf1d3739694826915cdf01642bab02958eb78 Mon Sep 17 00:00:00 2001 From: "Zhang, Liye" Date: Tue, 28 Apr 2015 10:24:00 -0700 Subject: [PATCH 448/591] [Core][test][minor] replace try finally block with tryWithSafeFinally Author: Zhang, Liye Closes #5739 from liyezhang556520/trySafeFinally and squashes the following commits: 55683e5 [Zhang, Liye] replace try finally block with tryWithSafeFinally --- .../apache/spark/deploy/history/FsHistoryProviderSuite.scala | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala b/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala index fcae603c7d18e..9e367a0d9af0d 100644 --- a/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala @@ -224,9 +224,9 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers EventLoggingListener.initEventLog(new FileOutputStream(file)) } val writer = new OutputStreamWriter(bstream, "UTF-8") - try { + Utils.tryWithSafeFinally { events.foreach(e => writer.write(compact(render(JsonProtocol.sparkEventToJson(e))) + "\n")) - } finally { + } { writer.close() } } From 8aab94d8984e9d12194dbda47b2e7d9dbc036889 Mon Sep 17 00:00:00 2001 From: Iulian Dragos Date: Tue, 28 Apr 2015 12:08:18 -0700 Subject: [PATCH 449/591] [SPARK-4286] Add an external shuffle service that can be run as a daemon. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit This allows Mesos deployments to use the shuffle service (and implicitly dynamic allocation). It does so by adding a new "main" class and two corresponding scripts in `sbin`: - `sbin/start-shuffle-service.sh` - `sbin/stop-shuffle-service.sh` Specific options can be passed in `SPARK_SHUFFLE_OPTS`. This is picking up work from #3861 /cc tnachen Author: Iulian Dragos Closes #4990 from dragos/feature/external-shuffle-service and squashes the following commits: 6c2b148 [Iulian Dragos] Import order and wrong name fixup. 07804ad [Iulian Dragos] Moved ExternalShuffleService to the `deploy` package + other minor tweaks. 4dc1f91 [Iulian Dragos] Reviewer’s comments: 8145429 [Iulian Dragos] Add an external shuffle service that can be run as a daemon. --- conf/spark-env.sh.template | 3 +- ...ice.scala => ExternalShuffleService.scala} | 59 ++++++++++++++++--- .../apache/spark/deploy/worker/Worker.scala | 13 ++-- docs/job-scheduling.md | 2 +- .../launcher/SparkClassCommandBuilder.java | 4 ++ sbin/start-shuffle-service.sh | 33 +++++++++++ sbin/stop-shuffle-service.sh | 25 ++++++++ 7 files changed, 124 insertions(+), 15 deletions(-) rename core/src/main/scala/org/apache/spark/deploy/{worker/StandaloneWorkerShuffleService.scala => ExternalShuffleService.scala} (59%) create mode 100755 sbin/start-shuffle-service.sh create mode 100755 sbin/stop-shuffle-service.sh diff --git a/conf/spark-env.sh.template b/conf/spark-env.sh.template index 67f81d33361e1..43c4288912b18 100755 --- a/conf/spark-env.sh.template +++ b/conf/spark-env.sh.template @@ -3,7 +3,7 @@ # This file is sourced when running various Spark programs. # Copy it as spark-env.sh and edit that to configure Spark for your site. -# Options read when launching programs locally with +# Options read when launching programs locally with # ./bin/run-example or ./bin/spark-submit # - HADOOP_CONF_DIR, to point Spark towards Hadoop configuration files # - SPARK_LOCAL_IP, to set the IP address Spark binds to on this node @@ -39,6 +39,7 @@ # - SPARK_WORKER_DIR, to set the working directory of worker processes # - SPARK_WORKER_OPTS, to set config properties only for the worker (e.g. "-Dx=y") # - SPARK_HISTORY_OPTS, to set config properties only for the history server (e.g. "-Dx=y") +# - SPARK_SHUFFLE_OPTS, to set config properties only for the external shuffle service (e.g. "-Dx=y") # - SPARK_DAEMON_JAVA_OPTS, to set config properties for all daemons (e.g. "-Dx=y") # - SPARK_PUBLIC_DNS, to set the public dns name of the master or workers diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/StandaloneWorkerShuffleService.scala b/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala similarity index 59% rename from core/src/main/scala/org/apache/spark/deploy/worker/StandaloneWorkerShuffleService.scala rename to core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala index b9798963bab0a..cd16f992a3c0a 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/StandaloneWorkerShuffleService.scala +++ b/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala @@ -15,7 +15,9 @@ * limitations under the License. */ -package org.apache.spark.deploy.worker +package org.apache.spark.deploy + +import java.util.concurrent.CountDownLatch import org.apache.spark.{Logging, SparkConf, SecurityManager} import org.apache.spark.network.TransportContext @@ -23,6 +25,7 @@ import org.apache.spark.network.netty.SparkTransportConf import org.apache.spark.network.sasl.SaslRpcHandler import org.apache.spark.network.server.TransportServer import org.apache.spark.network.shuffle.ExternalShuffleBlockHandler +import org.apache.spark.util.Utils /** * Provides a server from which Executors can read shuffle files (rather than reading directly from @@ -31,8 +34,8 @@ import org.apache.spark.network.shuffle.ExternalShuffleBlockHandler * * Optionally requires SASL authentication in order to read. See [[SecurityManager]]. */ -private[worker] -class StandaloneWorkerShuffleService(sparkConf: SparkConf, securityManager: SecurityManager) +private[deploy] +class ExternalShuffleService(sparkConf: SparkConf, securityManager: SecurityManager) extends Logging { private val enabled = sparkConf.getBoolean("spark.shuffle.service.enabled", false) @@ -51,16 +54,58 @@ class StandaloneWorkerShuffleService(sparkConf: SparkConf, securityManager: Secu /** Starts the external shuffle service if the user has configured us to. */ def startIfEnabled() { if (enabled) { - require(server == null, "Shuffle server already started") - logInfo(s"Starting shuffle service on port $port with useSasl = $useSasl") - server = transportContext.createServer(port) + start() } } + /** Start the external shuffle service */ + def start() { + require(server == null, "Shuffle server already started") + logInfo(s"Starting shuffle service on port $port with useSasl = $useSasl") + server = transportContext.createServer(port) + } + def stop() { - if (enabled && server != null) { + if (server != null) { server.close() server = null } } } + +/** + * A main class for running the external shuffle service. + */ +object ExternalShuffleService extends Logging { + @volatile + private var server: ExternalShuffleService = _ + + private val barrier = new CountDownLatch(1) + + def main(args: Array[String]): Unit = { + val sparkConf = new SparkConf + Utils.loadDefaultSparkProperties(sparkConf) + val securityManager = new SecurityManager(sparkConf) + + // we override this value since this service is started from the command line + // and we assume the user really wants it to be running + sparkConf.set("spark.shuffle.service.enabled", "true") + server = new ExternalShuffleService(sparkConf, securityManager) + server.start() + + installShutdownHook() + + // keep running until the process is terminated + barrier.await() + } + + private def installShutdownHook(): Unit = { + Runtime.getRuntime.addShutdownHook(new Thread("External Shuffle Service shutdown thread") { + override def run() { + logInfo("Shutting down shuffle service.") + server.stop() + barrier.countDown() + } + }) + } +} diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala index 3ee2eb69e8a4e..8f3cc54051048 100755 --- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala @@ -34,6 +34,7 @@ import akka.remote.{DisassociatedEvent, RemotingLifecycleEvent} import org.apache.spark.{Logging, SecurityManager, SparkConf} import org.apache.spark.deploy.{Command, ExecutorDescription, ExecutorState} import org.apache.spark.deploy.DeployMessages._ +import org.apache.spark.deploy.ExternalShuffleService import org.apache.spark.deploy.master.{DriverState, Master} import org.apache.spark.deploy.worker.ui.WorkerWebUI import org.apache.spark.metrics.MetricsSystem @@ -61,7 +62,7 @@ private[worker] class Worker( assert (port > 0) // For worker and executor IDs - private def createDateFormat = new SimpleDateFormat("yyyyMMddHHmmss") + private def createDateFormat = new SimpleDateFormat("yyyyMMddHHmmss") // Send a heartbeat every (heartbeat timeout) / 4 milliseconds private val HEARTBEAT_MILLIS = conf.getLong("spark.worker.timeout", 60) * 1000 / 4 @@ -85,10 +86,10 @@ private[worker] class Worker( private val CLEANUP_ENABLED = conf.getBoolean("spark.worker.cleanup.enabled", false) // How often worker will clean up old app folders - private val CLEANUP_INTERVAL_MILLIS = + private val CLEANUP_INTERVAL_MILLIS = conf.getLong("spark.worker.cleanup.interval", 60 * 30) * 1000 // TTL for app folders/data; after TTL expires it will be cleaned up - private val APP_DATA_RETENTION_SECS = + private val APP_DATA_RETENTION_SECS = conf.getLong("spark.worker.cleanup.appDataTtl", 7 * 24 * 3600) private val testing: Boolean = sys.props.contains("spark.testing") @@ -112,7 +113,7 @@ private[worker] class Worker( } else { new File(sys.env.get("SPARK_HOME").getOrElse(".")) } - + var workDir: File = null val finishedExecutors = new HashMap[String, ExecutorRunner] val drivers = new HashMap[String, DriverRunner] @@ -122,7 +123,7 @@ private[worker] class Worker( val finishedApps = new HashSet[String] // The shuffle service is not actually started unless configured. - private val shuffleService = new StandaloneWorkerShuffleService(conf, securityMgr) + private val shuffleService = new ExternalShuffleService(conf, securityMgr) private val publicAddress = { val envVar = conf.getenv("SPARK_PUBLIC_DNS") @@ -134,7 +135,7 @@ private[worker] class Worker( private val metricsSystem = MetricsSystem.createMetricsSystem("worker", conf, securityMgr) private val workerSource = new WorkerSource(this) - + private var registrationRetryTimer: Option[Cancellable] = None var coresUsed = 0 diff --git a/docs/job-scheduling.md b/docs/job-scheduling.md index 963e88a3e1d8f..8d9c2ba2041b2 100644 --- a/docs/job-scheduling.md +++ b/docs/job-scheduling.md @@ -32,7 +32,7 @@ Resource allocation can be configured as follows, based on the cluster type: * **Standalone mode:** By default, applications submitted to the standalone mode cluster will run in FIFO (first-in-first-out) order, and each application will try to use all available nodes. You can limit the number of nodes an application uses by setting the `spark.cores.max` configuration property in it, - or change the default for applications that don't set this setting through `spark.deploy.defaultCores`. + or change the default for applications that don't set this setting through `spark.deploy.defaultCores`. Finally, in addition to controlling cores, each application's `spark.executor.memory` setting controls its memory use. * **Mesos:** To use static partitioning on Mesos, set the `spark.mesos.coarse` configuration property to `true`, diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java index e601a0a19f368..d80abf2a8676e 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java +++ b/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java @@ -69,6 +69,10 @@ public List buildCommand(Map env) throws IOException { } else if (className.equals("org.apache.spark.executor.MesosExecutorBackend")) { javaOptsKeys.add("SPARK_EXECUTOR_OPTS"); memKey = "SPARK_EXECUTOR_MEMORY"; + } else if (className.equals("org.apache.spark.deploy.ExternalShuffleService")) { + javaOptsKeys.add("SPARK_DAEMON_JAVA_OPTS"); + javaOptsKeys.add("SPARK_SHUFFLE_OPTS"); + memKey = "SPARK_DAEMON_MEMORY"; } else if (className.startsWith("org.apache.spark.tools.")) { String sparkHome = getSparkHome(); File toolsDir = new File(join(File.separator, sparkHome, "tools", "target", diff --git a/sbin/start-shuffle-service.sh b/sbin/start-shuffle-service.sh new file mode 100755 index 0000000000000..4fddcf7f95d40 --- /dev/null +++ b/sbin/start-shuffle-service.sh @@ -0,0 +1,33 @@ +#!/usr/bin/env bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Starts the external shuffle server on the machine this script is executed on. +# +# Usage: start-shuffle-server.sh +# +# Use the SPARK_SHUFFLE_OPTS environment variable to set shuffle server configuration. +# + +sbin="`dirname "$0"`" +sbin="`cd "$sbin"; pwd`" + +. "$sbin/spark-config.sh" +. "$SPARK_PREFIX/bin/load-spark-env.sh" + +exec "$sbin"/spark-daemon.sh start org.apache.spark.deploy.ExternalShuffleService 1 diff --git a/sbin/stop-shuffle-service.sh b/sbin/stop-shuffle-service.sh new file mode 100755 index 0000000000000..4cb6891ae27fa --- /dev/null +++ b/sbin/stop-shuffle-service.sh @@ -0,0 +1,25 @@ +#!/usr/bin/env bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# Stops the external shuffle service on the machine this script is executed on. + +sbin="`dirname "$0"`" +sbin="`cd "$sbin"; pwd`" + +"$sbin"/spark-daemon.sh stop org.apache.spark.deploy.ExternalShuffleService 1 From 2d222fb39dd978e5a33cde6ceb59307cbdf7b171 Mon Sep 17 00:00:00 2001 From: Ilya Ganelin Date: Tue, 28 Apr 2015 12:18:55 -0700 Subject: [PATCH 450/591] [SPARK-5932] [CORE] Use consistent naming for size properties I've added an interface to JavaUtils to do byte conversion and added hooks within Utils.scala to handle conversion within Spark code (like for time strings). I've added matching tests for size conversion, and then updated all deprecated configs and documentation as per SPARK-5933. Author: Ilya Ganelin Closes #5574 from ilganeli/SPARK-5932 and squashes the following commits: 11f6999 [Ilya Ganelin] Nit fixes 49a8720 [Ilya Ganelin] Whitespace fix 2ab886b [Ilya Ganelin] Scala style fc85733 [Ilya Ganelin] Got rid of floating point math 852a407 [Ilya Ganelin] [SPARK-5932] Added much improved overflow handling. Can now handle sizes up to Long.MAX_VALUE Petabytes instead of being capped at Long.MAX_VALUE Bytes 9ee779c [Ilya Ganelin] Simplified fraction matches 22413b1 [Ilya Ganelin] Made MAX private 3dfae96 [Ilya Ganelin] Fixed some nits. Added automatic conversion of old paramter for kryoserializer.mb to new values. e428049 [Ilya Ganelin] resolving merge conflict 8b43748 [Ilya Ganelin] Fixed error in pattern matching for doubles 84a2581 [Ilya Ganelin] Added smoother handling of fractional values for size parameters. This now throws an exception and added a warning for old spark.kryoserializer.buffer d3d09b6 [Ilya Ganelin] [SPARK-5932] Fixing error in KryoSerializer fe286b4 [Ilya Ganelin] Resolved merge conflict c7803cd [Ilya Ganelin] Empty lines 54b78b4 [Ilya Ganelin] Simplified byteUnit class 69e2f20 [Ilya Ganelin] Updates to code f32bc01 [Ilya Ganelin] [SPARK-5932] Fixed error in API in SparkConf.scala where Kb conversion wasn't being done properly (was Mb). Added test cases for both timeUnit and ByteUnit conversion f15f209 [Ilya Ganelin] Fixed conversion of kryo buffer size 0f4443e [Ilya Ganelin] Merge remote-tracking branch 'upstream/master' into SPARK-5932 35a7fa7 [Ilya Ganelin] Minor formatting 928469e [Ilya Ganelin] [SPARK-5932] Converted some longs to ints 5d29f90 [Ilya Ganelin] [SPARK-5932] Finished documentation updates 7a6c847 [Ilya Ganelin] [SPARK-5932] Updated spark.shuffle.file.buffer afc9a38 [Ilya Ganelin] [SPARK-5932] Updated spark.broadcast.blockSize and spark.storage.memoryMapThreshold ae7e9f6 [Ilya Ganelin] [SPARK-5932] Updated spark.io.compression.snappy.block.size 2d15681 [Ilya Ganelin] [SPARK-5932] Updated spark.executor.logs.rolling.size.maxBytes 1fbd435 [Ilya Ganelin] [SPARK-5932] Updated spark.broadcast.blockSize eba4de6 [Ilya Ganelin] [SPARK-5932] Updated spark.shuffle.file.buffer.kb b809a78 [Ilya Ganelin] [SPARK-5932] Updated spark.kryoserializer.buffer.max 0cdff35 [Ilya Ganelin] [SPARK-5932] Updated to use bibibytes in method names. Updated spark.kryoserializer.buffer.mb and spark.reducer.maxMbInFlight 475370a [Ilya Ganelin] [SPARK-5932] Simplified ByteUnit code, switched to using longs. Updated docs to clarify that we use kibi, mebi etc instead of kilo, mega 851d691 [Ilya Ganelin] [SPARK-5932] Updated memoryStringToMb to use new interfaces a9f4fcf [Ilya Ganelin] [SPARK-5932] Added unit tests for unit conversion 747393a [Ilya Ganelin] [SPARK-5932] Added unit tests for ByteString conversion 09ea450 [Ilya Ganelin] [SPARK-5932] Added byte string conversion to Jav utils 5390fd9 [Ilya Ganelin] Merge remote-tracking branch 'upstream/master' into SPARK-5932 db9a963 [Ilya Ganelin] Closing second spark context 1dc0444 [Ilya Ganelin] Added ref equality check 8c884fa [Ilya Ganelin] Made getOrCreate synchronized cb0c6b7 [Ilya Ganelin] Doc updates and code cleanup 270cfe3 [Ilya Ganelin] [SPARK-6703] Documentation fixes 15e8dea [Ilya Ganelin] Updated comments and added MiMa Exclude 0e1567c [Ilya Ganelin] Got rid of unecessary option for AtomicReference dfec4da [Ilya Ganelin] Changed activeContext to AtomicReference 733ec9f [Ilya Ganelin] Fixed some bugs in test code 8be2f83 [Ilya Ganelin] Replaced match with if e92caf7 [Ilya Ganelin] [SPARK-6703] Added test to ensure that getOrCreate both allows creation, retrieval, and a second context if desired a99032f [Ilya Ganelin] Spacing fix d7a06b8 [Ilya Ganelin] Updated SparkConf class to add getOrCreate method. Started test suite implementation --- .../scala/org/apache/spark/SparkConf.scala | 90 ++++++++++++++- .../spark/broadcast/TorrentBroadcast.scala | 3 +- .../apache/spark/io/CompressionCodec.scala | 8 +- .../spark/serializer/KryoSerializer.scala | 17 +-- .../shuffle/FileShuffleBlockManager.scala | 3 +- .../hash/BlockStoreShuffleFetcher.scala | 3 +- .../org/apache/spark/storage/DiskStore.scala | 3 +- .../scala/org/apache/spark/util/Utils.scala | 53 ++++++--- .../collection/ExternalAppendOnlyMap.scala | 6 +- .../util/collection/ExternalSorter.scala | 4 +- .../util/logging/RollingFileAppender.scala | 2 +- .../org/apache/spark/DistributedSuite.scala | 2 +- .../org/apache/spark/SparkConfSuite.scala | 19 ++++ .../KryoSerializerResizableOutputSuite.scala | 8 +- .../serializer/KryoSerializerSuite.scala | 2 +- .../BlockManagerReplicationSuite.scala | 2 +- .../spark/storage/BlockManagerSuite.scala | 6 +- .../org/apache/spark/util/UtilsSuite.scala | 100 +++++++++++++++- docs/configuration.md | 60 ++++++---- docs/tuning.md | 2 +- .../spark/examples/mllib/MovieLensALS.scala | 2 +- .../apache/spark/network/util/ByteUnit.java | 67 +++++++++++ .../apache/spark/network/util/JavaUtils.java | 107 ++++++++++++++++-- 23 files changed, 488 insertions(+), 81 deletions(-) create mode 100644 network/common/src/main/java/org/apache/spark/network/util/ByteUnit.java diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala index c1996e08756a6..a8fc90ad2050e 100644 --- a/core/src/main/scala/org/apache/spark/SparkConf.scala +++ b/core/src/main/scala/org/apache/spark/SparkConf.scala @@ -211,7 +211,74 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { Utils.timeStringAsMs(get(key, defaultValue)) } + /** + * Get a size parameter as bytes; throws a NoSuchElementException if it's not set. If no + * suffix is provided then bytes are assumed. + * @throws NoSuchElementException + */ + def getSizeAsBytes(key: String): Long = { + Utils.byteStringAsBytes(get(key)) + } + + /** + * Get a size parameter as bytes, falling back to a default if not set. If no + * suffix is provided then bytes are assumed. + */ + def getSizeAsBytes(key: String, defaultValue: String): Long = { + Utils.byteStringAsBytes(get(key, defaultValue)) + } + + /** + * Get a size parameter as Kibibytes; throws a NoSuchElementException if it's not set. If no + * suffix is provided then Kibibytes are assumed. + * @throws NoSuchElementException + */ + def getSizeAsKb(key: String): Long = { + Utils.byteStringAsKb(get(key)) + } + + /** + * Get a size parameter as Kibibytes, falling back to a default if not set. If no + * suffix is provided then Kibibytes are assumed. + */ + def getSizeAsKb(key: String, defaultValue: String): Long = { + Utils.byteStringAsKb(get(key, defaultValue)) + } + + /** + * Get a size parameter as Mebibytes; throws a NoSuchElementException if it's not set. If no + * suffix is provided then Mebibytes are assumed. + * @throws NoSuchElementException + */ + def getSizeAsMb(key: String): Long = { + Utils.byteStringAsMb(get(key)) + } + + /** + * Get a size parameter as Mebibytes, falling back to a default if not set. If no + * suffix is provided then Mebibytes are assumed. + */ + def getSizeAsMb(key: String, defaultValue: String): Long = { + Utils.byteStringAsMb(get(key, defaultValue)) + } + + /** + * Get a size parameter as Gibibytes; throws a NoSuchElementException if it's not set. If no + * suffix is provided then Gibibytes are assumed. + * @throws NoSuchElementException + */ + def getSizeAsGb(key: String): Long = { + Utils.byteStringAsGb(get(key)) + } + /** + * Get a size parameter as Gibibytes, falling back to a default if not set. If no + * suffix is provided then Gibibytes are assumed. + */ + def getSizeAsGb(key: String, defaultValue: String): Long = { + Utils.byteStringAsGb(get(key, defaultValue)) + } + /** Get a parameter as an Option */ def getOption(key: String): Option[String] = { Option(settings.get(key)).orElse(getDeprecatedConfig(key, this)) @@ -407,7 +474,13 @@ private[spark] object SparkConf extends Logging { "The spark.cache.class property is no longer being used! Specify storage levels using " + "the RDD.persist() method instead."), DeprecatedConfig("spark.yarn.user.classpath.first", "1.3", - "Please use spark.{driver,executor}.userClassPathFirst instead.")) + "Please use spark.{driver,executor}.userClassPathFirst instead."), + DeprecatedConfig("spark.kryoserializer.buffer.mb", "1.4", + "Please use spark.kryoserializer.buffer instead. The default value for " + + "spark.kryoserializer.buffer.mb was previously specified as '0.064'. Fractional values " + + "are no longer accepted. To specify the equivalent now, one may use '64k'.") + ) + Map(configs.map { cfg => (cfg.key -> cfg) }:_*) } @@ -432,6 +505,21 @@ private[spark] object SparkConf extends Logging { AlternateConfig("spark.yarn.applicationMaster.waitTries", "1.3", // Translate old value to a duration, with 10s wait time per try. translation = s => s"${s.toLong * 10}s")), + "spark.reducer.maxSizeInFlight" -> Seq( + AlternateConfig("spark.reducer.maxMbInFlight", "1.4")), + "spark.kryoserializer.buffer" -> + Seq(AlternateConfig("spark.kryoserializer.buffer.mb", "1.4", + translation = s => s"${s.toDouble * 1000}k")), + "spark.kryoserializer.buffer.max" -> Seq( + AlternateConfig("spark.kryoserializer.buffer.max.mb", "1.4")), + "spark.shuffle.file.buffer" -> Seq( + AlternateConfig("spark.shuffle.file.buffer.kb", "1.4")), + "spark.executor.logs.rolling.maxSize" -> Seq( + AlternateConfig("spark.executor.logs.rolling.size.maxBytes", "1.4")), + "spark.io.compression.snappy.blockSize" -> Seq( + AlternateConfig("spark.io.compression.snappy.block.size", "1.4")), + "spark.io.compression.lz4.blockSize" -> Seq( + AlternateConfig("spark.io.compression.lz4.block.size", "1.4")), "spark.rpc.numRetries" -> Seq( AlternateConfig("spark.akka.num.retries", "1.4")), "spark.rpc.retry.wait" -> Seq( diff --git a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala index 23b02e60338fb..a0c9b5e63c744 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala @@ -74,7 +74,8 @@ private[spark] class TorrentBroadcast[T: ClassTag](obj: T, id: Long) } else { None } - blockSize = conf.getInt("spark.broadcast.blockSize", 4096) * 1024 + // Note: use getSizeAsKb (not bytes) to maintain compatiblity if no units are provided + blockSize = conf.getSizeAsKb("spark.broadcast.blockSize", "4m").toInt * 1024 } setConf(SparkEnv.get.conf) diff --git a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala index 0709b6d689e86..0756cdb2ed8e6 100644 --- a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala +++ b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala @@ -97,7 +97,7 @@ private[spark] object CompressionCodec { /** * :: DeveloperApi :: * LZ4 implementation of [[org.apache.spark.io.CompressionCodec]]. - * Block size can be configured by `spark.io.compression.lz4.block.size`. + * Block size can be configured by `spark.io.compression.lz4.blockSize`. * * Note: The wire protocol for this codec is not guaranteed to be compatible across versions * of Spark. This is intended for use as an internal compression utility within a single Spark @@ -107,7 +107,7 @@ private[spark] object CompressionCodec { class LZ4CompressionCodec(conf: SparkConf) extends CompressionCodec { override def compressedOutputStream(s: OutputStream): OutputStream = { - val blockSize = conf.getInt("spark.io.compression.lz4.block.size", 32768) + val blockSize = conf.getSizeAsBytes("spark.io.compression.lz4.blockSize", "32k").toInt new LZ4BlockOutputStream(s, blockSize) } @@ -137,7 +137,7 @@ class LZFCompressionCodec(conf: SparkConf) extends CompressionCodec { /** * :: DeveloperApi :: * Snappy implementation of [[org.apache.spark.io.CompressionCodec]]. - * Block size can be configured by `spark.io.compression.snappy.block.size`. + * Block size can be configured by `spark.io.compression.snappy.blockSize`. * * Note: The wire protocol for this codec is not guaranteed to be compatible across versions * of Spark. This is intended for use as an internal compression utility within a single Spark @@ -153,7 +153,7 @@ class SnappyCompressionCodec(conf: SparkConf) extends CompressionCodec { } override def compressedOutputStream(s: OutputStream): OutputStream = { - val blockSize = conf.getInt("spark.io.compression.snappy.block.size", 32768) + val blockSize = conf.getSizeAsBytes("spark.io.compression.snappy.blockSize", "32k").toInt new SnappyOutputStream(s, blockSize) } diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala index 579fb6624e692..754832b8a4ca7 100644 --- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala +++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala @@ -49,16 +49,17 @@ class KryoSerializer(conf: SparkConf) with Logging with Serializable { - private val bufferSizeMb = conf.getDouble("spark.kryoserializer.buffer.mb", 0.064) - if (bufferSizeMb >= 2048) { - throw new IllegalArgumentException("spark.kryoserializer.buffer.mb must be less than " + - s"2048 mb, got: + $bufferSizeMb mb.") + private val bufferSizeKb = conf.getSizeAsKb("spark.kryoserializer.buffer", "64k") + + if (bufferSizeKb >= 2048) { + throw new IllegalArgumentException("spark.kryoserializer.buffer must be less than " + + s"2048 mb, got: + $bufferSizeKb mb.") } - private val bufferSize = (bufferSizeMb * 1024 * 1024).toInt + private val bufferSize = (bufferSizeKb * 1024).toInt - val maxBufferSizeMb = conf.getInt("spark.kryoserializer.buffer.max.mb", 64) + val maxBufferSizeMb = conf.getSizeAsMb("spark.kryoserializer.buffer.max", "64m").toInt if (maxBufferSizeMb >= 2048) { - throw new IllegalArgumentException("spark.kryoserializer.buffer.max.mb must be less than " + + throw new IllegalArgumentException("spark.kryoserializer.buffer.max must be less than " + s"2048 mb, got: + $maxBufferSizeMb mb.") } private val maxBufferSize = maxBufferSizeMb * 1024 * 1024 @@ -173,7 +174,7 @@ private[spark] class KryoSerializerInstance(ks: KryoSerializer) extends Serializ } catch { case e: KryoException if e.getMessage.startsWith("Buffer overflow") => throw new SparkException(s"Kryo serialization failed: ${e.getMessage}. To avoid this, " + - "increase spark.kryoserializer.buffer.max.mb value.") + "increase spark.kryoserializer.buffer.max value.") } ByteBuffer.wrap(output.toBytes) } diff --git a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala index 538e150ead05a..e9b4e2b955dc8 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala @@ -78,7 +78,8 @@ class FileShuffleBlockManager(conf: SparkConf) private val consolidateShuffleFiles = conf.getBoolean("spark.shuffle.consolidateFiles", false) - private val bufferSize = conf.getInt("spark.shuffle.file.buffer.kb", 32) * 1024 + // Use getSizeAsKb (not bytes) to maintain backwards compatibility of on units are provided + private val bufferSize = conf.getSizeAsKb("spark.shuffle.file.buffer", "32k").toInt * 1024 /** * Contains all the state related to a particular shuffle. This includes a pool of unused diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala index 7a2c5ae32d98b..80374adc44296 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/hash/BlockStoreShuffleFetcher.scala @@ -79,7 +79,8 @@ private[hash] object BlockStoreShuffleFetcher extends Logging { blockManager, blocksByAddress, serializer, - SparkEnv.get.conf.getLong("spark.reducer.maxMbInFlight", 48) * 1024 * 1024) + // Note: we use getSizeAsMb when no suffix is provided for backwards compatibility + SparkEnv.get.conf.getSizeAsMb("spark.reducer.maxSizeInFlight", "48m") * 1024 * 1024) val itr = blockFetcherItr.flatMap(unpackBlock) val completionIter = CompletionIterator[T, Iterator[T]](itr, { diff --git a/core/src/main/scala/org/apache/spark/storage/DiskStore.scala b/core/src/main/scala/org/apache/spark/storage/DiskStore.scala index 4b232ae7d3180..1f45956282166 100644 --- a/core/src/main/scala/org/apache/spark/storage/DiskStore.scala +++ b/core/src/main/scala/org/apache/spark/storage/DiskStore.scala @@ -31,8 +31,7 @@ import org.apache.spark.util.Utils private[spark] class DiskStore(blockManager: BlockManager, diskManager: DiskBlockManager) extends BlockStore(blockManager) with Logging { - val minMemoryMapBytes = blockManager.conf.getLong( - "spark.storage.memoryMapThreshold", 2 * 1024L * 1024L) + val minMemoryMapBytes = blockManager.conf.getSizeAsBytes("spark.storage.memoryMapThreshold", "2m") override def getSize(blockId: BlockId): Long = { diskManager.getFile(blockId.name).length diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index 342bc9a06db47..4c028c06a5138 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -1020,21 +1020,48 @@ private[spark] object Utils extends Logging { } /** - * Convert a Java memory parameter passed to -Xmx (such as 300m or 1g) to a number of megabytes. + * Convert a passed byte string (e.g. 50b, 100k, or 250m) to bytes for internal use. + * + * If no suffix is provided, the passed number is assumed to be in bytes. + */ + def byteStringAsBytes(str: String): Long = { + JavaUtils.byteStringAsBytes(str) + } + + /** + * Convert a passed byte string (e.g. 50b, 100k, or 250m) to kibibytes for internal use. + * + * If no suffix is provided, the passed number is assumed to be in kibibytes. + */ + def byteStringAsKb(str: String): Long = { + JavaUtils.byteStringAsKb(str) + } + + /** + * Convert a passed byte string (e.g. 50b, 100k, or 250m) to mebibytes for internal use. + * + * If no suffix is provided, the passed number is assumed to be in mebibytes. + */ + def byteStringAsMb(str: String): Long = { + JavaUtils.byteStringAsMb(str) + } + + /** + * Convert a passed byte string (e.g. 50b, 100k, or 250m, 500g) to gibibytes for internal use. + * + * If no suffix is provided, the passed number is assumed to be in gibibytes. + */ + def byteStringAsGb(str: String): Long = { + JavaUtils.byteStringAsGb(str) + } + + /** + * Convert a Java memory parameter passed to -Xmx (such as 300m or 1g) to a number of mebibytes. */ def memoryStringToMb(str: String): Int = { - val lower = str.toLowerCase - if (lower.endsWith("k")) { - (lower.substring(0, lower.length-1).toLong / 1024).toInt - } else if (lower.endsWith("m")) { - lower.substring(0, lower.length-1).toInt - } else if (lower.endsWith("g")) { - lower.substring(0, lower.length-1).toInt * 1024 - } else if (lower.endsWith("t")) { - lower.substring(0, lower.length-1).toInt * 1024 * 1024 - } else {// no suffix, so it's just a number in bytes - (lower.toLong / 1024 / 1024).toInt - } + // Convert to bytes, rather than directly to MB, because when no units are specified the unit + // is assumed to be bytes + (JavaUtils.byteStringAsBytes(str) / 1024 / 1024).toInt } /** diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala index 30dd7f22e494f..f912049563906 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala @@ -89,8 +89,10 @@ class ExternalAppendOnlyMap[K, V, C]( // Number of bytes spilled in total private var _diskBytesSpilled = 0L - - private val fileBufferSize = sparkConf.getInt("spark.shuffle.file.buffer.kb", 32) * 1024 + + // Use getSizeAsKb (not bytes) to maintain backwards compatibility of on units are provided + private val fileBufferSize = + sparkConf.getSizeAsKb("spark.shuffle.file.buffer", "32k").toInt * 1024 // Write metrics for current spill private var curWriteMetrics: ShuffleWriteMetrics = _ diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala index 79a695fb62086..ef3cac622505e 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala @@ -108,7 +108,9 @@ private[spark] class ExternalSorter[K, V, C]( private val conf = SparkEnv.get.conf private val spillingEnabled = conf.getBoolean("spark.shuffle.spill", true) - private val fileBufferSize = conf.getInt("spark.shuffle.file.buffer.kb", 32) * 1024 + + // Use getSizeAsKb (not bytes) to maintain backwards compatibility of on units are provided + private val fileBufferSize = conf.getSizeAsKb("spark.shuffle.file.buffer", "32k").toInt * 1024 private val transferToEnabled = conf.getBoolean("spark.file.transferTo", true) // Size of object batches when reading/writing from serializers. diff --git a/core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala b/core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala index e579421676343..7138b4b8e4533 100644 --- a/core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala +++ b/core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala @@ -138,7 +138,7 @@ private[spark] object RollingFileAppender { val STRATEGY_DEFAULT = "" val INTERVAL_PROPERTY = "spark.executor.logs.rolling.time.interval" val INTERVAL_DEFAULT = "daily" - val SIZE_PROPERTY = "spark.executor.logs.rolling.size.maxBytes" + val SIZE_PROPERTY = "spark.executor.logs.rolling.maxSize" val SIZE_DEFAULT = (1024 * 1024).toString val RETAINED_FILES_PROPERTY = "spark.executor.logs.rolling.maxRetainedFiles" val DEFAULT_BUFFER_SIZE = 8192 diff --git a/core/src/test/scala/org/apache/spark/DistributedSuite.scala b/core/src/test/scala/org/apache/spark/DistributedSuite.scala index 97ea3578aa8ba..96a9c207ad022 100644 --- a/core/src/test/scala/org/apache/spark/DistributedSuite.scala +++ b/core/src/test/scala/org/apache/spark/DistributedSuite.scala @@ -77,7 +77,7 @@ class DistributedSuite extends FunSuite with Matchers with LocalSparkContext { } test("groupByKey where map output sizes exceed maxMbInFlight") { - val conf = new SparkConf().set("spark.reducer.maxMbInFlight", "1") + val conf = new SparkConf().set("spark.reducer.maxSizeInFlight", "1m") sc = new SparkContext(clusterUrl, "test", conf) // This data should be around 20 MB, so even with 4 mappers and 2 reducers, each map output // file should be about 2.5 MB diff --git a/core/src/test/scala/org/apache/spark/SparkConfSuite.scala b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala index 272e6af0514e4..68d08e32f9aa4 100644 --- a/core/src/test/scala/org/apache/spark/SparkConfSuite.scala +++ b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala @@ -24,11 +24,30 @@ import scala.language.postfixOps import scala.util.{Try, Random} import org.scalatest.FunSuite +import org.apache.spark.network.util.ByteUnit import org.apache.spark.serializer.{KryoRegistrator, KryoSerializer} import org.apache.spark.util.{RpcUtils, ResetSystemProperties} import com.esotericsoftware.kryo.Kryo class SparkConfSuite extends FunSuite with LocalSparkContext with ResetSystemProperties { + test("Test byteString conversion") { + val conf = new SparkConf() + // Simply exercise the API, we don't need a complete conversion test since that's handled in + // UtilsSuite.scala + assert(conf.getSizeAsBytes("fake","1k") === ByteUnit.KiB.toBytes(1)) + assert(conf.getSizeAsKb("fake","1k") === ByteUnit.KiB.toKiB(1)) + assert(conf.getSizeAsMb("fake","1k") === ByteUnit.KiB.toMiB(1)) + assert(conf.getSizeAsGb("fake","1k") === ByteUnit.KiB.toGiB(1)) + } + + test("Test timeString conversion") { + val conf = new SparkConf() + // Simply exercise the API, we don't need a complete conversion test since that's handled in + // UtilsSuite.scala + assert(conf.getTimeAsMs("fake","1ms") === TimeUnit.MILLISECONDS.toMillis(1)) + assert(conf.getTimeAsSeconds("fake","1000ms") === TimeUnit.MILLISECONDS.toSeconds(1000)) + } + test("loading from system properties") { System.setProperty("spark.test.testProperty", "2") val conf = new SparkConf() diff --git a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerResizableOutputSuite.scala b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerResizableOutputSuite.scala index 967c9e9899c9d..da98d09184735 100644 --- a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerResizableOutputSuite.scala +++ b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerResizableOutputSuite.scala @@ -33,8 +33,8 @@ class KryoSerializerResizableOutputSuite extends FunSuite { test("kryo without resizable output buffer should fail on large array") { val conf = new SparkConf(false) conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer") - conf.set("spark.kryoserializer.buffer.mb", "1") - conf.set("spark.kryoserializer.buffer.max.mb", "1") + conf.set("spark.kryoserializer.buffer", "1m") + conf.set("spark.kryoserializer.buffer.max", "1m") val sc = new SparkContext("local", "test", conf) intercept[SparkException](sc.parallelize(x).collect()) LocalSparkContext.stop(sc) @@ -43,8 +43,8 @@ class KryoSerializerResizableOutputSuite extends FunSuite { test("kryo with resizable output buffer should succeed on large array") { val conf = new SparkConf(false) conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer") - conf.set("spark.kryoserializer.buffer.mb", "1") - conf.set("spark.kryoserializer.buffer.max.mb", "2") + conf.set("spark.kryoserializer.buffer", "1m") + conf.set("spark.kryoserializer.buffer.max", "2m") val sc = new SparkContext("local", "test", conf) assert(sc.parallelize(x).collect() === x) LocalSparkContext.stop(sc) diff --git a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala index b070a54aa989b..1b13559e77cb8 100644 --- a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala +++ b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala @@ -269,7 +269,7 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext { test("serialization buffer overflow reporting") { import org.apache.spark.SparkException - val kryoBufferMaxProperty = "spark.kryoserializer.buffer.max.mb" + val kryoBufferMaxProperty = "spark.kryoserializer.buffer.max" val largeObject = (1 to 1000000).toArray diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala index ffa5162a31841..f647200402ecb 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala @@ -50,7 +50,7 @@ class BlockManagerReplicationSuite extends FunSuite with Matchers with BeforeAnd val allStores = new ArrayBuffer[BlockManager] // Reuse a serializer across tests to avoid creating a new thread-local buffer on each test - conf.set("spark.kryoserializer.buffer.mb", "1") + conf.set("spark.kryoserializer.buffer", "1m") val serializer = new KryoSerializer(conf) // Implicitly convert strings to BlockIds for test clarity. diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala index 7d82a7c66ad1a..6957bc72e9903 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala @@ -55,7 +55,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach val shuffleManager = new HashShuffleManager(conf) // Reuse a serializer across tests to avoid creating a new thread-local buffer on each test - conf.set("spark.kryoserializer.buffer.mb", "1") + conf.set("spark.kryoserializer.buffer", "1m") val serializer = new KryoSerializer(conf) // Implicitly convert strings to BlockIds for test clarity. @@ -814,14 +814,14 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach // be nice to refactor classes involved in disk storage in a way that // allows for easier testing. val blockManager = mock(classOf[BlockManager]) - when(blockManager.conf).thenReturn(conf.clone.set(confKey, 0.toString)) + when(blockManager.conf).thenReturn(conf.clone.set(confKey, "0")) val diskBlockManager = new DiskBlockManager(blockManager, conf) val diskStoreMapped = new DiskStore(blockManager, diskBlockManager) diskStoreMapped.putBytes(blockId, byteBuffer, StorageLevel.DISK_ONLY) val mapped = diskStoreMapped.getBytes(blockId).get - when(blockManager.conf).thenReturn(conf.clone.set(confKey, (1000 * 1000).toString)) + when(blockManager.conf).thenReturn(conf.clone.set(confKey, "1m")) val diskStoreNotMapped = new DiskStore(blockManager, diskBlockManager) diskStoreNotMapped.putBytes(blockId, byteBuffer, StorageLevel.DISK_ONLY) val notMapped = diskStoreNotMapped.getBytes(blockId).get diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala index 1ba99803f5a0e..62a3cbcdf69ea 100644 --- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala @@ -23,7 +23,6 @@ import java.nio.{ByteBuffer, ByteOrder} import java.text.DecimalFormatSymbols import java.util.concurrent.TimeUnit import java.util.Locale -import java.util.PriorityQueue import scala.collection.mutable.ListBuffer import scala.util.Random @@ -35,6 +34,7 @@ import org.scalatest.FunSuite import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path +import org.apache.spark.network.util.ByteUnit import org.apache.spark.SparkConf class UtilsSuite extends FunSuite with ResetSystemProperties { @@ -65,6 +65,10 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { assert(Utils.timeStringAsMs("1d") === TimeUnit.DAYS.toMillis(1)) // Test invalid strings + intercept[NumberFormatException] { + Utils.timeStringAsMs("600l") + } + intercept[NumberFormatException] { Utils.timeStringAsMs("This breaks 600s") } @@ -82,6 +86,100 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { } } + test("Test byteString conversion") { + // Test zero + assert(Utils.byteStringAsBytes("0") === 0) + + assert(Utils.byteStringAsGb("1") === 1) + assert(Utils.byteStringAsGb("1g") === 1) + assert(Utils.byteStringAsGb("1023m") === 0) + assert(Utils.byteStringAsGb("1024m") === 1) + assert(Utils.byteStringAsGb("1048575k") === 0) + assert(Utils.byteStringAsGb("1048576k") === 1) + assert(Utils.byteStringAsGb("1k") === 0) + assert(Utils.byteStringAsGb("1t") === ByteUnit.TiB.toGiB(1)) + assert(Utils.byteStringAsGb("1p") === ByteUnit.PiB.toGiB(1)) + + assert(Utils.byteStringAsMb("1") === 1) + assert(Utils.byteStringAsMb("1m") === 1) + assert(Utils.byteStringAsMb("1048575b") === 0) + assert(Utils.byteStringAsMb("1048576b") === 1) + assert(Utils.byteStringAsMb("1023k") === 0) + assert(Utils.byteStringAsMb("1024k") === 1) + assert(Utils.byteStringAsMb("3645k") === 3) + assert(Utils.byteStringAsMb("1024gb") === 1048576) + assert(Utils.byteStringAsMb("1g") === ByteUnit.GiB.toMiB(1)) + assert(Utils.byteStringAsMb("1t") === ByteUnit.TiB.toMiB(1)) + assert(Utils.byteStringAsMb("1p") === ByteUnit.PiB.toMiB(1)) + + assert(Utils.byteStringAsKb("1") === 1) + assert(Utils.byteStringAsKb("1k") === 1) + assert(Utils.byteStringAsKb("1m") === ByteUnit.MiB.toKiB(1)) + assert(Utils.byteStringAsKb("1g") === ByteUnit.GiB.toKiB(1)) + assert(Utils.byteStringAsKb("1t") === ByteUnit.TiB.toKiB(1)) + assert(Utils.byteStringAsKb("1p") === ByteUnit.PiB.toKiB(1)) + + assert(Utils.byteStringAsBytes("1") === 1) + assert(Utils.byteStringAsBytes("1k") === ByteUnit.KiB.toBytes(1)) + assert(Utils.byteStringAsBytes("1m") === ByteUnit.MiB.toBytes(1)) + assert(Utils.byteStringAsBytes("1g") === ByteUnit.GiB.toBytes(1)) + assert(Utils.byteStringAsBytes("1t") === ByteUnit.TiB.toBytes(1)) + assert(Utils.byteStringAsBytes("1p") === ByteUnit.PiB.toBytes(1)) + + // Overflow handling, 1073741824p exceeds Long.MAX_VALUE if converted straight to Bytes + // This demonstrates that we can have e.g 1024^3 PB without overflowing. + assert(Utils.byteStringAsGb("1073741824p") === ByteUnit.PiB.toGiB(1073741824)) + assert(Utils.byteStringAsMb("1073741824p") === ByteUnit.PiB.toMiB(1073741824)) + + // Run this to confirm it doesn't throw an exception + assert(Utils.byteStringAsBytes("9223372036854775807") === 9223372036854775807L) + assert(ByteUnit.PiB.toPiB(9223372036854775807L) === 9223372036854775807L) + + // Test overflow exception + intercept[IllegalArgumentException] { + // This value exceeds Long.MAX when converted to bytes + Utils.byteStringAsBytes("9223372036854775808") + } + + // Test overflow exception + intercept[IllegalArgumentException] { + // This value exceeds Long.MAX when converted to TB + ByteUnit.PiB.toTiB(9223372036854775807L) + } + + // Test fractional string + intercept[NumberFormatException] { + Utils.byteStringAsMb("0.064") + } + + // Test fractional string + intercept[NumberFormatException] { + Utils.byteStringAsMb("0.064m") + } + + // Test invalid strings + intercept[NumberFormatException] { + Utils.byteStringAsBytes("500ub") + } + + // Test invalid strings + intercept[NumberFormatException] { + Utils.byteStringAsBytes("This breaks 600b") + } + + intercept[NumberFormatException] { + Utils.byteStringAsBytes("This breaks 600") + } + + intercept[NumberFormatException] { + Utils.byteStringAsBytes("600gb This breaks") + } + + intercept[NumberFormatException] { + Utils.byteStringAsBytes("This 123mb breaks") + } + } + test("bytesToString") { assert(Utils.bytesToString(10) === "10.0 B") assert(Utils.bytesToString(1500) === "1500.0 B") diff --git a/docs/configuration.md b/docs/configuration.md index d587b91124cb8..72105feba4919 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -48,6 +48,17 @@ The following format is accepted: 5d (days) 1y (years) + +Properties that specify a byte size should be configured with a unit of size. +The following format is accepted: + + 1b (bytes) + 1k or 1kb (kibibytes = 1024 bytes) + 1m or 1mb (mebibytes = 1024 kibibytes) + 1g or 1gb (gibibytes = 1024 mebibytes) + 1t or 1tb (tebibytes = 1024 gibibytes) + 1p or 1pb (pebibytes = 1024 tebibytes) + ## Dynamically Loading Spark Properties In some cases, you may want to avoid hard-coding certain configurations in a `SparkConf`. For instance, if you'd like to run the same application with different masters or different @@ -272,12 +283,11 @@ Apart from these, the following properties are also available, and may be useful - + @@ -366,10 +376,10 @@ Apart from these, the following properties are also available, and may be useful
    spark.executor.logs.rolling.size.maxBytesspark.executor.logs.rolling.maxSize (none) Set the max size of the file by which the executor logs will be rolled over. - Rolling is disabled by default. Value is set in terms of bytes. - See spark.executor.logs.rolling.maxRetainedFiles + Rolling is disabled by default. See spark.executor.logs.rolling.maxRetainedFiles for automatic cleaning of old logs.
    - - + + @@ -403,10 +413,10 @@ Apart from these, the following properties are also available, and may be useful - - + + @@ -582,18 +592,18 @@ Apart from these, the following properties are also available, and may be useful - - + + - - + + @@ -641,19 +651,19 @@ Apart from these, the following properties are also available, and may be useful - - + + - - + + @@ -698,9 +708,9 @@ Apart from these, the following properties are also available, and may be useful - + @@ -816,9 +826,9 @@ Apart from these, the following properties are also available, and may be useful - + diff --git a/docs/tuning.md b/docs/tuning.md index cbd227868b248..1cb223e74f382 100644 --- a/docs/tuning.md +++ b/docs/tuning.md @@ -60,7 +60,7 @@ val sc = new SparkContext(conf) The [Kryo documentation](https://github.com/EsotericSoftware/kryo) describes more advanced registration options, such as adding custom serialization code. -If your objects are large, you may also need to increase the `spark.kryoserializer.buffer.mb` +If your objects are large, you may also need to increase the `spark.kryoserializer.buffer` config property. The default is 2, but this value needs to be large enough to hold the *largest* object you will serialize. diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala index 0bc36ea65e1ab..99588b0984ab2 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala @@ -100,7 +100,7 @@ object MovieLensALS { val conf = new SparkConf().setAppName(s"MovieLensALS with $params") if (params.kryo) { conf.registerKryoClasses(Array(classOf[mutable.BitSet], classOf[Rating])) - .set("spark.kryoserializer.buffer.mb", "8") + .set("spark.kryoserializer.buffer", "8m") } val sc = new SparkContext(conf) diff --git a/network/common/src/main/java/org/apache/spark/network/util/ByteUnit.java b/network/common/src/main/java/org/apache/spark/network/util/ByteUnit.java new file mode 100644 index 0000000000000..36d655017fb0d --- /dev/null +++ b/network/common/src/main/java/org/apache/spark/network/util/ByteUnit.java @@ -0,0 +1,67 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.spark.network.util; + +public enum ByteUnit { + BYTE (1), + KiB (1024L), + MiB ((long) Math.pow(1024L, 2L)), + GiB ((long) Math.pow(1024L, 3L)), + TiB ((long) Math.pow(1024L, 4L)), + PiB ((long) Math.pow(1024L, 5L)); + + private ByteUnit(long multiplier) { + this.multiplier = multiplier; + } + + // Interpret the provided number (d) with suffix (u) as this unit type. + // E.g. KiB.interpret(1, MiB) interprets 1MiB as its KiB representation = 1024k + public long convertFrom(long d, ByteUnit u) { + return u.convertTo(d, this); + } + + // Convert the provided number (d) interpreted as this unit type to unit type (u). + public long convertTo(long d, ByteUnit u) { + if (multiplier > u.multiplier) { + long ratio = multiplier / u.multiplier; + if (Long.MAX_VALUE / ratio < d) { + throw new IllegalArgumentException("Conversion of " + d + " exceeds Long.MAX_VALUE in " + + name() + ". Try a larger unit (e.g. MiB instead of KiB)"); + } + return d * ratio; + } else { + // Perform operations in this order to avoid potential overflow + // when computing d * multiplier + return d / (u.multiplier / multiplier); + } + } + + public double toBytes(long d) { + if (d < 0) { + throw new IllegalArgumentException("Negative size value. Size must be positive: " + d); + } + return d * multiplier; + } + + public long toKiB(long d) { return convertTo(d, KiB); } + public long toMiB(long d) { return convertTo(d, MiB); } + public long toGiB(long d) { return convertTo(d, GiB); } + public long toTiB(long d) { return convertTo(d, TiB); } + public long toPiB(long d) { return convertTo(d, PiB); } + + private final long multiplier; +} diff --git a/network/common/src/main/java/org/apache/spark/network/util/JavaUtils.java b/network/common/src/main/java/org/apache/spark/network/util/JavaUtils.java index b6fbace509a0e..6b514aaa1290d 100644 --- a/network/common/src/main/java/org/apache/spark/network/util/JavaUtils.java +++ b/network/common/src/main/java/org/apache/spark/network/util/JavaUtils.java @@ -126,7 +126,7 @@ private static boolean isSymlink(File file) throws IOException { return !fileInCanonicalDir.getCanonicalFile().equals(fileInCanonicalDir.getAbsoluteFile()); } - private static ImmutableMap timeSuffixes = + private static final ImmutableMap timeSuffixes = ImmutableMap.builder() .put("us", TimeUnit.MICROSECONDS) .put("ms", TimeUnit.MILLISECONDS) @@ -137,6 +137,21 @@ private static boolean isSymlink(File file) throws IOException { .put("d", TimeUnit.DAYS) .build(); + private static final ImmutableMap byteSuffixes = + ImmutableMap.builder() + .put("b", ByteUnit.BYTE) + .put("k", ByteUnit.KiB) + .put("kb", ByteUnit.KiB) + .put("m", ByteUnit.MiB) + .put("mb", ByteUnit.MiB) + .put("g", ByteUnit.GiB) + .put("gb", ByteUnit.GiB) + .put("t", ByteUnit.TiB) + .put("tb", ByteUnit.TiB) + .put("p", ByteUnit.PiB) + .put("pb", ByteUnit.PiB) + .build(); + /** * Convert a passed time string (e.g. 50s, 100ms, or 250us) to a time count for * internal use. If no suffix is provided a direct conversion is attempted. @@ -145,16 +160,14 @@ private static long parseTimeString(String str, TimeUnit unit) { String lower = str.toLowerCase().trim(); try { - String suffix; - long val; Matcher m = Pattern.compile("(-?[0-9]+)([a-z]+)?").matcher(lower); - if (m.matches()) { - val = Long.parseLong(m.group(1)); - suffix = m.group(2); - } else { + if (!m.matches()) { throw new NumberFormatException("Failed to parse time string: " + str); } + long val = Long.parseLong(m.group(1)); + String suffix = m.group(2); + // Check for invalid suffixes if (suffix != null && !timeSuffixes.containsKey(suffix)) { throw new NumberFormatException("Invalid suffix: \"" + suffix + "\""); @@ -164,7 +177,7 @@ private static long parseTimeString(String str, TimeUnit unit) { return unit.convert(val, suffix != null ? timeSuffixes.get(suffix) : unit); } catch (NumberFormatException e) { String timeError = "Time must be specified as seconds (s), " + - "milliseconds (ms), microseconds (us), minutes (m or min) hour (h), or day (d). " + + "milliseconds (ms), microseconds (us), minutes (m or min), hour (h), or day (d). " + "E.g. 50s, 100ms, or 250us."; throw new NumberFormatException(timeError + "\n" + e.getMessage()); @@ -186,5 +199,83 @@ public static long timeStringAsMs(String str) { public static long timeStringAsSec(String str) { return parseTimeString(str, TimeUnit.SECONDS); } + + /** + * Convert a passed byte string (e.g. 50b, 100kb, or 250mb) to a ByteUnit for + * internal use. If no suffix is provided a direct conversion of the provided default is + * attempted. + */ + private static long parseByteString(String str, ByteUnit unit) { + String lower = str.toLowerCase().trim(); + + try { + Matcher m = Pattern.compile("([0-9]+)([a-z]+)?").matcher(lower); + Matcher fractionMatcher = Pattern.compile("([0-9]+\\.[0-9]+)([a-z]+)?").matcher(lower); + + if (m.matches()) { + long val = Long.parseLong(m.group(1)); + String suffix = m.group(2); + + // Check for invalid suffixes + if (suffix != null && !byteSuffixes.containsKey(suffix)) { + throw new NumberFormatException("Invalid suffix: \"" + suffix + "\""); + } + + // If suffix is valid use that, otherwise none was provided and use the default passed + return unit.convertFrom(val, suffix != null ? byteSuffixes.get(suffix) : unit); + } else if (fractionMatcher.matches()) { + throw new NumberFormatException("Fractional values are not supported. Input was: " + + fractionMatcher.group(1)); + } else { + throw new NumberFormatException("Failed to parse byte string: " + str); + } + + } catch (NumberFormatException e) { + String timeError = "Size must be specified as bytes (b), " + + "kibibytes (k), mebibytes (m), gibibytes (g), tebibytes (t), or pebibytes(p). " + + "E.g. 50b, 100k, or 250m."; + throw new NumberFormatException(timeError + "\n" + e.getMessage()); + } + } + + /** + * Convert a passed byte string (e.g. 50b, 100k, or 250m) to bytes for + * internal use. + * + * If no suffix is provided, the passed number is assumed to be in bytes. + */ + public static long byteStringAsBytes(String str) { + return parseByteString(str, ByteUnit.BYTE); + } + + /** + * Convert a passed byte string (e.g. 50b, 100k, or 250m) to kibibytes for + * internal use. + * + * If no suffix is provided, the passed number is assumed to be in kibibytes. + */ + public static long byteStringAsKb(String str) { + return parseByteString(str, ByteUnit.KiB); + } + + /** + * Convert a passed byte string (e.g. 50b, 100k, or 250m) to mebibytes for + * internal use. + * + * If no suffix is provided, the passed number is assumed to be in mebibytes. + */ + public static long byteStringAsMb(String str) { + return parseByteString(str, ByteUnit.MiB); + } + + /** + * Convert a passed byte string (e.g. 50b, 100k, or 250m) to gibibytes for + * internal use. + * + * If no suffix is provided, the passed number is assumed to be in gibibytes. + */ + public static long byteStringAsGb(String str) { + return parseByteString(str, ByteUnit.GiB); + } } From 80098109d908b738b43d397e024756ff617d0af4 Mon Sep 17 00:00:00 2001 From: "Zhang, Liye" Date: Tue, 28 Apr 2015 12:33:48 -0700 Subject: [PATCH 451/591] [SPARK-6314] [CORE] handle JsonParseException for history server This is handled in the same way with [SPARK-6197](https://issues.apache.org/jira/browse/SPARK-6197). The result of this PR is that exception showed in history server log will be replaced by a warning, and the application that with un-complete history log file will be listed on history server webUI Author: Zhang, Liye Closes #5736 from liyezhang556520/SPARK-6314 and squashes the following commits: b8d2d88 [Zhang, Liye] handle JsonParseException for history server --- .../org/apache/spark/deploy/history/FsHistoryProvider.scala | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala index a94ebf6e53750..fb2cbbcccc54b 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala @@ -333,8 +333,9 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis } try { val appListener = new ApplicationEventListener + val appCompleted = isApplicationCompleted(eventLog) bus.addListener(appListener) - bus.replay(logInput, logPath.toString) + bus.replay(logInput, logPath.toString, !appCompleted) new FsApplicationHistoryInfo( logPath.getName(), appListener.appId.getOrElse(logPath.getName()), @@ -343,7 +344,7 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis appListener.endTime.getOrElse(-1L), getModificationTime(eventLog).get, appListener.sparkUser.getOrElse(NOT_STARTED), - isApplicationCompleted(eventLog)) + appCompleted) } finally { logInput.close() } From 53befacced828bbac53c6e3a4976ec3f036bae9e Mon Sep 17 00:00:00 2001 From: Timothy Chen Date: Tue, 28 Apr 2015 13:31:08 -0700 Subject: [PATCH 452/591] [SPARK-5338] [MESOS] Add cluster mode support for Mesos This patch adds the support for cluster mode to run on Mesos. It introduces a new Mesos framework dedicated to launch new apps/drivers, and can be called with the spark-submit script and specifying --master flag to the cluster mode REST interface instead of Mesos master. Example: ./bin/spark-submit --deploy-mode cluster --class org.apache.spark.examples.SparkPi --master mesos://10.0.0.206:8077 --executor-memory 1G --total-executor-cores 100 examples/target/spark-examples_2.10-1.3.0-SNAPSHOT.jar 30 Part of this patch is also to abstract the StandaloneRestServer so it can have different implementations of the REST endpoints. Features of the cluster mode in this PR: - Supports supervise mode where scheduler will keep trying to reschedule exited job. - Adds a new UI for the cluster mode scheduler to see all the running jobs, finished jobs, and supervise jobs waiting to be retried - Supports state persistence to ZK, so when the cluster scheduler fails over it can pick up all the queued and running jobs Author: Timothy Chen Author: Luc Bourlier Closes #5144 from tnachen/mesos_cluster_mode and squashes the following commits: 069e946 [Timothy Chen] Fix rebase. e24b512 [Timothy Chen] Persist submitted driver. 390c491 [Timothy Chen] Fix zk conf key for mesos zk engine. e324ac1 [Timothy Chen] Fix merge. fd5259d [Timothy Chen] Address review comments. 1553230 [Timothy Chen] Address review comments. c6c6b73 [Timothy Chen] Pass spark properties to mesos cluster tasks. f7d8046 [Timothy Chen] Change app name to spark cluster. 17f93a2 [Timothy Chen] Fix head of line blocking in scheduling drivers. 6ff8e5c [Timothy Chen] Address comments and add logging. df355cd [Timothy Chen] Add metrics to mesos cluster scheduler. 20f7284 [Timothy Chen] Address review comments 7252612 [Timothy Chen] Fix tests. a46ad66 [Timothy Chen] Allow zk cli param override. 920fc4b [Timothy Chen] Fix scala style issues. 862b5b5 [Timothy Chen] Support asking driver status when it's retrying. 7f214c2 [Timothy Chen] Fix RetryState visibility e0f33f7 [Timothy Chen] Add supervise support and persist retries. 371ce65 [Timothy Chen] Handle cluster mode recovery and state persistence. 3d4dfa1 [Luc Bourlier] Adds support to kill submissions febfaba [Timothy Chen] Bound the finished drivers in memory 543a98d [Timothy Chen] Schedule multiple jobs 6887e5e [Timothy Chen] Support looking at SPARK_EXECUTOR_URI env variable in schedulers 8ec76bc [Timothy Chen] Fix Mesos dispatcher UI. d57d77d [Timothy Chen] Add documentation 825afa0 [Luc Bourlier] Supports more spark-submit parameters b8e7181 [Luc Bourlier] Adds a shutdown latch to keep the deamon running 0fa7780 [Luc Bourlier] Launch task through the mesos scheduler 5b7a12b [Timothy Chen] WIP: Making a cluster mode a mesos framework. 4b2f5ef [Timothy Chen] Specify user jar in command to be replaced with local. e775001 [Timothy Chen] Support fetching remote uris in driver runner. 7179495 [Timothy Chen] Change Driver page output and add logging 880bc27 [Timothy Chen] Add Mesos Cluster UI to display driver results 9986731 [Timothy Chen] Kill drivers when shutdown 67cbc18 [Timothy Chen] Rename StandaloneRestClient to RestClient and add sbin scripts e3facdd [Timothy Chen] Add Mesos Cluster dispatcher --- .../spark/deploy/FaultToleranceTest.scala | 2 +- .../{master => }/SparkCuratorUtil.scala | 10 +- .../org/apache/spark/deploy/SparkSubmit.scala | 48 +- .../spark/deploy/SparkSubmitArguments.scala | 11 +- .../apache/spark/deploy/master/Master.scala | 2 +- .../master/ZooKeeperLeaderElectionAgent.scala | 1 + .../master/ZooKeeperPersistenceEngine.scala | 1 + .../deploy/mesos/MesosClusterDispatcher.scala | 116 ++++ .../MesosClusterDispatcherArguments.scala | 101 +++ .../deploy/mesos/MesosDriverDescription.scala | 65 ++ .../deploy/mesos/ui/MesosClusterPage.scala | 114 ++++ .../deploy/mesos/ui/MesosClusterUI.scala | 48 ++ ...lient.scala => RestSubmissionClient.scala} | 35 +- .../deploy/rest/RestSubmissionServer.scala | 318 +++++++++ .../deploy/rest/StandaloneRestServer.scala | 344 ++-------- .../rest/SubmitRestProtocolRequest.scala | 2 +- .../rest/SubmitRestProtocolResponse.scala | 6 +- .../deploy/rest/mesos/MesosRestServer.scala | 158 +++++ .../mesos/CoarseMesosSchedulerBackend.scala | 82 +-- .../mesos/MesosClusterPersistenceEngine.scala | 134 ++++ .../cluster/mesos/MesosClusterScheduler.scala | 608 ++++++++++++++++++ .../mesos/MesosClusterSchedulerSource.scala | 40 ++ .../cluster/mesos/MesosSchedulerBackend.scala | 85 +-- .../cluster/mesos/MesosSchedulerUtils.scala | 95 +++ .../spark/deploy/SparkSubmitSuite.scala | 2 +- .../rest/StandaloneRestSubmitSuite.scala | 46 +- .../mesos/MesosClusterSchedulerSuite.scala | 76 +++ docs/running-on-mesos.md | 23 +- sbin/start-mesos-dispatcher.sh | 40 ++ sbin/stop-mesos-dispatcher.sh | 27 + 30 files changed, 2147 insertions(+), 493 deletions(-) rename core/src/main/scala/org/apache/spark/deploy/{master => }/SparkCuratorUtil.scala (89%) create mode 100644 core/src/main/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcher.scala create mode 100644 core/src/main/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcherArguments.scala create mode 100644 core/src/main/scala/org/apache/spark/deploy/mesos/MesosDriverDescription.scala create mode 100644 core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterPage.scala create mode 100644 core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterUI.scala rename core/src/main/scala/org/apache/spark/deploy/rest/{StandaloneRestClient.scala => RestSubmissionClient.scala} (93%) create mode 100644 core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionServer.scala create mode 100644 core/src/main/scala/org/apache/spark/deploy/rest/mesos/MesosRestServer.scala create mode 100644 core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterPersistenceEngine.scala create mode 100644 core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala create mode 100644 core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterSchedulerSource.scala create mode 100644 core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala create mode 100644 core/src/test/scala/org/apache/spark/scheduler/mesos/MesosClusterSchedulerSuite.scala create mode 100755 sbin/start-mesos-dispatcher.sh create mode 100755 sbin/stop-mesos-dispatcher.sh diff --git a/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala b/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala index a7c89276a045e..c048b78910f38 100644 --- a/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala +++ b/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala @@ -32,7 +32,7 @@ import org.json4s._ import org.json4s.jackson.JsonMethods import org.apache.spark.{Logging, SparkConf, SparkContext} -import org.apache.spark.deploy.master.{RecoveryState, SparkCuratorUtil} +import org.apache.spark.deploy.master.RecoveryState import org.apache.spark.util.Utils /** diff --git a/core/src/main/scala/org/apache/spark/deploy/master/SparkCuratorUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkCuratorUtil.scala similarity index 89% rename from core/src/main/scala/org/apache/spark/deploy/master/SparkCuratorUtil.scala rename to core/src/main/scala/org/apache/spark/deploy/SparkCuratorUtil.scala index 5b22481ea8c5f..b8d3993540220 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/SparkCuratorUtil.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkCuratorUtil.scala @@ -15,7 +15,7 @@ * limitations under the License. */ -package org.apache.spark.deploy.master +package org.apache.spark.deploy import scala.collection.JavaConversions._ @@ -25,15 +25,17 @@ import org.apache.zookeeper.KeeperException import org.apache.spark.{Logging, SparkConf} -private[deploy] object SparkCuratorUtil extends Logging { +private[spark] object SparkCuratorUtil extends Logging { private val ZK_CONNECTION_TIMEOUT_MILLIS = 15000 private val ZK_SESSION_TIMEOUT_MILLIS = 60000 private val RETRY_WAIT_MILLIS = 5000 private val MAX_RECONNECT_ATTEMPTS = 3 - def newClient(conf: SparkConf): CuratorFramework = { - val ZK_URL = conf.get("spark.deploy.zookeeper.url") + def newClient( + conf: SparkConf, + zkUrlConf: String = "spark.deploy.zookeeper.url"): CuratorFramework = { + val ZK_URL = conf.get(zkUrlConf) val zk = CuratorFrameworkFactory.newClient(ZK_URL, ZK_SESSION_TIMEOUT_MILLIS, ZK_CONNECTION_TIMEOUT_MILLIS, new ExponentialBackoffRetry(RETRY_WAIT_MILLIS, MAX_RECONNECT_ATTEMPTS)) diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala index 296a0764b8baf..f4f572e1e256e 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -36,11 +36,11 @@ import org.apache.ivy.core.retrieve.RetrieveOptions import org.apache.ivy.core.settings.IvySettings import org.apache.ivy.plugins.matcher.GlobPatternMatcher import org.apache.ivy.plugins.resolver.{ChainResolver, IBiblioResolver} - import org.apache.spark.SPARK_VERSION import org.apache.spark.deploy.rest._ import org.apache.spark.util.{ChildFirstURLClassLoader, MutableURLClassLoader, Utils} + /** * Whether to submit, kill, or request the status of an application. * The latter two operations are currently supported only for standalone cluster mode. @@ -114,18 +114,20 @@ object SparkSubmit { } } - /** Kill an existing submission using the REST protocol. Standalone cluster mode only. */ + /** + * Kill an existing submission using the REST protocol. Standalone and Mesos cluster mode only. + */ private def kill(args: SparkSubmitArguments): Unit = { - new StandaloneRestClient() + new RestSubmissionClient() .killSubmission(args.master, args.submissionToKill) } /** * Request the status of an existing submission using the REST protocol. - * Standalone cluster mode only. + * Standalone and Mesos cluster mode only. */ private def requestStatus(args: SparkSubmitArguments): Unit = { - new StandaloneRestClient() + new RestSubmissionClient() .requestSubmissionStatus(args.master, args.submissionToRequestStatusFor) } @@ -252,6 +254,7 @@ object SparkSubmit { } val isYarnCluster = clusterManager == YARN && deployMode == CLUSTER + val isMesosCluster = clusterManager == MESOS && deployMode == CLUSTER // Resolve maven dependencies if there are any and add classpath to jars. Add them to py-files // too for packages that include Python code @@ -294,8 +297,9 @@ object SparkSubmit { // The following modes are not supported or applicable (clusterManager, deployMode) match { - case (MESOS, CLUSTER) => - printErrorAndExit("Cluster deploy mode is currently not supported for Mesos clusters.") + case (MESOS, CLUSTER) if args.isPython => + printErrorAndExit("Cluster deploy mode is currently not supported for python " + + "applications on Mesos clusters.") case (STANDALONE, CLUSTER) if args.isPython => printErrorAndExit("Cluster deploy mode is currently not supported for python " + "applications on standalone clusters.") @@ -377,15 +381,6 @@ object SparkSubmit { OptionAssigner(args.driverExtraLibraryPath, ALL_CLUSTER_MGRS, ALL_DEPLOY_MODES, sysProp = "spark.driver.extraLibraryPath"), - // Standalone cluster only - // Do not set CL arguments here because there are multiple possibilities for the main class - OptionAssigner(args.jars, STANDALONE, CLUSTER, sysProp = "spark.jars"), - OptionAssigner(args.ivyRepoPath, STANDALONE, CLUSTER, sysProp = "spark.jars.ivy"), - OptionAssigner(args.driverMemory, STANDALONE, CLUSTER, sysProp = "spark.driver.memory"), - OptionAssigner(args.driverCores, STANDALONE, CLUSTER, sysProp = "spark.driver.cores"), - OptionAssigner(args.supervise.toString, STANDALONE, CLUSTER, - sysProp = "spark.driver.supervise"), - // Yarn client only OptionAssigner(args.queue, YARN, CLIENT, sysProp = "spark.yarn.queue"), OptionAssigner(args.numExecutors, YARN, CLIENT, sysProp = "spark.executor.instances"), @@ -413,7 +408,15 @@ object SparkSubmit { OptionAssigner(args.totalExecutorCores, STANDALONE | MESOS, ALL_DEPLOY_MODES, sysProp = "spark.cores.max"), OptionAssigner(args.files, LOCAL | STANDALONE | MESOS, ALL_DEPLOY_MODES, - sysProp = "spark.files") + sysProp = "spark.files"), + OptionAssigner(args.jars, STANDALONE | MESOS, CLUSTER, sysProp = "spark.jars"), + OptionAssigner(args.driverMemory, STANDALONE | MESOS, CLUSTER, + sysProp = "spark.driver.memory"), + OptionAssigner(args.driverCores, STANDALONE | MESOS, CLUSTER, + sysProp = "spark.driver.cores"), + OptionAssigner(args.supervise.toString, STANDALONE | MESOS, CLUSTER, + sysProp = "spark.driver.supervise"), + OptionAssigner(args.ivyRepoPath, STANDALONE, CLUSTER, sysProp = "spark.jars.ivy") ) // In client mode, launch the application main class directly @@ -452,7 +455,7 @@ object SparkSubmit { // All Spark parameters are expected to be passed to the client through system properties. if (args.isStandaloneCluster) { if (args.useRest) { - childMainClass = "org.apache.spark.deploy.rest.StandaloneRestClient" + childMainClass = "org.apache.spark.deploy.rest.RestSubmissionClient" childArgs += (args.primaryResource, args.mainClass) } else { // In legacy standalone cluster mode, use Client as a wrapper around the user class @@ -496,6 +499,15 @@ object SparkSubmit { } } + if (isMesosCluster) { + assert(args.useRest, "Mesos cluster mode is only supported through the REST submission API") + childMainClass = "org.apache.spark.deploy.rest.RestSubmissionClient" + childArgs += (args.primaryResource, args.mainClass) + if (args.childArgs != null) { + childArgs ++= args.childArgs + } + } + // Load any properties specified through --conf and the default properties file for ((k, v) <- args.sparkProperties) { sysProps.getOrElseUpdate(k, v) diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala index c896842943f2b..c621b8fc86f94 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala @@ -241,8 +241,9 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S } private def validateKillArguments(): Unit = { - if (!master.startsWith("spark://")) { - SparkSubmit.printErrorAndExit("Killing submissions is only supported in standalone mode!") + if (!master.startsWith("spark://") && !master.startsWith("mesos://")) { + SparkSubmit.printErrorAndExit( + "Killing submissions is only supported in standalone or Mesos mode!") } if (submissionToKill == null) { SparkSubmit.printErrorAndExit("Please specify a submission to kill.") @@ -250,9 +251,9 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S } private def validateStatusRequestArguments(): Unit = { - if (!master.startsWith("spark://")) { + if (!master.startsWith("spark://") && !master.startsWith("mesos://")) { SparkSubmit.printErrorAndExit( - "Requesting submission statuses is only supported in standalone mode!") + "Requesting submission statuses is only supported in standalone or Mesos mode!") } if (submissionToRequestStatusFor == null) { SparkSubmit.printErrorAndExit("Please specify a submission to request status for.") @@ -485,6 +486,8 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S | | Spark standalone with cluster deploy mode only: | --driver-cores NUM Cores for driver (Default: 1). + | + | Spark standalone or Mesos with cluster deploy mode only: | --supervise If given, restarts the driver on failure. | --kill SUBMISSION_ID If given, kills the driver specified. | --status SUBMISSION_ID If given, requests the status of the driver specified. diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala index ff2eed6dee70a..1c21c179562ac 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala @@ -130,7 +130,7 @@ private[master] class Master( private val restServer = if (restServerEnabled) { val port = conf.getInt("spark.master.rest.port", 6066) - Some(new StandaloneRestServer(host, port, self, masterUrl, conf)) + Some(new StandaloneRestServer(host, port, conf, self, masterUrl)) } else { None } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala index 4823fd7cac0cb..52758d6a7c4be 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala @@ -23,6 +23,7 @@ import org.apache.spark.{Logging, SparkConf} import org.apache.spark.deploy.master.MasterMessages._ import org.apache.curator.framework.CuratorFramework import org.apache.curator.framework.recipes.leader.{LeaderLatchListener, LeaderLatch} +import org.apache.spark.deploy.SparkCuratorUtil private[master] class ZooKeeperLeaderElectionAgent(val masterActor: LeaderElectable, conf: SparkConf) extends LeaderLatchListener with LeaderElectionAgent with Logging { diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala index a285783f72000..80db6d474b5c1 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala @@ -26,6 +26,7 @@ import org.apache.curator.framework.CuratorFramework import org.apache.zookeeper.CreateMode import org.apache.spark.{Logging, SparkConf} +import org.apache.spark.deploy.SparkCuratorUtil private[master] class ZooKeeperPersistenceEngine(conf: SparkConf, val serialization: Serialization) diff --git a/core/src/main/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcher.scala b/core/src/main/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcher.scala new file mode 100644 index 0000000000000..5d4e5b899dfdc --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcher.scala @@ -0,0 +1,116 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.mesos + +import java.util.concurrent.CountDownLatch + +import org.apache.spark.deploy.mesos.ui.MesosClusterUI +import org.apache.spark.deploy.rest.mesos.MesosRestServer +import org.apache.spark.scheduler.cluster.mesos._ +import org.apache.spark.util.SignalLogger +import org.apache.spark.{Logging, SecurityManager, SparkConf} + +/* + * A dispatcher that is responsible for managing and launching drivers, and is intended to be + * used for Mesos cluster mode. The dispatcher is a long-running process started by the user in + * the cluster independently of Spark applications. + * It contains a [[MesosRestServer]] that listens for requests to submit drivers and a + * [[MesosClusterScheduler]] that processes these requests by negotiating with the Mesos master + * for resources. + * + * A typical new driver lifecycle is the following: + * - Driver submitted via spark-submit talking to the [[MesosRestServer]] + * - [[MesosRestServer]] queues the driver request to [[MesosClusterScheduler]] + * - [[MesosClusterScheduler]] gets resource offers and launches the drivers that are in queue + * + * This dispatcher supports both Mesos fine-grain or coarse-grain mode as the mode is configurable + * per driver launched. + * This class is needed since Mesos doesn't manage frameworks, so the dispatcher acts as + * a daemon to launch drivers as Mesos frameworks upon request. The dispatcher is also started and + * stopped by sbin/start-mesos-dispatcher and sbin/stop-mesos-dispatcher respectively. + */ +private[mesos] class MesosClusterDispatcher( + args: MesosClusterDispatcherArguments, + conf: SparkConf) + extends Logging { + + private val publicAddress = Option(conf.getenv("SPARK_PUBLIC_DNS")).getOrElse(args.host) + private val recoveryMode = conf.get("spark.mesos.deploy.recoveryMode", "NONE").toUpperCase() + logInfo("Recovery mode in Mesos dispatcher set to: " + recoveryMode) + + private val engineFactory = recoveryMode match { + case "NONE" => new BlackHoleMesosClusterPersistenceEngineFactory + case "ZOOKEEPER" => new ZookeeperMesosClusterPersistenceEngineFactory(conf) + case _ => throw new IllegalArgumentException("Unsupported recovery mode: " + recoveryMode) + } + + private val scheduler = new MesosClusterScheduler(engineFactory, conf) + + private val server = new MesosRestServer(args.host, args.port, conf, scheduler) + private val webUi = new MesosClusterUI( + new SecurityManager(conf), + args.webUiPort, + conf, + publicAddress, + scheduler) + + private val shutdownLatch = new CountDownLatch(1) + + def start(): Unit = { + webUi.bind() + scheduler.frameworkUrl = webUi.activeWebUiUrl + scheduler.start() + server.start() + } + + def awaitShutdown(): Unit = { + shutdownLatch.await() + } + + def stop(): Unit = { + webUi.stop() + server.stop() + scheduler.stop() + shutdownLatch.countDown() + } +} + +private[mesos] object MesosClusterDispatcher extends Logging { + def main(args: Array[String]) { + SignalLogger.register(log) + val conf = new SparkConf + val dispatcherArgs = new MesosClusterDispatcherArguments(args, conf) + conf.setMaster(dispatcherArgs.masterUrl) + conf.setAppName(dispatcherArgs.name) + dispatcherArgs.zookeeperUrl.foreach { z => + conf.set("spark.mesos.deploy.recoveryMode", "ZOOKEEPER") + conf.set("spark.mesos.deploy.zookeeper.url", z) + } + val dispatcher = new MesosClusterDispatcher(dispatcherArgs, conf) + dispatcher.start() + val shutdownHook = new Thread() { + override def run() { + logInfo("Shutdown hook is shutting down dispatcher") + dispatcher.stop() + dispatcher.awaitShutdown() + } + } + Runtime.getRuntime.addShutdownHook(shutdownHook) + dispatcher.awaitShutdown() + } +} diff --git a/core/src/main/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcherArguments.scala b/core/src/main/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcherArguments.scala new file mode 100644 index 0000000000000..894cb78d8591a --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcherArguments.scala @@ -0,0 +1,101 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.mesos + +import org.apache.spark.SparkConf +import org.apache.spark.util.{IntParam, Utils} + + +private[mesos] class MesosClusterDispatcherArguments(args: Array[String], conf: SparkConf) { + var host = Utils.localHostName() + var port = 7077 + var name = "Spark Cluster" + var webUiPort = 8081 + var masterUrl: String = _ + var zookeeperUrl: Option[String] = None + var propertiesFile: String = _ + + parse(args.toList) + + propertiesFile = Utils.loadDefaultSparkProperties(conf, propertiesFile) + + private def parse(args: List[String]): Unit = args match { + case ("--host" | "-h") :: value :: tail => + Utils.checkHost(value, "Please use hostname " + value) + host = value + parse(tail) + + case ("--port" | "-p") :: IntParam(value) :: tail => + port = value + parse(tail) + + case ("--webui-port" | "-p") :: IntParam(value) :: tail => + webUiPort = value + parse(tail) + + case ("--zk" | "-z") :: value :: tail => + zookeeperUrl = Some(value) + parse(tail) + + case ("--master" | "-m") :: value :: tail => + if (!value.startsWith("mesos://")) { + System.err.println("Cluster dispatcher only supports mesos (uri begins with mesos://)") + System.exit(1) + } + masterUrl = value.stripPrefix("mesos://") + parse(tail) + + case ("--name") :: value :: tail => + name = value + parse(tail) + + case ("--properties-file") :: value :: tail => + propertiesFile = value + parse(tail) + + case ("--help") :: tail => + printUsageAndExit(0) + + case Nil => { + if (masterUrl == null) { + System.err.println("--master is required") + printUsageAndExit(1) + } + } + + case _ => + printUsageAndExit(1) + } + + private def printUsageAndExit(exitCode: Int): Unit = { + System.err.println( + "Usage: MesosClusterDispatcher [options]\n" + + "\n" + + "Options:\n" + + " -h HOST, --host HOST Hostname to listen on\n" + + " -p PORT, --port PORT Port to listen on (default: 7077)\n" + + " --webui-port WEBUI_PORT WebUI Port to listen on (default: 8081)\n" + + " --name NAME Framework name to show in Mesos UI\n" + + " -m --master MASTER URI for connecting to Mesos master\n" + + " -z --zk ZOOKEEPER Comma delimited URLs for connecting to \n" + + " Zookeeper for persistence\n" + + " --properties-file FILE Path to a custom Spark properties file.\n" + + " Default is conf/spark-defaults.conf.") + System.exit(exitCode) + } +} diff --git a/core/src/main/scala/org/apache/spark/deploy/mesos/MesosDriverDescription.scala b/core/src/main/scala/org/apache/spark/deploy/mesos/MesosDriverDescription.scala new file mode 100644 index 0000000000000..1948226800afe --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/mesos/MesosDriverDescription.scala @@ -0,0 +1,65 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.mesos + +import java.util.Date + +import org.apache.spark.deploy.Command +import org.apache.spark.scheduler.cluster.mesos.MesosClusterRetryState + +/** + * Describes a Spark driver that is submitted from the + * [[org.apache.spark.deploy.rest.mesos.MesosRestServer]], to be launched by + * [[org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler]]. + * @param jarUrl URL to the application jar + * @param mem Amount of memory for the driver + * @param cores Number of cores for the driver + * @param supervise Supervise the driver for long running app + * @param command The command to launch the driver. + * @param schedulerProperties Extra properties to pass the Mesos scheduler + */ +private[spark] class MesosDriverDescription( + val name: String, + val jarUrl: String, + val mem: Int, + val cores: Double, + val supervise: Boolean, + val command: Command, + val schedulerProperties: Map[String, String], + val submissionId: String, + val submissionDate: Date, + val retryState: Option[MesosClusterRetryState] = None) + extends Serializable { + + def copy( + name: String = name, + jarUrl: String = jarUrl, + mem: Int = mem, + cores: Double = cores, + supervise: Boolean = supervise, + command: Command = command, + schedulerProperties: Map[String, String] = schedulerProperties, + submissionId: String = submissionId, + submissionDate: Date = submissionDate, + retryState: Option[MesosClusterRetryState] = retryState): MesosDriverDescription = { + new MesosDriverDescription(name, jarUrl, mem, cores, supervise, command, schedulerProperties, + submissionId, submissionDate, retryState) + } + + override def toString: String = s"MesosDriverDescription (${command.mainClass})" +} diff --git a/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterPage.scala b/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterPage.scala new file mode 100644 index 0000000000000..7b2005e0f1237 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterPage.scala @@ -0,0 +1,114 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.mesos.ui + +import javax.servlet.http.HttpServletRequest + +import scala.xml.Node + +import org.apache.mesos.Protos.TaskStatus +import org.apache.spark.deploy.mesos.MesosDriverDescription +import org.apache.spark.scheduler.cluster.mesos.MesosClusterSubmissionState +import org.apache.spark.ui.{UIUtils, WebUIPage} + +private[mesos] class MesosClusterPage(parent: MesosClusterUI) extends WebUIPage("") { + def render(request: HttpServletRequest): Seq[Node] = { + val state = parent.scheduler.getSchedulerState() + val queuedHeaders = Seq("Driver ID", "Submit Date", "Main Class", "Driver Resources") + val driverHeaders = queuedHeaders ++ + Seq("Start Date", "Mesos Slave ID", "State") + val retryHeaders = Seq("Driver ID", "Submit Date", "Description") ++ + Seq("Last Failed Status", "Next Retry Time", "Attempt Count") + val queuedTable = UIUtils.listingTable(queuedHeaders, queuedRow, state.queuedDrivers) + val launchedTable = UIUtils.listingTable(driverHeaders, driverRow, state.launchedDrivers) + val finishedTable = UIUtils.listingTable(driverHeaders, driverRow, state.finishedDrivers) + val retryTable = UIUtils.listingTable(retryHeaders, retryRow, state.pendingRetryDrivers) + val content = +

    Mesos Framework ID: {state.frameworkId}

    +
    +
    +

    Queued Drivers:

    + {queuedTable} +

    Launched Drivers:

    + {launchedTable} +

    Finished Drivers:

    + {finishedTable} +

    Supervise drivers waiting for retry:

    + {retryTable} +
    +
    ; + UIUtils.basicSparkPage(content, "Spark Drivers for Mesos cluster") + } + + private def queuedRow(submission: MesosDriverDescription): Seq[Node] = { +     + + + + + + } + + private def driverRow(state: MesosClusterSubmissionState): Seq[Node] = { + + + + + + + + + + } + + private def retryRow(submission: MesosDriverDescription): Seq[Node] = { + + + + + + + + + } + + private def stateString(status: Option[TaskStatus]): String = { + if (status.isEmpty) { + return "" + } + val sb = new StringBuilder + val s = status.get + sb.append(s"State: ${s.getState}") + if (status.get.hasMessage) { + sb.append(s", Message: ${s.getMessage}") + } + if (status.get.hasHealthy) { + sb.append(s", Healthy: ${s.getHealthy}") + } + if (status.get.hasSource) { + sb.append(s", Source: ${s.getSource}") + } + if (status.get.hasReason) { + sb.append(s", Reason: ${s.getReason}") + } + if (status.get.hasTimestamp) { + sb.append(s", Time: ${s.getTimestamp}") + } + sb.toString() + } +} diff --git a/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterUI.scala b/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterUI.scala new file mode 100644 index 0000000000000..4865d46dbc4ab --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterUI.scala @@ -0,0 +1,48 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.mesos.ui + +import org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler +import org.apache.spark.{SecurityManager, SparkConf} +import org.apache.spark.ui.JettyUtils._ +import org.apache.spark.ui.{SparkUI, WebUI} + +/** + * UI that displays driver results from the [[org.apache.spark.deploy.mesos.MesosClusterDispatcher]] + */ +private[spark] class MesosClusterUI( + securityManager: SecurityManager, + port: Int, + conf: SparkConf, + dispatcherPublicAddress: String, + val scheduler: MesosClusterScheduler) + extends WebUI(securityManager, port, conf) { + + initialize() + + def activeWebUiUrl: String = "http://" + dispatcherPublicAddress + ":" + boundPort + + override def initialize() { + attachPage(new MesosClusterPage(this)) + attachHandler(createStaticHandler(MesosClusterUI.STATIC_RESOURCE_DIR, "/static")) + } +} + +private object MesosClusterUI { + val STATIC_RESOURCE_DIR = SparkUI.STATIC_RESOURCE_DIR +} diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestClient.scala b/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala similarity index 93% rename from core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestClient.scala rename to core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala index b8fd406fb6f9a..307cebfb4bd09 100644 --- a/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestClient.scala +++ b/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala @@ -30,9 +30,7 @@ import org.apache.spark.{Logging, SparkConf, SPARK_VERSION => sparkVersion} import org.apache.spark.util.Utils /** - * A client that submits applications to the standalone Master using a REST protocol. - * This client is intended to communicate with the [[StandaloneRestServer]] and is - * currently used for cluster mode only. + * A client that submits applications to a [[RestSubmissionServer]]. * * In protocol version v1, the REST URL takes the form http://[host:port]/v1/submissions/[action], * where [action] can be one of create, kill, or status. Each type of request is represented in @@ -53,8 +51,10 @@ import org.apache.spark.util.Utils * implementation of this client can use that information to retry using the version specified * by the server. */ -private[deploy] class StandaloneRestClient extends Logging { - import StandaloneRestClient._ +private[spark] class RestSubmissionClient extends Logging { + import RestSubmissionClient._ + + private val supportedMasterPrefixes = Seq("spark://", "mesos://") /** * Submit an application specified by the parameters in the provided request. @@ -62,7 +62,7 @@ private[deploy] class StandaloneRestClient extends Logging { * If the submission was successful, poll the status of the submission and report * it to the user. Otherwise, report the error message provided by the server. */ - private[rest] def createSubmission( + def createSubmission( master: String, request: CreateSubmissionRequest): SubmitRestProtocolResponse = { logInfo(s"Submitting a request to launch an application in $master.") @@ -107,7 +107,7 @@ private[deploy] class StandaloneRestClient extends Logging { } /** Construct a message that captures the specified parameters for submitting an application. */ - private[rest] def constructSubmitRequest( + def constructSubmitRequest( appResource: String, mainClass: String, appArgs: Array[String], @@ -219,14 +219,23 @@ private[deploy] class StandaloneRestClient extends Logging { /** Return the base URL for communicating with the server, including the protocol version. */ private def getBaseUrl(master: String): String = { - val masterUrl = master.stripPrefix("spark://").stripSuffix("/") + var masterUrl = master + supportedMasterPrefixes.foreach { prefix => + if (master.startsWith(prefix)) { + masterUrl = master.stripPrefix(prefix) + } + } + masterUrl = masterUrl.stripSuffix("/") s"http://$masterUrl/$PROTOCOL_VERSION/submissions" } /** Throw an exception if this is not standalone mode. */ private def validateMaster(master: String): Unit = { - if (!master.startsWith("spark://")) { - throw new IllegalArgumentException("This REST client is only supported in standalone mode.") + val valid = supportedMasterPrefixes.exists { prefix => master.startsWith(prefix) } + if (!valid) { + throw new IllegalArgumentException( + "This REST client only supports master URLs that start with " + + "one of the following: " + supportedMasterPrefixes.mkString(",")) } } @@ -295,7 +304,7 @@ private[deploy] class StandaloneRestClient extends Logging { } } -private[rest] object StandaloneRestClient { +private[spark] object RestSubmissionClient { private val REPORT_DRIVER_STATUS_INTERVAL = 1000 private val REPORT_DRIVER_STATUS_MAX_TRIES = 10 val PROTOCOL_VERSION = "v1" @@ -315,7 +324,7 @@ private[rest] object StandaloneRestClient { } val sparkProperties = conf.getAll.toMap val environmentVariables = env.filter { case (k, _) => k.startsWith("SPARK_") } - val client = new StandaloneRestClient + val client = new RestSubmissionClient val submitRequest = client.constructSubmitRequest( appResource, mainClass, appArgs, sparkProperties, environmentVariables) client.createSubmission(master, submitRequest) @@ -323,7 +332,7 @@ private[rest] object StandaloneRestClient { def main(args: Array[String]): Unit = { if (args.size < 2) { - sys.error("Usage: StandaloneRestClient [app resource] [main class] [app args*]") + sys.error("Usage: RestSubmissionClient [app resource] [main class] [app args*]") sys.exit(1) } val appResource = args(0) diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionServer.scala b/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionServer.scala new file mode 100644 index 0000000000000..2e78d03e5c0cc --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionServer.scala @@ -0,0 +1,318 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.rest + +import java.net.InetSocketAddress +import javax.servlet.http.{HttpServlet, HttpServletRequest, HttpServletResponse} + +import scala.io.Source +import com.fasterxml.jackson.core.JsonProcessingException +import org.eclipse.jetty.server.Server +import org.eclipse.jetty.servlet.{ServletHolder, ServletContextHandler} +import org.eclipse.jetty.util.thread.QueuedThreadPool +import org.json4s._ +import org.json4s.jackson.JsonMethods._ + +import org.apache.spark.{Logging, SparkConf, SPARK_VERSION => sparkVersion} +import org.apache.spark.util.Utils + +/** + * A server that responds to requests submitted by the [[RestSubmissionClient]]. + * + * This server responds with different HTTP codes depending on the situation: + * 200 OK - Request was processed successfully + * 400 BAD REQUEST - Request was malformed, not successfully validated, or of unexpected type + * 468 UNKNOWN PROTOCOL VERSION - Request specified a protocol this server does not understand + * 500 INTERNAL SERVER ERROR - Server throws an exception internally while processing the request + * + * The server always includes a JSON representation of the relevant [[SubmitRestProtocolResponse]] + * in the HTTP body. If an error occurs, however, the server will include an [[ErrorResponse]] + * instead of the one expected by the client. If the construction of this error response itself + * fails, the response will consist of an empty body with a response code that indicates internal + * server error. + */ +private[spark] abstract class RestSubmissionServer( + val host: String, + val requestedPort: Int, + val masterConf: SparkConf) extends Logging { + protected val submitRequestServlet: SubmitRequestServlet + protected val killRequestServlet: KillRequestServlet + protected val statusRequestServlet: StatusRequestServlet + + private var _server: Option[Server] = None + + // A mapping from URL prefixes to servlets that serve them. Exposed for testing. + protected val baseContext = s"/${RestSubmissionServer.PROTOCOL_VERSION}/submissions" + protected lazy val contextToServlet = Map[String, RestServlet]( + s"$baseContext/create/*" -> submitRequestServlet, + s"$baseContext/kill/*" -> killRequestServlet, + s"$baseContext/status/*" -> statusRequestServlet, + "/*" -> new ErrorServlet // default handler + ) + + /** Start the server and return the bound port. */ + def start(): Int = { + val (server, boundPort) = Utils.startServiceOnPort[Server](requestedPort, doStart, masterConf) + _server = Some(server) + logInfo(s"Started REST server for submitting applications on port $boundPort") + boundPort + } + + /** + * Map the servlets to their corresponding contexts and attach them to a server. + * Return a 2-tuple of the started server and the bound port. + */ + private def doStart(startPort: Int): (Server, Int) = { + val server = new Server(new InetSocketAddress(host, startPort)) + val threadPool = new QueuedThreadPool + threadPool.setDaemon(true) + server.setThreadPool(threadPool) + val mainHandler = new ServletContextHandler + mainHandler.setContextPath("/") + contextToServlet.foreach { case (prefix, servlet) => + mainHandler.addServlet(new ServletHolder(servlet), prefix) + } + server.setHandler(mainHandler) + server.start() + val boundPort = server.getConnectors()(0).getLocalPort + (server, boundPort) + } + + def stop(): Unit = { + _server.foreach(_.stop()) + } +} + +private[rest] object RestSubmissionServer { + val PROTOCOL_VERSION = RestSubmissionClient.PROTOCOL_VERSION + val SC_UNKNOWN_PROTOCOL_VERSION = 468 +} + +/** + * An abstract servlet for handling requests passed to the [[RestSubmissionServer]]. + */ +private[rest] abstract class RestServlet extends HttpServlet with Logging { + + /** + * Serialize the given response message to JSON and send it through the response servlet. + * This validates the response before sending it to ensure it is properly constructed. + */ + protected def sendResponse( + responseMessage: SubmitRestProtocolResponse, + responseServlet: HttpServletResponse): Unit = { + val message = validateResponse(responseMessage, responseServlet) + responseServlet.setContentType("application/json") + responseServlet.setCharacterEncoding("utf-8") + responseServlet.getWriter.write(message.toJson) + } + + /** + * Return any fields in the client request message that the server does not know about. + * + * The mechanism for this is to reconstruct the JSON on the server side and compare the + * diff between this JSON and the one generated on the client side. Any fields that are + * only in the client JSON are treated as unexpected. + */ + protected def findUnknownFields( + requestJson: String, + requestMessage: SubmitRestProtocolMessage): Array[String] = { + val clientSideJson = parse(requestJson) + val serverSideJson = parse(requestMessage.toJson) + val Diff(_, _, unknown) = clientSideJson.diff(serverSideJson) + unknown match { + case j: JObject => j.obj.map { case (k, _) => k }.toArray + case _ => Array.empty[String] // No difference + } + } + + /** Return a human readable String representation of the exception. */ + protected def formatException(e: Throwable): String = { + val stackTraceString = e.getStackTrace.map { "\t" + _ }.mkString("\n") + s"$e\n$stackTraceString" + } + + /** Construct an error message to signal the fact that an exception has been thrown. */ + protected def handleError(message: String): ErrorResponse = { + val e = new ErrorResponse + e.serverSparkVersion = sparkVersion + e.message = message + e + } + + /** + * Parse a submission ID from the relative path, assuming it is the first part of the path. + * For instance, we expect the path to take the form /[submission ID]/maybe/something/else. + * The returned submission ID cannot be empty. If the path is unexpected, return None. + */ + protected def parseSubmissionId(path: String): Option[String] = { + if (path == null || path.isEmpty) { + None + } else { + path.stripPrefix("/").split("/").headOption.filter(_.nonEmpty) + } + } + + /** + * Validate the response to ensure that it is correctly constructed. + * + * If it is, simply return the message as is. Otherwise, return an error response instead + * to propagate the exception back to the client and set the appropriate error code. + */ + private def validateResponse( + responseMessage: SubmitRestProtocolResponse, + responseServlet: HttpServletResponse): SubmitRestProtocolResponse = { + try { + responseMessage.validate() + responseMessage + } catch { + case e: Exception => + responseServlet.setStatus(HttpServletResponse.SC_INTERNAL_SERVER_ERROR) + handleError("Internal server error: " + formatException(e)) + } + } +} + +/** + * A servlet for handling kill requests passed to the [[RestSubmissionServer]]. + */ +private[rest] abstract class KillRequestServlet extends RestServlet { + + /** + * If a submission ID is specified in the URL, have the Master kill the corresponding + * driver and return an appropriate response to the client. Otherwise, return error. + */ + protected override def doPost( + request: HttpServletRequest, + response: HttpServletResponse): Unit = { + val submissionId = parseSubmissionId(request.getPathInfo) + val responseMessage = submissionId.map(handleKill).getOrElse { + response.setStatus(HttpServletResponse.SC_BAD_REQUEST) + handleError("Submission ID is missing in kill request.") + } + sendResponse(responseMessage, response) + } + + protected def handleKill(submissionId: String): KillSubmissionResponse +} + +/** + * A servlet for handling status requests passed to the [[RestSubmissionServer]]. + */ +private[rest] abstract class StatusRequestServlet extends RestServlet { + + /** + * If a submission ID is specified in the URL, request the status of the corresponding + * driver from the Master and include it in the response. Otherwise, return error. + */ + protected override def doGet( + request: HttpServletRequest, + response: HttpServletResponse): Unit = { + val submissionId = parseSubmissionId(request.getPathInfo) + val responseMessage = submissionId.map(handleStatus).getOrElse { + response.setStatus(HttpServletResponse.SC_BAD_REQUEST) + handleError("Submission ID is missing in status request.") + } + sendResponse(responseMessage, response) + } + + protected def handleStatus(submissionId: String): SubmissionStatusResponse +} + +/** + * A servlet for handling submit requests passed to the [[RestSubmissionServer]]. + */ +private[rest] abstract class SubmitRequestServlet extends RestServlet { + + /** + * Submit an application to the Master with parameters specified in the request. + * + * The request is assumed to be a [[SubmitRestProtocolRequest]] in the form of JSON. + * If the request is successfully processed, return an appropriate response to the + * client indicating so. Otherwise, return error instead. + */ + protected override def doPost( + requestServlet: HttpServletRequest, + responseServlet: HttpServletResponse): Unit = { + val responseMessage = + try { + val requestMessageJson = Source.fromInputStream(requestServlet.getInputStream).mkString + val requestMessage = SubmitRestProtocolMessage.fromJson(requestMessageJson) + // The response should have already been validated on the client. + // In case this is not true, validate it ourselves to avoid potential NPEs. + requestMessage.validate() + handleSubmit(requestMessageJson, requestMessage, responseServlet) + } catch { + // The client failed to provide a valid JSON, so this is not our fault + case e @ (_: JsonProcessingException | _: SubmitRestProtocolException) => + responseServlet.setStatus(HttpServletResponse.SC_BAD_REQUEST) + handleError("Malformed request: " + formatException(e)) + } + sendResponse(responseMessage, responseServlet) + } + + protected def handleSubmit( + requestMessageJson: String, + requestMessage: SubmitRestProtocolMessage, + responseServlet: HttpServletResponse): SubmitRestProtocolResponse +} + +/** + * A default servlet that handles error cases that are not captured by other servlets. + */ +private class ErrorServlet extends RestServlet { + private val serverVersion = RestSubmissionServer.PROTOCOL_VERSION + + /** Service a faulty request by returning an appropriate error message to the client. */ + protected override def service( + request: HttpServletRequest, + response: HttpServletResponse): Unit = { + val path = request.getPathInfo + val parts = path.stripPrefix("/").split("/").filter(_.nonEmpty).toList + var versionMismatch = false + var msg = + parts match { + case Nil => + // http://host:port/ + "Missing protocol version." + case `serverVersion` :: Nil => + // http://host:port/correct-version + "Missing the /submissions prefix." + case `serverVersion` :: "submissions" :: tail => + // http://host:port/correct-version/submissions/* + "Missing an action: please specify one of /create, /kill, or /status." + case unknownVersion :: tail => + // http://host:port/unknown-version/* + versionMismatch = true + s"Unknown protocol version '$unknownVersion'." + case _ => + // never reached + s"Malformed path $path." + } + msg += s" Please submit requests through http://[host]:[port]/$serverVersion/submissions/..." + val error = handleError(msg) + // If there is a version mismatch, include the highest protocol version that + // this server supports in case the client wants to retry with our version + if (versionMismatch) { + error.highestProtocolVersion = serverVersion + response.setStatus(RestSubmissionServer.SC_UNKNOWN_PROTOCOL_VERSION) + } else { + response.setStatus(HttpServletResponse.SC_BAD_REQUEST) + } + sendResponse(error, response) + } +} diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestServer.scala b/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestServer.scala index 2d6b8d4204795..502b9bb701ccf 100644 --- a/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestServer.scala +++ b/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestServer.scala @@ -18,26 +18,16 @@ package org.apache.spark.deploy.rest import java.io.File -import java.net.InetSocketAddress -import javax.servlet.http.{HttpServlet, HttpServletRequest, HttpServletResponse} - -import scala.io.Source +import javax.servlet.http.HttpServletResponse import akka.actor.ActorRef -import com.fasterxml.jackson.core.JsonProcessingException -import org.eclipse.jetty.server.Server -import org.eclipse.jetty.servlet.{ServletHolder, ServletContextHandler} -import org.eclipse.jetty.util.thread.QueuedThreadPool -import org.json4s._ -import org.json4s.jackson.JsonMethods._ - -import org.apache.spark.{Logging, SparkConf, SPARK_VERSION => sparkVersion} -import org.apache.spark.util.{AkkaUtils, RpcUtils, Utils} -import org.apache.spark.deploy.{Command, DeployMessages, DriverDescription} import org.apache.spark.deploy.ClientArguments._ +import org.apache.spark.deploy.{Command, DeployMessages, DriverDescription} +import org.apache.spark.util.{AkkaUtils, RpcUtils, Utils} +import org.apache.spark.{SPARK_VERSION => sparkVersion, SparkConf} /** - * A server that responds to requests submitted by the [[StandaloneRestClient]]. + * A server that responds to requests submitted by the [[RestSubmissionClient]]. * This is intended to be embedded in the standalone Master and used in cluster mode only. * * This server responds with different HTTP codes depending on the situation: @@ -54,173 +44,31 @@ import org.apache.spark.deploy.ClientArguments._ * * @param host the address this server should bind to * @param requestedPort the port this server will attempt to bind to + * @param masterConf the conf used by the Master * @param masterActor reference to the Master actor to which requests can be sent * @param masterUrl the URL of the Master new drivers will attempt to connect to - * @param masterConf the conf used by the Master */ private[deploy] class StandaloneRestServer( host: String, requestedPort: Int, + masterConf: SparkConf, masterActor: ActorRef, - masterUrl: String, - masterConf: SparkConf) - extends Logging { - - import StandaloneRestServer._ - - private var _server: Option[Server] = None - - // A mapping from URL prefixes to servlets that serve them. Exposed for testing. - protected val baseContext = s"/$PROTOCOL_VERSION/submissions" - protected val contextToServlet = Map[String, StandaloneRestServlet]( - s"$baseContext/create/*" -> new SubmitRequestServlet(masterActor, masterUrl, masterConf), - s"$baseContext/kill/*" -> new KillRequestServlet(masterActor, masterConf), - s"$baseContext/status/*" -> new StatusRequestServlet(masterActor, masterConf), - "/*" -> new ErrorServlet // default handler - ) - - /** Start the server and return the bound port. */ - def start(): Int = { - val (server, boundPort) = Utils.startServiceOnPort[Server](requestedPort, doStart, masterConf) - _server = Some(server) - logInfo(s"Started REST server for submitting applications on port $boundPort") - boundPort - } - - /** - * Map the servlets to their corresponding contexts and attach them to a server. - * Return a 2-tuple of the started server and the bound port. - */ - private def doStart(startPort: Int): (Server, Int) = { - val server = new Server(new InetSocketAddress(host, startPort)) - val threadPool = new QueuedThreadPool - threadPool.setDaemon(true) - server.setThreadPool(threadPool) - val mainHandler = new ServletContextHandler - mainHandler.setContextPath("/") - contextToServlet.foreach { case (prefix, servlet) => - mainHandler.addServlet(new ServletHolder(servlet), prefix) - } - server.setHandler(mainHandler) - server.start() - val boundPort = server.getConnectors()(0).getLocalPort - (server, boundPort) - } - - def stop(): Unit = { - _server.foreach(_.stop()) - } -} - -private[rest] object StandaloneRestServer { - val PROTOCOL_VERSION = StandaloneRestClient.PROTOCOL_VERSION - val SC_UNKNOWN_PROTOCOL_VERSION = 468 -} - -/** - * An abstract servlet for handling requests passed to the [[StandaloneRestServer]]. - */ -private[rest] abstract class StandaloneRestServlet extends HttpServlet with Logging { - - /** - * Serialize the given response message to JSON and send it through the response servlet. - * This validates the response before sending it to ensure it is properly constructed. - */ - protected def sendResponse( - responseMessage: SubmitRestProtocolResponse, - responseServlet: HttpServletResponse): Unit = { - val message = validateResponse(responseMessage, responseServlet) - responseServlet.setContentType("application/json") - responseServlet.setCharacterEncoding("utf-8") - responseServlet.getWriter.write(message.toJson) - } - - /** - * Return any fields in the client request message that the server does not know about. - * - * The mechanism for this is to reconstruct the JSON on the server side and compare the - * diff between this JSON and the one generated on the client side. Any fields that are - * only in the client JSON are treated as unexpected. - */ - protected def findUnknownFields( - requestJson: String, - requestMessage: SubmitRestProtocolMessage): Array[String] = { - val clientSideJson = parse(requestJson) - val serverSideJson = parse(requestMessage.toJson) - val Diff(_, _, unknown) = clientSideJson.diff(serverSideJson) - unknown match { - case j: JObject => j.obj.map { case (k, _) => k }.toArray - case _ => Array.empty[String] // No difference - } - } - - /** Return a human readable String representation of the exception. */ - protected def formatException(e: Throwable): String = { - val stackTraceString = e.getStackTrace.map { "\t" + _ }.mkString("\n") - s"$e\n$stackTraceString" - } - - /** Construct an error message to signal the fact that an exception has been thrown. */ - protected def handleError(message: String): ErrorResponse = { - val e = new ErrorResponse - e.serverSparkVersion = sparkVersion - e.message = message - e - } - - /** - * Parse a submission ID from the relative path, assuming it is the first part of the path. - * For instance, we expect the path to take the form /[submission ID]/maybe/something/else. - * The returned submission ID cannot be empty. If the path is unexpected, return None. - */ - protected def parseSubmissionId(path: String): Option[String] = { - if (path == null || path.isEmpty) { - None - } else { - path.stripPrefix("/").split("/").headOption.filter(_.nonEmpty) - } - } - - /** - * Validate the response to ensure that it is correctly constructed. - * - * If it is, simply return the message as is. Otherwise, return an error response instead - * to propagate the exception back to the client and set the appropriate error code. - */ - private def validateResponse( - responseMessage: SubmitRestProtocolResponse, - responseServlet: HttpServletResponse): SubmitRestProtocolResponse = { - try { - responseMessage.validate() - responseMessage - } catch { - case e: Exception => - responseServlet.setStatus(HttpServletResponse.SC_INTERNAL_SERVER_ERROR) - handleError("Internal server error: " + formatException(e)) - } - } + masterUrl: String) + extends RestSubmissionServer(host, requestedPort, masterConf) { + + protected override val submitRequestServlet = + new StandaloneSubmitRequestServlet(masterActor, masterUrl, masterConf) + protected override val killRequestServlet = + new StandaloneKillRequestServlet(masterActor, masterConf) + protected override val statusRequestServlet = + new StandaloneStatusRequestServlet(masterActor, masterConf) } /** * A servlet for handling kill requests passed to the [[StandaloneRestServer]]. */ -private[rest] class KillRequestServlet(masterActor: ActorRef, conf: SparkConf) - extends StandaloneRestServlet { - - /** - * If a submission ID is specified in the URL, have the Master kill the corresponding - * driver and return an appropriate response to the client. Otherwise, return error. - */ - protected override def doPost( - request: HttpServletRequest, - response: HttpServletResponse): Unit = { - val submissionId = parseSubmissionId(request.getPathInfo) - val responseMessage = submissionId.map(handleKill).getOrElse { - response.setStatus(HttpServletResponse.SC_BAD_REQUEST) - handleError("Submission ID is missing in kill request.") - } - sendResponse(responseMessage, response) - } +private[rest] class StandaloneKillRequestServlet(masterActor: ActorRef, conf: SparkConf) + extends KillRequestServlet { protected def handleKill(submissionId: String): KillSubmissionResponse = { val askTimeout = RpcUtils.askTimeout(conf) @@ -238,23 +86,8 @@ private[rest] class KillRequestServlet(masterActor: ActorRef, conf: SparkConf) /** * A servlet for handling status requests passed to the [[StandaloneRestServer]]. */ -private[rest] class StatusRequestServlet(masterActor: ActorRef, conf: SparkConf) - extends StandaloneRestServlet { - - /** - * If a submission ID is specified in the URL, request the status of the corresponding - * driver from the Master and include it in the response. Otherwise, return error. - */ - protected override def doGet( - request: HttpServletRequest, - response: HttpServletResponse): Unit = { - val submissionId = parseSubmissionId(request.getPathInfo) - val responseMessage = submissionId.map(handleStatus).getOrElse { - response.setStatus(HttpServletResponse.SC_BAD_REQUEST) - handleError("Submission ID is missing in status request.") - } - sendResponse(responseMessage, response) - } +private[rest] class StandaloneStatusRequestServlet(masterActor: ActorRef, conf: SparkConf) + extends StatusRequestServlet { protected def handleStatus(submissionId: String): SubmissionStatusResponse = { val askTimeout = RpcUtils.askTimeout(conf) @@ -276,71 +109,11 @@ private[rest] class StatusRequestServlet(masterActor: ActorRef, conf: SparkConf) /** * A servlet for handling submit requests passed to the [[StandaloneRestServer]]. */ -private[rest] class SubmitRequestServlet( +private[rest] class StandaloneSubmitRequestServlet( masterActor: ActorRef, masterUrl: String, conf: SparkConf) - extends StandaloneRestServlet { - - /** - * Submit an application to the Master with parameters specified in the request. - * - * The request is assumed to be a [[SubmitRestProtocolRequest]] in the form of JSON. - * If the request is successfully processed, return an appropriate response to the - * client indicating so. Otherwise, return error instead. - */ - protected override def doPost( - requestServlet: HttpServletRequest, - responseServlet: HttpServletResponse): Unit = { - val responseMessage = - try { - val requestMessageJson = Source.fromInputStream(requestServlet.getInputStream).mkString - val requestMessage = SubmitRestProtocolMessage.fromJson(requestMessageJson) - // The response should have already been validated on the client. - // In case this is not true, validate it ourselves to avoid potential NPEs. - requestMessage.validate() - handleSubmit(requestMessageJson, requestMessage, responseServlet) - } catch { - // The client failed to provide a valid JSON, so this is not our fault - case e @ (_: JsonProcessingException | _: SubmitRestProtocolException) => - responseServlet.setStatus(HttpServletResponse.SC_BAD_REQUEST) - handleError("Malformed request: " + formatException(e)) - } - sendResponse(responseMessage, responseServlet) - } - - /** - * Handle the submit request and construct an appropriate response to return to the client. - * - * This assumes that the request message is already successfully validated. - * If the request message is not of the expected type, return error to the client. - */ - private def handleSubmit( - requestMessageJson: String, - requestMessage: SubmitRestProtocolMessage, - responseServlet: HttpServletResponse): SubmitRestProtocolResponse = { - requestMessage match { - case submitRequest: CreateSubmissionRequest => - val askTimeout = RpcUtils.askTimeout(conf) - val driverDescription = buildDriverDescription(submitRequest) - val response = AkkaUtils.askWithReply[DeployMessages.SubmitDriverResponse]( - DeployMessages.RequestSubmitDriver(driverDescription), masterActor, askTimeout) - val submitResponse = new CreateSubmissionResponse - submitResponse.serverSparkVersion = sparkVersion - submitResponse.message = response.message - submitResponse.success = response.success - submitResponse.submissionId = response.driverId.orNull - val unknownFields = findUnknownFields(requestMessageJson, requestMessage) - if (unknownFields.nonEmpty) { - // If there are fields that the server does not know about, warn the client - submitResponse.unknownFields = unknownFields - } - submitResponse - case unexpected => - responseServlet.setStatus(HttpServletResponse.SC_BAD_REQUEST) - handleError(s"Received message of unexpected type ${unexpected.messageType}.") - } - } + extends SubmitRequestServlet { /** * Build a driver description from the fields specified in the submit request. @@ -389,50 +162,37 @@ private[rest] class SubmitRequestServlet( new DriverDescription( appResource, actualDriverMemory, actualDriverCores, actualSuperviseDriver, command) } -} -/** - * A default servlet that handles error cases that are not captured by other servlets. - */ -private class ErrorServlet extends StandaloneRestServlet { - private val serverVersion = StandaloneRestServer.PROTOCOL_VERSION - - /** Service a faulty request by returning an appropriate error message to the client. */ - protected override def service( - request: HttpServletRequest, - response: HttpServletResponse): Unit = { - val path = request.getPathInfo - val parts = path.stripPrefix("/").split("/").filter(_.nonEmpty).toList - var versionMismatch = false - var msg = - parts match { - case Nil => - // http://host:port/ - "Missing protocol version." - case `serverVersion` :: Nil => - // http://host:port/correct-version - "Missing the /submissions prefix." - case `serverVersion` :: "submissions" :: tail => - // http://host:port/correct-version/submissions/* - "Missing an action: please specify one of /create, /kill, or /status." - case unknownVersion :: tail => - // http://host:port/unknown-version/* - versionMismatch = true - s"Unknown protocol version '$unknownVersion'." - case _ => - // never reached - s"Malformed path $path." - } - msg += s" Please submit requests through http://[host]:[port]/$serverVersion/submissions/..." - val error = handleError(msg) - // If there is a version mismatch, include the highest protocol version that - // this server supports in case the client wants to retry with our version - if (versionMismatch) { - error.highestProtocolVersion = serverVersion - response.setStatus(StandaloneRestServer.SC_UNKNOWN_PROTOCOL_VERSION) - } else { - response.setStatus(HttpServletResponse.SC_BAD_REQUEST) + /** + * Handle the submit request and construct an appropriate response to return to the client. + * + * This assumes that the request message is already successfully validated. + * If the request message is not of the expected type, return error to the client. + */ + protected override def handleSubmit( + requestMessageJson: String, + requestMessage: SubmitRestProtocolMessage, + responseServlet: HttpServletResponse): SubmitRestProtocolResponse = { + requestMessage match { + case submitRequest: CreateSubmissionRequest => + val askTimeout = RpcUtils.askTimeout(conf) + val driverDescription = buildDriverDescription(submitRequest) + val response = AkkaUtils.askWithReply[DeployMessages.SubmitDriverResponse]( + DeployMessages.RequestSubmitDriver(driverDescription), masterActor, askTimeout) + val submitResponse = new CreateSubmissionResponse + submitResponse.serverSparkVersion = sparkVersion + submitResponse.message = response.message + submitResponse.success = response.success + submitResponse.submissionId = response.driverId.orNull + val unknownFields = findUnknownFields(requestMessageJson, requestMessage) + if (unknownFields.nonEmpty) { + // If there are fields that the server does not know about, warn the client + submitResponse.unknownFields = unknownFields + } + submitResponse + case unexpected => + responseServlet.setStatus(HttpServletResponse.SC_BAD_REQUEST) + handleError(s"Received message of unexpected type ${unexpected.messageType}.") } - sendResponse(error, response) } } diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolRequest.scala b/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolRequest.scala index d80abdf15fb34..0d50a768942ed 100644 --- a/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolRequest.scala +++ b/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolRequest.scala @@ -61,7 +61,7 @@ private[rest] class CreateSubmissionRequest extends SubmitRestProtocolRequest { assertProperty[Boolean](key, "boolean", _.toBoolean) private def assertPropertyIsNumeric(key: String): Unit = - assertProperty[Int](key, "numeric", _.toInt) + assertProperty[Double](key, "numeric", _.toDouble) private def assertPropertyIsMemory(key: String): Unit = assertProperty[Int](key, "memory", Utils.memoryStringToMb) diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolResponse.scala b/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolResponse.scala index 8fde8c142a4c1..0e226ee294cab 100644 --- a/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolResponse.scala +++ b/core/src/main/scala/org/apache/spark/deploy/rest/SubmitRestProtocolResponse.scala @@ -35,7 +35,7 @@ private[rest] abstract class SubmitRestProtocolResponse extends SubmitRestProtoc /** * A response to a [[CreateSubmissionRequest]] in the REST application submission protocol. */ -private[rest] class CreateSubmissionResponse extends SubmitRestProtocolResponse { +private[spark] class CreateSubmissionResponse extends SubmitRestProtocolResponse { var submissionId: String = null protected override def doValidate(): Unit = { super.doValidate() @@ -46,7 +46,7 @@ private[rest] class CreateSubmissionResponse extends SubmitRestProtocolResponse /** * A response to a kill request in the REST application submission protocol. */ -private[rest] class KillSubmissionResponse extends SubmitRestProtocolResponse { +private[spark] class KillSubmissionResponse extends SubmitRestProtocolResponse { var submissionId: String = null protected override def doValidate(): Unit = { super.doValidate() @@ -58,7 +58,7 @@ private[rest] class KillSubmissionResponse extends SubmitRestProtocolResponse { /** * A response to a status request in the REST application submission protocol. */ -private[rest] class SubmissionStatusResponse extends SubmitRestProtocolResponse { +private[spark] class SubmissionStatusResponse extends SubmitRestProtocolResponse { var submissionId: String = null var driverState: String = null var workerId: String = null diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/mesos/MesosRestServer.scala b/core/src/main/scala/org/apache/spark/deploy/rest/mesos/MesosRestServer.scala new file mode 100644 index 0000000000000..fd17a980c9319 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/rest/mesos/MesosRestServer.scala @@ -0,0 +1,158 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.rest.mesos + +import java.io.File +import java.text.SimpleDateFormat +import java.util.Date +import java.util.concurrent.atomic.AtomicLong +import javax.servlet.http.HttpServletResponse + +import org.apache.spark.deploy.Command +import org.apache.spark.deploy.mesos.MesosDriverDescription +import org.apache.spark.deploy.rest._ +import org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler +import org.apache.spark.util.Utils +import org.apache.spark.{SPARK_VERSION => sparkVersion, SparkConf} + + +/** + * A server that responds to requests submitted by the [[RestSubmissionClient]]. + * All requests are forwarded to + * [[org.apache.spark.scheduler.cluster.mesos.MesosClusterScheduler]]. + * This is intended to be used in Mesos cluster mode only. + * For more details about the REST submission please refer to [[RestSubmissionServer]] javadocs. + */ +private[spark] class MesosRestServer( + host: String, + requestedPort: Int, + masterConf: SparkConf, + scheduler: MesosClusterScheduler) + extends RestSubmissionServer(host, requestedPort, masterConf) { + + protected override val submitRequestServlet = + new MesosSubmitRequestServlet(scheduler, masterConf) + protected override val killRequestServlet = + new MesosKillRequestServlet(scheduler, masterConf) + protected override val statusRequestServlet = + new MesosStatusRequestServlet(scheduler, masterConf) +} + +private[deploy] class MesosSubmitRequestServlet( + scheduler: MesosClusterScheduler, + conf: SparkConf) + extends SubmitRequestServlet { + + private val DEFAULT_SUPERVISE = false + private val DEFAULT_MEMORY = 512 // mb + private val DEFAULT_CORES = 1.0 + + private val nextDriverNumber = new AtomicLong(0) + private def createDateFormat = new SimpleDateFormat("yyyyMMddHHmmss") // For application IDs + private def newDriverId(submitDate: Date): String = { + "driver-%s-%04d".format( + createDateFormat.format(submitDate), nextDriverNumber.incrementAndGet()) + } + + /** + * Build a driver description from the fields specified in the submit request. + * + * This involves constructing a command that launches a mesos framework for the job. + * This does not currently consider fields used by python applications since python + * is not supported in mesos cluster mode yet. + */ + private def buildDriverDescription(request: CreateSubmissionRequest): MesosDriverDescription = { + // Required fields, including the main class because python is not yet supported + val appResource = Option(request.appResource).getOrElse { + throw new SubmitRestMissingFieldException("Application jar is missing.") + } + val mainClass = Option(request.mainClass).getOrElse { + throw new SubmitRestMissingFieldException("Main class is missing.") + } + + // Optional fields + val sparkProperties = request.sparkProperties + val driverExtraJavaOptions = sparkProperties.get("spark.driver.extraJavaOptions") + val driverExtraClassPath = sparkProperties.get("spark.driver.extraClassPath") + val driverExtraLibraryPath = sparkProperties.get("spark.driver.extraLibraryPath") + val superviseDriver = sparkProperties.get("spark.driver.supervise") + val driverMemory = sparkProperties.get("spark.driver.memory") + val driverCores = sparkProperties.get("spark.driver.cores") + val appArgs = request.appArgs + val environmentVariables = request.environmentVariables + val name = request.sparkProperties.get("spark.app.name").getOrElse(mainClass) + + // Construct driver description + val conf = new SparkConf(false).setAll(sparkProperties) + val extraClassPath = driverExtraClassPath.toSeq.flatMap(_.split(File.pathSeparator)) + val extraLibraryPath = driverExtraLibraryPath.toSeq.flatMap(_.split(File.pathSeparator)) + val extraJavaOpts = driverExtraJavaOptions.map(Utils.splitCommandString).getOrElse(Seq.empty) + val sparkJavaOpts = Utils.sparkJavaOpts(conf) + val javaOpts = sparkJavaOpts ++ extraJavaOpts + val command = new Command( + mainClass, appArgs, environmentVariables, extraClassPath, extraLibraryPath, javaOpts) + val actualSuperviseDriver = superviseDriver.map(_.toBoolean).getOrElse(DEFAULT_SUPERVISE) + val actualDriverMemory = driverMemory.map(Utils.memoryStringToMb).getOrElse(DEFAULT_MEMORY) + val actualDriverCores = driverCores.map(_.toDouble).getOrElse(DEFAULT_CORES) + val submitDate = new Date() + val submissionId = newDriverId(submitDate) + + new MesosDriverDescription( + name, appResource, actualDriverMemory, actualDriverCores, actualSuperviseDriver, + command, request.sparkProperties, submissionId, submitDate) + } + + protected override def handleSubmit( + requestMessageJson: String, + requestMessage: SubmitRestProtocolMessage, + responseServlet: HttpServletResponse): SubmitRestProtocolResponse = { + requestMessage match { + case submitRequest: CreateSubmissionRequest => + val driverDescription = buildDriverDescription(submitRequest) + val s = scheduler.submitDriver(driverDescription) + s.serverSparkVersion = sparkVersion + val unknownFields = findUnknownFields(requestMessageJson, requestMessage) + if (unknownFields.nonEmpty) { + // If there are fields that the server does not know about, warn the client + s.unknownFields = unknownFields + } + s + case unexpected => + responseServlet.setStatus(HttpServletResponse.SC_BAD_REQUEST) + handleError(s"Received message of unexpected type ${unexpected.messageType}.") + } + } +} + +private[deploy] class MesosKillRequestServlet(scheduler: MesosClusterScheduler, conf: SparkConf) + extends KillRequestServlet { + protected override def handleKill(submissionId: String): KillSubmissionResponse = { + val k = scheduler.killDriver(submissionId) + k.serverSparkVersion = sparkVersion + k + } +} + +private[deploy] class MesosStatusRequestServlet(scheduler: MesosClusterScheduler, conf: SparkConf) + extends StatusRequestServlet { + protected override def handleStatus(submissionId: String): SubmissionStatusResponse = { + val d = scheduler.getDriverStatus(submissionId) + d.serverSparkVersion = sparkVersion + d + } +} diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala index 82f652dae0378..3412301e64fd7 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala @@ -18,20 +18,17 @@ package org.apache.spark.scheduler.cluster.mesos import java.io.File -import java.util.{List => JList} -import java.util.Collections +import java.util.{Collections, List => JList} import scala.collection.JavaConversions._ import scala.collection.mutable.{HashMap, HashSet} -import org.apache.mesos.{Scheduler => MScheduler} -import org.apache.mesos._ -import org.apache.mesos.Protos.{TaskInfo => MesosTaskInfo, TaskState => MesosTaskState, _} - -import org.apache.spark.{Logging, SparkContext, SparkEnv, SparkException, TaskState} +import org.apache.mesos.Protos.{TaskInfo => MesosTaskInfo, _} +import org.apache.mesos.{Scheduler => MScheduler, _} import org.apache.spark.scheduler.TaskSchedulerImpl import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend -import org.apache.spark.util.{Utils, AkkaUtils} +import org.apache.spark.util.{AkkaUtils, Utils} +import org.apache.spark.{SparkContext, SparkEnv, SparkException, TaskState} /** * A SchedulerBackend that runs tasks on Mesos, but uses "coarse-grained" tasks, where it holds @@ -49,17 +46,10 @@ private[spark] class CoarseMesosSchedulerBackend( master: String) extends CoarseGrainedSchedulerBackend(scheduler, sc.env.rpcEnv) with MScheduler - with Logging { + with MesosSchedulerUtils { val MAX_SLAVE_FAILURES = 2 // Blacklist a slave after this many failures - // Lock used to wait for scheduler to be registered - var isRegistered = false - val registeredLock = new Object() - - // Driver for talking to Mesos - var driver: SchedulerDriver = null - // Maximum number of cores to acquire (TODO: we'll need more flexible controls here) val maxCores = conf.get("spark.cores.max", Int.MaxValue.toString).toInt @@ -87,26 +77,8 @@ private[spark] class CoarseMesosSchedulerBackend( override def start() { super.start() - - synchronized { - new Thread("CoarseMesosSchedulerBackend driver") { - setDaemon(true) - override def run() { - val scheduler = CoarseMesosSchedulerBackend.this - val fwInfo = FrameworkInfo.newBuilder().setUser(sc.sparkUser).setName(sc.appName).build() - driver = new MesosSchedulerDriver(scheduler, fwInfo, master) - try { { - val ret = driver.run() - logInfo("driver.run() returned with code " + ret) - } - } catch { - case e: Exception => logError("driver.run() failed", e) - } - } - }.start() - - waitForRegister() - } + val fwInfo = FrameworkInfo.newBuilder().setUser(sc.sparkUser).setName(sc.appName).build() + startScheduler(master, CoarseMesosSchedulerBackend.this, fwInfo) } def createCommand(offer: Offer, numCores: Int): CommandInfo = { @@ -150,8 +122,10 @@ private[spark] class CoarseMesosSchedulerBackend( conf.get("spark.driver.port"), CoarseGrainedSchedulerBackend.ENDPOINT_NAME) - val uri = conf.get("spark.executor.uri", null) - if (uri == null) { + val uri = conf.getOption("spark.executor.uri") + .orElse(Option(System.getenv("SPARK_EXECUTOR_URI"))) + + if (uri.isEmpty) { val runScript = new File(executorSparkHome, "./bin/spark-class").getCanonicalPath command.setValue( "%s \"%s\" org.apache.spark.executor.CoarseGrainedExecutorBackend" @@ -164,7 +138,7 @@ private[spark] class CoarseMesosSchedulerBackend( } else { // Grab everything to the first '.'. We'll use that and '*' to // glob the directory "correctly". - val basename = uri.split('/').last.split('.').head + val basename = uri.get.split('/').last.split('.').head command.setValue( s"cd $basename*; $prefixEnv " + "./bin/spark-class org.apache.spark.executor.CoarseGrainedExecutorBackend" + @@ -173,7 +147,7 @@ private[spark] class CoarseMesosSchedulerBackend( s" --hostname ${offer.getHostname}" + s" --cores $numCores" + s" --app-id $appId") - command.addUris(CommandInfo.URI.newBuilder().setValue(uri)) + command.addUris(CommandInfo.URI.newBuilder().setValue(uri.get)) } command.build() } @@ -183,18 +157,7 @@ private[spark] class CoarseMesosSchedulerBackend( override def registered(d: SchedulerDriver, frameworkId: FrameworkID, masterInfo: MasterInfo) { appId = frameworkId.getValue logInfo("Registered as framework ID " + appId) - registeredLock.synchronized { - isRegistered = true - registeredLock.notifyAll() - } - } - - def waitForRegister() { - registeredLock.synchronized { - while (!isRegistered) { - registeredLock.wait() - } - } + markRegistered() } override def disconnected(d: SchedulerDriver) {} @@ -245,14 +208,6 @@ private[spark] class CoarseMesosSchedulerBackend( } } - /** Helper function to pull out a resource from a Mesos Resources protobuf */ - private def getResource(res: JList[Resource], name: String): Double = { - for (r <- res if r.getName == name) { - return r.getScalar.getValue - } - 0 - } - /** Build a Mesos resource protobuf object */ private def createResource(resourceName: String, quantity: Double): Protos.Resource = { Resource.newBuilder() @@ -284,7 +239,8 @@ private[spark] class CoarseMesosSchedulerBackend( "is Spark installed on it?") } } - driver.reviveOffers() // In case we'd rejected everything before but have now lost a node + // In case we'd rejected everything before but have now lost a node + mesosDriver.reviveOffers() } } } @@ -296,8 +252,8 @@ private[spark] class CoarseMesosSchedulerBackend( override def stop() { super.stop() - if (driver != null) { - driver.stop() + if (mesosDriver != null) { + mesosDriver.stop() } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterPersistenceEngine.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterPersistenceEngine.scala new file mode 100644 index 0000000000000..3efc536f1456c --- /dev/null +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterPersistenceEngine.scala @@ -0,0 +1,134 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler.cluster.mesos + +import scala.collection.JavaConversions._ + +import org.apache.curator.framework.CuratorFramework +import org.apache.zookeeper.CreateMode +import org.apache.zookeeper.KeeperException.NoNodeException + +import org.apache.spark.{Logging, SparkConf} +import org.apache.spark.deploy.SparkCuratorUtil +import org.apache.spark.util.Utils + +/** + * Persistence engine factory that is responsible for creating new persistence engines + * to store Mesos cluster mode state. + */ +private[spark] abstract class MesosClusterPersistenceEngineFactory(conf: SparkConf) { + def createEngine(path: String): MesosClusterPersistenceEngine +} + +/** + * Mesos cluster persistence engine is responsible for persisting Mesos cluster mode + * specific state, so that on failover all the state can be recovered and the scheduler + * can resume managing the drivers. + */ +private[spark] trait MesosClusterPersistenceEngine { + def persist(name: String, obj: Object): Unit + def expunge(name: String): Unit + def fetch[T](name: String): Option[T] + def fetchAll[T](): Iterable[T] +} + +/** + * Zookeeper backed persistence engine factory. + * All Zk engines created from this factory shares the same Zookeeper client, so + * all of them reuses the same connection pool. + */ +private[spark] class ZookeeperMesosClusterPersistenceEngineFactory(conf: SparkConf) + extends MesosClusterPersistenceEngineFactory(conf) { + + lazy val zk = SparkCuratorUtil.newClient(conf, "spark.mesos.deploy.zookeeper.url") + + def createEngine(path: String): MesosClusterPersistenceEngine = { + new ZookeeperMesosClusterPersistenceEngine(path, zk, conf) + } +} + +/** + * Black hole persistence engine factory that creates black hole + * persistence engines, which stores nothing. + */ +private[spark] class BlackHoleMesosClusterPersistenceEngineFactory + extends MesosClusterPersistenceEngineFactory(null) { + def createEngine(path: String): MesosClusterPersistenceEngine = { + new BlackHoleMesosClusterPersistenceEngine + } +} + +/** + * Black hole persistence engine that stores nothing. + */ +private[spark] class BlackHoleMesosClusterPersistenceEngine extends MesosClusterPersistenceEngine { + override def persist(name: String, obj: Object): Unit = {} + override def fetch[T](name: String): Option[T] = None + override def expunge(name: String): Unit = {} + override def fetchAll[T](): Iterable[T] = Iterable.empty[T] +} + +/** + * Zookeeper based Mesos cluster persistence engine, that stores cluster mode state + * into Zookeeper. Each engine object is operating under one folder in Zookeeper, but + * reuses a shared Zookeeper client. + */ +private[spark] class ZookeeperMesosClusterPersistenceEngine( + baseDir: String, + zk: CuratorFramework, + conf: SparkConf) + extends MesosClusterPersistenceEngine with Logging { + private val WORKING_DIR = + conf.get("spark.deploy.zookeeper.dir", "/spark_mesos_dispatcher") + "/" + baseDir + + SparkCuratorUtil.mkdir(zk, WORKING_DIR) + + def path(name: String): String = { + WORKING_DIR + "/" + name + } + + override def expunge(name: String): Unit = { + zk.delete().forPath(path(name)) + } + + override def persist(name: String, obj: Object): Unit = { + val serialized = Utils.serialize(obj) + val zkPath = path(name) + zk.create().withMode(CreateMode.PERSISTENT).forPath(zkPath, serialized) + } + + override def fetch[T](name: String): Option[T] = { + val zkPath = path(name) + + try { + val fileData = zk.getData().forPath(zkPath) + Some(Utils.deserialize[T](fileData)) + } catch { + case e: NoNodeException => None + case e: Exception => { + logWarning("Exception while reading persisted file, deleting", e) + zk.delete().forPath(zkPath) + None + } + } + } + + override def fetchAll[T](): Iterable[T] = { + zk.getChildren.forPath(WORKING_DIR).map(fetch[T]).flatten + } +} diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala new file mode 100644 index 0000000000000..0396e62be5309 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala @@ -0,0 +1,608 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler.cluster.mesos + +import java.io.File +import java.util.concurrent.locks.ReentrantLock +import java.util.{Collections, Date, List => JList} + +import scala.collection.JavaConversions._ +import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer + +import org.apache.mesos.Protos.Environment.Variable +import org.apache.mesos.Protos.TaskStatus.Reason +import org.apache.mesos.Protos.{TaskState => MesosTaskState, _} +import org.apache.mesos.{Scheduler, SchedulerDriver} +import org.apache.spark.deploy.mesos.MesosDriverDescription +import org.apache.spark.deploy.rest.{CreateSubmissionResponse, KillSubmissionResponse, SubmissionStatusResponse} +import org.apache.spark.metrics.MetricsSystem +import org.apache.spark.util.Utils +import org.apache.spark.{SecurityManager, SparkConf, SparkException, TaskState} + + +/** + * Tracks the current state of a Mesos Task that runs a Spark driver. + * @param driverDescription Submitted driver description from + * [[org.apache.spark.deploy.rest.mesos.MesosRestServer]] + * @param taskId Mesos TaskID generated for the task + * @param slaveId Slave ID that the task is assigned to + * @param mesosTaskStatus The last known task status update. + * @param startDate The date the task was launched + */ +private[spark] class MesosClusterSubmissionState( + val driverDescription: MesosDriverDescription, + val taskId: TaskID, + val slaveId: SlaveID, + var mesosTaskStatus: Option[TaskStatus], + var startDate: Date) + extends Serializable { + + def copy(): MesosClusterSubmissionState = { + new MesosClusterSubmissionState( + driverDescription, taskId, slaveId, mesosTaskStatus, startDate) + } +} + +/** + * Tracks the retry state of a driver, which includes the next time it should be scheduled + * and necessary information to do exponential backoff. + * This class is not thread-safe, and we expect the caller to handle synchronizing state. + * @param lastFailureStatus Last Task status when it failed. + * @param retries Number of times it has been retried. + * @param nextRetry Time at which it should be retried next + * @param waitTime The amount of time driver is scheduled to wait until next retry. + */ +private[spark] class MesosClusterRetryState( + val lastFailureStatus: TaskStatus, + val retries: Int, + val nextRetry: Date, + val waitTime: Int) extends Serializable { + def copy(): MesosClusterRetryState = + new MesosClusterRetryState(lastFailureStatus, retries, nextRetry, waitTime) +} + +/** + * The full state of the cluster scheduler, currently being used for displaying + * information on the UI. + * @param frameworkId Mesos Framework id for the cluster scheduler. + * @param masterUrl The Mesos master url + * @param queuedDrivers All drivers queued to be launched + * @param launchedDrivers All launched or running drivers + * @param finishedDrivers All terminated drivers + * @param pendingRetryDrivers All drivers pending to be retried + */ +private[spark] class MesosClusterSchedulerState( + val frameworkId: String, + val masterUrl: Option[String], + val queuedDrivers: Iterable[MesosDriverDescription], + val launchedDrivers: Iterable[MesosClusterSubmissionState], + val finishedDrivers: Iterable[MesosClusterSubmissionState], + val pendingRetryDrivers: Iterable[MesosDriverDescription]) + +/** + * A Mesos scheduler that is responsible for launching submitted Spark drivers in cluster mode + * as Mesos tasks in a Mesos cluster. + * All drivers are launched asynchronously by the framework, which will eventually be launched + * by one of the slaves in the cluster. The results of the driver will be stored in slave's task + * sandbox which is accessible by visiting the Mesos UI. + * This scheduler supports recovery by persisting all its state and performs task reconciliation + * on recover, which gets all the latest state for all the drivers from Mesos master. + */ +private[spark] class MesosClusterScheduler( + engineFactory: MesosClusterPersistenceEngineFactory, + conf: SparkConf) + extends Scheduler with MesosSchedulerUtils { + var frameworkUrl: String = _ + private val metricsSystem = + MetricsSystem.createMetricsSystem("mesos_cluster", conf, new SecurityManager(conf)) + private val master = conf.get("spark.master") + private val appName = conf.get("spark.app.name") + private val queuedCapacity = conf.getInt("spark.mesos.maxDrivers", 200) + private val retainedDrivers = conf.getInt("spark.mesos.retainedDrivers", 200) + private val maxRetryWaitTime = conf.getInt("spark.mesos.cluster.retry.wait.max", 60) // 1 minute + private val schedulerState = engineFactory.createEngine("scheduler") + private val stateLock = new ReentrantLock() + private val finishedDrivers = + new mutable.ArrayBuffer[MesosClusterSubmissionState](retainedDrivers) + private var frameworkId: String = null + // Holds all the launched drivers and current launch state, keyed by driver id. + private val launchedDrivers = new mutable.HashMap[String, MesosClusterSubmissionState]() + // Holds a map of driver id to expected slave id that is passed to Mesos for reconciliation. + // All drivers that are loaded after failover are added here, as we need get the latest + // state of the tasks from Mesos. + private val pendingRecover = new mutable.HashMap[String, SlaveID]() + // Stores all the submitted drivers that hasn't been launched. + private val queuedDrivers = new ArrayBuffer[MesosDriverDescription]() + // All supervised drivers that are waiting to retry after termination. + private val pendingRetryDrivers = new ArrayBuffer[MesosDriverDescription]() + private val queuedDriversState = engineFactory.createEngine("driverQueue") + private val launchedDriversState = engineFactory.createEngine("launchedDrivers") + private val pendingRetryDriversState = engineFactory.createEngine("retryList") + // Flag to mark if the scheduler is ready to be called, which is until the scheduler + // is registered with Mesos master. + @volatile protected var ready = false + private var masterInfo: Option[MasterInfo] = None + + def submitDriver(desc: MesosDriverDescription): CreateSubmissionResponse = { + val c = new CreateSubmissionResponse + if (!ready) { + c.success = false + c.message = "Scheduler is not ready to take requests" + return c + } + + stateLock.synchronized { + if (isQueueFull()) { + c.success = false + c.message = "Already reached maximum submission size" + return c + } + c.submissionId = desc.submissionId + queuedDriversState.persist(desc.submissionId, desc) + queuedDrivers += desc + c.success = true + } + c + } + + def killDriver(submissionId: String): KillSubmissionResponse = { + val k = new KillSubmissionResponse + if (!ready) { + k.success = false + k.message = "Scheduler is not ready to take requests" + return k + } + k.submissionId = submissionId + stateLock.synchronized { + // We look for the requested driver in the following places: + // 1. Check if submission is running or launched. + // 2. Check if it's still queued. + // 3. Check if it's in the retry list. + // 4. Check if it has already completed. + if (launchedDrivers.contains(submissionId)) { + val task = launchedDrivers(submissionId) + mesosDriver.killTask(task.taskId) + k.success = true + k.message = "Killing running driver" + } else if (removeFromQueuedDrivers(submissionId)) { + k.success = true + k.message = "Removed driver while it's still pending" + } else if (removeFromPendingRetryDrivers(submissionId)) { + k.success = true + k.message = "Removed driver while it's being retried" + } else if (finishedDrivers.exists(_.driverDescription.submissionId.equals(submissionId))) { + k.success = false + k.message = "Driver already terminated" + } else { + k.success = false + k.message = "Cannot find driver" + } + } + k + } + + def getDriverStatus(submissionId: String): SubmissionStatusResponse = { + val s = new SubmissionStatusResponse + if (!ready) { + s.success = false + s.message = "Scheduler is not ready to take requests" + return s + } + s.submissionId = submissionId + stateLock.synchronized { + if (queuedDrivers.exists(_.submissionId.equals(submissionId))) { + s.success = true + s.driverState = "QUEUED" + } else if (launchedDrivers.contains(submissionId)) { + s.success = true + s.driverState = "RUNNING" + launchedDrivers(submissionId).mesosTaskStatus.foreach(state => s.message = state.toString) + } else if (finishedDrivers.exists(_.driverDescription.submissionId.equals(submissionId))) { + s.success = true + s.driverState = "FINISHED" + finishedDrivers + .find(d => d.driverDescription.submissionId.equals(submissionId)).get.mesosTaskStatus + .foreach(state => s.message = state.toString) + } else if (pendingRetryDrivers.exists(_.submissionId.equals(submissionId))) { + val status = pendingRetryDrivers.find(_.submissionId.equals(submissionId)) + .get.retryState.get.lastFailureStatus + s.success = true + s.driverState = "RETRYING" + s.message = status.toString + } else { + s.success = false + s.driverState = "NOT_FOUND" + } + } + s + } + + private def isQueueFull(): Boolean = launchedDrivers.size >= queuedCapacity + + /** + * Recover scheduler state that is persisted. + * We still need to do task reconciliation to be up to date of the latest task states + * as it might have changed while the scheduler is failing over. + */ + private def recoverState(): Unit = { + stateLock.synchronized { + launchedDriversState.fetchAll[MesosClusterSubmissionState]().foreach { state => + launchedDrivers(state.taskId.getValue) = state + pendingRecover(state.taskId.getValue) = state.slaveId + } + queuedDriversState.fetchAll[MesosDriverDescription]().foreach(d => queuedDrivers += d) + // There is potential timing issue where a queued driver might have been launched + // but the scheduler shuts down before the queued driver was able to be removed + // from the queue. We try to mitigate this issue by walking through all queued drivers + // and remove if they're already launched. + queuedDrivers + .filter(d => launchedDrivers.contains(d.submissionId)) + .foreach(d => removeFromQueuedDrivers(d.submissionId)) + pendingRetryDriversState.fetchAll[MesosDriverDescription]() + .foreach(s => pendingRetryDrivers += s) + // TODO: Consider storing finished drivers so we can show them on the UI after + // failover. For now we clear the history on each recovery. + finishedDrivers.clear() + } + } + + /** + * Starts the cluster scheduler and wait until the scheduler is registered. + * This also marks the scheduler to be ready for requests. + */ + def start(): Unit = { + // TODO: Implement leader election to make sure only one framework running in the cluster. + val fwId = schedulerState.fetch[String]("frameworkId") + val builder = FrameworkInfo.newBuilder() + .setUser(Utils.getCurrentUserName()) + .setName(appName) + .setWebuiUrl(frameworkUrl) + .setCheckpoint(true) + .setFailoverTimeout(Integer.MAX_VALUE) // Setting to max so tasks keep running on crash + fwId.foreach { id => + builder.setId(FrameworkID.newBuilder().setValue(id).build()) + frameworkId = id + } + recoverState() + metricsSystem.registerSource(new MesosClusterSchedulerSource(this)) + metricsSystem.start() + startScheduler(master, MesosClusterScheduler.this, builder.build()) + ready = true + } + + def stop(): Unit = { + ready = false + metricsSystem.report() + metricsSystem.stop() + mesosDriver.stop(true) + } + + override def registered( + driver: SchedulerDriver, + newFrameworkId: FrameworkID, + masterInfo: MasterInfo): Unit = { + logInfo("Registered as framework ID " + newFrameworkId.getValue) + if (newFrameworkId.getValue != frameworkId) { + frameworkId = newFrameworkId.getValue + schedulerState.persist("frameworkId", frameworkId) + } + markRegistered() + + stateLock.synchronized { + this.masterInfo = Some(masterInfo) + if (!pendingRecover.isEmpty) { + // Start task reconciliation if we need to recover. + val statuses = pendingRecover.collect { + case (taskId, slaveId) => + val newStatus = TaskStatus.newBuilder() + .setTaskId(TaskID.newBuilder().setValue(taskId).build()) + .setSlaveId(slaveId) + .setState(MesosTaskState.TASK_STAGING) + .build() + launchedDrivers.get(taskId).map(_.mesosTaskStatus.getOrElse(newStatus)) + .getOrElse(newStatus) + } + // TODO: Page the status updates to avoid trying to reconcile + // a large amount of tasks at once. + driver.reconcileTasks(statuses) + } + } + } + + private def buildDriverCommand(desc: MesosDriverDescription): CommandInfo = { + val appJar = CommandInfo.URI.newBuilder() + .setValue(desc.jarUrl.stripPrefix("file:").stripPrefix("local:")).build() + val builder = CommandInfo.newBuilder().addUris(appJar) + val entries = + (conf.getOption("spark.executor.extraLibraryPath").toList ++ + desc.command.libraryPathEntries) + val prefixEnv = if (!entries.isEmpty) { + Utils.libraryPathEnvPrefix(entries) + } else { + "" + } + val envBuilder = Environment.newBuilder() + desc.command.environment.foreach { case (k, v) => + envBuilder.addVariables(Variable.newBuilder().setName(k).setValue(v).build()) + } + // Pass all spark properties to executor. + val executorOpts = desc.schedulerProperties.map { case (k, v) => s"-D$k=$v" }.mkString(" ") + envBuilder.addVariables( + Variable.newBuilder().setName("SPARK_EXECUTOR_OPTS").setValue(executorOpts)) + val cmdOptions = generateCmdOption(desc) + val executorUri = desc.schedulerProperties.get("spark.executor.uri") + .orElse(desc.command.environment.get("SPARK_EXECUTOR_URI")) + val appArguments = desc.command.arguments.mkString(" ") + val cmd = if (executorUri.isDefined) { + builder.addUris(CommandInfo.URI.newBuilder().setValue(executorUri.get).build()) + val folderBasename = executorUri.get.split('/').last.split('.').head + val cmdExecutable = s"cd $folderBasename*; $prefixEnv bin/spark-submit" + val cmdJar = s"../${desc.jarUrl.split("/").last}" + s"$cmdExecutable ${cmdOptions.mkString(" ")} $cmdJar $appArguments" + } else { + val executorSparkHome = desc.schedulerProperties.get("spark.mesos.executor.home") + .orElse(conf.getOption("spark.home")) + .orElse(Option(System.getenv("SPARK_HOME"))) + .getOrElse { + throw new SparkException("Executor Spark home `spark.mesos.executor.home` is not set!") + } + val cmdExecutable = new File(executorSparkHome, "./bin/spark-submit").getCanonicalPath + val cmdJar = desc.jarUrl.split("/").last + s"$cmdExecutable ${cmdOptions.mkString(" ")} $cmdJar $appArguments" + } + builder.setValue(cmd) + builder.setEnvironment(envBuilder.build()) + builder.build() + } + + private def generateCmdOption(desc: MesosDriverDescription): Seq[String] = { + var options = Seq( + "--name", desc.schedulerProperties("spark.app.name"), + "--class", desc.command.mainClass, + "--master", s"mesos://${conf.get("spark.master")}", + "--driver-cores", desc.cores.toString, + "--driver-memory", s"${desc.mem}M") + desc.schedulerProperties.get("spark.executor.memory").map { v => + options ++= Seq("--executor-memory", v) + } + desc.schedulerProperties.get("spark.cores.max").map { v => + options ++= Seq("--total-executor-cores", v) + } + options + } + + private class ResourceOffer(val offer: Offer, var cpu: Double, var mem: Double) { + override def toString(): String = { + s"Offer id: ${offer.getId.getValue}, cpu: $cpu, mem: $mem" + } + } + + /** + * This method takes all the possible candidates and attempt to schedule them with Mesos offers. + * Every time a new task is scheduled, the afterLaunchCallback is called to perform post scheduled + * logic on each task. + */ + private def scheduleTasks( + candidates: Seq[MesosDriverDescription], + afterLaunchCallback: (String) => Boolean, + currentOffers: List[ResourceOffer], + tasks: mutable.HashMap[OfferID, ArrayBuffer[TaskInfo]]): Unit = { + for (submission <- candidates) { + val driverCpu = submission.cores + val driverMem = submission.mem + logTrace(s"Finding offer to launch driver with cpu: $driverCpu, mem: $driverMem") + val offerOption = currentOffers.find { o => + o.cpu >= driverCpu && o.mem >= driverMem + } + if (offerOption.isEmpty) { + logDebug(s"Unable to find offer to launch driver id: ${submission.submissionId}, " + + s"cpu: $driverCpu, mem: $driverMem") + } else { + val offer = offerOption.get + offer.cpu -= driverCpu + offer.mem -= driverMem + val taskId = TaskID.newBuilder().setValue(submission.submissionId).build() + val cpuResource = Resource.newBuilder() + .setName("cpus").setType(Value.Type.SCALAR) + .setScalar(Value.Scalar.newBuilder().setValue(driverCpu)).build() + val memResource = Resource.newBuilder() + .setName("mem").setType(Value.Type.SCALAR) + .setScalar(Value.Scalar.newBuilder().setValue(driverMem)).build() + val commandInfo = buildDriverCommand(submission) + val appName = submission.schedulerProperties("spark.app.name") + val taskInfo = TaskInfo.newBuilder() + .setTaskId(taskId) + .setName(s"Driver for $appName") + .setSlaveId(offer.offer.getSlaveId) + .setCommand(commandInfo) + .addResources(cpuResource) + .addResources(memResource) + .build() + val queuedTasks = tasks.getOrElseUpdate(offer.offer.getId, new ArrayBuffer[TaskInfo]) + queuedTasks += taskInfo + logTrace(s"Using offer ${offer.offer.getId.getValue} to launch driver " + + submission.submissionId) + val newState = new MesosClusterSubmissionState(submission, taskId, offer.offer.getSlaveId, + None, new Date()) + launchedDrivers(submission.submissionId) = newState + launchedDriversState.persist(submission.submissionId, newState) + afterLaunchCallback(submission.submissionId) + } + } + } + + override def resourceOffers(driver: SchedulerDriver, offers: JList[Offer]): Unit = { + val currentOffers = offers.map { o => + new ResourceOffer( + o, getResource(o.getResourcesList, "cpus"), getResource(o.getResourcesList, "mem")) + }.toList + logTrace(s"Received offers from Mesos: \n${currentOffers.mkString("\n")}") + val tasks = new mutable.HashMap[OfferID, ArrayBuffer[TaskInfo]]() + val currentTime = new Date() + + stateLock.synchronized { + // We first schedule all the supervised drivers that are ready to retry. + // This list will be empty if none of the drivers are marked as supervise. + val driversToRetry = pendingRetryDrivers.filter { d => + d.retryState.get.nextRetry.before(currentTime) + } + scheduleTasks( + driversToRetry, + removeFromPendingRetryDrivers, + currentOffers, + tasks) + // Then we walk through the queued drivers and try to schedule them. + scheduleTasks( + queuedDrivers, + removeFromQueuedDrivers, + currentOffers, + tasks) + } + tasks.foreach { case (offerId, tasks) => + driver.launchTasks(Collections.singleton(offerId), tasks) + } + offers + .filter(o => !tasks.keySet.contains(o.getId)) + .foreach(o => driver.declineOffer(o.getId)) + } + + def getSchedulerState(): MesosClusterSchedulerState = { + def copyBuffer( + buffer: ArrayBuffer[MesosDriverDescription]): ArrayBuffer[MesosDriverDescription] = { + val newBuffer = new ArrayBuffer[MesosDriverDescription](buffer.size) + buffer.copyToBuffer(newBuffer) + newBuffer + } + stateLock.synchronized { + new MesosClusterSchedulerState( + frameworkId, + masterInfo.map(m => s"http://${m.getIp}:${m.getPort}"), + copyBuffer(queuedDrivers), + launchedDrivers.values.map(_.copy()).toList, + finishedDrivers.map(_.copy()).toList, + copyBuffer(pendingRetryDrivers)) + } + } + + override def offerRescinded(driver: SchedulerDriver, offerId: OfferID): Unit = {} + override def disconnected(driver: SchedulerDriver): Unit = {} + override def reregistered(driver: SchedulerDriver, masterInfo: MasterInfo): Unit = { + logInfo(s"Framework re-registered with master ${masterInfo.getId}") + } + override def slaveLost(driver: SchedulerDriver, slaveId: SlaveID): Unit = {} + override def error(driver: SchedulerDriver, error: String): Unit = { + logError("Error received: " + error) + } + + /** + * Check if the task state is a recoverable state that we can relaunch the task. + * Task state like TASK_ERROR are not relaunchable state since it wasn't able + * to be validated by Mesos. + */ + private def shouldRelaunch(state: MesosTaskState): Boolean = { + state == MesosTaskState.TASK_FAILED || + state == MesosTaskState.TASK_KILLED || + state == MesosTaskState.TASK_LOST + } + + override def statusUpdate(driver: SchedulerDriver, status: TaskStatus): Unit = { + val taskId = status.getTaskId.getValue + stateLock.synchronized { + if (launchedDrivers.contains(taskId)) { + if (status.getReason == Reason.REASON_RECONCILIATION && + !pendingRecover.contains(taskId)) { + // Task has already received update and no longer requires reconciliation. + return + } + val state = launchedDrivers(taskId) + // Check if the driver is supervise enabled and can be relaunched. + if (state.driverDescription.supervise && shouldRelaunch(status.getState)) { + removeFromLaunchedDrivers(taskId) + val retryState: Option[MesosClusterRetryState] = state.driverDescription.retryState + val (retries, waitTimeSec) = retryState + .map { rs => (rs.retries + 1, Math.min(maxRetryWaitTime, rs.waitTime * 2)) } + .getOrElse{ (1, 1) } + val nextRetry = new Date(new Date().getTime + waitTimeSec * 1000L) + + val newDriverDescription = state.driverDescription.copy( + retryState = Some(new MesosClusterRetryState(status, retries, nextRetry, waitTimeSec))) + pendingRetryDrivers += newDriverDescription + pendingRetryDriversState.persist(taskId, newDriverDescription) + } else if (TaskState.isFinished(TaskState.fromMesos(status.getState))) { + removeFromLaunchedDrivers(taskId) + if (finishedDrivers.size >= retainedDrivers) { + val toRemove = math.max(retainedDrivers / 10, 1) + finishedDrivers.trimStart(toRemove) + } + finishedDrivers += state + } + state.mesosTaskStatus = Option(status) + } else { + logError(s"Unable to find driver $taskId in status update") + } + } + } + + override def frameworkMessage( + driver: SchedulerDriver, + executorId: ExecutorID, + slaveId: SlaveID, + message: Array[Byte]): Unit = {} + + override def executorLost( + driver: SchedulerDriver, + executorId: ExecutorID, + slaveId: SlaveID, + status: Int): Unit = {} + + private def removeFromQueuedDrivers(id: String): Boolean = { + val index = queuedDrivers.indexWhere(_.submissionId.equals(id)) + if (index != -1) { + queuedDrivers.remove(index) + queuedDriversState.expunge(id) + true + } else { + false + } + } + + private def removeFromLaunchedDrivers(id: String): Boolean = { + if (launchedDrivers.remove(id).isDefined) { + launchedDriversState.expunge(id) + true + } else { + false + } + } + + private def removeFromPendingRetryDrivers(id: String): Boolean = { + val index = pendingRetryDrivers.indexWhere(_.submissionId.equals(id)) + if (index != -1) { + pendingRetryDrivers.remove(index) + pendingRetryDriversState.expunge(id) + true + } else { + false + } + } + + def getQueuedDriversSize: Int = queuedDrivers.size + def getLaunchedDriversSize: Int = launchedDrivers.size + def getPendingRetryDriversSize: Int = pendingRetryDrivers.size +} diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterSchedulerSource.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterSchedulerSource.scala new file mode 100644 index 0000000000000..1fe94974c8e36 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterSchedulerSource.scala @@ -0,0 +1,40 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler.cluster.mesos + +import com.codahale.metrics.{Gauge, MetricRegistry} + +import org.apache.spark.metrics.source.Source + +private[mesos] class MesosClusterSchedulerSource(scheduler: MesosClusterScheduler) + extends Source { + override def sourceName: String = "mesos_cluster" + override def metricRegistry: MetricRegistry = new MetricRegistry() + + metricRegistry.register(MetricRegistry.name("waitingDrivers"), new Gauge[Int] { + override def getValue: Int = scheduler.getQueuedDriversSize + }) + + metricRegistry.register(MetricRegistry.name("launchedDrivers"), new Gauge[Int] { + override def getValue: Int = scheduler.getLaunchedDriversSize + }) + + metricRegistry.register(MetricRegistry.name("retryDrivers"), new Gauge[Int] { + override def getValue: Int = scheduler.getPendingRetryDriversSize + }) +} diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala index d9d62b0e287ed..8346a2407489f 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala @@ -18,23 +18,19 @@ package org.apache.spark.scheduler.cluster.mesos import java.io.File -import java.util.{ArrayList => JArrayList, List => JList} -import java.util.Collections +import java.util.{ArrayList => JArrayList, Collections, List => JList} import scala.collection.JavaConversions._ import scala.collection.mutable.{HashMap, HashSet} +import org.apache.mesos.Protos.{ExecutorInfo => MesosExecutorInfo, TaskInfo => MesosTaskInfo, TaskState => MesosTaskState, _} import org.apache.mesos.protobuf.ByteString -import org.apache.mesos.{Scheduler => MScheduler} -import org.apache.mesos._ -import org.apache.mesos.Protos.{TaskInfo => MesosTaskInfo, TaskState => MesosTaskState, - ExecutorInfo => MesosExecutorInfo, _} - +import org.apache.mesos.{Scheduler => MScheduler, _} import org.apache.spark.executor.MesosExecutorBackend -import org.apache.spark.{Logging, SparkContext, SparkException, TaskState} -import org.apache.spark.scheduler.cluster.ExecutorInfo import org.apache.spark.scheduler._ +import org.apache.spark.scheduler.cluster.ExecutorInfo import org.apache.spark.util.Utils +import org.apache.spark.{SparkContext, SparkException, TaskState} /** * A SchedulerBackend for running fine-grained tasks on Mesos. Each Spark task is mapped to a @@ -47,14 +43,7 @@ private[spark] class MesosSchedulerBackend( master: String) extends SchedulerBackend with MScheduler - with Logging { - - // Lock used to wait for scheduler to be registered - var isRegistered = false - val registeredLock = new Object() - - // Driver for talking to Mesos - var driver: SchedulerDriver = null + with MesosSchedulerUtils { // Which slave IDs we have executors on val slaveIdsWithExecutors = new HashSet[String] @@ -73,26 +62,9 @@ private[spark] class MesosSchedulerBackend( @volatile var appId: String = _ override def start() { - synchronized { - classLoader = Thread.currentThread.getContextClassLoader - - new Thread("MesosSchedulerBackend driver") { - setDaemon(true) - override def run() { - val scheduler = MesosSchedulerBackend.this - val fwInfo = FrameworkInfo.newBuilder().setUser(sc.sparkUser).setName(sc.appName).build() - driver = new MesosSchedulerDriver(scheduler, fwInfo, master) - try { - val ret = driver.run() - logInfo("driver.run() returned with code " + ret) - } catch { - case e: Exception => logError("driver.run() failed", e) - } - } - }.start() - - waitForRegister() - } + val fwInfo = FrameworkInfo.newBuilder().setUser(sc.sparkUser).setName(sc.appName).build() + classLoader = Thread.currentThread.getContextClassLoader + startScheduler(master, MesosSchedulerBackend.this, fwInfo) } def createExecutorInfo(execId: String): MesosExecutorInfo = { @@ -125,17 +97,19 @@ private[spark] class MesosSchedulerBackend( } val command = CommandInfo.newBuilder() .setEnvironment(environment) - val uri = sc.conf.get("spark.executor.uri", null) + val uri = sc.conf.getOption("spark.executor.uri") + .orElse(Option(System.getenv("SPARK_EXECUTOR_URI"))) + val executorBackendName = classOf[MesosExecutorBackend].getName - if (uri == null) { + if (uri.isEmpty) { val executorPath = new File(executorSparkHome, "/bin/spark-class").getCanonicalPath command.setValue(s"$prefixEnv $executorPath $executorBackendName") } else { // Grab everything to the first '.'. We'll use that and '*' to // glob the directory "correctly". - val basename = uri.split('/').last.split('.').head + val basename = uri.get.split('/').last.split('.').head command.setValue(s"cd ${basename}*; $prefixEnv ./bin/spark-class $executorBackendName") - command.addUris(CommandInfo.URI.newBuilder().setValue(uri)) + command.addUris(CommandInfo.URI.newBuilder().setValue(uri.get)) } val cpus = Resource.newBuilder() .setName("cpus") @@ -181,18 +155,7 @@ private[spark] class MesosSchedulerBackend( inClassLoader() { appId = frameworkId.getValue logInfo("Registered as framework ID " + appId) - registeredLock.synchronized { - isRegistered = true - registeredLock.notifyAll() - } - } - } - - def waitForRegister() { - registeredLock.synchronized { - while (!isRegistered) { - registeredLock.wait() - } + markRegistered() } } @@ -287,14 +250,6 @@ private[spark] class MesosSchedulerBackend( } } - /** Helper function to pull out a resource from a Mesos Resources protobuf */ - def getResource(res: JList[Resource], name: String): Double = { - for (r <- res if r.getName == name) { - return r.getScalar.getValue - } - 0 - } - /** Turn a Spark TaskDescription into a Mesos task */ def createMesosTask(task: TaskDescription, slaveId: String): MesosTaskInfo = { val taskId = TaskID.newBuilder().setValue(task.taskId.toString).build() @@ -339,13 +294,13 @@ private[spark] class MesosSchedulerBackend( } override def stop() { - if (driver != null) { - driver.stop() + if (mesosDriver != null) { + mesosDriver.stop() } } override def reviveOffers() { - driver.reviveOffers() + mesosDriver.reviveOffers() } override def frameworkMessage(d: SchedulerDriver, e: ExecutorID, s: SlaveID, b: Array[Byte]) {} @@ -380,7 +335,7 @@ private[spark] class MesosSchedulerBackend( } override def killTask(taskId: Long, executorId: String, interruptThread: Boolean): Unit = { - driver.killTask( + mesosDriver.killTask( TaskID.newBuilder() .setValue(taskId.toString).build() ) diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala new file mode 100644 index 0000000000000..d11228f3d016a --- /dev/null +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala @@ -0,0 +1,95 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler.cluster.mesos + +import java.util.List +import java.util.concurrent.CountDownLatch + +import scala.collection.JavaConversions._ + +import org.apache.mesos.Protos.{FrameworkInfo, Resource, Status} +import org.apache.mesos.{MesosSchedulerDriver, Scheduler} +import org.apache.spark.Logging +import org.apache.spark.util.Utils + +/** + * Shared trait for implementing a Mesos Scheduler. This holds common state and helper + * methods and Mesos scheduler will use. + */ +private[mesos] trait MesosSchedulerUtils extends Logging { + // Lock used to wait for scheduler to be registered + private final val registerLatch = new CountDownLatch(1) + + // Driver for talking to Mesos + protected var mesosDriver: MesosSchedulerDriver = null + + /** + * Starts the MesosSchedulerDriver with the provided information. This method returns + * only after the scheduler has registered with Mesos. + * @param masterUrl Mesos master connection URL + * @param scheduler Scheduler object + * @param fwInfo FrameworkInfo to pass to the Mesos master + */ + def startScheduler(masterUrl: String, scheduler: Scheduler, fwInfo: FrameworkInfo): Unit = { + synchronized { + if (mesosDriver != null) { + registerLatch.await() + return + } + + new Thread(Utils.getFormattedClassName(this) + "-mesos-driver") { + setDaemon(true) + + override def run() { + mesosDriver = new MesosSchedulerDriver(scheduler, fwInfo, masterUrl) + try { + val ret = mesosDriver.run() + logInfo("driver.run() returned with code " + ret) + if (ret.equals(Status.DRIVER_ABORTED)) { + System.exit(1) + } + } catch { + case e: Exception => { + logError("driver.run() failed", e) + System.exit(1) + } + } + } + }.start() + + registerLatch.await() + } + } + + /** + * Signal that the scheduler has registered with Mesos. + */ + protected def markRegistered(): Unit = { + registerLatch.countDown() + } + + /** + * Get the amount of resources for the specified type from the resource list + */ + protected def getResource(res: List[Resource], name: String): Double = { + for (r <- res if r.getName == name) { + return r.getScalar.getValue + } + 0.0 + } +} diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala index 4561e5b8e9663..c4e6f06146b0a 100644 --- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala @@ -231,7 +231,7 @@ class SparkSubmitSuite extends FunSuite with Matchers with ResetSystemProperties val childArgsStr = childArgs.mkString(" ") if (useRest) { childArgsStr should endWith ("thejar.jar org.SomeClass arg1 arg2") - mainClass should be ("org.apache.spark.deploy.rest.StandaloneRestClient") + mainClass should be ("org.apache.spark.deploy.rest.RestSubmissionClient") } else { childArgsStr should startWith ("--supervise --memory 4g --cores 5") childArgsStr should include regex "launch spark://h:p .*thejar.jar org.SomeClass arg1 arg2" diff --git a/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala index 8e09976636386..0a318a27ac212 100644 --- a/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala @@ -39,9 +39,9 @@ import org.apache.spark.deploy.master.DriverState._ * Tests for the REST application submission protocol used in standalone cluster mode. */ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { - private val client = new StandaloneRestClient + private val client = new RestSubmissionClient private var actorSystem: Option[ActorSystem] = None - private var server: Option[StandaloneRestServer] = None + private var server: Option[RestSubmissionServer] = None override def afterEach() { actorSystem.foreach(_.shutdown()) @@ -89,7 +89,7 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { conf.set("spark.app.name", "dreamer") val appArgs = Array("one", "two", "six") // main method calls this - val response = StandaloneRestClient.run("app-resource", "main-class", appArgs, conf) + val response = RestSubmissionClient.run("app-resource", "main-class", appArgs, conf) val submitResponse = getSubmitResponse(response) assert(submitResponse.action === Utils.getFormattedClassName(submitResponse)) assert(submitResponse.serverSparkVersion === SPARK_VERSION) @@ -208,7 +208,7 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { test("good request paths") { val masterUrl = startSmartServer() val httpUrl = masterUrl.replace("spark://", "http://") - val v = StandaloneRestServer.PROTOCOL_VERSION + val v = RestSubmissionServer.PROTOCOL_VERSION val json = constructSubmitRequest(masterUrl).toJson val submitRequestPath = s"$httpUrl/$v/submissions/create" val killRequestPath = s"$httpUrl/$v/submissions/kill" @@ -238,7 +238,7 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { test("good request paths, bad requests") { val masterUrl = startSmartServer() val httpUrl = masterUrl.replace("spark://", "http://") - val v = StandaloneRestServer.PROTOCOL_VERSION + val v = RestSubmissionServer.PROTOCOL_VERSION val submitRequestPath = s"$httpUrl/$v/submissions/create" val killRequestPath = s"$httpUrl/$v/submissions/kill" val statusRequestPath = s"$httpUrl/$v/submissions/status" @@ -276,7 +276,7 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { test("bad request paths") { val masterUrl = startSmartServer() val httpUrl = masterUrl.replace("spark://", "http://") - val v = StandaloneRestServer.PROTOCOL_VERSION + val v = RestSubmissionServer.PROTOCOL_VERSION val (response1, code1) = sendHttpRequestWithResponse(httpUrl, "GET") val (response2, code2) = sendHttpRequestWithResponse(s"$httpUrl/", "GET") val (response3, code3) = sendHttpRequestWithResponse(s"$httpUrl/$v", "GET") @@ -292,7 +292,7 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { assert(code5 === HttpServletResponse.SC_BAD_REQUEST) assert(code6 === HttpServletResponse.SC_BAD_REQUEST) assert(code7 === HttpServletResponse.SC_BAD_REQUEST) - assert(code8 === StandaloneRestServer.SC_UNKNOWN_PROTOCOL_VERSION) + assert(code8 === RestSubmissionServer.SC_UNKNOWN_PROTOCOL_VERSION) // all responses should be error responses val errorResponse1 = getErrorResponse(response1) val errorResponse2 = getErrorResponse(response2) @@ -310,13 +310,13 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { assert(errorResponse5.highestProtocolVersion === null) assert(errorResponse6.highestProtocolVersion === null) assert(errorResponse7.highestProtocolVersion === null) - assert(errorResponse8.highestProtocolVersion === StandaloneRestServer.PROTOCOL_VERSION) + assert(errorResponse8.highestProtocolVersion === RestSubmissionServer.PROTOCOL_VERSION) } test("server returns unknown fields") { val masterUrl = startSmartServer() val httpUrl = masterUrl.replace("spark://", "http://") - val v = StandaloneRestServer.PROTOCOL_VERSION + val v = RestSubmissionServer.PROTOCOL_VERSION val submitRequestPath = s"$httpUrl/$v/submissions/create" val oldJson = constructSubmitRequest(masterUrl).toJson val oldFields = parse(oldJson).asInstanceOf[JObject].obj @@ -340,7 +340,7 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { test("client handles faulty server") { val masterUrl = startFaultyServer() val httpUrl = masterUrl.replace("spark://", "http://") - val v = StandaloneRestServer.PROTOCOL_VERSION + val v = RestSubmissionServer.PROTOCOL_VERSION val submitRequestPath = s"$httpUrl/$v/submissions/create" val killRequestPath = s"$httpUrl/$v/submissions/kill/anything" val statusRequestPath = s"$httpUrl/$v/submissions/status/anything" @@ -400,9 +400,9 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { val fakeMasterRef = _actorSystem.actorOf(Props(makeFakeMaster)) val _server = if (faulty) { - new FaultyStandaloneRestServer(localhost, 0, fakeMasterRef, "spark://fake:7077", conf) + new FaultyStandaloneRestServer(localhost, 0, conf, fakeMasterRef, "spark://fake:7077") } else { - new StandaloneRestServer(localhost, 0, fakeMasterRef, "spark://fake:7077", conf) + new StandaloneRestServer(localhost, 0, conf, fakeMasterRef, "spark://fake:7077") } val port = _server.start() // set these to clean them up after every test @@ -563,20 +563,18 @@ private class SmarterMaster extends Actor { private class FaultyStandaloneRestServer( host: String, requestedPort: Int, + masterConf: SparkConf, masterActor: ActorRef, - masterUrl: String, - masterConf: SparkConf) - extends StandaloneRestServer(host, requestedPort, masterActor, masterUrl, masterConf) { + masterUrl: String) + extends RestSubmissionServer(host, requestedPort, masterConf) { - protected override val contextToServlet = Map[String, StandaloneRestServlet]( - s"$baseContext/create/*" -> new MalformedSubmitServlet, - s"$baseContext/kill/*" -> new InvalidKillServlet, - s"$baseContext/status/*" -> new ExplodingStatusServlet, - "/*" -> new ErrorServlet - ) + protected override val submitRequestServlet = new MalformedSubmitServlet + protected override val killRequestServlet = new InvalidKillServlet + protected override val statusRequestServlet = new ExplodingStatusServlet /** A faulty servlet that produces malformed responses. */ - class MalformedSubmitServlet extends SubmitRequestServlet(masterActor, masterUrl, masterConf) { + class MalformedSubmitServlet + extends StandaloneSubmitRequestServlet(masterActor, masterUrl, masterConf) { protected override def sendResponse( responseMessage: SubmitRestProtocolResponse, responseServlet: HttpServletResponse): Unit = { @@ -586,7 +584,7 @@ private class FaultyStandaloneRestServer( } /** A faulty servlet that produces invalid responses. */ - class InvalidKillServlet extends KillRequestServlet(masterActor, masterConf) { + class InvalidKillServlet extends StandaloneKillRequestServlet(masterActor, masterConf) { protected override def handleKill(submissionId: String): KillSubmissionResponse = { val k = super.handleKill(submissionId) k.submissionId = null @@ -595,7 +593,7 @@ private class FaultyStandaloneRestServer( } /** A faulty status servlet that explodes. */ - class ExplodingStatusServlet extends StatusRequestServlet(masterActor, masterConf) { + class ExplodingStatusServlet extends StandaloneStatusRequestServlet(masterActor, masterConf) { private def explode: Int = 1 / 0 protected override def handleStatus(submissionId: String): SubmissionStatusResponse = { val s = super.handleStatus(submissionId) diff --git a/core/src/test/scala/org/apache/spark/scheduler/mesos/MesosClusterSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/mesos/MesosClusterSchedulerSuite.scala new file mode 100644 index 0000000000000..f28e29e9b8d8e --- /dev/null +++ b/core/src/test/scala/org/apache/spark/scheduler/mesos/MesosClusterSchedulerSuite.scala @@ -0,0 +1,76 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler.mesos + +import java.util.Date + +import org.scalatest.FunSuite +import org.scalatest.mock.MockitoSugar + +import org.apache.spark.deploy.Command +import org.apache.spark.deploy.mesos.MesosDriverDescription +import org.apache.spark.scheduler.cluster.mesos._ +import org.apache.spark.{LocalSparkContext, SparkConf} + + +class MesosClusterSchedulerSuite extends FunSuite with LocalSparkContext with MockitoSugar { + + private val command = new Command("mainClass", Seq("arg"), null, null, null, null) + + test("can queue drivers") { + val conf = new SparkConf() + conf.setMaster("mesos://localhost:5050") + conf.setAppName("spark mesos") + val scheduler = new MesosClusterScheduler( + new BlackHoleMesosClusterPersistenceEngineFactory, conf) { + override def start(): Unit = { ready = true } + } + scheduler.start() + val response = scheduler.submitDriver( + new MesosDriverDescription("d1", "jar", 1000, 1, true, + command, Map[String, String](), "s1", new Date())) + assert(response.success) + val response2 = + scheduler.submitDriver(new MesosDriverDescription( + "d1", "jar", 1000, 1, true, command, Map[String, String](), "s2", new Date())) + assert(response2.success) + val state = scheduler.getSchedulerState() + val queuedDrivers = state.queuedDrivers.toList + assert(queuedDrivers(0).submissionId == response.submissionId) + assert(queuedDrivers(1).submissionId == response2.submissionId) + } + + test("can kill queued drivers") { + val conf = new SparkConf() + conf.setMaster("mesos://localhost:5050") + conf.setAppName("spark mesos") + val scheduler = new MesosClusterScheduler( + new BlackHoleMesosClusterPersistenceEngineFactory, conf) { + override def start(): Unit = { ready = true } + } + scheduler.start() + val response = scheduler.submitDriver( + new MesosDriverDescription("d1", "jar", 1000, 1, true, + command, Map[String, String](), "s1", new Date())) + assert(response.success) + val killResponse = scheduler.killDriver(response.submissionId) + assert(killResponse.success) + val state = scheduler.getSchedulerState() + assert(state.queuedDrivers.isEmpty) + } +} diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md index 594bf78b67713..8f53d8201a089 100644 --- a/docs/running-on-mesos.md +++ b/docs/running-on-mesos.md @@ -78,6 +78,9 @@ To verify that the Mesos cluster is ready for Spark, navigate to the Mesos maste To use Mesos from Spark, you need a Spark binary package available in a place accessible by Mesos, and a Spark driver program configured to connect to Mesos. +Alternatively, you can also install Spark in the same location in all the Mesos slaves, and configure +`spark.mesos.executor.home` (defaults to SPARK_HOME) to point to that location. + ## Uploading Spark Package When Mesos runs a task on a Mesos slave for the first time, that slave must have a Spark binary @@ -107,7 +110,11 @@ the `make-distribution.sh` script included in a Spark source tarball/checkout. The Master URLs for Mesos are in the form `mesos://host:5050` for a single-master Mesos cluster, or `mesos://zk://host:2181` for a multi-master Mesos cluster using ZooKeeper. -The driver also needs some configuration in `spark-env.sh` to interact properly with Mesos: +## Client Mode + +In client mode, a Spark Mesos framework is launched directly on the client machine and waits for the driver output. + +The driver needs some configuration in `spark-env.sh` to interact properly with Mesos: 1. In `spark-env.sh` set some environment variables: * `export MESOS_NATIVE_JAVA_LIBRARY=`. This path is typically @@ -129,8 +136,7 @@ val sc = new SparkContext(conf) {% endhighlight %} (You can also use [`spark-submit`](submitting-applications.html) and configure `spark.executor.uri` -in the [conf/spark-defaults.conf](configuration.html#loading-default-configurations) file. Note -that `spark-submit` currently only supports deploying the Spark driver in `client` mode for Mesos.) +in the [conf/spark-defaults.conf](configuration.html#loading-default-configurations) file.) When running a shell, the `spark.executor.uri` parameter is inherited from `SPARK_EXECUTOR_URI`, so it does not need to be redundantly passed in as a system property. @@ -139,6 +145,17 @@ it does not need to be redundantly passed in as a system property. ./bin/spark-shell --master mesos://host:5050 {% endhighlight %} +## Cluster mode + +Spark on Mesos also supports cluster mode, where the driver is launched in the cluster and the client +can find the results of the driver from the Mesos Web UI. + +To use cluster mode, you must start the MesosClusterDispatcher in your cluster via the `sbin/start-mesos-dispatcher.sh` script, +passing in the Mesos master url (e.g: mesos://host:5050). + +From the client, you can submit a job to Mesos cluster by running `spark-submit` and specifying the master url +to the url of the MesosClusterDispatcher (e.g: mesos://dispatcher:7077). You can view driver statuses on the +Spark cluster Web UI. # Mesos Run Modes diff --git a/sbin/start-mesos-dispatcher.sh b/sbin/start-mesos-dispatcher.sh new file mode 100755 index 0000000000000..ef1fc573d5c65 --- /dev/null +++ b/sbin/start-mesos-dispatcher.sh @@ -0,0 +1,40 @@ +#!/usr/bin/env bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Starts the Mesos Cluster Dispatcher on the machine this script is executed on. +# The Mesos Cluster Dispatcher is responsible for launching the Mesos framework and +# Rest server to handle driver requests for Mesos cluster mode. +# Only one cluster dispatcher is needed per Mesos cluster. + +sbin="`dirname "$0"`" +sbin="`cd "$sbin"; pwd`" + +. "$sbin/spark-config.sh" + +. "$SPARK_PREFIX/bin/load-spark-env.sh" + +if [ "$SPARK_MESOS_DISPATCHER_PORT" = "" ]; then + SPARK_MESOS_DISPATCHER_PORT=7077 +fi + +if [ "$SPARK_MESOS_DISPATCHER_HOST" = "" ]; then + SPARK_MESOS_DISPATCHER_HOST=`hostname` +fi + + +"$sbin"/spark-daemon.sh start org.apache.spark.deploy.mesos.MesosClusterDispatcher 1 --host $SPARK_MESOS_DISPATCHER_HOST --port $SPARK_MESOS_DISPATCHER_PORT "$@" diff --git a/sbin/stop-mesos-dispatcher.sh b/sbin/stop-mesos-dispatcher.sh new file mode 100755 index 0000000000000..cb65d95b5e524 --- /dev/null +++ b/sbin/stop-mesos-dispatcher.sh @@ -0,0 +1,27 @@ +#!/usr/bin/env bash + +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Stop the Mesos Cluster dispatcher on the machine this script is executed on. + +sbin=`dirname "$0"` +sbin=`cd "$sbin"; pwd` + +. "$sbin/spark-config.sh" + +"$sbin"/spark-daemon.sh stop org.apache.spark.deploy.mesos.MesosClusterDispatcher 1 + From 28b1af7420e0b5e7e2dfc09eafc45fe2ffcde5ec Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Tue, 28 Apr 2015 13:49:29 -0700 Subject: [PATCH 453/591] [MINOR] [CORE] Warn users who try to cache RDDs with dynamic allocation on. Author: Marcelo Vanzin Closes #5751 from vanzin/cached-rdd-warning and squashes the following commits: 554cc07 [Marcelo Vanzin] Change message. 9efb9da [Marcelo Vanzin] [minor] [core] Warn users who try to cache RDDs with dynamic allocation on. --- core/src/main/scala/org/apache/spark/SparkContext.scala | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 65b903a55d5bd..d0cf2a8dd01cd 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -1396,6 +1396,11 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * Register an RDD to be persisted in memory and/or disk storage */ private[spark] def persistRDD(rdd: RDD[_]) { + _executorAllocationManager.foreach { _ => + logWarning( + s"Dynamic allocation currently does not support cached RDDs. Cached data for RDD " + + s"${rdd.id} will be lost when executors are removed.") + } persistentRdds(rdd.id) = rdd } From f0a1f90f53b447c61f405cdb2c553f3dc067bf8d Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Tue, 28 Apr 2015 14:07:26 -0700 Subject: [PATCH 454/591] [SPARK-7201] [MLLIB] move Identifiable to ml.util It shouldn't live directly under `spark.ml`. Author: Xiangrui Meng Closes #5749 from mengxr/SPARK-7201 and squashes the following commits: 53847f9 [Xiangrui Meng] move Identifiable to ml.util --- mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala | 1 + mllib/src/main/scala/org/apache/spark/ml/param/params.scala | 2 +- .../scala/org/apache/spark/ml/{ => util}/Identifiable.scala | 2 +- 3 files changed, 3 insertions(+), 2 deletions(-) rename mllib/src/main/scala/org/apache/spark/ml/{ => util}/Identifiable.scala (97%) diff --git a/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala index d2ca2e6871e6b..8b4b5fd8af986 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala @@ -19,6 +19,7 @@ package org.apache.spark.ml import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.param.ParamMap +import org.apache.spark.ml.util.Identifiable import org.apache.spark.sql.DataFrame /** diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala index ddc5907e7facd..014e124e440a3 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala @@ -24,7 +24,7 @@ import scala.annotation.varargs import scala.collection.mutable import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} -import org.apache.spark.ml.Identifiable +import org.apache.spark.ml.util.Identifiable /** * :: AlphaComponent :: diff --git a/mllib/src/main/scala/org/apache/spark/ml/Identifiable.scala b/mllib/src/main/scala/org/apache/spark/ml/util/Identifiable.scala similarity index 97% rename from mllib/src/main/scala/org/apache/spark/ml/Identifiable.scala rename to mllib/src/main/scala/org/apache/spark/ml/util/Identifiable.scala index a1d49095c24ac..8a56748ab0a02 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Identifiable.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/util/Identifiable.scala @@ -15,7 +15,7 @@ * limitations under the License. */ -package org.apache.spark.ml +package org.apache.spark.ml.util import java.util.UUID From 555213ebbf2be2ee523be8665bd5b9a47ae4bec8 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Tue, 28 Apr 2015 14:21:25 -0700 Subject: [PATCH 455/591] Closes #4807 Closes #5055 Closes #3583 From d36e67350c516a96d58abd50a0d5d22b3b22f291 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Tue, 28 Apr 2015 17:41:09 -0700 Subject: [PATCH 456/591] [SPARK-6965] [MLLIB] StringIndexer handles numeric input. Cast numeric types to String for indexing. Boolean type is not handled in this PR. jkbradley Author: Xiangrui Meng Closes #5753 from mengxr/SPARK-6965 and squashes the following commits: 2e34f3c [Xiangrui Meng] add actual type in the error message ad938bf [Xiangrui Meng] StringIndexer handles numeric input. --- .../spark/ml/feature/StringIndexer.scala | 17 ++++++++++++----- .../spark/ml/feature/StringIndexerSuite.scala | 19 +++++++++++++++++++ 2 files changed, 31 insertions(+), 5 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala index 23956c512c8a6..9db3b29e10d69 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala @@ -23,10 +23,9 @@ import org.apache.spark.ml.{Estimator, Model} import org.apache.spark.ml.attribute.NominalAttribute import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared._ -import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.sql.DataFrame import org.apache.spark.sql.functions._ -import org.apache.spark.sql.types.{StringType, StructType} +import org.apache.spark.sql.types.{NumericType, StringType, StructType} import org.apache.spark.util.collection.OpenHashMap /** @@ -37,7 +36,11 @@ private[feature] trait StringIndexerBase extends Params with HasInputCol with Ha /** Validates and transforms the input schema. */ protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = { val map = extractParamMap(paramMap) - SchemaUtils.checkColumnType(schema, map(inputCol), StringType) + val inputColName = map(inputCol) + val inputDataType = schema(inputColName).dataType + require(inputDataType == StringType || inputDataType.isInstanceOf[NumericType], + s"The input column $inputColName must be either string type or numeric type, " + + s"but got $inputDataType.") val inputFields = schema.fields val outputColName = map(outputCol) require(inputFields.forall(_.name != outputColName), @@ -51,6 +54,7 @@ private[feature] trait StringIndexerBase extends Params with HasInputCol with Ha /** * :: AlphaComponent :: * A label indexer that maps a string column of labels to an ML column of label indices. + * If the input column is numeric, we cast it to string and index the string values. * The indices are in [0, numLabels), ordered by label frequencies. * So the most frequent label gets index 0. */ @@ -67,7 +71,9 @@ class StringIndexer extends Estimator[StringIndexerModel] with StringIndexerBase override def fit(dataset: DataFrame, paramMap: ParamMap): StringIndexerModel = { val map = extractParamMap(paramMap) - val counts = dataset.select(map(inputCol)).map(_.getString(0)).countByValue() + val counts = dataset.select(col(map(inputCol)).cast(StringType)) + .map(_.getString(0)) + .countByValue() val labels = counts.toSeq.sortBy(-_._2).map(_._1).toArray val model = new StringIndexerModel(this, map, labels) Params.inheritValues(map, this, model) @@ -119,7 +125,8 @@ class StringIndexerModel private[ml] ( val outputColName = map(outputCol) val metadata = NominalAttribute.defaultAttr .withName(outputColName).withValues(labels).toMetadata() - dataset.select(col("*"), indexer(dataset(map(inputCol))).as(outputColName, metadata)) + dataset.select(col("*"), + indexer(dataset(map(inputCol)).cast(StringType)).as(outputColName, metadata)) } override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala index 00b5d094d82f1..b6939e5870410 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala @@ -49,4 +49,23 @@ class StringIndexerSuite extends FunSuite with MLlibTestSparkContext { val expected = Set((0, 0.0), (1, 2.0), (2, 1.0), (3, 0.0), (4, 0.0), (5, 1.0)) assert(output === expected) } + + test("StringIndexer with a numeric input column") { + val data = sc.parallelize(Seq((0, 100), (1, 200), (2, 300), (3, 100), (4, 100), (5, 300)), 2) + val df = sqlContext.createDataFrame(data).toDF("id", "label") + val indexer = new StringIndexer() + .setInputCol("label") + .setOutputCol("labelIndex") + .fit(df) + val transformed = indexer.transform(df) + val attr = Attribute.fromStructField(transformed.schema("labelIndex")) + .asInstanceOf[NominalAttribute] + assert(attr.values.get === Array("100", "300", "200")) + val output = transformed.select("id", "labelIndex").map { r => + (r.getInt(0), r.getDouble(1)) + }.collect().toSet + // 100 -> 0, 200 -> 2, 300 -> 1 + val expected = Set((0, 0.0), (1, 2.0), (2, 1.0), (3, 0.0), (4, 0.0), (5, 1.0)) + assert(output === expected) + } } From 5c8f4bd5fae539ab5fb992573d5357ed34e2f4d0 Mon Sep 17 00:00:00 2001 From: Tathagata Das Date: Tue, 28 Apr 2015 19:31:57 -0700 Subject: [PATCH 457/591] [SPARK-7138] [STREAMING] Add method to BlockGenerator to add multiple records to BlockGenerator with single callback This is to ensure that receivers that receive data in small batches (like Kinesis) and want to add them but want the callback function to be called only once. This is for internal use only for improvement to Kinesis Receiver that we are planning to do. Author: Tathagata Das Closes #5695 from tdas/SPARK-7138 and squashes the following commits: a35cf7d [Tathagata Das] Fixed style. a7a4cb9 [Tathagata Das] Added extra method to BlockGenerator. --- .../spark/streaming/receiver/BlockGenerator.scala | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala index f4963a78e1d18..4bebcc5aa7ca0 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala @@ -126,6 +126,20 @@ private[streaming] class BlockGenerator( listener.onAddData(data, metadata) } + /** + * Push multiple data items into the buffer. After buffering the data, the + * `BlockGeneratorListener.onAddData` callback will be called. All received data items + * will be periodically pushed into BlockManager. Note that all the data items is guaranteed + * to be present in a single block. + */ + def addMultipleDataWithCallback(dataIterator: Iterator[Any], metadata: Any): Unit = synchronized { + dataIterator.foreach { data => + waitToPush() + currentBuffer += data + } + listener.onAddData(dataIterator, metadata) + } + /** Change the buffer to which single records are added to. */ private def updateCurrentBuffer(time: Long): Unit = synchronized { try { From a8aeadb7d4a2dc308a75a50fdd8065f9a32ef336 Mon Sep 17 00:00:00 2001 From: "Joseph K. Bradley" Date: Tue, 28 Apr 2015 21:15:47 -0700 Subject: [PATCH 458/591] [SPARK-7208] [ML] [PYTHON] Added Matrix, SparseMatrix to __all__ list in linalg.py Added Matrix, SparseMatrix to __all__ list in linalg.py CC: mengxr Author: Joseph K. Bradley Closes #5759 from jkbradley/SPARK-7208 and squashes the following commits: deb51a2 [Joseph K. Bradley] Added Matrix, SparseMatrix to __all__ list in linalg.py --- python/pyspark/mllib/linalg.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py index cc9a4cf8ba170..a57c0b3ae0d00 100644 --- a/python/pyspark/mllib/linalg.py +++ b/python/pyspark/mllib/linalg.py @@ -39,7 +39,8 @@ IntegerType, ByteType -__all__ = ['Vector', 'DenseVector', 'SparseVector', 'Vectors', 'DenseMatrix', 'Matrices'] +__all__ = ['Vector', 'DenseVector', 'SparseVector', 'Vectors', + 'Matrix', 'DenseMatrix', 'SparseMatrix', 'Matrices'] if sys.version_info[:2] == (2, 7): From 5ef006fc4d010905e02cb905c9115b95ba55282b Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Tue, 28 Apr 2015 21:49:53 -0700 Subject: [PATCH 459/591] [SPARK-6756] [MLLIB] add toSparse, toDense, numActives, numNonzeros, and compressed to Vector Add `compressed` to `Vector` with some other methods: `numActives`, `numNonzeros`, `toSparse`, and `toDense`. jkbradley Author: Xiangrui Meng Closes #5756 from mengxr/SPARK-6756 and squashes the following commits: 8d4ecbd [Xiangrui Meng] address comment and add mima excludes da54179 [Xiangrui Meng] add toSparse, toDense, numActives, numNonzeros, and compressed to Vector --- .../apache/spark/mllib/linalg/Vectors.scala | 93 +++++++++++++++++++ .../spark/mllib/linalg/VectorsSuite.scala | 44 +++++++++ project/MimaExcludes.scala | 12 +++ 3 files changed, 149 insertions(+) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala index 34833e90d4af0..188d1e542b5b5 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala @@ -116,6 +116,40 @@ sealed trait Vector extends Serializable { * with type `Double`. */ private[spark] def foreachActive(f: (Int, Double) => Unit) + + /** + * Number of active entries. An "active entry" is an element which is explicitly stored, + * regardless of its value. Note that inactive entries have value 0. + */ + def numActives: Int + + /** + * Number of nonzero elements. This scans all active values and count nonzeros. + */ + def numNonzeros: Int + + /** + * Converts this vector to a sparse vector with all explicit zeros removed. + */ + def toSparse: SparseVector + + /** + * Converts this vector to a dense vector. + */ + def toDense: DenseVector = new DenseVector(this.toArray) + + /** + * Returns a vector in either dense or sparse format, whichever uses less storage. + */ + def compressed: Vector = { + val nnz = numNonzeros + // A dense vector needs 8 * size + 8 bytes, while a sparse vector needs 12 * nnz + 20 bytes. + if (1.5 * (nnz + 1.0) < size) { + toSparse + } else { + toDense + } + } } /** @@ -525,6 +559,34 @@ class DenseVector(val values: Array[Double]) extends Vector { } result } + + override def numActives: Int = size + + override def numNonzeros: Int = { + // same as values.count(_ != 0.0) but faster + var nnz = 0 + values.foreach { v => + if (v != 0.0) { + nnz += 1 + } + } + nnz + } + + override def toSparse: SparseVector = { + val nnz = numNonzeros + val ii = new Array[Int](nnz) + val vv = new Array[Double](nnz) + var k = 0 + foreachActive { (i, v) => + if (v != 0) { + ii(k) = i + vv(k) = v + k += 1 + } + } + new SparseVector(size, ii, vv) + } } object DenseVector { @@ -602,6 +664,37 @@ class SparseVector( } result } + + override def numActives: Int = values.length + + override def numNonzeros: Int = { + var nnz = 0 + values.foreach { v => + if (v != 0.0) { + nnz += 1 + } + } + nnz + } + + override def toSparse: SparseVector = { + val nnz = numNonzeros + if (nnz == numActives) { + this + } else { + val ii = new Array[Int](nnz) + val vv = new Array[Double](nnz) + var k = 0 + foreachActive { (i, v) => + if (v != 0.0) { + ii(k) = i + vv(k) = v + k += 1 + } + } + new SparseVector(size, ii, vv) + } + } } object SparseVector { diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala index 2839c4c289b2d..24755e9ff46fc 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala @@ -270,4 +270,48 @@ class VectorsSuite extends FunSuite { assert(Vectors.norm(sv, 3.7) ~== math.pow(sv.toArray.foldLeft(0.0)((a, v) => a + math.pow(math.abs(v), 3.7)), 1.0 / 3.7) relTol 1E-8) } + + test("Vector numActive and numNonzeros") { + val dv = Vectors.dense(0.0, 2.0, 3.0, 0.0) + assert(dv.numActives === 4) + assert(dv.numNonzeros === 2) + + val sv = Vectors.sparse(4, Array(0, 1, 2), Array(0.0, 2.0, 3.0)) + assert(sv.numActives === 3) + assert(sv.numNonzeros === 2) + } + + test("Vector toSparse and toDense") { + val dv0 = Vectors.dense(0.0, 2.0, 3.0, 0.0) + assert(dv0.toDense === dv0) + val dv0s = dv0.toSparse + assert(dv0s.numActives === 2) + assert(dv0s === dv0) + + val sv0 = Vectors.sparse(4, Array(0, 1, 2), Array(0.0, 2.0, 3.0)) + assert(sv0.toDense === sv0) + val sv0s = sv0.toSparse + assert(sv0s.numActives === 2) + assert(sv0s === sv0) + } + + test("Vector.compressed") { + val dv0 = Vectors.dense(1.0, 2.0, 3.0, 0.0) + val dv0c = dv0.compressed.asInstanceOf[DenseVector] + assert(dv0c === dv0) + + val dv1 = Vectors.dense(0.0, 2.0, 0.0, 0.0) + val dv1c = dv1.compressed.asInstanceOf[SparseVector] + assert(dv1 === dv1c) + assert(dv1c.numActives === 1) + + val sv0 = Vectors.sparse(4, Array(1, 2), Array(2.0, 0.0)) + val sv0c = sv0.compressed.asInstanceOf[SparseVector] + assert(sv0 === sv0c) + assert(sv0c.numActives === 1) + + val sv1 = Vectors.sparse(4, Array(0, 1, 2), Array(1.0, 2.0, 3.0)) + val sv1c = sv1.compressed.asInstanceOf[DenseVector] + assert(sv1 === sv1c) + } } diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala index 967961c2bf5c3..3beafa158eb97 100644 --- a/project/MimaExcludes.scala +++ b/project/MimaExcludes.scala @@ -76,6 +76,18 @@ object MimaExcludes { // SPARK-7090 Introduce LDAOptimizer to LDA to further improve extensibility ProblemFilters.exclude[MissingClassProblem]( "org.apache.spark.mllib.clustering.LDA$EMOptimizer") + ) ++ Seq( + // SPARK-6756 add toSparse, toDense, numActives, numNonzeros, and compressed to Vector + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.linalg.Vector.compressed"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.linalg.Vector.toDense"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.linalg.Vector.numNonzeros"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.linalg.Vector.toSparse"), + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.mllib.linalg.Vector.numActives") ) case v if v.startsWith("1.3") => From 271c4c621d91d3f610ae89e5d2e5dab1a2009ca6 Mon Sep 17 00:00:00 2001 From: Burak Yavuz Date: Tue, 28 Apr 2015 22:48:04 -0700 Subject: [PATCH 460/591] [SPARK-7215] made coalesce and repartition a part of the query plan Coalesce and repartition now show up as part of the query plan, rather than resulting in a new `DataFrame`. cc rxin Author: Burak Yavuz Closes #5762 from brkyvz/df-repartition and squashes the following commits: b1e76dd [Burak Yavuz] added documentation on repartitions 5807e35 [Burak Yavuz] renamed coalescepartitions fa4509f [Burak Yavuz] rename coalesce 2c349b5 [Burak Yavuz] address comments f2e6af1 [Burak Yavuz] add ticks 686c90b [Burak Yavuz] made coalesce and repartition a part of the query plan --- .../catalyst/plans/logical/basicOperators.scala | 11 +++++++++++ .../sql/catalyst/plans/logical/partitioning.scala | 8 +++++++- .../scala/org/apache/spark/sql/DataFrame.scala | 9 ++------- .../spark/sql/execution/SparkStrategies.scala | 5 +++-- .../spark/sql/execution/basicOperators.scala | 14 ++++++++++++++ .../scala/org/apache/spark/sql/hive/HiveQl.scala | 6 +++--- 6 files changed, 40 insertions(+), 13 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala index bbc94a7ab3398..608e272da7784 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala @@ -310,6 +310,17 @@ case class Distinct(child: LogicalPlan) extends UnaryNode { override def output: Seq[Attribute] = child.output } +/** + * Return a new RDD that has exactly `numPartitions` partitions. Differs from + * [[RepartitionByExpression]] as this method is called directly by DataFrame's, because the user + * asked for `coalesce` or `repartition`. [[RepartitionByExpression]] is used when the consumer + * of the output requires some specific ordering or distribution of the data. + */ +case class Repartition(numPartitions: Int, shuffle: Boolean, child: LogicalPlan) + extends UnaryNode { + override def output: Seq[Attribute] = child.output +} + /** * A relation with one row. This is used in "SELECT ..." without a from clause. */ diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala index e737418d9c3bc..63df2c1ee72ff 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/partitioning.scala @@ -32,5 +32,11 @@ abstract class RedistributeData extends UnaryNode { case class SortPartitions(sortExpressions: Seq[SortOrder], child: LogicalPlan) extends RedistributeData -case class Repartition(partitionExpressions: Seq[Expression], child: LogicalPlan) +/** + * This method repartitions data using [[Expression]]s, and receives information about the + * number of partitions during execution. Used when a specific ordering or distribution is + * expected by the consumer of the query result. Use [[Repartition]] for RDD-like + * `coalesce` and `repartition`. + */ +case class RepartitionByExpression(partitionExpressions: Seq[Expression], child: LogicalPlan) extends RedistributeData diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index ca6ae482eb2ab..2affba7d42cc8 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -961,9 +961,7 @@ class DataFrame private[sql]( * @group rdd */ override def repartition(numPartitions: Int): DataFrame = { - sqlContext.createDataFrame( - queryExecution.toRdd.map(_.copy()).repartition(numPartitions), - schema, needsConversion = false) + Repartition(numPartitions, shuffle = true, logicalPlan) } /** @@ -974,10 +972,7 @@ class DataFrame private[sql]( * @group rdd */ override def coalesce(numPartitions: Int): DataFrame = { - sqlContext.createDataFrame( - queryExecution.toRdd.coalesce(numPartitions), - schema, - needsConversion = false) + Repartition(numPartitions, shuffle = false, logicalPlan) } /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala index 030ef118f75d4..3a0a6c86700a8 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala @@ -283,7 +283,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { case logical.Distinct(child) => execution.Distinct(partial = false, execution.Distinct(partial = true, planLater(child))) :: Nil - + case logical.Repartition(numPartitions, shuffle, child) => + execution.Repartition(numPartitions, shuffle, planLater(child)) :: Nil case logical.SortPartitions(sortExprs, child) => // This sort only sorts tuples within a partition. Its requiredDistribution will be // an UnspecifiedDistribution. @@ -317,7 +318,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { generator, join = join, outer = outer, g.output, planLater(child)) :: Nil case logical.OneRowRelation => execution.PhysicalRDD(Nil, singleRowRdd) :: Nil - case logical.Repartition(expressions, child) => + case logical.RepartitionByExpression(expressions, child) => execution.Exchange( HashPartitioning(expressions, numPartitions), Nil, planLater(child)) :: Nil case e @ EvaluatePython(udf, child, _) => diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala index d286fe81bee5f..1afdb409417ce 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala @@ -245,6 +245,20 @@ case class Distinct(partial: Boolean, child: SparkPlan) extends UnaryNode { } } +/** + * :: DeveloperApi :: + * Return a new RDD that has exactly `numPartitions` partitions. + */ +@DeveloperApi +case class Repartition(numPartitions: Int, shuffle: Boolean, child: SparkPlan) + extends UnaryNode { + override def output: Seq[Attribute] = child.output + + override def execute(): RDD[Row] = { + child.execute().map(_.copy()).coalesce(numPartitions, shuffle) + } +} + /** * :: DeveloperApi :: diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala index 0ea6d57b816c6..2dc6463abafa7 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala @@ -783,13 +783,13 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C case (None, Some(perPartitionOrdering), None, None) => Sort(perPartitionOrdering.getChildren.map(nodeToSortOrder), false, withHaving) case (None, None, Some(partitionExprs), None) => - Repartition(partitionExprs.getChildren.map(nodeToExpr), withHaving) + RepartitionByExpression(partitionExprs.getChildren.map(nodeToExpr), withHaving) case (None, Some(perPartitionOrdering), Some(partitionExprs), None) => Sort(perPartitionOrdering.getChildren.map(nodeToSortOrder), false, - Repartition(partitionExprs.getChildren.map(nodeToExpr), withHaving)) + RepartitionByExpression(partitionExprs.getChildren.map(nodeToExpr), withHaving)) case (None, None, None, Some(clusterExprs)) => Sort(clusterExprs.getChildren.map(nodeToExpr).map(SortOrder(_, Ascending)), false, - Repartition(clusterExprs.getChildren.map(nodeToExpr), withHaving)) + RepartitionByExpression(clusterExprs.getChildren.map(nodeToExpr), withHaving)) case (None, None, None, None) => withHaving case _ => sys.error("Unsupported set of ordering / distribution clauses.") } From f98773a90ded0e408af6bbd85fafbaffbc5b825f Mon Sep 17 00:00:00 2001 From: Burak Yavuz Date: Tue, 28 Apr 2015 23:05:02 -0700 Subject: [PATCH 461/591] [SPARK-7205] Support `.ivy2/local` and `.m2/repositories/` in --packages In addition, I made a small change that will allow users to import 2 different artifacts with the same name. That change is made in `[organization]_[artifact]-[revision].[ext]`. This used to be only `[artifact].[ext]` which might have caused collisions between artifacts with the same artifactId, but different groupId's. cc pwendell Author: Burak Yavuz Closes #5755 from brkyvz/local-caches and squashes the following commits: c47c9c5 [Burak Yavuz] Small fixes to --packages --- .../org/apache/spark/deploy/SparkSubmit.scala | 34 ++++++++++++++----- .../spark/deploy/SparkSubmitUtilsSuite.scala | 27 ++++++++------- 2 files changed, 40 insertions(+), 21 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala index f4f572e1e256e..b8ae4af18d1d1 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -734,13 +734,31 @@ private[deploy] object SparkSubmitUtils { /** * Extracts maven coordinates from a comma-delimited string * @param remoteRepos Comma-delimited string of remote repositories + * @param ivySettings The Ivy settings for this session * @return A ChainResolver used by Ivy to search for and resolve dependencies. */ - def createRepoResolvers(remoteRepos: Option[String]): ChainResolver = { + def createRepoResolvers(remoteRepos: Option[String], ivySettings: IvySettings): ChainResolver = { // We need a chain resolver if we want to check multiple repositories val cr = new ChainResolver cr.setName("list") + val localM2 = new IBiblioResolver + localM2.setM2compatible(true) + val m2Path = ".m2" + File.separator + "repository" + File.separator + localM2.setRoot(new File(System.getProperty("user.home"), m2Path).toURI.toString) + localM2.setUsepoms(true) + localM2.setName("local-m2-cache") + cr.add(localM2) + + val localIvy = new IBiblioResolver + localIvy.setRoot(new File(ivySettings.getDefaultIvyUserDir, + "local" + File.separator).toURI.toString) + val ivyPattern = Seq("[organisation]", "[module]", "[revision]", "[type]s", + "[artifact](-[classifier]).[ext]").mkString(File.separator) + localIvy.setPattern(ivyPattern) + localIvy.setName("local-ivy-cache") + cr.add(localIvy) + // the biblio resolver resolves POM declared dependencies val br: IBiblioResolver = new IBiblioResolver br.setM2compatible(true) @@ -773,8 +791,7 @@ private[deploy] object SparkSubmitUtils { /** * Output a comma-delimited list of paths for the downloaded jars to be added to the classpath - * (will append to jars in SparkSubmit). The name of the jar is given - * after a '!' by Ivy. It also sometimes contains '(bundle)' after '.jar'. Remove that as well. + * (will append to jars in SparkSubmit). * @param artifacts Sequence of dependencies that were resolved and retrieved * @param cacheDirectory directory where jars are cached * @return a comma-delimited list of paths for the dependencies @@ -783,10 +800,9 @@ private[deploy] object SparkSubmitUtils { artifacts: Array[AnyRef], cacheDirectory: File): String = { artifacts.map { artifactInfo => - val artifactString = artifactInfo.toString - val jarName = artifactString.drop(artifactString.lastIndexOf("!") + 1) + val artifact = artifactInfo.asInstanceOf[Artifact].getModuleRevisionId cacheDirectory.getAbsolutePath + File.separator + - jarName.substring(0, jarName.lastIndexOf(".jar") + 4) + s"${artifact.getOrganisation}_${artifact.getName}-${artifact.getRevision}.jar" }.mkString(",") } @@ -868,6 +884,7 @@ private[deploy] object SparkSubmitUtils { if (alternateIvyCache.trim.isEmpty) { new File(ivySettings.getDefaultIvyUserDir, "jars") } else { + ivySettings.setDefaultIvyUserDir(new File(alternateIvyCache)) ivySettings.setDefaultCache(new File(alternateIvyCache, "cache")) new File(alternateIvyCache, "jars") } @@ -877,7 +894,7 @@ private[deploy] object SparkSubmitUtils { // create a pattern matcher ivySettings.addMatcher(new GlobPatternMatcher) // create the dependency resolvers - val repoResolver = createRepoResolvers(remoteRepos) + val repoResolver = createRepoResolvers(remoteRepos, ivySettings) ivySettings.addResolver(repoResolver) ivySettings.setDefaultResolver(repoResolver.getName) @@ -911,7 +928,8 @@ private[deploy] object SparkSubmitUtils { } // retrieve all resolved dependencies ivy.retrieve(rr.getModuleDescriptor.getModuleRevisionId, - packagesDirectory.getAbsolutePath + File.separator + "[artifact](-[classifier]).[ext]", + packagesDirectory.getAbsolutePath + File.separator + + "[organization]_[artifact]-[revision].[ext]", retrieveOptions.setConfs(Array(ivyConfName))) System.setOut(sysOut) resolveDependencyPaths(rr.getArtifacts.toArray, packagesDirectory) diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala index 8bcca926097a1..1b2b699cb11e6 100644 --- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala @@ -19,6 +19,8 @@ package org.apache.spark.deploy import java.io.{PrintStream, OutputStream, File} +import org.apache.ivy.core.settings.IvySettings + import scala.collection.mutable.ArrayBuffer import org.scalatest.{BeforeAndAfterAll, FunSuite} @@ -56,24 +58,23 @@ class SparkSubmitUtilsSuite extends FunSuite with BeforeAndAfterAll { } test("create repo resolvers") { - val resolver1 = SparkSubmitUtils.createRepoResolvers(None) + val settings = new IvySettings + val res1 = SparkSubmitUtils.createRepoResolvers(None, settings) // should have central and spark-packages by default - assert(resolver1.getResolvers.size() === 2) - assert(resolver1.getResolvers.get(0).asInstanceOf[IBiblioResolver].getName === "central") - assert(resolver1.getResolvers.get(1).asInstanceOf[IBiblioResolver].getName === "spark-packages") + assert(res1.getResolvers.size() === 4) + assert(res1.getResolvers.get(0).asInstanceOf[IBiblioResolver].getName === "local-m2-cache") + assert(res1.getResolvers.get(1).asInstanceOf[IBiblioResolver].getName === "local-ivy-cache") + assert(res1.getResolvers.get(2).asInstanceOf[IBiblioResolver].getName === "central") + assert(res1.getResolvers.get(3).asInstanceOf[IBiblioResolver].getName === "spark-packages") val repos = "a/1,b/2,c/3" - val resolver2 = SparkSubmitUtils.createRepoResolvers(Option(repos)) - assert(resolver2.getResolvers.size() === 5) + val resolver2 = SparkSubmitUtils.createRepoResolvers(Option(repos), settings) + assert(resolver2.getResolvers.size() === 7) val expected = repos.split(",").map(r => s"$r/") resolver2.getResolvers.toArray.zipWithIndex.foreach { case (resolver: IBiblioResolver, i) => - if (i == 0) { - assert(resolver.getName === "central") - } else if (i == 1) { - assert(resolver.getName === "spark-packages") - } else { - assert(resolver.getName === s"repo-${i - 1}") - assert(resolver.getRoot === expected(i - 2)) + if (i > 3) { + assert(resolver.getName === s"repo-${i - 3}") + assert(resolver.getRoot === expected(i - 4)) } } } From 8dee2746b5857bb4c32b96f38840dd1b63574ab2 Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Tue, 28 Apr 2015 23:38:59 -0700 Subject: [PATCH 462/591] MAINTENANCE: Automated closing of pull requests. This commit exists to close the following pull requests on Github: Closes #3205 (close requested by 'srowen') Closes #5478 (close requested by 'andrewor14') Closes #4910 (close requested by 'srowen') Closes #5080 (close requested by 'marmbrus') Closes #537 (close requested by 'srowen') Closes #5691 (close requested by 'srowen') Closes #5469 (close requested by 'marmbrus') From fe917f5ec9be8c8424416f7b5423ddb4318e03a0 Mon Sep 17 00:00:00 2001 From: Burak Yavuz Date: Wed, 29 Apr 2015 00:09:24 -0700 Subject: [PATCH 463/591] [SPARK-7188] added python support for math DataFrame functions Adds support for the math functions for DataFrames in PySpark. rxin I love Davies. Author: Burak Yavuz Closes #5750 from brkyvz/python-math-udfs and squashes the following commits: 7c4f563 [Burak Yavuz] removed is_math 3c4adde [Burak Yavuz] cleanup imports d5dca3f [Burak Yavuz] moved math functions to mathfunctions 25e6534 [Burak Yavuz] addressed comments v2.0 d3f7e0f [Burak Yavuz] addressed comments and added tests 7b7d7c4 [Burak Yavuz] remove tests for removed methods 33c2c15 [Burak Yavuz] fixed python style 3ee0c05 [Burak Yavuz] added python functions --- python/pyspark/sql/functions.py | 2 +- python/pyspark/sql/mathfunctions.py | 101 +++++ python/pyspark/sql/tests.py | 29 ++ .../expressions/mathfuncs/binary.scala | 12 +- .../expressions/mathfuncs/unary.scala | 122 +----- .../ExpressionEvaluationSuite.scala | 12 - .../org/apache/spark/sql/mathfunctions.scala | 409 +++++------------- .../spark/sql/MathExpressionsSuite.scala | 12 - 8 files changed, 276 insertions(+), 423 deletions(-) create mode 100644 python/pyspark/sql/mathfunctions.py diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py index 7b86655d9c82f..555c2fa5e7071 100644 --- a/python/pyspark/sql/functions.py +++ b/python/pyspark/sql/functions.py @@ -54,7 +54,7 @@ def _(col): 'upper': 'Converts a string expression to upper case.', 'lower': 'Converts a string expression to upper case.', 'sqrt': 'Computes the square root of the specified float value.', - 'abs': 'Computes the absolutle value.', + 'abs': 'Computes the absolute value.', 'max': 'Aggregate function: returns the maximum value of the expression in a group.', 'min': 'Aggregate function: returns the minimum value of the expression in a group.', diff --git a/python/pyspark/sql/mathfunctions.py b/python/pyspark/sql/mathfunctions.py new file mode 100644 index 0000000000000..7dbcab8694293 --- /dev/null +++ b/python/pyspark/sql/mathfunctions.py @@ -0,0 +1,101 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +""" +A collection of builtin math functions +""" + +from pyspark import SparkContext +from pyspark.sql.dataframe import Column + +__all__ = [] + + +def _create_unary_mathfunction(name, doc=""): + """ Create a unary mathfunction by name""" + def _(col): + sc = SparkContext._active_spark_context + jc = getattr(sc._jvm.mathfunctions, name)(col._jc if isinstance(col, Column) else col) + return Column(jc) + _.__name__ = name + _.__doc__ = doc + return _ + + +def _create_binary_mathfunction(name, doc=""): + """ Create a binary mathfunction by name""" + def _(col1, col2): + sc = SparkContext._active_spark_context + # users might write ints for simplicity. This would throw an error on the JVM side. + if type(col1) is int: + col1 = col1 * 1.0 + if type(col2) is int: + col2 = col2 * 1.0 + jc = getattr(sc._jvm.mathfunctions, name)(col1._jc if isinstance(col1, Column) else col1, + col2._jc if isinstance(col2, Column) else col2) + return Column(jc) + _.__name__ = name + _.__doc__ = doc + return _ + + +# math functions are found under another object therefore, they need to be handled separately +_mathfunctions = { + 'acos': 'Computes the cosine inverse of the given value; the returned angle is in the range' + + '0.0 through pi.', + 'asin': 'Computes the sine inverse of the given value; the returned angle is in the range' + + '-pi/2 through pi/2.', + 'atan': 'Computes the tangent inverse of the given value.', + 'cbrt': 'Computes the cube-root of the given value.', + 'ceil': 'Computes the ceiling of the given value.', + 'cos': 'Computes the cosine of the given value.', + 'cosh': 'Computes the hyperbolic cosine of the given value.', + 'exp': 'Computes the exponential of the given value.', + 'expm1': 'Computes the exponential of the given value minus one.', + 'floor': 'Computes the floor of the given value.', + 'log': 'Computes the natural logarithm of the given value.', + 'log10': 'Computes the logarithm of the given value in Base 10.', + 'log1p': 'Computes the natural logarithm of the given value plus one.', + 'rint': 'Returns the double value that is closest in value to the argument and' + + ' is equal to a mathematical integer.', + 'signum': 'Computes the signum of the given value.', + 'sin': 'Computes the sine of the given value.', + 'sinh': 'Computes the hyperbolic sine of the given value.', + 'tan': 'Computes the tangent of the given value.', + 'tanh': 'Computes the hyperbolic tangent of the given value.', + 'toDeg': 'Converts an angle measured in radians to an approximately equivalent angle ' + + 'measured in degrees.', + 'toRad': 'Converts an angle measured in degrees to an approximately equivalent angle ' + + 'measured in radians.' +} + +# math functions that take two arguments as input +_binary_mathfunctions = { + 'atan2': 'Returns the angle theta from the conversion of rectangular coordinates (x, y) to' + + 'polar coordinates (r, theta).', + 'hypot': 'Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.', + 'pow': 'Returns the value of the first argument raised to the power of the second argument.' +} + +for _name, _doc in _mathfunctions.items(): + globals()[_name] = _create_unary_mathfunction(_name, _doc) +for _name, _doc in _binary_mathfunctions.items(): + globals()[_name] = _create_binary_mathfunction(_name, _doc) +del _name, _doc +__all__ += _mathfunctions.keys() +__all__ += _binary_mathfunctions.keys() +__all__.sort() diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index fe43c374f1cb1..2ffd18ebd7c89 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -387,6 +387,35 @@ def test_aggregator(self): self.assertTrue(95 < g.agg(functions.approxCountDistinct(df.key)).first()[0]) self.assertEqual(100, g.agg(functions.countDistinct(df.value)).first()[0]) + def test_math_functions(self): + df = self.sc.parallelize([Row(a=i, b=2 * i) for i in range(10)]).toDF() + from pyspark.sql import mathfunctions as functions + import math + + def get_values(l): + return [j[0] for j in l] + + def assert_close(a, b): + c = get_values(b) + diff = [abs(v - c[k]) < 1e-6 for k, v in enumerate(a)] + return sum(diff) == len(a) + assert_close([math.cos(i) for i in range(10)], + df.select(functions.cos(df.a)).collect()) + assert_close([math.cos(i) for i in range(10)], + df.select(functions.cos("a")).collect()) + assert_close([math.sin(i) for i in range(10)], + df.select(functions.sin(df.a)).collect()) + assert_close([math.sin(i) for i in range(10)], + df.select(functions.sin(df['a'])).collect()) + assert_close([math.pow(i, 2 * i) for i in range(10)], + df.select(functions.pow(df.a, df.b)).collect()) + assert_close([math.pow(i, 2) for i in range(10)], + df.select(functions.pow(df.a, 2)).collect()) + assert_close([math.pow(i, 2) for i in range(10)], + df.select(functions.pow(df.a, 2.0)).collect()) + assert_close([math.hypot(i, 2 * i) for i in range(10)], + df.select(functions.hypot(df.a, df.b)).collect()) + def test_save_and_load(self): df = self.df tmpPath = tempfile.mkdtemp() diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/binary.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/binary.scala index 5b4d912a64f71..fcc06d3aa1036 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/binary.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/binary.scala @@ -65,12 +65,6 @@ abstract class BinaryMathExpression(f: (Double, Double) => Double, name: String) } } -case class Pow(left: Expression, right: Expression) extends BinaryMathExpression(math.pow, "POWER") - -case class Hypot( - left: Expression, - right: Expression) extends BinaryMathExpression(math.hypot, "HYPOT") - case class Atan2( left: Expression, right: Expression) extends BinaryMathExpression(math.atan2, "ATAN2") { @@ -91,3 +85,9 @@ case class Atan2( } } } + +case class Hypot( + left: Expression, + right: Expression) extends BinaryMathExpression(math.hypot, "HYPOT") + +case class Pow(left: Expression, right: Expression) extends BinaryMathExpression(math.pow, "POWER") diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/unary.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/unary.scala index 96cb77d487529..dc68469e060cb 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/unary.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/unary.scala @@ -25,27 +25,16 @@ import org.apache.spark.sql.types._ * input format, therefore these functions extend `ExpectsInputTypes`. * @param name The short name of the function */ -abstract class MathematicalExpression(name: String) +abstract class MathematicalExpression(f: Double => Double, name: String) extends UnaryExpression with Serializable with ExpectsInputTypes { self: Product => type EvaluatedType = Any + override def expectedChildTypes: Seq[DataType] = Seq(DoubleType) override def dataType: DataType = DoubleType override def foldable: Boolean = child.foldable override def nullable: Boolean = true override def toString: String = s"$name($child)" -} - -/** - * A unary expression specifically for math functions that take a `Double` as input and return - * a `Double`. - * @param f The math function. - * @param name The short name of the function - */ -abstract class MathematicalExpressionForDouble(f: Double => Double, name: String) - extends MathematicalExpression(name) { self: Product => - - override def expectedChildTypes: Seq[DataType] = Seq(DoubleType) override def eval(input: Row): Any = { val evalE = child.eval(input) @@ -58,111 +47,46 @@ abstract class MathematicalExpressionForDouble(f: Double => Double, name: String } } -/** - * A unary expression specifically for math functions that take an `Int` as input and return - * an `Int`. - * @param f The math function. - * @param name The short name of the function - */ -abstract class MathematicalExpressionForInt(f: Int => Int, name: String) - extends MathematicalExpression(name) { self: Product => +case class Acos(child: Expression) extends MathematicalExpression(math.acos, "ACOS") - override def dataType: DataType = IntegerType - override def expectedChildTypes: Seq[DataType] = Seq(IntegerType) +case class Asin(child: Expression) extends MathematicalExpression(math.asin, "ASIN") - override def eval(input: Row): Any = { - val evalE = child.eval(input) - if (evalE == null) null else f(evalE.asInstanceOf[Int]) - } -} +case class Atan(child: Expression) extends MathematicalExpression(math.atan, "ATAN") -/** - * A unary expression specifically for math functions that take a `Float` as input and return - * a `Float`. - * @param f The math function. - * @param name The short name of the function - */ -abstract class MathematicalExpressionForFloat(f: Float => Float, name: String) - extends MathematicalExpression(name) { self: Product => +case class Cbrt(child: Expression) extends MathematicalExpression(math.cbrt, "CBRT") - override def dataType: DataType = FloatType - override def expectedChildTypes: Seq[DataType] = Seq(FloatType) +case class Ceil(child: Expression) extends MathematicalExpression(math.ceil, "CEIL") - override def eval(input: Row): Any = { - val evalE = child.eval(input) - if (evalE == null) { - null - } else { - val result = f(evalE.asInstanceOf[Float]) - if (result.isNaN) null else result - } - } -} - -/** - * A unary expression specifically for math functions that take a `Long` as input and return - * a `Long`. - * @param f The math function. - * @param name The short name of the function - */ -abstract class MathematicalExpressionForLong(f: Long => Long, name: String) - extends MathematicalExpression(name) { self: Product => - - override def dataType: DataType = LongType - override def expectedChildTypes: Seq[DataType] = Seq(LongType) - - override def eval(input: Row): Any = { - val evalE = child.eval(input) - if (evalE == null) null else f(evalE.asInstanceOf[Long]) - } -} - -case class Sin(child: Expression) extends MathematicalExpressionForDouble(math.sin, "SIN") - -case class Asin(child: Expression) extends MathematicalExpressionForDouble(math.asin, "ASIN") - -case class Sinh(child: Expression) extends MathematicalExpressionForDouble(math.sinh, "SINH") - -case class Cos(child: Expression) extends MathematicalExpressionForDouble(math.cos, "COS") +case class Cos(child: Expression) extends MathematicalExpression(math.cos, "COS") -case class Acos(child: Expression) extends MathematicalExpressionForDouble(math.acos, "ACOS") +case class Cosh(child: Expression) extends MathematicalExpression(math.cosh, "COSH") -case class Cosh(child: Expression) extends MathematicalExpressionForDouble(math.cosh, "COSH") +case class Exp(child: Expression) extends MathematicalExpression(math.exp, "EXP") -case class Tan(child: Expression) extends MathematicalExpressionForDouble(math.tan, "TAN") +case class Expm1(child: Expression) extends MathematicalExpression(math.expm1, "EXPM1") -case class Atan(child: Expression) extends MathematicalExpressionForDouble(math.atan, "ATAN") +case class Floor(child: Expression) extends MathematicalExpression(math.floor, "FLOOR") -case class Tanh(child: Expression) extends MathematicalExpressionForDouble(math.tanh, "TANH") +case class Log(child: Expression) extends MathematicalExpression(math.log, "LOG") -case class Ceil(child: Expression) extends MathematicalExpressionForDouble(math.ceil, "CEIL") +case class Log10(child: Expression) extends MathematicalExpression(math.log10, "LOG10") -case class Floor(child: Expression) extends MathematicalExpressionForDouble(math.floor, "FLOOR") +case class Log1p(child: Expression) extends MathematicalExpression(math.log1p, "LOG1P") -case class Rint(child: Expression) extends MathematicalExpressionForDouble(math.rint, "ROUND") +case class Rint(child: Expression) extends MathematicalExpression(math.rint, "ROUND") -case class Cbrt(child: Expression) extends MathematicalExpressionForDouble(math.cbrt, "CBRT") +case class Signum(child: Expression) extends MathematicalExpression(math.signum, "SIGNUM") -case class Signum(child: Expression) extends MathematicalExpressionForDouble(math.signum, "SIGNUM") +case class Sin(child: Expression) extends MathematicalExpression(math.sin, "SIN") -case class ISignum(child: Expression) extends MathematicalExpressionForInt(math.signum, "ISIGNUM") +case class Sinh(child: Expression) extends MathematicalExpression(math.sinh, "SINH") -case class FSignum(child: Expression) extends MathematicalExpressionForFloat(math.signum, "FSIGNUM") +case class Tan(child: Expression) extends MathematicalExpression(math.tan, "TAN") -case class LSignum(child: Expression) extends MathematicalExpressionForLong(math.signum, "LSIGNUM") +case class Tanh(child: Expression) extends MathematicalExpression(math.tanh, "TANH") case class ToDegrees(child: Expression) - extends MathematicalExpressionForDouble(math.toDegrees, "DEGREES") + extends MathematicalExpression(math.toDegrees, "DEGREES") case class ToRadians(child: Expression) - extends MathematicalExpressionForDouble(math.toRadians, "RADIANS") - -case class Log(child: Expression) extends MathematicalExpressionForDouble(math.log, "LOG") - -case class Log10(child: Expression) extends MathematicalExpressionForDouble(math.log10, "LOG10") - -case class Log1p(child: Expression) extends MathematicalExpressionForDouble(math.log1p, "LOG1P") - -case class Exp(child: Expression) extends MathematicalExpressionForDouble(math.exp, "EXP") - -case class Expm1(child: Expression) extends MathematicalExpressionForDouble(math.expm1, "EXPM1") + extends MathematicalExpression(math.toRadians, "RADIANS") diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala index 5390ce43c6639..fa71001c9336e 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala @@ -1253,18 +1253,6 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { unaryMathFunctionEvaluation[Double](Signum, math.signum) } - test("isignum") { - unaryMathFunctionEvaluation[Int](ISignum, math.signum, (-5 to 5)) - } - - test("fsignum") { - unaryMathFunctionEvaluation[Float](FSignum, math.signum, (-5 to 5).map(_.toFloat)) - } - - test("lsignum") { - unaryMathFunctionEvaluation[Long](LSignum, math.signum, (5 to 5).map(_.toLong)) - } - test("log") { unaryMathFunctionEvaluation(Log, math.log, (0 to 20).map(_ * 0.1)) unaryMathFunctionEvaluation(Log, math.log, (-5 to -1).map(_ * 0.1), true) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/mathfunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/mathfunctions.scala index 84f62bf47f955..d901542b7eaf3 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/mathfunctions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/mathfunctions.scala @@ -29,9 +29,6 @@ import org.apache.spark.sql.functions.lit * Mathematical Functions available for [[DataFrame]]. * * @groupname double_funcs Functions that require DoubleType as an input - * @groupname int_funcs Functions that require IntegerType as an input - * @groupname float_funcs Functions that require FloatType as an input - * @groupname long_funcs Functions that require LongType as an input */ @Experimental // scalastyle:off @@ -41,522 +38,348 @@ object mathfunctions { private[this] implicit def toColumn(expr: Expression): Column = Column(expr) /** - * Computes the sine of the given value. - * - * @group double_funcs + * Computes the cosine inverse of the given value; the returned angle is in the range + * 0.0 through pi. */ - def sin(e: Column): Column = Sin(e.expr) + def acos(e: Column): Column = Acos(e.expr) /** - * Computes the sine of the given column. - * - * @group double_funcs + * Computes the cosine inverse of the given column; the returned angle is in the range + * 0.0 through pi. */ - def sin(columnName: String): Column = sin(Column(columnName)) + def acos(columnName: String): Column = acos(Column(columnName)) /** * Computes the sine inverse of the given value; the returned angle is in the range * -pi/2 through pi/2. - * - * @group double_funcs */ def asin(e: Column): Column = Asin(e.expr) /** * Computes the sine inverse of the given column; the returned angle is in the range * -pi/2 through pi/2. - * - * @group double_funcs */ def asin(columnName: String): Column = asin(Column(columnName)) /** - * Computes the hyperbolic sine of the given value. - * - * @group double_funcs - */ - def sinh(e: Column): Column = Sinh(e.expr) - - /** - * Computes the hyperbolic sine of the given column. - * - * @group double_funcs + * Computes the tangent inverse of the given value. */ - def sinh(columnName: String): Column = sinh(Column(columnName)) + def atan(e: Column): Column = Atan(e.expr) /** - * Computes the cosine of the given value. - * - * @group double_funcs + * Computes the tangent inverse of the given column. */ - def cos(e: Column): Column = Cos(e.expr) + def atan(columnName: String): Column = atan(Column(columnName)) /** - * Computes the cosine of the given column. - * - * @group double_funcs + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). */ - def cos(columnName: String): Column = cos(Column(columnName)) + def atan2(l: Column, r: Column): Column = Atan2(l.expr, r.expr) /** - * Computes the cosine inverse of the given value; the returned angle is in the range - * 0.0 through pi. - * - * @group double_funcs + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). */ - def acos(e: Column): Column = Acos(e.expr) + def atan2(l: Column, rightName: String): Column = atan2(l, Column(rightName)) /** - * Computes the cosine inverse of the given column; the returned angle is in the range - * 0.0 through pi. - * - * @group double_funcs + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). */ - def acos(columnName: String): Column = acos(Column(columnName)) + def atan2(leftName: String, r: Column): Column = atan2(Column(leftName), r) /** - * Computes the hyperbolic cosine of the given value. - * - * @group double_funcs + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). */ - def cosh(e: Column): Column = Cosh(e.expr) + def atan2(leftName: String, rightName: String): Column = + atan2(Column(leftName), Column(rightName)) /** - * Computes the hyperbolic cosine of the given column. - * - * @group double_funcs - */ - def cosh(columnName: String): Column = cosh(Column(columnName)) - - /** - * Computes the tangent of the given value. - * - * @group double_funcs + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). */ - def tan(e: Column): Column = Tan(e.expr) + def atan2(l: Column, r: Double): Column = atan2(l, lit(r).expr) /** - * Computes the tangent of the given column. - * - * @group double_funcs + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta).= */ - def tan(columnName: String): Column = tan(Column(columnName)) + def atan2(leftName: String, r: Double): Column = atan2(Column(leftName), r) /** - * Computes the tangent inverse of the given value. - * - * @group double_funcs + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). */ - def atan(e: Column): Column = Atan(e.expr) + def atan2(l: Double, r: Column): Column = atan2(lit(l).expr, r) /** - * Computes the tangent inverse of the given column. - * - * @group double_funcs + * Returns the angle theta from the conversion of rectangular coordinates (x, y) to + * polar coordinates (r, theta). */ - def atan(columnName: String): Column = atan(Column(columnName)) + def atan2(l: Double, rightName: String): Column = atan2(l, Column(rightName)) /** - * Computes the hyperbolic tangent of the given value. - * - * @group double_funcs + * Computes the cube-root of the given value. */ - def tanh(e: Column): Column = Tanh(e.expr) + def cbrt(e: Column): Column = Cbrt(e.expr) /** - * Computes the hyperbolic tangent of the given column. - * - * @group double_funcs + * Computes the cube-root of the given column. */ - def tanh(columnName: String): Column = tanh(Column(columnName)) + def cbrt(columnName: String): Column = cbrt(Column(columnName)) /** - * Converts an angle measured in radians to an approximately equivalent angle measured in degrees. - * - * @group double_funcs + * Computes the ceiling of the given value. */ - def toDeg(e: Column): Column = ToDegrees(e.expr) + def ceil(e: Column): Column = Ceil(e.expr) /** - * Converts an angle measured in radians to an approximately equivalent angle measured in degrees. - * - * @group double_funcs + * Computes the ceiling of the given column. */ - def toDeg(columnName: String): Column = toDeg(Column(columnName)) + def ceil(columnName: String): Column = ceil(Column(columnName)) /** - * Converts an angle measured in degrees to an approximately equivalent angle measured in radians. - * - * @group double_funcs + * Computes the cosine of the given value. */ - def toRad(e: Column): Column = ToRadians(e.expr) + def cos(e: Column): Column = Cos(e.expr) /** - * Converts an angle measured in degrees to an approximately equivalent angle measured in radians. - * - * @group double_funcs + * Computes the cosine of the given column. */ - def toRad(columnName: String): Column = toRad(Column(columnName)) + def cos(columnName: String): Column = cos(Column(columnName)) /** - * Computes the ceiling of the given value. - * - * @group double_funcs + * Computes the hyperbolic cosine of the given value. */ - def ceil(e: Column): Column = Ceil(e.expr) + def cosh(e: Column): Column = Cosh(e.expr) /** - * Computes the ceiling of the given column. - * - * @group double_funcs + * Computes the hyperbolic cosine of the given column. */ - def ceil(columnName: String): Column = ceil(Column(columnName)) + def cosh(columnName: String): Column = cosh(Column(columnName)) /** - * Computes the floor of the given value. - * - * @group double_funcs + * Computes the exponential of the given value. */ - def floor(e: Column): Column = Floor(e.expr) + def exp(e: Column): Column = Exp(e.expr) /** - * Computes the floor of the given column. - * - * @group double_funcs + * Computes the exponential of the given column. */ - def floor(columnName: String): Column = floor(Column(columnName)) + def exp(columnName: String): Column = exp(Column(columnName)) /** - * Returns the double value that is closest in value to the argument and - * is equal to a mathematical integer. - * - * @group double_funcs + * Computes the exponential of the given value minus one. */ - def rint(e: Column): Column = Rint(e.expr) + def expm1(e: Column): Column = Expm1(e.expr) /** - * Returns the double value that is closest in value to the argument and - * is equal to a mathematical integer. - * - * @group double_funcs + * Computes the exponential of the given column. */ - def rint(columnName: String): Column = rint(Column(columnName)) + def expm1(columnName: String): Column = expm1(Column(columnName)) /** - * Computes the cube-root of the given value. - * - * @group double_funcs + * Computes the floor of the given value. */ - def cbrt(e: Column): Column = Cbrt(e.expr) + def floor(e: Column): Column = Floor(e.expr) /** - * Computes the cube-root of the given column. - * - * @group double_funcs + * Computes the floor of the given column. */ - def cbrt(columnName: String): Column = cbrt(Column(columnName)) + def floor(columnName: String): Column = floor(Column(columnName)) /** - * Computes the signum of the given value. - * - * @group double_funcs + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. */ - def signum(e: Column): Column = Signum(e.expr) + def hypot(l: Column, r: Column): Column = Hypot(l.expr, r.expr) /** - * Computes the signum of the given column. - * - * @group double_funcs + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. */ - def signum(columnName: String): Column = signum(Column(columnName)) + def hypot(l: Column, rightName: String): Column = hypot(l, Column(rightName)) /** - * Computes the signum of the given value. For IntegerType. - * - * @group int_funcs + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. */ - def isignum(e: Column): Column = ISignum(e.expr) + def hypot(leftName: String, r: Column): Column = hypot(Column(leftName), r) /** - * Computes the signum of the given column. For IntegerType. - * - * @group int_funcs + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. */ - def isignum(columnName: String): Column = isignum(Column(columnName)) + def hypot(leftName: String, rightName: String): Column = + hypot(Column(leftName), Column(rightName)) /** - * Computes the signum of the given value. For FloatType. - * - * @group float_funcs + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. */ - def fsignum(e: Column): Column = FSignum(e.expr) + def hypot(l: Column, r: Double): Column = hypot(l, lit(r).expr) /** - * Computes the signum of the given column. For FloatType. - * - * @group float_funcs + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. */ - def fsignum(columnName: String): Column = fsignum(Column(columnName)) + def hypot(leftName: String, r: Double): Column = hypot(Column(leftName), r) /** - * Computes the signum of the given value. For LongType. - * - * @group long_funcs + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. */ - def lsignum(e: Column): Column = LSignum(e.expr) + def hypot(l: Double, r: Column): Column = hypot(lit(l).expr, r) /** - * Computes the signum of the given column. For FloatType. - * - * @group long_funcs + * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. */ - def lsignum(columnName: String): Column = lsignum(Column(columnName)) + def hypot(l: Double, rightName: String): Column = hypot(l, Column(rightName)) /** * Computes the natural logarithm of the given value. - * - * @group double_funcs */ def log(e: Column): Column = Log(e.expr) /** * Computes the natural logarithm of the given column. - * - * @group double_funcs */ def log(columnName: String): Column = log(Column(columnName)) /** * Computes the logarithm of the given value in Base 10. - * - * @group double_funcs */ def log10(e: Column): Column = Log10(e.expr) /** * Computes the logarithm of the given value in Base 10. - * - * @group double_funcs */ def log10(columnName: String): Column = log10(Column(columnName)) /** * Computes the natural logarithm of the given value plus one. - * - * @group double_funcs */ def log1p(e: Column): Column = Log1p(e.expr) /** * Computes the natural logarithm of the given column plus one. - * - * @group double_funcs */ def log1p(columnName: String): Column = log1p(Column(columnName)) - /** - * Computes the exponential of the given value. - * - * @group double_funcs - */ - def exp(e: Column): Column = Exp(e.expr) - - /** - * Computes the exponential of the given column. - * - * @group double_funcs - */ - def exp(columnName: String): Column = exp(Column(columnName)) - - /** - * Computes the exponential of the given value minus one. - * - * @group double_funcs - */ - def expm1(e: Column): Column = Expm1(e.expr) - - /** - * Computes the exponential of the given column. - * - * @group double_funcs - */ - def expm1(columnName: String): Column = expm1(Column(columnName)) - /** * Returns the value of the first argument raised to the power of the second argument. - * - * @group double_funcs */ def pow(l: Column, r: Column): Column = Pow(l.expr, r.expr) /** * Returns the value of the first argument raised to the power of the second argument. - * - * @group double_funcs */ def pow(l: Column, rightName: String): Column = pow(l, Column(rightName)) /** * Returns the value of the first argument raised to the power of the second argument. - * - * @group double_funcs */ def pow(leftName: String, r: Column): Column = pow(Column(leftName), r) /** * Returns the value of the first argument raised to the power of the second argument. - * - * @group double_funcs */ def pow(leftName: String, rightName: String): Column = pow(Column(leftName), Column(rightName)) /** * Returns the value of the first argument raised to the power of the second argument. - * - * @group double_funcs */ def pow(l: Column, r: Double): Column = pow(l, lit(r).expr) /** * Returns the value of the first argument raised to the power of the second argument. - * - * @group double_funcs */ def pow(leftName: String, r: Double): Column = pow(Column(leftName), r) /** * Returns the value of the first argument raised to the power of the second argument. - * - * @group double_funcs */ def pow(l: Double, r: Column): Column = pow(lit(l).expr, r) /** * Returns the value of the first argument raised to the power of the second argument. - * - * @group double_funcs */ def pow(l: Double, rightName: String): Column = pow(l, Column(rightName)) /** - * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. - * - * @group double_funcs + * Returns the double value that is closest in value to the argument and + * is equal to a mathematical integer. */ - def hypot(l: Column, r: Column): Column = Hypot(l.expr, r.expr) + def rint(e: Column): Column = Rint(e.expr) /** - * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. - * - * @group double_funcs + * Returns the double value that is closest in value to the argument and + * is equal to a mathematical integer. */ - def hypot(l: Column, rightName: String): Column = hypot(l, Column(rightName)) + def rint(columnName: String): Column = rint(Column(columnName)) /** - * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. - * - * @group double_funcs + * Computes the signum of the given value. */ - def hypot(leftName: String, r: Column): Column = hypot(Column(leftName), r) + def signum(e: Column): Column = Signum(e.expr) /** - * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. - * - * @group double_funcs + * Computes the signum of the given column. */ - def hypot(leftName: String, rightName: String): Column = - hypot(Column(leftName), Column(rightName)) + def signum(columnName: String): Column = signum(Column(columnName)) /** - * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. - * - * @group double_funcs + * Computes the sine of the given value. */ - def hypot(l: Column, r: Double): Column = hypot(l, lit(r).expr) + def sin(e: Column): Column = Sin(e.expr) /** - * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. - * - * @group double_funcs + * Computes the sine of the given column. */ - def hypot(leftName: String, r: Double): Column = hypot(Column(leftName), r) + def sin(columnName: String): Column = sin(Column(columnName)) /** - * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. - * - * @group double_funcs + * Computes the hyperbolic sine of the given value. */ - def hypot(l: Double, r: Column): Column = hypot(lit(l).expr, r) + def sinh(e: Column): Column = Sinh(e.expr) /** - * Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow. - * - * @group double_funcs + * Computes the hyperbolic sine of the given column. */ - def hypot(l: Double, rightName: String): Column = hypot(l, Column(rightName)) - + def sinh(columnName: String): Column = sinh(Column(columnName)) + /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). - * - * @group double_funcs + * Computes the tangent of the given value. */ - def atan2(l: Column, r: Column): Column = Atan2(l.expr, r.expr) + def tan(e: Column): Column = Tan(e.expr) /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). - * - * @group double_funcs + * Computes the tangent of the given column. */ - def atan2(l: Column, rightName: String): Column = atan2(l, Column(rightName)) - + def tan(columnName: String): Column = tan(Column(columnName)) + /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). - * - * @group double_funcs + * Computes the hyperbolic tangent of the given value. */ - def atan2(leftName: String, r: Column): Column = atan2(Column(leftName), r) + def tanh(e: Column): Column = Tanh(e.expr) /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). - * - * @group double_funcs + * Computes the hyperbolic tangent of the given column. */ - def atan2(leftName: String, rightName: String): Column = - atan2(Column(leftName), Column(rightName)) + def tanh(columnName: String): Column = tanh(Column(columnName)) /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). - * - * @group double_funcs + * Converts an angle measured in radians to an approximately equivalent angle measured in degrees. */ - def atan2(l: Column, r: Double): Column = atan2(l, lit(r).expr) + def toDeg(e: Column): Column = ToDegrees(e.expr) /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). - * - * @group double_funcs + * Converts an angle measured in radians to an approximately equivalent angle measured in degrees. */ - def atan2(leftName: String, r: Double): Column = atan2(Column(leftName), r) + def toDeg(columnName: String): Column = toDeg(Column(columnName)) /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). - * - * @group double_funcs + * Converts an angle measured in degrees to an approximately equivalent angle measured in radians. */ - def atan2(l: Double, r: Column): Column = atan2(lit(l).expr, r) + def toRad(e: Column): Column = ToRadians(e.expr) /** - * Returns the angle theta from the conversion of rectangular coordinates (x, y) to - * polar coordinates (r, theta). - * - * @group double_funcs + * Converts an angle measured in degrees to an approximately equivalent angle measured in radians. */ - def atan2(l: Double, rightName: String): Column = atan2(l, Column(rightName)) + def toRad(columnName: String): Column = toRad(Column(columnName)) } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala index 561553cc925cb..9e19bb7482e9b 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala @@ -194,18 +194,6 @@ class MathExpressionsSuite extends QueryTest { testOneToOneMathFunction[Double](signum, math.signum) } - test("isignum") { - testOneToOneMathFunction[Int](isignum, math.signum) - } - - test("fsignum") { - testOneToOneMathFunction[Float](fsignum, math.signum) - } - - test("lsignum") { - testOneToOneMathFunction[Long](lsignum, math.signum) - } - test("pow") { testTwoToOneMathFunction(pow, pow, math.pow) } From 1fd6ed9a56ac4671f4a3d25a42823ba3bf01f60f Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Wed, 29 Apr 2015 00:35:08 -0700 Subject: [PATCH 464/591] [SPARK-7204] [SQL] Fix callSite for Dataframe and SQL operations This patch adds SQL to the set of excluded libraries when generating a callSite. This makes the callSite mechanism work properly for the data frame API. I also added a small improvement for JDBC queries where we just use the string "Spark JDBC Server Query" instead of trying to give a callsite that doesn't make any sense to the user. Before (DF): ![screen shot 2015-04-28 at 1 29 26 pm](https://cloud.githubusercontent.com/assets/320616/7380170/ef63bfb0-edae-11e4-989c-f88a5ba6bbee.png) After (DF): ![screen shot 2015-04-28 at 1 34 58 pm](https://cloud.githubusercontent.com/assets/320616/7380181/fa7f6d90-edae-11e4-9559-26f163ed63b8.png) After (JDBC): ![screen shot 2015-04-28 at 2 00 10 pm](https://cloud.githubusercontent.com/assets/320616/7380185/02f5b2a4-edaf-11e4-8e5b-99bdc3df66dd.png) Author: Patrick Wendell Closes #5757 from pwendell/dataframes and squashes the following commits: 0d931a4 [Patrick Wendell] Attempting to fix PySpark tests 85bf740 [Patrick Wendell] [SPARK-7204] Fix callsite for dataframe operations. --- .../scala/org/apache/spark/util/Utils.scala | 28 +++++++++++++------ python/pyspark/sql/dataframe.py | 3 +- 2 files changed, 21 insertions(+), 10 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index 4c028c06a5138..4b5a5df5ef7b7 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -1299,16 +1299,18 @@ private[spark] object Utils extends Logging { } /** Default filtering function for finding call sites using `getCallSite`. */ - private def coreExclusionFunction(className: String): Boolean = { - // A regular expression to match classes of the "core" Spark API that we want to skip when - // finding the call site of a method. + private def sparkInternalExclusionFunction(className: String): Boolean = { + // A regular expression to match classes of the internal Spark API's + // that we want to skip when finding the call site of a method. val SPARK_CORE_CLASS_REGEX = """^org\.apache\.spark(\.api\.java)?(\.util)?(\.rdd)?(\.broadcast)?\.[A-Z]""".r + val SPARK_SQL_CLASS_REGEX = """^org\.apache\.spark\.sql.*""".r val SCALA_CORE_CLASS_PREFIX = "scala" - val isSparkCoreClass = SPARK_CORE_CLASS_REGEX.findFirstIn(className).isDefined + val isSparkClass = SPARK_CORE_CLASS_REGEX.findFirstIn(className).isDefined || + SPARK_SQL_CLASS_REGEX.findFirstIn(className).isDefined val isScalaClass = className.startsWith(SCALA_CORE_CLASS_PREFIX) // If the class is a Spark internal class or a Scala class, then exclude. - isSparkCoreClass || isScalaClass + isSparkClass || isScalaClass } /** @@ -1318,7 +1320,7 @@ private[spark] object Utils extends Logging { * * @param skipClass Function that is used to exclude non-user-code classes. */ - def getCallSite(skipClass: String => Boolean = coreExclusionFunction): CallSite = { + def getCallSite(skipClass: String => Boolean = sparkInternalExclusionFunction): CallSite = { // Keep crawling up the stack trace until we find the first function not inside of the spark // package. We track the last (shallowest) contiguous Spark method. This might be an RDD // transformation, a SparkContext function (such as parallelize), or anything else that leads @@ -1357,9 +1359,17 @@ private[spark] object Utils extends Logging { } val callStackDepth = System.getProperty("spark.callstack.depth", "20").toInt - CallSite( - shortForm = s"$lastSparkMethod at $firstUserFile:$firstUserLine", - longForm = callStack.take(callStackDepth).mkString("\n")) + val shortForm = + if (firstUserFile == "HiveSessionImpl.java") { + // To be more user friendly, show a nicer string for queries submitted from the JDBC + // server. + "Spark JDBC Server Query" + } else { + s"$lastSparkMethod at $firstUserFile:$firstUserLine" + } + val longForm = callStack.take(callStackDepth).mkString("\n") + + CallSite(shortForm, longForm) } /** Return a string containing part of a file from byte 'start' to 'end'. */ diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index 4759f5fe783ad..6879fe0805c58 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -237,7 +237,8 @@ def explain(self, extended=False): :param extended: boolean, default ``False``. If ``False``, prints only the physical plan. >>> df.explain() - PhysicalRDD [age#0,name#1], MapPartitionsRDD[...] at mapPartitions at SQLContext.scala:... + PhysicalRDD [age#0,name#1], MapPartitionsRDD[...] at applySchemaToPythonRDD at\ + NativeMethodAccessorImpl.java:... >>> df.explain(True) == Parsed Logical Plan == From f49284b5bf3a69ed91a5e3e6e0ed3be93a6ab9e4 Mon Sep 17 00:00:00 2001 From: Josh Rosen Date: Wed, 29 Apr 2015 01:07:26 -0700 Subject: [PATCH 465/591] [SPARK-7076][SPARK-7077][SPARK-7080][SQL] Use managed memory for aggregations MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit This patch adds managed-memory-based aggregation to Spark SQL / DataFrames. Instead of working with Java objects, this new aggregation path uses `sun.misc.Unsafe` to manipulate raw memory. This reduces the memory footprint for aggregations, resulting in fewer spills, OutOfMemoryErrors, and garbage collection pauses. As a result, this allows for higher memory utilization. It can also result in better cache locality since objects will be stored closer together in memory. This feature can be eanbled by setting `spark.sql.unsafe.enabled=true`. For now, this feature is only supported when codegen is enabled and only supports aggregations for which the grouping columns are primitive numeric types or strings and aggregated values are numeric. ### Managing memory with sun.misc.Unsafe This patch supports both on- and off-heap managed memory. - In on-heap mode, memory addresses are identified by the combination of a base Object and an offset within that object. - In off-heap mode, memory is addressed directly with 64-bit long addresses. To support both modes, functions that manipulate memory accept both `baseObject` and `baseOffset` fields. In off-heap mode, we simply pass `null` as `baseObject`. We allocate memory in large chunks, so memory fragmentation and allocation speed are not significant bottlenecks. By default, we use on-heap mode. To enable off-heap mode, set `spark.unsafe.offHeap=true`. To track allocated memory, this patch extends `SparkEnv` with an `ExecutorMemoryManager` and supplies each `TaskContext` with a `TaskMemoryManager`. These classes work together to track allocations and detect memory leaks. ### Compact tuple format This patch introduces `UnsafeRow`, a compact row layout. In this format, each tuple has three parts: a null bit set, fixed length values, and variable-length values: ![image](https://cloud.githubusercontent.com/assets/50748/7328538/2fdb65ce-ea8b-11e4-9743-6c0f02bb7d1f.png) - Rows are always 8-byte word aligned (so their sizes will always be a multiple of 8 bytes) - The bit set is used for null tracking: - Position _i_ is set if and only if field _i_ is null - The bit set is aligned to an 8-byte word boundary. - Every field appears as an 8-byte word in the fixed-length values part: - If a field is null, we zero out the values. - If a field is variable-length, the word stores a relative offset (w.r.t. the base of the tuple) that points to the beginning of the field's data in the variable-length part. - Each variable-length data type can have its own encoding: - For strings, the first word stores the length of the string and is followed by UTF-8 encoded bytes. If necessary, the end of the string is padded with empty bytes in order to ensure word-alignment. For example, a tuple that consists 3 fields of type (int, string, string), with value (null, “data”, “bricks”) would look like this: ![image](https://cloud.githubusercontent.com/assets/50748/7328526/1e21959c-ea8b-11e4-9a28-a4350fe4a7b5.png) This format allows us to compare tuples for equality by directly comparing their raw bytes. This also enables fast hashing of tuples. ### Hash map for performing aggregations This patch introduces `UnsafeFixedWidthAggregationMap`, a hash map for performing aggregations where the aggregation result columns are fixed-with. This map's keys and values are `Row` objects. `UnsafeFixedWidthAggregationMap` is implemented on top of `BytesToBytesMap`, an append-only map which supports byte-array keys and values. `BytesToBytesMap` stores pointers to key and value tuples. For each record with a new key, we copy the key and create the aggregation value buffer for that key and put them in a buffer. The hash table then simply stores pointers to the key and value. For each record with an existing key, we simply run the aggregation function to update the values in place. This map is implemented using open hashing with triangular sequence probing. Each entry stores two words in a long array: the first word stores the address of the key and the second word stores the relative offset from the key tuple to the value tuple, as well as the key's 32-bit hashcode. By storing the full hashcode, we reduce the number of equality checks that need to be performed to handle position collisions ()since the chance of hashcode collision is much lower than position collision). `UnsafeFixedWidthAggregationMap` allows regular Spark SQL `Row` objects to be used when probing the map. Internally, it encodes these rows into `UnsafeRow` format using `UnsafeRowConverter`. This conversion has a small overhead that can be eliminated in the future once we use UnsafeRows in other operators. [Review on Reviewable](https://reviewable.io/reviews/apache/spark/5725) Author: Josh Rosen Closes #5725 from JoshRosen/unsafe and squashes the following commits: eeee512 [Josh Rosen] Add converters for Null, Boolean, Byte, and Short columns. 81f34f8 [Josh Rosen] Follow 'place children last' convention for GeneratedAggregate 1bc36cc [Josh Rosen] Refactor UnsafeRowConverter to avoid unnecessary boxing. 017b2dc [Josh Rosen] Remove BytesToBytesMap.finalize() 50e9671 [Josh Rosen] Throw memory leak warning even in case of error; add warning about code duplication 70a39e4 [Josh Rosen] Split MemoryManager into ExecutorMemoryManager and TaskMemoryManager: 6e4b192 [Josh Rosen] Remove an unused method from ByteArrayMethods. de5e001 [Josh Rosen] Fix debug vs. trace in logging message. a19e066 [Josh Rosen] Rename unsafe Java test suites to match Scala test naming convention. 78a5b84 [Josh Rosen] Add logging to MemoryManager ce3c565 [Josh Rosen] More comments, formatting, and code cleanup. 529e571 [Josh Rosen] Measure timeSpentResizing in nanoseconds instead of milliseconds. 3ca84b2 [Josh Rosen] Only zero the used portion of groupingKeyConversionScratchSpace 162caf7 [Josh Rosen] Fix test compilation b45f070 [Josh Rosen] Don't redundantly store the offset from key to value, since we can compute this from the key size. a8e4a3f [Josh Rosen] Introduce MemoryManager interface; add to SparkEnv. 0925847 [Josh Rosen] Disable MiMa checks for new unsafe module cde4132 [Josh Rosen] Add missing pom.xml 9c19fc0 [Josh Rosen] Add configuration options for heap vs. offheap 6ffdaa1 [Josh Rosen] Null handling improvements in UnsafeRow. 31eaabc [Josh Rosen] Lots of TODO and doc cleanup. a95291e [Josh Rosen] Cleanups to string handling code afe8dca [Josh Rosen] Some Javadoc cleanup f3dcbfe [Josh Rosen] More mod replacement 854201a [Josh Rosen] Import and comment cleanup 06e929d [Josh Rosen] More warning cleanup ef6b3d3 [Josh Rosen] Fix a bunch of FindBugs and IntelliJ inspections 29a7575 [Josh Rosen] Remove debug logging 49aed30 [Josh Rosen] More long -> int conversion. b26f1d3 [Josh Rosen] Fix bug in murmur hash implementation. 765243d [Josh Rosen] Enable optional performance metrics for hash map. 23a440a [Josh Rosen] Bump up default hash map size 628f936 [Josh Rosen] Use ints intead of longs for indexing. 92d5a06 [Josh Rosen] Address a number of minor code review comments. 1f4b716 [Josh Rosen] Merge Unsafe code into the regular GeneratedAggregate, guarded by a configuration flag; integrate planner support and re-enable all tests. d85eeff [Josh Rosen] Add basic sanity test for UnsafeFixedWidthAggregationMap bade966 [Josh Rosen] Comment update (bumping to refresh GitHub cache...) b3eaccd [Josh Rosen] Extract aggregation map into its own class. d2bb986 [Josh Rosen] Update to implement new Row methods added upstream 58ac393 [Josh Rosen] Use UNSAFE allocator in GeneratedAggregate (TODO: make this configurable) 7df6008 [Josh Rosen] Optimizations related to zeroing out memory: c1b3813 [Josh Rosen] Fix bug in UnsafeMemoryAllocator.free(): 738fa33 [Josh Rosen] Add feature flag to guard UnsafeGeneratedAggregate c55bf66 [Josh Rosen] Free buffer once iterator has been fully consumed. 62ab054 [Josh Rosen] Optimize for fact that get() is only called on String columns. c7f0b56 [Josh Rosen] Reuse UnsafeRow pointer in UnsafeRowConverter ae39694 [Josh Rosen] Add finalizer as "cleanup method of last resort" c754ae1 [Josh Rosen] Now that the store*() contract has been stregthened, we can remove an extra lookup f764d13 [Josh Rosen] Simplify address + length calculation in Location. 079f1bf [Josh Rosen] Some clarification of the BytesToBytesMap.lookup() / set() contract. 1a483c5 [Josh Rosen] First version that passes some aggregation tests: fc4c3a8 [Josh Rosen] Sketch how the converters will be used in UnsafeGeneratedAggregate 53ba9b7 [Josh Rosen] Start prototyping Java Row -> UnsafeRow converters 1ff814d [Josh Rosen] Add reminder to free memory on iterator completion 8a8f9df [Josh Rosen] Add skeleton for GeneratedAggregate integration. 5d55cef [Josh Rosen] Add skeleton for Row implementation. f03e9c1 [Josh Rosen] Play around with Unsafe implementations of more string methods. ab68e08 [Josh Rosen] Begin merging the UTF8String implementations. 480a74a [Josh Rosen] Initial import of code from Databricks unsafe utils repo. --- core/pom.xml | 5 + .../scala/org/apache/spark/SparkEnv.scala | 12 + .../scala/org/apache/spark/TaskContext.scala | 6 + .../org/apache/spark/TaskContextImpl.scala | 2 + .../org/apache/spark/executor/Executor.scala | 19 +- .../apache/spark/scheduler/DAGScheduler.scala | 22 +- .../org/apache/spark/scheduler/Task.scala | 16 +- .../java/org/apache/spark/JavaAPISuite.java | 2 +- .../org/apache/spark/CacheManagerSuite.scala | 8 +- .../org/apache/spark/rdd/PipedRDDSuite.scala | 2 +- .../spark/scheduler/TaskContextSuite.scala | 2 +- .../ShuffleBlockFetcherIteratorSuite.scala | 6 +- pom.xml | 2 + project/SparkBuild.scala | 7 +- sql/catalyst/pom.xml | 5 + .../UnsafeFixedWidthAggregationMap.java | 259 +++++++++ .../sql/catalyst/expressions/UnsafeRow.java | 435 ++++++++++++++ .../expressions/UnsafeRowConverter.scala | 223 +++++++ .../UnsafeFixedWidthAggregationMapSuite.scala | 119 ++++ .../expressions/UnsafeRowConverterSuite.scala | 153 +++++ .../scala/org/apache/spark/sql/SQLConf.scala | 9 + .../org/apache/spark/sql/SQLContext.scala | 2 + .../sql/execution/GeneratedAggregate.scala | 60 ++ .../spark/sql/execution/SparkStrategies.scala | 2 + unsafe/pom.xml | 69 +++ .../spark/unsafe/PlatformDependent.java | 87 +++ .../spark/unsafe/array/ByteArrayMethods.java | 56 ++ .../apache/spark/unsafe/array/LongArray.java | 78 +++ .../apache/spark/unsafe/bitset/BitSet.java | 105 ++++ .../spark/unsafe/bitset/BitSetMethods.java | 129 ++++ .../spark/unsafe/hash/Murmur3_x86_32.java | 96 +++ .../spark/unsafe/map/BytesToBytesMap.java | 549 ++++++++++++++++++ .../unsafe/map/HashMapGrowthStrategy.java | 39 ++ .../unsafe/memory/ExecutorMemoryManager.java | 58 ++ .../unsafe/memory/HeapMemoryAllocator.java | 35 ++ .../spark/unsafe/memory/MemoryAllocator.java | 33 ++ .../spark/unsafe/memory/MemoryBlock.java | 63 ++ .../spark/unsafe/memory/MemoryLocation.java | 54 ++ .../unsafe/memory/TaskMemoryManager.java | 237 ++++++++ .../unsafe/memory/UnsafeMemoryAllocator.java | 39 ++ .../spark/unsafe/array/LongArraySuite.java | 38 ++ .../spark/unsafe/bitset/BitSetSuite.java | 82 +++ .../unsafe/hash/Murmur3_x86_32Suite.java | 119 ++++ .../map/AbstractBytesToBytesMapSuite.java | 250 ++++++++ .../map/BytesToBytesMapOffHeapSuite.java | 29 + .../map/BytesToBytesMapOnHeapSuite.java | 29 + .../unsafe/memory/TaskMemoryManagerSuite.java | 41 ++ 47 files changed, 3675 insertions(+), 18 deletions(-) create mode 100644 sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java create mode 100644 sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala create mode 100644 unsafe/pom.xml create mode 100644 unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java create mode 100644 unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java create mode 100644 unsafe/src/main/java/org/apache/spark/unsafe/array/LongArray.java create mode 100644 unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSet.java create mode 100644 unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java create mode 100644 unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java create mode 100644 unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java create mode 100644 unsafe/src/main/java/org/apache/spark/unsafe/map/HashMapGrowthStrategy.java create mode 100644 unsafe/src/main/java/org/apache/spark/unsafe/memory/ExecutorMemoryManager.java create mode 100644 unsafe/src/main/java/org/apache/spark/unsafe/memory/HeapMemoryAllocator.java create mode 100644 unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryAllocator.java create mode 100644 unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java create mode 100644 unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryLocation.java create mode 100644 unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java create mode 100644 unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java create mode 100644 unsafe/src/test/java/org/apache/spark/unsafe/array/LongArraySuite.java create mode 100644 unsafe/src/test/java/org/apache/spark/unsafe/bitset/BitSetSuite.java create mode 100644 unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java create mode 100644 unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java create mode 100644 unsafe/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOffHeapSuite.java create mode 100644 unsafe/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOnHeapSuite.java create mode 100644 unsafe/src/test/java/org/apache/spark/unsafe/memory/TaskMemoryManagerSuite.java diff --git a/core/pom.xml b/core/pom.xml index 459ef66712c36..2dfb00d7ecf26 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -95,6 +95,11 @@ spark-network-shuffle_${scala.binary.version} ${project.version} + + org.apache.spark + spark-unsafe_${scala.binary.version} + ${project.version} + net.java.dev.jets3t jets3t diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala index 959aefabd8de4..0c4d28f786edd 100644 --- a/core/src/main/scala/org/apache/spark/SparkEnv.scala +++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala @@ -40,6 +40,7 @@ import org.apache.spark.scheduler.OutputCommitCoordinator.OutputCommitCoordinato import org.apache.spark.serializer.Serializer import org.apache.spark.shuffle.{ShuffleMemoryManager, ShuffleManager} import org.apache.spark.storage._ +import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator} import org.apache.spark.util.{RpcUtils, Utils} /** @@ -69,6 +70,7 @@ class SparkEnv ( val sparkFilesDir: String, val metricsSystem: MetricsSystem, val shuffleMemoryManager: ShuffleMemoryManager, + val executorMemoryManager: ExecutorMemoryManager, val outputCommitCoordinator: OutputCommitCoordinator, val conf: SparkConf) extends Logging { @@ -382,6 +384,15 @@ object SparkEnv extends Logging { new OutputCommitCoordinatorEndpoint(rpcEnv, outputCommitCoordinator)) outputCommitCoordinator.coordinatorRef = Some(outputCommitCoordinatorRef) + val executorMemoryManager: ExecutorMemoryManager = { + val allocator = if (conf.getBoolean("spark.unsafe.offHeap", false)) { + MemoryAllocator.UNSAFE + } else { + MemoryAllocator.HEAP + } + new ExecutorMemoryManager(allocator) + } + val envInstance = new SparkEnv( executorId, rpcEnv, @@ -398,6 +409,7 @@ object SparkEnv extends Logging { sparkFilesDir, metricsSystem, shuffleMemoryManager, + executorMemoryManager, outputCommitCoordinator, conf) diff --git a/core/src/main/scala/org/apache/spark/TaskContext.scala b/core/src/main/scala/org/apache/spark/TaskContext.scala index 7d7fe1a446313..d09e17dea0911 100644 --- a/core/src/main/scala/org/apache/spark/TaskContext.scala +++ b/core/src/main/scala/org/apache/spark/TaskContext.scala @@ -21,6 +21,7 @@ import java.io.Serializable import org.apache.spark.annotation.DeveloperApi import org.apache.spark.executor.TaskMetrics +import org.apache.spark.unsafe.memory.TaskMemoryManager import org.apache.spark.util.TaskCompletionListener @@ -133,4 +134,9 @@ abstract class TaskContext extends Serializable { /** ::DeveloperApi:: */ @DeveloperApi def taskMetrics(): TaskMetrics + + /** + * Returns the manager for this task's managed memory. + */ + private[spark] def taskMemoryManager(): TaskMemoryManager } diff --git a/core/src/main/scala/org/apache/spark/TaskContextImpl.scala b/core/src/main/scala/org/apache/spark/TaskContextImpl.scala index 337c8e4ebebcd..b4d572cb52313 100644 --- a/core/src/main/scala/org/apache/spark/TaskContextImpl.scala +++ b/core/src/main/scala/org/apache/spark/TaskContextImpl.scala @@ -18,6 +18,7 @@ package org.apache.spark import org.apache.spark.executor.TaskMetrics +import org.apache.spark.unsafe.memory.TaskMemoryManager import org.apache.spark.util.{TaskCompletionListener, TaskCompletionListenerException} import scala.collection.mutable.ArrayBuffer @@ -27,6 +28,7 @@ private[spark] class TaskContextImpl( val partitionId: Int, override val taskAttemptId: Long, override val attemptNumber: Int, + override val taskMemoryManager: TaskMemoryManager, val runningLocally: Boolean = false, val taskMetrics: TaskMetrics = TaskMetrics.empty) extends TaskContext diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala index f57e215c3f2ed..dd1c48e6cb953 100644 --- a/core/src/main/scala/org/apache/spark/executor/Executor.scala +++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala @@ -32,6 +32,7 @@ import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.scheduler.{DirectTaskResult, IndirectTaskResult, Task} import org.apache.spark.shuffle.FetchFailedException import org.apache.spark.storage.{StorageLevel, TaskResultBlockId} +import org.apache.spark.unsafe.memory.TaskMemoryManager import org.apache.spark.util._ /** @@ -178,6 +179,7 @@ private[spark] class Executor( } override def run(): Unit = { + val taskMemoryManager = new TaskMemoryManager(env.executorMemoryManager) val deserializeStartTime = System.currentTimeMillis() Thread.currentThread.setContextClassLoader(replClassLoader) val ser = env.closureSerializer.newInstance() @@ -190,6 +192,7 @@ private[spark] class Executor( val (taskFiles, taskJars, taskBytes) = Task.deserializeWithDependencies(serializedTask) updateDependencies(taskFiles, taskJars) task = ser.deserialize[Task[Any]](taskBytes, Thread.currentThread.getContextClassLoader) + task.setTaskMemoryManager(taskMemoryManager) // If this task has been killed before we deserialized it, let's quit now. Otherwise, // continue executing the task. @@ -206,7 +209,21 @@ private[spark] class Executor( // Run the actual task and measure its runtime. taskStart = System.currentTimeMillis() - val value = task.run(taskAttemptId = taskId, attemptNumber = attemptNumber) + val value = try { + task.run(taskAttemptId = taskId, attemptNumber = attemptNumber) + } finally { + // Note: this memory freeing logic is duplicated in DAGScheduler.runLocallyWithinThread; + // when changing this, make sure to update both copies. + val freedMemory = taskMemoryManager.cleanUpAllAllocatedMemory() + if (freedMemory > 0) { + val errMsg = s"Managed memory leak detected; size = $freedMemory bytes, TID = $taskId" + if (conf.getBoolean("spark.unsafe.exceptionOnMemoryLeak", false)) { + throw new SparkException(errMsg) + } else { + logError(errMsg) + } + } + } val taskFinish = System.currentTimeMillis() // If the task has been killed, let's fail it. diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala index 8c4bff4e83afc..b7901c06a16e0 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -34,6 +34,7 @@ import org.apache.spark.executor.TaskMetrics import org.apache.spark.partial.{ApproximateActionListener, ApproximateEvaluator, PartialResult} import org.apache.spark.rdd.RDD import org.apache.spark.storage._ +import org.apache.spark.unsafe.memory.TaskMemoryManager import org.apache.spark.util._ import org.apache.spark.storage.BlockManagerMessages.BlockManagerHeartbeat @@ -643,8 +644,15 @@ class DAGScheduler( try { val rdd = job.finalStage.rdd val split = rdd.partitions(job.partitions(0)) - val taskContext = new TaskContextImpl(job.finalStage.id, job.partitions(0), taskAttemptId = 0, - attemptNumber = 0, runningLocally = true) + val taskMemoryManager = new TaskMemoryManager(env.executorMemoryManager) + val taskContext = + new TaskContextImpl( + job.finalStage.id, + job.partitions(0), + taskAttemptId = 0, + attemptNumber = 0, + taskMemoryManager = taskMemoryManager, + runningLocally = true) TaskContext.setTaskContext(taskContext) try { val result = job.func(taskContext, rdd.iterator(split, taskContext)) @@ -652,6 +660,16 @@ class DAGScheduler( } finally { taskContext.markTaskCompleted() TaskContext.unset() + // Note: this memory freeing logic is duplicated in Executor.run(); when changing this, + // make sure to update both copies. + val freedMemory = taskMemoryManager.cleanUpAllAllocatedMemory() + if (freedMemory > 0) { + if (sc.getConf.getBoolean("spark.unsafe.exceptionOnMemoryLeak", false)) { + throw new SparkException(s"Managed memory leak detected; size = $freedMemory bytes") + } else { + logError(s"Managed memory leak detected; size = $freedMemory bytes") + } + } } } catch { case e: Exception => diff --git a/core/src/main/scala/org/apache/spark/scheduler/Task.scala b/core/src/main/scala/org/apache/spark/scheduler/Task.scala index b09b19e2ac9e7..586d1e06204c1 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/Task.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/Task.scala @@ -25,6 +25,7 @@ import scala.collection.mutable.HashMap import org.apache.spark.{TaskContextImpl, TaskContext} import org.apache.spark.executor.TaskMetrics import org.apache.spark.serializer.SerializerInstance +import org.apache.spark.unsafe.memory.TaskMemoryManager import org.apache.spark.util.ByteBufferInputStream import org.apache.spark.util.Utils @@ -52,8 +53,13 @@ private[spark] abstract class Task[T](val stageId: Int, var partitionId: Int) ex * @return the result of the task */ final def run(taskAttemptId: Long, attemptNumber: Int): T = { - context = new TaskContextImpl(stageId = stageId, partitionId = partitionId, - taskAttemptId = taskAttemptId, attemptNumber = attemptNumber, runningLocally = false) + context = new TaskContextImpl( + stageId = stageId, + partitionId = partitionId, + taskAttemptId = taskAttemptId, + attemptNumber = attemptNumber, + taskMemoryManager = taskMemoryManager, + runningLocally = false) TaskContext.setTaskContext(context) context.taskMetrics.setHostname(Utils.localHostName()) taskThread = Thread.currentThread() @@ -68,6 +74,12 @@ private[spark] abstract class Task[T](val stageId: Int, var partitionId: Int) ex } } + private var taskMemoryManager: TaskMemoryManager = _ + + def setTaskMemoryManager(taskMemoryManager: TaskMemoryManager): Unit = { + this.taskMemoryManager = taskMemoryManager + } + def runTask(context: TaskContext): T def preferredLocations: Seq[TaskLocation] = Nil diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java index 8a4f2a08fe701..34ac9361d46c6 100644 --- a/core/src/test/java/org/apache/spark/JavaAPISuite.java +++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java @@ -1009,7 +1009,7 @@ public void persist() { @Test public void iterator() { JavaRDD rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5), 2); - TaskContext context = new TaskContextImpl(0, 0, 0L, 0, false, new TaskMetrics()); + TaskContext context = new TaskContextImpl(0, 0, 0L, 0, null, false, new TaskMetrics()); Assert.assertEquals(1, rdd.iterator(rdd.partitions().get(0), context).next().intValue()); } diff --git a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala index 70529d9216591..668ddf9f5f0a9 100644 --- a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala @@ -65,7 +65,7 @@ class CacheManagerSuite extends FunSuite with LocalSparkContext with BeforeAndAf // in blockManager.put is a losing battle. You have been warned. blockManager = sc.env.blockManager cacheManager = sc.env.cacheManager - val context = new TaskContextImpl(0, 0, 0, 0) + val context = new TaskContextImpl(0, 0, 0, 0, null) val computeValue = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY) val getValue = blockManager.get(RDDBlockId(rdd.id, split.index)) assert(computeValue.toList === List(1, 2, 3, 4)) @@ -77,7 +77,7 @@ class CacheManagerSuite extends FunSuite with LocalSparkContext with BeforeAndAf val result = new BlockResult(Array(5, 6, 7).iterator, DataReadMethod.Memory, 12) when(blockManager.get(RDDBlockId(0, 0))).thenReturn(Some(result)) - val context = new TaskContextImpl(0, 0, 0, 0) + val context = new TaskContextImpl(0, 0, 0, 0, null) val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY) assert(value.toList === List(5, 6, 7)) } @@ -86,14 +86,14 @@ class CacheManagerSuite extends FunSuite with LocalSparkContext with BeforeAndAf // Local computation should not persist the resulting value, so don't expect a put(). when(blockManager.get(RDDBlockId(0, 0))).thenReturn(None) - val context = new TaskContextImpl(0, 0, 0, 0, true) + val context = new TaskContextImpl(0, 0, 0, 0, null, true) val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY) assert(value.toList === List(1, 2, 3, 4)) } test("verify task metrics updated correctly") { cacheManager = sc.env.cacheManager - val context = new TaskContextImpl(0, 0, 0, 0) + val context = new TaskContextImpl(0, 0, 0, 0, null) cacheManager.getOrCompute(rdd3, split, context, StorageLevel.MEMORY_ONLY) assert(context.taskMetrics.updatedBlocks.getOrElse(Seq()).size === 2) } diff --git a/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala index aea76c1adcc09..85eb2a1d07ba4 100644 --- a/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala @@ -176,7 +176,7 @@ class PipedRDDSuite extends FunSuite with SharedSparkContext { } val hadoopPart1 = generateFakeHadoopPartition() val pipedRdd = new PipedRDD(nums, "printenv " + varName) - val tContext = new TaskContextImpl(0, 0, 0, 0) + val tContext = new TaskContextImpl(0, 0, 0, 0, null) val rddIter = pipedRdd.compute(hadoopPart1, tContext) val arr = rddIter.toArray assert(arr(0) == "/some/path") diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala index 057e226916027..83ae8701243e5 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala @@ -51,7 +51,7 @@ class TaskContextSuite extends FunSuite with BeforeAndAfter with LocalSparkConte } test("all TaskCompletionListeners should be called even if some fail") { - val context = new TaskContextImpl(0, 0, 0, 0) + val context = new TaskContextImpl(0, 0, 0, 0, null) val listener = mock(classOf[TaskCompletionListener]) context.addTaskCompletionListener(_ => throw new Exception("blah")) context.addTaskCompletionListener(listener) diff --git a/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala b/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala index 37b593b2c5f79..2080c432d77db 100644 --- a/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala @@ -89,7 +89,7 @@ class ShuffleBlockFetcherIteratorSuite extends FunSuite { ) val iterator = new ShuffleBlockFetcherIterator( - new TaskContextImpl(0, 0, 0, 0), + new TaskContextImpl(0, 0, 0, 0, null), transfer, blockManager, blocksByAddress, @@ -154,7 +154,7 @@ class ShuffleBlockFetcherIteratorSuite extends FunSuite { val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])]( (remoteBmId, blocks.keys.map(blockId => (blockId, 1.asInstanceOf[Long])).toSeq)) - val taskContext = new TaskContextImpl(0, 0, 0, 0) + val taskContext = new TaskContextImpl(0, 0, 0, 0, null) val iterator = new ShuffleBlockFetcherIterator( taskContext, transfer, @@ -217,7 +217,7 @@ class ShuffleBlockFetcherIteratorSuite extends FunSuite { val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])]( (remoteBmId, blocks.keys.map(blockId => (blockId, 1.asInstanceOf[Long])).toSeq)) - val taskContext = new TaskContextImpl(0, 0, 0, 0) + val taskContext = new TaskContextImpl(0, 0, 0, 0, null) val iterator = new ShuffleBlockFetcherIterator( taskContext, transfer, diff --git a/pom.xml b/pom.xml index 928f5d0f5efad..c85c5feeaf383 100644 --- a/pom.xml +++ b/pom.xml @@ -97,6 +97,7 @@ sql/catalyst sql/core sql/hive + unsafe assembly external/twitter external/flume @@ -1215,6 +1216,7 @@ false false true + true false diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index 09b4976d10c26..b7dbcd9bc562a 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -34,11 +34,11 @@ object BuildCommons { val allProjects@Seq(bagel, catalyst, core, graphx, hive, hiveThriftServer, mllib, repl, sql, networkCommon, networkShuffle, streaming, streamingFlumeSink, streamingFlume, streamingKafka, - streamingMqtt, streamingTwitter, streamingZeromq, launcher) = + streamingMqtt, streamingTwitter, streamingZeromq, launcher, unsafe) = Seq("bagel", "catalyst", "core", "graphx", "hive", "hive-thriftserver", "mllib", "repl", "sql", "network-common", "network-shuffle", "streaming", "streaming-flume-sink", "streaming-flume", "streaming-kafka", "streaming-mqtt", "streaming-twitter", - "streaming-zeromq", "launcher").map(ProjectRef(buildLocation, _)) + "streaming-zeromq", "launcher", "unsafe").map(ProjectRef(buildLocation, _)) val optionallyEnabledProjects@Seq(yarn, yarnStable, java8Tests, sparkGangliaLgpl, sparkKinesisAsl) = Seq("yarn", "yarn-stable", "java8-tests", "ganglia-lgpl", @@ -159,7 +159,7 @@ object SparkBuild extends PomBuild { // TODO: Add Sql to mima checks // TODO: remove launcher from this list after 1.3. allProjects.filterNot(x => Seq(spark, sql, hive, hiveThriftServer, catalyst, repl, - networkCommon, networkShuffle, networkYarn, launcher).contains(x)).foreach { + networkCommon, networkShuffle, networkYarn, launcher, unsafe).contains(x)).foreach { x => enable(MimaBuild.mimaSettings(sparkHome, x))(x) } @@ -496,6 +496,7 @@ object TestSettings { javaOptions in Test += "-Dspark.ui.enabled=false", javaOptions in Test += "-Dspark.ui.showConsoleProgress=false", javaOptions in Test += "-Dspark.driver.allowMultipleContexts=true", + javaOptions in Test += "-Dspark.unsafe.exceptionOnMemoryLeak=true", javaOptions in Test += "-Dsun.io.serialization.extendedDebugInfo=true", javaOptions in Test ++= System.getProperties.filter(_._1 startsWith "spark") .map { case (k,v) => s"-D$k=$v" }.toSeq, diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml index 3dea2ee76542f..5c322d032d474 100644 --- a/sql/catalyst/pom.xml +++ b/sql/catalyst/pom.xml @@ -50,6 +50,11 @@ spark-core_${scala.binary.version} ${project.version} + + org.apache.spark + spark-unsafe_${scala.binary.version} + ${project.version} + org.scalacheck scalacheck_${scala.binary.version} diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java new file mode 100644 index 0000000000000..299ff3728a6d9 --- /dev/null +++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMap.java @@ -0,0 +1,259 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions; + +import java.util.Arrays; +import java.util.Iterator; + +import org.apache.spark.sql.Row; +import org.apache.spark.sql.types.StructField; +import org.apache.spark.sql.types.StructType; +import org.apache.spark.unsafe.PlatformDependent; +import org.apache.spark.unsafe.map.BytesToBytesMap; +import org.apache.spark.unsafe.memory.MemoryLocation; +import org.apache.spark.unsafe.memory.TaskMemoryManager; + +/** + * Unsafe-based HashMap for performing aggregations where the aggregated values are fixed-width. + * + * This map supports a maximum of 2 billion keys. + */ +public final class UnsafeFixedWidthAggregationMap { + + /** + * An empty aggregation buffer, encoded in UnsafeRow format. When inserting a new key into the + * map, we copy this buffer and use it as the value. + */ + private final long[] emptyAggregationBuffer; + + private final StructType aggregationBufferSchema; + + private final StructType groupingKeySchema; + + /** + * Encodes grouping keys as UnsafeRows. + */ + private final UnsafeRowConverter groupingKeyToUnsafeRowConverter; + + /** + * A hashmap which maps from opaque bytearray keys to bytearray values. + */ + private final BytesToBytesMap map; + + /** + * Re-used pointer to the current aggregation buffer + */ + private final UnsafeRow currentAggregationBuffer = new UnsafeRow(); + + /** + * Scratch space that is used when encoding grouping keys into UnsafeRow format. + * + * By default, this is a 1MB array, but it will grow as necessary in case larger keys are + * encountered. + */ + private long[] groupingKeyConversionScratchSpace = new long[1024 / 8]; + + private final boolean enablePerfMetrics; + + /** + * @return true if UnsafeFixedWidthAggregationMap supports grouping keys with the given schema, + * false otherwise. + */ + public static boolean supportsGroupKeySchema(StructType schema) { + for (StructField field: schema.fields()) { + if (!UnsafeRow.readableFieldTypes.contains(field.dataType())) { + return false; + } + } + return true; + } + + /** + * @return true if UnsafeFixedWidthAggregationMap supports aggregation buffers with the given + * schema, false otherwise. + */ + public static boolean supportsAggregationBufferSchema(StructType schema) { + for (StructField field: schema.fields()) { + if (!UnsafeRow.settableFieldTypes.contains(field.dataType())) { + return false; + } + } + return true; + } + + /** + * Create a new UnsafeFixedWidthAggregationMap. + * + * @param emptyAggregationBuffer the default value for new keys (a "zero" of the agg. function) + * @param aggregationBufferSchema the schema of the aggregation buffer, used for row conversion. + * @param groupingKeySchema the schema of the grouping key, used for row conversion. + * @param memoryManager the memory manager used to allocate our Unsafe memory structures. + * @param initialCapacity the initial capacity of the map (a sizing hint to avoid re-hashing). + * @param enablePerfMetrics if true, performance metrics will be recorded (has minor perf impact) + */ + public UnsafeFixedWidthAggregationMap( + Row emptyAggregationBuffer, + StructType aggregationBufferSchema, + StructType groupingKeySchema, + TaskMemoryManager memoryManager, + int initialCapacity, + boolean enablePerfMetrics) { + this.emptyAggregationBuffer = + convertToUnsafeRow(emptyAggregationBuffer, aggregationBufferSchema); + this.aggregationBufferSchema = aggregationBufferSchema; + this.groupingKeyToUnsafeRowConverter = new UnsafeRowConverter(groupingKeySchema); + this.groupingKeySchema = groupingKeySchema; + this.map = new BytesToBytesMap(memoryManager, initialCapacity, enablePerfMetrics); + this.enablePerfMetrics = enablePerfMetrics; + } + + /** + * Convert a Java object row into an UnsafeRow, allocating it into a new long array. + */ + private static long[] convertToUnsafeRow(Row javaRow, StructType schema) { + final UnsafeRowConverter converter = new UnsafeRowConverter(schema); + final long[] unsafeRow = new long[converter.getSizeRequirement(javaRow)]; + final long writtenLength = + converter.writeRow(javaRow, unsafeRow, PlatformDependent.LONG_ARRAY_OFFSET); + assert (writtenLength == unsafeRow.length): "Size requirement calculation was wrong!"; + return unsafeRow; + } + + /** + * Return the aggregation buffer for the current group. For efficiency, all calls to this method + * return the same object. + */ + public UnsafeRow getAggregationBuffer(Row groupingKey) { + final int groupingKeySize = groupingKeyToUnsafeRowConverter.getSizeRequirement(groupingKey); + // Make sure that the buffer is large enough to hold the key. If it's not, grow it: + if (groupingKeySize > groupingKeyConversionScratchSpace.length) { + // This new array will be initially zero, so there's no need to zero it out here + groupingKeyConversionScratchSpace = new long[groupingKeySize]; + } else { + // Zero out the buffer that's used to hold the current row. This is necessary in order + // to ensure that rows hash properly, since garbage data from the previous row could + // otherwise end up as padding in this row. As a performance optimization, we only zero out + // the portion of the buffer that we'll actually write to. + Arrays.fill(groupingKeyConversionScratchSpace, 0, groupingKeySize, 0); + } + final long actualGroupingKeySize = groupingKeyToUnsafeRowConverter.writeRow( + groupingKey, + groupingKeyConversionScratchSpace, + PlatformDependent.LONG_ARRAY_OFFSET); + assert (groupingKeySize == actualGroupingKeySize) : "Size requirement calculation was wrong!"; + + // Probe our map using the serialized key + final BytesToBytesMap.Location loc = map.lookup( + groupingKeyConversionScratchSpace, + PlatformDependent.LONG_ARRAY_OFFSET, + groupingKeySize); + if (!loc.isDefined()) { + // This is the first time that we've seen this grouping key, so we'll insert a copy of the + // empty aggregation buffer into the map: + loc.putNewKey( + groupingKeyConversionScratchSpace, + PlatformDependent.LONG_ARRAY_OFFSET, + groupingKeySize, + emptyAggregationBuffer, + PlatformDependent.LONG_ARRAY_OFFSET, + emptyAggregationBuffer.length + ); + } + + // Reset the pointer to point to the value that we just stored or looked up: + final MemoryLocation address = loc.getValueAddress(); + currentAggregationBuffer.pointTo( + address.getBaseObject(), + address.getBaseOffset(), + aggregationBufferSchema.length(), + aggregationBufferSchema + ); + return currentAggregationBuffer; + } + + /** + * Mutable pair object returned by {@link UnsafeFixedWidthAggregationMap#iterator()}. + */ + public static class MapEntry { + private MapEntry() { }; + public final UnsafeRow key = new UnsafeRow(); + public final UnsafeRow value = new UnsafeRow(); + } + + /** + * Returns an iterator over the keys and values in this map. + * + * For efficiency, each call returns the same object. + */ + public Iterator iterator() { + return new Iterator() { + + private final MapEntry entry = new MapEntry(); + private final Iterator mapLocationIterator = map.iterator(); + + @Override + public boolean hasNext() { + return mapLocationIterator.hasNext(); + } + + @Override + public MapEntry next() { + final BytesToBytesMap.Location loc = mapLocationIterator.next(); + final MemoryLocation keyAddress = loc.getKeyAddress(); + final MemoryLocation valueAddress = loc.getValueAddress(); + entry.key.pointTo( + keyAddress.getBaseObject(), + keyAddress.getBaseOffset(), + groupingKeySchema.length(), + groupingKeySchema + ); + entry.value.pointTo( + valueAddress.getBaseObject(), + valueAddress.getBaseOffset(), + aggregationBufferSchema.length(), + aggregationBufferSchema + ); + return entry; + } + + @Override + public void remove() { + throw new UnsupportedOperationException(); + } + }; + } + + /** + * Free the unsafe memory associated with this map. + */ + public void free() { + map.free(); + } + + @SuppressWarnings("UseOfSystemOutOrSystemErr") + public void printPerfMetrics() { + if (!enablePerfMetrics) { + throw new IllegalStateException("Perf metrics not enabled"); + } + System.out.println("Average probes per lookup: " + map.getAverageProbesPerLookup()); + System.out.println("Number of hash collisions: " + map.getNumHashCollisions()); + System.out.println("Time spent resizing (ns): " + map.getTimeSpentResizingNs()); + System.out.println("Total memory consumption (bytes): " + map.getTotalMemoryConsumption()); + } + +} diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java new file mode 100644 index 0000000000000..0a358ed408aa1 --- /dev/null +++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java @@ -0,0 +1,435 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions; + +import scala.collection.Map; +import scala.collection.Seq; +import scala.collection.mutable.ArraySeq; + +import javax.annotation.Nullable; +import java.math.BigDecimal; +import java.sql.Date; +import java.util.*; + +import org.apache.spark.sql.Row; +import org.apache.spark.sql.types.DataType; +import static org.apache.spark.sql.types.DataTypes.*; +import org.apache.spark.sql.types.StructType; +import org.apache.spark.sql.types.UTF8String; +import org.apache.spark.unsafe.PlatformDependent; +import org.apache.spark.unsafe.bitset.BitSetMethods; + +/** + * An Unsafe implementation of Row which is backed by raw memory instead of Java objects. + * + * Each tuple has three parts: [null bit set] [values] [variable length portion] + * + * The bit set is used for null tracking and is aligned to 8-byte word boundaries. It stores + * one bit per field. + * + * In the `values` region, we store one 8-byte word per field. For fields that hold fixed-length + * primitive types, such as long, double, or int, we store the value directly in the word. For + * fields with non-primitive or variable-length values, we store a relative offset (w.r.t. the + * base address of the row) that points to the beginning of the variable-length field. + * + * Instances of `UnsafeRow` act as pointers to row data stored in this format. + */ +public final class UnsafeRow implements MutableRow { + + private Object baseObject; + private long baseOffset; + + Object getBaseObject() { return baseObject; } + long getBaseOffset() { return baseOffset; } + + /** The number of fields in this row, used for calculating the bitset width (and in assertions) */ + private int numFields; + + /** The width of the null tracking bit set, in bytes */ + private int bitSetWidthInBytes; + /** + * This optional schema is required if you want to call generic get() and set() methods on + * this UnsafeRow, but is optional if callers will only use type-specific getTYPE() and setTYPE() + * methods. This should be removed after the planned InternalRow / Row split; right now, it's only + * needed by the generic get() method, which is only called internally by code that accesses + * UTF8String-typed columns. + */ + @Nullable + private StructType schema; + + private long getFieldOffset(int ordinal) { + return baseOffset + bitSetWidthInBytes + ordinal * 8L; + } + + public static int calculateBitSetWidthInBytes(int numFields) { + return ((numFields / 64) + (numFields % 64 == 0 ? 0 : 1)) * 8; + } + + /** + * Field types that can be updated in place in UnsafeRows (e.g. we support set() for these types) + */ + public static final Set settableFieldTypes; + + /** + * Fields types can be read(but not set (e.g. set() will throw UnsupportedOperationException). + */ + public static final Set readableFieldTypes; + + static { + settableFieldTypes = Collections.unmodifiableSet( + new HashSet( + Arrays.asList(new DataType[] { + NullType, + BooleanType, + ByteType, + ShortType, + IntegerType, + LongType, + FloatType, + DoubleType + }))); + + // We support get() on a superset of the types for which we support set(): + final Set _readableFieldTypes = new HashSet( + Arrays.asList(new DataType[]{ + StringType + })); + _readableFieldTypes.addAll(settableFieldTypes); + readableFieldTypes = Collections.unmodifiableSet(_readableFieldTypes); + } + + /** + * Construct a new UnsafeRow. The resulting row won't be usable until `pointTo()` has been called, + * since the value returned by this constructor is equivalent to a null pointer. + */ + public UnsafeRow() { } + + /** + * Update this UnsafeRow to point to different backing data. + * + * @param baseObject the base object + * @param baseOffset the offset within the base object + * @param numFields the number of fields in this row + * @param schema an optional schema; this is necessary if you want to call generic get() or set() + * methods on this row, but is optional if the caller will only use type-specific + * getTYPE() and setTYPE() methods. + */ + public void pointTo( + Object baseObject, + long baseOffset, + int numFields, + @Nullable StructType schema) { + assert numFields >= 0 : "numFields should >= 0"; + assert schema == null || schema.fields().length == numFields; + this.bitSetWidthInBytes = calculateBitSetWidthInBytes(numFields); + this.baseObject = baseObject; + this.baseOffset = baseOffset; + this.numFields = numFields; + this.schema = schema; + } + + private void assertIndexIsValid(int index) { + assert index >= 0 : "index (" + index + ") should >= 0"; + assert index < numFields : "index (" + index + ") should <= " + numFields; + } + + @Override + public void setNullAt(int i) { + assertIndexIsValid(i); + BitSetMethods.set(baseObject, baseOffset, i); + // To preserve row equality, zero out the value when setting the column to null. + // Since this row does does not currently support updates to variable-length values, we don't + // have to worry about zeroing out that data. + PlatformDependent.UNSAFE.putLong(baseObject, getFieldOffset(i), 0); + } + + private void setNotNullAt(int i) { + assertIndexIsValid(i); + BitSetMethods.unset(baseObject, baseOffset, i); + } + + @Override + public void update(int ordinal, Object value) { + throw new UnsupportedOperationException(); + } + + @Override + public void setInt(int ordinal, int value) { + assertIndexIsValid(ordinal); + setNotNullAt(ordinal); + PlatformDependent.UNSAFE.putInt(baseObject, getFieldOffset(ordinal), value); + } + + @Override + public void setLong(int ordinal, long value) { + assertIndexIsValid(ordinal); + setNotNullAt(ordinal); + PlatformDependent.UNSAFE.putLong(baseObject, getFieldOffset(ordinal), value); + } + + @Override + public void setDouble(int ordinal, double value) { + assertIndexIsValid(ordinal); + setNotNullAt(ordinal); + PlatformDependent.UNSAFE.putDouble(baseObject, getFieldOffset(ordinal), value); + } + + @Override + public void setBoolean(int ordinal, boolean value) { + assertIndexIsValid(ordinal); + setNotNullAt(ordinal); + PlatformDependent.UNSAFE.putBoolean(baseObject, getFieldOffset(ordinal), value); + } + + @Override + public void setShort(int ordinal, short value) { + assertIndexIsValid(ordinal); + setNotNullAt(ordinal); + PlatformDependent.UNSAFE.putShort(baseObject, getFieldOffset(ordinal), value); + } + + @Override + public void setByte(int ordinal, byte value) { + assertIndexIsValid(ordinal); + setNotNullAt(ordinal); + PlatformDependent.UNSAFE.putByte(baseObject, getFieldOffset(ordinal), value); + } + + @Override + public void setFloat(int ordinal, float value) { + assertIndexIsValid(ordinal); + setNotNullAt(ordinal); + PlatformDependent.UNSAFE.putFloat(baseObject, getFieldOffset(ordinal), value); + } + + @Override + public void setString(int ordinal, String value) { + throw new UnsupportedOperationException(); + } + + @Override + public int size() { + return numFields; + } + + @Override + public int length() { + return size(); + } + + @Override + public StructType schema() { + return schema; + } + + @Override + public Object apply(int i) { + return get(i); + } + + @Override + public Object get(int i) { + assertIndexIsValid(i); + assert (schema != null) : "Schema must be defined when calling generic get() method"; + final DataType dataType = schema.fields()[i].dataType(); + // UnsafeRow is only designed to be invoked by internal code, which only invokes this generic + // get() method when trying to access UTF8String-typed columns. If we refactor the codebase to + // separate the internal and external row interfaces, then internal code can fetch strings via + // a new getUTF8String() method and we'll be able to remove this method. + if (isNullAt(i)) { + return null; + } else if (dataType == StringType) { + return getUTF8String(i); + } else { + throw new UnsupportedOperationException(); + } + } + + @Override + public boolean isNullAt(int i) { + assertIndexIsValid(i); + return BitSetMethods.isSet(baseObject, baseOffset, i); + } + + @Override + public boolean getBoolean(int i) { + assertIndexIsValid(i); + return PlatformDependent.UNSAFE.getBoolean(baseObject, getFieldOffset(i)); + } + + @Override + public byte getByte(int i) { + assertIndexIsValid(i); + return PlatformDependent.UNSAFE.getByte(baseObject, getFieldOffset(i)); + } + + @Override + public short getShort(int i) { + assertIndexIsValid(i); + return PlatformDependent.UNSAFE.getShort(baseObject, getFieldOffset(i)); + } + + @Override + public int getInt(int i) { + assertIndexIsValid(i); + return PlatformDependent.UNSAFE.getInt(baseObject, getFieldOffset(i)); + } + + @Override + public long getLong(int i) { + assertIndexIsValid(i); + return PlatformDependent.UNSAFE.getLong(baseObject, getFieldOffset(i)); + } + + @Override + public float getFloat(int i) { + assertIndexIsValid(i); + if (isNullAt(i)) { + return Float.NaN; + } else { + return PlatformDependent.UNSAFE.getFloat(baseObject, getFieldOffset(i)); + } + } + + @Override + public double getDouble(int i) { + assertIndexIsValid(i); + if (isNullAt(i)) { + return Float.NaN; + } else { + return PlatformDependent.UNSAFE.getDouble(baseObject, getFieldOffset(i)); + } + } + + public UTF8String getUTF8String(int i) { + assertIndexIsValid(i); + final UTF8String str = new UTF8String(); + final long offsetToStringSize = getLong(i); + final int stringSizeInBytes = + (int) PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offsetToStringSize); + final byte[] strBytes = new byte[stringSizeInBytes]; + PlatformDependent.copyMemory( + baseObject, + baseOffset + offsetToStringSize + 8, // The `+ 8` is to skip past the size to get the data + strBytes, + PlatformDependent.BYTE_ARRAY_OFFSET, + stringSizeInBytes + ); + str.set(strBytes); + return str; + } + + @Override + public String getString(int i) { + return getUTF8String(i).toString(); + } + + @Override + public BigDecimal getDecimal(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public Date getDate(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public Seq getSeq(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public List getList(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public Map getMap(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public scala.collection.immutable.Map getValuesMap(Seq fieldNames) { + throw new UnsupportedOperationException(); + } + + @Override + public java.util.Map getJavaMap(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public Row getStruct(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public T getAs(int i) { + throw new UnsupportedOperationException(); + } + + @Override + public T getAs(String fieldName) { + throw new UnsupportedOperationException(); + } + + @Override + public int fieldIndex(String name) { + throw new UnsupportedOperationException(); + } + + @Override + public Row copy() { + throw new UnsupportedOperationException(); + } + + @Override + public boolean anyNull() { + return BitSetMethods.anySet(baseObject, baseOffset, bitSetWidthInBytes); + } + + @Override + public Seq toSeq() { + final ArraySeq values = new ArraySeq(numFields); + for (int fieldNumber = 0; fieldNumber < numFields; fieldNumber++) { + values.update(fieldNumber, get(fieldNumber)); + } + return values; + } + + @Override + public String toString() { + return mkString("[", ",", "]"); + } + + @Override + public String mkString() { + return toSeq().mkString(); + } + + @Override + public String mkString(String sep) { + return toSeq().mkString(sep); + } + + @Override + public String mkString(String start, String sep, String end) { + return toSeq().mkString(start, sep, end); + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala new file mode 100644 index 0000000000000..5b2c8572784bd --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverter.scala @@ -0,0 +1,223 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.PlatformDependent +import org.apache.spark.unsafe.array.ByteArrayMethods + +/** + * Converts Rows into UnsafeRow format. This class is NOT thread-safe. + * + * @param fieldTypes the data types of the row's columns. + */ +class UnsafeRowConverter(fieldTypes: Array[DataType]) { + + def this(schema: StructType) { + this(schema.fields.map(_.dataType)) + } + + /** Re-used pointer to the unsafe row being written */ + private[this] val unsafeRow = new UnsafeRow() + + /** Functions for encoding each column */ + private[this] val writers: Array[UnsafeColumnWriter] = { + fieldTypes.map(t => UnsafeColumnWriter.forType(t)) + } + + /** The size, in bytes, of the fixed-length portion of the row, including the null bitmap */ + private[this] val fixedLengthSize: Int = + (8 * fieldTypes.length) + UnsafeRow.calculateBitSetWidthInBytes(fieldTypes.length) + + /** + * Compute the amount of space, in bytes, required to encode the given row. + */ + def getSizeRequirement(row: Row): Int = { + var fieldNumber = 0 + var variableLengthFieldSize: Int = 0 + while (fieldNumber < writers.length) { + if (!row.isNullAt(fieldNumber)) { + variableLengthFieldSize += writers(fieldNumber).getSize(row, fieldNumber) + } + fieldNumber += 1 + } + fixedLengthSize + variableLengthFieldSize + } + + /** + * Convert the given row into UnsafeRow format. + * + * @param row the row to convert + * @param baseObject the base object of the destination address + * @param baseOffset the base offset of the destination address + * @return the number of bytes written. This should be equal to `getSizeRequirement(row)`. + */ + def writeRow(row: Row, baseObject: Object, baseOffset: Long): Long = { + unsafeRow.pointTo(baseObject, baseOffset, writers.length, null) + var fieldNumber = 0 + var appendCursor: Int = fixedLengthSize + while (fieldNumber < writers.length) { + if (row.isNullAt(fieldNumber)) { + unsafeRow.setNullAt(fieldNumber) + } else { + appendCursor += writers(fieldNumber).write(row, unsafeRow, fieldNumber, appendCursor) + } + fieldNumber += 1 + } + appendCursor + } + +} + +/** + * Function for writing a column into an UnsafeRow. + */ +private abstract class UnsafeColumnWriter { + /** + * Write a value into an UnsafeRow. + * + * @param source the row being converted + * @param target a pointer to the converted unsafe row + * @param column the column to write + * @param appendCursor the offset from the start of the unsafe row to the end of the row; + * used for calculating where variable-length data should be written + * @return the number of variable-length bytes written + */ + def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int + + /** + * Return the number of bytes that are needed to write this variable-length value. + */ + def getSize(source: Row, column: Int): Int +} + +private object UnsafeColumnWriter { + + def forType(dataType: DataType): UnsafeColumnWriter = { + dataType match { + case NullType => NullUnsafeColumnWriter + case BooleanType => BooleanUnsafeColumnWriter + case ByteType => ByteUnsafeColumnWriter + case ShortType => ShortUnsafeColumnWriter + case IntegerType => IntUnsafeColumnWriter + case LongType => LongUnsafeColumnWriter + case FloatType => FloatUnsafeColumnWriter + case DoubleType => DoubleUnsafeColumnWriter + case StringType => StringUnsafeColumnWriter + case t => + throw new UnsupportedOperationException(s"Do not know how to write columns of type $t") + } + } +} + +// ------------------------------------------------------------------------------------------------ + +private object NullUnsafeColumnWriter extends NullUnsafeColumnWriter +private object BooleanUnsafeColumnWriter extends BooleanUnsafeColumnWriter +private object ByteUnsafeColumnWriter extends ByteUnsafeColumnWriter +private object ShortUnsafeColumnWriter extends ShortUnsafeColumnWriter +private object IntUnsafeColumnWriter extends IntUnsafeColumnWriter +private object LongUnsafeColumnWriter extends LongUnsafeColumnWriter +private object FloatUnsafeColumnWriter extends FloatUnsafeColumnWriter +private object DoubleUnsafeColumnWriter extends DoubleUnsafeColumnWriter +private object StringUnsafeColumnWriter extends StringUnsafeColumnWriter + +private abstract class PrimitiveUnsafeColumnWriter extends UnsafeColumnWriter { + // Primitives don't write to the variable-length region: + def getSize(sourceRow: Row, column: Int): Int = 0 +} + +private class NullUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { + override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + target.setNullAt(column) + 0 + } +} + +private class BooleanUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { + override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + target.setBoolean(column, source.getBoolean(column)) + 0 + } +} + +private class ByteUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { + override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + target.setByte(column, source.getByte(column)) + 0 + } +} + +private class ShortUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { + override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + target.setShort(column, source.getShort(column)) + 0 + } +} + +private class IntUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { + override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + target.setInt(column, source.getInt(column)) + 0 + } +} + +private class LongUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { + override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + target.setLong(column, source.getLong(column)) + 0 + } +} + +private class FloatUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { + override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + target.setFloat(column, source.getFloat(column)) + 0 + } +} + +private class DoubleUnsafeColumnWriter private() extends PrimitiveUnsafeColumnWriter { + override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + target.setDouble(column, source.getDouble(column)) + 0 + } +} + +private class StringUnsafeColumnWriter private() extends UnsafeColumnWriter { + def getSize(source: Row, column: Int): Int = { + val numBytes = source.get(column).asInstanceOf[UTF8String].getBytes.length + 8 + ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes) + } + + override def write(source: Row, target: UnsafeRow, column: Int, appendCursor: Int): Int = { + val value = source.get(column).asInstanceOf[UTF8String] + val baseObject = target.getBaseObject + val baseOffset = target.getBaseOffset + val numBytes = value.getBytes.length + PlatformDependent.UNSAFE.putLong(baseObject, baseOffset + appendCursor, numBytes) + PlatformDependent.copyMemory( + value.getBytes, + PlatformDependent.BYTE_ARRAY_OFFSET, + baseObject, + baseOffset + appendCursor + 8, + numBytes + ) + target.setLong(column, appendCursor) + 8 + ByteArrayMethods.roundNumberOfBytesToNearestWord(numBytes) + } +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala new file mode 100644 index 0000000000000..7a19e511eb8b5 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeFixedWidthAggregationMapSuite.scala @@ -0,0 +1,119 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import scala.collection.JavaConverters._ +import scala.util.Random + +import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, TaskMemoryManager, MemoryAllocator} +import org.scalatest.{BeforeAndAfterEach, FunSuite, Matchers} + +import org.apache.spark.sql.types._ + +class UnsafeFixedWidthAggregationMapSuite extends FunSuite with Matchers with BeforeAndAfterEach { + + import UnsafeFixedWidthAggregationMap._ + + private val groupKeySchema = StructType(StructField("product", StringType) :: Nil) + private val aggBufferSchema = StructType(StructField("salePrice", IntegerType) :: Nil) + private def emptyAggregationBuffer: Row = new GenericRow(Array[Any](0)) + + private var memoryManager: TaskMemoryManager = null + + override def beforeEach(): Unit = { + memoryManager = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP)) + } + + override def afterEach(): Unit = { + if (memoryManager != null) { + memoryManager.cleanUpAllAllocatedMemory() + memoryManager = null + } + } + + test("supported schemas") { + assert(!supportsAggregationBufferSchema(StructType(StructField("x", StringType) :: Nil))) + assert(supportsGroupKeySchema(StructType(StructField("x", StringType) :: Nil))) + + assert( + !supportsAggregationBufferSchema(StructType(StructField("x", ArrayType(IntegerType)) :: Nil))) + assert( + !supportsGroupKeySchema(StructType(StructField("x", ArrayType(IntegerType)) :: Nil))) + } + + test("empty map") { + val map = new UnsafeFixedWidthAggregationMap( + emptyAggregationBuffer, + aggBufferSchema, + groupKeySchema, + memoryManager, + 1024, // initial capacity + false // disable perf metrics + ) + assert(!map.iterator().hasNext) + map.free() + } + + test("updating values for a single key") { + val map = new UnsafeFixedWidthAggregationMap( + emptyAggregationBuffer, + aggBufferSchema, + groupKeySchema, + memoryManager, + 1024, // initial capacity + false // disable perf metrics + ) + val groupKey = new GenericRow(Array[Any](UTF8String("cats"))) + + // Looking up a key stores a zero-entry in the map (like Python Counters or DefaultDicts) + map.getAggregationBuffer(groupKey) + val iter = map.iterator() + val entry = iter.next() + assert(!iter.hasNext) + entry.key.getString(0) should be ("cats") + entry.value.getInt(0) should be (0) + + // Modifications to rows retrieved from the map should update the values in the map + entry.value.setInt(0, 42) + map.getAggregationBuffer(groupKey).getInt(0) should be (42) + + map.free() + } + + test("inserting large random keys") { + val map = new UnsafeFixedWidthAggregationMap( + emptyAggregationBuffer, + aggBufferSchema, + groupKeySchema, + memoryManager, + 128, // initial capacity + false // disable perf metrics + ) + val rand = new Random(42) + val groupKeys: Set[String] = Seq.fill(512)(rand.nextString(1024)).toSet + groupKeys.foreach { keyString => + map.getAggregationBuffer(new GenericRow(Array[Any](UTF8String(keyString)))) + } + val seenKeys: Set[String] = map.iterator().asScala.map { entry => + entry.key.getString(0) + }.toSet + seenKeys.size should be (groupKeys.size) + seenKeys should be (groupKeys) + } + +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala new file mode 100644 index 0000000000000..3a60c7fd32675 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala @@ -0,0 +1,153 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import java.util.Arrays + +import org.scalatest.{FunSuite, Matchers} + +import org.apache.spark.sql.types._ +import org.apache.spark.unsafe.PlatformDependent +import org.apache.spark.unsafe.array.ByteArrayMethods + +class UnsafeRowConverterSuite extends FunSuite with Matchers { + + test("basic conversion with only primitive types") { + val fieldTypes: Array[DataType] = Array(LongType, LongType, IntegerType) + val converter = new UnsafeRowConverter(fieldTypes) + + val row = new SpecificMutableRow(fieldTypes) + row.setLong(0, 0) + row.setLong(1, 1) + row.setInt(2, 2) + + val sizeRequired: Int = converter.getSizeRequirement(row) + sizeRequired should be (8 + (3 * 8)) + val buffer: Array[Long] = new Array[Long](sizeRequired / 8) + val numBytesWritten = converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET) + numBytesWritten should be (sizeRequired) + + val unsafeRow = new UnsafeRow() + unsafeRow.pointTo(buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, null) + unsafeRow.getLong(0) should be (0) + unsafeRow.getLong(1) should be (1) + unsafeRow.getInt(2) should be (2) + } + + test("basic conversion with primitive and string types") { + val fieldTypes: Array[DataType] = Array(LongType, StringType, StringType) + val converter = new UnsafeRowConverter(fieldTypes) + + val row = new SpecificMutableRow(fieldTypes) + row.setLong(0, 0) + row.setString(1, "Hello") + row.setString(2, "World") + + val sizeRequired: Int = converter.getSizeRequirement(row) + sizeRequired should be (8 + (8 * 3) + + ByteArrayMethods.roundNumberOfBytesToNearestWord("Hello".getBytes.length + 8) + + ByteArrayMethods.roundNumberOfBytesToNearestWord("World".getBytes.length + 8)) + val buffer: Array[Long] = new Array[Long](sizeRequired / 8) + val numBytesWritten = converter.writeRow(row, buffer, PlatformDependent.LONG_ARRAY_OFFSET) + numBytesWritten should be (sizeRequired) + + val unsafeRow = new UnsafeRow() + unsafeRow.pointTo(buffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, null) + unsafeRow.getLong(0) should be (0) + unsafeRow.getString(1) should be ("Hello") + unsafeRow.getString(2) should be ("World") + } + + test("null handling") { + val fieldTypes: Array[DataType] = Array( + NullType, + BooleanType, + ByteType, + ShortType, + IntegerType, + LongType, + FloatType, + DoubleType) + val converter = new UnsafeRowConverter(fieldTypes) + + val rowWithAllNullColumns: Row = { + val r = new SpecificMutableRow(fieldTypes) + for (i <- 0 to fieldTypes.length - 1) { + r.setNullAt(i) + } + r + } + + val sizeRequired: Int = converter.getSizeRequirement(rowWithAllNullColumns) + val createdFromNullBuffer: Array[Long] = new Array[Long](sizeRequired / 8) + val numBytesWritten = converter.writeRow( + rowWithAllNullColumns, createdFromNullBuffer, PlatformDependent.LONG_ARRAY_OFFSET) + numBytesWritten should be (sizeRequired) + + val createdFromNull = new UnsafeRow() + createdFromNull.pointTo( + createdFromNullBuffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, null) + for (i <- 0 to fieldTypes.length - 1) { + assert(createdFromNull.isNullAt(i)) + } + createdFromNull.getBoolean(1) should be (false) + createdFromNull.getByte(2) should be (0) + createdFromNull.getShort(3) should be (0) + createdFromNull.getInt(4) should be (0) + createdFromNull.getLong(5) should be (0) + assert(java.lang.Float.isNaN(createdFromNull.getFloat(6))) + assert(java.lang.Double.isNaN(createdFromNull.getFloat(7))) + + // If we have an UnsafeRow with columns that are initially non-null and we null out those + // columns, then the serialized row representation should be identical to what we would get by + // creating an entirely null row via the converter + val rowWithNoNullColumns: Row = { + val r = new SpecificMutableRow(fieldTypes) + r.setNullAt(0) + r.setBoolean(1, false) + r.setByte(2, 20) + r.setShort(3, 30) + r.setInt(4, 400) + r.setLong(5, 500) + r.setFloat(6, 600) + r.setDouble(7, 700) + r + } + val setToNullAfterCreationBuffer: Array[Long] = new Array[Long](sizeRequired / 8) + converter.writeRow( + rowWithNoNullColumns, setToNullAfterCreationBuffer, PlatformDependent.LONG_ARRAY_OFFSET) + val setToNullAfterCreation = new UnsafeRow() + setToNullAfterCreation.pointTo( + setToNullAfterCreationBuffer, PlatformDependent.LONG_ARRAY_OFFSET, fieldTypes.length, null) + + setToNullAfterCreation.isNullAt(0) should be (rowWithNoNullColumns.isNullAt(0)) + setToNullAfterCreation.getBoolean(1) should be (rowWithNoNullColumns.getBoolean(1)) + setToNullAfterCreation.getByte(2) should be (rowWithNoNullColumns.getByte(2)) + setToNullAfterCreation.getShort(3) should be (rowWithNoNullColumns.getShort(3)) + setToNullAfterCreation.getInt(4) should be (rowWithNoNullColumns.getInt(4)) + setToNullAfterCreation.getLong(5) should be (rowWithNoNullColumns.getLong(5)) + setToNullAfterCreation.getFloat(6) should be (rowWithNoNullColumns.getFloat(6)) + setToNullAfterCreation.getDouble(7) should be (rowWithNoNullColumns.getDouble(7)) + + for (i <- 0 to fieldTypes.length - 1) { + setToNullAfterCreation.setNullAt(i) + } + assert(Arrays.equals(createdFromNullBuffer, setToNullAfterCreationBuffer)) + } + +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala index 4fc5de7e824fe..2fa602a6082dc 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala @@ -30,6 +30,7 @@ private[spark] object SQLConf { val DEFAULT_SIZE_IN_BYTES = "spark.sql.defaultSizeInBytes" val SHUFFLE_PARTITIONS = "spark.sql.shuffle.partitions" val CODEGEN_ENABLED = "spark.sql.codegen" + val UNSAFE_ENABLED = "spark.sql.unsafe.enabled" val DIALECT = "spark.sql.dialect" val PARQUET_BINARY_AS_STRING = "spark.sql.parquet.binaryAsString" @@ -149,6 +150,14 @@ private[sql] class SQLConf extends Serializable { */ private[spark] def codegenEnabled: Boolean = getConf(CODEGEN_ENABLED, "false").toBoolean + /** + * When set to true, Spark SQL will use managed memory for certain operations. This option only + * takes effect if codegen is enabled. + * + * Defaults to false as this feature is currently experimental. + */ + private[spark] def unsafeEnabled: Boolean = getConf(UNSAFE_ENABLED, "false").toBoolean + private[spark] def useSqlSerializer2: Boolean = getConf(USE_SQL_SERIALIZER2, "true").toBoolean /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala index a279b0f07c38a..bd4a55fa132fb 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala @@ -1011,6 +1011,8 @@ class SQLContext(@transient val sparkContext: SparkContext) def codegenEnabled: Boolean = self.conf.codegenEnabled + def unsafeEnabled: Boolean = self.conf.unsafeEnabled + def numPartitions: Int = self.conf.numShufflePartitions def strategies: Seq[Strategy] = diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala index b1ef6556de1e9..5d9f202681045 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala @@ -17,6 +17,7 @@ package org.apache.spark.sql.execution +import org.apache.spark.TaskContext import org.apache.spark.annotation.DeveloperApi import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.trees._ @@ -40,6 +41,7 @@ case class AggregateEvaluation( * ensure all values where `groupingExpressions` are equal are present. * @param groupingExpressions expressions that are evaluated to determine grouping. * @param aggregateExpressions expressions that are computed for each group. + * @param unsafeEnabled whether to allow Unsafe-based aggregation buffers to be used. * @param child the input data source. */ @DeveloperApi @@ -47,6 +49,7 @@ case class GeneratedAggregate( partial: Boolean, groupingExpressions: Seq[Expression], aggregateExpressions: Seq[NamedExpression], + unsafeEnabled: Boolean, child: SparkPlan) extends UnaryNode { @@ -225,6 +228,21 @@ case class GeneratedAggregate( case e: Expression if groupMap.contains(e) => groupMap(e) }) + val aggregationBufferSchema: StructType = StructType.fromAttributes(computationSchema) + + val groupKeySchema: StructType = { + val fields = groupingExpressions.zipWithIndex.map { case (expr, idx) => + // This is a dummy field name + StructField(idx.toString, expr.dataType, expr.nullable) + } + StructType(fields) + } + + val schemaSupportsUnsafe: Boolean = { + UnsafeFixedWidthAggregationMap.supportsAggregationBufferSchema(aggregationBufferSchema) && + UnsafeFixedWidthAggregationMap.supportsGroupKeySchema(groupKeySchema) + } + child.execute().mapPartitions { iter => // Builds a new custom class for holding the results of aggregation for a group. val initialValues = computeFunctions.flatMap(_.initialValues) @@ -265,7 +283,49 @@ case class GeneratedAggregate( val resultProjection = resultProjectionBuilder() Iterator(resultProjection(buffer)) + } else if (unsafeEnabled && schemaSupportsUnsafe) { + log.info("Using Unsafe-based aggregator") + val aggregationMap = new UnsafeFixedWidthAggregationMap( + newAggregationBuffer(EmptyRow), + aggregationBufferSchema, + groupKeySchema, + TaskContext.get.taskMemoryManager(), + 1024 * 16, // initial capacity + false // disable tracking of performance metrics + ) + + while (iter.hasNext) { + val currentRow: Row = iter.next() + val groupKey: Row = groupProjection(currentRow) + val aggregationBuffer = aggregationMap.getAggregationBuffer(groupKey) + updateProjection.target(aggregationBuffer)(joinedRow(aggregationBuffer, currentRow)) + } + + new Iterator[Row] { + private[this] val mapIterator = aggregationMap.iterator() + private[this] val resultProjection = resultProjectionBuilder() + + def hasNext: Boolean = mapIterator.hasNext + + def next(): Row = { + val entry = mapIterator.next() + val result = resultProjection(joinedRow(entry.key, entry.value)) + if (hasNext) { + result + } else { + // This is the last element in the iterator, so let's free the buffer. Before we do, + // though, we need to make a defensive copy of the result so that we don't return an + // object that might contain dangling pointers to the freed memory + val resultCopy = result.copy() + aggregationMap.free() + resultCopy + } + } + } } else { + if (unsafeEnabled) { + log.info("Not using Unsafe-based aggregator because it is not supported for this schema") + } val buffers = new java.util.HashMap[Row, MutableRow]() var currentRow: Row = null diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala index 3a0a6c86700a8..af58911cc0e6a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala @@ -136,10 +136,12 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { partial = false, namedGroupingAttributes, rewrittenAggregateExpressions, + unsafeEnabled, execution.GeneratedAggregate( partial = true, groupingExpressions, partialComputation, + unsafeEnabled, planLater(child))) :: Nil // Cases where some aggregate can not be codegened diff --git a/unsafe/pom.xml b/unsafe/pom.xml new file mode 100644 index 0000000000000..8901d77591932 --- /dev/null +++ b/unsafe/pom.xml @@ -0,0 +1,69 @@ + + + + + 4.0.0 + + org.apache.spark + spark-parent_2.10 + 1.4.0-SNAPSHOT + ../pom.xml + + + org.apache.spark + spark-unsafe_2.10 + jar + Spark Project Unsafe + http://spark.apache.org/ + + unsafe + + + + + + + com.google.code.findbugs + jsr305 + + + + + org.slf4j + slf4j-api + provided + + + + + junit + junit + test + + + com.novocode + junit-interface + test + + + + target/scala-${scala.binary.version}/classes + target/scala-${scala.binary.version}/test-classes + + diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java b/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java new file mode 100644 index 0000000000000..91b2f9aa43921 --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java @@ -0,0 +1,87 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe; + +import java.lang.reflect.Field; + +import sun.misc.Unsafe; + +public final class PlatformDependent { + + public static final Unsafe UNSAFE; + + public static final int BYTE_ARRAY_OFFSET; + + public static final int INT_ARRAY_OFFSET; + + public static final int LONG_ARRAY_OFFSET; + + public static final int DOUBLE_ARRAY_OFFSET; + + /** + * Limits the number of bytes to copy per {@link Unsafe#copyMemory(long, long, long)} to + * allow safepoint polling during a large copy. + */ + private static final long UNSAFE_COPY_THRESHOLD = 1024L * 1024L; + + static { + sun.misc.Unsafe unsafe; + try { + Field unsafeField = Unsafe.class.getDeclaredField("theUnsafe"); + unsafeField.setAccessible(true); + unsafe = (sun.misc.Unsafe) unsafeField.get(null); + } catch (Throwable cause) { + unsafe = null; + } + UNSAFE = unsafe; + + if (UNSAFE != null) { + BYTE_ARRAY_OFFSET = UNSAFE.arrayBaseOffset(byte[].class); + INT_ARRAY_OFFSET = UNSAFE.arrayBaseOffset(int[].class); + LONG_ARRAY_OFFSET = UNSAFE.arrayBaseOffset(long[].class); + DOUBLE_ARRAY_OFFSET = UNSAFE.arrayBaseOffset(double[].class); + } else { + BYTE_ARRAY_OFFSET = 0; + INT_ARRAY_OFFSET = 0; + LONG_ARRAY_OFFSET = 0; + DOUBLE_ARRAY_OFFSET = 0; + } + } + + static public void copyMemory( + Object src, + long srcOffset, + Object dst, + long dstOffset, + long length) { + while (length > 0) { + long size = Math.min(length, UNSAFE_COPY_THRESHOLD); + UNSAFE.copyMemory(src, srcOffset, dst, dstOffset, size); + length -= size; + srcOffset += size; + dstOffset += size; + } + } + + /** + * Raises an exception bypassing compiler checks for checked exceptions. + */ + public static void throwException(Throwable t) { + UNSAFE.throwException(t); + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java b/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java new file mode 100644 index 0000000000000..53eadf96a6b52 --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java @@ -0,0 +1,56 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.array; + +import org.apache.spark.unsafe.PlatformDependent; + +public class ByteArrayMethods { + + private ByteArrayMethods() { + // Private constructor, since this class only contains static methods. + } + + public static int roundNumberOfBytesToNearestWord(int numBytes) { + int remainder = numBytes & 0x07; // This is equivalent to `numBytes % 8` + if (remainder == 0) { + return numBytes; + } else { + return numBytes + (8 - remainder); + } + } + + /** + * Optimized byte array equality check for 8-byte-word-aligned byte arrays. + * @return true if the arrays are equal, false otherwise + */ + public static boolean wordAlignedArrayEquals( + Object leftBaseObject, + long leftBaseOffset, + Object rightBaseObject, + long rightBaseOffset, + long arrayLengthInBytes) { + for (int i = 0; i < arrayLengthInBytes; i += 8) { + final long left = + PlatformDependent.UNSAFE.getLong(leftBaseObject, leftBaseOffset + i); + final long right = + PlatformDependent.UNSAFE.getLong(rightBaseObject, rightBaseOffset + i); + if (left != right) return false; + } + return true; + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/array/LongArray.java b/unsafe/src/main/java/org/apache/spark/unsafe/array/LongArray.java new file mode 100644 index 0000000000000..18d1f0d2d7eb2 --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/array/LongArray.java @@ -0,0 +1,78 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.array; + +import org.apache.spark.unsafe.PlatformDependent; +import org.apache.spark.unsafe.memory.MemoryBlock; + +/** + * An array of long values. Compared with native JVM arrays, this: + *
      + *
    • supports using both in-heap and off-heap memory
      • + *
    • has no bound checking, and thus can crash the JVM process when assert is turned off
      • + *
    + */ +public final class LongArray { + + // This is a long so that we perform long multiplications when computing offsets. + private static final long WIDTH = 8; + + private final MemoryBlock memory; + private final Object baseObj; + private final long baseOffset; + + private final long length; + + public LongArray(MemoryBlock memory) { + assert memory.size() % WIDTH == 0 : "Memory not aligned (" + memory.size() + ")"; + assert memory.size() < (long) Integer.MAX_VALUE * 8: "Array size > 4 billion elements"; + this.memory = memory; + this.baseObj = memory.getBaseObject(); + this.baseOffset = memory.getBaseOffset(); + this.length = memory.size() / WIDTH; + } + + public MemoryBlock memoryBlock() { + return memory; + } + + /** + * Returns the number of elements this array can hold. + */ + public long size() { + return length; + } + + /** + * Sets the value at position {@code index}. + */ + public void set(int index, long value) { + assert index >= 0 : "index (" + index + ") should >= 0"; + assert index < length : "index (" + index + ") should < length (" + length + ")"; + PlatformDependent.UNSAFE.putLong(baseObj, baseOffset + index * WIDTH, value); + } + + /** + * Returns the value at position {@code index}. + */ + public long get(int index) { + assert index >= 0 : "index (" + index + ") should >= 0"; + assert index < length : "index (" + index + ") should < length (" + length + ")"; + return PlatformDependent.UNSAFE.getLong(baseObj, baseOffset + index * WIDTH); + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSet.java b/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSet.java new file mode 100644 index 0000000000000..f72e07fce92fd --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSet.java @@ -0,0 +1,105 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.bitset; + +import org.apache.spark.unsafe.array.LongArray; +import org.apache.spark.unsafe.memory.MemoryBlock; + +/** + * A fixed size uncompressed bit set backed by a {@link LongArray}. + * + * Each bit occupies exactly one bit of storage. + */ +public final class BitSet { + + /** A long array for the bits. */ + private final LongArray words; + + /** Length of the long array. */ + private final int numWords; + + private final Object baseObject; + private final long baseOffset; + + /** + * Creates a new {@link BitSet} using the specified memory block. Size of the memory block must be + * multiple of 8 bytes (i.e. 64 bits). + */ + public BitSet(MemoryBlock memory) { + words = new LongArray(memory); + assert (words.size() <= Integer.MAX_VALUE); + numWords = (int) words.size(); + baseObject = words.memoryBlock().getBaseObject(); + baseOffset = words.memoryBlock().getBaseOffset(); + } + + public MemoryBlock memoryBlock() { + return words.memoryBlock(); + } + + /** + * Returns the number of bits in this {@code BitSet}. + */ + public long capacity() { + return numWords * 64; + } + + /** + * Sets the bit at the specified index to {@code true}. + */ + public void set(int index) { + assert index < numWords * 64 : "index (" + index + ") should < length (" + numWords * 64 + ")"; + BitSetMethods.set(baseObject, baseOffset, index); + } + + /** + * Sets the bit at the specified index to {@code false}. + */ + public void unset(int index) { + assert index < numWords * 64 : "index (" + index + ") should < length (" + numWords * 64 + ")"; + BitSetMethods.unset(baseObject, baseOffset, index); + } + + /** + * Returns {@code true} if the bit is set at the specified index. + */ + public boolean isSet(int index) { + assert index < numWords * 64 : "index (" + index + ") should < length (" + numWords * 64 + ")"; + return BitSetMethods.isSet(baseObject, baseOffset, index); + } + + /** + * Returns the index of the first bit that is set to true that occurs on or after the + * specified starting index. If no such bit exists then {@code -1} is returned. + *

    + * To iterate over the true bits in a BitSet, use the following loop: + * 

    +   * 
+   *  for (long i = bs.nextSetBit(0); i >= 0; i = bs.nextSetBit(i + 1)) {
+   *    // operate on index i here
+   *  }
+   * 
+   *
    + * + * @param fromIndex the index to start checking from (inclusive) + * @return the index of the next set bit, or -1 if there is no such bit + */ + public int nextSetBit(int fromIndex) { + return BitSetMethods.nextSetBit(baseObject, baseOffset, fromIndex, numWords); + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java b/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java new file mode 100644 index 0000000000000..f30626d8f4317 --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java @@ -0,0 +1,129 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.bitset; + +import org.apache.spark.unsafe.PlatformDependent; + +/** + * Methods for working with fixed-size uncompressed bitsets. + * + * We assume that the bitset data is word-aligned (that is, a multiple of 8 bytes in length). + * + * Each bit occupies exactly one bit of storage. + */ +public final class BitSetMethods { + + private static final long WORD_SIZE = 8; + + private BitSetMethods() { + // Make the default constructor private, since this only holds static methods. + } + + /** + * Sets the bit at the specified index to {@code true}. + */ + public static void set(Object baseObject, long baseOffset, int index) { + assert index >= 0 : "index (" + index + ") should >= 0"; + final long mask = 1L << (index & 0x3f); // mod 64 and shift + final long wordOffset = baseOffset + (index >> 6) * WORD_SIZE; + final long word = PlatformDependent.UNSAFE.getLong(baseObject, wordOffset); + PlatformDependent.UNSAFE.putLong(baseObject, wordOffset, word | mask); + } + + /** + * Sets the bit at the specified index to {@code false}. + */ + public static void unset(Object baseObject, long baseOffset, int index) { + assert index >= 0 : "index (" + index + ") should >= 0"; + final long mask = 1L << (index & 0x3f); // mod 64 and shift + final long wordOffset = baseOffset + (index >> 6) * WORD_SIZE; + final long word = PlatformDependent.UNSAFE.getLong(baseObject, wordOffset); + PlatformDependent.UNSAFE.putLong(baseObject, wordOffset, word & ~mask); + } + + /** + * Returns {@code true} if the bit is set at the specified index. + */ + public static boolean isSet(Object baseObject, long baseOffset, int index) { + assert index >= 0 : "index (" + index + ") should >= 0"; + final long mask = 1L << (index & 0x3f); // mod 64 and shift + final long wordOffset = baseOffset + (index >> 6) * WORD_SIZE; + final long word = PlatformDependent.UNSAFE.getLong(baseObject, wordOffset); + return (word & mask) != 0; + } + + /** + * Returns {@code true} if any bit is set. + */ + public static boolean anySet(Object baseObject, long baseOffset, long bitSetWidthInBytes) { + for (int i = 0; i <= bitSetWidthInBytes; i++) { + if (PlatformDependent.UNSAFE.getByte(baseObject, baseOffset + i) != 0) { + return true; + } + } + return false; + } + + /** + * Returns the index of the first bit that is set to true that occurs on or after the + * specified starting index. If no such bit exists then {@code -1} is returned. + *

    + * To iterate over the true bits in a BitSet, use the following loop: + * 

    +   * 
+   *  for (long i = bs.nextSetBit(0, sizeInWords); i >= 0; i = bs.nextSetBit(i + 1, sizeInWords)) {
+   *    // operate on index i here
+   *  }
+   * 
+   *
    + * + * @param fromIndex the index to start checking from (inclusive) + * @param bitsetSizeInWords the size of the bitset, measured in 8-byte words + * @return the index of the next set bit, or -1 if there is no such bit + */ + public static int nextSetBit( + Object baseObject, + long baseOffset, + int fromIndex, + int bitsetSizeInWords) { + int wi = fromIndex >> 6; + if (wi >= bitsetSizeInWords) { + return -1; + } + + // Try to find the next set bit in the current word + final int subIndex = fromIndex & 0x3f; + long word = + PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + wi * WORD_SIZE) >> subIndex; + if (word != 0) { + return (wi << 6) + subIndex + java.lang.Long.numberOfTrailingZeros(word); + } + + // Find the next set bit in the rest of the words + wi += 1; + while (wi < bitsetSizeInWords) { + word = PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + wi * WORD_SIZE); + if (word != 0) { + return (wi << 6) + java.lang.Long.numberOfTrailingZeros(word); + } + wi += 1; + } + + return -1; + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java b/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java new file mode 100644 index 0000000000000..85cd02469adb7 --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java @@ -0,0 +1,96 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.hash; + +import org.apache.spark.unsafe.PlatformDependent; + +/** + * 32-bit Murmur3 hasher. This is based on Guava's Murmur3_32HashFunction. + */ +public final class Murmur3_x86_32 { + private static final int C1 = 0xcc9e2d51; + private static final int C2 = 0x1b873593; + + private final int seed; + + public Murmur3_x86_32(int seed) { + this.seed = seed; + } + + @Override + public String toString() { + return "Murmur3_32(seed=" + seed + ")"; + } + + public int hashInt(int input) { + int k1 = mixK1(input); + int h1 = mixH1(seed, k1); + + return fmix(h1, 4); + } + + public int hashUnsafeWords(Object baseObject, long baseOffset, int lengthInBytes) { + // This is based on Guava's `Murmur32_Hasher.processRemaining(ByteBuffer)` method. + assert (lengthInBytes % 8 == 0): "lengthInBytes must be a multiple of 8 (word-aligned)"; + int h1 = seed; + for (int offset = 0; offset < lengthInBytes; offset += 4) { + int halfWord = PlatformDependent.UNSAFE.getInt(baseObject, baseOffset + offset); + int k1 = mixK1(halfWord); + h1 = mixH1(h1, k1); + } + return fmix(h1, lengthInBytes); + } + + public int hashLong(long input) { + int low = (int) input; + int high = (int) (input >>> 32); + + int k1 = mixK1(low); + int h1 = mixH1(seed, k1); + + k1 = mixK1(high); + h1 = mixH1(h1, k1); + + return fmix(h1, 8); + } + + private static int mixK1(int k1) { + k1 *= C1; + k1 = Integer.rotateLeft(k1, 15); + k1 *= C2; + return k1; + } + + private static int mixH1(int h1, int k1) { + h1 ^= k1; + h1 = Integer.rotateLeft(h1, 13); + h1 = h1 * 5 + 0xe6546b64; + return h1; + } + + // Finalization mix - force all bits of a hash block to avalanche + private static int fmix(int h1, int length) { + h1 ^= length; + h1 ^= h1 >>> 16; + h1 *= 0x85ebca6b; + h1 ^= h1 >>> 13; + h1 *= 0xc2b2ae35; + h1 ^= h1 >>> 16; + return h1; + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java new file mode 100644 index 0000000000000..85b64c0833803 --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java @@ -0,0 +1,549 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.map; + +import java.lang.Override; +import java.lang.UnsupportedOperationException; +import java.util.Iterator; +import java.util.LinkedList; +import java.util.List; + +import org.apache.spark.unsafe.*; +import org.apache.spark.unsafe.array.ByteArrayMethods; +import org.apache.spark.unsafe.array.LongArray; +import org.apache.spark.unsafe.bitset.BitSet; +import org.apache.spark.unsafe.hash.Murmur3_x86_32; +import org.apache.spark.unsafe.memory.*; + +/** + * An append-only hash map where keys and values are contiguous regions of bytes. + *

    + * This is backed by a power-of-2-sized hash table, using quadratic probing with triangular numbers, + * which is guaranteed to exhaust the space. + *

    + * The map can support up to 2^31 keys because we use 32 bit MurmurHash. If the key cardinality is + * higher than this, you should probably be using sorting instead of hashing for better cache + * locality. + *

    + * This class is not thread safe. + */ +public final class BytesToBytesMap { + + private static final Murmur3_x86_32 HASHER = new Murmur3_x86_32(0); + + private static final HashMapGrowthStrategy growthStrategy = HashMapGrowthStrategy.DOUBLING; + + private final TaskMemoryManager memoryManager; + + /** + * A linked list for tracking all allocated data pages so that we can free all of our memory. + */ + private final List dataPages = new LinkedList(); + + /** + * The data page that will be used to store keys and values for new hashtable entries. When this + * page becomes full, a new page will be allocated and this pointer will change to point to that + * new page. + */ + private MemoryBlock currentDataPage = null; + + /** + * Offset into `currentDataPage` that points to the location where new data can be inserted into + * the page. + */ + private long pageCursor = 0; + + /** + * The size of the data pages that hold key and value data. Map entries cannot span multiple + * pages, so this limits the maximum entry size. + */ + private static final long PAGE_SIZE_BYTES = 1L << 26; // 64 megabytes + + // This choice of page table size and page size means that we can address up to 500 gigabytes + // of memory. + + /** + * A single array to store the key and value. + * + * Position {@code 2 * i} in the array is used to track a pointer to the key at index {@code i}, + * while position {@code 2 * i + 1} in the array holds key's full 32-bit hashcode. + */ + private LongArray longArray; + // TODO: we're wasting 32 bits of space here; we can probably store fewer bits of the hashcode + // and exploit word-alignment to use fewer bits to hold the address. This might let us store + // only one long per map entry, increasing the chance that this array will fit in cache at the + // expense of maybe performing more lookups if we have hash collisions. Say that we stored only + // 27 bits of the hashcode and 37 bits of the address. 37 bits is enough to address 1 terabyte + // of RAM given word-alignment. If we use 13 bits of this for our page table, that gives us a + // maximum page size of 2^24 * 8 = ~134 megabytes per page. This change will require us to store + // full base addresses in the page table for off-heap mode so that we can reconstruct the full + // absolute memory addresses. + + /** + * A {@link BitSet} used to track location of the map where the key is set. + * Size of the bitset should be half of the size of the long array. + */ + private BitSet bitset; + + private final double loadFactor; + + /** + * Number of keys defined in the map. + */ + private int size; + + /** + * The map will be expanded once the number of keys exceeds this threshold. + */ + private int growthThreshold; + + /** + * Mask for truncating hashcodes so that they do not exceed the long array's size. + * This is a strength reduction optimization; we're essentially performing a modulus operation, + * but doing so with a bitmask because this is a power-of-2-sized hash map. + */ + private int mask; + + /** + * Return value of {@link BytesToBytesMap#lookup(Object, long, int)}. + */ + private final Location loc; + + private final boolean enablePerfMetrics; + + private long timeSpentResizingNs = 0; + + private long numProbes = 0; + + private long numKeyLookups = 0; + + private long numHashCollisions = 0; + + public BytesToBytesMap( + TaskMemoryManager memoryManager, + int initialCapacity, + double loadFactor, + boolean enablePerfMetrics) { + this.memoryManager = memoryManager; + this.loadFactor = loadFactor; + this.loc = new Location(); + this.enablePerfMetrics = enablePerfMetrics; + allocate(initialCapacity); + } + + public BytesToBytesMap(TaskMemoryManager memoryManager, int initialCapacity) { + this(memoryManager, initialCapacity, 0.70, false); + } + + public BytesToBytesMap( + TaskMemoryManager memoryManager, + int initialCapacity, + boolean enablePerfMetrics) { + this(memoryManager, initialCapacity, 0.70, enablePerfMetrics); + } + + /** + * Returns the number of keys defined in the map. + */ + public int size() { return size; } + + /** + * Returns an iterator for iterating over the entries of this map. + * + * For efficiency, all calls to `next()` will return the same {@link Location} object. + * + * If any other lookups or operations are performed on this map while iterating over it, including + * `lookup()`, the behavior of the returned iterator is undefined. + */ + public Iterator iterator() { + return new Iterator() { + + private int nextPos = bitset.nextSetBit(0); + + @Override + public boolean hasNext() { + return nextPos != -1; + } + + @Override + public Location next() { + final int pos = nextPos; + nextPos = bitset.nextSetBit(nextPos + 1); + return loc.with(pos, 0, true); + } + + @Override + public void remove() { + throw new UnsupportedOperationException(); + } + }; + } + + /** + * Looks up a key, and return a {@link Location} handle that can be used to test existence + * and read/write values. + * + * This function always return the same {@link Location} instance to avoid object allocation. + */ + public Location lookup( + Object keyBaseObject, + long keyBaseOffset, + int keyRowLengthBytes) { + if (enablePerfMetrics) { + numKeyLookups++; + } + final int hashcode = HASHER.hashUnsafeWords(keyBaseObject, keyBaseOffset, keyRowLengthBytes); + int pos = hashcode & mask; + int step = 1; + while (true) { + if (enablePerfMetrics) { + numProbes++; + } + if (!bitset.isSet(pos)) { + // This is a new key. + return loc.with(pos, hashcode, false); + } else { + long stored = longArray.get(pos * 2 + 1); + if ((int) (stored) == hashcode) { + // Full hash code matches. Let's compare the keys for equality. + loc.with(pos, hashcode, true); + if (loc.getKeyLength() == keyRowLengthBytes) { + final MemoryLocation keyAddress = loc.getKeyAddress(); + final Object storedKeyBaseObject = keyAddress.getBaseObject(); + final long storedKeyBaseOffset = keyAddress.getBaseOffset(); + final boolean areEqual = ByteArrayMethods.wordAlignedArrayEquals( + keyBaseObject, + keyBaseOffset, + storedKeyBaseObject, + storedKeyBaseOffset, + keyRowLengthBytes + ); + if (areEqual) { + return loc; + } else { + if (enablePerfMetrics) { + numHashCollisions++; + } + } + } + } + } + pos = (pos + step) & mask; + step++; + } + } + + /** + * Handle returned by {@link BytesToBytesMap#lookup(Object, long, int)} function. + */ + public final class Location { + /** An index into the hash map's Long array */ + private int pos; + /** True if this location points to a position where a key is defined, false otherwise */ + private boolean isDefined; + /** + * The hashcode of the most recent key passed to + * {@link BytesToBytesMap#lookup(Object, long, int)}. Caching this hashcode here allows us to + * avoid re-hashing the key when storing a value for that key. + */ + private int keyHashcode; + private final MemoryLocation keyMemoryLocation = new MemoryLocation(); + private final MemoryLocation valueMemoryLocation = new MemoryLocation(); + private int keyLength; + private int valueLength; + + private void updateAddressesAndSizes(long fullKeyAddress) { + final Object page = memoryManager.getPage(fullKeyAddress); + final long keyOffsetInPage = memoryManager.getOffsetInPage(fullKeyAddress); + long position = keyOffsetInPage; + keyLength = (int) PlatformDependent.UNSAFE.getLong(page, position); + position += 8; // word used to store the key size + keyMemoryLocation.setObjAndOffset(page, position); + position += keyLength; + valueLength = (int) PlatformDependent.UNSAFE.getLong(page, position); + position += 8; // word used to store the key size + valueMemoryLocation.setObjAndOffset(page, position); + } + + Location with(int pos, int keyHashcode, boolean isDefined) { + this.pos = pos; + this.isDefined = isDefined; + this.keyHashcode = keyHashcode; + if (isDefined) { + final long fullKeyAddress = longArray.get(pos * 2); + updateAddressesAndSizes(fullKeyAddress); + } + return this; + } + + /** + * Returns true if the key is defined at this position, and false otherwise. + */ + public boolean isDefined() { + return isDefined; + } + + /** + * Returns the address of the key defined at this position. + * This points to the first byte of the key data. + * Unspecified behavior if the key is not defined. + * For efficiency reasons, calls to this method always returns the same MemoryLocation object. + */ + public MemoryLocation getKeyAddress() { + assert (isDefined); + return keyMemoryLocation; + } + + /** + * Returns the length of the key defined at this position. + * Unspecified behavior if the key is not defined. + */ + public int getKeyLength() { + assert (isDefined); + return keyLength; + } + + /** + * Returns the address of the value defined at this position. + * This points to the first byte of the value data. + * Unspecified behavior if the key is not defined. + * For efficiency reasons, calls to this method always returns the same MemoryLocation object. + */ + public MemoryLocation getValueAddress() { + assert (isDefined); + return valueMemoryLocation; + } + + /** + * Returns the length of the value defined at this position. + * Unspecified behavior if the key is not defined. + */ + public int getValueLength() { + assert (isDefined); + return valueLength; + } + + /** + * Store a new key and value. This method may only be called once for a given key; if you want + * to update the value associated with a key, then you can directly manipulate the bytes stored + * at the value address. + *

    + * It is only valid to call this method immediately after calling `lookup()` using the same key. + *

    + * After calling this method, calls to `get[Key|Value]Address()` and `get[Key|Value]Length` + * will return information on the data stored by this `putNewKey` call. + *

    + * As an example usage, here's the proper way to store a new key: + *

    + * 

    +     *   Location loc = map.lookup(keyBaseOffset, keyBaseObject, keyLengthInBytes);
+     *   if (!loc.isDefined()) {
+     *     loc.putNewKey(keyBaseOffset, keyBaseObject, keyLengthInBytes, ...)
+     *   }
+     *
    + *

    + * Unspecified behavior if the key is not defined. + */ + public void putNewKey( + Object keyBaseObject, + long keyBaseOffset, + int keyLengthBytes, + Object valueBaseObject, + long valueBaseOffset, + int valueLengthBytes) { + assert (!isDefined) : "Can only set value once for a key"; + isDefined = true; + assert (keyLengthBytes % 8 == 0); + assert (valueLengthBytes % 8 == 0); + // Here, we'll copy the data into our data pages. Because we only store a relative offset from + // the key address instead of storing the absolute address of the value, the key and value + // must be stored in the same memory page. + // (8 byte key length) (key) (8 byte value length) (value) + final long requiredSize = 8 + keyLengthBytes + 8 + valueLengthBytes; + assert(requiredSize <= PAGE_SIZE_BYTES); + size++; + bitset.set(pos); + + // If there's not enough space in the current page, allocate a new page: + if (currentDataPage == null || PAGE_SIZE_BYTES - pageCursor < requiredSize) { + MemoryBlock newPage = memoryManager.allocatePage(PAGE_SIZE_BYTES); + dataPages.add(newPage); + pageCursor = 0; + currentDataPage = newPage; + } + + // Compute all of our offsets up-front: + final Object pageBaseObject = currentDataPage.getBaseObject(); + final long pageBaseOffset = currentDataPage.getBaseOffset(); + final long keySizeOffsetInPage = pageBaseOffset + pageCursor; + pageCursor += 8; // word used to store the key size + final long keyDataOffsetInPage = pageBaseOffset + pageCursor; + pageCursor += keyLengthBytes; + final long valueSizeOffsetInPage = pageBaseOffset + pageCursor; + pageCursor += 8; // word used to store the value size + final long valueDataOffsetInPage = pageBaseOffset + pageCursor; + pageCursor += valueLengthBytes; + + // Copy the key + PlatformDependent.UNSAFE.putLong(pageBaseObject, keySizeOffsetInPage, keyLengthBytes); + PlatformDependent.UNSAFE.copyMemory( + keyBaseObject, keyBaseOffset, pageBaseObject, keyDataOffsetInPage, keyLengthBytes); + // Copy the value + PlatformDependent.UNSAFE.putLong(pageBaseObject, valueSizeOffsetInPage, valueLengthBytes); + PlatformDependent.UNSAFE.copyMemory( + valueBaseObject, valueBaseOffset, pageBaseObject, valueDataOffsetInPage, valueLengthBytes); + + final long storedKeyAddress = memoryManager.encodePageNumberAndOffset( + currentDataPage, keySizeOffsetInPage); + longArray.set(pos * 2, storedKeyAddress); + longArray.set(pos * 2 + 1, keyHashcode); + updateAddressesAndSizes(storedKeyAddress); + isDefined = true; + if (size > growthThreshold) { + growAndRehash(); + } + } + } + + /** + * Allocate new data structures for this map. When calling this outside of the constructor, + * make sure to keep references to the old data structures so that you can free them. + * + * @param capacity the new map capacity + */ + private void allocate(int capacity) { + capacity = Math.max((int) Math.min(Integer.MAX_VALUE, nextPowerOf2(capacity)), 64); + longArray = new LongArray(memoryManager.allocate(capacity * 8 * 2)); + bitset = new BitSet(memoryManager.allocate(capacity / 8).zero()); + + this.growthThreshold = (int) (capacity * loadFactor); + this.mask = capacity - 1; + } + + /** + * Free all allocated memory associated with this map, including the storage for keys and values + * as well as the hash map array itself. + * + * This method is idempotent. + */ + public void free() { + if (longArray != null) { + memoryManager.free(longArray.memoryBlock()); + longArray = null; + } + if (bitset != null) { + memoryManager.free(bitset.memoryBlock()); + bitset = null; + } + Iterator dataPagesIterator = dataPages.iterator(); + while (dataPagesIterator.hasNext()) { + memoryManager.freePage(dataPagesIterator.next()); + dataPagesIterator.remove(); + } + assert(dataPages.isEmpty()); + } + + /** Returns the total amount of memory, in bytes, consumed by this map's managed structures. */ + public long getTotalMemoryConsumption() { + return ( + dataPages.size() * PAGE_SIZE_BYTES + + bitset.memoryBlock().size() + + longArray.memoryBlock().size()); + } + + /** + * Returns the total amount of time spent resizing this map (in nanoseconds). + */ + public long getTimeSpentResizingNs() { + if (!enablePerfMetrics) { + throw new IllegalStateException(); + } + return timeSpentResizingNs; + } + + + /** + * Returns the average number of probes per key lookup. + */ + public double getAverageProbesPerLookup() { + if (!enablePerfMetrics) { + throw new IllegalStateException(); + } + return (1.0 * numProbes) / numKeyLookups; + } + + public long getNumHashCollisions() { + if (!enablePerfMetrics) { + throw new IllegalStateException(); + } + return numHashCollisions; + } + + /** + * Grows the size of the hash table and re-hash everything. + */ + private void growAndRehash() { + long resizeStartTime = -1; + if (enablePerfMetrics) { + resizeStartTime = System.nanoTime(); + } + // Store references to the old data structures to be used when we re-hash + final LongArray oldLongArray = longArray; + final BitSet oldBitSet = bitset; + final int oldCapacity = (int) oldBitSet.capacity(); + + // Allocate the new data structures + allocate(Math.min(Integer.MAX_VALUE, growthStrategy.nextCapacity(oldCapacity))); + + // Re-mask (we don't recompute the hashcode because we stored all 32 bits of it) + for (int pos = oldBitSet.nextSetBit(0); pos >= 0; pos = oldBitSet.nextSetBit(pos + 1)) { + final long keyPointer = oldLongArray.get(pos * 2); + final int hashcode = (int) oldLongArray.get(pos * 2 + 1); + int newPos = hashcode & mask; + int step = 1; + boolean keepGoing = true; + + // No need to check for equality here when we insert so this has one less if branch than + // the similar code path in addWithoutResize. + while (keepGoing) { + if (!bitset.isSet(newPos)) { + bitset.set(newPos); + longArray.set(newPos * 2, keyPointer); + longArray.set(newPos * 2 + 1, hashcode); + keepGoing = false; + } else { + newPos = (newPos + step) & mask; + step++; + } + } + } + + // Deallocate the old data structures. + memoryManager.free(oldLongArray.memoryBlock()); + memoryManager.free(oldBitSet.memoryBlock()); + if (enablePerfMetrics) { + timeSpentResizingNs += System.nanoTime() - resizeStartTime; + } + } + + /** Returns the next number greater or equal num that is power of 2. */ + private static long nextPowerOf2(long num) { + final long highBit = Long.highestOneBit(num); + return (highBit == num) ? num : highBit << 1; + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/map/HashMapGrowthStrategy.java b/unsafe/src/main/java/org/apache/spark/unsafe/map/HashMapGrowthStrategy.java new file mode 100644 index 0000000000000..7c321baffe82d --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/map/HashMapGrowthStrategy.java @@ -0,0 +1,39 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.map; + +/** + * Interface that defines how we can grow the size of a hash map when it is over a threshold. + */ +public interface HashMapGrowthStrategy { + + int nextCapacity(int currentCapacity); + + /** + * Double the size of the hash map every time. + */ + HashMapGrowthStrategy DOUBLING = new Doubling(); + + class Doubling implements HashMapGrowthStrategy { + @Override + public int nextCapacity(int currentCapacity) { + return currentCapacity * 2; + } + } + +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/ExecutorMemoryManager.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/ExecutorMemoryManager.java new file mode 100644 index 0000000000000..62c29c8cc1e4d --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/ExecutorMemoryManager.java @@ -0,0 +1,58 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.memory; + +/** + * Manages memory for an executor. Individual operators / tasks allocate memory through + * {@link TaskMemoryManager} objects, which obtain their memory from ExecutorMemoryManager. + */ +public class ExecutorMemoryManager { + + /** + * Allocator, exposed for enabling untracked allocations of temporary data structures. + */ + public final MemoryAllocator allocator; + + /** + * Tracks whether memory will be allocated on the JVM heap or off-heap using sun.misc.Unsafe. + */ + final boolean inHeap; + + /** + * Construct a new ExecutorMemoryManager. + * + * @param allocator the allocator that will be used + */ + public ExecutorMemoryManager(MemoryAllocator allocator) { + this.inHeap = allocator instanceof HeapMemoryAllocator; + this.allocator = allocator; + } + + /** + * Allocates a contiguous block of memory. Note that the allocated memory is not guaranteed + * to be zeroed out (call `zero()` on the result if this is necessary). + */ + MemoryBlock allocate(long size) throws OutOfMemoryError { + return allocator.allocate(size); + } + + void free(MemoryBlock memory) { + allocator.free(memory); + } + +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/HeapMemoryAllocator.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/HeapMemoryAllocator.java new file mode 100644 index 0000000000000..bbe83d36cf36b --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/HeapMemoryAllocator.java @@ -0,0 +1,35 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.memory; + +/** + * A simple {@link MemoryAllocator} that can allocate up to 16GB using a JVM long primitive array. + */ +public class HeapMemoryAllocator implements MemoryAllocator { + + @Override + public MemoryBlock allocate(long size) throws OutOfMemoryError { + long[] array = new long[(int) (size / 8)]; + return MemoryBlock.fromLongArray(array); + } + + @Override + public void free(MemoryBlock memory) { + // Do nothing + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryAllocator.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryAllocator.java new file mode 100644 index 0000000000000..5192f68c862cf --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryAllocator.java @@ -0,0 +1,33 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.memory; + +public interface MemoryAllocator { + + /** + * Allocates a contiguous block of memory. Note that the allocated memory is not guaranteed + * to be zeroed out (call `zero()` on the result if this is necessary). + */ + MemoryBlock allocate(long size) throws OutOfMemoryError; + + void free(MemoryBlock memory); + + MemoryAllocator UNSAFE = new UnsafeMemoryAllocator(); + + MemoryAllocator HEAP = new HeapMemoryAllocator(); +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java new file mode 100644 index 0000000000000..0beb743e5644e --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java @@ -0,0 +1,63 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.memory; + +import javax.annotation.Nullable; + +import org.apache.spark.unsafe.PlatformDependent; + +/** + * A consecutive block of memory, starting at a {@link MemoryLocation} with a fixed size. + */ +public class MemoryBlock extends MemoryLocation { + + private final long length; + + /** + * Optional page number; used when this MemoryBlock represents a page allocated by a + * MemoryManager. This is package-private and is modified by MemoryManager. + */ + int pageNumber = -1; + + MemoryBlock(@Nullable Object obj, long offset, long length) { + super(obj, offset); + this.length = length; + } + + /** + * Returns the size of the memory block. + */ + public long size() { + return length; + } + + /** + * Clear the contents of this memory block. Returns `this` to facilitate chaining. + */ + public MemoryBlock zero() { + PlatformDependent.UNSAFE.setMemory(obj, offset, length, (byte) 0); + return this; + } + + /** + * Creates a memory block pointing to the memory used by the long array. + */ + public static MemoryBlock fromLongArray(final long[] array) { + return new MemoryBlock(array, PlatformDependent.LONG_ARRAY_OFFSET, array.length * 8); + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryLocation.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryLocation.java new file mode 100644 index 0000000000000..74ebc87dc978c --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryLocation.java @@ -0,0 +1,54 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.memory; + +import javax.annotation.Nullable; + +/** + * A memory location. Tracked either by a memory address (with off-heap allocation), + * or by an offset from a JVM object (in-heap allocation). + */ +public class MemoryLocation { + + @Nullable + Object obj; + + long offset; + + public MemoryLocation(@Nullable Object obj, long offset) { + this.obj = obj; + this.offset = offset; + } + + public MemoryLocation() { + this(null, 0); + } + + public void setObjAndOffset(Object newObj, long newOffset) { + this.obj = newObj; + this.offset = newOffset; + } + + public final Object getBaseObject() { + return obj; + } + + public final long getBaseOffset() { + return offset; + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java new file mode 100644 index 0000000000000..9224988e6ad69 --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java @@ -0,0 +1,237 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.memory; + +import java.util.*; + +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +/** + * Manages the memory allocated by an individual task. + *

    + * Most of the complexity in this class deals with encoding of off-heap addresses into 64-bit longs. + * In off-heap mode, memory can be directly addressed with 64-bit longs. In on-heap mode, memory is + * addressed by the combination of a base Object reference and a 64-bit offset within that object. + * This is a problem when we want to store pointers to data structures inside of other structures, + * such as record pointers inside hashmaps or sorting buffers. Even if we decided to use 128 bits + * to address memory, we can't just store the address of the base object since it's not guaranteed + * to remain stable as the heap gets reorganized due to GC. + *

    + * Instead, we use the following approach to encode record pointers in 64-bit longs: for off-heap + * mode, just store the raw address, and for on-heap mode use the upper 13 bits of the address to + * store a "page number" and the lower 51 bits to store an offset within this page. These page + * numbers are used to index into a "page table" array inside of the MemoryManager in order to + * retrieve the base object. + *

    + * This allows us to address 8192 pages. In on-heap mode, the maximum page size is limited by the + * maximum size of a long[] array, allowing us to address 8192 * 2^32 * 8 bytes, which is + * approximately 35 terabytes of memory. + */ +public final class TaskMemoryManager { + + private final Logger logger = LoggerFactory.getLogger(TaskMemoryManager.class); + + /** + * The number of entries in the page table. + */ + private static final int PAGE_TABLE_SIZE = 1 << 13; + + /** Bit mask for the lower 51 bits of a long. */ + private static final long MASK_LONG_LOWER_51_BITS = 0x7FFFFFFFFFFFFL; + + /** Bit mask for the upper 13 bits of a long */ + private static final long MASK_LONG_UPPER_13_BITS = ~MASK_LONG_LOWER_51_BITS; + + /** + * Similar to an operating system's page table, this array maps page numbers into base object + * pointers, allowing us to translate between the hashtable's internal 64-bit address + * representation and the baseObject+offset representation which we use to support both in- and + * off-heap addresses. When using an off-heap allocator, every entry in this map will be `null`. + * When using an in-heap allocator, the entries in this map will point to pages' base objects. + * Entries are added to this map as new data pages are allocated. + */ + private final MemoryBlock[] pageTable = new MemoryBlock[PAGE_TABLE_SIZE]; + + /** + * Bitmap for tracking free pages. + */ + private final BitSet allocatedPages = new BitSet(PAGE_TABLE_SIZE); + + /** + * Tracks memory allocated with {@link TaskMemoryManager#allocate(long)}, used to detect / clean + * up leaked memory. + */ + private final HashSet allocatedNonPageMemory = new HashSet(); + + private final ExecutorMemoryManager executorMemoryManager; + + /** + * Tracks whether we're in-heap or off-heap. For off-heap, we short-circuit most of these methods + * without doing any masking or lookups. Since this branching should be well-predicted by the JIT, + * this extra layer of indirection / abstraction hopefully shouldn't be too expensive. + */ + private final boolean inHeap; + + /** + * Construct a new MemoryManager. + */ + public TaskMemoryManager(ExecutorMemoryManager executorMemoryManager) { + this.inHeap = executorMemoryManager.inHeap; + this.executorMemoryManager = executorMemoryManager; + } + + /** + * Allocate a block of memory that will be tracked in the MemoryManager's page table; this is + * intended for allocating large blocks of memory that will be shared between operators. + */ + public MemoryBlock allocatePage(long size) { + if (logger.isTraceEnabled()) { + logger.trace("Allocating {} byte page", size); + } + if (size >= (1L << 51)) { + throw new IllegalArgumentException("Cannot allocate a page with more than 2^51 bytes"); + } + + final int pageNumber; + synchronized (this) { + pageNumber = allocatedPages.nextClearBit(0); + if (pageNumber >= PAGE_TABLE_SIZE) { + throw new IllegalStateException( + "Have already allocated a maximum of " + PAGE_TABLE_SIZE + " pages"); + } + allocatedPages.set(pageNumber); + } + final MemoryBlock page = executorMemoryManager.allocate(size); + page.pageNumber = pageNumber; + pageTable[pageNumber] = page; + if (logger.isDebugEnabled()) { + logger.debug("Allocate page number {} ({} bytes)", pageNumber, size); + } + return page; + } + + /** + * Free a block of memory allocated via {@link TaskMemoryManager#allocatePage(long)}. + */ + public void freePage(MemoryBlock page) { + if (logger.isTraceEnabled()) { + logger.trace("Freeing page number {} ({} bytes)", page.pageNumber, page.size()); + } + assert (page.pageNumber != -1) : + "Called freePage() on memory that wasn't allocated with allocatePage()"; + executorMemoryManager.free(page); + synchronized (this) { + allocatedPages.clear(page.pageNumber); + } + pageTable[page.pageNumber] = null; + if (logger.isDebugEnabled()) { + logger.debug("Freed page number {} ({} bytes)", page.pageNumber, page.size()); + } + } + + /** + * Allocates a contiguous block of memory. Note that the allocated memory is not guaranteed + * to be zeroed out (call `zero()` on the result if this is necessary). This method is intended + * to be used for allocating operators' internal data structures. For data pages that you want to + * exchange between operators, consider using {@link TaskMemoryManager#allocatePage(long)}, since + * that will enable intra-memory pointers (see + * {@link TaskMemoryManager#encodePageNumberAndOffset(MemoryBlock, long)} and this class's + * top-level Javadoc for more details). + */ + public MemoryBlock allocate(long size) throws OutOfMemoryError { + final MemoryBlock memory = executorMemoryManager.allocate(size); + allocatedNonPageMemory.add(memory); + return memory; + } + + /** + * Free memory allocated by {@link TaskMemoryManager#allocate(long)}. + */ + public void free(MemoryBlock memory) { + assert (memory.pageNumber == -1) : "Should call freePage() for pages, not free()"; + executorMemoryManager.free(memory); + final boolean wasAlreadyRemoved = !allocatedNonPageMemory.remove(memory); + assert (!wasAlreadyRemoved) : "Called free() on memory that was already freed!"; + } + + /** + * Given a memory page and offset within that page, encode this address into a 64-bit long. + * This address will remain valid as long as the corresponding page has not been freed. + */ + public long encodePageNumberAndOffset(MemoryBlock page, long offsetInPage) { + if (inHeap) { + assert (page.pageNumber != -1) : "encodePageNumberAndOffset called with invalid page"; + return (((long) page.pageNumber) << 51) | (offsetInPage & MASK_LONG_LOWER_51_BITS); + } else { + return offsetInPage; + } + } + + /** + * Get the page associated with an address encoded by + * {@link TaskMemoryManager#encodePageNumberAndOffset(MemoryBlock, long)} + */ + public Object getPage(long pagePlusOffsetAddress) { + if (inHeap) { + final int pageNumber = (int) ((pagePlusOffsetAddress & MASK_LONG_UPPER_13_BITS) >>> 51); + assert (pageNumber >= 0 && pageNumber < PAGE_TABLE_SIZE); + final Object page = pageTable[pageNumber].getBaseObject(); + assert (page != null); + return page; + } else { + return null; + } + } + + /** + * Get the offset associated with an address encoded by + * {@link TaskMemoryManager#encodePageNumberAndOffset(MemoryBlock, long)} + */ + public long getOffsetInPage(long pagePlusOffsetAddress) { + if (inHeap) { + return (pagePlusOffsetAddress & MASK_LONG_LOWER_51_BITS); + } else { + return pagePlusOffsetAddress; + } + } + + /** + * Clean up all allocated memory and pages. Returns the number of bytes freed. A non-zero return + * value can be used to detect memory leaks. + */ + public long cleanUpAllAllocatedMemory() { + long freedBytes = 0; + for (MemoryBlock page : pageTable) { + if (page != null) { + freedBytes += page.size(); + freePage(page); + } + } + final Iterator iter = allocatedNonPageMemory.iterator(); + while (iter.hasNext()) { + final MemoryBlock memory = iter.next(); + freedBytes += memory.size(); + // We don't call free() here because that calls Set.remove, which would lead to a + // ConcurrentModificationException here. + executorMemoryManager.free(memory); + iter.remove(); + } + return freedBytes; + } +} diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java new file mode 100644 index 0000000000000..15898771fef25 --- /dev/null +++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java @@ -0,0 +1,39 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.memory; + +import org.apache.spark.unsafe.PlatformDependent; + +/** + * A simple {@link MemoryAllocator} that uses {@code Unsafe} to allocate off-heap memory. + */ +public class UnsafeMemoryAllocator implements MemoryAllocator { + + @Override + public MemoryBlock allocate(long size) throws OutOfMemoryError { + long address = PlatformDependent.UNSAFE.allocateMemory(size); + return new MemoryBlock(null, address, size); + } + + @Override + public void free(MemoryBlock memory) { + assert (memory.obj == null) : + "baseObject not null; are you trying to use the off-heap allocator to free on-heap memory?"; + PlatformDependent.UNSAFE.freeMemory(memory.offset); + } +} diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/array/LongArraySuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/array/LongArraySuite.java new file mode 100644 index 0000000000000..5974cf91ff993 --- /dev/null +++ b/unsafe/src/test/java/org/apache/spark/unsafe/array/LongArraySuite.java @@ -0,0 +1,38 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.array; + +import org.junit.Assert; +import org.junit.Test; + +import org.apache.spark.unsafe.memory.MemoryBlock; + +public class LongArraySuite { + + @Test + public void basicTest() { + long[] bytes = new long[2]; + LongArray arr = new LongArray(MemoryBlock.fromLongArray(bytes)); + arr.set(0, 1L); + arr.set(1, 2L); + arr.set(1, 3L); + Assert.assertEquals(2, arr.size()); + Assert.assertEquals(1L, arr.get(0)); + Assert.assertEquals(3L, arr.get(1)); + } +} diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/bitset/BitSetSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/bitset/BitSetSuite.java new file mode 100644 index 0000000000000..4bf132fd4053e --- /dev/null +++ b/unsafe/src/test/java/org/apache/spark/unsafe/bitset/BitSetSuite.java @@ -0,0 +1,82 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.bitset; + +import junit.framework.Assert; +import org.apache.spark.unsafe.bitset.BitSet; +import org.junit.Test; + +import org.apache.spark.unsafe.memory.MemoryBlock; + +public class BitSetSuite { + + private static BitSet createBitSet(int capacity) { + assert capacity % 64 == 0; + return new BitSet(MemoryBlock.fromLongArray(new long[capacity / 64]).zero()); + } + + @Test + public void basicOps() { + BitSet bs = createBitSet(64); + Assert.assertEquals(64, bs.capacity()); + + // Make sure the bit set starts empty. + for (int i = 0; i < bs.capacity(); i++) { + Assert.assertFalse(bs.isSet(i)); + } + + // Set every bit and check it. + for (int i = 0; i < bs.capacity(); i++) { + bs.set(i); + Assert.assertTrue(bs.isSet(i)); + } + + // Unset every bit and check it. + for (int i = 0; i < bs.capacity(); i++) { + Assert.assertTrue(bs.isSet(i)); + bs.unset(i); + Assert.assertFalse(bs.isSet(i)); + } + } + + @Test + public void traversal() { + BitSet bs = createBitSet(256); + + Assert.assertEquals(-1, bs.nextSetBit(0)); + Assert.assertEquals(-1, bs.nextSetBit(10)); + Assert.assertEquals(-1, bs.nextSetBit(64)); + + bs.set(10); + Assert.assertEquals(10, bs.nextSetBit(0)); + Assert.assertEquals(10, bs.nextSetBit(1)); + Assert.assertEquals(10, bs.nextSetBit(10)); + Assert.assertEquals(-1, bs.nextSetBit(11)); + + bs.set(11); + Assert.assertEquals(10, bs.nextSetBit(10)); + Assert.assertEquals(11, bs.nextSetBit(11)); + + // Skip a whole word and find it + bs.set(190); + Assert.assertEquals(190, bs.nextSetBit(12)); + + Assert.assertEquals(-1, bs.nextSetBit(191)); + Assert.assertEquals(-1, bs.nextSetBit(256)); + } +} diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java b/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java new file mode 100644 index 0000000000000..3b9175835229c --- /dev/null +++ b/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java @@ -0,0 +1,119 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.hash; + +import java.util.HashSet; +import java.util.Random; +import java.util.Set; + +import junit.framework.Assert; +import org.apache.spark.unsafe.PlatformDependent; +import org.junit.Test; + +/** + * Test file based on Guava's Murmur3Hash32Test. + */ +public class Murmur3_x86_32Suite { + + private static final Murmur3_x86_32 hasher = new Murmur3_x86_32(0); + + @Test + public void testKnownIntegerInputs() { + Assert.assertEquals(593689054, hasher.hashInt(0)); + Assert.assertEquals(-189366624, hasher.hashInt(-42)); + Assert.assertEquals(-1134849565, hasher.hashInt(42)); + Assert.assertEquals(-1718298732, hasher.hashInt(Integer.MIN_VALUE)); + Assert.assertEquals(-1653689534, hasher.hashInt(Integer.MAX_VALUE)); + } + + @Test + public void testKnownLongInputs() { + Assert.assertEquals(1669671676, hasher.hashLong(0L)); + Assert.assertEquals(-846261623, hasher.hashLong(-42L)); + Assert.assertEquals(1871679806, hasher.hashLong(42L)); + Assert.assertEquals(1366273829, hasher.hashLong(Long.MIN_VALUE)); + Assert.assertEquals(-2106506049, hasher.hashLong(Long.MAX_VALUE)); + } + + @Test + public void randomizedStressTest() { + int size = 65536; + Random rand = new Random(); + + // A set used to track collision rate. + Set hashcodes = new HashSet(); + for (int i = 0; i < size; i++) { + int vint = rand.nextInt(); + long lint = rand.nextLong(); + Assert.assertEquals(hasher.hashInt(vint), hasher.hashInt(vint)); + Assert.assertEquals(hasher.hashLong(lint), hasher.hashLong(lint)); + + hashcodes.add(hasher.hashLong(lint)); + } + + // A very loose bound. + Assert.assertTrue(hashcodes.size() > size * 0.95); + } + + @Test + public void randomizedStressTestBytes() { + int size = 65536; + Random rand = new Random(); + + // A set used to track collision rate. + Set hashcodes = new HashSet(); + for (int i = 0; i < size; i++) { + int byteArrSize = rand.nextInt(100) * 8; + byte[] bytes = new byte[byteArrSize]; + rand.nextBytes(bytes); + + Assert.assertEquals( + hasher.hashUnsafeWords(bytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize), + hasher.hashUnsafeWords(bytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize)); + + hashcodes.add(hasher.hashUnsafeWords( + bytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize)); + } + + // A very loose bound. + Assert.assertTrue(hashcodes.size() > size * 0.95); + } + + @Test + public void randomizedStressTestPaddedStrings() { + int size = 64000; + // A set used to track collision rate. + Set hashcodes = new HashSet(); + for (int i = 0; i < size; i++) { + int byteArrSize = 8; + byte[] strBytes = ("" + i).getBytes(); + byte[] paddedBytes = new byte[byteArrSize]; + System.arraycopy(strBytes, 0, paddedBytes, 0, strBytes.length); + + Assert.assertEquals( + hasher.hashUnsafeWords(paddedBytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize), + hasher.hashUnsafeWords(paddedBytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize)); + + hashcodes.add(hasher.hashUnsafeWords( + paddedBytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize)); + } + + // A very loose bound. + Assert.assertTrue(hashcodes.size() > size * 0.95); + } +} diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java new file mode 100644 index 0000000000000..9038cf567f1e2 --- /dev/null +++ b/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java @@ -0,0 +1,250 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.map; + +import java.lang.Exception; +import java.nio.ByteBuffer; +import java.util.*; + +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.unsafe.array.ByteArrayMethods; +import org.apache.spark.unsafe.PlatformDependent; +import static org.apache.spark.unsafe.PlatformDependent.BYTE_ARRAY_OFFSET; +import org.apache.spark.unsafe.memory.ExecutorMemoryManager; +import org.apache.spark.unsafe.memory.MemoryAllocator; +import org.apache.spark.unsafe.memory.MemoryLocation; +import org.apache.spark.unsafe.memory.TaskMemoryManager; + +public abstract class AbstractBytesToBytesMapSuite { + + private final Random rand = new Random(42); + + private TaskMemoryManager memoryManager; + + @Before + public void setup() { + memoryManager = new TaskMemoryManager(new ExecutorMemoryManager(getMemoryAllocator())); + } + + @After + public void tearDown() { + if (memoryManager != null) { + memoryManager.cleanUpAllAllocatedMemory(); + memoryManager = null; + } + } + + protected abstract MemoryAllocator getMemoryAllocator(); + + private static byte[] getByteArray(MemoryLocation loc, int size) { + final byte[] arr = new byte[size]; + PlatformDependent.UNSAFE.copyMemory( + loc.getBaseObject(), + loc.getBaseOffset(), + arr, + BYTE_ARRAY_OFFSET, + size + ); + return arr; + } + + private byte[] getRandomByteArray(int numWords) { + Assert.assertTrue(numWords > 0); + final int lengthInBytes = numWords * 8; + final byte[] bytes = new byte[lengthInBytes]; + rand.nextBytes(bytes); + return bytes; + } + + /** + * Fast equality checking for byte arrays, since these comparisons are a bottleneck + * in our stress tests. + */ + private static boolean arrayEquals( + byte[] expected, + MemoryLocation actualAddr, + long actualLengthBytes) { + return (actualLengthBytes == expected.length) && ByteArrayMethods.wordAlignedArrayEquals( + expected, + BYTE_ARRAY_OFFSET, + actualAddr.getBaseObject(), + actualAddr.getBaseOffset(), + expected.length + ); + } + + @Test + public void emptyMap() { + BytesToBytesMap map = new BytesToBytesMap(memoryManager, 64); + try { + Assert.assertEquals(0, map.size()); + final int keyLengthInWords = 10; + final int keyLengthInBytes = keyLengthInWords * 8; + final byte[] key = getRandomByteArray(keyLengthInWords); + Assert.assertFalse(map.lookup(key, BYTE_ARRAY_OFFSET, keyLengthInBytes).isDefined()); + } finally { + map.free(); + } + } + + @Test + public void setAndRetrieveAKey() { + BytesToBytesMap map = new BytesToBytesMap(memoryManager, 64); + final int recordLengthWords = 10; + final int recordLengthBytes = recordLengthWords * 8; + final byte[] keyData = getRandomByteArray(recordLengthWords); + final byte[] valueData = getRandomByteArray(recordLengthWords); + try { + final BytesToBytesMap.Location loc = + map.lookup(keyData, BYTE_ARRAY_OFFSET, recordLengthBytes); + Assert.assertFalse(loc.isDefined()); + loc.putNewKey( + keyData, + BYTE_ARRAY_OFFSET, + recordLengthBytes, + valueData, + BYTE_ARRAY_OFFSET, + recordLengthBytes + ); + // After storing the key and value, the other location methods should return results that + // reflect the result of this store without us having to call lookup() again on the same key. + Assert.assertEquals(recordLengthBytes, loc.getKeyLength()); + Assert.assertEquals(recordLengthBytes, loc.getValueLength()); + Assert.assertArrayEquals(keyData, getByteArray(loc.getKeyAddress(), recordLengthBytes)); + Assert.assertArrayEquals(valueData, getByteArray(loc.getValueAddress(), recordLengthBytes)); + + // After calling lookup() the location should still point to the correct data. + Assert.assertTrue(map.lookup(keyData, BYTE_ARRAY_OFFSET, recordLengthBytes).isDefined()); + Assert.assertEquals(recordLengthBytes, loc.getKeyLength()); + Assert.assertEquals(recordLengthBytes, loc.getValueLength()); + Assert.assertArrayEquals(keyData, getByteArray(loc.getKeyAddress(), recordLengthBytes)); + Assert.assertArrayEquals(valueData, getByteArray(loc.getValueAddress(), recordLengthBytes)); + + try { + loc.putNewKey( + keyData, + BYTE_ARRAY_OFFSET, + recordLengthBytes, + valueData, + BYTE_ARRAY_OFFSET, + recordLengthBytes + ); + Assert.fail("Should not be able to set a new value for a key"); + } catch (AssertionError e) { + // Expected exception; do nothing. + } + } finally { + map.free(); + } + } + + @Test + public void iteratorTest() throws Exception { + final int size = 128; + BytesToBytesMap map = new BytesToBytesMap(memoryManager, size / 2); + try { + for (long i = 0; i < size; i++) { + final long[] value = new long[] { i }; + final BytesToBytesMap.Location loc = + map.lookup(value, PlatformDependent.LONG_ARRAY_OFFSET, 8); + Assert.assertFalse(loc.isDefined()); + loc.putNewKey( + value, + PlatformDependent.LONG_ARRAY_OFFSET, + 8, + value, + PlatformDependent.LONG_ARRAY_OFFSET, + 8 + ); + } + final java.util.BitSet valuesSeen = new java.util.BitSet(size); + final Iterator iter = map.iterator(); + while (iter.hasNext()) { + final BytesToBytesMap.Location loc = iter.next(); + Assert.assertTrue(loc.isDefined()); + final MemoryLocation keyAddress = loc.getKeyAddress(); + final MemoryLocation valueAddress = loc.getValueAddress(); + final long key = PlatformDependent.UNSAFE.getLong( + keyAddress.getBaseObject(), keyAddress.getBaseOffset()); + final long value = PlatformDependent.UNSAFE.getLong( + valueAddress.getBaseObject(), valueAddress.getBaseOffset()); + Assert.assertEquals(key, value); + valuesSeen.set((int) value); + } + Assert.assertEquals(size, valuesSeen.cardinality()); + } finally { + map.free(); + } + } + + @Test + public void randomizedStressTest() { + final int size = 65536; + // Java arrays' hashCodes() aren't based on the arrays' contents, so we need to wrap arrays + // into ByteBuffers in order to use them as keys here. + final Map expected = new HashMap(); + final BytesToBytesMap map = new BytesToBytesMap(memoryManager, size); + + try { + // Fill the map to 90% full so that we can trigger probing + for (int i = 0; i < size * 0.9; i++) { + final byte[] key = getRandomByteArray(rand.nextInt(256) + 1); + final byte[] value = getRandomByteArray(rand.nextInt(512) + 1); + if (!expected.containsKey(ByteBuffer.wrap(key))) { + expected.put(ByteBuffer.wrap(key), value); + final BytesToBytesMap.Location loc = map.lookup( + key, + BYTE_ARRAY_OFFSET, + key.length + ); + Assert.assertFalse(loc.isDefined()); + loc.putNewKey( + key, + BYTE_ARRAY_OFFSET, + key.length, + value, + BYTE_ARRAY_OFFSET, + value.length + ); + // After calling putNewKey, the following should be true, even before calling + // lookup(): + Assert.assertTrue(loc.isDefined()); + Assert.assertEquals(key.length, loc.getKeyLength()); + Assert.assertEquals(value.length, loc.getValueLength()); + Assert.assertTrue(arrayEquals(key, loc.getKeyAddress(), key.length)); + Assert.assertTrue(arrayEquals(value, loc.getValueAddress(), value.length)); + } + } + + for (Map.Entry entry : expected.entrySet()) { + final byte[] key = entry.getKey().array(); + final byte[] value = entry.getValue(); + final BytesToBytesMap.Location loc = map.lookup(key, BYTE_ARRAY_OFFSET, key.length); + Assert.assertTrue(loc.isDefined()); + Assert.assertTrue(arrayEquals(key, loc.getKeyAddress(), loc.getKeyLength())); + Assert.assertTrue(arrayEquals(value, loc.getValueAddress(), loc.getValueLength())); + } + } finally { + map.free(); + } + } +} diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOffHeapSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOffHeapSuite.java new file mode 100644 index 0000000000000..5a10de49f54fe --- /dev/null +++ b/unsafe/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOffHeapSuite.java @@ -0,0 +1,29 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.map; + +import org.apache.spark.unsafe.memory.MemoryAllocator; + +public class BytesToBytesMapOffHeapSuite extends AbstractBytesToBytesMapSuite { + + @Override + protected MemoryAllocator getMemoryAllocator() { + return MemoryAllocator.UNSAFE; + } + +} diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOnHeapSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOnHeapSuite.java new file mode 100644 index 0000000000000..12cc9b25d93b3 --- /dev/null +++ b/unsafe/src/test/java/org/apache/spark/unsafe/map/BytesToBytesMapOnHeapSuite.java @@ -0,0 +1,29 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.map; + +import org.apache.spark.unsafe.memory.MemoryAllocator; + +public class BytesToBytesMapOnHeapSuite extends AbstractBytesToBytesMapSuite { + + @Override + protected MemoryAllocator getMemoryAllocator() { + return MemoryAllocator.HEAP; + } + +} diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/memory/TaskMemoryManagerSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/memory/TaskMemoryManagerSuite.java new file mode 100644 index 0000000000000..932882f1ca248 --- /dev/null +++ b/unsafe/src/test/java/org/apache/spark/unsafe/memory/TaskMemoryManagerSuite.java @@ -0,0 +1,41 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.unsafe.memory; + +import org.junit.Assert; +import org.junit.Test; + +public class TaskMemoryManagerSuite { + + @Test + public void leakedNonPageMemoryIsDetected() { + final TaskMemoryManager manager = + new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP)); + manager.allocate(1024); // leak memory + Assert.assertEquals(1024, manager.cleanUpAllAllocatedMemory()); + } + + @Test + public void leakedPageMemoryIsDetected() { + final TaskMemoryManager manager = + new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP)); + manager.allocatePage(4096); // leak memory + Assert.assertEquals(4096, manager.cleanUpAllAllocatedMemory()); + } + +} From baed3f2c73afd9c7d9de34f0485c507ac6a498b0 Mon Sep 17 00:00:00 2001 From: Dean Chen Date: Wed, 29 Apr 2015 08:58:33 -0500 Subject: [PATCH 466/591] [SPARK-6918] [YARN] Secure HBase support. Obtain HBase security token with Kerberos credentials locally to be sent to executors. Tested on eBay's secure HBase cluster. Similar to obtainTokenForNamenodes and fails gracefully if HBase classes are not included in path. Requires hbase-site.xml to be in the classpath(typically via conf dir) for the zookeeper configuration. Should that go in the docs somewhere? Did not see an HBase section. Author: Dean Chen Closes #5586 from deanchen/master and squashes the following commits: 0c190ef [Dean Chen] [SPARK-6918][YARN] Secure HBase support. --- .../org/apache/spark/deploy/yarn/Client.scala | 38 ++++++++++++++++++- 1 file changed, 37 insertions(+), 1 deletion(-) diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 741239c953794..4abcf7307a388 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -39,7 +39,7 @@ import org.apache.hadoop.io.Text import org.apache.hadoop.mapred.Master import org.apache.hadoop.mapreduce.MRJobConfig import org.apache.hadoop.security.{Credentials, UserGroupInformation} -import org.apache.hadoop.security.token.Token +import org.apache.hadoop.security.token.{TokenIdentifier, Token} import org.apache.hadoop.util.StringUtils import org.apache.hadoop.yarn.api._ import org.apache.hadoop.yarn.api.ApplicationConstants.Environment @@ -226,6 +226,7 @@ private[spark] class Client( val distributedUris = new HashSet[String] obtainTokensForNamenodes(nns, hadoopConf, credentials) obtainTokenForHiveMetastore(hadoopConf, credentials) + obtainTokenForHBase(hadoopConf, credentials) val replication = sparkConf.getInt("spark.yarn.submit.file.replication", fs.getDefaultReplication(dst)).toShort @@ -1084,6 +1085,41 @@ object Client extends Logging { } } + /** + * Obtain security token for HBase. + */ + def obtainTokenForHBase(conf: Configuration, credentials: Credentials): Unit = { + if (UserGroupInformation.isSecurityEnabled) { + val mirror = universe.runtimeMirror(getClass.getClassLoader) + + try { + val confCreate = mirror.classLoader. + loadClass("org.apache.hadoop.hbase.HBaseConfiguration"). + getMethod("create", classOf[Configuration]) + val obtainToken = mirror.classLoader. + loadClass("org.apache.hadoop.hbase.security.token.TokenUtil"). + getMethod("obtainToken", classOf[Configuration]) + + logDebug("Attempting to fetch HBase security token.") + + val hbaseConf = confCreate.invoke(null, conf) + val token = obtainToken.invoke(null, hbaseConf).asInstanceOf[Token[TokenIdentifier]] + credentials.addToken(token.getService, token) + + logInfo("Added HBase security token to credentials.") + } catch { + case e:java.lang.NoSuchMethodException => + logInfo("HBase Method not found: " + e) + case e:java.lang.ClassNotFoundException => + logDebug("HBase Class not found: " + e) + case e:java.lang.NoClassDefFoundError => + logDebug("HBase Class not found: " + e) + case e:Exception => + logError("Exception when obtaining HBase security token: " + e) + } + } + } + /** * Return whether the two file systems are the same. */ From 687273d9150e1c89a74aa1473f0c6495f56509af Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Wed, 29 Apr 2015 09:46:37 -0700 Subject: [PATCH 467/591] [SPARK-7223] Rename RPC askWithReply -> askWithReply, sendWithReply -> ask. The old naming scheme was very confusing between askWithReply and sendWithReply. I also divided RpcEnv.scala into multiple files. Author: Reynold Xin Closes #5768 from rxin/rpc-rename and squashes the following commits: a84058e [Reynold Xin] [SPARK-7223] Rename RPC askWithReply -> askWithReply, sendWithReply -> ask. --- .../org/apache/spark/MapOutputTracker.scala | 2 +- .../scala/org/apache/spark/SparkContext.scala | 2 +- .../CoarseGrainedExecutorBackend.scala | 4 +- .../org/apache/spark/executor/Executor.scala | 2 +- .../org/apache/spark/rpc/RpcCallContext.scala | 41 +++ .../org/apache/spark/rpc/RpcEndpoint.scala | 148 +++++++++ .../org/apache/spark/rpc/RpcEndpointRef.scala | 119 +++++++ .../scala/org/apache/spark/rpc/RpcEnv.scala | 313 ++---------------- .../apache/spark/rpc/akka/AkkaRpcEnv.scala | 2 +- .../apache/spark/scheduler/DAGScheduler.scala | 2 +- .../scheduler/OutputCommitCoordinator.scala | 2 +- .../CoarseGrainedSchedulerBackend.scala | 6 +- .../cluster/YarnSchedulerBackend.scala | 8 +- .../spark/scheduler/local/LocalBackend.scala | 2 +- .../spark/storage/BlockManagerMaster.scala | 28 +- .../storage/BlockManagerMasterEndpoint.scala | 12 +- .../apache/spark/HeartbeatReceiverSuite.scala | 4 +- .../org/apache/spark/rpc/RpcEnvSuite.scala | 20 +- .../spark/storage/BlockManagerSuite.scala | 2 +- .../receiver/ReceiverSupervisorImpl.scala | 6 +- 20 files changed, 394 insertions(+), 331 deletions(-) create mode 100644 core/src/main/scala/org/apache/spark/rpc/RpcCallContext.scala create mode 100644 core/src/main/scala/org/apache/spark/rpc/RpcEndpoint.scala create mode 100644 core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala diff --git a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala index d65c94e410662..16072283edbe9 100644 --- a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala +++ b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala @@ -106,7 +106,7 @@ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging */ protected def askTracker[T: ClassTag](message: Any): T = { try { - trackerEndpoint.askWithReply[T](message) + trackerEndpoint.askWithRetry[T](message) } catch { case e: Exception => logError("Error communicating with MapOutputTracker", e) diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index d0cf2a8dd01cd..5ae8fb81de809 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -555,7 +555,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli SparkEnv.executorActorSystemName, RpcAddress(host, port), ExecutorEndpoint.EXECUTOR_ENDPOINT_NAME) - Some(endpointRef.askWithReply[Array[ThreadStackTrace]](TriggerThreadDump)) + Some(endpointRef.askWithRetry[Array[ThreadStackTrace]](TriggerThreadDump)) } } catch { case e: Exception => diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala index 8af46f3327adb..79aed90b53e2f 100644 --- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala +++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala @@ -57,7 +57,7 @@ private[spark] class CoarseGrainedExecutorBackend( logInfo("Connecting to driver: " + driverUrl) rpcEnv.asyncSetupEndpointRefByURI(driverUrl).flatMap { ref => driver = Some(ref) - ref.sendWithReply[RegisteredExecutor.type]( + ref.ask[RegisteredExecutor.type]( RegisterExecutor(executorId, self, hostPort, cores, extractLogUrls)) } onComplete { case Success(msg) => Utils.tryLogNonFatalError { @@ -154,7 +154,7 @@ private[spark] object CoarseGrainedExecutorBackend extends Logging { executorConf, new SecurityManager(executorConf)) val driver = fetcher.setupEndpointRefByURI(driverUrl) - val props = driver.askWithReply[Seq[(String, String)]](RetrieveSparkProps) ++ + val props = driver.askWithRetry[Seq[(String, String)]](RetrieveSparkProps) ++ Seq[(String, String)](("spark.app.id", appId)) fetcher.shutdown() diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala index dd1c48e6cb953..8f916e0502ecb 100644 --- a/core/src/main/scala/org/apache/spark/executor/Executor.scala +++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala @@ -441,7 +441,7 @@ private[spark] class Executor( val message = Heartbeat(executorId, tasksMetrics.toArray, env.blockManager.blockManagerId) try { - val response = heartbeatReceiverRef.askWithReply[HeartbeatResponse](message) + val response = heartbeatReceiverRef.askWithRetry[HeartbeatResponse](message) if (response.reregisterBlockManager) { logWarning("Told to re-register on heartbeat") env.blockManager.reregister() diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcCallContext.scala b/core/src/main/scala/org/apache/spark/rpc/RpcCallContext.scala new file mode 100644 index 0000000000000..3e5b64265e919 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/rpc/RpcCallContext.scala @@ -0,0 +1,41 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rpc + +/** + * A callback that [[RpcEndpoint]] can use it to send back a message or failure. It's thread-safe + * and can be called in any thread. + */ +private[spark] trait RpcCallContext { + + /** + * Reply a message to the sender. If the sender is [[RpcEndpoint]], its [[RpcEndpoint.receive]] + * will be called. + */ + def reply(response: Any): Unit + + /** + * Report a failure to the sender. + */ + def sendFailure(e: Throwable): Unit + + /** + * The sender of this message. + */ + def sender: RpcEndpointRef +} diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEndpoint.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEndpoint.scala new file mode 100644 index 0000000000000..d2b2baef1d8c4 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/rpc/RpcEndpoint.scala @@ -0,0 +1,148 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rpc + +import org.apache.spark.SparkException + +/** + * A factory class to create the [[RpcEnv]]. It must have an empty constructor so that it can be + * created using Reflection. + */ +private[spark] trait RpcEnvFactory { + + def create(config: RpcEnvConfig): RpcEnv +} + +/** + * A trait that requires RpcEnv thread-safely sending messages to it. + * + * Thread-safety means processing of one message happens before processing of the next message by + * the same [[ThreadSafeRpcEndpoint]]. In the other words, changes to internal fields of a + * [[ThreadSafeRpcEndpoint]] are visible when processing the next message, and fields in the + * [[ThreadSafeRpcEndpoint]] need not be volatile or equivalent. + * + * However, there is no guarantee that the same thread will be executing the same + * [[ThreadSafeRpcEndpoint]] for different messages. + */ +private[spark] trait ThreadSafeRpcEndpoint extends RpcEndpoint + + +/** + * An end point for the RPC that defines what functions to trigger given a message. + * + * It is guaranteed that `onStart`, `receive` and `onStop` will be called in sequence. + * + * The lift-cycle will be: + * + * constructor onStart receive* onStop + * + * Note: `receive` can be called concurrently. If you want `receive` is thread-safe, please use + * [[ThreadSafeRpcEndpoint]] + * + * If any error is thrown from one of [[RpcEndpoint]] methods except `onError`, `onError` will be + * invoked with the cause. If `onError` throws an error, [[RpcEnv]] will ignore it. + */ +private[spark] trait RpcEndpoint { + + /** + * The [[RpcEnv]] that this [[RpcEndpoint]] is registered to. + */ + val rpcEnv: RpcEnv + + /** + * The [[RpcEndpointRef]] of this [[RpcEndpoint]]. `self` will become valid when `onStart` is + * called. And `self` will become `null` when `onStop` is called. + * + * Note: Because before `onStart`, [[RpcEndpoint]] has not yet been registered and there is not + * valid [[RpcEndpointRef]] for it. So don't call `self` before `onStart` is called. + */ + final def self: RpcEndpointRef = { + require(rpcEnv != null, "rpcEnv has not been initialized") + rpcEnv.endpointRef(this) + } + + /** + * Process messages from [[RpcEndpointRef.send]] or [[RpcCallContext.reply)]]. If receiving a + * unmatched message, [[SparkException]] will be thrown and sent to `onError`. + */ + def receive: PartialFunction[Any, Unit] = { + case _ => throw new SparkException(self + " does not implement 'receive'") + } + + /** + * Process messages from [[RpcEndpointRef.ask]]. If receiving a unmatched message, + * [[SparkException]] will be thrown and sent to `onError`. + */ + def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case _ => context.sendFailure(new SparkException(self + " won't reply anything")) + } + + /** + * Invoked when any exception is thrown during handling messages. + */ + def onError(cause: Throwable): Unit = { + // By default, throw e and let RpcEnv handle it + throw cause + } + + /** + * Invoked before [[RpcEndpoint]] starts to handle any message. + */ + def onStart(): Unit = { + // By default, do nothing. + } + + /** + * Invoked when [[RpcEndpoint]] is stopping. + */ + def onStop(): Unit = { + // By default, do nothing. + } + + /** + * Invoked when `remoteAddress` is connected to the current node. + */ + def onConnected(remoteAddress: RpcAddress): Unit = { + // By default, do nothing. + } + + /** + * Invoked when `remoteAddress` is lost. + */ + def onDisconnected(remoteAddress: RpcAddress): Unit = { + // By default, do nothing. + } + + /** + * Invoked when some network error happens in the connection between the current node and + * `remoteAddress`. + */ + def onNetworkError(cause: Throwable, remoteAddress: RpcAddress): Unit = { + // By default, do nothing. + } + + /** + * A convenient method to stop [[RpcEndpoint]]. + */ + final def stop(): Unit = { + val _self = self + if (_self != null) { + rpcEnv.stop(_self) + } + } +} diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala new file mode 100644 index 0000000000000..69181edb9ad44 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala @@ -0,0 +1,119 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rpc + +import scala.concurrent.{Await, Future} +import scala.concurrent.duration.FiniteDuration +import scala.reflect.ClassTag + +import org.apache.spark.util.RpcUtils +import org.apache.spark.{SparkException, Logging, SparkConf} + +/** + * A reference for a remote [[RpcEndpoint]]. [[RpcEndpointRef]] is thread-safe. + */ +private[spark] abstract class RpcEndpointRef(@transient conf: SparkConf) + extends Serializable with Logging { + + private[this] val maxRetries = RpcUtils.numRetries(conf) + private[this] val retryWaitMs = RpcUtils.retryWaitMs(conf) + private[this] val defaultAskTimeout = RpcUtils.askTimeout(conf) + + /** + * return the address for the [[RpcEndpointRef]] + */ + def address: RpcAddress + + def name: String + + /** + * Sends a one-way asynchronous message. Fire-and-forget semantics. + */ + def send(message: Any): Unit + + /** + * Send a message to the corresponding [[RpcEndpoint.receiveAndReply)]] and return a [[Future]] to + * receive the reply within the specified timeout. + * + * This method only sends the message once and never retries. + */ + def ask[T: ClassTag](message: Any, timeout: FiniteDuration): Future[T] + + /** + * Send a message to the corresponding [[RpcEndpoint.receiveAndReply)]] and return a [[Future]] to + * receive the reply within a default timeout. + * + * This method only sends the message once and never retries. + */ + def ask[T: ClassTag](message: Any): Future[T] = ask(message, defaultAskTimeout) + + /** + * Send a message to the corresponding [[RpcEndpoint]] and get its result within a default + * timeout, or throw a SparkException if this fails even after the default number of retries. + * The default `timeout` will be used in every trial of calling `sendWithReply`. Because this + * method retries, the message handling in the receiver side should be idempotent. + * + * Note: this is a blocking action which may cost a lot of time, so don't call it in an message + * loop of [[RpcEndpoint]]. + * + * @param message the message to send + * @tparam T type of the reply message + * @return the reply message from the corresponding [[RpcEndpoint]] + */ + def askWithRetry[T: ClassTag](message: Any): T = askWithRetry(message, defaultAskTimeout) + + /** + * Send a message to the corresponding [[RpcEndpoint.receive]] and get its result within a + * specified timeout, throw a SparkException if this fails even after the specified number of + * retries. `timeout` will be used in every trial of calling `sendWithReply`. Because this method + * retries, the message handling in the receiver side should be idempotent. + * + * Note: this is a blocking action which may cost a lot of time, so don't call it in an message + * loop of [[RpcEndpoint]]. + * + * @param message the message to send + * @param timeout the timeout duration + * @tparam T type of the reply message + * @return the reply message from the corresponding [[RpcEndpoint]] + */ + def askWithRetry[T: ClassTag](message: Any, timeout: FiniteDuration): T = { + // TODO: Consider removing multiple attempts + var attempts = 0 + var lastException: Exception = null + while (attempts < maxRetries) { + attempts += 1 + try { + val future = ask[T](message, timeout) + val result = Await.result(future, timeout) + if (result == null) { + throw new SparkException("Actor returned null") + } + return result + } catch { + case ie: InterruptedException => throw ie + case e: Exception => + lastException = e + logWarning(s"Error sending message [message = $message] in $attempts attempts", e) + } + Thread.sleep(retryWaitMs) + } + + throw new SparkException( + s"Error sending message [message = $message]", lastException) + } +} diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala index a5336b7563802..12b6b28d4d7ec 100644 --- a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala +++ b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala @@ -20,13 +20,41 @@ package org.apache.spark.rpc import java.net.URI import scala.concurrent.{Await, Future} -import scala.concurrent.duration._ import scala.language.postfixOps -import scala.reflect.ClassTag -import org.apache.spark.{Logging, SparkException, SecurityManager, SparkConf} +import org.apache.spark.{SecurityManager, SparkConf} import org.apache.spark.util.{RpcUtils, Utils} + +/** + * A RpcEnv implementation must have a [[RpcEnvFactory]] implementation with an empty constructor + * so that it can be created via Reflection. + */ +private[spark] object RpcEnv { + + private def getRpcEnvFactory(conf: SparkConf): RpcEnvFactory = { + // Add more RpcEnv implementations here + val rpcEnvNames = Map("akka" -> "org.apache.spark.rpc.akka.AkkaRpcEnvFactory") + val rpcEnvName = conf.get("spark.rpc", "akka") + val rpcEnvFactoryClassName = rpcEnvNames.getOrElse(rpcEnvName.toLowerCase, rpcEnvName) + Class.forName(rpcEnvFactoryClassName, true, Utils.getContextOrSparkClassLoader). + newInstance().asInstanceOf[RpcEnvFactory] + } + + def create( + name: String, + host: String, + port: Int, + conf: SparkConf, + securityManager: SecurityManager): RpcEnv = { + // Using Reflection to create the RpcEnv to avoid to depend on Akka directly + val config = RpcEnvConfig(conf, name, host, port, securityManager) + getRpcEnvFactory(conf).create(config) + } + +} + + /** * An RPC environment. [[RpcEndpoint]]s need to register itself with a name to [[RpcEnv]] to * receives messages. Then [[RpcEnv]] will process messages sent from [[RpcEndpointRef]] or remote @@ -112,6 +140,7 @@ private[spark] abstract class RpcEnv(conf: SparkConf) { def uriOf(systemName: String, address: RpcAddress, endpointName: String): String } + private[spark] case class RpcEnvConfig( conf: SparkConf, name: String, @@ -119,261 +148,9 @@ private[spark] case class RpcEnvConfig( port: Int, securityManager: SecurityManager) -/** - * A RpcEnv implementation must have a [[RpcEnvFactory]] implementation with an empty constructor - * so that it can be created via Reflection. - */ -private[spark] object RpcEnv { - - private def getRpcEnvFactory(conf: SparkConf): RpcEnvFactory = { - // Add more RpcEnv implementations here - val rpcEnvNames = Map("akka" -> "org.apache.spark.rpc.akka.AkkaRpcEnvFactory") - val rpcEnvName = conf.get("spark.rpc", "akka") - val rpcEnvFactoryClassName = rpcEnvNames.getOrElse(rpcEnvName.toLowerCase, rpcEnvName) - Class.forName(rpcEnvFactoryClassName, true, Utils.getContextOrSparkClassLoader). - newInstance().asInstanceOf[RpcEnvFactory] - } - - def create( - name: String, - host: String, - port: Int, - conf: SparkConf, - securityManager: SecurityManager): RpcEnv = { - // Using Reflection to create the RpcEnv to avoid to depend on Akka directly - val config = RpcEnvConfig(conf, name, host, port, securityManager) - getRpcEnvFactory(conf).create(config) - } - -} - -/** - * A factory class to create the [[RpcEnv]]. It must have an empty constructor so that it can be - * created using Reflection. - */ -private[spark] trait RpcEnvFactory { - - def create(config: RpcEnvConfig): RpcEnv -} /** - * An end point for the RPC that defines what functions to trigger given a message. - * - * It is guaranteed that `onStart`, `receive` and `onStop` will be called in sequence. - * - * The lift-cycle will be: - * - * constructor onStart receive* onStop - * - * Note: `receive` can be called concurrently. If you want `receive` is thread-safe, please use - * [[ThreadSafeRpcEndpoint]] - * - * If any error is thrown from one of [[RpcEndpoint]] methods except `onError`, `onError` will be - * invoked with the cause. If `onError` throws an error, [[RpcEnv]] will ignore it. - */ -private[spark] trait RpcEndpoint { - - /** - * The [[RpcEnv]] that this [[RpcEndpoint]] is registered to. - */ - val rpcEnv: RpcEnv - - /** - * The [[RpcEndpointRef]] of this [[RpcEndpoint]]. `self` will become valid when `onStart` is - * called. And `self` will become `null` when `onStop` is called. - * - * Note: Because before `onStart`, [[RpcEndpoint]] has not yet been registered and there is not - * valid [[RpcEndpointRef]] for it. So don't call `self` before `onStart` is called. - */ - final def self: RpcEndpointRef = { - require(rpcEnv != null, "rpcEnv has not been initialized") - rpcEnv.endpointRef(this) - } - - /** - * Process messages from [[RpcEndpointRef.send]] or [[RpcCallContext.reply)]]. If receiving a - * unmatched message, [[SparkException]] will be thrown and sent to `onError`. - */ - def receive: PartialFunction[Any, Unit] = { - case _ => throw new SparkException(self + " does not implement 'receive'") - } - - /** - * Process messages from [[RpcEndpointRef.sendWithReply]]. If receiving a unmatched message, - * [[SparkException]] will be thrown and sent to `onError`. - */ - def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { - case _ => context.sendFailure(new SparkException(self + " won't reply anything")) - } - - /** - * Call onError when any exception is thrown during handling messages. - * - * @param cause - */ - def onError(cause: Throwable): Unit = { - // By default, throw e and let RpcEnv handle it - throw cause - } - - /** - * Invoked before [[RpcEndpoint]] starts to handle any message. - */ - def onStart(): Unit = { - // By default, do nothing. - } - - /** - * Invoked when [[RpcEndpoint]] is stopping. - */ - def onStop(): Unit = { - // By default, do nothing. - } - - /** - * Invoked when `remoteAddress` is connected to the current node. - */ - def onConnected(remoteAddress: RpcAddress): Unit = { - // By default, do nothing. - } - - /** - * Invoked when `remoteAddress` is lost. - */ - def onDisconnected(remoteAddress: RpcAddress): Unit = { - // By default, do nothing. - } - - /** - * Invoked when some network error happens in the connection between the current node and - * `remoteAddress`. - */ - def onNetworkError(cause: Throwable, remoteAddress: RpcAddress): Unit = { - // By default, do nothing. - } - - /** - * A convenient method to stop [[RpcEndpoint]]. - */ - final def stop(): Unit = { - val _self = self - if (_self != null) { - rpcEnv.stop(_self) - } - } -} - -/** - * A trait that requires RpcEnv thread-safely sending messages to it. - * - * Thread-safety means processing of one message happens before processing of the next message by - * the same [[ThreadSafeRpcEndpoint]]. In the other words, changes to internal fields of a - * [[ThreadSafeRpcEndpoint]] are visible when processing the next message, and fields in the - * [[ThreadSafeRpcEndpoint]] need not be volatile or equivalent. - * - * However, there is no guarantee that the same thread will be executing the same - * [[ThreadSafeRpcEndpoint]] for different messages. - */ -trait ThreadSafeRpcEndpoint extends RpcEndpoint - -/** - * A reference for a remote [[RpcEndpoint]]. [[RpcEndpointRef]] is thread-safe. - */ -private[spark] abstract class RpcEndpointRef(@transient conf: SparkConf) - extends Serializable with Logging { - - private[this] val maxRetries = RpcUtils.numRetries(conf) - private[this] val retryWaitMs = RpcUtils.retryWaitMs(conf) - private[this] val defaultAskTimeout = RpcUtils.askTimeout(conf) - - /** - * return the address for the [[RpcEndpointRef]] - */ - def address: RpcAddress - - def name: String - - /** - * Sends a one-way asynchronous message. Fire-and-forget semantics. - */ - def send(message: Any): Unit - - /** - * Send a message to the corresponding [[RpcEndpoint.receiveAndReply)]] and return a `Future` to - * receive the reply within a default timeout. - * - * This method only sends the message once and never retries. - */ - def sendWithReply[T: ClassTag](message: Any): Future[T] = - sendWithReply(message, defaultAskTimeout) - - /** - * Send a message to the corresponding [[RpcEndpoint.receiveAndReply)]] and return a `Future` to - * receive the reply within the specified timeout. - * - * This method only sends the message once and never retries. - */ - def sendWithReply[T: ClassTag](message: Any, timeout: FiniteDuration): Future[T] - - /** - * Send a message to the corresponding [[RpcEndpoint]] and get its result within a default - * timeout, or throw a SparkException if this fails even after the default number of retries. - * The default `timeout` will be used in every trial of calling `sendWithReply`. Because this - * method retries, the message handling in the receiver side should be idempotent. - * - * Note: this is a blocking action which may cost a lot of time, so don't call it in an message - * loop of [[RpcEndpoint]]. - * - * @param message the message to send - * @tparam T type of the reply message - * @return the reply message from the corresponding [[RpcEndpoint]] - */ - def askWithReply[T: ClassTag](message: Any): T = askWithReply(message, defaultAskTimeout) - - /** - * Send a message to the corresponding [[RpcEndpoint.receive]] and get its result within a - * specified timeout, throw a SparkException if this fails even after the specified number of - * retries. `timeout` will be used in every trial of calling `sendWithReply`. Because this method - * retries, the message handling in the receiver side should be idempotent. - * - * Note: this is a blocking action which may cost a lot of time, so don't call it in an message - * loop of [[RpcEndpoint]]. - * - * @param message the message to send - * @param timeout the timeout duration - * @tparam T type of the reply message - * @return the reply message from the corresponding [[RpcEndpoint]] - */ - def askWithReply[T: ClassTag](message: Any, timeout: FiniteDuration): T = { - // TODO: Consider removing multiple attempts - var attempts = 0 - var lastException: Exception = null - while (attempts < maxRetries) { - attempts += 1 - try { - val future = sendWithReply[T](message, timeout) - val result = Await.result(future, timeout) - if (result == null) { - throw new SparkException("Actor returned null") - } - return result - } catch { - case ie: InterruptedException => throw ie - case e: Exception => - lastException = e - logWarning(s"Error sending message [message = $message] in $attempts attempts", e) - } - Thread.sleep(retryWaitMs) - } - - throw new SparkException( - s"Error sending message [message = $message]", lastException) - } - -} - -/** - * Represent a host with a port + * Represents a host and port. */ private[spark] case class RpcAddress(host: String, port: Int) { // TODO do we need to add the type of RpcEnv in the address? @@ -383,6 +160,7 @@ private[spark] case class RpcAddress(host: String, port: Int) { override val toString: String = hostPort } + private[spark] object RpcAddress { /** @@ -404,26 +182,3 @@ private[spark] object RpcAddress { RpcAddress(host, port) } } - -/** - * A callback that [[RpcEndpoint]] can use it to send back a message or failure. It's thread-safe - * and can be called in any thread. - */ -private[spark] trait RpcCallContext { - - /** - * Reply a message to the sender. If the sender is [[RpcEndpoint]], its [[RpcEndpoint.receive]] - * will be called. - */ - def reply(response: Any): Unit - - /** - * Report a failure to the sender. - */ - def sendFailure(e: Throwable): Unit - - /** - * The sender of this message. - */ - def sender: RpcEndpointRef -} diff --git a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala index 652e52f2b2e73..ba0d468f111ef 100644 --- a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala +++ b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala @@ -293,7 +293,7 @@ private[akka] class AkkaRpcEndpointRef( actorRef ! AkkaMessage(message, false) } - override def sendWithReply[T: ClassTag](message: Any, timeout: FiniteDuration): Future[T] = { + override def ask[T: ClassTag](message: Any, timeout: FiniteDuration): Future[T] = { import scala.concurrent.ExecutionContext.Implicits.global actorRef.ask(AkkaMessage(message, true))(timeout).flatMap { case msg @ AkkaMessage(message, reply) => diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala index b7901c06a16e0..b511c306ca508 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -167,7 +167,7 @@ class DAGScheduler( taskMetrics: Array[(Long, Int, Int, TaskMetrics)], // (taskId, stageId, stateAttempt, metrics) blockManagerId: BlockManagerId): Boolean = { listenerBus.post(SparkListenerExecutorMetricsUpdate(execId, taskMetrics)) - blockManagerMaster.driverEndpoint.askWithReply[Boolean]( + blockManagerMaster.driverEndpoint.askWithRetry[Boolean]( BlockManagerHeartbeat(blockManagerId), 600 seconds) } diff --git a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala index 7c184b1dcb308..0b1d47cff3746 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala @@ -85,7 +85,7 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf) extends Logging { val msg = AskPermissionToCommitOutput(stage, partition, attempt) coordinatorRef match { case Some(endpointRef) => - endpointRef.askWithReply[Boolean](msg) + endpointRef.askWithRetry[Boolean](msg) case None => logError( "canCommit called after coordinator was stopped (is SparkEnv shutdown in progress)?") diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala index 9656fb76858ea..7352fa1fe9ebd 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala @@ -252,7 +252,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp try { if (driverEndpoint != null) { logInfo("Shutting down all executors") - driverEndpoint.askWithReply[Boolean](StopExecutors) + driverEndpoint.askWithRetry[Boolean](StopExecutors) } } catch { case e: Exception => @@ -264,7 +264,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp stopExecutors() try { if (driverEndpoint != null) { - driverEndpoint.askWithReply[Boolean](StopDriver) + driverEndpoint.askWithRetry[Boolean](StopDriver) } } catch { case e: Exception => @@ -287,7 +287,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp // Called by subclasses when notified of a lost worker def removeExecutor(executorId: String, reason: String) { try { - driverEndpoint.askWithReply[Boolean](RemoveExecutor(executorId, reason)) + driverEndpoint.askWithRetry[Boolean](RemoveExecutor(executorId, reason)) } catch { case e: Exception => throw new SparkException("Error notifying standalone scheduler's driver endpoint", e) diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala index d987c7d563579..2a3a5d925d06f 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala @@ -53,14 +53,14 @@ private[spark] abstract class YarnSchedulerBackend( * This includes executors already pending or running. */ override def doRequestTotalExecutors(requestedTotal: Int): Boolean = { - yarnSchedulerEndpoint.askWithReply[Boolean](RequestExecutors(requestedTotal)) + yarnSchedulerEndpoint.askWithRetry[Boolean](RequestExecutors(requestedTotal)) } /** * Request that the ApplicationMaster kill the specified executors. */ override def doKillExecutors(executorIds: Seq[String]): Boolean = { - yarnSchedulerEndpoint.askWithReply[Boolean](KillExecutors(executorIds)) + yarnSchedulerEndpoint.askWithRetry[Boolean](KillExecutors(executorIds)) } override def sufficientResourcesRegistered(): Boolean = { @@ -115,7 +115,7 @@ private[spark] abstract class YarnSchedulerBackend( amEndpoint match { case Some(am) => Future { - context.reply(am.askWithReply[Boolean](r)) + context.reply(am.askWithRetry[Boolean](r)) } onFailure { case NonFatal(e) => logError(s"Sending $r to AM was unsuccessful", e) @@ -130,7 +130,7 @@ private[spark] abstract class YarnSchedulerBackend( amEndpoint match { case Some(am) => Future { - context.reply(am.askWithReply[Boolean](k)) + context.reply(am.askWithRetry[Boolean](k)) } onFailure { case NonFatal(e) => logError(s"Sending $k to AM was unsuccessful", e) diff --git a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala index ac5b524517818..e64d06c4d3cfc 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala @@ -123,7 +123,7 @@ private[spark] class LocalBackend( } override def stop() { - localEndpoint.sendWithReply(StopExecutor) + localEndpoint.ask(StopExecutor) } override def reviveOffers() { diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala index c798843bd5d8a..9bfc4201d37c0 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala @@ -55,7 +55,7 @@ class BlockManagerMaster( memSize: Long, diskSize: Long, tachyonSize: Long): Boolean = { - val res = driverEndpoint.askWithReply[Boolean]( + val res = driverEndpoint.askWithRetry[Boolean]( UpdateBlockInfo(blockManagerId, blockId, storageLevel, memSize, diskSize, tachyonSize)) logDebug(s"Updated info of block $blockId") res @@ -63,12 +63,12 @@ class BlockManagerMaster( /** Get locations of the blockId from the driver */ def getLocations(blockId: BlockId): Seq[BlockManagerId] = { - driverEndpoint.askWithReply[Seq[BlockManagerId]](GetLocations(blockId)) + driverEndpoint.askWithRetry[Seq[BlockManagerId]](GetLocations(blockId)) } /** Get locations of multiple blockIds from the driver */ def getLocations(blockIds: Array[BlockId]): Seq[Seq[BlockManagerId]] = { - driverEndpoint.askWithReply[Seq[Seq[BlockManagerId]]](GetLocationsMultipleBlockIds(blockIds)) + driverEndpoint.askWithRetry[Seq[Seq[BlockManagerId]]](GetLocationsMultipleBlockIds(blockIds)) } /** @@ -81,11 +81,11 @@ class BlockManagerMaster( /** Get ids of other nodes in the cluster from the driver */ def getPeers(blockManagerId: BlockManagerId): Seq[BlockManagerId] = { - driverEndpoint.askWithReply[Seq[BlockManagerId]](GetPeers(blockManagerId)) + driverEndpoint.askWithRetry[Seq[BlockManagerId]](GetPeers(blockManagerId)) } def getRpcHostPortForExecutor(executorId: String): Option[(String, Int)] = { - driverEndpoint.askWithReply[Option[(String, Int)]](GetRpcHostPortForExecutor(executorId)) + driverEndpoint.askWithRetry[Option[(String, Int)]](GetRpcHostPortForExecutor(executorId)) } /** @@ -93,12 +93,12 @@ class BlockManagerMaster( * blocks that the driver knows about. */ def removeBlock(blockId: BlockId) { - driverEndpoint.askWithReply[Boolean](RemoveBlock(blockId)) + driverEndpoint.askWithRetry[Boolean](RemoveBlock(blockId)) } /** Remove all blocks belonging to the given RDD. */ def removeRdd(rddId: Int, blocking: Boolean) { - val future = driverEndpoint.askWithReply[Future[Seq[Int]]](RemoveRdd(rddId)) + val future = driverEndpoint.askWithRetry[Future[Seq[Int]]](RemoveRdd(rddId)) future.onFailure { case e: Exception => logWarning(s"Failed to remove RDD $rddId - ${e.getMessage}}") @@ -110,7 +110,7 @@ class BlockManagerMaster( /** Remove all blocks belonging to the given shuffle. */ def removeShuffle(shuffleId: Int, blocking: Boolean) { - val future = driverEndpoint.askWithReply[Future[Seq[Boolean]]](RemoveShuffle(shuffleId)) + val future = driverEndpoint.askWithRetry[Future[Seq[Boolean]]](RemoveShuffle(shuffleId)) future.onFailure { case e: Exception => logWarning(s"Failed to remove shuffle $shuffleId - ${e.getMessage}}") @@ -122,7 +122,7 @@ class BlockManagerMaster( /** Remove all blocks belonging to the given broadcast. */ def removeBroadcast(broadcastId: Long, removeFromMaster: Boolean, blocking: Boolean) { - val future = driverEndpoint.askWithReply[Future[Seq[Int]]]( + val future = driverEndpoint.askWithRetry[Future[Seq[Int]]]( RemoveBroadcast(broadcastId, removeFromMaster)) future.onFailure { case e: Exception => @@ -141,11 +141,11 @@ class BlockManagerMaster( * amount of remaining memory. */ def getMemoryStatus: Map[BlockManagerId, (Long, Long)] = { - driverEndpoint.askWithReply[Map[BlockManagerId, (Long, Long)]](GetMemoryStatus) + driverEndpoint.askWithRetry[Map[BlockManagerId, (Long, Long)]](GetMemoryStatus) } def getStorageStatus: Array[StorageStatus] = { - driverEndpoint.askWithReply[Array[StorageStatus]](GetStorageStatus) + driverEndpoint.askWithRetry[Array[StorageStatus]](GetStorageStatus) } /** @@ -166,7 +166,7 @@ class BlockManagerMaster( * master endpoint for a response to a prior message. */ val response = driverEndpoint. - askWithReply[Map[BlockManagerId, Future[Option[BlockStatus]]]](msg) + askWithRetry[Map[BlockManagerId, Future[Option[BlockStatus]]]](msg) val (blockManagerIds, futures) = response.unzip val result = Await.result(Future.sequence(futures), timeout) if (result == null) { @@ -190,7 +190,7 @@ class BlockManagerMaster( filter: BlockId => Boolean, askSlaves: Boolean): Seq[BlockId] = { val msg = GetMatchingBlockIds(filter, askSlaves) - val future = driverEndpoint.askWithReply[Future[Seq[BlockId]]](msg) + val future = driverEndpoint.askWithRetry[Future[Seq[BlockId]]](msg) Await.result(future, timeout) } @@ -205,7 +205,7 @@ class BlockManagerMaster( /** Send a one-way message to the master endpoint, to which we expect it to reply with true. */ private def tell(message: Any) { - if (!driverEndpoint.askWithReply[Boolean](message)) { + if (!driverEndpoint.askWithRetry[Boolean](message)) { throw new SparkException("BlockManagerMasterEndpoint returned false, expected true.") } } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala index 4682167912ff0..7212362df5d71 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala @@ -132,7 +132,7 @@ class BlockManagerMasterEndpoint( val removeMsg = RemoveRdd(rddId) Future.sequence( blockManagerInfo.values.map { bm => - bm.slaveEndpoint.sendWithReply[Int](removeMsg) + bm.slaveEndpoint.ask[Int](removeMsg) }.toSeq ) } @@ -142,7 +142,7 @@ class BlockManagerMasterEndpoint( val removeMsg = RemoveShuffle(shuffleId) Future.sequence( blockManagerInfo.values.map { bm => - bm.slaveEndpoint.sendWithReply[Boolean](removeMsg) + bm.slaveEndpoint.ask[Boolean](removeMsg) }.toSeq ) } @@ -159,7 +159,7 @@ class BlockManagerMasterEndpoint( } Future.sequence( requiredBlockManagers.map { bm => - bm.slaveEndpoint.sendWithReply[Int](removeMsg) + bm.slaveEndpoint.ask[Int](removeMsg) }.toSeq ) } @@ -214,7 +214,7 @@ class BlockManagerMasterEndpoint( // Remove the block from the slave's BlockManager. // Doesn't actually wait for a confirmation and the message might get lost. // If message loss becomes frequent, we should add retry logic here. - blockManager.get.slaveEndpoint.sendWithReply[Boolean](RemoveBlock(blockId)) + blockManager.get.slaveEndpoint.ask[Boolean](RemoveBlock(blockId)) } } } @@ -253,7 +253,7 @@ class BlockManagerMasterEndpoint( blockManagerInfo.values.map { info => val blockStatusFuture = if (askSlaves) { - info.slaveEndpoint.sendWithReply[Option[BlockStatus]](getBlockStatus) + info.slaveEndpoint.ask[Option[BlockStatus]](getBlockStatus) } else { Future { info.getStatus(blockId) } } @@ -277,7 +277,7 @@ class BlockManagerMasterEndpoint( blockManagerInfo.values.map { info => val future = if (askSlaves) { - info.slaveEndpoint.sendWithReply[Seq[BlockId]](getMatchingBlockIds) + info.slaveEndpoint.ask[Seq[BlockId]](getMatchingBlockIds) } else { Future { info.blocks.keys.filter(filter).toSeq } } diff --git a/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala b/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala index 0fd570e5297d9..b789912e9ebef 100644 --- a/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala +++ b/core/src/test/scala/org/apache/spark/HeartbeatReceiverSuite.scala @@ -48,7 +48,7 @@ class HeartbeatReceiverSuite extends FunSuite with LocalSparkContext { val metrics = new TaskMetrics val blockManagerId = BlockManagerId("executor-1", "localhost", 12345) - val response = receiverRef.askWithReply[HeartbeatResponse]( + val response = receiverRef.askWithRetry[HeartbeatResponse]( Heartbeat("executor-1", Array(1L -> metrics), blockManagerId)) verify(scheduler).executorHeartbeatReceived( @@ -71,7 +71,7 @@ class HeartbeatReceiverSuite extends FunSuite with LocalSparkContext { val metrics = new TaskMetrics val blockManagerId = BlockManagerId("executor-1", "localhost", 12345) - val response = receiverRef.askWithReply[HeartbeatResponse]( + val response = receiverRef.askWithRetry[HeartbeatResponse]( Heartbeat("executor-1", Array(1L -> metrics), blockManagerId)) verify(scheduler).executorHeartbeatReceived( diff --git a/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala b/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala index 44c88b00c442a..ae3339d80f9c6 100644 --- a/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala +++ b/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala @@ -100,8 +100,8 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { } val rpcEndpointRef = env.setupEndpoint("send-ref", endpoint) - val newRpcEndpointRef = rpcEndpointRef.askWithReply[RpcEndpointRef]("Hello") - val reply = newRpcEndpointRef.askWithReply[String]("Echo") + val newRpcEndpointRef = rpcEndpointRef.askWithRetry[RpcEndpointRef]("Hello") + val reply = newRpcEndpointRef.askWithRetry[String]("Echo") assert("Echo" === reply) } @@ -115,7 +115,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { } } }) - val reply = rpcEndpointRef.askWithReply[String]("hello") + val reply = rpcEndpointRef.askWithRetry[String]("hello") assert("hello" === reply) } @@ -134,7 +134,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { // Use anotherEnv to find out the RpcEndpointRef val rpcEndpointRef = anotherEnv.setupEndpointRef("local", env.address, "ask-remotely") try { - val reply = rpcEndpointRef.askWithReply[String]("hello") + val reply = rpcEndpointRef.askWithRetry[String]("hello") assert("hello" === reply) } finally { anotherEnv.shutdown() @@ -162,7 +162,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { val rpcEndpointRef = anotherEnv.setupEndpointRef("local", env.address, "ask-timeout") try { val e = intercept[Exception] { - rpcEndpointRef.askWithReply[String]("hello", 1 millis) + rpcEndpointRef.askWithRetry[String]("hello", 1 millis) } assert(e.isInstanceOf[TimeoutException] || e.getCause.isInstanceOf[TimeoutException]) } finally { @@ -399,7 +399,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { } }) - val f = endpointRef.sendWithReply[String]("Hi") + val f = endpointRef.ask[String]("Hi") val ack = Await.result(f, 5 seconds) assert("ack" === ack) @@ -419,7 +419,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { // Use anotherEnv to find out the RpcEndpointRef val rpcEndpointRef = anotherEnv.setupEndpointRef("local", env.address, "sendWithReply-remotely") try { - val f = rpcEndpointRef.sendWithReply[String]("hello") + val f = rpcEndpointRef.ask[String]("hello") val ack = Await.result(f, 5 seconds) assert("ack" === ack) } finally { @@ -437,7 +437,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { } }) - val f = endpointRef.sendWithReply[String]("Hi") + val f = endpointRef.ask[String]("Hi") val e = intercept[SparkException] { Await.result(f, 5 seconds) } @@ -460,7 +460,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { val rpcEndpointRef = anotherEnv.setupEndpointRef( "local", env.address, "sendWithReply-remotely-error") try { - val f = rpcEndpointRef.sendWithReply[String]("hello") + val f = rpcEndpointRef.ask[String]("hello") val e = intercept[SparkException] { Await.result(f, 5 seconds) } @@ -529,7 +529,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { val rpcEndpointRef = anotherEnv.setupEndpointRef( "local", env.address, "sendWithReply-unserializable-error") try { - val f = rpcEndpointRef.sendWithReply[String]("hello") + val f = rpcEndpointRef.ask[String]("hello") intercept[TimeoutException] { Await.result(f, 1 seconds) } diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala index 6957bc72e9903..f5b410f41da27 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala @@ -356,7 +356,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach master.removeExecutor(store.blockManagerId.executorId) assert(master.getLocations("a1").size == 0, "a1 was not removed from master") - val reregister = !master.driverEndpoint.askWithReply[Boolean]( + val reregister = !master.driverEndpoint.askWithRetry[Boolean]( BlockManagerHeartbeat(store.blockManagerId)) assert(reregister == true) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala index 89af40330b9d9..f2379366f36ef 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala @@ -146,7 +146,7 @@ private[streaming] class ReceiverSupervisorImpl( logDebug(s"Pushed block $blockId in ${(System.currentTimeMillis - time)} ms") val blockInfo = ReceivedBlockInfo(streamId, numRecords, blockStoreResult) - trackerEndpoint.askWithReply[Boolean](AddBlock(blockInfo)) + trackerEndpoint.askWithRetry[Boolean](AddBlock(blockInfo)) logDebug(s"Reported block $blockId") } @@ -169,13 +169,13 @@ private[streaming] class ReceiverSupervisorImpl( override protected def onReceiverStart() { val msg = RegisterReceiver( streamId, receiver.getClass.getSimpleName, Utils.localHostName(), endpoint) - trackerEndpoint.askWithReply[Boolean](msg) + trackerEndpoint.askWithRetry[Boolean](msg) } override protected def onReceiverStop(message: String, error: Option[Throwable]) { logInfo("Deregistering receiver " + streamId) val errorString = error.map(Throwables.getStackTraceAsString).getOrElse("") - trackerEndpoint.askWithReply[Boolean](DeregisterReceiver(streamId, message, errorString)) + trackerEndpoint.askWithRetry[Boolean](DeregisterReceiver(streamId, message, errorString)) logInfo("Stopped receiver " + streamId) } From 3df9c5dd0ce9ce82f9029c2846ad2f6164c501f3 Mon Sep 17 00:00:00 2001 From: ksonj Date: Wed, 29 Apr 2015 09:48:47 -0700 Subject: [PATCH 468/591] Better error message on access to non-existing attribute I believe column access via `__getattr__` is bad and shouldn't be implicitly encouraged by the error message when accessing a non-existing attribute on DataFrame. This patch changes the error message from 'no such column' to the more generic 'no such attribute', which is also what Pandas DFs will throw. Author: ksonj Closes #5771 from ksonj/master and squashes the following commits: bcc2220 [ksonj] Better error message on access to non-existing attribute --- python/pyspark/sql/dataframe.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index 6879fe0805c58..d9cbbc68b3bf0 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -633,7 +633,8 @@ def __getattr__(self, name): [Row(age=2), Row(age=5)] """ if name not in self.columns: - raise AttributeError("No such column: %s" % name) + raise AttributeError( + "'%s' object has no attribute '%s'" % (self.__class__.__name__, name)) jc = self._jdf.apply(name) return Column(jc) From 81ea42bf39db7366e266bc1e5ce7e459fa2ef409 Mon Sep 17 00:00:00 2001 From: Wenchen Fan Date: Wed, 29 Apr 2015 09:49:24 -0700 Subject: [PATCH 469/591] [SQL][Minor] fix java doc for DataFrame.agg Author: Wenchen Fan Closes #5712 from cloud-fan/minor and squashes the following commits: be23064 [Wenchen Fan] fix java doc for DataFrame.agg --- .../scala/org/apache/spark/sql/DataFrame.scala | 17 ++++++----------- 1 file changed, 6 insertions(+), 11 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 2affba7d42cc8..0e896e5693b98 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -623,17 +623,12 @@ class DataFrame private[sql]( } /** - * (Scala-specific) Compute aggregates by specifying a map from column name to - * aggregate methods. The resulting [[DataFrame]] will also contain the grouping columns. - * - * The available aggregate methods are `avg`, `max`, `min`, `sum`, `count`. - * {{{ - * // Selects the age of the oldest employee and the aggregate expense for each department - * df.groupBy("department").agg( - * "age" -> "max", - * "expense" -> "sum" - * ) - * }}} + * (Scala-specific) Aggregates on the entire [[DataFrame]] without groups. + * {{ + * // df.agg(...) is a shorthand for df.groupBy().agg(...) + * df.agg("age" -> "max", "salary" -> "avg") + * df.groupBy().agg("age" -> "max", "salary" -> "avg") + * }} * @group dfops */ def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = { From c0c0ba6d2a11febc8e874c437c4f676dd36ae059 Mon Sep 17 00:00:00 2001 From: Xusen Yin Date: Wed, 29 Apr 2015 10:13:48 -0700 Subject: [PATCH 470/591] Fix a typo of "threshold" mengxr Author: Xusen Yin Closes #5769 from yinxusen/patch-1 and squashes the following commits: 43235f4 [Xusen Yin] Update PearsonCorrelation.scala f7287ee [Xusen Yin] Fix a typo of "threshold" --- .../spark/mllib/stat/correlation/PearsonCorrelation.scala | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/PearsonCorrelation.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/PearsonCorrelation.scala index 23b291eee070b..8a821d1b23bab 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/PearsonCorrelation.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/PearsonCorrelation.scala @@ -101,7 +101,7 @@ private[stat] object PearsonCorrelation extends Correlation with Logging { Matrices.fromBreeze(cov) } - private def closeToZero(value: Double, threshhold: Double = 1e-12): Boolean = { - math.abs(value) <= threshhold + private def closeToZero(value: Double, threshold: Double = 1e-12): Boolean = { + math.abs(value) <= threshold } } From 1868bd40dcce23990b98748b0239bd00452b1ca5 Mon Sep 17 00:00:00 2001 From: Tathagata Das Date: Wed, 29 Apr 2015 13:06:11 -0700 Subject: [PATCH 471/591] [SPARK-7056] [STREAMING] Make the Write Ahead Log pluggable Users may want the WAL data to be written to non-HDFS data storage systems. To allow that, we have to make the WAL pluggable. The following design doc outlines the plan. https://docs.google.com/a/databricks.com/document/d/1A2XaOLRFzvIZSi18i_luNw5Rmm9j2j4AigktXxIYxmY/edit?usp=sharing Things to add. * Unit tests for WriteAheadLogUtils Author: Tathagata Das Closes #5645 from tdas/wal-pluggable and squashes the following commits: 2c431fd [Tathagata Das] Minor fixes. c2bc7384 [Tathagata Das] More changes based on PR comments. 569a416 [Tathagata Das] fixed long line bde26b1 [Tathagata Das] Renamed segment to record handle everywhere b65e155 [Tathagata Das] More changes based on PR comments. d7cd15b [Tathagata Das] Fixed test 1a32a4b [Tathagata Das] Fixed test e0d19fb [Tathagata Das] Fixed defaults 9310cbf [Tathagata Das] style fix. 86abcb1 [Tathagata Das] Refactored WriteAheadLogUtils, and consolidated all WAL related configuration into it. 84ce469 [Tathagata Das] Added unit test and fixed compilation error. bce5e75 [Tathagata Das] Fixed long lines. 837c4f5 [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into wal-pluggable 754fbf8 [Tathagata Das] Added license and docs. 09bc6fe [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into wal-pluggable 7dd2d4b [Tathagata Das] Added pluggable WriteAheadLog interface, and refactored all code along with it --- .../spark/streaming/kafka/KafkaUtils.scala | 3 +- .../spark/streaming/util/WriteAheadLog.java | 60 ++++++ .../util/WriteAheadLogRecordHandle.java | 30 +++ .../dstream/ReceiverInputDStream.scala | 2 +- .../rdd/WriteAheadLogBackedBlockRDD.scala | 79 +++++-- .../receiver/ReceivedBlockHandler.scala | 38 ++-- .../receiver/ReceiverSupervisorImpl.scala | 5 +- .../scheduler/ReceivedBlockTracker.scala | 38 ++-- .../streaming/scheduler/ReceiverTracker.scala | 3 +- ...ger.scala => FileBasedWriteAheadLog.scala} | 76 ++++--- ... FileBasedWriteAheadLogRandomReader.scala} | 8 +- ...ala => FileBasedWriteAheadLogReader.scala} | 4 +- ...la => FileBasedWriteAheadLogSegment.scala} | 3 +- ...ala => FileBasedWriteAheadLogWriter.scala} | 9 +- .../streaming/util/WriteAheadLogUtils.scala | 129 ++++++++++++ .../streaming/JavaWriteAheadLogSuite.java | 129 ++++++++++++ .../streaming/ReceivedBlockHandlerSuite.scala | 18 +- .../streaming/ReceivedBlockTrackerSuite.scala | 28 +-- .../spark/streaming/ReceiverSuite.scala | 2 +- .../streaming/StreamingContextSuite.scala | 4 +- .../WriteAheadLogBackedBlockRDDSuite.scala | 31 +-- .../streaming/util/WriteAheadLogSuite.scala | 194 ++++++++++++------ 22 files changed, 686 insertions(+), 207 deletions(-) create mode 100644 streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLog.java create mode 100644 streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLogRecordHandle.java rename streaming/src/main/scala/org/apache/spark/streaming/util/{WriteAheadLogManager.scala => FileBasedWriteAheadLog.scala} (79%) rename streaming/src/main/scala/org/apache/spark/streaming/util/{WriteAheadLogRandomReader.scala => FileBasedWriteAheadLogRandomReader.scala} (83%) rename streaming/src/main/scala/org/apache/spark/streaming/util/{WriteAheadLogReader.scala => FileBasedWriteAheadLogReader.scala} (93%) rename streaming/src/main/scala/org/apache/spark/streaming/util/{WriteAheadLogFileSegment.scala => FileBasedWriteAheadLogSegment.scala} (86%) rename streaming/src/main/scala/org/apache/spark/streaming/util/{WriteAheadLogWriter.scala => FileBasedWriteAheadLogWriter.scala} (88%) create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogUtils.scala create mode 100644 streaming/src/test/java/org/apache/spark/streaming/JavaWriteAheadLogSuite.java diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala index 0721ddaf7055a..d7cf500577c2a 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala @@ -31,6 +31,7 @@ import kafka.message.MessageAndMetadata import kafka.serializer.{DefaultDecoder, Decoder, StringDecoder} import org.apache.spark.api.java.function.{Function => JFunction} +import org.apache.spark.streaming.util.WriteAheadLogUtils import org.apache.spark.{SparkContext, SparkException} import org.apache.spark.annotation.Experimental import org.apache.spark.rdd.RDD @@ -80,7 +81,7 @@ object KafkaUtils { topics: Map[String, Int], storageLevel: StorageLevel ): ReceiverInputDStream[(K, V)] = { - val walEnabled = ssc.conf.getBoolean("spark.streaming.receiver.writeAheadLog.enable", false) + val walEnabled = WriteAheadLogUtils.enableReceiverLog(ssc.conf) new KafkaInputDStream[K, V, U, T](ssc, kafkaParams, topics, walEnabled, storageLevel) } diff --git a/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLog.java b/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLog.java new file mode 100644 index 0000000000000..8c0fdfa9c7478 --- /dev/null +++ b/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLog.java @@ -0,0 +1,60 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.util; + +import java.nio.ByteBuffer; +import java.util.Iterator; + +/** + * This abstract class represents a write ahead log (aka journal) that is used by Spark Streaming + * to save the received data (by receivers) and associated metadata to a reliable storage, so that + * they can be recovered after driver failures. See the Spark documentation for more information + * on how to plug in your own custom implementation of a write ahead log. + */ +@org.apache.spark.annotation.DeveloperApi +public abstract class WriteAheadLog { + /** + * Write the record to the log and return a record handle, which contains all the information + * necessary to read back the written record. The time is used to the index the record, + * such that it can be cleaned later. Note that implementations of this abstract class must + * ensure that the written data is durable and readable (using the record handle) by the + * time this function returns. + */ + abstract public WriteAheadLogRecordHandle write(ByteBuffer record, long time); + + /** + * Read a written record based on the given record handle. + */ + abstract public ByteBuffer read(WriteAheadLogRecordHandle handle); + + /** + * Read and return an iterator of all the records that have been written but not yet cleaned up. + */ + abstract public Iterator readAll(); + + /** + * Clean all the records that are older than the threshold time. It can wait for + * the completion of the deletion. + */ + abstract public void clean(long threshTime, boolean waitForCompletion); + + /** + * Close this log and release any resources. + */ + abstract public void close(); +} diff --git a/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLogRecordHandle.java b/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLogRecordHandle.java new file mode 100644 index 0000000000000..02324189b7822 --- /dev/null +++ b/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLogRecordHandle.java @@ -0,0 +1,30 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.util; + +/** + * This abstract class represents a handle that refers to a record written in a + * {@link org.apache.spark.streaming.util.WriteAheadLog WriteAheadLog}. + * It must contain all the information necessary for the record to be read and returned by + * an implemenation of the WriteAheadLog class. + * + * @see org.apache.spark.streaming.util.WriteAheadLog + */ +@org.apache.spark.annotation.DeveloperApi +public abstract class WriteAheadLogRecordHandle implements java.io.Serializable { +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala index 8be04314c4285..4c7fd2c57c006 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala @@ -82,7 +82,7 @@ abstract class ReceiverInputDStream[T: ClassTag](@transient ssc_ : StreamingCont // WriteAheadLogBackedBlockRDD else create simple BlockRDD. if (resultTypes.size == 1 && resultTypes.head == classOf[WriteAheadLogBasedStoreResult]) { val logSegments = blockStoreResults.map { - _.asInstanceOf[WriteAheadLogBasedStoreResult].segment + _.asInstanceOf[WriteAheadLogBasedStoreResult].walRecordHandle }.toArray // Since storeInBlockManager = false, the storage level does not matter. new WriteAheadLogBackedBlockRDD[T](ssc.sparkContext, diff --git a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala index 93caa4ba35c7f..ebdf418f4ab6a 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala @@ -16,14 +16,17 @@ */ package org.apache.spark.streaming.rdd +import java.nio.ByteBuffer + import scala.reflect.ClassTag +import scala.util.control.NonFatal -import org.apache.hadoop.conf.Configuration +import org.apache.commons.io.FileUtils import org.apache.spark._ import org.apache.spark.rdd.BlockRDD import org.apache.spark.storage.{BlockId, StorageLevel} -import org.apache.spark.streaming.util.{HdfsUtils, WriteAheadLogFileSegment, WriteAheadLogRandomReader} +import org.apache.spark.streaming.util._ /** * Partition class for [[org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD]]. @@ -31,26 +34,27 @@ import org.apache.spark.streaming.util.{HdfsUtils, WriteAheadLogFileSegment, Wri * the segment of the write ahead log that backs the partition. * @param index index of the partition * @param blockId id of the block having the partition data - * @param segment segment of the write ahead log having the partition data + * @param walRecordHandle Handle of the record in a write ahead log having the partition data */ private[streaming] class WriteAheadLogBackedBlockRDDPartition( val index: Int, val blockId: BlockId, - val segment: WriteAheadLogFileSegment) + val walRecordHandle: WriteAheadLogRecordHandle) extends Partition /** * This class represents a special case of the BlockRDD where the data blocks in - * the block manager are also backed by segments in write ahead logs. For reading + * the block manager are also backed by data in write ahead logs. For reading * the data, this RDD first looks up the blocks by their ids in the block manager. - * If it does not find them, it looks up the corresponding file segment. + * If it does not find them, it looks up the corresponding data in the write ahead log. * * @param sc SparkContext * @param blockIds Ids of the blocks that contains this RDD's data - * @param segments Segments in write ahead logs that contain this RDD's data - * @param storeInBlockManager Whether to store in the block manager after reading from the segment + * @param walRecordHandles Record handles in write ahead logs that contain this RDD's data + * @param storeInBlockManager Whether to store in the block manager after reading + * from the WAL record * @param storageLevel storage level to store when storing in block manager * (applicable when storeInBlockManager = true) */ @@ -58,15 +62,15 @@ private[streaming] class WriteAheadLogBackedBlockRDD[T: ClassTag]( @transient sc: SparkContext, @transient blockIds: Array[BlockId], - @transient segments: Array[WriteAheadLogFileSegment], + @transient walRecordHandles: Array[WriteAheadLogRecordHandle], storeInBlockManager: Boolean, storageLevel: StorageLevel) extends BlockRDD[T](sc, blockIds) { require( - blockIds.length == segments.length, + blockIds.length == walRecordHandles.length, s"Number of block ids (${blockIds.length}) must be " + - s"the same as number of segments (${segments.length}})!") + s"the same as number of WAL record handles (${walRecordHandles.length}})!") // Hadoop configuration is not serializable, so broadcast it as a serializable. @transient private val hadoopConfig = sc.hadoopConfiguration @@ -75,13 +79,13 @@ class WriteAheadLogBackedBlockRDD[T: ClassTag]( override def getPartitions: Array[Partition] = { assertValid() Array.tabulate(blockIds.size) { i => - new WriteAheadLogBackedBlockRDDPartition(i, blockIds(i), segments(i)) + new WriteAheadLogBackedBlockRDDPartition(i, blockIds(i), walRecordHandles(i)) } } /** * Gets the partition data by getting the corresponding block from the block manager. - * If the block does not exist, then the data is read from the corresponding segment + * If the block does not exist, then the data is read from the corresponding record * in write ahead log files. */ override def compute(split: Partition, context: TaskContext): Iterator[T] = { @@ -96,10 +100,35 @@ class WriteAheadLogBackedBlockRDD[T: ClassTag]( logDebug(s"Read partition data of $this from block manager, block $blockId") iterator case None => // Data not found in Block Manager, grab it from write ahead log file - val reader = new WriteAheadLogRandomReader(partition.segment.path, hadoopConf) - val dataRead = reader.read(partition.segment) - reader.close() - logInfo(s"Read partition data of $this from write ahead log, segment ${partition.segment}") + var dataRead: ByteBuffer = null + var writeAheadLog: WriteAheadLog = null + try { + // The WriteAheadLogUtils.createLog*** method needs a directory to create a + // WriteAheadLog object as the default FileBasedWriteAheadLog needs a directory for + // writing log data. However, the directory is not needed if data needs to be read, hence + // a dummy path is provided to satisfy the method parameter requirements. + // FileBasedWriteAheadLog will not create any file or directory at that path. + val dummyDirectory = FileUtils.getTempDirectoryPath() + writeAheadLog = WriteAheadLogUtils.createLogForReceiver( + SparkEnv.get.conf, dummyDirectory, hadoopConf) + dataRead = writeAheadLog.read(partition.walRecordHandle) + } catch { + case NonFatal(e) => + throw new SparkException( + s"Could not read data from write ahead log record ${partition.walRecordHandle}", e) + } finally { + if (writeAheadLog != null) { + writeAheadLog.close() + writeAheadLog = null + } + } + if (dataRead == null) { + throw new SparkException( + s"Could not read data from write ahead log record ${partition.walRecordHandle}, " + + s"read returned null") + } + logInfo(s"Read partition data of $this from write ahead log, record handle " + + partition.walRecordHandle) if (storeInBlockManager) { blockManager.putBytes(blockId, dataRead, storageLevel) logDebug(s"Stored partition data of $this into block manager with level $storageLevel") @@ -111,14 +140,20 @@ class WriteAheadLogBackedBlockRDD[T: ClassTag]( /** * Get the preferred location of the partition. This returns the locations of the block - * if it is present in the block manager, else it returns the location of the - * corresponding segment in HDFS. + * if it is present in the block manager, else if FileBasedWriteAheadLogSegment is used, + * it returns the location of the corresponding file segment in HDFS . */ override def getPreferredLocations(split: Partition): Seq[String] = { val partition = split.asInstanceOf[WriteAheadLogBackedBlockRDDPartition] val blockLocations = getBlockIdLocations().get(partition.blockId) - blockLocations.getOrElse( - HdfsUtils.getFileSegmentLocations( - partition.segment.path, partition.segment.offset, partition.segment.length, hadoopConfig)) + blockLocations.getOrElse { + partition.walRecordHandle match { + case fileSegment: FileBasedWriteAheadLogSegment => + HdfsUtils.getFileSegmentLocations( + fileSegment.path, fileSegment.offset, fileSegment.length, hadoopConfig) + case _ => + Seq.empty + } + } } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala index 297bf04c0c25e..4b3d9ee4b0090 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala @@ -17,18 +17,18 @@ package org.apache.spark.streaming.receiver -import scala.concurrent.{Await, ExecutionContext, Future} import scala.concurrent.duration._ +import scala.concurrent.{Await, ExecutionContext, Future} import scala.language.{existentials, postfixOps} -import WriteAheadLogBasedBlockHandler._ import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path -import org.apache.spark.{Logging, SparkConf, SparkException} import org.apache.spark.storage._ -import org.apache.spark.streaming.util.{WriteAheadLogFileSegment, WriteAheadLogManager} -import org.apache.spark.util.{ThreadUtils, Clock, SystemClock} +import org.apache.spark.streaming.receiver.WriteAheadLogBasedBlockHandler._ +import org.apache.spark.streaming.util.{WriteAheadLogRecordHandle, WriteAheadLogUtils} +import org.apache.spark.util.{Clock, SystemClock, ThreadUtils} +import org.apache.spark.{Logging, SparkConf, SparkException} /** Trait that represents the metadata related to storage of blocks */ private[streaming] trait ReceivedBlockStoreResult { @@ -96,7 +96,7 @@ private[streaming] class BlockManagerBasedBlockHandler( */ private[streaming] case class WriteAheadLogBasedStoreResult( blockId: StreamBlockId, - segment: WriteAheadLogFileSegment + walRecordHandle: WriteAheadLogRecordHandle ) extends ReceivedBlockStoreResult @@ -116,10 +116,6 @@ private[streaming] class WriteAheadLogBasedBlockHandler( private val blockStoreTimeout = conf.getInt( "spark.streaming.receiver.blockStoreTimeout", 30).seconds - private val rollingInterval = conf.getInt( - "spark.streaming.receiver.writeAheadLog.rollingInterval", 60) - private val maxFailures = conf.getInt( - "spark.streaming.receiver.writeAheadLog.maxFailures", 3) private val effectiveStorageLevel = { if (storageLevel.deserialized) { @@ -139,13 +135,9 @@ private[streaming] class WriteAheadLogBasedBlockHandler( s"$effectiveStorageLevel when write ahead log is enabled") } - // Manages rolling log files - private val logManager = new WriteAheadLogManager( - checkpointDirToLogDir(checkpointDir, streamId), - hadoopConf, rollingInterval, maxFailures, - callerName = this.getClass.getSimpleName, - clock = clock - ) + // Write ahead log manages + private val writeAheadLog = WriteAheadLogUtils.createLogForReceiver( + conf, checkpointDirToLogDir(checkpointDir, streamId), hadoopConf) // For processing futures used in parallel block storing into block manager and write ahead log // # threads = 2, so that both writing to BM and WAL can proceed in parallel @@ -183,21 +175,21 @@ private[streaming] class WriteAheadLogBasedBlockHandler( // Store the block in write ahead log val storeInWriteAheadLogFuture = Future { - logManager.writeToLog(serializedBlock) + writeAheadLog.write(serializedBlock, clock.getTimeMillis()) } - // Combine the futures, wait for both to complete, and return the write ahead log segment + // Combine the futures, wait for both to complete, and return the write ahead log record handle val combinedFuture = storeInBlockManagerFuture.zip(storeInWriteAheadLogFuture).map(_._2) - val segment = Await.result(combinedFuture, blockStoreTimeout) - WriteAheadLogBasedStoreResult(blockId, segment) + val walRecordHandle = Await.result(combinedFuture, blockStoreTimeout) + WriteAheadLogBasedStoreResult(blockId, walRecordHandle) } def cleanupOldBlocks(threshTime: Long) { - logManager.cleanupOldLogs(threshTime, waitForCompletion = false) + writeAheadLog.clean(threshTime, false) } def stop() { - logManager.stop() + writeAheadLog.close() } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala index f2379366f36ef..93f047b91018f 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala @@ -25,12 +25,13 @@ import scala.collection.mutable.ArrayBuffer import com.google.common.base.Throwables import org.apache.hadoop.conf.Configuration -import org.apache.spark.{Logging, SparkEnv, SparkException} import org.apache.spark.rpc.{RpcEnv, ThreadSafeRpcEndpoint} import org.apache.spark.storage.StreamBlockId import org.apache.spark.streaming.Time import org.apache.spark.streaming.scheduler._ +import org.apache.spark.streaming.util.WriteAheadLogUtils import org.apache.spark.util.{RpcUtils, Utils} +import org.apache.spark.{Logging, SparkEnv, SparkException} /** * Concrete implementation of [[org.apache.spark.streaming.receiver.ReceiverSupervisor]] @@ -46,7 +47,7 @@ private[streaming] class ReceiverSupervisorImpl( ) extends ReceiverSupervisor(receiver, env.conf) with Logging { private val receivedBlockHandler: ReceivedBlockHandler = { - if (env.conf.getBoolean("spark.streaming.receiver.writeAheadLog.enable", false)) { + if (WriteAheadLogUtils.enableReceiverLog(env.conf)) { if (checkpointDirOption.isEmpty) { throw new SparkException( "Cannot enable receiver write-ahead log without checkpoint directory set. " + diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala index 200cf4ef4b0f1..14e769a281f51 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala @@ -25,10 +25,10 @@ import scala.language.implicitConversions import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path -import org.apache.spark.{SparkException, Logging, SparkConf} import org.apache.spark.streaming.Time -import org.apache.spark.streaming.util.WriteAheadLogManager +import org.apache.spark.streaming.util.{WriteAheadLog, WriteAheadLogUtils} import org.apache.spark.util.{Clock, Utils} +import org.apache.spark.{Logging, SparkConf, SparkException} /** Trait representing any event in the ReceivedBlockTracker that updates its state. */ private[streaming] sealed trait ReceivedBlockTrackerLogEvent @@ -70,7 +70,7 @@ private[streaming] class ReceivedBlockTracker( private val streamIdToUnallocatedBlockQueues = new mutable.HashMap[Int, ReceivedBlockQueue] private val timeToAllocatedBlocks = new mutable.HashMap[Time, AllocatedBlocks] - private val logManagerOption = createLogManager() + private val writeAheadLogOption = createWriteAheadLog() private var lastAllocatedBatchTime: Time = null @@ -155,12 +155,12 @@ private[streaming] class ReceivedBlockTracker( logInfo("Deleting batches " + timesToCleanup) writeToLog(BatchCleanupEvent(timesToCleanup)) timeToAllocatedBlocks --= timesToCleanup - logManagerOption.foreach(_.cleanupOldLogs(cleanupThreshTime.milliseconds, waitForCompletion)) + writeAheadLogOption.foreach(_.clean(cleanupThreshTime.milliseconds, waitForCompletion)) } /** Stop the block tracker. */ def stop() { - logManagerOption.foreach { _.stop() } + writeAheadLogOption.foreach { _.close() } } /** @@ -190,9 +190,10 @@ private[streaming] class ReceivedBlockTracker( timeToAllocatedBlocks --= batchTimes } - logManagerOption.foreach { logManager => + writeAheadLogOption.foreach { writeAheadLog => logInfo(s"Recovering from write ahead logs in ${checkpointDirOption.get}") - logManager.readFromLog().foreach { byteBuffer => + import scala.collection.JavaConversions._ + writeAheadLog.readAll().foreach { byteBuffer => logTrace("Recovering record " + byteBuffer) Utils.deserialize[ReceivedBlockTrackerLogEvent](byteBuffer.array) match { case BlockAdditionEvent(receivedBlockInfo) => @@ -208,10 +209,10 @@ private[streaming] class ReceivedBlockTracker( /** Write an update to the tracker to the write ahead log */ private def writeToLog(record: ReceivedBlockTrackerLogEvent) { - if (isLogManagerEnabled) { + if (isWriteAheadLogEnabled) { logDebug(s"Writing to log $record") - logManagerOption.foreach { logManager => - logManager.writeToLog(ByteBuffer.wrap(Utils.serialize(record))) + writeAheadLogOption.foreach { logManager => + logManager.write(ByteBuffer.wrap(Utils.serialize(record)), clock.getTimeMillis()) } } } @@ -222,8 +223,8 @@ private[streaming] class ReceivedBlockTracker( } /** Optionally create the write ahead log manager only if the feature is enabled */ - private def createLogManager(): Option[WriteAheadLogManager] = { - if (conf.getBoolean("spark.streaming.receiver.writeAheadLog.enable", false)) { + private def createWriteAheadLog(): Option[WriteAheadLog] = { + if (WriteAheadLogUtils.enableReceiverLog(conf)) { if (checkpointDirOption.isEmpty) { throw new SparkException( "Cannot enable receiver write-ahead log without checkpoint directory set. " + @@ -231,19 +232,16 @@ private[streaming] class ReceivedBlockTracker( "See documentation for more details.") } val logDir = ReceivedBlockTracker.checkpointDirToLogDir(checkpointDirOption.get) - val rollingIntervalSecs = conf.getInt( - "spark.streaming.receivedBlockTracker.writeAheadLog.rotationIntervalSecs", 60) - val logManager = new WriteAheadLogManager(logDir, hadoopConf, - rollingIntervalSecs = rollingIntervalSecs, clock = clock, - callerName = "ReceivedBlockHandlerMaster") - Some(logManager) + + val log = WriteAheadLogUtils.createLogForDriver(conf, logDir, hadoopConf) + Some(log) } else { None } } - /** Check if the log manager is enabled. This is only used for testing purposes. */ - private[streaming] def isLogManagerEnabled: Boolean = logManagerOption.nonEmpty + /** Check if the write ahead log is enabled. This is only used for testing purposes. */ + private[streaming] def isWriteAheadLogEnabled: Boolean = writeAheadLogOption.nonEmpty } private[streaming] object ReceivedBlockTracker { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala index c4ead6f30a63d..1af65716d3003 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala @@ -20,6 +20,7 @@ package org.apache.spark.streaming.scheduler import scala.collection.mutable.{HashMap, SynchronizedMap} import scala.language.existentials +import org.apache.spark.streaming.util.WriteAheadLogUtils import org.apache.spark.{Logging, SerializableWritable, SparkEnv, SparkException} import org.apache.spark.rpc._ import org.apache.spark.streaming.{StreamingContext, Time} @@ -125,7 +126,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false receivedBlockTracker.cleanupOldBatches(cleanupThreshTime, waitForCompletion = false) // Signal the receivers to delete old block data - if (ssc.conf.getBoolean("spark.streaming.receiver.writeAheadLog.enable", false)) { + if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) { logInfo(s"Cleanup old received batch data: $cleanupThreshTime") receiverInfo.values.flatMap { info => Option(info.endpoint) } .foreach { _.send(CleanupOldBlocks(cleanupThreshTime)) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogManager.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala similarity index 79% rename from streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogManager.scala rename to streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala index 38a93cc3c9a1f..9985fedc35141 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogManager.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala @@ -17,6 +17,7 @@ package org.apache.spark.streaming.util import java.nio.ByteBuffer +import java.util.{Iterator => JIterator} import scala.collection.mutable.ArrayBuffer import scala.concurrent.{Await, ExecutionContext, Future} @@ -24,9 +25,9 @@ import scala.language.postfixOps import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path -import org.apache.spark.Logging -import org.apache.spark.util.{ThreadUtils, Clock, SystemClock} -import WriteAheadLogManager._ + +import org.apache.spark.util.ThreadUtils +import org.apache.spark.{Logging, SparkConf} /** * This class manages write ahead log files. @@ -34,37 +35,32 @@ import WriteAheadLogManager._ * - Recovers the log files and the reads the recovered records upon failures. * - Cleans up old log files. * - * Uses [[org.apache.spark.streaming.util.WriteAheadLogWriter]] to write - * and [[org.apache.spark.streaming.util.WriteAheadLogReader]] to read. + * Uses [[org.apache.spark.streaming.util.FileBasedWriteAheadLogWriter]] to write + * and [[org.apache.spark.streaming.util.FileBasedWriteAheadLogReader]] to read. * * @param logDirectory Directory when rotating log files will be created. * @param hadoopConf Hadoop configuration for reading/writing log files. - * @param rollingIntervalSecs The interval in seconds with which logs will be rolled over. - * Default is one minute. - * @param maxFailures Max number of failures that is tolerated for every attempt to write to log. - * Default is three. - * @param callerName Optional name of the class who is using this manager. - * @param clock Optional clock that is used to check for rotation interval. */ -private[streaming] class WriteAheadLogManager( +private[streaming] class FileBasedWriteAheadLog( + conf: SparkConf, logDirectory: String, hadoopConf: Configuration, - rollingIntervalSecs: Int = 60, - maxFailures: Int = 3, - callerName: String = "", - clock: Clock = new SystemClock - ) extends Logging { + rollingIntervalSecs: Int, + maxFailures: Int + ) extends WriteAheadLog with Logging { + + import FileBasedWriteAheadLog._ private val pastLogs = new ArrayBuffer[LogInfo] - private val callerNameTag = - if (callerName.nonEmpty) s" for $callerName" else "" + private val callerNameTag = getCallerName.map(c => s" for $c").getOrElse("") + private val threadpoolName = s"WriteAheadLogManager $callerNameTag" implicit private val executionContext = ExecutionContext.fromExecutorService( ThreadUtils.newDaemonSingleThreadExecutor(threadpoolName)) override protected val logName = s"WriteAheadLogManager $callerNameTag" private var currentLogPath: Option[String] = None - private var currentLogWriter: WriteAheadLogWriter = null + private var currentLogWriter: FileBasedWriteAheadLogWriter = null private var currentLogWriterStartTime: Long = -1L private var currentLogWriterStopTime: Long = -1L @@ -75,14 +71,14 @@ private[streaming] class WriteAheadLogManager( * ByteBuffer to HDFS. When this method returns, the data is guaranteed to have been flushed * to HDFS, and will be available for readers to read. */ - def writeToLog(byteBuffer: ByteBuffer): WriteAheadLogFileSegment = synchronized { - var fileSegment: WriteAheadLogFileSegment = null + def write(byteBuffer: ByteBuffer, time: Long): FileBasedWriteAheadLogSegment = synchronized { + var fileSegment: FileBasedWriteAheadLogSegment = null var failures = 0 var lastException: Exception = null var succeeded = false while (!succeeded && failures < maxFailures) { try { - fileSegment = getLogWriter(clock.getTimeMillis()).write(byteBuffer) + fileSegment = getLogWriter(time).write(byteBuffer) succeeded = true } catch { case ex: Exception => @@ -99,6 +95,19 @@ private[streaming] class WriteAheadLogManager( fileSegment } + def read(segment: WriteAheadLogRecordHandle): ByteBuffer = { + val fileSegment = segment.asInstanceOf[FileBasedWriteAheadLogSegment] + var reader: FileBasedWriteAheadLogRandomReader = null + var byteBuffer: ByteBuffer = null + try { + reader = new FileBasedWriteAheadLogRandomReader(fileSegment.path, hadoopConf) + byteBuffer = reader.read(fileSegment) + } finally { + reader.close() + } + byteBuffer + } + /** * Read all the existing logs from the log directory. * @@ -108,12 +117,14 @@ private[streaming] class WriteAheadLogManager( * the latest the records. This does not deal with currently active log files, and * hence the implementation is kept simple. */ - def readFromLog(): Iterator[ByteBuffer] = synchronized { + def readAll(): JIterator[ByteBuffer] = synchronized { + import scala.collection.JavaConversions._ val logFilesToRead = pastLogs.map{ _.path} ++ currentLogPath logInfo("Reading from the logs: " + logFilesToRead.mkString("\n")) + logFilesToRead.iterator.map { file => logDebug(s"Creating log reader with $file") - new WriteAheadLogReader(file, hadoopConf) + new FileBasedWriteAheadLogReader(file, hadoopConf) } flatMap { x => x } } @@ -129,7 +140,7 @@ private[streaming] class WriteAheadLogManager( * deleted. This should be set to true only for testing. Else the files will be deleted * asynchronously. */ - def cleanupOldLogs(threshTime: Long, waitForCompletion: Boolean): Unit = { + def clean(threshTime: Long, waitForCompletion: Boolean): Unit = { val oldLogFiles = synchronized { pastLogs.filter { _.endTime < threshTime } } logInfo(s"Attempting to clear ${oldLogFiles.size} old log files in $logDirectory " + s"older than $threshTime: ${oldLogFiles.map { _.path }.mkString("\n")}") @@ -160,7 +171,7 @@ private[streaming] class WriteAheadLogManager( /** Stop the manager, close any open log writer */ - def stop(): Unit = synchronized { + def close(): Unit = synchronized { if (currentLogWriter != null) { currentLogWriter.close() } @@ -169,7 +180,7 @@ private[streaming] class WriteAheadLogManager( } /** Get the current log writer while taking care of rotation */ - private def getLogWriter(currentTime: Long): WriteAheadLogWriter = synchronized { + private def getLogWriter(currentTime: Long): FileBasedWriteAheadLogWriter = synchronized { if (currentLogWriter == null || currentTime > currentLogWriterStopTime) { resetWriter() currentLogPath.foreach { @@ -180,7 +191,7 @@ private[streaming] class WriteAheadLogManager( val newLogPath = new Path(logDirectory, timeToLogFile(currentLogWriterStartTime, currentLogWriterStopTime)) currentLogPath = Some(newLogPath.toString) - currentLogWriter = new WriteAheadLogWriter(currentLogPath.get, hadoopConf) + currentLogWriter = new FileBasedWriteAheadLogWriter(currentLogPath.get, hadoopConf) } currentLogWriter } @@ -207,7 +218,7 @@ private[streaming] class WriteAheadLogManager( } } -private[util] object WriteAheadLogManager { +private[streaming] object FileBasedWriteAheadLog { case class LogInfo(startTime: Long, endTime: Long, path: String) @@ -217,6 +228,11 @@ private[util] object WriteAheadLogManager { s"log-$startTime-$stopTime" } + def getCallerName(): Option[String] = { + val stackTraceClasses = Thread.currentThread.getStackTrace().map(_.getClassName) + stackTraceClasses.find(!_.contains("WriteAheadLog")).flatMap(_.split(".").lastOption) + } + /** Convert a sequence of files to a sequence of sorted LogInfo objects */ def logFilesTologInfo(files: Seq[Path]): Seq[LogInfo] = { files.flatMap { file => diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogRandomReader.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogRandomReader.scala similarity index 83% rename from streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogRandomReader.scala rename to streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogRandomReader.scala index 003989092a42a..f7168229ec15a 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogRandomReader.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogRandomReader.scala @@ -23,16 +23,16 @@ import org.apache.hadoop.conf.Configuration /** * A random access reader for reading write ahead log files written using - * [[org.apache.spark.streaming.util.WriteAheadLogWriter]]. Given the file segment info, - * this reads the record (bytebuffer) from the log file. + * [[org.apache.spark.streaming.util.FileBasedWriteAheadLogWriter]]. Given the file segment info, + * this reads the record (ByteBuffer) from the log file. */ -private[streaming] class WriteAheadLogRandomReader(path: String, conf: Configuration) +private[streaming] class FileBasedWriteAheadLogRandomReader(path: String, conf: Configuration) extends Closeable { private val instream = HdfsUtils.getInputStream(path, conf) private var closed = false - def read(segment: WriteAheadLogFileSegment): ByteBuffer = synchronized { + def read(segment: FileBasedWriteAheadLogSegment): ByteBuffer = synchronized { assertOpen() instream.seek(segment.offset) val nextLength = instream.readInt() diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogReader.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogReader.scala similarity index 93% rename from streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogReader.scala rename to streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogReader.scala index 2afc0d1551acf..c3bb59f3fef94 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogReader.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogReader.scala @@ -24,11 +24,11 @@ import org.apache.spark.Logging /** * A reader for reading write ahead log files written using - * [[org.apache.spark.streaming.util.WriteAheadLogWriter]]. This reads + * [[org.apache.spark.streaming.util.FileBasedWriteAheadLogWriter]]. This reads * the records (bytebuffers) in the log file sequentially and return them as an * iterator of bytebuffers. */ -private[streaming] class WriteAheadLogReader(path: String, conf: Configuration) +private[streaming] class FileBasedWriteAheadLogReader(path: String, conf: Configuration) extends Iterator[ByteBuffer] with Closeable with Logging { private val instream = HdfsUtils.getInputStream(path, conf) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogFileSegment.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogSegment.scala similarity index 86% rename from streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogFileSegment.scala rename to streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogSegment.scala index 1005a2c8ec303..2e1f1528fad20 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogFileSegment.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogSegment.scala @@ -17,4 +17,5 @@ package org.apache.spark.streaming.util /** Class for representing a segment of data in a write ahead log file */ -private[streaming] case class WriteAheadLogFileSegment (path: String, offset: Long, length: Int) +private[streaming] case class FileBasedWriteAheadLogSegment(path: String, offset: Long, length: Int) + extends WriteAheadLogRecordHandle diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogWriter.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogWriter.scala similarity index 88% rename from streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogWriter.scala rename to streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogWriter.scala index 679f6a6dfd7c1..e146bec32a456 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogWriter.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLogWriter.scala @@ -17,18 +17,17 @@ package org.apache.spark.streaming.util import java.io._ -import java.net.URI import java.nio.ByteBuffer import scala.util.Try import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.{FSDataOutputStream, FileSystem} +import org.apache.hadoop.fs.FSDataOutputStream /** * A writer for writing byte-buffers to a write ahead log file. */ -private[streaming] class WriteAheadLogWriter(path: String, hadoopConf: Configuration) +private[streaming] class FileBasedWriteAheadLogWriter(path: String, hadoopConf: Configuration) extends Closeable { private lazy val stream = HdfsUtils.getOutputStream(path, hadoopConf) @@ -43,11 +42,11 @@ private[streaming] class WriteAheadLogWriter(path: String, hadoopConf: Configura private var closed = false /** Write the bytebuffer to the log file */ - def write(data: ByteBuffer): WriteAheadLogFileSegment = synchronized { + def write(data: ByteBuffer): FileBasedWriteAheadLogSegment = synchronized { assertOpen() data.rewind() // Rewind to ensure all data in the buffer is retrieved val lengthToWrite = data.remaining() - val segment = new WriteAheadLogFileSegment(path, nextOffset, lengthToWrite) + val segment = new FileBasedWriteAheadLogSegment(path, nextOffset, lengthToWrite) stream.writeInt(lengthToWrite) if (data.hasArray) { stream.write(data.array()) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogUtils.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogUtils.scala new file mode 100644 index 0000000000000..7f6ff12c58d47 --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogUtils.scala @@ -0,0 +1,129 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.util + +import scala.util.control.NonFatal + +import org.apache.hadoop.conf.Configuration + +import org.apache.spark.util.Utils +import org.apache.spark.{Logging, SparkConf, SparkException} + +/** A helper class with utility functions related to the WriteAheadLog interface */ +private[streaming] object WriteAheadLogUtils extends Logging { + val RECEIVER_WAL_ENABLE_CONF_KEY = "spark.streaming.receiver.writeAheadLog.enable" + val RECEIVER_WAL_CLASS_CONF_KEY = "spark.streaming.receiver.writeAheadLog.class" + val RECEIVER_WAL_ROLLING_INTERVAL_CONF_KEY = + "spark.streaming.receiver.writeAheadLog.rollingIntervalSecs" + val RECEIVER_WAL_MAX_FAILURES_CONF_KEY = "spark.streaming.receiver.writeAheadLog.maxFailures" + + val DRIVER_WAL_CLASS_CONF_KEY = "spark.streaming.driver.writeAheadLog.class" + val DRIVER_WAL_ROLLING_INTERVAL_CONF_KEY = + "spark.streaming.driver.writeAheadLog.rollingIntervalSecs" + val DRIVER_WAL_MAX_FAILURES_CONF_KEY = "spark.streaming.driver.writeAheadLog.maxFailures" + + val DEFAULT_ROLLING_INTERVAL_SECS = 60 + val DEFAULT_MAX_FAILURES = 3 + + def enableReceiverLog(conf: SparkConf): Boolean = { + conf.getBoolean(RECEIVER_WAL_ENABLE_CONF_KEY, false) + } + + def getRollingIntervalSecs(conf: SparkConf, isDriver: Boolean): Int = { + if (isDriver) { + conf.getInt(DRIVER_WAL_ROLLING_INTERVAL_CONF_KEY, DEFAULT_ROLLING_INTERVAL_SECS) + } else { + conf.getInt(RECEIVER_WAL_ROLLING_INTERVAL_CONF_KEY, DEFAULT_ROLLING_INTERVAL_SECS) + } + } + + def getMaxFailures(conf: SparkConf, isDriver: Boolean): Int = { + if (isDriver) { + conf.getInt(DRIVER_WAL_MAX_FAILURES_CONF_KEY, DEFAULT_MAX_FAILURES) + } else { + conf.getInt(RECEIVER_WAL_MAX_FAILURES_CONF_KEY, DEFAULT_MAX_FAILURES) + } + } + + /** + * Create a WriteAheadLog for the driver. If configured with custom WAL class, it will try + * to create instance of that class, otherwise it will create the default FileBasedWriteAheadLog. + */ + def createLogForDriver( + sparkConf: SparkConf, + fileWalLogDirectory: String, + fileWalHadoopConf: Configuration + ): WriteAheadLog = { + createLog(true, sparkConf, fileWalLogDirectory, fileWalHadoopConf) + } + + /** + * Create a WriteAheadLog for the receiver. If configured with custom WAL class, it will try + * to create instance of that class, otherwise it will create the default FileBasedWriteAheadLog. + */ + def createLogForReceiver( + sparkConf: SparkConf, + fileWalLogDirectory: String, + fileWalHadoopConf: Configuration + ): WriteAheadLog = { + createLog(false, sparkConf, fileWalLogDirectory, fileWalHadoopConf) + } + + /** + * Create a WriteAheadLog based on the value of the given config key. The config key is used + * to get the class name from the SparkConf. If the class is configured, it will try to + * create instance of that class by first trying `new CustomWAL(sparkConf, logDir)` then trying + * `new CustomWAL(sparkConf)`. If either fails, it will fail. If no class is configured, then + * it will create the default FileBasedWriteAheadLog. + */ + private def createLog( + isDriver: Boolean, + sparkConf: SparkConf, + fileWalLogDirectory: String, + fileWalHadoopConf: Configuration + ): WriteAheadLog = { + + val classNameOption = if (isDriver) { + sparkConf.getOption(DRIVER_WAL_CLASS_CONF_KEY) + } else { + sparkConf.getOption(RECEIVER_WAL_CLASS_CONF_KEY) + } + classNameOption.map { className => + try { + instantiateClass( + Utils.classForName(className).asInstanceOf[Class[_ <: WriteAheadLog]], sparkConf) + } catch { + case NonFatal(e) => + throw new SparkException(s"Could not create a write ahead log of class $className", e) + } + }.getOrElse { + new FileBasedWriteAheadLog(sparkConf, fileWalLogDirectory, fileWalHadoopConf, + getRollingIntervalSecs(sparkConf, isDriver), getMaxFailures(sparkConf, isDriver)) + } + } + + /** Instantiate the class, either using single arg constructor or zero arg constructor */ + private def instantiateClass(cls: Class[_ <: WriteAheadLog], conf: SparkConf): WriteAheadLog = { + try { + cls.getConstructor(classOf[SparkConf]).newInstance(conf) + } catch { + case nsme: NoSuchMethodException => + cls.getConstructor().newInstance() + } + } +} diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaWriteAheadLogSuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaWriteAheadLogSuite.java new file mode 100644 index 0000000000000..50e8f9fc159c8 --- /dev/null +++ b/streaming/src/test/java/org/apache/spark/streaming/JavaWriteAheadLogSuite.java @@ -0,0 +1,129 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming; + +import java.util.ArrayList; +import java.nio.ByteBuffer; +import java.util.Arrays; +import java.util.Collection; + +import org.apache.commons.collections.CollectionUtils; +import org.apache.commons.collections.Transformer; +import org.apache.spark.SparkConf; +import org.apache.spark.streaming.util.WriteAheadLog; +import org.apache.spark.streaming.util.WriteAheadLogRecordHandle; +import org.apache.spark.streaming.util.WriteAheadLogUtils; + +import org.junit.Test; +import org.junit.Assert; + +class JavaWriteAheadLogSuiteHandle extends WriteAheadLogRecordHandle { + int index = -1; + public JavaWriteAheadLogSuiteHandle(int idx) { + index = idx; + } +} + +public class JavaWriteAheadLogSuite extends WriteAheadLog { + + class Record { + long time; + int index; + ByteBuffer buffer; + + public Record(long tym, int idx, ByteBuffer buf) { + index = idx; + time = tym; + buffer = buf; + } + } + private int index = -1; + private ArrayList records = new ArrayList(); + + + // Methods for WriteAheadLog + @Override + public WriteAheadLogRecordHandle write(java.nio.ByteBuffer record, long time) { + index += 1; + records.add(new org.apache.spark.streaming.JavaWriteAheadLogSuite.Record(time, index, record)); + return new JavaWriteAheadLogSuiteHandle(index); + } + + @Override + public java.nio.ByteBuffer read(WriteAheadLogRecordHandle handle) { + if (handle instanceof JavaWriteAheadLogSuiteHandle) { + int reqdIndex = ((JavaWriteAheadLogSuiteHandle) handle).index; + for (Record record: records) { + if (record.index == reqdIndex) { + return record.buffer; + } + } + } + return null; + } + + @Override + public java.util.Iterator readAll() { + Collection buffers = CollectionUtils.collect(records, new Transformer() { + @Override + public Object transform(Object input) { + return ((Record) input).buffer; + } + }); + return buffers.iterator(); + } + + @Override + public void clean(long threshTime, boolean waitForCompletion) { + for (int i = 0; i < records.size(); i++) { + if (records.get(i).time < threshTime) { + records.remove(i); + i--; + } + } + } + + @Override + public void close() { + records.clear(); + } + + @Test + public void testCustomWAL() { + SparkConf conf = new SparkConf(); + conf.set("spark.streaming.driver.writeAheadLog.class", JavaWriteAheadLogSuite.class.getName()); + WriteAheadLog wal = WriteAheadLogUtils.createLogForDriver(conf, null, null); + + String data1 = "data1"; + WriteAheadLogRecordHandle handle = wal.write(ByteBuffer.wrap(data1.getBytes()), 1234); + Assert.assertTrue(handle instanceof JavaWriteAheadLogSuiteHandle); + Assert.assertTrue(new String(wal.read(handle).array()).equals(data1)); + + wal.write(ByteBuffer.wrap("data2".getBytes()), 1235); + wal.write(ByteBuffer.wrap("data3".getBytes()), 1236); + wal.write(ByteBuffer.wrap("data4".getBytes()), 1237); + wal.clean(1236, false); + + java.util.Iterator dataIterator = wal.readAll(); + ArrayList readData = new ArrayList(); + while (dataIterator.hasNext()) { + readData.add(new String(dataIterator.next().array())); + } + Assert.assertTrue(readData.equals(Arrays.asList("data3", "data4"))); + } +} diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala index c090eaec2928d..23804237bda80 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala @@ -43,7 +43,7 @@ import WriteAheadLogSuite._ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matchers with Logging { - val conf = new SparkConf().set("spark.streaming.receiver.writeAheadLog.rollingInterval", "1") + val conf = new SparkConf().set("spark.streaming.receiver.writeAheadLog.rollingIntervalSecs", "1") val hadoopConf = new Configuration() val storageLevel = StorageLevel.MEMORY_ONLY_SER val streamId = 1 @@ -130,10 +130,13 @@ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matche "Unexpected store result type" ) // Verify the data in write ahead log files is correct - val fileSegments = storeResults.map { _.asInstanceOf[WriteAheadLogBasedStoreResult].segment} - val loggedData = fileSegments.flatMap { segment => - val reader = new WriteAheadLogRandomReader(segment.path, hadoopConf) - val bytes = reader.read(segment) + val walSegments = storeResults.map { result => + result.asInstanceOf[WriteAheadLogBasedStoreResult].walRecordHandle + } + val loggedData = walSegments.flatMap { walSegment => + val fileSegment = walSegment.asInstanceOf[FileBasedWriteAheadLogSegment] + val reader = new FileBasedWriteAheadLogRandomReader(fileSegment.path, hadoopConf) + val bytes = reader.read(fileSegment) reader.close() blockManager.dataDeserialize(generateBlockId(), bytes).toList } @@ -148,13 +151,13 @@ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matche } } - test("WriteAheadLogBasedBlockHandler - cleanup old blocks") { + test("WriteAheadLogBasedBlockHandler - clean old blocks") { withWriteAheadLogBasedBlockHandler { handler => val blocks = Seq.tabulate(10) { i => IteratorBlock(Iterator(1 to i)) } storeBlocks(handler, blocks) val preCleanupLogFiles = getWriteAheadLogFiles() - preCleanupLogFiles.size should be > 1 + require(preCleanupLogFiles.size > 1) // this depends on the number of blocks inserted using generateAndStoreData() manualClock.getTimeMillis() shouldEqual 5000L @@ -218,6 +221,7 @@ class ReceivedBlockHandlerSuite extends FunSuite with BeforeAndAfter with Matche /** Instantiate a WriteAheadLogBasedBlockHandler and run a code with it */ private def withWriteAheadLogBasedBlockHandler(body: WriteAheadLogBasedBlockHandler => Unit) { + require(WriteAheadLogUtils.getRollingIntervalSecs(conf, isDriver = false) === 1) val receivedBlockHandler = new WriteAheadLogBasedBlockHandler(blockManager, 1, storageLevel, conf, hadoopConf, tempDirectory.toString, manualClock) try { diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala index b63b37d9f9cef..8317fb9720416 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockTrackerSuite.scala @@ -32,7 +32,7 @@ import org.apache.spark.{Logging, SparkConf, SparkException} import org.apache.spark.storage.StreamBlockId import org.apache.spark.streaming.receiver.BlockManagerBasedStoreResult import org.apache.spark.streaming.scheduler._ -import org.apache.spark.streaming.util.WriteAheadLogReader +import org.apache.spark.streaming.util.{WriteAheadLogUtils, FileBasedWriteAheadLogReader} import org.apache.spark.streaming.util.WriteAheadLogSuite._ import org.apache.spark.util.{Clock, ManualClock, SystemClock, Utils} @@ -59,7 +59,7 @@ class ReceivedBlockTrackerSuite test("block addition, and block to batch allocation") { val receivedBlockTracker = createTracker(setCheckpointDir = false) - receivedBlockTracker.isLogManagerEnabled should be (false) // should be disable by default + receivedBlockTracker.isWriteAheadLogEnabled should be (false) // should be disable by default receivedBlockTracker.getUnallocatedBlocks(streamId) shouldEqual Seq.empty val blockInfos = generateBlockInfos() @@ -88,7 +88,7 @@ class ReceivedBlockTrackerSuite receivedBlockTracker.getUnallocatedBlocks(streamId) shouldEqual blockInfos } - test("block addition, block to batch allocation and cleanup with write ahead log") { + test("block addition, block to batch allocation and clean up with write ahead log") { val manualClock = new ManualClock // Set the time increment level to twice the rotation interval so that every increment creates // a new log file @@ -113,11 +113,15 @@ class ReceivedBlockTrackerSuite logInfo(s"\n\n=====================\n$message\n$fileContents\n=====================\n") } - // Start tracker and add blocks + // Set WAL configuration conf.set("spark.streaming.receiver.writeAheadLog.enable", "true") - conf.set("spark.streaming.receivedBlockTracker.writeAheadLog.rotationIntervalSecs", "1") + conf.set("spark.streaming.driver.writeAheadLog.rollingIntervalSecs", "1") + require(WriteAheadLogUtils.enableReceiverLog(conf)) + require(WriteAheadLogUtils.getRollingIntervalSecs(conf, isDriver = true) === 1) + + // Start tracker and add blocks val tracker1 = createTracker(clock = manualClock) - tracker1.isLogManagerEnabled should be (true) + tracker1.isWriteAheadLogEnabled should be (true) val blockInfos1 = addBlockInfos(tracker1) tracker1.getUnallocatedBlocks(streamId).toList shouldEqual blockInfos1 @@ -171,7 +175,7 @@ class ReceivedBlockTrackerSuite eventually(timeout(10 seconds), interval(10 millisecond)) { getWriteAheadLogFiles() should not contain oldestLogFile } - printLogFiles("After cleanup") + printLogFiles("After clean") // Restart tracker and verify recovered state, specifically whether info about the first // batch has been removed, but not the second batch @@ -192,17 +196,17 @@ class ReceivedBlockTrackerSuite test("setting checkpoint dir but not enabling write ahead log") { // When WAL config is not set, log manager should not be enabled val tracker1 = createTracker(setCheckpointDir = true) - tracker1.isLogManagerEnabled should be (false) + tracker1.isWriteAheadLogEnabled should be (false) // When WAL is explicitly disabled, log manager should not be enabled conf.set("spark.streaming.receiver.writeAheadLog.enable", "false") val tracker2 = createTracker(setCheckpointDir = true) - tracker2.isLogManagerEnabled should be(false) + tracker2.isWriteAheadLogEnabled should be(false) } /** * Create tracker object with the optional provided clock. Use fake clock if you - * want to control time by manually incrementing it to test log cleanup. + * want to control time by manually incrementing it to test log clean. */ def createTracker( setCheckpointDir: Boolean = true, @@ -231,7 +235,7 @@ class ReceivedBlockTrackerSuite def getWrittenLogData(logFiles: Seq[String] = getWriteAheadLogFiles) : Seq[ReceivedBlockTrackerLogEvent] = { logFiles.flatMap { - file => new WriteAheadLogReader(file, hadoopConf).toSeq + file => new FileBasedWriteAheadLogReader(file, hadoopConf).toSeq }.map { byteBuffer => Utils.deserialize[ReceivedBlockTrackerLogEvent](byteBuffer.array) }.toList @@ -250,7 +254,7 @@ class ReceivedBlockTrackerSuite BatchAllocationEvent(time, AllocatedBlocks(Map((streamId -> blockInfos)))) } - /** Create batch cleanup object from the given info */ + /** Create batch clean object from the given info */ def createBatchCleanup(time: Long, moreTimes: Long*): BatchCleanupEvent = { BatchCleanupEvent((Seq(time) ++ moreTimes).map(Time.apply)) } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala index b84129fd70dd4..393a360cfe150 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala @@ -225,7 +225,7 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable { .setAppName(framework) .set("spark.ui.enabled", "true") .set("spark.streaming.receiver.writeAheadLog.enable", "true") - .set("spark.streaming.receiver.writeAheadLog.rollingInterval", "1") + .set("spark.streaming.receiver.writeAheadLog.rollingIntervalSecs", "1") val batchDuration = Milliseconds(500) val tempDirectory = Utils.createTempDir() val logDirectory1 = new File(checkpointDirToLogDir(tempDirectory.getAbsolutePath, 0)) diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala index 58353a5f97c8a..09440b1e791ab 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala @@ -363,7 +363,7 @@ class TestReceiver extends Receiver[Int](StorageLevel.MEMORY_ONLY) with Logging } def onStop() { - // no cleanup to be done, the receiving thread should stop on it own + // no clean to be done, the receiving thread should stop on it own } } @@ -396,7 +396,7 @@ class SlowTestReceiver(totalRecords: Int, recordsPerSecond: Int) def onStop() { // Simulate slow receiver by waiting for all records to be produced while(!SlowTestReceiver.receivedAllRecords) Thread.sleep(100) - // no cleanup to be done, the receiving thread should stop on it own + // no clean to be done, the receiving thread should stop on it own } } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala index c3602a5b73732..8b300d8dd3fbe 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDDSuite.scala @@ -21,12 +21,12 @@ import java.io.File import scala.util.Random import org.apache.hadoop.conf.Configuration -import org.scalatest.{BeforeAndAfterEach, BeforeAndAfterAll, FunSuite} +import org.scalatest.{BeforeAndAfterAll, BeforeAndAfterEach, FunSuite} -import org.apache.spark.{SparkConf, SparkContext} import org.apache.spark.storage.{BlockId, BlockManager, StorageLevel, StreamBlockId} -import org.apache.spark.streaming.util.{WriteAheadLogFileSegment, WriteAheadLogWriter} +import org.apache.spark.streaming.util.{FileBasedWriteAheadLogSegment, FileBasedWriteAheadLogWriter} import org.apache.spark.util.Utils +import org.apache.spark.{SparkConf, SparkContext} class WriteAheadLogBackedBlockRDDSuite extends FunSuite with BeforeAndAfterAll with BeforeAndAfterEach { @@ -100,9 +100,10 @@ class WriteAheadLogBackedBlockRDDSuite blockManager.putIterator(blockId, block.iterator, StorageLevel.MEMORY_ONLY_SER) } - // Generate write ahead log segments - val segments = generateFakeSegments(numPartitionsInBM) ++ - writeLogSegments(data.takeRight(numPartitionsInWAL), blockIds.takeRight(numPartitionsInWAL)) + // Generate write ahead log file segments + val recordHandles = generateFakeRecordHandles(numPartitionsInBM) ++ + generateWALRecordHandles(data.takeRight(numPartitionsInWAL), + blockIds.takeRight(numPartitionsInWAL)) // Make sure that the left `numPartitionsInBM` blocks are in block manager, and others are not require( @@ -116,24 +117,24 @@ class WriteAheadLogBackedBlockRDDSuite // Make sure that the right `numPartitionsInWAL` blocks are in WALs, and other are not require( - segments.takeRight(numPartitionsInWAL).forall(s => + recordHandles.takeRight(numPartitionsInWAL).forall(s => new File(s.path.stripPrefix("file://")).exists()), "Expected blocks not in write ahead log" ) require( - segments.take(numPartitionsInBM).forall(s => + recordHandles.take(numPartitionsInBM).forall(s => !new File(s.path.stripPrefix("file://")).exists()), "Unexpected blocks in write ahead log" ) // Create the RDD and verify whether the returned data is correct val rdd = new WriteAheadLogBackedBlockRDD[String](sparkContext, blockIds.toArray, - segments.toArray, storeInBlockManager = false, StorageLevel.MEMORY_ONLY) + recordHandles.toArray, storeInBlockManager = false, StorageLevel.MEMORY_ONLY) assert(rdd.collect() === data.flatten) if (testStoreInBM) { val rdd2 = new WriteAheadLogBackedBlockRDD[String](sparkContext, blockIds.toArray, - segments.toArray, storeInBlockManager = true, StorageLevel.MEMORY_ONLY) + recordHandles.toArray, storeInBlockManager = true, StorageLevel.MEMORY_ONLY) assert(rdd2.collect() === data.flatten) assert( blockIds.forall(blockManager.get(_).nonEmpty), @@ -142,12 +143,12 @@ class WriteAheadLogBackedBlockRDDSuite } } - private def writeLogSegments( + private def generateWALRecordHandles( blockData: Seq[Seq[String]], blockIds: Seq[BlockId] - ): Seq[WriteAheadLogFileSegment] = { + ): Seq[FileBasedWriteAheadLogSegment] = { require(blockData.size === blockIds.size) - val writer = new WriteAheadLogWriter(new File(dir, "logFile").toString, hadoopConf) + val writer = new FileBasedWriteAheadLogWriter(new File(dir, "logFile").toString, hadoopConf) val segments = blockData.zip(blockIds).map { case (data, id) => writer.write(blockManager.dataSerialize(id, data.iterator)) } @@ -155,7 +156,7 @@ class WriteAheadLogBackedBlockRDDSuite segments } - private def generateFakeSegments(count: Int): Seq[WriteAheadLogFileSegment] = { - Array.fill(count)(new WriteAheadLogFileSegment("random", 0L, 0)) + private def generateFakeRecordHandles(count: Int): Seq[FileBasedWriteAheadLogSegment] = { + Array.fill(count)(new FileBasedWriteAheadLogSegment("random", 0L, 0)) } } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala index a3919c43b95b4..79098bcf4861c 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala @@ -18,33 +18,38 @@ package org.apache.spark.streaming.util import java.io._ import java.nio.ByteBuffer +import java.util import scala.collection.mutable.ArrayBuffer import scala.concurrent.duration._ import scala.language.{implicitConversions, postfixOps} +import scala.reflect.ClassTag -import WriteAheadLogSuite._ import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path -import org.apache.spark.util.{ManualClock, Utils} -import org.scalatest.{BeforeAndAfter, FunSuite} import org.scalatest.concurrent.Eventually._ +import org.scalatest.{BeforeAndAfter, FunSuite} + +import org.apache.spark.util.{ManualClock, Utils} +import org.apache.spark.{SparkConf, SparkException} class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { + import WriteAheadLogSuite._ + val hadoopConf = new Configuration() var tempDir: File = null var testDir: String = null var testFile: String = null - var manager: WriteAheadLogManager = null + var writeAheadLog: FileBasedWriteAheadLog = null before { tempDir = Utils.createTempDir() testDir = tempDir.toString testFile = new File(tempDir, "testFile").toString - if (manager != null) { - manager.stop() - manager = null + if (writeAheadLog != null) { + writeAheadLog.close() + writeAheadLog = null } } @@ -52,16 +57,60 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { Utils.deleteRecursively(tempDir) } - test("WriteAheadLogWriter - writing data") { + test("WriteAheadLogUtils - log selection and creation") { + val logDir = Utils.createTempDir().getAbsolutePath() + + def assertDriverLogClass[T <: WriteAheadLog: ClassTag](conf: SparkConf): WriteAheadLog = { + val log = WriteAheadLogUtils.createLogForDriver(conf, logDir, hadoopConf) + assert(log.getClass === implicitly[ClassTag[T]].runtimeClass) + log + } + + def assertReceiverLogClass[T: ClassTag](conf: SparkConf): WriteAheadLog = { + val log = WriteAheadLogUtils.createLogForReceiver(conf, logDir, hadoopConf) + assert(log.getClass === implicitly[ClassTag[T]].runtimeClass) + log + } + + val emptyConf = new SparkConf() // no log configuration + assertDriverLogClass[FileBasedWriteAheadLog](emptyConf) + assertReceiverLogClass[FileBasedWriteAheadLog](emptyConf) + + // Verify setting driver WAL class + val conf1 = new SparkConf().set("spark.streaming.driver.writeAheadLog.class", + classOf[MockWriteAheadLog0].getName()) + assertDriverLogClass[MockWriteAheadLog0](conf1) + assertReceiverLogClass[FileBasedWriteAheadLog](conf1) + + // Verify setting receiver WAL class + val receiverWALConf = new SparkConf().set("spark.streaming.receiver.writeAheadLog.class", + classOf[MockWriteAheadLog0].getName()) + assertDriverLogClass[FileBasedWriteAheadLog](receiverWALConf) + assertReceiverLogClass[MockWriteAheadLog0](receiverWALConf) + + // Verify setting receiver WAL class with 1-arg constructor + val receiverWALConf2 = new SparkConf().set("spark.streaming.receiver.writeAheadLog.class", + classOf[MockWriteAheadLog1].getName()) + assertReceiverLogClass[MockWriteAheadLog1](receiverWALConf2) + + // Verify failure setting receiver WAL class with 2-arg constructor + intercept[SparkException] { + val receiverWALConf3 = new SparkConf().set("spark.streaming.receiver.writeAheadLog.class", + classOf[MockWriteAheadLog2].getName()) + assertReceiverLogClass[MockWriteAheadLog1](receiverWALConf3) + } + } + + test("FileBasedWriteAheadLogWriter - writing data") { val dataToWrite = generateRandomData() val segments = writeDataUsingWriter(testFile, dataToWrite) val writtenData = readDataManually(segments) assert(writtenData === dataToWrite) } - test("WriteAheadLogWriter - syncing of data by writing and reading immediately") { + test("FileBasedWriteAheadLogWriter - syncing of data by writing and reading immediately") { val dataToWrite = generateRandomData() - val writer = new WriteAheadLogWriter(testFile, hadoopConf) + val writer = new FileBasedWriteAheadLogWriter(testFile, hadoopConf) dataToWrite.foreach { data => val segment = writer.write(stringToByteBuffer(data)) val dataRead = readDataManually(Seq(segment)).head @@ -70,10 +119,10 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { writer.close() } - test("WriteAheadLogReader - sequentially reading data") { + test("FileBasedWriteAheadLogReader - sequentially reading data") { val writtenData = generateRandomData() writeDataManually(writtenData, testFile) - val reader = new WriteAheadLogReader(testFile, hadoopConf) + val reader = new FileBasedWriteAheadLogReader(testFile, hadoopConf) val readData = reader.toSeq.map(byteBufferToString) assert(readData === writtenData) assert(reader.hasNext === false) @@ -83,14 +132,14 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { reader.close() } - test("WriteAheadLogReader - sequentially reading data written with writer") { + test("FileBasedWriteAheadLogReader - sequentially reading data written with writer") { val dataToWrite = generateRandomData() writeDataUsingWriter(testFile, dataToWrite) val readData = readDataUsingReader(testFile) assert(readData === dataToWrite) } - test("WriteAheadLogReader - reading data written with writer after corrupted write") { + test("FileBasedWriteAheadLogReader - reading data written with writer after corrupted write") { // Write data manually for testing the sequential reader val dataToWrite = generateRandomData() writeDataUsingWriter(testFile, dataToWrite) @@ -113,38 +162,38 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { assert(readDataUsingReader(testFile) === (dataToWrite.dropRight(1))) } - test("WriteAheadLogRandomReader - reading data using random reader") { + test("FileBasedWriteAheadLogRandomReader - reading data using random reader") { // Write data manually for testing the random reader val writtenData = generateRandomData() val segments = writeDataManually(writtenData, testFile) // Get a random order of these segments and read them back val writtenDataAndSegments = writtenData.zip(segments).toSeq.permutations.take(10).flatten - val reader = new WriteAheadLogRandomReader(testFile, hadoopConf) + val reader = new FileBasedWriteAheadLogRandomReader(testFile, hadoopConf) writtenDataAndSegments.foreach { case (data, segment) => assert(data === byteBufferToString(reader.read(segment))) } reader.close() } - test("WriteAheadLogRandomReader - reading data using random reader written with writer") { + test("FileBasedWriteAheadLogRandomReader- reading data using random reader written with writer") { // Write data using writer for testing the random reader val data = generateRandomData() val segments = writeDataUsingWriter(testFile, data) // Read a random sequence of segments and verify read data val dataAndSegments = data.zip(segments).toSeq.permutations.take(10).flatten - val reader = new WriteAheadLogRandomReader(testFile, hadoopConf) + val reader = new FileBasedWriteAheadLogRandomReader(testFile, hadoopConf) dataAndSegments.foreach { case (data, segment) => assert(data === byteBufferToString(reader.read(segment))) } reader.close() } - test("WriteAheadLogManager - write rotating logs") { - // Write data using manager + test("FileBasedWriteAheadLog - write rotating logs") { + // Write data with rotation using WriteAheadLog class val dataToWrite = generateRandomData() - writeDataUsingManager(testDir, dataToWrite) + writeDataUsingWriteAheadLog(testDir, dataToWrite) // Read data manually to verify the written data val logFiles = getLogFilesInDirectory(testDir) @@ -153,8 +202,8 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { assert(writtenData === dataToWrite) } - test("WriteAheadLogManager - read rotating logs") { - // Write data manually for testing reading through manager + test("FileBasedWriteAheadLog - read rotating logs") { + // Write data manually for testing reading through WriteAheadLog val writtenData = (1 to 10).map { i => val data = generateRandomData() val file = testDir + s"/log-$i-$i" @@ -167,25 +216,25 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { assert(fileSystem.exists(logDirectoryPath) === true) // Read data using manager and verify - val readData = readDataUsingManager(testDir) + val readData = readDataUsingWriteAheadLog(testDir) assert(readData === writtenData) } - test("WriteAheadLogManager - recover past logs when creating new manager") { + test("FileBasedWriteAheadLog - recover past logs when creating new manager") { // Write data with manager, recover with new manager and verify val dataToWrite = generateRandomData() - writeDataUsingManager(testDir, dataToWrite) + writeDataUsingWriteAheadLog(testDir, dataToWrite) val logFiles = getLogFilesInDirectory(testDir) assert(logFiles.size > 1) - val readData = readDataUsingManager(testDir) + val readData = readDataUsingWriteAheadLog(testDir) assert(dataToWrite === readData) } - test("WriteAheadLogManager - cleanup old logs") { + test("FileBasedWriteAheadLog - clean old logs") { logCleanUpTest(waitForCompletion = false) } - test("WriteAheadLogManager - cleanup old logs synchronously") { + test("FileBasedWriteAheadLog - clean old logs synchronously") { logCleanUpTest(waitForCompletion = true) } @@ -193,11 +242,11 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { // Write data with manager, recover with new manager and verify val manualClock = new ManualClock val dataToWrite = generateRandomData() - manager = writeDataUsingManager(testDir, dataToWrite, manualClock, stopManager = false) + writeAheadLog = writeDataUsingWriteAheadLog(testDir, dataToWrite, manualClock, closeLog = false) val logFiles = getLogFilesInDirectory(testDir) assert(logFiles.size > 1) - manager.cleanupOldLogs(manualClock.getTimeMillis() / 2, waitForCompletion) + writeAheadLog.clean(manualClock.getTimeMillis() / 2, waitForCompletion) if (waitForCompletion) { assert(getLogFilesInDirectory(testDir).size < logFiles.size) @@ -208,11 +257,11 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { } } - test("WriteAheadLogManager - handling file errors while reading rotating logs") { + test("FileBasedWriteAheadLog - handling file errors while reading rotating logs") { // Generate a set of log files val manualClock = new ManualClock val dataToWrite1 = generateRandomData() - writeDataUsingManager(testDir, dataToWrite1, manualClock) + writeDataUsingWriteAheadLog(testDir, dataToWrite1, manualClock) val logFiles1 = getLogFilesInDirectory(testDir) assert(logFiles1.size > 1) @@ -220,12 +269,12 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { // Recover old files and generate a second set of log files val dataToWrite2 = generateRandomData() manualClock.advance(100000) - writeDataUsingManager(testDir, dataToWrite2, manualClock) + writeDataUsingWriteAheadLog(testDir, dataToWrite2, manualClock) val logFiles2 = getLogFilesInDirectory(testDir) assert(logFiles2.size > logFiles1.size) // Read the files and verify that all the written data can be read - val readData1 = readDataUsingManager(testDir) + val readData1 = readDataUsingWriteAheadLog(testDir) assert(readData1 === (dataToWrite1 ++ dataToWrite2)) // Corrupt the first set of files so that they are basically unreadable @@ -236,25 +285,51 @@ class WriteAheadLogSuite extends FunSuite with BeforeAndAfter { } // Verify that the corrupted files do not prevent reading of the second set of data - val readData = readDataUsingManager(testDir) + val readData = readDataUsingWriteAheadLog(testDir) assert(readData === dataToWrite2) } + + test("FileBasedWriteAheadLog - do not create directories or files unless write") { + val nonexistentTempPath = File.createTempFile("test", "") + nonexistentTempPath.delete() + assert(!nonexistentTempPath.exists()) + + val writtenSegment = writeDataManually(generateRandomData(), testFile) + val wal = new FileBasedWriteAheadLog( + new SparkConf(), tempDir.getAbsolutePath, new Configuration(), 1, 1) + assert(!nonexistentTempPath.exists(), "Directory created just by creating log object") + wal.read(writtenSegment.head) + assert(!nonexistentTempPath.exists(), "Directory created just by attempting to read segment") + } } object WriteAheadLogSuite { + class MockWriteAheadLog0() extends WriteAheadLog { + override def write(record: ByteBuffer, time: Long): WriteAheadLogRecordHandle = { null } + override def read(handle: WriteAheadLogRecordHandle): ByteBuffer = { null } + override def readAll(): util.Iterator[ByteBuffer] = { null } + override def clean(threshTime: Long, waitForCompletion: Boolean): Unit = { } + override def close(): Unit = { } + } + + class MockWriteAheadLog1(val conf: SparkConf) extends MockWriteAheadLog0() + + class MockWriteAheadLog2(val conf: SparkConf, x: Int) extends MockWriteAheadLog0() + + private val hadoopConf = new Configuration() /** Write data to a file directly and return an array of the file segments written. */ - def writeDataManually(data: Seq[String], file: String): Seq[WriteAheadLogFileSegment] = { - val segments = new ArrayBuffer[WriteAheadLogFileSegment]() + def writeDataManually(data: Seq[String], file: String): Seq[FileBasedWriteAheadLogSegment] = { + val segments = new ArrayBuffer[FileBasedWriteAheadLogSegment]() val writer = HdfsUtils.getOutputStream(file, hadoopConf) data.foreach { item => val offset = writer.getPos val bytes = Utils.serialize(item) writer.writeInt(bytes.size) writer.write(bytes) - segments += WriteAheadLogFileSegment(file, offset, bytes.size) + segments += FileBasedWriteAheadLogSegment(file, offset, bytes.size) } writer.close() segments @@ -263,8 +338,11 @@ object WriteAheadLogSuite { /** * Write data to a file using the writer class and return an array of the file segments written. */ - def writeDataUsingWriter(filePath: String, data: Seq[String]): Seq[WriteAheadLogFileSegment] = { - val writer = new WriteAheadLogWriter(filePath, hadoopConf) + def writeDataUsingWriter( + filePath: String, + data: Seq[String] + ): Seq[FileBasedWriteAheadLogSegment] = { + val writer = new FileBasedWriteAheadLogWriter(filePath, hadoopConf) val segments = data.map { item => writer.write(item) } @@ -272,27 +350,27 @@ object WriteAheadLogSuite { segments } - /** Write data to rotating files in log directory using the manager class. */ - def writeDataUsingManager( + /** Write data to rotating files in log directory using the WriteAheadLog class. */ + def writeDataUsingWriteAheadLog( logDirectory: String, data: Seq[String], manualClock: ManualClock = new ManualClock, - stopManager: Boolean = true - ): WriteAheadLogManager = { + closeLog: Boolean = true + ): FileBasedWriteAheadLog = { if (manualClock.getTimeMillis() < 100000) manualClock.setTime(10000) - val manager = new WriteAheadLogManager(logDirectory, hadoopConf, - rollingIntervalSecs = 1, callerName = "WriteAheadLogSuite", clock = manualClock) + val wal = new FileBasedWriteAheadLog(new SparkConf(), logDirectory, hadoopConf, 1, 1) + // Ensure that 500 does not get sorted after 2000, so put a high base value. data.foreach { item => manualClock.advance(500) - manager.writeToLog(item) + wal.write(item, manualClock.getTimeMillis()) } - if (stopManager) manager.stop() - manager + if (closeLog) wal.close() + wal } /** Read data from a segments of a log file directly and return the list of byte buffers. */ - def readDataManually(segments: Seq[WriteAheadLogFileSegment]): Seq[String] = { + def readDataManually(segments: Seq[FileBasedWriteAheadLogSegment]): Seq[String] = { segments.map { segment => val reader = HdfsUtils.getInputStream(segment.path, hadoopConf) try { @@ -331,18 +409,18 @@ object WriteAheadLogSuite { /** Read all the data from a log file using reader class and return the list of byte buffers. */ def readDataUsingReader(file: String): Seq[String] = { - val reader = new WriteAheadLogReader(file, hadoopConf) + val reader = new FileBasedWriteAheadLogReader(file, hadoopConf) val readData = reader.toList.map(byteBufferToString) reader.close() readData } - /** Read all the data in the log file in a directory using the manager class. */ - def readDataUsingManager(logDirectory: String): Seq[String] = { - val manager = new WriteAheadLogManager(logDirectory, hadoopConf, - callerName = "WriteAheadLogSuite") - val data = manager.readFromLog().map(byteBufferToString).toSeq - manager.stop() + /** Read all the data in the log file in a directory using the WriteAheadLog class. */ + def readDataUsingWriteAheadLog(logDirectory: String): Seq[String] = { + import scala.collection.JavaConversions._ + val wal = new FileBasedWriteAheadLog(new SparkConf(), logDirectory, hadoopConf, 1, 1) + val data = wal.readAll().map(byteBufferToString).toSeq + wal.close() data } From a9c4e29950a14e32acaac547e9a0e8879fd37fc9 Mon Sep 17 00:00:00 2001 From: Tathagata Das Date: Wed, 29 Apr 2015 13:10:31 -0700 Subject: [PATCH 472/591] [SPARK-6752] [STREAMING] [REOPENED] Allow StreamingContext to be recreated from checkpoint and existing SparkContext Original PR #5428 got reverted due to issues between MutableBoolean and Hadoop 1.0.4 (see JIRA). This replaces MutableBoolean with AtomicBoolean. srowen pwendell Author: Tathagata Das Closes #5773 from tdas/SPARK-6752 and squashes the following commits: a0c0ead [Tathagata Das] Fix for hadoop 1.0.4 70ae85b [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into SPARK-6752 94db63c [Tathagata Das] Fix long line. 524f519 [Tathagata Das] Many changes based on PR comments. eabd092 [Tathagata Das] Added Function0, Java API and unit tests for StreamingContext.getOrCreate 36a7823 [Tathagata Das] Minor changes. 204814e [Tathagata Das] Added StreamingContext.getOrCreate with existing SparkContext --- .../spark/api/java/function/Function0.java | 27 +++ .../apache/spark/streaming/Checkpoint.scala | 26 ++- .../spark/streaming/StreamingContext.scala | 85 ++++++++-- .../api/java/JavaStreamingContext.scala | 119 ++++++++++++- .../apache/spark/streaming/JavaAPISuite.java | 145 ++++++++++++---- .../spark/streaming/CheckpointSuite.scala | 3 +- .../streaming/StreamingContextSuite.scala | 159 ++++++++++++++++++ 7 files changed, 503 insertions(+), 61 deletions(-) create mode 100644 core/src/main/java/org/apache/spark/api/java/function/Function0.java diff --git a/core/src/main/java/org/apache/spark/api/java/function/Function0.java b/core/src/main/java/org/apache/spark/api/java/function/Function0.java new file mode 100644 index 0000000000000..38e410c5debe6 --- /dev/null +++ b/core/src/main/java/org/apache/spark/api/java/function/Function0.java @@ -0,0 +1,27 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.api.java.function; + +import java.io.Serializable; + +/** + * A zero-argument function that returns an R. + */ +public interface Function0 extends Serializable { + public R call() throws Exception; +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala index 0a50485118588..7bfae253c3a0c 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala @@ -77,7 +77,8 @@ object Checkpoint extends Logging { } /** Get checkpoint files present in the give directory, ordered by oldest-first */ - def getCheckpointFiles(checkpointDir: String, fs: FileSystem): Seq[Path] = { + def getCheckpointFiles(checkpointDir: String, fsOption: Option[FileSystem] = None): Seq[Path] = { + def sortFunc(path1: Path, path2: Path): Boolean = { val (time1, bk1) = path1.getName match { case REGEX(x, y) => (x.toLong, !y.isEmpty) } val (time2, bk2) = path2.getName match { case REGEX(x, y) => (x.toLong, !y.isEmpty) } @@ -85,6 +86,7 @@ object Checkpoint extends Logging { } val path = new Path(checkpointDir) + val fs = fsOption.getOrElse(path.getFileSystem(new Configuration())) if (fs.exists(path)) { val statuses = fs.listStatus(path) if (statuses != null) { @@ -160,7 +162,7 @@ class CheckpointWriter( } // Delete old checkpoint files - val allCheckpointFiles = Checkpoint.getCheckpointFiles(checkpointDir, fs) + val allCheckpointFiles = Checkpoint.getCheckpointFiles(checkpointDir, Some(fs)) if (allCheckpointFiles.size > 10) { allCheckpointFiles.take(allCheckpointFiles.size - 10).foreach(file => { logInfo("Deleting " + file) @@ -234,15 +236,24 @@ class CheckpointWriter( private[streaming] object CheckpointReader extends Logging { - def read(checkpointDir: String, conf: SparkConf, hadoopConf: Configuration): Option[Checkpoint] = - { + /** + * Read checkpoint files present in the given checkpoint directory. If there are no checkpoint + * files, then return None, else try to return the latest valid checkpoint object. If no + * checkpoint files could be read correctly, then return None (if ignoreReadError = true), + * or throw exception (if ignoreReadError = false). + */ + def read( + checkpointDir: String, + conf: SparkConf, + hadoopConf: Configuration, + ignoreReadError: Boolean = false): Option[Checkpoint] = { val checkpointPath = new Path(checkpointDir) // TODO(rxin): Why is this a def?! def fs: FileSystem = checkpointPath.getFileSystem(hadoopConf) // Try to find the checkpoint files - val checkpointFiles = Checkpoint.getCheckpointFiles(checkpointDir, fs).reverse + val checkpointFiles = Checkpoint.getCheckpointFiles(checkpointDir, Some(fs)).reverse if (checkpointFiles.isEmpty) { return None } @@ -282,7 +293,10 @@ object CheckpointReader extends Logging { }) // If none of checkpoint files could be read, then throw exception - throw new SparkException("Failed to read checkpoint from directory " + checkpointPath) + if (!ignoreReadError) { + throw new SparkException(s"Failed to read checkpoint from directory $checkpointPath") + } + None } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala index f57f295874645..90c8b47aebce0 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala @@ -107,6 +107,19 @@ class StreamingContext private[streaming] ( */ def this(path: String) = this(path, new Configuration) + /** + * Recreate a StreamingContext from a checkpoint file using an existing SparkContext. + * @param path Path to the directory that was specified as the checkpoint directory + * @param sparkContext Existing SparkContext + */ + def this(path: String, sparkContext: SparkContext) = { + this( + sparkContext, + CheckpointReader.read(path, sparkContext.conf, sparkContext.hadoopConfiguration).get, + null) + } + + if (sc_ == null && cp_ == null) { throw new Exception("Spark Streaming cannot be initialized with " + "both SparkContext and checkpoint as null") @@ -115,10 +128,12 @@ class StreamingContext private[streaming] ( private[streaming] val isCheckpointPresent = (cp_ != null) private[streaming] val sc: SparkContext = { - if (isCheckpointPresent) { + if (sc_ != null) { + sc_ + } else if (isCheckpointPresent) { new SparkContext(cp_.createSparkConf()) } else { - sc_ + throw new SparkException("Cannot create StreamingContext without a SparkContext") } } @@ -129,7 +144,7 @@ class StreamingContext private[streaming] ( private[streaming] val conf = sc.conf - private[streaming] val env = SparkEnv.get + private[streaming] val env = sc.env private[streaming] val graph: DStreamGraph = { if (isCheckpointPresent) { @@ -174,7 +189,9 @@ class StreamingContext private[streaming] ( /** Register streaming source to metrics system */ private val streamingSource = new StreamingSource(this) - SparkEnv.get.metricsSystem.registerSource(streamingSource) + assert(env != null) + assert(env.metricsSystem != null) + env.metricsSystem.registerSource(streamingSource) /** Enumeration to identify current state of the StreamingContext */ private[streaming] object StreamingContextState extends Enumeration { @@ -621,19 +638,59 @@ object StreamingContext extends Logging { hadoopConf: Configuration = new Configuration(), createOnError: Boolean = false ): StreamingContext = { - val checkpointOption = try { - CheckpointReader.read(checkpointPath, new SparkConf(), hadoopConf) - } catch { - case e: Exception => - if (createOnError) { - None - } else { - throw e - } - } + val checkpointOption = CheckpointReader.read( + checkpointPath, new SparkConf(), hadoopConf, createOnError) checkpointOption.map(new StreamingContext(null, _, null)).getOrElse(creatingFunc()) } + + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the StreamingContext + * will be created by called the provided `creatingFunc` on the provided `sparkContext`. Note + * that the SparkConf configuration in the checkpoint data will not be restored as the + * SparkContext has already been created. + * + * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program + * @param creatingFunc Function to create a new StreamingContext using the given SparkContext + * @param sparkContext SparkContext using which the StreamingContext will be created + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: SparkContext => StreamingContext, + sparkContext: SparkContext + ): StreamingContext = { + getOrCreate(checkpointPath, creatingFunc, sparkContext, createOnError = false) + } + + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the StreamingContext + * will be created by called the provided `creatingFunc` on the provided `sparkContext`. Note + * that the SparkConf configuration in the checkpoint data will not be restored as the + * SparkContext has already been created. + * + * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program + * @param creatingFunc Function to create a new StreamingContext using the given SparkContext + * @param sparkContext SparkContext using which the StreamingContext will be created + * @param createOnError Whether to create a new StreamingContext if there is an + * error in reading checkpoint data. By default, an exception will be + * thrown on error. + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: SparkContext => StreamingContext, + sparkContext: SparkContext, + createOnError: Boolean + ): StreamingContext = { + val checkpointOption = CheckpointReader.read( + checkpointPath, sparkContext.conf, sparkContext.hadoopConfiguration, createOnError) + checkpointOption.map(new StreamingContext(sparkContext, _, null)) + .getOrElse(creatingFunc(sparkContext)) + } + /** * Find the JAR from which a given class was loaded, to make it easy for users to pass * their JARs to StreamingContext. diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala index 4095a7cc84946..572d7d8e8753d 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala @@ -32,13 +32,14 @@ import org.apache.spark.{SparkConf, SparkContext} import org.apache.spark.annotation.Experimental import org.apache.spark.api.java.{JavaPairRDD, JavaRDD, JavaSparkContext} import org.apache.spark.api.java.function.{Function => JFunction, Function2 => JFunction2} +import org.apache.spark.api.java.function.{Function0 => JFunction0} import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming._ import org.apache.spark.streaming.scheduler.StreamingListener -import org.apache.hadoop.conf.Configuration -import org.apache.spark.streaming.dstream.{PluggableInputDStream, ReceiverInputDStream, DStream} +import org.apache.spark.streaming.dstream.DStream import org.apache.spark.streaming.receiver.Receiver +import org.apache.hadoop.conf.Configuration /** * A Java-friendly version of [[org.apache.spark.streaming.StreamingContext]] which is the main @@ -655,6 +656,7 @@ object JavaStreamingContext { * @param checkpointPath Checkpoint directory used in an earlier JavaStreamingContext program * @param factory JavaStreamingContextFactory object to create a new JavaStreamingContext */ + @deprecated("use getOrCreate without JavaStreamingContextFactor", "1.4.0") def getOrCreate( checkpointPath: String, factory: JavaStreamingContextFactory @@ -676,6 +678,7 @@ object JavaStreamingContext { * @param hadoopConf Hadoop configuration if necessary for reading from any HDFS compatible * file system */ + @deprecated("use getOrCreate without JavaStreamingContextFactory", "1.4.0") def getOrCreate( checkpointPath: String, hadoopConf: Configuration, @@ -700,6 +703,7 @@ object JavaStreamingContext { * @param createOnError Whether to create a new JavaStreamingContext if there is an * error in reading checkpoint data. */ + @deprecated("use getOrCreate without JavaStreamingContextFactory", "1.4.0") def getOrCreate( checkpointPath: String, hadoopConf: Configuration, @@ -712,6 +716,117 @@ object JavaStreamingContext { new JavaStreamingContext(ssc) } + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the provided factory + * will be used to create a JavaStreamingContext. + * + * @param checkpointPath Checkpoint directory used in an earlier JavaStreamingContext program + * @param creatingFunc Function to create a new JavaStreamingContext + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: JFunction0[JavaStreamingContext] + ): JavaStreamingContext = { + val ssc = StreamingContext.getOrCreate(checkpointPath, () => { + creatingFunc.call().ssc + }) + new JavaStreamingContext(ssc) + } + + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the provided factory + * will be used to create a JavaStreamingContext. + * + * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program + * @param creatingFunc Function to create a new JavaStreamingContext + * @param hadoopConf Hadoop configuration if necessary for reading from any HDFS compatible + * file system + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: JFunction0[JavaStreamingContext], + hadoopConf: Configuration + ): JavaStreamingContext = { + val ssc = StreamingContext.getOrCreate(checkpointPath, () => { + creatingFunc.call().ssc + }, hadoopConf) + new JavaStreamingContext(ssc) + } + + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the provided factory + * will be used to create a JavaStreamingContext. + * + * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program + * @param creatingFunc Function to create a new JavaStreamingContext + * @param hadoopConf Hadoop configuration if necessary for reading from any HDFS compatible + * file system + * @param createOnError Whether to create a new JavaStreamingContext if there is an + * error in reading checkpoint data. + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: JFunction0[JavaStreamingContext], + hadoopConf: Configuration, + createOnError: Boolean + ): JavaStreamingContext = { + val ssc = StreamingContext.getOrCreate(checkpointPath, () => { + creatingFunc.call().ssc + }, hadoopConf, createOnError) + new JavaStreamingContext(ssc) + } + + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the provided factory + * will be used to create a JavaStreamingContext. + * + * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program + * @param creatingFunc Function to create a new JavaStreamingContext + * @param sparkContext SparkContext using which the StreamingContext will be created + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: JFunction[JavaSparkContext, JavaStreamingContext], + sparkContext: JavaSparkContext + ): JavaStreamingContext = { + val ssc = StreamingContext.getOrCreate(checkpointPath, (sparkContext: SparkContext) => { + creatingFunc.call(new JavaSparkContext(sparkContext)).ssc + }, sparkContext.sc) + new JavaStreamingContext(ssc) + } + + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the provided factory + * will be used to create a JavaStreamingContext. + * + * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program + * @param creatingFunc Function to create a new JavaStreamingContext + * @param sparkContext SparkContext using which the StreamingContext will be created + * @param createOnError Whether to create a new JavaStreamingContext if there is an + * error in reading checkpoint data. + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: JFunction[JavaSparkContext, JavaStreamingContext], + sparkContext: JavaSparkContext, + createOnError: Boolean + ): JavaStreamingContext = { + val ssc = StreamingContext.getOrCreate(checkpointPath, (sparkContext: SparkContext) => { + creatingFunc.call(new JavaSparkContext(sparkContext)).ssc + }, sparkContext.sc, createOnError) + new JavaStreamingContext(ssc) + } + /** * Find the JAR from which a given class was loaded, to make it easy for users to pass * their JARs to StreamingContext. diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java index 90340753a4eed..b1adf881dd0f5 100644 --- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java +++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java @@ -21,11 +21,13 @@ import java.lang.Iterable; import java.nio.charset.Charset; import java.util.*; +import java.util.concurrent.atomic.AtomicBoolean; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.lib.input.TextInputFormat; + import scala.Tuple2; import org.junit.Assert; @@ -45,6 +47,7 @@ import org.apache.spark.storage.StorageLevel; import org.apache.spark.streaming.api.java.*; import org.apache.spark.util.Utils; +import org.apache.spark.SparkConf; // The test suite itself is Serializable so that anonymous Function implementations can be // serialized, as an alternative to converting these anonymous classes to static inner classes; @@ -929,7 +932,7 @@ public void testPairMap() { // Maps pair -> pair of different type public Tuple2 call(Tuple2 in) throws Exception { return in.swap(); } - }); + }); JavaTestUtils.attachTestOutputStream(reversed); List>> result = JavaTestUtils.runStreams(ssc, 2, 2); @@ -987,12 +990,12 @@ public void testPairMap2() { // Maps pair -> single JavaDStream> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); JavaPairDStream pairStream = JavaPairDStream.fromJavaDStream(stream); JavaDStream reversed = pairStream.map( - new Function, Integer>() { - @Override - public Integer call(Tuple2 in) throws Exception { - return in._2(); - } - }); + new Function, Integer>() { + @Override + public Integer call(Tuple2 in) throws Exception { + return in._2(); + } + }); JavaTestUtils.attachTestOutputStream(reversed); List> result = JavaTestUtils.runStreams(ssc, 2, 2); @@ -1123,7 +1126,7 @@ public void testCombineByKey() { JavaPairDStream combined = pairStream.combineByKey( new Function() { - @Override + @Override public Integer call(Integer i) throws Exception { return i; } @@ -1144,14 +1147,14 @@ public void testCountByValue() { Arrays.asList("hello")); List>> expected = Arrays.asList( - Arrays.asList( - new Tuple2("hello", 1L), - new Tuple2("world", 1L)), - Arrays.asList( - new Tuple2("hello", 1L), - new Tuple2("moon", 1L)), - Arrays.asList( - new Tuple2("hello", 1L))); + Arrays.asList( + new Tuple2("hello", 1L), + new Tuple2("world", 1L)), + Arrays.asList( + new Tuple2("hello", 1L), + new Tuple2("moon", 1L)), + Arrays.asList( + new Tuple2("hello", 1L))); JavaDStream stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); JavaPairDStream counted = stream.countByValue(); @@ -1249,17 +1252,17 @@ public void testUpdateStateByKey() { JavaPairDStream updated = pairStream.updateStateByKey( new Function2, Optional, Optional>() { - @Override - public Optional call(List values, Optional state) { - int out = 0; - if (state.isPresent()) { - out = out + state.get(); - } - for (Integer v: values) { - out = out + v; + @Override + public Optional call(List values, Optional state) { + int out = 0; + if (state.isPresent()) { + out = out + state.get(); + } + for (Integer v : values) { + out = out + v; + } + return Optional.of(out); } - return Optional.of(out); - } }); JavaTestUtils.attachTestOutputStream(updated); List>> result = JavaTestUtils.runStreams(ssc, 3, 3); @@ -1292,17 +1295,17 @@ public void testUpdateStateByKeyWithInitial() { JavaPairDStream updated = pairStream.updateStateByKey( new Function2, Optional, Optional>() { - @Override - public Optional call(List values, Optional state) { - int out = 0; - if (state.isPresent()) { - out = out + state.get(); - } - for (Integer v: values) { - out = out + v; + @Override + public Optional call(List values, Optional state) { + int out = 0; + if (state.isPresent()) { + out = out + state.get(); + } + for (Integer v : values) { + out = out + v; + } + return Optional.of(out); } - return Optional.of(out); - } }, new HashPartitioner(1), initialRDD); JavaTestUtils.attachTestOutputStream(updated); List>> result = JavaTestUtils.runStreams(ssc, 3, 3); @@ -1328,7 +1331,7 @@ public void testReduceByKeyAndWindowWithInverse() { JavaPairDStream reduceWindowed = pairStream.reduceByKeyAndWindow(new IntegerSum(), new IntegerDifference(), - new Duration(2000), new Duration(1000)); + new Duration(2000), new Duration(1000)); JavaTestUtils.attachTestOutputStream(reduceWindowed); List>> result = JavaTestUtils.runStreams(ssc, 3, 3); @@ -1707,6 +1710,74 @@ public Integer call(String s) throws Exception { Utils.deleteRecursively(tempDir); } + @SuppressWarnings("unchecked") + @Test + public void testContextGetOrCreate() throws InterruptedException { + + final SparkConf conf = new SparkConf() + .setMaster("local[2]") + .setAppName("test") + .set("newContext", "true"); + + File emptyDir = Files.createTempDir(); + emptyDir.deleteOnExit(); + StreamingContextSuite contextSuite = new StreamingContextSuite(); + String corruptedCheckpointDir = contextSuite.createCorruptedCheckpoint(); + String checkpointDir = contextSuite.createValidCheckpoint(); + + // Function to create JavaStreamingContext without any output operations + // (used to detect the new context) + final AtomicBoolean newContextCreated = new AtomicBoolean(false); + Function0 creatingFunc = new Function0() { + public JavaStreamingContext call() { + newContextCreated.set(true); + return new JavaStreamingContext(conf, Seconds.apply(1)); + } + }; + + newContextCreated.set(false); + ssc = JavaStreamingContext.getOrCreate(emptyDir.getAbsolutePath(), creatingFunc); + Assert.assertTrue("new context not created", newContextCreated.get()); + ssc.stop(); + + newContextCreated.set(false); + ssc = JavaStreamingContext.getOrCreate(corruptedCheckpointDir, creatingFunc, + new org.apache.hadoop.conf.Configuration(), true); + Assert.assertTrue("new context not created", newContextCreated.get()); + ssc.stop(); + + newContextCreated.set(false); + ssc = JavaStreamingContext.getOrCreate(checkpointDir, creatingFunc, + new org.apache.hadoop.conf.Configuration()); + Assert.assertTrue("old context not recovered", !newContextCreated.get()); + ssc.stop(); + + // Function to create JavaStreamingContext using existing JavaSparkContext + // without any output operations (used to detect the new context) + Function creatingFunc2 = + new Function() { + public JavaStreamingContext call(JavaSparkContext context) { + newContextCreated.set(true); + return new JavaStreamingContext(context, Seconds.apply(1)); + } + }; + + JavaSparkContext sc = new JavaSparkContext(conf); + newContextCreated.set(false); + ssc = JavaStreamingContext.getOrCreate(emptyDir.getAbsolutePath(), creatingFunc2, sc); + Assert.assertTrue("new context not created", newContextCreated.get()); + ssc.stop(false); + + newContextCreated.set(false); + ssc = JavaStreamingContext.getOrCreate(corruptedCheckpointDir, creatingFunc2, sc, true); + Assert.assertTrue("new context not created", newContextCreated.get()); + ssc.stop(false); + + newContextCreated.set(false); + ssc = JavaStreamingContext.getOrCreate(checkpointDir, creatingFunc2, sc); + Assert.assertTrue("old context not recovered", !newContextCreated.get()); + ssc.stop(); + } /* TEST DISABLED: Pending a discussion about checkpoint() semantics with TD @SuppressWarnings("unchecked") diff --git a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala index 54c30440a6e8d..6b0a3f91d4d06 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala @@ -430,9 +430,8 @@ class CheckpointSuite extends TestSuiteBase { assert(recordedFiles(ssc) === Seq(1, 2, 3) && batchCounter.getNumStartedBatches === 3) } // Wait for a checkpoint to be written - val fs = new Path(checkpointDir).getFileSystem(ssc.sc.hadoopConfiguration) eventually(eventuallyTimeout) { - assert(Checkpoint.getCheckpointFiles(checkpointDir, fs).size === 6) + assert(Checkpoint.getCheckpointFiles(checkpointDir).size === 6) } ssc.stop() // Check that we shut down while the third batch was being processed diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala index 09440b1e791ab..5207b7109e69b 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala @@ -17,8 +17,10 @@ package org.apache.spark.streaming +import java.io.File import java.util.concurrent.atomic.AtomicInteger +import org.apache.commons.io.FileUtils import org.scalatest.{Assertions, BeforeAndAfter, FunSuite} import org.scalatest.concurrent.Timeouts import org.scalatest.concurrent.Eventually._ @@ -330,6 +332,139 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w } } + test("getOrCreate") { + val conf = new SparkConf().setMaster(master).setAppName(appName) + + // Function to create StreamingContext that has a config to identify it to be new context + var newContextCreated = false + def creatingFunction(): StreamingContext = { + newContextCreated = true + new StreamingContext(conf, batchDuration) + } + + // Call ssc.stop after a body of code + def testGetOrCreate(body: => Unit): Unit = { + newContextCreated = false + try { + body + } finally { + if (ssc != null) { + ssc.stop() + } + ssc = null + } + } + + val emptyPath = Utils.createTempDir().getAbsolutePath() + + // getOrCreate should create new context with empty path + testGetOrCreate { + ssc = StreamingContext.getOrCreate(emptyPath, creatingFunction _) + assert(ssc != null, "no context created") + assert(newContextCreated, "new context not created") + } + + val corrutedCheckpointPath = createCorruptedCheckpoint() + + // getOrCreate should throw exception with fake checkpoint file and createOnError = false + intercept[Exception] { + ssc = StreamingContext.getOrCreate(corrutedCheckpointPath, creatingFunction _) + } + + // getOrCreate should throw exception with fake checkpoint file + intercept[Exception] { + ssc = StreamingContext.getOrCreate( + corrutedCheckpointPath, creatingFunction _, createOnError = false) + } + + // getOrCreate should create new context with fake checkpoint file and createOnError = true + testGetOrCreate { + ssc = StreamingContext.getOrCreate( + corrutedCheckpointPath, creatingFunction _, createOnError = true) + assert(ssc != null, "no context created") + assert(newContextCreated, "new context not created") + } + + val checkpointPath = createValidCheckpoint() + + // getOrCreate should recover context with checkpoint path, and recover old configuration + testGetOrCreate { + ssc = StreamingContext.getOrCreate(checkpointPath, creatingFunction _) + assert(ssc != null, "no context created") + assert(!newContextCreated, "old context not recovered") + assert(ssc.conf.get("someKey") === "someValue") + } + } + + test("getOrCreate with existing SparkContext") { + val conf = new SparkConf().setMaster(master).setAppName(appName) + sc = new SparkContext(conf) + + // Function to create StreamingContext that has a config to identify it to be new context + var newContextCreated = false + def creatingFunction(sparkContext: SparkContext): StreamingContext = { + newContextCreated = true + new StreamingContext(sparkContext, batchDuration) + } + + // Call ssc.stop(stopSparkContext = false) after a body of cody + def testGetOrCreate(body: => Unit): Unit = { + newContextCreated = false + try { + body + } finally { + if (ssc != null) { + ssc.stop(stopSparkContext = false) + } + ssc = null + } + } + + val emptyPath = Utils.createTempDir().getAbsolutePath() + + // getOrCreate should create new context with empty path + testGetOrCreate { + ssc = StreamingContext.getOrCreate(emptyPath, creatingFunction _, sc, createOnError = true) + assert(ssc != null, "no context created") + assert(newContextCreated, "new context not created") + assert(ssc.sparkContext === sc, "new StreamingContext does not use existing SparkContext") + } + + val corrutedCheckpointPath = createCorruptedCheckpoint() + + // getOrCreate should throw exception with fake checkpoint file and createOnError = false + intercept[Exception] { + ssc = StreamingContext.getOrCreate(corrutedCheckpointPath, creatingFunction _, sc) + } + + // getOrCreate should throw exception with fake checkpoint file + intercept[Exception] { + ssc = StreamingContext.getOrCreate( + corrutedCheckpointPath, creatingFunction _, sc, createOnError = false) + } + + // getOrCreate should create new context with fake checkpoint file and createOnError = true + testGetOrCreate { + ssc = StreamingContext.getOrCreate( + corrutedCheckpointPath, creatingFunction _, sc, createOnError = true) + assert(ssc != null, "no context created") + assert(newContextCreated, "new context not created") + assert(ssc.sparkContext === sc, "new StreamingContext does not use existing SparkContext") + } + + val checkpointPath = createValidCheckpoint() + + // StreamingContext.getOrCreate should recover context with checkpoint path + testGetOrCreate { + ssc = StreamingContext.getOrCreate(checkpointPath, creatingFunction _, sc) + assert(ssc != null, "no context created") + assert(!newContextCreated, "old context not recovered") + assert(ssc.sparkContext === sc, "new StreamingContext does not use existing SparkContext") + assert(!ssc.conf.contains("someKey"), + "recovered StreamingContext unexpectedly has old config") + } + } + test("DStream and generated RDD creation sites") { testPackage.test() } @@ -339,6 +474,30 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w val inputStream = new TestInputStream(s, input, 1) inputStream } + + def createValidCheckpoint(): String = { + val testDirectory = Utils.createTempDir().getAbsolutePath() + val checkpointDirectory = Utils.createTempDir().getAbsolutePath() + val conf = new SparkConf().setMaster(master).setAppName(appName) + conf.set("someKey", "someValue") + ssc = new StreamingContext(conf, batchDuration) + ssc.checkpoint(checkpointDirectory) + ssc.textFileStream(testDirectory).foreachRDD { rdd => rdd.count() } + ssc.start() + eventually(timeout(10000 millis)) { + assert(Checkpoint.getCheckpointFiles(checkpointDirectory).size > 1) + } + ssc.stop() + checkpointDirectory + } + + def createCorruptedCheckpoint(): String = { + val checkpointDirectory = Utils.createTempDir().getAbsolutePath() + val fakeCheckpointFile = Checkpoint.checkpointFile(checkpointDirectory, Time(1000)) + FileUtils.write(new File(fakeCheckpointFile.toString()), "blablabla") + assert(Checkpoint.getCheckpointFiles(checkpointDirectory).nonEmpty) + checkpointDirectory + } } class TestException(msg: String) extends Exception(msg) From 3a180c19a4ef165366186e23d8e8844c5baaecdd Mon Sep 17 00:00:00 2001 From: Josh Rosen Date: Wed, 29 Apr 2015 13:31:52 -0700 Subject: [PATCH 473/591] [SPARK-6629] cancelJobGroup() may not work for jobs whose job groups are inherited from parent threads When a job is submitted with a job group and that job group is inherited from a parent thread, there are multiple bugs that may prevent this job from being cancelable via `SparkContext.cancelJobGroup()`: - When filtering jobs based on their job group properties, DAGScheduler calls `get()` instead of `getProperty()`, which does not respect inheritance, so it will skip over jobs whose job group properties were inherited. - `Properties` objects are mutable, but we do not make defensive copies / snapshots, so modifications of the parent thread's job group will cause running jobs' groups to change; this also breaks cancelation. Both of these issues are easy to fix: use `getProperty()` and perform defensive copying. Author: Josh Rosen Closes #5288 from JoshRosen/localProperties-mutability-race and squashes the following commits: 9e29654 [Josh Rosen] Fix style issue 5d90750 [Josh Rosen] Merge remote-tracking branch 'origin/master' into localProperties-mutability-race 3f7b9e8 [Josh Rosen] Add JIRA reference; move clone into DAGScheduler 707e417 [Josh Rosen] Clone local properties to prevent mutations from breaking job cancellation. b376114 [Josh Rosen] Fix bug that prevented jobs with inherited job group properties from being cancelled. --- .../apache/spark/scheduler/DAGScheduler.scala | 15 ++++-- .../apache/spark/JobCancellationSuite.scala | 35 ++++++++++++++ .../org/apache/spark/ThreadingSuite.scala | 46 ++++++++++++++++++- 3 files changed, 91 insertions(+), 5 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala index b511c306ca508..05b8ab0d0a1f9 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -28,6 +28,8 @@ import scala.language.existentials import scala.language.postfixOps import scala.util.control.NonFatal +import org.apache.commons.lang3.SerializationUtils + import org.apache.spark._ import org.apache.spark.broadcast.Broadcast import org.apache.spark.executor.TaskMetrics @@ -510,7 +512,8 @@ class DAGScheduler( val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _] val waiter = new JobWaiter(this, jobId, partitions.size, resultHandler) eventProcessLoop.post(JobSubmitted( - jobId, rdd, func2, partitions.toArray, allowLocal, callSite, waiter, properties)) + jobId, rdd, func2, partitions.toArray, allowLocal, callSite, waiter, + SerializationUtils.clone(properties))) waiter } @@ -547,7 +550,8 @@ class DAGScheduler( val partitions = (0 until rdd.partitions.size).toArray val jobId = nextJobId.getAndIncrement() eventProcessLoop.post(JobSubmitted( - jobId, rdd, func2, partitions, allowLocal = false, callSite, listener, properties)) + jobId, rdd, func2, partitions, allowLocal = false, callSite, listener, + SerializationUtils.clone(properties))) listener.awaitResult() // Will throw an exception if the job fails } @@ -704,8 +708,11 @@ class DAGScheduler( private[scheduler] def handleJobGroupCancelled(groupId: String) { // Cancel all jobs belonging to this job group. // First finds all active jobs with this group id, and then kill stages for them. - val activeInGroup = activeJobs.filter(activeJob => - Option(activeJob.properties).exists(_.get(SparkContext.SPARK_JOB_GROUP_ID) == groupId)) + val activeInGroup = activeJobs.filter { activeJob => + Option(activeJob.properties).exists { + _.getProperty(SparkContext.SPARK_JOB_GROUP_ID) == groupId + } + } val jobIds = activeInGroup.map(_.jobId) jobIds.foreach(handleJobCancellation(_, "part of cancelled job group %s".format(groupId))) submitWaitingStages() diff --git a/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala b/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala index 4d3e09793faff..ae17fc60e4a43 100644 --- a/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala +++ b/core/src/test/scala/org/apache/spark/JobCancellationSuite.scala @@ -141,6 +141,41 @@ class JobCancellationSuite extends FunSuite with Matchers with BeforeAndAfter assert(jobB.get() === 100) } + test("inherited job group (SPARK-6629)") { + sc = new SparkContext("local[2]", "test") + + // Add a listener to release the semaphore once any tasks are launched. + val sem = new Semaphore(0) + sc.addSparkListener(new SparkListener { + override def onTaskStart(taskStart: SparkListenerTaskStart) { + sem.release() + } + }) + + sc.setJobGroup("jobA", "this is a job to be cancelled") + @volatile var exception: Exception = null + val jobA = new Thread() { + // The job group should be inherited by this thread + override def run(): Unit = { + exception = intercept[SparkException] { + sc.parallelize(1 to 10000, 2).map { i => Thread.sleep(10); i }.count() + } + } + } + jobA.start() + + // Block until both tasks of job A have started and cancel job A. + sem.acquire(2) + sc.cancelJobGroup("jobA") + jobA.join(10000) + assert(!jobA.isAlive) + assert(exception.getMessage contains "cancel") + + // Once A is cancelled, job B should finish fairly quickly. + val jobB = sc.parallelize(1 to 100, 2).countAsync() + assert(jobB.get() === 100) + } + test("job group with interruption") { sc = new SparkContext("local[2]", "test") diff --git a/core/src/test/scala/org/apache/spark/ThreadingSuite.scala b/core/src/test/scala/org/apache/spark/ThreadingSuite.scala index b5383d553add1..10917c866cc7d 100644 --- a/core/src/test/scala/org/apache/spark/ThreadingSuite.scala +++ b/core/src/test/scala/org/apache/spark/ThreadingSuite.scala @@ -17,10 +17,11 @@ package org.apache.spark -import java.util.concurrent.Semaphore +import java.util.concurrent.{TimeUnit, Semaphore} import java.util.concurrent.atomic.AtomicBoolean import java.util.concurrent.atomic.AtomicInteger +import org.apache.spark.scheduler._ import org.scalatest.FunSuite /** @@ -189,4 +190,47 @@ class ThreadingSuite extends FunSuite with LocalSparkContext { assert(sc.getLocalProperty("test") === "parent") assert(sc.getLocalProperty("Foo") === null) } + + test("mutations to local properties should not affect submitted jobs (SPARK-6629)") { + val jobStarted = new Semaphore(0) + val jobEnded = new Semaphore(0) + @volatile var jobResult: JobResult = null + + sc = new SparkContext("local", "test") + sc.setJobGroup("originalJobGroupId", "description") + sc.addSparkListener(new SparkListener { + override def onJobStart(jobStart: SparkListenerJobStart): Unit = { + jobStarted.release() + } + override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = { + jobResult = jobEnd.jobResult + jobEnded.release() + } + }) + + // Create a new thread which will inherit the current thread's properties + val thread = new Thread() { + override def run(): Unit = { + assert(sc.getLocalProperty(SparkContext.SPARK_JOB_GROUP_ID) === "originalJobGroupId") + // Sleeps for a total of 10 seconds, but allows cancellation to interrupt the task + try { + sc.parallelize(1 to 100).foreach { x => + Thread.sleep(100) + } + } catch { + case s: SparkException => // ignored so that we don't print noise in test logs + } + } + } + thread.start() + // Wait for the job to start, then mutate the original properties, which should have been + // inherited by the running job but hopefully defensively copied or snapshotted: + jobStarted.tryAcquire(10, TimeUnit.SECONDS) + sc.setJobGroup("modifiedJobGroupId", "description") + // Canceling the original job group should cancel the running job. In other words, the + // modification of the properties object should not affect the properties of running jobs + sc.cancelJobGroup("originalJobGroupId") + jobEnded.tryAcquire(10, TimeUnit.SECONDS) + assert(jobResult.isInstanceOf[JobFailed]) + } } From 15995c883aa248235fdebf0cbeeaa3ef12c97e9c Mon Sep 17 00:00:00 2001 From: DB Tsai Date: Wed, 29 Apr 2015 14:53:37 -0700 Subject: [PATCH 474/591] [SPARK-7222] [ML] Added mathematical derivation in comment and compressed the model, removed the correction terms in LinearRegression with ElasticNet Added detailed mathematical derivation of how scaling and LeastSquaresAggregator work. Refactored the code so the model is compressed based on the storage. We may try compression based on the prediction time. Also, I found that diffSum will be always zero mathematically, so no corrections are required. Author: DB Tsai Closes #5767 from dbtsai/lir-doc and squashes the following commits: 5e346c9 [DB Tsai] refactoring fc9f582 [DB Tsai] doc 58456d8 [DB Tsai] address feedback 69757b8 [DB Tsai] actually diffSum is mathematically zero! No correction is needed. 5929e49 [DB Tsai] typo 63f7d1e [DB Tsai] Added compression to the model based on storage 203a295 [DB Tsai] Add more documentation to LinearRegression in new ML framework. --- .../ml/regression/LinearRegression.scala | 115 ++++++++++++++---- .../GeneralizedLinearAlgorithm.scala | 2 +- .../mllib/util/LinearDataGenerator.scala | 21 ++-- 3 files changed, 101 insertions(+), 37 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala index f92c6816eb54c..cc9ad22cb860f 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala @@ -34,6 +34,7 @@ import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame import org.apache.spark.storage.StorageLevel import org.apache.spark.util.StatCounter +import org.apache.spark.Logging /** * Params for linear regression. @@ -48,7 +49,7 @@ private[regression] trait LinearRegressionParams extends RegressorParams */ @AlphaComponent class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegressionModel] - with LinearRegressionParams { + with LinearRegressionParams with Logging { /** * Set the regularization parameter. @@ -110,6 +111,15 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress val yMean = statCounter.mean val yStd = math.sqrt(statCounter.variance) + // If the yStd is zero, then the intercept is yMean with zero weights; + // as a result, training is not needed. + if (yStd == 0.0) { + logWarning(s"The standard deviation of the label is zero, so the weights will be zeros " + + s"and the intercept will be the mean of the label; as a result, training is not needed.") + if (handlePersistence) instances.unpersist() + return new LinearRegressionModel(this, paramMap, Vectors.sparse(numFeatures, Seq()), yMean) + } + val featuresMean = summarizer.mean.toArray val featuresStd = summarizer.variance.toArray.map(math.sqrt) @@ -141,7 +151,6 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress } lossHistory += state.value - // TODO: Based on the sparsity of weights, we may convert the weights to the sparse vector. // The weights are trained in the scaled space; we're converting them back to // the original space. val weights = { @@ -158,11 +167,10 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress // converged. See the following discussion for detail. // http://stats.stackexchange.com/questions/13617/how-is-the-intercept-computed-in-glmnet val intercept = yMean - dot(weights, Vectors.dense(featuresMean)) + if (handlePersistence) instances.unpersist() - if (handlePersistence) { - instances.unpersist() - } - new LinearRegressionModel(this, paramMap, weights, intercept) + // TODO: Converts to sparse format based on the storage, but may base on the scoring speed. + new LinearRegressionModel(this, paramMap, weights.compressed, intercept) } } @@ -198,15 +206,84 @@ class LinearRegressionModel private[ml] ( * Two LeastSquaresAggregator can be merged together to have a summary of loss and gradient of * the corresponding joint dataset. * - - * * Compute gradient and loss for a Least-squared loss function, as used in linear regression. - * This is correct for the averaged least squares loss function (mean squared error) - * L = 1/2n ||A weights-y||^2 - * See also the documentation for the precise formulation. + * For improving the convergence rate during the optimization process, and also preventing against + * features with very large variances exerting an overly large influence during model training, + * package like R's GLMNET performs the scaling to unit variance and removing the mean to reduce + * the condition number, and then trains the model in scaled space but returns the weights in + * the original scale. See page 9 in http://cran.r-project.org/web/packages/glmnet/glmnet.pdf + * + * However, we don't want to apply the `StandardScaler` on the training dataset, and then cache + * the standardized dataset since it will create a lot of overhead. As a result, we perform the + * scaling implicitly when we compute the objective function. The following is the mathematical + * derivation. + * + * Note that we don't deal with intercept by adding bias here, because the intercept + * can be computed using closed form after the coefficients are converged. + * See this discussion for detail. + * http://stats.stackexchange.com/questions/13617/how-is-the-intercept-computed-in-glmnet + * + * The objective function in the scaled space is given by + * {{{ + * L = 1/2n ||\sum_i w_i(x_i - \bar{x_i}) / \hat{x_i} - (y - \bar{y}) / \hat{y}||^2, + * }}} + * where \bar{x_i} is the mean of x_i, \hat{x_i} is the standard deviation of x_i, + * \bar{y} is the mean of label, and \hat{y} is the standard deviation of label. + * + * This can be rewritten as + * {{{ + * L = 1/2n ||\sum_i (w_i/\hat{x_i})x_i - \sum_i (w_i/\hat{x_i})\bar{x_i} - y / \hat{y} + * + \bar{y} / \hat{y}||^2 + * = 1/2n ||\sum_i w_i^\prime x_i - y / \hat{y} + offset||^2 = 1/2n diff^2 + * }}} + * where w_i^\prime is the effective weights defined by w_i/\hat{x_i}, offset is + * {{{ + * - \sum_i (w_i/\hat{x_i})\bar{x_i} + \bar{y} / \hat{y}. + * }}}, and diff is + * {{{ + * \sum_i w_i^\prime x_i - y / \hat{y} + offset + * }}} * - * @param weights weights/coefficients corresponding to features + * Note that the effective weights and offset don't depend on training dataset, + * so they can be precomputed. * - * @param updater Updater to be used to update weights after every iteration. + * Now, the first derivative of the objective function in scaled space is + * {{{ + * \frac{\partial L}{\partial\w_i} = diff/N (x_i - \bar{x_i}) / \hat{x_i} + * }}} + * However, ($x_i - \bar{x_i}$) will densify the computation, so it's not + * an ideal formula when the training dataset is sparse format. + * + * This can be addressed by adding the dense \bar{x_i} / \har{x_i} terms + * in the end by keeping the sum of diff. The first derivative of total + * objective function from all the samples is + * {{{ + * \frac{\partial L}{\partial\w_i} = + * 1/N \sum_j diff_j (x_{ij} - \bar{x_i}) / \hat{x_i} + * = 1/N ((\sum_j diff_j x_{ij} / \hat{x_i}) - diffSum \bar{x_i}) / \hat{x_i}) + * = 1/N ((\sum_j diff_j x_{ij} / \hat{x_i}) + correction_i) + * }}}, + * where correction_i = - diffSum \bar{x_i}) / \hat{x_i} + * + * A simple math can show that diffSum is actually zero, so we don't even + * need to add the correction terms in the end. From the definition of diff, + * {{{ + * diffSum = \sum_j (\sum_i w_i(x_{ij} - \bar{x_i}) / \hat{x_i} - (y_j - \bar{y}) / \hat{y}) + * = N * (\sum_i w_i(\bar{x_i} - \bar{x_i}) / \hat{x_i} - (\bar{y_j} - \bar{y}) / \hat{y}) + * = 0 + * }}} + * + * As a result, the first derivative of the total objective function only depends on + * the training dataset, which can be easily computed in distributed fashion, and is + * sparse format friendly. + * {{{ + * \frac{\partial L}{\partial\w_i} = 1/N ((\sum_j diff_j x_{ij} / \hat{x_i}) + * }}}, + * + * @param weights The weights/coefficients corresponding to the features. + * @param labelStd The standard deviation value of the label. + * @param labelMean The mean value of the label. + * @param featuresStd The standard deviation values of the features. + * @param featuresMean The mean values of the features. */ private class LeastSquaresAggregator( weights: Vector, @@ -302,18 +379,6 @@ private class LeastSquaresAggregator( def gradient: Vector = { val result = Vectors.dense(gradientSumArray.clone()) - - val correction = { - val temp = effectiveWeightsArray.clone() - var i = 0 - while (i < temp.length) { - temp(i) *= featuresMean(i) - i += 1 - } - Vectors.dense(temp) - } - - axpy(-diffSum, correction, result) scal(1.0 / totalCnt, result) result } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala index 9fd60ff7a0c79..26be30ff9d6fd 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala @@ -225,7 +225,7 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel] throw new SparkException("Input validation failed.") } - /* + /** * Scaling columns to unit variance as a heuristic to reduce the condition number: * * During the optimization process, the convergence (rate) depends on the condition number of diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala index d7bb943e84f53..91c2f4ccd3d17 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala @@ -103,17 +103,16 @@ object LinearDataGenerator { val rnd = new Random(seed) val x = Array.fill[Array[Double]](nPoints)( - Array.fill[Double](weights.length)(rnd.nextDouble)) - - x.map(vector => { - // This doesn't work if `vector` is a sparse vector. - val vectorArray = vector.toArray - var i = 0 - while (i < vectorArray.size) { - vectorArray(i) = (vectorArray(i) - 0.5) * math.sqrt(12.0 * xVariance(i)) + xMean(i) - i += 1 - } - }) + Array.fill[Double](weights.length)(rnd.nextDouble())) + + x.foreach { + case v => + var i = 0 + while (i < v.length) { + v(i) = (v(i) - 0.5) * math.sqrt(12.0 * xVariance(i)) + xMean(i) + i += 1 + } + } val y = x.map { xi => blas.ddot(weights.length, xi, 1, weights, 1) + intercept + eps * rnd.nextGaussian() From c9d530e2e5123dbd4fd13fc487c890d6076b24bf Mon Sep 17 00:00:00 2001 From: Xusen Yin Date: Wed, 29 Apr 2015 14:55:32 -0700 Subject: [PATCH 475/591] [SPARK-6529] [ML] Add Word2Vec transformer See JIRA issue [here](https://issues.apache.org/jira/browse/SPARK-6529). There are some notes: 1. I add `learningRate` in sharedParams since it is a common parameter for ML algorithms. 2. We will not support transform of finding synonyms from a `Vector`, which will support in further JIRA issues. 3. Word2Vec is different with other ML models that its training set and transformed set are different. Its training set is an `RDD[Iterable[String]]` which represents documents, but the transformed set we want is an `RDD[String]` that represents unique words. So you have to switch your `inputCol` in these two stages. Author: Xusen Yin Closes #5596 from yinxusen/SPARK-6529 and squashes the following commits: ee2b37a [Xusen Yin] merge with former HEAD 4945462 [Xusen Yin] merge with #5626 3bc2cbd [Xusen Yin] change foldLeft to for loop and use blas 5dd4ee7 [Xusen Yin] fix scala style 743e0d5 [Xusen Yin] fix comments and code style 04c48e9 [Xusen Yin] ensure the functionality a190f2c [Xusen Yin] fix code style and refine the transform function of word2vec 02848fa [Xusen Yin] refine comments 34a55c0 [Xusen Yin] fix errors 109d124 [Xusen Yin] add test suite and pass it 04dde06 [Xusen Yin] add shared params c594095 [Xusen Yin] add word2vec transformer 23d77fa [Xusen Yin] merge with #5626 e8cfaf7 [Xusen Yin] fix conflict with master 66e7bd3 [Xusen Yin] change foldLeft to for loop and use blas 566ec20 [Xusen Yin] fix scala style b54399f [Xusen Yin] fix comments and code style 1211e86 [Xusen Yin] ensure the functionality 6b97ec8 [Xusen Yin] fix code style and refine the transform function of word2vec 7cde18f [Xusen Yin] rm sharedParams 618abd0 [Xusen Yin] refine comments e29680a [Xusen Yin] fix errors fe3afe9 [Xusen Yin] add test suite and pass it 02767fb [Xusen Yin] add shared params 6a514f1 [Xusen Yin] add word2vec transformer --- .../apache/spark/ml/feature/Word2Vec.scala | 185 ++++++++++++++++++ .../ml/param/shared/SharedParamsCodeGen.scala | 3 +- .../spark/ml/param/shared/sharedParams.scala | 17 ++ .../spark/ml/feature/Word2VecSuite.scala | 63 ++++++ 4 files changed, 267 insertions(+), 1 deletion(-) create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala new file mode 100644 index 0000000000000..0163fa8bd8a5b --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala @@ -0,0 +1,185 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared._ +import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.ml.{Estimator, Model} +import org.apache.spark.mllib.feature +import org.apache.spark.mllib.linalg.BLAS._ +import org.apache.spark.mllib.linalg.{VectorUDT, Vectors} +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types._ +import org.apache.spark.sql.{DataFrame, Row} + +/** + * Params for [[Word2Vec]] and [[Word2VecModel]]. + */ +private[feature] trait Word2VecBase extends Params + with HasInputCol with HasOutputCol with HasMaxIter with HasStepSize with HasSeed { + + /** + * The dimension of the code that you want to transform from words. + */ + final val vectorSize = new IntParam( + this, "vectorSize", "the dimension of codes after transforming from words") + setDefault(vectorSize -> 100) + + /** @group getParam */ + def getVectorSize: Int = getOrDefault(vectorSize) + + /** + * Number of partitions for sentences of words. + */ + final val numPartitions = new IntParam( + this, "numPartitions", "number of partitions for sentences of words") + setDefault(numPartitions -> 1) + + /** @group getParam */ + def getNumPartitions: Int = getOrDefault(numPartitions) + + /** + * The minimum number of times a token must appear to be included in the word2vec model's + * vocabulary. + */ + final val minCount = new IntParam(this, "minCount", "the minimum number of times a token must " + + "appear to be included in the word2vec model's vocabulary") + setDefault(minCount -> 5) + + /** @group getParam */ + def getMinCount: Int = getOrDefault(minCount) + + setDefault(stepSize -> 0.025) + setDefault(maxIter -> 1) + setDefault(seed -> 42L) + + /** + * Validate and transform the input schema. + */ + protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = { + val map = extractParamMap(paramMap) + SchemaUtils.checkColumnType(schema, map(inputCol), new ArrayType(StringType, true)) + SchemaUtils.appendColumn(schema, map(outputCol), new VectorUDT) + } +} + +/** + * :: AlphaComponent :: + * Word2Vec trains a model of `Map(String, Vector)`, i.e. transforms a word into a code for further + * natural language processing or machine learning process. + */ +@AlphaComponent +final class Word2Vec extends Estimator[Word2VecModel] with Word2VecBase { + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + /** @group setParam */ + def setVectorSize(value: Int): this.type = set(vectorSize, value) + + /** @group setParam */ + def setStepSize(value: Double): this.type = set(stepSize, value) + + /** @group setParam */ + def setNumPartitions(value: Int): this.type = set(numPartitions, value) + + /** @group setParam */ + def setMaxIter(value: Int): this.type = set(maxIter, value) + + /** @group setParam */ + def setSeed(value: Long): this.type = set(seed, value) + + /** @group setParam */ + def setMinCount(value: Int): this.type = set(minCount, value) + + override def fit(dataset: DataFrame, paramMap: ParamMap): Word2VecModel = { + transformSchema(dataset.schema, paramMap, logging = true) + val map = extractParamMap(paramMap) + val input = dataset.select(map(inputCol)).map { case Row(v: Seq[String]) => v } + val wordVectors = new feature.Word2Vec() + .setLearningRate(map(stepSize)) + .setMinCount(map(minCount)) + .setNumIterations(map(maxIter)) + .setNumPartitions(map(numPartitions)) + .setSeed(map(seed)) + .setVectorSize(map(vectorSize)) + .fit(input) + val model = new Word2VecModel(this, map, wordVectors) + Params.inheritValues(map, this, model) + model + } + + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + validateAndTransformSchema(schema, paramMap) + } +} + +/** + * :: AlphaComponent :: + * Model fitted by [[Word2Vec]]. + */ +@AlphaComponent +class Word2VecModel private[ml] ( + override val parent: Word2Vec, + override val fittingParamMap: ParamMap, + wordVectors: feature.Word2VecModel) + extends Model[Word2VecModel] with Word2VecBase { + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + /** + * Transform a sentence column to a vector column to represent the whole sentence. The transform + * is performed by averaging all word vectors it contains. + */ + override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + transformSchema(dataset.schema, paramMap, logging = true) + val map = extractParamMap(paramMap) + val bWordVectors = dataset.sqlContext.sparkContext.broadcast(wordVectors) + val word2Vec = udf { sentence: Seq[String] => + if (sentence.size == 0) { + Vectors.sparse(map(vectorSize), Array.empty[Int], Array.empty[Double]) + } else { + val cum = Vectors.zeros(map(vectorSize)) + val model = bWordVectors.value.getVectors + for (word <- sentence) { + if (model.contains(word)) { + axpy(1.0, bWordVectors.value.transform(word), cum) + } else { + // pass words which not belong to model + } + } + scal(1.0 / sentence.size, cum) + cum + } + } + dataset.withColumn(map(outputCol), word2Vec(col(map(inputCol)))) + } + + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + validateAndTransformSchema(schema, paramMap) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala index 3f7e8f5a0b22c..654cd72d53074 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala @@ -48,7 +48,8 @@ private[shared] object SharedParamsCodeGen { ParamDesc[Boolean]("fitIntercept", "whether to fit an intercept term", Some("true")), ParamDesc[Long]("seed", "random seed", Some("Utils.random.nextLong()")), ParamDesc[Double]("elasticNetParam", "the ElasticNet mixing parameter"), - ParamDesc[Double]("tol", "the convergence tolerance for iterative algorithms")) + ParamDesc[Double]("tol", "the convergence tolerance for iterative algorithms"), + ParamDesc[Double]("stepSize", "Step size to be used for each iteration of optimization.")) val code = genSharedParams(params) val file = "src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala" diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala index 7d2c76d6c62c8..96d11ed76fa8e 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala @@ -310,4 +310,21 @@ trait HasTol extends Params { /** @group getParam */ final def getTol: Double = getOrDefault(tol) } + +/** + * :: DeveloperApi :: + * Trait for shared param stepSize. + */ +@DeveloperApi +trait HasStepSize extends Params { + + /** + * Param for Step size to be used for each iteration of optimization.. + * @group param + */ + final val stepSize: DoubleParam = new DoubleParam(this, "stepSize", "Step size to be used for each iteration of optimization.") + + /** @group getParam */ + final def getStepSize: Double = getOrDefault(stepSize) +} // scalastyle:on diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala new file mode 100644 index 0000000000000..03ba86670d453 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala @@ -0,0 +1,63 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.scalatest.FunSuite + +import org.apache.spark.mllib.linalg.{Vector, Vectors} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.sql.{Row, SQLContext} + +class Word2VecSuite extends FunSuite with MLlibTestSparkContext { + + test("Word2Vec") { + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + + val sentence = "a b " * 100 + "a c " * 10 + val numOfWords = sentence.split(" ").size + val doc = sc.parallelize(Seq(sentence, sentence)).map(line => line.split(" ")) + + val codes = Map( + "a" -> Array(-0.2811822295188904,-0.6356269121170044,-0.3020961284637451), + "b" -> Array(1.0309048891067505,-1.29472815990448,0.22276712954044342), + "c" -> Array(-0.08456747233867645,0.5137411952018738,0.11731560528278351) + ) + + val expected = doc.map { sentence => + Vectors.dense(sentence.map(codes.apply).reduce((word1, word2) => + word1.zip(word2).map { case (v1, v2) => v1 + v2 } + ).map(_ / numOfWords)) + } + + val docDF = doc.zip(expected).toDF("text", "expected") + + val model = new Word2Vec() + .setVectorSize(3) + .setInputCol("text") + .setOutputCol("result") + .fit(docDF) + + model.transform(docDF).select("result", "expected").collect().foreach { + case Row(vector1: Vector, vector2: Vector) => + assert(vector1 ~== vector2 absTol 1E-5, "Transformed vector is different with expected.") + } + } +} + From d7dbce8f7da8a7fd01df6633a6043f51161b7d18 Mon Sep 17 00:00:00 2001 From: Burak Yavuz Date: Wed, 29 Apr 2015 15:34:05 -0700 Subject: [PATCH 476/591] [SPARK-7156][SQL] support RandomSplit in DataFrames This is built on top of kaka1992 's PR #5711 using Logical plans. Author: Burak Yavuz Closes #5761 from brkyvz/random-sample and squashes the following commits: a1fb0aa [Burak Yavuz] remove unrelated file 69669c3 [Burak Yavuz] fix broken test 1ddb3da [Burak Yavuz] copy base 6000328 [Burak Yavuz] added python api and fixed test 3c11d1b [Burak Yavuz] fixed broken test f400ade [Burak Yavuz] fix build errors 2384266 [Burak Yavuz] addressed comments v0.1 e98ebac [Burak Yavuz] [SPARK-7156][SQL] support RandomSplit in DataFrames --- .../main/scala/org/apache/spark/rdd/RDD.scala | 19 +++++++++- .../java/org/apache/spark/JavaAPISuite.java | 8 ++-- python/pyspark/sql/dataframe.py | 18 ++++++++- .../spark/sql/catalyst/dsl/package.scala | 6 --- .../plans/logical/basicOperators.scala | 18 ++++++++- .../org/apache/spark/sql/DataFrame.scala | 38 ++++++++++++++++++- .../spark/sql/execution/SparkStrategies.scala | 4 +- .../spark/sql/execution/basicOperators.scala | 20 +++++++++- .../org/apache/spark/sql/DataFrameSuite.scala | 17 +++++++++ .../org/apache/spark/sql/hive/HiveQl.scala | 4 +- 10 files changed, 130 insertions(+), 22 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala index d80d94a588346..330255f89247f 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -407,11 +407,26 @@ abstract class RDD[T: ClassTag]( val sum = weights.sum val normalizedCumWeights = weights.map(_ / sum).scanLeft(0.0d)(_ + _) normalizedCumWeights.sliding(2).map { x => - new PartitionwiseSampledRDD[T, T]( - this, new BernoulliCellSampler[T](x(0), x(1)), true, seed) + randomSampleWithRange(x(0), x(1), seed) }.toArray } + /** + * Internal method exposed for Random Splits in DataFrames. Samples an RDD given a probability + * range. + * @param lb lower bound to use for the Bernoulli sampler + * @param ub upper bound to use for the Bernoulli sampler + * @param seed the seed for the Random number generator + * @return A random sub-sample of the RDD without replacement. + */ + private[spark] def randomSampleWithRange(lb: Double, ub: Double, seed: Long): RDD[T] = { + this.mapPartitionsWithIndex { case (index, partition) => + val sampler = new BernoulliCellSampler[T](lb, ub) + sampler.setSeed(seed + index) + sampler.sample(partition) + } + } + /** * Return a fixed-size sampled subset of this RDD in an array * diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java index 34ac9361d46c6..c2089b0e56a1f 100644 --- a/core/src/test/java/org/apache/spark/JavaAPISuite.java +++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java @@ -157,11 +157,11 @@ public void sample() { public void randomSplit() { List ints = Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9, 10); JavaRDD rdd = sc.parallelize(ints); - JavaRDD[] splits = rdd.randomSplit(new double[] { 0.4, 0.6, 1.0 }, 11); + JavaRDD[] splits = rdd.randomSplit(new double[] { 0.4, 0.6, 1.0 }, 31); Assert.assertEquals(3, splits.length); - Assert.assertEquals(2, splits[0].count()); - Assert.assertEquals(3, splits[1].count()); - Assert.assertEquals(5, splits[2].count()); + Assert.assertEquals(1, splits[0].count()); + Assert.assertEquals(2, splits[1].count()); + Assert.assertEquals(7, splits[2].count()); } @Test diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index d9cbbc68b3bf0..3074af3ed2e83 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -426,7 +426,7 @@ def distinct(self): def sample(self, withReplacement, fraction, seed=None): """Returns a sampled subset of this :class:`DataFrame`. - >>> df.sample(False, 0.5, 97).count() + >>> df.sample(False, 0.5, 42).count() 1 """ assert fraction >= 0.0, "Negative fraction value: %s" % fraction @@ -434,6 +434,22 @@ def sample(self, withReplacement, fraction, seed=None): rdd = self._jdf.sample(withReplacement, fraction, long(seed)) return DataFrame(rdd, self.sql_ctx) + def randomSplit(self, weights, seed=None): + """Randomly splits this :class:`DataFrame` with the provided weights. + + >>> splits = df4.randomSplit([1.0, 2.0], 24) + >>> splits[0].count() + 1 + + >>> splits[1].count() + 3 + """ + for w in weights: + assert w >= 0.0, "Negative weight value: %s" % w + seed = seed if seed is not None else random.randint(0, sys.maxsize) + rdd_array = self._jdf.randomSplit(_to_seq(self.sql_ctx._sc, weights), long(seed)) + return [DataFrame(rdd, self.sql_ctx) for rdd in rdd_array] + @property def dtypes(self): """Returns all column names and their data types as a list. diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala index 5d5aba9644ff7..fa6cc7a1a36cf 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala @@ -278,12 +278,6 @@ package object dsl { def sfilter[T1](arg1: Symbol)(udf: (T1) => Boolean): LogicalPlan = Filter(ScalaUdf(udf, BooleanType, Seq(UnresolvedAttribute(arg1.name))), logicalPlan) - def sample( - fraction: Double, - withReplacement: Boolean = true, - seed: Int = (math.random * 1000).toInt): LogicalPlan = - Sample(fraction, withReplacement, seed, logicalPlan) - // TODO specify the output column names def generate( generator: Generator, diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala index 608e272da7784..21208c8a5c281 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala @@ -300,8 +300,22 @@ case class Subquery(alias: String, child: LogicalPlan) extends UnaryNode { override def output: Seq[Attribute] = child.output.map(_.withQualifiers(alias :: Nil)) } -case class Sample(fraction: Double, withReplacement: Boolean, seed: Long, child: LogicalPlan) - extends UnaryNode { +/** + * Sample the dataset. + * + * @param lowerBound Lower-bound of the sampling probability (usually 0.0) + * @param upperBound Upper-bound of the sampling probability. The expected fraction sampled + * will be ub - lb. + * @param withReplacement Whether to sample with replacement. + * @param seed the random seed + * @param child the LogicalPlan + */ +case class Sample( + lowerBound: Double, + upperBound: Double, + withReplacement: Boolean, + seed: Long, + child: LogicalPlan) extends UnaryNode { override def output: Seq[Attribute] = child.output } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 0e896e5693b98..0d02e14c21be0 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -706,7 +706,7 @@ class DataFrame private[sql]( * @group dfops */ def sample(withReplacement: Boolean, fraction: Double, seed: Long): DataFrame = { - Sample(fraction, withReplacement, seed, logicalPlan) + Sample(0.0, fraction, withReplacement, seed, logicalPlan) } /** @@ -720,6 +720,42 @@ class DataFrame private[sql]( sample(withReplacement, fraction, Utils.random.nextLong) } + /** + * Randomly splits this [[DataFrame]] with the provided weights. + * + * @param weights weights for splits, will be normalized if they don't sum to 1. + * @param seed Seed for sampling. + * @group dfops + */ + def randomSplit(weights: Array[Double], seed: Long): Array[DataFrame] = { + val sum = weights.sum + val normalizedCumWeights = weights.map(_ / sum).scanLeft(0.0d)(_ + _) + normalizedCumWeights.sliding(2).map { x => + new DataFrame(sqlContext, Sample(x(0), x(1), false, seed, logicalPlan)) + }.toArray + } + + /** + * Randomly splits this [[DataFrame]] with the provided weights. + * + * @param weights weights for splits, will be normalized if they don't sum to 1. + * @group dfops + */ + def randomSplit(weights: Array[Double]): Array[DataFrame] = { + randomSplit(weights, Utils.random.nextLong) + } + + /** + * Randomly splits this [[DataFrame]] with the provided weights. Provided for the Python Api. + * + * @param weights weights for splits, will be normalized if they don't sum to 1. + * @param seed Seed for sampling. + * @group dfops + */ + def randomSplit(weights: List[Double], seed: Long): Array[DataFrame] = { + randomSplit(weights.toArray, seed) + } + /** * (Scala-specific) Returns a new [[DataFrame]] where each row has been expanded to zero or more * rows by the provided function. This is similar to a `LATERAL VIEW` in HiveQL. The columns of diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala index af58911cc0e6a..326e8ce4ca524 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala @@ -303,8 +303,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { execution.Expand(projections, output, planLater(child)) :: Nil case logical.Aggregate(group, agg, child) => execution.Aggregate(partial = false, group, agg, planLater(child)) :: Nil - case logical.Sample(fraction, withReplacement, seed, child) => - execution.Sample(fraction, withReplacement, seed, planLater(child)) :: Nil + case logical.Sample(lb, ub, withReplacement, seed, child) => + execution.Sample(lb, ub, withReplacement, seed, planLater(child)) :: Nil case logical.LocalRelation(output, data) => LocalTableScan(output, data) :: Nil case logical.Limit(IntegerLiteral(limit), child) => diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala index 1afdb409417ce..5ca11e67a9434 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala @@ -63,16 +63,32 @@ case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode { /** * :: DeveloperApi :: + * Sample the dataset. + * @param lowerBound Lower-bound of the sampling probability (usually 0.0) + * @param upperBound Upper-bound of the sampling probability. The expected fraction sampled + * will be ub - lb. + * @param withReplacement Whether to sample with replacement. + * @param seed the random seed + * @param child the QueryPlan */ @DeveloperApi -case class Sample(fraction: Double, withReplacement: Boolean, seed: Long, child: SparkPlan) +case class Sample( + lowerBound: Double, + upperBound: Double, + withReplacement: Boolean, + seed: Long, + child: SparkPlan) extends UnaryNode { override def output: Seq[Attribute] = child.output // TODO: How to pick seed? override def execute(): RDD[Row] = { - child.execute().map(_.copy()).sample(withReplacement, fraction, seed) + if (withReplacement) { + child.execute().map(_.copy()).sample(withReplacement, upperBound - lowerBound, seed) + } else { + child.execute().map(_.copy()).randomSampleWithRange(lowerBound, upperBound, seed) + } } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index 5ec06d448e50f..b70e127b4ed1a 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -510,6 +510,23 @@ class DataFrameSuite extends QueryTest { assert(df.schema.map(_.name).toSeq === Seq("key", "valueRenamed", "newCol")) } + test("randomSplit") { + val n = 600 + val data = TestSQLContext.sparkContext.parallelize(1 to n, 2).toDF("id") + for (seed <- 1 to 5) { + val splits = data.randomSplit(Array[Double](1, 2, 3), seed) + assert(splits.length == 3, "wrong number of splits") + + assert(splits.reduce((a, b) => a.unionAll(b)).sort("id").collect().toList == + data.collect().toList, "incomplete or wrong split") + + val s = splits.map(_.count()) + assert(math.abs(s(0) - 100) < 50) // std = 9.13 + assert(math.abs(s(1) - 200) < 50) // std = 11.55 + assert(math.abs(s(2) - 300) < 50) // std = 12.25 + } + } + test("describe") { val describeTestData = Seq( ("Bob", 16, 176), diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala index 2dc6463abafa7..0a86519e1412b 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala @@ -887,13 +887,13 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C fraction.toDouble >= (0.0 - RandomSampler.roundingEpsilon) && fraction.toDouble <= (100.0 + RandomSampler.roundingEpsilon), s"Sampling fraction ($fraction) must be on interval [0, 100]") - Sample(fraction.toDouble / 100, withReplacement = false, (math.random * 1000).toInt, + Sample(0.0, fraction.toDouble / 100, withReplacement = false, (math.random * 1000).toInt, relation) case Token("TOK_TABLEBUCKETSAMPLE", Token(numerator, Nil) :: Token(denominator, Nil) :: Nil) => val fraction = numerator.toDouble / denominator.toDouble - Sample(fraction, withReplacement = false, (math.random * 1000).toInt, relation) + Sample(0.0, fraction, withReplacement = false, (math.random * 1000).toInt, relation) case a: ASTNode => throw new NotImplementedError( s"""No parse rules for sampling clause: ${a.getType}, text: ${a.getText} : From 7f4b583733714bbecb43fb0823134bf2ec720a17 Mon Sep 17 00:00:00 2001 From: Qiping Li Date: Wed, 29 Apr 2015 23:52:16 +0100 Subject: [PATCH 477/591] [SPARK-7181] [CORE] fix inifite loop in Externalsorter's mergeWithAggregation see [SPARK-7181](https://issues.apache.org/jira/browse/SPARK-7181). Author: Qiping Li Closes #5737 from chouqin/externalsorter and squashes the following commits: 2924b93 [Qiping Li] fix inifite loop in Externalsorter's mergeWithAggregation --- .../org/apache/spark/util/collection/ExternalSorter.scala | 3 ++- .../apache/spark/util/collection/ExternalSorterSuite.scala | 5 ++++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala index ef3cac622505e..4ed8a740f99db 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala @@ -527,7 +527,8 @@ private[spark] class ExternalSorter[K, V, C]( val k = elem._1 var c = elem._2 while (sorted.hasNext && sorted.head._1 == k) { - c = mergeCombiners(c, sorted.head._2) + val pair = sorted.next() + c = mergeCombiners(c, pair._2) } (k, c) } diff --git a/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala index 9ff067f86af44..de26aa351b0d2 100644 --- a/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala @@ -506,7 +506,10 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe val agg = new Aggregator[Int, Int, Int](i => i, (i, j) => i + j, (i, j) => i + j) val ord = implicitly[Ordering[Int]] val sorter = new ExternalSorter(Some(agg), Some(new HashPartitioner(3)), Some(ord), None) - sorter.insertAll((0 until 100000).iterator.map(i => (i / 2, i))) + + // avoid combine before spill + sorter.insertAll((0 until 50000).iterator.map(i => (i , 2 * i))) + sorter.insertAll((0 until 50000).iterator.map(i => (i, 2 * i + 1))) val results = sorter.partitionedIterator.map{case (p, vs) => (p, vs.toSet)}.toSet val expected = (0 until 3).map(p => { (p, (0 until 50000).map(i => (i, i * 4 + 1)).filter(_._1 % 3 == p).toSet) From 3fc6cfd079d8cdd35574605cb9a4178ca7f2613d Mon Sep 17 00:00:00 2001 From: yongtang Date: Wed, 29 Apr 2015 23:55:51 +0100 Subject: [PATCH 478/591] [SPARK-7155] [CORE] Allow newAPIHadoopFile to support comma-separated list of files as input See JIRA: https://issues.apache.org/jira/browse/SPARK-7155 SparkContext's newAPIHadoopFile() does not support comma-separated list of files. For example, the following: ```scala sc.newAPIHadoopFile("/root/file1.txt,/root/file2.txt", classOf[TextInputFormat], classOf[LongWritable], classOf[Text]) ``` will throw ``` org.apache.hadoop.mapreduce.lib.input.InvalidInputException: Input path does not exist: file:/root/file1.txt,/root/file2.txt ``` However, the other API hadoopFile() is able to process comma-separated list of files correctly. In addition, since sc.textFile() uses hadoopFile(), it is also able to process comma-separated list of files correctly. That means the behaviors of hadoopFile() and newAPIHadoopFile() are not aligned. This pull request fix this issue and allows newAPIHadoopFile() to support comma-separated list of files as input. A unit test has also been added in SparkContextSuite.scala. It creates two temporary text files as the input and tested against sc.textFile(), sc.hadoopFile(), and sc.newAPIHadoopFile(). Note: The contribution is my original work and that I license the work to the project under the project's open source license. Author: yongtang Closes #5708 from yongtang/SPARK-7155 and squashes the following commits: 654c80c [yongtang] [SPARK-7155] [CORE] Remove unneeded temp file deletion in unit test as parent dir is already temporary. 26faa6a [yongtang] [SPARK-7155] [CORE] Support comma-separated list of files as input for newAPIHadoopFile, wholeTextFiles, and binaryFiles. Use setInputPaths for consistency. 73e1f16 [yongtang] [SPARK-7155] [CORE] Allow newAPIHadoopFile to support comma-separated list of files as input. --- .../scala/org/apache/spark/SparkContext.scala | 12 +++- .../org/apache/spark/SparkContextSuite.scala | 63 ++++++++++++++++++- 2 files changed, 71 insertions(+), 4 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 5ae8fb81de809..bae951f388337 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -713,7 +713,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli RDD[(String, String)] = { assertNotStopped() val job = new NewHadoopJob(hadoopConfiguration) - NewFileInputFormat.addInputPath(job, new Path(path)) + // Use setInputPaths so that wholeTextFiles aligns with hadoopFile/textFile in taking + // comma separated files as input. (see SPARK-7155) + NewFileInputFormat.setInputPaths(job, path) val updateConf = job.getConfiguration new WholeTextFileRDD( this, @@ -759,7 +761,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli RDD[(String, PortableDataStream)] = { assertNotStopped() val job = new NewHadoopJob(hadoopConfiguration) - NewFileInputFormat.addInputPath(job, new Path(path)) + // Use setInputPaths so that binaryFiles aligns with hadoopFile/textFile in taking + // comma separated files as input. (see SPARK-7155) + NewFileInputFormat.setInputPaths(job, path) val updateConf = job.getConfiguration new BinaryFileRDD( this, @@ -935,7 +939,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli // The call to new NewHadoopJob automatically adds security credentials to conf, // so we don't need to explicitly add them ourselves val job = new NewHadoopJob(conf) - NewFileInputFormat.addInputPath(job, new Path(path)) + // Use setInputPaths so that newAPIHadoopFile aligns with hadoopFile/textFile in taking + // comma separated files as input. (see SPARK-7155) + NewFileInputFormat.setInputPaths(job, path) val updatedConf = job.getConfiguration new NewHadoopRDD(this, fClass, kClass, vClass, updatedConf).setName(path) } diff --git a/core/src/test/scala/org/apache/spark/SparkContextSuite.scala b/core/src/test/scala/org/apache/spark/SparkContextSuite.scala index 728558a424780..9049db7755358 100644 --- a/core/src/test/scala/org/apache/spark/SparkContextSuite.scala +++ b/core/src/test/scala/org/apache/spark/SparkContextSuite.scala @@ -25,7 +25,9 @@ import com.google.common.io.Files import org.scalatest.FunSuite -import org.apache.hadoop.io.BytesWritable +import org.apache.hadoop.io.{BytesWritable, LongWritable, Text} +import org.apache.hadoop.mapred.TextInputFormat +import org.apache.hadoop.mapreduce.lib.input.{TextInputFormat => NewTextInputFormat} import org.apache.spark.util.Utils import scala.concurrent.Await @@ -213,4 +215,63 @@ class SparkContextSuite extends FunSuite with LocalSparkContext { sc.stop() } } + + test("Comma separated paths for newAPIHadoopFile/wholeTextFiles/binaryFiles (SPARK-7155)") { + // Regression test for SPARK-7155 + // dir1 and dir2 are used for wholeTextFiles and binaryFiles + val dir1 = Utils.createTempDir() + val dir2 = Utils.createTempDir() + + val dirpath1=dir1.getAbsolutePath + val dirpath2=dir2.getAbsolutePath + + // file1 and file2 are placed inside dir1, they are also used for + // textFile, hadoopFile, and newAPIHadoopFile + // file3, file4 and file5 are placed inside dir2, they are used for + // textFile, hadoopFile, and newAPIHadoopFile as well + val file1 = new File(dir1, "part-00000") + val file2 = new File(dir1, "part-00001") + val file3 = new File(dir2, "part-00000") + val file4 = new File(dir2, "part-00001") + val file5 = new File(dir2, "part-00002") + + val filepath1=file1.getAbsolutePath + val filepath2=file2.getAbsolutePath + val filepath3=file3.getAbsolutePath + val filepath4=file4.getAbsolutePath + val filepath5=file5.getAbsolutePath + + + try { + // Create 5 text files. + Files.write("someline1 in file1\nsomeline2 in file1\nsomeline3 in file1", file1, UTF_8) + Files.write("someline1 in file2\nsomeline2 in file2", file2, UTF_8) + Files.write("someline1 in file3", file3, UTF_8) + Files.write("someline1 in file4\nsomeline2 in file4", file4, UTF_8) + Files.write("someline1 in file2\nsomeline2 in file5", file5, UTF_8) + + sc = new SparkContext(new SparkConf().setAppName("test").setMaster("local")) + + // Test textFile, hadoopFile, and newAPIHadoopFile for file1 and file2 + assert(sc.textFile(filepath1 + "," + filepath2).count() == 5L) + assert(sc.hadoopFile(filepath1 + "," + filepath2, + classOf[TextInputFormat], classOf[LongWritable], classOf[Text]).count() == 5L) + assert(sc.newAPIHadoopFile(filepath1 + "," + filepath2, + classOf[NewTextInputFormat], classOf[LongWritable], classOf[Text]).count() == 5L) + + // Test textFile, hadoopFile, and newAPIHadoopFile for file3, file4, and file5 + assert(sc.textFile(filepath3 + "," + filepath4 + "," + filepath5).count() == 5L) + assert(sc.hadoopFile(filepath3 + "," + filepath4 + "," + filepath5, + classOf[TextInputFormat], classOf[LongWritable], classOf[Text]).count() == 5L) + assert(sc.newAPIHadoopFile(filepath3 + "," + filepath4 + "," + filepath5, + classOf[NewTextInputFormat], classOf[LongWritable], classOf[Text]).count() == 5L) + + // Test wholeTextFiles, and binaryFiles for dir1 and dir2 + assert(sc.wholeTextFiles(dirpath1 + "," + dirpath2).count() == 5L) + assert(sc.binaryFiles(dirpath1 + "," + dirpath2).count() == 5L) + + } finally { + sc.stop() + } + } } From f8cbb0a4b37b0d4ba49515d888cb52dea9eb01f1 Mon Sep 17 00:00:00 2001 From: Cheng Hao Date: Wed, 29 Apr 2015 16:23:34 -0700 Subject: [PATCH 479/591] [SPARK-7229] [SQL] SpecificMutableRow should take integer type as internal representation for Date Author: Cheng Hao Closes #5772 from chenghao-intel/specific_row and squashes the following commits: 2cd064d [Cheng Hao] scala style issue 60347a2 [Cheng Hao] SpecificMutableRow should take integer type as internal representation for DateType --- .../sql/catalyst/expressions/SpecificMutableRow.scala | 1 + .../test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala | 9 +++++++++ 2 files changed, 10 insertions(+) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala index 3475ed05f4454..aa4099e4d7bf9 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala @@ -202,6 +202,7 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR case DoubleType => new MutableDouble case BooleanType => new MutableBoolean case LongType => new MutableLong + case DateType => new MutableInt // We use INT for DATE internally case _ => new MutableAny }.toArray) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala index db096af4535a9..856a806781ec4 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala @@ -256,6 +256,15 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { assert(cachedRows(0).getAs[java.sql.Date](1) === java.sql.Date.valueOf("1996-01-01")) } + test("test DATE types in cache") { + val rows = TestSQLContext.jdbc(urlWithUserAndPass, "TEST.TIMETYPES").collect() + TestSQLContext + .jdbc(urlWithUserAndPass, "TEST.TIMETYPES").cache().registerTempTable("mycached_date") + val cachedRows = sql("select * from mycached_date").collect() + assert(rows(0).getAs[java.sql.Date](1) === java.sql.Date.valueOf("1996-01-01")) + assert(cachedRows(0).getAs[java.sql.Date](1) === java.sql.Date.valueOf("1996-01-01")) + } + test("H2 floating-point types") { val rows = sql("SELECT * FROM flttypes").collect() assert(rows(0).getDouble(0) === 1.00000000000000022) // Yes, I meant ==. From b1ef6a60ff6ea2adb43c6544e5311c11f4364f64 Mon Sep 17 00:00:00 2001 From: "Joseph K. Bradley" Date: Wed, 29 Apr 2015 16:35:17 -0700 Subject: [PATCH 480/591] [SPARK-7259] [ML] VectorIndexer: do not copy non-ML metadata to output column Changed VectorIndexer so it does not carry non-ML metadata from the input to the output column. Removed ml.util.TestingUtils since VectorIndexer was the only use. CC: mengxr Author: Joseph K. Bradley Closes #5789 from jkbradley/vector-indexer-metadata and squashes the following commits: b28e159 [Joseph K. Bradley] Changed VectorIndexer so it does not carry non-ML metadata from the input to the output column. Removed ml.util.TestingUtils since VectorIndexer was the only use. --- .../spark/ml/feature/VectorIndexer.scala | 69 ++++++++++--------- .../spark/ml/feature/VectorIndexerSuite.scala | 7 +- .../apache/spark/ml/util/TestingUtils.scala | 60 ---------------- 3 files changed, 37 insertions(+), 99 deletions(-) delete mode 100644 mllib/src/test/scala/org/apache/spark/ml/util/TestingUtils.scala diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala index 452faa06e2021..1e5ffd15afa51 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala @@ -233,6 +233,7 @@ private object VectorIndexer { * - Continuous features (columns) are left unchanged. * This also appends metadata to the output column, marking features as Numeric (continuous), * Nominal (categorical), or Binary (either continuous or categorical). + * Non-ML metadata is not carried over from the input to the output column. * * This maintains vector sparsity. * @@ -283,34 +284,40 @@ class VectorIndexerModel private[ml] ( // TODO: Check more carefully about whether this whole class will be included in a closure. + /** Per-vector transform function */ private val transformFunc: Vector => Vector = { - val sortedCategoricalFeatureIndices = categoryMaps.keys.toArray.sorted + val sortedCatFeatureIndices = categoryMaps.keys.toArray.sorted val localVectorMap = categoryMaps - val f: Vector => Vector = { - case dv: DenseVector => - val tmpv = dv.copy - localVectorMap.foreach { case (featureIndex: Int, categoryMap: Map[Double, Int]) => - tmpv.values(featureIndex) = categoryMap(tmpv(featureIndex)) - } - tmpv - case sv: SparseVector => - // We use the fact that categorical value 0 is always mapped to index 0. - val tmpv = sv.copy - var catFeatureIdx = 0 // index into sortedCategoricalFeatureIndices - var k = 0 // index into non-zero elements of sparse vector - while (catFeatureIdx < sortedCategoricalFeatureIndices.length && k < tmpv.indices.length) { - val featureIndex = sortedCategoricalFeatureIndices(catFeatureIdx) - if (featureIndex < tmpv.indices(k)) { - catFeatureIdx += 1 - } else if (featureIndex > tmpv.indices(k)) { - k += 1 - } else { - tmpv.values(k) = localVectorMap(featureIndex)(tmpv.values(k)) - catFeatureIdx += 1 - k += 1 + val localNumFeatures = numFeatures + val f: Vector => Vector = { (v: Vector) => + assert(v.size == localNumFeatures, "VectorIndexerModel expected vector of length" + + s" $numFeatures but found length ${v.size}") + v match { + case dv: DenseVector => + val tmpv = dv.copy + localVectorMap.foreach { case (featureIndex: Int, categoryMap: Map[Double, Int]) => + tmpv.values(featureIndex) = categoryMap(tmpv(featureIndex)) } - } - tmpv + tmpv + case sv: SparseVector => + // We use the fact that categorical value 0 is always mapped to index 0. + val tmpv = sv.copy + var catFeatureIdx = 0 // index into sortedCatFeatureIndices + var k = 0 // index into non-zero elements of sparse vector + while (catFeatureIdx < sortedCatFeatureIndices.length && k < tmpv.indices.length) { + val featureIndex = sortedCatFeatureIndices(catFeatureIdx) + if (featureIndex < tmpv.indices(k)) { + catFeatureIdx += 1 + } else if (featureIndex > tmpv.indices(k)) { + k += 1 + } else { + tmpv.values(k) = localVectorMap(featureIndex)(tmpv.values(k)) + catFeatureIdx += 1 + k += 1 + } + } + tmpv + } } f } @@ -326,13 +333,6 @@ class VectorIndexerModel private[ml] ( val map = extractParamMap(paramMap) val newField = prepOutputField(dataset.schema, map) val newCol = callUDF(transformFunc, new VectorUDT, dataset(map(inputCol))) - // For now, just check the first row of inputCol for vector length. - val firstRow = dataset.select(map(inputCol)).take(1) - if (firstRow.length != 0) { - val actualNumFeatures = firstRow(0).getAs[Vector](0).size - require(numFeatures == actualNumFeatures, "VectorIndexerModel expected vector of length" + - s" $numFeatures but found length $actualNumFeatures") - } dataset.withColumn(map(outputCol), newCol.as(map(outputCol), newField.metadata)) } @@ -345,6 +345,7 @@ class VectorIndexerModel private[ml] ( s"VectorIndexerModel requires output column parameter: $outputCol") SchemaUtils.checkColumnType(schema, map(inputCol), dataType) + // If the input metadata specifies numFeatures, compare with expected numFeatures. val origAttrGroup = AttributeGroup.fromStructField(schema(map(inputCol))) val origNumFeatures: Option[Int] = if (origAttrGroup.attributes.nonEmpty) { Some(origAttrGroup.attributes.get.length) @@ -364,7 +365,7 @@ class VectorIndexerModel private[ml] ( * Prepare the output column field, including per-feature metadata. * @param schema Input schema * @param map Parameter map (with this class' embedded parameter map folded in) - * @return Output column field + * @return Output column field. This field does not contain non-ML metadata. */ private def prepOutputField(schema: StructType, map: ParamMap): StructField = { val origAttrGroup = AttributeGroup.fromStructField(schema(map(inputCol))) @@ -391,6 +392,6 @@ class VectorIndexerModel private[ml] ( partialFeatureAttributes } val newAttributeGroup = new AttributeGroup(map(outputCol), featureAttributes) - newAttributeGroup.toStructField(schema(map(inputCol)).metadata) + newAttributeGroup.toStructField() } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala index 1b261b2643854..38dc83b1241cf 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala @@ -23,7 +23,6 @@ import org.scalatest.FunSuite import org.apache.spark.SparkException import org.apache.spark.ml.attribute._ -import org.apache.spark.ml.util.TestingUtils import org.apache.spark.mllib.linalg.{SparseVector, Vector, Vectors} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.rdd.RDD @@ -111,8 +110,8 @@ class VectorIndexerSuite extends FunSuite with MLlibTestSparkContext { val model = vectorIndexer.fit(densePoints1) // vectors of length 3 model.transform(densePoints1) // should work model.transform(sparsePoints1) // should work - intercept[IllegalArgumentException] { - model.transform(densePoints2) + intercept[SparkException] { + model.transform(densePoints2).collect() println("Did not throw error when fit, transform were called on vectors of different lengths") } intercept[SparkException] { @@ -245,8 +244,6 @@ class VectorIndexerSuite extends FunSuite with MLlibTestSparkContext { // TODO: Once input features marked as categorical are handled correctly, check that here. } } - // Check that non-ML metadata are preserved. - TestingUtils.testPreserveMetadata(densePoints1WithMeta, model, "features", "indexed") } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/util/TestingUtils.scala b/mllib/src/test/scala/org/apache/spark/ml/util/TestingUtils.scala deleted file mode 100644 index c44cb61b34171..0000000000000 --- a/mllib/src/test/scala/org/apache/spark/ml/util/TestingUtils.scala +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.ml.util - -import org.apache.spark.ml.Transformer -import org.apache.spark.sql.DataFrame -import org.apache.spark.sql.types.MetadataBuilder -import org.scalatest.FunSuite - -private[ml] object TestingUtils extends FunSuite { - - /** - * Test whether unrelated metadata are preserved for this transformer. - * This attaches extra metadata to a column, transforms the column, and check to ensure the - * extra metadata have not changed. - * @param data Input dataset - * @param transformer Transformer to test - * @param inputCol Unique input column for Transformer. This must be the ONLY input column. - * @param outputCol Output column to test for metadata presence. - */ - def testPreserveMetadata( - data: DataFrame, - transformer: Transformer, - inputCol: String, - outputCol: String): Unit = { - // Create some fake metadata - val origMetadata = data.schema(inputCol).metadata - val metaKey = "__testPreserveMetadata__fake_key" - val metaValue = 12345 - assert(!origMetadata.contains(metaKey), - s"Unit test with testPreserveMetadata will fail since metadata key was present: $metaKey") - val newMetadata = - new MetadataBuilder().withMetadata(origMetadata).putLong(metaKey, metaValue).build() - // Add metadata to the inputCol - val withMetadata = data.select(data(inputCol).as(inputCol, newMetadata)) - // Transform, and ensure extra metadata was not affected - val transformed = transformer.transform(withMetadata) - val transMetadata = transformed.schema(outputCol).metadata - assert(transMetadata.contains(metaKey), - "Unit test with testPreserveMetadata failed; extra metadata key was not present.") - assert(transMetadata.getLong(metaKey) === metaValue, - "Unit test with testPreserveMetadata failed; extra metadata value was wrong." + - s" Expected $metaValue but found ${transMetadata.getLong(metaKey)}") - } -} From 1fdfdb47b44315ff8ccb0ef92e56d3f2a070f1f1 Mon Sep 17 00:00:00 2001 From: wangfei Date: Wed, 29 Apr 2015 17:00:24 -0700 Subject: [PATCH 481/591] [SQL] [Minor] Print detail query execution info when spark answer is not right Print detail query execution info including parsed/analyzed/optimized/Physical plan for query when spak answer is not rignt. ``` Results do not match for query: == Parsed Logical Plan == 'Aggregate ['x.str], ['x.str,SUM('x.strCount) AS c1#46] 'Join Inner, Some(('x.str = 'y.str)) 'UnresolvedRelation [df], Some(x) 'UnresolvedRelation [df], Some(y) == Analyzed Logical Plan == Aggregate [str#44], [str#44,SUM(strCount#45L) AS c1#46L] Join Inner, Some((str#44 = str#51)) Subquery x Subquery df Aggregate [str#44], [str#44,COUNT(str#44) AS strCount#45L] Project [_1#41 AS int#43,_2#42 AS str#44] LocalRelation [_1#41,_2#42], [[1,1],[2,2],[3,3]] Subquery y Subquery df Aggregate [str#51], [str#51,COUNT(str#51) AS strCount#47L] Project [_1#41 AS int#50,_2#42 AS str#51] LocalRelation [_1#41,_2#42], [[1,1],[2,2],[3,3]] == Optimized Logical Plan == Aggregate [str#44], [str#44,SUM(strCount#45L) AS c1#46L] Project [str#44,strCount#45L] Join Inner, Some((str#44 = str#51)) Aggregate [str#44], [str#44,COUNT(str#44) AS strCount#45L] LocalRelation [str#44], [[1],[2],[3]] Aggregate [str#51], [str#51] LocalRelation [str#51], [[1],[2],[3]] == Physical Plan == Aggregate false, [str#44], [str#44,CombineSum(PartialSum#53L) AS c1#46L] Aggregate true, [str#44], [str#44,SUM(strCount#45L) AS PartialSum#53L] Project [str#44,strCount#45L] BroadcastHashJoin [str#44], [str#51], BuildRight Aggregate false, [str#44], [str#44,Coalesce(SUM(PartialCount#55L),0) AS strCount#45L] Exchange (HashPartitioning [str#44], 5), [] Aggregate true, [str#44], [str#44,COUNT(str#44) AS PartialCount#55L] LocalTableScan [str#44], [[1],[2],[3]] Aggregate false, [str#51], [str#51] Exchange (HashPartitioning [str#51], 5), [] Aggregate true, [str#51], [str#51] LocalTableScan [str#51], [[1],[2],[3]] Code Generation: false == RDD == == Results == !== Correct Answer - 3 == == Spark Answer - 3 == [1,1] [1,1] ![2,3] [2,1] [3,1] [3,1] ``` Author: wangfei Closes #5774 from scwf/checkanswer and squashes the following commits: 5be6f78 [wangfei] print detail query execution info when Spark Answer is not right --- .../src/test/scala/org/apache/spark/sql/QueryTest.scala | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala index 59f9508444f25..bbf9ab113ca43 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala @@ -132,11 +132,7 @@ object QueryTest { val errorMessage = s""" |Results do not match for query: - |${df.logicalPlan} - |== Analyzed Plan == - |${df.queryExecution.analyzed} - |== Physical Plan == - |${df.queryExecution.executedPlan} + |${df.queryExecution} |== Results == |${sideBySide( s"== Correct Answer - ${expectedAnswer.size} ==" +: From 114bad606e7a17f980ea6c99e31c8ab0179fec2e Mon Sep 17 00:00:00 2001 From: "Joseph K. Bradley" Date: Wed, 29 Apr 2015 17:26:46 -0700 Subject: [PATCH 482/591] [SPARK-7176] [ML] Add validation functionality to Param Main change: Added isValid field to Param. Modified all usages to use isValid when relevant. Added helper methods in ParamValidate. Also overrode Params.validate() in: * CrossValidator + model * Pipeline + model I made a few updates for the elastic net patch: * I changed "tol" to "convergenceTol" * I added some documentation This PR is Scala + Java only. Python will be in a follow-up PR. CC: mengxr Author: Joseph K. Bradley Closes #5740 from jkbradley/enforce-validate and squashes the following commits: ad9c6c1 [Joseph K. Bradley] re-generated sharedParams after merging with current master 76415e8 [Joseph K. Bradley] reverted convergenceTol to tol af62f4b [Joseph K. Bradley] Removed changes to SparkBuild, python linalg. Fixed test failures. Renamed ParamValidate to ParamValidators. Removed explicit type from ParamValidators calls where possible. bb2665a [Joseph K. Bradley] merged with elastic net pr ecda302 [Joseph K. Bradley] fix rat tests, plus add a little doc 6895dfc [Joseph K. Bradley] small cleanups 069ac6d [Joseph K. Bradley] many cleanups 928fb84 [Joseph K. Bradley] Maybe done a910ac7 [Joseph K. Bradley] still workin 6d60e2e [Joseph K. Bradley] Still workin b987319 [Joseph K. Bradley] Partly done with adding checks, but blocking on adding checking functionality to Param dbc9fb2 [Joseph K. Bradley] merged with master. enforcing Params.validate --- .../examples/ml/JavaDeveloperApiExample.java | 14 +- .../scala/org/apache/spark/ml/Pipeline.scala | 19 +- .../ml/classification/GBTClassifier.scala | 13 +- .../apache/spark/ml/feature/HashingTF.scala | 12 +- .../apache/spark/ml/feature/Normalizer.scala | 11 +- .../ml/feature/PolynomialExpansion.scala | 9 +- .../spark/ml/feature/StandardScaler.scala | 10 +- .../apache/spark/ml/feature/Tokenizer.scala | 20 +- .../spark/ml/feature/VectorIndexer.scala | 18 +- .../spark/ml/impl/tree/treeParams.scala | 115 ++++------- .../org/apache/spark/ml/param/params.scala | 179 ++++++++++++++++-- .../ml/param/shared/SharedParamsCodeGen.scala | 35 ++-- .../spark/ml/param/shared/sharedParams.scala | 122 +++++------- .../apache/spark/ml/recommendation/ALS.scala | 35 ++-- .../spark/ml/regression/GBTRegressor.scala | 13 +- .../ml/regression/LinearRegression.scala | 16 +- .../spark/ml/tuning/CrossValidator.scala | 22 ++- .../mllib/tree/GradientBoostedTrees.scala | 4 +- .../spark/ml/param/JavaParamsSuite.java | 66 +++++++ .../apache/spark/ml/param/JavaTestParams.java | 63 ++++++ .../apache/spark/ml/param/ParamsSuite.scala | 69 ++++++- .../apache/spark/ml/param/TestParams.scala | 2 +- 22 files changed, 593 insertions(+), 274 deletions(-) create mode 100644 mllib/src/test/java/org/apache/spark/ml/param/JavaParamsSuite.java create mode 100644 mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java index eaf00d09f550d..46377a99c4857 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java @@ -28,7 +28,6 @@ import org.apache.spark.ml.classification.ClassificationModel; import org.apache.spark.ml.param.IntParam; import org.apache.spark.ml.param.ParamMap; -import org.apache.spark.ml.param.Params; import org.apache.spark.ml.param.Params$; import org.apache.spark.mllib.linalg.BLAS; import org.apache.spark.mllib.linalg.Vector; @@ -100,11 +99,12 @@ public static void main(String[] args) throws Exception { /** * Example of defining a type of {@link Classifier}. * - * NOTE: This is private since it is an example. In practice, you may not want it to be private. + * Note: Some IDEs (e.g., IntelliJ) will complain that this will not compile due to + * {@link org.apache.spark.ml.param.Params#set} using incompatible return types. + * However, this should still compile and run successfully. */ class MyJavaLogisticRegression - extends Classifier - implements Params { + extends Classifier { /** * Param for max number of iterations @@ -145,10 +145,12 @@ public MyJavaLogisticRegressionModel train(DataFrame dataset, ParamMap paramMap) /** * Example of defining a type of {@link ClassificationModel}. * - * NOTE: This is private since it is an example. In practice, you may not want it to be private. + * Note: Some IDEs (e.g., IntelliJ) will complain that this will not compile due to + * {@link org.apache.spark.ml.param.Params#set} using incompatible return types. + * However, this should still compile and run successfully. */ class MyJavaLogisticRegressionModel - extends ClassificationModel implements Params { + extends ClassificationModel { private MyJavaLogisticRegression parent_; public MyJavaLogisticRegression parent() { return parent_; } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala index 8eddf79cdfe28..6bfeecd764d75 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala @@ -21,7 +21,7 @@ import scala.collection.mutable.ListBuffer import org.apache.spark.Logging import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} -import org.apache.spark.ml.param.{Param, ParamMap} +import org.apache.spark.ml.param.{Params, Param, ParamMap} import org.apache.spark.sql.DataFrame import org.apache.spark.sql.types.StructType @@ -86,6 +86,14 @@ class Pipeline extends Estimator[PipelineModel] { def setStages(value: Array[PipelineStage]): this.type = { set(stages, value); this } def getStages: Array[PipelineStage] = getOrDefault(stages) + override def validate(paramMap: ParamMap): Unit = { + val map = extractParamMap(paramMap) + getStages.foreach { + case pStage: Params => pStage.validate(map) + case _ => + } + } + /** * Fits the pipeline to the input dataset with additional parameters. If a stage is an * [[Estimator]], its [[Estimator#fit]] method will be called on the input dataset to fit a model. @@ -140,7 +148,7 @@ class Pipeline extends Estimator[PipelineModel] { override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { val map = extractParamMap(paramMap) val theStages = map(stages) - require(theStages.toSet.size == theStages.size, + require(theStages.toSet.size == theStages.length, "Cannot have duplicate components in a pipeline.") theStages.foldLeft(schema)((cur, stage) => stage.transformSchema(cur, paramMap)) } @@ -157,6 +165,11 @@ class PipelineModel private[ml] ( private[ml] val stages: Array[Transformer]) extends Model[PipelineModel] with Logging { + override def validate(paramMap: ParamMap): Unit = { + val map = fittingParamMap ++ extractParamMap(paramMap) + stages.foreach(_.validate(map)) + } + /** * Gets the model produced by the input estimator. Throws an NoSuchElementException is the input * estimator does not exist in the pipeline. @@ -168,7 +181,7 @@ class PipelineModel private[ml] ( } if (matched.isEmpty) { throw new NoSuchElementException(s"Cannot find stage $stage from the pipeline.") - } else if (matched.size > 1) { + } else if (matched.length > 1) { throw new IllegalStateException(s"Cannot have duplicate estimators in the sample pipeline.") } else { matched.head.asInstanceOf[M] diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala index d2e052fbbbf22..3d849867d4c47 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala @@ -103,21 +103,16 @@ final class GBTClassifier */ val lossType: Param[String] = new Param[String](this, "lossType", "Loss function which GBT" + " tries to minimize (case-insensitive). Supported options:" + - s" ${GBTClassifier.supportedLossTypes.mkString(", ")}") + s" ${GBTClassifier.supportedLossTypes.mkString(", ")}", + (value: String) => GBTClassifier.supportedLossTypes.contains(value.toLowerCase)) setDefault(lossType -> "logistic") /** @group setParam */ - def setLossType(value: String): this.type = { - val lossStr = value.toLowerCase - require(GBTClassifier.supportedLossTypes.contains(lossStr), "GBTClassifier was given bad loss" + - s" type: $value. Supported options: ${GBTClassifier.supportedLossTypes.mkString(", ")}") - set(lossType, lossStr) - this - } + def setLossType(value: String): this.type = set(lossType, value) /** @group getParam */ - def getLossType: String = getOrDefault(lossType) + def getLossType: String = getOrDefault(lossType).toLowerCase /** (private[ml]) Convert new loss to old loss. */ override private[ml] def getOldLossType: OldLoss = { diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala index b20f2fc49a8f6..0b3128f9ee8cd 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala @@ -19,7 +19,7 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.UnaryTransformer -import org.apache.spark.ml.param.{IntParam, ParamMap} +import org.apache.spark.ml.param.{ParamValidators, IntParam, ParamMap} import org.apache.spark.mllib.feature import org.apache.spark.mllib.linalg.{VectorUDT, Vector} import org.apache.spark.sql.types.DataType @@ -32,10 +32,14 @@ import org.apache.spark.sql.types.DataType class HashingTF extends UnaryTransformer[Iterable[_], Vector, HashingTF] { /** - * number of features + * Number of features. Should be > 0. + * (default = 2^18^) * @group param */ - val numFeatures = new IntParam(this, "numFeatures", "number of features") + val numFeatures = new IntParam(this, "numFeatures", "number of features (> 0)", + ParamValidators.gt(0)) + + setDefault(numFeatures -> (1 << 18)) /** @group getParam */ def getNumFeatures: Int = getOrDefault(numFeatures) @@ -43,8 +47,6 @@ class HashingTF extends UnaryTransformer[Iterable[_], Vector, HashingTF] { /** @group setParam */ def setNumFeatures(value: Int): this.type = set(numFeatures, value) - setDefault(numFeatures -> (1 << 18)) - override protected def createTransformFunc(paramMap: ParamMap): Iterable[_] => Vector = { val hashingTF = new feature.HashingTF(paramMap(numFeatures)) hashingTF.transform diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala index decaeb0da6246..bd2b5f6067e2d 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala @@ -19,7 +19,7 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.UnaryTransformer -import org.apache.spark.ml.param.{DoubleParam, ParamMap} +import org.apache.spark.ml.param.{ParamValidators, DoubleParam, ParamMap} import org.apache.spark.mllib.feature import org.apache.spark.mllib.linalg.{VectorUDT, Vector} import org.apache.spark.sql.types.DataType @@ -32,10 +32,13 @@ import org.apache.spark.sql.types.DataType class Normalizer extends UnaryTransformer[Vector, Vector, Normalizer] { /** - * Normalization in L^p^ space, p = 2 by default. + * Normalization in L^p^ space. Must be >= 1. + * (default: p = 2) * @group param */ - val p = new DoubleParam(this, "p", "the p norm value") + val p = new DoubleParam(this, "p", "the p norm value", ParamValidators.gtEq(1)) + + setDefault(p -> 2.0) /** @group getParam */ def getP: Double = getOrDefault(p) @@ -43,8 +46,6 @@ class Normalizer extends UnaryTransformer[Vector, Vector, Normalizer] { /** @group setParam */ def setP(value: Double): this.type = set(p, value) - setDefault(p -> 2.0) - override protected def createTransformFunc(paramMap: ParamMap): Vector => Vector = { val normalizer = new feature.Normalizer(paramMap(p)) normalizer.transform diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala index d855f04799ae7..1b7c939c2dffe 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala @@ -21,7 +21,7 @@ import scala.collection.mutable import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.UnaryTransformer -import org.apache.spark.ml.param.{IntParam, ParamMap} +import org.apache.spark.ml.param.{ParamValidators, IntParam, ParamMap} import org.apache.spark.mllib.linalg._ import org.apache.spark.sql.types.DataType @@ -37,10 +37,13 @@ import org.apache.spark.sql.types.DataType class PolynomialExpansion extends UnaryTransformer[Vector, Vector, PolynomialExpansion] { /** - * The polynomial degree to expand, which should be larger than 1. + * The polynomial degree to expand, which should be >= 1. A value of 1 means no expansion. + * Default: 2 * @group param */ - val degree = new IntParam(this, "degree", "the polynomial degree to expand") + val degree = new IntParam(this, "degree", "the polynomial degree to expand (>= 1)", + ParamValidators.gt(1)) + setDefault(degree -> 2) /** @group getParam */ diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala index 447851ec034d6..a0e9ed32e0e4c 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala @@ -31,17 +31,19 @@ import org.apache.spark.sql.types.{StructField, StructType} * Params for [[StandardScaler]] and [[StandardScalerModel]]. */ private[feature] trait StandardScalerParams extends Params with HasInputCol with HasOutputCol { - + /** - * False by default. Centers the data with mean before scaling. + * Centers the data with mean before scaling. * It will build a dense output, so this does not work on sparse input * and will raise an exception. + * Default: false * @group param */ val withMean: BooleanParam = new BooleanParam(this, "withMean", "Center data with mean") /** - * True by default. Scales the data to unit standard deviation. + * Scales the data to unit standard deviation. + * Default: true * @group param */ val withStd: BooleanParam = new BooleanParam(this, "withStd", "Scale to unit standard deviation") @@ -56,7 +58,7 @@ private[feature] trait StandardScalerParams extends Params with HasInputCol with class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerParams { setDefault(withMean -> false, withStd -> true) - + /** @group setParam */ def setInputCol(value: String): this.type = set(inputCol, value) diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala index 376a004858b4c..01752ba482d0c 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala @@ -19,7 +19,7 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.UnaryTransformer -import org.apache.spark.ml.param.{ParamMap, IntParam, BooleanParam, Param} +import org.apache.spark.ml.param._ import org.apache.spark.sql.types.{DataType, StringType, ArrayType} /** @@ -43,20 +43,20 @@ class Tokenizer extends UnaryTransformer[String, Seq[String], Tokenizer] { /** * :: AlphaComponent :: * A regex based tokenizer that extracts tokens either by repeatedly matching the regex(default) - * or using it to split the text (set matching to false). Optional parameters also allow to fold - * the text to lowercase prior to it being tokenized and to filer tokens using a minimal length. + * or using it to split the text (set matching to false). Optional parameters also allow filtering + * tokens using a minimal length. * It returns an array of strings that can be empty. - * The default parameters are regex = "\\p{L}+|[^\\p{L}\\s]+", matching = true, - * lowercase = false, minTokenLength = 1 */ @AlphaComponent class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenizer] { /** - * param for minimum token length, default is one to avoid returning empty strings + * Minimum token length, >= 0. + * Default: 1, to avoid returning empty strings * @group param */ - val minTokenLength: IntParam = new IntParam(this, "minLength", "minimum token length") + val minTokenLength: IntParam = new IntParam(this, "minLength", "minimum token length (>= 0)", + ParamValidators.gtEq(0)) /** @group setParam */ def setMinTokenLength(value: Int): this.type = set(minTokenLength, value) @@ -65,7 +65,8 @@ class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenize def getMinTokenLength: Int = getOrDefault(minTokenLength) /** - * param sets regex as splitting on gaps (true) or matching tokens (false) + * Indicates whether regex splits on gaps (true) or matching tokens (false). + * Default: false * @group param */ val gaps: BooleanParam = new BooleanParam(this, "gaps", "Set regex to match gaps or tokens") @@ -77,7 +78,8 @@ class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenize def getGaps: Boolean = getOrDefault(gaps) /** - * param sets regex pattern used by tokenizer + * Regex pattern used by tokenizer. + * Default: `"\\p{L}+|[^\\p{L}\\s]+"` * @group param */ val pattern: Param[String] = new Param(this, "pattern", "regex pattern used for tokenizing") diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala index 1e5ffd15afa51..ed833c63c7ef1 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala @@ -22,7 +22,7 @@ import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.ml.{Estimator, Model} import org.apache.spark.ml.attribute.{BinaryAttribute, NumericAttribute, NominalAttribute, Attribute, AttributeGroup} -import org.apache.spark.ml.param.{IntParam, ParamMap, Params} +import org.apache.spark.ml.param.{ParamValidators, IntParam, ParamMap, Params} import org.apache.spark.ml.param.shared._ import org.apache.spark.mllib.linalg.{SparseVector, DenseVector, Vector, VectorUDT} import org.apache.spark.sql.{Row, DataFrame} @@ -37,17 +37,19 @@ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOu /** * Threshold for the number of values a categorical feature can take. * If a feature is found to have > maxCategories values, then it is declared continuous. + * Must be >= 2. * * (default = 20) */ val maxCategories = new IntParam(this, "maxCategories", - "Threshold for the number of values a categorical feature can take." + - " If a feature is found to have > maxCategories values, then it is declared continuous.") + "Threshold for the number of values a categorical feature can take (>= 2)." + + " If a feature is found to have > maxCategories values, then it is declared continuous.", + ParamValidators.gtEq(2)) + + setDefault(maxCategories -> 20) /** @group getParam */ def getMaxCategories: Int = getOrDefault(maxCategories) - - setDefault(maxCategories -> 20) } /** @@ -90,11 +92,7 @@ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOu class VectorIndexer extends Estimator[VectorIndexerModel] with VectorIndexerParams { /** @group setParam */ - def setMaxCategories(value: Int): this.type = { - require(value > 1, - s"DatasetIndexer given maxCategories = value, but requires maxCategories > 1.") - set(maxCategories, value) - } + def setMaxCategories(value: Int): this.type = set(maxCategories, value) /** @group setParam */ def setInputCol(value: String): this.type = set(inputCol, value) diff --git a/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala b/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala index ab6281b9b2e34..fb770622e71f0 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala @@ -38,14 +38,15 @@ import org.apache.spark.mllib.tree.loss.{Loss => OldLoss} private[ml] trait DecisionTreeParams extends PredictorParams { /** - * Maximum depth of the tree. + * Maximum depth of the tree (>= 0). * E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. * (default = 5) * @group param */ final val maxDepth: IntParam = - new IntParam(this, "maxDepth", "Maximum depth of the tree." + - " E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.") + new IntParam(this, "maxDepth", "Maximum depth of the tree. (>= 0)" + + " E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.", + ParamValidators.gtEq(0)) /** * Maximum number of bins used for discretizing continuous features and for choosing how to split @@ -56,7 +57,7 @@ private[ml] trait DecisionTreeParams extends PredictorParams { */ final val maxBins: IntParam = new IntParam(this, "maxBins", "Max number of bins for" + " discretizing continuous features. Must be >=2 and >= number of categories for any" + - " categorical feature.") + " categorical feature.", ParamValidators.gtEq(2)) /** * Minimum number of instances each child must have after split. @@ -69,7 +70,7 @@ private[ml] trait DecisionTreeParams extends PredictorParams { final val minInstancesPerNode: IntParam = new IntParam(this, "minInstancesPerNode", "Minimum" + " number of instances each child must have after split. If a split causes the left or right" + " child to have fewer than minInstancesPerNode, the split will be discarded as invalid." + - " Should be >= 1.") + " Should be >= 1.", ParamValidators.gtEq(1)) /** * Minimum information gain for a split to be considered at a tree node. @@ -85,7 +86,8 @@ private[ml] trait DecisionTreeParams extends PredictorParams { * @group expertParam */ final val maxMemoryInMB: IntParam = new IntParam(this, "maxMemoryInMB", - "Maximum memory in MB allocated to histogram aggregation.") + "Maximum memory in MB allocated to histogram aggregation.", + ParamValidators.gtEq(0)) /** * If false, the algorithm will pass trees to executors to match instances with nodes. @@ -111,34 +113,26 @@ private[ml] trait DecisionTreeParams extends PredictorParams { final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "Specifies" + " how often to checkpoint the cached node IDs. E.g. 10 means that the cache will get" + " checkpointed every 10 iterations. This is only used if cacheNodeIds is true and if the" + - " checkpoint directory is set in the SparkContext. Must be >= 1.") + " checkpoint directory is set in the SparkContext. Must be >= 1.", + ParamValidators.gtEq(1)) setDefault(maxDepth -> 5, maxBins -> 32, minInstancesPerNode -> 1, minInfoGain -> 0.0, maxMemoryInMB -> 256, cacheNodeIds -> false, checkpointInterval -> 10) /** @group setParam */ - def setMaxDepth(value: Int): this.type = { - require(value >= 0, s"maxDepth parameter must be >= 0. Given bad value: $value") - set(maxDepth, value) - } + def setMaxDepth(value: Int): this.type = set(maxDepth, value) /** @group getParam */ final def getMaxDepth: Int = getOrDefault(maxDepth) /** @group setParam */ - def setMaxBins(value: Int): this.type = { - require(value >= 2, s"maxBins parameter must be >= 2. Given bad value: $value") - set(maxBins, value) - } + def setMaxBins(value: Int): this.type = set(maxBins, value) /** @group getParam */ final def getMaxBins: Int = getOrDefault(maxBins) /** @group setParam */ - def setMinInstancesPerNode(value: Int): this.type = { - require(value >= 1, s"minInstancesPerNode parameter must be >= 1. Given bad value: $value") - set(minInstancesPerNode, value) - } + def setMinInstancesPerNode(value: Int): this.type = set(minInstancesPerNode, value) /** @group getParam */ final def getMinInstancesPerNode: Int = getOrDefault(minInstancesPerNode) @@ -150,10 +144,7 @@ private[ml] trait DecisionTreeParams extends PredictorParams { final def getMinInfoGain: Double = getOrDefault(minInfoGain) /** @group expertSetParam */ - def setMaxMemoryInMB(value: Int): this.type = { - require(value > 0, s"maxMemoryInMB parameter must be > 0. Given bad value: $value") - set(maxMemoryInMB, value) - } + def setMaxMemoryInMB(value: Int): this.type = set(maxMemoryInMB, value) /** @group expertGetParam */ final def getMaxMemoryInMB: Int = getOrDefault(maxMemoryInMB) @@ -165,10 +156,7 @@ private[ml] trait DecisionTreeParams extends PredictorParams { final def getCacheNodeIds: Boolean = getOrDefault(cacheNodeIds) /** @group expertSetParam */ - def setCheckpointInterval(value: Int): this.type = { - require(value >= 1, s"checkpointInterval parameter must be >= 1. Given bad value: $value") - set(checkpointInterval, value) - } + def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value) /** @group expertGetParam */ final def getCheckpointInterval: Int = getOrDefault(checkpointInterval) @@ -209,21 +197,16 @@ private[ml] trait TreeClassifierParams extends Params { */ final val impurity: Param[String] = new Param[String](this, "impurity", "Criterion used for" + " information gain calculation (case-insensitive). Supported options:" + - s" ${TreeClassifierParams.supportedImpurities.mkString(", ")}") + s" ${TreeClassifierParams.supportedImpurities.mkString(", ")}", + (value: String) => TreeClassifierParams.supportedImpurities.contains(value.toLowerCase)) setDefault(impurity -> "gini") /** @group setParam */ - def setImpurity(value: String): this.type = { - val impurityStr = value.toLowerCase - require(TreeClassifierParams.supportedImpurities.contains(impurityStr), - s"Tree-based classifier was given unrecognized impurity: $value." + - s" Supported options: ${TreeClassifierParams.supportedImpurities.mkString(", ")}") - set(impurity, impurityStr) - } + def setImpurity(value: String): this.type = set(impurity, value) /** @group getParam */ - final def getImpurity: String = getOrDefault(impurity) + final def getImpurity: String = getOrDefault(impurity).toLowerCase /** Convert new impurity to old impurity. */ private[ml] def getOldImpurity: OldImpurity = { @@ -256,21 +239,16 @@ private[ml] trait TreeRegressorParams extends Params { */ final val impurity: Param[String] = new Param[String](this, "impurity", "Criterion used for" + " information gain calculation (case-insensitive). Supported options:" + - s" ${TreeRegressorParams.supportedImpurities.mkString(", ")}") + s" ${TreeRegressorParams.supportedImpurities.mkString(", ")}", + (value: String) => TreeRegressorParams.supportedImpurities.contains(value.toLowerCase)) setDefault(impurity -> "variance") /** @group setParam */ - def setImpurity(value: String): this.type = { - val impurityStr = value.toLowerCase - require(TreeRegressorParams.supportedImpurities.contains(impurityStr), - s"Tree-based regressor was given unrecognized impurity: $value." + - s" Supported options: ${TreeRegressorParams.supportedImpurities.mkString(", ")}") - set(impurity, impurityStr) - } + def setImpurity(value: String): this.type = set(impurity, value) /** @group getParam */ - final def getImpurity: String = getOrDefault(impurity) + final def getImpurity: String = getOrDefault(impurity).toLowerCase /** Convert new impurity to old impurity. */ private[ml] def getOldImpurity: OldImpurity = { @@ -299,21 +277,18 @@ private[ml] object TreeRegressorParams { private[ml] trait TreeEnsembleParams extends DecisionTreeParams with HasSeed { /** - * Fraction of the training data used for learning each decision tree. + * Fraction of the training data used for learning each decision tree, in range (0, 1]. * (default = 1.0) * @group param */ final val subsamplingRate: DoubleParam = new DoubleParam(this, "subsamplingRate", - "Fraction of the training data used for learning each decision tree.") + "Fraction of the training data used for learning each decision tree, in range (0, 1].", + ParamValidators.inRange(0, 1, lowerInclusive = false, upperInclusive = true)) setDefault(subsamplingRate -> 1.0) /** @group setParam */ - def setSubsamplingRate(value: Double): this.type = { - require(value > 0.0 && value <= 1.0, - s"Subsampling rate must be in range (0,1]. Bad rate: $value") - set(subsamplingRate, value) - } + def setSubsamplingRate(value: Double): this.type = set(subsamplingRate, value) /** @group getParam */ final def getSubsamplingRate: Double = getOrDefault(subsamplingRate) @@ -350,7 +325,8 @@ private[ml] trait RandomForestParams extends TreeEnsembleParams { * (default = 20) * @group param */ - final val numTrees: IntParam = new IntParam(this, "numTrees", "Number of trees to train (>= 1)") + final val numTrees: IntParam = new IntParam(this, "numTrees", "Number of trees to train (>= 1)", + ParamValidators.gtEq(1)) /** * The number of features to consider for splits at each tree node. @@ -378,30 +354,23 @@ private[ml] trait RandomForestParams extends TreeEnsembleParams { */ final val featureSubsetStrategy: Param[String] = new Param[String](this, "featureSubsetStrategy", "The number of features to consider for splits at each tree node." + - s" Supported options: ${RandomForestParams.supportedFeatureSubsetStrategies.mkString(", ")}") + s" Supported options: ${RandomForestParams.supportedFeatureSubsetStrategies.mkString(", ")}", + (value: String) => + RandomForestParams.supportedFeatureSubsetStrategies.contains(value.toLowerCase)) setDefault(numTrees -> 20, featureSubsetStrategy -> "auto") /** @group setParam */ - def setNumTrees(value: Int): this.type = { - require(value >= 1, s"Random Forest numTrees parameter cannot be $value; it must be >= 1.") - set(numTrees, value) - } + def setNumTrees(value: Int): this.type = set(numTrees, value) /** @group getParam */ final def getNumTrees: Int = getOrDefault(numTrees) /** @group setParam */ - def setFeatureSubsetStrategy(value: String): this.type = { - val strategyStr = value.toLowerCase - require(RandomForestParams.supportedFeatureSubsetStrategies.contains(strategyStr), - s"RandomForestParams was given unrecognized featureSubsetStrategy: $value. Supported" + - s" options: ${RandomForestParams.supportedFeatureSubsetStrategies.mkString(", ")}") - set(featureSubsetStrategy, strategyStr) - } + def setFeatureSubsetStrategy(value: String): this.type = set(featureSubsetStrategy, value) /** @group getParam */ - final def getFeatureSubsetStrategy: String = getOrDefault(featureSubsetStrategy) + final def getFeatureSubsetStrategy: String = getOrDefault(featureSubsetStrategy).toLowerCase } private[ml] object RandomForestParams { @@ -426,7 +395,8 @@ private[ml] trait GBTParams extends TreeEnsembleParams with HasMaxIter { * @group param */ final val stepSize: DoubleParam = new DoubleParam(this, "stepSize", "Step size (a.k.a." + - " learning rate) in interval (0, 1] for shrinking the contribution of each estimator") + " learning rate) in interval (0, 1] for shrinking the contribution of each estimator", + ParamValidators.inRange(0, 1, lowerInclusive = false, upperInclusive = true)) /* TODO: Add this doc when we add this param. SPARK-7132 * Threshold for stopping early when runWithValidation is used. @@ -442,17 +412,10 @@ private[ml] trait GBTParams extends TreeEnsembleParams with HasMaxIter { setDefault(maxIter -> 20, stepSize -> 0.1) /** @group setParam */ - def setMaxIter(value: Int): this.type = { - require(value >= 1, s"Gradient Boosting maxIter parameter cannot be $value; it must be >= 1.") - set(maxIter, value) - } + def setMaxIter(value: Int): this.type = set(maxIter, value) /** @group setParam */ - def setStepSize(value: Double): this.type = { - require(value > 0.0 && value <= 1.0, - s"GBT given invalid step size ($value). Value should be in (0,1].") - set(stepSize, value) - } + def setStepSize(value: Double): this.type = set(stepSize, value) /** @group getParam */ final def getStepSize: Double = getOrDefault(stepSize) diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala index 014e124e440a3..df6360dce6013 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala @@ -34,10 +34,35 @@ import org.apache.spark.ml.util.Identifiable * @param parent parent object * @param name param name * @param doc documentation + * @param isValid optional validation method which indicates if a value is valid. + * See [[ParamValidators]] for factory methods for common validation functions. * @tparam T param value type */ @AlphaComponent -class Param[T] (val parent: Params, val name: String, val doc: String) extends Serializable { +class Param[T] (val parent: Params, val name: String, val doc: String, val isValid: T => Boolean) + extends Serializable { + + def this(parent: Params, name: String, doc: String) = + this(parent, name, doc, ParamValidators.alwaysTrue[T]) + + /** + * Assert that the given value is valid for this parameter. + * + * Note: Parameter checks involving interactions between multiple parameters should be + * implemented in [[Params.validate()]]. Checks for input/output columns should be + * implemented in [[org.apache.spark.ml.PipelineStage.transformSchema()]]. + * + * DEVELOPERS: This method is only called by [[ParamPair]], which means that all parameters + * should be specified via [[ParamPair]]. + * + * @throws IllegalArgumentException if the value is invalid + */ + private[param] def validate(value: T): Unit = { + if (!isValid(value)) { + throw new IllegalArgumentException(s"$parent parameter $name given invalid value $value." + + s" Parameter description: $toString") + } + } /** * Creates a param pair with the given value (for Java). @@ -65,38 +90,129 @@ class Param[T] (val parent: Params, val name: String, val doc: String) extends S } } +/** + * Factory methods for common validation functions for [[Param.isValid]]. + * The numerical methods only support Int, Long, Float, and Double. + */ +object ParamValidators { + + /** (private[param]) Default validation always return true */ + private[param] def alwaysTrue[T]: T => Boolean = (_: T) => true + + /** + * Private method for checking numerical types and converting to Double. + * This is mainly for the sake of compilation; type checks are really handled + * by [[Params]] setters and the [[ParamPair]] constructor. + */ + private def getDouble[T](value: T): Double = value match { + case x: Int => x.toDouble + case x: Long => x.toDouble + case x: Float => x.toDouble + case x: Double => x.toDouble + case _ => + // The type should be checked before this is ever called. + throw new IllegalArgumentException("Numerical Param validation failed because" + + s" of unexpected input type: ${value.getClass}") + } + + /** Check if value > lowerBound */ + def gt[T](lowerBound: Double): T => Boolean = { (value: T) => + getDouble(value) > lowerBound + } + + /** Check if value >= lowerBound */ + def gtEq[T](lowerBound: Double): T => Boolean = { (value: T) => + getDouble(value) >= lowerBound + } + + /** Check if value < upperBound */ + def lt[T](upperBound: Double): T => Boolean = { (value: T) => + getDouble(value) < upperBound + } + + /** Check if value <= upperBound */ + def ltEq[T](upperBound: Double): T => Boolean = { (value: T) => + getDouble(value) <= upperBound + } + + /** + * Check for value in range lowerBound to upperBound. + * @param lowerInclusive If true, check for value >= lowerBound. + * If false, check for value > lowerBound. + * @param upperInclusive If true, check for value <= upperBound. + * If false, check for value < upperBound. + */ + def inRange[T]( + lowerBound: Double, + upperBound: Double, + lowerInclusive: Boolean, + upperInclusive: Boolean): T => Boolean = { (value: T) => + val x: Double = getDouble(value) + val lowerValid = if (lowerInclusive) x >= lowerBound else x > lowerBound + val upperValid = if (upperInclusive) x <= upperBound else x < upperBound + lowerValid && upperValid + } + + /** Version of [[inRange()]] which uses inclusive be default: [lowerBound, upperBound] */ + def inRange[T](lowerBound: Double, upperBound: Double): T => Boolean = { + inRange[T](lowerBound, upperBound, lowerInclusive = true, upperInclusive = true) + } + + /** Check for value in an allowed set of values. */ + def inArray[T](allowed: Array[T]): T => Boolean = { (value: T) => + allowed.contains(value) + } + + /** Check for value in an allowed set of values. */ + def inArray[T](allowed: java.util.List[T]): T => Boolean = { (value: T) => + allowed.contains(value) + } +} + // specialize primitive-typed params because Java doesn't recognize scala.Double, scala.Int, ... /** Specialized version of [[Param[Double]]] for Java. */ -class DoubleParam(parent: Params, name: String, doc: String) - extends Param[Double](parent, name, doc) { +class DoubleParam(parent: Params, name: String, doc: String, isValid: Double => Boolean) + extends Param[Double](parent, name, doc, isValid) { + + def this(parent: Params, name: String, doc: String) = + this(parent, name, doc, ParamValidators.alwaysTrue) override def w(value: Double): ParamPair[Double] = super.w(value) } /** Specialized version of [[Param[Int]]] for Java. */ -class IntParam(parent: Params, name: String, doc: String) - extends Param[Int](parent, name, doc) { +class IntParam(parent: Params, name: String, doc: String, isValid: Int => Boolean) + extends Param[Int](parent, name, doc, isValid) { + + def this(parent: Params, name: String, doc: String) = + this(parent, name, doc, ParamValidators.alwaysTrue) override def w(value: Int): ParamPair[Int] = super.w(value) } /** Specialized version of [[Param[Float]]] for Java. */ -class FloatParam(parent: Params, name: String, doc: String) - extends Param[Float](parent, name, doc) { +class FloatParam(parent: Params, name: String, doc: String, isValid: Float => Boolean) + extends Param[Float](parent, name, doc, isValid) { + + def this(parent: Params, name: String, doc: String) = + this(parent, name, doc, ParamValidators.alwaysTrue) override def w(value: Float): ParamPair[Float] = super.w(value) } /** Specialized version of [[Param[Long]]] for Java. */ -class LongParam(parent: Params, name: String, doc: String) - extends Param[Long](parent, name, doc) { +class LongParam(parent: Params, name: String, doc: String, isValid: Long => Boolean) + extends Param[Long](parent, name, doc, isValid) { + + def this(parent: Params, name: String, doc: String) = + this(parent, name, doc, ParamValidators.alwaysTrue) override def w(value: Long): ParamPair[Long] = super.w(value) } /** Specialized version of [[Param[Boolean]]] for Java. */ -class BooleanParam(parent: Params, name: String, doc: String) +class BooleanParam(parent: Params, name: String, doc: String) // No need for isValid extends Param[Boolean](parent, name, doc) { override def w(value: Boolean): ParamPair[Boolean] = super.w(value) @@ -105,7 +221,11 @@ class BooleanParam(parent: Params, name: String, doc: String) /** * A param amd its value. */ -case class ParamPair[T](param: Param[T], value: T) +case class ParamPair[T](param: Param[T], value: T) { + // This is *the* place Param.validate is called. Whenever a parameter is specified, we should + // always construct a ParamPair so that validate is called. + param.validate(value) +} /** * :: AlphaComponent :: @@ -132,12 +252,22 @@ trait Params extends Identifiable with Serializable { /** * Validates parameter values stored internally plus the input parameter map. * Raises an exception if any parameter is invalid. + * + * This only needs to check for interactions between parameters. + * Parameter value checks which do not depend on other parameters are handled by + * [[Param.validate()]]. This method does not handle input/output column parameters; + * those are checked during schema validation. */ - def validate(paramMap: ParamMap): Unit = {} + def validate(paramMap: ParamMap): Unit = { } /** * Validates parameter values stored internally. * Raise an exception if any parameter value is invalid. + * + * This only needs to check for interactions between parameters. + * Parameter value checks which do not depend on other parameters are handled by + * [[Param.validate()]]. This method does not handle input/output column parameters; + * those are checked during schema validation. */ def validate(): Unit = validate(ParamMap.empty) @@ -221,6 +351,10 @@ trait Params extends Identifiable with Serializable { /** * Sets default values for a list of params. + * + * Note: Java developers should use the single-parameter [[setDefault()]]. + * Annotating this with varargs causes compilation failures. + * * @param paramPairs a list of param pairs that specify params and their default values to set * respectively. Make sure that the params are initialized before this method * gets called. @@ -305,6 +439,14 @@ private[spark] object Params { } } +/** + * Java-friendly wrapper for [[Params]]. + * Java developers who need to extend [[Params]] should use this class instead. + * If you need to extend a abstract class which already extends [[Params]], then that abstract + * class should be Java-friendly as well. + */ +abstract class JavaParams extends Params + /** * :: AlphaComponent :: * A param to value map. @@ -313,6 +455,12 @@ private[spark] object Params { final class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) extends Serializable { + /* DEVELOPERS: About validating parameter values + * This and ParamPair are the only two collections of parameters. + * This class should always create ParamPairs when + * specifying new parameter values. ParamPair will then call Param.validate(). + */ + /** * Creates an empty param map. */ @@ -321,10 +469,7 @@ final class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) /** * Puts a (param, value) pair (overwrites if the input param exists). */ - def put[T](param: Param[T], value: T): this.type = { - map(param.asInstanceOf[Param[Any]]) = value - this - } + def put[T](param: Param[T], value: T): this.type = put(ParamPair(param, value)) /** * Puts a list of param pairs (overwrites if the input params exists). @@ -332,7 +477,7 @@ final class ParamMap private[ml] (private val map: mutable.Map[Param[Any], Any]) @varargs def put(paramPairs: ParamPair[_]*): this.type = { paramPairs.foreach { p => - put(p.param.asInstanceOf[Param[Any]], p.value) + map(p.param.asInstanceOf[Param[Any]]) = p.value } this } diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala index 654cd72d53074..7da4bb4b4bf25 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala @@ -21,6 +21,8 @@ import java.io.PrintWriter import scala.reflect.ClassTag +import org.apache.spark.ml.param.ParamValidators + /** * Code generator for shared params (sharedParams.scala). Run under the Spark folder with * {{{ @@ -31,8 +33,10 @@ private[shared] object SharedParamsCodeGen { def main(args: Array[String]): Unit = { val params = Seq( - ParamDesc[Double]("regParam", "regularization parameter"), - ParamDesc[Int]("maxIter", "max number of iterations"), + ParamDesc[Double]("regParam", "regularization parameter (>= 0)", + isValid = "ParamValidators.gtEq(0)"), + ParamDesc[Int]("maxIter", "max number of iterations (>= 0)", + isValid = "ParamValidators.gtEq(0)"), ParamDesc[String]("featuresCol", "features column name", Some("\"features\"")), ParamDesc[String]("labelCol", "label column name", Some("\"label\"")), ParamDesc[String]("predictionCol", "prediction column name", Some("\"prediction\"")), @@ -40,14 +44,19 @@ private[shared] object SharedParamsCodeGen { Some("\"rawPrediction\"")), ParamDesc[String]("probabilityCol", "column name for predicted class conditional probabilities", Some("\"probability\"")), - ParamDesc[Double]("threshold", "threshold in binary classification prediction"), + ParamDesc[Double]("threshold", + "threshold in binary classification prediction, in range [0, 1]", + isValid = "ParamValidators.inRange(0, 1)"), ParamDesc[String]("inputCol", "input column name"), ParamDesc[Array[String]]("inputCols", "input column names"), ParamDesc[String]("outputCol", "output column name"), - ParamDesc[Int]("checkpointInterval", "checkpoint interval"), + ParamDesc[Int]("checkpointInterval", "checkpoint interval (>= 1)", + isValid = "ParamValidators.gtEq(1)"), ParamDesc[Boolean]("fitIntercept", "whether to fit an intercept term", Some("true")), ParamDesc[Long]("seed", "random seed", Some("Utils.random.nextLong()")), - ParamDesc[Double]("elasticNetParam", "the ElasticNet mixing parameter"), + ParamDesc[Double]("elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]." + + " For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.", + isValid = "ParamValidators.inRange(0, 1)"), ParamDesc[Double]("tol", "the convergence tolerance for iterative algorithms"), ParamDesc[Double]("stepSize", "Step size to be used for each iteration of optimization.")) @@ -62,7 +71,8 @@ private[shared] object SharedParamsCodeGen { private case class ParamDesc[T: ClassTag]( name: String, doc: String, - defaultValueStr: Option[String] = None) { + defaultValueStr: Option[String] = None, + isValid: String = "") { require(name.matches("[a-z][a-zA-Z0-9]*"), s"Param name $name is invalid.") require(doc.nonEmpty) // TODO: more rigorous on doc @@ -113,20 +123,23 @@ private[shared] object SharedParamsCodeGen { | setDefault($name, $v) |""".stripMargin }.getOrElse("") + val isValid = if (param.isValid != "") { + ", " + param.isValid + } else { + "" + } s""" |/** - | * :: DeveloperApi :: - | * Trait for shared param $name$defaultValueDoc. + | * (private[ml]) Trait for shared param $name$defaultValueDoc. | */ - |@DeveloperApi - |trait Has$Name extends Params { + |private[ml] trait Has$Name extends Params { | | /** | * Param for $doc. | * @group param | */ - | final val $name: $Param = new $Param(this, "$name", "$doc") + | final val $name: $Param = new $Param(this, "$name", "$doc"$isValid) |$setDefault | /** @group getParam */ | final def get$Name: $T = getOrDefault($name) diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala index 96d11ed76fa8e..e1549f46a68d4 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala @@ -26,45 +26,39 @@ import org.apache.spark.util.Utils // scalastyle:off /** - * :: DeveloperApi :: - * Trait for shared param regParam. + * (private[ml]) Trait for shared param regParam. */ -@DeveloperApi -trait HasRegParam extends Params { +private[ml] trait HasRegParam extends Params { /** - * Param for regularization parameter. + * Param for regularization parameter (>= 0). * @group param */ - final val regParam: DoubleParam = new DoubleParam(this, "regParam", "regularization parameter") + final val regParam: DoubleParam = new DoubleParam(this, "regParam", "regularization parameter (>= 0)", ParamValidators.gtEq(0)) /** @group getParam */ final def getRegParam: Double = getOrDefault(regParam) } /** - * :: DeveloperApi :: - * Trait for shared param maxIter. + * (private[ml]) Trait for shared param maxIter. */ -@DeveloperApi -trait HasMaxIter extends Params { +private[ml] trait HasMaxIter extends Params { /** - * Param for max number of iterations. + * Param for max number of iterations (>= 0). * @group param */ - final val maxIter: IntParam = new IntParam(this, "maxIter", "max number of iterations") + final val maxIter: IntParam = new IntParam(this, "maxIter", "max number of iterations (>= 0)", ParamValidators.gtEq(0)) /** @group getParam */ final def getMaxIter: Int = getOrDefault(maxIter) } /** - * :: DeveloperApi :: - * Trait for shared param featuresCol (default: "features"). + * (private[ml]) Trait for shared param featuresCol (default: "features"). */ -@DeveloperApi -trait HasFeaturesCol extends Params { +private[ml] trait HasFeaturesCol extends Params { /** * Param for features column name. @@ -79,11 +73,9 @@ trait HasFeaturesCol extends Params { } /** - * :: DeveloperApi :: - * Trait for shared param labelCol (default: "label"). + * (private[ml]) Trait for shared param labelCol (default: "label"). */ -@DeveloperApi -trait HasLabelCol extends Params { +private[ml] trait HasLabelCol extends Params { /** * Param for label column name. @@ -98,11 +90,9 @@ trait HasLabelCol extends Params { } /** - * :: DeveloperApi :: - * Trait for shared param predictionCol (default: "prediction"). + * (private[ml]) Trait for shared param predictionCol (default: "prediction"). */ -@DeveloperApi -trait HasPredictionCol extends Params { +private[ml] trait HasPredictionCol extends Params { /** * Param for prediction column name. @@ -117,11 +107,9 @@ trait HasPredictionCol extends Params { } /** - * :: DeveloperApi :: - * Trait for shared param rawPredictionCol (default: "rawPrediction"). + * (private[ml]) Trait for shared param rawPredictionCol (default: "rawPrediction"). */ -@DeveloperApi -trait HasRawPredictionCol extends Params { +private[ml] trait HasRawPredictionCol extends Params { /** * Param for raw prediction (a.k.a. confidence) column name. @@ -136,11 +124,9 @@ trait HasRawPredictionCol extends Params { } /** - * :: DeveloperApi :: - * Trait for shared param probabilityCol (default: "probability"). + * (private[ml]) Trait for shared param probabilityCol (default: "probability"). */ -@DeveloperApi -trait HasProbabilityCol extends Params { +private[ml] trait HasProbabilityCol extends Params { /** * Param for column name for predicted class conditional probabilities. @@ -155,28 +141,24 @@ trait HasProbabilityCol extends Params { } /** - * :: DeveloperApi :: - * Trait for shared param threshold. + * (private[ml]) Trait for shared param threshold. */ -@DeveloperApi -trait HasThreshold extends Params { +private[ml] trait HasThreshold extends Params { /** - * Param for threshold in binary classification prediction. + * Param for threshold in binary classification prediction, in range [0, 1]. * @group param */ - final val threshold: DoubleParam = new DoubleParam(this, "threshold", "threshold in binary classification prediction") + final val threshold: DoubleParam = new DoubleParam(this, "threshold", "threshold in binary classification prediction, in range [0, 1]", ParamValidators.inRange(0, 1)) /** @group getParam */ final def getThreshold: Double = getOrDefault(threshold) } /** - * :: DeveloperApi :: - * Trait for shared param inputCol. + * (private[ml]) Trait for shared param inputCol. */ -@DeveloperApi -trait HasInputCol extends Params { +private[ml] trait HasInputCol extends Params { /** * Param for input column name. @@ -189,11 +171,9 @@ trait HasInputCol extends Params { } /** - * :: DeveloperApi :: - * Trait for shared param inputCols. + * (private[ml]) Trait for shared param inputCols. */ -@DeveloperApi -trait HasInputCols extends Params { +private[ml] trait HasInputCols extends Params { /** * Param for input column names. @@ -206,11 +186,9 @@ trait HasInputCols extends Params { } /** - * :: DeveloperApi :: - * Trait for shared param outputCol. + * (private[ml]) Trait for shared param outputCol. */ -@DeveloperApi -trait HasOutputCol extends Params { +private[ml] trait HasOutputCol extends Params { /** * Param for output column name. @@ -223,28 +201,24 @@ trait HasOutputCol extends Params { } /** - * :: DeveloperApi :: - * Trait for shared param checkpointInterval. + * (private[ml]) Trait for shared param checkpointInterval. */ -@DeveloperApi -trait HasCheckpointInterval extends Params { +private[ml] trait HasCheckpointInterval extends Params { /** - * Param for checkpoint interval. + * Param for checkpoint interval (>= 1). * @group param */ - final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "checkpoint interval") + final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "checkpoint interval (>= 1)", ParamValidators.gtEq(1)) /** @group getParam */ final def getCheckpointInterval: Int = getOrDefault(checkpointInterval) } /** - * :: DeveloperApi :: - * Trait for shared param fitIntercept (default: true). + * (private[ml]) Trait for shared param fitIntercept (default: true). */ -@DeveloperApi -trait HasFitIntercept extends Params { +private[ml] trait HasFitIntercept extends Params { /** * Param for whether to fit an intercept term. @@ -259,11 +233,9 @@ trait HasFitIntercept extends Params { } /** - * :: DeveloperApi :: - * Trait for shared param seed (default: Utils.random.nextLong()). + * (private[ml]) Trait for shared param seed (default: Utils.random.nextLong()). */ -@DeveloperApi -trait HasSeed extends Params { +private[ml] trait HasSeed extends Params { /** * Param for random seed. @@ -278,28 +250,24 @@ trait HasSeed extends Params { } /** - * :: DeveloperApi :: - * Trait for shared param elasticNetParam. + * (private[ml]) Trait for shared param elasticNetParam. */ -@DeveloperApi -trait HasElasticNetParam extends Params { +private[ml] trait HasElasticNetParam extends Params { /** - * Param for the ElasticNet mixing parameter. + * Param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.. * @group param */ - final val elasticNetParam: DoubleParam = new DoubleParam(this, "elasticNetParam", "the ElasticNet mixing parameter") + final val elasticNetParam: DoubleParam = new DoubleParam(this, "elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.", ParamValidators.inRange(0, 1)) /** @group getParam */ final def getElasticNetParam: Double = getOrDefault(elasticNetParam) } /** - * :: DeveloperApi :: - * Trait for shared param tol. + * (private[ml]) Trait for shared param tol. */ -@DeveloperApi -trait HasTol extends Params { +private[ml] trait HasTol extends Params { /** * Param for the convergence tolerance for iterative algorithms. @@ -312,11 +280,9 @@ trait HasTol extends Params { } /** - * :: DeveloperApi :: - * Trait for shared param stepSize. + * (private[ml]) Trait for shared param stepSize. */ -@DeveloperApi -trait HasStepSize extends Params { +private[ml] trait HasStepSize extends Params { /** * Param for Step size to be used for each iteration of optimization.. diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala index bd793beba35b6..f9f2b2764ddb1 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala @@ -52,35 +52,40 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR with HasPredictionCol with HasCheckpointInterval { /** - * Param for rank of the matrix factorization. + * Param for rank of the matrix factorization (>= 1). + * Default: 10 * @group param */ - val rank = new IntParam(this, "rank", "rank of the factorization") + val rank = new IntParam(this, "rank", "rank of the factorization", ParamValidators.gtEq(1)) /** @group getParam */ def getRank: Int = getOrDefault(rank) /** - * Param for number of user blocks. + * Param for number of user blocks (>= 1). + * Default: 10 * @group param */ - val numUserBlocks = new IntParam(this, "numUserBlocks", "number of user blocks") + val numUserBlocks = new IntParam(this, "numUserBlocks", "number of user blocks", + ParamValidators.gtEq(1)) /** @group getParam */ def getNumUserBlocks: Int = getOrDefault(numUserBlocks) /** - * Param for number of item blocks. + * Param for number of item blocks (>= 1). + * Default: 10 * @group param */ - val numItemBlocks = - new IntParam(this, "numItemBlocks", "number of item blocks") + val numItemBlocks = new IntParam(this, "numItemBlocks", "number of item blocks", + ParamValidators.gtEq(1)) /** @group getParam */ def getNumItemBlocks: Int = getOrDefault(numItemBlocks) /** * Param to decide whether to use implicit preference. + * Default: false * @group param */ val implicitPrefs = new BooleanParam(this, "implicitPrefs", "whether to use implicit preference") @@ -89,16 +94,19 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR def getImplicitPrefs: Boolean = getOrDefault(implicitPrefs) /** - * Param for the alpha parameter in the implicit preference formulation. + * Param for the alpha parameter in the implicit preference formulation (>= 0). + * Default: 1.0 * @group param */ - val alpha = new DoubleParam(this, "alpha", "alpha for implicit preference") + val alpha = new DoubleParam(this, "alpha", "alpha for implicit preference", + ParamValidators.gtEq(0)) /** @group getParam */ def getAlpha: Double = getOrDefault(alpha) /** * Param for the column name for user ids. + * Default: "user" * @group param */ val userCol = new Param[String](this, "userCol", "column name for user ids") @@ -108,6 +116,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR /** * Param for the column name for item ids. + * Default: "item" * @group param */ val itemCol = new Param[String](this, "itemCol", "column name for item ids") @@ -117,6 +126,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR /** * Param for the column name for ratings. + * Default: "rating" * @group param */ val ratingCol = new Param[String](this, "ratingCol", "column name for ratings") @@ -126,6 +136,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR /** * Param for whether to apply nonnegativity constraints. + * Default: false * @group param */ val nonnegative = new BooleanParam( @@ -136,7 +147,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR setDefault(rank -> 10, maxIter -> 10, regParam -> 0.1, numUserBlocks -> 10, numItemBlocks -> 10, implicitPrefs -> false, alpha -> 1.0, userCol -> "user", itemCol -> "item", - ratingCol -> "rating", nonnegative -> false) + ratingCol -> "rating", nonnegative -> false, checkpointInterval -> 10) /** * Validates and transforms the input schema. @@ -281,10 +292,6 @@ class ALS extends Estimator[ALSModel] with ALSParams { this } - setMaxIter(20) - setRegParam(1.0) - setCheckpointInterval(10) - override def fit(dataset: DataFrame, paramMap: ParamMap): ALSModel = { val map = extractParamMap(paramMap) val ratings = dataset diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala index c784cf39ed31a..76c98376930c5 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala @@ -102,21 +102,16 @@ final class GBTRegressor */ val lossType: Param[String] = new Param[String](this, "lossType", "Loss function which GBT" + " tries to minimize (case-insensitive). Supported options:" + - s" ${GBTRegressor.supportedLossTypes.mkString(", ")}") + s" ${GBTRegressor.supportedLossTypes.mkString(", ")}", + (value: String) => GBTRegressor.supportedLossTypes.contains(value.toLowerCase)) setDefault(lossType -> "squared") /** @group setParam */ - def setLossType(value: String): this.type = { - val lossStr = value.toLowerCase - require(GBTRegressor.supportedLossTypes.contains(lossStr), "GBTRegressor was given bad loss" + - s" type: $value. Supported options: ${GBTRegressor.supportedLossTypes.mkString(", ")}") - set(lossType, lossStr) - this - } + def setLossType(value: String): this.type = set(lossType, value) /** @group getParam */ - def getLossType: String = getOrDefault(lossType) + def getLossType: String = getOrDefault(lossType).toLowerCase /** (private[ml]) Convert new loss to old loss. */ override private[ml] def getOldLossType: OldLoss = { diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala index cc9ad22cb860f..11c6cea0f8c8c 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala @@ -25,7 +25,8 @@ import breeze.optimize.{CachedDiffFunction, DiffFunction} import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.param.{Params, ParamMap} -import org.apache.spark.ml.param.shared.{HasElasticNetParam, HasMaxIter, HasRegParam, HasTol} +import org.apache.spark.ml.param.shared.{HasTol, HasElasticNetParam, HasMaxIter, + HasRegParam} import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.mllib.linalg.BLAS._ @@ -46,6 +47,16 @@ private[regression] trait LinearRegressionParams extends RegressorParams * :: AlphaComponent :: * * Linear regression. + * + * The learning objective is to minimize the squared error, with regularization. + * The specific squared error loss function used is: + * L = 1/2n ||A weights - y||^2^ + * + * This support multiple types of regularization: + * - none (a.k.a. ordinary least squares) + * - L2 (ridge regression) + * - L1 (Lasso) + * - L2 + L1 (elastic net) */ @AlphaComponent class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegressionModel] @@ -135,7 +146,8 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress val optimizer = if (paramMap(elasticNetParam) == 0.0 || effectiveRegParam == 0.0) { new BreezeLBFGS[BDV[Double]](paramMap(maxIter), 10, paramMap(tol)) } else { - new BreezeOWLQN[Int, BDV[Double]](paramMap(maxIter), 10, effectiveL1RegParam, paramMap(tol)) + new BreezeOWLQN[Int, BDV[Double]](paramMap(maxIter), 10, effectiveL1RegParam, + paramMap(tol)) } val initialWeights = Vectors.zeros(numFeatures) diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala index 4bb4ed813c006..d1ad0893cd044 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala @@ -22,7 +22,7 @@ import com.github.fommil.netlib.F2jBLAS import org.apache.spark.Logging import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml._ -import org.apache.spark.ml.param.{IntParam, Param, ParamMap, Params} +import org.apache.spark.ml.param._ import org.apache.spark.mllib.util.MLUtils import org.apache.spark.sql.DataFrame import org.apache.spark.sql.types.StructType @@ -61,10 +61,12 @@ private[ml] trait CrossValidatorParams extends Params { def getEvaluator: Evaluator = getOrDefault(evaluator) /** - * param for number of folds for cross validation + * Param for number of folds for cross validation. Must be >= 2. + * Default: 3 * @group param */ - val numFolds: IntParam = new IntParam(this, "numFolds", "number of folds for cross validation") + val numFolds: IntParam = new IntParam(this, "numFolds", + "number of folds for cross validation (>= 2)", ParamValidators.gtEq(2)) /** @group getParam */ def getNumFolds: Int = getOrDefault(numFolds) @@ -93,6 +95,12 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP /** @group setParam */ def setNumFolds(value: Int): this.type = set(numFolds, value) + override def validate(paramMap: ParamMap): Unit = { + getEstimatorParamMaps.foreach { eMap => + getEstimator.validate(eMap ++ paramMap) + } + } + override def fit(dataset: DataFrame, paramMap: ParamMap): CrossValidatorModel = { val map = extractParamMap(paramMap) val schema = dataset.schema @@ -101,8 +109,8 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP val est = map(estimator) val eval = map(evaluator) val epm = map(estimatorParamMaps) - val numModels = epm.size - val metrics = new Array[Double](epm.size) + val numModels = epm.length + val metrics = new Array[Double](epm.length) val splits = MLUtils.kFold(dataset.rdd, map(numFolds), 0) splits.zipWithIndex.foreach { case ((training, validation), splitIndex) => val trainingDataset = sqlCtx.createDataFrame(training, schema).cache() @@ -148,6 +156,10 @@ class CrossValidatorModel private[ml] ( val bestModel: Model[_]) extends Model[CrossValidatorModel] with CrossValidatorParams { + override def validate(paramMap: ParamMap): Unit = { + bestModel.validate(paramMap) + } + override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { bestModel.transform(dataset, paramMap) } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala index deac390130128..1f779584dcffd 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala @@ -180,7 +180,9 @@ object GradientBoostedTrees extends Logging { val persistedInput = if (input.getStorageLevel == StorageLevel.NONE) { input.persist(StorageLevel.MEMORY_AND_DISK) true - } else false + } else { + false + } timer.stop("init") diff --git a/mllib/src/test/java/org/apache/spark/ml/param/JavaParamsSuite.java b/mllib/src/test/java/org/apache/spark/ml/param/JavaParamsSuite.java new file mode 100644 index 0000000000000..e7df10dfa63ac --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/param/JavaParamsSuite.java @@ -0,0 +1,66 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.param; + +import com.google.common.collect.Lists; +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaSparkContext; + +/** + * Test Param and related classes in Java + */ +public class JavaParamsSuite { + + private transient JavaSparkContext jsc; + + @Before + public void setUp() { + jsc = new JavaSparkContext("local", "JavaParamsSuite"); + } + + @After + public void tearDown() { + jsc.stop(); + jsc = null; + } + + @Test + public void testParams() { + JavaTestParams testParams = new JavaTestParams(); + Assert.assertEquals(testParams.getMyIntParam(), 1); + testParams.setMyIntParam(2).setMyDoubleParam(0.4).setMyStringParam("a"); + Assert.assertEquals(testParams.getMyDoubleParam(), 0.4, 0.0); + Assert.assertEquals(testParams.getMyStringParam(), "a"); + } + + @Test + public void testParamValidate() { + ParamValidators.gt(1.0); + ParamValidators.gtEq(1.0); + ParamValidators.lt(1.0); + ParamValidators.ltEq(1.0); + ParamValidators.inRange(0, 1, true, false); + ParamValidators.inRange(0, 1); + ParamValidators.inArray(Lists.newArrayList(0, 1, 3)); + ParamValidators.inArray(Lists.newArrayList("a", "b")); + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java b/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java new file mode 100644 index 0000000000000..8abe575610d19 --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java @@ -0,0 +1,63 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.param; + +import java.util.List; + +import com.google.common.collect.Lists; + +/** + * A subclass of Params for testing. + */ +public class JavaTestParams extends JavaParams { + + public IntParam myIntParam; + + public int getMyIntParam() { return (Integer)getOrDefault(myIntParam); } + + public JavaTestParams setMyIntParam(int value) { + set(myIntParam, value); return this; + } + + public DoubleParam myDoubleParam; + + public double getMyDoubleParam() { return (Double)getOrDefault(myDoubleParam); } + + public JavaTestParams setMyDoubleParam(double value) { + set(myDoubleParam, value); return this; + } + + public Param myStringParam; + + public String getMyStringParam() { return (String)getOrDefault(myStringParam); } + + public JavaTestParams setMyStringParam(String value) { + set(myStringParam, value); return this; + } + + public JavaTestParams() { + myIntParam = new IntParam(this, "myIntParam", "this is an int param", ParamValidators.gt(0)); + myDoubleParam = new DoubleParam(this, "myDoubleParam", "this is a double param", + ParamValidators.inRange(0.0, 1.0)); + List validStrings = Lists.newArrayList("a", "b"); + myStringParam = new Param(this, "myStringParam", "this is a string param", + ParamValidators.inArray(validStrings)); + setDefault(myIntParam, 1); + setDefault(myDoubleParam, 0.5); + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala index 88ea679eeaad5..f8852606abbf2 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala @@ -26,14 +26,22 @@ class ParamsSuite extends FunSuite { import solver.{maxIter, inputCol} assert(maxIter.name === "maxIter") - assert(maxIter.doc === "max number of iterations") + assert(maxIter.doc === "max number of iterations (>= 0)") assert(maxIter.parent.eq(solver)) - assert(maxIter.toString === "maxIter: max number of iterations (default: 10)") + assert(maxIter.toString === "maxIter: max number of iterations (>= 0) (default: 10)") + assert(!maxIter.isValid(-1)) + assert(maxIter.isValid(0)) + assert(maxIter.isValid(1)) solver.setMaxIter(5) - assert(maxIter.toString === "maxIter: max number of iterations (default: 10, current: 5)") + assert(maxIter.toString === + "maxIter: max number of iterations (>= 0) (default: 10, current: 5)") assert(inputCol.toString === "inputCol: input column name (undefined)") + + intercept[IllegalArgumentException] { + solver.setMaxIter(-1) + } } test("param pair") { @@ -47,6 +55,9 @@ class ParamsSuite extends FunSuite { assert(pair.param.eq(maxIter)) assert(pair.value === 5) } + intercept[IllegalArgumentException] { + val pair = maxIter -> -1 + } } test("param map") { @@ -59,6 +70,9 @@ class ParamsSuite extends FunSuite { map0.put(maxIter, 10) assert(map0.contains(maxIter)) assert(map0(maxIter) === 10) + intercept[IllegalArgumentException] { + map0.put(maxIter, -1) + } assert(!map0.contains(inputCol)) intercept[NoSuchElementException] { @@ -122,14 +136,57 @@ class ParamsSuite extends FunSuite { assert(solver.getInputCol === "input") solver.validate() intercept[IllegalArgumentException] { - solver.validate(ParamMap(maxIter -> -10)) + ParamMap(maxIter -> -10) } - solver.setMaxIter(-10) intercept[IllegalArgumentException] { - solver.validate() + solver.setMaxIter(-10) } solver.clearMaxIter() assert(!solver.isSet(maxIter)) } + + test("ParamValidate") { + val alwaysTrue = ParamValidators.alwaysTrue[Int] + assert(alwaysTrue(1)) + + val gt1Int = ParamValidators.gt[Int](1) + assert(!gt1Int(1) && gt1Int(2)) + val gt1Double = ParamValidators.gt[Double](1) + assert(!gt1Double(1.0) && gt1Double(1.1)) + + val gtEq1Int = ParamValidators.gtEq[Int](1) + assert(!gtEq1Int(0) && gtEq1Int(1)) + val gtEq1Double = ParamValidators.gtEq[Double](1) + assert(!gtEq1Double(0.9) && gtEq1Double(1.0)) + + val lt1Int = ParamValidators.lt[Int](1) + assert(lt1Int(0) && !lt1Int(1)) + val lt1Double = ParamValidators.lt[Double](1) + assert(lt1Double(0.9) && !lt1Double(1.0)) + + val ltEq1Int = ParamValidators.ltEq[Int](1) + assert(ltEq1Int(1) && !ltEq1Int(2)) + val ltEq1Double = ParamValidators.ltEq[Double](1) + assert(ltEq1Double(1.0) && !ltEq1Double(1.1)) + + val inRange02IntInclusive = ParamValidators.inRange[Int](0, 2) + assert(inRange02IntInclusive(0) && inRange02IntInclusive(1) && inRange02IntInclusive(2) && + !inRange02IntInclusive(-1) && !inRange02IntInclusive(3)) + val inRange02IntExclusive = + ParamValidators.inRange[Int](0, 2, lowerInclusive = false, upperInclusive = false) + assert(!inRange02IntExclusive(0) && inRange02IntExclusive(1) && !inRange02IntExclusive(2)) + + val inRange02DoubleInclusive = ParamValidators.inRange[Double](0, 2) + assert(inRange02DoubleInclusive(0) && inRange02DoubleInclusive(1) && + inRange02DoubleInclusive(2) && + !inRange02DoubleInclusive(-0.1) && !inRange02DoubleInclusive(2.1)) + val inRange02DoubleExclusive = + ParamValidators.inRange[Double](0, 2, lowerInclusive = false, upperInclusive = false) + assert(!inRange02DoubleExclusive(0) && inRange02DoubleExclusive(1) && + !inRange02DoubleExclusive(2)) + + val inArray = ParamValidators.inArray[Int](Array(1, 2)) + assert(inArray(1) && inArray(2) && !inArray(0)) + } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala index 641b64b42a5e7..6f9c9cb5166ae 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala @@ -29,7 +29,7 @@ class TestParams extends Params with HasMaxIter with HasInputCol { override def validate(paramMap: ParamMap): Unit = { val m = extractParamMap(paramMap) - require(m(maxIter) >= 0) + // Note: maxIter is validated when it is set. require(m.contains(inputCol)) } From 1b7106b867bc0aa4d64b669d79b646f862acaf47 Mon Sep 17 00:00:00 2001 From: zsxwing Date: Wed, 29 Apr 2015 18:22:14 -0700 Subject: [PATCH 483/591] [SPARK-6862] [STREAMING] [WEBUI] Add BatchPage to display details of a batch This is an initial commit for SPARK-6862. Once SPARK-6796 is merged, I will add the links to StreamingPage so that the user can jump to BatchPage. Screenshots: ![success](https://cloud.githubusercontent.com/assets/1000778/7102439/bbe75406-e0b3-11e4-84fe-3e6de629a49a.png) ![failure](https://cloud.githubusercontent.com/assets/1000778/7102440/bc124454-e0b3-11e4-921a-c8b39d6b61bc.png) Author: zsxwing Closes #5473 from zsxwing/SPARK-6862 and squashes the following commits: 0727d35 [zsxwing] Change BatchUIData to a case class b380cfb [zsxwing] Add createJobStart to eliminate duplicate codes 9a3083d [zsxwing] Rename XxxDatas -> XxxData 087ba98 [zsxwing] Refactor BatchInfo to store only necessary fields cb62e4f [zsxwing] Use Seq[(OutputOpId, SparkJobId)] to store the id relations 72f8e7e [zsxwing] Add unit tests for BatchPage 1282b10 [zsxwing] Handle some corner cases and add tests for StreamingJobProgressListener 77a69ae [zsxwing] Refactor codes as per TD's comments 35ffd80 [zsxwing] Merge branch 'master' into SPARK-6862 15bdf9b [zsxwing] Add batch links and unit tests 4bf66b6 [zsxwing] Merge branch 'master' into SPARK-6862 7168807 [zsxwing] Limit the max width of the error message and fix nits in the UI 0b226f9 [zsxwing] Change 'Last Error' to 'Error' fc98a43 [zsxwing] Put clearing local properties to finally and remove redundant private[streaming] 0c7b2eb [zsxwing] Add BatchPage to display details of a batch --- .../org/apache/spark/ui/jobs/UIData.scala | 2 +- .../spark/streaming/dstream/DStream.scala | 2 +- .../spark/streaming/scheduler/Job.scala | 44 ++- .../streaming/scheduler/JobScheduler.scala | 28 +- .../spark/streaming/scheduler/JobSet.scala | 2 +- .../spark/streaming/ui/AllBatchesTable.scala | 26 +- .../apache/spark/streaming/ui/BatchPage.scala | 264 ++++++++++++++++++ .../spark/streaming/ui/BatchUIData.scala | 75 +++++ .../ui/StreamingJobProgressListener.scala | 161 ++++++++--- .../spark/streaming/ui/StreamingTab.scala | 4 +- .../spark/streaming/UISeleniumSuite.scala | 83 +++++- .../StreamingJobProgressListenerSuite.scala | 100 ++++++- 12 files changed, 710 insertions(+), 81 deletions(-) create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/ui/BatchUIData.scala diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala b/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala index 711a3697bda15..935c8a4f80e7b 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala @@ -24,7 +24,7 @@ import org.apache.spark.util.collection.OpenHashSet import scala.collection.mutable.HashMap -private[jobs] object UIData { +private[spark] object UIData { class ExecutorSummary { var taskTime : Long = 0 diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala index 24f99a2b929f5..83d41f5762444 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala @@ -626,7 +626,7 @@ abstract class DStream[T: ClassTag] ( println("Time: " + time) println("-------------------------------------------") firstNum.take(num).foreach(println) - if (firstNum.size > num) println("...") + if (firstNum.length > num) println("...") println() } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala index 30cf87f5b7dd1..3c481bf3491f9 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala @@ -25,15 +25,49 @@ import scala.util.Try */ private[streaming] class Job(val time: Time, func: () => _) { - var id: String = _ - var result: Try[_] = null + private var _id: String = _ + private var _outputOpId: Int = _ + private var isSet = false + private var _result: Try[_] = null def run() { - result = Try(func()) + _result = Try(func()) } - def setId(number: Int) { - id = "streaming job " + time + "." + number + def result: Try[_] = { + if (_result == null) { + throw new IllegalStateException("Cannot access result before job finishes") + } + _result + } + + /** + * @return the global unique id of this Job. + */ + def id: String = { + if (!isSet) { + throw new IllegalStateException("Cannot access id before calling setId") + } + _id + } + + /** + * @return the output op id of this Job. Each Job has a unique output op id in the same JobSet. + */ + def outputOpId: Int = { + if (!isSet) { + throw new IllegalStateException("Cannot access number before calling setId") + } + _outputOpId + } + + def setOutputOpId(outputOpId: Int) { + if (isSet) { + throw new IllegalStateException("Cannot call setOutputOpId more than once") + } + isSet = true + _id = s"streaming job $time.$outputOpId" + _outputOpId = outputOpId } override def toString: String = id diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala index 508b89278dcba..c7a2c1141a128 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala @@ -172,16 +172,28 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { ssc.waiter.notifyError(e) } - private class JobHandler(job: Job) extends Runnable { + private class JobHandler(job: Job) extends Runnable with Logging { def run() { - eventLoop.post(JobStarted(job)) - // Disable checks for existing output directories in jobs launched by the streaming scheduler, - // since we may need to write output to an existing directory during checkpoint recovery; - // see SPARK-4835 for more details. - PairRDDFunctions.disableOutputSpecValidation.withValue(true) { - job.run() + ssc.sc.setLocalProperty(JobScheduler.BATCH_TIME_PROPERTY_KEY, job.time.milliseconds.toString) + ssc.sc.setLocalProperty(JobScheduler.OUTPUT_OP_ID_PROPERTY_KEY, job.outputOpId.toString) + try { + eventLoop.post(JobStarted(job)) + // Disable checks for existing output directories in jobs launched by the streaming + // scheduler, since we may need to write output to an existing directory during checkpoint + // recovery; see SPARK-4835 for more details. + PairRDDFunctions.disableOutputSpecValidation.withValue(true) { + job.run() + } + eventLoop.post(JobCompleted(job)) + } finally { + ssc.sc.setLocalProperty(JobScheduler.BATCH_TIME_PROPERTY_KEY, null) + ssc.sc.setLocalProperty(JobScheduler.OUTPUT_OP_ID_PROPERTY_KEY, null) } - eventLoop.post(JobCompleted(job)) } } } + +private[streaming] object JobScheduler { + val BATCH_TIME_PROPERTY_KEY = "spark.streaming.internal.batchTime" + val OUTPUT_OP_ID_PROPERTY_KEY = "spark.streaming.internal.outputOpId" +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala index 5b134877d0b2d..24b3794236ea5 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala @@ -35,7 +35,7 @@ case class JobSet( private var processingStartTime = -1L // when the first job of this jobset started processing private var processingEndTime = -1L // when the last job of this jobset finished processing - jobs.zipWithIndex.foreach { case (job, i) => job.setId(i) } + jobs.zipWithIndex.foreach { case (job, i) => job.setOutputOpId(i) } incompleteJobs ++= jobs def handleJobStart(job: Job) { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala index df1c0a10704c3..e219e27785533 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/AllBatchesTable.scala @@ -19,7 +19,6 @@ package org.apache.spark.streaming.ui import scala.xml.Node -import org.apache.spark.streaming.scheduler.BatchInfo import org.apache.spark.ui.UIUtils private[ui] abstract class BatchTableBase(tableId: String) { @@ -31,18 +30,20 @@ private[ui] abstract class BatchTableBase(tableId: String) {

    } - protected def baseRow(batch: BatchInfo): Seq[Node] = { + protected def baseRow(batch: BatchUIData): Seq[Node] = { val batchTime = batch.batchTime.milliseconds val formattedBatchTime = UIUtils.formatDate(batch.batchTime.milliseconds) - val eventCount = batch.receivedBlockInfo.values.map { - receivers => receivers.map(_.numRecords).sum - }.sum + val eventCount = batch.numRecords val schedulingDelay = batch.schedulingDelay val formattedSchedulingDelay = schedulingDelay.map(UIUtils.formatDuration).getOrElse("-") val processingTime = batch.processingDelay val formattedProcessingTime = processingTime.map(UIUtils.formatDuration).getOrElse("-") - + @@ -85,16 +87,16 @@ private[ui] class ActiveBatchTable(runningBatches: Seq[BatchInfo], waitingBatche runningBatches.flatMap(batch => {runningBatchRow(batch)}) } - private def runningBatchRow(batch: BatchInfo): Seq[Node] = { + private def runningBatchRow(batch: BatchUIData): Seq[Node] = { baseRow(batch) ++ } - private def waitingBatchRow(batch: BatchInfo): Seq[Node] = { + private def waitingBatchRow(batch: BatchUIData): Seq[Node] = { baseRow(batch) ++ } } -private[ui] class CompletedBatchTable(batches: Seq[BatchInfo]) +private[ui] class CompletedBatchTable(batches: Seq[BatchUIData]) extends BatchTableBase("completed-batches-table") { override protected def columns: Seq[Node] = super.columns ++ @@ -103,7 +105,7 @@ private[ui] class CompletedBatchTable(batches: Seq[BatchInfo]) batches.flatMap(batch => {completedBatchRow(batch)}) } - private def completedBatchRow(batch: BatchInfo): Seq[Node] = { + private def completedBatchRow(batch: BatchUIData): Seq[Node] = { val totalDelay = batch.totalDelay val formattedTotalDelay = totalDelay.map(UIUtils.formatDuration).getOrElse("-") baseRow(batch) ++ diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala new file mode 100644 index 0000000000000..2da9a29e2529e --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala @@ -0,0 +1,264 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.ui + +import javax.servlet.http.HttpServletRequest + +import scala.xml.{NodeSeq, Node} + +import org.apache.commons.lang3.StringEscapeUtils + +import org.apache.spark.streaming.Time +import org.apache.spark.ui.{UIUtils, WebUIPage} +import org.apache.spark.streaming.ui.StreamingJobProgressListener.{SparkJobId, OutputOpId} +import org.apache.spark.ui.jobs.UIData.JobUIData + + +private[ui] class BatchPage(parent: StreamingTab) extends WebUIPage("batch") { + private val streamingListener = parent.listener + private val sparkListener = parent.ssc.sc.jobProgressListener + + private def columns: Seq[Node] = { + + + + + + + + + } + + /** + * Generate a row for a Spark Job. Because duplicated output op infos needs to be collapsed into + * one cell, we use "rowspan" for the first row of a output op. + */ + def generateJobRow( + outputOpId: OutputOpId, + formattedOutputOpDuration: String, + numSparkJobRowsInOutputOp: Int, + isFirstRow: Boolean, + sparkJob: JobUIData): Seq[Node] = { + val lastStageInfo = Option(sparkJob.stageIds) + .filter(_.nonEmpty) + .flatMap { ids => sparkListener.stageIdToInfo.get(ids.max) } + val lastStageData = lastStageInfo.flatMap { s => + sparkListener.stageIdToData.get((s.stageId, s.attemptId)) + } + + val lastStageName = lastStageInfo.map(_.name).getOrElse("(Unknown Stage Name)") + val lastStageDescription = lastStageData.flatMap(_.description).getOrElse("") + val duration: Option[Long] = { + sparkJob.submissionTime.map { start => + val end = sparkJob.completionTime.getOrElse(System.currentTimeMillis()) + end - start + } + } + val lastFailureReason = + sparkJob.stageIds.sorted.reverse.flatMap(sparkListener.stageIdToInfo.get). + dropWhile(_.failureReason == None).take(1). // get the first info that contains failure + flatMap(info => info.failureReason).headOption.getOrElse("") + val formattedDuration = duration.map(d => UIUtils.formatDuration(d)).getOrElse("-") + val detailUrl = s"${UIUtils.prependBaseUri(parent.basePath)}/jobs/job?id=${sparkJob.jobId}" + + // In the first row, output op id and its information needs to be shown. In other rows, these + // cells will be taken up due to "rowspan". + // scalastyle:off + val prefixCells = + if (isFirstRow) { + + + + } else { + Nil + } + // scalastyle:on + + + {prefixCells} + + + + + {failureReasonCell(lastFailureReason)} + + } + + private def generateOutputOpIdRow( + outputOpId: OutputOpId, sparkJobs: Seq[JobUIData]): Seq[Node] = { + val sparkjobDurations = sparkJobs.map(sparkJob => { + sparkJob.submissionTime.map { start => + val end = sparkJob.completionTime.getOrElse(System.currentTimeMillis()) + end - start + } + }) + val formattedOutputOpDuration = + if (sparkjobDurations.exists(_ == None)) { + // If any job does not finish, set "formattedOutputOpDuration" to "-" + "-" + } else { + UIUtils.formatDuration(sparkjobDurations.flatMap(x => x).sum) + } + generateJobRow(outputOpId, formattedOutputOpDuration, sparkJobs.size, true, sparkJobs.head) ++ + sparkJobs.tail.map { sparkJob => + generateJobRow(outputOpId, formattedOutputOpDuration, sparkJobs.size, false, sparkJob) + }.flatMap(x => x) + } + + private def failureReasonCell(failureReason: String): Seq[Node] = { + val isMultiline = failureReason.indexOf('\n') >= 0 + // Display the first line by default + val failureReasonSummary = StringEscapeUtils.escapeHtml4( + if (isMultiline) { + failureReason.substring(0, failureReason.indexOf('\n')) + } else { + failureReason + }) + val details = if (isMultiline) { + // scalastyle:off + + +details + ++ + + // scalastyle:on + } else { + "" + } + + } + + private def getJobData(sparkJobId: SparkJobId): Option[JobUIData] = { + sparkListener.activeJobs.get(sparkJobId).orElse { + sparkListener.completedJobs.find(_.jobId == sparkJobId).orElse { + sparkListener.failedJobs.find(_.jobId == sparkJobId) + } + } + } + + /** + * Generate the job table for the batch. + */ + private def generateJobTable(batchUIData: BatchUIData): Seq[Node] = { + val outputOpIdToSparkJobIds = batchUIData.outputOpIdSparkJobIdPairs.groupBy(_.outputOpId).toSeq. + sortBy(_._1). // sorted by OutputOpId + map { case (outputOpId, outputOpIdAndSparkJobIds) => + // sort SparkJobIds for each OutputOpId + (outputOpId, outputOpIdAndSparkJobIds.map(_.sparkJobId).sorted) + } + sparkListener.synchronized { + val outputOpIdWithJobs: Seq[(OutputOpId, Seq[JobUIData])] = + outputOpIdToSparkJobIds.map { case (outputOpId, sparkJobIds) => + // Filter out spark Job ids that don't exist in sparkListener + (outputOpId, sparkJobIds.flatMap(getJobData)) + } + +
    Property NameDefaultMeaning
    spark.reducer.maxMbInFlight48spark.reducer.maxSizeInFlight48m - Maximum size (in megabytes) of map outputs to fetch simultaneously from each reduce task. Since + Maximum size of map outputs to fetch simultaneously from each reduce task. Since each output requires us to create a buffer to receive it, this represents a fixed memory overhead per reduce task, so keep it small unless you have a large amount of memory.
    spark.shuffle.file.buffer.kb32spark.shuffle.file.buffer32k - Size of the in-memory buffer for each shuffle file output stream, in kilobytes. These buffers + Size of the in-memory buffer for each shuffle file output stream. These buffers reduce the number of disk seeks and system calls made in creating intermediate shuffle files.
    spark.io.compression.lz4.block.size32768spark.io.compression.lz4.blockSize32k - Block size (in bytes) used in LZ4 compression, in the case when LZ4 compression codec + Block size used in LZ4 compression, in the case when LZ4 compression codec is used. Lowering this block size will also lower shuffle memory usage when LZ4 is used.
    spark.io.compression.snappy.block.size32768spark.io.compression.snappy.blockSize32k - Block size (in bytes) used in Snappy compression, in the case when Snappy compression codec + Block size used in Snappy compression, in the case when Snappy compression codec is used. Lowering this block size will also lower shuffle memory usage when Snappy is used.
    spark.kryoserializer.buffer.max.mb64spark.kryoserializer.buffer.max64m - Maximum allowable size of Kryo serialization buffer, in megabytes. This must be larger than any + Maximum allowable size of Kryo serialization buffer. This must be larger than any object you attempt to serialize. Increase this if you get a "buffer limit exceeded" exception inside Kryo.
    spark.kryoserializer.buffer.mb0.064spark.kryoserializer.buffer64k - Initial size of Kryo's serialization buffer, in megabytes. Note that there will be one buffer + Initial size of Kryo's serialization buffer. Note that there will be one buffer per core on each worker. This buffer will grow up to spark.kryoserializer.buffer.max.mb if needed.
    Property NameDefaultMeaning
    spark.broadcast.blockSize40964m - Size of each piece of a block in kilobytes for TorrentBroadcastFactory. + Size of each piece of a block for TorrentBroadcastFactory. Too large a value decreases parallelism during broadcast (makes it slower); however, if it is too small, BlockManager might take a performance hit.
    spark.storage.memoryMapThreshold20971522m - Size of a block, in bytes, above which Spark memory maps when reading a block from disk. + Size of a block above which Spark memory maps when reading a block from disk. This prevents Spark from memory mapping very small blocks. In general, memory mapping has high overhead for blocks close to or below the page size of the operating system.
    {submission.submissionId}{submission.submissionDate}{submission.command.mainClass}cpus: {submission.cores}, mem: {submission.mem}
    {state.driverDescription.submissionId}{state.driverDescription.submissionDate}{state.driverDescription.command.mainClass}cpus: {state.driverDescription.cores}, mem: {state.driverDescription.mem}{state.startDate}{state.slaveId.getValue}{stateString(state.mesosTaskStatus)}
    {submission.submissionId}{submission.submissionDate}{submission.command.mainClass}{submission.retryState.get.lastFailureStatus}{submission.retryState.get.nextRetry}{submission.retryState.get.retries}
    Processing Time{formattedBatchTime} + + {formattedBatchTime} + + {eventCount.toString} events {formattedSchedulingDelay} @@ -73,8 +74,9 @@ private[ui] abstract class BatchTableBase(tableId: String) { protected def renderRows: Seq[Node] } -private[ui] class ActiveBatchTable(runningBatches: Seq[BatchInfo], waitingBatches: Seq[BatchInfo]) - extends BatchTableBase("active-batches-table") { +private[ui] class ActiveBatchTable( + runningBatches: Seq[BatchUIData], + waitingBatches: Seq[BatchUIData]) extends BatchTableBase("active-batches-table") { override protected def columns: Seq[Node] = super.columns ++ Status
    processingqueuedTotal Delay
    Output Op IdDescriptionDurationJob IdDurationStages: Succeeded/TotalTasks (for all stages): Succeeded/TotalError{outputOpId.toString} + + {lastStageDescription} + {lastStageName} + {formattedOutputOpDuration}
    + + {sparkJob.jobId}{sparkJob.jobGroup.map(id => s"($id)").getOrElse("")} + + + {formattedDuration} + + {sparkJob.completedStageIndices.size}/{sparkJob.stageIds.size - sparkJob.numSkippedStages} + {if (sparkJob.numFailedStages > 0) s"(${sparkJob.numFailedStages} failed)"} + {if (sparkJob.numSkippedStages > 0) s"(${sparkJob.numSkippedStages} skipped)"} + + { + UIUtils.makeProgressBar( + started = sparkJob.numActiveTasks, + completed = sparkJob.numCompletedTasks, + failed = sparkJob.numFailedTasks, + skipped = sparkJob.numSkippedTasks, + total = sparkJob.numTasks - sparkJob.numSkippedTasks) + } +
    {failureReasonSummary}{details}
    + + {columns} + + + { + outputOpIdWithJobs.map { + case (outputOpId, jobs) => generateOutputOpIdRow(outputOpId, jobs) + } + } + +
    + } + } + + def render(request: HttpServletRequest): Seq[Node] = { + val batchTime = Option(request.getParameter("id")).map(id => Time(id.toLong)).getOrElse { + throw new IllegalArgumentException(s"Missing id parameter") + } + val formattedBatchTime = UIUtils.formatDate(batchTime.milliseconds) + + val batchUIData = streamingListener.getBatchUIData(batchTime).getOrElse { + throw new IllegalArgumentException(s"Batch $formattedBatchTime does not exist") + } + + val formattedSchedulingDelay = + batchUIData.schedulingDelay.map(UIUtils.formatDuration).getOrElse("-") + val formattedProcessingTime = + batchUIData.processingDelay.map(UIUtils.formatDuration).getOrElse("-") + val formattedTotalDelay = batchUIData.totalDelay.map(UIUtils.formatDuration).getOrElse("-") + + val summary: NodeSeq = +
    +
      +
    • + Batch Duration: + {UIUtils.formatDuration(streamingListener.batchDuration)} +
      • +
    • + Input data size: + {batchUIData.numRecords} records +
      • +
    • + Scheduling delay: + {formattedSchedulingDelay} +
      • +
    • + Processing time: + {formattedProcessingTime} +
      • +
    • + Total delay: + {formattedTotalDelay} +
      • +
    +
    + + val jobTable = + if (batchUIData.outputOpIdSparkJobIdPairs.isEmpty) { +
    Cannot find any job for Batch {formattedBatchTime}.
    + } else { + generateJobTable(batchUIData) + } + + val content = summary ++ jobTable + + UIUtils.headerSparkPage(s"Details of batch at $formattedBatchTime", content, parent) + } +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchUIData.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchUIData.scala new file mode 100644 index 0000000000000..f45c291b7c0fe --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchUIData.scala @@ -0,0 +1,75 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + + +package org.apache.spark.streaming.ui + +import org.apache.spark.streaming.Time +import org.apache.spark.streaming.scheduler.BatchInfo +import org.apache.spark.streaming.ui.StreamingJobProgressListener._ + +private[ui] case class OutputOpIdAndSparkJobId(outputOpId: OutputOpId, sparkJobId: SparkJobId) + +private[ui] case class BatchUIData( + val batchTime: Time, + val receiverNumRecords: Map[Int, Long], + val submissionTime: Long, + val processingStartTime: Option[Long], + val processingEndTime: Option[Long], + var outputOpIdSparkJobIdPairs: Seq[OutputOpIdAndSparkJobId] = Seq.empty) { + + /** + * Time taken for the first job of this batch to start processing from the time this batch + * was submitted to the streaming scheduler. Essentially, it is + * `processingStartTime` - `submissionTime`. + */ + def schedulingDelay: Option[Long] = processingStartTime.map(_ - submissionTime) + + /** + * Time taken for the all jobs of this batch to finish processing from the time they started + * processing. Essentially, it is `processingEndTime` - `processingStartTime`. + */ + def processingDelay: Option[Long] = { + for (start <- processingStartTime; + end <- processingEndTime) + yield end - start + } + + /** + * Time taken for all the jobs of this batch to finish processing from the time they + * were submitted. Essentially, it is `processingDelay` + `schedulingDelay`. + */ + def totalDelay: Option[Long] = processingEndTime.map(_ - submissionTime) + + /** + * The number of recorders received by the receivers in this batch. + */ + def numRecords: Long = receiverNumRecords.map(_._2).sum +} + +private[ui] object BatchUIData { + + def apply(batchInfo: BatchInfo): BatchUIData = { + new BatchUIData( + batchInfo.batchTime, + batchInfo.receivedBlockInfo.mapValues(_.map(_.numRecords).sum), + batchInfo.submissionTime, + batchInfo.processingStartTime, + batchInfo.processingEndTime + ) + } +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala index be1e8686cf9fa..34b55717a1db2 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala @@ -17,29 +17,58 @@ package org.apache.spark.streaming.ui -import scala.collection.mutable.{Queue, HashMap} +import java.util.LinkedHashMap +import java.util.{Map => JMap} +import java.util.Properties +import scala.collection.mutable.{ArrayBuffer, Queue, HashMap, SynchronizedBuffer} + +import org.apache.spark.scheduler._ import org.apache.spark.streaming.{Time, StreamingContext} import org.apache.spark.streaming.scheduler._ import org.apache.spark.streaming.scheduler.StreamingListenerReceiverStarted import org.apache.spark.streaming.scheduler.StreamingListenerBatchStarted -import org.apache.spark.streaming.scheduler.BatchInfo import org.apache.spark.streaming.scheduler.StreamingListenerBatchSubmitted import org.apache.spark.util.Distribution private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) - extends StreamingListener { + extends StreamingListener with SparkListener { - private val waitingBatchInfos = new HashMap[Time, BatchInfo] - private val runningBatchInfos = new HashMap[Time, BatchInfo] - private val completedBatchInfos = new Queue[BatchInfo] - private val batchInfoLimit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 100) + private val waitingBatchUIData = new HashMap[Time, BatchUIData] + private val runningBatchUIData = new HashMap[Time, BatchUIData] + private val completedBatchUIData = new Queue[BatchUIData] + private val batchUIDataLimit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 100) private var totalCompletedBatches = 0L private var totalReceivedRecords = 0L private var totalProcessedRecords = 0L private val receiverInfos = new HashMap[Int, ReceiverInfo] + // Because onJobStart and onBatchXXX messages are processed in different threads, + // we may not be able to get the corresponding BatchUIData when receiving onJobStart. So here we + // cannot use a map of (Time, BatchUIData). + private[ui] val batchTimeToOutputOpIdSparkJobIdPair = + new LinkedHashMap[Time, SynchronizedBuffer[OutputOpIdAndSparkJobId]] { + override def removeEldestEntry( + p1: JMap.Entry[Time, SynchronizedBuffer[OutputOpIdAndSparkJobId]]): Boolean = { + // If a lot of "onBatchCompleted"s happen before "onJobStart" (image if + // SparkContext.listenerBus is very slow), "batchTimeToOutputOpIdToSparkJobIds" + // may add some information for a removed batch when processing "onJobStart". It will be a + // memory leak. + // + // To avoid the memory leak, we control the size of "batchTimeToOutputOpIdToSparkJobIds" and + // evict the eldest one. + // + // Note: if "onJobStart" happens before "onBatchSubmitted", the size of + // "batchTimeToOutputOpIdToSparkJobIds" may be greater than the number of the retained + // batches temporarily, so here we use "10" to handle such case. This is not a perfect + // solution, but at least it can handle most of cases. + size() > + waitingBatchUIData.size + runningBatchUIData.size + completedBatchUIData.size + 10 + } + } + + val batchDuration = ssc.graph.batchDuration.milliseconds override def onReceiverStarted(receiverStarted: StreamingListenerReceiverStarted) { @@ -62,37 +91,62 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) override def onBatchSubmitted(batchSubmitted: StreamingListenerBatchSubmitted): Unit = { synchronized { - waitingBatchInfos(batchSubmitted.batchInfo.batchTime) = batchSubmitted.batchInfo + waitingBatchUIData(batchSubmitted.batchInfo.batchTime) = + BatchUIData(batchSubmitted.batchInfo) } } override def onBatchStarted(batchStarted: StreamingListenerBatchStarted): Unit = synchronized { - runningBatchInfos(batchStarted.batchInfo.batchTime) = batchStarted.batchInfo - waitingBatchInfos.remove(batchStarted.batchInfo.batchTime) + val batchUIData = BatchUIData(batchStarted.batchInfo) + runningBatchUIData(batchStarted.batchInfo.batchTime) = BatchUIData(batchStarted.batchInfo) + waitingBatchUIData.remove(batchStarted.batchInfo.batchTime) - batchStarted.batchInfo.receivedBlockInfo.foreach { case (_, infos) => - totalReceivedRecords += infos.map(_.numRecords).sum - } + totalReceivedRecords += batchUIData.numRecords } override def onBatchCompleted(batchCompleted: StreamingListenerBatchCompleted): Unit = { synchronized { - waitingBatchInfos.remove(batchCompleted.batchInfo.batchTime) - runningBatchInfos.remove(batchCompleted.batchInfo.batchTime) - completedBatchInfos.enqueue(batchCompleted.batchInfo) - if (completedBatchInfos.size > batchInfoLimit) completedBatchInfos.dequeue() + waitingBatchUIData.remove(batchCompleted.batchInfo.batchTime) + runningBatchUIData.remove(batchCompleted.batchInfo.batchTime) + val batchUIData = BatchUIData(batchCompleted.batchInfo) + completedBatchUIData.enqueue(batchUIData) + if (completedBatchUIData.size > batchUIDataLimit) { + val removedBatch = completedBatchUIData.dequeue() + batchTimeToOutputOpIdSparkJobIdPair.remove(removedBatch.batchTime) + } totalCompletedBatches += 1L - batchCompleted.batchInfo.receivedBlockInfo.foreach { case (_, infos) => - totalProcessedRecords += infos.map(_.numRecords).sum + totalProcessedRecords += batchUIData.numRecords + } + } + + override def onJobStart(jobStart: SparkListenerJobStart): Unit = synchronized { + getBatchTimeAndOutputOpId(jobStart.properties).foreach { case (batchTime, outputOpId) => + var outputOpIdToSparkJobIds = batchTimeToOutputOpIdSparkJobIdPair.get(batchTime) + if (outputOpIdToSparkJobIds == null) { + outputOpIdToSparkJobIds = + new ArrayBuffer[OutputOpIdAndSparkJobId]() + with SynchronizedBuffer[OutputOpIdAndSparkJobId] + batchTimeToOutputOpIdSparkJobIdPair.put(batchTime, outputOpIdToSparkJobIds) } + outputOpIdToSparkJobIds += OutputOpIdAndSparkJobId(outputOpId, jobStart.jobId) } } - def numReceivers: Int = synchronized { - ssc.graph.getReceiverInputStreams().size + private def getBatchTimeAndOutputOpId(properties: Properties): Option[(Time, Int)] = { + val batchTime = properties.getProperty(JobScheduler.BATCH_TIME_PROPERTY_KEY) + if (batchTime == null) { + // Not submitted from JobScheduler + None + } else { + val outputOpId = properties.getProperty(JobScheduler.OUTPUT_OP_ID_PROPERTY_KEY) + assert(outputOpId != null) + Some(Time(batchTime.toLong) -> outputOpId.toInt) + } } + def numReceivers: Int = ssc.graph.getReceiverInputStreams().size + def numTotalCompletedBatches: Long = synchronized { totalCompletedBatches } @@ -106,19 +160,19 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) } def numUnprocessedBatches: Long = synchronized { - waitingBatchInfos.size + runningBatchInfos.size + waitingBatchUIData.size + runningBatchUIData.size } - def waitingBatches: Seq[BatchInfo] = synchronized { - waitingBatchInfos.values.toSeq + def waitingBatches: Seq[BatchUIData] = synchronized { + waitingBatchUIData.values.toSeq } - def runningBatches: Seq[BatchInfo] = synchronized { - runningBatchInfos.values.toSeq + def runningBatches: Seq[BatchUIData] = synchronized { + runningBatchUIData.values.toSeq } - def retainedCompletedBatches: Seq[BatchInfo] = synchronized { - completedBatchInfos.toSeq + def retainedCompletedBatches: Seq[BatchUIData] = synchronized { + completedBatchUIData.toSeq } def processingDelayDistribution: Option[Distribution] = synchronized { @@ -134,15 +188,11 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) } def receivedRecordsDistributions: Map[Int, Option[Distribution]] = synchronized { - val latestBatchInfos = retainedBatches.reverse.take(batchInfoLimit) - val latestBlockInfos = latestBatchInfos.map(_.receivedBlockInfo) + val latestBatches = retainedBatches.reverse.take(batchUIDataLimit) (0 until numReceivers).map { receiverId => - val blockInfoOfParticularReceiver = latestBlockInfos.map { batchInfo => - batchInfo.get(receiverId).getOrElse(Array.empty) - } - val recordsOfParticularReceiver = blockInfoOfParticularReceiver.map { blockInfo => - // calculate records per second for each batch - blockInfo.map(_.numRecords).sum.toDouble * 1000 / batchDuration + val recordsOfParticularReceiver = latestBatches.map { batch => + // calculate records per second for each batch + batch.receiverNumRecords.get(receiverId).sum.toDouble * 1000 / batchDuration } val distributionOption = Distribution(recordsOfParticularReceiver) (receiverId, distributionOption) @@ -150,10 +200,10 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) } def lastReceivedBatchRecords: Map[Int, Long] = synchronized { - val lastReceivedBlockInfoOption = lastReceivedBatch.map(_.receivedBlockInfo) + val lastReceivedBlockInfoOption = lastReceivedBatch.map(_.receiverNumRecords) lastReceivedBlockInfoOption.map { lastReceivedBlockInfo => (0 until numReceivers).map { receiverId => - (receiverId, lastReceivedBlockInfo(receiverId).map(_.numRecords).sum) + (receiverId, lastReceivedBlockInfo.getOrElse(receiverId, 0L)) }.toMap }.getOrElse { (0 until numReceivers).map(receiverId => (receiverId, 0L)).toMap @@ -164,20 +214,39 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) receiverInfos.get(receiverId) } - def lastCompletedBatch: Option[BatchInfo] = synchronized { - completedBatchInfos.sortBy(_.batchTime)(Time.ordering).lastOption + def lastCompletedBatch: Option[BatchUIData] = synchronized { + completedBatchUIData.sortBy(_.batchTime)(Time.ordering).lastOption } - def lastReceivedBatch: Option[BatchInfo] = synchronized { + def lastReceivedBatch: Option[BatchUIData] = synchronized { retainedBatches.lastOption } - private def retainedBatches: Seq[BatchInfo] = { - (waitingBatchInfos.values.toSeq ++ - runningBatchInfos.values.toSeq ++ completedBatchInfos).sortBy(_.batchTime)(Time.ordering) + private def retainedBatches: Seq[BatchUIData] = { + (waitingBatchUIData.values.toSeq ++ + runningBatchUIData.values.toSeq ++ completedBatchUIData).sortBy(_.batchTime)(Time.ordering) + } + + private def extractDistribution(getMetric: BatchUIData => Option[Long]): Option[Distribution] = { + Distribution(completedBatchUIData.flatMap(getMetric(_)).map(_.toDouble)) } - private def extractDistribution(getMetric: BatchInfo => Option[Long]): Option[Distribution] = { - Distribution(completedBatchInfos.flatMap(getMetric(_)).map(_.toDouble)) + def getBatchUIData(batchTime: Time): Option[BatchUIData] = synchronized { + val batchUIData = waitingBatchUIData.get(batchTime).orElse { + runningBatchUIData.get(batchTime).orElse { + completedBatchUIData.find(batch => batch.batchTime == batchTime) + } + } + batchUIData.foreach { _batchUIData => + val outputOpIdToSparkJobIds = + Option(batchTimeToOutputOpIdSparkJobIdPair.get(batchTime)).getOrElse(Seq.empty) + _batchUIData.outputOpIdSparkJobIdPairs = outputOpIdToSparkJobIds + } + batchUIData } } + +private[streaming] object StreamingJobProgressListener { + type SparkJobId = Int + type OutputOpId = Int +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala index 9a860ea4a6c68..e4039639adbad 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingTab.scala @@ -27,14 +27,16 @@ import StreamingTab._ * Spark Web UI tab that shows statistics of a streaming job. * This assumes the given SparkContext has enabled its SparkUI. */ -private[spark] class StreamingTab(ssc: StreamingContext) +private[spark] class StreamingTab(val ssc: StreamingContext) extends SparkUITab(getSparkUI(ssc), "streaming") with Logging { val parent = getSparkUI(ssc) val listener = ssc.progressListener ssc.addStreamingListener(listener) + ssc.sc.addSparkListener(listener) attachPage(new StreamingPage(this)) + attachPage(new BatchPage(this)) parent.attachTab(this) def detach() { diff --git a/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala index 205ddf6dbe9b0..8de43baabc21d 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala @@ -17,6 +17,8 @@ package org.apache.spark.streaming +import scala.collection.mutable.Queue + import org.openqa.selenium.WebDriver import org.openqa.selenium.htmlunit.HtmlUnitDriver import org.scalatest._ @@ -60,8 +62,28 @@ class UISeleniumSuite ssc } + private def setupStreams(ssc: StreamingContext): Unit = { + val rdds = Queue(ssc.sc.parallelize(1 to 4, 4)) + val inputStream = ssc.queueStream(rdds) + inputStream.foreachRDD { rdd => + rdd.foreach(_ => {}) + rdd.foreach(_ => {}) + } + inputStream.foreachRDD { rdd => + rdd.foreach(_ => {}) + try { + rdd.foreach(_ => throw new RuntimeException("Oops")) + } catch { + case e: SparkException if e.getMessage.contains("Oops") => + } + } + } + test("attaching and detaching a Streaming tab") { withStreamingContext(newSparkStreamingContext()) { ssc => + setupStreams(ssc) + ssc.start() + val sparkUI = ssc.sparkContext.ui.get eventually(timeout(10 seconds), interval(50 milliseconds)) { @@ -77,8 +99,8 @@ class UISeleniumSuite statisticText should contain("Batch interval:") val h4Text = findAll(cssSelector("h4")).map(_.text).toSeq - h4Text should contain("Active Batches (0)") - h4Text should contain("Completed Batches (last 0 out of 0)") + h4Text.exists(_.matches("Active Batches \\(\\d+\\)")) should be (true) + h4Text.exists(_.matches("Completed Batches \\(last \\d+ out of \\d+\\)")) should be (true) findAll(cssSelector("""#active-batches-table th""")).map(_.text).toSeq should be { List("Batch Time", "Input Size", "Scheduling Delay", "Processing Time", "Status") @@ -86,6 +108,63 @@ class UISeleniumSuite findAll(cssSelector("""#completed-batches-table th""")).map(_.text).toSeq should be { List("Batch Time", "Input Size", "Scheduling Delay", "Processing Time", "Total Delay") } + + val batchLinks = + findAll(cssSelector("""#completed-batches-table a""")).flatMap(_.attribute("href")).toSeq + batchLinks.size should be >= 1 + + // Check a normal batch page + go to (batchLinks.last) // Last should be the first batch, so it will have some jobs + val summaryText = findAll(cssSelector("li strong")).map(_.text).toSeq + summaryText should contain ("Batch Duration:") + summaryText should contain ("Input data size:") + summaryText should contain ("Scheduling delay:") + summaryText should contain ("Processing time:") + summaryText should contain ("Total delay:") + + findAll(cssSelector("""#batch-job-table th""")).map(_.text).toSeq should be { + List("Output Op Id", "Description", "Duration", "Job Id", "Duration", + "Stages: Succeeded/Total", "Tasks (for all stages): Succeeded/Total", "Error") + } + + // Check we have 2 output op ids + val outputOpIds = findAll(cssSelector(".output-op-id-cell")).toSeq + outputOpIds.map(_.attribute("rowspan")) should be (List(Some("2"), Some("2"))) + outputOpIds.map(_.text) should be (List("0", "1")) + + // Check job ids + val jobIdCells = findAll(cssSelector( """#batch-job-table a""")).toSeq + jobIdCells.map(_.text) should be (List("0", "1", "2", "3")) + + val jobLinks = jobIdCells.flatMap(_.attribute("href")) + jobLinks.size should be (4) + + // Check stage progress + findAll(cssSelector(""".stage-progress-cell""")).map(_.text).toSeq should be + (List("1/1", "1/1", "1/1", "0/1 (1 failed)")) + + // Check job progress + findAll(cssSelector(""".progress-cell""")).map(_.text).toSeq should be + (List("1/1", "1/1", "1/1", "0/1 (1 failed)")) + + // Check stacktrace + val errorCells = findAll(cssSelector(""".stacktrace-details""")).map(_.text).toSeq + errorCells should have size 1 + errorCells(0) should include("java.lang.RuntimeException: Oops") + + // Check the job link in the batch page is right + go to (jobLinks(0)) + val jobDetails = findAll(cssSelector("li strong")).map(_.text).toSeq + jobDetails should contain("Status:") + jobDetails should contain("Completed Stages:") + + // Check a batch page without id + go to (sparkUI.appUIAddress.stripSuffix("/") + "/streaming/batch/") + webDriver.getPageSource should include ("Missing id parameter") + + // Check a non-exist batch + go to (sparkUI.appUIAddress.stripSuffix("/") + "/streaming/batch/?id=12345") + webDriver.getPageSource should include ("does not exist") } ssc.stop(false) diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala index 94b1985116feb..fa89536de4054 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala @@ -17,8 +17,11 @@ package org.apache.spark.streaming.ui +import java.util.Properties + import org.scalatest.Matchers +import org.apache.spark.scheduler.SparkListenerJobStart import org.apache.spark.streaming.dstream.DStream import org.apache.spark.streaming.scheduler._ import org.apache.spark.streaming.{Duration, Time, Milliseconds, TestSuiteBase} @@ -28,6 +31,17 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { val input = (1 to 4).map(Seq(_)).toSeq val operation = (d: DStream[Int]) => d.map(x => x) + private def createJobStart( + batchTime: Time, outputOpId: Int, jobId: Int): SparkListenerJobStart = { + val properties = new Properties() + properties.setProperty(JobScheduler.BATCH_TIME_PROPERTY_KEY, batchTime.milliseconds.toString) + properties.setProperty(JobScheduler.OUTPUT_OP_ID_PROPERTY_KEY, outputOpId.toString) + SparkListenerJobStart(jobId = jobId, + 0L, // unused + Nil, // unused + properties) + } + override def batchDuration: Duration = Milliseconds(100) test("onBatchSubmitted, onBatchStarted, onBatchCompleted, " + @@ -43,7 +57,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { // onBatchSubmitted val batchInfoSubmitted = BatchInfo(Time(1000), receivedBlockInfo, 1000, None, None) listener.onBatchSubmitted(StreamingListenerBatchSubmitted(batchInfoSubmitted)) - listener.waitingBatches should be (List(batchInfoSubmitted)) + listener.waitingBatches should be (List(BatchUIData(batchInfoSubmitted))) listener.runningBatches should be (Nil) listener.retainedCompletedBatches should be (Nil) listener.lastCompletedBatch should be (None) @@ -56,7 +70,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { val batchInfoStarted = BatchInfo(Time(1000), receivedBlockInfo, 1000, Some(2000), None) listener.onBatchStarted(StreamingListenerBatchStarted(batchInfoStarted)) listener.waitingBatches should be (Nil) - listener.runningBatches should be (List(batchInfoStarted)) + listener.runningBatches should be (List(BatchUIData(batchInfoStarted))) listener.retainedCompletedBatches should be (Nil) listener.lastCompletedBatch should be (None) listener.numUnprocessedBatches should be (1) @@ -64,13 +78,40 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { listener.numTotalProcessedRecords should be (0) listener.numTotalReceivedRecords should be (600) + // onJobStart + val jobStart1 = createJobStart(Time(1000), outputOpId = 0, jobId = 0) + listener.onJobStart(jobStart1) + + val jobStart2 = createJobStart(Time(1000), outputOpId = 0, jobId = 1) + listener.onJobStart(jobStart2) + + val jobStart3 = createJobStart(Time(1000), outputOpId = 1, jobId = 0) + listener.onJobStart(jobStart3) + + val jobStart4 = createJobStart(Time(1000), outputOpId = 1, jobId = 1) + listener.onJobStart(jobStart4) + + val batchUIData = listener.getBatchUIData(Time(1000)) + batchUIData should not be None + batchUIData.get.batchTime should be (batchInfoStarted.batchTime) + batchUIData.get.schedulingDelay should be (batchInfoStarted.schedulingDelay) + batchUIData.get.processingDelay should be (batchInfoStarted.processingDelay) + batchUIData.get.totalDelay should be (batchInfoStarted.totalDelay) + batchUIData.get.receiverNumRecords should be (Map(0 -> 300L, 1 -> 300L)) + batchUIData.get.numRecords should be(600) + batchUIData.get.outputOpIdSparkJobIdPairs should be + Seq(OutputOpIdAndSparkJobId(0, 0), + OutputOpIdAndSparkJobId(0, 1), + OutputOpIdAndSparkJobId(1, 0), + OutputOpIdAndSparkJobId(1, 1)) + // onBatchCompleted val batchInfoCompleted = BatchInfo(Time(1000), receivedBlockInfo, 1000, Some(2000), None) listener.onBatchCompleted(StreamingListenerBatchCompleted(batchInfoCompleted)) listener.waitingBatches should be (Nil) listener.runningBatches should be (Nil) - listener.retainedCompletedBatches should be (List(batchInfoCompleted)) - listener.lastCompletedBatch should be (Some(batchInfoCompleted)) + listener.retainedCompletedBatches should be (List(BatchUIData(batchInfoCompleted))) + listener.lastCompletedBatch should be (Some(BatchUIData(batchInfoCompleted))) listener.numUnprocessedBatches should be (0) listener.numTotalCompletedBatches should be (1) listener.numTotalProcessedRecords should be (600) @@ -116,4 +157,55 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { listener.retainedCompletedBatches.size should be (limit) listener.numTotalCompletedBatches should be(limit + 10) } + + test("out-of-order onJobStart and onBatchXXX") { + val ssc = setupStreams(input, operation) + val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 100) + val listener = new StreamingJobProgressListener(ssc) + + // fulfill completedBatchInfos + for(i <- 0 until limit) { + val batchInfoCompleted = + BatchInfo(Time(1000 + i * 100), Map.empty, 1000 + i * 100, Some(2000 + i * 100), None) + listener.onBatchCompleted(StreamingListenerBatchCompleted(batchInfoCompleted)) + val jobStart = createJobStart(Time(1000 + i * 100), outputOpId = 0, jobId = 1) + listener.onJobStart(jobStart) + } + + // onJobStart happens before onBatchSubmitted + val jobStart = createJobStart(Time(1000 + limit * 100), outputOpId = 0, jobId = 0) + listener.onJobStart(jobStart) + + val batchInfoSubmitted = + BatchInfo(Time(1000 + limit * 100), Map.empty, (1000 + limit * 100), None, None) + listener.onBatchSubmitted(StreamingListenerBatchSubmitted(batchInfoSubmitted)) + + // We still can see the info retrieved from onJobStart + val batchUIData = listener.getBatchUIData(Time(1000 + limit * 100)) + batchUIData should not be None + batchUIData.get.batchTime should be (batchInfoSubmitted.batchTime) + batchUIData.get.schedulingDelay should be (batchInfoSubmitted.schedulingDelay) + batchUIData.get.processingDelay should be (batchInfoSubmitted.processingDelay) + batchUIData.get.totalDelay should be (batchInfoSubmitted.totalDelay) + batchUIData.get.receiverNumRecords should be (Map.empty) + batchUIData.get.numRecords should be (0) + batchUIData.get.outputOpIdSparkJobIdPairs should be (Seq(OutputOpIdAndSparkJobId(0, 0))) + + // A lot of "onBatchCompleted"s happen before "onJobStart" + for(i <- limit + 1 to limit * 2) { + val batchInfoCompleted = + BatchInfo(Time(1000 + i * 100), Map.empty, 1000 + i * 100, Some(2000 + i * 100), None) + listener.onBatchCompleted(StreamingListenerBatchCompleted(batchInfoCompleted)) + } + + for(i <- limit + 1 to limit * 2) { + val jobStart = createJobStart(Time(1000 + i * 100), outputOpId = 0, jobId = 1) + listener.onJobStart(jobStart) + } + + // We should not leak memory + listener.batchTimeToOutputOpIdSparkJobIdPair.size() should be <= + (listener.waitingBatches.size + listener.runningBatches.size + + listener.retainedCompletedBatches.size + 10) + } } From 7143f6e9718bae9cffa0a73df03ba8c9860ee129 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=BA=91=E5=B3=A4?= Date: Wed, 29 Apr 2015 18:23:42 -0700 Subject: [PATCH 484/591] [SPARK-7234][SQL] Fix DateType mismatch when codegen on. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Author: 云峤 Closes #5778 from kaka1992/fix_codegenon_datetype_mismatch and squashes the following commits: 1ad4cff [云峤] SPARK-7234 fix dateType mismatch --- .../spark/sql/catalyst/expressions/codegen/CodeGenerator.scala | 1 + 1 file changed, 1 insertion(+) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala index dbc92fb93e95e..d17af0e7ff87e 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala @@ -672,6 +672,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin case DoubleType => ru.Literal(Constant(-1.toDouble)) case DecimalType() => q"org.apache.spark.sql.types.Decimal(-1)" case IntegerType => ru.Literal(Constant(-1)) + case DateType => ru.Literal(Constant(-1)) case _ => ru.Literal(Constant(null)) } From 5553198fe521fb38b600b7687f7780d89a6e1cb9 Mon Sep 17 00:00:00 2001 From: Burak Yavuz Date: Wed, 29 Apr 2015 19:13:47 -0700 Subject: [PATCH 485/591] [SPARK-7156][SQL] Addressed follow up comments for randomSplit small fixes regarding comments in PR #5761 cc rxin Author: Burak Yavuz Closes #5795 from brkyvz/split-followup and squashes the following commits: 369c522 [Burak Yavuz] changed wording a little 1ea456f [Burak Yavuz] Addressed follow up comments --- python/pyspark/sql/dataframe.py | 7 ++++++- .../src/main/scala/org/apache/spark/sql/DataFrame.scala | 2 +- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index 3074af3ed2e83..5908ebc990a56 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -437,6 +437,10 @@ def sample(self, withReplacement, fraction, seed=None): def randomSplit(self, weights, seed=None): """Randomly splits this :class:`DataFrame` with the provided weights. + :param weights: list of doubles as weights with which to split the DataFrame. Weights will + be normalized if they don't sum up to 1.0. + :param seed: The seed for sampling. + >>> splits = df4.randomSplit([1.0, 2.0], 24) >>> splits[0].count() 1 @@ -445,7 +449,8 @@ def randomSplit(self, weights, seed=None): 3 """ for w in weights: - assert w >= 0.0, "Negative weight value: %s" % w + if w < 0.0: + raise ValueError("Weights must be positive. Found weight value: %s" % w) seed = seed if seed is not None else random.randint(0, sys.maxsize) rdd_array = self._jdf.randomSplit(_to_seq(self.sql_ctx._sc, weights), long(seed)) return [DataFrame(rdd, self.sql_ctx) for rdd in rdd_array] diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 0d02e14c21be0..2669300029545 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -752,7 +752,7 @@ class DataFrame private[sql]( * @param seed Seed for sampling. * @group dfops */ - def randomSplit(weights: List[Double], seed: Long): Array[DataFrame] = { + private[spark] def randomSplit(weights: List[Double], seed: Long): Array[DataFrame] = { randomSplit(weights.toArray, seed) } From ba49eb1625b1190d8aaf2c55dc1f6309ac3e080c Mon Sep 17 00:00:00 2001 From: DB Tsai Date: Wed, 29 Apr 2015 21:44:41 -0700 Subject: [PATCH 486/591] Some code clean up. Author: DB Tsai Closes #5794 from dbtsai/clean and squashes the following commits: ad639dd [DB Tsai] Indentation 834d527 [DB Tsai] Some code clean up. --- .../spark/mllib/util/LinearDataGenerator.scala | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala index 91c2f4ccd3d17..b1a4517344970 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala @@ -75,8 +75,8 @@ object LinearDataGenerator { seed: Int, eps: Double = 0.1): Seq[LabeledPoint] = { generateLinearInput(intercept, weights, - Array.fill[Double](weights.size)(0.0), - Array.fill[Double](weights.size)(1.0 / 3.0), + Array.fill[Double](weights.length)(0.0), + Array.fill[Double](weights.length)(1.0 / 3.0), nPoints, seed, eps)} /** @@ -105,13 +105,12 @@ object LinearDataGenerator { val x = Array.fill[Array[Double]](nPoints)( Array.fill[Double](weights.length)(rnd.nextDouble())) - x.foreach { - case v => - var i = 0 - while (i < v.length) { - v(i) = (v(i) - 0.5) * math.sqrt(12.0 * xVariance(i)) + xMean(i) - i += 1 - } + x.foreach { v => + var i = 0 + while (i < v.length) { + v(i) = (v(i) - 0.5) * math.sqrt(12.0 * xVariance(i)) + xMean(i) + i += 1 + } } val y = x.map { xi => From 4459514497eb76e6f2465d071857854390453805 Mon Sep 17 00:00:00 2001 From: Zhongshuai Pei <799203320@qq.com> Date: Wed, 29 Apr 2015 22:44:14 -0700 Subject: [PATCH 487/591] [SPARK-7225][SQL] CombineLimits optimizer does not work SQL ``` select key from (select key from src limit 100) t2 limit 10 ``` Optimized Logical Plan before modifying ``` == Optimized Logical Plan == Limit 10 Limit 100 Project key#3 MetastoreRelation default, src, None ``` Optimized Logical Plan after modifying ``` == Optimized Logical Plan == Limit 10 Project [key#1] MetastoreRelation default, src, None ``` Author: Zhongshuai Pei <799203320@qq.com> Author: DoingDone9 <799203320@qq.com> Closes #5770 from DoingDone9/limitOptimizer and squashes the following commits: c68eaa7 [Zhongshuai Pei] Update CombiningLimitsSuite.scala 97e18cf [Zhongshuai Pei] Update Optimizer.scala 19ab875 [Zhongshuai Pei] Update CombiningLimitsSuite.scala 7db4566 [Zhongshuai Pei] Update CombiningLimitsSuite.scala e2a491d [Zhongshuai Pei] Update Optimizer.scala f03fe7f [Zhongshuai Pei] Merge pull request #12 from apache/master f12fa50 [Zhongshuai Pei] Merge pull request #10 from apache/master f61210c [Zhongshuai Pei] Merge pull request #9 from apache/master 34b1a9a [Zhongshuai Pei] Merge pull request #8 from apache/master 802261c [DoingDone9] Merge pull request #7 from apache/master d00303b [DoingDone9] Merge pull request #6 from apache/master 98b134f [DoingDone9] Merge pull request #5 from apache/master 161cae3 [DoingDone9] Merge pull request #4 from apache/master c87e8b6 [DoingDone9] Merge pull request #3 from apache/master cb1852d [DoingDone9] Merge pull request #2 from apache/master c3f046f [DoingDone9] Merge pull request #1 from apache/master --- .../sql/catalyst/optimizer/Optimizer.scala | 15 +++++++-------- .../optimizer/CombiningLimitsSuite.scala | 19 +++++++++++++++++++ 2 files changed, 26 insertions(+), 8 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala index 2d03fbfb0d311..485761dd8538b 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala @@ -36,7 +36,13 @@ object DefaultOptimizer extends Optimizer { // SubQueries are only needed for analysis and can be removed before execution. Batch("Remove SubQueries", FixedPoint(100), EliminateSubQueries) :: - Batch("Combine Limits", FixedPoint(100), + Batch("Operator Reordering", FixedPoint(100), + UnionPushdown, + CombineFilters, + PushPredicateThroughProject, + PushPredicateThroughJoin, + PushPredicateThroughGenerate, + ColumnPruning, CombineLimits) :: Batch("ConstantFolding", FixedPoint(100), NullPropagation, @@ -49,13 +55,6 @@ object DefaultOptimizer extends Optimizer { OptimizeIn) :: Batch("Decimal Optimizations", FixedPoint(100), DecimalAggregates) :: - Batch("Filter Pushdown", FixedPoint(100), - UnionPushdown, - CombineFilters, - PushPredicateThroughProject, - PushPredicateThroughJoin, - PushPredicateThroughGenerate, - ColumnPruning) :: Batch("LocalRelation", FixedPoint(100), ConvertToLocalRelation) :: Nil } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala index 2d16d668fd522..a30052b38fc11 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala @@ -27,6 +27,8 @@ class CombiningLimitsSuite extends PlanTest { object Optimize extends RuleExecutor[LogicalPlan] { val batches = + Batch("Filter Pushdown", FixedPoint(100), + ColumnPruning) :: Batch("Combine Limit", FixedPoint(10), CombineLimits) :: Batch("Constant Folding", FixedPoint(10), @@ -69,4 +71,21 @@ class CombiningLimitsSuite extends PlanTest { comparePlans(optimized, correctAnswer) } + + test("limits: combines two limits after ColumnPruning") { + val originalQuery = + testRelation + .select('a) + .limit(2) + .select('a) + .limit(5) + + val optimized = Optimize.execute(originalQuery.analyze) + val correctAnswer = + testRelation + .select('a) + .limit(2).analyze + + comparePlans(optimized, correctAnswer) + } } From 254e0509762937acc9c72b432d5d953bf72c3c52 Mon Sep 17 00:00:00 2001 From: Vincenzo Selvaggio Date: Wed, 29 Apr 2015 23:21:21 -0700 Subject: [PATCH 488/591] [SPARK-1406] Mllib pmml model export See PDF attached to the JIRA issue 1406. The contribution is my original work and I license the work to the project under the project's open source license. Author: Vincenzo Selvaggio Author: Xiangrui Meng Author: selvinsource Closes #3062 from selvinsource/mllib_pmml_model_export_SPARK-1406 and squashes the following commits: 852aac6 [Vincenzo Selvaggio] [SPARK-1406] Update JPMML version to 1.1.15 in LICENSE file 085cf42 [Vincenzo Selvaggio] [SPARK-1406] Added Double Min and Max Fixed scala style 30165c4 [Vincenzo Selvaggio] [SPARK-1406] Fixed extreme cases for logit 7a5e0ec [Vincenzo Selvaggio] [SPARK-1406] Binary classification for SVM and Logistic Regression cfcb596 [Vincenzo Selvaggio] [SPARK-1406] Throw IllegalArgumentException when exporting a multinomial logistic regression 25dce33 [Vincenzo Selvaggio] [SPARK-1406] Update code to latest pmml model dea98ca [Vincenzo Selvaggio] [SPARK-1406] Exclude transitive dependency for pmml model 66b7c12 [Vincenzo Selvaggio] [SPARK-1406] Updated pmml model lib to 1.1.15, latest Java 6 compatible a0a55f7 [Vincenzo Selvaggio] Merge pull request #2 from mengxr/SPARK-1406 3c22f79 [Xiangrui Meng] more code style e2313df [Vincenzo Selvaggio] Merge pull request #1 from mengxr/SPARK-1406 472d757 [Xiangrui Meng] fix code style 1676e15 [Vincenzo Selvaggio] fixed scala issue e2ffae8 [Vincenzo Selvaggio] fixed scala style b8823b0 [Vincenzo Selvaggio] Merge remote-tracking branch 'upstream/master' into mllib_pmml_model_export_SPARK-1406 b25bbf7 [Vincenzo Selvaggio] [SPARK-1406] Added export of pmml to distributed file system using the spark context 7a949d0 [Vincenzo Selvaggio] [SPARK-1406] Fixed scala style f46c75c [Vincenzo Selvaggio] [SPARK-1406] Added PMMLExportable to supported models 7b33b4e [Vincenzo Selvaggio] [SPARK-1406] Added a PMMLExportable interface Restructured code in a new package mllib.pmml Supported models implements the new PMMLExportable interface: LogisticRegression, SVM, KMeansModel, LinearRegression, RidgeRegression, Lasso d559ec5 [Vincenzo Selvaggio] Merge remote-tracking branch 'upstream/master' into mllib_pmml_model_export_SPARK-1406 8fe12bb [Vincenzo Selvaggio] [SPARK-1406] Adjusted logistic regression export description and target categories 03bc3a5 [Vincenzo Selvaggio] added logistic regression da2ec11 [Vincenzo Selvaggio] [SPARK-1406] added linear SVM PMML export 82f2131 [Vincenzo Selvaggio] Merge remote-tracking branch 'upstream/master' into mllib_pmml_model_export_SPARK-1406 19adf29 [Vincenzo Selvaggio] [SPARK-1406] Fixed scala style 1faf985 [Vincenzo Selvaggio] [SPARK-1406] Added target field to the regression model for completeness Adjusted unit test to deal with this change 3ae8ae5 [Vincenzo Selvaggio] [SPARK-1406] Adjusted imported order according to the guidelines c67ce81 [Vincenzo Selvaggio] Merge remote-tracking branch 'upstream/master' into mllib_pmml_model_export_SPARK-1406 78515ec [Vincenzo Selvaggio] [SPARK-1406] added pmml export for LinearRegressionModel, RidgeRegressionModel and LassoModel e29dfb9 [Vincenzo Selvaggio] removed version, by default is set to 4.2 (latest from jpmml) removed copyright ae8b993 [Vincenzo Selvaggio] updated some commented tests to use the new ModelExporter object reordered the imports df8a89e [Vincenzo Selvaggio] added pmml version to pmml model changed the copyright to spark a1b4dc3 [Vincenzo Selvaggio] updated imports 834ca44 [Vincenzo Selvaggio] reordered the import accordingly to the guidelines 349a76b [Vincenzo Selvaggio] new helper object to serialize the models to pmml format c3ef9b8 [Vincenzo Selvaggio] set it to private 6357b98 [Vincenzo Selvaggio] set it to private e1eb251 [Vincenzo Selvaggio] removed serialization part, this will be part of the ModelExporter helper object aba5ee1 [Vincenzo Selvaggio] fixed cluster export cd6c07c [Vincenzo Selvaggio] fixed scala style to run tests f75b988 [Vincenzo Selvaggio] Merge remote-tracking branch 'origin/master' into mllib_pmml_model_export_SPARK-1406 07a29bf [selvinsource] Update LICENSE 8841439 [Vincenzo Selvaggio] adjust scala style in order to compile 1433b11 [Vincenzo Selvaggio] complete suite tests 8e71b8d [Vincenzo Selvaggio] kmeans pmml export implementation 9bc494f [Vincenzo Selvaggio] added scala suite tests added saveLocalFile to ModelExport trait 226e184 [Vincenzo Selvaggio] added javadoc and export model type in case there is a need to support other types of export (not just PMML) a0e3679 [Vincenzo Selvaggio] export and pmml export traits kmeans test implementation --- LICENSE | 1 + mllib/pom.xml | 15 +++ .../classification/LogisticRegression.scala | 3 +- .../spark/mllib/classification/SVM.scala | 3 +- .../spark/mllib/clustering/KMeansModel.scala | 4 +- .../spark/mllib/pmml/PMMLExportable.scala | 74 +++++++++++++++ .../BinaryClassificationPMMLModelExport.scala | 90 ++++++++++++++++++ .../GeneralizedLinearPMMLModelExport.scala | 75 +++++++++++++++ .../pmml/export/KMeansPMMLModelExport.scala | 83 ++++++++++++++++ .../mllib/pmml/export/PMMLModelExport.scala | 47 +++++++++ .../pmml/export/PMMLModelExportFactory.scala | 64 +++++++++++++ .../apache/spark/mllib/regression/Lasso.scala | 3 +- .../mllib/regression/LinearRegression.scala | 3 +- .../mllib/regression/RidgeRegression.scala | 3 +- ...ryClassificationPMMLModelExportSuite.scala | 84 ++++++++++++++++ ...eneralizedLinearPMMLModelExportSuite.scala | 84 ++++++++++++++++ .../export/KMeansPMMLModelExportSuite.scala | 49 ++++++++++ .../export/PMMLModelExportFactorySuite.scala | 95 +++++++++++++++++++ 18 files changed, 774 insertions(+), 6 deletions(-) create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExport.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExport.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExport.scala create mode 100644 mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactory.scala create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExportSuite.scala create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExportSuite.scala create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExportSuite.scala create mode 100644 mllib/src/test/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactorySuite.scala diff --git a/LICENSE b/LICENSE index 9b364a4d00079..21c42e9a20fa3 100644 --- a/LICENSE +++ b/LICENSE @@ -814,6 +814,7 @@ BSD-style licenses The following components are provided under a BSD-style license. See project link for details. (BSD 3 Clause) core (com.github.fommil.netlib:core:1.1.2 - https://github.com/fommil/netlib-java/core) + (BSD 3 Clause) JPMML-Model (org.jpmml:pmml-model:1.1.15 - https://github.com/jpmml/jpmml-model) (BSD 3-clause style license) jblas (org.jblas:jblas:1.2.3 - http://jblas.org/) (BSD License) AntLR Parser Generator (antlr:antlr:2.7.7 - http://www.antlr.org/) (BSD License) Javolution (javolution:javolution:5.5.1 - http://javolution.org) diff --git a/mllib/pom.xml b/mllib/pom.xml index 5dfab36c76907..a3c57ae26000b 100644 --- a/mllib/pom.xml +++ b/mllib/pom.xml @@ -109,6 +109,21 @@ test-jar test     + + org.jpmml + pmml-model + 1.1.15 + + + com.sun.xml.fastinfoset + FastInfoset + + + com.sun.istack + istack-commons-runtime + + + diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala index 057b628c6a586..bd2e9079ce1ae 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala @@ -23,6 +23,7 @@ import org.apache.spark.mllib.classification.impl.GLMClassificationModel import org.apache.spark.mllib.linalg.BLAS.dot import org.apache.spark.mllib.linalg.{DenseVector, Vector} import org.apache.spark.mllib.optimization._ +import org.apache.spark.mllib.pmml.PMMLExportable import org.apache.spark.mllib.regression._ import org.apache.spark.mllib.util.{DataValidators, Saveable, Loader} import org.apache.spark.rdd.RDD @@ -46,7 +47,7 @@ class LogisticRegressionModel ( val numFeatures: Int, val numClasses: Int) extends GeneralizedLinearModel(weights, intercept) with ClassificationModel with Serializable - with Saveable { + with Saveable with PMMLExportable { if (numClasses == 2) { require(weights.size == numFeatures, diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala index 52fb62dcff1b4..33104cf06c6ea 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala @@ -22,6 +22,7 @@ import org.apache.spark.annotation.Experimental import org.apache.spark.mllib.classification.impl.GLMClassificationModel import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.optimization._ +import org.apache.spark.mllib.pmml.PMMLExportable import org.apache.spark.mllib.regression._ import org.apache.spark.mllib.util.{DataValidators, Loader, Saveable} import org.apache.spark.rdd.RDD @@ -36,7 +37,7 @@ class SVMModel ( override val weights: Vector, override val intercept: Double) extends GeneralizedLinearModel(weights, intercept) with ClassificationModel with Serializable - with Saveable { + with Saveable with PMMLExportable { private var threshold: Option[Double] = Some(0.0) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala index e4e411a3c8b42..ba228b11fcec3 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala @@ -25,6 +25,7 @@ import org.json4s.jackson.JsonMethods._ import org.apache.spark.api.java.JavaRDD import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.pmml.PMMLExportable import org.apache.spark.mllib.util.{Loader, Saveable} import org.apache.spark.rdd.RDD import org.apache.spark.SparkContext @@ -34,7 +35,8 @@ import org.apache.spark.sql.Row /** * A clustering model for K-means. Each point belongs to the cluster with the closest center. */ -class KMeansModel (val clusterCenters: Array[Vector]) extends Saveable with Serializable { +class KMeansModel ( + val clusterCenters: Array[Vector]) extends Saveable with Serializable with PMMLExportable { /** A Java-friendly constructor that takes an Iterable of Vectors. */ def this(centers: java.lang.Iterable[Vector]) = this(centers.asScala.toArray) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala new file mode 100644 index 0000000000000..354e90f3eeaa6 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala @@ -0,0 +1,74 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.pmml + +import java.io.{File, OutputStream, StringWriter} +import javax.xml.transform.stream.StreamResult + +import org.jpmml.model.JAXBUtil + +import org.apache.spark.SparkContext +import org.apache.spark.mllib.pmml.export.PMMLModelExportFactory + +/** + * Export model to the PMML format + * Predictive Model Markup Language (PMML) is an XML-based file format + * developed by the Data Mining Group (www.dmg.org). + */ +trait PMMLExportable { + + /** + * Export the model to the stream result in PMML format + */ + private def toPMML(streamResult: StreamResult): Unit = { + val pmmlModelExport = PMMLModelExportFactory.createPMMLModelExport(this) + JAXBUtil.marshalPMML(pmmlModelExport.getPmml, streamResult) + } + + /** + * Export the model to a local file in PMML format + */ + def toPMML(localPath: String): Unit = { + toPMML(new StreamResult(new File(localPath))) + } + + /** + * Export the model to a directory on a distributed file system in PMML format + */ + def toPMML(sc: SparkContext, path: String): Unit = { + val pmml = toPMML() + sc.parallelize(Array(pmml), 1).saveAsTextFile(path) + } + + /** + * Export the model to the OutputStream in PMML format + */ + def toPMML(outputStream: OutputStream): Unit = { + toPMML(new StreamResult(outputStream)) + } + + /** + * Export the model to a String in PMML format + */ + def toPMML(): String = { + val writer = new StringWriter + toPMML(new StreamResult(writer)) + writer.toString + } + +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala new file mode 100644 index 0000000000000..34b447584e521 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala @@ -0,0 +1,90 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.pmml.export + +import scala.{Array => SArray} + +import org.dmg.pmml._ + +import org.apache.spark.mllib.regression.GeneralizedLinearModel + +/** + * PMML Model Export for GeneralizedLinearModel class with binary ClassificationModel + */ +private[mllib] class BinaryClassificationPMMLModelExport( + model : GeneralizedLinearModel, + description : String, + normalizationMethod : RegressionNormalizationMethodType, + threshold: Double) + extends PMMLModelExport { + + populateBinaryClassificationPMML() + + /** + * Export the input LogisticRegressionModel or SVMModel to PMML format. + */ + private def populateBinaryClassificationPMML(): Unit = { + pmml.getHeader.setDescription(description) + + if (model.weights.size > 0) { + val fields = new SArray[FieldName](model.weights.size) + val dataDictionary = new DataDictionary + val miningSchema = new MiningSchema + val regressionTableYES = new RegressionTable(model.intercept).withTargetCategory("1") + var interceptNO = threshold + if (RegressionNormalizationMethodType.LOGIT == normalizationMethod) { + if (threshold <= 0) { + interceptNO = Double.MinValue + } else if (threshold >= 1) { + interceptNO = Double.MaxValue + } else { + interceptNO = -math.log(1 / threshold - 1) + } + } + val regressionTableNO = new RegressionTable(interceptNO).withTargetCategory("0") + val regressionModel = new RegressionModel() + .withFunctionName(MiningFunctionType.CLASSIFICATION) + .withMiningSchema(miningSchema) + .withModelName(description) + .withNormalizationMethod(normalizationMethod) + .withRegressionTables(regressionTableYES, regressionTableNO) + + for (i <- 0 until model.weights.size) { + fields(i) = FieldName.create("field_" + i) + dataDictionary.withDataFields(new DataField(fields(i), OpType.CONTINUOUS, DataType.DOUBLE)) + miningSchema + .withMiningFields(new MiningField(fields(i)) + .withUsageType(FieldUsageType.ACTIVE)) + regressionTableYES.withNumericPredictors(new NumericPredictor(fields(i), model.weights(i))) + } + + // add target field + val targetField = FieldName.create("target") + dataDictionary + .withDataFields(new DataField(targetField, OpType.CATEGORICAL, DataType.STRING)) + miningSchema + .withMiningFields(new MiningField(targetField) + .withUsageType(FieldUsageType.TARGET)) + + dataDictionary.withNumberOfFields(dataDictionary.getDataFields.size) + + pmml.setDataDictionary(dataDictionary) + pmml.withModels(regressionModel) + } + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExport.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExport.scala new file mode 100644 index 0000000000000..1874786af0002 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExport.scala @@ -0,0 +1,75 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.pmml.export + +import scala.{Array => SArray} + +import org.dmg.pmml._ + +import org.apache.spark.mllib.regression.GeneralizedLinearModel + +/** + * PMML Model Export for GeneralizedLinearModel abstract class + */ +private[mllib] class GeneralizedLinearPMMLModelExport( + model: GeneralizedLinearModel, + description: String) + extends PMMLModelExport { + + populateGeneralizedLinearPMML(model) + + /** + * Export the input GeneralizedLinearModel model to PMML format. + */ + private def populateGeneralizedLinearPMML(model: GeneralizedLinearModel): Unit = { + pmml.getHeader.setDescription(description) + + if (model.weights.size > 0) { + val fields = new SArray[FieldName](model.weights.size) + val dataDictionary = new DataDictionary + val miningSchema = new MiningSchema + val regressionTable = new RegressionTable(model.intercept) + val regressionModel = new RegressionModel() + .withFunctionName(MiningFunctionType.REGRESSION) + .withMiningSchema(miningSchema) + .withModelName(description) + .withRegressionTables(regressionTable) + + for (i <- 0 until model.weights.size) { + fields(i) = FieldName.create("field_" + i) + dataDictionary.withDataFields(new DataField(fields(i), OpType.CONTINUOUS, DataType.DOUBLE)) + miningSchema + .withMiningFields(new MiningField(fields(i)) + .withUsageType(FieldUsageType.ACTIVE)) + regressionTable.withNumericPredictors(new NumericPredictor(fields(i), model.weights(i))) + } + + // for completeness add target field + val targetField = FieldName.create("target") + dataDictionary.withDataFields(new DataField(targetField, OpType.CONTINUOUS, DataType.DOUBLE)) + miningSchema + .withMiningFields(new MiningField(targetField) + .withUsageType(FieldUsageType.TARGET)) + + dataDictionary.withNumberOfFields(dataDictionary.getDataFields.size) + + pmml.setDataDictionary(dataDictionary) + pmml.withModels(regressionModel) + } + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExport.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExport.scala new file mode 100644 index 0000000000000..069e7afc9fca0 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExport.scala @@ -0,0 +1,83 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.pmml.export + +import scala.{Array => SArray} + +import org.dmg.pmml._ + +import org.apache.spark.mllib.clustering.KMeansModel + +/** + * PMML Model Export for KMeansModel class + */ +private[mllib] class KMeansPMMLModelExport(model : KMeansModel) extends PMMLModelExport{ + + populateKMeansPMML(model) + + /** + * Export the input KMeansModel model to PMML format. + */ + private def populateKMeansPMML(model : KMeansModel): Unit = { + pmml.getHeader.setDescription("k-means clustering") + + if (model.clusterCenters.length > 0) { + val clusterCenter = model.clusterCenters(0) + val fields = new SArray[FieldName](clusterCenter.size) + val dataDictionary = new DataDictionary + val miningSchema = new MiningSchema + val comparisonMeasure = new ComparisonMeasure() + .withKind(ComparisonMeasure.Kind.DISTANCE) + .withMeasure(new SquaredEuclidean()) + val clusteringModel = new ClusteringModel() + .withModelName("k-means") + .withMiningSchema(miningSchema) + .withComparisonMeasure(comparisonMeasure) + .withFunctionName(MiningFunctionType.CLUSTERING) + .withModelClass(ClusteringModel.ModelClass.CENTER_BASED) + .withNumberOfClusters(model.clusterCenters.length) + + for (i <- 0 until clusterCenter.size) { + fields(i) = FieldName.create("field_" + i) + dataDictionary.withDataFields(new DataField(fields(i), OpType.CONTINUOUS, DataType.DOUBLE)) + miningSchema + .withMiningFields(new MiningField(fields(i)) + .withUsageType(FieldUsageType.ACTIVE)) + clusteringModel.withClusteringFields( + new ClusteringField(fields(i)).withCompareFunction(CompareFunctionType.ABS_DIFF)) + } + + dataDictionary.withNumberOfFields(dataDictionary.getDataFields.size) + + for (i <- 0 until model.clusterCenters.length) { + val cluster = new Cluster() + .withName("cluster_" + i) + .withArray(new org.dmg.pmml.Array() + .withType(Array.Type.REAL) + .withN(clusterCenter.size) + .withValue(model.clusterCenters(i).toArray.mkString(" "))) + // we don't have the size of the single cluster but only the centroids (withValue) + // .withSize(value) + clusteringModel.withClusters(cluster) + } + + pmml.setDataDictionary(dataDictionary) + pmml.withModels(clusteringModel) + } + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExport.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExport.scala new file mode 100644 index 0000000000000..ebdeae50bb32f --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExport.scala @@ -0,0 +1,47 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.pmml.export + +import java.text.SimpleDateFormat +import java.util.Date + +import scala.beans.BeanProperty + +import org.dmg.pmml.{Application, Header, PMML, Timestamp} + +private[mllib] trait PMMLModelExport { + + /** + * Holder of the exported model in PMML format + */ + @BeanProperty + val pmml: PMML = new PMML + + setHeader(pmml) + + private def setHeader(pmml: PMML): Unit = { + val version = getClass.getPackage.getImplementationVersion + val app = new Application().withName("Apache Spark MLlib").withVersion(version) + val timestamp = new Timestamp() + .withContent(new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss").format(new Date())) + val header = new Header() + .withApplication(app) + .withTimestamp(timestamp) + pmml.setHeader(header) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactory.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactory.scala new file mode 100644 index 0000000000000..c16e83d6a067d --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactory.scala @@ -0,0 +1,64 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.pmml.export + +import org.dmg.pmml.RegressionNormalizationMethodType + +import org.apache.spark.mllib.classification.LogisticRegressionModel +import org.apache.spark.mllib.classification.SVMModel +import org.apache.spark.mllib.clustering.KMeansModel +import org.apache.spark.mllib.regression.LassoModel +import org.apache.spark.mllib.regression.LinearRegressionModel +import org.apache.spark.mllib.regression.RidgeRegressionModel + +private[mllib] object PMMLModelExportFactory { + + /** + * Factory object to help creating the necessary PMMLModelExport implementation + * taking as input the machine learning model (for example KMeansModel). + */ + def createPMMLModelExport(model: Any): PMMLModelExport = { + model match { + case kmeans: KMeansModel => + new KMeansPMMLModelExport(kmeans) + case linear: LinearRegressionModel => + new GeneralizedLinearPMMLModelExport(linear, "linear regression") + case ridge: RidgeRegressionModel => + new GeneralizedLinearPMMLModelExport(ridge, "ridge regression") + case lasso: LassoModel => + new GeneralizedLinearPMMLModelExport(lasso, "lasso regression") + case svm: SVMModel => + new BinaryClassificationPMMLModelExport( + svm, "linear SVM", RegressionNormalizationMethodType.NONE, + svm.getThreshold.getOrElse(0.0)) + case logistic: LogisticRegressionModel => + if (logistic.numClasses == 2) { + new BinaryClassificationPMMLModelExport( + logistic, "logistic regression", RegressionNormalizationMethodType.LOGIT, + logistic.getThreshold.getOrElse(0.5)) + } else { + throw new IllegalArgumentException( + "PMML Export not supported for Multinomial Logistic Regression") + } + case _ => + throw new IllegalArgumentException( + "PMML Export not supported for model: " + model.getClass.getName) + } + } + +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala index e8b03816573cf..4f482384f0f38 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala @@ -20,6 +20,7 @@ package org.apache.spark.mllib.regression import org.apache.spark.SparkContext import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.optimization._ +import org.apache.spark.mllib.pmml.PMMLExportable import org.apache.spark.mllib.regression.impl.GLMRegressionModel import org.apache.spark.mllib.util.{Saveable, Loader} import org.apache.spark.rdd.RDD @@ -34,7 +35,7 @@ class LassoModel ( override val weights: Vector, override val intercept: Double) extends GeneralizedLinearModel(weights, intercept) - with RegressionModel with Serializable with Saveable { + with RegressionModel with Serializable with Saveable with PMMLExportable { override protected def predictPoint( dataMatrix: Vector, diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala index 6fa7ad52a5b33..9453c4f66c216 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala @@ -20,6 +20,7 @@ package org.apache.spark.mllib.regression import org.apache.spark.SparkContext import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.optimization._ +import org.apache.spark.mllib.pmml.PMMLExportable import org.apache.spark.mllib.regression.impl.GLMRegressionModel import org.apache.spark.mllib.util.{Saveable, Loader} import org.apache.spark.rdd.RDD @@ -34,7 +35,7 @@ class LinearRegressionModel ( override val weights: Vector, override val intercept: Double) extends GeneralizedLinearModel(weights, intercept) with RegressionModel with Serializable - with Saveable { + with Saveable with PMMLExportable { override protected def predictPoint( dataMatrix: Vector, diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala index 309f9af466457..e0c03d8180c7a 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala @@ -20,6 +20,7 @@ package org.apache.spark.mllib.regression import org.apache.spark.SparkContext import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.optimization._ +import org.apache.spark.mllib.pmml.PMMLExportable import org.apache.spark.mllib.regression.impl.GLMRegressionModel import org.apache.spark.mllib.util.{Loader, Saveable} import org.apache.spark.rdd.RDD @@ -35,7 +36,7 @@ class RidgeRegressionModel ( override val weights: Vector, override val intercept: Double) extends GeneralizedLinearModel(weights, intercept) - with RegressionModel with Serializable with Saveable { + with RegressionModel with Serializable with Saveable with PMMLExportable { override protected def predictPoint( dataMatrix: Vector, diff --git a/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExportSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExportSuite.scala new file mode 100644 index 0000000000000..0b646cf1ce6c4 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExportSuite.scala @@ -0,0 +1,84 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.pmml.export + +import org.dmg.pmml.RegressionModel +import org.dmg.pmml.RegressionNormalizationMethodType +import org.scalatest.FunSuite + +import org.apache.spark.mllib.classification.LogisticRegressionModel +import org.apache.spark.mllib.classification.SVMModel +import org.apache.spark.mllib.util.LinearDataGenerator + +class BinaryClassificationPMMLModelExportSuite extends FunSuite { + + test("logistic regression PMML export") { + val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17) + val logisticRegressionModel = + new LogisticRegressionModel(linearInput(0).features, linearInput(0).label) + + val logisticModelExport = PMMLModelExportFactory.createPMMLModelExport(logisticRegressionModel) + + // assert that the PMML format is as expected + assert(logisticModelExport.isInstanceOf[PMMLModelExport]) + val pmml = logisticModelExport.asInstanceOf[PMMLModelExport].getPmml + assert(pmml.getHeader.getDescription === "logistic regression") + // check that the number of fields match the weights size + assert(pmml.getDataDictionary.getNumberOfFields === logisticRegressionModel.weights.size + 1) + // This verify that there is a model attached to the pmml object and the model is a regression + // one. It also verifies that the pmml model has a regression table (for target category 1) + // with the same number of predictors of the model weights. + val pmmlRegressionModel = pmml.getModels.get(0).asInstanceOf[RegressionModel] + assert(pmmlRegressionModel.getRegressionTables.get(0).getTargetCategory === "1") + assert(pmmlRegressionModel.getRegressionTables.get(0).getNumericPredictors.size + === logisticRegressionModel.weights.size) + // verify if there is a second table with target category 0 and no predictors + assert(pmmlRegressionModel.getRegressionTables.get(1).getTargetCategory === "0") + assert(pmmlRegressionModel.getRegressionTables.get(1).getNumericPredictors.size === 0) + // ensure logistic regression has normalization method set to LOGIT + assert(pmmlRegressionModel.getNormalizationMethod() == RegressionNormalizationMethodType.LOGIT) + } + + test("linear SVM PMML export") { + val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17) + val svmModel = new SVMModel(linearInput(0).features, linearInput(0).label) + + val svmModelExport = PMMLModelExportFactory.createPMMLModelExport(svmModel) + + // assert that the PMML format is as expected + assert(svmModelExport.isInstanceOf[PMMLModelExport]) + val pmml = svmModelExport.getPmml + assert(pmml.getHeader.getDescription + === "linear SVM") + // check that the number of fields match the weights size + assert(pmml.getDataDictionary.getNumberOfFields === svmModel.weights.size + 1) + // This verify that there is a model attached to the pmml object and the model is a regression + // one. It also verifies that the pmml model has a regression table (for target category 1) + // with the same number of predictors of the model weights. + val pmmlRegressionModel = pmml.getModels.get(0).asInstanceOf[RegressionModel] + assert(pmmlRegressionModel.getRegressionTables.get(0).getTargetCategory === "1") + assert(pmmlRegressionModel.getRegressionTables.get(0).getNumericPredictors.size + === svmModel.weights.size) + // verify if there is a second table with target category 0 and no predictors + assert(pmmlRegressionModel.getRegressionTables.get(1).getTargetCategory === "0") + assert(pmmlRegressionModel.getRegressionTables.get(1).getNumericPredictors.size === 0) + // ensure linear SVM has normalization method set to NONE + assert(pmmlRegressionModel.getNormalizationMethod() == RegressionNormalizationMethodType.NONE) + } + +} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExportSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExportSuite.scala new file mode 100644 index 0000000000000..f9afbd888dfc5 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExportSuite.scala @@ -0,0 +1,84 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.pmml.export + +import org.dmg.pmml.RegressionModel +import org.scalatest.FunSuite + +import org.apache.spark.mllib.regression.{LassoModel, LinearRegressionModel, RidgeRegressionModel} +import org.apache.spark.mllib.util.LinearDataGenerator + +class GeneralizedLinearPMMLModelExportSuite extends FunSuite { + + test("linear regression PMML export") { + val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17) + val linearRegressionModel = + new LinearRegressionModel(linearInput(0).features, linearInput(0).label) + val linearModelExport = PMMLModelExportFactory.createPMMLModelExport(linearRegressionModel) + // assert that the PMML format is as expected + assert(linearModelExport.isInstanceOf[PMMLModelExport]) + val pmml = linearModelExport.getPmml + assert(pmml.getHeader.getDescription === "linear regression") + // check that the number of fields match the weights size + assert(pmml.getDataDictionary.getNumberOfFields === linearRegressionModel.weights.size + 1) + // This verifies that there is a model attached to the pmml object and the model is a regression + // one. It also verifies that the pmml model has a regression table with the same number of + // predictors of the model weights. + val pmmlRegressionModel = pmml.getModels.get(0).asInstanceOf[RegressionModel] + assert(pmmlRegressionModel.getRegressionTables.get(0).getNumericPredictors.size + === linearRegressionModel.weights.size) + } + + test("ridge regression PMML export") { + val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17) + val ridgeRegressionModel = + new RidgeRegressionModel(linearInput(0).features, linearInput(0).label) + val ridgeModelExport = PMMLModelExportFactory.createPMMLModelExport(ridgeRegressionModel) + // assert that the PMML format is as expected + assert(ridgeModelExport.isInstanceOf[PMMLModelExport]) + val pmml = ridgeModelExport.getPmml + assert(pmml.getHeader.getDescription === "ridge regression") + // check that the number of fields match the weights size + assert(pmml.getDataDictionary.getNumberOfFields === ridgeRegressionModel.weights.size + 1) + // This verify that there is a model attached to the pmml object and the model is a regression + // one. It also verifies that the pmml model has a regression table with the same number of + // predictors of the model weights. + val pmmlRegressionModel = pmml.getModels.get(0).asInstanceOf[RegressionModel] + assert(pmmlRegressionModel.getRegressionTables.get(0).getNumericPredictors.size + === ridgeRegressionModel.weights.size) + } + + test("lasso PMML export") { + val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17) + val lassoModel = new LassoModel(linearInput(0).features, linearInput(0).label) + val lassoModelExport = PMMLModelExportFactory.createPMMLModelExport(lassoModel) + // assert that the PMML format is as expected + assert(lassoModelExport.isInstanceOf[PMMLModelExport]) + val pmml = lassoModelExport.getPmml + assert(pmml.getHeader.getDescription === "lasso regression") + // check that the number of fields match the weights size + assert(pmml.getDataDictionary.getNumberOfFields === lassoModel.weights.size + 1) + // This verify that there is a model attached to the pmml object and the model is a regression + // one. It also verifies that the pmml model has a regression table with the same number of + // predictors of the model weights. + val pmmlRegressionModel = pmml.getModels.get(0).asInstanceOf[RegressionModel] + assert(pmmlRegressionModel.getRegressionTables.get(0).getNumericPredictors.size + === lassoModel.weights.size) + } + +} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExportSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExportSuite.scala new file mode 100644 index 0000000000000..b985d0446d7b0 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExportSuite.scala @@ -0,0 +1,49 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.pmml.export + +import org.dmg.pmml.ClusteringModel +import org.scalatest.FunSuite + +import org.apache.spark.mllib.clustering.KMeansModel +import org.apache.spark.mllib.linalg.Vectors + +class KMeansPMMLModelExportSuite extends FunSuite { + + test("KMeansPMMLModelExport generate PMML format") { + val clusterCenters = Array( + Vectors.dense(1.0, 2.0, 6.0), + Vectors.dense(1.0, 3.0, 0.0), + Vectors.dense(1.0, 4.0, 6.0)) + val kmeansModel = new KMeansModel(clusterCenters) + + val modelExport = PMMLModelExportFactory.createPMMLModelExport(kmeansModel) + + // assert that the PMML format is as expected + assert(modelExport.isInstanceOf[PMMLModelExport]) + val pmml = modelExport.asInstanceOf[PMMLModelExport].getPmml + assert(pmml.getHeader.getDescription === "k-means clustering") + // check that the number of fields match the single vector size + assert(pmml.getDataDictionary.getNumberOfFields === clusterCenters(0).size) + // This verify that there is a model attached to the pmml object and the model is a clustering + // one. It also verifies that the pmml model has the same number of clusters of the spark model. + val pmmlClusteringModel = pmml.getModels.get(0).asInstanceOf[ClusteringModel] + assert(pmmlClusteringModel.getNumberOfClusters === clusterCenters.length) + } + +} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactorySuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactorySuite.scala new file mode 100644 index 0000000000000..f28a4ac8ad01f --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/mllib/pmml/export/PMMLModelExportFactorySuite.scala @@ -0,0 +1,95 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.mllib.pmml.export + +import org.scalatest.FunSuite + +import org.apache.spark.mllib.classification.{LogisticRegressionModel, SVMModel} +import org.apache.spark.mllib.clustering.KMeansModel +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.mllib.regression.{LassoModel, LinearRegressionModel, RidgeRegressionModel} +import org.apache.spark.mllib.util.LinearDataGenerator + +class PMMLModelExportFactorySuite extends FunSuite { + + test("PMMLModelExportFactory create KMeansPMMLModelExport when passing a KMeansModel") { + val clusterCenters = Array( + Vectors.dense(1.0, 2.0, 6.0), + Vectors.dense(1.0, 3.0, 0.0), + Vectors.dense(1.0, 4.0, 6.0)) + val kmeansModel = new KMeansModel(clusterCenters) + + val modelExport = PMMLModelExportFactory.createPMMLModelExport(kmeansModel) + + assert(modelExport.isInstanceOf[KMeansPMMLModelExport]) + } + + test("PMMLModelExportFactory create GeneralizedLinearPMMLModelExport when passing a " + + "LinearRegressionModel, RidgeRegressionModel or LassoModel") { + val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17) + + val linearRegressionModel = + new LinearRegressionModel(linearInput(0).features, linearInput(0).label) + val linearModelExport = PMMLModelExportFactory.createPMMLModelExport(linearRegressionModel) + assert(linearModelExport.isInstanceOf[GeneralizedLinearPMMLModelExport]) + + val ridgeRegressionModel = + new RidgeRegressionModel(linearInput(0).features, linearInput(0).label) + val ridgeModelExport = PMMLModelExportFactory.createPMMLModelExport(ridgeRegressionModel) + assert(ridgeModelExport.isInstanceOf[GeneralizedLinearPMMLModelExport]) + + val lassoModel = new LassoModel(linearInput(0).features, linearInput(0).label) + val lassoModelExport = PMMLModelExportFactory.createPMMLModelExport(lassoModel) + assert(lassoModelExport.isInstanceOf[GeneralizedLinearPMMLModelExport]) + } + + test("PMMLModelExportFactory create BinaryClassificationPMMLModelExport " + + "when passing a LogisticRegressionModel or SVMModel") { + val linearInput = LinearDataGenerator.generateLinearInput(3.0, Array(10.0, 10.0), 1, 17) + + val logisticRegressionModel = + new LogisticRegressionModel(linearInput(0).features, linearInput(0).label) + val logisticRegressionModelExport = + PMMLModelExportFactory.createPMMLModelExport(logisticRegressionModel) + assert(logisticRegressionModelExport.isInstanceOf[BinaryClassificationPMMLModelExport]) + + val svmModel = new SVMModel(linearInput(0).features, linearInput(0).label) + val svmModelExport = PMMLModelExportFactory.createPMMLModelExport(svmModel) + assert(svmModelExport.isInstanceOf[BinaryClassificationPMMLModelExport]) + } + + test("PMMLModelExportFactory throw IllegalArgumentException " + + "when passing a Multinomial Logistic Regression") { + /** 3 classes, 2 features */ + val multiclassLogisticRegressionModel = new LogisticRegressionModel( + weights = Vectors.dense(0.1, 0.2, 0.3, 0.4), intercept = 1.0, + numFeatures = 2, numClasses = 3) + + intercept[IllegalArgumentException] { + PMMLModelExportFactory.createPMMLModelExport(multiclassLogisticRegressionModel) + } + } + + test("PMMLModelExportFactory throw IllegalArgumentException when passing an unsupported model") { + val invalidModel = new Object + + intercept[IllegalArgumentException] { + PMMLModelExportFactory.createPMMLModelExport(invalidModel) + } + } +} From 47bf406d608c4777f5f383ba439608f673034a1d Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Thu, 30 Apr 2015 01:02:33 -0700 Subject: [PATCH 489/591] [HOTFIX] Disabling flaky test (fix in progress as part of SPARK-7224) --- .../test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala index c4e6f06146b0a..029a1156fda6b 100644 --- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala @@ -334,7 +334,7 @@ class SparkSubmitSuite extends FunSuite with Matchers with ResetSystemProperties runSparkSubmit(args) } - test("includes jars passed in through --packages") { + ignore("includes jars passed in through --packages") { val unusedJar = TestUtils.createJarWithClasses(Seq.empty) val packagesString = "com.databricks:spark-csv_2.10:0.1,com.databricks:spark-avro_2.10:0.1" val args = Seq( From 7dacc08ab36188991a001df23880167433844767 Mon Sep 17 00:00:00 2001 From: Burak Yavuz Date: Thu, 30 Apr 2015 10:19:08 -0700 Subject: [PATCH 490/591] [SPARK-7224] added mock repository generator for --packages tests This patch contains an `IvyTestUtils` file, which dynamically generates jars and pom files to test the `--packages` feature without having to rely on the internet, and Maven Central. cc pwendell I know that there existed Util functions to create Jars and stuff already, but they didn't really serve my purposes as they appended random prefixes that was breaking things. I also added the local repository tests. Notice that they work without passing the `repo` to `resolveMavenCoordinates`. Author: Burak Yavuz Closes #5790 from brkyvz/maven-utils and squashes the following commits: 3ec79b7 [Burak Yavuz] addressed comments v0.2 a39151b [Burak Yavuz] address comments v0.1 172dfef [Burak Yavuz] use Ivy format 7476d06 [Burak Yavuz] added mock repository generator --- .../scala/org/apache/spark/TestUtils.scala | 27 +- .../org/apache/spark/deploy/SparkSubmit.scala | 129 +++++---- .../apache/spark/deploy/IvyTestUtils.scala | 262 ++++++++++++++++++ .../spark/deploy/SparkSubmitSuite.scala | 25 +- .../spark/deploy/SparkSubmitUtilsSuite.scala | 57 ++-- 5 files changed, 403 insertions(+), 97 deletions(-) create mode 100644 core/src/test/scala/org/apache/spark/deploy/IvyTestUtils.scala diff --git a/core/src/main/scala/org/apache/spark/TestUtils.scala b/core/src/main/scala/org/apache/spark/TestUtils.scala index 398ca41e16151..fe6320b504e15 100644 --- a/core/src/main/scala/org/apache/spark/TestUtils.scala +++ b/core/src/main/scala/org/apache/spark/TestUtils.scala @@ -105,23 +105,18 @@ private[spark] object TestUtils { URI.create(s"string:///${name.replace(".", "/")}${SOURCE.extension}") } - private class JavaSourceFromString(val name: String, val code: String) + private[spark] class JavaSourceFromString(val name: String, val code: String) extends SimpleJavaFileObject(createURI(name), SOURCE) { override def getCharContent(ignoreEncodingErrors: Boolean): String = code } - /** Creates a compiled class with the given name. Class file will be placed in destDir. */ + /** Creates a compiled class with the source file. Class file will be placed in destDir. */ def createCompiledClass( className: String, destDir: File, - toStringValue: String = "", - baseClass: String = null, - classpathUrls: Seq[URL] = Seq()): File = { + sourceFile: JavaSourceFromString, + classpathUrls: Seq[URL]): File = { val compiler = ToolProvider.getSystemJavaCompiler - val extendsText = Option(baseClass).map { c => s" extends ${c}" }.getOrElse("") - val sourceFile = new JavaSourceFromString(className, - "public class " + className + extendsText + " implements java.io.Serializable {" + - " @Override public String toString() { return \"" + toStringValue + "\"; }}") // Calling this outputs a class file in pwd. It's easier to just rename the file than // build a custom FileManager that controls the output location. @@ -144,4 +139,18 @@ private[spark] object TestUtils { assert(out.exists(), "Destination file not moved: " + out.getAbsolutePath()) out } + + /** Creates a compiled class with the given name. Class file will be placed in destDir. */ + def createCompiledClass( + className: String, + destDir: File, + toStringValue: String = "", + baseClass: String = null, + classpathUrls: Seq[URL] = Seq()): File = { + val extendsText = Option(baseClass).map { c => s" extends ${c}" }.getOrElse("") + val sourceFile = new JavaSourceFromString(className, + "public class " + className + extendsText + " implements java.io.Serializable {" + + " @Override public String toString() { return \"" + toStringValue + "\"; }}") + createCompiledClass(className, destDir, sourceFile, classpathUrls) + } } diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala index b8ae4af18d1d1..0d149e703aff2 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -20,6 +20,7 @@ package org.apache.spark.deploy import java.io.{File, PrintStream} import java.lang.reflect.{InvocationTargetException, Modifier, UndeclaredThrowableException} import java.net.URL +import java.nio.file.{Path => JavaPath} import java.security.PrivilegedExceptionAction import scala.collection.mutable.{ArrayBuffer, HashMap, Map} @@ -708,7 +709,9 @@ private[deploy] object SparkSubmitUtils { * @param artifactId the artifactId of the coordinate * @param version the version of the coordinate */ - private[deploy] case class MavenCoordinate(groupId: String, artifactId: String, version: String) + private[deploy] case class MavenCoordinate(groupId: String, artifactId: String, version: String) { + override def toString: String = s"$groupId:$artifactId:$version" + } /** * Extracts maven coordinates from a comma-delimited string. Coordinates should be provided @@ -731,6 +734,10 @@ private[deploy] object SparkSubmitUtils { } } + /** Path of the local Maven cache. */ + private[spark] def m2Path: JavaPath = new File(System.getProperty("user.home"), + ".m2" + File.separator + "repository" + File.separator).toPath + /** * Extracts maven coordinates from a comma-delimited string * @param remoteRepos Comma-delimited string of remote repositories @@ -744,8 +751,7 @@ private[deploy] object SparkSubmitUtils { val localM2 = new IBiblioResolver localM2.setM2compatible(true) - val m2Path = ".m2" + File.separator + "repository" + File.separator - localM2.setRoot(new File(System.getProperty("user.home"), m2Path).toURI.toString) + localM2.setRoot(m2Path.toUri.toString) localM2.setUsepoms(true) localM2.setName("local-m2-cache") cr.add(localM2) @@ -870,69 +876,72 @@ private[deploy] object SparkSubmitUtils { "" } else { val sysOut = System.out - // To prevent ivy from logging to system out - System.setOut(printStream) - val artifacts = extractMavenCoordinates(coordinates) - // Default configuration name for ivy - val ivyConfName = "default" - // set ivy settings for location of cache - val ivySettings: IvySettings = new IvySettings - // Directories for caching downloads through ivy and storing the jars when maven coordinates - // are supplied to spark-submit - val alternateIvyCache = ivyPath.getOrElse("") - val packagesDirectory: File = - if (alternateIvyCache.trim.isEmpty) { - new File(ivySettings.getDefaultIvyUserDir, "jars") + try { + // To prevent ivy from logging to system out + System.setOut(printStream) + val artifacts = extractMavenCoordinates(coordinates) + // Default configuration name for ivy + val ivyConfName = "default" + // set ivy settings for location of cache + val ivySettings: IvySettings = new IvySettings + // Directories for caching downloads through ivy and storing the jars when maven coordinates + // are supplied to spark-submit + val alternateIvyCache = ivyPath.getOrElse("") + val packagesDirectory: File = + if (alternateIvyCache.trim.isEmpty) { + new File(ivySettings.getDefaultIvyUserDir, "jars") + } else { + ivySettings.setDefaultIvyUserDir(new File(alternateIvyCache)) + ivySettings.setDefaultCache(new File(alternateIvyCache, "cache")) + new File(alternateIvyCache, "jars") + } + printStream.println( + s"Ivy Default Cache set to: ${ivySettings.getDefaultCache.getAbsolutePath}") + printStream.println(s"The jars for the packages stored in: $packagesDirectory") + // create a pattern matcher + ivySettings.addMatcher(new GlobPatternMatcher) + // create the dependency resolvers + val repoResolver = createRepoResolvers(remoteRepos, ivySettings) + ivySettings.addResolver(repoResolver) + ivySettings.setDefaultResolver(repoResolver.getName) + + val ivy = Ivy.newInstance(ivySettings) + // Set resolve options to download transitive dependencies as well + val resolveOptions = new ResolveOptions + resolveOptions.setTransitive(true) + val retrieveOptions = new RetrieveOptions + // Turn downloading and logging off for testing + if (isTest) { + resolveOptions.setDownload(false) + resolveOptions.setLog(LogOptions.LOG_QUIET) + retrieveOptions.setLog(LogOptions.LOG_QUIET) } else { - ivySettings.setDefaultIvyUserDir(new File(alternateIvyCache)) - ivySettings.setDefaultCache(new File(alternateIvyCache, "cache")) - new File(alternateIvyCache, "jars") + resolveOptions.setDownload(true) } - printStream.println( - s"Ivy Default Cache set to: ${ivySettings.getDefaultCache.getAbsolutePath}") - printStream.println(s"The jars for the packages stored in: $packagesDirectory") - // create a pattern matcher - ivySettings.addMatcher(new GlobPatternMatcher) - // create the dependency resolvers - val repoResolver = createRepoResolvers(remoteRepos, ivySettings) - ivySettings.addResolver(repoResolver) - ivySettings.setDefaultResolver(repoResolver.getName) - - val ivy = Ivy.newInstance(ivySettings) - // Set resolve options to download transitive dependencies as well - val resolveOptions = new ResolveOptions - resolveOptions.setTransitive(true) - val retrieveOptions = new RetrieveOptions - // Turn downloading and logging off for testing - if (isTest) { - resolveOptions.setDownload(false) - resolveOptions.setLog(LogOptions.LOG_QUIET) - retrieveOptions.setLog(LogOptions.LOG_QUIET) - } else { - resolveOptions.setDownload(true) - } - // A Module descriptor must be specified. Entries are dummy strings - val md = getModuleDescriptor - md.setDefaultConf(ivyConfName) + // A Module descriptor must be specified. Entries are dummy strings + val md = getModuleDescriptor + md.setDefaultConf(ivyConfName) - // Add exclusion rules for Spark and Scala Library - addExclusionRules(ivySettings, ivyConfName, md) - // add all supplied maven artifacts as dependencies - addDependenciesToIvy(md, artifacts, ivyConfName) + // Add exclusion rules for Spark and Scala Library + addExclusionRules(ivySettings, ivyConfName, md) + // add all supplied maven artifacts as dependencies + addDependenciesToIvy(md, artifacts, ivyConfName) - // resolve dependencies - val rr: ResolveReport = ivy.resolve(md, resolveOptions) - if (rr.hasError) { - throw new RuntimeException(rr.getAllProblemMessages.toString) + // resolve dependencies + val rr: ResolveReport = ivy.resolve(md, resolveOptions) + if (rr.hasError) { + throw new RuntimeException(rr.getAllProblemMessages.toString) + } + // retrieve all resolved dependencies + ivy.retrieve(rr.getModuleDescriptor.getModuleRevisionId, + packagesDirectory.getAbsolutePath + File.separator + + "[organization]_[artifact]-[revision].[ext]", + retrieveOptions.setConfs(Array(ivyConfName))) + resolveDependencyPaths(rr.getArtifacts.toArray, packagesDirectory) + } finally { + System.setOut(sysOut) } - // retrieve all resolved dependencies - ivy.retrieve(rr.getModuleDescriptor.getModuleRevisionId, - packagesDirectory.getAbsolutePath + File.separator + - "[organization]_[artifact]-[revision].[ext]", - retrieveOptions.setConfs(Array(ivyConfName))) - System.setOut(sysOut) - resolveDependencyPaths(rr.getArtifacts.toArray, packagesDirectory) } } } diff --git a/core/src/test/scala/org/apache/spark/deploy/IvyTestUtils.scala b/core/src/test/scala/org/apache/spark/deploy/IvyTestUtils.scala new file mode 100644 index 0000000000000..529f91e8eaf9e --- /dev/null +++ b/core/src/test/scala/org/apache/spark/deploy/IvyTestUtils.scala @@ -0,0 +1,262 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy + +import java.io.{File, FileInputStream, FileOutputStream} +import java.nio.file.{Files, Path} +import java.util.jar.{JarEntry, JarOutputStream} + +import org.apache.spark.TestUtils.{createCompiledClass, JavaSourceFromString} + +import com.google.common.io.ByteStreams + +import org.apache.commons.io.FileUtils + +import org.apache.spark.deploy.SparkSubmitUtils.MavenCoordinate + +private[deploy] object IvyTestUtils { + + /** + * Create the path for the jar and pom from the maven coordinate. Extension should be `jar` + * or `pom`. + */ + private def pathFromCoordinate( + artifact: MavenCoordinate, + prefix: Path, + ext: String, + useIvyLayout: Boolean): Path = { + val groupDirs = artifact.groupId.replace(".", File.separator) + val artifactDirs = artifact.artifactId + val artifactPath = + if (!useIvyLayout) { + Seq(groupDirs, artifactDirs, artifact.version).mkString(File.separator) + } else { + Seq(groupDirs, artifactDirs, artifact.version, ext + "s").mkString(File.separator) + } + new File(prefix.toFile, artifactPath).toPath + } + + private def artifactName(artifact: MavenCoordinate, ext: String = ".jar"): String = { + s"${artifact.artifactId}-${artifact.version}$ext" + } + + /** Write the contents to a file to the supplied directory. */ + private def writeFile(dir: File, fileName: String, contents: String): File = { + val outputFile = new File(dir, fileName) + val outputStream = new FileOutputStream(outputFile) + outputStream.write(contents.toCharArray.map(_.toByte)) + outputStream.close() + outputFile + } + + /** Create an example Python file. */ + private def createPythonFile(dir: File): File = { + val contents = + """def myfunc(x): + | return x + 1 + """.stripMargin + writeFile(dir, "mylib.py", contents) + } + + /** Create a simple testable Class. */ + private def createJavaClass(dir: File, className: String, packageName: String): File = { + val contents = + s"""package $packageName; + | + |import java.lang.Integer; + | + |class $className implements java.io.Serializable { + | + | public $className() {} + | + | public Integer myFunc(Integer x) { + | return x + 1; + | } + |} + """.stripMargin + val sourceFile = + new JavaSourceFromString(new File(dir, className + ".java").getAbsolutePath, contents) + createCompiledClass(className, dir, sourceFile, Seq.empty) + } + + /** Helper method to write artifact information in the pom. */ + private def pomArtifactWriter(artifact: MavenCoordinate, tabCount: Int = 1): String = { + var result = "\n" + " " * tabCount + s"${artifact.groupId}" + result += "\n" + " " * tabCount + s"${artifact.artifactId}" + result += "\n" + " " * tabCount + s"${artifact.version}" + result + } + + /** Create a pom file for this artifact. */ + private def createPom( + dir: File, + artifact: MavenCoordinate, + dependencies: Option[Seq[MavenCoordinate]]): File = { + var content = """ + | + | + | 4.0.0 + """.stripMargin.trim + content += pomArtifactWriter(artifact) + content += dependencies.map { deps => + val inside = deps.map { dep => + "\t" + pomArtifactWriter(dep, 3) + "\n\t" + }.mkString("\n") + "\n \n" + inside + "\n " + }.getOrElse("") + content += "\n" + writeFile(dir, artifactName(artifact, ".pom"), content.trim) + } + + /** Create the jar for the given maven coordinate, using the supplied files. */ + private def packJar( + dir: File, + artifact: MavenCoordinate, + files: Seq[(String, File)]): File = { + val jarFile = new File(dir, artifactName(artifact)) + val jarFileStream = new FileOutputStream(jarFile) + val jarStream = new JarOutputStream(jarFileStream, new java.util.jar.Manifest()) + + for (file <- files) { + val jarEntry = new JarEntry(file._1) + jarStream.putNextEntry(jarEntry) + + val in = new FileInputStream(file._2) + ByteStreams.copy(in, jarStream) + in.close() + } + jarStream.close() + jarFileStream.close() + + jarFile + } + + /** + * Creates a jar and pom file, mocking a Maven repository. The root path can be supplied with + * `tempDir`, dependencies can be created into the same repo, and python files can also be packed + * inside the jar. + * + * @param artifact The maven coordinate to generate the jar and pom for. + * @param dependencies List of dependencies this artifact might have to also create jars and poms. + * @param tempDir The root folder of the repository + * @param useIvyLayout whether to mock the Ivy layout for local repository testing + * @param withPython Whether to pack python files inside the jar for extensive testing. + * @return Root path of the repository + */ + private def createLocalRepository( + artifact: MavenCoordinate, + dependencies: Option[Seq[MavenCoordinate]] = None, + tempDir: Option[Path] = None, + useIvyLayout: Boolean = false, + withPython: Boolean = false): Path = { + // Where the root of the repository exists, and what Ivy will search in + val tempPath = tempDir.getOrElse(Files.createTempDirectory(null)) + // Create directory if it doesn't exist + Files.createDirectories(tempPath) + // Where to create temporary class files and such + val root = Files.createTempDirectory(tempPath, null).toFile + try { + val jarPath = pathFromCoordinate(artifact, tempPath, "jar", useIvyLayout) + Files.createDirectories(jarPath) + val className = "MyLib" + + val javaClass = createJavaClass(root, className, artifact.groupId) + // A tuple of files representation in the jar, and the file + val javaFile = (artifact.groupId.replace(".", "/") + "/" + javaClass.getName, javaClass) + val allFiles = + if (withPython) { + val pythonFile = createPythonFile(root) + Seq(javaFile, (pythonFile.getName, pythonFile)) + } else { + Seq(javaFile) + } + val jarFile = packJar(jarPath.toFile, artifact, allFiles) + assert(jarFile.exists(), "Problem creating Jar file") + val pomPath = pathFromCoordinate(artifact, tempPath, "pom", useIvyLayout) + Files.createDirectories(pomPath) + val pomFile = createPom(pomPath.toFile, artifact, dependencies) + assert(pomFile.exists(), "Problem creating Pom file") + } finally { + FileUtils.deleteDirectory(root) + } + tempPath + } + + /** + * Creates a suite of jars and poms, with or without dependencies, mocking a maven repository. + * @param artifact The main maven coordinate to generate the jar and pom for. + * @param dependencies List of dependencies this artifact might have to also create jars and poms. + * @param rootDir The root folder of the repository (like `~/.m2/repositories`) + * @param useIvyLayout whether to mock the Ivy layout for local repository testing + * @param withPython Whether to pack python files inside the jar for extensive testing. + * @return Root path of the repository. Will be `rootDir` if supplied. + */ + private[deploy] def createLocalRepositoryForTests( + artifact: MavenCoordinate, + dependencies: Option[String], + rootDir: Option[Path], + useIvyLayout: Boolean = false, + withPython: Boolean = false): Path = { + val deps = dependencies.map(SparkSubmitUtils.extractMavenCoordinates) + val mainRepo = createLocalRepository(artifact, deps, rootDir, useIvyLayout, withPython) + deps.foreach { seq => seq.foreach { dep => + createLocalRepository(dep, None, Some(mainRepo), useIvyLayout, withPython = false) + }} + mainRepo + } + + /** + * Creates a repository for a test, and cleans it up afterwards. + * + * @param artifact The main maven coordinate to generate the jar and pom for. + * @param dependencies List of dependencies this artifact might have to also create jars and poms. + * @param rootDir The root folder of the repository (like `~/.m2/repositories`) + * @param useIvyLayout whether to mock the Ivy layout for local repository testing + * @param withPython Whether to pack python files inside the jar for extensive testing. + * @return Root path of the repository. Will be `rootDir` if supplied. + */ + private[deploy] def withRepository( + artifact: MavenCoordinate, + dependencies: Option[String], + rootDir: Option[Path], + useIvyLayout: Boolean = false, + withPython: Boolean = false)(f: String => Unit): Unit = { + val repo = createLocalRepositoryForTests(artifact, dependencies, rootDir, useIvyLayout, + withPython) + try { + f(repo.toUri.toString) + } finally { + // Clean up + if (repo.toString.contains(".m2") || repo.toString.contains(".ivy2")) { + FileUtils.deleteDirectory(new File(repo.toFile, + artifact.groupId.replace(".", File.separator) + File.separator + artifact.artifactId)) + dependencies.map(SparkSubmitUtils.extractMavenCoordinates).foreach { seq => + seq.foreach { dep => + FileUtils.deleteDirectory(new File(repo.toFile, + dep.artifactId.replace(".", File.separator))) + } + } + } else { + FileUtils.deleteDirectory(repo.toFile) + } + } + } +} diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala index 029a1156fda6b..8360b94599547 100644 --- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala @@ -30,6 +30,7 @@ import org.scalatest.time.SpanSugar._ import org.apache.spark._ import org.apache.spark.deploy.SparkSubmit._ +import org.apache.spark.deploy.SparkSubmitUtils.MavenCoordinate import org.apache.spark.util.{ResetSystemProperties, Utils} // Note: this suite mixes in ResetSystemProperties because SparkSubmit.main() sets a bunch @@ -336,16 +337,20 @@ class SparkSubmitSuite extends FunSuite with Matchers with ResetSystemProperties ignore("includes jars passed in through --packages") { val unusedJar = TestUtils.createJarWithClasses(Seq.empty) - val packagesString = "com.databricks:spark-csv_2.10:0.1,com.databricks:spark-avro_2.10:0.1" - val args = Seq( - "--class", JarCreationTest.getClass.getName.stripSuffix("$"), - "--name", "testApp", - "--master", "local-cluster[2,1,512]", - "--packages", packagesString, - "--conf", "spark.ui.enabled=false", - unusedJar.toString, - "com.databricks.spark.csv.DefaultSource", "com.databricks.spark.avro.DefaultSource") - runSparkSubmit(args) + val main = MavenCoordinate("my.great.lib", "mylib", "0.1") + val dep = MavenCoordinate("my.great.dep", "mylib", "0.1") + IvyTestUtils.withRepository(main, Some(dep.toString), None) { repo => + val args = Seq( + "--class", JarCreationTest.getClass.getName.stripSuffix("$"), + "--name", "testApp", + "--master", "local-cluster[2,1,512]", + "--packages", Seq(main, dep).mkString(","), + "--repositories", repo, + "--conf", "spark.ui.enabled=false", + unusedJar.toString, + "my.great.lib.MyLib", "my.great.dep.MyLib") + runSparkSubmit(args) + } } test("resolves command line argument paths correctly") { diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala index 1b2b699cb11e6..cc79ee7ea20b4 100644 --- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala @@ -19,15 +19,15 @@ package org.apache.spark.deploy import java.io.{PrintStream, OutputStream, File} -import org.apache.ivy.core.settings.IvySettings - import scala.collection.mutable.ArrayBuffer - import org.scalatest.{BeforeAndAfterAll, FunSuite} import org.apache.ivy.core.module.descriptor.MDArtifact +import org.apache.ivy.core.settings.IvySettings import org.apache.ivy.plugins.resolver.IBiblioResolver +import org.apache.spark.deploy.SparkSubmitUtils.MavenCoordinate + class SparkSubmitUtilsSuite extends FunSuite with BeforeAndAfterAll { private val noOpOutputStream = new OutputStream { @@ -89,7 +89,7 @@ class SparkSubmitUtilsSuite extends FunSuite with BeforeAndAfterAll { } test("ivy path works correctly") { - val ivyPath = "dummy/ivy" + val ivyPath = "dummy" + File.separator + "ivy" val md = SparkSubmitUtils.getModuleDescriptor val artifacts = for (i <- 0 until 3) yield new MDArtifact(md, s"jar-$i", "jar", "jar") var jPaths = SparkSubmitUtils.resolveDependencyPaths(artifacts.toArray, new File(ivyPath)) @@ -98,17 +98,38 @@ class SparkSubmitUtilsSuite extends FunSuite with BeforeAndAfterAll { assert(index >= 0) jPaths = jPaths.substring(index + ivyPath.length) } - // end to end - val jarPath = SparkSubmitUtils.resolveMavenCoordinates( - "com.databricks:spark-csv_2.10:0.1", None, Option(ivyPath), true) - assert(jarPath.indexOf(ivyPath) >= 0, "should use non-default ivy path") + val main = MavenCoordinate("my.awesome.lib", "mylib", "0.1") + IvyTestUtils.withRepository(main, None, None) { repo => + // end to end + val jarPath = SparkSubmitUtils.resolveMavenCoordinates(main.toString, Option(repo), + Option(ivyPath), true) + assert(jarPath.indexOf(ivyPath) >= 0, "should use non-default ivy path") + } } - test("search for artifact at other repositories") { - val path = SparkSubmitUtils.resolveMavenCoordinates("com.agimatec:agimatec-validation:0.9.3", - Option("https://oss.sonatype.org/content/repositories/agimatec/"), None, true) - assert(path.indexOf("agimatec-validation") >= 0, "should find package. If it doesn't, check" + - "if package still exists. If it has been removed, replace the example in this test.") + test("search for artifact at local repositories") { + val main = new MavenCoordinate("my.awesome.lib", "mylib", "0.1") + // Local M2 repository + IvyTestUtils.withRepository(main, None, Some(SparkSubmitUtils.m2Path)) { repo => + val jarPath = SparkSubmitUtils.resolveMavenCoordinates(main.toString, None, None, true) + assert(jarPath.indexOf("mylib") >= 0, "should find artifact") + } + // Local Ivy Repository + val settings = new IvySettings + val ivyLocal = new File(settings.getDefaultIvyUserDir, "local" + File.separator) + IvyTestUtils.withRepository(main, None, Some(ivyLocal.toPath), true) { repo => + val jarPath = SparkSubmitUtils.resolveMavenCoordinates(main.toString, None, None, true) + assert(jarPath.indexOf("mylib") >= 0, "should find artifact") + } + // Local ivy repository with modified home + val dummyIvyPath = "dummy" + File.separator + "ivy" + val dummyIvyLocal = new File(dummyIvyPath, "local" + File.separator) + IvyTestUtils.withRepository(main, None, Some(dummyIvyLocal.toPath), true) { repo => + val jarPath = SparkSubmitUtils.resolveMavenCoordinates(main.toString, None, + Some(dummyIvyPath), true) + assert(jarPath.indexOf("mylib") >= 0, "should find artifact") + assert(jarPath.indexOf(dummyIvyPath) >= 0, "should be in new ivy path") + } } test("dependency not found throws RuntimeException") { @@ -127,11 +148,11 @@ class SparkSubmitUtilsSuite extends FunSuite with BeforeAndAfterAll { val path = SparkSubmitUtils.resolveMavenCoordinates(coordinates, None, None, true) assert(path === "", "should return empty path") - // Should not exclude the following dependency. Will throw an error, because it doesn't exist, - // but the fact that it is checking means that it wasn't excluded. - intercept[RuntimeException] { - SparkSubmitUtils.resolveMavenCoordinates(coordinates + - ",org.apache.spark:spark-streaming-kafka-assembly_2.10:1.2.0", None, None, true) + val main = MavenCoordinate("org.apache.spark", "spark-streaming-kafka-assembly_2.10", "1.2.0") + IvyTestUtils.withRepository(main, None, None) { repo => + val files = SparkSubmitUtils.resolveMavenCoordinates(coordinates + "," + main.toString, + Some(repo), None, true) + assert(files.indexOf(main.artifactId) >= 0, "Did not return artifact") } } } From 6c65da6bb7d1213e6a4a9f7fd1597d029d87d07c Mon Sep 17 00:00:00 2001 From: Hari Shreedharan Date: Thu, 30 Apr 2015 13:03:23 -0500 Subject: [PATCH 491/591] [SPARK-5342] [YARN] Allow long running Spark apps to run on secure YARN/HDFS Current Spark apps running on Secure YARN/HDFS would not be able to write data to HDFS after 7 days, since delegation tokens cannot be renewed beyond that. This means Spark Streaming apps will not be able to run on Secure YARN. This commit adds basic functionality to fix this issue. In this patch: - new parameters are added - principal and keytab, which can be used to login to a KDC - the client logs in, and then get tokens to start the AM - the keytab is copied to the staging directory - the AM waits for 60% of the time till expiry of the tokens and then logs in using the keytab - each time after 60% of the time, new tokens are created and sent to the executors Currently, to avoid complicating the architecture, we set the keytab and principal in the SparkHadoopUtil singleton, and schedule a login. Once the login is completed, a callback is scheduled. This is being posted for feedback, so I can gather feedback on the general implementation. There are currently a bunch of things to do: - [x] logging - [x] testing - I plan to manually test this soon. If you have ideas of how to add unit tests, comment. - [x] add code to ensure that if these params are set in non-YARN cluster mode, we complain - [x] documentation - [x] Have the executors request for credentials from the AM, so that retries are possible. Author: Hari Shreedharan Closes #4688 from harishreedharan/kerberos-longrunning and squashes the following commits: 36eb8a9 [Hari Shreedharan] Change the renewal interval config param. Fix a bunch of comments. 611923a [Hari Shreedharan] Make sure the namenodes are listed correctly for creating tokens. 09fe224 [Hari Shreedharan] Use token.renew to get token's renewal interval rather than using hdfs-site.xml 6963bbc [Hari Shreedharan] Schedule renewal in AM before starting user class. Else, a restarted AM cannot access HDFS if the user class tries to. 072659e [Hari Shreedharan] Fix build failure caused by thread factory getting moved to ThreadUtils. f041dd3 [Hari Shreedharan] Merge branch 'master' into kerberos-longrunning 42eead4 [Hari Shreedharan] Remove RPC part. Refactor and move methods around, use renewal interval rather than max lifetime to create new tokens. ebb36f5 [Hari Shreedharan] Merge branch 'master' into kerberos-longrunning bc083e3 [Hari Shreedharan] Overload RegisteredExecutor to send tokens. Minor doc updates. 7b19643 [Hari Shreedharan] Merge branch 'master' into kerberos-longrunning 8a4f268 [Hari Shreedharan] Added docs in the security guide. Changed some code to ensure that the renewer objects are created only if required. e800c8b [Hari Shreedharan] Restore original RegisteredExecutor message, and send new tokens via NewTokens message. 0e9507e [Hari Shreedharan] Merge branch 'master' into kerberos-longrunning 7f1bc58 [Hari Shreedharan] Minor fixes, cleanup. bcd11f9 [Hari Shreedharan] Refactor AM and Executor token update code into separate classes, also send tokens via akka on executor startup. f74303c [Hari Shreedharan] Move the new logic into specialized classes. Add cleanup for old credentials files. 2f9975c [Hari Shreedharan] Ensure new tokens are written out immediately on AM restart. Also, pikc up the latest suffix from HDFS if the AM is restarted. 61b2b27 [Hari Shreedharan] Account for AM restarts by making sure lastSuffix is read from the files on HDFS. 62c45ce [Hari Shreedharan] Relogin from keytab periodically. fa233bd [Hari Shreedharan] Adding logging, fixing minor formatting and ordering issues. 42813b4 [Hari Shreedharan] Remove utils.sh, which was re-added due to merge with master. 0de27ee [Hari Shreedharan] Merge branch 'master' into kerberos-longrunning 55522e3 [Hari Shreedharan] Fix failure caused by Preconditions ambiguity. 9ef5f1b [Hari Shreedharan] Added explanation of how the credentials refresh works, some other minor fixes. f4fd711 [Hari Shreedharan] Fix SparkConf usage. 2debcea [Hari Shreedharan] Change the file structure for credentials files. I will push a followup patch which adds a cleanup mechanism for old credentials files. The credentials files are small and few enough for it to cause issues on HDFS. af6d5f0 [Hari Shreedharan] Cleaning up files where changes weren't required. f0f54cb [Hari Shreedharan] Be more defensive when updating the credentials file. f6954da [Hari Shreedharan] Got rid of Akka communication to renew, instead the executors check a known file's modification time to read the credentials. 5c11c3e [Hari Shreedharan] Move tests to YarnSparkHadoopUtil to fix compile issues. b4cb917 [Hari Shreedharan] Send keytab to AM via DistributedCache rather than directly via HDFS 0985b4e [Hari Shreedharan] Write tokens to HDFS and read them back when required, rather than sending them over the wire. d79b2b9 [Hari Shreedharan] Make sure correct credentials are passed to FileSystem#addDelegationTokens() 8c6928a [Hari Shreedharan] Fix issue caused by direct creation of Actor object. fb27f46 [Hari Shreedharan] Make sure principal and keytab are set before CoarseGrainedSchedulerBackend is started. Also schedule re-logins in CoarseGrainedSchedulerBackend#start() 41efde0 [Hari Shreedharan] Merge branch 'master' into kerberos-longrunning d282d7a [Hari Shreedharan] Fix ClientSuite to set YARN mode, so that the correct class is used in tests. bcfc374 [Hari Shreedharan] Fix Hadoop-1 build by adding no-op methods in SparkHadoopUtil, with impl in YarnSparkHadoopUtil. f8fe694 [Hari Shreedharan] Handle None if keytab-login is not scheduled. 2b0d745 [Hari Shreedharan] [SPARK-5342][YARN] Allow long running Spark apps to run on secure YARN/HDFS. ccba5bc [Hari Shreedharan] WIP: More changes wrt kerberos 77914dd [Hari Shreedharan] WIP: Add kerberos principal and keytab to YARN client. --- .../ExecutorDelegationTokenUpdater.scala | 105 +++++++++ .../apache/spark/deploy/SparkHadoopUtil.scala | 69 +++++- .../org/apache/spark/deploy/SparkSubmit.scala | 4 + .../spark/deploy/SparkSubmitArguments.scala | 15 ++ .../CoarseGrainedExecutorBackend.scala | 13 +- .../CoarseGrainedSchedulerBackend.scala | 4 +- docs/security.md | 2 + .../launcher/SparkSubmitOptionParser.java | 6 +- .../yarn/AMDelegationTokenRenewer.scala | 203 ++++++++++++++++++ .../spark/deploy/yarn/ApplicationMaster.scala | 17 +- .../org/apache/spark/deploy/yarn/Client.scala | 123 ++++++----- .../spark/deploy/yarn/ClientArguments.scala | 10 + .../deploy/yarn/YarnSparkHadoopUtil.scala | 60 +++++- .../spark/deploy/yarn/ClientSuite.scala | 51 ----- .../spark/deploy/yarn/YarnClusterSuite.scala | 2 + .../yarn/YarnSparkHadoopUtilSuite.scala | 62 +++++- 16 files changed, 635 insertions(+), 111 deletions(-) create mode 100644 core/src/main/scala/org/apache/spark/deploy/ExecutorDelegationTokenUpdater.scala create mode 100644 yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala diff --git a/core/src/main/scala/org/apache/spark/deploy/ExecutorDelegationTokenUpdater.scala b/core/src/main/scala/org/apache/spark/deploy/ExecutorDelegationTokenUpdater.scala new file mode 100644 index 0000000000000..80363aa3a1e0b --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/ExecutorDelegationTokenUpdater.scala @@ -0,0 +1,105 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.spark.deploy + +import java.util.concurrent.{Executors, TimeUnit} + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileSystem, Path} +import org.apache.hadoop.security.{Credentials, UserGroupInformation} + +import org.apache.spark.{Logging, SparkConf} +import org.apache.spark.util.{ThreadUtils, Utils} + +import scala.util.control.NonFatal + +private[spark] class ExecutorDelegationTokenUpdater( + sparkConf: SparkConf, + hadoopConf: Configuration) extends Logging { + + @volatile private var lastCredentialsFileSuffix = 0 + + private val credentialsFile = sparkConf.get("spark.yarn.credentials.file") + + private val delegationTokenRenewer = + Executors.newSingleThreadScheduledExecutor( + ThreadUtils.namedThreadFactory("Delegation Token Refresh Thread")) + + // On the executor, this thread wakes up and picks up new tokens from HDFS, if any. + private val executorUpdaterRunnable = + new Runnable { + override def run(): Unit = Utils.logUncaughtExceptions(updateCredentialsIfRequired()) + } + + def updateCredentialsIfRequired(): Unit = { + try { + val credentialsFilePath = new Path(credentialsFile) + val remoteFs = FileSystem.get(hadoopConf) + SparkHadoopUtil.get.listFilesSorted( + remoteFs, credentialsFilePath.getParent, + credentialsFilePath.getName, SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) + .lastOption.foreach { credentialsStatus => + val suffix = SparkHadoopUtil.get.getSuffixForCredentialsPath(credentialsStatus.getPath) + if (suffix > lastCredentialsFileSuffix) { + logInfo("Reading new delegation tokens from " + credentialsStatus.getPath) + val newCredentials = getCredentialsFromHDFSFile(remoteFs, credentialsStatus.getPath) + lastCredentialsFileSuffix = suffix + UserGroupInformation.getCurrentUser.addCredentials(newCredentials) + logInfo("Tokens updated from credentials file.") + } else { + // Check every hour to see if new credentials arrived. + logInfo("Updated delegation tokens were expected, but the driver has not updated the " + + "tokens yet, will check again in an hour.") + delegationTokenRenewer.schedule(executorUpdaterRunnable, 1, TimeUnit.HOURS) + return + } + } + val timeFromNowToRenewal = + SparkHadoopUtil.get.getTimeFromNowToRenewal( + sparkConf, 0.8, UserGroupInformation.getCurrentUser.getCredentials) + if (timeFromNowToRenewal <= 0) { + executorUpdaterRunnable.run() + } else { + logInfo(s"Scheduling token refresh from HDFS in $timeFromNowToRenewal millis.") + delegationTokenRenewer.schedule( + executorUpdaterRunnable, timeFromNowToRenewal, TimeUnit.MILLISECONDS) + } + } catch { + // Since the file may get deleted while we are reading it, catch the Exception and come + // back in an hour to try again + case NonFatal(e) => + logWarning("Error while trying to update credentials, will try again in 1 hour", e) + delegationTokenRenewer.schedule(executorUpdaterRunnable, 1, TimeUnit.HOURS) + } + } + + private def getCredentialsFromHDFSFile(remoteFs: FileSystem, tokenPath: Path): Credentials = { + val stream = remoteFs.open(tokenPath) + try { + val newCredentials = new Credentials() + newCredentials.readTokenStorageStream(stream) + newCredentials + } finally { + stream.close() + } + } + + def stop(): Unit = { + delegationTokenRenewer.shutdown() + } + +} diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala index cfaebf9ea5050..00194fba84ff7 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala @@ -17,12 +17,16 @@ package org.apache.spark.deploy +import java.io.{ByteArrayInputStream, DataInputStream} import java.lang.reflect.Method import java.security.PrivilegedExceptionAction +import java.util.{Arrays, Comparator} +import com.google.common.primitives.Longs import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.{FileStatus, FileSystem, Path} +import org.apache.hadoop.fs.{FileStatus, FileSystem, Path, PathFilter} import org.apache.hadoop.fs.FileSystem.Statistics +import org.apache.hadoop.hdfs.security.token.delegation.DelegationTokenIdentifier import org.apache.hadoop.mapred.JobConf import org.apache.hadoop.mapreduce.JobContext import org.apache.hadoop.security.{Credentials, UserGroupInformation} @@ -32,6 +36,7 @@ import org.apache.spark.annotation.DeveloperApi import org.apache.spark.util.Utils import scala.collection.JavaConversions._ +import scala.concurrent.duration._ /** * :: DeveloperApi :: @@ -39,7 +44,8 @@ import scala.collection.JavaConversions._ */ @DeveloperApi class SparkHadoopUtil extends Logging { - val conf: Configuration = newConfiguration(new SparkConf()) + private val sparkConf = new SparkConf() + val conf: Configuration = newConfiguration(sparkConf) UserGroupInformation.setConfiguration(conf) /** @@ -201,6 +207,61 @@ class SparkHadoopUtil extends Logging { if (baseStatus.isDir) recurse(basePath) else Array(baseStatus) } + /** + * Lists all the files in a directory with the specified prefix, and does not end with the + * given suffix. The returned {{FileStatus}} instances are sorted by the modification times of + * the respective files. + */ + def listFilesSorted( + remoteFs: FileSystem, + dir: Path, + prefix: String, + exclusionSuffix: String): Array[FileStatus] = { + val fileStatuses = remoteFs.listStatus(dir, + new PathFilter { + override def accept(path: Path): Boolean = { + val name = path.getName + name.startsWith(prefix) && !name.endsWith(exclusionSuffix) + } + }) + Arrays.sort(fileStatuses, new Comparator[FileStatus] { + override def compare(o1: FileStatus, o2: FileStatus): Int = { + Longs.compare(o1.getModificationTime, o2.getModificationTime) + } + }) + fileStatuses + } + + /** + * How much time is remaining (in millis) from now to (fraction * renewal time for the token that + * is valid the latest)? + * This will return -ve (or 0) value if the fraction of validity has already expired. + */ + def getTimeFromNowToRenewal( + sparkConf: SparkConf, + fraction: Double, + credentials: Credentials): Long = { + val now = System.currentTimeMillis() + + val renewalInterval = + sparkConf.getLong("spark.yarn.token.renewal.interval", (24 hours).toMillis) + + credentials.getAllTokens.filter(_.getKind == DelegationTokenIdentifier.HDFS_DELEGATION_KIND) + .map { t => + val identifier = new DelegationTokenIdentifier() + identifier.readFields(new DataInputStream(new ByteArrayInputStream(t.getIdentifier))) + (identifier.getIssueDate + fraction * renewalInterval).toLong - now + }.foldLeft(0L)(math.max) + } + + + private[spark] def getSuffixForCredentialsPath(credentialsPath: Path): Int = { + val fileName = credentialsPath.getName + fileName.substring( + fileName.lastIndexOf(SparkHadoopUtil.SPARK_YARN_CREDS_COUNTER_DELIM) + 1).toInt + } + + private val HADOOP_CONF_PATTERN = "(\\$\\{hadoopconf-[^\\}\\$\\s]+\\})".r.unanchored /** @@ -251,6 +312,10 @@ object SparkHadoopUtil { } } + val SPARK_YARN_CREDS_TEMP_EXTENSION = ".tmp" + + val SPARK_YARN_CREDS_COUNTER_DELIM = "-" + def get: SparkHadoopUtil = { hadoop } diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala index 0d149e703aff2..4b89ede427486 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -401,6 +401,10 @@ object SparkSubmit { OptionAssigner(args.archives, YARN, CLUSTER, clOption = "--archives"), OptionAssigner(args.jars, YARN, CLUSTER, clOption = "--addJars"), + // Yarn client or cluster + OptionAssigner(args.principal, YARN, ALL_DEPLOY_MODES, clOption = "--principal"), + OptionAssigner(args.keytab, YARN, ALL_DEPLOY_MODES, clOption = "--keytab"), + // Other options OptionAssigner(args.executorCores, STANDALONE, ALL_DEPLOY_MODES, sysProp = "spark.executor.cores"), diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala index c621b8fc86f94..c0e4c771908b3 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala @@ -63,6 +63,8 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S var action: SparkSubmitAction = null val sparkProperties: HashMap[String, String] = new HashMap[String, String]() var proxyUser: String = null + var principal: String = null + var keytab: String = null // Standalone cluster mode only var supervise: Boolean = false @@ -393,6 +395,12 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S case PROXY_USER => proxyUser = value + case PRINCIPAL => + principal = value + + case KEYTAB => + keytab = value + case HELP => printUsageAndExit(0) @@ -506,6 +514,13 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S | --num-executors NUM Number of executors to launch (Default: 2). | --archives ARCHIVES Comma separated list of archives to be extracted into the | working directory of each executor. + | --principal PRINCIPAL Principal to be used to login to KDC, while running on + | secure HDFS. + | --keytab KEYTAB The full path to the file that contains the keytab for the + | principal specified above. This keytab will be copied to + | the node running the Application Master via the Secure + | Distributed Cache, for renewing the login tickets and the + | delegation tokens periodically. """.stripMargin ) SparkSubmit.exitFn() diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala index 79aed90b53e2f..9e0f06ab765f1 100644 --- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala +++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala @@ -26,7 +26,7 @@ import scala.util.{Failure, Success} import org.apache.spark.rpc._ import org.apache.spark._ import org.apache.spark.TaskState.TaskState -import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.deploy.{ExecutorDelegationTokenUpdater, SparkHadoopUtil} import org.apache.spark.deploy.worker.WorkerWatcher import org.apache.spark.scheduler.TaskDescription import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ @@ -168,6 +168,16 @@ private[spark] object CoarseGrainedExecutorBackend extends Logging { driverConf.set(key, value) } } + var tokenUpdaterOption: Option[ExecutorDelegationTokenUpdater] = None + if (driverConf.contains("spark.yarn.credentials.file")) { + logInfo("Will periodically update credentials from: " + + driverConf.get("spark.yarn.credentials.file")) + // Periodically update the credentials for this user to ensure HDFS tokens get updated. + tokenUpdaterOption = + Some(new ExecutorDelegationTokenUpdater(driverConf, SparkHadoopUtil.get.conf)) + tokenUpdaterOption.get.updateCredentialsIfRequired() + } + val env = SparkEnv.createExecutorEnv( driverConf, executorId, hostname, port, cores, isLocal = false) @@ -183,6 +193,7 @@ private[spark] object CoarseGrainedExecutorBackend extends Logging { env.rpcEnv.setupEndpoint("WorkerWatcher", new WorkerWatcher(env.rpcEnv, url)) } env.rpcEnv.awaitTermination() + tokenUpdaterOption.foreach(_.stop()) } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala index 7352fa1fe9ebd..f107148f3b8c6 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala @@ -68,6 +68,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp class DriverEndpoint(override val rpcEnv: RpcEnv, sparkProperties: Seq[(String, String)]) extends ThreadSafeRpcEndpoint with Logging { + override protected def log = CoarseGrainedSchedulerBackend.this.log private val addressToExecutorId = new HashMap[RpcAddress, String] @@ -112,6 +113,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp // Ignoring the task kill since the executor is not registered. logWarning(s"Attempted to kill task $taskId for unknown executor $executorId.") } + } override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { @@ -122,7 +124,6 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp } else { logInfo("Registered executor: " + executorRef + " with ID " + executorId) context.reply(RegisteredExecutor) - addressToExecutorId(executorRef.address) = executorId totalCoreCount.addAndGet(cores) totalRegisteredExecutors.addAndGet(1) @@ -243,6 +244,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp properties += ((key, value)) } } + // TODO (prashant) send conf instead of properties driverEndpoint = rpcEnv.setupEndpoint( CoarseGrainedSchedulerBackend.ENDPOINT_NAME, new DriverEndpoint(rpcEnv, properties)) diff --git a/docs/security.md b/docs/security.md index c034ba12ff1fc..d4ffa60e59a33 100644 --- a/docs/security.md +++ b/docs/security.md @@ -32,6 +32,8 @@ SSL must be configured on each node and configured for each component involved i ### YARN mode The key-store can be prepared on the client side and then distributed and used by the executors as the part of the application. It is possible because the user is able to deploy files before the application is started in YARN by using `spark.yarn.dist.files` or `spark.yarn.dist.archives` configuration settings. The responsibility for encryption of transferring these files is on YARN side and has nothing to do with Spark. +For long-running apps like Spark Streaming apps to be able to write to HDFS, it is possible to pass a principal and keytab to `spark-submit` via the `--principal` and `--keytab` parameters respectively. The keytab passed in will be copied over to the machine running the Application Master via the Hadoop Distributed Cache (securely - if YARN is configured with SSL and HDFS encryption is enabled). The Kerberos login will be periodically renewed using this principal and keytab and the delegation tokens required for HDFS will be generated periodically so the application can continue writing to HDFS. + ### Standalone mode The user needs to provide key-stores and configuration options for master and workers. They have to be set by attaching appropriate Java system properties in `SPARK_MASTER_OPTS` and in `SPARK_WORKER_OPTS` environment variables, or just in `SPARK_DAEMON_JAVA_OPTS`. In this mode, the user may allow the executors to use the SSL settings inherited from the worker which spawned that executor. It can be accomplished by setting `spark.ssl.useNodeLocalConf` to `true`. If that parameter is set, the settings provided by user on the client side, are not used by the executors. diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java index 8526d2e7cfa3f..229000087688f 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java +++ b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java @@ -69,8 +69,10 @@ class SparkSubmitOptionParser { // YARN-only options. protected final String ARCHIVES = "--archives"; protected final String EXECUTOR_CORES = "--executor-cores"; - protected final String QUEUE = "--queue"; + protected final String KEYTAB = "--keytab"; protected final String NUM_EXECUTORS = "--num-executors"; + protected final String PRINCIPAL = "--principal"; + protected final String QUEUE = "--queue"; /** * This is the canonical list of spark-submit options. Each entry in the array contains the @@ -96,11 +98,13 @@ class SparkSubmitOptionParser { { EXECUTOR_MEMORY }, { FILES }, { JARS }, + { KEYTAB }, { KILL_SUBMISSION }, { MASTER }, { NAME }, { NUM_EXECUTORS }, { PACKAGES }, + { PRINCIPAL }, { PROPERTIES_FILE }, { PROXY_USER }, { PY_FILES }, diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala new file mode 100644 index 0000000000000..9ff02046de805 --- /dev/null +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala @@ -0,0 +1,203 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.spark.deploy.yarn + +import java.security.PrivilegedExceptionAction +import java.util.concurrent.{Executors, TimeUnit} + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileSystem, Path} +import org.apache.hadoop.security.UserGroupInformation +import org.apache.spark.deploy.SparkHadoopUtil + +import org.apache.spark.{Logging, SparkConf} +import org.apache.spark.util.ThreadUtils + +/* + * The following methods are primarily meant to make sure long-running apps like Spark + * Streaming apps can run without interruption while writing to secure HDFS. The + * scheduleLoginFromKeytab method is called on the driver when the + * CoarseGrainedScheduledBackend starts up. This method wakes up a thread that logs into the KDC + * once 75% of the renewal interval of the original delegation tokens used for the container + * has elapsed. It then creates new delegation tokens and writes them to HDFS in a + * pre-specified location - the prefix of which is specified in the sparkConf by + * spark.yarn.credentials.file (so the file(s) would be named c-1, c-2 etc. - each update goes + * to a new file, with a monotonically increasing suffix). After this, the credentials are + * updated once 75% of the new tokens renewal interval has elapsed. + * + * On the executor side, the updateCredentialsIfRequired method is called once 80% of the + * validity of the original tokens has elapsed. At that time the executor finds the + * credentials file with the latest timestamp and checks if it has read those credentials + * before (by keeping track of the suffix of the last file it read). If a new file has + * appeared, it will read the credentials and update the currently running UGI with it. This + * process happens again once 80% of the validity of this has expired. + */ +private[yarn] class AMDelegationTokenRenewer( + sparkConf: SparkConf, + hadoopConf: Configuration) extends Logging { + + private var lastCredentialsFileSuffix = 0 + + private val delegationTokenRenewer = + Executors.newSingleThreadScheduledExecutor( + ThreadUtils.namedThreadFactory("Delegation Token Refresh Thread")) + + private val hadoopUtil = YarnSparkHadoopUtil.get + + private val daysToKeepFiles = sparkConf.getInt("spark.yarn.credentials.file.retention.days", 5) + private val numFilesToKeep = sparkConf.getInt("spark.yarn.credentials.file.retention.count", 5) + + /** + * Schedule a login from the keytab and principal set using the --principal and --keytab + * arguments to spark-submit. This login happens only when the credentials of the current user + * are about to expire. This method reads spark.yarn.principal and spark.yarn.keytab from + * SparkConf to do the login. This method is a no-op in non-YARN mode. + * + */ + private[spark] def scheduleLoginFromKeytab(): Unit = { + val principal = sparkConf.get("spark.yarn.principal") + val keytab = sparkConf.get("spark.yarn.keytab") + + /** + * Schedule re-login and creation of new tokens. If tokens have already expired, this method + * will synchronously create new ones. + */ + def scheduleRenewal(runnable: Runnable): Unit = { + val credentials = UserGroupInformation.getCurrentUser.getCredentials + val renewalInterval = hadoopUtil.getTimeFromNowToRenewal(sparkConf, 0.75, credentials) + // Run now! + if (renewalInterval <= 0) { + logInfo("HDFS tokens have expired, creating new tokens now.") + runnable.run() + } else { + logInfo(s"Scheduling login from keytab in $renewalInterval millis.") + delegationTokenRenewer.schedule(runnable, renewalInterval, TimeUnit.MILLISECONDS) + } + } + + // This thread periodically runs on the driver to update the delegation tokens on HDFS. + val driverTokenRenewerRunnable = + new Runnable { + override def run(): Unit = { + try { + writeNewTokensToHDFS(principal, keytab) + cleanupOldFiles() + } catch { + case e: Exception => + // Log the error and try to write new tokens back in an hour + logWarning("Failed to write out new credentials to HDFS, will try again in an " + + "hour! If this happens too often tasks will fail.", e) + delegationTokenRenewer.schedule(this, 1, TimeUnit.HOURS) + return + } + scheduleRenewal(this) + } + } + // Schedule update of credentials. This handles the case of updating the tokens right now + // as well, since the renenwal interval will be 0, and the thread will get scheduled + // immediately. + scheduleRenewal(driverTokenRenewerRunnable) + } + + // Keeps only files that are newer than daysToKeepFiles days, and deletes everything else. At + // least numFilesToKeep files are kept for safety + private def cleanupOldFiles(): Unit = { + import scala.concurrent.duration._ + try { + val remoteFs = FileSystem.get(hadoopConf) + val credentialsPath = new Path(sparkConf.get("spark.yarn.credentials.file")) + val thresholdTime = System.currentTimeMillis() - (daysToKeepFiles days).toMillis + hadoopUtil.listFilesSorted( + remoteFs, credentialsPath.getParent, + credentialsPath.getName, SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) + .dropRight(numFilesToKeep) + .takeWhile(_.getModificationTime < thresholdTime) + .foreach(x => remoteFs.delete(x.getPath, true)) + } catch { + // Such errors are not fatal, so don't throw. Make sure they are logged though + case e: Exception => + logWarning("Error while attempting to cleanup old tokens. If you are seeing many such " + + "warnings there may be an issue with your HDFS cluster.") + } + } + + private def writeNewTokensToHDFS(principal: String, keytab: String): Unit = { + // Keytab is copied by YARN to the working directory of the AM, so full path is + // not needed. + + // HACK: + // HDFS will not issue new delegation tokens, if the Credentials object + // passed in already has tokens for that FS even if the tokens are expired (it really only + // checks if there are tokens for the service, and not if they are valid). So the only real + // way to get new tokens is to make sure a different Credentials object is used each time to + // get new tokens and then the new tokens are copied over the the current user's Credentials. + // So: + // - we login as a different user and get the UGI + // - use that UGI to get the tokens (see doAs block below) + // - copy the tokens over to the current user's credentials (this will overwrite the tokens + // in the current user's Credentials object for this FS). + // The login to KDC happens each time new tokens are required, but this is rare enough to not + // have to worry about (like once every day or so). This makes this code clearer than having + // to login and then relogin every time (the HDFS API may not relogin since we don't use this + // UGI directly for HDFS communication. + logInfo(s"Attempting to login to KDC using principal: $principal") + val keytabLoggedInUGI = UserGroupInformation.loginUserFromKeytabAndReturnUGI(principal, keytab) + logInfo("Successfully logged into KDC.") + val tempCreds = keytabLoggedInUGI.getCredentials + val credentialsPath = new Path(sparkConf.get("spark.yarn.credentials.file")) + val dst = credentialsPath.getParent + keytabLoggedInUGI.doAs(new PrivilegedExceptionAction[Void] { + // Get a copy of the credentials + override def run(): Void = { + val nns = YarnSparkHadoopUtil.get.getNameNodesToAccess(sparkConf) + dst + hadoopUtil.obtainTokensForNamenodes(nns, hadoopConf, tempCreds) + null + } + }) + // Add the temp credentials back to the original ones. + UserGroupInformation.getCurrentUser.addCredentials(tempCreds) + val remoteFs = FileSystem.get(hadoopConf) + // If lastCredentialsFileSuffix is 0, then the AM is either started or restarted. If the AM + // was restarted, then the lastCredentialsFileSuffix might be > 0, so find the newest file + // and update the lastCredentialsFileSuffix. + if (lastCredentialsFileSuffix == 0) { + hadoopUtil.listFilesSorted( + remoteFs, credentialsPath.getParent, + credentialsPath.getName, SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) + .lastOption.foreach { status => + lastCredentialsFileSuffix = hadoopUtil.getSuffixForCredentialsPath(status.getPath) + } + } + val nextSuffix = lastCredentialsFileSuffix + 1 + val tokenPathStr = + sparkConf.get("spark.yarn.credentials.file") + + SparkHadoopUtil.SPARK_YARN_CREDS_COUNTER_DELIM + nextSuffix + val tokenPath = new Path(tokenPathStr) + val tempTokenPath = new Path(tokenPathStr + SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) + logInfo("Writing out delegation tokens to " + tempTokenPath.toString) + val credentials = UserGroupInformation.getCurrentUser.getCredentials + credentials.writeTokenStorageFile(tempTokenPath, hadoopConf) + logInfo(s"Delegation Tokens written out successfully. Renaming file to $tokenPathStr") + remoteFs.rename(tempTokenPath, tokenPath) + logInfo("Delegation token file rename complete.") + lastCredentialsFileSuffix = nextSuffix + } + + def stop(): Unit = { + delegationTokenRenewer.shutdown() + } +} diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala index 70cb57ffd8c69..82f2b7e1dd288 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -75,6 +75,8 @@ private[spark] class ApplicationMaster( // Fields used in cluster mode. private val sparkContextRef = new AtomicReference[SparkContext](null) + private var delegationTokenRenewerOption: Option[AMDelegationTokenRenewer] = None + final def run(): Int = { try { val appAttemptId = client.getAttemptId() @@ -125,6 +127,15 @@ private[spark] class ApplicationMaster( // doAs in order for the credentials to be passed on to the executor containers. val securityMgr = new SecurityManager(sparkConf) + // If the credentials file config is present, we must periodically renew tokens. So create + // a new AMDelegationTokenRenewer + if (sparkConf.contains("spark.yarn.credentials.file")) { + delegationTokenRenewerOption = Some(new AMDelegationTokenRenewer(sparkConf, yarnConf)) + // If a principal and keytab have been set, use that to create new credentials for executors + // periodically + delegationTokenRenewerOption.foreach(_.scheduleLoginFromKeytab()) + } + if (isClusterMode) { runDriver(securityMgr) } else { @@ -189,6 +200,7 @@ private[spark] class ApplicationMaster( logDebug("shutting down user thread") userClassThread.interrupt() } + if (!inShutdown) delegationTokenRenewerOption.foreach(_.stop()) } } } @@ -235,12 +247,12 @@ private[spark] class ApplicationMaster( host: String, port: String, isClusterMode: Boolean): Unit = { - val driverEndpont = rpcEnv.setupEndpointRef( + val driverEndpoint = rpcEnv.setupEndpointRef( SparkEnv.driverActorSystemName, RpcAddress(host, port.toInt), YarnSchedulerBackend.ENDPOINT_NAME) amEndpoint = - rpcEnv.setupEndpoint("YarnAM", new AMEndpoint(rpcEnv, driverEndpont, isClusterMode)) + rpcEnv.setupEndpoint("YarnAM", new AMEndpoint(rpcEnv, driverEndpoint, isClusterMode)) } private def runDriver(securityMgr: SecurityManager): Unit = { @@ -494,6 +506,7 @@ private[spark] class ApplicationMaster( override def onStart(): Unit = { driver.send(RegisterClusterManager(self)) + } override def receive: PartialFunction[Any, Unit] = { diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 4abcf7307a388..bb126a46374d5 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -17,9 +17,11 @@ package org.apache.spark.deploy.yarn -import java.io.{File, FileOutputStream} +import java.io.{ByteArrayInputStream, DataInputStream, File, FileOutputStream} import java.net.{InetAddress, UnknownHostException, URI, URISyntaxException} import java.nio.ByteBuffer +import java.security.PrivilegedExceptionAction +import java.util.UUID import java.util.zip.{ZipEntry, ZipOutputStream} import scala.collection.JavaConversions._ @@ -36,7 +38,6 @@ import org.apache.hadoop.hdfs.security.token.delegation.DelegationTokenIdentifie import org.apache.hadoop.fs._ import org.apache.hadoop.fs.permission.FsPermission import org.apache.hadoop.io.Text -import org.apache.hadoop.mapred.Master import org.apache.hadoop.mapreduce.MRJobConfig import org.apache.hadoop.security.{Credentials, UserGroupInformation} import org.apache.hadoop.security.token.{TokenIdentifier, Token} @@ -50,8 +51,8 @@ import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.exceptions.ApplicationNotFoundException import org.apache.hadoop.yarn.util.Records -import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext, SparkException} import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext, SparkException} import org.apache.spark.util.Utils private[spark] class Client( @@ -69,11 +70,13 @@ private[spark] class Client( private val yarnClient = YarnClient.createYarnClient private val yarnConf = new YarnConfiguration(hadoopConf) - private val credentials = UserGroupInformation.getCurrentUser.getCredentials + private var credentials: Credentials = null private val amMemoryOverhead = args.amMemoryOverhead // MB private val executorMemoryOverhead = args.executorMemoryOverhead // MB private val distCacheMgr = new ClientDistributedCacheManager() private val isClusterMode = args.isClusterMode + + private var loginFromKeytab = false private val fireAndForget = isClusterMode && !sparkConf.getBoolean("spark.yarn.submit.waitAppCompletion", true) @@ -88,6 +91,8 @@ private[spark] class Client( * available in the alpha API. */ def submitApplication(): ApplicationId = { + // Setup the credentials before doing anything else, so we have don't have issues at any point. + setupCredentials() yarnClient.init(yarnConf) yarnClient.start() @@ -219,12 +224,12 @@ private[spark] class Client( // and add them as local resources to the application master. val fs = FileSystem.get(hadoopConf) val dst = new Path(fs.getHomeDirectory(), appStagingDir) - val nns = getNameNodesToAccess(sparkConf) + dst + val nns = YarnSparkHadoopUtil.get.getNameNodesToAccess(sparkConf) + dst + YarnSparkHadoopUtil.get.obtainTokensForNamenodes(nns, hadoopConf, credentials) // Used to keep track of URIs added to the distributed cache. If the same URI is added // multiple times, YARN will fail to launch containers for the app with an internal // error. val distributedUris = new HashSet[String] - obtainTokensForNamenodes(nns, hadoopConf, credentials) obtainTokenForHiveMetastore(hadoopConf, credentials) obtainTokenForHBase(hadoopConf, credentials) @@ -243,6 +248,20 @@ private[spark] class Client( "for alternatives.") } + // If we passed in a keytab, make sure we copy the keytab to the staging directory on + // HDFS, and setup the relevant environment vars, so the AM can login again. + if (loginFromKeytab) { + logInfo("To enable the AM to login from keytab, credentials are being copied over to the AM" + + " via the YARN Secure Distributed Cache.") + val localUri = new URI(args.keytab) + val localPath = getQualifiedLocalPath(localUri, hadoopConf) + val destinationPath = copyFileToRemote(dst, localPath, replication) + val destFs = FileSystem.get(destinationPath.toUri(), hadoopConf) + distCacheMgr.addResource( + destFs, hadoopConf, destinationPath, localResources, LocalResourceType.FILE, + sparkConf.get("spark.yarn.keytab"), statCache, appMasterOnly = true) + } + def addDistributedUri(uri: URI): Boolean = { val uriStr = uri.toString() if (distributedUris.contains(uriStr)) { @@ -385,6 +404,28 @@ private[spark] class Client( } } + /** + * Get the renewal interval for tokens. + */ + private def getTokenRenewalInterval(stagingDirPath: Path): Long = { + // We cannot use the tokens generated above since those have renewer yarn. Trying to renew + // those will fail with an access control issue. So create new tokens with the logged in + // user as renewer. + val creds = new Credentials() + val nns = YarnSparkHadoopUtil.get.getNameNodesToAccess(sparkConf) + stagingDirPath + YarnSparkHadoopUtil.get.obtainTokensForNamenodes( + nns, hadoopConf, creds, Some(sparkConf.get("spark.yarn.principal"))) + val t = creds.getAllTokens + .filter(_.getKind == DelegationTokenIdentifier.HDFS_DELEGATION_KIND) + .head + val newExpiration = t.renew(hadoopConf) + val identifier = new DelegationTokenIdentifier() + identifier.readFields(new DataInputStream(new ByteArrayInputStream(t.getIdentifier))) + val interval = newExpiration - identifier.getIssueDate + logInfo(s"Renewal Interval set to $interval") + interval + } + /** * Set up the environment for launching our ApplicationMaster container. */ @@ -396,7 +437,16 @@ private[spark] class Client( env("SPARK_YARN_MODE") = "true" env("SPARK_YARN_STAGING_DIR") = stagingDir env("SPARK_USER") = UserGroupInformation.getCurrentUser().getShortUserName() - + if (loginFromKeytab) { + val remoteFs = FileSystem.get(hadoopConf) + val stagingDirPath = new Path(remoteFs.getHomeDirectory, stagingDir) + val credentialsFile = "credentials-" + UUID.randomUUID().toString + sparkConf.set( + "spark.yarn.credentials.file", new Path(stagingDirPath, credentialsFile).toString) + logInfo(s"Credentials file set to: $credentialsFile") + val renewalInterval = getTokenRenewalInterval(stagingDirPath) + sparkConf.set("spark.yarn.token.renewal.interval", renewalInterval.toString) + } // Set the environment variables to be passed on to the executors. distCacheMgr.setDistFilesEnv(env) distCacheMgr.setDistArchivesEnv(env) @@ -461,7 +511,6 @@ private[spark] class Client( private def createContainerLaunchContext(newAppResponse: GetNewApplicationResponse) : ContainerLaunchContext = { logInfo("Setting up container launch context for our AM") - val appId = newAppResponse.getApplicationId val appStagingDir = getAppStagingDir(appId) val localResources = prepareLocalResources(appStagingDir) @@ -632,6 +681,24 @@ private[spark] class Client( amContainer } + def setupCredentials(): Unit = { + if (args.principal != null) { + require(args.keytab != null, "Keytab must be specified when principal is specified.") + logInfo("Attempting to login to the Kerberos" + + s" using principal: ${args.principal} and keytab: ${args.keytab}") + val f = new File(args.keytab) + // Generate a file name that can be used for the keytab file, that does not conflict + // with any user file. + val keytabFileName = f.getName + "-" + UUID.randomUUID().toString + UserGroupInformation.loginUserFromKeytab(args.principal, args.keytab) + loginFromKeytab = true + sparkConf.set("spark.yarn.keytab", keytabFileName) + sparkConf.set("spark.yarn.principal", args.principal) + logInfo("Successfully logged into the KDC.") + } + credentials = UserGroupInformation.getCurrentUser.getCredentials + } + /** * Report the state of an application until it has exited, either successfully or * due to some failure, then return a pair of the yarn application state (FINISHED, FAILED, @@ -987,46 +1054,6 @@ object Client extends Logging { private def addClasspathEntry(path: String, env: HashMap[String, String]): Unit = YarnSparkHadoopUtil.addPathToEnvironment(env, Environment.CLASSPATH.name, path) - /** - * Get the list of namenodes the user may access. - */ - private[yarn] def getNameNodesToAccess(sparkConf: SparkConf): Set[Path] = { - sparkConf.get("spark.yarn.access.namenodes", "") - .split(",") - .map(_.trim()) - .filter(!_.isEmpty) - .map(new Path(_)) - .toSet - } - - private[yarn] def getTokenRenewer(conf: Configuration): String = { - val delegTokenRenewer = Master.getMasterPrincipal(conf) - logDebug("delegation token renewer is: " + delegTokenRenewer) - if (delegTokenRenewer == null || delegTokenRenewer.length() == 0) { - val errorMessage = "Can't get Master Kerberos principal for use as renewer" - logError(errorMessage) - throw new SparkException(errorMessage) - } - delegTokenRenewer - } - - /** - * Obtains tokens for the namenodes passed in and adds them to the credentials. - */ - private def obtainTokensForNamenodes( - paths: Set[Path], - conf: Configuration, - creds: Credentials): Unit = { - if (UserGroupInformation.isSecurityEnabled()) { - val delegTokenRenewer = getTokenRenewer(conf) - paths.foreach { dst => - val dstFs = dst.getFileSystem(conf) - logDebug("getting token for namenode: " + dst) - dstFs.addDelegationTokens(delegTokenRenewer, creds) - } - } - } - /** * Obtains token for the Hive metastore and adds them to the credentials. */ diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala index 1423533470fc0..5653c9f14dc6d 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala @@ -42,6 +42,8 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) var amCores: Int = 1 var appName: String = "Spark" var priority = 0 + var principal: String = null + var keytab: String = null def isClusterMode: Boolean = userClass != null private var driverMemory: Int = 512 // MB @@ -231,6 +233,14 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) archives = value args = tail + case ("--principal") :: value :: tail => + principal = value + args = tail + + case ("--keytab") :: value :: tail => + keytab = value + args = tail + case Nil => case _ => diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala index 5881dc5ffa3ad..a5c454e2e5c5e 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala @@ -17,25 +17,26 @@ package org.apache.spark.deploy.yarn -import java.io.File +import java.io._ import java.util.regex.Matcher import java.util.regex.Pattern import scala.collection.mutable.HashMap import scala.util.Try +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs._ import org.apache.hadoop.io.Text -import org.apache.hadoop.mapred.JobConf +import org.apache.hadoop.mapred.{Master, JobConf} import org.apache.hadoop.security.Credentials import org.apache.hadoop.security.UserGroupInformation import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.api.ApplicationConstants import org.apache.hadoop.yarn.api.ApplicationConstants.Environment import org.apache.hadoop.yarn.api.records.{Priority, ApplicationAccessType} -import org.apache.hadoop.conf.Configuration -import org.apache.spark.{SecurityManager, SparkConf} import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.{SecurityManager, SparkConf, SparkException} import org.apache.spark.util.Utils /** @@ -82,6 +83,48 @@ class YarnSparkHadoopUtil extends SparkHadoopUtil { if (credentials != null) credentials.getSecretKey(new Text(key)) else null } + /** + * Get the list of namenodes the user may access. + */ + def getNameNodesToAccess(sparkConf: SparkConf): Set[Path] = { + sparkConf.get("spark.yarn.access.namenodes", "") + .split(",") + .map(_.trim()) + .filter(!_.isEmpty) + .map(new Path(_)) + .toSet + } + + def getTokenRenewer(conf: Configuration): String = { + val delegTokenRenewer = Master.getMasterPrincipal(conf) + logDebug("delegation token renewer is: " + delegTokenRenewer) + if (delegTokenRenewer == null || delegTokenRenewer.length() == 0) { + val errorMessage = "Can't get Master Kerberos principal for use as renewer" + logError(errorMessage) + throw new SparkException(errorMessage) + } + delegTokenRenewer + } + + /** + * Obtains tokens for the namenodes passed in and adds them to the credentials. + */ + def obtainTokensForNamenodes( + paths: Set[Path], + conf: Configuration, + creds: Credentials, + renewer: Option[String] = None + ): Unit = { + if (UserGroupInformation.isSecurityEnabled()) { + val delegTokenRenewer = renewer.getOrElse(getTokenRenewer(conf)) + paths.foreach { dst => + val dstFs = dst.getFileSystem(conf) + logInfo("getting token for namenode: " + dst) + dstFs.addDelegationTokens(delegTokenRenewer, creds) + } + } + } + } object YarnSparkHadoopUtil { @@ -100,6 +143,14 @@ object YarnSparkHadoopUtil { // request types (like map/reduce in hadoop for example) val RM_REQUEST_PRIORITY = Priority.newInstance(1) + def get: YarnSparkHadoopUtil = { + val yarnMode = java.lang.Boolean.valueOf( + System.getProperty("SPARK_YARN_MODE", System.getenv("SPARK_YARN_MODE"))) + if (!yarnMode) { + throw new SparkException("YarnSparkHadoopUtil is not available in non-YARN mode!") + } + SparkHadoopUtil.get.asInstanceOf[YarnSparkHadoopUtil] + } /** * Add a path variable to the given environment map. * If the map already contains this key, append the value to the existing value instead. @@ -212,3 +263,4 @@ object YarnSparkHadoopUtil { classPathSeparatorField.get(null).asInstanceOf[String] } } + diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala index a51c2005cb472..508819e242a26 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala @@ -151,57 +151,6 @@ class ClientSuite extends FunSuite with Matchers with BeforeAndAfterAll { } } - test("check access nns empty") { - val sparkConf = new SparkConf() - sparkConf.set("spark.yarn.access.namenodes", "") - val nns = Client.getNameNodesToAccess(sparkConf) - nns should be(Set()) - } - - test("check access nns unset") { - val sparkConf = new SparkConf() - val nns = Client.getNameNodesToAccess(sparkConf) - nns should be(Set()) - } - - test("check access nns") { - val sparkConf = new SparkConf() - sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032") - val nns = Client.getNameNodesToAccess(sparkConf) - nns should be(Set(new Path("hdfs://nn1:8032"))) - } - - test("check access nns space") { - val sparkConf = new SparkConf() - sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032, ") - val nns = Client.getNameNodesToAccess(sparkConf) - nns should be(Set(new Path("hdfs://nn1:8032"))) - } - - test("check access two nns") { - val sparkConf = new SparkConf() - sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032,hdfs://nn2:8032") - val nns = Client.getNameNodesToAccess(sparkConf) - nns should be(Set(new Path("hdfs://nn1:8032"), new Path("hdfs://nn2:8032"))) - } - - test("check token renewer") { - val hadoopConf = new Configuration() - hadoopConf.set("yarn.resourcemanager.address", "myrm:8033") - hadoopConf.set("yarn.resourcemanager.principal", "yarn/myrm:8032@SPARKTEST.COM") - val renewer = Client.getTokenRenewer(hadoopConf) - renewer should be ("yarn/myrm:8032@SPARKTEST.COM") - } - - test("check token renewer default") { - val hadoopConf = new Configuration() - val caught = - intercept[SparkException] { - Client.getTokenRenewer(hadoopConf) - } - assert(caught.getMessage === "Can't get Master Kerberos principal for use as renewer") - } - object Fixtures { val knownDefYarnAppCP: Seq[String] = diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala index 3877da4120e7c..d3c606e0ed998 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala @@ -86,6 +86,7 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit tempDir = Utils.createTempDir() logConfDir = new File(tempDir, "log4j") logConfDir.mkdir() + System.setProperty("SPARK_YARN_MODE", "true") val logConfFile = new File(logConfDir, "log4j.properties") Files.write(LOG4J_CONF, logConfFile, UTF_8) @@ -128,6 +129,7 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit override def afterAll() { yarnCluster.stop() + System.clearProperty("SPARK_YARN_MODE") super.afterAll() } diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala index 9395316b71ff4..e10b985c3c236 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala @@ -20,6 +20,8 @@ package org.apache.spark.deploy.yarn import java.io.{File, IOException} import com.google.common.io.{ByteStreams, Files} +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.Path import org.apache.hadoop.yarn.api.ApplicationConstants import org.apache.hadoop.yarn.api.ApplicationConstants.Environment import org.apache.hadoop.yarn.conf.YarnConfiguration @@ -27,7 +29,7 @@ import org.scalatest.{FunSuite, Matchers} import org.apache.hadoop.yarn.api.records.ApplicationAccessType -import org.apache.spark.{Logging, SecurityManager, SparkConf} +import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException} import org.apache.spark.util.Utils @@ -173,4 +175,62 @@ class YarnSparkHadoopUtilSuite extends FunSuite with Matchers with Logging { YarnSparkHadoopUtil.getClassPathSeparator() should be (":") } } + + test("check access nns empty") { + val sparkConf = new SparkConf() + val util = new YarnSparkHadoopUtil + sparkConf.set("spark.yarn.access.namenodes", "") + val nns = util.getNameNodesToAccess(sparkConf) + nns should be(Set()) + } + + test("check access nns unset") { + val sparkConf = new SparkConf() + val util = new YarnSparkHadoopUtil + val nns = util.getNameNodesToAccess(sparkConf) + nns should be(Set()) + } + + test("check access nns") { + val sparkConf = new SparkConf() + sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032") + val util = new YarnSparkHadoopUtil + val nns = util.getNameNodesToAccess(sparkConf) + nns should be(Set(new Path("hdfs://nn1:8032"))) + } + + test("check access nns space") { + val sparkConf = new SparkConf() + sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032, ") + val util = new YarnSparkHadoopUtil + val nns = util.getNameNodesToAccess(sparkConf) + nns should be(Set(new Path("hdfs://nn1:8032"))) + } + + test("check access two nns") { + val sparkConf = new SparkConf() + sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032,hdfs://nn2:8032") + val util = new YarnSparkHadoopUtil + val nns = util.getNameNodesToAccess(sparkConf) + nns should be(Set(new Path("hdfs://nn1:8032"), new Path("hdfs://nn2:8032"))) + } + + test("check token renewer") { + val hadoopConf = new Configuration() + hadoopConf.set("yarn.resourcemanager.address", "myrm:8033") + hadoopConf.set("yarn.resourcemanager.principal", "yarn/myrm:8032@SPARKTEST.COM") + val util = new YarnSparkHadoopUtil + val renewer = util.getTokenRenewer(hadoopConf) + renewer should be ("yarn/myrm:8032@SPARKTEST.COM") + } + + test("check token renewer default") { + val hadoopConf = new Configuration() + val util = new YarnSparkHadoopUtil + val caught = + intercept[SparkException] { + util.getTokenRenewer(hadoopConf) + } + assert(caught.getMessage === "Can't get Master Kerberos principal for use as renewer") + } } From adbdb19a7d2cc939795f0cecbdc07c605dc946c1 Mon Sep 17 00:00:00 2001 From: "Joseph K. Bradley" Date: Thu, 30 Apr 2015 14:39:27 -0700 Subject: [PATCH 492/591] [SPARK-7207] [ML] [BUILD] Added ml.recommendation, ml.regression to SparkBuild Added ml.recommendation, ml.regression to SparkBuild CC: mengxr Author: Joseph K. Bradley Closes #5758 from jkbradley/SPARK-7207 and squashes the following commits: a28158a [Joseph K. Bradley] Added ml.recommendation, ml.regression to SparkBuild --- project/SparkBuild.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index b7dbcd9bc562a..4bfa8cede7a85 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -467,7 +467,7 @@ object Unidoc { "mllib.evaluation", "mllib.feature", "mllib.random", "mllib.stat.correlation", "mllib.stat.test", "mllib.tree.impl", "mllib.tree.loss", "ml", "ml.attribute", "ml.classification", "ml.evaluation", "ml.feature", "ml.param", - "ml.tuning" + "ml.recommendation", "ml.regression", "ml.tuning" ), "-group", "Spark SQL", packageList("sql.api.java", "sql.api.java.types", "sql.hive.api.java"), "-noqualifier", "java.lang" From e0628f2fae7f99d096f9dd625876a60d11020d9b Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Thu, 30 Apr 2015 14:59:20 -0700 Subject: [PATCH 493/591] Revert "[SPARK-5342] [YARN] Allow long running Spark apps to run on secure YARN/HDFS" This reverts commit 6c65da6bb7d1213e6a4a9f7fd1597d029d87d07c. --- .../ExecutorDelegationTokenUpdater.scala | 105 --------- .../apache/spark/deploy/SparkHadoopUtil.scala | 69 +----- .../org/apache/spark/deploy/SparkSubmit.scala | 4 - .../spark/deploy/SparkSubmitArguments.scala | 15 -- .../CoarseGrainedExecutorBackend.scala | 13 +- .../CoarseGrainedSchedulerBackend.scala | 4 +- docs/security.md | 2 - .../launcher/SparkSubmitOptionParser.java | 6 +- .../yarn/AMDelegationTokenRenewer.scala | 203 ------------------ .../spark/deploy/yarn/ApplicationMaster.scala | 17 +- .../org/apache/spark/deploy/yarn/Client.scala | 123 +++++------ .../spark/deploy/yarn/ClientArguments.scala | 10 - .../deploy/yarn/YarnSparkHadoopUtil.scala | 60 +----- .../spark/deploy/yarn/ClientSuite.scala | 51 +++++ .../spark/deploy/yarn/YarnClusterSuite.scala | 2 - .../yarn/YarnSparkHadoopUtilSuite.scala | 62 +----- 16 files changed, 111 insertions(+), 635 deletions(-) delete mode 100644 core/src/main/scala/org/apache/spark/deploy/ExecutorDelegationTokenUpdater.scala delete mode 100644 yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala diff --git a/core/src/main/scala/org/apache/spark/deploy/ExecutorDelegationTokenUpdater.scala b/core/src/main/scala/org/apache/spark/deploy/ExecutorDelegationTokenUpdater.scala deleted file mode 100644 index 80363aa3a1e0b..0000000000000 --- a/core/src/main/scala/org/apache/spark/deploy/ExecutorDelegationTokenUpdater.scala +++ /dev/null @@ -1,105 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package org.apache.spark.deploy - -import java.util.concurrent.{Executors, TimeUnit} - -import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.{FileSystem, Path} -import org.apache.hadoop.security.{Credentials, UserGroupInformation} - -import org.apache.spark.{Logging, SparkConf} -import org.apache.spark.util.{ThreadUtils, Utils} - -import scala.util.control.NonFatal - -private[spark] class ExecutorDelegationTokenUpdater( - sparkConf: SparkConf, - hadoopConf: Configuration) extends Logging { - - @volatile private var lastCredentialsFileSuffix = 0 - - private val credentialsFile = sparkConf.get("spark.yarn.credentials.file") - - private val delegationTokenRenewer = - Executors.newSingleThreadScheduledExecutor( - ThreadUtils.namedThreadFactory("Delegation Token Refresh Thread")) - - // On the executor, this thread wakes up and picks up new tokens from HDFS, if any. - private val executorUpdaterRunnable = - new Runnable { - override def run(): Unit = Utils.logUncaughtExceptions(updateCredentialsIfRequired()) - } - - def updateCredentialsIfRequired(): Unit = { - try { - val credentialsFilePath = new Path(credentialsFile) - val remoteFs = FileSystem.get(hadoopConf) - SparkHadoopUtil.get.listFilesSorted( - remoteFs, credentialsFilePath.getParent, - credentialsFilePath.getName, SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) - .lastOption.foreach { credentialsStatus => - val suffix = SparkHadoopUtil.get.getSuffixForCredentialsPath(credentialsStatus.getPath) - if (suffix > lastCredentialsFileSuffix) { - logInfo("Reading new delegation tokens from " + credentialsStatus.getPath) - val newCredentials = getCredentialsFromHDFSFile(remoteFs, credentialsStatus.getPath) - lastCredentialsFileSuffix = suffix - UserGroupInformation.getCurrentUser.addCredentials(newCredentials) - logInfo("Tokens updated from credentials file.") - } else { - // Check every hour to see if new credentials arrived. - logInfo("Updated delegation tokens were expected, but the driver has not updated the " + - "tokens yet, will check again in an hour.") - delegationTokenRenewer.schedule(executorUpdaterRunnable, 1, TimeUnit.HOURS) - return - } - } - val timeFromNowToRenewal = - SparkHadoopUtil.get.getTimeFromNowToRenewal( - sparkConf, 0.8, UserGroupInformation.getCurrentUser.getCredentials) - if (timeFromNowToRenewal <= 0) { - executorUpdaterRunnable.run() - } else { - logInfo(s"Scheduling token refresh from HDFS in $timeFromNowToRenewal millis.") - delegationTokenRenewer.schedule( - executorUpdaterRunnable, timeFromNowToRenewal, TimeUnit.MILLISECONDS) - } - } catch { - // Since the file may get deleted while we are reading it, catch the Exception and come - // back in an hour to try again - case NonFatal(e) => - logWarning("Error while trying to update credentials, will try again in 1 hour", e) - delegationTokenRenewer.schedule(executorUpdaterRunnable, 1, TimeUnit.HOURS) - } - } - - private def getCredentialsFromHDFSFile(remoteFs: FileSystem, tokenPath: Path): Credentials = { - val stream = remoteFs.open(tokenPath) - try { - val newCredentials = new Credentials() - newCredentials.readTokenStorageStream(stream) - newCredentials - } finally { - stream.close() - } - } - - def stop(): Unit = { - delegationTokenRenewer.shutdown() - } - -} diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala index 00194fba84ff7..cfaebf9ea5050 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala @@ -17,16 +17,12 @@ package org.apache.spark.deploy -import java.io.{ByteArrayInputStream, DataInputStream} import java.lang.reflect.Method import java.security.PrivilegedExceptionAction -import java.util.{Arrays, Comparator} -import com.google.common.primitives.Longs import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.{FileStatus, FileSystem, Path, PathFilter} +import org.apache.hadoop.fs.{FileStatus, FileSystem, Path} import org.apache.hadoop.fs.FileSystem.Statistics -import org.apache.hadoop.hdfs.security.token.delegation.DelegationTokenIdentifier import org.apache.hadoop.mapred.JobConf import org.apache.hadoop.mapreduce.JobContext import org.apache.hadoop.security.{Credentials, UserGroupInformation} @@ -36,7 +32,6 @@ import org.apache.spark.annotation.DeveloperApi import org.apache.spark.util.Utils import scala.collection.JavaConversions._ -import scala.concurrent.duration._ /** * :: DeveloperApi :: @@ -44,8 +39,7 @@ import scala.concurrent.duration._ */ @DeveloperApi class SparkHadoopUtil extends Logging { - private val sparkConf = new SparkConf() - val conf: Configuration = newConfiguration(sparkConf) + val conf: Configuration = newConfiguration(new SparkConf()) UserGroupInformation.setConfiguration(conf) /** @@ -207,61 +201,6 @@ class SparkHadoopUtil extends Logging { if (baseStatus.isDir) recurse(basePath) else Array(baseStatus) } - /** - * Lists all the files in a directory with the specified prefix, and does not end with the - * given suffix. The returned {{FileStatus}} instances are sorted by the modification times of - * the respective files. - */ - def listFilesSorted( - remoteFs: FileSystem, - dir: Path, - prefix: String, - exclusionSuffix: String): Array[FileStatus] = { - val fileStatuses = remoteFs.listStatus(dir, - new PathFilter { - override def accept(path: Path): Boolean = { - val name = path.getName - name.startsWith(prefix) && !name.endsWith(exclusionSuffix) - } - }) - Arrays.sort(fileStatuses, new Comparator[FileStatus] { - override def compare(o1: FileStatus, o2: FileStatus): Int = { - Longs.compare(o1.getModificationTime, o2.getModificationTime) - } - }) - fileStatuses - } - - /** - * How much time is remaining (in millis) from now to (fraction * renewal time for the token that - * is valid the latest)? - * This will return -ve (or 0) value if the fraction of validity has already expired. - */ - def getTimeFromNowToRenewal( - sparkConf: SparkConf, - fraction: Double, - credentials: Credentials): Long = { - val now = System.currentTimeMillis() - - val renewalInterval = - sparkConf.getLong("spark.yarn.token.renewal.interval", (24 hours).toMillis) - - credentials.getAllTokens.filter(_.getKind == DelegationTokenIdentifier.HDFS_DELEGATION_KIND) - .map { t => - val identifier = new DelegationTokenIdentifier() - identifier.readFields(new DataInputStream(new ByteArrayInputStream(t.getIdentifier))) - (identifier.getIssueDate + fraction * renewalInterval).toLong - now - }.foldLeft(0L)(math.max) - } - - - private[spark] def getSuffixForCredentialsPath(credentialsPath: Path): Int = { - val fileName = credentialsPath.getName - fileName.substring( - fileName.lastIndexOf(SparkHadoopUtil.SPARK_YARN_CREDS_COUNTER_DELIM) + 1).toInt - } - - private val HADOOP_CONF_PATTERN = "(\\$\\{hadoopconf-[^\\}\\$\\s]+\\})".r.unanchored /** @@ -312,10 +251,6 @@ object SparkHadoopUtil { } } - val SPARK_YARN_CREDS_TEMP_EXTENSION = ".tmp" - - val SPARK_YARN_CREDS_COUNTER_DELIM = "-" - def get: SparkHadoopUtil = { hadoop } diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala index 4b89ede427486..0d149e703aff2 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -401,10 +401,6 @@ object SparkSubmit { OptionAssigner(args.archives, YARN, CLUSTER, clOption = "--archives"), OptionAssigner(args.jars, YARN, CLUSTER, clOption = "--addJars"), - // Yarn client or cluster - OptionAssigner(args.principal, YARN, ALL_DEPLOY_MODES, clOption = "--principal"), - OptionAssigner(args.keytab, YARN, ALL_DEPLOY_MODES, clOption = "--keytab"), - // Other options OptionAssigner(args.executorCores, STANDALONE, ALL_DEPLOY_MODES, sysProp = "spark.executor.cores"), diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala index c0e4c771908b3..c621b8fc86f94 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala @@ -63,8 +63,6 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S var action: SparkSubmitAction = null val sparkProperties: HashMap[String, String] = new HashMap[String, String]() var proxyUser: String = null - var principal: String = null - var keytab: String = null // Standalone cluster mode only var supervise: Boolean = false @@ -395,12 +393,6 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S case PROXY_USER => proxyUser = value - case PRINCIPAL => - principal = value - - case KEYTAB => - keytab = value - case HELP => printUsageAndExit(0) @@ -514,13 +506,6 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S | --num-executors NUM Number of executors to launch (Default: 2). | --archives ARCHIVES Comma separated list of archives to be extracted into the | working directory of each executor. - | --principal PRINCIPAL Principal to be used to login to KDC, while running on - | secure HDFS. - | --keytab KEYTAB The full path to the file that contains the keytab for the - | principal specified above. This keytab will be copied to - | the node running the Application Master via the Secure - | Distributed Cache, for renewing the login tickets and the - | delegation tokens periodically. """.stripMargin ) SparkSubmit.exitFn() diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala index 9e0f06ab765f1..79aed90b53e2f 100644 --- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala +++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala @@ -26,7 +26,7 @@ import scala.util.{Failure, Success} import org.apache.spark.rpc._ import org.apache.spark._ import org.apache.spark.TaskState.TaskState -import org.apache.spark.deploy.{ExecutorDelegationTokenUpdater, SparkHadoopUtil} +import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.deploy.worker.WorkerWatcher import org.apache.spark.scheduler.TaskDescription import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ @@ -168,16 +168,6 @@ private[spark] object CoarseGrainedExecutorBackend extends Logging { driverConf.set(key, value) } } - var tokenUpdaterOption: Option[ExecutorDelegationTokenUpdater] = None - if (driverConf.contains("spark.yarn.credentials.file")) { - logInfo("Will periodically update credentials from: " + - driverConf.get("spark.yarn.credentials.file")) - // Periodically update the credentials for this user to ensure HDFS tokens get updated. - tokenUpdaterOption = - Some(new ExecutorDelegationTokenUpdater(driverConf, SparkHadoopUtil.get.conf)) - tokenUpdaterOption.get.updateCredentialsIfRequired() - } - val env = SparkEnv.createExecutorEnv( driverConf, executorId, hostname, port, cores, isLocal = false) @@ -193,7 +183,6 @@ private[spark] object CoarseGrainedExecutorBackend extends Logging { env.rpcEnv.setupEndpoint("WorkerWatcher", new WorkerWatcher(env.rpcEnv, url)) } env.rpcEnv.awaitTermination() - tokenUpdaterOption.foreach(_.stop()) } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala index f107148f3b8c6..7352fa1fe9ebd 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala @@ -68,7 +68,6 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp class DriverEndpoint(override val rpcEnv: RpcEnv, sparkProperties: Seq[(String, String)]) extends ThreadSafeRpcEndpoint with Logging { - override protected def log = CoarseGrainedSchedulerBackend.this.log private val addressToExecutorId = new HashMap[RpcAddress, String] @@ -113,7 +112,6 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp // Ignoring the task kill since the executor is not registered. logWarning(s"Attempted to kill task $taskId for unknown executor $executorId.") } - } override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { @@ -124,6 +122,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp } else { logInfo("Registered executor: " + executorRef + " with ID " + executorId) context.reply(RegisteredExecutor) + addressToExecutorId(executorRef.address) = executorId totalCoreCount.addAndGet(cores) totalRegisteredExecutors.addAndGet(1) @@ -244,7 +243,6 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp properties += ((key, value)) } } - // TODO (prashant) send conf instead of properties driverEndpoint = rpcEnv.setupEndpoint( CoarseGrainedSchedulerBackend.ENDPOINT_NAME, new DriverEndpoint(rpcEnv, properties)) diff --git a/docs/security.md b/docs/security.md index d4ffa60e59a33..c034ba12ff1fc 100644 --- a/docs/security.md +++ b/docs/security.md @@ -32,8 +32,6 @@ SSL must be configured on each node and configured for each component involved i ### YARN mode The key-store can be prepared on the client side and then distributed and used by the executors as the part of the application. It is possible because the user is able to deploy files before the application is started in YARN by using `spark.yarn.dist.files` or `spark.yarn.dist.archives` configuration settings. The responsibility for encryption of transferring these files is on YARN side and has nothing to do with Spark. -For long-running apps like Spark Streaming apps to be able to write to HDFS, it is possible to pass a principal and keytab to `spark-submit` via the `--principal` and `--keytab` parameters respectively. The keytab passed in will be copied over to the machine running the Application Master via the Hadoop Distributed Cache (securely - if YARN is configured with SSL and HDFS encryption is enabled). The Kerberos login will be periodically renewed using this principal and keytab and the delegation tokens required for HDFS will be generated periodically so the application can continue writing to HDFS. - ### Standalone mode The user needs to provide key-stores and configuration options for master and workers. They have to be set by attaching appropriate Java system properties in `SPARK_MASTER_OPTS` and in `SPARK_WORKER_OPTS` environment variables, or just in `SPARK_DAEMON_JAVA_OPTS`. In this mode, the user may allow the executors to use the SSL settings inherited from the worker which spawned that executor. It can be accomplished by setting `spark.ssl.useNodeLocalConf` to `true`. If that parameter is set, the settings provided by user on the client side, are not used by the executors. diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java index 229000087688f..8526d2e7cfa3f 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java +++ b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java @@ -69,10 +69,8 @@ class SparkSubmitOptionParser { // YARN-only options. protected final String ARCHIVES = "--archives"; protected final String EXECUTOR_CORES = "--executor-cores"; - protected final String KEYTAB = "--keytab"; - protected final String NUM_EXECUTORS = "--num-executors"; - protected final String PRINCIPAL = "--principal"; protected final String QUEUE = "--queue"; + protected final String NUM_EXECUTORS = "--num-executors"; /** * This is the canonical list of spark-submit options. Each entry in the array contains the @@ -98,13 +96,11 @@ class SparkSubmitOptionParser { { EXECUTOR_MEMORY }, { FILES }, { JARS }, - { KEYTAB }, { KILL_SUBMISSION }, { MASTER }, { NAME }, { NUM_EXECUTORS }, { PACKAGES }, - { PRINCIPAL }, { PROPERTIES_FILE }, { PROXY_USER }, { PY_FILES }, diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala deleted file mode 100644 index 9ff02046de805..0000000000000 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala +++ /dev/null @@ -1,203 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -package org.apache.spark.deploy.yarn - -import java.security.PrivilegedExceptionAction -import java.util.concurrent.{Executors, TimeUnit} - -import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.{FileSystem, Path} -import org.apache.hadoop.security.UserGroupInformation -import org.apache.spark.deploy.SparkHadoopUtil - -import org.apache.spark.{Logging, SparkConf} -import org.apache.spark.util.ThreadUtils - -/* - * The following methods are primarily meant to make sure long-running apps like Spark - * Streaming apps can run without interruption while writing to secure HDFS. The - * scheduleLoginFromKeytab method is called on the driver when the - * CoarseGrainedScheduledBackend starts up. This method wakes up a thread that logs into the KDC - * once 75% of the renewal interval of the original delegation tokens used for the container - * has elapsed. It then creates new delegation tokens and writes them to HDFS in a - * pre-specified location - the prefix of which is specified in the sparkConf by - * spark.yarn.credentials.file (so the file(s) would be named c-1, c-2 etc. - each update goes - * to a new file, with a monotonically increasing suffix). After this, the credentials are - * updated once 75% of the new tokens renewal interval has elapsed. - * - * On the executor side, the updateCredentialsIfRequired method is called once 80% of the - * validity of the original tokens has elapsed. At that time the executor finds the - * credentials file with the latest timestamp and checks if it has read those credentials - * before (by keeping track of the suffix of the last file it read). If a new file has - * appeared, it will read the credentials and update the currently running UGI with it. This - * process happens again once 80% of the validity of this has expired. - */ -private[yarn] class AMDelegationTokenRenewer( - sparkConf: SparkConf, - hadoopConf: Configuration) extends Logging { - - private var lastCredentialsFileSuffix = 0 - - private val delegationTokenRenewer = - Executors.newSingleThreadScheduledExecutor( - ThreadUtils.namedThreadFactory("Delegation Token Refresh Thread")) - - private val hadoopUtil = YarnSparkHadoopUtil.get - - private val daysToKeepFiles = sparkConf.getInt("spark.yarn.credentials.file.retention.days", 5) - private val numFilesToKeep = sparkConf.getInt("spark.yarn.credentials.file.retention.count", 5) - - /** - * Schedule a login from the keytab and principal set using the --principal and --keytab - * arguments to spark-submit. This login happens only when the credentials of the current user - * are about to expire. This method reads spark.yarn.principal and spark.yarn.keytab from - * SparkConf to do the login. This method is a no-op in non-YARN mode. - * - */ - private[spark] def scheduleLoginFromKeytab(): Unit = { - val principal = sparkConf.get("spark.yarn.principal") - val keytab = sparkConf.get("spark.yarn.keytab") - - /** - * Schedule re-login and creation of new tokens. If tokens have already expired, this method - * will synchronously create new ones. - */ - def scheduleRenewal(runnable: Runnable): Unit = { - val credentials = UserGroupInformation.getCurrentUser.getCredentials - val renewalInterval = hadoopUtil.getTimeFromNowToRenewal(sparkConf, 0.75, credentials) - // Run now! - if (renewalInterval <= 0) { - logInfo("HDFS tokens have expired, creating new tokens now.") - runnable.run() - } else { - logInfo(s"Scheduling login from keytab in $renewalInterval millis.") - delegationTokenRenewer.schedule(runnable, renewalInterval, TimeUnit.MILLISECONDS) - } - } - - // This thread periodically runs on the driver to update the delegation tokens on HDFS. - val driverTokenRenewerRunnable = - new Runnable { - override def run(): Unit = { - try { - writeNewTokensToHDFS(principal, keytab) - cleanupOldFiles() - } catch { - case e: Exception => - // Log the error and try to write new tokens back in an hour - logWarning("Failed to write out new credentials to HDFS, will try again in an " + - "hour! If this happens too often tasks will fail.", e) - delegationTokenRenewer.schedule(this, 1, TimeUnit.HOURS) - return - } - scheduleRenewal(this) - } - } - // Schedule update of credentials. This handles the case of updating the tokens right now - // as well, since the renenwal interval will be 0, and the thread will get scheduled - // immediately. - scheduleRenewal(driverTokenRenewerRunnable) - } - - // Keeps only files that are newer than daysToKeepFiles days, and deletes everything else. At - // least numFilesToKeep files are kept for safety - private def cleanupOldFiles(): Unit = { - import scala.concurrent.duration._ - try { - val remoteFs = FileSystem.get(hadoopConf) - val credentialsPath = new Path(sparkConf.get("spark.yarn.credentials.file")) - val thresholdTime = System.currentTimeMillis() - (daysToKeepFiles days).toMillis - hadoopUtil.listFilesSorted( - remoteFs, credentialsPath.getParent, - credentialsPath.getName, SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) - .dropRight(numFilesToKeep) - .takeWhile(_.getModificationTime < thresholdTime) - .foreach(x => remoteFs.delete(x.getPath, true)) - } catch { - // Such errors are not fatal, so don't throw. Make sure they are logged though - case e: Exception => - logWarning("Error while attempting to cleanup old tokens. If you are seeing many such " + - "warnings there may be an issue with your HDFS cluster.") - } - } - - private def writeNewTokensToHDFS(principal: String, keytab: String): Unit = { - // Keytab is copied by YARN to the working directory of the AM, so full path is - // not needed. - - // HACK: - // HDFS will not issue new delegation tokens, if the Credentials object - // passed in already has tokens for that FS even if the tokens are expired (it really only - // checks if there are tokens for the service, and not if they are valid). So the only real - // way to get new tokens is to make sure a different Credentials object is used each time to - // get new tokens and then the new tokens are copied over the the current user's Credentials. - // So: - // - we login as a different user and get the UGI - // - use that UGI to get the tokens (see doAs block below) - // - copy the tokens over to the current user's credentials (this will overwrite the tokens - // in the current user's Credentials object for this FS). - // The login to KDC happens each time new tokens are required, but this is rare enough to not - // have to worry about (like once every day or so). This makes this code clearer than having - // to login and then relogin every time (the HDFS API may not relogin since we don't use this - // UGI directly for HDFS communication. - logInfo(s"Attempting to login to KDC using principal: $principal") - val keytabLoggedInUGI = UserGroupInformation.loginUserFromKeytabAndReturnUGI(principal, keytab) - logInfo("Successfully logged into KDC.") - val tempCreds = keytabLoggedInUGI.getCredentials - val credentialsPath = new Path(sparkConf.get("spark.yarn.credentials.file")) - val dst = credentialsPath.getParent - keytabLoggedInUGI.doAs(new PrivilegedExceptionAction[Void] { - // Get a copy of the credentials - override def run(): Void = { - val nns = YarnSparkHadoopUtil.get.getNameNodesToAccess(sparkConf) + dst - hadoopUtil.obtainTokensForNamenodes(nns, hadoopConf, tempCreds) - null - } - }) - // Add the temp credentials back to the original ones. - UserGroupInformation.getCurrentUser.addCredentials(tempCreds) - val remoteFs = FileSystem.get(hadoopConf) - // If lastCredentialsFileSuffix is 0, then the AM is either started or restarted. If the AM - // was restarted, then the lastCredentialsFileSuffix might be > 0, so find the newest file - // and update the lastCredentialsFileSuffix. - if (lastCredentialsFileSuffix == 0) { - hadoopUtil.listFilesSorted( - remoteFs, credentialsPath.getParent, - credentialsPath.getName, SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) - .lastOption.foreach { status => - lastCredentialsFileSuffix = hadoopUtil.getSuffixForCredentialsPath(status.getPath) - } - } - val nextSuffix = lastCredentialsFileSuffix + 1 - val tokenPathStr = - sparkConf.get("spark.yarn.credentials.file") + - SparkHadoopUtil.SPARK_YARN_CREDS_COUNTER_DELIM + nextSuffix - val tokenPath = new Path(tokenPathStr) - val tempTokenPath = new Path(tokenPathStr + SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) - logInfo("Writing out delegation tokens to " + tempTokenPath.toString) - val credentials = UserGroupInformation.getCurrentUser.getCredentials - credentials.writeTokenStorageFile(tempTokenPath, hadoopConf) - logInfo(s"Delegation Tokens written out successfully. Renaming file to $tokenPathStr") - remoteFs.rename(tempTokenPath, tokenPath) - logInfo("Delegation token file rename complete.") - lastCredentialsFileSuffix = nextSuffix - } - - def stop(): Unit = { - delegationTokenRenewer.shutdown() - } -} diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala index 82f2b7e1dd288..70cb57ffd8c69 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -75,8 +75,6 @@ private[spark] class ApplicationMaster( // Fields used in cluster mode. private val sparkContextRef = new AtomicReference[SparkContext](null) - private var delegationTokenRenewerOption: Option[AMDelegationTokenRenewer] = None - final def run(): Int = { try { val appAttemptId = client.getAttemptId() @@ -127,15 +125,6 @@ private[spark] class ApplicationMaster( // doAs in order for the credentials to be passed on to the executor containers. val securityMgr = new SecurityManager(sparkConf) - // If the credentials file config is present, we must periodically renew tokens. So create - // a new AMDelegationTokenRenewer - if (sparkConf.contains("spark.yarn.credentials.file")) { - delegationTokenRenewerOption = Some(new AMDelegationTokenRenewer(sparkConf, yarnConf)) - // If a principal and keytab have been set, use that to create new credentials for executors - // periodically - delegationTokenRenewerOption.foreach(_.scheduleLoginFromKeytab()) - } - if (isClusterMode) { runDriver(securityMgr) } else { @@ -200,7 +189,6 @@ private[spark] class ApplicationMaster( logDebug("shutting down user thread") userClassThread.interrupt() } - if (!inShutdown) delegationTokenRenewerOption.foreach(_.stop()) } } } @@ -247,12 +235,12 @@ private[spark] class ApplicationMaster( host: String, port: String, isClusterMode: Boolean): Unit = { - val driverEndpoint = rpcEnv.setupEndpointRef( + val driverEndpont = rpcEnv.setupEndpointRef( SparkEnv.driverActorSystemName, RpcAddress(host, port.toInt), YarnSchedulerBackend.ENDPOINT_NAME) amEndpoint = - rpcEnv.setupEndpoint("YarnAM", new AMEndpoint(rpcEnv, driverEndpoint, isClusterMode)) + rpcEnv.setupEndpoint("YarnAM", new AMEndpoint(rpcEnv, driverEndpont, isClusterMode)) } private def runDriver(securityMgr: SecurityManager): Unit = { @@ -506,7 +494,6 @@ private[spark] class ApplicationMaster( override def onStart(): Unit = { driver.send(RegisterClusterManager(self)) - } override def receive: PartialFunction[Any, Unit] = { diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index bb126a46374d5..4abcf7307a388 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -17,11 +17,9 @@ package org.apache.spark.deploy.yarn -import java.io.{ByteArrayInputStream, DataInputStream, File, FileOutputStream} +import java.io.{File, FileOutputStream} import java.net.{InetAddress, UnknownHostException, URI, URISyntaxException} import java.nio.ByteBuffer -import java.security.PrivilegedExceptionAction -import java.util.UUID import java.util.zip.{ZipEntry, ZipOutputStream} import scala.collection.JavaConversions._ @@ -38,6 +36,7 @@ import org.apache.hadoop.hdfs.security.token.delegation.DelegationTokenIdentifie import org.apache.hadoop.fs._ import org.apache.hadoop.fs.permission.FsPermission import org.apache.hadoop.io.Text +import org.apache.hadoop.mapred.Master import org.apache.hadoop.mapreduce.MRJobConfig import org.apache.hadoop.security.{Credentials, UserGroupInformation} import org.apache.hadoop.security.token.{TokenIdentifier, Token} @@ -51,8 +50,8 @@ import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.exceptions.ApplicationNotFoundException import org.apache.hadoop.yarn.util.Records -import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext, SparkException} +import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.util.Utils private[spark] class Client( @@ -70,13 +69,11 @@ private[spark] class Client( private val yarnClient = YarnClient.createYarnClient private val yarnConf = new YarnConfiguration(hadoopConf) - private var credentials: Credentials = null + private val credentials = UserGroupInformation.getCurrentUser.getCredentials private val amMemoryOverhead = args.amMemoryOverhead // MB private val executorMemoryOverhead = args.executorMemoryOverhead // MB private val distCacheMgr = new ClientDistributedCacheManager() private val isClusterMode = args.isClusterMode - - private var loginFromKeytab = false private val fireAndForget = isClusterMode && !sparkConf.getBoolean("spark.yarn.submit.waitAppCompletion", true) @@ -91,8 +88,6 @@ private[spark] class Client( * available in the alpha API. */ def submitApplication(): ApplicationId = { - // Setup the credentials before doing anything else, so we have don't have issues at any point. - setupCredentials() yarnClient.init(yarnConf) yarnClient.start() @@ -224,12 +219,12 @@ private[spark] class Client( // and add them as local resources to the application master. val fs = FileSystem.get(hadoopConf) val dst = new Path(fs.getHomeDirectory(), appStagingDir) - val nns = YarnSparkHadoopUtil.get.getNameNodesToAccess(sparkConf) + dst - YarnSparkHadoopUtil.get.obtainTokensForNamenodes(nns, hadoopConf, credentials) + val nns = getNameNodesToAccess(sparkConf) + dst // Used to keep track of URIs added to the distributed cache. If the same URI is added // multiple times, YARN will fail to launch containers for the app with an internal // error. val distributedUris = new HashSet[String] + obtainTokensForNamenodes(nns, hadoopConf, credentials) obtainTokenForHiveMetastore(hadoopConf, credentials) obtainTokenForHBase(hadoopConf, credentials) @@ -248,20 +243,6 @@ private[spark] class Client( "for alternatives.") } - // If we passed in a keytab, make sure we copy the keytab to the staging directory on - // HDFS, and setup the relevant environment vars, so the AM can login again. - if (loginFromKeytab) { - logInfo("To enable the AM to login from keytab, credentials are being copied over to the AM" + - " via the YARN Secure Distributed Cache.") - val localUri = new URI(args.keytab) - val localPath = getQualifiedLocalPath(localUri, hadoopConf) - val destinationPath = copyFileToRemote(dst, localPath, replication) - val destFs = FileSystem.get(destinationPath.toUri(), hadoopConf) - distCacheMgr.addResource( - destFs, hadoopConf, destinationPath, localResources, LocalResourceType.FILE, - sparkConf.get("spark.yarn.keytab"), statCache, appMasterOnly = true) - } - def addDistributedUri(uri: URI): Boolean = { val uriStr = uri.toString() if (distributedUris.contains(uriStr)) { @@ -404,28 +385,6 @@ private[spark] class Client( } } - /** - * Get the renewal interval for tokens. - */ - private def getTokenRenewalInterval(stagingDirPath: Path): Long = { - // We cannot use the tokens generated above since those have renewer yarn. Trying to renew - // those will fail with an access control issue. So create new tokens with the logged in - // user as renewer. - val creds = new Credentials() - val nns = YarnSparkHadoopUtil.get.getNameNodesToAccess(sparkConf) + stagingDirPath - YarnSparkHadoopUtil.get.obtainTokensForNamenodes( - nns, hadoopConf, creds, Some(sparkConf.get("spark.yarn.principal"))) - val t = creds.getAllTokens - .filter(_.getKind == DelegationTokenIdentifier.HDFS_DELEGATION_KIND) - .head - val newExpiration = t.renew(hadoopConf) - val identifier = new DelegationTokenIdentifier() - identifier.readFields(new DataInputStream(new ByteArrayInputStream(t.getIdentifier))) - val interval = newExpiration - identifier.getIssueDate - logInfo(s"Renewal Interval set to $interval") - interval - } - /** * Set up the environment for launching our ApplicationMaster container. */ @@ -437,16 +396,7 @@ private[spark] class Client( env("SPARK_YARN_MODE") = "true" env("SPARK_YARN_STAGING_DIR") = stagingDir env("SPARK_USER") = UserGroupInformation.getCurrentUser().getShortUserName() - if (loginFromKeytab) { - val remoteFs = FileSystem.get(hadoopConf) - val stagingDirPath = new Path(remoteFs.getHomeDirectory, stagingDir) - val credentialsFile = "credentials-" + UUID.randomUUID().toString - sparkConf.set( - "spark.yarn.credentials.file", new Path(stagingDirPath, credentialsFile).toString) - logInfo(s"Credentials file set to: $credentialsFile") - val renewalInterval = getTokenRenewalInterval(stagingDirPath) - sparkConf.set("spark.yarn.token.renewal.interval", renewalInterval.toString) - } + // Set the environment variables to be passed on to the executors. distCacheMgr.setDistFilesEnv(env) distCacheMgr.setDistArchivesEnv(env) @@ -511,6 +461,7 @@ private[spark] class Client( private def createContainerLaunchContext(newAppResponse: GetNewApplicationResponse) : ContainerLaunchContext = { logInfo("Setting up container launch context for our AM") + val appId = newAppResponse.getApplicationId val appStagingDir = getAppStagingDir(appId) val localResources = prepareLocalResources(appStagingDir) @@ -681,24 +632,6 @@ private[spark] class Client( amContainer } - def setupCredentials(): Unit = { - if (args.principal != null) { - require(args.keytab != null, "Keytab must be specified when principal is specified.") - logInfo("Attempting to login to the Kerberos" + - s" using principal: ${args.principal} and keytab: ${args.keytab}") - val f = new File(args.keytab) - // Generate a file name that can be used for the keytab file, that does not conflict - // with any user file. - val keytabFileName = f.getName + "-" + UUID.randomUUID().toString - UserGroupInformation.loginUserFromKeytab(args.principal, args.keytab) - loginFromKeytab = true - sparkConf.set("spark.yarn.keytab", keytabFileName) - sparkConf.set("spark.yarn.principal", args.principal) - logInfo("Successfully logged into the KDC.") - } - credentials = UserGroupInformation.getCurrentUser.getCredentials - } - /** * Report the state of an application until it has exited, either successfully or * due to some failure, then return a pair of the yarn application state (FINISHED, FAILED, @@ -1054,6 +987,46 @@ object Client extends Logging { private def addClasspathEntry(path: String, env: HashMap[String, String]): Unit = YarnSparkHadoopUtil.addPathToEnvironment(env, Environment.CLASSPATH.name, path) + /** + * Get the list of namenodes the user may access. + */ + private[yarn] def getNameNodesToAccess(sparkConf: SparkConf): Set[Path] = { + sparkConf.get("spark.yarn.access.namenodes", "") + .split(",") + .map(_.trim()) + .filter(!_.isEmpty) + .map(new Path(_)) + .toSet + } + + private[yarn] def getTokenRenewer(conf: Configuration): String = { + val delegTokenRenewer = Master.getMasterPrincipal(conf) + logDebug("delegation token renewer is: " + delegTokenRenewer) + if (delegTokenRenewer == null || delegTokenRenewer.length() == 0) { + val errorMessage = "Can't get Master Kerberos principal for use as renewer" + logError(errorMessage) + throw new SparkException(errorMessage) + } + delegTokenRenewer + } + + /** + * Obtains tokens for the namenodes passed in and adds them to the credentials. + */ + private def obtainTokensForNamenodes( + paths: Set[Path], + conf: Configuration, + creds: Credentials): Unit = { + if (UserGroupInformation.isSecurityEnabled()) { + val delegTokenRenewer = getTokenRenewer(conf) + paths.foreach { dst => + val dstFs = dst.getFileSystem(conf) + logDebug("getting token for namenode: " + dst) + dstFs.addDelegationTokens(delegTokenRenewer, creds) + } + } + } + /** * Obtains token for the Hive metastore and adds them to the credentials. */ diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala index 5653c9f14dc6d..1423533470fc0 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala @@ -42,8 +42,6 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) var amCores: Int = 1 var appName: String = "Spark" var priority = 0 - var principal: String = null - var keytab: String = null def isClusterMode: Boolean = userClass != null private var driverMemory: Int = 512 // MB @@ -233,14 +231,6 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) archives = value args = tail - case ("--principal") :: value :: tail => - principal = value - args = tail - - case ("--keytab") :: value :: tail => - keytab = value - args = tail - case Nil => case _ => diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala index a5c454e2e5c5e..5881dc5ffa3ad 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala @@ -17,26 +17,25 @@ package org.apache.spark.deploy.yarn -import java.io._ +import java.io.File import java.util.regex.Matcher import java.util.regex.Pattern import scala.collection.mutable.HashMap import scala.util.Try -import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs._ import org.apache.hadoop.io.Text -import org.apache.hadoop.mapred.{Master, JobConf} +import org.apache.hadoop.mapred.JobConf import org.apache.hadoop.security.Credentials import org.apache.hadoop.security.UserGroupInformation import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.api.ApplicationConstants import org.apache.hadoop.yarn.api.ApplicationConstants.Environment import org.apache.hadoop.yarn.api.records.{Priority, ApplicationAccessType} +import org.apache.hadoop.conf.Configuration +import org.apache.spark.{SecurityManager, SparkConf} import org.apache.spark.deploy.SparkHadoopUtil -import org.apache.spark.{SecurityManager, SparkConf, SparkException} import org.apache.spark.util.Utils /** @@ -83,48 +82,6 @@ class YarnSparkHadoopUtil extends SparkHadoopUtil { if (credentials != null) credentials.getSecretKey(new Text(key)) else null } - /** - * Get the list of namenodes the user may access. - */ - def getNameNodesToAccess(sparkConf: SparkConf): Set[Path] = { - sparkConf.get("spark.yarn.access.namenodes", "") - .split(",") - .map(_.trim()) - .filter(!_.isEmpty) - .map(new Path(_)) - .toSet - } - - def getTokenRenewer(conf: Configuration): String = { - val delegTokenRenewer = Master.getMasterPrincipal(conf) - logDebug("delegation token renewer is: " + delegTokenRenewer) - if (delegTokenRenewer == null || delegTokenRenewer.length() == 0) { - val errorMessage = "Can't get Master Kerberos principal for use as renewer" - logError(errorMessage) - throw new SparkException(errorMessage) - } - delegTokenRenewer - } - - /** - * Obtains tokens for the namenodes passed in and adds them to the credentials. - */ - def obtainTokensForNamenodes( - paths: Set[Path], - conf: Configuration, - creds: Credentials, - renewer: Option[String] = None - ): Unit = { - if (UserGroupInformation.isSecurityEnabled()) { - val delegTokenRenewer = renewer.getOrElse(getTokenRenewer(conf)) - paths.foreach { dst => - val dstFs = dst.getFileSystem(conf) - logInfo("getting token for namenode: " + dst) - dstFs.addDelegationTokens(delegTokenRenewer, creds) - } - } - } - } object YarnSparkHadoopUtil { @@ -143,14 +100,6 @@ object YarnSparkHadoopUtil { // request types (like map/reduce in hadoop for example) val RM_REQUEST_PRIORITY = Priority.newInstance(1) - def get: YarnSparkHadoopUtil = { - val yarnMode = java.lang.Boolean.valueOf( - System.getProperty("SPARK_YARN_MODE", System.getenv("SPARK_YARN_MODE"))) - if (!yarnMode) { - throw new SparkException("YarnSparkHadoopUtil is not available in non-YARN mode!") - } - SparkHadoopUtil.get.asInstanceOf[YarnSparkHadoopUtil] - } /** * Add a path variable to the given environment map. * If the map already contains this key, append the value to the existing value instead. @@ -263,4 +212,3 @@ object YarnSparkHadoopUtil { classPathSeparatorField.get(null).asInstanceOf[String] } } - diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala index 508819e242a26..a51c2005cb472 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala @@ -151,6 +151,57 @@ class ClientSuite extends FunSuite with Matchers with BeforeAndAfterAll { } } + test("check access nns empty") { + val sparkConf = new SparkConf() + sparkConf.set("spark.yarn.access.namenodes", "") + val nns = Client.getNameNodesToAccess(sparkConf) + nns should be(Set()) + } + + test("check access nns unset") { + val sparkConf = new SparkConf() + val nns = Client.getNameNodesToAccess(sparkConf) + nns should be(Set()) + } + + test("check access nns") { + val sparkConf = new SparkConf() + sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032") + val nns = Client.getNameNodesToAccess(sparkConf) + nns should be(Set(new Path("hdfs://nn1:8032"))) + } + + test("check access nns space") { + val sparkConf = new SparkConf() + sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032, ") + val nns = Client.getNameNodesToAccess(sparkConf) + nns should be(Set(new Path("hdfs://nn1:8032"))) + } + + test("check access two nns") { + val sparkConf = new SparkConf() + sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032,hdfs://nn2:8032") + val nns = Client.getNameNodesToAccess(sparkConf) + nns should be(Set(new Path("hdfs://nn1:8032"), new Path("hdfs://nn2:8032"))) + } + + test("check token renewer") { + val hadoopConf = new Configuration() + hadoopConf.set("yarn.resourcemanager.address", "myrm:8033") + hadoopConf.set("yarn.resourcemanager.principal", "yarn/myrm:8032@SPARKTEST.COM") + val renewer = Client.getTokenRenewer(hadoopConf) + renewer should be ("yarn/myrm:8032@SPARKTEST.COM") + } + + test("check token renewer default") { + val hadoopConf = new Configuration() + val caught = + intercept[SparkException] { + Client.getTokenRenewer(hadoopConf) + } + assert(caught.getMessage === "Can't get Master Kerberos principal for use as renewer") + } + object Fixtures { val knownDefYarnAppCP: Seq[String] = diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala index d3c606e0ed998..3877da4120e7c 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala @@ -86,7 +86,6 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit tempDir = Utils.createTempDir() logConfDir = new File(tempDir, "log4j") logConfDir.mkdir() - System.setProperty("SPARK_YARN_MODE", "true") val logConfFile = new File(logConfDir, "log4j.properties") Files.write(LOG4J_CONF, logConfFile, UTF_8) @@ -129,7 +128,6 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit override def afterAll() { yarnCluster.stop() - System.clearProperty("SPARK_YARN_MODE") super.afterAll() } diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala index e10b985c3c236..9395316b71ff4 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala @@ -20,8 +20,6 @@ package org.apache.spark.deploy.yarn import java.io.{File, IOException} import com.google.common.io.{ByteStreams, Files} -import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.Path import org.apache.hadoop.yarn.api.ApplicationConstants import org.apache.hadoop.yarn.api.ApplicationConstants.Environment import org.apache.hadoop.yarn.conf.YarnConfiguration @@ -29,7 +27,7 @@ import org.scalatest.{FunSuite, Matchers} import org.apache.hadoop.yarn.api.records.ApplicationAccessType -import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException} +import org.apache.spark.{Logging, SecurityManager, SparkConf} import org.apache.spark.util.Utils @@ -175,62 +173,4 @@ class YarnSparkHadoopUtilSuite extends FunSuite with Matchers with Logging { YarnSparkHadoopUtil.getClassPathSeparator() should be (":") } } - - test("check access nns empty") { - val sparkConf = new SparkConf() - val util = new YarnSparkHadoopUtil - sparkConf.set("spark.yarn.access.namenodes", "") - val nns = util.getNameNodesToAccess(sparkConf) - nns should be(Set()) - } - - test("check access nns unset") { - val sparkConf = new SparkConf() - val util = new YarnSparkHadoopUtil - val nns = util.getNameNodesToAccess(sparkConf) - nns should be(Set()) - } - - test("check access nns") { - val sparkConf = new SparkConf() - sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032") - val util = new YarnSparkHadoopUtil - val nns = util.getNameNodesToAccess(sparkConf) - nns should be(Set(new Path("hdfs://nn1:8032"))) - } - - test("check access nns space") { - val sparkConf = new SparkConf() - sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032, ") - val util = new YarnSparkHadoopUtil - val nns = util.getNameNodesToAccess(sparkConf) - nns should be(Set(new Path("hdfs://nn1:8032"))) - } - - test("check access two nns") { - val sparkConf = new SparkConf() - sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032,hdfs://nn2:8032") - val util = new YarnSparkHadoopUtil - val nns = util.getNameNodesToAccess(sparkConf) - nns should be(Set(new Path("hdfs://nn1:8032"), new Path("hdfs://nn2:8032"))) - } - - test("check token renewer") { - val hadoopConf = new Configuration() - hadoopConf.set("yarn.resourcemanager.address", "myrm:8033") - hadoopConf.set("yarn.resourcemanager.principal", "yarn/myrm:8032@SPARKTEST.COM") - val util = new YarnSparkHadoopUtil - val renewer = util.getTokenRenewer(hadoopConf) - renewer should be ("yarn/myrm:8032@SPARKTEST.COM") - } - - test("check token renewer default") { - val hadoopConf = new Configuration() - val util = new YarnSparkHadoopUtil - val caught = - intercept[SparkException] { - util.getTokenRenewer(hadoopConf) - } - assert(caught.getMessage === "Can't get Master Kerberos principal for use as renewer") - } } From 6702324b60f99dab55912c08ccd3d03805f6b7b2 Mon Sep 17 00:00:00 2001 From: Liang-Chi Hsieh Date: Thu, 30 Apr 2015 15:13:43 -0700 Subject: [PATCH 494/591] [SPARK-7196][SQL] Support precision and scale of decimal type for JDBC JIRA: https://issues.apache.org/jira/browse/SPARK-7196 Author: Liang-Chi Hsieh Closes #5777 from viirya/jdbc_precision and squashes the following commits: f40f5e6 [Liang-Chi Hsieh] Support precision and scale for NUMERIC type. 49acbf9 [Liang-Chi Hsieh] Add unit test. a509e19 [Liang-Chi Hsieh] Support precision and scale of decimal type for JDBC. --- .../main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala | 10 ++++++++-- .../scala/org/apache/spark/sql/jdbc/JDBCSuite.scala | 2 ++ 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala index f3b5455574d1a..cef92abbdc586 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala @@ -37,7 +37,7 @@ private[sql] object JDBCRDD extends Logging { * @param sqlType - A field of java.sql.Types * @return The Catalyst type corresponding to sqlType. */ - private def getCatalystType(sqlType: Int): DataType = { + private def getCatalystType(sqlType: Int, precision: Int, scale: Int): DataType = { val answer = sqlType match { case java.sql.Types.ARRAY => null case java.sql.Types.BIGINT => LongType @@ -49,6 +49,8 @@ private[sql] object JDBCRDD extends Logging { case java.sql.Types.CLOB => StringType case java.sql.Types.DATALINK => null case java.sql.Types.DATE => DateType + case java.sql.Types.DECIMAL + if precision != 0 || scale != 0 => DecimalType(precision, scale) case java.sql.Types.DECIMAL => DecimalType.Unlimited case java.sql.Types.DISTINCT => null case java.sql.Types.DOUBLE => DoubleType @@ -61,6 +63,8 @@ private[sql] object JDBCRDD extends Logging { case java.sql.Types.NCHAR => StringType case java.sql.Types.NCLOB => StringType case java.sql.Types.NULL => null + case java.sql.Types.NUMERIC + if precision != 0 || scale != 0 => DecimalType(precision, scale) case java.sql.Types.NUMERIC => DecimalType.Unlimited case java.sql.Types.NVARCHAR => StringType case java.sql.Types.OTHER => null @@ -109,10 +113,11 @@ private[sql] object JDBCRDD extends Logging { val dataType = rsmd.getColumnType(i + 1) val typeName = rsmd.getColumnTypeName(i + 1) val fieldSize = rsmd.getPrecision(i + 1) + val fieldScale = rsmd.getScale(i + 1) val nullable = rsmd.isNullable(i + 1) != ResultSetMetaData.columnNoNulls val metadata = new MetadataBuilder().putString("name", columnName) var columnType = quirks.getCatalystType(dataType, typeName, fieldSize, metadata) - if (columnType == null) columnType = getCatalystType(dataType) + if (columnType == null) columnType = getCatalystType(dataType, fieldSize, fieldScale) fields(i) = StructField(columnName, columnType, nullable, metadata.build()) i = i + 1 } @@ -307,6 +312,7 @@ private[sql] class JDBCRDD( case BooleanType => BooleanConversion case DateType => DateConversion case DecimalType.Unlimited => DecimalConversion + case DecimalType.Fixed(d) => DecimalConversion case DoubleType => DoubleConversion case FloatType => FloatConversion case IntegerType => IntegerConversion diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala index 856a806781ec4..b165ab2b1deb3 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala @@ -22,6 +22,7 @@ import java.sql.DriverManager import java.util.{Calendar, GregorianCalendar, Properties} import org.apache.spark.sql.test._ +import org.apache.spark.sql.types._ import org.h2.jdbc.JdbcSQLException import org.scalatest.{FunSuite, BeforeAndAfter} import TestSQLContext._ @@ -271,6 +272,7 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { assert(rows(0).getDouble(1) === 1.00000011920928955) // Yes, I meant ==. assert(rows(0).getAs[BigDecimal](2) .equals(new BigDecimal("123456789012345.54321543215432100000"))) + assert(rows(0).schema.fields(2).dataType === DecimalType(40, 20)) } test("SQL query as table name") { From 07a86205f9efc43ea1ec5edb97c21c32abe7fb8a Mon Sep 17 00:00:00 2001 From: Josh Rosen Date: Thu, 30 Apr 2015 15:21:00 -0700 Subject: [PATCH 495/591] [SPARK-7288] Suppress compiler warnings due to use of sun.misc.Unsafe; add facade in front of Unsafe; remove use of Unsafe.setMemory This patch suppresses compiler warnings due to our use of `sun.misc.Unsafe` (introduced in #5725). These warnings can only be suppressed via the `-XDignore.symbol.file` javac flag; the `SuppressWarnings` annotation won't work for these. In order to restrict uses of this compiler flag to the `unsafe` module, I placed a facade in front of `Unsafe` so that other modules won't call it directly. This facade also will also help us to avoid accidental usage of deprecated Unsafe methods or methods that aren't supported in Java 6. I also removed an unnecessary use of `Unsafe.setMemory`, which isn't present in certain versions of Java 6, and excluded the new `unsafe` module from Javadoc. Author: Josh Rosen Closes #5814 from JoshRosen/unsafe-compiler-warnings-fixes and squashes the following commits: 9e8c483 [Josh Rosen] Exclude new unsafe module from Javadoc ba75ecf [Josh Rosen] Only apply -XDignore.symbol.file flag in unsafe project. 7403345 [Josh Rosen] Put facade in front of Unsafe. 50230c0 [Josh Rosen] Remove usage of Unsafe.setMemory 96d41c9 [Josh Rosen] Use -XDignore.symbol.file to suppress warnings about sun.misc.Unsafe usage --- project/SparkBuild.scala | 11 +++ unsafe/pom.xml | 24 +++++ .../spark/unsafe/PlatformDependent.java | 93 +++++++++++++++++-- .../spark/unsafe/map/BytesToBytesMap.java | 9 +- .../spark/unsafe/memory/MemoryBlock.java | 8 -- .../spark/unsafe/bitset/BitSetSuite.java | 2 +- .../map/AbstractBytesToBytesMapSuite.java | 2 +- 7 files changed, 125 insertions(+), 24 deletions(-) diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index 4bfa8cede7a85..a93aa12ce1aac 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -163,6 +163,9 @@ object SparkBuild extends PomBuild { x => enable(MimaBuild.mimaSettings(sparkHome, x))(x) } + /* Unsafe settings */ + enable(Unsafe.settings)(unsafe) + /* Enable Assembly for all assembly projects */ assemblyProjects.foreach(enable(Assembly.settings)) @@ -216,6 +219,13 @@ object SparkBuild extends PomBuild { } +object Unsafe { + lazy val settings = Seq( + // This option is needed to suppress warnings from sun.misc.Unsafe usage + javacOptions in Compile += "-XDignore.symbol.file" + ) +} + object Flume { lazy val settings = sbtavro.SbtAvro.avroSettings } @@ -424,6 +434,7 @@ object Unidoc { .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/network"))) .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/shuffle"))) .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/executor"))) + .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/unsafe"))) .map(_.filterNot(_.getCanonicalPath.contains("python"))) .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/util/collection"))) .map(_.filterNot(_.getCanonicalPath.contains("org/apache/spark/sql/catalyst"))) diff --git a/unsafe/pom.xml b/unsafe/pom.xml index 8901d77591932..5b0733206b2bc 100644 --- a/unsafe/pom.xml +++ b/unsafe/pom.xml @@ -65,5 +65,29 @@ target/scala-${scala.binary.version}/classes target/scala-${scala.binary.version}/test-classes + + + + net.alchim31.maven + scala-maven-plugin + + + + -XDignore.symbol.file + + + + + org.apache.maven.plugins + maven-compiler-plugin + + + + -XDignore.symbol.file + + + + + diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java b/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java index 91b2f9aa43921..24b2892098059 100644 --- a/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java +++ b/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java @@ -23,7 +23,82 @@ public final class PlatformDependent { - public static final Unsafe UNSAFE; + /** + * Facade in front of {@link sun.misc.Unsafe}, used to avoid directly exposing Unsafe outside of + * this package. This also lets us aovid accidental use of deprecated methods or methods that + * aren't present in Java 6. + */ + public static final class UNSAFE { + + private UNSAFE() { } + + public static int getInt(Object object, long offset) { + return _UNSAFE.getInt(object, offset); + } + + public static void putInt(Object object, long offset, int value) { + _UNSAFE.putInt(object, offset, value); + } + + public static boolean getBoolean(Object object, long offset) { + return _UNSAFE.getBoolean(object, offset); + } + + public static void putBoolean(Object object, long offset, boolean value) { + _UNSAFE.putBoolean(object, offset, value); + } + + public static byte getByte(Object object, long offset) { + return _UNSAFE.getByte(object, offset); + } + + public static void putByte(Object object, long offset, byte value) { + _UNSAFE.putByte(object, offset, value); + } + + public static short getShort(Object object, long offset) { + return _UNSAFE.getShort(object, offset); + } + + public static void putShort(Object object, long offset, short value) { + _UNSAFE.putShort(object, offset, value); + } + + public static long getLong(Object object, long offset) { + return _UNSAFE.getLong(object, offset); + } + + public static void putLong(Object object, long offset, long value) { + _UNSAFE.putLong(object, offset, value); + } + + public static float getFloat(Object object, long offset) { + return _UNSAFE.getFloat(object, offset); + } + + public static void putFloat(Object object, long offset, float value) { + _UNSAFE.putFloat(object, offset, value); + } + + public static double getDouble(Object object, long offset) { + return _UNSAFE.getDouble(object, offset); + } + + public static void putDouble(Object object, long offset, double value) { + _UNSAFE.putDouble(object, offset, value); + } + + public static long allocateMemory(long size) { + return _UNSAFE.allocateMemory(size); + } + + public static void freeMemory(long address) { + _UNSAFE.freeMemory(address); + } + + } + + private static final Unsafe _UNSAFE; public static final int BYTE_ARRAY_OFFSET; @@ -48,13 +123,13 @@ public final class PlatformDependent { } catch (Throwable cause) { unsafe = null; } - UNSAFE = unsafe; + _UNSAFE = unsafe; - if (UNSAFE != null) { - BYTE_ARRAY_OFFSET = UNSAFE.arrayBaseOffset(byte[].class); - INT_ARRAY_OFFSET = UNSAFE.arrayBaseOffset(int[].class); - LONG_ARRAY_OFFSET = UNSAFE.arrayBaseOffset(long[].class); - DOUBLE_ARRAY_OFFSET = UNSAFE.arrayBaseOffset(double[].class); + if (_UNSAFE != null) { + BYTE_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(byte[].class); + INT_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(int[].class); + LONG_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(long[].class); + DOUBLE_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(double[].class); } else { BYTE_ARRAY_OFFSET = 0; INT_ARRAY_OFFSET = 0; @@ -71,7 +146,7 @@ static public void copyMemory( long length) { while (length > 0) { long size = Math.min(length, UNSAFE_COPY_THRESHOLD); - UNSAFE.copyMemory(src, srcOffset, dst, dstOffset, size); + _UNSAFE.copyMemory(src, srcOffset, dst, dstOffset, size); length -= size; srcOffset += size; dstOffset += size; @@ -82,6 +157,6 @@ static public void copyMemory( * Raises an exception bypassing compiler checks for checked exceptions. */ public static void throwException(Throwable t) { - UNSAFE.throwException(t); + _UNSAFE.throwException(t); } } diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java index 85b64c0833803..4e5ebc402be35 100644 --- a/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java +++ b/unsafe/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java @@ -401,11 +401,11 @@ public void putNewKey( // Copy the key PlatformDependent.UNSAFE.putLong(pageBaseObject, keySizeOffsetInPage, keyLengthBytes); - PlatformDependent.UNSAFE.copyMemory( + PlatformDependent.copyMemory( keyBaseObject, keyBaseOffset, pageBaseObject, keyDataOffsetInPage, keyLengthBytes); // Copy the value PlatformDependent.UNSAFE.putLong(pageBaseObject, valueSizeOffsetInPage, valueLengthBytes); - PlatformDependent.UNSAFE.copyMemory( + PlatformDependent.copyMemory( valueBaseObject, valueBaseOffset, pageBaseObject, valueDataOffsetInPage, valueLengthBytes); final long storedKeyAddress = memoryManager.encodePageNumberAndOffset( @@ -429,7 +429,7 @@ public void putNewKey( private void allocate(int capacity) { capacity = Math.max((int) Math.min(Integer.MAX_VALUE, nextPowerOf2(capacity)), 64); longArray = new LongArray(memoryManager.allocate(capacity * 8 * 2)); - bitset = new BitSet(memoryManager.allocate(capacity / 8).zero()); + bitset = new BitSet(MemoryBlock.fromLongArray(new long[capacity / 64])); this.growthThreshold = (int) (capacity * loadFactor); this.mask = capacity - 1; @@ -447,7 +447,7 @@ public void free() { longArray = null; } if (bitset != null) { - memoryManager.free(bitset.memoryBlock()); + // The bitset's heap memory isn't managed by a memory manager, so no need to free it here. bitset = null; } Iterator dataPagesIterator = dataPages.iterator(); @@ -535,7 +535,6 @@ private void growAndRehash() { // Deallocate the old data structures. memoryManager.free(oldLongArray.memoryBlock()); - memoryManager.free(oldBitSet.memoryBlock()); if (enablePerfMetrics) { timeSpentResizingNs += System.nanoTime() - resizeStartTime; } diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java index 0beb743e5644e..3dc82d8c2eb39 100644 --- a/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java +++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java @@ -46,14 +46,6 @@ public long size() { return length; } - /** - * Clear the contents of this memory block. Returns `this` to facilitate chaining. - */ - public MemoryBlock zero() { - PlatformDependent.UNSAFE.setMemory(obj, offset, length, (byte) 0); - return this; - } - /** * Creates a memory block pointing to the memory used by the long array. */ diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/bitset/BitSetSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/bitset/BitSetSuite.java index 4bf132fd4053e..e3a824e29b768 100644 --- a/unsafe/src/test/java/org/apache/spark/unsafe/bitset/BitSetSuite.java +++ b/unsafe/src/test/java/org/apache/spark/unsafe/bitset/BitSetSuite.java @@ -27,7 +27,7 @@ public class BitSetSuite { private static BitSet createBitSet(int capacity) { assert capacity % 64 == 0; - return new BitSet(MemoryBlock.fromLongArray(new long[capacity / 64]).zero()); + return new BitSet(MemoryBlock.fromLongArray(new long[capacity / 64])); } @Test diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java index 9038cf567f1e2..7a5c0622d1ffb 100644 --- a/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java +++ b/unsafe/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java @@ -57,7 +57,7 @@ public void tearDown() { private static byte[] getByteArray(MemoryLocation loc, int size) { final byte[] arr = new byte[size]; - PlatformDependent.UNSAFE.copyMemory( + PlatformDependent.copyMemory( loc.getBaseObject(), loc.getBaseOffset(), arr, From 77cc25fb7473d8a06b727d2ba5ee62db1c651cf8 Mon Sep 17 00:00:00 2001 From: Zhongshuai Pei <799203320@qq.com> Date: Thu, 30 Apr 2015 15:22:13 -0700 Subject: [PATCH 496/591] [SPARK-7267][SQL]Push down Project when it's child is Limit SQL ``` select key from (select key,value from t1 limit 100) t2 limit 10 ``` Optimized Logical Plan before modifying ``` == Optimized Logical Plan == Limit 10 Project key#228 Limit 100 MetastoreRelation default, t1, None ``` Optimized Logical Plan after modifying ``` == Optimized Logical Plan == Limit 10 Limit 100 Project key#228 MetastoreRelation default, t1, None ``` After this, we can combine limits Author: Zhongshuai Pei <799203320@qq.com> Author: DoingDone9 <799203320@qq.com> Closes #5797 from DoingDone9/ProjectLimit and squashes the following commits: 70d0fca [Zhongshuai Pei] Update FilterPushdownSuite.scala dc83ae9 [Zhongshuai Pei] Update FilterPushdownSuite.scala 485c61c [Zhongshuai Pei] Update Optimizer.scala f03fe7f [Zhongshuai Pei] Merge pull request #12 from apache/master f12fa50 [Zhongshuai Pei] Merge pull request #10 from apache/master f61210c [Zhongshuai Pei] Merge pull request #9 from apache/master 34b1a9a [Zhongshuai Pei] Merge pull request #8 from apache/master 802261c [DoingDone9] Merge pull request #7 from apache/master d00303b [DoingDone9] Merge pull request #6 from apache/master 98b134f [DoingDone9] Merge pull request #5 from apache/master 161cae3 [DoingDone9] Merge pull request #4 from apache/master c87e8b6 [DoingDone9] Merge pull request #3 from apache/master cb1852d [DoingDone9] Merge pull request #2 from apache/master c3f046f [DoingDone9] Merge pull request #1 from apache/master --- .../spark/sql/catalyst/optimizer/Optimizer.scala | 3 +++ .../catalyst/optimizer/FilterPushdownSuite.scala | 16 ++++++++++++++++ 2 files changed, 19 insertions(+) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala index 485761dd8538b..63669e970b1ec 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala @@ -170,6 +170,9 @@ object ColumnPruning extends Rule[LogicalPlan] { Project(substitutedProjection, child) + case Project(projectList, Limit(exp, child)) => + Limit(exp, Project(projectList, child)) + // Eliminate no-op Projects case Project(projectList, child) if child.output == projectList => child } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala index aa9708b164efa..2ad73941ab978 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala @@ -90,7 +90,23 @@ class FilterPushdownSuite extends PlanTest { comparePlans(optimized, correctAnswer) } + + test("column pruning for Project(ne, Limit)") { + val originalQuery = + testRelation + .select('a,'b) + .limit(2) + .select('a) + + val optimized = Optimize.execute(originalQuery.analyze) + val correctAnswer = + testRelation + .select('a) + .limit(2).analyze + comparePlans(optimized, correctAnswer) + } + // After this line is unimplemented. test("simple push down") { val originalQuery = From fa01bec484fc000e0a31645b722ffde48556c4df Mon Sep 17 00:00:00 2001 From: Josh Rosen Date: Thu, 30 Apr 2015 16:23:01 -0700 Subject: [PATCH 497/591] [Build] Enable MiMa checks for SQL Now that 1.3 has been released, we should enable MiMa checks for the `sql` subproject. Author: Josh Rosen Closes #5727 from JoshRosen/enable-more-mima-checks and squashes the following commits: 3ad302b [Josh Rosen] Merge remote-tracking branch 'origin/master' into enable-more-mima-checks 0c48e4d [Josh Rosen] Merge remote-tracking branch 'origin/master' into enable-more-mima-checks e276cee [Josh Rosen] Fix SQL MiMa checks via excludes and private[sql] 44d0d01 [Josh Rosen] Add back 'launcher' exclude 1aae027 [Josh Rosen] Enable MiMa checks for launcher and sql projects. --- project/MimaExcludes.scala | 16 ++++++++++++++++ project/SparkBuild.scala | 5 ++--- .../apache/spark/sql/execution/ExistingRDD.scala | 5 +++-- .../spark/sql/execution/LocalTableScan.scala | 2 +- .../apache/spark/sql/execution/commands.scala | 2 +- 5 files changed, 23 insertions(+), 7 deletions(-) diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala index 3beafa158eb97..bf343d4b7e40b 100644 --- a/project/MimaExcludes.scala +++ b/project/MimaExcludes.scala @@ -88,6 +88,22 @@ object MimaExcludes { "org.apache.spark.mllib.linalg.Vector.toSparse"), ProblemFilters.exclude[MissingMethodProblem]( "org.apache.spark.mllib.linalg.Vector.numActives") + ) ++ Seq( + // This `protected[sql]` method was removed in 1.3.1 + ProblemFilters.exclude[MissingMethodProblem]( + "org.apache.spark.sql.SQLContext.checkAnalysis"), + // This `private[sql]` class was removed in 1.4.0: + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.sql.execution.AddExchange"), + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.sql.execution.AddExchange$"), + // These test support classes were moved out of src/main and into src/test: + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.sql.parquet.ParquetTestData"), + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.sql.parquet.ParquetTestData$"), + ProblemFilters.exclude[MissingClassProblem]( + "org.apache.spark.sql.parquet.TestGroupWriteSupport") ) case v if v.startsWith("1.3") => diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index a93aa12ce1aac..b4431c7ee05b6 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -156,9 +156,8 @@ object SparkBuild extends PomBuild { /* Enable tests settings for all projects except examples, assembly and tools */ (allProjects ++ optionallyEnabledProjects).foreach(enable(TestSettings.settings)) - // TODO: Add Sql to mima checks - // TODO: remove launcher from this list after 1.3. - allProjects.filterNot(x => Seq(spark, sql, hive, hiveThriftServer, catalyst, repl, + // TODO: remove launcher from this list after 1.4.0 + allProjects.filterNot(x => Seq(spark, hive, hiveThriftServer, catalyst, repl, networkCommon, networkShuffle, networkYarn, launcher, unsafe).contains(x)).foreach { x => enable(MimaBuild.mimaSettings(sparkHome, x))(x) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala index 1fd387eec7e57..57effbf7ec501 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala @@ -84,7 +84,7 @@ object RDDConversions { } /** Logical plan node for scanning data from an RDD. */ -case class LogicalRDD(output: Seq[Attribute], rdd: RDD[Row])(sqlContext: SQLContext) +private[sql] case class LogicalRDD(output: Seq[Attribute], rdd: RDD[Row])(sqlContext: SQLContext) extends LogicalPlan with MultiInstanceRelation { override def children: Seq[LogicalPlan] = Nil @@ -105,11 +105,12 @@ case class LogicalRDD(output: Seq[Attribute], rdd: RDD[Row])(sqlContext: SQLCont } /** Physical plan node for scanning data from an RDD. */ -case class PhysicalRDD(output: Seq[Attribute], rdd: RDD[Row]) extends LeafNode { +private[sql] case class PhysicalRDD(output: Seq[Attribute], rdd: RDD[Row]) extends LeafNode { override def execute(): RDD[Row] = rdd } /** Logical plan node for scanning data from a local collection. */ +private[sql] case class LogicalLocalTable(output: Seq[Attribute], rows: Seq[Row])(sqlContext: SQLContext) extends LogicalPlan with MultiInstanceRelation { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala index 8a8c3a404323a..ace9af5f384c9 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala @@ -26,7 +26,7 @@ import org.apache.spark.sql.catalyst.expressions.Attribute /** * Physical plan node for scanning data from a local collection. */ -case class LocalTableScan(output: Seq[Attribute], rows: Seq[Row]) extends LeafNode { +private[sql] case class LocalTableScan(output: Seq[Attribute], rows: Seq[Row]) extends LeafNode { private lazy val rdd = sqlContext.sparkContext.parallelize(rows) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala index 99f24910fd61f..98df5bef34efa 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala @@ -42,7 +42,7 @@ trait RunnableCommand extends logical.Command { * A physical operator that executes the run method of a `RunnableCommand` and * saves the result to prevent multiple executions. */ -case class ExecutedCommand(cmd: RunnableCommand) extends SparkPlan { +private[sql] case class ExecutedCommand(cmd: RunnableCommand) extends SparkPlan { /** * A concrete command should override this lazy field to wrap up any side effects caused by the * command or any other computation that should be evaluated exactly once. The value of this field From 1c3e402e669d047410b00de9193adf3c329844a2 Mon Sep 17 00:00:00 2001 From: DB Tsai Date: Thu, 30 Apr 2015 16:26:51 -0700 Subject: [PATCH 498/591] [SPARK-7279] Removed diffSum which is theoretical zero in LinearRegression and coding formating Author: DB Tsai Closes #5809 from dbtsai/format and squashes the following commits: 6904eed [DB Tsai] triger jenkins 9146e19 [DB Tsai] initial commit --- .../spark/ml/regression/LinearRegression.scala | 18 ++++++------------ 1 file changed, 6 insertions(+), 12 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala index 11c6cea0f8c8c..0b81c48466be9 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala @@ -25,8 +25,7 @@ import breeze.optimize.{CachedDiffFunction, DiffFunction} import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.param.{Params, ParamMap} -import org.apache.spark.ml.param.shared.{HasTol, HasElasticNetParam, HasMaxIter, - HasRegParam} +import org.apache.spark.ml.param.shared.{HasTol, HasElasticNetParam, HasMaxIter, HasRegParam} import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.mllib.linalg.BLAS._ @@ -103,9 +102,7 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress case LabeledPoint(label: Double, features: Vector) => (label, features) } val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE - if (handlePersistence) { - instances.persist(StorageLevel.MEMORY_AND_DISK) - } + if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK) val (summarizer, statCounter) = instances.treeAggregate( (new MultivariateOnlineSummarizer, new StatCounter))( { @@ -146,8 +143,7 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress val optimizer = if (paramMap(elasticNetParam) == 0.0 || effectiveRegParam == 0.0) { new BreezeLBFGS[BDV[Double]](paramMap(maxIter), 10, paramMap(tol)) } else { - new BreezeOWLQN[Int, BDV[Double]](paramMap(maxIter), 10, effectiveL1RegParam, - paramMap(tol)) + new BreezeOWLQN[Int, BDV[Double]](paramMap(maxIter), 10, effectiveL1RegParam, paramMap(tol)) } val initialWeights = Vectors.zeros(numFeatures) @@ -304,9 +300,8 @@ private class LeastSquaresAggregator( featuresStd: Array[Double], featuresMean: Array[Double]) extends Serializable { - private var totalCnt: Long = 0 + private var totalCnt: Long = 0L private var lossSum = 0.0 - private var diffSum = 0.0 private val (effectiveWeightsArray: Array[Double], offset: Double, dim: Int) = { val weightsArray = weights.toArray.clone() @@ -323,9 +318,10 @@ private class LeastSquaresAggregator( } (weightsArray, -sum + labelMean / labelStd, weightsArray.length) } + private val effectiveWeightsVector = Vectors.dense(effectiveWeightsArray) - private val gradientSumArray: Array[Double] = Array.ofDim[Double](dim) + private val gradientSumArray = Array.ofDim[Double](dim) /** * Add a new training data to this LeastSquaresAggregator, and update the loss and gradient @@ -350,7 +346,6 @@ private class LeastSquaresAggregator( } } lossSum += diff * diff / 2.0 - diffSum += diff } totalCnt += 1 @@ -372,7 +367,6 @@ private class LeastSquaresAggregator( if (other.totalCnt != 0) { totalCnt += other.totalCnt lossSum += other.lossSum - diffSum += other.diffSum var i = 0 val localThisGradientSumArray = this.gradientSumArray From 149b3ee2dac992355adbe44e989570726c1f35d0 Mon Sep 17 00:00:00 2001 From: Burak Yavuz Date: Thu, 30 Apr 2015 16:40:32 -0700 Subject: [PATCH 499/591] [SPARK-7242][SQL][MLLIB] Frequent items for DataFrames Finding frequent items with possibly false positives, using the algorithm described in `http://www.cs.umd.edu/~samir/498/karp.pdf`. public API under: ``` df.stat.freqItems(cols: Array[String], support: Double = 0.001): DataFrame ``` The output is a local DataFrame having the input column names with `-freqItems` appended to it. This is a single pass algorithm that may return false positives, but no false negatives. cc mengxr rxin Let's get the implementations in, I can add python API in a follow up PR. Author: Burak Yavuz Closes #5799 from brkyvz/freq-items and squashes the following commits: a6ec82c [Burak Yavuz] addressed comments v? 39b1bba [Burak Yavuz] removed toSeq 0915e23 [Burak Yavuz] addressed comments v2.1 3a5c177 [Burak Yavuz] addressed comments v2.0 482e741 [Burak Yavuz] removed old import 38e784d [Burak Yavuz] addressed comments v1.0 8279d4d [Burak Yavuz] added default value for support 3d82168 [Burak Yavuz] made base implementation --- .../org/apache/spark/sql/DataFrame.scala | 11 ++ .../spark/sql/DataFrameStatFunctions.scala | 68 ++++++++++ .../sql/execution/stat/FrequentItems.scala | 121 ++++++++++++++++++ .../apache/spark/sql/JavaDataFrameSuite.java | 14 +- .../apache/spark/sql/DataFrameStatSuite.scala | 47 +++++++ 5 files changed, 256 insertions(+), 5 deletions(-) create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 2669300029545..7be2a01da17e8 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -330,6 +330,17 @@ class DataFrame private[sql]( */ def na: DataFrameNaFunctions = new DataFrameNaFunctions(this) + /** + * Returns a [[DataFrameStatFunctions]] for working statistic functions support. + * {{{ + * // Finding frequent items in column with name 'a'. + * df.stat.freqItems(Seq("a")) + * }}} + * + * @group dfops + */ + def stat: DataFrameStatFunctions = new DataFrameStatFunctions(this) + /** * Cartesian join with another [[DataFrame]]. * diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala new file mode 100644 index 0000000000000..42e5cbc05e1e0 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala @@ -0,0 +1,68 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql + +import org.apache.spark.annotation.Experimental +import org.apache.spark.sql.execution.stat.FrequentItems + +/** + * :: Experimental :: + * Statistic functions for [[DataFrame]]s. + */ +@Experimental +final class DataFrameStatFunctions private[sql](df: DataFrame) { + + /** + * Finding frequent items for columns, possibly with false positives. Using the + * frequent element count algorithm described in + * [[http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou]]. + * The `support` should be greater than 1e-4. + * + * @param cols the names of the columns to search frequent items in. + * @param support The minimum frequency for an item to be considered `frequent`. Should be greater + * than 1e-4. + * @return A Local DataFrame with the Array of frequent items for each column. + */ + def freqItems(cols: Array[String], support: Double): DataFrame = { + FrequentItems.singlePassFreqItems(df, cols, support) + } + + /** + * Runs `freqItems` with a default `support` of 1%. + * + * @param cols the names of the columns to search frequent items in. + * @return A Local DataFrame with the Array of frequent items for each column. + */ + def freqItems(cols: Array[String]): DataFrame = { + FrequentItems.singlePassFreqItems(df, cols, 0.01) + } + + /** + * Python friendly implementation for `freqItems` + */ + def freqItems(cols: List[String], support: Double): DataFrame = { + FrequentItems.singlePassFreqItems(df, cols, support) + } + + /** + * Python friendly implementation for `freqItems` with a default `support` of 1%. + */ + def freqItems(cols: List[String]): DataFrame = { + FrequentItems.singlePassFreqItems(df, cols, 0.01) + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala new file mode 100644 index 0000000000000..5ae7e107544f8 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala @@ -0,0 +1,121 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql.execution.stat + +import scala.collection.mutable.{Map => MutableMap} + +import org.apache.spark.Logging +import org.apache.spark.sql.{Column, DataFrame, Row} +import org.apache.spark.sql.catalyst.plans.logical.LocalRelation +import org.apache.spark.sql.types.{ArrayType, StructField, StructType} + +private[sql] object FrequentItems extends Logging { + + /** A helper class wrapping `MutableMap[Any, Long]` for simplicity. */ + private class FreqItemCounter(size: Int) extends Serializable { + val baseMap: MutableMap[Any, Long] = MutableMap.empty[Any, Long] + + /** + * Add a new example to the counts if it exists, otherwise deduct the count + * from existing items. + */ + def add(key: Any, count: Long): this.type = { + if (baseMap.contains(key)) { + baseMap(key) += count + } else { + if (baseMap.size < size) { + baseMap += key -> count + } else { + // TODO: Make this more efficient... A flatMap? + baseMap.retain((k, v) => v > count) + baseMap.transform((k, v) => v - count) + } + } + this + } + + /** + * Merge two maps of counts. + * @param other The map containing the counts for that partition + */ + def merge(other: FreqItemCounter): this.type = { + other.baseMap.foreach { case (k, v) => + add(k, v) + } + this + } + } + + /** + * Finding frequent items for columns, possibly with false positives. Using the + * frequent element count algorithm described in + * [[http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou]]. + * The `support` should be greater than 1e-4. + * For Internal use only. + * + * @param df The input DataFrame + * @param cols the names of the columns to search frequent items in + * @param support The minimum frequency for an item to be considered `frequent`. Should be greater + * than 1e-4. + * @return A Local DataFrame with the Array of frequent items for each column. + */ + private[sql] def singlePassFreqItems( + df: DataFrame, + cols: Seq[String], + support: Double): DataFrame = { + require(support >= 1e-4, s"support ($support) must be greater than 1e-4.") + val numCols = cols.length + // number of max items to keep counts for + val sizeOfMap = (1 / support).toInt + val countMaps = Seq.tabulate(numCols)(i => new FreqItemCounter(sizeOfMap)) + val originalSchema = df.schema + val colInfo = cols.map { name => + val index = originalSchema.fieldIndex(name) + (name, originalSchema.fields(index).dataType) + } + + val freqItems = df.select(cols.map(Column(_)):_*).rdd.aggregate(countMaps)( + seqOp = (counts, row) => { + var i = 0 + while (i < numCols) { + val thisMap = counts(i) + val key = row.get(i) + thisMap.add(key, 1L) + i += 1 + } + counts + }, + combOp = (baseCounts, counts) => { + var i = 0 + while (i < numCols) { + baseCounts(i).merge(counts(i)) + i += 1 + } + baseCounts + } + ) + val justItems = freqItems.map(m => m.baseMap.keys.toSeq) + val resultRow = Row(justItems:_*) + // append frequent Items to the column name for easy debugging + val outputCols = colInfo.map { v => + StructField(v._1 + "_freqItems", ArrayType(v._2, false)) + } + val schema = StructType(outputCols).toAttributes + new DataFrame(df.sqlContext, LocalRelation(schema, Seq(resultRow))) + } +} diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java index e5c9504d21042..966d879e1fc9f 100644 --- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java @@ -22,10 +22,7 @@ import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.sql.DataFrame; -import org.apache.spark.sql.Row; -import org.apache.spark.sql.SQLContext; -import org.apache.spark.sql.TestData$; +import org.apache.spark.sql.*; import org.apache.spark.sql.test.TestSQLContext; import org.apache.spark.sql.test.TestSQLContext$; import org.apache.spark.sql.types.*; @@ -178,5 +175,12 @@ public void testCreateDataFrameFromJavaBeans() { Assert.assertEquals(bean.getD().get(i), d.apply(i)); } } - + + @Test + public void testFrequentItems() { + DataFrame df = context.table("testData2"); + String[] cols = new String[]{"a"}; + DataFrame results = df.stat().freqItems(cols, 0.2); + Assert.assertTrue(results.collect()[0].getSeq(0).contains(1)); + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala new file mode 100644 index 0000000000000..bb1d29c71d23b --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala @@ -0,0 +1,47 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import org.scalatest.FunSuite +import org.scalatest.Matchers._ + +import org.apache.spark.sql.test.TestSQLContext +import org.apache.spark.sql.test.TestSQLContext.implicits._ + +class DataFrameStatSuite extends FunSuite { + + val sqlCtx = TestSQLContext + + test("Frequent Items") { + def toLetter(i: Int): String = (i + 96).toChar.toString + val rows = Array.tabulate(1000) { i => + if (i % 3 == 0) (1, toLetter(1), -1.0) else (i, toLetter(i), i * -1.0) + } + val df = sqlCtx.sparkContext.parallelize(rows).toDF("numbers", "letters", "negDoubles") + + val results = df.stat.freqItems(Array("numbers", "letters"), 0.1) + val items = results.collect().head + items.getSeq[Int](0) should contain (1) + items.getSeq[String](1) should contain (toLetter(1)) + + val singleColResults = df.stat.freqItems(Array("negDoubles"), 0.1) + val items2 = singleColResults.collect().head + items2.getSeq[Double](0) should contain (-1.0) + + } +} From ee04413935f74b3178adbb6d8dee19b3320803e9 Mon Sep 17 00:00:00 2001 From: rakeshchalasani Date: Thu, 30 Apr 2015 17:42:50 -0700 Subject: [PATCH 500/591] [SPARK-7280][SQL] Add "drop" column/s on a data frame Takes a column name/s and returns a new DataFrame that drops a column/s. Author: rakeshchalasani Closes #5818 from rakeshchalasani/SPARK-7280 and squashes the following commits: ce2ec09 [rakeshchalasani] Minor edit 45c06f1 [rakeshchalasani] Change withColumnRename and format changes f68945a [rakeshchalasani] Minor fix 0b9104d [rakeshchalasani] Drop one column at a time 289afd2 [rakeshchalasani] [SPARK-7280][SQL] Add "drop" column/s on a data frame --- .../org/apache/spark/sql/DataFrame.scala | 33 ++++++++++++++++--- .../org/apache/spark/sql/DataFrameSuite.scala | 16 +++++++++ 2 files changed, 45 insertions(+), 4 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 7be2a01da17e8..c421006c8fd2d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -851,15 +851,40 @@ class DataFrame private[sql]( /** * Returns a new [[DataFrame]] with a column renamed. + * This is a no-op if schema doesn't contain existingName. * @group dfops */ def withColumnRenamed(existingName: String, newName: String): DataFrame = { val resolver = sqlContext.analyzer.resolver - val colNames = schema.map { field => - val name = field.name - if (resolver(name, existingName)) Column(name).as(newName) else Column(name) + val shouldRename = schema.exists(f => resolver(f.name, existingName)) + if (shouldRename) { + val colNames = schema.map { field => + val name = field.name + if (resolver(name, existingName)) Column(name).as(newName) else Column(name) + } + select(colNames : _*) + } else { + this + } + } + + /** + * Returns a new [[DataFrame]] with a column dropped. + * This is a no-op if schema doesn't contain column name. + * @group dfops + */ + def drop(colName: String): DataFrame = { + val resolver = sqlContext.analyzer.resolver + val shouldDrop = schema.exists(f => resolver(f.name, colName)) + if (shouldDrop) { + val colsAfterDrop = schema.filter { field => + val name = field.name + !resolver(name, colName) + }.map(f => Column(f.name)) + select(colsAfterDrop : _*) + } else { + this } - select(colNames :_*) } /** diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index b70e127b4ed1a..e286fef23caa4 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -499,6 +499,22 @@ class DataFrameSuite extends QueryTest { Row(2) :: Row(3) :: Row(4) :: Nil) } + test("drop column using drop") { + val df = testData.drop("key") + checkAnswer( + df, + testData.collect().map(x => Row(x.getString(1))).toSeq) + assert(df.schema.map(_.name) === Seq("value")) + } + + test("drop unknown column (no-op)") { + val df = testData.drop("random") + checkAnswer( + df, + testData.collect().toSeq) + assert(df.schema.map(_.name) === Seq("key","value")) + } + test("withColumnRenamed") { val df = testData.toDF().withColumn("newCol", col("key") + 1) .withColumnRenamed("value", "valueRenamed") From 079733817f02c61ef814f5d9c0c8227498ff0058 Mon Sep 17 00:00:00 2001 From: scwf Date: Thu, 30 Apr 2015 18:15:56 -0700 Subject: [PATCH 501/591] [SPARK-7093] [SQL] Using newPredicate in NestedLoopJoin to enable code generation Using newPredicate in NestedLoopJoin instead of InterpretedPredicate to make it can make use of code generation Author: scwf Closes #5665 from scwf/NLP and squashes the following commits: d19dd31 [scwf] improvement a887c02 [scwf] improve for NLP boundCondition --- .../spark/sql/execution/joins/BroadcastNestedLoopJoin.scala | 5 +---- .../apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala | 5 +---- 2 files changed, 2 insertions(+), 8 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala index 56200f6b8c8a9..6aaf35fb429e2 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala @@ -59,10 +59,7 @@ case class BroadcastNestedLoopJoin( } @transient private lazy val boundCondition = - InterpretedPredicate.create( - condition - .map(c => BindReferences.bindReference(c, left.output ++ right.output)) - .getOrElse(Literal(true))) + newPredicate(condition.getOrElse(Literal(true)), left.output ++ right.output) override def execute(): RDD[Row] = { val broadcastedRelation = diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala index e06f63f94b78b..b03af410dca08 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala @@ -45,10 +45,7 @@ case class LeftSemiJoinBNL( override def right: SparkPlan = broadcast @transient private lazy val boundCondition = - InterpretedPredicate.create( - condition - .map(c => BindReferences.bindReference(c, left.output ++ right.output)) - .getOrElse(Literal(true))) + newPredicate(condition.getOrElse(Literal(true)), left.output ++ right.output) override def execute(): RDD[Row] = { val broadcastedRelation = From a0d8a61ab198b8c0ddbb3072bbe1d0e1dabc3e45 Mon Sep 17 00:00:00 2001 From: wangfei Date: Thu, 30 Apr 2015 18:18:54 -0700 Subject: [PATCH 502/591] [SPARK-7109] [SQL] Push down left side filter for left semi join Now in spark sql optimizer we only push down right side filter for left semi join, actually we can push down left side filter because left semi join is doing filter on left table essentially. Author: wangfei Author: scwf Closes #5677 from scwf/leftsemi and squashes the following commits: 483d205 [wangfei] update with master to fix compile issue 82df0e1 [wangfei] Merge branch 'master' of https://github.com/apache/spark into leftsemi d68a053 [wangfei] added apply 8f48a3d [scwf] added test ebadaa9 [wangfei] left filter push down for left semi join --- .../sql/catalyst/optimizer/Optimizer.scala | 8 +++---- .../optimizer/FilterPushdownSuite.scala | 21 ++++++++++++++++++- 2 files changed, 24 insertions(+), 5 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala index 63669e970b1ec..709f7d672d931 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala @@ -571,7 +571,7 @@ object PushPredicateThroughJoin extends Rule[LogicalPlan] with PredicateHelper { split(joinCondition.map(splitConjunctivePredicates).getOrElse(Nil), left, right) joinType match { - case Inner => + case _ @ (Inner | LeftSemi) => // push down the single side only join filter for both sides sub queries val newLeft = leftJoinConditions. reduceLeftOption(And).map(Filter(_, left)).getOrElse(left) @@ -579,7 +579,7 @@ object PushPredicateThroughJoin extends Rule[LogicalPlan] with PredicateHelper { reduceLeftOption(And).map(Filter(_, right)).getOrElse(right) val newJoinCond = commonJoinCondition.reduceLeftOption(And) - Join(newLeft, newRight, Inner, newJoinCond) + Join(newLeft, newRight, joinType, newJoinCond) case RightOuter => // push down the left side only join filter for left side sub query val newLeft = leftJoinConditions. @@ -588,14 +588,14 @@ object PushPredicateThroughJoin extends Rule[LogicalPlan] with PredicateHelper { val newJoinCond = (rightJoinConditions ++ commonJoinCondition).reduceLeftOption(And) Join(newLeft, newRight, RightOuter, newJoinCond) - case _ @ (LeftOuter | LeftSemi) => + case LeftOuter => // push down the right side only join filter for right sub query val newLeft = left val newRight = rightJoinConditions. reduceLeftOption(And).map(Filter(_, right)).getOrElse(right) val newJoinCond = (leftJoinConditions ++ commonJoinCondition).reduceLeftOption(And) - Join(newLeft, newRight, joinType, newJoinCond) + Join(newLeft, newRight, LeftOuter, newJoinCond) case FullOuter => f } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala index 2ad73941ab978..58d415d9011e1 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala @@ -21,7 +21,7 @@ import org.apache.spark.sql.catalyst.analysis import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries import org.apache.spark.sql.catalyst.expressions.{Count, Explode} import org.apache.spark.sql.catalyst.plans.logical._ -import org.apache.spark.sql.catalyst.plans.{PlanTest, LeftOuter, RightOuter} +import org.apache.spark.sql.catalyst.plans.{LeftSemi, PlanTest, LeftOuter, RightOuter} import org.apache.spark.sql.catalyst.rules._ import org.apache.spark.sql.catalyst.dsl.plans._ import org.apache.spark.sql.catalyst.dsl.expressions._ @@ -43,6 +43,8 @@ class FilterPushdownSuite extends PlanTest { val testRelation = LocalRelation('a.int, 'b.int, 'c.int) + val testRelation1 = LocalRelation('d.int) + // This test already passes. test("eliminate subqueries") { val originalQuery = @@ -213,6 +215,23 @@ class FilterPushdownSuite extends PlanTest { comparePlans(optimized, correctAnswer) } + test("joins: push down left semi join") { + val x = testRelation.subquery('x) + val y = testRelation1.subquery('y) + + val originalQuery = { + x.join(y, LeftSemi, Option("x.a".attr === "y.d".attr && "x.b".attr >= 1 && "y.d".attr >= 2)) + } + + val optimized = Optimize.execute(originalQuery.analyze) + val left = testRelation.where('b >= 1) + val right = testRelation1.where('d >= 2) + val correctAnswer = + left.join(right, LeftSemi, Option("a".attr === "d".attr)).analyze + + comparePlans(optimized, correctAnswer) + } + test("joins: push down left outer join #1") { val x = testRelation.subquery('x) val y = testRelation.subquery('y) From e991255e7203a0f7080efbd71f57574f46076711 Mon Sep 17 00:00:00 2001 From: Vyacheslav Baranov Date: Thu, 30 Apr 2015 18:45:14 -0700 Subject: [PATCH 503/591] [SPARK-6913][SQL] Fixed "java.sql.SQLException: No suitable driver found" Fixed `java.sql.SQLException: No suitable driver found` when loading DataFrame into Spark SQL if the driver is supplied with `--jars` argument. The problem is in `java.sql.DriverManager` class that can't access drivers loaded by Spark ClassLoader. Wrappers that forward requests are created for these drivers. Also, it's not necessary any more to include JDBC drivers in `--driver-class-path` in local mode, specifying in `--jars` argument is sufficient. Author: Vyacheslav Baranov Closes #5782 from SlavikBaranov/SPARK-6913 and squashes the following commits: 510c43f [Vyacheslav Baranov] [SPARK-6913] Fixed review comments b2a727c [Vyacheslav Baranov] [SPARK-6913] Fixed thread race on driver registration c8294ae [Vyacheslav Baranov] [SPARK-6913] Fixed "No suitable driver found" when using using JDBC driver added with SparkContext.addJar --- .../org/apache/spark/sql/jdbc/JDBCRDD.scala | 2 +- .../apache/spark/sql/jdbc/JDBCRelation.scala | 4 +- .../org/apache/spark/sql/jdbc/jdbc.scala | 60 ++++++++++++++++++- 3 files changed, 62 insertions(+), 4 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala index cef92abbdc586..2f6ba48dbc3d9 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala @@ -159,7 +159,7 @@ private[sql] object JDBCRDD extends Logging { def getConnector(driver: String, url: String, properties: Properties): () => Connection = { () => { try { - if (driver != null) Utils.getContextOrSparkClassLoader.loadClass(driver) + if (driver != null) DriverRegistry.register(driver) } catch { case e: ClassNotFoundException => { logWarning(s"Couldn't find class $driver", e); diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala index 5f480083d5a49..d6b3fb3291a2e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala @@ -100,7 +100,7 @@ private[sql] class DefaultSource extends RelationProvider { val upperBound = parameters.getOrElse("upperBound", null) val numPartitions = parameters.getOrElse("numPartitions", null) - if (driver != null) Utils.getContextOrSparkClassLoader.loadClass(driver) + if (driver != null) DriverRegistry.register(driver) if (partitionColumn != null && (lowerBound == null || upperBound == null || numPartitions == null)) { @@ -136,7 +136,7 @@ private[sql] case class JDBCRelation( override val schema: StructType = JDBCRDD.resolveTable(url, table, properties) override def buildScan(requiredColumns: Array[String], filters: Array[Filter]): RDD[Row] = { - val driver: String = DriverManager.getDriver(url).getClass.getCanonicalName + val driver: String = DriverRegistry.getDriverClassName(url) JDBCRDD.scanTable( sqlContext.sparkContext, schema, diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala index d4e0abc040bc6..ae9af1eabe68e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala @@ -17,10 +17,14 @@ package org.apache.spark.sql -import java.sql.{Connection, DriverManager, PreparedStatement} +import java.sql.{Connection, Driver, DriverManager, DriverPropertyInfo, PreparedStatement} +import java.util.Properties + +import scala.collection.mutable import org.apache.spark.Logging import org.apache.spark.sql.types._ +import org.apache.spark.util.Utils package object jdbc { private[sql] object JDBCWriteDetails extends Logging { @@ -179,4 +183,58 @@ package object jdbc { } } + + private [sql] class DriverWrapper(val wrapped: Driver) extends Driver { + override def acceptsURL(url: String): Boolean = wrapped.acceptsURL(url) + + override def jdbcCompliant(): Boolean = wrapped.jdbcCompliant() + + override def getPropertyInfo(url: String, info: Properties): Array[DriverPropertyInfo] = { + wrapped.getPropertyInfo(url, info) + } + + override def getMinorVersion: Int = wrapped.getMinorVersion + + override def getParentLogger: java.util.logging.Logger = wrapped.getParentLogger + + override def connect(url: String, info: Properties): Connection = wrapped.connect(url, info) + + override def getMajorVersion: Int = wrapped.getMajorVersion + } + + /** + * java.sql.DriverManager is always loaded by bootstrap classloader, + * so it can't load JDBC drivers accessible by Spark ClassLoader. + * + * To solve the problem, drivers from user-supplied jars are wrapped + * into thin wrapper. + */ + private [sql] object DriverRegistry extends Logging { + + private val wrapperMap: mutable.Map[String, DriverWrapper] = mutable.Map.empty + + def register(className: String): Unit = { + val cls = Utils.getContextOrSparkClassLoader.loadClass(className) + if (cls.getClassLoader == null) { + logTrace(s"$className has been loaded with bootstrap ClassLoader, wrapper is not required") + } else if (wrapperMap.get(className).isDefined) { + logTrace(s"Wrapper for $className already exists") + } else { + synchronized { + if (wrapperMap.get(className).isEmpty) { + val wrapper = new DriverWrapper(cls.newInstance().asInstanceOf[Driver]) + DriverManager.registerDriver(wrapper) + wrapperMap(className) = wrapper + logTrace(s"Wrapper for $className registered") + } + } + } + } + + def getDriverClassName(url: String): String = DriverManager.getDriver(url) match { + case wrapper: DriverWrapper => wrapper.wrapped.getClass.getCanonicalName + case driver => driver.getClass.getCanonicalName + } + } + } // package object jdbc From 3ba5aaab8266822545ac82b9e733fd25cc215a77 Mon Sep 17 00:00:00 2001 From: Cheng Hao Date: Thu, 30 Apr 2015 18:49:06 -0700 Subject: [PATCH 504/591] [SPARK-5213] [SQL] Pluggable SQL Parser Support This PR aims to make the SQL Parser Pluggable, and user can register it's own parser via Spark SQL CLI. ``` # add the jar into the classpath $hchengmydesktop:spark>bin/spark-sql --jars sql99.jar -- switch to "hiveql" dialect spark-sql>SET spark.sql.dialect=hiveql; spark-sql>SELECT * FROM src LIMIT 1; -- switch to "sql" dialect spark-sql>SET spark.sql.dialect=sql; spark-sql>SELECT * FROM src LIMIT 1; -- switch to a custom dialect spark-sql>SET spark.sql.dialect=com.xxx.xxx.SQL99Dialect; spark-sql>SELECT * FROM src LIMIT 1; -- register the non-exist SQL dialect spark-sql> SET spark.sql.dialect=NotExistedClass; spark-sql> SELECT * FROM src LIMIT 1; -- Exception will be thrown and switch to default sql dialect ("sql" for SQLContext and "hiveql" for HiveContext) ``` Author: Cheng Hao Closes #4015 from chenghao-intel/sqlparser and squashes the following commits: 493775c [Cheng Hao] update the code as feedback 81a731f [Cheng Hao] remove the unecessary comment aab0b0b [Cheng Hao] polish the code a little bit 49b9d81 [Cheng Hao] shrink the comment for rebasing --- .../sql/catalyst/AbstractSparkSQLParser.scala | 11 ++- .../apache/spark/sql/catalyst/Dialect.scala | 33 ++++++++ .../spark/sql/catalyst/errors/package.scala | 2 + .../org/apache/spark/sql/SQLContext.scala | 82 +++++++++++++++---- .../org/apache/spark/sql/sources/ddl.scala | 6 +- .../org/apache/spark/sql/SQLQuerySuite.scala | 22 +++++ .../apache/spark/sql/hive/HiveContext.scala | 41 ++++++---- .../apache/spark/sql/hive/test/TestHive.scala | 5 +- .../sql/hive/execution/SQLQuerySuite.scala | 39 ++++++++- 9 files changed, 199 insertions(+), 42 deletions(-) create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/Dialect.scala diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala index 1f3c02478bd68..2eb3e167baad5 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala @@ -25,10 +25,6 @@ import scala.util.parsing.input.CharArrayReader.EofCh import org.apache.spark.sql.catalyst.plans.logical._ -private[sql] object KeywordNormalizer { - def apply(str: String): String = str.toLowerCase() -} - private[sql] abstract class AbstractSparkSQLParser extends StandardTokenParsers with PackratParsers { @@ -42,7 +38,7 @@ private[sql] abstract class AbstractSparkSQLParser } protected case class Keyword(str: String) { - def normalize: String = KeywordNormalizer(str) + def normalize: String = lexical.normalizeKeyword(str) def parser: Parser[String] = normalize } @@ -90,13 +86,16 @@ class SqlLexical extends StdLexical { reserved ++= keywords } + /* Normal the keyword string */ + def normalizeKeyword(str: String): String = str.toLowerCase + delimiters += ( "@", "*", "+", "-", "<", "=", "<>", "!=", "<=", ">=", ">", "/", "(", ")", ",", ";", "%", "{", "}", ":", "[", "]", ".", "&", "|", "^", "~", "<=>" ) protected override def processIdent(name: String) = { - val token = KeywordNormalizer(name) + val token = normalizeKeyword(name) if (reserved contains token) Keyword(token) else Identifier(name) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/Dialect.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/Dialect.scala new file mode 100644 index 0000000000000..977003493d471 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/Dialect.scala @@ -0,0 +1,33 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan + +/** + * Root class of SQL Parser Dialect, and we don't guarantee the binary + * compatibility for the future release, let's keep it as the internal + * interface for advanced user. + * + */ +@DeveloperApi +abstract class Dialect { + // this is the main function that will be implemented by sql parser. + def parse(sqlText: String): LogicalPlan +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/errors/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/errors/package.scala index bdeb660b1ecb7..0fd4f9b374ee0 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/errors/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/errors/package.scala @@ -38,6 +38,8 @@ package object errors { } } + class DialectException(msg: String, cause: Throwable) extends Exception(msg, cause) + /** * Wraps any exceptions that are thrown while executing `f` in a * [[catalyst.errors.TreeNodeException TreeNodeException]], attaching the provided `tree`. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala index bd4a55fa132fb..77f51dfd88d6f 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala @@ -24,6 +24,7 @@ import scala.collection.JavaConversions._ import scala.collection.immutable import scala.language.implicitConversions import scala.reflect.runtime.universe.TypeTag +import scala.util.control.NonFatal import com.google.common.reflect.TypeToken @@ -32,9 +33,11 @@ import org.apache.spark.api.java.{JavaRDD, JavaSparkContext} import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.analysis._ import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.errors.DialectException import org.apache.spark.sql.catalyst.optimizer.{DefaultOptimizer, Optimizer} import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan} import org.apache.spark.sql.catalyst.rules.RuleExecutor +import org.apache.spark.sql.catalyst.Dialect import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, expressions} import org.apache.spark.sql.execution.{Filter, _} import org.apache.spark.sql.jdbc.{JDBCPartition, JDBCPartitioningInfo, JDBCRelation} @@ -44,6 +47,45 @@ import org.apache.spark.sql.types._ import org.apache.spark.util.Utils import org.apache.spark.{Partition, SparkContext} +/** + * Currently we support the default dialect named "sql", associated with the class + * [[DefaultDialect]] + * + * And we can also provide custom SQL Dialect, for example in Spark SQL CLI: + * {{{ + *-- switch to "hiveql" dialect + * spark-sql>SET spark.sql.dialect=hiveql; + * spark-sql>SELECT * FROM src LIMIT 1; + * + *-- switch to "sql" dialect + * spark-sql>SET spark.sql.dialect=sql; + * spark-sql>SELECT * FROM src LIMIT 1; + * + *-- register the new SQL dialect + * spark-sql> SET spark.sql.dialect=com.xxx.xxx.SQL99Dialect; + * spark-sql> SELECT * FROM src LIMIT 1; + * + *-- register the non-exist SQL dialect + * spark-sql> SET spark.sql.dialect=NotExistedClass; + * spark-sql> SELECT * FROM src LIMIT 1; + * + *-- Exception will be thrown and switch to dialect + *-- "sql" (for SQLContext) or + *-- "hiveql" (for HiveContext) + * }}} + */ +private[spark] class DefaultDialect extends Dialect { + @transient + protected val sqlParser = { + val catalystSqlParser = new catalyst.SqlParser + new SparkSQLParser(catalystSqlParser.parse) + } + + override def parse(sqlText: String): LogicalPlan = { + sqlParser.parse(sqlText) + } +} + /** * The entry point for working with structured data (rows and columns) in Spark. Allows the * creation of [[DataFrame]] objects as well as the execution of SQL queries. @@ -132,17 +174,27 @@ class SQLContext(@transient val sparkContext: SparkContext) protected[sql] lazy val optimizer: Optimizer = DefaultOptimizer @transient - protected[sql] val ddlParser = new DDLParser(sqlParser.parse(_)) - - @transient - protected[sql] val sqlParser = { - val fallback = new catalyst.SqlParser - new SparkSQLParser(fallback.parse(_)) + protected[sql] val ddlParser = new DDLParser((sql: String) => { getSQLDialect().parse(sql) }) + + protected[sql] def getSQLDialect(): Dialect = { + try { + val clazz = Utils.classForName(dialectClassName) + clazz.newInstance().asInstanceOf[Dialect] + } catch { + case NonFatal(e) => + // Since we didn't find the available SQL Dialect, it will fail even for SET command: + // SET spark.sql.dialect=sql; Let's reset as default dialect automatically. + val dialect = conf.dialect + // reset the sql dialect + conf.unsetConf(SQLConf.DIALECT) + // throw out the exception, and the default sql dialect will take effect for next query. + throw new DialectException( + s"""Instantiating dialect '$dialect' failed. + |Reverting to default dialect '${conf.dialect}'""".stripMargin, e) + } } - protected[sql] def parseSql(sql: String): LogicalPlan = { - ddlParser.parse(sql, false).getOrElse(sqlParser.parse(sql)) - } + protected[sql] def parseSql(sql: String): LogicalPlan = ddlParser.parse(sql, false) protected[sql] def executeSql(sql: String): this.QueryExecution = executePlan(parseSql(sql)) @@ -156,6 +208,12 @@ class SQLContext(@transient val sparkContext: SparkContext) @transient protected[sql] val defaultSession = createSession() + protected[sql] def dialectClassName = if (conf.dialect == "sql") { + classOf[DefaultDialect].getCanonicalName + } else { + conf.dialect + } + sparkContext.getConf.getAll.foreach { case (key, value) if key.startsWith("spark.sql") => setConf(key, value) case _ => @@ -945,11 +1003,7 @@ class SQLContext(@transient val sparkContext: SparkContext) * @group basic */ def sql(sqlText: String): DataFrame = { - if (conf.dialect == "sql") { - DataFrame(this, parseSql(sqlText)) - } else { - sys.error(s"Unsupported SQL dialect: ${conf.dialect}") - } + DataFrame(this, parseSql(sqlText)) } /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala index e7a0685e013d8..1abf3aa51cb25 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala @@ -38,12 +38,12 @@ private[sql] class DDLParser( parseQuery: String => LogicalPlan) extends AbstractSparkSQLParser with DataTypeParser with Logging { - def parse(input: String, exceptionOnError: Boolean): Option[LogicalPlan] = { + def parse(input: String, exceptionOnError: Boolean): LogicalPlan = { try { - Some(parse(input)) + parse(input) } catch { case ddlException: DDLException => throw ddlException - case _ if !exceptionOnError => None + case _ if !exceptionOnError => parseQuery(input) case x: Throwable => throw x } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala index 9e02e69fda3f2..255f8c3982cdc 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala @@ -19,13 +19,18 @@ package org.apache.spark.sql import org.scalatest.BeforeAndAfterAll +import org.apache.spark.sql.catalyst.errors.DialectException import org.apache.spark.sql.execution.GeneratedAggregate import org.apache.spark.sql.functions._ import org.apache.spark.sql.TestData._ import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.test.TestSQLContext.{udf => _, _} + import org.apache.spark.sql.types._ +/** A SQL Dialect for testing purpose, and it can not be nested type */ +class MyDialect extends DefaultDialect + class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { // Make sure the tables are loaded. TestData @@ -64,6 +69,23 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil) } + test("SQL Dialect Switching to a new SQL parser") { + val newContext = new SQLContext(TestSQLContext.sparkContext) + newContext.setConf("spark.sql.dialect", classOf[MyDialect].getCanonicalName()) + assert(newContext.getSQLDialect().getClass === classOf[MyDialect]) + assert(newContext.sql("SELECT 1").collect() === Array(Row(1))) + } + + test("SQL Dialect Switch to an invalid parser with alias") { + val newContext = new SQLContext(TestSQLContext.sparkContext) + newContext.sql("SET spark.sql.dialect=MyTestClass") + intercept[DialectException] { + newContext.sql("SELECT 1") + } + // test if the dialect set back to DefaultSQLDialect + assert(newContext.getSQLDialect().getClass === classOf[DefaultDialect]) + } + test("SPARK-4625 support SORT BY in SimpleSQLParser & DSL") { checkAnswer( sql("SELECT a FROM testData2 SORT BY a"), diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala index dd06b2620c5ee..1d8d0b5c322ad 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala @@ -20,6 +20,9 @@ package org.apache.spark.sql.hive import java.io.{BufferedReader, InputStreamReader, PrintStream} import java.sql.Timestamp +import org.apache.hadoop.hive.ql.parse.VariableSubstitution +import org.apache.spark.sql.catalyst.Dialect + import scala.collection.JavaConversions._ import scala.language.implicitConversions @@ -42,6 +45,15 @@ import org.apache.spark.sql.hive.execution.{DescribeHiveTableCommand, HiveNative import org.apache.spark.sql.sources.{DDLParser, DataSourceStrategy} import org.apache.spark.sql.types._ +/** + * This is the HiveQL Dialect, this dialect is strongly bind with HiveContext + */ +private[hive] class HiveQLDialect extends Dialect { + override def parse(sqlText: String): LogicalPlan = { + HiveQl.parseSql(sqlText) + } +} + /** * An instance of the Spark SQL execution engine that integrates with data stored in Hive. * Configuration for Hive is read from hive-site.xml on the classpath. @@ -81,25 +93,16 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { protected[sql] def convertCTAS: Boolean = getConf("spark.sql.hive.convertCTAS", "false").toBoolean - override protected[sql] def executePlan(plan: LogicalPlan): this.QueryExecution = - new this.QueryExecution(plan) - @transient - protected[sql] val ddlParserWithHiveQL = new DDLParser(HiveQl.parseSql(_)) - - override def sql(sqlText: String): DataFrame = { - val substituted = new VariableSubstitution().substitute(hiveconf, sqlText) - // TODO: Create a framework for registering parsers instead of just hardcoding if statements. - if (conf.dialect == "sql") { - super.sql(substituted) - } else if (conf.dialect == "hiveql") { - val ddlPlan = ddlParserWithHiveQL.parse(sqlText, exceptionOnError = false) - DataFrame(this, ddlPlan.getOrElse(HiveQl.parseSql(substituted))) - } else { - sys.error(s"Unsupported SQL dialect: ${conf.dialect}. Try 'sql' or 'hiveql'") - } + protected[sql] lazy val substitutor = new VariableSubstitution() + + protected[sql] override def parseSql(sql: String): LogicalPlan = { + super.parseSql(substitutor.substitute(hiveconf, sql)) } + override protected[sql] def executePlan(plan: LogicalPlan): this.QueryExecution = + new this.QueryExecution(plan) + /** * Invalidate and refresh all the cached the metadata of the given table. For performance reasons, * Spark SQL or the external data source library it uses might cache certain metadata about a @@ -356,6 +359,12 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { } } + override protected[sql] def dialectClassName = if (conf.dialect == "hiveql") { + classOf[HiveQLDialect].getCanonicalName + } else { + super.dialectClassName + } + @transient private val hivePlanner = new SparkPlanner with HiveStrategies { val hiveContext = self diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala index 9f17bca083d13..edeab5158df62 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala @@ -107,7 +107,10 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { /** Fewer partitions to speed up testing. */ protected[sql] override lazy val conf: SQLConf = new SQLConf { override def numShufflePartitions: Int = getConf(SQLConf.SHUFFLE_PARTITIONS, "5").toInt - override def dialect: String = getConf(SQLConf.DIALECT, "hiveql") + + // TODO as in unit test, conf.clear() probably be called, all of the value will be cleared. + // The super.getConf(SQLConf.DIALECT) is "sql" by default, we need to set it as "hiveql" + override def dialect: String = super.getConf(SQLConf.DIALECT, "hiveql") } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala index 4f8d0ac0e7656..630dec8fa05a0 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala @@ -18,14 +18,17 @@ package org.apache.spark.sql.hive.execution import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries -import org.apache.spark.sql.hive.{MetastoreRelation, HiveShim} +import org.apache.spark.sql.catalyst.errors.DialectException +import org.apache.spark.sql.DefaultDialect +import org.apache.spark.sql.{AnalysisException, QueryTest, Row, SQLConf} +import org.apache.spark.sql.hive.MetastoreRelation import org.apache.spark.sql.hive.test.TestHive import org.apache.spark.sql.hive.test.TestHive._ import org.apache.spark.sql.hive.test.TestHive.implicits._ +import org.apache.spark.sql.hive.{HiveQLDialect, HiveShim} import org.apache.spark.sql.parquet.ParquetRelation2 import org.apache.spark.sql.sources.LogicalRelation import org.apache.spark.sql.types._ -import org.apache.spark.sql.{AnalysisException, QueryTest, Row, SQLConf} case class Nested1(f1: Nested2) case class Nested2(f2: Nested3) @@ -45,6 +48,9 @@ case class Order( state: String, month: Int) +/** A SQL Dialect for testing purpose, and it can not be nested type */ +class MyDialect extends DefaultDialect + /** * A collection of hive query tests where we generate the answers ourselves instead of depending on * Hive to generate them (in contrast to HiveQuerySuite). Often this is because the query is @@ -229,6 +235,35 @@ class SQLQuerySuite extends QueryTest { setConf("spark.sql.hive.convertCTAS", originalConf) } + test("SQL Dialect Switching") { + assert(getSQLDialect().getClass === classOf[HiveQLDialect]) + setConf("spark.sql.dialect", classOf[MyDialect].getCanonicalName()) + assert(getSQLDialect().getClass === classOf[MyDialect]) + assert(sql("SELECT 1").collect() === Array(Row(1))) + + // set the dialect back to the DefaultSQLDialect + sql("SET spark.sql.dialect=sql") + assert(getSQLDialect().getClass === classOf[DefaultDialect]) + sql("SET spark.sql.dialect=hiveql") + assert(getSQLDialect().getClass === classOf[HiveQLDialect]) + + // set invalid dialect + sql("SET spark.sql.dialect.abc=MyTestClass") + sql("SET spark.sql.dialect=abc") + intercept[Exception] { + sql("SELECT 1") + } + // test if the dialect set back to HiveQLDialect + getSQLDialect().getClass === classOf[HiveQLDialect] + + sql("SET spark.sql.dialect=MyTestClass") + intercept[DialectException] { + sql("SELECT 1") + } + // test if the dialect set back to HiveQLDialect + assert(getSQLDialect().getClass === classOf[HiveQLDialect]) + } + test("CTAS with serde") { sql("CREATE TABLE ctas1 AS SELECT key k, value FROM src ORDER BY k, value").collect() sql( From 473552fa5db9fa81f1a800f4ebacd23472e8c212 Mon Sep 17 00:00:00 2001 From: scwf Date: Thu, 30 Apr 2015 18:50:14 -0700 Subject: [PATCH 505/591] [SPARK-7123] [SQL] support table.star in sqlcontext Run following sql get error `SELECT r.* FROM testData l join testData2 r on (l.key = r.a)` Author: scwf Closes #5690 from scwf/tablestar and squashes the following commits: 3b2e2b6 [scwf] support table.star --- .../org/apache/spark/sql/catalyst/SqlParser.scala | 1 + .../scala/org/apache/spark/sql/SQLQuerySuite.scala | 10 ++++++++++ 2 files changed, 11 insertions(+) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala index 0af969cc5cc67..1d3a2dc0d9bb0 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala @@ -365,6 +365,7 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser { protected lazy val baseExpression: Parser[Expression] = ( "*" ^^^ UnresolvedStar(None) + | ident <~ "." ~ "*" ^^ { case tableName => UnresolvedStar(Option(tableName)) } | primary ) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala index 255f8c3982cdc..0ab8558c1db13 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala @@ -51,6 +51,16 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil) } + test("support table.star") { + checkAnswer( + sql( + """ + |SELECT r.* + |FROM testData l join testData2 r on (l.key = r.a) + """.stripMargin), + Row(1, 1) :: Row(1, 2) :: Row(2, 1) :: Row(2, 2) :: Row(3, 1) :: Row(3, 2) :: Nil) + } + test("self join with alias in agg") { Seq(1,2,3) .map(i => (i, i.toString)) From beeafcfd6ee1e460c4d564cd1515d8781989b422 Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Thu, 30 Apr 2015 20:33:36 -0700 Subject: [PATCH 506/591] Revert "[SPARK-5213] [SQL] Pluggable SQL Parser Support" This reverts commit 3ba5aaab8266822545ac82b9e733fd25cc215a77. --- .../sql/catalyst/AbstractSparkSQLParser.scala | 11 +-- .../apache/spark/sql/catalyst/Dialect.scala | 33 -------- .../spark/sql/catalyst/errors/package.scala | 2 - .../org/apache/spark/sql/SQLContext.scala | 82 ++++--------------- .../org/apache/spark/sql/sources/ddl.scala | 6 +- .../org/apache/spark/sql/SQLQuerySuite.scala | 22 ----- .../apache/spark/sql/hive/HiveContext.scala | 41 ++++------ .../apache/spark/sql/hive/test/TestHive.scala | 5 +- .../sql/hive/execution/SQLQuerySuite.scala | 39 +-------- 9 files changed, 42 insertions(+), 199 deletions(-) delete mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/Dialect.scala diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala index 2eb3e167baad5..1f3c02478bd68 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala @@ -25,6 +25,10 @@ import scala.util.parsing.input.CharArrayReader.EofCh import org.apache.spark.sql.catalyst.plans.logical._ +private[sql] object KeywordNormalizer { + def apply(str: String): String = str.toLowerCase() +} + private[sql] abstract class AbstractSparkSQLParser extends StandardTokenParsers with PackratParsers { @@ -38,7 +42,7 @@ private[sql] abstract class AbstractSparkSQLParser } protected case class Keyword(str: String) { - def normalize: String = lexical.normalizeKeyword(str) + def normalize: String = KeywordNormalizer(str) def parser: Parser[String] = normalize } @@ -86,16 +90,13 @@ class SqlLexical extends StdLexical { reserved ++= keywords } - /* Normal the keyword string */ - def normalizeKeyword(str: String): String = str.toLowerCase - delimiters += ( "@", "*", "+", "-", "<", "=", "<>", "!=", "<=", ">=", ">", "/", "(", ")", ",", ";", "%", "{", "}", ":", "[", "]", ".", "&", "|", "^", "~", "<=>" ) protected override def processIdent(name: String) = { - val token = normalizeKeyword(name) + val token = KeywordNormalizer(name) if (reserved contains token) Keyword(token) else Identifier(name) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/Dialect.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/Dialect.scala deleted file mode 100644 index 977003493d471..0000000000000 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/Dialect.scala +++ /dev/null @@ -1,33 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.catalyst - -import org.apache.spark.annotation.DeveloperApi -import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan - -/** - * Root class of SQL Parser Dialect, and we don't guarantee the binary - * compatibility for the future release, let's keep it as the internal - * interface for advanced user. - * - */ -@DeveloperApi -abstract class Dialect { - // this is the main function that will be implemented by sql parser. - def parse(sqlText: String): LogicalPlan -} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/errors/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/errors/package.scala index 0fd4f9b374ee0..bdeb660b1ecb7 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/errors/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/errors/package.scala @@ -38,8 +38,6 @@ package object errors { } } - class DialectException(msg: String, cause: Throwable) extends Exception(msg, cause) - /** * Wraps any exceptions that are thrown while executing `f` in a * [[catalyst.errors.TreeNodeException TreeNodeException]], attaching the provided `tree`. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala index 77f51dfd88d6f..bd4a55fa132fb 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala @@ -24,7 +24,6 @@ import scala.collection.JavaConversions._ import scala.collection.immutable import scala.language.implicitConversions import scala.reflect.runtime.universe.TypeTag -import scala.util.control.NonFatal import com.google.common.reflect.TypeToken @@ -33,11 +32,9 @@ import org.apache.spark.api.java.{JavaRDD, JavaSparkContext} import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.analysis._ import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.catalyst.errors.DialectException import org.apache.spark.sql.catalyst.optimizer.{DefaultOptimizer, Optimizer} import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan} import org.apache.spark.sql.catalyst.rules.RuleExecutor -import org.apache.spark.sql.catalyst.Dialect import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, expressions} import org.apache.spark.sql.execution.{Filter, _} import org.apache.spark.sql.jdbc.{JDBCPartition, JDBCPartitioningInfo, JDBCRelation} @@ -47,45 +44,6 @@ import org.apache.spark.sql.types._ import org.apache.spark.util.Utils import org.apache.spark.{Partition, SparkContext} -/** - * Currently we support the default dialect named "sql", associated with the class - * [[DefaultDialect]] - * - * And we can also provide custom SQL Dialect, for example in Spark SQL CLI: - * {{{ - *-- switch to "hiveql" dialect - * spark-sql>SET spark.sql.dialect=hiveql; - * spark-sql>SELECT * FROM src LIMIT 1; - * - *-- switch to "sql" dialect - * spark-sql>SET spark.sql.dialect=sql; - * spark-sql>SELECT * FROM src LIMIT 1; - * - *-- register the new SQL dialect - * spark-sql> SET spark.sql.dialect=com.xxx.xxx.SQL99Dialect; - * spark-sql> SELECT * FROM src LIMIT 1; - * - *-- register the non-exist SQL dialect - * spark-sql> SET spark.sql.dialect=NotExistedClass; - * spark-sql> SELECT * FROM src LIMIT 1; - * - *-- Exception will be thrown and switch to dialect - *-- "sql" (for SQLContext) or - *-- "hiveql" (for HiveContext) - * }}} - */ -private[spark] class DefaultDialect extends Dialect { - @transient - protected val sqlParser = { - val catalystSqlParser = new catalyst.SqlParser - new SparkSQLParser(catalystSqlParser.parse) - } - - override def parse(sqlText: String): LogicalPlan = { - sqlParser.parse(sqlText) - } -} - /** * The entry point for working with structured data (rows and columns) in Spark. Allows the * creation of [[DataFrame]] objects as well as the execution of SQL queries. @@ -174,27 +132,17 @@ class SQLContext(@transient val sparkContext: SparkContext) protected[sql] lazy val optimizer: Optimizer = DefaultOptimizer @transient - protected[sql] val ddlParser = new DDLParser((sql: String) => { getSQLDialect().parse(sql) }) - - protected[sql] def getSQLDialect(): Dialect = { - try { - val clazz = Utils.classForName(dialectClassName) - clazz.newInstance().asInstanceOf[Dialect] - } catch { - case NonFatal(e) => - // Since we didn't find the available SQL Dialect, it will fail even for SET command: - // SET spark.sql.dialect=sql; Let's reset as default dialect automatically. - val dialect = conf.dialect - // reset the sql dialect - conf.unsetConf(SQLConf.DIALECT) - // throw out the exception, and the default sql dialect will take effect for next query. - throw new DialectException( - s"""Instantiating dialect '$dialect' failed. - |Reverting to default dialect '${conf.dialect}'""".stripMargin, e) - } + protected[sql] val ddlParser = new DDLParser(sqlParser.parse(_)) + + @transient + protected[sql] val sqlParser = { + val fallback = new catalyst.SqlParser + new SparkSQLParser(fallback.parse(_)) } - protected[sql] def parseSql(sql: String): LogicalPlan = ddlParser.parse(sql, false) + protected[sql] def parseSql(sql: String): LogicalPlan = { + ddlParser.parse(sql, false).getOrElse(sqlParser.parse(sql)) + } protected[sql] def executeSql(sql: String): this.QueryExecution = executePlan(parseSql(sql)) @@ -208,12 +156,6 @@ class SQLContext(@transient val sparkContext: SparkContext) @transient protected[sql] val defaultSession = createSession() - protected[sql] def dialectClassName = if (conf.dialect == "sql") { - classOf[DefaultDialect].getCanonicalName - } else { - conf.dialect - } - sparkContext.getConf.getAll.foreach { case (key, value) if key.startsWith("spark.sql") => setConf(key, value) case _ => @@ -1003,7 +945,11 @@ class SQLContext(@transient val sparkContext: SparkContext) * @group basic */ def sql(sqlText: String): DataFrame = { - DataFrame(this, parseSql(sqlText)) + if (conf.dialect == "sql") { + DataFrame(this, parseSql(sqlText)) + } else { + sys.error(s"Unsupported SQL dialect: ${conf.dialect}") + } } /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala index 1abf3aa51cb25..e7a0685e013d8 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala @@ -38,12 +38,12 @@ private[sql] class DDLParser( parseQuery: String => LogicalPlan) extends AbstractSparkSQLParser with DataTypeParser with Logging { - def parse(input: String, exceptionOnError: Boolean): LogicalPlan = { + def parse(input: String, exceptionOnError: Boolean): Option[LogicalPlan] = { try { - parse(input) + Some(parse(input)) } catch { case ddlException: DDLException => throw ddlException - case _ if !exceptionOnError => parseQuery(input) + case _ if !exceptionOnError => None case x: Throwable => throw x } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala index 0ab8558c1db13..d8e7cdbd3a94e 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala @@ -19,18 +19,13 @@ package org.apache.spark.sql import org.scalatest.BeforeAndAfterAll -import org.apache.spark.sql.catalyst.errors.DialectException import org.apache.spark.sql.execution.GeneratedAggregate import org.apache.spark.sql.functions._ import org.apache.spark.sql.TestData._ import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.test.TestSQLContext.{udf => _, _} - import org.apache.spark.sql.types._ -/** A SQL Dialect for testing purpose, and it can not be nested type */ -class MyDialect extends DefaultDialect - class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { // Make sure the tables are loaded. TestData @@ -79,23 +74,6 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil) } - test("SQL Dialect Switching to a new SQL parser") { - val newContext = new SQLContext(TestSQLContext.sparkContext) - newContext.setConf("spark.sql.dialect", classOf[MyDialect].getCanonicalName()) - assert(newContext.getSQLDialect().getClass === classOf[MyDialect]) - assert(newContext.sql("SELECT 1").collect() === Array(Row(1))) - } - - test("SQL Dialect Switch to an invalid parser with alias") { - val newContext = new SQLContext(TestSQLContext.sparkContext) - newContext.sql("SET spark.sql.dialect=MyTestClass") - intercept[DialectException] { - newContext.sql("SELECT 1") - } - // test if the dialect set back to DefaultSQLDialect - assert(newContext.getSQLDialect().getClass === classOf[DefaultDialect]) - } - test("SPARK-4625 support SORT BY in SimpleSQLParser & DSL") { checkAnswer( sql("SELECT a FROM testData2 SORT BY a"), diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala index 1d8d0b5c322ad..dd06b2620c5ee 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala @@ -20,9 +20,6 @@ package org.apache.spark.sql.hive import java.io.{BufferedReader, InputStreamReader, PrintStream} import java.sql.Timestamp -import org.apache.hadoop.hive.ql.parse.VariableSubstitution -import org.apache.spark.sql.catalyst.Dialect - import scala.collection.JavaConversions._ import scala.language.implicitConversions @@ -45,15 +42,6 @@ import org.apache.spark.sql.hive.execution.{DescribeHiveTableCommand, HiveNative import org.apache.spark.sql.sources.{DDLParser, DataSourceStrategy} import org.apache.spark.sql.types._ -/** - * This is the HiveQL Dialect, this dialect is strongly bind with HiveContext - */ -private[hive] class HiveQLDialect extends Dialect { - override def parse(sqlText: String): LogicalPlan = { - HiveQl.parseSql(sqlText) - } -} - /** * An instance of the Spark SQL execution engine that integrates with data stored in Hive. * Configuration for Hive is read from hive-site.xml on the classpath. @@ -93,16 +81,25 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { protected[sql] def convertCTAS: Boolean = getConf("spark.sql.hive.convertCTAS", "false").toBoolean - @transient - protected[sql] lazy val substitutor = new VariableSubstitution() - - protected[sql] override def parseSql(sql: String): LogicalPlan = { - super.parseSql(substitutor.substitute(hiveconf, sql)) - } - override protected[sql] def executePlan(plan: LogicalPlan): this.QueryExecution = new this.QueryExecution(plan) + @transient + protected[sql] val ddlParserWithHiveQL = new DDLParser(HiveQl.parseSql(_)) + + override def sql(sqlText: String): DataFrame = { + val substituted = new VariableSubstitution().substitute(hiveconf, sqlText) + // TODO: Create a framework for registering parsers instead of just hardcoding if statements. + if (conf.dialect == "sql") { + super.sql(substituted) + } else if (conf.dialect == "hiveql") { + val ddlPlan = ddlParserWithHiveQL.parse(sqlText, exceptionOnError = false) + DataFrame(this, ddlPlan.getOrElse(HiveQl.parseSql(substituted))) + } else { + sys.error(s"Unsupported SQL dialect: ${conf.dialect}. Try 'sql' or 'hiveql'") + } + } + /** * Invalidate and refresh all the cached the metadata of the given table. For performance reasons, * Spark SQL or the external data source library it uses might cache certain metadata about a @@ -359,12 +356,6 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { } } - override protected[sql] def dialectClassName = if (conf.dialect == "hiveql") { - classOf[HiveQLDialect].getCanonicalName - } else { - super.dialectClassName - } - @transient private val hivePlanner = new SparkPlanner with HiveStrategies { val hiveContext = self diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala index edeab5158df62..9f17bca083d13 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala @@ -107,10 +107,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { /** Fewer partitions to speed up testing. */ protected[sql] override lazy val conf: SQLConf = new SQLConf { override def numShufflePartitions: Int = getConf(SQLConf.SHUFFLE_PARTITIONS, "5").toInt - - // TODO as in unit test, conf.clear() probably be called, all of the value will be cleared. - // The super.getConf(SQLConf.DIALECT) is "sql" by default, we need to set it as "hiveql" - override def dialect: String = super.getConf(SQLConf.DIALECT, "hiveql") + override def dialect: String = getConf(SQLConf.DIALECT, "hiveql") } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala index 630dec8fa05a0..4f8d0ac0e7656 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala @@ -18,17 +18,14 @@ package org.apache.spark.sql.hive.execution import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries -import org.apache.spark.sql.catalyst.errors.DialectException -import org.apache.spark.sql.DefaultDialect -import org.apache.spark.sql.{AnalysisException, QueryTest, Row, SQLConf} -import org.apache.spark.sql.hive.MetastoreRelation +import org.apache.spark.sql.hive.{MetastoreRelation, HiveShim} import org.apache.spark.sql.hive.test.TestHive import org.apache.spark.sql.hive.test.TestHive._ import org.apache.spark.sql.hive.test.TestHive.implicits._ -import org.apache.spark.sql.hive.{HiveQLDialect, HiveShim} import org.apache.spark.sql.parquet.ParquetRelation2 import org.apache.spark.sql.sources.LogicalRelation import org.apache.spark.sql.types._ +import org.apache.spark.sql.{AnalysisException, QueryTest, Row, SQLConf} case class Nested1(f1: Nested2) case class Nested2(f2: Nested3) @@ -48,9 +45,6 @@ case class Order( state: String, month: Int) -/** A SQL Dialect for testing purpose, and it can not be nested type */ -class MyDialect extends DefaultDialect - /** * A collection of hive query tests where we generate the answers ourselves instead of depending on * Hive to generate them (in contrast to HiveQuerySuite). Often this is because the query is @@ -235,35 +229,6 @@ class SQLQuerySuite extends QueryTest { setConf("spark.sql.hive.convertCTAS", originalConf) } - test("SQL Dialect Switching") { - assert(getSQLDialect().getClass === classOf[HiveQLDialect]) - setConf("spark.sql.dialect", classOf[MyDialect].getCanonicalName()) - assert(getSQLDialect().getClass === classOf[MyDialect]) - assert(sql("SELECT 1").collect() === Array(Row(1))) - - // set the dialect back to the DefaultSQLDialect - sql("SET spark.sql.dialect=sql") - assert(getSQLDialect().getClass === classOf[DefaultDialect]) - sql("SET spark.sql.dialect=hiveql") - assert(getSQLDialect().getClass === classOf[HiveQLDialect]) - - // set invalid dialect - sql("SET spark.sql.dialect.abc=MyTestClass") - sql("SET spark.sql.dialect=abc") - intercept[Exception] { - sql("SELECT 1") - } - // test if the dialect set back to HiveQLDialect - getSQLDialect().getClass === classOf[HiveQLDialect] - - sql("SET spark.sql.dialect=MyTestClass") - intercept[DialectException] { - sql("SELECT 1") - } - // test if the dialect set back to HiveQLDialect - assert(getSQLDialect().getClass === classOf[HiveQLDialect]) - } - test("CTAS with serde") { sql("CREATE TABLE ctas1 AS SELECT key k, value FROM src ORDER BY k, value").collect() sql( From 69a739c7f5fd002432ece203957e1458deb2f4c3 Mon Sep 17 00:00:00 2001 From: zsxwing Date: Thu, 30 Apr 2015 21:32:11 -0700 Subject: [PATCH 507/591] [SPARK-7282] [STREAMING] Fix the race conditions in StreamingListenerSuite Fixed the following flaky test ```Scala [info] StreamingListenerSuite: [info] - batch info reporting (782 milliseconds) [info] - receiver info reporting *** FAILED *** (3 seconds, 911 milliseconds) [info] The code passed to eventually never returned normally. Attempted 10 times over 3.4735783689999997 seconds. Last failure message: 0 did not equal 1. (StreamingListenerSuite.scala:104) [info] org.scalatest.exceptions.TestFailedDueToTimeoutException: [info] at org.scalatest.concurrent.Eventually$class.tryTryAgain$1(Eventually.scala:420) [info] at org.scalatest.concurrent.Eventually$class.eventually(Eventually.scala:438) [info] at org.scalatest.concurrent.Eventually$.eventually(Eventually.scala:478) [info] at org.scalatest.concurrent.Eventually$class.eventually(Eventually.scala:307) [info] at org.scalatest.concurrent.Eventually$.eventually(Eventually.scala:478) [info] at org.apache.spark.streaming.StreamingListenerSuite$$anonfun$2.apply$mcV$sp(StreamingListenerSuite.scala:104) [info] at org.apache.spark.streaming.StreamingListenerSuite$$anonfun$2.apply(StreamingListenerSuite.scala:94) [info] at org.apache.spark.streaming.StreamingListenerSuite$$anonfun$2.apply(StreamingListenerSuite.scala:94) [info] at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22) [info] at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85) [info] at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104) [info] at org.scalatest.Transformer.apply(Transformer.scala:22) [info] at org.scalatest.Transformer.apply(Transformer.scala:20) [info] at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:166) [info] at org.scalatest.Suite$class.withFixture(Suite.scala:1122) [info] at org.scalatest.FunSuite.withFixture(FunSuite.scala:1555) [info] at org.scalatest.FunSuiteLike$class.invokeWithFixture$1(FunSuiteLike.scala:163) [info] at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175) [info] at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175) [info] at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306) [info] at org.scalatest.FunSuiteLike$class.runTest(FunSuiteLike.scala:175) [info] at org.apache.spark.streaming.StreamingListenerSuite.org$scalatest$BeforeAndAfter$$super$runTest(StreamingListenerSuite.scala:34) [info] at org.scalatest.BeforeAndAfter$class.runTest(BeforeAndAfter.scala:200) [info] at org.apache.spark.streaming.StreamingListenerSuite.runTest(StreamingListenerSuite.scala:34) [info] at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208) [info] at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208) [info] at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:413) [info] at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:401) [info] at scala.collection.immutable.List.foreach(List.scala:318) [info] at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:401) [info] at org.scalatest.SuperEngine.org$scalatest$SuperEngine$$runTestsInBranch(Engine.scala:396) [info] at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:483) [info] at org.scalatest.FunSuiteLike$class.runTests(FunSuiteLike.scala:208) [info] at org.scalatest.FunSuite.runTests(FunSuite.scala:1555) [info] at org.scalatest.Suite$class.run(Suite.scala:1424) [info] at org.scalatest.FunSuite.org$scalatest$FunSuiteLike$$super$run(FunSuite.scala:1555) [info] at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:212) [info] at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:212) [info] at org.scalatest.SuperEngine.runImpl(Engine.scala:545) [info] at org.scalatest.FunSuiteLike$class.run(FunSuiteLike.scala:212) [info] at org.apache.spark.streaming.StreamingListenerSuite.org$scalatest$BeforeAndAfter$$super$run(StreamingListenerSuite.scala:34) [info] at org.scalatest.BeforeAndAfter$class.run(BeforeAndAfter.scala:241) [info] at org.apache.spark.streaming.StreamingListenerSuite.run(StreamingListenerSuite.scala:34) [info] at org.scalatest.tools.Framework.org$scalatest$tools$Framework$$runSuite(Framework.scala:462) [info] at org.scalatest.tools.Framework$ScalaTestTask.execute(Framework.scala:671) [info] at sbt.ForkMain$Run$2.call(ForkMain.java:294) [info] at sbt.ForkMain$Run$2.call(ForkMain.java:284) [info] at java.util.concurrent.FutureTask.run(FutureTask.java:262) [info] at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) [info] at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) [info] at java.lang.Thread.run(Thread.java:745) [info] Cause: org.scalatest.exceptions.TestFailedException: 0 did not equal 1 [info] at org.scalatest.MatchersHelper$.newTestFailedException(MatchersHelper.scala:160) [info] at org.scalatest.Matchers$ShouldMethodHelper$.shouldMatcher(Matchers.scala:6231) [info] at org.scalatest.Matchers$AnyShouldWrapper.should(Matchers.scala:6277) [info] at org.apache.spark.streaming.StreamingListenerSuite$$anonfun$2$$anonfun$apply$mcV$sp$1.apply$mcV$sp(StreamingListenerSuite.scala:105) [info] at org.apache.spark.streaming.StreamingListenerSuite$$anonfun$2$$anonfun$apply$mcV$sp$1.apply(StreamingListenerSuite.scala:104) [info] at org.apache.spark.streaming.StreamingListenerSuite$$anonfun$2$$anonfun$apply$mcV$sp$1.apply(StreamingListenerSuite.scala:104) [info] at org.scalatest.concurrent.Eventually$class.makeAValiantAttempt$1(Eventually.scala:394) [info] at org.scalatest.concurrent.Eventually$class.tryTryAgain$1(Eventually.scala:408) [info] at org.scalatest.concurrent.Eventually$class.eventually(Eventually.scala:438) [info] at org.scalatest.concurrent.Eventually$.eventually(Eventually.scala:478) [info] at org.scalatest.concurrent.Eventually$class.eventually(Eventually.scala:307) [info] at org.scalatest.concurrent.Eventually$.eventually(Eventually.scala:478) [info] at org.apache.spark.streaming.StreamingListenerSuite$$anonfun$2.apply$mcV$sp(StreamingListenerSuite.scala:104) [info] at org.apache.spark.streaming.StreamingListenerSuite$$anonfun$2.apply(StreamingListenerSuite.scala:94) [info] at org.apache.spark.streaming.StreamingListenerSuite$$anonfun$2.apply(StreamingListenerSuite.scala:94) [info] at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22) [info] at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85) [info] at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104) [info] at org.scalatest.Transformer.apply(Transformer.scala:22) [info] at org.scalatest.Transformer.apply(Transformer.scala:20) [info] at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:166) [info] at org.scalatest.Suite$class.withFixture(Suite.scala:1122) [info] at org.scalatest.FunSuite.withFixture(FunSuite.scala:1555) [info] at org.scalatest.FunSuiteLike$class.invokeWithFixture$1(FunSuiteLike.scala:163) [info] at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175) [info] at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175) [info] at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306) [info] at org.scalatest.FunSuiteLike$class.runTest(FunSuiteLike.scala:175) [info] at org.apache.spark.streaming.StreamingListenerSuite.org$scalatest$BeforeAndAfter$$super$runTest(StreamingListenerSuite.scala:34) [info] at org.scalatest.BeforeAndAfter$class.runTest(BeforeAndAfter.scala:200) [info] at org.apache.spark.streaming.StreamingListenerSuite.runTest(StreamingListenerSuite.scala:34) [info] at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208) [info] at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208) [info] at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:413) [info] at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:401) [info] at scala.collection.immutable.List.foreach(List.scala:318) [info] at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:401) [info] at org.scalatest.SuperEngine.org$scalatest$SuperEngine$$runTestsInBranch(Engine.scala:396) [info] at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:483) [info] at org.scalatest.FunSuiteLike$class.runTests(FunSuiteLike.scala:208) [info] at org.scalatest.FunSuite.runTests(FunSuite.scala:1555) [info] at org.scalatest.Suite$class.run(Suite.scala:1424) [info] at org.scalatest.FunSuite.org$scalatest$FunSuiteLike$$super$run(FunSuite.scala:1555) [info] at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:212) [info] at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:212) [info] at org.scalatest.SuperEngine.runImpl(Engine.scala:545) [info] at org.scalatest.FunSuiteLike$class.run(FunSuiteLike.scala:212) [info] at org.apache.spark.streaming.StreamingListenerSuite.org$scalatest$BeforeAndAfter$$super$run(StreamingListenerSuite.scala:34) [info] at org.scalatest.BeforeAndAfter$class.run(BeforeAndAfter.scala:241) [info] at org.apache.spark.streaming.StreamingListenerSuite.run(StreamingListenerSuite.scala:34) [info] at org.scalatest.tools.Framework.org$scalatest$tools$Framework$$runSuite(Framework.scala:462) [info] at org.scalatest.tools.Framework$ScalaTestTask.execute(Framework.scala:671) [info] at sbt.ForkMain$Run$2.call(ForkMain.java:294) [info] at sbt.ForkMain$Run$2.call(ForkMain.java:284) [info] at java.util.concurrent.FutureTask.run(FutureTask.java:262) [info] at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) [info] at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) [info] at java.lang.Thread.run(Thread.java:745) ``` The original codes didn't have a memory barrier in the `eventually` closure, which might fail the test, because JVM doesn't guarantee the memory consistency between different threads without a memory barrier. This PR used `ConcurrentLinkedQueue` to set up the memory barrier. Author: zsxwing Closes #5812 from zsxwing/SPARK-7282 and squashes the following commits: 59115ef [zsxwing] Use SynchronizedBuffer 014dd2b [zsxwing] Fix the race conditions in StreamingListenerSuite --- .../spark/streaming/StreamingListenerSuite.scala | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala index 7210439509541..9020be166acf0 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala @@ -17,7 +17,7 @@ package org.apache.spark.streaming -import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.{ArrayBuffer, SynchronizedBuffer} import scala.concurrent.Future import scala.concurrent.ExecutionContext.Implicits.global @@ -127,9 +127,9 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers { /** Listener that collects information on processed batches */ class BatchInfoCollector extends StreamingListener { - val batchInfosCompleted = new ArrayBuffer[BatchInfo] - val batchInfosStarted = new ArrayBuffer[BatchInfo] - val batchInfosSubmitted = new ArrayBuffer[BatchInfo] + val batchInfosCompleted = new ArrayBuffer[BatchInfo] with SynchronizedBuffer[BatchInfo] + val batchInfosStarted = new ArrayBuffer[BatchInfo] with SynchronizedBuffer[BatchInfo] + val batchInfosSubmitted = new ArrayBuffer[BatchInfo] with SynchronizedBuffer[BatchInfo] override def onBatchSubmitted(batchSubmitted: StreamingListenerBatchSubmitted) { batchInfosSubmitted += batchSubmitted.batchInfo @@ -146,9 +146,10 @@ class BatchInfoCollector extends StreamingListener { /** Listener that collects information on processed batches */ class ReceiverInfoCollector extends StreamingListener { - val startedReceiverStreamIds = new ArrayBuffer[Int] - val stoppedReceiverStreamIds = new ArrayBuffer[Int]() - val receiverErrors = new ArrayBuffer[(Int, String, String)]() + val startedReceiverStreamIds = new ArrayBuffer[Int] with SynchronizedBuffer[Int] + val stoppedReceiverStreamIds = new ArrayBuffer[Int] with SynchronizedBuffer[Int] + val receiverErrors = + new ArrayBuffer[(Int, String, String)] with SynchronizedBuffer[(Int, String, String)] override def onReceiverStarted(receiverStarted: StreamingListenerReceiverStarted) { startedReceiverStreamIds += receiverStarted.receiverInfo.streamId From b5347a4664625ede6ab9d8ef6558457a34ae423f Mon Sep 17 00:00:00 2001 From: Burak Yavuz Date: Thu, 30 Apr 2015 21:56:03 -0700 Subject: [PATCH 508/591] [SPARK-7248] implemented random number generators for DataFrames Adds the functions `rand` (Uniform Dist) and `randn` (Normal Dist.) as expressions to DataFrames. cc mengxr rxin Author: Burak Yavuz Closes #5819 from brkyvz/df-rng and squashes the following commits: 50d69d4 [Burak Yavuz] add seed for test that failed 4234c3a [Burak Yavuz] fix Rand expression 13cad5c [Burak Yavuz] couple fixes 7d53953 [Burak Yavuz] waiting for hive tests b453716 [Burak Yavuz] move radn with seed down 03637f0 [Burak Yavuz] fix broken hive func c5909eb [Burak Yavuz] deleted old implementation of Rand 6d43895 [Burak Yavuz] implemented random generators --- python/pyspark/sql/functions.py | 25 ++++++++- python/pyspark/sql/tests.py | 10 ++++ .../spark/sql/catalyst/expressions/Rand.scala | 36 ------------ .../sql/catalyst/expressions/random.scala | 56 +++++++++++++++++++ .../optimizer/ConstantFoldingSuite.scala | 4 +- .../org/apache/spark/sql/functions.scala | 30 +++++++++- .../org/apache/spark/sql/mathfunctions.scala | 2 - .../apache/spark/sql/JavaDataFrameSuite.java | 3 + .../spark/sql/ColumnExpressionSuite.scala | 22 ++++++++ .../org/apache/spark/sql/hive/HiveQl.scala | 7 +-- 10 files changed, 149 insertions(+), 46 deletions(-) delete mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py index 555c2fa5e7071..241f82175726f 100644 --- a/python/pyspark/sql/functions.py +++ b/python/pyspark/sql/functions.py @@ -67,7 +67,6 @@ def _(col): 'sumDistinct': 'Aggregate function: returns the sum of distinct values in the expression.', } - for _name, _doc in _functions.items(): globals()[_name] = _create_function(_name, _doc) del _name, _doc @@ -75,6 +74,30 @@ def _(col): __all__.sort() +def rand(seed=None): + """ + Generate a random column with i.i.d. samples from U[0.0, 1.0]. + """ + sc = SparkContext._active_spark_context + if seed: + jc = sc._jvm.functions.rand(seed) + else: + jc = sc._jvm.functions.rand() + return Column(jc) + + +def randn(seed=None): + """ + Generate a column with i.i.d. samples from the standard normal distribution. + """ + sc = SparkContext._active_spark_context + if seed: + jc = sc._jvm.functions.randn(seed) + else: + jc = sc._jvm.functions.randn() + return Column(jc) + + def approxCountDistinct(col, rsd=None): """Returns a new :class:`Column` for approximate distinct count of ``col``. diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index 2ffd18ebd7c89..5640bb5ea2346 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -416,6 +416,16 @@ def assert_close(a, b): assert_close([math.hypot(i, 2 * i) for i in range(10)], df.select(functions.hypot(df.a, df.b)).collect()) + def test_rand_functions(self): + df = self.df + from pyspark.sql import functions + rnd = df.select('key', functions.rand()).collect() + for row in rnd: + assert row[1] >= 0.0 and row[1] <= 1.0, "got: %s" % row[1] + rndn = df.select('key', functions.randn(5)).collect() + for row in rndn: + assert row[1] >= -4.0 and row[1] <= 4.0, "got: %s" % row[1] + def test_save_and_load(self): df = self.df tmpPath = tempfile.mkdtemp() diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala deleted file mode 100644 index f5fea3f015dc4..0000000000000 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Rand.scala +++ /dev/null @@ -1,36 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.catalyst.expressions - -import java.util.Random - -import org.apache.spark.sql.types.{DataType, DoubleType} - - -case object Rand extends LeafExpression { - override def dataType: DataType = DoubleType - override def nullable: Boolean = false - - private[this] lazy val rand = new Random - - override def eval(input: Row = null): EvaluatedType = { - rand.nextDouble().asInstanceOf[EvaluatedType] - } - - override def toString: String = "RAND()" -} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala new file mode 100644 index 0000000000000..66d7c8b07cce8 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala @@ -0,0 +1,56 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.expressions + +import org.apache.spark.TaskContext +import org.apache.spark.sql.types.{DataType, DoubleType} +import org.apache.spark.util.Utils +import org.apache.spark.util.random.XORShiftRandom + +/** + * A Random distribution generating expression. + * TODO: This can be made generic to generate any type of random distribution, or any type of + * StructType. + * + * Since this expression is stateful, it cannot be a case object. + */ +abstract class RDG(seed: Long) extends LeafExpression with Serializable { + self: Product => + + /** + * Record ID within each partition. By being transient, the Random Number Generator is + * reset every time we serialize and deserialize it. + */ + @transient protected lazy val rng = new XORShiftRandom(seed + TaskContext.get().partitionId()) + + override type EvaluatedType = Double + + override def nullable: Boolean = false + + override def dataType: DataType = DoubleType +} + +/** Generate a random column with i.i.d. uniformly distributed values in [0, 1). */ +case class Rand(seed: Long = Utils.random.nextLong()) extends RDG(seed) { + override def eval(input: Row): Double = rng.nextDouble() +} + +/** Generate a random column with i.i.d. gaussian random distribution. */ +case class Randn(seed: Long = Utils.random.nextLong()) extends RDG(seed) { + override def eval(input: Row): Double = rng.nextGaussian() +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala index 14b28e8402610..18f92150b0966 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala @@ -160,7 +160,7 @@ class ConstantFoldingSuite extends PlanTest { val originalQuery = testRelation .select( - Rand + Literal(1) as Symbol("c1"), + Rand(5L) + Literal(1) as Symbol("c1"), Sum('a) as Symbol("c2")) val optimized = Optimize.execute(originalQuery.analyze) @@ -168,7 +168,7 @@ class ConstantFoldingSuite extends PlanTest { val correctAnswer = testRelation .select( - Rand + Literal(1.0) as Symbol("c1"), + Rand(5L) + Literal(1.0) as Symbol("c1"), Sum('a) as Symbol("c2")) .analyze diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala index aa31d04a0cbe4..242e64d3ff881 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala @@ -25,7 +25,7 @@ import org.apache.spark.sql.catalyst.ScalaReflection import org.apache.spark.sql.catalyst.analysis.{UnresolvedFunction, Star} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.types._ - +import org.apache.spark.util.Utils /** * :: Experimental :: @@ -346,6 +346,34 @@ object functions { */ def not(e: Column): Column = !e + /** + * Generate a random column with i.i.d. samples from U[0.0, 1.0]. + * + * @group normal_funcs + */ + def rand(seed: Long): Column = Rand(seed) + + /** + * Generate a random column with i.i.d. samples from U[0.0, 1.0]. + * + * @group normal_funcs + */ + def rand(): Column = rand(Utils.random.nextLong) + + /** + * Generate a column with i.i.d. samples from the standard normal distribution. + * + * @group normal_funcs + */ + def randn(seed: Long): Column = Randn(seed) + + /** + * Generate a column with i.i.d. samples from the standard normal distribution. + * + * @group normal_funcs + */ + def randn(): Column = randn(Utils.random.nextLong) + /** * Partition ID of the Spark task. * diff --git a/sql/core/src/main/scala/org/apache/spark/sql/mathfunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/mathfunctions.scala index d901542b7eaf3..db47480c3864d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/mathfunctions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/mathfunctions.scala @@ -27,8 +27,6 @@ import org.apache.spark.sql.functions.lit /** * :: Experimental :: * Mathematical Functions available for [[DataFrame]]. - * - * @groupname double_funcs Functions that require DoubleType as an input */ @Experimental // scalastyle:off diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java index 966d879e1fc9f..ebe96e649d940 100644 --- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java @@ -104,6 +104,9 @@ public void testVarargMethods() { df2.select(pow("a", "a"), pow("b", 2.0)); df2.select(pow(col("a"), col("b")), exp("b")); df2.select(sin("a"), acos("b")); + + df2.select(rand(), acos("b")); + df2.select(col("*"), randn(5L)); } @Ignore diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala index 2ba5fc21ff57c..6322faf4d9907 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala @@ -17,6 +17,8 @@ package org.apache.spark.sql +import org.scalatest.Matchers._ + import org.apache.spark.sql.functions._ import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.test.TestSQLContext.implicits._ @@ -349,4 +351,24 @@ class ColumnExpressionSuite extends QueryTest { assert(schema("value").metadata === Metadata.empty) assert(schema("abc").metadata === metadata) } + + test("rand") { + val randCol = testData.select('key, rand(5L).as("rand")) + randCol.columns.length should be (2) + val rows = randCol.collect() + rows.foreach { row => + assert(row.getDouble(1) <= 1.0) + assert(row.getDouble(1) >= 0.0) + } + } + + test("randn") { + val randCol = testData.select('key, randn(5L).as("rand")) + randCol.columns.length should be (2) + val rows = randCol.collect() + rows.foreach { row => + assert(row.getDouble(1) <= 4.0) + assert(row.getDouble(1) >= -4.0) + } + } } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala index 0a86519e1412b..63a8c05f775b8 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala @@ -19,13 +19,11 @@ package org.apache.spark.sql.hive import java.sql.Date - -import org.apache.hadoop.hive.ql.exec.{FunctionRegistry, FunctionInfo} - import scala.collection.mutable.ArrayBuffer import org.apache.hadoop.hive.conf.HiveConf import org.apache.hadoop.hive.ql.Context +import org.apache.hadoop.hive.ql.exec.{FunctionRegistry, FunctionInfo} import org.apache.hadoop.hive.ql.lib.Node import org.apache.hadoop.hive.ql.metadata.Table import org.apache.hadoop.hive.ql.parse._ @@ -1244,7 +1242,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C /* Other functions */ case Token("TOK_FUNCTION", Token(ARRAY(), Nil) :: children) => CreateArray(children.map(nodeToExpr)) - case Token("TOK_FUNCTION", Token(RAND(), Nil) :: Nil) => Rand + case Token("TOK_FUNCTION", Token(RAND(), Nil) :: Nil) => Rand() + case Token("TOK_FUNCTION", Token(RAND(), Nil) :: seed :: Nil) => Rand(seed.toString.toLong) case Token("TOK_FUNCTION", Token(SUBSTR(), Nil) :: string :: pos :: Nil) => Substring(nodeToExpr(string), nodeToExpr(pos), Literal.create(Integer.MAX_VALUE, IntegerType)) case Token("TOK_FUNCTION", Token(SUBSTR(), Nil) :: string :: pos :: length :: Nil) => From 36a7a6807ee58c8ba309c0e09e547919246b7f82 Mon Sep 17 00:00:00 2001 From: Zhan Zhang Date: Thu, 30 Apr 2015 22:24:31 -0700 Subject: [PATCH 509/591] [SPARK-6479] [BLOCK MANAGER] Create off-heap block storage API This is the classes for creating off-heap block storage API. It also includes the migration for Tachyon. The diff seems to be big, but it mainly just rename tachyon to offheap. New implementation for hdfs will be submit for review in spark-6112. Author: Zhan Zhang Closes #5430 from zhzhan/SPARK-6479 and squashes the following commits: 60acd84 [Zhan Zhang] minor change to kickoff the test 12f54c9 [Zhan Zhang] solve merge conflicts a54132c [Zhan Zhang] solve review comments ffb8e00 [Zhan Zhang] rebase to sparkcontext change 6e121e0 [Zhan Zhang] resolve review comments and restructure blockmanasger code a7aed6c [Zhan Zhang] add Tachyon migration code 186de31 [Zhan Zhang] initial commit for off-heap block storage api --- .../scala/org/apache/spark/SparkContext.scala | 8 +- .../spark/executor/ExecutorExitCode.scala | 16 +- .../main/scala/org/apache/spark/rdd/RDD.scala | 4 +- .../apache/spark/storage/BlockManager.scala | 63 +++--- .../spark/storage/BlockManagerMaster.scala | 5 +- .../storage/BlockManagerMasterEndpoint.scala | 29 +-- .../spark/storage/BlockManagerMessages.scala | 6 +- .../spark/storage/ExternalBlockManager.scala | 102 ++++++++++ .../spark/storage/ExternalBlockStore.scala | 181 ++++++++++++++++++ .../org/apache/spark/storage/RDDInfo.scala | 9 +- .../apache/spark/storage/StorageLevel.scala | 8 +- .../apache/spark/storage/StorageUtils.scala | 19 +- .../spark/storage/TachyonBlockManager.scala | 138 +++++++++---- .../spark/storage/TachyonFileSegment.scala | 30 --- .../apache/spark/storage/TachyonStore.scala | 128 ------------- .../apache/spark/ui/storage/StoragePage.scala | 4 +- .../org/apache/spark/util/JsonProtocol.scala | 24 ++- .../spark/storage/BlockManagerSuite.scala | 1 + .../apache/spark/storage/StorageSuite.scala | 8 +- .../spark/ui/storage/StorageTabSuite.scala | 6 +- .../apache/spark/util/JsonProtocolSuite.scala | 56 +++--- docs/configuration.md | 22 ++- 22 files changed, 536 insertions(+), 331 deletions(-) create mode 100644 core/src/main/scala/org/apache/spark/storage/ExternalBlockManager.scala create mode 100644 core/src/main/scala/org/apache/spark/storage/ExternalBlockStore.scala delete mode 100644 core/src/main/scala/org/apache/spark/storage/TachyonFileSegment.scala delete mode 100644 core/src/main/scala/org/apache/spark/storage/TachyonStore.scala diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index bae951f388337..fe24260cdb4c5 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -247,9 +247,11 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli private[spark] def eventLogDir: Option[URI] = _eventLogDir private[spark] def eventLogCodec: Option[String] = _eventLogCodec - // Generate the random name for a temp folder in Tachyon + // Generate the random name for a temp folder in external block store. // Add a timestamp as the suffix here to make it more safe - val tachyonFolderName = "spark-" + randomUUID.toString() + val externalBlockStoreFolderName = "spark-" + randomUUID.toString() + @deprecated("Use externalBlockStoreFolderName instead.", "1.4.0") + val tachyonFolderName = externalBlockStoreFolderName def isLocal: Boolean = (master == "local" || master.startsWith("local[")) @@ -386,7 +388,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli } } - _conf.set("spark.tachyonStore.folderName", tachyonFolderName) + _conf.set("spark.externalBlockStore.folderName", externalBlockStoreFolderName) if (master == "yarn-client") System.setProperty("SPARK_YARN_MODE", "true") diff --git a/core/src/main/scala/org/apache/spark/executor/ExecutorExitCode.scala b/core/src/main/scala/org/apache/spark/executor/ExecutorExitCode.scala index 52862ae0ca5e4..ea36fb60bd540 100644 --- a/core/src/main/scala/org/apache/spark/executor/ExecutorExitCode.scala +++ b/core/src/main/scala/org/apache/spark/executor/ExecutorExitCode.scala @@ -33,11 +33,11 @@ object ExecutorExitCode { /** DiskStore failed to create a local temporary directory after many attempts. */ val DISK_STORE_FAILED_TO_CREATE_DIR = 53 - /** TachyonStore failed to initialize after many attempts. */ - val TACHYON_STORE_FAILED_TO_INITIALIZE = 54 + /** ExternalBlockStore failed to initialize after many attempts. */ + val EXTERNAL_BLOCK_STORE_FAILED_TO_INITIALIZE = 54 - /** TachyonStore failed to create a local temporary directory after many attempts. */ - val TACHYON_STORE_FAILED_TO_CREATE_DIR = 55 + /** ExternalBlockStore failed to create a local temporary directory after many attempts. */ + val EXTERNAL_BLOCK_STORE_FAILED_TO_CREATE_DIR = 55 def explainExitCode(exitCode: Int): String = { exitCode match { @@ -46,9 +46,11 @@ object ExecutorExitCode { case OOM => "OutOfMemoryError" case DISK_STORE_FAILED_TO_CREATE_DIR => "Failed to create local directory (bad spark.local.dir?)" - case TACHYON_STORE_FAILED_TO_INITIALIZE => "TachyonStore failed to initialize." - case TACHYON_STORE_FAILED_TO_CREATE_DIR => - "TachyonStore failed to create a local temporary directory." + // TODO: replace external block store with concrete implementation name + case EXTERNAL_BLOCK_STORE_FAILED_TO_INITIALIZE => "ExternalBlockStore failed to initialize." + // TODO: replace external block store with concrete implementation name + case EXTERNAL_BLOCK_STORE_FAILED_TO_CREATE_DIR => + "ExternalBlockStore failed to create a local temporary directory." case _ => "Unknown executor exit code (" + exitCode + ")" + ( if (exitCode > 128) { diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala index 330255f89247f..31c07c73fe07b 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -1475,9 +1475,9 @@ abstract class RDD[T: ClassTag]( val persistence = if (storageLevel != StorageLevel.NONE) storageLevel.description else "" val storageInfo = rdd.context.getRDDStorageInfo.filter(_.id == rdd.id).map(info => - " CachedPartitions: %d; MemorySize: %s; TachyonSize: %s; DiskSize: %s".format( + " CachedPartitions: %d; MemorySize: %s; ExternalBlockStoreSize: %s; DiskSize: %s".format( info.numCachedPartitions, bytesToString(info.memSize), - bytesToString(info.tachyonSize), bytesToString(info.diskSize))) + bytesToString(info.externalBlockStoreSize), bytesToString(info.diskSize))) s"$rdd [$persistence]" +: storageInfo } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala index 55718e584c195..402ee1c7648c5 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala @@ -78,19 +78,11 @@ private[spark] class BlockManager( private val blockInfo = new TimeStampedHashMap[BlockId, BlockInfo] // Actual storage of where blocks are kept - private var tachyonInitialized = false + private var externalBlockStoreInitialized = false private[spark] val memoryStore = new MemoryStore(this, maxMemory) private[spark] val diskStore = new DiskStore(this, diskBlockManager) - private[spark] lazy val tachyonStore: TachyonStore = { - val storeDir = conf.get("spark.tachyonStore.baseDir", "/tmp_spark_tachyon") - val appFolderName = conf.get("spark.tachyonStore.folderName") - val tachyonStorePath = s"$storeDir/$appFolderName/${this.executorId}" - val tachyonMaster = conf.get("spark.tachyonStore.url", "tachyon://localhost:19998") - val tachyonBlockManager = - new TachyonBlockManager(this, tachyonStorePath, tachyonMaster) - tachyonInitialized = true - new TachyonStore(this, tachyonBlockManager) - } + private[spark] lazy val externalBlockStore: ExternalBlockStore = + new ExternalBlockStore(this, executorId) private[spark] val externalShuffleServiceEnabled = conf.getBoolean("spark.shuffle.service.enabled", false) @@ -320,13 +312,13 @@ private[spark] class BlockManager( /** * Get the BlockStatus for the block identified by the given ID, if it exists. - * NOTE: This is mainly for testing, and it doesn't fetch information from Tachyon. + * NOTE: This is mainly for testing, and it doesn't fetch information from external block store. */ def getStatus(blockId: BlockId): Option[BlockStatus] = { blockInfo.get(blockId).map { info => val memSize = if (memoryStore.contains(blockId)) memoryStore.getSize(blockId) else 0L val diskSize = if (diskStore.contains(blockId)) diskStore.getSize(blockId) else 0L - // Assume that block is not in Tachyon + // Assume that block is not in external block store BlockStatus(info.level, memSize, diskSize, 0L) } } @@ -376,10 +368,10 @@ private[spark] class BlockManager( if (info.tellMaster) { val storageLevel = status.storageLevel val inMemSize = Math.max(status.memSize, droppedMemorySize) - val inTachyonSize = status.tachyonSize + val inExternalBlockStoreSize = status.externalBlockStoreSize val onDiskSize = status.diskSize master.updateBlockInfo( - blockManagerId, blockId, storageLevel, inMemSize, onDiskSize, inTachyonSize) + blockManagerId, blockId, storageLevel, inMemSize, onDiskSize, inExternalBlockStoreSize) } else { true } @@ -397,15 +389,17 @@ private[spark] class BlockManager( BlockStatus(StorageLevel.NONE, 0L, 0L, 0L) case level => val inMem = level.useMemory && memoryStore.contains(blockId) - val inTachyon = level.useOffHeap && tachyonStore.contains(blockId) + val inExternalBlockStore = level.useOffHeap && externalBlockStore.contains(blockId) val onDisk = level.useDisk && diskStore.contains(blockId) val deserialized = if (inMem) level.deserialized else false - val replication = if (inMem || inTachyon || onDisk) level.replication else 1 - val storageLevel = StorageLevel(onDisk, inMem, inTachyon, deserialized, replication) + val replication = if (inMem || inExternalBlockStore || onDisk) level.replication else 1 + val storageLevel = + StorageLevel(onDisk, inMem, inExternalBlockStore, deserialized, replication) val memSize = if (inMem) memoryStore.getSize(blockId) else 0L - val tachyonSize = if (inTachyon) tachyonStore.getSize(blockId) else 0L + val externalBlockStoreSize = + if (inExternalBlockStore) externalBlockStore.getSize(blockId) else 0L val diskSize = if (onDisk) diskStore.getSize(blockId) else 0L - BlockStatus(storageLevel, memSize, diskSize, tachyonSize) + BlockStatus(storageLevel, memSize, diskSize, externalBlockStoreSize) } } } @@ -485,11 +479,11 @@ private[spark] class BlockManager( } } - // Look for the block in Tachyon + // Look for the block in external block store if (level.useOffHeap) { - logDebug(s"Getting block $blockId from tachyon") - if (tachyonStore.contains(blockId)) { - tachyonStore.getBytes(blockId) match { + logDebug(s"Getting block $blockId from ExternalBlockStore") + if (externalBlockStore.contains(blockId)) { + externalBlockStore.getBytes(blockId) match { case Some(bytes) => if (!asBlockResult) { return Some(bytes) @@ -498,7 +492,7 @@ private[spark] class BlockManager( dataDeserialize(blockId, bytes), DataReadMethod.Memory, info.size)) } case None => - logDebug(s"Block $blockId not found in tachyon") + logDebug(s"Block $blockId not found in externalBlockStore") } } } @@ -766,8 +760,8 @@ private[spark] class BlockManager( // We will drop it to disk later if the memory store can't hold it. (true, memoryStore) } else if (putLevel.useOffHeap) { - // Use tachyon for off-heap storage - (false, tachyonStore) + // Use external block store + (false, externalBlockStore) } else if (putLevel.useDisk) { // Don't get back the bytes from put unless we replicate them (putLevel.replication > 1, diskStore) @@ -802,7 +796,7 @@ private[spark] class BlockManager( val putBlockStatus = getCurrentBlockStatus(blockId, putBlockInfo) if (putBlockStatus.storageLevel != StorageLevel.NONE) { - // Now that the block is in either the memory, tachyon, or disk store, + // Now that the block is in either the memory, externalBlockStore, or disk store, // let other threads read it, and tell the master about it. marked = true putBlockInfo.markReady(size) @@ -1099,10 +1093,11 @@ private[spark] class BlockManager( // Removals are idempotent in disk store and memory store. At worst, we get a warning. val removedFromMemory = memoryStore.remove(blockId) val removedFromDisk = diskStore.remove(blockId) - val removedFromTachyon = if (tachyonInitialized) tachyonStore.remove(blockId) else false - if (!removedFromMemory && !removedFromDisk && !removedFromTachyon) { + val removedFromExternalBlockStore = + if (externalBlockStoreInitialized) externalBlockStore.remove(blockId) else false + if (!removedFromMemory && !removedFromDisk && !removedFromExternalBlockStore) { logWarning(s"Block $blockId could not be removed as it was not found in either " + - "the disk, memory, or tachyon store") + "the disk, memory, or external block store") } blockInfo.remove(blockId) if (tellMaster && info.tellMaster) { @@ -1136,7 +1131,7 @@ private[spark] class BlockManager( val level = info.level if (level.useMemory) { memoryStore.remove(id) } if (level.useDisk) { diskStore.remove(id) } - if (level.useOffHeap) { tachyonStore.remove(id) } + if (level.useOffHeap) { externalBlockStore.remove(id) } iterator.remove() logInfo(s"Dropped block $id") } @@ -1216,8 +1211,8 @@ private[spark] class BlockManager( blockInfo.clear() memoryStore.clear() diskStore.clear() - if (tachyonInitialized) { - tachyonStore.clear() + if (externalBlockStoreInitialized) { + externalBlockStore.clear() } metadataCleaner.cancel() broadcastCleaner.cancel() diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala index 9bfc4201d37c0..a85e1c7632973 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala @@ -54,9 +54,10 @@ class BlockManagerMaster( storageLevel: StorageLevel, memSize: Long, diskSize: Long, - tachyonSize: Long): Boolean = { + externalBlockStoreSize: Long): Boolean = { val res = driverEndpoint.askWithRetry[Boolean]( - UpdateBlockInfo(blockManagerId, blockId, storageLevel, memSize, diskSize, tachyonSize)) + UpdateBlockInfo(blockManagerId, blockId, storageLevel, + memSize, diskSize, externalBlockStoreSize)) logDebug(s"Updated info of block $blockId") res } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala index 7212362df5d71..3afb4c3c02e2d 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala @@ -60,9 +60,9 @@ class BlockManagerMasterEndpoint( context.reply(true) case UpdateBlockInfo( - blockManagerId, blockId, storageLevel, deserializedSize, size, tachyonSize) => + blockManagerId, blockId, storageLevel, deserializedSize, size, externalBlockStoreSize) => context.reply(updateBlockInfo( - blockManagerId, blockId, storageLevel, deserializedSize, size, tachyonSize)) + blockManagerId, blockId, storageLevel, deserializedSize, size, externalBlockStoreSize)) case GetLocations(blockId) => context.reply(getLocations(blockId)) @@ -314,7 +314,7 @@ class BlockManagerMasterEndpoint( storageLevel: StorageLevel, memSize: Long, diskSize: Long, - tachyonSize: Long): Boolean = { + externalBlockStoreSize: Long): Boolean = { if (!blockManagerInfo.contains(blockManagerId)) { if (blockManagerId.isDriver && !isLocal) { @@ -332,7 +332,7 @@ class BlockManagerMasterEndpoint( } blockManagerInfo(blockManagerId).updateBlockInfo( - blockId, storageLevel, memSize, diskSize, tachyonSize) + blockId, storageLevel, memSize, diskSize, externalBlockStoreSize) var locations: mutable.HashSet[BlockManagerId] = null if (blockLocations.containsKey(blockId)) { @@ -396,8 +396,8 @@ case class BlockStatus( storageLevel: StorageLevel, memSize: Long, diskSize: Long, - tachyonSize: Long) { - def isCached: Boolean = memSize + diskSize + tachyonSize > 0 + externalBlockStoreSize: Long) { + def isCached: Boolean = memSize + diskSize + externalBlockStoreSize > 0 } @DeveloperApi @@ -429,7 +429,7 @@ private[spark] class BlockManagerInfo( storageLevel: StorageLevel, memSize: Long, diskSize: Long, - tachyonSize: Long) { + externalBlockStoreSize: Long) { updateLastSeenMs() @@ -445,9 +445,9 @@ private[spark] class BlockManagerInfo( } if (storageLevel.isValid) { - /* isValid means it is either stored in-memory, on-disk or on-Tachyon. + /* isValid means it is either stored in-memory, on-disk or on-externalBlockStore. * The memSize here indicates the data size in or dropped from memory, - * tachyonSize here indicates the data size in or dropped from Tachyon, + * externalBlockStoreSize here indicates the data size in or dropped from externalBlockStore, * and the diskSize here indicates the data size in or dropped to disk. * They can be both larger than 0, when a block is dropped from memory to disk. * Therefore, a safe way to set BlockStatus is to set its info in accurate modes. */ @@ -464,9 +464,9 @@ private[spark] class BlockManagerInfo( blockId, blockManagerId.hostPort, Utils.bytesToString(diskSize))) } if (storageLevel.useOffHeap) { - _blocks.put(blockId, BlockStatus(storageLevel, 0, 0, tachyonSize)) - logInfo("Added %s on tachyon on %s (size: %s)".format( - blockId, blockManagerId.hostPort, Utils.bytesToString(tachyonSize))) + _blocks.put(blockId, BlockStatus(storageLevel, 0, 0, externalBlockStoreSize)) + logInfo("Added %s on ExternalBlockStore on %s (size: %s)".format( + blockId, blockManagerId.hostPort, Utils.bytesToString(externalBlockStoreSize))) } } else if (_blocks.containsKey(blockId)) { // If isValid is not true, drop the block. @@ -482,8 +482,9 @@ private[spark] class BlockManagerInfo( blockId, blockManagerId.hostPort, Utils.bytesToString(blockStatus.diskSize))) } if (blockStatus.storageLevel.useOffHeap) { - logInfo("Removed %s on %s on tachyon (size: %s)".format( - blockId, blockManagerId.hostPort, Utils.bytesToString(blockStatus.tachyonSize))) + logInfo("Removed %s on %s on externalBlockStore (size: %s)".format( + blockId, blockManagerId.hostPort, + Utils.bytesToString(blockStatus.externalBlockStoreSize))) } } } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala index f89d8d7493f7c..1683576067fe8 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMessages.scala @@ -60,7 +60,7 @@ private[spark] object BlockManagerMessages { var storageLevel: StorageLevel, var memSize: Long, var diskSize: Long, - var tachyonSize: Long) + var externalBlockStoreSize: Long) extends ToBlockManagerMaster with Externalizable { @@ -72,7 +72,7 @@ private[spark] object BlockManagerMessages { storageLevel.writeExternal(out) out.writeLong(memSize) out.writeLong(diskSize) - out.writeLong(tachyonSize) + out.writeLong(externalBlockStoreSize) } override def readExternal(in: ObjectInput): Unit = Utils.tryOrIOException { @@ -81,7 +81,7 @@ private[spark] object BlockManagerMessages { storageLevel = StorageLevel(in) memSize = in.readLong() diskSize = in.readLong() - tachyonSize = in.readLong() + externalBlockStoreSize = in.readLong() } } diff --git a/core/src/main/scala/org/apache/spark/storage/ExternalBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/ExternalBlockManager.scala new file mode 100644 index 0000000000000..8964762df6af3 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/storage/ExternalBlockManager.scala @@ -0,0 +1,102 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.storage + +import java.nio.ByteBuffer + +/** + * An abstract class that the concrete external block manager has to inherit. + * The class has to have a no-argument constructor, and will be initialized by init, + * which is invoked by ExternalBlockStore. The main input parameter is blockId for all + * the methods, which is the unique identifier for Block in one Spark application. + * + * The underlying external block manager should avoid any name space conflicts among multiple + * Spark applications. For example, creating different directory for different applications + * by randomUUID + * + */ +private[spark] abstract class ExternalBlockManager { + + override def toString: String = {"External Block Store"} + + /** + * Initialize a concrete block manager implementation. Subclass should initialize its internal + * data structure, e.g, file system, in this function, which is invoked by ExternalBlockStore + * right after the class is constructed. The function should throw IOException on failure + * + * @throws java.io.IOException if there is any file system failure during the initialization. + */ + def init(blockManager: BlockManager, executorId: String): Unit + + /** + * Drop the block from underlying external block store, if it exists.. + * @return true on successfully removing the block + * false if the block could not be removed as it was not found + * + * @throws java.io.IOException if there is any file system failure in removing the block. + */ + def removeBlock(blockId: BlockId): Boolean + + /** + * Used by BlockManager to check the existence of the block in the underlying external + * block store. + * @return true if the block exists. + * false if the block does not exists. + * + * @throws java.io.IOException if there is any file system failure in checking + * the block existence. + */ + def blockExists(blockId: BlockId): Boolean + + /** + * Put the given block to the underlying external block store. Note that in normal case, + * putting a block should never fail unless something wrong happens to the underlying + * external block store, e.g., file system failure, etc. In this case, IOException + * should be thrown. + * + * @throws java.io.IOException if there is any file system failure in putting the block. + */ + def putBytes(blockId: BlockId, bytes: ByteBuffer): Unit + + /** + * Retrieve the block bytes. + * @return Some(ByteBuffer) if the block bytes is successfully retrieved + * None if the block does not exist in the external block store. + * + * @throws java.io.IOException if there is any file system failure in getting the block. + */ + def getBytes(blockId: BlockId): Option[ByteBuffer] + + /** + * Get the size of the block saved in the underlying external block store, + * which is saved before by putBytes. + * @return size of the block + * 0 if the block does not exist + * + * @throws java.io.IOException if there is any file system failure in getting the block size. + */ + def getSize(blockId: BlockId): Long + + /** + * Clean up any information persisted in the underlying external block store, + * e.g., the directory, files, etc,which is invoked by the shutdown hook of ExternalBlockStore + * during system shutdown. + * + */ + def shutdown() +} diff --git a/core/src/main/scala/org/apache/spark/storage/ExternalBlockStore.scala b/core/src/main/scala/org/apache/spark/storage/ExternalBlockStore.scala new file mode 100644 index 0000000000000..0bf770306ae9b --- /dev/null +++ b/core/src/main/scala/org/apache/spark/storage/ExternalBlockStore.scala @@ -0,0 +1,181 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.storage + +import java.nio.ByteBuffer +import org.apache.spark.Logging +import org.apache.spark.util.Utils +import scala.util.control.NonFatal + + +/** + * Stores BlockManager blocks on ExternalBlockStore. + * We capture any potential exception from underlying implementation + * and return with the expected failure value + */ +private[spark] class ExternalBlockStore(blockManager: BlockManager, executorId: String) + extends BlockStore(blockManager: BlockManager) with Logging { + + lazy val externalBlockManager: Option[ExternalBlockManager] = createBlkManager() + + logInfo("ExternalBlockStore started") + + override def getSize(blockId: BlockId): Long = { + try { + externalBlockManager.map(_.getSize(blockId)).getOrElse(0) + } catch { + case NonFatal(t) => + logError(s"error in getSize from $blockId", t) + 0L + } + } + + override def putBytes(blockId: BlockId, bytes: ByteBuffer, level: StorageLevel): PutResult = { + putIntoExternalBlockStore(blockId, bytes, returnValues = true) + } + + override def putArray( + blockId: BlockId, + values: Array[Any], + level: StorageLevel, + returnValues: Boolean): PutResult = { + putIterator(blockId, values.toIterator, level, returnValues) + } + + override def putIterator( + blockId: BlockId, + values: Iterator[Any], + level: StorageLevel, + returnValues: Boolean): PutResult = { + logDebug(s"Attempting to write values for block $blockId") + val bytes = blockManager.dataSerialize(blockId, values) + putIntoExternalBlockStore(blockId, bytes, returnValues) + } + + private def putIntoExternalBlockStore( + blockId: BlockId, + bytes: ByteBuffer, + returnValues: Boolean): PutResult = { + // So that we do not modify the input offsets ! + // duplicate does not copy buffer, so inexpensive + val byteBuffer = bytes.duplicate() + byteBuffer.rewind() + logDebug(s"Attempting to put block $blockId into ExtBlk store") + // we should never hit here if externalBlockManager is None. Handle it anyway for safety. + try { + val startTime = System.currentTimeMillis + if (externalBlockManager.isDefined) { + externalBlockManager.get.putBytes(blockId, bytes) + val finishTime = System.currentTimeMillis + logDebug("Block %s stored as %s file in ExternalBlockStore in %d ms".format( + blockId, Utils.bytesToString(byteBuffer.limit), finishTime - startTime)) + + if (returnValues) { + PutResult(bytes.limit(), Right(bytes.duplicate())) + } else { + PutResult(bytes.limit(), null) + } + } else { + logError(s"error in putBytes $blockId") + PutResult(bytes.limit(), null, Seq((blockId, BlockStatus.empty))) + } + } catch { + case NonFatal(t) => + logError(s"error in putBytes $blockId", t) + PutResult(bytes.limit(), null, Seq((blockId, BlockStatus.empty))) + } + } + + // We assume the block is removed even if exception thrown + override def remove(blockId: BlockId): Boolean = { + try { + externalBlockManager.map(_.removeBlock(blockId)).getOrElse(true) + } catch { + case NonFatal(t) => + logError(s"error in removing $blockId", t) + true + } + } + + override def getValues(blockId: BlockId): Option[Iterator[Any]] = { + getBytes(blockId).map(buffer => blockManager.dataDeserialize(blockId, buffer)) + } + + override def getBytes(blockId: BlockId): Option[ByteBuffer] = { + try { + externalBlockManager.flatMap(_.getBytes(blockId)) + } catch { + case NonFatal(t) => + logError(s"error in getBytes from $blockId", t) + None + } + } + + override def contains(blockId: BlockId): Boolean = { + try { + val ret = externalBlockManager.map(_.blockExists(blockId)).getOrElse(false) + if (!ret) { + logInfo(s"remove block $blockId") + blockManager.removeBlock(blockId, true) + } + ret + } catch { + case NonFatal(t) => + logError(s"error in getBytes from $blockId", t) + false + } + } + + private def addShutdownHook() { + Runtime.getRuntime.addShutdownHook(new Thread("ExternalBlockStore shutdown hook") { + override def run(): Unit = Utils.logUncaughtExceptions { + logDebug("Shutdown hook called") + externalBlockManager.map(_.shutdown()) + } + }) + } + + // Create concrete block manager and fall back to Tachyon by default for backward compatibility. + private def createBlkManager(): Option[ExternalBlockManager] = { + val clsName = blockManager.conf.getOption(ExternalBlockStore.BLOCK_MANAGER_NAME) + .getOrElse(ExternalBlockStore.DEFAULT_BLOCK_MANAGER_NAME) + + try { + val instance = Class.forName(clsName) + .newInstance() + .asInstanceOf[ExternalBlockManager] + instance.init(blockManager, executorId) + addShutdownHook(); + Some(instance) + } catch { + case NonFatal(t) => + logError("Cannot initialize external block store", t) + None + } + } +} + +private[spark] object ExternalBlockStore extends Logging { + val MAX_DIR_CREATION_ATTEMPTS = 10 + val SUB_DIRS_PER_DIR = "64" + val BASE_DIR = "spark.externalBlockStore.baseDir" + val FOLD_NAME = "spark.externalBlockStore.folderName" + val MASTER_URL = "spark.externalBlockStore.url" + val BLOCK_MANAGER_NAME = "spark.externalBlockStore.blockManager" + val DEFAULT_BLOCK_MANAGER_NAME = "org.apache.spark.storage.TachyonBlockManager" +} diff --git a/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala b/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala index 034525b56f59c..ad53a3edc7cc1 100644 --- a/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala +++ b/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala @@ -32,16 +32,17 @@ class RDDInfo( var numCachedPartitions = 0 var memSize = 0L var diskSize = 0L - var tachyonSize = 0L + var externalBlockStoreSize = 0L - def isCached: Boolean = (memSize + diskSize + tachyonSize > 0) && numCachedPartitions > 0 + def isCached: Boolean = + (memSize + diskSize + externalBlockStoreSize > 0) && numCachedPartitions > 0 override def toString: String = { import Utils.bytesToString ("RDD \"%s\" (%d) StorageLevel: %s; CachedPartitions: %d; TotalPartitions: %d; " + - "MemorySize: %s; TachyonSize: %s; DiskSize: %s").format( + "MemorySize: %s; ExternalBlockStoreSize: %s; DiskSize: %s").format( name, id, storageLevel.toString, numCachedPartitions, numPartitions, - bytesToString(memSize), bytesToString(tachyonSize), bytesToString(diskSize)) + bytesToString(memSize), bytesToString(externalBlockStoreSize), bytesToString(diskSize)) } override def compare(that: RDDInfo): Int = { diff --git a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala index 134abea866218..703bce3e6b85b 100644 --- a/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala +++ b/core/src/main/scala/org/apache/spark/storage/StorageLevel.scala @@ -26,9 +26,9 @@ import org.apache.spark.util.Utils /** * :: DeveloperApi :: * Flags for controlling the storage of an RDD. Each StorageLevel records whether to use memory, - * or Tachyon, whether to drop the RDD to disk if it falls out of memory or Tachyon , whether to - * keep the data in memory in a serialized format, and whether to replicate the RDD partitions on - * multiple nodes. + * or ExternalBlockStore, whether to drop the RDD to disk if it falls out of memory or + * ExternalBlockStore, whether to keep the data in memory in a serialized format, and whether + * to replicate the RDD partitions on multiple nodes. * * The [[org.apache.spark.storage.StorageLevel$]] singleton object contains some static constants * for commonly useful storage levels. To create your own storage level object, use the @@ -126,7 +126,7 @@ class StorageLevel private( var result = "" result += (if (useDisk) "Disk " else "") result += (if (useMemory) "Memory " else "") - result += (if (useOffHeap) "Tachyon " else "") + result += (if (useOffHeap) "ExternalBlockStore " else "") result += (if (deserialized) "Deserialized " else "Serialized ") result += s"${replication}x Replicated" result diff --git a/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala b/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala index 2bd6b749be261..c4ac30092f807 100644 --- a/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala +++ b/core/src/main/scala/org/apache/spark/storage/StorageUtils.scala @@ -199,33 +199,34 @@ class StorageStatus(val blockManagerId: BlockManagerId, val maxMem: Long) { val oldBlockStatus = getBlock(blockId).getOrElse(BlockStatus.empty) val changeInMem = newBlockStatus.memSize - oldBlockStatus.memSize val changeInDisk = newBlockStatus.diskSize - oldBlockStatus.diskSize - val changeInTachyon = newBlockStatus.tachyonSize - oldBlockStatus.tachyonSize + val changeInExternalBlockStore = + newBlockStatus.externalBlockStoreSize - oldBlockStatus.externalBlockStoreSize val level = newBlockStatus.storageLevel // Compute new info from old info - val (oldMem, oldDisk, oldTachyon) = blockId match { + val (oldMem, oldDisk, oldExternalBlockStore) = blockId match { case RDDBlockId(rddId, _) => _rddStorageInfo.get(rddId) - .map { case (mem, disk, tachyon, _) => (mem, disk, tachyon) } + .map { case (mem, disk, externalBlockStore, _) => (mem, disk, externalBlockStore) } .getOrElse((0L, 0L, 0L)) case _ => _nonRddStorageInfo } val newMem = math.max(oldMem + changeInMem, 0L) val newDisk = math.max(oldDisk + changeInDisk, 0L) - val newTachyon = math.max(oldTachyon + changeInTachyon, 0L) + val newExternalBlockStore = math.max(oldExternalBlockStore + changeInExternalBlockStore, 0L) // Set the correct info blockId match { case RDDBlockId(rddId, _) => // If this RDD is no longer persisted, remove it - if (newMem + newDisk + newTachyon == 0) { + if (newMem + newDisk + newExternalBlockStore == 0) { _rddStorageInfo.remove(rddId) } else { - _rddStorageInfo(rddId) = (newMem, newDisk, newTachyon, level) + _rddStorageInfo(rddId) = (newMem, newDisk, newExternalBlockStore, level) } case _ => - _nonRddStorageInfo = (newMem, newDisk, newTachyon) + _nonRddStorageInfo = (newMem, newDisk, newExternalBlockStore) } } @@ -247,13 +248,13 @@ private[spark] object StorageUtils { val numCachedPartitions = statuses.map(_.numRddBlocksById(rddId)).sum val memSize = statuses.map(_.memUsedByRdd(rddId)).sum val diskSize = statuses.map(_.diskUsedByRdd(rddId)).sum - val tachyonSize = statuses.map(_.offHeapUsedByRdd(rddId)).sum + val externalBlockStoreSize = statuses.map(_.offHeapUsedByRdd(rddId)).sum rddInfo.storageLevel = storageLevel rddInfo.numCachedPartitions = numCachedPartitions rddInfo.memSize = memSize rddInfo.diskSize = diskSize - rddInfo.tachyonSize = tachyonSize + rddInfo.externalBlockStoreSize = externalBlockStoreSize } } diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala index 583f1fdf0475b..bdc6276e41915 100644 --- a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala @@ -17,13 +17,16 @@ package org.apache.spark.storage +import java.io.IOException +import java.nio.ByteBuffer import java.text.SimpleDateFormat import java.util.{Date, Random} +import com.google.common.io.ByteStreams +import tachyon.client.{ReadType, WriteType, TachyonFS, TachyonFile} import tachyon.TachyonURI -import tachyon.client.{TachyonFile, TachyonFS} -import org.apache.spark.Logging +import org.apache.spark.{SparkException, SparkConf, Logging} import org.apache.spark.executor.ExecutorExitCode import org.apache.spark.util.Utils @@ -32,32 +35,94 @@ import org.apache.spark.util.Utils * Creates and maintains the logical mapping between logical blocks and tachyon fs locations. By * default, one block is mapped to one file with a name given by its BlockId. * - * @param rootDirs The directories to use for storing block files. Data will be hashed among these. */ -private[spark] class TachyonBlockManager( - blockManager: BlockManager, - rootDirs: String, - val master: String) - extends Logging { +private[spark] class TachyonBlockManager() extends ExternalBlockManager with Logging { - val client = if (master != null && master != "") TachyonFS.get(new TachyonURI(master)) else null - - if (client == null) { - logError("Failed to connect to the Tachyon as the master address is not configured") - System.exit(ExecutorExitCode.TACHYON_STORE_FAILED_TO_INITIALIZE) - } - - private val MAX_DIR_CREATION_ATTEMPTS = 10 - private val subDirsPerTachyonDir = - blockManager.conf.get("spark.tachyonStore.subDirectories", "64").toInt + var blockManager: BlockManager =_ + var rootDirs: String = _ + var master: String = _ + var client: tachyon.client.TachyonFS = _ + private var subDirsPerTachyonDir: Int = _ // Create one Tachyon directory for each path mentioned in spark.tachyonStore.folderName; // then, inside this directory, create multiple subdirectories that we will hash files into, // in order to avoid having really large inodes at the top level in Tachyon. - private val tachyonDirs: Array[TachyonFile] = createTachyonDirs() - private val subDirs = Array.fill(tachyonDirs.length)(new Array[TachyonFile](subDirsPerTachyonDir)) + private var tachyonDirs: Array[TachyonFile] = _ + private var subDirs: Array[Array[tachyon.client.TachyonFile]] = _ + + + override def init(blockManager: BlockManager, executorId: String): Unit = { + this.blockManager = blockManager + val storeDir = blockManager.conf.get(ExternalBlockStore.BASE_DIR, "/tmp_spark_tachyon") + val appFolderName = blockManager.conf.get(ExternalBlockStore.FOLD_NAME) + + rootDirs = s"$storeDir/$appFolderName/$executorId" + master = blockManager.conf.get(ExternalBlockStore.MASTER_URL, "tachyon://localhost:19998") + client = if (master != null && master != "") TachyonFS.get(new TachyonURI(master)) else null + // original implementation call System.exit, we change it to run without extblkstore support + if (client == null) { + logError("Failed to connect to the Tachyon as the master address is not configured") + throw new IOException("Failed to connect to the Tachyon as the master " + + "address is not configured") + } + subDirsPerTachyonDir = blockManager.conf.get("spark.externalBlockStore.subDirectories", + ExternalBlockStore.SUB_DIRS_PER_DIR).toInt + + // Create one Tachyon directory for each path mentioned in spark.tachyonStore.folderName; + // then, inside this directory, create multiple subdirectories that we will hash files into, + // in order to avoid having really large inodes at the top level in Tachyon. + tachyonDirs = createTachyonDirs() + subDirs = Array.fill(tachyonDirs.length)(new Array[TachyonFile](subDirsPerTachyonDir)) + tachyonDirs.foreach(tachyonDir => Utils.registerShutdownDeleteDir(tachyonDir)) + } + + override def toString: String = {"ExternalBlockStore-Tachyon"} + + override def removeBlock(blockId: BlockId): Boolean = { + val file = getFile(blockId) + if (fileExists(file)) { + removeFile(file) + } else { + false + } + } + + override def blockExists(blockId: BlockId): Boolean = { + val file = getFile(blockId) + fileExists(file) + } - addShutdownHook() + override def putBytes(blockId: BlockId, bytes: ByteBuffer): Unit = { + val file = getFile(blockId) + val os = file.getOutStream(WriteType.TRY_CACHE) + os.write(bytes.array()) + os.close() + } + + override def getBytes(blockId: BlockId): Option[ByteBuffer] = { + val file = getFile(blockId) + if (file == null || file.getLocationHosts.size == 0) { + return None + } + val is = file.getInStream(ReadType.CACHE) + assert (is != null) + try { + val size = file.length + val bs = new Array[Byte](size.asInstanceOf[Int]) + ByteStreams.readFully(is, bs) + Some(ByteBuffer.wrap(bs)) + } catch { + case ioe: IOException => + logWarning(s"Failed to fetch the block $blockId from Tachyon", ioe) + None + } finally { + is.close() + } + } + + override def getSize(blockId: BlockId): Long = { + getFile(blockId.name).length + } def removeFile(file: TachyonFile): Boolean = { client.delete(new TachyonURI(file.getPath()), false) @@ -109,7 +174,7 @@ private[spark] class TachyonBlockManager( var tachyonDirId: String = null var tries = 0 val rand = new Random() - while (!foundLocalDir && tries < MAX_DIR_CREATION_ATTEMPTS) { + while (!foundLocalDir && tries < ExternalBlockStore.MAX_DIR_CREATION_ATTEMPTS) { tries += 1 try { tachyonDirId = "%s-%04x".format(dateFormat.format(new Date), rand.nextInt(65536)) @@ -124,30 +189,27 @@ private[spark] class TachyonBlockManager( } } if (!foundLocalDir) { - logError("Failed " + MAX_DIR_CREATION_ATTEMPTS + " attempts to create tachyon dir in " + - rootDir) - System.exit(ExecutorExitCode.TACHYON_STORE_FAILED_TO_CREATE_DIR) + logError("Failed " + ExternalBlockStore.MAX_DIR_CREATION_ATTEMPTS + + " attempts to create tachyon dir in " + rootDir) + System.exit(ExecutorExitCode.EXTERNAL_BLOCK_STORE_FAILED_TO_CREATE_DIR) } logInfo("Created tachyon directory at " + tachyonDir) tachyonDir } } - private def addShutdownHook() { - tachyonDirs.foreach(tachyonDir => Utils.registerShutdownDeleteDir(tachyonDir)) - Utils.addShutdownHook { () => - logDebug("Shutdown hook called") - tachyonDirs.foreach { tachyonDir => - try { - if (!Utils.hasRootAsShutdownDeleteDir(tachyonDir)) { - Utils.deleteRecursively(tachyonDir, client) - } - } catch { - case e: Exception => - logError("Exception while deleting tachyon spark dir: " + tachyonDir, e) + override def shutdown() { + logDebug("Shutdown hook called") + tachyonDirs.foreach { tachyonDir => + try { + if (!Utils.hasRootAsShutdownDeleteDir(tachyonDir)) { + Utils.deleteRecursively(tachyonDir, client) } + } catch { + case e: Exception => + logError("Exception while deleting tachyon spark dir: " + tachyonDir, e) } - client.close() } + client.close() } } diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonFileSegment.scala b/core/src/main/scala/org/apache/spark/storage/TachyonFileSegment.scala deleted file mode 100644 index 65fa81704c365..0000000000000 --- a/core/src/main/scala/org/apache/spark/storage/TachyonFileSegment.scala +++ /dev/null @@ -1,30 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.storage - -import tachyon.client.TachyonFile - -/** - * References a particular segment of a file (potentially the entire file), based off an offset and - * a length. - */ -private[spark] class TachyonFileSegment(val file: TachyonFile, val offset: Long, val length: Long) { - override def toString: String = { - "(name=%s, offset=%d, length=%d)".format(file.getPath(), offset, length) - } -} diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonStore.scala b/core/src/main/scala/org/apache/spark/storage/TachyonStore.scala deleted file mode 100644 index 233d1e2b7c616..0000000000000 --- a/core/src/main/scala/org/apache/spark/storage/TachyonStore.scala +++ /dev/null @@ -1,128 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.storage - -import java.io.IOException -import java.nio.ByteBuffer - -import com.google.common.io.ByteStreams -import tachyon.client.{ReadType, WriteType} - -import org.apache.spark.Logging -import org.apache.spark.util.Utils - -/** - * Stores BlockManager blocks on Tachyon. - */ -private[spark] class TachyonStore( - blockManager: BlockManager, - tachyonManager: TachyonBlockManager) - extends BlockStore(blockManager: BlockManager) with Logging { - - logInfo("TachyonStore started") - - override def getSize(blockId: BlockId): Long = { - tachyonManager.getFile(blockId.name).length - } - - override def putBytes(blockId: BlockId, bytes: ByteBuffer, level: StorageLevel): PutResult = { - putIntoTachyonStore(blockId, bytes, returnValues = true) - } - - override def putArray( - blockId: BlockId, - values: Array[Any], - level: StorageLevel, - returnValues: Boolean): PutResult = { - putIterator(blockId, values.toIterator, level, returnValues) - } - - override def putIterator( - blockId: BlockId, - values: Iterator[Any], - level: StorageLevel, - returnValues: Boolean): PutResult = { - logDebug(s"Attempting to write values for block $blockId") - val bytes = blockManager.dataSerialize(blockId, values) - putIntoTachyonStore(blockId, bytes, returnValues) - } - - private def putIntoTachyonStore( - blockId: BlockId, - bytes: ByteBuffer, - returnValues: Boolean): PutResult = { - // So that we do not modify the input offsets ! - // duplicate does not copy buffer, so inexpensive - val byteBuffer = bytes.duplicate() - byteBuffer.rewind() - logDebug(s"Attempting to put block $blockId into Tachyon") - val startTime = System.currentTimeMillis - val file = tachyonManager.getFile(blockId) - val os = file.getOutStream(WriteType.TRY_CACHE) - os.write(byteBuffer.array()) - os.close() - val finishTime = System.currentTimeMillis - logDebug("Block %s stored as %s file in Tachyon in %d ms".format( - blockId, Utils.bytesToString(byteBuffer.limit), finishTime - startTime)) - - if (returnValues) { - PutResult(bytes.limit(), Right(bytes.duplicate())) - } else { - PutResult(bytes.limit(), null) - } - } - - override def remove(blockId: BlockId): Boolean = { - val file = tachyonManager.getFile(blockId) - if (tachyonManager.fileExists(file)) { - tachyonManager.removeFile(file) - } else { - false - } - } - - override def getValues(blockId: BlockId): Option[Iterator[Any]] = { - getBytes(blockId).map(buffer => blockManager.dataDeserialize(blockId, buffer)) - } - - override def getBytes(blockId: BlockId): Option[ByteBuffer] = { - val file = tachyonManager.getFile(blockId) - if (file == null || file.getLocationHosts.size == 0) { - return None - } - val is = file.getInStream(ReadType.CACHE) - assert (is != null) - try { - val size = file.length - val bs = new Array[Byte](size.asInstanceOf[Int]) - ByteStreams.readFully(is, bs) - Some(ByteBuffer.wrap(bs)) - } catch { - case ioe: IOException => - logWarning(s"Failed to fetch the block $blockId from Tachyon", ioe) - None - } finally { - is.close() - } - } - - override def contains(blockId: BlockId): Boolean = { - val file = tachyonManager.getFile(blockId) - tachyonManager.fileExists(file) - } -} diff --git a/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala b/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala index 6ced6052d2b18..59dc6b547c1d8 100644 --- a/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala +++ b/core/src/main/scala/org/apache/spark/ui/storage/StoragePage.scala @@ -42,7 +42,7 @@ private[ui] class StoragePage(parent: StorageTab) extends WebUIPage("") { "Cached Partitions", "Fraction Cached", "Size in Memory", - "Size in Tachyon", + "Size in ExternalBlockStore", "Size on Disk") /** Render an HTML row representing an RDD */ @@ -59,7 +59,7 @@ private[ui] class StoragePage(parent: StorageTab) extends WebUIPage("") { {rdd.numCachedPartitions} {"%.0f%%".format(rdd.numCachedPartitions * 100.0 / rdd.numPartitions)} {Utils.bytesToString(rdd.memSize)} - {Utils.bytesToString(rdd.tachyonSize)} + {Utils.bytesToString(rdd.externalBlockStoreSize)} {Utils.bytesToString(rdd.diskSize)} // scalastyle:on diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala index 474f79fb756f6..44d274956d693 100644 --- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala +++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala @@ -373,14 +373,14 @@ private[spark] object JsonProtocol { ("Number of Partitions" -> rddInfo.numPartitions) ~ ("Number of Cached Partitions" -> rddInfo.numCachedPartitions) ~ ("Memory Size" -> rddInfo.memSize) ~ - ("Tachyon Size" -> rddInfo.tachyonSize) ~ + ("ExternalBlockStore Size" -> rddInfo.externalBlockStoreSize) ~ ("Disk Size" -> rddInfo.diskSize) } def storageLevelToJson(storageLevel: StorageLevel): JValue = { ("Use Disk" -> storageLevel.useDisk) ~ ("Use Memory" -> storageLevel.useMemory) ~ - ("Use Tachyon" -> storageLevel.useOffHeap) ~ + ("Use ExternalBlockStore" -> storageLevel.useOffHeap) ~ ("Deserialized" -> storageLevel.deserialized) ~ ("Replication" -> storageLevel.replication) } @@ -389,7 +389,7 @@ private[spark] object JsonProtocol { val storageLevel = storageLevelToJson(blockStatus.storageLevel) ("Storage Level" -> storageLevel) ~ ("Memory Size" -> blockStatus.memSize) ~ - ("Tachyon Size" -> blockStatus.tachyonSize) ~ + ("ExternalBlockStore Size" -> blockStatus.externalBlockStoreSize) ~ ("Disk Size" -> blockStatus.diskSize) } @@ -787,13 +787,15 @@ private[spark] object JsonProtocol { val numPartitions = (json \ "Number of Partitions").extract[Int] val numCachedPartitions = (json \ "Number of Cached Partitions").extract[Int] val memSize = (json \ "Memory Size").extract[Long] - val tachyonSize = (json \ "Tachyon Size").extract[Long] + // fallback to tachyon for backward compatability + val externalBlockStoreSize = (json \ "ExternalBlockStore Size").toSome + .getOrElse(json \ "Tachyon Size").extract[Long] val diskSize = (json \ "Disk Size").extract[Long] val rddInfo = new RDDInfo(rddId, name, numPartitions, storageLevel) rddInfo.numCachedPartitions = numCachedPartitions rddInfo.memSize = memSize - rddInfo.tachyonSize = tachyonSize + rddInfo.externalBlockStoreSize = externalBlockStoreSize rddInfo.diskSize = diskSize rddInfo } @@ -801,18 +803,22 @@ private[spark] object JsonProtocol { def storageLevelFromJson(json: JValue): StorageLevel = { val useDisk = (json \ "Use Disk").extract[Boolean] val useMemory = (json \ "Use Memory").extract[Boolean] - val useTachyon = (json \ "Use Tachyon").extract[Boolean] + // fallback to tachyon for backward compatability + val useExternalBlockStore = (json \ "Use ExternalBlockStore").toSome + .getOrElse(json \ "Use Tachyon").extract[Boolean] val deserialized = (json \ "Deserialized").extract[Boolean] val replication = (json \ "Replication").extract[Int] - StorageLevel(useDisk, useMemory, useTachyon, deserialized, replication) + StorageLevel(useDisk, useMemory, useExternalBlockStore, deserialized, replication) } def blockStatusFromJson(json: JValue): BlockStatus = { val storageLevel = storageLevelFromJson(json \ "Storage Level") val memorySize = (json \ "Memory Size").extract[Long] val diskSize = (json \ "Disk Size").extract[Long] - val tachyonSize = (json \ "Tachyon Size").extract[Long] - BlockStatus(storageLevel, memorySize, diskSize, tachyonSize) + // fallback to tachyon for backward compatability + val externalBlockStoreSize = (json \ "ExternalBlockStore Size").toSome + .getOrElse(json \ "Tachyon Size").extract[Long] + BlockStatus(storageLevel, memorySize, diskSize, externalBlockStoreSize) } def executorInfoFromJson(json: JValue): ExecutorInfo = { diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala index f5b410f41da27..151955ef7f435 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala @@ -526,6 +526,7 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach test("tachyon storage") { // TODO Make the spark.test.tachyon.enable true after using tachyon 0.5.0 testing jar. val tachyonUnitTestEnabled = conf.getBoolean("spark.test.tachyon.enable", false) + conf.set(ExternalBlockStore.BLOCK_MANAGER_NAME, ExternalBlockStore.DEFAULT_BLOCK_MANAGER_NAME) if (tachyonUnitTestEnabled) { store = makeBlockManager(1200) val a1 = new Array[Byte](400) diff --git a/core/src/test/scala/org/apache/spark/storage/StorageSuite.scala b/core/src/test/scala/org/apache/spark/storage/StorageSuite.scala index ef5c55f91c39a..ee1071cbcde69 100644 --- a/core/src/test/scala/org/apache/spark/storage/StorageSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/StorageSuite.scala @@ -240,11 +240,11 @@ class StorageSuite extends FunSuite { assert(status.rddBlocksById(1000).size === status.numRddBlocksById(1000)) } - test("storage status memUsed, diskUsed, tachyonUsed") { + test("storage status memUsed, diskUsed, externalBlockStoreUsed") { val status = storageStatus2 def actualMemUsed: Long = status.blocks.values.map(_.memSize).sum def actualDiskUsed: Long = status.blocks.values.map(_.diskSize).sum - def actualOffHeapUsed: Long = status.blocks.values.map(_.tachyonSize).sum + def actualOffHeapUsed: Long = status.blocks.values.map(_.externalBlockStoreSize).sum assert(status.memUsed === actualMemUsed) assert(status.diskUsed === actualDiskUsed) assert(status.offHeapUsed === actualOffHeapUsed) @@ -300,12 +300,12 @@ class StorageSuite extends FunSuite { assert(rddInfos(0).numCachedPartitions === 5) assert(rddInfos(0).memSize === 5L) assert(rddInfos(0).diskSize === 10L) - assert(rddInfos(0).tachyonSize === 0L) + assert(rddInfos(0).externalBlockStoreSize === 0L) assert(rddInfos(1).storageLevel === memAndDisk) assert(rddInfos(1).numCachedPartitions === 3) assert(rddInfos(1).memSize === 3L) assert(rddInfos(1).diskSize === 6L) - assert(rddInfos(1).tachyonSize === 0L) + assert(rddInfos(1).externalBlockStoreSize === 0L) } test("StorageUtils.getRddBlockLocations") { diff --git a/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala b/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala index 3744e479d2f05..0ea9ea47b4288 100644 --- a/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala +++ b/core/src/test/scala/org/apache/spark/ui/storage/StorageTabSuite.scala @@ -133,12 +133,12 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter { bus.postToAll(SparkListenerTaskEnd(1, 0, "obliteration", Success, taskInfo, metrics1)) assert(storageListener._rddInfoMap(0).memSize === 800L) assert(storageListener._rddInfoMap(0).diskSize === 400L) - assert(storageListener._rddInfoMap(0).tachyonSize === 200L) + assert(storageListener._rddInfoMap(0).externalBlockStoreSize === 200L) assert(storageListener._rddInfoMap(0).numCachedPartitions === 3) assert(storageListener._rddInfoMap(0).isCached) assert(storageListener._rddInfoMap(1).memSize === 0L) assert(storageListener._rddInfoMap(1).diskSize === 240L) - assert(storageListener._rddInfoMap(1).tachyonSize === 0L) + assert(storageListener._rddInfoMap(1).externalBlockStoreSize === 0L) assert(storageListener._rddInfoMap(1).numCachedPartitions === 1) assert(storageListener._rddInfoMap(1).isCached) assert(!storageListener._rddInfoMap(2).isCached) @@ -155,7 +155,7 @@ class StorageTabSuite extends FunSuite with BeforeAndAfter { bus.postToAll(SparkListenerTaskEnd(2, 0, "obliteration", Success, taskInfo, metrics2)) assert(storageListener._rddInfoMap(0).memSize === 400L) assert(storageListener._rddInfoMap(0).diskSize === 400L) - assert(storageListener._rddInfoMap(0).tachyonSize === 200L) + assert(storageListener._rddInfoMap(0).externalBlockStoreSize === 200L) assert(storageListener._rddInfoMap(0).numCachedPartitions === 2) assert(storageListener._rddInfoMap(0).isCached) assert(!storageListener._rddInfoMap(1).isCached) diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala index a2be724254d7c..2d039cb75a6e0 100644 --- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala @@ -785,14 +785,14 @@ class JsonProtocolSuite extends FunSuite { | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 201, | "Number of Cached Partitions": 301, | "Memory Size": 401, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 501 | } | ], @@ -969,12 +969,12 @@ class JsonProtocolSuite extends FunSuite { | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": false, | "Replication": 2 | }, | "Memory Size": 0, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 0 | } | } @@ -1052,12 +1052,12 @@ class JsonProtocolSuite extends FunSuite { | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": false, | "Replication": 2 | }, | "Memory Size": 0, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 0 | } | } @@ -1135,12 +1135,12 @@ class JsonProtocolSuite extends FunSuite { | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": false, | "Replication": 2 | }, | "Memory Size": 0, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 0 | } | } @@ -1168,14 +1168,14 @@ class JsonProtocolSuite extends FunSuite { | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 200, | "Number of Cached Partitions": 300, | "Memory Size": 400, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 500 | } | ], @@ -1207,14 +1207,14 @@ class JsonProtocolSuite extends FunSuite { | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 400, | "Number of Cached Partitions": 600, | "Memory Size": 800, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 1000 | }, | { @@ -1223,14 +1223,14 @@ class JsonProtocolSuite extends FunSuite { | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 401, | "Number of Cached Partitions": 601, | "Memory Size": 801, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 1001 | } | ], @@ -1262,14 +1262,14 @@ class JsonProtocolSuite extends FunSuite { | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 600, | "Number of Cached Partitions": 900, | "Memory Size": 1200, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 1500 | }, | { @@ -1278,14 +1278,14 @@ class JsonProtocolSuite extends FunSuite { | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 601, | "Number of Cached Partitions": 901, | "Memory Size": 1201, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 1501 | }, | { @@ -1294,14 +1294,14 @@ class JsonProtocolSuite extends FunSuite { | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 602, | "Number of Cached Partitions": 902, | "Memory Size": 1202, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 1502 | } | ], @@ -1333,14 +1333,14 @@ class JsonProtocolSuite extends FunSuite { | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 800, | "Number of Cached Partitions": 1200, | "Memory Size": 1600, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 2000 | }, | { @@ -1349,14 +1349,14 @@ class JsonProtocolSuite extends FunSuite { | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 801, | "Number of Cached Partitions": 1201, | "Memory Size": 1601, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 2001 | }, | { @@ -1365,14 +1365,14 @@ class JsonProtocolSuite extends FunSuite { | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 802, | "Number of Cached Partitions": 1202, | "Memory Size": 1602, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 2002 | }, | { @@ -1381,14 +1381,14 @@ class JsonProtocolSuite extends FunSuite { | "Storage Level": { | "Use Disk": true, | "Use Memory": true, - | "Use Tachyon": false, + | "Use ExternalBlockStore": false, | "Deserialized": true, | "Replication": 1 | }, | "Number of Partitions": 803, | "Number of Cached Partitions": 1203, | "Memory Size": 1603, - | "Tachyon Size": 0, + | "ExternalBlockStore Size": 0, | "Disk Size": 2003 | } | ], diff --git a/docs/configuration.md b/docs/configuration.md index 72105feba4919..7239b252a9fcc 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -1,4 +1,4 @@ ---- +-- layout: global displayTitle: Spark Configuration title: Configuration @@ -843,19 +843,27 @@ Apart from these, the following properties are also available, and may be useful - spark.tachyonStore.baseDir + spark.externalBlockStore.blockManager + org.apache.spark.storage.TachyonBlockManager + + Implementation of external block manager (file system) that store RDDs. The file system's URL is set by + spark.externalBlockStore.url. + + + + spark.externalBlockStore.baseDir System.getProperty("java.io.tmpdir") - Directories of the Tachyon File System that store RDDs. The Tachyon file system's URL is set by - spark.tachyonStore.url. It can also be a comma-separated list of multiple + Directories of the external block store that store RDDs. The file system's URL is set by + spark.externalBlockStore.url It can also be a comma-separated list of multiple directories on Tachyon file system. - spark.tachyonStore.url - tachyon://localhost:19998 + spark.externalBlockStore.url + tachyon://localhost:19998 for Tachyon - The URL of the underlying Tachyon file system in the TachyonStore. + The URL of the underlying external blocker file system in the external block store. From a9fc50552ec96cd7817dfd19fc681b3368545ee3 Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Thu, 30 Apr 2015 22:39:58 -0700 Subject: [PATCH 510/591] HOTFIX: Disable buggy dependency checker --- dev/run-tests-jenkins | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dev/run-tests-jenkins b/dev/run-tests-jenkins index 030f2cdddb350..f452ab66efcd8 100755 --- a/dev/run-tests-jenkins +++ b/dev/run-tests-jenkins @@ -62,7 +62,7 @@ TESTS_TIMEOUT="150m" # format: http://linux.die.net/man/1/timeout PR_TESTS=( "pr_merge_ability" "pr_public_classes" - "pr_new_dependencies" +# DISABLED (pwendell) "pr_new_dependencies" ) function post_message () { From 0a2b15ce43cf6096e1a7ae060b7c8a4010ce3b92 Mon Sep 17 00:00:00 2001 From: Sandy Ryza Date: Thu, 30 Apr 2015 23:14:14 -0700 Subject: [PATCH 511/591] [SPARK-4550] In sort-based shuffle, store map outputs in serialized form Refer to the JIRA for the design doc and some perf results. I wanted to call out some of the more possibly controversial changes up front: * Map outputs are only stored in serialized form when Kryo is in use. I'm still unsure whether Java-serialized objects can be relocated. At the very least, Java serialization writes out a stream header which causes problems with the current approach, so I decided to leave investigating this to future work. * The shuffle now explicitly operates on key-value pairs instead of any object. Data is written to shuffle files in alternating keys and values instead of key-value tuples. `BlockObjectWriter.write` now accepts a key argument and a value argument instead of any object. * The map output buffer can hold a max of Integer.MAX_VALUE bytes. Though this wouldn't be terribly difficult to change. * When spilling occurs, the objects that still in memory at merge time end up serialized and deserialized an extra time. Author: Sandy Ryza Closes #4450 from sryza/sandy-spark-4550 and squashes the following commits: 8c70dd9 [Sandy Ryza] Fix serialization 9c16fe6 [Sandy Ryza] Fix a couple tests and move getAutoReset to KryoSerializerInstance 6c54e06 [Sandy Ryza] Fix scalastyle d8462d8 [Sandy Ryza] SPARK-4550 --- .../spark/serializer/KryoSerializer.scala | 10 + .../apache/spark/serializer/Serializer.scala | 31 +++ .../shuffle/hash/HashShuffleWriter.scala | 2 +- .../spark/storage/BlockObjectWriter.scala | 37 ++- .../storage/ShuffleBlockFetcherIterator.scala | 6 +- .../spark/util/collection/ChainedBuffer.scala | 144 ++++++++++ .../collection/ExternalAppendOnlyMap.scala | 6 +- .../util/collection/ExternalSorter.scala | 144 +++++----- ...airCollection.scala => PairIterator.scala} | 16 +- .../collection/PartitionedAppendOnlyMap.scala | 44 +++ ...ffer.scala => PartitionedPairBuffer.scala} | 58 ++-- .../PartitionedSerializedPairBuffer.scala | 254 ++++++++++++++++++ .../SizeTrackingAppendOnlyMap.scala | 2 +- .../WritablePartitionedPairCollection.scala | 113 ++++++++ .../serializer/KryoSerializerSuite.scala | 15 ++ .../spark/serializer/TestSerializer.scala | 4 +- .../hash/HashShuffleManagerSuite.scala | 12 +- .../storage/BlockObjectWriterSuite.scala | 8 +- .../util/collection/ChainedBufferSuite.scala | 143 ++++++++++ .../util/collection/ExternalSorterSuite.scala | 189 +++++++++---- ...PartitionedSerializedPairBufferSuite.scala | 149 ++++++++++ .../sql/execution/SparkSqlSerializer2.scala | 38 ++- .../spark/tools/StoragePerfTester.scala | 5 +- 23 files changed, 1240 insertions(+), 190 deletions(-) create mode 100644 core/src/main/scala/org/apache/spark/util/collection/ChainedBuffer.scala rename core/src/main/scala/org/apache/spark/util/collection/{SizeTrackingPairCollection.scala => PairIterator.scala} (55%) create mode 100644 core/src/main/scala/org/apache/spark/util/collection/PartitionedAppendOnlyMap.scala rename core/src/main/scala/org/apache/spark/util/collection/{SizeTrackingPairBuffer.scala => PartitionedPairBuffer.scala} (66%) create mode 100644 core/src/main/scala/org/apache/spark/util/collection/PartitionedSerializedPairBuffer.scala create mode 100644 core/src/main/scala/org/apache/spark/util/collection/WritablePartitionedPairCollection.scala create mode 100644 core/src/test/scala/org/apache/spark/util/collection/ChainedBufferSuite.scala create mode 100644 core/src/test/scala/org/apache/spark/util/collection/PartitionedSerializedPairBufferSuite.scala diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala index 754832b8a4ca7..b7bc087855b9f 100644 --- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala +++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala @@ -200,6 +200,16 @@ private[spark] class KryoSerializerInstance(ks: KryoSerializer) extends Serializ override def deserializeStream(s: InputStream): DeserializationStream = { new KryoDeserializationStream(kryo, s) } + + /** + * Returns true if auto-reset is on. The only reason this would be false is if the user-supplied + * registrator explicitly turns auto-reset off. + */ + def getAutoReset(): Boolean = { + val field = classOf[Kryo].getDeclaredField("autoReset") + field.setAccessible(true) + field.get(kryo).asInstanceOf[Boolean] + } } /** diff --git a/core/src/main/scala/org/apache/spark/serializer/Serializer.scala b/core/src/main/scala/org/apache/spark/serializer/Serializer.scala index ca6e971d227fb..c381672a4f588 100644 --- a/core/src/main/scala/org/apache/spark/serializer/Serializer.scala +++ b/core/src/main/scala/org/apache/spark/serializer/Serializer.scala @@ -101,7 +101,12 @@ abstract class SerializerInstance { */ @DeveloperApi abstract class SerializationStream { + /** The most general-purpose method to write an object. */ def writeObject[T: ClassTag](t: T): SerializationStream + /** Writes the object representing the key of a key-value pair. */ + def writeKey[T: ClassTag](key: T): SerializationStream = writeObject(key) + /** Writes the object representing the value of a key-value pair. */ + def writeValue[T: ClassTag](value: T): SerializationStream = writeObject(value) def flush(): Unit def close(): Unit @@ -120,7 +125,12 @@ abstract class SerializationStream { */ @DeveloperApi abstract class DeserializationStream { + /** The most general-purpose method to read an object. */ def readObject[T: ClassTag](): T + /** Reads the object representing the key of a key-value pair. */ + def readKey[T: ClassTag](): T = readObject[T]() + /** Reads the object representing the value of a key-value pair. */ + def readValue[T: ClassTag](): T = readObject[T]() def close(): Unit /** @@ -141,4 +151,25 @@ abstract class DeserializationStream { DeserializationStream.this.close() } } + + /** + * Read the elements of this stream through an iterator over key-value pairs. This can only be + * called once, as reading each element will consume data from the input source. + */ + def asKeyValueIterator: Iterator[(Any, Any)] = new NextIterator[(Any, Any)] { + override protected def getNext() = { + try { + (readKey[Any](), readValue[Any]()) + } catch { + case eof: EOFException => { + finished = true + null + } + } + } + + override protected def close() { + DeserializationStream.this.close() + } + } } diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala index 755f17d6aa15a..cd27c9e07a3cd 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleWriter.scala @@ -63,7 +63,7 @@ private[spark] class HashShuffleWriter[K, V]( for (elem <- iter) { val bucketId = dep.partitioner.getPartition(elem._1) - shuffle.writers(bucketId).write(elem) + shuffle.writers(bucketId).write(elem._1, elem._2) } } diff --git a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala index 14833791f7a4d..499dd97c0656a 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala @@ -33,7 +33,7 @@ import org.apache.spark.util.Utils * This interface does not support concurrent writes. Also, once the writer has * been opened, it cannot be reopened again. */ -private[spark] abstract class BlockObjectWriter(val blockId: BlockId) { +private[spark] abstract class BlockObjectWriter(val blockId: BlockId) extends OutputStream { def open(): BlockObjectWriter @@ -54,9 +54,14 @@ private[spark] abstract class BlockObjectWriter(val blockId: BlockId) { def revertPartialWritesAndClose() /** - * Writes an object. + * Writes a key-value pair. */ - def write(value: Any) + def write(key: Any, value: Any) + + /** + * Notify the writer that a record worth of bytes has been written with writeBytes. + */ + def recordWritten() /** * Returns the file segment of committed data that this Writer has written. @@ -203,12 +208,32 @@ private[spark] class DiskBlockObjectWriter( } } - override def write(value: Any) { + override def write(key: Any, value: Any) { + if (!initialized) { + open() + } + + objOut.writeKey(key) + objOut.writeValue(value) + numRecordsWritten += 1 + writeMetrics.incShuffleRecordsWritten(1) + + if (numRecordsWritten % 32 == 0) { + updateBytesWritten() + } + } + + override def write(b: Int): Unit = throw new UnsupportedOperationException() + + override def write(kvBytes: Array[Byte], offs: Int, len: Int): Unit = { if (!initialized) { open() } - objOut.writeObject(value) + bs.write(kvBytes, offs, len) + } + + override def recordWritten(): Unit = { numRecordsWritten += 1 writeMetrics.incShuffleRecordsWritten(1) @@ -238,7 +263,7 @@ private[spark] class DiskBlockObjectWriter( } // For testing - private[spark] def flush() { + private[spark] override def flush() { objOut.flush() bs.flush() } diff --git a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala index f3379521d55e2..d0faab62c9e9e 100644 --- a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala +++ b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala @@ -17,14 +17,12 @@ package org.apache.spark.storage -import java.io.{InputStream, IOException} import java.util.concurrent.LinkedBlockingQueue import scala.collection.mutable.{ArrayBuffer, HashSet, Queue} -import scala.util.{Failure, Success, Try} +import scala.util.{Failure, Try} import org.apache.spark.{Logging, TaskContext} -import org.apache.spark.network.BlockTransferService import org.apache.spark.network.shuffle.{BlockFetchingListener, ShuffleClient} import org.apache.spark.network.buffer.ManagedBuffer import org.apache.spark.serializer.{SerializerInstance, Serializer} @@ -301,7 +299,7 @@ final class ShuffleBlockFetcherIterator( // the scheduler gets a FetchFailedException. Try(buf.createInputStream()).map { is0 => val is = blockManager.wrapForCompression(blockId, is0) - val iter = serializerInstance.deserializeStream(is).asIterator + val iter = serializerInstance.deserializeStream(is).asKeyValueIterator CompletionIterator[Any, Iterator[Any]](iter, { // Once the iterator is exhausted, release the buffer and set currentResult to null // so we don't release it again in cleanup. diff --git a/core/src/main/scala/org/apache/spark/util/collection/ChainedBuffer.scala b/core/src/main/scala/org/apache/spark/util/collection/ChainedBuffer.scala new file mode 100644 index 0000000000000..a60bffe611f14 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/collection/ChainedBuffer.scala @@ -0,0 +1,144 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import java.io.OutputStream + +import scala.collection.mutable.ArrayBuffer + +/** + * A logical byte buffer that wraps a list of byte arrays. All the byte arrays have equal size. The + * advantage of this over a standard ArrayBuffer is that it can grow without claiming large amounts + * of memory and needing to copy the full contents. The disadvantage is that the contents don't + * occupy a contiguous segment of memory. + */ +private[spark] class ChainedBuffer(chunkSize: Int) { + private val chunkSizeLog2 = (math.log(chunkSize) / math.log(2)).toInt + assert(math.pow(2, chunkSizeLog2).toInt == chunkSize, + s"ChainedBuffer chunk size $chunkSize must be a power of two") + private val chunks: ArrayBuffer[Array[Byte]] = new ArrayBuffer[Array[Byte]]() + private var _size: Int = _ + + /** + * Feed bytes from this buffer into a BlockObjectWriter. + * + * @param pos Offset in the buffer to read from. + * @param os OutputStream to read into. + * @param len Number of bytes to read. + */ + def read(pos: Int, os: OutputStream, len: Int): Unit = { + if (pos + len > _size) { + throw new IndexOutOfBoundsException( + s"Read of $len bytes at position $pos would go past size ${_size} of buffer") + } + var chunkIndex = pos >> chunkSizeLog2 + var posInChunk = pos - (chunkIndex << chunkSizeLog2) + var written = 0 + while (written < len) { + val toRead = math.min(len - written, chunkSize - posInChunk) + os.write(chunks(chunkIndex), posInChunk, toRead) + written += toRead + chunkIndex += 1 + posInChunk = 0 + } + } + + /** + * Read bytes from this buffer into a byte array. + * + * @param pos Offset in the buffer to read from. + * @param bytes Byte array to read into. + * @param offs Offset in the byte array to read to. + * @param len Number of bytes to read. + */ + def read(pos: Int, bytes: Array[Byte], offs: Int, len: Int): Unit = { + if (pos + len > _size) { + throw new IndexOutOfBoundsException( + s"Read of $len bytes at position $pos would go past size of buffer") + } + var chunkIndex = pos >> chunkSizeLog2 + var posInChunk = pos - (chunkIndex << chunkSizeLog2) + var written = 0 + while (written < len) { + val toRead = math.min(len - written, chunkSize - posInChunk) + System.arraycopy(chunks(chunkIndex), posInChunk, bytes, offs + written, toRead) + written += toRead + chunkIndex += 1 + posInChunk = 0 + } + } + + /** + * Write bytes from a byte array into this buffer. + * + * @param pos Offset in the buffer to write to. + * @param bytes Byte array to write from. + * @param offs Offset in the byte array to write from. + * @param len Number of bytes to write. + */ + def write(pos: Int, bytes: Array[Byte], offs: Int, len: Int): Unit = { + if (pos > _size) { + throw new IndexOutOfBoundsException( + s"Write at position $pos starts after end of buffer ${_size}") + } + // Grow if needed + val endChunkIndex = (pos + len - 1) >> chunkSizeLog2 + while (endChunkIndex >= chunks.length) { + chunks += new Array[Byte](chunkSize) + } + + var chunkIndex = pos >> chunkSizeLog2 + var posInChunk = pos - (chunkIndex << chunkSizeLog2) + var written = 0 + while (written < len) { + val toWrite = math.min(len - written, chunkSize - posInChunk) + System.arraycopy(bytes, offs + written, chunks(chunkIndex), posInChunk, toWrite) + written += toWrite + chunkIndex += 1 + posInChunk = 0 + } + + _size = math.max(_size, pos + len) + } + + /** + * Total size of buffer that can be written to without allocating additional memory. + */ + def capacity: Int = chunks.size * chunkSize + + /** + * Size of the logical buffer. + */ + def size: Int = _size +} + +/** + * Output stream that writes to a ChainedBuffer. + */ +private[spark] class ChainedBufferOutputStream(chainedBuffer: ChainedBuffer) extends OutputStream { + private var pos = 0 + + override def write(b: Int): Unit = { + throw new UnsupportedOperationException() + } + + override def write(bytes: Array[Byte], offs: Int, len: Int): Unit = { + chainedBuffer.write(pos, bytes, offs, len) + pos += len + } +} diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala index f912049563906..b850973145077 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala @@ -174,7 +174,7 @@ class ExternalAppendOnlyMap[K, V, C]( val it = currentMap.destructiveSortedIterator(keyComparator) while (it.hasNext) { val kv = it.next() - writer.write(kv) + writer.write(kv._1, kv._2) objectsWritten += 1 if (objectsWritten == serializerBatchSize) { @@ -435,7 +435,9 @@ class ExternalAppendOnlyMap[K, V, C]( */ private def readNextItem(): (K, C) = { try { - val item = deserializeStream.readObject().asInstanceOf[(K, C)] + val k = deserializeStream.readKey().asInstanceOf[K] + val c = deserializeStream.readValue().asInstanceOf[C] + val item = (k, c) objectsRead += 1 if (objectsRead == serializerBatchSize) { objectsRead = 0 diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala index 4ed8a740f99db..b7306cd551918 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala @@ -26,7 +26,7 @@ import scala.collection.mutable import com.google.common.io.ByteStreams import org.apache.spark._ -import org.apache.spark.serializer.{DeserializationStream, Serializer} +import org.apache.spark.serializer._ import org.apache.spark.executor.ShuffleWriteMetrics import org.apache.spark.storage.{BlockObjectWriter, BlockId} @@ -66,10 +66,11 @@ import org.apache.spark.storage.{BlockObjectWriter, BlockId} * * At a high level, this class works internally as follows: * - * - We repeatedly fill up buffers of in-memory data, using either a SizeTrackingAppendOnlyMap if - * we want to combine by key, or an simple SizeTrackingBuffer if we don't. Inside these buffers, - * we sort elements of type ((Int, K), C) where the Int is the partition ID. This is done to - * avoid calling the partitioner multiple times on the same key (e.g. for RangePartitioner). + * - We repeatedly fill up buffers of in-memory data, using either a PartitionedAppendOnlyMap if + * we want to combine by key, or a PartitionedSerializedPairBuffer or PartitionedPairBuffer if we + * don't. Inside these buffers, we sort elements by partition ID and then possibly also by key. + * To avoid calling the partitioner multiple times with each key, we store the partition ID + * alongside each record. * * - When each buffer reaches our memory limit, we spill it to a file. This file is sorted first * by partition ID and possibly second by key or by hash code of the key, if we want to do @@ -96,7 +97,7 @@ private[spark] class ExternalSorter[K, V, C]( partitioner: Option[Partitioner] = None, ordering: Option[Ordering[K]] = None, serializer: Option[Serializer] = None) - extends Logging with Spillable[SizeTrackingPairCollection[(Int, K), C]] { + extends Logging with Spillable[WritablePartitionedPairCollection[K, C]] { private val numPartitions = partitioner.map(_.numPartitions).getOrElse(1) private val shouldPartition = numPartitions > 1 @@ -126,11 +127,22 @@ private[spark] class ExternalSorter[K, V, C]( if (shouldPartition) partitioner.get.getPartition(key) else 0 } + private val metaInitialRecords = 256 + private val kvChunkSize = conf.getInt("spark.shuffle.sort.kvChunkSize", 1 << 22) // 4 MB + private val useSerializedPairBuffer = + !ordering.isDefined && conf.getBoolean("spark.shuffle.sort.serializeMapOutputs", true) && + ser.isInstanceOf[KryoSerializer] && + serInstance.asInstanceOf[KryoSerializerInstance].getAutoReset + // Data structures to store in-memory objects before we spill. Depending on whether we have an // Aggregator set, we either put objects into an AppendOnlyMap where we combine them, or we // store them in an array buffer. - private var map = new SizeTrackingAppendOnlyMap[(Int, K), C] - private var buffer = new SizeTrackingPairBuffer[(Int, K), C] + private var map = new PartitionedAppendOnlyMap[K, C] + private var buffer = if (useSerializedPairBuffer) { + new PartitionedSerializedPairBuffer[K, C](metaInitialRecords, kvChunkSize, serInstance) + } else { + new PartitionedPairBuffer[K, C] + } // Total spilling statistics private var _diskBytesSpilled = 0L @@ -163,33 +175,6 @@ private[spark] class ExternalSorter[K, V, C]( } }) - // A comparator for (Int, K) pairs that orders them by only their partition ID - private val partitionComparator: Comparator[(Int, K)] = new Comparator[(Int, K)] { - override def compare(a: (Int, K), b: (Int, K)): Int = { - a._1 - b._1 - } - } - - // A comparator that orders (Int, K) pairs by partition ID and then possibly by key - private val partitionKeyComparator: Comparator[(Int, K)] = { - if (ordering.isDefined || aggregator.isDefined) { - // Sort by partition ID then key comparator - new Comparator[(Int, K)] { - override def compare(a: (Int, K), b: (Int, K)): Int = { - val partitionDiff = a._1 - b._1 - if (partitionDiff != 0) { - partitionDiff - } else { - keyComparator.compare(a._2, b._2) - } - } - } - } else { - // Just sort it by partition ID - partitionComparator - } - } - // Information about a spilled file. Includes sizes in bytes of "batches" written by the // serializer as we periodically reset its stream, as well as number of elements in each // partition, used to efficiently keep track of partitions when merging. @@ -221,16 +206,18 @@ private[spark] class ExternalSorter[K, V, C]( } else if (bypassMergeSort) { // SPARK-4479: Also bypass buffering if merge sort is bypassed to avoid defensive copies if (records.hasNext) { - spillToPartitionFiles(records.map { kv => - ((getPartition(kv._1), kv._1), kv._2.asInstanceOf[C]) - }) + spillToPartitionFiles( + WritablePartitionedIterator.fromIterator(records.map { kv => + ((getPartition(kv._1), kv._1), kv._2.asInstanceOf[C]) + }) + ) } } else { // Stick values into our buffer while (records.hasNext) { addElementsRead() val kv = records.next() - buffer.insert((getPartition(kv._1), kv._1), kv._2.asInstanceOf[C]) + buffer.insert(getPartition(kv._1), kv._1, kv._2.asInstanceOf[C]) maybeSpillCollection(usingMap = false) } } @@ -248,11 +235,15 @@ private[spark] class ExternalSorter[K, V, C]( if (usingMap) { if (maybeSpill(map, map.estimateSize())) { - map = new SizeTrackingAppendOnlyMap[(Int, K), C] + map = new PartitionedAppendOnlyMap[K, C] } } else { if (maybeSpill(buffer, buffer.estimateSize())) { - buffer = new SizeTrackingPairBuffer[(Int, K), C] + buffer = if (useSerializedPairBuffer) { + new PartitionedSerializedPairBuffer[K, C](metaInitialRecords, kvChunkSize, serInstance) + } else { + new PartitionedPairBuffer[K, C] + } } } } @@ -260,7 +251,7 @@ private[spark] class ExternalSorter[K, V, C]( /** * Spill the current in-memory collection to disk, adding a new file to spills, and clear it. */ - override protected[this] def spill(collection: SizeTrackingPairCollection[(Int, K), C]): Unit = { + override protected[this] def spill(collection: WritablePartitionedPairCollection[K, C]): Unit = { if (bypassMergeSort) { spillToPartitionFiles(collection) } else { @@ -277,7 +268,7 @@ private[spark] class ExternalSorter[K, V, C]( * * @param collection whichever collection we're using (map or buffer) */ - private def spillToMergeableFile(collection: SizeTrackingPairCollection[(Int, K), C]): Unit = { + private def spillToMergeableFile(collection: WritablePartitionedPairCollection[K, C]): Unit = { assert(!bypassMergeSort) // Because these files may be read during shuffle, their compression must be controlled by @@ -308,14 +299,10 @@ private[spark] class ExternalSorter[K, V, C]( var success = false try { - val it = collection.destructiveSortedIterator(partitionKeyComparator) + val it = collection.destructiveSortedWritablePartitionedIterator(comparator) while (it.hasNext) { - val elem = it.next() - val partitionId = elem._1._1 - val key = elem._1._2 - val value = elem._2 - writer.write(key) - writer.write(value) + val partitionId = it.nextPartition() + it.writeNext(writer) elementsPerPartition(partitionId) += 1 objectsWritten += 1 @@ -357,11 +344,11 @@ private[spark] class ExternalSorter[K, V, C]( * * @param collection whichever collection we're using (map or buffer) */ - private def spillToPartitionFiles(collection: SizeTrackingPairCollection[(Int, K), C]): Unit = { - spillToPartitionFiles(collection.iterator) + private def spillToPartitionFiles(collection: WritablePartitionedPairCollection[K, C]): Unit = { + spillToPartitionFiles(collection.writablePartitionedIterator()) } - private def spillToPartitionFiles(iterator: Iterator[((Int, K), C)]): Unit = { + private def spillToPartitionFiles(iterator: WritablePartitionedIterator): Unit = { assert(bypassMergeSort) // Create our file writers if we haven't done so yet @@ -385,11 +372,8 @@ private[spark] class ExternalSorter[K, V, C]( // No need to sort stuff, just write each element out while (iterator.hasNext) { - val elem = iterator.next() - val partitionId = elem._1._1 - val key = elem._1._2 - val value = elem._2 - partitionWriters(partitionId).write((key, value)) + val partitionId = iterator.nextPartition() + iterator.writeNext(partitionWriters(partitionId)) } } @@ -618,8 +602,8 @@ private[spark] class ExternalSorter[K, V, C]( if (finished || deserializeStream == null) { return null } - val k = deserializeStream.readObject().asInstanceOf[K] - val c = deserializeStream.readObject().asInstanceOf[C] + val k = deserializeStream.readKey().asInstanceOf[K] + val c = deserializeStream.readValue().asInstanceOf[C] lastPartitionId = partitionId // Start reading the next batch if we're done with this one indexInBatch += 1 @@ -695,27 +679,27 @@ private[spark] class ExternalSorter[K, V, C]( */ def partitionedIterator: Iterator[(Int, Iterator[Product2[K, C]])] = { val usingMap = aggregator.isDefined - val collection: SizeTrackingPairCollection[(Int, K), C] = if (usingMap) map else buffer + val collection: WritablePartitionedPairCollection[K, C] = if (usingMap) map else buffer if (spills.isEmpty && partitionWriters == null) { // Special case: if we have only in-memory data, we don't need to merge streams, and perhaps // we don't even need to sort by anything other than partition ID if (!ordering.isDefined) { // The user hasn't requested sorted keys, so only sort by partition ID, not key - groupByPartition(collection.destructiveSortedIterator(partitionComparator)) + groupByPartition(collection.partitionedDestructiveSortedIterator(None)) } else { // We do need to sort by both partition ID and key - groupByPartition(collection.destructiveSortedIterator(partitionKeyComparator)) + groupByPartition(collection.partitionedDestructiveSortedIterator(Some(keyComparator))) } } else if (bypassMergeSort) { // Read data from each partition file and merge it together with the data in memory; // note that there's no ordering or aggregator in this case -- we just partition objects - val collIter = groupByPartition(collection.destructiveSortedIterator(partitionComparator)) + val collIter = groupByPartition(collection.partitionedDestructiveSortedIterator(None)) collIter.map { case (partitionId, values) => (partitionId, values ++ readPartitionFile(partitionWriters(partitionId))) } } else { // Merge spilled and in-memory data - merge(spills, collection.destructiveSortedIterator(partitionKeyComparator)) + merge(spills, collection.partitionedDestructiveSortedIterator(comparator)) } } @@ -762,15 +746,29 @@ private[spark] class ExternalSorter[K, V, C]( context.taskMetrics.shuffleWriteMetrics.foreach( _.incShuffleWriteTime(System.nanoTime - writeStartTime)) } + } else if (spills.isEmpty && partitionWriters == null) { + // Case where we only have in-memory data + val collection = if (aggregator.isDefined) map else buffer + val it = collection.destructiveSortedWritablePartitionedIterator(comparator) + while (it.hasNext) { + val writer = blockManager.getDiskWriter(blockId, outputFile, serInstance, fileBufferSize, + context.taskMetrics.shuffleWriteMetrics.get) + val partitionId = it.nextPartition() + while (it.hasNext && it.nextPartition() == partitionId) { + it.writeNext(writer) + } + writer.commitAndClose() + val segment = writer.fileSegment() + lengths(partitionId) = segment.length + } } else { - // Either we're not bypassing merge-sort or we have only in-memory data; get an iterator by - // partition and just write everything directly. + // Not bypassing merge-sort; get an iterator by partition and just write everything directly. for ((id, elements) <- this.partitionedIterator) { if (elements.hasNext) { val writer = blockManager.getDiskWriter(blockId, outputFile, serInstance, fileBufferSize, context.taskMetrics.shuffleWriteMetrics.get) for (elem <- elements) { - writer.write(elem) + writer.write(elem._1, elem._2) } writer.commitAndClose() val segment = writer.fileSegment() @@ -799,7 +797,7 @@ private[spark] class ExternalSorter[K, V, C]( if (writer.isOpen) { writer.commitAndClose() } - blockManager.diskStore.getValues(writer.blockId, ser).get.asInstanceOf[Iterator[Product2[K, C]]] + new PairIterator[K, C](blockManager.diskStore.getValues(writer.blockId, ser).get) } def stop(): Unit = { @@ -829,6 +827,14 @@ private[spark] class ExternalSorter[K, V, C]( (0 until numPartitions).iterator.map(p => (p, new IteratorForPartition(p, buffered))) } + private def comparator: Option[Comparator[K]] = { + if (ordering.isDefined || aggregator.isDefined) { + Some(keyComparator) + } else { + None + } + } + /** * An iterator that reads only the elements for a given partition ID from an underlying buffered * stream, assuming this partition is the next one to be read. Used to make it easier to return diff --git a/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingPairCollection.scala b/core/src/main/scala/org/apache/spark/util/collection/PairIterator.scala similarity index 55% rename from core/src/main/scala/org/apache/spark/util/collection/SizeTrackingPairCollection.scala rename to core/src/main/scala/org/apache/spark/util/collection/PairIterator.scala index faa4e2b12ddb6..d75959f480756 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingPairCollection.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/PairIterator.scala @@ -17,18 +17,8 @@ package org.apache.spark.util.collection -import java.util.Comparator +private[spark] class PairIterator[K, V](iter: Iterator[Any]) extends Iterator[(K, V)] { + def hasNext: Boolean = iter.hasNext -/** - * A common interface for our size-tracking collections of key-value pairs, which are used in - * external operations. These all support estimating the size and obtaining a memory-efficient - * sorted iterator. - */ -// TODO: should extend Iterable[Product2[K, V]] instead of (K, V) -private[spark] trait SizeTrackingPairCollection[K, V] extends Iterable[(K, V)] { - /** Estimate the collection's current memory usage in bytes. */ - def estimateSize(): Long - - /** Iterate through the data in a given key order. This may destroy the underlying collection. */ - def destructiveSortedIterator(keyComparator: Comparator[K]): Iterator[(K, V)] + def next(): (K, V) = (iter.next().asInstanceOf[K], iter.next().asInstanceOf[V]) } diff --git a/core/src/main/scala/org/apache/spark/util/collection/PartitionedAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/PartitionedAppendOnlyMap.scala new file mode 100644 index 0000000000000..e2e2f1faae9d1 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/collection/PartitionedAppendOnlyMap.scala @@ -0,0 +1,44 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import java.util.Comparator + +import org.apache.spark.util.collection.WritablePartitionedPairCollection._ + +/** + * Implementation of WritablePartitionedPairCollection that wraps a map in which the keys are tuples + * of (partition ID, K) + */ +private[spark] class PartitionedAppendOnlyMap[K, V] + extends SizeTrackingAppendOnlyMap[(Int, K), V] with WritablePartitionedPairCollection[K, V] { + + def partitionedDestructiveSortedIterator(keyComparator: Option[Comparator[K]]) + : Iterator[((Int, K), V)] = { + val comparator = keyComparator.map(partitionKeyComparator).getOrElse(partitionComparator) + destructiveSortedIterator(comparator) + } + + def writablePartitionedIterator(): WritablePartitionedIterator = { + WritablePartitionedIterator.fromIterator(super.iterator) + } + + def insert(partition: Int, key: K, value: V): Unit = { + update((partition, key), value) + } +} diff --git a/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingPairBuffer.scala b/core/src/main/scala/org/apache/spark/util/collection/PartitionedPairBuffer.scala similarity index 66% rename from core/src/main/scala/org/apache/spark/util/collection/SizeTrackingPairBuffer.scala rename to core/src/main/scala/org/apache/spark/util/collection/PartitionedPairBuffer.scala index 9e9c16c5a2962..e8332e1a87eac 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingPairBuffer.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/PartitionedPairBuffer.scala @@ -19,11 +19,15 @@ package org.apache.spark.util.collection import java.util.Comparator +import org.apache.spark.storage.BlockObjectWriter +import org.apache.spark.util.collection.WritablePartitionedPairCollection._ + /** - * Append-only buffer of key-value pairs that keeps track of its estimated size in bytes. + * Append-only buffer of key-value pairs, each with a corresponding partition ID, that keeps track + * of its estimated size in bytes. */ -private[spark] class SizeTrackingPairBuffer[K, V](initialCapacity: Int = 64) - extends SizeTracker with SizeTrackingPairCollection[K, V] +private[spark] class PartitionedPairBuffer[K, V](initialCapacity: Int = 64) + extends WritablePartitionedPairCollection[K, V] with SizeTracker { require(initialCapacity <= (1 << 29), "Can't make capacity bigger than 2^29 elements") require(initialCapacity >= 1, "Invalid initial capacity") @@ -35,35 +39,16 @@ private[spark] class SizeTrackingPairBuffer[K, V](initialCapacity: Int = 64) private var data = new Array[AnyRef](2 * initialCapacity) /** Add an element into the buffer */ - def insert(key: K, value: V): Unit = { + def insert(partition: Int, key: K, value: V): Unit = { if (curSize == capacity) { growArray() } - data(2 * curSize) = key.asInstanceOf[AnyRef] + data(2 * curSize) = (partition, key.asInstanceOf[AnyRef]) data(2 * curSize + 1) = value.asInstanceOf[AnyRef] curSize += 1 afterUpdate() } - /** Total number of elements in buffer */ - override def size: Int = curSize - - /** Iterate over the elements of the buffer */ - override def iterator: Iterator[(K, V)] = new Iterator[(K, V)] { - var pos = 0 - - override def hasNext: Boolean = pos < curSize - - override def next(): (K, V) = { - if (!hasNext) { - throw new NoSuchElementException - } - val pair = (data(2 * pos).asInstanceOf[K], data(2 * pos + 1).asInstanceOf[V]) - pos += 1 - pair - } - } - /** Double the size of the array because we've reached capacity */ private def growArray(): Unit = { if (capacity == (1 << 29)) { @@ -79,8 +64,29 @@ private[spark] class SizeTrackingPairBuffer[K, V](initialCapacity: Int = 64) } /** Iterate through the data in a given order. For this class this is not really destructive. */ - override def destructiveSortedIterator(keyComparator: Comparator[K]): Iterator[(K, V)] = { - new Sorter(new KVArraySortDataFormat[K, AnyRef]).sort(data, 0, curSize, keyComparator) + override def partitionedDestructiveSortedIterator(keyComparator: Option[Comparator[K]]) + : Iterator[((Int, K), V)] = { + val comparator = keyComparator.map(partitionKeyComparator).getOrElse(partitionComparator) + new Sorter(new KVArraySortDataFormat[(Int, K), AnyRef]).sort(data, 0, curSize, comparator) iterator } + + override def writablePartitionedIterator(): WritablePartitionedIterator = { + WritablePartitionedIterator.fromIterator(iterator) + } + + private def iterator(): Iterator[((Int, K), V)] = new Iterator[((Int, K), V)] { + var pos = 0 + + override def hasNext: Boolean = pos < curSize + + override def next(): ((Int, K), V) = { + if (!hasNext) { + throw new NoSuchElementException + } + val pair = (data(2 * pos).asInstanceOf[(Int, K)], data(2 * pos + 1).asInstanceOf[V]) + pos += 1 + pair + } + } } diff --git a/core/src/main/scala/org/apache/spark/util/collection/PartitionedSerializedPairBuffer.scala b/core/src/main/scala/org/apache/spark/util/collection/PartitionedSerializedPairBuffer.scala new file mode 100644 index 0000000000000..b5ca0c62a04f2 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/collection/PartitionedSerializedPairBuffer.scala @@ -0,0 +1,254 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import java.io.InputStream +import java.nio.IntBuffer +import java.util.Comparator + +import org.apache.spark.SparkEnv +import org.apache.spark.serializer.{JavaSerializerInstance, SerializerInstance} +import org.apache.spark.storage.BlockObjectWriter +import org.apache.spark.util.collection.PartitionedSerializedPairBuffer._ + +/** + * Append-only buffer of key-value pairs, each with a corresponding partition ID, that serializes + * its records upon insert and stores them as raw bytes. + * + * We use two data-structures to store the contents. The serialized records are stored in a + * ChainedBuffer that can expand gracefully as records are added. This buffer is accompanied by a + * metadata buffer that stores pointers into the data buffer as well as the partition ID of each + * record. Each entry in the metadata buffer takes up a fixed amount of space. + * + * Sorting the collection means swapping entries in the metadata buffer - the record buffer need not + * be modified at all. Storing the partition IDs in the metadata buffer means that comparisons can + * happen without following any pointers, which should minimize cache misses. + * + * Currently, only sorting by partition is supported. + * + * @param metaInitialRecords The initial number of entries in the metadata buffer. + * @param kvBlockSize The size of each byte buffer in the ChainedBuffer used to store the records. + * @param serializerInstance the serializer used for serializing inserted records. + */ +private[spark] class PartitionedSerializedPairBuffer[K, V]( + metaInitialRecords: Int, + kvBlockSize: Int, + serializerInstance: SerializerInstance) + extends WritablePartitionedPairCollection[K, V] with SizeTracker { + + if (serializerInstance.isInstanceOf[JavaSerializerInstance]) { + throw new IllegalArgumentException("PartitionedSerializedPairBuffer does not support" + + " Java-serialized objects.") + } + + private var metaBuffer = IntBuffer.allocate(metaInitialRecords * RECORD_SIZE) + + private val kvBuffer: ChainedBuffer = new ChainedBuffer(kvBlockSize) + private val kvOutputStream = new ChainedBufferOutputStream(kvBuffer) + private val kvSerializationStream = serializerInstance.serializeStream(kvOutputStream) + + def insert(partition: Int, key: K, value: V): Unit = { + if (metaBuffer.position == metaBuffer.capacity) { + growMetaBuffer() + } + + val keyStart = kvBuffer.size + if (keyStart < 0) { + throw new Exception(s"Can't grow buffer beyond ${1 << 31} bytes") + } + kvSerializationStream.writeObject[Any](key) + kvSerializationStream.flush() + val valueStart = kvBuffer.size + kvSerializationStream.writeObject[Any](value) + kvSerializationStream.flush() + val valueEnd = kvBuffer.size + + metaBuffer.put(keyStart) + metaBuffer.put(valueStart) + metaBuffer.put(valueEnd) + metaBuffer.put(partition) + } + + /** Double the size of the array because we've reached capacity */ + private def growMetaBuffer(): Unit = { + if (metaBuffer.capacity.toLong * 2 > Int.MaxValue) { + // Doubling the capacity would create an array bigger than Int.MaxValue, so don't + throw new Exception(s"Can't grow buffer beyond ${Int.MaxValue} bytes") + } + val newMetaBuffer = IntBuffer.allocate(metaBuffer.capacity * 2) + newMetaBuffer.put(metaBuffer.array) + metaBuffer = newMetaBuffer + } + + /** Iterate through the data in a given order. For this class this is not really destructive. */ + override def partitionedDestructiveSortedIterator(keyComparator: Option[Comparator[K]]) + : Iterator[((Int, K), V)] = { + sort(keyComparator) + val is = orderedInputStream + val deserStream = serializerInstance.deserializeStream(is) + new Iterator[((Int, K), V)] { + var metaBufferPos = 0 + def hasNext: Boolean = metaBufferPos < metaBuffer.position + def next(): ((Int, K), V) = { + val key = deserStream.readKey[Any]().asInstanceOf[K] + val value = deserStream.readValue[Any]().asInstanceOf[V] + val partition = metaBuffer.get(metaBufferPos + PARTITION) + metaBufferPos += RECORD_SIZE + ((partition, key), value) + } + } + } + + override def estimateSize: Long = metaBuffer.capacity * 4 + kvBuffer.capacity + + override def destructiveSortedWritablePartitionedIterator(keyComparator: Option[Comparator[K]]) + : WritablePartitionedIterator = { + sort(keyComparator) + writablePartitionedIterator + } + + override def writablePartitionedIterator(): WritablePartitionedIterator = { + new WritablePartitionedIterator { + // current position in the meta buffer in ints + var pos = 0 + + def writeNext(writer: BlockObjectWriter): Unit = { + val keyStart = metaBuffer.get(pos + KEY_START) + val valueEnd = metaBuffer.get(pos + VAL_END) + pos += RECORD_SIZE + kvBuffer.read(keyStart, writer, valueEnd - keyStart) + writer.recordWritten() + } + def nextPartition(): Int = metaBuffer.get(pos + PARTITION) + def hasNext(): Boolean = pos < metaBuffer.position + } + } + + // Visible for testing + def orderedInputStream: OrderedInputStream = { + new OrderedInputStream(metaBuffer, kvBuffer) + } + + private def sort(keyComparator: Option[Comparator[K]]): Unit = { + val comparator = if (keyComparator.isEmpty) { + new Comparator[Int]() { + def compare(partition1: Int, partition2: Int): Int = { + partition1 - partition2 + } + } + } else { + throw new UnsupportedOperationException() + } + + val sorter = new Sorter(new SerializedSortDataFormat) + sorter.sort(metaBuffer, 0, metaBuffer.position / RECORD_SIZE, comparator) + } +} + +private[spark] class OrderedInputStream(metaBuffer: IntBuffer, kvBuffer: ChainedBuffer) + extends InputStream { + + private var metaBufferPos = 0 + private var kvBufferPos = + if (metaBuffer.position > 0) metaBuffer.get(metaBufferPos + KEY_START) else 0 + + override def read(bytes: Array[Byte]): Int = read(bytes, 0, bytes.length) + + override def read(bytes: Array[Byte], offs: Int, len: Int): Int = { + if (metaBufferPos >= metaBuffer.position) { + return -1 + } + val bytesRemainingInRecord = metaBuffer.get(metaBufferPos + VAL_END) - kvBufferPos + val toRead = math.min(bytesRemainingInRecord, len) + kvBuffer.read(kvBufferPos, bytes, offs, toRead) + if (toRead == bytesRemainingInRecord) { + metaBufferPos += RECORD_SIZE + if (metaBufferPos < metaBuffer.position) { + kvBufferPos = metaBuffer.get(metaBufferPos + KEY_START) + } + } else { + kvBufferPos += toRead + } + toRead + } + + override def read(): Int = { + throw new UnsupportedOperationException() + } +} + +private[spark] class SerializedSortDataFormat extends SortDataFormat[Int, IntBuffer] { + + private val META_BUFFER_TMP = new Array[Int](RECORD_SIZE) + + /** Return the sort key for the element at the given index. */ + override protected def getKey(metaBuffer: IntBuffer, pos: Int): Int = { + metaBuffer.get(pos * RECORD_SIZE + PARTITION) + } + + /** Swap two elements. */ + override def swap(metaBuffer: IntBuffer, pos0: Int, pos1: Int): Unit = { + val iOff = pos0 * RECORD_SIZE + val jOff = pos1 * RECORD_SIZE + System.arraycopy(metaBuffer.array, iOff, META_BUFFER_TMP, 0, RECORD_SIZE) + System.arraycopy(metaBuffer.array, jOff, metaBuffer.array, iOff, RECORD_SIZE) + System.arraycopy(META_BUFFER_TMP, 0, metaBuffer.array, jOff, RECORD_SIZE) + } + + /** Copy a single element from src(srcPos) to dst(dstPos). */ + override def copyElement( + src: IntBuffer, + srcPos: Int, + dst: IntBuffer, + dstPos: Int): Unit = { + val srcOff = srcPos * RECORD_SIZE + val dstOff = dstPos * RECORD_SIZE + System.arraycopy(src.array, srcOff, dst.array, dstOff, RECORD_SIZE) + } + + /** + * Copy a range of elements starting at src(srcPos) to dst, starting at dstPos. + * Overlapping ranges are allowed. + */ + override def copyRange( + src: IntBuffer, + srcPos: Int, + dst: IntBuffer, + dstPos: Int, + length: Int): Unit = { + val srcOff = srcPos * RECORD_SIZE + val dstOff = dstPos * RECORD_SIZE + System.arraycopy(src.array, srcOff, dst.array, dstOff, RECORD_SIZE * length) + } + + /** + * Allocates a Buffer that can hold up to 'length' elements. + * All elements of the buffer should be considered invalid until data is explicitly copied in. + */ + override def allocate(length: Int): IntBuffer = { + IntBuffer.allocate(length * RECORD_SIZE) + } +} + +private[spark] object PartitionedSerializedPairBuffer { + val KEY_START = 0 + val VAL_START = 1 + val VAL_END = 2 + val PARTITION = 3 + val RECORD_SIZE = Seq(KEY_START, VAL_START, VAL_END, PARTITION).size // num ints of metadata +} diff --git a/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingAppendOnlyMap.scala index eb4de413867a0..722f78bd15986 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingAppendOnlyMap.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/SizeTrackingAppendOnlyMap.scala @@ -21,7 +21,7 @@ package org.apache.spark.util.collection * An append-only map that keeps track of its estimated size in bytes. */ private[spark] class SizeTrackingAppendOnlyMap[K, V] - extends AppendOnlyMap[K, V] with SizeTracker with SizeTrackingPairCollection[K, V] + extends AppendOnlyMap[K, V] with SizeTracker { override def update(key: K, value: V): Unit = { super.update(key, value) diff --git a/core/src/main/scala/org/apache/spark/util/collection/WritablePartitionedPairCollection.scala b/core/src/main/scala/org/apache/spark/util/collection/WritablePartitionedPairCollection.scala new file mode 100644 index 0000000000000..f26d1618c9200 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/collection/WritablePartitionedPairCollection.scala @@ -0,0 +1,113 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import java.util.Comparator + +import org.apache.spark.storage.BlockObjectWriter + +/** + * A common interface for size-tracking collections of key-value pairs that + * - Have an associated partition for each key-value pair. + * - Support a memory-efficient sorted iterator + * - Support a WritablePartitionedIterator for writing the contents directly as bytes. + */ +private[spark] trait WritablePartitionedPairCollection[K, V] { + /** + * Insert a key-value pair with a partition into the collection + */ + def insert(partition: Int, key: K, value: V): Unit + + /** + * Iterate through the data in order of partition ID and then the given comparator. This may + * destroy the underlying collection. + */ + def partitionedDestructiveSortedIterator(keyComparator: Option[Comparator[K]]) + : Iterator[((Int, K), V)] + + /** + * Iterate through the data and write out the elements instead of returning them. Records are + * returned in order of their partition ID and then the given comparator. + * This may destroy the underlying collection. + */ + def destructiveSortedWritablePartitionedIterator(keyComparator: Option[Comparator[K]]) + : WritablePartitionedIterator = { + WritablePartitionedIterator.fromIterator(partitionedDestructiveSortedIterator(keyComparator)) + } + + /** + * Iterate through the data and write out the elements instead of returning them. + */ + def writablePartitionedIterator(): WritablePartitionedIterator +} + +private[spark] object WritablePartitionedPairCollection { + /** + * A comparator for (Int, K) pairs that orders them by only their partition ID. + */ + def partitionComparator[K]: Comparator[(Int, K)] = new Comparator[(Int, K)] { + override def compare(a: (Int, K), b: (Int, K)): Int = { + a._1 - b._1 + } + } + + /** + * A comparator for (Int, K) pairs that orders them both by their partition ID and a key ordering. + */ + def partitionKeyComparator[K](keyComparator: Comparator[K]): Comparator[(Int, K)] = { + new Comparator[(Int, K)] { + override def compare(a: (Int, K), b: (Int, K)): Int = { + val partitionDiff = a._1 - b._1 + if (partitionDiff != 0) { + partitionDiff + } else { + keyComparator.compare(a._2, b._2) + } + } + } + } +} + +/** + * Iterator that writes elements to a BlockObjectWriter instead of returning them. Each element + * has an associated partition. + */ +private[spark] trait WritablePartitionedIterator { + def writeNext(writer: BlockObjectWriter): Unit + + def hasNext(): Boolean + + def nextPartition(): Int +} + +private[spark] object WritablePartitionedIterator { + def fromIterator(it: Iterator[((Int, _), _)]): WritablePartitionedIterator = { + new WritablePartitionedIterator { + var cur = if (it.hasNext) it.next() else null + + def writeNext(writer: BlockObjectWriter): Unit = { + writer.write(cur._1._2, cur._2) + cur = if (it.hasNext) it.next() else null + } + + def hasNext(): Boolean = cur != null + + def nextPartition(): Int = cur._1._1 + } + } +} diff --git a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala index 1b13559e77cb8..778a7eee73b23 100644 --- a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala +++ b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala @@ -280,6 +280,15 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext { val thrown = intercept[SparkException](ser.serialize(largeObject)) assert(thrown.getMessage.contains(kryoBufferMaxProperty)) } + + test("getAutoReset") { + val ser = new KryoSerializer(new SparkConf).newInstance().asInstanceOf[KryoSerializerInstance] + assert(ser.getAutoReset) + val conf = new SparkConf().set("spark.kryo.registrator", + classOf[RegistratorWithoutAutoReset].getName) + val ser2 = new KryoSerializer(conf).newInstance().asInstanceOf[KryoSerializerInstance] + assert(!ser2.getAutoReset) + } } @@ -313,4 +322,10 @@ object KryoTest { k.register(classOf[java.util.HashMap[_, _]]) } } + + class RegistratorWithoutAutoReset extends KryoRegistrator { + override def registerClasses(k: Kryo) { + k.setAutoReset(false) + } + } } diff --git a/core/src/test/scala/org/apache/spark/serializer/TestSerializer.scala b/core/src/test/scala/org/apache/spark/serializer/TestSerializer.scala index 963264cef3a71..86fcf447287f7 100644 --- a/core/src/test/scala/org/apache/spark/serializer/TestSerializer.scala +++ b/core/src/test/scala/org/apache/spark/serializer/TestSerializer.scala @@ -24,7 +24,7 @@ import scala.reflect.ClassTag /** - * A serializer implementation that always return a single element in a deserialization stream. + * A serializer implementation that always returns two elements in a deserialization stream. */ class TestSerializer extends Serializer { override def newInstance(): TestSerializerInstance = new TestSerializerInstance @@ -51,7 +51,7 @@ class TestDeserializationStream extends DeserializationStream { override def readObject[T: ClassTag](): T = { count += 1 - if (count == 2) { + if (count == 3) { throw new EOFException } new Object().asInstanceOf[T] diff --git a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala index 7d76435cd75e7..84384bb48999a 100644 --- a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleManagerSuite.scala @@ -59,8 +59,8 @@ class HashShuffleManagerSuite extends FunSuite with LocalSparkContext { val shuffle1 = shuffleBlockManager.forMapTask(1, 1, 1, new JavaSerializer(conf), new ShuffleWriteMetrics) for (writer <- shuffle1.writers) { - writer.write("test1") - writer.write("test2") + writer.write("test1", "value") + writer.write("test2", "value") } for (writer <- shuffle1.writers) { writer.commitAndClose() @@ -73,8 +73,8 @@ class HashShuffleManagerSuite extends FunSuite with LocalSparkContext { new ShuffleWriteMetrics) for (writer <- shuffle2.writers) { - writer.write("test3") - writer.write("test4") + writer.write("test3", "value") + writer.write("test4", "vlue") } for (writer <- shuffle2.writers) { writer.commitAndClose() @@ -91,8 +91,8 @@ class HashShuffleManagerSuite extends FunSuite with LocalSparkContext { val shuffle3 = shuffleBlockManager.forMapTask(1, 3, 1, new JavaSerializer(testConf), new ShuffleWriteMetrics) for (writer <- shuffle3.writers) { - writer.write("test3") - writer.write("test4") + writer.write("test3", "value") + writer.write("test4", "value") } for (writer <- shuffle3.writers) { writer.commitAndClose() diff --git a/core/src/test/scala/org/apache/spark/storage/BlockObjectWriterSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockObjectWriterSuite.scala index 003a728cb84a0..43ef469c1fd48 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockObjectWriterSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockObjectWriterSuite.scala @@ -32,7 +32,7 @@ class BlockObjectWriterSuite extends FunSuite { val writer = new DiskBlockObjectWriter(new TestBlockId("0"), file, new JavaSerializer(new SparkConf()).newInstance(), 1024, os => os, true, writeMetrics) - writer.write(Long.box(20)) + writer.write(Long.box(20), Long.box(30)) // Record metrics update on every write assert(writeMetrics.shuffleRecordsWritten === 1) // Metrics don't update on every write @@ -40,7 +40,7 @@ class BlockObjectWriterSuite extends FunSuite { // After 32 writes, metrics should update for (i <- 0 until 32) { writer.flush() - writer.write(Long.box(i)) + writer.write(Long.box(i), Long.box(i)) } assert(writeMetrics.shuffleBytesWritten > 0) assert(writeMetrics.shuffleRecordsWritten === 33) @@ -54,7 +54,7 @@ class BlockObjectWriterSuite extends FunSuite { val writer = new DiskBlockObjectWriter(new TestBlockId("0"), file, new JavaSerializer(new SparkConf()).newInstance(), 1024, os => os, true, writeMetrics) - writer.write(Long.box(20)) + writer.write(Long.box(20), Long.box(30)) // Record metrics update on every write assert(writeMetrics.shuffleRecordsWritten === 1) // Metrics don't update on every write @@ -62,7 +62,7 @@ class BlockObjectWriterSuite extends FunSuite { // After 32 writes, metrics should update for (i <- 0 until 32) { writer.flush() - writer.write(Long.box(i)) + writer.write(Long.box(i), Long.box(i)) } assert(writeMetrics.shuffleBytesWritten > 0) assert(writeMetrics.shuffleRecordsWritten === 33) diff --git a/core/src/test/scala/org/apache/spark/util/collection/ChainedBufferSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ChainedBufferSuite.scala new file mode 100644 index 0000000000000..c0c38cd4ac4ad --- /dev/null +++ b/core/src/test/scala/org/apache/spark/util/collection/ChainedBufferSuite.scala @@ -0,0 +1,143 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import java.nio.ByteBuffer + +import org.scalatest.FunSuite +import org.scalatest.Matchers._ + +class ChainedBufferSuite extends FunSuite { + test("write and read at start") { + // write from start of source array + val buffer = new ChainedBuffer(8) + buffer.capacity should be (0) + verifyWriteAndRead(buffer, 0, 0, 0, 4) + buffer.capacity should be (8) + + // write from middle of source array + verifyWriteAndRead(buffer, 0, 5, 0, 4) + buffer.capacity should be (8) + + // read to middle of target array + verifyWriteAndRead(buffer, 0, 0, 5, 4) + buffer.capacity should be (8) + + // write up to border + verifyWriteAndRead(buffer, 0, 0, 0, 8) + buffer.capacity should be (8) + + // expand into second buffer + verifyWriteAndRead(buffer, 0, 0, 0, 12) + buffer.capacity should be (16) + + // expand into multiple buffers + verifyWriteAndRead(buffer, 0, 0, 0, 28) + buffer.capacity should be (32) + } + + test("write and read at middle") { + val buffer = new ChainedBuffer(8) + + // fill to a middle point + verifyWriteAndRead(buffer, 0, 0, 0, 3) + + // write from start of source array + verifyWriteAndRead(buffer, 3, 0, 0, 4) + buffer.capacity should be (8) + + // write from middle of source array + verifyWriteAndRead(buffer, 3, 5, 0, 4) + buffer.capacity should be (8) + + // read to middle of target array + verifyWriteAndRead(buffer, 3, 0, 5, 4) + buffer.capacity should be (8) + + // write up to border + verifyWriteAndRead(buffer, 3, 0, 0, 5) + buffer.capacity should be (8) + + // expand into second buffer + verifyWriteAndRead(buffer, 3, 0, 0, 12) + buffer.capacity should be (16) + + // expand into multiple buffers + verifyWriteAndRead(buffer, 3, 0, 0, 28) + buffer.capacity should be (32) + } + + test("write and read at later buffer") { + val buffer = new ChainedBuffer(8) + + // fill to a middle point + verifyWriteAndRead(buffer, 0, 0, 0, 11) + + // write from start of source array + verifyWriteAndRead(buffer, 11, 0, 0, 4) + buffer.capacity should be (16) + + // write from middle of source array + verifyWriteAndRead(buffer, 11, 5, 0, 4) + buffer.capacity should be (16) + + // read to middle of target array + verifyWriteAndRead(buffer, 11, 0, 5, 4) + buffer.capacity should be (16) + + // write up to border + verifyWriteAndRead(buffer, 11, 0, 0, 5) + buffer.capacity should be (16) + + // expand into second buffer + verifyWriteAndRead(buffer, 11, 0, 0, 12) + buffer.capacity should be (24) + + // expand into multiple buffers + verifyWriteAndRead(buffer, 11, 0, 0, 28) + buffer.capacity should be (40) + } + + + // Used to make sure we're writing different bytes each time + var rangeStart = 0 + + /** + * @param buffer The buffer to write to and read from. + * @param offsetInBuffer The offset to write to in the buffer. + * @param offsetInSource The offset in the array that the bytes are written from. + * @param offsetInTarget The offset in the array to read the bytes into. + * @param length The number of bytes to read and write + */ + def verifyWriteAndRead( + buffer: ChainedBuffer, + offsetInBuffer: Int, + offsetInSource: Int, + offsetInTarget: Int, + length: Int): Unit = { + val source = new Array[Byte](offsetInSource + length) + (rangeStart until rangeStart + length).map(_.toByte).copyToArray(source, offsetInSource) + buffer.write(offsetInBuffer, source, offsetInSource, length) + val target = new Array[Byte](offsetInTarget + length) + buffer.read(offsetInBuffer, target, offsetInTarget, length) + ByteBuffer.wrap(source, offsetInSource, length) should be + (ByteBuffer.wrap(target, offsetInTarget, length)) + + rangeStart += 100 + } +} diff --git a/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala index de26aa351b0d2..20fd22b78ef5d 100644 --- a/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala @@ -19,19 +19,24 @@ package org.apache.spark.util.collection import scala.collection.mutable.ArrayBuffer -import org.scalatest.{PrivateMethodTester, FunSuite} - -import org.apache.spark._ +import org.scalatest.{FunSuite, PrivateMethodTester} import scala.util.Random +import org.apache.spark._ +import org.apache.spark.serializer.{JavaSerializer, KryoSerializer} + class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMethodTester { - private def createSparkConf(loadDefaults: Boolean): SparkConf = { + private def createSparkConf(loadDefaults: Boolean, kryo: Boolean): SparkConf = { val conf = new SparkConf(loadDefaults) - // Make the Java serializer write a reset instruction (TC_RESET) after each object to test - // for a bug we had with bytes written past the last object in a batch (SPARK-2792) - conf.set("spark.serializer.objectStreamReset", "1") - conf.set("spark.serializer", "org.apache.spark.serializer.JavaSerializer") + if (kryo) { + conf.set("spark.serializer", classOf[KryoSerializer].getName) + } else { + // Make the Java serializer write a reset instruction (TC_RESET) after each object to test + // for a bug we had with bytes written past the last object in a batch (SPARK-2792) + conf.set("spark.serializer.objectStreamReset", "1") + conf.set("spark.serializer", classOf[JavaSerializer].getName) + } // Ensure that we actually have multiple batches per spill file conf.set("spark.shuffle.spill.batchSize", "10") conf @@ -47,8 +52,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe assert(!sorter.invokePrivate(bypassMergeSort()), "sorter bypassed merge-sort") } - test("empty data stream") { - val conf = new SparkConf(false) + test("empty data stream with kryo ser") { + emptyDataStream(createSparkConf(false, true)) + } + + test("empty data stream with java ser") { + emptyDataStream(createSparkConf(false, false)) + } + + def emptyDataStream(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -81,8 +93,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe sorter4.stop() } - test("few elements per partition") { - val conf = createSparkConf(false) + test("few elements per partition with kryo ser") { + fewElementsPerPartition(createSparkConf(false, true)) + } + + test("few elements per partition with java ser") { + fewElementsPerPartition(createSparkConf(false, false)) + } + + def fewElementsPerPartition(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -123,8 +142,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe sorter4.stop() } - test("empty partitions with spilling") { - val conf = createSparkConf(false) + test("empty partitions with spilling with kryo ser") { + emptyPartitionsWithSpilling(createSparkConf(false, true)) + } + + test("empty partitions with spilling with java ser") { + emptyPartitionsWithSpilling(createSparkConf(false, false)) + } + + def emptyPartitionsWithSpilling(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.spill.initialMemoryThreshold", "512") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") @@ -149,8 +175,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe sorter.stop() } - test("empty partitions with spilling, bypass merge-sort") { - val conf = createSparkConf(false) + test("empty partitions with spilling, bypass merge-sort with kryo ser") { + emptyPartitionerWithSpillingBypassMergeSort(createSparkConf(false, true)) + } + + test("empty partitions with spilling, bypass merge-sort with java ser") { + emptyPartitionerWithSpillingBypassMergeSort(createSparkConf(false, false)) + } + + def emptyPartitionerWithSpillingBypassMergeSort(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.spill.initialMemoryThreshold", "512") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") @@ -174,8 +207,17 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe sorter.stop() } - test("spilling in local cluster") { - val conf = createSparkConf(true) // Load defaults, otherwise SPARK_HOME is not found + test("spilling in local cluster with kryo ser") { + // Load defaults, otherwise SPARK_HOME is not found + testSpillingInLocalCluster(createSparkConf(true, true)) + } + + test("spilling in local cluster with java ser") { + // Load defaults, otherwise SPARK_HOME is not found + testSpillingInLocalCluster(createSparkConf(true, false)) + } + + def testSpillingInLocalCluster(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local-cluster[1,1,512]", "test", conf) @@ -245,8 +287,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe assert(resultE === (0 until 100000).map(i => (i/4, i)).toSeq) } - test("spilling in local cluster with many reduce tasks") { - val conf = createSparkConf(true) // Load defaults, otherwise SPARK_HOME is not found + test("spilling in local cluster with many reduce tasks with kryo ser") { + spillingInLocalClusterWithManyReduceTasks(createSparkConf(true, true)) + } + + test("spilling in local cluster with many reduce tasks with java ser") { + spillingInLocalClusterWithManyReduceTasks(createSparkConf(true, false)) + } + + def spillingInLocalClusterWithManyReduceTasks(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local-cluster[2,1,512]", "test", conf) @@ -317,7 +366,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("cleanup of intermediate files in sorter") { - val conf = createSparkConf(true) // Load defaults, otherwise SPARK_HOME is not found + val conf = createSparkConf(true, false) // Load defaults, otherwise SPARK_HOME is not found conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -344,7 +393,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("cleanup of intermediate files in sorter, bypass merge-sort") { - val conf = createSparkConf(true) // Load defaults, otherwise SPARK_HOME is not found + val conf = createSparkConf(true, false) // Load defaults, otherwise SPARK_HOME is not found conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -367,7 +416,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("cleanup of intermediate files in sorter if there are errors") { - val conf = createSparkConf(true) // Load defaults, otherwise SPARK_HOME is not found + val conf = createSparkConf(true, false) // Load defaults, otherwise SPARK_HOME is not found conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -392,7 +441,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("cleanup of intermediate files in sorter if there are errors, bypass merge-sort") { - val conf = createSparkConf(true) // Load defaults, otherwise SPARK_HOME is not found + val conf = createSparkConf(true, false) // Load defaults, otherwise SPARK_HOME is not found conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -414,7 +463,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("cleanup of intermediate files in shuffle") { - val conf = createSparkConf(false) + val conf = createSparkConf(false, false) conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -429,7 +478,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("cleanup of intermediate files in shuffle with errors") { - val conf = createSparkConf(false) + val conf = createSparkConf(false, false) conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -450,8 +499,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe assert(diskBlockManager.getAllFiles().length === 2) } - test("no partial aggregation or sorting") { - val conf = createSparkConf(false) + test("no partial aggregation or sorting with kryo ser") { + noPartialAggregationOrSorting(createSparkConf(false, true)) + } + + test("no partial aggregation or sorting with java ser") { + noPartialAggregationOrSorting(createSparkConf(false, false)) + } + + def noPartialAggregationOrSorting(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -465,8 +521,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe assert(results === expected) } - test("partial aggregation without spill") { - val conf = createSparkConf(false) + test("partial aggregation without spill with kryo ser") { + partialAggregationWithoutSpill(createSparkConf(false, true)) + } + + test("partial aggregation without spill with java ser") { + partialAggregationWithoutSpill(createSparkConf(false, false)) + } + + def partialAggregationWithoutSpill(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -481,8 +544,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe assert(results === expected) } - test("partial aggregation with spill, no ordering") { - val conf = createSparkConf(false) + test("partial aggregation with spill, no ordering with kryo ser") { + partialAggregationWIthSpillNoOrdering(createSparkConf(false, true)) + } + + test("partial aggregation with spill, no ordering with java ser") { + partialAggregationWIthSpillNoOrdering(createSparkConf(false, false)) + } + + def partialAggregationWIthSpillNoOrdering(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -497,8 +567,16 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe assert(results === expected) } - test("partial aggregation with spill, with ordering") { - val conf = createSparkConf(false) + test("partial aggregation with spill, with ordering with kryo ser") { + partialAggregationWithSpillWithOrdering(createSparkConf(false, true)) + } + + + test("partial aggregation with spill, with ordering with java ser") { + partialAggregationWithSpillWithOrdering(createSparkConf(false, false)) + } + + def partialAggregationWithSpillWithOrdering(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -517,8 +595,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe assert(results === expected) } - test("sorting without aggregation, no spill") { - val conf = createSparkConf(false) + test("sorting without aggregation, no spill with kryo ser") { + sortingWithoutAggregationNoSpill(createSparkConf(false, true)) + } + + test("sorting without aggregation, no spill with java ser") { + sortingWithoutAggregationNoSpill(createSparkConf(false, false)) + } + + def sortingWithoutAggregationNoSpill(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -534,8 +619,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe assert(results === expected) } - test("sorting without aggregation, with spill") { - val conf = createSparkConf(false) + test("sorting without aggregation, with spill with kryo ser") { + sortingWithoutAggregationWithSpill(createSparkConf(false, true)) + } + + test("sorting without aggregation, with spill with java ser") { + sortingWithoutAggregationWithSpill(createSparkConf(false, false)) + } + + def sortingWithoutAggregationWithSpill(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -552,7 +644,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("spilling with hash collisions") { - val conf = createSparkConf(true) + val conf = createSparkConf(true, false) conf.set("spark.shuffle.memoryFraction", "0.001") sc = new SparkContext("local-cluster[1,1,512]", "test", conf) @@ -609,7 +701,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("spilling with many hash collisions") { - val conf = createSparkConf(true) + val conf = createSparkConf(true, false) conf.set("spark.shuffle.memoryFraction", "0.0001") sc = new SparkContext("local-cluster[1,1,512]", "test", conf) @@ -632,7 +724,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("spilling with hash collisions using the Int.MaxValue key") { - val conf = createSparkConf(true) + val conf = createSparkConf(true, false) conf.set("spark.shuffle.memoryFraction", "0.001") sc = new SparkContext("local-cluster[1,1,512]", "test", conf) @@ -656,7 +748,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("spilling with null keys and values") { - val conf = createSparkConf(true) + val conf = createSparkConf(true, false) conf.set("spark.shuffle.memoryFraction", "0.001") sc = new SparkContext("local-cluster[1,1,512]", "test", conf) @@ -685,7 +777,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe } test("conditions for bypassing merge-sort") { - val conf = createSparkConf(false) + val conf = createSparkConf(false, false) conf.set("spark.shuffle.memoryFraction", "0.001") conf.set("spark.shuffle.manager", "org.apache.spark.shuffle.sort.SortShuffleManager") sc = new SparkContext("local", "test", conf) @@ -718,8 +810,15 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe assertDidNotBypassMergeSort(sorter4) } - test("sort without breaking sorting contracts") { - val conf = createSparkConf(true) + test("sort without breaking sorting contracts with kryo ser") { + sortWithoutBreakingSortingContracts(createSparkConf(true, true)) + } + + test("sort without breaking sorting contracts with java ser") { + sortWithoutBreakingSortingContracts(createSparkConf(true, false)) + } + + def sortWithoutBreakingSortingContracts(conf: SparkConf) { conf.set("spark.shuffle.memoryFraction", "0.01") conf.set("spark.shuffle.manager", "sort") sc = new SparkContext("local-cluster[1,1,512]", "test", conf) diff --git a/core/src/test/scala/org/apache/spark/util/collection/PartitionedSerializedPairBufferSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/PartitionedSerializedPairBufferSuite.scala new file mode 100644 index 0000000000000..b5a2d9ef720c1 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/util/collection/PartitionedSerializedPairBufferSuite.scala @@ -0,0 +1,149 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util.collection + +import java.io.{ByteArrayInputStream, ByteArrayOutputStream, InputStream} + +import com.google.common.io.ByteStreams + +import org.scalatest.FunSuite +import org.scalatest.Matchers._ + +import org.apache.spark.SparkConf +import org.apache.spark.serializer.KryoSerializer +import org.apache.spark.storage.{FileSegment, BlockObjectWriter} + +class PartitionedSerializedPairBufferSuite extends FunSuite { + test("OrderedInputStream single record") { + val serializerInstance = new KryoSerializer(new SparkConf()).newInstance + + val buffer = new PartitionedSerializedPairBuffer[Int, SomeStruct](4, 32, serializerInstance) + val struct = SomeStruct("something", 5) + buffer.insert(4, 10, struct) + + val bytes = ByteStreams.toByteArray(buffer.orderedInputStream) + + val baos = new ByteArrayOutputStream() + val stream = serializerInstance.serializeStream(baos) + stream.writeObject(10) + stream.writeObject(struct) + stream.close() + + baos.toByteArray should be (bytes) + } + + test("insert single record") { + val serializerInstance = new KryoSerializer(new SparkConf()).newInstance + val buffer = new PartitionedSerializedPairBuffer[Int, SomeStruct](4, 32, serializerInstance) + val struct = SomeStruct("something", 5) + buffer.insert(4, 10, struct) + val elements = buffer.partitionedDestructiveSortedIterator(None).toArray + elements.size should be (1) + elements.head should be (((4, 10), struct)) + } + + test("insert multiple records") { + val serializerInstance = new KryoSerializer(new SparkConf()).newInstance + val buffer = new PartitionedSerializedPairBuffer[Int, SomeStruct](4, 32, serializerInstance) + val struct1 = SomeStruct("something1", 8) + buffer.insert(6, 1, struct1) + val struct2 = SomeStruct("something2", 9) + buffer.insert(4, 2, struct2) + val struct3 = SomeStruct("something3", 10) + buffer.insert(5, 3, struct3) + + val elements = buffer.partitionedDestructiveSortedIterator(None).toArray + elements.size should be (3) + elements(0) should be (((4, 2), struct2)) + elements(1) should be (((5, 3), struct3)) + elements(2) should be (((6, 1), struct1)) + } + + test("write single record") { + val serializerInstance = new KryoSerializer(new SparkConf()).newInstance + val buffer = new PartitionedSerializedPairBuffer[Int, SomeStruct](4, 32, serializerInstance) + val struct = SomeStruct("something", 5) + buffer.insert(4, 10, struct) + val it = buffer.destructiveSortedWritablePartitionedIterator(None) + val writer = new SimpleBlockObjectWriter + assert(it.hasNext) + it.nextPartition should be (4) + it.writeNext(writer) + assert(!it.hasNext) + + val stream = serializerInstance.deserializeStream(writer.getInputStream) + stream.readObject[AnyRef]() should be (10) + stream.readObject[AnyRef]() should be (struct) + } + + test("write multiple records") { + val serializerInstance = new KryoSerializer(new SparkConf()).newInstance + val buffer = new PartitionedSerializedPairBuffer[Int, SomeStruct](4, 32, serializerInstance) + val struct1 = SomeStruct("something1", 8) + buffer.insert(6, 1, struct1) + val struct2 = SomeStruct("something2", 9) + buffer.insert(4, 2, struct2) + val struct3 = SomeStruct("something3", 10) + buffer.insert(5, 3, struct3) + + val it = buffer.destructiveSortedWritablePartitionedIterator(None) + val writer = new SimpleBlockObjectWriter + assert(it.hasNext) + it.nextPartition should be (4) + it.writeNext(writer) + assert(it.hasNext) + it.nextPartition should be (5) + it.writeNext(writer) + assert(it.hasNext) + it.nextPartition should be (6) + it.writeNext(writer) + assert(!it.hasNext) + + val stream = serializerInstance.deserializeStream(writer.getInputStream) + val iter = stream.asIterator + iter.next() should be (2) + iter.next() should be (struct2) + iter.next() should be (3) + iter.next() should be (struct3) + iter.next() should be (1) + iter.next() should be (struct1) + assert(!iter.hasNext) + } +} + +case class SomeStruct(val str: String, val num: Int) + +class SimpleBlockObjectWriter extends BlockObjectWriter(null) { + val baos = new ByteArrayOutputStream() + + override def write(bytes: Array[Byte], offs: Int, len: Int): Unit = { + baos.write(bytes, offs, len) + } + + def getInputStream(): InputStream = new ByteArrayInputStream(baos.toByteArray) + + override def open(): BlockObjectWriter = this + override def close(): Unit = { } + override def isOpen: Boolean = true + override def commitAndClose(): Unit = { } + override def revertPartialWritesAndClose(): Unit = { } + override def fileSegment(): FileSegment = null + override def write(key: Any, value: Any): Unit = { } + override def recordWritten(): Unit = { } + override def write(b: Int): Unit = { } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala index cec97de2cd8e4..9552f41115866 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala @@ -50,10 +50,10 @@ private[sql] class Serializer2SerializationStream( extends SerializationStream with Logging { val rowOut = new DataOutputStream(out) - val writeKey = SparkSqlSerializer2.createSerializationFunction(keySchema, rowOut) - val writeValue = SparkSqlSerializer2.createSerializationFunction(valueSchema, rowOut) + val writeKeyFunc = SparkSqlSerializer2.createSerializationFunction(keySchema, rowOut) + val writeValueFunc = SparkSqlSerializer2.createSerializationFunction(valueSchema, rowOut) - def writeObject[T: ClassTag](t: T): SerializationStream = { + override def writeObject[T: ClassTag](t: T): SerializationStream = { val kv = t.asInstanceOf[Product2[Row, Row]] writeKey(kv._1) writeValue(kv._2) @@ -61,6 +61,16 @@ private[sql] class Serializer2SerializationStream( this } + override def writeKey[T: ClassTag](t: T): SerializationStream = { + writeKeyFunc(t.asInstanceOf[Row]) + this + } + + override def writeValue[T: ClassTag](t: T): SerializationStream = { + writeValueFunc(t.asInstanceOf[Row]) + this + } + def flush(): Unit = { rowOut.flush() } @@ -83,17 +93,27 @@ private[sql] class Serializer2DeserializationStream( val key = if (keySchema != null) new SpecificMutableRow(keySchema) else null val value = if (valueSchema != null) new SpecificMutableRow(valueSchema) else null - val readKey = SparkSqlSerializer2.createDeserializationFunction(keySchema, rowIn, key) - val readValue = SparkSqlSerializer2.createDeserializationFunction(valueSchema, rowIn, value) + val readKeyFunc = SparkSqlSerializer2.createDeserializationFunction(keySchema, rowIn, key) + val readValueFunc = SparkSqlSerializer2.createDeserializationFunction(valueSchema, rowIn, value) - def readObject[T: ClassTag](): T = { - readKey() - readValue() + override def readObject[T: ClassTag](): T = { + readKeyFunc() + readValueFunc() (key, value).asInstanceOf[T] } - def close(): Unit = { + override def readKey[T: ClassTag](): T = { + readKeyFunc() + key.asInstanceOf[T] + } + + override def readValue[T: ClassTag](): T = { + readValueFunc() + value.asInstanceOf[T] + } + + override def close(): Unit = { rowIn.close() } } diff --git a/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala b/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala index f2d135397ce2f..baa97616eaff3 100644 --- a/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala +++ b/tools/src/main/scala/org/apache/spark/tools/StoragePerfTester.scala @@ -46,7 +46,8 @@ object StoragePerfTester { val totalRecords = dataSizeMb * 1000 val recordsPerMap = totalRecords / numMaps - val writeData = "1" * recordLength + val writeKey = "1" * (recordLength / 2) + val writeValue = "1" * (recordLength / 2) val executor = Executors.newFixedThreadPool(numMaps) val conf = new SparkConf() @@ -63,7 +64,7 @@ object StoragePerfTester { new KryoSerializer(sc.conf), new ShuffleWriteMetrics()) val writers = shuffle.writers for (i <- 1 to recordsPerMap) { - writers(i % numOutputSplits).write(writeData) + writers(i % numOutputSplits).write(writeKey, writeValue) } writers.map { w => w.commitAndClose() From 7cf1eb79b1fa290aa1d867a8a1eaaea86d6b2239 Mon Sep 17 00:00:00 2001 From: Burak Yavuz Date: Thu, 30 Apr 2015 23:39:58 -0700 Subject: [PATCH 512/591] [SPARK-7287] enabled fixed test andrewor14 pwendell I reenabled the test. Let's see if it's fixed. I did also notice that `--jars` started to fail after this was ignored though in the JIRA. like [here](https://amplab.cs.berkeley.edu/jenkins/job/Spark-Master-SBT/AMPLAB_JENKINS_BUILD_PROFILE=hadoop2.0,label=centos/2238/consoleFull), you see that --jars fails for the same exact reason. Has any change been made to Spark Submit recently? Did the test setup on Jenkins change? If we look into flaky tests last month, you wouldn't find this test among them. Author: Burak Yavuz Closes #5826 from brkyvz/restart-test and squashes the following commits: f509f68 [Burak Yavuz] enabled fixed test --- .../test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala index 8360b94599547..35382be7e0ef1 100644 --- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala @@ -335,7 +335,7 @@ class SparkSubmitSuite extends FunSuite with Matchers with ResetSystemProperties runSparkSubmit(args) } - ignore("includes jars passed in through --packages") { + test("includes jars passed in through --packages") { val unusedJar = TestUtils.createJarWithClasses(Seq.empty) val main = MavenCoordinate("my.great.lib", "mylib", "0.1") val dep = MavenCoordinate("my.great.dep", "mylib", "0.1") From 14b32886fa01aef6cc0dfbc263eb6d4c9d2876fa Mon Sep 17 00:00:00 2001 From: zsxwing Date: Thu, 30 Apr 2015 23:44:33 -0700 Subject: [PATCH 513/591] [SPARK-7291] [CORE] Fix a flaky test in AkkaRpcEnvSuite Read the port from RpcEnv to check the result so that it will success even if port conflicts Author: zsxwing Closes #5822 from zsxwing/SPARK-7291 and squashes the following commits: e521b84 [zsxwing] Fix a flaky test in AkkaRpcEnvSuite --- .../test/scala/org/apache/spark/rpc/akka/AkkaRpcEnvSuite.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/src/test/scala/org/apache/spark/rpc/akka/AkkaRpcEnvSuite.scala b/core/src/test/scala/org/apache/spark/rpc/akka/AkkaRpcEnvSuite.scala index 58214c0637235..a33a83db7bc9e 100644 --- a/core/src/test/scala/org/apache/spark/rpc/akka/AkkaRpcEnvSuite.scala +++ b/core/src/test/scala/org/apache/spark/rpc/akka/AkkaRpcEnvSuite.scala @@ -40,7 +40,7 @@ class AkkaRpcEnvSuite extends RpcEnvSuite { RpcEnvConfig(conf, "test", "localhost", 12346, new SecurityManager(conf))) try { val newRef = newRpcEnv.setupEndpointRef("local", ref.address, "test_endpoint") - assert("akka.tcp://local@localhost:12345/user/test_endpoint" === + assert(s"akka.tcp://local@${env.address}/user/test_endpoint" === newRef.asInstanceOf[AkkaRpcEndpointRef].actorRef.path.toString) } finally { newRpcEnv.shutdown() From c24aeb6a310b49dba8db1f4642531780a2e27253 Mon Sep 17 00:00:00 2001 From: MechCoder Date: Thu, 30 Apr 2015 23:51:00 -0700 Subject: [PATCH 514/591] [SPARK-6257] [PYSPARK] [MLLIB] MLlib API missing items in Recommendation Adds rank, recommendUsers and RecommendProducts to MatrixFactorizationModel in PySpark. Author: MechCoder Closes #5807 from MechCoder/spark-6257 and squashes the following commits: 09629c6 [MechCoder] doc 953b326 [MechCoder] [SPARK-6257] MLlib API missing items in Recommendation --- docs/mllib-collaborative-filtering.md | 2 +- python/pyspark/mllib/recommendation.py | 39 ++++++++++++++++++++++++++ 2 files changed, 40 insertions(+), 1 deletion(-) diff --git a/docs/mllib-collaborative-filtering.md b/docs/mllib-collaborative-filtering.md index 76140282a2dd0..7b397e30b2d90 100644 --- a/docs/mllib-collaborative-filtering.md +++ b/docs/mllib-collaborative-filtering.md @@ -216,7 +216,7 @@ model = ALS.train(ratings, rank, numIterations) testdata = ratings.map(lambda p: (p[0], p[1])) predictions = model.predictAll(testdata).map(lambda r: ((r[0], r[1]), r[2])) ratesAndPreds = ratings.map(lambda r: ((r[0], r[1]), r[2])).join(predictions) -MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).reduce(lambda x, y: x + y) / ratesAndPreds.count() +MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean() print("Mean Squared Error = " + str(MSE)) # Save and load model diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py index 4b7d17d64e947..9c4647ddfdcfd 100644 --- a/python/pyspark/mllib/recommendation.py +++ b/python/pyspark/mllib/recommendation.py @@ -65,6 +65,13 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): >>> model.userFeatures().collect() [(1, array('d', [...])), (2, array('d', [...]))] + >>> model.recommendUsers(1, 2) + [Rating(user=2, product=1, rating=1.9...), Rating(user=1, product=1, rating=1.0...)] + >>> model.recommendProducts(1, 2) + [Rating(user=1, product=2, rating=1.9...), Rating(user=1, product=1, rating=1.0...)] + >>> model.rank + 4 + >>> first_user = model.userFeatures().take(1)[0] >>> latents = first_user[1] >>> len(latents) == 4 @@ -105,9 +112,15 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): ... pass """ def predict(self, user, product): + """ + Predicts rating for the given user and product. + """ return self._java_model.predict(int(user), int(product)) def predictAll(self, user_product): + """ + Returns a list of predicted ratings for input user and product pairs. + """ assert isinstance(user_product, RDD), "user_product should be RDD of (user, product)" first = user_product.first() assert len(first) == 2, "user_product should be RDD of (user, product)" @@ -115,11 +128,37 @@ def predictAll(self, user_product): return self.call("predict", user_product) def userFeatures(self): + """ + Returns a paired RDD, where the first element is the user and the + second is an array of features corresponding to that user. + """ return self.call("getUserFeatures").mapValues(lambda v: array.array('d', v)) def productFeatures(self): + """ + Returns a paired RDD, where the first element is the product and the + second is an array of features corresponding to that product. + """ return self.call("getProductFeatures").mapValues(lambda v: array.array('d', v)) + def recommendUsers(self, product, num): + """ + Recommends the top "num" number of users for a given product and returns a list + of Rating objects sorted by the predicted rating in descending order. + """ + return list(self.call("recommendUsers", product, num)) + + def recommendProducts(self, user, num): + """ + Recommends the top "num" number of products for a given user and returns a list + of Rating objects sorted by the predicted rating in descending order. + """ + return list(self.call("recommendProducts", user, num)) + + @property + def rank(self): + return self.call("rank") + @classmethod def load(cls, sc, path): model = cls._load_java(sc, path) From 7fe0f3f2b46c61a5cc4af9166781624409fda8a4 Mon Sep 17 00:00:00 2001 From: Kousuke Saruta Date: Fri, 1 May 2015 01:39:56 -0700 Subject: [PATCH 515/591] [SPARK-3468] [WEBUI] Timeline-View feature I sometimes trouble-shoot and analyse the cause of long time spending job. At the time, I find the stages which spends long time or fails, then I find the tasks which spends long time or fails, next I analyse the proportion of each phase in a task. Another case, I find executors which spends long time for running a task and analyse the details of a task. In such situation, I think it's helpful to visualize timeline for each application, job, task and the details of proportion of activity for each task. I added 3 timeline features to existing Web UI. [Application Timeline View] This view shows following things. * When each executor was added/removed and the reason why it's removed. * When each job was started/finished. * Status of each job. ![screenshot from 2015-04-01 16 49 25](https://cloud.githubusercontent.com/assets/4736016/6936886/e35fd582-d891-11e4-980d-8de13f50e442.png) [Stage Timeline View] Similar to Application Timeline View, this view shows following things. * When each executor was added/removed and the reason why it's removed. * When each job was started/finished. * Status of each stage. ![screenshot from 2015-04-01 16 50 59](https://cloud.githubusercontent.com/assets/4736016/6936900/0dca6526-d892-11e4-84a8-efd9037af444.png) [Task Assignment Timeline View] This view shows following things. * When each task started/finished * How long each task spent and the proportion. * Status of each task. * Where each task ran on. ![screenshot from 2015-04-01 16 51 54](https://cloud.githubusercontent.com/assets/4736016/6936910/20fd5acc-d892-11e4-9018-80e463881fc2.png) All the view above is zoomable by mouse wheel action and scrollable by drag action. Author: Kousuke Saruta Closes #2342 from sarutak/timeline-viewer-feature and squashes the following commits: 11fe67d [Kousuke Saruta] Fixed conflict 79ac03d [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into timeline-viewer-feature a91abd3 [Kousuke Saruta] Merge branch 'master' of https://github.com/apache/spark into timeline-viewer-feature ef34a5b [Kousuke Saruta] Implement tooltip using bootstrap b09d0c5 [Kousuke Saruta] Move `stroke` and `fill` attribute of rect elements to css d3c63c8 [Kousuke Saruta] Fixed a little bit bugs a36291b [Kousuke Saruta] Merge branch 'master' of https://github.com/apache/spark into timeline-viewer-feature 28714b6 [Kousuke Saruta] Fixed highlight issue 0dc4278 [Kousuke Saruta] Addressed most of Patrics's feedbacks 8110acf [Kousuke Saruta] Added scroll limit to Job timeline 974a64a [Kousuke Saruta] Removed unused function ee7a7f0 [Kousuke Saruta] Refactored 6a91872 [Kousuke Saruta] Temporary commit 6693f34 [Kousuke Saruta] Added link to job/stage box in the timeline in order to move to corresponding row when we click 8f88222 [Kousuke Saruta] Added job/stage description aeed4b1 [Kousuke Saruta] Removed stage timeline fc1696c [Kousuke Saruta] Merge branch 'timeline-viewer-feature' of github.com:sarutak/spark into timeline-viewer-feature 999ccd4 [Kousuke Saruta] Improved scalability 0fc6a31 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into timeline-viewer-feature 19815ae [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into timeline-viewer-feature 68b7540 [Kousuke Saruta] Merge branch 'timeline-viewer-feature' of github.com:sarutak/spark into timeline-viewer-feature 52b5f0b [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into timeline-viewer-feature dec85db [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into timeline-viewer-feature fcdab7d [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into timeline-viewer-feature dab7cc1 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into timeline-viewer-feature 09cce97 [Kousuke Saruta] Cleanuped 16f82cf [Kousuke Saruta] Cleanuped 9fb522e [Kousuke Saruta] Cleanuped d05f2c2 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into timeline-viewer-feature e85e9aa [Kousuke Saruta] Cleanup: Added TimelineViewUtils.scala a76e569 [Kousuke Saruta] Removed unused setting in timeline-view.css 5ce1b21 [Kousuke Saruta] Added vis.min.js, vis.min.css and vis.map to .rat-exclude 082f709 [Kousuke Saruta] Added Timeline-View feature for Applications, Jobs and Stages --- .rat-excludes | 3 + .../apache/spark/ui/static/timeline-view.css | 182 ++++++++++++++++ .../apache/spark/ui/static/timeline-view.js | 168 +++++++++++++++ .../org/apache/spark/ui/static/vis.map | 1 + .../org/apache/spark/ui/static/vis.min.css | 1 + .../org/apache/spark/ui/static/vis.min.js | 39 ++++ .../apache/spark/scheduler/StageInfo.scala | 12 ++ .../scala/org/apache/spark/ui/UIUtils.scala | 5 + .../apache/spark/ui/exec/ExecutorsTab.scala | 11 + .../apache/spark/ui/jobs/AllJobsPage.scala | 200 ++++++++++++++++-- .../org/apache/spark/ui/jobs/JobPage.scala | 174 ++++++++++++++- .../spark/ui/jobs/JobProgressListener.scala | 7 + .../org/apache/spark/ui/jobs/JobsTab.scala | 6 +- .../org/apache/spark/ui/jobs/StageTable.scala | 3 +- .../org/apache/spark/ui/jobs/UIData.scala | 5 + 15 files changed, 785 insertions(+), 32 deletions(-) create mode 100644 core/src/main/resources/org/apache/spark/ui/static/timeline-view.css create mode 100644 core/src/main/resources/org/apache/spark/ui/static/timeline-view.js create mode 100644 core/src/main/resources/org/apache/spark/ui/static/vis.map create mode 100644 core/src/main/resources/org/apache/spark/ui/static/vis.min.css create mode 100644 core/src/main/resources/org/apache/spark/ui/static/vis.min.js diff --git a/.rat-excludes b/.rat-excludes index 8aca5a7f7a967..4468da19008bc 100644 --- a/.rat-excludes +++ b/.rat-excludes @@ -30,6 +30,9 @@ log4j-defaults.properties bootstrap-tooltip.js jquery-1.11.1.min.js sorttable.js +vis.min.js +vis.min.css +vis.map .*avsc .*txt .*json diff --git a/core/src/main/resources/org/apache/spark/ui/static/timeline-view.css b/core/src/main/resources/org/apache/spark/ui/static/timeline-view.css new file mode 100644 index 0000000000000..35ef14e5aaf1a --- /dev/null +++ b/core/src/main/resources/org/apache/spark/ui/static/timeline-view.css @@ -0,0 +1,182 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +div#application-timeline, div#job-timeline { + margin-bottom: 30px; +} + +#application-timeline div.legend-area { + margin-top: 5px; +} + +.vis.timeline div.content { + width: 100%; +} + +.vis.timeline .item.stage { + cursor: pointer; +} + +.vis.timeline .item.stage.succeeded { + background-color: #D5DDF6; +} + +.vis.timeline .item.stage.succeeded.selected { + background-color: #D5DDF6; + border-color: #97B0F8; + z-index: auto; +} + +.legend-area rect.completed-stage-legend { + fill: #D5DDF6; + stroke: #97B0F8; +} + +.vis.timeline .item.stage.failed { + background-color: #FF5475; +} + +.vis.timeline .item.stage.failed.selected { + background-color: #FF5475; + border-color: #97B0F8; + z-index: auto; +} + +.legend-area rect.failed-stage-legend { + fill: #FF5475; + stroke: #97B0F8; +} + +.vis.timeline .item.stage.running { + background-color: #FDFFCA; +} + +.vis.timeline .item.stage.running.selected { + background-color: #FDFFCA; + border-color: #97B0F8; + z-index: auto; +} + +.legend-area rect.active-stage-legend { + fill: #FDFFCA; + stroke: #97B0F8; +} + +.vis.timeline .item.job { + cursor: pointer; +} + +.vis.timeline .item.job.succeeded { + background-color: #D5DDF6; +} + +.vis.timeline .item.job.succeeded.selected { + background-color: #D5DDF6; + border-color: #97B0F8; + z-index: auto; +} + +.legend-area rect.succeeded-job-legend { + fill: #D5DDF6; + stroke: #97B0F8; +} + +.vis.timeline .item.job.failed { + background-color: #FF5475; +} + +.vis.timeline .item.job.failed.selected { + background-color: #FF5475; + border-color: #97B0F8; + z-index: auto; +} + +.legend-area rect.failed-job-legend { + fill: #FF5475; + stroke: #97B0F8; +} + +.vis.timeline .item.job.running { + background-color: #FDFFCA; +} + +.vis.timeline .item.job.running.selected { + background-color: #FDFFCA; + border-color: #97B0F8; + z-index: auto; +} + +.legend-area rect.running-job-legend { + fill: #FDFFCA; + stroke: #97B0F8; +} + +.vis.timeline .item.executor.added { + background-color: #D5DDF6; +} + +.legend-area rect.executor-added-legend { + fill: #D5DDF6; + stroke: #97B0F8; +} + +.vis.timeline .item.executor.removed { + background-color: #EBCA59; +} + +.legend-area rect.executor-removed-legend { + fill: #EBCA59; + stroke: #97B0F8; +} + +.vis.timeline .item.executor.selected { + border-color: #FFC200; + background-color: #FFF785; + z-index: 2; +} + +tr.corresponding-item-hover>td, tr.corresponding-item-hover>th { + background-color: #FFE1FA !important; +} + +#application-timeline.collapsed { + display: none; +} + +#job-timeline.collapsed { + display: none; +} + +.control-panel { + margin-bottom: 5px; +} + +span.expand-application-timeline, span.expand-job-timeline { + cursor: pointer; +} + +.control-panel input+span { + cursor: pointer; +} + +.vis.timeline .item.range .content { + position: unset; +} + +.vis.timeline .item .tooltip-inner { + max-width: unset !important; +} diff --git a/core/src/main/resources/org/apache/spark/ui/static/timeline-view.js b/core/src/main/resources/org/apache/spark/ui/static/timeline-view.js new file mode 100644 index 0000000000000..e4a891d47f035 --- /dev/null +++ b/core/src/main/resources/org/apache/spark/ui/static/timeline-view.js @@ -0,0 +1,168 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +function drawApplicationTimeline(groupArray, eventObjArray, startTime) { + var groups = new vis.DataSet(groupArray); + var items = new vis.DataSet(eventObjArray); + var container = $("#application-timeline")[0]; + var options = { + groupOrder: function(a, b) { + return a.value - b.value + }, + editable: false, + showCurrentTime: false, + min: startTime, + zoomable: false + }; + + var applicationTimeline = new vis.Timeline(container); + applicationTimeline.setOptions(options); + applicationTimeline.setGroups(groups); + applicationTimeline.setItems(items); + + setupZoomable("#application-timeline-zoom-lock", applicationTimeline); + setupExecutorEventAction(); + + function setupJobEventAction() { + $(".item.range.job.application-timeline-object").each(function() { + var getJobId = function(baseElem) { + var jobIdText = $($(baseElem).find(".application-timeline-content")[0]).text(); + var jobId = jobIdText.match("\\(Job (\\d+)\\)")[1]; + return jobId; + }; + + $(this).click(function() { + window.location.href = "job/?id=" + getJobId(this); + }); + + $(this).hover( + function() { + $("#job-" + getJobId(this)).addClass("corresponding-item-hover"); + $($(this).find("div.application-timeline-content")[0]).tooltip("show"); + }, + function() { + $("#job-" + getJobId(this)).removeClass("corresponding-item-hover"); + $($(this).find("div.application-timeline-content")[0]).tooltip("hide"); + } + ); + }); + } + + setupJobEventAction(); + + $("span.expand-application-timeline").click(function() { + $("#application-timeline").toggleClass('collapsed'); + + // Switch the class of the arrow from open to closed. + $(this).find('.expand-application-timeline-arrow').toggleClass('arrow-open'); + $(this).find('.expand-application-timeline-arrow').toggleClass('arrow-closed'); + }); +} + +function drawJobTimeline(groupArray, eventObjArray, startTime) { + var groups = new vis.DataSet(groupArray); + var items = new vis.DataSet(eventObjArray); + var container = $('#job-timeline')[0]; + var options = { + groupOrder: function(a, b) { + return a.value - b.value; + }, + editable: false, + showCurrentTime: false, + min: startTime, + zoomable: false, + }; + + var jobTimeline = new vis.Timeline(container); + jobTimeline.setOptions(options); + jobTimeline.setGroups(groups); + jobTimeline.setItems(items); + + setupZoomable("#job-timeline-zoom-lock", jobTimeline); + setupExecutorEventAction(); + + function setupStageEventAction() { + $(".item.range.stage.job-timeline-object").each(function() { + var getStageIdAndAttempt = function(baseElem) { + var stageIdText = $($(baseElem).find(".job-timeline-content")[0]).text(); + var stageIdAndAttempt = stageIdText.match("\\(Stage (\\d+\\.\\d+)\\)")[1].split("."); + return stageIdAndAttempt; + }; + + $(this).click(function() { + var idAndAttempt = getStageIdAndAttempt(this); + var id = idAndAttempt[0]; + var attempt = idAndAttempt[1]; + window.location.href = "../../stages/stage/?id=" + id + "&attempt=" + attempt; + }); + + $(this).hover( + function() { + var idAndAttempt = getStageIdAndAttempt(this); + var id = idAndAttempt[0]; + var attempt = idAndAttempt[1]; + $("#stage-" + id + "-" + attempt).addClass("corresponding-item-hover"); + $($(this).find("div.job-timeline-content")[0]).tooltip("show"); + }, + function() { + var idAndAttempt = getStageIdAndAttempt(this); + var id = idAndAttempt[0]; + var attempt = idAndAttempt[1]; + $("#stage-" + id + "-" + attempt).removeClass("corresponding-item-hover"); + $($(this).find("div.job-timeline-content")[0]).tooltip("hide"); + } + ); + }); + } + + setupStageEventAction(); + + $("span.expand-job-timeline").click(function() { + $("#job-timeline").toggleClass('collapsed'); + + // Switch the class of the arrow from open to closed. + $(this).find('.expand-job-timeline-arrow').toggleClass('arrow-open'); + $(this).find('.expand-job-timeline-arrow').toggleClass('arrow-closed'); + }); +} + +function setupExecutorEventAction() { + $(".item.box.executor").each(function () { + $(this).hover( + function() { + $($(this).find(".executor-event-content")[0]).tooltip("show"); + }, + function() { + $($(this).find(".executor-event-content")[0]).tooltip("hide"); + } + ); + }); +} + +function setupZoomable(id, timeline) { + $(id + '>input[type="checkbox"]').click(function() { + if (this.checked) { + timeline.setOptions({zoomable: false}); + } else { + timeline.setOptions({zoomable: true}); + } + }); + + $(id + ">span").click(function() { + $(this).parent().find('input:checkbox').trigger('click'); + }); +} diff --git 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m=(t-p)/(u-p),g=240*m,v=this._hsv2rgb(g,1,1);c.strokeStyle=v,c.beginPath(),c.moveTo(h,r+t),c.lineTo(a,r+t),c.stroke()}c.strokeStyle=this.colorAxis,c.strokeRect(h,r,i,n)}if(this.style===s.STYLE.DOTSIZE&&(c.strokeStyle=this.colorAxis,c.fillStyle=this.colorDot,c.beginPath(),c.moveTo(h,r),c.lineTo(a,r),c.lineTo(a-i+e,d),c.lineTo(h,d),c.closePath(),c.fill(),c.stroke()),this.style===s.STYLE.DOTCOLOR||this.style===s.STYLE.DOTSIZE){var y=5,b=new f(this.valueMin,this.valueMax,(this.valueMax-this.valueMin)/5,!0);for(b.start(),b.getCurrent()0?this.yMin:this.yMax,o=this._convert3Dto2D(new l(x,r,this.zMin)),Math.cos(2*_)>0?(g.textAlign="center",g.textBaseline="top",o.y+=b):Math.sin(2*_)<0?(g.textAlign="right",g.textBaseline="middle"):(g.textAlign="left",g.textBaseline="middle"),g.fillStyle=this.colorAxis,g.fillText(" "+this.xValueLabel(i.getCurrent())+" ",o.x,o.y),i.next()}for(g.lineWidth=1,s=void 0===this.defaultYStep,i=new f(this.yMin,this.yMax,this.yStep,s),i.start(),i.getCurrent()0?this.xMin:this.xMax,o=this._convert3Dto2D(new l(n,i.getCurrent(),this.zMin)),Math.cos(2*_)<0?(g.textAlign="center",g.textBaseline="top",o.y+=b):Math.sin(2*_)>0?(g.textAlign="right",g.textBaseline="middle"):(g.textAlign="left",g.textBaseline="middle"),g.fillStyle=this.colorAxis,g.fillText(" "+this.yValueLabel(i.getCurrent())+" ",o.x,o.y),i.next();for(g.lineWidth=1,s=void 0===this.defaultZStep,i=new f(this.zMin,this.zMax,this.zStep,s),i.start(),i.getCurrent()0?this.xMin:this.xMax,r=Math.sin(_)<0?this.yMin:this.yMax;!i.end();)t=this._convert3Dto2D(new l(n,r,i.getCurrent())),g.strokeStyle=this.colorAxis,g.beginPath(),g.moveTo(t.x,t.y),g.lineTo(t.x-b,t.y),g.stroke(),g.textAlign="right",g.textBaseline="middle",g.fillStyle=this.colorAxis,g.fillText(this.zValueLabel(i.getCurrent())+" ",t.x-5,t.y),i.next();g.lineWidth=1,t=this._convert3Dto2D(new l(n,r,this.zMin)),e=this._convert3Dto2D(new l(n,r,this.zMax)),g.strokeStyle=this.colorAxis,g.beginPath(),g.moveTo(t.x,t.y),g.lineTo(e.x,e.y),g.stroke(),g.lineWidth=1,p=this._convert3Dto2D(new l(this.xMin,this.yMin,this.zMin)),u=this._convert3Dto2D(new l(this.xMax,this.yMin,this.zMin)),g.strokeStyle=this.colorAxis,g.beginPath(),g.moveTo(p.x,p.y),g.lineTo(u.x,u.y),g.stroke(),p=this._convert3Dto2D(new l(this.xMin,this.yMax,this.zMin)),u=this._convert3Dto2D(new l(this.xMax,this.yMax,this.zMin)),g.strokeStyle=this.colorAxis,g.beginPath(),g.moveTo(p.x,p.y),g.lineTo(u.x,u.y),g.stroke(),g.lineWidth=1,t=this._convert3Dto2D(new l(this.xMin,this.yMin,this.zMin)),e=this._convert3Dto2D(new l(this.xMin,this.yMax,this.zMin)),g.strokeStyle=this.colorAxis,g.beginPath(),g.moveTo(t.x,t.y),g.lineTo(e.x,e.y),g.stroke(),t=this._convert3Dto2D(new l(this.xMax,this.yMin,this.zMin)),e=this._convert3Dto2D(new l(this.xMax,this.yMax,this.zMin)),g.strokeStyle=this.colorAxis,g.beginPath(),g.moveTo(t.x,t.y),g.lineTo(e.x,e.y),g.stroke();var w=this.xLabel;w.length>0&&(c=.1/this.scale.y,n=(this.xMin+this.xMax)/2,r=Math.cos(_)>0?this.yMin-c:this.yMax+c,o=this._convert3Dto2D(new l(n,r,this.zMin)),Math.cos(2*_)>0?(g.textAlign="center",g.textBaseline="top"):Math.sin(2*_)<0?(g.textAlign="right",g.textBaseline="middle"):(g.textAlign="left",g.textBaseline="middle"),g.fillStyle=this.colorAxis,g.fillText(w,o.x,o.y));var S=this.yLabel;S.length>0&&(d=.1/this.scale.x,n=Math.sin(_)>0?this.xMin-d:this.xMax+d,r=(this.yMin+this.yMax)/2,o=this._convert3Dto2D(new l(n,r,this.zMin)),Math.cos(2*_)<0?(g.textAlign="center",g.textBaseline="top"):Math.sin(2*_)>0?(g.textAlign="right",g.textBaseline="middle"):(g.textAlign="left",g.textBaseline="middle"),g.fillStyle=this.colorAxis,g.fillText(S,o.x,o.y));var M=this.zLabel;M.length>0&&(h=30,n=Math.cos(_)>0?this.xMin:this.xMax,r=Math.sin(_)<0?this.yMin:this.yMax,a=(this.zMin+this.zMax)/2,o=this._convert3Dto2D(new l(n,r,a)),g.textAlign="right",g.textBaseline="middle",g.fillStyle=this.colorAxis,g.fillText(M,o.x-h,o.y))},s.prototype._hsv2rgb=function(t,e,i){var s,o,n,r,a,h;switch(r=i*e,a=Math.floor(t/60),h=r*(1-Math.abs(t/60%2-1)),a){case 0:s=r,o=h,n=0;break;case 1:s=h,o=r,n=0;break;case 2:s=0,o=r,n=h;break;case 3:s=0,o=h,n=r;break;case 4:s=h,o=0,n=r;break;case 5:s=r,o=0,n=h;break;default:s=0,o=0,n=0}return"RGB("+parseInt(255*s)+","+parseInt(255*o)+","+parseInt(255*n)+")"},s.prototype._redrawDataGrid=function(){var t,e,i,o,n,r,a,h,d,c,p,u,m,f=this.frame.canvas,g=f.getContext("2d");if(!(void 0===this.dataPoints||this.dataPoints.length<=0)){for(n=0;n0}else r=!0;r?(m=(t.point.z+e.point.z+i.point.z+o.point.z)/4,c=240*(1-(m-this.zMin)*this.scale.z/this.verticalRatio),p=1,this.showShadow?(u=Math.min(1+S.x/M/2,1),a=this._hsv2rgb(c,p,u),h=a):(u=1,a=this._hsv2rgb(c,p,u),h=this.colorAxis)):(a="gray",h=this.colorAxis),d=.5,g.lineWidth=d,g.fillStyle=a,g.strokeStyle=h,g.beginPath(),g.moveTo(t.screen.x,t.screen.y),g.lineTo(e.screen.x,e.screen.y),g.lineTo(o.screen.x,o.screen.y),g.lineTo(i.screen.x,i.screen.y),g.closePath(),g.fill(),g.stroke()}}else for(n=0;np&&(p=0);var u,m,f;this.style===s.STYLE.DOTCOLOR?(u=240*(1-(d.point.value-this.valueMin)*this.scale.value),m=this._hsv2rgb(u,1,1),f=this._hsv2rgb(u,1,.8)):this.style===s.STYLE.DOTSIZE?(m=this.colorDot,f=this.colorDotBorder):(u=240*(1-(d.point.z-this.zMin)*this.scale.z/this.verticalRatio),m=this._hsv2rgb(u,1,1),f=this._hsv2rgb(u,1,.8)),i.lineWidth=1,i.strokeStyle=f,i.fillStyle=m,i.beginPath(),i.arc(d.screen.x,d.screen.y,p,0,2*Math.PI,!0),i.fill(),i.stroke()}}},s.prototype._redrawDataBar=function(){var t,e,i,o,n=this.frame.canvas,r=n.getContext("2d");if(!(void 0===this.dataPoints||this.dataPoints.length<=0)){for(t=0;t0&&(t=this.dataPoints[0],s.lineWidth=1,s.strokeStyle="blue",s.beginPath(),s.moveTo(t.screen.x,t.screen.y)),e=1;e0&&s.stroke()}},s.prototype._onMouseDown=function(t){if(t=t||window.event,this.leftButtonDown&&this._onMouseUp(t),this.leftButtonDown=t.which?1===t.which:1===t.button,this.leftButtonDown||this.touchDown){this.startMouseX=o(t),this.startMouseY=n(t),this.startStart=new Date(this.start),this.startEnd=new Date(this.end),this.startArmRotation=this.camera.getArmRotation(),this.frame.style.cursor="move";var e=this;this.onmousemove=function(t){e._onMouseMove(t)},this.onmouseup=function(t){e._onMouseUp(t)},d.addEventListener(document,"mousemove",e.onmousemove),d.addEventListener(document,"mouseup",e.onmouseup),d.preventDefault(t)}},s.prototype._onMouseMove=function(t){t=t||window.event;var e=parseFloat(o(t))-this.startMouseX,i=parseFloat(n(t))-this.startMouseY,s=this.startArmRotation.horizontal+e/200,r=this.startArmRotation.vertical+i/200,a=4,h=Math.sin(a/360*2*Math.PI);Math.abs(Math.sin(s))0?1:0>t?-1:0}var s=e[0],o=e[1],n=e[2],r=i((o.x-s.x)*(t.y-s.y)-(o.y-s.y)*(t.x-s.x)),a=i((n.x-o.x)*(t.y-o.y)-(n.y-o.y)*(t.x-o.x)),h=i((s.x-n.x)*(t.y-n.y)-(s.y-n.y)*(t.x-n.x));return!(0!=r&&0!=a&&r!=a||0!=a&&0!=h&&a!=h||0!=r&&0!=h&&r!=h)},s.prototype._dataPointFromXY=function(t,e){var i,o=100,n=null,r=null,a=null,h=new c(t,e);if(this.style===s.STYLE.BAR||this.style===s.STYLE.BARCOLOR||this.style===s.STYLE.BARSIZE)for(i=this.dataPoints.length-1;i>=0;i--){n=this.dataPoints[i];var d=n.surfaces;if(d)for(var l=d.length-1;l>=0;l--){var p=d[l],u=p.corners,m=[u[0].screen,u[1].screen,u[2].screen],f=[u[2].screen,u[3].screen,u[0].screen];if(this._insideTriangle(h,m)||this._insideTriangle(h,f))return n}}else for(i=0;ib)&&o>b&&(a=b,r=n)}}return r},s.prototype._showTooltip=function(t){var e,i,s;this.tooltip?(e=this.tooltip.dom.content,i=this.tooltip.dom.line,s=this.tooltip.dom.dot):(e=document.createElement("div"),e.style.position="absolute",e.style.padding="10px",e.style.border="1px solid #4d4d4d",e.style.color="#1a1a1a",e.style.background="rgba(255,255,255,0.7)",e.style.borderRadius="2px",e.style.boxShadow="5px 5px 10px rgba(128,128,128,0.5)",i=document.createElement("div"),i.style.position="absolute",i.style.height="40px",i.style.width="0",i.style.borderLeft="1px solid #4d4d4d",s=document.createElement("div"),s.style.position="absolute",s.style.height="0",s.style.width="0",s.style.border="5px solid #4d4d4d",s.style.borderRadius="5px",this.tooltip={dataPoint:null,dom:{content:e,line:i,dot:s}}),this._hideTooltip(),this.tooltip.dataPoint=t,e.innerHTML="function"==typeof this.showTooltip?this.showTooltip(t.point):"
    x:"+t.point.x+"
    y:"+t.point.y+"
    z:"+t.point.z+"
    ",e.style.left="0",e.style.top="0",this.frame.appendChild(e),this.frame.appendChild(i),this.frame.appendChild(s);var o=e.offsetWidth,n=e.offsetHeight,r=i.offsetHeight,a=s.offsetWidth,h=s.offsetHeight,d=t.screen.x-o/2;d=Math.min(Math.max(d,10),this.frame.clientWidth-10-o),i.style.left=t.screen.x+"px",i.style.top=t.screen.y-r+"px",e.style.left=d+"px",e.style.top=t.screen.y-r-n+"px",s.style.left=t.screen.x-a/2+"px",s.style.top=t.screen.y-h/2+"px"},s.prototype._hideTooltip=function(){if(this.tooltip){this.tooltip.dataPoint=null;for(var t in this.tooltip.dom)if(this.tooltip.dom.hasOwnProperty(t)){var e=this.tooltip.dom[t];e&&e.parentNode&&e.parentNode.removeChild(e)}}},t.exports=s},function(t,e,i){function s(){this.armLocation=new o,this.armRotation={},this.armRotation.horizontal=0,this.armRotation.vertical=0,this.armLength=1.7,this.cameraLocation=new o,this.cameraRotation=new o(.5*Math.PI,0,0),this.calculateCameraOrientation()}var o=i(10);s.prototype.setArmLocation=function(t,e,i){this.armLocation.x=t,this.armLocation.y=e,this.armLocation.z=i,this.calculateCameraOrientation()},s.prototype.setArmRotation=function(t,e){void 0!==t&&(this.armRotation.horizontal=t),void 0!==e&&(this.armRotation.vertical=e,this.armRotation.vertical<0&&(this.armRotation.vertical=0),this.armRotation.vertical>.5*Math.PI&&(this.armRotation.vertical=.5*Math.PI)),(void 0!==t||void 0!==e)&&this.calculateCameraOrientation()},s.prototype.getArmRotation=function(){var t={};return t.horizontal=this.armRotation.horizontal,t.vertical=this.armRotation.vertical,t},s.prototype.setArmLength=function(t){void 0!==t&&(this.armLength=t,this.armLength<.71&&(this.armLength=.71),this.armLength>5&&(this.armLength=5),this.calculateCameraOrientation())},s.prototype.getArmLength=function(){return this.armLength},s.prototype.getCameraLocation=function(){return this.cameraLocation},s.prototype.getCameraRotation=function(){return this.cameraRotation},s.prototype.calculateCameraOrientation=function(){this.cameraLocation.x=this.armLocation.x-this.armLength*Math.sin(this.armRotation.horizontal)*Math.cos(this.armRotation.vertical),this.cameraLocation.y=this.armLocation.y-this.armLength*Math.cos(this.armRotation.horizontal)*Math.cos(this.armRotation.vertical),this.cameraLocation.z=this.armLocation.z+this.armLength*Math.sin(this.armRotation.vertical),this.cameraRotation.x=Math.PI/2-this.armRotation.vertical,this.cameraRotation.y=0,this.cameraRotation.z=-this.armRotation.horizontal},t.exports=s},function(t,e,i){function s(t,e,i){this.data=t,this.column=e,this.graph=i,this.index=void 0,this.value=void 0,this.values=i.getDistinctValues(t.get(),this.column),this.values.sort(function(t,e){return t>e?1:e>t?-1:0}),this.values.length>0&&this.selectValue(0),this.dataPoints=[],this.loaded=!1,this.onLoadCallback=void 0,i.animationPreload?(this.loaded=!1,this.loadInBackground()):this.loaded=!0}var o=i(4);s.prototype.isLoaded=function(){return this.loaded},s.prototype.getLoadedProgress=function(){for(var t=this.values.length,e=0;this.dataPoints[e];)e++;return Math.round(e/t*100)},s.prototype.getLabel=function(){return this.graph.filterLabel},s.prototype.getColumn=function(){return this.column},s.prototype.getSelectedValue=function(){return void 0===this.index?void 0:this.values[this.index]},s.prototype.getValues=function(){return this.values},s.prototype.getValue=function(t){if(t>=this.values.length)throw"Error: index out of range";return this.values[t]},s.prototype._getDataPoints=function(t){if(void 0===t&&(t=this.index),void 0===t)return[];var e;if(this.dataPoints[t])e=this.dataPoints[t];else{var i={};i.column=this.column,i.value=this.values[t];var s=new o(this.data,{filter:function(t){return t[i.column]==i.value}}).get();e=this.graph._getDataPoints(s),this.dataPoints[t]=e}return e},s.prototype.setOnLoadCallback=function(t){this.onLoadCallback=t},s.prototype.selectValue=function(t){if(t>=this.values.length)throw"Error: index out of range";this.index=t,this.value=this.values[t]},s.prototype.loadInBackground=function(t){void 0===t&&(t=0);var e=this.graph.frame;if(t0&&(t--,this.setIndex(t))},s.prototype.next=function(){var t=this.getIndex();t0?this.setIndex(0):this.index=void 0},s.prototype.setIndex=function(t){if(!(ts&&(s=0),s>this.values.length-1&&(s=this.values.length-1),s},s.prototype.indexToLeft=function(t){var e=parseFloat(this.frame.bar.style.width)-this.frame.slide.clientWidth-10,i=t/(this.values.length-1)*e,s=i+3;return s},s.prototype._onMouseMove=function(t){var e=t.clientX-this.startClientX,i=this.startSlideX+e,s=this.leftToIndex(i);this.setIndex(s),o.preventDefault()},s.prototype._onMouseUp=function(){this.frame.style.cursor="auto",o.removeEventListener(document,"mousemove",this.onmousemove),o.removeEventListener(document,"mouseup",this.onmouseup),o.preventDefault()},t.exports=s},function(t){function e(t,e,i,s){this._start=0,this._end=0,this._step=1,this.prettyStep=!0,this.precision=5,this._current=0,this.setRange(t,e,i,s)}e.prototype.setRange=function(t,e,i,s){this._start=t?t:0,this._end=e?e:0,this.setStep(i,s)},e.prototype.setStep=function(t,i){void 0===t||0>=t||(void 0!==i&&(this.prettyStep=i),this._step=this.prettyStep===!0?e.calculatePrettyStep(t):t)},e.calculatePrettyStep=function(t){var e=function(t){return Math.log(t)/Math.LN10},i=Math.pow(10,Math.round(e(t))),s=2*Math.pow(10,Math.round(e(t/2))),o=5*Math.pow(10,Math.round(e(t/5))),n=i;return Math.abs(s-t)<=Math.abs(n-t)&&(n=s),Math.abs(o-t)<=Math.abs(n-t)&&(n=o),0>=n&&(n=1),n},e.prototype.getCurrent=function(){return parseFloat(this._current.toPrecision(this.precision))},e.prototype.getStep=function(){return this._step},e.prototype.start=function(){this._current=this._start-this._start%this._step},e.prototype.next=function(){this._current+=this._step},e.prototype.end=function(){return this._current>this._end},t.exports=e},function(t,e,i){function s(t,e,i,r){if(!(this instanceof s))throw new SyntaxError("Constructor must be called with the new operator");if(!(Array.isArray(i)||i instanceof n)&&i instanceof Object){var h=r;r=i,i=h}var u=this;this.defaultOptions={start:null,end:null,autoResize:!0,orientation:"bottom",width:null,height:null,maxHeight:null,minHeight:null},this.options=o.deepExtend({},this.defaultOptions),this._create(t),this.components=[],this.body={dom:this.dom,domProps:this.props,emitter:{on:this.on.bind(this),off:this.off.bind(this),emit:this.emit.bind(this)},hiddenDates:[],util:{snap:null,toScreen:u._toScreen.bind(u),toGlobalScreen:u._toGlobalScreen.bind(u),toTime:u._toTime.bind(u),toGlobalTime:u._toGlobalTime.bind(u)}},this.range=new a(this.body),this.components.push(this.range),this.body.range=this.range,this.timeAxis=new d(this.body),this.components.push(this.timeAxis),this.body.util.snap=this.timeAxis.snap.bind(this.timeAxis),this.currentTime=new l(this.body),this.components.push(this.currentTime),this.customTime=new c(this.body),this.components.push(this.customTime),this.itemSet=new p(this.body),this.components.push(this.itemSet),this.itemsData=null,this.groupsData=null,r&&this.setOptions(r),i&&this.setGroups(i),e?this.setItems(e):this.redraw()}var o=(i(56),i(45),i(1)),n=i(3),r=i(4),a=i(17),h=i(46),d=i(30),l=i(21),c=i(22),p=i(27);s.prototype=new h,s.prototype.setItems=function(t){var e,i=null==this.itemsData;if(e=t?t instanceof n||t instanceof r?t:new n(t,{type:{start:"Date",end:"Date"}}):null,this.itemsData=e,this.itemSet&&this.itemSet.setItems(e),i)if(void 0!=this.options.start||void 0!=this.options.end){if(void 0==this.options.start||void 0==this.options.end)var s=this._getDataRange();var o=void 0!=this.options.start?this.options.start:s.start,a=void 0!=this.options.end?this.options.end:s.end;this.setWindow(o,a,{animate:!1})}else this.fit({animate:!1})},s.prototype.setGroups=function(t){var e;e=t?t instanceof n||t instanceof r?t:new n(t):null,this.groupsData=e,this.itemSet.setGroups(e)},s.prototype.setSelection=function(t,e){this.itemSet&&this.itemSet.setSelection(t),e&&e.focus&&this.focus(t,e)},s.prototype.getSelection=function(){return this.itemSet&&this.itemSet.getSelection()||[]},s.prototype.focus=function(t,e){if(this.itemsData&&void 0!=t){var i=Array.isArray(t)?t:[t],s=this.itemsData.getDataSet().get(i,{type:{start:"Date",end:"Date"}}),o=null,n=null;if(s.forEach(function(t){var e=t.start.valueOf(),i="end"in t?t.end.valueOf():t.start.valueOf();(null===o||o>e)&&(o=e),(null===n||i>n)&&(n=i)}),null!==o&&null!==n){var r=(o+n)/2,a=Math.max(this.range.end-this.range.start,1.1*(n-o)),h=e&&void 0!==e.animate?e.animate:!0;this.range.setRange(r-a/2,r+a/2,h)}}},s.prototype.getItemRange=function(){var t=this.itemsData.getDataSet(),e=null,i=null;if(t){var s=t.min("start");e=s?o.convert(s.start,"Date").valueOf():null;var n=t.max("start");n&&(i=o.convert(n.start,"Date").valueOf());var r=t.max("end");r&&(i=null==i?o.convert(r.end,"Date").valueOf():Math.max(i,o.convert(r.end,"Date").valueOf()))}return{min:null!=e?new Date(e):null,max:null!=i?new Date(i):null}},t.exports=s},function(t,e,i){function s(t,e,i,s){if(!(Array.isArray(i)||i instanceof n)&&i instanceof Object){var r=s;s=i,i=r}var h=this;this.defaultOptions={start:null,end:null,autoResize:!0,orientation:"bottom",width:null,height:null,maxHeight:null,minHeight:null},this.options=o.deepExtend({},this.defaultOptions),this._create(t),this.components=[],this.body={dom:this.dom,domProps:this.props,emitter:{on:this.on.bind(this),off:this.off.bind(this),emit:this.emit.bind(this)},hiddenDates:[],util:{snap:null,toScreen:h._toScreen.bind(h),toGlobalScreen:h._toGlobalScreen.bind(h),toTime:h._toTime.bind(h),toGlobalTime:h._toGlobalTime.bind(h)}},this.range=new a(this.body),this.components.push(this.range),this.body.range=this.range,this.timeAxis=new d(this.body),this.components.push(this.timeAxis),this.body.util.snap=this.timeAxis.snap.bind(this.timeAxis),this.currentTime=new l(this.body),this.components.push(this.currentTime),this.customTime=new c(this.body),this.components.push(this.customTime),this.linegraph=new p(this.body),this.components.push(this.linegraph),this.itemsData=null,this.groupsData=null,s&&this.setOptions(s),i&&this.setGroups(i),e?this.setItems(e):this.redraw()}var o=(i(56),i(45),i(1)),n=i(3),r=i(4),a=i(17),h=i(46),d=i(30),l=i(21),c=i(22),p=i(29);s.prototype=new h,s.prototype.setItems=function(t){var e,i=null==this.itemsData;if(e=t?t instanceof n||t instanceof r?t:new n(t,{type:{start:"Date",end:"Date"}}):null,this.itemsData=e,this.linegraph&&this.linegraph.setItems(e),i)if(void 0!=this.options.start||void 0!=this.options.end){var s=void 0!=this.options.start?this.options.start:null,o=void 0!=this.options.end?this.options.end:null;this.setWindow(s,o,{animate:!1})}else this.fit({animate:!1})},s.prototype.setGroups=function(t){var e;e=t?t instanceof n||t instanceof r?t:new n(t):null,this.groupsData=e,this.linegraph.setGroups(e)},s.prototype.getLegend=function(t,e,i){return void 0===e&&(e=15),void 0===i&&(i=15),void 0!==this.linegraph.groups[t]?this.linegraph.groups[t].getLegend(e,i):"cannot find group:"+t},s.prototype.isGroupVisible=function(t){return void 0!==this.linegraph.groups[t]?this.linegraph.groups[t].visible&&(void 0===this.linegraph.options.groups.visibility[t]||1==this.linegraph.options.groups.visibility[t]):!1},s.prototype.getItemRange=function(){var t=null,e=null;for(var i in this.linegraph.groups)if(this.linegraph.groups.hasOwnProperty(i)&&1==this.linegraph.groups[i].visible)for(var s=0;sr?r:t,e=null==e?r:r>e?r:e}return{min:null!=t?new Date(t):null,max:null!=e?new Date(e):null}},t.exports=s},function(t,e,i){var s=i(44);e.convertHiddenOptions=function(t,e){if(t.hiddenDates=[],e&&1==Array.isArray(e)){for(var i=0;i=4*a){var p=0,u=n.clone();switch(i[h].repeat){case"daily":d.day()!=l.day()&&(p=1),d.dayOfYear(o.dayOfYear()),d.year(o.year()),d.subtract(7,"days"),l.dayOfYear(o.dayOfYear()),l.year(o.year()),l.subtract(7-p,"days"),u.add(1,"weeks");break;case"weekly":var m=l.diff(d,"days"),f=d.day();d.date(o.date()),d.month(o.month()),d.year(o.year()),l=d.clone(),d.day(f),l.day(f),l.add(m,"days"),d.subtract(1,"weeks"),l.subtract(1,"weeks"),u.add(1,"weeks");break;case"monthly":d.month()!=l.month()&&(p=1),d.month(o.month()),d.year(o.year()),d.subtract(1,"months"),l.month(o.month()),l.year(o.year()),l.subtract(1,"months"),l.add(p,"months"),u.add(1,"months");break;case"yearly":d.year()!=l.year()&&(p=1),d.year(o.year()),d.subtract(1,"years"),l.year(o.year()),l.subtract(1,"years"),l.add(p,"years"),u.add(1,"years");break;default:return void console.log("Wrong repeat format, allowed are: daily, weekly, monthly, yearly. Given:",i[h].repeat)}for(;u>d;)switch(t.hiddenDates.push({start:d.valueOf(),end:l.valueOf()}),i[h].repeat){case"daily":d.add(1,"days"),l.add(1,"days");break;case"weekly":d.add(1,"weeks"),l.add(1,"weeks");break;case"monthly":d.add(1,"months"),l.add(1,"months");break;case"yearly":d.add(1,"y"),l.add(1,"y");break;default:return void console.log("Wrong repeat format, allowed are: daily, weekly, monthly, yearly. Given:",i[h].repeat)}t.hiddenDates.push({start:d.valueOf(),end:l.valueOf()})}}e.removeDuplicates(t);var g=e.isHidden(t.range.start,t.hiddenDates),v=e.isHidden(t.range.end,t.hiddenDates),y=t.range.start,b=t.range.end;1==g.hidden&&(y=1==t.range.startToFront?g.startDate-1:g.endDate+1),1==v.hidden&&(b=1==t.range.endToFront?v.startDate-1:v.endDate+1),(1==g.hidden||1==v.hidden)&&t.range._applyRange(y,b)}},e.removeDuplicates=function(t){for(var e=t.hiddenDates,i=[],s=0;s=e[s].start&&e[o].end<=e[s].end?e[o].remove=!0:e[o].start>=e[s].start&&e[o].start<=e[s].end?(e[s].end=e[o].end,e[o].remove=!0):e[o].end>=e[s].start&&e[o].end<=e[s].end&&(e[s].start=e[o].start,e[o].remove=!0));for(var s=0;s=r&&a>o){i=!0;break}}if(1==i&&o=e&&i>r&&(s+=r-n)}return s},e.correctTimeForHidden=function(t,i,o){return o=s(o).toDate().valueOf(),o-=e.getHiddenDurationBefore(t,i,o)},e.getHiddenDurationBefore=function(t,e,i){var o=0;i=s(i).toDate().valueOf();for(var n=0;n=e.start&&a=a&&(o+=a-r)}return o},e.getAccumulatedHiddenDuration=function(t,e,i){for(var s=0,o=0,n=e.start,r=0;r=e.start&&h=i)break;s+=h-a}}return s},e.snapAwayFromHidden=function(t,i,s,o){var n=e.isHidden(i,t);return 1==n.hidden?0>s?1==o?n.startDate-(n.endDate-i)-1:n.startDate-1:1==o?n.endDate+(i-n.startDate)+1:n.endDate+1:i},e.isHidden=function(t,e){for(var i=0;i=s&&o>t)return{hidden:!0,startDate:s,endDate:o}}return{hidden:!1,startDate:s,endDate:o}}},function(t){function e(t,e,i,s,o,n){this.current=0,this.autoScale=!0,this.stepIndex=0,this.step=1,this.scale=1,this.marginStart,this.marginEnd,this.deadSpace=0,this.majorSteps=[1,2,5,10],this.minorSteps=[.25,.5,1,2],this.alignZeros=n,this.setRange(t,e,i,s,o)}e.prototype.setRange=function(t,e,i,s,o){this._start=void 0===o.min?t:o.min,this._end=void 0===o.max?e:o.max,this._start==this._end&&(this._start-=.75,this._end+=1),1==this.autoScale&&this.setMinimumStep(i,s),this.setFirst(o)},e.prototype.setMinimumStep=function(t,e){var i=this._end-this._start,s=1.2*i,o=t*(s/e),n=Math.round(Math.log(s)/Math.LN10),r=-1,a=Math.pow(10,n),h=0;0>n&&(h=n);for(var d=!1,l=h;Math.abs(l)<=Math.abs(n);l++){a=Math.pow(10,l);for(var c=0;c=o){d=!0,r=c;break}}if(1==d)break}this.stepIndex=r,this.scale=a,this.step=a*this.minorSteps[r]},e.prototype.setFirst=function(t){void 0===t&&(t={});var e=void 0===t.min?this._start-2*this.scale*this.minorSteps[this.stepIndex]:t.min,i=void 0===t.max?this._end+this.scale*this.minorSteps[this.stepIndex]:t.max;this.marginEnd=void 0===t.max?this.roundToMinor(i):t.max,this.marginStart=void 0===t.min?this.roundToMinor(e):t.min,1==this.alignZeros&&(this.marginEnd-this.marginStart)%this.step!=0&&(this.marginEnd+=this.marginEnd%this.step),this.deadSpace=this.roundToMinor(i)-i+this.roundToMinor(e)-e,this.marginRange=this.marginEnd-this.marginStart,this.current=this.marginEnd},e.prototype.roundToMinor=function(t){var e=t-t%(this.scale*this.minorSteps[this.stepIndex]);return t%(this.scale*this.minorSteps[this.stepIndex])>.5*this.scale*this.minorSteps[this.stepIndex]?e+this.scale*this.minorSteps[this.stepIndex]:e},e.prototype.hasNext=function(){return this.current>=this.marginStart},e.prototype.next=function(){var t=this.current;this.current-=this.step,this.current==t&&(this.current=this._end)},e.prototype.previous=function(){this.current+=this.step,this.marginEnd+=this.step,this.marginRange=this.marginEnd-this.marginStart},e.prototype.getCurrent=function(t){var e=Math.abs(this.current)0;s--){if("0"!=i[s]){if("."==i[s]||","==i[s]){i=i.slice(0,s);break}break}i=i.slice(0,s)}}else{var o="",n=i.indexOf("e");if(-1!=n&&(o=i.slice(n),i=i.slice(0,n)),n=Math.max(i.indexOf(","),i.indexOf(".")),-1===n?(0!==t&&(i+="."),n=i.length+t):0!==t&&(n+=t+1),n>i.length)for(var r=n-i.length;r>0;r--)i+="0";else i=i.slice(0,n);i+=o}return i},e.prototype.snap=function(){},e.prototype.isMajor=function(){return this.current%(this.scale*this.majorSteps[this.stepIndex])==0},t.exports=e},function(t,e,i){function s(t,e){var i=a().hours(0).minutes(0).seconds(0).milliseconds(0);this.start=i.clone().add(-3,"days").valueOf(),this.end=i.clone().add(4,"days").valueOf(),this.body=t,this.deltaDifference=0,this.scaleOffset=0,this.startToFront=!1,this.endToFront=!0,this.defaultOptions={start:null,end:null,direction:"horizontal",moveable:!0,zoomable:!0,min:null,max:null,zoomMin:10,zoomMax:31536e10},this.options=r.extend({},this.defaultOptions),this.props={touch:{}},this.animateTimer=null,this.body.emitter.on("panstart",this._onDragStart.bind(this)),this.body.emitter.on("panmove",this._onDrag.bind(this)),this.body.emitter.on("panend",this._onDragEnd.bind(this)),this.body.emitter.on("press",this._onHold.bind(this)),this.body.emitter.on("mousewheel",this._onMouseWheel.bind(this)),this.body.emitter.on("touch",this._onTouch.bind(this)),this.body.emitter.on("pinch",this._onPinch.bind(this)),this.setOptions(e)}function o(t){if("horizontal"!=t&&"vertical"!=t)throw new TypeError('Unknown direction "'+t+'". Choose "horizontal" or "vertical".')}function n(t,e){return{x:t.x-r.getAbsoluteLeft(e),y:t.y-r.getAbsoluteTop(e)}}var r=i(1),a=(i(47),i(44)),h=i(20),d=i(15);s.prototype=new h,s.prototype.setOptions=function(t){if(t){var e=["direction","min","max","zoomMin","zoomMax","moveable","zoomable","activate","hiddenDates"];r.selectiveExtend(e,this.options,t),("start"in t||"end"in t)&&this.setRange(t.start,t.end)}},s.prototype.setRange=function(t,e,i,s){s!==!0&&(s=!1);var o=void 0!=t?r.convert(t,"Date").valueOf():null,n=void 0!=e?r.convert(e,"Date").valueOf():null;if(this._cancelAnimation(),i){var a=this,h=this.start,l=this.end,c="number"==typeof i?i:500,p=(new Date).valueOf(),u=!1,m=function(){if(!a.props.touch.dragging){var t=(new Date).valueOf(),e=t-p,i=e>c,g=i||null===o?o:r.easeInOutQuad(e,h,o,c),v=i||null===n?n:r.easeInOutQuad(e,l,n,c);f=a._applyRange(g,v),d.updateHiddenDates(a.body,a.options.hiddenDates),u=u||f,f&&a.body.emitter.emit("rangechange",{start:new Date(a.start),end:new Date(a.end),byUser:s}),i?u&&a.body.emitter.emit("rangechanged",{start:new Date(a.start),end:new Date(a.end),byUser:s}):a.animateTimer=setTimeout(m,20)}};return m()}var f=this._applyRange(o,n);if(d.updateHiddenDates(this.body,this.options.hiddenDates),f){var g={start:new Date(this.start),end:new Date(this.end),byUser:s};this.body.emitter.emit("rangechange",g),this.body.emitter.emit("rangechanged",g)}},s.prototype._cancelAnimation=function(){this.animateTimer&&(clearTimeout(this.animateTimer),this.animateTimer=null)},s.prototype._applyRange=function(t,e){var i,s=null!=t?r.convert(t,"Date").valueOf():this.start,o=null!=e?r.convert(e,"Date").valueOf():this.end,n=null!=this.options.max?r.convert(this.options.max,"Date").valueOf():null,a=null!=this.options.min?r.convert(this.options.min,"Date").valueOf():null;if(isNaN(s)||null===s)throw new Error('Invalid start "'+t+'"');if(isNaN(o)||null===o)throw new Error('Invalid end "'+e+'"');if(s>o&&(o=s),null!==a&&a>s&&(i=a-s,s+=i,o+=i,null!=n&&o>n&&(o=n)),null!==n&&o>n&&(i=o-n,s-=i,o-=i,null!=a&&a>s&&(s=a)),null!==this.options.zoomMin){var h=parseFloat(this.options.zoomMin);0>h&&(h=0),h>o-s&&(this.end-this.start===h?(s=this.start,o=this.end):(i=h-(o-s),s-=i/2,o+=i/2))}if(null!==this.options.zoomMax){var d=parseFloat(this.options.zoomMax);0>d&&(d=0),o-s>d&&(this.end-this.start===d?(s=this.start,o=this.end):(i=o-s-d,s+=i/2,o-=i/2))}var l=this.start!=s||this.end!=o;return s>=this.start&&s<=this.end||o>=this.start&&o<=this.end||this.start>=s&&this.start<=o||this.end>=s&&this.end<=o||this.body.emitter.emit("checkRangedItems"),this.start=s,this.end=o,l},s.prototype.getRange=function(){return{start:this.start,end:this.end}},s.prototype.conversion=function(t,e){return s.conversion(this.start,this.end,t,e)},s.conversion=function(t,e,i,s){return void 0===s&&(s=0),0!=i&&e-t!=0?{offset:t,scale:i/(e-t-s)}:{offset:0,scale:1}},s.prototype._onDragStart=function(t){this.deltaDifference=0,this.previousDelta=0,this.options.moveable&&this.props.touch.allowDragging&&(this.props.touch.start=this.start,this.props.touch.end=this.end,this.props.touch.dragging=!0,this.body.dom.root&&(this.body.dom.root.style.cursor="move"),t.preventDefault())},s.prototype._onDrag=function(t){if(this.options.moveable&&this.props.touch.allowDragging){var e=this.options.direction;o(e);var i="horizontal"==e?t.deltaX:t.deltaY;i-=this.deltaDifference;var s=this.props.touch.end-this.props.touch.start,n=d.getHiddenDurationBetween(this.body.hiddenDates,this.start,this.end);s-=n;var r="horizontal"==e?this.body.domProps.center.width:this.body.domProps.center.height,a=-i/r*s,h=this.props.touch.start+a,l=this.props.touch.end+a,c=d.snapAwayFromHidden(this.body.hiddenDates,h,this.previousDelta-i,!0),p=d.snapAwayFromHidden(this.body.hiddenDates,l,this.previousDelta-i,!0);if(c!=h||p!=l)return this.deltaDifference+=i,this.props.touch.start=c,this.props.touch.end=p,void this._onDrag(t);this.previousDelta=i,this._applyRange(h,l),this.body.emitter.emit("rangechange",{start:new Date(this.start),end:new Date(this.end),byUser:!0}),t.preventDefault()}},s.prototype._onDragEnd=function(){this.options.moveable&&this.props.touch.allowDragging&&(this.props.touch.dragging=!1,this.body.dom.root&&(this.body.dom.root.style.cursor="auto"),this.body.emitter.emit("rangechanged",{start:new Date(this.start),end:new Date(this.end),byUser:!0}))},s.prototype._onMouseWheel=function(t){if(this.options.zoomable&&this.options.moveable){var e=0;if(t.wheelDelta?e=t.wheelDelta/120:t.detail&&(e=-t.detail/3),e){var i;i=0>e?1-e/5:1/(1+e/5);var s=n({x:t.pageX,y:t.pageY},this.body.dom.center),o=this._pointerToDate(s);this.zoom(i,o,e)}t.preventDefault()}},s.prototype._onTouch=function(){this.props.touch.start=this.start,this.props.touch.end=this.end,this.props.touch.allowDragging=!0,this.props.touch.center=null,this.scaleOffset=0,this.deltaDifference=0},s.prototype._onHold=function(){this.props.touch.allowDragging=!1},s.prototype._onPinch=function(t){if(this.options.zoomable&&this.options.moveable){this.props.touch.allowDragging=!1,this.props.touch.center||(this.props.touch.center=n(t.center,this.body.dom.center));var e=1/(t.scale+this.scaleOffset),i=this._pointerToDate(this.props.touch.center),s=d.getHiddenDurationBetween(this.body.hiddenDates,this.start,this.end),o=d.getHiddenDurationBefore(this.body.hiddenDates,this,i),r=s-o,a=i-o+(this.props.touch.start-(i-o))*e,h=i+r+(this.props.touch.end-(i+r))*e;this.startToFront=0>=1-e,this.endToFront=0>=e-1;var l=d.snapAwayFromHidden(this.body.hiddenDates,a,1-e,!0),c=d.snapAwayFromHidden(this.body.hiddenDates,h,e-1,!0);(l!=a||c!=h)&&(this.props.touch.start=l,this.props.touch.end=c,this.scaleOffset=1-t.scale,a=l,h=c),this.setRange(a,h,!1,!0),this.startToFront=!1,this.endToFront=!0,t.preventDefault()}},s.prototype._pointerToDate=function(t){var e,i=this.options.direction;if(o(i),"horizontal"==i)return this.body.util.toTime(t.x).valueOf();var s=this.body.domProps.center.height;return e=this.conversion(s),t.y/e.scale+e.offset},s.prototype.zoom=function(t,e,i){null==e&&(e=(this.start+this.end)/2);var s=d.getHiddenDurationBetween(this.body.hiddenDates,this.start,this.end),o=d.getHiddenDurationBefore(this.body.hiddenDates,this,e),n=s-o,r=e-o+(this.start-(e-o))*t,a=e+n+(this.end-(e+n))*t;this.startToFront=i>0?!1:!0,this.endToFront=-i>0?!1:!0;var h=d.snapAwayFromHidden(this.body.hiddenDates,r,i,!0),l=d.snapAwayFromHidden(this.body.hiddenDates,a,-i,!0);(h!=r||l!=a)&&(r=h,a=l),this.setRange(r,a,!1,!0),this.startToFront=!1,this.endToFront=!0},s.prototype.move=function(t){var e=this.end-this.start,i=this.start+e*t,s=this.end+e*t;this.start=i,this.end=s},s.prototype.moveTo=function(t){var e=(this.start+this.end)/2,i=e-t,s=this.start-i,o=this.end-i;this.setRange(s,o)},t.exports=s},function(t,e){var i=.001;e.orderByStart=function(t){t.sort(function(t,e){return t.data.start-e.data.start})},e.orderByEnd=function(t){t.sort(function(t,e){var i="end"in t.data?t.data.end:t.data.start,s="end"in e.data?e.data.end:e.data.start;return i-s})},e.stack=function(t,i,s){var o,n;if(s)for(o=0,n=t.length;n>o;o++)t[o].top=null;for(o=0,n=t.length;n>o;o++){var r=t[o];if(r.stack&&null===r.top){r.top=i.axis;do{for(var a=null,h=0,d=t.length;d>h;h++){var l=t[h];if(null!==l.top&&l!==r&&l.stack&&e.collision(r,l,i.item)){a=l;break}}null!=a&&(r.top=a.top+a.height+i.item.vertical)}while(a)}}},e.nostack=function(t,e,i){var s,o,n;for(s=0,o=t.length;o>s;s++)if(void 0!==t[s].data.subgroup){n=e.axis;for(var r in i)i.hasOwnProperty(r)&&1==i[r].visible&&i[r].indexe.left&&t.top-s.vertical+ie.top}},function(t,e,i){function s(t,e,i,o){this.current=new Date,this._start=new Date,this._end=new Date,this.autoScale=!0,this.scale="day",this.step=1,this.setRange(t,e,i),this.switchedDay=!1,this.switchedMonth=!1,this.switchedYear=!1,this.hiddenDates=o,void 0===o&&(this.hiddenDates=[]),this.format=s.FORMAT}var o=i(44),n=i(15),r=i(1);s.FORMAT={minorLabels:{millisecond:"SSS",second:"s",minute:"HH:mm",hour:"HH:mm",weekday:"ddd D",day:"D",month:"MMM",year:"YYYY"},majorLabels:{millisecond:"HH:mm:ss",second:"D MMMM HH:mm",minute:"ddd D MMMM",hour:"ddd D MMMM",weekday:"MMMM YYYY",day:"MMMM YYYY",month:"YYYY",year:""}},s.prototype.setFormat=function(t){var e=r.deepExtend({},s.FORMAT);this.format=r.deepExtend(e,t)},s.prototype.setRange=function(t,e,i){if(!(t instanceof Date&&e instanceof Date))throw"No legal start or end date in method setRange";this._start=void 0!=t?new Date(t.valueOf()):new Date,this._end=void 0!=e?new Date(e.valueOf()):new Date,this.autoScale&&this.setMinimumStep(i)},s.prototype.first=function(){this.current=new Date(this._start.valueOf()),this.roundToMinor()},s.prototype.roundToMinor=function(){switch(this.scale){case"year":this.current.setFullYear(this.step*Math.floor(this.current.getFullYear()/this.step)),this.current.setMonth(0);case"month":this.current.setDate(1);case"day":case"weekday":this.current.setHours(0);case"hour":this.current.setMinutes(0);case"minute":this.current.setSeconds(0);case"second":this.current.setMilliseconds(0)}if(1!=this.step)switch(this.scale){case"millisecond":this.current.setMilliseconds(this.current.getMilliseconds()-this.current.getMilliseconds()%this.step);break;case"second":this.current.setSeconds(this.current.getSeconds()-this.current.getSeconds()%this.step);break;case"minute":this.current.setMinutes(this.current.getMinutes()-this.current.getMinutes()%this.step);break;case"hour":this.current.setHours(this.current.getHours()-this.current.getHours()%this.step);break;case"weekday":case"day":this.current.setDate(this.current.getDate()-1-(this.current.getDate()-1)%this.step+1);break;case"month":this.current.setMonth(this.current.getMonth()-this.current.getMonth()%this.step);break;case"year":this.current.setFullYear(this.current.getFullYear()-this.current.getFullYear()%this.step)}},s.prototype.hasNext=function(){return this.current.valueOf()<=this._end.valueOf()},s.prototype.next=function(){var t=this.current.valueOf();if(this.current.getMonth()<6)switch(this.scale){case"millisecond":this.current=new Date(this.current.valueOf()+this.step);break;case"second":this.current=new Date(this.current.valueOf()+1e3*this.step);break;case"minute":this.current=new Date(this.current.valueOf()+1e3*this.step*60);break;case"hour":this.current=new Date(this.current.valueOf()+1e3*this.step*60*60);var e=this.current.getHours();this.current.setHours(e-e%this.step);break;case"weekday":case"day":this.current.setDate(this.current.getDate()+this.step);break;case"month":this.current.setMonth(this.current.getMonth()+this.step);break;case"year":this.current.setFullYear(this.current.getFullYear()+this.step)}else switch(this.scale){case"millisecond":this.current=new Date(this.current.valueOf()+this.step);break;case"second":this.current.setSeconds(this.current.getSeconds()+this.step);break;case"minute":this.current.setMinutes(this.current.getMinutes()+this.step); +break;case"hour":this.current.setHours(this.current.getHours()+this.step);break;case"weekday":case"day":this.current.setDate(this.current.getDate()+this.step);break;case"month":this.current.setMonth(this.current.getMonth()+this.step);break;case"year":this.current.setFullYear(this.current.getFullYear()+this.step)}if(1!=this.step)switch(this.scale){case"millisecond":this.current.getMilliseconds()0&&(this.step=e),this.autoScale=!1},s.prototype.setAutoScale=function(t){this.autoScale=t},s.prototype.setMinimumStep=function(t){if(void 0!=t){var e=31104e6,i=2592e6,s=864e5,o=36e5,n=6e4,r=1e3,a=1;1e3*e>t&&(this.scale="year",this.step=1e3),500*e>t&&(this.scale="year",this.step=500),100*e>t&&(this.scale="year",this.step=100),50*e>t&&(this.scale="year",this.step=50),10*e>t&&(this.scale="year",this.step=10),5*e>t&&(this.scale="year",this.step=5),e>t&&(this.scale="year",this.step=1),3*i>t&&(this.scale="month",this.step=3),i>t&&(this.scale="month",this.step=1),5*s>t&&(this.scale="day",this.step=5),2*s>t&&(this.scale="day",this.step=2),s>t&&(this.scale="day",this.step=1),s/2>t&&(this.scale="weekday",this.step=1),4*o>t&&(this.scale="hour",this.step=4),o>t&&(this.scale="hour",this.step=1),15*n>t&&(this.scale="minute",this.step=15),10*n>t&&(this.scale="minute",this.step=10),5*n>t&&(this.scale="minute",this.step=5),n>t&&(this.scale="minute",this.step=1),15*r>t&&(this.scale="second",this.step=15),10*r>t&&(this.scale="second",this.step=10),5*r>t&&(this.scale="second",this.step=5),r>t&&(this.scale="second",this.step=1),200*a>t&&(this.scale="millisecond",this.step=200),100*a>t&&(this.scale="millisecond",this.step=100),50*a>t&&(this.scale="millisecond",this.step=50),10*a>t&&(this.scale="millisecond",this.step=10),5*a>t&&(this.scale="millisecond",this.step=5),a>t&&(this.scale="millisecond",this.step=1)}},s.prototype.snap=function(t){var e=new Date(t.valueOf());if("year"==this.scale){var i=e.getFullYear()+Math.round(e.getMonth()/12);e.setFullYear(Math.round(i/this.step)*this.step),e.setMonth(0),e.setDate(0),e.setHours(0),e.setMinutes(0),e.setSeconds(0),e.setMilliseconds(0)}else if("month"==this.scale)e.getDate()>15?(e.setDate(1),e.setMonth(e.getMonth()+1)):e.setDate(1),e.setHours(0),e.setMinutes(0),e.setSeconds(0),e.setMilliseconds(0);else if("day"==this.scale){switch(this.step){case 5:case 2:e.setHours(24*Math.round(e.getHours()/24));break;default:e.setHours(12*Math.round(e.getHours()/12))}e.setMinutes(0),e.setSeconds(0),e.setMilliseconds(0)}else if("weekday"==this.scale){switch(this.step){case 5:case 2:e.setHours(12*Math.round(e.getHours()/12));break;default:e.setHours(6*Math.round(e.getHours()/6))}e.setMinutes(0),e.setSeconds(0),e.setMilliseconds(0)}else if("hour"==this.scale){switch(this.step){case 4:e.setMinutes(60*Math.round(e.getMinutes()/60));break;default:e.setMinutes(30*Math.round(e.getMinutes()/30))}e.setSeconds(0),e.setMilliseconds(0)}else if("minute"==this.scale){switch(this.step){case 15:case 10:e.setMinutes(5*Math.round(e.getMinutes()/5)),e.setSeconds(0);break;case 5:e.setSeconds(60*Math.round(e.getSeconds()/60));break;default:e.setSeconds(30*Math.round(e.getSeconds()/30))}e.setMilliseconds(0)}else if("second"==this.scale)switch(this.step){case 15:case 10:e.setSeconds(5*Math.round(e.getSeconds()/5)),e.setMilliseconds(0);break;case 5:e.setMilliseconds(1e3*Math.round(e.getMilliseconds()/1e3));break;default:e.setMilliseconds(500*Math.round(e.getMilliseconds()/500))}else if("millisecond"==this.scale){var s=this.step>5?this.step/2:1;e.setMilliseconds(Math.round(e.getMilliseconds()/s)*s)}return e},s.prototype.isMajor=function(){if(1==this.switchedYear)switch(this.switchedYear=!1,this.scale){case"year":case"month":case"weekday":case"day":case"hour":case"minute":case"second":case"millisecond":return!0;default:return!1}else if(1==this.switchedMonth)switch(this.switchedMonth=!1,this.scale){case"weekday":case"day":case"hour":case"minute":case"second":case"millisecond":return!0;default:return!1}else if(1==this.switchedDay)switch(this.switchedDay=!1,this.scale){case"millisecond":case"second":case"minute":case"hour":return!0;default:return!1}switch(this.scale){case"millisecond":return 0==this.current.getMilliseconds();case"second":return 0==this.current.getSeconds();case"minute":return 0==this.current.getHours()&&0==this.current.getMinutes();case"hour":return 0==this.current.getHours();case"weekday":case"day":return 1==this.current.getDate();case"month":return 0==this.current.getMonth();case"year":return!1;default:return!1}},s.prototype.getLabelMinor=function(t){void 0==t&&(t=this.current);var e=this.format.minorLabels[this.scale];return e&&e.length>0?o(t).format(e):""},s.prototype.getLabelMajor=function(t){void 0==t&&(t=this.current);var e=this.format.majorLabels[this.scale];return e&&e.length>0?o(t).format(e):""},s.prototype.getClassName=function(){function t(t){return t/h%2==0?" even":" odd"}function e(t){return t.isSame(new Date,"day")?" today":t.isSame(o().add(1,"day"),"day")?" tomorrow":t.isSame(o().add(-1,"day"),"day")?" yesterday":""}function i(t){return t.isSame(new Date,"week")?" current-week":""}function s(t){return t.isSame(new Date,"month")?" current-month":""}function n(t){return t.isSame(new Date,"year")?" current-year":""}var r=o(this.current),a=r.locale?r.locale("en"):r.lang("en"),h=this.step;switch(this.scale){case"millisecond":return t(a.milliseconds()).trim();case"second":return t(a.seconds()).trim();case"minute":return t(a.minutes()).trim();case"hour":var d=a.hours();return 4==this.step&&(d=d+"-"+(d+4)),d+"h"+e(a)+t(a.hours());case"weekday":return a.format("dddd").toLowerCase()+e(a)+i(a)+t(a.date());case"day":var l=a.date(),c=a.format("MMMM").toLowerCase();return"day"+l+" "+c+s(a)+t(l-1);case"month":return a.format("MMMM").toLowerCase()+s(a)+t(a.month());case"year":var p=a.year();return"year"+p+n(a)+t(p);default:return""}},t.exports=s},function(t){function e(){this.options=null,this.props=null}e.prototype.setOptions=function(t){t&&util.extend(this.options,t)},e.prototype.redraw=function(){return!1},e.prototype.destroy=function(){},e.prototype._isResized=function(){var t=this.props._previousWidth!==this.props.width||this.props._previousHeight!==this.props.height;return this.props._previousWidth=this.props.width,this.props._previousHeight=this.props.height,t},t.exports=e},function(t,e,i){function s(t,e){this.body=t,this.defaultOptions={showCurrentTime:!0,locales:a,locale:"en"},this.options=o.extend({},this.defaultOptions),this.offset=0,this._create(),this.setOptions(e)}var o=i(1),n=i(20),r=i(44),a=i(48);s.prototype=new n,s.prototype._create=function(){var t=document.createElement("div");t.className="currenttime",t.style.position="absolute",t.style.top="0px",t.style.height="100%",this.bar=t},s.prototype.destroy=function(){this.options.showCurrentTime=!1,this.redraw(),this.body=null},s.prototype.setOptions=function(t){t&&o.selectiveExtend(["showCurrentTime","locale","locales"],this.options,t)},s.prototype.redraw=function(){if(this.options.showCurrentTime){var t=this.body.dom.backgroundVertical;this.bar.parentNode!=t&&(this.bar.parentNode&&this.bar.parentNode.removeChild(this.bar),t.appendChild(this.bar),this.start());var e=new Date((new Date).valueOf()+this.offset),i=this.body.util.toScreen(e),s=this.options.locales[this.options.locale],o=s.current+" "+s.time+": "+r(e).format("dddd, MMMM Do YYYY, H:mm:ss");o=o.charAt(0).toUpperCase()+o.substring(1),this.bar.style.left=i+"px",this.bar.title=o}else this.bar.parentNode&&this.bar.parentNode.removeChild(this.bar),this.stop();return!1},s.prototype.start=function(){function t(){e.stop();var i=e.body.range.conversion(e.body.domProps.center.width).scale,s=1/i/10;30>s&&(s=30),s>1e3&&(s=1e3),e.redraw(),e.currentTimeTimer=setTimeout(t,s)}var e=this;t()},s.prototype.stop=function(){void 0!==this.currentTimeTimer&&(clearTimeout(this.currentTimeTimer),delete this.currentTimeTimer)},s.prototype.setCurrentTime=function(t){var e=o.convert(t,"Date").valueOf(),i=(new Date).valueOf();this.offset=e-i,this.redraw()},s.prototype.getCurrentTime=function(){return new Date((new Date).valueOf()+this.offset)},t.exports=s},function(t,e,i){function s(t,e){this.body=t,this.defaultOptions={showCustomTime:!1,locales:h,locale:"en"},this.options=n.extend({},this.defaultOptions),this.customTime=new Date,this.eventParams={},this._create(),this.setOptions(e)}var o=i(45),n=i(1),r=i(20),a=i(44),h=i(48);s.prototype=new r,s.prototype.setOptions=function(t){t&&n.selectiveExtend(["showCustomTime","locale","locales"],this.options,t)},s.prototype._create=function(){var t=document.createElement("div");t.className="customtime",t.style.position="absolute",t.style.top="0px",t.style.height="100%",this.bar=t;var e=document.createElement("div");e.style.position="relative",e.style.top="0px",e.style.left="-10px",e.style.height="100%",e.style.width="20px",t.appendChild(e),this.hammer=new o(e),this.hammer.on("panstart",this._onDragStart.bind(this)),this.hammer.on("panmove",this._onDrag.bind(this)),this.hammer.on("panend",this._onDragEnd.bind(this)),this.hammer.on("pan",function(t){t.preventDefault()})},s.prototype.destroy=function(){this.options.showCustomTime=!1,this.redraw(),this.hammer.enable(!1),this.hammer=null,this.body=null},s.prototype.redraw=function(){if(this.options.showCustomTime){var t=this.body.dom.backgroundVertical;this.bar.parentNode!=t&&(this.bar.parentNode&&this.bar.parentNode.removeChild(this.bar),t.appendChild(this.bar));var e=this.body.util.toScreen(this.customTime),i=this.options.locales[this.options.locale],s=i.time+": "+a(this.customTime).format("dddd, MMMM Do YYYY, H:mm:ss");s=s.charAt(0).toUpperCase()+s.substring(1),this.bar.style.left=e+"px",this.bar.title=s}else this.bar.parentNode&&this.bar.parentNode.removeChild(this.bar);return!1},s.prototype.setCustomTime=function(t){this.customTime=n.convert(t,"Date"),this.redraw()},s.prototype.getCustomTime=function(){return new Date(this.customTime.valueOf())},s.prototype._onDragStart=function(t){this.eventParams.dragging=!0,this.eventParams.customTime=this.customTime,t.stopPropagation(),t.preventDefault()},s.prototype._onDrag=function(t){if(this.eventParams.dragging){var e=this.body.util.toScreen(this.eventParams.customTime)+t.deltaX,i=this.body.util.toTime(e);this.setCustomTime(i),this.body.emitter.emit("timechange",{time:new Date(this.customTime.valueOf())}),t.stopPropagation(),t.preventDefault()}},s.prototype._onDragEnd=function(t){this.eventParams.dragging&&(this.body.emitter.emit("timechanged",{time:new Date(this.customTime.valueOf())}),t.stopPropagation(),t.preventDefault())},t.exports=s},function(t,e,i){function s(t,e,i,s){this.id=o.randomUUID(),this.body=t,this.defaultOptions={orientation:"left",showMinorLabels:!0,showMajorLabels:!0,icons:!0,majorLinesOffset:7,minorLinesOffset:4,labelOffsetX:10,labelOffsetY:2,iconWidth:20,width:"40px",visible:!0,alignZeros:!0,customRange:{left:{min:void 0,max:void 0},right:{min:void 0,max:void 0}},title:{left:{text:void 0},right:{text:void 0}},format:{left:{decimals:void 0},right:{decimals:void 0}}},this.linegraphOptions=s,this.linegraphSVG=i,this.props={},this.DOMelements={lines:{},labels:{},title:{}},this.dom={},this.range={start:0,end:0},this.options=o.extend({},this.defaultOptions),this.conversionFactor=1,this.setOptions(e),this.width=Number((""+this.options.width).replace("px","")),this.minWidth=this.width,this.height=this.linegraphSVG.offsetHeight,this.hidden=!1,this.stepPixels=25,this.stepPixelsForced=25,this.zeroCrossing=-1,this.lineOffset=0,this.master=!0,this.svgElements={},this.iconsRemoved=!1,this.groups={},this.amountOfGroups=0,this._create();var n=this;this.body.emitter.on("verticalDrag",function(){n.dom.lineContainer.style.top=n.body.domProps.scrollTop+"px"})}var o=i(1),n=i(2),r=i(20),a=i(16);s.prototype=new r,s.prototype.addGroup=function(t,e){this.groups.hasOwnProperty(t)||(this.groups[t]=e),this.amountOfGroups+=1},s.prototype.updateGroup=function(t,e){this.groups[t]=e},s.prototype.removeGroup=function(t){this.groups.hasOwnProperty(t)&&(delete this.groups[t],this.amountOfGroups-=1)},s.prototype.setOptions=function(t){if(t){var e=!1;this.options.orientation!=t.orientation&&void 0!==t.orientation&&(e=!0);var i=["orientation","showMinorLabels","showMajorLabels","icons","majorLinesOffset","minorLinesOffset","labelOffsetX","labelOffsetY","iconWidth","width","visible","customRange","title","format","alignZeros"];o.selectiveExtend(i,this.options,t),this.minWidth=Number((""+this.options.width).replace("px","")),1==e&&this.dom.frame&&(this.hide(),this.show())}},s.prototype._create=function(){this.dom.frame=document.createElement("div"),this.dom.frame.style.width=this.options.width,this.dom.frame.style.height=this.height,this.dom.lineContainer=document.createElement("div"),this.dom.lineContainer.style.width="100%",this.dom.lineContainer.style.height=this.height,this.dom.lineContainer.style.position="relative",this.svg=document.createElementNS("http://www.w3.org/2000/svg","svg"),this.svg.style.position="absolute",this.svg.style.top="0px",this.svg.style.height="100%",this.svg.style.width="100%",this.svg.style.display="block",this.dom.frame.appendChild(this.svg)},s.prototype._redrawGroupIcons=function(){n.prepareElements(this.svgElements);var t,e=this.options.iconWidth,i=15,s=4,o=s+.5*i;t="left"==this.options.orientation?s:this.width-e-s;for(var r in this.groups)this.groups.hasOwnProperty(r)&&(1!=this.groups[r].visible||void 0!==this.linegraphOptions.visibility[r]&&1!=this.linegraphOptions.visibility[r]||(this.groups[r].drawIcon(t,o,this.svgElements,this.svg,e,i),o+=i+s));n.cleanupElements(this.svgElements),this.iconsRemoved=!1},s.prototype._cleanupIcons=function(){0==this.iconsRemoved&&(n.prepareElements(this.svgElements),n.cleanupElements(this.svgElements),this.iconsRemoved=!0)},s.prototype.show=function(){this.hidden=!1,this.dom.frame.parentNode||("left"==this.options.orientation?this.body.dom.left.appendChild(this.dom.frame):this.body.dom.right.appendChild(this.dom.frame)),this.dom.lineContainer.parentNode||this.body.dom.backgroundHorizontal.appendChild(this.dom.lineContainer)},s.prototype.hide=function(){this.hidden=!0,this.dom.frame.parentNode&&this.dom.frame.parentNode.removeChild(this.dom.frame),this.dom.lineContainer.parentNode&&this.dom.lineContainer.parentNode.removeChild(this.dom.lineContainer)},s.prototype.setRange=function(t,e){0==this.master&&1==this.options.alignZeros&&-1!=this.zeroCrossing&&t>0&&(t=0),this.range.start=t,this.range.end=e},s.prototype.redraw=function(){var t=!1,e=0;this.dom.lineContainer.style.top=this.body.domProps.scrollTop+"px";for(var i in this.groups)this.groups.hasOwnProperty(i)&&(1!=this.groups[i].visible||void 0!==this.linegraphOptions.visibility[i]&&1!=this.linegraphOptions.visibility[i]||e++);if(0==this.amountOfGroups||0==e)this.hide();else{this.show(),this.height=Number(this.linegraphSVG.style.height.replace("px","")),this.dom.lineContainer.style.height=this.height+"px",this.width=1==this.options.visible?Number((""+this.options.width).replace("px","")):0;var s=this.props,o=this.dom.frame;o.className="dataaxis",this._calculateCharSize();var n=this.options.orientation,r=this.options.showMinorLabels,a=this.options.showMajorLabels;s.minorLabelHeight=r?s.minorCharHeight:0,s.majorLabelHeight=a?s.majorCharHeight:0,s.minorLineWidth=this.body.dom.backgroundHorizontal.offsetWidth-this.lineOffset-this.width+2*this.options.minorLinesOffset,s.minorLineHeight=1,s.majorLineWidth=this.body.dom.backgroundHorizontal.offsetWidth-this.lineOffset-this.width+2*this.options.majorLinesOffset,s.majorLineHeight=1,"left"==n?(o.style.top="0",o.style.left="0",o.style.bottom="",o.style.width=this.width+"px",o.style.height=this.height+"px",this.props.width=this.body.domProps.left.width,this.props.height=this.body.domProps.left.height):(o.style.top="",o.style.bottom="0",o.style.left="0",o.style.width=this.width+"px",o.style.height=this.height+"px",this.props.width=this.body.domProps.right.width,this.props.height=this.body.domProps.right.height),t=this._redrawLabels(),t=this._isResized()||t,1==this.options.icons?this._redrawGroupIcons():this._cleanupIcons(),this._redrawTitle(n)}return t},s.prototype._redrawLabels=function(){var t=!1;n.prepareElements(this.DOMelements.lines),n.prepareElements(this.DOMelements.labels);var e=this.options.orientation,i=this.master?this.props.majorCharHeight||10:this.stepPixelsForced,s=new a(this.range.start,this.range.end,i,this.dom.frame.offsetHeight,this.options.customRange[this.options.orientation],0==this.master&&this.options.alignZeros);this.step=s;var o=(this.dom.frame.offsetHeight-s.deadSpace*(this.dom.frame.offsetHeight/s.marginRange))/((s.marginRange-s.deadSpace)/s.step);this.stepPixels=o;var r=this.height/o,h=0;if(0==this.master){o=this.stepPixelsForced,h=Math.round(this.dom.frame.offsetHeight/o-r);for(var d=0;.5*h>d;d++)s.previous();if(r=this.height/o,-1!=this.zeroCrossing&&1==this.options.alignZeros){var l=s.marginEnd/s.step-this.zeroCrossing;if(l>0)for(var d=0;l>d;d++)s.next();else if(0>l)for(var d=0;-l>d;d++)s.previous()}}else r+=.25;this.valueAtZero=s.marginEnd;var c,p=0,u=1;void 0!==this.options.format[e]&&(c=this.options.format[e].decimals),this.maxLabelSize=0;for(var m=0;u=0&&this._redrawLabel(m-2,s.getCurrent(c),e,"yAxis major",this.props.majorCharHeight),this._redrawLine(m,e,"grid horizontal major",this.options.majorLinesOffset,this.props.majorLineWidth)):this._redrawLine(m,e,"grid horizontal minor",this.options.minorLinesOffset,this.props.minorLineWidth),1==this.master&&0==s.current&&(this.zeroCrossing=u),u++}this.conversionFactor=0==this.master?m/(this.valueAtZero-s.current):this.dom.frame.offsetHeight/s.marginRange;var g=0;void 0!==this.options.title[e]&&void 0!==this.options.title[e].text&&(g=this.props.titleCharHeight);var v=1==this.options.icons?Math.max(this.options.iconWidth,g)+this.options.labelOffsetX+15:g+this.options.labelOffsetX+15;return this.maxLabelSize>this.width-v&&1==this.options.visible?(this.width=this.maxLabelSize+v,this.options.width=this.width+"px",n.cleanupElements(this.DOMelements.lines),n.cleanupElements(this.DOMelements.labels),this.redraw(),t=!0):this.maxLabelSizethis.minWidth?(this.width=Math.max(this.minWidth,this.maxLabelSize+v),this.options.width=this.width+"px",n.cleanupElements(this.DOMelements.lines),n.cleanupElements(this.DOMelements.labels),this.redraw(),t=!0):(n.cleanupElements(this.DOMelements.lines),n.cleanupElements(this.DOMelements.labels),t=!1),t},s.prototype.convertValue=function(t){var e=this.valueAtZero-t,i=e*this.conversionFactor;return i},s.prototype._redrawLabel=function(t,e,i,s,o){var r=n.getDOMElement("div",this.DOMelements.labels,this.dom.frame);r.className=s,r.innerHTML=e,"left"==i?(r.style.left="-"+this.options.labelOffsetX+"px",r.style.textAlign="right"):(r.style.right="-"+this.options.labelOffsetX+"px",r.style.textAlign="left"),r.style.top=t-.5*o+this.options.labelOffsetY+"px",e+="";var a=Math.max(this.props.majorCharWidth,this.props.minorCharWidth);this.maxLabelSized;d++){var c=this.visibleItems[d];c.repositionY(e)}return s},s.prototype._calculateHeight=function(t){var e,i=this.visibleItems;this.resetSubgroups();var s=this;if(i.length){var n=i[0].top,r=i[0].top+i[0].height;if(o.forEach(i,function(t){n=Math.min(n,t.top),r=Math.max(r,t.top+t.height),void 0!==t.data.subgroup&&(s.subgroups[t.data.subgroup].height=Math.max(s.subgroups[t.data.subgroup].height,t.height),s.subgroups[t.data.subgroup].visible=!0)}),n>t.axis){var a=n-t.axis;r-=a,o.forEach(i,function(t){t.top-=a})}e=r+t.item.vertical/2}else e=t.axis+t.item.vertical;return e=Math.max(e,this.props.label.height)},s.prototype.show=function(){this.dom.label.parentNode||this.itemSet.dom.labelSet.appendChild(this.dom.label),this.dom.foreground.parentNode||this.itemSet.dom.foreground.appendChild(this.dom.foreground),this.dom.background.parentNode||this.itemSet.dom.background.appendChild(this.dom.background),this.dom.axis.parentNode||this.itemSet.dom.axis.appendChild(this.dom.axis)},s.prototype.hide=function(){var t=this.dom.label;t.parentNode&&t.parentNode.removeChild(t);var e=this.dom.foreground;e.parentNode&&e.parentNode.removeChild(e);var i=this.dom.background;i.parentNode&&i.parentNode.removeChild(i);var s=this.dom.axis;s.parentNode&&s.parentNode.removeChild(s)},s.prototype.add=function(t){if(this.items[t.id]=t,t.setParent(this),void 0!==t.data.subgroup&&(void 0===this.subgroups[t.data.subgroup]&&(this.subgroups[t.data.subgroup]={height:0,visible:!1,index:this.subgroupIndex,items:[]},this.subgroupIndex++),this.subgroups[t.data.subgroup].items.push(t)),this.orderSubgroups(),-1==this.visibleItems.indexOf(t)){var e=this.itemSet.body.range;this._checkIfVisible(t,this.visibleItems,e)}},s.prototype.orderSubgroups=function(){if(void 0!==this.subgroupOrderer){var t=[];if("string"==typeof this.subgroupOrderer){for(var e in this.subgroups)t.push({subgroup:e,sortField:this.subgroups[e].items[0].data[this.subgroupOrderer]});t.sort(function(t,e){return t.sortField-e.sortField})}else if("function"==typeof this.subgroupOrderer){for(var e in this.subgroups)t.push(this.subgroups[e].items[0].data);t.sort(this.subgroupOrderer)}if(t.length>0)for(var i=0;it?-1:l>=t?0:1};if(e.length>0)for(n=0;nl}),1==this.checkRangedItems)for(this.checkRangedItems=!1,n=0;nl})}for(n=0;n=0&&(n=e[r],!o(n));r--)void 0===s[n.id]&&(s[n.id]=!0,i.push(n));for(r=t+1;rs;s++){var n=this.visibleItems[s];n.repositionY(e)}return i},s.prototype.show=function(){this.dom.background.parentNode||this.itemSet.dom.background.appendChild(this.dom.background)},t.exports=s},function(t,e,i){function s(t,e){this.body=t,this.defaultOptions={type:null,orientation:"bottom",align:"auto",stack:!0,groupOrder:null,selectable:!0,editable:{updateTime:!1,updateGroup:!1,add:!1,remove:!1},onAdd:function(t,e){e(t)},onUpdate:function(t,e){e(t)},onMove:function(t,e){e(t)},onRemove:function(t,e){e(t)},onMoving:function(t,e){e(t)},margin:{item:{horizontal:10,vertical:10},axis:20},padding:5},this.options=n.extend({},this.defaultOptions),this.itemOptions={type:{start:"Date",end:"Date"}},this.conversion={toScreen:t.util.toScreen,toTime:t.util.toTime},this.dom={},this.props={},this.hammer=null;var i=this;this.itemsData=null,this.groupsData=null,this.itemListeners={add:function(t,e){i._onAdd(e.items)},update:function(t,e){i._onUpdate(e.items)},remove:function(t,e){i._onRemove(e.items)}},this.groupListeners={add:function(t,e){i._onAddGroups(e.items)},update:function(t,e){i._onUpdateGroups(e.items)},remove:function(t,e){i._onRemoveGroups(e.items)}},this.items={},this.groups={},this.groupIds=[],this.selection=[],this.stackDirty=!0,this.touchParams={},this._create(),this.setOptions(e)}var o=i(45),n=i(1),r=i(3),a=i(4),h=i(20),d=i(25),l=i(26),c=i(33),p=i(34),u=i(35),m=i(32),f="__ungrouped__",g="__background__";s.prototype=new h,s.types={background:m,box:c,range:u,point:p},s.prototype._create=function(){var t=document.createElement("div");t.className="itemset",t["timeline-itemset"]=this,this.dom.frame=t;var e=document.createElement("div");e.className="background",t.appendChild(e),this.dom.background=e;var i=document.createElement("div");i.className="foreground",t.appendChild(i),this.dom.foreground=i;var s=document.createElement("div");s.className="axis",this.dom.axis=s;var n=document.createElement("div");n.className="labelset",this.dom.labelSet=n,this._updateUngrouped();var r=new l(g,null,this);r.show(),this.groups[g]=r,this.hammer=new o(this.body.dom.centerContainer),this.hammer.on("hammer.input",function(t){t.isFirst&&this._onTouch(t)}.bind(this)),this.hammer.on("panstart",this._onDragStart.bind(this)),this.hammer.on("panmove",this._onDrag.bind(this)),this.hammer.on("panend",this._onDragEnd.bind(this)),this.hammer.on("tap",this._onSelectItem.bind(this)),this.hammer.on("press",this._onMultiSelectItem.bind(this)),this.hammer.on("doubletap",this._onAddItem.bind(this)),this.show()},s.prototype.setOptions=function(t){if(t){var e=["type","align","orientation","padding","stack","selectable","groupOrder","dataAttributes","template","hide"];n.selectiveExtend(e,this.options,t),"margin"in t&&("number"==typeof t.margin?(this.options.margin.axis=t.margin,this.options.margin.item.horizontal=t.margin,this.options.margin.item.vertical=t.margin):"object"==typeof t.margin&&(n.selectiveExtend(["axis"],this.options.margin,t.margin),"item"in t.margin&&("number"==typeof t.margin.item?(this.options.margin.item.horizontal=t.margin.item,this.options.margin.item.vertical=t.margin.item):"object"==typeof t.margin.item&&n.selectiveExtend(["horizontal","vertical"],this.options.margin.item,t.margin.item)))),"editable"in t&&("boolean"==typeof t.editable?(this.options.editable.updateTime=t.editable,this.options.editable.updateGroup=t.editable,this.options.editable.add=t.editable,this.options.editable.remove=t.editable):"object"==typeof t.editable&&n.selectiveExtend(["updateTime","updateGroup","add","remove"],this.options.editable,t.editable));var i=function(e){var i=t[e];if(i){if(!(i instanceof Function))throw new Error("option "+e+" must be a function "+e+"(item, callback)");this.options[e]=i}}.bind(this);["onAdd","onUpdate","onRemove","onMove","onMoving"].forEach(i),this.markDirty()}},s.prototype.markDirty=function(){this.groupIds=[],this.stackDirty=!0},s.prototype.destroy=function(){this.hide(),this.setItems(null),this.setGroups(null),this.hammer=null,this.body=null,this.conversion=null},s.prototype.hide=function(){this.dom.frame.parentNode&&this.dom.frame.parentNode.removeChild(this.dom.frame),this.dom.axis.parentNode&&this.dom.axis.parentNode.removeChild(this.dom.axis),this.dom.labelSet.parentNode&&this.dom.labelSet.parentNode.removeChild(this.dom.labelSet)},s.prototype.show=function(){this.dom.frame.parentNode||this.body.dom.center.appendChild(this.dom.frame),this.dom.axis.parentNode||this.body.dom.backgroundVertical.appendChild(this.dom.axis),this.dom.labelSet.parentNode||this.body.dom.left.appendChild(this.dom.labelSet)},s.prototype.setSelection=function(t){var e,i,s,o;for(void 0==t&&(t=[]),Array.isArray(t)||(t=[t]),e=0,i=this.selection.length;i>e;e++)s=this.selection[e],o=this.items[s],o&&o.unselect();for(this.selection=[],e=0,i=t.length;i>e;e++)s=t[e],o=this.items[s],o&&(this.selection.push(s),o.select())},s.prototype.getSelection=function(){return this.selection.concat([])},s.prototype.getVisibleItems=function(){var t=this.body.range.getRange(),e=this.body.util.toScreen(t.start),i=this.body.util.toScreen(t.end),s=[];for(var o in this.groups)if(this.groups.hasOwnProperty(o))for(var n=this.groups[o],r=n.visibleItems,a=0;ae&&s.push(h.id)}return s},s.prototype._deselect=function(t){for(var e=this.selection,i=0,s=e.length;s>i;i++)if(e[i]==t){e.splice(i,1);break}},s.prototype.redraw=function(){var t=this.options.margin,e=this.body.range,i=n.option.asSize,s=this.options,o=s.orientation,r=!1,a=this.dom.frame,h=s.editable.updateTime||s.editable.updateGroup;this.props.top=this.body.domProps.top.height+this.body.domProps.border.top,this.props.left=this.body.domProps.left.width+this.body.domProps.border.left,a.className="itemset"+(h?" editable":""),r=this._orderGroups()||r;var d=e.end-e.start,l=d!=this.lastVisibleInterval||this.props.width!=this.props.lastWidth;l&&(this.stackDirty=!0),this.lastVisibleInterval=d,this.props.lastWidth=this.props.width;var c=this.stackDirty,p=this._firstGroup(),u={item:t.item,axis:t.axis},m={item:t.item,axis:t.item.vertical/2},f=0,v=t.axis+t.item.vertical;return this.groups[g].redraw(e,m,c),n.forEach(this.groups,function(t){var i=t==p?u:m,s=t.redraw(e,i,c);r=s||r,f+=t.height}),f=Math.max(f,v),this.stackDirty=!1,a.style.height=i(f),this.props.width=a.offsetWidth,this.props.height=f,this.dom.axis.style.top=i("top"==o?this.body.domProps.top.height+this.body.domProps.border.top:this.body.domProps.top.height+this.body.domProps.centerContainer.height),this.dom.axis.style.left="0",r=this._isResized()||r},s.prototype._firstGroup=function(){var t="top"==this.options.orientation?0:this.groupIds.length-1,e=this.groupIds[t],i=this.groups[e]||this.groups[f];return i||null},s.prototype._updateUngrouped=function(){{var t,e,i=this.groups[f];this.groups[g]}if(this.groupsData){if(i){i.hide(),delete this.groups[f];for(e in this.items)if(this.items.hasOwnProperty(e)){t=this.items[e],t.parent&&t.parent.remove(t);var s=this._getGroupId(t.data),o=this.groups[s];o&&o.add(t)||t.hide()}}}else if(!i){var n=null,r=null;i=new d(n,r,this),this.groups[f]=i;for(e in this.items)this.items.hasOwnProperty(e)&&(t=this.items[e],i.add(t));i.show()}},s.prototype.getLabelSet=function(){return this.dom.labelSet},s.prototype.setItems=function(t){var e,i=this,s=this.itemsData;if(t){if(!(t instanceof r||t instanceof a))throw new TypeError("Data must be an instance of DataSet or DataView");this.itemsData=t}else this.itemsData=null;if(s&&(n.forEach(this.itemListeners,function(t,e){s.off(e,t)}),e=s.getIds(),this._onRemove(e)),this.itemsData){var o=this.id;n.forEach(this.itemListeners,function(t,e){i.itemsData.on(e,t,o)}),e=this.itemsData.getIds(),this._onAdd(e),this._updateUngrouped()}},s.prototype.getItems=function(){return this.itemsData},s.prototype.setGroups=function(t){var e,i=this;if(this.groupsData&&(n.forEach(this.groupListeners,function(t,e){i.groupsData.unsubscribe(e,t)}),e=this.groupsData.getIds(),this.groupsData=null,this._onRemoveGroups(e)),t){if(!(t instanceof r||t instanceof a))throw new TypeError("Data must be an instance of DataSet or DataView");this.groupsData=t}else this.groupsData=null;if(this.groupsData){var s=this.id;n.forEach(this.groupListeners,function(t,e){i.groupsData.on(e,t,s)}),e=this.groupsData.getIds(),this._onAddGroups(e)}this._updateUngrouped(),this._order(),this.body.emitter.emit("change",{queue:!0})},s.prototype.getGroups=function(){return this.groupsData},s.prototype.removeItem=function(t){var e=this.itemsData.get(t),i=this.itemsData.getDataSet();e&&this.options.onRemove(e,function(e){e&&i.remove(t)})},s.prototype._getType=function(t){return t.type||this.options.type||(t.end?"range":"box")},s.prototype._getGroupId=function(t){var e=this._getType(t);return"background"==e&&void 0==t.group?g:this.groupsData?t.group:f},s.prototype._onUpdate=function(t){var e=this;t.forEach(function(t){var i=e.itemsData.get(t,e.itemOptions),o=e.items[t],n=e._getType(i),r=s.types[n];if(o&&(r&&o instanceof r?e._updateItem(o,i):(e._removeItem(o),o=null)),!o){if(!r)throw new TypeError("rangeoverflow"==n?'Item type "rangeoverflow" is deprecated. Use css styling instead: .vis.timeline .item.range .content {overflow: visible;}':'Unknown item type "'+n+'"');o=new r(i,e.conversion,e.options),o.id=t,e._addItem(o)}}),this._order(),this.stackDirty=!0,this.body.emitter.emit("change",{queue:!0})},s.prototype._onAdd=s.prototype._onUpdate,s.prototype._onRemove=function(t){var e=0,i=this;t.forEach(function(t){var s=i.items[t];s&&(e++,i._removeItem(s))}),e&&(this._order(),this.stackDirty=!0,this.body.emitter.emit("change",{queue:!0}))},s.prototype._order=function(){n.forEach(this.groups,function(t){t.order()})},s.prototype._onUpdateGroups=function(t){this._onAddGroups(t)},s.prototype._onAddGroups=function(t){var e=this;t.forEach(function(t){var i=e.groupsData.get(t),s=e.groups[t];if(s)s.setData(i);else{if(t==f||t==g)throw new Error("Illegal group id. "+t+" is a reserved id.");var o=Object.create(e.options);n.extend(o,{height:null}),s=new d(t,i,e),e.groups[t]=s;for(var r in e.items)if(e.items.hasOwnProperty(r)){var a=e.items[r];a.data.group==t&&s.add(a)}s.order(),s.show()}}),this.body.emitter.emit("change",{queue:!0})},s.prototype._onRemoveGroups=function(t){var e=this.groups;t.forEach(function(t){var i=e[t];i&&(i.hide(),delete e[t])}),this.markDirty(),this.body.emitter.emit("change",{queue:!0})},s.prototype._orderGroups=function(){if(this.groupsData){var t=this.groupsData.getIds({order:this.options.groupOrder}),e=!n.equalArray(t,this.groupIds);if(e){var i=this.groups;t.forEach(function(t){i[t].hide()}),t.forEach(function(t){i[t].show()}),this.groupIds=t}return e}return!1},s.prototype._addItem=function(t){this.items[t.id]=t;var e=this._getGroupId(t.data),i=this.groups[e];i&&i.add(t)},s.prototype._updateItem=function(t,e){var i=t.data.group;if(t.setData(e),i!=t.data.group){var s=this.groups[i];s&&s.remove(t);var o=this._getGroupId(t.data),n=this.groups[o];n&&n.add(t)}},s.prototype._removeItem=function(t){t.hide(),delete this.items[t.id];var e=this.selection.indexOf(t.id);-1!=e&&this.selection.splice(e,1),t.parent&&t.parent.remove(t)},s.prototype._constructByEndArray=function(t){for(var e=[],i=0;i0||o.length>0)&&this.body.emitter.emit("select",{items:a})}},s.prototype._onAddItem=function(t){if(this.options.selectable&&this.options.editable.add){var e=this,i=this.body.util.snap||null,o=s.itemFromTarget(t);if(o){var r=e.itemsData.get(o.id);this.options.onUpdate(r,function(t){t&&e.itemsData.getDataSet().update(t)})}else{var a=n.getAbsoluteLeft(this.dom.frame),h=t.center.x-a,d=this.body.util.toTime(h),l={start:i?i(d):d,content:"new item"};if("range"===this.options.type){var c=this.body.util.toTime(h+this.props.width/5);l.end=i?i(c):c}l[this.itemsData._fieldId]=n.randomUUID();var p=s.groupFromTarget(t);p&&(l.group=p.groupId),this.options.onAdd(l,function(t){t&&e.itemsData.getDataSet().add(t)})}}},s.prototype._onMultiSelectItem=function(t){if(this.options.selectable){var e,i=s.itemFromTarget(t);if(i){e=this.getSelection();var o=t.srcEvent&&t.srcEvent.shiftKey||!1;if(o){e.push(i.id);var n=s._getItemRange(this.itemsData.get(e,this.itemOptions));e=[];for(var r in this.items)if(this.items.hasOwnProperty(r)){var a=this.items[r],h=a.data.start,d=void 0!==a.data.end?a.data.end:h;h>=n.min&&d<=n.max&&e.push(a.id)}}else{var l=e.indexOf(i.id);-1==l?e.push(i.id):e.splice(l,1)}this.setSelection(e),this.body.emitter.emit("select",{items:this.getSelection()})}}},s._getItemRange=function(t){var e=null,i=null;return t.forEach(function(t){(null==i||t.starte)&&(e=t.end):(null==e||t.start>e)&&(e=t.start)}),{min:i,max:e}},s.itemFromTarget=function(t){for(var e=t.target;e;){if(e.hasOwnProperty("timeline-item"))return e["timeline-item"];e=e.parentNode}return null},s.groupFromTarget=function(t){for(var e=t.target;e;){if(e.hasOwnProperty("timeline-group"))return e["timeline-group"];e=e.parentNode}return null},s.itemSetFromTarget=function(t){for(var e=t.target;e;){if(e.hasOwnProperty("timeline-itemset"))return e["timeline-itemset"];e=e.parentNode}return null},t.exports=s},function(t,e,i){function s(t,e,i,s){this.body=t,this.defaultOptions={enabled:!0,icons:!0,iconSize:20,iconSpacing:6,left:{visible:!0,position:"top-left"},right:{visible:!0,position:"top-left"}},this.side=i,this.options=o.extend({},this.defaultOptions),this.linegraphOptions=s,this.svgElements={},this.dom={},this.groups={},this.amountOfGroups=0,this._create(),this.setOptions(e)}var o=i(1),n=i(2),r=i(20);s.prototype=new r,s.prototype.clear=function(){this.groups={},this.amountOfGroups=0},s.prototype.addGroup=function(t,e){this.groups.hasOwnProperty(t)||(this.groups[t]=e),this.amountOfGroups+=1},s.prototype.updateGroup=function(t,e){this.groups[t]=e},s.prototype.removeGroup=function(t){this.groups.hasOwnProperty(t)&&(delete this.groups[t],this.amountOfGroups-=1)},s.prototype._create=function(){this.dom.frame=document.createElement("div"),this.dom.frame.className="legend",this.dom.frame.style.position="absolute",this.dom.frame.style.top="10px",this.dom.frame.style.display="block",this.dom.textArea=document.createElement("div"),this.dom.textArea.className="legendText",this.dom.textArea.style.position="relative",this.dom.textArea.style.top="0px",this.svg=document.createElementNS("http://www.w3.org/2000/svg","svg"),this.svg.style.position="absolute",this.svg.style.top="0px",this.svg.style.width=this.options.iconSize+5+"px",this.svg.style.height="100%",this.dom.frame.appendChild(this.svg),this.dom.frame.appendChild(this.dom.textArea)},s.prototype.hide=function(){this.dom.frame.parentNode&&this.dom.frame.parentNode.removeChild(this.dom.frame)},s.prototype.show=function(){this.dom.frame.parentNode||this.body.dom.center.appendChild(this.dom.frame)},s.prototype.setOptions=function(t){var e=["enabled","orientation","icons","left","right"];o.selectiveDeepExtend(e,this.options,t)},s.prototype.redraw=function(){var t=0;for(var e in this.groups)this.groups.hasOwnProperty(e)&&(1!=this.groups[e].visible||void 0!==this.linegraphOptions.visibility[e]&&1!=this.linegraphOptions.visibility[e]||t++);if(0==this.options[this.side].visible||0==this.amountOfGroups||0==this.options.enabled||0==t)this.hide();else{if(this.show(),"top-left"==this.options[this.side].position||"bottom-left"==this.options[this.side].position?(this.dom.frame.style.left="4px",this.dom.frame.style.textAlign="left",this.dom.textArea.style.textAlign="left",this.dom.textArea.style.left=this.options.iconSize+15+"px",this.dom.textArea.style.right="",this.svg.style.left="0px",this.svg.style.right=""):(this.dom.frame.style.right="4px",this.dom.frame.style.textAlign="right",this.dom.textArea.style.textAlign="right",this.dom.textArea.style.right=this.options.iconSize+15+"px",this.dom.textArea.style.left="",this.svg.style.right="0px",this.svg.style.left=""),"top-left"==this.options[this.side].position||"top-right"==this.options[this.side].position)this.dom.frame.style.top=4-Number(this.body.dom.center.style.top.replace("px",""))+"px",this.dom.frame.style.bottom="";else{var i=this.body.domProps.center.height-this.body.domProps.centerContainer.height;this.dom.frame.style.bottom=4+i+Number(this.body.dom.center.style.top.replace("px",""))+"px",this.dom.frame.style.top=""}0==this.options.icons?(this.dom.frame.style.width=this.dom.textArea.offsetWidth+10+"px",this.dom.textArea.style.right="",this.dom.textArea.style.left="",this.svg.style.width="0px"):(this.dom.frame.style.width=this.options.iconSize+15+this.dom.textArea.offsetWidth+10+"px",this.drawLegendIcons());var s="";for(var e in this.groups)this.groups.hasOwnProperty(e)&&(1!=this.groups[e].visible||void 0!==this.linegraphOptions.visibility[e]&&1!=this.linegraphOptions.visibility[e]||(s+=this.groups[e].content+"
    "));this.dom.textArea.innerHTML=s,this.dom.textArea.style.lineHeight=.75*this.options.iconSize+this.options.iconSpacing+"px"}},s.prototype.drawLegendIcons=function(){if(this.dom.frame.parentNode){n.prepareElements(this.svgElements);var t=window.getComputedStyle(this.dom.frame).paddingTop,e=Number(t.replace("px","")),i=e,s=this.options.iconSize,o=.75*this.options.iconSize,r=e+.5*o+3;this.svg.style.width=s+5+e+"px";for(var a in this.groups)this.groups.hasOwnProperty(a)&&(1!=this.groups[a].visible||void 0!==this.linegraphOptions.visibility[a]&&1!=this.linegraphOptions.visibility[a]||(this.groups[a].drawIcon(i,r,this.svgElements,this.svg,s,o),r+=o+this.options.iconSpacing));n.cleanupElements(this.svgElements)}},t.exports=s},function(t,e,i){function s(t,e){this.id=o.randomUUID(),this.body=t,this.defaultOptions={yAxisOrientation:"left",defaultGroup:"default",sort:!0,sampling:!0,graphHeight:"400px",shaded:{enabled:!1,orientation:"bottom"},style:"line",barChart:{width:50,handleOverlap:"overlap",align:"center"},catmullRom:{enabled:!0,parametrization:"centripetal",alpha:.5},drawPoints:{enabled:!0,size:6,style:"square"},dataAxis:{showMinorLabels:!0,showMajorLabels:!0,icons:!1,width:"40px",visible:!0,alignZeros:!0,customRange:{left:{min:void 0,max:void 0},right:{min:void 0,max:void 0}}},legend:{enabled:!1,icons:!0,left:{visible:!0,position:"top-left"},right:{visible:!0,position:"top-right"}},groups:{visibility:{}}},this.options=o.extend({},this.defaultOptions),this.dom={},this.props={},this.hammer=null,this.groups={},this.abortedGraphUpdate=!1,this.updateSVGheight=!1,this.updateSVGheightOnResize=!1;var i=this;this.itemsData=null,this.groupsData=null,this.itemListeners={add:function(t,e){i._onAdd(e.items)},update:function(t,e){i._onUpdate(e.items)},remove:function(t,e){i._onRemove(e.items)}},this.groupListeners={add:function(t,e){i._onAddGroups(e.items)},update:function(t,e){i._onUpdateGroups(e.items)},remove:function(t,e){i._onRemoveGroups(e.items)}},this.items={},this.selection=[],this.lastStart=this.body.range.start,this.touchParams={},this.svgElements={},this.setOptions(e),this.groupsUsingDefaultStyles=[0],this.COUNTER=0,this.body.emitter.on("rangechanged",function(){i.lastStart=i.body.range.start,i.svg.style.left=o.option.asSize(-i.props.width),i.redraw.call(i,!0)}),this._create(),this.framework={svg:this.svg,svgElements:this.svgElements,options:this.options,groups:this.groups},this.body.emitter.emit("change")}var o=i(1),n=i(2),r=i(3),a=i(4),h=i(20),d=i(23),l=i(24),c=i(28),p=i(52),u="__ungrouped__";s.prototype=new h,s.prototype._create=function(){var t=document.createElement("div");t.className="LineGraph",this.dom.frame=t,this.svg=document.createElementNS("http://www.w3.org/2000/svg","svg"),this.svg.style.position="relative",this.svg.style.height=(""+this.options.graphHeight).replace("px","")+"px",this.svg.style.display="block",t.appendChild(this.svg),this.options.dataAxis.orientation="left",this.yAxisLeft=new d(this.body,this.options.dataAxis,this.svg,this.options.groups),this.options.dataAxis.orientation="right",this.yAxisRight=new d(this.body,this.options.dataAxis,this.svg,this.options.groups),delete this.options.dataAxis.orientation,this.legendLeft=new c(this.body,this.options.legend,"left",this.options.groups),this.legendRight=new c(this.body,this.options.legend,"right",this.options.groups),this.show()},s.prototype.setOptions=function(t){if(t){var e=["sampling","defaultGroup","height","graphHeight","yAxisOrientation","style","barChart","dataAxis","sort","groups"];void 0===t.graphHeight&&void 0!==t.height&&void 0!==this.body.domProps.centerContainer.height?(this.updateSVGheight=!0,this.updateSVGheightOnResize=!0):void 0!==this.body.domProps.centerContainer.height&&void 0!==t.graphHeight&&parseInt((t.graphHeight+"").replace("px",""))0){var d=this.body.util.toGlobalTime(-this.body.domProps.root.width),l=this.body.util.toGlobalTime(2*this.body.domProps.root.width),c={};for(this._getRelevantData(a,c,d,l),this._applySampling(a,c),e=0;eu&&console.log("WARNING: there may be an infinite loop in the _updateGraph emitter cycle."),this.COUNTER=0,this.abortedGraphUpdate=!1,e=0;e0)for(r=0;rs){d.push(h);break}d.push(h)}}else for(a=0;ai&&h.x0)for(var s=0;s0){var n=1,r=o.length,a=this.body.util.toGlobalScreen(o[o.length-1].x)-this.body.util.toGlobalScreen(o[0].x),h=r/a;n=Math.min(Math.ceil(.2*r),Math.max(1,Math.round(h)));for(var d=[],l=0;r>l;l+=n)d.push(o[l]);e[t[s]]=d}}},s.prototype._getYRanges=function(t,e,i){var s,o,n,r,a=[],h=[];if(t.length>0){for(n=0;n0&&(o=this.groups[t[n]],"stack"==r.barChart.handleOverlap&&"bar"==r.style?"left"==r.yAxisOrientation?a=a.concat(o.getYRange(s)):h=h.concat(o.getYRange(s)):i[t[n]]=o.getYRange(s,t[n]));p.getStackedBarYRange(a,i,t,"__barchartLeft","left"),p.getStackedBarYRange(h,i,t,"__barchartRight","right")}},s.prototype._updateYAxis=function(t,e){var i,s,o=!1,n=!1,r=!1,a=1e9,h=1e9,d=-1e9,l=-1e9;if(t.length>0){for(var c=0;ci?i:a,d=s>d?s:d):(r=!0,h=h>i?i:h,l=s>l?s:l));1==n&&this.yAxisLeft.setRange(a,d),1==r&&this.yAxisRight.setRange(h,l)}return o=this._toggleAxisVisiblity(n,this.yAxisLeft)||o,o=this._toggleAxisVisiblity(r,this.yAxisRight)||o,1==r&&1==n?(this.yAxisLeft.drawIcons=!0,this.yAxisRight.drawIcons=!0):(this.yAxisLeft.drawIcons=!1,this.yAxisRight.drawIcons=!1),this.yAxisRight.master=!n,0==this.yAxisRight.master?(this.yAxisLeft.lineOffset=1==r?this.yAxisRight.width:0,o=this.yAxisLeft.redraw()||o,this.yAxisRight.stepPixelsForced=this.yAxisLeft.stepPixels,this.yAxisRight.zeroCrossing=this.yAxisLeft.zeroCrossing,o=this.yAxisRight.redraw()||o):o=this.yAxisRight.redraw()||o,-1!=t.indexOf("__barchartLeft")&&t.splice(t.indexOf("__barchartLeft"),1),-1!=t.indexOf("__barchartRight")&&t.splice(t.indexOf("__barchartRight"),1),o},s.prototype._toggleAxisVisiblity=function(t,e){var i=!1;return 0==t?e.dom.frame.parentNode&&0==e.hidden&&(e.hide(),i=!0):e.dom.frame.parentNode||1!=e.hidden||(e.show(),i=!0),i},s.prototype._convertXcoordinates=function(t){for(var e,i,s=[],o=this.body.util.toScreen,n=0;ny;)y++,l=h.getCurrent(),c=h.isMajor(),u=h.getClassName(),f=m,m=this.body.util.toScreen(l),g=m-f,p&&(p.style.width=g+"px"),this.options.showMinorLabels&&this._repaintMinorText(m,h.getLabelMinor(),t,u),c&&this.options.showMajorLabels?(m>0&&(void 0==v&&(v=m),this._repaintMajorText(m,h.getLabelMajor(),t,u)),p=this._repaintMajorLine(m,t,u)):p=this._repaintMinorLine(m,t,u),h.next();if(this.options.showMajorLabels){var b=this.body.util.toTime(0),_=h.getLabelMajor(b),x=_.length*(this.props.majorCharWidth||10)+10;(void 0==v||v>x)&&this._repaintMajorText(0,_,t,u)}o.forEach(this.dom.redundant,function(t){for(;t.length;){var e=t.pop();e&&e.parentNode&&e.parentNode.removeChild(e)}})},s.prototype._repaintMinorText=function(t,e,i,s){var o=this.dom.redundant.minorTexts.shift();if(!o){var n=document.createTextNode("");o=document.createElement("div"),o.appendChild(n),this.dom.foreground.appendChild(o)}this.dom.minorTexts.push(o),o.childNodes[0].nodeValue=e,o.style.top="top"==i?this.props.majorLabelHeight+"px":"0",o.style.left=t+"px",o.className="text minor "+s},s.prototype._repaintMajorText=function(t,e,i,s){var o=this.dom.redundant.majorTexts.shift();if(!o){var n=document.createTextNode(e);o=document.createElement("div"),o.appendChild(n),this.dom.foreground.appendChild(o)}this.dom.majorTexts.push(o),o.childNodes[0].nodeValue=e,o.className="text major "+s,o.style.top="top"==i?"0":this.props.minorLabelHeight+"px",o.style.left=t+"px"},s.prototype._repaintMinorLine=function(t,e,i){var s=this.dom.redundant.lines.shift();s||(s=document.createElement("div"),this.dom.background.appendChild(s)),this.dom.lines.push(s);var o=this.props;return s.style.top="top"==e?o.majorLabelHeight+"px":this.body.domProps.top.height+"px",s.style.height=o.minorLineHeight+"px",s.style.left=t-o.minorLineWidth/2+"px",s.className="grid vertical minor "+i,s},s.prototype._repaintMajorLine=function(t,e,i){var s=this.dom.redundant.lines.shift();s||(s=document.createElement("div"),this.dom.background.appendChild(s)),this.dom.lines.push(s);var o=this.props;return s.style.top="top"==e?"0":this.body.domProps.top.height+"px",s.style.left=t-o.majorLineWidth/2+"px",s.style.height=o.majorLineHeight+"px",s.className="grid vertical major "+i,s},s.prototype._calculateCharSize=function(){this.dom.measureCharMinor||(this.dom.measureCharMinor=document.createElement("DIV"),this.dom.measureCharMinor.className="text minor measure",this.dom.measureCharMinor.style.position="absolute",this.dom.measureCharMinor.appendChild(document.createTextNode("0")),this.dom.foreground.appendChild(this.dom.measureCharMinor)),this.props.minorCharHeight=this.dom.measureCharMinor.clientHeight,this.props.minorCharWidth=this.dom.measureCharMinor.clientWidth,this.dom.measureCharMajor||(this.dom.measureCharMajor=document.createElement("DIV"),this.dom.measureCharMajor.className="text major measure",this.dom.measureCharMajor.style.position="absolute",this.dom.measureCharMajor.appendChild(document.createTextNode("0")),this.dom.foreground.appendChild(this.dom.measureCharMajor)),this.props.majorCharHeight=this.dom.measureCharMajor.clientHeight,this.props.majorCharWidth=this.dom.measureCharMajor.clientWidth},s.prototype.snap=function(t){return this.step.snap(t)},t.exports=s},function(t,e,i){function s(t,e,i){this.id=null,this.parent=null,this.data=t,this.dom=null,this.conversion=e||{},this.options=i||{},this.selected=!1,this.displayed=!1,this.dirty=!0,this.top=null,this.left=null,this.width=null,this.height=null}var o=i(45),n=i(1);s.prototype.stack=!0,s.prototype.select=function(){this.selected=!0,this.dirty=!0,this.displayed&&this.redraw()},s.prototype.unselect=function(){this.selected=!1,this.dirty=!0,this.displayed&&this.redraw()},s.prototype.setData=function(t){this.data=t,this.dirty=!0,this.displayed&&this.redraw()},s.prototype.setParent=function(t){this.displayed?(this.hide(),this.parent=t,this.parent&&this.show()):this.parent=t},s.prototype.isVisible=function(){return!1},s.prototype.show=function(){return!1},s.prototype.hide=function(){return!1},s.prototype.redraw=function(){},s.prototype.repositionX=function(){},s.prototype.repositionY=function(){},s.prototype._repaintDeleteButton=function(t){if(this.selected&&this.options.editable.remove&&!this.dom.deleteButton){var e=this,i=document.createElement("div");i.className="delete",i.title="Delete this item",new o(i).on("tap",function(t){e.parent.removeFromDataSet(e),t.stopPropagation(),t.preventDefault()}),t.appendChild(i),this.dom.deleteButton=i}else!this.selected&&this.dom.deleteButton&&(this.dom.deleteButton.parentNode&&this.dom.deleteButton.parentNode.removeChild(this.dom.deleteButton),this.dom.deleteButton=null)},s.prototype._updateContents=function(t){var e;if(this.options.template){var i=this.parent.itemSet.itemsData.get(this.id);e=this.options.template(i)}else e=this.data.content;if(e!==this.content){if(e instanceof Element)t.innerHTML="",t.appendChild(e);else if(void 0!=e)t.innerHTML=e;else if("background"!=this.data.type||void 0!==this.data.content)throw new Error('Property "content" missing in item '+this.id);this.content=e}},s.prototype._updateTitle=function(t){null!=this.data.title?t.title=this.data.title||"":t.removeAttribute("title")},s.prototype._updateDataAttributes=function(t){if(this.options.dataAttributes&&this.options.dataAttributes.length>0){var e=[];if(Array.isArray(this.options.dataAttributes))e=this.options.dataAttributes;else{if("all"!=this.options.dataAttributes)return;e=Object.keys(this.data)}for(var i=0;it.start},s.prototype.redraw=function(){var t=this.dom;if(t||(this.dom={},t=this.dom,t.box=document.createElement("div"),t.content=document.createElement("div"),t.content.className="content",t.box.appendChild(t.content),this.dirty=!0),!this.parent)throw new Error("Cannot redraw item: no parent attached");if(!t.box.parentNode){var e=this.parent.dom.background;if(!e)throw new Error("Cannot redraw item: parent has no background container element");e.appendChild(t.box)}if(this.displayed=!0,this.dirty){this._updateContents(this.dom.content),this._updateTitle(this.dom.content),this._updateDataAttributes(this.dom.content),this._updateStyle(this.dom.box);var i=(this.data.className?" "+this.data.className:"")+(this.selected?" selected":"");t.box.className=this.baseClassName+i,this.overflow="hidden"!==window.getComputedStyle(t.content).overflow,this.props.content.width=this.dom.content.offsetWidth,this.height=0,this.dirty=!1}},s.prototype.show=r.prototype.show,s.prototype.hide=r.prototype.hide,s.prototype.repositionX=r.prototype.repositionX,s.prototype.repositionY=function(t){var e="top"===this.options.orientation;this.dom.content.style.top=e?"":"0",this.dom.content.style.bottom=e?"0":"";var i;if(void 0!==this.data.subgroup){var s=this.data.subgroup,o=this.parent.subgroups,r=o[s].index;if(1==e){i=this.parent.subgroups[s].height+t.item.vertical,i+=0==r?t.axis-.5*t.item.vertical:0;var a=this.parent.top;for(var h in o)o.hasOwnProperty(h)&&1==o[h].visible&&o[h].indexr&&(a+=o[h].height+t.item.vertical);i=this.parent.subgroups[s].height+t.item.vertical,this.dom.box.style.top=a+"px",this.dom.box.style.bottom=""}}else this.parent instanceof n?(i=Math.max(this.parent.height,this.parent.itemSet.body.domProps.center.height,this.parent.itemSet.body.domProps.centerContainer.height),this.dom.box.style.top=e?"0":"",this.dom.box.style.bottom=e?"":"0"):(i=this.parent.height,this.dom.box.style.top=this.parent.top+"px",this.dom.box.style.bottom="");this.dom.box.style.height=i+"px"},t.exports=s},function(t,e,i){function s(t,e,i){if(this.props={dot:{width:0,height:0},line:{width:0,height:0}},t&&void 0==t.start)throw new Error('Property "start" missing in item '+t);o.call(this,t,e,i)}{var o=i(31);i(1)}s.prototype=new o(null,null,null),s.prototype.isVisible=function(t){var e=(t.end-t.start)/4;return this.data.start>t.start-e&&this.data.startt.start-e&&this.data.startt.start},s.prototype.redraw=function(){var t=this.dom;if(t||(this.dom={},t=this.dom,t.box=document.createElement("div"),t.content=document.createElement("div"),t.content.className="content",t.box.appendChild(t.content),t.box["timeline-item"]=this,this.dirty=!0),!this.parent)throw new Error("Cannot redraw item: no parent attached");if(!t.box.parentNode){var e=this.parent.dom.foreground;if(!e)throw new Error("Cannot redraw item: parent has no foreground container element");e.appendChild(t.box)}if(this.displayed=!0,this.dirty){this._updateContents(this.dom.content),this._updateTitle(this.dom.box),this._updateDataAttributes(this.dom.box),this._updateStyle(this.dom.box);var i=(this.data.className?" "+this.data.className:"")+(this.selected?" selected":"");t.box.className=this.baseClassName+i,this.overflow="hidden"!==window.getComputedStyle(t.content).overflow,this.dom.content.style.maxWidth="none",this.props.content.width=this.dom.content.offsetWidth,this.height=this.dom.box.offsetHeight,this.dom.content.style.maxWidth="",this.dirty=!1}this._repaintDeleteButton(t.box),this._repaintDragLeft(),this._repaintDragRight()},s.prototype.show=function(){this.displayed||this.redraw()},s.prototype.hide=function(){if(this.displayed){var t=this.dom.box;t.parentNode&&t.parentNode.removeChild(t),this.top=null,this.left=null,this.displayed=!1}},s.prototype.repositionX=function(){var t,e,i=this.parent.width,s=this.conversion.toScreen(this.data.start),o=this.conversion.toScreen(this.data.end);-i>s&&(s=-i),o>2*i&&(o=2*i);var n=Math.max(o-s,1);switch(this.overflow?(this.left=s,this.width=n+this.props.content.width,e=this.props.content.width):(this.left=s,this.width=n,e=Math.min(o-s-2*this.options.padding,this.props.content.width)),this.dom.box.style.left=this.left+"px",this.dom.box.style.width=n+"px",this.options.align){case"left":this.dom.content.style.left="0";break;case"right":this.dom.content.style.left=Math.max(n-e-2*this.options.padding,0)+"px";break;case"center":this.dom.content.style.left=Math.max((n-e-2*this.options.padding)/2,0)+"px";break;default:t=this.overflow?o>0?Math.max(-s,0):-e:0>s?Math.min(-s,o-s-e-2*this.options.padding):0,this.dom.content.style.left=t+"px"}},s.prototype.repositionY=function(){var t=this.options.orientation,e=this.dom.box;e.style.top="top"==t?this.top+"px":this.parent.height-this.top-this.height+"px"},s.prototype._repaintDragLeft=function(){if(this.selected&&this.options.editable.updateTime&&!this.dom.dragLeft){var t=document.createElement("div");t.className="drag-left",t.dragLeftItem=this,this.dom.box.appendChild(t),this.dom.dragLeft=t}else!this.selected&&this.dom.dragLeft&&(this.dom.dragLeft.parentNode&&this.dom.dragLeft.parentNode.removeChild(this.dom.dragLeft),this.dom.dragLeft=null)},s.prototype._repaintDragRight=function(){if(this.selected&&this.options.editable.updateTime&&!this.dom.dragRight){var t=document.createElement("div");t.className="drag-right",t.dragRightItem=this,this.dom.box.appendChild(t),this.dom.dragRight=t}else!this.selected&&this.dom.dragRight&&(this.dom.dragRight.parentNode&&this.dom.dragRight.parentNode.removeChild(this.dom.dragRight),this.dom.dragRight=null)},t.exports=s},function(t,e,i){function s(t,e,i){if(!(this instanceof s))throw new SyntaxError("Constructor must be called with the new operator");this._determineBrowserMethod(),this._initializeMixinLoaders(),this.containerElement=t,this.renderRefreshRate=60,this.renderTimestep=1e3/this.renderRefreshRate,this.renderTime=0,this.physicsTime=0,this.runDoubleSpeed=!1,this.physicsDiscreteStepsize=.5,this.initializing=!0,this.triggerFunctions={add:null,edit:null,editEdge:null,connect:null,del:null},this.defaultOptions={nodes:{mass:1,radiusMin:10,radiusMax:30,radius:10,shape:"ellipse",image:void 0,widthMin:16,widthMax:64,fontColor:"black",fontSize:14,fontFace:"verdana",fontFill:void 0,fontStrokeWidth:0,fontStrokeColor:"white",level:-1,color:{border:"#2B7CE9",background:"#97C2FC",highlight:{border:"#2B7CE9",background:"#D2E5FF"},hover:{border:"#2B7CE9",background:"#D2E5FF"}},group:void 0,borderWidth:1,borderWidthSelected:void 0},edges:{widthMin:1,widthMax:15,width:1,widthSelectionMultiplier:2,hoverWidth:1.5,style:"line",color:{color:"#848484",highlight:"#848484",hover:"#848484"},fontColor:"#343434",fontSize:14,fontFace:"arial",fontFill:"white",fontStrokeWidth:0,fontStrokeColor:"white",labelAlignment:"horizontal",arrowScaleFactor:1,dash:{length:10,gap:5,altLength:void 0},inheritColor:"from"},configurePhysics:!1,physics:{barnesHut:{enabled:!0,thetaInverted:2,gravitationalConstant:-2e3,centralGravity:.3,springLength:95,springConstant:.04,damping:.09},repulsion:{centralGravity:0,springLength:200,springConstant:.05,nodeDistance:100,damping:.09},hierarchicalRepulsion:{enabled:!1,centralGravity:0,springLength:100,springConstant:.01,nodeDistance:150,damping:.09},damping:null,centralGravity:null,springLength:null,springConstant:null},clustering:{enabled:!1,initialMaxNodes:100,clusterThreshold:500,reduceToNodes:300,chainThreshold:.4,clusterEdgeThreshold:20,sectorThreshold:100,screenSizeThreshold:.2,fontSizeMultiplier:4,maxFontSize:1e3,forceAmplification:.1,distanceAmplification:.1,edgeGrowth:20,nodeScaling:{width:1,height:1,radius:1},maxNodeSizeIncrements:600,activeAreaBoxSize:80,clusterLevelDifference:2},navigation:{enabled:!1},keyboard:{enabled:!1,speed:{x:10,y:10,zoom:.02}},dataManipulation:{enabled:!1,initiallyVisible:!1},hierarchicalLayout:{enabled:!1,levelSeparation:150,nodeSpacing:100,direction:"UD",layout:"hubsize"},freezeForStabilization:!1,smoothCurves:{enabled:!0,dynamic:!0,type:"continuous",roundness:.5},maxVelocity:30,minVelocity:.1,stabilize:!0,stabilizationIterations:1e3,zoomExtentOnStabilize:!0,locale:"en",locales:_,tooltip:{delay:300,fontColor:"black",fontSize:14,fontFace:"verdana",color:{border:"#666",background:"#FFFFC6"}},dragNetwork:!0,dragNodes:!0,zoomable:!0,hover:!1,hideEdgesOnDrag:!1,hideNodesOnDrag:!1,width:"100%",height:"100%",selectable:!0},this.constants=a.extend({},this.defaultOptions),this.pixelRatio=1,this.hoverObj={nodes:{},edges:{}},this.controlNodesActive=!1,this.navigationHammers={existing:[],_new:[]},this.animationSpeed=1/this.renderRefreshRate,this.animationEasingFunction="easeInOutQuint",this.easingTime=0,this.sourceScale=0,this.targetScale=0,this.sourceTranslation=0,this.targetTranslation=0,this.lockedOnNodeId=null,this.lockedOnNodeOffset=null,this.touchTime=0;var o=this;this.groups=new u,this.images=new m,this.images.setOnloadCallback(function(){o._redraw()}),this.xIncrement=0,this.yIncrement=0,this.zoomIncrement=0,this._loadPhysicsSystem(),this._create(),this._loadSectorSystem(),this._loadClusterSystem(),this._loadSelectionSystem(),this._loadHierarchySystem(),this._setTranslation(this.frame.clientWidth/2,this.frame.clientHeight/2),this._setScale(1),this.setOptions(i),this.freezeSimulation=!1,this.cachedFunctions={},this.startedStabilization=!1,this.stabilized=!1,this.stabilizationIterations=null,this.draggingNodes=!1,this.calculationNodes={},this.calculationNodeIndices=[],this.nodeIndices=[],this.nodes={},this.edges={},this.canvasTopLeft={x:0,y:0},this.canvasBottomRight={x:0,y:0},this.pointerPosition={x:0,y:0},this.areaCenter={},this.scale=1,this.previousScale=this.scale,this.nodesData=null,this.edgesData=null,this.nodesListeners={add:function(t,e){o._addNodes(e.items),o.start()},update:function(t,e){o._updateNodes(e.items,e.data),o.start()},remove:function(t,e){o._removeNodes(e.items),o.start()}},this.edgesListeners={add:function(t,e){o._addEdges(e.items),o.start()},update:function(t,e){o._updateEdges(e.items),o.start()},remove:function(t,e){o._removeEdges(e.items),o.start()}},this.moving=!0,this.timer=void 0,this.setData(e,this.constants.clustering.enabled||this.constants.hierarchicalLayout.enabled),this.initializing=!1,1==this.constants.hierarchicalLayout.enabled?this._setupHierarchicalLayout():0==this.constants.stabilize&&this.zoomExtent(void 0,!0,this.constants.clustering.enabled),this.constants.clustering.enabled&&this.startWithClustering()}var o=i(56),n=i(45),r=i(58),a=i(1),h=i(47),d=i(3),l=i(4),c=i(42),p=i(43),u=i(38),m=i(39),f=i(40),g=i(37),v=i(41),y=i(54),b=i(55),_=i(49);i(50),o(s.prototype),s.prototype._determineBrowserMethod=function(){var t=navigator.userAgent.toLowerCase();this.requiresTimeout=!1,-1!=t.indexOf("msie 9.0")?this.requiresTimeout=!0:-1!=t.indexOf("safari")&&t.indexOf("chrome")<=-1&&(this.requiresTimeout=!0)},s.prototype._getScriptPath=function(){for(var t=document.getElementsByTagName("script"),e=0;et.boundingBox.left&&(s=t.boundingBox.left),ot.boundingBox.bottom&&(e=t.boundingBox.bottom),i=this.constants.clustering.initialMaxNodes?49.07548/(n+142.05338)+91444e-8:12.662/(n+7.4147)+.0964822:1==this.constants.clustering.enabled&&n>=this.constants.clustering.initialMaxNodes?77.5271985/(n+187.266146)+476710517e-13:30.5062972/(n+19.93597763)+.08413486;var r=Math.min(this.frame.canvas.clientWidth/600,this.frame.canvas.clientHeight/600);s*=r}else{var a=1.1*Math.abs(o.maxX-o.minX),h=1.1*Math.abs(o.maxY-o.minY),d=this.frame.canvas.clientWidth/a,l=this.frame.canvas.clientHeight/h;s=l>=d?d:l}s>1&&(s=1);var c=this._findCenter(o);if(0==i){var p={position:c,scale:s,animation:t};this.moveTo(p),this.moving=!0,this.start()}else c.x*=s,c.y*=s,c.x-=.5*this.frame.canvas.clientWidth,c.y-=.5*this.frame.canvas.clientHeight,this._setScale(s),this._setTranslation(-c.x,-c.y)},s.prototype._updateNodeIndexList=function(){this._clearNodeIndexList();for(var t in this.nodes)this.nodes.hasOwnProperty(t)&&this.nodeIndices.push(t)},s.prototype.setData=function(t,e){if(void 0===e&&(e=!1),this.initializing=!0,t&&t.dot&&(t.nodes||t.edges))throw new SyntaxError('Data must contain either parameter "dot" or parameter pair "nodes" and "edges", but not both.');if(1==this.constants.dataManipulation.enabled&&this._createManipulatorBar(),this.setOptions(t&&t.options),t&&t.dot){if(t&&t.dot){var i=c.DOTToGraph(t.dot);return void this.setData(i)}}else if(t&&t.gephi){if(t&&t.gephi){var s=p.parseGephi(t.gephi);return void this.setData(s)}}else this._setNodes(t&&t.nodes),this._setEdges(t&&t.edges);this._putDataInSector(),0==e&&(1==this.constants.hierarchicalLayout.enabled?(this._resetLevels(),this._setupHierarchicalLayout()):this.constants.stabilize&&this._stabilize(),this.start()),this.initializing=!1},s.prototype.setOptions=function(t){if(t){var e,i=["nodes","edges","smoothCurves","hierarchicalLayout","clustering","navigation","keyboard","dataManipulation","onAdd","onEdit","onEditEdge","onConnect","onDelete","clickToUse"];if(a.selectiveNotDeepExtend(i,this.constants,t),a.selectiveNotDeepExtend(["color"],this.constants.nodes,t.nodes),a.selectiveNotDeepExtend(["color","length"],this.constants.edges,t.edges),t.physics&&(a.mergeOptions(this.constants.physics,t.physics,"barnesHut"),a.mergeOptions(this.constants.physics,t.physics,"repulsion"),t.physics.hierarchicalRepulsion)){this.constants.hierarchicalLayout.enabled=!0,this.constants.physics.hierarchicalRepulsion.enabled=!0,this.constants.physics.barnesHut.enabled=!1;for(e in t.physics.hierarchicalRepulsion)t.physics.hierarchicalRepulsion.hasOwnProperty(e)&&(this.constants.physics.hierarchicalRepulsion[e]=t.physics.hierarchicalRepulsion[e]) +}if(t.onAdd&&(this.triggerFunctions.add=t.onAdd),t.onEdit&&(this.triggerFunctions.edit=t.onEdit),t.onEditEdge&&(this.triggerFunctions.editEdge=t.onEditEdge),t.onConnect&&(this.triggerFunctions.connect=t.onConnect),t.onDelete&&(this.triggerFunctions.del=t.onDelete),a.mergeOptions(this.constants,t,"smoothCurves"),a.mergeOptions(this.constants,t,"hierarchicalLayout"),a.mergeOptions(this.constants,t,"clustering"),a.mergeOptions(this.constants,t,"navigation"),a.mergeOptions(this.constants,t,"keyboard"),a.mergeOptions(this.constants,t,"dataManipulation"),t.dataManipulation&&(this.editMode=this.constants.dataManipulation.initiallyVisible),t.edges&&(void 0!==t.edges.color&&(a.isString(t.edges.color)?(this.constants.edges.color={},this.constants.edges.color.color=t.edges.color,this.constants.edges.color.highlight=t.edges.color,this.constants.edges.color.hover=t.edges.color):(void 0!==t.edges.color.color&&(this.constants.edges.color.color=t.edges.color.color),void 0!==t.edges.color.highlight&&(this.constants.edges.color.highlight=t.edges.color.highlight),void 0!==t.edges.color.hover&&(this.constants.edges.color.hover=t.edges.color.hover)),this.constants.edges.inheritColor=!1),t.edges.fontColor||void 0!==t.edges.color&&(a.isString(t.edges.color)?this.constants.edges.fontColor=t.edges.color:void 0!==t.edges.color.color&&(this.constants.edges.fontColor=t.edges.color.color))),t.nodes&&t.nodes.color){var s=a.parseColor(t.nodes.color);this.constants.nodes.color.background=s.background,this.constants.nodes.color.border=s.border,this.constants.nodes.color.highlight.background=s.highlight.background,this.constants.nodes.color.highlight.border=s.highlight.border,this.constants.nodes.color.hover.background=s.hover.background,this.constants.nodes.color.hover.border=s.hover.border}if(t.groups)for(var o in t.groups)if(t.groups.hasOwnProperty(o)){var n=t.groups[o];this.groups.add(o,n)}if(t.tooltip){for(e in t.tooltip)t.tooltip.hasOwnProperty(e)&&(this.constants.tooltip[e]=t.tooltip[e]);t.tooltip.color&&(this.constants.tooltip.color=a.parseColor(t.tooltip.color))}if("clickToUse"in t&&(t.clickToUse?this.activator||(this.activator=new b(this.frame),this.activator.on("change",this._createKeyBinds.bind(this))):this.activator&&(this.activator.destroy(),delete this.activator)),t.labels)throw new Error('Option "labels" is deprecated. Use options "locale" and "locales" instead.');this._loadPhysicsSystem(),this._loadNavigationControls(),this._loadManipulationSystem(),this._configureSmoothCurves(),this._createKeyBinds(),this.setSize(this.constants.width,this.constants.height),this.moving=!0,this.start()}},s.prototype._create=function(){for(;this.containerElement.hasChildNodes();)this.containerElement.removeChild(this.containerElement.firstChild);if(this.frame=document.createElement("div"),this.frame.className="vis network-frame",this.frame.style.position="relative",this.frame.style.overflow="hidden",this.frame.canvas=document.createElement("canvas"),this.frame.canvas.style.position="relative",this.frame.appendChild(this.frame.canvas),this.frame.canvas.getContext){var t=this.frame.canvas.getContext("2d");this.pixelRatio=(window.devicePixelRatio||1)/(t.webkitBackingStorePixelRatio||t.mozBackingStorePixelRatio||t.msBackingStorePixelRatio||t.oBackingStorePixelRatio||t.backingStorePixelRatio||1),this.frame.canvas.getContext("2d").setTransform(this.pixelRatio,0,0,this.pixelRatio,0,0)}else{var e=document.createElement("DIV");e.style.color="red",e.style.fontWeight="bold",e.style.padding="10px",e.innerHTML="Error: your browser does not support HTML canvas",this.frame.canvas.appendChild(e)}var i=this;this.drag={},this.pinch={},this.hammer=new n(this.frame.canvas),this.hammer.get("pinch").set({enable:!0}),this.hammer.on("tap",i._onTap.bind(i)),this.hammer.on("doubletap",i._onDoubleTap.bind(i)),this.hammer.on("press",i._onHold.bind(i)),this.hammer.on("pinch",i._onPinch.bind(i)),h.onTouch(this.hammer,i._onTouch.bind(i)),this.hammer.on("panstart",i._onDragStart.bind(i)),this.hammer.on("panmove",i._onDrag.bind(i)),this.hammer.on("panend",i._onDragEnd.bind(i)),this.frame.canvas.addEventListener("mousemove",i._onMouseMoveTitle.bind(i)),this.frame.canvas.addEventListener("mousewheel",i._onMouseWheel.bind(i)),this.frame.canvas.addEventListener("DOMMouseScroll",i._onMouseWheel.bind(i)),this.containerElement.appendChild(this.frame)},s.prototype._createKeyBinds=function(){var t=this;void 0!==this.keycharm&&this.keycharm.destroy(),this.keycharm=r(),this.keycharm.reset(),this.constants.keyboard.enabled&&this.isActive()&&(this.keycharm.bind("up",this._moveUp.bind(t),"keydown"),this.keycharm.bind("up",this._yStopMoving.bind(t),"keyup"),this.keycharm.bind("down",this._moveDown.bind(t),"keydown"),this.keycharm.bind("down",this._yStopMoving.bind(t),"keyup"),this.keycharm.bind("left",this._moveLeft.bind(t),"keydown"),this.keycharm.bind("left",this._xStopMoving.bind(t),"keyup"),this.keycharm.bind("right",this._moveRight.bind(t),"keydown"),this.keycharm.bind("right",this._xStopMoving.bind(t),"keyup"),this.keycharm.bind("=",this._zoomIn.bind(t),"keydown"),this.keycharm.bind("=",this._stopZoom.bind(t),"keyup"),this.keycharm.bind("num+",this._zoomIn.bind(t),"keydown"),this.keycharm.bind("num+",this._stopZoom.bind(t),"keyup"),this.keycharm.bind("num-",this._zoomOut.bind(t),"keydown"),this.keycharm.bind("num-",this._stopZoom.bind(t),"keyup"),this.keycharm.bind("-",this._zoomOut.bind(t),"keydown"),this.keycharm.bind("-",this._stopZoom.bind(t),"keyup"),this.keycharm.bind("[",this._zoomIn.bind(t),"keydown"),this.keycharm.bind("[",this._stopZoom.bind(t),"keyup"),this.keycharm.bind("]",this._zoomOut.bind(t),"keydown"),this.keycharm.bind("]",this._stopZoom.bind(t),"keyup"),this.keycharm.bind("pageup",this._zoomIn.bind(t),"keydown"),this.keycharm.bind("pageup",this._stopZoom.bind(t),"keyup"),this.keycharm.bind("pagedown",this._zoomOut.bind(t),"keydown"),this.keycharm.bind("pagedown",this._stopZoom.bind(t),"keyup")),1==this.constants.dataManipulation.enabled&&(this.keycharm.bind("esc",this._createManipulatorBar.bind(t)),this.keycharm.bind("delete",this._deleteSelected.bind(t)))},s.prototype.destroy=function(){this.start=function(){},this.redraw=function(){},this.timer=!1,this._cleanupPhysicsConfiguration(),this.keycharm.reset(),this.hammer.destroy(),this.off(),this._recursiveDOMDelete(this.containerElement)},s.prototype._recursiveDOMDelete=function(t){for(;1==t.hasChildNodes();)this._recursiveDOMDelete(t.firstChild),t.removeChild(t.firstChild)},s.prototype._getPointer=function(t){return{x:t.x-a.getAbsoluteLeft(this.frame.canvas),y:t.y-a.getAbsoluteTop(this.frame.canvas)}},s.prototype._onTouch=function(t){(new Date).valueOf()-this.touchTime>100&&(this.drag.pointer=this._getPointer(t.center),this.drag.pinched=!1,this.pinch.scale=this._getScale(),this.touchTime=(new Date).valueOf(),this._handleTouch(this.drag.pointer))},s.prototype._onDragStart=function(t){this._handleDragStart(t)},s.prototype._handleDragStart=function(t){void 0===this.drag.pointer&&this._onTouch(t);var e=this._getNodeAt(this.drag.pointer);if(this.drag.dragging=!0,this.drag.selection=[],this.drag.translation=this._getTranslation(),this.drag.nodeId=null,this.draggingNodes=!1,null!=e&&1==this.constants.dragNodes){this.draggingNodes=!0,this.drag.nodeId=e.id,e.isSelected()||this._selectObject(e,!1),this.emit("dragStart",{nodeIds:this.getSelection().nodes});for(var i in this.selectionObj.nodes)if(this.selectionObj.nodes.hasOwnProperty(i)){var s=this.selectionObj.nodes[i],o={id:s.id,node:s,x:s.x,y:s.y,xFixed:s.xFixed,yFixed:s.yFixed};s.xFixed=!0,s.yFixed=!0,this.drag.selection.push(o)}}t.preventDefault()},s.prototype._onDrag=function(t){this._handleOnDrag(t)},s.prototype._handleOnDrag=function(t){if(!this.drag.pinched){this.releaseNode();var e=this._getPointer(t.center),i=this,s=this.drag,o=s.selection;if(o&&o.length&&1==this.constants.dragNodes){var n=e.x-s.pointer.x,r=e.y-s.pointer.y;o.forEach(function(t){var e=t.node;t.xFixed||(e.x=i._XconvertDOMtoCanvas(i._XconvertCanvasToDOM(t.x)+n)),t.yFixed||(e.y=i._YconvertDOMtoCanvas(i._YconvertCanvasToDOM(t.y)+r))}),this.moving||(this.moving=!0,this.start())}else if(1==this.constants.dragNetwork){if(void 0===this.drag.pointer)return void this._handleDragStart(t);var a=e.x-this.drag.pointer.x,h=e.y-this.drag.pointer.y;this._setTranslation(this.drag.translation.x+a,this.drag.translation.y+h),this._redraw()}t.preventDefault()}},s.prototype._onDragEnd=function(t){this._handleDragEnd(t)},s.prototype._handleDragEnd=function(t){this.drag.dragging=!1;var e=this.drag.selection;e&&e.length?(e.forEach(function(t){t.node.xFixed=t.xFixed,t.node.yFixed=t.yFixed}),this.moving=!0,this.start()):this._redraw(),0==this.draggingNodes?this.emit("dragEnd",{nodeIds:[]}):this.emit("dragEnd",{nodeIds:this.getSelection().nodes}),t.preventDefault()},s.prototype._onTap=function(t){var e=this._getPointer(t.center);this.pointerPosition=e,this._handleTap(e)},s.prototype._onDoubleTap=function(t){var e=this._getPointer(t.center);this._handleDoubleTap(e)},s.prototype._onHold=function(t){var e=this._getPointer(t.center);this.pointerPosition=e,this._handleOnHold(e)},s.prototype._onRelease=function(t){var e=this._getPointer(t.center);this._handleOnRelease(e)},s.prototype._onPinch=function(t){var e=this._getPointer(t.center);this.drag.pinched=!0,"scale"in this.pinch||(this.pinch.scale=1);var i=this.pinch.scale*t.scale;this._zoom(i,e)},s.prototype._zoom=function(t,e){if(1==this.constants.zoomable){var i=this._getScale();1e-5>t&&(t=1e-5),t>10&&(t=10);var s=null;void 0!==this.drag&&1==this.drag.dragging&&(s=this.DOMtoCanvas(this.drag.pointer));var o=this._getTranslation(),n=t/i,r=(1-n)*e.x+o.x*n,a=(1-n)*e.y+o.y*n;if(this.areaCenter={x:this._XconvertDOMtoCanvas(e.x),y:this._YconvertDOMtoCanvas(e.y)},this._setScale(t),this._setTranslation(r,a),this.updateClustersDefault(),null!=s){var h=this.canvasToDOM(s);this.drag.pointer.x=h.x,this.drag.pointer.y=h.y}return this._redraw(),t>i?this.emit("zoom",{direction:"+"}):this.emit("zoom",{direction:"-"}),t}},s.prototype._onMouseWheel=function(t){var e=0;if(t.wheelDelta?e=t.wheelDelta/120:t.detail&&(e=-t.detail/3),e){var i=this._getScale(),s=e/10;0>e&&(s/=1-s),i*=1+s;var o=this._getPointer({x:t.pageX,y:t.pageY});this._zoom(i,o)}t.preventDefault()},s.prototype._onMouseMoveTitle=function(t){var e=this._getPointer({x:t.pageX,y:t.pageY});this.popupObj&&this._checkHidePopup(e);var i=this,s=function(){i._checkShowPopup(e)};if(this.popupTimer&&clearInterval(this.popupTimer),this.drag.dragging||(this.popupTimer=setTimeout(s,this.constants.tooltip.delay)),1==this.constants.hover){for(var o in this.hoverObj.edges)this.hoverObj.edges.hasOwnProperty(o)&&(this.hoverObj.edges[o].hover=!1,delete this.hoverObj.edges[o]);var n=this._getNodeAt(e);null==n&&(n=this._getEdgeAt(e)),null!=n&&this._hoverObject(n);for(var r in this.hoverObj.nodes)this.hoverObj.nodes.hasOwnProperty(r)&&(n instanceof f&&n.id!=r||n instanceof g||null==n)&&(this._blurObject(this.hoverObj.nodes[r]),delete this.hoverObj.nodes[r]);this.redraw()}},s.prototype._checkShowPopup=function(t){var e,i={left:this._XconvertDOMtoCanvas(t.x),top:this._YconvertDOMtoCanvas(t.y),right:this._XconvertDOMtoCanvas(t.x),bottom:this._YconvertDOMtoCanvas(t.y)},s=this.popupObj,o=!1;if(void 0==this.popupObj){var n=this.nodes,r=[];for(e in n)if(n.hasOwnProperty(e)){var a=n[e];a.isOverlappingWith(i)&&void 0!==a.getTitle()&&r.push(e)}r.length>0&&(this.popupObj=this.nodes[r[r.length-1]],o=!0)}if(void 0===this.popupObj&&0==o){var h=this.edges,d=[];for(e in h)if(h.hasOwnProperty(e)){var l=h[e];l.connected&&void 0!==l.getTitle()&&l.isOverlappingWith(i)&&d.push(e)}d.length>0&&(this.popupObj=this.edges[d[d.length-1]])}if(this.popupObj){if(this.popupObj!=s){var c=this;c.popup||(c.popup=new v(c.frame,c.constants.tooltip)),c.popup.setPosition(t.x-3,t.y-3),c.popup.setText(c.popupObj.getTitle()),c.popup.show()}}else this.popup&&this.popup.hide()},s.prototype._checkHidePopup=function(t){this.popupObj&&this._getNodeAt(t)||(this.popupObj=void 0,this.popup&&this.popup.hide())},s.prototype.setSize=function(t,e){var i=!1,s=this.frame.canvas.width,o=this.frame.canvas.height;t!=this.constants.width||e!=this.constants.height||this.frame.style.width!=t||this.frame.style.height!=e?(this.frame.style.width=t,this.frame.style.height=e,this.frame.canvas.style.width="100%",this.frame.canvas.style.height="100%",this.frame.canvas.width=this.frame.canvas.clientWidth*this.pixelRatio,this.frame.canvas.height=this.frame.canvas.clientHeight*this.pixelRatio,this.constants.width=t,this.constants.height=e,i=!0):(this.frame.canvas.width!=this.frame.canvas.clientWidth*this.pixelRatio&&(this.frame.canvas.width=this.frame.canvas.clientWidth*this.pixelRatio,i=!0),this.frame.canvas.height!=this.frame.canvas.clientHeight*this.pixelRatio&&(this.frame.canvas.height=this.frame.canvas.clientHeight*this.pixelRatio,i=!0)),1==i&&this.emit("resize",{width:this.frame.canvas.width*this.pixelRatio,height:this.frame.canvas.height*this.pixelRatio,oldWidth:s*this.pixelRatio,oldHeight:o*this.pixelRatio})},s.prototype._setNodes=function(t){var e=this.nodesData;if(t instanceof d||t instanceof l)this.nodesData=t;else if(Array.isArray(t))this.nodesData=new d,this.nodesData.add(t);else{if(t)throw new TypeError("Array or DataSet expected");this.nodesData=new d}if(e&&a.forEach(this.nodesListeners,function(t,i){e.off(i,t)}),this.nodes={},this.nodesData){var i=this;a.forEach(this.nodesListeners,function(t,e){i.nodesData.on(e,t)});var s=this.nodesData.getIds();this._addNodes(s)}this._updateSelection()},s.prototype._addNodes=function(t){for(var e,i=0,s=t.length;s>i;i++){e=t[i];var o=this.nodesData.get(e),n=new f(o,this.images,this.groups,this.constants);if(this.nodes[e]=n,!(0!=n.xFixed&&0!=n.yFixed||null!==n.x&&null!==n.y)){var r=1*t.length+10,a=2*Math.PI*Math.random();0==n.xFixed&&(n.x=r*Math.cos(a)),0==n.yFixed&&(n.y=r*Math.sin(a))}this.moving=!0}this._updateNodeIndexList(),1==this.constants.hierarchicalLayout.enabled&&0==this.initializing&&(this._resetLevels(),this._setupHierarchicalLayout()),this._updateCalculationNodes(),this._reconnectEdges(),this._updateValueRange(this.nodes),this.updateLabels()},s.prototype._updateNodes=function(t,e){for(var i=this.nodes,s=0,o=t.length;o>s;s++){var n=t[s],r=i[n],a=e[s];r?r.setProperties(a,this.constants):(r=new f(properties,this.images,this.groups,this.constants),i[n]=r)}this.moving=!0,1==this.constants.hierarchicalLayout.enabled&&0==this.initializing&&(this._resetLevels(),this._setupHierarchicalLayout()),this._updateNodeIndexList(),this._updateValueRange(i)},s.prototype._removeNodes=function(t){for(var e=this.nodes,i=0,s=t.length;s>i;i++){var o=t[i];delete e[o]}this._updateNodeIndexList(),1==this.constants.hierarchicalLayout.enabled&&0==this.initializing&&(this._resetLevels(),this._setupHierarchicalLayout()),this._updateCalculationNodes(),this._reconnectEdges(),this._updateSelection(),this._updateValueRange(e)},s.prototype._setEdges=function(t){var e=this.edgesData;if(t instanceof d||t instanceof l)this.edgesData=t;else if(Array.isArray(t))this.edgesData=new d,this.edgesData.add(t);else{if(t)throw new TypeError("Array or DataSet expected");this.edgesData=new d}if(e&&a.forEach(this.edgesListeners,function(t,i){e.off(i,t)}),this.edges={},this.edgesData){var i=this;a.forEach(this.edgesListeners,function(t,e){i.edgesData.on(e,t)});var s=this.edgesData.getIds();this._addEdges(s)}this._reconnectEdges()},s.prototype._addEdges=function(t){for(var e=this.edges,i=this.edgesData,s=0,o=t.length;o>s;s++){var n=t[s],r=e[n];r&&r.disconnect();var a=i.get(n,{showInternalIds:!0});e[n]=new g(a,this,this.constants)}this.moving=!0,this._updateValueRange(e),this._createBezierNodes(),this._updateCalculationNodes(),1==this.constants.hierarchicalLayout.enabled&&0==this.initializing&&(this._resetLevels(),this._setupHierarchicalLayout())},s.prototype._updateEdges=function(t){for(var e=this.edges,i=this.edgesData,s=0,o=t.length;o>s;s++){var n=t[s],r=i.get(n),a=e[n];a?(a.disconnect(),a.setProperties(r,this.constants),a.connect()):(a=new g(r,this,this.constants),this.edges[n]=a)}this._createBezierNodes(),1==this.constants.hierarchicalLayout.enabled&&0==this.initializing&&(this._resetLevels(),this._setupHierarchicalLayout()),this.moving=!0,this._updateValueRange(e)},s.prototype._removeEdges=function(t){for(var e=this.edges,i=0,s=t.length;s>i;i++){var o=t[i],n=e[o];n&&(null!=n.via&&delete this.sectors.support.nodes[n.via.id],n.disconnect(),delete e[o])}this.moving=!0,this._updateValueRange(e),1==this.constants.hierarchicalLayout.enabled&&0==this.initializing&&(this._resetLevels(),this._setupHierarchicalLayout()),this._updateCalculationNodes()},s.prototype._reconnectEdges=function(){var t,e=this.nodes,i=this.edges;for(t in e)e.hasOwnProperty(t)&&(e[t].edges=[],e[t].dynamicEdges=[]);for(t in i)if(i.hasOwnProperty(t)){var s=i[t];s.from=null,s.to=null,s.connect()}},s.prototype._updateValueRange=function(t){var e,i=void 0,s=void 0;for(e in t)if(t.hasOwnProperty(e)){var o=t[e].getValue();void 0!==o&&(i=void 0===i?o:Math.min(o,i),s=void 0===s?o:Math.max(o,s))}if(void 0!==i&&void 0!==s)for(e in t)t.hasOwnProperty(e)&&t[e].setValueRange(i,s)},s.prototype.redraw=function(){this.setSize(this.constants.width,this.constants.height),this._redraw()},s.prototype._redraw=function(t){var e=this.frame.canvas.getContext("2d");e.setTransform(this.pixelRatio,0,0,this.pixelRatio,0,0);var i=this.frame.canvas.width*this.pixelRatio,s=this.frame.canvas.height*this.pixelRatio;e.clearRect(0,0,i,s),e.save(),e.translate(this.translation.x,this.translation.y),e.scale(this.scale,this.scale),this.canvasTopLeft={x:this._XconvertDOMtoCanvas(0),y:this._YconvertDOMtoCanvas(0)},this.canvasBottomRight={x:this._XconvertDOMtoCanvas(this.frame.canvas.clientWidth*this.pixelRatio),y:this._YconvertDOMtoCanvas(this.frame.canvas.clientHeight*this.pixelRatio)},1!=t&&(this._doInAllSectors("_drawAllSectorNodes",e),(0==this.drag.dragging||void 0===this.drag.dragging||0==this.constants.hideEdgesOnDrag)&&this._doInAllSectors("_drawEdges",e)),(0==this.drag.dragging||void 0===this.drag.dragging||0==this.constants.hideNodesOnDrag)&&this._doInAllSectors("_drawNodes",e,!1),1!=t&&1==this.controlNodesActive&&this._doInAllSectors("_drawControlNodes",e),e.restore(),1==t&&e.clearRect(0,0,i,s)},s.prototype._setTranslation=function(t,e){void 0===this.translation&&(this.translation={x:0,y:0}),void 0!==t&&(this.translation.x=t),void 0!==e&&(this.translation.y=e),this.emit("viewChanged")},s.prototype._getTranslation=function(){return{x:this.translation.x,y:this.translation.y}},s.prototype._setScale=function(t){this.scale=t},s.prototype._getScale=function(){return this.scale},s.prototype._XconvertDOMtoCanvas=function(t){return(t-this.translation.x)/this.scale},s.prototype._XconvertCanvasToDOM=function(t){return t*this.scale+this.translation.x},s.prototype._YconvertDOMtoCanvas=function(t){return(t-this.translation.y)/this.scale},s.prototype._YconvertCanvasToDOM=function(t){return t*this.scale+this.translation.y},s.prototype.canvasToDOM=function(t){return{x:this._XconvertCanvasToDOM(t.x),y:this._YconvertCanvasToDOM(t.y)}},s.prototype.DOMtoCanvas=function(t){return{x:this._XconvertDOMtoCanvas(t.x),y:this._YconvertDOMtoCanvas(t.y)}},s.prototype._drawNodes=function(t,e){void 0===e&&(e=!1);var i=this.nodes,s=[];for(var o in i)i.hasOwnProperty(o)&&(i[o].setScaleAndPos(this.scale,this.canvasTopLeft,this.canvasBottomRight),i[o].isSelected()?s.push(o):(i[o].inArea()||e)&&i[o].draw(t));for(var n=0,r=s.length;r>n;n++)(i[s[n]].inArea()||e)&&i[s[n]].draw(t)},s.prototype._drawEdges=function(t){var e=this.edges;for(var i in e)if(e.hasOwnProperty(i)){var s=e[i];s.setScale(this.scale),s.connected&&e[i].draw(t)}},s.prototype._drawControlNodes=function(t){var e=this.edges;for(var i in e)e.hasOwnProperty(i)&&e[i]._drawControlNodes(t)},s.prototype._stabilize=function(){1==this.constants.freezeForStabilization&&this._freezeDefinedNodes();for(var t=0;this.moving&&t0)for(t in i)i.hasOwnProperty(t)&&(i[t].discreteStepLimited(e,this.constants.maxVelocity),s=!0);else for(t in i)i.hasOwnProperty(t)&&(i[t].discreteStep(e),s=!0);if(1==s){var o=this.constants.minVelocity/Math.max(this.scale,.05);return o>.5*this.constants.maxVelocity?!0:this._isMoving(o)}return!1},s.prototype._revertPhysicsState=function(){var t=this.nodes;for(var e in t)t.hasOwnProperty(e)&&t[e].revertPosition()},s.prototype._revertPhysicsTick=function(){this._doInAllActiveSectors("_revertPhysicsState"),1==this.constants.smoothCurves.enabled&&1==this.constants.smoothCurves.dynamic&&this._doInSupportSector("_revertPhysicsState")},s.prototype._physicsTick=function(){if(!this.freezeSimulation&&1==this.moving){var t=!1,e=!1;this._doInAllActiveSectors("_initializeForceCalculation");var i=this._doInAllActiveSectors("_discreteStepNodes");1==this.constants.smoothCurves.enabled&&1==this.constants.smoothCurves.dynamic&&(e=this._doInSupportSector("_discreteStepNodes"));for(var s=0;s2*e||1==this.runDoubleSpeed)&&1==this.moving&&(this._physicsTick(),0!=this.renderTime&&(this.runDoubleSpeed=!0));var i=Date.now();this._redraw(),this.renderTime=Date.now()-i,this.start()},"undefined"!=typeof window&&(window.requestAnimationFrame=window.requestAnimationFrame||window.mozRequestAnimationFrame||window.webkitRequestAnimationFrame||window.msRequestAnimationFrame),s.prototype.start=function(){if(1==this.moving||0!=this.xIncrement||0!=this.yIncrement||0!=this.zoomIncrement)this.timer||(this.timer=1==this.requiresTimeout?window.setTimeout(this._animationStep.bind(this),this.renderTimestep):window.requestAnimationFrame(this._animationStep.bind(this)));else if(this._redraw(),this.stabilizationIterations>1){var t=this,e={iterations:t.stabilizationIterations};this.stabilizationIterations=0,this.startedStabilization=!1,setTimeout(function(){t.emit("stabilized",e)},0)}else this.stabilizationIterations=0},s.prototype._handleNavigation=function(){if(0!=this.xIncrement||0!=this.yIncrement){var t=this._getTranslation();this._setTranslation(t.x+this.xIncrement,t.y+this.yIncrement)}if(0!=this.zoomIncrement){var e={x:this.frame.canvas.clientWidth/2,y:this.frame.canvas.clientHeight/2};this._zoom(this.scale*(1+this.zoomIncrement),e)}},s.prototype.toggleFreeze=function(){0==this.freezeSimulation?this.freezeSimulation=!0:(this.freezeSimulation=!1,this.start())},s.prototype._configureSmoothCurves=function(t){if(void 0===t&&(t=!0),1==this.constants.smoothCurves.enabled&&1==this.constants.smoothCurves.dynamic){this._createBezierNodes();for(var e in this.sectors.support.nodes)this.sectors.support.nodes.hasOwnProperty(e)&&void 0===this.edges[this.sectors.support.nodes[e].parentEdgeId]&&delete this.sectors.support.nodes[e]}else{this.sectors.support.nodes={};for(var i in this.edges)this.edges.hasOwnProperty(i)&&(this.edges[i].via=null)}this._updateCalculationNodes(),t||(this.moving=!0,this.start())},s.prototype._createBezierNodes=function(){if(1==this.constants.smoothCurves.enabled&&1==this.constants.smoothCurves.dynamic)for(var t in this.edges)if(this.edges.hasOwnProperty(t)){var e=this.edges[t];if(null==e.via){var i="edgeId:".concat(e.id);this.sectors.support.nodes[i]=new f({id:i,mass:1,shape:"circle",image:"",internalMultiplier:1},{},{},this.constants),e.via=this.sectors.support.nodes[i],e.via.parentEdgeId=e.id,e.positionBezierNode()}}},s.prototype._initializeMixinLoaders=function(){for(var t in y)y.hasOwnProperty(t)&&(s.prototype[t]=y[t])},s.prototype.storePosition=function(){console.log("storePosition is deprecated: use .storePositions() from now on."),this.storePositions()},s.prototype.storePositions=function(){var t=[];for(var e in this.nodes)if(this.nodes.hasOwnProperty(e)){var i=this.nodes[e],s=!this.nodes.xFixed,o=!this.nodes.yFixed;(this.nodesData._data[e].x!=Math.round(i.x)||this.nodesData._data[e].y!=Math.round(i.y))&&t.push({id:e,x:Math.round(i.x),y:Math.round(i.y),allowedToMoveX:s,allowedToMoveY:o})}this.nodesData.update(t)},s.prototype.getPositions=function(t){var e={};if(void 0!==t){if(1==Array.isArray(t)){for(var i=0;i=1&&(this.easingTime=0,this._redraw=null!=this.lockedOnNodeId?this._lockedRedraw:this._classicRedraw,this.emit("animationFinished"))},s.prototype._classicRedraw=function(){},s.prototype.isActive=function(){return!this.activator||this.activator.active},s.prototype.setScale=function(){return this._setScale()},s.prototype.getScale=function(){return this._getScale()},s.prototype.getCenterCoordinates=function(){return this.DOMtoCanvas({x:.5*this.frame.canvas.clientWidth,y:.5*this.frame.canvas.clientHeight})},s.prototype.getBoundingBox=function(t){return void 0!==this.nodes[t]?this.nodes[t].boundingBox:void 0},t.exports=s},function(t,e,i){function s(t,e,i){if(!e)throw"No network provided";var s=["edges","physics"],n=o.selectiveBridgeObject(s,i);this.options=n.edges,this.physics=n.physics,this.options.smoothCurves=i.smoothCurves,this.network=e,this.id=void 0,this.fromId=void 0,this.toId=void 0,this.title=void 0,this.widthSelected=this.options.width*this.options.widthSelectionMultiplier,this.value=void 0,this.selected=!1,this.hover=!1,this.labelDimensions={top:0,left:0,width:0,height:0,yLine:0},this.dirtyLabel=!0,this.from=null,this.to=null,this.via=null,this.fromBackup=null,this.toBackup=null,this.originalFromId=[],this.originalToId=[],this.connected=!1,this.widthFixed=!1,this.lengthFixed=!1,this.setProperties(t),this.controlNodesEnabled=!1,this.controlNodes={from:null,to:null,positions:{}},this.connectedNode=null}var o=i(1),n=i(40);s.prototype.setProperties=function(t){if(t){var e=["style","fontSize","fontFace","fontColor","fontFill","fontStrokeWidth","fontStrokeColor","width","widthSelectionMultiplier","hoverWidth","arrowScaleFactor","dash","inheritColor","labelAlignment"];switch(o.selectiveDeepExtend(e,this.options,t),void 0!==t.from&&(this.fromId=t.from),void 0!==t.to&&(this.toId=t.to),void 0!==t.id&&(this.id=t.id),void 0!==t.label&&(this.label=t.label,this.dirtyLabel=!0),void 0!==t.title&&(this.title=t.title),void 0!==t.value&&(this.value=t.value),void 0!==t.length&&(this.physics.springLength=t.length),void 0!==t.color&&(this.options.inheritColor=!1,o.isString(t.color)?(this.options.color.color=t.color,this.options.color.highlight=t.color):(void 0!==t.color.color&&(this.options.color.color=t.color.color),void 0!==t.color.highlight&&(this.options.color.highlight=t.color.highlight),void 0!==t.color.hover&&(this.options.color.hover=t.color.hover))),this.connect(),this.widthFixed=this.widthFixed||void 0!==t.width,this.lengthFixed=this.lengthFixed||void 0!==t.length,this.widthSelected=this.options.width*this.options.widthSelectionMultiplier,this.options.style){case"line":this.draw=this._drawLine;break;case"arrow":this.draw=this._drawArrow;break;case"arrow-center":this.draw=this._drawArrowCenter;break;case"dash-line":this.draw=this._drawDashLine;break;default:this.draw=this._drawLine}}},s.prototype.connect=function(){this.disconnect(),this.from=this.network.nodes[this.fromId]||null,this.to=this.network.nodes[this.toId]||null,this.connected=this.from&&this.to,this.connected?(this.from.attachEdge(this),this.to.attachEdge(this)):(this.from&&this.from.detachEdge(this),this.to&&this.to.detachEdge(this))},s.prototype.disconnect=function(){this.from&&(this.from.detachEdge(this),this.from=null),this.to&&(this.to.detachEdge(this),this.to=null),this.connected=!1},s.prototype.getTitle=function(){return"function"==typeof this.title?this.title():this.title +},s.prototype.getValue=function(){return this.value},s.prototype.setValueRange=function(t,e){if(!this.widthFixed&&void 0!==this.value){var i=(this.options.widthMax-this.options.widthMin)/(e-t);this.options.width=(this.value-t)*i+this.options.widthMin,this.widthSelected=this.options.width*this.options.widthSelectionMultiplier}},s.prototype.draw=function(){throw"Method draw not initialized in edge"},s.prototype.isOverlappingWith=function(t){if(this.connected){var e=10,i=this.from.x,s=this.from.y,o=this.to.x,n=this.to.y,r=t.left,a=t.top,h=this._getDistanceToEdge(i,s,o,n,r,a);return e>h}return!1},s.prototype._getColor=function(){var t=this.options.color;return"to"==this.options.inheritColor?t={highlight:this.to.options.color.highlight.border,hover:this.to.options.color.hover.border,color:this.to.options.color.border}:("from"==this.options.inheritColor||1==this.options.inheritColor)&&(t={highlight:this.from.options.color.highlight.border,hover:this.from.options.color.hover.border,color:this.from.options.color.border}),1==this.selected?t.highlight:1==this.hover?t.hover:t.color},s.prototype._drawLine=function(t){if(t.strokeStyle=this._getColor(),t.lineWidth=this._getLineWidth(),this.from!=this.to){var e,i=this._line(t);if(this.label){if(1==this.options.smoothCurves.enabled&&null!=i){var s=.5*(.5*(this.from.x+i.x)+.5*(this.to.x+i.x)),o=.5*(.5*(this.from.y+i.y)+.5*(this.to.y+i.y));e={x:s,y:o}}else e=this._pointOnLine(.5);this._label(t,this.label,e.x,e.y)}}else{var n,r,a=this.physics.springLength/4,h=this.from;h.width||h.resize(t),h.width>h.height?(n=h.x+h.width/2,r=h.y-a):(n=h.x+a,r=h.y-h.height/2),this._circle(t,n,r,a),e=this._pointOnCircle(n,r,a,.5),this._label(t,this.label,e.x,e.y)}},s.prototype._getLineWidth=function(){return 1==this.selected?Math.max(Math.min(this.widthSelected,this.options.widthMax),.3*this.networkScaleInv):1==this.hover?Math.max(Math.min(this.options.hoverWidth,this.options.widthMax),.3*this.networkScaleInv):Math.max(this.options.width,.3*this.networkScaleInv)},s.prototype._getViaCoordinates=function(){if(1==this.options.smoothCurves.dynamic&&1==this.options.smoothCurves.enabled)return this.via;if(0==this.options.smoothCurves.enabled)return{x:0,y:0};var t=null,e=null,i=this.options.smoothCurves.roundness,s=this.options.smoothCurves.type,o=Math.abs(this.from.x-this.to.x),n=Math.abs(this.from.y-this.to.y);return"discrete"==s||"diagonalCross"==s?Math.abs(this.from.x-this.to.x)this.to.y?this.from.xthis.to.x&&(t=this.from.x-i*n,e=this.from.y-i*n):this.from.ythis.to.x&&(t=this.from.x-i*n,e=this.from.y+i*n)),"discrete"==s&&(t=i*n>o?this.from.x:t)):Math.abs(this.from.x-this.to.x)>Math.abs(this.from.y-this.to.y)&&(this.from.y>this.to.y?this.from.xthis.to.x&&(t=this.from.x-i*o,e=this.from.y-i*o):this.from.ythis.to.x&&(t=this.from.x-i*o,e=this.from.y+i*o)),"discrete"==s&&(e=i*o>n?this.from.y:e)):"straightCross"==s?Math.abs(this.from.x-this.to.x)Math.abs(this.from.y-this.to.y)&&(t=this.from.xthis.to.y?this.from.xthis.to.x&&(t=this.from.x-i*n,e=this.from.y-i*n,t=this.to.x>t?this.to.x:t):this.from.ythis.to.x&&(t=this.from.x-i*n,e=this.from.y+i*n,t=this.to.x>t?this.to.x:t)):Math.abs(this.from.x-this.to.x)>Math.abs(this.from.y-this.to.y)&&(this.from.y>this.to.y?this.from.xe?this.to.y:e):this.from.x>this.to.x&&(t=this.from.x-i*o,e=this.from.y-i*o,e=this.to.y>e?this.to.y:e):this.from.ythis.to.x&&(t=this.from.x-i*o,e=this.from.y+i*o,e=this.to.yd;d++){var l=t.measureText(n[d]).width;h=l>h?l:h}var c=this.options.fontSize*r,p=i-h/2,u=s-c/2;this.labelDimensions={top:u,left:p,width:h,height:c,yLine:o}}var o=this.labelDimensions.yLine;t.save(),"horizontal"!=this.options.labelAlignment&&(t.translate(i,o),this._rotateForLabelAlignment(t),i=0,o=0),this._drawLabelRect(t),this._drawLabelText(t,i,o,n,r,a),t.restore()}},s.prototype._rotateForLabelAlignment=function(t){var e=this.from.y-this.to.y,i=this.from.x-this.to.x,s=Math.atan2(e,i);(-1>s&&0>i||s>0&&0>i)&&(s+=Math.PI),t.rotate(s)},s.prototype._drawLabelRect=function(t){if(void 0!==this.options.fontFill&&null!==this.options.fontFill&&"none"!==this.options.fontFill){t.fillStyle=this.options.fontFill;var e=2;"line-center"==this.options.labelAlignment?t.fillRect(.5*-this.labelDimensions.width,.5*-this.labelDimensions.height,this.labelDimensions.width,this.labelDimensions.height):"line-above"==this.options.labelAlignment?t.fillRect(.5*-this.labelDimensions.width,-(this.labelDimensions.height+e),this.labelDimensions.width,this.labelDimensions.height):"line-below"==this.options.labelAlignment?t.fillRect(.5*-this.labelDimensions.width,e,this.labelDimensions.width,this.labelDimensions.height):t.fillRect(this.labelDimensions.left,this.labelDimensions.top,this.labelDimensions.width,this.labelDimensions.height)}},s.prototype._drawLabelText=function(t,e,i,s,o,n){if(t.fillStyle=this.options.fontColor||"black",t.textAlign="center","horizontal"!=this.options.labelAlignment){var r=2;"line-above"==this.options.labelAlignment?(t.textBaseline="alphabetic",i-=2*r):"line-below"==this.options.labelAlignment?(t.textBaseline="hanging",i+=2*r):t.textBaseline="middle"}else t.textBaseline="middle";this.options.fontStrokeWidth>0&&(t.lineWidth=this.options.fontStrokeWidth,t.strokeStyle=this.options.fontStrokeColor,t.lineJoin="round");for(var a=0;o>a;a++)this.options.fontStrokeWidth>0&&t.strokeText(s[a],e,i),t.fillText(s[a],e,i),i+=n},s.prototype._drawDashLine=function(t){t.strokeStyle=this._getColor(),t.lineWidth=this._getLineWidth();var e=null;if(void 0!==t.setLineDash){t.save();var i=[0];i=void 0!==this.options.dash.length&&void 0!==this.options.dash.gap?[this.options.dash.length,this.options.dash.gap]:[5,5],t.setLineDash(i),t.lineDashOffset=0,e=this._line(t),t.setLineDash([0]),t.lineDashOffset=0,t.restore()}else t.beginPath(),t.lineCap="round",void 0!==this.options.dash.altLength?t.dashedLine(this.from.x,this.from.y,this.to.x,this.to.y,[this.options.dash.length,this.options.dash.gap,this.options.dash.altLength,this.options.dash.gap]):void 0!==this.options.dash.length&&void 0!==this.options.dash.gap?t.dashedLine(this.from.x,this.from.y,this.to.x,this.to.y,[this.options.dash.length,this.options.dash.gap]):(t.moveTo(this.from.x,this.from.y),t.lineTo(this.to.x,this.to.y)),t.stroke();if(this.label){var s;if(1==this.options.smoothCurves.enabled&&null!=e){var o=.5*(.5*(this.from.x+e.x)+.5*(this.to.x+e.x)),n=.5*(.5*(this.from.y+e.y)+.5*(this.to.y+e.y));s={x:o,y:n}}else s=this._pointOnLine(.5);this._label(t,this.label,s.x,s.y)}},s.prototype._pointOnLine=function(t){return{x:(1-t)*this.from.x+t*this.to.x,y:(1-t)*this.from.y+t*this.to.y}},s.prototype._pointOnCircle=function(t,e,i,s){var o=2*(s-3/8)*Math.PI;return{x:t+i*Math.cos(o),y:e-i*Math.sin(o)}},s.prototype._drawArrowCenter=function(t){var e;if(t.strokeStyle=this._getColor(),t.fillStyle=t.strokeStyle,t.lineWidth=this._getLineWidth(),this.from!=this.to){var i=this._line(t),s=Math.atan2(this.to.y-this.from.y,this.to.x-this.from.x),o=(10+5*this.options.width)*this.options.arrowScaleFactor;if(1==this.options.smoothCurves.enabled&&null!=i){var n=.5*(.5*(this.from.x+i.x)+.5*(this.to.x+i.x)),r=.5*(.5*(this.from.y+i.y)+.5*(this.to.y+i.y));e={x:n,y:r}}else e=this._pointOnLine(.5);t.arrow(e.x,e.y,s,o),t.fill(),t.stroke(),this.label&&this._label(t,this.label,e.x,e.y)}else{var a,h,d=.25*Math.max(100,this.physics.springLength),l=this.from;l.width||l.resize(t),l.width>l.height?(a=l.x+.5*l.width,h=l.y-d):(a=l.x+d,h=l.y-.5*l.height),this._circle(t,a,h,d);var s=.2*Math.PI,o=(10+5*this.options.width)*this.options.arrowScaleFactor;e=this._pointOnCircle(a,h,d,.5),t.arrow(e.x,e.y,s,o),t.fill(),t.stroke(),this.label&&(e=this._pointOnCircle(a,h,d,.5),this._label(t,this.label,e.x,e.y))}},s.prototype._pointOnBezier=function(t){var e=this._getViaCoordinates(),i=Math.pow(1-t,2)*this.from.x+2*t*(1-t)*e.x+Math.pow(t,2)*this.to.x,s=Math.pow(1-t,2)*this.from.y+2*t*(1-t)*e.y+Math.pow(t,2)*this.to.y;return{x:i,y:s}},s.prototype._findBorderPosition=function(t,e){var i,s,o,n,r,a=10,h=0,d=0,l=1,c=.2,p=this.to;for(1==t&&(p=this.from);l>=d&&a>h;){var u=.5*(d+l);if(i=this._pointOnBezier(u),s=Math.atan2(p.y-i.y,p.x-i.x),o=p.distanceToBorder(e,s),n=Math.sqrt(Math.pow(i.x-p.x,2)+Math.pow(i.y-p.y,2)),r=o-n,Math.abs(r)r?0==t?d=u:l=u:0==t?l=u:d=u,h++}return i.t=u,i},s.prototype._drawArrow=function(t){t.strokeStyle=this._getColor(),t.fillStyle=t.strokeStyle,t.lineWidth=this._getLineWidth();var e,i,s;if(this.from!=this.to){if(this._line(t),1==this.options.smoothCurves.enabled){var o=this._getViaCoordinates();s=this._findBorderPosition(!1,t);var n=this._pointOnBezier(Math.max(0,s.t-.1));e=Math.atan2(s.y-n.y,s.x-n.x)}else{e=Math.atan2(this.to.y-this.from.y,this.to.x-this.from.x);var r=this.to.x-this.from.x,a=this.to.y-this.from.y,h=Math.sqrt(r*r+a*a),d=this.to.distanceToBorder(t,e),l=(h-d)/h;s={},s.x=(1-l)*this.from.x+l*this.to.x,s.y=(1-l)*this.from.y+l*this.to.y}if(i=(10+5*this.options.width)*this.options.arrowScaleFactor,t.arrow(s.x,s.y,e,i),t.fill(),t.stroke(),this.label){var c;c=1==this.options.smoothCurves.enabled&&null!=o?this._pointOnBezier(.5):this._pointOnLine(.5),this._label(t,this.label,c.x,c.y)}}else{var p,u,m,f=this.from,g=.25*Math.max(100,this.physics.springLength);f.width||f.resize(t),f.width>f.height?(p=f.x+.5*f.width,u=f.y-g,m={x:p,y:f.y,angle:.9*Math.PI}):(p=f.x+g,u=f.y-.5*f.height,m={x:f.x,y:u,angle:.6*Math.PI}),t.beginPath(),t.arc(p,u,g,0,2*Math.PI,!1),t.stroke();var i=(10+5*this.options.width)*this.options.arrowScaleFactor;t.arrow(m.x,m.y,m.angle,i),t.fill(),t.stroke(),this.label&&(c=this._pointOnCircle(p,u,g,.5),this._label(t,this.label,c.x,c.y))}},s.prototype._getDistanceToEdge=function(t,e,i,s,o,n){var r=0;if(this.from!=this.to)if(1==this.options.smoothCurves.enabled){var a,h;if(1==this.options.smoothCurves.enabled&&1==this.options.smoothCurves.dynamic)a=this.via.x,h=this.via.y;else{var d=this._getViaCoordinates();a=d.x,h=d.y}var l,c,p,u,m,f,g,v=1e9;for(c=0;10>c;c++)p=.1*c,u=Math.pow(1-p,2)*t+2*p*(1-p)*a+Math.pow(p,2)*i,m=Math.pow(1-p,2)*e+2*p*(1-p)*h+Math.pow(p,2)*s,c>0&&(l=this._getDistanceToLine(f,g,u,m,o,n),v=v>l?l:v),f=u,g=m;r=v}else r=this._getDistanceToLine(t,e,i,s,o,n);else{var u,m,y,b,_=.25*this.physics.springLength,x=this.from;x.width>x.height?(u=x.x+.5*x.width,m=x.y-_):(u=x.x+_,m=x.y-.5*x.height),y=u-o,b=m-n,r=Math.abs(Math.sqrt(y*y+b*b)-_)}return this.labelDimensions.lefto&&this.labelDimensions.topn?0:r},s.prototype._getDistanceToLine=function(t,e,i,s,o,n){var r=i-t,a=s-e,h=r*r+a*a,d=((o-t)*r+(n-e)*a)/h;d>1?d=1:0>d&&(d=0);var l=t+d*r,c=e+d*a,p=l-o,u=c-n;return Math.sqrt(p*p+u*u)},s.prototype.setScale=function(t){this.networkScaleInv=1/t},s.prototype.select=function(){this.selected=!0},s.prototype.unselect=function(){this.selected=!1},s.prototype.positionBezierNode=function(){null!==this.via&&null!==this.from&&null!==this.to?(this.via.x=.5*(this.from.x+this.to.x),this.via.y=.5*(this.from.y+this.to.y)):(this.via.x=0,this.via.y=0)},s.prototype._drawControlNodes=function(t){if(1==this.controlNodesEnabled){if(null===this.controlNodes.from&&null===this.controlNodes.to){var e="edgeIdFrom:".concat(this.id),i="edgeIdTo:".concat(this.id),s={nodes:{group:"",radius:7,borderWidth:2,borderWidthSelected:2},physics:{damping:0},clustering:{maxNodeSizeIncrements:0,nodeScaling:{width:0,height:0,radius:0}}};this.controlNodes.from=new n({id:e,shape:"dot",color:{background:"#ff0000",border:"#3c3c3c",highlight:{background:"#07f968"}}},{},{},s),this.controlNodes.to=new n({id:i,shape:"dot",color:{background:"#ff0000",border:"#3c3c3c",highlight:{background:"#07f968"}}},{},{},s)}this.controlNodes.positions={},0==this.controlNodes.from.selected&&(this.controlNodes.positions.from=this.getControlNodeFromPosition(t),this.controlNodes.from.x=this.controlNodes.positions.from.x,this.controlNodes.from.y=this.controlNodes.positions.from.y),0==this.controlNodes.to.selected&&(this.controlNodes.positions.to=this.getControlNodeToPosition(t),this.controlNodes.to.x=this.controlNodes.positions.to.x,this.controlNodes.to.y=this.controlNodes.positions.to.y),this.controlNodes.from.draw(t),this.controlNodes.to.draw(t)}else this.controlNodes={from:null,to:null,positions:{}}},s.prototype._enableControlNodes=function(){this.fromBackup=this.from,this.toBackup=this.to,this.controlNodesEnabled=!0},s.prototype._disableControlNodes=function(){this.fromId=this.from.id,this.toId=this.to.id,this.fromId!=this.fromBackup.id?this.fromBackup.detachEdge(this):this.toId!=this.toBackup.id&&this.toBackup.detachEdge(this),this.fromBackup=null,this.toBackup=null,this.controlNodesEnabled=!1},s.prototype._getSelectedControlNode=function(t,e){var i=this.controlNodes.positions,s=Math.sqrt(Math.pow(t-i.from.x,2)+Math.pow(e-i.from.y,2)),o=Math.sqrt(Math.pow(t-i.to.x,2)+Math.pow(e-i.to.y,2));return 15>s?(this.connectedNode=this.from,this.from=this.controlNodes.from,this.controlNodes.from):15>o?(this.connectedNode=this.to,this.to=this.controlNodes.to,this.controlNodes.to):null},s.prototype._restoreControlNodes=function(){1==this.controlNodes.from.selected?(this.from=this.connectedNode,this.connectedNode=null,this.controlNodes.from.unselect()):1==this.controlNodes.to.selected&&(this.to=this.connectedNode,this.connectedNode=null,this.controlNodes.to.unselect())},s.prototype.getControlNodeFromPosition=function(t){var e;if(1==this.options.smoothCurves.enabled)e=this._findBorderPosition(!0,t);else{var i=Math.atan2(this.to.y-this.from.y,this.to.x-this.from.x),s=this.to.x-this.from.x,o=this.to.y-this.from.y,n=Math.sqrt(s*s+o*o),r=this.from.distanceToBorder(t,i+Math.PI),a=(n-r)/n;e={},e.x=a*this.from.x+(1-a)*this.to.x,e.y=a*this.from.y+(1-a)*this.to.y}return e},s.prototype.getControlNodeToPosition=function(t){var e;if(1==this.options.smoothCurves.enabled)e=this._findBorderPosition(!1,t);else{var i=Math.atan2(this.to.y-this.from.y,this.to.x-this.from.x),s=this.to.x-this.from.x,o=this.to.y-this.from.y,n=Math.sqrt(s*s+o*o),r=this.to.distanceToBorder(t,i),a=(n-r)/n;e={},e.x=(1-a)*this.from.x+a*this.to.x,e.y=(1-a)*this.from.y+a*this.to.y}return e},t.exports=s},function(t,e,i){function s(){this.clear(),this.defaultIndex=0}i(1);s.DEFAULT=[{border:"#2B7CE9",background:"#97C2FC",highlight:{border:"#2B7CE9",background:"#D2E5FF"},hover:{border:"#2B7CE9",background:"#D2E5FF"}},{border:"#FFA500",background:"#FFFF00",highlight:{border:"#FFA500",background:"#FFFFA3"},hover:{border:"#FFA500",background:"#FFFFA3"}},{border:"#FA0A10",background:"#FB7E81",highlight:{border:"#FA0A10",background:"#FFAFB1"},hover:{border:"#FA0A10",background:"#FFAFB1"}},{border:"#41A906",background:"#7BE141",highlight:{border:"#41A906",background:"#A1EC76"},hover:{border:"#41A906",background:"#A1EC76"}},{border:"#E129F0",background:"#EB7DF4",highlight:{border:"#E129F0",background:"#F0B3F5"},hover:{border:"#E129F0",background:"#F0B3F5"}},{border:"#7C29F0",background:"#AD85E4",highlight:{border:"#7C29F0",background:"#D3BDF0"},hover:{border:"#7C29F0",background:"#D3BDF0"}},{border:"#C37F00",background:"#FFA807",highlight:{border:"#C37F00",background:"#FFCA66"},hover:{border:"#C37F00",background:"#FFCA66"}},{border:"#4220FB",background:"#6E6EFD",highlight:{border:"#4220FB",background:"#9B9BFD"},hover:{border:"#4220FB",background:"#9B9BFD"}},{border:"#FD5A77",background:"#FFC0CB",highlight:{border:"#FD5A77",background:"#FFD1D9"},hover:{border:"#FD5A77",background:"#FFD1D9"}},{border:"#4AD63A",background:"#C2FABC",highlight:{border:"#4AD63A",background:"#E6FFE3"},hover:{border:"#4AD63A",background:"#E6FFE3"}}],s.prototype.clear=function(){this.groups={},this.groups.length=function(){var t=0;for(var e in this)this.hasOwnProperty(e)&&t++;return t}},s.prototype.get=function(t){var e=this.groups[t];if(void 0==e){var i=this.defaultIndex%s.DEFAULT.length;this.defaultIndex++,e={},e.color=s.DEFAULT[i],this.groups[t]=e}return e},s.prototype.add=function(t,e){return this.groups[t]=e,e},t.exports=s},function(t){function e(){this.images={},this.imageBroken={},this.callback=void 0}e.prototype.setOnloadCallback=function(t){this.callback=t},e.prototype.load=function(t,e){var i=this.images[t];if(void 0===i){var s=this;i=new Image,i.onload=function(){0==this.width&&(document.body.appendChild(this),this.width=this.offsetWidth,this.height=this.offsetHeight,document.body.removeChild(this)),s.callback&&(s.images[t]=i,s.callback(this))},i.onerror=function(){void 0===e?(console.error("Could not load image:",t),delete this.src,s.callback&&s.callback(this)):s.imageBroken[t]===!0?(console.error("Could not load brokenImage:",e),delete this.src,s.callback&&s.callback(this)):(this.src=e,s.imageBroken[t]=!0)},i.src=t}return i},t.exports=e},function(t,e,i){function s(t,e,i,s){var n=o.selectiveBridgeObject(["nodes"],s);this.options=n.nodes,this.selected=!1,this.hover=!1,this.edges=[],this.dynamicEdges=[],this.reroutedEdges={},this.fontDrawThreshold=3,this.id=void 0,this.allowedToMoveX=!1,this.allowedToMoveY=!1,this.xFixed=!1,this.yFixed=!1,this.horizontalAlignLeft=!0,this.verticalAlignTop=!0,this.baseRadiusValue=s.nodes.radius,this.radiusFixed=!1,this.level=-1,this.preassignedLevel=!1,this.hierarchyEnumerated=!1,this.labelDimensions={top:0,left:0,width:0,height:0,yLine:0},this.boundingBox={top:0,left:0,right:0,bottom:0},this.imagelist=e,this.grouplist=i,this.fx=0,this.fy=0,this.vx=0,this.vy=0,this.x=null,this.y=null,this.previousState={vx:0,vy:0,x:0,y:0},this.damping=s.physics.damping,this.fixedData={x:null,y:null},this.setProperties(t,n),this.resetCluster(),this.dynamicEdgesLength=0,this.clusterSession=0,this.clusterSizeWidthFactor=s.clustering.nodeScaling.width,this.clusterSizeHeightFactor=s.clustering.nodeScaling.height,this.clusterSizeRadiusFactor=s.clustering.nodeScaling.radius,this.maxNodeSizeIncrements=s.clustering.maxNodeSizeIncrements,this.growthIndicator=0,this.networkScaleInv=1,this.networkScale=1,this.canvasTopLeft={x:-300,y:-300},this.canvasBottomRight={x:300,y:300},this.parentEdgeId=null}var o=i(1);s.prototype.revertPosition=function(){this.x=this.previousState.x,this.y=this.previousState.y,this.vx=this.previousState.vx,this.vy=this.previousState.vy},s.prototype.resetCluster=function(){this.formationScale=void 0,this.clusterSize=1,this.containedNodes={},this.containedEdges={},this.clusterSessions=[]},s.prototype.attachEdge=function(t){-1==this.edges.indexOf(t)&&this.edges.push(t),-1==this.dynamicEdges.indexOf(t)&&this.dynamicEdges.push(t),this.dynamicEdgesLength=this.dynamicEdges.length},s.prototype.detachEdge=function(t){var e=this.edges.indexOf(t);-1!=e&&this.edges.splice(e,1),e=this.dynamicEdges.indexOf(t),-1!=e&&this.dynamicEdges.splice(e,1),this.dynamicEdgesLength=this.dynamicEdges.length},s.prototype.setProperties=function(t,e){if(t){var i=["borderWidth","borderWidthSelected","shape","image","brokenImage","radius","fontColor","fontSize","fontFace","fontFill","fontStrokeWidth","fontStrokeColor","group","mass"];if(o.selectiveDeepExtend(i,this.options,t),void 0!==t.id&&(this.id=t.id),void 0!==t.label&&(this.label=t.label,this.originalLabel=t.label),void 0!==t.title&&(this.title=t.title),void 0!==t.x&&(this.x=t.x),void 0!==t.y&&(this.y=t.y),void 0!==t.value&&(this.value=t.value),void 0!==t.level&&(this.level=t.level,this.preassignedLevel=!0),void 0!==t.horizontalAlignLeft&&(this.horizontalAlignLeft=t.horizontalAlignLeft),void 0!==t.verticalAlignTop&&(this.verticalAlignTop=t.verticalAlignTop),void 0!==t.triggerFunction&&(this.triggerFunction=t.triggerFunction),void 0===this.id)throw"Node must have an id";if("number"==typeof this.options.group||"string"==typeof this.options.group&&""!=this.options.group){var s=this.grouplist.get(this.options.group);o.deepExtend(this.options,s),this.options.color=o.parseColor(this.options.color)}if(void 0!==t.radius&&(this.baseRadiusValue=this.options.radius),void 0!==t.color&&(this.options.color=o.parseColor(t.color)),void 0!==this.options.image&&""!=this.options.image){if(!this.imagelist)throw"No imagelist provided";this.imageObj=this.imagelist.load(this.options.image,this.options.brokenImage)}switch(void 0!==t.allowedToMoveX?(this.xFixed=!t.allowedToMoveX,this.allowedToMoveX=t.allowedToMoveX):void 0!==t.x&&0==this.allowedToMoveX&&(this.xFixed=!0),void 0!==t.allowedToMoveY?(this.yFixed=!t.allowedToMoveY,this.allowedToMoveY=t.allowedToMoveY):void 0!==t.y&&0==this.allowedToMoveY&&(this.yFixed=!0),this.radiusFixed=this.radiusFixed||void 0!==t.radius,("image"===this.options.shape||"circularImage"===this.options.shape)&&(this.options.radiusMin=e.nodes.widthMin,this.options.radiusMax=e.nodes.widthMax),this.options.shape){case"database":this.draw=this._drawDatabase,this.resize=this._resizeDatabase;break;case"box":this.draw=this._drawBox,this.resize=this._resizeBox;break;case"circle":this.draw=this._drawCircle,this.resize=this._resizeCircle;break;case"ellipse":this.draw=this._drawEllipse,this.resize=this._resizeEllipse;break;case"image":this.draw=this._drawImage,this.resize=this._resizeImage;break;case"circularImage":this.draw=this._drawCircularImage,this.resize=this._resizeCircularImage;break;case"text":this.draw=this._drawText,this.resize=this._resizeText;break;case"dot":this.draw=this._drawDot,this.resize=this._resizeShape;break;case"square":this.draw=this._drawSquare,this.resize=this._resizeShape;break;case"triangle":this.draw=this._drawTriangle,this.resize=this._resizeShape;break;case"triangleDown":this.draw=this._drawTriangleDown,this.resize=this._resizeShape;break;case"star":this.draw=this._drawStar,this.resize=this._resizeShape;break;default:this.draw=this._drawEllipse,this.resize=this._resizeEllipse}this._reset()}},s.prototype.select=function(){this.selected=!0,this._reset()},s.prototype.unselect=function(){this.selected=!1,this._reset()},s.prototype.clearSizeCache=function(){this._reset()},s.prototype._reset=function(){this.width=void 0,this.height=void 0},s.prototype.getTitle=function(){return"function"==typeof this.title?this.title():this.title},s.prototype.distanceToBorder=function(t,e){var i=1;switch(this.width||this.resize(t),this.options.shape){case"circle":case"dot":return this.options.radius+i;case"ellipse":var s=this.width/2,o=this.height/2,n=Math.sin(e)*s,r=Math.cos(e)*o;return s*o/Math.sqrt(n*n+r*r);case"box":case"image":case"text":default:return this.width?Math.min(Math.abs(this.width/2/Math.cos(e)),Math.abs(this.height/2/Math.sin(e)))+i:0}},s.prototype._setForce=function(t,e){this.fx=t,this.fy=e},s.prototype._addForce=function(t,e){this.fx+=t,this.fy+=e},s.prototype.storeState=function(){this.previousState.x=this.x,this.previousState.y=this.y,this.previousState.vx=this.vx,this.previousState.vy=this.vy},s.prototype.discreteStep=function(t){if(this.storeState(),this.xFixed)this.fx=0,this.vx=0;else{var e=this.damping*this.vx,i=(this.fx-e)/this.options.mass;this.vx+=i*t,this.x+=this.vx*t}if(this.yFixed)this.fy=0,this.vy=0;else{var s=this.damping*this.vy,o=(this.fy-s)/this.options.mass;this.vy+=o*t,this.y+=this.vy*t}},s.prototype.discreteStepLimited=function(t,e){if(this.storeState(),this.xFixed)this.fx=0,this.vx=0;else{var i=this.damping*this.vx,s=(this.fx-i)/this.options.mass;this.vx+=s*t,this.vx=Math.abs(this.vx)>e?this.vx>0?e:-e:this.vx,this.x+=this.vx*t}if(this.yFixed)this.fy=0,this.vy=0;else{var o=this.damping*this.vy,n=(this.fy-o)/this.options.mass;this.vy+=n*t,this.vy=Math.abs(this.vy)>e?this.vy>0?e:-e:this.vy,this.y+=this.vy*t}},s.prototype.isFixed=function(){return this.xFixed&&this.yFixed},s.prototype.isMoving=function(t){var e=Math.sqrt(Math.pow(this.vx,2)+Math.pow(this.vy,2));return e>t},s.prototype.isSelected=function(){return this.selected},s.prototype.getValue=function(){return this.value},s.prototype.getDistance=function(t,e){var i=this.x-t,s=this.y-e;return Math.sqrt(i*i+s*s)},s.prototype.setValueRange=function(t,e){if(!this.radiusFixed&&void 0!==this.value)if(e==t)this.options.radius=(this.options.radiusMin+this.options.radiusMax)/2;else{var i=(this.options.radiusMax-this.options.radiusMin)/(e-t);this.options.radius=(this.value-t)*i+this.options.radiusMin}this.baseRadiusValue=this.options.radius},s.prototype.draw=function(){throw"Draw method not initialized for node"},s.prototype.resize=function(){throw"Resize method not initialized for node"},s.prototype.isOverlappingWith=function(t){return this.leftt.left&&this.topt.top},s.prototype._resizeImage=function(){if(!this.width||!this.height){var t,e;if(this.value){this.options.radius=this.baseRadiusValue;var i=this.imageObj.height/this.imageObj.width;void 0!==i?(t=this.options.radius||this.imageObj.width,e=this.options.radius*i||this.imageObj.height):(t=0,e=0)}else t=this.imageObj.width,e=this.imageObj.height;this.width=t,this.height=e,this.growthIndicator=0,this.width>0&&this.height>0&&(this.width+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeWidthFactor,this.height+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeHeightFactor,this.options.radius+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeRadiusFactor,this.growthIndicator=this.width-t)}},s.prototype._drawImageAtPosition=function(t){if(0!=this.imageObj.width){if(this.clusterSize>1){var e=this.clusterSize>1?10:0;e*=this.networkScaleInv,e=Math.min(.2*this.width,e),t.globalAlpha=.5,t.drawImage(this.imageObj,this.left-e,this.top-e,this.width+2*e,this.height+2*e)}t.globalAlpha=1,t.drawImage(this.imageObj,this.left,this.top,this.width,this.height)}},s.prototype._drawImageLabel=function(t){var e,i=0;if(this.height){i=this.height/2;var s=this.getTextSize(t);s.lineCount>=1&&(i+=s.height/2,i+=3)}e=this.y+i,this._label(t,this.label,this.x,e,void 0)},s.prototype._drawImage=function(t){this._resizeImage(t),this.left=this.x-this.width/2,this.top=this.y-this.height/2,this._drawImageAtPosition(t),this.boundingBox.top=this.top,this.boundingBox.left=this.left,this.boundingBox.right=this.left+this.width,this.boundingBox.bottom=this.top+this.height,this._drawImageLabel(t),this.boundingBox.left=Math.min(this.boundingBox.left,this.labelDimensions.left),this.boundingBox.right=Math.max(this.boundingBox.right,this.labelDimensions.left+this.labelDimensions.width),this.boundingBox.bottom=Math.max(this.boundingBox.bottom,this.boundingBox.bottom+this.labelDimensions.height)},s.prototype._resizeCircularImage=function(t){if(this.imageObj.src&&this.imageObj.width&&this.imageObj.height)this._swapToImageResizeWhenImageLoaded&&(this.width=0,this.height=0,delete this._swapToImageResizeWhenImageLoaded),this._resizeImage(t);else if(!this.width){var e=2*this.options.radius;this.width=e,this.height=e,this.options.radius+=.5*Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeRadiusFactor,this.growthIndicator=this.options.radius-.5*e,this._swapToImageResizeWhenImageLoaded=!0}},s.prototype._drawCircularImage=function(t){this._resizeCircularImage(t),this.left=this.x-this.width/2,this.top=this.y-this.height/2;var e=this.left+this.width/2,i=this.top+this.height/2,s=Math.abs(this.height/2);this._drawRawCircle(t,e,i,s),t.save(),t.circle(this.x,this.y,s),t.stroke(),t.clip(),this._drawImageAtPosition(t),t.restore(),this.boundingBox.top=this.y-this.options.radius,this.boundingBox.left=this.x-this.options.radius,this.boundingBox.right=this.x+this.options.radius,this.boundingBox.bottom=this.y+this.options.radius,this._drawImageLabel(t),this.boundingBox.left=Math.min(this.boundingBox.left,this.labelDimensions.left),this.boundingBox.right=Math.max(this.boundingBox.right,this.labelDimensions.left+this.labelDimensions.width),this.boundingBox.bottom=Math.max(this.boundingBox.bottom,this.boundingBox.bottom+this.labelDimensions.height)},s.prototype._resizeBox=function(t){if(!this.width){var e=5,i=this.getTextSize(t);this.width=i.width+2*e,this.height=i.height+2*e,this.width+=.5*Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeWidthFactor,this.height+=.5*Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeHeightFactor,this.growthIndicator=this.width-(i.width+2*e)}},s.prototype._drawBox=function(t){this._resizeBox(t),this.left=this.x-this.width/2,this.top=this.y-this.height/2;var e=2.5,i=this.options.borderWidth,s=this.options.borderWidthSelected||2*this.options.borderWidth;t.strokeStyle=this.selected?this.options.color.highlight.border:this.hover?this.options.color.hover.border:this.options.color.border,this.clusterSize>1&&(t.lineWidth=(this.selected?s:i)+(this.clusterSize>1?e:0),t.lineWidth*=this.networkScaleInv,t.lineWidth=Math.min(this.width,t.lineWidth),t.roundRect(this.left-2*t.lineWidth,this.top-2*t.lineWidth,this.width+4*t.lineWidth,this.height+4*t.lineWidth,this.options.radius),t.stroke()),t.lineWidth=(this.selected?s:i)+(this.clusterSize>1?e:0),t.lineWidth*=this.networkScaleInv,t.lineWidth=Math.min(this.width,t.lineWidth),t.fillStyle=this.selected?this.options.color.highlight.background:this.hover?this.options.color.hover.background:this.options.color.background,t.roundRect(this.left,this.top,this.width,this.height,this.options.radius),t.fill(),t.stroke(),this.boundingBox.top=this.top,this.boundingBox.left=this.left,this.boundingBox.right=this.left+this.width,this.boundingBox.bottom=this.top+this.height,this._label(t,this.label,this.x,this.y)},s.prototype._resizeDatabase=function(t){if(!this.width){var e=5,i=this.getTextSize(t),s=i.width+2*e;this.width=s,this.height=s,this.width+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeWidthFactor,this.height+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeHeightFactor,this.options.radius+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeRadiusFactor,this.growthIndicator=this.width-s}},s.prototype._drawDatabase=function(t){this._resizeDatabase(t),this.left=this.x-this.width/2,this.top=this.y-this.height/2;var e=2.5,i=this.options.borderWidth,s=this.options.borderWidthSelected||2*this.options.borderWidth;t.strokeStyle=this.selected?this.options.color.highlight.border:this.hover?this.options.color.hover.border:this.options.color.border,this.clusterSize>1&&(t.lineWidth=(this.selected?s:i)+(this.clusterSize>1?e:0),t.lineWidth*=this.networkScaleInv,t.lineWidth=Math.min(this.width,t.lineWidth),t.database(this.x-this.width/2-2*t.lineWidth,this.y-.5*this.height-2*t.lineWidth,this.width+4*t.lineWidth,this.height+4*t.lineWidth),t.stroke()),t.lineWidth=(this.selected?s:i)+(this.clusterSize>1?e:0),t.lineWidth*=this.networkScaleInv,t.lineWidth=Math.min(this.width,t.lineWidth),t.fillStyle=this.selected?this.options.color.highlight.background:this.hover?this.options.color.hover.background:this.options.color.background,t.database(this.x-this.width/2,this.y-.5*this.height,this.width,this.height),t.fill(),t.stroke(),this.boundingBox.top=this.top,this.boundingBox.left=this.left,this.boundingBox.right=this.left+this.width,this.boundingBox.bottom=this.top+this.height,this._label(t,this.label,this.x,this.y) +},s.prototype._resizeCircle=function(t){if(!this.width){var e=5,i=this.getTextSize(t),s=Math.max(i.width,i.height)+2*e;this.options.radius=s/2,this.width=s,this.height=s,this.options.radius+=.5*Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeRadiusFactor,this.growthIndicator=this.options.radius-.5*s}},s.prototype._drawRawCircle=function(t,e,i,s){var o=2.5,n=this.options.borderWidth,r=this.options.borderWidthSelected||2*this.options.borderWidth;t.strokeStyle=this.selected?this.options.color.highlight.border:this.hover?this.options.color.hover.border:this.options.color.border,this.clusterSize>1&&(t.lineWidth=(this.selected?r:n)+(this.clusterSize>1?o:0),t.lineWidth*=this.networkScaleInv,t.lineWidth=Math.min(this.width,t.lineWidth),t.circle(e,i,s+2*t.lineWidth),t.stroke()),t.lineWidth=(this.selected?r:n)+(this.clusterSize>1?o:0),t.lineWidth*=this.networkScaleInv,t.lineWidth=Math.min(this.width,t.lineWidth),t.fillStyle=this.selected?this.options.color.highlight.background:this.hover?this.options.color.hover.background:this.options.color.background,t.circle(this.x,this.y,s),t.fill(),t.stroke()},s.prototype._drawCircle=function(t){this._resizeCircle(t),this.left=this.x-this.width/2,this.top=this.y-this.height/2,this._drawRawCircle(t,this.x,this.y,this.options.radius),this.boundingBox.top=this.y-this.options.radius,this.boundingBox.left=this.x-this.options.radius,this.boundingBox.right=this.x+this.options.radius,this.boundingBox.bottom=this.y+this.options.radius,this._label(t,this.label,this.x,this.y)},s.prototype._resizeEllipse=function(t){if(!this.width){var e=this.getTextSize(t);this.width=1.5*e.width,this.height=2*e.height,this.width1&&(t.lineWidth=(this.selected?s:i)+(this.clusterSize>1?e:0),t.lineWidth*=this.networkScaleInv,t.lineWidth=Math.min(this.width,t.lineWidth),t.ellipse(this.left-2*t.lineWidth,this.top-2*t.lineWidth,this.width+4*t.lineWidth,this.height+4*t.lineWidth),t.stroke()),t.lineWidth=(this.selected?s:i)+(this.clusterSize>1?e:0),t.lineWidth*=this.networkScaleInv,t.lineWidth=Math.min(this.width,t.lineWidth),t.fillStyle=this.selected?this.options.color.highlight.background:this.hover?this.options.color.hover.background:this.options.color.background,t.ellipse(this.left,this.top,this.width,this.height),t.fill(),t.stroke(),this.boundingBox.top=this.top,this.boundingBox.left=this.left,this.boundingBox.right=this.left+this.width,this.boundingBox.bottom=this.top+this.height,this._label(t,this.label,this.x,this.y)},s.prototype._drawDot=function(t){this._drawShape(t,"circle")},s.prototype._drawTriangle=function(t){this._drawShape(t,"triangle")},s.prototype._drawTriangleDown=function(t){this._drawShape(t,"triangleDown")},s.prototype._drawSquare=function(t){this._drawShape(t,"square")},s.prototype._drawStar=function(t){this._drawShape(t,"star")},s.prototype._resizeShape=function(){if(!this.width){this.options.radius=this.baseRadiusValue;var t=2*this.options.radius;this.width=t,this.height=t,this.width+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeWidthFactor,this.height+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeHeightFactor,this.options.radius+=.5*Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeRadiusFactor,this.growthIndicator=this.width-t}},s.prototype._drawShape=function(t,e){this._resizeShape(t),this.left=this.x-this.width/2,this.top=this.y-this.height/2;var i=2.5,s=this.options.borderWidth,o=this.options.borderWidthSelected||2*this.options.borderWidth,n=2;switch(e){case"dot":n=2;break;case"square":n=2;break;case"triangle":n=3;break;case"triangleDown":n=3;break;case"star":n=4}t.strokeStyle=this.selected?this.options.color.highlight.border:this.hover?this.options.color.hover.border:this.options.color.border,this.clusterSize>1&&(t.lineWidth=(this.selected?o:s)+(this.clusterSize>1?i:0),t.lineWidth*=this.networkScaleInv,t.lineWidth=Math.min(this.width,t.lineWidth),t[e](this.x,this.y,this.options.radius+n*t.lineWidth),t.stroke()),t.lineWidth=(this.selected?o:s)+(this.clusterSize>1?i:0),t.lineWidth*=this.networkScaleInv,t.lineWidth=Math.min(this.width,t.lineWidth),t.fillStyle=this.selected?this.options.color.highlight.background:this.hover?this.options.color.hover.background:this.options.color.background,t[e](this.x,this.y,this.options.radius),t.fill(),t.stroke(),this.boundingBox.top=this.y-this.options.radius,this.boundingBox.left=this.x-this.options.radius,this.boundingBox.right=this.x+this.options.radius,this.boundingBox.bottom=this.y+this.options.radius,this.label&&(this._label(t,this.label,this.x,this.y+this.height/2,void 0,"hanging",!0),this.boundingBox.left=Math.min(this.boundingBox.left,this.labelDimensions.left),this.boundingBox.right=Math.max(this.boundingBox.right,this.labelDimensions.left+this.labelDimensions.width),this.boundingBox.bottom=Math.max(this.boundingBox.bottom,this.boundingBox.bottom+this.labelDimensions.height))},s.prototype._resizeText=function(t){if(!this.width){var e=5,i=this.getTextSize(t);this.width=i.width+2*e,this.height=i.height+2*e,this.width+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeWidthFactor,this.height+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeHeightFactor,this.options.radius+=Math.min(this.clusterSize-1,this.maxNodeSizeIncrements)*this.clusterSizeRadiusFactor,this.growthIndicator=this.width-(i.width+2*e)}},s.prototype._drawText=function(t){this._resizeText(t),this.left=this.x-this.width/2,this.top=this.y-this.height/2,this._label(t,this.label,this.x,this.y),this.boundingBox.top=this.top,this.boundingBox.left=this.left,this.boundingBox.right=this.left+this.width,this.boundingBox.bottom=this.top+this.height},s.prototype._label=function(t,e,i,s,o,n,r){if(e&&Number(this.options.fontSize)*this.networkScale>this.fontDrawThreshold){t.font=(this.selected?"bold ":"")+this.options.fontSize+"px "+this.options.fontFace;var a=e.split("\n"),h=a.length,d=Number(this.options.fontSize),l=s+(1-h)/2*d;1==r&&(l=s+(1-h)/(2*d));for(var c=t.measureText(a[0]).width,p=1;h>p;p++){var u=t.measureText(a[p]).width;c=u>c?u:c}var m=this.options.fontSize*h,f=i-c/2,g=s-m/2;"hanging"==n&&(g+=.5*d,g+=4,l+=4),this.labelDimensions={top:g,left:f,width:c,height:m,yLine:l},void 0!==this.options.fontFill&&null!==this.options.fontFill&&"none"!==this.options.fontFill&&(t.fillStyle=this.options.fontFill,t.fillRect(f,g,c,m)),t.fillStyle=this.options.fontColor||"black",t.textAlign=o||"center",t.textBaseline=n||"middle",this.options.fontStrokeWidth>0&&(t.lineWidth=this.options.fontStrokeWidth,t.strokeStyle=this.options.fontStrokeColor,t.lineJoin="round");for(var p=0;h>p;p++)this.options.fontStrokeWidth&&t.strokeText(a[p],i,l),t.fillText(a[p],i,l),l+=d}},s.prototype.getTextSize=function(t){if(void 0!==this.label){t.font=(this.selected?"bold ":"")+this.options.fontSize+"px "+this.options.fontFace;for(var e=this.label.split("\n"),i=(Number(this.options.fontSize)+4)*e.length,s=0,o=0,n=e.length;n>o;o++)s=Math.max(s,t.measureText(e[o]).width);return{width:s,height:i,lineCount:e.length}}return{width:0,height:0,lineCount:0}},s.prototype.inArea=function(){return void 0!==this.width?this.x+this.width*this.networkScaleInv>=this.canvasTopLeft.x&&this.x-this.width*this.networkScaleInv=this.canvasTopLeft.y&&this.y-this.height*this.networkScaleInv=this.canvasTopLeft.x&&this.x=this.canvasTopLeft.y&&this.ys&&(n=s-e-this.padding),no&&(r=o-i-this.padding),ri;i++)if(e.id===r.nodes[i].id){o=r.nodes[i];break}for(o||(o={id:e.id},t.node&&(o.attr=a(o.attr,t.node))),i=n.length-1;i>=0;i--){var h=n[i];h.nodes||(h.nodes=[]),-1==h.nodes.indexOf(o)&&h.nodes.push(o)}e.attr&&(o.attr=a(o.attr,e.attr))}function l(t,e){if(t.edges||(t.edges=[]),t.edges.push(e),t.edge){var i=a({},t.edge);e.attr=a(i,e.attr)}}function c(t,e,i,s,o){var n={from:e,to:i,type:s};return t.edge&&(n.attr=a({},t.edge)),n.attr=a(n.attr||{},o),n}function p(){for(N=D.NULL,k="";" "==E||" "==E||"\n"==E||"\r"==E;)o();do{var t=!1;if("#"==E){for(var e=O-1;" "==T.charAt(e)||" "==T.charAt(e);)e--;if("\n"==T.charAt(e)||""==T.charAt(e)){for(;""!=E&&"\n"!=E;)o();t=!0}}if("/"==E&&"/"==n()){for(;""!=E&&"\n"!=E;)o();t=!0}if("/"==E&&"*"==n()){for(;""!=E;){if("*"==E&&"/"==n()){o(),o();break}o()}t=!0}for(;" "==E||" "==E||"\n"==E||"\r"==E;)o()}while(t);if(""==E)return void(N=D.DELIMITER);var i=E+n();if(C[i])return N=D.DELIMITER,k=i,o(),void o();if(C[E])return N=D.DELIMITER,k=E,void o();if(r(E)||"-"==E){for(k+=E,o();r(E);)k+=E,o();return"false"==k?k=!1:"true"==k?k=!0:isNaN(Number(k))||(k=Number(k)),void(N=D.IDENTIFIER)}if('"'==E){for(o();""!=E&&('"'!=E||'"'==E&&'"'==n());)k+=E,'"'==E&&o(),o();if('"'!=E)throw x('End of string " expected');return o(),void(N=D.IDENTIFIER)}for(N=D.UNKNOWN;""!=E;)k+=E,o();throw new SyntaxError('Syntax error in part "'+w(k,30)+'"')}function u(){var t={};if(s(),p(),"strict"==k&&(t.strict=!0,p()),("graph"==k||"digraph"==k)&&(t.type=k,p()),N==D.IDENTIFIER&&(t.id=k,p()),"{"!=k)throw x("Angle bracket { expected");if(p(),m(t),"}"!=k)throw x("Angle bracket } expected");if(p(),""!==k)throw x("End of file expected");return p(),delete t.node,delete t.edge,delete t.graph,t}function m(t){for(;""!==k&&"}"!=k;)f(t),";"==k&&p()}function f(t){var e=g(t);if(e)return void b(t,e);var i=v(t);if(!i){if(N!=D.IDENTIFIER)throw x("Identifier expected");var s=k;if(p(),"="==k){if(p(),N!=D.IDENTIFIER)throw x("Identifier expected");t[s]=k,p()}else y(t,s)}}function g(t){var e=null;if("subgraph"==k&&(e={},e.type="subgraph",p(),N==D.IDENTIFIER&&(e.id=k,p())),"{"==k){if(p(),e||(e={}),e.parent=t,e.node=t.node,e.edge=t.edge,e.graph=t.graph,m(e),"}"!=k)throw x("Angle bracket } expected");p(),delete e.node,delete e.edge,delete e.graph,delete e.parent,t.subgraphs||(t.subgraphs=[]),t.subgraphs.push(e)}return e}function v(t){return"node"==k?(p(),t.node=_(),"node"):"edge"==k?(p(),t.edge=_(),"edge"):"graph"==k?(p(),t.graph=_(),"graph"):null}function y(t,e){var i={id:e},s=_();s&&(i.attr=s),d(t,i),b(t,e)}function b(t,e){for(;"->"==k||"--"==k;){var i,s=k;p();var o=g(t);if(o)i=o;else{if(N!=D.IDENTIFIER)throw x("Identifier or subgraph expected");i=k,d(t,{id:i}),p()}var n=_(),r=c(t,e,i,s,n);l(t,r),e=i}}function _(){for(var t=null;"["==k;){for(p(),t={};""!==k&&"]"!=k;){if(N!=D.IDENTIFIER)throw x("Attribute name expected");var e=k;if(p(),"="!=k)throw x("Equal sign = expected");if(p(),N!=D.IDENTIFIER)throw x("Attribute value expected");var i=k;h(t,e,i),p(),","==k&&p()}if("]"!=k)throw x("Bracket ] expected");p()}return t}function x(t){return new SyntaxError(t+', got "'+w(k,30)+'" (char '+O+")")}function w(t,e){return t.length<=e?t:t.substr(0,27)+"..."}function 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D={NULL:0,DELIMITER:1,IDENTIFIER:2,UNKNOWN:3},C={"{":!0,"}":!0,"[":!0,"]":!0,";":!0,"=":!0,",":!0,"->":!0,"--":!0},T="",O=0,E="",k="",N=D.NULL,I=/[a-zA-Z_0-9.:#]/;e.parseDOT=i,e.DOTToGraph=M},function(t,e){function i(t,e){var i=[],s=[];this.options={edges:{inheritColor:!0},nodes:{allowedToMove:!1,parseColor:!1}},void 0!==e&&(this.options.nodes.allowedToMove=e.allowedToMove|!1,this.options.nodes.parseColor=e.parseColor|!1,this.options.edges.inheritColor=e.inheritColor|!0);for(var o=t.edges,n=t.nodes,r=0;r=s&&(s=864e5),e=new Date(e.valueOf()-.05*s),i=new Date(i.valueOf()+.05*s)}return{start:e,end:i}},s.prototype.setWindow=function(t,e,i){var s=i&&void 0!==i.animate?i.animate:!0;if(1==arguments.length){var o=arguments[0];this.range.setRange(o.start,o.end,s)}else this.range.setRange(t,e,s)},s.prototype.moveTo=function(t,e){var i=this.range.end-this.range.start,s=r.convert(t,"Date").valueOf(),o=s-i/2,n=s+i/2,a=e&&void 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o=s.root.offsetHeight-s.root.clientHeight,n=s.root.offsetWidth-s.root.clientWidth;0===s.centerContainer.clientHeight&&(i.border.left=i.border.top,i.border.right=i.border.left),0===s.root.clientHeight&&(n=o),i.center.height=s.center.offsetHeight,i.left.height=s.left.offsetHeight,i.right.height=s.right.offsetHeight,i.top.height=s.top.clientHeight||-i.border.top,i.bottom.height=s.bottom.clientHeight||-i.border.bottom;var a=Math.max(i.left.height,i.center.height,i.right.height),d=i.top.height+a+i.bottom.height+o+i.border.top+i.border.bottom;s.root.style.height=r.option.asSize(e.height,d+"px"),i.root.height=s.root.offsetHeight,i.background.height=i.root.height-o;var l=i.root.height-i.top.height-i.bottom.height-o;i.centerContainer.height=l,i.leftContainer.height=l,i.rightContainer.height=i.leftContainer.height,i.root.width=s.root.offsetWidth,i.background.width=i.root.width-n,i.left.width=s.leftContainer.clientWidth||-i.border.left,i.leftContainer.width=i.left.width,i.right.width=s.rightContainer.clientWidth||-i.border.right,i.rightContainer.width=i.right.width;var c=i.root.width-i.left.width-i.right.width-n;i.center.width=c,i.centerContainer.width=c,i.top.width=c,i.bottom.width=c,s.background.style.height=i.background.height+"px",s.backgroundVertical.style.height=i.background.height+"px",s.backgroundHorizontal.style.height=i.centerContainer.height+"px",s.centerContainer.style.height=i.centerContainer.height+"px",s.leftContainer.style.height=i.leftContainer.height+"px",s.rightContainer.style.height=i.rightContainer.height+"px",s.background.style.width=i.background.width+"px",s.backgroundVertical.style.width=i.centerContainer.width+"px",s.backgroundHorizontal.style.width=i.background.width+"px",s.centerContainer.style.width=i.center.width+"px",s.top.style.width=i.top.width+"px",s.bottom.style.width=i.bottom.width+"px",s.background.style.left="0",s.background.style.top="0",s.backgroundVertical.style.left=i.left.width+i.border.left+"px",s.backgroundVertical.style.top="0",s.backgroundHorizontal.style.left="0",s.backgroundHorizontal.style.top=i.top.height+"px",s.centerContainer.style.left=i.left.width+"px",s.centerContainer.style.top=i.top.height+"px",s.leftContainer.style.left="0",s.leftContainer.style.top=i.top.height+"px",s.rightContainer.style.left=i.left.width+i.center.width+"px",s.rightContainer.style.top=i.top.height+"px",s.top.style.left=i.left.width+"px",s.top.style.top="0",s.bottom.style.left=i.left.width+"px",s.bottom.style.top=i.top.height+i.centerContainer.height+"px",this._updateScrollTop();var p=this.props.scrollTop;"bottom"==e.orientation&&(p+=Math.max(this.props.centerContainer.height-this.props.center.height-this.props.border.top-this.props.border.bottom,0)),s.center.style.left="0",s.center.style.top=p+"px",s.left.style.left="0",s.left.style.top=p+"px",s.right.style.left="0",s.right.style.top=p+"px";var u=0==this.props.scrollTop?"hidden":"",m=this.props.scrollTop==this.props.scrollTopMin?"hidden":"";if(s.shadowTop.style.visibility=u,s.shadowBottom.style.visibility=m,s.shadowTopLeft.style.visibility=u,s.shadowBottomLeft.style.visibility=m,s.shadowTopRight.style.visibility=u,s.shadowBottomRight.style.visibility=m,this.components.forEach(function(e){t=e.redraw()||t}),t){var f=3;this.redrawCount0&&(this.props.scrollTop=0),this.props.scrollTops;s++){var o=s%2===0?1.3*i:.5*i;this.lineTo(t+o*Math.sin(2*s*Math.PI/10),e-o*Math.cos(2*s*Math.PI/10))}this.closePath()},CanvasRenderingContext2D.prototype.roundRect=function(t,e,i,s,o){var n=Math.PI/180;0>i-2*o&&(o=i/2),0>s-2*o&&(o=s/2),this.beginPath(),this.moveTo(t+o,e),this.lineTo(t+i-o,e),this.arc(t+i-o,e+o,o,270*n,360*n,!1),this.lineTo(t+i,e+s-o),this.arc(t+i-o,e+s-o,o,0,90*n,!1),this.lineTo(t+o,e+s),this.arc(t+o,e+s-o,o,90*n,180*n,!1),this.lineTo(t,e+o),this.arc(t+o,e+o,o,180*n,270*n,!1)},CanvasRenderingContext2D.prototype.ellipse=function(t,e,i,s){var o=.5522848,n=i/2*o,r=s/2*o,a=t+i,h=e+s,d=t+i/2,l=e+s/2; +this.beginPath(),this.moveTo(t,l),this.bezierCurveTo(t,l-r,d-n,e,d,e),this.bezierCurveTo(d+n,e,a,l-r,a,l),this.bezierCurveTo(a,l+r,d+n,h,d,h),this.bezierCurveTo(d-n,h,t,l+r,t,l)},CanvasRenderingContext2D.prototype.database=function(t,e,i,s){var o=1/3,n=i,r=s*o,a=.5522848,h=n/2*a,d=r/2*a,l=t+n,c=e+r,p=t+n/2,u=e+r/2,m=e+(s-r/2),f=e+s;this.beginPath(),this.moveTo(l,u),this.bezierCurveTo(l,u+d,p+h,c,p,c),this.bezierCurveTo(p-h,c,t,u+d,t,u),this.bezierCurveTo(t,u-d,p-h,e,p,e),this.bezierCurveTo(p+h,e,l,u-d,l,u),this.lineTo(l,m),this.bezierCurveTo(l,m+d,p+h,f,p,f),this.bezierCurveTo(p-h,f,t,m+d,t,m),this.lineTo(t,u)},CanvasRenderingContext2D.prototype.arrow=function(t,e,i,s){var o=t-s*Math.cos(i),n=e-s*Math.sin(i),r=t-.9*s*Math.cos(i),a=e-.9*s*Math.sin(i),h=o+s/3*Math.cos(i+.5*Math.PI),d=n+s/3*Math.sin(i+.5*Math.PI),l=o+s/3*Math.cos(i-.5*Math.PI),c=n+s/3*Math.sin(i-.5*Math.PI);this.beginPath(),this.moveTo(t,e),this.lineTo(h,d),this.lineTo(r,a),this.lineTo(l,c),this.closePath()},CanvasRenderingContext2D.prototype.dashedLine=function(t,e,i,s,o){o||(o=[10,5]),0==p&&(p=.001);var n=o.length;this.moveTo(t,e);for(var r=i-t,a=s-e,h=a/r,d=Math.sqrt(r*r+a*a),l=0,c=!0;d>=.1;){var p=o[l++%n];p>d&&(p=d);var u=Math.sqrt(p*p/(1+h*h));0>r&&(u=-u),t+=u,e+=h*u,this[c?"lineTo":"moveTo"](t,e),d-=p,c=!c}})},function(t,e,i){function s(t,e){this.groupId=t,this.options=e}var o=i(2),n=i(53);s.prototype.getYRange=function(t){for(var e=t[0].y,i=t[0].y,s=0;st[s].y?t[s].y:e,i=i0){var r,a,h=Number(i.svg.style.height.replace("px",""));if(r=o.getSVGElement("path",i.svgElements,i.svg),r.setAttributeNS(null,"class",e.className),void 0!==e.style&&r.setAttributeNS(null,"style",e.style),a=1==e.options.catmullRom.enabled?s._catmullRom(t,e):s._linear(t),1==e.options.shaded.enabled){var d,l=o.getSVGElement("path",i.svgElements,i.svg);d="top"==e.options.shaded.orientation?"M"+t[0].x+",0 "+a+"L"+t[t.length-1].x+",0":"M"+t[0].x+","+h+" "+a+"L"+t[t.length-1].x+","+h,l.setAttributeNS(null,"class",e.className+" fill"),void 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g=d+(p[r].amount-p[r].resolved),v=d-(p[r].resolved+1);g0&&(n=Math.min(n,Math.abs(c[v].x-r))),a=s._getSafeDrawData(n,h,m),p[r].resolved+=1,"stack"==h.options.barChart.handleOverlap?(f=p[r].accumulated,p[r].accumulated+=h.zeroPosition-c[d].y):"sideBySide"==h.options.barChart.handleOverlap&&(a.width=a.width/p[r].amount,a.offset+=p[r].resolved*a.width-.5*a.width*(p[r].amount+1),"left"==h.options.barChart.align?a.offset-=.5*a.width:"right"==h.options.barChart.align&&(a.offset+=.5*a.width))}o.drawBar(c[d].x+a.offset,c[d].y-f,a.width,h.zeroPosition-c[d].y,h.className+" bar",i.svgElements,i.svg),1==h.options.drawPoints.enabled&&o.drawPoint(c[d].x+a.offset,c[d].y,h,i.svgElements,i.svg)}},s._getDataIntersections=function(t,e){for(var i,s=0;s0&&(i=Math.min(i,Math.abs(e[s-1].x-e[s].x))),0==i&&(void 0===t[e[s].x]&&(t[e[s].x]={amount:0,resolved:0,accumulated:0}),t[e[s].x].amount+=1)},s._getSafeDrawData=function(t,e,i){var s,o;return t0?(s=i>t?i:t,o=0,"left"==e.options.barChart.align?o-=.5*t:"right"==e.options.barChart.align&&(o+=.5*t)):(s=e.options.barChart.width,o=0,"left"==e.options.barChart.align?o-=.5*e.options.barChart.width:"right"==e.options.barChart.align&&(o+=.5*e.options.barChart.width)),{width:s,offset:o}},s.getStackedBarYRange=function(t,e,i,o,n){if(t.length>0){t.sort(function(t,e){return t.x==e.x?t.groupId-e.groupId:t.x-e.x});var r={};s._getDataIntersections(r,t),e[o]=s._getStackedBarYRange(r,t),e[o].yAxisOrientation=n,i.push(o)}},s._getStackedBarYRange=function(t,e){for(var i,s=e[0].y,o=e[0].y,n=0;ne[n].y?e[n].y:s,o=ot[r].accumulated?t[r].accumulated:s,o=ot[s].y?t[s].y:e,i=is;++s)i[s].apply(this,e)}return this},e.prototype.listeners=function(t){return this._callbacks=this._callbacks||{},this._callbacks[t]||[]},e.prototype.hasListeners=function(t){return!!this.listeners(t).length}},function(t,e,i){var s;(function(t,o){(function(n){function r(t,e,i){switch(arguments.length){case 2:return 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e=t.srcEvent,i=t.offsetDirection;if(this.manager.session.prevented)return void e.preventDefault();var s=this.actions,o=_(s,ni),n=_(s,ai),r=_(s,ri);return o||n&&i&Re||r&&i&Fe?this.preventSrc(e):void 0}},preventSrc:function(t){this.manager.session.prevented=!0,t.preventDefault()}};var hi=1,di=2,li=4,ci=8,pi=ci,ui=16,mi=32;K.prototype={defaults:{},set:function(t){return c(this.options,t),this.manager&&this.manager.touchAction.update(),this},recognizeWith:function(t){if(d(t,"recognizeWith",this))return this;var e=this.simultaneous;return t=te(t,this),e[t.id]||(e[t.id]=t,t.recognizeWith(this)),this},dropRecognizeWith:function(t){return d(t,"dropRecognizeWith",this)?this:(t=te(t,this),delete this.simultaneous[t.id],this)},requireFailure:function(t){if(d(t,"requireFailure",this))return this;var e=this.requireFail;return t=te(t,this),-1===w(e,t)&&(e.push(t),t.requireFailure(this)),this},dropRequireFailure:function(t){if(d(t,"dropRequireFailure",this))return this;t=te(t,this);var e=w(this.requireFail,t);return e>-1&&this.requireFail.splice(e,1),this},hasRequireFailures:function(){return this.requireFail.length>0},canRecognizeWith:function(t){return!!this.simultaneous[t.id]},emit:function(t){function e(e){i.manager.emit(i.options.event+(e?$(s):""),t)}var i=this,s=this.state;ci>s&&e(!0),e(),s>=ci&&e(!0)},tryEmit:function(t){return this.canEmit()?this.emit(t):void(this.state=mi)},canEmit:function(){for(var t=0;tn?Le:ze,i=n!=this.pX,s=Math.abs(t.deltaX)):(o=0===r?Ie:0>r?Pe:Ae,i=r!=this.pY,s=Math.abs(t.deltaY))),t.direction=o,i&&s>e.threshold&&o&e.direction},attrTest:function(t){return ee.prototype.attrTest.call(this,t)&&(this.state&di||!(this.state&di)&&this.directionTest(t))},emit:function(t){this.pX=t.deltaX,this.pY=t.deltaY;var e=J(t.direction);e&&this.manager.emit(this.options.event+e,t),this._super.emit.call(this,t)}}),u(se,ee,{defaults:{event:"pinch",threshold:0,pointers:2},getTouchAction:function(){return[ni]},attrTest:function(t){return this._super.attrTest.call(this,t)&&(Math.abs(t.scale-1)>this.options.threshold||this.state&di)},emit:function(t){if(this._super.emit.call(this,t),1!==t.scale){var e=t.scale<1?"in":"out";this.manager.emit(this.options.event+e,t)}}}),u(oe,K,{defaults:{event:"press",pointers:1,time:500,threshold:5},getTouchAction:function(){return[si]},process:function(t){var e=this.options,i=t.pointers.length===e.pointers,s=t.distancee.time;if(this._input=t,!s||!i||t.eventType&(ke|Ne)&&!o)this.reset();else if(t.eventType&Oe)this.reset(),this._timer=h(function(){this.state=pi,this.tryEmit() +},e.time,this);else if(t.eventType&ke)return pi;return mi},reset:function(){clearTimeout(this._timer)},emit:function(t){this.state===pi&&(t&&t.eventType&ke?this.manager.emit(this.options.event+"up",t):(this._input.timeStamp=ve(),this.manager.emit(this.options.event,this._input)))}}),u(ne,ee,{defaults:{event:"rotate",threshold:0,pointers:2},getTouchAction:function(){return[ni]},attrTest:function(t){return this._super.attrTest.call(this,t)&&(Math.abs(t.rotation)>this.options.threshold||this.state&di)}}),u(re,ee,{defaults:{event:"swipe",threshold:10,velocity:.65,direction:Re|Fe,pointers:1},getTouchAction:function(){return ie.prototype.getTouchAction.call(this)},attrTest:function(t){var e,i=this.options.direction;return i&(Re|Fe)?e=t.velocity:i&Re?e=t.velocityX:i&Fe&&(e=t.velocityY),this._super.attrTest.call(this,t)&&i&t.direction&&t.distance>this.options.threshold&&ge(e)>this.options.velocity&&t.eventType&ke},emit:function(t){var e=J(t.direction);e&&this.manager.emit(this.options.event+e,t),this.manager.emit(this.options.event,t)}}),u(ae,K,{defaults:{event:"tap",pointers:1,taps:1,interval:300,time:250,threshold:2,posThreshold:10},getTouchAction:function(){return[oi]},process:function(t){var e=this.options,i=t.pointers.length===e.pointers,s=t.distancet&&s>o;)o%3==0?(this.forceAggregateHubs(!0),this.normalizeClusterLevels()):this.increaseClusterLevel(),i=this.nodeIndices.length,o+=1;o>0&&1==e&&this.repositionNodes(),this._updateCalculationNodes()},e.openCluster=function(t){var e=this.moving;if(t.clusterSize>this.constants.clustering.sectorThreshold&&this._nodeInActiveArea(t)&&("default"!=this._sector()||1!=this.nodeIndices.length)){this._addSector(t);for(var i=0;this.nodeIndices.lengthi;)this.decreaseClusterLevel(),i+=1}else this._expandClusterNode(t,!1,!0),this._updateNodeIndexList(),this._updateDynamicEdges(),this._updateCalculationNodes(),this.updateLabels();this.moving!=e&&this.start()},e.updateClustersDefault=function(){1==this.constants.clustering.enabled&&this.updateClusters(0,!1,!1)},e.increaseClusterLevel=function(){this.updateClusters(-1,!1,!0)},e.decreaseClusterLevel=function(){this.updateClusters(1,!1,!0)},e.updateClusters=function(t,e,i,s){var o=this.moving,n=this.nodeIndices.length;this.previousScale>this.scale&&0==t&&this._collapseSector(),this.previousScale>this.scale||-1==t?this._formClusters(i):(this.previousScalethis.scale||-1==t)&&(this._aggregateHubs(i),this._updateNodeIndexList()),(this.previousScale>this.scale||-1==t)&&(this.handleChains(),this._updateNodeIndexList()),this.previousScale=this.scale,this._updateDynamicEdges(),this.updateLabels(),this.nodeIndices.lengththis.constants.clustering.chainThreshold&&this._reduceAmountOfChains(1-this.constants.clustering.chainThreshold/t)},e._aggregateHubs=function(t){this._getHubSize(),this._formClustersByHub(t,!1)},e.forceAggregateHubs=function(t){var e=this.moving,i=this.nodeIndices.length;this._aggregateHubs(!0),this._updateNodeIndexList(),this._updateDynamicEdges(),this.updateLabels(),this.nodeIndices.length!=i&&(this.clusterSession+=1),(0==t||void 0===t)&&this.moving!=e&&this.start()},e._openClustersBySize=function(){for(var t in this.nodes)if(this.nodes.hasOwnProperty(t)){var e=this.nodes[t];1==e.inView()&&(e.width*this.scale>this.constants.clustering.screenSizeThreshold*this.frame.canvas.clientWidth||e.height*this.scale>this.constants.clustering.screenSizeThreshold*this.frame.canvas.clientHeight)&&this.openCluster(e)}},e._openClusters=function(t,e){for(var i=0;i1&&(t.clusterSizei)){var r=n.from,a=n.to;n.to.options.mass>n.from.options.mass&&(r=n.to,a=n.from),1==a.dynamicEdgesLength?this._addToCluster(r,a,!1):1==r.dynamicEdgesLength&&this._addToCluster(a,r,!1)}}},e._forceClustersByZoom=function(){for(var t in this.nodes)if(this.nodes.hasOwnProperty(t)){var e=this.nodes[t];if(1==e.dynamicEdgesLength&&0!=e.dynamicEdges.length){var i=e.dynamicEdges[0],s=i.toId==e.id?this.nodes[i.fromId]:this.nodes[i.toId];e.id!=s.id&&(s.options.mass>e.options.mass?this._addToCluster(s,e,!0):this._addToCluster(e,s,!0))}}},e._clusterToSmallestNeighbour=function(t){for(var e=-1,i=null,s=0;so.clusterSessions.length&&(e=o.clusterSessions.length,i=o)}null!=o&&void 0!==this.nodes[o.id]&&this._addToCluster(o,t,!0)},e._formClustersByHub=function(t,e){for(var i in this.nodes)this.nodes.hasOwnProperty(i)&&this._formClusterFromHub(this.nodes[i],t,e)},e._formClusterFromHub=function(t,e,i,s){if(void 0===s&&(s=0),t.dynamicEdgesLength>=this.hubThreshold&&0==i||t.dynamicEdgesLength==this.hubThreshold&&1==i){for(var o,n,r,a=this.constants.clustering.clusterEdgeThreshold/this.scale,h=!1,d=[],l=t.dynamicEdges.length,c=0;l>c;c++)d.push(t.dynamicEdges[c].id);if(0==e)for(h=!1,c=0;l>c;c++){var p=this.edges[d[c]];if(void 0!==p&&p.connected&&p.toId!=p.fromId&&(o=p.to.x-p.from.x,n=p.to.y-p.from.y,r=Math.sqrt(o*o+n*n),a>r)){h=!0;break}}if(!e&&h||e)for(c=0;l>c;c++)if(p=this.edges[d[c]],void 0!==p){var u=this.nodes[p.fromId==t.id?p.toId:p.fromId];u.dynamicEdges.length<=this.hubThreshold+s&&u.id!=t.id&&this._addToCluster(t,u,e)}}},e._addToCluster=function(t,e,i){t.containedNodes[e.id]=e;for(var s=0;s1)for(var s=0;s1&&(e.label="[".concat(String(e.clusterSize),"]"))}for(t in this.nodes)this.nodes.hasOwnProperty(t)&&(e=this.nodes[t],1==e.clusterSize&&(e.label=void 0!==e.originalLabel?e.originalLabel:String(e.id)))},e.normalizeClusterLevels=function(){var t,e=0,i=1e9,s=0;for(t in this.nodes)this.nodes.hasOwnProperty(t)&&(s=this.nodes[t].clusterSessions.length,s>e&&(e=s),i>s&&(i=s));if(e-i>this.constants.clustering.clusterLevelDifference){var o=this.nodeIndices.length,n=e-this.constants.clustering.clusterLevelDifference;for(t in this.nodes)this.nodes.hasOwnProperty(t)&&this.nodes[t].clusterSessions.lengths&&(s=n.dynamicEdgesLength),t+=n.dynamicEdgesLength,e+=Math.pow(n.dynamicEdgesLength,2),i+=1}t/=i,e/=i;var r=e-Math.pow(t,2),a=Math.sqrt(r);this.hubThreshold=Math.floor(t+2*a),this.hubThreshold>s&&(this.hubThreshold=s)},e._reduceAmountOfChains=function(t){this.hubThreshold=2;var e=Math.floor(this.nodeIndices.length*t);for(var i in this.nodes)this.nodes.hasOwnProperty(i)&&2==this.nodes[i].dynamicEdgesLength&&this.nodes[i].dynamicEdges.length>=2&&e>0&&(this._formClusterFromHub(this.nodes[i],!0,!0,1),e-=1)},e._getChainFraction=function(){var t=0,e=0;for(var i in this.nodes)this.nodes.hasOwnProperty(i)&&(2==this.nodes[i].dynamicEdgesLength&&this.nodes[i].dynamicEdges.length>=2&&(t+=1),e+=1);return t/e}},function(t,e,i){var s=i(1),o=i(40);e._putDataInSector=function(){this.sectors.active[this._sector()].nodes=this.nodes,this.sectors.active[this._sector()].edges=this.edges,this.sectors.active[this._sector()].nodeIndices=this.nodeIndices},e._switchToSector=function(t,e){void 0===e||"active"==e?this._switchToActiveSector(t):this._switchToFrozenSector(t)},e._switchToActiveSector=function(t){this.nodeIndices=this.sectors.active[t].nodeIndices,this.nodes=this.sectors.active[t].nodes,this.edges=this.sectors.active[t].edges},e._switchToSupportSector=function(){this.nodeIndices=this.sectors.support.nodeIndices,this.nodes=this.sectors.support.nodes,this.edges=this.sectors.support.edges},e._switchToFrozenSector=function(t){this.nodeIndices=this.sectors.frozen[t].nodeIndices,this.nodes=this.sectors.frozen[t].nodes,this.edges=this.sectors.frozen[t].edges},e._loadLatestSector=function(){this._switchToSector(this._sector())},e._sector=function(){return this.activeSector[this.activeSector.length-1]},e._previousSector=function(){if(this.activeSector.length>1)return this.activeSector[this.activeSector.length-2];throw new TypeError("there are not enough sectors in the this.activeSector array.")},e._setActiveSector=function(t){this.activeSector.push(t)},e._forgetLastSector=function(){this.activeSector.pop()},e._createNewSector=function(t){this.sectors.active[t]={nodes:{},edges:{},nodeIndices:[],formationScale:this.scale,drawingNode:void 0},this.sectors.active[t].drawingNode=new o({id:t,color:{background:"#eaefef",border:"495c5e"}},{},{},this.constants),this.sectors.active[t].drawingNode.clusterSize=2},e._deleteActiveSector=function(t){delete this.sectors.active[t]},e._deleteFrozenSector=function(t){delete this.sectors.frozen[t]},e._freezeSector=function(t){this.sectors.frozen[t]=this.sectors.active[t],this._deleteActiveSector(t)},e._activateSector=function(t){this.sectors.active[t]=this.sectors.frozen[t],this._deleteFrozenSector(t)},e._mergeThisWithFrozen=function(t){for(var e in this.nodes)this.nodes.hasOwnProperty(e)&&(this.sectors.frozen[t].nodes[e]=this.nodes[e]);for(var i in this.edges)this.edges.hasOwnProperty(i)&&(this.sectors.frozen[t].edges[i]=this.edges[i]);for(var s=0;s1?this[t](o[0],o[1]):this[t](e))}return this._loadLatestSector(),i},e._doInSupportSector=function(t,e){var i=!1;if(void 0===e)this._switchToSupportSector(),i=this[t]();else{this._switchToSupportSector();var s=Array.prototype.splice.call(arguments,1);i=s.length>1?this[t](s[0],s[1]):this[t](e)}return this._loadLatestSector(),i},e._doInAllFrozenSectors=function(t,e){if(void 0===e)for(var i in this.sectors.frozen)this.sectors.frozen.hasOwnProperty(i)&&(this._switchToFrozenSector(i),this[t]());else for(var i in this.sectors.frozen)if(this.sectors.frozen.hasOwnProperty(i)){this._switchToFrozenSector(i);var s=Array.prototype.splice.call(arguments,1);s.length>1?this[t](s[0],s[1]):this[t](e)}this._loadLatestSector()},e._doInAllSectors=function(t,e){var i=Array.prototype.splice.call(arguments,1);void 0===e?(this._doInAllActiveSectors(t),this._doInAllFrozenSectors(t)):i.length>1?(this._doInAllActiveSectors(t,i[0],i[1]),this._doInAllFrozenSectors(t,i[0],i[1])):(this._doInAllActiveSectors(t,e),this._doInAllFrozenSectors(t,e))},e._clearNodeIndexList=function(){var t=this._sector();this.sectors.active[t].nodeIndices=[],this.nodeIndices=this.sectors.active[t].nodeIndices},e._drawSectorNodes=function(t,e){var i,s=1e9,o=-1e9,n=1e9,r=-1e9;for(var a in this.sectors[e])if(this.sectors[e].hasOwnProperty(a)&&void 0!==this.sectors[e][a].drawingNode){this._switchToSector(a,e),s=1e9,o=-1e9,n=1e9,r=-1e9;for(var h in this.nodes)this.nodes.hasOwnProperty(h)&&(i=this.nodes[h],i.resize(t),n>i.x-.5*i.width&&(n=i.x-.5*i.width),ri.y-.5*i.height&&(s=i.y-.5*i.height),o0?this.nodes[i[i.length-1]]:null},e._getEdgesOverlappingWith=function(t,e){var i=this.edges;for(var s in i)i.hasOwnProperty(s)&&i[s].isOverlappingWith(t)&&e.push(s)},e._getAllEdgesOverlappingWith=function(t){var e=[];return this._doInAllActiveSectors("_getEdgesOverlappingWith",t,e),e},e._getEdgeAt=function(t){var e=this._pointerToPositionObject(t),i=this._getAllEdgesOverlappingWith(e);return i.length>0?this.edges[i[i.length-1]]:null},e._addToSelection=function(t){t instanceof s?this.selectionObj.nodes[t.id]=t:this.selectionObj.edges[t.id]=t},e._addToHover=function(t){t instanceof s?this.hoverObj.nodes[t.id]=t:this.hoverObj.edges[t.id]=t},e._removeFromSelection=function(t){t instanceof s?delete this.selectionObj.nodes[t.id]:delete this.selectionObj.edges[t.id]},e._unselectAll=function(t){void 0===t&&(t=!1);for(var e in this.selectionObj.nodes)this.selectionObj.nodes.hasOwnProperty(e)&&this.selectionObj.nodes[e].unselect();for(var i in this.selectionObj.edges)this.selectionObj.edges.hasOwnProperty(i)&&this.selectionObj.edges[i].unselect();this.selectionObj={nodes:{},edges:{}},0==t&&this.emit("select",this.getSelection())},e._unselectClusters=function(t){void 0===t&&(t=!1);for(var e in this.selectionObj.nodes)this.selectionObj.nodes.hasOwnProperty(e)&&this.selectionObj.nodes[e].clusterSize>1&&(this.selectionObj.nodes[e].unselect(),this._removeFromSelection(this.selectionObj.nodes[e]));0==t&&this.emit("select",this.getSelection())},e._getSelectedNodeCount=function(){var t=0;for(var e in this.selectionObj.nodes)this.selectionObj.nodes.hasOwnProperty(e)&&(t+=1);return t},e._getSelectedNode=function(){for(var t in this.selectionObj.nodes)if(this.selectionObj.nodes.hasOwnProperty(t))return this.selectionObj.nodes[t];return null},e._getSelectedEdge=function(){for(var t in this.selectionObj.edges)if(this.selectionObj.edges.hasOwnProperty(t))return this.selectionObj.edges[t];return null},e._getSelectedEdgeCount=function(){var t=0;for(var e in this.selectionObj.edges)this.selectionObj.edges.hasOwnProperty(e)&&(t+=1);return t},e._getSelectedObjectCount=function(){var t=0;for(var e in this.selectionObj.nodes)this.selectionObj.nodes.hasOwnProperty(e)&&(t+=1);for(var i in this.selectionObj.edges)this.selectionObj.edges.hasOwnProperty(i)&&(t+=1);return t},e._selectionIsEmpty=function(){for(var t in this.selectionObj.nodes)if(this.selectionObj.nodes.hasOwnProperty(t))return!1;for(var e in this.selectionObj.edges)if(this.selectionObj.edges.hasOwnProperty(e))return!1;return!0},e._clusterInSelection=function(){for(var t in this.selectionObj.nodes)if(this.selectionObj.nodes.hasOwnProperty(t)&&this.selectionObj.nodes[t].clusterSize>1)return!0;return!1},e._selectConnectedEdges=function(t){for(var e=0;ei;i++){o=t[i];var n=this.nodes[o];if(!n)throw new RangeError('Node with id "'+o+'" not found');this._selectObject(n,!0,!0,e,!0)}this.redraw()},e.selectEdges=function(t){var e,i,s;if(!t||void 0==t.length)throw"Selection must be an array with ids";for(this._unselectAll(!0),e=0,i=t.length;i>e;e++){s=t[e];var o=this.edges[s];if(!o)throw new RangeError('Edge with id "'+s+'" not found');this._selectObject(o,!0,!0,!1,!0)}this.redraw()},e._updateSelection=function(){for(var t in this.selectionObj.nodes)this.selectionObj.nodes.hasOwnProperty(t)&&(this.nodes.hasOwnProperty(t)||delete this.selectionObj.nodes[t]);for(var e in this.selectionObj.edges)this.selectionObj.edges.hasOwnProperty(e)&&(this.edges.hasOwnProperty(e)||delete this.selectionObj.edges[e])}},function(t,e,i){var s=i(1),o=i(40),n=i(37);e._clearManipulatorBar=function(){this._recursiveDOMDelete(this.manipulationDiv),this.manipulationDOM={},this._manipulationReleaseOverload=function(){},delete this.sectors.support.nodes.targetNode,delete this.sectors.support.nodes.targetViaNode,this.controlNodesActive=!1,this.freezeSimulation=!1},e._restoreOverloadedFunctions=function(){for(var t in this.cachedFunctions)this.cachedFunctions.hasOwnProperty(t)&&(this[t]=this.cachedFunctions[t],delete this.cachedFunctions[t])},e._toggleEditMode=function(){this.editMode=!this.editMode;var t=this.manipulationDiv,e=this.closeDiv,i=this.editModeDiv;1==this.editMode?(t.style.display="block",e.style.display="block",i.style.display="none",e.onclick=this._toggleEditMode.bind(this)):(t.style.display="none",e.style.display="none",i.style.display="block",e.onclick=null),this._createManipulatorBar()},e._createManipulatorBar=function(){this.boundFunction&&this.off("select",this.boundFunction);var t=this.constants.locales[this.constants.locale];if(void 0!==this.edgeBeingEdited&&(this.edgeBeingEdited._disableControlNodes(),this.edgeBeingEdited=void 0,this.selectedControlNode=null,this.controlNodesActive=!1,this._redraw()),this._restoreOverloadedFunctions(),this.freezeSimulation=!1,this.blockConnectingEdgeSelection=!1,this.forceAppendSelection=!1,this.manipulationDOM={},1==this.editMode){for(;this.manipulationDiv.hasChildNodes();)this.manipulationDiv.removeChild(this.manipulationDiv.firstChild);this.manipulationDOM.addNodeSpan=document.createElement("span"),this.manipulationDOM.addNodeSpan.className="network-manipulationUI add",this.manipulationDOM.addNodeLabelSpan=document.createElement("span"),this.manipulationDOM.addNodeLabelSpan.className="network-manipulationLabel",this.manipulationDOM.addNodeLabelSpan.innerHTML=t.addNode,this.manipulationDOM.addNodeSpan.appendChild(this.manipulationDOM.addNodeLabelSpan),this.manipulationDOM.seperatorLineDiv1=document.createElement("div"),this.manipulationDOM.seperatorLineDiv1.className="network-seperatorLine",this.manipulationDOM.addEdgeSpan=document.createElement("span"),this.manipulationDOM.addEdgeSpan.className="network-manipulationUI connect",this.manipulationDOM.addEdgeLabelSpan=document.createElement("span"),this.manipulationDOM.addEdgeLabelSpan.className="network-manipulationLabel",this.manipulationDOM.addEdgeLabelSpan.innerHTML=t.addEdge,this.manipulationDOM.addEdgeSpan.appendChild(this.manipulationDOM.addEdgeLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.addNodeSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv1),this.manipulationDiv.appendChild(this.manipulationDOM.addEdgeSpan),1==this._getSelectedNodeCount()&&this.triggerFunctions.edit?(this.manipulationDOM.seperatorLineDiv2=document.createElement("div"),this.manipulationDOM.seperatorLineDiv2.className="network-seperatorLine",this.manipulationDOM.editNodeSpan=document.createElement("span"),this.manipulationDOM.editNodeSpan.className="network-manipulationUI edit",this.manipulationDOM.editNodeLabelSpan=document.createElement("span"),this.manipulationDOM.editNodeLabelSpan.className="network-manipulationLabel",this.manipulationDOM.editNodeLabelSpan.innerHTML=t.editNode,this.manipulationDOM.editNodeSpan.appendChild(this.manipulationDOM.editNodeLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv2),this.manipulationDiv.appendChild(this.manipulationDOM.editNodeSpan)):1==this._getSelectedEdgeCount()&&0==this._getSelectedNodeCount()&&(this.manipulationDOM.seperatorLineDiv3=document.createElement("div"),this.manipulationDOM.seperatorLineDiv3.className="network-seperatorLine",this.manipulationDOM.editEdgeSpan=document.createElement("span"),this.manipulationDOM.editEdgeSpan.className="network-manipulationUI edit",this.manipulationDOM.editEdgeLabelSpan=document.createElement("span"),this.manipulationDOM.editEdgeLabelSpan.className="network-manipulationLabel",this.manipulationDOM.editEdgeLabelSpan.innerHTML=t.editEdge,this.manipulationDOM.editEdgeSpan.appendChild(this.manipulationDOM.editEdgeLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv3),this.manipulationDiv.appendChild(this.manipulationDOM.editEdgeSpan)),0==this._selectionIsEmpty()&&(this.manipulationDOM.seperatorLineDiv4=document.createElement("div"),this.manipulationDOM.seperatorLineDiv4.className="network-seperatorLine",this.manipulationDOM.deleteSpan=document.createElement("span"),this.manipulationDOM.deleteSpan.className="network-manipulationUI delete",this.manipulationDOM.deleteLabelSpan=document.createElement("span"),this.manipulationDOM.deleteLabelSpan.className="network-manipulationLabel",this.manipulationDOM.deleteLabelSpan.innerHTML=t.del,this.manipulationDOM.deleteSpan.appendChild(this.manipulationDOM.deleteLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv4),this.manipulationDiv.appendChild(this.manipulationDOM.deleteSpan)),this.manipulationDOM.addNodeSpan.onclick=this._createAddNodeToolbar.bind(this),this.manipulationDOM.addEdgeSpan.onclick=this._createAddEdgeToolbar.bind(this),1==this._getSelectedNodeCount()&&this.triggerFunctions.edit?this.manipulationDOM.editNodeSpan.onclick=this._editNode.bind(this):1==this._getSelectedEdgeCount()&&0==this._getSelectedNodeCount()&&(this.manipulationDOM.editEdgeSpan.onclick=this._createEditEdgeToolbar.bind(this)),0==this._selectionIsEmpty()&&(this.manipulationDOM.deleteSpan.onclick=this._deleteSelected.bind(this)),this.closeDiv.onclick=this._toggleEditMode.bind(this); +var e=this;this.boundFunction=e._createManipulatorBar,this.on("select",this.boundFunction)}else{for(;this.editModeDiv.hasChildNodes();)this.editModeDiv.removeChild(this.editModeDiv.firstChild);this.manipulationDOM.editModeSpan=document.createElement("span"),this.manipulationDOM.editModeSpan.className="network-manipulationUI edit editmode",this.manipulationDOM.editModeLabelSpan=document.createElement("span"),this.manipulationDOM.editModeLabelSpan.className="network-manipulationLabel",this.manipulationDOM.editModeLabelSpan.innerHTML=t.edit,this.manipulationDOM.editModeSpan.appendChild(this.manipulationDOM.editModeLabelSpan),this.editModeDiv.appendChild(this.manipulationDOM.editModeSpan),this.manipulationDOM.editModeSpan.onclick=this._toggleEditMode.bind(this)}},e._createAddNodeToolbar=function(){this._clearManipulatorBar(),this.boundFunction&&this.off("select",this.boundFunction);var t=this.constants.locales[this.constants.locale];this.manipulationDOM={},this.manipulationDOM.backSpan=document.createElement("span"),this.manipulationDOM.backSpan.className="network-manipulationUI back",this.manipulationDOM.backLabelSpan=document.createElement("span"),this.manipulationDOM.backLabelSpan.className="network-manipulationLabel",this.manipulationDOM.backLabelSpan.innerHTML=t.back,this.manipulationDOM.backSpan.appendChild(this.manipulationDOM.backLabelSpan),this.manipulationDOM.seperatorLineDiv1=document.createElement("div"),this.manipulationDOM.seperatorLineDiv1.className="network-seperatorLine",this.manipulationDOM.descriptionSpan=document.createElement("span"),this.manipulationDOM.descriptionSpan.className="network-manipulationUI none",this.manipulationDOM.descriptionLabelSpan=document.createElement("span"),this.manipulationDOM.descriptionLabelSpan.className="network-manipulationLabel",this.manipulationDOM.descriptionLabelSpan.innerHTML=t.addDescription,this.manipulationDOM.descriptionSpan.appendChild(this.manipulationDOM.descriptionLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.backSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv1),this.manipulationDiv.appendChild(this.manipulationDOM.descriptionSpan),this.manipulationDOM.backSpan.onclick=this._createManipulatorBar.bind(this);var e=this;this.boundFunction=e._addNode,this.on("select",this.boundFunction)},e._createAddEdgeToolbar=function(){this._clearManipulatorBar(),this._unselectAll(!0),this.freezeSimulation=!0,this.boundFunction&&this.off("select",this.boundFunction);var t=this.constants.locales[this.constants.locale];this._unselectAll(),this.forceAppendSelection=!1,this.blockConnectingEdgeSelection=!0,this.manipulationDOM={},this.manipulationDOM.backSpan=document.createElement("span"),this.manipulationDOM.backSpan.className="network-manipulationUI back",this.manipulationDOM.backLabelSpan=document.createElement("span"),this.manipulationDOM.backLabelSpan.className="network-manipulationLabel",this.manipulationDOM.backLabelSpan.innerHTML=t.back,this.manipulationDOM.backSpan.appendChild(this.manipulationDOM.backLabelSpan),this.manipulationDOM.seperatorLineDiv1=document.createElement("div"),this.manipulationDOM.seperatorLineDiv1.className="network-seperatorLine",this.manipulationDOM.descriptionSpan=document.createElement("span"),this.manipulationDOM.descriptionSpan.className="network-manipulationUI none",this.manipulationDOM.descriptionLabelSpan=document.createElement("span"),this.manipulationDOM.descriptionLabelSpan.className="network-manipulationLabel",this.manipulationDOM.descriptionLabelSpan.innerHTML=t.edgeDescription,this.manipulationDOM.descriptionSpan.appendChild(this.manipulationDOM.descriptionLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.backSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv1),this.manipulationDiv.appendChild(this.manipulationDOM.descriptionSpan),this.manipulationDOM.backSpan.onclick=this._createManipulatorBar.bind(this);var e=this;this.boundFunction=e._handleConnect,this.on("select",this.boundFunction),this.cachedFunctions._handleTouch=this._handleTouch,this.cachedFunctions._manipulationReleaseOverload=this._manipulationReleaseOverload,this.cachedFunctions._handleDragStart=this._handleDragStart,this.cachedFunctions._handleDragEnd=this._handleDragEnd,this._handleTouch=this._handleConnect,this._manipulationReleaseOverload=function(){},this._handleDragStart=function(){},this._handleDragEnd=this._finishConnect,this._redraw()},e._createEditEdgeToolbar=function(){this._clearManipulatorBar(),this.controlNodesActive=!0,this.boundFunction&&this.off("select",this.boundFunction),this.edgeBeingEdited=this._getSelectedEdge(),this.edgeBeingEdited._enableControlNodes();var t=this.constants.locales[this.constants.locale];this.manipulationDOM={},this.manipulationDOM.backSpan=document.createElement("span"),this.manipulationDOM.backSpan.className="network-manipulationUI back",this.manipulationDOM.backLabelSpan=document.createElement("span"),this.manipulationDOM.backLabelSpan.className="network-manipulationLabel",this.manipulationDOM.backLabelSpan.innerHTML=t.back,this.manipulationDOM.backSpan.appendChild(this.manipulationDOM.backLabelSpan),this.manipulationDOM.seperatorLineDiv1=document.createElement("div"),this.manipulationDOM.seperatorLineDiv1.className="network-seperatorLine",this.manipulationDOM.descriptionSpan=document.createElement("span"),this.manipulationDOM.descriptionSpan.className="network-manipulationUI none",this.manipulationDOM.descriptionLabelSpan=document.createElement("span"),this.manipulationDOM.descriptionLabelSpan.className="network-manipulationLabel",this.manipulationDOM.descriptionLabelSpan.innerHTML=t.editEdgeDescription,this.manipulationDOM.descriptionSpan.appendChild(this.manipulationDOM.descriptionLabelSpan),this.manipulationDiv.appendChild(this.manipulationDOM.backSpan),this.manipulationDiv.appendChild(this.manipulationDOM.seperatorLineDiv1),this.manipulationDiv.appendChild(this.manipulationDOM.descriptionSpan),this.manipulationDOM.backSpan.onclick=this._createManipulatorBar.bind(this),this.cachedFunctions._handleTouch=this._handleTouch,this.cachedFunctions._manipulationReleaseOverload=this._manipulationReleaseOverload,this.cachedFunctions._handleTap=this._handleTap,this.cachedFunctions._handleDragStart=this._handleDragStart,this.cachedFunctions._handleOnDrag=this._handleOnDrag,this._handleTouch=this._selectControlNode,this._handleTap=function(){},this._handleOnDrag=this._controlNodeDrag,this._handleDragStart=function(){},this._manipulationReleaseOverload=this._releaseControlNode,this._redraw()},e._selectControlNode=function(t){this.edgeBeingEdited.controlNodes.from.unselect(),this.edgeBeingEdited.controlNodes.to.unselect(),this.selectedControlNode=this.edgeBeingEdited._getSelectedControlNode(this._XconvertDOMtoCanvas(t.x),this._YconvertDOMtoCanvas(t.y)),null!==this.selectedControlNode&&(this.selectedControlNode.select(),this.freezeSimulation=!0),this._redraw()},e._controlNodeDrag=function(t){var e=this._getPointer(t.center);null!==this.selectedControlNode&&void 0!==this.selectedControlNode&&(this.selectedControlNode.x=this._XconvertDOMtoCanvas(e.x),this.selectedControlNode.y=this._YconvertDOMtoCanvas(e.y)),this._redraw()},e._releaseControlNode=function(t){var e=this._getNodeAt(t);null!==e?(1==this.edgeBeingEdited.controlNodes.from.selected&&(this.edgeBeingEdited._restoreControlNodes(),this._editEdge(e.id,this.edgeBeingEdited.to.id),this.edgeBeingEdited.controlNodes.from.unselect()),1==this.edgeBeingEdited.controlNodes.to.selected&&(this.edgeBeingEdited._restoreControlNodes(),this._editEdge(this.edgeBeingEdited.from.id,e.id),this.edgeBeingEdited.controlNodes.to.unselect())):this.edgeBeingEdited._restoreControlNodes(),this.freezeSimulation=!1,this._redraw()},e._handleConnect=function(t){if(0==this._getSelectedNodeCount()){var e=this._getNodeAt(t);if(null!=e)if(e.clusterSize>1)alert(this.constants.locales[this.constants.locale].createEdgeError);else{this._selectObject(e,!1);var i=this.sectors.support.nodes;i.targetNode=new o({id:"targetNode"},{},{},this.constants);var s=i.targetNode;s.x=e.x,s.y=e.y,this.edges.connectionEdge=new n({id:"connectionEdge",from:e.id,to:s.id},this,this.constants);var r=this.edges.connectionEdge;r.from=e,r.connected=!0,r.options.smoothCurves={enabled:!0,dynamic:!1,type:"continuous",roundness:.5},r.selected=!0,r.to=s,this.cachedFunctions._handleOnDrag=this._handleOnDrag,this._handleOnDrag=function(t){var e=this._getPointer(t.center),i=this.edges.connectionEdge;i.to.x=this._XconvertDOMtoCanvas(e.x),i.to.y=this._YconvertDOMtoCanvas(e.y)},this.moving=!0,this.start()}}},e._finishConnect=function(t){if(1==this._getSelectedNodeCount()){var e=this._getPointer(t.center);this._handleOnDrag=this.cachedFunctions._handleOnDrag,delete this.cachedFunctions._handleOnDrag;var i=this.edges.connectionEdge.fromId;delete this.edges.connectionEdge,delete this.sectors.support.nodes.targetNode,delete this.sectors.support.nodes.targetViaNode;var s=this._getNodeAt(e);null!=s&&(s.clusterSize>1?alert(this.constants.locales[this.constants.locale].createEdgeError):(this._createEdge(i,s.id),this._createManipulatorBar())),this._unselectAll()}},e._addNode=function(){if(this._selectionIsEmpty()&&1==this.editMode){var t=this._pointerToPositionObject(this.pointerPosition),e={id:s.randomUUID(),x:t.left,y:t.top,label:"new",allowedToMoveX:!0,allowedToMoveY:!0};if(this.triggerFunctions.add){if(2!=this.triggerFunctions.add.length)throw new Error("The function for add does not support two arguments (data,callback)");var i=this;this.triggerFunctions.add(e,function(t){i.nodesData.add(t),i._createManipulatorBar(),i.moving=!0,i.start()})}else this.nodesData.add(e),this._createManipulatorBar(),this.moving=!0,this.start()}},e._createEdge=function(t,e){if(1==this.editMode){var i={from:t,to:e};if(this.triggerFunctions.connect){if(2!=this.triggerFunctions.connect.length)throw new Error("The function for connect does not support two arguments (data,callback)");var s=this;this.triggerFunctions.connect(i,function(t){s.edgesData.add(t),s.moving=!0,s.start()})}else this.edgesData.add(i),this.moving=!0,this.start()}},e._editEdge=function(t,e){if(1==this.editMode){var i={id:this.edgeBeingEdited.id,from:t,to:e};if(this.triggerFunctions.editEdge){if(2!=this.triggerFunctions.editEdge.length)throw new Error("The function for edit does not support two arguments (data, callback)");var s=this;this.triggerFunctions.editEdge(i,function(t){s.edgesData.update(t),s.moving=!0,s.start()})}else this.edgesData.update(i),this.moving=!0,this.start()}},e._editNode=function(){if(!this.triggerFunctions.edit||1!=this.editMode)throw new Error("No edit function has been bound to this button");var t=this._getSelectedNode(),e={id:t.id,label:t.label,group:t.options.group,shape:t.options.shape,color:{background:t.options.color.background,border:t.options.color.border,highlight:{background:t.options.color.highlight.background,border:t.options.color.highlight.border}}};if(2!=this.triggerFunctions.edit.length)throw new Error("The function for edit does not support two arguments (data, callback)");var i=this;this.triggerFunctions.edit(e,function(t){i.nodesData.update(t),i._createManipulatorBar(),i.moving=!0,i.start()})},e._deleteSelected=function(){if(!this._selectionIsEmpty()&&1==this.editMode)if(this._clusterInSelection())alert(this.constants.locales[this.constants.locale].deleteClusterError);else{var t=this.getSelectedNodes(),e=this.getSelectedEdges();if(this.triggerFunctions.del){var i=this,s={nodes:t,edges:e};if(2!=this.triggerFunctions.del.length)throw new Error("The function for delete does not support two arguments (data, callback)");this.triggerFunctions.del(s,function(t){i.edgesData.remove(t.edges),i.nodesData.remove(t.nodes),i._unselectAll(),i.moving=!0,i.start()})}else this.edgesData.remove(e),this.nodesData.remove(t),this._unselectAll(),this.moving=!0,this.start()}}},function(t,e,i){var s=(i(1),i(47)),o=i(45);e._cleanNavigation=function(){if(0!=this.navigationHammers.existing.length){for(var t=0;t0){var t,e,i=0,s=!1,o=!1;for(e in this.nodes)this.nodes.hasOwnProperty(e)&&(t=this.nodes[e],-1!=t.level?s=!0:o=!0,is&&(n.xFixed=!1,n.x=i[n.level].minPos,r=!0):n.yFixed&&n.level>s&&(n.yFixed=!1,n.y=i[n.level].minPos,r=!0),1==r&&(i[n.level].minPos+=i[n.level].nodeSpacing,n.edges.length>1&&this._placeBranchNodes(n.edges,n.id,i,n.level))}},e._setLevel=function(t,e,i){for(var s=0;st)&&(o.level=t,o.edges.length>1&&this._setLevel(t+1,o.edges,o.id))}},e._setLevelDirected=function(t,e,i){this.nodes[i].hierarchyEnumerated=!0;for(var s,o,n=0;n1&&s.hierarchyEnumerated===!1&&this._setLevelDirected(s.level,s.edges,s.id)},e._restoreNodes=function(){for(var t in this.nodes)this.nodes.hasOwnProperty(t)&&(this.nodes[t].xFixed=!1,this.nodes[t].yFixed=!1)}},function(t,e,i){function s(){this.constants.smoothCurves.enabled=!this.constants.smoothCurves.enabled;var t=document.getElementById("graph_toggleSmooth");t.style.background=1==this.constants.smoothCurves.enabled?"#A4FF56":"#FF8532",this._configureSmoothCurves(!1)}function o(){for(var t in this.calculationNodes)this.calculationNodes.hasOwnProperty(t)&&(this.calculationNodes[t].vx=0,this.calculationNodes[t].vy=0,this.calculationNodes[t].fx=0,this.calculationNodes[t].fy=0);1==this.constants.hierarchicalLayout.enabled?(this._setupHierarchicalLayout(),a.call(this,"graph_H_nd",1,"physics_hierarchicalRepulsion_nodeDistance"),a.call(this,"graph_H_cg",1,"physics_centralGravity"),a.call(this,"graph_H_sc",1,"physics_springConstant"),a.call(this,"graph_H_sl",1,"physics_springLength"),a.call(this,"graph_H_damp",1,"physics_damping")):this.repositionNodes(),this.moving=!0,this.start()}function n(){var t="No options are required, default values used.",e=[],i=document.getElementById("graph_physicsMethod1"),s=document.getElementById("graph_physicsMethod2");if(1==i.checked){if(this.constants.physics.barnesHut.gravitationalConstant!=this.backupConstants.physics.barnesHut.gravitationalConstant&&e.push("gravitationalConstant: "+this.constants.physics.barnesHut.gravitationalConstant),this.constants.physics.centralGravity!=this.backupConstants.physics.barnesHut.centralGravity&&e.push("centralGravity: "+this.constants.physics.centralGravity),this.constants.physics.springLength!=this.backupConstants.physics.barnesHut.springLength&&e.push("springLength: "+this.constants.physics.springLength),this.constants.physics.springConstant!=this.backupConstants.physics.barnesHut.springConstant&&e.push("springConstant: "+this.constants.physics.springConstant),this.constants.physics.damping!=this.backupConstants.physics.barnesHut.damping&&e.push("damping: "+this.constants.physics.damping),0!=e.length){t="var options = {",t+="physics: {barnesHut: {";for(var o=0;othis.constants.clustering.clusterThreshold&&1==this.constants.clustering.enabled&&this.clusterToFit(this.constants.clustering.reduceToNodes,!1),this._calculateForces())},e._calculateForces=function(){this._calculateGravitationalForces(),this._calculateNodeForces(),this.constants.physics.springConstant>0&&(1==this.constants.smoothCurves.enabled&&1==this.constants.smoothCurves.dynamic?this._calculateSpringForcesWithSupport():1==this.constants.physics.hierarchicalRepulsion.enabled?this._calculateHierarchicalSpringForces():this._calculateSpringForces())},e._updateCalculationNodes=function(){if(1==this.constants.smoothCurves.enabled&&1==this.constants.smoothCurves.dynamic){this.calculationNodes={},this.calculationNodeIndices=[];for(var t in this.nodes)this.nodes.hasOwnProperty(t)&&(this.calculationNodes[t]=this.nodes[t]);var e=this.sectors.support.nodes;for(var i in e)e.hasOwnProperty(i)&&(this.edges.hasOwnProperty(e[i].parentEdgeId)?this.calculationNodes[i]=e[i]:e[i]._setForce(0,0));for(var s in this.calculationNodes)this.calculationNodes.hasOwnProperty(s)&&this.calculationNodeIndices.push(s)}else this.calculationNodes=this.nodes,this.calculationNodeIndices=this.nodeIndices},e._calculateGravitationalForces=function(){var t,e,i,s,o,n=this.calculationNodes,r=this.constants.physics.centralGravity,a=0;for(o=0;oSimulation Mode:Barnes HutRepulsionHierarchical
    Options:
    ',this.containerElement.parentElement.insertBefore(this.physicsConfiguration,this.containerElement),this.optionsDiv=document.createElement("div"),this.optionsDiv.style.fontSize="14px",this.optionsDiv.style.fontFamily="verdana",this.containerElement.parentElement.insertBefore(this.optionsDiv,this.containerElement); +var e;e=document.getElementById("graph_BH_gc"),e.onchange=a.bind(this,"graph_BH_gc",-1,"physics_barnesHut_gravitationalConstant"),e=document.getElementById("graph_BH_cg"),e.onchange=a.bind(this,"graph_BH_cg",1,"physics_centralGravity"),e=document.getElementById("graph_BH_sc"),e.onchange=a.bind(this,"graph_BH_sc",1,"physics_springConstant"),e=document.getElementById("graph_BH_sl"),e.onchange=a.bind(this,"graph_BH_sl",1,"physics_springLength"),e=document.getElementById("graph_BH_damp"),e.onchange=a.bind(this,"graph_BH_damp",1,"physics_damping"),e=document.getElementById("graph_R_nd"),e.onchange=a.bind(this,"graph_R_nd",1,"physics_repulsion_nodeDistance"),e=document.getElementById("graph_R_cg"),e.onchange=a.bind(this,"graph_R_cg",1,"physics_centralGravity"),e=document.getElementById("graph_R_sc"),e.onchange=a.bind(this,"graph_R_sc",1,"physics_springConstant"),e=document.getElementById("graph_R_sl"),e.onchange=a.bind(this,"graph_R_sl",1,"physics_springLength"),e=document.getElementById("graph_R_damp"),e.onchange=a.bind(this,"graph_R_damp",1,"physics_damping"),e=document.getElementById("graph_H_nd"),e.onchange=a.bind(this,"graph_H_nd",1,"physics_hierarchicalRepulsion_nodeDistance"),e=document.getElementById("graph_H_cg"),e.onchange=a.bind(this,"graph_H_cg",1,"physics_centralGravity"),e=document.getElementById("graph_H_sc"),e.onchange=a.bind(this,"graph_H_sc",1,"physics_springConstant"),e=document.getElementById("graph_H_sl"),e.onchange=a.bind(this,"graph_H_sl",1,"physics_springLength"),e=document.getElementById("graph_H_damp"),e.onchange=a.bind(this,"graph_H_damp",1,"physics_damping"),e=document.getElementById("graph_H_direction"),e.onchange=a.bind(this,"graph_H_direction",t,"hierarchicalLayout_direction"),e=document.getElementById("graph_H_levsep"),e.onchange=a.bind(this,"graph_H_levsep",1,"hierarchicalLayout_levelSeparation"),e=document.getElementById("graph_H_nspac"),e.onchange=a.bind(this,"graph_H_nspac",1,"hierarchicalLayout_nodeSpacing");var i=document.getElementById("graph_physicsMethod1"),d=document.getElementById("graph_physicsMethod2"),l=document.getElementById("graph_physicsMethod3");d.checked=!0,this.constants.physics.barnesHut.enabled&&(i.checked=!0),this.constants.hierarchicalLayout.enabled&&(l.checked=!0);var c=document.getElementById("graph_toggleSmooth"),p=document.getElementById("graph_repositionNodes"),u=document.getElementById("graph_generateOptions");c.onclick=s.bind(this),p.onclick=o.bind(this),u.onclick=n.bind(this),c.style.background=1==this.constants.smoothCurves&&0==this.constants.dynamicSmoothCurves?"#A4FF56":"#FF8532",r.apply(this),i.onchange=r.bind(this),d.onchange=r.bind(this),l.onchange=r.bind(this)}},e._overWriteGraphConstants=function(t,e){var i=t.split("_");1==i.length?this.constants[i[0]]=e:2==i.length?this.constants[i[0]][i[1]]=e:3==i.length&&(this.constants[i[0]][i[1]][i[2]]=e)}},function(t){function e(t){throw new Error("Cannot find module '"+t+"'.")}e.keys=function(){return[]},e.resolve=e,t.exports=e,e.id=68},function(t,e){e._calculateNodeForces=function(){var t,e,i,s,o,n,r,a,h,d,l,c=this.calculationNodes,p=this.calculationNodeIndices,u=-2/3,m=4/3,f=this.constants.physics.repulsion.nodeDistance,g=f;for(d=0;di&&(r=.5*g>i?1:v*i+m,r*=0==n?1:1+n*this.constants.clustering.forceAmplification,r/=Math.max(i,.01*g),s=t*r,o=e*r,a.fx-=s,a.fy-=o,h.fx+=s,h.fy+=o)}}},function(t,e){e._calculateNodeForces=function(){var t,e,i,s,o,n,r,a,h,d,l=this.calculationNodes,c=this.calculationNodeIndices,p=this.constants.physics.hierarchicalRepulsion.nodeDistance;for(h=0;hi?-Math.pow(u*i,2)+Math.pow(u*p,2):0,0==i?i=.01:n/=i,s=t*n,o=e*n,r.fx-=s,r.fy-=o,a.fx+=s,a.fy+=o}},e._calculateHierarchicalSpringForces=function(){for(var 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0===i&&(i="#FF0000"),4==t.childrenCount&&(this._drawBranch(t.children.NW,e),this._drawBranch(t.children.NE,e),this._drawBranch(t.children.SE,e),this._drawBranch(t.children.SW,e)),e.strokeStyle=i,e.beginPath(),e.moveTo(t.range.minX,t.range.minY),e.lineTo(t.range.maxX,t.range.minY),e.stroke(),e.beginPath(),e.moveTo(t.range.maxX,t.range.minY),e.lineTo(t.range.maxX,t.range.maxY),e.stroke(),e.beginPath(),e.moveTo(t.range.maxX,t.range.maxY),e.lineTo(t.range.minX,t.range.maxY),e.stroke(),e.beginPath(),e.moveTo(t.range.minX,t.range.maxY),e.lineTo(t.range.minX,t.range.minY),e.stroke()}},function(t){t.exports=function(t){return t.webpackPolyfill||(t.deprecate=function(){},t.paths=[],t.children=[],t.webpackPolyfill=1),t}},function(t,e){(function(e){t.exports=e}).call(e,{})}])}); +//# sourceMappingURL=vis.map diff --git a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala index c6dc3369ba5cc..cf3db0b94a0b3 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala @@ -47,6 +47,18 @@ class StageInfo( failureReason = Some(reason) completionTime = Some(System.currentTimeMillis) } + + private[spark] def getStatusString: String = { + if (completionTime.isDefined) { + if (failureReason.isDefined) { + "failed" + } else { + "succeeded" + } + } else { + "running" + } + } } private[spark] object StageInfo { diff --git a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala index f07864141a21c..395af2ea30b9d 100644 --- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala +++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala @@ -159,12 +159,17 @@ private[spark] object UIUtils extends Logging { type="text/css" /> + + + + } /** Returns a spark page with correctly formatted headers */ diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala index 69053fe44d7e4..0a08b000e2d03 100644 --- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala +++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorsTab.scala @@ -24,6 +24,7 @@ import org.apache.spark.annotation.DeveloperApi import org.apache.spark.scheduler._ import org.apache.spark.storage.{StorageStatus, StorageStatusListener} import org.apache.spark.ui.{SparkUI, SparkUITab} +import org.apache.spark.ui.jobs.UIData.ExecutorUIData private[ui] class ExecutorsTab(parent: SparkUI) extends SparkUITab(parent, "executors") { val listener = parent.executorsListener @@ -54,12 +55,22 @@ class ExecutorsListener(storageStatusListener: StorageStatusListener) extends Sp val executorToShuffleRead = HashMap[String, Long]() val executorToShuffleWrite = HashMap[String, Long]() val executorToLogUrls = HashMap[String, Map[String, String]]() + val executorIdToData = HashMap[String, ExecutorUIData]() def storageStatusList: Seq[StorageStatus] = storageStatusListener.storageStatusList override def onExecutorAdded(executorAdded: SparkListenerExecutorAdded): Unit = synchronized { val eid = executorAdded.executorId executorToLogUrls(eid) = executorAdded.executorInfo.logUrlMap + executorIdToData(eid) = ExecutorUIData(executorAdded.time) + } + + override def onExecutorRemoved( + executorRemoved: SparkListenerExecutorRemoved): Unit = synchronized { + val eid = executorRemoved.executorId + val uiData = executorIdToData(eid) + uiData.finishTime = Some(executorRemoved.time) + uiData.finishReason = Some(executorRemoved.reason) } override def onTaskStart(taskStart: SparkListenerTaskStart): Unit = synchronized { diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala index bd923d78a86ce..a7ea12b1655fe 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala @@ -17,17 +17,183 @@ package org.apache.spark.ui.jobs -import scala.xml.{Node, NodeSeq} +import scala.collection.mutable.{HashMap, ListBuffer} +import scala.xml.{Node, NodeSeq, Unparsed} +import java.util.Date import javax.servlet.http.HttpServletRequest -import org.apache.spark.ui.{WebUIPage, UIUtils} -import org.apache.spark.ui.jobs.UIData.JobUIData +import org.apache.spark.ui.{UIUtils, WebUIPage} +import org.apache.spark.ui.jobs.UIData.{ExecutorUIData, JobUIData} +import org.apache.spark.JobExecutionStatus /** Page showing list of all ongoing and recently finished jobs */ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") { - private val startTime: Option[Long] = parent.sc.map(_.startTime) - private val listener = parent.listener + private val JOBS_LEGEND = +
    + + Succeeded + + Failed + + Running +
    .toString.filter(_ != '\n') + + private val EXECUTORS_LEGEND = +
    + + Added + + Removed +
    .toString.filter(_ != '\n') + + private def getLastStageNameAndDescription(job: JobUIData): (String, String) = { + val lastStageInfo = Option(job.stageIds) + .filter(_.nonEmpty) + .flatMap { ids => parent.jobProgresslistener.stageIdToInfo.get(ids.max)} + val lastStageData = lastStageInfo.flatMap { s => + parent.jobProgresslistener.stageIdToData.get((s.stageId, s.attemptId)) + } + val name = lastStageInfo.map(_.name).getOrElse("(Unknown Stage Name)") + val description = lastStageData.flatMap(_.description).getOrElse("") + (name, description) + } + + private def makeJobEvent(jobUIDatas: Seq[JobUIData]): Seq[String] = { + jobUIDatas.filter { jobUIData => + jobUIData.status != JobExecutionStatus.UNKNOWN && jobUIData.submissionTime.isDefined + }.map { jobUIData => + val jobId = jobUIData.jobId + val status = jobUIData.status + val (jobName, jobDescription) = getLastStageNameAndDescription(jobUIData) + val displayJobDescription = if (jobDescription.isEmpty) jobName else jobDescription + val submissionTime = jobUIData.submissionTime.get + val completionTimeOpt = jobUIData.completionTime + val completionTime = completionTimeOpt.getOrElse(System.currentTimeMillis()) + val classNameByStatus = status match { + case JobExecutionStatus.SUCCEEDED => "succeeded" + case JobExecutionStatus.FAILED => "failed" + case JobExecutionStatus.RUNNING => "running" + } + + val jobEventJsonAsStr = + s""" + |{ + | 'className': 'job application-timeline-object ${classNameByStatus}', + | 'group': 'jobs', + | 'start': new Date(${submissionTime}), + | 'end': new Date(${completionTime}), + | 'content': '
    Completion Time: ${UIUtils.formatDate(new Date(completionTime))}""" + } else { + "" + } + }">' + + | '${displayJobDescription} (Job ${jobId})
    ' + |} + """.stripMargin + jobEventJsonAsStr + } + } + + private def makeExecutorEvent(executorUIDatas: HashMap[String, ExecutorUIData]): Seq[String] = { + val events = ListBuffer[String]() + executorUIDatas.foreach { + case (executorId, event) => + val addedEvent = + s""" + |{ + | 'className': 'executor added', + | 'group': 'executors', + | 'start': new Date(${event.startTime}), + | 'content': '
    Executor ${executorId} added
    ' + |} + """.stripMargin + events += addedEvent + + if (event.finishTime.isDefined) { + val removedEvent = + s""" + |{ + | 'className': 'executor removed', + | 'group': 'executors', + | 'start': new Date(${event.finishTime.get}), + | 'content': '
    Reason: ${event.finishReason.get}""" + } else { + "" + } + }"' + + | 'data-html="true">Executor ${executorId} removed
    ' + |} + """.stripMargin + events += removedEvent + } + } + events.toSeq + } + + private def makeTimeline( + jobs: Seq[JobUIData], + executors: HashMap[String, ExecutorUIData], + startTime: Long): Seq[Node] = { + + val jobEventJsonAsStrSeq = makeJobEvent(jobs) + val executorEventJsonAsStrSeq = makeExecutorEvent(executors) + + val groupJsonArrayAsStr = + s""" + |[ + | { + | 'id': 'executors', + | 'content': '
    Executors
    ${EXECUTORS_LEGEND}', + | }, + | { + | 'id': 'jobs', + | 'content': '
    Jobs
    ${JOBS_LEGEND}', + | } + |] + """.stripMargin + + val eventArrayAsStr = + (jobEventJsonAsStrSeq ++ executorEventJsonAsStrSeq).mkString("[", ",", "]") + + + + Event Timeline + ++ + ++ + + } private def jobsTable(jobs: Seq[JobUIData]): Seq[Node] = { val someJobHasJobGroup = jobs.exists(_.jobGroup.isDefined) @@ -42,15 +208,7 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") { } def makeRow(job: JobUIData): Seq[Node] = { - val lastStageInfo = Option(job.stageIds) - .filter(_.nonEmpty) - .flatMap { ids => listener.stageIdToInfo.get(ids.max) } - val lastStageData = lastStageInfo.flatMap { s => - listener.stageIdToData.get((s.stageId, s.attemptId)) - } - - val lastStageName = lastStageInfo.map(_.name).getOrElse("(Unknown Stage Name)") - val lastStageDescription = lastStageData.flatMap(_.description).getOrElse("") + val (lastStageName, lastStageDescription) = getLastStageNameAndDescription(job) val duration: Option[Long] = { job.submissionTime.map { start => val end = job.completionTime.getOrElse(System.currentTimeMillis()) @@ -61,7 +219,7 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") { val formattedSubmissionTime = job.submissionTime.map(UIUtils.formatDate).getOrElse("Unknown") val detailUrl = "%s/jobs/job?id=%s".format(UIUtils.prependBaseUri(parent.basePath), job.jobId) - + {job.jobId} {job.jobGroup.map(id => s"($id)").getOrElse("")} @@ -95,11 +253,12 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") { } def render(request: HttpServletRequest): Seq[Node] = { + val listener = parent.jobProgresslistener listener.synchronized { + val startTime = listener.startTime val activeJobs = listener.activeJobs.values.toSeq val completedJobs = listener.completedJobs.reverse.toSeq val failedJobs = listener.failedJobs.reverse.toSeq - val now = System.currentTimeMillis val activeJobsTable = jobsTable(activeJobs.sortBy(_.submissionTime.getOrElse(-1L)).reverse) @@ -115,11 +274,11 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") { val summary: NodeSeq =
      - {if (startTime.isDefined) { + {if (parent.sc.isDefined) { // Total duration is not meaningful unless the UI is live
    • Total Duration: - {UIUtils.formatDuration(now - startTime.get)} + {UIUtils.formatDuration(System.currentTimeMillis() - startTime)}
      • }}
    • @@ -154,6 +313,10 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") {
    var content = summary + val executorListener = parent.executorListener + content ++= makeTimeline(activeJobs ++ completedJobs ++ failedJobs, + executorListener.executorIdToData, startTime) + if (shouldShowActiveJobs) { content ++=

    Active Jobs ({activeJobs.size})

    ++ activeJobsTable @@ -166,6 +329,7 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") { content ++=

    Failed Jobs ({failedJobs.size})

    ++ failedJobsTable } + val helpText = """A job is triggered by an action, like "count()" or "saveAsTextFile()".""" + " Click on a job's title to see information about the stages of tasks associated with" + " the job." diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala index 7541d3e9c72e7..dd968e124738e 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobPage.scala @@ -17,20 +17,167 @@ package org.apache.spark.ui.jobs -import scala.collection.mutable -import scala.xml.{NodeSeq, Node} +import java.util.Date + +import scala.collection.mutable.{Buffer, HashMap, ListBuffer} +import scala.xml.{NodeSeq, Node, Unparsed} import javax.servlet.http.HttpServletRequest import org.apache.spark.JobExecutionStatus import org.apache.spark.scheduler.StageInfo import org.apache.spark.ui.{UIUtils, WebUIPage} +import org.apache.spark.ui.jobs.UIData.ExecutorUIData /** Page showing statistics and stage list for a given job */ private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") { - private val listener = parent.listener + private val STAGES_LEGEND = +
    + + Completed + + Failed + + Active +
    .toString.filter(_ != '\n') + + private val EXECUTORS_LEGEND = +
    + + Added + + Removed +
    .toString.filter(_ != '\n') + + private def makeStageEvent(stageInfos: Seq[StageInfo]): Seq[String] = { + stageInfos.map { stage => + val stageId = stage.stageId + val attemptId = stage.attemptId + val name = stage.name + val status = stage.getStatusString + val submissionTime = stage.submissionTime.get + val completionTime = stage.completionTime.getOrElse(System.currentTimeMillis()) + + s""" + |{ + | 'className': 'stage job-timeline-object ${status}', + | 'group': 'stages', + | 'start': new Date(${submissionTime}), + | 'end': new Date(${completionTime}), + | 'content': '
    Completion Time: ${UIUtils.formatDate(new Date(completionTime))}""" + } else { + "" + } + }">' + + | '${name} (Stage ${stageId}.${attemptId})
    ', + |} + """.stripMargin + } + } + + def makeExecutorEvent(executorUIDatas: HashMap[String, ExecutorUIData]): Seq[String] = { + val events = ListBuffer[String]() + executorUIDatas.foreach { + case (executorId, event) => + val addedEvent = + s""" + |{ + | 'className': 'executor added', + | 'group': 'executors', + | 'start': new Date(${event.startTime}), + | 'content': '
    Executor ${executorId} added
    ' + |} + """.stripMargin + events += addedEvent + + if (event.finishTime.isDefined) { + val removedEvent = + s""" + |{ + | 'className': 'executor removed', + | 'group': 'executors', + | 'start': new Date(${event.finishTime.get}), + | 'content': '
    Reason: ${event.finishReason.get}""" + } else { + "" + } + }"' + + | 'data-html="true">Executor ${executorId} removed
    ' + |} + """.stripMargin + events += removedEvent + } + } + events.toSeq + } + + private def makeTimeline( + stages: Seq[StageInfo], + executors: HashMap[String, ExecutorUIData], + appStartTime: Long): Seq[Node] = { + + val stageEventJsonAsStrSeq = makeStageEvent(stages) + val executorsJsonAsStrSeq = makeExecutorEvent(executors) + + val groupJsonArrayAsStr = + s""" + |[ + | { + | 'id': 'executors', + | 'content': '
    Executors
    ${EXECUTORS_LEGEND}', + | }, + | { + | 'id': 'stages', + | 'content': '
    Stages
    ${STAGES_LEGEND}', + | } + |] + """.stripMargin + + val eventArrayAsStr = + (stageEventJsonAsStrSeq ++ executorsJsonAsStrSeq).mkString("[", ",", "]") + + + + Event Timeline + ++ + ++ + + } def render(request: HttpServletRequest): Seq[Node] = { + val listener = parent.jobProgresslistener + listener.synchronized { val parameterId = request.getParameter("id") require(parameterId != null && parameterId.nonEmpty, "Missing id parameter") @@ -54,11 +201,11 @@ private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") { new StageInfo(stageId, 0, "Unknown", 0, Seq.empty, "Unknown")) } - val activeStages = mutable.Buffer[StageInfo]() - val completedStages = mutable.Buffer[StageInfo]() + val activeStages = Buffer[StageInfo]() + val completedStages = Buffer[StageInfo]() // If the job is completed, then any pending stages are displayed as "skipped": - val pendingOrSkippedStages = mutable.Buffer[StageInfo]() - val failedStages = mutable.Buffer[StageInfo]() + val pendingOrSkippedStages = Buffer[StageInfo]() + val failedStages = Buffer[StageInfo]() for (stage <- stages) { if (stage.submissionTime.isEmpty) { pendingOrSkippedStages += stage @@ -75,18 +222,18 @@ private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") { val activeStagesTable = new StageTableBase(activeStages.sortBy(_.submissionTime).reverse, - parent.basePath, parent.listener, isFairScheduler = parent.isFairScheduler, + parent.basePath, parent.jobProgresslistener, isFairScheduler = parent.isFairScheduler, killEnabled = parent.killEnabled) val pendingOrSkippedStagesTable = new StageTableBase(pendingOrSkippedStages.sortBy(_.stageId).reverse, - parent.basePath, parent.listener, isFairScheduler = parent.isFairScheduler, + parent.basePath, parent.jobProgresslistener, isFairScheduler = parent.isFairScheduler, killEnabled = false) val completedStagesTable = new StageTableBase(completedStages.sortBy(_.submissionTime).reverse, parent.basePath, - parent.listener, isFairScheduler = parent.isFairScheduler, killEnabled = false) + parent.jobProgresslistener, isFairScheduler = parent.isFairScheduler, killEnabled = false) val failedStagesTable = new FailedStageTable(failedStages.sortBy(_.submissionTime).reverse, parent.basePath, - parent.listener, isFairScheduler = parent.isFairScheduler) + parent.jobProgresslistener, isFairScheduler = parent.isFairScheduler) val shouldShowActiveStages = activeStages.nonEmpty val shouldShowPendingStages = !isComplete && pendingOrSkippedStages.nonEmpty @@ -154,6 +301,11 @@ private[ui] class JobPage(parent: JobsTab) extends WebUIPage("job") {
    var content = summary + val appStartTime = listener.startTime + val executorListener = parent.executorListener + content ++= makeTimeline(activeStages ++ completedStages ++ failedStages, + executorListener.executorIdToData, appStartTime) + if (shouldShowActiveStages) { content ++=

    Active Stages ({activeStages.size})

    ++ activeStagesTable.toNodeSeq diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala index 625596885faa1..d6d716de25920 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala @@ -50,6 +50,9 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { type PoolName = String type ExecutorId = String + // Applicatin: + @volatile var startTime = -1L + // Jobs: val activeJobs = new HashMap[JobId, JobUIData] val completedJobs = ListBuffer[JobUIData]() @@ -75,6 +78,7 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { // Misc: val executorIdToBlockManagerId = HashMap[ExecutorId, BlockManagerId]() + def blockManagerIds: Seq[BlockManagerId] = executorIdToBlockManagerId.values.toSeq var schedulingMode: Option[SchedulingMode] = None @@ -516,6 +520,9 @@ class JobProgressListener(conf: SparkConf) extends SparkListener with Logging { } } + override def onApplicationStart(appStarted: SparkListenerApplicationStart) { + startTime = appStarted.time + } } private object JobProgressListener { diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala index 7ffcf291b5cc6..342787f1a3c2d 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobsTab.scala @@ -24,8 +24,10 @@ import org.apache.spark.ui.{SparkUI, SparkUITab} private[ui] class JobsTab(parent: SparkUI) extends SparkUITab(parent, "jobs") { val sc = parent.sc val killEnabled = parent.killEnabled - def isFairScheduler: Boolean = listener.schedulingMode.exists(_ == SchedulingMode.FAIR) - val listener = parent.jobProgressListener + def isFairScheduler: Boolean = + jobProgresslistener.schedulingMode.exists(_ == SchedulingMode.FAIR) + val jobProgresslistener = parent.jobProgressListener + val executorListener = parent.executorsListener attachPage(new AllJobsPage(this)) attachPage(new JobPage(this)) diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala index cb72890a0fd20..6d8c7e1fda8d8 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala @@ -174,7 +174,8 @@ private[ui] class StageTableBase( } /** Render an HTML row that represents a stage */ - private def renderStageRow(s: StageInfo): Seq[Node] = {stageRow(s)} + private def renderStageRow(s: StageInfo): Seq[Node] = + {stageRow(s)} } private[ui] class FailedStageTable( diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala b/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala index 935c8a4f80e7b..3d96113aa5fe9 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/UIData.scala @@ -108,4 +108,9 @@ private[spark] object UIData { var taskInfo: TaskInfo, var taskMetrics: Option[TaskMetrics] = None, var errorMessage: Option[String] = None) + + case class ExecutorUIData( + val startTime: Long, + var finishTime: Option[Long] = None, + var finishReason: Option[String] = None) } From 3052f4916e7f2c7fbc4837f00f4463b7d0b34718 Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Fri, 1 May 2015 09:50:55 -0500 Subject: [PATCH 516/591] [SPARK-4705] Handle multiple app attempts event logs, history server. This change modifies the event logging listener to write the logs for different application attempts to different files. The attempt ID is set by the scheduler backend, so as long as the backend returns that ID to SparkContext, things should work. Currently, the YARN backend does that. The history server was also modified to model multiple attempts per application. Each attempt has its own UI and a separate row in the listing table, so that users can look at all the attempts separately. The UI "adapts" itself to avoid showing attempt-specific info when all the applications being shown have a single attempt. Author: Marcelo Vanzin Author: twinkle sachdeva Author: twinkle.sachdeva Author: twinkle sachdeva Closes #5432 from vanzin/SPARK-4705 and squashes the following commits: 7e289fa [Marcelo Vanzin] Review feedback. f66dcc5 [Marcelo Vanzin] Merge branch 'master' into SPARK-4705 bc885b7 [Marcelo Vanzin] Review feedback. 76a3651 [Marcelo Vanzin] Fix log cleaner, add test. 7c381ec [Marcelo Vanzin] Merge branch 'master' into SPARK-4705 1aa309d [Marcelo Vanzin] Improve sorting of app attempts. 2ad77e7 [Marcelo Vanzin] Missed a reference to the old property name. 9d59d92 [Marcelo Vanzin] Scalastyle... d5a9c37 [Marcelo Vanzin] Update JsonProtocol test, make property name consistent. ba34b69 [Marcelo Vanzin] Use Option[String] for attempt id. f1cb9b3 [Marcelo Vanzin] Merge branch 'master' into SPARK-4705 c14ec19 [Marcelo Vanzin] Merge branch 'master' into SPARK-4705 9092d39 [Marcelo Vanzin] Merge branch 'master' into SPARK-4705 86de638 [Marcelo Vanzin] Merge branch 'master' into SPARK-4705 07446c6 [Marcelo Vanzin] Disable striping for app id / name when multiple attempts exist. 9092af5 [Marcelo Vanzin] Fix HistoryServer test. 3a14503 [Marcelo Vanzin] Argh scalastyle. 657ec18 [Marcelo Vanzin] Fix yarn history URL, app links. c3e0a82 [Marcelo Vanzin] Move app name to app info, more UI fixes. ce5ee5d [Marcelo Vanzin] Misc UI, test, style fixes. cbe8bba [Marcelo Vanzin] Attempt ID in listener event should be an option. 88b1de8 [Marcelo Vanzin] Add a test for apps with multiple attempts. 3245aa2 [Marcelo Vanzin] Make app attempts part of the history server model. 5fd5c6f [Marcelo Vanzin] Fix my broken rebase. 318525a [twinkle.sachdeva] SPARK-4705: 1) moved from directory structure to single file, as per the master branch. 2) Added the attempt id inside the SparkListenerApplicationStart, to make the info available independent of directory structure. 3) Changes in History Server to render the UI as per the snaphot II 6b2e521 [twinkle sachdeva] SPARK-4705 Incorporating the review comments regarding formatting, will do the rest of the changes after this 4c1fc26 [twinkle sachdeva] SPARK-4705 Incorporating the review comments regarding formatting, will do the rest of the changes after this 0eb7722 [twinkle sachdeva] SPARK-4705: Doing cherry-pick of fix into master --- .../scala/org/apache/spark/SparkContext.scala | 8 +- .../history/ApplicationHistoryProvider.scala | 13 +- .../deploy/history/FsHistoryProvider.scala | 211 +++++++++------ .../spark/deploy/history/HistoryPage.scala | 111 ++++++-- .../spark/deploy/history/HistoryServer.scala | 24 +- .../apache/spark/deploy/master/Master.scala | 20 +- .../scheduler/ApplicationEventListener.scala | 2 + .../scheduler/EventLoggingListener.scala | 28 +- .../spark/scheduler/SchedulerBackend.scala | 8 + .../spark/scheduler/SparkListener.scala | 4 +- .../spark/scheduler/TaskScheduler.scala | 10 +- .../spark/scheduler/TaskSchedulerImpl.scala | 2 + .../org/apache/spark/util/JsonProtocol.scala | 6 +- .../history/FsHistoryProviderSuite.scala | 243 ++++++++++++++---- .../deploy/history/HistoryServerSuite.scala | 3 +- .../spark/scheduler/DAGSchedulerSuite.scala | 2 + .../scheduler/EventLoggingListenerSuite.scala | 18 +- .../spark/scheduler/ReplayListenerSuite.scala | 4 +- .../apache/spark/util/JsonProtocolSuite.scala | 11 +- .../spark/deploy/yarn/ApplicationMaster.scala | 7 +- .../cluster/YarnClusterSchedulerBackend.scala | 12 +- 21 files changed, 546 insertions(+), 201 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index fe24260cdb4c5..3f7cba6dbcdb5 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -217,6 +217,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli private var _heartbeatReceiver: RpcEndpointRef = _ @volatile private var _dagScheduler: DAGScheduler = _ private var _applicationId: String = _ + private var _applicationAttemptId: Option[String] = None private var _eventLogger: Option[EventLoggingListener] = None private var _executorAllocationManager: Option[ExecutorAllocationManager] = None private var _cleaner: Option[ContextCleaner] = None @@ -315,6 +316,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli } def applicationId: String = _applicationId + def applicationAttemptId: Option[String] = _applicationAttemptId def metricsSystem: MetricsSystem = if (_env != null) _env.metricsSystem else null @@ -472,6 +474,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli _taskScheduler.start() _applicationId = _taskScheduler.applicationId() + _applicationAttemptId = taskScheduler.applicationAttemptId() _conf.set("spark.app.id", _applicationId) _env.blockManager.initialize(_applicationId) @@ -484,7 +487,8 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli _eventLogger = if (isEventLogEnabled) { val logger = - new EventLoggingListener(_applicationId, _eventLogDir.get, _conf, _hadoopConfiguration) + new EventLoggingListener(_applicationId, _applicationAttemptId, _eventLogDir.get, + _conf, _hadoopConfiguration) logger.start() listenerBus.addListener(logger) Some(logger) @@ -1868,7 +1872,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli // Note: this code assumes that the task scheduler has been initialized and has contacted // the cluster manager to get an application ID (in case the cluster manager provides one). listenerBus.post(SparkListenerApplicationStart(appName, Some(applicationId), - startTime, sparkUser)) + startTime, sparkUser, applicationAttemptId)) } /** Post the application end event */ diff --git a/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala index ea6c85ee511d5..6a5011af17458 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/ApplicationHistoryProvider.scala @@ -19,15 +19,19 @@ package org.apache.spark.deploy.history import org.apache.spark.ui.SparkUI -private[history] case class ApplicationHistoryInfo( - id: String, - name: String, +private[history] case class ApplicationAttemptInfo( + attemptId: Option[String], startTime: Long, endTime: Long, lastUpdated: Long, sparkUser: String, completed: Boolean = false) +private[history] case class ApplicationHistoryInfo( + id: String, + name: String, + attempts: List[ApplicationAttemptInfo]) + private[history] abstract class ApplicationHistoryProvider { /** @@ -41,9 +45,10 @@ private[history] abstract class ApplicationHistoryProvider { * Returns the Spark UI for a specific application. * * @param appId The application ID. + * @param attemptId The application attempt ID (or None if there is no attempt ID). * @return The application's UI, or None if application is not found. */ - def getAppUI(appId: String): Option[SparkUI] + def getAppUI(appId: String, attemptId: Option[String]): Option[SparkUI] /** * Called when the server is shutting down. diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala index fb2cbbcccc54b..993763f3aa092 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala @@ -32,7 +32,7 @@ import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.io.CompressionCodec import org.apache.spark.scheduler._ import org.apache.spark.ui.SparkUI -import org.apache.spark.util.{ThreadUtils, Utils} +import org.apache.spark.util.{Clock, SystemClock, ThreadUtils, Utils} import org.apache.spark.{Logging, SecurityManager, SparkConf} /** @@ -40,8 +40,12 @@ import org.apache.spark.{Logging, SecurityManager, SparkConf} * This provider checks for new finished applications in the background periodically and * renders the history application UI by parsing the associated event logs. */ -private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHistoryProvider - with Logging { +private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock) + extends ApplicationHistoryProvider with Logging { + + def this(conf: SparkConf) = { + this(conf, new SystemClock()) + } import FsHistoryProvider._ @@ -75,8 +79,8 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis @volatile private var applications: mutable.LinkedHashMap[String, FsApplicationHistoryInfo] = new mutable.LinkedHashMap() - // List of applications to be deleted by event log cleaner. - private var appsToClean = new mutable.ListBuffer[FsApplicationHistoryInfo] + // List of application logs to be deleted by event log cleaner. + private var attemptsToClean = new mutable.ListBuffer[FsApplicationAttemptInfo] // Constants used to parse Spark 1.0.0 log directories. private[history] val LOG_PREFIX = "EVENT_LOG_" @@ -138,31 +142,33 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis override def getListing(): Iterable[FsApplicationHistoryInfo] = applications.values - override def getAppUI(appId: String): Option[SparkUI] = { + override def getAppUI(appId: String, attemptId: Option[String]): Option[SparkUI] = { try { - applications.get(appId).map { info => - val replayBus = new ReplayListenerBus() - val ui = { - val conf = this.conf.clone() - val appSecManager = new SecurityManager(conf) - SparkUI.createHistoryUI(conf, replayBus, appSecManager, appId, - s"${HistoryServer.UI_PATH_PREFIX}/$appId") - // Do not call ui.bind() to avoid creating a new server for each application - } + applications.get(appId).flatMap { appInfo => + appInfo.attempts.find(_.attemptId == attemptId).map { attempt => + val replayBus = new ReplayListenerBus() + val ui = { + val conf = this.conf.clone() + val appSecManager = new SecurityManager(conf) + SparkUI.createHistoryUI(conf, replayBus, appSecManager, appId, + HistoryServer.getAttemptURI(appId, attempt.attemptId)) + // Do not call ui.bind() to avoid creating a new server for each application + } - val appListener = new ApplicationEventListener() - replayBus.addListener(appListener) - val appInfo = replay(fs.getFileStatus(new Path(logDir, info.logPath)), replayBus) + val appListener = new ApplicationEventListener() + replayBus.addListener(appListener) + val appInfo = replay(fs.getFileStatus(new Path(logDir, attempt.logPath)), replayBus) - ui.setAppName(s"${appInfo.name} ($appId)") + ui.setAppName(s"${appInfo.name} ($appId)") - val uiAclsEnabled = conf.getBoolean("spark.history.ui.acls.enable", false) - ui.getSecurityManager.setAcls(uiAclsEnabled) - // make sure to set admin acls before view acls so they are properly picked up - ui.getSecurityManager.setAdminAcls(appListener.adminAcls.getOrElse("")) - ui.getSecurityManager.setViewAcls(appInfo.sparkUser, - appListener.viewAcls.getOrElse("")) - ui + val uiAclsEnabled = conf.getBoolean("spark.history.ui.acls.enable", false) + ui.getSecurityManager.setAcls(uiAclsEnabled) + // make sure to set admin acls before view acls so they are properly picked up + ui.getSecurityManager.setAdminAcls(appListener.adminAcls.getOrElse("")) + ui.getSecurityManager.setViewAcls(attempt.sparkUser, + appListener.viewAcls.getOrElse("")) + ui + } } } catch { case e: FileNotFoundException => None @@ -220,7 +226,7 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis */ private def mergeApplicationListing(logs: Seq[FileStatus]): Unit = { val bus = new ReplayListenerBus() - val newApps = logs.flatMap { fileStatus => + val newAttempts = logs.flatMap { fileStatus => try { val res = replay(fileStatus, bus) logInfo(s"Application log ${res.logPath} loaded successfully.") @@ -232,76 +238,104 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis e) None } - }.toSeq.sortWith(compareAppInfo) - - // When there are new logs, merge the new list with the existing one, maintaining - // the expected ordering (descending end time). Maintaining the order is important - // to avoid having to sort the list every time there is a request for the log list. - if (newApps.nonEmpty) { - val mergedApps = new mutable.LinkedHashMap[String, FsApplicationHistoryInfo]() - def addIfAbsent(info: FsApplicationHistoryInfo): Unit = { - if (!mergedApps.contains(info.id) || - mergedApps(info.id).logPath.endsWith(EventLoggingListener.IN_PROGRESS) && - !info.logPath.endsWith(EventLoggingListener.IN_PROGRESS)) { - mergedApps += (info.id -> info) - } - } + } - val newIterator = newApps.iterator.buffered - val oldIterator = applications.values.iterator.buffered - while (newIterator.hasNext && oldIterator.hasNext) { - if (compareAppInfo(newIterator.head, oldIterator.head)) { - addIfAbsent(newIterator.next()) - } else { - addIfAbsent(oldIterator.next()) + if (newAttempts.isEmpty) { + return + } + + // Build a map containing all apps that contain new attempts. The app information in this map + // contains both the new app attempt, and those that were already loaded in the existing apps + // map. If an attempt has been updated, it replaces the old attempt in the list. + val newAppMap = new mutable.HashMap[String, FsApplicationHistoryInfo]() + newAttempts.foreach { attempt => + val appInfo = newAppMap.get(attempt.appId) + .orElse(applications.get(attempt.appId)) + .map { app => + val attempts = + app.attempts.filter(_.attemptId != attempt.attemptId).toList ++ List(attempt) + new FsApplicationHistoryInfo(attempt.appId, attempt.name, + attempts.sortWith(compareAttemptInfo)) } + .getOrElse(new FsApplicationHistoryInfo(attempt.appId, attempt.name, List(attempt))) + newAppMap(attempt.appId) = appInfo + } + + // Merge the new app list with the existing one, maintaining the expected ordering (descending + // end time). Maintaining the order is important to avoid having to sort the list every time + // there is a request for the log list. + val newApps = newAppMap.values.toSeq.sortWith(compareAppInfo) + val mergedApps = new mutable.LinkedHashMap[String, FsApplicationHistoryInfo]() + def addIfAbsent(info: FsApplicationHistoryInfo): Unit = { + if (!mergedApps.contains(info.id)) { + mergedApps += (info.id -> info) } - newIterator.foreach(addIfAbsent) - oldIterator.foreach(addIfAbsent) + } - applications = mergedApps + val newIterator = newApps.iterator.buffered + val oldIterator = applications.values.iterator.buffered + while (newIterator.hasNext && oldIterator.hasNext) { + if (newAppMap.contains(oldIterator.head.id)) { + oldIterator.next() + } else if (compareAppInfo(newIterator.head, oldIterator.head)) { + addIfAbsent(newIterator.next()) + } else { + addIfAbsent(oldIterator.next()) + } } + newIterator.foreach(addIfAbsent) + oldIterator.foreach(addIfAbsent) + + applications = mergedApps } /** * Delete event logs from the log directory according to the clean policy defined by the user. */ - private def cleanLogs(): Unit = { + private[history] def cleanLogs(): Unit = { try { val maxAge = conf.getTimeAsSeconds("spark.history.fs.cleaner.maxAge", "7d") * 1000 - val now = System.currentTimeMillis() + val now = clock.getTimeMillis() val appsToRetain = new mutable.LinkedHashMap[String, FsApplicationHistoryInfo]() + def shouldClean(attempt: FsApplicationAttemptInfo): Boolean = { + now - attempt.lastUpdated > maxAge && attempt.completed + } + // Scan all logs from the log directory. // Only completed applications older than the specified max age will be deleted. - applications.values.foreach { info => - if (now - info.lastUpdated <= maxAge || !info.completed) { - appsToRetain += (info.id -> info) - } else { - appsToClean += info + applications.values.foreach { app => + val (toClean, toRetain) = app.attempts.partition(shouldClean) + attemptsToClean ++= toClean + + if (toClean.isEmpty) { + appsToRetain += (app.id -> app) + } else if (toRetain.nonEmpty) { + appsToRetain += (app.id -> + new FsApplicationHistoryInfo(app.id, app.name, toRetain.toList)) } } applications = appsToRetain - val leftToClean = new mutable.ListBuffer[FsApplicationHistoryInfo] - appsToClean.foreach { info => + val leftToClean = new mutable.ListBuffer[FsApplicationAttemptInfo] + attemptsToClean.foreach { attempt => try { - val path = new Path(logDir, info.logPath) + val path = new Path(logDir, attempt.logPath) if (fs.exists(path)) { fs.delete(path, true) } } catch { case e: AccessControlException => - logInfo(s"No permission to delete ${info.logPath}, ignoring.") + logInfo(s"No permission to delete ${attempt.logPath}, ignoring.") case t: IOException => - logError(s"IOException in cleaning logs of ${info.logPath}", t) - leftToClean += info + logError(s"IOException in cleaning ${attempt.logPath}", t) + leftToClean += attempt } } - appsToClean = leftToClean + attemptsToClean = leftToClean } catch { case t: Exception => logError("Exception in cleaning logs", t) } @@ -315,14 +349,36 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis private def compareAppInfo( i1: FsApplicationHistoryInfo, i2: FsApplicationHistoryInfo): Boolean = { - if (i1.endTime != i2.endTime) i1.endTime >= i2.endTime else i1.startTime >= i2.startTime + val a1 = i1.attempts.head + val a2 = i2.attempts.head + if (a1.endTime != a2.endTime) a1.endTime >= a2.endTime else a1.startTime >= a2.startTime + } + + /** + * Comparison function that defines the sort order for application attempts within the same + * application. Order is: running attempts before complete attempts, running attempts sorted + * by start time, completed attempts sorted by end time. + * + * Normally applications should have a single running attempt; but failure to call sc.stop() + * may cause multiple running attempts to show up. + * + * @return Whether `a1` should precede `a2`. + */ + private def compareAttemptInfo( + a1: FsApplicationAttemptInfo, + a2: FsApplicationAttemptInfo): Boolean = { + if (a1.completed == a2.completed) { + if (a1.completed) a1.endTime >= a2.endTime else a1.startTime >= a2.startTime + } else { + !a1.completed + } } /** * Replays the events in the specified log file and returns information about the associated * application. */ - private def replay(eventLog: FileStatus, bus: ReplayListenerBus): FsApplicationHistoryInfo = { + private def replay(eventLog: FileStatus, bus: ReplayListenerBus): FsApplicationAttemptInfo = { val logPath = eventLog.getPath() logInfo(s"Replaying log path: $logPath") val logInput = @@ -336,10 +392,11 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis val appCompleted = isApplicationCompleted(eventLog) bus.addListener(appListener) bus.replay(logInput, logPath.toString, !appCompleted) - new FsApplicationHistoryInfo( + new FsApplicationAttemptInfo( logPath.getName(), - appListener.appId.getOrElse(logPath.getName()), appListener.appName.getOrElse(NOT_STARTED), + appListener.appId.getOrElse(logPath.getName()), + appListener.appAttemptId, appListener.startTime.getOrElse(-1L), appListener.endTime.getOrElse(-1L), getModificationTime(eventLog).get, @@ -425,13 +482,21 @@ private object FsHistoryProvider { val DEFAULT_LOG_DIR = "file:/tmp/spark-events" } -private class FsApplicationHistoryInfo( +private class FsApplicationAttemptInfo( val logPath: String, - id: String, - name: String, + val name: String, + val appId: String, + attemptId: Option[String], startTime: Long, endTime: Long, lastUpdated: Long, sparkUser: String, completed: Boolean = true) - extends ApplicationHistoryInfo(id, name, startTime, endTime, lastUpdated, sparkUser, completed) + extends ApplicationAttemptInfo( + attemptId, startTime, endTime, lastUpdated, sparkUser, completed) + +private class FsApplicationHistoryInfo( + id: String, + override val name: String, + override val attempts: List[FsApplicationAttemptInfo]) + extends ApplicationHistoryInfo(id, name, attempts) diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala index 3781b4e8c12bd..0830cc1ba1245 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryPage.scala @@ -34,18 +34,28 @@ private[history] class HistoryPage(parent: HistoryServer) extends WebUIPage("") val requestedIncomplete = Option(request.getParameter("showIncomplete")).getOrElse("false").toBoolean - val allApps = parent.getApplicationList().filter(_.completed != requestedIncomplete) - val actualFirst = if (requestedFirst < allApps.size) requestedFirst else 0 - val apps = allApps.slice(actualFirst, Math.min(actualFirst + pageSize, allApps.size)) + val allApps = parent.getApplicationList() + .filter(_.attempts.head.completed != requestedIncomplete) + val allAppsSize = allApps.size + + val actualFirst = if (requestedFirst < allAppsSize) requestedFirst else 0 + val appsToShow = allApps.slice(actualFirst, actualFirst + pageSize) val actualPage = (actualFirst / pageSize) + 1 - val last = Math.min(actualFirst + pageSize, allApps.size) - 1 - val pageCount = allApps.size / pageSize + (if (allApps.size % pageSize > 0) 1 else 0) + val last = Math.min(actualFirst + pageSize, allAppsSize) - 1 + val pageCount = allAppsSize / pageSize + (if (allAppsSize % pageSize > 0) 1 else 0) val secondPageFromLeft = 2 val secondPageFromRight = pageCount - 1 - val appTable = UIUtils.listingTable(appHeader, appRow, apps) + val hasMultipleAttempts = appsToShow.exists(_.attempts.size > 1) + val appTable = + if (hasMultipleAttempts) { + UIUtils.listingTable(appWithAttemptHeader, appWithAttemptRow, appsToShow) + } else { + UIUtils.listingTable(appHeader, appRow, appsToShow) + } + val providerConfig = parent.getProviderConfig() val content =
    @@ -59,7 +69,7 @@ private[history] class HistoryPage(parent: HistoryServer) extends WebUIPage("") // to the first and last page. If the current page +/- `plusOrMinus` is greater // than the 2nd page from the first page or less than the 2nd page from the last // page, `...` will be displayed. - if (allApps.size > 0) { + if (allAppsSize > 0) { val leftSideIndices = rangeIndices(actualPage - plusOrMinus until actualPage, 1 < _, requestedIncomplete) val rightSideIndices = @@ -67,7 +77,7 @@ private[history] class HistoryPage(parent: HistoryServer) extends WebUIPage("") requestedIncomplete)

    - Showing {actualFirst + 1}-{last + 1} of {allApps.size} + Showing {actualFirst + 1}-{last + 1} of {allAppsSize} {if (requestedIncomplete) "(Incomplete applications)"} { @@ -125,30 +135,85 @@ private[history] class HistoryPage(parent: HistoryServer) extends WebUIPage("") "Spark User", "Last Updated") - private def rangeIndices(range: Seq[Int], condition: Int => Boolean, showIncomplete: Boolean): - Seq[Node] = { + private val appWithAttemptHeader = Seq( + "App ID", + "App Name", + "Attempt ID", + "Started", + "Completed", + "Duration", + "Spark User", + "Last Updated") + + private def rangeIndices( + range: Seq[Int], + condition: Int => Boolean, + showIncomplete: Boolean): Seq[Node] = { range.filter(condition).map(nextPage => {nextPage} ) } - private def appRow(info: ApplicationHistoryInfo): Seq[Node] = { - val uiAddress = HistoryServer.UI_PATH_PREFIX + s"/${info.id}" - val startTime = UIUtils.formatDate(info.startTime) - val endTime = if (info.endTime > 0) UIUtils.formatDate(info.endTime) else "-" + private def attemptRow( + renderAttemptIdColumn: Boolean, + info: ApplicationHistoryInfo, + attempt: ApplicationAttemptInfo, + isFirst: Boolean): Seq[Node] = { + val uiAddress = HistoryServer.getAttemptURI(info.id, attempt.attemptId) + val startTime = UIUtils.formatDate(attempt.startTime) + val endTime = if (attempt.endTime > 0) UIUtils.formatDate(attempt.endTime) else "-" val duration = - if (info.endTime > 0) UIUtils.formatDuration(info.endTime - info.startTime) else "-" - val lastUpdated = UIUtils.formatDate(info.lastUpdated) + if (attempt.endTime > 0) { + UIUtils.formatDuration(attempt.endTime - attempt.startTime) + } else { + "-" + } + val lastUpdated = UIUtils.formatDate(attempt.lastUpdated) - {info.id} - {info.name} - {startTime} - {endTime} - {duration} - {info.sparkUser} - {lastUpdated} + { + if (isFirst) { + if (info.attempts.size > 1 || renderAttemptIdColumn) { + + {info.id} + + {info.name} + } else { + {info.id} + {info.name} + } + } else { + Nil + } + } + { + if (renderAttemptIdColumn) { + if (info.attempts.size > 1 && attempt.attemptId.isDefined) { + + {attempt.attemptId.get} + } else { +   + } + } else { + Nil + } + } + {startTime} + {endTime} + + {duration} + {attempt.sparkUser} + {lastUpdated} } + private def appRow(info: ApplicationHistoryInfo): Seq[Node] = { + attemptRow(false, info, info.attempts.head, true) + } + + private def appWithAttemptRow(info: ApplicationHistoryInfo): Seq[Node] = { + attemptRow(true, info, info.attempts.head, true) ++ + info.attempts.drop(1).flatMap(attemptRow(true, info, _, false)) + } + private def makePageLink(linkPage: Int, showIncomplete: Boolean): String = { "/?" + Array( "page=" + linkPage, diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala index 56bef57e55392..754c8e9b6668b 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala @@ -52,7 +52,11 @@ class HistoryServer( private val appLoader = new CacheLoader[String, SparkUI] { override def load(key: String): SparkUI = { - val ui = provider.getAppUI(key).getOrElse(throw new NoSuchElementException()) + val parts = key.split("/") + require(parts.length == 1 || parts.length == 2, s"Invalid app key $key") + val ui = provider + .getAppUI(parts(0), if (parts.length > 1) Some(parts(1)) else None) + .getOrElse(throw new NoSuchElementException()) attachSparkUI(ui) ui } @@ -69,6 +73,8 @@ class HistoryServer( private val loaderServlet = new HttpServlet { protected override def doGet(req: HttpServletRequest, res: HttpServletResponse): Unit = { + // Parse the URI created by getAttemptURI(). It contains an app ID and an optional + // attempt ID (separated by a slash). val parts = Option(req.getPathInfo()).getOrElse("").split("/") if (parts.length < 2) { res.sendError(HttpServletResponse.SC_BAD_REQUEST, @@ -76,18 +82,23 @@ class HistoryServer( return } - val appId = parts(1) + val appKey = + if (parts.length == 3) { + s"${parts(1)}/${parts(2)}" + } else { + parts(1) + } // Note we don't use the UI retrieved from the cache; the cache loader above will register // the app's UI, and all we need to do is redirect the user to the same URI that was // requested, and the proper data should be served at that point. try { - appCache.get(appId) + appCache.get(appKey) res.sendRedirect(res.encodeRedirectURL(req.getRequestURI())) } catch { case e: Exception => e.getCause() match { case nsee: NoSuchElementException => - val msg =
    Application {appId} not found.
    + val msg =
    Application {appKey} not found.
    res.setStatus(HttpServletResponse.SC_NOT_FOUND) UIUtils.basicSparkPage(msg, "Not Found").foreach( n => res.getWriter().write(n.toString)) @@ -213,4 +224,9 @@ object HistoryServer extends Logging { } } + private[history] def getAttemptURI(appId: String, attemptId: Option[String]): String = { + val attemptSuffix = attemptId.map { id => s"/$id" }.getOrElse("") + s"${HistoryServer.UI_PATH_PREFIX}/${appId}${attemptSuffix}" + } + } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala index 1c21c179562ac..dc6077f3d132b 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala @@ -62,7 +62,7 @@ private[master] class Master( private val hadoopConf = SparkHadoopUtil.get.newConfiguration(conf) private def createDateFormat = new SimpleDateFormat("yyyyMMddHHmmss") // For application IDs - + private val WORKER_TIMEOUT = conf.getLong("spark.worker.timeout", 60) * 1000 private val RETAINED_APPLICATIONS = conf.getInt("spark.deploy.retainedApplications", 200) private val RETAINED_DRIVERS = conf.getInt("spark.deploy.retainedDrivers", 200) @@ -86,7 +86,7 @@ private[master] class Master( private val drivers = new HashSet[DriverInfo] private val completedDrivers = new ArrayBuffer[DriverInfo] // Drivers currently spooled for scheduling - private val waitingDrivers = new ArrayBuffer[DriverInfo] + private val waitingDrivers = new ArrayBuffer[DriverInfo] private var nextDriverNumber = 0 Utils.checkHost(host, "Expected hostname") @@ -758,24 +758,24 @@ private[master] class Master( app.desc.appUiUrl = notFoundBasePath return false } - + val eventLogFilePrefix = EventLoggingListener.getLogPath( - eventLogDir, app.id, app.desc.eventLogCodec) + eventLogDir, app.id, None, app.desc.eventLogCodec) val fs = Utils.getHadoopFileSystem(eventLogDir, hadoopConf) - val inProgressExists = fs.exists(new Path(eventLogFilePrefix + + val inProgressExists = fs.exists(new Path(eventLogFilePrefix + EventLoggingListener.IN_PROGRESS)) - + if (inProgressExists) { // Event logging is enabled for this application, but the application is still in progress logWarning(s"Application $appName is still in progress, it may be terminated abnormally.") } - + val (eventLogFile, status) = if (inProgressExists) { (eventLogFilePrefix + EventLoggingListener.IN_PROGRESS, " (in progress)") } else { (eventLogFilePrefix, " (completed)") } - + val logInput = EventLoggingListener.openEventLog(new Path(eventLogFile), fs) val replayBus = new ReplayListenerBus() val ui = SparkUI.createHistoryUI(new SparkConf, replayBus, new SecurityManager(conf), @@ -859,8 +859,8 @@ private[master] class Master( } private def removeDriver( - driverId: String, - finalState: DriverState, + driverId: String, + finalState: DriverState, exception: Option[Exception]) { drivers.find(d => d.id == driverId) match { case Some(driver) => diff --git a/core/src/main/scala/org/apache/spark/scheduler/ApplicationEventListener.scala b/core/src/main/scala/org/apache/spark/scheduler/ApplicationEventListener.scala index 6d39a5e3fa64c..9f218c64cac2d 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ApplicationEventListener.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ApplicationEventListener.scala @@ -26,6 +26,7 @@ package org.apache.spark.scheduler private[spark] class ApplicationEventListener extends SparkListener { var appName: Option[String] = None var appId: Option[String] = None + var appAttemptId: Option[String] = None var sparkUser: Option[String] = None var startTime: Option[Long] = None var endTime: Option[Long] = None @@ -35,6 +36,7 @@ private[spark] class ApplicationEventListener extends SparkListener { override def onApplicationStart(applicationStart: SparkListenerApplicationStart) { appName = Some(applicationStart.appName) appId = applicationStart.appId + appAttemptId = applicationStart.appAttemptId startTime = Some(applicationStart.time) sparkUser = Some(applicationStart.sparkUser) } diff --git a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala index 08e7727db2fde..529a5b2bf1a0d 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/EventLoggingListener.scala @@ -47,6 +47,7 @@ import org.apache.spark.util.{JsonProtocol, Utils} */ private[spark] class EventLoggingListener( appId: String, + appAttemptId : Option[String], logBaseDir: URI, sparkConf: SparkConf, hadoopConf: Configuration) @@ -54,8 +55,9 @@ private[spark] class EventLoggingListener( import EventLoggingListener._ - def this(appId: String, logBaseDir: URI, sparkConf: SparkConf) = - this(appId, logBaseDir, sparkConf, SparkHadoopUtil.get.newConfiguration(sparkConf)) + def this(appId: String, appAttemptId : Option[String], logBaseDir: URI, sparkConf: SparkConf) = + this(appId, appAttemptId, logBaseDir, sparkConf, + SparkHadoopUtil.get.newConfiguration(sparkConf)) private val shouldCompress = sparkConf.getBoolean("spark.eventLog.compress", false) private val shouldOverwrite = sparkConf.getBoolean("spark.eventLog.overwrite", false) @@ -89,7 +91,7 @@ private[spark] class EventLoggingListener( private[scheduler] val loggedEvents = new ArrayBuffer[JValue] // Visible for tests only. - private[scheduler] val logPath = getLogPath(logBaseDir, appId, compressionCodecName) + private[scheduler] val logPath = getLogPath(logBaseDir, appId, appAttemptId, compressionCodecName) /** * Creates the log file in the configured log directory. @@ -252,8 +254,12 @@ private[spark] object EventLoggingListener extends Logging { * we won't know which codec to use to decompress the metadata needed to open the file in * the first place. * + * The log file name will identify the compression codec used for the contents, if any. + * For example, app_123 for an uncompressed log, app_123.lzf for an LZF-compressed log. + * * @param logBaseDir Directory where the log file will be written. * @param appId A unique app ID. + * @param appAttemptId A unique attempt id of appId. May be the empty string. * @param compressionCodecName Name to identify the codec used to compress the contents * of the log, or None if compression is not enabled. * @return A path which consists of file-system-safe characters. @@ -261,11 +267,19 @@ private[spark] object EventLoggingListener extends Logging { def getLogPath( logBaseDir: URI, appId: String, + appAttemptId: Option[String], compressionCodecName: Option[String] = None): String = { - val sanitizedAppId = appId.replaceAll("[ :/]", "-").replaceAll("[.${}'\"]", "_").toLowerCase - // e.g. app_123, app_123.lzf - val logName = sanitizedAppId + compressionCodecName.map { "." + _ }.getOrElse("") - logBaseDir.toString.stripSuffix("/") + "/" + logName + val base = logBaseDir.toString.stripSuffix("/") + "/" + sanitize(appId) + val codec = compressionCodecName.map("." + _).getOrElse("") + if (appAttemptId.isDefined) { + base + "_" + sanitize(appAttemptId.get) + codec + } else { + base + codec + } + } + + private def sanitize(str: String): String = { + str.replaceAll("[ :/]", "-").replaceAll("[.${}'\"]", "_").toLowerCase } /** diff --git a/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala index 992c477493d8e..646820520ea1b 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala @@ -41,4 +41,12 @@ private[spark] trait SchedulerBackend { */ def applicationId(): String = appId + /** + * Get the attempt ID for this run, if the cluster manager supports multiple + * attempts. Applications run in client mode will not have attempt IDs. + * + * @return The application attempt id, if available. + */ + def applicationAttemptId(): Option[String] = None + } diff --git a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala index b711ff209af94..169d4fd3a94f0 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/SparkListener.scala @@ -110,8 +110,8 @@ case class SparkListenerExecutorMetricsUpdate( extends SparkListenerEvent @DeveloperApi -case class SparkListenerApplicationStart(appName: String, appId: Option[String], time: Long, - sparkUser: String) extends SparkListenerEvent +case class SparkListenerApplicationStart(appName: String, appId: Option[String], + time: Long, sparkUser: String, appAttemptId: Option[String]) extends SparkListenerEvent @DeveloperApi case class SparkListenerApplicationEnd(time: Long) extends SparkListenerEvent diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala index ed3418676e077..f25f3ed0d9037 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskScheduler.scala @@ -73,9 +73,17 @@ private[spark] trait TaskScheduler { * @return An application ID */ def applicationId(): String = appId - + /** * Process a lost executor */ def executorLost(executorId: String, reason: ExecutorLossReason): Unit + + /** + * Get an application's attempt ID associated with the job. + * + * @return An application's Attempt ID + */ + def applicationAttemptId(): Option[String] + } diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala index 13a52d836f32f..b4b8a630694bb 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala @@ -513,6 +513,8 @@ private[spark] class TaskSchedulerImpl( override def applicationId(): String = backend.applicationId() + override def applicationAttemptId(): Option[String] = backend.applicationAttemptId() + } diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala index 44d274956d693..ee02fbd9ce069 100644 --- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala +++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala @@ -194,7 +194,8 @@ private[spark] object JsonProtocol { ("App Name" -> applicationStart.appName) ~ ("App ID" -> applicationStart.appId.map(JString(_)).getOrElse(JNothing)) ~ ("Timestamp" -> applicationStart.time) ~ - ("User" -> applicationStart.sparkUser) + ("User" -> applicationStart.sparkUser) ~ + ("App Attempt ID" -> applicationStart.appAttemptId.map(JString(_)).getOrElse(JNothing)) } def applicationEndToJson(applicationEnd: SparkListenerApplicationEnd): JValue = { @@ -562,7 +563,8 @@ private[spark] object JsonProtocol { val appId = Utils.jsonOption(json \ "App ID").map(_.extract[String]) val time = (json \ "Timestamp").extract[Long] val sparkUser = (json \ "User").extract[String] - SparkListenerApplicationStart(appName, appId, time, sparkUser) + val appAttemptId = Utils.jsonOption(json \ "App Attempt ID").map(_.extract[String]) + SparkListenerApplicationStart(appName, appId, time, sparkUser, appAttemptId) } def applicationEndFromJson(json: JValue): SparkListenerApplicationEnd = { diff --git a/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala b/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala index 9e367a0d9af0d..a0a0afa48833e 100644 --- a/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/history/FsHistoryProviderSuite.scala @@ -19,6 +19,7 @@ package org.apache.spark.deploy.history import java.io.{BufferedOutputStream, File, FileOutputStream, OutputStreamWriter} import java.net.URI +import java.util.concurrent.TimeUnit import scala.io.Source @@ -30,7 +31,7 @@ import org.scalatest.Matchers import org.apache.spark.{Logging, SparkConf} import org.apache.spark.io._ import org.apache.spark.scheduler._ -import org.apache.spark.util.{JsonProtocol, Utils} +import org.apache.spark.util.{JsonProtocol, ManualClock, Utils} class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers with Logging { @@ -47,10 +48,11 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers /** Create a fake log file using the new log format used in Spark 1.3+ */ private def newLogFile( appId: String, + appAttemptId: Option[String], inProgress: Boolean, codec: Option[String] = None): File = { val ip = if (inProgress) EventLoggingListener.IN_PROGRESS else "" - val logUri = EventLoggingListener.getLogPath(testDir.toURI, appId) + val logUri = EventLoggingListener.getLogPath(testDir.toURI, appId, appAttemptId) val logPath = new URI(logUri).getPath + ip new File(logPath) } @@ -59,22 +61,23 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers val provider = new FsHistoryProvider(createTestConf()) // Write a new-style application log. - val newAppComplete = newLogFile("new1", inProgress = false) + val newAppComplete = newLogFile("new1", None, inProgress = false) writeFile(newAppComplete, true, None, - SparkListenerApplicationStart("new-app-complete", None, 1L, "test"), + SparkListenerApplicationStart("new-app-complete", None, 1L, "test", None), SparkListenerApplicationEnd(5L) ) // Write a new-style application log. - val newAppCompressedComplete = newLogFile("new1compressed", inProgress = false, Some("lzf")) + val newAppCompressedComplete = newLogFile("new1compressed", None, inProgress = false, + Some("lzf")) writeFile(newAppCompressedComplete, true, None, - SparkListenerApplicationStart("new-app-compressed-complete", None, 1L, "test"), + SparkListenerApplicationStart("new-app-compressed-complete", None, 1L, "test", None), SparkListenerApplicationEnd(4L)) // Write an unfinished app, new-style. - val newAppIncomplete = newLogFile("new2", inProgress = true) + val newAppIncomplete = newLogFile("new2", None, inProgress = true) writeFile(newAppIncomplete, true, None, - SparkListenerApplicationStart("new-app-incomplete", None, 1L, "test") + SparkListenerApplicationStart("new-app-incomplete", None, 1L, "test", None) ) // Write an old-style application log. @@ -82,7 +85,7 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers oldAppComplete.mkdir() createEmptyFile(new File(oldAppComplete, provider.SPARK_VERSION_PREFIX + "1.0")) writeFile(new File(oldAppComplete, provider.LOG_PREFIX + "1"), false, None, - SparkListenerApplicationStart("old-app-complete", None, 2L, "test"), + SparkListenerApplicationStart("old-app-complete", None, 2L, "test", None), SparkListenerApplicationEnd(3L) ) createEmptyFile(new File(oldAppComplete, provider.APPLICATION_COMPLETE)) @@ -96,33 +99,45 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers oldAppIncomplete.mkdir() createEmptyFile(new File(oldAppIncomplete, provider.SPARK_VERSION_PREFIX + "1.0")) writeFile(new File(oldAppIncomplete, provider.LOG_PREFIX + "1"), false, None, - SparkListenerApplicationStart("old-app-incomplete", None, 2L, "test") + SparkListenerApplicationStart("old-app-incomplete", None, 2L, "test", None) ) // Force a reload of data from the log directory, and check that both logs are loaded. // Take the opportunity to check that the offset checks work as expected. - provider.checkForLogs() + updateAndCheck(provider) { list => + list.size should be (5) + list.count(_.attempts.head.completed) should be (3) + + def makeAppInfo( + id: String, + name: String, + start: Long, + end: Long, + lastMod: Long, + user: String, + completed: Boolean): ApplicationHistoryInfo = { + ApplicationHistoryInfo(id, name, + List(ApplicationAttemptInfo(None, start, end, lastMod, user, completed))) + } - val list = provider.getListing().toSeq - list should not be (null) - list.size should be (5) - list.count(_.completed) should be (3) - - list(0) should be (ApplicationHistoryInfo(newAppComplete.getName(), "new-app-complete", 1L, 5L, - newAppComplete.lastModified(), "test", true)) - list(1) should be (ApplicationHistoryInfo(newAppCompressedComplete.getName(), - "new-app-compressed-complete", 1L, 4L, newAppCompressedComplete.lastModified(), "test", true)) - list(2) should be (ApplicationHistoryInfo(oldAppComplete.getName(), "old-app-complete", 2L, 3L, - oldAppComplete.lastModified(), "test", true)) - list(3) should be (ApplicationHistoryInfo(oldAppIncomplete.getName(), "old-app-incomplete", 2L, - -1L, oldAppIncomplete.lastModified(), "test", false)) - list(4) should be (ApplicationHistoryInfo(newAppIncomplete.getName(), "new-app-incomplete", 1L, - -1L, newAppIncomplete.lastModified(), "test", false)) - - // Make sure the UI can be rendered. - list.foreach { case info => - val appUi = provider.getAppUI(info.id) - appUi should not be null + list(0) should be (makeAppInfo(newAppComplete.getName(), "new-app-complete", 1L, 5L, + newAppComplete.lastModified(), "test", true)) + list(1) should be (makeAppInfo(newAppCompressedComplete.getName(), + "new-app-compressed-complete", 1L, 4L, newAppCompressedComplete.lastModified(), "test", + true)) + list(2) should be (makeAppInfo(oldAppComplete.getName(), "old-app-complete", 2L, 3L, + oldAppComplete.lastModified(), "test", true)) + list(3) should be (makeAppInfo(oldAppIncomplete.getName(), "old-app-incomplete", 2L, -1L, + oldAppIncomplete.lastModified(), "test", false)) + list(4) should be (makeAppInfo(newAppIncomplete.getName(), "new-app-incomplete", 1L, -1L, + newAppIncomplete.lastModified(), "test", false)) + + // Make sure the UI can be rendered. + list.foreach { case info => + val appUi = provider.getAppUI(info.id, None) + appUi should not be null + appUi should not be None + } } } @@ -138,7 +153,7 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers logDir.mkdir() createEmptyFile(new File(logDir, provider.SPARK_VERSION_PREFIX + "1.0")) writeFile(new File(logDir, provider.LOG_PREFIX + "1"), false, Option(codec), - SparkListenerApplicationStart("app2", None, 2L, "test"), + SparkListenerApplicationStart("app2", None, 2L, "test", None), SparkListenerApplicationEnd(3L) ) createEmptyFile(new File(logDir, provider.COMPRESSION_CODEC_PREFIX + codecName)) @@ -159,52 +174,52 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers } test("SPARK-3697: ignore directories that cannot be read.") { - val logFile1 = newLogFile("new1", inProgress = false) + val logFile1 = newLogFile("new1", None, inProgress = false) writeFile(logFile1, true, None, - SparkListenerApplicationStart("app1-1", None, 1L, "test"), + SparkListenerApplicationStart("app1-1", None, 1L, "test", None), SparkListenerApplicationEnd(2L) ) - val logFile2 = newLogFile("new2", inProgress = false) + val logFile2 = newLogFile("new2", None, inProgress = false) writeFile(logFile2, true, None, - SparkListenerApplicationStart("app1-2", None, 1L, "test"), + SparkListenerApplicationStart("app1-2", None, 1L, "test", None), SparkListenerApplicationEnd(2L) ) logFile2.setReadable(false, false) val provider = new FsHistoryProvider(createTestConf()) - provider.checkForLogs() - - val list = provider.getListing().toSeq - list should not be (null) - list.size should be (1) + updateAndCheck(provider) { list => + list.size should be (1) + } } test("history file is renamed from inprogress to completed") { val provider = new FsHistoryProvider(createTestConf()) - val logFile1 = newLogFile("app1", inProgress = true) + val logFile1 = newLogFile("app1", None, inProgress = true) writeFile(logFile1, true, None, - SparkListenerApplicationStart("app1", Some("app1"), 1L, "test"), + SparkListenerApplicationStart("app1", Some("app1"), 1L, "test", None), SparkListenerApplicationEnd(2L) ) - provider.checkForLogs() - val appListBeforeRename = provider.getListing() - appListBeforeRename.size should be (1) - appListBeforeRename.head.logPath should endWith(EventLoggingListener.IN_PROGRESS) + updateAndCheck(provider) { list => + list.size should be (1) + list.head.attempts.head.asInstanceOf[FsApplicationAttemptInfo].logPath should + endWith(EventLoggingListener.IN_PROGRESS) + } - logFile1.renameTo(newLogFile("app1", inProgress = false)) - provider.checkForLogs() - val appListAfterRename = provider.getListing() - appListAfterRename.size should be (1) - appListAfterRename.head.logPath should not endWith(EventLoggingListener.IN_PROGRESS) + logFile1.renameTo(newLogFile("app1", None, inProgress = false)) + updateAndCheck(provider) { list => + list.size should be (1) + list.head.attempts.head.asInstanceOf[FsApplicationAttemptInfo].logPath should not + endWith(EventLoggingListener.IN_PROGRESS) + } } test("SPARK-5582: empty log directory") { val provider = new FsHistoryProvider(createTestConf()) - val logFile1 = newLogFile("app1", inProgress = true) + val logFile1 = newLogFile("app1", None, inProgress = true) writeFile(logFile1, true, None, - SparkListenerApplicationStart("app1", Some("app1"), 1L, "test"), + SparkListenerApplicationStart("app1", Some("app1"), 1L, "test", None), SparkListenerApplicationEnd(2L)) val oldLog = new File(testDir, "old1") @@ -215,6 +230,126 @@ class FsHistoryProviderSuite extends FunSuite with BeforeAndAfter with Matchers appListAfterRename.size should be (1) } + test("apps with multiple attempts") { + val provider = new FsHistoryProvider(createTestConf()) + + val attempt1 = newLogFile("app1", Some("attempt1"), inProgress = false) + writeFile(attempt1, true, None, + SparkListenerApplicationStart("app1", Some("app1"), 1L, "test", Some("attempt1")), + SparkListenerApplicationEnd(2L) + ) + + updateAndCheck(provider) { list => + list.size should be (1) + list.head.attempts.size should be (1) + } + + val attempt2 = newLogFile("app1", Some("attempt2"), inProgress = true) + writeFile(attempt2, true, None, + SparkListenerApplicationStart("app1", Some("app1"), 3L, "test", Some("attempt2")) + ) + + updateAndCheck(provider) { list => + list.size should be (1) + list.head.attempts.size should be (2) + list.head.attempts.head.attemptId should be (Some("attempt2")) + } + + val completedAttempt2 = newLogFile("app1", Some("attempt2"), inProgress = false) + attempt2.delete() + writeFile(attempt2, true, None, + SparkListenerApplicationStart("app1", Some("app1"), 3L, "test", Some("attempt2")), + SparkListenerApplicationEnd(4L) + ) + + updateAndCheck(provider) { list => + list should not be (null) + list.size should be (1) + list.head.attempts.size should be (2) + list.head.attempts.head.attemptId should be (Some("attempt2")) + } + + val app2Attempt1 = newLogFile("app2", Some("attempt1"), inProgress = false) + writeFile(attempt2, true, None, + SparkListenerApplicationStart("app2", Some("app2"), 5L, "test", Some("attempt1")), + SparkListenerApplicationEnd(6L) + ) + + updateAndCheck(provider) { list => + list.size should be (2) + list.head.attempts.size should be (1) + list.last.attempts.size should be (2) + list.head.attempts.head.attemptId should be (Some("attempt1")) + + list.foreach { case app => + app.attempts.foreach { attempt => + val appUi = provider.getAppUI(app.id, attempt.attemptId) + appUi should not be null + } + } + + } + } + + test("log cleaner") { + val maxAge = TimeUnit.SECONDS.toMillis(10) + val clock = new ManualClock(maxAge / 2) + val provider = new FsHistoryProvider( + createTestConf().set("spark.history.fs.cleaner.maxAge", s"${maxAge}ms"), clock) + + val log1 = newLogFile("app1", Some("attempt1"), inProgress = false) + writeFile(log1, true, None, + SparkListenerApplicationStart("app1", Some("app1"), 1L, "test", Some("attempt1")), + SparkListenerApplicationEnd(2L) + ) + log1.setLastModified(0L) + + val log2 = newLogFile("app1", Some("attempt2"), inProgress = false) + writeFile(log2, true, None, + SparkListenerApplicationStart("app1", Some("app1"), 3L, "test", Some("attempt2")), + SparkListenerApplicationEnd(4L) + ) + log2.setLastModified(clock.getTimeMillis()) + + updateAndCheck(provider) { list => + list.size should be (1) + list.head.attempts.size should be (2) + } + + // Move the clock forward so log1 exceeds the max age. + clock.advance(maxAge) + + updateAndCheck(provider) { list => + list.size should be (1) + list.head.attempts.size should be (1) + list.head.attempts.head.attemptId should be (Some("attempt2")) + } + assert(!log1.exists()) + + // Do the same for the other log. + clock.advance(maxAge) + + updateAndCheck(provider) { list => + list.size should be (0) + } + assert(!log2.exists()) + } + + /** + * Asks the provider to check for logs and calls a function to perform checks on the updated + * app list. Example: + * + * updateAndCheck(provider) { list => + * // asserts + * } + */ + private def updateAndCheck(provider: FsHistoryProvider) + (checkFn: Seq[ApplicationHistoryInfo] => Unit): Unit = { + provider.checkForLogs() + provider.cleanLogs() + checkFn(provider.getListing().toSeq) + } + private def writeFile(file: File, isNewFormat: Boolean, codec: Option[CompressionCodec], events: SparkListenerEvent*) = { val fstream = new FileOutputStream(file) diff --git a/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala b/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala index 20de46fdab909..71ba9c18257b8 100644 --- a/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/history/HistoryServerSuite.scala @@ -36,7 +36,8 @@ class HistoryServerSuite extends FunSuite with Matchers with MockitoSugar { val request = mock[HttpServletRequest] val ui = mock[SparkUI] val link = "/history/app1" - val info = new ApplicationHistoryInfo("app1", "app1", 0, 2, 1, "xxx", true) + val info = new ApplicationHistoryInfo("app1", "app1", + List(ApplicationAttemptInfo(None, 0, 2, 1, "xxx", true))) when(historyServer.getApplicationList()).thenReturn(Seq(info)) when(ui.basePath).thenReturn(link) when(historyServer.getProviderConfig()).thenReturn(Map[String, String]()) diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala index 3c52a8c4460c6..2482603f420ee 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala @@ -95,6 +95,7 @@ class DAGSchedulerSuite override def setDAGScheduler(dagScheduler: DAGScheduler) = {} override def defaultParallelism() = 2 override def executorLost(executorId: String, reason: ExecutorLossReason): Unit = {} + override def applicationAttemptId(): Option[String] = None } /** Length of time to wait while draining listener events. */ @@ -404,6 +405,7 @@ class DAGSchedulerSuite taskMetrics: Array[(Long, TaskMetrics)], blockManagerId: BlockManagerId): Boolean = true override def executorLost(executorId: String, reason: ExecutorLossReason): Unit = {} + override def applicationAttemptId(): Option[String] = None } val noKillScheduler = new DAGScheduler( sc, diff --git a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala index 6d25edb7d20dc..b52a8d11d147d 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala @@ -61,7 +61,7 @@ class EventLoggingListenerSuite extends FunSuite with LocalSparkContext with Bef test("Verify log file exist") { // Verify logging directory exists val conf = getLoggingConf(testDirPath) - val eventLogger = new EventLoggingListener("test", testDirPath.toUri(), conf) + val eventLogger = new EventLoggingListener("test", None, testDirPath.toUri(), conf) eventLogger.start() val logPath = new Path(eventLogger.logPath + EventLoggingListener.IN_PROGRESS) @@ -95,7 +95,7 @@ class EventLoggingListenerSuite extends FunSuite with LocalSparkContext with Bef } test("Log overwriting") { - val logUri = EventLoggingListener.getLogPath(testDir.toURI, "test") + val logUri = EventLoggingListener.getLogPath(testDir.toURI, "test", None) val logPath = new URI(logUri).getPath // Create file before writing the event log new FileOutputStream(new File(logPath)).close() @@ -108,18 +108,18 @@ class EventLoggingListenerSuite extends FunSuite with LocalSparkContext with Bef test("Event log name") { // without compression assert(s"file:/base-dir/app1" === EventLoggingListener.getLogPath( - Utils.resolveURI("/base-dir"), "app1")) + Utils.resolveURI("/base-dir"), "app1", None)) // with compression assert(s"file:/base-dir/app1.lzf" === - EventLoggingListener.getLogPath(Utils.resolveURI("/base-dir"), "app1", Some("lzf"))) + EventLoggingListener.getLogPath(Utils.resolveURI("/base-dir"), "app1", None, Some("lzf"))) // illegal characters in app ID assert(s"file:/base-dir/a-fine-mind_dollar_bills__1" === EventLoggingListener.getLogPath(Utils.resolveURI("/base-dir"), - "a fine:mind$dollar{bills}.1")) + "a fine:mind$dollar{bills}.1", None)) // illegal characters in app ID with compression assert(s"file:/base-dir/a-fine-mind_dollar_bills__1.lz4" === EventLoggingListener.getLogPath(Utils.resolveURI("/base-dir"), - "a fine:mind$dollar{bills}.1", Some("lz4"))) + "a fine:mind$dollar{bills}.1", None, Some("lz4"))) } /* ----------------- * @@ -140,10 +140,10 @@ class EventLoggingListenerSuite extends FunSuite with LocalSparkContext with Bef val conf = getLoggingConf(testDirPath, compressionCodec) extraConf.foreach { case (k, v) => conf.set(k, v) } val logName = compressionCodec.map("test-" + _).getOrElse("test") - val eventLogger = new EventLoggingListener(logName, testDirPath.toUri(), conf) + val eventLogger = new EventLoggingListener(logName, None, testDirPath.toUri(), conf) val listenerBus = new LiveListenerBus val applicationStart = SparkListenerApplicationStart("Greatest App (N)ever", None, - 125L, "Mickey") + 125L, "Mickey", None) val applicationEnd = SparkListenerApplicationEnd(1000L) // A comprehensive test on JSON de/serialization of all events is in JsonProtocolSuite @@ -186,7 +186,7 @@ class EventLoggingListenerSuite extends FunSuite with LocalSparkContext with Bef val eventLogPath = eventLogger.logPath val expectedLogDir = testDir.toURI() assert(eventLogPath === EventLoggingListener.getLogPath( - expectedLogDir, sc.applicationId, compressionCodec.map(CompressionCodec.getShortName))) + expectedLogDir, sc.applicationId, None, compressionCodec.map(CompressionCodec.getShortName))) // Begin listening for events that trigger asserts val eventExistenceListener = new EventExistenceListener(eventLogger) diff --git a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala index 6de6d2fec622a..dabe4574b6456 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/ReplayListenerSuite.scala @@ -50,7 +50,7 @@ class ReplayListenerSuite extends FunSuite with BeforeAndAfter { val fstream = fileSystem.create(logFilePath) val writer = new PrintWriter(fstream) val applicationStart = SparkListenerApplicationStart("Greatest App (N)ever", None, - 125L, "Mickey") + 125L, "Mickey", None) val applicationEnd = SparkListenerApplicationEnd(1000L) writer.println(compact(render(JsonProtocol.sparkEventToJson(applicationStart)))) writer.println(compact(render(JsonProtocol.sparkEventToJson(applicationEnd)))) @@ -146,7 +146,7 @@ class ReplayListenerSuite extends FunSuite with BeforeAndAfter { * log the events. */ private class EventMonster(conf: SparkConf) - extends EventLoggingListener("test", new URI("testdir"), conf) { + extends EventLoggingListener("test", None, new URI("testdir"), conf) { override def start() { } diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala index 2d039cb75a6e0..0c9cf5bc685c1 100644 --- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala @@ -74,7 +74,8 @@ class JsonProtocolSuite extends FunSuite { val blockManagerRemoved = SparkListenerBlockManagerRemoved(2L, BlockManagerId("Scarce", "to be counted...", 100)) val unpersistRdd = SparkListenerUnpersistRDD(12345) - val applicationStart = SparkListenerApplicationStart("The winner of all", None, 42L, "Garfield") + val applicationStart = SparkListenerApplicationStart("The winner of all", Some("appId"), + 42L, "Garfield", Some("appAttempt")) val applicationEnd = SparkListenerApplicationEnd(42L) val logUrlMap = Map("stderr" -> "mystderr", "stdout" -> "mystdout").toMap val executorAdded = SparkListenerExecutorAdded(executorAddedTime, "exec1", @@ -274,9 +275,11 @@ class JsonProtocolSuite extends FunSuite { test("SparkListenerApplicationStart backwards compatibility") { // SparkListenerApplicationStart in Spark 1.0.0 do not have an "appId" property. - val applicationStart = SparkListenerApplicationStart("test", None, 1L, "user") + // SparkListenerApplicationStart pre-Spark 1.4 does not have "appAttemptId". + val applicationStart = SparkListenerApplicationStart("test", None, 1L, "user", None) val oldEvent = JsonProtocol.applicationStartToJson(applicationStart) .removeField({ _._1 == "App ID" }) + .removeField({ _._1 == "App Attempt ID" }) assert(applicationStart === JsonProtocol.applicationStartFromJson(oldEvent)) } @@ -1497,8 +1500,10 @@ class JsonProtocolSuite extends FunSuite { |{ | "Event": "SparkListenerApplicationStart", | "App Name": "The winner of all", + | "App ID": "appId", | "Timestamp": 42, - | "User": "Garfield" + | "User": "Garfield", + | "App Attempt ID": "appAttempt" |} """ diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala index 70cb57ffd8c69..27f804782f355 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -89,6 +89,10 @@ private[spark] class ApplicationMaster( // Propagate the application ID so that YarnClusterSchedulerBackend can pick it up. System.setProperty("spark.yarn.app.id", appAttemptId.getApplicationId().toString()) + + // Propagate the attempt if, so that in case of event logging, + // different attempt's logs gets created in different directory + System.setProperty("spark.yarn.app.attemptId", appAttemptId.getAttemptId().toString()) } logInfo("ApplicationAttemptId: " + appAttemptId) @@ -208,10 +212,11 @@ private[spark] class ApplicationMaster( val sc = sparkContextRef.get() val appId = client.getAttemptId().getApplicationId().toString() + val attemptId = client.getAttemptId().getAttemptId().toString() val historyAddress = sparkConf.getOption("spark.yarn.historyServer.address") .map { text => SparkHadoopUtil.get.substituteHadoopVariables(text, yarnConf) } - .map { address => s"${address}${HistoryServer.UI_PATH_PREFIX}/${appId}" } + .map { address => s"${address}${HistoryServer.UI_PATH_PREFIX}/${appId}/${attemptId}" } .getOrElse("") allocator = client.register(yarnConf, diff --git a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala index b1de81e6a8b0f..aeb218a575455 100644 --- a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala +++ b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClusterSchedulerBackend.scala @@ -39,12 +39,18 @@ private[spark] class YarnClusterSchedulerBackend( } override def applicationId(): String = - // In YARN Cluster mode, spark.yarn.app.id is expect to be set - // before user application is launched. - // So, if spark.yarn.app.id is not set, it is something wrong. + // In YARN Cluster mode, the application ID is expected to be set, so log an error if it's + // not found. sc.getConf.getOption("spark.yarn.app.id").getOrElse { logError("Application ID is not set.") super.applicationId } + override def applicationAttemptId(): Option[String] = + // In YARN Cluster mode, the attempt ID is expected to be set, so log an error if it's + // not found. + sc.getConf.getOption("spark.yarn.app.attemptId").orElse { + logError("Application attempt ID is not set.") + super.applicationAttemptId + } } From 3b514af8a0c2ca496315b99a2b09bc887ac6c5e1 Mon Sep 17 00:00:00 2001 From: Debasish Das Date: Fri, 1 May 2015 08:27:46 -0700 Subject: [PATCH 517/591] [SPARK-3066] [MLLIB] Support recommendAll in matrix factorization model This is based on #3098 from debasish83. 1. BLAS' GEMM is used to compute inner products. 2. Reverted changes to MovieLensALS. SPARK-4231 should be addressed in a separate PR. 3. ~~Fixed a bug in topByKey~~ Closes #3098 debasish83 coderxiang Author: Debasish Das Author: Xiangrui Meng Closes #5829 from mengxr/SPARK-3066 and squashes the following commits: 22e6a87 [Xiangrui Meng] topByKey was correct. update its usage 389b381 [Xiangrui Meng] fix indentation 49953de [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-3066 cb9799a [Xiangrui Meng] revert MovieLensALS f864f5e [Xiangrui Meng] update test and fix a bug in topByKey c5e0181 [Xiangrui Meng] use GEMM and topByKey 3a0c4eb [Debasish Das] updated with spark master 98fa424 [Debasish Das] updated with master ee99571 [Debasish Das] addressed initial review comments;merged with master;added tests for batch predict APIs in matrix factorization 3f97c49 [Debasish Das] fixed spark coding style for imports 7163a5c [Debasish Das] Added API for batch user and product recommendation; MAP calculation for product recommendation per user using randomized split d144f57 [Debasish Das] recommendAll API to MatrixFactorizationModel, uses topK finding using BoundedPriorityQueue similar to RDD.top f38a1b5 [Debasish Das] use sampleByKey for per user sampling 10cbb37 [Debasish Das] provide ratio for topN product validation; generate MAP and prec@k metric for movielens dataset 9fa063e [Debasish Das] import scala.math.round 4bbae0f [Debasish Das] comments fixed as per scalastyle cd3ab31 [Debasish Das] merged with AbstractParams serialization bug 9b3951f [Debasish Das] validate user/product on MovieLens dataset through user input and compute map measure along with rmse --- .../spark/mllib/rdd/MLPairRDDFunctions.scala | 4 +- .../MatrixFactorizationModel.scala | 132 +++++++++++++++--- .../mllib/rdd/MLPairRDDFunctionsSuite.scala | 4 +- .../MatrixFactorizationModelSuite.scala | 20 +++ 4 files changed, 138 insertions(+), 22 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala b/mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala index 9213fd3f595c3..5af55aaf84802 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctions.scala @@ -42,13 +42,11 @@ class MLPairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) extends Se self.aggregateByKey(new BoundedPriorityQueue[V](num)(ord))( seqOp = (queue, item) => { queue += item - queue }, combOp = (queue1, queue2) => { queue1 ++= queue2 - queue1 } - ).mapValues(_.toArray.sorted(ord.reverse)) + ).mapValues(_.toArray.reverse) // This is an min-heap, so we reverse the order. } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala index 36cbf060d9998..88c2148403313 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala @@ -20,14 +20,18 @@ package org.apache.spark.mllib.recommendation import java.io.IOException import java.lang.{Integer => JavaInteger} +import scala.collection.mutable + +import com.github.fommil.netlib.BLAS.{getInstance => blas} import org.apache.hadoop.fs.Path import org.json4s._ import org.json4s.JsonDSL._ import org.json4s.jackson.JsonMethods._ -import com.github.fommil.netlib.BLAS.{getInstance => blas} import org.apache.spark.{Logging, SparkContext} import org.apache.spark.api.java.{JavaPairRDD, JavaRDD} +import org.apache.spark.mllib.linalg._ +import org.apache.spark.mllib.rdd.MLPairRDDFunctions._ import org.apache.spark.mllib.util.{Loader, Saveable} import org.apache.spark.rdd.RDD import org.apache.spark.sql.{Row, SQLContext} @@ -57,7 +61,7 @@ class MatrixFactorizationModel( /** Validates factors and warns users if there are performance concerns. */ private def validateFeatures(name: String, features: RDD[(Int, Array[Double])]): Unit = { - require(features.first()._2.size == rank, + require(features.first()._2.length == rank, s"$name feature dimension does not match the rank $rank.") if (features.partitioner.isEmpty) { logWarning(s"$name factor does not have a partitioner. " @@ -72,19 +76,19 @@ class MatrixFactorizationModel( def predict(user: Int, product: Int): Double = { val userVector = userFeatures.lookup(user).head val productVector = productFeatures.lookup(product).head - blas.ddot(userVector.length, userVector, 1, productVector, 1) + blas.ddot(rank, userVector, 1, productVector, 1) } /** - * Predict the rating of many users for many products. - * The output RDD has an element per each element in the input RDD (including all duplicates) - * unless a user or product is missing in the training set. - * - * @param usersProducts RDD of (user, product) pairs. - * @return RDD of Ratings. - */ + * Predict the rating of many users for many products. + * The output RDD has an element per each element in the input RDD (including all duplicates) + * unless a user or product is missing in the training set. + * + * @param usersProducts RDD of (user, product) pairs. + * @return RDD of Ratings. + */ def predict(usersProducts: RDD[(Int, Int)]): RDD[Rating] = { - val users = userFeatures.join(usersProducts).map{ + val users = userFeatures.join(usersProducts).map { case (user, (uFeatures, product)) => (product, (user, uFeatures)) } users.join(productFeatures).map { @@ -112,7 +116,7 @@ class MatrixFactorizationModel( * recommended the product is. */ def recommendProducts(user: Int, num: Int): Array[Rating] = - recommend(userFeatures.lookup(user).head, productFeatures, num) + MatrixFactorizationModel.recommend(userFeatures.lookup(user).head, productFeatures, num) .map(t => Rating(user, t._1, t._2)) /** @@ -128,7 +132,7 @@ class MatrixFactorizationModel( * recommended the user is. */ def recommendUsers(product: Int, num: Int): Array[Rating] = - recommend(productFeatures.lookup(product).head, userFeatures, num) + MatrixFactorizationModel.recommend(productFeatures.lookup(product).head, userFeatures, num) .map(t => Rating(t._1, product, t._2)) protected override val formatVersion: String = "1.0" @@ -137,20 +141,113 @@ class MatrixFactorizationModel( MatrixFactorizationModel.SaveLoadV1_0.save(this, path) } + /** + * Recommends topK products for all users. + * + * @param num how many products to return for every user. + * @return [(Int, Array[Rating])] objects, where every tuple contains a userID and an array of + * rating objects which contains the same userId, recommended productID and a "score" in the + * rating field. Semantics of score is same as recommendProducts API + */ + def recommendProductsForUsers(num: Int): RDD[(Int, Array[Rating])] = { + MatrixFactorizationModel.recommendForAll(rank, userFeatures, productFeatures, num).map { + case (user, top) => + val ratings = top.map { case (product, rating) => Rating(user, product, rating) } + (user, ratings) + } + } + + + /** + * Recommends topK users for all products. + * + * @param num how many users to return for every product. + * @return [(Int, Array[Rating])] objects, where every tuple contains a productID and an array + * of rating objects which contains the recommended userId, same productID and a "score" in the + * rating field. Semantics of score is same as recommendUsers API + */ + def recommendUsersForProducts(num: Int): RDD[(Int, Array[Rating])] = { + MatrixFactorizationModel.recommendForAll(rank, productFeatures, userFeatures, num).map { + case (product, top) => + val ratings = top.map { case (user, rating) => Rating(user, product, rating) } + (product, ratings) + } + } +} + +object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] { + + import org.apache.spark.mllib.util.Loader._ + + /** + * Makes recommendations for a single user (or product). + */ private def recommend( recommendToFeatures: Array[Double], recommendableFeatures: RDD[(Int, Array[Double])], num: Int): Array[(Int, Double)] = { - val scored = recommendableFeatures.map { case (id,features) => + val scored = recommendableFeatures.map { case (id, features) => (id, blas.ddot(features.length, recommendToFeatures, 1, features, 1)) } scored.top(num)(Ordering.by(_._2)) } -} -object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] { + /** + * Makes recommendations for all users (or products). + * @param rank rank + * @param srcFeatures src features to receive recommendations + * @param dstFeatures dst features used to make recommendations + * @param num number of recommendations for each record + * @return an RDD of (srcId: Int, recommendations), where recommendations are stored as an array + * of (dstId, rating) pairs. + */ + private def recommendForAll( + rank: Int, + srcFeatures: RDD[(Int, Array[Double])], + dstFeatures: RDD[(Int, Array[Double])], + num: Int): RDD[(Int, Array[(Int, Double)])] = { + val srcBlocks = blockify(rank, srcFeatures) + val dstBlocks = blockify(rank, dstFeatures) + val ratings = srcBlocks.cartesian(dstBlocks).flatMap { + case ((srcIds, srcFactors), (dstIds, dstFactors)) => + val m = srcIds.length + val n = dstIds.length + val ratings = srcFactors.transpose.multiply(dstFactors) + val output = new Array[(Int, (Int, Double))](m * n) + var k = 0 + ratings.foreachActive { (i, j, r) => + output(k) = (srcIds(i), (dstIds(j), r)) + k += 1 + } + output.toSeq + } + ratings.topByKey(num)(Ordering.by(_._2)) + } - import org.apache.spark.mllib.util.Loader._ + /** + * Blockifies features to use Level-3 BLAS. + */ + private def blockify( + rank: Int, + features: RDD[(Int, Array[Double])]): RDD[(Array[Int], DenseMatrix)] = { + val blockSize = 4096 // TODO: tune the block size + val blockStorage = rank * blockSize + features.mapPartitions { iter => + iter.grouped(blockSize).map { grouped => + val ids = mutable.ArrayBuilder.make[Int] + ids.sizeHint(blockSize) + val factors = mutable.ArrayBuilder.make[Double] + factors.sizeHint(blockStorage) + var i = 0 + grouped.foreach { case (id, factor) => + ids += id + factors ++= factor + i += 1 + } + (ids.result(), new DenseMatrix(rank, i, factors.result())) + } + } + } override def load(sc: SparkContext, path: String): MatrixFactorizationModel = { val (loadedClassName, formatVersion, _) = loadMetadata(sc, path) @@ -214,4 +311,5 @@ object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] { new Path(dataPath(path), "product").toUri.toString } } + } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctionsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctionsSuite.scala index 1ac7c12c4e8e6..cb8fe4dba96f5 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctionsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/rdd/MLPairRDDFunctionsSuite.scala @@ -24,13 +24,13 @@ import org.apache.spark.mllib.rdd.MLPairRDDFunctions._ class MLPairRDDFunctionsSuite extends FunSuite with MLlibTestSparkContext { test("topByKey") { - val topMap = sc.parallelize(Array((1, 1), (1, 2), (3, 2), (3, 7), (3, 5), (5, 1), (5, 3)), 2) + val topMap = sc.parallelize(Array((1, 1), (1, 2), (3, 2), (3, 7), (5, 1), (3, 5)), 2) .topByKey(2) .collectAsMap() assert(topMap.size === 3) assert(topMap(1) === Array(2, 1)) assert(topMap(3) === Array(7, 5)) - assert(topMap(5) === Array(3, 1)) + assert(topMap(5) === Array(1)) } } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala index 9801e87576744..2c92866f3893d 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModelSuite.scala @@ -72,4 +72,24 @@ class MatrixFactorizationModelSuite extends FunSuite with MLlibTestSparkContext Utils.deleteRecursively(tempDir) } } + + test("batch predict API recommendProductsForUsers") { + val model = new MatrixFactorizationModel(rank, userFeatures, prodFeatures) + val topK = 10 + val recommendations = model.recommendProductsForUsers(topK).collectAsMap() + + assert(recommendations(0)(0).rating ~== 17.0 relTol 1e-14) + assert(recommendations(1)(0).rating ~== 39.0 relTol 1e-14) + } + + test("batch predict API recommendUsersForProducts") { + val model = new MatrixFactorizationModel(rank, userFeatures, prodFeatures) + val topK = 10 + val recommendations = model.recommendUsersForProducts(topK).collectAsMap() + + assert(recommendations(2)(0).user == 1) + assert(recommendations(2)(0).rating ~== 39.0 relTol 1e-14) + assert(recommendations(2)(1).user == 0) + assert(recommendations(2)(1).rating ~== 17.0 relTol 1e-14) + } } From 7630213cab1f653212828f045cf1d7d1870abea0 Mon Sep 17 00:00:00 2001 From: Liang-Chi Hsieh Date: Fri, 1 May 2015 08:31:01 -0700 Subject: [PATCH 518/591] [SPARK-5891] [ML] Add Binarizer ML Transformer JIRA: https://issues.apache.org/jira/browse/SPARK-5891 Author: Liang-Chi Hsieh Closes #5699 from viirya/add_binarizer and squashes the following commits: 1a0b9a4 [Liang-Chi Hsieh] For comments. bc397f2 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into add_binarizer cc4f03c [Liang-Chi Hsieh] Implement threshold param and use merged params map. 7564c63 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into add_binarizer 1682f8c [Liang-Chi Hsieh] Add Binarizer ML Transformer. --- .../apache/spark/ml/feature/Binarizer.scala | 85 +++++++++++++++++++ .../spark/ml/feature/BinarizerSuite.scala | 69 +++++++++++++++ 2 files changed, 154 insertions(+) create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/BinarizerSuite.scala diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala new file mode 100644 index 0000000000000..f3ce6dfca2c1c --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala @@ -0,0 +1,85 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.Transformer +import org.apache.spark.ml.attribute.BinaryAttribute +import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol} +import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.sql._ +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types.{DoubleType, StructType} + +/** + * :: AlphaComponent :: + * Binarize a column of continuous features given a threshold. + */ +@AlphaComponent +final class Binarizer extends Transformer with HasInputCol with HasOutputCol { + + /** + * Param for threshold used to binarize continuous features. + * The features greater than the threshold, will be binarized to 1.0. + * The features equal to or less than the threshold, will be binarized to 0.0. + * @group param + */ + val threshold: DoubleParam = + new DoubleParam(this, "threshold", "threshold used to binarize continuous features") + + /** @group getParam */ + def getThreshold: Double = getOrDefault(threshold) + + /** @group setParam */ + def setThreshold(value: Double): this.type = set(threshold, value) + + setDefault(threshold -> 0.0) + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + transformSchema(dataset.schema, paramMap, logging = true) + val map = extractParamMap(paramMap) + val td = map(threshold) + val binarizer = udf { in: Double => if (in > td) 1.0 else 0.0 } + val outputColName = map(outputCol) + val metadata = BinaryAttribute.defaultAttr.withName(outputColName).toMetadata() + dataset.select(col("*"), + binarizer(col(map(inputCol))).as(outputColName, metadata)) + } + + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + val map = extractParamMap(paramMap) + SchemaUtils.checkColumnType(schema, map(inputCol), DoubleType) + + val inputFields = schema.fields + val outputColName = map(outputCol) + + require(inputFields.forall(_.name != outputColName), + s"Output column $outputColName already exists.") + + val attr = BinaryAttribute.defaultAttr.withName(outputColName) + val outputFields = inputFields :+ attr.toStructField() + StructType(outputFields) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/BinarizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/BinarizerSuite.scala new file mode 100644 index 0000000000000..caf1b759593f3 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/BinarizerSuite.scala @@ -0,0 +1,69 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.scalatest.FunSuite + +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.sql.{DataFrame, Row, SQLContext} + + +class BinarizerSuite extends FunSuite with MLlibTestSparkContext { + + @transient var data: Array[Double] = _ + @transient var sqlContext: SQLContext = _ + + override def beforeAll(): Unit = { + super.beforeAll() + sqlContext = new SQLContext(sc) + data = Array(0.1, -0.5, 0.2, -0.3, 0.8, 0.7, -0.1, -0.4) + } + + test("Binarize continuous features with default parameter") { + val defaultBinarized: Array[Double] = data.map(x => if (x > 0.0) 1.0 else 0.0) + val dataFrame: DataFrame = sqlContext.createDataFrame( + data.zip(defaultBinarized)).toDF("feature", "expected") + + val binarizer: Binarizer = new Binarizer() + .setInputCol("feature") + .setOutputCol("binarized_feature") + + binarizer.transform(dataFrame).select("binarized_feature", "expected").collect().foreach { + case Row(x: Double, y: Double) => + assert(x === y, "The feature value is not correct after binarization.") + } + } + + test("Binarize continuous features with setter") { + val threshold: Double = 0.2 + val thresholdBinarized: Array[Double] = data.map(x => if (x > threshold) 1.0 else 0.0) + val dataFrame: DataFrame = sqlContext.createDataFrame( + data.zip(thresholdBinarized)).toDF("feature", "expected") + + val binarizer: Binarizer = new Binarizer() + .setInputCol("feature") + .setOutputCol("binarized_feature") + .setThreshold(threshold) + + binarizer.transform(dataFrame).select("binarized_feature", "expected").collect().foreach { + case Row(x: Double, y: Double) => + assert(x === y, "The feature value is not correct after binarization.") + } + } +} From c8c481da18688e684d4e34f14c5afa0b5d37a618 Mon Sep 17 00:00:00 2001 From: Chris Biow Date: Fri, 1 May 2015 19:26:55 +0100 Subject: [PATCH 519/591] Limit help option regex Added word-boundary delimiters so that embedded text such as "-h" within command line options and values doesn't trigger the usage script and exit. Author: Chris Biow Closes #5816 from cbiow/patch-1 and squashes the following commits: 36b3726 [Chris Biow] Limit help option regex --- bin/spark-shell2.cmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/bin/spark-shell2.cmd b/bin/spark-shell2.cmd index 02f51fe59a911..00fd30fa38d36 100644 --- a/bin/spark-shell2.cmd +++ b/bin/spark-shell2.cmd @@ -19,7 +19,7 @@ rem set SPARK_HOME=%~dp0.. -echo "%*" | findstr " --help -h" >nul +echo "%*" | findstr " \<--help\> \<-h\>" >nul if %ERRORLEVEL% equ 0 ( call :usage exit /b 0 From 27de6fef6a852c0801b5d55d0e69096878bd7909 Mon Sep 17 00:00:00 2001 From: niranda Date: Fri, 1 May 2015 11:27:45 -0700 Subject: [PATCH 520/591] changing persistence engine trait to an abstract class Author: niranda Closes #5832 from nirandaperera/PersistanceEngine_abstract_class and squashes the following commits: 67b9d5a [niranda] changing persistence engine trait to an abstract class --- .../org/apache/spark/deploy/master/PersistenceEngine.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala index da5060778edeb..a03d460509e03 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala @@ -33,7 +33,7 @@ import scala.reflect.ClassTag * The implementation of this trait defines how name-object pairs are stored or retrieved. */ @DeveloperApi -trait PersistenceEngine { +abstract class PersistenceEngine { /** * Defines how the object is serialized and persisted. Implementation will From 7d427222dca4807ec55e8d9a7de6ffe861cd0d24 Mon Sep 17 00:00:00 2001 From: Dan McClary Date: Fri, 1 May 2015 11:55:43 -0700 Subject: [PATCH 521/591] [SPARK-5854] personalized page rank Here's a modification to PageRank which does personalized PageRank. The approach is basically similar to that outlined by Bahmani et al. from 2010 (http://arxiv.org/pdf/1006.2880.pdf). I'm sure this needs tuning up or other considerations, so let me know how I can improve this. Author: Dan McClary Author: dwmclary Closes #4774 from dwmclary/SPARK-5854-Personalized-PageRank and squashes the following commits: 8b907db [dwmclary] fixed scalastyle errors in PageRankSuite 2c20e5d [dwmclary] merged with upstream master d6cebac [dwmclary] updated as per style requests 7d00c23 [Dan McClary] fixed line overrun in personalizedVertexPageRank d711677 [Dan McClary] updated vertexProgram to restore binary compatibility for inner method bb8d507 [Dan McClary] Merge branch 'master' of https://github.com/apache/spark into SPARK-5854-Personalized-PageRank fba0edd [Dan McClary] fixed silly mistakes de51be2 [Dan McClary] cleaned up whitespace between comments and methods 0c30d0c [Dan McClary] updated to maintain binary compatibility aaf0b4b [Dan McClary] Merge branch 'master' of https://github.com/apache/spark into SPARK-5854-Personalized-PageRank 76773f6 [Dan McClary] Merge branch 'master' of https://github.com/apache/spark into SPARK-5854-Personalized-PageRank 44ada8e [Dan McClary] updated tolerance on chain PPR 1ffed95 [Dan McClary] updated tolerance on chain PPR b67ac69 [Dan McClary] updated tolerance on chain PPR a560942 [Dan McClary] rolled PPR into pregel code for PageRank 6dc2c29 [Dan McClary] initial implementation of personalized page rank --- .../org/apache/spark/graphx/GraphOps.scala | 25 +++++ .../apache/spark/graphx/lib/PageRank.scala | 93 +++++++++++++++++-- .../spark/graphx/lib/PageRankSuite.scala | 47 ++++++++++ 3 files changed, 159 insertions(+), 6 deletions(-) diff --git a/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala b/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala index 86f611d55aa8a..7edd627b20918 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/GraphOps.scala @@ -372,6 +372,31 @@ class GraphOps[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED]) extends Seriali PageRank.runUntilConvergence(graph, tol, resetProb) } + + /** + * Run personalized PageRank for a given vertex, such that all random walks + * are started relative to the source node. + * + * @see [[org.apache.spark.graphx.lib.PageRank$#runUntilConvergenceWithOptions]] + */ + def personalizedPageRank(src: VertexId, tol: Double, + resetProb: Double = 0.15) : Graph[Double, Double] = { + PageRank.runUntilConvergenceWithOptions(graph, tol, resetProb, Some(src)) + } + + /** + * Run Personalized PageRank for a fixed number of iterations with + * with all iterations originating at the source node + * returning a graph with vertex attributes + * containing the PageRank and edge attributes the normalized edge weight. + * + * @see [[org.apache.spark.graphx.lib.PageRank$#runWithOptions]] + */ + def staticPersonalizedPageRank(src: VertexId, numIter: Int, + resetProb: Double = 0.15) : Graph[Double, Double] = { + PageRank.runWithOptions(graph, numIter, resetProb, Some(src)) + } + /** * Run PageRank for a fixed number of iterations returning a graph with vertex attributes * containing the PageRank and edge attributes the normalized edge weight. diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala index 042e366a29f58..bc974b2f04e70 100644 --- a/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala +++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/PageRank.scala @@ -18,6 +18,7 @@ package org.apache.spark.graphx.lib import scala.reflect.ClassTag +import scala.language.postfixOps import org.apache.spark.Logging import org.apache.spark.graphx._ @@ -60,6 +61,7 @@ import org.apache.spark.graphx._ */ object PageRank extends Logging { + /** * Run PageRank for a fixed number of iterations returning a graph * with vertex attributes containing the PageRank and edge @@ -74,10 +76,33 @@ object PageRank extends Logging { * * @return the graph containing with each vertex containing the PageRank and each edge * containing the normalized weight. + */ + def run[VD: ClassTag, ED: ClassTag](graph: Graph[VD, ED], numIter: Int, + resetProb: Double = 0.15): Graph[Double, Double] = + { + runWithOptions(graph, numIter, resetProb) + } + + /** + * Run PageRank for a fixed number of iterations returning a graph + * with vertex attributes containing the PageRank and edge + * attributes the normalized edge weight. + * + * @tparam VD the original vertex attribute (not used) + * @tparam ED the original edge attribute (not used) + * + * @param graph the graph on which to compute PageRank + * @param numIter the number of iterations of PageRank to run + * @param resetProb the random reset probability (alpha) + * @param srcId the source vertex for a Personalized Page Rank (optional) + * + * @return the graph containing with each vertex containing the PageRank and each edge + * containing the normalized weight. * */ - def run[VD: ClassTag, ED: ClassTag]( - graph: Graph[VD, ED], numIter: Int, resetProb: Double = 0.15): Graph[Double, Double] = + def runWithOptions[VD: ClassTag, ED: ClassTag]( + graph: Graph[VD, ED], numIter: Int, resetProb: Double = 0.15, + srcId: Option[VertexId] = None): Graph[Double, Double] = { // Initialize the PageRank graph with each edge attribute having // weight 1/outDegree and each vertex with attribute 1.0. @@ -89,6 +114,10 @@ object PageRank extends Logging { // Set the vertex attributes to the initial pagerank values .mapVertices( (id, attr) => resetProb ) + val personalized = srcId isDefined + val src: VertexId = srcId.getOrElse(-1L) + def delta(u: VertexId, v: VertexId):Double = { if (u == v) 1.0 else 0.0 } + var iteration = 0 var prevRankGraph: Graph[Double, Double] = null while (iteration < numIter) { @@ -103,8 +132,14 @@ object PageRank extends Logging { // that didn't receive a message. Requires a shuffle for broadcasting updated ranks to the // edge partitions. prevRankGraph = rankGraph + val rPrb = if (personalized) { + (src: VertexId ,id: VertexId) => resetProb * delta(src,id) + } else { + (src: VertexId, id: VertexId) => resetProb + } + rankGraph = rankGraph.joinVertices(rankUpdates) { - (id, oldRank, msgSum) => resetProb + (1.0 - resetProb) * msgSum + (id, oldRank, msgSum) => rPrb(src,id) + (1.0 - resetProb) * msgSum }.cache() rankGraph.edges.foreachPartition(x => {}) // also materializes rankGraph.vertices @@ -133,7 +168,29 @@ object PageRank extends Logging { * containing the normalized weight. */ def runUntilConvergence[VD: ClassTag, ED: ClassTag]( - graph: Graph[VD, ED], tol: Double, resetProb: Double = 0.15): Graph[Double, Double] = + graph: Graph[VD, ED], tol: Double, resetProb: Double = 0.15): Graph[Double, Double] = + { + runUntilConvergenceWithOptions(graph, tol, resetProb) + } + + /** + * Run a dynamic version of PageRank returning a graph with vertex attributes containing the + * PageRank and edge attributes containing the normalized edge weight. + * + * @tparam VD the original vertex attribute (not used) + * @tparam ED the original edge attribute (not used) + * + * @param graph the graph on which to compute PageRank + * @param tol the tolerance allowed at convergence (smaller => more accurate). + * @param resetProb the random reset probability (alpha) + * @param srcId the source vertex for a Personalized Page Rank (optional) + * + * @return the graph containing with each vertex containing the PageRank and each edge + * containing the normalized weight. + */ + def runUntilConvergenceWithOptions[VD: ClassTag, ED: ClassTag]( + graph: Graph[VD, ED], tol: Double, resetProb: Double = 0.15, + srcId: Option[VertexId] = None): Graph[Double, Double] = { // Initialize the pagerankGraph with each edge attribute // having weight 1/outDegree and each vertex with attribute 1.0. @@ -148,6 +205,10 @@ object PageRank extends Logging { .mapVertices( (id, attr) => (0.0, 0.0) ) .cache() + val personalized = srcId.isDefined + val src: VertexId = srcId.getOrElse(-1L) + + // Define the three functions needed to implement PageRank in the GraphX // version of Pregel def vertexProgram(id: VertexId, attr: (Double, Double), msgSum: Double): (Double, Double) = { @@ -156,7 +217,18 @@ object PageRank extends Logging { (newPR, newPR - oldPR) } - def sendMessage(edge: EdgeTriplet[(Double, Double), Double]): Iterator[(VertexId, Double)] = { + def personalizedVertexProgram(id: VertexId, attr: (Double, Double), + msgSum: Double): (Double, Double) = { + val (oldPR, lastDelta) = attr + var teleport = oldPR + val delta = if (src==id) 1.0 else 0.0 + teleport = oldPR*delta + + val newPR = teleport + (1.0 - resetProb) * msgSum + (newPR, newPR - oldPR) + } + + def sendMessage(edge: EdgeTriplet[(Double, Double), Double]) = { if (edge.srcAttr._2 > tol) { Iterator((edge.dstId, edge.srcAttr._2 * edge.attr)) } else { @@ -170,8 +242,17 @@ object PageRank extends Logging { val initialMessage = resetProb / (1.0 - resetProb) // Execute a dynamic version of Pregel. + val vp = if (personalized) { + (id: VertexId, attr: (Double, Double),msgSum: Double) => + personalizedVertexProgram(id, attr, msgSum) + } else { + (id: VertexId, attr: (Double, Double), msgSum: Double) => + vertexProgram(id, attr, msgSum) + } + Pregel(pagerankGraph, initialMessage, activeDirection = EdgeDirection.Out)( - vertexProgram, sendMessage, messageCombiner) + vp, sendMessage, messageCombiner) .mapVertices((vid, attr) => attr._1) } // end of deltaPageRank + } diff --git a/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala b/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala index 95804b07b1db0..3f3c9dfd7b3dd 100644 --- a/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala +++ b/graphx/src/test/scala/org/apache/spark/graphx/lib/PageRankSuite.scala @@ -92,6 +92,36 @@ class PageRankSuite extends FunSuite with LocalSparkContext { } } // end of test Star PageRank + test("Star PersonalPageRank") { + withSpark { sc => + val nVertices = 100 + val starGraph = GraphGenerators.starGraph(sc, nVertices).cache() + val resetProb = 0.15 + val errorTol = 1.0e-5 + + val staticRanks1 = starGraph.staticPersonalizedPageRank(0,numIter = 1, resetProb).vertices + val staticRanks2 = starGraph.staticPersonalizedPageRank(0,numIter = 2, resetProb) + .vertices.cache() + + // Static PageRank should only take 2 iterations to converge + val notMatching = staticRanks1.innerZipJoin(staticRanks2) { (vid, pr1, pr2) => + if (pr1 != pr2) 1 else 0 + }.map { case (vid, test) => test }.sum + assert(notMatching === 0) + + val staticErrors = staticRanks2.map { case (vid, pr) => + val correct = (vid > 0 && pr == resetProb) || + (vid == 0 && math.abs(pr - (resetProb + (1.0 - resetProb) * (resetProb * + (nVertices - 1)) )) < 1.0E-5) + if (!correct) 1 else 0 + } + assert(staticErrors.sum === 0) + + val dynamicRanks = starGraph.personalizedPageRank(0,0, resetProb).vertices.cache() + assert(compareRanks(staticRanks2, dynamicRanks) < errorTol) + } + } // end of test Star PageRank + test("Grid PageRank") { withSpark { sc => val rows = 10 @@ -128,4 +158,21 @@ class PageRankSuite extends FunSuite with LocalSparkContext { assert(compareRanks(staticRanks, dynamicRanks) < errorTol) } } + + test("Chain PersonalizedPageRank") { + withSpark { sc => + val chain1 = (0 until 9).map(x => (x, x + 1) ) + val rawEdges = sc.parallelize(chain1, 1).map { case (s,d) => (s.toLong, d.toLong) } + val chain = Graph.fromEdgeTuples(rawEdges, 1.0).cache() + val resetProb = 0.15 + val tol = 0.0001 + val numIter = 10 + val errorTol = 1.0e-1 + + val staticRanks = chain.staticPersonalizedPageRank(4, numIter, resetProb).vertices + val dynamicRanks = chain.personalizedPageRank(4, tol, resetProb).vertices + + assert(compareRanks(staticRanks, dynamicRanks) < errorTol) + } + } } From 1262e310cd294c8fd936c55c3281ed855824ea27 Mon Sep 17 00:00:00 2001 From: Sean Owen Date: Fri, 1 May 2015 19:57:37 +0100 Subject: [PATCH 522/591] [SPARK-6846] [WEBUI] [HOTFIX] return to GET for kill link in UI since YARN AM won't proxy POST Partial undoing of SPARK-6846; YARN AM proxy won't forward POSTs, so go back to GET for kill links in Spark UI. Standalone UIs are not affected. Author: Sean Owen Closes #5837 from srowen/SPARK-6846.2 and squashes the following commits: c17c386 [Sean Owen] Partial undoing of SPARK-6846; YARN AM proxy won't forward POSTs, so go back to GET for kill links in Spark UI. Standalone UIs are not affected. --- .../spark/deploy/master/ui/MasterWebUI.scala | 4 ++-- .../scala/org/apache/spark/ui/JettyUtils.scala | 16 +++++++++------- .../main/scala/org/apache/spark/ui/SparkUI.scala | 4 +++- .../org/apache/spark/ui/jobs/StageTable.scala | 7 ++++++- .../org/apache/spark/ui/UISeleniumSuite.scala | 5 +++-- 5 files changed, 23 insertions(+), 13 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala index aad9c87bdb987..dea0a65eeeaa6 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala @@ -44,9 +44,9 @@ class MasterWebUI(val master: Master, requestedPort: Int) attachPage(masterPage) attachHandler(createStaticHandler(MasterWebUI.STATIC_RESOURCE_DIR, "/static")) attachHandler(createRedirectHandler( - "/app/kill", "/", masterPage.handleAppKillRequest, httpMethod = "POST")) + "/app/kill", "/", masterPage.handleAppKillRequest, httpMethods = Set("POST"))) attachHandler(createRedirectHandler( - "/driver/kill", "/", masterPage.handleDriverKillRequest, httpMethod = "POST")) + "/driver/kill", "/", masterPage.handleDriverKillRequest, httpMethods = Set("POST"))) } /** Attach a reconstructed UI to this Master UI. Only valid after bind(). */ diff --git a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala index a091ca650c60c..dfd6fdb5e9993 100644 --- a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala +++ b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala @@ -115,19 +115,21 @@ private[spark] object JettyUtils extends Logging { destPath: String, beforeRedirect: HttpServletRequest => Unit = x => (), basePath: String = "", - httpMethod: String = "GET"): ServletContextHandler = { + httpMethods: Set[String] = Set("GET")): ServletContextHandler = { val prefixedDestPath = attachPrefix(basePath, destPath) val servlet = new HttpServlet { override def doGet(request: HttpServletRequest, response: HttpServletResponse): Unit = { - httpMethod match { - case "GET" => doRequest(request, response) - case _ => response.sendError(HttpServletResponse.SC_METHOD_NOT_ALLOWED) + if (httpMethods.contains("GET")) { + doRequest(request, response) + } else { + response.sendError(HttpServletResponse.SC_METHOD_NOT_ALLOWED) } } override def doPost(request: HttpServletRequest, response: HttpServletResponse): Unit = { - httpMethod match { - case "POST" => doRequest(request, response) - case _ => response.sendError(HttpServletResponse.SC_METHOD_NOT_ALLOWED) + if (httpMethods.contains("POST")) { + doRequest(request, response) + } else { + response.sendError(HttpServletResponse.SC_METHOD_NOT_ALLOWED) } } private def doRequest(request: HttpServletRequest, response: HttpServletResponse): Unit = { diff --git a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala index 580ab8b1325f8..06fce86bd38d2 100644 --- a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala +++ b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala @@ -55,8 +55,10 @@ private[spark] class SparkUI private ( attachTab(new ExecutorsTab(this)) attachHandler(createStaticHandler(SparkUI.STATIC_RESOURCE_DIR, "/static")) attachHandler(createRedirectHandler("/", "/jobs", basePath = basePath)) + // This should be POST only, but, the YARN AM proxy won't proxy POSTs attachHandler(createRedirectHandler( - "/stages/stage/kill", "/stages", stagesTab.handleKillRequest, httpMethod = "POST")) + "/stages/stage/kill", "/stages", stagesTab.handleKillRequest, + httpMethods = Set("GET", "POST"))) } initialize() diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala index 6d8c7e1fda8d8..a33243d4252bf 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala @@ -76,15 +76,20 @@ private[ui] class StageTableBase( val basePathUri = UIUtils.prependBaseUri(basePath) val killLink = if (killEnabled) { - val killLinkUri = s"$basePathUri/stages/stage/kill/" val confirm = s"if (window.confirm('Are you sure you want to kill stage ${s.stageId} ?')) " + "{ this.parentNode.submit(); return true; } else { return false; }" + // SPARK-6846 this should be POST-only but YARN AM won't proxy POST + /* + val killLinkUri = s"$basePathUri/stages/stage/kill/"
    (kill)
    + */ + val killLinkUri = s"$basePathUri/stages/stage/kill/?id=${s.stageId}&terminate=true" + (kill) } val nameLinkUri = s"$basePathUri/stages/stage?id=${s.stageId}&attempt=${s.attemptId}" diff --git a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala index eb9db550fd74c..d53d7f3ba5ae7 100644 --- a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala +++ b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala @@ -350,7 +350,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before } } - test("kill stage is POST only") { + test("kill stage POST/GET response is correct") { def getResponseCode(url: URL, method: String): Int = { val connection = url.openConnection().asInstanceOf[HttpURLConnection] connection.setRequestMethod(method) @@ -365,7 +365,8 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before eventually(timeout(5 seconds), interval(50 milliseconds)) { val url = new URL( sc.ui.get.appUIAddress.stripSuffix("/") + "/stages/stage/kill/?id=0&terminate=true") - getResponseCode(url, "GET") should be (405) + // SPARK-6846: should be POST only but YARN AM doesn't proxy POST + getResponseCode(url, "GET") should be (200) getResponseCode(url, "POST") should be (200) } } From 16860327286bc08b4e2283d51b4c8fe024ba5006 Mon Sep 17 00:00:00 2001 From: Liang-Chi Hsieh Date: Fri, 1 May 2015 11:59:12 -0700 Subject: [PATCH 523/591] [SPARK-7183] [NETWORK] Fix memory leak of TransportRequestHandler.streamIds JIRA: https://issues.apache.org/jira/browse/SPARK-7183 Author: Liang-Chi Hsieh Closes #5743 from viirya/fix_requesthandler_memory_leak and squashes the following commits: cf2c086 [Liang-Chi Hsieh] For comments. 97e205c [Liang-Chi Hsieh] Remove unused import. d35f19a [Liang-Chi Hsieh] For comments. f9a0c37 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into fix_requesthandler_memory_leak 45908b7 [Liang-Chi Hsieh] for style. 17f020f [Liang-Chi Hsieh] Remove unused import. 37a4b6c [Liang-Chi Hsieh] Remove streamIds from TransportRequestHandler. 3b3f38a [Liang-Chi Hsieh] Fix memory leak of TransportRequestHandler.streamIds. --- .../server/OneForOneStreamManager.java | 35 ++++++++++++++----- .../spark/network/server/StreamManager.java | 19 +++++++--- .../server/TransportRequestHandler.java | 14 ++------ 3 files changed, 44 insertions(+), 24 deletions(-) diff --git a/network/common/src/main/java/org/apache/spark/network/server/OneForOneStreamManager.java b/network/common/src/main/java/org/apache/spark/network/server/OneForOneStreamManager.java index a6d390e13f396..c95e64e8e2cda 100644 --- a/network/common/src/main/java/org/apache/spark/network/server/OneForOneStreamManager.java +++ b/network/common/src/main/java/org/apache/spark/network/server/OneForOneStreamManager.java @@ -20,14 +20,18 @@ import java.util.Iterator; import java.util.Map; import java.util.Random; +import java.util.Set; import java.util.concurrent.ConcurrentHashMap; import java.util.concurrent.atomic.AtomicLong; +import io.netty.channel.Channel; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import org.apache.spark.network.buffer.ManagedBuffer; +import com.google.common.base.Preconditions; + /** * StreamManager which allows registration of an Iterator<ManagedBuffer>, which are individually * fetched as chunks by the client. Each registered buffer is one chunk. @@ -36,18 +40,21 @@ public class OneForOneStreamManager extends StreamManager { private final Logger logger = LoggerFactory.getLogger(OneForOneStreamManager.class); private final AtomicLong nextStreamId; - private final Map streams; + private final ConcurrentHashMap streams; /** State of a single stream. */ private static class StreamState { final Iterator buffers; + // The channel associated to the stream + Channel associatedChannel = null; + // Used to keep track of the index of the buffer that the user has retrieved, just to ensure // that the caller only requests each chunk one at a time, in order. int curChunk = 0; StreamState(Iterator buffers) { - this.buffers = buffers; + this.buffers = Preconditions.checkNotNull(buffers); } } @@ -58,6 +65,13 @@ public OneForOneStreamManager() { streams = new ConcurrentHashMap(); } + @Override + public void registerChannel(Channel channel, long streamId) { + if (streams.containsKey(streamId)) { + streams.get(streamId).associatedChannel = channel; + } + } + @Override public ManagedBuffer getChunk(long streamId, int chunkIndex) { StreamState state = streams.get(streamId); @@ -80,12 +94,17 @@ public ManagedBuffer getChunk(long streamId, int chunkIndex) { } @Override - public void connectionTerminated(long streamId) { - // Release all remaining buffers. - StreamState state = streams.remove(streamId); - if (state != null && state.buffers != null) { - while (state.buffers.hasNext()) { - state.buffers.next().release(); + public void connectionTerminated(Channel channel) { + // Close all streams which have been associated with the channel. + for (Map.Entry entry: streams.entrySet()) { + StreamState state = entry.getValue(); + if (state.associatedChannel == channel) { + streams.remove(entry.getKey()); + + // Release all remaining buffers. + while (state.buffers.hasNext()) { + state.buffers.next().release(); + } } } } diff --git a/network/common/src/main/java/org/apache/spark/network/server/StreamManager.java b/network/common/src/main/java/org/apache/spark/network/server/StreamManager.java index 5a9a14a180c10..929f789bf9d24 100644 --- a/network/common/src/main/java/org/apache/spark/network/server/StreamManager.java +++ b/network/common/src/main/java/org/apache/spark/network/server/StreamManager.java @@ -17,6 +17,8 @@ package org.apache.spark.network.server; +import io.netty.channel.Channel; + import org.apache.spark.network.buffer.ManagedBuffer; /** @@ -44,9 +46,18 @@ public abstract class StreamManager { public abstract ManagedBuffer getChunk(long streamId, int chunkIndex); /** - * Indicates that the TCP connection that was tied to the given stream has been terminated. After - * this occurs, we are guaranteed not to read from the stream again, so any state can be cleaned - * up. + * Associates a stream with a single client connection, which is guaranteed to be the only reader + * of the stream. The getChunk() method will be called serially on this connection and once the + * connection is closed, the stream will never be used again, enabling cleanup. + * + * This must be called before the first getChunk() on the stream, but it may be invoked multiple + * times with the same channel and stream id. + */ + public void registerChannel(Channel channel, long streamId) { } + + /** + * Indicates that the given channel has been terminated. After this occurs, we are guaranteed not + * to read from the associated streams again, so any state can be cleaned up. */ - public void connectionTerminated(long streamId) { } + public void connectionTerminated(Channel channel) { } } diff --git a/network/common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java b/network/common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java index 1580180cc17e9..e5159ab56d0d4 100644 --- a/network/common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java +++ b/network/common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java @@ -17,10 +17,7 @@ package org.apache.spark.network.server; -import java.util.Set; - import com.google.common.base.Throwables; -import com.google.common.collect.Sets; import io.netty.channel.Channel; import io.netty.channel.ChannelFuture; import io.netty.channel.ChannelFutureListener; @@ -62,9 +59,6 @@ public class TransportRequestHandler extends MessageHandler { /** Returns each chunk part of a stream. */ private final StreamManager streamManager; - /** List of all stream ids that have been read on this handler, used for cleanup. */ - private final Set streamIds; - public TransportRequestHandler( Channel channel, TransportClient reverseClient, @@ -73,7 +67,6 @@ public TransportRequestHandler( this.reverseClient = reverseClient; this.rpcHandler = rpcHandler; this.streamManager = rpcHandler.getStreamManager(); - this.streamIds = Sets.newHashSet(); } @Override @@ -82,10 +75,7 @@ public void exceptionCaught(Throwable cause) { @Override public void channelUnregistered() { - // Inform the StreamManager that these streams will no longer be read from. - for (long streamId : streamIds) { - streamManager.connectionTerminated(streamId); - } + streamManager.connectionTerminated(channel); rpcHandler.connectionTerminated(reverseClient); } @@ -102,12 +92,12 @@ public void handle(RequestMessage request) { private void processFetchRequest(final ChunkFetchRequest req) { final String client = NettyUtils.getRemoteAddress(channel); - streamIds.add(req.streamChunkId.streamId); logger.trace("Received req from {} to fetch block {}", client, req.streamChunkId); ManagedBuffer buf; try { + streamManager.registerChannel(channel, req.streamChunkId.streamId); buf = streamManager.getChunk(req.streamChunkId.streamId, req.streamChunkId.chunkIndex); } catch (Exception e) { logger.error(String.format( From 37537760d19eab878a5e1a48641cc49e6cb4b989 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Fri, 1 May 2015 12:49:02 -0700 Subject: [PATCH 524/591] [SPARK-7274] [SQL] Create Column expression for array/struct creation. Author: Reynold Xin Closes #5802 from rxin/SPARK-7274 and squashes the following commits: 19aecaa [Reynold Xin] Fixed unicode tests. bfc1538 [Reynold Xin] Export all Python functions. 2517b8c [Reynold Xin] Code review. 23da335 [Reynold Xin] Fixed Python bug. 132002e [Reynold Xin] Fixed tests. 56fce26 [Reynold Xin] Added Python support. b0d591a [Reynold Xin] Fixed debug error. 86926a6 [Reynold Xin] Added test suite. 7dbb9ab [Reynold Xin] Ok one more. 470e2f5 [Reynold Xin] One more MLlib ... e2d14f0 [Reynold Xin] [SPARK-7274][SQL] Create Column expression for array/struct creation. --- .../spark/ml/feature/VectorAssembler.scala | 13 ++- python/pyspark/sql/functions.py | 80 +++++++++++++----- .../catalyst/expressions/BoundAttribute.scala | 10 ++- .../org/apache/spark/sql/functions.scala | 41 ++++++++- .../spark/sql/DataFrameFunctionsSuite.scala | 84 +++++++++++++++++++ 5 files changed, 199 insertions(+), 29 deletions(-) create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala index 7b2a451ca5ee5..5e781a326d98c 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala @@ -25,9 +25,7 @@ import org.apache.spark.ml.Transformer import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.param.shared._ import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors} -import org.apache.spark.sql.{Column, DataFrame, Row} -import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute -import org.apache.spark.sql.catalyst.expressions.{Alias, Cast, CreateStruct} +import org.apache.spark.sql.{DataFrame, Row} import org.apache.spark.sql.functions._ import org.apache.spark.sql.types._ @@ -53,13 +51,12 @@ class VectorAssembler extends Transformer with HasInputCols with HasOutputCol { val inputColNames = map(inputCols) val args = inputColNames.map { c => schema(c).dataType match { - case DoubleType => UnresolvedAttribute(c) - case t if t.isInstanceOf[VectorUDT] => UnresolvedAttribute(c) - case _: NumericType | BooleanType => - Alias(Cast(UnresolvedAttribute(c), DoubleType), s"${c}_double_$uid")() + case DoubleType => dataset(c) + case _: VectorUDT => dataset(c) + case _: NumericType | BooleanType => dataset(c).cast(DoubleType).as(s"${c}_double_$uid") } } - dataset.select(col("*"), assembleFunc(new Column(CreateStruct(args))).as(map(outputCol))) + dataset.select(col("*"), assembleFunc(struct(args : _*)).as(map(outputCol))) } override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py index 241f82175726f..641220a264295 100644 --- a/python/pyspark/sql/functions.py +++ b/python/pyspark/sql/functions.py @@ -24,13 +24,20 @@ from itertools import imap as map from pyspark import SparkContext -from pyspark.rdd import _prepare_for_python_RDD +from pyspark.rdd import _prepare_for_python_RDD, ignore_unicode_prefix from pyspark.serializers import PickleSerializer, AutoBatchedSerializer from pyspark.sql.types import StringType from pyspark.sql.dataframe import Column, _to_java_column, _to_seq -__all__ = ['countDistinct', 'approxCountDistinct', 'udf'] +__all__ = [ + 'approxCountDistinct', + 'countDistinct', + 'monotonicallyIncreasingId', + 'rand', + 'randn', + 'sparkPartitionId', + 'udf'] def _create_function(name, doc=""): @@ -74,27 +81,21 @@ def _(col): __all__.sort() -def rand(seed=None): - """ - Generate a random column with i.i.d. samples from U[0.0, 1.0]. - """ - sc = SparkContext._active_spark_context - if seed: - jc = sc._jvm.functions.rand(seed) - else: - jc = sc._jvm.functions.rand() - return Column(jc) +def array(*cols): + """Creates a new array column. + :param cols: list of column names (string) or list of :class:`Column` expressions that have + the same data type. -def randn(seed=None): - """ - Generate a column with i.i.d. samples from the standard normal distribution. + >>> df.select(array('age', 'age').alias("arr")).collect() + [Row(arr=[2, 2]), Row(arr=[5, 5])] + >>> df.select(array([df.age, df.age]).alias("arr")).collect() + [Row(arr=[2, 2]), Row(arr=[5, 5])] """ sc = SparkContext._active_spark_context - if seed: - jc = sc._jvm.functions.randn(seed) - else: - jc = sc._jvm.functions.randn() + if len(cols) == 1 and isinstance(cols[0], (list, set)): + cols = cols[0] + jc = sc._jvm.functions.array(_to_seq(sc, cols, _to_java_column)) return Column(jc) @@ -146,6 +147,28 @@ def monotonicallyIncreasingId(): return Column(sc._jvm.functions.monotonicallyIncreasingId()) +def rand(seed=None): + """Generates a random column with i.i.d. samples from U[0.0, 1.0]. + """ + sc = SparkContext._active_spark_context + if seed: + jc = sc._jvm.functions.rand(seed) + else: + jc = sc._jvm.functions.rand() + return Column(jc) + + +def randn(seed=None): + """Generates a column with i.i.d. samples from the standard normal distribution. + """ + sc = SparkContext._active_spark_context + if seed: + jc = sc._jvm.functions.randn(seed) + else: + jc = sc._jvm.functions.randn() + return Column(jc) + + def sparkPartitionId(): """A column for partition ID of the Spark task. @@ -158,6 +181,25 @@ def sparkPartitionId(): return Column(sc._jvm.functions.sparkPartitionId()) +@ignore_unicode_prefix +def struct(*cols): + """Creates a new struct column. + + :param cols: list of column names (string) or list of :class:`Column` expressions + that are named or aliased. + + >>> df.select(struct('age', 'name').alias("struct")).collect() + [Row(struct=Row(age=2, name=u'Alice')), Row(struct=Row(age=5, name=u'Bob'))] + >>> df.select(struct([df.age, df.name]).alias("struct")).collect() + [Row(struct=Row(age=2, name=u'Alice')), Row(struct=Row(age=5, name=u'Bob'))] + """ + sc = SparkContext._active_spark_context + if len(cols) == 1 and isinstance(cols[0], (list, set)): + cols = cols[0] + jc = sc._jvm.functions.struct(_to_seq(sc, cols, _to_java_column)) + return Column(jc) + + class UserDefinedFunction(object): """ User defined function in Python diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala index 2225621dbaabd..c6217f07c452d 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/BoundAttribute.scala @@ -28,13 +28,21 @@ import org.apache.spark.sql.catalyst.trees * the layout of intermediate tuples, BindReferences should be run after all such transformations. */ case class BoundReference(ordinal: Int, dataType: DataType, nullable: Boolean) - extends Expression with trees.LeafNode[Expression] { + extends NamedExpression with trees.LeafNode[Expression] { type EvaluatedType = Any override def toString: String = s"input[$ordinal]" override def eval(input: Row): Any = input(ordinal) + + override def name: String = s"i[$ordinal]" + + override def toAttribute: Attribute = throw new UnsupportedOperationException + + override def qualifiers: Seq[String] = throw new UnsupportedOperationException + + override def exprId: ExprId = throw new UnsupportedOperationException } object BindReferences extends Logging { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala index 242e64d3ff881..7e283393d0563 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala @@ -22,7 +22,7 @@ import scala.reflect.runtime.universe.{TypeTag, typeTag} import org.apache.spark.annotation.Experimental import org.apache.spark.sql.catalyst.ScalaReflection -import org.apache.spark.sql.catalyst.analysis.{UnresolvedFunction, Star} +import org.apache.spark.sql.catalyst.analysis.{UnresolvedAttribute, UnresolvedFunction, Star} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.types._ import org.apache.spark.util.Utils @@ -283,6 +283,23 @@ object functions { */ def abs(e: Column): Column = Abs(e.expr) + /** + * Creates a new array column. The input columns must all have the same data type. + * + * @group normal_funcs + */ + @scala.annotation.varargs + def array(cols: Column*): Column = CreateArray(cols.map(_.expr)) + + /** + * Creates a new array column. The input columns must all have the same data type. + * + * @group normal_funcs + */ + def array(colName: String, colNames: String*): Column = { + array((colName +: colNames).map(col) : _*) + } + /** * Returns the first column that is not null. * {{{ @@ -390,6 +407,28 @@ object functions { */ def sqrt(e: Column): Column = Sqrt(e.expr) + /** + * Creates a new struct column. The input column must be a column in a [[DataFrame]], or + * a derived column expression that is named (i.e. aliased). + * + * @group normal_funcs + */ + @scala.annotation.varargs + def struct(cols: Column*): Column = { + require(cols.forall(_.expr.isInstanceOf[NamedExpression]), + s"struct input columns must all be named or aliased ($cols)") + CreateStruct(cols.map(_.expr.asInstanceOf[NamedExpression])) + } + + /** + * Creates a new struct column that composes multiple input columns. + * + * @group normal_funcs + */ + def struct(colName: String, colNames: String*): Column = { + struct((colName +: colNames).map(col) : _*) + } + /** * Converts a string expression to upper case. * diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala new file mode 100644 index 0000000000000..ca03713ef4658 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala @@ -0,0 +1,84 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.test.TestSQLContext.implicits._ +import org.apache.spark.sql.types._ + +/** + * Test suite for functions in [[org.apache.spark.sql.functions]]. + */ +class DataFrameFunctionsSuite extends QueryTest { + + test("array with column name") { + val df = Seq((0, 1)).toDF("a", "b") + val row = df.select(array("a", "b")).first() + + val expectedType = ArrayType(IntegerType, containsNull = false) + assert(row.schema(0).dataType === expectedType) + assert(row.getAs[Seq[Int]](0) === Seq(0, 1)) + } + + test("array with column expression") { + val df = Seq((0, 1)).toDF("a", "b") + val row = df.select(array(col("a"), col("b") + col("b"))).first() + + val expectedType = ArrayType(IntegerType, containsNull = false) + assert(row.schema(0).dataType === expectedType) + assert(row.getAs[Seq[Int]](0) === Seq(0, 2)) + } + + // Turn this on once we add a rule to the analyzer to throw a friendly exception + ignore("array: throw exception if putting columns of different types into an array") { + val df = Seq((0, "str")).toDF("a", "b") + intercept[AnalysisException] { + df.select(array("a", "b")) + } + } + + test("struct with column name") { + val df = Seq((1, "str")).toDF("a", "b") + val row = df.select(struct("a", "b")).first() + + val expectedType = StructType(Seq( + StructField("a", IntegerType, nullable = false), + StructField("b", StringType) + )) + assert(row.schema(0).dataType === expectedType) + assert(row.getAs[Row](0) === Row(1, "str")) + } + + test("struct with column expression") { + val df = Seq((1, "str")).toDF("a", "b") + val row = df.select(struct((col("a") * 2).as("c"), col("b"))).first() + + val expectedType = StructType(Seq( + StructField("c", IntegerType, nullable = false), + StructField("b", StringType) + )) + assert(row.schema(0).dataType === expectedType) + assert(row.getAs[Row](0) === Row(2, "str")) + } + + test("struct: must use named column expression") { + intercept[IllegalArgumentException] { + struct(col("a") * 2) + } + } +} From 58d6584d349d5208a994a074b4cfa8a6ec4d1665 Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Fri, 1 May 2015 13:01:14 -0700 Subject: [PATCH 525/591] Revert "[SPARK-7287] enabled fixed test" This reverts commit 7cf1eb79b1fa290aa1d867a8a1eaaea86d6b2239. --- .../test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala index 35382be7e0ef1..8360b94599547 100644 --- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala @@ -335,7 +335,7 @@ class SparkSubmitSuite extends FunSuite with Matchers with ResetSystemProperties runSparkSubmit(args) } - test("includes jars passed in through --packages") { + ignore("includes jars passed in through --packages") { val unusedJar = TestUtils.createJarWithClasses(Seq.empty) val main = MavenCoordinate("my.great.lib", "mylib", "0.1") val dep = MavenCoordinate("my.great.dep", "mylib", "0.1") From c6d9a429421561508e8adbb4892954381bc33a90 Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Fri, 1 May 2015 13:01:43 -0700 Subject: [PATCH 526/591] Revert "[SPARK-7224] added mock repository generator for --packages tests" This reverts commit 7dacc08ab36188991a001df23880167433844767. --- .../scala/org/apache/spark/TestUtils.scala | 27 +- .../org/apache/spark/deploy/SparkSubmit.scala | 129 ++++----- .../apache/spark/deploy/IvyTestUtils.scala | 262 ------------------ .../spark/deploy/SparkSubmitSuite.scala | 25 +- .../spark/deploy/SparkSubmitUtilsSuite.scala | 57 ++-- 5 files changed, 97 insertions(+), 403 deletions(-) delete mode 100644 core/src/test/scala/org/apache/spark/deploy/IvyTestUtils.scala diff --git a/core/src/main/scala/org/apache/spark/TestUtils.scala b/core/src/main/scala/org/apache/spark/TestUtils.scala index fe6320b504e15..398ca41e16151 100644 --- a/core/src/main/scala/org/apache/spark/TestUtils.scala +++ b/core/src/main/scala/org/apache/spark/TestUtils.scala @@ -105,18 +105,23 @@ private[spark] object TestUtils { URI.create(s"string:///${name.replace(".", "/")}${SOURCE.extension}") } - private[spark] class JavaSourceFromString(val name: String, val code: String) + private class JavaSourceFromString(val name: String, val code: String) extends SimpleJavaFileObject(createURI(name), SOURCE) { override def getCharContent(ignoreEncodingErrors: Boolean): String = code } - /** Creates a compiled class with the source file. Class file will be placed in destDir. */ + /** Creates a compiled class with the given name. Class file will be placed in destDir. */ def createCompiledClass( className: String, destDir: File, - sourceFile: JavaSourceFromString, - classpathUrls: Seq[URL]): File = { + toStringValue: String = "", + baseClass: String = null, + classpathUrls: Seq[URL] = Seq()): File = { val compiler = ToolProvider.getSystemJavaCompiler + val extendsText = Option(baseClass).map { c => s" extends ${c}" }.getOrElse("") + val sourceFile = new JavaSourceFromString(className, + "public class " + className + extendsText + " implements java.io.Serializable {" + + " @Override public String toString() { return \"" + toStringValue + "\"; }}") // Calling this outputs a class file in pwd. It's easier to just rename the file than // build a custom FileManager that controls the output location. @@ -139,18 +144,4 @@ private[spark] object TestUtils { assert(out.exists(), "Destination file not moved: " + out.getAbsolutePath()) out } - - /** Creates a compiled class with the given name. Class file will be placed in destDir. */ - def createCompiledClass( - className: String, - destDir: File, - toStringValue: String = "", - baseClass: String = null, - classpathUrls: Seq[URL] = Seq()): File = { - val extendsText = Option(baseClass).map { c => s" extends ${c}" }.getOrElse("") - val sourceFile = new JavaSourceFromString(className, - "public class " + className + extendsText + " implements java.io.Serializable {" + - " @Override public String toString() { return \"" + toStringValue + "\"; }}") - createCompiledClass(className, destDir, sourceFile, classpathUrls) - } } diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala index 0d149e703aff2..b8ae4af18d1d1 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -20,7 +20,6 @@ package org.apache.spark.deploy import java.io.{File, PrintStream} import java.lang.reflect.{InvocationTargetException, Modifier, UndeclaredThrowableException} import java.net.URL -import java.nio.file.{Path => JavaPath} import java.security.PrivilegedExceptionAction import scala.collection.mutable.{ArrayBuffer, HashMap, Map} @@ -709,9 +708,7 @@ private[deploy] object SparkSubmitUtils { * @param artifactId the artifactId of the coordinate * @param version the version of the coordinate */ - private[deploy] case class MavenCoordinate(groupId: String, artifactId: String, version: String) { - override def toString: String = s"$groupId:$artifactId:$version" - } + private[deploy] case class MavenCoordinate(groupId: String, artifactId: String, version: String) /** * Extracts maven coordinates from a comma-delimited string. Coordinates should be provided @@ -734,10 +731,6 @@ private[deploy] object SparkSubmitUtils { } } - /** Path of the local Maven cache. */ - private[spark] def m2Path: JavaPath = new File(System.getProperty("user.home"), - ".m2" + File.separator + "repository" + File.separator).toPath - /** * Extracts maven coordinates from a comma-delimited string * @param remoteRepos Comma-delimited string of remote repositories @@ -751,7 +744,8 @@ private[deploy] object SparkSubmitUtils { val localM2 = new IBiblioResolver localM2.setM2compatible(true) - localM2.setRoot(m2Path.toUri.toString) + val m2Path = ".m2" + File.separator + "repository" + File.separator + localM2.setRoot(new File(System.getProperty("user.home"), m2Path).toURI.toString) localM2.setUsepoms(true) localM2.setName("local-m2-cache") cr.add(localM2) @@ -876,72 +870,69 @@ private[deploy] object SparkSubmitUtils { "" } else { val sysOut = System.out - try { - // To prevent ivy from logging to system out - System.setOut(printStream) - val artifacts = extractMavenCoordinates(coordinates) - // Default configuration name for ivy - val ivyConfName = "default" - // set ivy settings for location of cache - val ivySettings: IvySettings = new IvySettings - // Directories for caching downloads through ivy and storing the jars when maven coordinates - // are supplied to spark-submit - val alternateIvyCache = ivyPath.getOrElse("") - val packagesDirectory: File = - if (alternateIvyCache.trim.isEmpty) { - new File(ivySettings.getDefaultIvyUserDir, "jars") - } else { - ivySettings.setDefaultIvyUserDir(new File(alternateIvyCache)) - ivySettings.setDefaultCache(new File(alternateIvyCache, "cache")) - new File(alternateIvyCache, "jars") - } - printStream.println( - s"Ivy Default Cache set to: ${ivySettings.getDefaultCache.getAbsolutePath}") - printStream.println(s"The jars for the packages stored in: $packagesDirectory") - // create a pattern matcher - ivySettings.addMatcher(new GlobPatternMatcher) - // create the dependency resolvers - val repoResolver = createRepoResolvers(remoteRepos, ivySettings) - ivySettings.addResolver(repoResolver) - ivySettings.setDefaultResolver(repoResolver.getName) - - val ivy = Ivy.newInstance(ivySettings) - // Set resolve options to download transitive dependencies as well - val resolveOptions = new ResolveOptions - resolveOptions.setTransitive(true) - val retrieveOptions = new RetrieveOptions - // Turn downloading and logging off for testing - if (isTest) { - resolveOptions.setDownload(false) - resolveOptions.setLog(LogOptions.LOG_QUIET) - retrieveOptions.setLog(LogOptions.LOG_QUIET) + // To prevent ivy from logging to system out + System.setOut(printStream) + val artifacts = extractMavenCoordinates(coordinates) + // Default configuration name for ivy + val ivyConfName = "default" + // set ivy settings for location of cache + val ivySettings: IvySettings = new IvySettings + // Directories for caching downloads through ivy and storing the jars when maven coordinates + // are supplied to spark-submit + val alternateIvyCache = ivyPath.getOrElse("") + val packagesDirectory: File = + if (alternateIvyCache.trim.isEmpty) { + new File(ivySettings.getDefaultIvyUserDir, "jars") } else { - resolveOptions.setDownload(true) + ivySettings.setDefaultIvyUserDir(new File(alternateIvyCache)) + ivySettings.setDefaultCache(new File(alternateIvyCache, "cache")) + new File(alternateIvyCache, "jars") } + printStream.println( + s"Ivy Default Cache set to: ${ivySettings.getDefaultCache.getAbsolutePath}") + printStream.println(s"The jars for the packages stored in: $packagesDirectory") + // create a pattern matcher + ivySettings.addMatcher(new GlobPatternMatcher) + // create the dependency resolvers + val repoResolver = createRepoResolvers(remoteRepos, ivySettings) + ivySettings.addResolver(repoResolver) + ivySettings.setDefaultResolver(repoResolver.getName) + + val ivy = Ivy.newInstance(ivySettings) + // Set resolve options to download transitive dependencies as well + val resolveOptions = new ResolveOptions + resolveOptions.setTransitive(true) + val retrieveOptions = new RetrieveOptions + // Turn downloading and logging off for testing + if (isTest) { + resolveOptions.setDownload(false) + resolveOptions.setLog(LogOptions.LOG_QUIET) + retrieveOptions.setLog(LogOptions.LOG_QUIET) + } else { + resolveOptions.setDownload(true) + } - // A Module descriptor must be specified. Entries are dummy strings - val md = getModuleDescriptor - md.setDefaultConf(ivyConfName) + // A Module descriptor must be specified. Entries are dummy strings + val md = getModuleDescriptor + md.setDefaultConf(ivyConfName) - // Add exclusion rules for Spark and Scala Library - addExclusionRules(ivySettings, ivyConfName, md) - // add all supplied maven artifacts as dependencies - addDependenciesToIvy(md, artifacts, ivyConfName) + // Add exclusion rules for Spark and Scala Library + addExclusionRules(ivySettings, ivyConfName, md) + // add all supplied maven artifacts as dependencies + addDependenciesToIvy(md, artifacts, ivyConfName) - // resolve dependencies - val rr: ResolveReport = ivy.resolve(md, resolveOptions) - if (rr.hasError) { - throw new RuntimeException(rr.getAllProblemMessages.toString) - } - // retrieve all resolved dependencies - ivy.retrieve(rr.getModuleDescriptor.getModuleRevisionId, - packagesDirectory.getAbsolutePath + File.separator + - "[organization]_[artifact]-[revision].[ext]", - retrieveOptions.setConfs(Array(ivyConfName))) - resolveDependencyPaths(rr.getArtifacts.toArray, packagesDirectory) - } finally { - System.setOut(sysOut) + // resolve dependencies + val rr: ResolveReport = ivy.resolve(md, resolveOptions) + if (rr.hasError) { + throw new RuntimeException(rr.getAllProblemMessages.toString) } + // retrieve all resolved dependencies + ivy.retrieve(rr.getModuleDescriptor.getModuleRevisionId, + packagesDirectory.getAbsolutePath + File.separator + + "[organization]_[artifact]-[revision].[ext]", + retrieveOptions.setConfs(Array(ivyConfName))) + System.setOut(sysOut) + resolveDependencyPaths(rr.getArtifacts.toArray, packagesDirectory) } } } diff --git a/core/src/test/scala/org/apache/spark/deploy/IvyTestUtils.scala b/core/src/test/scala/org/apache/spark/deploy/IvyTestUtils.scala deleted file mode 100644 index 529f91e8eaf9e..0000000000000 --- a/core/src/test/scala/org/apache/spark/deploy/IvyTestUtils.scala +++ /dev/null @@ -1,262 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.deploy - -import java.io.{File, FileInputStream, FileOutputStream} -import java.nio.file.{Files, Path} -import java.util.jar.{JarEntry, JarOutputStream} - -import org.apache.spark.TestUtils.{createCompiledClass, JavaSourceFromString} - -import com.google.common.io.ByteStreams - -import org.apache.commons.io.FileUtils - -import org.apache.spark.deploy.SparkSubmitUtils.MavenCoordinate - -private[deploy] object IvyTestUtils { - - /** - * Create the path for the jar and pom from the maven coordinate. Extension should be `jar` - * or `pom`. - */ - private def pathFromCoordinate( - artifact: MavenCoordinate, - prefix: Path, - ext: String, - useIvyLayout: Boolean): Path = { - val groupDirs = artifact.groupId.replace(".", File.separator) - val artifactDirs = artifact.artifactId - val artifactPath = - if (!useIvyLayout) { - Seq(groupDirs, artifactDirs, artifact.version).mkString(File.separator) - } else { - Seq(groupDirs, artifactDirs, artifact.version, ext + "s").mkString(File.separator) - } - new File(prefix.toFile, artifactPath).toPath - } - - private def artifactName(artifact: MavenCoordinate, ext: String = ".jar"): String = { - s"${artifact.artifactId}-${artifact.version}$ext" - } - - /** Write the contents to a file to the supplied directory. */ - private def writeFile(dir: File, fileName: String, contents: String): File = { - val outputFile = new File(dir, fileName) - val outputStream = new FileOutputStream(outputFile) - outputStream.write(contents.toCharArray.map(_.toByte)) - outputStream.close() - outputFile - } - - /** Create an example Python file. */ - private def createPythonFile(dir: File): File = { - val contents = - """def myfunc(x): - | return x + 1 - """.stripMargin - writeFile(dir, "mylib.py", contents) - } - - /** Create a simple testable Class. */ - private def createJavaClass(dir: File, className: String, packageName: String): File = { - val contents = - s"""package $packageName; - | - |import java.lang.Integer; - | - |class $className implements java.io.Serializable { - | - | public $className() {} - | - | public Integer myFunc(Integer x) { - | return x + 1; - | } - |} - """.stripMargin - val sourceFile = - new JavaSourceFromString(new File(dir, className + ".java").getAbsolutePath, contents) - createCompiledClass(className, dir, sourceFile, Seq.empty) - } - - /** Helper method to write artifact information in the pom. */ - private def pomArtifactWriter(artifact: MavenCoordinate, tabCount: Int = 1): String = { - var result = "\n" + " " * tabCount + s"${artifact.groupId}" - result += "\n" + " " * tabCount + s"${artifact.artifactId}" - result += "\n" + " " * tabCount + s"${artifact.version}" - result - } - - /** Create a pom file for this artifact. */ - private def createPom( - dir: File, - artifact: MavenCoordinate, - dependencies: Option[Seq[MavenCoordinate]]): File = { - var content = """ - | - | - | 4.0.0 - """.stripMargin.trim - content += pomArtifactWriter(artifact) - content += dependencies.map { deps => - val inside = deps.map { dep => - "\t" + pomArtifactWriter(dep, 3) + "\n\t" - }.mkString("\n") - "\n \n" + inside + "\n " - }.getOrElse("") - content += "\n" - writeFile(dir, artifactName(artifact, ".pom"), content.trim) - } - - /** Create the jar for the given maven coordinate, using the supplied files. */ - private def packJar( - dir: File, - artifact: MavenCoordinate, - files: Seq[(String, File)]): File = { - val jarFile = new File(dir, artifactName(artifact)) - val jarFileStream = new FileOutputStream(jarFile) - val jarStream = new JarOutputStream(jarFileStream, new java.util.jar.Manifest()) - - for (file <- files) { - val jarEntry = new JarEntry(file._1) - jarStream.putNextEntry(jarEntry) - - val in = new FileInputStream(file._2) - ByteStreams.copy(in, jarStream) - in.close() - } - jarStream.close() - jarFileStream.close() - - jarFile - } - - /** - * Creates a jar and pom file, mocking a Maven repository. The root path can be supplied with - * `tempDir`, dependencies can be created into the same repo, and python files can also be packed - * inside the jar. - * - * @param artifact The maven coordinate to generate the jar and pom for. - * @param dependencies List of dependencies this artifact might have to also create jars and poms. - * @param tempDir The root folder of the repository - * @param useIvyLayout whether to mock the Ivy layout for local repository testing - * @param withPython Whether to pack python files inside the jar for extensive testing. - * @return Root path of the repository - */ - private def createLocalRepository( - artifact: MavenCoordinate, - dependencies: Option[Seq[MavenCoordinate]] = None, - tempDir: Option[Path] = None, - useIvyLayout: Boolean = false, - withPython: Boolean = false): Path = { - // Where the root of the repository exists, and what Ivy will search in - val tempPath = tempDir.getOrElse(Files.createTempDirectory(null)) - // Create directory if it doesn't exist - Files.createDirectories(tempPath) - // Where to create temporary class files and such - val root = Files.createTempDirectory(tempPath, null).toFile - try { - val jarPath = pathFromCoordinate(artifact, tempPath, "jar", useIvyLayout) - Files.createDirectories(jarPath) - val className = "MyLib" - - val javaClass = createJavaClass(root, className, artifact.groupId) - // A tuple of files representation in the jar, and the file - val javaFile = (artifact.groupId.replace(".", "/") + "/" + javaClass.getName, javaClass) - val allFiles = - if (withPython) { - val pythonFile = createPythonFile(root) - Seq(javaFile, (pythonFile.getName, pythonFile)) - } else { - Seq(javaFile) - } - val jarFile = packJar(jarPath.toFile, artifact, allFiles) - assert(jarFile.exists(), "Problem creating Jar file") - val pomPath = pathFromCoordinate(artifact, tempPath, "pom", useIvyLayout) - Files.createDirectories(pomPath) - val pomFile = createPom(pomPath.toFile, artifact, dependencies) - assert(pomFile.exists(), "Problem creating Pom file") - } finally { - FileUtils.deleteDirectory(root) - } - tempPath - } - - /** - * Creates a suite of jars and poms, with or without dependencies, mocking a maven repository. - * @param artifact The main maven coordinate to generate the jar and pom for. - * @param dependencies List of dependencies this artifact might have to also create jars and poms. - * @param rootDir The root folder of the repository (like `~/.m2/repositories`) - * @param useIvyLayout whether to mock the Ivy layout for local repository testing - * @param withPython Whether to pack python files inside the jar for extensive testing. - * @return Root path of the repository. Will be `rootDir` if supplied. - */ - private[deploy] def createLocalRepositoryForTests( - artifact: MavenCoordinate, - dependencies: Option[String], - rootDir: Option[Path], - useIvyLayout: Boolean = false, - withPython: Boolean = false): Path = { - val deps = dependencies.map(SparkSubmitUtils.extractMavenCoordinates) - val mainRepo = createLocalRepository(artifact, deps, rootDir, useIvyLayout, withPython) - deps.foreach { seq => seq.foreach { dep => - createLocalRepository(dep, None, Some(mainRepo), useIvyLayout, withPython = false) - }} - mainRepo - } - - /** - * Creates a repository for a test, and cleans it up afterwards. - * - * @param artifact The main maven coordinate to generate the jar and pom for. - * @param dependencies List of dependencies this artifact might have to also create jars and poms. - * @param rootDir The root folder of the repository (like `~/.m2/repositories`) - * @param useIvyLayout whether to mock the Ivy layout for local repository testing - * @param withPython Whether to pack python files inside the jar for extensive testing. - * @return Root path of the repository. Will be `rootDir` if supplied. - */ - private[deploy] def withRepository( - artifact: MavenCoordinate, - dependencies: Option[String], - rootDir: Option[Path], - useIvyLayout: Boolean = false, - withPython: Boolean = false)(f: String => Unit): Unit = { - val repo = createLocalRepositoryForTests(artifact, dependencies, rootDir, useIvyLayout, - withPython) - try { - f(repo.toUri.toString) - } finally { - // Clean up - if (repo.toString.contains(".m2") || repo.toString.contains(".ivy2")) { - FileUtils.deleteDirectory(new File(repo.toFile, - artifact.groupId.replace(".", File.separator) + File.separator + artifact.artifactId)) - dependencies.map(SparkSubmitUtils.extractMavenCoordinates).foreach { seq => - seq.foreach { dep => - FileUtils.deleteDirectory(new File(repo.toFile, - dep.artifactId.replace(".", File.separator))) - } - } - } else { - FileUtils.deleteDirectory(repo.toFile) - } - } - } -} diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala index 8360b94599547..029a1156fda6b 100644 --- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala @@ -30,7 +30,6 @@ import org.scalatest.time.SpanSugar._ import org.apache.spark._ import org.apache.spark.deploy.SparkSubmit._ -import org.apache.spark.deploy.SparkSubmitUtils.MavenCoordinate import org.apache.spark.util.{ResetSystemProperties, Utils} // Note: this suite mixes in ResetSystemProperties because SparkSubmit.main() sets a bunch @@ -337,20 +336,16 @@ class SparkSubmitSuite extends FunSuite with Matchers with ResetSystemProperties ignore("includes jars passed in through --packages") { val unusedJar = TestUtils.createJarWithClasses(Seq.empty) - val main = MavenCoordinate("my.great.lib", "mylib", "0.1") - val dep = MavenCoordinate("my.great.dep", "mylib", "0.1") - IvyTestUtils.withRepository(main, Some(dep.toString), None) { repo => - val args = Seq( - "--class", JarCreationTest.getClass.getName.stripSuffix("$"), - "--name", "testApp", - "--master", "local-cluster[2,1,512]", - "--packages", Seq(main, dep).mkString(","), - "--repositories", repo, - "--conf", "spark.ui.enabled=false", - unusedJar.toString, - "my.great.lib.MyLib", "my.great.dep.MyLib") - runSparkSubmit(args) - } + val packagesString = "com.databricks:spark-csv_2.10:0.1,com.databricks:spark-avro_2.10:0.1" + val args = Seq( + "--class", JarCreationTest.getClass.getName.stripSuffix("$"), + "--name", "testApp", + "--master", "local-cluster[2,1,512]", + "--packages", packagesString, + "--conf", "spark.ui.enabled=false", + unusedJar.toString, + "com.databricks.spark.csv.DefaultSource", "com.databricks.spark.avro.DefaultSource") + runSparkSubmit(args) } test("resolves command line argument paths correctly") { diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala index cc79ee7ea20b4..1b2b699cb11e6 100644 --- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala @@ -19,15 +19,15 @@ package org.apache.spark.deploy import java.io.{PrintStream, OutputStream, File} +import org.apache.ivy.core.settings.IvySettings + import scala.collection.mutable.ArrayBuffer + import org.scalatest.{BeforeAndAfterAll, FunSuite} import org.apache.ivy.core.module.descriptor.MDArtifact -import org.apache.ivy.core.settings.IvySettings import org.apache.ivy.plugins.resolver.IBiblioResolver -import org.apache.spark.deploy.SparkSubmitUtils.MavenCoordinate - class SparkSubmitUtilsSuite extends FunSuite with BeforeAndAfterAll { private val noOpOutputStream = new OutputStream { @@ -89,7 +89,7 @@ class SparkSubmitUtilsSuite extends FunSuite with BeforeAndAfterAll { } test("ivy path works correctly") { - val ivyPath = "dummy" + File.separator + "ivy" + val ivyPath = "dummy/ivy" val md = SparkSubmitUtils.getModuleDescriptor val artifacts = for (i <- 0 until 3) yield new MDArtifact(md, s"jar-$i", "jar", "jar") var jPaths = SparkSubmitUtils.resolveDependencyPaths(artifacts.toArray, new File(ivyPath)) @@ -98,38 +98,17 @@ class SparkSubmitUtilsSuite extends FunSuite with BeforeAndAfterAll { assert(index >= 0) jPaths = jPaths.substring(index + ivyPath.length) } - val main = MavenCoordinate("my.awesome.lib", "mylib", "0.1") - IvyTestUtils.withRepository(main, None, None) { repo => - // end to end - val jarPath = SparkSubmitUtils.resolveMavenCoordinates(main.toString, Option(repo), - Option(ivyPath), true) - assert(jarPath.indexOf(ivyPath) >= 0, "should use non-default ivy path") - } + // end to end + val jarPath = SparkSubmitUtils.resolveMavenCoordinates( + "com.databricks:spark-csv_2.10:0.1", None, Option(ivyPath), true) + assert(jarPath.indexOf(ivyPath) >= 0, "should use non-default ivy path") } - test("search for artifact at local repositories") { - val main = new MavenCoordinate("my.awesome.lib", "mylib", "0.1") - // Local M2 repository - IvyTestUtils.withRepository(main, None, Some(SparkSubmitUtils.m2Path)) { repo => - val jarPath = SparkSubmitUtils.resolveMavenCoordinates(main.toString, None, None, true) - assert(jarPath.indexOf("mylib") >= 0, "should find artifact") - } - // Local Ivy Repository - val settings = new IvySettings - val ivyLocal = new File(settings.getDefaultIvyUserDir, "local" + File.separator) - IvyTestUtils.withRepository(main, None, Some(ivyLocal.toPath), true) { repo => - val jarPath = SparkSubmitUtils.resolveMavenCoordinates(main.toString, None, None, true) - assert(jarPath.indexOf("mylib") >= 0, "should find artifact") - } - // Local ivy repository with modified home - val dummyIvyPath = "dummy" + File.separator + "ivy" - val dummyIvyLocal = new File(dummyIvyPath, "local" + File.separator) - IvyTestUtils.withRepository(main, None, Some(dummyIvyLocal.toPath), true) { repo => - val jarPath = SparkSubmitUtils.resolveMavenCoordinates(main.toString, None, - Some(dummyIvyPath), true) - assert(jarPath.indexOf("mylib") >= 0, "should find artifact") - assert(jarPath.indexOf(dummyIvyPath) >= 0, "should be in new ivy path") - } + test("search for artifact at other repositories") { + val path = SparkSubmitUtils.resolveMavenCoordinates("com.agimatec:agimatec-validation:0.9.3", + Option("https://oss.sonatype.org/content/repositories/agimatec/"), None, true) + assert(path.indexOf("agimatec-validation") >= 0, "should find package. If it doesn't, check" + + "if package still exists. If it has been removed, replace the example in this test.") } test("dependency not found throws RuntimeException") { @@ -148,11 +127,11 @@ class SparkSubmitUtilsSuite extends FunSuite with BeforeAndAfterAll { val path = SparkSubmitUtils.resolveMavenCoordinates(coordinates, None, None, true) assert(path === "", "should return empty path") - val main = MavenCoordinate("org.apache.spark", "spark-streaming-kafka-assembly_2.10", "1.2.0") - IvyTestUtils.withRepository(main, None, None) { repo => - val files = SparkSubmitUtils.resolveMavenCoordinates(coordinates + "," + main.toString, - Some(repo), None, true) - assert(files.indexOf(main.artifactId) >= 0, "Did not return artifact") + // Should not exclude the following dependency. Will throw an error, because it doesn't exist, + // but the fact that it is checking means that it wasn't excluded. + intercept[RuntimeException] { + SparkSubmitUtils.resolveMavenCoordinates(coordinates + + ",org.apache.spark:spark-streaming-kafka-assembly_2.10:1.2.0", None, None, true) } } } From f53a48827ef024f91b292132075e5598c9cb94bb Mon Sep 17 00:00:00 2001 From: Nishkam Ravi Date: Fri, 1 May 2015 21:14:16 +0100 Subject: [PATCH 527/591] [SPARK-7213] [YARN] Check for read permissions before copying a Hadoop config file Author: Nishkam Ravi Author: nishkamravi2 Author: nravi Closes #5760 from nishkamravi2/master_nravi and squashes the following commits: eaa13b5 [nishkamravi2] Update Client.scala 981afd2 [Nishkam Ravi] Check for read permission before initiating copy 1b81383 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 0f1abd0 [nishkamravi2] Update Utils.scala 474e3bf [nishkamravi2] Update DiskBlockManager.scala 97c383e [nishkamravi2] Update Utils.scala 8691e0c [Nishkam Ravi] Add a try/catch block around Utils.removeShutdownHook 2be1e76 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 1c13b79 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi bad4349 [nishkamravi2] Update Main.java 36a6f87 [Nishkam Ravi] Minor changes and bug fixes b7f4ae7 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 4a45d6a [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 458af39 [Nishkam Ravi] Locate the jar using getLocation, obviates the need to pass assembly path as an argument d9658d6 [Nishkam Ravi] Changes for SPARK-6406 ccdc334 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 3faa7a4 [Nishkam Ravi] Launcher library changes (SPARK-6406) 345206a [Nishkam Ravi] spark-class merge Merge branch 'master_nravi' of https://github.com/nishkamravi2/spark into master_nravi ac58975 [Nishkam Ravi] spark-class changes 06bfeb0 [nishkamravi2] Update spark-class 35af990 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 32c3ab3 [nishkamravi2] Update AbstractCommandBuilder.java 4bd4489 [nishkamravi2] Update AbstractCommandBuilder.java 746f35b [Nishkam Ravi] "hadoop" string in the assembly name should not be mandatory (everywhere else in spark we mandate spark-assembly*hadoop*.jar) bfe96e0 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi ee902fa [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi d453197 [nishkamravi2] Update NewHadoopRDD.scala 6f41a1d [nishkamravi2] Update NewHadoopRDD.scala 0ce2c32 [nishkamravi2] Update HadoopRDD.scala f7e33c2 [Nishkam Ravi] Merge branch 'master_nravi' of https://github.com/nishkamravi2/spark into master_nravi ba1eb8b [Nishkam Ravi] Try-catch block around the two occurrences of removeShutDownHook. Deletion of semi-redundant occurrences of expensive operation inShutDown. 71d0e17 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 494d8c0 [nishkamravi2] Update DiskBlockManager.scala 3c5ddba [nishkamravi2] Update DiskBlockManager.scala f0d12de [Nishkam Ravi] Workaround for IllegalStateException caused by recent changes to BlockManager.stop 79ea8b4 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi b446edc [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 5c9a4cb [nishkamravi2] Update TaskSetManagerSuite.scala 535295a [nishkamravi2] Update TaskSetManager.scala 3e1b616 [Nishkam Ravi] Modify test for maxResultSize 9f6583e [Nishkam Ravi] Changes to maxResultSize code (improve error message and add condition to check if maxResultSize > 0) 5f8f9ed [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi 636a9ff [nishkamravi2] Update YarnAllocator.scala 8f76c8b [Nishkam Ravi] Doc change for yarn memory overhead 35daa64 [Nishkam Ravi] Slight change in the doc for yarn memory overhead 5ac2ec1 [Nishkam Ravi] Remove out dac1047 [Nishkam Ravi] Additional documentation for yarn memory overhead issue 42c2c3d [Nishkam Ravi] Additional changes for yarn memory overhead issue 362da5e [Nishkam Ravi] Additional changes for yarn memory overhead c726bd9 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi f00fa31 [Nishkam Ravi] Improving logging for AM memoryOverhead 1cf2d1e [nishkamravi2] Update YarnAllocator.scala ebcde10 [Nishkam Ravi] Modify default YARN memory_overhead-- from an additive constant to a multiplier (redone to resolve merge conflicts) 2e69f11 [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark into master_nravi efd688a [Nishkam Ravi] Merge branch 'master' of https://github.com/apache/spark 2b630f9 [nravi] Accept memory input as "30g", "512M" instead of an int value, to be consistent with rest of Spark 3bf8fad [nravi] Merge branch 'master' of https://github.com/apache/spark 5423a03 [nravi] Merge branch 'master' of https://github.com/apache/spark eb663ca [nravi] Merge branch 'master' of https://github.com/apache/spark df2aeb1 [nravi] Improved fix for ConcurrentModificationIssue (Spark-1097, Hadoop-10456) 6b840f0 [nravi] Undo the fix for SPARK-1758 (the problem is fixed) 5108700 [nravi] Fix in Spark for the Concurrent thread modification issue (SPARK-1097, HADOOP-10456) 681b36f [nravi] Fix for SPARK-1758: failing test org.apache.spark.JavaAPISuite.wholeTextFiles --- .../main/scala/org/apache/spark/deploy/yarn/Client.scala | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 4abcf7307a388..b945395f24ea6 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -371,9 +371,11 @@ private[spark] class Client( try { hadoopConfStream.setLevel(0) hadoopConfFiles.foreach { case (name, file) => - hadoopConfStream.putNextEntry(new ZipEntry(name)) - Files.copy(file, hadoopConfStream) - hadoopConfStream.closeEntry() + if (file.canRead()) { + hadoopConfStream.putNextEntry(new ZipEntry(name)) + Files.copy(file, hadoopConfStream) + hadoopConfStream.closeEntry() + } } } finally { hadoopConfStream.close() From 7b5dd3e3c0030087eea5a8224789352c03717c1d Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Fri, 1 May 2015 21:20:46 +0100 Subject: [PATCH 528/591] [SPARK-7281] [YARN] Add option to set AM's lib path in client mode. Author: Marcelo Vanzin Closes #5813 from vanzin/SPARK-7281 and squashes the following commits: 1cb6f42 [Marcelo Vanzin] [SPARK-7281] [yarn] Add option to set AM's lib path in client mode. --- docs/running-on-yarn.md | 7 +++++++ .../main/scala/org/apache/spark/deploy/yarn/Client.scala | 4 ++++ 2 files changed, 11 insertions(+) diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md index 0968fc5ad632b..b6701b64c2925 100644 --- a/docs/running-on-yarn.md +++ b/docs/running-on-yarn.md @@ -189,6 +189,13 @@ Most of the configs are the same for Spark on YARN as for other deployment modes In cluster mode, use spark.driver.extraJavaOptions instead. + + spark.yarn.am.extraLibraryPath + (none) + + Set a special library path to use when launching the application master in client mode. + + spark.yarn.maxAppAttempts yarn.resourcemanager.am.max-attempts in YARN diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index b945395f24ea6..31ab6b491ec2a 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -544,6 +544,10 @@ private[spark] class Client( } javaOpts ++= Utils.splitCommandString(opts).map(YarnSparkHadoopUtil.escapeForShell) } + + sparkConf.getOption("spark.yarn.am.extraLibraryPath").foreach { paths => + prefixEnv = Some(Utils.libraryPathEnvPrefix(Seq(paths))) + } } // For log4j configuration to reference From 4dc8d74491b101a794cf8d386d8c5ebc6019b75f Mon Sep 17 00:00:00 2001 From: Burak Yavuz Date: Fri, 1 May 2015 13:29:17 -0700 Subject: [PATCH 529/591] [SPARK-7240][SQL] Single pass covariance calculation for dataframes Added the calculation of covariance between two columns to DataFrames. cc mengxr rxin Author: Burak Yavuz Closes #5825 from brkyvz/df-cov and squashes the following commits: cb18046 [Burak Yavuz] changed to sample covariance f2e862b [Burak Yavuz] fixed failed test 51e39b8 [Burak Yavuz] moved implementation 0c6a759 [Burak Yavuz] addressed math comments 8456eca [Burak Yavuz] fix pyStyle3 aa2ad29 [Burak Yavuz] fix pyStyle2 4e97a50 [Burak Yavuz] Merge branch 'master' of github.com:apache/spark into df-cov e3b0b85 [Burak Yavuz] addressed comments v0.1 a7115f1 [Burak Yavuz] fix python style 7dc6dbc [Burak Yavuz] reorder imports 408cb77 [Burak Yavuz] initial commit --- python/pyspark/sql/__init__.py | 4 +- python/pyspark/sql/dataframe.py | 36 ++++++++- python/pyspark/sql/tests.py | 5 ++ .../spark/sql/DataFrameStatFunctions.scala | 12 ++- .../sql/execution/stat/StatFunctions.scala | 80 +++++++++++++++++++ .../apache/spark/sql/JavaDataFrameSuite.java | 7 ++ .../apache/spark/sql/DataFrameStatSuite.scala | 18 ++++- 7 files changed, 157 insertions(+), 5 deletions(-) create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala diff --git a/python/pyspark/sql/__init__.py b/python/pyspark/sql/__init__.py index 6d54b9e49ed10..b60b991dd4d8b 100644 --- a/python/pyspark/sql/__init__.py +++ b/python/pyspark/sql/__init__.py @@ -54,7 +54,9 @@ from pyspark.sql.types import Row from pyspark.sql.context import SQLContext, HiveContext from pyspark.sql.dataframe import DataFrame, GroupedData, Column, SchemaRDD, DataFrameNaFunctions +from pyspark.sql.dataframe import DataFrameStatFunctions __all__ = [ - 'SQLContext', 'HiveContext', 'DataFrame', 'GroupedData', 'Column', 'Row', 'DataFrameNaFunctions' + 'SQLContext', 'HiveContext', 'DataFrame', 'GroupedData', 'Column', 'Row', + 'DataFrameNaFunctions', 'DataFrameStatFunctions' ] diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index 5908ebc990a56..1f08c2df9305b 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -34,7 +34,8 @@ from pyspark.sql.types import _create_cls, _parse_datatype_json_string -__all__ = ["DataFrame", "GroupedData", "Column", "SchemaRDD", "DataFrameNaFunctions"] +__all__ = ["DataFrame", "GroupedData", "Column", "SchemaRDD", "DataFrameNaFunctions", + "DataFrameStatFunctions"] class DataFrame(object): @@ -93,6 +94,12 @@ def na(self): """ return DataFrameNaFunctions(self) + @property + def stat(self): + """Returns a :class:`DataFrameStatFunctions` for statistic functions. + """ + return DataFrameStatFunctions(self) + @ignore_unicode_prefix def toJSON(self, use_unicode=True): """Converts a :class:`DataFrame` into a :class:`RDD` of string. @@ -868,6 +875,20 @@ def fillna(self, value, subset=None): return DataFrame(self._jdf.na().fill(value, self._jseq(subset)), self.sql_ctx) + def cov(self, col1, col2): + """ + Calculate the sample covariance for the given columns, specified by their names, as a + double value. :func:`DataFrame.cov` and :func:`DataFrameStatFunctions.cov` are aliases. + + :param col1: The name of the first column + :param col2: The name of the second column + """ + if not isinstance(col1, str): + raise ValueError("col1 should be a string.") + if not isinstance(col2, str): + raise ValueError("col2 should be a string.") + return self._jdf.stat().cov(col1, col2) + @ignore_unicode_prefix def withColumn(self, colName, col): """Returns a new :class:`DataFrame` by adding a column. @@ -1311,6 +1332,19 @@ def fill(self, value, subset=None): fill.__doc__ = DataFrame.fillna.__doc__ +class DataFrameStatFunctions(object): + """Functionality for statistic functions with :class:`DataFrame`. + """ + + def __init__(self, df): + self.df = df + + def cov(self, col1, col2): + return self.df.cov(col1, col2) + + cov.__doc__ = DataFrame.cov.__doc__ + + def _test(): import doctest from pyspark.context import SparkContext diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index 5640bb5ea2346..44c8b6a1aac13 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -387,6 +387,11 @@ def test_aggregator(self): self.assertTrue(95 < g.agg(functions.approxCountDistinct(df.key)).first()[0]) self.assertEqual(100, g.agg(functions.countDistinct(df.value)).first()[0]) + def test_cov(self): + df = self.sc.parallelize([Row(a=i, b=2 * i) for i in range(10)]).toDF() + cov = df.stat.cov("a", "b") + self.assertTrue(abs(cov - 55.0 / 3) < 1e-6) + def test_math_functions(self): df = self.sc.parallelize([Row(a=i, b=2 * i) for i in range(10)]).toDF() from pyspark.sql import mathfunctions as functions diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala index 42e5cbc05e1e0..23652aeb7c7bc 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala @@ -18,7 +18,7 @@ package org.apache.spark.sql import org.apache.spark.annotation.Experimental -import org.apache.spark.sql.execution.stat.FrequentItems +import org.apache.spark.sql.execution.stat._ /** * :: Experimental :: @@ -65,4 +65,14 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) { def freqItems(cols: List[String]): DataFrame = { FrequentItems.singlePassFreqItems(df, cols, 0.01) } + + /** + * Calculate the sample covariance of two numerical columns of a DataFrame. + * @param col1 the name of the first column + * @param col2 the name of the second column + * @return the covariance of the two columns. + */ + def cov(col1: String, col2: String): Double = { + StatFunctions.calculateCov(df, Seq(col1, col2)) + } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala new file mode 100644 index 0000000000000..d4a94c24d9866 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala @@ -0,0 +1,80 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.stat + +import org.apache.spark.sql.catalyst.expressions.Cast +import org.apache.spark.sql.{Column, DataFrame} +import org.apache.spark.sql.types.{DoubleType, NumericType} + +private[sql] object StatFunctions { + + /** Helper class to simplify tracking and merging counts. */ + private class CovarianceCounter extends Serializable { + var xAvg = 0.0 + var yAvg = 0.0 + var Ck = 0.0 + var count = 0L + // add an example to the calculation + def add(x: Double, y: Double): this.type = { + val oldX = xAvg + count += 1 + xAvg += (x - xAvg) / count + yAvg += (y - yAvg) / count + Ck += (y - yAvg) * (x - oldX) + this + } + // merge counters from other partitions. Formula can be found at: + // http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Covariance + def merge(other: CovarianceCounter): this.type = { + val totalCount = count + other.count + Ck += other.Ck + + (xAvg - other.xAvg) * (yAvg - other.yAvg) * count / totalCount * other.count + xAvg = (xAvg * count + other.xAvg * other.count) / totalCount + yAvg = (yAvg * count + other.yAvg * other.count) / totalCount + count = totalCount + this + } + // return the sample covariance for the observed examples + def cov: Double = Ck / (count - 1) + } + + /** + * Calculate the covariance of two numerical columns of a DataFrame. + * @param df The DataFrame + * @param cols the column names + * @return the covariance of the two columns. + */ + private[sql] def calculateCov(df: DataFrame, cols: Seq[String]): Double = { + require(cols.length == 2, "Currently cov supports calculating the covariance " + + "between two columns.") + cols.map(name => (name, df.schema.fields.find(_.name == name))).foreach { case (name, data) => + require(data.nonEmpty, s"Couldn't find column with name $name") + require(data.get.dataType.isInstanceOf[NumericType], "Covariance calculation for columns " + + s"with dataType ${data.get.dataType} not supported.") + } + val columns = cols.map(n => Column(Cast(Column(n).expr, DoubleType))) + val counts = df.select(columns:_*).rdd.aggregate(new CovarianceCounter)( + seqOp = (counter, row) => { + counter.add(row.getDouble(0), row.getDouble(1)) + }, + combOp = (baseCounter, other) => { + baseCounter.merge(other) + }) + counts.cov + } +} diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java index ebe96e649d940..96fe66d0b84a6 100644 --- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java @@ -186,4 +186,11 @@ public void testFrequentItems() { DataFrame results = df.stat().freqItems(cols, 0.2); Assert.assertTrue(results.collect()[0].getSeq(0).contains(1)); } + + @Test + public void testCovariance() { + DataFrame df = context.table("testData2"); + Double result = df.stat().cov("a", "b"); + Assert.assertTrue(Math.abs(result) < 1e-6); + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala index bb1d29c71d23b..4f5a2ff696789 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala @@ -25,10 +25,11 @@ import org.apache.spark.sql.test.TestSQLContext.implicits._ class DataFrameStatSuite extends FunSuite { + import TestData._ val sqlCtx = TestSQLContext - + def toLetter(i: Int): String = (i + 97).toChar.toString + test("Frequent Items") { - def toLetter(i: Int): String = (i + 96).toChar.toString val rows = Array.tabulate(1000) { i => if (i % 3 == 0) (1, toLetter(1), -1.0) else (i, toLetter(i), i * -1.0) } @@ -44,4 +45,17 @@ class DataFrameStatSuite extends FunSuite { items2.getSeq[Double](0) should contain (-1.0) } + + test("covariance") { + val rows = Array.tabulate(10)(i => (i, 2.0 * i, toLetter(i))) + val df = sqlCtx.sparkContext.parallelize(rows).toDF("singles", "doubles", "letters") + + val results = df.stat.cov("singles", "doubles") + assert(math.abs(results - 55.0 / 3) < 1e-6) + intercept[IllegalArgumentException] { + df.stat.cov("singles", "letters") // doesn't accept non-numerical dataTypes + } + val decimalRes = decimalData.stat.cov("a", "b") + assert(math.abs(decimalRes) < 1e-6) + } } From b1f4ca82d170935d15f1fe6beb9af0743b4d81cd Mon Sep 17 00:00:00 2001 From: Hari Shreedharan Date: Fri, 1 May 2015 15:32:09 -0500 Subject: [PATCH 530/591] [SPARK-5342] [YARN] Allow long running Spark apps to run on secure YARN/HDFS Take 2. Does the same thing as #4688, but fixes Hadoop-1 build. Author: Hari Shreedharan Closes #5823 from harishreedharan/kerberos-longrunning and squashes the following commits: 3c86bba [Hari Shreedharan] Import fixes. Import postfixOps explicitly. 4d04301 [Hari Shreedharan] Minor formatting fixes. b5e7a72 [Hari Shreedharan] Remove reflection, use a method in SparkHadoopUtil to update the token renewer. 7bff6e9 [Hari Shreedharan] Make sure all required classes are present in the jar. Fix import order. e851f70 [Hari Shreedharan] Move the ExecutorDelegationTokenRenewer to yarn module. Use reflection to use it. 36eb8a9 [Hari Shreedharan] Change the renewal interval config param. Fix a bunch of comments. 611923a [Hari Shreedharan] Make sure the namenodes are listed correctly for creating tokens. 09fe224 [Hari Shreedharan] Use token.renew to get token's renewal interval rather than using hdfs-site.xml 6963bbc [Hari Shreedharan] Schedule renewal in AM before starting user class. Else, a restarted AM cannot access HDFS if the user class tries to. 072659e [Hari Shreedharan] Fix build failure caused by thread factory getting moved to ThreadUtils. f041dd3 [Hari Shreedharan] Merge branch 'master' into kerberos-longrunning 42eead4 [Hari Shreedharan] Remove RPC part. Refactor and move methods around, use renewal interval rather than max lifetime to create new tokens. ebb36f5 [Hari Shreedharan] Merge branch 'master' into kerberos-longrunning bc083e3 [Hari Shreedharan] Overload RegisteredExecutor to send tokens. Minor doc updates. 7b19643 [Hari Shreedharan] Merge branch 'master' into kerberos-longrunning 8a4f268 [Hari Shreedharan] Added docs in the security guide. Changed some code to ensure that the renewer objects are created only if required. e800c8b [Hari Shreedharan] Restore original RegisteredExecutor message, and send new tokens via NewTokens message. 0e9507e [Hari Shreedharan] Merge branch 'master' into kerberos-longrunning 7f1bc58 [Hari Shreedharan] Minor fixes, cleanup. bcd11f9 [Hari Shreedharan] Refactor AM and Executor token update code into separate classes, also send tokens via akka on executor startup. f74303c [Hari Shreedharan] Move the new logic into specialized classes. Add cleanup for old credentials files. 2f9975c [Hari Shreedharan] Ensure new tokens are written out immediately on AM restart. Also, pikc up the latest suffix from HDFS if the AM is restarted. 61b2b27 [Hari Shreedharan] Account for AM restarts by making sure lastSuffix is read from the files on HDFS. 62c45ce [Hari Shreedharan] Relogin from keytab periodically. fa233bd [Hari Shreedharan] Adding logging, fixing minor formatting and ordering issues. 42813b4 [Hari Shreedharan] Remove utils.sh, which was re-added due to merge with master. 0de27ee [Hari Shreedharan] Merge branch 'master' into kerberos-longrunning 55522e3 [Hari Shreedharan] Fix failure caused by Preconditions ambiguity. 9ef5f1b [Hari Shreedharan] Added explanation of how the credentials refresh works, some other minor fixes. f4fd711 [Hari Shreedharan] Fix SparkConf usage. 2debcea [Hari Shreedharan] Change the file structure for credentials files. I will push a followup patch which adds a cleanup mechanism for old credentials files. The credentials files are small and few enough for it to cause issues on HDFS. af6d5f0 [Hari Shreedharan] Cleaning up files where changes weren't required. f0f54cb [Hari Shreedharan] Be more defensive when updating the credentials file. f6954da [Hari Shreedharan] Got rid of Akka communication to renew, instead the executors check a known file's modification time to read the credentials. 5c11c3e [Hari Shreedharan] Move tests to YarnSparkHadoopUtil to fix compile issues. b4cb917 [Hari Shreedharan] Send keytab to AM via DistributedCache rather than directly via HDFS 0985b4e [Hari Shreedharan] Write tokens to HDFS and read them back when required, rather than sending them over the wire. d79b2b9 [Hari Shreedharan] Make sure correct credentials are passed to FileSystem#addDelegationTokens() 8c6928a [Hari Shreedharan] Fix issue caused by direct creation of Actor object. fb27f46 [Hari Shreedharan] Make sure principal and keytab are set before CoarseGrainedSchedulerBackend is started. Also schedule re-logins in CoarseGrainedSchedulerBackend#start() 41efde0 [Hari Shreedharan] Merge branch 'master' into kerberos-longrunning d282d7a [Hari Shreedharan] Fix ClientSuite to set YARN mode, so that the correct class is used in tests. bcfc374 [Hari Shreedharan] Fix Hadoop-1 build by adding no-op methods in SparkHadoopUtil, with impl in YarnSparkHadoopUtil. f8fe694 [Hari Shreedharan] Handle None if keytab-login is not scheduled. 2b0d745 [Hari Shreedharan] [SPARK-5342][YARN] Allow long running Spark apps to run on secure YARN/HDFS. ccba5bc [Hari Shreedharan] WIP: More changes wrt kerberos 77914dd [Hari Shreedharan] WIP: Add kerberos principal and keytab to YARN client. --- .../apache/spark/deploy/SparkHadoopUtil.scala | 81 ++++++- .../org/apache/spark/deploy/SparkSubmit.scala | 4 + .../spark/deploy/SparkSubmitArguments.scala | 15 ++ .../CoarseGrainedExecutorBackend.scala | 9 + .../CoarseGrainedSchedulerBackend.scala | 4 +- docs/security.md | 2 + .../launcher/SparkSubmitOptionParser.java | 6 +- .../yarn/AMDelegationTokenRenewer.scala | 205 ++++++++++++++++++ .../spark/deploy/yarn/ApplicationMaster.scala | 17 +- .../org/apache/spark/deploy/yarn/Client.scala | 123 +++++++---- .../spark/deploy/yarn/ClientArguments.scala | 10 + .../yarn/ExecutorDelegationTokenUpdater.scala | 106 +++++++++ .../deploy/yarn/YarnSparkHadoopUtil.scala | 69 +++++- .../spark/deploy/yarn/ClientSuite.scala | 51 ----- .../spark/deploy/yarn/YarnClusterSuite.scala | 2 + .../yarn/YarnSparkHadoopUtilSuite.scala | 62 +++++- 16 files changed, 657 insertions(+), 109 deletions(-) create mode 100644 yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala create mode 100644 yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorDelegationTokenUpdater.scala diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala index cfaebf9ea5050..b563034457a91 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala @@ -17,12 +17,16 @@ package org.apache.spark.deploy +import java.io.{ByteArrayInputStream, DataInputStream} import java.lang.reflect.Method import java.security.PrivilegedExceptionAction +import java.util.{Arrays, Comparator} +import com.google.common.primitives.Longs import org.apache.hadoop.conf.Configuration -import org.apache.hadoop.fs.{FileStatus, FileSystem, Path} +import org.apache.hadoop.fs.{FileStatus, FileSystem, Path, PathFilter} import org.apache.hadoop.fs.FileSystem.Statistics +import org.apache.hadoop.hdfs.security.token.delegation.DelegationTokenIdentifier import org.apache.hadoop.mapred.JobConf import org.apache.hadoop.mapreduce.JobContext import org.apache.hadoop.security.{Credentials, UserGroupInformation} @@ -32,6 +36,8 @@ import org.apache.spark.annotation.DeveloperApi import org.apache.spark.util.Utils import scala.collection.JavaConversions._ +import scala.concurrent.duration._ +import scala.language.postfixOps /** * :: DeveloperApi :: @@ -39,7 +45,8 @@ import scala.collection.JavaConversions._ */ @DeveloperApi class SparkHadoopUtil extends Logging { - val conf: Configuration = newConfiguration(new SparkConf()) + private val sparkConf = new SparkConf() + val conf: Configuration = newConfiguration(sparkConf) UserGroupInformation.setConfiguration(conf) /** @@ -201,6 +208,61 @@ class SparkHadoopUtil extends Logging { if (baseStatus.isDir) recurse(basePath) else Array(baseStatus) } + /** + * Lists all the files in a directory with the specified prefix, and does not end with the + * given suffix. The returned {{FileStatus}} instances are sorted by the modification times of + * the respective files. + */ + def listFilesSorted( + remoteFs: FileSystem, + dir: Path, + prefix: String, + exclusionSuffix: String): Array[FileStatus] = { + val fileStatuses = remoteFs.listStatus(dir, + new PathFilter { + override def accept(path: Path): Boolean = { + val name = path.getName + name.startsWith(prefix) && !name.endsWith(exclusionSuffix) + } + }) + Arrays.sort(fileStatuses, new Comparator[FileStatus] { + override def compare(o1: FileStatus, o2: FileStatus): Int = { + Longs.compare(o1.getModificationTime, o2.getModificationTime) + } + }) + fileStatuses + } + + /** + * How much time is remaining (in millis) from now to (fraction * renewal time for the token that + * is valid the latest)? + * This will return -ve (or 0) value if the fraction of validity has already expired. + */ + def getTimeFromNowToRenewal( + sparkConf: SparkConf, + fraction: Double, + credentials: Credentials): Long = { + val now = System.currentTimeMillis() + + val renewalInterval = + sparkConf.getLong("spark.yarn.token.renewal.interval", (24 hours).toMillis) + + credentials.getAllTokens.filter(_.getKind == DelegationTokenIdentifier.HDFS_DELEGATION_KIND) + .map { t => + val identifier = new DelegationTokenIdentifier() + identifier.readFields(new DataInputStream(new ByteArrayInputStream(t.getIdentifier))) + (identifier.getIssueDate + fraction * renewalInterval).toLong - now + }.foldLeft(0L)(math.max) + } + + + private[spark] def getSuffixForCredentialsPath(credentialsPath: Path): Int = { + val fileName = credentialsPath.getName + fileName.substring( + fileName.lastIndexOf(SparkHadoopUtil.SPARK_YARN_CREDS_COUNTER_DELIM) + 1).toInt + } + + private val HADOOP_CONF_PATTERN = "(\\$\\{hadoopconf-[^\\}\\$\\s]+\\})".r.unanchored /** @@ -231,6 +293,17 @@ class SparkHadoopUtil extends Logging { } } } + + /** + * Start a thread to periodically update the current user's credentials with new delegation + * tokens so that writes to HDFS do not fail. + */ + private[spark] def startExecutorDelegationTokenRenewer(conf: SparkConf) {} + + /** + * Stop the thread that does the delegation token updates. + */ + private[spark] def stopExecutorDelegationTokenRenewer() {} } object SparkHadoopUtil { @@ -251,6 +324,10 @@ object SparkHadoopUtil { } } + val SPARK_YARN_CREDS_TEMP_EXTENSION = ".tmp" + + val SPARK_YARN_CREDS_COUNTER_DELIM = "-" + def get: SparkHadoopUtil = { hadoop } diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala index b8ae4af18d1d1..af38bf80e4f0b 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -400,6 +400,10 @@ object SparkSubmit { OptionAssigner(args.archives, YARN, CLUSTER, clOption = "--archives"), OptionAssigner(args.jars, YARN, CLUSTER, clOption = "--addJars"), + // Yarn client or cluster + OptionAssigner(args.principal, YARN, ALL_DEPLOY_MODES, clOption = "--principal"), + OptionAssigner(args.keytab, YARN, ALL_DEPLOY_MODES, clOption = "--keytab"), + // Other options OptionAssigner(args.executorCores, STANDALONE, ALL_DEPLOY_MODES, sysProp = "spark.executor.cores"), diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala index c621b8fc86f94..c0e4c771908b3 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala @@ -63,6 +63,8 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S var action: SparkSubmitAction = null val sparkProperties: HashMap[String, String] = new HashMap[String, String]() var proxyUser: String = null + var principal: String = null + var keytab: String = null // Standalone cluster mode only var supervise: Boolean = false @@ -393,6 +395,12 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S case PROXY_USER => proxyUser = value + case PRINCIPAL => + principal = value + + case KEYTAB => + keytab = value + case HELP => printUsageAndExit(0) @@ -506,6 +514,13 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S | --num-executors NUM Number of executors to launch (Default: 2). | --archives ARCHIVES Comma separated list of archives to be extracted into the | working directory of each executor. + | --principal PRINCIPAL Principal to be used to login to KDC, while running on + | secure HDFS. + | --keytab KEYTAB The full path to the file that contains the keytab for the + | principal specified above. This keytab will be copied to + | the node running the Application Master via the Secure + | Distributed Cache, for renewing the login tickets and the + | delegation tokens periodically. """.stripMargin ) SparkSubmit.exitFn() diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala index 79aed90b53e2f..ed159dec4f998 100644 --- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala +++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala @@ -20,6 +20,8 @@ package org.apache.spark.executor import java.net.URL import java.nio.ByteBuffer +import org.apache.hadoop.conf.Configuration + import scala.collection.mutable import scala.util.{Failure, Success} @@ -168,6 +170,12 @@ private[spark] object CoarseGrainedExecutorBackend extends Logging { driverConf.set(key, value) } } + if (driverConf.contains("spark.yarn.credentials.file")) { + logInfo("Will periodically update credentials from: " + + driverConf.get("spark.yarn.credentials.file")) + SparkHadoopUtil.get.startExecutorDelegationTokenRenewer(driverConf) + } + val env = SparkEnv.createExecutorEnv( driverConf, executorId, hostname, port, cores, isLocal = false) @@ -183,6 +191,7 @@ private[spark] object CoarseGrainedExecutorBackend extends Logging { env.rpcEnv.setupEndpoint("WorkerWatcher", new WorkerWatcher(env.rpcEnv, url)) } env.rpcEnv.awaitTermination() + SparkHadoopUtil.get.stopExecutorDelegationTokenRenewer() } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala index 7352fa1fe9ebd..f107148f3b8c6 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala @@ -68,6 +68,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp class DriverEndpoint(override val rpcEnv: RpcEnv, sparkProperties: Seq[(String, String)]) extends ThreadSafeRpcEndpoint with Logging { + override protected def log = CoarseGrainedSchedulerBackend.this.log private val addressToExecutorId = new HashMap[RpcAddress, String] @@ -112,6 +113,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp // Ignoring the task kill since the executor is not registered. logWarning(s"Attempted to kill task $taskId for unknown executor $executorId.") } + } override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { @@ -122,7 +124,6 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp } else { logInfo("Registered executor: " + executorRef + " with ID " + executorId) context.reply(RegisteredExecutor) - addressToExecutorId(executorRef.address) = executorId totalCoreCount.addAndGet(cores) totalRegisteredExecutors.addAndGet(1) @@ -243,6 +244,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp properties += ((key, value)) } } + // TODO (prashant) send conf instead of properties driverEndpoint = rpcEnv.setupEndpoint( CoarseGrainedSchedulerBackend.ENDPOINT_NAME, new DriverEndpoint(rpcEnv, properties)) diff --git a/docs/security.md b/docs/security.md index c034ba12ff1fc..d4ffa60e59a33 100644 --- a/docs/security.md +++ b/docs/security.md @@ -32,6 +32,8 @@ SSL must be configured on each node and configured for each component involved i ### YARN mode The key-store can be prepared on the client side and then distributed and used by the executors as the part of the application. It is possible because the user is able to deploy files before the application is started in YARN by using `spark.yarn.dist.files` or `spark.yarn.dist.archives` configuration settings. The responsibility for encryption of transferring these files is on YARN side and has nothing to do with Spark. +For long-running apps like Spark Streaming apps to be able to write to HDFS, it is possible to pass a principal and keytab to `spark-submit` via the `--principal` and `--keytab` parameters respectively. The keytab passed in will be copied over to the machine running the Application Master via the Hadoop Distributed Cache (securely - if YARN is configured with SSL and HDFS encryption is enabled). The Kerberos login will be periodically renewed using this principal and keytab and the delegation tokens required for HDFS will be generated periodically so the application can continue writing to HDFS. + ### Standalone mode The user needs to provide key-stores and configuration options for master and workers. They have to be set by attaching appropriate Java system properties in `SPARK_MASTER_OPTS` and in `SPARK_WORKER_OPTS` environment variables, or just in `SPARK_DAEMON_JAVA_OPTS`. In this mode, the user may allow the executors to use the SSL settings inherited from the worker which spawned that executor. It can be accomplished by setting `spark.ssl.useNodeLocalConf` to `true`. If that parameter is set, the settings provided by user on the client side, are not used by the executors. diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java index 8526d2e7cfa3f..229000087688f 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java +++ b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java @@ -69,8 +69,10 @@ class SparkSubmitOptionParser { // YARN-only options. protected final String ARCHIVES = "--archives"; protected final String EXECUTOR_CORES = "--executor-cores"; - protected final String QUEUE = "--queue"; + protected final String KEYTAB = "--keytab"; protected final String NUM_EXECUTORS = "--num-executors"; + protected final String PRINCIPAL = "--principal"; + protected final String QUEUE = "--queue"; /** * This is the canonical list of spark-submit options. Each entry in the array contains the @@ -96,11 +98,13 @@ class SparkSubmitOptionParser { { EXECUTOR_MEMORY }, { FILES }, { JARS }, + { KEYTAB }, { KILL_SUBMISSION }, { MASTER }, { NAME }, { NUM_EXECUTORS }, { PACKAGES }, + { PRINCIPAL }, { PROPERTIES_FILE }, { PROXY_USER }, { PY_FILES }, diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala new file mode 100644 index 0000000000000..aaae6f9734a85 --- /dev/null +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/AMDelegationTokenRenewer.scala @@ -0,0 +1,205 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.spark.deploy.yarn + +import java.security.PrivilegedExceptionAction +import java.util.concurrent.{Executors, TimeUnit} + +import scala.language.postfixOps + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileSystem, Path} +import org.apache.hadoop.security.UserGroupInformation +import org.apache.spark.deploy.SparkHadoopUtil + +import org.apache.spark.{Logging, SparkConf} +import org.apache.spark.util.ThreadUtils + +/* + * The following methods are primarily meant to make sure long-running apps like Spark + * Streaming apps can run without interruption while writing to secure HDFS. The + * scheduleLoginFromKeytab method is called on the driver when the + * CoarseGrainedScheduledBackend starts up. This method wakes up a thread that logs into the KDC + * once 75% of the renewal interval of the original delegation tokens used for the container + * has elapsed. It then creates new delegation tokens and writes them to HDFS in a + * pre-specified location - the prefix of which is specified in the sparkConf by + * spark.yarn.credentials.file (so the file(s) would be named c-1, c-2 etc. - each update goes + * to a new file, with a monotonically increasing suffix). After this, the credentials are + * updated once 75% of the new tokens renewal interval has elapsed. + * + * On the executor side, the updateCredentialsIfRequired method is called once 80% of the + * validity of the original tokens has elapsed. At that time the executor finds the + * credentials file with the latest timestamp and checks if it has read those credentials + * before (by keeping track of the suffix of the last file it read). If a new file has + * appeared, it will read the credentials and update the currently running UGI with it. This + * process happens again once 80% of the validity of this has expired. + */ +private[yarn] class AMDelegationTokenRenewer( + sparkConf: SparkConf, + hadoopConf: Configuration) extends Logging { + + private var lastCredentialsFileSuffix = 0 + + private val delegationTokenRenewer = + Executors.newSingleThreadScheduledExecutor( + ThreadUtils.namedThreadFactory("Delegation Token Refresh Thread")) + + private val hadoopUtil = YarnSparkHadoopUtil.get + + private val daysToKeepFiles = sparkConf.getInt("spark.yarn.credentials.file.retention.days", 5) + private val numFilesToKeep = sparkConf.getInt("spark.yarn.credentials.file.retention.count", 5) + + /** + * Schedule a login from the keytab and principal set using the --principal and --keytab + * arguments to spark-submit. This login happens only when the credentials of the current user + * are about to expire. This method reads spark.yarn.principal and spark.yarn.keytab from + * SparkConf to do the login. This method is a no-op in non-YARN mode. + * + */ + private[spark] def scheduleLoginFromKeytab(): Unit = { + val principal = sparkConf.get("spark.yarn.principal") + val keytab = sparkConf.get("spark.yarn.keytab") + + /** + * Schedule re-login and creation of new tokens. If tokens have already expired, this method + * will synchronously create new ones. + */ + def scheduleRenewal(runnable: Runnable): Unit = { + val credentials = UserGroupInformation.getCurrentUser.getCredentials + val renewalInterval = hadoopUtil.getTimeFromNowToRenewal(sparkConf, 0.75, credentials) + // Run now! + if (renewalInterval <= 0) { + logInfo("HDFS tokens have expired, creating new tokens now.") + runnable.run() + } else { + logInfo(s"Scheduling login from keytab in $renewalInterval millis.") + delegationTokenRenewer.schedule(runnable, renewalInterval, TimeUnit.MILLISECONDS) + } + } + + // This thread periodically runs on the driver to update the delegation tokens on HDFS. + val driverTokenRenewerRunnable = + new Runnable { + override def run(): Unit = { + try { + writeNewTokensToHDFS(principal, keytab) + cleanupOldFiles() + } catch { + case e: Exception => + // Log the error and try to write new tokens back in an hour + logWarning("Failed to write out new credentials to HDFS, will try again in an " + + "hour! If this happens too often tasks will fail.", e) + delegationTokenRenewer.schedule(this, 1, TimeUnit.HOURS) + return + } + scheduleRenewal(this) + } + } + // Schedule update of credentials. This handles the case of updating the tokens right now + // as well, since the renenwal interval will be 0, and the thread will get scheduled + // immediately. + scheduleRenewal(driverTokenRenewerRunnable) + } + + // Keeps only files that are newer than daysToKeepFiles days, and deletes everything else. At + // least numFilesToKeep files are kept for safety + private def cleanupOldFiles(): Unit = { + import scala.concurrent.duration._ + try { + val remoteFs = FileSystem.get(hadoopConf) + val credentialsPath = new Path(sparkConf.get("spark.yarn.credentials.file")) + val thresholdTime = System.currentTimeMillis() - (daysToKeepFiles days).toMillis + hadoopUtil.listFilesSorted( + remoteFs, credentialsPath.getParent, + credentialsPath.getName, SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) + .dropRight(numFilesToKeep) + .takeWhile(_.getModificationTime < thresholdTime) + .foreach(x => remoteFs.delete(x.getPath, true)) + } catch { + // Such errors are not fatal, so don't throw. Make sure they are logged though + case e: Exception => + logWarning("Error while attempting to cleanup old tokens. If you are seeing many such " + + "warnings there may be an issue with your HDFS cluster.", e) + } + } + + private def writeNewTokensToHDFS(principal: String, keytab: String): Unit = { + // Keytab is copied by YARN to the working directory of the AM, so full path is + // not needed. + + // HACK: + // HDFS will not issue new delegation tokens, if the Credentials object + // passed in already has tokens for that FS even if the tokens are expired (it really only + // checks if there are tokens for the service, and not if they are valid). So the only real + // way to get new tokens is to make sure a different Credentials object is used each time to + // get new tokens and then the new tokens are copied over the the current user's Credentials. + // So: + // - we login as a different user and get the UGI + // - use that UGI to get the tokens (see doAs block below) + // - copy the tokens over to the current user's credentials (this will overwrite the tokens + // in the current user's Credentials object for this FS). + // The login to KDC happens each time new tokens are required, but this is rare enough to not + // have to worry about (like once every day or so). This makes this code clearer than having + // to login and then relogin every time (the HDFS API may not relogin since we don't use this + // UGI directly for HDFS communication. + logInfo(s"Attempting to login to KDC using principal: $principal") + val keytabLoggedInUGI = UserGroupInformation.loginUserFromKeytabAndReturnUGI(principal, keytab) + logInfo("Successfully logged into KDC.") + val tempCreds = keytabLoggedInUGI.getCredentials + val credentialsPath = new Path(sparkConf.get("spark.yarn.credentials.file")) + val dst = credentialsPath.getParent + keytabLoggedInUGI.doAs(new PrivilegedExceptionAction[Void] { + // Get a copy of the credentials + override def run(): Void = { + val nns = YarnSparkHadoopUtil.get.getNameNodesToAccess(sparkConf) + dst + hadoopUtil.obtainTokensForNamenodes(nns, hadoopConf, tempCreds) + null + } + }) + // Add the temp credentials back to the original ones. + UserGroupInformation.getCurrentUser.addCredentials(tempCreds) + val remoteFs = FileSystem.get(hadoopConf) + // If lastCredentialsFileSuffix is 0, then the AM is either started or restarted. If the AM + // was restarted, then the lastCredentialsFileSuffix might be > 0, so find the newest file + // and update the lastCredentialsFileSuffix. + if (lastCredentialsFileSuffix == 0) { + hadoopUtil.listFilesSorted( + remoteFs, credentialsPath.getParent, + credentialsPath.getName, SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) + .lastOption.foreach { status => + lastCredentialsFileSuffix = hadoopUtil.getSuffixForCredentialsPath(status.getPath) + } + } + val nextSuffix = lastCredentialsFileSuffix + 1 + val tokenPathStr = + sparkConf.get("spark.yarn.credentials.file") + + SparkHadoopUtil.SPARK_YARN_CREDS_COUNTER_DELIM + nextSuffix + val tokenPath = new Path(tokenPathStr) + val tempTokenPath = new Path(tokenPathStr + SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) + logInfo("Writing out delegation tokens to " + tempTokenPath.toString) + val credentials = UserGroupInformation.getCurrentUser.getCredentials + credentials.writeTokenStorageFile(tempTokenPath, hadoopConf) + logInfo(s"Delegation Tokens written out successfully. Renaming file to $tokenPathStr") + remoteFs.rename(tempTokenPath, tokenPath) + logInfo("Delegation token file rename complete.") + lastCredentialsFileSuffix = nextSuffix + } + + def stop(): Unit = { + delegationTokenRenewer.shutdown() + } +} diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala index 27f804782f355..e1694c1f64a9f 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -75,6 +75,8 @@ private[spark] class ApplicationMaster( // Fields used in cluster mode. private val sparkContextRef = new AtomicReference[SparkContext](null) + private var delegationTokenRenewerOption: Option[AMDelegationTokenRenewer] = None + final def run(): Int = { try { val appAttemptId = client.getAttemptId() @@ -129,6 +131,15 @@ private[spark] class ApplicationMaster( // doAs in order for the credentials to be passed on to the executor containers. val securityMgr = new SecurityManager(sparkConf) + // If the credentials file config is present, we must periodically renew tokens. So create + // a new AMDelegationTokenRenewer + if (sparkConf.contains("spark.yarn.credentials.file")) { + delegationTokenRenewerOption = Some(new AMDelegationTokenRenewer(sparkConf, yarnConf)) + // If a principal and keytab have been set, use that to create new credentials for executors + // periodically + delegationTokenRenewerOption.foreach(_.scheduleLoginFromKeytab()) + } + if (isClusterMode) { runDriver(securityMgr) } else { @@ -193,6 +204,7 @@ private[spark] class ApplicationMaster( logDebug("shutting down user thread") userClassThread.interrupt() } + if (!inShutdown) delegationTokenRenewerOption.foreach(_.stop()) } } } @@ -240,12 +252,12 @@ private[spark] class ApplicationMaster( host: String, port: String, isClusterMode: Boolean): Unit = { - val driverEndpont = rpcEnv.setupEndpointRef( + val driverEndpoint = rpcEnv.setupEndpointRef( SparkEnv.driverActorSystemName, RpcAddress(host, port.toInt), YarnSchedulerBackend.ENDPOINT_NAME) amEndpoint = - rpcEnv.setupEndpoint("YarnAM", new AMEndpoint(rpcEnv, driverEndpont, isClusterMode)) + rpcEnv.setupEndpoint("YarnAM", new AMEndpoint(rpcEnv, driverEndpoint, isClusterMode)) } private def runDriver(securityMgr: SecurityManager): Unit = { @@ -499,6 +511,7 @@ private[spark] class ApplicationMaster( override def onStart(): Unit = { driver.send(RegisterClusterManager(self)) + } override def receive: PartialFunction[Any, Unit] = { diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 31ab6b491ec2a..20ecaf092e3f8 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -17,9 +17,11 @@ package org.apache.spark.deploy.yarn -import java.io.{File, FileOutputStream} +import java.io.{ByteArrayInputStream, DataInputStream, File, FileOutputStream} import java.net.{InetAddress, UnknownHostException, URI, URISyntaxException} import java.nio.ByteBuffer +import java.security.PrivilegedExceptionAction +import java.util.UUID import java.util.zip.{ZipEntry, ZipOutputStream} import scala.collection.JavaConversions._ @@ -36,7 +38,6 @@ import org.apache.hadoop.hdfs.security.token.delegation.DelegationTokenIdentifie import org.apache.hadoop.fs._ import org.apache.hadoop.fs.permission.FsPermission import org.apache.hadoop.io.Text -import org.apache.hadoop.mapred.Master import org.apache.hadoop.mapreduce.MRJobConfig import org.apache.hadoop.security.{Credentials, UserGroupInformation} import org.apache.hadoop.security.token.{TokenIdentifier, Token} @@ -50,8 +51,8 @@ import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.exceptions.ApplicationNotFoundException import org.apache.hadoop.yarn.util.Records -import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext, SparkException} import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext, SparkException} import org.apache.spark.util.Utils private[spark] class Client( @@ -69,11 +70,13 @@ private[spark] class Client( private val yarnClient = YarnClient.createYarnClient private val yarnConf = new YarnConfiguration(hadoopConf) - private val credentials = UserGroupInformation.getCurrentUser.getCredentials + private var credentials: Credentials = null private val amMemoryOverhead = args.amMemoryOverhead // MB private val executorMemoryOverhead = args.executorMemoryOverhead // MB private val distCacheMgr = new ClientDistributedCacheManager() private val isClusterMode = args.isClusterMode + + private var loginFromKeytab = false private val fireAndForget = isClusterMode && !sparkConf.getBoolean("spark.yarn.submit.waitAppCompletion", true) @@ -88,6 +91,8 @@ private[spark] class Client( * available in the alpha API. */ def submitApplication(): ApplicationId = { + // Setup the credentials before doing anything else, so we have don't have issues at any point. + setupCredentials() yarnClient.init(yarnConf) yarnClient.start() @@ -219,12 +224,12 @@ private[spark] class Client( // and add them as local resources to the application master. val fs = FileSystem.get(hadoopConf) val dst = new Path(fs.getHomeDirectory(), appStagingDir) - val nns = getNameNodesToAccess(sparkConf) + dst + val nns = YarnSparkHadoopUtil.get.getNameNodesToAccess(sparkConf) + dst + YarnSparkHadoopUtil.get.obtainTokensForNamenodes(nns, hadoopConf, credentials) // Used to keep track of URIs added to the distributed cache. If the same URI is added // multiple times, YARN will fail to launch containers for the app with an internal // error. val distributedUris = new HashSet[String] - obtainTokensForNamenodes(nns, hadoopConf, credentials) obtainTokenForHiveMetastore(hadoopConf, credentials) obtainTokenForHBase(hadoopConf, credentials) @@ -243,6 +248,20 @@ private[spark] class Client( "for alternatives.") } + // If we passed in a keytab, make sure we copy the keytab to the staging directory on + // HDFS, and setup the relevant environment vars, so the AM can login again. + if (loginFromKeytab) { + logInfo("To enable the AM to login from keytab, credentials are being copied over to the AM" + + " via the YARN Secure Distributed Cache.") + val localUri = new URI(args.keytab) + val localPath = getQualifiedLocalPath(localUri, hadoopConf) + val destinationPath = copyFileToRemote(dst, localPath, replication) + val destFs = FileSystem.get(destinationPath.toUri(), hadoopConf) + distCacheMgr.addResource( + destFs, hadoopConf, destinationPath, localResources, LocalResourceType.FILE, + sparkConf.get("spark.yarn.keytab"), statCache, appMasterOnly = true) + } + def addDistributedUri(uri: URI): Boolean = { val uriStr = uri.toString() if (distributedUris.contains(uriStr)) { @@ -387,6 +406,28 @@ private[spark] class Client( } } + /** + * Get the renewal interval for tokens. + */ + private def getTokenRenewalInterval(stagingDirPath: Path): Long = { + // We cannot use the tokens generated above since those have renewer yarn. Trying to renew + // those will fail with an access control issue. So create new tokens with the logged in + // user as renewer. + val creds = new Credentials() + val nns = YarnSparkHadoopUtil.get.getNameNodesToAccess(sparkConf) + stagingDirPath + YarnSparkHadoopUtil.get.obtainTokensForNamenodes( + nns, hadoopConf, creds, Some(sparkConf.get("spark.yarn.principal"))) + val t = creds.getAllTokens + .filter(_.getKind == DelegationTokenIdentifier.HDFS_DELEGATION_KIND) + .head + val newExpiration = t.renew(hadoopConf) + val identifier = new DelegationTokenIdentifier() + identifier.readFields(new DataInputStream(new ByteArrayInputStream(t.getIdentifier))) + val interval = newExpiration - identifier.getIssueDate + logInfo(s"Renewal Interval set to $interval") + interval + } + /** * Set up the environment for launching our ApplicationMaster container. */ @@ -398,7 +439,16 @@ private[spark] class Client( env("SPARK_YARN_MODE") = "true" env("SPARK_YARN_STAGING_DIR") = stagingDir env("SPARK_USER") = UserGroupInformation.getCurrentUser().getShortUserName() - + if (loginFromKeytab) { + val remoteFs = FileSystem.get(hadoopConf) + val stagingDirPath = new Path(remoteFs.getHomeDirectory, stagingDir) + val credentialsFile = "credentials-" + UUID.randomUUID().toString + sparkConf.set( + "spark.yarn.credentials.file", new Path(stagingDirPath, credentialsFile).toString) + logInfo(s"Credentials file set to: $credentialsFile") + val renewalInterval = getTokenRenewalInterval(stagingDirPath) + sparkConf.set("spark.yarn.token.renewal.interval", renewalInterval.toString) + } // Set the environment variables to be passed on to the executors. distCacheMgr.setDistFilesEnv(env) distCacheMgr.setDistArchivesEnv(env) @@ -463,7 +513,6 @@ private[spark] class Client( private def createContainerLaunchContext(newAppResponse: GetNewApplicationResponse) : ContainerLaunchContext = { logInfo("Setting up container launch context for our AM") - val appId = newAppResponse.getApplicationId val appStagingDir = getAppStagingDir(appId) val localResources = prepareLocalResources(appStagingDir) @@ -638,6 +687,24 @@ private[spark] class Client( amContainer } + def setupCredentials(): Unit = { + if (args.principal != null) { + require(args.keytab != null, "Keytab must be specified when principal is specified.") + logInfo("Attempting to login to the Kerberos" + + s" using principal: ${args.principal} and keytab: ${args.keytab}") + val f = new File(args.keytab) + // Generate a file name that can be used for the keytab file, that does not conflict + // with any user file. + val keytabFileName = f.getName + "-" + UUID.randomUUID().toString + UserGroupInformation.loginUserFromKeytab(args.principal, args.keytab) + loginFromKeytab = true + sparkConf.set("spark.yarn.keytab", keytabFileName) + sparkConf.set("spark.yarn.principal", args.principal) + logInfo("Successfully logged into the KDC.") + } + credentials = UserGroupInformation.getCurrentUser.getCredentials + } + /** * Report the state of an application until it has exited, either successfully or * due to some failure, then return a pair of the yarn application state (FINISHED, FAILED, @@ -993,46 +1060,6 @@ object Client extends Logging { private def addClasspathEntry(path: String, env: HashMap[String, String]): Unit = YarnSparkHadoopUtil.addPathToEnvironment(env, Environment.CLASSPATH.name, path) - /** - * Get the list of namenodes the user may access. - */ - private[yarn] def getNameNodesToAccess(sparkConf: SparkConf): Set[Path] = { - sparkConf.get("spark.yarn.access.namenodes", "") - .split(",") - .map(_.trim()) - .filter(!_.isEmpty) - .map(new Path(_)) - .toSet - } - - private[yarn] def getTokenRenewer(conf: Configuration): String = { - val delegTokenRenewer = Master.getMasterPrincipal(conf) - logDebug("delegation token renewer is: " + delegTokenRenewer) - if (delegTokenRenewer == null || delegTokenRenewer.length() == 0) { - val errorMessage = "Can't get Master Kerberos principal for use as renewer" - logError(errorMessage) - throw new SparkException(errorMessage) - } - delegTokenRenewer - } - - /** - * Obtains tokens for the namenodes passed in and adds them to the credentials. - */ - private def obtainTokensForNamenodes( - paths: Set[Path], - conf: Configuration, - creds: Credentials): Unit = { - if (UserGroupInformation.isSecurityEnabled()) { - val delegTokenRenewer = getTokenRenewer(conf) - paths.foreach { dst => - val dstFs = dst.getFileSystem(conf) - logDebug("getting token for namenode: " + dst) - dstFs.addDelegationTokens(delegTokenRenewer, creds) - } - } - } - /** * Obtains token for the Hive metastore and adds them to the credentials. */ diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala index 1423533470fc0..5653c9f14dc6d 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala @@ -42,6 +42,8 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) var amCores: Int = 1 var appName: String = "Spark" var priority = 0 + var principal: String = null + var keytab: String = null def isClusterMode: Boolean = userClass != null private var driverMemory: Int = 512 // MB @@ -231,6 +233,14 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) archives = value args = tail + case ("--principal") :: value :: tail => + principal = value + args = tail + + case ("--keytab") :: value :: tail => + keytab = value + args = tail + case Nil => case _ => diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorDelegationTokenUpdater.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorDelegationTokenUpdater.scala new file mode 100644 index 0000000000000..229c2c4d5eb36 --- /dev/null +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorDelegationTokenUpdater.scala @@ -0,0 +1,106 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +package org.apache.spark.deploy.yarn + +import java.util.concurrent.{Executors, TimeUnit} + +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileSystem, Path} +import org.apache.hadoop.security.{Credentials, UserGroupInformation} + +import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.{Logging, SparkConf} +import org.apache.spark.util.{ThreadUtils, Utils} + +import scala.util.control.NonFatal + +private[spark] class ExecutorDelegationTokenUpdater( + sparkConf: SparkConf, + hadoopConf: Configuration) extends Logging { + + @volatile private var lastCredentialsFileSuffix = 0 + + private val credentialsFile = sparkConf.get("spark.yarn.credentials.file") + + private val delegationTokenRenewer = + Executors.newSingleThreadScheduledExecutor( + ThreadUtils.namedThreadFactory("Delegation Token Refresh Thread")) + + // On the executor, this thread wakes up and picks up new tokens from HDFS, if any. + private val executorUpdaterRunnable = + new Runnable { + override def run(): Unit = Utils.logUncaughtExceptions(updateCredentialsIfRequired()) + } + + def updateCredentialsIfRequired(): Unit = { + try { + val credentialsFilePath = new Path(credentialsFile) + val remoteFs = FileSystem.get(hadoopConf) + SparkHadoopUtil.get.listFilesSorted( + remoteFs, credentialsFilePath.getParent, + credentialsFilePath.getName, SparkHadoopUtil.SPARK_YARN_CREDS_TEMP_EXTENSION) + .lastOption.foreach { credentialsStatus => + val suffix = SparkHadoopUtil.get.getSuffixForCredentialsPath(credentialsStatus.getPath) + if (suffix > lastCredentialsFileSuffix) { + logInfo("Reading new delegation tokens from " + credentialsStatus.getPath) + val newCredentials = getCredentialsFromHDFSFile(remoteFs, credentialsStatus.getPath) + lastCredentialsFileSuffix = suffix + UserGroupInformation.getCurrentUser.addCredentials(newCredentials) + logInfo("Tokens updated from credentials file.") + } else { + // Check every hour to see if new credentials arrived. + logInfo("Updated delegation tokens were expected, but the driver has not updated the " + + "tokens yet, will check again in an hour.") + delegationTokenRenewer.schedule(executorUpdaterRunnable, 1, TimeUnit.HOURS) + return + } + } + val timeFromNowToRenewal = + SparkHadoopUtil.get.getTimeFromNowToRenewal( + sparkConf, 0.8, UserGroupInformation.getCurrentUser.getCredentials) + if (timeFromNowToRenewal <= 0) { + executorUpdaterRunnable.run() + } else { + logInfo(s"Scheduling token refresh from HDFS in $timeFromNowToRenewal millis.") + delegationTokenRenewer.schedule( + executorUpdaterRunnable, timeFromNowToRenewal, TimeUnit.MILLISECONDS) + } + } catch { + // Since the file may get deleted while we are reading it, catch the Exception and come + // back in an hour to try again + case NonFatal(e) => + logWarning("Error while trying to update credentials, will try again in 1 hour", e) + delegationTokenRenewer.schedule(executorUpdaterRunnable, 1, TimeUnit.HOURS) + } + } + + private def getCredentialsFromHDFSFile(remoteFs: FileSystem, tokenPath: Path): Credentials = { + val stream = remoteFs.open(tokenPath) + try { + val newCredentials = new Credentials() + newCredentials.readTokenStorageStream(stream) + newCredentials + } finally { + stream.close() + } + } + + def stop(): Unit = { + delegationTokenRenewer.shutdown() + } + +} diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala index 5881dc5ffa3ad..ba91872107d0c 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala @@ -24,18 +24,19 @@ import java.util.regex.Pattern import scala.collection.mutable.HashMap import scala.util.Try +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.Path import org.apache.hadoop.io.Text -import org.apache.hadoop.mapred.JobConf +import org.apache.hadoop.mapred.{Master, JobConf} import org.apache.hadoop.security.Credentials import org.apache.hadoop.security.UserGroupInformation import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.api.ApplicationConstants import org.apache.hadoop.yarn.api.ApplicationConstants.Environment import org.apache.hadoop.yarn.api.records.{Priority, ApplicationAccessType} -import org.apache.hadoop.conf.Configuration -import org.apache.spark.{SecurityManager, SparkConf} import org.apache.spark.deploy.SparkHadoopUtil +import org.apache.spark.{SecurityManager, SparkConf, SparkException} import org.apache.spark.util.Utils /** @@ -43,6 +44,8 @@ import org.apache.spark.util.Utils */ class YarnSparkHadoopUtil extends SparkHadoopUtil { + private var tokenRenewer: Option[ExecutorDelegationTokenUpdater] = None + override def transferCredentials(source: UserGroupInformation, dest: UserGroupInformation) { dest.addCredentials(source.getCredentials()) } @@ -82,6 +85,57 @@ class YarnSparkHadoopUtil extends SparkHadoopUtil { if (credentials != null) credentials.getSecretKey(new Text(key)) else null } + /** + * Get the list of namenodes the user may access. + */ + def getNameNodesToAccess(sparkConf: SparkConf): Set[Path] = { + sparkConf.get("spark.yarn.access.namenodes", "") + .split(",") + .map(_.trim()) + .filter(!_.isEmpty) + .map(new Path(_)) + .toSet + } + + def getTokenRenewer(conf: Configuration): String = { + val delegTokenRenewer = Master.getMasterPrincipal(conf) + logDebug("delegation token renewer is: " + delegTokenRenewer) + if (delegTokenRenewer == null || delegTokenRenewer.length() == 0) { + val errorMessage = "Can't get Master Kerberos principal for use as renewer" + logError(errorMessage) + throw new SparkException(errorMessage) + } + delegTokenRenewer + } + + /** + * Obtains tokens for the namenodes passed in and adds them to the credentials. + */ + def obtainTokensForNamenodes( + paths: Set[Path], + conf: Configuration, + creds: Credentials, + renewer: Option[String] = None + ): Unit = { + if (UserGroupInformation.isSecurityEnabled()) { + val delegTokenRenewer = renewer.getOrElse(getTokenRenewer(conf)) + paths.foreach { dst => + val dstFs = dst.getFileSystem(conf) + logInfo("getting token for namenode: " + dst) + dstFs.addDelegationTokens(delegTokenRenewer, creds) + } + } + } + + private[spark] override def startExecutorDelegationTokenRenewer(sparkConf: SparkConf): Unit = { + tokenRenewer = Some(new ExecutorDelegationTokenUpdater(sparkConf, conf)) + tokenRenewer.get.updateCredentialsIfRequired() + } + + private[spark] override def stopExecutorDelegationTokenRenewer(): Unit = { + tokenRenewer.foreach(_.stop()) + } + } object YarnSparkHadoopUtil { @@ -100,6 +154,14 @@ object YarnSparkHadoopUtil { // request types (like map/reduce in hadoop for example) val RM_REQUEST_PRIORITY = Priority.newInstance(1) + def get: YarnSparkHadoopUtil = { + val yarnMode = java.lang.Boolean.valueOf( + System.getProperty("SPARK_YARN_MODE", System.getenv("SPARK_YARN_MODE"))) + if (!yarnMode) { + throw new SparkException("YarnSparkHadoopUtil is not available in non-YARN mode!") + } + SparkHadoopUtil.get.asInstanceOf[YarnSparkHadoopUtil] + } /** * Add a path variable to the given environment map. * If the map already contains this key, append the value to the existing value instead. @@ -212,3 +274,4 @@ object YarnSparkHadoopUtil { classPathSeparatorField.get(null).asInstanceOf[String] } } + diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala index a51c2005cb472..508819e242a26 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala @@ -151,57 +151,6 @@ class ClientSuite extends FunSuite with Matchers with BeforeAndAfterAll { } } - test("check access nns empty") { - val sparkConf = new SparkConf() - sparkConf.set("spark.yarn.access.namenodes", "") - val nns = Client.getNameNodesToAccess(sparkConf) - nns should be(Set()) - } - - test("check access nns unset") { - val sparkConf = new SparkConf() - val nns = Client.getNameNodesToAccess(sparkConf) - nns should be(Set()) - } - - test("check access nns") { - val sparkConf = new SparkConf() - sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032") - val nns = Client.getNameNodesToAccess(sparkConf) - nns should be(Set(new Path("hdfs://nn1:8032"))) - } - - test("check access nns space") { - val sparkConf = new SparkConf() - sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032, ") - val nns = Client.getNameNodesToAccess(sparkConf) - nns should be(Set(new Path("hdfs://nn1:8032"))) - } - - test("check access two nns") { - val sparkConf = new SparkConf() - sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032,hdfs://nn2:8032") - val nns = Client.getNameNodesToAccess(sparkConf) - nns should be(Set(new Path("hdfs://nn1:8032"), new Path("hdfs://nn2:8032"))) - } - - test("check token renewer") { - val hadoopConf = new Configuration() - hadoopConf.set("yarn.resourcemanager.address", "myrm:8033") - hadoopConf.set("yarn.resourcemanager.principal", "yarn/myrm:8032@SPARKTEST.COM") - val renewer = Client.getTokenRenewer(hadoopConf) - renewer should be ("yarn/myrm:8032@SPARKTEST.COM") - } - - test("check token renewer default") { - val hadoopConf = new Configuration() - val caught = - intercept[SparkException] { - Client.getTokenRenewer(hadoopConf) - } - assert(caught.getMessage === "Can't get Master Kerberos principal for use as renewer") - } - object Fixtures { val knownDefYarnAppCP: Seq[String] = diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala index 3877da4120e7c..d3c606e0ed998 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala @@ -86,6 +86,7 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit tempDir = Utils.createTempDir() logConfDir = new File(tempDir, "log4j") logConfDir.mkdir() + System.setProperty("SPARK_YARN_MODE", "true") val logConfFile = new File(logConfDir, "log4j.properties") Files.write(LOG4J_CONF, logConfFile, UTF_8) @@ -128,6 +129,7 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit override def afterAll() { yarnCluster.stop() + System.clearProperty("SPARK_YARN_MODE") super.afterAll() } diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala index 9395316b71ff4..e10b985c3c236 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtilSuite.scala @@ -20,6 +20,8 @@ package org.apache.spark.deploy.yarn import java.io.{File, IOException} import com.google.common.io.{ByteStreams, Files} +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.Path import org.apache.hadoop.yarn.api.ApplicationConstants import org.apache.hadoop.yarn.api.ApplicationConstants.Environment import org.apache.hadoop.yarn.conf.YarnConfiguration @@ -27,7 +29,7 @@ import org.scalatest.{FunSuite, Matchers} import org.apache.hadoop.yarn.api.records.ApplicationAccessType -import org.apache.spark.{Logging, SecurityManager, SparkConf} +import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException} import org.apache.spark.util.Utils @@ -173,4 +175,62 @@ class YarnSparkHadoopUtilSuite extends FunSuite with Matchers with Logging { YarnSparkHadoopUtil.getClassPathSeparator() should be (":") } } + + test("check access nns empty") { + val sparkConf = new SparkConf() + val util = new YarnSparkHadoopUtil + sparkConf.set("spark.yarn.access.namenodes", "") + val nns = util.getNameNodesToAccess(sparkConf) + nns should be(Set()) + } + + test("check access nns unset") { + val sparkConf = new SparkConf() + val util = new YarnSparkHadoopUtil + val nns = util.getNameNodesToAccess(sparkConf) + nns should be(Set()) + } + + test("check access nns") { + val sparkConf = new SparkConf() + sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032") + val util = new YarnSparkHadoopUtil + val nns = util.getNameNodesToAccess(sparkConf) + nns should be(Set(new Path("hdfs://nn1:8032"))) + } + + test("check access nns space") { + val sparkConf = new SparkConf() + sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032, ") + val util = new YarnSparkHadoopUtil + val nns = util.getNameNodesToAccess(sparkConf) + nns should be(Set(new Path("hdfs://nn1:8032"))) + } + + test("check access two nns") { + val sparkConf = new SparkConf() + sparkConf.set("spark.yarn.access.namenodes", "hdfs://nn1:8032,hdfs://nn2:8032") + val util = new YarnSparkHadoopUtil + val nns = util.getNameNodesToAccess(sparkConf) + nns should be(Set(new Path("hdfs://nn1:8032"), new Path("hdfs://nn2:8032"))) + } + + test("check token renewer") { + val hadoopConf = new Configuration() + hadoopConf.set("yarn.resourcemanager.address", "myrm:8033") + hadoopConf.set("yarn.resourcemanager.principal", "yarn/myrm:8032@SPARKTEST.COM") + val util = new YarnSparkHadoopUtil + val renewer = util.getTokenRenewer(hadoopConf) + renewer should be ("yarn/myrm:8032@SPARKTEST.COM") + } + + test("check token renewer default") { + val hadoopConf = new Configuration() + val util = new YarnSparkHadoopUtil + val caught = + intercept[SparkException] { + util.getTokenRenewer(hadoopConf) + } + assert(caught.getMessage === "Can't get Master Kerberos principal for use as renewer") + } } From 5c1fabafabdb87de7a92acbefbf294b24d0713fc Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Fri, 1 May 2015 14:42:58 -0700 Subject: [PATCH 531/591] Ignore flakey test in SparkSubmitUtilsSuite --- .../scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala index 1b2b699cb11e6..2df2597e058cd 100644 --- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala @@ -117,7 +117,7 @@ class SparkSubmitUtilsSuite extends FunSuite with BeforeAndAfterAll { } } - test("neglects Spark and Spark's dependencies") { + ignore("neglects Spark and Spark's dependencies") { val components = Seq("bagel_", "catalyst_", "core_", "graphx_", "hive_", "mllib_", "repl_", "sql_", "streaming_", "yarn_", "network-common_", "network-shuffle_", "network-yarn_") From 41c6a44b1a6ae5c70a8e8dc82d0062de9bdee5b3 Mon Sep 17 00:00:00 2001 From: Yin Huai Date: Fri, 1 May 2015 16:47:00 -0700 Subject: [PATCH 532/591] [SPARK-7312][SQL] SPARK-6913 broke jdk6 build JIRA: https://issues.apache.org/jira/browse/SPARK-7312 Author: Yin Huai Closes #5847 from yhuai/jdbcJava6 and squashes the following commits: 68433a2 [Yin Huai] compile with Java 6 --- .../src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala index ae9af1eabe68e..3a6c2c1e9101f 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql -import java.sql.{Connection, Driver, DriverManager, DriverPropertyInfo, PreparedStatement} +import java.sql.{Connection, Driver, DriverManager, DriverPropertyInfo, PreparedStatement, SQLFeatureNotSupportedException} import java.util.Properties import scala.collection.mutable @@ -195,7 +195,9 @@ package object jdbc { override def getMinorVersion: Int = wrapped.getMinorVersion - override def getParentLogger: java.util.logging.Logger = wrapped.getParentLogger + def getParentLogger: java.util.logging.Logger = + throw new SQLFeatureNotSupportedException( + s"${this.getClass().getName}.getParentLogger is not yet implemented.") override def connect(url: String, info: Properties): Connection = wrapped.connect(url, info) From e6fb37712eb1762d8184edc897bf2d468db8d254 Mon Sep 17 00:00:00 2001 From: "Rajendra Gokhale (rvgcentos)" Date: Fri, 1 May 2015 17:01:36 -0700 Subject: [PATCH 533/591] [SPARK-7304] [BUILD] Include $@ in call to mvn consistently in make-distribution.sh Adding the $ allows the caller of this script to supply additional arguments to the mvn command and is consistent with how mvn is being invoked elsewhere in the scripts Author: Rajendra Gokhale (rvgcentos) Closes #5846 from palamau/master and squashes the following commits: e5f2adb [Rajendra Gokhale (rvgcentos)] Add $@ in call to mvn consistently in make-distribution.sh --- make-distribution.sh | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/make-distribution.sh b/make-distribution.sh index cb65932b4abc0..92177e19fe6be 100755 --- a/make-distribution.sh +++ b/make-distribution.sh @@ -26,6 +26,7 @@ set -o pipefail set -e +set -x # Figure out where the Spark framework is installed SPARK_HOME="$(cd "`dirname "$0"`"; pwd)" @@ -126,7 +127,7 @@ if [ ! $(command -v "$MVN") ] ; then exit -1; fi -VERSION=$("$MVN" help:evaluate -Dexpression=project.version 2>/dev/null | grep -v "INFO" | tail -n 1) +VERSION=$("$MVN" help:evaluate -Dexpression=project.version $@ 2>/dev/null | grep -v "INFO" | tail -n 1) SCALA_VERSION=$("$MVN" help:evaluate -Dexpression=scala.binary.version $@ 2>/dev/null\ | grep -v "INFO"\ | tail -n 1) From 98e7045805282988da907793a844fa53d4c293c9 Mon Sep 17 00:00:00 2001 From: Cheng Hao Date: Fri, 1 May 2015 19:39:30 -0500 Subject: [PATCH 534/591] [SPARK-6999] [SQL] Remove the infinite recursive method (useless) Remove the method, since it causes infinite recursive calls. And seems it's a dummy method, since we have the API: `def createDataFrame(rowRDD: JavaRDD[Row], schema: StructType): DataFrame` Author: Cheng Hao Closes #5804 from chenghao-intel/spark_6999 and squashes the following commits: 63220a8 [Cheng Hao] remove the infinite recursive method (useless) --- .../scala/org/apache/spark/sql/SQLContext.scala | 14 -------------- 1 file changed, 14 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala index bd4a55fa132fb..5116fcefd4bf2 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala @@ -428,20 +428,6 @@ class SQLContext(@transient val sparkContext: SparkContext) createDataFrame(rowRDD.rdd, schema) } - /** - * Creates a [[DataFrame]] from an [[JavaRDD]] containing [[Row]]s by applying - * a seq of names of columns to this RDD, the data type for each column will - * be inferred by the first row. - * - * @param rowRDD an JavaRDD of Row - * @param columns names for each column - * @return DataFrame - * @group dataframes - */ - def createDataFrame(rowRDD: JavaRDD[Row], columns: java.util.List[String]): DataFrame = { - createDataFrame(rowRDD.rdd, columns.toSeq) - } - /** * Applies a schema to an RDD of Java Beans. * From ebc25a4ddfe07a67668217cec59893bc3b8cf730 Mon Sep 17 00:00:00 2001 From: zsxwing Date: Fri, 1 May 2015 17:41:55 -0700 Subject: [PATCH 535/591] [SPARK-7309] [CORE] [STREAMING] Shutdown the thread pools in ReceivedBlockHandler and DAGScheduler Shutdown the thread pools in ReceivedBlockHandler and DAGScheduler when stopping them. Author: zsxwing Closes #5845 from zsxwing/SPARK-7309 and squashes the following commits: 6c004fd [zsxwing] Shutdown the thread pools in ReceivedBlockHandler and DAGScheduler --- .../src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala | 1 + .../apache/spark/streaming/receiver/ReceivedBlockHandler.scala | 1 + 2 files changed, 2 insertions(+) diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala index 05b8ab0d0a1f9..5d812918a13d1 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -1399,6 +1399,7 @@ class DAGScheduler( def stop() { logInfo("Stopping DAGScheduler") + messageScheduler.shutdownNow() eventProcessLoop.stop() taskScheduler.stop() } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala index 4b3d9ee4b0090..651b534ac1900 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala @@ -190,6 +190,7 @@ private[streaming] class WriteAheadLogBasedBlockHandler( def stop() { writeAheadLog.close() + executionContext.shutdown() } } From b88c275e6ef6b17cd34d1c2c780b8959b41222c0 Mon Sep 17 00:00:00 2001 From: jerryshao Date: Fri, 1 May 2015 17:46:06 -0700 Subject: [PATCH 536/591] [SPARK-7112][Streaming][WIP] Add a InputInfoTracker to track all the input streams Author: jerryshao Author: Saisai Shao Closes #5680 from jerryshao/SPARK-7111 and squashes the following commits: 339f854 [Saisai Shao] Add an end-to-end test 812bcaf [jerryshao] Continue address the comments abd0036 [jerryshao] Address the comments 727264e [jerryshao] Fix comment typo 6682bef [jerryshao] Fix compile issue 8325787 [jerryshao] Fix rebase issue 17fa251 [jerryshao] Refactor to build InputInfoTracker ee1b536 [jerryshao] Add DirectStreamTracker to track the direct streams --- .../spark/streaming/StreamingContext.scala | 4 +- .../streaming/dstream/InputDStream.scala | 3 + .../dstream/ReceiverInputDStream.scala | 9 ++- .../spark/streaming/scheduler/BatchInfo.scala | 8 +- .../scheduler/InputInfoTracker.scala | 62 +++++++++++++++ .../streaming/scheduler/JobGenerator.scala | 8 +- .../streaming/scheduler/JobScheduler.scala | 4 + .../spark/streaming/scheduler/JobSet.scala | 4 +- .../spark/streaming/ui/BatchUIData.scala | 2 +- .../ui/StreamingJobProgressListener.scala | 31 +++++--- .../spark/streaming/ui/StreamingPage.scala | 4 +- .../spark/streaming/InputStreamsSuite.scala | 33 +++++++- .../streaming/StreamingListenerSuite.scala | 15 ++++ .../spark/streaming/TestSuiteBase.scala | 8 +- .../scheduler/InputInfoTrackerSuite.scala | 79 +++++++++++++++++++ .../StreamingJobProgressListenerSuite.scala | 19 ++--- 16 files changed, 247 insertions(+), 46 deletions(-) create mode 100644 streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala create mode 100644 streaming/src/test/scala/org/apache/spark/streaming/scheduler/InputInfoTrackerSuite.scala diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala index 90c8b47aebce0..117cb59fb61c9 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala @@ -159,7 +159,7 @@ class StreamingContext private[streaming] ( } } - private val nextReceiverInputStreamId = new AtomicInteger(0) + private val nextInputStreamId = new AtomicInteger(0) private[streaming] var checkpointDir: String = { if (isCheckpointPresent) { @@ -241,7 +241,7 @@ class StreamingContext private[streaming] ( if (isCheckpointPresent) cp_ else null } - private[streaming] def getNewReceiverStreamId() = nextReceiverInputStreamId.getAndIncrement() + private[streaming] def getNewInputStreamId() = nextInputStreamId.getAndIncrement() /** * Create an input stream with any arbitrary user implemented receiver. diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala index e652702e213ef..e4ad4b509d8d8 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala @@ -41,6 +41,9 @@ abstract class InputDStream[T: ClassTag] (@transient ssc_ : StreamingContext) ssc.graph.addInputStream(this) + /** This is an unique identifier for the input stream. */ + val id = ssc.getNewInputStreamId() + /** * Checks whether the 'time' is valid wrt slideDuration for generating RDD. * Additionally it also ensures valid times are in strictly increasing order. diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala index 4c7fd2c57c006..ba88416ef4009 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala @@ -24,7 +24,7 @@ import org.apache.spark.storage.{BlockId, StorageLevel} import org.apache.spark.streaming._ import org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD import org.apache.spark.streaming.receiver.{Receiver, WriteAheadLogBasedStoreResult} -import org.apache.spark.streaming.scheduler.ReceivedBlockInfo +import org.apache.spark.streaming.scheduler.{InputInfo, ReceivedBlockInfo} /** * Abstract class for defining any [[org.apache.spark.streaming.dstream.InputDStream]] @@ -39,9 +39,6 @@ import org.apache.spark.streaming.scheduler.ReceivedBlockInfo abstract class ReceiverInputDStream[T: ClassTag](@transient ssc_ : StreamingContext) extends InputDStream[T](ssc_) { - /** This is an unique identifier for the receiver input stream. */ - val id = ssc.getNewReceiverStreamId() - /** * Gets the receiver object that will be sent to the worker nodes * to receive data. This method needs to defined by any specific implementation @@ -72,6 +69,10 @@ abstract class ReceiverInputDStream[T: ClassTag](@transient ssc_ : StreamingCont val blockStoreResults = blockInfos.map { _.blockStoreResult } val blockIds = blockStoreResults.map { _.blockId.asInstanceOf[BlockId] }.toArray + // Register the input blocks information into InputInfoTracker + val inputInfo = InputInfo(id, blockInfos.map(_.numRecords).sum) + ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo) + // Check whether all the results are of the same type val resultTypes = blockStoreResults.map { _.getClass }.distinct if (resultTypes.size > 1) { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/BatchInfo.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/BatchInfo.scala index 92dc113f397ca..5b9bfbf9b01e3 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/BatchInfo.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/BatchInfo.scala @@ -24,6 +24,7 @@ import org.apache.spark.streaming.Time * :: DeveloperApi :: * Class having information on completed batches. * @param batchTime Time of the batch + * @param streamIdToNumRecords A map of input stream id to record number * @param submissionTime Clock time of when jobs of this batch was submitted to * the streaming scheduler queue * @param processingStartTime Clock time of when the first job of this batch started processing @@ -32,7 +33,7 @@ import org.apache.spark.streaming.Time @DeveloperApi case class BatchInfo( batchTime: Time, - receivedBlockInfo: Map[Int, Array[ReceivedBlockInfo]], + streamIdToNumRecords: Map[Int, Long], submissionTime: Long, processingStartTime: Option[Long], processingEndTime: Option[Long] @@ -58,4 +59,9 @@ case class BatchInfo( */ def totalDelay: Option[Long] = schedulingDelay.zip(processingDelay) .map(x => x._1 + x._2).headOption + + /** + * The number of recorders received by the receivers in this batch. + */ + def numRecords: Long = streamIdToNumRecords.values.sum } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala new file mode 100644 index 0000000000000..a72efccf2f994 --- /dev/null +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala @@ -0,0 +1,62 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.scheduler + +import scala.collection.mutable + +import org.apache.spark.Logging +import org.apache.spark.streaming.{Time, StreamingContext} + +/** To track the information of input stream at specified batch time. */ +private[streaming] case class InputInfo(inputStreamId: Int, numRecords: Long) + +/** + * This class manages all the input streams as well as their input data statistics. The information + * will be exposed through StreamingListener for monitoring. + */ +private[streaming] class InputInfoTracker(ssc: StreamingContext) extends Logging { + + // Map to track all the InputInfo related to specific batch time and input stream. + private val batchTimeToInputInfos = new mutable.HashMap[Time, mutable.HashMap[Int, InputInfo]] + + /** Report the input information with batch time to the tracker */ + def reportInfo(batchTime: Time, inputInfo: InputInfo): Unit = synchronized { + val inputInfos = batchTimeToInputInfos.getOrElseUpdate(batchTime, + new mutable.HashMap[Int, InputInfo]()) + + if (inputInfos.contains(inputInfo.inputStreamId)) { + throw new IllegalStateException(s"Input stream ${inputInfo.inputStreamId}} for batch" + + s"$batchTime is already added into InputInfoTracker, this is a illegal state") + } + inputInfos += ((inputInfo.inputStreamId, inputInfo)) + } + + /** Get the all the input stream's information of specified batch time */ + def getInfo(batchTime: Time): Map[Int, InputInfo] = synchronized { + val inputInfos = batchTimeToInputInfos.get(batchTime) + // Convert mutable HashMap to immutable Map for the caller + inputInfos.map(_.toMap).getOrElse(Map[Int, InputInfo]()) + } + + /** Cleanup the tracked input information older than threshold batch time */ + def cleanup(batchThreshTime: Time): Unit = synchronized { + val timesToCleanup = batchTimeToInputInfos.keys.filter(_ < batchThreshTime) + logInfo(s"remove old batch metadata: ${timesToCleanup.mkString(" ")}") + batchTimeToInputInfos --= timesToCleanup + } +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala index 2467d50839add..9f93d6cbc3c20 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala @@ -243,9 +243,9 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { graph.generateJobs(time) // generate jobs using allocated block } match { case Success(jobs) => - val receivedBlockInfos = - jobScheduler.receiverTracker.getBlocksOfBatch(time).mapValues { _.toArray } - jobScheduler.submitJobSet(JobSet(time, jobs, receivedBlockInfos)) + val streamIdToInputInfos = jobScheduler.inputInfoTracker.getInfo(time) + val streamIdToNumRecords = streamIdToInputInfos.mapValues(_.numRecords) + jobScheduler.submitJobSet(JobSet(time, jobs, streamIdToNumRecords)) case Failure(e) => jobScheduler.reportError("Error generating jobs for time " + time, e) } @@ -266,6 +266,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { // checkpointing of this batch to complete. val maxRememberDuration = graph.getMaxInputStreamRememberDuration() jobScheduler.receiverTracker.cleanupOldBlocksAndBatches(time - maxRememberDuration) + jobScheduler.inputInfoTracker.cleanup(time - maxRememberDuration) markBatchFullyProcessed(time) } } @@ -278,6 +279,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { // been saved to checkpoints, so its safe to delete block metadata and data WAL files val maxRememberDuration = graph.getMaxInputStreamRememberDuration() jobScheduler.receiverTracker.cleanupOldBlocksAndBatches(time - maxRememberDuration) + jobScheduler.inputInfoTracker.cleanup(time - maxRememberDuration) markBatchFullyProcessed(time) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala index c7a2c1141a128..1d1ddaaccf217 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala @@ -50,6 +50,9 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { // These two are created only when scheduler starts. // eventLoop not being null means the scheduler has been started and not stopped var receiverTracker: ReceiverTracker = null + // A tracker to track all the input stream information as well as processed record number + var inputInfoTracker: InputInfoTracker = null + private var eventLoop: EventLoop[JobSchedulerEvent] = null def start(): Unit = synchronized { @@ -65,6 +68,7 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { listenerBus.start(ssc.sparkContext) receiverTracker = new ReceiverTracker(ssc) + inputInfoTracker = new InputInfoTracker(ssc) receiverTracker.start() jobGenerator.start() logInfo("Started JobScheduler") diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala index 24b3794236ea5..e6be63b2ddbdc 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobSet.scala @@ -28,7 +28,7 @@ private[streaming] case class JobSet( time: Time, jobs: Seq[Job], - receivedBlockInfo: Map[Int, Array[ReceivedBlockInfo]] = Map.empty) { + streamIdToNumRecords: Map[Int, Long] = Map.empty) { private val incompleteJobs = new HashSet[Job]() private val submissionTime = System.currentTimeMillis() // when this jobset was submitted @@ -64,7 +64,7 @@ case class JobSet( def toBatchInfo: BatchInfo = { new BatchInfo( time, - receivedBlockInfo, + streamIdToNumRecords, submissionTime, if (processingStartTime >= 0 ) Some(processingStartTime) else None, if (processingEndTime >= 0 ) Some(processingEndTime) else None diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchUIData.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchUIData.scala index f45c291b7c0fe..99e10d2b0be12 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchUIData.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchUIData.scala @@ -66,7 +66,7 @@ private[ui] object BatchUIData { def apply(batchInfo: BatchInfo): BatchUIData = { new BatchUIData( batchInfo.batchTime, - batchInfo.receivedBlockInfo.mapValues(_.map(_.numRecords).sum), + batchInfo.streamIdToNumRecords, batchInfo.submissionTime, batchInfo.processingStartTime, batchInfo.processingEndTime diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala index 34b55717a1db2..d2729fa70d6d2 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala @@ -188,25 +188,26 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) } def receivedRecordsDistributions: Map[Int, Option[Distribution]] = synchronized { - val latestBatches = retainedBatches.reverse.take(batchUIDataLimit) - (0 until numReceivers).map { receiverId => - val recordsOfParticularReceiver = latestBatches.map { batch => - // calculate records per second for each batch - batch.receiverNumRecords.get(receiverId).sum.toDouble * 1000 / batchDuration - } - val distributionOption = Distribution(recordsOfParticularReceiver) - (receiverId, distributionOption) + val latestBatchInfos = retainedBatches.reverse.take(batchUIDataLimit) + val latestReceiverNumRecords = latestBatchInfos.map(_.receiverNumRecords) + val streamIds = ssc.graph.getInputStreams().map(_.id) + streamIds.map { id => + val recordsOfParticularReceiver = + latestReceiverNumRecords.map(v => v.getOrElse(id, 0L).toDouble * 1000 / batchDuration) + val distribution = Distribution(recordsOfParticularReceiver) + (id, distribution) }.toMap } def lastReceivedBatchRecords: Map[Int, Long] = synchronized { - val lastReceivedBlockInfoOption = lastReceivedBatch.map(_.receiverNumRecords) - lastReceivedBlockInfoOption.map { lastReceivedBlockInfo => - (0 until numReceivers).map { receiverId => - (receiverId, lastReceivedBlockInfo.getOrElse(receiverId, 0L)) + val lastReceiverNumRecords = lastReceivedBatch.map(_.receiverNumRecords) + val streamIds = ssc.graph.getInputStreams().map(_.id) + lastReceiverNumRecords.map { receiverNumRecords => + streamIds.map { id => + (id, receiverNumRecords.getOrElse(id, 0L)) }.toMap }.getOrElse { - (0 until numReceivers).map(receiverId => (receiverId, 0L)).toMap + streamIds.map(id => (id, 0L)).toMap } } @@ -214,6 +215,10 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext) receiverInfos.get(receiverId) } + def receiverIds(): Iterable[Int] = synchronized { + receiverInfos.keys + } + def lastCompletedBatch: Option[BatchUIData] = synchronized { completedBatchUIData.sortBy(_.batchTime)(Time.ordering).lastOption } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala index 07fa285642eec..db37ae815bdf5 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala @@ -95,7 +95,7 @@ private[ui] class StreamingPage(parent: StreamingTab) "Maximum rate\n[events/sec]", "Last Error" ) - val dataRows = (0 until listener.numReceivers).map { receiverId => + val dataRows = listener.receiverIds().map { receiverId => val receiverInfo = listener.receiverInfo(receiverId) val receiverName = receiverInfo.map(_.name).getOrElse(s"Receiver-$receiverId") val receiverActive = receiverInfo.map { info => @@ -114,7 +114,7 @@ private[ui] class StreamingPage(parent: StreamingTab) }.getOrElse(emptyCell) Seq(receiverName, receiverActive, receiverLocation, receiverLastBatchRecords) ++ receivedRecordStats ++ Seq(receiverLastError) - } + }.toSeq Some(listingTable(headerRow, dataRows)) } else { None diff --git a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala index e6ac4975c5e68..eb136758249d5 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala @@ -27,17 +27,18 @@ import scala.collection.mutable.{SynchronizedBuffer, ArrayBuffer, SynchronizedQu import scala.language.postfixOps import com.google.common.io.Files +import org.apache.hadoop.io.{Text, LongWritable} +import org.apache.hadoop.mapreduce.lib.input.TextInputFormat +import org.apache.hadoop.fs.Path import org.scalatest.BeforeAndAfter import org.scalatest.concurrent.Eventually._ import org.apache.spark.Logging +import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel import org.apache.spark.util.{ManualClock, Utils} +import org.apache.spark.streaming.dstream.{InputDStream, ReceiverInputDStream} import org.apache.spark.streaming.receiver.Receiver -import org.apache.spark.rdd.RDD -import org.apache.hadoop.io.{Text, LongWritable} -import org.apache.hadoop.mapreduce.lib.input.TextInputFormat -import org.apache.hadoop.fs.Path class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { @@ -278,6 +279,30 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { } } + test("test track the number of input stream") { + val ssc = new StreamingContext(conf, batchDuration) + + class TestInputDStream extends InputDStream[String](ssc) { + def start() { } + def stop() { } + def compute(validTime: Time): Option[RDD[String]] = None + } + + class TestReceiverInputDStream extends ReceiverInputDStream[String](ssc) { + def getReceiver: Receiver[String] = null + } + + // Register input streams + val receiverInputStreams = Array(new TestReceiverInputDStream, new TestReceiverInputDStream) + val inputStreams = Array(new TestInputDStream, new TestInputDStream, new TestInputDStream) + + assert(ssc.graph.getInputStreams().length == receiverInputStreams.length + inputStreams.length) + assert(ssc.graph.getReceiverInputStreams().length == receiverInputStreams.length) + assert(ssc.graph.getReceiverInputStreams() === receiverInputStreams) + assert(ssc.graph.getInputStreams().map(_.id) === Array.tabulate(5)(i => i)) + assert(receiverInputStreams.map(_.id) === Array(0, 1)) + } + def testFileStream(newFilesOnly: Boolean) { val testDir: File = null try { diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala index 9020be166acf0..312cce408cfe7 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala @@ -57,6 +57,11 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers { info.totalDelay should be (None) }) + batchInfosSubmitted.foreach { info => + info.numRecords should be (1L) + info.streamIdToNumRecords should be (Map(0 -> 1L)) + } + isInIncreasingOrder(batchInfosSubmitted.map(_.submissionTime)) should be (true) // SPARK-6766: processingStartTime of batch info should not be None when starting @@ -70,6 +75,11 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers { info.totalDelay should be (None) }) + batchInfosStarted.foreach { info => + info.numRecords should be (1L) + info.streamIdToNumRecords should be (Map(0 -> 1L)) + } + isInIncreasingOrder(batchInfosStarted.map(_.submissionTime)) should be (true) isInIncreasingOrder(batchInfosStarted.map(_.processingStartTime.get)) should be (true) @@ -86,6 +96,11 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers { info.totalDelay.get should be >= 0L }) + batchInfosCompleted.foreach { info => + info.numRecords should be (1L) + info.streamIdToNumRecords should be (Map(0 -> 1L)) + } + isInIncreasingOrder(batchInfosCompleted.map(_.submissionTime)) should be (true) isInIncreasingOrder(batchInfosCompleted.map(_.processingStartTime.get)) should be (true) isInIncreasingOrder(batchInfosCompleted.map(_.processingEndTime.get)) should be (true) diff --git a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala index c3cae8aeb6d15..2ba86aeaf9d51 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala @@ -29,10 +29,10 @@ import org.scalatest.time.{Span, Seconds => ScalaTestSeconds} import org.scalatest.concurrent.Eventually.timeout import org.scalatest.concurrent.PatienceConfiguration -import org.apache.spark.streaming.dstream.{DStream, InputDStream, ForEachDStream} -import org.apache.spark.streaming.scheduler.{StreamingListenerBatchStarted, StreamingListenerBatchCompleted, StreamingListener} import org.apache.spark.{SparkConf, Logging} import org.apache.spark.rdd.RDD +import org.apache.spark.streaming.dstream.{DStream, InputDStream, ForEachDStream} +import org.apache.spark.streaming.scheduler._ import org.apache.spark.util.{ManualClock, Utils} /** @@ -57,6 +57,10 @@ class TestInputStream[T: ClassTag](ssc_ : StreamingContext, input: Seq[Seq[T]], return None } + // Report the input data's information to InputInfoTracker for testing + val inputInfo = InputInfo(id, selectedInput.length.toLong) + ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo) + val rdd = ssc.sc.makeRDD(selectedInput, numPartitions) logInfo("Created RDD " + rdd.id + " with " + selectedInput) Some(rdd) diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/InputInfoTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/InputInfoTrackerSuite.scala new file mode 100644 index 0000000000000..5478b41845943 --- /dev/null +++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/InputInfoTrackerSuite.scala @@ -0,0 +1,79 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.streaming.scheduler + +import org.scalatest.{BeforeAndAfter, FunSuite} + +import org.apache.spark.SparkConf +import org.apache.spark.streaming.{Time, Duration, StreamingContext} + +class InputInfoTrackerSuite extends FunSuite with BeforeAndAfter { + + private var ssc: StreamingContext = _ + + before { + val conf = new SparkConf().setMaster("local[2]").setAppName("DirectStreamTacker") + if (ssc == null) { + ssc = new StreamingContext(conf, Duration(1000)) + } + } + + after { + if (ssc != null) { + ssc.stop() + ssc = null + } + } + + test("test report and get InputInfo from InputInfoTracker") { + val inputInfoTracker = new InputInfoTracker(ssc) + + val streamId1 = 0 + val streamId2 = 1 + val time = Time(0L) + val inputInfo1 = InputInfo(streamId1, 100L) + val inputInfo2 = InputInfo(streamId2, 300L) + inputInfoTracker.reportInfo(time, inputInfo1) + inputInfoTracker.reportInfo(time, inputInfo2) + + val batchTimeToInputInfos = inputInfoTracker.getInfo(time) + assert(batchTimeToInputInfos.size == 2) + assert(batchTimeToInputInfos.keys === Set(streamId1, streamId2)) + assert(batchTimeToInputInfos(streamId1) === inputInfo1) + assert(batchTimeToInputInfos(streamId2) === inputInfo2) + assert(inputInfoTracker.getInfo(time)(streamId1) === inputInfo1) + } + + test("test cleanup InputInfo from InputInfoTracker") { + val inputInfoTracker = new InputInfoTracker(ssc) + + val streamId1 = 0 + val inputInfo1 = InputInfo(streamId1, 100L) + val inputInfo2 = InputInfo(streamId1, 300L) + inputInfoTracker.reportInfo(Time(0), inputInfo1) + inputInfoTracker.reportInfo(Time(1), inputInfo2) + + inputInfoTracker.cleanup(Time(0)) + assert(inputInfoTracker.getInfo(Time(0))(streamId1) === inputInfo1) + assert(inputInfoTracker.getInfo(Time(1))(streamId1) === inputInfo2) + + inputInfoTracker.cleanup(Time(1)) + assert(inputInfoTracker.getInfo(Time(0)).get(streamId1) === None) + assert(inputInfoTracker.getInfo(Time(1))(streamId1) === inputInfo2) + } +} diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala index fa89536de4054..e874536e63518 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala @@ -49,13 +49,10 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { val ssc = setupStreams(input, operation) val listener = new StreamingJobProgressListener(ssc) - val receivedBlockInfo = Map( - 0 -> Array(ReceivedBlockInfo(0, 100, null), ReceivedBlockInfo(0, 200, null)), - 1 -> Array(ReceivedBlockInfo(1, 300, null)) - ) + val streamIdToNumRecords = Map(0 -> 300L, 1 -> 300L) // onBatchSubmitted - val batchInfoSubmitted = BatchInfo(Time(1000), receivedBlockInfo, 1000, None, None) + val batchInfoSubmitted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, None, None) listener.onBatchSubmitted(StreamingListenerBatchSubmitted(batchInfoSubmitted)) listener.waitingBatches should be (List(BatchUIData(batchInfoSubmitted))) listener.runningBatches should be (Nil) @@ -67,7 +64,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { listener.numTotalReceivedRecords should be (0) // onBatchStarted - val batchInfoStarted = BatchInfo(Time(1000), receivedBlockInfo, 1000, Some(2000), None) + val batchInfoStarted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, Some(2000), None) listener.onBatchStarted(StreamingListenerBatchStarted(batchInfoStarted)) listener.waitingBatches should be (Nil) listener.runningBatches should be (List(BatchUIData(batchInfoStarted))) @@ -106,7 +103,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { OutputOpIdAndSparkJobId(1, 1)) // onBatchCompleted - val batchInfoCompleted = BatchInfo(Time(1000), receivedBlockInfo, 1000, Some(2000), None) + val batchInfoCompleted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, Some(2000), None) listener.onBatchCompleted(StreamingListenerBatchCompleted(batchInfoCompleted)) listener.waitingBatches should be (Nil) listener.runningBatches should be (Nil) @@ -144,11 +141,9 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 100) val listener = new StreamingJobProgressListener(ssc) - val receivedBlockInfo = Map( - 0 -> Array(ReceivedBlockInfo(0, 100, null), ReceivedBlockInfo(0, 200, null)), - 1 -> Array(ReceivedBlockInfo(1, 300, null)) - ) - val batchInfoCompleted = BatchInfo(Time(1000), receivedBlockInfo, 1000, Some(2000), None) + val streamIdToNumRecords = Map(0 -> 300L, 1 -> 300L) + + val batchInfoCompleted = BatchInfo(Time(1000), streamIdToNumRecords, 1000, Some(2000), None) for(_ <- 0 until (limit + 10)) { listener.onBatchCompleted(StreamingListenerBatchCompleted(batchInfoCompleted)) From 4786484076865c56c3fc23c49819b9be2933d287 Mon Sep 17 00:00:00 2001 From: cody koeninger Date: Fri, 1 May 2015 17:54:56 -0700 Subject: [PATCH 537/591] [SPARK-2808][Streaming][Kafka] update kafka to 0.8.2 i don't think this should be merged until after 1.3.0 is final Author: cody koeninger Author: Helena Edelson Closes #4537 from koeninger/wip-2808-kafka-0.8.2-upgrade and squashes the following commits: 803aa2c [cody koeninger] [SPARK-2808][Streaming][Kafka] code cleanup per TD e6dfaf6 [cody koeninger] [SPARK-2808][Streaming][Kafka] pointless whitespace change to trigger jenkins again 1770abc [cody koeninger] [SPARK-2808][Streaming][Kafka] make waitUntilLeaderOffset easier to call, call it from python tests as well d4267e9 [cody koeninger] [SPARK-2808][Streaming][Kafka] fix stderr redirect in python test script 30d991d [cody koeninger] [SPARK-2808][Streaming][Kafka] remove stderr prints since it breaks python 3 syntax 1d896e2 [cody koeninger] [SPARK-2808][Streaming][Kafka] add even even more logging to python test 4c4557f [cody koeninger] [SPARK-2808][Streaming][Kafka] add even more logging to python test 115aeee [cody koeninger] Merge branch 'master' into wip-2808-kafka-0.8.2-upgrade 2712649 [cody koeninger] [SPARK-2808][Streaming][Kafka] add more logging to python test, see why its timing out in jenkins 2b92d3f [cody koeninger] [SPARK-2808][Streaming][Kafka] wait for leader offsets in the java test as well 3824ce3 [cody koeninger] [SPARK-2808][Streaming][Kafka] naming / comments per tdas 61b3464 [cody koeninger] [SPARK-2808][Streaming][Kafka] delay for second send in boundary condition test af6f3ec [cody koeninger] [SPARK-2808][Streaming][Kafka] delay test until latest leader offset matches expected value 9edab4c [cody koeninger] [SPARK-2808][Streaming][Kafka] more shots in the dark on jenkins failing test c70ee43 [cody koeninger] [SPARK-2808][Streaming][Kafka] add more asserts to test, try to figure out why it fails on jenkins but not locally 1d10751 [cody koeninger] Merge branch 'master' into wip-2808-kafka-0.8.2-upgrade ed02d2c [cody koeninger] [SPARK-2808][Streaming][Kafka] move default argument for api version to overloaded method, for binary compat 407382e [cody koeninger] [SPARK-2808][Streaming][Kafka] update kafka to 0.8.2.1 77de6c2 [cody koeninger] Merge branch 'master' into wip-2808-kafka-0.8.2-upgrade 6953429 [cody koeninger] [SPARK-2808][Streaming][Kafka] update kafka to 0.8.2 2e67c66 [Helena Edelson] #SPARK-2808 Update to Kafka 0.8.2.0 GA from beta. d9dc2bc [Helena Edelson] Merge remote-tracking branch 'upstream/master' into wip-2808-kafka-0.8.2-upgrade e768164 [Helena Edelson] #2808 update kafka to version 0.8.2 --- external/kafka/pom.xml | 2 +- .../spark/streaming/kafka/KafkaCluster.scala | 51 +++++++++++++++---- .../streaming/kafka/KafkaTestUtils.scala | 35 +++++++++++-- .../streaming/kafka/JavaKafkaRDDSuite.java | 3 ++ .../spark/streaming/kafka/KafkaRDDSuite.scala | 32 ++++++++---- python/pyspark/streaming/tests.py | 8 +-- 6 files changed, 104 insertions(+), 27 deletions(-) diff --git a/external/kafka/pom.xml b/external/kafka/pom.xml index f695cff410a18..243ce6eaca658 100644 --- a/external/kafka/pom.xml +++ b/external/kafka/pom.xml @@ -44,7 +44,7 @@ org.apache.kafka kafka_${scala.binary.version} - 0.8.1.1 + 0.8.2.1 com.sun.jmx diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala index bd767031c1849..6cf254a7b69cb 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaCluster.scala @@ -20,9 +20,10 @@ package org.apache.spark.streaming.kafka import scala.util.control.NonFatal import scala.util.Random import scala.collection.mutable.ArrayBuffer +import scala.collection.JavaConverters._ import java.util.Properties import kafka.api._ -import kafka.common.{ErrorMapping, OffsetMetadataAndError, TopicAndPartition} +import kafka.common.{ErrorMapping, OffsetAndMetadata, OffsetMetadataAndError, TopicAndPartition} import kafka.consumer.{ConsumerConfig, SimpleConsumer} import org.apache.spark.SparkException @@ -220,12 +221,22 @@ class KafkaCluster(val kafkaParams: Map[String, String]) extends Serializable { // https://cwiki.apache.org/confluence/display/KAFKA/A+Guide+To+The+Kafka+Protocol#AGuideToTheKafkaProtocol-OffsetCommit/FetchAPI // scalastyle:on + // this 0 here indicates api version, in this case the original ZK backed api. + private def defaultConsumerApiVersion: Short = 0 + /** Requires Kafka >= 0.8.1.1 */ def getConsumerOffsets( groupId: String, topicAndPartitions: Set[TopicAndPartition] + ): Either[Err, Map[TopicAndPartition, Long]] = + getConsumerOffsets(groupId, topicAndPartitions, defaultConsumerApiVersion) + + def getConsumerOffsets( + groupId: String, + topicAndPartitions: Set[TopicAndPartition], + consumerApiVersion: Short ): Either[Err, Map[TopicAndPartition, Long]] = { - getConsumerOffsetMetadata(groupId, topicAndPartitions).right.map { r => + getConsumerOffsetMetadata(groupId, topicAndPartitions, consumerApiVersion).right.map { r => r.map { kv => kv._1 -> kv._2.offset } @@ -236,9 +247,16 @@ class KafkaCluster(val kafkaParams: Map[String, String]) extends Serializable { def getConsumerOffsetMetadata( groupId: String, topicAndPartitions: Set[TopicAndPartition] + ): Either[Err, Map[TopicAndPartition, OffsetMetadataAndError]] = + getConsumerOffsetMetadata(groupId, topicAndPartitions, defaultConsumerApiVersion) + + def getConsumerOffsetMetadata( + groupId: String, + topicAndPartitions: Set[TopicAndPartition], + consumerApiVersion: Short ): Either[Err, Map[TopicAndPartition, OffsetMetadataAndError]] = { var result = Map[TopicAndPartition, OffsetMetadataAndError]() - val req = OffsetFetchRequest(groupId, topicAndPartitions.toSeq) + val req = OffsetFetchRequest(groupId, topicAndPartitions.toSeq, consumerApiVersion) val errs = new Err withBrokers(Random.shuffle(config.seedBrokers), errs) { consumer => val resp = consumer.fetchOffsets(req) @@ -266,24 +284,39 @@ class KafkaCluster(val kafkaParams: Map[String, String]) extends Serializable { def setConsumerOffsets( groupId: String, offsets: Map[TopicAndPartition, Long] + ): Either[Err, Map[TopicAndPartition, Short]] = + setConsumerOffsets(groupId, offsets, defaultConsumerApiVersion) + + def setConsumerOffsets( + groupId: String, + offsets: Map[TopicAndPartition, Long], + consumerApiVersion: Short ): Either[Err, Map[TopicAndPartition, Short]] = { - setConsumerOffsetMetadata(groupId, offsets.map { kv => - kv._1 -> OffsetMetadataAndError(kv._2) - }) + val meta = offsets.map { kv => + kv._1 -> OffsetAndMetadata(kv._2) + } + setConsumerOffsetMetadata(groupId, meta, consumerApiVersion) } /** Requires Kafka >= 0.8.1.1 */ def setConsumerOffsetMetadata( groupId: String, - metadata: Map[TopicAndPartition, OffsetMetadataAndError] + metadata: Map[TopicAndPartition, OffsetAndMetadata] + ): Either[Err, Map[TopicAndPartition, Short]] = + setConsumerOffsetMetadata(groupId, metadata, defaultConsumerApiVersion) + + def setConsumerOffsetMetadata( + groupId: String, + metadata: Map[TopicAndPartition, OffsetAndMetadata], + consumerApiVersion: Short ): Either[Err, Map[TopicAndPartition, Short]] = { var result = Map[TopicAndPartition, Short]() - val req = OffsetCommitRequest(groupId, metadata) + val req = OffsetCommitRequest(groupId, metadata, consumerApiVersion) val errs = new Err val topicAndPartitions = metadata.keySet withBrokers(Random.shuffle(config.seedBrokers), errs) { consumer => val resp = consumer.commitOffsets(req) - val respMap = resp.requestInfo + val respMap = resp.commitStatus val needed = topicAndPartitions.diff(result.keySet) needed.foreach { tp: TopicAndPartition => respMap.get(tp).foreach { err: Short => diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala index 13e9475065979..6dc4e9517d5a4 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala @@ -29,10 +29,12 @@ import scala.language.postfixOps import scala.util.control.NonFatal import kafka.admin.AdminUtils +import kafka.api.Request +import kafka.common.TopicAndPartition import kafka.producer.{KeyedMessage, Producer, ProducerConfig} import kafka.serializer.StringEncoder import kafka.server.{KafkaConfig, KafkaServer} -import kafka.utils.ZKStringSerializer +import kafka.utils.{ZKStringSerializer, ZkUtils} import org.apache.zookeeper.server.{NIOServerCnxnFactory, ZooKeeperServer} import org.I0Itec.zkclient.ZkClient @@ -227,12 +229,35 @@ private class KafkaTestUtils extends Logging { tryAgain(1) } - private def waitUntilMetadataIsPropagated(topic: String, partition: Int): Unit = { + /** Wait until the leader offset for the given topic/partition equals the specified offset */ + def waitUntilLeaderOffset( + topic: String, + partition: Int, + offset: Long): Unit = { eventually(Time(10000), Time(100)) { + val kc = new KafkaCluster(Map("metadata.broker.list" -> brokerAddress)) + val tp = TopicAndPartition(topic, partition) + val llo = kc.getLatestLeaderOffsets(Set(tp)).right.get.apply(tp).offset assert( - server.apis.metadataCache.containsTopicAndPartition(topic, partition), - s"Partition [$topic, $partition] metadata not propagated after timeout" - ) + llo == offset, + s"$topic $partition $offset not reached after timeout") + } + } + + private def waitUntilMetadataIsPropagated(topic: String, partition: Int): Unit = { + def isPropagated = server.apis.metadataCache.getPartitionInfo(topic, partition) match { + case Some(partitionState) => + val leaderAndInSyncReplicas = partitionState.leaderIsrAndControllerEpoch.leaderAndIsr + + ZkUtils.getLeaderForPartition(zkClient, topic, partition).isDefined && + Request.isValidBrokerId(leaderAndInSyncReplicas.leader) && + leaderAndInSyncReplicas.isr.size >= 1 + + case _ => + false + } + eventually(Time(10000), Time(100)) { + assert(isPropagated, s"Partition [$topic, $partition] metadata not propagated after timeout") } } diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java index a9dc6e50613ca..5cf379635354f 100644 --- a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java +++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java @@ -72,6 +72,9 @@ public void testKafkaRDD() throws InterruptedException { HashMap kafkaParams = new HashMap(); kafkaParams.put("metadata.broker.list", kafkaTestUtils.brokerAddress()); + kafkaTestUtils.waitUntilLeaderOffset(topic1, 0, topic1data.length); + kafkaTestUtils.waitUntilLeaderOffset(topic2, 0, topic2data.length); + OffsetRange[] offsetRanges = { OffsetRange.create(topic1, 0, 0, 1), OffsetRange.create(topic2, 0, 0, 1) diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaRDDSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaRDDSuite.scala index 7d26ce50875b3..39c3fb448ff57 100644 --- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaRDDSuite.scala +++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaRDDSuite.scala @@ -53,14 +53,15 @@ class KafkaRDDSuite extends FunSuite with BeforeAndAfterAll { } test("basic usage") { - val topic = "topicbasic" + val topic = s"topicbasic-${Random.nextInt}" kafkaTestUtils.createTopic(topic) val messages = Set("the", "quick", "brown", "fox") kafkaTestUtils.sendMessages(topic, messages.toArray) - val kafkaParams = Map("metadata.broker.list" -> kafkaTestUtils.brokerAddress, - "group.id" -> s"test-consumer-${Random.nextInt(10000)}") + "group.id" -> s"test-consumer-${Random.nextInt}") + + kafkaTestUtils.waitUntilLeaderOffset(topic, 0, messages.size) val offsetRanges = Array(OffsetRange(topic, 0, 0, messages.size)) @@ -73,27 +74,38 @@ class KafkaRDDSuite extends FunSuite with BeforeAndAfterAll { test("iterator boundary conditions") { // the idea is to find e.g. off-by-one errors between what kafka has available and the rdd - val topic = "topic1" + val topic = s"topicboundary-${Random.nextInt}" val sent = Map("a" -> 5, "b" -> 3, "c" -> 10) kafkaTestUtils.createTopic(topic) val kafkaParams = Map("metadata.broker.list" -> kafkaTestUtils.brokerAddress, - "group.id" -> s"test-consumer-${Random.nextInt(10000)}") + "group.id" -> s"test-consumer-${Random.nextInt}") val kc = new KafkaCluster(kafkaParams) // this is the "lots of messages" case kafkaTestUtils.sendMessages(topic, sent) + val sentCount = sent.values.sum + kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sentCount) + // rdd defined from leaders after sending messages, should get the number sent val rdd = getRdd(kc, Set(topic)) assert(rdd.isDefined) - assert(rdd.get.count === sent.values.sum, "didn't get all sent messages") - val ranges = rdd.get.asInstanceOf[HasOffsetRanges] - .offsetRanges.map(o => TopicAndPartition(o.topic, o.partition) -> o.untilOffset).toMap + val ranges = rdd.get.asInstanceOf[HasOffsetRanges].offsetRanges + val rangeCount = ranges.map(o => o.untilOffset - o.fromOffset).sum - kc.setConsumerOffsets(kafkaParams("group.id"), ranges) + assert(rangeCount === sentCount, "offset range didn't include all sent messages") + assert(rdd.get.count === sentCount, "didn't get all sent messages") + + val rangesMap = ranges.map(o => TopicAndPartition(o.topic, o.partition) -> o.untilOffset).toMap + + // make sure consumer offsets are committed before the next getRdd call + kc.setConsumerOffsets(kafkaParams("group.id"), rangesMap).fold( + err => throw new Exception(err.mkString("\n")), + _ => () + ) // this is the "0 messages" case val rdd2 = getRdd(kc, Set(topic)) @@ -101,6 +113,8 @@ class KafkaRDDSuite extends FunSuite with BeforeAndAfterAll { val sentOnlyOne = Map("d" -> 1) kafkaTestUtils.sendMessages(topic, sentOnlyOne) + kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sentCount + 1) + assert(rdd2.isDefined) assert(rdd2.get.count === 0, "got messages when there shouldn't be any") diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py index 7c06c203455d9..33ea8c9293d74 100644 --- a/python/pyspark/streaming/tests.py +++ b/python/pyspark/streaming/tests.py @@ -606,7 +606,6 @@ def _validateRddResult(self, sendData, rdd): result = {} for i in rdd.map(lambda x: x[1]).collect(): result[i] = result.get(i, 0) + 1 - self.assertEqual(sendData, result) def test_kafka_stream(self): @@ -616,6 +615,7 @@ def test_kafka_stream(self): self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) + self._kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sum(sendData.values())) stream = KafkaUtils.createStream(self.ssc, self._kafkaTestUtils.zkAddress(), "test-streaming-consumer", {topic: 1}, @@ -631,6 +631,7 @@ def test_kafka_direct_stream(self): self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) + self._kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sum(sendData.values())) stream = KafkaUtils.createDirectStream(self.ssc, [topic], kafkaParams) self._validateStreamResult(sendData, stream) @@ -645,6 +646,7 @@ def test_kafka_direct_stream_from_offset(self): self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) + self._kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sum(sendData.values())) stream = KafkaUtils.createDirectStream(self.ssc, [topic], kafkaParams, fromOffsets) self._validateStreamResult(sendData, stream) @@ -659,7 +661,7 @@ def test_kafka_rdd(self): self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) - + self._kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sum(sendData.values())) rdd = KafkaUtils.createRDD(self.sc, kafkaParams, offsetRanges) self._validateRddResult(sendData, rdd) @@ -675,7 +677,7 @@ def test_kafka_rdd_with_leaders(self): self._kafkaTestUtils.createTopic(topic) self._kafkaTestUtils.sendMessages(topic, sendData) - + self._kafkaTestUtils.waitUntilLeaderOffset(topic, 0, sum(sendData.values())) rdd = KafkaUtils.createRDD(self.sc, kafkaParams, offsetRanges, leaders) self._validateRddResult(sendData, rdd) From ae98eec730125c1153dcac9ea941959cc79e4f42 Mon Sep 17 00:00:00 2001 From: Holden Karau Date: Fri, 1 May 2015 18:02:10 -0700 Subject: [PATCH 538/591] [SPARK-3444] Provide an easy way to change log level Add support for changing the log level at run time through the SparkContext. Based on an earlier PR, #2433 includes CR feedback from pwendel & davies Author: Holden Karau Closes #5791 from holdenk/SPARK-3444-provide-an-easy-way-to-change-log-level-r2 and squashes the following commits: 3bf3be9 [Holden Karau] fix exception 42ba873 [Holden Karau] fix exception 9117244 [Holden Karau] Only allow valid log levels, throw exception if invalid log level. 338d7bf [Holden Karau] rename setLoggingLevel to setLogLevel fac14a0 [Holden Karau] Fix style errors d9d03f3 [Holden Karau] Add support for changing the log level at run time through the SparkContext. Based on an earlier PR, #2433 includes CR feedback from @pwendel & @davies --- .../scala/org/apache/spark/SparkContext.scala | 13 ++++++ .../spark/api/java/JavaSparkContext.scala | 8 ++++ .../scala/org/apache/spark/util/Utils.scala | 7 ++++ .../org/apache/spark/util/UtilsSuite.scala | 40 ++++++++++++------- python/pyspark/context.py | 7 ++++ python/pyspark/sql/dataframe.py | 2 +- 6 files changed, 61 insertions(+), 16 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 3f7cba6dbcdb5..4ef90546a2452 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -347,6 +347,19 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli value } + /** Control our logLevel. This overrides any user-defined log settings. + * @param logLevel The desired log level as a string. + * Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, WARN + */ + def setLogLevel(logLevel: String) { + val validLevels = Seq("ALL", "DEBUG", "ERROR", "FATAL", "INFO", "OFF", "TRACE", "WARN") + if (!validLevels.contains(logLevel)) { + throw new IllegalArgumentException( + s"Supplied level $logLevel did not match one of: ${validLevels.mkString(",")}") + } + Utils.setLogLevel(org.apache.log4j.Level.toLevel(logLevel)) + } + try { _conf = config.clone() _conf.validateSettings() diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala index 3be6783bba49d..02e49a853c5f7 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala @@ -755,6 +755,14 @@ class JavaSparkContext(val sc: SparkContext) */ def getLocalProperty(key: String): String = sc.getLocalProperty(key) + /** Control our logLevel. This overrides any user-defined log settings. + * @param logLevel The desired log level as a string. + * Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, WARN + */ + def setLogLevel(logLevel: String) { + sc.setLogLevel(logLevel) + } + /** * Assigns a group ID to all the jobs started by this thread until the group ID is set to a * different value or cleared. diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index 4b5a5df5ef7b7..844f0cd22d95d 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -2022,6 +2022,13 @@ private[spark] object Utils extends Logging { } } + /** + * configure a new log4j level + */ + def setLogLevel(l: org.apache.log4j.Level) { + org.apache.log4j.Logger.getRootLogger().setLevel(l) + } + /** * config a log4j properties used for testsuite */ diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala index 62a3cbcdf69ea..651ead6ff1de2 100644 --- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala @@ -35,9 +35,10 @@ import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.apache.spark.network.util.ByteUnit +import org.apache.spark.Logging import org.apache.spark.SparkConf -class UtilsSuite extends FunSuite with ResetSystemProperties { +class UtilsSuite extends FunSuite with ResetSystemProperties with Logging { test("timeConversion") { // Test -1 @@ -68,7 +69,7 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { intercept[NumberFormatException] { Utils.timeStringAsMs("600l") } - + intercept[NumberFormatException] { Utils.timeStringAsMs("This breaks 600s") } @@ -99,7 +100,7 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { assert(Utils.byteStringAsGb("1k") === 0) assert(Utils.byteStringAsGb("1t") === ByteUnit.TiB.toGiB(1)) assert(Utils.byteStringAsGb("1p") === ByteUnit.PiB.toGiB(1)) - + assert(Utils.byteStringAsMb("1") === 1) assert(Utils.byteStringAsMb("1m") === 1) assert(Utils.byteStringAsMb("1048575b") === 0) @@ -118,7 +119,7 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { assert(Utils.byteStringAsKb("1g") === ByteUnit.GiB.toKiB(1)) assert(Utils.byteStringAsKb("1t") === ByteUnit.TiB.toKiB(1)) assert(Utils.byteStringAsKb("1p") === ByteUnit.PiB.toKiB(1)) - + assert(Utils.byteStringAsBytes("1") === 1) assert(Utils.byteStringAsBytes("1k") === ByteUnit.KiB.toBytes(1)) assert(Utils.byteStringAsBytes("1m") === ByteUnit.MiB.toBytes(1)) @@ -127,17 +128,17 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { assert(Utils.byteStringAsBytes("1p") === ByteUnit.PiB.toBytes(1)) // Overflow handling, 1073741824p exceeds Long.MAX_VALUE if converted straight to Bytes - // This demonstrates that we can have e.g 1024^3 PB without overflowing. + // This demonstrates that we can have e.g 1024^3 PB without overflowing. assert(Utils.byteStringAsGb("1073741824p") === ByteUnit.PiB.toGiB(1073741824)) assert(Utils.byteStringAsMb("1073741824p") === ByteUnit.PiB.toMiB(1073741824)) - + // Run this to confirm it doesn't throw an exception - assert(Utils.byteStringAsBytes("9223372036854775807") === 9223372036854775807L) + assert(Utils.byteStringAsBytes("9223372036854775807") === 9223372036854775807L) assert(ByteUnit.PiB.toPiB(9223372036854775807L) === 9223372036854775807L) - + // Test overflow exception intercept[IllegalArgumentException] { - // This value exceeds Long.MAX when converted to bytes + // This value exceeds Long.MAX when converted to bytes Utils.byteStringAsBytes("9223372036854775808") } @@ -146,22 +147,22 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { // This value exceeds Long.MAX when converted to TB ByteUnit.PiB.toTiB(9223372036854775807L) } - + // Test fractional string intercept[NumberFormatException] { Utils.byteStringAsMb("0.064") } - + // Test fractional string intercept[NumberFormatException] { Utils.byteStringAsMb("0.064m") } - + // Test invalid strings intercept[NumberFormatException] { Utils.byteStringAsBytes("500ub") } - + // Test invalid strings intercept[NumberFormatException] { Utils.byteStringAsBytes("This breaks 600b") @@ -174,12 +175,12 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { intercept[NumberFormatException] { Utils.byteStringAsBytes("600gb This breaks") } - + intercept[NumberFormatException] { Utils.byteStringAsBytes("This 123mb breaks") } } - + test("bytesToString") { assert(Utils.bytesToString(10) === "10.0 B") assert(Utils.bytesToString(1500) === "1500.0 B") @@ -475,6 +476,15 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { } } + // Test for using the util function to change our log levels. + test("log4j log level change") { + Utils.setLogLevel(org.apache.log4j.Level.ALL) + assert(log.isInfoEnabled()) + Utils.setLogLevel(org.apache.log4j.Level.ERROR) + assert(!log.isInfoEnabled()) + assert(log.isErrorEnabled()) + } + test("deleteRecursively") { val tempDir1 = Utils.createTempDir() assert(tempDir1.exists()) diff --git a/python/pyspark/context.py b/python/pyspark/context.py index b006120eb266d..31992795a9e45 100644 --- a/python/pyspark/context.py +++ b/python/pyspark/context.py @@ -267,6 +267,13 @@ def __exit__(self, type, value, trace): """ self.stop() + def setLogLevel(self, logLevel): + """ + Control our logLevel. This overrides any user-defined log settings. + Valid log levels include: ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, WARN + """ + self._jsc.setLogLevel(logLevel) + @classmethod def setSystemProperty(cls, key, value): """ diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index 1f08c2df9305b..5ff49cac5522b 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -1,6 +1,6 @@ # # Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with +# contir[butor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with From 099327d5376554134c9af49bc2045add4cfb024d Mon Sep 17 00:00:00 2001 From: Sandy Ryza Date: Fri, 1 May 2015 18:32:46 -0700 Subject: [PATCH 539/591] [SPARK-6954] [YARN] ExecutorAllocationManager can end up requesting a negative n... ...umber of executors Author: Sandy Ryza Closes #5704 from sryza/sandy-spark-6954 and squashes the following commits: b7890fb [Sandy Ryza] Avoid ramping up to an existing number of executors 6eb516a [Sandy Ryza] SPARK-6954. ExecutorAllocationManager can end up requesting a negative number of executors --- .../spark/ExecutorAllocationManager.scala | 101 +++++------ .../ExecutorAllocationManagerSuite.scala | 171 ++++++++++-------- 2 files changed, 148 insertions(+), 124 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala index b986fa87dc2f4..228d9149df2a2 100644 --- a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala +++ b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala @@ -27,13 +27,20 @@ import org.apache.spark.util.{ThreadUtils, Clock, SystemClock, Utils} /** * An agent that dynamically allocates and removes executors based on the workload. * - * The add policy depends on whether there are backlogged tasks waiting to be scheduled. If - * the scheduler queue is not drained in N seconds, then new executors are added. If the queue - * persists for another M seconds, then more executors are added and so on. The number added - * in each round increases exponentially from the previous round until an upper bound on the - * number of executors has been reached. The upper bound is based both on a configured property - * and on the number of tasks pending: the policy will never increase the number of executor - * requests past the number needed to handle all pending tasks. + * The ExecutorAllocationManager maintains a moving target number of executors which is periodically + * synced to the cluster manager. The target starts at a configured initial value and changes with + * the number of pending and running tasks. + * + * Decreasing the target number of executors happens when the current target is more than needed to + * handle the current load. The target number of executors is always truncated to the number of + * executors that could run all current running and pending tasks at once. + * + * Increasing the target number of executors happens in response to backlogged tasks waiting to be + * scheduled. If the scheduler queue is not drained in N seconds, then new executors are added. If + * the queue persists for another M seconds, then more executors are added and so on. The number + * added in each round increases exponentially from the previous round until an upper bound has been + * reached. The upper bound is based both on a configured property and on the current number of + * running and pending tasks, as described above. * * The rationale for the exponential increase is twofold: (1) Executors should be added slowly * in the beginning in case the number of extra executors needed turns out to be small. Otherwise, @@ -105,8 +112,10 @@ private[spark] class ExecutorAllocationManager( // Number of executors to add in the next round private var numExecutorsToAdd = 1 - // Number of executors that have been requested but have not registered yet - private var numExecutorsPending = 0 + // The desired number of executors at this moment in time. If all our executors were to die, this + // is the number of executors we would immediately want from the cluster manager. + private var numExecutorsTarget = + conf.getInt("spark.dynamicAllocation.initialExecutors", minNumExecutors) // Executors that have been requested to be removed but have not been killed yet private val executorsPendingToRemove = new mutable.HashSet[String] @@ -199,13 +208,6 @@ private[spark] class ExecutorAllocationManager( executor.awaitTermination(10, TimeUnit.SECONDS) } - /** - * The number of executors we would have if the cluster manager were to fulfill all our existing - * requests. - */ - private def targetNumExecutors(): Int = - numExecutorsPending + executorIds.size - executorsPendingToRemove.size - /** * The maximum number of executors we would need under the current load to satisfy all running * and pending tasks, rounded up. @@ -227,7 +229,7 @@ private[spark] class ExecutorAllocationManager( private def schedule(): Unit = synchronized { val now = clock.getTimeMillis - addOrCancelExecutorRequests(now) + updateAndSyncNumExecutorsTarget(now) removeTimes.retain { case (executorId, expireTime) => val expired = now >= expireTime @@ -239,26 +241,28 @@ private[spark] class ExecutorAllocationManager( } /** + * Updates our target number of executors and syncs the result with the cluster manager. + * * Check to see whether our existing allocation and the requests we've made previously exceed our - * current needs. If so, let the cluster manager know so that it can cancel pending requests that - * are unneeded. + * current needs. If so, truncate our target and let the cluster manager know so that it can + * cancel pending requests that are unneeded. * * If not, and the add time has expired, see if we can request new executors and refresh the add * time. * * @return the delta in the target number of executors. */ - private def addOrCancelExecutorRequests(now: Long): Int = synchronized { - val currentTarget = targetNumExecutors + private def updateAndSyncNumExecutorsTarget(now: Long): Int = synchronized { val maxNeeded = maxNumExecutorsNeeded - if (maxNeeded < currentTarget) { + if (maxNeeded < numExecutorsTarget) { // The target number exceeds the number we actually need, so stop adding new - // executors and inform the cluster manager to cancel the extra pending requests. - val newTotalExecutors = math.max(maxNeeded, minNumExecutors) - client.requestTotalExecutors(newTotalExecutors) + // executors and inform the cluster manager to cancel the extra pending requests + val oldNumExecutorsTarget = numExecutorsTarget + numExecutorsTarget = math.max(maxNeeded, minNumExecutors) + client.requestTotalExecutors(numExecutorsTarget) numExecutorsToAdd = 1 - updateNumExecutorsPending(newTotalExecutors) + numExecutorsTarget - oldNumExecutorsTarget } else if (addTime != NOT_SET && now >= addTime) { val delta = addExecutors(maxNeeded) logDebug(s"Starting timer to add more executors (to " + @@ -281,21 +285,30 @@ private[spark] class ExecutorAllocationManager( */ private def addExecutors(maxNumExecutorsNeeded: Int): Int = { // Do not request more executors if it would put our target over the upper bound - val currentTarget = targetNumExecutors - if (currentTarget >= maxNumExecutors) { - logDebug(s"Not adding executors because there are already ${executorIds.size} " + - s"registered and $numExecutorsPending pending executor(s) (limit $maxNumExecutors)") + if (numExecutorsTarget >= maxNumExecutors) { + val numExecutorsPending = numExecutorsTarget - executorIds.size + logDebug(s"Not adding executors because there are already ${executorIds.size} registered " + + s"and ${numExecutorsPending} pending executor(s) (limit $maxNumExecutors)") numExecutorsToAdd = 1 return 0 } - val actualMaxNumExecutors = math.min(maxNumExecutors, maxNumExecutorsNeeded) - val newTotalExecutors = math.min(currentTarget + numExecutorsToAdd, actualMaxNumExecutors) - val addRequestAcknowledged = testing || client.requestTotalExecutors(newTotalExecutors) + val oldNumExecutorsTarget = numExecutorsTarget + // There's no point in wasting time ramping up to the number of executors we already have, so + // make sure our target is at least as much as our current allocation: + numExecutorsTarget = math.max(numExecutorsTarget, executorIds.size) + // Boost our target with the number to add for this round: + numExecutorsTarget += numExecutorsToAdd + // Ensure that our target doesn't exceed what we need at the present moment: + numExecutorsTarget = math.min(numExecutorsTarget, maxNumExecutorsNeeded) + // Ensure that our target fits within configured bounds: + numExecutorsTarget = math.max(math.min(numExecutorsTarget, maxNumExecutors), minNumExecutors) + + val addRequestAcknowledged = testing || client.requestTotalExecutors(numExecutorsTarget) if (addRequestAcknowledged) { - val delta = updateNumExecutorsPending(newTotalExecutors) + val delta = numExecutorsTarget - oldNumExecutorsTarget logInfo(s"Requesting $delta new executor(s) because tasks are backlogged" + - s" (new desired total will be $newTotalExecutors)") + s" (new desired total will be $numExecutorsTarget)") numExecutorsToAdd = if (delta == numExecutorsToAdd) { numExecutorsToAdd * 2 } else { @@ -304,23 +317,11 @@ private[spark] class ExecutorAllocationManager( delta } else { logWarning( - s"Unable to reach the cluster manager to request $newTotalExecutors total executors!") + s"Unable to reach the cluster manager to request $numExecutorsTarget total executors!") 0 } } - /** - * Given the new target number of executors, update the number of pending executor requests, - * and return the delta from the old number of pending requests. - */ - private def updateNumExecutorsPending(newTotalExecutors: Int): Int = { - val newNumExecutorsPending = - newTotalExecutors - executorIds.size + executorsPendingToRemove.size - val delta = newNumExecutorsPending - numExecutorsPending - numExecutorsPending = newNumExecutorsPending - delta - } - /** * Request the cluster manager to remove the given executor. * Return whether the request is received. @@ -372,10 +373,6 @@ private[spark] class ExecutorAllocationManager( // as idle again so as not to forget that it is a candidate for removal. (see SPARK-4951) executorIds.filter(listener.isExecutorIdle).foreach(onExecutorIdle) logInfo(s"New executor $executorId has registered (new total is ${executorIds.size})") - if (numExecutorsPending > 0) { - numExecutorsPending -= 1 - logDebug(s"Decremented number of pending executors ($numExecutorsPending left)") - } } else { logWarning(s"Duplicate executor $executorId has registered") } diff --git a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala index 22acc270b983e..49e6de4e0bafd 100644 --- a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala @@ -78,7 +78,7 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext wit test("starting state") { sc = createSparkContext() val manager = sc.executorAllocationManager.get - assert(numExecutorsPending(manager) === 0) + assert(numExecutorsTarget(manager) === 1) assert(executorsPendingToRemove(manager).isEmpty) assert(executorIds(manager).isEmpty) assert(addTime(manager) === ExecutorAllocationManager.NOT_SET) @@ -91,108 +91,108 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext wit sc.listenerBus.postToAll(SparkListenerStageSubmitted(createStageInfo(0, 1000))) // Keep adding until the limit is reached - assert(numExecutorsPending(manager) === 0) + assert(numExecutorsTarget(manager) === 1) assert(numExecutorsToAdd(manager) === 1) assert(addExecutors(manager) === 1) - assert(numExecutorsPending(manager) === 1) + assert(numExecutorsTarget(manager) === 2) assert(numExecutorsToAdd(manager) === 2) assert(addExecutors(manager) === 2) - assert(numExecutorsPending(manager) === 3) + assert(numExecutorsTarget(manager) === 4) assert(numExecutorsToAdd(manager) === 4) assert(addExecutors(manager) === 4) - assert(numExecutorsPending(manager) === 7) + assert(numExecutorsTarget(manager) === 8) assert(numExecutorsToAdd(manager) === 8) - assert(addExecutors(manager) === 3) // reached the limit of 10 - assert(numExecutorsPending(manager) === 10) + assert(addExecutors(manager) === 2) // reached the limit of 10 + assert(numExecutorsTarget(manager) === 10) assert(numExecutorsToAdd(manager) === 1) assert(addExecutors(manager) === 0) - assert(numExecutorsPending(manager) === 10) + assert(numExecutorsTarget(manager) === 10) assert(numExecutorsToAdd(manager) === 1) // Register previously requested executors onExecutorAdded(manager, "first") - assert(numExecutorsPending(manager) === 9) + assert(numExecutorsTarget(manager) === 10) onExecutorAdded(manager, "second") onExecutorAdded(manager, "third") onExecutorAdded(manager, "fourth") - assert(numExecutorsPending(manager) === 6) + assert(numExecutorsTarget(manager) === 10) onExecutorAdded(manager, "first") // duplicates should not count onExecutorAdded(manager, "second") - assert(numExecutorsPending(manager) === 6) + assert(numExecutorsTarget(manager) === 10) // Try adding again // This should still fail because the number pending + running is still at the limit assert(addExecutors(manager) === 0) - assert(numExecutorsPending(manager) === 6) + assert(numExecutorsTarget(manager) === 10) assert(numExecutorsToAdd(manager) === 1) assert(addExecutors(manager) === 0) - assert(numExecutorsPending(manager) === 6) + assert(numExecutorsTarget(manager) === 10) assert(numExecutorsToAdd(manager) === 1) } test("add executors capped by num pending tasks") { - sc = createSparkContext(1, 10) + sc = createSparkContext(0, 10) val manager = sc.executorAllocationManager.get sc.listenerBus.postToAll(SparkListenerStageSubmitted(createStageInfo(0, 5))) // Verify that we're capped at number of tasks in the stage - assert(numExecutorsPending(manager) === 0) + assert(numExecutorsTarget(manager) === 0) assert(numExecutorsToAdd(manager) === 1) assert(addExecutors(manager) === 1) - assert(numExecutorsPending(manager) === 1) + assert(numExecutorsTarget(manager) === 1) assert(numExecutorsToAdd(manager) === 2) assert(addExecutors(manager) === 2) - assert(numExecutorsPending(manager) === 3) + assert(numExecutorsTarget(manager) === 3) assert(numExecutorsToAdd(manager) === 4) assert(addExecutors(manager) === 2) - assert(numExecutorsPending(manager) === 5) + assert(numExecutorsTarget(manager) === 5) assert(numExecutorsToAdd(manager) === 1) - // Verify that running a task reduces the cap + // Verify that running a task doesn't affect the target sc.listenerBus.postToAll(SparkListenerStageSubmitted(createStageInfo(1, 3))) sc.listenerBus.postToAll(SparkListenerExecutorAdded( 0L, "executor-1", new ExecutorInfo("host1", 1, Map.empty))) sc.listenerBus.postToAll(SparkListenerTaskStart(1, 0, createTaskInfo(0, 0, "executor-1"))) - assert(numExecutorsPending(manager) === 4) + assert(numExecutorsTarget(manager) === 5) assert(addExecutors(manager) === 1) - assert(numExecutorsPending(manager) === 5) + assert(numExecutorsTarget(manager) === 6) assert(numExecutorsToAdd(manager) === 2) assert(addExecutors(manager) === 2) - assert(numExecutorsPending(manager) === 7) + assert(numExecutorsTarget(manager) === 8) assert(numExecutorsToAdd(manager) === 4) assert(addExecutors(manager) === 0) - assert(numExecutorsPending(manager) === 7) + assert(numExecutorsTarget(manager) === 8) assert(numExecutorsToAdd(manager) === 1) - // Verify that re-running a task doesn't reduce the cap further + // Verify that re-running a task doesn't blow things up sc.listenerBus.postToAll(SparkListenerStageSubmitted(createStageInfo(2, 3))) sc.listenerBus.postToAll(SparkListenerTaskStart(2, 0, createTaskInfo(0, 0, "executor-1"))) sc.listenerBus.postToAll(SparkListenerTaskStart(2, 0, createTaskInfo(1, 0, "executor-1"))) assert(addExecutors(manager) === 1) - assert(numExecutorsPending(manager) === 8) + assert(numExecutorsTarget(manager) === 9) assert(numExecutorsToAdd(manager) === 2) assert(addExecutors(manager) === 1) - assert(numExecutorsPending(manager) === 9) + assert(numExecutorsTarget(manager) === 10) assert(numExecutorsToAdd(manager) === 1) // Verify that running a task once we're at our limit doesn't blow things up sc.listenerBus.postToAll(SparkListenerTaskStart(2, 0, createTaskInfo(0, 1, "executor-1"))) assert(addExecutors(manager) === 0) - assert(numExecutorsPending(manager) === 9) + assert(numExecutorsTarget(manager) === 10) } test("cancel pending executors when no longer needed") { - sc = createSparkContext(1, 10) + sc = createSparkContext(0, 10) val manager = sc.executorAllocationManager.get sc.listenerBus.postToAll(SparkListenerStageSubmitted(createStageInfo(2, 5))) - assert(numExecutorsPending(manager) === 0) + assert(numExecutorsTarget(manager) === 0) assert(numExecutorsToAdd(manager) === 1) assert(addExecutors(manager) === 1) - assert(numExecutorsPending(manager) === 1) + assert(numExecutorsTarget(manager) === 1) assert(numExecutorsToAdd(manager) === 2) assert(addExecutors(manager) === 2) - assert(numExecutorsPending(manager) === 3) + assert(numExecutorsTarget(manager) === 3) val task1Info = createTaskInfo(0, 0, "executor-1") sc.listenerBus.postToAll(SparkListenerTaskStart(2, 0, task1Info)) @@ -266,7 +266,6 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext wit // Add a few executors assert(addExecutors(manager) === 1) assert(addExecutors(manager) === 2) - assert(addExecutors(manager) === 4) onExecutorAdded(manager, "1") onExecutorAdded(manager, "2") onExecutorAdded(manager, "3") @@ -274,55 +273,57 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext wit onExecutorAdded(manager, "5") onExecutorAdded(manager, "6") onExecutorAdded(manager, "7") - assert(executorIds(manager).size === 7) + onExecutorAdded(manager, "8") + assert(executorIds(manager).size === 8) // Remove until limit assert(removeExecutor(manager, "1")) assert(removeExecutor(manager, "2")) - assert(!removeExecutor(manager, "3")) // lower limit reached - assert(!removeExecutor(manager, "4")) + assert(removeExecutor(manager, "3")) + assert(!removeExecutor(manager, "4")) // lower limit reached + assert(!removeExecutor(manager, "5")) onExecutorRemoved(manager, "1") onExecutorRemoved(manager, "2") + onExecutorRemoved(manager, "3") assert(executorIds(manager).size === 5) // Add until limit - assert(addExecutors(manager) === 5) // upper limit reached + assert(addExecutors(manager) === 2) // upper limit reached assert(addExecutors(manager) === 0) - assert(!removeExecutor(manager, "3")) // still at lower limit - assert(!removeExecutor(manager, "4")) - onExecutorAdded(manager, "8") + assert(!removeExecutor(manager, "4")) // still at lower limit + assert(!removeExecutor(manager, "5")) onExecutorAdded(manager, "9") onExecutorAdded(manager, "10") onExecutorAdded(manager, "11") onExecutorAdded(manager, "12") + onExecutorAdded(manager, "13") assert(executorIds(manager).size === 10) // Remove succeeds again, now that we are no longer at the lower limit - assert(removeExecutor(manager, "3")) assert(removeExecutor(manager, "4")) assert(removeExecutor(manager, "5")) assert(removeExecutor(manager, "6")) + assert(removeExecutor(manager, "7")) assert(executorIds(manager).size === 10) - assert(addExecutors(manager) === 1) - onExecutorRemoved(manager, "3") + assert(addExecutors(manager) === 0) onExecutorRemoved(manager, "4") + onExecutorRemoved(manager, "5") assert(executorIds(manager).size === 8) - // Add succeeds again, now that we are no longer at the upper limit - // Number of executors added restarts at 1 - assert(addExecutors(manager) === 2) - assert(addExecutors(manager) === 1) // upper limit reached + // Number of executors pending restarts at 1 + assert(numExecutorsToAdd(manager) === 1) assert(addExecutors(manager) === 0) assert(executorIds(manager).size === 8) - onExecutorRemoved(manager, "5") onExecutorRemoved(manager, "6") - onExecutorAdded(manager, "13") + onExecutorRemoved(manager, "7") onExecutorAdded(manager, "14") + onExecutorAdded(manager, "15") assert(executorIds(manager).size === 8) assert(addExecutors(manager) === 0) // still at upper limit - onExecutorAdded(manager, "15") onExecutorAdded(manager, "16") + onExecutorAdded(manager, "17") assert(executorIds(manager).size === 10) + assert(numExecutorsTarget(manager) === 10) } test("starting/canceling add timer") { @@ -405,33 +406,33 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext wit } test("mock polling loop with no events") { - sc = createSparkContext(1, 20) + sc = createSparkContext(0, 20) val manager = sc.executorAllocationManager.get val clock = new ManualClock(2020L) manager.setClock(clock) // No events - we should not be adding or removing - assert(numExecutorsPending(manager) === 0) + assert(numExecutorsTarget(manager) === 0) assert(executorsPendingToRemove(manager).isEmpty) schedule(manager) - assert(numExecutorsPending(manager) === 0) + assert(numExecutorsTarget(manager) === 0) assert(executorsPendingToRemove(manager).isEmpty) clock.advance(100L) schedule(manager) - assert(numExecutorsPending(manager) === 0) + assert(numExecutorsTarget(manager) === 0) assert(executorsPendingToRemove(manager).isEmpty) clock.advance(1000L) schedule(manager) - assert(numExecutorsPending(manager) === 0) + assert(numExecutorsTarget(manager) === 0) assert(executorsPendingToRemove(manager).isEmpty) clock.advance(10000L) schedule(manager) - assert(numExecutorsPending(manager) === 0) + assert(numExecutorsTarget(manager) === 0) assert(executorsPendingToRemove(manager).isEmpty) } test("mock polling loop add behavior") { - sc = createSparkContext(1, 20) + sc = createSparkContext(0, 20) val clock = new ManualClock(2020L) val manager = sc.executorAllocationManager.get manager.setClock(clock) @@ -441,43 +442,43 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext wit onSchedulerBacklogged(manager) clock.advance(schedulerBacklogTimeout * 1000 / 2) schedule(manager) - assert(numExecutorsPending(manager) === 0) // timer not exceeded yet + assert(numExecutorsTarget(manager) === 0) // timer not exceeded yet clock.advance(schedulerBacklogTimeout * 1000) schedule(manager) - assert(numExecutorsPending(manager) === 1) // first timer exceeded + assert(numExecutorsTarget(manager) === 1) // first timer exceeded clock.advance(sustainedSchedulerBacklogTimeout * 1000 / 2) schedule(manager) - assert(numExecutorsPending(manager) === 1) // second timer not exceeded yet + assert(numExecutorsTarget(manager) === 1) // second timer not exceeded yet clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) - assert(numExecutorsPending(manager) === 1 + 2) // second timer exceeded + assert(numExecutorsTarget(manager) === 1 + 2) // second timer exceeded clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) - assert(numExecutorsPending(manager) === 1 + 2 + 4) // third timer exceeded + assert(numExecutorsTarget(manager) === 1 + 2 + 4) // third timer exceeded // Scheduler queue drained onSchedulerQueueEmpty(manager) clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) - assert(numExecutorsPending(manager) === 7) // timer is canceled + assert(numExecutorsTarget(manager) === 7) // timer is canceled clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) - assert(numExecutorsPending(manager) === 7) + assert(numExecutorsTarget(manager) === 7) // Scheduler queue backlogged again onSchedulerBacklogged(manager) clock.advance(schedulerBacklogTimeout * 1000) schedule(manager) - assert(numExecutorsPending(manager) === 7 + 1) // timer restarted + assert(numExecutorsTarget(manager) === 7 + 1) // timer restarted clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) - assert(numExecutorsPending(manager) === 7 + 1 + 2) + assert(numExecutorsTarget(manager) === 7 + 1 + 2) clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) - assert(numExecutorsPending(manager) === 7 + 1 + 2 + 4) + assert(numExecutorsTarget(manager) === 7 + 1 + 2 + 4) clock.advance(sustainedSchedulerBacklogTimeout * 1000) schedule(manager) - assert(numExecutorsPending(manager) === 20) // limit reached + assert(numExecutorsTarget(manager) === 20) // limit reached } test("mock polling loop remove behavior") { @@ -671,6 +672,31 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext wit assert(!removeTimes(manager).contains("executor-1")) } + test("avoid ramp up when target < running executors") { + sc = createSparkContext(0, 100000) + val manager = sc.executorAllocationManager.get + val stage1 = createStageInfo(0, 1000) + sc.listenerBus.postToAll(SparkListenerStageSubmitted(stage1)) + + assert(addExecutors(manager) === 1) + assert(addExecutors(manager) === 2) + assert(addExecutors(manager) === 4) + assert(addExecutors(manager) === 8) + assert(numExecutorsTarget(manager) === 15) + (0 until 15).foreach { i => + onExecutorAdded(manager, s"executor-$i") + } + assert(executorIds(manager).size === 15) + sc.listenerBus.postToAll(SparkListenerStageCompleted(stage1)) + + adjustRequestedExecutors(manager) + assert(numExecutorsTarget(manager) === 0) + + sc.listenerBus.postToAll(SparkListenerStageSubmitted(createStageInfo(1, 1000))) + addExecutors(manager) + assert(numExecutorsTarget(manager) === 16) + } + private def createSparkContext(minExecutors: Int = 1, maxExecutors: Int = 5): SparkContext = { val conf = new SparkConf() .setMaster("local") @@ -713,7 +739,7 @@ private object ExecutorAllocationManagerSuite extends PrivateMethodTester { * ------------------------------------------------------- */ private val _numExecutorsToAdd = PrivateMethod[Int]('numExecutorsToAdd) - private val _numExecutorsPending = PrivateMethod[Int]('numExecutorsPending) + private val _numExecutorsTarget = PrivateMethod[Int]('numExecutorsTarget) private val _maxNumExecutorsNeeded = PrivateMethod[Int]('maxNumExecutorsNeeded) private val _executorsPendingToRemove = PrivateMethod[collection.Set[String]]('executorsPendingToRemove) @@ -722,7 +748,8 @@ private object ExecutorAllocationManagerSuite extends PrivateMethodTester { private val _removeTimes = PrivateMethod[collection.Map[String, Long]]('removeTimes) private val _schedule = PrivateMethod[Unit]('schedule) private val _addExecutors = PrivateMethod[Int]('addExecutors) - private val _addOrCancelExecutorRequests = PrivateMethod[Int]('addOrCancelExecutorRequests) + private val _updateAndSyncNumExecutorsTarget = + PrivateMethod[Int]('updateAndSyncNumExecutorsTarget) private val _removeExecutor = PrivateMethod[Boolean]('removeExecutor) private val _onExecutorAdded = PrivateMethod[Unit]('onExecutorAdded) private val _onExecutorRemoved = PrivateMethod[Unit]('onExecutorRemoved) @@ -735,8 +762,8 @@ private object ExecutorAllocationManagerSuite extends PrivateMethodTester { manager invokePrivate _numExecutorsToAdd() } - private def numExecutorsPending(manager: ExecutorAllocationManager): Int = { - manager invokePrivate _numExecutorsPending() + private def numExecutorsTarget(manager: ExecutorAllocationManager): Int = { + manager invokePrivate _numExecutorsTarget() } private def executorsPendingToRemove( @@ -766,7 +793,7 @@ private object ExecutorAllocationManagerSuite extends PrivateMethodTester { } private def adjustRequestedExecutors(manager: ExecutorAllocationManager): Int = { - manager invokePrivate _addOrCancelExecutorRequests(0L) + manager invokePrivate _updateAndSyncNumExecutorsTarget(0L) } private def removeExecutor(manager: ExecutorAllocationManager, id: String): Boolean = { From 2022193412e832393a29b94609841c3ffe8e3d66 Mon Sep 17 00:00:00 2001 From: Timothy Chen Date: Fri, 1 May 2015 18:36:42 -0700 Subject: [PATCH 540/591] [SPARK-7216] [MESOS] Add driver details page to Mesos cluster UI. Add a details page that displays Mesos driver in the Mesos cluster UI Author: Timothy Chen Closes #5763 from tnachen/mesos_cluster_page and squashes the following commits: 55f36eb [Timothy Chen] Add driver details page to Mesos cluster UI. --- .../spark/deploy/mesos/ui/DriverPage.scala | 180 ++++++++++++++++++ .../deploy/mesos/ui/MesosClusterPage.scala | 9 +- .../deploy/mesos/ui/MesosClusterUI.scala | 1 + .../deploy/rest/mesos/MesosRestServer.scala | 6 +- .../cluster/mesos/MesosClusterScheduler.scala | 33 +++- .../cluster/mesos/MesosSchedulerBackend.scala | 4 +- 6 files changed, 222 insertions(+), 11 deletions(-) create mode 100644 core/src/main/scala/org/apache/spark/deploy/mesos/ui/DriverPage.scala diff --git a/core/src/main/scala/org/apache/spark/deploy/mesos/ui/DriverPage.scala b/core/src/main/scala/org/apache/spark/deploy/mesos/ui/DriverPage.scala new file mode 100644 index 0000000000000..be8560d10fc62 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/mesos/ui/DriverPage.scala @@ -0,0 +1,180 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.deploy.mesos.ui + +import javax.servlet.http.HttpServletRequest + +import scala.xml.Node + +import org.apache.spark.deploy.Command +import org.apache.spark.deploy.mesos.MesosDriverDescription +import org.apache.spark.scheduler.cluster.mesos.{MesosClusterSubmissionState, MesosClusterRetryState} +import org.apache.spark.ui.{UIUtils, WebUIPage} + + +private[ui] class DriverPage(parent: MesosClusterUI) extends WebUIPage("driver") { + + override def render(request: HttpServletRequest): Seq[Node] = { + val driverId = request.getParameter("id") + require(driverId != null && driverId.nonEmpty, "Missing id parameter") + + val state = parent.scheduler.getDriverState(driverId) + if (state.isEmpty) { + val content = +
    +

    Cannot find driver {driverId}

    +
    + return UIUtils.basicSparkPage(content, s"Details for Job $driverId") + } + val driverState = state.get + val driverHeaders = Seq("Driver property", "Value") + val schedulerHeaders = Seq("Scheduler property", "Value") + val commandEnvHeaders = Seq("Command environment variable", "Value") + val launchedHeaders = Seq("Launched property", "Value") + val commandHeaders = Seq("Comamnd property", "Value") + val retryHeaders = Seq("Last failed status", "Next retry time", "Retry count") + val driverDescription = Iterable.apply(driverState.description) + val submissionState = Iterable.apply(driverState.submissionState) + val command = Iterable.apply(driverState.description.command) + val schedulerProperties = Iterable.apply(driverState.description.schedulerProperties) + val commandEnv = Iterable.apply(driverState.description.command.environment) + val driverTable = + UIUtils.listingTable(driverHeaders, driverRow, driverDescription) + val commandTable = + UIUtils.listingTable(commandHeaders, commandRow, command) + val commandEnvTable = + UIUtils.listingTable(commandEnvHeaders, propertiesRow, commandEnv) + val schedulerTable = + UIUtils.listingTable(schedulerHeaders, propertiesRow, schedulerProperties) + val launchedTable = + UIUtils.listingTable(launchedHeaders, launchedRow, submissionState) + val retryTable = + UIUtils.listingTable( + retryHeaders, retryRow, Iterable.apply(driverState.description.retryState)) + val content = +

    Driver state information for driver id {driverId}

    + Back to Drivers +
    +
    +

    Driver state: {driverState.state}

    +

    Driver properties

    + {driverTable} +

    Driver command

    + {commandTable} +

    Driver command environment

    + {commandEnvTable} +

    Scheduler properties

    + {schedulerTable} +

    Launched state

    + {launchedTable} +

    Retry state

    + {retryTable} +
    +
    ; + + UIUtils.basicSparkPage(content, s"Details for Job $driverId") + } + + private def launchedRow(submissionState: Option[MesosClusterSubmissionState]): Seq[Node] = { + submissionState.map { state => + + Mesos Slave ID + {state.slaveId.getValue} + + + Mesos Task ID + {state.taskId.getValue} + + + Launch Time + {state.startDate} + + + Finish Time + {state.finishDate.map(_.toString).getOrElse("")} + + + Last Task Status + {state.mesosTaskStatus.map(_.toString).getOrElse("")} + + }.getOrElse(Seq[Node]()) + } + + private def propertiesRow(properties: collection.Map[String, String]): Seq[Node] = { + properties.map { case (k, v) => + + {k}{v} + + }.toSeq + } + + private def commandRow(command: Command): Seq[Node] = { + + Main class{command.mainClass} + + + Arguments{command.arguments.mkString(" ")} + + + Class path entries{command.classPathEntries.mkString(" ")} + + + Java options{command.javaOpts.mkString((" "))} + + + Library path entries{command.libraryPathEntries.mkString((" "))} + + } + + private def driverRow(driver: MesosDriverDescription): Seq[Node] = { + + Name{driver.name} + + + Id{driver.submissionId} + + + Cores{driver.cores} + + + Memory{driver.mem} + + + Submitted{driver.submissionDate} + + + Supervise{driver.supervise} + + } + + private def retryRow(retryState: Option[MesosClusterRetryState]): Seq[Node] = { + retryState.map { state => + + + {state.lastFailureStatus} + + + {state.nextRetry} + + + {state.retries} + + + }.getOrElse(Seq[Node]()) + } +} diff --git a/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterPage.scala b/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterPage.scala index 7b2005e0f1237..7419fa9699648 100644 --- a/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterPage.scala @@ -56,8 +56,9 @@ private[mesos] class MesosClusterPage(parent: MesosClusterUI) extends WebUIPage( } private def queuedRow(submission: MesosDriverDescription): Seq[Node] = { + val id = submission.submissionId - {submission.submissionId} + {id} {submission.submissionDate} {submission.command.mainClass} cpus: {submission.cores}, mem: {submission.mem} @@ -65,8 +66,9 @@ private[mesos] class MesosClusterPage(parent: MesosClusterUI) extends WebUIPage( } private def driverRow(state: MesosClusterSubmissionState): Seq[Node] = { + val id = state.driverDescription.submissionId - {state.driverDescription.submissionId} + {id} {state.driverDescription.submissionDate} {state.driverDescription.command.mainClass} cpus: {state.driverDescription.cores}, mem: {state.driverDescription.mem} @@ -77,8 +79,9 @@ private[mesos] class MesosClusterPage(parent: MesosClusterUI) extends WebUIPage( } private def retryRow(submission: MesosDriverDescription): Seq[Node] = { + val id = submission.submissionId - {submission.submissionId} + {id} {submission.submissionDate} {submission.command.mainClass} {submission.retryState.get.lastFailureStatus} diff --git a/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterUI.scala b/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterUI.scala index 4865d46dbc4ab..3f693545a0349 100644 --- a/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterUI.scala +++ b/core/src/main/scala/org/apache/spark/deploy/mesos/ui/MesosClusterUI.scala @@ -39,6 +39,7 @@ private[spark] class MesosClusterUI( override def initialize() { attachPage(new MesosClusterPage(this)) + attachPage(new DriverPage(this)) attachHandler(createStaticHandler(MesosClusterUI.STATIC_RESOURCE_DIR, "/static")) } } diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/mesos/MesosRestServer.scala b/core/src/main/scala/org/apache/spark/deploy/rest/mesos/MesosRestServer.scala index fd17a980c9319..8198296eeb341 100644 --- a/core/src/main/scala/org/apache/spark/deploy/rest/mesos/MesosRestServer.scala +++ b/core/src/main/scala/org/apache/spark/deploy/rest/mesos/MesosRestServer.scala @@ -53,7 +53,7 @@ private[spark] class MesosRestServer( new MesosStatusRequestServlet(scheduler, masterConf) } -private[deploy] class MesosSubmitRequestServlet( +private[mesos] class MesosSubmitRequestServlet( scheduler: MesosClusterScheduler, conf: SparkConf) extends SubmitRequestServlet { @@ -139,7 +139,7 @@ private[deploy] class MesosSubmitRequestServlet( } } -private[deploy] class MesosKillRequestServlet(scheduler: MesosClusterScheduler, conf: SparkConf) +private[mesos] class MesosKillRequestServlet(scheduler: MesosClusterScheduler, conf: SparkConf) extends KillRequestServlet { protected override def handleKill(submissionId: String): KillSubmissionResponse = { val k = scheduler.killDriver(submissionId) @@ -148,7 +148,7 @@ private[deploy] class MesosKillRequestServlet(scheduler: MesosClusterScheduler, } } -private[deploy] class MesosStatusRequestServlet(scheduler: MesosClusterScheduler, conf: SparkConf) +private[mesos] class MesosStatusRequestServlet(scheduler: MesosClusterScheduler, conf: SparkConf) extends StatusRequestServlet { protected override def handleStatus(submissionId: String): SubmissionStatusResponse = { val d = scheduler.getDriverStatus(submissionId) diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala index 0396e62be5309..06f0e2881c344 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala @@ -50,12 +50,13 @@ private[spark] class MesosClusterSubmissionState( val taskId: TaskID, val slaveId: SlaveID, var mesosTaskStatus: Option[TaskStatus], - var startDate: Date) + var startDate: Date, + var finishDate: Option[Date]) extends Serializable { def copy(): MesosClusterSubmissionState = { new MesosClusterSubmissionState( - driverDescription, taskId, slaveId, mesosTaskStatus, startDate) + driverDescription, taskId, slaveId, mesosTaskStatus, startDate, finishDate) } } @@ -95,6 +96,14 @@ private[spark] class MesosClusterSchedulerState( val finishedDrivers: Iterable[MesosClusterSubmissionState], val pendingRetryDrivers: Iterable[MesosDriverDescription]) +/** + * The full state of a Mesos driver, that is being used to display driver information on the UI. + */ +private[spark] class MesosDriverState( + val state: String, + val description: MesosDriverDescription, + val submissionState: Option[MesosClusterSubmissionState] = None) + /** * A Mesos scheduler that is responsible for launching submitted Spark drivers in cluster mode * as Mesos tasks in a Mesos cluster. @@ -233,6 +242,22 @@ private[spark] class MesosClusterScheduler( s } + /** + * Gets the driver state to be displayed on the Web UI. + */ + def getDriverState(submissionId: String): Option[MesosDriverState] = { + stateLock.synchronized { + queuedDrivers.find(_.submissionId.equals(submissionId)) + .map(d => new MesosDriverState("QUEUED", d)) + .orElse(launchedDrivers.get(submissionId) + .map(d => new MesosDriverState("RUNNING", d.driverDescription, Some(d)))) + .orElse(finishedDrivers.find(_.driverDescription.submissionId.equals(submissionId)) + .map(d => new MesosDriverState("FINISHED", d.driverDescription, Some(d)))) + .orElse(pendingRetryDrivers.find(_.submissionId.equals(submissionId)) + .map(d => new MesosDriverState("RETRYING", d))) + } + } + private def isQueueFull(): Boolean = launchedDrivers.size >= queuedCapacity /** @@ -439,7 +464,7 @@ private[spark] class MesosClusterScheduler( logTrace(s"Using offer ${offer.offer.getId.getValue} to launch driver " + submission.submissionId) val newState = new MesosClusterSubmissionState(submission, taskId, offer.offer.getSlaveId, - None, new Date()) + None, new Date(), None) launchedDrivers(submission.submissionId) = newState launchedDriversState.persist(submission.submissionId, newState) afterLaunchCallback(submission.submissionId) @@ -534,6 +559,7 @@ private[spark] class MesosClusterScheduler( // Check if the driver is supervise enabled and can be relaunched. if (state.driverDescription.supervise && shouldRelaunch(status.getState)) { removeFromLaunchedDrivers(taskId) + state.finishDate = Some(new Date()) val retryState: Option[MesosClusterRetryState] = state.driverDescription.retryState val (retries, waitTimeSec) = retryState .map { rs => (rs.retries + 1, Math.min(maxRetryWaitTime, rs.waitTime * 2)) } @@ -546,6 +572,7 @@ private[spark] class MesosClusterScheduler( pendingRetryDriversState.persist(taskId, newDriverDescription) } else if (TaskState.isFinished(TaskState.fromMesos(status.getState))) { removeFromLaunchedDrivers(taskId) + state.finishDate = Some(new Date()) if (finishedDrivers.size >= retainedDrivers) { val toRemove = math.max(retainedDrivers / 10, 1) finishedDrivers.trimStart(toRemove) diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala index 8346a2407489f..86a7d0fb587e4 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala @@ -23,7 +23,7 @@ import java.util.{ArrayList => JArrayList, Collections, List => JList} import scala.collection.JavaConversions._ import scala.collection.mutable.{HashMap, HashSet} -import org.apache.mesos.Protos.{ExecutorInfo => MesosExecutorInfo, TaskInfo => MesosTaskInfo, TaskState => MesosTaskState, _} +import org.apache.mesos.Protos.{ExecutorInfo => MesosExecutorInfo, TaskInfo => MesosTaskInfo, _} import org.apache.mesos.protobuf.ByteString import org.apache.mesos.{Scheduler => MScheduler, _} import org.apache.spark.executor.MesosExecutorBackend @@ -56,7 +56,7 @@ private[spark] class MesosSchedulerBackend( // The listener bus to publish executor added/removed events. val listenerBus = sc.listenerBus - + private[mesos] val mesosExecutorCores = sc.conf.getDouble("spark.mesos.mesosExecutor.cores", 1) @volatile var appId: String = _ From b4b43df8a338a30c0eadcf10cbe3ba203dc3f861 Mon Sep 17 00:00:00 2001 From: WangTaoTheTonic Date: Fri, 1 May 2015 18:38:20 -0700 Subject: [PATCH 541/591] [SPARK-6443] [SPARK SUBMIT] Could not submit app in standalone cluster mode when HA is enabled **3/26 update:** * Akka-based: Use an array of `ActorSelection` to represent multiple master. Add an `activeMasterActor` for query status of driver. And will add lost masters( including the standby one) to `lostMasters`. When size of `lostMasters` equals or greater than # of all masters, we should give an error that all masters are not avalible. * Rest-based: When all masters are not available(throw an exception), we use akka gateway to submit apps. I have tested simply on standalone HA cluster(with two masters alive and one alive/one dead), it worked. There might remains some issues on style or message print, but we can check the solution then fix them together. /cc srowen andrewor14 Author: WangTaoTheTonic Closes #5116 from WangTaoTheTonic/SPARK-6443 and squashes the following commits: 2a28aab [WangTaoTheTonic] based the newest change https://github.com/apache/spark/pull/5144 76fd411 [WangTaoTheTonic] rebase f4f972b [WangTaoTheTonic] rebase...again a41de0b [WangTaoTheTonic] rebase 220cb3c [WangTaoTheTonic] move connect exception inside 35119a0 [WangTaoTheTonic] style and compile issues 9d636be [WangTaoTheTonic] per Andrew's comments 979760c [WangTaoTheTonic] rebase e4f4ece [WangTaoTheTonic] fix failed test 5d23958 [WangTaoTheTonic] refact some duplicated code, style and comments 7a881b3 [WangTaoTheTonic] when one of masters is gone, we still can submit 2b011c9 [WangTaoTheTonic] fix broken tests 60d97a4 [WangTaoTheTonic] rebase fa1fa80 [WangTaoTheTonic] submit app to HA cluster in standalone cluster mode --- .../org/apache/spark/deploy/Client.scala | 73 ++++++--- .../apache/spark/deploy/ClientArguments.scala | 9 +- .../org/apache/spark/deploy/SparkSubmit.scala | 8 +- .../apache/spark/deploy/master/Master.scala | 24 ++- .../deploy/rest/RestSubmissionClient.scala | 152 +++++++++++++----- .../spark/deploy/worker/WorkerArguments.scala | 2 +- .../scala/org/apache/spark/util/Utils.scala | 16 ++ .../rest/StandaloneRestSubmitSuite.scala | 40 ++--- 8 files changed, 229 insertions(+), 95 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/deploy/Client.scala b/core/src/main/scala/org/apache/spark/deploy/Client.scala index c2c3e9a9e4827..848b62f9de71b 100644 --- a/core/src/main/scala/org/apache/spark/deploy/Client.scala +++ b/core/src/main/scala/org/apache/spark/deploy/Client.scala @@ -17,6 +17,7 @@ package org.apache.spark.deploy +import scala.collection.mutable.HashSet import scala.concurrent._ import akka.actor._ @@ -31,21 +32,24 @@ import org.apache.spark.util.{ActorLogReceive, AkkaUtils, RpcUtils, Utils} /** * Proxy that relays messages to the driver. + * + * We currently don't support retry if submission fails. In HA mode, client will submit request to + * all masters and see which one could handle it. */ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) extends Actor with ActorLogReceive with Logging { - var masterActor: ActorSelection = _ + private val masterActors = driverArgs.masters.map { m => + context.actorSelection(Master.toAkkaUrl(m, AkkaUtils.protocol(context.system))) + } + private val lostMasters = new HashSet[Address] + private var activeMasterActor: ActorSelection = null + val timeout = RpcUtils.askTimeout(conf) override def preStart(): Unit = { - masterActor = context.actorSelection( - Master.toAkkaUrl(driverArgs.master, AkkaUtils.protocol(context.system))) - context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) - println(s"Sending ${driverArgs.cmd} command to ${driverArgs.master}") - driverArgs.cmd match { case "launch" => // TODO: We could add an env variable here and intercept it in `sc.addJar` that would @@ -79,11 +83,17 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) driverArgs.supervise, command) - masterActor ! RequestSubmitDriver(driverDescription) + // This assumes only one Master is active at a time + for (masterActor <- masterActors) { + masterActor ! RequestSubmitDriver(driverDescription) + } case "kill" => val driverId = driverArgs.driverId - masterActor ! RequestKillDriver(driverId) + // This assumes only one Master is active at a time + for (masterActor <- masterActors) { + masterActor ! RequestKillDriver(driverId) + } } } @@ -92,10 +102,9 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) println("... waiting before polling master for driver state") Thread.sleep(5000) println("... polling master for driver state") - val statusFuture = (masterActor ? RequestDriverStatus(driverId))(timeout) + val statusFuture = (activeMasterActor ? RequestDriverStatus(driverId))(timeout) .mapTo[DriverStatusResponse] val statusResponse = Await.result(statusFuture, timeout) - statusResponse.found match { case false => println(s"ERROR: Cluster master did not recognize $driverId") @@ -122,20 +131,46 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) case SubmitDriverResponse(success, driverId, message) => println(message) - if (success) pollAndReportStatus(driverId.get) else System.exit(-1) + if (success) { + activeMasterActor = context.actorSelection(sender.path) + pollAndReportStatus(driverId.get) + } else if (!Utils.responseFromBackup(message)) { + System.exit(-1) + } + case KillDriverResponse(driverId, success, message) => println(message) - if (success) pollAndReportStatus(driverId) else System.exit(-1) + if (success) { + activeMasterActor = context.actorSelection(sender.path) + pollAndReportStatus(driverId) + } else if (!Utils.responseFromBackup(message)) { + System.exit(-1) + } case DisassociatedEvent(_, remoteAddress, _) => - println(s"Error connecting to master ${driverArgs.master} ($remoteAddress), exiting.") - System.exit(-1) + if (!lostMasters.contains(remoteAddress)) { + println(s"Error connecting to master $remoteAddress.") + lostMasters += remoteAddress + // Note that this heuristic does not account for the fact that a Master can recover within + // the lifetime of this client. Thus, once a Master is lost it is lost to us forever. This + // is not currently a concern, however, because this client does not retry submissions. + if (lostMasters.size >= masterActors.size) { + println("No master is available, exiting.") + System.exit(-1) + } + } case AssociationErrorEvent(cause, _, remoteAddress, _, _) => - println(s"Error connecting to master ${driverArgs.master} ($remoteAddress), exiting.") - println(s"Cause was: $cause") - System.exit(-1) + if (!lostMasters.contains(remoteAddress)) { + println(s"Error connecting to master ($remoteAddress).") + println(s"Cause was: $cause") + lostMasters += remoteAddress + if (lostMasters.size >= masterActors.size) { + println("No master is available, exiting.") + System.exit(-1) + } + } } } @@ -163,7 +198,9 @@ object Client { "driverClient", Utils.localHostName(), 0, conf, new SecurityManager(conf)) // Verify driverArgs.master is a valid url so that we can use it in ClientActor safely - Master.toAkkaUrl(driverArgs.master, AkkaUtils.protocol(actorSystem)) + for (m <- driverArgs.masters) { + Master.toAkkaUrl(m, AkkaUtils.protocol(actorSystem)) + } actorSystem.actorOf(Props(classOf[ClientActor], driverArgs, conf)) actorSystem.awaitTermination() diff --git a/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala b/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala index 5cbac787dceeb..316e2d59f01b8 100644 --- a/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala @@ -22,8 +22,7 @@ import java.net.{URI, URISyntaxException} import scala.collection.mutable.ListBuffer import org.apache.log4j.Level - -import org.apache.spark.util.{IntParam, MemoryParam} +import org.apache.spark.util.{IntParam, MemoryParam, Utils} /** * Command-line parser for the driver client. @@ -35,7 +34,7 @@ private[deploy] class ClientArguments(args: Array[String]) { var logLevel = Level.WARN // launch parameters - var master: String = "" + var masters: Array[String] = null var jarUrl: String = "" var mainClass: String = "" var supervise: Boolean = DEFAULT_SUPERVISE @@ -80,13 +79,13 @@ private[deploy] class ClientArguments(args: Array[String]) { } jarUrl = _jarUrl - master = _master + masters = Utils.parseStandaloneMasterUrls(_master) mainClass = _mainClass _driverOptions ++= tail case "kill" :: _master :: _driverId :: tail => cmd = "kill" - master = _master + masters = Utils.parseStandaloneMasterUrls(_master) driverId = _driverId case _ => diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala index af38bf80e4f0b..42b5d41b7b526 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -118,8 +118,8 @@ object SparkSubmit { * Kill an existing submission using the REST protocol. Standalone and Mesos cluster mode only. */ private def kill(args: SparkSubmitArguments): Unit = { - new RestSubmissionClient() - .killSubmission(args.master, args.submissionToKill) + new RestSubmissionClient(args.master) + .killSubmission(args.submissionToKill) } /** @@ -127,8 +127,8 @@ object SparkSubmit { * Standalone and Mesos cluster mode only. */ private def requestStatus(args: SparkSubmitArguments): Unit = { - new RestSubmissionClient() - .requestSubmissionStatus(args.master, args.submissionToRequestStatusFor) + new RestSubmissionClient(args.master) + .requestSubmissionStatus(args.submissionToRequestStatusFor) } /** diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala index dc6077f3d132b..0fac3cdcf55e7 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala @@ -254,7 +254,8 @@ private[master] class Master( case RequestSubmitDriver(description) => { if (state != RecoveryState.ALIVE) { - val msg = s"Can only accept driver submissions in ALIVE state. Current state: $state." + val msg = s"${Utils.BACKUP_STANDALONE_MASTER_PREFIX}: $state. " + + "Can only accept driver submissions in ALIVE state." sender ! SubmitDriverResponse(false, None, msg) } else { logInfo("Driver submitted " + description.command.mainClass) @@ -274,7 +275,8 @@ private[master] class Master( case RequestKillDriver(driverId) => { if (state != RecoveryState.ALIVE) { - val msg = s"Can only kill drivers in ALIVE state. Current state: $state." + val msg = s"${Utils.BACKUP_STANDALONE_MASTER_PREFIX}: $state. " + + s"Can only kill drivers in ALIVE state." sender ! KillDriverResponse(driverId, success = false, msg) } else { logInfo("Asked to kill driver " + driverId) @@ -305,12 +307,18 @@ private[master] class Master( } case RequestDriverStatus(driverId) => { - (drivers ++ completedDrivers).find(_.id == driverId) match { - case Some(driver) => - sender ! DriverStatusResponse(found = true, Some(driver.state), - driver.worker.map(_.id), driver.worker.map(_.hostPort), driver.exception) - case None => - sender ! DriverStatusResponse(found = false, None, None, None, None) + if (state != RecoveryState.ALIVE) { + val msg = s"${Utils.BACKUP_STANDALONE_MASTER_PREFIX}: $state. " + + "Can only request driver status in ALIVE state." + sender ! DriverStatusResponse(found = false, None, None, None, Some(new Exception(msg))) + } else { + (drivers ++ completedDrivers).find(_.id == driverId) match { + case Some(driver) => + sender ! DriverStatusResponse(found = true, Some(driver.state), + driver.worker.map(_.id), driver.worker.map(_.hostPort), driver.exception) + case None => + sender ! DriverStatusResponse(found = false, None, None, None, None) + } } } diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala b/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala index 307cebfb4bd09..6078f50518ba4 100644 --- a/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala +++ b/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala @@ -18,9 +18,10 @@ package org.apache.spark.deploy.rest import java.io.{DataOutputStream, FileNotFoundException} -import java.net.{HttpURLConnection, SocketException, URL} +import java.net.{ConnectException, HttpURLConnection, SocketException, URL} import javax.servlet.http.HttpServletResponse +import scala.collection.mutable import scala.io.Source import com.fasterxml.jackson.core.JsonProcessingException @@ -51,57 +52,109 @@ import org.apache.spark.util.Utils * implementation of this client can use that information to retry using the version specified * by the server. */ -private[spark] class RestSubmissionClient extends Logging { +private[spark] class RestSubmissionClient(master: String) extends Logging { import RestSubmissionClient._ private val supportedMasterPrefixes = Seq("spark://", "mesos://") + private val masters: Array[String] = Utils.parseStandaloneMasterUrls(master) + + // Set of masters that lost contact with us, used to keep track of + // whether there are masters still alive for us to communicate with + private val lostMasters = new mutable.HashSet[String] + /** * Submit an application specified by the parameters in the provided request. * * If the submission was successful, poll the status of the submission and report * it to the user. Otherwise, report the error message provided by the server. */ - def createSubmission( - master: String, - request: CreateSubmissionRequest): SubmitRestProtocolResponse = { + def createSubmission(request: CreateSubmissionRequest): SubmitRestProtocolResponse = { logInfo(s"Submitting a request to launch an application in $master.") - validateMaster(master) - val url = getSubmitUrl(master) - val response = postJson(url, request.toJson) - response match { - case s: CreateSubmissionResponse => - reportSubmissionStatus(master, s) - handleRestResponse(s) - case unexpected => - handleUnexpectedRestResponse(unexpected) + var handled: Boolean = false + var response: SubmitRestProtocolResponse = null + for (m <- masters if !handled) { + validateMaster(m) + val url = getSubmitUrl(m) + try { + response = postJson(url, request.toJson) + response match { + case s: CreateSubmissionResponse => + if (s.success) { + reportSubmissionStatus(s) + handleRestResponse(s) + handled = true + } + case unexpected => + handleUnexpectedRestResponse(unexpected) + } + } catch { + case e: SubmitRestConnectionException => + if (handleConnectionException(m)) { + throw new SubmitRestConnectionException("Unable to connect to server", e) + } + } } response } /** Request that the server kill the specified submission. */ - def killSubmission(master: String, submissionId: String): SubmitRestProtocolResponse = { + def killSubmission(submissionId: String): SubmitRestProtocolResponse = { logInfo(s"Submitting a request to kill submission $submissionId in $master.") - validateMaster(master) - val response = post(getKillUrl(master, submissionId)) - response match { - case k: KillSubmissionResponse => handleRestResponse(k) - case unexpected => handleUnexpectedRestResponse(unexpected) + var handled: Boolean = false + var response: SubmitRestProtocolResponse = null + for (m <- masters if !handled) { + validateMaster(m) + val url = getKillUrl(m, submissionId) + try { + response = post(url) + response match { + case k: KillSubmissionResponse => + if (!Utils.responseFromBackup(k.message)) { + handleRestResponse(k) + handled = true + } + case unexpected => + handleUnexpectedRestResponse(unexpected) + } + } catch { + case e: SubmitRestConnectionException => + if (handleConnectionException(m)) { + throw new SubmitRestConnectionException("Unable to connect to server", e) + } + } } response } /** Request the status of a submission from the server. */ def requestSubmissionStatus( - master: String, submissionId: String, quiet: Boolean = false): SubmitRestProtocolResponse = { logInfo(s"Submitting a request for the status of submission $submissionId in $master.") - validateMaster(master) - val response = get(getStatusUrl(master, submissionId)) - response match { - case s: SubmissionStatusResponse => if (!quiet) { handleRestResponse(s) } - case unexpected => handleUnexpectedRestResponse(unexpected) + + var handled: Boolean = false + var response: SubmitRestProtocolResponse = null + for (m <- masters if !handled) { + validateMaster(m) + val url = getStatusUrl(m, submissionId) + try { + response = get(url) + response match { + case s: SubmissionStatusResponse if s.success => + if (!quiet) { + handleRestResponse(s) + } + handled = true + case unexpected => + handleUnexpectedRestResponse(unexpected) + } + } catch { + case e: SubmitRestConnectionException => + if (handleConnectionException(m)) { + throw new SubmitRestConnectionException("Unable to connect to server", e) + } + } } response } @@ -148,11 +201,16 @@ private[spark] class RestSubmissionClient extends Logging { conn.setRequestProperty("Content-Type", "application/json") conn.setRequestProperty("charset", "utf-8") conn.setDoOutput(true) - val out = new DataOutputStream(conn.getOutputStream) - Utils.tryWithSafeFinally { - out.write(json.getBytes(Charsets.UTF_8)) - } { - out.close() + try { + val out = new DataOutputStream(conn.getOutputStream) + Utils.tryWithSafeFinally { + out.write(json.getBytes(Charsets.UTF_8)) + } { + out.close() + } + } catch { + case e: ConnectException => + throw new SubmitRestConnectionException("Connect Exception when connect to server", e) } readResponse(conn) } @@ -191,11 +249,9 @@ private[spark] class RestSubmissionClient extends Logging { } } catch { case unreachable @ (_: FileNotFoundException | _: SocketException) => - throw new SubmitRestConnectionException( - s"Unable to connect to server ${connection.getURL}", unreachable) + throw new SubmitRestConnectionException("Unable to connect to server", unreachable) case malformed @ (_: JsonProcessingException | _: SubmitRestProtocolException) => - throw new SubmitRestProtocolException( - "Malformed response received from server", malformed) + throw new SubmitRestProtocolException("Malformed response received from server", malformed) } } @@ -241,13 +297,12 @@ private[spark] class RestSubmissionClient extends Logging { /** Report the status of a newly created submission. */ private def reportSubmissionStatus( - master: String, submitResponse: CreateSubmissionResponse): Unit = { if (submitResponse.success) { val submissionId = submitResponse.submissionId if (submissionId != null) { logInfo(s"Submission successfully created as $submissionId. Polling submission state...") - pollSubmissionStatus(master, submissionId) + pollSubmissionStatus(submissionId) } else { // should never happen logError("Application successfully submitted, but submission ID was not provided!") @@ -262,9 +317,9 @@ private[spark] class RestSubmissionClient extends Logging { * Poll the status of the specified submission and log it. * This retries up to a fixed number of times before giving up. */ - private def pollSubmissionStatus(master: String, submissionId: String): Unit = { + private def pollSubmissionStatus(submissionId: String): Unit = { (1 to REPORT_DRIVER_STATUS_MAX_TRIES).foreach { _ => - val response = requestSubmissionStatus(master, submissionId, quiet = true) + val response = requestSubmissionStatus(submissionId, quiet = true) val statusResponse = response match { case s: SubmissionStatusResponse => s case _ => return // unexpected type, let upstream caller handle it @@ -302,6 +357,21 @@ private[spark] class RestSubmissionClient extends Logging { private def handleUnexpectedRestResponse(unexpected: SubmitRestProtocolResponse): Unit = { logError(s"Error: Server responded with message of unexpected type ${unexpected.messageType}.") } + + /** + * When a connection exception is caught, return true if all masters are lost. + * Note that the heuristic used here does not take into account that masters + * can recover during the lifetime of this client. This assumption should be + * harmless because this client currently does not support retrying submission + * on failure yet (SPARK-6443). + */ + private def handleConnectionException(masterUrl: String): Boolean = { + if (!lostMasters.contains(masterUrl)) { + logWarning(s"Unable to connect to server ${masterUrl}.") + lostMasters += masterUrl + } + lostMasters.size >= masters.size + } } private[spark] object RestSubmissionClient { @@ -324,10 +394,10 @@ private[spark] object RestSubmissionClient { } val sparkProperties = conf.getAll.toMap val environmentVariables = env.filter { case (k, _) => k.startsWith("SPARK_") } - val client = new RestSubmissionClient + val client = new RestSubmissionClient(master) val submitRequest = client.constructSubmitRequest( appResource, mainClass, appArgs, sparkProperties, environmentVariables) - client.createSubmission(master, submitRequest) + client.createSubmission(submitRequest) } def main(args: Array[String]): Unit = { diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala index 88f9d880ac209..9678631da9f6f 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerArguments.scala @@ -105,7 +105,7 @@ private[worker] class WorkerArguments(args: Array[String], conf: SparkConf) { if (masters != null) { // Two positional arguments were given printUsageAndExit(1) } - masters = value.stripPrefix("spark://").split(",").map("spark://" + _) + masters = Utils.parseStandaloneMasterUrls(value) parse(tail) case Nil => diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index 844f0cd22d95d..be4db02ab86d0 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -2159,6 +2159,22 @@ private[spark] object Utils extends Logging { .getOrElse(UserGroupInformation.getCurrentUser().getShortUserName()) } + /** + * Split the comma delimited string of master URLs into a list. + * For instance, "spark://abc,def" becomes [spark://abc, spark://def]. + */ + def parseStandaloneMasterUrls(masterUrls: String): Array[String] = { + masterUrls.stripPrefix("spark://").split(",").map("spark://" + _) + } + + /** An identifier that backup masters use in their responses. */ + val BACKUP_STANDALONE_MASTER_PREFIX = "Current state is not alive" + + /** Return true if the response message is sent from a backup Master on standby. */ + def responseFromBackup(msg: String): Boolean = { + msg.startsWith(BACKUP_STANDALONE_MASTER_PREFIX) + } + /** * Adds a shutdown hook with default priority. * diff --git a/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala index 0a318a27ac212..f4d548d9e7720 100644 --- a/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala @@ -39,7 +39,6 @@ import org.apache.spark.deploy.master.DriverState._ * Tests for the REST application submission protocol used in standalone cluster mode. */ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { - private val client = new RestSubmissionClient private var actorSystem: Option[ActorSystem] = None private var server: Option[RestSubmissionServer] = None @@ -52,7 +51,7 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { val appArgs = Array("one", "two", "three") val sparkProperties = Map("spark.app.name" -> "pi") val environmentVariables = Map("SPARK_ONE" -> "UN", "SPARK_TWO" -> "DEUX") - val request = client.constructSubmitRequest( + val request = new RestSubmissionClient("spark://host:port").constructSubmitRequest( "my-app-resource", "my-main-class", appArgs, sparkProperties, environmentVariables) assert(request.action === Utils.getFormattedClassName(request)) assert(request.clientSparkVersion === SPARK_VERSION) @@ -71,7 +70,7 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { val request = constructSubmitRequest(masterUrl, appArgs) assert(request.appArgs === appArgs) assert(request.sparkProperties("spark.master") === masterUrl) - val response = client.createSubmission(masterUrl, request) + val response = new RestSubmissionClient(masterUrl).createSubmission(request) val submitResponse = getSubmitResponse(response) assert(submitResponse.action === Utils.getFormattedClassName(submitResponse)) assert(submitResponse.serverSparkVersion === SPARK_VERSION) @@ -102,7 +101,7 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { val submissionId = "my-lyft-driver" val killMessage = "your driver is killed" val masterUrl = startDummyServer(killMessage = killMessage) - val response = client.killSubmission(masterUrl, submissionId) + val response = new RestSubmissionClient(masterUrl).killSubmission(submissionId) val killResponse = getKillResponse(response) assert(killResponse.action === Utils.getFormattedClassName(killResponse)) assert(killResponse.serverSparkVersion === SPARK_VERSION) @@ -116,7 +115,7 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { val submissionState = KILLED val submissionException = new Exception("there was an irresponsible mix of alcohol and cars") val masterUrl = startDummyServer(state = submissionState, exception = Some(submissionException)) - val response = client.requestSubmissionStatus(masterUrl, submissionId) + val response = new RestSubmissionClient(masterUrl).requestSubmissionStatus(submissionId) val statusResponse = getStatusResponse(response) assert(statusResponse.action === Utils.getFormattedClassName(statusResponse)) assert(statusResponse.serverSparkVersion === SPARK_VERSION) @@ -129,13 +128,14 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { test("create then kill") { val masterUrl = startSmartServer() val request = constructSubmitRequest(masterUrl) - val response1 = client.createSubmission(masterUrl, request) + val client = new RestSubmissionClient(masterUrl) + val response1 = client.createSubmission(request) val submitResponse = getSubmitResponse(response1) assert(submitResponse.success) assert(submitResponse.submissionId != null) // kill submission that was just created val submissionId = submitResponse.submissionId - val response2 = client.killSubmission(masterUrl, submissionId) + val response2 = client.killSubmission(submissionId) val killResponse = getKillResponse(response2) assert(killResponse.success) assert(killResponse.submissionId === submissionId) @@ -144,13 +144,14 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { test("create then request status") { val masterUrl = startSmartServer() val request = constructSubmitRequest(masterUrl) - val response1 = client.createSubmission(masterUrl, request) + val client = new RestSubmissionClient(masterUrl) + val response1 = client.createSubmission(request) val submitResponse = getSubmitResponse(response1) assert(submitResponse.success) assert(submitResponse.submissionId != null) // request status of submission that was just created val submissionId = submitResponse.submissionId - val response2 = client.requestSubmissionStatus(masterUrl, submissionId) + val response2 = client.requestSubmissionStatus(submissionId) val statusResponse = getStatusResponse(response2) assert(statusResponse.success) assert(statusResponse.submissionId === submissionId) @@ -160,8 +161,9 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { test("create then kill then request status") { val masterUrl = startSmartServer() val request = constructSubmitRequest(masterUrl) - val response1 = client.createSubmission(masterUrl, request) - val response2 = client.createSubmission(masterUrl, request) + val client = new RestSubmissionClient(masterUrl) + val response1 = client.createSubmission(request) + val response2 = client.createSubmission(request) val submitResponse1 = getSubmitResponse(response1) val submitResponse2 = getSubmitResponse(response2) assert(submitResponse1.success) @@ -171,13 +173,13 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { val submissionId1 = submitResponse1.submissionId val submissionId2 = submitResponse2.submissionId // kill only submission 1, but not submission 2 - val response3 = client.killSubmission(masterUrl, submissionId1) + val response3 = client.killSubmission(submissionId1) val killResponse = getKillResponse(response3) assert(killResponse.success) assert(killResponse.submissionId === submissionId1) // request status for both submissions: 1 should be KILLED but 2 should be RUNNING still - val response4 = client.requestSubmissionStatus(masterUrl, submissionId1) - val response5 = client.requestSubmissionStatus(masterUrl, submissionId2) + val response4 = client.requestSubmissionStatus(submissionId1) + val response5 = client.requestSubmissionStatus(submissionId2) val statusResponse1 = getStatusResponse(response4) val statusResponse2 = getStatusResponse(response5) assert(statusResponse1.submissionId === submissionId1) @@ -189,13 +191,14 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { test("kill or request status before create") { val masterUrl = startSmartServer() val doesNotExist = "does-not-exist" + val client = new RestSubmissionClient(masterUrl) // kill a non-existent submission - val response1 = client.killSubmission(masterUrl, doesNotExist) + val response1 = client.killSubmission(doesNotExist) val killResponse = getKillResponse(response1) assert(!killResponse.success) assert(killResponse.submissionId === doesNotExist) // request status for a non-existent submission - val response2 = client.requestSubmissionStatus(masterUrl, doesNotExist) + val response2 = client.requestSubmissionStatus(doesNotExist) val statusResponse = getStatusResponse(response2) assert(!statusResponse.success) assert(statusResponse.submissionId === doesNotExist) @@ -339,6 +342,7 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { test("client handles faulty server") { val masterUrl = startFaultyServer() + val client = new RestSubmissionClient(masterUrl) val httpUrl = masterUrl.replace("spark://", "http://") val v = RestSubmissionServer.PROTOCOL_VERSION val submitRequestPath = s"$httpUrl/$v/submissions/create" @@ -425,7 +429,7 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { mainJar) ++ appArgs val args = new SparkSubmitArguments(commandLineArgs) val (_, _, sparkProperties, _) = SparkSubmit.prepareSubmitEnvironment(args) - client.constructSubmitRequest( + new RestSubmissionClient("spark://host:port").constructSubmitRequest( mainJar, mainClass, appArgs, sparkProperties.toMap, Map.empty) } @@ -492,7 +496,7 @@ class StandaloneRestSubmitSuite extends FunSuite with BeforeAndAfterEach { method: String, body: String = ""): (SubmitRestProtocolResponse, Int) = { val conn = sendHttpRequest(url, method, body) - (client.readResponse(conn), conn.getResponseCode) + (new RestSubmissionClient("spark://host:port").readResponse(conn), conn.getResponseCode) } } From 8f50a07d2188ccc5315d979755188b1e5d5b5471 Mon Sep 17 00:00:00 2001 From: Chris Heller Date: Fri, 1 May 2015 18:41:22 -0700 Subject: [PATCH 542/591] [SPARK-2691] [MESOS] Support for Mesos DockerInfo This patch adds partial support for running spark on mesos inside of a docker container. Only fine-grained mode is presently supported, and there is no checking done to ensure that the version of libmesos is recent enough to have a DockerInfo structure in the protobuf (other than pinning a mesos version in the pom.xml). Author: Chris Heller Closes #3074 from hellertime/SPARK-2691 and squashes the following commits: d504af6 [Chris Heller] Assist type inference f64885d [Chris Heller] Fix errant line length 17c41c0 [Chris Heller] Base Dockerfile on mesosphere/mesos image 8aebda4 [Chris Heller] Simplfy Docker image docs 1ae7f4f [Chris Heller] Style points 974bd56 [Chris Heller] Convert map to flatMap 5d8bdf7 [Chris Heller] Factor out the DockerInfo construction. 7b75a3d [Chris Heller] Align to styleguide 80108e7 [Chris Heller] Bend to the will of RAT ba77056 [Chris Heller] Explicit RAT exclude abda5e5 [Chris Heller] Wildcard .rat-excludes 2f2873c [Chris Heller] Exclude spark-mesos from RAT a589a5b [Chris Heller] Add example Dockerfile b6825ce [Chris Heller] Remove use of EasyMock eae1b86 [Chris Heller] Move properties under 'spark.mesos.' c184d00 [Chris Heller] Use map on Option to be consistent with non-coarse code fb9501a [Chris Heller] Bumped mesos version to current release fa11879 [Chris Heller] Add listenerBus to EasyMock 882151e [Chris Heller] Changes to scala style b22d42d [Chris Heller] Exclude template from RAT db536cf [Chris Heller] Remove unneeded mocks dea1bd5 [Chris Heller] Force default protocol 7dac042 [Chris Heller] Add test for DockerInfo 5456c0c [Chris Heller] Adjust syntax style 521c194 [Chris Heller] Adjust version info 6e38f70 [Chris Heller] Document Mesos Docker properties 29572ab [Chris Heller] Support all DockerInfo fields b8c0dea [Chris Heller] Support for mesos DockerInfo in coarse-mode. 482a9fd [Chris Heller] Support for mesos DockerInfo in fine-grained mode. --- .rat-excludes | 1 + conf/docker.properties.template | 3 + .../mesos/CoarseMesosSchedulerBackend.scala | 9 +- .../cluster/mesos/MesosSchedulerBackend.scala | 10 +- .../mesos/MesosSchedulerBackendUtil.scala | 142 ++++++++++++++++++ .../mesos/MesosSchedulerBackendSuite.scala | 46 ++++++ docker/spark-mesos/Dockerfile | 30 ++++ docs/running-on-mesos.md | 42 ++++++ pom.xml | 2 +- 9 files changed, 280 insertions(+), 5 deletions(-) create mode 100644 conf/docker.properties.template create mode 100644 core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala create mode 100644 docker/spark-mesos/Dockerfile diff --git a/.rat-excludes b/.rat-excludes index 4468da19008bc..2238a5b68e359 100644 --- a/.rat-excludes +++ b/.rat-excludes @@ -15,6 +15,7 @@ TAGS RELEASE control docs +docker.properties.template fairscheduler.xml.template spark-defaults.conf.template log4j.properties diff --git a/conf/docker.properties.template b/conf/docker.properties.template new file mode 100644 index 0000000000000..26e3bfd9c5b9b --- /dev/null +++ b/conf/docker.properties.template @@ -0,0 +1,3 @@ +spark.mesos.executor.docker.image: +spark.mesos.executor.docker.volumes: /usr/local/lib:/host/usr/local/lib:ro +spark.mesos.executor.home: /opt/spark diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala index 3412301e64fd7..dc59545b43314 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala @@ -196,9 +196,14 @@ private[spark] class CoarseMesosSchedulerBackend( .addResources(createResource("cpus", cpusToUse)) .addResources(createResource("mem", MemoryUtils.calculateTotalMemory(sc))) - .build() + + sc.conf.getOption("spark.mesos.executor.docker.image").foreach { image => + MesosSchedulerBackendUtil + .setupContainerBuilderDockerInfo(image, sc.conf, task.getContainerBuilder()) + } + d.launchTasks( - Collections.singleton(offer.getId), Collections.singletonList(task), filters) + Collections.singleton(offer.getId), Collections.singletonList(task.build()), filters) } else { // Filter it out d.launchTasks( diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala index 86a7d0fb587e4..db0a080b3b0c0 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala @@ -124,13 +124,19 @@ private[spark] class MesosSchedulerBackend( Value.Scalar.newBuilder() .setValue(MemoryUtils.calculateTotalMemory(sc)).build()) .build() - MesosExecutorInfo.newBuilder() + val executorInfo = MesosExecutorInfo.newBuilder() .setExecutorId(ExecutorID.newBuilder().setValue(execId).build()) .setCommand(command) .setData(ByteString.copyFrom(createExecArg())) .addResources(cpus) .addResources(memory) - .build() + + sc.conf.getOption("spark.mesos.executor.docker.image").foreach { image => + MesosSchedulerBackendUtil + .setupContainerBuilderDockerInfo(image, sc.conf, executorInfo.getContainerBuilder()) + } + + executorInfo.build() } /** diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala new file mode 100644 index 0000000000000..928c5cfed417a --- /dev/null +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala @@ -0,0 +1,142 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.scheduler.cluster.mesos + +import org.apache.mesos.Protos.{ContainerInfo, Volume} +import org.apache.mesos.Protos.ContainerInfo.DockerInfo + +import org.apache.spark.{Logging, SparkConf} + +/** + * A collection of utility functions which can be used by both the + * MesosSchedulerBackend and the CoarseMesosSchedulerBackend. + */ +private[mesos] object MesosSchedulerBackendUtil extends Logging { + /** + * Parse a comma-delimited list of volume specs, each of which + * takes the form [host-dir:]container-dir[:rw|:ro]. + */ + def parseVolumesSpec(volumes: String): List[Volume] = { + volumes.split(",").map(_.split(":")).flatMap { spec => + val vol: Volume.Builder = Volume + .newBuilder() + .setMode(Volume.Mode.RW) + spec match { + case Array(container_path) => + Some(vol.setContainerPath(container_path)) + case Array(container_path, "rw") => + Some(vol.setContainerPath(container_path)) + case Array(container_path, "ro") => + Some(vol.setContainerPath(container_path) + .setMode(Volume.Mode.RO)) + case Array(host_path, container_path) => + Some(vol.setContainerPath(container_path) + .setHostPath(host_path)) + case Array(host_path, container_path, "rw") => + Some(vol.setContainerPath(container_path) + .setHostPath(host_path)) + case Array(host_path, container_path, "ro") => + Some(vol.setContainerPath(container_path) + .setHostPath(host_path) + .setMode(Volume.Mode.RO)) + case spec => { + logWarning(s"Unable to parse volume specs: $volumes. " + + "Expected form: \"[host-dir:]container-dir[:rw|:ro](, ...)\"") + None + } + } + } + .map { _.build() } + .toList + } + + /** + * Parse a comma-delimited list of port mapping specs, each of which + * takes the form host_port:container_port[:udp|:tcp] + * + * Note: + * the docker form is [ip:]host_port:container_port, but the DockerInfo + * message has no field for 'ip', and instead has a 'protocol' field. + * Docker itself only appears to support TCP, so this alternative form + * anticipates the expansion of the docker form to allow for a protocol + * and leaves open the chance for mesos to begin to accept an 'ip' field + */ + def parsePortMappingsSpec(portmaps: String): List[DockerInfo.PortMapping] = { + portmaps.split(",").map(_.split(":")).flatMap { spec: Array[String] => + val portmap: DockerInfo.PortMapping.Builder = DockerInfo.PortMapping + .newBuilder() + .setProtocol("tcp") + spec match { + case Array(host_port, container_port) => + Some(portmap.setHostPort(host_port.toInt) + .setContainerPort(container_port.toInt)) + case Array(host_port, container_port, protocol) => + Some(portmap.setHostPort(host_port.toInt) + .setContainerPort(container_port.toInt) + .setProtocol(protocol)) + case spec => { + logWarning(s"Unable to parse port mapping specs: $portmaps. " + + "Expected form: \"host_port:container_port[:udp|:tcp](, ...)\"") + None + } + } + } + .map { _.build() } + .toList + } + + /** + * Construct a DockerInfo structure and insert it into a ContainerInfo + */ + def addDockerInfo( + container: ContainerInfo.Builder, + image: String, + volumes: Option[List[Volume]] = None, + network: Option[ContainerInfo.DockerInfo.Network] = None, + portmaps: Option[List[ContainerInfo.DockerInfo.PortMapping]] = None):Unit = { + + val docker = ContainerInfo.DockerInfo.newBuilder().setImage(image) + + network.foreach(docker.setNetwork) + portmaps.foreach(_.foreach(docker.addPortMappings)) + container.setType(ContainerInfo.Type.DOCKER) + container.setDocker(docker.build()) + volumes.foreach(_.foreach(container.addVolumes)) + } + + /** + * Setup a docker containerizer + */ + def setupContainerBuilderDockerInfo( + imageName: String, + conf: SparkConf, + builder: ContainerInfo.Builder): Unit = { + val volumes = conf + .getOption("spark.mesos.executor.docker.volumes") + .map(parseVolumesSpec) + val portmaps = conf + .getOption("spark.mesos.executor.docker.portmaps") + .map(parsePortMappingsSpec) + addDockerInfo( + builder, + imageName, + volumes = volumes, + portmaps = portmaps) + logDebug("setupContainerDockerInfo: using docker image: " + imageName) + } +} diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala index cdd7be0fbe5dd..ab863f3d8d672 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala @@ -73,6 +73,52 @@ class MesosSchedulerBackendSuite extends FunSuite with LocalSparkContext with Mo s"cd test-app-1*; ./bin/spark-class ${classOf[MesosExecutorBackend].getName}") } + test("spark docker properties correctly populate the DockerInfo message") { + val taskScheduler = mock[TaskSchedulerImpl] + + val conf = new SparkConf() + .set("spark.mesos.executor.docker.image", "spark/mock") + .set("spark.mesos.executor.docker.volumes", "/a,/b:/b,/c:/c:rw,/d:ro,/e:/e:ro") + .set("spark.mesos.executor.docker.portmaps", "80:8080,53:53:tcp") + + val listenerBus = mock[LiveListenerBus] + listenerBus.post( + SparkListenerExecutorAdded(anyLong, "s1", new ExecutorInfo("host1", 2, Map.empty))) + + val sc = mock[SparkContext] + when(sc.executorMemory).thenReturn(100) + when(sc.getSparkHome()).thenReturn(Option("/spark-home")) + when(sc.executorEnvs).thenReturn(new mutable.HashMap[String, String]) + when(sc.conf).thenReturn(conf) + when(sc.listenerBus).thenReturn(listenerBus) + + val backend = new MesosSchedulerBackend(taskScheduler, sc, "master") + + val execInfo = backend.createExecutorInfo("mockExecutor") + assert(execInfo.getContainer.getDocker.getImage.equals("spark/mock")) + val portmaps = execInfo.getContainer.getDocker.getPortMappingsList + assert(portmaps.get(0).getHostPort.equals(80)) + assert(portmaps.get(0).getContainerPort.equals(8080)) + assert(portmaps.get(0).getProtocol.equals("tcp")) + assert(portmaps.get(1).getHostPort.equals(53)) + assert(portmaps.get(1).getContainerPort.equals(53)) + assert(portmaps.get(1).getProtocol.equals("tcp")) + val volumes = execInfo.getContainer.getVolumesList + assert(volumes.get(0).getContainerPath.equals("/a")) + assert(volumes.get(0).getMode.equals(Volume.Mode.RW)) + assert(volumes.get(1).getContainerPath.equals("/b")) + assert(volumes.get(1).getHostPath.equals("/b")) + assert(volumes.get(1).getMode.equals(Volume.Mode.RW)) + assert(volumes.get(2).getContainerPath.equals("/c")) + assert(volumes.get(2).getHostPath.equals("/c")) + assert(volumes.get(2).getMode.equals(Volume.Mode.RW)) + assert(volumes.get(3).getContainerPath.equals("/d")) + assert(volumes.get(3).getMode.equals(Volume.Mode.RO)) + assert(volumes.get(4).getContainerPath.equals("/e")) + assert(volumes.get(4).getHostPath.equals("/e")) + assert(volumes.get(4).getMode.equals(Volume.Mode.RO)) + } + test("mesos resource offers result in launching tasks") { def createOffer(id: Int, mem: Int, cpu: Int): Offer = { val builder = Offer.newBuilder() diff --git a/docker/spark-mesos/Dockerfile b/docker/spark-mesos/Dockerfile new file mode 100644 index 0000000000000..b90aef3655dee --- /dev/null +++ b/docker/spark-mesos/Dockerfile @@ -0,0 +1,30 @@ +# This is an example Dockerfile for creating a Spark image which can be +# references by the Spark property 'spark.mesos.executor.docker.image' +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +FROM mesosphere/mesos:0.20.1 + +# Update the base ubuntu image with dependencies needed for Spark +RUN apt-get update && \ + apt-get install -y python libnss3 openjdk-7-jre-headless curl + +RUN mkdir /opt/spark && \ + curl http://www.apache.org/dyn/closer.cgi/spark/spark-1.4.0/spark-1.4.0-bin-hadoop2.4.tgz \ + | tar -xzC /opt +ENV SPARK_HOME /opt/spark +ENV MESOS_NATIVE_JAVA_LIBRARY /usr/local/lib/libmesos.so diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md index 8f53d8201a089..5f1d6daeb27f0 100644 --- a/docs/running-on-mesos.md +++ b/docs/running-on-mesos.md @@ -184,6 +184,16 @@ acquire. By default, it will acquire *all* cores in the cluster (that get offere only makes sense if you run just one application at a time. You can cap the maximum number of cores using `conf.set("spark.cores.max", "10")` (for example). +# Mesos Docker Support + +Spark can make use of a Mesos Docker containerizer by setting the property `spark.mesos.executor.docker.image` +in your [SparkConf](configuration.html#spark-properties). + +The Docker image used must have an appropriate version of Spark already part of the image, or you can +have Mesos download Spark via the usual methods. + +Requires Mesos version 0.20.1 or later. + # Running Alongside Hadoop You can run Spark and Mesos alongside your existing Hadoop cluster by just launching them as a @@ -237,6 +247,38 @@ See the [configuration page](configuration.html) for information on Spark config The value can be a floating point number. + + spark.mesos.executor.docker.image + (none) + + Set the name of the docker image that the Spark executors will run in. The selected + image must have Spark installed, as well as a compatible version of the Mesos library. + The installed path of Spark in the image can be specified with spark.mesos.executor.home; + the installed path of the Mesos library can be specified with spark.executorEnv.MESOS_NATIVE_LIBRARY. + + + + spark.mesos.executor.docker.volumes + (none) + + Set the list of volumes which will be mounted into the Docker image, which was set using + spark.mesos.executor.docker.image. The format of this property is a comma-separated list of + mappings following the form passed to docker run -v. That is they take the form: + + 
    [host_path:]container_path[:ro|:rw]
    + + + + spark.mesos.executor.docker.portmaps + (none) + + Set the list of incoming ports exposed by the Docker image, which was set using + spark.mesos.executor.docker.image. The format of this property is a comma-separated list of + mappings which take the form: + + 
    host_port:container_port[:tcp|:udp]
    + + spark.mesos.executor.home driver side SPARK_HOME diff --git a/pom.xml b/pom.xml index c85c5feeaf383..4313f940036c8 100644 --- a/pom.xml +++ b/pom.xml @@ -117,7 +117,7 @@ 1.6 spark 2.0.1 - 0.21.0 + 0.21.1 shaded-protobuf 1.7.10 1.2.17 From 38d4e9e446b425ca6a8fe8d8080f387b08683842 Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Fri, 1 May 2015 19:01:46 -0700 Subject: [PATCH 543/591] [SPARK-6229] Add SASL encryption to network library. There are two main parts of this change: - Extending the bootstrap mechanism in the network library to add a server-side bootstrap (which works a little bit differently than the client-side bootstrap), and to allow the bootstraps to modify the underlying channel. - Use SASL to encrypt data going through the RPC channel. The second item requires some non-optimal code to be able to work around the fact that the outbound path in netty is not thread-safe, and ordering is very important when encryption is in the picture. A lot of the changes outside the network/common library are just to adjust to the changed API for initializing the RPC server. Author: Marcelo Vanzin Closes #5377 from vanzin/SPARK-6229 and squashes the following commits: ff01966 [Marcelo Vanzin] Use fancy new size config style. be53f32 [Marcelo Vanzin] Merge branch 'master' into SPARK-6229 47d4aff [Marcelo Vanzin] Merge branch 'master' into SPARK-6229 7a2a805 [Marcelo Vanzin] Clean up some unneeded changes. 2f92237 [Marcelo Vanzin] Add comment. 67bb0c6 [Marcelo Vanzin] Revert "Avoid exposing ByteArrayWritableChannel outside of test code." 065f684 [Marcelo Vanzin] Add test to verify chunking. 3d1695d [Marcelo Vanzin] Minor cleanups. 73cff0e [Marcelo Vanzin] Skip bytes in decode path too. 318ad23 [Marcelo Vanzin] Avoid exposing ByteArrayWritableChannel outside of test code. 346f829 [Marcelo Vanzin] Avoid trip through channel selector by not reporting 0 bytes written. a4a5938 [Marcelo Vanzin] Review feedback. 4797519 [Marcelo Vanzin] Remove unused import. 9908ada [Marcelo Vanzin] Fix test, SASL backend disposal. 7fe1489 [Marcelo Vanzin] Add a test that makes sure encryption is actually enabled. adb6f9d [Marcelo Vanzin] Review feedback. cf2a605 [Marcelo Vanzin] Clean up some code. 8584323 [Marcelo Vanzin] Fix a comment. e98bc55 [Marcelo Vanzin] Add option to only allow encrypted connections to the server. dad42fc [Marcelo Vanzin] Make encryption thread-safe, less memory-intensive. b00999a [Marcelo Vanzin] Consolidate ByteArrayWritableChannel, fix SASL code to match master changes. b923cae [Marcelo Vanzin] Make SASL encryption handler thread-safe, handle FileRegion messages. 39539a7 [Marcelo Vanzin] Add config option to enable SASL encryption. 351a86f [Marcelo Vanzin] Add SASL encryption to network library. fbe6ccb [Marcelo Vanzin] Add TransportServerBootstrap, make SASL code use it. --- .../org/apache/spark/SecurityManager.scala | 17 +- .../spark/deploy/ExternalShuffleService.scala | 17 +- .../netty/NettyBlockTransferService.scala | 22 +- .../spark/network/nio/ConnectionManager.scala | 4 +- .../apache/spark/storage/BlockManager.scala | 3 +- .../spark/network/TransportContext.java | 26 +- .../client/TransportClientBootstrap.java | 4 +- .../client/TransportClientFactory.java | 5 +- .../network/sasl/SaslClientBootstrap.java | 41 +- .../spark/network/sasl/SaslEncryption.java | 291 ++++++++++++++ .../network/sasl/SaslEncryptionBackend.java | 33 ++ .../spark/network/sasl/SaslRpcHandler.java | 56 ++- .../network/sasl/SaslServerBootstrap.java | 49 +++ .../spark/network/sasl/SparkSaslClient.java | 33 +- .../spark/network/sasl/SparkSaslServer.java | 49 ++- .../spark/network/server/TransportServer.java | 19 +- .../server/TransportServerBootstrap.java | 36 ++ .../util}/ByteArrayWritableChannel.java | 26 +- .../spark/network/util/TransportConf.java | 18 + .../apache/spark/network/ProtocolSuite.java | 1 + .../protocol/MessageWithHeaderSuite.java | 2 +- .../spark/network/sasl/SparkSaslSuite.java | 358 +++++++++++++++++- .../shuffle/ExternalShuffleClient.java | 11 +- .../network/sasl/SaslIntegrationSuite.java | 9 +- .../ExternalShuffleIntegrationSuite.java | 4 +- .../shuffle/ExternalShuffleSecuritySuite.java | 27 +- .../network/yarn/YarnShuffleService.java | 15 +- 27 files changed, 1070 insertions(+), 106 deletions(-) create mode 100644 network/common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java create mode 100644 network/common/src/main/java/org/apache/spark/network/sasl/SaslEncryptionBackend.java create mode 100644 network/common/src/main/java/org/apache/spark/network/sasl/SaslServerBootstrap.java create mode 100644 network/common/src/main/java/org/apache/spark/network/server/TransportServerBootstrap.java rename network/common/src/{test/java/org/apache/spark/network => main/java/org/apache/spark/network/util}/ByteArrayWritableChannel.java (70%) diff --git a/core/src/main/scala/org/apache/spark/SecurityManager.scala b/core/src/main/scala/org/apache/spark/SecurityManager.scala index 3653f724ba192..8aed1e20e0686 100644 --- a/core/src/main/scala/org/apache/spark/SecurityManager.scala +++ b/core/src/main/scala/org/apache/spark/SecurityManager.scala @@ -150,8 +150,13 @@ import org.apache.spark.util.Utils * authorization. If not filter is in place the user is generally null and no authorization * can take place. * - * Connection encryption (SSL) configuration is organized hierarchically. The user can configure - * the default SSL settings which will be used for all the supported communication protocols unless + * When authentication is being used, encryption can also be enabled by setting the option + * spark.authenticate.enableSaslEncryption to true. This is only supported by communication + * channels that use the network-common library, and can be used as an alternative to SSL in those + * cases. + * + * SSL can be used for encryption for certain communication channels. The user can configure the + * default SSL settings which will be used for all the supported communication protocols unless * they are overwritten by protocol specific settings. This way the user can easily provide the * common settings for all the protocols without disabling the ability to configure each one * individually. @@ -412,6 +417,14 @@ private[spark] class SecurityManager(sparkConf: SparkConf) */ def isAuthenticationEnabled(): Boolean = authOn + /** + * Checks whether SASL encryption should be enabled. + * @return Whether to enable SASL encryption when connecting to services that support it. + */ + def isSaslEncryptionEnabled(): Boolean = { + sparkConf.getBoolean("spark.authenticate.enableSaslEncryption", false) + } + /** * Gets the user used for authenticating HTTP connections. * For now use a single hardcoded user. diff --git a/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala b/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala index cd16f992a3c0a..09973a0a2c998 100644 --- a/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala +++ b/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala @@ -19,10 +19,12 @@ package org.apache.spark.deploy import java.util.concurrent.CountDownLatch +import scala.collection.JavaConversions._ + import org.apache.spark.{Logging, SparkConf, SecurityManager} import org.apache.spark.network.TransportContext import org.apache.spark.network.netty.SparkTransportConf -import org.apache.spark.network.sasl.SaslRpcHandler +import org.apache.spark.network.sasl.SaslServerBootstrap import org.apache.spark.network.server.TransportServer import org.apache.spark.network.shuffle.ExternalShuffleBlockHandler import org.apache.spark.util.Utils @@ -44,10 +46,7 @@ class ExternalShuffleService(sparkConf: SparkConf, securityManager: SecurityMana private val transportConf = SparkTransportConf.fromSparkConf(sparkConf, numUsableCores = 0) private val blockHandler = new ExternalShuffleBlockHandler(transportConf) - private val transportContext: TransportContext = { - val handler = if (useSasl) new SaslRpcHandler(blockHandler, securityManager) else blockHandler - new TransportContext(transportConf, handler) - } + private val transportContext: TransportContext = new TransportContext(transportConf, blockHandler) private var server: TransportServer = _ @@ -62,7 +61,13 @@ class ExternalShuffleService(sparkConf: SparkConf, securityManager: SecurityMana def start() { require(server == null, "Shuffle server already started") logInfo(s"Starting shuffle service on port $port with useSasl = $useSasl") - server = transportContext.createServer(port) + val bootstraps = + if (useSasl) { + Seq(new SaslServerBootstrap(transportConf, securityManager)) + } else { + Nil + } + server = transportContext.createServer(port, bootstraps) } def stop() { diff --git a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala index 3f0950dae1f24..6181c0ee9fa2b 100644 --- a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala +++ b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala @@ -24,7 +24,7 @@ import org.apache.spark.{SecurityManager, SparkConf} import org.apache.spark.network._ import org.apache.spark.network.buffer.ManagedBuffer import org.apache.spark.network.client.{TransportClientBootstrap, RpcResponseCallback, TransportClientFactory} -import org.apache.spark.network.sasl.{SaslRpcHandler, SaslClientBootstrap} +import org.apache.spark.network.sasl.{SaslClientBootstrap, SaslServerBootstrap} import org.apache.spark.network.server._ import org.apache.spark.network.shuffle.{RetryingBlockFetcher, BlockFetchingListener, OneForOneBlockFetcher} import org.apache.spark.network.shuffle.protocol.UploadBlock @@ -49,18 +49,18 @@ class NettyBlockTransferService(conf: SparkConf, securityManager: SecurityManage private[this] var appId: String = _ override def init(blockDataManager: BlockDataManager): Unit = { - val (rpcHandler: RpcHandler, bootstrap: Option[TransportClientBootstrap]) = { - val nettyRpcHandler = new NettyBlockRpcServer(serializer, blockDataManager) - if (!authEnabled) { - (nettyRpcHandler, None) - } else { - (new SaslRpcHandler(nettyRpcHandler, securityManager), - Some(new SaslClientBootstrap(transportConf, conf.getAppId, securityManager))) - } + val rpcHandler = new NettyBlockRpcServer(serializer, blockDataManager) + var serverBootstrap: Option[TransportServerBootstrap] = None + var clientBootstrap: Option[TransportClientBootstrap] = None + if (authEnabled) { + serverBootstrap = Some(new SaslServerBootstrap(transportConf, securityManager)) + clientBootstrap = Some(new SaslClientBootstrap(transportConf, conf.getAppId, securityManager, + securityManager.isSaslEncryptionEnabled())) } transportContext = new TransportContext(transportConf, rpcHandler) - clientFactory = transportContext.createClientFactory(bootstrap.toList) - server = transportContext.createServer(conf.getInt("spark.blockManager.port", 0)) + clientFactory = transportContext.createClientFactory(clientBootstrap.toList) + server = transportContext.createServer(conf.getInt("spark.blockManager.port", 0), + serverBootstrap.toList) appId = conf.getAppId logInfo("Server created on " + server.getPort) } diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala index 16e905982cf64..497871ed6d5e5 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala @@ -656,7 +656,7 @@ private[nio] class ConnectionManager( connection.synchronized { if (connection.sparkSaslServer == null) { logDebug("Creating sasl Server") - connection.sparkSaslServer = new SparkSaslServer(conf.getAppId, securityManager) + connection.sparkSaslServer = new SparkSaslServer(conf.getAppId, securityManager, false) } } replyToken = connection.sparkSaslServer.response(securityMsg.getToken) @@ -800,7 +800,7 @@ private[nio] class ConnectionManager( if (!conn.isSaslComplete()) { conn.synchronized { if (conn.sparkSaslClient == null) { - conn.sparkSaslClient = new SparkSaslClient(conf.getAppId, securityManager) + conn.sparkSaslClient = new SparkSaslClient(conf.getAppId, securityManager, false) var firstResponse: Array[Byte] = null try { firstResponse = conn.sparkSaslClient.firstToken() diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala index 402ee1c7648c5..a46fecd2274ef 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala @@ -111,7 +111,8 @@ private[spark] class BlockManager( // standard BlockTransferService to directly connect to other Executors. private[spark] val shuffleClient = if (externalShuffleServiceEnabled) { val transConf = SparkTransportConf.fromSparkConf(conf, numUsableCores) - new ExternalShuffleClient(transConf, securityManager, securityManager.isAuthenticationEnabled()) + new ExternalShuffleClient(transConf, securityManager, securityManager.isAuthenticationEnabled(), + securityManager.isSaslEncryptionEnabled()) } else { blockTransferService } diff --git a/network/common/src/main/java/org/apache/spark/network/TransportContext.java b/network/common/src/main/java/org/apache/spark/network/TransportContext.java index 3fe69b1bd8851..b8d073fa16b4b 100644 --- a/network/common/src/main/java/org/apache/spark/network/TransportContext.java +++ b/network/common/src/main/java/org/apache/spark/network/TransportContext.java @@ -36,6 +36,7 @@ import org.apache.spark.network.server.TransportChannelHandler; import org.apache.spark.network.server.TransportRequestHandler; import org.apache.spark.network.server.TransportServer; +import org.apache.spark.network.server.TransportServerBootstrap; import org.apache.spark.network.util.NettyUtils; import org.apache.spark.network.util.TransportConf; @@ -82,13 +83,21 @@ public TransportClientFactory createClientFactory() { } /** Create a server which will attempt to bind to a specific port. */ - public TransportServer createServer(int port) { - return new TransportServer(this, port); + public TransportServer createServer(int port, List bootstraps) { + return new TransportServer(this, port, rpcHandler, bootstraps); } /** Creates a new server, binding to any available ephemeral port. */ + public TransportServer createServer(List bootstraps) { + return createServer(0, bootstraps); + } + public TransportServer createServer() { - return new TransportServer(this, 0); + return createServer(0, Lists.newArrayList()); + } + + public TransportChannelHandler initializePipeline(SocketChannel channel) { + return initializePipeline(channel, rpcHandler); } /** @@ -96,13 +105,18 @@ public TransportServer createServer() { * has a {@link org.apache.spark.network.server.TransportChannelHandler} to handle request or * response messages. * + * @param channel The channel to initialize. + * @param channelRpcHandler The RPC handler to use for the channel. + * * @return Returns the created TransportChannelHandler, which includes a TransportClient that can * be used to communicate on this channel. The TransportClient is directly associated with a * ChannelHandler to ensure all users of the same channel get the same TransportClient object. */ - public TransportChannelHandler initializePipeline(SocketChannel channel) { + public TransportChannelHandler initializePipeline( + SocketChannel channel, + RpcHandler channelRpcHandler) { try { - TransportChannelHandler channelHandler = createChannelHandler(channel); + TransportChannelHandler channelHandler = createChannelHandler(channel, channelRpcHandler); channel.pipeline() .addLast("encoder", encoder) .addLast("frameDecoder", NettyUtils.createFrameDecoder()) @@ -123,7 +137,7 @@ public TransportChannelHandler initializePipeline(SocketChannel channel) { * ResponseMessages. The channel is expected to have been successfully created, though certain * properties (such as the remoteAddress()) may not be available yet. */ - private TransportChannelHandler createChannelHandler(Channel channel) { + private TransportChannelHandler createChannelHandler(Channel channel, RpcHandler rpcHandler) { TransportResponseHandler responseHandler = new TransportResponseHandler(channel); TransportClient client = new TransportClient(channel, responseHandler); TransportRequestHandler requestHandler = new TransportRequestHandler(channel, client, diff --git a/network/common/src/main/java/org/apache/spark/network/client/TransportClientBootstrap.java b/network/common/src/main/java/org/apache/spark/network/client/TransportClientBootstrap.java index 65e8020e34121..eaae2ee043c5a 100644 --- a/network/common/src/main/java/org/apache/spark/network/client/TransportClientBootstrap.java +++ b/network/common/src/main/java/org/apache/spark/network/client/TransportClientBootstrap.java @@ -17,6 +17,8 @@ package org.apache.spark.network.client; +import io.netty.channel.Channel; + /** * A bootstrap which is executed on a TransportClient before it is returned to the user. * This enables an initial exchange of information (e.g., SASL authentication tokens) on a once-per- @@ -28,5 +30,5 @@ */ public interface TransportClientBootstrap { /** Performs the bootstrapping operation, throwing an exception on failure. */ - public void doBootstrap(TransportClient client) throws RuntimeException; + void doBootstrap(TransportClient client, Channel channel) throws RuntimeException; } diff --git a/network/common/src/main/java/org/apache/spark/network/client/TransportClientFactory.java b/network/common/src/main/java/org/apache/spark/network/client/TransportClientFactory.java index d26b9b4d6055f..4952ffb44bb8b 100644 --- a/network/common/src/main/java/org/apache/spark/network/client/TransportClientFactory.java +++ b/network/common/src/main/java/org/apache/spark/network/client/TransportClientFactory.java @@ -172,12 +172,14 @@ private TransportClient createClient(InetSocketAddress address) throws IOExcepti .option(ChannelOption.ALLOCATOR, pooledAllocator); final AtomicReference clientRef = new AtomicReference(); + final AtomicReference channelRef = new AtomicReference(); bootstrap.handler(new ChannelInitializer() { @Override public void initChannel(SocketChannel ch) { TransportChannelHandler clientHandler = context.initializePipeline(ch); clientRef.set(clientHandler.getClient()); + channelRef.set(ch); } }); @@ -192,6 +194,7 @@ public void initChannel(SocketChannel ch) { } TransportClient client = clientRef.get(); + Channel channel = channelRef.get(); assert client != null : "Channel future completed successfully with null client"; // Execute any client bootstraps synchronously before marking the Client as successful. @@ -199,7 +202,7 @@ public void initChannel(SocketChannel ch) { logger.debug("Connection to {} successful, running bootstraps...", address); try { for (TransportClientBootstrap clientBootstrap : clientBootstraps) { - clientBootstrap.doBootstrap(client); + clientBootstrap.doBootstrap(client, channel); } } catch (Exception e) { // catch non-RuntimeExceptions too as bootstrap may be written in Scala long bootstrapTimeMs = (System.nanoTime() - preBootstrap) / 1000000; diff --git a/network/common/src/main/java/org/apache/spark/network/sasl/SaslClientBootstrap.java b/network/common/src/main/java/org/apache/spark/network/sasl/SaslClientBootstrap.java index 33aa1344345ff..185ba2ef3bb1f 100644 --- a/network/common/src/main/java/org/apache/spark/network/sasl/SaslClientBootstrap.java +++ b/network/common/src/main/java/org/apache/spark/network/sasl/SaslClientBootstrap.java @@ -17,8 +17,12 @@ package org.apache.spark.network.sasl; +import javax.security.sasl.Sasl; +import javax.security.sasl.SaslException; + import io.netty.buffer.ByteBuf; import io.netty.buffer.Unpooled; +import io.netty.channel.Channel; import org.slf4j.Logger; import org.slf4j.LoggerFactory; @@ -33,14 +37,24 @@ public class SaslClientBootstrap implements TransportClientBootstrap { private final Logger logger = LoggerFactory.getLogger(SaslClientBootstrap.class); + private final boolean encrypt; private final TransportConf conf; private final String appId; private final SecretKeyHolder secretKeyHolder; public SaslClientBootstrap(TransportConf conf, String appId, SecretKeyHolder secretKeyHolder) { + this(conf, appId, secretKeyHolder, false); + } + + public SaslClientBootstrap( + TransportConf conf, + String appId, + SecretKeyHolder secretKeyHolder, + boolean encrypt) { this.conf = conf; this.appId = appId; this.secretKeyHolder = secretKeyHolder; + this.encrypt = encrypt; } /** @@ -49,8 +63,8 @@ public SaslClientBootstrap(TransportConf conf, String appId, SecretKeyHolder sec * due to mismatch. */ @Override - public void doBootstrap(TransportClient client) { - SparkSaslClient saslClient = new SparkSaslClient(appId, secretKeyHolder); + public void doBootstrap(TransportClient client, Channel channel) { + SparkSaslClient saslClient = new SparkSaslClient(appId, secretKeyHolder, encrypt); try { byte[] payload = saslClient.firstToken(); @@ -62,13 +76,26 @@ public void doBootstrap(TransportClient client) { byte[] response = client.sendRpcSync(buf.array(), conf.saslRTTimeoutMs()); payload = saslClient.response(response); } + + if (encrypt) { + if (!SparkSaslServer.QOP_AUTH_CONF.equals(saslClient.getNegotiatedProperty(Sasl.QOP))) { + throw new RuntimeException( + new SaslException("Encryption requests by negotiated non-encrypted connection.")); + } + SaslEncryption.addToChannel(channel, saslClient, conf.maxSaslEncryptedBlockSize()); + saslClient = null; + logger.debug("Channel {} configured for SASL encryption.", client); + } } finally { - try { - // Once authentication is complete, the server will trust all remaining communication. - saslClient.dispose(); - } catch (RuntimeException e) { - logger.error("Error while disposing SASL client", e); + if (saslClient != null) { + try { + // Once authentication is complete, the server will trust all remaining communication. + saslClient.dispose(); + } catch (RuntimeException e) { + logger.error("Error while disposing SASL client", e); + } } } } + } diff --git a/network/common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java b/network/common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java new file mode 100644 index 0000000000000..127335e4d35fb --- /dev/null +++ b/network/common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java @@ -0,0 +1,291 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.network.sasl; + +import java.io.IOException; +import java.nio.ByteBuffer; +import java.nio.channels.WritableByteChannel; +import java.util.List; + +import com.google.common.annotations.VisibleForTesting; +import com.google.common.base.Preconditions; +import io.netty.buffer.ByteBuf; +import io.netty.buffer.Unpooled; +import io.netty.channel.Channel; +import io.netty.channel.ChannelHandlerContext; +import io.netty.channel.ChannelOutboundHandlerAdapter; +import io.netty.channel.ChannelPromise; +import io.netty.channel.FileRegion; +import io.netty.handler.codec.MessageToMessageDecoder; +import io.netty.util.AbstractReferenceCounted; +import io.netty.util.ReferenceCountUtil; + +import org.apache.spark.network.util.ByteArrayWritableChannel; +import org.apache.spark.network.util.NettyUtils; + +/** + * Provides SASL-based encription for transport channels. The single method exposed by this + * class installs the needed channel handlers on a connected channel. + */ +class SaslEncryption { + + @VisibleForTesting + static final String ENCRYPTION_HANDLER_NAME = "saslEncryption"; + + /** + * Adds channel handlers that perform encryption / decryption of data using SASL. + * + * @param channel The channel. + * @param backend The SASL backend. + * @param maxOutboundBlockSize Max size in bytes of outgoing encrypted blocks, to control + * memory usage. + */ + static void addToChannel( + Channel channel, + SaslEncryptionBackend backend, + int maxOutboundBlockSize) { + channel.pipeline() + .addFirst(ENCRYPTION_HANDLER_NAME, new EncryptionHandler(backend, maxOutboundBlockSize)) + .addFirst("saslDecryption", new DecryptionHandler(backend)) + .addFirst("saslFrameDecoder", NettyUtils.createFrameDecoder()); + } + + private static class EncryptionHandler extends ChannelOutboundHandlerAdapter { + + private final int maxOutboundBlockSize; + private final SaslEncryptionBackend backend; + + EncryptionHandler(SaslEncryptionBackend backend, int maxOutboundBlockSize) { + this.backend = backend; + this.maxOutboundBlockSize = maxOutboundBlockSize; + } + + /** + * Wrap the incoming message in an implementation that will perform encryption lazily. This is + * needed to guarantee ordering of the outgoing encrypted packets - they need to be decrypted in + * the same order, and netty doesn't have an atomic ChannelHandlerContext.write() API, so it + * does not guarantee any ordering. + */ + @Override + public void write(ChannelHandlerContext ctx, Object msg, ChannelPromise promise) + throws Exception { + + ctx.write(new EncryptedMessage(backend, msg, maxOutboundBlockSize), promise); + } + + @Override + public void handlerRemoved(ChannelHandlerContext ctx) throws Exception { + try { + backend.dispose(); + } finally { + super.handlerRemoved(ctx); + } + } + + } + + private static class DecryptionHandler extends MessageToMessageDecoder { + + private final SaslEncryptionBackend backend; + + DecryptionHandler(SaslEncryptionBackend backend) { + this.backend = backend; + } + + @Override + protected void decode(ChannelHandlerContext ctx, ByteBuf msg, List out) + throws Exception { + + byte[] data; + int offset; + int length = msg.readableBytes(); + if (msg.hasArray()) { + data = msg.array(); + offset = msg.arrayOffset(); + msg.skipBytes(length); + } else { + data = new byte[length]; + msg.readBytes(data); + offset = 0; + } + + out.add(Unpooled.wrappedBuffer(backend.unwrap(data, offset, length))); + } + + } + + @VisibleForTesting + static class EncryptedMessage extends AbstractReferenceCounted implements FileRegion { + + private final SaslEncryptionBackend backend; + private final boolean isByteBuf; + private final ByteBuf buf; + private final FileRegion region; + + /** + * A channel used to buffer input data for encryption. The channel has an upper size bound + * so that if the input is larger than the allowed buffer, it will be broken into multiple + * chunks. + */ + private final ByteArrayWritableChannel byteChannel; + + private ByteBuf currentHeader; + private ByteBuffer currentChunk; + private long currentChunkSize; + private long currentReportedBytes; + private long unencryptedChunkSize; + private long transferred; + + EncryptedMessage(SaslEncryptionBackend backend, Object msg, int maxOutboundBlockSize) { + Preconditions.checkArgument(msg instanceof ByteBuf || msg instanceof FileRegion, + "Unrecognized message type: %s", msg.getClass().getName()); + this.backend = backend; + this.isByteBuf = msg instanceof ByteBuf; + this.buf = isByteBuf ? (ByteBuf) msg : null; + this.region = isByteBuf ? null : (FileRegion) msg; + this.byteChannel = new ByteArrayWritableChannel(maxOutboundBlockSize); + } + + /** + * Returns the size of the original (unencrypted) message. + * + * This makes assumptions about how netty treats FileRegion instances, because there's no way + * to know beforehand what will be the size of the encrypted message. Namely, it assumes + * that netty will try to transfer data from this message while + * transfered() < count(). So these two methods return, technically, wrong data, + * but netty doesn't know better. + */ + @Override + public long count() { + return isByteBuf ? buf.readableBytes() : region.count(); + } + + @Override + public long position() { + return 0; + } + + /** + * Returns an approximation of the amount of data transferred. See {@link #count()}. + */ + @Override + public long transfered() { + return transferred; + } + + /** + * Transfers data from the original message to the channel, encrypting it in the process. + * + * This method also breaks down the original message into smaller chunks when needed. This + * is done to keep memory usage under control. This avoids having to copy the whole message + * data into memory at once, and can avoid ballooning memory usage when transferring large + * messages such as shuffle blocks. + * + * The {@link #transfered()} counter also behaves a little funny, in that it won't go forward + * until a whole chunk has been written. This is done because the code can't use the actual + * number of bytes written to the channel as the transferred count (see {@link #count()}). + * Instead, once an encrypted chunk is written to the output (including its header), the + * size of the original block will be added to the {@link #transfered()} amount. + */ + @Override + public long transferTo(final WritableByteChannel target, final long position) + throws IOException { + + Preconditions.checkArgument(position == transfered(), "Invalid position."); + + long reportedWritten = 0L; + long actuallyWritten = 0L; + do { + if (currentChunk == null) { + nextChunk(); + } + + if (currentHeader.readableBytes() > 0) { + int bytesWritten = target.write(currentHeader.nioBuffer()); + currentHeader.skipBytes(bytesWritten); + actuallyWritten += bytesWritten; + if (currentHeader.readableBytes() > 0) { + // Break out of loop if there are still header bytes left to write. + break; + } + } + + actuallyWritten += target.write(currentChunk); + if (!currentChunk.hasRemaining()) { + // Only update the count of written bytes once a full chunk has been written. + // See method javadoc. + long chunkBytesRemaining = unencryptedChunkSize - currentReportedBytes; + reportedWritten += chunkBytesRemaining; + transferred += chunkBytesRemaining; + currentHeader.release(); + currentHeader = null; + currentChunk = null; + currentChunkSize = 0; + currentReportedBytes = 0; + } + } while (currentChunk == null && transfered() + reportedWritten < count()); + + // Returning 0 triggers a backoff mechanism in netty which may harm performance. Instead, + // we return 1 until we can (i.e. until the reported count would actually match the size + // of the current chunk), at which point we resort to returning 0 so that the counts still + // match, at the cost of some performance. That situation should be rare, though. + if (reportedWritten != 0L) { + return reportedWritten; + } + + if (actuallyWritten > 0 && currentReportedBytes < currentChunkSize - 1) { + transferred += 1L; + currentReportedBytes += 1L; + return 1L; + } + + return 0L; + } + + private void nextChunk() throws IOException { + byteChannel.reset(); + if (isByteBuf) { + int copied = byteChannel.write(buf.nioBuffer()); + buf.skipBytes(copied); + } else { + region.transferTo(byteChannel, region.transfered()); + } + + byte[] encrypted = backend.wrap(byteChannel.getData(), 0, byteChannel.length()); + this.currentChunk = ByteBuffer.wrap(encrypted); + this.currentChunkSize = encrypted.length; + this.currentHeader = Unpooled.copyLong(8 + currentChunkSize); + this.unencryptedChunkSize = byteChannel.length(); + } + + @Override + protected void deallocate() { + if (currentHeader != null) { + currentHeader.release(); + } + if (buf != null) { + buf.release(); + } + if (region != null) { + region.release(); + } + } + + } + +} diff --git a/network/common/src/main/java/org/apache/spark/network/sasl/SaslEncryptionBackend.java b/network/common/src/main/java/org/apache/spark/network/sasl/SaslEncryptionBackend.java new file mode 100644 index 0000000000000..89b78bc7e1df1 --- /dev/null +++ b/network/common/src/main/java/org/apache/spark/network/sasl/SaslEncryptionBackend.java @@ -0,0 +1,33 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.network.sasl; + +import javax.security.sasl.SaslException; + +interface SaslEncryptionBackend { + + /** Disposes of resources used by the backend. */ + void dispose(); + + /** Encrypt data. */ + byte[] wrap(byte[] data, int offset, int len) throws SaslException; + + /** Decrypt data. */ + byte[] unwrap(byte[] data, int offset, int len) throws SaslException; + +} diff --git a/network/common/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java b/network/common/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java index 026cbd260d16c..be6165caf3c74 100644 --- a/network/common/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java +++ b/network/common/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java @@ -17,10 +17,10 @@ package org.apache.spark.network.sasl; -import java.util.concurrent.ConcurrentMap; +import javax.security.sasl.Sasl; -import com.google.common.collect.Maps; import io.netty.buffer.Unpooled; +import io.netty.channel.Channel; import org.slf4j.Logger; import org.slf4j.LoggerFactory; @@ -28,6 +28,7 @@ import org.apache.spark.network.client.TransportClient; import org.apache.spark.network.server.RpcHandler; import org.apache.spark.network.server.StreamManager; +import org.apache.spark.network.util.TransportConf; /** * RPC Handler which performs SASL authentication before delegating to a child RPC handler. @@ -37,8 +38,14 @@ * Note that the authentication process consists of multiple challenge-response pairs, each of * which are individual RPCs. */ -public class SaslRpcHandler extends RpcHandler { - private final Logger logger = LoggerFactory.getLogger(SaslRpcHandler.class); +class SaslRpcHandler extends RpcHandler { + private static final Logger logger = LoggerFactory.getLogger(SaslRpcHandler.class); + + /** Transport configuration. */ + private final TransportConf conf; + + /** The client channel. */ + private final Channel channel; /** RpcHandler we will delegate to for authenticated connections. */ private final RpcHandler delegate; @@ -46,19 +53,25 @@ public class SaslRpcHandler extends RpcHandler { /** Class which provides secret keys which are shared by server and client on a per-app basis. */ private final SecretKeyHolder secretKeyHolder; - /** Maps each channel to its SASL authentication state. */ - private final ConcurrentMap channelAuthenticationMap; + private SparkSaslServer saslServer; + private boolean isComplete; - public SaslRpcHandler(RpcHandler delegate, SecretKeyHolder secretKeyHolder) { + SaslRpcHandler( + TransportConf conf, + Channel channel, + RpcHandler delegate, + SecretKeyHolder secretKeyHolder) { + this.conf = conf; + this.channel = channel; this.delegate = delegate; this.secretKeyHolder = secretKeyHolder; - this.channelAuthenticationMap = Maps.newConcurrentMap(); + this.saslServer = null; + this.isComplete = false; } @Override public void receive(TransportClient client, byte[] message, RpcResponseCallback callback) { - SparkSaslServer saslServer = channelAuthenticationMap.get(client); - if (saslServer != null && saslServer.isComplete()) { + if (isComplete) { // Authentication complete, delegate to base handler. delegate.receive(client, message, callback); return; @@ -68,15 +81,30 @@ public void receive(TransportClient client, byte[] message, RpcResponseCallback if (saslServer == null) { // First message in the handshake, setup the necessary state. - saslServer = new SparkSaslServer(saslMessage.appId, secretKeyHolder); - channelAuthenticationMap.put(client, saslServer); + saslServer = new SparkSaslServer(saslMessage.appId, secretKeyHolder, + conf.saslServerAlwaysEncrypt()); } byte[] response = saslServer.response(saslMessage.payload); + callback.onSuccess(response); + + // Setup encryption after the SASL response is sent, otherwise the client can't parse the + // response. It's ok to change the channel pipeline here since we are processing an incoming + // message, so the pipeline is busy and no new incoming messages will be fed to it before this + // method returns. This assumes that the code ensures, through other means, that no outbound + // messages are being written to the channel while negotiation is still going on. if (saslServer.isComplete()) { logger.debug("SASL authentication successful for channel {}", client); + isComplete = true; + if (SparkSaslServer.QOP_AUTH_CONF.equals(saslServer.getNegotiatedProperty(Sasl.QOP))) { + logger.debug("Enabling encryption for channel {}", client); + SaslEncryption.addToChannel(channel, saslServer, conf.maxSaslEncryptedBlockSize()); + saslServer = null; + } else { + saslServer.dispose(); + saslServer = null; + } } - callback.onSuccess(response); } @Override @@ -86,9 +114,9 @@ public StreamManager getStreamManager() { @Override public void connectionTerminated(TransportClient client) { - SparkSaslServer saslServer = channelAuthenticationMap.remove(client); if (saslServer != null) { saslServer.dispose(); } } + } diff --git a/network/common/src/main/java/org/apache/spark/network/sasl/SaslServerBootstrap.java b/network/common/src/main/java/org/apache/spark/network/sasl/SaslServerBootstrap.java new file mode 100644 index 0000000000000..f2f983856f444 --- /dev/null +++ b/network/common/src/main/java/org/apache/spark/network/sasl/SaslServerBootstrap.java @@ -0,0 +1,49 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.network.sasl; + +import io.netty.channel.Channel; + +import org.apache.spark.network.server.RpcHandler; +import org.apache.spark.network.server.TransportServerBootstrap; +import org.apache.spark.network.util.TransportConf; + +/** + * A bootstrap which is executed on a TransportServer's client channel once a client connects + * to the server. This allows customizing the client channel to allow for things such as SASL + * authentication. + */ +public class SaslServerBootstrap implements TransportServerBootstrap { + + private final TransportConf conf; + private final SecretKeyHolder secretKeyHolder; + + public SaslServerBootstrap(TransportConf conf, SecretKeyHolder secretKeyHolder) { + this.conf = conf; + this.secretKeyHolder = secretKeyHolder; + } + + /** + * Wrap the given application handler in a SaslRpcHandler that will handle the initial SASL + * negotiation. + */ + public RpcHandler doBootstrap(Channel channel, RpcHandler rpcHandler) { + return new SaslRpcHandler(conf, channel, rpcHandler, secretKeyHolder); + } + +} diff --git a/network/common/src/main/java/org/apache/spark/network/sasl/SparkSaslClient.java b/network/common/src/main/java/org/apache/spark/network/sasl/SparkSaslClient.java index 9abad1f30a259..94685e91b862e 100644 --- a/network/common/src/main/java/org/apache/spark/network/sasl/SparkSaslClient.java +++ b/network/common/src/main/java/org/apache/spark/network/sasl/SparkSaslClient.java @@ -17,6 +17,8 @@ package org.apache.spark.network.sasl; +import java.io.IOException; +import java.util.Map; import javax.security.auth.callback.Callback; import javax.security.auth.callback.CallbackHandler; import javax.security.auth.callback.NameCallback; @@ -27,9 +29,9 @@ import javax.security.sasl.Sasl; import javax.security.sasl.SaslClient; import javax.security.sasl.SaslException; -import java.io.IOException; import com.google.common.base.Throwables; +import com.google.common.collect.ImmutableMap; import org.slf4j.Logger; import org.slf4j.LoggerFactory; @@ -40,19 +42,25 @@ * initial state to the "authenticated" state. This client initializes the protocol via a * firstToken, which is then followed by a set of challenges and responses. */ -public class SparkSaslClient { +public class SparkSaslClient implements SaslEncryptionBackend { private final Logger logger = LoggerFactory.getLogger(SparkSaslClient.class); private final String secretKeyId; private final SecretKeyHolder secretKeyHolder; + private final String expectedQop; private SaslClient saslClient; - public SparkSaslClient(String secretKeyId, SecretKeyHolder secretKeyHolder) { + public SparkSaslClient(String secretKeyId, SecretKeyHolder secretKeyHolder, boolean encrypt) { this.secretKeyId = secretKeyId; this.secretKeyHolder = secretKeyHolder; + this.expectedQop = encrypt ? QOP_AUTH_CONF : QOP_AUTH; + + Map saslProps = ImmutableMap.builder() + .put(Sasl.QOP, expectedQop) + .build(); try { this.saslClient = Sasl.createSaslClient(new String[] { DIGEST }, null, null, DEFAULT_REALM, - SASL_PROPS, new ClientCallbackHandler()); + saslProps, new ClientCallbackHandler()); } catch (SaslException e) { throw Throwables.propagate(e); } @@ -76,6 +84,11 @@ public synchronized boolean isComplete() { return saslClient != null && saslClient.isComplete(); } + /** Returns the value of a negotiated property. */ + public Object getNegotiatedProperty(String name) { + return saslClient.getNegotiatedProperty(name); + } + /** * Respond to server's SASL token. * @param token contains server's SASL token @@ -93,6 +106,7 @@ public synchronized byte[] response(byte[] token) { * Disposes of any system resources or security-sensitive information the * SaslClient might be using. */ + @Override public synchronized void dispose() { if (saslClient != null) { try { @@ -134,4 +148,15 @@ public void handle(Callback[] callbacks) throws IOException, UnsupportedCallback } } } + + @Override + public byte[] wrap(byte[] data, int offset, int len) throws SaslException { + return saslClient.wrap(data, offset, len); + } + + @Override + public byte[] unwrap(byte[] data, int offset, int len) throws SaslException { + return saslClient.unwrap(data, offset, len); + } + } diff --git a/network/common/src/main/java/org/apache/spark/network/sasl/SparkSaslServer.java b/network/common/src/main/java/org/apache/spark/network/sasl/SparkSaslServer.java index e87b17ead1e1a..431cb67a2ae0b 100644 --- a/network/common/src/main/java/org/apache/spark/network/sasl/SparkSaslServer.java +++ b/network/common/src/main/java/org/apache/spark/network/sasl/SparkSaslServer.java @@ -44,7 +44,7 @@ * initial state to the "authenticated" state. (It is not a server in the sense of accepting * connections on some socket.) */ -public class SparkSaslServer { +public class SparkSaslServer implements SaslEncryptionBackend { private final Logger logger = LoggerFactory.getLogger(SparkSaslServer.class); /** @@ -60,26 +60,37 @@ public class SparkSaslServer { static final String DIGEST = "DIGEST-MD5"; /** - * The quality of protection is just "auth". This means that we are doing - * authentication only, we are not supporting integrity or privacy protection of the - * communication channel after authentication. This could be changed to be configurable - * in the future. + * Quality of protection value that includes encryption. */ - static final Map SASL_PROPS = ImmutableMap.builder() - .put(Sasl.QOP, "auth") - .put(Sasl.SERVER_AUTH, "true") - .build(); + static final String QOP_AUTH_CONF = "auth-conf"; + + /** + * Quality of protection value that does not include encryption. + */ + static final String QOP_AUTH = "auth"; /** Identifier for a certain secret key within the secretKeyHolder. */ private final String secretKeyId; private final SecretKeyHolder secretKeyHolder; private SaslServer saslServer; - public SparkSaslServer(String secretKeyId, SecretKeyHolder secretKeyHolder) { + public SparkSaslServer( + String secretKeyId, + SecretKeyHolder secretKeyHolder, + boolean alwaysEncrypt) { this.secretKeyId = secretKeyId; this.secretKeyHolder = secretKeyHolder; + + // Sasl.QOP is a comma-separated list of supported values. The value that allows encryption + // is listed first since it's preferred over the non-encrypted one (if the client also + // lists both in the request). + String qop = alwaysEncrypt ? QOP_AUTH_CONF : String.format("%s,%s", QOP_AUTH_CONF, QOP_AUTH); + Map saslProps = ImmutableMap.builder() + .put(Sasl.SERVER_AUTH, "true") + .put(Sasl.QOP, qop) + .build(); try { - this.saslServer = Sasl.createSaslServer(DIGEST, null, DEFAULT_REALM, SASL_PROPS, + this.saslServer = Sasl.createSaslServer(DIGEST, null, DEFAULT_REALM, saslProps, new DigestCallbackHandler()); } catch (SaslException e) { throw Throwables.propagate(e); @@ -93,6 +104,11 @@ public synchronized boolean isComplete() { return saslServer != null && saslServer.isComplete(); } + /** Returns the value of a negotiated property. */ + public Object getNegotiatedProperty(String name) { + return saslServer.getNegotiatedProperty(name); + } + /** * Used to respond to server SASL tokens. * @param token Server's SASL token @@ -110,6 +126,7 @@ public synchronized byte[] response(byte[] token) { * Disposes of any system resources or security-sensitive information the * SaslServer might be using. */ + @Override public synchronized void dispose() { if (saslServer != null) { try { @@ -122,6 +139,16 @@ public synchronized void dispose() { } } + @Override + public byte[] wrap(byte[] data, int offset, int len) throws SaslException { + return saslServer.wrap(data, offset, len); + } + + @Override + public byte[] unwrap(byte[] data, int offset, int len) throws SaslException { + return saslServer.unwrap(data, offset, len); + } + /** * Implementation of javax.security.auth.callback.CallbackHandler for SASL DIGEST-MD5 mechanism. */ diff --git a/network/common/src/main/java/org/apache/spark/network/server/TransportServer.java b/network/common/src/main/java/org/apache/spark/network/server/TransportServer.java index b7ce8541e565e..941ef95772e16 100644 --- a/network/common/src/main/java/org/apache/spark/network/server/TransportServer.java +++ b/network/common/src/main/java/org/apache/spark/network/server/TransportServer.java @@ -19,8 +19,11 @@ import java.io.Closeable; import java.net.InetSocketAddress; +import java.util.List; import java.util.concurrent.TimeUnit; +import com.google.common.base.Preconditions; +import com.google.common.collect.Lists; import io.netty.bootstrap.ServerBootstrap; import io.netty.buffer.PooledByteBufAllocator; import io.netty.channel.ChannelFuture; @@ -44,15 +47,23 @@ public class TransportServer implements Closeable { private final TransportContext context; private final TransportConf conf; + private final RpcHandler appRpcHandler; + private final List bootstraps; private ServerBootstrap bootstrap; private ChannelFuture channelFuture; private int port = -1; /** Creates a TransportServer that binds to the given port, or to any available if 0. */ - public TransportServer(TransportContext context, int portToBind) { + public TransportServer( + TransportContext context, + int portToBind, + RpcHandler appRpcHandler, + List bootstraps) { this.context = context; this.conf = context.getConf(); + this.appRpcHandler = appRpcHandler; + this.bootstraps = Lists.newArrayList(Preconditions.checkNotNull(bootstraps)); init(portToBind); } @@ -95,7 +106,11 @@ private void init(int portToBind) { bootstrap.childHandler(new ChannelInitializer() { @Override protected void initChannel(SocketChannel ch) throws Exception { - context.initializePipeline(ch); + RpcHandler rpcHandler = appRpcHandler; + for (TransportServerBootstrap bootstrap : bootstraps) { + rpcHandler = bootstrap.doBootstrap(ch, rpcHandler); + } + context.initializePipeline(ch, rpcHandler); } }); diff --git a/network/common/src/main/java/org/apache/spark/network/server/TransportServerBootstrap.java b/network/common/src/main/java/org/apache/spark/network/server/TransportServerBootstrap.java new file mode 100644 index 0000000000000..05803ab1bb059 --- /dev/null +++ b/network/common/src/main/java/org/apache/spark/network/server/TransportServerBootstrap.java @@ -0,0 +1,36 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.network.server; + +import io.netty.channel.Channel; + +/** + * A bootstrap which is executed on a TransportServer's client channel once a client connects + * to the server. This allows customizing the client channel to allow for things such as SASL + * authentication. + */ +public interface TransportServerBootstrap { + /** + * Customizes the channel to include new features, if needed. + * + * @param channel The connected channel opened by the client. + * @param rpcHandler The RPC handler for the server. + * @return The RPC handler to use for the channel. + */ + RpcHandler doBootstrap(Channel channel, RpcHandler rpcHandler); +} diff --git a/network/common/src/test/java/org/apache/spark/network/ByteArrayWritableChannel.java b/network/common/src/main/java/org/apache/spark/network/util/ByteArrayWritableChannel.java similarity index 70% rename from network/common/src/test/java/org/apache/spark/network/ByteArrayWritableChannel.java rename to network/common/src/main/java/org/apache/spark/network/util/ByteArrayWritableChannel.java index b525ed69fc9fb..b1415720045e2 100644 --- a/network/common/src/test/java/org/apache/spark/network/ByteArrayWritableChannel.java +++ b/network/common/src/main/java/org/apache/spark/network/util/ByteArrayWritableChannel.java @@ -15,11 +15,14 @@ * limitations under the License. */ -package org.apache.spark.network; +package org.apache.spark.network.util; import java.nio.ByteBuffer; import java.nio.channels.WritableByteChannel; +/** + * A writable channel that stores the written data in a byte array in memory. + */ public class ByteArrayWritableChannel implements WritableByteChannel { private final byte[] data; @@ -27,19 +30,30 @@ public class ByteArrayWritableChannel implements WritableByteChannel { public ByteArrayWritableChannel(int size) { this.data = new byte[size]; - this.offset = 0; } public byte[] getData() { return data; } + public int length() { + return offset; + } + + /** Resets the channel so that writing to it will overwrite the existing buffer. */ + public void reset() { + offset = 0; + } + + /** + * Reads from the given buffer into the internal byte array. + */ @Override public int write(ByteBuffer src) { - int available = src.remaining(); - src.get(data, offset, available); - offset += available; - return available; + int toTransfer = Math.min(src.remaining(), data.length - offset); + src.get(data, offset, toTransfer); + offset += toTransfer; + return toTransfer; } @Override diff --git a/network/common/src/main/java/org/apache/spark/network/util/TransportConf.java b/network/common/src/main/java/org/apache/spark/network/util/TransportConf.java index 0aef7f1987315..3b2eff377955a 100644 --- a/network/common/src/main/java/org/apache/spark/network/util/TransportConf.java +++ b/network/common/src/main/java/org/apache/spark/network/util/TransportConf.java @@ -17,6 +17,8 @@ package org.apache.spark.network.util; +import com.google.common.primitives.Ints; + /** * A central location that tracks all the settings we expose to users. */ @@ -112,4 +114,20 @@ public boolean lazyFileDescriptor() { public int portMaxRetries() { return conf.getInt("spark.port.maxRetries", 16); } + + /** + * Maximum number of bytes to be encrypted at a time when SASL encryption is enabled. + */ + public int maxSaslEncryptedBlockSize() { + return Ints.checkedCast(JavaUtils.byteStringAsBytes( + conf.get("spark.network.sasl.maxEncryptedBlockSize", "64k"))); + } + + /** + * Whether the server should enforce encryption on SASL-authenticated connections. + */ + public boolean saslServerAlwaysEncrypt() { + return conf.getBoolean("spark.network.sasl.serverAlwaysEncrypt", false); + } + } diff --git a/network/common/src/test/java/org/apache/spark/network/ProtocolSuite.java b/network/common/src/test/java/org/apache/spark/network/ProtocolSuite.java index 860dd6d9b3915..d500bc3c98a78 100644 --- a/network/common/src/test/java/org/apache/spark/network/ProtocolSuite.java +++ b/network/common/src/test/java/org/apache/spark/network/ProtocolSuite.java @@ -39,6 +39,7 @@ import org.apache.spark.network.protocol.RpcRequest; import org.apache.spark.network.protocol.RpcResponse; import org.apache.spark.network.protocol.StreamChunkId; +import org.apache.spark.network.util.ByteArrayWritableChannel; import org.apache.spark.network.util.NettyUtils; public class ProtocolSuite { diff --git a/network/common/src/test/java/org/apache/spark/network/protocol/MessageWithHeaderSuite.java b/network/common/src/test/java/org/apache/spark/network/protocol/MessageWithHeaderSuite.java index ff985096d72d5..6c98e733b462f 100644 --- a/network/common/src/test/java/org/apache/spark/network/protocol/MessageWithHeaderSuite.java +++ b/network/common/src/test/java/org/apache/spark/network/protocol/MessageWithHeaderSuite.java @@ -29,7 +29,7 @@ import static org.junit.Assert.*; -import org.apache.spark.network.ByteArrayWritableChannel; +import org.apache.spark.network.util.ByteArrayWritableChannel; public class MessageWithHeaderSuite { diff --git a/network/common/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java b/network/common/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java index 23b4e06f064e1..be6632bb8cf49 100644 --- a/network/common/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java +++ b/network/common/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java @@ -17,12 +17,47 @@ package org.apache.spark.network.sasl; -import static org.junit.Assert.assertFalse; -import static org.junit.Assert.assertTrue; -import static org.junit.Assert.fail; +import static com.google.common.base.Charsets.UTF_8; +import static org.junit.Assert.*; +import static org.mockito.Mockito.*; +import java.io.File; +import java.util.Arrays; +import java.util.List; +import java.util.Random; +import java.util.concurrent.TimeoutException; +import java.util.concurrent.TimeUnit; +import java.util.concurrent.atomic.AtomicReference; +import javax.security.sasl.SaslException; + +import com.google.common.collect.Lists; +import com.google.common.io.ByteStreams; +import com.google.common.io.Files; +import io.netty.buffer.ByteBuf; +import io.netty.buffer.Unpooled; +import io.netty.channel.Channel; +import io.netty.channel.ChannelHandlerContext; +import io.netty.channel.ChannelOutboundHandlerAdapter; +import io.netty.channel.ChannelPromise; import org.junit.Test; +import org.mockito.invocation.InvocationOnMock; +import org.mockito.stubbing.Answer; +import org.apache.spark.network.TestUtils; +import org.apache.spark.network.TransportContext; +import org.apache.spark.network.buffer.FileSegmentManagedBuffer; +import org.apache.spark.network.buffer.ManagedBuffer; +import org.apache.spark.network.client.ChunkReceivedCallback; +import org.apache.spark.network.client.RpcResponseCallback; +import org.apache.spark.network.client.TransportClient; +import org.apache.spark.network.client.TransportClientBootstrap; +import org.apache.spark.network.server.RpcHandler; +import org.apache.spark.network.server.StreamManager; +import org.apache.spark.network.server.TransportServer; +import org.apache.spark.network.server.TransportServerBootstrap; +import org.apache.spark.network.util.ByteArrayWritableChannel; +import org.apache.spark.network.util.SystemPropertyConfigProvider; +import org.apache.spark.network.util.TransportConf; /** * Jointly tests SparkSaslClient and SparkSaslServer, as both are black boxes. @@ -44,8 +79,8 @@ public String getSecretKey(String appId) { @Test public void testMatching() { - SparkSaslClient client = new SparkSaslClient("shared-secret", secretKeyHolder); - SparkSaslServer server = new SparkSaslServer("shared-secret", secretKeyHolder); + SparkSaslClient client = new SparkSaslClient("shared-secret", secretKeyHolder, false); + SparkSaslServer server = new SparkSaslServer("shared-secret", secretKeyHolder, false); assertFalse(client.isComplete()); assertFalse(server.isComplete()); @@ -64,11 +99,10 @@ public void testMatching() { assertFalse(client.isComplete()); } - @Test public void testNonMatching() { - SparkSaslClient client = new SparkSaslClient("my-secret", secretKeyHolder); - SparkSaslServer server = new SparkSaslServer("your-secret", secretKeyHolder); + SparkSaslClient client = new SparkSaslClient("my-secret", secretKeyHolder, false); + SparkSaslServer server = new SparkSaslServer("your-secret", secretKeyHolder, false); assertFalse(client.isComplete()); assertFalse(server.isComplete()); @@ -86,4 +120,312 @@ public void testNonMatching() { assertFalse(server.isComplete()); } } + + @Test + public void testSaslAuthentication() throws Exception { + testBasicSasl(false); + } + + @Test + public void testSaslEncryption() throws Exception { + testBasicSasl(true); + } + + private void testBasicSasl(boolean encrypt) throws Exception { + RpcHandler rpcHandler = mock(RpcHandler.class); + doAnswer(new Answer() { + @Override + public Void answer(InvocationOnMock invocation) { + byte[] message = (byte[]) invocation.getArguments()[1]; + RpcResponseCallback cb = (RpcResponseCallback) invocation.getArguments()[2]; + assertEquals("Ping", new String(message, UTF_8)); + cb.onSuccess("Pong".getBytes(UTF_8)); + return null; + } + }) + .when(rpcHandler) + .receive(any(TransportClient.class), any(byte[].class), any(RpcResponseCallback.class)); + + SaslTestCtx ctx = new SaslTestCtx(rpcHandler, encrypt, false); + try { + byte[] response = ctx.client.sendRpcSync("Ping".getBytes(UTF_8), TimeUnit.SECONDS.toMillis(10)); + assertEquals("Pong", new String(response, UTF_8)); + } finally { + ctx.close(); + } + } + + @Test + public void testEncryptedMessage() throws Exception { + SaslEncryptionBackend backend = mock(SaslEncryptionBackend.class); + byte[] data = new byte[1024]; + new Random().nextBytes(data); + when(backend.wrap(any(byte[].class), anyInt(), anyInt())).thenReturn(data); + + ByteBuf msg = Unpooled.buffer(); + try { + msg.writeBytes(data); + + // Create a channel with a really small buffer compared to the data. This means that on each + // call, the outbound data will not be fully written, so the write() method should return a + // dummy count to keep the channel alive when possible. + ByteArrayWritableChannel channel = new ByteArrayWritableChannel(32); + + SaslEncryption.EncryptedMessage emsg = + new SaslEncryption.EncryptedMessage(backend, msg, 1024); + long count = emsg.transferTo(channel, emsg.transfered()); + assertTrue(count < data.length); + assertTrue(count > 0); + + // Here, the output buffer is full so nothing should be transferred. + assertEquals(0, emsg.transferTo(channel, emsg.transfered())); + + // Now there's room in the buffer, but not enough to transfer all the remaining data, + // so the dummy count should be returned. + channel.reset(); + assertEquals(1, emsg.transferTo(channel, emsg.transfered())); + + // Eventually, the whole message should be transferred. + for (int i = 0; i < data.length / 32 - 2; i++) { + channel.reset(); + assertEquals(1, emsg.transferTo(channel, emsg.transfered())); + } + + channel.reset(); + count = emsg.transferTo(channel, emsg.transfered()); + assertTrue("Unexpected count: " + count, count > 1 && count < data.length); + assertEquals(data.length, emsg.transfered()); + } finally { + msg.release(); + } + } + + @Test + public void testEncryptedMessageChunking() throws Exception { + File file = File.createTempFile("sasltest", ".txt"); + try { + TransportConf conf = new TransportConf(new SystemPropertyConfigProvider()); + + byte[] data = new byte[8 * 1024]; + new Random().nextBytes(data); + Files.write(data, file); + + SaslEncryptionBackend backend = mock(SaslEncryptionBackend.class); + // It doesn't really matter what we return here, as long as it's not null. + when(backend.wrap(any(byte[].class), anyInt(), anyInt())).thenReturn(data); + + FileSegmentManagedBuffer msg = new FileSegmentManagedBuffer(conf, file, 0, file.length()); + SaslEncryption.EncryptedMessage emsg = + new SaslEncryption.EncryptedMessage(backend, msg.convertToNetty(), data.length / 8); + + ByteArrayWritableChannel channel = new ByteArrayWritableChannel(data.length); + while (emsg.transfered() < emsg.count()) { + channel.reset(); + emsg.transferTo(channel, emsg.transfered()); + } + + verify(backend, times(8)).wrap(any(byte[].class), anyInt(), anyInt()); + } finally { + file.delete(); + } + } + + @Test + public void testFileRegionEncryption() throws Exception { + final String blockSizeConf = "spark.network.sasl.maxEncryptedBlockSize"; + System.setProperty(blockSizeConf, "1k"); + + final AtomicReference response = new AtomicReference(); + final File file = File.createTempFile("sasltest", ".txt"); + SaslTestCtx ctx = null; + try { + final TransportConf conf = new TransportConf(new SystemPropertyConfigProvider()); + StreamManager sm = mock(StreamManager.class); + when(sm.getChunk(anyLong(), anyInt())).thenAnswer(new Answer() { + @Override + public ManagedBuffer answer(InvocationOnMock invocation) { + return new FileSegmentManagedBuffer(conf, file, 0, file.length()); + } + }); + + RpcHandler rpcHandler = mock(RpcHandler.class); + when(rpcHandler.getStreamManager()).thenReturn(sm); + + byte[] data = new byte[8 * 1024]; + new Random().nextBytes(data); + Files.write(data, file); + + ctx = new SaslTestCtx(rpcHandler, true, false); + + final Object lock = new Object(); + + ChunkReceivedCallback callback = mock(ChunkReceivedCallback.class); + doAnswer(new Answer() { + @Override + public Void answer(InvocationOnMock invocation) { + response.set((ManagedBuffer) invocation.getArguments()[1]); + response.get().retain(); + synchronized (lock) { + lock.notifyAll(); + } + return null; + } + }).when(callback).onSuccess(anyInt(), any(ManagedBuffer.class)); + + synchronized (lock) { + ctx.client.fetchChunk(0, 0, callback); + lock.wait(10 * 1000); + } + + verify(callback, times(1)).onSuccess(anyInt(), any(ManagedBuffer.class)); + verify(callback, never()).onFailure(anyInt(), any(Throwable.class)); + + byte[] received = ByteStreams.toByteArray(response.get().createInputStream()); + assertTrue(Arrays.equals(data, received)); + } finally { + file.delete(); + if (ctx != null) { + ctx.close(); + } + if (response.get() != null) { + response.get().release(); + } + System.clearProperty(blockSizeConf); + } + } + + @Test + public void testServerAlwaysEncrypt() throws Exception { + final String alwaysEncryptConfName = "spark.network.sasl.serverAlwaysEncrypt"; + System.setProperty(alwaysEncryptConfName, "true"); + + SaslTestCtx ctx = null; + try { + ctx = new SaslTestCtx(mock(RpcHandler.class), false, false); + fail("Should have failed to connect without encryption."); + } catch (Exception e) { + assertTrue(e.getCause() instanceof SaslException); + } finally { + if (ctx != null) { + ctx.close(); + } + System.clearProperty(alwaysEncryptConfName); + } + } + + @Test + public void testDataEncryptionIsActuallyEnabled() throws Exception { + // This test sets up an encrypted connection but then, using a client bootstrap, removes + // the encryption handler from the client side. This should cause the server to not be + // able to understand RPCs sent to it and thus close the connection. + SaslTestCtx ctx = null; + try { + ctx = new SaslTestCtx(mock(RpcHandler.class), true, true); + ctx.client.sendRpcSync("Ping".getBytes(UTF_8), TimeUnit.SECONDS.toMillis(10)); + fail("Should have failed to send RPC to server."); + } catch (Exception e) { + assertFalse(e.getCause() instanceof TimeoutException); + } finally { + if (ctx != null) { + ctx.close(); + } + } + } + + private static class SaslTestCtx { + + final TransportClient client; + final TransportServer server; + + private final boolean encrypt; + private final boolean disableClientEncryption; + private final EncryptionCheckerBootstrap checker; + + SaslTestCtx( + RpcHandler rpcHandler, + boolean encrypt, + boolean disableClientEncryption) + throws Exception { + + TransportConf conf = new TransportConf(new SystemPropertyConfigProvider()); + + SecretKeyHolder keyHolder = mock(SecretKeyHolder.class); + when(keyHolder.getSaslUser(anyString())).thenReturn("user"); + when(keyHolder.getSecretKey(anyString())).thenReturn("secret"); + + TransportContext ctx = new TransportContext(conf, rpcHandler); + + this.checker = new EncryptionCheckerBootstrap(); + this.server = ctx.createServer(Arrays.asList(new SaslServerBootstrap(conf, keyHolder), + checker)); + + try { + List clientBootstraps = Lists.newArrayList(); + clientBootstraps.add(new SaslClientBootstrap(conf, "user", keyHolder, encrypt)); + if (disableClientEncryption) { + clientBootstraps.add(new EncryptionDisablerBootstrap()); + } + + this.client = ctx.createClientFactory(clientBootstraps) + .createClient(TestUtils.getLocalHost(), server.getPort()); + } catch (Exception e) { + close(); + throw e; + } + + this.encrypt = encrypt; + this.disableClientEncryption = disableClientEncryption; + } + + void close() { + if (!disableClientEncryption) { + assertEquals(encrypt, checker.foundEncryptionHandler); + } + if (client != null) { + client.close(); + } + if (server != null) { + server.close(); + } + } + + } + + private static class EncryptionCheckerBootstrap extends ChannelOutboundHandlerAdapter + implements TransportServerBootstrap { + + boolean foundEncryptionHandler; + + @Override + public void write(ChannelHandlerContext ctx, Object msg, ChannelPromise promise) + throws Exception { + if (!foundEncryptionHandler) { + foundEncryptionHandler = + ctx.channel().pipeline().get(SaslEncryption.ENCRYPTION_HANDLER_NAME) != null; + } + ctx.write(msg, promise); + } + + @Override + public void handlerRemoved(ChannelHandlerContext ctx) throws Exception { + super.handlerRemoved(ctx); + } + + @Override + public RpcHandler doBootstrap(Channel channel, RpcHandler rpcHandler) { + channel.pipeline().addFirst("encryptionChecker", this); + return rpcHandler; + } + + } + + private static class EncryptionDisablerBootstrap implements TransportClientBootstrap { + + @Override + public void doBootstrap(TransportClient client, Channel channel) { + channel.pipeline().remove(SaslEncryption.ENCRYPTION_HANDLER_NAME); + } + + } + } diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleClient.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleClient.java index 6e8018b723dc6..612bce571a493 100644 --- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleClient.java +++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleClient.java @@ -20,6 +20,7 @@ import java.io.IOException; import java.util.List; +import com.google.common.base.Preconditions; import com.google.common.collect.Lists; import org.slf4j.Logger; import org.slf4j.LoggerFactory; @@ -46,6 +47,7 @@ public class ExternalShuffleClient extends ShuffleClient { private final TransportConf conf; private final boolean saslEnabled; + private final boolean saslEncryptionEnabled; private final SecretKeyHolder secretKeyHolder; private TransportClientFactory clientFactory; @@ -58,10 +60,15 @@ public class ExternalShuffleClient extends ShuffleClient { public ExternalShuffleClient( TransportConf conf, SecretKeyHolder secretKeyHolder, - boolean saslEnabled) { + boolean saslEnabled, + boolean saslEncryptionEnabled) { + Preconditions.checkArgument( + !saslEncryptionEnabled || saslEnabled, + "SASL encryption can only be enabled if SASL is also enabled."); this.conf = conf; this.secretKeyHolder = secretKeyHolder; this.saslEnabled = saslEnabled; + this.saslEncryptionEnabled = saslEncryptionEnabled; } @Override @@ -70,7 +77,7 @@ public void init(String appId) { TransportContext context = new TransportContext(conf, new NoOpRpcHandler()); List bootstraps = Lists.newArrayList(); if (saslEnabled) { - bootstraps.add(new SaslClientBootstrap(conf, appId, secretKeyHolder)); + bootstraps.add(new SaslClientBootstrap(conf, appId, secretKeyHolder, saslEncryptionEnabled)); } clientFactory = context.createClientFactory(bootstraps); } diff --git a/network/shuffle/src/test/java/org/apache/spark/network/sasl/SaslIntegrationSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/sasl/SaslIntegrationSuite.java index d25283e46ef96..382f613ecbb1b 100644 --- a/network/shuffle/src/test/java/org/apache/spark/network/sasl/SaslIntegrationSuite.java +++ b/network/shuffle/src/test/java/org/apache/spark/network/sasl/SaslIntegrationSuite.java @@ -18,6 +18,7 @@ package org.apache.spark.network.sasl; import java.io.IOException; +import java.util.Arrays; import com.google.common.collect.Lists; import org.junit.After; @@ -37,6 +38,7 @@ import org.apache.spark.network.server.RpcHandler; import org.apache.spark.network.server.StreamManager; import org.apache.spark.network.server.TransportServer; +import org.apache.spark.network.server.TransportServerBootstrap; import org.apache.spark.network.shuffle.ExternalShuffleBlockHandler; import org.apache.spark.network.util.SystemPropertyConfigProvider; import org.apache.spark.network.util.TransportConf; @@ -72,10 +74,11 @@ public String getSecretKey(String appId) { @BeforeClass public static void beforeAll() throws IOException { SecretKeyHolder secretKeyHolder = new TestSecretKeyHolder("good-key"); - SaslRpcHandler handler = new SaslRpcHandler(new TestRpcHandler(), secretKeyHolder); conf = new TransportConf(new SystemPropertyConfigProvider()); - context = new TransportContext(conf, handler); - server = context.createServer(); + context = new TransportContext(conf, new TestRpcHandler()); + + TransportServerBootstrap bootstrap = new SaslServerBootstrap(conf, secretKeyHolder); + server = context.createServer(Arrays.asList(bootstrap)); } diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleIntegrationSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleIntegrationSuite.java index 02c10bcb7b261..39aa49911d9cb 100644 --- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleIntegrationSuite.java +++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleIntegrationSuite.java @@ -136,7 +136,7 @@ private FetchResult fetchBlocks(String execId, String[] blockIds, int port) thro final Semaphore requestsRemaining = new Semaphore(0); - ExternalShuffleClient client = new ExternalShuffleClient(conf, null, false); + ExternalShuffleClient client = new ExternalShuffleClient(conf, null, false, false); client.init(APP_ID); client.fetchBlocks(TestUtils.getLocalHost(), port, execId, blockIds, new BlockFetchingListener() { @@ -274,7 +274,7 @@ public void testFetchNoServer() throws Exception { private void registerExecutor(String executorId, ExecutorShuffleInfo executorInfo) throws IOException { - ExternalShuffleClient client = new ExternalShuffleClient(conf, null, false); + ExternalShuffleClient client = new ExternalShuffleClient(conf, null, false, false); client.init(APP_ID); client.registerWithShuffleServer(TestUtils.getLocalHost(), server.getPort(), executorId, executorInfo); diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleSecuritySuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleSecuritySuite.java index 759a12910c94d..d4ec1956c1e29 100644 --- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleSecuritySuite.java +++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleSecuritySuite.java @@ -18,6 +18,7 @@ package org.apache.spark.network.shuffle; import java.io.IOException; +import java.util.Arrays; import org.junit.After; import org.junit.Before; @@ -27,10 +28,11 @@ import org.apache.spark.network.TestUtils; import org.apache.spark.network.TransportContext; -import org.apache.spark.network.sasl.SaslRpcHandler; +import org.apache.spark.network.sasl.SaslServerBootstrap; import org.apache.spark.network.sasl.SecretKeyHolder; import org.apache.spark.network.server.RpcHandler; import org.apache.spark.network.server.TransportServer; +import org.apache.spark.network.server.TransportServerBootstrap; import org.apache.spark.network.shuffle.protocol.ExecutorShuffleInfo; import org.apache.spark.network.util.SystemPropertyConfigProvider; import org.apache.spark.network.util.TransportConf; @@ -42,10 +44,10 @@ public class ExternalShuffleSecuritySuite { @Before public void beforeEach() { - RpcHandler handler = new SaslRpcHandler(new ExternalShuffleBlockHandler(conf), - new TestSecretKeyHolder("my-app-id", "secret")); - TransportContext context = new TransportContext(conf, handler); - this.server = context.createServer(); + TransportContext context = new TransportContext(conf, new ExternalShuffleBlockHandler(conf)); + TransportServerBootstrap bootstrap = new SaslServerBootstrap(conf, + new TestSecretKeyHolder("my-app-id", "secret")); + this.server = context.createServer(Arrays.asList(bootstrap)); } @After @@ -58,13 +60,13 @@ public void afterEach() { @Test public void testValid() throws IOException { - validate("my-app-id", "secret"); + validate("my-app-id", "secret", false); } @Test public void testBadAppId() { try { - validate("wrong-app-id", "secret"); + validate("wrong-app-id", "secret", false); } catch (Exception e) { assertTrue(e.getMessage(), e.getMessage().contains("Wrong appId!")); } @@ -73,16 +75,21 @@ public void testBadAppId() { @Test public void testBadSecret() { try { - validate("my-app-id", "bad-secret"); + validate("my-app-id", "bad-secret", false); } catch (Exception e) { assertTrue(e.getMessage(), e.getMessage().contains("Mismatched response")); } } + @Test + public void testEncryption() throws IOException { + validate("my-app-id", "secret", true); + } + /** Creates an ExternalShuffleClient and attempts to register with the server. */ - private void validate(String appId, String secretKey) throws IOException { + private void validate(String appId, String secretKey, boolean encrypt) throws IOException { ExternalShuffleClient client = - new ExternalShuffleClient(conf, new TestSecretKeyHolder(appId, secretKey), true); + new ExternalShuffleClient(conf, new TestSecretKeyHolder(appId, secretKey), true, encrypt); client.init(appId); // Registration either succeeds or throws an exception. client.registerWithShuffleServer(TestUtils.getLocalHost(), server.getPort(), "exec0", diff --git a/network/yarn/src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java b/network/yarn/src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java index 63b21222e7b77..463f99ef3352d 100644 --- a/network/yarn/src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java +++ b/network/yarn/src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java @@ -17,9 +17,10 @@ package org.apache.spark.network.yarn; -import java.lang.Override; import java.nio.ByteBuffer; +import java.util.List; +import com.google.common.collect.Lists; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.yarn.api.records.ApplicationId; import org.apache.hadoop.yarn.api.records.ContainerId; @@ -32,10 +33,11 @@ import org.slf4j.LoggerFactory; import org.apache.spark.network.TransportContext; -import org.apache.spark.network.sasl.SaslRpcHandler; +import org.apache.spark.network.sasl.SaslServerBootstrap; import org.apache.spark.network.sasl.ShuffleSecretManager; import org.apache.spark.network.server.RpcHandler; import org.apache.spark.network.server.TransportServer; +import org.apache.spark.network.server.TransportServerBootstrap; import org.apache.spark.network.shuffle.ExternalShuffleBlockHandler; import org.apache.spark.network.util.TransportConf; import org.apache.spark.network.yarn.util.HadoopConfigProvider; @@ -103,16 +105,17 @@ protected void serviceInit(Configuration conf) { // special RPC handler that filters out unauthenticated fetch requests boolean authEnabled = conf.getBoolean(SPARK_AUTHENTICATE_KEY, DEFAULT_SPARK_AUTHENTICATE); blockHandler = new ExternalShuffleBlockHandler(transportConf); - RpcHandler rpcHandler = blockHandler; + + List bootstraps = Lists.newArrayList(); if (authEnabled) { secretManager = new ShuffleSecretManager(); - rpcHandler = new SaslRpcHandler(rpcHandler, secretManager); + bootstraps.add(new SaslServerBootstrap(transportConf, secretManager)); } int port = conf.getInt( SPARK_SHUFFLE_SERVICE_PORT_KEY, DEFAULT_SPARK_SHUFFLE_SERVICE_PORT); - TransportContext transportContext = new TransportContext(transportConf, rpcHandler); - shuffleServer = transportContext.createServer(port); + TransportContext transportContext = new TransportContext(transportConf, blockHandler); + shuffleServer = transportContext.createServer(port, bootstraps); String authEnabledString = authEnabled ? "enabled" : "not enabled"; logger.info("Started YARN shuffle service for Spark on port {}. " + "Authentication is {}.", port, authEnabledString); From b79aeb95b45ab4ae811039d452cf028d7b844132 Mon Sep 17 00:00:00 2001 From: Mridul Muralidharan Date: Fri, 1 May 2015 21:23:42 -0700 Subject: [PATCH 544/591] [SPARK-7317] [Shuffle] Expose shuffle handle Details in JIRA, in a nut-shell, all machinary for custom RDD's to leverage spark shuffle directly (without exposing impl details of shuffle) exists - except for this small piece. Exposing this will allow for custom dependencies to get a handle to ShuffleHandle - which they can then leverage on reduce side. Author: Mridul Muralidharan Closes #5857 from mridulm/expose_shuffle_handle and squashes the following commits: d8b6bd4 [Mridul Muralidharan] Expose ShuffleHandle --- .../main/scala/org/apache/spark/shuffle/ShuffleHandle.scala | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleHandle.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleHandle.scala index 13c7115f88afa..e04c97fe61894 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/ShuffleHandle.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleHandle.scala @@ -17,9 +17,12 @@ package org.apache.spark.shuffle +import org.apache.spark.annotation.DeveloperApi + /** * An opaque handle to a shuffle, used by a ShuffleManager to pass information about it to tasks. * * @param shuffleId ID of the shuffle */ -private[spark] abstract class ShuffleHandle(val shuffleId: Int) extends Serializable {} +@DeveloperApi +abstract class ShuffleHandle(val shuffleId: Int) extends Serializable {} From 2e0f3579f1fa7139c2e79bde656cbac049abbc33 Mon Sep 17 00:00:00 2001 From: Burak Yavuz Date: Fri, 1 May 2015 23:43:24 -0700 Subject: [PATCH 545/591] [SPARK-7242] added python api for freqItems in DataFrames The python api for DataFrame's plus addressed your comments from previous PR. rxin Author: Burak Yavuz Closes #5859 from brkyvz/df-freq-py2 and squashes the following commits: f9aa9ce [Burak Yavuz] addressed comments v0.1 4b25056 [Burak Yavuz] added python api for freqItems --- python/pyspark/sql/dataframe.py | 25 +++++++++++++++++++ python/pyspark/sql/tests.py | 7 ++++++ .../spark/sql/DataFrameStatFunctions.scala | 9 ++++--- 3 files changed, 38 insertions(+), 3 deletions(-) diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index 5ff49cac5522b..e9fd17ed4ce94 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -889,6 +889,26 @@ def cov(self, col1, col2): raise ValueError("col2 should be a string.") return self._jdf.stat().cov(col1, col2) + def freqItems(self, cols, support=None): + """ + Finding frequent items for columns, possibly with false positives. Using the + frequent element count algorithm described in + "http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou". + :func:`DataFrame.freqItems` and :func:`DataFrameStatFunctions.freqItems` are aliases. + + :param cols: Names of the columns to calculate frequent items for as a list or tuple of + strings. + :param support: The frequency with which to consider an item 'frequent'. Default is 1%. + The support must be greater than 1e-4. + """ + if isinstance(cols, tuple): + cols = list(cols) + if not isinstance(cols, list): + raise ValueError("cols must be a list or tuple of column names as strings.") + if not support: + support = 0.01 + return DataFrame(self._jdf.stat().freqItems(_to_seq(self._sc, cols), support), self.sql_ctx) + @ignore_unicode_prefix def withColumn(self, colName, col): """Returns a new :class:`DataFrame` by adding a column. @@ -1344,6 +1364,11 @@ def cov(self, col1, col2): cov.__doc__ = DataFrame.cov.__doc__ + def freqItems(self, cols, support=None): + return self.df.freqItems(cols, support) + + freqItems.__doc__ = DataFrame.freqItems.__doc__ + def _test(): import doctest diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index 44c8b6a1aac13..613efc0ac029d 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -375,6 +375,13 @@ def test_column_select(self): self.assertEqual(self.testData, df.select(df.key, df.value).collect()) self.assertEqual([Row(value='1')], df.where(df.key == 1).select(df.value).collect()) + def test_freqItems(self): + vals = [Row(a=1, b=-2.0) if i % 2 == 0 else Row(a=i, b=i * 1.0) for i in range(100)] + df = self.sc.parallelize(vals).toDF() + items = df.stat.freqItems(("a", "b"), 0.4).collect()[0] + self.assertTrue(1 in items[0]) + self.assertTrue(-2.0 in items[1]) + def test_aggregator(self): df = self.df g = df.groupBy() diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala index 23652aeb7c7bc..e8fa82947759b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala @@ -43,7 +43,10 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) { } /** - * Runs `freqItems` with a default `support` of 1%. + * Finding frequent items for columns, possibly with false positives. Using the + * frequent element count algorithm described in + * [[http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou]]. + * Uses a `default` support of 1%. * * @param cols the names of the columns to search frequent items in. * @return A Local DataFrame with the Array of frequent items for each column. @@ -55,14 +58,14 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) { /** * Python friendly implementation for `freqItems` */ - def freqItems(cols: List[String], support: Double): DataFrame = { + def freqItems(cols: Seq[String], support: Double): DataFrame = { FrequentItems.singlePassFreqItems(df, cols, support) } /** * Python friendly implementation for `freqItems` with a default `support` of 1%. */ - def freqItems(cols: List[String]): DataFrame = { + def freqItems(cols: Seq[String]): DataFrame = { FrequentItems.singlePassFreqItems(df, cols, 0.01) } From 7394e7adeb03df159978f1d10061d9ec6a913968 Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Fri, 1 May 2015 23:57:58 -0700 Subject: [PATCH 546/591] [SPARK-7120] [SPARK-7121] Closure cleaner nesting + documentation + tests Note: ~600 lines of this is test code, and ~100 lines documentation. **[SPARK-7121]** ClosureCleaner does not handle nested closures properly. For instance, in SparkContext, I tried to do the following: ``` def scope[T](body: => T): T = body // no-op def myCoolMethod(path: String): RDD[String] = scope { parallelize(1 to 10).map { _ => path } } ``` and I got an exception complaining that SparkContext is not serializable. The issue here is that the inner closure is getting its path from the outer closure (the scope), but the outer closure references the SparkContext object itself to get the `parallelize` method. Note, however, that the inner closure doesn't actually need the SparkContext; it just needs a field from the outer closure. If we modify ClosureCleaner to clean the outer closure recursively using only the fields accessed by the inner closure, then we can serialize the inner closure. **[SPARK-7120]** Also, the other thing is that this file is one of the least understood, partly because it is very low level and is written a long time ago. This patch attempts to change that by adding the missing documentation. This is blocking my effort on a separate task #5729. Author: Andrew Or Closes #5685 from andrewor14/closure-cleaner and squashes the following commits: cd46230 [Andrew Or] Revert a small change that affected streaming 0bbe77f [Andrew Or] Fix style ea874bc [Andrew Or] Fix tests 26c5072 [Andrew Or] Address comments 16fbcfd [Andrew Or] Merge branch 'master' of github.com:apache/spark into closure-cleaner 26c7aba [Andrew Or] Revert "In sc.runJob, actually clean the inner closure" 6f75784 [Andrew Or] Revert "Guard against NPE if CC is used outside of an application" e909a42 [Andrew Or] Guard against NPE if CC is used outside of an application 3998168 [Andrew Or] In sc.runJob, actually clean the inner closure 9187066 [Andrew Or] Merge branch 'master' of github.com:apache/spark into closure-cleaner d889950 [Andrew Or] Revert "Bypass SerializationDebugger for now (SPARK-7180)" 9419efe [Andrew Or] Bypass SerializationDebugger for now (SPARK-7180) 6d4d3f1 [Andrew Or] Fix scala style? 4aab379 [Andrew Or] Merge branch 'master' of github.com:apache/spark into closure-cleaner e45e904 [Andrew Or] More minor updates (wording, renaming etc.) 8b71cdb [Andrew Or] Update a few comments eb127e5 [Andrew Or] Use private method tester for a few things a3aa465 [Andrew Or] Add more tests for individual closure cleaner operations e672170 [Andrew Or] Guard against potential infinite cycles in method visitor 6d36f38 [Andrew Or] Fix closure cleaner visibility 2106f12 [Andrew Or] Merge branch 'master' of github.com:apache/spark into closure-cleaner 263593d [Andrew Or] Finalize tests 06fd668 [Andrew Or] Make closure cleaning idempotent a4866e3 [Andrew Or] Add tests (still WIP) 438c68f [Andrew Or] Minor changes 2390a60 [Andrew Or] Feature flag this new behavior 86f7823 [Andrew Or] Implement transitive cleaning + add missing documentation --- .../apache/spark/util/ClosureCleaner.scala | 305 ++++++++-- .../spark/util/ClosureCleanerSuite.scala | 13 +- .../spark/util/ClosureCleanerSuite2.scala | 571 ++++++++++++++++++ 3 files changed, 831 insertions(+), 58 deletions(-) create mode 100644 core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite2.scala diff --git a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala index e3f52f6ff1e63..4ac0382d80815 100644 --- a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala +++ b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala @@ -19,17 +19,20 @@ package org.apache.spark.util import java.io.{ByteArrayInputStream, ByteArrayOutputStream} -import scala.collection.mutable.Map -import scala.collection.mutable.Set +import scala.collection.mutable.{Map, Set} import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm.{ClassReader, ClassVisitor, MethodVisitor, Type} import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm.Opcodes._ import org.apache.spark.{Logging, SparkEnv, SparkException} +/** + * A cleaner that renders closures serializable if they can be done so safely. + */ private[spark] object ClosureCleaner extends Logging { + // Get an ASM class reader for a given class from the JAR that loaded it - private def getClassReader(cls: Class[_]): ClassReader = { + private[util] def getClassReader(cls: Class[_]): ClassReader = { // Copy data over, before delegating to ClassReader - else we can run out of open file handles. val className = cls.getName.replaceFirst("^.*\\.", "") + ".class" val resourceStream = cls.getResourceAsStream(className) @@ -55,10 +58,14 @@ private[spark] object ClosureCleaner extends Logging { private def getOuterClasses(obj: AnyRef): List[Class[_]] = { for (f <- obj.getClass.getDeclaredFields if f.getName == "$outer") { f.setAccessible(true) - if (isClosure(f.getType)) { - return f.getType :: getOuterClasses(f.get(obj)) - } else { - return f.getType :: Nil // Stop at the first $outer that is not a closure + val outer = f.get(obj) + // The outer pointer may be null if we have cleaned this closure before + if (outer != null) { + if (isClosure(f.getType)) { + return f.getType :: getOuterClasses(outer) + } else { + return f.getType :: Nil // Stop at the first $outer that is not a closure + } } } Nil @@ -68,16 +75,23 @@ private[spark] object ClosureCleaner extends Logging { private def getOuterObjects(obj: AnyRef): List[AnyRef] = { for (f <- obj.getClass.getDeclaredFields if f.getName == "$outer") { f.setAccessible(true) - if (isClosure(f.getType)) { - return f.get(obj) :: getOuterObjects(f.get(obj)) - } else { - return f.get(obj) :: Nil // Stop at the first $outer that is not a closure + val outer = f.get(obj) + // The outer pointer may be null if we have cleaned this closure before + if (outer != null) { + if (isClosure(f.getType)) { + return outer :: getOuterObjects(outer) + } else { + return outer :: Nil // Stop at the first $outer that is not a closure + } } } Nil } - private def getInnerClasses(obj: AnyRef): List[Class[_]] = { + /** + * Return a list of classes that represent closures enclosed in the given closure object. + */ + private def getInnerClosureClasses(obj: AnyRef): List[Class[_]] = { val seen = Set[Class[_]](obj.getClass) var stack = List[Class[_]](obj.getClass) while (!stack.isEmpty) { @@ -90,7 +104,7 @@ private[spark] object ClosureCleaner extends Logging { stack = cls :: stack } } - return (seen - obj.getClass).toList + (seen - obj.getClass).toList } private def createNullValue(cls: Class[_]): AnyRef = { @@ -101,21 +115,124 @@ private[spark] object ClosureCleaner extends Logging { } } - def clean(func: AnyRef, checkSerializable: Boolean = true) { + /** + * Clean the given closure in place. + * + * More specifically, this renders the given closure serializable as long as it does not + * explicitly reference unserializable objects. + * + * @param closure the closure to clean + * @param checkSerializable whether to verify that the closure is serializable after cleaning + * @param cleanTransitively whether to clean enclosing closures transitively + */ + def clean( + closure: AnyRef, + checkSerializable: Boolean = true, + cleanTransitively: Boolean = true): Unit = { + clean(closure, checkSerializable, cleanTransitively, Map.empty) + } + + /** + * Helper method to clean the given closure in place. + * + * The mechanism is to traverse the hierarchy of enclosing closures and null out any + * references along the way that are not actually used by the starting closure, but are + * nevertheless included in the compiled anonymous classes. Note that it is unsafe to + * simply mutate the enclosing closures in place, as other code paths may depend on them. + * Instead, we clone each enclosing closure and set the parent pointers accordingly. + * + * By default, closures are cleaned transitively. This means we detect whether enclosing + * objects are actually referenced by the starting one, either directly or transitively, + * and, if not, sever these closures from the hierarchy. In other words, in addition to + * nulling out unused field references, we also null out any parent pointers that refer + * to enclosing objects not actually needed by the starting closure. We determine + * transitivity by tracing through the tree of all methods ultimately invoked by the + * inner closure and record all the fields referenced in the process. + * + * For instance, transitive cleaning is necessary in the following scenario: + * + * class SomethingNotSerializable { + * def someValue = 1 + * def scope(name: String)(body: => Unit) = body + * def someMethod(): Unit = scope("one") { + * def x = someValue + * def y = 2 + * scope("two") { println(y + 1) } + * } + * } + * + * In this example, scope "two" is not serializable because it references scope "one", which + * references SomethingNotSerializable. Note that, however, the body of scope "two" does not + * actually depend on SomethingNotSerializable. This means we can safely null out the parent + * pointer of a cloned scope "one" and set it the parent of scope "two", such that scope "two" + * no longer references SomethingNotSerializable transitively. + * + * @param func the starting closure to clean + * @param checkSerializable whether to verify that the closure is serializable after cleaning + * @param cleanTransitively whether to clean enclosing closures transitively + * @param accessedFields a map from a class to a set of its fields that are accessed by + * the starting closure + */ + private def clean( + func: AnyRef, + checkSerializable: Boolean, + cleanTransitively: Boolean, + accessedFields: Map[Class[_], Set[String]]): Unit = { + + // TODO: clean all inner closures first. This requires us to find the inner objects. // TODO: cache outerClasses / innerClasses / accessedFields + + if (func == null) { + return + } + + logDebug(s"+++ Cleaning closure $func (${func.getClass.getName}}) +++") + + // A list of classes that represents closures enclosed in the given one + val innerClasses = getInnerClosureClasses(func) + + // A list of enclosing objects and their respective classes, from innermost to outermost + // An outer object at a given index is of type outer class at the same index val outerClasses = getOuterClasses(func) - val innerClasses = getInnerClasses(func) val outerObjects = getOuterObjects(func) - val accessedFields = Map[Class[_], Set[String]]() - + // For logging purposes only + val declaredFields = func.getClass.getDeclaredFields + val declaredMethods = func.getClass.getDeclaredMethods + + logDebug(" + declared fields: " + declaredFields.size) + declaredFields.foreach { f => logDebug(" " + f) } + logDebug(" + declared methods: " + declaredMethods.size) + declaredMethods.foreach { m => logDebug(" " + m) } + logDebug(" + inner classes: " + innerClasses.size) + innerClasses.foreach { c => logDebug(" " + c.getName) } + logDebug(" + outer classes: " + outerClasses.size) + outerClasses.foreach { c => logDebug(" " + c.getName) } + logDebug(" + outer objects: " + outerObjects.size) + outerObjects.foreach { o => logDebug(" " + o) } + + // Fail fast if we detect return statements in closures getClassReader(func.getClass).accept(new ReturnStatementFinder(), 0) - - for (cls <- outerClasses) - accessedFields(cls) = Set[String]() - for (cls <- func.getClass :: innerClasses) - getClassReader(cls).accept(new FieldAccessFinder(accessedFields), 0) - // logInfo("accessedFields: " + accessedFields) + + // If accessed fields is not populated yet, we assume that + // the closure we are trying to clean is the starting one + if (accessedFields.isEmpty) { + logDebug(s" + populating accessed fields because this is the starting closure") + // Initialize accessed fields with the outer classes first + // This step is needed to associate the fields to the correct classes later + for (cls <- outerClasses) { + accessedFields(cls) = Set[String]() + } + // Populate accessed fields by visiting all fields and methods accessed by this and + // all of its inner closures. If transitive cleaning is enabled, this may recursively + // visits methods that belong to other classes in search of transitively referenced fields. + for (cls <- func.getClass :: innerClasses) { + getClassReader(cls).accept(new FieldAccessFinder(accessedFields, cleanTransitively), 0) + } + } + + logDebug(s" + fields accessed by starting closure: " + accessedFields.size) + accessedFields.foreach { f => logDebug(" " + f) } val inInterpreter = { try { @@ -126,34 +243,68 @@ private[spark] object ClosureCleaner extends Logging { } } + // List of outer (class, object) pairs, ordered from outermost to innermost + // Note that all outer objects but the outermost one (first one in this list) must be closures var outerPairs: List[(Class[_], AnyRef)] = (outerClasses zip outerObjects).reverse - var outer: AnyRef = null + var parent: AnyRef = null if (outerPairs.size > 0 && !isClosure(outerPairs.head._1)) { // The closure is ultimately nested inside a class; keep the object of that // class without cloning it since we don't want to clone the user's objects. - outer = outerPairs.head._2 + // Note that we still need to keep around the outermost object itself because + // we need it to clone its child closure later (see below). + logDebug(s" + outermost object is not a closure, so do not clone it: ${outerPairs.head}") + parent = outerPairs.head._2 // e.g. SparkContext outerPairs = outerPairs.tail + } else if (outerPairs.size > 0) { + logDebug(s" + outermost object is a closure, so we just keep it: ${outerPairs.head}") + } else { + logDebug(" + there are no enclosing objects!") } + // Clone the closure objects themselves, nulling out any fields that are not // used in the closure we're working on or any of its inner closures. for ((cls, obj) <- outerPairs) { - outer = instantiateClass(cls, outer, inInterpreter) + logDebug(s" + cloning the object $obj of class ${cls.getName}") + // We null out these unused references by cloning each object and then filling in all + // required fields from the original object. We need the parent here because the Java + // language specification requires the first constructor parameter of any closure to be + // its enclosing object. + val clone = instantiateClass(cls, parent, inInterpreter) for (fieldName <- accessedFields(cls)) { val field = cls.getDeclaredField(fieldName) field.setAccessible(true) val value = field.get(obj) - // logInfo("1: Setting " + fieldName + " on " + cls + " to " + value); - field.set(outer, value) + field.set(clone, value) + } + // If transitive cleaning is enabled, we recursively clean any enclosing closure using + // the already populated accessed fields map of the starting closure + if (cleanTransitively && isClosure(clone.getClass)) { + logDebug(s" + cleaning cloned closure $clone recursively (${cls.getName})") + // No need to check serializable here for the outer closures because we're + // only interested in the serializability of the starting closure + clean(clone, checkSerializable = false, cleanTransitively, accessedFields) } + parent = clone } - if (outer != null) { - // logInfo("2: Setting $outer on " + func.getClass + " to " + outer); + // Update the parent pointer ($outer) of this closure + if (parent != null) { val field = func.getClass.getDeclaredField("$outer") field.setAccessible(true) - field.set(func, outer) + // If the starting closure doesn't actually need our enclosing object, then just null it out + if (accessedFields.contains(func.getClass) && + !accessedFields(func.getClass).contains("$outer")) { + logDebug(s" + the starting closure doesn't actually need $parent, so we null it out") + field.set(func, null) + } else { + // Update this closure's parent pointer to point to our enclosing object, + // which could either be a cloned closure or the original user object + field.set(func, parent) + } } - + + logDebug(s" +++ closure $func (${func.getClass.getName}) is now cleaned +++") + if (checkSerializable) { ensureSerializable(func) } @@ -167,15 +318,17 @@ private[spark] object ClosureCleaner extends Logging { } } - private def instantiateClass(cls: Class[_], outer: AnyRef, inInterpreter: Boolean): AnyRef = { - // logInfo("Creating a " + cls + " with outer = " + outer) + private def instantiateClass( + cls: Class[_], + enclosingObject: AnyRef, + inInterpreter: Boolean): AnyRef = { if (!inInterpreter) { // This is a bona fide closure class, whose constructor has no effects // other than to set its fields, so use its constructor val cons = cls.getConstructors()(0) val params = cons.getParameterTypes.map(createNullValue).toArray - if (outer != null) { - params(0) = outer // First param is always outer object + if (enclosingObject != null) { + params(0) = enclosingObject // First param is always enclosing object } return cons.newInstance(params: _*).asInstanceOf[AnyRef] } else { @@ -184,19 +337,17 @@ private[spark] object ClosureCleaner extends Logging { val parentCtor = classOf[java.lang.Object].getDeclaredConstructor() val newCtor = rf.newConstructorForSerialization(cls, parentCtor) val obj = newCtor.newInstance().asInstanceOf[AnyRef] - if (outer != null) { - // logInfo("3: Setting $outer on " + cls + " to " + outer); + if (enclosingObject != null) { val field = cls.getDeclaredField("$outer") field.setAccessible(true) - field.set(obj, outer) + field.set(obj, enclosingObject) } obj } } } -private[spark] -class ReturnStatementFinder extends ClassVisitor(ASM4) { +private class ReturnStatementFinder extends ClassVisitor(ASM4) { override def visitMethod(access: Int, name: String, desc: String, sig: String, exceptions: Array[String]): MethodVisitor = { if (name.contains("apply")) { @@ -213,26 +364,65 @@ class ReturnStatementFinder extends ClassVisitor(ASM4) { } } -private[spark] -class FieldAccessFinder(output: Map[Class[_], Set[String]]) extends ClassVisitor(ASM4) { - override def visitMethod(access: Int, name: String, desc: String, - sig: String, exceptions: Array[String]): MethodVisitor = { +/** Helper class to identify a method. */ +private case class MethodIdentifier[T](cls: Class[T], name: String, desc: String) + +/** + * Find the fields accessed by a given class. + * + * The resulting fields are stored in the mutable map passed in through the constructor. + * This map is assumed to have its keys already populated with the classes of interest. + * + * @param fields the mutable map that stores the fields to return + * @param findTransitively if true, find fields indirectly referenced through method calls + * @param specificMethod if not empty, visit only this specific method + * @param visitedMethods a set of visited methods to avoid cycles + */ +private[util] class FieldAccessFinder( + fields: Map[Class[_], Set[String]], + findTransitively: Boolean, + specificMethod: Option[MethodIdentifier[_]] = None, + visitedMethods: Set[MethodIdentifier[_]] = Set.empty) + extends ClassVisitor(ASM4) { + + override def visitMethod( + access: Int, + name: String, + desc: String, + sig: String, + exceptions: Array[String]): MethodVisitor = { + + // If we are told to visit only a certain method and this is not the one, ignore it + if (specificMethod.isDefined && + (specificMethod.get.name != name || specificMethod.get.desc != desc)) { + return null + } + new MethodVisitor(ASM4) { override def visitFieldInsn(op: Int, owner: String, name: String, desc: String) { if (op == GETFIELD) { - for (cl <- output.keys if cl.getName == owner.replace('/', '.')) { - output(cl) += name + for (cl <- fields.keys if cl.getName == owner.replace('/', '.')) { + fields(cl) += name } } } - override def visitMethodInsn(op: Int, owner: String, name: String, - desc: String) { - // Check for calls a getter method for a variable in an interpreter wrapper object. - // This means that the corresponding field will be accessed, so we should save it. - if (op == INVOKEVIRTUAL && owner.endsWith("$iwC") && !name.endsWith("$outer")) { - for (cl <- output.keys if cl.getName == owner.replace('/', '.')) { - output(cl) += name + override def visitMethodInsn(op: Int, owner: String, name: String, desc: String) { + for (cl <- fields.keys if cl.getName == owner.replace('/', '.')) { + // Check for calls a getter method for a variable in an interpreter wrapper object. + // This means that the corresponding field will be accessed, so we should save it. + if (op == INVOKEVIRTUAL && owner.endsWith("$iwC") && !name.endsWith("$outer")) { + fields(cl) += name + } + // Optionally visit other methods to find fields that are transitively referenced + if (findTransitively) { + val m = MethodIdentifier(cl, name, desc) + if (!visitedMethods.contains(m)) { + // Keep track of visited methods to avoid potential infinite cycles + visitedMethods += m + ClosureCleaner.getClassReader(cl).accept( + new FieldAccessFinder(fields, findTransitively, Some(m), visitedMethods), 0) + } } } } @@ -240,9 +430,14 @@ class FieldAccessFinder(output: Map[Class[_], Set[String]]) extends ClassVisitor } } -private[spark] class InnerClosureFinder(output: Set[Class[_]]) extends ClassVisitor(ASM4) { +private class InnerClosureFinder(output: Set[Class[_]]) extends ClassVisitor(ASM4) { var myName: String = null + // TODO: Recursively find inner closures that we indirectly reference, e.g. + // val closure1 = () = { () => 1 } + // val closure2 = () => { (1 to 5).map(closure1) } + // The second closure technically has two inner closures, but this finder only finds one + override def visit(version: Int, access: Int, name: String, sig: String, superName: String, interfaces: Array[String]) { myName = name diff --git a/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala index c47162779bbba..ff1bfe0774a2f 100644 --- a/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala @@ -50,7 +50,7 @@ class ClosureCleanerSuite extends FunSuite { val obj = new TestClassWithNesting(1) assert(obj.run() === 96) // 4 * (1+2+3+4) + 4 * (1+2+3+4) + 16 * 1 } - + test("toplevel return statements in closures are identified at cleaning time") { val ex = intercept[SparkException] { TestObjectWithBogusReturns.run() @@ -61,13 +61,20 @@ class ClosureCleanerSuite extends FunSuite { test("return statements from named functions nested in closures don't raise exceptions") { val result = TestObjectWithNestedReturns.run() - assert(result == 1) + assert(result === 1) } } // A non-serializable class we create in closures to make sure that we aren't // keeping references to unneeded variables from our outer closures. -class NonSerializable {} +class NonSerializable(val id: Int = -1) { + override def equals(other: Any): Boolean = { + other match { + case o: NonSerializable => id == o.id + case _ => false + } + } +} object TestObject { def run(): Int = { diff --git a/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite2.scala b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite2.scala new file mode 100644 index 0000000000000..59456790e89f0 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite2.scala @@ -0,0 +1,571 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.util + +import java.io.NotSerializableException + +import scala.collection.mutable + +import org.scalatest.{BeforeAndAfterAll, FunSuite, PrivateMethodTester} + +import org.apache.spark.{SparkContext, SparkException} +import org.apache.spark.serializer.SerializerInstance + +/** + * Another test suite for the closure cleaner that is finer-grained. + * For tests involving end-to-end Spark jobs, see {{ClosureCleanerSuite}}. + */ +class ClosureCleanerSuite2 extends FunSuite with BeforeAndAfterAll with PrivateMethodTester { + + // Start a SparkContext so that the closure serializer is accessible + // We do not actually use this explicitly otherwise + private var sc: SparkContext = null + private var closureSerializer: SerializerInstance = null + + override def beforeAll(): Unit = { + sc = new SparkContext("local", "test") + closureSerializer = sc.env.closureSerializer.newInstance() + } + + override def afterAll(): Unit = { + sc.stop() + sc = null + closureSerializer = null + } + + // Some fields and methods to reference in inner closures later + private val someSerializableValue = 1 + private val someNonSerializableValue = new NonSerializable + private def someSerializableMethod() = 1 + private def someNonSerializableMethod() = new NonSerializable + + /** Assert that the given closure is serializable (or not). */ + private def assertSerializable(closure: AnyRef, serializable: Boolean): Unit = { + if (serializable) { + closureSerializer.serialize(closure) + } else { + intercept[NotSerializableException] { + closureSerializer.serialize(closure) + } + } + } + + /** + * Helper method for testing whether closure cleaning works as expected. + * This cleans the given closure twice, with and without transitive cleaning. + * + * @param closure closure to test cleaning with + * @param serializableBefore if true, verify that the closure is serializable + * before cleaning, otherwise assert that it is not + * @param serializableAfter if true, assert that the closure is serializable + * after cleaning otherwise assert that it is not + */ + private def verifyCleaning( + closure: AnyRef, + serializableBefore: Boolean, + serializableAfter: Boolean): Unit = { + verifyCleaning(closure, serializableBefore, serializableAfter, transitive = true) + verifyCleaning(closure, serializableBefore, serializableAfter, transitive = false) + } + + /** Helper method for testing whether closure cleaning works as expected. */ + private def verifyCleaning( + closure: AnyRef, + serializableBefore: Boolean, + serializableAfter: Boolean, + transitive: Boolean): Unit = { + assertSerializable(closure, serializableBefore) + // If the resulting closure is not serializable even after + // cleaning, we expect ClosureCleaner to throw a SparkException + if (serializableAfter) { + ClosureCleaner.clean(closure, checkSerializable = true, transitive) + } else { + intercept[SparkException] { + ClosureCleaner.clean(closure, checkSerializable = true, transitive) + } + } + assertSerializable(closure, serializableAfter) + } + + /** + * Return the fields accessed by the given closure by class. + * This also optionally finds the fields transitively referenced through methods invocations. + */ + private def findAccessedFields( + closure: AnyRef, + outerClasses: Seq[Class[_]], + findTransitively: Boolean): Map[Class[_], Set[String]] = { + val fields = new mutable.HashMap[Class[_], mutable.Set[String]] + outerClasses.foreach { c => fields(c) = new mutable.HashSet[String] } + ClosureCleaner.getClassReader(closure.getClass) + .accept(new FieldAccessFinder(fields, findTransitively), 0) + fields.mapValues(_.toSet).toMap + } + + // Accessors for private methods + private val _isClosure = PrivateMethod[Boolean]('isClosure) + private val _getInnerClosureClasses = PrivateMethod[List[Class[_]]]('getInnerClosureClasses) + private val _getOuterClasses = PrivateMethod[List[Class[_]]]('getOuterClasses) + private val _getOuterObjects = PrivateMethod[List[AnyRef]]('getOuterObjects) + + private def isClosure(obj: AnyRef): Boolean = { + ClosureCleaner invokePrivate _isClosure(obj) + } + + private def getInnerClosureClasses(closure: AnyRef): List[Class[_]] = { + ClosureCleaner invokePrivate _getInnerClosureClasses(closure) + } + + private def getOuterClasses(closure: AnyRef): List[Class[_]] = { + ClosureCleaner invokePrivate _getOuterClasses(closure) + } + + private def getOuterObjects(closure: AnyRef): List[AnyRef] = { + ClosureCleaner invokePrivate _getOuterObjects(closure) + } + + test("get inner closure classes") { + val closure1 = () => 1 + val closure2 = () => { () => 1 } + val closure3 = (i: Int) => { + (1 to i).map { x => x + 1 }.filter { x => x > 5 } + } + val closure4 = (j: Int) => { + (1 to j).flatMap { x => + (1 to x).flatMap { y => + (1 to y).map { z => z + 1 } + } + } + } + val inner1 = getInnerClosureClasses(closure1) + val inner2 = getInnerClosureClasses(closure2) + val inner3 = getInnerClosureClasses(closure3) + val inner4 = getInnerClosureClasses(closure4) + assert(inner1.isEmpty) + assert(inner2.size === 1) + assert(inner3.size === 2) + assert(inner4.size === 3) + assert(inner2.forall(isClosure)) + assert(inner3.forall(isClosure)) + assert(inner4.forall(isClosure)) + } + + test("get outer classes and objects") { + val localValue = someSerializableValue + val closure1 = () => 1 + val closure2 = () => localValue + val closure3 = () => someSerializableValue + val closure4 = () => someSerializableMethod() + val outerClasses1 = getOuterClasses(closure1) + val outerClasses2 = getOuterClasses(closure2) + val outerClasses3 = getOuterClasses(closure3) + val outerClasses4 = getOuterClasses(closure4) + val outerObjects1 = getOuterObjects(closure1) + val outerObjects2 = getOuterObjects(closure2) + val outerObjects3 = getOuterObjects(closure3) + val outerObjects4 = getOuterObjects(closure4) + + // The classes and objects should have the same size + assert(outerClasses1.size === outerObjects1.size) + assert(outerClasses2.size === outerObjects2.size) + assert(outerClasses3.size === outerObjects3.size) + assert(outerClasses4.size === outerObjects4.size) + + // These do not have $outer pointers because they reference only local variables + assert(outerClasses1.isEmpty) + assert(outerClasses2.isEmpty) + + // These closures do have $outer pointers because they ultimately reference `this` + // The first $outer pointer refers to the closure defines this test (see FunSuite#test) + // The second $outer pointer refers to ClosureCleanerSuite2 + assert(outerClasses3.size === 2) + assert(outerClasses4.size === 2) + assert(isClosure(outerClasses3(0))) + assert(isClosure(outerClasses4(0))) + assert(outerClasses3(0) === outerClasses4(0)) // part of the same "FunSuite#test" scope + assert(outerClasses3(1) === this.getClass) + assert(outerClasses4(1) === this.getClass) + assert(outerObjects3(1) === this) + assert(outerObjects4(1) === this) + } + + test("get outer classes and objects with nesting") { + val localValue = someSerializableValue + + val test1 = () => { + val x = 1 + val closure1 = () => 1 + val closure2 = () => x + val outerClasses1 = getOuterClasses(closure1) + val outerClasses2 = getOuterClasses(closure2) + val outerObjects1 = getOuterObjects(closure1) + val outerObjects2 = getOuterObjects(closure2) + assert(outerClasses1.size === outerObjects1.size) + assert(outerClasses2.size === outerObjects2.size) + // These inner closures only reference local variables, and so do not have $outer pointers + assert(outerClasses1.isEmpty) + assert(outerClasses2.isEmpty) + } + + val test2 = () => { + def y = 1 + val closure1 = () => 1 + val closure2 = () => y + val closure3 = () => localValue + val outerClasses1 = getOuterClasses(closure1) + val outerClasses2 = getOuterClasses(closure2) + val outerClasses3 = getOuterClasses(closure3) + val outerObjects1 = getOuterObjects(closure1) + val outerObjects2 = getOuterObjects(closure2) + val outerObjects3 = getOuterObjects(closure3) + assert(outerClasses1.size === outerObjects1.size) + assert(outerClasses2.size === outerObjects2.size) + assert(outerClasses3.size === outerObjects3.size) + // Same as above, this closure only references local variables + assert(outerClasses1.isEmpty) + // This closure references the "test2" scope because it needs to find the method `y` + // Scope hierarchy: "test2" < "FunSuite#test" < ClosureCleanerSuite2 + assert(outerClasses2.size === 3) + // This closure references the "test2" scope because it needs to find the `localValue` + // defined outside of this scope + assert(outerClasses3.size === 3) + assert(isClosure(outerClasses2(0))) + assert(isClosure(outerClasses3(0))) + assert(isClosure(outerClasses2(1))) + assert(isClosure(outerClasses3(1))) + assert(outerClasses2(0) === outerClasses3(0)) // part of the same "test2" scope + assert(outerClasses2(1) === outerClasses3(1)) // part of the same "FunSuite#test" scope + assert(outerClasses2(2) === this.getClass) + assert(outerClasses3(2) === this.getClass) + assert(outerObjects2(2) === this) + assert(outerObjects3(2) === this) + } + + test1() + test2() + } + + test("find accessed fields") { + val localValue = someSerializableValue + val closure1 = () => 1 + val closure2 = () => localValue + val closure3 = () => someSerializableValue + val outerClasses1 = getOuterClasses(closure1) + val outerClasses2 = getOuterClasses(closure2) + val outerClasses3 = getOuterClasses(closure3) + + val fields1 = findAccessedFields(closure1, outerClasses1, findTransitively = false) + val fields2 = findAccessedFields(closure2, outerClasses2, findTransitively = false) + val fields3 = findAccessedFields(closure3, outerClasses3, findTransitively = false) + assert(fields1.isEmpty) + assert(fields2.isEmpty) + assert(fields3.size === 2) + // This corresponds to the "FunSuite#test" closure. This is empty because the + // `someSerializableValue` belongs to its parent (i.e. ClosureCleanerSuite2). + assert(fields3(outerClasses3(0)).isEmpty) + // This corresponds to the ClosureCleanerSuite2. This is also empty, however, + // because accessing a `ClosureCleanerSuite2#someSerializableValue` actually involves a + // method call. Since we do not find fields transitively, we will not recursively trace + // through the fields referenced by this method. + assert(fields3(outerClasses3(1)).isEmpty) + + val fields1t = findAccessedFields(closure1, outerClasses1, findTransitively = true) + val fields2t = findAccessedFields(closure2, outerClasses2, findTransitively = true) + val fields3t = findAccessedFields(closure3, outerClasses3, findTransitively = true) + assert(fields1t.isEmpty) + assert(fields2t.isEmpty) + assert(fields3t.size === 2) + // Because we find fields transitively now, we are able to detect that we need the + // $outer pointer to get the field from the ClosureCleanerSuite2 + assert(fields3t(outerClasses3(0)).size === 1) + assert(fields3t(outerClasses3(0)).head === "$outer") + assert(fields3t(outerClasses3(1)).size === 1) + assert(fields3t(outerClasses3(1)).head.contains("someSerializableValue")) + } + + test("find accessed fields with nesting") { + val localValue = someSerializableValue + + val test1 = () => { + def a = localValue + 1 + val closure1 = () => 1 + val closure2 = () => a + val closure3 = () => localValue + val closure4 = () => someSerializableValue + val outerClasses1 = getOuterClasses(closure1) + val outerClasses2 = getOuterClasses(closure2) + val outerClasses3 = getOuterClasses(closure3) + val outerClasses4 = getOuterClasses(closure4) + + // First, find only fields accessed directly, not transitively, by these closures + val fields1 = findAccessedFields(closure1, outerClasses1, findTransitively = false) + val fields2 = findAccessedFields(closure2, outerClasses2, findTransitively = false) + val fields3 = findAccessedFields(closure3, outerClasses3, findTransitively = false) + val fields4 = findAccessedFields(closure4, outerClasses4, findTransitively = false) + assert(fields1.isEmpty) + // Note that the size here represents the number of outer classes, not the number of fields + // "test1" < parameter of "FunSuite#test" < ClosureCleanerSuite2 + assert(fields2.size === 3) + // Since we do not find fields transitively here, we do not look into what `def a` references + assert(fields2(outerClasses2(0)).isEmpty) // This corresponds to the "test1" scope + assert(fields2(outerClasses2(1)).isEmpty) // This corresponds to the "FunSuite#test" scope + assert(fields2(outerClasses2(2)).isEmpty) // This corresponds to the ClosureCleanerSuite2 + assert(fields3.size === 3) + // Note that `localValue` is a field of the "test1" scope because `def a` references it, + // but NOT a field of the "FunSuite#test" scope because it is only a local variable there + assert(fields3(outerClasses3(0)).size === 1) + assert(fields3(outerClasses3(0)).head.contains("localValue")) + assert(fields3(outerClasses3(1)).isEmpty) + assert(fields3(outerClasses3(2)).isEmpty) + assert(fields4.size === 3) + // Because `val someSerializableValue` is an instance variable, even an explicit reference + // here actually involves a method call to access the underlying value of the variable. + // Because we are not finding fields transitively here, we do not consider the fields + // accessed by this "method" (i.e. the val's accessor). + assert(fields4(outerClasses4(0)).isEmpty) + assert(fields4(outerClasses4(1)).isEmpty) + assert(fields4(outerClasses4(2)).isEmpty) + + // Now do the same, but find fields that the closures transitively reference + val fields1t = findAccessedFields(closure1, outerClasses1, findTransitively = true) + val fields2t = findAccessedFields(closure2, outerClasses2, findTransitively = true) + val fields3t = findAccessedFields(closure3, outerClasses3, findTransitively = true) + val fields4t = findAccessedFields(closure4, outerClasses4, findTransitively = true) + assert(fields1t.isEmpty) + assert(fields2t.size === 3) + assert(fields2t(outerClasses2(0)).size === 1) // `def a` references `localValue` + assert(fields2t(outerClasses2(0)).head.contains("localValue")) + assert(fields2t(outerClasses2(1)).isEmpty) + assert(fields2t(outerClasses2(2)).isEmpty) + assert(fields3t.size === 3) + assert(fields3t(outerClasses3(0)).size === 1) // as before + assert(fields3t(outerClasses3(0)).head.contains("localValue")) + assert(fields3t(outerClasses3(1)).isEmpty) + assert(fields3t(outerClasses3(2)).isEmpty) + assert(fields4t.size === 3) + // Through a series of method calls, we are able to detect that we ultimately access + // ClosureCleanerSuite2's field `someSerializableValue`. Along the way, we also accessed + // a few $outer parent pointers to get to the outermost object. + assert(fields4t(outerClasses4(0)) === Set("$outer")) + assert(fields4t(outerClasses4(1)) === Set("$outer")) + assert(fields4t(outerClasses4(2)).size === 1) + assert(fields4t(outerClasses4(2)).head.contains("someSerializableValue")) + } + + test1() + } + + test("clean basic serializable closures") { + val localValue = someSerializableValue + val closure1 = () => 1 + val closure2 = () => Array[String]("a", "b", "c") + val closure3 = (s: String, arr: Array[Long]) => s + arr.mkString(", ") + val closure4 = () => localValue + val closure5 = () => new NonSerializable(5) // we're just serializing the class information + val closure1r = closure1() + val closure2r = closure2() + val closure3r = closure3("g", Array(1, 5, 8)) + val closure4r = closure4() + val closure5r = closure5() + + verifyCleaning(closure1, serializableBefore = true, serializableAfter = true) + verifyCleaning(closure2, serializableBefore = true, serializableAfter = true) + verifyCleaning(closure3, serializableBefore = true, serializableAfter = true) + verifyCleaning(closure4, serializableBefore = true, serializableAfter = true) + verifyCleaning(closure5, serializableBefore = true, serializableAfter = true) + + // Verify that closures can still be invoked and the result still the same + assert(closure1() === closure1r) + assert(closure2() === closure2r) + assert(closure3("g", Array(1, 5, 8)) === closure3r) + assert(closure4() === closure4r) + assert(closure5() === closure5r) + } + + test("clean basic non-serializable closures") { + val closure1 = () => this // ClosureCleanerSuite2 is not serializable + val closure5 = () => someSerializableValue + val closure3 = () => someSerializableMethod() + val closure4 = () => someNonSerializableValue + val closure2 = () => someNonSerializableMethod() + + // These are not cleanable because they ultimately reference the ClosureCleanerSuite2 + verifyCleaning(closure1, serializableBefore = false, serializableAfter = false) + verifyCleaning(closure2, serializableBefore = false, serializableAfter = false) + verifyCleaning(closure3, serializableBefore = false, serializableAfter = false) + verifyCleaning(closure4, serializableBefore = false, serializableAfter = false) + verifyCleaning(closure5, serializableBefore = false, serializableAfter = false) + } + + test("clean basic nested serializable closures") { + val localValue = someSerializableValue + val closure1 = (i: Int) => { + (1 to i).map { x => x + localValue } // 1 level of nesting + } + val closure2 = (j: Int) => { + (1 to j).flatMap { x => + (1 to x).map { y => y + localValue } // 2 levels + } + } + val closure3 = (k: Int, l: Int, m: Int) => { + (1 to k).flatMap(closure2) ++ // 4 levels + (1 to l).flatMap(closure1) ++ // 3 levels + (1 to m).map { x => x + 1 } // 2 levels + } + val closure1r = closure1(1) + val closure2r = closure2(2) + val closure3r = closure3(3, 4, 5) + + verifyCleaning(closure1, serializableBefore = true, serializableAfter = true) + verifyCleaning(closure2, serializableBefore = true, serializableAfter = true) + verifyCleaning(closure3, serializableBefore = true, serializableAfter = true) + + // Verify that closures can still be invoked and the result still the same + assert(closure1(1) === closure1r) + assert(closure2(2) === closure2r) + assert(closure3(3, 4, 5) === closure3r) + } + + test("clean basic nested non-serializable closures") { + def localSerializableMethod(): Int = someSerializableValue + val localNonSerializableValue = someNonSerializableValue + // These closures ultimately reference the ClosureCleanerSuite2 + // Note that even accessing `val` that is an instance variable involves a method call + val closure1 = (i: Int) => { (1 to i).map { x => x + someSerializableValue } } + val closure2 = (j: Int) => { (1 to j).map { x => x + someSerializableMethod() } } + val closure4 = (k: Int) => { (1 to k).map { x => x + localSerializableMethod() } } + // This closure references a local non-serializable value + val closure3 = (l: Int) => { (1 to l).map { x => localNonSerializableValue } } + // This is non-serializable no matter how many levels we nest it + val closure5 = (m: Int) => { + (1 to m).foreach { x => + (1 to x).foreach { y => + (1 to y).foreach { z => + someSerializableValue + } + } + } + } + + verifyCleaning(closure1, serializableBefore = false, serializableAfter = false) + verifyCleaning(closure2, serializableBefore = false, serializableAfter = false) + verifyCleaning(closure3, serializableBefore = false, serializableAfter = false) + verifyCleaning(closure4, serializableBefore = false, serializableAfter = false) + verifyCleaning(closure5, serializableBefore = false, serializableAfter = false) + } + + test("clean complicated nested serializable closures") { + val localValue = someSerializableValue + + // Here we assume that if the outer closure is serializable, + // then all inner closures must also be serializable + + // Reference local fields from all levels + val closure1 = (i: Int) => { + val a = 1 + (1 to i).flatMap { x => + val b = a + 1 + (1 to x).map { y => + y + a + b + localValue + } + } + } + + // Reference local fields and methods from all levels within the outermost closure + val closure2 = (i: Int) => { + val a1 = 1 + def a2 = 2 + (1 to i).flatMap { x => + val b1 = a1 + 1 + def b2 = a2 + 1 + (1 to x).map { y => + // If this references a method outside the outermost closure, then it will try to pull + // in the ClosureCleanerSuite2. This is why `localValue` here must be a local `val`. + y + a1 + a2 + b1 + b2 + localValue + } + } + } + + val closure1r = closure1(1) + val closure2r = closure2(2) + verifyCleaning(closure1, serializableBefore = true, serializableAfter = true) + verifyCleaning(closure2, serializableBefore = true, serializableAfter = true) + assert(closure1(1) == closure1r) + assert(closure2(2) == closure2r) + } + + test("clean complicated nested non-serializable closures") { + val localValue = someSerializableValue + + // Note that we are not interested in cleaning the outer closures here (they are not cleanable) + // The only reason why they exist is to nest the inner closures + + val test1 = () => { + val a = localValue + val b = sc + val inner1 = (x: Int) => x + a + b.hashCode() + val inner2 = (x: Int) => x + a + + // This closure explicitly references a non-serializable field + // There is no way to clean it + verifyCleaning(inner1, serializableBefore = false, serializableAfter = false) + + // This closure is serializable to begin with since it does not need a pointer to + // the outer closure (it only references local variables) + verifyCleaning(inner2, serializableBefore = true, serializableAfter = true) + } + + // Same as above, but the `val a` becomes `def a` + // The difference here is that all inner closures now have pointers to the outer closure + val test2 = () => { + def a = localValue + val b = sc + val inner1 = (x: Int) => x + a + b.hashCode() + val inner2 = (x: Int) => x + a + + // As before, this closure is neither serializable nor cleanable + verifyCleaning(inner1, serializableBefore = false, serializableAfter = false) + + // This closure is no longer serializable because it now has a pointer to the outer closure, + // which is itself not serializable because it has a pointer to the ClosureCleanerSuite2. + // If we do not clean transitively, we will not null out this indirect reference. + verifyCleaning( + inner2, serializableBefore = false, serializableAfter = false, transitive = false) + + // If we clean transitively, we will find that method `a` does not actually reference the + // outer closure's parent (i.e. the ClosureCleanerSuite), so we can additionally null out + // the outer closure's parent pointer. This will make `inner2` serializable. + verifyCleaning( + inner2, serializableBefore = false, serializableAfter = true, transitive = true) + } + + // Same as above, but with more levels of nesting + val test3 = () => { () => test1() } + val test4 = () => { () => test2() } + val test5 = () => { () => { () => test3() } } + val test6 = () => { () => { () => test4() } } + + test1() + test2() + test3()() + test4()() + test5()()() + test6()()() + } + +} From ecc6eb50a59172dc132bd8f97957734f6f009024 Mon Sep 17 00:00:00 2001 From: Tathagata Das Date: Sat, 2 May 2015 01:53:14 -0700 Subject: [PATCH 547/591] [SPARK-7315] [STREAMING] [TEST] Fix flaky WALBackedBlockRDDSuite `FileUtils.getTempDirectoryPath()` path may or may not exist. We want to make sure that it does not exist. Author: Tathagata Das Closes #5853 from tdas/SPARK-7315 and squashes the following commits: 141afd5 [Tathagata Das] Removed use of FileUtils b08d4f1 [Tathagata Das] Fix flaky WALBackedBlockRDDSuite --- .../streaming/rdd/WriteAheadLogBackedBlockRDD.scala | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala index ebdf418f4ab6a..f4c8046e8a1a8 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala @@ -16,13 +16,13 @@ */ package org.apache.spark.streaming.rdd +import java.io.File import java.nio.ByteBuffer +import java.util.UUID import scala.reflect.ClassTag import scala.util.control.NonFatal -import org.apache.commons.io.FileUtils - import org.apache.spark._ import org.apache.spark.rdd.BlockRDD import org.apache.spark.storage.{BlockId, StorageLevel} @@ -108,9 +108,13 @@ class WriteAheadLogBackedBlockRDD[T: ClassTag]( // writing log data. However, the directory is not needed if data needs to be read, hence // a dummy path is provided to satisfy the method parameter requirements. // FileBasedWriteAheadLog will not create any file or directory at that path. - val dummyDirectory = FileUtils.getTempDirectoryPath() + // FileBasedWriteAheadLog will not create any file or directory at that path. Also, + // this dummy directory should not already exist otherwise the WAL will try to recover + // past events from the directory and throw errors. + val nonExistentDirectory = new File( + System.getProperty("java.io.tmpdir"), UUID.randomUUID().toString).getAbsolutePath writeAheadLog = WriteAheadLogUtils.createLogForReceiver( - SparkEnv.get.conf, dummyDirectory, hadoopConf) + SparkEnv.get.conf, nonExistentDirectory, hadoopConf) dataRead = writeAheadLog.read(partition.walRecordHandle) } catch { case NonFatal(e) => From 856a571ef4f0338ccefb3b792e1e96b8f15a0884 Mon Sep 17 00:00:00 2001 From: Dean Chen Date: Sat, 2 May 2015 23:04:13 +0100 Subject: [PATCH 548/591] [SPARK-3444] Fix typo in Dataframes.py introduced in [] Author: Dean Chen Closes #5866 from deanchen/patch-1 and squashes the following commits: 0028bc4 [Dean Chen] Fix typo in Dataframes.py introduced in [SPARK-3444] --- python/pyspark/sql/dataframe.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index e9fd17ed4ce94..8ddcff8fcdf98 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -1,6 +1,6 @@ # # Licensed to the Apache Software Foundation (ASF) under one or more -# contir[butor license agreements. See the NOTICE file distributed with +# contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with From da303526e54e9a0adfedb49417f383cde7870a69 Mon Sep 17 00:00:00 2001 From: Mridul Muralidharan Date: Sat, 2 May 2015 23:05:51 +0100 Subject: [PATCH 549/591] [SPARK-7323] [SPARK CORE] Use insertAll instead of insert while merging combiners in reducer Author: Mridul Muralidharan Closes #5862 from mridulm/optimize_aggregator and squashes the following commits: 61cf43a [Mridul Muralidharan] Use insertAll instead of insert - much more expensive to do it per tuple --- core/src/main/scala/org/apache/spark/Aggregator.scala | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/Aggregator.scala b/core/src/main/scala/org/apache/spark/Aggregator.scala index 3b684bbeceaf2..af9765d313e9e 100644 --- a/core/src/main/scala/org/apache/spark/Aggregator.scala +++ b/core/src/main/scala/org/apache/spark/Aggregator.scala @@ -88,10 +88,7 @@ case class Aggregator[K, V, C] ( combiners.iterator } else { val combiners = new ExternalAppendOnlyMap[K, C, C](identity, mergeCombiners, mergeCombiners) - while (iter.hasNext) { - val pair = iter.next() - combiners.insert(pair._1, pair._2) - } + combiners.insertAll(iter) // Update task metrics if context is not null // TODO: Make context non-optional in a future release Option(context).foreach { c => From bfcd528d6f5a5ebe61e0fcca890143e9a3c7f7f9 Mon Sep 17 00:00:00 2001 From: Ye Xianjin Date: Sat, 2 May 2015 23:08:09 +0100 Subject: [PATCH 550/591] [SPARK-6030] [CORE] Using simulated field layout method to compute class shellSize SizeEstimator gives wrong result for Integer on 64bit JVM with UseCompressedOops on, this pr fixes that. For more details, please refer [SPARK-6030](https://issues.apache.org/jira/browse/SPARK-6030) sryza, I noticed there is a pr to expose SizeEstimator, maybe that should be waited by this pr get merged if we confirm this problem. And shivaram would you mind to review this pr since you contribute related code. Also cc to srowen and mateiz Author: Ye Xianjin Closes #4783 from advancedxy/SPARK-6030 and squashes the following commits: c4dcb41 [Ye Xianjin] Add super.beforeEach in the beforeEach method to make the trait stackable.. Remove useless leading whitespace. 3f80640 [Ye Xianjin] The size of Integer class changes from 24 to 16 on a 64-bit JVM with -UseCompressedOops flag on after the fix. I don't how 100000 was originally calculated, It looks like 100000 is the magic number which makes sure spilling. Because of the size change, It fails because there is no spilling at all. Change the number to a slightly larger number fixes that. e849d2d [Ye Xianjin] Merge two shellSize assignments into one. Add some explanation to alignSizeUp method. 85a0b51 [Ye Xianjin] Fix typos and update wording in comments. Using alignSizeUp to compute alignSize. d27eb77 [Ye Xianjin] Add some detailed comments in the code. Add some test cases. It's very difficult to design test cases as the final object alignment will hide a lot of filed layout details if we just considering the whole size. 842aed1 [Ye Xianjin] primitiveSize(cls) can just return Int. Use a simplified class field layout method to calculate class instance size. Will add more documents and test cases. Add a new alignSizeUp function which uses bitwise operators to speedup. 62e8ab4 [Ye Xianjin] Don't alignSize for objects' shellSize, alignSize when added to state.size. Add some primitive wrapper objects size tests. --- .../org/apache/spark/util/SizeEstimator.scala | 61 ++++++++++++++++--- .../spark/util/SizeEstimatorSuite.scala | 47 +++++++++++++- .../util/collection/ExternalSorterSuite.scala | 10 +-- 3 files changed, 100 insertions(+), 18 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala b/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala index 4dd7ab9e0767b..d91c3294ddb8b 100644 --- a/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala +++ b/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala @@ -47,6 +47,11 @@ private[spark] object SizeEstimator extends Logging { private val FLOAT_SIZE = 4 private val DOUBLE_SIZE = 8 + // Fields can be primitive types, sizes are: 1, 2, 4, 8. Or fields can be pointers. The size of + // a pointer is 4 or 8 depending on the JVM (32-bit or 64-bit) and UseCompressedOops flag. + // The sizes should be in descending order, as we will use that information for fields placement. + private val fieldSizes = List(8, 4, 2, 1) + // Alignment boundary for objects // TODO: Is this arch dependent ? private val ALIGN_SIZE = 8 @@ -171,7 +176,7 @@ private[spark] object SizeEstimator extends Logging { // general all ClassLoaders and Classes will be shared between objects anyway. } else { val classInfo = getClassInfo(cls) - state.size += classInfo.shellSize + state.size += alignSize(classInfo.shellSize) for (field <- classInfo.pointerFields) { state.enqueue(field.get(obj)) } @@ -237,8 +242,8 @@ private[spark] object SizeEstimator extends Logging { } size } - - private def primitiveSize(cls: Class[_]): Long = { + + private def primitiveSize(cls: Class[_]): Int = { if (cls == classOf[Byte]) { BYTE_SIZE } else if (cls == classOf[Boolean]) { @@ -274,21 +279,50 @@ private[spark] object SizeEstimator extends Logging { val parent = getClassInfo(cls.getSuperclass) var shellSize = parent.shellSize var pointerFields = parent.pointerFields + val sizeCount = Array.fill(fieldSizes.max + 1)(0) + // iterate through the fields of this class and gather information. for (field <- cls.getDeclaredFields) { if (!Modifier.isStatic(field.getModifiers)) { val fieldClass = field.getType if (fieldClass.isPrimitive) { - shellSize += primitiveSize(fieldClass) + sizeCount(primitiveSize(fieldClass)) += 1 } else { field.setAccessible(true) // Enable future get()'s on this field - shellSize += pointerSize + sizeCount(pointerSize) += 1 pointerFields = field :: pointerFields } } } - shellSize = alignSize(shellSize) + // Based on the simulated field layout code in Aleksey Shipilev's report: + // http://cr.openjdk.java.net/~shade/papers/2013-shipilev-fieldlayout-latest.pdf + // The code is in Figure 9. + // The simplified idea of field layout consists of 4 parts (see more details in the report): + // + // 1. field alignment: HotSpot lays out the fields aligned by their size. + // 2. object alignment: HotSpot rounds instance size up to 8 bytes + // 3. consistent fields layouts throughout the hierarchy: This means we should layout + // superclass first. And we can use superclass's shellSize as a starting point to layout the + // other fields in this class. + // 4. class alignment: HotSpot rounds field blocks up to to HeapOopSize not 4 bytes, confirmed + // with Aleksey. see https://bugs.openjdk.java.net/browse/CODETOOLS-7901322 + // + // The real world field layout is much more complicated. There are three kinds of fields + // order in Java 8. And we don't consider the @contended annotation introduced by Java 8. + // see the HotSpot classloader code, layout_fields method for more details. + // hg.openjdk.java.net/jdk8/jdk8/hotspot/file/tip/src/share/vm/classfile/classFileParser.cpp + var alignedSize = shellSize + for (size <- fieldSizes if sizeCount(size) > 0) { + val count = sizeCount(size) + // If there are internal gaps, smaller field can fit in. + alignedSize = math.max(alignedSize, alignSizeUp(shellSize, size) + size * count) + shellSize += size * count + } + + // Should choose a larger size to be new shellSize and clearly alignedSize >= shellSize, and + // round up the instance filed blocks + shellSize = alignSizeUp(alignedSize, pointerSize) // Create and cache a new ClassInfo val newInfo = new ClassInfo(shellSize, pointerFields) @@ -296,8 +330,15 @@ private[spark] object SizeEstimator extends Logging { newInfo } - private def alignSize(size: Long): Long = { - val rem = size % ALIGN_SIZE - if (rem == 0) size else (size + ALIGN_SIZE - rem) - } + private def alignSize(size: Long): Long = alignSizeUp(size, ALIGN_SIZE) + + /** + * Compute aligned size. The alignSize must be 2^n, otherwise the result will be wrong. + * When alignSize = 2^n, alignSize - 1 = 2^n - 1. The binary representation of (alignSize - 1) + * will only have n trailing 1s(0b00...001..1). ~(alignSize - 1) will be 0b11..110..0. Hence, + * (size + alignSize - 1) & ~(alignSize - 1) will set the last n bits to zeros, which leads to + * multiple of alignSize. + */ + private def alignSizeUp(size: Long, alignSize: Int): Long = + (size + alignSize - 1) & ~(alignSize - 1) } diff --git a/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala b/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala index 28915bd53354e..133a76f28e000 100644 --- a/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala @@ -36,6 +36,15 @@ class DummyClass4(val d: DummyClass3) { val x: Int = 0 } +// dummy class to show class field blocks alignment. +class DummyClass5 extends DummyClass1 { + val x: Boolean = true +} + +class DummyClass6 extends DummyClass5 { + val y: Boolean = true +} + object DummyString { def apply(str: String) : DummyString = new DummyString(str.toArray) } @@ -50,6 +59,7 @@ class SizeEstimatorSuite override def beforeEach() { // Set the arch to 64-bit and compressedOops to true to get a deterministic test-case + super.beforeEach() System.setProperty("os.arch", "amd64") System.setProperty("spark.test.useCompressedOops", "true") } @@ -62,6 +72,22 @@ class SizeEstimatorSuite assertResult(48)(SizeEstimator.estimate(new DummyClass4(new DummyClass3))) } + test("primitive wrapper objects") { + assertResult(16)(SizeEstimator.estimate(new java.lang.Boolean(true))) + assertResult(16)(SizeEstimator.estimate(new java.lang.Byte("1"))) + assertResult(16)(SizeEstimator.estimate(new java.lang.Character('1'))) + assertResult(16)(SizeEstimator.estimate(new java.lang.Short("1"))) + assertResult(16)(SizeEstimator.estimate(new java.lang.Integer(1))) + assertResult(24)(SizeEstimator.estimate(new java.lang.Long(1))) + assertResult(16)(SizeEstimator.estimate(new java.lang.Float(1.0))) + assertResult(24)(SizeEstimator.estimate(new java.lang.Double(1.0d))) + } + + test("class field blocks rounding") { + assertResult(16)(SizeEstimator.estimate(new DummyClass5)) + assertResult(24)(SizeEstimator.estimate(new DummyClass6)) + } + // NOTE: The String class definition varies across JDK versions (1.6 vs. 1.7) and vendors // (Sun vs IBM). Use a DummyString class to make tests deterministic. test("strings") { @@ -102,18 +128,18 @@ class SizeEstimatorSuite val arr = new Array[Char](100000) assertResult(200016)(SizeEstimator.estimate(arr)) assertResult(480032)(SizeEstimator.estimate(Array.fill(10000)(new DummyString(arr)))) - + val buf = new ArrayBuffer[DummyString]() for (i <- 0 until 5000) { buf.append(new DummyString(new Array[Char](10))) } assertResult(340016)(SizeEstimator.estimate(buf.toArray)) - + for (i <- 0 until 5000) { buf.append(new DummyString(arr)) } assertResult(683912)(SizeEstimator.estimate(buf.toArray)) - + // If an array contains the *same* element many times, we should only count it once. val d1 = new DummyClass1 // 10 pointers plus 8-byte object @@ -155,5 +181,20 @@ class SizeEstimatorSuite assertResult(64)(SizeEstimator.estimate(DummyString("a"))) assertResult(64)(SizeEstimator.estimate(DummyString("ab"))) assertResult(72)(SizeEstimator.estimate(DummyString("abcdefgh"))) + + // primitive wrapper classes + assertResult(24)(SizeEstimator.estimate(new java.lang.Boolean(true))) + assertResult(24)(SizeEstimator.estimate(new java.lang.Byte("1"))) + assertResult(24)(SizeEstimator.estimate(new java.lang.Character('1'))) + assertResult(24)(SizeEstimator.estimate(new java.lang.Short("1"))) + assertResult(24)(SizeEstimator.estimate(new java.lang.Integer(1))) + assertResult(24)(SizeEstimator.estimate(new java.lang.Long(1))) + assertResult(24)(SizeEstimator.estimate(new java.lang.Float(1.0))) + assertResult(24)(SizeEstimator.estimate(new java.lang.Double(1.0d))) + } + + test("class field blocks rounding on 64-bit VM without useCompressedOops") { + assertResult(24)(SizeEstimator.estimate(new DummyClass5)) + assertResult(32)(SizeEstimator.estimate(new DummyClass6)) } } diff --git a/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala index 20fd22b78ef5d..7a98723bc6472 100644 --- a/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala @@ -377,7 +377,7 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe val sorter = new ExternalSorter[Int, Int, Int]( None, Some(new HashPartitioner(3)), Some(ord), None) assertDidNotBypassMergeSort(sorter) - sorter.insertAll((0 until 100000).iterator.map(i => (i, i))) + sorter.insertAll((0 until 120000).iterator.map(i => (i, i))) assert(diskBlockManager.getAllFiles().length > 0) sorter.stop() assert(diskBlockManager.getAllBlocks().length === 0) @@ -385,9 +385,9 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe val sorter2 = new ExternalSorter[Int, Int, Int]( None, Some(new HashPartitioner(3)), Some(ord), None) assertDidNotBypassMergeSort(sorter2) - sorter2.insertAll((0 until 100000).iterator.map(i => (i, i))) + sorter2.insertAll((0 until 120000).iterator.map(i => (i, i))) assert(diskBlockManager.getAllFiles().length > 0) - assert(sorter2.iterator.toSet === (0 until 100000).map(i => (i, i)).toSet) + assert(sorter2.iterator.toSet === (0 until 120000).map(i => (i, i)).toSet) sorter2.stop() assert(diskBlockManager.getAllBlocks().length === 0) } @@ -428,8 +428,8 @@ class ExternalSorterSuite extends FunSuite with LocalSparkContext with PrivateMe None, Some(new HashPartitioner(3)), Some(ord), None) assertDidNotBypassMergeSort(sorter) intercept[SparkException] { - sorter.insertAll((0 until 100000).iterator.map(i => { - if (i == 99990) { + sorter.insertAll((0 until 120000).iterator.map(i => { + if (i == 119990) { throw new SparkException("Intentional failure") } (i, i) From 82c8c37c098e5886da65cea3108737744e270b91 Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Sat, 2 May 2015 23:10:35 +0100 Subject: [PATCH 551/591] [MINOR] [HIVE] Fix QueryPartitionSuite. At least in the version of Hive I tested on, the test was deleting a temp directory generated by Hive instead of one containing partition data. So fix the filter to only consider partition directories when deciding what to delete. Author: Marcelo Vanzin Closes #5854 from vanzin/hive-test-fix and squashes the following commits: 7594ae9 [Marcelo Vanzin] Fix typo. 729fa80 [Marcelo Vanzin] [minor] [hive] Fix QueryPartitionSuite. --- .../org/apache/spark/sql/hive/QueryPartitionSuite.scala | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala index a787fa5546e76..4990092df6a99 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala @@ -28,7 +28,7 @@ import org.apache.spark.util.Utils class QueryPartitionSuite extends QueryTest { import org.apache.spark.sql.hive.test.TestHive.implicits._ - test("SPARK-5068: query data when path doesn't exists"){ + test("SPARK-5068: query data when path doesn't exist"){ val testData = TestHive.sparkContext.parallelize( (1 to 10).map(i => TestData(i, i.toString))).toDF() testData.registerTempTable("testData") @@ -52,8 +52,9 @@ class QueryPartitionSuite extends QueryTest { ++ testData.toSchemaRDD.collect ++ testData.toSchemaRDD.collect) // delete the path of one partition - val folders = tmpDir.listFiles.filter(_.isDirectory) - Utils.deleteRecursively(folders(0)) + tmpDir.listFiles + .find { f => f.isDirectory && f.getName().startsWith("ds=") } + .foreach { f => Utils.deleteRecursively(f) } // test for after delete the path checkAnswer(sql("select key,value from table_with_partition"), From 5d6b90d939d281130c786be38fd1794c74391b08 Mon Sep 17 00:00:00 2001 From: Cheng Hao Date: Sat, 2 May 2015 15:20:07 -0700 Subject: [PATCH 552/591] [SPARK-5213] [SQL] Pluggable SQL Parser Support based on #4015, we should not delete `sqlParser` from sqlcontext, that leads to mima failed. Users implement dialect to give a fallback for `sqlParser` and we should construct `sqlParser` in sqlcontext according to the dialect `protected[sql] val sqlParser = new SparkSQLParser(getSQLDialect().parse(_))` Author: Cheng Hao Author: scwf Closes #5827 from scwf/sqlparser1 and squashes the following commits: 81b9737 [scwf] comment fix 0878bd1 [scwf] remove comments c19780b [scwf] fix mima tests c2895cf [scwf] Merge branch 'master' of https://github.com/apache/spark into sqlparser1 493775c [Cheng Hao] update the code as feedback 81a731f [Cheng Hao] remove the unecessary comment aab0b0b [Cheng Hao] polish the code a little bit 49b9d81 [Cheng Hao] shrink the comment for rebasing --- .../sql/catalyst/AbstractSparkSQLParser.scala | 11 ++- .../apache/spark/sql/catalyst/Dialect.scala | 33 ++++++++ .../spark/sql/catalyst/errors/package.scala | 2 + .../org/apache/spark/sql/SQLContext.scala | 76 ++++++++++++++++--- .../org/apache/spark/sql/sources/ddl.scala | 6 +- .../org/apache/spark/sql/SQLQuerySuite.scala | 22 ++++++ .../apache/spark/sql/hive/HiveContext.scala | 41 ++++++---- .../apache/spark/sql/hive/test/TestHive.scala | 5 +- .../sql/hive/execution/SQLQuerySuite.scala | 39 +++++++++- 9 files changed, 196 insertions(+), 39 deletions(-) create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/Dialect.scala diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala index 1f3c02478bd68..2eb3e167baad5 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala @@ -25,10 +25,6 @@ import scala.util.parsing.input.CharArrayReader.EofCh import org.apache.spark.sql.catalyst.plans.logical._ -private[sql] object KeywordNormalizer { - def apply(str: String): String = str.toLowerCase() -} - private[sql] abstract class AbstractSparkSQLParser extends StandardTokenParsers with PackratParsers { @@ -42,7 +38,7 @@ private[sql] abstract class AbstractSparkSQLParser } protected case class Keyword(str: String) { - def normalize: String = KeywordNormalizer(str) + def normalize: String = lexical.normalizeKeyword(str) def parser: Parser[String] = normalize } @@ -90,13 +86,16 @@ class SqlLexical extends StdLexical { reserved ++= keywords } + /* Normal the keyword string */ + def normalizeKeyword(str: String): String = str.toLowerCase + delimiters += ( "@", "*", "+", "-", "<", "=", "<>", "!=", "<=", ">=", ">", "/", "(", ")", ",", ";", "%", "{", "}", ":", "[", "]", ".", "&", "|", "^", "~", "<=>" ) protected override def processIdent(name: String) = { - val token = KeywordNormalizer(name) + val token = normalizeKeyword(name) if (reserved contains token) Keyword(token) else Identifier(name) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/Dialect.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/Dialect.scala new file mode 100644 index 0000000000000..977003493d471 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/Dialect.scala @@ -0,0 +1,33 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan + +/** + * Root class of SQL Parser Dialect, and we don't guarantee the binary + * compatibility for the future release, let's keep it as the internal + * interface for advanced user. + * + */ +@DeveloperApi +abstract class Dialect { + // this is the main function that will be implemented by sql parser. + def parse(sqlText: String): LogicalPlan +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/errors/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/errors/package.scala index bdeb660b1ecb7..0fd4f9b374ee0 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/errors/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/errors/package.scala @@ -38,6 +38,8 @@ package object errors { } } + class DialectException(msg: String, cause: Throwable) extends Exception(msg, cause) + /** * Wraps any exceptions that are thrown while executing `f` in a * [[catalyst.errors.TreeNodeException TreeNodeException]], attaching the provided `tree`. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala index 5116fcefd4bf2..7eabb93c1e3d6 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala @@ -24,6 +24,7 @@ import scala.collection.JavaConversions._ import scala.collection.immutable import scala.language.implicitConversions import scala.reflect.runtime.universe.TypeTag +import scala.util.control.NonFatal import com.google.common.reflect.TypeToken @@ -32,9 +33,11 @@ import org.apache.spark.api.java.{JavaRDD, JavaSparkContext} import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.analysis._ import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.errors.DialectException import org.apache.spark.sql.catalyst.optimizer.{DefaultOptimizer, Optimizer} import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan} import org.apache.spark.sql.catalyst.rules.RuleExecutor +import org.apache.spark.sql.catalyst.Dialect import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, expressions} import org.apache.spark.sql.execution.{Filter, _} import org.apache.spark.sql.jdbc.{JDBCPartition, JDBCPartitioningInfo, JDBCRelation} @@ -44,6 +47,42 @@ import org.apache.spark.sql.types._ import org.apache.spark.util.Utils import org.apache.spark.{Partition, SparkContext} +/** + * Currently we support the default dialect named "sql", associated with the class + * [[DefaultDialect]] + * + * And we can also provide custom SQL Dialect, for example in Spark SQL CLI: + * {{{ + *-- switch to "hiveql" dialect + * spark-sql>SET spark.sql.dialect=hiveql; + * spark-sql>SELECT * FROM src LIMIT 1; + * + *-- switch to "sql" dialect + * spark-sql>SET spark.sql.dialect=sql; + * spark-sql>SELECT * FROM src LIMIT 1; + * + *-- register the new SQL dialect + * spark-sql> SET spark.sql.dialect=com.xxx.xxx.SQL99Dialect; + * spark-sql> SELECT * FROM src LIMIT 1; + * + *-- register the non-exist SQL dialect + * spark-sql> SET spark.sql.dialect=NotExistedClass; + * spark-sql> SELECT * FROM src LIMIT 1; + * + *-- Exception will be thrown and switch to dialect + *-- "sql" (for SQLContext) or + *-- "hiveql" (for HiveContext) + * }}} + */ +private[spark] class DefaultDialect extends Dialect { + @transient + protected val sqlParser = new catalyst.SqlParser + + override def parse(sqlText: String): LogicalPlan = { + sqlParser.parse(sqlText) + } +} + /** * The entry point for working with structured data (rows and columns) in Spark. Allows the * creation of [[DataFrame]] objects as well as the execution of SQL queries. @@ -135,14 +174,27 @@ class SQLContext(@transient val sparkContext: SparkContext) protected[sql] val ddlParser = new DDLParser(sqlParser.parse(_)) @transient - protected[sql] val sqlParser = { - val fallback = new catalyst.SqlParser - new SparkSQLParser(fallback.parse(_)) + protected[sql] val sqlParser = new SparkSQLParser(getSQLDialect().parse(_)) + + protected[sql] def getSQLDialect(): Dialect = { + try { + val clazz = Utils.classForName(dialectClassName) + clazz.newInstance().asInstanceOf[Dialect] + } catch { + case NonFatal(e) => + // Since we didn't find the available SQL Dialect, it will fail even for SET command: + // SET spark.sql.dialect=sql; Let's reset as default dialect automatically. + val dialect = conf.dialect + // reset the sql dialect + conf.unsetConf(SQLConf.DIALECT) + // throw out the exception, and the default sql dialect will take effect for next query. + throw new DialectException( + s"""Instantiating dialect '$dialect' failed. + |Reverting to default dialect '${conf.dialect}'""".stripMargin, e) + } } - protected[sql] def parseSql(sql: String): LogicalPlan = { - ddlParser.parse(sql, false).getOrElse(sqlParser.parse(sql)) - } + protected[sql] def parseSql(sql: String): LogicalPlan = ddlParser.parse(sql, false) protected[sql] def executeSql(sql: String): this.QueryExecution = executePlan(parseSql(sql)) @@ -156,6 +208,12 @@ class SQLContext(@transient val sparkContext: SparkContext) @transient protected[sql] val defaultSession = createSession() + protected[sql] def dialectClassName = if (conf.dialect == "sql") { + classOf[DefaultDialect].getCanonicalName + } else { + conf.dialect + } + sparkContext.getConf.getAll.foreach { case (key, value) if key.startsWith("spark.sql") => setConf(key, value) case _ => @@ -931,11 +989,7 @@ class SQLContext(@transient val sparkContext: SparkContext) * @group basic */ def sql(sqlText: String): DataFrame = { - if (conf.dialect == "sql") { - DataFrame(this, parseSql(sqlText)) - } else { - sys.error(s"Unsupported SQL dialect: ${conf.dialect}") - } + DataFrame(this, parseSql(sqlText)) } /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala index e7a0685e013d8..1abf3aa51cb25 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala @@ -38,12 +38,12 @@ private[sql] class DDLParser( parseQuery: String => LogicalPlan) extends AbstractSparkSQLParser with DataTypeParser with Logging { - def parse(input: String, exceptionOnError: Boolean): Option[LogicalPlan] = { + def parse(input: String, exceptionOnError: Boolean): LogicalPlan = { try { - Some(parse(input)) + parse(input) } catch { case ddlException: DDLException => throw ddlException - case _ if !exceptionOnError => None + case _ if !exceptionOnError => parseQuery(input) case x: Throwable => throw x } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala index d8e7cdbd3a94e..0ab8558c1db13 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala @@ -19,13 +19,18 @@ package org.apache.spark.sql import org.scalatest.BeforeAndAfterAll +import org.apache.spark.sql.catalyst.errors.DialectException import org.apache.spark.sql.execution.GeneratedAggregate import org.apache.spark.sql.functions._ import org.apache.spark.sql.TestData._ import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.test.TestSQLContext.{udf => _, _} + import org.apache.spark.sql.types._ +/** A SQL Dialect for testing purpose, and it can not be nested type */ +class MyDialect extends DefaultDialect + class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { // Make sure the tables are loaded. TestData @@ -74,6 +79,23 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil) } + test("SQL Dialect Switching to a new SQL parser") { + val newContext = new SQLContext(TestSQLContext.sparkContext) + newContext.setConf("spark.sql.dialect", classOf[MyDialect].getCanonicalName()) + assert(newContext.getSQLDialect().getClass === classOf[MyDialect]) + assert(newContext.sql("SELECT 1").collect() === Array(Row(1))) + } + + test("SQL Dialect Switch to an invalid parser with alias") { + val newContext = new SQLContext(TestSQLContext.sparkContext) + newContext.sql("SET spark.sql.dialect=MyTestClass") + intercept[DialectException] { + newContext.sql("SELECT 1") + } + // test if the dialect set back to DefaultSQLDialect + assert(newContext.getSQLDialect().getClass === classOf[DefaultDialect]) + } + test("SPARK-4625 support SORT BY in SimpleSQLParser & DSL") { checkAnswer( sql("SELECT a FROM testData2 SORT BY a"), diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala index dd06b2620c5ee..1d8d0b5c322ad 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala @@ -20,6 +20,9 @@ package org.apache.spark.sql.hive import java.io.{BufferedReader, InputStreamReader, PrintStream} import java.sql.Timestamp +import org.apache.hadoop.hive.ql.parse.VariableSubstitution +import org.apache.spark.sql.catalyst.Dialect + import scala.collection.JavaConversions._ import scala.language.implicitConversions @@ -42,6 +45,15 @@ import org.apache.spark.sql.hive.execution.{DescribeHiveTableCommand, HiveNative import org.apache.spark.sql.sources.{DDLParser, DataSourceStrategy} import org.apache.spark.sql.types._ +/** + * This is the HiveQL Dialect, this dialect is strongly bind with HiveContext + */ +private[hive] class HiveQLDialect extends Dialect { + override def parse(sqlText: String): LogicalPlan = { + HiveQl.parseSql(sqlText) + } +} + /** * An instance of the Spark SQL execution engine that integrates with data stored in Hive. * Configuration for Hive is read from hive-site.xml on the classpath. @@ -81,25 +93,16 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { protected[sql] def convertCTAS: Boolean = getConf("spark.sql.hive.convertCTAS", "false").toBoolean - override protected[sql] def executePlan(plan: LogicalPlan): this.QueryExecution = - new this.QueryExecution(plan) - @transient - protected[sql] val ddlParserWithHiveQL = new DDLParser(HiveQl.parseSql(_)) - - override def sql(sqlText: String): DataFrame = { - val substituted = new VariableSubstitution().substitute(hiveconf, sqlText) - // TODO: Create a framework for registering parsers instead of just hardcoding if statements. - if (conf.dialect == "sql") { - super.sql(substituted) - } else if (conf.dialect == "hiveql") { - val ddlPlan = ddlParserWithHiveQL.parse(sqlText, exceptionOnError = false) - DataFrame(this, ddlPlan.getOrElse(HiveQl.parseSql(substituted))) - } else { - sys.error(s"Unsupported SQL dialect: ${conf.dialect}. Try 'sql' or 'hiveql'") - } + protected[sql] lazy val substitutor = new VariableSubstitution() + + protected[sql] override def parseSql(sql: String): LogicalPlan = { + super.parseSql(substitutor.substitute(hiveconf, sql)) } + override protected[sql] def executePlan(plan: LogicalPlan): this.QueryExecution = + new this.QueryExecution(plan) + /** * Invalidate and refresh all the cached the metadata of the given table. For performance reasons, * Spark SQL or the external data source library it uses might cache certain metadata about a @@ -356,6 +359,12 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { } } + override protected[sql] def dialectClassName = if (conf.dialect == "hiveql") { + classOf[HiveQLDialect].getCanonicalName + } else { + super.dialectClassName + } + @transient private val hivePlanner = new SparkPlanner with HiveStrategies { val hiveContext = self diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala index 9f17bca083d13..edeab5158df62 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala @@ -107,7 +107,10 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { /** Fewer partitions to speed up testing. */ protected[sql] override lazy val conf: SQLConf = new SQLConf { override def numShufflePartitions: Int = getConf(SQLConf.SHUFFLE_PARTITIONS, "5").toInt - override def dialect: String = getConf(SQLConf.DIALECT, "hiveql") + + // TODO as in unit test, conf.clear() probably be called, all of the value will be cleared. + // The super.getConf(SQLConf.DIALECT) is "sql" by default, we need to set it as "hiveql" + override def dialect: String = super.getConf(SQLConf.DIALECT, "hiveql") } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala index 4f8d0ac0e7656..630dec8fa05a0 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala @@ -18,14 +18,17 @@ package org.apache.spark.sql.hive.execution import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries -import org.apache.spark.sql.hive.{MetastoreRelation, HiveShim} +import org.apache.spark.sql.catalyst.errors.DialectException +import org.apache.spark.sql.DefaultDialect +import org.apache.spark.sql.{AnalysisException, QueryTest, Row, SQLConf} +import org.apache.spark.sql.hive.MetastoreRelation import org.apache.spark.sql.hive.test.TestHive import org.apache.spark.sql.hive.test.TestHive._ import org.apache.spark.sql.hive.test.TestHive.implicits._ +import org.apache.spark.sql.hive.{HiveQLDialect, HiveShim} import org.apache.spark.sql.parquet.ParquetRelation2 import org.apache.spark.sql.sources.LogicalRelation import org.apache.spark.sql.types._ -import org.apache.spark.sql.{AnalysisException, QueryTest, Row, SQLConf} case class Nested1(f1: Nested2) case class Nested2(f2: Nested3) @@ -45,6 +48,9 @@ case class Order( state: String, month: Int) +/** A SQL Dialect for testing purpose, and it can not be nested type */ +class MyDialect extends DefaultDialect + /** * A collection of hive query tests where we generate the answers ourselves instead of depending on * Hive to generate them (in contrast to HiveQuerySuite). Often this is because the query is @@ -229,6 +235,35 @@ class SQLQuerySuite extends QueryTest { setConf("spark.sql.hive.convertCTAS", originalConf) } + test("SQL Dialect Switching") { + assert(getSQLDialect().getClass === classOf[HiveQLDialect]) + setConf("spark.sql.dialect", classOf[MyDialect].getCanonicalName()) + assert(getSQLDialect().getClass === classOf[MyDialect]) + assert(sql("SELECT 1").collect() === Array(Row(1))) + + // set the dialect back to the DefaultSQLDialect + sql("SET spark.sql.dialect=sql") + assert(getSQLDialect().getClass === classOf[DefaultDialect]) + sql("SET spark.sql.dialect=hiveql") + assert(getSQLDialect().getClass === classOf[HiveQLDialect]) + + // set invalid dialect + sql("SET spark.sql.dialect.abc=MyTestClass") + sql("SET spark.sql.dialect=abc") + intercept[Exception] { + sql("SELECT 1") + } + // test if the dialect set back to HiveQLDialect + getSQLDialect().getClass === classOf[HiveQLDialect] + + sql("SET spark.sql.dialect=MyTestClass") + intercept[DialectException] { + sql("SELECT 1") + } + // test if the dialect set back to HiveQLDialect + assert(getSQLDialect().getClass === classOf[HiveQLDialect]) + } + test("CTAS with serde") { sql("CREATE TABLE ctas1 AS SELECT key k, value FROM src ORDER BY k, value").collect() sql( From ea841efc5a67e9a64a4ec803d31e4023b565c327 Mon Sep 17 00:00:00 2001 From: BenFradet Date: Sat, 2 May 2015 23:41:14 +0100 Subject: [PATCH 553/591] [SPARK-7255] [STREAMING] [DOCUMENTATION] Added documentation for spark.streaming.kafka.maxRetries Added documentation for spark.streaming.kafka.maxRetries Author: BenFradet Closes #5808 from BenFradet/master and squashes the following commits: cc72e7a [BenFradet] updated doc for spark.streaming.kafka.maxRetries to explain the default value 18f823e [BenFradet] Added "consecutive" to the spark.streaming.kafka.maxRetries doc 597fdeb [BenFradet] Mention that spark.streaming.kafka.maxRetries only applies to the direct kafka api 0efad39 [BenFradet] Added documentation for spark.streaming.kafka.maxRetries --- docs/configuration.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/docs/configuration.md b/docs/configuration.md index 7239b252a9fcc..64066bc0d70cd 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -1464,6 +1464,16 @@ Apart from these, the following properties are also available, and may be useful for more details. + + spark.streaming.kafka.maxRetries + 1 + + Maximum number of consecutive retries the driver will make in order to find + the latest offsets on the leader of each partition (a default value of 1 + means that the driver will make a maximum of 2 attempts). Only applies to + the new Kafka direct stream API. + + #### Cluster Managers From 49549d5a1a867c3ba25f5e4aec351d4102444bc0 Mon Sep 17 00:00:00 2001 From: WangTaoTheTonic Date: Sun, 3 May 2015 00:47:47 +0100 Subject: [PATCH 554/591] [SPARK-7031] [THRIFTSERVER] let thrift server take SPARK_DAEMON_MEMORY and SPARK_DAEMON_JAVA_OPTS We should let Thrift Server take these two parameters as it is a daemon. And it is better to read driver-related configs as an app submited by spark-submit. https://issues.apache.org/jira/browse/SPARK-7031 Author: WangTaoTheTonic Closes #5609 from WangTaoTheTonic/SPARK-7031 and squashes the following commits: 8d3fc16 [WangTaoTheTonic] indent 035069b [WangTaoTheTonic] better code style d3ddfb6 [WangTaoTheTonic] revert the unnecessary changes in suite 624e652 [WangTaoTheTonic] fix break tests 0565831 [WangTaoTheTonic] fix failed tests 4fb25ed [WangTaoTheTonic] let thrift server take SPARK_DAEMON_MEMORY and SPARK_DAEMON_JAVA_OPTS --- .../launcher/SparkSubmitCommandBuilder.java | 19 ++++++++++++++++++- 1 file changed, 18 insertions(+), 1 deletion(-) diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java index a73c9c87e3126..7d387d406edae 100644 --- a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java +++ b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java @@ -190,6 +190,10 @@ private List buildSparkSubmitCommand(Map env) throws IOE firstNonEmptyValue(SparkLauncher.DRIVER_EXTRA_CLASSPATH, conf, props) : null; List cmd = buildJavaCommand(extraClassPath); + // Take Thrift Server as daemon + if (isThriftServer(mainClass)) { + addOptionString(cmd, System.getenv("SPARK_DAEMON_JAVA_OPTS")); + } addOptionString(cmd, System.getenv("SPARK_SUBMIT_OPTS")); addOptionString(cmd, System.getenv("SPARK_JAVA_OPTS")); @@ -201,7 +205,11 @@ private List buildSparkSubmitCommand(Map env) throws IOE // - SPARK_DRIVER_MEMORY env variable // - SPARK_MEM env variable // - default value (512m) - String memory = firstNonEmpty(firstNonEmptyValue(SparkLauncher.DRIVER_MEMORY, conf, props), + // Take Thrift Server as daemon + String tsMemory = + isThriftServer(mainClass) ? System.getenv("SPARK_DAEMON_MEMORY") : null; + String memory = firstNonEmpty(tsMemory, + firstNonEmptyValue(SparkLauncher.DRIVER_MEMORY, conf, props), System.getenv("SPARK_DRIVER_MEMORY"), System.getenv("SPARK_MEM"), DEFAULT_MEM); cmd.add("-Xms" + memory); cmd.add("-Xmx" + memory); @@ -292,6 +300,15 @@ private boolean isClientMode(Properties userProps) { (!userMaster.equals("yarn-cluster") && deployMode == null); } + /** + * Return whether the given main class represents a thrift server. + */ + private boolean isThriftServer(String mainClass) { + return (mainClass != null && + mainClass.equals("org.apache.spark.sql.hive.thriftserver.HiveThriftServer2")); + } + + private class OptionParser extends SparkSubmitOptionParser { @Override From f4af92550cb90e47a12d4625fa615dd2b1587d42 Mon Sep 17 00:00:00 2001 From: Omede Firouz Date: Sun, 3 May 2015 11:42:02 -0700 Subject: [PATCH 555/591] [SPARK-7022] [PYSPARK] [ML] Add ML.Tuning.ParamGridBuilder to PySpark Author: Omede Firouz Author: Omede Closes #5601 from oefirouz/paramgrid and squashes the following commits: c9e2481 [Omede Firouz] Make test a doctest 9a8ce22 [Omede] Fix linter issues 8b8a6d2 [Omede Firouz] [SPARK-7022][PySpark][ML] Add ML.Tuning.ParamGridBuilder to PySpark --- python/pyspark/ml/tuning.py | 94 +++++++++++++++++++++++++++++++++++++ python/run-tests | 1 + 2 files changed, 95 insertions(+) create mode 100644 python/pyspark/ml/tuning.py diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py new file mode 100644 index 0000000000000..a383bd0c0d26f --- /dev/null +++ b/python/pyspark/ml/tuning.py @@ -0,0 +1,94 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +__all__ = ['ParamGridBuilder'] + + +class ParamGridBuilder(object): + """ + Builder for a param grid used in grid search-based model selection. + + >>> from classification import LogisticRegression + >>> lr = LogisticRegression() + >>> output = ParamGridBuilder().baseOn({lr.labelCol: 'l'}) \ + .baseOn([lr.predictionCol, 'p']) \ + .addGrid(lr.regParam, [1.0, 2.0, 3.0]) \ + .addGrid(lr.maxIter, [1, 5]) \ + .addGrid(lr.featuresCol, ['f']) \ + .build() + >>> expected = [ \ +{lr.regParam: 1.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ +{lr.regParam: 2.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ +{lr.regParam: 3.0, lr.featuresCol: 'f', lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ +{lr.regParam: 1.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ +{lr.regParam: 2.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ +{lr.regParam: 3.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}] + >>> fail_count = 0 + >>> for e in expected: + ... if e not in output: + ... fail_count += 1 + >>> if len(expected) != len(output): + ... fail_count += 1 + >>> fail_count + 0 + """ + + def __init__(self): + self._param_grid = {} + + def addGrid(self, param, values): + """ + Sets the given parameters in this grid to fixed values. + """ + self._param_grid[param] = values + + return self + + def baseOn(self, *args): + """ + Sets the given parameters in this grid to fixed values. + Accepts either a parameter dictionary or a list of (parameter, value) pairs. + """ + if isinstance(args[0], dict): + self.baseOn(*args[0].items()) + else: + for (param, value) in args: + self.addGrid(param, [value]) + + return self + + def build(self): + """ + Builds and returns all combinations of parameters specified + by the param grid. + """ + param_maps = [{}] + for (param, values) in self._param_grid.items(): + new_param_maps = [] + for value in values: + for old_map in param_maps: + copied_map = old_map.copy() + copied_map[param] = value + new_param_maps.append(copied_map) + param_maps = new_param_maps + + return param_maps + + +if __name__ == "__main__": + import doctest + doctest.testmod() diff --git a/python/run-tests b/python/run-tests index 88b63b84fdc27..0e0eee3564e7c 100755 --- a/python/run-tests +++ b/python/run-tests @@ -98,6 +98,7 @@ function run_ml_tests() { echo "Run ml tests ..." run_test "pyspark/ml/feature.py" run_test "pyspark/ml/classification.py" + run_test "pyspark/ml/tuning.py" run_test "pyspark/ml/tests.py" } From daa70bf135f23381f5f410aa95a1c0e5a2888568 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Sun, 3 May 2015 13:12:50 -0700 Subject: [PATCH 556/591] [SPARK-6907] [SQL] Isolated client for HiveMetastore This PR adds initial support for loading multiple versions of Hive in a single JVM and provides a common interface for extracting metadata from the `HiveMetastoreClient` for a given version. This is accomplished by creating an isolated `ClassLoader` that operates according to the following rules: - __Shared Classes__: Java, Scala, logging, and Spark classes are delegated to `baseClassLoader` allowing the results of calls to the `ClientInterface` to be visible externally. - __Hive Classes__: new instances are loaded from `execJars`. These classes are not accessible externally due to their custom loading. - __Barrier Classes__: Classes such as `ClientWrapper` are defined in Spark but must link to a specific version of Hive. As a result, the bytecode is acquired from the Spark `ClassLoader` but a new copy is created for each instance of `IsolatedClientLoader`. This new instance is able to see a specific version of hive without using reflection where ever hive is consistent across versions. Since this is a unique instance, it is not visible externally other than as a generic `ClientInterface`, unless `isolationOn` is set to `false`. In addition to the unit tests, I have also tested this locally against mysql instances of the Hive Metastore. I've also successfully ported Spark SQL to run with this client, but due to the size of the changes, that will come in a follow-up PR. By default, Hive jars are currently downloaded from Maven automatically for a given version to ease packaging and testing. However, there is also support for specifying their location manually for deployments without internet. Author: Michael Armbrust Closes #5851 from marmbrus/isolatedClient and squashes the following commits: c72f6ac [Michael Armbrust] rxins comments 1e271fa [Michael Armbrust] [SPARK-6907][SQL] Isolated client for HiveMetastore --- .../org/apache/spark/deploy/SparkSubmit.scala | 2 +- .../spark/sql/catalyst/analysis/Catalog.scala | 2 + .../spark/sql/catalyst/util/package.scala | 37 +- .../sql/hive/client/ClientInterface.scala | 149 +++++++ .../spark/sql/hive/client/ClientWrapper.scala | 395 ++++++++++++++++++ .../hive/client/IsolatedClientLoader.scala | 172 ++++++++ .../sql/hive/client/ReflectionMagic.scala | 200 +++++++++ .../spark/sql/hive/client/package.scala | 33 ++ .../spark/sql/hive/client/VersionsSuite.scala | 105 +++++ 9 files changed, 1088 insertions(+), 7 deletions(-) create mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala create mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala create mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala create mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala create mode 100644 sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala index 42b5d41b7b526..8a0327984e195 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala @@ -701,7 +701,7 @@ object SparkSubmit { } /** Provides utility functions to be used inside SparkSubmit. */ -private[deploy] object SparkSubmitUtils { +private[spark] object SparkSubmitUtils { // Exposed for testing var printStream = SparkSubmit.printStream diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala index b2f8157a1a61f..18c24b651921a 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala @@ -27,6 +27,8 @@ import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Subquery} */ class NoSuchTableException extends Exception +class NoSuchDatabaseException extends Exception + /** * An interface for looking up relations by name. Used by an [[Analyzer]]. */ diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala index c86214a2aa944..9d613a940ee86 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/package.scala @@ -17,12 +17,31 @@ package org.apache.spark.sql.catalyst -import java.io.{PrintWriter, ByteArrayOutputStream, FileInputStream, File} +import java.io._ import org.apache.spark.util.Utils package object util { + /** Silences output to stderr or stdout for the duration of f */ + def quietly[A](f: => A): A = { + val origErr = System.err + val origOut = System.out + try { + System.setErr(new PrintStream(new OutputStream { + def write(b: Int) = {} + })) + System.setOut(new PrintStream(new OutputStream { + def write(b: Int) = {} + })) + + f + } finally { + System.setErr(origErr) + System.setOut(origOut) + } + } + def fileToString(file: File, encoding: String = "UTF-8"): String = { val inStream = new FileInputStream(file) val outStream = new ByteArrayOutputStream @@ -42,10 +61,9 @@ package object util { new String(outStream.toByteArray, encoding) } - def resourceToString( - resource:String, - encoding: String = "UTF-8", - classLoader: ClassLoader = Utils.getSparkClassLoader): String = { + def resourceToBytes( + resource: String, + classLoader: ClassLoader = Utils.getSparkClassLoader): Array[Byte] = { val inStream = classLoader.getResourceAsStream(resource) val outStream = new ByteArrayOutputStream try { @@ -61,7 +79,14 @@ package object util { finally { inStream.close() } - new String(outStream.toByteArray, encoding) + outStream.toByteArray + } + + def resourceToString( + resource:String, + encoding: String = "UTF-8", + classLoader: ClassLoader = Utils.getSparkClassLoader): String = { + new String(resourceToBytes(resource, classLoader), encoding) } def stringToFile(file: File, str: String): File = { diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala new file mode 100644 index 0000000000000..a863aa77cb7e0 --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala @@ -0,0 +1,149 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.client + +import org.apache.spark.sql.catalyst.analysis.{NoSuchDatabaseException, NoSuchTableException} + +case class HiveDatabase( + name: String, + location: String) + +abstract class TableType { val name: String } +case object ExternalTable extends TableType { override val name = "EXTERNAL_TABLE" } +case object IndexTable extends TableType { override val name = "INDEX_TABLE" } +case object ManagedTable extends TableType { override val name = "MANAGED_TABLE" } +case object VirtualView extends TableType { override val name = "VIRTUAL_VIEW" } + +case class HiveStorageDescriptor( + location: String, + inputFormat: String, + outputFormat: String, + serde: String) + +case class HivePartition( + values: Seq[String], + storage: HiveStorageDescriptor) + +case class HiveColumn(name: String, hiveType: String, comment: String) +case class HiveTable( + specifiedDatabase: Option[String], + name: String, + schema: Seq[HiveColumn], + partitionColumns: Seq[HiveColumn], + properties: Map[String, String], + serdeProperties: Map[String, String], + tableType: TableType, + location: Option[String] = None, + inputFormat: Option[String] = None, + outputFormat: Option[String] = None, + serde: Option[String] = None) { + + @transient + private[client] var client: ClientInterface = _ + + private[client] def withClient(ci: ClientInterface): this.type = { + client = ci + this + } + + def database: String = specifiedDatabase.getOrElse(sys.error("database not resolved")) + + def isPartitioned: Boolean = partitionColumns.nonEmpty + + def getAllPartitions: Seq[HivePartition] = client.getAllPartitions(this) + + // Hive does not support backticks when passing names to the client. + def qualifiedName: String = s"$database.$name" +} + +/** + * An externally visible interface to the Hive client. This interface is shared across both the + * internal and external classloaders for a given version of Hive and thus must expose only + * shared classes. + */ +trait ClientInterface { + /** + * Runs a HiveQL command using Hive, returning the results as a list of strings. Each row will + * result in one string. + */ + def runSqlHive(sql: String): Seq[String] + + /** Returns the names of all tables in the given database. */ + def listTables(dbName: String): Seq[String] + + /** Returns the name of the active database. */ + def currentDatabase: String + + /** Returns the metadata for specified database, throwing an exception if it doesn't exist */ + def getDatabase(name: String): HiveDatabase = { + getDatabaseOption(name).getOrElse(throw new NoSuchDatabaseException) + } + + /** Returns the metadata for a given database, or None if it doesn't exist. */ + def getDatabaseOption(name: String): Option[HiveDatabase] + + /** Returns the specified table, or throws [[NoSuchTableException]]. */ + def getTable(dbName: String, tableName: String): HiveTable = { + getTableOption(dbName, tableName).getOrElse(throw new NoSuchTableException) + } + + /** Returns the metadata for the specified table or None if it doens't exist. */ + def getTableOption(dbName: String, tableName: String): Option[HiveTable] + + /** Creates a table with the given metadata. */ + def createTable(table: HiveTable): Unit + + /** Updates the given table with new metadata. */ + def alterTable(table: HiveTable): Unit + + /** Creates a new database with the given name. */ + def createDatabase(database: HiveDatabase): Unit + + /** Returns all partitions for the given table. */ + def getAllPartitions(hTable: HiveTable): Seq[HivePartition] + + /** Loads a static partition into an existing table. */ + def loadPartition( + loadPath: String, + tableName: String, + partSpec: java.util.LinkedHashMap[String, String], // Hive relies on LinkedHashMap ordering + replace: Boolean, + holdDDLTime: Boolean, + inheritTableSpecs: Boolean, + isSkewedStoreAsSubdir: Boolean): Unit + + /** Loads data into an existing table. */ + def loadTable( + loadPath: String, // TODO URI + tableName: String, + replace: Boolean, + holdDDLTime: Boolean): Unit + + /** Loads new dynamic partitions into an existing table. */ + def loadDynamicPartitions( + loadPath: String, + tableName: String, + partSpec: java.util.LinkedHashMap[String, String], // Hive relies on LinkedHashMap ordering + replace: Boolean, + numDP: Int, + holdDDLTime: Boolean, + listBucketingEnabled: Boolean): Unit + + /** Used for testing only. Removes all metadata from this instance of Hive. */ + def reset(): Unit +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala new file mode 100644 index 0000000000000..ea52fea037f1f --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala @@ -0,0 +1,395 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.client + +import java.io.{BufferedReader, InputStreamReader, File, PrintStream} +import java.net.URI +import java.util.{ArrayList => JArrayList} + +import scala.collection.JavaConversions._ +import scala.language.reflectiveCalls + +import org.apache.hadoop.fs.Path +import org.apache.hadoop.hive.metastore.api.Database +import org.apache.hadoop.hive.conf.HiveConf +import org.apache.hadoop.hive.metastore.api +import org.apache.hadoop.hive.metastore.api.FieldSchema +import org.apache.hadoop.hive.ql.metadata +import org.apache.hadoop.hive.ql.metadata.Hive +import org.apache.hadoop.hive.ql.session.SessionState +import org.apache.hadoop.hive.ql.processors._ +import org.apache.hadoop.hive.ql.Driver + +import org.apache.spark.Logging +import org.apache.spark.sql.execution.QueryExecutionException + + +/** + * A class that wraps the HiveClient and converts its responses to externally visible classes. + * Note that this class is typically loaded with an internal classloader for each instantiation, + * allowing it to interact directly with a specific isolated version of Hive. Loading this class + * with the isolated classloader however will result in it only being visible as a ClientInterface, + * not a ClientWrapper. + * + * This class needs to interact with multiple versions of Hive, but will always be compiled with + * the 'native', execution version of Hive. Therefore, any places where hive breaks compatibility + * must use reflection after matching on `version`. + * + * @param version the version of hive used when pick function calls that are not compatible. + * @param config a collection of configuration options that will be added to the hive conf before + * opening the hive client. + */ +class ClientWrapper( + version: HiveVersion, + config: Map[String, String]) + extends ClientInterface + with Logging + with ReflectionMagic { + + private val conf = new HiveConf(classOf[SessionState]) + config.foreach { case (k, v) => + logDebug(s"Hive Config: $k=$v") + conf.set(k, v) + } + + // Circular buffer to hold what hive prints to STDOUT and ERR. Only printed when failures occur. + private val outputBuffer = new java.io.OutputStream { + var pos: Int = 0 + var buffer = new Array[Int](10240) + def write(i: Int): Unit = { + buffer(pos) = i + pos = (pos + 1) % buffer.size + } + + override def toString: String = { + val (end, start) = buffer.splitAt(pos) + val input = new java.io.InputStream { + val iterator = (start ++ end).iterator + + def read(): Int = if (iterator.hasNext) iterator.next() else -1 + } + val reader = new BufferedReader(new InputStreamReader(input)) + val stringBuilder = new StringBuilder + var line = reader.readLine() + while(line != null) { + stringBuilder.append(line) + stringBuilder.append("\n") + line = reader.readLine() + } + stringBuilder.toString() + } + } + + val state = { + val original = Thread.currentThread().getContextClassLoader + Thread.currentThread().setContextClassLoader(getClass.getClassLoader) + val ret = try { + val newState = new SessionState(conf) + SessionState.start(newState) + newState.out = new PrintStream(outputBuffer, true, "UTF-8") + newState.err = new PrintStream(outputBuffer, true, "UTF-8") + newState + } finally { + Thread.currentThread().setContextClassLoader(original) + } + ret + } + + private val client = Hive.get(conf) + + /** + * Runs `f` with ThreadLocal session state and classloaders configured for this version of hive. + */ + private def withHiveState[A](f: => A): A = synchronized { + val original = Thread.currentThread().getContextClassLoader + Thread.currentThread().setContextClassLoader(getClass.getClassLoader) + Hive.set(client) + version match { + case hive.v12 => + classOf[SessionState] + .callStatic[SessionState, SessionState]("start", state) + case hive.v13 => + classOf[SessionState] + .callStatic[SessionState, SessionState]("setCurrentSessionState", state) + } + val ret = try f finally { + Thread.currentThread().setContextClassLoader(original) + } + ret + } + + override def currentDatabase: String = withHiveState { + state.getCurrentDatabase + } + + override def createDatabase(database: HiveDatabase): Unit = withHiveState { + client.createDatabase( + new Database( + database.name, + "", + new File(database.location).toURI.toString, + new java.util.HashMap), + true) + } + + override def getDatabaseOption(name: String): Option[HiveDatabase] = withHiveState { + Option(client.getDatabase(name)).map { d => + HiveDatabase( + name = d.getName, + location = d.getLocationUri) + } + } + + override def getTableOption( + dbName: String, + tableName: String): Option[HiveTable] = withHiveState { + + logDebug(s"Looking up $dbName.$tableName") + + val hiveTable = Option(client.getTable(dbName, tableName, false)) + val converted = hiveTable.map { h => + + HiveTable( + name = h.getTableName, + specifiedDatabase = Option(h.getDbName), + schema = h.getCols.map(f => HiveColumn(f.getName, f.getType, f.getComment)), + partitionColumns = h.getPartCols.map(f => HiveColumn(f.getName, f.getType, f.getComment)), + properties = h.getParameters.toMap, + serdeProperties = h.getTTable.getSd.getSerdeInfo.getParameters.toMap, + tableType = ManagedTable, // TODO + location = version match { + case hive.v12 => Option(h.call[URI]("getDataLocation")).map(_.toString) + case hive.v13 => Option(h.call[Path]("getDataLocation")).map(_.toString) + }, + inputFormat = Option(h.getInputFormatClass).map(_.getName), + outputFormat = Option(h.getOutputFormatClass).map(_.getName), + serde = Option(h.getSerializationLib)).withClient(this) + } + converted + } + + private def toInputFormat(name: String) = + Class.forName(name).asInstanceOf[Class[_ <: org.apache.hadoop.mapred.InputFormat[_, _]]] + + private def toOutputFormat(name: String) = + Class.forName(name) + .asInstanceOf[Class[_ <: org.apache.hadoop.hive.ql.io.HiveOutputFormat[_, _]]] + + private def toQlTable(table: HiveTable): metadata.Table = { + val qlTable = new metadata.Table(table.database, table.name) + + qlTable.setFields(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment))) + qlTable.setPartCols( + table.partitionColumns.map(c => new FieldSchema(c.name, c.hiveType, c.comment))) + table.properties.foreach { case (k, v) => qlTable.setProperty(k, v) } + table.serdeProperties.foreach { case (k, v) => qlTable.setSerdeParam(k, v) } + version match { + case hive.v12 => + table.location.map(new URI(_)).foreach(u => qlTable.call[URI, Unit]("setDataLocation", u)) + case hive.v13 => + table.location + .map(new org.apache.hadoop.fs.Path(_)) + .foreach(qlTable.call[Path, Unit]("setDataLocation", _)) + } + table.inputFormat.map(toInputFormat).foreach(qlTable.setInputFormatClass) + table.outputFormat.map(toOutputFormat).foreach(qlTable.setOutputFormatClass) + table.serde.foreach(qlTable.setSerializationLib) + + qlTable + } + + override def createTable(table: HiveTable): Unit = withHiveState { + val qlTable = toQlTable(table) + client.createTable(qlTable) + } + + override def alterTable(table: HiveTable): Unit = withHiveState { + val qlTable = toQlTable(table) + client.alterTable(table.qualifiedName, qlTable) + } + + override def getAllPartitions(hTable: HiveTable): Seq[HivePartition] = withHiveState { + val qlTable = toQlTable(hTable) + val qlPartitions = version match { + case hive.v12 => + client.call[metadata.Table, Set[metadata.Partition]]("getAllPartitionsForPruner", qlTable) + case hive.v13 => + client.call[metadata.Table, Set[metadata.Partition]]("getAllPartitionsOf", qlTable) + } + qlPartitions.map(_.getTPartition).map { p => + HivePartition( + values = Option(p.getValues).map(_.toSeq).getOrElse(Seq.empty), + storage = HiveStorageDescriptor( + location = p.getSd.getLocation, + inputFormat = p.getSd.getInputFormat, + outputFormat = p.getSd.getOutputFormat, + serde = p.getSd.getSerdeInfo.getSerializationLib)) + }.toSeq + } + + override def listTables(dbName: String): Seq[String] = withHiveState { + client.getAllTables + } + + /** + * Runs the specified SQL query using Hive. + */ + override def runSqlHive(sql: String): Seq[String] = { + val maxResults = 100000 + val results = runHive(sql, maxResults) + // It is very confusing when you only get back some of the results... + if (results.size == maxResults) sys.error("RESULTS POSSIBLY TRUNCATED") + results + } + + /** + * Execute the command using Hive and return the results as a sequence. Each element + * in the sequence is one row. + */ + protected def runHive(cmd: String, maxRows: Int = 1000): Seq[String] = withHiveState { + logDebug(s"Running hiveql '$cmd'") + if (cmd.toLowerCase.startsWith("set")) { logDebug(s"Changing config: $cmd") } + try { + val cmd_trimmed: String = cmd.trim() + val tokens: Array[String] = cmd_trimmed.split("\\s+") + val cmd_1: String = cmd_trimmed.substring(tokens(0).length()).trim() + val proc: CommandProcessor = version match { + case hive.v12 => + classOf[CommandProcessorFactory] + .callStatic[String, HiveConf, CommandProcessor]("get", cmd_1, conf) + case hive.v13 => + classOf[CommandProcessorFactory] + .callStatic[Array[String], HiveConf, CommandProcessor]("get", Array(tokens(0)), conf) + } + + proc match { + case driver: Driver => + val response: CommandProcessorResponse = driver.run(cmd) + // Throw an exception if there is an error in query processing. + if (response.getResponseCode != 0) { + driver.close() + throw new QueryExecutionException(response.getErrorMessage) + } + driver.setMaxRows(maxRows) + + val results = version match { + case hive.v12 => + val res = new JArrayList[String] + driver.call[JArrayList[String], Boolean]("getResults", res) + res.toSeq + case hive.v13 => + val res = new JArrayList[Object] + driver.call[JArrayList[Object], Boolean]("getResults", res) + res.map { r => + r match { + case s: String => s + case a: Array[Object] => a(0).asInstanceOf[String] + } + } + } + driver.close() + results + + case _ => + if (state.out != null) { + state.out.println(tokens(0) + " " + cmd_1) + } + Seq(proc.run(cmd_1).getResponseCode.toString) + } + } catch { + case e: Exception => + logError( + s""" + |====================== + |HIVE FAILURE OUTPUT + |====================== + |${outputBuffer.toString} + |====================== + |END HIVE FAILURE OUTPUT + |====================== + """.stripMargin) + throw e + } + } + + def loadPartition( + loadPath: String, + tableName: String, + partSpec: java.util.LinkedHashMap[String, String], + replace: Boolean, + holdDDLTime: Boolean, + inheritTableSpecs: Boolean, + isSkewedStoreAsSubdir: Boolean): Unit = withHiveState { + + client.loadPartition( + new Path(loadPath), // TODO: Use URI + tableName, + partSpec, + replace, + holdDDLTime, + inheritTableSpecs, + isSkewedStoreAsSubdir) + } + + def loadTable( + loadPath: String, // TODO URI + tableName: String, + replace: Boolean, + holdDDLTime: Boolean): Unit = withHiveState { + client.loadTable( + new Path(loadPath), + tableName, + replace, + holdDDLTime) + } + + def loadDynamicPartitions( + loadPath: String, + tableName: String, + partSpec: java.util.LinkedHashMap[String, String], + replace: Boolean, + numDP: Int, + holdDDLTime: Boolean, + listBucketingEnabled: Boolean): Unit = withHiveState { + client.loadDynamicPartitions( + new Path(loadPath), + tableName, + partSpec, + replace, + numDP, + holdDDLTime, + listBucketingEnabled) + } + + def reset(): Unit = withHiveState { + client.getAllTables("default").foreach { t => + logDebug(s"Deleting table $t") + val table = client.getTable("default", t) + client.getIndexes("default", t, 255).foreach { index => + client.dropIndex("default", t, index.getIndexName, true) + } + if (!table.isIndexTable) { + client.dropTable("default", t) + } + } + client.getAllDatabases.filterNot(_ == "default").foreach { db => + logDebug(s"Dropping Database: $db") + client.dropDatabase(db, true, false, true) + } + } +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala new file mode 100644 index 0000000000000..710dbca6e3c66 --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala @@ -0,0 +1,172 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.client + +import java.io.File +import java.net.URLClassLoader +import java.util + +import scala.language.reflectiveCalls +import scala.util.Try + +import org.apache.commons.io.{FileUtils, IOUtils} + +import org.apache.spark.Logging +import org.apache.spark.deploy.SparkSubmitUtils + +import org.apache.spark.sql.catalyst.util.quietly + +/** Factory for `IsolatedClientLoader` with specific versions of hive. */ +object IsolatedClientLoader { + /** + * Creates isolated Hive client loaders by downloading the requested version from maven. + */ + def forVersion( + version: String, + config: Map[String, String] = Map.empty): IsolatedClientLoader = synchronized { + val resolvedVersion = hiveVersion(version) + val files = resolvedVersions.getOrElseUpdate(resolvedVersion, downloadVersion(resolvedVersion)) + new IsolatedClientLoader(hiveVersion(version), files, config) + } + + def hiveVersion(version: String): HiveVersion = version match { + case "12" | "0.12" | "0.12.0" => hive.v12 + case "13" | "0.13" | "0.13.0" | "0.13.1" => hive.v13 + } + + private def downloadVersion(version: HiveVersion): Seq[File] = { + val hiveArtifacts = + (Seq("hive-metastore", "hive-exec", "hive-common", "hive-serde") ++ + (if (version.hasBuiltinsJar) "hive-builtins" :: Nil else Nil)) + .map(a => s"org.apache.hive:$a:${version.fullVersion}") :+ + "com.google.guava:guava:14.0.1" :+ + "org.apache.hadoop:hadoop-client:2.4.0" :+ + "mysql:mysql-connector-java:5.1.12" + + val classpath = quietly { + SparkSubmitUtils.resolveMavenCoordinates( + hiveArtifacts.mkString(","), + Some("http://www.datanucleus.org/downloads/maven2"), + None) + } + val allFiles = classpath.split(",").map(new File(_)).toSet + + // TODO: Remove copy logic. + val tempDir = File.createTempFile("hive", "v" + version.toString) + tempDir.delete() + tempDir.mkdir() + + allFiles.foreach(f => FileUtils.copyFileToDirectory(f, tempDir)) + tempDir.listFiles() + } + + private def resolvedVersions = new scala.collection.mutable.HashMap[HiveVersion, Seq[File]] +} + +/** + * Creates a Hive `ClientInterface` using a classloader that works according to the following rules: + * - Shared classes: Java, Scala, logging, and Spark classes are delegated to `baseClassLoader` + * allowing the results of calls to the `ClientInterface` to be visible externally. + * - Hive classes: new instances are loaded from `execJars`. These classes are not + * accessible externally due to their custom loading. + * - ClientWrapper: a new copy is created for each instance of `IsolatedClassLoader`. + * This new instance is able to see a specific version of hive without using reflection. Since + * this is a unique instance, it is not visible externally other than as a generic + * `ClientInterface`, unless `isolationOn` is set to `false`. + * + * @param version The version of hive on the classpath. used to pick specific function signatures + * that are not compatibile accross versions. + * @param execJars A collection of jar files that must include hive and hadoop. + * @param config A set of options that will be added to the HiveConf of the constructed client. + * @param isolationOn When true, custom versions of barrier classes will be constructed. Must be + * true unless loading the version of hive that is on Sparks classloader. + * @param rootClassLoader The system root classloader. Must not know about hive classes. + * @param baseClassLoader The spark classloader that is used to load shared classes. + * + */ +class IsolatedClientLoader( + val version: HiveVersion, + val execJars: Seq[File] = Seq.empty, + val config: Map[String, String] = Map.empty, + val isolationOn: Boolean = true, + val rootClassLoader: ClassLoader = ClassLoader.getSystemClassLoader.getParent.getParent, + val baseClassLoader: ClassLoader = Thread.currentThread().getContextClassLoader) + extends Logging { + + // Check to make sure that the root classloader does not know about Hive. + assert(Try(baseClassLoader.loadClass("org.apache.hive.HiveConf")).isFailure) + + /** All jars used by the hive specific classloader. */ + protected def allJars = execJars.map(_.toURI.toURL).toArray + + protected def isSharedClass(name: String): Boolean = + name.contains("slf4j") || + name.contains("log4j") || + name.startsWith("org.apache.spark.") || + name.startsWith("scala.") || + name.startsWith("com.google") || + name.startsWith("java.lang.") || + name.startsWith("java.net") + + /** True if `name` refers to a spark class that must see specific version of Hive. */ + protected def isBarrierClass(name: String): Boolean = + name.startsWith("org.apache.spark.sql.hive.execution.PairSerDe") || + name.startsWith(classOf[ClientWrapper].getName) || + name.startsWith(classOf[ReflectionMagic].getName) + + protected def classToPath(name: String): String = + name.replaceAll("\\.", "/") + ".class" + + /** The classloader that is used to load an isolated version of Hive. */ + protected val classLoader: ClassLoader = new URLClassLoader(allJars, rootClassLoader) { + override def loadClass(name: String, resolve: Boolean): Class[_] = { + val loaded = findLoadedClass(name) + if (loaded == null) doLoadClass(name, resolve) else loaded + } + + def doLoadClass(name: String, resolve: Boolean): Class[_] = { + val classFileName = name.replaceAll("\\.", "/") + ".class" + if (isBarrierClass(name) && isolationOn) { + val bytes = IOUtils.toByteArray(baseClassLoader.getResourceAsStream(classFileName)) + logDebug(s"custom defining: $name - ${util.Arrays.hashCode(bytes)}") + defineClass(name, bytes, 0, bytes.length) + } else if (!isSharedClass(name)) { + logDebug(s"hive class: $name - ${getResource(classToPath(name))}") + super.loadClass(name, resolve) + } else { + logDebug(s"shared class: $name") + baseClassLoader.loadClass(name) + } + } + } + + // Pre-reflective instantiation setup. + logDebug("Initializing the logger to avoid disaster...") + Thread.currentThread.setContextClassLoader(classLoader) + + /** The isolated client interface to Hive. */ + val client: ClientInterface = try { + classLoader + .loadClass(classOf[ClientWrapper].getName) + .getConstructors.head + .newInstance(version, config) + .asInstanceOf[ClientInterface] + } finally { + Thread.currentThread.setContextClassLoader(baseClassLoader) + } +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala new file mode 100644 index 0000000000000..90d03049356b5 --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ReflectionMagic.scala @@ -0,0 +1,200 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.client + +import scala.reflect._ + +/** + * Provides implicit functions on any object for calling methods reflectively. + */ +protected trait ReflectionMagic { + /** code for InstanceMagic + println( + (1 to 22).map { n => + def repeat(str: String => String) = (1 to n).map(i => str(i.toString)).mkString(", ") + val types = repeat(n => s"A$n <: AnyRef : ClassTag") + val inArgs = repeat(n => s"a$n: A$n") + val erasure = repeat(n => s"classTag[A$n].erasure") + val outArgs = repeat(n => s"a$n") + s"""|def call[$types, R](name: String, $inArgs): R = { + | clazz.getMethod(name, $erasure).invoke(a, $outArgs).asInstanceOf[R] + |}""".stripMargin + }.mkString("\n") + ) + */ + + // scalastyle:off + protected implicit class InstanceMagic(a: Any) { + private val clazz = a.getClass + + def call[R](name: String): R = { + clazz.getMethod(name).invoke(a).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, R](name: String, a1: A1): R = { + clazz.getMethod(name, classTag[A1].erasure).invoke(a, a1).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure).invoke(a, a1, a2).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure).invoke(a, a1, a2, a3).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure).invoke(a, a1, a2, a3, a4).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure).invoke(a, a1, a2, a3, a4, a5).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure).invoke(a, a1, a2, a3, a4, a5, a6).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, A21 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20, a21: A21): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure, classTag[A21].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21).asInstanceOf[R] + } + def call[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, A21 <: AnyRef : ClassTag, A22 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20, a21: A21, a22: A22): R = { + clazz.getMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure, classTag[A21].erasure, classTag[A22].erasure).invoke(a, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21, a22).asInstanceOf[R] + } + } + + /** code for StaticMagic + println( + (1 to 22).map { n => + def repeat(str: String => String) = (1 to n).map(i => str(i.toString)).mkString(", ") + val types = repeat(n => s"A$n <: AnyRef : ClassTag") + val inArgs = repeat(n => s"a$n: A$n") + val erasure = repeat(n => s"classTag[A$n].erasure") + val outArgs = repeat(n => s"a$n") + s"""|def callStatic[$types, R](name: String, $inArgs): R = { + | c.getDeclaredMethod(name, $erasure).invoke(c, $outArgs).asInstanceOf[R] + |}""".stripMargin + }.mkString("\n") + ) + */ + + protected implicit class StaticMagic(c: Class[_]) { + def callStatic[A1 <: AnyRef : ClassTag, R](name: String, a1: A1): R = { + c.getDeclaredMethod(name, classTag[A1].erasure).invoke(c, a1).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure).invoke(c, a1, a2).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure).invoke(c, a1, a2, a3).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure).invoke(c, a1, a2, a3, a4).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure).invoke(c, a1, a2, a3, a4, a5).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure).invoke(c, a1, a2, a3, a4, a5, a6).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, A21 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20, a21: A21): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure, classTag[A21].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21).asInstanceOf[R] + } + def callStatic[A1 <: AnyRef : ClassTag, A2 <: AnyRef : ClassTag, A3 <: AnyRef : ClassTag, A4 <: AnyRef : ClassTag, A5 <: AnyRef : ClassTag, A6 <: AnyRef : ClassTag, A7 <: AnyRef : ClassTag, A8 <: AnyRef : ClassTag, A9 <: AnyRef : ClassTag, A10 <: AnyRef : ClassTag, A11 <: AnyRef : ClassTag, A12 <: AnyRef : ClassTag, A13 <: AnyRef : ClassTag, A14 <: AnyRef : ClassTag, A15 <: AnyRef : ClassTag, A16 <: AnyRef : ClassTag, A17 <: AnyRef : ClassTag, A18 <: AnyRef : ClassTag, A19 <: AnyRef : ClassTag, A20 <: AnyRef : ClassTag, A21 <: AnyRef : ClassTag, A22 <: AnyRef : ClassTag, R](name: String, a1: A1, a2: A2, a3: A3, a4: A4, a5: A5, a6: A6, a7: A7, a8: A8, a9: A9, a10: A10, a11: A11, a12: A12, a13: A13, a14: A14, a15: A15, a16: A16, a17: A17, a18: A18, a19: A19, a20: A20, a21: A21, a22: A22): R = { + c.getDeclaredMethod(name, classTag[A1].erasure, classTag[A2].erasure, classTag[A3].erasure, classTag[A4].erasure, classTag[A5].erasure, classTag[A6].erasure, classTag[A7].erasure, classTag[A8].erasure, classTag[A9].erasure, classTag[A10].erasure, classTag[A11].erasure, classTag[A12].erasure, classTag[A13].erasure, classTag[A14].erasure, classTag[A15].erasure, classTag[A16].erasure, classTag[A17].erasure, classTag[A18].erasure, classTag[A19].erasure, classTag[A20].erasure, classTag[A21].erasure, classTag[A22].erasure).invoke(c, a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15, a16, a17, a18, a19, a20, a21, a22).asInstanceOf[R] + } + } + // scalastyle:on +} diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala new file mode 100644 index 0000000000000..7db9200d47440 --- /dev/null +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala @@ -0,0 +1,33 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive + +/** Support for interacting with different versions of the HiveMetastoreClient */ +package object client { + private[client] abstract class HiveVersion(val fullVersion: String, val hasBuiltinsJar: Boolean) + + // scalastyle:off + private[client] object hive { + case object v10 extends HiveVersion("0.10.0", true) + case object v11 extends HiveVersion("0.11.0", false) + case object v12 extends HiveVersion("0.12.0", false) + case object v13 extends HiveVersion("0.13.1", false) + } + // scalastyle:on + +} \ No newline at end of file diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala new file mode 100644 index 0000000000000..81e77ba257bf1 --- /dev/null +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala @@ -0,0 +1,105 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.client + +import org.apache.spark.Logging +import org.apache.spark.sql.catalyst.util.quietly +import org.apache.spark.util.Utils +import org.scalatest.FunSuite + +class VersionsSuite extends FunSuite with Logging { + val testType = "derby" + + private def buildConf() = { + lazy val warehousePath = Utils.createTempDir() + lazy val metastorePath = Utils.createTempDir() + metastorePath.delete() + Map( + "javax.jdo.option.ConnectionURL" -> s"jdbc:derby:;databaseName=$metastorePath;create=true", + "hive.metastore.warehouse.dir" -> warehousePath.toString) + } + + test("success sanity check") { + val badClient = IsolatedClientLoader.forVersion("13", buildConf()).client + val db = new HiveDatabase("default", "") + badClient.createDatabase(db) + } + + private def getNestedMessages(e: Throwable): String = { + var causes = "" + var lastException = e + while (lastException != null) { + causes += lastException.toString + "\n" + lastException = lastException.getCause + } + causes + } + + // Its actually pretty easy to mess things up and have all of your tests "pass" by accidentally + // connecting to an auto-populated, in-process metastore. Let's make sure we are getting the + // versions right by forcing a known compatibility failure. + // TODO: currently only works on mysql where we manually create the schema... + ignore("failure sanity check") { + val e = intercept[Throwable] { + val badClient = quietly { IsolatedClientLoader.forVersion("13", buildConf()).client } + } + assert(getNestedMessages(e) contains "Unknown column 'A0.OWNER_NAME' in 'field list'") + } + + private val versions = Seq("12", "13") + + private var client: ClientInterface = null + + versions.foreach { version => + test(s"$version: listTables") { + client = null + client = IsolatedClientLoader.forVersion(version, buildConf()).client + client.listTables("default") + } + + test(s"$version: createDatabase") { + val db = HiveDatabase("default", "") + client.createDatabase(db) + } + + test(s"$version: createTable") { + val table = + HiveTable( + specifiedDatabase = Option("default"), + name = "src", + schema = Seq(HiveColumn("key", "int", "")), + partitionColumns = Seq.empty, + properties = Map.empty, + serdeProperties = Map.empty, + tableType = ManagedTable, + location = None, + inputFormat = + Some(classOf[org.apache.hadoop.mapred.TextInputFormat].getName), + outputFormat = + Some(classOf[org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat[_, _]].getName), + serde = + Some(classOf[org.apache.hadoop.hive.serde2.`lazy`.LazySimpleSerDe].getName())) + + client.createTable(table) + } + + test(s"$version: getTable") { + client.getTable("default", "src") + } + } +} From 9e25b09f8809378777ae8bbe75dca12d2c45ff4c Mon Sep 17 00:00:00 2001 From: Sean Owen Date: Sun, 3 May 2015 21:22:31 +0100 Subject: [PATCH 557/591] [SPARK-7302] [DOCS] SPARK building documentation still mentions building for yarn 0.23 Remove references to Hadoop 0.23 CC tgravescs Is this what you had in mind? basically all refs to 0.23? We don't support YARN 0.23, but also don't support Hadoop 0.23 anymore AFAICT. There are no builds or releases for it. In fact, on a related note, refs to CDH3 (Hadoop 0.20.2) should be removed as this certainly isn't supported either. Author: Sean Owen Closes #5863 from srowen/SPARK-7302 and squashes the following commits: 42f5d1e [Sean Owen] Remove CDH3 (Hadoop 0.20.2) refs too dad02e3 [Sean Owen] Remove references to Hadoop 0.23 --- docs/building-spark.md | 4 ---- docs/hadoop-third-party-distributions.md | 3 --- make-distribution.sh | 2 +- pom.xml | 14 -------------- 4 files changed, 1 insertion(+), 22 deletions(-) diff --git a/docs/building-spark.md b/docs/building-spark.md index ea79c5bc276d3..287fcd3c4034f 100644 --- a/docs/building-spark.md +++ b/docs/building-spark.md @@ -66,7 +66,6 @@ Because HDFS is not protocol-compatible across versions, if you want to read fro Hadoop versionProfile required - 0.23.xhadoop-0.23 1.x to 2.1.x(none) 2.2.xhadoop-2.2 2.3.xhadoop-2.3 @@ -82,9 +81,6 @@ mvn -Dhadoop.version=1.2.1 -DskipTests clean package # Cloudera CDH 4.2.0 with MapReduce v1 mvn -Dhadoop.version=2.0.0-mr1-cdh4.2.0 -DskipTests clean package - -# Apache Hadoop 0.23.x -mvn -Phadoop-0.23 -Dhadoop.version=0.23.7 -DskipTests clean package {% endhighlight %} You can enable the "yarn" profile and optionally set the "yarn.version" property if it is different from "hadoop.version". Spark only supports YARN versions 2.2.0 and later. diff --git a/docs/hadoop-third-party-distributions.md b/docs/hadoop-third-party-distributions.md index 87dcc58feb494..96bd69ca3b33b 100644 --- a/docs/hadoop-third-party-distributions.md +++ b/docs/hadoop-third-party-distributions.md @@ -29,9 +29,6 @@ the _exact_ Hadoop version you are running to avoid any compatibility errors. ReleaseVersion code CDH 4.X.X (YARN mode)2.0.0-cdh4.X.X CDH 4.X.X2.0.0-mr1-cdh4.X.X - CDH 3u60.20.2-cdh3u6 - CDH 3u50.20.2-cdh3u5 - CDH 3u40.20.2-cdh3u4 diff --git a/make-distribution.sh b/make-distribution.sh index 92177e19fe6be..1bfa9acb1fe6e 100755 --- a/make-distribution.sh +++ b/make-distribution.sh @@ -58,7 +58,7 @@ while (( "$#" )); do --hadoop) echo "Error: '--hadoop' is no longer supported:" echo "Error: use Maven profiles and options -Dhadoop.version and -Dyarn.version instead." - echo "Error: Related profiles include hadoop-0.23, hdaoop-2.2, hadoop-2.3 and hadoop-2.4." + echo "Error: Related profiles include hadoop-2.2, hadoop-2.3 and hadoop-2.4." exit_with_usage ;; --with-yarn) diff --git a/pom.xml b/pom.xml index 4313f940036c8..de18741feae3a 100644 --- a/pom.xml +++ b/pom.xml @@ -1614,20 +1614,6 @@ http://hadoop.apache.org/docs/ra.b.c/hadoop-project-dist/hadoop-common/dependency-analysis.html --> - - hadoop-0.23 - - - - org.apache.avro - avro - - - - 0.23.10 - - - hadoop-2.2 From 1ffa8cb91f8badf12a8aa190dc25920715a00db7 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Sun, 3 May 2015 18:06:48 -0700 Subject: [PATCH 558/591] [SPARK-7329] [MLLIB] simplify ParamGridBuilder impl as suggested by justinuang on #5601. Author: Xiangrui Meng Closes #5873 from mengxr/SPARK-7329 and squashes the following commits: d08f9cf [Xiangrui Meng] simplify tests b7a7b9b [Xiangrui Meng] simplify grid build --- python/pyspark/ml/tuning.py | 28 +++++++++------------------- 1 file changed, 9 insertions(+), 19 deletions(-) diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py index a383bd0c0d26f..1773ab5bdcdb1 100644 --- a/python/pyspark/ml/tuning.py +++ b/python/pyspark/ml/tuning.py @@ -15,6 +15,8 @@ # limitations under the License. # +import itertools + __all__ = ['ParamGridBuilder'] @@ -37,14 +39,10 @@ class ParamGridBuilder(object): {lr.regParam: 1.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ {lr.regParam: 2.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}, \ {lr.regParam: 3.0, lr.featuresCol: 'f', lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}] - >>> fail_count = 0 - >>> for e in expected: - ... if e not in output: - ... fail_count += 1 - >>> if len(expected) != len(output): - ... fail_count += 1 - >>> fail_count - 0 + >>> len(output) == len(expected) + True + >>> all([m in expected for m in output]) + True """ def __init__(self): @@ -76,17 +74,9 @@ def build(self): Builds and returns all combinations of parameters specified by the param grid. """ - param_maps = [{}] - for (param, values) in self._param_grid.items(): - new_param_maps = [] - for value in values: - for old_map in param_maps: - copied_map = old_map.copy() - copied_map[param] = value - new_param_maps.append(copied_map) - param_maps = new_param_maps - - return param_maps + keys = self._param_grid.keys() + grid_values = self._param_grid.values() + return [dict(zip(keys, prod)) for prod in itertools.product(*grid_values)] if __name__ == "__main__": From 9646018bb4466433521b4e602b808f16e8d0ffdb Mon Sep 17 00:00:00 2001 From: Burak Yavuz Date: Sun, 3 May 2015 21:44:39 -0700 Subject: [PATCH 559/591] [SPARK-7241] Pearson correlation for DataFrames submitting this PR from a phone, excuse the brevity. adds Pearson correlation to Dataframes, reusing the covariance calculation code cc mengxr rxin Author: Burak Yavuz Closes #5858 from brkyvz/df-corr and squashes the following commits: 285b838 [Burak Yavuz] addressed comments v2.0 d10babb [Burak Yavuz] addressed comments v0.2 4b74b24 [Burak Yavuz] Merge branch 'master' of github.com:apache/spark into df-corr 4fe693b [Burak Yavuz] addressed comments v0.1 a682d06 [Burak Yavuz] ready for PR --- python/pyspark/sql/dataframe.py | 26 +++++++++ python/pyspark/sql/tests.py | 6 ++ .../spark/sql/DataFrameStatFunctions.scala | 26 +++++++++ .../sql/execution/stat/StatFunctions.scala | 58 ++++++++++++------- .../apache/spark/sql/JavaDataFrameSuite.java | 7 +++ .../apache/spark/sql/DataFrameStatSuite.scala | 33 +++++++++-- 6 files changed, 130 insertions(+), 26 deletions(-) diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index 8ddcff8fcdf98..aac5b8c4c5770 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -875,6 +875,27 @@ def fillna(self, value, subset=None): return DataFrame(self._jdf.na().fill(value, self._jseq(subset)), self.sql_ctx) + def corr(self, col1, col2, method=None): + """ + Calculates the correlation of two columns of a DataFrame as a double value. Currently only + supports the Pearson Correlation Coefficient. + :func:`DataFrame.corr` and :func:`DataFrameStatFunctions.corr` are aliases. + + :param col1: The name of the first column + :param col2: The name of the second column + :param method: The correlation method. Currently only supports "pearson" + """ + if not isinstance(col1, str): + raise ValueError("col1 should be a string.") + if not isinstance(col2, str): + raise ValueError("col2 should be a string.") + if not method: + method = "pearson" + if not method == "pearson": + raise ValueError("Currently only the calculation of the Pearson Correlation " + + "coefficient is supported.") + return self._jdf.stat().corr(col1, col2, method) + def cov(self, col1, col2): """ Calculate the sample covariance for the given columns, specified by their names, as a @@ -1359,6 +1380,11 @@ class DataFrameStatFunctions(object): def __init__(self, df): self.df = df + def corr(self, col1, col2, method=None): + return self.df.corr(col1, col2, method) + + corr.__doc__ = DataFrame.corr.__doc__ + def cov(self, col1, col2): return self.df.cov(col1, col2) diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index 613efc0ac029d..d652c302a54ba 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -394,6 +394,12 @@ def test_aggregator(self): self.assertTrue(95 < g.agg(functions.approxCountDistinct(df.key)).first()[0]) self.assertEqual(100, g.agg(functions.countDistinct(df.value)).first()[0]) + def test_corr(self): + import math + df = self.sc.parallelize([Row(a=i, b=math.sqrt(i)) for i in range(10)]).toDF() + corr = df.stat.corr("a", "b") + self.assertTrue(abs(corr - 0.95734012) < 1e-6) + def test_cov(self): df = self.sc.parallelize([Row(a=i, b=2 * i) for i in range(10)]).toDF() cov = df.stat.cov("a", "b") diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala index e8fa82947759b..903532105284e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala @@ -27,6 +27,32 @@ import org.apache.spark.sql.execution.stat._ @Experimental final class DataFrameStatFunctions private[sql](df: DataFrame) { + /** + * Calculates the correlation of two columns of a DataFrame. Currently only supports the Pearson + * Correlation Coefficient. For Spearman Correlation, consider using RDD methods found in + * MLlib's Statistics. + * + * @param col1 the name of the column + * @param col2 the name of the column to calculate the correlation against + * @return The Pearson Correlation Coefficient as a Double. + */ + def corr(col1: String, col2: String, method: String): Double = { + require(method == "pearson", "Currently only the calculation of the Pearson Correlation " + + "coefficient is supported.") + StatFunctions.pearsonCorrelation(df, Seq(col1, col2)) + } + + /** + * Calculates the Pearson Correlation Coefficient of two columns of a DataFrame. + * + * @param col1 the name of the column + * @param col2 the name of the column to calculate the correlation against + * @return The Pearson Correlation Coefficient as a Double. + */ + def corr(col1: String, col2: String): Double = { + corr(col1, col2, "pearson") + } + /** * Finding frequent items for columns, possibly with false positives. Using the * frequent element count algorithm described in diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala index d4a94c24d9866..67b48e58b17ab 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/StatFunctions.scala @@ -23,29 +23,43 @@ import org.apache.spark.sql.types.{DoubleType, NumericType} private[sql] object StatFunctions { + /** Calculate the Pearson Correlation Coefficient for the given columns */ + private[sql] def pearsonCorrelation(df: DataFrame, cols: Seq[String]): Double = { + val counts = collectStatisticalData(df, cols) + counts.Ck / math.sqrt(counts.MkX * counts.MkY) + } + /** Helper class to simplify tracking and merging counts. */ private class CovarianceCounter extends Serializable { - var xAvg = 0.0 - var yAvg = 0.0 - var Ck = 0.0 - var count = 0L + var xAvg = 0.0 // the mean of all examples seen so far in col1 + var yAvg = 0.0 // the mean of all examples seen so far in col2 + var Ck = 0.0 // the co-moment after k examples + var MkX = 0.0 // sum of squares of differences from the (current) mean for col1 + var MkY = 0.0 // sum of squares of differences from the (current) mean for col1 + var count = 0L // count of observed examples // add an example to the calculation def add(x: Double, y: Double): this.type = { - val oldX = xAvg + val deltaX = x - xAvg + val deltaY = y - yAvg count += 1 - xAvg += (x - xAvg) / count - yAvg += (y - yAvg) / count - Ck += (y - yAvg) * (x - oldX) + xAvg += deltaX / count + yAvg += deltaY / count + Ck += deltaX * (y - yAvg) + MkX += deltaX * (x - xAvg) + MkY += deltaY * (y - yAvg) this } // merge counters from other partitions. Formula can be found at: - // http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Covariance + // http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance def merge(other: CovarianceCounter): this.type = { val totalCount = count + other.count - Ck += other.Ck + - (xAvg - other.xAvg) * (yAvg - other.yAvg) * count / totalCount * other.count + val deltaX = xAvg - other.xAvg + val deltaY = yAvg - other.yAvg + Ck += other.Ck + deltaX * deltaY * count / totalCount * other.count xAvg = (xAvg * count + other.xAvg * other.count) / totalCount yAvg = (yAvg * count + other.yAvg * other.count) / totalCount + MkX += other.MkX + deltaX * deltaX * count / totalCount * other.count + MkY += other.MkY + deltaY * deltaY * count / totalCount * other.count count = totalCount this } @@ -53,13 +67,7 @@ private[sql] object StatFunctions { def cov: Double = Ck / (count - 1) } - /** - * Calculate the covariance of two numerical columns of a DataFrame. - * @param df The DataFrame - * @param cols the column names - * @return the covariance of the two columns. - */ - private[sql] def calculateCov(df: DataFrame, cols: Seq[String]): Double = { + private def collectStatisticalData(df: DataFrame, cols: Seq[String]): CovarianceCounter = { require(cols.length == 2, "Currently cov supports calculating the covariance " + "between two columns.") cols.map(name => (name, df.schema.fields.find(_.name == name))).foreach { case (name, data) => @@ -68,13 +76,23 @@ private[sql] object StatFunctions { s"with dataType ${data.get.dataType} not supported.") } val columns = cols.map(n => Column(Cast(Column(n).expr, DoubleType))) - val counts = df.select(columns:_*).rdd.aggregate(new CovarianceCounter)( + df.select(columns: _*).rdd.aggregate(new CovarianceCounter)( seqOp = (counter, row) => { counter.add(row.getDouble(0), row.getDouble(1)) }, combOp = (baseCounter, other) => { baseCounter.merge(other) - }) + }) + } + + /** + * Calculate the covariance of two numerical columns of a DataFrame. + * @param df The DataFrame + * @param cols the column names + * @return the covariance of the two columns. + */ + private[sql] def calculateCov(df: DataFrame, cols: Seq[String]): Double = { + val counts = collectStatisticalData(df, cols) counts.cov } } diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java index 96fe66d0b84a6..78e847239f405 100644 --- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java @@ -187,6 +187,13 @@ public void testFrequentItems() { Assert.assertTrue(results.collect()[0].getSeq(0).contains(1)); } + @Test + public void testCorrelation() { + DataFrame df = context.table("testData2"); + Double pearsonCorr = df.stat().corr("a", "b", "pearson"); + Assert.assertTrue(Math.abs(pearsonCorr) < 1e-6); + } + @Test public void testCovariance() { DataFrame df = context.table("testData2"); diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala index 4f5a2ff696789..06764d2a122f1 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala @@ -30,10 +30,10 @@ class DataFrameStatSuite extends FunSuite { def toLetter(i: Int): String = (i + 97).toChar.toString test("Frequent Items") { - val rows = Array.tabulate(1000) { i => + val rows = Seq.tabulate(1000) { i => if (i % 3 == 0) (1, toLetter(1), -1.0) else (i, toLetter(i), i * -1.0) } - val df = sqlCtx.sparkContext.parallelize(rows).toDF("numbers", "letters", "negDoubles") + val df = rows.toDF("numbers", "letters", "negDoubles") val results = df.stat.freqItems(Array("numbers", "letters"), 0.1) val items = results.collect().head @@ -43,19 +43,40 @@ class DataFrameStatSuite extends FunSuite { val singleColResults = df.stat.freqItems(Array("negDoubles"), 0.1) val items2 = singleColResults.collect().head items2.getSeq[Double](0) should contain (-1.0) + } + test("pearson correlation") { + val df = Seq.tabulate(10)(i => (i, 2 * i, i * -1.0)).toDF("a", "b", "c") + val corr1 = df.stat.corr("a", "b", "pearson") + assert(math.abs(corr1 - 1.0) < 1e-12) + val corr2 = df.stat.corr("a", "c", "pearson") + assert(math.abs(corr2 + 1.0) < 1e-12) + // non-trivial example. To reproduce in python, use: + // >>> from scipy.stats import pearsonr + // >>> import numpy as np + // >>> a = np.array(range(20)) + // >>> b = np.array([x * x - 2 * x + 3.5 for x in range(20)]) + // >>> pearsonr(a, b) + // (0.95723391394758572, 3.8902121417802199e-11) + // In R, use: + // > a <- 0:19 + // > b <- mapply(function(x) x * x - 2 * x + 3.5, a) + // > cor(a, b) + // [1] 0.957233913947585835 + val df2 = Seq.tabulate(20)(x => (x, x * x - 2 * x + 3.5)).toDF("a", "b") + val corr3 = df2.stat.corr("a", "b", "pearson") + assert(math.abs(corr3 - 0.95723391394758572) < 1e-12) } test("covariance") { - val rows = Array.tabulate(10)(i => (i, 2.0 * i, toLetter(i))) - val df = sqlCtx.sparkContext.parallelize(rows).toDF("singles", "doubles", "letters") + val df = Seq.tabulate(10)(i => (i, 2.0 * i, toLetter(i))).toDF("singles", "doubles", "letters") val results = df.stat.cov("singles", "doubles") - assert(math.abs(results - 55.0 / 3) < 1e-6) + assert(math.abs(results - 55.0 / 3) < 1e-12) intercept[IllegalArgumentException] { df.stat.cov("singles", "letters") // doesn't accept non-numerical dataTypes } val decimalRes = decimalData.stat.cov("a", "b") - assert(math.abs(decimalRes) < 1e-6) + assert(math.abs(decimalRes) < 1e-12) } } From 3539cb7d20f5f878132407ec3b854011b183b2ad Mon Sep 17 00:00:00 2001 From: Yuhao Yang Date: Mon, 4 May 2015 00:06:25 -0700 Subject: [PATCH 560/591] [SPARK-5563] [MLLIB] LDA with online variational inference MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit JIRA: https://issues.apache.org/jira/browse/SPARK-5563 The PR contains the implementation for [Online LDA] (https://www.cs.princeton.edu/~blei/papers/HoffmanBleiBach2010b.pdf) based on the research of Matt Hoffman and David M. Blei, which provides an efficient option for LDA users. Major advantages for the algorithm are the stream compatibility and economic time/memory consumption due to the corpus split. For more details, please refer to the jira. Online LDA can act as a fast option for LDA, and will be especially helpful for the users who needs a quick result or with large corpus. Correctness test. I have tested current PR with https://github.com/Blei-Lab/onlineldavb and the results are identical. I've uploaded the result and code to https://github.com/hhbyyh/LDACrossValidation. Author: Yuhao Yang Author: Joseph K. Bradley Closes #4419 from hhbyyh/ldaonline and squashes the following commits: 1045eec [Yuhao Yang] Merge pull request #2 from jkbradley/hhbyyh-ldaonline2 cf376ff [Joseph K. Bradley] For private vars needed for testing, I made them private and added accessors. Java doesn’t understand package-private tags, so this minimizes the issues Java users might encounter. 6149ca6 [Yuhao Yang] fix for setOptimizer cf0007d [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline 54cf8da [Yuhao Yang] some style change 68c2318 [Yuhao Yang] add a java ut 4041723 [Yuhao Yang] add ut 138bfed [Yuhao Yang] Merge pull request #1 from jkbradley/hhbyyh-ldaonline-update 9e910d9 [Joseph K. Bradley] small fix 61d60df [Joseph K. Bradley] Minor cleanups: * Update *Concentration parameter documentation * EM Optimizer: createVertices() does not need to be a function * OnlineLDAOptimizer: typos in doc * Clean up the core code for online LDA (Scala style) a996a82 [Yuhao Yang] respond to comments b1178cf [Yuhao Yang] fit into the optimizer framework dbe3cff [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline 15be071 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline b29193b [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline d19ef55 [Yuhao Yang] change OnlineLDA to class 97b9e1a [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline e7bf3b0 [Yuhao Yang] move to seperate file f367cc9 [Yuhao Yang] change to optimization 8cb16a6 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline 62405cc [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline 02d0373 [Yuhao Yang] fix style in comment f6d47ca [Yuhao Yang] Merge branch 'ldaonline' of https://github.com/hhbyyh/spark into ldaonline d86cdec [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline a570c9a [Yuhao Yang] use sample to pick up batch 4a3f27e [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline e271eb1 [Yuhao Yang] remove non ascii 581c623 [Yuhao Yang] seperate API and adjust batch split 37af91a [Yuhao Yang] iMerge remote-tracking branch 'upstream/master' into ldaonline 20328d1 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline i aa365d1 [Yuhao Yang] merge upstream master 3a06526 [Yuhao Yang] merge with new example 0dd3947 [Yuhao Yang] kMerge remote-tracking branch 'upstream/master' into ldaonline 0d0f3ee [Yuhao Yang] replace random split with sliding fa408a8 [Yuhao Yang] ssMerge remote-tracking branch 'upstream/master' into ldaonline 45884ab [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline s f41c5ca [Yuhao Yang] style fix 26dca1b [Yuhao Yang] style fix and make class private 043e786 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into ldaonline s Conflicts: mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala d640d9c [Yuhao Yang] online lda initial checkin --- .../apache/spark/mllib/clustering/LDA.scala | 65 ++-- .../spark/mllib/clustering/LDAOptimizer.scala | 320 ++++++++++++++++-- .../spark/mllib/clustering/JavaLDASuite.java | 38 ++- .../spark/mllib/clustering/LDASuite.scala | 89 ++++- 4 files changed, 438 insertions(+), 74 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala index 37bf88b73b911..c8daa2388e868 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala @@ -78,35 +78,29 @@ class LDA private ( * * This is the parameter to a symmetric Dirichlet distribution. */ - def getDocConcentration: Double = { - if (this.docConcentration == -1) { - (50.0 / k) + 1.0 - } else { - this.docConcentration - } - } + def getDocConcentration: Double = this.docConcentration /** * Concentration parameter (commonly named "alpha") for the prior placed on documents' * distributions over topics ("theta"). * - * This is the parameter to a symmetric Dirichlet distribution. + * This is the parameter to a symmetric Dirichlet distribution, where larger values + * mean more smoothing (more regularization). * - * This value should be > 1.0, where larger values mean more smoothing (more regularization). * If set to -1, then docConcentration is set automatically. * (default = -1 = automatic) * - * Automatic setting of parameter: - * - For EM: default = (50 / k) + 1. - * - The 50/k is common in LDA libraries. - * - The +1 follows Asuncion et al. (2009), who recommend a +1 adjustment for EM. - * - * Note: The restriction > 1.0 may be relaxed in the future (allowing sparse solutions), - * but values in (0,1) are not yet supported. + * Optimizer-specific parameter settings: + * - EM + * - Value should be > 1.0 + * - default = (50 / k) + 1, where 50/k is common in LDA libraries and +1 follows + * Asuncion et al. (2009), who recommend a +1 adjustment for EM. + * - Online + * - Value should be >= 0 + * - default = (1.0 / k), following the implementation from + * [[https://github.com/Blei-Lab/onlineldavb]]. */ def setDocConcentration(docConcentration: Double): this.type = { - require(docConcentration > 1.0 || docConcentration == -1.0, - s"LDA docConcentration must be > 1.0 (or -1 for auto), but was set to $docConcentration") this.docConcentration = docConcentration this } @@ -126,13 +120,7 @@ class LDA private ( * Note: The topics' distributions over terms are called "beta" in the original LDA paper * by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009. */ - def getTopicConcentration: Double = { - if (this.topicConcentration == -1) { - 1.1 - } else { - this.topicConcentration - } - } + def getTopicConcentration: Double = this.topicConcentration /** * Concentration parameter (commonly named "beta" or "eta") for the prior placed on topics' @@ -143,21 +131,20 @@ class LDA private ( * Note: The topics' distributions over terms are called "beta" in the original LDA paper * by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009. * - * This value should be > 0.0. * If set to -1, then topicConcentration is set automatically. * (default = -1 = automatic) * - * Automatic setting of parameter: - * - For EM: default = 0.1 + 1. - * - The 0.1 gives a small amount of smoothing. - * - The +1 follows Asuncion et al. (2009), who recommend a +1 adjustment for EM. - * - * Note: The restriction > 1.0 may be relaxed in the future (allowing sparse solutions), - * but values in (0,1) are not yet supported. + * Optimizer-specific parameter settings: + * - EM + * - Value should be > 1.0 + * - default = 0.1 + 1, where 0.1 gives a small amount of smoothing and +1 follows + * Asuncion et al. (2009), who recommend a +1 adjustment for EM. + * - Online + * - Value should be >= 0 + * - default = (1.0 / k), following the implementation from + * [[https://github.com/Blei-Lab/onlineldavb]]. */ def setTopicConcentration(topicConcentration: Double): this.type = { - require(topicConcentration > 1.0 || topicConcentration == -1.0, - s"LDA topicConcentration must be > 1.0 (or -1 for auto), but was set to $topicConcentration") this.topicConcentration = topicConcentration this } @@ -223,14 +210,15 @@ class LDA private ( /** * Set the LDAOptimizer used to perform the actual calculation by algorithm name. - * Currently "em" is supported. + * Currently "em", "online" is supported. */ def setOptimizer(optimizerName: String): this.type = { this.ldaOptimizer = optimizerName.toLowerCase match { case "em" => new EMLDAOptimizer + case "online" => new OnlineLDAOptimizer case other => - throw new IllegalArgumentException(s"Only em is supported but got $other.") + throw new IllegalArgumentException(s"Only em, online are supported but got $other.") } this } @@ -245,8 +233,7 @@ class LDA private ( * @return Inferred LDA model */ def run(documents: RDD[(Long, Vector)]): LDAModel = { - val state = ldaOptimizer.initialState(documents, k, getDocConcentration, getTopicConcentration, - seed, checkpointInterval) + val state = ldaOptimizer.initialize(documents, this) var iter = 0 val iterationTimes = Array.fill[Double](maxIterations)(0) while (iter < maxIterations) { diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala index ffd72a294c6c6..093aa0f315ab2 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala @@ -19,13 +19,15 @@ package org.apache.spark.mllib.clustering import java.util.Random -import breeze.linalg.{DenseVector => BDV, normalize} +import breeze.linalg.{DenseVector => BDV, DenseMatrix => BDM, sum, normalize, kron} +import breeze.numerics.{digamma, exp, abs} +import breeze.stats.distributions.{Gamma, RandBasis} import org.apache.spark.annotation.Experimental import org.apache.spark.graphx._ import org.apache.spark.graphx.impl.GraphImpl import org.apache.spark.mllib.impl.PeriodicGraphCheckpointer -import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.linalg.{Matrices, SparseVector, DenseVector, Vector} import org.apache.spark.rdd.RDD /** @@ -35,7 +37,7 @@ import org.apache.spark.rdd.RDD * hold optimizer-specific parameters for users to set. */ @Experimental -trait LDAOptimizer{ +trait LDAOptimizer { /* DEVELOPERS NOTE: @@ -49,13 +51,7 @@ trait LDAOptimizer{ * Initializer for the optimizer. LDA passes the common parameters to the optimizer and * the internal structure can be initialized properly. */ - private[clustering] def initialState( - docs: RDD[(Long, Vector)], - k: Int, - docConcentration: Double, - topicConcentration: Double, - randomSeed: Long, - checkpointInterval: Int): LDAOptimizer + private[clustering] def initialize(docs: RDD[(Long, Vector)], lda: LDA): LDAOptimizer private[clustering] def next(): LDAOptimizer @@ -80,12 +76,12 @@ trait LDAOptimizer{ * */ @Experimental -class EMLDAOptimizer extends LDAOptimizer{ +class EMLDAOptimizer extends LDAOptimizer { import LDA._ /** - * Following fields will only be initialized through initialState method + * The following fields will only be initialized through the initialize() method */ private[clustering] var graph: Graph[TopicCounts, TokenCount] = null private[clustering] var k: Int = 0 @@ -98,13 +94,23 @@ class EMLDAOptimizer extends LDAOptimizer{ /** * Compute bipartite term/doc graph. */ - private[clustering] override def initialState( - docs: RDD[(Long, Vector)], - k: Int, - docConcentration: Double, - topicConcentration: Double, - randomSeed: Long, - checkpointInterval: Int): LDAOptimizer = { + override private[clustering] def initialize(docs: RDD[(Long, Vector)], lda: LDA): LDAOptimizer = { + + val docConcentration = lda.getDocConcentration + val topicConcentration = lda.getTopicConcentration + val k = lda.getK + + // Note: The restriction > 1.0 may be relaxed in the future (allowing sparse solutions), + // but values in (0,1) are not yet supported. + require(docConcentration > 1.0 || docConcentration == -1.0, s"LDA docConcentration must be" + + s" > 1.0 (or -1 for auto) for EM Optimizer, but was set to $docConcentration") + require(topicConcentration > 1.0 || topicConcentration == -1.0, s"LDA topicConcentration " + + s"must be > 1.0 (or -1 for auto) for EM Optimizer, but was set to $topicConcentration") + + this.docConcentration = if (docConcentration == -1) (50.0 / k) + 1.0 else docConcentration + this.topicConcentration = if (topicConcentration == -1) 1.1 else topicConcentration + val randomSeed = lda.getSeed + // For each document, create an edge (Document -> Term) for each unique term in the document. val edges: RDD[Edge[TokenCount]] = docs.flatMap { case (docID: Long, termCounts: Vector) => // Add edges for terms with non-zero counts. @@ -113,11 +119,9 @@ class EMLDAOptimizer extends LDAOptimizer{ } } - val vocabSize = docs.take(1).head._2.size - // Create vertices. // Initially, we use random soft assignments of tokens to topics (random gamma). - def createVertices(): RDD[(VertexId, TopicCounts)] = { + val docTermVertices: RDD[(VertexId, TopicCounts)] = { val verticesTMP: RDD[(VertexId, TopicCounts)] = edges.mapPartitionsWithIndex { case (partIndex, partEdges) => val random = new Random(partIndex + randomSeed) @@ -130,22 +134,18 @@ class EMLDAOptimizer extends LDAOptimizer{ verticesTMP.reduceByKey(_ + _) } - val docTermVertices = createVertices() - // Partition such that edges are grouped by document this.graph = Graph(docTermVertices, edges).partitionBy(PartitionStrategy.EdgePartition1D) this.k = k - this.vocabSize = vocabSize - this.docConcentration = docConcentration - this.topicConcentration = topicConcentration - this.checkpointInterval = checkpointInterval + this.vocabSize = docs.take(1).head._2.size + this.checkpointInterval = lda.getCheckpointInterval this.graphCheckpointer = new PeriodicGraphCheckpointer[TopicCounts, TokenCount](graph, checkpointInterval) this.globalTopicTotals = computeGlobalTopicTotals() this } - private[clustering] override def next(): EMLDAOptimizer = { + override private[clustering] def next(): EMLDAOptimizer = { require(graph != null, "graph is null, EMLDAOptimizer not initialized.") val eta = topicConcentration @@ -202,9 +202,269 @@ class EMLDAOptimizer extends LDAOptimizer{ graph.vertices.filter(isTermVertex).values.fold(BDV.zeros[Double](numTopics))(_ += _) } - private[clustering] override def getLDAModel(iterationTimes: Array[Double]): LDAModel = { + override private[clustering] def getLDAModel(iterationTimes: Array[Double]): LDAModel = { require(graph != null, "graph is null, EMLDAOptimizer not initialized.") this.graphCheckpointer.deleteAllCheckpoints() new DistributedLDAModel(this, iterationTimes) } } + + +/** + * :: Experimental :: + * + * An online optimizer for LDA. The Optimizer implements the Online variational Bayes LDA + * algorithm, which processes a subset of the corpus on each iteration, and updates the term-topic + * distribution adaptively. + * + * Original Online LDA paper: + * Hoffman, Blei and Bach, "Online Learning for Latent Dirichlet Allocation." NIPS, 2010. + */ +@Experimental +class OnlineLDAOptimizer extends LDAOptimizer { + + // LDA common parameters + private var k: Int = 0 + private var corpusSize: Long = 0 + private var vocabSize: Int = 0 + + /** alias for docConcentration */ + private var alpha: Double = 0 + + /** (private[clustering] for debugging) Get docConcentration */ + private[clustering] def getAlpha: Double = alpha + + /** alias for topicConcentration */ + private var eta: Double = 0 + + /** (private[clustering] for debugging) Get topicConcentration */ + private[clustering] def getEta: Double = eta + + private var randomGenerator: java.util.Random = null + + // Online LDA specific parameters + // Learning rate is: (tau_0 + t)^{-kappa} + private var tau_0: Double = 1024 + private var kappa: Double = 0.51 + private var miniBatchFraction: Double = 0.05 + + // internal data structure + private var docs: RDD[(Long, Vector)] = null + + /** Dirichlet parameter for the posterior over topics */ + private var lambda: BDM[Double] = null + + /** (private[clustering] for debugging) Get parameter for topics */ + private[clustering] def getLambda: BDM[Double] = lambda + + /** Current iteration (count of invocations of [[next()]]) */ + private var iteration: Int = 0 + private var gammaShape: Double = 100 + + /** + * A (positive) learning parameter that downweights early iterations. Larger values make early + * iterations count less. + */ + def getTau_0: Double = this.tau_0 + + /** + * A (positive) learning parameter that downweights early iterations. Larger values make early + * iterations count less. + * Default: 1024, following the original Online LDA paper. + */ + def setTau_0(tau_0: Double): this.type = { + require(tau_0 > 0, s"LDA tau_0 must be positive, but was set to $tau_0") + this.tau_0 = tau_0 + this + } + + /** + * Learning rate: exponential decay rate + */ + def getKappa: Double = this.kappa + + /** + * Learning rate: exponential decay rate---should be between + * (0.5, 1.0] to guarantee asymptotic convergence. + * Default: 0.51, based on the original Online LDA paper. + */ + def setKappa(kappa: Double): this.type = { + require(kappa >= 0, s"Online LDA kappa must be nonnegative, but was set to $kappa") + this.kappa = kappa + this + } + + /** + * Mini-batch fraction, which sets the fraction of document sampled and used in each iteration + */ + def getMiniBatchFraction: Double = this.miniBatchFraction + + /** + * Mini-batch fraction in (0, 1], which sets the fraction of document sampled and used in + * each iteration. + * + * Note that this should be adjusted in synch with [[LDA.setMaxIterations()]] + * so the entire corpus is used. Specifically, set both so that + * maxIterations * miniBatchFraction >= 1. + * + * Default: 0.05, i.e., 5% of total documents. + */ + def setMiniBatchFraction(miniBatchFraction: Double): this.type = { + require(miniBatchFraction > 0.0 && miniBatchFraction <= 1.0, + s"Online LDA miniBatchFraction must be in range (0,1], but was set to $miniBatchFraction") + this.miniBatchFraction = miniBatchFraction + this + } + + /** + * (private[clustering]) + * Set the Dirichlet parameter for the posterior over topics. + * This is only used for testing now. In the future, it can help support training stop/resume. + */ + private[clustering] def setLambda(lambda: BDM[Double]): this.type = { + this.lambda = lambda + this + } + + /** + * (private[clustering]) + * Used for random initialization of the variational parameters. + * Larger value produces values closer to 1.0. + * This is only used for testing currently. + */ + private[clustering] def setGammaShape(shape: Double): this.type = { + this.gammaShape = shape + this + } + + override private[clustering] def initialize( + docs: RDD[(Long, Vector)], + lda: LDA): OnlineLDAOptimizer = { + this.k = lda.getK + this.corpusSize = docs.count() + this.vocabSize = docs.first()._2.size + this.alpha = if (lda.getDocConcentration == -1) 1.0 / k else lda.getDocConcentration + this.eta = if (lda.getTopicConcentration == -1) 1.0 / k else lda.getTopicConcentration + this.randomGenerator = new Random(lda.getSeed) + + this.docs = docs + + // Initialize the variational distribution q(beta|lambda) + this.lambda = getGammaMatrix(k, vocabSize) + this.iteration = 0 + this + } + + override private[clustering] def next(): OnlineLDAOptimizer = { + val batch = docs.sample(withReplacement = true, miniBatchFraction, randomGenerator.nextLong()) + if (batch.isEmpty()) return this + submitMiniBatch(batch) + } + + /** + * Submit a subset (like 1%, decide by the miniBatchFraction) of the corpus to the Online LDA + * model, and it will update the topic distribution adaptively for the terms appearing in the + * subset. + */ + private[clustering] def submitMiniBatch(batch: RDD[(Long, Vector)]): OnlineLDAOptimizer = { + iteration += 1 + val k = this.k + val vocabSize = this.vocabSize + val Elogbeta = dirichletExpectation(lambda) + val expElogbeta = exp(Elogbeta) + val alpha = this.alpha + val gammaShape = this.gammaShape + + val stats: RDD[BDM[Double]] = batch.mapPartitions { docs => + val stat = BDM.zeros[Double](k, vocabSize) + docs.foreach { doc => + val termCounts = doc._2 + val (ids: List[Int], cts: Array[Double]) = termCounts match { + case v: DenseVector => ((0 until v.size).toList, v.values) + case v: SparseVector => (v.indices.toList, v.values) + case v => throw new IllegalArgumentException("Online LDA does not support vector type " + + v.getClass) + } + + // Initialize the variational distribution q(theta|gamma) for the mini-batch + var gammad = new Gamma(gammaShape, 1.0 / gammaShape).samplesVector(k).t // 1 * K + var Elogthetad = digamma(gammad) - digamma(sum(gammad)) // 1 * K + var expElogthetad = exp(Elogthetad) // 1 * K + val expElogbetad = expElogbeta(::, ids).toDenseMatrix // K * ids + + var phinorm = expElogthetad * expElogbetad + 1e-100 // 1 * ids + var meanchange = 1D + val ctsVector = new BDV[Double](cts).t // 1 * ids + + // Iterate between gamma and phi until convergence + while (meanchange > 1e-3) { + val lastgamma = gammad + // 1*K 1 * ids ids * k + gammad = (expElogthetad :* ((ctsVector / phinorm) * expElogbetad.t)) + alpha + Elogthetad = digamma(gammad) - digamma(sum(gammad)) + expElogthetad = exp(Elogthetad) + phinorm = expElogthetad * expElogbetad + 1e-100 + meanchange = sum(abs(gammad - lastgamma)) / k + } + + val m1 = expElogthetad.t + val m2 = (ctsVector / phinorm).t.toDenseVector + var i = 0 + while (i < ids.size) { + stat(::, ids(i)) := stat(::, ids(i)) + m1 * m2(i) + i += 1 + } + } + Iterator(stat) + } + + val statsSum: BDM[Double] = stats.reduce(_ += _) + val batchResult = statsSum :* expElogbeta + + // Note that this is an optimization to avoid batch.count + update(batchResult, iteration, (miniBatchFraction * corpusSize).ceil.toInt) + this + } + + override private[clustering] def getLDAModel(iterationTimes: Array[Double]): LDAModel = { + new LocalLDAModel(Matrices.fromBreeze(lambda).transpose) + } + + /** + * Update lambda based on the batch submitted. batchSize can be different for each iteration. + */ + private[clustering] def update(stat: BDM[Double], iter: Int, batchSize: Int): Unit = { + val tau_0 = this.getTau_0 + val kappa = this.getKappa + + // weight of the mini-batch. + val weight = math.pow(tau_0 + iter, -kappa) + + // Update lambda based on documents. + lambda = lambda * (1 - weight) + + (stat * (corpusSize.toDouble / batchSize.toDouble) + eta) * weight + } + + /** + * Get a random matrix to initialize lambda + */ + private def getGammaMatrix(row: Int, col: Int): BDM[Double] = { + val randBasis = new RandBasis(new org.apache.commons.math3.random.MersenneTwister( + randomGenerator.nextLong())) + val gammaRandomGenerator = new Gamma(gammaShape, 1.0 / gammaShape)(randBasis) + val temp = gammaRandomGenerator.sample(row * col).toArray + new BDM[Double](col, row, temp).t + } + + /** + * For theta ~ Dir(alpha), computes E[log(theta)] given alpha. Currently the implementation + * uses digamma which is accurate but expensive. + */ + private def dirichletExpectation(alpha: BDM[Double]): BDM[Double] = { + val rowSum = sum(alpha(breeze.linalg.*, ::)) + val digAlpha = digamma(alpha) + val digRowSum = digamma(rowSum) + val result = digAlpha(::, breeze.linalg.*) - digRowSum + result + } +} diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java index fbe171b4b1ab1..f394d903966de 100644 --- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java +++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java @@ -20,7 +20,6 @@ import java.io.Serializable; import java.util.ArrayList; -import org.apache.spark.api.java.JavaRDD; import scala.Tuple2; import org.junit.After; @@ -30,6 +29,7 @@ import org.junit.Test; import org.apache.spark.api.java.JavaPairRDD; +import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.mllib.linalg.Matrix; import org.apache.spark.mllib.linalg.Vector; @@ -109,11 +109,45 @@ public void distributedLDAModel() { assert(model.logPrior() < 0.0); } + @Test + public void OnlineOptimizerCompatibility() { + int k = 3; + double topicSmoothing = 1.2; + double termSmoothing = 1.2; + + // Train a model + OnlineLDAOptimizer op = new OnlineLDAOptimizer() + .setTau_0(1024) + .setKappa(0.51) + .setGammaShape(1e40) + .setMiniBatchFraction(0.5); + + LDA lda = new LDA(); + lda.setK(k) + .setDocConcentration(topicSmoothing) + .setTopicConcentration(termSmoothing) + .setMaxIterations(5) + .setSeed(12345) + .setOptimizer(op); + + LDAModel model = lda.run(corpus); + + // Check: basic parameters + assertEquals(model.k(), k); + assertEquals(model.vocabSize(), tinyVocabSize); + + // Check: topic summaries + Tuple2[] roundedTopicSummary = model.describeTopics(); + assertEquals(roundedTopicSummary.length, k); + Tuple2[] roundedLocalTopicSummary = model.describeTopics(); + assertEquals(roundedLocalTopicSummary.length, k); + } + private static int tinyK = LDASuite$.MODULE$.tinyK(); private static int tinyVocabSize = LDASuite$.MODULE$.tinyVocabSize(); private static Matrix tinyTopics = LDASuite$.MODULE$.tinyTopics(); private static Tuple2[] tinyTopicDescription = LDASuite$.MODULE$.tinyTopicDescription(); - JavaPairRDD corpus; + private JavaPairRDD corpus; } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala index 41ec794146c69..2dcc881f5abd2 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala @@ -17,6 +17,8 @@ package org.apache.spark.mllib.clustering +import breeze.linalg.{DenseMatrix => BDM} + import org.scalatest.FunSuite import org.apache.spark.mllib.linalg.{Vector, DenseMatrix, Matrix, Vectors} @@ -37,7 +39,7 @@ class LDASuite extends FunSuite with MLlibTestSparkContext { // Check: describeTopics() with all terms val fullTopicSummary = model.describeTopics() - assert(fullTopicSummary.size === tinyK) + assert(fullTopicSummary.length === tinyK) fullTopicSummary.zip(tinyTopicDescription).foreach { case ((algTerms, algTermWeights), (terms, termWeights)) => assert(algTerms === terms) @@ -54,7 +56,7 @@ class LDASuite extends FunSuite with MLlibTestSparkContext { } } - test("running and DistributedLDAModel") { + test("running and DistributedLDAModel with default Optimizer (EM)") { val k = 3 val topicSmoothing = 1.2 val termSmoothing = 1.2 @@ -99,7 +101,7 @@ class LDASuite extends FunSuite with MLlibTestSparkContext { // Check: per-doc topic distributions val topicDistributions = model.topicDistributions.collect() // Ensure all documents are covered. - assert(topicDistributions.size === tinyCorpus.size) + assert(topicDistributions.length === tinyCorpus.length) assert(tinyCorpus.map(_._1).toSet === topicDistributions.map(_._1).toSet) // Ensure we have proper distributions topicDistributions.foreach { case (docId, topicDistribution) => @@ -131,6 +133,87 @@ class LDASuite extends FunSuite with MLlibTestSparkContext { assert(lda.getBeta === 3.0) assert(lda.getTopicConcentration === 3.0) } + + test("OnlineLDAOptimizer initialization") { + val lda = new LDA().setK(2) + val corpus = sc.parallelize(tinyCorpus, 2) + val op = new OnlineLDAOptimizer().initialize(corpus, lda) + op.setKappa(0.9876).setMiniBatchFraction(0.123).setTau_0(567) + assert(op.getAlpha == 0.5) // default 1.0 / k + assert(op.getEta == 0.5) // default 1.0 / k + assert(op.getKappa == 0.9876) + assert(op.getMiniBatchFraction == 0.123) + assert(op.getTau_0 == 567) + } + + test("OnlineLDAOptimizer one iteration") { + // run OnlineLDAOptimizer for 1 iteration to verify it's consistency with Blei-lab, + // [[https://github.com/Blei-Lab/onlineldavb]] + val k = 2 + val vocabSize = 6 + + def docs: Array[(Long, Vector)] = Array( + Vectors.sparse(vocabSize, Array(0, 1, 2), Array(1, 1, 1)), // apple, orange, banana + Vectors.sparse(vocabSize, Array(3, 4, 5), Array(1, 1, 1)) // tiger, cat, dog + ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) } + val corpus = sc.parallelize(docs, 2) + + // Set GammaShape large to avoid the stochastic impact. + val op = new OnlineLDAOptimizer().setTau_0(1024).setKappa(0.51).setGammaShape(1e40) + .setMiniBatchFraction(1) + val lda = new LDA().setK(k).setMaxIterations(1).setOptimizer(op).setSeed(12345) + + val state = op.initialize(corpus, lda) + // override lambda to simulate an intermediate state + // [[ 1.1 1.2 1.3 0.9 0.8 0.7] + // [ 0.9 0.8 0.7 1.1 1.2 1.3]] + op.setLambda(new BDM[Double](k, vocabSize, + Array(1.1, 0.9, 1.2, 0.8, 1.3, 0.7, 0.9, 1.1, 0.8, 1.2, 0.7, 1.3))) + + // run for one iteration + state.submitMiniBatch(corpus) + + // verify the result, Note this generate the identical result as + // [[https://github.com/Blei-Lab/onlineldavb]] + val topic1 = op.getLambda(0, ::).inner.toArray.map("%.4f".format(_)).mkString(", ") + val topic2 = op.getLambda(1, ::).inner.toArray.map("%.4f".format(_)).mkString(", ") + assert("1.1101, 1.2076, 1.3050, 0.8899, 0.7924, 0.6950" == topic1) + assert("0.8899, 0.7924, 0.6950, 1.1101, 1.2076, 1.3050" == topic2) + } + + test("OnlineLDAOptimizer with toy data") { + def toydata: Array[(Long, Vector)] = Array( + Vectors.sparse(6, Array(0, 1), Array(1, 1)), + Vectors.sparse(6, Array(1, 2), Array(1, 1)), + Vectors.sparse(6, Array(0, 2), Array(1, 1)), + Vectors.sparse(6, Array(3, 4), Array(1, 1)), + Vectors.sparse(6, Array(3, 5), Array(1, 1)), + Vectors.sparse(6, Array(4, 5), Array(1, 1)) + ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) } + + val docs = sc.parallelize(toydata) + val op = new OnlineLDAOptimizer().setMiniBatchFraction(1).setTau_0(1024).setKappa(0.51) + .setGammaShape(1e10) + val lda = new LDA().setK(2) + .setDocConcentration(0.01) + .setTopicConcentration(0.01) + .setMaxIterations(100) + .setOptimizer(op) + .setSeed(12345) + + val ldaModel = lda.run(docs) + val topicIndices = ldaModel.describeTopics(maxTermsPerTopic = 10) + val topics = topicIndices.map { case (terms, termWeights) => + terms.zip(termWeights) + } + + // check distribution for each topic, typical distribution is (0.3, 0.3, 0.3, 0.02, 0.02, 0.02) + topics.foreach { topic => + val smalls = topic.filter(t => t._2 < 0.1).map(_._2) + assert(smalls.length == 3 && smalls.sum < 0.2) + } + } + } private[clustering] object LDASuite { From 343d3bfafd449a0371feb6a88f78e07302fa7143 Mon Sep 17 00:00:00 2001 From: tianyi Date: Mon, 4 May 2015 16:59:34 +0800 Subject: [PATCH 561/591] [SPARK-5100] [SQL] add webui for thriftserver This PR is a rebased version of #3946 , and mainly focused on creating an independent tab for the thrift server in spark web UI. Features: 1. Session related statistics ( username and IP are only supported in hive-0.13.1 ) 2. List all the SQL executing or executed on this server 3. Provide links to the job generated by SQL 4. Provide link to show all SQL executing or executed in a specified session Prototype snapshots: This is the main page for thrift server ![image](https://cloud.githubusercontent.com/assets/1411869/7361379/df7dcc64-ed89-11e4-9964-4df0b32f475e.png) Author: tianyi Closes #5730 from tianyi/SPARK-5100 and squashes the following commits: cfd14c7 [tianyi] style fix 0efe3d5 [tianyi] revert part of pom change c0f2fa0 [tianyi] extends HiveThriftJdbcTest to start/stop thriftserver for UI test aa20408 [tianyi] fix style problem c9df6f9 [tianyi] add testsuite for thriftserver ui and fix some style issue 9830199 [tianyi] add webui for thriftserver --- .../scala/org/apache/spark/sql/SQLConf.scala | 2 + sql/hive-thriftserver/pom.xml | 12 ++ .../hive/thriftserver/HiveThriftServer2.scala | 161 +++++++++++++- .../thriftserver/ui/ThriftServerPage.scala | 190 +++++++++++++++++ .../ui/ThriftServerSessionPage.scala | 197 ++++++++++++++++++ .../thriftserver/ui/ThriftServerTab.scala | 50 +++++ .../HiveThriftServer2Suites.scala | 12 +- .../hive/thriftserver/UISeleniumSuite.scala | 105 ++++++++++ .../spark/sql/hive/thriftserver/Shim12.scala | 18 +- .../spark/sql/hive/thriftserver/Shim13.scala | 26 ++- 10 files changed, 751 insertions(+), 22 deletions(-) create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala create mode 100644 sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala index 2fa602a6082dc..99db959a8741c 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala @@ -52,6 +52,8 @@ private[spark] object SQLConf { // This is only used for the thriftserver val THRIFTSERVER_POOL = "spark.sql.thriftserver.scheduler.pool" + val THRIFTSERVER_UI_STATEMENT_LIMIT = "spark.sql.thriftserver.ui.retainedStatements" + val THRIFTSERVER_UI_SESSION_LIMIT = "spark.sql.thriftserver.ui.retainedSessions" // This is used to set the default data source val DEFAULT_DATA_SOURCE_NAME = "spark.sql.sources.default" diff --git a/sql/hive-thriftserver/pom.xml b/sql/hive-thriftserver/pom.xml index f38c796241df1..437f697d25bf3 100644 --- a/sql/hive-thriftserver/pom.xml +++ b/sql/hive-thriftserver/pom.xml @@ -57,6 +57,18 @@ ${hive.group} hive-beeline + + + org.seleniumhq.selenium + selenium-java + test + + + io.netty + netty + + + target/scala-${scala.binary.version}/classes diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala index 832596fc8bee5..0be5a92c2546c 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala @@ -22,20 +22,27 @@ import org.apache.hadoop.hive.conf.HiveConf import org.apache.hadoop.hive.conf.HiveConf.ConfVars import org.apache.hive.service.cli.thrift.{ThriftBinaryCLIService, ThriftHttpCLIService} import org.apache.hive.service.server.{HiveServer2, ServerOptionsProcessor} +import org.apache.spark.sql.SQLConf -import org.apache.spark.Logging +import org.apache.spark.{SparkContext, SparkConf, Logging} import org.apache.spark.annotation.DeveloperApi import org.apache.spark.sql.hive.HiveContext import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._ -import org.apache.spark.scheduler.{SparkListenerApplicationEnd, SparkListener} +import org.apache.spark.scheduler.{SparkListenerJobStart, SparkListenerApplicationEnd, SparkListener} +import org.apache.spark.sql.hive.thriftserver.ui.ThriftServerTab import org.apache.spark.util.Utils +import scala.collection.mutable +import scala.collection.mutable.ArrayBuffer + /** * The main entry point for the Spark SQL port of HiveServer2. Starts up a `SparkSQLContext` and a * `HiveThriftServer2` thrift server. */ object HiveThriftServer2 extends Logging { var LOG = LogFactory.getLog(classOf[HiveServer2]) + var uiTab: Option[ThriftServerTab] = _ + var listener: HiveThriftServer2Listener = _ /** * :: DeveloperApi :: @@ -46,7 +53,13 @@ object HiveThriftServer2 extends Logging { val server = new HiveThriftServer2(sqlContext) server.init(sqlContext.hiveconf) server.start() - sqlContext.sparkContext.addSparkListener(new HiveThriftServer2Listener(server)) + listener = new HiveThriftServer2Listener(server, sqlContext.conf) + sqlContext.sparkContext.addSparkListener(listener) + uiTab = if (sqlContext.sparkContext.getConf.getBoolean("spark.ui.enabled", true)) { + Some(new ThriftServerTab(sqlContext.sparkContext)) + } else { + None + } } def main(args: Array[String]) { @@ -58,14 +71,23 @@ object HiveThriftServer2 extends Logging { logInfo("Starting SparkContext") SparkSQLEnv.init() - Utils.addShutdownHook { () => SparkSQLEnv.stop() } + Utils.addShutdownHook { () => + SparkSQLEnv.stop() + uiTab.foreach(_.detach()) + } try { val server = new HiveThriftServer2(SparkSQLEnv.hiveContext) server.init(SparkSQLEnv.hiveContext.hiveconf) server.start() logInfo("HiveThriftServer2 started") - SparkSQLEnv.sparkContext.addSparkListener(new HiveThriftServer2Listener(server)) + listener = new HiveThriftServer2Listener(server, SparkSQLEnv.hiveContext.conf) + SparkSQLEnv.sparkContext.addSparkListener(listener) + uiTab = if (SparkSQLEnv.sparkContext.getConf.getBoolean("spark.ui.enabled", true)) { + Some(new ThriftServerTab(SparkSQLEnv.sparkContext)) + } else { + None + } } catch { case e: Exception => logError("Error starting HiveThriftServer2", e) @@ -73,15 +95,140 @@ object HiveThriftServer2 extends Logging { } } + private[thriftserver] class SessionInfo( + val sessionId: String, + val startTimestamp: Long, + val ip: String, + val userName: String) { + var finishTimestamp: Long = 0L + var totalExecution: Int = 0 + def totalTime: Long = { + if (finishTimestamp == 0L) { + System.currentTimeMillis - startTimestamp + } else { + finishTimestamp - startTimestamp + } + } + } + + private[thriftserver] object ExecutionState extends Enumeration { + val STARTED, COMPILED, FAILED, FINISHED = Value + type ExecutionState = Value + } + + private[thriftserver] class ExecutionInfo( + val statement: String, + val sessionId: String, + val startTimestamp: Long, + val userName: String) { + var finishTimestamp: Long = 0L + var executePlan: String = "" + var detail: String = "" + var state: ExecutionState.Value = ExecutionState.STARTED + val jobId: ArrayBuffer[String] = ArrayBuffer[String]() + var groupId: String = "" + def totalTime: Long = { + if (finishTimestamp == 0L) { + System.currentTimeMillis - startTimestamp + } else { + finishTimestamp - startTimestamp + } + } + } + + /** * A inner sparkListener called in sc.stop to clean up the HiveThriftServer2 */ - class HiveThriftServer2Listener(val server: HiveServer2) extends SparkListener { + private[thriftserver] class HiveThriftServer2Listener( + val server: HiveServer2, + val conf: SQLConf) extends SparkListener { + override def onApplicationEnd(applicationEnd: SparkListenerApplicationEnd): Unit = { server.stop() } - } + val sessionList = new mutable.LinkedHashMap[String, SessionInfo] + val executionList = new mutable.LinkedHashMap[String, ExecutionInfo] + val retainedStatements = + conf.getConf(SQLConf.THRIFTSERVER_UI_STATEMENT_LIMIT, "200").toInt + val retainedSessions = + conf.getConf(SQLConf.THRIFTSERVER_UI_SESSION_LIMIT, "200").toInt + var totalRunning = 0 + + override def onJobStart(jobStart: SparkListenerJobStart): Unit = { + for { + props <- Option(jobStart.properties) + groupId <- Option(props.getProperty(SparkContext.SPARK_JOB_GROUP_ID)) + (_, info) <- executionList if info.groupId == groupId + } { + info.jobId += jobStart.jobId.toString + info.groupId = groupId + } + } + + def onSessionCreated(ip: String, sessionId: String, userName: String = "UNKNOWN"): Unit = { + val info = new SessionInfo(sessionId, System.currentTimeMillis, ip, userName) + sessionList.put(sessionId, info) + trimSessionIfNecessary() + } + + def onSessionClosed(sessionId: String): Unit = { + sessionList(sessionId).finishTimestamp = System.currentTimeMillis + } + + def onStatementStart( + id: String, + sessionId: String, + statement: String, + groupId: String, + userName: String = "UNKNOWN"): Unit = { + val info = new ExecutionInfo(statement, sessionId, System.currentTimeMillis, userName) + info.state = ExecutionState.STARTED + executionList.put(id, info) + trimExecutionIfNecessary() + sessionList(sessionId).totalExecution += 1 + executionList(id).groupId = groupId + totalRunning += 1 + } + + def onStatementParsed(id: String, executionPlan: String): Unit = { + executionList(id).executePlan = executionPlan + executionList(id).state = ExecutionState.COMPILED + } + + def onStatementError(id: String, errorMessage: String, errorTrace: String): Unit = { + executionList(id).finishTimestamp = System.currentTimeMillis + executionList(id).detail = errorMessage + executionList(id).state = ExecutionState.FAILED + totalRunning -= 1 + } + + def onStatementFinish(id: String): Unit = { + executionList(id).finishTimestamp = System.currentTimeMillis + executionList(id).state = ExecutionState.FINISHED + totalRunning -= 1 + } + + private def trimExecutionIfNecessary() = synchronized { + if (executionList.size > retainedStatements) { + val toRemove = math.max(retainedStatements / 10, 1) + executionList.take(toRemove).foreach { s => + executionList.remove(s._1) + } + } + } + + private def trimSessionIfNecessary() = synchronized { + if (sessionList.size > retainedSessions) { + val toRemove = math.max(retainedSessions / 10, 1) + sessionList.take(toRemove).foreach { s => + sessionList.remove(s._1) + } + } + + } + } } private[hive] class HiveThriftServer2(hiveContext: HiveContext) diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala new file mode 100644 index 0000000000000..71b16b6bebffb --- /dev/null +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala @@ -0,0 +1,190 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.thriftserver.ui + +import java.util.Calendar +import javax.servlet.http.HttpServletRequest + +import scala.xml.Node + +import org.apache.commons.lang3.StringEscapeUtils +import org.apache.spark.Logging +import org.apache.spark.sql.hive.thriftserver.HiveThriftServer2.{SessionInfo, ExecutionState, ExecutionInfo} +import org.apache.spark.ui.UIUtils._ +import org.apache.spark.ui._ + + +/** Page for Spark Web UI that shows statistics of a streaming job */ +private[ui] class ThriftServerPage(parent: ThriftServerTab) extends WebUIPage("") with Logging { + + private val listener = parent.listener + private val startTime = Calendar.getInstance().getTime() + private val emptyCell = "-" + + /** Render the page */ + def render(request: HttpServletRequest): Seq[Node] = { + val content = + generateBasicStats() ++ +
    ++ +

    + {listener.sessionList.size} session(s) are online, + running {listener.totalRunning} SQL statement(s) +

    ++ + generateSessionStatsTable() ++ + generateSQLStatsTable() + UIUtils.headerSparkPage("ThriftServer", content, parent, Some(5000)) + } + + /** Generate basic stats of the streaming program */ + private def generateBasicStats(): Seq[Node] = { + val timeSinceStart = System.currentTimeMillis() - startTime.getTime +
      +
    • + Started at: {startTime.toString} +
      • +
    • + Time since start: {formatDurationVerbose(timeSinceStart)} +
      • +
    + } + + /** Generate stats of batch statements of the thrift server program */ + private def generateSQLStatsTable(): Seq[Node] = { + val numStatement = listener.executionList.size + val table = if (numStatement > 0) { + val headerRow = Seq("User", "JobID", "GroupID", "Start Time", "Finish Time", "Duration", + "Statement", "State", "Detail") + val dataRows = listener.executionList.values + + def generateDataRow(info: ExecutionInfo): Seq[Node] = { + val jobLink = info.jobId.map { id: String => + + [{id}] + + } + val detail = if(info.state == ExecutionState.FAILED) info.detail else info.executePlan + + {info.userName} + + {jobLink} + + {info.groupId} + {formatDate(info.startTimestamp)} + {if(info.finishTimestamp > 0) formatDate(info.finishTimestamp)} + {formatDurationOption(Some(info.totalTime))} + {info.statement} + {info.state} + {errorMessageCell(detail)} + + } + + Some(UIUtils.listingTable(headerRow, generateDataRow, + dataRows, false, None, Seq(null), false)) + } else { + None + } + + val content = +
    SQL Statistics
    ++ +
    +
      + {table.getOrElse("No statistics have been generated yet.")} +
    +
    + + content + } + + private def errorMessageCell(errorMessage: String): Seq[Node] = { + val isMultiline = errorMessage.indexOf('\n') >= 0 + val errorSummary = StringEscapeUtils.escapeHtml4( + if (isMultiline) { + errorMessage.substring(0, errorMessage.indexOf('\n')) + } else { + errorMessage + }) + val details = if (isMultiline) { + // scalastyle:off + + + details + ++ + + // scalastyle:on + } else { + "" + } + {errorSummary}{details} + } + + /** Generate stats of batch sessions of the thrift server program */ + private def generateSessionStatsTable(): Seq[Node] = { + val numBatches = listener.sessionList.size + val table = if (numBatches > 0) { + val dataRows = + listener.sessionList.values + val headerRow = Seq("User", "IP", "Session ID", "Start Time", "Finish Time", "Duration", + "Total Execute") + def generateDataRow(session: SessionInfo): Seq[Node] = { + val sessionLink = "%s/ThriftServer/session?id=%s" + .format(UIUtils.prependBaseUri(parent.basePath), session.sessionId) + + {session.userName} + {session.ip} + {session.sessionId} , + {formatDate(session.startTimestamp)} + {if(session.finishTimestamp > 0) formatDate(session.finishTimestamp)} + {formatDurationOption(Some(session.totalTime))} + {session.totalExecution.toString} + + } + Some(UIUtils.listingTable(headerRow, generateDataRow, dataRows, true, None, Seq(null), false)) + } else { + None + } + + val content = +
    Session Statistics
    ++ +
    +
      + {table.getOrElse("No statistics have been generated yet.")} +
    +
    + + content + } + + + /** + * Returns a human-readable string representing a duration such as "5 second 35 ms" + */ + private def formatDurationOption(msOption: Option[Long]): String = { + msOption.map(formatDurationVerbose).getOrElse(emptyCell) + } + + /** Generate HTML table from string data */ + private def listingTable(headers: Seq[String], data: Seq[Seq[String]]) = { + def generateDataRow(data: Seq[String]): Seq[Node] = { + {data.map(d => {d})} + } + UIUtils.listingTable(headers, generateDataRow, data, fixedWidth = true) + } +} + diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala new file mode 100644 index 0000000000000..33ba038ecce73 --- /dev/null +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala @@ -0,0 +1,197 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.thriftserver.ui + +import java.util.Calendar +import javax.servlet.http.HttpServletRequest + +import scala.xml.Node + +import org.apache.commons.lang3.StringEscapeUtils +import org.apache.spark.Logging +import org.apache.spark.sql.hive.thriftserver.HiveThriftServer2.{ExecutionInfo, ExecutionState} +import org.apache.spark.ui.UIUtils._ +import org.apache.spark.ui._ + +/** Page for Spark Web UI that shows statistics of a streaming job */ +private[ui] class ThriftServerSessionPage(parent: ThriftServerTab) + extends WebUIPage("session") with Logging { + + private val listener = parent.listener + private val startTime = Calendar.getInstance().getTime() + private val emptyCell = "-" + + /** Render the page */ + def render(request: HttpServletRequest): Seq[Node] = { + val parameterId = request.getParameter("id") + require(parameterId != null && parameterId.nonEmpty, "Missing id parameter") + val sessionStat = listener.sessionList.find(stat => { + stat._1 == parameterId + }).getOrElse(null) + require(sessionStat != null, "Invalid sessionID[" + parameterId + "]") + + val content = + generateBasicStats() ++ +
    ++ +

    + User {sessionStat._2.userName}, + IP {sessionStat._2.ip}, + Session created at {formatDate(sessionStat._2.startTimestamp)}, + Total run {sessionStat._2.totalExecution} SQL +

    ++ + generateSQLStatsTable(sessionStat._2.sessionId) + UIUtils.headerSparkPage("ThriftServer", content, parent, Some(5000)) + } + + /** Generate basic stats of the streaming program */ + private def generateBasicStats(): Seq[Node] = { + val timeSinceStart = System.currentTimeMillis() - startTime.getTime +
      +
    • + Started at: {startTime.toString} +
      • +
    • + Time since start: {formatDurationVerbose(timeSinceStart)} +
      • +
    + } + + /** Generate stats of batch statements of the thrift server program */ + private def generateSQLStatsTable(sessionID: String): Seq[Node] = { + val executionList = listener.executionList + .filter(_._2.sessionId == sessionID) + val numStatement = executionList.size + val table = if (numStatement > 0) { + val headerRow = Seq("User", "JobID", "GroupID", "Start Time", "Finish Time", "Duration", + "Statement", "State", "Detail") + val dataRows = executionList.values.toSeq.sortBy(_.startTimestamp).reverse + + def generateDataRow(info: ExecutionInfo): Seq[Node] = { + val jobLink = info.jobId.map { id: String => + + [{id}] + + } + val detail = if(info.state == ExecutionState.FAILED) info.detail else info.executePlan + + {info.userName} + + {jobLink} + + {info.groupId} + {formatDate(info.startTimestamp)} + {formatDate(info.finishTimestamp)} + {formatDurationOption(Some(info.totalTime))} + {info.statement} + {info.state} + {errorMessageCell(detail)} + + } + + Some(UIUtils.listingTable(headerRow, generateDataRow, + dataRows, false, None, Seq(null), false)) + } else { + None + } + + val content = +
    SQL Statistics
    ++ +
    +
      + {table.getOrElse("No statistics have been generated yet.")} +
    +
    + + content + } + + private def errorMessageCell(errorMessage: String): Seq[Node] = { + val isMultiline = errorMessage.indexOf('\n') >= 0 + val errorSummary = StringEscapeUtils.escapeHtml4( + if (isMultiline) { + errorMessage.substring(0, errorMessage.indexOf('\n')) + } else { + errorMessage + }) + val details = if (isMultiline) { + // scalastyle:off + + + details + ++ + + // scalastyle:on + } else { + "" + } + {errorSummary}{details} + } + + /** Generate stats of batch sessions of the thrift server program */ + private def generateSessionStatsTable(): Seq[Node] = { + val numBatches = listener.sessionList.size + val table = if (numBatches > 0) { + val dataRows = + listener.sessionList.values.toSeq.sortBy(_.startTimestamp).reverse.map ( session => + Seq( + session.userName, + session.ip, + session.sessionId, + formatDate(session.startTimestamp), + formatDate(session.finishTimestamp), + formatDurationOption(Some(session.totalTime)), + session.totalExecution.toString + ) + ).toSeq + val headerRow = Seq("User", "IP", "Session ID", "Start Time", "Finish Time", "Duration", + "Total Execute") + Some(listingTable(headerRow, dataRows)) + } else { + None + } + + val content = +
    Session Statistics
    ++ +
    +
      + {table.getOrElse("No statistics have been generated yet.")} +
    +
    + + content + } + + + /** + * Returns a human-readable string representing a duration such as "5 second 35 ms" + */ + private def formatDurationOption(msOption: Option[Long]): String = { + msOption.map(formatDurationVerbose).getOrElse(emptyCell) + } + + /** Generate HTML table from string data */ + private def listingTable(headers: Seq[String], data: Seq[Seq[String]]) = { + def generateDataRow(data: Seq[String]): Seq[Node] = { + {data.map(d => {d})} + } + UIUtils.listingTable(headers, generateDataRow, data, fixedWidth = true) + } +} + diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala new file mode 100644 index 0000000000000..343031f10c75c --- /dev/null +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala @@ -0,0 +1,50 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.thriftserver.ui + +import org.apache.spark.sql.hive.thriftserver.{HiveThriftServer2, SparkSQLEnv} +import org.apache.spark.sql.hive.thriftserver.ui.ThriftServerTab._ +import org.apache.spark.ui.{SparkUI, SparkUITab} +import org.apache.spark.{SparkContext, Logging, SparkException} + +/** + * Spark Web UI tab that shows statistics of a streaming job. + * This assumes the given SparkContext has enabled its SparkUI. + */ +private[thriftserver] class ThriftServerTab(sparkContext: SparkContext) + extends SparkUITab(getSparkUI(sparkContext), "ThriftServer") with Logging { + + val parent = getSparkUI(sparkContext) + val listener = HiveThriftServer2.listener + + attachPage(new ThriftServerPage(this)) + attachPage(new ThriftServerSessionPage(this)) + parent.attachTab(this) + + def detach() { + getSparkUI(sparkContext).detachTab(this) + } +} + +private[thriftserver] object ThriftServerTab { + def getSparkUI(sparkContext: SparkContext): SparkUI = { + sparkContext.ui.getOrElse { + throw new SparkException("Parent SparkUI to attach this tab to not found!") + } + } +} diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala index 4cf95e7bdfb2b..1fadea97fd07f 100644 --- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala +++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala @@ -409,24 +409,24 @@ abstract class HiveThriftServer2Test extends FunSuite with BeforeAndAfterAll wit private val CLASS_NAME = HiveThriftServer2.getClass.getCanonicalName.stripSuffix("$") private val LOG_FILE_MARK = s"starting $CLASS_NAME, logging to " - private val startScript = "../../sbin/start-thriftserver.sh".split("/").mkString(File.separator) - private val stopScript = "../../sbin/stop-thriftserver.sh".split("/").mkString(File.separator) + protected val startScript = "../../sbin/start-thriftserver.sh".split("/").mkString(File.separator) + protected val stopScript = "../../sbin/stop-thriftserver.sh".split("/").mkString(File.separator) private var listeningPort: Int = _ protected def serverPort: Int = listeningPort protected def user = System.getProperty("user.name") - private var warehousePath: File = _ - private var metastorePath: File = _ - private def metastoreJdbcUri = s"jdbc:derby:;databaseName=$metastorePath;create=true" + protected var warehousePath: File = _ + protected var metastorePath: File = _ + protected def metastoreJdbcUri = s"jdbc:derby:;databaseName=$metastorePath;create=true" private val pidDir: File = Utils.createTempDir("thriftserver-pid") private var logPath: File = _ private var logTailingProcess: Process = _ private var diagnosisBuffer: ArrayBuffer[String] = ArrayBuffer.empty[String] - private def serverStartCommand(port: Int) = { + protected def serverStartCommand(port: Int) = { val portConf = if (mode == ServerMode.binary) { ConfVars.HIVE_SERVER2_THRIFT_PORT } else { diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala new file mode 100644 index 0000000000000..47541015a3611 --- /dev/null +++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala @@ -0,0 +1,105 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.thriftserver + + + +import scala.util.Random + +import org.openqa.selenium.WebDriver +import org.openqa.selenium.htmlunit.HtmlUnitDriver +import org.scalatest.{Matchers, BeforeAndAfterAll} +import org.scalatest.concurrent.Eventually._ +import org.scalatest.selenium.WebBrowser +import org.scalatest.time.SpanSugar._ + +import org.apache.hadoop.hive.conf.HiveConf.ConfVars +import org.apache.spark.sql.hive.HiveContext + + +class UISeleniumSuite + extends HiveThriftJdbcTest + with WebBrowser with Matchers with BeforeAndAfterAll { + + implicit var webDriver: WebDriver = _ + var server: HiveThriftServer2 = _ + var hc: HiveContext = _ + val uiPort = 20000 + Random.nextInt(10000) + override def mode: ServerMode.Value = ServerMode.binary + + override def beforeAll(): Unit = { + webDriver = new HtmlUnitDriver + super.beforeAll() + } + + override def afterAll(): Unit = { + if (webDriver != null) { + webDriver.quit() + } + super.afterAll() + } + + override protected def serverStartCommand(port: Int) = { + val portConf = if (mode == ServerMode.binary) { + ConfVars.HIVE_SERVER2_THRIFT_PORT + } else { + ConfVars.HIVE_SERVER2_THRIFT_HTTP_PORT + } + + s"""$startScript + | --master local + | --hiveconf hive.root.logger=INFO,console + | --hiveconf ${ConfVars.METASTORECONNECTURLKEY}=$metastoreJdbcUri + | --hiveconf ${ConfVars.METASTOREWAREHOUSE}=$warehousePath + | --hiveconf ${ConfVars.HIVE_SERVER2_THRIFT_BIND_HOST}=localhost + | --hiveconf ${ConfVars.HIVE_SERVER2_TRANSPORT_MODE}=$mode + | --hiveconf $portConf=$port + | --driver-class-path ${sys.props("java.class.path")} + | --conf spark.ui.enabled=true + | --conf spark.ui.port=$uiPort + """.stripMargin.split("\\s+").toSeq + } + + test("thrift server ui test") { + withJdbcStatement(statement =>{ + val baseURL = s"http://localhost:${uiPort}" + + val queries = Seq( + "CREATE TABLE test_map(key INT, value STRING)", + s"LOAD DATA LOCAL INPATH '${TestData.smallKv}' OVERWRITE INTO TABLE test_map") + + queries.foreach(statement.execute) + + eventually(timeout(10 seconds), interval(50 milliseconds)) { + go to (baseURL) + find(cssSelector("""ul li a[href*="ThriftServer"]""")) should not be(None) + } + + eventually(timeout(10 seconds), interval(50 milliseconds)) { + go to (baseURL + "/ThriftServer") + find(id("sessionstat")) should not be(None) + find(id("sqlstat")) should not be(None) + + // check whether statements exists + queries.foreach { line => + findAll(cssSelector("""ul table tbody tr td""")).map(_.text).toList should contain (line) + } + } + }) + } +} diff --git a/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala b/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala index 95a6e86d0546d..b3a79ba1c7d6b 100644 --- a/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala +++ b/sql/hive-thriftserver/v0.12.0/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim12.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.hive.thriftserver import java.sql.{Date, Timestamp} import java.util.concurrent.Executors -import java.util.{ArrayList => JArrayList, Map => JMap} +import java.util.{ArrayList => JArrayList, Map => JMap, UUID} import org.apache.commons.logging.Log import org.apache.hadoop.hive.conf.HiveConf @@ -190,9 +190,12 @@ private[hive] class SparkExecuteStatementOperation( } def run(): Unit = { + val statementId = UUID.randomUUID().toString logInfo(s"Running query '$statement'") setState(OperationState.RUNNING) - hiveContext.sparkContext.setJobDescription(statement) + HiveThriftServer2.listener.onStatementStart( + statementId, parentSession.getSessionHandle.getSessionId.toString, statement, statementId) + hiveContext.sparkContext.setJobGroup(statementId, statement) sessionToActivePool.get(parentSession.getSessionHandle).foreach { pool => hiveContext.sparkContext.setLocalProperty("spark.scheduler.pool", pool) } @@ -205,6 +208,7 @@ private[hive] class SparkExecuteStatementOperation( logInfo(s"Setting spark.scheduler.pool=$value for future statements in this session.") case _ => } + HiveThriftServer2.listener.onStatementParsed(statementId, result.queryExecution.toString()) iter = { val useIncrementalCollect = hiveContext.getConf("spark.sql.thriftServer.incrementalCollect", "false").toBoolean @@ -221,10 +225,13 @@ private[hive] class SparkExecuteStatementOperation( // HiveServer will silently swallow them. case e: Throwable => setState(OperationState.ERROR) + HiveThriftServer2.listener.onStatementError( + statementId, e.getMessage, e.getStackTraceString) logError("Error executing query:",e) throw new HiveSQLException(e.toString) } setState(OperationState.FINISHED) + HiveThriftServer2.listener.onStatementFinish(statementId) } } @@ -255,11 +262,14 @@ private[hive] class SparkSQLSessionManager(hiveContext: HiveContext) withImpersonation: Boolean, delegationToken: String): SessionHandle = { hiveContext.openSession() - - super.openSession(username, passwd, sessionConf, withImpersonation, delegationToken) + val sessionHandle = super.openSession( + username, passwd, sessionConf, withImpersonation, delegationToken) + HiveThriftServer2.listener.onSessionCreated("UNKNOWN", sessionHandle.getSessionId.toString) + sessionHandle } override def closeSession(sessionHandle: SessionHandle) { + HiveThriftServer2.listener.onSessionClosed(sessionHandle.getSessionId.toString) super.closeSession(sessionHandle) sparkSqlOperationManager.sessionToActivePool -= sessionHandle diff --git a/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala b/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala index 178eb1af7cdcd..b9d4f1c58c982 100644 --- a/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala +++ b/sql/hive-thriftserver/v0.13.1/src/main/scala/org/apache/spark/sql/hive/thriftserver/Shim13.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.hive.thriftserver import java.sql.{Date, Timestamp} import java.util.concurrent.Executors -import java.util.{ArrayList => JArrayList, List => JList, Map => JMap} +import java.util.{ArrayList => JArrayList, List => JList, Map => JMap, UUID} import org.apache.commons.logging.Log import org.apache.hadoop.hive.conf.HiveConf @@ -36,7 +36,7 @@ import org.apache.hive.service.cli._ import org.apache.hive.service.cli.operation.ExecuteStatementOperation import org.apache.hive.service.cli.session.{SessionManager, HiveSession} -import org.apache.spark.Logging +import org.apache.spark.{SparkContext, Logging} import org.apache.spark.sql.{DataFrame, Row => SparkRow, SQLConf} import org.apache.spark.sql.execution.SetCommand import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._ @@ -161,9 +161,16 @@ private[hive] class SparkExecuteStatementOperation( } def run(): Unit = { + val statementId = UUID.randomUUID().toString logInfo(s"Running query '$statement'") setState(OperationState.RUNNING) - hiveContext.sparkContext.setJobDescription(statement) + HiveThriftServer2.listener.onStatementStart( + statementId, + parentSession.getSessionHandle.getSessionId.toString, + statement, + statementId, + parentSession.getUsername) + hiveContext.sparkContext.setJobGroup(statementId, statement) sessionToActivePool.get(parentSession.getSessionHandle).foreach { pool => hiveContext.sparkContext.setLocalProperty("spark.scheduler.pool", pool) } @@ -176,6 +183,7 @@ private[hive] class SparkExecuteStatementOperation( logInfo(s"Setting spark.scheduler.pool=$value for future statements in this session.") case _ => } + HiveThriftServer2.listener.onStatementParsed(statementId, result.queryExecution.toString()) iter = { val useIncrementalCollect = hiveContext.getConf("spark.sql.thriftServer.incrementalCollect", "false").toBoolean @@ -192,10 +200,13 @@ private[hive] class SparkExecuteStatementOperation( // HiveServer will silently swallow them. case e: Throwable => setState(OperationState.ERROR) + HiveThriftServer2.listener.onStatementError( + statementId, e.getMessage, e.getStackTraceString) logError("Error executing query:", e) throw new HiveSQLException(e.toString) } setState(OperationState.FINISHED) + HiveThriftServer2.listener.onStatementFinish(statementId) } } @@ -227,11 +238,16 @@ private[hive] class SparkSQLSessionManager(hiveContext: HiveContext) withImpersonation: Boolean, delegationToken: String): SessionHandle = { hiveContext.openSession() - - super.openSession(protocol, username, passwd, sessionConf, withImpersonation, delegationToken) + val sessionHandle = super.openSession( + protocol, username, passwd, sessionConf, withImpersonation, delegationToken) + val session = super.getSession(sessionHandle) + HiveThriftServer2.listener.onSessionCreated( + session.getIpAddress, sessionHandle.getSessionId.toString, session.getUsername) + sessionHandle } override def closeSession(sessionHandle: SessionHandle) { + HiveThriftServer2.listener.onSessionClosed(sessionHandle.getSessionId.toString) super.closeSession(sessionHandle) sparkSqlOperationManager.sessionToActivePool -= sessionHandle From 5a1a1075a607be683f008ef92fa227803370c45f Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Mon, 4 May 2015 17:17:55 +0100 Subject: [PATCH 562/591] [MINOR] Fix python test typo? I suspect haven't been using anaconda in tests in a while. I wonder if this change actually does anything but this line as it stands looks strictly less correct. Author: Andrew Or Closes #5883 from andrewor14/fix-run-tests-typo and squashes the following commits: a3ad720 [Andrew Or] Fix typo? --- dev/run-tests | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dev/run-tests b/dev/run-tests index 861d1671182c2..05c63bce4d40d 100755 --- a/dev/run-tests +++ b/dev/run-tests @@ -236,7 +236,7 @@ echo "=========================================================================" CURRENT_BLOCK=$BLOCK_PYSPARK_UNIT_TESTS # add path for python 3 in jenkins -export PATH="${PATH}:/home/anaonda/envs/py3k/bin" +export PATH="${PATH}:/home/anaconda/envs/py3k/bin" ./python/run-tests echo "" From e0833c5958bbd73ff27cfe6865648d7b6e5a99bc Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Mon, 4 May 2015 11:28:59 -0700 Subject: [PATCH 563/591] [SPARK-5956] [MLLIB] Pipeline components should be copyable. This PR added `copy(extra: ParamMap): Params` to `Params`, which makes a copy of the current instance with a randomly generated uid and some extra param values. With this change, we only need to implement `fit` and `transform` without extra param values given the default implementation of `fit(dataset, extra)`: ~~~scala def fit(dataset: DataFrame, extra: ParamMap): Model = { copy(extra).fit(dataset) } ~~~ Inside `fit` and `transform`, since only the embedded values are used, I added `$` as an alias for `getOrDefault` to make the code easier to read. For example, in `LinearRegression.fit` we have: ~~~scala val effectiveRegParam = $(regParam) / yStd val effectiveL1RegParam = $(elasticNetParam) * effectiveRegParam val effectiveL2RegParam = (1.0 - $(elasticNetParam)) * effectiveRegParam ~~~ Meta-algorithm like `Pipeline` implements its own `copy(extra)`. So the fitted pipeline model stored all copied stages (no matter whether it is a transformer or a model). Other changes: * `Params$.inheritValues` is moved to `Params!.copyValues` and returns the target instance. * `fittingParamMap` was removed because the `parent` carries this information. * `validate` was renamed to `validateParams` to be more precise. TODOs: * [x] add tests for newly added methods * [ ] update documentation jkbradley dbtsai Author: Xiangrui Meng Closes #5820 from mengxr/SPARK-5956 and squashes the following commits: 7bef88d [Xiangrui Meng] address comments 05229c3 [Xiangrui Meng] assert -> assertEquals b2927b1 [Xiangrui Meng] organize imports f14456b [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-5956 93e7924 [Xiangrui Meng] add tests for hasParam & copy 463ecae [Xiangrui Meng] merge master 2b954c3 [Xiangrui Meng] update Binarizer 465dd12 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-5956 282a1a8 [Xiangrui Meng] fix test 819dd2d [Xiangrui Meng] merge master b642872 [Xiangrui Meng] example code runs 5a67779 [Xiangrui Meng] examples compile c76b4d1 [Xiangrui Meng] fix all unit tests 0f4fd64 [Xiangrui Meng] fix some tests 9286a22 [Xiangrui Meng] copyValues to trained models 53e0973 [Xiangrui Meng] move inheritValues to Params and rename it to copyValues 9ee004e [Xiangrui Meng] merge copy and copyWith; rename validate to validateParams d882afc [Xiangrui Meng] test compile f082a31 [Xiangrui Meng] make Params copyable and simply handling of extra params in all spark.ml components --- .../examples/ml/JavaDeveloperApiExample.java | 24 ++-- .../examples/ml/JavaSimpleParamsExample.java | 4 +- .../examples/ml/DecisionTreeExample.scala | 6 +- .../examples/ml/DeveloperApiExample.scala | 22 ++-- .../apache/spark/examples/ml/GBTExample.scala | 4 +- .../examples/ml/RandomForestExample.scala | 6 +- .../examples/ml/SimpleParamsExample.scala | 4 +- .../scala/org/apache/spark/ml/Estimator.scala | 26 ++++- .../scala/org/apache/spark/ml/Evaluator.scala | 20 +++- .../scala/org/apache/spark/ml/Model.scala | 9 +- .../scala/org/apache/spark/ml/Pipeline.scala | 106 ++++++++---------- .../org/apache/spark/ml/Transformer.scala | 46 +++++--- .../spark/ml/classification/Classifier.scala | 49 +++----- .../DecisionTreeClassifier.scala | 29 ++--- .../ml/classification/GBTClassifier.scala | 33 +++--- .../classification/LogisticRegression.scala | 58 +++++----- .../ProbabilisticClassifier.scala | 33 ++---- .../RandomForestClassifier.scala | 31 ++--- .../BinaryClassificationEvaluator.scala | 17 ++- .../apache/spark/ml/feature/Binarizer.scala | 20 ++-- .../apache/spark/ml/feature/HashingTF.scala | 10 +- .../org/apache/spark/ml/feature/IDF.scala | 38 +++---- .../apache/spark/ml/feature/Normalizer.scala | 10 +- .../ml/feature/PolynomialExpansion.scala | 9 +- .../spark/ml/feature/StandardScaler.scala | 49 ++++---- .../spark/ml/feature/StringIndexer.scala | 34 +++--- .../apache/spark/ml/feature/Tokenizer.scala | 18 +-- .../spark/ml/feature/VectorAssembler.scala | 15 +-- .../spark/ml/feature/VectorIndexer.scala | 74 ++++++------ .../apache/spark/ml/feature/Word2Vec.scala | 62 +++++----- .../spark/ml/impl/estimator/Predictor.scala | 72 ++++-------- .../spark/ml/impl/tree/treeParams.scala | 35 +++--- .../org/apache/spark/ml/param/params.scala | 75 ++++++++----- .../ml/param/shared/SharedParamsCodeGen.scala | 5 +- .../spark/ml/param/shared/sharedParams.scala | 35 +++--- .../apache/spark/ml/recommendation/ALS.scala | 73 ++++++------ .../ml/regression/DecisionTreeRegressor.scala | 23 ++-- .../spark/ml/regression/GBTRegressor.scala | 30 ++--- .../ml/regression/LinearRegression.scala | 41 ++++--- .../ml/regression/RandomForestRegressor.scala | 27 ++--- .../spark/ml/regression/Regressor.scala | 2 +- .../spark/ml/tuning/CrossValidator.scala | 51 ++++----- .../JavaLogisticRegressionSuite.java | 14 ++- .../regression/JavaLinearRegressionSuite.java | 21 ++-- .../ml/tuning/JavaCrossValidatorSuite.java | 6 +- .../org/apache/spark/ml/PipelineSuite.scala | 26 +++-- .../DecisionTreeClassifierSuite.scala | 4 +- .../classification/GBTClassifierSuite.scala | 4 +- .../LogisticRegressionSuite.scala | 18 +-- .../RandomForestClassifierSuite.scala | 4 +- .../apache/spark/ml/param/ParamsSuite.scala | 13 ++- .../apache/spark/ml/param/TestParams.scala | 14 ++- .../DecisionTreeRegressorSuite.scala | 4 +- .../ml/regression/GBTRegressorSuite.scala | 3 +- .../RandomForestRegressorSuite.scala | 4 +- .../spark/ml/tuning/CrossValidatorSuite.scala | 6 +- 56 files changed, 671 insertions(+), 805 deletions(-) diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java index 46377a99c4857..eac4f898a475d 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java @@ -28,7 +28,6 @@ import org.apache.spark.ml.classification.ClassificationModel; import org.apache.spark.ml.param.IntParam; import org.apache.spark.ml.param.ParamMap; -import org.apache.spark.ml.param.Params$; import org.apache.spark.mllib.linalg.BLAS; import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.mllib.linalg.Vectors; @@ -129,16 +128,16 @@ MyJavaLogisticRegression setMaxIter(int value) { // This method is used by fit(). // In Java, we have to make it public since Java does not understand Scala's protected modifier. - public MyJavaLogisticRegressionModel train(DataFrame dataset, ParamMap paramMap) { + public MyJavaLogisticRegressionModel train(DataFrame dataset) { // Extract columns from data using helper method. - JavaRDD oldDataset = extractLabeledPoints(dataset, paramMap).toJavaRDD(); + JavaRDD oldDataset = extractLabeledPoints(dataset).toJavaRDD(); // Do learning to estimate the weight vector. int numFeatures = oldDataset.take(1).get(0).features().size(); Vector weights = Vectors.zeros(numFeatures); // Learning would happen here. // Create a model, and return it. - return new MyJavaLogisticRegressionModel(this, paramMap, weights); + return new MyJavaLogisticRegressionModel(this, weights); } } @@ -155,18 +154,11 @@ class MyJavaLogisticRegressionModel private MyJavaLogisticRegression parent_; public MyJavaLogisticRegression parent() { return parent_; } - private ParamMap fittingParamMap_; - public ParamMap fittingParamMap() { return fittingParamMap_; } - private Vector weights_; public Vector weights() { return weights_; } - public MyJavaLogisticRegressionModel( - MyJavaLogisticRegression parent_, - ParamMap fittingParamMap_, - Vector weights_) { + public MyJavaLogisticRegressionModel(MyJavaLogisticRegression parent_, Vector weights_) { this.parent_ = parent_; - this.fittingParamMap_ = fittingParamMap_; this.weights_ = weights_; } @@ -210,10 +202,8 @@ public Vector predictRaw(Vector features) { * In Java, we have to make this method public since Java does not understand Scala's protected * modifier. */ - public MyJavaLogisticRegressionModel copy() { - MyJavaLogisticRegressionModel m = - new MyJavaLogisticRegressionModel(parent_, fittingParamMap_, weights_); - Params$.MODULE$.inheritValues(this.extractParamMap(), this, m); - return m; + @Override + public MyJavaLogisticRegressionModel copy(ParamMap extra) { + return copyValues(new MyJavaLogisticRegressionModel(parent_, weights_), extra); } } diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java index 4e02acce696e6..29158d5c85651 100644 --- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java @@ -71,7 +71,7 @@ public static void main(String[] args) { // we can view the parameters it used during fit(). // This prints the parameter (name: value) pairs, where names are unique IDs for this // LogisticRegression instance. - System.out.println("Model 1 was fit using parameters: " + model1.fittingParamMap()); + System.out.println("Model 1 was fit using parameters: " + model1.parent().extractParamMap()); // We may alternatively specify parameters using a ParamMap. ParamMap paramMap = new ParamMap(); @@ -87,7 +87,7 @@ public static void main(String[] args) { // Now learn a new model using the paramMapCombined parameters. // paramMapCombined overrides all parameters set earlier via lr.set* methods. LogisticRegressionModel model2 = lr.fit(training, paramMapCombined); - System.out.println("Model 2 was fit using parameters: " + model2.fittingParamMap()); + System.out.println("Model 2 was fit using parameters: " + model2.parent().extractParamMap()); // Prepare test documents. List localTest = Lists.newArrayList( diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala index 9002e99d82ad3..8340d91101ab3 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala @@ -276,16 +276,14 @@ object DecisionTreeExample { // Get the trained Decision Tree from the fitted PipelineModel algo match { case "classification" => - val treeModel = pipelineModel.getModel[DecisionTreeClassificationModel]( - dt.asInstanceOf[DecisionTreeClassifier]) + val treeModel = pipelineModel.stages.last.asInstanceOf[DecisionTreeClassificationModel] if (treeModel.numNodes < 20) { println(treeModel.toDebugString) // Print full model. } else { println(treeModel) // Print model summary. } case "regression" => - val treeModel = pipelineModel.getModel[DecisionTreeRegressionModel]( - dt.asInstanceOf[DecisionTreeRegressor]) + val treeModel = pipelineModel.stages.last.asInstanceOf[DecisionTreeRegressionModel] if (treeModel.numNodes < 20) { println(treeModel.toDebugString) // Print full model. } else { diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala index 2245fa429fda3..2a2d0677272a0 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala @@ -18,13 +18,12 @@ package org.apache.spark.examples.ml import org.apache.spark.{SparkConf, SparkContext} -import org.apache.spark.ml.classification.{Classifier, ClassifierParams, ClassificationModel} -import org.apache.spark.ml.param.{Params, IntParam, ParamMap} +import org.apache.spark.ml.classification.{ClassificationModel, Classifier, ClassifierParams} +import org.apache.spark.ml.param.{IntParam, ParamMap} import org.apache.spark.mllib.linalg.{BLAS, Vector, Vectors} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.sql.{DataFrame, Row, SQLContext} - /** * A simple example demonstrating how to write your own learning algorithm using Estimator, * Transformer, and other abstractions. @@ -99,7 +98,7 @@ private trait MyLogisticRegressionParams extends ClassifierParams { * class since the maxIter parameter is only used during training (not in the Model). */ val maxIter: IntParam = new IntParam(this, "maxIter", "max number of iterations") - def getMaxIter: Int = getOrDefault(maxIter) + def getMaxIter: Int = $(maxIter) } /** @@ -117,18 +116,16 @@ private class MyLogisticRegression def setMaxIter(value: Int): this.type = set(maxIter, value) // This method is used by fit() - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): MyLogisticRegressionModel = { + override protected def train(dataset: DataFrame): MyLogisticRegressionModel = { // Extract columns from data using helper method. - val oldDataset = extractLabeledPoints(dataset, paramMap) + val oldDataset = extractLabeledPoints(dataset) // Do learning to estimate the weight vector. val numFeatures = oldDataset.take(1)(0).features.size val weights = Vectors.zeros(numFeatures) // Learning would happen here. // Create a model, and return it. - new MyLogisticRegressionModel(this, paramMap, weights) + new MyLogisticRegressionModel(this, weights) } } @@ -139,7 +136,6 @@ private class MyLogisticRegression */ private class MyLogisticRegressionModel( override val parent: MyLogisticRegression, - override val fittingParamMap: ParamMap, val weights: Vector) extends ClassificationModel[Vector, MyLogisticRegressionModel] with MyLogisticRegressionParams { @@ -176,9 +172,7 @@ private class MyLogisticRegressionModel( * * This is used for the default implementation of [[transform()]]. */ - override protected def copy(): MyLogisticRegressionModel = { - val m = new MyLogisticRegressionModel(parent, fittingParamMap, weights) - Params.inheritValues(extractParamMap(), this, m) - m + override def copy(extra: ParamMap): MyLogisticRegressionModel = { + copyValues(new MyLogisticRegressionModel(parent, weights), extra) } } diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala index 5fccb142d4c3d..c5899b6683c79 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/GBTExample.scala @@ -201,14 +201,14 @@ object GBTExample { // Get the trained GBT from the fitted PipelineModel algo match { case "classification" => - val rfModel = pipelineModel.getModel[GBTClassificationModel](dt.asInstanceOf[GBTClassifier]) + val rfModel = pipelineModel.stages.last.asInstanceOf[GBTClassificationModel] if (rfModel.totalNumNodes < 30) { println(rfModel.toDebugString) // Print full model. } else { println(rfModel) // Print model summary. } case "regression" => - val rfModel = pipelineModel.getModel[GBTRegressionModel](dt.asInstanceOf[GBTRegressor]) + val rfModel = pipelineModel.stages.last.asInstanceOf[GBTRegressionModel] if (rfModel.totalNumNodes < 30) { println(rfModel.toDebugString) // Print full model. } else { diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala index 9b909324ec82a..7f88d2681bcaa 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/RandomForestExample.scala @@ -209,16 +209,14 @@ object RandomForestExample { // Get the trained Random Forest from the fitted PipelineModel algo match { case "classification" => - val rfModel = pipelineModel.getModel[RandomForestClassificationModel]( - dt.asInstanceOf[RandomForestClassifier]) + val rfModel = pipelineModel.stages.last.asInstanceOf[RandomForestClassificationModel] if (rfModel.totalNumNodes < 30) { println(rfModel.toDebugString) // Print full model. } else { println(rfModel) // Print model summary. } case "regression" => - val rfModel = pipelineModel.getModel[RandomForestRegressionModel]( - dt.asInstanceOf[RandomForestRegressor]) + val rfModel = pipelineModel.stages.last.asInstanceOf[RandomForestRegressionModel] if (rfModel.totalNumNodes < 30) { println(rfModel.toDebugString) // Print full model. } else { diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala index bf805149d0af6..e8a991f50e338 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala @@ -63,7 +63,7 @@ object SimpleParamsExample { // we can view the parameters it used during fit(). // This prints the parameter (name: value) pairs, where names are unique IDs for this // LogisticRegression instance. - println("Model 1 was fit using parameters: " + model1.fittingParamMap) + println("Model 1 was fit using parameters: " + model1.parent.extractParamMap()) // We may alternatively specify parameters using a ParamMap, // which supports several methods for specifying parameters. @@ -78,7 +78,7 @@ object SimpleParamsExample { // Now learn a new model using the paramMapCombined parameters. // paramMapCombined overrides all parameters set earlier via lr.set* methods. val model2 = lr.fit(training.toDF(), paramMapCombined) - println("Model 2 was fit using parameters: " + model2.fittingParamMap) + println("Model 2 was fit using parameters: " + model2.parent.extractParamMap()) // Prepare test data. val test = sc.parallelize(Seq( diff --git a/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala b/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala index d6b3503ebdd9a..7f3f3262a644f 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Estimator.scala @@ -34,13 +34,16 @@ abstract class Estimator[M <: Model[M]] extends PipelineStage with Params { * Fits a single model to the input data with optional parameters. * * @param dataset input dataset - * @param paramPairs Optional list of param pairs. - * These values override any specified in this Estimator's embedded ParamMap. + * @param firstParamPair the first param pair, overrides embedded params + * @param otherParamPairs other param pairs. These values override any specified in this + * Estimator's embedded ParamMap. * @return fitted model */ @varargs - def fit(dataset: DataFrame, paramPairs: ParamPair[_]*): M = { - val map = ParamMap(paramPairs: _*) + def fit(dataset: DataFrame, firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): M = { + val map = new ParamMap() + .put(firstParamPair) + .put(otherParamPairs: _*) fit(dataset, map) } @@ -52,12 +55,19 @@ abstract class Estimator[M <: Model[M]] extends PipelineStage with Params { * These values override any specified in this Estimator's embedded ParamMap. * @return fitted model */ - def fit(dataset: DataFrame, paramMap: ParamMap): M + def fit(dataset: DataFrame, paramMap: ParamMap): M = { + copy(paramMap).fit(dataset) + } + + /** + * Fits a model to the input data. + */ + def fit(dataset: DataFrame): M /** * Fits multiple models to the input data with multiple sets of parameters. * The default implementation uses a for loop on each parameter map. - * Subclasses could overwrite this to optimize multi-model training. + * Subclasses could override this to optimize multi-model training. * * @param dataset input dataset * @param paramMaps An array of parameter maps. @@ -67,4 +77,8 @@ abstract class Estimator[M <: Model[M]] extends PipelineStage with Params { def fit(dataset: DataFrame, paramMaps: Array[ParamMap]): Seq[M] = { paramMaps.map(fit(dataset, _)) } + + override def copy(extra: ParamMap): Estimator[M] = { + super.copy(extra).asInstanceOf[Estimator[M]] + } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala index 8b4b5fd8af986..5f2f8c94e9ff7 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Evaluator.scala @@ -18,8 +18,7 @@ package org.apache.spark.ml import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.param.ParamMap -import org.apache.spark.ml.util.Identifiable +import org.apache.spark.ml.param.{ParamMap, Params} import org.apache.spark.sql.DataFrame /** @@ -27,7 +26,7 @@ import org.apache.spark.sql.DataFrame * Abstract class for evaluators that compute metrics from predictions. */ @AlphaComponent -abstract class Evaluator extends Identifiable { +abstract class Evaluator extends Params { /** * Evaluates the output. @@ -36,5 +35,18 @@ abstract class Evaluator extends Identifiable { * @param paramMap parameter map that specifies the input columns and output metrics * @return metric */ - def evaluate(dataset: DataFrame, paramMap: ParamMap): Double + def evaluate(dataset: DataFrame, paramMap: ParamMap): Double = { + this.copy(paramMap).evaluate(dataset) + } + + /** + * Evaluates the output. + * @param dataset a dataset that contains labels/observations and predictions. + * @return metric + */ + def evaluate(dataset: DataFrame): Double + + override def copy(extra: ParamMap): Evaluator = { + super.copy(extra).asInstanceOf[Evaluator] + } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Model.scala b/mllib/src/main/scala/org/apache/spark/ml/Model.scala index a491bc7ee8295..9974efe7b1d25 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Model.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Model.scala @@ -34,9 +34,8 @@ abstract class Model[M <: Model[M]] extends Transformer { */ val parent: Estimator[M] - /** - * Fitting parameters, such that parent.fit(..., fittingParamMap) could reproduce the model. - * Note: For ensembles' component Models, this value can be null. - */ - val fittingParamMap: ParamMap + override def copy(extra: ParamMap): M = { + // The default implementation of Params.copy doesn't work for models. + throw new NotImplementedError(s"${this.getClass} doesn't implement copy(extra: ParamMap)") + } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala index 6bfeecd764d75..33d430f5671ee 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala @@ -21,7 +21,7 @@ import scala.collection.mutable.ListBuffer import org.apache.spark.Logging import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} -import org.apache.spark.ml.param.{Params, Param, ParamMap} +import org.apache.spark.ml.param.{Param, ParamMap, Params} import org.apache.spark.sql.DataFrame import org.apache.spark.sql.types.StructType @@ -30,40 +30,41 @@ import org.apache.spark.sql.types.StructType * A stage in a pipeline, either an [[Estimator]] or a [[Transformer]]. */ @AlphaComponent -abstract class PipelineStage extends Serializable with Logging { +abstract class PipelineStage extends Params with Logging { /** * :: DeveloperApi :: * - * Derives the output schema from the input schema and parameters. - * The schema describes the columns and types of the data. - * - * @param schema Input schema to this stage - * @param paramMap Parameters passed to this stage - * @return Output schema from this stage + * Derives the output schema from the input schema. */ @DeveloperApi - def transformSchema(schema: StructType, paramMap: ParamMap): StructType + def transformSchema(schema: StructType): StructType /** + * :: DeveloperApi :: + * * Derives the output schema from the input schema and parameters, optionally with logging. * * This should be optimistic. If it is unclear whether the schema will be valid, then it should * be assumed valid until proven otherwise. */ + @DeveloperApi protected def transformSchema( schema: StructType, - paramMap: ParamMap, logging: Boolean): StructType = { if (logging) { logDebug(s"Input schema: ${schema.json}") } - val outputSchema = transformSchema(schema, paramMap) + val outputSchema = transformSchema(schema) if (logging) { logDebug(s"Expected output schema: ${outputSchema.json}") } outputSchema } + + override def copy(extra: ParamMap): PipelineStage = { + super.copy(extra).asInstanceOf[PipelineStage] + } } /** @@ -81,15 +82,22 @@ abstract class PipelineStage extends Serializable with Logging { @AlphaComponent class Pipeline extends Estimator[PipelineModel] { - /** param for pipeline stages */ + /** + * param for pipeline stages + * @group param + */ val stages: Param[Array[PipelineStage]] = new Param(this, "stages", "stages of the pipeline") + + /** @group setParam */ def setStages(value: Array[PipelineStage]): this.type = { set(stages, value); this } - def getStages: Array[PipelineStage] = getOrDefault(stages) - override def validate(paramMap: ParamMap): Unit = { + /** @group getParam */ + def getStages: Array[PipelineStage] = $(stages).clone() + + override def validateParams(paramMap: ParamMap): Unit = { val map = extractParamMap(paramMap) getStages.foreach { - case pStage: Params => pStage.validate(map) + case pStage: Params => pStage.validateParams(map) case _ => } } @@ -104,13 +112,11 @@ class Pipeline extends Estimator[PipelineModel] { * pipeline stages. If there are no stages, the output model acts as an identity transformer. * * @param dataset input dataset - * @param paramMap parameter map * @return fitted pipeline */ - override def fit(dataset: DataFrame, paramMap: ParamMap): PipelineModel = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val theStages = map(stages) + override def fit(dataset: DataFrame): PipelineModel = { + transformSchema(dataset.schema, logging = true) + val theStages = $(stages) // Search for the last estimator. var indexOfLastEstimator = -1 theStages.view.zipWithIndex.foreach { case (stage, index) => @@ -126,7 +132,7 @@ class Pipeline extends Estimator[PipelineModel] { if (index <= indexOfLastEstimator) { val transformer = stage match { case estimator: Estimator[_] => - estimator.fit(curDataset, paramMap) + estimator.fit(curDataset) case t: Transformer => t case _ => @@ -134,7 +140,7 @@ class Pipeline extends Estimator[PipelineModel] { s"Do not support stage $stage of type ${stage.getClass}") } if (index < indexOfLastEstimator) { - curDataset = transformer.transform(curDataset, paramMap) + curDataset = transformer.transform(curDataset) } transformers += transformer } else { @@ -142,15 +148,20 @@ class Pipeline extends Estimator[PipelineModel] { } } - new PipelineModel(this, map, transformers.toArray) + new PipelineModel(this, transformers.toArray) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - val theStages = map(stages) + override def copy(extra: ParamMap): Pipeline = { + val map = extractParamMap(extra) + val newStages = map(stages).map(_.copy(extra)) + new Pipeline().setStages(newStages) + } + + override def transformSchema(schema: StructType): StructType = { + val theStages = $(stages) require(theStages.toSet.size == theStages.length, "Cannot have duplicate components in a pipeline.") - theStages.foldLeft(schema)((cur, stage) => stage.transformSchema(cur, paramMap)) + theStages.foldLeft(schema)((cur, stage) => stage.transformSchema(cur)) } } @@ -161,43 +172,24 @@ class Pipeline extends Estimator[PipelineModel] { @AlphaComponent class PipelineModel private[ml] ( override val parent: Pipeline, - override val fittingParamMap: ParamMap, - private[ml] val stages: Array[Transformer]) + val stages: Array[Transformer]) extends Model[PipelineModel] with Logging { - override def validate(paramMap: ParamMap): Unit = { - val map = fittingParamMap ++ extractParamMap(paramMap) - stages.foreach(_.validate(map)) + override def validateParams(): Unit = { + super.validateParams() + stages.foreach(_.validateParams()) } - /** - * Gets the model produced by the input estimator. Throws an NoSuchElementException is the input - * estimator does not exist in the pipeline. - */ - def getModel[M <: Model[M]](stage: Estimator[M]): M = { - val matched = stages.filter { - case m: Model[_] => m.parent.eq(stage) - case _ => false - } - if (matched.isEmpty) { - throw new NoSuchElementException(s"Cannot find stage $stage from the pipeline.") - } else if (matched.length > 1) { - throw new IllegalStateException(s"Cannot have duplicate estimators in the sample pipeline.") - } else { - matched.head.asInstanceOf[M] - } + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) + stages.foldLeft(dataset)((cur, transformer) => transformer.transform(cur)) } - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - // Precedence of ParamMaps: paramMap > this.paramMap > fittingParamMap - val map = fittingParamMap ++ extractParamMap(paramMap) - transformSchema(dataset.schema, map, logging = true) - stages.foldLeft(dataset)((cur, transformer) => transformer.transform(cur, map)) + override def transformSchema(schema: StructType): StructType = { + stages.foldLeft(schema)((cur, transformer) => transformer.transformSchema(cur)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - // Precedence of ParamMaps: paramMap > this.paramMap > fittingParamMap - val map = fittingParamMap ++ extractParamMap(paramMap) - stages.foldLeft(schema)((cur, transformer) => transformer.transformSchema(cur, map)) + override def copy(extra: ParamMap): PipelineModel = { + new PipelineModel(parent, stages) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala index 0acda71ec6045..d96b54e511e9c 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala @@ -37,13 +37,18 @@ abstract class Transformer extends PipelineStage with Params { /** * Transforms the dataset with optional parameters * @param dataset input dataset - * @param paramPairs optional list of param pairs, overwrite embedded params + * @param firstParamPair the first param pair, overwrite embedded params + * @param otherParamPairs other param pairs, overwrite embedded params * @return transformed dataset */ @varargs - def transform(dataset: DataFrame, paramPairs: ParamPair[_]*): DataFrame = { + def transform( + dataset: DataFrame, + firstParamPair: ParamPair[_], + otherParamPairs: ParamPair[_]*): DataFrame = { val map = new ParamMap() - paramPairs.foreach(map.put(_)) + .put(firstParamPair) + .put(otherParamPairs: _*) transform(dataset, map) } @@ -53,7 +58,18 @@ abstract class Transformer extends PipelineStage with Params { * @param paramMap additional parameters, overwrite embedded params * @return transformed dataset */ - def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame + def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + this.copy(paramMap).transform(dataset) + } + + /** + * Transforms the input dataset. + */ + def transform(dataset: DataFrame): DataFrame + + override def copy(extra: ParamMap): Transformer = { + super.copy(extra).asInstanceOf[Transformer] + } } /** @@ -74,7 +90,7 @@ private[ml] abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, O * account of the embedded param map. So the param values should be determined solely by the input * param map. */ - protected def createTransformFunc(paramMap: ParamMap): IN => OUT + protected def createTransformFunc: IN => OUT /** * Returns the data type of the output column. @@ -86,22 +102,20 @@ private[ml] abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, O */ protected def validateInputType(inputType: DataType): Unit = {} - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - val inputType = schema(map(inputCol)).dataType + override def transformSchema(schema: StructType): StructType = { + val inputType = schema($(inputCol)).dataType validateInputType(inputType) - if (schema.fieldNames.contains(map(outputCol))) { - throw new IllegalArgumentException(s"Output column ${map(outputCol)} already exists.") + if (schema.fieldNames.contains($(outputCol))) { + throw new IllegalArgumentException(s"Output column ${$(outputCol)} already exists.") } val outputFields = schema.fields :+ - StructField(map(outputCol), outputDataType, nullable = false) + StructField($(outputCol), outputDataType, nullable = false) StructType(outputFields) } - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - dataset.withColumn(map(outputCol), - callUDF(this.createTransformFunc(map), outputDataType, dataset(map(inputCol)))) + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) + dataset.withColumn($(outputCol), + callUDF(this.createTransformFunc, outputDataType, dataset($(inputCol)))) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala index 29339c98f51cf..d3361e24705c8 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala @@ -19,7 +19,6 @@ package org.apache.spark.ml.classification import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor, PredictorParams} -import org.apache.spark.ml.param.{ParamMap, Params} import org.apache.spark.ml.param.shared.HasRawPredictionCol import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.mllib.linalg.{Vector, VectorUDT} @@ -27,7 +26,6 @@ import org.apache.spark.sql.DataFrame import org.apache.spark.sql.functions._ import org.apache.spark.sql.types.{DataType, DoubleType, StructType} - /** * :: DeveloperApi :: * Params for classification. @@ -40,12 +38,10 @@ private[spark] trait ClassifierParams extends PredictorParams override protected def validateAndTransformSchema( schema: StructType, - paramMap: ParamMap, fitting: Boolean, featuresDataType: DataType): StructType = { - val parentSchema = super.validateAndTransformSchema(schema, paramMap, fitting, featuresDataType) - val map = extractParamMap(paramMap) - SchemaUtils.appendColumn(parentSchema, map(rawPredictionCol), new VectorUDT) + val parentSchema = super.validateAndTransformSchema(schema, fitting, featuresDataType) + SchemaUtils.appendColumn(parentSchema, $(rawPredictionCol), new VectorUDT) } } @@ -102,27 +98,16 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur * - raw predictions (confidences) as [[rawPredictionCol]] of type [[Vector]]. * * @param dataset input dataset - * @param paramMap additional parameters, overwrite embedded params * @return transformed dataset */ - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + override def transform(dataset: DataFrame): DataFrame = { // This default implementation should be overridden as needed. // Check schema - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - - // Prepare model - val tmpModel = if (paramMap.size != 0) { - val tmpModel = this.copy() - Params.inheritValues(paramMap, parent, tmpModel) - tmpModel - } else { - this - } + transformSchema(dataset.schema, logging = true) val (numColsOutput, outputData) = - ClassificationModel.transformColumnsImpl[FeaturesType](dataset, tmpModel, map) + ClassificationModel.transformColumnsImpl[FeaturesType](dataset, this) if (numColsOutput == 0) { logWarning(s"$uid: ClassificationModel.transform() was called as NOOP" + " since no output columns were set.") @@ -158,7 +143,6 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur */ @DeveloperApi protected def predictRaw(features: FeaturesType): Vector - } private[ml] object ClassificationModel { @@ -167,38 +151,35 @@ private[ml] object ClassificationModel { * Added prediction column(s). This is separated from [[ClassificationModel.transform()]] * since it is used by [[org.apache.spark.ml.classification.ProbabilisticClassificationModel]]. * @param dataset Input dataset - * @param map Parameter map. This will NOT be merged with the embedded paramMap; the merge - * should already be done. * @return (number of columns added, transformed dataset) */ def transformColumnsImpl[FeaturesType]( dataset: DataFrame, - model: ClassificationModel[FeaturesType, _], - map: ParamMap): (Int, DataFrame) = { + model: ClassificationModel[FeaturesType, _]): (Int, DataFrame) = { // Output selected columns only. // This is a bit complicated since it tries to avoid repeated computation. var tmpData = dataset var numColsOutput = 0 - if (map(model.rawPredictionCol) != "") { + if (model.getRawPredictionCol != "") { // output raw prediction val features2raw: FeaturesType => Vector = model.predictRaw - tmpData = tmpData.withColumn(map(model.rawPredictionCol), - callUDF(features2raw, new VectorUDT, col(map(model.featuresCol)))) + tmpData = tmpData.withColumn(model.getRawPredictionCol, + callUDF(features2raw, new VectorUDT, col(model.getFeaturesCol))) numColsOutput += 1 - if (map(model.predictionCol) != "") { + if (model.getPredictionCol != "") { val raw2pred: Vector => Double = (rawPred) => { rawPred.toArray.zipWithIndex.maxBy(_._1)._2 } - tmpData = tmpData.withColumn(map(model.predictionCol), - callUDF(raw2pred, DoubleType, col(map(model.rawPredictionCol)))) + tmpData = tmpData.withColumn(model.getPredictionCol, + callUDF(raw2pred, DoubleType, col(model.getRawPredictionCol))) numColsOutput += 1 } - } else if (map(model.predictionCol) != "") { + } else if (model.getPredictionCol != "") { // output prediction val features2pred: FeaturesType => Double = model.predict - tmpData = tmpData.withColumn(map(model.predictionCol), - callUDF(features2pred, DoubleType, col(map(model.featuresCol)))) + tmpData = tmpData.withColumn(model.getPredictionCol, + callUDF(features2pred, DoubleType, col(model.getFeaturesCol))) numColsOutput += 1 } (numColsOutput, tmpData) diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala index ee2a8dc6db171..419e5ba05d38a 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala @@ -18,9 +18,9 @@ package org.apache.spark.ml.classification import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.impl.estimator.{Predictor, PredictionModel} +import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} import org.apache.spark.ml.impl.tree._ -import org.apache.spark.ml.param.{Params, ParamMap} +import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.tree.{DecisionTreeModel, Node} import org.apache.spark.ml.util.MetadataUtils import org.apache.spark.mllib.linalg.Vector @@ -31,7 +31,6 @@ import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeMo import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame - /** * :: AlphaComponent :: * @@ -64,22 +63,20 @@ final class DecisionTreeClassifier override def setImpurity(value: String): this.type = super.setImpurity(value) - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): DecisionTreeClassificationModel = { + override protected def train(dataset: DataFrame): DecisionTreeClassificationModel = { val categoricalFeatures: Map[Int, Int] = - MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) - val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema(paramMap(labelCol))) match { + MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol))) + val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema($(labelCol))) match { case Some(n: Int) => n case None => throw new IllegalArgumentException("DecisionTreeClassifier was given input" + - s" with invalid label column ${paramMap(labelCol)}, without the number of classes" + + s" with invalid label column ${$(labelCol)}, without the number of classes" + " specified. See StringIndexer.") // TODO: Automatically index labels: SPARK-7126 } - val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset) val strategy = getOldStrategy(categoricalFeatures, numClasses) val oldModel = OldDecisionTree.train(oldDataset, strategy) - DecisionTreeClassificationModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + DecisionTreeClassificationModel.fromOld(oldModel, this, categoricalFeatures) } /** (private[ml]) Create a Strategy instance to use with the old API. */ @@ -106,7 +103,6 @@ object DecisionTreeClassifier { @AlphaComponent final class DecisionTreeClassificationModel private[ml] ( override val parent: DecisionTreeClassifier, - override val fittingParamMap: ParamMap, override val rootNode: Node) extends PredictionModel[Vector, DecisionTreeClassificationModel] with DecisionTreeModel with Serializable { @@ -118,10 +114,8 @@ final class DecisionTreeClassificationModel private[ml] ( rootNode.predict(features) } - override protected def copy(): DecisionTreeClassificationModel = { - val m = new DecisionTreeClassificationModel(parent, fittingParamMap, rootNode) - Params.inheritValues(this.extractParamMap(), this, m) - m + override def copy(extra: ParamMap): DecisionTreeClassificationModel = { + copyValues(new DecisionTreeClassificationModel(parent, rootNode), extra) } override def toString: String = { @@ -140,12 +134,11 @@ private[ml] object DecisionTreeClassificationModel { def fromOld( oldModel: OldDecisionTreeModel, parent: DecisionTreeClassifier, - fittingParamMap: ParamMap, categoricalFeatures: Map[Int, Int]): DecisionTreeClassificationModel = { require(oldModel.algo == OldAlgo.Classification, s"Cannot convert non-classification DecisionTreeModel (old API) to" + s" DecisionTreeClassificationModel (new API). Algo is: ${oldModel.algo}") val rootNode = Node.fromOld(oldModel.topNode, categoricalFeatures) - new DecisionTreeClassificationModel(parent, fittingParamMap, rootNode) + new DecisionTreeClassificationModel(parent, rootNode) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala index 3d849867d4c47..534ea95b1c538 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala @@ -23,7 +23,7 @@ import org.apache.spark.Logging import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} import org.apache.spark.ml.impl.tree._ -import org.apache.spark.ml.param.{Param, Params, ParamMap} +import org.apache.spark.ml.param.{Param, ParamMap} import org.apache.spark.ml.regression.DecisionTreeRegressionModel import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel} import org.apache.spark.ml.util.MetadataUtils @@ -31,12 +31,11 @@ import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{GradientBoostedTrees => OldGBT} import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} -import org.apache.spark.mllib.tree.loss.{Loss => OldLoss, LogLoss => OldLogLoss} +import org.apache.spark.mllib.tree.loss.{LogLoss => OldLogLoss, Loss => OldLoss} import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel} import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame - /** * :: AlphaComponent :: * @@ -112,7 +111,7 @@ final class GBTClassifier def setLossType(value: String): this.type = set(lossType, value) /** @group getParam */ - def getLossType: String = getOrDefault(lossType).toLowerCase + def getLossType: String = $(lossType).toLowerCase /** (private[ml]) Convert new loss to old loss. */ override private[ml] def getOldLossType: OldLoss = { @@ -124,25 +123,23 @@ final class GBTClassifier } } - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): GBTClassificationModel = { + override protected def train(dataset: DataFrame): GBTClassificationModel = { val categoricalFeatures: Map[Int, Int] = - MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) - val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema(paramMap(labelCol))) match { + MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol))) + val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema($(labelCol))) match { case Some(n: Int) => n case None => throw new IllegalArgumentException("GBTClassifier was given input" + - s" with invalid label column ${paramMap(labelCol)}, without the number of classes" + + s" with invalid label column ${$(labelCol)}, without the number of classes" + " specified. See StringIndexer.") // TODO: Automatically index labels: SPARK-7126 } require(numClasses == 2, s"GBTClassifier only supports binary classification but was given numClasses = $numClasses") - val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset) val boostingStrategy = super.getOldBoostingStrategy(categoricalFeatures, OldAlgo.Classification) val oldGBT = new OldGBT(boostingStrategy) val oldModel = oldGBT.run(oldDataset) - GBTClassificationModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + GBTClassificationModel.fromOld(oldModel, this, categoricalFeatures) } } @@ -165,7 +162,6 @@ object GBTClassifier { @AlphaComponent final class GBTClassificationModel( override val parent: GBTClassifier, - override val fittingParamMap: ParamMap, private val _trees: Array[DecisionTreeRegressionModel], private val _treeWeights: Array[Double]) extends PredictionModel[Vector, GBTClassificationModel] @@ -188,10 +184,8 @@ final class GBTClassificationModel( if (prediction > 0.0) 1.0 else 0.0 } - override protected def copy(): GBTClassificationModel = { - val m = new GBTClassificationModel(parent, fittingParamMap, _trees, _treeWeights) - Params.inheritValues(this.extractParamMap(), this, m) - m + override def copy(extra: ParamMap): GBTClassificationModel = { + copyValues(new GBTClassificationModel(parent, _trees, _treeWeights), extra) } override def toString: String = { @@ -210,14 +204,13 @@ private[ml] object GBTClassificationModel { def fromOld( oldModel: OldGBTModel, parent: GBTClassifier, - fittingParamMap: ParamMap, categoricalFeatures: Map[Int, Int]): GBTClassificationModel = { require(oldModel.algo == OldAlgo.Classification, "Cannot convert GradientBoostedTreesModel" + s" with algo=${oldModel.algo} (old API) to GBTClassificationModel (new API).") val newTrees = oldModel.trees.map { tree => // parent, fittingParamMap for each tree is null since there are no good ways to set these. - DecisionTreeRegressionModel.fromOld(tree, null, null, categoricalFeatures) + DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures) } - new GBTClassificationModel(parent, fittingParamMap, newTrees, oldModel.treeWeights) + new GBTClassificationModel(parent, newTrees, oldModel.treeWeights) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala index cc8b0721cf2b6..b73be035e29b5 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala @@ -21,12 +21,11 @@ import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared._ import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS -import org.apache.spark.mllib.linalg.{VectorUDT, BLAS, Vector, Vectors} +import org.apache.spark.mllib.linalg.{BLAS, Vector, VectorUDT, Vectors} import org.apache.spark.sql.DataFrame import org.apache.spark.sql.functions._ import org.apache.spark.storage.StorageLevel - /** * Params for logistic regression. */ @@ -59,9 +58,9 @@ class LogisticRegression /** @group setParam */ def setThreshold(value: Double): this.type = set(threshold, value) - override protected def train(dataset: DataFrame, paramMap: ParamMap): LogisticRegressionModel = { + override protected def train(dataset: DataFrame): LogisticRegressionModel = { // Extract columns from data. If dataset is persisted, do not persist oldDataset. - val oldDataset = extractLabeledPoints(dataset, paramMap) + val oldDataset = extractLabeledPoints(dataset) val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE if (handlePersistence) { oldDataset.persist(StorageLevel.MEMORY_AND_DISK) @@ -69,17 +68,17 @@ class LogisticRegression // Train model val lr = new LogisticRegressionWithLBFGS() - .setIntercept(paramMap(fitIntercept)) + .setIntercept($(fitIntercept)) lr.optimizer - .setRegParam(paramMap(regParam)) - .setNumIterations(paramMap(maxIter)) + .setRegParam($(regParam)) + .setNumIterations($(maxIter)) val oldModel = lr.run(oldDataset) - val lrm = new LogisticRegressionModel(this, paramMap, oldModel.weights, oldModel.intercept) + val lrm = new LogisticRegressionModel(this, oldModel.weights, oldModel.intercept) if (handlePersistence) { oldDataset.unpersist() } - lrm + copyValues(lrm) } } @@ -92,7 +91,6 @@ class LogisticRegression @AlphaComponent class LogisticRegressionModel private[ml] ( override val parent: LogisticRegression, - override val fittingParamMap: ParamMap, val weights: Vector, val intercept: Double) extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel] @@ -110,16 +108,14 @@ class LogisticRegressionModel private[ml] ( 1.0 / (1.0 + math.exp(-m)) } - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + override def transform(dataset: DataFrame): DataFrame = { // This is overridden (a) to be more efficient (avoiding re-computing values when creating // multiple output columns) and (b) to handle threshold, which the abstractions do not use. // TODO: We should abstract away the steps defined by UDFs below so that the abstractions // can call whichever UDFs are needed to create the output columns. // Check schema - transformSchema(dataset.schema, paramMap, logging = true) - - val map = extractParamMap(paramMap) + transformSchema(dataset.schema, logging = true) // Output selected columns only. // This is a bit complicated since it tries to avoid repeated computation. @@ -128,41 +124,41 @@ class LogisticRegressionModel private[ml] ( // prediction (max margin) var tmpData = dataset var numColsOutput = 0 - if (map(rawPredictionCol) != "") { + if ($(rawPredictionCol) != "") { val features2raw: Vector => Vector = (features) => predictRaw(features) - tmpData = tmpData.withColumn(map(rawPredictionCol), - callUDF(features2raw, new VectorUDT, col(map(featuresCol)))) + tmpData = tmpData.withColumn($(rawPredictionCol), + callUDF(features2raw, new VectorUDT, col($(featuresCol)))) numColsOutput += 1 } - if (map(probabilityCol) != "") { - if (map(rawPredictionCol) != "") { + if ($(probabilityCol) != "") { + if ($(rawPredictionCol) != "") { val raw2prob = udf { (rawPreds: Vector) => val prob1 = 1.0 / (1.0 + math.exp(-rawPreds(1))) Vectors.dense(1.0 - prob1, prob1): Vector } - tmpData = tmpData.withColumn(map(probabilityCol), raw2prob(col(map(rawPredictionCol)))) + tmpData = tmpData.withColumn($(probabilityCol), raw2prob(col($(rawPredictionCol)))) } else { val features2prob = udf { (features: Vector) => predictProbabilities(features) : Vector } - tmpData = tmpData.withColumn(map(probabilityCol), features2prob(col(map(featuresCol)))) + tmpData = tmpData.withColumn($(probabilityCol), features2prob(col($(featuresCol)))) } numColsOutput += 1 } - if (map(predictionCol) != "") { - val t = map(threshold) - if (map(probabilityCol) != "") { + if ($(predictionCol) != "") { + val t = $(threshold) + if ($(probabilityCol) != "") { val predict = udf { probs: Vector => if (probs(1) > t) 1.0 else 0.0 } - tmpData = tmpData.withColumn(map(predictionCol), predict(col(map(probabilityCol)))) - } else if (map(rawPredictionCol) != "") { + tmpData = tmpData.withColumn($(predictionCol), predict(col($(probabilityCol)))) + } else if ($(rawPredictionCol) != "") { val predict = udf { rawPreds: Vector => val prob1 = 1.0 / (1.0 + math.exp(-rawPreds(1))) if (prob1 > t) 1.0 else 0.0 } - tmpData = tmpData.withColumn(map(predictionCol), predict(col(map(rawPredictionCol)))) + tmpData = tmpData.withColumn($(predictionCol), predict(col($(rawPredictionCol)))) } else { val predict = udf { features: Vector => this.predict(features) } - tmpData = tmpData.withColumn(map(predictionCol), predict(col(map(featuresCol)))) + tmpData = tmpData.withColumn($(predictionCol), predict(col($(featuresCol)))) } numColsOutput += 1 } @@ -193,9 +189,7 @@ class LogisticRegressionModel private[ml] ( Vectors.dense(0.0, m) } - override protected def copy(): LogisticRegressionModel = { - val m = new LogisticRegressionModel(parent, fittingParamMap, weights, intercept) - Params.inheritValues(this.extractParamMap(), this, m) - m + override def copy(extra: ParamMap): LogisticRegressionModel = { + copyValues(new LogisticRegressionModel(parent, weights, intercept), extra) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala index 10404548ccfde..8519841c5c26c 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala @@ -18,7 +18,6 @@ package org.apache.spark.ml.classification import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} -import org.apache.spark.ml.param.{ParamMap, Params} import org.apache.spark.ml.param.shared._ import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.mllib.linalg.{Vector, VectorUDT} @@ -34,12 +33,10 @@ private[classification] trait ProbabilisticClassifierParams override protected def validateAndTransformSchema( schema: StructType, - paramMap: ParamMap, fitting: Boolean, featuresDataType: DataType): StructType = { - val parentSchema = super.validateAndTransformSchema(schema, paramMap, fitting, featuresDataType) - val map = extractParamMap(paramMap) - SchemaUtils.appendColumn(parentSchema, map(probabilityCol), new VectorUDT) + val parentSchema = super.validateAndTransformSchema(schema, fitting, featuresDataType) + SchemaUtils.appendColumn(parentSchema, $(probabilityCol), new VectorUDT) } } @@ -95,36 +92,22 @@ private[spark] abstract class ProbabilisticClassificationModel[ * - probability of each class as [[probabilityCol]] of type [[Vector]]. * * @param dataset input dataset - * @param paramMap additional parameters, overwrite embedded params * @return transformed dataset */ - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + override def transform(dataset: DataFrame): DataFrame = { // This default implementation should be overridden as needed. // Check schema - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - - // Prepare model - val tmpModel = if (paramMap.size != 0) { - val tmpModel = this.copy() - Params.inheritValues(paramMap, parent, tmpModel) - tmpModel - } else { - this - } + transformSchema(dataset.schema, logging = true) val (numColsOutput, outputData) = - ClassificationModel.transformColumnsImpl[FeaturesType](dataset, tmpModel, map) + ClassificationModel.transformColumnsImpl[FeaturesType](dataset, this) // Output selected columns only. - if (map(probabilityCol) != "") { + if ($(probabilityCol) != "") { // output probabilities - val features2probs: FeaturesType => Vector = (features) => { - tmpModel.predictProbabilities(features) - } - outputData.withColumn(map(probabilityCol), - callUDF(features2probs, new VectorUDT, col(map(featuresCol)))) + outputData.withColumn($(probabilityCol), + callUDF(predictProbabilities _, new VectorUDT, col($(featuresCol)))) } else { if (numColsOutput == 0) { this.logWarning(s"$uid: ProbabilisticClassificationModel.transform() was called as NOOP" + diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala index cfd6508fce890..17f59bb42e129 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala @@ -22,18 +22,17 @@ import scala.collection.mutable import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} import org.apache.spark.ml.impl.tree._ -import org.apache.spark.ml.param.{Params, ParamMap} +import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel} import org.apache.spark.ml.util.MetadataUtils import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{RandomForest => OldRandomForest} -import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel} import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame - /** * :: AlphaComponent :: * @@ -81,24 +80,22 @@ final class RandomForestClassifier override def setFeatureSubsetStrategy(value: String): this.type = super.setFeatureSubsetStrategy(value) - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): RandomForestClassificationModel = { + override protected def train(dataset: DataFrame): RandomForestClassificationModel = { val categoricalFeatures: Map[Int, Int] = - MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) - val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema(paramMap(labelCol))) match { + MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol))) + val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema($(labelCol))) match { case Some(n: Int) => n case None => throw new IllegalArgumentException("RandomForestClassifier was given input" + - s" with invalid label column ${paramMap(labelCol)}, without the number of classes" + + s" with invalid label column ${$(labelCol)}, without the number of classes" + " specified. See StringIndexer.") // TODO: Automatically index labels: SPARK-7126 } - val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset) val strategy = super.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, getOldImpurity) val oldModel = OldRandomForest.trainClassifier( oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed.toInt) - RandomForestClassificationModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + RandomForestClassificationModel.fromOld(oldModel, this, categoricalFeatures) } } @@ -123,7 +120,6 @@ object RandomForestClassifier { @AlphaComponent final class RandomForestClassificationModel private[ml] ( override val parent: RandomForestClassifier, - override val fittingParamMap: ParamMap, private val _trees: Array[DecisionTreeClassificationModel]) extends PredictionModel[Vector, RandomForestClassificationModel] with TreeEnsembleModel with Serializable { @@ -150,10 +146,8 @@ final class RandomForestClassificationModel private[ml] ( votes.maxBy(_._2)._1 } - override protected def copy(): RandomForestClassificationModel = { - val m = new RandomForestClassificationModel(parent, fittingParamMap, _trees) - Params.inheritValues(this.extractParamMap(), this, m) - m + override def copy(extra: ParamMap): RandomForestClassificationModel = { + copyValues(new RandomForestClassificationModel(parent, _trees), extra) } override def toString: String = { @@ -172,14 +166,13 @@ private[ml] object RandomForestClassificationModel { def fromOld( oldModel: OldRandomForestModel, parent: RandomForestClassifier, - fittingParamMap: ParamMap, categoricalFeatures: Map[Int, Int]): RandomForestClassificationModel = { require(oldModel.algo == OldAlgo.Classification, "Cannot convert RandomForestModel" + s" with algo=${oldModel.algo} (old API) to RandomForestClassificationModel (new API).") val newTrees = oldModel.trees.map { tree => // parent, fittingParamMap for each tree is null since there are no good ways to set these. - DecisionTreeClassificationModel.fromOld(tree, null, null, categoricalFeatures) + DecisionTreeClassificationModel.fromOld(tree, null, categoricalFeatures) } - new RandomForestClassificationModel(parent, fittingParamMap, newTrees) + new RandomForestClassificationModel(parent, newTrees) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala index c865eb9fe092d..e5a73c6087a11 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala @@ -33,8 +33,7 @@ import org.apache.spark.sql.types.DoubleType * Evaluator for binary classification, which expects two input columns: score and label. */ @AlphaComponent -class BinaryClassificationEvaluator extends Evaluator with Params - with HasRawPredictionCol with HasLabelCol { +class BinaryClassificationEvaluator extends Evaluator with HasRawPredictionCol with HasLabelCol { /** * param for metric name in evaluation @@ -44,7 +43,7 @@ class BinaryClassificationEvaluator extends Evaluator with Params "metric name in evaluation (areaUnderROC|areaUnderPR)") /** @group getParam */ - def getMetricName: String = getOrDefault(metricName) + def getMetricName: String = $(metricName) /** @group setParam */ def setMetricName(value: String): this.type = set(metricName, value) @@ -57,20 +56,18 @@ class BinaryClassificationEvaluator extends Evaluator with Params setDefault(metricName -> "areaUnderROC") - override def evaluate(dataset: DataFrame, paramMap: ParamMap): Double = { - val map = extractParamMap(paramMap) - + override def evaluate(dataset: DataFrame): Double = { val schema = dataset.schema - SchemaUtils.checkColumnType(schema, map(rawPredictionCol), new VectorUDT) - SchemaUtils.checkColumnType(schema, map(labelCol), DoubleType) + SchemaUtils.checkColumnType(schema, $(rawPredictionCol), new VectorUDT) + SchemaUtils.checkColumnType(schema, $(labelCol), DoubleType) // TODO: When dataset metadata has been implemented, check rawPredictionCol vector length = 2. - val scoreAndLabels = dataset.select(map(rawPredictionCol), map(labelCol)) + val scoreAndLabels = dataset.select($(rawPredictionCol), $(labelCol)) .map { case Row(rawPrediction: Vector, label: Double) => (rawPrediction(1), label) } val metrics = new BinaryClassificationMetrics(scoreAndLabels) - val metric = map(metricName) match { + val metric = $(metricName) match { case "areaUnderROC" => metrics.areaUnderROC() case "areaUnderPR" => diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala index f3ce6dfca2c1c..6eb1db6971111 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala @@ -44,7 +44,7 @@ final class Binarizer extends Transformer with HasInputCol with HasOutputCol { new DoubleParam(this, "threshold", "threshold used to binarize continuous features") /** @group getParam */ - def getThreshold: Double = getOrDefault(threshold) + def getThreshold: Double = $(threshold) /** @group setParam */ def setThreshold(value: Double): this.type = set(threshold, value) @@ -57,23 +57,21 @@ final class Binarizer extends Transformer with HasInputCol with HasOutputCol { /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val td = map(threshold) + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) + val td = $(threshold) val binarizer = udf { in: Double => if (in > td) 1.0 else 0.0 } - val outputColName = map(outputCol) + val outputColName = $(outputCol) val metadata = BinaryAttribute.defaultAttr.withName(outputColName).toMetadata() dataset.select(col("*"), - binarizer(col(map(inputCol))).as(outputColName, metadata)) + binarizer(col($(inputCol))).as(outputColName, metadata)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - SchemaUtils.checkColumnType(schema, map(inputCol), DoubleType) + override def transformSchema(schema: StructType): StructType = { + SchemaUtils.checkColumnType(schema, $(inputCol), DoubleType) val inputFields = schema.fields - val outputColName = map(outputCol) + val outputColName = $(outputCol) require(inputFields.forall(_.name != outputColName), s"Output column $outputColName already exists.") diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala index 0b3128f9ee8cd..c305a819a8966 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/HashingTF.scala @@ -19,9 +19,9 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.UnaryTransformer -import org.apache.spark.ml.param.{ParamValidators, IntParam, ParamMap} +import org.apache.spark.ml.param.{IntParam, ParamValidators} import org.apache.spark.mllib.feature -import org.apache.spark.mllib.linalg.{VectorUDT, Vector} +import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.sql.types.DataType /** @@ -42,13 +42,13 @@ class HashingTF extends UnaryTransformer[Iterable[_], Vector, HashingTF] { setDefault(numFeatures -> (1 << 18)) /** @group getParam */ - def getNumFeatures: Int = getOrDefault(numFeatures) + def getNumFeatures: Int = $(numFeatures) /** @group setParam */ def setNumFeatures(value: Int): this.type = set(numFeatures, value) - override protected def createTransformFunc(paramMap: ParamMap): Iterable[_] => Vector = { - val hashingTF = new feature.HashingTF(paramMap(numFeatures)) + override protected def createTransformFunc: Iterable[_] => Vector = { + val hashingTF = new feature.HashingTF($(numFeatures)) hashingTF.transform } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala index e6a62d998bb97..d901a20aed002 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala @@ -43,7 +43,7 @@ private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol setDefault(minDocFreq -> 0) /** @group getParam */ - def getMinDocFreq: Int = getOrDefault(minDocFreq) + def getMinDocFreq: Int = $(minDocFreq) /** @group setParam */ def setMinDocFreq(value: Int): this.type = set(minDocFreq, value) @@ -51,10 +51,9 @@ private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol /** * Validate and transform the input schema. */ - protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - SchemaUtils.checkColumnType(schema, map(inputCol), new VectorUDT) - SchemaUtils.appendColumn(schema, map(outputCol), new VectorUDT) + protected def validateAndTransformSchema(schema: StructType): StructType = { + SchemaUtils.checkColumnType(schema, $(inputCol), new VectorUDT) + SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT) } } @@ -71,18 +70,15 @@ final class IDF extends Estimator[IDFModel] with IDFBase { /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def fit(dataset: DataFrame, paramMap: ParamMap): IDFModel = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val input = dataset.select(map(inputCol)).map { case Row(v: Vector) => v } - val idf = new feature.IDF(map(minDocFreq)).fit(input) - val model = new IDFModel(this, map, idf) - Params.inheritValues(map, this, model) - model + override def fit(dataset: DataFrame): IDFModel = { + transformSchema(dataset.schema, logging = true) + val input = dataset.select($(inputCol)).map { case Row(v: Vector) => v } + val idf = new feature.IDF($(minDocFreq)).fit(input) + copyValues(new IDFModel(this, idf)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } @@ -93,7 +89,6 @@ final class IDF extends Estimator[IDFModel] with IDFBase { @AlphaComponent class IDFModel private[ml] ( override val parent: IDF, - override val fittingParamMap: ParamMap, idfModel: feature.IDFModel) extends Model[IDFModel] with IDFBase { @@ -103,14 +98,13 @@ class IDFModel private[ml] ( /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) val idf = udf { vec: Vector => idfModel.transform(vec) } - dataset.withColumn(map(outputCol), idf(col(map(inputCol)))) + dataset.withColumn($(outputCol), idf(col($(inputCol)))) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala index bd2b5f6067e2d..755b46a64c7f1 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Normalizer.scala @@ -19,9 +19,9 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.UnaryTransformer -import org.apache.spark.ml.param.{ParamValidators, DoubleParam, ParamMap} +import org.apache.spark.ml.param.{DoubleParam, ParamValidators} import org.apache.spark.mllib.feature -import org.apache.spark.mllib.linalg.{VectorUDT, Vector} +import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.sql.types.DataType /** @@ -41,13 +41,13 @@ class Normalizer extends UnaryTransformer[Vector, Vector, Normalizer] { setDefault(p -> 2.0) /** @group getParam */ - def getP: Double = getOrDefault(p) + def getP: Double = $(p) /** @group setParam */ def setP(value: Double): this.type = set(p, value) - override protected def createTransformFunc(paramMap: ParamMap): Vector => Vector = { - val normalizer = new feature.Normalizer(paramMap(p)) + override protected def createTransformFunc: Vector => Vector = { + val normalizer = new feature.Normalizer($(p)) normalizer.transform } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala index 1b7c939c2dffe..63e190c8aae53 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala @@ -21,7 +21,7 @@ import scala.collection.mutable import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.UnaryTransformer -import org.apache.spark.ml.param.{ParamValidators, IntParam, ParamMap} +import org.apache.spark.ml.param.{IntParam, ParamValidators} import org.apache.spark.mllib.linalg._ import org.apache.spark.sql.types.DataType @@ -47,14 +47,13 @@ class PolynomialExpansion extends UnaryTransformer[Vector, Vector, PolynomialExp setDefault(degree -> 2) /** @group getParam */ - def getDegree: Int = getOrDefault(degree) + def getDegree: Int = $(degree) /** @group setParam */ def setDegree(value: Int): this.type = set(degree, value) - override protected def createTransformFunc(paramMap: ParamMap): Vector => Vector = { v => - val d = paramMap(degree) - PolynomialExpansion.expand(v, d) + override protected def createTransformFunc: Vector => Vector = { v => + PolynomialExpansion.expand(v, $(degree)) } override protected def outputDataType: DataType = new VectorUDT() diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala index a0e9ed32e0e4c..7cad59ff3fa37 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala @@ -71,25 +71,21 @@ class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerP /** @group setParam */ def setWithStd(value: Boolean): this.type = set(withStd, value) - override def fit(dataset: DataFrame, paramMap: ParamMap): StandardScalerModel = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val input = dataset.select(map(inputCol)).map { case Row(v: Vector) => v } - val scaler = new feature.StandardScaler(withMean = map(withMean), withStd = map(withStd)) + override def fit(dataset: DataFrame): StandardScalerModel = { + transformSchema(dataset.schema, logging = true) + val input = dataset.select($(inputCol)).map { case Row(v: Vector) => v } + val scaler = new feature.StandardScaler(withMean = $(withMean), withStd = $(withStd)) val scalerModel = scaler.fit(input) - val model = new StandardScalerModel(this, map, scalerModel) - Params.inheritValues(map, this, model) - model + copyValues(new StandardScalerModel(this, scalerModel)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - val inputType = schema(map(inputCol)).dataType + override def transformSchema(schema: StructType): StructType = { + val inputType = schema($(inputCol)).dataType require(inputType.isInstanceOf[VectorUDT], - s"Input column ${map(inputCol)} must be a vector column") - require(!schema.fieldNames.contains(map(outputCol)), - s"Output column ${map(outputCol)} already exists.") - val outputFields = schema.fields :+ StructField(map(outputCol), new VectorUDT, false) + s"Input column ${$(inputCol)} must be a vector column") + require(!schema.fieldNames.contains($(outputCol)), + s"Output column ${$(outputCol)} already exists.") + val outputFields = schema.fields :+ StructField($(outputCol), new VectorUDT, false) StructType(outputFields) } } @@ -101,7 +97,6 @@ class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerP @AlphaComponent class StandardScalerModel private[ml] ( override val parent: StandardScaler, - override val fittingParamMap: ParamMap, scaler: feature.StandardScalerModel) extends Model[StandardScalerModel] with StandardScalerParams { @@ -111,21 +106,19 @@ class StandardScalerModel private[ml] ( /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val scale = udf((v: Vector) => { scaler.transform(v) } : Vector) - dataset.withColumn(map(outputCol), scale(col(map(inputCol)))) + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) + val scale = udf { scaler.transform _ } + dataset.withColumn($(outputCol), scale(col($(inputCol)))) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - val inputType = schema(map(inputCol)).dataType + override def transformSchema(schema: StructType): StructType = { + val inputType = schema($(inputCol)).dataType require(inputType.isInstanceOf[VectorUDT], - s"Input column ${map(inputCol)} must be a vector column") - require(!schema.fieldNames.contains(map(outputCol)), - s"Output column ${map(outputCol)} already exists.") - val outputFields = schema.fields :+ StructField(map(outputCol), new VectorUDT, false) + s"Input column ${$(inputCol)} must be a vector column") + require(!schema.fieldNames.contains($(outputCol)), + s"Output column ${$(outputCol)} already exists.") + val outputFields = schema.fields :+ StructField($(outputCol), new VectorUDT, false) StructType(outputFields) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala index 9db3b29e10d69..3d78537ad84cb 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala @@ -34,18 +34,17 @@ import org.apache.spark.util.collection.OpenHashMap private[feature] trait StringIndexerBase extends Params with HasInputCol with HasOutputCol { /** Validates and transforms the input schema. */ - protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - val inputColName = map(inputCol) + protected def validateAndTransformSchema(schema: StructType): StructType = { + val inputColName = $(inputCol) val inputDataType = schema(inputColName).dataType require(inputDataType == StringType || inputDataType.isInstanceOf[NumericType], s"The input column $inputColName must be either string type or numeric type, " + s"but got $inputDataType.") val inputFields = schema.fields - val outputColName = map(outputCol) + val outputColName = $(outputCol) require(inputFields.forall(_.name != outputColName), s"Output column $outputColName already exists.") - val attr = NominalAttribute.defaultAttr.withName(map(outputCol)) + val attr = NominalAttribute.defaultAttr.withName($(outputCol)) val outputFields = inputFields :+ attr.toStructField() StructType(outputFields) } @@ -69,19 +68,16 @@ class StringIndexer extends Estimator[StringIndexerModel] with StringIndexerBase // TODO: handle unseen labels - override def fit(dataset: DataFrame, paramMap: ParamMap): StringIndexerModel = { - val map = extractParamMap(paramMap) - val counts = dataset.select(col(map(inputCol)).cast(StringType)) + override def fit(dataset: DataFrame): StringIndexerModel = { + val counts = dataset.select(col($(inputCol)).cast(StringType)) .map(_.getString(0)) .countByValue() val labels = counts.toSeq.sortBy(-_._2).map(_._1).toArray - val model = new StringIndexerModel(this, map, labels) - Params.inheritValues(map, this, model) - model + copyValues(new StringIndexerModel(this, labels)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } @@ -92,7 +88,6 @@ class StringIndexer extends Estimator[StringIndexerModel] with StringIndexerBase @AlphaComponent class StringIndexerModel private[ml] ( override val parent: StringIndexer, - override val fittingParamMap: ParamMap, labels: Array[String]) extends Model[StringIndexerModel] with StringIndexerBase { private val labelToIndex: OpenHashMap[String, Double] = { @@ -112,8 +107,7 @@ class StringIndexerModel private[ml] ( /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - val map = extractParamMap(paramMap) + override def transform(dataset: DataFrame): DataFrame = { val indexer = udf { label: String => if (labelToIndex.contains(label)) { labelToIndex(label) @@ -122,14 +116,14 @@ class StringIndexerModel private[ml] ( throw new SparkException(s"Unseen label: $label.") } } - val outputColName = map(outputCol) + val outputColName = $(outputCol) val metadata = NominalAttribute.defaultAttr .withName(outputColName).withValues(labels).toMetadata() dataset.select(col("*"), - indexer(dataset(map(inputCol)).cast(StringType)).as(outputColName, metadata)) + indexer(dataset($(inputCol)).cast(StringType)).as(outputColName, metadata)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala index 01752ba482d0c..2863b7621526e 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Tokenizer.scala @@ -20,7 +20,7 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.UnaryTransformer import org.apache.spark.ml.param._ -import org.apache.spark.sql.types.{DataType, StringType, ArrayType} +import org.apache.spark.sql.types.{ArrayType, DataType, StringType} /** * :: AlphaComponent :: @@ -29,7 +29,7 @@ import org.apache.spark.sql.types.{DataType, StringType, ArrayType} @AlphaComponent class Tokenizer extends UnaryTransformer[String, Seq[String], Tokenizer] { - override protected def createTransformFunc(paramMap: ParamMap): String => Seq[String] = { + override protected def createTransformFunc: String => Seq[String] = { _.toLowerCase.split("\\s") } @@ -62,7 +62,7 @@ class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenize def setMinTokenLength(value: Int): this.type = set(minTokenLength, value) /** @group getParam */ - def getMinTokenLength: Int = getOrDefault(minTokenLength) + def getMinTokenLength: Int = $(minTokenLength) /** * Indicates whether regex splits on gaps (true) or matching tokens (false). @@ -75,7 +75,7 @@ class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenize def setGaps(value: Boolean): this.type = set(gaps, value) /** @group getParam */ - def getGaps: Boolean = getOrDefault(gaps) + def getGaps: Boolean = $(gaps) /** * Regex pattern used by tokenizer. @@ -88,14 +88,14 @@ class RegexTokenizer extends UnaryTransformer[String, Seq[String], RegexTokenize def setPattern(value: String): this.type = set(pattern, value) /** @group getParam */ - def getPattern: String = getOrDefault(pattern) + def getPattern: String = $(pattern) setDefault(minTokenLength -> 1, gaps -> false, pattern -> "\\p{L}+|[^\\p{L}\\s]+") - override protected def createTransformFunc(paramMap: ParamMap): String => Seq[String] = { str => - val re = paramMap(pattern).r - val tokens = if (paramMap(gaps)) re.split(str).toSeq else re.findAllIn(str).toSeq - val minLength = paramMap(minTokenLength) + override protected def createTransformFunc: String => Seq[String] = { str => + val re = $(pattern).r + val tokens = if ($(gaps)) re.split(str).toSeq else re.findAllIn(str).toSeq + val minLength = $(minTokenLength) tokens.filter(_.length >= minLength) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala index 5e781a326d98c..8f2e62a8e2081 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala @@ -22,7 +22,6 @@ import scala.collection.mutable.ArrayBuilder import org.apache.spark.SparkException import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.Transformer -import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.param.shared._ import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors} import org.apache.spark.sql.{DataFrame, Row} @@ -42,13 +41,12 @@ class VectorAssembler extends Transformer with HasInputCols with HasOutputCol { /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - val map = extractParamMap(paramMap) + override def transform(dataset: DataFrame): DataFrame = { val assembleFunc = udf { r: Row => VectorAssembler.assemble(r.toSeq: _*) } val schema = dataset.schema - val inputColNames = map(inputCols) + val inputColNames = $(inputCols) val args = inputColNames.map { c => schema(c).dataType match { case DoubleType => dataset(c) @@ -56,13 +54,12 @@ class VectorAssembler extends Transformer with HasInputCols with HasOutputCol { case _: NumericType | BooleanType => dataset(c).cast(DoubleType).as(s"${c}_double_$uid") } } - dataset.select(col("*"), assembleFunc(struct(args : _*)).as(map(outputCol))) + dataset.select(col("*"), assembleFunc(struct(args : _*)).as($(outputCol))) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - val inputColNames = map(inputCols) - val outputColName = map(outputCol) + override def transformSchema(schema: StructType): StructType = { + val inputColNames = $(inputCols) + val outputColName = $(outputCol) val inputDataTypes = inputColNames.map(name => schema(name).dataType) inputDataTypes.foreach { case _: NumericType | BooleanType => diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala index ed833c63c7ef1..07ea579d69893 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala @@ -18,19 +18,17 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.ml.{Estimator, Model} -import org.apache.spark.ml.attribute.{BinaryAttribute, NumericAttribute, NominalAttribute, - Attribute, AttributeGroup} -import org.apache.spark.ml.param.{ParamValidators, IntParam, ParamMap, Params} +import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, BinaryAttribute, NominalAttribute, NumericAttribute} +import org.apache.spark.ml.param.{IntParam, ParamValidators, Params} import org.apache.spark.ml.param.shared._ -import org.apache.spark.mllib.linalg.{SparseVector, DenseVector, Vector, VectorUDT} -import org.apache.spark.sql.{Row, DataFrame} +import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, VectorUDT} +import org.apache.spark.sql.{DataFrame, Row} import org.apache.spark.sql.functions.callUDF import org.apache.spark.sql.types.{StructField, StructType} import org.apache.spark.util.collection.OpenHashSet - /** Private trait for params for VectorIndexer and VectorIndexerModel */ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOutputCol { @@ -49,7 +47,7 @@ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOu setDefault(maxCategories -> 20) /** @group getParam */ - def getMaxCategories: Int = getOrDefault(maxCategories) + def getMaxCategories: Int = $(maxCategories) } /** @@ -100,33 +98,29 @@ class VectorIndexer extends Estimator[VectorIndexerModel] with VectorIndexerPara /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def fit(dataset: DataFrame, paramMap: ParamMap): VectorIndexerModel = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val firstRow = dataset.select(map(inputCol)).take(1) + override def fit(dataset: DataFrame): VectorIndexerModel = { + transformSchema(dataset.schema, logging = true) + val firstRow = dataset.select($(inputCol)).take(1) require(firstRow.length == 1, s"VectorIndexer cannot be fit on an empty dataset.") val numFeatures = firstRow(0).getAs[Vector](0).size - val vectorDataset = dataset.select(map(inputCol)).map { case Row(v: Vector) => v } - val maxCats = map(maxCategories) + val vectorDataset = dataset.select($(inputCol)).map { case Row(v: Vector) => v } + val maxCats = $(maxCategories) val categoryStats: VectorIndexer.CategoryStats = vectorDataset.mapPartitions { iter => val localCatStats = new VectorIndexer.CategoryStats(numFeatures, maxCats) iter.foreach(localCatStats.addVector) Iterator(localCatStats) }.reduce((stats1, stats2) => stats1.merge(stats2)) - val model = new VectorIndexerModel(this, map, numFeatures, categoryStats.getCategoryMaps) - Params.inheritValues(map, this, model) - model + copyValues(new VectorIndexerModel(this, numFeatures, categoryStats.getCategoryMaps)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + override def transformSchema(schema: StructType): StructType = { // We do not transfer feature metadata since we do not know what types of features we will // produce in transform(). - val map = extractParamMap(paramMap) val dataType = new VectorUDT - require(map.contains(inputCol), s"VectorIndexer requires input column parameter: $inputCol") - require(map.contains(outputCol), s"VectorIndexer requires output column parameter: $outputCol") - SchemaUtils.checkColumnType(schema, map(inputCol), dataType) - SchemaUtils.appendColumn(schema, map(outputCol), dataType) + require(isDefined(inputCol), s"VectorIndexer requires input column parameter: $inputCol") + require(isDefined(outputCol), s"VectorIndexer requires output column parameter: $outputCol") + SchemaUtils.checkColumnType(schema, $(inputCol), dataType) + SchemaUtils.appendColumn(schema, $(outputCol), dataType) } } @@ -243,7 +237,6 @@ private object VectorIndexer { @AlphaComponent class VectorIndexerModel private[ml] ( override val parent: VectorIndexer, - override val fittingParamMap: ParamMap, val numFeatures: Int, val categoryMaps: Map[Int, Map[Double, Int]]) extends Model[VectorIndexerModel] with VectorIndexerParams { @@ -326,35 +319,33 @@ class VectorIndexerModel private[ml] ( /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val newField = prepOutputField(dataset.schema, map) - val newCol = callUDF(transformFunc, new VectorUDT, dataset(map(inputCol))) - dataset.withColumn(map(outputCol), newCol.as(map(outputCol), newField.metadata)) + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) + val newField = prepOutputField(dataset.schema) + val newCol = callUDF(transformFunc, new VectorUDT, dataset($(inputCol))) + dataset.withColumn($(outputCol), newCol.as($(outputCol), newField.metadata)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) + override def transformSchema(schema: StructType): StructType = { val dataType = new VectorUDT - require(map.contains(inputCol), + require(isDefined(inputCol), s"VectorIndexerModel requires input column parameter: $inputCol") - require(map.contains(outputCol), + require(isDefined(outputCol), s"VectorIndexerModel requires output column parameter: $outputCol") - SchemaUtils.checkColumnType(schema, map(inputCol), dataType) + SchemaUtils.checkColumnType(schema, $(inputCol), dataType) // If the input metadata specifies numFeatures, compare with expected numFeatures. - val origAttrGroup = AttributeGroup.fromStructField(schema(map(inputCol))) + val origAttrGroup = AttributeGroup.fromStructField(schema($(inputCol))) val origNumFeatures: Option[Int] = if (origAttrGroup.attributes.nonEmpty) { Some(origAttrGroup.attributes.get.length) } else { origAttrGroup.numAttributes } require(origNumFeatures.forall(_ == numFeatures), "VectorIndexerModel expected" + - s" $numFeatures features, but input column ${map(inputCol)} had metadata specifying" + + s" $numFeatures features, but input column ${$(inputCol)} had metadata specifying" + s" ${origAttrGroup.numAttributes.get} features.") - val newField = prepOutputField(schema, map) + val newField = prepOutputField(schema) val outputFields = schema.fields :+ newField StructType(outputFields) } @@ -362,11 +353,10 @@ class VectorIndexerModel private[ml] ( /** * Prepare the output column field, including per-feature metadata. * @param schema Input schema - * @param map Parameter map (with this class' embedded parameter map folded in) * @return Output column field. This field does not contain non-ML metadata. */ - private def prepOutputField(schema: StructType, map: ParamMap): StructField = { - val origAttrGroup = AttributeGroup.fromStructField(schema(map(inputCol))) + private def prepOutputField(schema: StructType): StructField = { + val origAttrGroup = AttributeGroup.fromStructField(schema($(inputCol))) val featureAttributes: Array[Attribute] = if (origAttrGroup.attributes.nonEmpty) { // Convert original attributes to modified attributes val origAttrs: Array[Attribute] = origAttrGroup.attributes.get @@ -389,7 +379,7 @@ class VectorIndexerModel private[ml] ( } else { partialFeatureAttributes } - val newAttributeGroup = new AttributeGroup(map(outputCol), featureAttributes) + val newAttributeGroup = new AttributeGroup($(outputCol), featureAttributes) newAttributeGroup.toStructField() } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala index 0163fa8bd8a5b..34ff92970129f 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala @@ -18,16 +18,16 @@ package org.apache.spark.ml.feature import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.{Estimator, Model} import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared._ import org.apache.spark.ml.util.SchemaUtils -import org.apache.spark.ml.{Estimator, Model} import org.apache.spark.mllib.feature -import org.apache.spark.mllib.linalg.BLAS._ import org.apache.spark.mllib.linalg.{VectorUDT, Vectors} +import org.apache.spark.mllib.linalg.BLAS._ +import org.apache.spark.sql.DataFrame import org.apache.spark.sql.functions._ import org.apache.spark.sql.types._ -import org.apache.spark.sql.{DataFrame, Row} /** * Params for [[Word2Vec]] and [[Word2VecModel]]. @@ -43,7 +43,7 @@ private[feature] trait Word2VecBase extends Params setDefault(vectorSize -> 100) /** @group getParam */ - def getVectorSize: Int = getOrDefault(vectorSize) + def getVectorSize: Int = $(vectorSize) /** * Number of partitions for sentences of words. @@ -53,7 +53,7 @@ private[feature] trait Word2VecBase extends Params setDefault(numPartitions -> 1) /** @group getParam */ - def getNumPartitions: Int = getOrDefault(numPartitions) + def getNumPartitions: Int = $(numPartitions) /** * The minimum number of times a token must appear to be included in the word2vec model's @@ -64,7 +64,7 @@ private[feature] trait Word2VecBase extends Params setDefault(minCount -> 5) /** @group getParam */ - def getMinCount: Int = getOrDefault(minCount) + def getMinCount: Int = $(minCount) setDefault(stepSize -> 0.025) setDefault(maxIter -> 1) @@ -73,10 +73,9 @@ private[feature] trait Word2VecBase extends Params /** * Validate and transform the input schema. */ - protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - SchemaUtils.checkColumnType(schema, map(inputCol), new ArrayType(StringType, true)) - SchemaUtils.appendColumn(schema, map(outputCol), new VectorUDT) + protected def validateAndTransformSchema(schema: StructType): StructType = { + SchemaUtils.checkColumnType(schema, $(inputCol), new ArrayType(StringType, true)) + SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT) } } @@ -112,25 +111,22 @@ final class Word2Vec extends Estimator[Word2VecModel] with Word2VecBase { /** @group setParam */ def setMinCount(value: Int): this.type = set(minCount, value) - override def fit(dataset: DataFrame, paramMap: ParamMap): Word2VecModel = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val input = dataset.select(map(inputCol)).map { case Row(v: Seq[String]) => v } + override def fit(dataset: DataFrame): Word2VecModel = { + transformSchema(dataset.schema, logging = true) + val input = dataset.select($(inputCol)).map(_.getAs[Seq[String]](0)) val wordVectors = new feature.Word2Vec() - .setLearningRate(map(stepSize)) - .setMinCount(map(minCount)) - .setNumIterations(map(maxIter)) - .setNumPartitions(map(numPartitions)) - .setSeed(map(seed)) - .setVectorSize(map(vectorSize)) + .setLearningRate($(stepSize)) + .setMinCount($(minCount)) + .setNumIterations($(maxIter)) + .setNumPartitions($(numPartitions)) + .setSeed($(seed)) + .setVectorSize($(vectorSize)) .fit(input) - val model = new Word2VecModel(this, map, wordVectors) - Params.inheritValues(map, this, model) - model + copyValues(new Word2VecModel(this, wordVectors)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } @@ -141,7 +137,6 @@ final class Word2Vec extends Estimator[Word2VecModel] with Word2VecBase { @AlphaComponent class Word2VecModel private[ml] ( override val parent: Word2Vec, - override val fittingParamMap: ParamMap, wordVectors: feature.Word2VecModel) extends Model[Word2VecModel] with Word2VecBase { @@ -155,15 +150,14 @@ class Word2VecModel private[ml] ( * Transform a sentence column to a vector column to represent the whole sentence. The transform * is performed by averaging all word vectors it contains. */ - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) + override def transform(dataset: DataFrame): DataFrame = { + transformSchema(dataset.schema, logging = true) val bWordVectors = dataset.sqlContext.sparkContext.broadcast(wordVectors) val word2Vec = udf { sentence: Seq[String] => if (sentence.size == 0) { - Vectors.sparse(map(vectorSize), Array.empty[Int], Array.empty[Double]) + Vectors.sparse($(vectorSize), Array.empty[Int], Array.empty[Double]) } else { - val cum = Vectors.zeros(map(vectorSize)) + val cum = Vectors.zeros($(vectorSize)) val model = bWordVectors.value.getVectors for (word <- sentence) { if (model.contains(word)) { @@ -176,10 +170,10 @@ class Word2VecModel private[ml] ( cum } } - dataset.withColumn(map(outputCol), word2Vec(col(map(inputCol)))) + dataset.withColumn($(outputCol), word2Vec(col($(inputCol)))) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala b/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala index 195333a5cc47f..e8b3628140e99 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala @@ -18,18 +18,17 @@ package org.apache.spark.ml.impl.estimator import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} -import org.apache.spark.ml.util.SchemaUtils import org.apache.spark.ml.{Estimator, Model} import org.apache.spark.ml.param._ import org.apache.spark.ml.param.shared._ -import org.apache.spark.mllib.linalg.{VectorUDT, Vector} +import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.mllib.linalg.{Vector, VectorUDT} import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.rdd.RDD import org.apache.spark.sql.{DataFrame, Row} import org.apache.spark.sql.functions._ import org.apache.spark.sql.types.{DataType, DoubleType, StructType} - /** * :: DeveloperApi :: * @@ -44,7 +43,6 @@ private[spark] trait PredictorParams extends Params /** * Validates and transforms the input schema with the provided param map. * @param schema input schema - * @param paramMap additional parameters * @param fitting whether this is in fitting * @param featuresDataType SQL DataType for FeaturesType. * E.g., [[org.apache.spark.mllib.linalg.VectorUDT]] for vector features. @@ -52,17 +50,15 @@ private[spark] trait PredictorParams extends Params */ protected def validateAndTransformSchema( schema: StructType, - paramMap: ParamMap, fitting: Boolean, featuresDataType: DataType): StructType = { - val map = extractParamMap(paramMap) // TODO: Support casting Array[Double] and Array[Float] to Vector when FeaturesType = Vector - SchemaUtils.checkColumnType(schema, map(featuresCol), featuresDataType) + SchemaUtils.checkColumnType(schema, $(featuresCol), featuresDataType) if (fitting) { // TODO: Allow other numeric types - SchemaUtils.checkColumnType(schema, map(labelCol), DoubleType) + SchemaUtils.checkColumnType(schema, $(labelCol), DoubleType) } - SchemaUtils.appendColumn(schema, map(predictionCol), DoubleType) + SchemaUtils.appendColumn(schema, $(predictionCol), DoubleType) } } @@ -96,14 +92,15 @@ private[spark] abstract class Predictor[ /** @group setParam */ def setPredictionCol(value: String): Learner = set(predictionCol, value).asInstanceOf[Learner] - override def fit(dataset: DataFrame, paramMap: ParamMap): M = { + override def fit(dataset: DataFrame): M = { // This handles a few items such as schema validation. // Developers only need to implement train(). - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - val model = train(dataset, map) - Params.inheritValues(map, this, model) // copy params to model - model + transformSchema(dataset.schema, logging = true) + copyValues(train(dataset)) + } + + override def copy(extra: ParamMap): Learner = { + super.copy(extra).asInstanceOf[Learner] } /** @@ -114,12 +111,10 @@ private[spark] abstract class Predictor[ * and copying parameters into the model. * * @param dataset Training dataset - * @param paramMap Parameter map. Unlike [[fit()]]'s paramMap, this paramMap has already - * been combined with the embedded ParamMap. * @return Fitted model */ @DeveloperApi - protected def train(dataset: DataFrame, paramMap: ParamMap): M + protected def train(dataset: DataFrame): M /** * :: DeveloperApi :: @@ -134,17 +129,16 @@ private[spark] abstract class Predictor[ @DeveloperApi protected def featuresDataType: DataType = new VectorUDT - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap, fitting = true, featuresDataType) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema, fitting = true, featuresDataType) } /** * Extract [[labelCol]] and [[featuresCol]] from the given dataset, * and put it in an RDD with strong types. */ - protected def extractLabeledPoints(dataset: DataFrame, paramMap: ParamMap): RDD[LabeledPoint] = { - val map = extractParamMap(paramMap) - dataset.select(map(labelCol), map(featuresCol)) + protected def extractLabeledPoints(dataset: DataFrame): RDD[LabeledPoint] = { + dataset.select($(labelCol), $(featuresCol)) .map { case Row(label: Double, features: Vector) => LabeledPoint(label, features) } @@ -186,8 +180,8 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel @DeveloperApi protected def featuresDataType: DataType = new VectorUDT - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap, fitting = false, featuresDataType) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema, fitting = false, featuresDataType) } /** @@ -195,30 +189,16 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel * the predictions as a new column [[predictionCol]]. * * @param dataset input dataset - * @param paramMap additional parameters, overwrite embedded params * @return transformed dataset with [[predictionCol]] of type [[Double]] */ - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + override def transform(dataset: DataFrame): DataFrame = { // This default implementation should be overridden as needed. // Check schema - transformSchema(dataset.schema, paramMap, logging = true) - val map = extractParamMap(paramMap) - - // Prepare model - val tmpModel = if (paramMap.size != 0) { - val tmpModel = this.copy() - Params.inheritValues(paramMap, parent, tmpModel) - tmpModel - } else { - this - } + transformSchema(dataset.schema, logging = true) - if (map(predictionCol) != "") { - val pred: FeaturesType => Double = (features) => { - tmpModel.predict(features) - } - dataset.withColumn(map(predictionCol), callUDF(pred, DoubleType, col(map(featuresCol)))) + if ($(predictionCol) != "") { + dataset.withColumn($(predictionCol), callUDF(predict _, DoubleType, col($(featuresCol)))) } else { this.logWarning(s"$uid: Predictor.transform() was called as NOOP" + " since no output columns were set.") @@ -234,10 +214,4 @@ private[spark] abstract class PredictionModel[FeaturesType, M <: PredictionModel */ @DeveloperApi protected def predict(features: FeaturesType): Double - - /** - * Create a copy of the model. - * The copy is shallow, except for the embedded paramMap, which gets a deep copy. - */ - protected def copy(): M } diff --git a/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala b/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala index fb770622e71f0..0e225627d4ee3 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala @@ -20,14 +20,11 @@ package org.apache.spark.ml.impl.tree import org.apache.spark.annotation.DeveloperApi import org.apache.spark.ml.impl.estimator.PredictorParams import org.apache.spark.ml.param._ -import org.apache.spark.ml.param.shared.{HasSeed, HasMaxIter} -import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, - BoostingStrategy => OldBoostingStrategy, Strategy => OldStrategy} -import org.apache.spark.mllib.tree.impurity.{Gini => OldGini, Entropy => OldEntropy, - Impurity => OldImpurity, Variance => OldVariance} +import org.apache.spark.ml.param.shared.{HasMaxIter, HasSeed} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, BoostingStrategy => OldBoostingStrategy, Strategy => OldStrategy} +import org.apache.spark.mllib.tree.impurity.{Entropy => OldEntropy, Gini => OldGini, Impurity => OldImpurity, Variance => OldVariance} import org.apache.spark.mllib.tree.loss.{Loss => OldLoss} - /** * :: DeveloperApi :: * Parameters for Decision Tree-based algorithms. @@ -123,43 +120,43 @@ private[ml] trait DecisionTreeParams extends PredictorParams { def setMaxDepth(value: Int): this.type = set(maxDepth, value) /** @group getParam */ - final def getMaxDepth: Int = getOrDefault(maxDepth) + final def getMaxDepth: Int = $(maxDepth) /** @group setParam */ def setMaxBins(value: Int): this.type = set(maxBins, value) /** @group getParam */ - final def getMaxBins: Int = getOrDefault(maxBins) + final def getMaxBins: Int = $(maxBins) /** @group setParam */ def setMinInstancesPerNode(value: Int): this.type = set(minInstancesPerNode, value) /** @group getParam */ - final def getMinInstancesPerNode: Int = getOrDefault(minInstancesPerNode) + final def getMinInstancesPerNode: Int = $(minInstancesPerNode) /** @group setParam */ def setMinInfoGain(value: Double): this.type = set(minInfoGain, value) /** @group getParam */ - final def getMinInfoGain: Double = getOrDefault(minInfoGain) + final def getMinInfoGain: Double = $(minInfoGain) /** @group expertSetParam */ def setMaxMemoryInMB(value: Int): this.type = set(maxMemoryInMB, value) /** @group expertGetParam */ - final def getMaxMemoryInMB: Int = getOrDefault(maxMemoryInMB) + final def getMaxMemoryInMB: Int = $(maxMemoryInMB) /** @group expertSetParam */ def setCacheNodeIds(value: Boolean): this.type = set(cacheNodeIds, value) /** @group expertGetParam */ - final def getCacheNodeIds: Boolean = getOrDefault(cacheNodeIds) + final def getCacheNodeIds: Boolean = $(cacheNodeIds) /** @group expertSetParam */ def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value) /** @group expertGetParam */ - final def getCheckpointInterval: Int = getOrDefault(checkpointInterval) + final def getCheckpointInterval: Int = $(checkpointInterval) /** (private[ml]) Create a Strategy instance to use with the old API. */ private[ml] def getOldStrategy( @@ -206,7 +203,7 @@ private[ml] trait TreeClassifierParams extends Params { def setImpurity(value: String): this.type = set(impurity, value) /** @group getParam */ - final def getImpurity: String = getOrDefault(impurity).toLowerCase + final def getImpurity: String = $(impurity).toLowerCase /** Convert new impurity to old impurity. */ private[ml] def getOldImpurity: OldImpurity = { @@ -248,7 +245,7 @@ private[ml] trait TreeRegressorParams extends Params { def setImpurity(value: String): this.type = set(impurity, value) /** @group getParam */ - final def getImpurity: String = getOrDefault(impurity).toLowerCase + final def getImpurity: String = $(impurity).toLowerCase /** Convert new impurity to old impurity. */ private[ml] def getOldImpurity: OldImpurity = { @@ -291,7 +288,7 @@ private[ml] trait TreeEnsembleParams extends DecisionTreeParams with HasSeed { def setSubsamplingRate(value: Double): this.type = set(subsamplingRate, value) /** @group getParam */ - final def getSubsamplingRate: Double = getOrDefault(subsamplingRate) + final def getSubsamplingRate: Double = $(subsamplingRate) /** @group setParam */ def setSeed(value: Long): this.type = set(seed, value) @@ -364,13 +361,13 @@ private[ml] trait RandomForestParams extends TreeEnsembleParams { def setNumTrees(value: Int): this.type = set(numTrees, value) /** @group getParam */ - final def getNumTrees: Int = getOrDefault(numTrees) + final def getNumTrees: Int = $(numTrees) /** @group setParam */ def setFeatureSubsetStrategy(value: String): this.type = set(featureSubsetStrategy, value) /** @group getParam */ - final def getFeatureSubsetStrategy: String = getOrDefault(featureSubsetStrategy).toLowerCase + final def getFeatureSubsetStrategy: String = $(featureSubsetStrategy).toLowerCase } private[ml] object RandomForestParams { @@ -418,7 +415,7 @@ private[ml] trait GBTParams extends TreeEnsembleParams with HasMaxIter { def setStepSize(value: Double): this.type = set(stepSize, value) /** @group getParam */ - final def getStepSize: Double = getOrDefault(stepSize) + final def getStepSize: Double = $(stepSize) /** (private[ml]) Create a BoostingStrategy instance to use with the old API. */ private[ml] def getOldBoostingStrategy( diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala index df6360dce6013..51ce19d29cd29 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala @@ -23,7 +23,7 @@ import java.util.NoSuchElementException import scala.annotation.varargs import scala.collection.mutable -import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} +import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.util.Identifiable /** @@ -49,7 +49,7 @@ class Param[T] (val parent: Params, val name: String, val doc: String, val isVal * Assert that the given value is valid for this parameter. * * Note: Parameter checks involving interactions between multiple parameters should be - * implemented in [[Params.validate()]]. Checks for input/output columns should be + * implemented in [[Params.validateParams()]]. Checks for input/output columns should be * implemented in [[org.apache.spark.ml.PipelineStage.transformSchema()]]. * * DEVELOPERS: This method is only called by [[ParamPair]], which means that all parameters @@ -258,7 +258,9 @@ trait Params extends Identifiable with Serializable { * [[Param.validate()]]. This method does not handle input/output column parameters; * those are checked during schema validation. */ - def validate(paramMap: ParamMap): Unit = { } + def validateParams(paramMap: ParamMap): Unit = { + copy(paramMap).validateParams() + } /** * Validates parameter values stored internally. @@ -269,7 +271,11 @@ trait Params extends Identifiable with Serializable { * [[Param.validate()]]. This method does not handle input/output column parameters; * those are checked during schema validation. */ - def validate(): Unit = validate(ParamMap.empty) + def validateParams(): Unit = { + params.filter(isDefined _).foreach { param => + param.asInstanceOf[Param[Any]].validate($(param)) + } + } /** * Returns the documentation of all params. @@ -288,6 +294,11 @@ trait Params extends Identifiable with Serializable { defaultParamMap.contains(param) || paramMap.contains(param) } + /** Tests whether this instance contains a param with a given name. */ + def hasParam(paramName: String): Boolean = { + params.exists(_.name == paramName) + } + /** Gets a param by its name. */ def getParam(paramName: String): Param[Any] = { params.find(_.name == paramName).getOrElse { @@ -337,6 +348,9 @@ trait Params extends Identifiable with Serializable { get(param).orElse(getDefault(param)).get } + /** An alias for [[getOrDefault()]]. */ + protected final def $[T](param: Param[T]): T = getOrDefault(param) + /** * Sets a default value for a param. * @param param param to set the default value. Make sure that this param is initialized before @@ -382,19 +396,31 @@ trait Params extends Identifiable with Serializable { defaultParamMap.contains(param) } + /** + * Creates a copy of this instance with a randomly generated uid and some extra params. + * The default implementation calls the default constructor to create a new instance, then + * copies the embedded and extra parameters over and returns the new instance. + * Subclasses should override this method if the default approach is not sufficient. + */ + def copy(extra: ParamMap): Params = { + val that = this.getClass.newInstance() + copyValues(that, extra) + that + } + /** * Extracts the embedded default param values and user-supplied values, and then merges them with * extra values from input into a flat param map, where the latter value is used if there exist * conflicts, i.e., with ordering: default param values < user-supplied values < extraParamMap. */ - protected final def extractParamMap(extraParamMap: ParamMap): ParamMap = { + final def extractParamMap(extraParamMap: ParamMap): ParamMap = { defaultParamMap ++ paramMap ++ extraParamMap } /** * [[extractParamMap]] with no extra values. */ - protected final def extractParamMap(): ParamMap = { + final def extractParamMap(): ParamMap = { extractParamMap(ParamMap.empty) } @@ -408,34 +434,21 @@ trait Params extends Identifiable with Serializable { private def shouldOwn(param: Param[_]): Unit = { require(param.parent.eq(this), s"Param $param does not belong to $this.") } -} -/** - * :: DeveloperApi :: - * - * Helper functionality for developers. - * - * NOTE: This is currently private[spark] but will be made public later once it is stabilized. - */ -@DeveloperApi -private[spark] object Params { - - /** - * Copies parameter values from the parent estimator to the child model it produced. - * @param paramMap the param map that holds parameters of the parent - * @param parent the parent estimator - * @param child the child model - */ - def inheritValues[E <: Params, M <: E]( - paramMap: ParamMap, - parent: E, - child: M): Unit = { - val childParams = child.params.map(_.name).toSet - parent.params.foreach { param => - if (paramMap.contains(param) && childParams.contains(param.name)) { - child.set(child.getParam(param.name), paramMap(param)) + /** + * Copies param values from this instance to another instance for params shared by them. + * @param to the target instance + * @param extra extra params to be copied + * @return the target instance with param values copied + */ + protected def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T = { + val map = extractParamMap(extra) + params.foreach { param => + if (map.contains(param) && to.hasParam(param.name)) { + to.set(param.name, map(param)) } } + to } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala index 7da4bb4b4bf25..d379172e0bf53 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala @@ -21,8 +21,6 @@ import java.io.PrintWriter import scala.reflect.ClassTag -import org.apache.spark.ml.param.ParamValidators - /** * Code generator for shared params (sharedParams.scala). Run under the Spark folder with * {{{ @@ -142,7 +140,7 @@ private[shared] object SharedParamsCodeGen { | final val $name: $Param = new $Param(this, "$name", "$doc"$isValid) |$setDefault | /** @group getParam */ - | final def get$Name: $T = getOrDefault($name) + | final def get$Name: $T = $$($name) |} |""".stripMargin } @@ -169,7 +167,6 @@ private[shared] object SharedParamsCodeGen { | |package org.apache.spark.ml.param.shared | - |import org.apache.spark.annotation.DeveloperApi |import org.apache.spark.ml.param._ |import org.apache.spark.util.Utils | diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala index e1549f46a68d4..fb1874ccfc8dc 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala @@ -17,7 +17,6 @@ package org.apache.spark.ml.param.shared -import org.apache.spark.annotation.DeveloperApi import org.apache.spark.ml.param._ import org.apache.spark.util.Utils @@ -37,7 +36,7 @@ private[ml] trait HasRegParam extends Params { final val regParam: DoubleParam = new DoubleParam(this, "regParam", "regularization parameter (>= 0)", ParamValidators.gtEq(0)) /** @group getParam */ - final def getRegParam: Double = getOrDefault(regParam) + final def getRegParam: Double = $(regParam) } /** @@ -52,7 +51,7 @@ private[ml] trait HasMaxIter extends Params { final val maxIter: IntParam = new IntParam(this, "maxIter", "max number of iterations (>= 0)", ParamValidators.gtEq(0)) /** @group getParam */ - final def getMaxIter: Int = getOrDefault(maxIter) + final def getMaxIter: Int = $(maxIter) } /** @@ -69,7 +68,7 @@ private[ml] trait HasFeaturesCol extends Params { setDefault(featuresCol, "features") /** @group getParam */ - final def getFeaturesCol: String = getOrDefault(featuresCol) + final def getFeaturesCol: String = $(featuresCol) } /** @@ -86,7 +85,7 @@ private[ml] trait HasLabelCol extends Params { setDefault(labelCol, "label") /** @group getParam */ - final def getLabelCol: String = getOrDefault(labelCol) + final def getLabelCol: String = $(labelCol) } /** @@ -103,7 +102,7 @@ private[ml] trait HasPredictionCol extends Params { setDefault(predictionCol, "prediction") /** @group getParam */ - final def getPredictionCol: String = getOrDefault(predictionCol) + final def getPredictionCol: String = $(predictionCol) } /** @@ -120,7 +119,7 @@ private[ml] trait HasRawPredictionCol extends Params { setDefault(rawPredictionCol, "rawPrediction") /** @group getParam */ - final def getRawPredictionCol: String = getOrDefault(rawPredictionCol) + final def getRawPredictionCol: String = $(rawPredictionCol) } /** @@ -137,7 +136,7 @@ private[ml] trait HasProbabilityCol extends Params { setDefault(probabilityCol, "probability") /** @group getParam */ - final def getProbabilityCol: String = getOrDefault(probabilityCol) + final def getProbabilityCol: String = $(probabilityCol) } /** @@ -152,7 +151,7 @@ private[ml] trait HasThreshold extends Params { final val threshold: DoubleParam = new DoubleParam(this, "threshold", "threshold in binary classification prediction, in range [0, 1]", ParamValidators.inRange(0, 1)) /** @group getParam */ - final def getThreshold: Double = getOrDefault(threshold) + final def getThreshold: Double = $(threshold) } /** @@ -167,7 +166,7 @@ private[ml] trait HasInputCol extends Params { final val inputCol: Param[String] = new Param[String](this, "inputCol", "input column name") /** @group getParam */ - final def getInputCol: String = getOrDefault(inputCol) + final def getInputCol: String = $(inputCol) } /** @@ -182,7 +181,7 @@ private[ml] trait HasInputCols extends Params { final val inputCols: Param[Array[String]] = new Param[Array[String]](this, "inputCols", "input column names") /** @group getParam */ - final def getInputCols: Array[String] = getOrDefault(inputCols) + final def getInputCols: Array[String] = $(inputCols) } /** @@ -197,7 +196,7 @@ private[ml] trait HasOutputCol extends Params { final val outputCol: Param[String] = new Param[String](this, "outputCol", "output column name") /** @group getParam */ - final def getOutputCol: String = getOrDefault(outputCol) + final def getOutputCol: String = $(outputCol) } /** @@ -212,7 +211,7 @@ private[ml] trait HasCheckpointInterval extends Params { final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "checkpoint interval (>= 1)", ParamValidators.gtEq(1)) /** @group getParam */ - final def getCheckpointInterval: Int = getOrDefault(checkpointInterval) + final def getCheckpointInterval: Int = $(checkpointInterval) } /** @@ -229,7 +228,7 @@ private[ml] trait HasFitIntercept extends Params { setDefault(fitIntercept, true) /** @group getParam */ - final def getFitIntercept: Boolean = getOrDefault(fitIntercept) + final def getFitIntercept: Boolean = $(fitIntercept) } /** @@ -246,7 +245,7 @@ private[ml] trait HasSeed extends Params { setDefault(seed, Utils.random.nextLong()) /** @group getParam */ - final def getSeed: Long = getOrDefault(seed) + final def getSeed: Long = $(seed) } /** @@ -261,7 +260,7 @@ private[ml] trait HasElasticNetParam extends Params { final val elasticNetParam: DoubleParam = new DoubleParam(this, "elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.", ParamValidators.inRange(0, 1)) /** @group getParam */ - final def getElasticNetParam: Double = getOrDefault(elasticNetParam) + final def getElasticNetParam: Double = $(elasticNetParam) } /** @@ -276,7 +275,7 @@ private[ml] trait HasTol extends Params { final val tol: DoubleParam = new DoubleParam(this, "tol", "the convergence tolerance for iterative algorithms") /** @group getParam */ - final def getTol: Double = getOrDefault(tol) + final def getTol: Double = $(tol) } /** @@ -291,6 +290,6 @@ private[ml] trait HasStepSize extends Params { final val stepSize: DoubleParam = new DoubleParam(this, "stepSize", "Step size to be used for each iteration of optimization.") /** @group getParam */ - final def getStepSize: Double = getOrDefault(stepSize) + final def getStepSize: Double = $(stepSize) } // scalastyle:on diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala index f9f2b2764ddb1..6cf4b40075281 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala @@ -59,7 +59,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR val rank = new IntParam(this, "rank", "rank of the factorization", ParamValidators.gtEq(1)) /** @group getParam */ - def getRank: Int = getOrDefault(rank) + def getRank: Int = $(rank) /** * Param for number of user blocks (>= 1). @@ -70,7 +70,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR ParamValidators.gtEq(1)) /** @group getParam */ - def getNumUserBlocks: Int = getOrDefault(numUserBlocks) + def getNumUserBlocks: Int = $(numUserBlocks) /** * Param for number of item blocks (>= 1). @@ -81,7 +81,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR ParamValidators.gtEq(1)) /** @group getParam */ - def getNumItemBlocks: Int = getOrDefault(numItemBlocks) + def getNumItemBlocks: Int = $(numItemBlocks) /** * Param to decide whether to use implicit preference. @@ -91,7 +91,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR val implicitPrefs = new BooleanParam(this, "implicitPrefs", "whether to use implicit preference") /** @group getParam */ - def getImplicitPrefs: Boolean = getOrDefault(implicitPrefs) + def getImplicitPrefs: Boolean = $(implicitPrefs) /** * Param for the alpha parameter in the implicit preference formulation (>= 0). @@ -102,7 +102,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR ParamValidators.gtEq(0)) /** @group getParam */ - def getAlpha: Double = getOrDefault(alpha) + def getAlpha: Double = $(alpha) /** * Param for the column name for user ids. @@ -112,7 +112,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR val userCol = new Param[String](this, "userCol", "column name for user ids") /** @group getParam */ - def getUserCol: String = getOrDefault(userCol) + def getUserCol: String = $(userCol) /** * Param for the column name for item ids. @@ -122,7 +122,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR val itemCol = new Param[String](this, "itemCol", "column name for item ids") /** @group getParam */ - def getItemCol: String = getOrDefault(itemCol) + def getItemCol: String = $(itemCol) /** * Param for the column name for ratings. @@ -132,7 +132,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR val ratingCol = new Param[String](this, "ratingCol", "column name for ratings") /** @group getParam */ - def getRatingCol: String = getOrDefault(ratingCol) + def getRatingCol: String = $(ratingCol) /** * Param for whether to apply nonnegativity constraints. @@ -143,7 +143,7 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR this, "nonnegative", "whether to use nonnegative constraint for least squares") /** @group getParam */ - def getNonnegative: Boolean = getOrDefault(nonnegative) + def getNonnegative: Boolean = $(nonnegative) setDefault(rank -> 10, maxIter -> 10, regParam -> 0.1, numUserBlocks -> 10, numItemBlocks -> 10, implicitPrefs -> false, alpha -> 1.0, userCol -> "user", itemCol -> "item", @@ -152,19 +152,17 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR /** * Validates and transforms the input schema. * @param schema input schema - * @param paramMap extra params * @return output schema */ - protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - assert(schema(map(userCol)).dataType == IntegerType) - assert(schema(map(itemCol)).dataType== IntegerType) - val ratingType = schema(map(ratingCol)).dataType - assert(ratingType == FloatType || ratingType == DoubleType) - val predictionColName = map(predictionCol) - assert(!schema.fieldNames.contains(predictionColName), + protected def validateAndTransformSchema(schema: StructType): StructType = { + require(schema($(userCol)).dataType == IntegerType) + require(schema($(itemCol)).dataType== IntegerType) + val ratingType = schema($(ratingCol)).dataType + require(ratingType == FloatType || ratingType == DoubleType) + val predictionColName = $(predictionCol) + require(!schema.fieldNames.contains(predictionColName), s"Prediction column $predictionColName already exists.") - val newFields = schema.fields :+ StructField(map(predictionCol), FloatType, nullable = false) + val newFields = schema.fields :+ StructField($(predictionCol), FloatType, nullable = false) StructType(newFields) } } @@ -174,7 +172,6 @@ private[recommendation] trait ALSParams extends Params with HasMaxIter with HasR */ class ALSModel private[ml] ( override val parent: ALS, - override val fittingParamMap: ParamMap, k: Int, userFactors: RDD[(Int, Array[Float])], itemFactors: RDD[(Int, Array[Float])]) @@ -183,9 +180,8 @@ class ALSModel private[ml] ( /** @group setParam */ def setPredictionCol(value: String): this.type = set(predictionCol, value) - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + override def transform(dataset: DataFrame): DataFrame = { import dataset.sqlContext.implicits._ - val map = extractParamMap(paramMap) val users = userFactors.toDF("id", "features") val items = itemFactors.toDF("id", "features") @@ -199,13 +195,13 @@ class ALSModel private[ml] ( } } dataset - .join(users, dataset(map(userCol)) === users("id"), "left") - .join(items, dataset(map(itemCol)) === items("id"), "left") - .select(dataset("*"), predict(users("features"), items("features")).as(map(predictionCol))) + .join(users, dataset($(userCol)) === users("id"), "left") + .join(items, dataset($(itemCol)) === items("id"), "left") + .select(dataset("*"), predict(users("features"), items("features")).as($(predictionCol))) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } @@ -292,25 +288,22 @@ class ALS extends Estimator[ALSModel] with ALSParams { this } - override def fit(dataset: DataFrame, paramMap: ParamMap): ALSModel = { - val map = extractParamMap(paramMap) + override def fit(dataset: DataFrame): ALSModel = { val ratings = dataset - .select(col(map(userCol)), col(map(itemCol)), col(map(ratingCol)).cast(FloatType)) + .select(col($(userCol)), col($(itemCol)), col($(ratingCol)).cast(FloatType)) .map { row => Rating(row.getInt(0), row.getInt(1), row.getFloat(2)) } - val (userFactors, itemFactors) = ALS.train(ratings, rank = map(rank), - numUserBlocks = map(numUserBlocks), numItemBlocks = map(numItemBlocks), - maxIter = map(maxIter), regParam = map(regParam), implicitPrefs = map(implicitPrefs), - alpha = map(alpha), nonnegative = map(nonnegative), - checkpointInterval = map(checkpointInterval)) - val model = new ALSModel(this, map, map(rank), userFactors, itemFactors) - Params.inheritValues(map, this, model) - model + val (userFactors, itemFactors) = ALS.train(ratings, rank = $(rank), + numUserBlocks = $(numUserBlocks), numItemBlocks = $(numItemBlocks), + maxIter = $(maxIter), regParam = $(regParam), implicitPrefs = $(implicitPrefs), + alpha = $(alpha), nonnegative = $(nonnegative), + checkpointInterval = $(checkpointInterval)) + copyValues(new ALSModel(this, $(rank), userFactors, itemFactors)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - validateAndTransformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala index 756725a64b0f3..b07c26fe79b36 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala @@ -20,7 +20,7 @@ package org.apache.spark.ml.regression import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} import org.apache.spark.ml.impl.tree._ -import org.apache.spark.ml.param.{Params, ParamMap} +import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.tree.{DecisionTreeModel, Node} import org.apache.spark.ml.util.MetadataUtils import org.apache.spark.mllib.linalg.Vector @@ -31,7 +31,6 @@ import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeMo import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame - /** * :: AlphaComponent :: * @@ -63,15 +62,13 @@ final class DecisionTreeRegressor override def setImpurity(value: String): this.type = super.setImpurity(value) - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): DecisionTreeRegressionModel = { + override protected def train(dataset: DataFrame): DecisionTreeRegressionModel = { val categoricalFeatures: Map[Int, Int] = - MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) - val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol))) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset) val strategy = getOldStrategy(categoricalFeatures) val oldModel = OldDecisionTree.train(oldDataset, strategy) - DecisionTreeRegressionModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + DecisionTreeRegressionModel.fromOld(oldModel, this, categoricalFeatures) } /** (private[ml]) Create a Strategy instance to use with the old API. */ @@ -96,7 +93,6 @@ object DecisionTreeRegressor { @AlphaComponent final class DecisionTreeRegressionModel private[ml] ( override val parent: DecisionTreeRegressor, - override val fittingParamMap: ParamMap, override val rootNode: Node) extends PredictionModel[Vector, DecisionTreeRegressionModel] with DecisionTreeModel with Serializable { @@ -108,10 +104,8 @@ final class DecisionTreeRegressionModel private[ml] ( rootNode.predict(features) } - override protected def copy(): DecisionTreeRegressionModel = { - val m = new DecisionTreeRegressionModel(parent, fittingParamMap, rootNode) - Params.inheritValues(this.extractParamMap(), this, m) - m + override def copy(extra: ParamMap): DecisionTreeRegressionModel = { + copyValues(new DecisionTreeRegressionModel(parent, rootNode), extra) } override def toString: String = { @@ -130,12 +124,11 @@ private[ml] object DecisionTreeRegressionModel { def fromOld( oldModel: OldDecisionTreeModel, parent: DecisionTreeRegressor, - fittingParamMap: ParamMap, categoricalFeatures: Map[Int, Int]): DecisionTreeRegressionModel = { require(oldModel.algo == OldAlgo.Regression, s"Cannot convert non-regression DecisionTreeModel (old API) to" + s" DecisionTreeRegressionModel (new API). Algo is: ${oldModel.algo}") val rootNode = Node.fromOld(oldModel.topNode, categoricalFeatures) - new DecisionTreeRegressionModel(parent, fittingParamMap, rootNode) + new DecisionTreeRegressionModel(parent, rootNode) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala index 76c98376930c5..bc796958e4545 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala @@ -23,20 +23,18 @@ import org.apache.spark.Logging import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} import org.apache.spark.ml.impl.tree._ -import org.apache.spark.ml.param.{Params, ParamMap, Param} +import org.apache.spark.ml.param.{Param, ParamMap} import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel} import org.apache.spark.ml.util.MetadataUtils import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{GradientBoostedTrees => OldGBT} import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} -import org.apache.spark.mllib.tree.loss.{AbsoluteError => OldAbsoluteError, Loss => OldLoss, - SquaredError => OldSquaredError} +import org.apache.spark.mllib.tree.loss.{AbsoluteError => OldAbsoluteError, Loss => OldLoss, SquaredError => OldSquaredError} import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel => OldGBTModel} import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame - /** * :: AlphaComponent :: * @@ -111,7 +109,7 @@ final class GBTRegressor def setLossType(value: String): this.type = set(lossType, value) /** @group getParam */ - def getLossType: String = getOrDefault(lossType).toLowerCase + def getLossType: String = $(lossType).toLowerCase /** (private[ml]) Convert new loss to old loss. */ override private[ml] def getOldLossType: OldLoss = { @@ -124,16 +122,14 @@ final class GBTRegressor } } - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): GBTRegressionModel = { + override protected def train(dataset: DataFrame): GBTRegressionModel = { val categoricalFeatures: Map[Int, Int] = - MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) - val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol))) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset) val boostingStrategy = super.getOldBoostingStrategy(categoricalFeatures, OldAlgo.Regression) val oldGBT = new OldGBT(boostingStrategy) val oldModel = oldGBT.run(oldDataset) - GBTRegressionModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + GBTRegressionModel.fromOld(oldModel, this, categoricalFeatures) } } @@ -155,7 +151,6 @@ object GBTRegressor { @AlphaComponent final class GBTRegressionModel( override val parent: GBTRegressor, - override val fittingParamMap: ParamMap, private val _trees: Array[DecisionTreeRegressionModel], private val _treeWeights: Array[Double]) extends PredictionModel[Vector, GBTRegressionModel] @@ -178,10 +173,8 @@ final class GBTRegressionModel( if (prediction > 0.0) 1.0 else 0.0 } - override protected def copy(): GBTRegressionModel = { - val m = new GBTRegressionModel(parent, fittingParamMap, _trees, _treeWeights) - Params.inheritValues(this.extractParamMap(), this, m) - m + override def copy(extra: ParamMap): GBTRegressionModel = { + copyValues(new GBTRegressionModel(parent, _trees, _treeWeights), extra) } override def toString: String = { @@ -200,14 +193,13 @@ private[ml] object GBTRegressionModel { def fromOld( oldModel: OldGBTModel, parent: GBTRegressor, - fittingParamMap: ParamMap, categoricalFeatures: Map[Int, Int]): GBTRegressionModel = { require(oldModel.algo == OldAlgo.Regression, "Cannot convert GradientBoostedTreesModel" + s" with algo=${oldModel.algo} (old API) to GBTRegressionModel (new API).") val newTrees = oldModel.trees.map { tree => // parent, fittingParamMap for each tree is null since there are no good ways to set these. - DecisionTreeRegressionModel.fromOld(tree, null, null, categoricalFeatures) + DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures) } - new GBTRegressionModel(parent, fittingParamMap, newTrees, oldModel.treeWeights) + new GBTRegressionModel(parent, newTrees, oldModel.treeWeights) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala index 0b81c48466be9..66c475f2d9840 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala @@ -19,22 +19,22 @@ package org.apache.spark.ml.regression import scala.collection.mutable -import breeze.linalg.{norm => brzNorm, DenseVector => BDV} -import breeze.optimize.{LBFGS => BreezeLBFGS, OWLQN => BreezeOWLQN} -import breeze.optimize.{CachedDiffFunction, DiffFunction} +import breeze.linalg.{DenseVector => BDV, norm => brzNorm} +import breeze.optimize.{CachedDiffFunction, DiffFunction, LBFGS => BreezeLBFGS, + OWLQN => BreezeOWLQN} +import org.apache.spark.Logging import org.apache.spark.annotation.AlphaComponent -import org.apache.spark.ml.param.{Params, ParamMap} -import org.apache.spark.ml.param.shared.{HasTol, HasElasticNetParam, HasMaxIter, HasRegParam} -import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer +import org.apache.spark.ml.param.ParamMap +import org.apache.spark.ml.param.shared.{HasElasticNetParam, HasMaxIter, HasRegParam, HasTol} import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.mllib.linalg.BLAS._ import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame import org.apache.spark.storage.StorageLevel import org.apache.spark.util.StatCounter -import org.apache.spark.Logging /** * Params for linear regression. @@ -96,9 +96,9 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress def setTol(value: Double): this.type = set(tol, value) setDefault(tol -> 1E-6) - override protected def train(dataset: DataFrame, paramMap: ParamMap): LinearRegressionModel = { + override protected def train(dataset: DataFrame): LinearRegressionModel = { // Extract columns from data. If dataset is persisted, do not persist instances. - val instances = extractLabeledPoints(dataset, paramMap).map { + val instances = extractLabeledPoints(dataset).map { case LabeledPoint(label: Double, features: Vector) => (label, features) } val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE @@ -125,7 +125,7 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress logWarning(s"The standard deviation of the label is zero, so the weights will be zeros " + s"and the intercept will be the mean of the label; as a result, training is not needed.") if (handlePersistence) instances.unpersist() - return new LinearRegressionModel(this, paramMap, Vectors.sparse(numFeatures, Seq()), yMean) + return new LinearRegressionModel(this, Vectors.sparse(numFeatures, Seq()), yMean) } val featuresMean = summarizer.mean.toArray @@ -133,17 +133,17 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress // Since we implicitly do the feature scaling when we compute the cost function // to improve the convergence, the effective regParam will be changed. - val effectiveRegParam = paramMap(regParam) / yStd - val effectiveL1RegParam = paramMap(elasticNetParam) * effectiveRegParam - val effectiveL2RegParam = (1.0 - paramMap(elasticNetParam)) * effectiveRegParam + val effectiveRegParam = $(regParam) / yStd + val effectiveL1RegParam = $(elasticNetParam) * effectiveRegParam + val effectiveL2RegParam = (1.0 - $(elasticNetParam)) * effectiveRegParam val costFun = new LeastSquaresCostFun(instances, yStd, yMean, featuresStd, featuresMean, effectiveL2RegParam) - val optimizer = if (paramMap(elasticNetParam) == 0.0 || effectiveRegParam == 0.0) { - new BreezeLBFGS[BDV[Double]](paramMap(maxIter), 10, paramMap(tol)) + val optimizer = if ($(elasticNetParam) == 0.0 || effectiveRegParam == 0.0) { + new BreezeLBFGS[BDV[Double]]($(maxIter), 10, $(tol)) } else { - new BreezeOWLQN[Int, BDV[Double]](paramMap(maxIter), 10, effectiveL1RegParam, paramMap(tol)) + new BreezeOWLQN[Int, BDV[Double]]($(maxIter), 10, effectiveL1RegParam, $(tol)) } val initialWeights = Vectors.zeros(numFeatures) @@ -178,7 +178,7 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress if (handlePersistence) instances.unpersist() // TODO: Converts to sparse format based on the storage, but may base on the scoring speed. - new LinearRegressionModel(this, paramMap, weights.compressed, intercept) + new LinearRegressionModel(this, weights.compressed, intercept) } } @@ -190,7 +190,6 @@ class LinearRegression extends Regressor[Vector, LinearRegression, LinearRegress @AlphaComponent class LinearRegressionModel private[ml] ( override val parent: LinearRegression, - override val fittingParamMap: ParamMap, val weights: Vector, val intercept: Double) extends RegressionModel[Vector, LinearRegressionModel] @@ -200,10 +199,8 @@ class LinearRegressionModel private[ml] ( dot(features, weights) + intercept } - override protected def copy(): LinearRegressionModel = { - val m = new LinearRegressionModel(parent, fittingParamMap, weights, intercept) - Params.inheritValues(extractParamMap(), this, m) - m + override def copy(extra: ParamMap): LinearRegressionModel = { + copyValues(new LinearRegressionModel(parent, weights, intercept), extra) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala index 2171ef3d32c26..0468a1be1ba74 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala @@ -20,18 +20,17 @@ package org.apache.spark.ml.regression import org.apache.spark.annotation.AlphaComponent import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} import org.apache.spark.ml.impl.tree.{RandomForestParams, TreeRegressorParams} -import org.apache.spark.ml.param.{Params, ParamMap} +import org.apache.spark.ml.param.ParamMap import org.apache.spark.ml.tree.{DecisionTreeModel, TreeEnsembleModel} import org.apache.spark.ml.util.MetadataUtils import org.apache.spark.mllib.linalg.Vector import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.{RandomForest => OldRandomForest} -import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo} import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestModel} import org.apache.spark.rdd.RDD import org.apache.spark.sql.DataFrame - /** * :: AlphaComponent :: * @@ -77,17 +76,15 @@ final class RandomForestRegressor override def setFeatureSubsetStrategy(value: String): this.type = super.setFeatureSubsetStrategy(value) - override protected def train( - dataset: DataFrame, - paramMap: ParamMap): RandomForestRegressionModel = { + override protected def train(dataset: DataFrame): RandomForestRegressionModel = { val categoricalFeatures: Map[Int, Int] = - MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) - val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol))) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset) val strategy = super.getOldStrategy(categoricalFeatures, numClasses = 0, OldAlgo.Regression, getOldImpurity) val oldModel = OldRandomForest.trainRegressor( oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed.toInt) - RandomForestRegressionModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + RandomForestRegressionModel.fromOld(oldModel, this, categoricalFeatures) } } @@ -110,7 +107,6 @@ object RandomForestRegressor { @AlphaComponent final class RandomForestRegressionModel private[ml] ( override val parent: RandomForestRegressor, - override val fittingParamMap: ParamMap, private val _trees: Array[DecisionTreeRegressionModel]) extends PredictionModel[Vector, RandomForestRegressionModel] with TreeEnsembleModel with Serializable { @@ -132,10 +128,8 @@ final class RandomForestRegressionModel private[ml] ( _trees.map(_.rootNode.predict(features)).sum / numTrees } - override protected def copy(): RandomForestRegressionModel = { - val m = new RandomForestRegressionModel(parent, fittingParamMap, _trees) - Params.inheritValues(this.extractParamMap(), this, m) - m + override def copy(extra: ParamMap): RandomForestRegressionModel = { + copyValues(new RandomForestRegressionModel(parent, _trees), extra) } override def toString: String = { @@ -154,14 +148,13 @@ private[ml] object RandomForestRegressionModel { def fromOld( oldModel: OldRandomForestModel, parent: RandomForestRegressor, - fittingParamMap: ParamMap, categoricalFeatures: Map[Int, Int]): RandomForestRegressionModel = { require(oldModel.algo == OldAlgo.Regression, "Cannot convert RandomForestModel" + s" with algo=${oldModel.algo} (old API) to RandomForestRegressionModel (new API).") val newTrees = oldModel.trees.map { tree => // parent, fittingParamMap for each tree is null since there are no good ways to set these. - DecisionTreeRegressionModel.fromOld(tree, null, null, categoricalFeatures) + DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures) } - new RandomForestRegressionModel(parent, fittingParamMap, newTrees) + new RandomForestRegressionModel(parent, newTrees) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala index d679085eeafe1..c6b3327db6ad3 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/Regressor.scala @@ -17,7 +17,7 @@ package org.apache.spark.ml.regression -import org.apache.spark.annotation.{DeveloperApi, AlphaComponent} +import org.apache.spark.annotation.{AlphaComponent, DeveloperApi} import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor, PredictorParams} /** diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala index d1ad0893cd044..cee2aa6e85523 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala @@ -39,7 +39,7 @@ private[ml] trait CrossValidatorParams extends Params { val estimator: Param[Estimator[_]] = new Param(this, "estimator", "estimator for selection") /** @group getParam */ - def getEstimator: Estimator[_] = getOrDefault(estimator) + def getEstimator: Estimator[_] = $(estimator) /** * param for estimator param maps @@ -49,7 +49,7 @@ private[ml] trait CrossValidatorParams extends Params { new Param(this, "estimatorParamMaps", "param maps for the estimator") /** @group getParam */ - def getEstimatorParamMaps: Array[ParamMap] = getOrDefault(estimatorParamMaps) + def getEstimatorParamMaps: Array[ParamMap] = $(estimatorParamMaps) /** * param for the evaluator for selection @@ -58,7 +58,7 @@ private[ml] trait CrossValidatorParams extends Params { val evaluator: Param[Evaluator] = new Param(this, "evaluator", "evaluator for selection") /** @group getParam */ - def getEvaluator: Evaluator = getOrDefault(evaluator) + def getEvaluator: Evaluator = $(evaluator) /** * Param for number of folds for cross validation. Must be >= 2. @@ -69,7 +69,7 @@ private[ml] trait CrossValidatorParams extends Params { "number of folds for cross validation (>= 2)", ParamValidators.gtEq(2)) /** @group getParam */ - def getNumFolds: Int = getOrDefault(numFolds) + def getNumFolds: Int = $(numFolds) setDefault(numFolds -> 3) } @@ -95,23 +95,22 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP /** @group setParam */ def setNumFolds(value: Int): this.type = set(numFolds, value) - override def validate(paramMap: ParamMap): Unit = { + override def validateParams(paramMap: ParamMap): Unit = { getEstimatorParamMaps.foreach { eMap => - getEstimator.validate(eMap ++ paramMap) + getEstimator.validateParams(eMap ++ paramMap) } } - override def fit(dataset: DataFrame, paramMap: ParamMap): CrossValidatorModel = { - val map = extractParamMap(paramMap) + override def fit(dataset: DataFrame): CrossValidatorModel = { val schema = dataset.schema - transformSchema(dataset.schema, paramMap, logging = true) + transformSchema(dataset.schema, logging = true) val sqlCtx = dataset.sqlContext - val est = map(estimator) - val eval = map(evaluator) - val epm = map(estimatorParamMaps) + val est = $(estimator) + val eval = $(evaluator) + val epm = $(estimatorParamMaps) val numModels = epm.length val metrics = new Array[Double](epm.length) - val splits = MLUtils.kFold(dataset.rdd, map(numFolds), 0) + val splits = MLUtils.kFold(dataset.rdd, $(numFolds), 0) splits.zipWithIndex.foreach { case ((training, validation), splitIndex) => val trainingDataset = sqlCtx.createDataFrame(training, schema).cache() val validationDataset = sqlCtx.createDataFrame(validation, schema).cache() @@ -121,27 +120,24 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP trainingDataset.unpersist() var i = 0 while (i < numModels) { - val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)), map) + val metric = eval.evaluate(models(i).transform(validationDataset, epm(i))) logDebug(s"Got metric $metric for model trained with ${epm(i)}.") metrics(i) += metric i += 1 } validationDataset.unpersist() } - f2jBLAS.dscal(numModels, 1.0 / map(numFolds), metrics, 1) + f2jBLAS.dscal(numModels, 1.0 / $(numFolds), metrics, 1) logInfo(s"Average cross-validation metrics: ${metrics.toSeq}") val (bestMetric, bestIndex) = metrics.zipWithIndex.maxBy(_._1) logInfo(s"Best set of parameters:\n${epm(bestIndex)}") logInfo(s"Best cross-validation metric: $bestMetric.") val bestModel = est.fit(dataset, epm(bestIndex)).asInstanceOf[Model[_]] - val cvModel = new CrossValidatorModel(this, map, bestModel) - Params.inheritValues(map, this, cvModel) - cvModel + copyValues(new CrossValidatorModel(this, bestModel)) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - val map = extractParamMap(paramMap) - map(estimator).transformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + $(estimator).transformSchema(schema) } } @@ -152,19 +148,18 @@ class CrossValidator extends Estimator[CrossValidatorModel] with CrossValidatorP @AlphaComponent class CrossValidatorModel private[ml] ( override val parent: CrossValidator, - override val fittingParamMap: ParamMap, val bestModel: Model[_]) extends Model[CrossValidatorModel] with CrossValidatorParams { - override def validate(paramMap: ParamMap): Unit = { - bestModel.validate(paramMap) + override def validateParams(paramMap: ParamMap): Unit = { + bestModel.validateParams(paramMap) } - override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { - bestModel.transform(dataset, paramMap) + override def transform(dataset: DataFrame): DataFrame = { + bestModel.transform(dataset) } - override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { - bestModel.transformSchema(schema, paramMap) + override def transformSchema(schema: StructType): StructType = { + bestModel.transformSchema(schema) } } diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java index 3f8e59de0f05c..7e7189a2b1d53 100644 --- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java @@ -84,9 +84,10 @@ public void logisticRegressionWithSetters() { .setThreshold(0.6) .setProbabilityCol("myProbability"); LogisticRegressionModel model = lr.fit(dataset); - assert(model.fittingParamMap().apply(lr.maxIter()).equals(10)); - assert(model.fittingParamMap().apply(lr.regParam()).equals(1.0)); - assert(model.fittingParamMap().apply(lr.threshold()).equals(0.6)); + LogisticRegression parent = model.parent(); + assert(parent.getMaxIter() == 10); + assert(parent.getRegParam() == 1.0); + assert(parent.getThreshold() == 0.6); assert(model.getThreshold() == 0.6); // Modify model params, and check that the params worked. @@ -109,9 +110,10 @@ public void logisticRegressionWithSetters() { // Call fit() with new params, and check as many params as we can. LogisticRegressionModel model2 = lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1), lr.threshold().w(0.4), lr.probabilityCol().w("theProb")); - assert(model2.fittingParamMap().apply(lr.maxIter()).equals(5)); - assert(model2.fittingParamMap().apply(lr.regParam()).equals(0.1)); - assert(model2.fittingParamMap().apply(lr.threshold()).equals(0.4)); + LogisticRegression parent2 = model2.parent(); + assert(parent2.getMaxIter() == 5); + assert(parent2.getRegParam() == 0.1); + assert(parent2.getThreshold() == 0.4); assert(model2.getThreshold() == 0.4); assert(model2.getProbabilityCol().equals("theProb")); } diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java index 0cc36c8d56d70..a82b86d560b6e 100644 --- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java @@ -23,14 +23,15 @@ import org.junit.After; import org.junit.Before; import org.junit.Test; +import static org.junit.Assert.assertEquals; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; -import static org.apache.spark.mllib.classification.LogisticRegressionSuite - .generateLogisticInputAsList; import org.apache.spark.mllib.regression.LabeledPoint; import org.apache.spark.sql.DataFrame; import org.apache.spark.sql.SQLContext; +import static org.apache.spark.mllib.classification.LogisticRegressionSuite + .generateLogisticInputAsList; public class JavaLinearRegressionSuite implements Serializable { @@ -65,8 +66,8 @@ public void linearRegressionDefaultParams() { DataFrame predictions = jsql.sql("SELECT label, prediction FROM prediction"); predictions.collect(); // Check defaults - assert(model.getFeaturesCol().equals("features")); - assert(model.getPredictionCol().equals("prediction")); + assertEquals("features", model.getFeaturesCol()); + assertEquals("prediction", model.getPredictionCol()); } @Test @@ -76,14 +77,16 @@ public void linearRegressionWithSetters() { .setMaxIter(10) .setRegParam(1.0); LinearRegressionModel model = lr.fit(dataset); - assert(model.fittingParamMap().apply(lr.maxIter()).equals(10)); - assert(model.fittingParamMap().apply(lr.regParam()).equals(1.0)); + LinearRegression parent = model.parent(); + assertEquals(10, parent.getMaxIter()); + assertEquals(1.0, parent.getRegParam(), 0.0); // Call fit() with new params, and check as many params as we can. LinearRegressionModel model2 = lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1), lr.predictionCol().w("thePred")); - assert(model2.fittingParamMap().apply(lr.maxIter()).equals(5)); - assert(model2.fittingParamMap().apply(lr.regParam()).equals(0.1)); - assert(model2.getPredictionCol().equals("thePred")); + LinearRegression parent2 = model2.parent(); + assertEquals(5, parent2.getMaxIter()); + assertEquals(0.1, parent2.getRegParam(), 0.0); + assertEquals("thePred", model2.getPredictionCol()); } } diff --git a/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java b/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java index 0bb6b489f2757..08eeca53f0721 100644 --- a/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java +++ b/mllib/src/test/java/org/apache/spark/ml/tuning/JavaCrossValidatorSuite.java @@ -68,8 +68,8 @@ public void crossValidationWithLogisticRegression() { .setEvaluator(eval) .setNumFolds(3); CrossValidatorModel cvModel = cv.fit(dataset); - ParamMap bestParamMap = cvModel.bestModel().fittingParamMap(); - Assert.assertEquals(0.001, bestParamMap.apply(lr.regParam())); - Assert.assertEquals(10, bestParamMap.apply(lr.maxIter())); + LogisticRegression parent = (LogisticRegression) cvModel.bestModel().parent(); + Assert.assertEquals(0.001, parent.getRegParam(), 0.0); + Assert.assertEquals(10, parent.getMaxIter()); } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala index 2f175fb117941..2b04a3034782e 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala @@ -42,30 +42,32 @@ class PipelineSuite extends FunSuite { val dataset3 = mock[DataFrame] val dataset4 = mock[DataFrame] - when(estimator0.fit(meq(dataset0), any[ParamMap]())).thenReturn(model0) - when(model0.transform(meq(dataset0), any[ParamMap]())).thenReturn(dataset1) + when(estimator0.copy(any[ParamMap])).thenReturn(estimator0) + when(model0.copy(any[ParamMap])).thenReturn(model0) + when(transformer1.copy(any[ParamMap])).thenReturn(transformer1) + when(estimator2.copy(any[ParamMap])).thenReturn(estimator2) + when(model2.copy(any[ParamMap])).thenReturn(model2) + when(transformer3.copy(any[ParamMap])).thenReturn(transformer3) + + when(estimator0.fit(meq(dataset0))).thenReturn(model0) + when(model0.transform(meq(dataset0))).thenReturn(dataset1) when(model0.parent).thenReturn(estimator0) - when(transformer1.transform(meq(dataset1), any[ParamMap])).thenReturn(dataset2) - when(estimator2.fit(meq(dataset2), any[ParamMap]())).thenReturn(model2) - when(model2.transform(meq(dataset2), any[ParamMap]())).thenReturn(dataset3) + when(transformer1.transform(meq(dataset1))).thenReturn(dataset2) + when(estimator2.fit(meq(dataset2))).thenReturn(model2) + when(model2.transform(meq(dataset2))).thenReturn(dataset3) when(model2.parent).thenReturn(estimator2) - when(transformer3.transform(meq(dataset3), any[ParamMap]())).thenReturn(dataset4) + when(transformer3.transform(meq(dataset3))).thenReturn(dataset4) val pipeline = new Pipeline() .setStages(Array(estimator0, transformer1, estimator2, transformer3)) val pipelineModel = pipeline.fit(dataset0) - assert(pipelineModel.stages.size === 4) + assert(pipelineModel.stages.length === 4) assert(pipelineModel.stages(0).eq(model0)) assert(pipelineModel.stages(1).eq(transformer1)) assert(pipelineModel.stages(2).eq(model2)) assert(pipelineModel.stages(3).eq(transformer3)) - assert(pipelineModel.getModel(estimator0).eq(model0)) - assert(pipelineModel.getModel(estimator2).eq(model2)) - intercept[NoSuchElementException] { - pipelineModel.getModel(mock[Estimator[MyModel]]) - } val output = pipelineModel.transform(dataset0) assert(output.eq(dataset4)) } diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala index 9b31adecdcb1c..03af4ecd7a7e0 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala @@ -267,8 +267,8 @@ private[ml] object DecisionTreeClassifierSuite extends FunSuite { val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses) val newTree = dt.fit(newData) // Use parent, fittingParamMap from newTree since these are not checked anyways. - val oldTreeAsNew = DecisionTreeClassificationModel.fromOld(oldTree, newTree.parent, - newTree.fittingParamMap, categoricalFeatures) + val oldTreeAsNew = DecisionTreeClassificationModel.fromOld( + oldTree, newTree.parent, categoricalFeatures) TreeTests.checkEqual(oldTreeAsNew, newTree) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala index e6ccc2c93cba8..16c758b82c7cd 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala @@ -129,8 +129,8 @@ private object GBTClassifierSuite { val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 2) val newModel = gbt.fit(newData) // Use parent, fittingParamMap from newTree since these are not checked anyways. - val oldModelAsNew = GBTClassificationModel.fromOld(oldModel, newModel.parent, - newModel.fittingParamMap, categoricalFeatures) + val oldModelAsNew = GBTClassificationModel.fromOld( + oldModel, newModel.parent, categoricalFeatures) TreeTests.checkEqual(oldModelAsNew, newModel) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala index 35d8c2e16c6cd..6dd1fdf05514e 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala @@ -74,9 +74,10 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { .setThreshold(0.6) .setProbabilityCol("myProbability") val model = lr.fit(dataset) - assert(model.fittingParamMap.get(lr.maxIter) === Some(10)) - assert(model.fittingParamMap.get(lr.regParam) === Some(1.0)) - assert(model.fittingParamMap.get(lr.threshold) === Some(0.6)) + val parent = model.parent + assert(parent.getMaxIter === 10) + assert(parent.getRegParam === 1.0) + assert(parent.getThreshold === 0.6) assert(model.getThreshold === 0.6) // Modify model params, and check that the params worked. @@ -99,9 +100,10 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { // Call fit() with new params, and check as many params as we can. val model2 = lr.fit(dataset, lr.maxIter -> 5, lr.regParam -> 0.1, lr.threshold -> 0.4, lr.probabilityCol -> "theProb") - assert(model2.fittingParamMap.get(lr.maxIter).get === 5) - assert(model2.fittingParamMap.get(lr.regParam).get === 0.1) - assert(model2.fittingParamMap.get(lr.threshold).get === 0.4) + val parent2 = model2.parent + assert(parent2.getMaxIter === 5) + assert(parent2.getRegParam === 0.1) + assert(parent2.getThreshold === 0.4) assert(model2.getThreshold === 0.4) assert(model2.getProbabilityCol == "theProb") } @@ -117,7 +119,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { val results = model.transform(dataset) // Compare rawPrediction with probability - results.select("rawPrediction", "probability").collect().map { + results.select("rawPrediction", "probability").collect().foreach { case Row(raw: Vector, prob: Vector) => assert(raw.size === 2) assert(prob.size === 2) @@ -127,7 +129,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext { } // Compare prediction with probability - results.select("prediction", "probability").collect().map { + results.select("prediction", "probability").collect().foreach { case Row(pred: Double, prob: Vector) => val predFromProb = prob.toArray.zipWithIndex.maxBy(_._1)._2 assert(pred == predFromProb) diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala index ed41a9664f94f..c41def9330504 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala @@ -159,8 +159,8 @@ private object RandomForestClassifierSuite { val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses) val newModel = rf.fit(newData) // Use parent, fittingParamMap from newTree since these are not checked anyways. - val oldModelAsNew = RandomForestClassificationModel.fromOld(oldModel, newModel.parent, - newModel.fittingParamMap, categoricalFeatures) + val oldModelAsNew = RandomForestClassificationModel.fromOld( + oldModel, newModel.parent, categoricalFeatures) TreeTests.checkEqual(oldModelAsNew, newModel) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala index f8852606abbf2..6056e7d3f6ff8 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala @@ -122,19 +122,21 @@ class ParamsSuite extends FunSuite { assert(solver.getParam("inputCol").eq(inputCol)) assert(solver.getParam("maxIter").eq(maxIter)) + assert(solver.hasParam("inputCol")) + assert(!solver.hasParam("abc")) intercept[NoSuchElementException] { solver.getParam("abc") } intercept[IllegalArgumentException] { - solver.validate() + solver.validateParams() } - solver.validate(ParamMap(inputCol -> "input")) + solver.validateParams(ParamMap(inputCol -> "input")) solver.setInputCol("input") assert(solver.isSet(inputCol)) assert(solver.isDefined(inputCol)) assert(solver.getInputCol === "input") - solver.validate() + solver.validateParams() intercept[IllegalArgumentException] { ParamMap(maxIter -> -10) } @@ -144,6 +146,11 @@ class ParamsSuite extends FunSuite { solver.clearMaxIter() assert(!solver.isSet(maxIter)) + + val copied = solver.copy(ParamMap(solver.maxIter -> 50)) + assert(copied.uid !== solver.uid) + assert(copied.getInputCol === solver.getInputCol) + assert(copied.getMaxIter === 50) } test("ParamValidate") { diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala index 6f9c9cb5166ae..dc16073640407 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala @@ -23,15 +23,19 @@ import org.apache.spark.ml.param.shared.{HasInputCol, HasMaxIter} class TestParams extends Params with HasMaxIter with HasInputCol { def setMaxIter(value: Int): this.type = { set(maxIter, value); this } + def setInputCol(value: String): this.type = { set(inputCol, value); this } setDefault(maxIter -> 10) - override def validate(paramMap: ParamMap): Unit = { - val m = extractParamMap(paramMap) - // Note: maxIter is validated when it is set. - require(m.contains(inputCol)) + def clearMaxIter(): this.type = clear(maxIter) + + override def validateParams(): Unit = { + super.validateParams() + require(isDefined(inputCol)) } - def clearMaxIter(): this.type = clear(maxIter) + override def copy(extra: ParamMap): TestParams = { + super.copy(extra).asInstanceOf[TestParams] + } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala index c87a171b4b229..5aa81b44ddaf9 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala @@ -84,8 +84,8 @@ private[ml] object DecisionTreeRegressorSuite extends FunSuite { val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0) val newTree = dt.fit(newData) // Use parent, fittingParamMap from newTree since these are not checked anyways. - val oldTreeAsNew = DecisionTreeRegressionModel.fromOld(oldTree, newTree.parent, - newTree.fittingParamMap, categoricalFeatures) + val oldTreeAsNew = DecisionTreeRegressionModel.fromOld( + oldTree, newTree.parent, categoricalFeatures) TreeTests.checkEqual(oldTreeAsNew, newTree) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala index 4aec36948ac92..25b36ab08b67c 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala @@ -130,8 +130,7 @@ private object GBTRegressorSuite { val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0) val newModel = gbt.fit(newData) // Use parent, fittingParamMap from newTree since these are not checked anyways. - val oldModelAsNew = GBTRegressionModel.fromOld(oldModel, newModel.parent, - newModel.fittingParamMap, categoricalFeatures) + val oldModelAsNew = GBTRegressionModel.fromOld(oldModel, newModel.parent, categoricalFeatures) TreeTests.checkEqual(oldModelAsNew, newModel) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala index c6dc1cc29b6ff..45f09f4fdab81 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala @@ -115,8 +115,8 @@ private object RandomForestRegressorSuite extends FunSuite { val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0) val newModel = rf.fit(newData) // Use parent, fittingParamMap from newTree since these are not checked anyways. - val oldModelAsNew = RandomForestRegressionModel.fromOld(oldModel, newModel.parent, - newModel.fittingParamMap, categoricalFeatures) + val oldModelAsNew = RandomForestRegressionModel.fromOld( + oldModel, newModel.parent, categoricalFeatures) TreeTests.checkEqual(oldModelAsNew, newModel) } } diff --git a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala index 761ea821ef7c6..05313d440fbf6 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala @@ -49,8 +49,8 @@ class CrossValidatorSuite extends FunSuite with MLlibTestSparkContext { .setEvaluator(eval) .setNumFolds(3) val cvModel = cv.fit(dataset) - val bestParamMap = cvModel.bestModel.fittingParamMap - assert(bestParamMap(lr.regParam) === 0.001) - assert(bestParamMap(lr.maxIter) === 10) + val parent = cvModel.bestModel.parent.asInstanceOf[LogisticRegression] + assert(parent.getRegParam === 0.001) + assert(parent.getMaxIter === 10) } } From f32e69ecc333867fc966f65cd0aeaeddd43e0945 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=BA=91=E5=B3=A4?= Date: Mon, 4 May 2015 12:08:38 -0700 Subject: [PATCH 564/591] [SPARK-7319][SQL] Improve the output from DataFrame.show() MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Author: 云峤 Closes #5865 from kaka1992/df.show and squashes the following commits: c79204b [云峤] Update a1338f6 [云峤] Update python dataFrame show test and add empty df unit test. 734369c [云峤] Update python dataFrame show test and add empty df unit test. 84aec3e [云峤] Update python dataFrame show test and add empty df unit test. 159b3d5 [云峤] update 03ef434 [云峤] update 7394fd5 [云峤] update test show ced487a [云峤] update pep8 b6e690b [云峤] Merge remote-tracking branch 'upstream/master' into df.show 30ac311 [云峤] [SPARK-7294] ADD BETWEEN 7d62368 [云峤] [SPARK-7294] ADD BETWEEN baf839b [云峤] [SPARK-7294] ADD BETWEEN d11d5b9 [云峤] [SPARK-7294] ADD BETWEEN --- R/pkg/R/DataFrame.R | 2 +- R/pkg/inst/tests/test_sparkSQL.R | 2 +- python/pyspark/sql/dataframe.py | 105 ++++++++++++------ .../org/apache/spark/sql/DataFrame.scala | 28 ++++- .../org/apache/spark/sql/DataFrameSuite.scala | 19 ++++ 5 files changed, 112 insertions(+), 44 deletions(-) diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R index b59b700af5dc9..841e77e55e0d8 100644 --- a/R/pkg/R/DataFrame.R +++ b/R/pkg/R/DataFrame.R @@ -167,7 +167,7 @@ setMethod("isLocal", setMethod("showDF", signature(x = "DataFrame"), function(x, numRows = 20) { - cat(callJMethod(x@sdf, "showString", numToInt(numRows)), "\n") + callJMethod(x@sdf, "showString", numToInt(numRows)) }) #' show diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R index af7a6c582047a..f82e56fdd8278 100644 --- a/R/pkg/inst/tests/test_sparkSQL.R +++ b/R/pkg/inst/tests/test_sparkSQL.R @@ -641,7 +641,7 @@ test_that("toJSON() returns an RDD of the correct values", { test_that("showDF()", { df <- jsonFile(sqlCtx, jsonPath) - expect_output(showDF(df), "age name \nnull Michael\n30 Andy \n19 Justin ") + expect_output(showDF(df), "+----+-------+\n| age| name|\n+----+-------+\n|null|Michael|\n| 30| Andy|\n| 19| Justin|\n+----+-------+\n") }) test_that("isLocal()", { diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index aac5b8c4c5770..22762c5bbbcd0 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -275,9 +275,12 @@ def show(self, n=20): >>> df DataFrame[age: int, name: string] >>> df.show() - age name - 2 Alice - 5 Bob + +---+-----+ + |age| name| + +---+-----+ + | 2|Alice| + | 5| Bob| + +---+-----+ """ print(self._jdf.showString(n)) @@ -591,12 +594,15 @@ def describe(self, *cols): given, this function computes statistics for all numerical columns. >>> df.describe().show() - summary age - count 2 - mean 3.5 - stddev 1.5 - min 2 - max 5 + +-------+---+ + |summary|age| + +-------+---+ + | count| 2| + | mean|3.5| + | stddev|1.5| + | min| 2| + | max| 5| + +-------+---+ """ jdf = self._jdf.describe(self._jseq(cols)) return DataFrame(jdf, self.sql_ctx) @@ -801,12 +807,18 @@ def dropna(self, how='any', thresh=None, subset=None): :param subset: optional list of column names to consider. >>> df4.dropna().show() - age height name - 10 80 Alice + +---+------+-----+ + |age|height| name| + +---+------+-----+ + | 10| 80|Alice| + +---+------+-----+ >>> df4.na.drop().show() - age height name - 10 80 Alice + +---+------+-----+ + |age|height| name| + +---+------+-----+ + | 10| 80|Alice| + +---+------+-----+ """ if how is not None and how not in ['any', 'all']: raise ValueError("how ('" + how + "') should be 'any' or 'all'") @@ -837,25 +849,34 @@ def fillna(self, value, subset=None): then the non-string column is simply ignored. >>> df4.fillna(50).show() - age height name - 10 80 Alice - 5 50 Bob - 50 50 Tom - 50 50 null + +---+------+-----+ + |age|height| name| + +---+------+-----+ + | 10| 80|Alice| + | 5| 50| Bob| + | 50| 50| Tom| + | 50| 50| null| + +---+------+-----+ >>> df4.fillna({'age': 50, 'name': 'unknown'}).show() - age height name - 10 80 Alice - 5 null Bob - 50 null Tom - 50 null unknown + +---+------+-------+ + |age|height| name| + +---+------+-------+ + | 10| 80| Alice| + | 5| null| Bob| + | 50| null| Tom| + | 50| null|unknown| + +---+------+-------+ >>> df4.na.fill({'age': 50, 'name': 'unknown'}).show() - age height name - 10 80 Alice - 5 null Bob - 50 null Tom - 50 null unknown + +---+------+-------+ + |age|height| name| + +---+------+-------+ + | 10| 80| Alice| + | 5| null| Bob| + | 50| null| Tom| + | 50| null|unknown| + +---+------+-------+ """ if not isinstance(value, (float, int, long, basestring, dict)): raise ValueError("value should be a float, int, long, string, or dict") @@ -1241,11 +1262,17 @@ def getItem(self, key): >>> df = sc.parallelize([([1, 2], {"key": "value"})]).toDF(["l", "d"]) >>> df.select(df.l.getItem(0), df.d.getItem("key")).show() - l[0] d[key] - 1 value + +----+------+ + |l[0]|d[key]| + +----+------+ + | 1| value| + +----+------+ >>> df.select(df.l[0], df.d["key"]).show() - l[0] d[key] - 1 value + +----+------+ + |l[0]|d[key]| + +----+------+ + | 1| value| + +----+------+ """ return self[key] @@ -1255,11 +1282,17 @@ def getField(self, name): >>> from pyspark.sql import Row >>> df = sc.parallelize([Row(r=Row(a=1, b="b"))]).toDF() >>> df.select(df.r.getField("b")).show() - r.b - b + +---+ + |r.b| + +---+ + | b| + +---+ >>> df.select(df.r.a).show() - r.a - 1 + +---+ + |r.a| + +---+ + | 1| + +---+ """ return Column(self._jc.getField(name)) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index c421006c8fd2d..cf344710ff8b4 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -20,6 +20,7 @@ package org.apache.spark.sql import java.io.CharArrayWriter import java.sql.DriverManager + import scala.collection.JavaConversions._ import scala.language.implicitConversions import scala.reflect.ClassTag @@ -28,6 +29,7 @@ import scala.util.control.NonFatal import com.fasterxml.jackson.core.JsonFactory +import org.apache.commons.lang3.StringUtils import org.apache.spark.annotation.{DeveloperApi, Experimental} import org.apache.spark.api.java.JavaRDD import org.apache.spark.api.python.SerDeUtil @@ -175,6 +177,7 @@ class DataFrame private[sql]( * @param numRows Number of rows to show */ private[sql] def showString(numRows: Int): String = { + val sb = new StringBuilder val data = take(numRows) val numCols = schema.fieldNames.length @@ -194,12 +197,25 @@ class DataFrame private[sql]( } } - // Pad the cells - rows.map { row => - row.zipWithIndex.map { case (cell, i) => - String.format(s"%-${colWidths(i)}s", cell) - }.mkString(" ") - }.mkString("\n") + // Create SeparateLine + val sep: String = colWidths.map("-" * _).addString(sb, "+", "+", "+\n").toString() + + // column names + rows.head.zipWithIndex.map { case (cell, i) => + StringUtils.leftPad(cell.toString, colWidths(i)) + }.addString(sb, "|", "|", "|\n") + + sb.append(sep) + + // data + rows.tail.map { + _.zipWithIndex.map { case (cell, i) => + StringUtils.leftPad(cell.toString, colWidths(i)) + }.addString(sb, "|", "|", "|\n") + } + + sb.append(sep) + sb.toString() } override def toString: String = { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index e286fef23caa4..ff31e15e2d472 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -598,6 +598,25 @@ class DataFrameSuite extends QueryTest { testData.select($"*").show(1000) } + test("SPARK-7319 showString") { + val expectedAnswer = """+---+-----+ + ||key|value| + |+---+-----+ + || 1| 1| + |+---+-----+ + |""".stripMargin + assert(testData.select($"*").showString(1) === expectedAnswer) + } + + test("SPARK-7327 show with empty dataFrame") { + val expectedAnswer = """+---+-----+ + ||key|value| + |+---+-----+ + |+---+-----+ + |""".stripMargin + assert(testData.select($"*").filter($"key" < 0).showString(1) === expectedAnswer) + } + test("createDataFrame(RDD[Row], StructType) should convert UDTs (SPARK-6672)") { val rowRDD = TestSQLContext.sparkContext.parallelize(Seq(Row(new ExamplePoint(1.0, 2.0)))) val schema = StructType(Array(StructField("point", new ExamplePointUDT(), false))) From fc8b58195afa67fbb75b4c8303e022f703cbf007 Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Mon, 4 May 2015 16:21:36 -0700 Subject: [PATCH 565/591] [SPARK-6943] [SPARK-6944] DAG visualization on SparkUI This patch adds the functionality to display the RDD DAG on the SparkUI. This DAG describes the relationships between - an RDD and its dependencies, - an RDD and its operation scopes, and - an RDD's operation scopes and the stage / job hierarchy An operation scope here refers to the existing public APIs that created the RDDs (e.g. `textFile`, `treeAggregate`). In the future, we can expand this to include higher level operations like SQL queries. *Note: This blatantly stole a few lines of HTML and JavaScript from #5547 (thanks shroffpradyumn!)* Here's what the job page looks like: and the stage page: Author: Andrew Or Closes #5729 from andrewor14/viz2 and squashes the following commits: 666c03b [Andrew Or] Round corners of RDD boxes on stage page (minor) 01ba336 [Andrew Or] Change RDD cache color to red (minor) 6f9574a [Andrew Or] Add tests for RDDOperationScope 1c310e4 [Andrew Or] Wrap a few more RDD functions in an operation scope 3ffe566 [Andrew Or] Restore "null" as default for RDD name 5fdd89d [Andrew Or] children -> child (minor) 0d07a84 [Andrew Or] Fix python style afb98e2 [Andrew Or] Merge branch 'master' of github.com:apache/spark into viz2 0d7aa32 [Andrew Or] Fix python tests 3459ab2 [Andrew Or] Fix tests 832443c [Andrew Or] Merge branch 'master' of github.com:apache/spark into viz2 429e9e1 [Andrew Or] Display cached RDDs on the viz b1f0fd1 [Andrew Or] Rename OperatorScope -> RDDOperationScope 31aae06 [Andrew Or] Extract visualization logic from listener 83f9c58 [Andrew Or] Implement a programmatic representation of operator scopes 5a7faf4 [Andrew Or] Rename references to viz scopes to viz clusters ee33d52 [Andrew Or] Separate HTML generating code from listener f9830a2 [Andrew Or] Refactor + clean up + document JS visualization code b80cc52 [Andrew Or] Merge branch 'master' of github.com:apache/spark into viz2 0706992 [Andrew Or] Add link from jobs to stages deb48a0 [Andrew Or] Translate stage boxes taking into account the width 5c7ce16 [Andrew Or] Connect RDDs across stages + update style ab91416 [Andrew Or] Introduce visualization to the Job Page 5f07e9c [Andrew Or] Remove more return statements from scopes 5e388ea [Andrew Or] Fix line too long 43de96e [Andrew Or] Add parent IDs to StageInfo 6e2cfea [Andrew Or] Remove all return statements in `withScope` d19c4da [Andrew Or] Merge branch 'master' of github.com:apache/spark into viz2 7ef957c [Andrew Or] Fix scala style 4310271 [Andrew Or] Merge branch 'master' of github.com:apache/spark into viz2 aa868a9 [Andrew Or] Ensure that HadoopRDD is actually serializable c3bfcae [Andrew Or] Re-implement scopes using closures instead of annotations 52187fc [Andrew Or] Rat excludes 09d361e [Andrew Or] Add ID to node label (minor) 71281fa [Andrew Or] Embed the viz in the UI in a toggleable manner 8dd5af2 [Andrew Or] Fill in documentation + miscellaneous minor changes fe7816f [Andrew Or] Merge branch 'master' of github.com:apache/spark into viz 205f838 [Andrew Or] Reimplement rendering with dagre-d3 instead of viz.js 5e22946 [Andrew Or] Merge branch 'master' of github.com:apache/spark into viz 6a7cdca [Andrew Or] Move RDD scope util methods and logic to its own file 494d5c2 [Andrew Or] Revert a few unintended style changes 9fac6f3 [Andrew Or] Re-implement scopes through annotations instead f22f337 [Andrew Or] First working implementation of visualization with vis.js 2184348 [Andrew Or] Translate RDD information to dot file 5143523 [Andrew Or] Expose the necessary information in RDDInfo a9ed4f9 [Andrew Or] Add a few missing scopes to certain RDD methods 6b3403b [Andrew Or] Scope all RDD methods --- .rat-excludes | 3 + .../org/apache/spark/ui/static/d3.min.js | 5 + .../apache/spark/ui/static/dagre-d3.min.js | 29 ++ .../spark/ui/static/graphlib-dot.min.js | 4 + .../apache/spark/ui/static/spark-dag-viz.js | 392 ++++++++++++++++++ .../org/apache/spark/ui/static/webui.css | 2 +- .../scala/org/apache/spark/SparkContext.scala | 97 +++-- .../apache/spark/rdd/AsyncRDDActions.scala | 10 +- .../apache/spark/rdd/DoubleRDDFunctions.scala | 38 +- .../org/apache/spark/rdd/HadoopRDD.scala | 6 +- .../spark/rdd/OrderedRDDFunctions.scala | 6 +- .../apache/spark/rdd/PairRDDFunctions.scala | 167 +++++--- .../main/scala/org/apache/spark/rdd/RDD.scala | 341 +++++++++------ .../apache/spark/rdd/RDDOperationScope.scala | 137 ++++++ .../spark/rdd/SequenceFileRDDFunctions.scala | 4 +- .../apache/spark/scheduler/StageInfo.scala | 2 + .../org/apache/spark/storage/RDDInfo.scala | 11 +- .../scala/org/apache/spark/ui/SparkUI.scala | 10 +- .../scala/org/apache/spark/ui/UIUtils.scala | 55 ++- .../apache/spark/ui/jobs/AllJobsPage.scala | 2 +- .../apache/spark/ui/jobs/AllStagesPage.scala | 10 +- .../apache/spark/ui/jobs/ExecutorTable.scala | 2 +- .../org/apache/spark/ui/jobs/JobPage.scala | 14 +- .../spark/ui/jobs/JobProgressListener.scala | 17 +- .../org/apache/spark/ui/jobs/JobsTab.scala | 6 +- .../org/apache/spark/ui/jobs/PoolPage.scala | 4 +- .../org/apache/spark/ui/jobs/PoolTable.scala | 2 +- .../org/apache/spark/ui/jobs/StagePage.scala | 17 +- .../org/apache/spark/ui/jobs/StagesTab.scala | 7 +- .../spark/ui/scope/RDDOperationGraph.scala | 205 +++++++++ .../ui/scope/RDDOperationGraphListener.scala | 68 +++ .../org/apache/spark/util/JsonProtocol.scala | 28 +- .../ExecutorAllocationManagerSuite.scala | 2 +- .../spark/rdd/RDDOperationScopeSuite.scala | 133 ++++++ .../apache/spark/storage/StorageSuite.scala | 4 +- .../ui/jobs/JobProgressListenerSuite.scala | 6 +- .../spark/ui/storage/StorageTabSuite.scala | 30 +- .../apache/spark/util/JsonProtocolSuite.scala | 45 +- 38 files changed, 1584 insertions(+), 337 deletions(-) create mode 100644 core/src/main/resources/org/apache/spark/ui/static/d3.min.js create mode 100644 core/src/main/resources/org/apache/spark/ui/static/dagre-d3.min.js create mode 100644 core/src/main/resources/org/apache/spark/ui/static/graphlib-dot.min.js create mode 100644 core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js create mode 100644 core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala create mode 100644 core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala create mode 100644 core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraphListener.scala create mode 100644 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inputLabel=inputGraph.node(v),layoutLabel=layoutGraph.node(v);if(inputLabel){inputLabel.x=layoutLabel.x;inputLabel.y=layoutLabel.y;if(layoutGraph.children(v).length){inputLabel.width=layoutLabel.width;inputLabel.height=layoutLabel.height}}});_.each(inputGraph.edges(),function(e){var inputLabel=inputGraph.edge(e),layoutLabel=layoutGraph.edge(e);inputLabel.points=layoutLabel.points;if(_.has(layoutLabel,"x")){inputLabel.x=layoutLabel.x;inputLabel.y=layoutLabel.y}});inputGraph.graph().width=layoutGraph.graph().width;inputGraph.graph().height=layoutGraph.graph().height}var graphNumAttrs=["nodesep","edgesep","ranksep","marginx","marginy"],graphDefaults={ranksep:50,edgesep:20,nodesep:50,rankdir:"tb"},graphAttrs=["acyclicer","ranker","rankdir","align"],nodeNumAttrs=["width","height"],nodeDefaults={width:0,height:0},edgeNumAttrs=["minlen","weight","width","height","labeloffset"],edgeDefaults={minlen:1,weight:1,width:0,height:0,labeloffset:10,labelpos:"r"},edgeAttrs=["labelpos"];function buildLayoutGraph(inputGraph){var g=new Graph({multigraph:true,compound:true}),graph=canonicalize(inputGraph.graph());g.setGraph(_.merge({},graphDefaults,selectNumberAttrs(graph,graphNumAttrs),_.pick(graph,graphAttrs)));_.each(inputGraph.nodes(),function(v){var node=canonicalize(inputGraph.node(v));g.setNode(v,_.defaults(selectNumberAttrs(node,nodeNumAttrs),nodeDefaults));g.setParent(v,inputGraph.parent(v))});_.each(inputGraph.edges(),function(e){var edge=canonicalize(inputGraph.edge(e));g.setEdge(e,_.merge({},edgeDefaults,selectNumberAttrs(edge,edgeNumAttrs),_.pick(edge,edgeAttrs)))});return g}function makeSpaceForEdgeLabels(g){var graph=g.graph(); + +graph.ranksep/=2;_.each(g.edges(),function(e){var edge=g.edge(e);edge.minlen*=2;if(edge.labelpos.toLowerCase()!=="c"){if(graph.rankdir==="TB"||graph.rankdir==="BT"){edge.width+=edge.labeloffset}else{edge.height+=edge.labeloffset}}})}function injectEdgeLabelProxies(g){_.each(g.edges(),function(e){var edge=g.edge(e);if(edge.width&&edge.height){var v=g.node(e.v),w=g.node(e.w),label={rank:(w.rank-v.rank)/2+v.rank,e:e};util.addDummyNode(g,"edge-proxy",label,"_ep")}})}function assignRankMinMax(g){var maxRank=0;_.each(g.nodes(),function(v){var node=g.node(v);if(node.borderTop){node.minRank=g.node(node.borderTop).rank;node.maxRank=g.node(node.borderBottom).rank;maxRank=_.max(maxRank,node.maxRank)}});g.graph().maxRank=maxRank}function removeEdgeLabelProxies(g){_.each(g.nodes(),function(v){var node=g.node(v);if(node.dummy==="edge-proxy"){g.edge(node.e).labelRank=node.rank;g.removeNode(v)}})}function translateGraph(g){var minX=Number.POSITIVE_INFINITY,maxX=0,minY=Number.POSITIVE_INFINITY,maxY=0,graphLabel=g.graph(),marginX=graphLabel.marginx||0,marginY=graphLabel.marginy||0;function getExtremes(attrs){var x=attrs.x,y=attrs.y,w=attrs.width,h=attrs.height;minX=Math.min(minX,x-w/2);maxX=Math.max(maxX,x+w/2);minY=Math.min(minY,y-h/2);maxY=Math.max(maxY,y+h/2)}_.each(g.nodes(),function(v){getExtremes(g.node(v))});_.each(g.edges(),function(e){var edge=g.edge(e);if(_.has(edge,"x")){getExtremes(edge)}});minX-=marginX;minY-=marginY;_.each(g.nodes(),function(v){var node=g.node(v);node.x-=minX;node.y-=minY});_.each(g.edges(),function(e){var edge=g.edge(e);_.each(edge.points,function(p){p.x-=minX;p.y-=minY});if(_.has(edge,"x")){edge.x-=minX}if(_.has(edge,"y")){edge.y-=minY}});graphLabel.width=maxX-minX+marginX;graphLabel.height=maxY-minY+marginY}function assignNodeIntersects(g){_.each(g.edges(),function(e){var edge=g.edge(e),nodeV=g.node(e.v),nodeW=g.node(e.w),p1,p2;if(!edge.points){edge.points=[];p1=nodeW;p2=nodeV}else{p1=edge.points[0];p2=edge.points[edge.points.length-1]}edge.points.unshift(util.intersectRect(nodeV,p1));edge.points.push(util.intersectRect(nodeW,p2))})}function fixupEdgeLabelCoords(g){_.each(g.edges(),function(e){var edge=g.edge(e);if(_.has(edge,"x")){if(edge.labelpos==="l"||edge.labelpos==="r"){edge.width-=edge.labeloffset}switch(edge.labelpos){case"l":edge.x-=edge.width/2+edge.labeloffset;break;case"r":edge.x+=edge.width/2+edge.labeloffset;break}}})}function reversePointsForReversedEdges(g){_.each(g.edges(),function(e){var edge=g.edge(e);if(edge.reversed){edge.points.reverse()}})}function removeBorderNodes(g){_.each(g.nodes(),function(v){if(g.children(v).length){var node=g.node(v),t=g.node(node.borderTop),b=g.node(node.borderBottom),l=g.node(_.last(node.borderLeft)),r=g.node(_.last(node.borderRight));node.width=Math.abs(r.x-l.x);node.height=Math.abs(b.y-t.y);node.x=l.x+node.width/2;node.y=t.y+node.height/2}});_.each(g.nodes(),function(v){if(g.node(v).dummy==="border"){g.removeNode(v)}})}function removeSelfEdges(g){_.each(g.edges(),function(e){if(e.v===e.w){var node=g.node(e.v);if(!node.selfEdges){node.selfEdges=[]}node.selfEdges.push({e:e,label:g.edge(e)});g.removeEdge(e)}})}function insertSelfEdges(g){var layers=util.buildLayerMatrix(g);_.each(layers,function(layer){var orderShift=0;_.each(layer,function(v,i){var node=g.node(v);node.order=i+orderShift;_.each(node.selfEdges,function(selfEdge){util.addDummyNode(g,"selfedge",{width:selfEdge.label.width,height:selfEdge.label.height,rank:node.rank,order:i+ ++orderShift,e:selfEdge.e,label:selfEdge.label},"_se")});delete node.selfEdges})})}function positionSelfEdges(g){_.each(g.nodes(),function(v){var node=g.node(v);if(node.dummy==="selfedge"){var selfNode=g.node(node.e.v),x=selfNode.x+selfNode.width/2,y=selfNode.y,dx=node.x-x,dy=selfNode.height/2;g.setEdge(node.e,node.label);g.removeNode(v);node.label.points=[{x:x+2*dx/3,y:y-dy},{x:x+5*dx/6,y:y-dy},{x:x+dx,y:y},{x:x+5*dx/6,y:y+dy},{x:x+2*dx/3,y:y+dy}];node.label.x=node.x;node.label.y=node.y}})}function selectNumberAttrs(obj,attrs){return _.mapValues(_.pick(obj,attrs),Number)}function canonicalize(attrs){var newAttrs={};_.each(attrs,function(v,k){newAttrs[k.toLowerCase()]=v});return newAttrs}},{"./acyclic":28,"./add-border-segments":29,"./coordinate-system":30,"./graphlib":33,"./lodash":36,"./nesting-graph":37,"./normalize":38,"./order":43,"./parent-dummy-chains":48,"./position":50,"./rank":52,"./util":55}],36:[function(require,module,exports){arguments[4][20][0].apply(exports,arguments)},{dup:20,lodash:77}],37:[function(require,module,exports){var _=require("./lodash"),util=require("./util");module.exports={run:run,cleanup:cleanup};function run(g){var root=util.addDummyNode(g,"root",{},"_root"),depths=treeDepths(g),height=_.max(depths)-1,nodeSep=2*height+1;g.graph().nestingRoot=root;_.each(g.edges(),function(e){g.edge(e).minlen*=nodeSep});var weight=sumWeights(g)+1;_.each(g.children(),function(child){dfs(g,root,nodeSep,weight,height,depths,child)});g.graph().nodeRankFactor=nodeSep}function dfs(g,root,nodeSep,weight,height,depths,v){var children=g.children(v);if(!children.length){if(v!==root){g.setEdge(root,v,{weight:0,minlen:nodeSep})}return}var top=util.addBorderNode(g,"_bt"),bottom=util.addBorderNode(g,"_bb"),label=g.node(v);g.setParent(top,v);label.borderTop=top;g.setParent(bottom,v);label.borderBottom=bottom;_.each(children,function(child){dfs(g,root,nodeSep,weight,height,depths,child);var childNode=g.node(child),childTop=childNode.borderTop?childNode.borderTop:child,childBottom=childNode.borderBottom?childNode.borderBottom:child,thisWeight=childNode.borderTop?weight:2*weight,minlen=childTop!==childBottom?1:height-depths[v]+1;g.setEdge(top,childTop,{weight:thisWeight,minlen:minlen,nestingEdge:true});g.setEdge(childBottom,bottom,{weight:thisWeight,minlen:minlen,nestingEdge:true})});if(!g.parent(v)){g.setEdge(root,top,{weight:0,minlen:height+depths[v]})}}function treeDepths(g){var depths={};function dfs(v,depth){var children=g.children(v);if(children&&children.length){_.each(children,function(child){dfs(child,depth+1)})}depths[v]=depth}_.each(g.children(),function(v){dfs(v,1)});return depths}function sumWeights(g){return _.reduce(g.edges(),function(acc,e){return acc+g.edge(e).weight},0)}function cleanup(g){var graphLabel=g.graph();g.removeNode(graphLabel.nestingRoot);delete graphLabel.nestingRoot;_.each(g.edges(),function(e){var edge=g.edge(e);if(edge.nestingEdge){g.removeEdge(e)}})}},{"./lodash":36,"./util":55}],38:[function(require,module,exports){"use strict";var _=require("./lodash"),util=require("./util");module.exports={run:run,undo:undo};function run(g){g.graph().dummyChains=[];_.each(g.edges(),function(edge){normalizeEdge(g,edge)})}function normalizeEdge(g,e){var v=e.v,vRank=g.node(v).rank,w=e.w,wRank=g.node(w).rank,name=e.name,edgeLabel=g.edge(e),labelRank=edgeLabel.labelRank;if(wRank===vRank+1)return;g.removeEdge(e);var dummy,attrs,i;for(i=0,++vRank;vRank0){if(index%2){weightSum+=tree[index+1]}index=index-1>>1;tree[index]+=entry.weight}cc+=entry.weight*weightSum}));return cc}},{"../lodash":36}],43:[function(require,module,exports){"use strict";var _=require("../lodash"),initOrder=require("./init-order"),crossCount=require("./cross-count"),sortSubgraph=require("./sort-subgraph"),buildLayerGraph=require("./build-layer-graph"),addSubgraphConstraints=require("./add-subgraph-constraints"),Graph=require("../graphlib").Graph,util=require("../util");module.exports=order;function order(g){var maxRank=util.maxRank(g),downLayerGraphs=buildLayerGraphs(g,_.range(1,maxRank+1),"inEdges"),upLayerGraphs=buildLayerGraphs(g,_.range(maxRank-1,-1,-1),"outEdges");var layering=initOrder(g);assignOrder(g,layering);var bestCC=Number.POSITIVE_INFINITY,best;for(var i=0,lastBest=0;lastBest<4;++i,++lastBest){sweepLayerGraphs(i%2?downLayerGraphs:upLayerGraphs,i%4>=2);layering=util.buildLayerMatrix(g);var cc=crossCount(g,layering);if(cc=vEntry.barycenter){mergeEntries(vEntry,uEntry)}}}function handleOut(vEntry){return function(wEntry){wEntry["in"].push(vEntry);if(--wEntry.indegree===0){sourceSet.push(wEntry)}}}while(sourceSet.length){var entry=sourceSet.pop();entries.push(entry);_.each(entry["in"].reverse(),handleIn(entry));_.each(entry.out,handleOut(entry))}return _.chain(entries).filter(function(entry){return!entry.merged}).map(function(entry){return _.pick(entry,["vs","i","barycenter","weight"])}).value()}function mergeEntries(target,source){var sum=0,weight=0;if(target.weight){sum+=target.barycenter*target.weight;weight+=target.weight}if(source.weight){sum+=source.barycenter*source.weight;weight+=source.weight}target.vs=source.vs.concat(target.vs);target.barycenter=sum/weight;target.weight=weight;target.i=Math.min(source.i,target.i);source.merged=true}},{"../lodash":36}],46:[function(require,module,exports){var _=require("../lodash"),barycenter=require("./barycenter"),resolveConflicts=require("./resolve-conflicts"),sort=require("./sort");module.exports=sortSubgraph;function sortSubgraph(g,v,cg,biasRight){var movable=g.children(v),node=g.node(v),bl=node?node.borderLeft:undefined,br=node?node.borderRight:undefined,subgraphs={};if(bl){movable=_.filter(movable,function(w){return w!==bl&&w!==br})}var barycenters=barycenter(g,movable);_.each(barycenters,function(entry){if(g.children(entry.v).length){var subgraphResult=sortSubgraph(g,entry.v,cg,biasRight);subgraphs[entry.v]=subgraphResult;if(_.has(subgraphResult,"barycenter")){mergeBarycenters(entry,subgraphResult)}}});var entries=resolveConflicts(barycenters,cg);expandSubgraphs(entries,subgraphs);var result=sort(entries,biasRight);if(bl){result.vs=_.flatten([bl,result.vs,br],true);if(g.predecessors(bl).length){var blPred=g.node(g.predecessors(bl)[0]),brPred=g.node(g.predecessors(br)[0]);if(!_.has(result,"barycenter")){result.barycenter=0;result.weight=0}result.barycenter=(result.barycenter*result.weight+blPred.order+brPred.order)/(result.weight+2);result.weight+=2}}return result}function expandSubgraphs(entries,subgraphs){_.each(entries,function(entry){entry.vs=_.flatten(entry.vs.map(function(v){if(subgraphs[v]){return subgraphs[v].vs}return v}),true)})}function mergeBarycenters(target,other){if(!_.isUndefined(target.barycenter)){target.barycenter=(target.barycenter*target.weight+other.barycenter*other.weight)/(target.weight+other.weight);target.weight+=other.weight}else{target.barycenter=other.barycenter;target.weight=other.weight}}},{"../lodash":36,"./barycenter":40,"./resolve-conflicts":45,"./sort":47}],47:[function(require,module,exports){var _=require("../lodash"),util=require("../util");module.exports=sort;function sort(entries,biasRight){var parts=util.partition(entries,function(entry){return _.has(entry,"barycenter")});var sortable=parts.lhs,unsortable=_.sortBy(parts.rhs,function(entry){return-entry.i}),vs=[],sum=0,weight=0,vsIndex=0;sortable.sort(compareWithBias(!!biasRight));vsIndex=consumeUnsortable(vs,unsortable,vsIndex);_.each(sortable,function(entry){vsIndex+=entry.vs.length;vs.push(entry.vs);sum+=entry.barycenter*entry.weight;weight+=entry.weight;vsIndex=consumeUnsortable(vs,unsortable,vsIndex)});var result={vs:_.flatten(vs,true)};if(weight){result.barycenter=sum/weight;result.weight=weight}return result}function consumeUnsortable(vs,unsortable,index){var last;while(unsortable.length&&(last=_.last(unsortable)).i<=index){unsortable.pop();vs.push(last.vs);index++}return index}function compareWithBias(bias){return function(entryV,entryW){if(entryV.barycenterentryW.barycenter){return 1}return!bias?entryV.i-entryW.i:entryW.i-entryV.i}}},{"../lodash":36,"../util":55}],48:[function(require,module,exports){var _=require("./lodash");module.exports=parentDummyChains;function parentDummyChains(g){var postorderNums=postorder(g);_.each(g.graph().dummyChains,function(v){var node=g.node(v),edgeObj=node.edgeObj,pathData=findPath(g,postorderNums,edgeObj.v,edgeObj.w),path=pathData.path,lca=pathData.lca,pathIdx=0,pathV=path[pathIdx],ascending=true;while(v!==edgeObj.w){node=g.node(v);if(ascending){while((pathV=path[pathIdx])!==lca&&g.node(pathV).maxRanklow||lim>postorderNums[parent].lim));lca=parent;parent=w;while((parent=g.parent(parent))!==lca){wPath.push(parent)}return{path:vPath.concat(wPath.reverse()),lca:lca}}function postorder(g){var result={},lim=0;function dfs(v){var low=lim;_.each(g.children(v),dfs);result[v]={low:low,lim:lim++}}_.each(g.children(),dfs);return result}},{"./lodash":36}],49:[function(require,module,exports){"use strict";var _=require("../lodash"),Graph=require("../graphlib").Graph,util=require("../util");module.exports={positionX:positionX,findType1Conflicts:findType1Conflicts,findType2Conflicts:findType2Conflicts,addConflict:addConflict,hasConflict:hasConflict,verticalAlignment:verticalAlignment,horizontalCompaction:horizontalCompaction,alignCoordinates:alignCoordinates,findSmallestWidthAlignment:findSmallestWidthAlignment,balance:balance};function findType1Conflicts(g,layering){var conflicts={};function visitLayer(prevLayer,layer){var k0=0,scanPos=0,prevLayerLength=prevLayer.length,lastNode=_.last(layer);_.each(layer,function(v,i){var w=findOtherInnerSegmentNode(g,v),k1=w?g.node(w).order:prevLayerLength;if(w||v===lastNode){_.each(layer.slice(scanPos,i+1),function(scanNode){_.each(g.predecessors(scanNode),function(u){var uLabel=g.node(u),uPos=uLabel.order;if((uPosnextNorthBorder)){addConflict(conflicts,u,v)}})}})}function visitLayer(north,south){var prevNorthPos=-1,nextNorthPos,southPos=0;_.each(south,function(v,southLookahead){if(g.node(v).dummy==="border"){var predecessors=g.predecessors(v);if(predecessors.length){nextNorthPos=g.node(predecessors[0]).order;scan(south,southPos,southLookahead,prevNorthPos,nextNorthPos);southPos=southLookahead;prevNorthPos=nextNorthPos}}scan(south,southPos,south.length,nextNorthPos,north.length)});return south}_.reduce(layering,visitLayer);return conflicts}function findOtherInnerSegmentNode(g,v){if(g.node(v).dummy){return _.find(g.predecessors(v),function(u){return g.node(u).dummy})}}function addConflict(conflicts,v,w){if(v>w){var tmp=v;v=w;w=tmp}var conflictsV=conflicts[v];if(!conflictsV){conflicts[v]=conflictsV={}}conflictsV[w]=true}function hasConflict(conflicts,v,w){if(v>w){var tmp=v;v=w;w=tmp}return _.has(conflicts[v],w)}function verticalAlignment(g,layering,conflicts,neighborFn){var root={},align={},pos={};_.each(layering,function(layer){_.each(layer,function(v,order){root[v]=v;align[v]=v;pos[v]=order})});_.each(layering,function(layer){var prevIdx=-1;_.each(layer,function(v){var ws=neighborFn(v);if(ws.length){ws=_.sortBy(ws,function(w){return pos[w]});var mp=(ws.length-1)/2;for(var i=Math.floor(mp),il=Math.ceil(mp);i<=il;++i){var w=ws[i];if(align[v]===v&&prevIdxwLabel.lim){tailLabel=wLabel;flip=true}var candidates=_.filter(g.edges(),function(edge){return flip===isDescendant(t,t.node(edge.v),tailLabel)&&flip!==isDescendant(t,t.node(edge.w),tailLabel)});return _.min(candidates,function(edge){return slack(g,edge)})}function exchangeEdges(t,g,e,f){var v=e.v,w=e.w;t.removeEdge(v,w);t.setEdge(f.v,f.w,{});initLowLimValues(t);initCutValues(t,g);updateRanks(t,g)}function updateRanks(t,g){var root=_.find(t.nodes(),function(v){return!g.node(v).parent}),vs=preorder(t,root);vs=vs.slice(1);_.each(vs,function(v){var parent=t.node(v).parent,edge=g.edge(v,parent),flipped=false;if(!edge){edge=g.edge(parent,v);flipped=true}g.node(v).rank=g.node(parent).rank+(flipped?edge.minlen:-edge.minlen)})}function isTreeEdge(tree,u,v){return tree.hasEdge(u,v)}function isDescendant(tree,vLabel,rootLabel){return rootLabel.low<=vLabel.lim&&vLabel.lim<=rootLabel.lim}},{"../graphlib":33,"../lodash":36,"../util":55,"./feasible-tree":51,"./util":54}],54:[function(require,module,exports){"use strict";var _=require("../lodash");module.exports={longestPath:longestPath,slack:slack};function longestPath(g){var visited={};function dfs(v){var label=g.node(v);if(_.has(visited,v)){return label.rank}visited[v]=true;var rank=_.min(_.map(g.outEdges(v),function(e){return dfs(e.w)-g.edge(e).minlen}));if(rank===Number.POSITIVE_INFINITY){rank=0}return label.rank=rank}_.each(g.sources(),dfs)}function slack(g,e){return g.node(e.w).rank-g.node(e.v).rank-g.edge(e).minlen}},{"../lodash":36}],55:[function(require,module,exports){"use strict";var _=require("./lodash"),Graph=require("./graphlib").Graph;module.exports={addDummyNode:addDummyNode,simplify:simplify,asNonCompoundGraph:asNonCompoundGraph,successorWeights:successorWeights,predecessorWeights:predecessorWeights,intersectRect:intersectRect,buildLayerMatrix:buildLayerMatrix,normalizeRanks:normalizeRanks,removeEmptyRanks:removeEmptyRanks,addBorderNode:addBorderNode,maxRank:maxRank,partition:partition,time:time,notime:notime};function addDummyNode(g,type,attrs,name){var v;do{v=_.uniqueId(name)}while(g.hasNode(v));attrs.dummy=type;g.setNode(v,attrs);return v}function simplify(g){var simplified=(new Graph).setGraph(g.graph());_.each(g.nodes(),function(v){simplified.setNode(v,g.node(v))});_.each(g.edges(),function(e){var simpleLabel=simplified.edge(e.v,e.w)||{weight:0,minlen:1},label=g.edge(e);simplified.setEdge(e.v,e.w,{weight:simpleLabel.weight+label.weight,minlen:Math.max(simpleLabel.minlen,label.minlen)})});return simplified}function asNonCompoundGraph(g){ +var simplified=new Graph({multigraph:g.isMultigraph()}).setGraph(g.graph());_.each(g.nodes(),function(v){if(!g.children(v).length){simplified.setNode(v,g.node(v))}});_.each(g.edges(),function(e){simplified.setEdge(e,g.edge(e))});return simplified}function successorWeights(g){var weightMap=_.map(g.nodes(),function(v){var sucs={};_.each(g.outEdges(v),function(e){sucs[e.w]=(sucs[e.w]||0)+g.edge(e).weight});return sucs});return _.zipObject(g.nodes(),weightMap)}function predecessorWeights(g){var weightMap=_.map(g.nodes(),function(v){var preds={};_.each(g.inEdges(v),function(e){preds[e.v]=(preds[e.v]||0)+g.edge(e).weight});return preds});return _.zipObject(g.nodes(),weightMap)}function intersectRect(rect,point){var x=rect.x;var y=rect.y;var dx=point.x-x;var dy=point.y-y;var w=rect.width/2;var h=rect.height/2;if(!dx&&!dy){throw new Error("Not possible to find intersection inside of the rectangle")}var sx,sy;if(Math.abs(dy)*w>Math.abs(dx)*h){if(dy<0){h=-h}sx=h*dx/dy;sy=h}else{if(dx<0){w=-w}sx=w;sy=w*dy/dx}return{x:x+sx,y:y+sy}}function buildLayerMatrix(g){var layering=_.map(_.range(maxRank(g)+1),function(){return[]});_.each(g.nodes(),function(v){var node=g.node(v),rank=node.rank;if(!_.isUndefined(rank)){layering[rank][node.order]=v}});return layering}function normalizeRanks(g){var min=_.min(_.map(g.nodes(),function(v){return g.node(v).rank}));_.each(g.nodes(),function(v){var node=g.node(v);if(_.has(node,"rank")){node.rank-=min}})}function removeEmptyRanks(g){var offset=_.min(_.map(g.nodes(),function(v){return g.node(v).rank}));var layers=[];_.each(g.nodes(),function(v){var rank=g.node(v).rank-offset;if(!_.has(layers,rank)){layers[rank]=[]}layers[rank].push(v)});var delta=0,nodeRankFactor=g.graph().nodeRankFactor;_.each(layers,function(vs,i){if(_.isUndefined(vs)&&i%nodeRankFactor!==0){--delta}else if(delta){_.each(vs,function(v){g.node(v).rank+=delta})}})}function addBorderNode(g,prefix,rank,order){var node={width:0,height:0};if(arguments.length>=4){node.rank=rank;node.order=order}return addDummyNode(g,"border",node,prefix)}function maxRank(g){return _.max(_.map(g.nodes(),function(v){var rank=g.node(v).rank;if(!_.isUndefined(rank)){return rank}}))}function partition(collection,fn){var result={lhs:[],rhs:[]};_.each(collection,function(value){if(fn(value)){result.lhs.push(value)}else{result.rhs.push(value)}});return result}function time(name,fn){var start=_.now();try{return fn()}finally{console.log(name+" time: "+(_.now()-start)+"ms")}}function notime(name,fn){return fn()}},{"./graphlib":33,"./lodash":36}],56:[function(require,module,exports){module.exports="0.7.1"},{}],57:[function(require,module,exports){var lib=require("./lib");module.exports={Graph:lib.Graph,json:require("./lib/json"),alg:require("./lib/alg"),version:lib.version}},{"./lib":73,"./lib/alg":64,"./lib/json":74}],58:[function(require,module,exports){var _=require("../lodash");module.exports=components;function components(g){var visited={},cmpts=[],cmpt;function dfs(v){if(_.has(visited,v))return;visited[v]=true;cmpt.push(v);_.each(g.successors(v),dfs);_.each(g.predecessors(v),dfs)}_.each(g.nodes(),function(v){cmpt=[];dfs(v);if(cmpt.length){cmpts.push(cmpt)}});return cmpts}},{"../lodash":75}],59:[function(require,module,exports){var _=require("../lodash");module.exports=dfs;function dfs(g,vs,order){if(!_.isArray(vs)){vs=[vs]}var acc=[],visited={};_.each(vs,function(v){if(!g.hasNode(v)){throw new Error("Graph does not have node: "+v)}doDfs(g,v,order==="post",visited,acc)});return acc}function doDfs(g,v,postorder,visited,acc){if(!_.has(visited,v)){visited[v]=true;if(!postorder){acc.push(v)}_.each(g.neighbors(v),function(w){doDfs(g,w,postorder,visited,acc)});if(postorder){acc.push(v)}}}},{"../lodash":75}],60:[function(require,module,exports){var dijkstra=require("./dijkstra"),_=require("../lodash");module.exports=dijkstraAll;function 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this._nodes[v];if(this._isCompound){this._removeFromParentsChildList(v);delete this._parent[v];_.each(this.children(v),function(child){this.setParent(child)},this);delete this._children[v]}_.each(_.keys(this._in[v]),removeEdge);delete this._in[v];delete this._preds[v];_.each(_.keys(this._out[v]),removeEdge);delete this._out[v];delete this._sucs[v];--this._nodeCount}return this};Graph.prototype.setParent=function(v,parent){if(!this._isCompound){throw new Error("Cannot set parent in a non-compound graph")}if(_.isUndefined(parent)){parent=GRAPH_NODE}else{for(var ancestor=parent;!_.isUndefined(ancestor);ancestor=this.parent(ancestor)){if(ancestor===v){throw new Error("Setting "+parent+" as parent of "+v+" would create create a cycle")}}this.setNode(parent)}this.setNode(v);this._removeFromParentsChildList(v);this._parent[v]=parent;this._children[parent][v]=true;return this};Graph.prototype._removeFromParentsChildList=function(v){delete 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_.values(this._edgeObjs)};Graph.prototype.setPath=function(vs,value){var self=this,args=arguments;_.reduce(vs,function(v,w){if(args.length>1){self.setEdge(v,w,value)}else{self.setEdge(v,w)}return w});return this};Graph.prototype.setEdge=function(){var v,w,name,value,valueSpecified=false;if(_.isPlainObject(arguments[0])){v=arguments[0].v;w=arguments[0].w;name=arguments[0].name;if(arguments.length===2){value=arguments[1];valueSpecified=true}}else{v=arguments[0];w=arguments[1];name=arguments[3];if(arguments.length>2){value=arguments[2];valueSpecified=true}}v=""+v;w=""+w;if(!_.isUndefined(name)){name=""+name}var e=edgeArgsToId(this._isDirected,v,w,name);if(_.has(this._edgeLabels,e)){if(valueSpecified){this._edgeLabels[e]=value}return this}if(!_.isUndefined(name)&&!this._isMultigraph){throw new Error("Cannot set a named edge when isMultigraph = false")}this.setNode(v);this.setNode(w);this._edgeLabels[e]=valueSpecified?value:this._defaultEdgeLabelFn(v,w,name);var 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'' : __t) +\n'"}index=offset+match.length;return match});source+="';\n";var variable=options.variable,hasVariable=variable;if(!hasVariable){variable="obj";source="with ("+variable+") {\n"+source+"\n}\n"}source=(isEvaluating?source.replace(reEmptyStringLeading,""):source).replace(reEmptyStringMiddle,"$1").replace(reEmptyStringTrailing,"$1;");source="function("+variable+") {\n"+(hasVariable?"":variable+" || ("+variable+" = {});\n")+"var __t, __p = '', __e = _.escape"+(isEvaluating?", __j = Array.prototype.join;\n"+"function print() { __p += __j.call(arguments, '') }\n":";\n")+source+"return __p\n}";var sourceURL="\n/*\n//# sourceURL="+(options.sourceURL||"/lodash/template/source["+templateCounter++ +"]")+"\n*/";try{var result=Function(importsKeys,"return "+source+sourceURL).apply(undefined,importsValues)}catch(e){e.source=source;throw e}if(data){return result(data)}result.source=source;return result}function times(n,callback,thisArg){n=(n=+n)>-1?n:0;var index=-1,result=Array(n);callback=baseCreateCallback(callback,thisArg,1);while(++index + * div.stage-metadata > + * div.[dot-file | incoming-edge | outgoing-edge] + * + * Output DOM hierarchy: + * div#dag-viz-graph > + * svg > + * g#cluster_stage_[stageId] + * + * Note that the input metadata is populated by o.a.s.ui.UIUtils.showDagViz. + * Any changes in the input format here must be reflected there. + */ +function renderDagViz(forJob) { + + // If there is not a dot file to render, fail fast and report error + if (metadataContainer().empty()) { + graphContainer().append("div").text( + "No visualization information available for this " + (forJob ? "job" : "stage")); + return; + } + + var svg = graphContainer().append("svg"); + if (forJob) { + renderDagVizForJob(svg); + } else { + renderDagVizForStage(svg); + } + + // Find cached RDDs + metadataContainer().selectAll(".cached-rdd").each(function(v) { + var nodeId = VizConstants.nodePrefix + d3.select(this).text(); + graphContainer().selectAll("#" + nodeId).classed("cached", true); + }); + + // Set the appropriate SVG dimensions to ensure that all elements are displayed + var boundingBox = svg.node().getBBox(); + svg.style("width", (boundingBox.width + VizConstants.svgMarginX) + "px"); + svg.style("height", (boundingBox.height + VizConstants.svgMarginY) + "px"); + + // Add labels to clusters because dagre-d3 doesn't do this for us + svg.selectAll("g.cluster rect").each(function() { + var rect = d3.select(this); + var cluster = d3.select(this.parentNode); + // Shift the boxes up a little to make room for the labels + rect.attr("y", toFloat(rect.attr("y")) - 10); + rect.attr("height", toFloat(rect.attr("height")) + 10); + var labelX = toFloat(rect.attr("x")) + toFloat(rect.attr("width")) - 5; + var labelY = toFloat(rect.attr("y")) + 15; + var labelText = cluster.attr("name").replace(VizConstants.clusterPrefix, ""); + cluster.append("text") + .attr("x", labelX) + .attr("y", labelY) + .attr("text-anchor", "end") + .text(labelText); + }); + + // We have shifted a few elements upwards, so we should fix the SVG views + var startX = -VizConstants.svgMarginX; + var startY = -VizConstants.svgMarginY; + var endX = toFloat(svg.style("width")) + VizConstants.svgMarginX; + var endY = toFloat(svg.style("height")) + VizConstants.svgMarginY; + var newViewBox = startX + " " + startY + " " + endX + " " + endY; + svg.attr("viewBox", newViewBox); + + // Lastly, apply some custom style to the DAG + styleDagViz(forJob); +} + +/* Render the RDD DAG visualization for a stage. */ +function renderDagVizForStage(svgContainer) { + var metadata = metadataContainer().select(".stage-metadata"); + var dot = metadata.select(".dot-file").text(); + var containerId = VizConstants.graphPrefix + metadata.attr("stageId"); + var container = svgContainer.append("g").attr("id", containerId); + renderDot(dot, container); +} + +/* + * Render the RDD DAG visualization for a job. + * + * Due to limitations in dagre-d3, each stage is rendered independently so that + * we have more control on how to position them. Unfortunately, this means we + * cannot rely on dagre-d3 to render edges that cross stages and must render + * these manually on our own. + */ +function renderDagVizForJob(svgContainer) { + var crossStageEdges = []; + + metadataContainer().selectAll(".stage-metadata").each(function(d, i) { + var metadata = d3.select(this); + var dot = metadata.select(".dot-file").text(); + var stageId = metadata.attr("stageId"); + var containerId = VizConstants.graphPrefix + stageId; + // TODO: handle stage attempts + var stageLink = + "/stages/stage/?id=" + stageId.replace(VizConstants.stagePrefix, "") + "&attempt=0"; + var container = svgContainer + .append("a").attr("xlink:href", stageLink) + .append("g").attr("id", containerId); + // Now we need to shift the container for this stage so it doesn't overlap + // with existing ones. We do not need to do this for the first stage. + if (i > 0) { + // Take into account the position and width of the last stage's container + var existingStages = stageClusters(); + if (!existingStages.empty()) { + var lastStage = existingStages[0].pop(); + var lastStageId = d3.select(lastStage).attr("id"); + var lastStageWidth = toFloat(d3.select("#" + lastStageId + " rect").attr("width")); + var lastStagePosition = getAbsolutePosition(lastStageId); + var offset = lastStagePosition.x + lastStageWidth + VizConstants.stageSep; + container.attr("transform", "translate(" + offset + ", 0)"); + } + } + renderDot(dot, container); + // If there are any incoming edges into this graph, keep track of them to render + // them separately later. Note that we cannot draw them now because we need to + // put these edges in a separate container that is on top of all stage graphs. + metadata.selectAll(".incoming-edge").each(function(v) { + var edge = d3.select(this).text().split(","); // e.g. 3,4 => [3, 4] + crossStageEdges.push(edge); + }); + }); + + // Draw edges that cross stages + if (crossStageEdges.length > 0) { + var container = svgContainer.append("g").attr("id", "cross-stage-edges"); + for (var i = 0; i < crossStageEdges.length; i++) { + var fromRDDId = crossStageEdges[i][0]; + var toRDDId = crossStageEdges[i][1]; + connectRDDs(fromRDDId, toRDDId, container); + } + } +} + +/* Render the dot file as an SVG in the given container. */ +function renderDot(dot, container) { + var escaped_dot = dot + .replace(/</g, "<") + .replace(/>/g, ">") + .replace(/"/g, "\""); + var g = graphlibDot.read(escaped_dot); + var renderer = new dagreD3.render(); + renderer(container, g); +} + +/* Style the visualization we just rendered. */ +function styleDagViz(forJob) { + graphContainer().selectAll("svg g.cluster rect") + .style("fill", "white") + .style("stroke", VizConstants.rddOperationColor) + .style("stroke-width", "4px") + .style("stroke-opacity", "0.5"); + graphContainer().selectAll("svg g.cluster text") + .attr("fill", VizConstants.clusterLabelColor) + .attr("font-size", "11px"); + graphContainer().selectAll("svg path") + .style("stroke", VizConstants.edgeColor) + .style("stroke-width", VizConstants.edgeWidth); + stageClusters() + .select("rect") + .style("stroke", VizConstants.stageColor) + .style("strokeWidth", "6px"); + + // Put an arrow at the end of every edge + // We need to do this because we manually render some edges ourselves + // For these edges, we borrow the arrow marker generated by dagre-d3 + var dagreD3Marker = graphContainer().select("svg g.edgePaths marker").node(); + graphContainer().select("svg") + .append(function() { return dagreD3Marker.cloneNode(true); }) + .attr("id", "marker-arrow") + .select("path") + .attr("fill", VizConstants.edgeColor) + .attr("strokeWidth", "0px"); + graphContainer().selectAll("svg g > path").attr("marker-end", "url(#marker-arrow)"); + graphContainer().selectAll("svg g.edgePaths def").remove(); // We no longer need these + + // Apply any job or stage specific styles + if (forJob) { + styleDagVizForJob(); + } else { + styleDagVizForStage(); + } +} + +/* Apply job-page-specific style to the visualization. */ +function styleDagVizForJob() { + graphContainer().selectAll("svg g.node circle") + .style("fill", VizConstants.rddColor); + // TODO: add a legend to explain what a highlighted dot means + graphContainer().selectAll("svg g.cached circle") + .style("fill", VizConstants.rddCachedColor); + graphContainer().selectAll("svg g#cross-stage-edges path") + .style("fill", "none"); +} + +/* Apply stage-page-specific style to the visualization. */ +function styleDagVizForStage() { + graphContainer().selectAll("svg g.node rect") + .style("fill", "none") + .style("stroke", VizConstants.rddColor) + .style("stroke-width", "2px") + .attr("rx", "5") // round corners + .attr("ry", "5"); + // TODO: add a legend to explain what a highlighted RDD means + graphContainer().selectAll("svg g.cached rect") + .style("stroke", VizConstants.rddCachedColor); + graphContainer().selectAll("svg g.node g.label text tspan") + .style("fill", VizConstants.rddColor); +} + +/* + * (Job page only) Helper method to compute the absolute + * position of the group element identified by the given ID. + */ +function getAbsolutePosition(groupId) { + var obj = d3.select("#" + groupId).filter("g"); + var _x = 0, _y = 0; + while (!obj.empty()) { + var transformText = obj.attr("transform"); + var translate = d3.transform(transformText).translate + _x += translate[0]; + _y += translate[1]; + obj = d3.select(obj.node().parentNode).filter("g") + } + return { x: _x, y: _y }; +} + +/* (Job page only) Connect two RDD nodes with a curved edge. */ +function connectRDDs(fromRDDId, toRDDId, container) { + var fromNodeId = VizConstants.nodePrefix + fromRDDId; + var toNodeId = VizConstants.nodePrefix + toRDDId + var fromPos = getAbsolutePosition(fromNodeId); + var toPos = getAbsolutePosition(toNodeId); + + // On the job page, RDDs are rendered as dots (circles). When rendering the path, + // we need to account for the radii of these circles. Otherwise the arrow heads + // will bleed into the circle itself. + var delta = toFloat(graphContainer() + .select("g.node#" + toNodeId) + .select("circle") + .attr("r")); + if (fromPos.x < toPos.x) { + fromPos.x += delta; + toPos.x -= delta; + } else if (fromPos.x > toPos.x) { + fromPos.x -= delta; + toPos.x += delta; + } + + if (fromPos.y == toPos.y) { + // If they are on the same rank, curve the middle part of the edge + // upward a little to avoid interference with things in between + // e.g. _______ + // _____/ \_____ + var points = [ + [fromPos.x, fromPos.y], + [fromPos.x + (toPos.x - fromPos.x) * 0.2, fromPos.y], + [fromPos.x + (toPos.x - fromPos.x) * 0.3, fromPos.y - 20], + [fromPos.x + (toPos.x - fromPos.x) * 0.7, fromPos.y - 20], + [fromPos.x + (toPos.x - fromPos.x) * 0.8, toPos.y], + [toPos.x, toPos.y] + ]; + } else { + // Otherwise, draw a curved edge that flattens out on both ends + // e.g. _____ + // / + // | + // _____/ + var points = [ + [fromPos.x, fromPos.y], + [fromPos.x + (toPos.x - fromPos.x) * 0.4, fromPos.y], + [fromPos.x + (toPos.x - fromPos.x) * 0.6, toPos.y], + [toPos.x, toPos.y] + ]; + } + + var line = d3.svg.line().interpolate("basis"); + container.append("path").datum(points).attr("d", line); +} + +/* Helper d3 accessor to clusters that represent stages. */ +function stageClusters() { + return graphContainer().selectAll("g.cluster").filter(function() { + return d3.select(this).attr("id").indexOf(VizConstants.stageClusterPrefix) > -1; + }); +} + +/* Helper method to convert attributes to numeric values. */ +function toFloat(f) { + return parseFloat(f.replace(/px$/, "")); +} + diff --git a/core/src/main/resources/org/apache/spark/ui/static/webui.css b/core/src/main/resources/org/apache/spark/ui/static/webui.css index 4910744d1d790..669ad48937c05 100644 --- a/core/src/main/resources/org/apache/spark/ui/static/webui.css +++ b/core/src/main/resources/org/apache/spark/ui/static/webui.css @@ -145,7 +145,7 @@ pre { border: none; } -span.expand-additional-metrics { +span.expand-additional-metrics, span.expand-dag-viz { cursor: pointer; } diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 4ef90546a2452..b98a54b418cd0 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -659,6 +659,14 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli setLocalProperty(SparkContext.SPARK_JOB_INTERRUPT_ON_CANCEL, null) } + /** + * Execute a block of code in a scope such that all new RDDs created in this body will + * be part of the same scope. For more detail, see {{org.apache.spark.rdd.RDDOperationScope}}. + * + * Note: Return statements are NOT allowed in the given body. + */ + private def withScope[U](body: => U): U = RDDOperationScope.withScope[U](this)(body) + // Methods for creating RDDs /** Distribute a local Scala collection to form an RDD. @@ -669,7 +677,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * @note avoid using `parallelize(Seq())` to create an empty `RDD`. Consider `emptyRDD` for an * RDD with no partitions, or `parallelize(Seq[T]())` for an RDD of `T` with empty partitions. */ - def parallelize[T: ClassTag](seq: Seq[T], numSlices: Int = defaultParallelism): RDD[T] = { + def parallelize[T: ClassTag]( + seq: Seq[T], + numSlices: Int = defaultParallelism): RDD[T] = withScope { assertNotStopped() new ParallelCollectionRDD[T](this, seq, numSlices, Map[Int, Seq[String]]()) } @@ -678,14 +688,16 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * * This method is identical to `parallelize`. */ - def makeRDD[T: ClassTag](seq: Seq[T], numSlices: Int = defaultParallelism): RDD[T] = { + def makeRDD[T: ClassTag]( + seq: Seq[T], + numSlices: Int = defaultParallelism): RDD[T] = withScope { parallelize(seq, numSlices) } /** Distribute a local Scala collection to form an RDD, with one or more * location preferences (hostnames of Spark nodes) for each object. * Create a new partition for each collection item. */ - def makeRDD[T: ClassTag](seq: Seq[(T, Seq[String])]): RDD[T] = { + def makeRDD[T: ClassTag](seq: Seq[(T, Seq[String])]): RDD[T] = withScope { assertNotStopped() val indexToPrefs = seq.zipWithIndex.map(t => (t._2, t._1._2)).toMap new ParallelCollectionRDD[T](this, seq.map(_._1), seq.size, indexToPrefs) @@ -695,10 +707,12 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * Read a text file from HDFS, a local file system (available on all nodes), or any * Hadoop-supported file system URI, and return it as an RDD of Strings. */ - def textFile(path: String, minPartitions: Int = defaultMinPartitions): RDD[String] = { + def textFile( + path: String, + minPartitions: Int = defaultMinPartitions): RDD[String] = withScope { assertNotStopped() hadoopFile(path, classOf[TextInputFormat], classOf[LongWritable], classOf[Text], - minPartitions).map(pair => pair._2.toString).setName(path) + minPartitions).map(pair => pair._2.toString) } /** @@ -728,8 +742,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * * @param minPartitions A suggestion value of the minimal splitting number for input data. */ - def wholeTextFiles(path: String, minPartitions: Int = defaultMinPartitions): - RDD[(String, String)] = { + def wholeTextFiles( + path: String, + minPartitions: Int = defaultMinPartitions): RDD[(String, String)] = withScope { assertNotStopped() val job = new NewHadoopJob(hadoopConfiguration) // Use setInputPaths so that wholeTextFiles aligns with hadoopFile/textFile in taking @@ -776,8 +791,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * @note Small files are preferred; very large files may cause bad performance. */ @Experimental - def binaryFiles(path: String, minPartitions: Int = defaultMinPartitions): - RDD[(String, PortableDataStream)] = { + def binaryFiles( + path: String, + minPartitions: Int = defaultMinPartitions): RDD[(String, PortableDataStream)] = withScope { assertNotStopped() val job = new NewHadoopJob(hadoopConfiguration) // Use setInputPaths so that binaryFiles aligns with hadoopFile/textFile in taking @@ -806,8 +822,10 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * @return An RDD of data with values, represented as byte arrays */ @Experimental - def binaryRecords(path: String, recordLength: Int, conf: Configuration = hadoopConfiguration) - : RDD[Array[Byte]] = { + def binaryRecords( + path: String, + recordLength: Int, + conf: Configuration = hadoopConfiguration): RDD[Array[Byte]] = withScope { assertNotStopped() conf.setInt(FixedLengthBinaryInputFormat.RECORD_LENGTH_PROPERTY, recordLength) val br = newAPIHadoopFile[LongWritable, BytesWritable, FixedLengthBinaryInputFormat](path, @@ -848,8 +866,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli inputFormatClass: Class[_ <: InputFormat[K, V]], keyClass: Class[K], valueClass: Class[V], - minPartitions: Int = defaultMinPartitions - ): RDD[(K, V)] = { + minPartitions: Int = defaultMinPartitions): RDD[(K, V)] = withScope { assertNotStopped() // Add necessary security credentials to the JobConf before broadcasting it. SparkHadoopUtil.get.addCredentials(conf) @@ -869,8 +886,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli inputFormatClass: Class[_ <: InputFormat[K, V]], keyClass: Class[K], valueClass: Class[V], - minPartitions: Int = defaultMinPartitions - ): RDD[(K, V)] = { + minPartitions: Int = defaultMinPartitions): RDD[(K, V)] = withScope { assertNotStopped() // A Hadoop configuration can be about 10 KB, which is pretty big, so broadcast it. val confBroadcast = broadcast(new SerializableWritable(hadoopConfiguration)) @@ -901,7 +917,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli */ def hadoopFile[K, V, F <: InputFormat[K, V]] (path: String, minPartitions: Int) - (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = { + (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = withScope { hadoopFile(path, fm.runtimeClass.asInstanceOf[Class[F]], km.runtimeClass.asInstanceOf[Class[K]], @@ -924,13 +940,14 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * copy them using a `map` function. */ def hadoopFile[K, V, F <: InputFormat[K, V]](path: String) - (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = + (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = withScope { hadoopFile[K, V, F](path, defaultMinPartitions) + } /** Get an RDD for a Hadoop file with an arbitrary new API InputFormat. */ def newAPIHadoopFile[K, V, F <: NewInputFormat[K, V]] (path: String) - (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = { + (implicit km: ClassTag[K], vm: ClassTag[V], fm: ClassTag[F]): RDD[(K, V)] = withScope { newAPIHadoopFile( path, fm.runtimeClass.asInstanceOf[Class[F]], @@ -953,7 +970,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli fClass: Class[F], kClass: Class[K], vClass: Class[V], - conf: Configuration = hadoopConfiguration): RDD[(K, V)] = { + conf: Configuration = hadoopConfiguration): RDD[(K, V)] = withScope { assertNotStopped() // The call to new NewHadoopJob automatically adds security credentials to conf, // so we don't need to explicitly add them ourselves @@ -987,7 +1004,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli conf: Configuration = hadoopConfiguration, fClass: Class[F], kClass: Class[K], - vClass: Class[V]): RDD[(K, V)] = { + vClass: Class[V]): RDD[(K, V)] = withScope { assertNotStopped() // Add necessary security credentials to the JobConf. Required to access secure HDFS. val jconf = new JobConf(conf) @@ -1007,7 +1024,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli keyClass: Class[K], valueClass: Class[V], minPartitions: Int - ): RDD[(K, V)] = { + ): RDD[(K, V)] = withScope { assertNotStopped() val inputFormatClass = classOf[SequenceFileInputFormat[K, V]] hadoopFile(path, inputFormatClass, keyClass, valueClass, minPartitions) @@ -1021,7 +1038,10 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * If you plan to directly cache, sort, or aggregate Hadoop writable objects, you should first * copy them using a `map` function. * */ - def sequenceFile[K, V](path: String, keyClass: Class[K], valueClass: Class[V]): RDD[(K, V)] = { + def sequenceFile[K, V]( + path: String, + keyClass: Class[K], + valueClass: Class[V]): RDD[(K, V)] = withScope { assertNotStopped() sequenceFile(path, keyClass, valueClass, defaultMinPartitions) } @@ -1051,16 +1071,17 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli def sequenceFile[K, V] (path: String, minPartitions: Int = defaultMinPartitions) (implicit km: ClassTag[K], vm: ClassTag[V], - kcf: () => WritableConverter[K], vcf: () => WritableConverter[V]) - : RDD[(K, V)] = { - assertNotStopped() - val kc = kcf() - val vc = vcf() - val format = classOf[SequenceFileInputFormat[Writable, Writable]] - val writables = hadoopFile(path, format, + kcf: () => WritableConverter[K], vcf: () => WritableConverter[V]): RDD[(K, V)] = { + withScope { + assertNotStopped() + val kc = kcf() + val vc = vcf() + val format = classOf[SequenceFileInputFormat[Writable, Writable]] + val writables = hadoopFile(path, format, kc.writableClass(km).asInstanceOf[Class[Writable]], vc.writableClass(vm).asInstanceOf[Class[Writable]], minPartitions) - writables.map { case (k, v) => (kc.convert(k), vc.convert(v)) } + writables.map { case (k, v) => (kc.convert(k), vc.convert(v)) } + } } /** @@ -1073,21 +1094,18 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli */ def objectFile[T: ClassTag]( path: String, - minPartitions: Int = defaultMinPartitions - ): RDD[T] = { + minPartitions: Int = defaultMinPartitions): RDD[T] = withScope { assertNotStopped() sequenceFile(path, classOf[NullWritable], classOf[BytesWritable], minPartitions) .flatMap(x => Utils.deserialize[Array[T]](x._2.getBytes, Utils.getContextOrSparkClassLoader)) } - protected[spark] def checkpointFile[T: ClassTag]( - path: String - ): RDD[T] = { + protected[spark] def checkpointFile[T: ClassTag](path: String): RDD[T] = withScope { new CheckpointRDD[T](this, path) } /** Build the union of a list of RDDs. */ - def union[T: ClassTag](rdds: Seq[RDD[T]]): RDD[T] = { + def union[T: ClassTag](rdds: Seq[RDD[T]]): RDD[T] = withScope { val partitioners = rdds.flatMap(_.partitioner).toSet if (rdds.forall(_.partitioner.isDefined) && partitioners.size == 1) { new PartitionerAwareUnionRDD(this, rdds) @@ -1097,8 +1115,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli } /** Build the union of a list of RDDs passed as variable-length arguments. */ - def union[T: ClassTag](first: RDD[T], rest: RDD[T]*): RDD[T] = + def union[T: ClassTag](first: RDD[T], rest: RDD[T]*): RDD[T] = withScope { union(Seq(first) ++ rest) + } /** Get an RDD that has no partitions or elements. */ def emptyRDD[T: ClassTag]: EmptyRDD[T] = new EmptyRDD[T](this) @@ -2060,10 +2079,10 @@ object SparkContext extends Logging { } private[spark] val SPARK_JOB_DESCRIPTION = "spark.job.description" - private[spark] val SPARK_JOB_GROUP_ID = "spark.jobGroup.id" - private[spark] val SPARK_JOB_INTERRUPT_ON_CANCEL = "spark.job.interruptOnCancel" + private[spark] val RDD_SCOPE_KEY = "spark.rdd.scope" + private[spark] val RDD_SCOPE_NO_OVERRIDE_KEY = "spark.rdd.scope.noOverride" /** * Executor id for the driver. In earlier versions of Spark, this was ``, but this was diff --git a/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala b/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala index 3406a7e97e368..ec185340c3a2d 100644 --- a/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala @@ -33,7 +33,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi /** * Returns a future for counting the number of elements in the RDD. */ - def countAsync(): FutureAction[Long] = { + def countAsync(): FutureAction[Long] = self.withScope { val totalCount = new AtomicLong self.context.submitJob( self, @@ -53,7 +53,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi /** * Returns a future for retrieving all elements of this RDD. */ - def collectAsync(): FutureAction[Seq[T]] = { + def collectAsync(): FutureAction[Seq[T]] = self.withScope { val results = new Array[Array[T]](self.partitions.length) self.context.submitJob[T, Array[T], Seq[T]](self, _.toArray, Range(0, self.partitions.length), (index, data) => results(index) = data, results.flatten.toSeq) @@ -62,7 +62,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi /** * Returns a future for retrieving the first num elements of the RDD. */ - def takeAsync(num: Int): FutureAction[Seq[T]] = { + def takeAsync(num: Int): FutureAction[Seq[T]] = self.withScope { val f = new ComplexFutureAction[Seq[T]] f.run { @@ -109,7 +109,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi /** * Applies a function f to all elements of this RDD. */ - def foreachAsync(f: T => Unit): FutureAction[Unit] = { + def foreachAsync(f: T => Unit): FutureAction[Unit] = self.withScope { val cleanF = self.context.clean(f) self.context.submitJob[T, Unit, Unit](self, _.foreach(cleanF), Range(0, self.partitions.length), (index, data) => Unit, Unit) @@ -118,7 +118,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi /** * Applies a function f to each partition of this RDD. */ - def foreachPartitionAsync(f: Iterator[T] => Unit): FutureAction[Unit] = { + def foreachPartitionAsync(f: Iterator[T] => Unit): FutureAction[Unit] = self.withScope { self.context.submitJob[T, Unit, Unit](self, f, Range(0, self.partitions.length), (index, data) => Unit, Unit) } diff --git a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala index 843a893235e56..926bce6f15a2a 100644 --- a/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/DoubleRDDFunctions.scala @@ -30,7 +30,7 @@ import org.apache.spark.util.StatCounter */ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { /** Add up the elements in this RDD. */ - def sum(): Double = { + def sum(): Double = self.withScope { self.fold(0.0)(_ + _) } @@ -38,37 +38,49 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { * Return a [[org.apache.spark.util.StatCounter]] object that captures the mean, variance and * count of the RDD's elements in one operation. */ - def stats(): StatCounter = { + def stats(): StatCounter = self.withScope { self.mapPartitions(nums => Iterator(StatCounter(nums))).reduce((a, b) => a.merge(b)) } /** Compute the mean of this RDD's elements. */ - def mean(): Double = stats().mean + def mean(): Double = self.withScope { + stats().mean + } /** Compute the variance of this RDD's elements. */ - def variance(): Double = stats().variance + def variance(): Double = self.withScope { + stats().variance + } /** Compute the standard deviation of this RDD's elements. */ - def stdev(): Double = stats().stdev + def stdev(): Double = self.withScope { + stats().stdev + } /** * Compute the sample standard deviation of this RDD's elements (which corrects for bias in * estimating the standard deviation by dividing by N-1 instead of N). */ - def sampleStdev(): Double = stats().sampleStdev + def sampleStdev(): Double = self.withScope { + stats().sampleStdev + } /** * Compute the sample variance of this RDD's elements (which corrects for bias in * estimating the variance by dividing by N-1 instead of N). */ - def sampleVariance(): Double = stats().sampleVariance + def sampleVariance(): Double = self.withScope { + stats().sampleVariance + } /** * :: Experimental :: * Approximate operation to return the mean within a timeout. */ @Experimental - def meanApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = { + def meanApprox( + timeout: Long, + confidence: Double = 0.95): PartialResult[BoundedDouble] = self.withScope { val processPartition = (ctx: TaskContext, ns: Iterator[Double]) => StatCounter(ns) val evaluator = new MeanEvaluator(self.partitions.length, confidence) self.context.runApproximateJob(self, processPartition, evaluator, timeout) @@ -79,7 +91,9 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { * Approximate operation to return the sum within a timeout. */ @Experimental - def sumApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = { + def sumApprox( + timeout: Long, + confidence: Double = 0.95): PartialResult[BoundedDouble] = self.withScope { val processPartition = (ctx: TaskContext, ns: Iterator[Double]) => StatCounter(ns) val evaluator = new SumEvaluator(self.partitions.length, confidence) self.context.runApproximateJob(self, processPartition, evaluator, timeout) @@ -93,7 +107,7 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { * If the RDD contains infinity, NaN throws an exception * If the elements in RDD do not vary (max == min) always returns a single bucket. */ - def histogram(bucketCount: Int): Pair[Array[Double], Array[Long]] = { + def histogram(bucketCount: Int): Pair[Array[Double], Array[Long]] = self.withScope { // Scala's built-in range has issues. See #SI-8782 def customRange(min: Double, max: Double, steps: Int): IndexedSeq[Double] = { val span = max - min @@ -140,7 +154,9 @@ class DoubleRDDFunctions(self: RDD[Double]) extends Logging with Serializable { * the maximum value of the last position and all NaN entries will be counted * in that bucket. */ - def histogram(buckets: Array[Double], evenBuckets: Boolean = false): Array[Long] = { + def histogram( + buckets: Array[Double], + evenBuckets: Boolean = false): Array[Long] = self.withScope { if (buckets.length < 2) { throw new IllegalArgumentException("buckets array must have at least two elements") } diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala index f77abac42b623..2cefe63d44b20 100644 --- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala @@ -99,7 +99,7 @@ private[spark] class HadoopPartition(rddId: Int, idx: Int, @transient s: InputSp */ @DeveloperApi class HadoopRDD[K, V]( - sc: SparkContext, + @transient sc: SparkContext, broadcastedConf: Broadcast[SerializableWritable[Configuration]], initLocalJobConfFuncOpt: Option[JobConf => Unit], inputFormatClass: Class[_ <: InputFormat[K, V]], @@ -108,6 +108,10 @@ class HadoopRDD[K, V]( minPartitions: Int) extends RDD[(K, V)](sc, Nil) with Logging { + if (initLocalJobConfFuncOpt.isDefined) { + sc.clean(initLocalJobConfFuncOpt.get) + } + def this( sc: SparkContext, conf: JobConf, diff --git a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala index 6afe50161dacd..d71bb63000904 100644 --- a/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/OrderedRDDFunctions.scala @@ -57,7 +57,7 @@ class OrderedRDDFunctions[K : Ordering : ClassTag, */ // TODO: this currently doesn't work on P other than Tuple2! def sortByKey(ascending: Boolean = true, numPartitions: Int = self.partitions.length) - : RDD[(K, V)] = + : RDD[(K, V)] = self.withScope { val part = new RangePartitioner(numPartitions, self, ascending) new ShuffledRDD[K, V, V](self, part) @@ -71,7 +71,7 @@ class OrderedRDDFunctions[K : Ordering : ClassTag, * This is more efficient than calling `repartition` and then sorting within each partition * because it can push the sorting down into the shuffle machinery. */ - def repartitionAndSortWithinPartitions(partitioner: Partitioner): RDD[(K, V)] = { + def repartitionAndSortWithinPartitions(partitioner: Partitioner): RDD[(K, V)] = self.withScope { new ShuffledRDD[K, V, V](self, partitioner).setKeyOrdering(ordering) } @@ -81,7 +81,7 @@ class OrderedRDDFunctions[K : Ordering : ClassTag, * performed efficiently by only scanning the partitions that might contain matching elements. * Otherwise, a standard `filter` is applied to all partitions. */ - def filterByRange(lower: K, upper: K): RDD[P] = { + def filterByRange(lower: K, upper: K): RDD[P] = self.withScope { def inRange(k: K): Boolean = ordering.gteq(k, lower) && ordering.lteq(k, upper) diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala index 05351ba4ff76b..93d338fe0530c 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala @@ -29,7 +29,7 @@ import scala.util.DynamicVariable import com.clearspring.analytics.stream.cardinality.HyperLogLogPlus import org.apache.hadoop.conf.{Configurable, Configuration} -import org.apache.hadoop.fs.{FileSystem, Path} +import org.apache.hadoop.fs.FileSystem import org.apache.hadoop.io.SequenceFile.CompressionType import org.apache.hadoop.io.compress.CompressionCodec import org.apache.hadoop.mapred.{FileOutputCommitter, FileOutputFormat, JobConf, OutputFormat} @@ -75,7 +75,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) mergeCombiners: (C, C) => C, partitioner: Partitioner, mapSideCombine: Boolean = true, - serializer: Serializer = null): RDD[(K, C)] = { + serializer: Serializer = null): RDD[(K, C)] = self.withScope { require(mergeCombiners != null, "mergeCombiners must be defined") // required as of Spark 0.9.0 if (keyClass.isArray) { if (mapSideCombine) { @@ -108,7 +108,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) def combineByKey[C](createCombiner: V => C, mergeValue: (C, V) => C, mergeCombiners: (C, C) => C, - numPartitions: Int): RDD[(K, C)] = { + numPartitions: Int): RDD[(K, C)] = self.withScope { combineByKey(createCombiner, mergeValue, mergeCombiners, new HashPartitioner(numPartitions)) } @@ -122,7 +122,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * instead of creating a new U. */ def aggregateByKey[U: ClassTag](zeroValue: U, partitioner: Partitioner)(seqOp: (U, V) => U, - combOp: (U, U) => U): RDD[(K, U)] = { + combOp: (U, U) => U): RDD[(K, U)] = self.withScope { // Serialize the zero value to a byte array so that we can get a new clone of it on each key val zeroBuffer = SparkEnv.get.serializer.newInstance().serialize(zeroValue) val zeroArray = new Array[Byte](zeroBuffer.limit) @@ -144,7 +144,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * instead of creating a new U. */ def aggregateByKey[U: ClassTag](zeroValue: U, numPartitions: Int)(seqOp: (U, V) => U, - combOp: (U, U) => U): RDD[(K, U)] = { + combOp: (U, U) => U): RDD[(K, U)] = self.withScope { aggregateByKey(zeroValue, new HashPartitioner(numPartitions))(seqOp, combOp) } @@ -158,7 +158,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * instead of creating a new U. */ def aggregateByKey[U: ClassTag](zeroValue: U)(seqOp: (U, V) => U, - combOp: (U, U) => U): RDD[(K, U)] = { + combOp: (U, U) => U): RDD[(K, U)] = self.withScope { aggregateByKey(zeroValue, defaultPartitioner(self))(seqOp, combOp) } @@ -167,7 +167,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * may be added to the result an arbitrary number of times, and must not change the result * (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.). */ - def foldByKey(zeroValue: V, partitioner: Partitioner)(func: (V, V) => V): RDD[(K, V)] = { + def foldByKey( + zeroValue: V, + partitioner: Partitioner)(func: (V, V) => V): RDD[(K, V)] = self.withScope { // Serialize the zero value to a byte array so that we can get a new clone of it on each key val zeroBuffer = SparkEnv.get.serializer.newInstance().serialize(zeroValue) val zeroArray = new Array[Byte](zeroBuffer.limit) @@ -185,7 +187,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * may be added to the result an arbitrary number of times, and must not change the result * (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.). */ - def foldByKey(zeroValue: V, numPartitions: Int)(func: (V, V) => V): RDD[(K, V)] = { + def foldByKey(zeroValue: V, numPartitions: Int)(func: (V, V) => V): RDD[(K, V)] = self.withScope { foldByKey(zeroValue, new HashPartitioner(numPartitions))(func) } @@ -194,7 +196,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * may be added to the result an arbitrary number of times, and must not change the result * (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.). */ - def foldByKey(zeroValue: V)(func: (V, V) => V): RDD[(K, V)] = { + def foldByKey(zeroValue: V)(func: (V, V) => V): RDD[(K, V)] = self.withScope { foldByKey(zeroValue, defaultPartitioner(self))(func) } @@ -213,7 +215,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) */ def sampleByKey(withReplacement: Boolean, fractions: Map[K, Double], - seed: Long = Utils.random.nextLong): RDD[(K, V)] = { + seed: Long = Utils.random.nextLong): RDD[(K, V)] = self.withScope { require(fractions.values.forall(v => v >= 0.0), "Negative sampling rates.") @@ -242,9 +244,10 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * @return RDD containing the sampled subset */ @Experimental - def sampleByKeyExact(withReplacement: Boolean, + def sampleByKeyExact( + withReplacement: Boolean, fractions: Map[K, Double], - seed: Long = Utils.random.nextLong): RDD[(K, V)] = { + seed: Long = Utils.random.nextLong): RDD[(K, V)] = self.withScope { require(fractions.values.forall(v => v >= 0.0), "Negative sampling rates.") @@ -261,7 +264,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * the merging locally on each mapper before sending results to a reducer, similarly to a * "combiner" in MapReduce. */ - def reduceByKey(partitioner: Partitioner, func: (V, V) => V): RDD[(K, V)] = { + def reduceByKey(partitioner: Partitioner, func: (V, V) => V): RDD[(K, V)] = self.withScope { combineByKey[V]((v: V) => v, func, func, partitioner) } @@ -270,7 +273,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * the merging locally on each mapper before sending results to a reducer, similarly to a * "combiner" in MapReduce. Output will be hash-partitioned with numPartitions partitions. */ - def reduceByKey(func: (V, V) => V, numPartitions: Int): RDD[(K, V)] = { + def reduceByKey(func: (V, V) => V, numPartitions: Int): RDD[(K, V)] = self.withScope { reduceByKey(new HashPartitioner(numPartitions), func) } @@ -280,7 +283,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * "combiner" in MapReduce. Output will be hash-partitioned with the existing partitioner/ * parallelism level. */ - def reduceByKey(func: (V, V) => V): RDD[(K, V)] = { + def reduceByKey(func: (V, V) => V): RDD[(K, V)] = self.withScope { reduceByKey(defaultPartitioner(self), func) } @@ -289,7 +292,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * immediately to the master as a Map. This will also perform the merging locally on each mapper * before sending results to a reducer, similarly to a "combiner" in MapReduce. */ - def reduceByKeyLocally(func: (V, V) => V): Map[K, V] = { + def reduceByKeyLocally(func: (V, V) => V): Map[K, V] = self.withScope { if (keyClass.isArray) { throw new SparkException("reduceByKeyLocally() does not support array keys") @@ -317,7 +320,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) /** Alias for reduceByKeyLocally */ @deprecated("Use reduceByKeyLocally", "1.0.0") - def reduceByKeyToDriver(func: (V, V) => V): Map[K, V] = reduceByKeyLocally(func) + def reduceByKeyToDriver(func: (V, V) => V): Map[K, V] = self.withScope { + reduceByKeyLocally(func) + } /** * Count the number of elements for each key, collecting the results to a local Map. @@ -327,7 +332,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * To handle very large results, consider using rdd.mapValues(_ => 1L).reduceByKey(_ + _), which * returns an RDD[T, Long] instead of a map. */ - def countByKey(): Map[K, Long] = self.mapValues(_ => 1L).reduceByKey(_ + _).collect().toMap + def countByKey(): Map[K, Long] = self.withScope { + self.mapValues(_ => 1L).reduceByKey(_ + _).collect().toMap + } /** * :: Experimental :: @@ -336,7 +343,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) */ @Experimental def countByKeyApprox(timeout: Long, confidence: Double = 0.95) - : PartialResult[Map[K, BoundedDouble]] = { + : PartialResult[Map[K, BoundedDouble]] = self.withScope { self.map(_._1).countByValueApprox(timeout, confidence) } @@ -360,7 +367,10 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * @param partitioner Partitioner to use for the resulting RDD. */ @Experimental - def countApproxDistinctByKey(p: Int, sp: Int, partitioner: Partitioner): RDD[(K, Long)] = { + def countApproxDistinctByKey( + p: Int, + sp: Int, + partitioner: Partitioner): RDD[(K, Long)] = self.withScope { require(p >= 4, s"p ($p) must be >= 4") require(sp <= 32, s"sp ($sp) must be <= 32") require(sp == 0 || p <= sp, s"p ($p) cannot be greater than sp ($sp)") @@ -392,7 +402,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * It must be greater than 0.000017. * @param partitioner partitioner of the resulting RDD */ - def countApproxDistinctByKey(relativeSD: Double, partitioner: Partitioner): RDD[(K, Long)] = { + def countApproxDistinctByKey( + relativeSD: Double, + partitioner: Partitioner): RDD[(K, Long)] = self.withScope { require(relativeSD > 0.000017, s"accuracy ($relativeSD) must be greater than 0.000017") val p = math.ceil(2.0 * math.log(1.054 / relativeSD) / math.log(2)).toInt assert(p <= 32) @@ -410,7 +422,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * It must be greater than 0.000017. * @param numPartitions number of partitions of the resulting RDD */ - def countApproxDistinctByKey(relativeSD: Double, numPartitions: Int): RDD[(K, Long)] = { + def countApproxDistinctByKey( + relativeSD: Double, + numPartitions: Int): RDD[(K, Long)] = self.withScope { countApproxDistinctByKey(relativeSD, new HashPartitioner(numPartitions)) } @@ -424,7 +438,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * @param relativeSD Relative accuracy. Smaller values create counters that require more space. * It must be greater than 0.000017. */ - def countApproxDistinctByKey(relativeSD: Double = 0.05): RDD[(K, Long)] = { + def countApproxDistinctByKey(relativeSD: Double = 0.05): RDD[(K, Long)] = self.withScope { countApproxDistinctByKey(relativeSD, defaultPartitioner(self)) } @@ -441,7 +455,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Note: As currently implemented, groupByKey must be able to hold all the key-value pairs for any * key in memory. If a key has too many values, it can result in an [[OutOfMemoryError]]. */ - def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])] = { + def groupByKey(partitioner: Partitioner): RDD[(K, Iterable[V])] = self.withScope { // groupByKey shouldn't use map side combine because map side combine does not // reduce the amount of data shuffled and requires all map side data be inserted // into a hash table, leading to more objects in the old gen. @@ -465,14 +479,14 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Note: As currently implemented, groupByKey must be able to hold all the key-value pairs for any * key in memory. If a key has too many values, it can result in an [[OutOfMemoryError]]. */ - def groupByKey(numPartitions: Int): RDD[(K, Iterable[V])] = { + def groupByKey(numPartitions: Int): RDD[(K, Iterable[V])] = self.withScope { groupByKey(new HashPartitioner(numPartitions)) } /** * Return a copy of the RDD partitioned using the specified partitioner. */ - def partitionBy(partitioner: Partitioner): RDD[(K, V)] = { + def partitionBy(partitioner: Partitioner): RDD[(K, V)] = self.withScope { if (keyClass.isArray && partitioner.isInstanceOf[HashPartitioner]) { throw new SparkException("Default partitioner cannot partition array keys.") } @@ -488,7 +502,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and * (k, v2) is in `other`. Uses the given Partitioner to partition the output RDD. */ - def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))] = { + def join[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, W))] = self.withScope { this.cogroup(other, partitioner).flatMapValues( pair => for (v <- pair._1.iterator; w <- pair._2.iterator) yield (v, w) ) @@ -500,7 +514,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair (k, (v, None)) if no elements in `other` have key k. Uses the given Partitioner to * partition the output RDD. */ - def leftOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner): RDD[(K, (V, Option[W]))] = { + def leftOuterJoin[W]( + other: RDD[(K, W)], + partitioner: Partitioner): RDD[(K, (V, Option[W]))] = self.withScope { this.cogroup(other, partitioner).flatMapValues { pair => if (pair._2.isEmpty) { pair._1.iterator.map(v => (v, None)) @@ -517,7 +533,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * partition the output RDD. */ def rightOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner) - : RDD[(K, (Option[V], W))] = { + : RDD[(K, (Option[V], W))] = self.withScope { this.cogroup(other, partitioner).flatMapValues { pair => if (pair._1.isEmpty) { pair._2.iterator.map(w => (None, w)) @@ -536,7 +552,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * in `this` have key k. Uses the given Partitioner to partition the output RDD. */ def fullOuterJoin[W](other: RDD[(K, W)], partitioner: Partitioner) - : RDD[(K, (Option[V], Option[W]))] = { + : RDD[(K, (Option[V], Option[W]))] = self.withScope { this.cogroup(other, partitioner).flatMapValues { case (vs, Seq()) => vs.iterator.map(v => (Some(v), None)) case (Seq(), ws) => ws.iterator.map(w => (None, Some(w))) @@ -549,7 +565,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * existing partitioner/parallelism level. */ def combineByKey[C](createCombiner: V => C, mergeValue: (C, V) => C, mergeCombiners: (C, C) => C) - : RDD[(K, C)] = { + : RDD[(K, C)] = self.withScope { combineByKey(createCombiner, mergeValue, mergeCombiners, defaultPartitioner(self)) } @@ -563,7 +579,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]] * or [[PairRDDFunctions.reduceByKey]] will provide much better performance. */ - def groupByKey(): RDD[(K, Iterable[V])] = { + def groupByKey(): RDD[(K, Iterable[V])] = self.withScope { groupByKey(defaultPartitioner(self)) } @@ -572,7 +588,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and * (k, v2) is in `other`. Performs a hash join across the cluster. */ - def join[W](other: RDD[(K, W)]): RDD[(K, (V, W))] = { + def join[W](other: RDD[(K, W)]): RDD[(K, (V, W))] = self.withScope { join(other, defaultPartitioner(self, other)) } @@ -581,7 +597,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair of elements will be returned as a (k, (v1, v2)) tuple, where (k, v1) is in `this` and * (k, v2) is in `other`. Performs a hash join across the cluster. */ - def join[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (V, W))] = { + def join[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (V, W))] = self.withScope { join(other, new HashPartitioner(numPartitions)) } @@ -591,7 +607,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair (k, (v, None)) if no elements in `other` have key k. Hash-partitions the output * using the existing partitioner/parallelism level. */ - def leftOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (V, Option[W]))] = { + def leftOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (V, Option[W]))] = self.withScope { leftOuterJoin(other, defaultPartitioner(self, other)) } @@ -601,7 +617,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair (k, (v, None)) if no elements in `other` have key k. Hash-partitions the output * into `numPartitions` partitions. */ - def leftOuterJoin[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (V, Option[W]))] = { + def leftOuterJoin[W]( + other: RDD[(K, W)], + numPartitions: Int): RDD[(K, (V, Option[W]))] = self.withScope { leftOuterJoin(other, new HashPartitioner(numPartitions)) } @@ -611,7 +629,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair (k, (None, w)) if no elements in `this` have key k. Hash-partitions the resulting * RDD using the existing partitioner/parallelism level. */ - def rightOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], W))] = { + def rightOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], W))] = self.withScope { rightOuterJoin(other, defaultPartitioner(self, other)) } @@ -621,7 +639,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * pair (k, (None, w)) if no elements in `this` have key k. Hash-partitions the resulting * RDD into the given number of partitions. */ - def rightOuterJoin[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Option[V], W))] = { + def rightOuterJoin[W]( + other: RDD[(K, W)], + numPartitions: Int): RDD[(K, (Option[V], W))] = self.withScope { rightOuterJoin(other, new HashPartitioner(numPartitions)) } @@ -634,7 +654,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * in `this` have key k. Hash-partitions the resulting RDD using the existing partitioner/ * parallelism level. */ - def fullOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], Option[W]))] = { + def fullOuterJoin[W](other: RDD[(K, W)]): RDD[(K, (Option[V], Option[W]))] = self.withScope { fullOuterJoin(other, defaultPartitioner(self, other)) } @@ -646,7 +666,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * (k, (Some(v), Some(w))) for v in `this`, or the pair (k, (None, Some(w))) if no elements * in `this` have key k. Hash-partitions the resulting RDD into the given number of partitions. */ - def fullOuterJoin[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Option[V], Option[W]))] = { + def fullOuterJoin[W]( + other: RDD[(K, W)], + numPartitions: Int): RDD[(K, (Option[V], Option[W]))] = self.withScope { fullOuterJoin(other, new HashPartitioner(numPartitions)) } @@ -656,7 +678,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Warning: this doesn't return a multimap (so if you have multiple values to the same key, only * one value per key is preserved in the map returned) */ - def collectAsMap(): Map[K, V] = { + def collectAsMap(): Map[K, V] = self.withScope { val data = self.collect() val map = new mutable.HashMap[K, V] map.sizeHint(data.length) @@ -668,7 +690,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Pass each value in the key-value pair RDD through a map function without changing the keys; * this also retains the original RDD's partitioning. */ - def mapValues[U](f: V => U): RDD[(K, U)] = { + def mapValues[U](f: V => U): RDD[(K, U)] = self.withScope { val cleanF = self.context.clean(f) new MapPartitionsRDD[(K, U), (K, V)](self, (context, pid, iter) => iter.map { case (k, v) => (k, cleanF(v)) }, @@ -679,7 +701,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Pass each value in the key-value pair RDD through a flatMap function without changing the * keys; this also retains the original RDD's partitioning. */ - def flatMapValues[U](f: V => TraversableOnce[U]): RDD[(K, U)] = { + def flatMapValues[U](f: V => TraversableOnce[U]): RDD[(K, U)] = self.withScope { val cleanF = self.context.clean(f) new MapPartitionsRDD[(K, U), (K, V)](self, (context, pid, iter) => iter.flatMap { case (k, v) => @@ -697,7 +719,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) other2: RDD[(K, W2)], other3: RDD[(K, W3)], partitioner: Partitioner) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = self.withScope { if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) { throw new SparkException("Default partitioner cannot partition array keys.") } @@ -715,7 +737,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * list of values for that key in `this` as well as `other`. */ def cogroup[W](other: RDD[(K, W)], partitioner: Partitioner) - : RDD[(K, (Iterable[V], Iterable[W]))] = { + : RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope { if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) { throw new SparkException("Default partitioner cannot partition array keys.") } @@ -730,7 +752,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * tuple with the list of values for that key in `this`, `other1` and `other2`. */ def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], partitioner: Partitioner) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = self.withScope { if (partitioner.isInstanceOf[HashPartitioner] && keyClass.isArray) { throw new SparkException("Default partitioner cannot partition array keys.") } @@ -748,7 +770,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * for that key in `this`, `other1`, `other2` and `other3`. */ def cogroup[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)]) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = self.withScope { cogroup(other1, other2, other3, defaultPartitioner(self, other1, other2, other3)) } @@ -756,7 +778,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the * list of values for that key in `this` as well as `other`. */ - def cogroup[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = { + def cogroup[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope { cogroup(other, defaultPartitioner(self, other)) } @@ -765,7 +787,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * tuple with the list of values for that key in `this`, `other1` and `other2`. */ def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)]) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = self.withScope { cogroup(other1, other2, defaultPartitioner(self, other1, other2)) } @@ -773,7 +795,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * For each key k in `this` or `other`, return a resulting RDD that contains a tuple with the * list of values for that key in `this` as well as `other`. */ - def cogroup[W](other: RDD[(K, W)], numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W]))] = { + def cogroup[W]( + other: RDD[(K, W)], + numPartitions: Int): RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope { cogroup(other, new HashPartitioner(numPartitions)) } @@ -782,7 +806,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * tuple with the list of values for that key in `this`, `other1` and `other2`. */ def cogroup[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)], numPartitions: Int) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = self.withScope { cogroup(other1, other2, new HashPartitioner(numPartitions)) } @@ -795,24 +819,24 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) other2: RDD[(K, W2)], other3: RDD[(K, W3)], numPartitions: Int) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = self.withScope { cogroup(other1, other2, other3, new HashPartitioner(numPartitions)) } /** Alias for cogroup. */ - def groupWith[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = { + def groupWith[W](other: RDD[(K, W)]): RDD[(K, (Iterable[V], Iterable[W]))] = self.withScope { cogroup(other, defaultPartitioner(self, other)) } /** Alias for cogroup. */ def groupWith[W1, W2](other1: RDD[(K, W1)], other2: RDD[(K, W2)]) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))] = self.withScope { cogroup(other1, other2, defaultPartitioner(self, other1, other2)) } /** Alias for cogroup. */ def groupWith[W1, W2, W3](other1: RDD[(K, W1)], other2: RDD[(K, W2)], other3: RDD[(K, W3)]) - : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = { + : RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))] = self.withScope { cogroup(other1, other2, other3, defaultPartitioner(self, other1, other2, other3)) } @@ -822,22 +846,27 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting * RDD will be <= us. */ - def subtractByKey[W: ClassTag](other: RDD[(K, W)]): RDD[(K, V)] = + def subtractByKey[W: ClassTag](other: RDD[(K, W)]): RDD[(K, V)] = self.withScope { subtractByKey(other, self.partitioner.getOrElse(new HashPartitioner(self.partitions.length))) + } /** Return an RDD with the pairs from `this` whose keys are not in `other`. */ - def subtractByKey[W: ClassTag](other: RDD[(K, W)], numPartitions: Int): RDD[(K, V)] = + def subtractByKey[W: ClassTag]( + other: RDD[(K, W)], + numPartitions: Int): RDD[(K, V)] = self.withScope { subtractByKey(other, new HashPartitioner(numPartitions)) + } /** Return an RDD with the pairs from `this` whose keys are not in `other`. */ - def subtractByKey[W: ClassTag](other: RDD[(K, W)], p: Partitioner): RDD[(K, V)] = + def subtractByKey[W: ClassTag](other: RDD[(K, W)], p: Partitioner): RDD[(K, V)] = self.withScope { new SubtractedRDD[K, V, W](self, other, p) + } /** * Return the list of values in the RDD for key `key`. This operation is done efficiently if the * RDD has a known partitioner by only searching the partition that the key maps to. */ - def lookup(key: K): Seq[V] = { + def lookup(key: K): Seq[V] = self.withScope { self.partitioner match { case Some(p) => val index = p.getPartition(key) @@ -859,7 +888,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Output the RDD to any Hadoop-supported file system, using a Hadoop `OutputFormat` class * supporting the key and value types K and V in this RDD. */ - def saveAsHadoopFile[F <: OutputFormat[K, V]](path: String)(implicit fm: ClassTag[F]) { + def saveAsHadoopFile[F <: OutputFormat[K, V]]( + path: String)(implicit fm: ClassTag[F]): Unit = self.withScope { saveAsHadoopFile(path, keyClass, valueClass, fm.runtimeClass.asInstanceOf[Class[F]]) } @@ -869,7 +899,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * supplied codec. */ def saveAsHadoopFile[F <: OutputFormat[K, V]]( - path: String, codec: Class[_ <: CompressionCodec]) (implicit fm: ClassTag[F]) { + path: String, + codec: Class[_ <: CompressionCodec])(implicit fm: ClassTag[F]): Unit = self.withScope { val runtimeClass = fm.runtimeClass saveAsHadoopFile(path, keyClass, valueClass, runtimeClass.asInstanceOf[Class[F]], codec) } @@ -878,7 +909,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * Output the RDD to any Hadoop-supported file system, using a new Hadoop API `OutputFormat` * (mapreduce.OutputFormat) object supporting the key and value types K and V in this RDD. */ - def saveAsNewAPIHadoopFile[F <: NewOutputFormat[K, V]](path: String)(implicit fm: ClassTag[F]) { + def saveAsNewAPIHadoopFile[F <: NewOutputFormat[K, V]]( + path: String)(implicit fm: ClassTag[F]): Unit = self.withScope { saveAsNewAPIHadoopFile(path, keyClass, valueClass, fm.runtimeClass.asInstanceOf[Class[F]]) } @@ -891,8 +923,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[_ <: NewOutputFormat[_, _]], - conf: Configuration = self.context.hadoopConfiguration) - { + conf: Configuration = self.context.hadoopConfiguration): Unit = self.withScope { // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038). val hadoopConf = conf val job = new NewAPIHadoopJob(hadoopConf) @@ -912,7 +943,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[_ <: OutputFormat[_, _]], - codec: Class[_ <: CompressionCodec]) { + codec: Class[_ <: CompressionCodec]): Unit = self.withScope { saveAsHadoopFile(path, keyClass, valueClass, outputFormatClass, new JobConf(self.context.hadoopConfiguration), Some(codec)) } @@ -927,7 +958,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) valueClass: Class[_], outputFormatClass: Class[_ <: OutputFormat[_, _]], conf: JobConf = new JobConf(self.context.hadoopConfiguration), - codec: Option[Class[_ <: CompressionCodec]] = None) { + codec: Option[Class[_ <: CompressionCodec]] = None): Unit = self.withScope { // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038). val hadoopConf = conf hadoopConf.setOutputKeyClass(keyClass) @@ -960,7 +991,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * output paths required (e.g. a table name to write to) in the same way as it would be * configured for a Hadoop MapReduce job. */ - def saveAsNewAPIHadoopDataset(conf: Configuration) { + def saveAsNewAPIHadoopDataset(conf: Configuration): Unit = self.withScope { // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038). val hadoopConf = conf val job = new NewAPIHadoopJob(hadoopConf) @@ -1027,7 +1058,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)]) * (e.g. a table name to write to) in the same way as it would be configured for a Hadoop * MapReduce job. */ - def saveAsHadoopDataset(conf: JobConf) { + def saveAsHadoopDataset(conf: JobConf): Unit = self.withScope { // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038). val hadoopConf = conf val wrappedConf = new SerializableWritable(hadoopConf) diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala index 31c07c73fe07b..7f7c7ed144eb3 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -25,7 +25,7 @@ import scala.language.implicitConversions import scala.reflect.{classTag, ClassTag} import com.clearspring.analytics.stream.cardinality.HyperLogLogPlus -import org.apache.hadoop.io.{Writable, BytesWritable, NullWritable, Text} +import org.apache.hadoop.io.{BytesWritable, NullWritable, Text} import org.apache.hadoop.io.compress.CompressionCodec import org.apache.hadoop.mapred.TextOutputFormat @@ -277,12 +277,20 @@ abstract class RDD[T: ClassTag]( if (isCheckpointed) firstParent[T].iterator(split, context) else compute(split, context) } + /** + * Execute a block of code in a scope such that all new RDDs created in this body will + * be part of the same scope. For more detail, see {{org.apache.spark.rdd.RDDOperationScope}}. + * + * Note: Return statements are NOT allowed in the given body. + */ + private[spark] def withScope[U](body: => U): U = RDDOperationScope.withScope[U](sc)(body) + // Transformations (return a new RDD) /** * Return a new RDD by applying a function to all elements of this RDD. */ - def map[U: ClassTag](f: T => U): RDD[U] = { + def map[U: ClassTag](f: T => U): RDD[U] = withScope { val cleanF = sc.clean(f) new MapPartitionsRDD[U, T](this, (context, pid, iter) => iter.map(cleanF)) } @@ -291,7 +299,7 @@ abstract class RDD[T: ClassTag]( * Return a new RDD by first applying a function to all elements of this * RDD, and then flattening the results. */ - def flatMap[U: ClassTag](f: T => TraversableOnce[U]): RDD[U] = { + def flatMap[U: ClassTag](f: T => TraversableOnce[U]): RDD[U] = withScope { val cleanF = sc.clean(f) new MapPartitionsRDD[U, T](this, (context, pid, iter) => iter.flatMap(cleanF)) } @@ -299,7 +307,7 @@ abstract class RDD[T: ClassTag]( /** * Return a new RDD containing only the elements that satisfy a predicate. */ - def filter(f: T => Boolean): RDD[T] = { + def filter(f: T => Boolean): RDD[T] = withScope { val cleanF = sc.clean(f) new MapPartitionsRDD[T, T]( this, @@ -310,13 +318,16 @@ abstract class RDD[T: ClassTag]( /** * Return a new RDD containing the distinct elements in this RDD. */ - def distinct(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = + def distinct(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = withScope { map(x => (x, null)).reduceByKey((x, y) => x, numPartitions).map(_._1) + } /** * Return a new RDD containing the distinct elements in this RDD. */ - def distinct(): RDD[T] = distinct(partitions.length) + def distinct(): RDD[T] = withScope { + distinct(partitions.length) + } /** * Return a new RDD that has exactly numPartitions partitions. @@ -327,7 +338,7 @@ abstract class RDD[T: ClassTag]( * If you are decreasing the number of partitions in this RDD, consider using `coalesce`, * which can avoid performing a shuffle. */ - def repartition(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = { + def repartition(numPartitions: Int)(implicit ord: Ordering[T] = null): RDD[T] = withScope { coalesce(numPartitions, shuffle = true) } @@ -352,7 +363,7 @@ abstract class RDD[T: ClassTag]( * data distributed using a hash partitioner. */ def coalesce(numPartitions: Int, shuffle: Boolean = false)(implicit ord: Ordering[T] = null) - : RDD[T] = { + : RDD[T] = withScope { if (shuffle) { /** Distributes elements evenly across output partitions, starting from a random partition. */ val distributePartition = (index: Int, items: Iterator[T]) => { @@ -377,16 +388,17 @@ abstract class RDD[T: ClassTag]( /** * Return a sampled subset of this RDD. - * + * * @param withReplacement can elements be sampled multiple times (replaced when sampled out) * @param fraction expected size of the sample as a fraction of this RDD's size * without replacement: probability that each element is chosen; fraction must be [0, 1] * with replacement: expected number of times each element is chosen; fraction must be >= 0 * @param seed seed for the random number generator */ - def sample(withReplacement: Boolean, + def sample( + withReplacement: Boolean, fraction: Double, - seed: Long = Utils.random.nextLong): RDD[T] = { + seed: Long = Utils.random.nextLong): RDD[T] = withScope { require(fraction >= 0.0, "Negative fraction value: " + fraction) if (withReplacement) { new PartitionwiseSampledRDD[T, T](this, new PoissonSampler[T](fraction), true, seed) @@ -403,7 +415,9 @@ abstract class RDD[T: ClassTag]( * * @return split RDDs in an array */ - def randomSplit(weights: Array[Double], seed: Long = Utils.random.nextLong): Array[RDD[T]] = { + def randomSplit( + weights: Array[Double], + seed: Long = Utils.random.nextLong): Array[RDD[T]] = withScope { val sum = weights.sum val normalizedCumWeights = weights.map(_ / sum).scanLeft(0.0d)(_ + _) normalizedCumWeights.sliding(2).map { x => @@ -435,7 +449,9 @@ abstract class RDD[T: ClassTag]( * @param seed seed for the random number generator * @return sample of specified size in an array */ - def takeSample(withReplacement: Boolean, + // TODO: rewrite this without return statements so we can wrap it in a scope + def takeSample( + withReplacement: Boolean, num: Int, seed: Long = Utils.random.nextLong): Array[T] = { val numStDev = 10.0 @@ -483,7 +499,7 @@ abstract class RDD[T: ClassTag]( * Return the union of this RDD and another one. Any identical elements will appear multiple * times (use `.distinct()` to eliminate them). */ - def union(other: RDD[T]): RDD[T] = { + def union(other: RDD[T]): RDD[T] = withScope { if (partitioner.isDefined && other.partitioner == partitioner) { new PartitionerAwareUnionRDD(sc, Array(this, other)) } else { @@ -495,7 +511,9 @@ abstract class RDD[T: ClassTag]( * Return the union of this RDD and another one. Any identical elements will appear multiple * times (use `.distinct()` to eliminate them). */ - def ++(other: RDD[T]): RDD[T] = this.union(other) + def ++(other: RDD[T]): RDD[T] = withScope { + this.union(other) + } /** * Return this RDD sorted by the given key function. @@ -504,10 +522,11 @@ abstract class RDD[T: ClassTag]( f: (T) => K, ascending: Boolean = true, numPartitions: Int = this.partitions.length) - (implicit ord: Ordering[K], ctag: ClassTag[K]): RDD[T] = + (implicit ord: Ordering[K], ctag: ClassTag[K]): RDD[T] = withScope { this.keyBy[K](f) .sortByKey(ascending, numPartitions) .values + } /** * Return the intersection of this RDD and another one. The output will not contain any duplicate @@ -515,7 +534,7 @@ abstract class RDD[T: ClassTag]( * * Note that this method performs a shuffle internally. */ - def intersection(other: RDD[T]): RDD[T] = { + def intersection(other: RDD[T]): RDD[T] = withScope { this.map(v => (v, null)).cogroup(other.map(v => (v, null))) .filter { case (_, (leftGroup, rightGroup)) => leftGroup.nonEmpty && rightGroup.nonEmpty } .keys @@ -529,8 +548,9 @@ abstract class RDD[T: ClassTag]( * * @param partitioner Partitioner to use for the resulting RDD */ - def intersection(other: RDD[T], partitioner: Partitioner)(implicit ord: Ordering[T] = null) - : RDD[T] = { + def intersection( + other: RDD[T], + partitioner: Partitioner)(implicit ord: Ordering[T] = null): RDD[T] = withScope { this.map(v => (v, null)).cogroup(other.map(v => (v, null)), partitioner) .filter { case (_, (leftGroup, rightGroup)) => leftGroup.nonEmpty && rightGroup.nonEmpty } .keys @@ -544,16 +564,14 @@ abstract class RDD[T: ClassTag]( * * @param numPartitions How many partitions to use in the resulting RDD */ - def intersection(other: RDD[T], numPartitions: Int): RDD[T] = { - this.map(v => (v, null)).cogroup(other.map(v => (v, null)), new HashPartitioner(numPartitions)) - .filter { case (_, (leftGroup, rightGroup)) => leftGroup.nonEmpty && rightGroup.nonEmpty } - .keys + def intersection(other: RDD[T], numPartitions: Int): RDD[T] = withScope { + intersection(other, new HashPartitioner(numPartitions)) } /** * Return an RDD created by coalescing all elements within each partition into an array. */ - def glom(): RDD[Array[T]] = { + def glom(): RDD[Array[T]] = withScope { new MapPartitionsRDD[Array[T], T](this, (context, pid, iter) => Iterator(iter.toArray)) } @@ -561,7 +579,9 @@ abstract class RDD[T: ClassTag]( * Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of * elements (a, b) where a is in `this` and b is in `other`. */ - def cartesian[U: ClassTag](other: RDD[U]): RDD[(T, U)] = new CartesianRDD(sc, this, other) + def cartesian[U: ClassTag](other: RDD[U]): RDD[(T, U)] = withScope { + new CartesianRDD(sc, this, other) + } /** * Return an RDD of grouped items. Each group consists of a key and a sequence of elements @@ -572,8 +592,9 @@ abstract class RDD[T: ClassTag]( * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]] * or [[PairRDDFunctions.reduceByKey]] will provide much better performance. */ - def groupBy[K](f: T => K)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])] = + def groupBy[K](f: T => K)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])] = withScope { groupBy[K](f, defaultPartitioner(this)) + } /** * Return an RDD of grouped elements. Each group consists of a key and a sequence of elements @@ -584,8 +605,11 @@ abstract class RDD[T: ClassTag]( * aggregation (such as a sum or average) over each key, using [[PairRDDFunctions.aggregateByKey]] * or [[PairRDDFunctions.reduceByKey]] will provide much better performance. */ - def groupBy[K](f: T => K, numPartitions: Int)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])] = + def groupBy[K]( + f: T => K, + numPartitions: Int)(implicit kt: ClassTag[K]): RDD[(K, Iterable[T])] = withScope { groupBy(f, new HashPartitioner(numPartitions)) + } /** * Return an RDD of grouped items. Each group consists of a key and a sequence of elements @@ -597,7 +621,7 @@ abstract class RDD[T: ClassTag]( * or [[PairRDDFunctions.reduceByKey]] will provide much better performance. */ def groupBy[K](f: T => K, p: Partitioner)(implicit kt: ClassTag[K], ord: Ordering[K] = null) - : RDD[(K, Iterable[T])] = { + : RDD[(K, Iterable[T])] = withScope { val cleanF = sc.clean(f) this.map(t => (cleanF(t), t)).groupByKey(p) } @@ -605,13 +629,16 @@ abstract class RDD[T: ClassTag]( /** * Return an RDD created by piping elements to a forked external process. */ - def pipe(command: String): RDD[String] = new PipedRDD(this, command) + def pipe(command: String): RDD[String] = withScope { + new PipedRDD(this, command) + } /** * Return an RDD created by piping elements to a forked external process. */ - def pipe(command: String, env: Map[String, String]): RDD[String] = + def pipe(command: String, env: Map[String, String]): RDD[String] = withScope { new PipedRDD(this, command, env) + } /** * Return an RDD created by piping elements to a forked external process. @@ -619,7 +646,7 @@ abstract class RDD[T: ClassTag]( * * @param command command to run in forked process. * @param env environment variables to set. - * @param printPipeContext Before piping elements, this function is called as an oppotunity + * @param printPipeContext Before piping elements, this function is called as an opportunity * to pipe context data. Print line function (like out.println) will be * passed as printPipeContext's parameter. * @param printRDDElement Use this function to customize how to pipe elements. This function @@ -637,7 +664,7 @@ abstract class RDD[T: ClassTag]( env: Map[String, String] = Map(), printPipeContext: (String => Unit) => Unit = null, printRDDElement: (T, String => Unit) => Unit = null, - separateWorkingDir: Boolean = false): RDD[String] = { + separateWorkingDir: Boolean = false): RDD[String] = withScope { new PipedRDD(this, command, env, if (printPipeContext ne null) sc.clean(printPipeContext) else null, if (printRDDElement ne null) sc.clean(printRDDElement) else null, @@ -651,7 +678,7 @@ abstract class RDD[T: ClassTag]( * should be `false` unless this is a pair RDD and the input function doesn't modify the keys. */ def mapPartitions[U: ClassTag]( - f: Iterator[T] => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = { + f: Iterator[T] => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = withScope { val func = (context: TaskContext, index: Int, iter: Iterator[T]) => f(iter) new MapPartitionsRDD(this, sc.clean(func), preservesPartitioning) } @@ -664,7 +691,8 @@ abstract class RDD[T: ClassTag]( * should be `false` unless this is a pair RDD and the input function doesn't modify the keys. */ def mapPartitionsWithIndex[U: ClassTag]( - f: (Int, Iterator[T]) => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = { + f: (Int, Iterator[T]) => Iterator[U], + preservesPartitioning: Boolean = false): RDD[U] = withScope { val func = (context: TaskContext, index: Int, iter: Iterator[T]) => f(index, iter) new MapPartitionsRDD(this, sc.clean(func), preservesPartitioning) } @@ -681,7 +709,7 @@ abstract class RDD[T: ClassTag]( @deprecated("use TaskContext.get", "1.2.0") def mapPartitionsWithContext[U: ClassTag]( f: (TaskContext, Iterator[T]) => Iterator[U], - preservesPartitioning: Boolean = false): RDD[U] = { + preservesPartitioning: Boolean = false): RDD[U] = withScope { val func = (context: TaskContext, index: Int, iter: Iterator[T]) => f(context, iter) new MapPartitionsRDD(this, sc.clean(func), preservesPartitioning) } @@ -692,7 +720,8 @@ abstract class RDD[T: ClassTag]( */ @deprecated("use mapPartitionsWithIndex", "0.7.0") def mapPartitionsWithSplit[U: ClassTag]( - f: (Int, Iterator[T]) => Iterator[U], preservesPartitioning: Boolean = false): RDD[U] = { + f: (Int, Iterator[T]) => Iterator[U], + preservesPartitioning: Boolean = false): RDD[U] = withScope { mapPartitionsWithIndex(f, preservesPartitioning) } @@ -704,7 +733,7 @@ abstract class RDD[T: ClassTag]( @deprecated("use mapPartitionsWithIndex", "1.0.0") def mapWith[A, U: ClassTag] (constructA: Int => A, preservesPartitioning: Boolean = false) - (f: (T, A) => U): RDD[U] = { + (f: (T, A) => U): RDD[U] = withScope { mapPartitionsWithIndex((index, iter) => { val a = constructA(index) iter.map(t => f(t, a)) @@ -719,7 +748,7 @@ abstract class RDD[T: ClassTag]( @deprecated("use mapPartitionsWithIndex and flatMap", "1.0.0") def flatMapWith[A, U: ClassTag] (constructA: Int => A, preservesPartitioning: Boolean = false) - (f: (T, A) => Seq[U]): RDD[U] = { + (f: (T, A) => Seq[U]): RDD[U] = withScope { mapPartitionsWithIndex((index, iter) => { val a = constructA(index) iter.flatMap(t => f(t, a)) @@ -732,11 +761,11 @@ abstract class RDD[T: ClassTag]( * partition with the index of that partition. */ @deprecated("use mapPartitionsWithIndex and foreach", "1.0.0") - def foreachWith[A](constructA: Int => A)(f: (T, A) => Unit) { + def foreachWith[A](constructA: Int => A)(f: (T, A) => Unit): Unit = withScope { mapPartitionsWithIndex { (index, iter) => val a = constructA(index) iter.map(t => {f(t, a); t}) - }.foreach(_ => {}) + } } /** @@ -745,7 +774,7 @@ abstract class RDD[T: ClassTag]( * partition with the index of that partition. */ @deprecated("use mapPartitionsWithIndex and filter", "1.0.0") - def filterWith[A](constructA: Int => A)(p: (T, A) => Boolean): RDD[T] = { + def filterWith[A](constructA: Int => A)(p: (T, A) => Boolean): RDD[T] = withScope { mapPartitionsWithIndex((index, iter) => { val a = constructA(index) iter.filter(t => p(t, a)) @@ -758,7 +787,7 @@ abstract class RDD[T: ClassTag]( * partitions* and the *same number of elements in each partition* (e.g. one was made through * a map on the other). */ - def zip[U: ClassTag](other: RDD[U]): RDD[(T, U)] = { + def zip[U: ClassTag](other: RDD[U]): RDD[(T, U)] = withScope { zipPartitions(other, preservesPartitioning = false) { (thisIter, otherIter) => new Iterator[(T, U)] { def hasNext: Boolean = (thisIter.hasNext, otherIter.hasNext) match { @@ -780,33 +809,39 @@ abstract class RDD[T: ClassTag]( */ def zipPartitions[B: ClassTag, V: ClassTag] (rdd2: RDD[B], preservesPartitioning: Boolean) - (f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] = + (f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] = withScope { new ZippedPartitionsRDD2(sc, sc.clean(f), this, rdd2, preservesPartitioning) + } def zipPartitions[B: ClassTag, V: ClassTag] (rdd2: RDD[B]) - (f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] = - new ZippedPartitionsRDD2(sc, sc.clean(f), this, rdd2, false) + (f: (Iterator[T], Iterator[B]) => Iterator[V]): RDD[V] = withScope { + zipPartitions(rdd2, preservesPartitioning = false)(f) + } def zipPartitions[B: ClassTag, C: ClassTag, V: ClassTag] (rdd2: RDD[B], rdd3: RDD[C], preservesPartitioning: Boolean) - (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] = + (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] = withScope { new ZippedPartitionsRDD3(sc, sc.clean(f), this, rdd2, rdd3, preservesPartitioning) + } def zipPartitions[B: ClassTag, C: ClassTag, V: ClassTag] (rdd2: RDD[B], rdd3: RDD[C]) - (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] = - new ZippedPartitionsRDD3(sc, sc.clean(f), this, rdd2, rdd3, false) + (f: (Iterator[T], Iterator[B], Iterator[C]) => Iterator[V]): RDD[V] = withScope { + zipPartitions(rdd2, rdd3, preservesPartitioning = false)(f) + } def zipPartitions[B: ClassTag, C: ClassTag, D: ClassTag, V: ClassTag] (rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D], preservesPartitioning: Boolean) - (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] = + (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] = withScope { new ZippedPartitionsRDD4(sc, sc.clean(f), this, rdd2, rdd3, rdd4, preservesPartitioning) + } def zipPartitions[B: ClassTag, C: ClassTag, D: ClassTag, V: ClassTag] (rdd2: RDD[B], rdd3: RDD[C], rdd4: RDD[D]) - (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] = - new ZippedPartitionsRDD4(sc, sc.clean(f), this, rdd2, rdd3, rdd4, false) + (f: (Iterator[T], Iterator[B], Iterator[C], Iterator[D]) => Iterator[V]): RDD[V] = withScope { + zipPartitions(rdd2, rdd3, rdd4, preservesPartitioning = false)(f) + } // Actions (launch a job to return a value to the user program) @@ -814,7 +849,7 @@ abstract class RDD[T: ClassTag]( /** * Applies a function f to all elements of this RDD. */ - def foreach(f: T => Unit) { + def foreach(f: T => Unit): Unit = withScope { val cleanF = sc.clean(f) sc.runJob(this, (iter: Iterator[T]) => iter.foreach(cleanF)) } @@ -822,7 +857,7 @@ abstract class RDD[T: ClassTag]( /** * Applies a function f to each partition of this RDD. */ - def foreachPartition(f: Iterator[T] => Unit) { + def foreachPartition(f: Iterator[T] => Unit): Unit = withScope { val cleanF = sc.clean(f) sc.runJob(this, (iter: Iterator[T]) => cleanF(iter)) } @@ -830,7 +865,7 @@ abstract class RDD[T: ClassTag]( /** * Return an array that contains all of the elements in this RDD. */ - def collect(): Array[T] = { + def collect(): Array[T] = withScope { val results = sc.runJob(this, (iter: Iterator[T]) => iter.toArray) Array.concat(results: _*) } @@ -840,7 +875,7 @@ abstract class RDD[T: ClassTag]( * * The iterator will consume as much memory as the largest partition in this RDD. */ - def toLocalIterator: Iterator[T] = { + def toLocalIterator: Iterator[T] = withScope { def collectPartition(p: Int): Array[T] = { sc.runJob(this, (iter: Iterator[T]) => iter.toArray, Seq(p), allowLocal = false).head } @@ -851,12 +886,14 @@ abstract class RDD[T: ClassTag]( * Return an array that contains all of the elements in this RDD. */ @deprecated("use collect", "1.0.0") - def toArray(): Array[T] = collect() + def toArray(): Array[T] = withScope { + collect() + } /** * Return an RDD that contains all matching values by applying `f`. */ - def collect[U: ClassTag](f: PartialFunction[T, U]): RDD[U] = { + def collect[U: ClassTag](f: PartialFunction[T, U]): RDD[U] = withScope { filter(f.isDefinedAt).map(f) } @@ -866,19 +903,23 @@ abstract class RDD[T: ClassTag]( * Uses `this` partitioner/partition size, because even if `other` is huge, the resulting * RDD will be <= us. */ - def subtract(other: RDD[T]): RDD[T] = + def subtract(other: RDD[T]): RDD[T] = withScope { subtract(other, partitioner.getOrElse(new HashPartitioner(partitions.length))) + } /** * Return an RDD with the elements from `this` that are not in `other`. */ - def subtract(other: RDD[T], numPartitions: Int): RDD[T] = + def subtract(other: RDD[T], numPartitions: Int): RDD[T] = withScope { subtract(other, new HashPartitioner(numPartitions)) + } /** * Return an RDD with the elements from `this` that are not in `other`. */ - def subtract(other: RDD[T], p: Partitioner)(implicit ord: Ordering[T] = null): RDD[T] = { + def subtract( + other: RDD[T], + p: Partitioner)(implicit ord: Ordering[T] = null): RDD[T] = withScope { if (partitioner == Some(p)) { // Our partitioner knows how to handle T (which, since we have a partitioner, is // really (K, V)) so make a new Partitioner that will de-tuple our fake tuples @@ -900,7 +941,7 @@ abstract class RDD[T: ClassTag]( * Reduces the elements of this RDD using the specified commutative and * associative binary operator. */ - def reduce(f: (T, T) => T): T = { + def reduce(f: (T, T) => T): T = withScope { val cleanF = sc.clean(f) val reducePartition: Iterator[T] => Option[T] = iter => { if (iter.hasNext) { @@ -929,7 +970,7 @@ abstract class RDD[T: ClassTag]( * @param depth suggested depth of the tree (default: 2) * @see [[org.apache.spark.rdd.RDD#reduce]] */ - def treeReduce(f: (T, T) => T, depth: Int = 2): T = { + def treeReduce(f: (T, T) => T, depth: Int = 2): T = withScope { require(depth >= 1, s"Depth must be greater than or equal to 1 but got $depth.") val cleanF = context.clean(f) val reducePartition: Iterator[T] => Option[T] = iter => { @@ -961,7 +1002,7 @@ abstract class RDD[T: ClassTag]( * modify t1 and return it as its result value to avoid object allocation; however, it should not * modify t2. */ - def fold(zeroValue: T)(op: (T, T) => T): T = { + def fold(zeroValue: T)(op: (T, T) => T): T = withScope { // Clone the zero value since we will also be serializing it as part of tasks var jobResult = Utils.clone(zeroValue, sc.env.closureSerializer.newInstance()) val cleanOp = sc.clean(op) @@ -979,7 +1020,7 @@ abstract class RDD[T: ClassTag]( * allowed to modify and return their first argument instead of creating a new U to avoid memory * allocation. */ - def aggregate[U: ClassTag](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) => U): U = { + def aggregate[U: ClassTag](zeroValue: U)(seqOp: (U, T) => U, combOp: (U, U) => U): U = withScope { // Clone the zero value since we will also be serializing it as part of tasks var jobResult = Utils.clone(zeroValue, sc.env.serializer.newInstance()) val cleanSeqOp = sc.clean(seqOp) @@ -999,26 +1040,29 @@ abstract class RDD[T: ClassTag]( def treeAggregate[U: ClassTag](zeroValue: U)( seqOp: (U, T) => U, combOp: (U, U) => U, - depth: Int = 2): U = { + depth: Int = 2): U = withScope { require(depth >= 1, s"Depth must be greater than or equal to 1 but got $depth.") if (partitions.length == 0) { - return Utils.clone(zeroValue, context.env.closureSerializer.newInstance()) - } - val cleanSeqOp = context.clean(seqOp) - val cleanCombOp = context.clean(combOp) - val aggregatePartition = (it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp, cleanCombOp) - var partiallyAggregated = mapPartitions(it => Iterator(aggregatePartition(it))) - var numPartitions = partiallyAggregated.partitions.length - val scale = math.max(math.ceil(math.pow(numPartitions, 1.0 / depth)).toInt, 2) - // If creating an extra level doesn't help reduce the wall-clock time, we stop tree aggregation. - while (numPartitions > scale + numPartitions / scale) { - numPartitions /= scale - val curNumPartitions = numPartitions - partiallyAggregated = partiallyAggregated.mapPartitionsWithIndex { (i, iter) => - iter.map((i % curNumPartitions, _)) - }.reduceByKey(new HashPartitioner(curNumPartitions), cleanCombOp).values + Utils.clone(zeroValue, context.env.closureSerializer.newInstance()) + } else { + val cleanSeqOp = context.clean(seqOp) + val cleanCombOp = context.clean(combOp) + val aggregatePartition = + (it: Iterator[T]) => it.aggregate(zeroValue)(cleanSeqOp, cleanCombOp) + var partiallyAggregated = mapPartitions(it => Iterator(aggregatePartition(it))) + var numPartitions = partiallyAggregated.partitions.length + val scale = math.max(math.ceil(math.pow(numPartitions, 1.0 / depth)).toInt, 2) + // If creating an extra level doesn't help reduce + // the wall-clock time, we stop tree aggregation. + while (numPartitions > scale + numPartitions / scale) { + numPartitions /= scale + val curNumPartitions = numPartitions + partiallyAggregated = partiallyAggregated.mapPartitionsWithIndex { + (i, iter) => iter.map((i % curNumPartitions, _)) + }.reduceByKey(new HashPartitioner(curNumPartitions), cleanCombOp).values + } + partiallyAggregated.reduce(cleanCombOp) } - partiallyAggregated.reduce(cleanCombOp) } /** @@ -1032,7 +1076,9 @@ abstract class RDD[T: ClassTag]( * within a timeout, even if not all tasks have finished. */ @Experimental - def countApprox(timeout: Long, confidence: Double = 0.95): PartialResult[BoundedDouble] = { + def countApprox( + timeout: Long, + confidence: Double = 0.95): PartialResult[BoundedDouble] = withScope { val countElements: (TaskContext, Iterator[T]) => Long = { (ctx, iter) => var result = 0L while (iter.hasNext) { @@ -1053,7 +1099,7 @@ abstract class RDD[T: ClassTag]( * To handle very large results, consider using rdd.map(x => (x, 1L)).reduceByKey(_ + _), which * returns an RDD[T, Long] instead of a map. */ - def countByValue()(implicit ord: Ordering[T] = null): Map[T, Long] = { + def countByValue()(implicit ord: Ordering[T] = null): Map[T, Long] = withScope { map(value => (value, null)).countByKey() } @@ -1064,8 +1110,7 @@ abstract class RDD[T: ClassTag]( @Experimental def countByValueApprox(timeout: Long, confidence: Double = 0.95) (implicit ord: Ordering[T] = null) - : PartialResult[Map[T, BoundedDouble]] = - { + : PartialResult[Map[T, BoundedDouble]] = withScope { if (elementClassTag.runtimeClass.isArray) { throw new SparkException("countByValueApprox() does not support arrays") } @@ -1098,7 +1143,7 @@ abstract class RDD[T: ClassTag]( * If `sp` equals 0, the sparse representation is skipped. */ @Experimental - def countApproxDistinct(p: Int, sp: Int): Long = { + def countApproxDistinct(p: Int, sp: Int): Long = withScope { require(p >= 4, s"p ($p) must be at least 4") require(sp <= 32, s"sp ($sp) cannot be greater than 32") require(sp == 0 || p <= sp, s"p ($p) cannot be greater than sp ($sp)") @@ -1124,7 +1169,7 @@ abstract class RDD[T: ClassTag]( * @param relativeSD Relative accuracy. Smaller values create counters that require more space. * It must be greater than 0.000017. */ - def countApproxDistinct(relativeSD: Double = 0.05): Long = { + def countApproxDistinct(relativeSD: Double = 0.05): Long = withScope { val p = math.ceil(2.0 * math.log(1.054 / relativeSD) / math.log(2)).toInt countApproxDistinct(p, 0) } @@ -1142,7 +1187,9 @@ abstract class RDD[T: ClassTag]( * and may even change if the RDD is reevaluated. If a fixed ordering is required to guarantee * the same index assignments, you should sort the RDD with sortByKey() or save it to a file. */ - def zipWithIndex(): RDD[(T, Long)] = new ZippedWithIndexRDD(this) + def zipWithIndex(): RDD[(T, Long)] = withScope { + new ZippedWithIndexRDD(this) + } /** * Zips this RDD with generated unique Long ids. Items in the kth partition will get ids k, n+k, @@ -1154,7 +1201,7 @@ abstract class RDD[T: ClassTag]( * and may even change if the RDD is reevaluated. If a fixed ordering is required to guarantee * the same index assignments, you should sort the RDD with sortByKey() or save it to a file. */ - def zipWithUniqueId(): RDD[(T, Long)] = { + def zipWithUniqueId(): RDD[(T, Long)] = withScope { val n = this.partitions.length.toLong this.mapPartitionsWithIndex { case (k, iter) => iter.zipWithIndex.map { case (item, i) => @@ -1171,48 +1218,50 @@ abstract class RDD[T: ClassTag]( * @note due to complications in the internal implementation, this method will raise * an exception if called on an RDD of `Nothing` or `Null`. */ - def take(num: Int): Array[T] = { + def take(num: Int): Array[T] = withScope { if (num == 0) { - return new Array[T](0) - } - - val buf = new ArrayBuffer[T] - val totalParts = this.partitions.length - var partsScanned = 0 - while (buf.size < num && partsScanned < totalParts) { - // The number of partitions to try in this iteration. It is ok for this number to be - // greater than totalParts because we actually cap it at totalParts in runJob. - var numPartsToTry = 1 - if (partsScanned > 0) { - // If we didn't find any rows after the previous iteration, quadruple and retry. Otherwise, - // interpolate the number of partitions we need to try, but overestimate it by 50%. - // We also cap the estimation in the end. - if (buf.size == 0) { - numPartsToTry = partsScanned * 4 - } else { - // the left side of max is >=1 whenever partsScanned >= 2 - numPartsToTry = Math.max((1.5 * num * partsScanned / buf.size).toInt - partsScanned, 1) - numPartsToTry = Math.min(numPartsToTry, partsScanned * 4) + new Array[T](0) + } else { + val buf = new ArrayBuffer[T] + val totalParts = this.partitions.length + var partsScanned = 0 + while (buf.size < num && partsScanned < totalParts) { + // The number of partitions to try in this iteration. It is ok for this number to be + // greater than totalParts because we actually cap it at totalParts in runJob. + var numPartsToTry = 1 + if (partsScanned > 0) { + // If we didn't find any rows after the previous iteration, quadruple and retry. + // Otherwise, interpolate the number of partitions we need to try, but overestimate + // it by 50%. We also cap the estimation in the end. + if (buf.size == 0) { + numPartsToTry = partsScanned * 4 + } else { + // the left side of max is >=1 whenever partsScanned >= 2 + numPartsToTry = Math.max((1.5 * num * partsScanned / buf.size).toInt - partsScanned, 1) + numPartsToTry = Math.min(numPartsToTry, partsScanned * 4) + } } - } - val left = num - buf.size - val p = partsScanned until math.min(partsScanned + numPartsToTry, totalParts) - val res = sc.runJob(this, (it: Iterator[T]) => it.take(left).toArray, p, allowLocal = true) + val left = num - buf.size + val p = partsScanned until math.min(partsScanned + numPartsToTry, totalParts) + val res = sc.runJob(this, (it: Iterator[T]) => it.take(left).toArray, p, allowLocal = true) - res.foreach(buf ++= _.take(num - buf.size)) - partsScanned += numPartsToTry - } + res.foreach(buf ++= _.take(num - buf.size)) + partsScanned += numPartsToTry + } - buf.toArray + buf.toArray + } } /** * Return the first element in this RDD. */ - def first(): T = take(1) match { - case Array(t) => t - case _ => throw new UnsupportedOperationException("empty collection") + def first(): T = withScope { + take(1) match { + case Array(t) => t + case _ => throw new UnsupportedOperationException("empty collection") + } } /** @@ -1230,7 +1279,9 @@ abstract class RDD[T: ClassTag]( * @param ord the implicit ordering for T * @return an array of top elements */ - def top(num: Int)(implicit ord: Ordering[T]): Array[T] = takeOrdered(num)(ord.reverse) + def top(num: Int)(implicit ord: Ordering[T]): Array[T] = withScope { + takeOrdered(num)(ord.reverse) + } /** * Returns the first k (smallest) elements from this RDD as defined by the specified @@ -1248,7 +1299,7 @@ abstract class RDD[T: ClassTag]( * @param ord the implicit ordering for T * @return an array of top elements */ - def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T] = { + def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T] = withScope { if (num == 0) { Array.empty } else { @@ -1273,13 +1324,17 @@ abstract class RDD[T: ClassTag]( * Returns the max of this RDD as defined by the implicit Ordering[T]. * @return the maximum element of the RDD * */ - def max()(implicit ord: Ordering[T]): T = this.reduce(ord.max) + def max()(implicit ord: Ordering[T]): T = withScope { + this.reduce(ord.max) + } /** * Returns the min of this RDD as defined by the implicit Ordering[T]. * @return the minimum element of the RDD * */ - def min()(implicit ord: Ordering[T]): T = this.reduce(ord.min) + def min()(implicit ord: Ordering[T]): T = withScope { + this.reduce(ord.min) + } /** * @note due to complications in the internal implementation, this method will raise an @@ -1289,12 +1344,14 @@ abstract class RDD[T: ClassTag]( * @return true if and only if the RDD contains no elements at all. Note that an RDD * may be empty even when it has at least 1 partition. */ - def isEmpty(): Boolean = partitions.length == 0 || take(1).length == 0 + def isEmpty(): Boolean = withScope { + partitions.length == 0 || take(1).length == 0 + } /** * Save this RDD as a text file, using string representations of elements. */ - def saveAsTextFile(path: String) { + def saveAsTextFile(path: String): Unit = withScope { // https://issues.apache.org/jira/browse/SPARK-2075 // // NullWritable is a `Comparable` in Hadoop 1.+, so the compiler cannot find an implicit @@ -1321,7 +1378,7 @@ abstract class RDD[T: ClassTag]( /** * Save this RDD as a compressed text file, using string representations of elements. */ - def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]) { + def saveAsTextFile(path: String, codec: Class[_ <: CompressionCodec]): Unit = withScope { // https://issues.apache.org/jira/browse/SPARK-2075 val nullWritableClassTag = implicitly[ClassTag[NullWritable]] val textClassTag = implicitly[ClassTag[Text]] @@ -1339,7 +1396,7 @@ abstract class RDD[T: ClassTag]( /** * Save this RDD as a SequenceFile of serialized objects. */ - def saveAsObjectFile(path: String) { + def saveAsObjectFile(path: String): Unit = withScope { this.mapPartitions(iter => iter.grouped(10).map(_.toArray)) .map(x => (NullWritable.get(), new BytesWritable(Utils.serialize(x)))) .saveAsSequenceFile(path) @@ -1348,12 +1405,12 @@ abstract class RDD[T: ClassTag]( /** * Creates tuples of the elements in this RDD by applying `f`. */ - def keyBy[K](f: T => K): RDD[(K, T)] = { + def keyBy[K](f: T => K): RDD[(K, T)] = withScope { map(x => (f(x), x)) } /** A private method for tests, to look at the contents of each partition */ - private[spark] def collectPartitions(): Array[Array[T]] = { + private[spark] def collectPartitions(): Array[Array[T]] = withScope { sc.runJob(this, (iter: Iterator[T]) => iter.toArray) } @@ -1392,6 +1449,17 @@ abstract class RDD[T: ClassTag]( /** User code that created this RDD (e.g. `textFile`, `parallelize`). */ @transient private[spark] val creationSite = sc.getCallSite() + /** + * The scope associated with the operation that created this RDD. + * + * This is more flexible than the call site and can be defined hierarchically. For more + * detail, see the documentation of {{RDDOperationScope}}. This scope is not defined if the + * user instantiates this RDD himself without using any Spark operations. + */ + @transient private[spark] val scope: Option[RDDOperationScope] = { + Option(sc.getLocalProperty(SparkContext.RDD_SCOPE_KEY)).map(RDDOperationScope.fromJson) + } + private[spark] def getCreationSite: String = Option(creationSite).map(_.shortForm).getOrElse("") private[spark] def elementClassTag: ClassTag[T] = classTag[T] @@ -1470,7 +1538,7 @@ abstract class RDD[T: ClassTag]( /** A description of this RDD and its recursive dependencies for debugging. */ def toDebugString: String = { // Get a debug description of an rdd without its children - def debugSelf (rdd: RDD[_]): Seq[String] = { + def debugSelf(rdd: RDD[_]): Seq[String] = { import Utils.bytesToString val persistence = if (storageLevel != StorageLevel.NONE) storageLevel.description else "" @@ -1527,10 +1595,11 @@ abstract class RDD[T: ClassTag]( case (desc: String, _) => s"$nextPrefix$desc" } ++ debugChildren(rdd, nextPrefix) } - def debugString(rdd: RDD[_], - prefix: String = "", - isShuffle: Boolean = true, - isLastChild: Boolean = false): Seq[String] = { + def debugString( + rdd: RDD[_], + prefix: String = "", + isShuffle: Boolean = true, + isLastChild: Boolean = false): Seq[String] = { if (isShuffle) { shuffleDebugString(rdd, prefix, isLastChild) } else { diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala b/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala new file mode 100644 index 0000000000000..537b56b49f866 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala @@ -0,0 +1,137 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rdd + +import java.util.concurrent.atomic.AtomicInteger + +import com.fasterxml.jackson.annotation.{JsonIgnore, JsonInclude, JsonPropertyOrder} +import com.fasterxml.jackson.annotation.JsonInclude.Include +import com.fasterxml.jackson.databind.ObjectMapper +import com.fasterxml.jackson.module.scala.DefaultScalaModule + +import org.apache.spark.SparkContext + +/** + * A general, named code block representing an operation that instantiates RDDs. + * + * All RDDs instantiated in the corresponding code block will store a pointer to this object. + * Examples include, but will not be limited to, existing RDD operations, such as textFile, + * reduceByKey, and treeAggregate. + * + * An operation scope may be nested in other scopes. For instance, a SQL query may enclose + * scopes associated with the public RDD APIs it uses under the hood. + * + * There is no particular relationship between an operation scope and a stage or a job. + * A scope may live inside one stage (e.g. map) or span across multiple jobs (e.g. take). + */ +@JsonInclude(Include.NON_NULL) +@JsonPropertyOrder(Array("id", "name", "parent")) +private[spark] class RDDOperationScope( + val name: String, + val parent: Option[RDDOperationScope] = None) { + + val id: Int = RDDOperationScope.nextScopeId() + + def toJson: String = { + RDDOperationScope.jsonMapper.writeValueAsString(this) + } + + /** + * Return a list of scopes that this scope is a part of, including this scope itself. + * The result is ordered from the outermost scope (eldest ancestor) to this scope. + */ + @JsonIgnore + def getAllScopes: Seq[RDDOperationScope] = { + parent.map(_.getAllScopes).getOrElse(Seq.empty) ++ Seq(this) + } + + override def equals(other: Any): Boolean = { + other match { + case s: RDDOperationScope => + id == s.id && name == s.name && parent == s.parent + case _ => false + } + } + + override def toString: String = toJson +} + +/** + * A collection of utility methods to construct a hierarchical representation of RDD scopes. + * An RDD scope tracks the series of operations that created a given RDD. + */ +private[spark] object RDDOperationScope { + private val jsonMapper = new ObjectMapper().registerModule(DefaultScalaModule) + private val scopeCounter = new AtomicInteger(0) + + def fromJson(s: String): RDDOperationScope = { + jsonMapper.readValue(s, classOf[RDDOperationScope]) + } + + /** Return a globally unique operation scope ID. */ + def nextScopeId(): Int = scopeCounter.getAndIncrement + + /** + * Execute the given body such that all RDDs created in this body will have the same scope. + * The name of the scope will be the name of the method that immediately encloses this one. + * + * Note: Return statements are NOT allowed in body. + */ + private[spark] def withScope[T]( + sc: SparkContext, + allowNesting: Boolean = false)(body: => T): T = { + val callerMethodName = Thread.currentThread.getStackTrace()(3).getMethodName + withScope[T](sc, callerMethodName, allowNesting)(body) + } + + /** + * Execute the given body such that all RDDs created in this body will have the same scope. + * + * If nesting is allowed, this concatenates the previous scope with the new one in a way that + * signifies the hierarchy. Otherwise, if nesting is not allowed, then any children calls to + * this method executed in the body will have no effect. + * + * Note: Return statements are NOT allowed in body. + */ + private[spark] def withScope[T]( + sc: SparkContext, + name: String, + allowNesting: Boolean = false)(body: => T): T = { + // Save the old scope to restore it later + val scopeKey = SparkContext.RDD_SCOPE_KEY + val noOverrideKey = SparkContext.RDD_SCOPE_NO_OVERRIDE_KEY + val oldScopeJson = sc.getLocalProperty(scopeKey) + val oldScope = Option(oldScopeJson).map(RDDOperationScope.fromJson) + val oldNoOverride = sc.getLocalProperty(noOverrideKey) + try { + // Set the scope only if the higher level caller allows us to do so + if (sc.getLocalProperty(noOverrideKey) == null) { + sc.setLocalProperty(scopeKey, new RDDOperationScope(name, oldScope).toJson) + } + // Optionally disallow the child body to override our scope + if (!allowNesting) { + sc.setLocalProperty(noOverrideKey, "true") + } + body + } finally { + // Remember to restore any state that was modified before exiting + sc.setLocalProperty(scopeKey, oldScopeJson) + sc.setLocalProperty(noOverrideKey, oldNoOverride) + } + } +} diff --git a/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala index 059f8963691f0..3dfcf67f0eb66 100644 --- a/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/SequenceFileRDDFunctions.scala @@ -85,7 +85,9 @@ class SequenceFileRDDFunctions[K <% Writable: ClassTag, V <% Writable : ClassTag * byte arrays to BytesWritable, and Strings to Text. The `path` can be on any Hadoop-supported * file system. */ - def saveAsSequenceFile(path: String, codec: Option[Class[_ <: CompressionCodec]] = None) { + def saveAsSequenceFile( + path: String, + codec: Option[Class[_ <: CompressionCodec]] = None): Unit = self.withScope { def anyToWritable[U <% Writable](u: U): Writable = u // TODO We cannot force the return type of `anyToWritable` be same as keyWritableClass and diff --git a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala index cf3db0b94a0b3..e439d2a7e1229 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/StageInfo.scala @@ -33,6 +33,7 @@ class StageInfo( val name: String, val numTasks: Int, val rddInfos: Seq[RDDInfo], + val parentIds: Seq[Int], val details: String) { /** When this stage was submitted from the DAGScheduler to a TaskScheduler. */ var submissionTime: Option[Long] = None @@ -78,6 +79,7 @@ private[spark] object StageInfo { stage.name, numTasks.getOrElse(stage.numTasks), rddInfos, + stage.parents.map(_.id), stage.details) } } diff --git a/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala b/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala index ad53a3edc7cc1..96062626b5045 100644 --- a/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala +++ b/core/src/main/scala/org/apache/spark/storage/RDDInfo.scala @@ -18,7 +18,7 @@ package org.apache.spark.storage import org.apache.spark.annotation.DeveloperApi -import org.apache.spark.rdd.RDD +import org.apache.spark.rdd.{RDDOperationScope, RDD} import org.apache.spark.util.Utils @DeveloperApi @@ -26,7 +26,9 @@ class RDDInfo( val id: Int, val name: String, val numPartitions: Int, - var storageLevel: StorageLevel) + var storageLevel: StorageLevel, + val parentIds: Seq[Int], + val scope: Option[RDDOperationScope] = None) extends Ordered[RDDInfo] { var numCachedPartitions = 0 @@ -52,7 +54,8 @@ class RDDInfo( private[spark] object RDDInfo { def fromRdd(rdd: RDD[_]): RDDInfo = { - val rddName = Option(rdd.name).getOrElse(rdd.id.toString) - new RDDInfo(rdd.id, rddName, rdd.partitions.length, rdd.getStorageLevel) + val rddName = Option(rdd.name).getOrElse(Utils.getFormattedClassName(rdd)) + val parentIds = rdd.dependencies.map(_.rdd.id) + new RDDInfo(rdd.id, rddName, rdd.partitions.length, rdd.getStorageLevel, parentIds, rdd.scope) } } diff --git a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala index 06fce86bd38d2..a5271f0574e6c 100644 --- a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala +++ b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala @@ -25,6 +25,7 @@ import org.apache.spark.ui.env.{EnvironmentListener, EnvironmentTab} import org.apache.spark.ui.exec.{ExecutorsListener, ExecutorsTab} import org.apache.spark.ui.jobs.{JobsTab, JobProgressListener, StagesTab} import org.apache.spark.ui.storage.{StorageListener, StorageTab} +import org.apache.spark.ui.scope.RDDOperationGraphListener /** * Top level user interface for a Spark application. @@ -38,6 +39,7 @@ private[spark] class SparkUI private ( val executorsListener: ExecutorsListener, val jobProgressListener: JobProgressListener, val storageListener: StorageListener, + val operationGraphListener: RDDOperationGraphListener, var appName: String, val basePath: String) extends WebUI(securityManager, SparkUI.getUIPort(conf), conf, basePath, "SparkUI") @@ -93,6 +95,9 @@ private[spark] abstract class SparkUITab(parent: SparkUI, prefix: String) private[spark] object SparkUI { val DEFAULT_PORT = 4040 val STATIC_RESOURCE_DIR = "org/apache/spark/ui/static" + val DEFAULT_POOL_NAME = "default" + val DEFAULT_RETAINED_STAGES = 1000 + val DEFAULT_RETAINED_JOBS = 1000 def getUIPort(conf: SparkConf): Int = { conf.getInt("spark.ui.port", SparkUI.DEFAULT_PORT) @@ -144,13 +149,16 @@ private[spark] object SparkUI { val storageStatusListener = new StorageStatusListener val executorsListener = new ExecutorsListener(storageStatusListener) val storageListener = new StorageListener(storageStatusListener) + val operationGraphListener = new RDDOperationGraphListener(conf) listenerBus.addListener(environmentListener) listenerBus.addListener(storageStatusListener) listenerBus.addListener(executorsListener) listenerBus.addListener(storageListener) + listenerBus.addListener(operationGraphListener) new SparkUI(sc, conf, securityManager, environmentListener, storageStatusListener, - executorsListener, _jobProgressListener, storageListener, appName, basePath) + executorsListener, _jobProgressListener, storageListener, operationGraphListener, + appName, basePath) } } diff --git a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala index 395af2ea30b9d..2f3fb181e4026 100644 --- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala +++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala @@ -23,6 +23,7 @@ import java.util.{Locale, Date} import scala.xml.{Node, Text} import org.apache.spark.Logging +import org.apache.spark.ui.scope.RDDOperationGraph /** Utility functions for generating XML pages with spark content. */ private[spark] object UIUtils extends Logging { @@ -172,13 +173,21 @@ private[spark] object UIUtils extends Logging { } + def vizHeaderNodes: Seq[Node] = { + + + + + } + /** Returns a spark page with correctly formatted headers */ def headerSparkPage( title: String, content: => Seq[Node], activeTab: SparkUITab, refreshInterval: Option[Int] = None, - helpText: Option[String] = None): Seq[Node] = { + helpText: Option[String] = None, + showVisualization: Boolean = false): Seq[Node] = { val appName = activeTab.appName val shortAppName = if (appName.length < 36) appName else appName.take(32) + "..." @@ -196,6 +205,7 @@ private[spark] object UIUtils extends Logging { {commonHeaderNodes} + {if (showVisualization) vizHeaderNodes else Seq.empty} {appName} - {title} @@ -320,4 +330,47 @@ private[spark] object UIUtils extends Logging {
    } + + /** Return a "DAG visualization" DOM element that expands into a visualization for a stage. */ + def showDagVizForStage(stageId: Int, graph: Option[RDDOperationGraph]): Seq[Node] = { + showDagViz(graph.toSeq, forJob = false) + } + + /** Return a "DAG visualization" DOM element that expands into a visualization for a job. */ + def showDagVizForJob(jobId: Int, graphs: Seq[RDDOperationGraph]): Seq[Node] = { + showDagViz(graphs, forJob = true) + } + + /** + * Return a "DAG visualization" DOM element that expands into a visualization on the UI. + * + * This populates metadata necessary for generating the visualization on the front-end in + * a format that is expected by spark-dag-viz.js. Any changes in the format here must be + * reflected there. + */ + private def showDagViz(graphs: Seq[RDDOperationGraph], forJob: Boolean): Seq[Node] = { +
    + + + DAG visualization + +
    +
    + { + graphs.map { g => + + } + } +
    +
    + } } diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala index a7ea12b1655fe..f6abf27db49dd 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/AllJobsPage.scala @@ -179,7 +179,7 @@ private[ui] class AllJobsPage(parent: JobsTab) extends WebUIPage("") { - Event Timeline + Event timeline ++

    Did you specify the correct logging directory? From 6be918942c4078692d169d72fa9c358f6e98e85e Mon Sep 17 00:00:00 2001 From: Liang-Chi Hsieh Date: Tue, 14 Apr 2015 23:47:16 -0700 Subject: [PATCH 290/591] [SPARK-6871][SQL] WITH clause in CTE can not following another WITH clause JIRA https://issues.apache.org/jira/browse/SPARK-6871 Author: Liang-Chi Hsieh Closes #5480 from viirya/no_cte_after_cte and squashes the following commits: 4da3712 [Liang-Chi Hsieh] Create new test. 40b38ed [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into no_cte_after_cte 0edf568 [Liang-Chi Hsieh] for comments. 6591b79 [Liang-Chi Hsieh] WITH clause in CTE can not following another WITH clause. --- .../apache/spark/sql/catalyst/SqlParser.scala | 18 +++++++++--------- .../org/apache/spark/sql/SQLQuerySuite.scala | 6 ++++++ 2 files changed, 15 insertions(+), 9 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala index bc8d3751f6616..9a3531ceb3343 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala @@ -121,14 +121,14 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser { } protected lazy val start: Parser[LogicalPlan] = - ( (select | ("(" ~> select <~ ")")) * - ( UNION ~ ALL ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Union(q1, q2) } - | INTERSECT ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Intersect(q1, q2) } - | EXCEPT ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Except(q1, q2)} - | UNION ~ DISTINCT.? ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Distinct(Union(q1, q2)) } - ) - | insert - | cte + start1 | insert | cte + + protected lazy val start1: Parser[LogicalPlan] = + (select | ("(" ~> select <~ ")")) * + ( UNION ~ ALL ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Union(q1, q2) } + | INTERSECT ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Intersect(q1, q2) } + | EXCEPT ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Except(q1, q2)} + | UNION ~ DISTINCT.? ^^^ { (q1: LogicalPlan, q2: LogicalPlan) => Distinct(Union(q1, q2)) } ) protected lazy val select: Parser[LogicalPlan] = @@ -159,7 +159,7 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser { } protected lazy val cte: Parser[LogicalPlan] = - WITH ~> rep1sep(ident ~ ( AS ~ "(" ~> start <~ ")"), ",") ~ start ^^ { + WITH ~> rep1sep(ident ~ ( AS ~ "(" ~> start1 <~ ")"), ",") ~ (start1 | insert) ^^ { case r ~ s => With(s, r.map({case n ~ s => (n, Subquery(n, s))}).toMap) } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala index 73fb791c3ead7..0174aaee94246 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala @@ -431,6 +431,12 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { } + test("Allow only a single WITH clause per query") { + intercept[RuntimeException] { + sql("with q1 as (select * from testData) with q2 as (select * from q1) select * from q2") + } + } + test("date row") { checkAnswer(sql( """select cast("2015-01-28" as date) from testData limit 1"""), From 29aabdd6c20197adb16706823a8c7f48a0074352 Mon Sep 17 00:00:00 2001 From: Daoyuan Wang Date: Wed, 15 Apr 2015 10:23:53 +0100 Subject: [PATCH 291/591] [HOTFIX] [SPARK-6896] [SQL] fix compile error in hive-thriftserver SPARK-6440 #5424 import guava but did not promote guava dependency to compile level. [INFO] compiler plugin: BasicArtifact(org.scalamacros,paradise_2.10.4,2.0.1,null) [info] Compiling 8 Scala sources to /root/projects/spark/sql/hive-thriftserver/target/scala-2.10/classes... [error] bad symbolic reference. A signature in Utils.class refers to term util [error] in package com.google.common which is not available. [error] It may be completely missing from the current classpath, or the version on [error] the classpath might be incompatible with the version used when compiling Utils.class. [error] [error] while compiling: /root/projects/spark/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala [error] during phase: erasure [error] library version: version 2.10.4 [error] compiler version: version 2.10.4 [error] reconstructed args: -deprecation -classpath Author: Daoyuan Wang Closes #5507 from adrian-wang/guava and squashes the following commits: c337dad [Daoyuan Wang] fix compile error --- sql/hive-thriftserver/pom.xml | 1 - 1 file changed, 1 deletion(-) diff --git a/sql/hive-thriftserver/pom.xml b/sql/hive-thriftserver/pom.xml index a96b1ffc26966..f38c796241df1 100644 --- a/sql/hive-thriftserver/pom.xml +++ b/sql/hive-thriftserver/pom.xml @@ -44,7 +44,6 @@ com.google.guava guava - runtime ${hive.group} From 6c5ed8a6d552abd967d27cdb94b68d46ccb57221 Mon Sep 17 00:00:00 2001 From: Sean Owen Date: Wed, 15 Apr 2015 15:17:58 +0100 Subject: [PATCH 292/591] SPARK-6861 [BUILD] Scalastyle config prevents building Maven child modules alone Move scalastyle-config.xml to dev/ (SBT config still doesn't work) to fix running mvn targets from subdirs; make scalastyle a verify stage target again in Maven; output results in target not project root; update to scalastyle 0.7.0 Author: Sean Owen Closes #5471 from srowen/SPARK-6861 and squashes the following commits: acac637 [Sean Owen] Oops, add back execution but leave it at the default verify phase 35a4fd2 [Sean Owen] Revert change to scalastyle-config.xml location, but return scalastyle Maven check to verify phase instead of package to get it farther out of the way, since the Maven invocation is optional c4fb42c [Sean Owen] Move scalastyle-config.xml to dev/ (SBT config still doesn't work) to fix running mvn targets from subdirs; make scalastyle a verify stage target again in Maven; output results in target not project root; update to scalastyle 0.7.0 --- pom.xml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/pom.xml b/pom.xml index 261292d5b6cde..bcc2f57f1af5d 100644 --- a/pom.xml +++ b/pom.xml @@ -1447,7 +1447,7 @@ org.scalastyle scalastyle-maven-plugin - 0.4.0 + 0.7.0 false true @@ -1456,13 +1456,12 @@ ${basedir}/src/main/scala ${basedir}/src/test/scala scalastyle-config.xml - scalastyle-output.xml + ${basedir}/target/scalastyle-output.xml ${project.build.sourceEncoding} ${project.reporting.outputEncoding} - package check From f11288d5272bc18585b8cad4ee3bd59eade7c296 Mon Sep 17 00:00:00 2001 From: Davies Liu Date: Wed, 15 Apr 2015 12:58:02 -0700 Subject: [PATCH 293/591] [SPARK-6886] [PySpark] fix big closure with shuffle Currently, the created broadcast object will have same life cycle as RDD in Python. For multistage jobs, an PythonRDD will be created in JVM and the RDD in Python may be GCed, then the broadcast will be destroyed in JVM before the PythonRDD. This PR change to use PythonRDD to track the lifecycle of the broadcast object. It also have a refactor about getNumPartitions() to avoid unnecessary creation of PythonRDD, which could be heavy. cc JoshRosen Author: Davies Liu Closes #5496 from davies/big_closure and squashes the following commits: 9a0ea4c [Davies Liu] fix big closure with shuffle --- python/pyspark/rdd.py | 15 +++++---------- python/pyspark/tests.py | 6 ++---- 2 files changed, 7 insertions(+), 14 deletions(-) diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py index c9ac95d117574..93e658eded9e2 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.py @@ -1197,7 +1197,7 @@ def take(self, num): [91, 92, 93] """ items = [] - totalParts = self._jrdd.partitions().size() + totalParts = self.getNumPartitions() partsScanned = 0 while len(items) < num and partsScanned < totalParts: @@ -1260,7 +1260,7 @@ def isEmpty(self): >>> sc.parallelize([1]).isEmpty() False """ - return self._jrdd.partitions().size() == 0 or len(self.take(1)) == 0 + return self.getNumPartitions() == 0 or len(self.take(1)) == 0 def saveAsNewAPIHadoopDataset(self, conf, keyConverter=None, valueConverter=None): """ @@ -2235,11 +2235,9 @@ def _prepare_for_python_RDD(sc, command, obj=None): ser = CloudPickleSerializer() pickled_command = ser.dumps((command, sys.version_info[:2])) if len(pickled_command) > (1 << 20): # 1M + # The broadcast will have same life cycle as created PythonRDD broadcast = sc.broadcast(pickled_command) pickled_command = ser.dumps(broadcast) - # tracking the life cycle by obj - if obj is not None: - obj._broadcast = broadcast broadcast_vars = ListConverter().convert( [x._jbroadcast for x in sc._pickled_broadcast_vars], sc._gateway._gateway_client) @@ -2294,12 +2292,9 @@ def pipeline_func(split, iterator): self._jrdd_deserializer = self.ctx.serializer self._bypass_serializer = False self.partitioner = prev.partitioner if self.preservesPartitioning else None - self._broadcast = None - def __del__(self): - if self._broadcast: - self._broadcast.unpersist() - self._broadcast = None + def getNumPartitions(self): + return self._prev_jrdd.partitions().size() @property def _jrdd(self): diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py index b938b9ce12395..ee67e80d539f8 100644 --- a/python/pyspark/tests.py +++ b/python/pyspark/tests.py @@ -550,10 +550,8 @@ def test_large_closure(self): data = [float(i) for i in xrange(N)] rdd = self.sc.parallelize(range(1), 1).map(lambda x: len(data)) self.assertEquals(N, rdd.first()) - self.assertTrue(rdd._broadcast is not None) - rdd = self.sc.parallelize(range(1), 1).map(lambda x: 1) - self.assertEqual(1, rdd.first()) - self.assertTrue(rdd._broadcast is None) + # regression test for SPARK-6886 + self.assertEqual(1, rdd.map(lambda x: (x, 1)).groupByKey().count()) def test_zip_with_different_serializers(self): a = self.sc.parallelize(range(5)) From b75b3070740803480d235b0c9a86673721344f30 Mon Sep 17 00:00:00 2001 From: Liang-Chi Hsieh Date: Wed, 15 Apr 2015 13:00:19 -0700 Subject: [PATCH 294/591] [SPARK-6730][SQL] Allow using keyword as identifier in OPTIONS JIRA: https://issues.apache.org/jira/browse/SPARK-6730 It is very possible that keyword will be used as identifier in `OPTIONS`, this pr makes it works. However, another approach is that we can request that `OPTIONS` can't include keywords and has to use alternative identifier (e.g. table -> cassandraTable) if needed. If so, please let me know to close this pr. Thanks. Author: Liang-Chi Hsieh Closes #5520 from viirya/relax_options and squashes the following commits: 339fd68 [Liang-Chi Hsieh] Use regex parser. 92be11c [Liang-Chi Hsieh] Allow using keyword as identifier in OPTIONS. --- .../scala/org/apache/spark/sql/sources/ddl.scala | 15 ++++++++++++++- .../apache/spark/sql/sources/DDLTestSuite.scala | 11 ++++++----- 2 files changed, 20 insertions(+), 6 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala index 319de710fbc3e..2e861b84b7133 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala @@ -18,6 +18,7 @@ package org.apache.spark.sql.sources import scala.language.existentials +import scala.util.matching.Regex import scala.language.implicitConversions import org.apache.spark.Logging @@ -155,7 +156,19 @@ private[sql] class DDLParser( protected lazy val className: Parser[String] = repsep(ident, ".") ^^ { case s => s.mkString(".")} - protected lazy val pair: Parser[(String, String)] = ident ~ stringLit ^^ { case k ~ v => (k,v) } + override implicit def regexToParser(regex: Regex): Parser[String] = acceptMatch( + s"identifier matching regex ${regex}", { + case lexical.Identifier(str) if regex.unapplySeq(str).isDefined => str + case lexical.Keyword(str) if regex.unapplySeq(str).isDefined => str + } + ) + + protected lazy val optionName: Parser[String] = "[_a-zA-Z][a-zA-Z0-9]*".r ^^ { + case name => name + } + + protected lazy val pair: Parser[(String, String)] = + optionName ~ stringLit ^^ { case k ~ v => (k,v) } protected lazy val column: Parser[StructField] = ident ~ dataType ~ (COMMENT ~> stringLit).? ^^ { case columnName ~ typ ~ cm => diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala index 3f24a497390c1..ca25751b9583d 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala @@ -25,17 +25,17 @@ class DDLScanSource extends RelationProvider { override def createRelation( sqlContext: SQLContext, parameters: Map[String, String]): BaseRelation = { - SimpleDDLScan(parameters("from").toInt, parameters("TO").toInt)(sqlContext) + SimpleDDLScan(parameters("from").toInt, parameters("TO").toInt, parameters("Table"))(sqlContext) } } -case class SimpleDDLScan(from: Int, to: Int)(@transient val sqlContext: SQLContext) +case class SimpleDDLScan(from: Int, to: Int, table: String)(@transient val sqlContext: SQLContext) extends BaseRelation with TableScan { override def schema: StructType = StructType(Seq( StructField("intType", IntegerType, nullable = false, - new MetadataBuilder().putString("comment", "test comment").build()), + new MetadataBuilder().putString("comment", s"test comment $table").build()), StructField("stringType", StringType, nullable = false), StructField("dateType", DateType, nullable = false), StructField("timestampType", TimestampType, nullable = false), @@ -73,7 +73,8 @@ class DDLTestSuite extends DataSourceTest { |USING org.apache.spark.sql.sources.DDLScanSource |OPTIONS ( | From '1', - | To '10' + | To '10', + | Table 'test1' |) """.stripMargin) } @@ -81,7 +82,7 @@ class DDLTestSuite extends DataSourceTest { sqlTest( "describe ddlPeople", Seq( - Row("intType", "int", "test comment"), + Row("intType", "int", "test comment test1"), Row("stringType", "string", ""), Row("dateType", "date", ""), Row("timestampType", "timestamp", ""), From e3e4e9a38b25174ed8bb460ba2b375813ebf3b4b Mon Sep 17 00:00:00 2001 From: Liang-Chi Hsieh Date: Wed, 15 Apr 2015 13:01:29 -0700 Subject: [PATCH 295/591] [SPARK-6800][SQL] Update doc for JDBCRelation's columnPartition JIRA https://issues.apache.org/jira/browse/SPARK-6800 Author: Liang-Chi Hsieh Closes #5488 from viirya/fix_jdbc_where and squashes the following commits: 51386c8 [Liang-Chi Hsieh] Update code comment. 1dcc929 [Liang-Chi Hsieh] Update document. 3eb74d6 [Liang-Chi Hsieh] Revert and modify doc. df11783 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into fix_jdbc_where 3e7db15 [Liang-Chi Hsieh] Fix wrong logic to generate WHERE clause for JDBC. --- docs/sql-programming-guide.md | 5 ++++- .../src/main/scala/org/apache/spark/sql/SQLContext.scala | 4 ++-- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index 332618edf0c55..03500867df70f 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -1371,7 +1371,10 @@ the Data Sources API. The following options are supported: These options must all be specified if any of them is specified. They describe how to partition the table when reading in parallel from multiple workers. - partitionColumn must be a numeric column from the table in question. + partitionColumn must be a numeric column from the table in question. Notice + that lowerBound and upperBound are just used to decide the + partition stride, not for filtering the rows in table. So all rows in the table will be + partitioned and returned. diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala index c25ef58e6f62a..b237fe684cdc1 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala @@ -873,8 +873,8 @@ class SQLContext(@transient val sparkContext: SparkContext) * passed to this function. * * @param columnName the name of a column of integral type that will be used for partitioning. - * @param lowerBound the minimum value of `columnName` to retrieve - * @param upperBound the maximum value of `columnName` to retrieve + * @param lowerBound the minimum value of `columnName` used to decide partition stride + * @param upperBound the maximum value of `columnName` used to decide partition stride * @param numPartitions the number of partitions. the range `minValue`-`maxValue` will be split * evenly into this many partitions * From 785f95586b951d7b05481ee925fb95c20c4d6b6f Mon Sep 17 00:00:00 2001 From: Yin Huai Date: Wed, 15 Apr 2015 13:04:03 -0700 Subject: [PATCH 296/591] [SPARK-6887][SQL] ColumnBuilder misses FloatType https://issues.apache.org/jira/browse/SPARK-6887 Author: Yin Huai Closes #5499 from yhuai/inMemFloat and squashes the following commits: 84cba38 [Yin Huai] Add test. 4b75ba6 [Yin Huai] Add FloatType back. --- .../spark/sql/columnar/ColumnBuilder.scala | 1 + .../org/apache/spark/sql/QueryTest.scala | 3 + .../columnar/InMemoryColumnarQuerySuite.scala | 59 ++++++++++++++++++- 3 files changed, 62 insertions(+), 1 deletion(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala index c881747751520..00ed70430b84d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala @@ -153,6 +153,7 @@ private[sql] object ColumnBuilder { val builder: ColumnBuilder = dataType match { case IntegerType => new IntColumnBuilder case LongType => new LongColumnBuilder + case FloatType => new FloatColumnBuilder case DoubleType => new DoubleColumnBuilder case BooleanType => new BooleanColumnBuilder case ByteType => new ByteColumnBuilder diff --git a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala index 9a81fc5d72819..59f9508444f25 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala @@ -104,9 +104,12 @@ object QueryTest { // Converts data to types that we can do equality comparison using Scala collections. // For BigDecimal type, the Scala type has a better definition of equality test (similar to // Java's java.math.BigDecimal.compareTo). + // For binary arrays, we convert it to Seq to avoid of calling java.util.Arrays.equals for + // equality test. val converted: Seq[Row] = answer.map { s => Row.fromSeq(s.toSeq.map { case d: java.math.BigDecimal => BigDecimal(d) + case b: Array[Byte] => b.toSeq case o => o }) } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala index 479210d1c9c43..56591d9dba29e 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala @@ -17,11 +17,13 @@ package org.apache.spark.sql.columnar +import java.sql.{Date, Timestamp} + import org.apache.spark.sql.TestData._ import org.apache.spark.sql.catalyst.expressions.Row import org.apache.spark.sql.test.TestSQLContext._ import org.apache.spark.sql.test.TestSQLContext.implicits._ -import org.apache.spark.sql.types.{DecimalType, Decimal} +import org.apache.spark.sql.types._ import org.apache.spark.sql.{QueryTest, TestData} import org.apache.spark.storage.StorageLevel.MEMORY_ONLY @@ -132,4 +134,59 @@ class InMemoryColumnarQuerySuite extends QueryTest { sql("SELECT * FROM test_fixed_decimal"), (1 to 10).map(i => Row(Decimal(i, 15, 10).toJavaBigDecimal))) } + + test("test different data types") { + // Create the schema. + val struct = + StructType( + StructField("f1", FloatType, true) :: + StructField("f2", ArrayType(BooleanType), true) :: Nil) + val dataTypes = + Seq(StringType, BinaryType, NullType, BooleanType, + ByteType, ShortType, IntegerType, LongType, + FloatType, DoubleType, DecimalType.Unlimited, DecimalType(6, 5), + DateType, TimestampType, + ArrayType(IntegerType), MapType(StringType, LongType), struct) + val fields = dataTypes.zipWithIndex.map { case (dataType, index) => + StructField(s"col$index", dataType, true) + } + val allColumns = fields.map(_.name).mkString(",") + val schema = StructType(fields) + + // Create a RDD for the schema + val rdd = + sparkContext.parallelize((1 to 100), 10).map { i => + Row( + s"str${i}: test cache.", + s"binary${i}: test cache.".getBytes("UTF-8"), + null, + i % 2 == 0, + i.toByte, + i.toShort, + i, + Long.MaxValue - i.toLong, + (i + 0.25).toFloat, + (i + 0.75), + BigDecimal(Long.MaxValue.toString + ".12345"), + new java.math.BigDecimal(s"${i % 9 + 1}" + ".23456"), + new Date(i), + new Timestamp(i), + (1 to i).toSeq, + (0 to i).map(j => s"map_key_$j" -> (Long.MaxValue - j)).toMap, + Row((i - 0.25).toFloat, (1 to i).toSeq)) + } + createDataFrame(rdd, schema).registerTempTable("InMemoryCache_different_data_types") + // Cache the table. + sql("cache table InMemoryCache_different_data_types") + // Make sure the table is indeed cached. + val tableScan = table("InMemoryCache_different_data_types").queryExecution.executedPlan + assert( + isCached("InMemoryCache_different_data_types"), + "InMemoryCache_different_data_types should be cached.") + // Issue a query and check the results. + checkAnswer( + sql(s"SELECT DISTINCT ${allColumns} FROM InMemoryCache_different_data_types"), + table("InMemoryCache_different_data_types").collect()) + dropTempTable("InMemoryCache_different_data_types") + } } From 85842760dc4616577162f44cc0fa9db9bd23bd9c Mon Sep 17 00:00:00 2001 From: Davies Liu Date: Wed, 15 Apr 2015 13:06:38 -0700 Subject: [PATCH 297/591] [SPARK-6638] [SQL] Improve performance of StringType in SQL This PR change the internal representation for StringType from java.lang.String to UTF8String, which is implemented use ArrayByte. This PR should not break any public API, Row.getString() will still return java.lang.String. This is the first step of improve the performance of String in SQL. cc rxin Author: Davies Liu Closes #5350 from davies/string and squashes the following commits: 3b7bfa8 [Davies Liu] fix schema of AddJar 2772f0d [Davies Liu] fix new test failure 6d776a9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into string 59025c8 [Davies Liu] address comments from @marmbrus 341ec2c [Davies Liu] turn off scala style check in UTF8StringSuite 744788f [Davies Liu] Merge branch 'master' of github.com:apache/spark into string b04a19c [Davies Liu] add comment for getString/setString 08d897b [Davies Liu] Merge branch 'master' of github.com:apache/spark into string 5116b43 [Davies Liu] rollback unrelated changes 1314a37 [Davies Liu] address comments from Yin 867bf50 [Davies Liu] fix String filter push down 13d9d42 [Davies Liu] Merge branch 'master' of github.com:apache/spark into string 2089d24 [Davies Liu] add hashcode check back ac18ae6 [Davies Liu] address comment fd11364 [Davies Liu] optimize UTF8String 8d17f21 [Davies Liu] fix hive compatibility tests e5fa5b8 [Davies Liu] remove clone in UTF8String 28f3d81 [Davies Liu] Merge branch 'master' of github.com:apache/spark into string 28d6f32 [Davies Liu] refactor 537631c [Davies Liu] some comment about Date 9f4c194 [Davies Liu] convert data type for data source 956b0a4 [Davies Liu] fix hive tests 73e4363 [Davies Liu] Merge branch 'master' of github.com:apache/spark into string 9dc32d1 [Davies Liu] fix some hive tests 23a766c [Davies Liu] refactor 8b45864 [Davies Liu] fix codegen with UTF8String bb52e44 [Davies Liu] fix scala style c7dd4d2 [Davies Liu] fix some catalyst tests 38c303e [Davies Liu] fix python sql tests 5f9e120 [Davies Liu] fix sql tests 6b499ac [Davies Liu] fix style a85fb27 [Davies Liu] refactor d32abd1 [Davies Liu] fix utf8 for python api 4699c3a [Davies Liu] use Array[Byte] in UTF8String 21f67c6 [Davies Liu] cleanup 685fd07 [Davies Liu] use UTF8String instead of String for StringType --- python/pyspark/sql/dataframe.py | 10 +- .../main/scala/org/apache/spark/sql/Row.scala | 3 +- .../sql/catalyst/CatalystTypeConverters.scala | 37 +++ .../spark/sql/catalyst/ScalaReflection.scala | 1 + .../catalyst/analysis/HiveTypeCoercion.scala | 6 +- .../spark/sql/catalyst/expressions/Cast.scala | 36 +-- .../expressions/SpecificMutableRow.scala | 12 +- .../expressions/codegen/CodeGenerator.scala | 32 ++- .../codegen/GenerateProjection.scala | 46 ++-- .../sql/catalyst/expressions/generators.scala | 7 +- .../sql/catalyst/expressions/literals.scala | 7 +- .../sql/catalyst/expressions/predicates.scala | 3 +- .../spark/sql/catalyst/expressions/rows.scala | 14 +- .../expressions/stringOperations.scala | 90 ++++---- .../sql/catalyst/optimizer/Optimizer.scala | 21 +- .../apache/spark/sql/types/DateUtils.scala | 1 + .../apache/spark/sql/types/UTF8String.scala | 214 ++++++++++++++++++ .../apache/spark/sql/types/dataTypes.scala | 6 +- .../ExpressionEvaluationSuite.scala | 90 ++++---- .../GeneratedMutableEvaluationSuite.scala | 4 +- .../spark/sql/types/UTF8StringSuite.scala | 70 ++++++ .../org/apache/spark/sql/SQLContext.scala | 1 + .../spark/sql/columnar/ColumnStats.scala | 6 +- .../spark/sql/columnar/ColumnType.scala | 20 +- .../spark/sql/execution/ExistingRDD.scala | 31 ++- .../apache/spark/sql/execution/commands.scala | 13 +- .../spark/sql/execution/debug/package.scala | 2 +- .../spark/sql/execution/pythonUdfs.scala | 4 +- .../org/apache/spark/sql/jdbc/JDBCRDD.scala | 4 +- .../apache/spark/sql/jdbc/JDBCRelation.scala | 2 + .../org/apache/spark/sql/jdbc/jdbc.scala | 5 +- .../apache/spark/sql/json/JSONRelation.scala | 8 +- .../org/apache/spark/sql/json/JsonRDD.scala | 2 +- .../spark/sql/parquet/ParquetConverter.scala | 19 +- .../spark/sql/parquet/ParquetFilters.scala | 12 +- .../sql/parquet/ParquetTableSupport.scala | 7 +- .../apache/spark/sql/parquet/newParquet.scala | 11 +- .../sql/sources/DataSourceStrategy.scala | 37 +-- .../apache/spark/sql/sources/interfaces.scala | 10 + .../scala/org/apache/spark/sql/RowSuite.scala | 2 +- .../org/apache/spark/sql/SQLQuerySuite.scala | 10 +- .../spark/sql/columnar/ColumnTypeSuite.scala | 8 +- .../sql/columnar/ColumnarTestUtils.scala | 4 +- .../spark/sql/sources/TableScanSuite.scala | 10 +- .../spark/sql/hive/HiveInspectors.scala | 22 +- .../spark/sql/hive/HiveStrategies.scala | 13 +- .../hive/execution/ScriptTransformation.scala | 17 +- .../spark/sql/hive/execution/commands.scala | 10 +- .../org/apache/spark/sql/hive/Shim12.scala | 4 +- .../org/apache/spark/sql/hive/Shim13.scala | 36 ++- 50 files changed, 742 insertions(+), 298 deletions(-) create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/UTF8String.scala create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/types/UTF8StringSuite.scala diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index ef91a9c4f522d..f2c3b74a185cf 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -456,7 +456,7 @@ def join(self, other, joinExprs=None, joinType=None): One of `inner`, `outer`, `left_outer`, `right_outer`, `semijoin`. >>> df.join(df2, df.name == df2.name, 'outer').select(df.name, df2.height).collect() - [Row(name=None, height=80), Row(name=u'Bob', height=85), Row(name=u'Alice', height=None)] + [Row(name=None, height=80), Row(name=u'Alice', height=None), Row(name=u'Bob', height=85)] """ if joinExprs is None: @@ -637,9 +637,9 @@ def groupBy(self, *cols): >>> df.groupBy().avg().collect() [Row(AVG(age)=3.5)] >>> df.groupBy('name').agg({'age': 'mean'}).collect() - [Row(name=u'Bob', AVG(age)=5.0), Row(name=u'Alice', AVG(age)=2.0)] + [Row(name=u'Alice', AVG(age)=2.0), Row(name=u'Bob', AVG(age)=5.0)] >>> df.groupBy(df.name).avg().collect() - [Row(name=u'Bob', AVG(age)=5.0), Row(name=u'Alice', AVG(age)=2.0)] + [Row(name=u'Alice', AVG(age)=2.0), Row(name=u'Bob', AVG(age)=5.0)] """ jcols = ListConverter().convert([_to_java_column(c) for c in cols], self._sc._gateway._gateway_client) @@ -867,11 +867,11 @@ def agg(self, *exprs): >>> gdf = df.groupBy(df.name) >>> gdf.agg({"*": "count"}).collect() - [Row(name=u'Bob', COUNT(1)=1), Row(name=u'Alice', COUNT(1)=1)] + [Row(name=u'Alice', COUNT(1)=1), Row(name=u'Bob', COUNT(1)=1)] >>> from pyspark.sql import functions as F >>> gdf.agg(F.min(df.age)).collect() - [Row(MIN(age)=5), Row(MIN(age)=2)] + [Row(MIN(age)=2), Row(MIN(age)=5)] """ assert exprs, "exprs should not be empty" if len(exprs) == 1 and isinstance(exprs[0], dict): diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala index d794f034f5578..ac8a782976465 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala @@ -20,7 +20,7 @@ package org.apache.spark.sql import scala.util.hashing.MurmurHash3 import org.apache.spark.sql.catalyst.expressions.GenericRow -import org.apache.spark.sql.types.{StructType, DateUtils} +import org.apache.spark.sql.types.StructType object Row { /** @@ -257,6 +257,7 @@ trait Row extends Serializable { * * @throws ClassCastException when data type does not match. */ + // TODO(davies): This is not the right default implementation, we use Int as Date internally def getDate(i: Int): java.sql.Date = apply(i).asInstanceOf[java.sql.Date] /** diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala index 91976fef6dc0d..d4f9fdacda4fb 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala @@ -77,6 +77,9 @@ object CatalystTypeConverters { } new GenericRowWithSchema(ar, structType) + case (d: String, _) => + UTF8String(d) + case (d: BigDecimal, _) => Decimal(d) @@ -175,6 +178,11 @@ object CatalystTypeConverters { case other => other } + case dataType: StringType => (item: Any) => extractOption(item) match { + case s: String => UTF8String(s) + case other => other + } + case _ => (item: Any) => extractOption(item) match { case d: BigDecimal => Decimal(d) @@ -184,6 +192,26 @@ object CatalystTypeConverters { } } + /** + * Converts Scala objects to catalyst rows / types. + * + * Note: This should be called before do evaluation on Row + * (It does not support UDT) + * This is used to create an RDD or test results with correct types for Catalyst. + */ + def convertToCatalyst(a: Any): Any = a match { + case s: String => UTF8String(s) + case d: java.sql.Date => DateUtils.fromJavaDate(d) + case d: BigDecimal => Decimal(d) + case d: java.math.BigDecimal => Decimal(d) + case seq: Seq[Any] => seq.map(convertToCatalyst) + case r: Row => Row(r.toSeq.map(convertToCatalyst): _*) + case arr: Array[Any] => arr.toSeq.map(convertToCatalyst).toArray + case m: Map[Any, Any] => + m.map { case (k, v) => (convertToCatalyst(k), convertToCatalyst(v)) }.toMap + case other => other + } + /** * Converts Catalyst types used internally in rows to standard Scala types * This method is slow, and for batch conversion you should be using converter @@ -211,6 +239,9 @@ object CatalystTypeConverters { case (i: Int, DateType) => DateUtils.toJavaDate(i) + case (s: UTF8String, StringType) => + s.toString() + case (other, _) => other } @@ -262,6 +293,12 @@ object CatalystTypeConverters { case other => other } + case StringType => + (item: Any) => item match { + case s: UTF8String => s.toString() + case other => other + } + case other => (item: Any) => item } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala index 01d5c1512201a..d9521953cad73 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala @@ -138,6 +138,7 @@ trait ScalaReflection { // The data type can be determined without ambiguity. case obj: BooleanType.JvmType => BooleanType case obj: BinaryType.JvmType => BinaryType + case obj: String => StringType case obj: StringType.JvmType => StringType case obj: ByteType.JvmType => ByteType case obj: ShortType.JvmType => ShortType diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala index 3aeb964994d37..35c7f00d4e42a 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala @@ -115,7 +115,7 @@ trait HiveTypeCoercion { * the appropriate numeric equivalent. */ object ConvertNaNs extends Rule[LogicalPlan] { - val stringNaN = Literal.create("NaN", StringType) + val stringNaN = Literal("NaN") def apply(plan: LogicalPlan): LogicalPlan = plan transform { case q: LogicalPlan => q transformExpressions { @@ -563,6 +563,10 @@ trait HiveTypeCoercion { case Sum(e @ TimestampType()) => Sum(Cast(e, DoubleType)) case Average(e @ TimestampType()) => Average(Cast(e, DoubleType)) + // Compatible with Hive + case Substring(e, start, len) if e.dataType != StringType => + Substring(Cast(e, StringType), start, len) + // Coalesce should return the first non-null value, which could be any column // from the list. So we need to make sure the return type is deterministic and // compatible with every child column. diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala index 31f1a5fdc7e53..adf941ab2a45f 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala @@ -21,7 +21,6 @@ import java.sql.{Date, Timestamp} import java.text.{DateFormat, SimpleDateFormat} import org.apache.spark.Logging -import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.types._ /** Cast the child expression to the target data type. */ @@ -112,21 +111,21 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w // UDFToString private[this] def castToString(from: DataType): Any => Any = from match { - case BinaryType => buildCast[Array[Byte]](_, new String(_, "UTF-8")) - case DateType => buildCast[Int](_, d => DateUtils.toString(d)) - case TimestampType => buildCast[Timestamp](_, timestampToString) - case _ => buildCast[Any](_, _.toString) + case BinaryType => buildCast[Array[Byte]](_, UTF8String(_)) + case DateType => buildCast[Int](_, d => UTF8String(DateUtils.toString(d))) + case TimestampType => buildCast[Timestamp](_, t => UTF8String(timestampToString(t))) + case _ => buildCast[Any](_, o => UTF8String(o.toString)) } // BinaryConverter private[this] def castToBinary(from: DataType): Any => Any = from match { - case StringType => buildCast[String](_, _.getBytes("UTF-8")) + case StringType => buildCast[UTF8String](_, _.getBytes) } // UDFToBoolean private[this] def castToBoolean(from: DataType): Any => Any = from match { case StringType => - buildCast[String](_, _.length() != 0) + buildCast[UTF8String](_, _.length() != 0) case TimestampType => buildCast[Timestamp](_, t => t.getTime() != 0 || t.getNanos() != 0) case DateType => @@ -151,8 +150,9 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w // TimestampConverter private[this] def castToTimestamp(from: DataType): Any => Any = from match { case StringType => - buildCast[String](_, s => { + buildCast[UTF8String](_, utfs => { // Throw away extra if more than 9 decimal places + val s = utfs.toString val periodIdx = s.indexOf(".") var n = s if (periodIdx != -1 && n.length() - periodIdx > 9) { @@ -227,8 +227,8 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w // DateConverter private[this] def castToDate(from: DataType): Any => Any = from match { case StringType => - buildCast[String](_, s => - try DateUtils.fromJavaDate(Date.valueOf(s)) + buildCast[UTF8String](_, s => + try DateUtils.fromJavaDate(Date.valueOf(s.toString)) catch { case _: java.lang.IllegalArgumentException => null } ) case TimestampType => @@ -245,7 +245,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w // LongConverter private[this] def castToLong(from: DataType): Any => Any = from match { case StringType => - buildCast[String](_, s => try s.toLong catch { + buildCast[UTF8String](_, s => try s.toString.toLong catch { case _: NumberFormatException => null }) case BooleanType => @@ -261,7 +261,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w // IntConverter private[this] def castToInt(from: DataType): Any => Any = from match { case StringType => - buildCast[String](_, s => try s.toInt catch { + buildCast[UTF8String](_, s => try s.toString.toInt catch { case _: NumberFormatException => null }) case BooleanType => @@ -277,7 +277,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w // ShortConverter private[this] def castToShort(from: DataType): Any => Any = from match { case StringType => - buildCast[String](_, s => try s.toShort catch { + buildCast[UTF8String](_, s => try s.toString.toShort catch { case _: NumberFormatException => null }) case BooleanType => @@ -293,7 +293,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w // ByteConverter private[this] def castToByte(from: DataType): Any => Any = from match { case StringType => - buildCast[String](_, s => try s.toByte catch { + buildCast[UTF8String](_, s => try s.toString.toByte catch { case _: NumberFormatException => null }) case BooleanType => @@ -323,7 +323,9 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w private[this] def castToDecimal(from: DataType, target: DecimalType): Any => Any = from match { case StringType => - buildCast[String](_, s => try changePrecision(Decimal(s.toDouble), target) catch { + buildCast[UTF8String](_, s => try { + changePrecision(Decimal(s.toString.toDouble), target) + } catch { case _: NumberFormatException => null }) case BooleanType => @@ -348,7 +350,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w // DoubleConverter private[this] def castToDouble(from: DataType): Any => Any = from match { case StringType => - buildCast[String](_, s => try s.toDouble catch { + buildCast[UTF8String](_, s => try s.toString.toDouble catch { case _: NumberFormatException => null }) case BooleanType => @@ -364,7 +366,7 @@ case class Cast(child: Expression, dataType: DataType) extends UnaryExpression w // FloatConverter private[this] def castToFloat(from: DataType): Any => Any = from match { case StringType => - buildCast[String](_, s => try s.toFloat catch { + buildCast[UTF8String](_, s => try s.toString.toFloat catch { case _: NumberFormatException => null }) case BooleanType => diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala index 47b6f358ed1b1..3475ed05f4454 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala @@ -230,13 +230,17 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR new GenericRow(newValues) } - override def update(ordinal: Int, value: Any): Unit = { - if (value == null) setNullAt(ordinal) else values(ordinal).update(value) + override def update(ordinal: Int, value: Any) { + if (value == null) { + setNullAt(ordinal) + } else { + values(ordinal).update(value) + } } - override def setString(ordinal: Int, value: String): Unit = update(ordinal, value) + override def setString(ordinal: Int, value: String): Unit = update(ordinal, UTF8String(value)) - override def getString(ordinal: Int): String = apply(ordinal).asInstanceOf[String] + override def getString(ordinal: Int): String = apply(ordinal).toString override def setInt(ordinal: Int, value: Int): Unit = { val currentValue = values(ordinal).asInstanceOf[MutableInt] diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala index d141354a0f427..be2c101d63a63 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala @@ -216,10 +216,11 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin val $primitiveTerm: ${termForType(dataType)} = $value """.children - case expressions.Literal(value: String, dataType) => + case expressions.Literal(value: UTF8String, dataType) => q""" val $nullTerm = ${value == null} - val $primitiveTerm: ${termForType(dataType)} = $value + val $primitiveTerm: ${termForType(dataType)} = + org.apache.spark.sql.types.UTF8String(${value.getBytes}) """.children case expressions.Literal(value: Int, dataType) => @@ -243,11 +244,14 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin if($nullTerm) ${defaultPrimitive(StringType)} else - new String(${eval.primitiveTerm}.asInstanceOf[Array[Byte]]) + org.apache.spark.sql.types.UTF8String(${eval.primitiveTerm}.asInstanceOf[Array[Byte]]) """.children case Cast(child @ DateType(), StringType) => - child.castOrNull(c => q"org.apache.spark.sql.types.DateUtils.toString($c)", StringType) + child.castOrNull(c => + q"""org.apache.spark.sql.types.UTF8String( + org.apache.spark.sql.types.DateUtils.toString($c))""", + StringType) case Cast(child @ NumericType(), IntegerType) => child.castOrNull(c => q"$c.toInt", IntegerType) @@ -272,9 +276,18 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin if($nullTerm) ${defaultPrimitive(StringType)} else - ${eval.primitiveTerm}.toString + org.apache.spark.sql.types.UTF8String(${eval.primitiveTerm}.toString) """.children + case EqualTo(e1: BinaryType, e2: BinaryType) => + (e1, e2).evaluateAs (BooleanType) { + case (eval1, eval2) => + q""" + java.util.Arrays.equals($eval1.asInstanceOf[Array[Byte]], + $eval2.asInstanceOf[Array[Byte]]) + """ + } + case EqualTo(e1, e2) => (e1, e2).evaluateAs (BooleanType) { case (eval1, eval2) => q"$eval1 == $eval2" } @@ -597,7 +610,8 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin val localLogger = log val localLoggerTree = reify { localLogger } q""" - $localLoggerTree.debug(${e.toString} + ": " + (if($nullTerm) "null" else $primitiveTerm)) + $localLoggerTree.debug( + ${e.toString} + ": " + (if ($nullTerm) "null" else $primitiveTerm.toString)) """ :: Nil } else { Nil @@ -608,6 +622,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin protected def getColumn(inputRow: TermName, dataType: DataType, ordinal: Int) = { dataType match { + case StringType => q"$inputRow($ordinal).asInstanceOf[org.apache.spark.sql.types.UTF8String]" case dt @ NativeType() => q"$inputRow.${accessorForType(dt)}($ordinal)" case _ => q"$inputRow.apply($ordinal).asInstanceOf[${termForType(dataType)}]" } @@ -619,6 +634,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin ordinal: Int, value: TermName) = { dataType match { + case StringType => q"$destinationRow.update($ordinal, $value)" case dt @ NativeType() => q"$destinationRow.${mutatorForType(dt)}($ordinal, $value)" case _ => q"$destinationRow.update($ordinal, $value)" } @@ -642,13 +658,13 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin case DoubleType => "Double" case FloatType => "Float" case BooleanType => "Boolean" - case StringType => "String" + case StringType => "org.apache.spark.sql.types.UTF8String" } protected def defaultPrimitive(dt: DataType) = dt match { case BooleanType => ru.Literal(Constant(false)) case FloatType => ru.Literal(Constant(-1.0.toFloat)) - case StringType => ru.Literal(Constant("")) + case StringType => q"""org.apache.spark.sql.types.UTF8String("")""" case ShortType => ru.Literal(Constant(-1.toShort)) case LongType => ru.Literal(Constant(-1L)) case ByteType => ru.Literal(Constant(-1.toByte)) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala index 69397a73a8880..6f572ff959fb4 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala @@ -111,36 +111,54 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] { val specificAccessorFunctions = NativeType.all.map { dataType => val ifStatements = expressions.zipWithIndex.flatMap { - case (e, i) if e.dataType == dataType => + // getString() is not used by expressions + case (e, i) if e.dataType == dataType && dataType != StringType => val elementName = newTermName(s"c$i") // TODO: The string of ifs gets pretty inefficient as the row grows in size. // TODO: Optional null checks? q"if(i == $i) return $elementName" :: Nil case _ => Nil } - - q""" - override def ${accessorForType(dataType)}(i: Int):${termForType(dataType)} = { - ..$ifStatements; - $accessorFailure - }""" + dataType match { + // Row() need this interface to compile + case StringType => + q""" + override def getString(i: Int): String = { + $accessorFailure + }""" + case other => + q""" + override def ${accessorForType(dataType)}(i: Int): ${termForType(dataType)} = { + ..$ifStatements; + $accessorFailure + }""" + } } val specificMutatorFunctions = NativeType.all.map { dataType => val ifStatements = expressions.zipWithIndex.flatMap { - case (e, i) if e.dataType == dataType => + // setString() is not used by expressions + case (e, i) if e.dataType == dataType && dataType != StringType => val elementName = newTermName(s"c$i") // TODO: The string of ifs gets pretty inefficient as the row grows in size. // TODO: Optional null checks? q"if(i == $i) { nullBits($i) = false; $elementName = value; return }" :: Nil case _ => Nil } - - q""" - override def ${mutatorForType(dataType)}(i: Int, value: ${termForType(dataType)}): Unit = { - ..$ifStatements; - $accessorFailure - }""" + dataType match { + case StringType => + // MutableRow() need this interface to compile + q""" + override def setString(i: Int, value: String) { + $accessorFailure + }""" + case other => + q""" + override def ${mutatorForType(dataType)}(i: Int, value: ${termForType(dataType)}) { + ..$ifStatements; + $accessorFailure + }""" + } } val hashValues = expressions.zipWithIndex.map { case (e,i) => diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala index 860b72fad38b3..67caadb839ff9 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions import scala.collection.Map -import org.apache.spark.sql.catalyst.trees +import org.apache.spark.sql.catalyst.{CatalystTypeConverters, trees} import org.apache.spark.sql.types._ /** @@ -85,8 +85,11 @@ case class UserDefinedGenerator( override protected def makeOutput(): Seq[Attribute] = schema override def eval(input: Row): TraversableOnce[Row] = { + // TODO(davies): improve this + // Convert the objects into Scala Type before calling function, we need schema to support UDT + val inputSchema = StructType(children.map(e => StructField(e.simpleString, e.dataType, true))) val inputRow = new InterpretedProjection(children) - function(inputRow(input)) + function(CatalystTypeConverters.convertToScala(inputRow(input), inputSchema).asInstanceOf[Row]) } override def toString: String = s"UserDefinedGenerator(${children.mkString(",")})" diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala index 0e2d593e94124..18cba4cc46707 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala @@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.expressions import java.sql.{Date, Timestamp} +import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.types._ object Literal { @@ -29,7 +30,7 @@ object Literal { case f: Float => Literal(f, FloatType) case b: Byte => Literal(b, ByteType) case s: Short => Literal(s, ShortType) - case s: String => Literal(s, StringType) + case s: String => Literal(UTF8String(s), StringType) case b: Boolean => Literal(b, BooleanType) case d: BigDecimal => Literal(Decimal(d), DecimalType.Unlimited) case d: java.math.BigDecimal => Literal(Decimal(d), DecimalType.Unlimited) @@ -42,7 +43,9 @@ object Literal { throw new RuntimeException("Unsupported literal type " + v.getClass + " " + v) } - def create(v: Any, dataType: DataType): Literal = Literal(v, dataType) + def create(v: Any, dataType: DataType): Literal = { + Literal(CatalystTypeConverters.convertToCatalyst(v), dataType) + } } /** diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala index 7e47cb3fffe12..fcd6352079b4d 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala @@ -179,8 +179,7 @@ case class EqualTo(left: Expression, right: Expression) extends BinaryComparison val r = right.eval(input) if (r == null) null else if (left.dataType != BinaryType) l == r - else BinaryType.ordering.compare( - l.asInstanceOf[Array[Byte]], r.asInstanceOf[Array[Byte]]) == 0 + else java.util.Arrays.equals(l.asInstanceOf[Array[Byte]], r.asInstanceOf[Array[Byte]]) } } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala index 0a275b84086cf..1b62e17ff47fd 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql.catalyst.expressions -import org.apache.spark.sql.types.{StructType, NativeType} +import org.apache.spark.sql.types.{UTF8String, StructType, NativeType} /** @@ -37,6 +37,7 @@ trait MutableRow extends Row { def setByte(ordinal: Int, value: Byte) def setFloat(ordinal: Int, value: Float) def setString(ordinal: Int, value: String) + // TODO(davies): add setDate() and setDecimal() } /** @@ -114,9 +115,15 @@ class GenericRow(protected[sql] val values: Array[Any]) extends Row { } override def getString(i: Int): String = { - values(i).asInstanceOf[String] + values(i) match { + case null => null + case s: String => s + case utf8: UTF8String => utf8.toString + } } + // TODO(davies): add getDate and getDecimal + // Custom hashCode function that matches the efficient code generated version. override def hashCode: Int = { var result: Int = 37 @@ -189,8 +196,7 @@ class GenericMutableRow(v: Array[Any]) extends GenericRow(v) with MutableRow { override def setFloat(ordinal: Int, value: Float): Unit = { values(ordinal) = value } override def setInt(ordinal: Int, value: Int): Unit = { values(ordinal) = value } override def setLong(ordinal: Int, value: Long): Unit = { values(ordinal) = value } - override def setString(ordinal: Int, value: String): Unit = { values(ordinal) = value } - + override def setString(ordinal: Int, value: String) { values(ordinal) = UTF8String(value)} override def setNullAt(i: Int): Unit = { values(i) = null } override def setShort(ordinal: Int, value: Short): Unit = { values(ordinal) = value } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala index acfbbace608ef..d597bf7ce756a 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala @@ -19,11 +19,8 @@ package org.apache.spark.sql.catalyst.expressions import java.util.regex.Pattern -import scala.collection.IndexedSeqOptimized - - import org.apache.spark.sql.catalyst.analysis.UnresolvedException -import org.apache.spark.sql.types.{BinaryType, BooleanType, DataType, StringType} +import org.apache.spark.sql.types._ trait StringRegexExpression { self: BinaryExpression => @@ -60,38 +57,17 @@ trait StringRegexExpression { if(r == null) { null } else { - val regex = pattern(r.asInstanceOf[String]) + val regex = pattern(r.asInstanceOf[UTF8String].toString) if(regex == null) { null } else { - matches(regex, l.asInstanceOf[String]) + matches(regex, l.asInstanceOf[UTF8String].toString) } } } } } -trait CaseConversionExpression { - self: UnaryExpression => - - type EvaluatedType = Any - - def convert(v: String): String - - override def foldable: Boolean = child.foldable - def nullable: Boolean = child.nullable - def dataType: DataType = StringType - - override def eval(input: Row): Any = { - val evaluated = child.eval(input) - if (evaluated == null) { - null - } else { - convert(evaluated.toString) - } - } -} - /** * Simple RegEx pattern matching function */ @@ -134,12 +110,33 @@ case class RLike(left: Expression, right: Expression) override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).find(0) } +trait CaseConversionExpression { + self: UnaryExpression => + + type EvaluatedType = Any + + def convert(v: UTF8String): UTF8String + + override def foldable: Boolean = child.foldable + def nullable: Boolean = child.nullable + def dataType: DataType = StringType + + override def eval(input: Row): Any = { + val evaluated = child.eval(input) + if (evaluated == null) { + null + } else { + convert(evaluated.asInstanceOf[UTF8String]) + } + } +} + /** * A function that converts the characters of a string to uppercase. */ case class Upper(child: Expression) extends UnaryExpression with CaseConversionExpression { - override def convert(v: String): String = v.toUpperCase() + override def convert(v: UTF8String): UTF8String = v.toUpperCase override def toString: String = s"Upper($child)" } @@ -149,7 +146,7 @@ case class Upper(child: Expression) extends UnaryExpression with CaseConversionE */ case class Lower(child: Expression) extends UnaryExpression with CaseConversionExpression { - override def convert(v: String): String = v.toLowerCase() + override def convert(v: UTF8String): UTF8String = v.toLowerCase override def toString: String = s"Lower($child)" } @@ -162,15 +159,16 @@ trait StringComparison { override def nullable: Boolean = left.nullable || right.nullable - def compare(l: String, r: String): Boolean + def compare(l: UTF8String, r: UTF8String): Boolean override def eval(input: Row): Any = { - val leftEval = left.eval(input).asInstanceOf[String] + val leftEval = left.eval(input) if(leftEval == null) { null } else { - val rightEval = right.eval(input).asInstanceOf[String] - if (rightEval == null) null else compare(leftEval, rightEval) + val rightEval = right.eval(input) + if (rightEval == null) null + else compare(leftEval.asInstanceOf[UTF8String], rightEval.asInstanceOf[UTF8String]) } } @@ -184,7 +182,7 @@ trait StringComparison { */ case class Contains(left: Expression, right: Expression) extends BinaryPredicate with StringComparison { - override def compare(l: String, r: String): Boolean = l.contains(r) + override def compare(l: UTF8String, r: UTF8String): Boolean = l.contains(r) } /** @@ -192,7 +190,7 @@ case class Contains(left: Expression, right: Expression) */ case class StartsWith(left: Expression, right: Expression) extends BinaryPredicate with StringComparison { - override def compare(l: String, r: String): Boolean = l.startsWith(r) + override def compare(l: UTF8String, r: UTF8String): Boolean = l.startsWith(r) } /** @@ -200,7 +198,7 @@ case class StartsWith(left: Expression, right: Expression) */ case class EndsWith(left: Expression, right: Expression) extends BinaryPredicate with StringComparison { - override def compare(l: String, r: String): Boolean = l.endsWith(r) + override def compare(l: UTF8String, r: UTF8String): Boolean = l.endsWith(r) } /** @@ -224,9 +222,7 @@ case class Substring(str: Expression, pos: Expression, len: Expression) extends override def children: Seq[Expression] = str :: pos :: len :: Nil @inline - def slice[T, C <: Any](str: C, startPos: Int, sliceLen: Int) - (implicit ev: (C=>IndexedSeqOptimized[T,_])): Any = { - val len = str.length + def slicePos(startPos: Int, sliceLen: Int, length: () => Int): (Int, Int) = { // Hive and SQL use one-based indexing for SUBSTR arguments but also accept zero and // negative indices for start positions. If a start index i is greater than 0, it // refers to element i-1 in the sequence. If a start index i is less than 0, it refers @@ -235,7 +231,7 @@ case class Substring(str: Expression, pos: Expression, len: Expression) extends val start = startPos match { case pos if pos > 0 => pos - 1 - case neg if neg < 0 => len + neg + case neg if neg < 0 => length() + neg case _ => 0 } @@ -244,12 +240,11 @@ case class Substring(str: Expression, pos: Expression, len: Expression) extends case x => start + x } - str.slice(start, end) + (start, end) } override def eval(input: Row): Any = { val string = str.eval(input) - val po = pos.eval(input) val ln = len.eval(input) @@ -257,11 +252,14 @@ case class Substring(str: Expression, pos: Expression, len: Expression) extends null } else { val start = po.asInstanceOf[Int] - val length = ln.asInstanceOf[Int] - + val length = ln.asInstanceOf[Int] string match { - case ba: Array[Byte] => slice(ba, start, length) - case other => slice(other.toString, start, length) + case ba: Array[Byte] => + val (st, end) = slicePos(start, length, () => ba.length) + ba.slice(st, end) + case s: UTF8String => + val (st, end) = slicePos(start, length, () => s.length) + s.slice(st, end) } } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala index 93e69d409cb91..7c80634d2c852 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala @@ -198,14 +198,19 @@ object LikeSimplification extends Rule[LogicalPlan] { val equalTo = "([^_%]*)".r def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions { - case Like(l, Literal(startsWith(pattern), StringType)) if !pattern.endsWith("\\") => - StartsWith(l, Literal(pattern)) - case Like(l, Literal(endsWith(pattern), StringType)) => - EndsWith(l, Literal(pattern)) - case Like(l, Literal(contains(pattern), StringType)) if !pattern.endsWith("\\") => - Contains(l, Literal(pattern)) - case Like(l, Literal(equalTo(pattern), StringType)) => - EqualTo(l, Literal(pattern)) + case Like(l, Literal(utf, StringType)) => + utf.toString match { + case startsWith(pattern) if !pattern.endsWith("\\") => + StartsWith(l, Literal(pattern)) + case endsWith(pattern) => + EndsWith(l, Literal(pattern)) + case contains(pattern) if !pattern.endsWith("\\") => + Contains(l, Literal(pattern)) + case equalTo(pattern) => + EqualTo(l, Literal(pattern)) + case _ => + Like(l, Literal.create(utf, StringType)) + } } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateUtils.scala index 504fb05842505..d36a49159b87f 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateUtils.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateUtils.scala @@ -40,6 +40,7 @@ object DateUtils { millisToDays(d.getTime) } + // we should use the exact day as Int, for example, (year, month, day) -> day def millisToDays(millisLocal: Long): Int = { ((millisLocal + LOCAL_TIMEZONE.get().getOffset(millisLocal)) / MILLIS_PER_DAY).toInt } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UTF8String.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UTF8String.scala new file mode 100644 index 0000000000000..fc02ba6c9c43e --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UTF8String.scala @@ -0,0 +1,214 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql.types + +import java.util.Arrays + +/** + * A UTF-8 String, as internal representation of StringType in SparkSQL + * + * A String encoded in UTF-8 as an Array[Byte], which can be used for comparison, + * search, see http://en.wikipedia.org/wiki/UTF-8 for details. + * + * Note: This is not designed for general use cases, should not be used outside SQL. + */ + +final class UTF8String extends Ordered[UTF8String] with Serializable { + + private[this] var bytes: Array[Byte] = _ + + /** + * Update the UTF8String with String. + */ + def set(str: String): UTF8String = { + bytes = str.getBytes("utf-8") + this + } + + /** + * Update the UTF8String with Array[Byte], which should be encoded in UTF-8 + */ + def set(bytes: Array[Byte]): UTF8String = { + this.bytes = bytes + this + } + + /** + * Return the number of bytes for a code point with the first byte as `b` + * @param b The first byte of a code point + */ + @inline + private[this] def numOfBytes(b: Byte): Int = { + val offset = (b & 0xFF) - 192 + if (offset >= 0) UTF8String.bytesOfCodePointInUTF8(offset) else 1 + } + + /** + * Return the number of code points in it. + * + * This is only used by Substring() when `start` is negative. + */ + def length(): Int = { + var len = 0 + var i: Int = 0 + while (i < bytes.length) { + i += numOfBytes(bytes(i)) + len += 1 + } + len + } + + def getBytes: Array[Byte] = { + bytes + } + + /** + * Return a substring of this, + * @param start the position of first code point + * @param until the position after last code point + */ + def slice(start: Int, until: Int): UTF8String = { + if (until <= start || start >= bytes.length || bytes == null) { + new UTF8String + } + + var c = 0 + var i: Int = 0 + while (c < start && i < bytes.length) { + i += numOfBytes(bytes(i)) + c += 1 + } + var j = i + while (c < until && j < bytes.length) { + j += numOfBytes(bytes(j)) + c += 1 + } + UTF8String(Arrays.copyOfRange(bytes, i, j)) + } + + def contains(sub: UTF8String): Boolean = { + val b = sub.getBytes + if (b.length == 0) { + return true + } + var i: Int = 0 + while (i <= bytes.length - b.length) { + // In worst case, it's O(N*K), but should works fine with SQL + if (bytes(i) == b(0) && Arrays.equals(Arrays.copyOfRange(bytes, i, i + b.length), b)) { + return true + } + i += 1 + } + false + } + + def startsWith(prefix: UTF8String): Boolean = { + val b = prefix.getBytes + if (b.length > bytes.length) { + return false + } + Arrays.equals(Arrays.copyOfRange(bytes, 0, b.length), b) + } + + def endsWith(suffix: UTF8String): Boolean = { + val b = suffix.getBytes + if (b.length > bytes.length) { + return false + } + Arrays.equals(Arrays.copyOfRange(bytes, bytes.length - b.length, bytes.length), b) + } + + def toUpperCase(): UTF8String = { + // upper case depends on locale, fallback to String. + UTF8String(toString().toUpperCase) + } + + def toLowerCase(): UTF8String = { + // lower case depends on locale, fallback to String. + UTF8String(toString().toLowerCase) + } + + override def toString(): String = { + new String(bytes, "utf-8") + } + + override def clone(): UTF8String = new UTF8String().set(this.bytes) + + override def compare(other: UTF8String): Int = { + var i: Int = 0 + val b = other.getBytes + while (i < bytes.length && i < b.length) { + val res = bytes(i).compareTo(b(i)) + if (res != 0) return res + i += 1 + } + bytes.length - b.length + } + + override def compareTo(other: UTF8String): Int = { + compare(other) + } + + override def equals(other: Any): Boolean = other match { + case s: UTF8String => + Arrays.equals(bytes, s.getBytes) + case s: String => + // This is only used for Catalyst unit tests + // fail fast + bytes.length >= s.length && length() == s.length && toString() == s + case _ => + false + } + + override def hashCode(): Int = { + Arrays.hashCode(bytes) + } +} + +object UTF8String { + // number of tailing bytes in a UTF8 sequence for a code point + // see http://en.wikipedia.org/wiki/UTF-8, 192-256 of Byte 1 + private[types] val bytesOfCodePointInUTF8: Array[Int] = Array(2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, + 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, + 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, + 4, 4, 4, 4, 4, 4, 4, 4, + 5, 5, 5, 5, + 6, 6, 6, 6) + + /** + * Create a UTF-8 String from String + */ + def apply(s: String): UTF8String = { + if (s != null) { + new UTF8String().set(s) + } else{ + null + } + } + + /** + * Create a UTF-8 String from Array[Byte], which should be encoded in UTF-8 + */ + def apply(bytes: Array[Byte]): UTF8String = { + if (bytes != null) { + new UTF8String().set(bytes) + } else { + null + } + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala index cdf2bc68d9c5e..c6fb22c26bd3c 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala @@ -350,7 +350,7 @@ class StringType private() extends NativeType with PrimitiveType { // The companion object and this class is separated so the companion object also subclasses // this type. Otherwise, the companion object would be of type "StringType$" in byte code. // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type JvmType = String + private[sql] type JvmType = UTF8String @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } private[sql] val ordering = implicitly[Ordering[JvmType]] @@ -1196,8 +1196,8 @@ abstract class UserDefinedType[UserType] extends DataType with Serializable { /** * Convert the user type to a SQL datum * - * TODO: Can we make this take obj: UserType? The issue is in ScalaReflection.convertToCatalyst, - * where we need to convert Any to UserType. + * TODO: Can we make this take obj: UserType? The issue is in + * CatalystTypeConverters.convertToCatalyst, where we need to convert Any to UserType. */ def serialize(obj: Any): Any diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala index d4362a91d992c..76298f03c94ae 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala @@ -25,8 +25,9 @@ import org.scalactic.TripleEqualsSupport.Spread import org.scalatest.FunSuite import org.scalatest.Matchers._ -import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.catalyst.analysis.UnresolvedGetField +import org.apache.spark.sql.catalyst.dsl.expressions._ import org.apache.spark.sql.types._ @@ -59,6 +60,10 @@ class ExpressionEvaluationBaseSuite extends FunSuite { class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { + def create_row(values: Any*): Row = { + new GenericRow(values.map(CatalystTypeConverters.convertToCatalyst).toArray) + } + test("literals") { checkEvaluation(Literal(1), 1) checkEvaluation(Literal(true), true) @@ -265,24 +270,23 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { test("LIKE Non-literal Regular Expression") { val regEx = 'a.string.at(0) - checkEvaluation("abcd" like regEx, null, new GenericRow(Array[Any](null))) - checkEvaluation("abdef" like regEx, true, new GenericRow(Array[Any]("abdef"))) - checkEvaluation("a_%b" like regEx, true, new GenericRow(Array[Any]("a\\__b"))) - checkEvaluation("addb" like regEx, true, new GenericRow(Array[Any]("a_%b"))) - checkEvaluation("addb" like regEx, false, new GenericRow(Array[Any]("a\\__b"))) - checkEvaluation("addb" like regEx, false, new GenericRow(Array[Any]("a%\\%b"))) - checkEvaluation("a_%b" like regEx, true, new GenericRow(Array[Any]("a%\\%b"))) - checkEvaluation("addb" like regEx, true, new GenericRow(Array[Any]("a%"))) - checkEvaluation("addb" like regEx, false, new GenericRow(Array[Any]("**"))) - checkEvaluation("abc" like regEx, true, new GenericRow(Array[Any]("a%"))) - checkEvaluation("abc" like regEx, false, new GenericRow(Array[Any]("b%"))) - checkEvaluation("abc" like regEx, false, new GenericRow(Array[Any]("bc%"))) - checkEvaluation("a\nb" like regEx, true, new GenericRow(Array[Any]("a_b"))) - checkEvaluation("ab" like regEx, true, new GenericRow(Array[Any]("a%b"))) - checkEvaluation("a\nb" like regEx, true, new GenericRow(Array[Any]("a%b"))) - - checkEvaluation(Literal.create(null, StringType) like regEx, null, - new GenericRow(Array[Any]("bc%"))) + checkEvaluation("abcd" like regEx, null, create_row(null)) + checkEvaluation("abdef" like regEx, true, create_row("abdef")) + checkEvaluation("a_%b" like regEx, true, create_row("a\\__b")) + checkEvaluation("addb" like regEx, true, create_row("a_%b")) + checkEvaluation("addb" like regEx, false, create_row("a\\__b")) + checkEvaluation("addb" like regEx, false, create_row("a%\\%b")) + checkEvaluation("a_%b" like regEx, true, create_row("a%\\%b")) + checkEvaluation("addb" like regEx, true, create_row("a%")) + checkEvaluation("addb" like regEx, false, create_row("**")) + checkEvaluation("abc" like regEx, true, create_row("a%")) + checkEvaluation("abc" like regEx, false, create_row("b%")) + checkEvaluation("abc" like regEx, false, create_row("bc%")) + checkEvaluation("a\nb" like regEx, true, create_row("a_b")) + checkEvaluation("ab" like regEx, true, create_row("a%b")) + checkEvaluation("a\nb" like regEx, true, create_row("a%b")) + + checkEvaluation(Literal.create(null, StringType) like regEx, null, create_row("bc%")) } test("RLIKE literal Regular Expression") { @@ -313,14 +317,14 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { test("RLIKE Non-literal Regular Expression") { val regEx = 'a.string.at(0) - checkEvaluation("abdef" rlike regEx, true, new GenericRow(Array[Any]("abdef"))) - checkEvaluation("abbbbc" rlike regEx, true, new GenericRow(Array[Any]("a.*c"))) - checkEvaluation("fofo" rlike regEx, true, new GenericRow(Array[Any]("^fo"))) - checkEvaluation("fo\no" rlike regEx, true, new GenericRow(Array[Any]("^fo\no$"))) - checkEvaluation("Bn" rlike regEx, true, new GenericRow(Array[Any]("^Ba*n"))) + checkEvaluation("abdef" rlike regEx, true, create_row("abdef")) + checkEvaluation("abbbbc" rlike regEx, true, create_row("a.*c")) + checkEvaluation("fofo" rlike regEx, true, create_row("^fo")) + checkEvaluation("fo\no" rlike regEx, true, create_row("^fo\no$")) + checkEvaluation("Bn" rlike regEx, true, create_row("^Ba*n")) intercept[java.util.regex.PatternSyntaxException] { - evaluate("abbbbc" rlike regEx, new GenericRow(Array[Any]("**"))) + evaluate("abbbbc" rlike regEx, create_row("**")) } } @@ -763,7 +767,7 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { } test("null checking") { - val row = new GenericRow(Array[Any]("^Ba*n", null, true, null)) + val row = create_row("^Ba*n", null, true, null) val c1 = 'a.string.at(0) val c2 = 'a.string.at(1) val c3 = 'a.boolean.at(2) @@ -803,7 +807,7 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { } test("case when") { - val row = new GenericRow(Array[Any](null, false, true, "a", "b", "c")) + val row = create_row(null, false, true, "a", "b", "c") val c1 = 'a.boolean.at(0) val c2 = 'a.boolean.at(1) val c3 = 'a.boolean.at(2) @@ -846,13 +850,13 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { } test("complex type") { - val row = new GenericRow(Array[Any]( - "^Ba*n", // 0 - null.asInstanceOf[String], // 1 - new GenericRow(Array[Any]("aa", "bb")), // 2 - Map("aa"->"bb"), // 3 - Seq("aa", "bb") // 4 - )) + val row = create_row( + "^Ba*n", // 0 + null.asInstanceOf[UTF8String], // 1 + create_row("aa", "bb"), // 2 + Map("aa"->"bb"), // 3 + Seq("aa", "bb") // 4 + ) val typeS = StructType( StructField("a", StringType, true) :: StructField("b", StringType, true) :: Nil @@ -909,7 +913,7 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { } test("arithmetic") { - val row = new GenericRow(Array[Any](1, 2, 3, null)) + val row = create_row(1, 2, 3, null) val c1 = 'a.int.at(0) val c2 = 'a.int.at(1) val c3 = 'a.int.at(2) @@ -934,7 +938,7 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { } test("fractional arithmetic") { - val row = new GenericRow(Array[Any](1.1, 2.0, 3.1, null)) + val row = create_row(1.1, 2.0, 3.1, null) val c1 = 'a.double.at(0) val c2 = 'a.double.at(1) val c3 = 'a.double.at(2) @@ -958,7 +962,7 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { } test("BinaryComparison") { - val row = new GenericRow(Array[Any](1, 2, 3, null, 3, null)) + val row = create_row(1, 2, 3, null, 3, null) val c1 = 'a.int.at(0) val c2 = 'a.int.at(1) val c3 = 'a.int.at(2) @@ -988,7 +992,7 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { } test("StringComparison") { - val row = new GenericRow(Array[Any]("abc", null)) + val row = create_row("abc", null) val c1 = 'a.string.at(0) val c2 = 'a.string.at(1) @@ -1009,7 +1013,7 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { } test("Substring") { - val row = new GenericRow(Array[Any]("example", "example".toArray.map(_.toByte))) + val row = create_row("example", "example".toArray.map(_.toByte)) val s = 'a.string.at(0) @@ -1053,7 +1057,7 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { // substring(null, _, _) -> null checkEvaluation(Substring(s, Literal.create(100, IntegerType), Literal.create(4, IntegerType)), - null, new GenericRow(Array[Any](null))) + null, create_row(null)) // substring(_, null, _) -> null checkEvaluation(Substring(s, Literal.create(null, IntegerType), Literal.create(4, IntegerType)), @@ -1102,20 +1106,20 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite { test("SQRT") { val inputSequence = (1 to (1<<24) by 511).map(_ * (1L<<24)) val expectedResults = inputSequence.map(l => math.sqrt(l.toDouble)) - val rowSequence = inputSequence.map(l => new GenericRow(Array[Any](l.toDouble))) + val rowSequence = inputSequence.map(l => create_row(l.toDouble)) val d = 'a.double.at(0) for ((row, expected) <- rowSequence zip expectedResults) { checkEvaluation(Sqrt(d), expected, row) } - checkEvaluation(Sqrt(Literal.create(null, DoubleType)), null, new GenericRow(Array[Any](null))) + checkEvaluation(Sqrt(Literal.create(null, DoubleType)), null, create_row(null)) checkEvaluation(Sqrt(-1), null, EmptyRow) checkEvaluation(Sqrt(-1.5), null, EmptyRow) } test("Bitwise operations") { - val row = new GenericRow(Array[Any](1, 2, 3, null)) + val row = create_row(1, 2, 3, null) val c1 = 'a.int.at(0) val c2 = 'a.int.at(1) val c3 = 'a.int.at(2) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedMutableEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedMutableEvaluationSuite.scala index 275ea2627ebcd..bcc0c404d2cfb 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedMutableEvaluationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedMutableEvaluationSuite.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql.catalyst.expressions -import org.apache.spark.sql.catalyst.dsl.expressions._ +import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.catalyst.expressions.codegen._ /** @@ -43,7 +43,7 @@ class GeneratedMutableEvaluationSuite extends ExpressionEvaluationSuite { } val actual = plan(inputRow) - val expectedRow = new GenericRow(Array[Any](expected)) + val expectedRow = new GenericRow(Array[Any](CatalystTypeConverters.convertToCatalyst(expected))) if (actual.hashCode() != expectedRow.hashCode()) { fail( s""" diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/UTF8StringSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/UTF8StringSuite.scala new file mode 100644 index 0000000000000..a22aa6f244c48 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/UTF8StringSuite.scala @@ -0,0 +1,70 @@ +/* +* Licensed to the Apache Software Foundation (ASF) under one or more +* contributor license agreements. See the NOTICE file distributed with +* this work for additional information regarding copyright ownership. +* The ASF licenses this file to You under the Apache License, Version 2.0 +* (the "License"); you may not use this file except in compliance with +* the License. You may obtain a copy of the License at +* +* http://www.apache.org/licenses/LICENSE-2.0 +* +* Unless required by applicable law or agreed to in writing, software +* distributed under the License is distributed on an "AS IS" BASIS, +* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +* See the License for the specific language governing permissions and +* limitations under the License. +*/ + +package org.apache.spark.sql.types + +import org.scalatest.FunSuite + +// scalastyle:off +class UTF8StringSuite extends FunSuite { + test("basic") { + def check(str: String, len: Int) { + + assert(UTF8String(str).length == len) + assert(UTF8String(str.getBytes("utf8")).length() == len) + + assert(UTF8String(str) == str) + assert(UTF8String(str.getBytes("utf8")) == str) + assert(UTF8String(str).toString == str) + assert(UTF8String(str.getBytes("utf8")).toString == str) + assert(UTF8String(str.getBytes("utf8")) == UTF8String(str)) + + assert(UTF8String(str).hashCode() == UTF8String(str.getBytes("utf8")).hashCode()) + } + + check("hello", 5) + check("世 界", 3) + } + + test("contains") { + assert(UTF8String("hello").contains(UTF8String("ello"))) + assert(!UTF8String("hello").contains(UTF8String("vello"))) + assert(UTF8String("大千世界").contains(UTF8String("千世"))) + assert(!UTF8String("大千世界").contains(UTF8String("世千"))) + } + + test("prefix") { + assert(UTF8String("hello").startsWith(UTF8String("hell"))) + assert(!UTF8String("hello").startsWith(UTF8String("ell"))) + assert(UTF8String("大千世界").startsWith(UTF8String("大千"))) + assert(!UTF8String("大千世界").startsWith(UTF8String("千"))) + } + + test("suffix") { + assert(UTF8String("hello").endsWith(UTF8String("ello"))) + assert(!UTF8String("hello").endsWith(UTF8String("ellov"))) + assert(UTF8String("大千世界").endsWith(UTF8String("世界"))) + assert(!UTF8String("大千世界").endsWith(UTF8String("世"))) + } + + test("slice") { + assert(UTF8String("hello").slice(1, 3) == UTF8String("el")) + assert(UTF8String("大千世界").slice(0, 1) == UTF8String("大")) + assert(UTF8String("大千世界").slice(1, 3) == UTF8String("千世")) + assert(UTF8String("大千世界").slice(3, 5) == UTF8String("界")) + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala index b237fe684cdc1..89a4faf35e0d2 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala @@ -1195,6 +1195,7 @@ class SQLContext(@transient val sparkContext: SparkContext) case FloatType => true case DateType => true case TimestampType => true + case StringType => true case ArrayType(_, _) => true case MapType(_, _, _) => true case StructType(_) => true diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala index 87a6631da8300..b0f983c180673 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala @@ -216,13 +216,13 @@ private[sql] class IntColumnStats extends ColumnStats { } private[sql] class StringColumnStats extends ColumnStats { - protected var upper: String = null - protected var lower: String = null + protected var upper: UTF8String = null + protected var lower: UTF8String = null override def gatherStats(row: Row, ordinal: Int): Unit = { super.gatherStats(row, ordinal) if (!row.isNullAt(ordinal)) { - val value = row.getString(ordinal) + val value = row(ordinal).asInstanceOf[UTF8String] if (upper == null || value.compareTo(upper) > 0) upper = value if (lower == null || value.compareTo(lower) < 0) lower = value sizeInBytes += STRING.actualSize(row, ordinal) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala index c47497e0662d9..1b9e0df2dcb5e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala @@ -18,7 +18,7 @@ package org.apache.spark.sql.columnar import java.nio.ByteBuffer -import java.sql.{Date, Timestamp} +import java.sql.Timestamp import scala.reflect.runtime.universe.TypeTag @@ -312,26 +312,28 @@ private[sql] object STRING extends NativeColumnType(StringType, 7, 8) { row.getString(ordinal).getBytes("utf-8").length + 4 } - override def append(v: String, buffer: ByteBuffer): Unit = { - val stringBytes = v.getBytes("utf-8") + override def append(v: UTF8String, buffer: ByteBuffer): Unit = { + val stringBytes = v.getBytes buffer.putInt(stringBytes.length).put(stringBytes, 0, stringBytes.length) } - override def extract(buffer: ByteBuffer): String = { + override def extract(buffer: ByteBuffer): UTF8String = { val length = buffer.getInt() val stringBytes = new Array[Byte](length) buffer.get(stringBytes, 0, length) - new String(stringBytes, "utf-8") + UTF8String(stringBytes) } - override def setField(row: MutableRow, ordinal: Int, value: String): Unit = { - row.setString(ordinal, value) + override def setField(row: MutableRow, ordinal: Int, value: UTF8String): Unit = { + row.update(ordinal, value) } - override def getField(row: Row, ordinal: Int): String = row.getString(ordinal) + override def getField(row: Row, ordinal: Int): UTF8String = { + row(ordinal).asInstanceOf[UTF8String] + } override def copyField(from: Row, fromOrdinal: Int, to: MutableRow, toOrdinal: Int): Unit = { - to.setString(toOrdinal, from.getString(fromOrdinal)) + to.update(toOrdinal, from(fromOrdinal)) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala index 656bdd7212f56..1fd387eec7e57 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala @@ -19,12 +19,12 @@ package org.apache.spark.sql.execution import org.apache.spark.annotation.DeveloperApi import org.apache.spark.rdd.RDD -import org.apache.spark.sql.{Row, SQLContext} import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation -import org.apache.spark.sql.catalyst.expressions.{SpecificMutableRow, Attribute} +import org.apache.spark.sql.catalyst.expressions.{Attribute, GenericMutableRow, SpecificMutableRow} import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Statistics} import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.{Row, SQLContext} /** * :: DeveloperApi :: @@ -54,6 +54,33 @@ object RDDConversions { } } } + + /** + * Convert the objects inside Row into the types Catalyst expected. + */ + def rowToRowRdd(data: RDD[Row], schema: StructType): RDD[Row] = { + data.mapPartitions { iterator => + if (iterator.isEmpty) { + Iterator.empty + } else { + val bufferedIterator = iterator.buffered + val mutableRow = new GenericMutableRow(bufferedIterator.head.toSeq.toArray) + val schemaFields = schema.fields.toArray + val converters = schemaFields.map { + f => CatalystTypeConverters.createToCatalystConverter(f.dataType) + } + bufferedIterator.map { r => + var i = 0 + while (i < mutableRow.length) { + mutableRow(i) = converters(i)(r(i)) + i += 1 + } + + mutableRow + } + } + } + } } /** Logical plan node for scanning data from an RDD. */ diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala index fad7a281dc1e2..99f24910fd61f 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala @@ -20,12 +20,13 @@ package org.apache.spark.sql.execution import org.apache.spark.Logging import org.apache.spark.annotation.DeveloperApi import org.apache.spark.rdd.RDD -import org.apache.spark.sql.types.{BooleanType, StructField, StructType, StringType} -import org.apache.spark.sql.{DataFrame, SQLConf, SQLContext} +import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.catalyst.errors.TreeNodeException -import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Row, Attribute} +import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, Row} import org.apache.spark.sql.catalyst.plans.logical import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan +import org.apache.spark.sql.types._ +import org.apache.spark.sql.{DataFrame, SQLConf, SQLContext} /** * A logical command that is executed for its side-effects. `RunnableCommand`s are @@ -61,7 +62,11 @@ case class ExecutedCommand(cmd: RunnableCommand) extends SparkPlan { override def executeTake(limit: Int): Array[Row] = sideEffectResult.take(limit).toArray - override def execute(): RDD[Row] = sqlContext.sparkContext.parallelize(sideEffectResult, 1) + override def execute(): RDD[Row] = { + val converted = sideEffectResult.map(r => + CatalystTypeConverters.convertToCatalyst(r, schema).asInstanceOf[Row]) + sqlContext.sparkContext.parallelize(converted, 1) + } } /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala index e916e68e58b5d..710787096e6cb 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala @@ -164,7 +164,7 @@ package object debug { case (_: Long, LongType) => case (_: Int, IntegerType) => - case (_: String, StringType) => + case (_: UTF8String, StringType) => case (_: Float, FloatType) => case (_: Byte, ByteType) => case (_: Short, ShortType) => diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala index 5b308d88d4cdf..7a43bfd8bc8d9 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala @@ -140,6 +140,7 @@ object EvaluatePython { case (ud, udt: UserDefinedType[_]) => toJava(udt.serialize(ud), udt.sqlType) case (date: Int, DateType) => DateUtils.toJavaDate(date) + case (s: UTF8String, StringType) => s.toString // Pyrolite can handle Timestamp and Decimal case (other, _) => other @@ -192,7 +193,8 @@ object EvaluatePython { case (c: Long, IntegerType) => c.toInt case (c: Int, LongType) => c.toLong case (c: Double, FloatType) => c.toFloat - case (c, StringType) if !c.isInstanceOf[String] => c.toString + case (c: String, StringType) => UTF8String(c) + case (c, StringType) if !c.isInstanceOf[String] => UTF8String(c.toString) case (c, _) => c } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala index 463e1dcc268bc..b9022fcd9e3ad 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala @@ -233,7 +233,7 @@ private[sql] class JDBCRDD( * Converts value to SQL expression. */ private def compileValue(value: Any): Any = value match { - case stringValue: String => s"'${escapeSql(stringValue)}'" + case stringValue: UTF8String => s"'${escapeSql(stringValue.toString)}'" case _ => value } @@ -349,12 +349,14 @@ private[sql] class JDBCRDD( val pos = i + 1 conversions(i) match { case BooleanConversion => mutableRow.setBoolean(i, rs.getBoolean(pos)) + // TODO(davies): convert Date into Int case DateConversion => mutableRow.update(i, rs.getDate(pos)) case DecimalConversion => mutableRow.update(i, rs.getBigDecimal(pos)) case DoubleConversion => mutableRow.setDouble(i, rs.getDouble(pos)) case FloatConversion => mutableRow.setFloat(i, rs.getFloat(pos)) case IntegerConversion => mutableRow.setInt(i, rs.getInt(pos)) case LongConversion => mutableRow.setLong(i, rs.getLong(pos)) + // TODO(davies): use getBytes for better performance, if the encoding is UTF-8 case StringConversion => mutableRow.setString(i, rs.getString(pos)) case TimestampConversion => mutableRow.update(i, rs.getTimestamp(pos)) case BinaryConversion => mutableRow.update(i, rs.getBytes(pos)) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala index 4fa84dc076f7e..99b755c9f25d0 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala @@ -130,6 +130,8 @@ private[sql] case class JDBCRelation( extends BaseRelation with PrunedFilteredScan { + override val needConversion: Boolean = false + override val schema: StructType = JDBCRDD.resolveTable(url, table, properties) override def buildScan(requiredColumns: Array[String], filters: Array[Filter]): RDD[Row] = { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala index 34f864f5fda7a..d4e0abc040bc6 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala @@ -18,11 +18,8 @@ package org.apache.spark.sql import java.sql.{Connection, DriverManager, PreparedStatement} -import org.apache.spark.{Logging, Partition} -import org.apache.spark.sql._ -import org.apache.spark.sql.sources.LogicalRelation -import org.apache.spark.sql.jdbc.{JDBCPartitioningInfo, JDBCRelation, JDBCPartition} +import org.apache.spark.Logging import org.apache.spark.sql.types._ package object jdbc { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala index f4c99b4b56606..e3352d02787fd 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala @@ -20,12 +20,12 @@ package org.apache.spark.sql.json import java.io.IOException import org.apache.hadoop.fs.Path + import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.expressions.Row - -import org.apache.spark.sql.{SaveMode, DataFrame, SQLContext} import org.apache.spark.sql.sources._ -import org.apache.spark.sql.types.{DataType, StructType} +import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.{DataFrame, SQLContext, SaveMode} private[sql] class DefaultSource @@ -113,6 +113,8 @@ private[sql] case class JSONRelation( // TODO: Support partitioned JSON relation. private def baseRDD = sqlContext.sparkContext.textFile(path) + override val needConversion: Boolean = false + override val schema = userSpecifiedSchema.getOrElse( JsonRDD.nullTypeToStringType( JsonRDD.inferSchema( diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala index b1e8521383756..29de7401dda71 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala @@ -409,7 +409,7 @@ private[sql] object JsonRDD extends Logging { null } else { desiredType match { - case StringType => toString(value) + case StringType => UTF8String(toString(value)) case _ if value == null || value == "" => null // guard the non string type case IntegerType => value.asInstanceOf[IntegerType.JvmType] case LongType => toLong(value) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala index 43ca359b51735..bc108e37dfb0f 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala @@ -219,8 +219,8 @@ private[parquet] abstract class CatalystConverter extends GroupConverter { protected[parquet] def updateBinary(fieldIndex: Int, value: Binary): Unit = updateField(fieldIndex, value.getBytes) - protected[parquet] def updateString(fieldIndex: Int, value: String): Unit = - updateField(fieldIndex, value) + protected[parquet] def updateString(fieldIndex: Int, value: Array[Byte]): Unit = + updateField(fieldIndex, UTF8String(value)) protected[parquet] def updateTimestamp(fieldIndex: Int, value: Binary): Unit = updateField(fieldIndex, readTimestamp(value)) @@ -418,8 +418,8 @@ private[parquet] class CatalystPrimitiveRowConverter( override protected[parquet] def updateBinary(fieldIndex: Int, value: Binary): Unit = current.update(fieldIndex, value.getBytes) - override protected[parquet] def updateString(fieldIndex: Int, value: String): Unit = - current.setString(fieldIndex, value) + override protected[parquet] def updateString(fieldIndex: Int, value: Array[Byte]): Unit = + current.update(fieldIndex, UTF8String(value)) override protected[parquet] def updateTimestamp(fieldIndex: Int, value: Binary): Unit = current.update(fieldIndex, readTimestamp(value)) @@ -475,19 +475,18 @@ private[parquet] class CatalystPrimitiveConverter( private[parquet] class CatalystPrimitiveStringConverter(parent: CatalystConverter, fieldIndex: Int) extends CatalystPrimitiveConverter(parent, fieldIndex) { - private[this] var dict: Array[String] = null + private[this] var dict: Array[Array[Byte]] = null override def hasDictionarySupport: Boolean = true override def setDictionary(dictionary: Dictionary):Unit = - dict = Array.tabulate(dictionary.getMaxId + 1) {dictionary.decodeToBinary(_).toStringUsingUTF8} - + dict = Array.tabulate(dictionary.getMaxId + 1) { dictionary.decodeToBinary(_).getBytes } override def addValueFromDictionary(dictionaryId: Int): Unit = parent.updateString(fieldIndex, dict(dictionaryId)) override def addBinary(value: Binary): Unit = - parent.updateString(fieldIndex, value.toStringUsingUTF8) + parent.updateString(fieldIndex, value.getBytes) } private[parquet] object CatalystArrayConverter { @@ -714,9 +713,9 @@ private[parquet] class CatalystNativeArrayConverter( elements += 1 } - override protected[parquet] def updateString(fieldIndex: Int, value: String): Unit = { + override protected[parquet] def updateString(fieldIndex: Int, value: Array[Byte]): Unit = { checkGrowBuffer() - buffer(elements) = value.asInstanceOf[NativeType] + buffer(elements) = UTF8String(value).asInstanceOf[NativeType] elements += 1 } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala index 0357dcc4688be..5eb1c6abc2432 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala @@ -55,7 +55,7 @@ private[sql] object ParquetFilters { case StringType => (n: String, v: Any) => FilterApi.eq( binaryColumn(n), - Option(v).map(s => Binary.fromString(s.asInstanceOf[String])).orNull) + Option(v).map(s => Binary.fromByteArray(s.asInstanceOf[UTF8String].getBytes)).orNull) case BinaryType => (n: String, v: Any) => FilterApi.eq( binaryColumn(n), @@ -76,7 +76,7 @@ private[sql] object ParquetFilters { case StringType => (n: String, v: Any) => FilterApi.notEq( binaryColumn(n), - Option(v).map(s => Binary.fromString(s.asInstanceOf[String])).orNull) + Option(v).map(s => Binary.fromByteArray(s.asInstanceOf[UTF8String].getBytes)).orNull) case BinaryType => (n: String, v: Any) => FilterApi.notEq( binaryColumn(n), @@ -94,7 +94,7 @@ private[sql] object ParquetFilters { (n: String, v: Any) => FilterApi.lt(doubleColumn(n), v.asInstanceOf[java.lang.Double]) case StringType => (n: String, v: Any) => - FilterApi.lt(binaryColumn(n), Binary.fromString(v.asInstanceOf[String])) + FilterApi.lt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes)) case BinaryType => (n: String, v: Any) => FilterApi.lt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]])) @@ -111,7 +111,7 @@ private[sql] object ParquetFilters { (n: String, v: Any) => FilterApi.ltEq(doubleColumn(n), v.asInstanceOf[java.lang.Double]) case StringType => (n: String, v: Any) => - FilterApi.ltEq(binaryColumn(n), Binary.fromString(v.asInstanceOf[String])) + FilterApi.ltEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes)) case BinaryType => (n: String, v: Any) => FilterApi.ltEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]])) @@ -128,7 +128,7 @@ private[sql] object ParquetFilters { (n: String, v: Any) => FilterApi.gt(doubleColumn(n), v.asInstanceOf[java.lang.Double]) case StringType => (n: String, v: Any) => - FilterApi.gt(binaryColumn(n), Binary.fromString(v.asInstanceOf[String])) + FilterApi.gt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes)) case BinaryType => (n: String, v: Any) => FilterApi.gt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]])) @@ -145,7 +145,7 @@ private[sql] object ParquetFilters { (n: String, v: Any) => FilterApi.gtEq(doubleColumn(n), v.asInstanceOf[java.lang.Double]) case StringType => (n: String, v: Any) => - FilterApi.gtEq(binaryColumn(n), Binary.fromString(v.asInstanceOf[String])) + FilterApi.gtEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes)) case BinaryType => (n: String, v: Any) => FilterApi.gtEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]])) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala index 5a1b15490d273..e05a4c20b0d41 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala @@ -198,10 +198,7 @@ private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging { if (value != null) { schema match { case StringType => writer.addBinary( - Binary.fromByteArray( - value.asInstanceOf[String].getBytes("utf-8") - ) - ) + Binary.fromByteArray(value.asInstanceOf[UTF8String].getBytes)) case BinaryType => writer.addBinary( Binary.fromByteArray(value.asInstanceOf[Array[Byte]])) case IntegerType => writer.addInteger(value.asInstanceOf[Int]) @@ -349,7 +346,7 @@ private[parquet] class MutableRowWriteSupport extends RowWriteSupport { index: Int): Unit = { ctype match { case StringType => writer.addBinary( - Binary.fromByteArray(record(index).asInstanceOf[String].getBytes("utf-8"))) + Binary.fromByteArray(record(index).asInstanceOf[UTF8String].getBytes)) case BinaryType => writer.addBinary( Binary.fromByteArray(record(index).asInstanceOf[Array[Byte]])) case IntegerType => writer.addInteger(record.getInt(index)) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala index 20fdf5e58ef82..af7b3c81ae7b2 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala @@ -33,7 +33,6 @@ import org.apache.hadoop.io.Writable import org.apache.hadoop.mapreduce.lib.input.FileInputFormat import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat import org.apache.hadoop.mapreduce.{InputSplit, Job, JobContext} - import parquet.filter2.predicate.FilterApi import parquet.format.converter.ParquetMetadataConverter import parquet.hadoop.metadata.CompressionCodecName @@ -45,13 +44,13 @@ import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.mapred.SparkHadoopMapRedUtil import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil import org.apache.spark.rdd.{NewHadoopPartition, NewHadoopRDD, RDD} -import org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.{CatalystTypeConverters, expressions} import org.apache.spark.sql.parquet.ParquetTypesConverter._ import org.apache.spark.sql.sources._ import org.apache.spark.sql.types.{IntegerType, StructField, StructType, _} import org.apache.spark.sql.{DataFrame, Row, SQLConf, SQLContext, SaveMode} -import org.apache.spark.{Logging, Partition => SparkPartition, SerializableWritable, SparkException, TaskContext} +import org.apache.spark.{Logging, SerializableWritable, SparkException, TaskContext, Partition => SparkPartition} /** * Allows creation of Parquet based tables using the syntax: @@ -409,6 +408,9 @@ private[sql] case class ParquetRelation2( file.getName == ParquetFileWriter.PARQUET_METADATA_FILE } + // Skip type conversion + override val needConversion: Boolean = false + // TODO Should calculate per scan size // It's common that a query only scans a fraction of a large Parquet file. Returning size of the // whole Parquet file disables some optimizations in this case (e.g. broadcast join). @@ -550,7 +552,8 @@ private[sql] case class ParquetRelation2( baseRDD.mapPartitionsWithInputSplit { case (split: ParquetInputSplit, iterator) => val partValues = selectedPartitions.collectFirst { - case p if split.getPath.getParent.toString == p.path => p.values + case p if split.getPath.getParent.toString == p.path => + CatalystTypeConverters.convertToCatalyst(p.values).asInstanceOf[Row] }.get val requiredPartOrdinal = partitionKeyLocations.keys.toSeq diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala index 34d048e426d10..b3d71f687a60a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/DataSourceStrategy.scala @@ -23,7 +23,8 @@ import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.planning.PhysicalOperation import org.apache.spark.sql.catalyst.plans.logical import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan -import org.apache.spark.sql.types.StringType +import org.apache.spark.sql.execution.SparkPlan +import org.apache.spark.sql.types.{UTF8String, StringType} import org.apache.spark.sql.{Row, Strategy, execution, sources} /** @@ -53,7 +54,7 @@ private[sql] object DataSourceStrategy extends Strategy { (a, _) => t.buildScan(a)) :: Nil case l @ LogicalRelation(t: TableScan) => - execution.PhysicalRDD(l.output, t.buildScan()) :: Nil + createPhysicalRDD(l.relation, l.output, t.buildScan()) :: Nil case i @ logical.InsertIntoTable( l @ LogicalRelation(t: InsertableRelation), part, query, overwrite, false) if part.isEmpty => @@ -102,20 +103,30 @@ private[sql] object DataSourceStrategy extends Strategy { projectList.asInstanceOf[Seq[Attribute]] // Safe due to if above. .map(relation.attributeMap) // Match original case of attributes. - val scan = - execution.PhysicalRDD( - projectList.map(_.toAttribute), + val scan = createPhysicalRDD(relation.relation, projectList.map(_.toAttribute), scanBuilder(requestedColumns, pushedFilters)) filterCondition.map(execution.Filter(_, scan)).getOrElse(scan) } else { val requestedColumns = (projectSet ++ filterSet).map(relation.attributeMap).toSeq - val scan = - execution.PhysicalRDD(requestedColumns, scanBuilder(requestedColumns, pushedFilters)) + val scan = createPhysicalRDD(relation.relation, requestedColumns, + scanBuilder(requestedColumns, pushedFilters)) execution.Project(projectList, filterCondition.map(execution.Filter(_, scan)).getOrElse(scan)) } } + private[this] def createPhysicalRDD( + relation: BaseRelation, + output: Seq[Attribute], + rdd: RDD[Row]): SparkPlan = { + val converted = if (relation.needConversion) { + execution.RDDConversions.rowToRowRdd(rdd, relation.schema) + } else { + rdd + } + execution.PhysicalRDD(output, converted) + } + /** * Selects Catalyst predicate [[Expression]]s which are convertible into data source [[Filter]]s, * and convert them. @@ -167,14 +178,14 @@ private[sql] object DataSourceStrategy extends Strategy { case expressions.Not(child) => translate(child).map(sources.Not) - case expressions.StartsWith(a: Attribute, Literal(v: String, StringType)) => - Some(sources.StringStartsWith(a.name, v)) + case expressions.StartsWith(a: Attribute, Literal(v: UTF8String, StringType)) => + Some(sources.StringStartsWith(a.name, v.toString)) - case expressions.EndsWith(a: Attribute, Literal(v: String, StringType)) => - Some(sources.StringEndsWith(a.name, v)) + case expressions.EndsWith(a: Attribute, Literal(v: UTF8String, StringType)) => + Some(sources.StringEndsWith(a.name, v.toString)) - case expressions.Contains(a: Attribute, Literal(v: String, StringType)) => - Some(sources.StringContains(a.name, v)) + case expressions.Contains(a: Attribute, Literal(v: UTF8String, StringType)) => + Some(sources.StringContains(a.name, v.toString)) case _ => None } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala index 8f9946a5a801e..ca53dcdb92c52 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala @@ -126,6 +126,16 @@ abstract class BaseRelation { * could lead to execution plans that are suboptimal (i.e. broadcasting a very large table). */ def sizeInBytes: Long = sqlContext.conf.defaultSizeInBytes + + /** + * Whether does it need to convert the objects in Row to internal representation, for example: + * java.lang.String -> UTF8String + * java.lang.Decimal -> Decimal + * + * Note: The internal representation is not stable across releases and thus data sources outside + * of Spark SQL should leave this as true. + */ + def needConversion: Boolean = true } /** diff --git a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala index 36465cc2fa11a..bf6cf1321a056 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala @@ -30,7 +30,7 @@ class RowSuite extends FunSuite { test("create row") { val expected = new GenericMutableRow(4) expected.update(0, 2147483647) - expected.update(1, "this is a string") + expected.setString(1, "this is a string") expected.update(2, false) expected.update(3, null) val actual1 = Row(2147483647, "this is a string", false, null) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala index 0174aaee94246..4c48dca44498b 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala @@ -17,18 +17,14 @@ package org.apache.spark.sql -import org.apache.spark.sql.execution.GeneratedAggregate -import org.apache.spark.sql.test.TestSQLContext import org.scalatest.BeforeAndAfterAll +import org.apache.spark.sql.TestData._ import org.apache.spark.sql.execution.GeneratedAggregate import org.apache.spark.sql.functions._ -import org.apache.spark.sql.catalyst.errors.TreeNodeException -import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan -import org.apache.spark.sql.types._ - -import org.apache.spark.sql.TestData._ +import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.test.TestSQLContext.{udf => _, _} +import org.apache.spark.sql.types._ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala index 5f08834f73c6b..c86ef338fc644 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala @@ -65,7 +65,7 @@ class ColumnTypeSuite extends FunSuite with Logging { checkActualSize(FLOAT, Float.MaxValue, 4) checkActualSize(FIXED_DECIMAL(15, 10), Decimal(0, 15, 10), 8) checkActualSize(BOOLEAN, true, 1) - checkActualSize(STRING, "hello", 4 + "hello".getBytes("utf-8").length) + checkActualSize(STRING, UTF8String("hello"), 4 + "hello".getBytes("utf-8").length) checkActualSize(DATE, 0, 4) checkActualSize(TIMESTAMP, new Timestamp(0L), 12) @@ -108,8 +108,8 @@ class ColumnTypeSuite extends FunSuite with Logging { testNativeColumnType[StringType.type]( STRING, - (buffer: ByteBuffer, string: String) => { - val bytes = string.getBytes("utf-8") + (buffer: ByteBuffer, string: UTF8String) => { + val bytes = string.getBytes buffer.putInt(bytes.length) buffer.put(bytes) }, @@ -117,7 +117,7 @@ class ColumnTypeSuite extends FunSuite with Logging { val length = buffer.getInt() val bytes = new Array[Byte](length) buffer.get(bytes) - new String(bytes, "utf-8") + UTF8String(bytes) }) testColumnType[BinaryType.type, Array[Byte]]( diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala index b301818a008e7..f76314b9dab5e 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala @@ -24,7 +24,7 @@ import scala.util.Random import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.expressions.GenericMutableRow -import org.apache.spark.sql.types.{Decimal, DataType, NativeType} +import org.apache.spark.sql.types.{UTF8String, DataType, Decimal, NativeType} object ColumnarTestUtils { def makeNullRow(length: Int): GenericMutableRow = { @@ -48,7 +48,7 @@ object ColumnarTestUtils { case FLOAT => Random.nextFloat() case DOUBLE => Random.nextDouble() case FIXED_DECIMAL(precision, scale) => Decimal(Random.nextLong() % 100, precision, scale) - case STRING => Random.nextString(Random.nextInt(32)) + case STRING => UTF8String(Random.nextString(Random.nextInt(32))) case BOOLEAN => Random.nextBoolean() case BINARY => randomBytes(Random.nextInt(32)) case DATE => Random.nextInt() diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala index 60c8c00bda4d5..3b47b8adf313b 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala @@ -74,7 +74,7 @@ case class AllDataTypesScan( i.toDouble, new java.math.BigDecimal(i), new java.math.BigDecimal(i), - new Date((i + 1) * 8640000), + new Date(1970, 1, 1), new Timestamp(20000 + i), s"varchar_$i", Seq(i, i + 1), @@ -82,7 +82,7 @@ case class AllDataTypesScan( Map(i -> i.toString), Map(Map(s"str_$i" -> i.toFloat) -> Row(i.toLong)), Row(i, i.toString), - Row(Seq(s"str_$i", s"str_${i + 1}"), Row(Seq(new Date((i + 2) * 8640000))))) + Row(Seq(s"str_$i", s"str_${i + 1}"), Row(Seq(new Date(1970, 1, i + 1))))) } } } @@ -103,7 +103,7 @@ class TableScanSuite extends DataSourceTest { i.toDouble, new java.math.BigDecimal(i), new java.math.BigDecimal(i), - new Date((i + 1) * 8640000), + new Date(1970, 1, 1), new Timestamp(20000 + i), s"varchar_$i", Seq(i, i + 1), @@ -111,7 +111,7 @@ class TableScanSuite extends DataSourceTest { Map(i -> i.toString), Map(Map(s"str_$i" -> i.toFloat) -> Row(i.toLong)), Row(i, i.toString), - Row(Seq(s"str_$i", s"str_${i + 1}"), Row(Seq(new Date((i + 2) * 8640000))))) + Row(Seq(s"str_$i", s"str_${i + 1}"), Row(Seq(new Date(1970, 1, i + 1))))) }.toSeq before { @@ -266,7 +266,7 @@ class TableScanSuite extends DataSourceTest { sqlTest( "SELECT structFieldComplex.Value.`value_(2)` FROM tableWithSchema", - (1 to 10).map(i => Row(Seq(new Date((i + 2) * 8640000)))).toSeq) + (1 to 10).map(i => Row(Seq(new Date(1970, 1, i + 1)))).toSeq) test("Caching") { // Cached Query Execution diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala index 921c6194c7b76..74ae984f34866 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala @@ -34,7 +34,7 @@ import scala.collection.JavaConversions._ * 1. The Underlying data type in catalyst and in Hive * In catalyst: * Primitive => - * java.lang.String + * UTF8String * int / scala.Int * boolean / scala.Boolean * float / scala.Float @@ -239,9 +239,10 @@ private[hive] trait HiveInspectors { */ def unwrap(data: Any, oi: ObjectInspector): Any = oi match { case coi: ConstantObjectInspector if coi.getWritableConstantValue == null => null - case poi: WritableConstantStringObjectInspector => poi.getWritableConstantValue.toString + case poi: WritableConstantStringObjectInspector => + UTF8String(poi.getWritableConstantValue.toString) case poi: WritableConstantHiveVarcharObjectInspector => - poi.getWritableConstantValue.getHiveVarchar.getValue + UTF8String(poi.getWritableConstantValue.getHiveVarchar.getValue) case poi: WritableConstantHiveDecimalObjectInspector => HiveShim.toCatalystDecimal( PrimitiveObjectInspectorFactory.javaHiveDecimalObjectInspector, @@ -284,10 +285,13 @@ private[hive] trait HiveInspectors { case pi: PrimitiveObjectInspector => pi match { // We think HiveVarchar is also a String case hvoi: HiveVarcharObjectInspector if hvoi.preferWritable() => - hvoi.getPrimitiveWritableObject(data).getHiveVarchar.getValue - case hvoi: HiveVarcharObjectInspector => hvoi.getPrimitiveJavaObject(data).getValue + UTF8String(hvoi.getPrimitiveWritableObject(data).getHiveVarchar.getValue) + case hvoi: HiveVarcharObjectInspector => + UTF8String(hvoi.getPrimitiveJavaObject(data).getValue) case x: StringObjectInspector if x.preferWritable() => - x.getPrimitiveWritableObject(data).toString + UTF8String(x.getPrimitiveWritableObject(data).toString) + case x: StringObjectInspector => + UTF8String(x.getPrimitiveJavaObject(data)) case x: IntObjectInspector if x.preferWritable() => x.get(data) case x: BooleanObjectInspector if x.preferWritable() => x.get(data) case x: FloatObjectInspector if x.preferWritable() => x.get(data) @@ -340,7 +344,9 @@ private[hive] trait HiveInspectors { */ protected def wrapperFor(oi: ObjectInspector): Any => Any = oi match { case _: JavaHiveVarcharObjectInspector => - (o: Any) => new HiveVarchar(o.asInstanceOf[String], o.asInstanceOf[String].size) + (o: Any) => + val s = o.asInstanceOf[UTF8String].toString + new HiveVarchar(s, s.size) case _: JavaHiveDecimalObjectInspector => (o: Any) => HiveShim.createDecimal(o.asInstanceOf[Decimal].toJavaBigDecimal) @@ -409,7 +415,7 @@ private[hive] trait HiveInspectors { case x: PrimitiveObjectInspector => x match { // TODO we don't support the HiveVarcharObjectInspector yet. case _: StringObjectInspector if x.preferWritable() => HiveShim.getStringWritable(a) - case _: StringObjectInspector => a.asInstanceOf[java.lang.String] + case _: StringObjectInspector => a.asInstanceOf[UTF8String].toString() case _: IntObjectInspector if x.preferWritable() => HiveShim.getIntWritable(a) case _: IntObjectInspector => a.asInstanceOf[java.lang.Integer] case _: BooleanObjectInspector if x.preferWritable() => HiveShim.getBooleanWritable(a) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala index 1ccb0c279c60e..a6f4fbe8aba06 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala @@ -17,24 +17,21 @@ package org.apache.spark.sql.hive -import org.apache.spark.sql.catalyst.expressions.Row - import scala.collection.JavaConversions._ import org.apache.spark.annotation.Experimental import org.apache.spark.sql._ +import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute -import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate +import org.apache.spark.sql.catalyst.expressions.{Row, _} import org.apache.spark.sql.catalyst.planning._ import org.apache.spark.sql.catalyst.plans._ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan -import org.apache.spark.sql.sources.DescribeCommand -import org.apache.spark.sql.execution.{DescribeCommand => RunnableDescribeCommand} -import org.apache.spark.sql.execution._ +import org.apache.spark.sql.execution.{DescribeCommand => RunnableDescribeCommand, _} import org.apache.spark.sql.hive.execution._ import org.apache.spark.sql.parquet.ParquetRelation -import org.apache.spark.sql.sources.{CreateTableUsingAsSelect, CreateTableUsing} +import org.apache.spark.sql.sources.{CreateTableUsing, CreateTableUsingAsSelect, DescribeCommand} import org.apache.spark.sql.types.StringType @@ -131,7 +128,7 @@ private[hive] trait HiveStrategies { val partitionValues = part.getValues var i = 0 while (i < partitionValues.size()) { - inputData(i) = partitionValues(i) + inputData(i) = CatalystTypeConverters.convertToCatalyst(partitionValues(i)) i += 1 } pruningCondition(inputData) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala index 8efed7f0299bf..cab0fdd35723a 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala @@ -17,8 +17,7 @@ package org.apache.spark.sql.hive.execution -import java.io.{BufferedReader, InputStreamReader} -import java.io.{DataInputStream, DataOutputStream, EOFException} +import java.io.{BufferedReader, DataInputStream, DataOutputStream, EOFException, InputStreamReader} import java.util.Properties import scala.collection.JavaConversions._ @@ -28,12 +27,13 @@ import org.apache.hadoop.hive.serde2.AbstractSerDe import org.apache.hadoop.hive.serde2.objectinspector._ import org.apache.spark.rdd.RDD +import org.apache.spark.sql.catalyst.CatalystTypeConverters import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical.ScriptInputOutputSchema import org.apache.spark.sql.execution._ -import org.apache.spark.sql.types.DataType -import org.apache.spark.sql.hive.{HiveContext, HiveInspectors} import org.apache.spark.sql.hive.HiveShim._ +import org.apache.spark.sql.hive.{HiveContext, HiveInspectors} +import org.apache.spark.sql.types.DataType import org.apache.spark.util.Utils /** @@ -121,14 +121,13 @@ case class ScriptTransformation( if (outputSerde == null) { val prevLine = curLine curLine = reader.readLine() - if (!ioschema.schemaLess) { - new GenericRow( - prevLine.split(ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD")) + new GenericRow(CatalystTypeConverters.convertToCatalyst( + prevLine.split(ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD"))) .asInstanceOf[Array[Any]]) } else { - new GenericRow( - prevLine.split(ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD"), 2) + new GenericRow(CatalystTypeConverters.convertToCatalyst( + prevLine.split(ioschema.outputRowFormatMap("TOK_TABLEROWFORMATFIELD"), 2)) .asInstanceOf[Array[Any]]) } } else { diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala index 902a12785e3e9..a40a1e53117cd 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala @@ -22,11 +22,11 @@ import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries import org.apache.spark.sql.catalyst.util._ import org.apache.spark.sql.sources._ import org.apache.spark.sql.{SaveMode, DataFrame, SQLContext} -import org.apache.spark.sql.catalyst.expressions.Row +import org.apache.spark.sql.catalyst.expressions.{Attribute, Row} import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan import org.apache.spark.sql.execution.RunnableCommand import org.apache.spark.sql.hive.HiveContext -import org.apache.spark.sql.types.StructType +import org.apache.spark.sql.types._ /** * Analyzes the given table in the current database to generate statistics, which will be @@ -76,6 +76,12 @@ case class DropTable( private[hive] case class AddJar(path: String) extends RunnableCommand { + override val output: Seq[Attribute] = { + val schema = StructType( + StructField("result", IntegerType, false) :: Nil) + schema.toAttributes + } + override def run(sqlContext: SQLContext): Seq[Row] = { val hiveContext = sqlContext.asInstanceOf[HiveContext] hiveContext.runSqlHive(s"ADD JAR $path") diff --git a/sql/hive/v0.12.0/src/main/scala/org/apache/spark/sql/hive/Shim12.scala b/sql/hive/v0.12.0/src/main/scala/org/apache/spark/sql/hive/Shim12.scala index 0ed93c2c5b1fa..33e96eaabfbf6 100644 --- a/sql/hive/v0.12.0/src/main/scala/org/apache/spark/sql/hive/Shim12.scala +++ b/sql/hive/v0.12.0/src/main/scala/org/apache/spark/sql/hive/Shim12.scala @@ -41,7 +41,7 @@ import org.apache.hadoop.hive.serde2.typeinfo.{TypeInfo, TypeInfoFactory} import org.apache.hadoop.io.{NullWritable, Writable} import org.apache.hadoop.mapred.InputFormat -import org.apache.spark.sql.types.{Decimal, DecimalType} +import org.apache.spark.sql.types.{UTF8String, Decimal, DecimalType} private[hive] case class HiveFunctionWrapper(functionClassName: String) extends java.io.Serializable { @@ -135,7 +135,7 @@ private[hive] object HiveShim { PrimitiveCategory.VOID, null) def getStringWritable(value: Any): hadoopIo.Text = - if (value == null) null else new hadoopIo.Text(value.asInstanceOf[String]) + if (value == null) null else new hadoopIo.Text(value.asInstanceOf[UTF8String].toString) def getIntWritable(value: Any): hadoopIo.IntWritable = if (value == null) null else new hadoopIo.IntWritable(value.asInstanceOf[Int]) diff --git a/sql/hive/v0.13.1/src/main/scala/org/apache/spark/sql/hive/Shim13.scala b/sql/hive/v0.13.1/src/main/scala/org/apache/spark/sql/hive/Shim13.scala index 7577309900209..d331c210e8939 100644 --- a/sql/hive/v0.13.1/src/main/scala/org/apache/spark/sql/hive/Shim13.scala +++ b/sql/hive/v0.13.1/src/main/scala/org/apache/spark/sql/hive/Shim13.scala @@ -17,37 +17,35 @@ package org.apache.spark.sql.hive -import java.util -import java.util.{ArrayList => JArrayList} -import java.util.Properties import java.rmi.server.UID +import java.util.{Properties, ArrayList => JArrayList} import scala.collection.JavaConversions._ import scala.language.implicitConversions +import com.esotericsoftware.kryo.Kryo import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path -import org.apache.hadoop.io.{NullWritable, Writable} -import org.apache.hadoop.mapred.InputFormat import org.apache.hadoop.hive.common.StatsSetupConst -import org.apache.hadoop.hive.common.`type`.{HiveDecimal} +import org.apache.hadoop.hive.common.`type`.HiveDecimal import org.apache.hadoop.hive.conf.HiveConf import org.apache.hadoop.hive.ql.Context -import org.apache.hadoop.hive.ql.metadata.{Table, Hive, Partition} +import org.apache.hadoop.hive.ql.exec.{UDF, Utilities} +import org.apache.hadoop.hive.ql.metadata.{Hive, Partition, Table} import org.apache.hadoop.hive.ql.plan.{CreateTableDesc, FileSinkDesc, TableDesc} import org.apache.hadoop.hive.ql.processors.CommandProcessorFactory import org.apache.hadoop.hive.serde.serdeConstants -import org.apache.hadoop.hive.serde2.typeinfo.{TypeInfo, DecimalTypeInfo, TypeInfoFactory} -import org.apache.hadoop.hive.serde2.objectinspector.primitive.{HiveDecimalObjectInspector, PrimitiveObjectInspectorFactory} -import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspectorConverters, PrimitiveObjectInspector, ObjectInspector} -import org.apache.hadoop.hive.serde2.{Deserializer, ColumnProjectionUtils} -import org.apache.hadoop.hive.serde2.{io => hiveIo} import org.apache.hadoop.hive.serde2.avro.AvroGenericRecordWritable +import org.apache.hadoop.hive.serde2.objectinspector.primitive.{HiveDecimalObjectInspector, PrimitiveObjectInspectorFactory} +import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspector, ObjectInspectorConverters, PrimitiveObjectInspector} +import org.apache.hadoop.hive.serde2.typeinfo.{DecimalTypeInfo, TypeInfo, TypeInfoFactory} +import org.apache.hadoop.hive.serde2.{ColumnProjectionUtils, Deserializer, io => hiveIo} +import org.apache.hadoop.io.{NullWritable, Writable} +import org.apache.hadoop.mapred.InputFormat import org.apache.hadoop.{io => hadoopIo} import org.apache.spark.Logging -import org.apache.spark.sql.types.{Decimal, DecimalType} - +import org.apache.spark.sql.types.{Decimal, DecimalType, UTF8String} /** * This class provides the UDF creation and also the UDF instance serialization and @@ -63,18 +61,14 @@ private[hive] case class HiveFunctionWrapper(var functionClassName: String) // for Serialization def this() = this(null) - import java.io.{OutputStream, InputStream} - import com.esotericsoftware.kryo.Kryo import org.apache.spark.util.Utils._ - import org.apache.hadoop.hive.ql.exec.Utilities - import org.apache.hadoop.hive.ql.exec.UDF @transient private val methodDeSerialize = { val method = classOf[Utilities].getDeclaredMethod( "deserializeObjectByKryo", classOf[Kryo], - classOf[InputStream], + classOf[java.io.InputStream], classOf[Class[_]]) method.setAccessible(true) @@ -87,7 +81,7 @@ private[hive] case class HiveFunctionWrapper(var functionClassName: String) "serializeObjectByKryo", classOf[Kryo], classOf[Object], - classOf[OutputStream]) + classOf[java.io.OutputStream]) method.setAccessible(true) method @@ -224,7 +218,7 @@ private[hive] object HiveShim { TypeInfoFactory.voidTypeInfo, null) def getStringWritable(value: Any): hadoopIo.Text = - if (value == null) null else new hadoopIo.Text(value.asInstanceOf[String]) + if (value == null) null else new hadoopIo.Text(value.asInstanceOf[UTF8String].toString) def getIntWritable(value: Any): hadoopIo.IntWritable = if (value == null) null else new hadoopIo.IntWritable(value.asInstanceOf[Int]) From cf38fe04f8782ff4573ae106ec0de8e8d183cb2b Mon Sep 17 00:00:00 2001 From: Liang-Chi Hsieh Date: Wed, 15 Apr 2015 13:15:58 -0700 Subject: [PATCH 298/591] [SPARK-6844][SQL] Clean up accumulators used in InMemoryRelation when it is uncached JIRA: https://issues.apache.org/jira/browse/SPARK-6844 Author: Liang-Chi Hsieh Closes #5475 from viirya/cache_memory_leak and squashes the following commits: 0b41235 [Liang-Chi Hsieh] fix style. dc1d5d5 [Liang-Chi Hsieh] For comments. 78af229 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into cache_memory_leak 26c9bb6 [Liang-Chi Hsieh] Add configuration to enable in-memory table scan accumulators. 1c3b06e [Liang-Chi Hsieh] Clean up accumulators used in InMemoryRelation when it is uncached. --- .../org/apache/spark/sql/CacheManager.scala | 2 +- .../columnar/InMemoryColumnarTableScan.scala | 47 ++++++++++++++----- .../apache/spark/sql/CachedTableSuite.scala | 18 +++++++ .../columnar/PartitionBatchPruningSuite.scala | 2 + 4 files changed, 55 insertions(+), 14 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/CacheManager.scala b/sql/core/src/main/scala/org/apache/spark/sql/CacheManager.scala index ca4a127120b37..18584c2dcf797 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/CacheManager.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/CacheManager.scala @@ -112,7 +112,7 @@ private[sql] class CacheManager(sqlContext: SQLContext) extends Logging { val planToCache = query.queryExecution.analyzed val dataIndex = cachedData.indexWhere(cd => planToCache.sameResult(cd.plan)) require(dataIndex >= 0, s"Table $query is not cached.") - cachedData(dataIndex).cachedRepresentation.cachedColumnBuffers.unpersist(blocking) + cachedData(dataIndex).cachedRepresentation.uncache(blocking) cachedData.remove(dataIndex) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala index 6eee0c86d6a1c..d9b6fb43ab83d 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala @@ -19,13 +19,15 @@ package org.apache.spark.sql.columnar import java.nio.ByteBuffer -import org.apache.spark.Accumulator +import org.apache.spark.{Accumulable, Accumulator, Accumulators} import org.apache.spark.sql.catalyst.expressions import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.HashMap import org.apache.spark.rdd.RDD import org.apache.spark.sql.Row +import org.apache.spark.SparkContext import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation import org.apache.spark.sql.catalyst.dsl.expressions._ import org.apache.spark.sql.catalyst.expressions._ @@ -53,11 +55,16 @@ private[sql] case class InMemoryRelation( child: SparkPlan, tableName: Option[String])( private var _cachedColumnBuffers: RDD[CachedBatch] = null, - private var _statistics: Statistics = null) + private var _statistics: Statistics = null, + private var _batchStats: Accumulable[ArrayBuffer[Row], Row] = null) extends LogicalPlan with MultiInstanceRelation { - private val batchStats = - child.sqlContext.sparkContext.accumulableCollection(ArrayBuffer.empty[Row]) + private val batchStats: Accumulable[ArrayBuffer[Row], Row] = + if (_batchStats == null) { + child.sqlContext.sparkContext.accumulableCollection(ArrayBuffer.empty[Row]) + } else { + _batchStats + } val partitionStatistics = new PartitionStatistics(output) @@ -161,7 +168,7 @@ private[sql] case class InMemoryRelation( def withOutput(newOutput: Seq[Attribute]): InMemoryRelation = { InMemoryRelation( newOutput, useCompression, batchSize, storageLevel, child, tableName)( - _cachedColumnBuffers, statisticsToBePropagated) + _cachedColumnBuffers, statisticsToBePropagated, batchStats) } override def children: Seq[LogicalPlan] = Seq.empty @@ -175,13 +182,20 @@ private[sql] case class InMemoryRelation( child, tableName)( _cachedColumnBuffers, - statisticsToBePropagated).asInstanceOf[this.type] + statisticsToBePropagated, + batchStats).asInstanceOf[this.type] } def cachedColumnBuffers: RDD[CachedBatch] = _cachedColumnBuffers override protected def otherCopyArgs: Seq[AnyRef] = - Seq(_cachedColumnBuffers, statisticsToBePropagated) + Seq(_cachedColumnBuffers, statisticsToBePropagated, batchStats) + + private[sql] def uncache(blocking: Boolean): Unit = { + Accumulators.remove(batchStats.id) + cachedColumnBuffers.unpersist(blocking) + _cachedColumnBuffers = null + } } private[sql] case class InMemoryColumnarTableScan( @@ -244,15 +258,20 @@ private[sql] case class InMemoryColumnarTableScan( } } + lazy val enableAccumulators: Boolean = + sqlContext.getConf("spark.sql.inMemoryTableScanStatistics.enable", "false").toBoolean + // Accumulators used for testing purposes - val readPartitions: Accumulator[Int] = sparkContext.accumulator(0) - val readBatches: Accumulator[Int] = sparkContext.accumulator(0) + lazy val readPartitions: Accumulator[Int] = sparkContext.accumulator(0) + lazy val readBatches: Accumulator[Int] = sparkContext.accumulator(0) private val inMemoryPartitionPruningEnabled = sqlContext.conf.inMemoryPartitionPruning override def execute(): RDD[Row] = { - readPartitions.setValue(0) - readBatches.setValue(0) + if (enableAccumulators) { + readPartitions.setValue(0) + readBatches.setValue(0) + } relation.cachedColumnBuffers.mapPartitions { cachedBatchIterator => val partitionFilter = newPredicate( @@ -302,7 +321,7 @@ private[sql] case class InMemoryColumnarTableScan( } } - if (rows.hasNext) { + if (rows.hasNext && enableAccumulators) { readPartitions += 1 } @@ -321,7 +340,9 @@ private[sql] case class InMemoryColumnarTableScan( logInfo(s"Skipping partition based on stats $statsString") false } else { - readBatches += 1 + if (enableAccumulators) { + readBatches += 1 + } true } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala index f7b5f08beb92f..01e3b8671071e 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala @@ -22,6 +22,7 @@ import scala.language.{implicitConversions, postfixOps} import org.scalatest.concurrent.Eventually._ +import org.apache.spark.Accumulators import org.apache.spark.sql.TestData._ import org.apache.spark.sql.columnar._ import org.apache.spark.sql.test.TestSQLContext._ @@ -297,4 +298,21 @@ class CachedTableSuite extends QueryTest { sql("Clear CACHE") assert(cacheManager.isEmpty) } + + test("Clear accumulators when uncacheTable to prevent memory leaking") { + val accsSize = Accumulators.originals.size + + sql("SELECT key FROM testData LIMIT 10").registerTempTable("t1") + sql("SELECT key FROM testData LIMIT 5").registerTempTable("t2") + cacheTable("t1") + cacheTable("t2") + sql("SELECT * FROM t1").count() + sql("SELECT * FROM t2").count() + sql("SELECT * FROM t1").count() + sql("SELECT * FROM t2").count() + uncacheTable("t1") + uncacheTable("t2") + + assert(accsSize >= Accumulators.originals.size) + } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala index e57bb06e7263b..2a0b701cad7fa 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala @@ -39,6 +39,8 @@ class PartitionBatchPruningSuite extends FunSuite with BeforeAndAfterAll with Be // Enable in-memory partition pruning setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, "true") + // Enable in-memory table scan accumulators + setConf("spark.sql.inMemoryTableScanStatistics.enable", "true") } override protected def afterAll(): Unit = { From 557a797a273f1668065806cba53e19e6134a66d3 Mon Sep 17 00:00:00 2001 From: sboeschhuawei Date: Wed, 15 Apr 2015 13:28:10 -0700 Subject: [PATCH 299/591] [SPARK-6937][MLLIB] Fixed bug in PICExample in which the radius were not being accepted on c... Tiny bug in PowerIterationClusteringExample in which radius not accepted from command line Author: sboeschhuawei Closes #5531 from javadba/picsub and squashes the following commits: 2aab8cf [sboeschhuawei] Fixed bug in PICExample in which the radius were not being accepted on command line --- .../examples/mllib/PowerIterationClusteringExample.scala | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala index 9f22d40c15f3f..6d8b806569dfd 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala @@ -65,7 +65,7 @@ object PowerIterationClusteringExample { def main(args: Array[String]) { val defaultParams = Params() - val parser = new OptionParser[Params]("PIC Circles") { + val parser = new OptionParser[Params]("PowerIterationClusteringExample") { head("PowerIterationClusteringExample: an example PIC app using concentric circles.") opt[Int]('k', "k") .text(s"number of circles (/clusters), default: ${defaultParams.k}") @@ -76,9 +76,9 @@ object PowerIterationClusteringExample { opt[Int]("maxIterations") .text(s"number of iterations, default: ${defaultParams.maxIterations}") .action((x, c) => c.copy(maxIterations = x)) - opt[Int]('r', "r") + opt[Double]('r', "r") .text(s"radius of outermost circle, default: ${defaultParams.outerRadius}") - .action((x, c) => c.copy(numPoints = x)) + .action((x, c) => c.copy(outerRadius = x)) } parser.parse(args, defaultParams).map { params => @@ -154,3 +154,4 @@ object PowerIterationClusteringExample { coeff * math.exp(expCoeff * ssquares) } } + From 4754e16f4746ebd882b2ce7f1efc6e4d4408922c Mon Sep 17 00:00:00 2001 From: Wenchen Fan Date: Wed, 15 Apr 2015 13:39:12 -0700 Subject: [PATCH 300/591] [SPARK-6898][SQL] completely support special chars in column names Even if we wrap column names in backticks like `` `a#$b.c` ``, we still handle the "." inside column name specially. I think it's fragile to use a special char to split name parts, why not put name parts in `UnresolvedAttribute` directly? Author: Wenchen Fan This patch had conflicts when merged, resolved by Committer: Michael Armbrust Closes #5511 from cloud-fan/6898 and squashes the following commits: 48e3e57 [Wenchen Fan] more style fix 820dc45 [Wenchen Fan] do not ignore newName in UnresolvedAttribute d81ad43 [Wenchen Fan] fix style 11699d6 [Wenchen Fan] completely support special chars in column names --- .../apache/spark/sql/catalyst/SqlParser.scala | 4 +-- .../sql/catalyst/analysis/Analyzer.scala | 13 ++++----- .../sql/catalyst/analysis/CheckAnalysis.scala | 6 ++++- .../sql/catalyst/analysis/unresolved.scala | 14 ++++++++-- .../catalyst/plans/logical/LogicalPlan.scala | 27 +++++++++---------- .../sql/catalyst/analysis/AnalysisSuite.scala | 2 -- .../org/apache/spark/sql/DataFrame.scala | 4 +-- .../org/apache/spark/sql/SQLQuerySuite.scala | 13 ++++++--- .../org/apache/spark/sql/hive/HiveQl.scala | 2 +- 9 files changed, 52 insertions(+), 33 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala index 9a3531ceb3343..0af969cc5cc67 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/SqlParser.scala @@ -381,13 +381,13 @@ class SqlParser extends AbstractSparkSQLParser with DataTypeParser { | "(" ~> expression <~ ")" | function | dotExpressionHeader - | ident ^^ UnresolvedAttribute + | ident ^^ {case i => UnresolvedAttribute.quoted(i)} | signedPrimary | "~" ~> expression ^^ BitwiseNot ) protected lazy val dotExpressionHeader: Parser[Expression] = (ident <~ ".") ~ ident ~ rep("." ~> ident) ^^ { - case i1 ~ i2 ~ rest => UnresolvedAttribute((Seq(i1, i2) ++ rest).mkString(".")) + case i1 ~ i2 ~ rest => UnresolvedAttribute(Seq(i1, i2) ++ rest) } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala index 8b68b0df35f48..cb49e5ad5586f 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala @@ -297,14 +297,15 @@ class Analyzer( case q: LogicalPlan => logTrace(s"Attempting to resolve ${q.simpleString}") q transformExpressionsUp { - case u @ UnresolvedAttribute(name) if resolver(name, VirtualColumn.groupingIdName) && + case u @ UnresolvedAttribute(nameParts) if nameParts.length == 1 && + resolver(nameParts(0), VirtualColumn.groupingIdName) && q.isInstanceOf[GroupingAnalytics] => // Resolve the virtual column GROUPING__ID for the operator GroupingAnalytics q.asInstanceOf[GroupingAnalytics].gid - case u @ UnresolvedAttribute(name) => + case u @ UnresolvedAttribute(nameParts) => // Leave unchanged if resolution fails. Hopefully will be resolved next round. val result = - withPosition(u) { q.resolveChildren(name, resolver).getOrElse(u) } + withPosition(u) { q.resolveChildren(nameParts, resolver).getOrElse(u) } logDebug(s"Resolving $u to $result") result case UnresolvedGetField(child, fieldName) if child.resolved => @@ -383,12 +384,12 @@ class Analyzer( child: LogicalPlan, grandchild: LogicalPlan): (Seq[SortOrder], Seq[Attribute]) = { // Find any attributes that remain unresolved in the sort. - val unresolved: Seq[String] = - ordering.flatMap(_.collect { case UnresolvedAttribute(name) => name }) + val unresolved: Seq[Seq[String]] = + ordering.flatMap(_.collect { case UnresolvedAttribute(nameParts) => nameParts }) // Create a map from name, to resolved attributes, when the desired name can be found // prior to the projection. - val resolved: Map[String, NamedExpression] = + val resolved: Map[Seq[String], NamedExpression] = unresolved.flatMap(u => grandchild.resolve(u, resolver).map(a => u -> a)).toMap // Construct a set that contains all of the attributes that we need to evaluate the diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala index fa02111385c06..1155dac28fc78 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala @@ -46,8 +46,12 @@ trait CheckAnalysis { operator transformExpressionsUp { case a: Attribute if !a.resolved => if (operator.childrenResolved) { + val nameParts = a match { + case UnresolvedAttribute(nameParts) => nameParts + case _ => Seq(a.name) + } // Throw errors for specific problems with get field. - operator.resolveChildren(a.name, resolver, throwErrors = true) + operator.resolveChildren(nameParts, resolver, throwErrors = true) } val from = operator.inputSet.map(_.name).mkString(", ") diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala index 300e9ba187bc5..3f567e3e8b2a6 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala @@ -49,7 +49,12 @@ case class UnresolvedRelation( /** * Holds the name of an attribute that has yet to be resolved. */ -case class UnresolvedAttribute(name: String) extends Attribute with trees.LeafNode[Expression] { +case class UnresolvedAttribute(nameParts: Seq[String]) + extends Attribute with trees.LeafNode[Expression] { + + def name: String = + nameParts.map(n => if (n.contains(".")) s"`$n`" else n).mkString(".") + override def exprId: ExprId = throw new UnresolvedException(this, "exprId") override def dataType: DataType = throw new UnresolvedException(this, "dataType") override def nullable: Boolean = throw new UnresolvedException(this, "nullable") @@ -59,7 +64,7 @@ case class UnresolvedAttribute(name: String) extends Attribute with trees.LeafNo override def newInstance(): UnresolvedAttribute = this override def withNullability(newNullability: Boolean): UnresolvedAttribute = this override def withQualifiers(newQualifiers: Seq[String]): UnresolvedAttribute = this - override def withName(newName: String): UnresolvedAttribute = UnresolvedAttribute(name) + override def withName(newName: String): UnresolvedAttribute = UnresolvedAttribute.quoted(newName) // Unresolved attributes are transient at compile time and don't get evaluated during execution. override def eval(input: Row = null): EvaluatedType = @@ -68,6 +73,11 @@ case class UnresolvedAttribute(name: String) extends Attribute with trees.LeafNo override def toString: String = s"'$name" } +object UnresolvedAttribute { + def apply(name: String): UnresolvedAttribute = new UnresolvedAttribute(name.split("\\.")) + def quoted(name: String): UnresolvedAttribute = new UnresolvedAttribute(Seq(name)) +} + case class UnresolvedFunction(name: String, children: Seq[Expression]) extends Expression { override def dataType: DataType = throw new UnresolvedException(this, "dataType") override def foldable: Boolean = throw new UnresolvedException(this, "foldable") diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala index 579a0fb8d3f93..ae4620a4e5abf 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala @@ -19,12 +19,11 @@ package org.apache.spark.sql.catalyst.plans.logical import org.apache.spark.Logging import org.apache.spark.sql.AnalysisException -import org.apache.spark.sql.catalyst.analysis.{EliminateSubQueries, UnresolvedGetField, Resolver} +import org.apache.spark.sql.catalyst.analysis.{UnresolvedAttribute, EliminateSubQueries, Resolver} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.QueryPlan import org.apache.spark.sql.catalyst.trees.TreeNode import org.apache.spark.sql.catalyst.trees -import org.apache.spark.sql.types.{ArrayType, StructType, StructField} abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { @@ -111,10 +110,10 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { * as string in the following form: `[scope].AttributeName.[nested].[fields]...`. */ def resolveChildren( - name: String, + nameParts: Seq[String], resolver: Resolver, throwErrors: Boolean = false): Option[NamedExpression] = - resolve(name, children.flatMap(_.output), resolver, throwErrors) + resolve(nameParts, children.flatMap(_.output), resolver, throwErrors) /** * Optionally resolves the given string to a [[NamedExpression]] based on the output of this @@ -122,10 +121,10 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { * `[scope].AttributeName.[nested].[fields]...`. */ def resolve( - name: String, + nameParts: Seq[String], resolver: Resolver, throwErrors: Boolean = false): Option[NamedExpression] = - resolve(name, output, resolver, throwErrors) + resolve(nameParts, output, resolver, throwErrors) /** * Resolve the given `name` string against the given attribute, returning either 0 or 1 match. @@ -135,7 +134,7 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { * See the comment above `candidates` variable in resolve() for semantics the returned data. */ private def resolveAsTableColumn( - nameParts: Array[String], + nameParts: Seq[String], resolver: Resolver, attribute: Attribute): Option[(Attribute, List[String])] = { assert(nameParts.length > 1) @@ -155,7 +154,7 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { * See the comment above `candidates` variable in resolve() for semantics the returned data. */ private def resolveAsColumn( - nameParts: Array[String], + nameParts: Seq[String], resolver: Resolver, attribute: Attribute): Option[(Attribute, List[String])] = { if (resolver(attribute.name, nameParts.head)) { @@ -167,13 +166,11 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { /** Performs attribute resolution given a name and a sequence of possible attributes. */ protected def resolve( - name: String, + nameParts: Seq[String], input: Seq[Attribute], resolver: Resolver, throwErrors: Boolean): Option[NamedExpression] = { - val parts = name.split("\\.") - // A sequence of possible candidate matches. // Each candidate is a tuple. The first element is a resolved attribute, followed by a list // of parts that are to be resolved. @@ -182,9 +179,9 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { // and the second element will be List("c"). var candidates: Seq[(Attribute, List[String])] = { // If the name has 2 or more parts, try to resolve it as `table.column` first. - if (parts.length > 1) { + if (nameParts.length > 1) { input.flatMap { option => - resolveAsTableColumn(parts, resolver, option) + resolveAsTableColumn(nameParts, resolver, option) } } else { Seq.empty @@ -194,10 +191,12 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging { // If none of attributes match `table.column` pattern, we try to resolve it as a column. if (candidates.isEmpty) { candidates = input.flatMap { candidate => - resolveAsColumn(parts, resolver, candidate) + resolveAsColumn(nameParts, resolver, candidate) } } + def name = UnresolvedAttribute(nameParts).name + candidates.distinct match { // One match, no nested fields, use it. case Seq((a, Nil)) => Some(a) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala index 6e3d6b9263e86..e10ddfdf5127c 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala @@ -27,8 +27,6 @@ import org.apache.spark.sql.types._ import org.apache.spark.sql.catalyst.dsl.expressions._ import org.apache.spark.sql.catalyst.dsl.plans._ -import scala.collection.immutable - class AnalysisSuite extends FunSuite with BeforeAndAfter { val caseSensitiveCatalog = new SimpleCatalog(true) val caseInsensitiveCatalog = new SimpleCatalog(false) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 94ae2d65fd0e4..3235f85d5bbd2 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -158,7 +158,7 @@ class DataFrame private[sql]( } protected[sql] def resolve(colName: String): NamedExpression = { - queryExecution.analyzed.resolve(colName, sqlContext.analyzer.resolver).getOrElse { + queryExecution.analyzed.resolve(colName.split("\\."), sqlContext.analyzer.resolver).getOrElse { throw new AnalysisException( s"""Cannot resolve column name "$colName" among (${schema.fieldNames.mkString(", ")})""") } @@ -166,7 +166,7 @@ class DataFrame private[sql]( protected[sql] def numericColumns: Seq[Expression] = { schema.fields.filter(_.dataType.isInstanceOf[NumericType]).map { n => - queryExecution.analyzed.resolve(n.name, sqlContext.analyzer.resolver).get + queryExecution.analyzed.resolve(n.name.split("\\."), sqlContext.analyzer.resolver).get } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala index 4c48dca44498b..d739e550f3e56 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala @@ -19,14 +19,13 @@ package org.apache.spark.sql import org.scalatest.BeforeAndAfterAll -import org.apache.spark.sql.TestData._ import org.apache.spark.sql.execution.GeneratedAggregate import org.apache.spark.sql.functions._ +import org.apache.spark.sql.TestData._ import org.apache.spark.sql.test.TestSQLContext import org.apache.spark.sql.test.TestSQLContext.{udf => _, _} import org.apache.spark.sql.types._ - class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { // Make sure the tables are loaded. TestData @@ -1125,7 +1124,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { val data = sparkContext.parallelize( Seq("""{"key?number1": "value1", "key.number2": "value2"}""")) jsonRDD(data).registerTempTable("records") - sql("SELECT `key?number1` FROM records") + sql("SELECT `key?number1`, `key.number2` FROM records") } test("SPARK-3814 Support Bitwise & operator") { @@ -1225,4 +1224,12 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { checkAnswer(sql("SELECT a.b[0] FROM t ORDER BY c0.a"), Row(1)) checkAnswer(sql("SELECT b[0].a FROM t ORDER BY c0.a"), Row(1)) } + + test("SPARK-6898: complete support for special chars in column names") { + jsonRDD(sparkContext.makeRDD( + """{"a": {"c.b": 1}, "b.$q": [{"a@!.q": 1}], "q.w": {"w.i&": [1]}}""" :: Nil)) + .registerTempTable("t") + + checkAnswer(sql("SELECT a.`c.b`, `b.$q`[0].`a@!.q`, `q.w`.`w.i&`[0] FROM t"), Row(1, 1, 1)) + } } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala index 53a204b8c2932..fd305eb480e63 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala @@ -1101,7 +1101,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C case Token(".", qualifier :: Token(attr, Nil) :: Nil) => nodeToExpr(qualifier) match { case UnresolvedAttribute(qualifierName) => - UnresolvedAttribute(qualifierName + "." + cleanIdentifier(attr)) + UnresolvedAttribute(qualifierName :+ cleanIdentifier(attr)) case other => UnresolvedGetField(other, attr) } From 585638e81ce09a72b9e7f95d38e0d432cfa02456 Mon Sep 17 00:00:00 2001 From: Daoyuan Wang Date: Wed, 15 Apr 2015 14:06:10 -0700 Subject: [PATCH 301/591] [SPARK-2213] [SQL] sort merge join for spark sql Thanks for the initial work from Ishiihara in #3173 This PR introduce a new join method of sort merge join, which firstly ensure that keys of same value are in the same partition, and inside each partition the Rows are sorted by key. Then we can run down both sides together, find matched rows using [sort merge join](http://en.wikipedia.org/wiki/Sort-merge_join). In this way, we don't have to store the whole hash table of one side as hash join, thus we have less memory usage. Also, this PR would benefit from #3438 , making the sorting phrase much more efficient. We introduced a new configuration of "spark.sql.planner.sortMergeJoin" to switch between this(`true`) and ShuffledHashJoin(`false`), probably we want the default value of it be `false` at first. Author: Daoyuan Wang Author: Michael Armbrust This patch had conflicts when merged, resolved by Committer: Michael Armbrust Closes #5208 from adrian-wang/smj and squashes the following commits: 2493b9f [Daoyuan Wang] fix style 5049d88 [Daoyuan Wang] propagate rowOrdering for RangePartitioning f91a2ae [Daoyuan Wang] yin's comment: use external sort if option is enabled, add comments f515cd2 [Daoyuan Wang] yin's comment: outputOrdering, join suite refine ec8061b [Daoyuan Wang] minor change 413fd24 [Daoyuan Wang] Merge pull request #3 from marmbrus/pr/5208 952168a [Michael Armbrust] add type 5492884 [Michael Armbrust] copy when ordering 7ddd656 [Michael Armbrust] Cleanup addition of ordering requirements b198278 [Daoyuan Wang] inherit ordering in project c8e82a3 [Daoyuan Wang] fix style 6e897dd [Daoyuan Wang] hide boundReference from manually construct RowOrdering for key compare in smj 8681d73 [Daoyuan Wang] refactor Exchange and fix copy for sorting 2875ef2 [Daoyuan Wang] fix changed configuration 61d7f49 [Daoyuan Wang] add omitted comment 00a4430 [Daoyuan Wang] fix bug 078d69b [Daoyuan Wang] address comments: add comments, do sort in shuffle, and others 3af6ba5 [Daoyuan Wang] use buffer for only one side 171001f [Daoyuan Wang] change default outputordering 47455c9 [Daoyuan Wang] add apache license ... a28277f [Daoyuan Wang] fix style 645c70b [Daoyuan Wang] address comments using sort 068c35d [Daoyuan Wang] fix new style and add some tests 925203b [Daoyuan Wang] address comments 07ce92f [Daoyuan Wang] fix ArrayIndexOutOfBound 42fca0e [Daoyuan Wang] code clean e3ec096 [Daoyuan Wang] fix comment style.. 2edd235 [Daoyuan Wang] fix outputpartitioning 57baa40 [Daoyuan Wang] fix sort eval bug 303b6da [Daoyuan Wang] fix several errors 95db7ad [Daoyuan Wang] fix brackets for if-statement 4464f16 [Daoyuan Wang] fix error 880d8e9 [Daoyuan Wang] sort merge join for spark sql --- .../spark/sql/catalyst/expressions/rows.scala | 10 +- .../plans/physical/partitioning.scala | 13 ++ .../scala/org/apache/spark/sql/SQLConf.scala | 8 + .../org/apache/spark/sql/SQLContext.scala | 2 +- .../apache/spark/sql/execution/Exchange.scala | 148 ++++++++++++--- .../spark/sql/execution/SparkPlan.scala | 6 + .../spark/sql/execution/SparkStrategies.scala | 11 +- .../spark/sql/execution/basicOperators.scala | 10 ++ .../sql/execution/joins/SortMergeJoin.scala | 169 ++++++++++++++++++ .../org/apache/spark/sql/JoinSuite.scala | 28 ++- .../SortMergeCompatibilitySuite.scala | 162 +++++++++++++++++ 11 files changed, 534 insertions(+), 33 deletions(-) create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala create mode 100644 sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/SortMergeCompatibilitySuite.scala diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala index 1b62e17ff47fd..b6ec7d3417ef8 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala @@ -17,8 +17,7 @@ package org.apache.spark.sql.catalyst.expressions -import org.apache.spark.sql.types.{UTF8String, StructType, NativeType} - +import org.apache.spark.sql.types.{UTF8String, DataType, StructType, NativeType} /** * An extended interface to [[Row]] that allows the values for each column to be updated. Setting @@ -239,3 +238,10 @@ class RowOrdering(ordering: Seq[SortOrder]) extends Ordering[Row] { return 0 } } + +object RowOrdering { + def forSchema(dataTypes: Seq[DataType]): RowOrdering = + new RowOrdering(dataTypes.zipWithIndex.map { + case(dt, index) => new SortOrder(BoundReference(index, dt, nullable = true), Ascending) + }) +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala index 288c11f69fe22..fb4217a44807b 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala @@ -94,6 +94,9 @@ sealed trait Partitioning { * only compatible if the `numPartitions` of them is the same. */ def compatibleWith(other: Partitioning): Boolean + + /** Returns the expressions that are used to key the partitioning. */ + def keyExpressions: Seq[Expression] } case class UnknownPartitioning(numPartitions: Int) extends Partitioning { @@ -106,6 +109,8 @@ case class UnknownPartitioning(numPartitions: Int) extends Partitioning { case UnknownPartitioning(_) => true case _ => false } + + override def keyExpressions: Seq[Expression] = Nil } case object SinglePartition extends Partitioning { @@ -117,6 +122,8 @@ case object SinglePartition extends Partitioning { case SinglePartition => true case _ => false } + + override def keyExpressions: Seq[Expression] = Nil } case object BroadcastPartitioning extends Partitioning { @@ -128,6 +135,8 @@ case object BroadcastPartitioning extends Partitioning { case SinglePartition => true case _ => false } + + override def keyExpressions: Seq[Expression] = Nil } /** @@ -158,6 +167,8 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int) case _ => false } + override def keyExpressions: Seq[Expression] = expressions + override def eval(input: Row = null): EvaluatedType = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") } @@ -200,6 +211,8 @@ case class RangePartitioning(ordering: Seq[SortOrder], numPartitions: Int) case _ => false } + override def keyExpressions: Seq[Expression] = ordering.map(_.child) + override def eval(input: Row): EvaluatedType = throw new TreeNodeException(this, s"No function to evaluate expression. type: ${this.nodeName}") } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala index ee641bdfeb2d7..5c65f04ee8497 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala @@ -47,6 +47,7 @@ private[spark] object SQLConf { // Options that control which operators can be chosen by the query planner. These should be // considered hints and may be ignored by future versions of Spark SQL. val EXTERNAL_SORT = "spark.sql.planner.externalSort" + val SORTMERGE_JOIN = "spark.sql.planner.sortMergeJoin" // This is only used for the thriftserver val THRIFTSERVER_POOL = "spark.sql.thriftserver.scheduler.pool" @@ -128,6 +129,13 @@ private[sql] class SQLConf extends Serializable { /** When true the planner will use the external sort, which may spill to disk. */ private[spark] def externalSortEnabled: Boolean = getConf(EXTERNAL_SORT, "false").toBoolean + /** + * Sort merge join would sort the two side of join first, and then iterate both sides together + * only once to get all matches. Using sort merge join can save a lot of memory usage compared + * to HashJoin. + */ + private[spark] def sortMergeJoinEnabled: Boolean = getConf(SORTMERGE_JOIN, "false").toBoolean + /** * When set to true, Spark SQL will use the Scala compiler at runtime to generate custom bytecode * that evaluates expressions found in queries. In general this custom code runs much faster diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala index 89a4faf35e0d2..f9f3eb2e03817 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala @@ -1081,7 +1081,7 @@ class SQLContext(@transient val sparkContext: SparkContext) @transient protected[sql] val prepareForExecution = new RuleExecutor[SparkPlan] { val batches = - Batch("Add exchange", Once, AddExchange(self)) :: Nil + Batch("Add exchange", Once, EnsureRequirements(self)) :: Nil } protected[sql] def openSession(): SQLSession = { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala index 437408d30bfd2..518fc9e57c708 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala @@ -19,24 +19,42 @@ package org.apache.spark.sql.execution import org.apache.spark.annotation.DeveloperApi import org.apache.spark.shuffle.sort.SortShuffleManager -import org.apache.spark.sql.catalyst.expressions import org.apache.spark.{SparkEnv, HashPartitioner, RangePartitioner, SparkConf} import org.apache.spark.rdd.{RDD, ShuffledRDD} import org.apache.spark.sql.{SQLContext, Row} import org.apache.spark.sql.catalyst.errors.attachTree -import org.apache.spark.sql.catalyst.expressions.{Attribute, RowOrdering} +import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.physical._ import org.apache.spark.sql.catalyst.rules.Rule import org.apache.spark.util.MutablePair +object Exchange { + /** + * Returns true when the ordering expressions are a subset of the key. + * if true, ShuffledRDD can use `setKeyOrdering(orderingKey)` to sort within [[Exchange]]. + */ + def canSortWithShuffle(partitioning: Partitioning, desiredOrdering: Seq[SortOrder]): Boolean = { + desiredOrdering.map(_.child).toSet.subsetOf(partitioning.keyExpressions.toSet) + } +} + /** * :: DeveloperApi :: + * Performs a shuffle that will result in the desired `newPartitioning`. Optionally sorts each + * resulting partition based on expressions from the partition key. It is invalid to construct an + * exchange operator with a `newOrdering` that cannot be calculated using the partitioning key. */ @DeveloperApi -case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends UnaryNode { +case class Exchange( + newPartitioning: Partitioning, + newOrdering: Seq[SortOrder], + child: SparkPlan) + extends UnaryNode { override def outputPartitioning: Partitioning = newPartitioning + override def outputOrdering: Seq[SortOrder] = newOrdering + override def output: Seq[Attribute] = child.output /** We must copy rows when sort based shuffle is on */ @@ -45,6 +63,20 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una private val bypassMergeThreshold = child.sqlContext.sparkContext.conf.getInt("spark.shuffle.sort.bypassMergeThreshold", 200) + private val keyOrdering = { + if (newOrdering.nonEmpty) { + val key = newPartitioning.keyExpressions + val boundOrdering = newOrdering.map { o => + val ordinal = key.indexOf(o.child) + if (ordinal == -1) sys.error(s"Invalid ordering on $o requested for $newPartitioning") + o.copy(child = BoundReference(ordinal, o.child.dataType, o.child.nullable)) + } + new RowOrdering(boundOrdering) + } else { + null // Ordering will not be used + } + } + override def execute(): RDD[Row] = attachTree(this , "execute") { newPartitioning match { case HashPartitioning(expressions, numPartitions) => @@ -56,7 +88,9 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una // we can avoid the defensive copies to improve performance. In the long run, we probably // want to include information in shuffle dependencies to indicate whether elements in the // source RDD should be copied. - val rdd = if (sortBasedShuffleOn && numPartitions > bypassMergeThreshold) { + val willMergeSort = sortBasedShuffleOn && numPartitions > bypassMergeThreshold + + val rdd = if (willMergeSort || newOrdering.nonEmpty) { child.execute().mapPartitions { iter => val hashExpressions = newMutableProjection(expressions, child.output)() iter.map(r => (hashExpressions(r).copy(), r.copy())) @@ -69,12 +103,17 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una } } val part = new HashPartitioner(numPartitions) - val shuffled = new ShuffledRDD[Row, Row, Row](rdd, part) + val shuffled = + if (newOrdering.nonEmpty) { + new ShuffledRDD[Row, Row, Row](rdd, part).setKeyOrdering(keyOrdering) + } else { + new ShuffledRDD[Row, Row, Row](rdd, part) + } shuffled.setSerializer(new SparkSqlSerializer(new SparkConf(false))) shuffled.map(_._2) case RangePartitioning(sortingExpressions, numPartitions) => - val rdd = if (sortBasedShuffleOn) { + val rdd = if (sortBasedShuffleOn || newOrdering.nonEmpty) { child.execute().mapPartitions { iter => iter.map(row => (row.copy(), null))} } else { child.execute().mapPartitions { iter => @@ -87,7 +126,12 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una implicit val ordering = new RowOrdering(sortingExpressions, child.output) val part = new RangePartitioner(numPartitions, rdd, ascending = true) - val shuffled = new ShuffledRDD[Row, Null, Null](rdd, part) + val shuffled = + if (newOrdering.nonEmpty) { + new ShuffledRDD[Row, Null, Null](rdd, part).setKeyOrdering(keyOrdering) + } else { + new ShuffledRDD[Row, Null, Null](rdd, part) + } shuffled.setSerializer(new SparkSqlSerializer(new SparkConf(false))) shuffled.map(_._1) @@ -120,27 +164,34 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una * Ensures that the [[org.apache.spark.sql.catalyst.plans.physical.Partitioning Partitioning]] * of input data meets the * [[org.apache.spark.sql.catalyst.plans.physical.Distribution Distribution]] requirements for - * each operator by inserting [[Exchange]] Operators where required. + * each operator by inserting [[Exchange]] Operators where required. Also ensure that the + * required input partition ordering requirements are met. */ -private[sql] case class AddExchange(sqlContext: SQLContext) extends Rule[SparkPlan] { +private[sql] case class EnsureRequirements(sqlContext: SQLContext) extends Rule[SparkPlan] { // TODO: Determine the number of partitions. def numPartitions: Int = sqlContext.conf.numShufflePartitions def apply(plan: SparkPlan): SparkPlan = plan.transformUp { case operator: SparkPlan => - // Check if every child's outputPartitioning satisfies the corresponding + // True iff every child's outputPartitioning satisfies the corresponding // required data distribution. def meetsRequirements: Boolean = - !operator.requiredChildDistribution.zip(operator.children).map { + operator.requiredChildDistribution.zip(operator.children).forall { case (required, child) => val valid = child.outputPartitioning.satisfies(required) logDebug( s"${if (valid) "Valid" else "Invalid"} distribution," + s"required: $required current: ${child.outputPartitioning}") valid - }.exists(!_) + } - // Check if outputPartitionings of children are compatible with each other. + // True iff any of the children are incorrectly sorted. + def needsAnySort: Boolean = + operator.requiredChildOrdering.zip(operator.children).exists { + case (required, child) => required.nonEmpty && required != child.outputOrdering + } + + // True iff outputPartitionings of children are compatible with each other. // It is possible that every child satisfies its required data distribution // but two children have incompatible outputPartitionings. For example, // A dataset is range partitioned by "a.asc" (RangePartitioning) and another @@ -157,28 +208,69 @@ private[sql] case class AddExchange(sqlContext: SQLContext) extends Rule[SparkPl case Seq(a,b) => a compatibleWith b }.exists(!_) - // Check if the partitioning we want to ensure is the same as the child's output - // partitioning. If so, we do not need to add the Exchange operator. - def addExchangeIfNecessary(partitioning: Partitioning, child: SparkPlan): SparkPlan = - if (child.outputPartitioning != partitioning) Exchange(partitioning, child) else child + // Adds Exchange or Sort operators as required + def addOperatorsIfNecessary( + partitioning: Partitioning, + rowOrdering: Seq[SortOrder], + child: SparkPlan): SparkPlan = { + val needSort = rowOrdering.nonEmpty && child.outputOrdering != rowOrdering + val needsShuffle = child.outputPartitioning != partitioning + val canSortWithShuffle = Exchange.canSortWithShuffle(partitioning, rowOrdering) + + if (needSort && needsShuffle && canSortWithShuffle) { + Exchange(partitioning, rowOrdering, child) + } else { + val withShuffle = if (needsShuffle) { + Exchange(partitioning, Nil, child) + } else { + child + } - if (meetsRequirements && compatible) { + val withSort = if (needSort) { + if (sqlContext.conf.externalSortEnabled) { + ExternalSort(rowOrdering, global = false, withShuffle) + } else { + Sort(rowOrdering, global = false, withShuffle) + } + } else { + withShuffle + } + + withSort + } + } + + if (meetsRequirements && compatible && !needsAnySort) { operator } else { // At least one child does not satisfies its required data distribution or // at least one child's outputPartitioning is not compatible with another child's // outputPartitioning. In this case, we need to add Exchange operators. - val repartitionedChildren = operator.requiredChildDistribution.zip(operator.children).map { - case (AllTuples, child) => - addExchangeIfNecessary(SinglePartition, child) - case (ClusteredDistribution(clustering), child) => - addExchangeIfNecessary(HashPartitioning(clustering, numPartitions), child) - case (OrderedDistribution(ordering), child) => - addExchangeIfNecessary(RangePartitioning(ordering, numPartitions), child) - case (UnspecifiedDistribution, child) => child - case (dist, _) => sys.error(s"Don't know how to ensure $dist") + val requirements = + (operator.requiredChildDistribution, operator.requiredChildOrdering, operator.children) + + val fixedChildren = requirements.zipped.map { + case (AllTuples, rowOrdering, child) => + addOperatorsIfNecessary(SinglePartition, rowOrdering, child) + case (ClusteredDistribution(clustering), rowOrdering, child) => + addOperatorsIfNecessary(HashPartitioning(clustering, numPartitions), rowOrdering, child) + case (OrderedDistribution(ordering), rowOrdering, child) => + addOperatorsIfNecessary(RangePartitioning(ordering, numPartitions), rowOrdering, child) + + case (UnspecifiedDistribution, Seq(), child) => + child + case (UnspecifiedDistribution, rowOrdering, child) => + if (sqlContext.conf.externalSortEnabled) { + ExternalSort(rowOrdering, global = false, child) + } else { + Sort(rowOrdering, global = false, child) + } + + case (dist, ordering, _) => + sys.error(s"Don't know how to ensure $dist with ordering $ordering") } - operator.withNewChildren(repartitionedChildren) + + operator.withNewChildren(fixedChildren) } } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala index fabcf6b4a0570..e159ffe66cb24 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala @@ -72,6 +72,12 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ def requiredChildDistribution: Seq[Distribution] = Seq.fill(children.size)(UnspecifiedDistribution) + /** Specifies how data is ordered in each partition. */ + def outputOrdering: Seq[SortOrder] = Nil + + /** Specifies sort order for each partition requirements on the input data for this operator. */ + def requiredChildOrdering: Seq[Seq[SortOrder]] = Seq.fill(children.size)(Nil) + /** * Runs this query returning the result as an RDD. */ diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala index 5b99e40c2f491..e687d01f57520 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala @@ -90,6 +90,14 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { left.statistics.sizeInBytes <= sqlContext.conf.autoBroadcastJoinThreshold => makeBroadcastHashJoin(leftKeys, rightKeys, left, right, condition, joins.BuildLeft) + // If the sort merge join option is set, we want to use sort merge join prior to hashjoin + // for now let's support inner join first, then add outer join + case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right) + if sqlContext.conf.sortMergeJoinEnabled => + val mergeJoin = + joins.SortMergeJoin(leftKeys, rightKeys, planLater(left), planLater(right)) + condition.map(Filter(_, mergeJoin)).getOrElse(mergeJoin) :: Nil + case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right) => val buildSide = if (right.statistics.sizeInBytes <= left.statistics.sizeInBytes) { @@ -309,7 +317,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { case logical.OneRowRelation => execution.PhysicalRDD(Nil, singleRowRdd) :: Nil case logical.Repartition(expressions, child) => - execution.Exchange(HashPartitioning(expressions, numPartitions), planLater(child)) :: Nil + execution.Exchange( + HashPartitioning(expressions, numPartitions), Nil, planLater(child)) :: Nil case e @ EvaluatePython(udf, child, _) => BatchPythonEvaluation(udf, e.output, planLater(child)) :: Nil case LogicalRDD(output, rdd) => PhysicalRDD(output, rdd) :: Nil diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala index f8221f41bc6c3..308dae236a5ed 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala @@ -41,6 +41,8 @@ case class Project(projectList: Seq[NamedExpression], child: SparkPlan) extends val resuableProjection = buildProjection() iter.map(resuableProjection) } + + override def outputOrdering: Seq[SortOrder] = child.outputOrdering } /** @@ -55,6 +57,8 @@ case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode { override def execute(): RDD[Row] = child.execute().mapPartitions { iter => iter.filter(conditionEvaluator) } + + override def outputOrdering: Seq[SortOrder] = child.outputOrdering } /** @@ -147,6 +151,8 @@ case class TakeOrdered(limit: Int, sortOrder: Seq[SortOrder], child: SparkPlan) // TODO: Terminal split should be implemented differently from non-terminal split. // TODO: Pick num splits based on |limit|. override def execute(): RDD[Row] = sparkContext.makeRDD(collectData(), 1) + + override def outputOrdering: Seq[SortOrder] = sortOrder } /** @@ -172,6 +178,8 @@ case class Sort( } override def output: Seq[Attribute] = child.output + + override def outputOrdering: Seq[SortOrder] = sortOrder } /** @@ -202,6 +210,8 @@ case class ExternalSort( } override def output: Seq[Attribute] = child.output + + override def outputOrdering: Seq[SortOrder] = sortOrder } /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala new file mode 100644 index 0000000000000..b5123668ba11e --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala @@ -0,0 +1,169 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution.joins + +import java.util.NoSuchElementException + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.expressions._ +import org.apache.spark.sql.catalyst.plans._ +import org.apache.spark.sql.catalyst.plans.physical._ +import org.apache.spark.sql.execution.{BinaryNode, SparkPlan} +import org.apache.spark.util.collection.CompactBuffer + +/** + * :: DeveloperApi :: + * Performs an sort merge join of two child relations. + */ +@DeveloperApi +case class SortMergeJoin( + leftKeys: Seq[Expression], + rightKeys: Seq[Expression], + left: SparkPlan, + right: SparkPlan) extends BinaryNode { + + override def output: Seq[Attribute] = left.output ++ right.output + + override def outputPartitioning: Partitioning = left.outputPartitioning + + override def requiredChildDistribution: Seq[Distribution] = + ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil + + // this is to manually construct an ordering that can be used to compare keys from both sides + private val keyOrdering: RowOrdering = RowOrdering.forSchema(leftKeys.map(_.dataType)) + + override def outputOrdering: Seq[SortOrder] = requiredOrders(leftKeys) + + override def requiredChildOrdering: Seq[Seq[SortOrder]] = + requiredOrders(leftKeys) :: requiredOrders(rightKeys) :: Nil + + @transient protected lazy val leftKeyGenerator = newProjection(leftKeys, left.output) + @transient protected lazy val rightKeyGenerator = newProjection(rightKeys, right.output) + + private def requiredOrders(keys: Seq[Expression]): Seq[SortOrder] = + keys.map(SortOrder(_, Ascending)) + + override def execute(): RDD[Row] = { + val leftResults = left.execute().map(_.copy()) + val rightResults = right.execute().map(_.copy()) + + leftResults.zipPartitions(rightResults) { (leftIter, rightIter) => + new Iterator[Row] { + // Mutable per row objects. + private[this] val joinRow = new JoinedRow5 + private[this] var leftElement: Row = _ + private[this] var rightElement: Row = _ + private[this] var leftKey: Row = _ + private[this] var rightKey: Row = _ + private[this] var rightMatches: CompactBuffer[Row] = _ + private[this] var rightPosition: Int = -1 + private[this] var stop: Boolean = false + private[this] var matchKey: Row = _ + + // initialize iterator + initialize() + + override final def hasNext: Boolean = nextMatchingPair() + + override final def next(): Row = { + if (hasNext) { + // we are using the buffered right rows and run down left iterator + val joinedRow = joinRow(leftElement, rightMatches(rightPosition)) + rightPosition += 1 + if (rightPosition >= rightMatches.size) { + rightPosition = 0 + fetchLeft() + if (leftElement == null || keyOrdering.compare(leftKey, matchKey) != 0) { + stop = false + rightMatches = null + } + } + joinedRow + } else { + // no more result + throw new NoSuchElementException + } + } + + private def fetchLeft() = { + if (leftIter.hasNext) { + leftElement = leftIter.next() + leftKey = leftKeyGenerator(leftElement) + } else { + leftElement = null + } + } + + private def fetchRight() = { + if (rightIter.hasNext) { + rightElement = rightIter.next() + rightKey = rightKeyGenerator(rightElement) + } else { + rightElement = null + } + } + + private def initialize() = { + fetchLeft() + fetchRight() + } + + /** + * Searches the right iterator for the next rows that have matches in left side, and store + * them in a buffer. + * + * @return true if the search is successful, and false if the right iterator runs out of + * tuples. + */ + private def nextMatchingPair(): Boolean = { + if (!stop && rightElement != null) { + // run both side to get the first match pair + while (!stop && leftElement != null && rightElement != null) { + val comparing = keyOrdering.compare(leftKey, rightKey) + // for inner join, we need to filter those null keys + stop = comparing == 0 && !leftKey.anyNull + if (comparing > 0 || rightKey.anyNull) { + fetchRight() + } else if (comparing < 0 || leftKey.anyNull) { + fetchLeft() + } + } + rightMatches = new CompactBuffer[Row]() + if (stop) { + stop = false + // iterate the right side to buffer all rows that matches + // as the records should be ordered, exit when we meet the first that not match + while (!stop && rightElement != null) { + rightMatches += rightElement + fetchRight() + stop = keyOrdering.compare(leftKey, rightKey) != 0 + } + if (rightMatches.size > 0) { + rightPosition = 0 + matchKey = leftKey + } + } + } + rightMatches != null && rightMatches.size > 0 + } + } + } + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala index e4dee87849fd4..037d392c1f929 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala @@ -51,6 +51,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { case j: CartesianProduct => j case j: BroadcastNestedLoopJoin => j case j: BroadcastLeftSemiJoinHash => j + case j: SortMergeJoin => j } assert(operators.size === 1) @@ -62,6 +63,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { test("join operator selection") { cacheManager.clearCache() + val SORTMERGEJOIN_ENABLED: Boolean = conf.sortMergeJoinEnabled Seq( ("SELECT * FROM testData LEFT SEMI JOIN testData2 ON key = a", classOf[LeftSemiJoinHash]), ("SELECT * FROM testData LEFT SEMI JOIN testData2", classOf[LeftSemiJoinBNL]), @@ -91,17 +93,41 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach { ("SELECT * FROM testData full JOIN testData2 ON (key * a != key + a)", classOf[BroadcastNestedLoopJoin]) ).foreach { case (query, joinClass) => assertJoin(query, joinClass) } + try { + conf.setConf("spark.sql.planner.sortMergeJoin", "true") + Seq( + ("SELECT * FROM testData JOIN testData2 ON key = a", classOf[SortMergeJoin]), + ("SELECT * FROM testData JOIN testData2 ON key = a and key = 2", classOf[SortMergeJoin]), + ("SELECT * FROM testData JOIN testData2 ON key = a where key = 2", classOf[SortMergeJoin]) + ).foreach { case (query, joinClass) => assertJoin(query, joinClass) } + } finally { + conf.setConf("spark.sql.planner.sortMergeJoin", SORTMERGEJOIN_ENABLED.toString) + } } test("broadcasted hash join operator selection") { cacheManager.clearCache() sql("CACHE TABLE testData") + val SORTMERGEJOIN_ENABLED: Boolean = conf.sortMergeJoinEnabled Seq( ("SELECT * FROM testData join testData2 ON key = a", classOf[BroadcastHashJoin]), ("SELECT * FROM testData join testData2 ON key = a and key = 2", classOf[BroadcastHashJoin]), - ("SELECT * FROM testData join testData2 ON key = a where key = 2", classOf[BroadcastHashJoin]) + ("SELECT * FROM testData join testData2 ON key = a where key = 2", + classOf[BroadcastHashJoin]) ).foreach { case (query, joinClass) => assertJoin(query, joinClass) } + try { + conf.setConf("spark.sql.planner.sortMergeJoin", "true") + Seq( + ("SELECT * FROM testData join testData2 ON key = a", classOf[BroadcastHashJoin]), + ("SELECT * FROM testData join testData2 ON key = a and key = 2", + classOf[BroadcastHashJoin]), + ("SELECT * FROM testData join testData2 ON key = a where key = 2", + classOf[BroadcastHashJoin]) + ).foreach { case (query, joinClass) => assertJoin(query, joinClass) } + } finally { + conf.setConf("spark.sql.planner.sortMergeJoin", SORTMERGEJOIN_ENABLED.toString) + } sql("UNCACHE TABLE testData") } diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/SortMergeCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/SortMergeCompatibilitySuite.scala new file mode 100644 index 0000000000000..65d070bd3cbde --- /dev/null +++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/SortMergeCompatibilitySuite.scala @@ -0,0 +1,162 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.hive.execution + +import org.apache.spark.sql.SQLConf +import org.apache.spark.sql.hive.test.TestHive + +/** + * Runs the test cases that are included in the hive distribution with sort merge join is true. + */ +class SortMergeCompatibilitySuite extends HiveCompatibilitySuite { + override def beforeAll() { + super.beforeAll() + TestHive.setConf(SQLConf.SORTMERGE_JOIN, "true") + } + + override def afterAll() { + TestHive.setConf(SQLConf.SORTMERGE_JOIN, "false") + super.afterAll() + } + + override def whiteList = Seq( + "auto_join0", + "auto_join1", + "auto_join10", + "auto_join11", + "auto_join12", + "auto_join13", + "auto_join14", + "auto_join14_hadoop20", + "auto_join15", + "auto_join17", + "auto_join18", + "auto_join19", + "auto_join2", + "auto_join20", + "auto_join21", + "auto_join22", + "auto_join23", + "auto_join24", + "auto_join25", + "auto_join26", + "auto_join27", + "auto_join28", + "auto_join3", + "auto_join30", + "auto_join31", + "auto_join32", + "auto_join4", + "auto_join5", + "auto_join6", + "auto_join7", + "auto_join8", + "auto_join9", + "auto_join_filters", + "auto_join_nulls", + "auto_join_reordering_values", + "auto_smb_mapjoin_14", + "auto_sortmerge_join_1", + "auto_sortmerge_join_10", + "auto_sortmerge_join_11", + "auto_sortmerge_join_12", + "auto_sortmerge_join_13", + "auto_sortmerge_join_14", + "auto_sortmerge_join_15", + "auto_sortmerge_join_16", + "auto_sortmerge_join_2", + "auto_sortmerge_join_3", + "auto_sortmerge_join_4", + "auto_sortmerge_join_5", + "auto_sortmerge_join_6", + "auto_sortmerge_join_7", + "auto_sortmerge_join_8", + "auto_sortmerge_join_9", + "correlationoptimizer1", + "correlationoptimizer10", + "correlationoptimizer11", + "correlationoptimizer13", + "correlationoptimizer14", + "correlationoptimizer15", + "correlationoptimizer2", + "correlationoptimizer3", + "correlationoptimizer4", + "correlationoptimizer6", + "correlationoptimizer7", + "correlationoptimizer8", + "correlationoptimizer9", + "join0", + "join1", + "join10", + "join11", + "join12", + "join13", + "join14", + "join14_hadoop20", + "join15", + "join16", + "join17", + "join18", + "join19", + "join2", + "join20", + "join21", + "join22", + "join23", + "join24", + "join25", + "join26", + "join27", + "join28", + "join29", + "join3", + "join30", + "join31", + "join32", + "join32_lessSize", + "join33", + "join34", + "join35", + "join36", + "join37", + "join38", + "join39", + "join4", + "join40", + "join41", + "join5", + "join6", + "join7", + "join8", + "join9", + "join_1to1", + "join_array", + "join_casesensitive", + "join_empty", + "join_filters", + "join_hive_626", + "join_map_ppr", + "join_nulls", + "join_nullsafe", + "join_rc", + "join_reorder2", + "join_reorder3", + "join_reorder4", + "join_star" + ) +} From d5f1b9650b6e46cf6a9d61f01cda0df0cda5b1c9 Mon Sep 17 00:00:00 2001 From: Isaias Barroso Date: Wed, 15 Apr 2015 22:40:52 +0100 Subject: [PATCH 302/591] [SPARK-2312] Logging Unhandled messages The previous solution has changed based on https://github.com/apache/spark/pull/2048 discussions. Author: Isaias Barroso Closes #2055 from isaias/SPARK-2312 and squashes the following commits: f61d9e6 [Isaias Barroso] Change Log level for unhandled message to debug f341777 [Isaias Barroso] [SPARK-2312] Logging Unhandled messages --- .../scala/org/apache/spark/util/ActorLogReceive.scala | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/core/src/main/scala/org/apache/spark/util/ActorLogReceive.scala b/core/src/main/scala/org/apache/spark/util/ActorLogReceive.scala index 332d0cbb2dc0c..81a7cbde01ce5 100644 --- a/core/src/main/scala/org/apache/spark/util/ActorLogReceive.scala +++ b/core/src/main/scala/org/apache/spark/util/ActorLogReceive.scala @@ -43,7 +43,13 @@ private[spark] trait ActorLogReceive { private val _receiveWithLogging = receiveWithLogging - override def isDefinedAt(o: Any): Boolean = _receiveWithLogging.isDefinedAt(o) + override def isDefinedAt(o: Any): Boolean = { + val handled = _receiveWithLogging.isDefinedAt(o) + if (!handled) { + log.debug(s"Received unexpected actor system event: $o") + } + handled + } override def apply(o: Any): Unit = { if (log.isDebugEnabled) { From 8a53de16fc8208358b76d0f3d45538e0304bcc8e Mon Sep 17 00:00:00 2001 From: Max Seiden Date: Wed, 15 Apr 2015 16:15:11 -0700 Subject: [PATCH 303/591] [SPARK-5277][SQL] - SparkSqlSerializer doesn't always register user specified KryoRegistrators [SPARK-5277][SQL] - SparkSqlSerializer doesn't always register user specified KryoRegistrators There were a few places where new SparkSqlSerializer instances were created with new, empty SparkConfs resulting in user specified registrators sometimes not getting initialized. The fix is to try and pull a conf from the SparkEnv, and construct a new conf (that loads defaults) if one cannot be found. The changes touched: 1) SparkSqlSerializer's resource pool (this appears to fix the issue in the comment) 2) execution.Exchange (for all of the partitioners) 3) execution.Limit (for the HashPartitioner) A few tests were added to ColumnTypeSuite, ensuring that a custom registrator and serde is initialized and used when in-memory columns are written. Author: Max Seiden This patch had conflicts when merged, resolved by Committer: Michael Armbrust Closes #5237 from mhseiden/sql_udt_kryo and squashes the following commits: 3175c2f [Max Seiden] [SPARK-5277][SQL] - address code review comments e5011fb [Max Seiden] [SPARK-5277][SQL] - SparkSqlSerializer does not register user specified KryoRegistrators --- .../apache/spark/sql/execution/Exchange.scala | 9 +-- .../sql/execution/SparkSqlSerializer.scala | 7 +-- .../spark/sql/execution/basicOperators.scala | 2 +- .../spark/sql/columnar/ColumnTypeSuite.scala | 62 ++++++++++++++++++- 4 files changed, 68 insertions(+), 12 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala index 518fc9e57c708..69a620e1ec929 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala @@ -78,6 +78,8 @@ case class Exchange( } override def execute(): RDD[Row] = attachTree(this , "execute") { + lazy val sparkConf = child.sqlContext.sparkContext.getConf + newPartitioning match { case HashPartitioning(expressions, numPartitions) => // TODO: Eliminate redundant expressions in grouping key and value. @@ -109,7 +111,7 @@ case class Exchange( } else { new ShuffledRDD[Row, Row, Row](rdd, part) } - shuffled.setSerializer(new SparkSqlSerializer(new SparkConf(false))) + shuffled.setSerializer(new SparkSqlSerializer(sparkConf)) shuffled.map(_._2) case RangePartitioning(sortingExpressions, numPartitions) => @@ -132,8 +134,7 @@ case class Exchange( } else { new ShuffledRDD[Row, Null, Null](rdd, part) } - shuffled.setSerializer(new SparkSqlSerializer(new SparkConf(false))) - + shuffled.setSerializer(new SparkSqlSerializer(sparkConf)) shuffled.map(_._1) case SinglePartition => @@ -151,7 +152,7 @@ case class Exchange( } val partitioner = new HashPartitioner(1) val shuffled = new ShuffledRDD[Null, Row, Row](rdd, partitioner) - shuffled.setSerializer(new SparkSqlSerializer(new SparkConf(false))) + shuffled.setSerializer(new SparkSqlSerializer(sparkConf)) shuffled.map(_._2) case _ => sys.error(s"Exchange not implemented for $newPartitioning") diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala index 914f387dec78f..eea15aff5dbcf 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer.scala @@ -65,12 +65,9 @@ private[sql] class SparkSqlSerializer(conf: SparkConf) extends KryoSerializer(co private[execution] class KryoResourcePool(size: Int) extends ResourcePool[SerializerInstance](size) { - val ser: KryoSerializer = { + val ser: SparkSqlSerializer = { val sparkConf = Option(SparkEnv.get).map(_.conf).getOrElse(new SparkConf()) - // TODO (lian) Using KryoSerializer here is workaround, needs further investigation - // Using SparkSqlSerializer here makes BasicQuerySuite to fail because of Kryo serialization - // related error. - new KryoSerializer(sparkConf) + new SparkSqlSerializer(sparkConf) } def newInstance(): SerializerInstance = ser.newInstance() diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala index 308dae236a5ed..d286fe81bee5f 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala @@ -121,7 +121,7 @@ case class Limit(limit: Int, child: SparkPlan) } val part = new HashPartitioner(1) val shuffled = new ShuffledRDD[Boolean, Row, Row](rdd, part) - shuffled.setSerializer(new SparkSqlSerializer(new SparkConf(false))) + shuffled.setSerializer(new SparkSqlSerializer(child.sqlContext.sparkContext.getConf)) shuffled.mapPartitions(_.take(limit).map(_._2)) } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala index c86ef338fc644..b48bed1871c50 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala @@ -20,9 +20,12 @@ package org.apache.spark.sql.columnar import java.nio.ByteBuffer import java.sql.Timestamp +import com.esotericsoftware.kryo.{Serializer, Kryo} +import com.esotericsoftware.kryo.io.{Input, Output} +import org.apache.spark.serializer.KryoRegistrator import org.scalatest.FunSuite -import org.apache.spark.Logging +import org.apache.spark.{SparkConf, Logging} import org.apache.spark.sql.catalyst.expressions.GenericMutableRow import org.apache.spark.sql.columnar.ColumnarTestUtils._ import org.apache.spark.sql.execution.SparkSqlSerializer @@ -73,7 +76,7 @@ class ColumnTypeSuite extends FunSuite with Logging { checkActualSize(BINARY, binary, 4 + 4) val generic = Map(1 -> "a") - checkActualSize(GENERIC, SparkSqlSerializer.serialize(generic), 4 + 11) + checkActualSize(GENERIC, SparkSqlSerializer.serialize(generic), 4 + 8) } testNativeColumnType[BooleanType.type]( @@ -158,6 +161,41 @@ class ColumnTypeSuite extends FunSuite with Logging { } } + test("CUSTOM") { + val conf = new SparkConf() + conf.set("spark.kryo.registrator", "org.apache.spark.sql.columnar.Registrator") + val serializer = new SparkSqlSerializer(conf).newInstance() + + val buffer = ByteBuffer.allocate(512) + val obj = CustomClass(Int.MaxValue,Long.MaxValue) + val serializedObj = serializer.serialize(obj).array() + + GENERIC.append(serializer.serialize(obj).array(), buffer) + buffer.rewind() + + val length = buffer.getInt + assert(length === serializedObj.length) + assert(13 == length) // id (1) + int (4) + long (8) + + val genericSerializedObj = SparkSqlSerializer.serialize(obj) + assert(length != genericSerializedObj.length) + assert(length < genericSerializedObj.length) + + assertResult(obj, "Custom deserialized object didn't equal the original object") { + val bytes = new Array[Byte](length) + buffer.get(bytes, 0, length) + serializer.deserialize(ByteBuffer.wrap(bytes)) + } + + buffer.rewind() + buffer.putInt(serializedObj.length).put(serializedObj) + + assertResult(obj, "Custom deserialized object didn't equal the original object") { + buffer.rewind() + serializer.deserialize(ByteBuffer.wrap(GENERIC.extract(buffer))) + } + } + def testNativeColumnType[T <: NativeType]( columnType: NativeColumnType[T], putter: (ByteBuffer, T#JvmType) => Unit, @@ -229,3 +267,23 @@ class ColumnTypeSuite extends FunSuite with Logging { } } } + +private[columnar] final case class CustomClass(a: Int, b: Long) + +private[columnar] object CustomerSerializer extends Serializer[CustomClass] { + override def write(kryo: Kryo, output: Output, t: CustomClass) { + output.writeInt(t.a) + output.writeLong(t.b) + } + override def read(kryo: Kryo, input: Input, aClass: Class[CustomClass]): CustomClass = { + val a = input.readInt() + val b = input.readLong() + CustomClass(a,b) + } +} + +private[columnar] final class Registrator extends KryoRegistrator { + override def registerClasses(kryo: Kryo) { + kryo.register(classOf[CustomClass], CustomerSerializer) + } +} From 52c3439a8a107ce1fc10e4f0b59fd7881e851622 Mon Sep 17 00:00:00 2001 From: Juliet Hougland Date: Wed, 15 Apr 2015 21:52:25 -0700 Subject: [PATCH 304/591] SPARK-6938: All require statements now have an informative error message. This pr adds informative error messages to all require statements in the Vectors class that did not previously have them. This references [SPARK-6938](https://issues.apache.org/jira/browse/SPARK-6938). Author: Juliet Hougland Closes #5532 from jhlch/SPARK-6938 and squashes the following commits: ab321bb [Juliet Hougland] Remove braces from string interpolation when not required. 1221f94 [Juliet Hougland] All require statements now have an informative error message. --- .../org/apache/spark/mllib/linalg/Vectors.scala | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala index 328dbe2ce11fa..4ef171f4f0419 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala @@ -227,7 +227,7 @@ object Vectors { * @param elements vector elements in (index, value) pairs. */ def sparse(size: Int, elements: Seq[(Int, Double)]): Vector = { - require(size > 0) + require(size > 0, "The size of the requested sparse vector must be greater than 0.") val (indices, values) = elements.sortBy(_._1).unzip var prev = -1 @@ -235,7 +235,8 @@ object Vectors { require(prev < i, s"Found duplicate indices: $i.") prev = i } - require(prev < size) + require(prev < size, s"You may not write an element to index $prev because the declared " + + s"size of your vector is $size") new SparseVector(size, indices.toArray, values.toArray) } @@ -309,7 +310,8 @@ object Vectors { * @return norm in L^p^ space. */ def norm(vector: Vector, p: Double): Double = { - require(p >= 1.0) + require(p >= 1.0, "To compute the p-norm of the vector, we require that you specify a p>=1. " + + s"You specified p=$p.") val values = vector match { case DenseVector(vs) => vs case SparseVector(n, ids, vs) => vs @@ -360,7 +362,8 @@ object Vectors { * @return squared distance between two Vectors. */ def sqdist(v1: Vector, v2: Vector): Double = { - require(v1.size == v2.size, "vector dimension mismatch") + require(v1.size == v2.size, s"Vector dimensions do not match: Dim(v1)=${v1.size} and Dim(v2)" + + s"=${v2.size}.") var squaredDistance = 0.0 (v1, v2) match { case (v1: SparseVector, v2: SparseVector) => @@ -518,7 +521,9 @@ class SparseVector( val indices: Array[Int], val values: Array[Double]) extends Vector { - require(indices.length == values.length) + require(indices.length == values.length, "Sparse vectors require that the dimension of the" + + s" indices match the dimension of the values. You provided ${indices.size} indices and " + + s" ${values.size} values.") override def toString: String = "(%s,%s,%s)".format(size, indices.mkString("[", ",", "]"), values.mkString("[", ",", "]")) From 57cd1e86d1d450f85fc9e296aff498a940452113 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Wed, 15 Apr 2015 23:49:42 -0700 Subject: [PATCH 305/591] [SPARK-6893][ML] default pipeline parameter handling in python Same as #5431 but for Python. jkbradley Author: Xiangrui Meng Closes #5534 from mengxr/SPARK-6893 and squashes the following commits: d3b519b [Xiangrui Meng] address comments ebaccc6 [Xiangrui Meng] style update fce244e [Xiangrui Meng] update explainParams with test 4d6b07a [Xiangrui Meng] add tests 5294500 [Xiangrui Meng] update default param handling in python --- .../org/apache/spark/ml/Identifiable.scala | 2 +- .../apache/spark/ml/param/TestParams.scala | 9 +- python/pyspark/ml/classification.py | 3 +- python/pyspark/ml/feature.py | 19 +-- python/pyspark/ml/param/__init__.py | 146 +++++++++++++++--- ...d_params.py => _shared_params_code_gen.py} | 42 ++--- python/pyspark/ml/param/shared.py | 106 ++++++------- python/pyspark/ml/pipeline.py | 6 +- python/pyspark/ml/tests.py | 52 ++++++- python/pyspark/ml/util.py | 4 +- python/pyspark/ml/wrapper.py | 2 +- 11 files changed, 270 insertions(+), 121 deletions(-) rename python/pyspark/ml/param/{_gen_shared_params.py => _shared_params_code_gen.py} (70%) diff --git a/mllib/src/main/scala/org/apache/spark/ml/Identifiable.scala b/mllib/src/main/scala/org/apache/spark/ml/Identifiable.scala index a50090671ae48..a1d49095c24ac 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Identifiable.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Identifiable.scala @@ -25,7 +25,7 @@ import java.util.UUID private[ml] trait Identifiable extends Serializable { /** - * A unique id for the object. The default implementation concatenates the class name, "-", and 8 + * A unique id for the object. The default implementation concatenates the class name, "_", and 8 * random hex chars. */ private[ml] val uid: String = diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala index 8f9ab687c05cb..641b64b42a5e7 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala @@ -17,16 +17,13 @@ package org.apache.spark.ml.param +import org.apache.spark.ml.param.shared.{HasInputCol, HasMaxIter} + /** A subclass of Params for testing. */ -class TestParams extends Params { +class TestParams extends Params with HasMaxIter with HasInputCol { - val maxIter = new IntParam(this, "maxIter", "max number of iterations") def setMaxIter(value: Int): this.type = { set(maxIter, value); this } - def getMaxIter: Int = getOrDefault(maxIter) - - val inputCol = new Param[String](this, "inputCol", "input column name") def setInputCol(value: String): this.type = { set(inputCol, value); this } - def getInputCol: String = getOrDefault(inputCol) setDefault(maxIter -> 10) diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py index 7f42de531f3b4..d7bc09fd77adb 100644 --- a/python/pyspark/ml/classification.py +++ b/python/pyspark/ml/classification.py @@ -59,6 +59,7 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred maxIter=100, regParam=0.1) """ super(LogisticRegression, self).__init__() + self._setDefault(maxIter=100, regParam=0.1) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @@ -71,7 +72,7 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre Sets params for logistic regression. """ kwargs = self.setParams._input_kwargs - return self._set_params(**kwargs) + return self._set(**kwargs) def _create_model(self, java_model): return LogisticRegressionModel(java_model) diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py index 1cfcd019dfb18..263fe2a5bcc41 100644 --- a/python/pyspark/ml/feature.py +++ b/python/pyspark/ml/feature.py @@ -52,22 +52,22 @@ class Tokenizer(JavaTransformer, HasInputCol, HasOutputCol): _java_class = "org.apache.spark.ml.feature.Tokenizer" @keyword_only - def __init__(self, inputCol="input", outputCol="output"): + def __init__(self, inputCol=None, outputCol=None): """ - __init__(self, inputCol="input", outputCol="output") + __init__(self, inputCol=None, outputCol=None) """ super(Tokenizer, self).__init__() kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only - def setParams(self, inputCol="input", outputCol="output"): + def setParams(self, inputCol=None, outputCol=None): """ setParams(self, inputCol="input", outputCol="output") Sets params for this Tokenizer. """ kwargs = self.setParams._input_kwargs - return self._set_params(**kwargs) + return self._set(**kwargs) @inherit_doc @@ -91,22 +91,23 @@ class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures): _java_class = "org.apache.spark.ml.feature.HashingTF" @keyword_only - def __init__(self, numFeatures=1 << 18, inputCol="input", outputCol="output"): + def __init__(self, numFeatures=1 << 18, inputCol=None, outputCol=None): """ - __init__(self, numFeatures=1 << 18, inputCol="input", outputCol="output") + __init__(self, numFeatures=1 << 18, inputCol=None, outputCol=None) """ super(HashingTF, self).__init__() + self._setDefault(numFeatures=1 << 18) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only - def setParams(self, numFeatures=1 << 18, inputCol="input", outputCol="output"): + def setParams(self, numFeatures=1 << 18, inputCol=None, outputCol=None): """ - setParams(self, numFeatures=1 << 18, inputCol="input", outputCol="output") + setParams(self, numFeatures=1 << 18, inputCol=None, outputCol=None) Sets params for this HashingTF. """ kwargs = self.setParams._input_kwargs - return self._set_params(**kwargs) + return self._set(**kwargs) if __name__ == "__main__": diff --git a/python/pyspark/ml/param/__init__.py b/python/pyspark/ml/param/__init__.py index e3a53dd780c4c..5c62620562a84 100644 --- a/python/pyspark/ml/param/__init__.py +++ b/python/pyspark/ml/param/__init__.py @@ -25,23 +25,21 @@ class Param(object): """ - A param with self-contained documentation and optionally default value. + A param with self-contained documentation. """ - def __init__(self, parent, name, doc, defaultValue=None): - if not isinstance(parent, Identifiable): - raise ValueError("Parent must be identifiable but got type %s." % type(parent).__name__) + def __init__(self, parent, name, doc): + if not isinstance(parent, Params): + raise ValueError("Parent must be a Params but got type %s." % type(parent).__name__) self.parent = parent self.name = str(name) self.doc = str(doc) - self.defaultValue = defaultValue def __str__(self): - return str(self.parent) + "-" + self.name + return str(self.parent) + "__" + self.name def __repr__(self): - return "Param(parent=%r, name=%r, doc=%r, defaultValue=%r)" % \ - (self.parent, self.name, self.doc, self.defaultValue) + return "Param(parent=%r, name=%r, doc=%r)" % (self.parent, self.name, self.doc) class Params(Identifiable): @@ -52,26 +50,128 @@ class Params(Identifiable): __metaclass__ = ABCMeta - def __init__(self): - super(Params, self).__init__() - #: embedded param map - self.paramMap = {} + #: internal param map for user-supplied values param map + paramMap = {} + + #: internal param map for default values + defaultParamMap = {} @property def params(self): """ - Returns all params. The default implementation uses - :py:func:`dir` to get all attributes of type + Returns all params ordered by name. The default implementation + uses :py:func:`dir` to get all attributes of type :py:class:`Param`. """ return filter(lambda attr: isinstance(attr, Param), [getattr(self, x) for x in dir(self) if x != "params"]) - def _merge_params(self, params): - paramMap = self.paramMap.copy() - paramMap.update(params) + def _explain(self, param): + """ + Explains a single param and returns its name, doc, and optional + default value and user-supplied value in a string. + """ + param = self._resolveParam(param) + values = [] + if self.isDefined(param): + if param in self.defaultParamMap: + values.append("default: %s" % self.defaultParamMap[param]) + if param in self.paramMap: + values.append("current: %s" % self.paramMap[param]) + else: + values.append("undefined") + valueStr = "(" + ", ".join(values) + ")" + return "%s: %s %s" % (param.name, param.doc, valueStr) + + def explainParams(self): + """ + Returns the documentation of all params with their optionally + default values and user-supplied values. + """ + return "\n".join([self._explain(param) for param in self.params]) + + def getParam(self, paramName): + """ + Gets a param by its name. + """ + param = getattr(self, paramName) + if isinstance(param, Param): + return param + else: + raise ValueError("Cannot find param with name %s." % paramName) + + def isSet(self, param): + """ + Checks whether a param is explicitly set by user. + """ + param = self._resolveParam(param) + return param in self.paramMap + + def hasDefault(self, param): + """ + Checks whether a param has a default value. + """ + param = self._resolveParam(param) + return param in self.defaultParamMap + + def isDefined(self, param): + """ + Checks whether a param is explicitly set by user or has a default value. + """ + return self.isSet(param) or self.hasDefault(param) + + def getOrDefault(self, param): + """ + Gets the value of a param in the user-supplied param map or its + default value. Raises an error if either is set. + """ + if isinstance(param, Param): + if param in self.paramMap: + return self.paramMap[param] + else: + return self.defaultParamMap[param] + elif isinstance(param, str): + return self.getOrDefault(self.getParam(param)) + else: + raise KeyError("Cannot recognize %r as a param." % param) + + def extractParamMap(self, extraParamMap={}): + """ + Extracts the embedded default param values and user-supplied + values, and then merges them with extra values from input into + a flat param map, where the latter value is used if there exist + conflicts, i.e., with ordering: default param values < + user-supplied values < extraParamMap. + :param extraParamMap: extra param values + :return: merged param map + """ + paramMap = self.defaultParamMap.copy() + paramMap.update(self.paramMap) + paramMap.update(extraParamMap) return paramMap + def _shouldOwn(self, param): + """ + Validates that the input param belongs to this Params instance. + """ + if param.parent is not self: + raise ValueError("Param %r does not belong to %r." % (param, self)) + + def _resolveParam(self, param): + """ + Resolves a param and validates the ownership. + :param param: param name or the param instance, which must + belong to this Params instance + :return: resolved param instance + """ + if isinstance(param, Param): + self._shouldOwn(param) + return param + elif isinstance(param, str): + return self.getParam(param) + else: + raise ValueError("Cannot resolve %r as a param." % param) + @staticmethod def _dummy(): """ @@ -81,10 +181,18 @@ def _dummy(): dummy.uid = "undefined" return dummy - def _set_params(self, **kwargs): + def _set(self, **kwargs): """ - Sets params. + Sets user-supplied params. """ for param, value in kwargs.iteritems(): self.paramMap[getattr(self, param)] = value return self + + def _setDefault(self, **kwargs): + """ + Sets default params. + """ + for param, value in kwargs.iteritems(): + self.defaultParamMap[getattr(self, param)] = value + return self diff --git a/python/pyspark/ml/param/_gen_shared_params.py b/python/pyspark/ml/param/_shared_params_code_gen.py similarity index 70% rename from python/pyspark/ml/param/_gen_shared_params.py rename to python/pyspark/ml/param/_shared_params_code_gen.py index 5eb81106f116c..55f422497672f 100644 --- a/python/pyspark/ml/param/_gen_shared_params.py +++ b/python/pyspark/ml/param/_shared_params_code_gen.py @@ -32,29 +32,34 @@ # limitations under the License. #""" +# Code generator for shared params (shared.py). Run under this folder with: +# python _shared_params_code_gen.py > shared.py -def _gen_param_code(name, doc, defaultValue): + +def _gen_param_code(name, doc, defaultValueStr): """ Generates Python code for a shared param class. :param name: param name :param doc: param doc - :param defaultValue: string representation of the param + :param defaultValueStr: string representation of the default value :return: code string """ # TODO: How to correctly inherit instance attributes? template = '''class Has$Name(Params): """ - Params with $name. + Mixin for param $name: $doc. """ # a placeholder to make it appear in the generated doc - $name = Param(Params._dummy(), "$name", "$doc", $defaultValue) + $name = Param(Params._dummy(), "$name", "$doc") def __init__(self): super(Has$Name, self).__init__() #: param for $doc - self.$name = Param(self, "$name", "$doc", $defaultValue) + self.$name = Param(self, "$name", "$doc") + if $defaultValueStr is not None: + self._setDefault($name=$defaultValueStr) def set$Name(self, value): """ @@ -67,32 +72,29 @@ def get$Name(self): """ Gets the value of $name or its default value. """ - if self.$name in self.paramMap: - return self.paramMap[self.$name] - else: - return self.$name.defaultValue''' + return self.getOrDefault(self.$name)''' - upperCamelName = name[0].upper() + name[1:] + Name = name[0].upper() + name[1:] return template \ .replace("$name", name) \ - .replace("$Name", upperCamelName) \ + .replace("$Name", Name) \ .replace("$doc", doc) \ - .replace("$defaultValue", defaultValue) + .replace("$defaultValueStr", str(defaultValueStr)) if __name__ == "__main__": print header - print "\n# DO NOT MODIFY. The code is generated by _gen_shared_params.py.\n" + print "\n# DO NOT MODIFY THIS FILE! It was generated by _shared_params_code_gen.py.\n" print "from pyspark.ml.param import Param, Params\n\n" shared = [ - ("maxIter", "max number of iterations", "100"), - ("regParam", "regularization constant", "0.1"), + ("maxIter", "max number of iterations", None), + ("regParam", "regularization constant", None), ("featuresCol", "features column name", "'features'"), ("labelCol", "label column name", "'label'"), ("predictionCol", "prediction column name", "'prediction'"), - ("inputCol", "input column name", "'input'"), - ("outputCol", "output column name", "'output'"), - ("numFeatures", "number of features", "1 << 18")] + ("inputCol", "input column name", None), + ("outputCol", "output column name", None), + ("numFeatures", "number of features", None)] code = [] - for name, doc, defaultValue in shared: - code.append(_gen_param_code(name, doc, defaultValue)) + for name, doc, defaultValueStr in shared: + code.append(_gen_param_code(name, doc, defaultValueStr)) print "\n\n\n".join(code) diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py index 586822f2de423..13b6749998ad0 100644 --- a/python/pyspark/ml/param/shared.py +++ b/python/pyspark/ml/param/shared.py @@ -15,23 +15,25 @@ # limitations under the License. # -# DO NOT MODIFY. The code is generated by _gen_shared_params.py. +# DO NOT MODIFY THIS FILE! It was generated by _shared_params_code_gen.py. from pyspark.ml.param import Param, Params class HasMaxIter(Params): """ - Params with maxIter. + Mixin for param maxIter: max number of iterations. """ # a placeholder to make it appear in the generated doc - maxIter = Param(Params._dummy(), "maxIter", "max number of iterations", 100) + maxIter = Param(Params._dummy(), "maxIter", "max number of iterations") def __init__(self): super(HasMaxIter, self).__init__() #: param for max number of iterations - self.maxIter = Param(self, "maxIter", "max number of iterations", 100) + self.maxIter = Param(self, "maxIter", "max number of iterations") + if None is not None: + self._setDefault(maxIter=None) def setMaxIter(self, value): """ @@ -44,24 +46,23 @@ def getMaxIter(self): """ Gets the value of maxIter or its default value. """ - if self.maxIter in self.paramMap: - return self.paramMap[self.maxIter] - else: - return self.maxIter.defaultValue + return self.getOrDefault(self.maxIter) class HasRegParam(Params): """ - Params with regParam. + Mixin for param regParam: regularization constant. """ # a placeholder to make it appear in the generated doc - regParam = Param(Params._dummy(), "regParam", "regularization constant", 0.1) + regParam = Param(Params._dummy(), "regParam", "regularization constant") def __init__(self): super(HasRegParam, self).__init__() #: param for regularization constant - self.regParam = Param(self, "regParam", "regularization constant", 0.1) + self.regParam = Param(self, "regParam", "regularization constant") + if None is not None: + self._setDefault(regParam=None) def setRegParam(self, value): """ @@ -74,24 +75,23 @@ def getRegParam(self): """ Gets the value of regParam or its default value. """ - if self.regParam in self.paramMap: - return self.paramMap[self.regParam] - else: - return self.regParam.defaultValue + return self.getOrDefault(self.regParam) class HasFeaturesCol(Params): """ - Params with featuresCol. + Mixin for param featuresCol: features column name. """ # a placeholder to make it appear in the generated doc - featuresCol = Param(Params._dummy(), "featuresCol", "features column name", 'features') + featuresCol = Param(Params._dummy(), "featuresCol", "features column name") def __init__(self): super(HasFeaturesCol, self).__init__() #: param for features column name - self.featuresCol = Param(self, "featuresCol", "features column name", 'features') + self.featuresCol = Param(self, "featuresCol", "features column name") + if 'features' is not None: + self._setDefault(featuresCol='features') def setFeaturesCol(self, value): """ @@ -104,24 +104,23 @@ def getFeaturesCol(self): """ Gets the value of featuresCol or its default value. """ - if self.featuresCol in self.paramMap: - return self.paramMap[self.featuresCol] - else: - return self.featuresCol.defaultValue + return self.getOrDefault(self.featuresCol) class HasLabelCol(Params): """ - Params with labelCol. + Mixin for param labelCol: label column name. """ # a placeholder to make it appear in the generated doc - labelCol = Param(Params._dummy(), "labelCol", "label column name", 'label') + labelCol = Param(Params._dummy(), "labelCol", "label column name") def __init__(self): super(HasLabelCol, self).__init__() #: param for label column name - self.labelCol = Param(self, "labelCol", "label column name", 'label') + self.labelCol = Param(self, "labelCol", "label column name") + if 'label' is not None: + self._setDefault(labelCol='label') def setLabelCol(self, value): """ @@ -134,24 +133,23 @@ def getLabelCol(self): """ Gets the value of labelCol or its default value. """ - if self.labelCol in self.paramMap: - return self.paramMap[self.labelCol] - else: - return self.labelCol.defaultValue + return self.getOrDefault(self.labelCol) class HasPredictionCol(Params): """ - Params with predictionCol. + Mixin for param predictionCol: prediction column name. """ # a placeholder to make it appear in the generated doc - predictionCol = Param(Params._dummy(), "predictionCol", "prediction column name", 'prediction') + predictionCol = Param(Params._dummy(), "predictionCol", "prediction column name") def __init__(self): super(HasPredictionCol, self).__init__() #: param for prediction column name - self.predictionCol = Param(self, "predictionCol", "prediction column name", 'prediction') + self.predictionCol = Param(self, "predictionCol", "prediction column name") + if 'prediction' is not None: + self._setDefault(predictionCol='prediction') def setPredictionCol(self, value): """ @@ -164,24 +162,23 @@ def getPredictionCol(self): """ Gets the value of predictionCol or its default value. """ - if self.predictionCol in self.paramMap: - return self.paramMap[self.predictionCol] - else: - return self.predictionCol.defaultValue + return self.getOrDefault(self.predictionCol) class HasInputCol(Params): """ - Params with inputCol. + Mixin for param inputCol: input column name. """ # a placeholder to make it appear in the generated doc - inputCol = Param(Params._dummy(), "inputCol", "input column name", 'input') + inputCol = Param(Params._dummy(), "inputCol", "input column name") def __init__(self): super(HasInputCol, self).__init__() #: param for input column name - self.inputCol = Param(self, "inputCol", "input column name", 'input') + self.inputCol = Param(self, "inputCol", "input column name") + if None is not None: + self._setDefault(inputCol=None) def setInputCol(self, value): """ @@ -194,24 +191,23 @@ def getInputCol(self): """ Gets the value of inputCol or its default value. """ - if self.inputCol in self.paramMap: - return self.paramMap[self.inputCol] - else: - return self.inputCol.defaultValue + return self.getOrDefault(self.inputCol) class HasOutputCol(Params): """ - Params with outputCol. + Mixin for param outputCol: output column name. """ # a placeholder to make it appear in the generated doc - outputCol = Param(Params._dummy(), "outputCol", "output column name", 'output') + outputCol = Param(Params._dummy(), "outputCol", "output column name") def __init__(self): super(HasOutputCol, self).__init__() #: param for output column name - self.outputCol = Param(self, "outputCol", "output column name", 'output') + self.outputCol = Param(self, "outputCol", "output column name") + if None is not None: + self._setDefault(outputCol=None) def setOutputCol(self, value): """ @@ -224,24 +220,23 @@ def getOutputCol(self): """ Gets the value of outputCol or its default value. """ - if self.outputCol in self.paramMap: - return self.paramMap[self.outputCol] - else: - return self.outputCol.defaultValue + return self.getOrDefault(self.outputCol) class HasNumFeatures(Params): """ - Params with numFeatures. + Mixin for param numFeatures: number of features. """ # a placeholder to make it appear in the generated doc - numFeatures = Param(Params._dummy(), "numFeatures", "number of features", 1 << 18) + numFeatures = Param(Params._dummy(), "numFeatures", "number of features") def __init__(self): super(HasNumFeatures, self).__init__() #: param for number of features - self.numFeatures = Param(self, "numFeatures", "number of features", 1 << 18) + self.numFeatures = Param(self, "numFeatures", "number of features") + if None is not None: + self._setDefault(numFeatures=None) def setNumFeatures(self, value): """ @@ -254,7 +249,4 @@ def getNumFeatures(self): """ Gets the value of numFeatures or its default value. """ - if self.numFeatures in self.paramMap: - return self.paramMap[self.numFeatures] - else: - return self.numFeatures.defaultValue + return self.getOrDefault(self.numFeatures) diff --git a/python/pyspark/ml/pipeline.py b/python/pyspark/ml/pipeline.py index 83880a5afcd1d..d94ecfff09f66 100644 --- a/python/pyspark/ml/pipeline.py +++ b/python/pyspark/ml/pipeline.py @@ -124,10 +124,10 @@ def setParams(self, stages=[]): Sets params for Pipeline. """ kwargs = self.setParams._input_kwargs - return self._set_params(**kwargs) + return self._set(**kwargs) def fit(self, dataset, params={}): - paramMap = self._merge_params(params) + paramMap = self.extractParamMap(params) stages = paramMap[self.stages] for stage in stages: if not (isinstance(stage, Estimator) or isinstance(stage, Transformer)): @@ -164,7 +164,7 @@ def __init__(self, transformers): self.transformers = transformers def transform(self, dataset, params={}): - paramMap = self._merge_params(params) + paramMap = self.extractParamMap(params) for t in self.transformers: dataset = t.transform(dataset, paramMap) return dataset diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py index b627c2b4e930b..3a42bcf723894 100644 --- a/python/pyspark/ml/tests.py +++ b/python/pyspark/ml/tests.py @@ -33,6 +33,7 @@ from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase from pyspark.sql import DataFrame from pyspark.ml.param import Param +from pyspark.ml.param.shared import HasMaxIter, HasInputCol from pyspark.ml.pipeline import Transformer, Estimator, Pipeline @@ -46,7 +47,7 @@ class MockTransformer(Transformer): def __init__(self): super(MockTransformer, self).__init__() - self.fake = Param(self, "fake", "fake", None) + self.fake = Param(self, "fake", "fake") self.dataset_index = None self.fake_param_value = None @@ -62,7 +63,7 @@ class MockEstimator(Estimator): def __init__(self): super(MockEstimator, self).__init__() - self.fake = Param(self, "fake", "fake", None) + self.fake = Param(self, "fake", "fake") self.dataset_index = None self.fake_param_value = None self.model = None @@ -111,5 +112,52 @@ def test_pipeline(self): self.assertEqual(6, dataset.index) +class TestParams(HasMaxIter, HasInputCol): + """ + A subclass of Params mixed with HasMaxIter and HasInputCol. + """ + + def __init__(self): + super(TestParams, self).__init__() + self._setDefault(maxIter=10) + + +class ParamTests(PySparkTestCase): + + def test_param(self): + testParams = TestParams() + maxIter = testParams.maxIter + self.assertEqual(maxIter.name, "maxIter") + self.assertEqual(maxIter.doc, "max number of iterations") + self.assertTrue(maxIter.parent is testParams) + + def test_params(self): + testParams = TestParams() + maxIter = testParams.maxIter + inputCol = testParams.inputCol + + params = testParams.params + self.assertEqual(params, [inputCol, maxIter]) + + self.assertTrue(testParams.hasDefault(maxIter)) + self.assertFalse(testParams.isSet(maxIter)) + self.assertTrue(testParams.isDefined(maxIter)) + self.assertEqual(testParams.getMaxIter(), 10) + testParams.setMaxIter(100) + self.assertTrue(testParams.isSet(maxIter)) + self.assertEquals(testParams.getMaxIter(), 100) + + self.assertFalse(testParams.hasDefault(inputCol)) + self.assertFalse(testParams.isSet(inputCol)) + self.assertFalse(testParams.isDefined(inputCol)) + with self.assertRaises(KeyError): + testParams.getInputCol() + + self.assertEquals( + testParams.explainParams(), + "\n".join(["inputCol: input column name (undefined)", + "maxIter: max number of iterations (default: 10, current: 100)"])) + + if __name__ == "__main__": unittest.main() diff --git a/python/pyspark/ml/util.py b/python/pyspark/ml/util.py index 6f7f39c40eb5a..d3cb100a9efa5 100644 --- a/python/pyspark/ml/util.py +++ b/python/pyspark/ml/util.py @@ -40,8 +40,8 @@ class Identifiable(object): def __init__(self): #: A unique id for the object. The default implementation - #: concatenates the class name, "-", and 8 random hex chars. - self.uid = type(self).__name__ + "-" + uuid.uuid4().hex[:8] + #: concatenates the class name, "_", and 8 random hex chars. + self.uid = type(self).__name__ + "_" + uuid.uuid4().hex[:8] def __repr__(self): return self.uid diff --git a/python/pyspark/ml/wrapper.py b/python/pyspark/ml/wrapper.py index 31a66b3d2f730..394f23c5e9b12 100644 --- a/python/pyspark/ml/wrapper.py +++ b/python/pyspark/ml/wrapper.py @@ -64,7 +64,7 @@ def _transfer_params_to_java(self, params, java_obj): :param params: additional params (overwriting embedded values) :param java_obj: Java object to receive the params """ - paramMap = self._merge_params(params) + paramMap = self.extractParamMap(params) for param in self.params: if param in paramMap: java_obj.set(param.name, paramMap[param]) From 8370550593f3549e90ace446961281dad0cd7498 Mon Sep 17 00:00:00 2001 From: jerryshao Date: Thu, 16 Apr 2015 10:39:02 +0100 Subject: [PATCH 306/591] [Streaming][minor] Remove additional quote and unneeded imports Author: jerryshao Closes #5540 from jerryshao/minor-fix and squashes the following commits: ebaa646 [jerryshao] Minor fix --- .../apache/spark/examples/streaming/DirectKafkaWordCount.scala | 2 +- .../main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala | 3 +-- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala b/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala index 1c8a20bf8f1ae..11a8cf09533ce 100644 --- a/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala +++ b/examples/scala-2.10/src/main/scala/org/apache/spark/examples/streaming/DirectKafkaWordCount.scala @@ -41,7 +41,7 @@ object DirectKafkaWordCount { | is a list of one or more Kafka brokers | is a list of one or more kafka topics to consume from | - """".stripMargin) + """.stripMargin) System.exit(1) } diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala index a0b8a0c565210..a1b4a12e5d6a0 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala @@ -23,10 +23,9 @@ import org.apache.spark.{Logging, Partition, SparkContext, SparkException, TaskC import org.apache.spark.rdd.RDD import org.apache.spark.util.NextIterator -import java.util.Properties import kafka.api.{FetchRequestBuilder, FetchResponse} import kafka.common.{ErrorMapping, TopicAndPartition} -import kafka.consumer.{ConsumerConfig, SimpleConsumer} +import kafka.consumer.SimpleConsumer import kafka.message.{MessageAndMetadata, MessageAndOffset} import kafka.serializer.Decoder import kafka.utils.VerifiableProperties From 6179a948371897cecb7322ebda366c2de8ecaedd Mon Sep 17 00:00:00 2001 From: Sean Owen Date: Thu, 16 Apr 2015 10:45:32 +0100 Subject: [PATCH 307/591] SPARK-4783 [CORE] System.exit() calls in SparkContext disrupt applications embedding Spark Avoid `System.exit(1)` in `TaskSchedulerImpl` and convert to `SparkException`; ensure scheduler calls `sc.stop()` even when this exception is thrown. CC mateiz aarondav as those who may have last touched this code. Author: Sean Owen Closes #5492 from srowen/SPARK-4783 and squashes the following commits: 60dc682 [Sean Owen] Avoid System.exit(1) in TaskSchedulerImpl and convert to SparkException; ensure scheduler calls sc.stop() even when this exception is thrown --- .../org/apache/spark/scheduler/TaskSchedulerImpl.scala | 5 ++--- .../scheduler/cluster/SparkDeploySchedulerBackend.scala | 9 ++++++--- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala index 2362cc7240039..ecc8bf189986d 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala @@ -394,7 +394,7 @@ private[spark] class TaskSchedulerImpl( def error(message: String) { synchronized { - if (activeTaskSets.size > 0) { + if (activeTaskSets.nonEmpty) { // Have each task set throw a SparkException with the error for ((taskSetId, manager) <- activeTaskSets) { try { @@ -407,8 +407,7 @@ private[spark] class TaskSchedulerImpl( // No task sets are active but we still got an error. Just exit since this // must mean the error is during registration. // It might be good to do something smarter here in the future. - logError("Exiting due to error from cluster scheduler: " + message) - System.exit(1) + throw new SparkException(s"Exiting due to error from cluster scheduler: $message") } } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala index ed5b7c1088196..ccf1dc5af6120 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala @@ -118,9 +118,12 @@ private[spark] class SparkDeploySchedulerBackend( notifyContext() if (!stopping) { logError("Application has been killed. Reason: " + reason) - scheduler.error(reason) - // Ensure the application terminates, as we can no longer run jobs. - sc.stop() + try { + scheduler.error(reason) + } finally { + // Ensure the application terminates, as we can no longer run jobs. + sc.stop() + } } } From de4fa6b6d12e2bee0307ffba2abfca0c33f15e45 Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Thu, 16 Apr 2015 10:48:31 +0100 Subject: [PATCH 308/591] [SPARK-4194] [core] Make SparkContext initialization exception-safe. SparkContext has a very long constructor, where multiple things are initialized, multiple threads are spawned, and multiple opportunities for exceptions to be thrown exist. If one of these happens at an innoportune time, lots of garbage tends to stick around. This patch re-organizes SparkContext so that its internal state is initialized in a big "try" block. The fields keeping state are now completely private to SparkContext, and are "vars", because Scala doesn't allow you to initialize a val later. The existing API interface is kept by turning vals into defs (which works because Scala guarantees the same binary interface for those). On top of that, a few things in other areas were changed to avoid more things leaking: - Executor was changed to explicitly wait for the heartbeat thread to stop. LocalBackend was changed to wait for the "StopExecutor" message to be received, since otherwise there could be a race between that message arriving and the actor system being shut down. - ConnectionManager could possibly hang during shutdown, because an interrupt at the wrong moment could cause the selector thread to still call select and then wait forever. So also wake up the selector so that this situation is avoided. Author: Marcelo Vanzin Closes #5335 from vanzin/SPARK-4194 and squashes the following commits: 746b661 [Marcelo Vanzin] Fix borked merge. 80fc00e [Marcelo Vanzin] Merge branch 'master' into SPARK-4194 408dada [Marcelo Vanzin] Merge branch 'master' into SPARK-4194 2621609 [Marcelo Vanzin] Merge branch 'master' into SPARK-4194 6b73fcb [Marcelo Vanzin] Scalastyle. c671c46 [Marcelo Vanzin] Fix merge. 3979aad [Marcelo Vanzin] Merge branch 'master' into SPARK-4194 8caa8b3 [Marcelo Vanzin] [SPARK-4194] [core] Make SparkContext initialization exception-safe. 071f16e [Marcelo Vanzin] Nits. 27456b9 [Marcelo Vanzin] More exception safety. a0b0881 [Marcelo Vanzin] Stop alloc manager before scheduler. 5545d83 [Marcelo Vanzin] [SPARK-6650] [core] Stop ExecutorAllocationManager when context stops. --- .../scala/org/apache/spark/SparkContext.scala | 505 ++++++++++-------- .../org/apache/spark/executor/Executor.scala | 33 +- .../spark/network/nio/ConnectionManager.scala | 7 +- .../spark/scheduler/TaskSchedulerImpl.scala | 3 +- .../spark/scheduler/local/LocalBackend.scala | 19 +- .../ExecutorAllocationManagerSuite.scala | 6 - 6 files changed, 329 insertions(+), 244 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 3f1a7dd99d635..e106c5c4bef60 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -31,6 +31,7 @@ import scala.collection.JavaConversions._ import scala.collection.generic.Growable import scala.collection.mutable.HashMap import scala.reflect.{ClassTag, classTag} +import scala.util.control.NonFatal import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path @@ -50,9 +51,10 @@ import org.apache.spark.executor.{ExecutorEndpoint, TriggerThreadDump} import org.apache.spark.input.{StreamInputFormat, PortableDataStream, WholeTextFileInputFormat, FixedLengthBinaryInputFormat} import org.apache.spark.io.CompressionCodec +import org.apache.spark.metrics.MetricsSystem import org.apache.spark.partial.{ApproximateEvaluator, PartialResult} import org.apache.spark.rdd._ -import org.apache.spark.rpc.RpcAddress +import org.apache.spark.rpc.{RpcAddress, RpcEndpointRef} import org.apache.spark.scheduler._ import org.apache.spark.scheduler.cluster.{CoarseGrainedSchedulerBackend, SparkDeploySchedulerBackend, SimrSchedulerBackend} @@ -192,8 +194,42 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli // log out Spark Version in Spark driver log logInfo(s"Running Spark version $SPARK_VERSION") - private[spark] val conf = config.clone() - conf.validateSettings() + /* ------------------------------------------------------------------------------------- * + | Private variables. These variables keep the internal state of the context, and are | + | not accessible by the outside world. They're mutable since we want to initialize all | + | of them to some neutral value ahead of time, so that calling "stop()" while the | + | constructor is still running is safe. | + * ------------------------------------------------------------------------------------- */ + + private var _conf: SparkConf = _ + private var _eventLogDir: Option[URI] = None + private var _eventLogCodec: Option[String] = None + private var _env: SparkEnv = _ + private var _metadataCleaner: MetadataCleaner = _ + private var _jobProgressListener: JobProgressListener = _ + private var _statusTracker: SparkStatusTracker = _ + private var _progressBar: Option[ConsoleProgressBar] = None + private var _ui: Option[SparkUI] = None + private var _hadoopConfiguration: Configuration = _ + private var _executorMemory: Int = _ + private var _schedulerBackend: SchedulerBackend = _ + private var _taskScheduler: TaskScheduler = _ + private var _heartbeatReceiver: RpcEndpointRef = _ + @volatile private var _dagScheduler: DAGScheduler = _ + private var _applicationId: String = _ + private var _eventLogger: Option[EventLoggingListener] = None + private var _executorAllocationManager: Option[ExecutorAllocationManager] = None + private var _cleaner: Option[ContextCleaner] = None + private var _listenerBusStarted: Boolean = false + private var _jars: Seq[String] = _ + private var _files: Seq[String] = _ + + /* ------------------------------------------------------------------------------------- * + | Accessors and public fields. These provide access to the internal state of the | + | context. | + * ------------------------------------------------------------------------------------- */ + + private[spark] def conf: SparkConf = _conf /** * Return a copy of this SparkContext's configuration. The configuration ''cannot'' be @@ -201,65 +237,24 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli */ def getConf: SparkConf = conf.clone() - if (!conf.contains("spark.master")) { - throw new SparkException("A master URL must be set in your configuration") - } - if (!conf.contains("spark.app.name")) { - throw new SparkException("An application name must be set in your configuration") - } - - if (conf.getBoolean("spark.logConf", false)) { - logInfo("Spark configuration:\n" + conf.toDebugString) - } - - // Set Spark driver host and port system properties - conf.setIfMissing("spark.driver.host", Utils.localHostName()) - conf.setIfMissing("spark.driver.port", "0") - - val jars: Seq[String] = - conf.getOption("spark.jars").map(_.split(",")).map(_.filter(_.size != 0)).toSeq.flatten - - val files: Seq[String] = - conf.getOption("spark.files").map(_.split(",")).map(_.filter(_.size != 0)).toSeq.flatten - - val master = conf.get("spark.master") - val appName = conf.get("spark.app.name") + def jars: Seq[String] = _jars + def files: Seq[String] = _files + def master: String = _conf.get("spark.master") + def appName: String = _conf.get("spark.app.name") - private[spark] val isEventLogEnabled = conf.getBoolean("spark.eventLog.enabled", false) - private[spark] val eventLogDir: Option[URI] = { - if (isEventLogEnabled) { - val unresolvedDir = conf.get("spark.eventLog.dir", EventLoggingListener.DEFAULT_LOG_DIR) - .stripSuffix("/") - Some(Utils.resolveURI(unresolvedDir)) - } else { - None - } - } - private[spark] val eventLogCodec: Option[String] = { - val compress = conf.getBoolean("spark.eventLog.compress", false) - if (compress && isEventLogEnabled) { - Some(CompressionCodec.getCodecName(conf)).map(CompressionCodec.getShortName) - } else { - None - } - } + private[spark] def isEventLogEnabled: Boolean = _conf.getBoolean("spark.eventLog.enabled", false) + private[spark] def eventLogDir: Option[URI] = _eventLogDir + private[spark] def eventLogCodec: Option[String] = _eventLogCodec // Generate the random name for a temp folder in Tachyon // Add a timestamp as the suffix here to make it more safe val tachyonFolderName = "spark-" + randomUUID.toString() - conf.set("spark.tachyonStore.folderName", tachyonFolderName) - val isLocal = (master == "local" || master.startsWith("local[")) - - if (master == "yarn-client") System.setProperty("SPARK_YARN_MODE", "true") + def isLocal: Boolean = (master == "local" || master.startsWith("local[")) // An asynchronous listener bus for Spark events private[spark] val listenerBus = new LiveListenerBus - conf.set("spark.executor.id", SparkContext.DRIVER_IDENTIFIER) - - // Create the Spark execution environment (cache, map output tracker, etc) - // This function allows components created by SparkEnv to be mocked in unit tests: private[spark] def createSparkEnv( conf: SparkConf, @@ -268,8 +263,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli SparkEnv.createDriverEnv(conf, isLocal, listenerBus) } - private[spark] val env = createSparkEnv(conf, isLocal, listenerBus) - SparkEnv.set(env) + private[spark] def env: SparkEnv = _env // Used to store a URL for each static file/jar together with the file's local timestamp private[spark] val addedFiles = HashMap[String, Long]() @@ -277,35 +271,14 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli // Keeps track of all persisted RDDs private[spark] val persistentRdds = new TimeStampedWeakValueHashMap[Int, RDD[_]] - private[spark] val metadataCleaner = - new MetadataCleaner(MetadataCleanerType.SPARK_CONTEXT, this.cleanup, conf) - + private[spark] def metadataCleaner: MetadataCleaner = _metadataCleaner + private[spark] def jobProgressListener: JobProgressListener = _jobProgressListener - private[spark] val jobProgressListener = new JobProgressListener(conf) - listenerBus.addListener(jobProgressListener) + def statusTracker: SparkStatusTracker = _statusTracker - val statusTracker = new SparkStatusTracker(this) + private[spark] def progressBar: Option[ConsoleProgressBar] = _progressBar - private[spark] val progressBar: Option[ConsoleProgressBar] = - if (conf.getBoolean("spark.ui.showConsoleProgress", true) && !log.isInfoEnabled) { - Some(new ConsoleProgressBar(this)) - } else { - None - } - - // Initialize the Spark UI - private[spark] val ui: Option[SparkUI] = - if (conf.getBoolean("spark.ui.enabled", true)) { - Some(SparkUI.createLiveUI(this, conf, listenerBus, jobProgressListener, - env.securityManager,appName)) - } else { - // For tests, do not enable the UI - None - } - - // Bind the UI before starting the task scheduler to communicate - // the bound port to the cluster manager properly - ui.foreach(_.bind()) + private[spark] def ui: Option[SparkUI] = _ui /** * A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse. @@ -313,134 +286,248 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * '''Note:''' As it will be reused in all Hadoop RDDs, it's better not to modify it unless you * plan to set some global configurations for all Hadoop RDDs. */ - val hadoopConfiguration = SparkHadoopUtil.get.newConfiguration(conf) + def hadoopConfiguration: Configuration = _hadoopConfiguration + + private[spark] def executorMemory: Int = _executorMemory + + // Environment variables to pass to our executors. + private[spark] val executorEnvs = HashMap[String, String]() + + // Set SPARK_USER for user who is running SparkContext. + val sparkUser = Utils.getCurrentUserName() - // Add each JAR given through the constructor - if (jars != null) { - jars.foreach(addJar) + private[spark] def schedulerBackend: SchedulerBackend = _schedulerBackend + private[spark] def schedulerBackend_=(sb: SchedulerBackend): Unit = { + _schedulerBackend = sb } - if (files != null) { - files.foreach(addFile) + private[spark] def taskScheduler: TaskScheduler = _taskScheduler + private[spark] def taskScheduler_=(ts: TaskScheduler): Unit = { + _taskScheduler = ts } + private[spark] def dagScheduler: DAGScheduler = _dagScheduler + private[spark] def dagScheduler_=(ds: DAGScheduler): Unit = { + _dagScheduler = ds + } + + def applicationId: String = _applicationId + + def metricsSystem: MetricsSystem = if (_env != null) _env.metricsSystem else null + + private[spark] def eventLogger: Option[EventLoggingListener] = _eventLogger + + private[spark] def executorAllocationManager: Option[ExecutorAllocationManager] = + _executorAllocationManager + + private[spark] def cleaner: Option[ContextCleaner] = _cleaner + + private[spark] var checkpointDir: Option[String] = None + + // Thread Local variable that can be used by users to pass information down the stack + private val localProperties = new InheritableThreadLocal[Properties] { + override protected def childValue(parent: Properties): Properties = new Properties(parent) + override protected def initialValue(): Properties = new Properties() + } + + /* ------------------------------------------------------------------------------------- * + | Initialization. This code initializes the context in a manner that is exception-safe. | + | All internal fields holding state are initialized here, and any error prompts the | + | stop() method to be called. | + * ------------------------------------------------------------------------------------- */ + private def warnSparkMem(value: String): String = { logWarning("Using SPARK_MEM to set amount of memory to use per executor process is " + "deprecated, please use spark.executor.memory instead.") value } - private[spark] val executorMemory = conf.getOption("spark.executor.memory") - .orElse(Option(System.getenv("SPARK_EXECUTOR_MEMORY"))) - .orElse(Option(System.getenv("SPARK_MEM")).map(warnSparkMem)) - .map(Utils.memoryStringToMb) - .getOrElse(512) + try { + _conf = config.clone() + _conf.validateSettings() - // Environment variables to pass to our executors. - private[spark] val executorEnvs = HashMap[String, String]() + if (!_conf.contains("spark.master")) { + throw new SparkException("A master URL must be set in your configuration") + } + if (!_conf.contains("spark.app.name")) { + throw new SparkException("An application name must be set in your configuration") + } - // Convert java options to env vars as a work around - // since we can't set env vars directly in sbt. - for { (envKey, propKey) <- Seq(("SPARK_TESTING", "spark.testing")) - value <- Option(System.getenv(envKey)).orElse(Option(System.getProperty(propKey)))} { - executorEnvs(envKey) = value - } - Option(System.getenv("SPARK_PREPEND_CLASSES")).foreach { v => - executorEnvs("SPARK_PREPEND_CLASSES") = v - } - // The Mesos scheduler backend relies on this environment variable to set executor memory. - // TODO: Set this only in the Mesos scheduler. - executorEnvs("SPARK_EXECUTOR_MEMORY") = executorMemory + "m" - executorEnvs ++= conf.getExecutorEnv + if (_conf.getBoolean("spark.logConf", false)) { + logInfo("Spark configuration:\n" + _conf.toDebugString) + } - // Set SPARK_USER for user who is running SparkContext. - val sparkUser = Utils.getCurrentUserName() - executorEnvs("SPARK_USER") = sparkUser + // Set Spark driver host and port system properties + _conf.setIfMissing("spark.driver.host", Utils.localHostName()) + _conf.setIfMissing("spark.driver.port", "0") - // We need to register "HeartbeatReceiver" before "createTaskScheduler" because Executor will - // retrieve "HeartbeatReceiver" in the constructor. (SPARK-6640) - private val heartbeatReceiver = env.rpcEnv.setupEndpoint( - HeartbeatReceiver.ENDPOINT_NAME, new HeartbeatReceiver(this)) + _conf.set("spark.executor.id", SparkContext.DRIVER_IDENTIFIER) - // Create and start the scheduler - private[spark] var (schedulerBackend, taskScheduler) = - SparkContext.createTaskScheduler(this, master) + _jars =_conf.getOption("spark.jars").map(_.split(",")).map(_.filter(_.size != 0)).toSeq.flatten + _files = _conf.getOption("spark.files").map(_.split(",")).map(_.filter(_.size != 0)) + .toSeq.flatten - heartbeatReceiver.send(TaskSchedulerIsSet) + _eventLogDir = + if (isEventLogEnabled) { + val unresolvedDir = conf.get("spark.eventLog.dir", EventLoggingListener.DEFAULT_LOG_DIR) + .stripSuffix("/") + Some(Utils.resolveURI(unresolvedDir)) + } else { + None + } - @volatile private[spark] var dagScheduler: DAGScheduler = _ - try { - dagScheduler = new DAGScheduler(this) - } catch { - case e: Exception => { - try { - stop() - } finally { - throw new SparkException("Error while constructing DAGScheduler", e) + _eventLogCodec = { + val compress = _conf.getBoolean("spark.eventLog.compress", false) + if (compress && isEventLogEnabled) { + Some(CompressionCodec.getCodecName(_conf)).map(CompressionCodec.getShortName) + } else { + None } } - } - // start TaskScheduler after taskScheduler sets DAGScheduler reference in DAGScheduler's - // constructor - taskScheduler.start() + _conf.set("spark.tachyonStore.folderName", tachyonFolderName) - val applicationId: String = taskScheduler.applicationId() - conf.set("spark.app.id", applicationId) + if (master == "yarn-client") System.setProperty("SPARK_YARN_MODE", "true") - env.blockManager.initialize(applicationId) + // Create the Spark execution environment (cache, map output tracker, etc) + _env = createSparkEnv(_conf, isLocal, listenerBus) + SparkEnv.set(_env) - val metricsSystem = env.metricsSystem + _metadataCleaner = new MetadataCleaner(MetadataCleanerType.SPARK_CONTEXT, this.cleanup, _conf) - // The metrics system for Driver need to be set spark.app.id to app ID. - // So it should start after we get app ID from the task scheduler and set spark.app.id. - metricsSystem.start() - // Attach the driver metrics servlet handler to the web ui after the metrics system is started. - metricsSystem.getServletHandlers.foreach(handler => ui.foreach(_.attachHandler(handler))) + _jobProgressListener = new JobProgressListener(_conf) + listenerBus.addListener(jobProgressListener) - // Optionally log Spark events - private[spark] val eventLogger: Option[EventLoggingListener] = { - if (isEventLogEnabled) { - val logger = - new EventLoggingListener(applicationId, eventLogDir.get, conf, hadoopConfiguration) - logger.start() - listenerBus.addListener(logger) - Some(logger) - } else None - } + _statusTracker = new SparkStatusTracker(this) - // Optionally scale number of executors dynamically based on workload. Exposed for testing. - private val dynamicAllocationEnabled = conf.getBoolean("spark.dynamicAllocation.enabled", false) - private val dynamicAllocationTesting = conf.getBoolean("spark.dynamicAllocation.testing", false) - private[spark] val executorAllocationManager: Option[ExecutorAllocationManager] = - if (dynamicAllocationEnabled) { - assert(supportDynamicAllocation, - "Dynamic allocation of executors is currently only supported in YARN mode") - Some(new ExecutorAllocationManager(this, listenerBus, conf)) - } else { - None + _progressBar = + if (_conf.getBoolean("spark.ui.showConsoleProgress", true) && !log.isInfoEnabled) { + Some(new ConsoleProgressBar(this)) + } else { + None + } + + _ui = + if (conf.getBoolean("spark.ui.enabled", true)) { + Some(SparkUI.createLiveUI(this, _conf, listenerBus, _jobProgressListener, + _env.securityManager,appName)) + } else { + // For tests, do not enable the UI + None + } + // Bind the UI before starting the task scheduler to communicate + // the bound port to the cluster manager properly + _ui.foreach(_.bind()) + + _hadoopConfiguration = SparkHadoopUtil.get.newConfiguration(_conf) + + // Add each JAR given through the constructor + if (jars != null) { + jars.foreach(addJar) } - executorAllocationManager.foreach(_.start()) - private[spark] val cleaner: Option[ContextCleaner] = { - if (conf.getBoolean("spark.cleaner.referenceTracking", true)) { - Some(new ContextCleaner(this)) - } else { - None + if (files != null) { + files.foreach(addFile) } - } - cleaner.foreach(_.start()) - setupAndStartListenerBus() - postEnvironmentUpdate() - postApplicationStart() + _executorMemory = _conf.getOption("spark.executor.memory") + .orElse(Option(System.getenv("SPARK_EXECUTOR_MEMORY"))) + .orElse(Option(System.getenv("SPARK_MEM")) + .map(warnSparkMem)) + .map(Utils.memoryStringToMb) + .getOrElse(512) + + // Convert java options to env vars as a work around + // since we can't set env vars directly in sbt. + for { (envKey, propKey) <- Seq(("SPARK_TESTING", "spark.testing")) + value <- Option(System.getenv(envKey)).orElse(Option(System.getProperty(propKey)))} { + executorEnvs(envKey) = value + } + Option(System.getenv("SPARK_PREPEND_CLASSES")).foreach { v => + executorEnvs("SPARK_PREPEND_CLASSES") = v + } + // The Mesos scheduler backend relies on this environment variable to set executor memory. + // TODO: Set this only in the Mesos scheduler. + executorEnvs("SPARK_EXECUTOR_MEMORY") = executorMemory + "m" + executorEnvs ++= _conf.getExecutorEnv + executorEnvs("SPARK_USER") = sparkUser + + // We need to register "HeartbeatReceiver" before "createTaskScheduler" because Executor will + // retrieve "HeartbeatReceiver" in the constructor. (SPARK-6640) + _heartbeatReceiver = env.rpcEnv.setupEndpoint( + HeartbeatReceiver.ENDPOINT_NAME, new HeartbeatReceiver(this)) + + // Create and start the scheduler + val (sched, ts) = SparkContext.createTaskScheduler(this, master) + _schedulerBackend = sched + _taskScheduler = ts + _dagScheduler = new DAGScheduler(this) + _heartbeatReceiver.send(TaskSchedulerIsSet) + + // start TaskScheduler after taskScheduler sets DAGScheduler reference in DAGScheduler's + // constructor + _taskScheduler.start() + + _applicationId = _taskScheduler.applicationId() + _conf.set("spark.app.id", _applicationId) + _env.blockManager.initialize(_applicationId) + + // The metrics system for Driver need to be set spark.app.id to app ID. + // So it should start after we get app ID from the task scheduler and set spark.app.id. + metricsSystem.start() + // Attach the driver metrics servlet handler to the web ui after the metrics system is started. + metricsSystem.getServletHandlers.foreach(handler => ui.foreach(_.attachHandler(handler))) + + _eventLogger = + if (isEventLogEnabled) { + val logger = + new EventLoggingListener(_applicationId, _eventLogDir.get, _conf, _hadoopConfiguration) + logger.start() + listenerBus.addListener(logger) + Some(logger) + } else { + None + } - private[spark] var checkpointDir: Option[String] = None + // Optionally scale number of executors dynamically based on workload. Exposed for testing. + val dynamicAllocationEnabled = _conf.getBoolean("spark.dynamicAllocation.enabled", false) + _executorAllocationManager = + if (dynamicAllocationEnabled) { + assert(supportDynamicAllocation, + "Dynamic allocation of executors is currently only supported in YARN mode") + Some(new ExecutorAllocationManager(this, listenerBus, _conf)) + } else { + None + } + _executorAllocationManager.foreach(_.start()) - // Thread Local variable that can be used by users to pass information down the stack - private val localProperties = new InheritableThreadLocal[Properties] { - override protected def childValue(parent: Properties): Properties = new Properties(parent) - override protected def initialValue(): Properties = new Properties() + _cleaner = + if (_conf.getBoolean("spark.cleaner.referenceTracking", true)) { + Some(new ContextCleaner(this)) + } else { + None + } + _cleaner.foreach(_.start()) + + setupAndStartListenerBus() + postEnvironmentUpdate() + postApplicationStart() + + // Post init + _taskScheduler.postStartHook() + _env.metricsSystem.registerSource(new DAGSchedulerSource(dagScheduler)) + _env.metricsSystem.registerSource(new BlockManagerSource(_env.blockManager)) + } catch { + case NonFatal(e) => + logError("Error initializing SparkContext.", e) + try { + stop() + } catch { + case NonFatal(inner) => + logError("Error stopping SparkContext after init error.", inner) + } finally { + throw e + } } /** @@ -544,19 +631,6 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli setLocalProperty(SparkContext.SPARK_JOB_INTERRUPT_ON_CANCEL, null) } - // Post init - taskScheduler.postStartHook() - - private val dagSchedulerSource = new DAGSchedulerSource(this.dagScheduler) - private val blockManagerSource = new BlockManagerSource(SparkEnv.get.blockManager) - - private def initDriverMetrics() { - SparkEnv.get.metricsSystem.registerSource(dagSchedulerSource) - SparkEnv.get.metricsSystem.registerSource(blockManagerSource) - } - - initDriverMetrics() - // Methods for creating RDDs /** Distribute a local Scala collection to form an RDD. @@ -1146,7 +1220,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * this application is supported. This is currently only available for YARN. */ private[spark] def supportDynamicAllocation = - master.contains("yarn") || dynamicAllocationTesting + master.contains("yarn") || _conf.getBoolean("spark.dynamicAllocation.testing", false) /** * :: DeveloperApi :: @@ -1163,7 +1237,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli * This is currently only supported in YARN mode. Return whether the request is received. */ private[spark] override def requestTotalExecutors(numExecutors: Int): Boolean = { - assert(master.contains("yarn") || dynamicAllocationTesting, + assert(supportDynamicAllocation, "Requesting executors is currently only supported in YARN mode") schedulerBackend match { case b: CoarseGrainedSchedulerBackend => @@ -1403,28 +1477,40 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli def stop() { // Use the stopping variable to ensure no contention for the stop scenario. // Still track the stopped variable for use elsewhere in the code. - if (!stopped.compareAndSet(false, true)) { logInfo("SparkContext already stopped.") return } - + postApplicationEnd() - ui.foreach(_.stop()) - env.metricsSystem.report() - metadataCleaner.cancel() - cleaner.foreach(_.stop()) - executorAllocationManager.foreach(_.stop()) - dagScheduler.stop() - dagScheduler = null - listenerBus.stop() - eventLogger.foreach(_.stop()) - env.rpcEnv.stop(heartbeatReceiver) - progressBar.foreach(_.stop()) - taskScheduler = null + _ui.foreach(_.stop()) + if (env != null) { + env.metricsSystem.report() + } + if (metadataCleaner != null) { + metadataCleaner.cancel() + } + _cleaner.foreach(_.stop()) + _executorAllocationManager.foreach(_.stop()) + if (_dagScheduler != null) { + _dagScheduler.stop() + _dagScheduler = null + } + if (_listenerBusStarted) { + listenerBus.stop() + _listenerBusStarted = false + } + _eventLogger.foreach(_.stop()) + if (env != null && _heartbeatReceiver != null) { + env.rpcEnv.stop(_heartbeatReceiver) + } + _progressBar.foreach(_.stop()) + _taskScheduler = null // TODO: Cache.stop()? - env.stop() - SparkEnv.set(null) + if (_env != null) { + _env.stop() + SparkEnv.set(null) + } SparkContext.clearActiveContext() logInfo("Successfully stopped SparkContext") } @@ -1749,6 +1835,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli } listenerBus.start(this) + _listenerBusStarted = true } /** Post the application start event */ @@ -2152,7 +2239,7 @@ object SparkContext extends Logging { master match { case "local" => val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true) - val backend = new LocalBackend(scheduler, 1) + val backend = new LocalBackend(sc.getConf, scheduler, 1) scheduler.initialize(backend) (backend, scheduler) @@ -2164,7 +2251,7 @@ object SparkContext extends Logging { throw new SparkException(s"Asked to run locally with $threadCount threads") } val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true) - val backend = new LocalBackend(scheduler, threadCount) + val backend = new LocalBackend(sc.getConf, scheduler, threadCount) scheduler.initialize(backend) (backend, scheduler) @@ -2174,7 +2261,7 @@ object SparkContext extends Logging { // local[N, M] means exactly N threads with M failures val threadCount = if (threads == "*") localCpuCount else threads.toInt val scheduler = new TaskSchedulerImpl(sc, maxFailures.toInt, isLocal = true) - val backend = new LocalBackend(scheduler, threadCount) + val backend = new LocalBackend(sc.getConf, scheduler, threadCount) scheduler.initialize(backend) (backend, scheduler) diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala index 516f619529c48..1b5fdeba28ee2 100644 --- a/core/src/main/scala/org/apache/spark/executor/Executor.scala +++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala @@ -21,7 +21,7 @@ import java.io.File import java.lang.management.ManagementFactory import java.net.URL import java.nio.ByteBuffer -import java.util.concurrent.ConcurrentHashMap +import java.util.concurrent.{ConcurrentHashMap, Executors, TimeUnit} import scala.collection.JavaConversions._ import scala.collection.mutable.{ArrayBuffer, HashMap} @@ -60,8 +60,6 @@ private[spark] class Executor( private val conf = env.conf - @volatile private var isStopped = false - // No ip or host:port - just hostname Utils.checkHost(executorHostname, "Expected executed slave to be a hostname") // must not have port specified. @@ -114,6 +112,10 @@ private[spark] class Executor( // Maintains the list of running tasks. private val runningTasks = new ConcurrentHashMap[Long, TaskRunner] + // Executor for the heartbeat task. + private val heartbeater = Executors.newSingleThreadScheduledExecutor( + Utils.namedThreadFactory("driver-heartbeater")) + startDriverHeartbeater() def launchTask( @@ -138,7 +140,8 @@ private[spark] class Executor( def stop(): Unit = { env.metricsSystem.report() env.rpcEnv.stop(executorEndpoint) - isStopped = true + heartbeater.shutdown() + heartbeater.awaitTermination(10, TimeUnit.SECONDS) threadPool.shutdown() if (!isLocal) { env.stop() @@ -432,23 +435,17 @@ private[spark] class Executor( } /** - * Starts a thread to report heartbeat and partial metrics for active tasks to driver. - * This thread stops running when the executor is stopped. + * Schedules a task to report heartbeat and partial metrics for active tasks to driver. */ private def startDriverHeartbeater(): Unit = { val intervalMs = conf.getTimeAsMs("spark.executor.heartbeatInterval", "10s") - val thread = new Thread() { - override def run() { - // Sleep a random interval so the heartbeats don't end up in sync - Thread.sleep(intervalMs + (math.random * intervalMs).asInstanceOf[Int]) - while (!isStopped) { - reportHeartBeat() - Thread.sleep(intervalMs) - } - } + + // Wait a random interval so the heartbeats don't end up in sync + val initialDelay = intervalMs + (math.random * intervalMs).asInstanceOf[Int] + + val heartbeatTask = new Runnable() { + override def run(): Unit = Utils.logUncaughtExceptions(reportHeartBeat()) } - thread.setDaemon(true) - thread.setName("driver-heartbeater") - thread.start() + heartbeater.scheduleAtFixedRate(heartbeatTask, initialDelay, intervalMs, TimeUnit.MILLISECONDS) } } diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala index 8e3c30fc3d781..5a74c13b38bf7 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala @@ -86,11 +86,11 @@ private[nio] class ConnectionManager( conf.get("spark.network.timeout", "120s")) // Get the thread counts from the Spark Configuration. - // + // // Even though the ThreadPoolExecutor constructor takes both a minimum and maximum value, // we only query for the minimum value because we are using LinkedBlockingDeque. - // - // The JavaDoc for ThreadPoolExecutor points out that when using a LinkedBlockingDeque (which is + // + // The JavaDoc for ThreadPoolExecutor points out that when using a LinkedBlockingDeque (which is // an unbounded queue) no more than corePoolSize threads will ever be created, so only the "min" // parameter is necessary. private val handlerThreadCount = conf.getInt("spark.core.connection.handler.threads.min", 20) @@ -989,6 +989,7 @@ private[nio] class ConnectionManager( def stop() { ackTimeoutMonitor.stop() + selector.wakeup() selectorThread.interrupt() selectorThread.join() selector.close() diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala index ecc8bf189986d..13a52d836f32f 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala @@ -142,11 +142,10 @@ private[spark] class TaskSchedulerImpl( if (!isLocal && conf.getBoolean("spark.speculation", false)) { logInfo("Starting speculative execution thread") - import sc.env.actorSystem.dispatcher sc.env.actorSystem.scheduler.schedule(SPECULATION_INTERVAL_MS milliseconds, SPECULATION_INTERVAL_MS milliseconds) { Utils.tryOrStopSparkContext(sc) { checkSpeculatableTasks() } - } + }(sc.env.actorSystem.dispatcher) } } diff --git a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala index 70a477a6895cc..50ba0b9d5a612 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala @@ -20,12 +20,12 @@ package org.apache.spark.scheduler.local import java.nio.ByteBuffer import java.util.concurrent.{Executors, TimeUnit} -import org.apache.spark.rpc.{ThreadSafeRpcEndpoint, RpcEndpointRef, RpcEnv} -import org.apache.spark.util.Utils -import org.apache.spark.{Logging, SparkContext, SparkEnv, TaskState} +import org.apache.spark.{Logging, SparkConf, SparkContext, SparkEnv, TaskState} import org.apache.spark.TaskState.TaskState import org.apache.spark.executor.{Executor, ExecutorBackend} +import org.apache.spark.rpc.{ThreadSafeRpcEndpoint, RpcCallContext, RpcEndpointRef, RpcEnv} import org.apache.spark.scheduler.{SchedulerBackend, TaskSchedulerImpl, WorkerOffer} +import org.apache.spark.util.Utils private case class ReviveOffers() @@ -71,11 +71,15 @@ private[spark] class LocalEndpoint( case KillTask(taskId, interruptThread) => executor.killTask(taskId, interruptThread) + } + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { case StopExecutor => executor.stop() + context.reply(true) } + def reviveOffers() { val offers = Seq(new WorkerOffer(localExecutorId, localExecutorHostname, freeCores)) val tasks = scheduler.resourceOffers(offers).flatten @@ -104,8 +108,11 @@ private[spark] class LocalEndpoint( * master all run in the same JVM. It sits behind a TaskSchedulerImpl and handles launching tasks * on a single Executor (created by the LocalBackend) running locally. */ -private[spark] class LocalBackend(scheduler: TaskSchedulerImpl, val totalCores: Int) - extends SchedulerBackend with ExecutorBackend { +private[spark] class LocalBackend( + conf: SparkConf, + scheduler: TaskSchedulerImpl, + val totalCores: Int) + extends SchedulerBackend with ExecutorBackend with Logging { private val appId = "local-" + System.currentTimeMillis var localEndpoint: RpcEndpointRef = null @@ -116,7 +123,7 @@ private[spark] class LocalBackend(scheduler: TaskSchedulerImpl, val totalCores: } override def stop() { - localEndpoint.send(StopExecutor) + localEndpoint.sendWithReply(StopExecutor) } override def reviveOffers() { diff --git a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala index 6b3049b28cd5e..22acc270b983e 100644 --- a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala @@ -56,19 +56,13 @@ class ExecutorAllocationManagerSuite extends FunSuite with LocalSparkContext wit // Min < 0 val conf1 = conf.clone().set("spark.dynamicAllocation.minExecutors", "-1") intercept[SparkException] { contexts += new SparkContext(conf1) } - SparkEnv.get.stop() - SparkContext.clearActiveContext() // Max < 0 val conf2 = conf.clone().set("spark.dynamicAllocation.maxExecutors", "-1") intercept[SparkException] { contexts += new SparkContext(conf2) } - SparkEnv.get.stop() - SparkContext.clearActiveContext() // Both min and max, but min > max intercept[SparkException] { createSparkContext(2, 1) } - SparkEnv.get.stop() - SparkContext.clearActiveContext() // Both min and max, and min == max val sc1 = createSparkContext(1, 1) From 3ae37b93a7c299bd8b22a36248035bca5de3422f Mon Sep 17 00:00:00 2001 From: Jin Adachi Date: Thu, 16 Apr 2015 23:41:04 +0800 Subject: [PATCH 309/591] [SPARK-6694][SQL]SparkSQL CLI must be able to specify an option --database on the command line. SparkSQL CLI has an option --database as follows. But, the option --database is ignored. ``` $ spark-sql --help : CLI options: : --database Specify the database to use ``` Author: Jin Adachi Author: adachij Closes #5345 from adachij2002/SPARK-6694 and squashes the following commits: 8659084 [Jin Adachi] Merge branch 'master' of https://github.com/apache/spark into SPARK-6694 0301eb9 [Jin Adachi] Merge branch 'master' of https://github.com/apache/spark into SPARK-6694 df81086 [Jin Adachi] Modify code style. 846f83e [Jin Adachi] Merge branch 'master' of https://github.com/apache/spark into SPARK-6694 dbe8c63 [Jin Adachi] Change file permission to 644. 7b58f42 [Jin Adachi] Merge branch 'master' of https://github.com/apache/spark into SPARK-6694 c581d06 [Jin Adachi] Add an option --database test db56122 [Jin Adachi] Merge branch 'SPARK-6694' of https://github.com/adachij2002/spark into SPARK-6694 ee09fa5 [adachij] Merge branch 'master' into SPARK-6694 c804c03 [adachij] SparkSQL CLI must be able to specify an option --database on the command line. --- .../hive/thriftserver/SparkSQLCLIDriver.scala | 3 ++ .../sql/hive/thriftserver/CliSuite.scala | 45 +++++++++++++++---- 2 files changed, 39 insertions(+), 9 deletions(-) diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala index 62c061bef690a..85281c6d73a3b 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala @@ -145,6 +145,9 @@ private[hive] object SparkSQLCLIDriver { case e: UnsupportedEncodingException => System.exit(3) } + // use the specified database if specified + cli.processSelectDatabase(sessionState); + // Execute -i init files (always in silent mode) cli.processInitFiles(sessionState) diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala index 6d1d7c3a4e698..b070fa8eaa469 100644 --- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala +++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala @@ -25,22 +25,31 @@ import scala.concurrent.{Await, Promise} import scala.sys.process.{Process, ProcessLogger} import org.apache.hadoop.hive.conf.HiveConf.ConfVars -import org.scalatest.{BeforeAndAfterAll, FunSuite} +import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll, FunSuite} import org.apache.spark.Logging import org.apache.spark.util.Utils -class CliSuite extends FunSuite with BeforeAndAfterAll with Logging { +class CliSuite extends FunSuite with BeforeAndAfter with Logging { + val warehousePath = Utils.createTempDir() + val metastorePath = Utils.createTempDir() + + before { + warehousePath.delete() + metastorePath.delete() + } + + after { + warehousePath.delete() + metastorePath.delete() + } + def runCliWithin( timeout: FiniteDuration, extraArgs: Seq[String] = Seq.empty)( - queriesAndExpectedAnswers: (String, String)*) { + queriesAndExpectedAnswers: (String, String)*): Unit = { val (queries, expectedAnswers) = queriesAndExpectedAnswers.unzip - val warehousePath = Utils.createTempDir() - warehousePath.delete() - val metastorePath = Utils.createTempDir() - metastorePath.delete() val cliScript = "../../bin/spark-sql".split("/").mkString(File.separator) val command = { @@ -95,8 +104,6 @@ class CliSuite extends FunSuite with BeforeAndAfterAll with Logging { """.stripMargin, cause) throw cause } finally { - warehousePath.delete() - metastorePath.delete() process.destroy() } } @@ -124,4 +131,24 @@ class CliSuite extends FunSuite with BeforeAndAfterAll with Logging { test("Single command with -e") { runCliWithin(1.minute, Seq("-e", "SHOW DATABASES;"))("" -> "OK") } + + test("Single command with --database") { + runCliWithin(1.minute)( + "CREATE DATABASE hive_test_db;" + -> "OK", + "USE hive_test_db;" + -> "OK", + "CREATE TABLE hive_test(key INT, val STRING);" + -> "OK", + "SHOW TABLES;" + -> "Time taken: " + ) + + runCliWithin(1.minute, Seq("--database", "hive_test_db", "-e", "SHOW TABLES;"))( + "" + -> "OK", + "" + -> "hive_test" + ) + } } From ef3fb801ae971656ed9cd1b0ab95bc5a1548adbd Mon Sep 17 00:00:00 2001 From: zsxwing Date: Thu, 16 Apr 2015 13:45:55 -0500 Subject: [PATCH 310/591] [SPARK-6934][Core] Use 'spark.akka.askTimeout' for the ask timeout Fixed my mistake in #4588 Author: zsxwing Closes #5529 from zsxwing/SPARK-6934 and squashes the following commits: 9890b2d [zsxwing] Use 'spark.akka.askTimeout' for the ask timeout --- core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala index e259867c14040..f2c1c86af767e 100644 --- a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala +++ b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala @@ -284,7 +284,7 @@ private[spark] abstract class RpcEndpointRef(@transient conf: SparkConf) private[this] val maxRetries = conf.getInt("spark.akka.num.retries", 3) private[this] val retryWaitMs = conf.getLong("spark.akka.retry.wait", 3000) - private[this] val defaultTimeout = conf.getLong("spark.akka.lookupTimeout", 30) seconds + private[this] val defaultAskTimeout = conf.getLong("spark.akka.askTimeout", 30) seconds /** * return the address for the [[RpcEndpointRef]] @@ -304,7 +304,8 @@ private[spark] abstract class RpcEndpointRef(@transient conf: SparkConf) * * This method only sends the message once and never retries. */ - def sendWithReply[T: ClassTag](message: Any): Future[T] = sendWithReply(message, defaultTimeout) + def sendWithReply[T: ClassTag](message: Any): Future[T] = + sendWithReply(message, defaultAskTimeout) /** * Send a message to the corresponding [[RpcEndpoint.receiveAndReply)]] and return a `Future` to @@ -327,7 +328,7 @@ private[spark] abstract class RpcEndpointRef(@transient conf: SparkConf) * @tparam T type of the reply message * @return the reply message from the corresponding [[RpcEndpoint]] */ - def askWithReply[T: ClassTag](message: Any): T = askWithReply(message, defaultTimeout) + def askWithReply[T: ClassTag](message: Any): T = askWithReply(message, defaultAskTimeout) /** * Send a message to the corresponding [[RpcEndpoint.receive]] and get its result within a From 55f553a979db925aa0c3559f7e80b99d2bf3feb4 Mon Sep 17 00:00:00 2001 From: Shivaram Venkataraman Date: Thu, 16 Apr 2015 13:06:34 -0700 Subject: [PATCH 311/591] [SPARK-6855] [SPARKR] Set R includes to get the right collate order. This prevents tools like devtools::document creating invalid collate orders Author: Shivaram Venkataraman Closes #5462 from shivaram/collate-order and squashes the following commits: f3db562 [Shivaram Venkataraman] Set R includes to get the right collate order. This prevents tools like devtools::document creating invalid collate orders --- R/pkg/DESCRIPTION | 6 +++--- R/pkg/R/DataFrame.R | 2 +- R/pkg/R/column.R | 2 +- R/pkg/R/group.R | 3 +++ R/pkg/R/jobj.R | 3 +++ R/pkg/R/pairRDD.R | 2 ++ 6 files changed, 13 insertions(+), 5 deletions(-) diff --git a/R/pkg/DESCRIPTION b/R/pkg/DESCRIPTION index 1842b97d43651..052f68c6c24e2 100644 --- a/R/pkg/DESCRIPTION +++ b/R/pkg/DESCRIPTION @@ -17,19 +17,19 @@ License: Apache License (== 2.0) Collate: 'generics.R' 'jobj.R' - 'SQLTypes.R' 'RDD.R' 'pairRDD.R' + 'SQLTypes.R' 'column.R' 'group.R' 'DataFrame.R' 'SQLContext.R' + 'backend.R' 'broadcast.R' + 'client.R' 'context.R' 'deserialize.R' 'serialize.R' 'sparkR.R' - 'backend.R' - 'client.R' 'utils.R' 'zzz.R' diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R index feafd56909a67..044fdb4d01223 100644 --- a/R/pkg/R/DataFrame.R +++ b/R/pkg/R/DataFrame.R @@ -17,7 +17,7 @@ # DataFrame.R - DataFrame class and methods implemented in S4 OO classes -#' @include jobj.R SQLTypes.R RDD.R pairRDD.R column.R group.R +#' @include generics.R jobj.R SQLTypes.R RDD.R pairRDD.R column.R group.R NULL setOldClass("jobj") diff --git a/R/pkg/R/column.R b/R/pkg/R/column.R index e196305186b9a..b282001d8b6b5 100644 --- a/R/pkg/R/column.R +++ b/R/pkg/R/column.R @@ -17,7 +17,7 @@ # Column Class -#' @include generics.R jobj.R +#' @include generics.R jobj.R SQLTypes.R NULL setOldClass("jobj") diff --git a/R/pkg/R/group.R b/R/pkg/R/group.R index 09fc0a7abe48a..855fbdfc7c4ca 100644 --- a/R/pkg/R/group.R +++ b/R/pkg/R/group.R @@ -17,6 +17,9 @@ # group.R - GroupedData class and methods implemented in S4 OO classes +#' @include generics.R jobj.R SQLTypes.R column.R +NULL + setOldClass("jobj") #' @title S4 class that represents a GroupedData diff --git a/R/pkg/R/jobj.R b/R/pkg/R/jobj.R index 4180f146b7fbc..a8a25230b636d 100644 --- a/R/pkg/R/jobj.R +++ b/R/pkg/R/jobj.R @@ -18,6 +18,9 @@ # References to objects that exist on the JVM backend # are maintained using the jobj. +#' @include generics.R +NULL + # Maintain a reference count of Java object references # This allows us to GC the java object when it is safe .validJobjs <- new.env(parent = emptyenv()) diff --git a/R/pkg/R/pairRDD.R b/R/pkg/R/pairRDD.R index 739d399f0820f..5d64822859d1f 100644 --- a/R/pkg/R/pairRDD.R +++ b/R/pkg/R/pairRDD.R @@ -16,6 +16,8 @@ # # Operations supported on RDDs contains pairs (i.e key, value) +#' @include generics.R jobj.R RDD.R +NULL ############ Actions and Transformations ############ From 04e44b37cc04f62fbf9e08c7076349e0a4d12ea8 Mon Sep 17 00:00:00 2001 From: Davies Liu Date: Thu, 16 Apr 2015 16:20:57 -0700 Subject: [PATCH 312/591] [SPARK-4897] [PySpark] Python 3 support This PR update PySpark to support Python 3 (tested with 3.4). Known issue: unpickle array from Pyrolite is broken in Python 3, those tests are skipped. TODO: ec2/spark-ec2.py is not fully tested with python3. Author: Davies Liu Author: twneale Author: Josh Rosen Closes #5173 from davies/python3 and squashes the following commits: d7d6323 [Davies Liu] fix tests 6c52a98 [Davies Liu] fix mllib test 99e334f [Davies Liu] update timeout b716610 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 cafd5ec [Davies Liu] adddress comments from @mengxr bf225d7 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 179fc8d [Davies Liu] tuning flaky tests 8c8b957 [Davies Liu] fix ResourceWarning in Python 3 5c57c95 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 4006829 [Davies Liu] fix test 2fc0066 [Davies Liu] add python3 path 71535e9 [Davies Liu] fix xrange and divide 5a55ab4 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 125f12c [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 ed498c8 [Davies Liu] fix compatibility with python 3 820e649 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 e8ce8c9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 ad7c374 [Davies Liu] fix mllib test and warning ef1fc2f [Davies Liu] fix tests 4eee14a [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 20112ff [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 59bb492 [Davies Liu] fix tests 1da268c [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 ca0fdd3 [Davies Liu] fix code style 9563a15 [Davies Liu] add imap back for python 2 0b1ec04 [Davies Liu] make python examples work with Python 3 d2fd566 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 a716d34 [Davies Liu] test with python 3.4 f1700e8 [Davies Liu] fix test in python3 671b1db [Davies Liu] fix test in python3 692ff47 [Davies Liu] fix flaky test 7b9699f [Davies Liu] invalidate import cache for Python 3.3+ 9c58497 [Davies Liu] fix kill worker 309bfbf [Davies Liu] keep compatibility 5707476 [Davies Liu] cleanup, fix hash of string in 3.3+ 8662d5b [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3 f53e1f0 [Davies Liu] fix tests 70b6b73 [Davies Liu] compile ec2/spark_ec2.py in python 3 a39167e [Davies Liu] support customize class in __main__ 814c77b [Davies Liu] run unittests with python 3 7f4476e [Davies Liu] mllib tests passed d737924 [Davies Liu] pass ml tests 375ea17 [Davies Liu] SQL tests pass 6cc42a9 [Davies Liu] rename 431a8de [Davies Liu] streaming tests pass 78901a7 [Davies Liu] fix hash of serializer in Python 3 24b2f2e [Davies Liu] pass all RDD tests 35f48fe [Davies Liu] run future again 1eebac2 [Davies Liu] fix conflict in ec2/spark_ec2.py 6e3c21d [Davies Liu] make cloudpickle work with Python3 2fb2db3 [Josh Rosen] Guard more changes behind sys.version; still doesn't run 1aa5e8f [twneale] Turned out `pickle.DictionaryType is dict` == True, so swapped it out 7354371 [twneale] buffer --> memoryview I'm not super sure if this a valid change, but the 2.7 docs recommend using memoryview over buffer where possible, so hoping it'll work. b69ccdf [twneale] Uses the pure python pickle._Pickler instead of c-extension _pickle.Pickler. It appears pyspark 2.7 uses the pure python pickler as well, so this shouldn't degrade pickling performance (?). f40d925 [twneale] xrange --> range e104215 [twneale] Replaces 2.7 types.InstsanceType with 3.4 `object`....could be horribly wrong depending on how types.InstanceType is used elsewhere in the package--see http://bugs.python.org/issue8206 79de9d0 [twneale] Replaces python2.7 `file` with 3.4 _io.TextIOWrapper 2adb42d [Josh Rosen] Fix up some import differences between Python 2 and 3 854be27 [Josh Rosen] Run `futurize` on Python code: 7c5b4ce [Josh Rosen] Remove Python 3 check in shell.py. --- bin/pyspark | 1 + bin/spark-submit | 3 + bin/spark-submit2.cmd | 3 + dev/run-tests | 2 + dev/run-tests-jenkins | 2 +- ec2/spark_ec2.py | 262 ++++---- examples/src/main/python/als.py | 15 +- examples/src/main/python/avro_inputformat.py | 9 +- .../src/main/python/cassandra_inputformat.py | 8 +- .../src/main/python/cassandra_outputformat.py | 6 +- examples/src/main/python/hbase_inputformat.py | 8 +- .../src/main/python/hbase_outputformat.py | 6 +- examples/src/main/python/kmeans.py | 11 +- .../src/main/python/logistic_regression.py | 20 +- .../ml/simple_text_classification_pipeline.py | 20 +- .../src/main/python/mllib/correlations.py | 19 +- .../src/main/python/mllib/dataset_example.py | 13 +- .../main/python/mllib/decision_tree_runner.py | 29 +- .../python/mllib/gaussian_mixture_model.py | 9 +- .../python/mllib/gradient_boosted_trees.py | 7 +- examples/src/main/python/mllib/kmeans.py | 5 +- .../main/python/mllib/logistic_regression.py | 9 +- .../python/mllib/random_forest_example.py | 9 +- .../python/mllib/random_rdd_generation.py | 21 +- .../src/main/python/mllib/sampled_rdds.py | 29 +- examples/src/main/python/mllib/word2vec.py | 5 +- examples/src/main/python/pagerank.py | 16 +- .../src/main/python/parquet_inputformat.py | 7 +- examples/src/main/python/pi.py | 5 +- examples/src/main/python/sort.py | 6 +- examples/src/main/python/sql.py | 4 +- examples/src/main/python/status_api_demo.py | 10 +- .../main/python/streaming/hdfs_wordcount.py | 3 +- .../main/python/streaming/kafka_wordcount.py | 3 +- .../python/streaming/network_wordcount.py | 3 +- .../recoverable_network_wordcount.py | 11 +- .../python/streaming/sql_network_wordcount.py | 5 +- .../streaming/stateful_network_wordcount.py | 3 +- .../src/main/python/transitive_closure.py | 10 +- examples/src/main/python/wordcount.py | 6 +- .../MatrixFactorizationModelWrapper.scala | 9 +- .../mllib/api/python/PythonMLLibAPI.scala | 39 +- python/pyspark/accumulators.py | 9 +- python/pyspark/broadcast.py | 37 +- python/pyspark/cloudpickle.py | 577 +++++------------- python/pyspark/conf.py | 9 +- python/pyspark/context.py | 42 +- python/pyspark/daemon.py | 36 +- python/pyspark/heapq3.py | 24 +- python/pyspark/java_gateway.py | 2 +- python/pyspark/join.py | 1 + python/pyspark/ml/classification.py | 4 +- python/pyspark/ml/feature.py | 22 +- python/pyspark/ml/param/__init__.py | 8 +- .../ml/param/_shared_params_code_gen.py | 10 +- python/pyspark/mllib/__init__.py | 11 +- python/pyspark/mllib/classification.py | 7 +- python/pyspark/mllib/clustering.py | 18 +- python/pyspark/mllib/common.py | 19 +- python/pyspark/mllib/feature.py | 18 +- python/pyspark/mllib/fpm.py | 2 + python/pyspark/mllib/linalg.py | 48 +- python/pyspark/mllib/rand.py | 33 +- python/pyspark/mllib/recommendation.py | 7 +- python/pyspark/mllib/stat/_statistics.py | 25 +- python/pyspark/mllib/tests.py | 20 +- python/pyspark/mllib/tree.py | 15 +- python/pyspark/mllib/util.py | 26 +- python/pyspark/profiler.py | 10 +- python/pyspark/rdd.py | 189 +++--- python/pyspark/rddsampler.py | 4 +- python/pyspark/serializers.py | 101 ++- python/pyspark/shell.py | 16 +- python/pyspark/shuffle.py | 126 ++-- python/pyspark/sql/__init__.py | 15 +- python/pyspark/sql/{types.py => _types.py} | 49 +- python/pyspark/sql/context.py | 32 +- python/pyspark/sql/dataframe.py | 63 +- python/pyspark/sql/functions.py | 6 +- python/pyspark/sql/tests.py | 11 +- python/pyspark/statcounter.py | 4 +- python/pyspark/streaming/context.py | 5 +- python/pyspark/streaming/dstream.py | 51 +- python/pyspark/streaming/kafka.py | 8 +- python/pyspark/streaming/tests.py | 39 +- python/pyspark/streaming/util.py | 6 +- python/pyspark/tests.py | 327 +++++----- python/pyspark/worker.py | 16 +- python/run-tests | 15 +- python/test_support/userlib-0.1-py2.7.egg | Bin 1945 -> 0 bytes python/test_support/userlib-0.1.zip | Bin 0 -> 668 bytes 91 files changed, 1398 insertions(+), 1396 deletions(-) rename python/pyspark/sql/{types.py => _types.py} (97%) delete mode 100644 python/test_support/userlib-0.1-py2.7.egg create mode 100644 python/test_support/userlib-0.1.zip diff --git a/bin/pyspark b/bin/pyspark index 776b28dc41099..8acad6113797d 100755 --- a/bin/pyspark +++ b/bin/pyspark @@ -89,6 +89,7 @@ export PYTHONSTARTUP="$SPARK_HOME/python/pyspark/shell.py" if [[ -n "$SPARK_TESTING" ]]; then unset YARN_CONF_DIR unset HADOOP_CONF_DIR + export PYTHONHASHSEED=0 if [[ -n "$PYSPARK_DOC_TEST" ]]; then exec "$PYSPARK_DRIVER_PYTHON" -m doctest $1 else diff --git a/bin/spark-submit b/bin/spark-submit index bcff78edd51ca..0e0afe71a0f05 100755 --- a/bin/spark-submit +++ b/bin/spark-submit @@ -19,6 +19,9 @@ SPARK_HOME="$(cd "`dirname "$0"`"/..; pwd)" +# disable randomized hash for string in Python 3.3+ +export PYTHONHASHSEED=0 + # Only define a usage function if an upstream script hasn't done so. if ! type -t usage >/dev/null 2>&1; then usage() { diff --git a/bin/spark-submit2.cmd b/bin/spark-submit2.cmd index 08ddb185742d2..d3fc4a5cc3f6e 100644 --- a/bin/spark-submit2.cmd +++ b/bin/spark-submit2.cmd @@ -20,6 +20,9 @@ rem rem This is the entry point for running Spark submit. To avoid polluting the rem environment, it just launches a new cmd to do the real work. +rem disable randomized hash for string in Python 3.3+ +set PYTHONHASHSEED=0 + set CLASS=org.apache.spark.deploy.SparkSubmit call %~dp0spark-class2.cmd %CLASS% %* set SPARK_ERROR_LEVEL=%ERRORLEVEL% diff --git a/dev/run-tests b/dev/run-tests index bb21ab6c9aa04..861d1671182c2 100755 --- a/dev/run-tests +++ b/dev/run-tests @@ -235,6 +235,8 @@ echo "=========================================================================" CURRENT_BLOCK=$BLOCK_PYSPARK_UNIT_TESTS +# add path for python 3 in jenkins +export PATH="${PATH}:/home/anaonda/envs/py3k/bin" ./python/run-tests echo "" diff --git a/dev/run-tests-jenkins b/dev/run-tests-jenkins index 3c1c91a111357..030f2cdddb350 100755 --- a/dev/run-tests-jenkins +++ b/dev/run-tests-jenkins @@ -47,7 +47,7 @@ COMMIT_URL="https://github.com/apache/spark/commit/${ghprbActualCommit}" # GitHub doesn't auto-link short hashes when submitted via the API, unfortunately. :( SHORT_COMMIT_HASH="${ghprbActualCommit:0:7}" -TESTS_TIMEOUT="120m" # format: http://linux.die.net/man/1/timeout +TESTS_TIMEOUT="150m" # format: http://linux.die.net/man/1/timeout # Array to capture all tests to run on the pull request. These tests are held under the #+ dev/tests/ directory. diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py index 0c1f24761d0de..87c0818279713 100755 --- a/ec2/spark_ec2.py +++ b/ec2/spark_ec2.py @@ -19,7 +19,7 @@ # limitations under the License. # -from __future__ import with_statement +from __future__ import with_statement, print_function import hashlib import itertools @@ -37,12 +37,17 @@ import tempfile import textwrap import time -import urllib2 import warnings from datetime import datetime from optparse import OptionParser from sys import stderr +if sys.version < "3": + from urllib2 import urlopen, Request, HTTPError +else: + from urllib.request import urlopen, Request + from urllib.error import HTTPError + SPARK_EC2_VERSION = "1.2.1" SPARK_EC2_DIR = os.path.dirname(os.path.realpath(__file__)) @@ -88,10 +93,10 @@ def setup_external_libs(libs): SPARK_EC2_LIB_DIR = os.path.join(SPARK_EC2_DIR, "lib") if not os.path.exists(SPARK_EC2_LIB_DIR): - print "Downloading external libraries that spark-ec2 needs from PyPI to {path}...".format( + print("Downloading external libraries that spark-ec2 needs from PyPI to {path}...".format( path=SPARK_EC2_LIB_DIR - ) - print "This should be a one-time operation." + )) + print("This should be a one-time operation.") os.mkdir(SPARK_EC2_LIB_DIR) for lib in libs: @@ -100,8 +105,8 @@ def setup_external_libs(libs): if not os.path.isdir(lib_dir): tgz_file_path = os.path.join(SPARK_EC2_LIB_DIR, versioned_lib_name + ".tar.gz") - print " - Downloading {lib}...".format(lib=lib["name"]) - download_stream = urllib2.urlopen( + print(" - Downloading {lib}...".format(lib=lib["name"])) + download_stream = urlopen( "{prefix}/{first_letter}/{lib_name}/{lib_name}-{lib_version}.tar.gz".format( prefix=PYPI_URL_PREFIX, first_letter=lib["name"][:1], @@ -113,13 +118,13 @@ def setup_external_libs(libs): tgz_file.write(download_stream.read()) with open(tgz_file_path) as tar: if hashlib.md5(tar.read()).hexdigest() != lib["md5"]: - print >> stderr, "ERROR: Got wrong md5sum for {lib}.".format(lib=lib["name"]) + print("ERROR: Got wrong md5sum for {lib}.".format(lib=lib["name"]), file=stderr) sys.exit(1) tar = tarfile.open(tgz_file_path) tar.extractall(path=SPARK_EC2_LIB_DIR) tar.close() os.remove(tgz_file_path) - print " - Finished downloading {lib}.".format(lib=lib["name"]) + print(" - Finished downloading {lib}.".format(lib=lib["name"])) sys.path.insert(1, lib_dir) @@ -299,12 +304,12 @@ def parse_args(): if home_dir is None or not os.path.isfile(home_dir + '/.boto'): if not os.path.isfile('/etc/boto.cfg'): if os.getenv('AWS_ACCESS_KEY_ID') is None: - print >> stderr, ("ERROR: The environment variable AWS_ACCESS_KEY_ID " + - "must be set") + print("ERROR: The environment variable AWS_ACCESS_KEY_ID must be set", + file=stderr) sys.exit(1) if os.getenv('AWS_SECRET_ACCESS_KEY') is None: - print >> stderr, ("ERROR: The environment variable AWS_SECRET_ACCESS_KEY " + - "must be set") + print("ERROR: The environment variable AWS_SECRET_ACCESS_KEY must be set", + file=stderr) sys.exit(1) return (opts, action, cluster_name) @@ -316,7 +321,7 @@ def get_or_make_group(conn, name, vpc_id): if len(group) > 0: return group[0] else: - print "Creating security group " + name + print("Creating security group " + name) return conn.create_security_group(name, "Spark EC2 group", vpc_id) @@ -324,18 +329,19 @@ def get_validate_spark_version(version, repo): if "." in version: version = version.replace("v", "") if version not in VALID_SPARK_VERSIONS: - print >> stderr, "Don't know about Spark version: {v}".format(v=version) + print("Don't know about Spark version: {v}".format(v=version), file=stderr) sys.exit(1) return version else: github_commit_url = "{repo}/commit/{commit_hash}".format(repo=repo, commit_hash=version) - request = urllib2.Request(github_commit_url) + request = Request(github_commit_url) request.get_method = lambda: 'HEAD' try: - response = urllib2.urlopen(request) - except urllib2.HTTPError, e: - print >> stderr, "Couldn't validate Spark commit: {url}".format(url=github_commit_url) - print >> stderr, "Received HTTP response code of {code}.".format(code=e.code) + response = urlopen(request) + except HTTPError as e: + print("Couldn't validate Spark commit: {url}".format(url=github_commit_url), + file=stderr) + print("Received HTTP response code of {code}.".format(code=e.code), file=stderr) sys.exit(1) return version @@ -394,8 +400,7 @@ def get_spark_ami(opts): instance_type = EC2_INSTANCE_TYPES[opts.instance_type] else: instance_type = "pvm" - print >> stderr,\ - "Don't recognize %s, assuming type is pvm" % opts.instance_type + print("Don't recognize %s, assuming type is pvm" % opts.instance_type, file=stderr) # URL prefix from which to fetch AMI information ami_prefix = "{r}/{b}/ami-list".format( @@ -404,10 +409,10 @@ def get_spark_ami(opts): ami_path = "%s/%s/%s" % (ami_prefix, opts.region, instance_type) try: - ami = urllib2.urlopen(ami_path).read().strip() - print "Spark AMI: " + ami + ami = urlopen(ami_path).read().strip() + print("Spark AMI: " + ami) except: - print >> stderr, "Could not resolve AMI at: " + ami_path + print("Could not resolve AMI at: " + ami_path, file=stderr) sys.exit(1) return ami @@ -419,11 +424,11 @@ def get_spark_ami(opts): # Fails if there already instances running in the cluster's groups. def launch_cluster(conn, opts, cluster_name): if opts.identity_file is None: - print >> stderr, "ERROR: Must provide an identity file (-i) for ssh connections." + print("ERROR: Must provide an identity file (-i) for ssh connections.", file=stderr) sys.exit(1) if opts.key_pair is None: - print >> stderr, "ERROR: Must provide a key pair name (-k) to use on instances." + print("ERROR: Must provide a key pair name (-k) to use on instances.", file=stderr) sys.exit(1) user_data_content = None @@ -431,7 +436,7 @@ def launch_cluster(conn, opts, cluster_name): with open(opts.user_data) as user_data_file: user_data_content = user_data_file.read() - print "Setting up security groups..." + print("Setting up security groups...") master_group = get_or_make_group(conn, cluster_name + "-master", opts.vpc_id) slave_group = get_or_make_group(conn, cluster_name + "-slaves", opts.vpc_id) authorized_address = opts.authorized_address @@ -497,8 +502,8 @@ def launch_cluster(conn, opts, cluster_name): existing_masters, existing_slaves = get_existing_cluster(conn, opts, cluster_name, die_on_error=False) if existing_slaves or (existing_masters and not opts.use_existing_master): - print >> stderr, ("ERROR: There are already instances running in " + - "group %s or %s" % (master_group.name, slave_group.name)) + print("ERROR: There are already instances running in group %s or %s" % + (master_group.name, slave_group.name), file=stderr) sys.exit(1) # Figure out Spark AMI @@ -511,12 +516,12 @@ def launch_cluster(conn, opts, cluster_name): additional_group_ids = [sg.id for sg in conn.get_all_security_groups() if opts.additional_security_group in (sg.name, sg.id)] - print "Launching instances..." + print("Launching instances...") try: image = conn.get_all_images(image_ids=[opts.ami])[0] except: - print >> stderr, "Could not find AMI " + opts.ami + print("Could not find AMI " + opts.ami, file=stderr) sys.exit(1) # Create block device mapping so that we can add EBS volumes if asked to. @@ -542,8 +547,8 @@ def launch_cluster(conn, opts, cluster_name): # Launch slaves if opts.spot_price is not None: # Launch spot instances with the requested price - print ("Requesting %d slaves as spot instances with price $%.3f" % - (opts.slaves, opts.spot_price)) + print("Requesting %d slaves as spot instances with price $%.3f" % + (opts.slaves, opts.spot_price)) zones = get_zones(conn, opts) num_zones = len(zones) i = 0 @@ -566,7 +571,7 @@ def launch_cluster(conn, opts, cluster_name): my_req_ids += [req.id for req in slave_reqs] i += 1 - print "Waiting for spot instances to be granted..." + print("Waiting for spot instances to be granted...") try: while True: time.sleep(10) @@ -579,24 +584,24 @@ def launch_cluster(conn, opts, cluster_name): if i in id_to_req and id_to_req[i].state == "active": active_instance_ids.append(id_to_req[i].instance_id) if len(active_instance_ids) == opts.slaves: - print "All %d slaves granted" % opts.slaves + print("All %d slaves granted" % opts.slaves) reservations = conn.get_all_reservations(active_instance_ids) slave_nodes = [] for r in reservations: slave_nodes += r.instances break else: - print "%d of %d slaves granted, waiting longer" % ( - len(active_instance_ids), opts.slaves) + print("%d of %d slaves granted, waiting longer" % ( + len(active_instance_ids), opts.slaves)) except: - print "Canceling spot instance requests" + print("Canceling spot instance requests") conn.cancel_spot_instance_requests(my_req_ids) # Log a warning if any of these requests actually launched instances: (master_nodes, slave_nodes) = get_existing_cluster( conn, opts, cluster_name, die_on_error=False) running = len(master_nodes) + len(slave_nodes) if running: - print >> stderr, ("WARNING: %d instances are still running" % running) + print(("WARNING: %d instances are still running" % running), file=stderr) sys.exit(0) else: # Launch non-spot instances @@ -618,16 +623,16 @@ def launch_cluster(conn, opts, cluster_name): placement_group=opts.placement_group, user_data=user_data_content) slave_nodes += slave_res.instances - print "Launched {s} slave{plural_s} in {z}, regid = {r}".format( - s=num_slaves_this_zone, - plural_s=('' if num_slaves_this_zone == 1 else 's'), - z=zone, - r=slave_res.id) + print("Launched {s} slave{plural_s} in {z}, regid = {r}".format( + s=num_slaves_this_zone, + plural_s=('' if num_slaves_this_zone == 1 else 's'), + z=zone, + r=slave_res.id)) i += 1 # Launch or resume masters if existing_masters: - print "Starting master..." + print("Starting master...") for inst in existing_masters: if inst.state not in ["shutting-down", "terminated"]: inst.start() @@ -650,10 +655,10 @@ def launch_cluster(conn, opts, cluster_name): user_data=user_data_content) master_nodes = master_res.instances - print "Launched master in %s, regid = %s" % (zone, master_res.id) + print("Launched master in %s, regid = %s" % (zone, master_res.id)) # This wait time corresponds to SPARK-4983 - print "Waiting for AWS to propagate instance metadata..." + print("Waiting for AWS to propagate instance metadata...") time.sleep(5) # Give the instances descriptive names for master in master_nodes: @@ -674,8 +679,8 @@ def get_existing_cluster(conn, opts, cluster_name, die_on_error=True): Get the EC2 instances in an existing cluster if available. Returns a tuple of lists of EC2 instance objects for the masters and slaves. """ - print "Searching for existing cluster {c} in region {r}...".format( - c=cluster_name, r=opts.region) + print("Searching for existing cluster {c} in region {r}...".format( + c=cluster_name, r=opts.region)) def get_instances(group_names): """ @@ -693,16 +698,15 @@ def get_instances(group_names): slave_instances = get_instances([cluster_name + "-slaves"]) if any((master_instances, slave_instances)): - print "Found {m} master{plural_m}, {s} slave{plural_s}.".format( - m=len(master_instances), - plural_m=('' if len(master_instances) == 1 else 's'), - s=len(slave_instances), - plural_s=('' if len(slave_instances) == 1 else 's')) + print("Found {m} master{plural_m}, {s} slave{plural_s}.".format( + m=len(master_instances), + plural_m=('' if len(master_instances) == 1 else 's'), + s=len(slave_instances), + plural_s=('' if len(slave_instances) == 1 else 's'))) if not master_instances and die_on_error: - print >> sys.stderr, \ - "ERROR: Could not find a master for cluster {c} in region {r}.".format( - c=cluster_name, r=opts.region) + print("ERROR: Could not find a master for cluster {c} in region {r}.".format( + c=cluster_name, r=opts.region), file=sys.stderr) sys.exit(1) return (master_instances, slave_instances) @@ -713,7 +717,7 @@ def get_instances(group_names): def setup_cluster(conn, master_nodes, slave_nodes, opts, deploy_ssh_key): master = get_dns_name(master_nodes[0], opts.private_ips) if deploy_ssh_key: - print "Generating cluster's SSH key on master..." + print("Generating cluster's SSH key on master...") key_setup = """ [ -f ~/.ssh/id_rsa ] || (ssh-keygen -q -t rsa -N '' -f ~/.ssh/id_rsa && @@ -721,10 +725,10 @@ def setup_cluster(conn, master_nodes, slave_nodes, opts, deploy_ssh_key): """ ssh(master, opts, key_setup) dot_ssh_tar = ssh_read(master, opts, ['tar', 'c', '.ssh']) - print "Transferring cluster's SSH key to slaves..." + print("Transferring cluster's SSH key to slaves...") for slave in slave_nodes: slave_address = get_dns_name(slave, opts.private_ips) - print slave_address + print(slave_address) ssh_write(slave_address, opts, ['tar', 'x'], dot_ssh_tar) modules = ['spark', 'ephemeral-hdfs', 'persistent-hdfs', @@ -738,8 +742,8 @@ def setup_cluster(conn, master_nodes, slave_nodes, opts, deploy_ssh_key): # NOTE: We should clone the repository before running deploy_files to # prevent ec2-variables.sh from being overwritten - print "Cloning spark-ec2 scripts from {r}/tree/{b} on master...".format( - r=opts.spark_ec2_git_repo, b=opts.spark_ec2_git_branch) + print("Cloning spark-ec2 scripts from {r}/tree/{b} on master...".format( + r=opts.spark_ec2_git_repo, b=opts.spark_ec2_git_branch)) ssh( host=master, opts=opts, @@ -749,7 +753,7 @@ def setup_cluster(conn, master_nodes, slave_nodes, opts, deploy_ssh_key): b=opts.spark_ec2_git_branch) ) - print "Deploying files to master..." + print("Deploying files to master...") deploy_files( conn=conn, root_dir=SPARK_EC2_DIR + "/" + "deploy.generic", @@ -760,25 +764,25 @@ def setup_cluster(conn, master_nodes, slave_nodes, opts, deploy_ssh_key): ) if opts.deploy_root_dir is not None: - print "Deploying {s} to master...".format(s=opts.deploy_root_dir) + print("Deploying {s} to master...".format(s=opts.deploy_root_dir)) deploy_user_files( root_dir=opts.deploy_root_dir, opts=opts, master_nodes=master_nodes ) - print "Running setup on master..." + print("Running setup on master...") setup_spark_cluster(master, opts) - print "Done!" + print("Done!") def setup_spark_cluster(master, opts): ssh(master, opts, "chmod u+x spark-ec2/setup.sh") ssh(master, opts, "spark-ec2/setup.sh") - print "Spark standalone cluster started at http://%s:8080" % master + print("Spark standalone cluster started at http://%s:8080" % master) if opts.ganglia: - print "Ganglia started at http://%s:5080/ganglia" % master + print("Ganglia started at http://%s:5080/ganglia" % master) def is_ssh_available(host, opts, print_ssh_output=True): @@ -795,7 +799,7 @@ def is_ssh_available(host, opts, print_ssh_output=True): if s.returncode != 0 and print_ssh_output: # extra leading newline is for spacing in wait_for_cluster_state() - print textwrap.dedent("""\n + print(textwrap.dedent("""\n Warning: SSH connection error. (This could be temporary.) Host: {h} SSH return code: {r} @@ -804,7 +808,7 @@ def is_ssh_available(host, opts, print_ssh_output=True): h=host, r=s.returncode, o=cmd_output.strip() - ) + )) return s.returncode == 0 @@ -865,10 +869,10 @@ def wait_for_cluster_state(conn, opts, cluster_instances, cluster_state): sys.stdout.write("\n") end_time = datetime.now() - print "Cluster is now in '{s}' state. Waited {t} seconds.".format( + print("Cluster is now in '{s}' state. Waited {t} seconds.".format( s=cluster_state, t=(end_time - start_time).seconds - ) + )) # Get number of local disks available for a given EC2 instance type. @@ -916,8 +920,8 @@ def get_num_disks(instance_type): if instance_type in disks_by_instance: return disks_by_instance[instance_type] else: - print >> stderr, ("WARNING: Don't know number of disks on instance type %s; assuming 1" - % instance_type) + print("WARNING: Don't know number of disks on instance type %s; assuming 1" + % instance_type, file=stderr) return 1 @@ -951,7 +955,7 @@ def deploy_files(conn, root_dir, opts, master_nodes, slave_nodes, modules): # Spark-only custom deploy spark_v = "%s|%s" % (opts.spark_git_repo, opts.spark_version) tachyon_v = "" - print "Deploying Spark via git hash; Tachyon won't be set up" + print("Deploying Spark via git hash; Tachyon won't be set up") modules = filter(lambda x: x != "tachyon", modules) master_addresses = [get_dns_name(i, opts.private_ips) for i in master_nodes] @@ -1067,8 +1071,8 @@ def ssh(host, opts, command): "--key-pair parameters and try again.".format(host)) else: raise e - print >> stderr, \ - "Error executing remote command, retrying after 30 seconds: {0}".format(e) + print("Error executing remote command, retrying after 30 seconds: {0}".format(e), + file=stderr) time.sleep(30) tries = tries + 1 @@ -1107,8 +1111,8 @@ def ssh_write(host, opts, command, arguments): elif tries > 5: raise RuntimeError("ssh_write failed with error %s" % proc.returncode) else: - print >> stderr, \ - "Error {0} while executing remote command, retrying after 30 seconds".format(status) + print("Error {0} while executing remote command, retrying after 30 seconds". + format(status), file=stderr) time.sleep(30) tries = tries + 1 @@ -1162,42 +1166,41 @@ def real_main(): if opts.identity_file is not None: if not os.path.exists(opts.identity_file): - print >> stderr,\ - "ERROR: The identity file '{f}' doesn't exist.".format(f=opts.identity_file) + print("ERROR: The identity file '{f}' doesn't exist.".format(f=opts.identity_file), + file=stderr) sys.exit(1) file_mode = os.stat(opts.identity_file).st_mode if not (file_mode & S_IRUSR) or not oct(file_mode)[-2:] == '00': - print >> stderr, "ERROR: The identity file must be accessible only by you." - print >> stderr, 'You can fix this with: chmod 400 "{f}"'.format(f=opts.identity_file) + print("ERROR: The identity file must be accessible only by you.", file=stderr) + print('You can fix this with: chmod 400 "{f}"'.format(f=opts.identity_file), + file=stderr) sys.exit(1) if opts.instance_type not in EC2_INSTANCE_TYPES: - print >> stderr, "Warning: Unrecognized EC2 instance type for instance-type: {t}".format( - t=opts.instance_type) + print("Warning: Unrecognized EC2 instance type for instance-type: {t}".format( + t=opts.instance_type), file=stderr) if opts.master_instance_type != "": if opts.master_instance_type not in EC2_INSTANCE_TYPES: - print >> stderr, \ - "Warning: Unrecognized EC2 instance type for master-instance-type: {t}".format( - t=opts.master_instance_type) + print("Warning: Unrecognized EC2 instance type for master-instance-type: {t}".format( + t=opts.master_instance_type), file=stderr) # Since we try instance types even if we can't resolve them, we check if they resolve first # and, if they do, see if they resolve to the same virtualization type. if opts.instance_type in EC2_INSTANCE_TYPES and \ opts.master_instance_type in EC2_INSTANCE_TYPES: if EC2_INSTANCE_TYPES[opts.instance_type] != \ EC2_INSTANCE_TYPES[opts.master_instance_type]: - print >> stderr, \ - "Error: spark-ec2 currently does not support having a master and slaves " + \ - "with different AMI virtualization types." - print >> stderr, "master instance virtualization type: {t}".format( - t=EC2_INSTANCE_TYPES[opts.master_instance_type]) - print >> stderr, "slave instance virtualization type: {t}".format( - t=EC2_INSTANCE_TYPES[opts.instance_type]) + print("Error: spark-ec2 currently does not support having a master and slaves " + "with different AMI virtualization types.", file=stderr) + print("master instance virtualization type: {t}".format( + t=EC2_INSTANCE_TYPES[opts.master_instance_type]), file=stderr) + print("slave instance virtualization type: {t}".format( + t=EC2_INSTANCE_TYPES[opts.instance_type]), file=stderr) sys.exit(1) if opts.ebs_vol_num > 8: - print >> stderr, "ebs-vol-num cannot be greater than 8" + print("ebs-vol-num cannot be greater than 8", file=stderr) sys.exit(1) # Prevent breaking ami_prefix (/, .git and startswith checks) @@ -1206,23 +1209,22 @@ def real_main(): opts.spark_ec2_git_repo.endswith(".git") or \ not opts.spark_ec2_git_repo.startswith("https://github.com") or \ not opts.spark_ec2_git_repo.endswith("spark-ec2"): - print >> stderr, "spark-ec2-git-repo must be a github repo and it must not have a " \ - "trailing / or .git. " \ - "Furthermore, we currently only support forks named spark-ec2." + print("spark-ec2-git-repo must be a github repo and it must not have a trailing / or .git. " + "Furthermore, we currently only support forks named spark-ec2.", file=stderr) sys.exit(1) if not (opts.deploy_root_dir is None or (os.path.isabs(opts.deploy_root_dir) and os.path.isdir(opts.deploy_root_dir) and os.path.exists(opts.deploy_root_dir))): - print >> stderr, "--deploy-root-dir must be an absolute path to a directory that exists " \ - "on the local file system" + print("--deploy-root-dir must be an absolute path to a directory that exists " + "on the local file system", file=stderr) sys.exit(1) try: conn = ec2.connect_to_region(opts.region) except Exception as e: - print >> stderr, (e) + print((e), file=stderr) sys.exit(1) # Select an AZ at random if it was not specified. @@ -1231,7 +1233,7 @@ def real_main(): if action == "launch": if opts.slaves <= 0: - print >> sys.stderr, "ERROR: You have to start at least 1 slave" + print("ERROR: You have to start at least 1 slave", file=sys.stderr) sys.exit(1) if opts.resume: (master_nodes, slave_nodes) = get_existing_cluster(conn, opts, cluster_name) @@ -1250,18 +1252,18 @@ def real_main(): conn, opts, cluster_name, die_on_error=False) if any(master_nodes + slave_nodes): - print "The following instances will be terminated:" + print("The following instances will be terminated:") for inst in master_nodes + slave_nodes: - print "> %s" % get_dns_name(inst, opts.private_ips) - print "ALL DATA ON ALL NODES WILL BE LOST!!" + print("> %s" % get_dns_name(inst, opts.private_ips)) + print("ALL DATA ON ALL NODES WILL BE LOST!!") msg = "Are you sure you want to destroy the cluster {c}? (y/N) ".format(c=cluster_name) response = raw_input(msg) if response == "y": - print "Terminating master..." + print("Terminating master...") for inst in master_nodes: inst.terminate() - print "Terminating slaves..." + print("Terminating slaves...") for inst in slave_nodes: inst.terminate() @@ -1274,16 +1276,16 @@ def real_main(): cluster_instances=(master_nodes + slave_nodes), cluster_state='terminated' ) - print "Deleting security groups (this will take some time)..." + print("Deleting security groups (this will take some time)...") attempt = 1 while attempt <= 3: - print "Attempt %d" % attempt + print("Attempt %d" % attempt) groups = [g for g in conn.get_all_security_groups() if g.name in group_names] success = True # Delete individual rules in all groups before deleting groups to # remove dependencies between them for group in groups: - print "Deleting rules in security group " + group.name + print("Deleting rules in security group " + group.name) for rule in group.rules: for grant in rule.grants: success &= group.revoke(ip_protocol=rule.ip_protocol, @@ -1298,10 +1300,10 @@ def real_main(): try: # It is needed to use group_id to make it work with VPC conn.delete_security_group(group_id=group.id) - print "Deleted security group %s" % group.name + print("Deleted security group %s" % group.name) except boto.exception.EC2ResponseError: success = False - print "Failed to delete security group %s" % group.name + print("Failed to delete security group %s" % group.name) # Unfortunately, group.revoke() returns True even if a rule was not # deleted, so this needs to be rerun if something fails @@ -1311,17 +1313,16 @@ def real_main(): attempt += 1 if not success: - print "Failed to delete all security groups after 3 tries." - print "Try re-running in a few minutes." + print("Failed to delete all security groups after 3 tries.") + print("Try re-running in a few minutes.") elif action == "login": (master_nodes, slave_nodes) = get_existing_cluster(conn, opts, cluster_name) if not master_nodes[0].public_dns_name and not opts.private_ips: - print "Master has no public DNS name. Maybe you meant to specify " \ - "--private-ips?" + print("Master has no public DNS name. Maybe you meant to specify --private-ips?") else: master = get_dns_name(master_nodes[0], opts.private_ips) - print "Logging into master " + master + "..." + print("Logging into master " + master + "...") proxy_opt = [] if opts.proxy_port is not None: proxy_opt = ['-D', opts.proxy_port] @@ -1336,19 +1337,18 @@ def real_main(): if response == "y": (master_nodes, slave_nodes) = get_existing_cluster( conn, opts, cluster_name, die_on_error=False) - print "Rebooting slaves..." + print("Rebooting slaves...") for inst in slave_nodes: if inst.state not in ["shutting-down", "terminated"]: - print "Rebooting " + inst.id + print("Rebooting " + inst.id) inst.reboot() elif action == "get-master": (master_nodes, slave_nodes) = get_existing_cluster(conn, opts, cluster_name) if not master_nodes[0].public_dns_name and not opts.private_ips: - print "Master has no public DNS name. Maybe you meant to specify " \ - "--private-ips?" + print("Master has no public DNS name. Maybe you meant to specify --private-ips?") else: - print get_dns_name(master_nodes[0], opts.private_ips) + print(get_dns_name(master_nodes[0], opts.private_ips)) elif action == "stop": response = raw_input( @@ -1361,11 +1361,11 @@ def real_main(): if response == "y": (master_nodes, slave_nodes) = get_existing_cluster( conn, opts, cluster_name, die_on_error=False) - print "Stopping master..." + print("Stopping master...") for inst in master_nodes: if inst.state not in ["shutting-down", "terminated"]: inst.stop() - print "Stopping slaves..." + print("Stopping slaves...") for inst in slave_nodes: if inst.state not in ["shutting-down", "terminated"]: if inst.spot_instance_request_id: @@ -1375,11 +1375,11 @@ def real_main(): elif action == "start": (master_nodes, slave_nodes) = get_existing_cluster(conn, opts, cluster_name) - print "Starting slaves..." + print("Starting slaves...") for inst in slave_nodes: if inst.state not in ["shutting-down", "terminated"]: inst.start() - print "Starting master..." + print("Starting master...") for inst in master_nodes: if inst.state not in ["shutting-down", "terminated"]: inst.start() @@ -1403,15 +1403,15 @@ def real_main(): setup_cluster(conn, master_nodes, slave_nodes, opts, False) else: - print >> stderr, "Invalid action: %s" % action + print("Invalid action: %s" % action, file=stderr) sys.exit(1) def main(): try: real_main() - except UsageError, e: - print >> stderr, "\nError:\n", e + except UsageError as e: + print("\nError:\n", e, file=stderr) sys.exit(1) diff --git a/examples/src/main/python/als.py b/examples/src/main/python/als.py index 70b6146e39a87..1c3a787bd0e94 100755 --- a/examples/src/main/python/als.py +++ b/examples/src/main/python/als.py @@ -21,7 +21,8 @@ This example requires numpy (http://www.numpy.org/) """ -from os.path import realpath +from __future__ import print_function + import sys import numpy as np @@ -57,9 +58,9 @@ def update(i, vec, mat, ratings): Usage: als [M] [U] [F] [iterations] [partitions]" """ - print >> sys.stderr, """WARN: This is a naive implementation of ALS and is given as an + print("""WARN: This is a naive implementation of ALS and is given as an example. Please use the ALS method found in pyspark.mllib.recommendation for more - conventional use.""" + conventional use.""", file=sys.stderr) sc = SparkContext(appName="PythonALS") M = int(sys.argv[1]) if len(sys.argv) > 1 else 100 @@ -68,8 +69,8 @@ def update(i, vec, mat, ratings): ITERATIONS = int(sys.argv[4]) if len(sys.argv) > 4 else 5 partitions = int(sys.argv[5]) if len(sys.argv) > 5 else 2 - print "Running ALS with M=%d, U=%d, F=%d, iters=%d, partitions=%d\n" % \ - (M, U, F, ITERATIONS, partitions) + print("Running ALS with M=%d, U=%d, F=%d, iters=%d, partitions=%d\n" % + (M, U, F, ITERATIONS, partitions)) R = matrix(rand(M, F)) * matrix(rand(U, F).T) ms = matrix(rand(M, F)) @@ -95,7 +96,7 @@ def update(i, vec, mat, ratings): usb = sc.broadcast(us) error = rmse(R, ms, us) - print "Iteration %d:" % i - print "\nRMSE: %5.4f\n" % error + print("Iteration %d:" % i) + print("\nRMSE: %5.4f\n" % error) sc.stop() diff --git a/examples/src/main/python/avro_inputformat.py b/examples/src/main/python/avro_inputformat.py index 4626bbb7e3b02..da368ac628a49 100644 --- a/examples/src/main/python/avro_inputformat.py +++ b/examples/src/main/python/avro_inputformat.py @@ -15,9 +15,12 @@ # limitations under the License. # +from __future__ import print_function + import sys from pyspark import SparkContext +from functools import reduce """ Read data file users.avro in local Spark distro: @@ -49,7 +52,7 @@ """ if __name__ == "__main__": if len(sys.argv) != 2 and len(sys.argv) != 3: - print >> sys.stderr, """ + print(""" Usage: avro_inputformat [reader_schema_file] Run with example jar: @@ -57,7 +60,7 @@ /path/to/examples/avro_inputformat.py [reader_schema_file] Assumes you have Avro data stored in . Reader schema can be optionally specified in [reader_schema_file]. - """ + """, file=sys.stderr) exit(-1) path = sys.argv[1] @@ -77,6 +80,6 @@ conf=conf) output = avro_rdd.map(lambda x: x[0]).collect() for k in output: - print k + print(k) sc.stop() diff --git a/examples/src/main/python/cassandra_inputformat.py b/examples/src/main/python/cassandra_inputformat.py index 05f34b74df45a..93ca0cfcc9302 100644 --- a/examples/src/main/python/cassandra_inputformat.py +++ b/examples/src/main/python/cassandra_inputformat.py @@ -15,6 +15,8 @@ # limitations under the License. # +from __future__ import print_function + import sys from pyspark import SparkContext @@ -47,14 +49,14 @@ """ if __name__ == "__main__": if len(sys.argv) != 4: - print >> sys.stderr, """ + print(""" Usage: cassandra_inputformat Run with example jar: ./bin/spark-submit --driver-class-path /path/to/example/jar \ /path/to/examples/cassandra_inputformat.py Assumes you have some data in Cassandra already, running on , in and - """ + """, file=sys.stderr) exit(-1) host = sys.argv[1] @@ -77,6 +79,6 @@ conf=conf) output = cass_rdd.collect() for (k, v) in output: - print (k, v) + print((k, v)) sc.stop() diff --git a/examples/src/main/python/cassandra_outputformat.py b/examples/src/main/python/cassandra_outputformat.py index d144539e58b8f..5d643eac92f94 100644 --- a/examples/src/main/python/cassandra_outputformat.py +++ b/examples/src/main/python/cassandra_outputformat.py @@ -15,6 +15,8 @@ # limitations under the License. # +from __future__ import print_function + import sys from pyspark import SparkContext @@ -46,7 +48,7 @@ """ if __name__ == "__main__": if len(sys.argv) != 7: - print >> sys.stderr, """ + print(""" Usage: cassandra_outputformat Run with example jar: @@ -60,7 +62,7 @@ ... fname text, ... lname text ... ); - """ + """, file=sys.stderr) exit(-1) host = sys.argv[1] diff --git a/examples/src/main/python/hbase_inputformat.py b/examples/src/main/python/hbase_inputformat.py index 3b16010f1cb97..e17819d5feb76 100644 --- a/examples/src/main/python/hbase_inputformat.py +++ b/examples/src/main/python/hbase_inputformat.py @@ -15,6 +15,8 @@ # limitations under the License. # +from __future__ import print_function + import sys from pyspark import SparkContext @@ -47,14 +49,14 @@ """ if __name__ == "__main__": if len(sys.argv) != 3: - print >> sys.stderr, """ + print(""" Usage: hbase_inputformat Run with example jar: ./bin/spark-submit --driver-class-path /path/to/example/jar \ /path/to/examples/hbase_inputformat.py
    Assumes you have some data in HBase already, running on , in
    - """ + """, file=sys.stderr) exit(-1) host = sys.argv[1] @@ -74,6 +76,6 @@ conf=conf) output = hbase_rdd.collect() for (k, v) in output: - print (k, v) + print((k, v)) sc.stop() diff --git a/examples/src/main/python/hbase_outputformat.py b/examples/src/main/python/hbase_outputformat.py index abb425b1f886a..9e5641789a976 100644 --- a/examples/src/main/python/hbase_outputformat.py +++ b/examples/src/main/python/hbase_outputformat.py @@ -15,6 +15,8 @@ # limitations under the License. # +from __future__ import print_function + import sys from pyspark import SparkContext @@ -40,7 +42,7 @@ """ if __name__ == "__main__": if len(sys.argv) != 7: - print >> sys.stderr, """ + print(""" Usage: hbase_outputformat
    Run with example jar: @@ -48,7 +50,7 @@ /path/to/examples/hbase_outputformat.py Assumes you have created
    with column family in HBase running on already - """ + """, file=sys.stderr) exit(-1) host = sys.argv[1] diff --git a/examples/src/main/python/kmeans.py b/examples/src/main/python/kmeans.py index 86ef6f32c84e8..19391506463f0 100755 --- a/examples/src/main/python/kmeans.py +++ b/examples/src/main/python/kmeans.py @@ -22,6 +22,7 @@ This example requires NumPy (http://www.numpy.org/). """ +from __future__ import print_function import sys @@ -47,12 +48,12 @@ def closestPoint(p, centers): if __name__ == "__main__": if len(sys.argv) != 4: - print >> sys.stderr, "Usage: kmeans " + print("Usage: kmeans ", file=sys.stderr) exit(-1) - print >> sys.stderr, """WARN: This is a naive implementation of KMeans Clustering and is given + print("""WARN: This is a naive implementation of KMeans Clustering and is given as an example! Please refer to examples/src/main/python/mllib/kmeans.py for an example on - how to use MLlib's KMeans implementation.""" + how to use MLlib's KMeans implementation.""", file=sys.stderr) sc = SparkContext(appName="PythonKMeans") lines = sc.textFile(sys.argv[1]) @@ -69,13 +70,13 @@ def closestPoint(p, centers): pointStats = closest.reduceByKey( lambda (x1, y1), (x2, y2): (x1 + x2, y1 + y2)) newPoints = pointStats.map( - lambda (x, (y, z)): (x, y / z)).collect() + lambda xy: (xy[0], xy[1][0] / xy[1][1])).collect() tempDist = sum(np.sum((kPoints[x] - y) ** 2) for (x, y) in newPoints) for (x, y) in newPoints: kPoints[x] = y - print "Final centers: " + str(kPoints) + print("Final centers: " + str(kPoints)) sc.stop() diff --git a/examples/src/main/python/logistic_regression.py b/examples/src/main/python/logistic_regression.py index 3aa56b0528168..b318b7d87bfdc 100755 --- a/examples/src/main/python/logistic_regression.py +++ b/examples/src/main/python/logistic_regression.py @@ -22,10 +22,8 @@ In practice, one may prefer to use the LogisticRegression algorithm in MLlib, as shown in examples/src/main/python/mllib/logistic_regression.py. """ +from __future__ import print_function -from collections import namedtuple -from math import exp -from os.path import realpath import sys import numpy as np @@ -42,19 +40,19 @@ def readPointBatch(iterator): strs = list(iterator) matrix = np.zeros((len(strs), D + 1)) - for i in xrange(len(strs)): - matrix[i] = np.fromstring(strs[i].replace(',', ' '), dtype=np.float32, sep=' ') + for i, s in enumerate(strs): + matrix[i] = np.fromstring(s.replace(',', ' '), dtype=np.float32, sep=' ') return [matrix] if __name__ == "__main__": if len(sys.argv) != 3: - print >> sys.stderr, "Usage: logistic_regression " + print("Usage: logistic_regression ", file=sys.stderr) exit(-1) - print >> sys.stderr, """WARN: This is a naive implementation of Logistic Regression and is + print("""WARN: This is a naive implementation of Logistic Regression and is given as an example! Please refer to examples/src/main/python/mllib/logistic_regression.py - to see how MLlib's implementation is used.""" + to see how MLlib's implementation is used.""", file=sys.stderr) sc = SparkContext(appName="PythonLR") points = sc.textFile(sys.argv[1]).mapPartitions(readPointBatch).cache() @@ -62,7 +60,7 @@ def readPointBatch(iterator): # Initialize w to a random value w = 2 * np.random.ranf(size=D) - 1 - print "Initial w: " + str(w) + print("Initial w: " + str(w)) # Compute logistic regression gradient for a matrix of data points def gradient(matrix, w): @@ -76,9 +74,9 @@ def add(x, y): return x for i in range(iterations): - print "On iteration %i" % (i + 1) + print("On iteration %i" % (i + 1)) w -= points.map(lambda m: gradient(m, w)).reduce(add) - print "Final w: " + str(w) + print("Final w: " + str(w)) sc.stop() diff --git a/examples/src/main/python/ml/simple_text_classification_pipeline.py b/examples/src/main/python/ml/simple_text_classification_pipeline.py index c73edb7fd6b20..fab21f003b233 100644 --- a/examples/src/main/python/ml/simple_text_classification_pipeline.py +++ b/examples/src/main/python/ml/simple_text_classification_pipeline.py @@ -15,6 +15,8 @@ # limitations under the License. # +from __future__ import print_function + from pyspark import SparkContext from pyspark.ml import Pipeline from pyspark.ml.classification import LogisticRegression @@ -37,10 +39,10 @@ # Prepare training documents, which are labeled. LabeledDocument = Row("id", "text", "label") - training = sc.parallelize([(0L, "a b c d e spark", 1.0), - (1L, "b d", 0.0), - (2L, "spark f g h", 1.0), - (3L, "hadoop mapreduce", 0.0)]) \ + training = sc.parallelize([(0, "a b c d e spark", 1.0), + (1, "b d", 0.0), + (2, "spark f g h", 1.0), + (3, "hadoop mapreduce", 0.0)]) \ .map(lambda x: LabeledDocument(*x)).toDF() # Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr. @@ -54,16 +56,16 @@ # Prepare test documents, which are unlabeled. Document = Row("id", "text") - test = sc.parallelize([(4L, "spark i j k"), - (5L, "l m n"), - (6L, "mapreduce spark"), - (7L, "apache hadoop")]) \ + test = sc.parallelize([(4, "spark i j k"), + (5, "l m n"), + (6, "mapreduce spark"), + (7, "apache hadoop")]) \ .map(lambda x: Document(*x)).toDF() # Make predictions on test documents and print columns of interest. prediction = model.transform(test) selected = prediction.select("id", "text", "prediction") for row in selected.collect(): - print row + print(row) sc.stop() diff --git a/examples/src/main/python/mllib/correlations.py b/examples/src/main/python/mllib/correlations.py index 4218eca822a99..0e13546b88e67 100755 --- a/examples/src/main/python/mllib/correlations.py +++ b/examples/src/main/python/mllib/correlations.py @@ -18,6 +18,7 @@ """ Correlations using MLlib. """ +from __future__ import print_function import sys @@ -29,7 +30,7 @@ if __name__ == "__main__": if len(sys.argv) not in [1, 2]: - print >> sys.stderr, "Usage: correlations ()" + print("Usage: correlations ()", file=sys.stderr) exit(-1) sc = SparkContext(appName="PythonCorrelations") if len(sys.argv) == 2: @@ -41,20 +42,20 @@ points = MLUtils.loadLibSVMFile(sc, filepath)\ .map(lambda lp: LabeledPoint(lp.label, lp.features.toArray())) - print - print 'Summary of data file: ' + filepath - print '%d data points' % points.count() + print() + print('Summary of data file: ' + filepath) + print('%d data points' % points.count()) # Statistics (correlations) - print - print 'Correlation (%s) between label and each feature' % corrType - print 'Feature\tCorrelation' + print() + print('Correlation (%s) between label and each feature' % corrType) + print('Feature\tCorrelation') numFeatures = points.take(1)[0].features.size labelRDD = points.map(lambda lp: lp.label) for i in range(numFeatures): featureRDD = points.map(lambda lp: lp.features[i]) corr = Statistics.corr(labelRDD, featureRDD, corrType) - print '%d\t%g' % (i, corr) - print + print('%d\t%g' % (i, corr)) + print() sc.stop() diff --git a/examples/src/main/python/mllib/dataset_example.py b/examples/src/main/python/mllib/dataset_example.py index fcbf56cbf0c52..e23ecc0c5d302 100644 --- a/examples/src/main/python/mllib/dataset_example.py +++ b/examples/src/main/python/mllib/dataset_example.py @@ -19,6 +19,7 @@ An example of how to use DataFrame as a dataset for ML. Run with:: bin/spark-submit examples/src/main/python/mllib/dataset_example.py """ +from __future__ import print_function import os import sys @@ -32,16 +33,16 @@ def summarize(dataset): - print "schema: %s" % dataset.schema().json() + print("schema: %s" % dataset.schema().json()) labels = dataset.map(lambda r: r.label) - print "label average: %f" % labels.mean() + print("label average: %f" % labels.mean()) features = dataset.map(lambda r: r.features) summary = Statistics.colStats(features) - print "features average: %r" % summary.mean() + print("features average: %r" % summary.mean()) if __name__ == "__main__": if len(sys.argv) > 2: - print >> sys.stderr, "Usage: dataset_example.py " + print("Usage: dataset_example.py ", file=sys.stderr) exit(-1) sc = SparkContext(appName="DatasetExample") sqlContext = SQLContext(sc) @@ -54,9 +55,9 @@ def summarize(dataset): summarize(dataset0) tempdir = tempfile.NamedTemporaryFile(delete=False).name os.unlink(tempdir) - print "Save dataset as a Parquet file to %s." % tempdir + print("Save dataset as a Parquet file to %s." % tempdir) dataset0.saveAsParquetFile(tempdir) - print "Load it back and summarize it again." + print("Load it back and summarize it again.") dataset1 = sqlContext.parquetFile(tempdir).setName("dataset1").cache() summarize(dataset1) shutil.rmtree(tempdir) diff --git a/examples/src/main/python/mllib/decision_tree_runner.py b/examples/src/main/python/mllib/decision_tree_runner.py index fccabd841b139..513ed8fd51450 100755 --- a/examples/src/main/python/mllib/decision_tree_runner.py +++ b/examples/src/main/python/mllib/decision_tree_runner.py @@ -20,6 +20,7 @@ This example requires NumPy (http://www.numpy.org/). """ +from __future__ import print_function import numpy import os @@ -83,18 +84,17 @@ def reindexClassLabels(data): numClasses = len(classCounts) # origToNewLabels: class --> index in 0,...,numClasses-1 if (numClasses < 2): - print >> sys.stderr, \ - "Dataset for classification should have at least 2 classes." + \ - " The given dataset had only %d classes." % numClasses + print("Dataset for classification should have at least 2 classes." + " The given dataset had only %d classes." % numClasses, file=sys.stderr) exit(1) origToNewLabels = dict([(sortedClasses[i], i) for i in range(0, numClasses)]) - print "numClasses = %d" % numClasses - print "Per-class example fractions, counts:" - print "Class\tFrac\tCount" + print("numClasses = %d" % numClasses) + print("Per-class example fractions, counts:") + print("Class\tFrac\tCount") for c in sortedClasses: frac = classCounts[c] / (numExamples + 0.0) - print "%g\t%g\t%d" % (c, frac, classCounts[c]) + print("%g\t%g\t%d" % (c, frac, classCounts[c])) if (sortedClasses[0] == 0 and sortedClasses[-1] == numClasses - 1): return (data, origToNewLabels) @@ -105,8 +105,7 @@ def reindexClassLabels(data): def usage(): - print >> sys.stderr, \ - "Usage: decision_tree_runner [libsvm format data filepath]" + print("Usage: decision_tree_runner [libsvm format data filepath]", file=sys.stderr) exit(1) @@ -133,13 +132,13 @@ def usage(): model = DecisionTree.trainClassifier(reindexedData, numClasses=numClasses, categoricalFeaturesInfo=categoricalFeaturesInfo) # Print learned tree and stats. - print "Trained DecisionTree for classification:" - print " Model numNodes: %d" % model.numNodes() - print " Model depth: %d" % model.depth() - print " Training accuracy: %g" % getAccuracy(model, reindexedData) + print("Trained DecisionTree for classification:") + print(" Model numNodes: %d" % model.numNodes()) + print(" Model depth: %d" % model.depth()) + print(" Training accuracy: %g" % getAccuracy(model, reindexedData)) if model.numNodes() < 20: - print model.toDebugString() + print(model.toDebugString()) else: - print model + print(model) sc.stop() diff --git a/examples/src/main/python/mllib/gaussian_mixture_model.py b/examples/src/main/python/mllib/gaussian_mixture_model.py index a2cd626c9f19d..2cb8010cdc07f 100644 --- a/examples/src/main/python/mllib/gaussian_mixture_model.py +++ b/examples/src/main/python/mllib/gaussian_mixture_model.py @@ -18,7 +18,8 @@ """ A Gaussian Mixture Model clustering program using MLlib. """ -import sys +from __future__ import print_function + import random import argparse import numpy as np @@ -59,7 +60,7 @@ def parseVector(line): model = GaussianMixture.train(data, args.k, args.convergenceTol, args.maxIterations, args.seed) for i in range(args.k): - print ("weight = ", model.weights[i], "mu = ", model.gaussians[i].mu, - "sigma = ", model.gaussians[i].sigma.toArray()) - print ("Cluster labels (first 100): ", model.predict(data).take(100)) + print(("weight = ", model.weights[i], "mu = ", model.gaussians[i].mu, + "sigma = ", model.gaussians[i].sigma.toArray())) + print(("Cluster labels (first 100): ", model.predict(data).take(100))) sc.stop() diff --git a/examples/src/main/python/mllib/gradient_boosted_trees.py b/examples/src/main/python/mllib/gradient_boosted_trees.py index e647773ad9060..781bd61c9d2b5 100644 --- a/examples/src/main/python/mllib/gradient_boosted_trees.py +++ b/examples/src/main/python/mllib/gradient_boosted_trees.py @@ -18,6 +18,7 @@ """ Gradient boosted Trees classification and regression using MLlib. """ +from __future__ import print_function import sys @@ -34,7 +35,7 @@ def testClassification(trainingData, testData): # Evaluate model on test instances and compute test error predictions = model.predict(testData.map(lambda x: x.features)) labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions) - testErr = labelsAndPredictions.filter(lambda (v, p): v != p).count() \ + testErr = labelsAndPredictions.filter(lambda v_p: v_p[0] != v_p[1]).count() \ / float(testData.count()) print('Test Error = ' + str(testErr)) print('Learned classification ensemble model:') @@ -49,7 +50,7 @@ def testRegression(trainingData, testData): # Evaluate model on test instances and compute test error predictions = model.predict(testData.map(lambda x: x.features)) labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions) - testMSE = labelsAndPredictions.map(lambda (v, p): (v - p) * (v - p)).sum() \ + testMSE = labelsAndPredictions.map(lambda vp: (vp[0] - vp[1]) * (vp[0] - vp[1])).sum() \ / float(testData.count()) print('Test Mean Squared Error = ' + str(testMSE)) print('Learned regression ensemble model:') @@ -58,7 +59,7 @@ def testRegression(trainingData, testData): if __name__ == "__main__": if len(sys.argv) > 1: - print >> sys.stderr, "Usage: gradient_boosted_trees" + print("Usage: gradient_boosted_trees", file=sys.stderr) exit(1) sc = SparkContext(appName="PythonGradientBoostedTrees") diff --git a/examples/src/main/python/mllib/kmeans.py b/examples/src/main/python/mllib/kmeans.py index 2eeb1abeeb12b..f901a87fa63ac 100755 --- a/examples/src/main/python/mllib/kmeans.py +++ b/examples/src/main/python/mllib/kmeans.py @@ -20,6 +20,7 @@ This example requires NumPy (http://www.numpy.org/). """ +from __future__ import print_function import sys @@ -34,12 +35,12 @@ def parseVector(line): if __name__ == "__main__": if len(sys.argv) != 3: - print >> sys.stderr, "Usage: kmeans " + print("Usage: kmeans ", file=sys.stderr) exit(-1) sc = SparkContext(appName="KMeans") lines = sc.textFile(sys.argv[1]) data = lines.map(parseVector) k = int(sys.argv[2]) model = KMeans.train(data, k) - print "Final centers: " + str(model.clusterCenters) + print("Final centers: " + str(model.clusterCenters)) sc.stop() diff --git a/examples/src/main/python/mllib/logistic_regression.py b/examples/src/main/python/mllib/logistic_regression.py index 8cae27fc4a52d..d4f1d34e2d8cf 100755 --- a/examples/src/main/python/mllib/logistic_regression.py +++ b/examples/src/main/python/mllib/logistic_regression.py @@ -20,11 +20,10 @@ This example requires NumPy (http://www.numpy.org/). """ +from __future__ import print_function -from math import exp import sys -import numpy as np from pyspark import SparkContext from pyspark.mllib.regression import LabeledPoint from pyspark.mllib.classification import LogisticRegressionWithSGD @@ -42,12 +41,12 @@ def parsePoint(line): if __name__ == "__main__": if len(sys.argv) != 3: - print >> sys.stderr, "Usage: logistic_regression " + print("Usage: logistic_regression ", file=sys.stderr) exit(-1) sc = SparkContext(appName="PythonLR") points = sc.textFile(sys.argv[1]).map(parsePoint) iterations = int(sys.argv[2]) model = LogisticRegressionWithSGD.train(points, iterations) - print "Final weights: " + str(model.weights) - print "Final intercept: " + str(model.intercept) + print("Final weights: " + str(model.weights)) + print("Final intercept: " + str(model.intercept)) sc.stop() diff --git a/examples/src/main/python/mllib/random_forest_example.py b/examples/src/main/python/mllib/random_forest_example.py index d3c24f7664329..4cfdad868c66e 100755 --- a/examples/src/main/python/mllib/random_forest_example.py +++ b/examples/src/main/python/mllib/random_forest_example.py @@ -22,6 +22,7 @@ For information on multiclass classification, please refer to the decision_tree_runner.py example. """ +from __future__ import print_function import sys @@ -43,7 +44,7 @@ def testClassification(trainingData, testData): # Evaluate model on test instances and compute test error predictions = model.predict(testData.map(lambda x: x.features)) labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions) - testErr = labelsAndPredictions.filter(lambda (v, p): v != p).count()\ + testErr = labelsAndPredictions.filter(lambda v_p: v_p[0] != v_p[1]).count()\ / float(testData.count()) print('Test Error = ' + str(testErr)) print('Learned classification forest model:') @@ -62,8 +63,8 @@ def testRegression(trainingData, testData): # Evaluate model on test instances and compute test error predictions = model.predict(testData.map(lambda x: x.features)) labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions) - testMSE = labelsAndPredictions.map(lambda (v, p): (v - p) * (v - p)).sum()\ - / float(testData.count()) + testMSE = labelsAndPredictions.map(lambda v_p1: (v_p1[0] - v_p1[1]) * (v_p1[0] - v_p1[1]))\ + .sum() / float(testData.count()) print('Test Mean Squared Error = ' + str(testMSE)) print('Learned regression forest model:') print(model.toDebugString()) @@ -71,7 +72,7 @@ def testRegression(trainingData, testData): if __name__ == "__main__": if len(sys.argv) > 1: - print >> sys.stderr, "Usage: random_forest_example" + print("Usage: random_forest_example", file=sys.stderr) exit(1) sc = SparkContext(appName="PythonRandomForestExample") diff --git a/examples/src/main/python/mllib/random_rdd_generation.py b/examples/src/main/python/mllib/random_rdd_generation.py index 1e8892741e714..729bae30b152c 100755 --- a/examples/src/main/python/mllib/random_rdd_generation.py +++ b/examples/src/main/python/mllib/random_rdd_generation.py @@ -18,6 +18,7 @@ """ Randomly generated RDDs. """ +from __future__ import print_function import sys @@ -27,7 +28,7 @@ if __name__ == "__main__": if len(sys.argv) not in [1, 2]: - print >> sys.stderr, "Usage: random_rdd_generation" + print("Usage: random_rdd_generation", file=sys.stderr) exit(-1) sc = SparkContext(appName="PythonRandomRDDGeneration") @@ -37,19 +38,19 @@ # Example: RandomRDDs.normalRDD normalRDD = RandomRDDs.normalRDD(sc, numExamples) - print 'Generated RDD of %d examples sampled from the standard normal distribution'\ - % normalRDD.count() - print ' First 5 samples:' + print('Generated RDD of %d examples sampled from the standard normal distribution' + % normalRDD.count()) + print(' First 5 samples:') for sample in normalRDD.take(5): - print ' ' + str(sample) - print + print(' ' + str(sample)) + print() # Example: RandomRDDs.normalVectorRDD normalVectorRDD = RandomRDDs.normalVectorRDD(sc, numRows=numExamples, numCols=2) - print 'Generated RDD of %d examples of length-2 vectors.' % normalVectorRDD.count() - print ' First 5 samples:' + print('Generated RDD of %d examples of length-2 vectors.' % normalVectorRDD.count()) + print(' First 5 samples:') for sample in normalVectorRDD.take(5): - print ' ' + str(sample) - print + print(' ' + str(sample)) + print() sc.stop() diff --git a/examples/src/main/python/mllib/sampled_rdds.py b/examples/src/main/python/mllib/sampled_rdds.py index 92af3af5ebd1e..b7033ab7daeb3 100755 --- a/examples/src/main/python/mllib/sampled_rdds.py +++ b/examples/src/main/python/mllib/sampled_rdds.py @@ -18,6 +18,7 @@ """ Randomly sampled RDDs. """ +from __future__ import print_function import sys @@ -27,7 +28,7 @@ if __name__ == "__main__": if len(sys.argv) not in [1, 2]: - print >> sys.stderr, "Usage: sampled_rdds " + print("Usage: sampled_rdds ", file=sys.stderr) exit(-1) if len(sys.argv) == 2: datapath = sys.argv[1] @@ -41,24 +42,24 @@ examples = MLUtils.loadLibSVMFile(sc, datapath) numExamples = examples.count() if numExamples == 0: - print >> sys.stderr, "Error: Data file had no samples to load." + print("Error: Data file had no samples to load.", file=sys.stderr) exit(1) - print 'Loaded data with %d examples from file: %s' % (numExamples, datapath) + print('Loaded data with %d examples from file: %s' % (numExamples, datapath)) # Example: RDD.sample() and RDD.takeSample() expectedSampleSize = int(numExamples * fraction) - print 'Sampling RDD using fraction %g. Expected sample size = %d.' \ - % (fraction, expectedSampleSize) + print('Sampling RDD using fraction %g. Expected sample size = %d.' + % (fraction, expectedSampleSize)) sampledRDD = examples.sample(withReplacement=True, fraction=fraction) - print ' RDD.sample(): sample has %d examples' % sampledRDD.count() + print(' RDD.sample(): sample has %d examples' % sampledRDD.count()) sampledArray = examples.takeSample(withReplacement=True, num=expectedSampleSize) - print ' RDD.takeSample(): sample has %d examples' % len(sampledArray) + print(' RDD.takeSample(): sample has %d examples' % len(sampledArray)) - print + print() # Example: RDD.sampleByKey() keyedRDD = examples.map(lambda lp: (int(lp.label), lp.features)) - print ' Keyed data using label (Int) as key ==> Orig' + print(' Keyed data using label (Int) as key ==> Orig') # Count examples per label in original data. keyCountsA = keyedRDD.countByKey() @@ -69,18 +70,18 @@ sampledByKeyRDD = keyedRDD.sampleByKey(withReplacement=True, fractions=fractions) keyCountsB = sampledByKeyRDD.countByKey() sizeB = sum(keyCountsB.values()) - print ' Sampled %d examples using approximate stratified sampling (by label). ==> Sample' \ - % sizeB + print(' Sampled %d examples using approximate stratified sampling (by label). ==> Sample' + % sizeB) # Compare samples - print ' \tFractions of examples with key' - print 'Key\tOrig\tSample' + print(' \tFractions of examples with key') + print('Key\tOrig\tSample') for k in sorted(keyCountsA.keys()): fracA = keyCountsA[k] / float(numExamples) if sizeB != 0: fracB = keyCountsB.get(k, 0) / float(sizeB) else: fracB = 0 - print '%d\t%g\t%g' % (k, fracA, fracB) + print('%d\t%g\t%g' % (k, fracA, fracB)) sc.stop() diff --git a/examples/src/main/python/mllib/word2vec.py b/examples/src/main/python/mllib/word2vec.py index 99fef4276a369..40d1b887927e0 100644 --- a/examples/src/main/python/mllib/word2vec.py +++ b/examples/src/main/python/mllib/word2vec.py @@ -23,6 +23,7 @@ # grep -o -E '\w+(\W+\w+){0,15}' text8 > text8_lines # This was done so that the example can be run in local mode +from __future__ import print_function import sys @@ -34,7 +35,7 @@ if __name__ == "__main__": if len(sys.argv) < 2: - print USAGE + print(USAGE) sys.exit("Argument for file not provided") file_path = sys.argv[1] sc = SparkContext(appName='Word2Vec') @@ -46,5 +47,5 @@ synonyms = model.findSynonyms('china', 40) for word, cosine_distance in synonyms: - print "{}: {}".format(word, cosine_distance) + print("{}: {}".format(word, cosine_distance)) sc.stop() diff --git a/examples/src/main/python/pagerank.py b/examples/src/main/python/pagerank.py index a5f25d78c1146..2fdc9773d4eb1 100755 --- a/examples/src/main/python/pagerank.py +++ b/examples/src/main/python/pagerank.py @@ -19,6 +19,7 @@ This is an example implementation of PageRank. For more conventional use, Please refer to PageRank implementation provided by graphx """ +from __future__ import print_function import re import sys @@ -42,11 +43,12 @@ def parseNeighbors(urls): if __name__ == "__main__": if len(sys.argv) != 3: - print >> sys.stderr, "Usage: pagerank " + print("Usage: pagerank ", file=sys.stderr) exit(-1) - print >> sys.stderr, """WARN: This is a naive implementation of PageRank and is - given as an example! Please refer to PageRank implementation provided by graphx""" + print("""WARN: This is a naive implementation of PageRank and is + given as an example! Please refer to PageRank implementation provided by graphx""", + file=sys.stderr) # Initialize the spark context. sc = SparkContext(appName="PythonPageRank") @@ -62,19 +64,19 @@ def parseNeighbors(urls): links = lines.map(lambda urls: parseNeighbors(urls)).distinct().groupByKey().cache() # Loads all URLs with other URL(s) link to from input file and initialize ranks of them to one. - ranks = links.map(lambda (url, neighbors): (url, 1.0)) + ranks = links.map(lambda url_neighbors: (url_neighbors[0], 1.0)) # Calculates and updates URL ranks continuously using PageRank algorithm. - for iteration in xrange(int(sys.argv[2])): + for iteration in range(int(sys.argv[2])): # Calculates URL contributions to the rank of other URLs. contribs = links.join(ranks).flatMap( - lambda (url, (urls, rank)): computeContribs(urls, rank)) + lambda url_urls_rank: computeContribs(url_urls_rank[1][0], url_urls_rank[1][1])) # Re-calculates URL ranks based on neighbor contributions. ranks = contribs.reduceByKey(add).mapValues(lambda rank: rank * 0.85 + 0.15) # Collects all URL ranks and dump them to console. for (link, rank) in ranks.collect(): - print "%s has rank: %s." % (link, rank) + print("%s has rank: %s." % (link, rank)) sc.stop() diff --git a/examples/src/main/python/parquet_inputformat.py b/examples/src/main/python/parquet_inputformat.py index fa4c20ab20281..96ddac761d698 100644 --- a/examples/src/main/python/parquet_inputformat.py +++ b/examples/src/main/python/parquet_inputformat.py @@ -1,3 +1,4 @@ +from __future__ import print_function # # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with @@ -35,14 +36,14 @@ """ if __name__ == "__main__": if len(sys.argv) != 2: - print >> sys.stderr, """ + print(""" Usage: parquet_inputformat.py Run with example jar: ./bin/spark-submit --driver-class-path /path/to/example/jar \\ /path/to/examples/parquet_inputformat.py Assumes you have Parquet data stored in . - """ + """, file=sys.stderr) exit(-1) path = sys.argv[1] @@ -56,6 +57,6 @@ valueConverter='org.apache.spark.examples.pythonconverters.IndexedRecordToJavaConverter') output = parquet_rdd.map(lambda x: x[1]).collect() for k in output: - print k + print(k) sc.stop() diff --git a/examples/src/main/python/pi.py b/examples/src/main/python/pi.py index a7c74e969cdb9..92e5cf45abc8b 100755 --- a/examples/src/main/python/pi.py +++ b/examples/src/main/python/pi.py @@ -1,3 +1,4 @@ +from __future__ import print_function # # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with @@ -35,7 +36,7 @@ def f(_): y = random() * 2 - 1 return 1 if x ** 2 + y ** 2 < 1 else 0 - count = sc.parallelize(xrange(1, n + 1), partitions).map(f).reduce(add) - print "Pi is roughly %f" % (4.0 * count / n) + count = sc.parallelize(range(1, n + 1), partitions).map(f).reduce(add) + print("Pi is roughly %f" % (4.0 * count / n)) sc.stop() diff --git a/examples/src/main/python/sort.py b/examples/src/main/python/sort.py index bb686f17518a0..f6b0ecb02c100 100755 --- a/examples/src/main/python/sort.py +++ b/examples/src/main/python/sort.py @@ -15,6 +15,8 @@ # limitations under the License. # +from __future__ import print_function + import sys from pyspark import SparkContext @@ -22,7 +24,7 @@ if __name__ == "__main__": if len(sys.argv) != 2: - print >> sys.stderr, "Usage: sort " + print("Usage: sort ", file=sys.stderr) exit(-1) sc = SparkContext(appName="PythonSort") lines = sc.textFile(sys.argv[1], 1) @@ -33,6 +35,6 @@ # In reality, we wouldn't want to collect all the data to the driver node. output = sortedCount.collect() for (num, unitcount) in output: - print num + print(num) sc.stop() diff --git a/examples/src/main/python/sql.py b/examples/src/main/python/sql.py index d89361f324917..87d7b088f077b 100644 --- a/examples/src/main/python/sql.py +++ b/examples/src/main/python/sql.py @@ -15,6 +15,8 @@ # limitations under the License. # +from __future__ import print_function + import os from pyspark import SparkContext @@ -68,6 +70,6 @@ teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AND age <= 19") for each in teenagers.collect(): - print each[0] + print(each[0]) sc.stop() diff --git a/examples/src/main/python/status_api_demo.py b/examples/src/main/python/status_api_demo.py index a33bdc475a06d..49b7902185aaa 100644 --- a/examples/src/main/python/status_api_demo.py +++ b/examples/src/main/python/status_api_demo.py @@ -15,6 +15,8 @@ # limitations under the License. # +from __future__ import print_function + import time import threading import Queue @@ -52,15 +54,15 @@ def run(): ids = status.getJobIdsForGroup() for id in ids: job = status.getJobInfo(id) - print "Job", id, "status: ", job.status + print("Job", id, "status: ", job.status) for sid in job.stageIds: info = status.getStageInfo(sid) if info: - print "Stage %d: %d tasks total (%d active, %d complete)" % \ - (sid, info.numTasks, info.numActiveTasks, info.numCompletedTasks) + print("Stage %d: %d tasks total (%d active, %d complete)" % + (sid, info.numTasks, info.numActiveTasks, info.numCompletedTasks)) time.sleep(1) - print "Job results are:", result.get() + print("Job results are:", result.get()) sc.stop() if __name__ == "__main__": diff --git a/examples/src/main/python/streaming/hdfs_wordcount.py b/examples/src/main/python/streaming/hdfs_wordcount.py index f7ffb5379681e..f815dd26823d1 100644 --- a/examples/src/main/python/streaming/hdfs_wordcount.py +++ b/examples/src/main/python/streaming/hdfs_wordcount.py @@ -25,6 +25,7 @@ Then create a text file in `localdir` and the words in the file will get counted. """ +from __future__ import print_function import sys @@ -33,7 +34,7 @@ if __name__ == "__main__": if len(sys.argv) != 2: - print >> sys.stderr, "Usage: hdfs_wordcount.py " + print("Usage: hdfs_wordcount.py ", file=sys.stderr) exit(-1) sc = SparkContext(appName="PythonStreamingHDFSWordCount") diff --git a/examples/src/main/python/streaming/kafka_wordcount.py b/examples/src/main/python/streaming/kafka_wordcount.py index 51e1ff822fc55..b178e7899b5e1 100644 --- a/examples/src/main/python/streaming/kafka_wordcount.py +++ b/examples/src/main/python/streaming/kafka_wordcount.py @@ -27,6 +27,7 @@ spark-streaming-kafka-assembly-*.jar examples/src/main/python/streaming/kafka_wordcount.py \ localhost:2181 test` """ +from __future__ import print_function import sys @@ -36,7 +37,7 @@ if __name__ == "__main__": if len(sys.argv) != 3: - print >> sys.stderr, "Usage: kafka_wordcount.py " + print("Usage: kafka_wordcount.py ", file=sys.stderr) exit(-1) sc = SparkContext(appName="PythonStreamingKafkaWordCount") diff --git a/examples/src/main/python/streaming/network_wordcount.py b/examples/src/main/python/streaming/network_wordcount.py index cfa9c1ff5bfbc..2b48bcfd55db0 100644 --- a/examples/src/main/python/streaming/network_wordcount.py +++ b/examples/src/main/python/streaming/network_wordcount.py @@ -25,6 +25,7 @@ and then run the example `$ bin/spark-submit examples/src/main/python/streaming/network_wordcount.py localhost 9999` """ +from __future__ import print_function import sys @@ -33,7 +34,7 @@ if __name__ == "__main__": if len(sys.argv) != 3: - print >> sys.stderr, "Usage: network_wordcount.py " + print("Usage: network_wordcount.py ", file=sys.stderr) exit(-1) sc = SparkContext(appName="PythonStreamingNetworkWordCount") ssc = StreamingContext(sc, 1) diff --git a/examples/src/main/python/streaming/recoverable_network_wordcount.py b/examples/src/main/python/streaming/recoverable_network_wordcount.py index fc6827c82bf9b..ac91f0a06b172 100644 --- a/examples/src/main/python/streaming/recoverable_network_wordcount.py +++ b/examples/src/main/python/streaming/recoverable_network_wordcount.py @@ -35,6 +35,7 @@ checkpoint data exists in ~/checkpoint/, then it will create StreamingContext from the checkpoint data. """ +from __future__ import print_function import os import sys @@ -46,7 +47,7 @@ def createContext(host, port, outputPath): # If you do not see this printed, that means the StreamingContext has been loaded # from the new checkpoint - print "Creating new context" + print("Creating new context") if os.path.exists(outputPath): os.remove(outputPath) sc = SparkContext(appName="PythonStreamingRecoverableNetworkWordCount") @@ -60,8 +61,8 @@ def createContext(host, port, outputPath): def echo(time, rdd): counts = "Counts at time %s %s" % (time, rdd.collect()) - print counts - print "Appending to " + os.path.abspath(outputPath) + print(counts) + print("Appending to " + os.path.abspath(outputPath)) with open(outputPath, 'a') as f: f.write(counts + "\n") @@ -70,8 +71,8 @@ def echo(time, rdd): if __name__ == "__main__": if len(sys.argv) != 5: - print >> sys.stderr, "Usage: recoverable_network_wordcount.py "\ - " " + print("Usage: recoverable_network_wordcount.py " + " ", file=sys.stderr) exit(-1) host, port, checkpoint, output = sys.argv[1:] ssc = StreamingContext.getOrCreate(checkpoint, diff --git a/examples/src/main/python/streaming/sql_network_wordcount.py b/examples/src/main/python/streaming/sql_network_wordcount.py index f89bc562d856b..da90c07dbd82f 100644 --- a/examples/src/main/python/streaming/sql_network_wordcount.py +++ b/examples/src/main/python/streaming/sql_network_wordcount.py @@ -27,6 +27,7 @@ and then run the example `$ bin/spark-submit examples/src/main/python/streaming/sql_network_wordcount.py localhost 9999` """ +from __future__ import print_function import os import sys @@ -44,7 +45,7 @@ def getSqlContextInstance(sparkContext): if __name__ == "__main__": if len(sys.argv) != 3: - print >> sys.stderr, "Usage: sql_network_wordcount.py " + print("Usage: sql_network_wordcount.py ", file=sys.stderr) exit(-1) host, port = sys.argv[1:] sc = SparkContext(appName="PythonSqlNetworkWordCount") @@ -57,7 +58,7 @@ def getSqlContextInstance(sparkContext): # Convert RDDs of the words DStream to DataFrame and run SQL query def process(time, rdd): - print "========= %s =========" % str(time) + print("========= %s =========" % str(time)) try: # Get the singleton instance of SQLContext diff --git a/examples/src/main/python/streaming/stateful_network_wordcount.py b/examples/src/main/python/streaming/stateful_network_wordcount.py index 18a9a5a452ffb..16ef646b7c42e 100644 --- a/examples/src/main/python/streaming/stateful_network_wordcount.py +++ b/examples/src/main/python/streaming/stateful_network_wordcount.py @@ -29,6 +29,7 @@ `$ bin/spark-submit examples/src/main/python/streaming/stateful_network_wordcount.py \ localhost 9999` """ +from __future__ import print_function import sys @@ -37,7 +38,7 @@ if __name__ == "__main__": if len(sys.argv) != 3: - print >> sys.stderr, "Usage: stateful_network_wordcount.py " + print("Usage: stateful_network_wordcount.py ", file=sys.stderr) exit(-1) sc = SparkContext(appName="PythonStreamingStatefulNetworkWordCount") ssc = StreamingContext(sc, 1) diff --git a/examples/src/main/python/transitive_closure.py b/examples/src/main/python/transitive_closure.py index 00a281bfb6506..7bf5fb6ddfe29 100755 --- a/examples/src/main/python/transitive_closure.py +++ b/examples/src/main/python/transitive_closure.py @@ -15,6 +15,8 @@ # limitations under the License. # +from __future__ import print_function + import sys from random import Random @@ -49,20 +51,20 @@ def generateGraph(): # the graph to obtain the path (x, z). # Because join() joins on keys, the edges are stored in reversed order. - edges = tc.map(lambda (x, y): (y, x)) + edges = tc.map(lambda x_y: (x_y[1], x_y[0])) - oldCount = 0L + oldCount = 0 nextCount = tc.count() while True: oldCount = nextCount # Perform the join, obtaining an RDD of (y, (z, x)) pairs, # then project the result to obtain the new (x, z) paths. - new_edges = tc.join(edges).map(lambda (_, (a, b)): (b, a)) + new_edges = tc.join(edges).map(lambda __a_b: (__a_b[1][1], __a_b[1][0])) tc = tc.union(new_edges).distinct().cache() nextCount = tc.count() if nextCount == oldCount: break - print "TC has %i edges" % tc.count() + print("TC has %i edges" % tc.count()) sc.stop() diff --git a/examples/src/main/python/wordcount.py b/examples/src/main/python/wordcount.py index ae6cd13b83d92..7c0143607b61d 100755 --- a/examples/src/main/python/wordcount.py +++ b/examples/src/main/python/wordcount.py @@ -15,6 +15,8 @@ # limitations under the License. # +from __future__ import print_function + import sys from operator import add @@ -23,7 +25,7 @@ if __name__ == "__main__": if len(sys.argv) != 2: - print >> sys.stderr, "Usage: wordcount " + print("Usage: wordcount ", file=sys.stderr) exit(-1) sc = SparkContext(appName="PythonWordCount") lines = sc.textFile(sys.argv[1], 1) @@ -32,6 +34,6 @@ .reduceByKey(add) output = counts.collect() for (word, count) in output: - print "%s: %i" % (word, count) + print("%s: %i" % (word, count)) sc.stop() diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/MatrixFactorizationModelWrapper.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/MatrixFactorizationModelWrapper.scala index ecd3b16598438..534edac56bc5a 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/MatrixFactorizationModelWrapper.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/MatrixFactorizationModelWrapper.scala @@ -18,6 +18,7 @@ package org.apache.spark.mllib.api.python import org.apache.spark.api.java.JavaRDD +import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.recommendation.{MatrixFactorizationModel, Rating} import org.apache.spark.rdd.RDD @@ -31,10 +32,14 @@ private[python] class MatrixFactorizationModelWrapper(model: MatrixFactorization predict(SerDe.asTupleRDD(userAndProducts.rdd)) def getUserFeatures: RDD[Array[Any]] = { - SerDe.fromTuple2RDD(userFeatures.asInstanceOf[RDD[(Any, Any)]]) + SerDe.fromTuple2RDD(userFeatures.map { + case (user, feature) => (user, Vectors.dense(feature)) + }.asInstanceOf[RDD[(Any, Any)]]) } def getProductFeatures: RDD[Array[Any]] = { - SerDe.fromTuple2RDD(productFeatures.asInstanceOf[RDD[(Any, Any)]]) + SerDe.fromTuple2RDD(productFeatures.map { + case (product, feature) => (product, Vectors.dense(feature)) + }.asInstanceOf[RDD[(Any, Any)]]) } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index ab15f0f36a14b..f976d2f97b043 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -28,7 +28,6 @@ import scala.reflect.ClassTag import net.razorvine.pickle._ -import org.apache.spark.annotation.DeveloperApi import org.apache.spark.api.java.{JavaRDD, JavaSparkContext} import org.apache.spark.api.python.SerDeUtil import org.apache.spark.mllib.classification._ @@ -40,15 +39,15 @@ import org.apache.spark.mllib.optimization._ import org.apache.spark.mllib.random.{RandomRDDs => RG} import org.apache.spark.mllib.recommendation._ import org.apache.spark.mllib.regression._ -import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, Statistics} import org.apache.spark.mllib.stat.correlation.CorrelationNames import org.apache.spark.mllib.stat.distribution.MultivariateGaussian import org.apache.spark.mllib.stat.test.ChiSqTestResult -import org.apache.spark.mllib.tree.{GradientBoostedTrees, RandomForest, DecisionTree} -import org.apache.spark.mllib.tree.configuration.{BoostingStrategy, Algo, Strategy} +import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, Statistics} +import org.apache.spark.mllib.tree.configuration.{Algo, BoostingStrategy, Strategy} import org.apache.spark.mllib.tree.impurity._ import org.apache.spark.mllib.tree.loss.Losses -import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel, RandomForestModel, DecisionTreeModel} +import org.apache.spark.mllib.tree.model.{DecisionTreeModel, GradientBoostedTreesModel, RandomForestModel} +import org.apache.spark.mllib.tree.{DecisionTree, GradientBoostedTrees, RandomForest} import org.apache.spark.mllib.util.MLUtils import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel @@ -279,7 +278,7 @@ private[python] class PythonMLLibAPI extends Serializable { data: JavaRDD[LabeledPoint], lambda: Double): JList[Object] = { val model = NaiveBayes.train(data.rdd, lambda) - List(Vectors.dense(model.labels), Vectors.dense(model.pi), model.theta). + List(Vectors.dense(model.labels), Vectors.dense(model.pi), model.theta.map(Vectors.dense)). map(_.asInstanceOf[Object]).asJava } @@ -335,7 +334,7 @@ private[python] class PythonMLLibAPI extends Serializable { mu += model.gaussians(i).mu sigma += model.gaussians(i).sigma } - List(wt.toArray, mu.toArray, sigma.toArray).map(_.asInstanceOf[Object]).asJava + List(Vectors.dense(wt.toArray), mu.toArray, sigma.toArray).map(_.asInstanceOf[Object]).asJava } finally { data.rdd.unpersist(blocking = false) } @@ -346,20 +345,20 @@ private[python] class PythonMLLibAPI extends Serializable { */ def predictSoftGMM( data: JavaRDD[Vector], - wt: Object, + wt: Vector, mu: Array[Object], - si: Array[Object]): RDD[Array[Double]] = { + si: Array[Object]): RDD[Vector] = { - val weight = wt.asInstanceOf[Array[Double]] + val weight = wt.toArray val mean = mu.map(_.asInstanceOf[DenseVector]) val sigma = si.map(_.asInstanceOf[DenseMatrix]) val gaussians = Array.tabulate(weight.length){ i => new MultivariateGaussian(mean(i), sigma(i)) } val model = new GaussianMixtureModel(weight, gaussians) - model.predictSoft(data) + model.predictSoft(data).map(Vectors.dense) } - + /** * Java stub for Python mllib ALS.train(). This stub returns a handle * to the Java object instead of the content of the Java object. Extra care @@ -936,6 +935,14 @@ private[spark] object SerDe extends Serializable { out.write(code) } + protected def getBytes(obj: Object): Array[Byte] = { + if (obj.getClass.isArray) { + obj.asInstanceOf[Array[Byte]] + } else { + obj.asInstanceOf[String].getBytes(LATIN1) + } + } + private[python] def saveState(obj: Object, out: OutputStream, pickler: Pickler) } @@ -961,7 +968,7 @@ private[spark] object SerDe extends Serializable { if (args.length != 1) { throw new PickleException("should be 1") } - val bytes = args(0).asInstanceOf[String].getBytes(LATIN1) + val bytes = getBytes(args(0)) val bb = ByteBuffer.wrap(bytes, 0, bytes.length) bb.order(ByteOrder.nativeOrder()) val db = bb.asDoubleBuffer() @@ -994,7 +1001,7 @@ private[spark] object SerDe extends Serializable { if (args.length != 3) { throw new PickleException("should be 3") } - val bytes = args(2).asInstanceOf[String].getBytes(LATIN1) + val bytes = getBytes(args(2)) val n = bytes.length / 8 val values = new Array[Double](n) val order = ByteOrder.nativeOrder() @@ -1031,8 +1038,8 @@ private[spark] object SerDe extends Serializable { throw new PickleException("should be 3") } val size = args(0).asInstanceOf[Int] - val indiceBytes = args(1).asInstanceOf[String].getBytes(LATIN1) - val valueBytes = args(2).asInstanceOf[String].getBytes(LATIN1) + val indiceBytes = getBytes(args(1)) + val valueBytes = getBytes(args(2)) val n = indiceBytes.length / 4 val indices = new Array[Int](n) val values = new Array[Double](n) diff --git a/python/pyspark/accumulators.py b/python/pyspark/accumulators.py index ccbca67656c8d..7271809e43880 100644 --- a/python/pyspark/accumulators.py +++ b/python/pyspark/accumulators.py @@ -54,7 +54,7 @@ ... def zero(self, value): ... return [0.0] * len(value) ... def addInPlace(self, val1, val2): -... for i in xrange(len(val1)): +... for i in range(len(val1)): ... val1[i] += val2[i] ... return val1 >>> va = sc.accumulator([1.0, 2.0, 3.0], VectorAccumulatorParam()) @@ -86,9 +86,13 @@ Exception:... """ +import sys import select import struct -import SocketServer +if sys.version < '3': + import SocketServer +else: + import socketserver as SocketServer import threading from pyspark.cloudpickle import CloudPickler from pyspark.serializers import read_int, PickleSerializer @@ -247,6 +251,7 @@ class AccumulatorServer(SocketServer.TCPServer): def shutdown(self): self.server_shutdown = True SocketServer.TCPServer.shutdown(self) + self.server_close() def _start_update_server(): diff --git a/python/pyspark/broadcast.py b/python/pyspark/broadcast.py index 6b8a8b256a891..3de4615428bb6 100644 --- a/python/pyspark/broadcast.py +++ b/python/pyspark/broadcast.py @@ -16,10 +16,15 @@ # import os -import cPickle +import sys import gc from tempfile import NamedTemporaryFile +if sys.version < '3': + import cPickle as pickle +else: + import pickle + unicode = str __all__ = ['Broadcast'] @@ -70,33 +75,19 @@ def __init__(self, sc=None, value=None, pickle_registry=None, path=None): self._path = path def dump(self, value, f): - if isinstance(value, basestring): - if isinstance(value, unicode): - f.write('U') - value = value.encode('utf8') - else: - f.write('S') - f.write(value) - else: - f.write('P') - cPickle.dump(value, f, 2) + pickle.dump(value, f, 2) f.close() return f.name def load(self, path): with open(path, 'rb', 1 << 20) as f: - flag = f.read(1) - data = f.read() - if flag == 'P': - # cPickle.loads() may create lots of objects, disable GC - # temporary for better performance - gc.disable() - try: - return cPickle.loads(data) - finally: - gc.enable() - else: - return data.decode('utf8') if flag == 'U' else data + # pickle.load() may create lots of objects, disable GC + # temporary for better performance + gc.disable() + try: + return pickle.load(f) + finally: + gc.enable() @property def value(self): diff --git a/python/pyspark/cloudpickle.py b/python/pyspark/cloudpickle.py index bb0783555aa77..9ef93071d2e77 100644 --- a/python/pyspark/cloudpickle.py +++ b/python/pyspark/cloudpickle.py @@ -40,164 +40,126 @@ NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ - +from __future__ import print_function import operator import os +import io import pickle import struct import sys import types from functools import partial import itertools -from copy_reg import _extension_registry, _inverted_registry, _extension_cache -import new import dis import traceback -import platform - -PyImp = platform.python_implementation() - -import logging -cloudLog = logging.getLogger("Cloud.Transport") +if sys.version < '3': + from pickle import Pickler + try: + from cStringIO import StringIO + except ImportError: + from StringIO import StringIO + PY3 = False +else: + types.ClassType = type + from pickle import _Pickler as Pickler + from io import BytesIO as StringIO + PY3 = True #relevant opcodes -STORE_GLOBAL = chr(dis.opname.index('STORE_GLOBAL')) -DELETE_GLOBAL = chr(dis.opname.index('DELETE_GLOBAL')) -LOAD_GLOBAL = chr(dis.opname.index('LOAD_GLOBAL')) +STORE_GLOBAL = dis.opname.index('STORE_GLOBAL') +DELETE_GLOBAL = dis.opname.index('DELETE_GLOBAL') +LOAD_GLOBAL = dis.opname.index('LOAD_GLOBAL') GLOBAL_OPS = [STORE_GLOBAL, DELETE_GLOBAL, LOAD_GLOBAL] +HAVE_ARGUMENT = dis.HAVE_ARGUMENT +EXTENDED_ARG = dis.EXTENDED_ARG -HAVE_ARGUMENT = chr(dis.HAVE_ARGUMENT) -EXTENDED_ARG = chr(dis.EXTENDED_ARG) - -if PyImp == "PyPy": - # register builtin type in `new` - new.method = types.MethodType - -try: - from cStringIO import StringIO -except ImportError: - from StringIO import StringIO -# These helper functions were copied from PiCloud's util module. def islambda(func): - return getattr(func,'func_name') == '' + return getattr(func,'__name__') == '' -def xrange_params(xrangeobj): - """Returns a 3 element tuple describing the xrange start, step, and len - respectively - Note: Only guarentees that elements of xrange are the same. parameters may - be different. - e.g. xrange(1,1) is interpretted as xrange(0,0); both behave the same - though w/ iteration - """ - - xrange_len = len(xrangeobj) - if not xrange_len: #empty - return (0,1,0) - start = xrangeobj[0] - if xrange_len == 1: #one element - return start, 1, 1 - return (start, xrangeobj[1] - xrangeobj[0], xrange_len) - -#debug variables intended for developer use: -printSerialization = False -printMemoization = False +_BUILTIN_TYPE_NAMES = {} +for k, v in types.__dict__.items(): + if type(v) is type: + _BUILTIN_TYPE_NAMES[v] = k -useForcedImports = True #Should I use forced imports for tracking? +def _builtin_type(name): + return getattr(types, name) -class CloudPickler(pickle.Pickler): +class CloudPickler(Pickler): - dispatch = pickle.Pickler.dispatch.copy() - savedForceImports = False - savedDjangoEnv = False #hack tro transport django environment + dispatch = Pickler.dispatch.copy() - def __init__(self, file, protocol=None, min_size_to_save= 0): - pickle.Pickler.__init__(self,file,protocol) - self.modules = set() #set of modules needed to depickle - self.globals_ref = {} # map ids to dictionary. used to ensure that functions can share global env + def __init__(self, file, protocol=None): + Pickler.__init__(self, file, protocol) + # set of modules to unpickle + self.modules = set() + # map ids to dictionary. used to ensure that functions can share global env + self.globals_ref = {} def dump(self, obj): - # note: not thread safe - # minimal side-effects, so not fixing - recurse_limit = 3000 - base_recurse = sys.getrecursionlimit() - if base_recurse < recurse_limit: - sys.setrecursionlimit(recurse_limit) self.inject_addons() try: - return pickle.Pickler.dump(self, obj) - except RuntimeError, e: + return Pickler.dump(self, obj) + except RuntimeError as e: if 'recursion' in e.args[0]: - msg = """Could not pickle object as excessively deep recursion required. - Try _fast_serialization=2 or contact PiCloud support""" + msg = """Could not pickle object as excessively deep recursion required.""" raise pickle.PicklingError(msg) - finally: - new_recurse = sys.getrecursionlimit() - if new_recurse == recurse_limit: - sys.setrecursionlimit(base_recurse) + + def save_memoryview(self, obj): + """Fallback to save_string""" + Pickler.save_string(self, str(obj)) def save_buffer(self, obj): """Fallback to save_string""" - pickle.Pickler.save_string(self,str(obj)) - dispatch[buffer] = save_buffer + Pickler.save_string(self,str(obj)) + if PY3: + dispatch[memoryview] = save_memoryview + else: + dispatch[buffer] = save_buffer - #block broken objects - def save_unsupported(self, obj, pack=None): + def save_unsupported(self, obj): raise pickle.PicklingError("Cannot pickle objects of type %s" % type(obj)) dispatch[types.GeneratorType] = save_unsupported - #python2.6+ supports slice pickling. some py2.5 extensions might as well. We just test it - try: - slice(0,1).__reduce__() - except TypeError: #can't pickle - - dispatch[slice] = save_unsupported - - #itertools objects do not pickle! + # itertools objects do not pickle! for v in itertools.__dict__.values(): if type(v) is type: dispatch[v] = save_unsupported - - def save_dict(self, obj): - """hack fix - If the dict is a global, deal with it in a special way - """ - #print 'saving', obj - if obj is __builtins__: - self.save_reduce(_get_module_builtins, (), obj=obj) - else: - pickle.Pickler.save_dict(self, obj) - dispatch[pickle.DictionaryType] = save_dict - - - def save_module(self, obj, pack=struct.pack): + def save_module(self, obj): """ Save a module as an import """ - #print 'try save import', obj.__name__ self.modules.add(obj) - self.save_reduce(subimport,(obj.__name__,), obj=obj) - dispatch[types.ModuleType] = save_module #new type + self.save_reduce(subimport, (obj.__name__,), obj=obj) + dispatch[types.ModuleType] = save_module - def save_codeobject(self, obj, pack=struct.pack): + def save_codeobject(self, obj): """ Save a code object """ - #print 'try to save codeobj: ', obj - args = ( - obj.co_argcount, obj.co_nlocals, obj.co_stacksize, obj.co_flags, obj.co_code, - obj.co_consts, obj.co_names, obj.co_varnames, obj.co_filename, obj.co_name, - obj.co_firstlineno, obj.co_lnotab, obj.co_freevars, obj.co_cellvars - ) + if PY3: + args = ( + obj.co_argcount, obj.co_kwonlyargcount, obj.co_nlocals, obj.co_stacksize, + obj.co_flags, obj.co_code, obj.co_consts, obj.co_names, obj.co_varnames, + obj.co_filename, obj.co_name, obj.co_firstlineno, obj.co_lnotab, obj.co_freevars, + obj.co_cellvars + ) + else: + args = ( + obj.co_argcount, obj.co_nlocals, obj.co_stacksize, obj.co_flags, obj.co_code, + obj.co_consts, obj.co_names, obj.co_varnames, obj.co_filename, obj.co_name, + obj.co_firstlineno, obj.co_lnotab, obj.co_freevars, obj.co_cellvars + ) self.save_reduce(types.CodeType, args, obj=obj) - dispatch[types.CodeType] = save_codeobject #new type + dispatch[types.CodeType] = save_codeobject - def save_function(self, obj, name=None, pack=struct.pack): + def save_function(self, obj, name=None): """ Registered with the dispatch to handle all function types. Determines what kind of function obj is (e.g. lambda, defined at @@ -205,12 +167,14 @@ def save_function(self, obj, name=None, pack=struct.pack): """ write = self.write - name = obj.__name__ + if name is None: + name = obj.__name__ modname = pickle.whichmodule(obj, name) - #print 'which gives %s %s %s' % (modname, obj, name) + # print('which gives %s %s %s' % (modname, obj, name)) try: themodule = sys.modules[modname] - except KeyError: # eval'd items such as namedtuple give invalid items for their function __module__ + except KeyError: + # eval'd items such as namedtuple give invalid items for their function __module__ modname = '__main__' if modname == '__main__': @@ -221,37 +185,18 @@ def save_function(self, obj, name=None, pack=struct.pack): if getattr(themodule, name, None) is obj: return self.save_global(obj, name) - if not self.savedDjangoEnv: - #hack for django - if we detect the settings module, we transport it - django_settings = os.environ.get('DJANGO_SETTINGS_MODULE', '') - if django_settings: - django_mod = sys.modules.get(django_settings) - if django_mod: - cloudLog.debug('Transporting django settings %s during save of %s', django_mod, name) - self.savedDjangoEnv = True - self.modules.add(django_mod) - write(pickle.MARK) - self.save_reduce(django_settings_load, (django_mod.__name__,), obj=django_mod) - write(pickle.POP_MARK) - - # if func is lambda, def'ed at prompt, is in main, or is nested, then # we'll pickle the actual function object rather than simply saving a # reference (as is done in default pickler), via save_function_tuple. - if islambda(obj) or obj.func_code.co_filename == '' or themodule is None: - #Force server to import modules that have been imported in main - modList = None - if themodule is None and not self.savedForceImports: - mainmod = sys.modules['__main__'] - if useForcedImports and hasattr(mainmod,'___pyc_forcedImports__'): - modList = list(mainmod.___pyc_forcedImports__) - self.savedForceImports = True - self.save_function_tuple(obj, modList) + if islambda(obj) or obj.__code__.co_filename == '' or themodule is None: + #print("save global", islambda(obj), obj.__code__.co_filename, modname, themodule) + self.save_function_tuple(obj) return - else: # func is nested + else: + # func is nested klass = getattr(themodule, name, None) if klass is None or klass is not obj: - self.save_function_tuple(obj, [themodule]) + self.save_function_tuple(obj) return if obj.__dict__: @@ -266,7 +211,7 @@ def save_function(self, obj, name=None, pack=struct.pack): self.memoize(obj) dispatch[types.FunctionType] = save_function - def save_function_tuple(self, func, forced_imports): + def save_function_tuple(self, func): """ Pickles an actual func object. A func comprises: code, globals, defaults, closure, and dict. We @@ -281,19 +226,6 @@ def save_function_tuple(self, func, forced_imports): save = self.save write = self.write - # save the modules (if any) - if forced_imports: - write(pickle.MARK) - save(_modules_to_main) - #print 'forced imports are', forced_imports - - forced_names = map(lambda m: m.__name__, forced_imports) - save((forced_names,)) - - #save((forced_imports,)) - write(pickle.REDUCE) - write(pickle.POP_MARK) - code, f_globals, defaults, closure, dct, base_globals = self.extract_func_data(func) save(_fill_function) # skeleton function updater @@ -318,6 +250,8 @@ def extract_code_globals(co): Find all globals names read or written to by codeblock co """ code = co.co_code + if not PY3: + code = [ord(c) for c in code] names = co.co_names out_names = set() @@ -327,18 +261,18 @@ def extract_code_globals(co): while i < n: op = code[i] - i = i+1 + i += 1 if op >= HAVE_ARGUMENT: - oparg = ord(code[i]) + ord(code[i+1])*256 + extended_arg + oparg = code[i] + code[i+1] * 256 + extended_arg extended_arg = 0 - i = i+2 + i += 2 if op == EXTENDED_ARG: - extended_arg = oparg*65536L + extended_arg = oparg*65536 if op in GLOBAL_OPS: out_names.add(names[oparg]) - #print 'extracted', out_names, ' from ', names - if co.co_consts: # see if nested function have any global refs + # see if nested function have any global refs + if co.co_consts: for const in co.co_consts: if type(const) is types.CodeType: out_names |= CloudPickler.extract_code_globals(const) @@ -350,46 +284,28 @@ def extract_func_data(self, func): Turn the function into a tuple of data necessary to recreate it: code, globals, defaults, closure, dict """ - code = func.func_code + code = func.__code__ # extract all global ref's - func_global_refs = CloudPickler.extract_code_globals(code) + func_global_refs = self.extract_code_globals(code) # process all variables referenced by global environment f_globals = {} for var in func_global_refs: - #Some names, such as class functions are not global - we don't need them - if func.func_globals.has_key(var): - f_globals[var] = func.func_globals[var] + if var in func.__globals__: + f_globals[var] = func.__globals__[var] # defaults requires no processing - defaults = func.func_defaults - - def get_contents(cell): - try: - return cell.cell_contents - except ValueError, e: #cell is empty error on not yet assigned - raise pickle.PicklingError('Function to be pickled has free variables that are referenced before assignment in enclosing scope') - + defaults = func.__defaults__ # process closure - if func.func_closure: - closure = map(get_contents, func.func_closure) - else: - closure = [] + closure = [c.cell_contents for c in func.__closure__] if func.__closure__ else [] # save the dict - dct = func.func_dict - - if printSerialization: - outvars = ['code: ' + str(code) ] - outvars.append('globals: ' + str(f_globals)) - outvars.append('defaults: ' + str(defaults)) - outvars.append('closure: ' + str(closure)) - print 'function ', func, 'is extracted to: ', ', '.join(outvars) + dct = func.__dict__ - base_globals = self.globals_ref.get(id(func.func_globals), {}) - self.globals_ref[id(func.func_globals)] = base_globals + base_globals = self.globals_ref.get(id(func.__globals__), {}) + self.globals_ref[id(func.__globals__)] = base_globals return (code, f_globals, defaults, closure, dct, base_globals) @@ -400,8 +316,9 @@ def save_builtin_function(self, obj): dispatch[types.BuiltinFunctionType] = save_builtin_function def save_global(self, obj, name=None, pack=struct.pack): - write = self.write - memo = self.memo + if obj.__module__ == "__builtin__" or obj.__module__ == "builtins": + if obj in _BUILTIN_TYPE_NAMES: + return self.save_reduce(_builtin_type, (_BUILTIN_TYPE_NAMES[obj],), obj=obj) if name is None: name = obj.__name__ @@ -410,98 +327,57 @@ def save_global(self, obj, name=None, pack=struct.pack): if modname is None: modname = pickle.whichmodule(obj, name) - try: - __import__(modname) - themodule = sys.modules[modname] - except (ImportError, KeyError, AttributeError): #should never occur - raise pickle.PicklingError( - "Can't pickle %r: Module %s cannot be found" % - (obj, modname)) - if modname == '__main__': themodule = None - - if themodule: + else: + __import__(modname) + themodule = sys.modules[modname] self.modules.add(themodule) - sendRef = True - typ = type(obj) - #print 'saving', obj, typ - try: - try: #Deal with case when getattribute fails with exceptions - klass = getattr(themodule, name) - except (AttributeError): - if modname == '__builtin__': #new.* are misrepeported - modname = 'new' - __import__(modname) - themodule = sys.modules[modname] - try: - klass = getattr(themodule, name) - except AttributeError, a: - # print themodule, name, obj, type(obj) - raise pickle.PicklingError("Can't pickle builtin %s" % obj) - else: - raise + if hasattr(themodule, name) and getattr(themodule, name) is obj: + return Pickler.save_global(self, obj, name) - except (ImportError, KeyError, AttributeError): - if typ == types.TypeType or typ == types.ClassType: - sendRef = False - else: #we can't deal with this - raise - else: - if klass is not obj and (typ == types.TypeType or typ == types.ClassType): - sendRef = False - if not sendRef: - #note: Third party types might crash this - add better checks! - d = dict(obj.__dict__) #copy dict proxy to a dict - if not isinstance(d.get('__dict__', None), property): # don't extract dict that are properties - d.pop('__dict__',None) - d.pop('__weakref__',None) + typ = type(obj) + if typ is not obj and isinstance(obj, (type, types.ClassType)): + d = dict(obj.__dict__) # copy dict proxy to a dict + if not isinstance(d.get('__dict__', None), property): + # don't extract dict that are properties + d.pop('__dict__', None) + d.pop('__weakref__', None) # hack as __new__ is stored differently in the __dict__ new_override = d.get('__new__', None) if new_override: d['__new__'] = obj.__new__ - self.save_reduce(type(obj),(obj.__name__,obj.__bases__, - d),obj=obj) - #print 'internal reduce dask %s %s' % (obj, d) - return - - if self.proto >= 2: - code = _extension_registry.get((modname, name)) - if code: - assert code > 0 - if code <= 0xff: - write(pickle.EXT1 + chr(code)) - elif code <= 0xffff: - write("%c%c%c" % (pickle.EXT2, code&0xff, code>>8)) - else: - write(pickle.EXT4 + pack("> sys.stderr, 'Cloud not import django settings %s:' % (name) - print_exec(sys.stderr) - if modified_env: - del os.environ['DJANGO_SETTINGS_MODULE'] - else: - #add project directory to sys,path: - if hasattr(module,'__file__'): - dirname = os.path.split(module.__file__)[0] + '/' - sys.path.append(dirname) # restores function attributes def _restore_attr(obj, attr): @@ -851,13 +636,16 @@ def _restore_attr(obj, attr): setattr(obj, key, val) return obj + def _get_module_builtins(): return pickle.__builtins__ + def print_exec(stream): ei = sys.exc_info() traceback.print_exception(ei[0], ei[1], ei[2], None, stream) + def _modules_to_main(modList): """Force every module in modList to be placed into main""" if not modList: @@ -868,22 +656,16 @@ def _modules_to_main(modList): if type(modname) is str: try: mod = __import__(modname) - except Exception, i: #catch all... - sys.stderr.write('warning: could not import %s\n. Your function may unexpectedly error due to this import failing; \ -A version mismatch is likely. Specific error was:\n' % modname) + except Exception as e: + sys.stderr.write('warning: could not import %s\n. ' + 'Your function may unexpectedly error due to this import failing;' + 'A version mismatch is likely. Specific error was:\n' % modname) print_exec(sys.stderr) else: - setattr(main,mod.__name__, mod) - else: - #REVERSE COMPATIBILITY FOR CLOUD CLIENT 1.5 (WITH EPD) - #In old version actual module was sent - setattr(main,modname.__name__, modname) + setattr(main, mod.__name__, mod) -#object generators: -def _build_xrange(start, step, len): - """Built xrange explicitly""" - return xrange(start, start + step*len, step) +#object generators: def _genpartial(func, args, kwds): if not args: args = () @@ -891,22 +673,26 @@ def _genpartial(func, args, kwds): kwds = {} return partial(func, *args, **kwds) + def _fill_function(func, globals, defaults, dict): """ Fills in the rest of function data into the skeleton function object that were created via _make_skel_func(). """ - func.func_globals.update(globals) - func.func_defaults = defaults - func.func_dict = dict + func.__globals__.update(globals) + func.__defaults__ = defaults + func.__dict__ = dict return func + def _make_cell(value): - return (lambda: value).func_closure[0] + return (lambda: value).__closure__[0] + def _reconstruct_closure(values): return tuple([_make_cell(v) for v in values]) + def _make_skel_func(code, closures, base_globals = None): """ Creates a skeleton function object that contains just the provided code and the correct number of cells in func_closure. All other @@ -928,40 +714,3 @@ def _make_skel_func(code, closures, base_globals = None): def _getobject(modname, attribute): mod = __import__(modname, fromlist=[attribute]) return mod.__dict__[attribute] - -def _generateImage(size, mode, str_rep): - """Generate image from string representation""" - import Image - i = Image.new(mode, size) - i.fromstring(str_rep) - return i - -def _lazyloadImage(fp): - import Image - fp.seek(0) #works in almost any case - return Image.open(fp) - -"""Timeseries""" -def _genTimeSeries(reduce_args, state): - import scikits.timeseries.tseries as ts - from numpy import ndarray - from numpy.ma import MaskedArray - - - time_series = ts._tsreconstruct(*reduce_args) - - #from setstate modified - (ver, shp, typ, isf, raw, msk, flv, dsh, dtm, dtyp, frq, infodict) = state - #print 'regenerating %s' % dtyp - - MaskedArray.__setstate__(time_series, (ver, shp, typ, isf, raw, msk, flv)) - _dates = time_series._dates - #_dates.__setstate__((ver, dsh, typ, isf, dtm, frq)) #use remote typ - ndarray.__setstate__(_dates,(dsh,dtyp, isf, dtm)) - _dates.freq = frq - _dates._cachedinfo.update(dict(full=None, hasdups=None, steps=None, - toobj=None, toord=None, tostr=None)) - # Update the _optinfo dictionary - time_series._optinfo.update(infodict) - return time_series - diff --git a/python/pyspark/conf.py b/python/pyspark/conf.py index dc7cd0bce56f3..924da3eecf214 100644 --- a/python/pyspark/conf.py +++ b/python/pyspark/conf.py @@ -44,7 +44,7 @@ >>> conf.get("spark.executorEnv.VAR1") u'value1' ->>> print conf.toDebugString() +>>> print(conf.toDebugString()) spark.executorEnv.VAR1=value1 spark.executorEnv.VAR3=value3 spark.executorEnv.VAR4=value4 @@ -56,6 +56,13 @@ __all__ = ['SparkConf'] +import sys +import re + +if sys.version > '3': + unicode = str + __doc__ = re.sub(r"(\W|^)[uU](['])", r'\1\2', __doc__) + class SparkConf(object): diff --git a/python/pyspark/context.py b/python/pyspark/context.py index 78dccc40470e3..1dc2fec0ae5c8 100644 --- a/python/pyspark/context.py +++ b/python/pyspark/context.py @@ -15,6 +15,8 @@ # limitations under the License. # +from __future__ import print_function + import os import shutil import sys @@ -32,11 +34,14 @@ from pyspark.serializers import PickleSerializer, BatchedSerializer, UTF8Deserializer, \ PairDeserializer, AutoBatchedSerializer, NoOpSerializer from pyspark.storagelevel import StorageLevel -from pyspark.rdd import RDD, _load_from_socket +from pyspark.rdd import RDD, _load_from_socket, ignore_unicode_prefix from pyspark.traceback_utils import CallSite, first_spark_call from pyspark.status import StatusTracker from pyspark.profiler import ProfilerCollector, BasicProfiler +if sys.version > '3': + xrange = range + __all__ = ['SparkContext'] @@ -133,7 +138,7 @@ def _do_init(self, master, appName, sparkHome, pyFiles, environment, batchSize, if sparkHome: self._conf.setSparkHome(sparkHome) if environment: - for key, value in environment.iteritems(): + for key, value in environment.items(): self._conf.setExecutorEnv(key, value) for key, value in DEFAULT_CONFIGS.items(): self._conf.setIfMissing(key, value) @@ -153,6 +158,10 @@ def _do_init(self, master, appName, sparkHome, pyFiles, environment, batchSize, if k.startswith("spark.executorEnv."): varName = k[len("spark.executorEnv."):] self.environment[varName] = v + if sys.version >= '3.3' and 'PYTHONHASHSEED' not in os.environ: + # disable randomness of hash of string in worker, if this is not + # launched by spark-submit + self.environment["PYTHONHASHSEED"] = "0" # Create the Java SparkContext through Py4J self._jsc = jsc or self._initialize_context(self._conf._jconf) @@ -323,7 +332,7 @@ def parallelize(self, c, numSlices=None): start0 = c[0] def getStart(split): - return start0 + (split * size / numSlices) * step + return start0 + int((split * size / numSlices)) * step def f(split, iterator): return xrange(getStart(split), getStart(split + 1), step) @@ -357,6 +366,7 @@ def pickleFile(self, name, minPartitions=None): minPartitions = minPartitions or self.defaultMinPartitions return RDD(self._jsc.objectFile(name, minPartitions), self) + @ignore_unicode_prefix def textFile(self, name, minPartitions=None, use_unicode=True): """ Read a text file from HDFS, a local file system (available on all @@ -369,7 +379,7 @@ def textFile(self, name, minPartitions=None, use_unicode=True): >>> path = os.path.join(tempdir, "sample-text.txt") >>> with open(path, "w") as testFile: - ... testFile.write("Hello world!") + ... _ = testFile.write("Hello world!") >>> textFile = sc.textFile(path) >>> textFile.collect() [u'Hello world!'] @@ -378,6 +388,7 @@ def textFile(self, name, minPartitions=None, use_unicode=True): return RDD(self._jsc.textFile(name, minPartitions), self, UTF8Deserializer(use_unicode)) + @ignore_unicode_prefix def wholeTextFiles(self, path, minPartitions=None, use_unicode=True): """ Read a directory of text files from HDFS, a local file system @@ -411,9 +422,9 @@ def wholeTextFiles(self, path, minPartitions=None, use_unicode=True): >>> dirPath = os.path.join(tempdir, "files") >>> os.mkdir(dirPath) >>> with open(os.path.join(dirPath, "1.txt"), "w") as file1: - ... file1.write("1") + ... _ = file1.write("1") >>> with open(os.path.join(dirPath, "2.txt"), "w") as file2: - ... file2.write("2") + ... _ = file2.write("2") >>> textFiles = sc.wholeTextFiles(dirPath) >>> sorted(textFiles.collect()) [(u'.../1.txt', u'1'), (u'.../2.txt', u'2')] @@ -456,7 +467,7 @@ def _dictToJavaMap(self, d): jm = self._jvm.java.util.HashMap() if not d: d = {} - for k, v in d.iteritems(): + for k, v in d.items(): jm[k] = v return jm @@ -608,6 +619,7 @@ def _checkpointFile(self, name, input_deserializer): jrdd = self._jsc.checkpointFile(name) return RDD(jrdd, self, input_deserializer) + @ignore_unicode_prefix def union(self, rdds): """ Build the union of a list of RDDs. @@ -618,7 +630,7 @@ def union(self, rdds): >>> path = os.path.join(tempdir, "union-text.txt") >>> with open(path, "w") as testFile: - ... testFile.write("Hello") + ... _ = testFile.write("Hello") >>> textFile = sc.textFile(path) >>> textFile.collect() [u'Hello'] @@ -677,7 +689,7 @@ def addFile(self, path): >>> from pyspark import SparkFiles >>> path = os.path.join(tempdir, "test.txt") >>> with open(path, "w") as testFile: - ... testFile.write("100") + ... _ = testFile.write("100") >>> sc.addFile(path) >>> def func(iterator): ... with open(SparkFiles.get("test.txt")) as testFile: @@ -705,11 +717,13 @@ def addPyFile(self, path): """ self.addFile(path) (dirname, filename) = os.path.split(path) # dirname may be directory or HDFS/S3 prefix - if filename[-4:].lower() in self.PACKAGE_EXTENSIONS: self._python_includes.append(filename) # for tests in local mode sys.path.insert(1, os.path.join(SparkFiles.getRootDirectory(), filename)) + if sys.version > '3': + import importlib + importlib.invalidate_caches() def setCheckpointDir(self, dirName): """ @@ -744,7 +758,7 @@ def setJobGroup(self, groupId, description, interruptOnCancel=False): The application can use L{SparkContext.cancelJobGroup} to cancel all running jobs in this group. - >>> import thread, threading + >>> import threading >>> from time import sleep >>> result = "Not Set" >>> lock = threading.Lock() @@ -763,10 +777,10 @@ def setJobGroup(self, groupId, description, interruptOnCancel=False): ... sleep(5) ... sc.cancelJobGroup("job_to_cancel") >>> supress = lock.acquire() - >>> supress = thread.start_new_thread(start_job, (10,)) - >>> supress = thread.start_new_thread(stop_job, tuple()) + >>> supress = threading.Thread(target=start_job, args=(10,)).start() + >>> supress = threading.Thread(target=stop_job).start() >>> supress = lock.acquire() - >>> print result + >>> print(result) Cancelled If interruptOnCancel is set to true for the job group, then job cancellation will result diff --git a/python/pyspark/daemon.py b/python/pyspark/daemon.py index 93885985fe377..7f06d4288c872 100644 --- a/python/pyspark/daemon.py +++ b/python/pyspark/daemon.py @@ -24,9 +24,10 @@ import traceback import time import gc -from errno import EINTR, ECHILD, EAGAIN +from errno import EINTR, EAGAIN from socket import AF_INET, SOCK_STREAM, SOMAXCONN from signal import SIGHUP, SIGTERM, SIGCHLD, SIG_DFL, SIG_IGN, SIGINT + from pyspark.worker import main as worker_main from pyspark.serializers import read_int, write_int @@ -53,8 +54,8 @@ def worker(sock): # Read the socket using fdopen instead of socket.makefile() because the latter # seems to be very slow; note that we need to dup() the file descriptor because # otherwise writes also cause a seek that makes us miss data on the read side. - infile = os.fdopen(os.dup(sock.fileno()), "a+", 65536) - outfile = os.fdopen(os.dup(sock.fileno()), "a+", 65536) + infile = os.fdopen(os.dup(sock.fileno()), "rb", 65536) + outfile = os.fdopen(os.dup(sock.fileno()), "wb", 65536) exit_code = 0 try: worker_main(infile, outfile) @@ -68,17 +69,6 @@ def worker(sock): return exit_code -# Cleanup zombie children -def cleanup_dead_children(): - try: - while True: - pid, _ = os.waitpid(0, os.WNOHANG) - if not pid: - break - except: - pass - - def manager(): # Create a new process group to corral our children os.setpgid(0, 0) @@ -88,8 +78,12 @@ def manager(): listen_sock.bind(('127.0.0.1', 0)) listen_sock.listen(max(1024, SOMAXCONN)) listen_host, listen_port = listen_sock.getsockname() - write_int(listen_port, sys.stdout) - sys.stdout.flush() + + # re-open stdin/stdout in 'wb' mode + stdin_bin = os.fdopen(sys.stdin.fileno(), 'rb', 4) + stdout_bin = os.fdopen(sys.stdout.fileno(), 'wb', 4) + write_int(listen_port, stdout_bin) + stdout_bin.flush() def shutdown(code): signal.signal(SIGTERM, SIG_DFL) @@ -101,6 +95,7 @@ def handle_sigterm(*args): shutdown(1) signal.signal(SIGTERM, handle_sigterm) # Gracefully exit on SIGTERM signal.signal(SIGHUP, SIG_IGN) # Don't die on SIGHUP + signal.signal(SIGCHLD, SIG_IGN) reuse = os.environ.get("SPARK_REUSE_WORKER") @@ -115,12 +110,9 @@ def handle_sigterm(*args): else: raise - # cleanup in signal handler will cause deadlock - cleanup_dead_children() - if 0 in ready_fds: try: - worker_pid = read_int(sys.stdin) + worker_pid = read_int(stdin_bin) except EOFError: # Spark told us to exit by closing stdin shutdown(0) @@ -145,7 +137,7 @@ def handle_sigterm(*args): time.sleep(1) pid = os.fork() # error here will shutdown daemon else: - outfile = sock.makefile('w') + outfile = sock.makefile(mode='wb') write_int(e.errno, outfile) # Signal that the fork failed outfile.flush() outfile.close() @@ -157,7 +149,7 @@ def handle_sigterm(*args): listen_sock.close() try: # Acknowledge that the fork was successful - outfile = sock.makefile("w") + outfile = sock.makefile(mode="wb") write_int(os.getpid(), outfile) outfile.flush() outfile.close() diff --git a/python/pyspark/heapq3.py b/python/pyspark/heapq3.py index bc441f138f7fc..4ef2afe03544f 100644 --- a/python/pyspark/heapq3.py +++ b/python/pyspark/heapq3.py @@ -627,51 +627,49 @@ def merge(iterables, key=None, reverse=False): if key is None: for order, it in enumerate(map(iter, iterables)): try: - next = it.next - h_append([next(), order * direction, next]) + h_append([next(it), order * direction, it]) except StopIteration: pass _heapify(h) while len(h) > 1: try: while True: - value, order, next = s = h[0] + value, order, it = s = h[0] yield value - s[0] = next() # raises StopIteration when exhausted + s[0] = next(it) # raises StopIteration when exhausted _heapreplace(h, s) # restore heap condition except StopIteration: _heappop(h) # remove empty iterator if h: # fast case when only a single iterator remains - value, order, next = h[0] + value, order, it = h[0] yield value - for value in next.__self__: + for value in it: yield value return for order, it in enumerate(map(iter, iterables)): try: - next = it.next - value = next() - h_append([key(value), order * direction, value, next]) + value = next(it) + h_append([key(value), order * direction, value, it]) except StopIteration: pass _heapify(h) while len(h) > 1: try: while True: - key_value, order, value, next = s = h[0] + key_value, order, value, it = s = h[0] yield value - value = next() + value = next(it) s[0] = key(value) s[2] = value _heapreplace(h, s) except StopIteration: _heappop(h) if h: - key_value, order, value, next = h[0] + key_value, order, value, it = h[0] yield value - for value in next.__self__: + for value in it: yield value diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py index 2a5e84a7dfdb4..45bc38f7e61f8 100644 --- a/python/pyspark/java_gateway.py +++ b/python/pyspark/java_gateway.py @@ -69,7 +69,7 @@ def preexec_func(): if callback_socket in readable: gateway_connection = callback_socket.accept()[0] # Determine which ephemeral port the server started on: - gateway_port = read_int(gateway_connection.makefile()) + gateway_port = read_int(gateway_connection.makefile(mode="rb")) gateway_connection.close() callback_socket.close() if gateway_port is None: diff --git a/python/pyspark/join.py b/python/pyspark/join.py index c3491defb2b29..94df3990164d6 100644 --- a/python/pyspark/join.py +++ b/python/pyspark/join.py @@ -32,6 +32,7 @@ """ from pyspark.resultiterable import ResultIterable +from functools import reduce def _do_python_join(rdd, other, numPartitions, dispatch): diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py index d7bc09fd77adb..45754bc9d4b10 100644 --- a/python/pyspark/ml/classification.py +++ b/python/pyspark/ml/classification.py @@ -39,10 +39,10 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti >>> lr = LogisticRegression(maxIter=5, regParam=0.01) >>> model = lr.fit(df) >>> test0 = sc.parallelize([Row(features=Vectors.dense(-1.0))]).toDF() - >>> print model.transform(test0).head().prediction + >>> model.transform(test0).head().prediction 0.0 >>> test1 = sc.parallelize([Row(features=Vectors.sparse(1, [0], [1.0]))]).toDF() - >>> print model.transform(test1).head().prediction + >>> model.transform(test1).head().prediction 1.0 >>> lr.setParams("vector") Traceback (most recent call last): diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py index 263fe2a5bcc41..4e4614b859ac6 100644 --- a/python/pyspark/ml/feature.py +++ b/python/pyspark/ml/feature.py @@ -15,6 +15,7 @@ # limitations under the License. # +from pyspark.rdd import ignore_unicode_prefix from pyspark.ml.param.shared import HasInputCol, HasOutputCol, HasNumFeatures from pyspark.ml.util import keyword_only from pyspark.ml.wrapper import JavaTransformer @@ -24,6 +25,7 @@ @inherit_doc +@ignore_unicode_prefix class Tokenizer(JavaTransformer, HasInputCol, HasOutputCol): """ A tokenizer that converts the input string to lowercase and then @@ -32,15 +34,15 @@ class Tokenizer(JavaTransformer, HasInputCol, HasOutputCol): >>> from pyspark.sql import Row >>> df = sc.parallelize([Row(text="a b c")]).toDF() >>> tokenizer = Tokenizer(inputCol="text", outputCol="words") - >>> print tokenizer.transform(df).head() + >>> tokenizer.transform(df).head() Row(text=u'a b c', words=[u'a', u'b', u'c']) >>> # Change a parameter. - >>> print tokenizer.setParams(outputCol="tokens").transform(df).head() + >>> tokenizer.setParams(outputCol="tokens").transform(df).head() Row(text=u'a b c', tokens=[u'a', u'b', u'c']) >>> # Temporarily modify a parameter. - >>> print tokenizer.transform(df, {tokenizer.outputCol: "words"}).head() + >>> tokenizer.transform(df, {tokenizer.outputCol: "words"}).head() Row(text=u'a b c', words=[u'a', u'b', u'c']) - >>> print tokenizer.transform(df).head() + >>> tokenizer.transform(df).head() Row(text=u'a b c', tokens=[u'a', u'b', u'c']) >>> # Must use keyword arguments to specify params. >>> tokenizer.setParams("text") @@ -79,13 +81,13 @@ class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures): >>> from pyspark.sql import Row >>> df = sc.parallelize([Row(words=["a", "b", "c"])]).toDF() >>> hashingTF = HashingTF(numFeatures=10, inputCol="words", outputCol="features") - >>> print hashingTF.transform(df).head().features - (10,[7,8,9],[1.0,1.0,1.0]) - >>> print hashingTF.setParams(outputCol="freqs").transform(df).head().freqs - (10,[7,8,9],[1.0,1.0,1.0]) + >>> hashingTF.transform(df).head().features + SparseVector(10, {7: 1.0, 8: 1.0, 9: 1.0}) + >>> hashingTF.setParams(outputCol="freqs").transform(df).head().freqs + SparseVector(10, {7: 1.0, 8: 1.0, 9: 1.0}) >>> params = {hashingTF.numFeatures: 5, hashingTF.outputCol: "vector"} - >>> print hashingTF.transform(df, params).head().vector - (5,[2,3,4],[1.0,1.0,1.0]) + >>> hashingTF.transform(df, params).head().vector + SparseVector(5, {2: 1.0, 3: 1.0, 4: 1.0}) """ _java_class = "org.apache.spark.ml.feature.HashingTF" diff --git a/python/pyspark/ml/param/__init__.py b/python/pyspark/ml/param/__init__.py index 5c62620562a84..9fccb65675185 100644 --- a/python/pyspark/ml/param/__init__.py +++ b/python/pyspark/ml/param/__init__.py @@ -63,8 +63,8 @@ def params(self): uses :py:func:`dir` to get all attributes of type :py:class:`Param`. """ - return filter(lambda attr: isinstance(attr, Param), - [getattr(self, x) for x in dir(self) if x != "params"]) + return list(filter(lambda attr: isinstance(attr, Param), + [getattr(self, x) for x in dir(self) if x != "params"])) def _explain(self, param): """ @@ -185,7 +185,7 @@ def _set(self, **kwargs): """ Sets user-supplied params. """ - for param, value in kwargs.iteritems(): + for param, value in kwargs.items(): self.paramMap[getattr(self, param)] = value return self @@ -193,6 +193,6 @@ def _setDefault(self, **kwargs): """ Sets default params. """ - for param, value in kwargs.iteritems(): + for param, value in kwargs.items(): self.defaultParamMap[getattr(self, param)] = value return self diff --git a/python/pyspark/ml/param/_shared_params_code_gen.py b/python/pyspark/ml/param/_shared_params_code_gen.py index 55f422497672f..6a3192465d66d 100644 --- a/python/pyspark/ml/param/_shared_params_code_gen.py +++ b/python/pyspark/ml/param/_shared_params_code_gen.py @@ -15,6 +15,8 @@ # limitations under the License. # +from __future__ import print_function + header = """# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with @@ -82,9 +84,9 @@ def get$Name(self): .replace("$defaultValueStr", str(defaultValueStr)) if __name__ == "__main__": - print header - print "\n# DO NOT MODIFY THIS FILE! It was generated by _shared_params_code_gen.py.\n" - print "from pyspark.ml.param import Param, Params\n\n" + print(header) + print("\n# DO NOT MODIFY THIS FILE! It was generated by _shared_params_code_gen.py.\n") + print("from pyspark.ml.param import Param, Params\n\n") shared = [ ("maxIter", "max number of iterations", None), ("regParam", "regularization constant", None), @@ -97,4 +99,4 @@ def get$Name(self): code = [] for name, doc, defaultValueStr in shared: code.append(_gen_param_code(name, doc, defaultValueStr)) - print "\n\n\n".join(code) + print("\n\n\n".join(code)) diff --git a/python/pyspark/mllib/__init__.py b/python/pyspark/mllib/__init__.py index f2ef573fe9f6f..07507b2ad0d05 100644 --- a/python/pyspark/mllib/__init__.py +++ b/python/pyspark/mllib/__init__.py @@ -18,6 +18,7 @@ """ Python bindings for MLlib. """ +from __future__ import absolute_import # MLlib currently needs NumPy 1.4+, so complain if lower @@ -29,7 +30,9 @@ 'recommendation', 'regression', 'stat', 'tree', 'util'] import sys -import rand as random -random.__name__ = 'random' -random.RandomRDDs.__module__ = __name__ + '.random' -sys.modules[__name__ + '.random'] = random +from . import rand as random +modname = __name__ + '.random' +random.__name__ = modname +random.RandomRDDs.__module__ = modname +sys.modules[modname] = random +del modname, sys diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py index 2466e8ac43458..eda0b60f8b1e7 100644 --- a/python/pyspark/mllib/classification.py +++ b/python/pyspark/mllib/classification.py @@ -510,9 +510,10 @@ def save(self, sc, path): def load(cls, sc, path): java_model = sc._jvm.org.apache.spark.mllib.classification.NaiveBayesModel.load( sc._jsc.sc(), path) - py_labels = _java2py(sc, java_model.labels()) - py_pi = _java2py(sc, java_model.pi()) - py_theta = _java2py(sc, java_model.theta()) + # Can not unpickle array.array from Pyrolite in Python3 with "bytes" + py_labels = _java2py(sc, java_model.labels(), "latin1") + py_pi = _java2py(sc, java_model.pi(), "latin1") + py_theta = _java2py(sc, java_model.theta(), "latin1") return NaiveBayesModel(py_labels, py_pi, numpy.array(py_theta)) diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py index 464f49aeee3cd..abbb7cf60eece 100644 --- a/python/pyspark/mllib/clustering.py +++ b/python/pyspark/mllib/clustering.py @@ -15,6 +15,12 @@ # limitations under the License. # +import sys +import array as pyarray + +if sys.version > '3': + xrange = range + from numpy import array from pyspark import RDD @@ -55,8 +61,8 @@ class KMeansModel(Saveable, Loader): True >>> model.predict(sparse_data[2]) == model.predict(sparse_data[3]) True - >>> type(model.clusterCenters) - + >>> isinstance(model.clusterCenters, list) + True >>> import os, tempfile >>> path = tempfile.mkdtemp() >>> model.save(sc, path) @@ -90,7 +96,7 @@ def predict(self, x): return best def save(self, sc, path): - java_centers = _py2java(sc, map(_convert_to_vector, self.centers)) + java_centers = _py2java(sc, [_convert_to_vector(c) for c in self.centers]) java_model = sc._jvm.org.apache.spark.mllib.clustering.KMeansModel(java_centers) java_model.save(sc._jsc.sc(), path) @@ -133,7 +139,7 @@ class GaussianMixtureModel(object): ... 5.7048, 4.6567, 5.5026, ... 4.5605, 5.2043, 6.2734]).reshape(5, 3)) >>> model = GaussianMixture.train(clusterdata_2, 2, convergenceTol=0.0001, - ... maxIterations=150, seed=10) + ... maxIterations=150, seed=10) >>> labels = model.predict(clusterdata_2).collect() >>> labels[0]==labels[1]==labels[2] True @@ -168,8 +174,8 @@ def predictSoft(self, x): if isinstance(x, RDD): means, sigmas = zip(*[(g.mu, g.sigma) for g in self.gaussians]) membership_matrix = callMLlibFunc("predictSoftGMM", x.map(_convert_to_vector), - self.weights, means, sigmas) - return membership_matrix + _convert_to_vector(self.weights), means, sigmas) + return membership_matrix.map(lambda x: pyarray.array('d', x)) class GaussianMixture(object): diff --git a/python/pyspark/mllib/common.py b/python/pyspark/mllib/common.py index a539d2f2846f9..ba6058978880a 100644 --- a/python/pyspark/mllib/common.py +++ b/python/pyspark/mllib/common.py @@ -15,6 +15,11 @@ # limitations under the License. # +import sys +if sys.version >= '3': + long = int + unicode = str + import py4j.protocol from py4j.protocol import Py4JJavaError from py4j.java_gateway import JavaObject @@ -36,7 +41,7 @@ def _new_smart_decode(obj): if isinstance(obj, float): - s = unicode(obj) + s = str(obj) return _float_str_mapping.get(s, s) return _old_smart_decode(obj) @@ -74,15 +79,15 @@ def _py2java(sc, obj): obj = ListConverter().convert([_py2java(sc, x) for x in obj], sc._gateway._gateway_client) elif isinstance(obj, JavaObject): pass - elif isinstance(obj, (int, long, float, bool, basestring)): + elif isinstance(obj, (int, long, float, bool, bytes, unicode)): pass else: - bytes = bytearray(PickleSerializer().dumps(obj)) - obj = sc._jvm.SerDe.loads(bytes) + data = bytearray(PickleSerializer().dumps(obj)) + obj = sc._jvm.SerDe.loads(data) return obj -def _java2py(sc, r): +def _java2py(sc, r, encoding="bytes"): if isinstance(r, JavaObject): clsName = r.getClass().getSimpleName() # convert RDD into JavaRDD @@ -102,8 +107,8 @@ def _java2py(sc, r): except Py4JJavaError: pass # not pickable - if isinstance(r, bytearray): - r = PickleSerializer().loads(str(r)) + if isinstance(r, (bytearray, bytes)): + r = PickleSerializer().loads(bytes(r), encoding=encoding) return r diff --git a/python/pyspark/mllib/feature.py b/python/pyspark/mllib/feature.py index 8be819aceec24..1140539a24e95 100644 --- a/python/pyspark/mllib/feature.py +++ b/python/pyspark/mllib/feature.py @@ -23,12 +23,17 @@ import sys import warnings import random +import binascii +if sys.version >= '3': + basestring = str + unicode = str from py4j.protocol import Py4JJavaError -from pyspark import RDD, SparkContext +from pyspark import SparkContext +from pyspark.rdd import RDD, ignore_unicode_prefix from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper -from pyspark.mllib.linalg import Vectors, Vector, _convert_to_vector +from pyspark.mllib.linalg import Vectors, _convert_to_vector __all__ = ['Normalizer', 'StandardScalerModel', 'StandardScaler', 'HashingTF', 'IDFModel', 'IDF', 'Word2Vec', 'Word2VecModel'] @@ -206,7 +211,7 @@ class HashingTF(object): >>> htf = HashingTF(100) >>> doc = "a a b b c d".split(" ") >>> htf.transform(doc) - SparseVector(100, {1: 1.0, 14: 1.0, 31: 2.0, 44: 2.0}) + SparseVector(100, {...}) """ def __init__(self, numFeatures=1 << 20): """ @@ -360,6 +365,7 @@ def getVectors(self): return self.call("getVectors") +@ignore_unicode_prefix class Word2Vec(object): """ Word2Vec creates vector representation of words in a text corpus. @@ -382,7 +388,7 @@ class Word2Vec(object): >>> sentence = "a b " * 100 + "a c " * 10 >>> localDoc = [sentence, sentence] >>> doc = sc.parallelize(localDoc).map(lambda line: line.split(" ")) - >>> model = Word2Vec().setVectorSize(10).setSeed(42L).fit(doc) + >>> model = Word2Vec().setVectorSize(10).setSeed(42).fit(doc) >>> syms = model.findSynonyms("a", 2) >>> [s[0] for s in syms] @@ -400,7 +406,7 @@ def __init__(self): self.learningRate = 0.025 self.numPartitions = 1 self.numIterations = 1 - self.seed = random.randint(0, sys.maxint) + self.seed = random.randint(0, sys.maxsize) self.minCount = 5 def setVectorSize(self, vectorSize): @@ -459,7 +465,7 @@ def fit(self, data): raise TypeError("data should be an RDD of list of string") jmodel = callMLlibFunc("trainWord2Vec", data, int(self.vectorSize), float(self.learningRate), int(self.numPartitions), - int(self.numIterations), long(self.seed), + int(self.numIterations), int(self.seed), int(self.minCount)) return Word2VecModel(jmodel) diff --git a/python/pyspark/mllib/fpm.py b/python/pyspark/mllib/fpm.py index 3aa6d79d7093c..628ccc01cf3cc 100644 --- a/python/pyspark/mllib/fpm.py +++ b/python/pyspark/mllib/fpm.py @@ -16,12 +16,14 @@ # from pyspark import SparkContext +from pyspark.rdd import ignore_unicode_prefix from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, inherit_doc __all__ = ['FPGrowth', 'FPGrowthModel'] @inherit_doc +@ignore_unicode_prefix class FPGrowthModel(JavaModelWrapper): """ diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py index a80320c52d1d0..38b3aa3ad460e 100644 --- a/python/pyspark/mllib/linalg.py +++ b/python/pyspark/mllib/linalg.py @@ -25,7 +25,13 @@ import sys import array -import copy_reg + +if sys.version >= '3': + basestring = str + xrange = range + import copyreg as copy_reg +else: + import copy_reg import numpy as np @@ -57,7 +63,7 @@ def fast_pickle_array(ar): def _convert_to_vector(l): if isinstance(l, Vector): return l - elif type(l) in (array.array, np.array, np.ndarray, list, tuple): + elif type(l) in (array.array, np.array, np.ndarray, list, tuple, xrange): return DenseVector(l) elif _have_scipy and scipy.sparse.issparse(l): assert l.shape[1] == 1, "Expected column vector" @@ -88,7 +94,7 @@ def _vector_size(v): """ if isinstance(v, Vector): return len(v) - elif type(v) in (array.array, list, tuple): + elif type(v) in (array.array, list, tuple, xrange): return len(v) elif type(v) == np.ndarray: if v.ndim == 1 or (v.ndim == 2 and v.shape[1] == 1): @@ -193,7 +199,7 @@ class DenseVector(Vector): DenseVector([1.0, 0.0]) """ def __init__(self, ar): - if isinstance(ar, basestring): + if isinstance(ar, bytes): ar = np.frombuffer(ar, dtype=np.float64) elif not isinstance(ar, np.ndarray): ar = np.array(ar, dtype=np.float64) @@ -321,11 +327,13 @@ def func(self, other): __sub__ = _delegate("__sub__") __mul__ = _delegate("__mul__") __div__ = _delegate("__div__") + __truediv__ = _delegate("__truediv__") __mod__ = _delegate("__mod__") __radd__ = _delegate("__radd__") __rsub__ = _delegate("__rsub__") __rmul__ = _delegate("__rmul__") __rdiv__ = _delegate("__rdiv__") + __rtruediv__ = _delegate("__rtruediv__") __rmod__ = _delegate("__rmod__") @@ -344,12 +352,12 @@ def __init__(self, size, *args): :param args: Non-zero entries, as a dictionary, list of tupes, or two sorted lists containing indices and values. - >>> print SparseVector(4, {1: 1.0, 3: 5.5}) - (4,[1,3],[1.0,5.5]) - >>> print SparseVector(4, [(1, 1.0), (3, 5.5)]) - (4,[1,3],[1.0,5.5]) - >>> print SparseVector(4, [1, 3], [1.0, 5.5]) - (4,[1,3],[1.0,5.5]) + >>> SparseVector(4, {1: 1.0, 3: 5.5}) + SparseVector(4, {1: 1.0, 3: 5.5}) + >>> SparseVector(4, [(1, 1.0), (3, 5.5)]) + SparseVector(4, {1: 1.0, 3: 5.5}) + >>> SparseVector(4, [1, 3], [1.0, 5.5]) + SparseVector(4, {1: 1.0, 3: 5.5}) """ self.size = int(size) assert 1 <= len(args) <= 2, "must pass either 2 or 3 arguments" @@ -361,8 +369,8 @@ def __init__(self, size, *args): self.indices = np.array([p[0] for p in pairs], dtype=np.int32) self.values = np.array([p[1] for p in pairs], dtype=np.float64) else: - if isinstance(args[0], basestring): - assert isinstance(args[1], str), "values should be string too" + if isinstance(args[0], bytes): + assert isinstance(args[1], bytes), "values should be string too" if args[0]: self.indices = np.frombuffer(args[0], np.int32) self.values = np.frombuffer(args[1], np.float64) @@ -591,12 +599,12 @@ def sparse(size, *args): :param args: Non-zero entries, as a dictionary, list of tupes, or two sorted lists containing indices and values. - >>> print Vectors.sparse(4, {1: 1.0, 3: 5.5}) - (4,[1,3],[1.0,5.5]) - >>> print Vectors.sparse(4, [(1, 1.0), (3, 5.5)]) - (4,[1,3],[1.0,5.5]) - >>> print Vectors.sparse(4, [1, 3], [1.0, 5.5]) - (4,[1,3],[1.0,5.5]) + >>> Vectors.sparse(4, {1: 1.0, 3: 5.5}) + SparseVector(4, {1: 1.0, 3: 5.5}) + >>> Vectors.sparse(4, [(1, 1.0), (3, 5.5)]) + SparseVector(4, {1: 1.0, 3: 5.5}) + >>> Vectors.sparse(4, [1, 3], [1.0, 5.5]) + SparseVector(4, {1: 1.0, 3: 5.5}) """ return SparseVector(size, *args) @@ -645,7 +653,7 @@ def _convert_to_array(array_like, dtype): """ Convert Matrix attributes which are array-like or buffer to array. """ - if isinstance(array_like, basestring): + if isinstance(array_like, bytes): return np.frombuffer(array_like, dtype=dtype) return np.asarray(array_like, dtype=dtype) @@ -677,7 +685,7 @@ def toArray(self): def toSparse(self): """Convert to SparseMatrix""" indices = np.nonzero(self.values)[0] - colCounts = np.bincount(indices / self.numRows) + colCounts = np.bincount(indices // self.numRows) colPtrs = np.cumsum(np.hstack( (0, colCounts, np.zeros(self.numCols - colCounts.size)))) values = self.values[indices] diff --git a/python/pyspark/mllib/rand.py b/python/pyspark/mllib/rand.py index 20ee9d78bf5b0..06fbc0eb6aef0 100644 --- a/python/pyspark/mllib/rand.py +++ b/python/pyspark/mllib/rand.py @@ -88,10 +88,10 @@ def normalRDD(sc, size, numPartitions=None, seed=None): :param seed: Random seed (default: a random long integer). :return: RDD of float comprised of i.i.d. samples ~ N(0.0, 1.0). - >>> x = RandomRDDs.normalRDD(sc, 1000, seed=1L) + >>> x = RandomRDDs.normalRDD(sc, 1000, seed=1) >>> stats = x.stats() >>> stats.count() - 1000L + 1000 >>> abs(stats.mean() - 0.0) < 0.1 True >>> abs(stats.stdev() - 1.0) < 0.1 @@ -118,10 +118,10 @@ def logNormalRDD(sc, mean, std, size, numPartitions=None, seed=None): >>> std = 1.0 >>> expMean = exp(mean + 0.5 * std * std) >>> expStd = sqrt((exp(std * std) - 1.0) * exp(2.0 * mean + std * std)) - >>> x = RandomRDDs.logNormalRDD(sc, mean, std, 1000, seed=2L) + >>> x = RandomRDDs.logNormalRDD(sc, mean, std, 1000, seed=2) >>> stats = x.stats() >>> stats.count() - 1000L + 1000 >>> abs(stats.mean() - expMean) < 0.5 True >>> from math import sqrt @@ -145,10 +145,10 @@ def poissonRDD(sc, mean, size, numPartitions=None, seed=None): :return: RDD of float comprised of i.i.d. samples ~ Pois(mean). >>> mean = 100.0 - >>> x = RandomRDDs.poissonRDD(sc, mean, 1000, seed=2L) + >>> x = RandomRDDs.poissonRDD(sc, mean, 1000, seed=2) >>> stats = x.stats() >>> stats.count() - 1000L + 1000 >>> abs(stats.mean() - mean) < 0.5 True >>> from math import sqrt @@ -171,10 +171,10 @@ def exponentialRDD(sc, mean, size, numPartitions=None, seed=None): :return: RDD of float comprised of i.i.d. samples ~ Exp(mean). >>> mean = 2.0 - >>> x = RandomRDDs.exponentialRDD(sc, mean, 1000, seed=2L) + >>> x = RandomRDDs.exponentialRDD(sc, mean, 1000, seed=2) >>> stats = x.stats() >>> stats.count() - 1000L + 1000 >>> abs(stats.mean() - mean) < 0.5 True >>> from math import sqrt @@ -202,10 +202,10 @@ def gammaRDD(sc, shape, scale, size, numPartitions=None, seed=None): >>> scale = 2.0 >>> expMean = shape * scale >>> expStd = sqrt(shape * scale * scale) - >>> x = RandomRDDs.gammaRDD(sc, shape, scale, 1000, seed=2L) + >>> x = RandomRDDs.gammaRDD(sc, shape, scale, 1000, seed=2) >>> stats = x.stats() >>> stats.count() - 1000L + 1000 >>> abs(stats.mean() - expMean) < 0.5 True >>> abs(stats.stdev() - expStd) < 0.5 @@ -254,7 +254,7 @@ def normalVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None): :return: RDD of Vector with vectors containing i.i.d. samples ~ `N(0.0, 1.0)`. >>> import numpy as np - >>> mat = np.matrix(RandomRDDs.normalVectorRDD(sc, 100, 100, seed=1L).collect()) + >>> mat = np.matrix(RandomRDDs.normalVectorRDD(sc, 100, 100, seed=1).collect()) >>> mat.shape (100, 100) >>> abs(mat.mean() - 0.0) < 0.1 @@ -286,8 +286,8 @@ def logNormalVectorRDD(sc, mean, std, numRows, numCols, numPartitions=None, seed >>> std = 1.0 >>> expMean = exp(mean + 0.5 * std * std) >>> expStd = sqrt((exp(std * std) - 1.0) * exp(2.0 * mean + std * std)) - >>> mat = np.matrix(RandomRDDs.logNormalVectorRDD(sc, mean, std, \ - 100, 100, seed=1L).collect()) + >>> m = RandomRDDs.logNormalVectorRDD(sc, mean, std, 100, 100, seed=1).collect() + >>> mat = np.matrix(m) >>> mat.shape (100, 100) >>> abs(mat.mean() - expMean) < 0.1 @@ -315,7 +315,7 @@ def poissonVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None): >>> import numpy as np >>> mean = 100.0 - >>> rdd = RandomRDDs.poissonVectorRDD(sc, mean, 100, 100, seed=1L) + >>> rdd = RandomRDDs.poissonVectorRDD(sc, mean, 100, 100, seed=1) >>> mat = np.mat(rdd.collect()) >>> mat.shape (100, 100) @@ -345,7 +345,7 @@ def exponentialVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=No >>> import numpy as np >>> mean = 0.5 - >>> rdd = RandomRDDs.exponentialVectorRDD(sc, mean, 100, 100, seed=1L) + >>> rdd = RandomRDDs.exponentialVectorRDD(sc, mean, 100, 100, seed=1) >>> mat = np.mat(rdd.collect()) >>> mat.shape (100, 100) @@ -380,8 +380,7 @@ def gammaVectorRDD(sc, shape, scale, numRows, numCols, numPartitions=None, seed= >>> scale = 2.0 >>> expMean = shape * scale >>> expStd = sqrt(shape * scale * scale) - >>> mat = np.matrix(RandomRDDs.gammaVectorRDD(sc, shape, scale, \ - 100, 100, seed=1L).collect()) + >>> mat = np.matrix(RandomRDDs.gammaVectorRDD(sc, shape, scale, 100, 100, seed=1).collect()) >>> mat.shape (100, 100) >>> abs(mat.mean() - expMean) < 0.1 diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py index c5c4c13dae105..80e0a356bb78a 100644 --- a/python/pyspark/mllib/recommendation.py +++ b/python/pyspark/mllib/recommendation.py @@ -15,6 +15,7 @@ # limitations under the License. # +import array from collections import namedtuple from pyspark import SparkContext @@ -104,14 +105,14 @@ def predictAll(self, user_product): assert isinstance(user_product, RDD), "user_product should be RDD of (user, product)" first = user_product.first() assert len(first) == 2, "user_product should be RDD of (user, product)" - user_product = user_product.map(lambda (u, p): (int(u), int(p))) + user_product = user_product.map(lambda u_p: (int(u_p[0]), int(u_p[1]))) return self.call("predict", user_product) def userFeatures(self): - return self.call("getUserFeatures") + return self.call("getUserFeatures").mapValues(lambda v: array.array('d', v)) def productFeatures(self): - return self.call("getProductFeatures") + return self.call("getProductFeatures").mapValues(lambda v: array.array('d', v)) @classmethod def load(cls, sc, path): diff --git a/python/pyspark/mllib/stat/_statistics.py b/python/pyspark/mllib/stat/_statistics.py index 1d83e9d483f8e..b475be4b4d953 100644 --- a/python/pyspark/mllib/stat/_statistics.py +++ b/python/pyspark/mllib/stat/_statistics.py @@ -15,7 +15,7 @@ # limitations under the License. # -from pyspark import RDD +from pyspark.rdd import RDD, ignore_unicode_prefix from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper from pyspark.mllib.linalg import Matrix, _convert_to_vector from pyspark.mllib.regression import LabeledPoint @@ -38,7 +38,7 @@ def variance(self): return self.call("variance").toArray() def count(self): - return self.call("count") + return int(self.call("count")) def numNonzeros(self): return self.call("numNonzeros").toArray() @@ -78,7 +78,7 @@ def colStats(rdd): >>> cStats.variance() array([ 4., 13., 0., 25.]) >>> cStats.count() - 3L + 3 >>> cStats.numNonzeros() array([ 3., 2., 0., 3.]) >>> cStats.max() @@ -124,20 +124,20 @@ def corr(x, y=None, method=None): >>> rdd = sc.parallelize([Vectors.dense([1, 0, 0, -2]), Vectors.dense([4, 5, 0, 3]), ... Vectors.dense([6, 7, 0, 8]), Vectors.dense([9, 0, 0, 1])]) >>> pearsonCorr = Statistics.corr(rdd) - >>> print str(pearsonCorr).replace('nan', 'NaN') + >>> print(str(pearsonCorr).replace('nan', 'NaN')) [[ 1. 0.05564149 NaN 0.40047142] [ 0.05564149 1. NaN 0.91359586] [ NaN NaN 1. NaN] [ 0.40047142 0.91359586 NaN 1. ]] >>> spearmanCorr = Statistics.corr(rdd, method="spearman") - >>> print str(spearmanCorr).replace('nan', 'NaN') + >>> print(str(spearmanCorr).replace('nan', 'NaN')) [[ 1. 0.10540926 NaN 0.4 ] [ 0.10540926 1. NaN 0.9486833 ] [ NaN NaN 1. NaN] [ 0.4 0.9486833 NaN 1. ]] >>> try: ... Statistics.corr(rdd, "spearman") - ... print "Method name as second argument without 'method=' shouldn't be allowed." + ... print("Method name as second argument without 'method=' shouldn't be allowed.") ... except TypeError: ... pass """ @@ -153,6 +153,7 @@ def corr(x, y=None, method=None): return callMLlibFunc("corr", x.map(float), y.map(float), method) @staticmethod + @ignore_unicode_prefix def chiSqTest(observed, expected=None): """ .. note:: Experimental @@ -188,11 +189,11 @@ def chiSqTest(observed, expected=None): >>> from pyspark.mllib.linalg import Vectors, Matrices >>> observed = Vectors.dense([4, 6, 5]) >>> pearson = Statistics.chiSqTest(observed) - >>> print pearson.statistic + >>> print(pearson.statistic) 0.4 >>> pearson.degreesOfFreedom 2 - >>> print round(pearson.pValue, 4) + >>> print(round(pearson.pValue, 4)) 0.8187 >>> pearson.method u'pearson' @@ -202,12 +203,12 @@ def chiSqTest(observed, expected=None): >>> observed = Vectors.dense([21, 38, 43, 80]) >>> expected = Vectors.dense([3, 5, 7, 20]) >>> pearson = Statistics.chiSqTest(observed, expected) - >>> print round(pearson.pValue, 4) + >>> print(round(pearson.pValue, 4)) 0.0027 >>> data = [40.0, 24.0, 29.0, 56.0, 32.0, 42.0, 31.0, 10.0, 0.0, 30.0, 15.0, 12.0] >>> chi = Statistics.chiSqTest(Matrices.dense(3, 4, data)) - >>> print round(chi.statistic, 4) + >>> print(round(chi.statistic, 4)) 21.9958 >>> data = [LabeledPoint(0.0, Vectors.dense([0.5, 10.0])), @@ -218,9 +219,9 @@ def chiSqTest(observed, expected=None): ... LabeledPoint(1.0, Vectors.dense([3.5, 40.0])),] >>> rdd = sc.parallelize(data, 4) >>> chi = Statistics.chiSqTest(rdd) - >>> print chi[0].statistic + >>> print(chi[0].statistic) 0.75 - >>> print chi[1].statistic + >>> print(chi[1].statistic) 1.5 """ if isinstance(observed, RDD): diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 8eaddcf8b9b5e..c6ed5acd1770e 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -72,11 +72,11 @@ class VectorTests(PySparkTestCase): def _test_serialize(self, v): self.assertEqual(v, ser.loads(ser.dumps(v))) jvec = self.sc._jvm.SerDe.loads(bytearray(ser.dumps(v))) - nv = ser.loads(str(self.sc._jvm.SerDe.dumps(jvec))) + nv = ser.loads(bytes(self.sc._jvm.SerDe.dumps(jvec))) self.assertEqual(v, nv) vs = [v] * 100 jvecs = self.sc._jvm.SerDe.loads(bytearray(ser.dumps(vs))) - nvs = ser.loads(str(self.sc._jvm.SerDe.dumps(jvecs))) + nvs = ser.loads(bytes(self.sc._jvm.SerDe.dumps(jvecs))) self.assertEqual(vs, nvs) def test_serialize(self): @@ -412,11 +412,11 @@ def test_col_norms(self): self.assertEqual(10, len(summary.normL1())) self.assertEqual(10, len(summary.normL2())) - data2 = self.sc.parallelize(xrange(10)).map(lambda x: Vectors.dense(x)) + data2 = self.sc.parallelize(range(10)).map(lambda x: Vectors.dense(x)) summary2 = Statistics.colStats(data2) self.assertEqual(array([45.0]), summary2.normL1()) import math - expectedNormL2 = math.sqrt(sum(map(lambda x: x*x, xrange(10)))) + expectedNormL2 = math.sqrt(sum(map(lambda x: x*x, range(10)))) self.assertTrue(math.fabs(summary2.normL2()[0] - expectedNormL2) < 1e-14) @@ -438,11 +438,11 @@ def test_serialization(self): def test_infer_schema(self): sqlCtx = SQLContext(self.sc) rdd = self.sc.parallelize([LabeledPoint(1.0, self.dv1), LabeledPoint(0.0, self.sv1)]) - srdd = sqlCtx.inferSchema(rdd) - schema = srdd.schema + df = rdd.toDF() + schema = df.schema field = [f for f in schema.fields if f.name == "features"][0] self.assertEqual(field.dataType, self.udt) - vectors = srdd.map(lambda p: p.features).collect() + vectors = df.map(lambda p: p.features).collect() self.assertEqual(len(vectors), 2) for v in vectors: if isinstance(v, SparseVector): @@ -695,7 +695,7 @@ def test_right_number_of_results(self): class SerDeTest(PySparkTestCase): def test_to_java_object_rdd(self): # SPARK-6660 - data = RandomRDDs.uniformRDD(self.sc, 10, 5, seed=0L) + data = RandomRDDs.uniformRDD(self.sc, 10, 5, seed=0) self.assertEqual(_to_java_object_rdd(data).count(), 10) @@ -771,7 +771,7 @@ def test_model_transform(self): if __name__ == "__main__": if not _have_scipy: - print "NOTE: Skipping SciPy tests as it does not seem to be installed" + print("NOTE: Skipping SciPy tests as it does not seem to be installed") unittest.main() if not _have_scipy: - print "NOTE: SciPy tests were skipped as it does not seem to be installed" + print("NOTE: SciPy tests were skipped as it does not seem to be installed") diff --git a/python/pyspark/mllib/tree.py b/python/pyspark/mllib/tree.py index a7a4d2aaf855b..0fe6e4fabe43a 100644 --- a/python/pyspark/mllib/tree.py +++ b/python/pyspark/mllib/tree.py @@ -163,14 +163,16 @@ def trainClassifier(cls, data, numClasses, categoricalFeaturesInfo, ... LabeledPoint(1.0, [3.0]) ... ] >>> model = DecisionTree.trainClassifier(sc.parallelize(data), 2, {}) - >>> print model, # it already has newline + >>> print(model) DecisionTreeModel classifier of depth 1 with 3 nodes - >>> print model.toDebugString(), # it already has newline + + >>> print(model.toDebugString()) DecisionTreeModel classifier of depth 1 with 3 nodes If (feature 0 <= 0.0) Predict: 0.0 Else (feature 0 > 0.0) Predict: 1.0 + >>> model.predict(array([1.0])) 1.0 >>> model.predict(array([0.0])) @@ -318,9 +320,10 @@ def trainClassifier(cls, data, numClasses, categoricalFeaturesInfo, numTrees, 3 >>> model.totalNumNodes() 7 - >>> print model, + >>> print(model) TreeEnsembleModel classifier with 3 trees - >>> print model.toDebugString(), + + >>> print(model.toDebugString()) TreeEnsembleModel classifier with 3 trees Tree 0: @@ -335,6 +338,7 @@ def trainClassifier(cls, data, numClasses, categoricalFeaturesInfo, numTrees, Predict: 0.0 Else (feature 0 > 1.0) Predict: 1.0 + >>> model.predict([2.0]) 1.0 >>> model.predict([0.0]) @@ -483,8 +487,9 @@ def trainClassifier(cls, data, categoricalFeaturesInfo, 100 >>> model.totalNumNodes() 300 - >>> print model, # it already has newline + >>> print(model) # it already has newline TreeEnsembleModel classifier with 100 trees + >>> model.predict([2.0]) 1.0 >>> model.predict([0.0]) diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py index c5c3468eb95e9..16a90db146ef0 100644 --- a/python/pyspark/mllib/util.py +++ b/python/pyspark/mllib/util.py @@ -15,10 +15,14 @@ # limitations under the License. # +import sys import numpy as np import warnings -from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper, inherit_doc +if sys.version > '3': + xrange = range + +from pyspark.mllib.common import callMLlibFunc, inherit_doc from pyspark.mllib.linalg import Vectors, SparseVector, _convert_to_vector @@ -94,22 +98,16 @@ def loadLibSVMFile(sc, path, numFeatures=-1, minPartitions=None, multiclass=None >>> from pyspark.mllib.util import MLUtils >>> from pyspark.mllib.regression import LabeledPoint >>> tempFile = NamedTemporaryFile(delete=True) - >>> tempFile.write("+1 1:1.0 3:2.0 5:3.0\\n-1\\n-1 2:4.0 4:5.0 6:6.0") + >>> _ = tempFile.write(b"+1 1:1.0 3:2.0 5:3.0\\n-1\\n-1 2:4.0 4:5.0 6:6.0") >>> tempFile.flush() >>> examples = MLUtils.loadLibSVMFile(sc, tempFile.name).collect() >>> tempFile.close() - >>> type(examples[0]) == LabeledPoint - True - >>> print examples[0] - (1.0,(6,[0,2,4],[1.0,2.0,3.0])) - >>> type(examples[1]) == LabeledPoint - True - >>> print examples[1] - (-1.0,(6,[],[])) - >>> type(examples[2]) == LabeledPoint - True - >>> print examples[2] - (-1.0,(6,[1,3,5],[4.0,5.0,6.0])) + >>> examples[0] + LabeledPoint(1.0, (6,[0,2,4],[1.0,2.0,3.0])) + >>> examples[1] + LabeledPoint(-1.0, (6,[],[])) + >>> examples[2] + LabeledPoint(-1.0, (6,[1,3,5],[4.0,5.0,6.0])) """ from pyspark.mllib.regression import LabeledPoint if multiclass is not None: diff --git a/python/pyspark/profiler.py b/python/pyspark/profiler.py index 4408996db0790..d18daaabfcb3c 100644 --- a/python/pyspark/profiler.py +++ b/python/pyspark/profiler.py @@ -84,11 +84,11 @@ class Profiler(object): >>> from pyspark import BasicProfiler >>> class MyCustomProfiler(BasicProfiler): ... def show(self, id): - ... print "My custom profiles for RDD:%s" % id + ... print("My custom profiles for RDD:%s" % id) ... >>> conf = SparkConf().set("spark.python.profile", "true") >>> sc = SparkContext('local', 'test', conf=conf, profiler_cls=MyCustomProfiler) - >>> sc.parallelize(list(range(1000))).map(lambda x: 2 * x).take(10) + >>> sc.parallelize(range(1000)).map(lambda x: 2 * x).take(10) [0, 2, 4, 6, 8, 10, 12, 14, 16, 18] >>> sc.show_profiles() My custom profiles for RDD:1 @@ -111,9 +111,9 @@ def show(self, id): """ Print the profile stats to stdout, id is the RDD id """ stats = self.stats() if stats: - print "=" * 60 - print "Profile of RDD" % id - print "=" * 60 + print("=" * 60) + print("Profile of RDD" % id) + print("=" * 60) stats.sort_stats("time", "cumulative").print_stats() def dump(self, id, path): diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py index 93e658eded9e2..d9cdbb666f92a 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.py @@ -16,21 +16,29 @@ # import copy -from collections import defaultdict -from itertools import chain, ifilter, imap -import operator import sys +import os +import re +import operator import shlex -from subprocess import Popen, PIPE -from tempfile import NamedTemporaryFile -from threading import Thread import warnings import heapq import bisect import random import socket +from subprocess import Popen, PIPE +from tempfile import NamedTemporaryFile +from threading import Thread +from collections import defaultdict +from itertools import chain +from functools import reduce from math import sqrt, log, isinf, isnan, pow, ceil +if sys.version > '3': + basestring = unicode = str +else: + from itertools import imap as map, ifilter as filter + from pyspark.serializers import NoOpSerializer, CartesianDeserializer, \ BatchedSerializer, CloudPickleSerializer, PairDeserializer, \ PickleSerializer, pack_long, AutoBatchedSerializer @@ -50,20 +58,21 @@ __all__ = ["RDD"] -# TODO: for Python 3.3+, PYTHONHASHSEED should be reset to disable randomized -# hash for string def portable_hash(x): """ - This function returns consistant hash code for builtin types, especially + This function returns consistent hash code for builtin types, especially for None and tuple with None. - The algrithm is similar to that one used by CPython 2.7 + The algorithm is similar to that one used by CPython 2.7 >>> portable_hash(None) 0 >>> portable_hash((None, 1)) & 0xffffffff 219750521 """ + if sys.version >= '3.3' and 'PYTHONHASHSEED' not in os.environ: + raise Exception("Randomness of hash of string should be disabled via PYTHONHASHSEED") + if x is None: return 0 if isinstance(x, tuple): @@ -71,7 +80,7 @@ def portable_hash(x): for i in x: h ^= portable_hash(i) h *= 1000003 - h &= sys.maxint + h &= sys.maxsize h ^= len(x) if h == -1: h = -2 @@ -123,6 +132,19 @@ def _load_from_socket(port, serializer): sock.close() +def ignore_unicode_prefix(f): + """ + Ignore the 'u' prefix of string in doc tests, to make it works + in both python 2 and 3 + """ + if sys.version >= '3': + # the representation of unicode string in Python 3 does not have prefix 'u', + # so remove the prefix 'u' for doc tests + literal_re = re.compile(r"(\W|^)[uU](['])", re.UNICODE) + f.__doc__ = literal_re.sub(r'\1\2', f.__doc__) + return f + + class Partitioner(object): def __init__(self, numPartitions, partitionFunc): self.numPartitions = numPartitions @@ -251,7 +273,7 @@ def map(self, f, preservesPartitioning=False): [('a', 1), ('b', 1), ('c', 1)] """ def func(_, iterator): - return imap(f, iterator) + return map(f, iterator) return self.mapPartitionsWithIndex(func, preservesPartitioning) def flatMap(self, f, preservesPartitioning=False): @@ -266,7 +288,7 @@ def flatMap(self, f, preservesPartitioning=False): [(2, 2), (2, 2), (3, 3), (3, 3), (4, 4), (4, 4)] """ def func(s, iterator): - return chain.from_iterable(imap(f, iterator)) + return chain.from_iterable(map(f, iterator)) return self.mapPartitionsWithIndex(func, preservesPartitioning) def mapPartitions(self, f, preservesPartitioning=False): @@ -329,7 +351,7 @@ def filter(self, f): [2, 4] """ def func(iterator): - return ifilter(f, iterator) + return filter(f, iterator) return self.mapPartitions(func, True) def distinct(self, numPartitions=None): @@ -341,7 +363,7 @@ def distinct(self, numPartitions=None): """ return self.map(lambda x: (x, None)) \ .reduceByKey(lambda x, _: x, numPartitions) \ - .map(lambda (x, _): x) + .map(lambda x: x[0]) def sample(self, withReplacement, fraction, seed=None): """ @@ -354,8 +376,8 @@ def sample(self, withReplacement, fraction, seed=None): :param seed: seed for the random number generator >>> rdd = sc.parallelize(range(100), 4) - >>> rdd.sample(False, 0.1, 81).count() - 10 + >>> 6 <= rdd.sample(False, 0.1, 81).count() <= 14 + True """ assert fraction >= 0.0, "Negative fraction value: %s" % fraction return self.mapPartitionsWithIndex(RDDSampler(withReplacement, fraction, seed).func, True) @@ -368,12 +390,14 @@ def randomSplit(self, weights, seed=None): :param seed: random seed :return: split RDDs in a list - >>> rdd = sc.parallelize(range(5), 1) + >>> rdd = sc.parallelize(range(500), 1) >>> rdd1, rdd2 = rdd.randomSplit([2, 3], 17) - >>> rdd1.collect() - [1, 3] - >>> rdd2.collect() - [0, 2, 4] + >>> len(rdd1.collect() + rdd2.collect()) + 500 + >>> 150 < rdd1.count() < 250 + True + >>> 250 < rdd2.count() < 350 + True """ s = float(sum(weights)) cweights = [0.0] @@ -416,7 +440,7 @@ def takeSample(self, withReplacement, num, seed=None): rand.shuffle(samples) return samples - maxSampleSize = sys.maxint - int(numStDev * sqrt(sys.maxint)) + maxSampleSize = sys.maxsize - int(numStDev * sqrt(sys.maxsize)) if num > maxSampleSize: raise ValueError( "Sample size cannot be greater than %d." % maxSampleSize) @@ -430,7 +454,7 @@ def takeSample(self, withReplacement, num, seed=None): # See: scala/spark/RDD.scala while len(samples) < num: # TODO: add log warning for when more than one iteration was run - seed = rand.randint(0, sys.maxint) + seed = rand.randint(0, sys.maxsize) samples = self.sample(withReplacement, fraction, seed).collect() rand.shuffle(samples) @@ -507,7 +531,7 @@ def intersection(self, other): """ return self.map(lambda v: (v, None)) \ .cogroup(other.map(lambda v: (v, None))) \ - .filter(lambda (k, vs): all(vs)) \ + .filter(lambda k_vs: all(k_vs[1])) \ .keys() def _reserialize(self, serializer=None): @@ -549,7 +573,7 @@ def repartitionAndSortWithinPartitions(self, numPartitions=None, partitionFunc=p def sortPartition(iterator): sort = ExternalSorter(memory * 0.9, serializer).sorted if spill else sorted - return iter(sort(iterator, key=lambda (k, v): keyfunc(k), reverse=(not ascending))) + return iter(sort(iterator, key=lambda k_v: keyfunc(k_v[0]), reverse=(not ascending))) return self.partitionBy(numPartitions, partitionFunc).mapPartitions(sortPartition, True) @@ -579,7 +603,7 @@ def sortByKey(self, ascending=True, numPartitions=None, keyfunc=lambda x: x): def sortPartition(iterator): sort = ExternalSorter(memory * 0.9, serializer).sorted if spill else sorted - return iter(sort(iterator, key=lambda (k, v): keyfunc(k), reverse=(not ascending))) + return iter(sort(iterator, key=lambda kv: keyfunc(kv[0]), reverse=(not ascending))) if numPartitions == 1: if self.getNumPartitions() > 1: @@ -594,12 +618,12 @@ def sortPartition(iterator): return self # empty RDD maxSampleSize = numPartitions * 20.0 # constant from Spark's RangePartitioner fraction = min(maxSampleSize / max(rddSize, 1), 1.0) - samples = self.sample(False, fraction, 1).map(lambda (k, v): k).collect() + samples = self.sample(False, fraction, 1).map(lambda kv: kv[0]).collect() samples = sorted(samples, key=keyfunc) # we have numPartitions many parts but one of the them has # an implicit boundary - bounds = [samples[len(samples) * (i + 1) / numPartitions] + bounds = [samples[int(len(samples) * (i + 1) / numPartitions)] for i in range(0, numPartitions - 1)] def rangePartitioner(k): @@ -662,12 +686,13 @@ def groupBy(self, f, numPartitions=None): """ return self.map(lambda x: (f(x), x)).groupByKey(numPartitions) + @ignore_unicode_prefix def pipe(self, command, env={}): """ Return an RDD created by piping elements to a forked external process. >>> sc.parallelize(['1', '2', '', '3']).pipe('cat').collect() - ['1', '2', '', '3'] + [u'1', u'2', u'', u'3'] """ def func(iterator): pipe = Popen( @@ -675,17 +700,18 @@ def func(iterator): def pipe_objs(out): for obj in iterator: - out.write(str(obj).rstrip('\n') + '\n') + s = str(obj).rstrip('\n') + '\n' + out.write(s.encode('utf-8')) out.close() Thread(target=pipe_objs, args=[pipe.stdin]).start() - return (x.rstrip('\n') for x in iter(pipe.stdout.readline, '')) + return (x.rstrip(b'\n').decode('utf-8') for x in iter(pipe.stdout.readline, b'')) return self.mapPartitions(func) def foreach(self, f): """ Applies a function to all elements of this RDD. - >>> def f(x): print x + >>> def f(x): print(x) >>> sc.parallelize([1, 2, 3, 4, 5]).foreach(f) """ def processPartition(iterator): @@ -700,7 +726,7 @@ def foreachPartition(self, f): >>> def f(iterator): ... for x in iterator: - ... print x + ... print(x) >>> sc.parallelize([1, 2, 3, 4, 5]).foreachPartition(f) """ def func(it): @@ -874,7 +900,7 @@ def aggregatePartition(iterator): # aggregation. while numPartitions > scale + numPartitions / scale: numPartitions /= scale - curNumPartitions = numPartitions + curNumPartitions = int(numPartitions) def mapPartition(i, iterator): for obj in iterator: @@ -984,7 +1010,7 @@ def histogram(self, buckets): (('a', 'b', 'c'), [2, 2]) """ - if isinstance(buckets, (int, long)): + if isinstance(buckets, int): if buckets < 1: raise ValueError("number of buckets must be >= 1") @@ -1020,6 +1046,7 @@ def minmax(a, b): raise ValueError("Can not generate buckets with infinite value") # keep them as integer if possible + inc = int(inc) if inc * buckets != maxv - minv: inc = (maxv - minv) * 1.0 / buckets @@ -1137,7 +1164,7 @@ def countPartition(iterator): yield counts def mergeMaps(m1, m2): - for k, v in m2.iteritems(): + for k, v in m2.items(): m1[k] += v return m1 return self.mapPartitions(countPartition).reduce(mergeMaps) @@ -1378,8 +1405,8 @@ def saveAsPickleFile(self, path, batchSize=10): >>> tmpFile = NamedTemporaryFile(delete=True) >>> tmpFile.close() >>> sc.parallelize([1, 2, 'spark', 'rdd']).saveAsPickleFile(tmpFile.name, 3) - >>> sorted(sc.pickleFile(tmpFile.name, 5).collect()) - [1, 2, 'rdd', 'spark'] + >>> sorted(sc.pickleFile(tmpFile.name, 5).map(str).collect()) + ['1', '2', 'rdd', 'spark'] """ if batchSize == 0: ser = AutoBatchedSerializer(PickleSerializer()) @@ -1387,6 +1414,7 @@ def saveAsPickleFile(self, path, batchSize=10): ser = BatchedSerializer(PickleSerializer(), batchSize) self._reserialize(ser)._jrdd.saveAsObjectFile(path) + @ignore_unicode_prefix def saveAsTextFile(self, path, compressionCodecClass=None): """ Save this RDD as a text file, using string representations of elements. @@ -1418,12 +1446,13 @@ def saveAsTextFile(self, path, compressionCodecClass=None): >>> codec = "org.apache.hadoop.io.compress.GzipCodec" >>> sc.parallelize(['foo', 'bar']).saveAsTextFile(tempFile3.name, codec) >>> from fileinput import input, hook_compressed - >>> ''.join(sorted(input(glob(tempFile3.name + "/part*.gz"), openhook=hook_compressed))) - 'bar\\nfoo\\n' + >>> result = sorted(input(glob(tempFile3.name + "/part*.gz"), openhook=hook_compressed)) + >>> b''.join(result).decode('utf-8') + u'bar\\nfoo\\n' """ def func(split, iterator): for x in iterator: - if not isinstance(x, basestring): + if not isinstance(x, (unicode, bytes)): x = unicode(x) if isinstance(x, unicode): x = x.encode("utf-8") @@ -1458,7 +1487,7 @@ def keys(self): >>> m.collect() [1, 3] """ - return self.map(lambda (k, v): k) + return self.map(lambda x: x[0]) def values(self): """ @@ -1468,7 +1497,7 @@ def values(self): >>> m.collect() [2, 4] """ - return self.map(lambda (k, v): v) + return self.map(lambda x: x[1]) def reduceByKey(self, func, numPartitions=None): """ @@ -1507,7 +1536,7 @@ def reducePartition(iterator): yield m def mergeMaps(m1, m2): - for k, v in m2.iteritems(): + for k, v in m2.items(): m1[k] = func(m1[k], v) if k in m1 else v return m1 return self.mapPartitions(reducePartition).reduce(mergeMaps) @@ -1604,8 +1633,8 @@ def partitionBy(self, numPartitions, partitionFunc=portable_hash): >>> pairs = sc.parallelize([1, 2, 3, 4, 2, 4, 1]).map(lambda x: (x, x)) >>> sets = pairs.partitionBy(2).glom().collect() - >>> set(sets[0]).intersection(set(sets[1])) - set([]) + >>> len(set(sets[0]).intersection(set(sets[1]))) + 0 """ if numPartitions is None: numPartitions = self._defaultReducePartitions() @@ -1637,22 +1666,22 @@ def add_shuffle_key(split, iterator): if (c % 1000 == 0 and get_used_memory() > limit or c > batch): n, size = len(buckets), 0 - for split in buckets.keys(): + for split in list(buckets.keys()): yield pack_long(split) d = outputSerializer.dumps(buckets[split]) del buckets[split] yield d size += len(d) - avg = (size / n) >> 20 + avg = int(size / n) >> 20 # let 1M < avg < 10M if avg < 1: batch *= 1.5 elif avg > 10: - batch = max(batch / 1.5, 1) + batch = max(int(batch / 1.5), 1) c = 0 - for split, items in buckets.iteritems(): + for split, items in buckets.items(): yield pack_long(split) yield outputSerializer.dumps(items) @@ -1707,7 +1736,7 @@ def combineLocally(iterator): merger = ExternalMerger(agg, memory * 0.9, serializer) \ if spill else InMemoryMerger(agg) merger.mergeValues(iterator) - return merger.iteritems() + return merger.items() locally_combined = self.mapPartitions(combineLocally, preservesPartitioning=True) shuffled = locally_combined.partitionBy(numPartitions) @@ -1716,7 +1745,7 @@ def _mergeCombiners(iterator): merger = ExternalMerger(agg, memory, serializer) \ if spill else InMemoryMerger(agg) merger.mergeCombiners(iterator) - return merger.iteritems() + return merger.items() return shuffled.mapPartitions(_mergeCombiners, preservesPartitioning=True) @@ -1745,7 +1774,7 @@ def foldByKey(self, zeroValue, func, numPartitions=None): >>> rdd = sc.parallelize([("a", 1), ("b", 1), ("a", 1)]) >>> from operator import add - >>> rdd.foldByKey(0, add).collect() + >>> sorted(rdd.foldByKey(0, add).collect()) [('a', 2), ('b', 1)] """ def createZero(): @@ -1769,10 +1798,10 @@ def groupByKey(self, numPartitions=None): sum or average) over each key, using reduceByKey or aggregateByKey will provide much better performance. - >>> x = sc.parallelize([("a", 1), ("b", 1), ("a", 1)]) - >>> sorted(x.groupByKey().mapValues(len).collect()) + >>> rdd = sc.parallelize([("a", 1), ("b", 1), ("a", 1)]) + >>> sorted(rdd.groupByKey().mapValues(len).collect()) [('a', 2), ('b', 1)] - >>> sorted(x.groupByKey().mapValues(list).collect()) + >>> sorted(rdd.groupByKey().mapValues(list).collect()) [('a', [1, 1]), ('b', [1])] """ def createCombiner(x): @@ -1795,7 +1824,7 @@ def combine(iterator): merger = ExternalMerger(agg, memory * 0.9, serializer) \ if spill else InMemoryMerger(agg) merger.mergeValues(iterator) - return merger.iteritems() + return merger.items() locally_combined = self.mapPartitions(combine, preservesPartitioning=True) shuffled = locally_combined.partitionBy(numPartitions) @@ -1804,7 +1833,7 @@ def groupByKey(it): merger = ExternalGroupBy(agg, memory, serializer)\ if spill else InMemoryMerger(agg) merger.mergeCombiners(it) - return merger.iteritems() + return merger.items() return shuffled.mapPartitions(groupByKey, True).mapValues(ResultIterable) @@ -1819,7 +1848,7 @@ def flatMapValues(self, f): >>> x.flatMapValues(f).collect() [('a', 'x'), ('a', 'y'), ('a', 'z'), ('b', 'p'), ('b', 'r')] """ - flat_map_fn = lambda (k, v): ((k, x) for x in f(v)) + flat_map_fn = lambda kv: ((kv[0], x) for x in f(kv[1])) return self.flatMap(flat_map_fn, preservesPartitioning=True) def mapValues(self, f): @@ -1833,7 +1862,7 @@ def mapValues(self, f): >>> x.mapValues(f).collect() [('a', 3), ('b', 1)] """ - map_values_fn = lambda (k, v): (k, f(v)) + map_values_fn = lambda kv: (kv[0], f(kv[1])) return self.map(map_values_fn, preservesPartitioning=True) def groupWith(self, other, *others): @@ -1844,8 +1873,7 @@ def groupWith(self, other, *others): >>> x = sc.parallelize([("a", 1), ("b", 4)]) >>> y = sc.parallelize([("a", 2)]) >>> z = sc.parallelize([("b", 42)]) - >>> map((lambda (x,y): (x, (list(y[0]), list(y[1]), list(y[2]), list(y[3])))), \ - sorted(list(w.groupWith(x, y, z).collect()))) + >>> [(x, tuple(map(list, y))) for x, y in sorted(list(w.groupWith(x, y, z).collect()))] [('a', ([5], [1], [2], [])), ('b', ([6], [4], [], [42]))] """ @@ -1860,7 +1888,7 @@ def cogroup(self, other, numPartitions=None): >>> x = sc.parallelize([("a", 1), ("b", 4)]) >>> y = sc.parallelize([("a", 2)]) - >>> map((lambda (x,y): (x, (list(y[0]), list(y[1])))), sorted(list(x.cogroup(y).collect()))) + >>> [(x, tuple(map(list, y))) for x, y in sorted(list(x.cogroup(y).collect()))] [('a', ([1], [2])), ('b', ([4], []))] """ return python_cogroup((self, other), numPartitions) @@ -1896,8 +1924,9 @@ def subtractByKey(self, other, numPartitions=None): >>> sorted(x.subtractByKey(y).collect()) [('b', 4), ('b', 5)] """ - def filter_func((key, vals)): - return vals[0] and not vals[1] + def filter_func(pair): + key, (val1, val2) = pair + return val1 and not val2 return self.cogroup(other, numPartitions).filter(filter_func).flatMapValues(lambda x: x[0]) def subtract(self, other, numPartitions=None): @@ -1919,8 +1948,8 @@ def keyBy(self, f): >>> x = sc.parallelize(range(0,3)).keyBy(lambda x: x*x) >>> y = sc.parallelize(zip(range(0,5), range(0,5))) - >>> map((lambda (x,y): (x, (list(y[0]), (list(y[1]))))), sorted(x.cogroup(y).collect())) - [(0, ([0], [0])), (1, ([1], [1])), (2, ([], [2])), (3, ([], [3])), (4, ([2], [4]))] + >>> [(x, list(map(list, y))) for x, y in sorted(x.cogroup(y).collect())] + [(0, [[0], [0]]), (1, [[1], [1]]), (2, [[], [2]]), (3, [[], [3]]), (4, [[2], [4]])] """ return self.map(lambda x: (f(x), x)) @@ -2049,17 +2078,18 @@ def name(self): """ Return the name of this RDD. """ - name_ = self._jrdd.name() - if name_: - return name_.encode('utf-8') + n = self._jrdd.name() + if n: + return n + @ignore_unicode_prefix def setName(self, name): """ Assign a name to this RDD. - >>> rdd1 = sc.parallelize([1,2]) + >>> rdd1 = sc.parallelize([1, 2]) >>> rdd1.setName('RDD1').name() - 'RDD1' + u'RDD1' """ self._jrdd.setName(name) return self @@ -2121,7 +2151,7 @@ def lookup(self, key): >>> sorted.lookup(1024) [] """ - values = self.filter(lambda (k, v): k == key).values() + values = self.filter(lambda kv: kv[0] == key).values() if self.partitioner is not None: return self.ctx.runJob(values, lambda x: x, [self.partitioner(key)], False) @@ -2159,7 +2189,7 @@ def sumApprox(self, timeout, confidence=0.95): or meet the confidence. >>> rdd = sc.parallelize(range(1000), 10) - >>> r = sum(xrange(1000)) + >>> r = sum(range(1000)) >>> (rdd.sumApprox(1000) - r) / r < 0.05 True """ @@ -2176,7 +2206,7 @@ def meanApprox(self, timeout, confidence=0.95): or meet the confidence. >>> rdd = sc.parallelize(range(1000), 10) - >>> r = sum(xrange(1000)) / 1000.0 + >>> r = sum(range(1000)) / 1000.0 >>> (rdd.meanApprox(1000) - r) / r < 0.05 True """ @@ -2201,10 +2231,10 @@ def countApproxDistinct(self, relativeSD=0.05): It must be greater than 0.000017. >>> n = sc.parallelize(range(1000)).map(str).countApproxDistinct() - >>> 950 < n < 1050 + >>> 900 < n < 1100 True >>> n = sc.parallelize([i % 20 for i in range(1000)]).countApproxDistinct() - >>> 18 < n < 22 + >>> 16 < n < 24 True """ if relativeSD < 0.000017: @@ -2223,8 +2253,7 @@ def toLocalIterator(self): >>> [x for x in rdd.toLocalIterator()] [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] """ - partitions = xrange(self.getNumPartitions()) - for partition in partitions: + for partition in range(self.getNumPartitions()): rows = self.context.runJob(self, lambda x: x, [partition]) for row in rows: yield row diff --git a/python/pyspark/rddsampler.py b/python/pyspark/rddsampler.py index 459e1427803cb..fe8f87324804b 100644 --- a/python/pyspark/rddsampler.py +++ b/python/pyspark/rddsampler.py @@ -23,7 +23,7 @@ class RDDSamplerBase(object): def __init__(self, withReplacement, seed=None): - self._seed = seed if seed is not None else random.randint(0, sys.maxint) + self._seed = seed if seed is not None else random.randint(0, sys.maxsize) self._withReplacement = withReplacement self._random = None @@ -31,7 +31,7 @@ def initRandomGenerator(self, split): self._random = random.Random(self._seed ^ split) # mixing because the initial seeds are close to each other - for _ in xrange(10): + for _ in range(10): self._random.randint(0, 1) def getUniformSample(self): diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py index 4afa82f4b2973..d8cdcda3a3783 100644 --- a/python/pyspark/serializers.py +++ b/python/pyspark/serializers.py @@ -49,16 +49,24 @@ >>> sc.stop() """ -import cPickle -from itertools import chain, izip, product +import sys +from itertools import chain, product import marshal import struct -import sys import types import collections import zlib import itertools +if sys.version < '3': + import cPickle as pickle + protocol = 2 + from itertools import izip as zip +else: + import pickle + protocol = 3 + xrange = range + from pyspark import cloudpickle @@ -97,7 +105,7 @@ def _load_stream_without_unbatching(self, stream): # subclasses should override __eq__ as appropriate. def __eq__(self, other): - return isinstance(other, self.__class__) + return isinstance(other, self.__class__) and self.__dict__ == other.__dict__ def __ne__(self, other): return not self.__eq__(other) @@ -212,10 +220,6 @@ def load_stream(self, stream): def _load_stream_without_unbatching(self, stream): return self.serializer.load_stream(stream) - def __eq__(self, other): - return (isinstance(other, BatchedSerializer) and - other.serializer == self.serializer and other.batchSize == self.batchSize) - def __repr__(self): return "BatchedSerializer(%s, %d)" % (str(self.serializer), self.batchSize) @@ -233,14 +237,14 @@ def __init__(self, serializer, batchSize=10): def _batched(self, iterator): n = self.batchSize for key, values in iterator: - for i in xrange(0, len(values), n): + for i in range(0, len(values), n): yield key, values[i:i + n] def load_stream(self, stream): return self.serializer.load_stream(stream) def __repr__(self): - return "FlattenedValuesSerializer(%d)" % self.batchSize + return "FlattenedValuesSerializer(%s, %d)" % (self.serializer, self.batchSize) class AutoBatchedSerializer(BatchedSerializer): @@ -270,12 +274,8 @@ def dump_stream(self, iterator, stream): elif size > best * 10 and batch > 1: batch /= 2 - def __eq__(self, other): - return (isinstance(other, AutoBatchedSerializer) and - other.serializer == self.serializer and other.bestSize == self.bestSize) - def __repr__(self): - return "AutoBatchedSerializer(%s)" % str(self.serializer) + return "AutoBatchedSerializer(%s)" % self.serializer class CartesianDeserializer(FramedSerializer): @@ -285,6 +285,7 @@ class CartesianDeserializer(FramedSerializer): """ def __init__(self, key_ser, val_ser): + FramedSerializer.__init__(self) self.key_ser = key_ser self.val_ser = val_ser @@ -293,7 +294,7 @@ def prepare_keys_values(self, stream): val_stream = self.val_ser._load_stream_without_unbatching(stream) key_is_batched = isinstance(self.key_ser, BatchedSerializer) val_is_batched = isinstance(self.val_ser, BatchedSerializer) - for (keys, vals) in izip(key_stream, val_stream): + for (keys, vals) in zip(key_stream, val_stream): keys = keys if key_is_batched else [keys] vals = vals if val_is_batched else [vals] yield (keys, vals) @@ -303,10 +304,6 @@ def load_stream(self, stream): for pair in product(keys, vals): yield pair - def __eq__(self, other): - return (isinstance(other, CartesianDeserializer) and - self.key_ser == other.key_ser and self.val_ser == other.val_ser) - def __repr__(self): return "CartesianDeserializer(%s, %s)" % \ (str(self.key_ser), str(self.val_ser)) @@ -318,22 +315,14 @@ class PairDeserializer(CartesianDeserializer): Deserializes the JavaRDD zip() of two PythonRDDs. """ - def __init__(self, key_ser, val_ser): - self.key_ser = key_ser - self.val_ser = val_ser - def load_stream(self, stream): for (keys, vals) in self.prepare_keys_values(stream): if len(keys) != len(vals): raise ValueError("Can not deserialize RDD with different number of items" " in pair: (%d, %d)" % (len(keys), len(vals))) - for pair in izip(keys, vals): + for pair in zip(keys, vals): yield pair - def __eq__(self, other): - return (isinstance(other, PairDeserializer) and - self.key_ser == other.key_ser and self.val_ser == other.val_ser) - def __repr__(self): return "PairDeserializer(%s, %s)" % (str(self.key_ser), str(self.val_ser)) @@ -382,8 +371,8 @@ def _hijack_namedtuple(): global _old_namedtuple # or it will put in closure def _copy_func(f): - return types.FunctionType(f.func_code, f.func_globals, f.func_name, - f.func_defaults, f.func_closure) + return types.FunctionType(f.__code__, f.__globals__, f.__name__, + f.__defaults__, f.__closure__) _old_namedtuple = _copy_func(collections.namedtuple) @@ -392,15 +381,15 @@ def namedtuple(*args, **kwargs): return _hack_namedtuple(cls) # replace namedtuple with new one - collections.namedtuple.func_globals["_old_namedtuple"] = _old_namedtuple - collections.namedtuple.func_globals["_hack_namedtuple"] = _hack_namedtuple - collections.namedtuple.func_code = namedtuple.func_code + collections.namedtuple.__globals__["_old_namedtuple"] = _old_namedtuple + collections.namedtuple.__globals__["_hack_namedtuple"] = _hack_namedtuple + collections.namedtuple.__code__ = namedtuple.__code__ collections.namedtuple.__hijack = 1 # hack the cls already generated by namedtuple # those created in other module can be pickled as normal, # so only hack those in __main__ module - for n, o in sys.modules["__main__"].__dict__.iteritems(): + for n, o in sys.modules["__main__"].__dict__.items(): if (type(o) is type and o.__base__ is tuple and hasattr(o, "_fields") and "__reduce__" not in o.__dict__): @@ -413,7 +402,7 @@ def namedtuple(*args, **kwargs): class PickleSerializer(FramedSerializer): """ - Serializes objects using Python's cPickle serializer: + Serializes objects using Python's pickle serializer: http://docs.python.org/2/library/pickle.html @@ -422,10 +411,14 @@ class PickleSerializer(FramedSerializer): """ def dumps(self, obj): - return cPickle.dumps(obj, 2) + return pickle.dumps(obj, protocol) - def loads(self, obj): - return cPickle.loads(obj) + if sys.version >= '3': + def loads(self, obj, encoding="bytes"): + return pickle.loads(obj, encoding=encoding) + else: + def loads(self, obj, encoding=None): + return pickle.loads(obj) class CloudPickleSerializer(PickleSerializer): @@ -454,7 +447,7 @@ def loads(self, obj): class AutoSerializer(FramedSerializer): """ - Choose marshal or cPickle as serialization protocol automatically + Choose marshal or pickle as serialization protocol automatically """ def __init__(self): @@ -463,19 +456,19 @@ def __init__(self): def dumps(self, obj): if self._type is not None: - return 'P' + cPickle.dumps(obj, -1) + return b'P' + pickle.dumps(obj, -1) try: - return 'M' + marshal.dumps(obj) + return b'M' + marshal.dumps(obj) except Exception: - self._type = 'P' - return 'P' + cPickle.dumps(obj, -1) + self._type = b'P' + return b'P' + pickle.dumps(obj, -1) def loads(self, obj): _type = obj[0] - if _type == 'M': + if _type == b'M': return marshal.loads(obj[1:]) - elif _type == 'P': - return cPickle.loads(obj[1:]) + elif _type == b'P': + return pickle.loads(obj[1:]) else: raise ValueError("invalid sevialization type: %s" % _type) @@ -495,8 +488,8 @@ def dumps(self, obj): def loads(self, obj): return self.serializer.loads(zlib.decompress(obj)) - def __eq__(self, other): - return isinstance(other, CompressedSerializer) and self.serializer == other.serializer + def __repr__(self): + return "CompressedSerializer(%s)" % self.serializer class UTF8Deserializer(Serializer): @@ -505,7 +498,7 @@ class UTF8Deserializer(Serializer): Deserializes streams written by String.getBytes. """ - def __init__(self, use_unicode=False): + def __init__(self, use_unicode=True): self.use_unicode = use_unicode def loads(self, stream): @@ -526,13 +519,13 @@ def load_stream(self, stream): except EOFError: return - def __eq__(self, other): - return isinstance(other, UTF8Deserializer) and self.use_unicode == other.use_unicode + def __repr__(self): + return "UTF8Deserializer(%s)" % self.use_unicode def read_long(stream): length = stream.read(8) - if length == "": + if not length: raise EOFError return struct.unpack("!q", length)[0] @@ -547,7 +540,7 @@ def pack_long(value): def read_int(stream): length = stream.read(4) - if length == "": + if not length: raise EOFError return struct.unpack("!i", length)[0] diff --git a/python/pyspark/shell.py b/python/pyspark/shell.py index 81aa970a32f76..144cdf0b0cdd5 100644 --- a/python/pyspark/shell.py +++ b/python/pyspark/shell.py @@ -21,13 +21,6 @@ This file is designed to be launched as a PYTHONSTARTUP script. """ -import sys -if sys.version_info[0] != 2: - print("Error: Default Python used is Python%s" % sys.version_info.major) - print("\tSet env variable PYSPARK_PYTHON to Python2 binary and re-run it.") - sys.exit(1) - - import atexit import os import platform @@ -53,9 +46,14 @@ try: # Try to access HiveConf, it will raise exception if Hive is not added sc._jvm.org.apache.hadoop.hive.conf.HiveConf() - sqlCtx = sqlContext = HiveContext(sc) + sqlContext = HiveContext(sc) except py4j.protocol.Py4JError: - sqlCtx = sqlContext = SQLContext(sc) + sqlContext = SQLContext(sc) +except TypeError: + sqlContext = SQLContext(sc) + +# for compatibility +sqlCtx = sqlContext print("""Welcome to ____ __ diff --git a/python/pyspark/shuffle.py b/python/pyspark/shuffle.py index 8a6fc627eb383..b54baa57ec28a 100644 --- a/python/pyspark/shuffle.py +++ b/python/pyspark/shuffle.py @@ -78,8 +78,8 @@ def _get_local_dirs(sub): # global stats -MemoryBytesSpilled = 0L -DiskBytesSpilled = 0L +MemoryBytesSpilled = 0 +DiskBytesSpilled = 0 class Aggregator(object): @@ -126,7 +126,7 @@ def mergeCombiners(self, iterator): """ Merge the combined items by mergeCombiner """ raise NotImplementedError - def iteritems(self): + def items(self): """ Return the merged items ad iterator """ raise NotImplementedError @@ -156,9 +156,9 @@ def mergeCombiners(self, iterator): for k, v in iterator: d[k] = comb(d[k], v) if k in d else v - def iteritems(self): - """ Return the merged items as iterator """ - return self.data.iteritems() + def items(self): + """ Return the merged items ad iterator """ + return iter(self.data.items()) def _compressed_serializer(self, serializer=None): @@ -208,15 +208,15 @@ class ExternalMerger(Merger): >>> agg = SimpleAggregator(lambda x, y: x + y) >>> merger = ExternalMerger(agg, 10) >>> N = 10000 - >>> merger.mergeValues(zip(xrange(N), xrange(N))) + >>> merger.mergeValues(zip(range(N), range(N))) >>> assert merger.spills > 0 - >>> sum(v for k,v in merger.iteritems()) + >>> sum(v for k,v in merger.items()) 49995000 >>> merger = ExternalMerger(agg, 10) - >>> merger.mergeCombiners(zip(xrange(N), xrange(N))) + >>> merger.mergeCombiners(zip(range(N), range(N))) >>> assert merger.spills > 0 - >>> sum(v for k,v in merger.iteritems()) + >>> sum(v for k,v in merger.items()) 49995000 """ @@ -335,10 +335,10 @@ def _spill(self): # above limit at the first time. # open all the files for writing - streams = [open(os.path.join(path, str(i)), 'w') + streams = [open(os.path.join(path, str(i)), 'wb') for i in range(self.partitions)] - for k, v in self.data.iteritems(): + for k, v in self.data.items(): h = self._partition(k) # put one item in batch, make it compatible with load_stream # it will increase the memory if dump them in batch @@ -354,9 +354,9 @@ def _spill(self): else: for i in range(self.partitions): p = os.path.join(path, str(i)) - with open(p, "w") as f: + with open(p, "wb") as f: # dump items in batch - self.serializer.dump_stream(self.pdata[i].iteritems(), f) + self.serializer.dump_stream(iter(self.pdata[i].items()), f) self.pdata[i].clear() DiskBytesSpilled += os.path.getsize(p) @@ -364,10 +364,10 @@ def _spill(self): gc.collect() # release the memory as much as possible MemoryBytesSpilled += (used_memory - get_used_memory()) << 20 - def iteritems(self): + def items(self): """ Return all merged items as iterator """ if not self.pdata and not self.spills: - return self.data.iteritems() + return iter(self.data.items()) return self._external_items() def _external_items(self): @@ -398,7 +398,8 @@ def _merged_items(self, index): path = self._get_spill_dir(j) p = os.path.join(path, str(index)) # do not check memory during merging - self.mergeCombiners(self.serializer.load_stream(open(p)), 0) + with open(p, "rb") as f: + self.mergeCombiners(self.serializer.load_stream(f), 0) # limit the total partitions if (self.scale * self.partitions < self.MAX_TOTAL_PARTITIONS @@ -408,7 +409,7 @@ def _merged_items(self, index): gc.collect() # release the memory as much as possible return self._recursive_merged_items(index) - return self.data.iteritems() + return self.data.items() def _recursive_merged_items(self, index): """ @@ -426,7 +427,8 @@ def _recursive_merged_items(self, index): for j in range(self.spills): path = self._get_spill_dir(j) p = os.path.join(path, str(index)) - m.mergeCombiners(self.serializer.load_stream(open(p)), 0) + with open(p, 'rb') as f: + m.mergeCombiners(self.serializer.load_stream(f), 0) if get_used_memory() > limit: m._spill() @@ -451,7 +453,7 @@ class ExternalSorter(object): >>> sorter = ExternalSorter(1) # 1M >>> import random - >>> l = range(1024) + >>> l = list(range(1024)) >>> random.shuffle(l) >>> sorted(l) == list(sorter.sorted(l)) True @@ -499,9 +501,16 @@ def sorted(self, iterator, key=None, reverse=False): # sort them inplace will save memory current_chunk.sort(key=key, reverse=reverse) path = self._get_path(len(chunks)) - with open(path, 'w') as f: + with open(path, 'wb') as f: self.serializer.dump_stream(current_chunk, f) - chunks.append(self.serializer.load_stream(open(path))) + + def load(f): + for v in self.serializer.load_stream(f): + yield v + # close the file explicit once we consume all the items + # to avoid ResourceWarning in Python3 + f.close() + chunks.append(load(open(path, 'rb'))) current_chunk = [] gc.collect() limit = self._next_limit() @@ -527,7 +536,7 @@ class ExternalList(object): ExternalList can have many items which cannot be hold in memory in the same time. - >>> l = ExternalList(range(100)) + >>> l = ExternalList(list(range(100))) >>> len(l) 100 >>> l.append(10) @@ -555,11 +564,11 @@ def __init__(self, values): def __getstate__(self): if self._file is not None: self._file.flush() - f = os.fdopen(os.dup(self._file.fileno())) - f.seek(0) - serialized = f.read() + with os.fdopen(os.dup(self._file.fileno()), "rb") as f: + f.seek(0) + serialized = f.read() else: - serialized = '' + serialized = b'' return self.values, self.count, serialized def __setstate__(self, item): @@ -575,7 +584,7 @@ def __iter__(self): if self._file is not None: self._file.flush() # read all items from disks first - with os.fdopen(os.dup(self._file.fileno()), 'r') as f: + with os.fdopen(os.dup(self._file.fileno()), 'rb') as f: f.seek(0) for v in self._ser.load_stream(f): yield v @@ -598,11 +607,16 @@ def _open_file(self): d = dirs[id(self) % len(dirs)] if not os.path.exists(d): os.makedirs(d) - p = os.path.join(d, str(id)) - self._file = open(p, "w+", 65536) + p = os.path.join(d, str(id(self))) + self._file = open(p, "wb+", 65536) self._ser = BatchedSerializer(CompressedSerializer(PickleSerializer()), 1024) os.unlink(p) + def __del__(self): + if self._file: + self._file.close() + self._file = None + def _spill(self): """ dump the values into disk """ global MemoryBytesSpilled, DiskBytesSpilled @@ -651,33 +665,28 @@ class GroupByKey(object): """ Group a sorted iterator as [(k1, it1), (k2, it2), ...] - >>> k = [i/3 for i in range(6)] + >>> k = [i // 3 for i in range(6)] >>> v = [[i] for i in range(6)] - >>> g = GroupByKey(iter(zip(k, v))) + >>> g = GroupByKey(zip(k, v)) >>> [(k, list(it)) for k, it in g] [(0, [0, 1, 2]), (1, [3, 4, 5])] """ def __init__(self, iterator): - self.iterator = iter(iterator) - self.next_item = None + self.iterator = iterator def __iter__(self): - return self - - def next(self): - key, value = self.next_item if self.next_item else next(self.iterator) - values = ExternalListOfList([value]) - try: - while True: - k, v = next(self.iterator) - if k != key: - self.next_item = (k, v) - break + key, values = None, None + for k, v in self.iterator: + if values is not None and k == key: values.append(v) - except StopIteration: - self.next_item = None - return key, values + else: + if values is not None: + yield (key, values) + key = k + values = ExternalListOfList([v]) + if values is not None: + yield (key, values) class ExternalGroupBy(ExternalMerger): @@ -744,7 +753,7 @@ def _spill(self): # above limit at the first time. # open all the files for writing - streams = [open(os.path.join(path, str(i)), 'w') + streams = [open(os.path.join(path, str(i)), 'wb') for i in range(self.partitions)] # If the number of keys is small, then the overhead of sort is small @@ -756,7 +765,7 @@ def _spill(self): h = self._partition(k) self.serializer.dump_stream([(k, self.data[k])], streams[h]) else: - for k, v in self.data.iteritems(): + for k, v in self.data.items(): h = self._partition(k) self.serializer.dump_stream([(k, v)], streams[h]) @@ -771,14 +780,14 @@ def _spill(self): else: for i in range(self.partitions): p = os.path.join(path, str(i)) - with open(p, "w") as f: + with open(p, "wb") as f: # dump items in batch if self._sorted: # sort by key only (stable) - sorted_items = sorted(self.pdata[i].iteritems(), key=operator.itemgetter(0)) + sorted_items = sorted(self.pdata[i].items(), key=operator.itemgetter(0)) self.serializer.dump_stream(sorted_items, f) else: - self.serializer.dump_stream(self.pdata[i].iteritems(), f) + self.serializer.dump_stream(self.pdata[i].items(), f) self.pdata[i].clear() DiskBytesSpilled += os.path.getsize(p) @@ -792,7 +801,7 @@ def _merged_items(self, index): # if the memory can not hold all the partition, # then use sort based merge. Because of compression, # the data on disks will be much smaller than needed memory - if (size >> 20) >= self.memory_limit / 10: + if size >= self.memory_limit << 17: # * 1M / 8 return self._merge_sorted_items(index) self.data = {} @@ -800,15 +809,18 @@ def _merged_items(self, index): path = self._get_spill_dir(j) p = os.path.join(path, str(index)) # do not check memory during merging - self.mergeCombiners(self.serializer.load_stream(open(p)), 0) - return self.data.iteritems() + with open(p, "rb") as f: + self.mergeCombiners(self.serializer.load_stream(f), 0) + return self.data.items() def _merge_sorted_items(self, index): """ load a partition from disk, then sort and group by key """ def load_partition(j): path = self._get_spill_dir(j) p = os.path.join(path, str(index)) - return self.serializer.load_stream(open(p, 'r', 65536)) + with open(p, 'rb', 65536) as f: + for v in self.serializer.load_stream(f): + yield v disk_items = [load_partition(j) for j in range(self.spills)] diff --git a/python/pyspark/sql/__init__.py b/python/pyspark/sql/__init__.py index 65abb24eed823..6d54b9e49ed10 100644 --- a/python/pyspark/sql/__init__.py +++ b/python/pyspark/sql/__init__.py @@ -37,9 +37,22 @@ - L{types} List of data types available. """ +from __future__ import absolute_import + +# fix the module name conflict for Python 3+ +import sys +from . import _types as types +modname = __name__ + '.types' +types.__name__ = modname +# update the __module__ for all objects, make them picklable +for v in types.__dict__.values(): + if hasattr(v, "__module__") and v.__module__.endswith('._types'): + v.__module__ = modname +sys.modules[modname] = types +del modname, sys -from pyspark.sql.context import SQLContext, HiveContext from pyspark.sql.types import Row +from pyspark.sql.context import SQLContext, HiveContext from pyspark.sql.dataframe import DataFrame, GroupedData, Column, SchemaRDD, DataFrameNaFunctions __all__ = [ diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/_types.py similarity index 97% rename from python/pyspark/sql/types.py rename to python/pyspark/sql/_types.py index ef76d84c00481..492c0cbdcf693 100644 --- a/python/pyspark/sql/types.py +++ b/python/pyspark/sql/_types.py @@ -15,6 +15,7 @@ # limitations under the License. # +import sys import decimal import datetime import keyword @@ -25,6 +26,9 @@ from array import array from operator import itemgetter +if sys.version >= "3": + long = int + unicode = str __all__ = [ "DataType", "NullType", "StringType", "BinaryType", "BooleanType", "DateType", @@ -410,7 +414,7 @@ def fromJson(cls, json): split = pyUDT.rfind(".") pyModule = pyUDT[:split] pyClass = pyUDT[split+1:] - m = __import__(pyModule, globals(), locals(), [pyClass], -1) + m = __import__(pyModule, globals(), locals(), [pyClass]) UDT = getattr(m, pyClass) return UDT() @@ -419,10 +423,9 @@ def __eq__(self, other): _all_primitive_types = dict((v.typeName(), v) - for v in globals().itervalues() - if type(v) is PrimitiveTypeSingleton and - v.__base__ == PrimitiveType) - + for v in list(globals().values()) + if (type(v) is type or type(v) is PrimitiveTypeSingleton) + and v.__base__ == PrimitiveType) _all_complex_types = dict((v.typeName(), v) for v in [ArrayType, MapType, StructType]) @@ -486,10 +489,10 @@ def _parse_datatype_json_string(json_string): def _parse_datatype_json_value(json_value): - if type(json_value) is unicode: + if not isinstance(json_value, dict): if json_value in _all_primitive_types.keys(): return _all_primitive_types[json_value]() - elif json_value == u'decimal': + elif json_value == 'decimal': return DecimalType() elif _FIXED_DECIMAL.match(json_value): m = _FIXED_DECIMAL.match(json_value) @@ -511,10 +514,8 @@ def _parse_datatype_json_value(json_value): type(None): NullType, bool: BooleanType, int: LongType, - long: LongType, float: DoubleType, str: StringType, - unicode: StringType, bytearray: BinaryType, decimal.Decimal: DecimalType, datetime.date: DateType, @@ -522,6 +523,12 @@ def _parse_datatype_json_value(json_value): datetime.time: TimestampType, } +if sys.version < "3": + _type_mappings.update({ + unicode: StringType, + long: LongType, + }) + def _infer_type(obj): """Infer the DataType from obj @@ -541,7 +548,7 @@ def _infer_type(obj): return dataType() if isinstance(obj, dict): - for key, value in obj.iteritems(): + for key, value in obj.items(): if key is not None and value is not None: return MapType(_infer_type(key), _infer_type(value), True) else: @@ -565,10 +572,10 @@ def _infer_schema(row): items = sorted(row.items()) elif isinstance(row, (tuple, list)): - if hasattr(row, "_fields"): # namedtuple - items = zip(row._fields, tuple(row)) - elif hasattr(row, "__fields__"): # Row + if hasattr(row, "__fields__"): # Row items = zip(row.__fields__, tuple(row)) + elif hasattr(row, "_fields"): # namedtuple + items = zip(row._fields, tuple(row)) else: names = ['_%d' % i for i in range(1, len(row) + 1)] items = zip(names, row) @@ -647,7 +654,7 @@ def converter(obj): if isinstance(obj, dict): return tuple(c(obj.get(n)) for n, c in zip(names, converters)) elif isinstance(obj, tuple): - if hasattr(obj, "_fields") or hasattr(obj, "__fields__"): + if hasattr(obj, "__fields__") or hasattr(obj, "_fields"): return tuple(c(v) for c, v in zip(converters, obj)) elif all(isinstance(x, tuple) and len(x) == 2 for x in obj): # k-v pairs d = dict(obj) @@ -733,12 +740,12 @@ def _create_converter(dataType): if isinstance(dataType, ArrayType): conv = _create_converter(dataType.elementType) - return lambda row: map(conv, row) + return lambda row: [conv(v) for v in row] elif isinstance(dataType, MapType): kconv = _create_converter(dataType.keyType) vconv = _create_converter(dataType.valueType) - return lambda row: dict((kconv(k), vconv(v)) for k, v in row.iteritems()) + return lambda row: dict((kconv(k), vconv(v)) for k, v in row.items()) elif isinstance(dataType, NullType): return lambda x: None @@ -881,7 +888,7 @@ def _infer_schema_type(obj, dataType): >>> _infer_schema_type(row, schema) StructType...a,ArrayType...b,MapType(StringType,...c,LongType... """ - if dataType is NullType(): + if isinstance(dataType, NullType): return _infer_type(obj) if not obj: @@ -892,7 +899,7 @@ def _infer_schema_type(obj, dataType): return ArrayType(eType, True) elif isinstance(dataType, MapType): - k, v = obj.iteritems().next() + k, v = next(iter(obj.items())) return MapType(_infer_schema_type(k, dataType.keyType), _infer_schema_type(v, dataType.valueType)) @@ -935,7 +942,7 @@ def _verify_type(obj, dataType): >>> _verify_type(None, StructType([])) >>> _verify_type("", StringType()) >>> _verify_type(0, LongType()) - >>> _verify_type(range(3), ArrayType(ShortType())) + >>> _verify_type(list(range(3)), ArrayType(ShortType())) >>> _verify_type(set(), ArrayType(StringType())) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... @@ -976,7 +983,7 @@ def _verify_type(obj, dataType): _verify_type(i, dataType.elementType) elif isinstance(dataType, MapType): - for k, v in obj.iteritems(): + for k, v in obj.items(): _verify_type(k, dataType.keyType) _verify_type(v, dataType.valueType) @@ -1213,6 +1220,8 @@ def __getattr__(self, item): return self[idx] except IndexError: raise AttributeError(item) + except ValueError: + raise AttributeError(item) def __reduce__(self): if hasattr(self, "__fields__"): diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py index e8529a8f8e3a4..c90afc326ca0e 100644 --- a/python/pyspark/sql/context.py +++ b/python/pyspark/sql/context.py @@ -15,14 +15,19 @@ # limitations under the License. # +import sys import warnings import json -from itertools import imap + +if sys.version >= '3': + basestring = unicode = str +else: + from itertools import imap as map from py4j.protocol import Py4JError from py4j.java_collections import MapConverter -from pyspark.rdd import RDD, _prepare_for_python_RDD +from pyspark.rdd import RDD, _prepare_for_python_RDD, ignore_unicode_prefix from pyspark.serializers import AutoBatchedSerializer, PickleSerializer from pyspark.sql.types import Row, StringType, StructType, _verify_type, \ _infer_schema, _has_nulltype, _merge_type, _create_converter, _python_to_sql_converter @@ -62,31 +67,27 @@ class SQLContext(object): A SQLContext can be used create :class:`DataFrame`, register :class:`DataFrame` as tables, execute SQL over tables, cache tables, and read parquet files. - When created, :class:`SQLContext` adds a method called ``toDF`` to :class:`RDD`, - which could be used to convert an RDD into a DataFrame, it's a shorthand for - :func:`SQLContext.createDataFrame`. - :param sparkContext: The :class:`SparkContext` backing this SQLContext. :param sqlContext: An optional JVM Scala SQLContext. If set, we do not instantiate a new SQLContext in the JVM, instead we make all calls to this object. """ + @ignore_unicode_prefix def __init__(self, sparkContext, sqlContext=None): """Creates a new SQLContext. >>> from datetime import datetime >>> sqlContext = SQLContext(sc) - >>> allTypes = sc.parallelize([Row(i=1, s="string", d=1.0, l=1L, + >>> allTypes = sc.parallelize([Row(i=1, s="string", d=1.0, l=1, ... b=True, list=[1, 2, 3], dict={"s": 0}, row=Row(a=1), ... time=datetime(2014, 8, 1, 14, 1, 5))]) >>> df = allTypes.toDF() >>> df.registerTempTable("allTypes") >>> sqlContext.sql('select i+1, d+1, not b, list[1], dict["s"], time, row.a ' ... 'from allTypes where b and i > 0').collect() - [Row(c0=2, c1=2.0, c2=False, c3=2, c4=0...8, 1, 14, 1, 5), a=1)] - >>> df.map(lambda x: (x.i, x.s, x.d, x.l, x.b, x.time, - ... x.row.a, x.list)).collect() - [(1, u'string', 1.0, 1, True, ...(2014, 8, 1, 14, 1, 5), 1, [1, 2, 3])] + [Row(c0=2, c1=2.0, c2=False, c3=2, c4=0, time=datetime.datetime(2014, 8, 1, 14, 1, 5), a=1)] + >>> df.map(lambda x: (x.i, x.s, x.d, x.l, x.b, x.time, x.row.a, x.list)).collect() + [(1, u'string', 1.0, 1, True, datetime.datetime(2014, 8, 1, 14, 1, 5), 1, [1, 2, 3])] """ self._sc = sparkContext self._jsc = self._sc._jsc @@ -122,6 +123,7 @@ def udf(self): """Returns a :class:`UDFRegistration` for UDF registration.""" return UDFRegistration(self) + @ignore_unicode_prefix def registerFunction(self, name, f, returnType=StringType()): """Registers a lambda function as a UDF so it can be used in SQL statements. @@ -147,7 +149,7 @@ def registerFunction(self, name, f, returnType=StringType()): >>> sqlContext.sql("SELECT stringLengthInt('test')").collect() [Row(c0=4)] """ - func = lambda _, it: imap(lambda x: f(*x), it) + func = lambda _, it: map(lambda x: f(*x), it) ser = AutoBatchedSerializer(PickleSerializer()) command = (func, None, ser, ser) pickled_cmd, bvars, env, includes = _prepare_for_python_RDD(self._sc, command, self) @@ -185,6 +187,7 @@ def _inferSchema(self, rdd, samplingRatio=None): schema = rdd.map(_infer_schema).reduce(_merge_type) return schema + @ignore_unicode_prefix def inferSchema(self, rdd, samplingRatio=None): """::note: Deprecated in 1.3, use :func:`createDataFrame` instead. """ @@ -195,6 +198,7 @@ def inferSchema(self, rdd, samplingRatio=None): return self.createDataFrame(rdd, None, samplingRatio) + @ignore_unicode_prefix def applySchema(self, rdd, schema): """::note: Deprecated in 1.3, use :func:`createDataFrame` instead. """ @@ -208,6 +212,7 @@ def applySchema(self, rdd, schema): return self.createDataFrame(rdd, schema) + @ignore_unicode_prefix def createDataFrame(self, data, schema=None, samplingRatio=None): """ Creates a :class:`DataFrame` from an :class:`RDD` of :class:`tuple`/:class:`list`, @@ -380,6 +385,7 @@ def jsonFile(self, path, schema=None, samplingRatio=1.0): df = self._ssql_ctx.jsonFile(path, scala_datatype) return DataFrame(df, self) + @ignore_unicode_prefix def jsonRDD(self, rdd, schema=None, samplingRatio=1.0): """Loads an RDD storing one JSON object per string as a :class:`DataFrame`. @@ -477,6 +483,7 @@ def createExternalTable(self, tableName, path=None, source=None, joptions) return DataFrame(df, self) + @ignore_unicode_prefix def sql(self, sqlQuery): """Returns a :class:`DataFrame` representing the result of the given query. @@ -497,6 +504,7 @@ def table(self, tableName): """ return DataFrame(self._ssql_ctx.table(tableName), self) + @ignore_unicode_prefix def tables(self, dbName=None): """Returns a :class:`DataFrame` containing names of tables in the given database. diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index f2c3b74a185cf..d76504f986270 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -16,14 +16,19 @@ # import sys -import itertools import warnings import random +if sys.version >= '3': + basestring = unicode = str + long = int +else: + from itertools import imap as map + from py4j.java_collections import ListConverter, MapConverter from pyspark.context import SparkContext -from pyspark.rdd import RDD, _load_from_socket +from pyspark.rdd import RDD, _load_from_socket, ignore_unicode_prefix from pyspark.serializers import BatchedSerializer, PickleSerializer, UTF8Deserializer from pyspark.storagelevel import StorageLevel from pyspark.traceback_utils import SCCallSiteSync @@ -65,19 +70,20 @@ def __init__(self, jdf, sql_ctx): self._sc = sql_ctx and sql_ctx._sc self.is_cached = False self._schema = None # initialized lazily + self._lazy_rdd = None @property def rdd(self): """Returns the content as an :class:`pyspark.RDD` of :class:`Row`. """ - if not hasattr(self, '_lazy_rdd'): + if self._lazy_rdd is None: jrdd = self._jdf.javaToPython() rdd = RDD(jrdd, self.sql_ctx._sc, BatchedSerializer(PickleSerializer())) schema = self.schema def applySchema(it): cls = _create_cls(schema) - return itertools.imap(cls, it) + return map(cls, it) self._lazy_rdd = rdd.mapPartitions(applySchema) @@ -89,13 +95,14 @@ def na(self): """ return DataFrameNaFunctions(self) - def toJSON(self, use_unicode=False): + @ignore_unicode_prefix + def toJSON(self, use_unicode=True): """Converts a :class:`DataFrame` into a :class:`RDD` of string. Each row is turned into a JSON document as one element in the returned RDD. >>> df.toJSON().first() - '{"age":2,"name":"Alice"}' + u'{"age":2,"name":"Alice"}' """ rdd = self._jdf.toJSON() return RDD(rdd.toJavaRDD(), self._sc, UTF8Deserializer(use_unicode)) @@ -228,7 +235,7 @@ def printSchema(self): |-- name: string (nullable = true) """ - print (self._jdf.schema().treeString()) + print(self._jdf.schema().treeString()) def explain(self, extended=False): """Prints the (logical and physical) plans to the console for debugging purpose. @@ -250,9 +257,9 @@ def explain(self, extended=False): == RDD == """ if extended: - print self._jdf.queryExecution().toString() + print(self._jdf.queryExecution().toString()) else: - print self._jdf.queryExecution().executedPlan().toString() + print(self._jdf.queryExecution().executedPlan().toString()) def isLocal(self): """Returns ``True`` if the :func:`collect` and :func:`take` methods can be run locally @@ -270,7 +277,7 @@ def show(self, n=20): 2 Alice 5 Bob """ - print self._jdf.showString(n).encode('utf8', 'ignore') + print(self._jdf.showString(n)) def __repr__(self): return "DataFrame[%s]" % (", ".join("%s: %s" % c for c in self.dtypes)) @@ -279,10 +286,11 @@ def count(self): """Returns the number of rows in this :class:`DataFrame`. >>> df.count() - 2L + 2 """ - return self._jdf.count() + return int(self._jdf.count()) + @ignore_unicode_prefix def collect(self): """Returns all the records as a list of :class:`Row`. @@ -295,6 +303,7 @@ def collect(self): cls = _create_cls(self.schema) return [cls(r) for r in rs] + @ignore_unicode_prefix def limit(self, num): """Limits the result count to the number specified. @@ -306,6 +315,7 @@ def limit(self, num): jdf = self._jdf.limit(num) return DataFrame(jdf, self.sql_ctx) + @ignore_unicode_prefix def take(self, num): """Returns the first ``num`` rows as a :class:`list` of :class:`Row`. @@ -314,6 +324,7 @@ def take(self, num): """ return self.limit(num).collect() + @ignore_unicode_prefix def map(self, f): """ Returns a new :class:`RDD` by applying a the ``f`` function to each :class:`Row`. @@ -324,6 +335,7 @@ def map(self, f): """ return self.rdd.map(f) + @ignore_unicode_prefix def flatMap(self, f): """ Returns a new :class:`RDD` by first applying the ``f`` function to each :class:`Row`, and then flattening the results. @@ -353,7 +365,7 @@ def foreach(self, f): This is a shorthand for ``df.rdd.foreach()``. >>> def f(person): - ... print person.name + ... print(person.name) >>> df.foreach(f) """ return self.rdd.foreach(f) @@ -365,7 +377,7 @@ def foreachPartition(self, f): >>> def f(people): ... for person in people: - ... print person.name + ... print(person.name) >>> df.foreachPartition(f) """ return self.rdd.foreachPartition(f) @@ -412,7 +424,7 @@ def distinct(self): """Returns a new :class:`DataFrame` containing the distinct rows in this :class:`DataFrame`. >>> df.distinct().count() - 2L + 2 """ return DataFrame(self._jdf.distinct(), self.sql_ctx) @@ -420,10 +432,10 @@ def sample(self, withReplacement, fraction, seed=None): """Returns a sampled subset of this :class:`DataFrame`. >>> df.sample(False, 0.5, 97).count() - 1L + 1 """ assert fraction >= 0.0, "Negative fraction value: %s" % fraction - seed = seed if seed is not None else random.randint(0, sys.maxint) + seed = seed if seed is not None else random.randint(0, sys.maxsize) rdd = self._jdf.sample(withReplacement, fraction, long(seed)) return DataFrame(rdd, self.sql_ctx) @@ -437,6 +449,7 @@ def dtypes(self): return [(str(f.name), f.dataType.simpleString()) for f in self.schema.fields] @property + @ignore_unicode_prefix def columns(self): """Returns all column names as a list. @@ -445,6 +458,7 @@ def columns(self): """ return [f.name for f in self.schema.fields] + @ignore_unicode_prefix def join(self, other, joinExprs=None, joinType=None): """Joins with another :class:`DataFrame`, using the given join expression. @@ -470,6 +484,7 @@ def join(self, other, joinExprs=None, joinType=None): jdf = self._jdf.join(other._jdf, joinExprs._jc, joinType) return DataFrame(jdf, self.sql_ctx) + @ignore_unicode_prefix def sort(self, *cols): """Returns a new :class:`DataFrame` sorted by the specified column(s). @@ -513,6 +528,7 @@ def describe(self, *cols): jdf = self._jdf.describe(self.sql_ctx._sc._jvm.PythonUtils.toSeq(cols)) return DataFrame(jdf, self.sql_ctx) + @ignore_unicode_prefix def head(self, n=None): """ Returns the first ``n`` rows as a list of :class:`Row`, @@ -528,6 +544,7 @@ def head(self, n=None): return rs[0] if rs else None return self.take(n) + @ignore_unicode_prefix def first(self): """Returns the first row as a :class:`Row`. @@ -536,6 +553,7 @@ def first(self): """ return self.head() + @ignore_unicode_prefix def __getitem__(self, item): """Returns the column as a :class:`Column`. @@ -567,6 +585,7 @@ def __getattr__(self, name): jc = self._jdf.apply(name) return Column(jc) + @ignore_unicode_prefix def select(self, *cols): """Projects a set of expressions and returns a new :class:`DataFrame`. @@ -598,6 +617,7 @@ def selectExpr(self, *expr): jdf = self._jdf.selectExpr(self._sc._jvm.PythonUtils.toSeq(jexpr)) return DataFrame(jdf, self.sql_ctx) + @ignore_unicode_prefix def filter(self, condition): """Filters rows using the given condition. @@ -626,6 +646,7 @@ def filter(self, condition): where = filter + @ignore_unicode_prefix def groupBy(self, *cols): """Groups the :class:`DataFrame` using the specified columns, so we can run aggregation on them. See :class:`GroupedData` @@ -775,6 +796,7 @@ def fillna(self, value, subset=None): cols = self.sql_ctx._sc._jvm.PythonUtils.toSeq(cols) return DataFrame(self._jdf.na().fill(value, cols), self.sql_ctx) + @ignore_unicode_prefix def withColumn(self, colName, col): """Returns a new :class:`DataFrame` by adding a column. @@ -786,6 +808,7 @@ def withColumn(self, colName, col): """ return self.select('*', col.alias(colName)) + @ignore_unicode_prefix def withColumnRenamed(self, existing, new): """REturns a new :class:`DataFrame` by renaming an existing column. @@ -852,6 +875,7 @@ def __init__(self, jdf, sql_ctx): self._jdf = jdf self.sql_ctx = sql_ctx + @ignore_unicode_prefix def agg(self, *exprs): """Compute aggregates and returns the result as a :class:`DataFrame`. @@ -1041,11 +1065,13 @@ def __init__(self, jc): __sub__ = _bin_op("minus") __mul__ = _bin_op("multiply") __div__ = _bin_op("divide") + __truediv__ = _bin_op("divide") __mod__ = _bin_op("mod") __radd__ = _bin_op("plus") __rsub__ = _reverse_op("minus") __rmul__ = _bin_op("multiply") __rdiv__ = _reverse_op("divide") + __rtruediv__ = _reverse_op("divide") __rmod__ = _reverse_op("mod") # logistic operators @@ -1075,6 +1101,7 @@ def __init__(self, jc): startswith = _bin_op("startsWith") endswith = _bin_op("endsWith") + @ignore_unicode_prefix def substr(self, startPos, length): """ Return a :class:`Column` which is a substring of the column @@ -1097,6 +1124,7 @@ def substr(self, startPos, length): __getslice__ = substr + @ignore_unicode_prefix def inSet(self, *cols): """ A boolean expression that is evaluated to true if the value of this expression is contained by the evaluated values of the arguments. @@ -1131,6 +1159,7 @@ def alias(self, alias): """ return Column(getattr(self._jc, "as")(alias)) + @ignore_unicode_prefix def cast(self, dataType): """ Convert the column into type `dataType` diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py index daeb6916b58bc..1d6536952810f 100644 --- a/python/pyspark/sql/functions.py +++ b/python/pyspark/sql/functions.py @@ -18,8 +18,10 @@ """ A collections of builtin functions """ +import sys -from itertools import imap +if sys.version < "3": + from itertools import imap as map from py4j.java_collections import ListConverter @@ -116,7 +118,7 @@ def __init__(self, func, returnType): def _create_judf(self): f = self.func # put it in closure `func` - func = lambda _, it: imap(lambda x: f(*x), it) + func = lambda _, it: map(lambda x: f(*x), it) ser = AutoBatchedSerializer(PickleSerializer()) command = (func, None, ser, ser) sc = SparkContext._active_spark_context diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index b3a6a2c6a9229..7c09a0cfe30ab 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -157,13 +157,13 @@ def test_udf2(self): self.assertEqual(4, res[0]) def test_udf_with_array_type(self): - d = [Row(l=range(3), d={"key": range(5)})] + d = [Row(l=list(range(3)), d={"key": list(range(5))})] rdd = self.sc.parallelize(d) self.sqlCtx.createDataFrame(rdd).registerTempTable("test") self.sqlCtx.registerFunction("copylist", lambda l: list(l), ArrayType(IntegerType())) self.sqlCtx.registerFunction("maplen", lambda d: len(d), IntegerType()) [(l1, l2)] = self.sqlCtx.sql("select copylist(l), maplen(d) from test").collect() - self.assertEqual(range(3), l1) + self.assertEqual(list(range(3)), l1) self.assertEqual(1, l2) def test_broadcast_in_udf(self): @@ -266,7 +266,7 @@ def test_infer_nested_schema(self): def test_apply_schema(self): from datetime import date, datetime - rdd = self.sc.parallelize([(127, -128L, -32768, 32767, 2147483647L, 1.0, + rdd = self.sc.parallelize([(127, -128, -32768, 32767, 2147483647, 1.0, date(2010, 1, 1), datetime(2010, 1, 1, 1, 1, 1), {"a": 1}, (2,), [1, 2, 3], None)]) schema = StructType([ @@ -309,7 +309,7 @@ def test_apply_schema(self): def test_struct_in_map(self): d = [Row(m={Row(i=1): Row(s="")})] df = self.sc.parallelize(d).toDF() - k, v = df.head().m.items()[0] + k, v = list(df.head().m.items())[0] self.assertEqual(1, k.i) self.assertEqual("", v.s) @@ -554,6 +554,9 @@ def setUpClass(cls): except py4j.protocol.Py4JError: cls.sqlCtx = None return + except TypeError: + cls.sqlCtx = None + return os.unlink(cls.tempdir.name) _scala_HiveContext =\ cls.sc._jvm.org.apache.spark.sql.hive.test.TestHiveContext(cls.sc._jsc.sc()) diff --git a/python/pyspark/statcounter.py b/python/pyspark/statcounter.py index 1e597d64e03fe..944fa414b0c0e 100644 --- a/python/pyspark/statcounter.py +++ b/python/pyspark/statcounter.py @@ -31,7 +31,7 @@ class StatCounter(object): def __init__(self, values=[]): - self.n = 0L # Running count of our values + self.n = 0 # Running count of our values self.mu = 0.0 # Running mean of our values self.m2 = 0.0 # Running variance numerator (sum of (x - mean)^2) self.maxValue = float("-inf") @@ -87,7 +87,7 @@ def copy(self): return copy.deepcopy(self) def count(self): - return self.n + return int(self.n) def mean(self): return self.mu diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py index 2c73083c9f9a8..4590c58839266 100644 --- a/python/pyspark/streaming/context.py +++ b/python/pyspark/streaming/context.py @@ -14,6 +14,9 @@ # See the License for the specific language governing permissions and # limitations under the License. # + +from __future__ import print_function + import os import sys @@ -157,7 +160,7 @@ def getOrCreate(cls, checkpointPath, setupFunc): try: jssc = gw.jvm.JavaStreamingContext(checkpointPath) except Exception: - print >>sys.stderr, "failed to load StreamingContext from checkpoint" + print("failed to load StreamingContext from checkpoint", file=sys.stderr) raise jsc = jssc.sparkContext() diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py index 3fa42444239f7..ff097985fae3e 100644 --- a/python/pyspark/streaming/dstream.py +++ b/python/pyspark/streaming/dstream.py @@ -15,11 +15,15 @@ # limitations under the License. # -from itertools import chain, ifilter, imap +import sys import operator import time +from itertools import chain from datetime import datetime +if sys.version < "3": + from itertools import imap as map, ifilter as filter + from py4j.protocol import Py4JJavaError from pyspark import RDD @@ -76,7 +80,7 @@ def filter(self, f): Return a new DStream containing only the elements that satisfy predicate. """ def func(iterator): - return ifilter(f, iterator) + return filter(f, iterator) return self.mapPartitions(func, True) def flatMap(self, f, preservesPartitioning=False): @@ -85,7 +89,7 @@ def flatMap(self, f, preservesPartitioning=False): this DStream, and then flattening the results """ def func(s, iterator): - return chain.from_iterable(imap(f, iterator)) + return chain.from_iterable(map(f, iterator)) return self.mapPartitionsWithIndex(func, preservesPartitioning) def map(self, f, preservesPartitioning=False): @@ -93,7 +97,7 @@ def map(self, f, preservesPartitioning=False): Return a new DStream by applying a function to each element of DStream. """ def func(iterator): - return imap(f, iterator) + return map(f, iterator) return self.mapPartitions(func, preservesPartitioning) def mapPartitions(self, f, preservesPartitioning=False): @@ -150,7 +154,7 @@ def foreachRDD(self, func): """ Apply a function to each RDD in this DStream. """ - if func.func_code.co_argcount == 1: + if func.__code__.co_argcount == 1: old_func = func func = lambda t, rdd: old_func(rdd) jfunc = TransformFunction(self._sc, func, self._jrdd_deserializer) @@ -165,14 +169,14 @@ def pprint(self, num=10): """ def takeAndPrint(time, rdd): taken = rdd.take(num + 1) - print "-------------------------------------------" - print "Time: %s" % time - print "-------------------------------------------" + print("-------------------------------------------") + print("Time: %s" % time) + print("-------------------------------------------") for record in taken[:num]: - print record + print(record) if len(taken) > num: - print "..." - print + print("...") + print() self.foreachRDD(takeAndPrint) @@ -181,7 +185,7 @@ def mapValues(self, f): Return a new DStream by applying a map function to the value of each key-value pairs in this DStream without changing the key. """ - map_values_fn = lambda (k, v): (k, f(v)) + map_values_fn = lambda kv: (kv[0], f(kv[1])) return self.map(map_values_fn, preservesPartitioning=True) def flatMapValues(self, f): @@ -189,7 +193,7 @@ def flatMapValues(self, f): Return a new DStream by applying a flatmap function to the value of each key-value pairs in this DStream without changing the key. """ - flat_map_fn = lambda (k, v): ((k, x) for x in f(v)) + flat_map_fn = lambda kv: ((kv[0], x) for x in f(kv[1])) return self.flatMap(flat_map_fn, preservesPartitioning=True) def glom(self): @@ -286,10 +290,10 @@ def transform(self, func): `func` can have one argument of `rdd`, or have two arguments of (`time`, `rdd`) """ - if func.func_code.co_argcount == 1: + if func.__code__.co_argcount == 1: oldfunc = func func = lambda t, rdd: oldfunc(rdd) - assert func.func_code.co_argcount == 2, "func should take one or two arguments" + assert func.__code__.co_argcount == 2, "func should take one or two arguments" return TransformedDStream(self, func) def transformWith(self, func, other, keepSerializer=False): @@ -300,10 +304,10 @@ def transformWith(self, func, other, keepSerializer=False): `func` can have two arguments of (`rdd_a`, `rdd_b`) or have three arguments of (`time`, `rdd_a`, `rdd_b`) """ - if func.func_code.co_argcount == 2: + if func.__code__.co_argcount == 2: oldfunc = func func = lambda t, a, b: oldfunc(a, b) - assert func.func_code.co_argcount == 3, "func should take two or three arguments" + assert func.__code__.co_argcount == 3, "func should take two or three arguments" jfunc = TransformFunction(self._sc, func, self._jrdd_deserializer, other._jrdd_deserializer) dstream = self._sc._jvm.PythonTransformed2DStream(self._jdstream.dstream(), other._jdstream.dstream(), jfunc) @@ -460,7 +464,7 @@ def reduceByWindow(self, reduceFunc, invReduceFunc, windowDuration, slideDuratio keyed = self.map(lambda x: (1, x)) reduced = keyed.reduceByKeyAndWindow(reduceFunc, invReduceFunc, windowDuration, slideDuration, 1) - return reduced.map(lambda (k, v): v) + return reduced.map(lambda kv: kv[1]) def countByWindow(self, windowDuration, slideDuration): """ @@ -489,7 +493,7 @@ def countByValueAndWindow(self, windowDuration, slideDuration, numPartitions=Non keyed = self.map(lambda x: (x, 1)) counted = keyed.reduceByKeyAndWindow(operator.add, operator.sub, windowDuration, slideDuration, numPartitions) - return counted.filter(lambda (k, v): v > 0).count() + return counted.filter(lambda kv: kv[1] > 0).count() def groupByKeyAndWindow(self, windowDuration, slideDuration, numPartitions=None): """ @@ -548,7 +552,8 @@ def reduceFunc(t, a, b): def invReduceFunc(t, a, b): b = b.reduceByKey(func, numPartitions) joined = a.leftOuterJoin(b, numPartitions) - return joined.mapValues(lambda (v1, v2): invFunc(v1, v2) if v2 is not None else v1) + return joined.mapValues(lambda kv: invFunc(kv[0], kv[1]) + if kv[1] is not None else kv[0]) jreduceFunc = TransformFunction(self._sc, reduceFunc, reduced._jrdd_deserializer) if invReduceFunc: @@ -579,9 +584,9 @@ def reduceFunc(t, a, b): g = b.groupByKey(numPartitions).mapValues(lambda vs: (list(vs), None)) else: g = a.cogroup(b.partitionBy(numPartitions), numPartitions) - g = g.mapValues(lambda (va, vb): (list(vb), list(va)[0] if len(va) else None)) - state = g.mapValues(lambda (vs, s): updateFunc(vs, s)) - return state.filter(lambda (k, v): v is not None) + g = g.mapValues(lambda ab: (list(ab[1]), list(ab[0])[0] if len(ab[0]) else None)) + state = g.mapValues(lambda vs_s: updateFunc(vs_s[0], vs_s[1])) + return state.filter(lambda k_v: k_v[1] is not None) jreduceFunc = TransformFunction(self._sc, reduceFunc, self._sc.serializer, self._jrdd_deserializer) diff --git a/python/pyspark/streaming/kafka.py b/python/pyspark/streaming/kafka.py index f083ed149effb..7a7b6e1d9a527 100644 --- a/python/pyspark/streaming/kafka.py +++ b/python/pyspark/streaming/kafka.py @@ -67,10 +67,10 @@ def createStream(ssc, zkQuorum, groupId, topics, kafkaParams={}, .loadClass("org.apache.spark.streaming.kafka.KafkaUtilsPythonHelper") helper = helperClass.newInstance() jstream = helper.createStream(ssc._jssc, jparam, jtopics, jlevel) - except Py4JJavaError, e: + except Py4JJavaError as e: # TODO: use --jar once it also work on driver if 'ClassNotFoundException' in str(e.java_exception): - print """ + print(""" ________________________________________________________________________________________________ Spark Streaming's Kafka libraries not found in class path. Try one of the following. @@ -88,8 +88,8 @@ def createStream(ssc, zkQuorum, groupId, topics, kafkaParams={}, ________________________________________________________________________________________________ -""" % (ssc.sparkContext.version, ssc.sparkContext.version) +""" % (ssc.sparkContext.version, ssc.sparkContext.version)) raise e ser = PairDeserializer(NoOpSerializer(), NoOpSerializer()) stream = DStream(jstream, ssc, ser) - return stream.map(lambda (k, v): (keyDecoder(k), valueDecoder(v))) + return stream.map(lambda k_v: (keyDecoder(k_v[0]), valueDecoder(k_v[1]))) diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py index 9b4635e49020b..06d22154373bc 100644 --- a/python/pyspark/streaming/tests.py +++ b/python/pyspark/streaming/tests.py @@ -22,6 +22,7 @@ import unittest import tempfile import struct +from functools import reduce from py4j.java_collections import MapConverter @@ -51,7 +52,7 @@ def wait_for(self, result, n): while len(result) < n and time.time() - start_time < self.timeout: time.sleep(0.01) if len(result) < n: - print "timeout after", self.timeout + print("timeout after", self.timeout) def _take(self, dstream, n): """ @@ -131,7 +132,7 @@ def test_map(self): def func(dstream): return dstream.map(str) - expected = map(lambda x: map(str, x), input) + expected = [list(map(str, x)) for x in input] self._test_func(input, func, expected) def test_flatMap(self): @@ -140,8 +141,8 @@ def test_flatMap(self): def func(dstream): return dstream.flatMap(lambda x: (x, x * 2)) - expected = map(lambda x: list(chain.from_iterable((map(lambda y: [y, y * 2], x)))), - input) + expected = [list(chain.from_iterable((map(lambda y: [y, y * 2], x)))) + for x in input] self._test_func(input, func, expected) def test_filter(self): @@ -150,7 +151,7 @@ def test_filter(self): def func(dstream): return dstream.filter(lambda x: x % 2 == 0) - expected = map(lambda x: filter(lambda y: y % 2 == 0, x), input) + expected = [[y for y in x if y % 2 == 0] for x in input] self._test_func(input, func, expected) def test_count(self): @@ -159,7 +160,7 @@ def test_count(self): def func(dstream): return dstream.count() - expected = map(lambda x: [len(x)], input) + expected = [[len(x)] for x in input] self._test_func(input, func, expected) def test_reduce(self): @@ -168,7 +169,7 @@ def test_reduce(self): def func(dstream): return dstream.reduce(operator.add) - expected = map(lambda x: [reduce(operator.add, x)], input) + expected = [[reduce(operator.add, x)] for x in input] self._test_func(input, func, expected) def test_reduceByKey(self): @@ -185,27 +186,27 @@ def func(dstream): def test_mapValues(self): """Basic operation test for DStream.mapValues.""" input = [[("a", 2), ("b", 2), ("c", 1), ("d", 1)], - [("", 4), (1, 1), (2, 2), (3, 3)], + [(0, 4), (1, 1), (2, 2), (3, 3)], [(1, 1), (2, 1), (3, 1), (4, 1)]] def func(dstream): return dstream.mapValues(lambda x: x + 10) expected = [[("a", 12), ("b", 12), ("c", 11), ("d", 11)], - [("", 14), (1, 11), (2, 12), (3, 13)], + [(0, 14), (1, 11), (2, 12), (3, 13)], [(1, 11), (2, 11), (3, 11), (4, 11)]] self._test_func(input, func, expected, sort=True) def test_flatMapValues(self): """Basic operation test for DStream.flatMapValues.""" input = [[("a", 2), ("b", 2), ("c", 1), ("d", 1)], - [("", 4), (1, 1), (2, 1), (3, 1)], + [(0, 4), (1, 1), (2, 1), (3, 1)], [(1, 1), (2, 1), (3, 1), (4, 1)]] def func(dstream): return dstream.flatMapValues(lambda x: (x, x + 10)) expected = [[("a", 2), ("a", 12), ("b", 2), ("b", 12), ("c", 1), ("c", 11), ("d", 1), ("d", 11)], - [("", 4), ("", 14), (1, 1), (1, 11), (2, 1), (2, 11), (3, 1), (3, 11)], + [(0, 4), (0, 14), (1, 1), (1, 11), (2, 1), (2, 11), (3, 1), (3, 11)], [(1, 1), (1, 11), (2, 1), (2, 11), (3, 1), (3, 11), (4, 1), (4, 11)]] self._test_func(input, func, expected) @@ -233,7 +234,7 @@ def f(iterator): def test_countByValue(self): """Basic operation test for DStream.countByValue.""" - input = [range(1, 5) * 2, range(5, 7) + range(5, 9), ["a", "a", "b", ""]] + input = [list(range(1, 5)) * 2, list(range(5, 7)) + list(range(5, 9)), ["a", "a", "b", ""]] def func(dstream): return dstream.countByValue() @@ -285,7 +286,7 @@ def test_union(self): def func(d1, d2): return d1.union(d2) - expected = [range(6), range(6), range(6)] + expected = [list(range(6)), list(range(6)), list(range(6))] self._test_func(input1, func, expected, input2=input2) def test_cogroup(self): @@ -424,7 +425,7 @@ class StreamingContextTests(PySparkStreamingTestCase): duration = 0.1 def _add_input_stream(self): - inputs = map(lambda x: range(1, x), range(101)) + inputs = [range(1, x) for x in range(101)] stream = self.ssc.queueStream(inputs) self._collect(stream, 1, block=False) @@ -441,7 +442,7 @@ def test_stop_multiple_times(self): self.ssc.stop() def test_queue_stream(self): - input = [range(i + 1) for i in range(3)] + input = [list(range(i + 1)) for i in range(3)] dstream = self.ssc.queueStream(input) result = self._collect(dstream, 3) self.assertEqual(input, result) @@ -457,13 +458,13 @@ def test_text_file_stream(self): with open(os.path.join(d, name), "w") as f: f.writelines(["%d\n" % i for i in range(10)]) self.wait_for(result, 2) - self.assertEqual([range(10), range(10)], result) + self.assertEqual([list(range(10)), list(range(10))], result) def test_binary_records_stream(self): d = tempfile.mkdtemp() self.ssc = StreamingContext(self.sc, self.duration) dstream = self.ssc.binaryRecordsStream(d, 10).map( - lambda v: struct.unpack("10b", str(v))) + lambda v: struct.unpack("10b", bytes(v))) result = self._collect(dstream, 2, block=False) self.ssc.start() for name in ('a', 'b'): @@ -471,10 +472,10 @@ def test_binary_records_stream(self): with open(os.path.join(d, name), "wb") as f: f.write(bytearray(range(10))) self.wait_for(result, 2) - self.assertEqual([range(10), range(10)], map(lambda v: list(v[0]), result)) + self.assertEqual([list(range(10)), list(range(10))], [list(v[0]) for v in result]) def test_union(self): - input = [range(i + 1) for i in range(3)] + input = [list(range(i + 1)) for i in range(3)] dstream = self.ssc.queueStream(input) dstream2 = self.ssc.queueStream(input) dstream3 = self.ssc.union(dstream, dstream2) diff --git a/python/pyspark/streaming/util.py b/python/pyspark/streaming/util.py index 86ee5aa04f252..34291f30a5652 100644 --- a/python/pyspark/streaming/util.py +++ b/python/pyspark/streaming/util.py @@ -91,9 +91,9 @@ def dumps(self, id): except Exception: traceback.print_exc() - def loads(self, bytes): + def loads(self, data): try: - f, deserializers = self.serializer.loads(str(bytes)) + f, deserializers = self.serializer.loads(bytes(data)) return TransformFunction(self.ctx, f, *deserializers) except Exception: traceback.print_exc() @@ -116,7 +116,7 @@ def rddToFileName(prefix, suffix, timestamp): """ if isinstance(timestamp, datetime): seconds = time.mktime(timestamp.timetuple()) - timestamp = long(seconds * 1000) + timestamp.microsecond / 1000 + timestamp = int(seconds * 1000) + timestamp.microsecond // 1000 if suffix is None: return prefix + "-" + str(timestamp) else: diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py index ee67e80d539f8..75f39d9e75f38 100644 --- a/python/pyspark/tests.py +++ b/python/pyspark/tests.py @@ -19,8 +19,8 @@ Unit tests for PySpark; additional tests are implemented as doctests in individual modules. """ + from array import array -from fileinput import input from glob import glob import os import re @@ -45,6 +45,9 @@ sys.exit(1) else: import unittest + if sys.version_info[0] >= 3: + xrange = range + basestring = str from pyspark.conf import SparkConf @@ -52,7 +55,9 @@ from pyspark.rdd import RDD from pyspark.files import SparkFiles from pyspark.serializers import read_int, BatchedSerializer, MarshalSerializer, PickleSerializer, \ - CloudPickleSerializer, CompressedSerializer, UTF8Deserializer, NoOpSerializer + CloudPickleSerializer, CompressedSerializer, UTF8Deserializer, NoOpSerializer, \ + PairDeserializer, CartesianDeserializer, AutoBatchedSerializer, AutoSerializer, \ + FlattenedValuesSerializer from pyspark.shuffle import Aggregator, InMemoryMerger, ExternalMerger, ExternalSorter from pyspark import shuffle from pyspark.profiler import BasicProfiler @@ -81,7 +86,7 @@ class MergerTests(unittest.TestCase): def setUp(self): self.N = 1 << 12 self.l = [i for i in xrange(self.N)] - self.data = zip(self.l, self.l) + self.data = list(zip(self.l, self.l)) self.agg = Aggregator(lambda x: [x], lambda x, y: x.append(y) or x, lambda x, y: x.extend(y) or x) @@ -89,45 +94,45 @@ def setUp(self): def test_in_memory(self): m = InMemoryMerger(self.agg) m.mergeValues(self.data) - self.assertEqual(sum(sum(v) for k, v in m.iteritems()), + self.assertEqual(sum(sum(v) for k, v in m.items()), sum(xrange(self.N))) m = InMemoryMerger(self.agg) - m.mergeCombiners(map(lambda (x, y): (x, [y]), self.data)) - self.assertEqual(sum(sum(v) for k, v in m.iteritems()), + m.mergeCombiners(map(lambda x_y: (x_y[0], [x_y[1]]), self.data)) + self.assertEqual(sum(sum(v) for k, v in m.items()), sum(xrange(self.N))) def test_small_dataset(self): m = ExternalMerger(self.agg, 1000) m.mergeValues(self.data) self.assertEqual(m.spills, 0) - self.assertEqual(sum(sum(v) for k, v in m.iteritems()), + self.assertEqual(sum(sum(v) for k, v in m.items()), sum(xrange(self.N))) m = ExternalMerger(self.agg, 1000) - m.mergeCombiners(map(lambda (x, y): (x, [y]), self.data)) + m.mergeCombiners(map(lambda x_y1: (x_y1[0], [x_y1[1]]), self.data)) self.assertEqual(m.spills, 0) - self.assertEqual(sum(sum(v) for k, v in m.iteritems()), + self.assertEqual(sum(sum(v) for k, v in m.items()), sum(xrange(self.N))) def test_medium_dataset(self): - m = ExternalMerger(self.agg, 30) + m = ExternalMerger(self.agg, 20) m.mergeValues(self.data) self.assertTrue(m.spills >= 1) - self.assertEqual(sum(sum(v) for k, v in m.iteritems()), + self.assertEqual(sum(sum(v) for k, v in m.items()), sum(xrange(self.N))) m = ExternalMerger(self.agg, 10) - m.mergeCombiners(map(lambda (x, y): (x, [y]), self.data * 3)) + m.mergeCombiners(map(lambda x_y2: (x_y2[0], [x_y2[1]]), self.data * 3)) self.assertTrue(m.spills >= 1) - self.assertEqual(sum(sum(v) for k, v in m.iteritems()), + self.assertEqual(sum(sum(v) for k, v in m.items()), sum(xrange(self.N)) * 3) def test_huge_dataset(self): - m = ExternalMerger(self.agg, 10, partitions=3) - m.mergeCombiners(map(lambda (k, v): (k, [str(v)]), self.data * 10)) + m = ExternalMerger(self.agg, 5, partitions=3) + m.mergeCombiners(map(lambda k_v: (k_v[0], [str(k_v[1])]), self.data * 10)) self.assertTrue(m.spills >= 1) - self.assertEqual(sum(len(v) for k, v in m.iteritems()), + self.assertEqual(sum(len(v) for k, v in m.items()), self.N * 10) m._cleanup() @@ -144,55 +149,55 @@ def gen_gs(N, step=1): self.assertEqual(1, len(list(gen_gs(1)))) self.assertEqual(2, len(list(gen_gs(2)))) self.assertEqual(100, len(list(gen_gs(100)))) - self.assertEqual(range(1, 101), [k for k, _ in gen_gs(100)]) - self.assertTrue(all(range(k) == list(vs) for k, vs in gen_gs(100))) + self.assertEqual(list(range(1, 101)), [k for k, _ in gen_gs(100)]) + self.assertTrue(all(list(range(k)) == list(vs) for k, vs in gen_gs(100))) for k, vs in gen_gs(50002, 10000): self.assertEqual(k, len(vs)) - self.assertEqual(range(k), list(vs)) + self.assertEqual(list(range(k)), list(vs)) ser = PickleSerializer() l = ser.loads(ser.dumps(list(gen_gs(50002, 30000)))) for k, vs in l: self.assertEqual(k, len(vs)) - self.assertEqual(range(k), list(vs)) + self.assertEqual(list(range(k)), list(vs)) class SorterTests(unittest.TestCase): def test_in_memory_sort(self): - l = range(1024) + l = list(range(1024)) random.shuffle(l) sorter = ExternalSorter(1024) - self.assertEquals(sorted(l), list(sorter.sorted(l))) - self.assertEquals(sorted(l, reverse=True), list(sorter.sorted(l, reverse=True))) - self.assertEquals(sorted(l, key=lambda x: -x), list(sorter.sorted(l, key=lambda x: -x))) - self.assertEquals(sorted(l, key=lambda x: -x, reverse=True), - list(sorter.sorted(l, key=lambda x: -x, reverse=True))) + self.assertEqual(sorted(l), list(sorter.sorted(l))) + self.assertEqual(sorted(l, reverse=True), list(sorter.sorted(l, reverse=True))) + self.assertEqual(sorted(l, key=lambda x: -x), list(sorter.sorted(l, key=lambda x: -x))) + self.assertEqual(sorted(l, key=lambda x: -x, reverse=True), + list(sorter.sorted(l, key=lambda x: -x, reverse=True))) def test_external_sort(self): - l = range(1024) + l = list(range(1024)) random.shuffle(l) sorter = ExternalSorter(1) - self.assertEquals(sorted(l), list(sorter.sorted(l))) + self.assertEqual(sorted(l), list(sorter.sorted(l))) self.assertGreater(shuffle.DiskBytesSpilled, 0) last = shuffle.DiskBytesSpilled - self.assertEquals(sorted(l, reverse=True), list(sorter.sorted(l, reverse=True))) + self.assertEqual(sorted(l, reverse=True), list(sorter.sorted(l, reverse=True))) self.assertGreater(shuffle.DiskBytesSpilled, last) last = shuffle.DiskBytesSpilled - self.assertEquals(sorted(l, key=lambda x: -x), list(sorter.sorted(l, key=lambda x: -x))) + self.assertEqual(sorted(l, key=lambda x: -x), list(sorter.sorted(l, key=lambda x: -x))) self.assertGreater(shuffle.DiskBytesSpilled, last) last = shuffle.DiskBytesSpilled - self.assertEquals(sorted(l, key=lambda x: -x, reverse=True), - list(sorter.sorted(l, key=lambda x: -x, reverse=True))) + self.assertEqual(sorted(l, key=lambda x: -x, reverse=True), + list(sorter.sorted(l, key=lambda x: -x, reverse=True))) self.assertGreater(shuffle.DiskBytesSpilled, last) def test_external_sort_in_rdd(self): conf = SparkConf().set("spark.python.worker.memory", "1m") sc = SparkContext(conf=conf) - l = range(10240) + l = list(range(10240)) random.shuffle(l) - rdd = sc.parallelize(l, 10) - self.assertEquals(sorted(l), rdd.sortBy(lambda x: x).collect()) + rdd = sc.parallelize(l, 2) + self.assertEqual(sorted(l), rdd.sortBy(lambda x: x).collect()) sc.stop() @@ -200,11 +205,11 @@ class SerializationTestCase(unittest.TestCase): def test_namedtuple(self): from collections import namedtuple - from cPickle import dumps, loads + from pickle import dumps, loads P = namedtuple("P", "x y") p1 = P(1, 3) p2 = loads(dumps(p1, 2)) - self.assertEquals(p1, p2) + self.assertEqual(p1, p2) def test_itemgetter(self): from operator import itemgetter @@ -246,7 +251,7 @@ def test_pickling_file_handles(self): ser = CloudPickleSerializer() out1 = sys.stderr out2 = ser.loads(ser.dumps(out1)) - self.assertEquals(out1, out2) + self.assertEqual(out1, out2) def test_func_globals(self): @@ -263,19 +268,36 @@ def __reduce__(self): def foo(): sys.exit(0) - self.assertTrue("exit" in foo.func_code.co_names) + self.assertTrue("exit" in foo.__code__.co_names) ser.dumps(foo) def test_compressed_serializer(self): ser = CompressedSerializer(PickleSerializer()) - from StringIO import StringIO + try: + from StringIO import StringIO + except ImportError: + from io import BytesIO as StringIO io = StringIO() ser.dump_stream(["abc", u"123", range(5)], io) io.seek(0) self.assertEqual(["abc", u"123", range(5)], list(ser.load_stream(io))) ser.dump_stream(range(1000), io) io.seek(0) - self.assertEqual(["abc", u"123", range(5)] + range(1000), list(ser.load_stream(io))) + self.assertEqual(["abc", u"123", range(5)] + list(range(1000)), list(ser.load_stream(io))) + io.close() + + def test_hash_serializer(self): + hash(NoOpSerializer()) + hash(UTF8Deserializer()) + hash(PickleSerializer()) + hash(MarshalSerializer()) + hash(AutoSerializer()) + hash(BatchedSerializer(PickleSerializer())) + hash(AutoBatchedSerializer(MarshalSerializer())) + hash(PairDeserializer(NoOpSerializer(), UTF8Deserializer())) + hash(CartesianDeserializer(NoOpSerializer(), UTF8Deserializer())) + hash(CompressedSerializer(PickleSerializer())) + hash(FlattenedValuesSerializer(PickleSerializer())) class PySparkTestCase(unittest.TestCase): @@ -340,7 +362,7 @@ def test_checkpoint_and_restore(self): self.assertTrue(flatMappedRDD.getCheckpointFile() is not None) recovered = self.sc._checkpointFile(flatMappedRDD.getCheckpointFile(), flatMappedRDD._jrdd_deserializer) - self.assertEquals([1, 2, 3, 4], recovered.collect()) + self.assertEqual([1, 2, 3, 4], recovered.collect()) class AddFileTests(PySparkTestCase): @@ -356,8 +378,7 @@ def test_add_py_file(self): def func(x): from userlibrary import UserClass return UserClass().hello() - self.assertRaises(Exception, - self.sc.parallelize(range(2)).map(func).first) + self.assertRaises(Exception, self.sc.parallelize(range(2)).map(func).first) log4j.LogManager.getRootLogger().setLevel(old_level) # Add the file, so the job should now succeed: @@ -372,7 +393,7 @@ def test_add_file_locally(self): download_path = SparkFiles.get("hello.txt") self.assertNotEqual(path, download_path) with open(download_path) as test_file: - self.assertEquals("Hello World!\n", test_file.readline()) + self.assertEqual("Hello World!\n", test_file.readline()) def test_add_py_file_locally(self): # To ensure that we're actually testing addPyFile's effects, check that @@ -381,7 +402,7 @@ def func(): from userlibrary import UserClass self.assertRaises(ImportError, func) path = os.path.join(SPARK_HOME, "python/test_support/userlibrary.py") - self.sc.addFile(path) + self.sc.addPyFile(path) from userlibrary import UserClass self.assertEqual("Hello World!", UserClass().hello()) @@ -391,7 +412,7 @@ def test_add_egg_file_locally(self): def func(): from userlib import UserClass self.assertRaises(ImportError, func) - path = os.path.join(SPARK_HOME, "python/test_support/userlib-0.1-py2.7.egg") + path = os.path.join(SPARK_HOME, "python/test_support/userlib-0.1.zip") self.sc.addPyFile(path) from userlib import UserClass self.assertEqual("Hello World from inside a package!", UserClass().hello()) @@ -427,8 +448,9 @@ def test_save_as_textfile_with_unicode(self): tempFile = tempfile.NamedTemporaryFile(delete=True) tempFile.close() data.saveAsTextFile(tempFile.name) - raw_contents = ''.join(input(glob(tempFile.name + "/part-0000*"))) - self.assertEqual(x, unicode(raw_contents.strip(), "utf-8")) + raw_contents = b''.join(open(p, 'rb').read() + for p in glob(tempFile.name + "/part-0000*")) + self.assertEqual(x, raw_contents.strip().decode("utf-8")) def test_save_as_textfile_with_utf8(self): x = u"\u00A1Hola, mundo!" @@ -436,19 +458,20 @@ def test_save_as_textfile_with_utf8(self): tempFile = tempfile.NamedTemporaryFile(delete=True) tempFile.close() data.saveAsTextFile(tempFile.name) - raw_contents = ''.join(input(glob(tempFile.name + "/part-0000*"))) - self.assertEqual(x, unicode(raw_contents.strip(), "utf-8")) + raw_contents = b''.join(open(p, 'rb').read() + for p in glob(tempFile.name + "/part-0000*")) + self.assertEqual(x, raw_contents.strip().decode('utf8')) def test_transforming_cartesian_result(self): # Regression test for SPARK-1034 rdd1 = self.sc.parallelize([1, 2]) rdd2 = self.sc.parallelize([3, 4]) cart = rdd1.cartesian(rdd2) - result = cart.map(lambda (x, y): x + y).collect() + result = cart.map(lambda x_y3: x_y3[0] + x_y3[1]).collect() def test_transforming_pickle_file(self): # Regression test for SPARK-2601 - data = self.sc.parallelize(["Hello", "World!"]) + data = self.sc.parallelize([u"Hello", u"World!"]) tempFile = tempfile.NamedTemporaryFile(delete=True) tempFile.close() data.saveAsPickleFile(tempFile.name) @@ -461,13 +484,13 @@ def test_cartesian_on_textfile(self): a = self.sc.textFile(path) result = a.cartesian(a).collect() (x, y) = result[0] - self.assertEqual("Hello World!", x.strip()) - self.assertEqual("Hello World!", y.strip()) + self.assertEqual(u"Hello World!", x.strip()) + self.assertEqual(u"Hello World!", y.strip()) def test_deleting_input_files(self): # Regression test for SPARK-1025 tempFile = tempfile.NamedTemporaryFile(delete=False) - tempFile.write("Hello World!") + tempFile.write(b"Hello World!") tempFile.close() data = self.sc.textFile(tempFile.name) filtered_data = data.filter(lambda x: True) @@ -510,21 +533,21 @@ def test_namedtuple_in_rdd(self): jon = Person(1, "Jon", "Doe") jane = Person(2, "Jane", "Doe") theDoes = self.sc.parallelize([jon, jane]) - self.assertEquals([jon, jane], theDoes.collect()) + self.assertEqual([jon, jane], theDoes.collect()) def test_large_broadcast(self): N = 100000 data = [[float(i) for i in range(300)] for i in range(N)] bdata = self.sc.broadcast(data) # 270MB m = self.sc.parallelize(range(1), 1).map(lambda x: len(bdata.value)).sum() - self.assertEquals(N, m) + self.assertEqual(N, m) def test_multiple_broadcasts(self): N = 1 << 21 b1 = self.sc.broadcast(set(range(N))) # multiple blocks in JVM - r = range(1 << 15) + r = list(range(1 << 15)) random.shuffle(r) - s = str(r) + s = str(r).encode() checksum = hashlib.md5(s).hexdigest() b2 = self.sc.broadcast(s) r = list(set(self.sc.parallelize(range(10), 10).map( @@ -535,7 +558,7 @@ def test_multiple_broadcasts(self): self.assertEqual(checksum, csum) random.shuffle(r) - s = str(r) + s = str(r).encode() checksum = hashlib.md5(s).hexdigest() b2 = self.sc.broadcast(s) r = list(set(self.sc.parallelize(range(10), 10).map( @@ -549,7 +572,7 @@ def test_large_closure(self): N = 1000000 data = [float(i) for i in xrange(N)] rdd = self.sc.parallelize(range(1), 1).map(lambda x: len(data)) - self.assertEquals(N, rdd.first()) + self.assertEqual(N, rdd.first()) # regression test for SPARK-6886 self.assertEqual(1, rdd.map(lambda x: (x, 1)).groupByKey().count()) @@ -590,15 +613,15 @@ def test_zip_with_different_number_of_items(self): # same total number of items, but different distributions a = self.sc.parallelize([2, 3], 2).flatMap(range) b = self.sc.parallelize([3, 2], 2).flatMap(range) - self.assertEquals(a.count(), b.count()) + self.assertEqual(a.count(), b.count()) self.assertRaises(Exception, lambda: a.zip(b).count()) def test_count_approx_distinct(self): rdd = self.sc.parallelize(range(1000)) - self.assertTrue(950 < rdd.countApproxDistinct(0.04) < 1050) - self.assertTrue(950 < rdd.map(float).countApproxDistinct(0.04) < 1050) - self.assertTrue(950 < rdd.map(str).countApproxDistinct(0.04) < 1050) - self.assertTrue(950 < rdd.map(lambda x: (x, -x)).countApproxDistinct(0.04) < 1050) + self.assertTrue(950 < rdd.countApproxDistinct(0.03) < 1050) + self.assertTrue(950 < rdd.map(float).countApproxDistinct(0.03) < 1050) + self.assertTrue(950 < rdd.map(str).countApproxDistinct(0.03) < 1050) + self.assertTrue(950 < rdd.map(lambda x: (x, -x)).countApproxDistinct(0.03) < 1050) rdd = self.sc.parallelize([i % 20 for i in range(1000)], 7) self.assertTrue(18 < rdd.countApproxDistinct() < 22) @@ -612,59 +635,59 @@ def test_count_approx_distinct(self): def test_histogram(self): # empty rdd = self.sc.parallelize([]) - self.assertEquals([0], rdd.histogram([0, 10])[1]) - self.assertEquals([0, 0], rdd.histogram([0, 4, 10])[1]) + self.assertEqual([0], rdd.histogram([0, 10])[1]) + self.assertEqual([0, 0], rdd.histogram([0, 4, 10])[1]) self.assertRaises(ValueError, lambda: rdd.histogram(1)) # out of range rdd = self.sc.parallelize([10.01, -0.01]) - self.assertEquals([0], rdd.histogram([0, 10])[1]) - self.assertEquals([0, 0], rdd.histogram((0, 4, 10))[1]) + self.assertEqual([0], rdd.histogram([0, 10])[1]) + self.assertEqual([0, 0], rdd.histogram((0, 4, 10))[1]) # in range with one bucket rdd = self.sc.parallelize(range(1, 5)) - self.assertEquals([4], rdd.histogram([0, 10])[1]) - self.assertEquals([3, 1], rdd.histogram([0, 4, 10])[1]) + self.assertEqual([4], rdd.histogram([0, 10])[1]) + self.assertEqual([3, 1], rdd.histogram([0, 4, 10])[1]) # in range with one bucket exact match - self.assertEquals([4], rdd.histogram([1, 4])[1]) + self.assertEqual([4], rdd.histogram([1, 4])[1]) # out of range with two buckets rdd = self.sc.parallelize([10.01, -0.01]) - self.assertEquals([0, 0], rdd.histogram([0, 5, 10])[1]) + self.assertEqual([0, 0], rdd.histogram([0, 5, 10])[1]) # out of range with two uneven buckets rdd = self.sc.parallelize([10.01, -0.01]) - self.assertEquals([0, 0], rdd.histogram([0, 4, 10])[1]) + self.assertEqual([0, 0], rdd.histogram([0, 4, 10])[1]) # in range with two buckets rdd = self.sc.parallelize([1, 2, 3, 5, 6]) - self.assertEquals([3, 2], rdd.histogram([0, 5, 10])[1]) + self.assertEqual([3, 2], rdd.histogram([0, 5, 10])[1]) # in range with two bucket and None rdd = self.sc.parallelize([1, 2, 3, 5, 6, None, float('nan')]) - self.assertEquals([3, 2], rdd.histogram([0, 5, 10])[1]) + self.assertEqual([3, 2], rdd.histogram([0, 5, 10])[1]) # in range with two uneven buckets rdd = self.sc.parallelize([1, 2, 3, 5, 6]) - self.assertEquals([3, 2], rdd.histogram([0, 5, 11])[1]) + self.assertEqual([3, 2], rdd.histogram([0, 5, 11])[1]) # mixed range with two uneven buckets rdd = self.sc.parallelize([-0.01, 0.0, 1, 2, 3, 5, 6, 11.0, 11.01]) - self.assertEquals([4, 3], rdd.histogram([0, 5, 11])[1]) + self.assertEqual([4, 3], rdd.histogram([0, 5, 11])[1]) # mixed range with four uneven buckets rdd = self.sc.parallelize([-0.01, 0.0, 1, 2, 3, 5, 6, 11.01, 12.0, 199.0, 200.0, 200.1]) - self.assertEquals([4, 2, 1, 3], rdd.histogram([0.0, 5.0, 11.0, 12.0, 200.0])[1]) + self.assertEqual([4, 2, 1, 3], rdd.histogram([0.0, 5.0, 11.0, 12.0, 200.0])[1]) # mixed range with uneven buckets and NaN rdd = self.sc.parallelize([-0.01, 0.0, 1, 2, 3, 5, 6, 11.01, 12.0, 199.0, 200.0, 200.1, None, float('nan')]) - self.assertEquals([4, 2, 1, 3], rdd.histogram([0.0, 5.0, 11.0, 12.0, 200.0])[1]) + self.assertEqual([4, 2, 1, 3], rdd.histogram([0.0, 5.0, 11.0, 12.0, 200.0])[1]) # out of range with infinite buckets rdd = self.sc.parallelize([10.01, -0.01, float('nan'), float("inf")]) - self.assertEquals([1, 2], rdd.histogram([float('-inf'), 0, float('inf')])[1]) + self.assertEqual([1, 2], rdd.histogram([float('-inf'), 0, float('inf')])[1]) # invalid buckets self.assertRaises(ValueError, lambda: rdd.histogram([])) @@ -674,25 +697,25 @@ def test_histogram(self): # without buckets rdd = self.sc.parallelize(range(1, 5)) - self.assertEquals(([1, 4], [4]), rdd.histogram(1)) + self.assertEqual(([1, 4], [4]), rdd.histogram(1)) # without buckets single element rdd = self.sc.parallelize([1]) - self.assertEquals(([1, 1], [1]), rdd.histogram(1)) + self.assertEqual(([1, 1], [1]), rdd.histogram(1)) # without bucket no range rdd = self.sc.parallelize([1] * 4) - self.assertEquals(([1, 1], [4]), rdd.histogram(1)) + self.assertEqual(([1, 1], [4]), rdd.histogram(1)) # without buckets basic two rdd = self.sc.parallelize(range(1, 5)) - self.assertEquals(([1, 2.5, 4], [2, 2]), rdd.histogram(2)) + self.assertEqual(([1, 2.5, 4], [2, 2]), rdd.histogram(2)) # without buckets with more requested than elements rdd = self.sc.parallelize([1, 2]) buckets = [1 + 0.2 * i for i in range(6)] hist = [1, 0, 0, 0, 1] - self.assertEquals((buckets, hist), rdd.histogram(5)) + self.assertEqual((buckets, hist), rdd.histogram(5)) # invalid RDDs rdd = self.sc.parallelize([1, float('inf')]) @@ -702,15 +725,8 @@ def test_histogram(self): # string rdd = self.sc.parallelize(["ab", "ac", "b", "bd", "ef"], 2) - self.assertEquals([2, 2], rdd.histogram(["a", "b", "c"])[1]) - self.assertEquals((["ab", "ef"], [5]), rdd.histogram(1)) - self.assertRaises(TypeError, lambda: rdd.histogram(2)) - - # mixed RDD - rdd = self.sc.parallelize([1, 4, "ab", "ac", "b"], 2) - self.assertEquals([1, 1], rdd.histogram([0, 4, 10])[1]) - self.assertEquals([2, 1], rdd.histogram(["a", "b", "c"])[1]) - self.assertEquals(([1, "b"], [5]), rdd.histogram(1)) + self.assertEqual([2, 2], rdd.histogram(["a", "b", "c"])[1]) + self.assertEqual((["ab", "ef"], [5]), rdd.histogram(1)) self.assertRaises(TypeError, lambda: rdd.histogram(2)) def test_repartitionAndSortWithinPartitions(self): @@ -718,31 +734,31 @@ def test_repartitionAndSortWithinPartitions(self): repartitioned = rdd.repartitionAndSortWithinPartitions(2, lambda key: key % 2) partitions = repartitioned.glom().collect() - self.assertEquals(partitions[0], [(0, 5), (0, 8), (2, 6)]) - self.assertEquals(partitions[1], [(1, 3), (3, 8), (3, 8)]) + self.assertEqual(partitions[0], [(0, 5), (0, 8), (2, 6)]) + self.assertEqual(partitions[1], [(1, 3), (3, 8), (3, 8)]) def test_distinct(self): rdd = self.sc.parallelize((1, 2, 3)*10, 10) - self.assertEquals(rdd.getNumPartitions(), 10) - self.assertEquals(rdd.distinct().count(), 3) + self.assertEqual(rdd.getNumPartitions(), 10) + self.assertEqual(rdd.distinct().count(), 3) result = rdd.distinct(5) - self.assertEquals(result.getNumPartitions(), 5) - self.assertEquals(result.count(), 3) + self.assertEqual(result.getNumPartitions(), 5) + self.assertEqual(result.count(), 3) def test_external_group_by_key(self): - self.sc._conf.set("spark.python.worker.memory", "5m") + self.sc._conf.set("spark.python.worker.memory", "1m") N = 200001 kv = self.sc.parallelize(range(N)).map(lambda x: (x % 3, x)) gkv = kv.groupByKey().cache() self.assertEqual(3, gkv.count()) - filtered = gkv.filter(lambda (k, vs): k == 1) + filtered = gkv.filter(lambda kv: kv[0] == 1) self.assertEqual(1, filtered.count()) - self.assertEqual([(1, N/3)], filtered.mapValues(len).collect()) - self.assertEqual([(N/3, N/3)], + self.assertEqual([(1, N // 3)], filtered.mapValues(len).collect()) + self.assertEqual([(N // 3, N // 3)], filtered.values().map(lambda x: (len(x), len(list(x)))).collect()) result = filtered.collect()[0][1] - self.assertEqual(N/3, len(result)) - self.assertTrue(isinstance(result.data, shuffle.ExternalList)) + self.assertEqual(N // 3, len(result)) + self.assertTrue(isinstance(result.data, shuffle.ExternalListOfList)) def test_sort_on_empty_rdd(self): self.assertEqual([], self.sc.parallelize(zip([], [])).sortByKey().collect()) @@ -767,7 +783,7 @@ def test_null_in_rdd(self): rdd = RDD(jrdd, self.sc, UTF8Deserializer()) self.assertEqual([u"a", None, u"b"], rdd.collect()) rdd = RDD(jrdd, self.sc, NoOpSerializer()) - self.assertEqual(["a", None, "b"], rdd.collect()) + self.assertEqual([b"a", None, b"b"], rdd.collect()) def test_multiple_python_java_RDD_conversions(self): # Regression test for SPARK-5361 @@ -813,14 +829,14 @@ def test_narrow_dependency_in_join(self): self.sc.setJobGroup("test3", "test", True) d = sorted(parted.cogroup(parted).collect()) self.assertEqual(10, len(d)) - self.assertEqual([[0], [0]], map(list, d[0][1])) + self.assertEqual([[0], [0]], list(map(list, d[0][1]))) jobId = tracker.getJobIdsForGroup("test3")[0] self.assertEqual(2, len(tracker.getJobInfo(jobId).stageIds)) self.sc.setJobGroup("test4", "test", True) d = sorted(parted.cogroup(rdd).collect()) self.assertEqual(10, len(d)) - self.assertEqual([[0], [0]], map(list, d[0][1])) + self.assertEqual([[0], [0]], list(map(list, d[0][1]))) jobId = tracker.getJobIdsForGroup("test4")[0] self.assertEqual(3, len(tracker.getJobInfo(jobId).stageIds)) @@ -906,6 +922,7 @@ def tearDownClass(cls): ReusedPySparkTestCase.tearDownClass() shutil.rmtree(cls.tempdir.name) + @unittest.skipIf(sys.version >= "3", "serialize array of byte") def test_sequencefiles(self): basepath = self.tempdir.name ints = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfint/", @@ -954,15 +971,16 @@ def test_sequencefiles(self): en = [(1, None), (1, None), (2, None), (2, None), (2, None), (3, None)] self.assertEqual(nulls, en) - maps = sorted(self.sc.sequenceFile(basepath + "/sftestdata/sfmap/", - "org.apache.hadoop.io.IntWritable", - "org.apache.hadoop.io.MapWritable").collect()) + maps = self.sc.sequenceFile(basepath + "/sftestdata/sfmap/", + "org.apache.hadoop.io.IntWritable", + "org.apache.hadoop.io.MapWritable").collect() em = [(1, {}), (1, {3.0: u'bb'}), (2, {1.0: u'aa'}), (2, {1.0: u'cc'}), (3, {2.0: u'dd'})] - self.assertEqual(maps, em) + for v in maps: + self.assertTrue(v in em) # arrays get pickled to tuples by default tuples = sorted(self.sc.sequenceFile( @@ -1089,8 +1107,8 @@ def test_converters(self): def test_binary_files(self): path = os.path.join(self.tempdir.name, "binaryfiles") os.mkdir(path) - data = "short binary data" - with open(os.path.join(path, "part-0000"), 'w') as f: + data = b"short binary data" + with open(os.path.join(path, "part-0000"), 'wb') as f: f.write(data) [(p, d)] = self.sc.binaryFiles(path).collect() self.assertTrue(p.endswith("part-0000")) @@ -1103,7 +1121,7 @@ def test_binary_records(self): for i in range(100): f.write('%04d' % i) result = self.sc.binaryRecords(path, 4).map(int).collect() - self.assertEqual(range(100), result) + self.assertEqual(list(range(100)), result) class OutputFormatTests(ReusedPySparkTestCase): @@ -1115,6 +1133,7 @@ def setUp(self): def tearDown(self): shutil.rmtree(self.tempdir.name, ignore_errors=True) + @unittest.skipIf(sys.version >= "3", "serialize array of byte") def test_sequencefiles(self): basepath = self.tempdir.name ei = [(1, u'aa'), (1, u'aa'), (2, u'aa'), (2, u'bb'), (2, u'bb'), (3, u'cc')] @@ -1155,8 +1174,9 @@ def test_sequencefiles(self): (2, {1.0: u'cc'}), (3, {2.0: u'dd'})] self.sc.parallelize(em).saveAsSequenceFile(basepath + "/sfmap/") - maps = sorted(self.sc.sequenceFile(basepath + "/sfmap/").collect()) - self.assertEqual(maps, em) + maps = self.sc.sequenceFile(basepath + "/sfmap/").collect() + for v in maps: + self.assertTrue(v, em) def test_oldhadoop(self): basepath = self.tempdir.name @@ -1168,12 +1188,13 @@ def test_oldhadoop(self): "org.apache.hadoop.mapred.SequenceFileOutputFormat", "org.apache.hadoop.io.IntWritable", "org.apache.hadoop.io.MapWritable") - result = sorted(self.sc.hadoopFile( + result = self.sc.hadoopFile( basepath + "/oldhadoop/", "org.apache.hadoop.mapred.SequenceFileInputFormat", "org.apache.hadoop.io.IntWritable", - "org.apache.hadoop.io.MapWritable").collect()) - self.assertEqual(result, dict_data) + "org.apache.hadoop.io.MapWritable").collect() + for v in result: + self.assertTrue(v, dict_data) conf = { "mapred.output.format.class": "org.apache.hadoop.mapred.SequenceFileOutputFormat", @@ -1183,12 +1204,13 @@ def test_oldhadoop(self): } self.sc.parallelize(dict_data).saveAsHadoopDataset(conf) input_conf = {"mapred.input.dir": basepath + "/olddataset/"} - old_dataset = sorted(self.sc.hadoopRDD( + result = self.sc.hadoopRDD( "org.apache.hadoop.mapred.SequenceFileInputFormat", "org.apache.hadoop.io.IntWritable", "org.apache.hadoop.io.MapWritable", - conf=input_conf).collect()) - self.assertEqual(old_dataset, dict_data) + conf=input_conf).collect() + for v in result: + self.assertTrue(v, dict_data) def test_newhadoop(self): basepath = self.tempdir.name @@ -1223,6 +1245,7 @@ def test_newhadoop(self): conf=input_conf).collect()) self.assertEqual(new_dataset, data) + @unittest.skipIf(sys.version >= "3", "serialize of array") def test_newhadoop_with_array(self): basepath = self.tempdir.name # use custom ArrayWritable types and converters to handle arrays @@ -1303,7 +1326,7 @@ def test_reserialization(self): basepath = self.tempdir.name x = range(1, 5) y = range(1001, 1005) - data = zip(x, y) + data = list(zip(x, y)) rdd = self.sc.parallelize(x).zip(self.sc.parallelize(y)) rdd.saveAsSequenceFile(basepath + "/reserialize/sequence") result1 = sorted(self.sc.sequenceFile(basepath + "/reserialize/sequence").collect()) @@ -1354,7 +1377,7 @@ def connect(self, port): sock = socket(AF_INET, SOCK_STREAM) sock.connect(('127.0.0.1', port)) # send a split index of -1 to shutdown the worker - sock.send("\xFF\xFF\xFF\xFF") + sock.send(b"\xFF\xFF\xFF\xFF") sock.close() return True @@ -1395,7 +1418,6 @@ def test_termination_sigterm(self): class WorkerTests(PySparkTestCase): - def test_cancel_task(self): temp = tempfile.NamedTemporaryFile(delete=True) temp.close() @@ -1410,7 +1432,7 @@ def sleep(x): # start job in background thread def run(): - self.sc.parallelize(range(1)).foreach(sleep) + self.sc.parallelize(range(1), 1).foreach(sleep) import threading t = threading.Thread(target=run) t.daemon = True @@ -1419,7 +1441,8 @@ def run(): daemon_pid, worker_pid = 0, 0 while True: if os.path.exists(path): - data = open(path).read().split(' ') + with open(path) as f: + data = f.read().split(' ') daemon_pid, worker_pid = map(int, data) break time.sleep(0.1) @@ -1455,7 +1478,7 @@ def raise_exception(_): def test_after_jvm_exception(self): tempFile = tempfile.NamedTemporaryFile(delete=False) - tempFile.write("Hello World!") + tempFile.write(b"Hello World!") tempFile.close() data = self.sc.textFile(tempFile.name, 1) filtered_data = data.filter(lambda x: True) @@ -1577,12 +1600,12 @@ def test_single_script(self): |from pyspark import SparkContext | |sc = SparkContext() - |print sc.parallelize([1, 2, 3]).map(lambda x: x * 2).collect() + |print(sc.parallelize([1, 2, 3]).map(lambda x: x * 2).collect()) """) proc = subprocess.Popen([self.sparkSubmit, script], stdout=subprocess.PIPE) out, err = proc.communicate() self.assertEqual(0, proc.returncode) - self.assertIn("[2, 4, 6]", out) + self.assertIn("[2, 4, 6]", out.decode('utf-8')) def test_script_with_local_functions(self): """Submit and test a single script file calling a global function""" @@ -1593,12 +1616,12 @@ def test_script_with_local_functions(self): | return x * 3 | |sc = SparkContext() - |print sc.parallelize([1, 2, 3]).map(foo).collect() + |print(sc.parallelize([1, 2, 3]).map(foo).collect()) """) proc = subprocess.Popen([self.sparkSubmit, script], stdout=subprocess.PIPE) out, err = proc.communicate() self.assertEqual(0, proc.returncode) - self.assertIn("[3, 6, 9]", out) + self.assertIn("[3, 6, 9]", out.decode('utf-8')) def test_module_dependency(self): """Submit and test a script with a dependency on another module""" @@ -1607,7 +1630,7 @@ def test_module_dependency(self): |from mylib import myfunc | |sc = SparkContext() - |print sc.parallelize([1, 2, 3]).map(myfunc).collect() + |print(sc.parallelize([1, 2, 3]).map(myfunc).collect()) """) zip = self.createFileInZip("mylib.py", """ |def myfunc(x): @@ -1617,7 +1640,7 @@ def test_module_dependency(self): stdout=subprocess.PIPE) out, err = proc.communicate() self.assertEqual(0, proc.returncode) - self.assertIn("[2, 3, 4]", out) + self.assertIn("[2, 3, 4]", out.decode('utf-8')) def test_module_dependency_on_cluster(self): """Submit and test a script with a dependency on another module on a cluster""" @@ -1626,7 +1649,7 @@ def test_module_dependency_on_cluster(self): |from mylib import myfunc | |sc = SparkContext() - |print sc.parallelize([1, 2, 3]).map(myfunc).collect() + |print(sc.parallelize([1, 2, 3]).map(myfunc).collect()) """) zip = self.createFileInZip("mylib.py", """ |def myfunc(x): @@ -1637,7 +1660,7 @@ def test_module_dependency_on_cluster(self): stdout=subprocess.PIPE) out, err = proc.communicate() self.assertEqual(0, proc.returncode) - self.assertIn("[2, 3, 4]", out) + self.assertIn("[2, 3, 4]", out.decode('utf-8')) def test_package_dependency(self): """Submit and test a script with a dependency on a Spark Package""" @@ -1646,14 +1669,14 @@ def test_package_dependency(self): |from mylib import myfunc | |sc = SparkContext() - |print sc.parallelize([1, 2, 3]).map(myfunc).collect() + |print(sc.parallelize([1, 2, 3]).map(myfunc).collect()) """) self.create_spark_package("a:mylib:0.1") proc = subprocess.Popen([self.sparkSubmit, "--packages", "a:mylib:0.1", "--repositories", "file:" + self.programDir, script], stdout=subprocess.PIPE) out, err = proc.communicate() self.assertEqual(0, proc.returncode) - self.assertIn("[2, 3, 4]", out) + self.assertIn("[2, 3, 4]", out.decode('utf-8')) def test_package_dependency_on_cluster(self): """Submit and test a script with a dependency on a Spark Package on a cluster""" @@ -1662,7 +1685,7 @@ def test_package_dependency_on_cluster(self): |from mylib import myfunc | |sc = SparkContext() - |print sc.parallelize([1, 2, 3]).map(myfunc).collect() + |print(sc.parallelize([1, 2, 3]).map(myfunc).collect()) """) self.create_spark_package("a:mylib:0.1") proc = subprocess.Popen([self.sparkSubmit, "--packages", "a:mylib:0.1", "--repositories", @@ -1670,7 +1693,7 @@ def test_package_dependency_on_cluster(self): "local-cluster[1,1,512]", script], stdout=subprocess.PIPE) out, err = proc.communicate() self.assertEqual(0, proc.returncode) - self.assertIn("[2, 3, 4]", out) + self.assertIn("[2, 3, 4]", out.decode('utf-8')) def test_single_script_on_cluster(self): """Submit and test a single script on a cluster""" @@ -1681,7 +1704,7 @@ def test_single_script_on_cluster(self): | return x * 2 | |sc = SparkContext() - |print sc.parallelize([1, 2, 3]).map(foo).collect() + |print(sc.parallelize([1, 2, 3]).map(foo).collect()) """) # this will fail if you have different spark.executor.memory # in conf/spark-defaults.conf @@ -1690,7 +1713,7 @@ def test_single_script_on_cluster(self): stdout=subprocess.PIPE) out, err = proc.communicate() self.assertEqual(0, proc.returncode) - self.assertIn("[2, 4, 6]", out) + self.assertIn("[2, 4, 6]", out.decode('utf-8')) class ContextTests(unittest.TestCase): @@ -1765,7 +1788,7 @@ class SciPyTests(PySparkTestCase): def test_serialize(self): from scipy.special import gammaln x = range(1, 5) - expected = map(gammaln, x) + expected = list(map(gammaln, x)) observed = self.sc.parallelize(x).map(gammaln).collect() self.assertEqual(expected, observed) @@ -1786,11 +1809,11 @@ def test_statcounter_array(self): if __name__ == "__main__": if not _have_scipy: - print "NOTE: Skipping SciPy tests as it does not seem to be installed" + print("NOTE: Skipping SciPy tests as it does not seem to be installed") if not _have_numpy: - print "NOTE: Skipping NumPy tests as it does not seem to be installed" + print("NOTE: Skipping NumPy tests as it does not seem to be installed") unittest.main() if not _have_scipy: - print "NOTE: SciPy tests were skipped as it does not seem to be installed" + print("NOTE: SciPy tests were skipped as it does not seem to be installed") if not _have_numpy: - print "NOTE: NumPy tests were skipped as it does not seem to be installed" + print("NOTE: NumPy tests were skipped as it does not seem to be installed") diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py index 452d6fabdcc17..fbdaf3a5814cd 100644 --- a/python/pyspark/worker.py +++ b/python/pyspark/worker.py @@ -18,6 +18,7 @@ """ Worker that receives input from Piped RDD. """ +from __future__ import print_function import os import sys import time @@ -37,9 +38,9 @@ def report_times(outfile, boot, init, finish): write_int(SpecialLengths.TIMING_DATA, outfile) - write_long(1000 * boot, outfile) - write_long(1000 * init, outfile) - write_long(1000 * finish, outfile) + write_long(int(1000 * boot), outfile) + write_long(int(1000 * init), outfile) + write_long(int(1000 * finish), outfile) def add_path(path): @@ -72,6 +73,9 @@ def main(infile, outfile): for _ in range(num_python_includes): filename = utf8_deserializer.loads(infile) add_path(os.path.join(spark_files_dir, filename)) + if sys.version > '3': + import importlib + importlib.invalidate_caches() # fetch names and values of broadcast variables num_broadcast_variables = read_int(infile) @@ -106,14 +110,14 @@ def process(): except Exception: try: write_int(SpecialLengths.PYTHON_EXCEPTION_THROWN, outfile) - write_with_length(traceback.format_exc(), outfile) + write_with_length(traceback.format_exc().encode("utf-8"), outfile) except IOError: # JVM close the socket pass except Exception: # Write the error to stderr if it happened while serializing - print >> sys.stderr, "PySpark worker failed with exception:" - print >> sys.stderr, traceback.format_exc() + print("PySpark worker failed with exception:", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) exit(-1) finish_time = time.time() report_times(outfile, boot_time, init_time, finish_time) diff --git a/python/run-tests b/python/run-tests index f3a07d8aba562..ed3e819ef30c1 100755 --- a/python/run-tests +++ b/python/run-tests @@ -66,7 +66,7 @@ function run_core_tests() { function run_sql_tests() { echo "Run sql tests ..." - run_test "pyspark/sql/types.py" + run_test "pyspark/sql/_types.py" run_test "pyspark/sql/context.py" run_test "pyspark/sql/dataframe.py" run_test "pyspark/sql/functions.py" @@ -136,6 +136,19 @@ run_mllib_tests run_ml_tests run_streaming_tests +# Try to test with Python 3 +if [ $(which python3.4) ]; then + export PYSPARK_PYTHON="python3.4" + echo "Testing with Python3.4 version:" + $PYSPARK_PYTHON --version + + run_core_tests + run_sql_tests + run_mllib_tests + run_ml_tests + run_streaming_tests +fi + # Try to test with PyPy if [ $(which pypy) ]; then export PYSPARK_PYTHON="pypy" diff --git a/python/test_support/userlib-0.1-py2.7.egg b/python/test_support/userlib-0.1-py2.7.egg deleted file mode 100644 index 1674c9cb2227e160aec55c74e9ba1ddba850f580..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1945 zcmWIWW@Zs#U|`^2Sdq%-{OPC4zkVQZ3lQ@IacOaCQBG!(er{rBo?bzvZr?^OCPM+2 z-!(^Pyju5>Ifws9kif>ilh5?x&a}Ph`t|+UfpA{q4-(mHcXF|6ID3k0S`rmJ^Pu27 zX2pP|PDj;+_Dt5>5pw-nXZI!d?|Lntzt1q@eSGhRp0S;^U`hJyZ6+W7I>sGuc2o~I z;$-<*VantEcQTf&4@>aB-66eQMnKwrV%L&?hZb7x-!-%B+8>4hxZ7LO_?&A_9oL=$ zbn|W?2Kfl)_W1bByv&mLc%b`}lmDGLz4eV;d4LhKhymk9W&!bThE5TMq?8kh2`5rh zI+9Wnd=is5t|hQ}rTk-O;&oG4-6HJKBDP%2^0|tb`0vbyFVikRj(5)S6>%#*g z4>4Xznml`c(5%T>?3=boqzIt-qG4rZelQ~gLjn^6g8-5*pfQlVbi#eF!v-Slm#_J* zt#~NyvVK)UwfP*@J=O@3Im_WVolA58N~>g61`pSxe0_vE)< z^ZdsjUjEk5J^74tL*7Q7qYqkq3~ia@R@xk@{p5N=T`@m+{)dK?i}POB9N~!PDabP2 znxJE9d~uu8)sXNlS7bEW#Y9#FH7t}rbS~rCraZfArw&~@*7N!K)x8r0<{n>Yd*t

    waS|`|O?l3T$=A=7qdQyq0*MfDE zS-ZRgRd|K9^1{m+-Ws>d>^fX$@A>54M%x1?ijPX2D3<;<+wF7fk=$4Lh4pO9-@=?k}n_0dd;3x|M^$py6en$SghCm=SC!>>%c6)9BG#!3k-N^AQnc7 z0HkE(t$pfz=n3C5XM#_h(LJHB7099Mr(1N+Q%CO^6IygS{lCb@1vG^Nh{b@|)!kj! z)6dOcza+mPJ}0#-HAkN)2L?f8-Bp`(* z^5{v|nXIY_yAzriEjv{+f4c7E5CpoBkx7IZ6hzp|O`rrAY-t2hu#yOxhmkd7E4Uz9 zfrW769wg03=`R`G1oT1!VL}ry>7ZGUq8nR^N9bk-CO(*MB>T~=M^EGk0|J2tz!MQl zl1DcKJ*gwi=tnjKmhkau2c>%$*wT0qSv$5|fNm6eCO{bVAK56REP-wUdYVO;Fo^{z YJYXp}z?+o~q=XX)eSyWk87qhf04R`+K>z>% diff --git a/python/test_support/userlib-0.1.zip b/python/test_support/userlib-0.1.zip new file mode 100644 index 0000000000000000000000000000000000000000..496e1349aa9674cfa4f232ea8b5e17fcdb49f4fd GIT binary patch literal 668 zcmWIWW@h1H0D&_>m2O}Ll;B{HVJIz5Ey~GE(hm*cWMDr3PAF9Jy-;Xr1vdjD%U4DQ z1{M*Z+5or-kw6n(?`LOH28x2PFp>%J@tJv%$WKZ+A#ed zj*Lul%(&u00_Z;w5MX%g2%-@&!wQKRw17l54KrRKrU7G+VN0VEl4&Th1T+{+3_%RW k6#>WwZ$dH{9vwhKLD7NXT2?loY6fN?`~#%BKu%)-0QB3qpa1{> literal 0 HcmV?d00001 From 5fe43433529346788e8c343d338a5b7dc169cf58 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=BA=91=E5=B3=A4?= Date: Thu, 16 Apr 2015 17:32:42 -0700 Subject: [PATCH 313/591] SPARK-6927 [SQL] Sorting Error when codegen on MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Fix this error by adding BinaryType comparor in GenerateOrdering. JIRA https://issues.apache.org/jira/browse/SPARK-6927 Author: 云峤 Closes #5524 from kaka1992/fix-codegen-sort and squashes the following commits: d7e2afe [云峤] fix codegen sorting error --- .../codegen/GenerateOrdering.scala | 14 ++++++++++++- .../org/apache/spark/sql/SQLQuerySuite.scala | 20 +++++++++++++++++++ 2 files changed, 33 insertions(+), 1 deletion(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala index 0db29eb404bd1..fc2a2b60703e4 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala @@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions.codegen import org.apache.spark.Logging import org.apache.spark.sql.catalyst.expressions._ -import org.apache.spark.sql.types.{StringType, NumericType} +import org.apache.spark.sql.types.{BinaryType, StringType, NumericType} /** * Generates bytecode for an [[Ordering]] of [[Row Rows]] for a given set of @@ -43,6 +43,18 @@ object GenerateOrdering extends CodeGenerator[Seq[SortOrder], Ordering[Row]] wit val evalB = expressionEvaluator(order.child) val compare = order.child.dataType match { + case BinaryType => + q""" + val x = ${if (order.direction == Ascending) evalA.primitiveTerm else evalB.primitiveTerm} + val y = ${if (order.direction != Ascending) evalB.primitiveTerm else evalA.primitiveTerm} + var i = 0 + while (i < x.length && i < y.length) { + val res = x(i).compareTo(y(i)) + if (res != 0) return res + i = i+1 + } + return x.length - y.length + """ case _: NumericType => q""" val comp = ${evalA.primitiveTerm} - ${evalB.primitiveTerm} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala index d739e550f3e56..9e02e69fda3f2 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala @@ -398,6 +398,26 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll { setConf(SQLConf.EXTERNAL_SORT, before.toString) } + test("SPARK-6927 sorting with codegen on") { + val externalbefore = conf.externalSortEnabled + val codegenbefore = conf.codegenEnabled + setConf(SQLConf.EXTERNAL_SORT, "false") + setConf(SQLConf.CODEGEN_ENABLED, "true") + sortTest() + setConf(SQLConf.EXTERNAL_SORT, externalbefore.toString) + setConf(SQLConf.CODEGEN_ENABLED, codegenbefore.toString) + } + + test("SPARK-6927 external sorting with codegen on") { + val externalbefore = conf.externalSortEnabled + val codegenbefore = conf.codegenEnabled + setConf(SQLConf.CODEGEN_ENABLED, "true") + setConf(SQLConf.EXTERNAL_SORT, "true") + sortTest() + setConf(SQLConf.EXTERNAL_SORT, externalbefore.toString) + setConf(SQLConf.CODEGEN_ENABLED, codegenbefore.toString) + } + test("limit") { checkAnswer( sql("SELECT * FROM testData LIMIT 10"), From 6183b5e2caedd074073d0f6cb6609a634e2f5194 Mon Sep 17 00:00:00 2001 From: Davies Liu Date: Thu, 16 Apr 2015 17:33:57 -0700 Subject: [PATCH 314/591] [SPARK-6911] [SQL] improve accessor for nested types Support access columns by index in Python: ``` >>> df[df[0] > 3].collect() [Row(age=5, name=u'Bob')] ``` Access items in ArrayType or MapType ``` >>> df.select(df.l.getItem(0), df.d.getItem("key")).show() >>> df.select(df.l[0], df.d["key"]).show() ``` Access field in StructType ``` >>> df.select(df.r.getField("b")).show() >>> df.select(df.r.a).show() ``` Author: Davies Liu Closes #5513 from davies/access and squashes the following commits: e04d5a0 [Davies Liu] Update run-tests-jenkins 7ada9eb [Davies Liu] update timeout d125ac4 [Davies Liu] check column name, improve scala tests 6b62540 [Davies Liu] fix test db15b42 [Davies Liu] Merge branch 'master' of github.com:apache/spark into access 6c32e79 [Davies Liu] add scala tests 11f1df3 [Davies Liu] improve accessor for nested types --- python/pyspark/sql/dataframe.py | 49 +++++++++++++++++-- python/pyspark/sql/tests.py | 18 +++++++ .../scala/org/apache/spark/sql/Column.scala | 7 +-- .../org/apache/spark/sql/DataFrameSuite.scala | 6 +++ .../scala/org/apache/spark/sql/TestData.scala | 9 ++-- 5 files changed, 76 insertions(+), 13 deletions(-) diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index d76504f986270..b9a3e6cfe7f49 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -563,16 +563,23 @@ def __getitem__(self, item): [Row(name=u'Alice', age=2), Row(name=u'Bob', age=5)] >>> df[ df.age > 3 ].collect() [Row(age=5, name=u'Bob')] + >>> df[df[0] > 3].collect() + [Row(age=5, name=u'Bob')] """ if isinstance(item, basestring): + if item not in self.columns: + raise IndexError("no such column: %s" % item) jc = self._jdf.apply(item) return Column(jc) elif isinstance(item, Column): return self.filter(item) - elif isinstance(item, list): + elif isinstance(item, (list, tuple)): return self.select(*item) + elif isinstance(item, int): + jc = self._jdf.apply(self.columns[item]) + return Column(jc) else: - raise IndexError("unexpected index: %s" % item) + raise TypeError("unexpected type: %s" % type(item)) def __getattr__(self, name): """Returns the :class:`Column` denoted by ``name``. @@ -580,8 +587,8 @@ def __getattr__(self, name): >>> df.select(df.age).collect() [Row(age=2), Row(age=5)] """ - if name.startswith("__"): - raise AttributeError(name) + if name not in self.columns: + raise AttributeError("No such column: %s" % name) jc = self._jdf.apply(name) return Column(jc) @@ -1093,7 +1100,39 @@ def __init__(self, jc): # container operators __contains__ = _bin_op("contains") __getitem__ = _bin_op("getItem") - getField = _bin_op("getField", "An expression that gets a field by name in a StructField.") + + def getItem(self, key): + """An expression that gets an item at position `ordinal` out of a list, + or gets an item by key out of a dict. + + >>> df = sc.parallelize([([1, 2], {"key": "value"})]).toDF(["l", "d"]) + >>> df.select(df.l.getItem(0), df.d.getItem("key")).show() + l[0] d[key] + 1 value + >>> df.select(df.l[0], df.d["key"]).show() + l[0] d[key] + 1 value + """ + return self[key] + + def getField(self, name): + """An expression that gets a field by name in a StructField. + + >>> from pyspark.sql import Row + >>> df = sc.parallelize([Row(r=Row(a=1, b="b"))]).toDF() + >>> df.select(df.r.getField("b")).show() + r.b + b + >>> df.select(df.r.a).show() + r.a + 1 + """ + return Column(self._jc.getField(name)) + + def __getattr__(self, item): + if item.startswith("__"): + raise AttributeError(item) + return self.getField(item) # string methods rlike = _bin_op("rlike") diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index 7c09a0cfe30ab..6691e8c8dc44b 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -426,6 +426,24 @@ def test_help_command(self): pydoc.render_doc(df.foo) pydoc.render_doc(df.take(1)) + def test_access_column(self): + df = self.df + self.assertTrue(isinstance(df.key, Column)) + self.assertTrue(isinstance(df['key'], Column)) + self.assertTrue(isinstance(df[0], Column)) + self.assertRaises(IndexError, lambda: df[2]) + self.assertRaises(IndexError, lambda: df["bad_key"]) + self.assertRaises(TypeError, lambda: df[{}]) + + def test_access_nested_types(self): + df = self.sc.parallelize([Row(l=[1], r=Row(a=1, b="b"), d={"k": "v"})]).toDF() + self.assertEqual(1, df.select(df.l[0]).first()[0]) + self.assertEqual(1, df.select(df.l.getItem(0)).first()[0]) + self.assertEqual(1, df.select(df.r.a).first()[0]) + self.assertEqual("b", df.select(df.r.getField("b")).first()[0]) + self.assertEqual("v", df.select(df.d["k"]).first()[0]) + self.assertEqual("v", df.select(df.d.getItem("k")).first()[0]) + def test_infer_long_type(self): longrow = [Row(f1='a', f2=100000000000000)] df = self.sc.parallelize(longrow).toDF() diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala index 3cd7adf8cab5e..edb229c059e6b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala @@ -515,14 +515,15 @@ class Column(protected[sql] val expr: Expression) extends Logging { def rlike(literal: String): Column = RLike(expr, lit(literal).expr) /** - * An expression that gets an item at position `ordinal` out of an array. + * An expression that gets an item at position `ordinal` out of an array, + * or gets a value by key `key` in a [[MapType]]. * * @group expr_ops */ - def getItem(ordinal: Int): Column = GetItem(expr, Literal(ordinal)) + def getItem(key: Any): Column = GetItem(expr, Literal(key)) /** - * An expression that gets a field by name in a [[StructField]]. + * An expression that gets a field by name in a [[StructType]]. * * @group expr_ops */ diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index b26e22f6229fe..34b2cb054a3e7 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -86,6 +86,12 @@ class DataFrameSuite extends QueryTest { TestSQLContext.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, oldSetting.toString) } + test("access complex data") { + assert(complexData.filter(complexData("a").getItem(0) === 2).count() == 1) + assert(complexData.filter(complexData("m").getItem("1") === 1).count() == 1) + assert(complexData.filter(complexData("s").getField("key") === 1).count() == 1) + } + test("table scan") { checkAnswer( testData, diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala index 637f59b2e68ca..225b51bd73d6c 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala @@ -20,9 +20,8 @@ package org.apache.spark.sql import java.sql.Timestamp import org.apache.spark.sql.catalyst.plans.logical -import org.apache.spark.sql.functions._ -import org.apache.spark.sql.test._ import org.apache.spark.sql.test.TestSQLContext.implicits._ +import org.apache.spark.sql.test._ case class TestData(key: Int, value: String) @@ -199,11 +198,11 @@ object TestData { Salary(1, 1000.0) :: Nil).toDF() salary.registerTempTable("salary") - case class ComplexData(m: Map[Int, String], s: TestData, a: Seq[Int], b: Boolean) + case class ComplexData(m: Map[String, Int], s: TestData, a: Seq[Int], b: Boolean) val complexData = TestSQLContext.sparkContext.parallelize( - ComplexData(Map(1 -> "1"), TestData(1, "1"), Seq(1), true) - :: ComplexData(Map(2 -> "2"), TestData(2, "2"), Seq(2), false) + ComplexData(Map("1" -> 1), TestData(1, "1"), Seq(1), true) + :: ComplexData(Map("2" -> 2), TestData(2, "2"), Seq(2), false) :: Nil).toDF() complexData.registerTempTable("complexData") } From d96608674f6c2ff3abb13c65d80c1a3872206710 Mon Sep 17 00:00:00 2001 From: scwf Date: Thu, 16 Apr 2015 17:35:51 -0700 Subject: [PATCH 315/591] [SQL][Minor] Fix foreachUp of treenode `foreachUp` should runs the given function recursively on [[children]] then on this node(just like transformUp). The current implementation does not follow this. This will leads to checkanalysis do not check from bottom of logical tree. Author: scwf Author: Fei Wang Closes #5518 from scwf/patch-1 and squashes the following commits: 18e28b2 [scwf] added a test case 1ccbfa8 [Fei Wang] fix foreachUp --- .../apache/spark/sql/catalyst/trees/TreeNode.scala | 2 +- .../spark/sql/catalyst/trees/TreeNodeSuite.scala | 12 ++++++++++++ 2 files changed, 13 insertions(+), 1 deletion(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala index a2df51e598a2b..97502ed3afe72 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala @@ -85,7 +85,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] { * @param f the function to be applied to each node in the tree. */ def foreachUp(f: BaseType => Unit): Unit = { - children.foreach(_.foreach(f)) + children.foreach(_.foreachUp(f)) f(this) } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala index 4eb8708335dcf..6b393327cc97a 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/TreeNodeSuite.scala @@ -117,5 +117,17 @@ class TreeNodeSuite extends FunSuite { assert(transformed.origin.startPosition.isDefined) } + test("foreach up") { + val actual = new ArrayBuffer[String]() + val expected = Seq("1", "2", "3", "4", "-", "*", "+") + val expression = Add(Literal(1), Multiply(Literal(2), Subtract(Literal(3), Literal(4)))) + expression foreachUp { + case b: BinaryExpression => actual.append(b.symbol); + case l: Literal => actual.append(l.toString); + } + + assert(expected === actual) + } + } From 1e43851d6455f65b850ea0327d0e92f65395d23f Mon Sep 17 00:00:00 2001 From: Liang-Chi Hsieh Date: Thu, 16 Apr 2015 17:50:20 -0700 Subject: [PATCH 316/591] [SPARK-6899][SQL] Fix type mismatch when using codegen with Average on DecimalType JIRA https://issues.apache.org/jira/browse/SPARK-6899 Author: Liang-Chi Hsieh Closes #5517 from viirya/fix_codegen_average and squashes the following commits: 8ae5f65 [Liang-Chi Hsieh] Add the case of DecimalType.Unlimited to Average. --- .../spark/sql/catalyst/expressions/aggregates.scala | 2 +- .../test/scala/org/apache/spark/sql/DataFrameSuite.scala | 9 +++++++++ 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala index 14a855054b94d..f3830c6d3bcf2 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala @@ -326,7 +326,7 @@ case class Average(child: Expression) extends PartialAggregate with trees.UnaryN override def asPartial: SplitEvaluation = { child.dataType match { - case DecimalType.Fixed(_, _) => + case DecimalType.Fixed(_, _) | DecimalType.Unlimited => // Turn the child to unlimited decimals for calculation, before going back to fixed val partialSum = Alias(Sum(Cast(child, DecimalType.Unlimited)), "PartialSum")() val partialCount = Alias(Count(child), "PartialCount")() diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index 34b2cb054a3e7..44a7d1e7bbb6a 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -537,4 +537,13 @@ class DataFrameSuite extends QueryTest { val df = TestSQLContext.createDataFrame(rowRDD, schema) df.rdd.collect() } + + test("SPARK-6899") { + val originalValue = TestSQLContext.conf.codegenEnabled + TestSQLContext.setConf(SQLConf.CODEGEN_ENABLED, "true") + checkAnswer( + decimalData.agg(avg('a)), + Row(new java.math.BigDecimal(2.0))) + TestSQLContext.setConf(SQLConf.CODEGEN_ENABLED, originalValue.toString) + } } From e5949c287ed19e78b6eecc61c3e88a07ad452eb9 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Thu, 16 Apr 2015 17:59:49 -0700 Subject: [PATCH 317/591] [SPARK-6966][SQL] Use correct ClassLoader for JDBC Driver Otherwise we cannot add jars with drivers after the fact. Author: Michael Armbrust Closes #5543 from marmbrus/jdbcClassloader and squashes the following commits: d9930f3 [Michael Armbrust] fix imports 73d0614 [Michael Armbrust] [SPARK-6966][SQL] Use correct ClassLoader for JDBC Driver --- .../main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala index 99b755c9f25d0..5f480083d5a49 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala @@ -28,6 +28,7 @@ import org.apache.spark.sql.SQLContext import org.apache.spark.sql.catalyst.expressions.Row import org.apache.spark.sql.sources._ import org.apache.spark.sql.types.StructType +import org.apache.spark.util.Utils /** * Data corresponding to one partition of a JDBCRDD. @@ -99,7 +100,7 @@ private[sql] class DefaultSource extends RelationProvider { val upperBound = parameters.getOrElse("upperBound", null) val numPartitions = parameters.getOrElse("numPartitions", null) - if (driver != null) Class.forName(driver) + if (driver != null) Utils.getContextOrSparkClassLoader.loadClass(driver) if (partitionColumn != null && (lowerBound == null || upperBound == null || numPartitions == null)) { From 8220d5265f1bbea9dfdaeec4f2d06d7fe24c0bc3 Mon Sep 17 00:00:00 2001 From: Michael Armbrust Date: Thu, 16 Apr 2015 21:49:26 -0500 Subject: [PATCH 318/591] [SPARK-6972][SQL] Add Coalesce to DataFrame Author: Michael Armbrust Closes #5545 from marmbrus/addCoalesce and squashes the following commits: 9fdf3f6 [Michael Armbrust] [SPARK-6972][SQL] Add Coalesce to DataFrame --- .../scala/org/apache/spark/sql/DataFrame.scala | 14 ++++++++++++++ .../main/scala/org/apache/spark/sql/RDDApi.scala | 2 ++ .../org/apache/spark/sql/DataFrameSuite.scala | 8 ++++++++ 3 files changed, 24 insertions(+) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 3235f85d5bbd2..17c21f6e3a0e9 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -908,6 +908,20 @@ class DataFrame private[sql]( schema, needsConversion = false) } + /** + * Returns a new [[DataFrame]] that has exactly `numPartitions` partitions. + * Similar to coalesce defined on an [[RDD]], this operation results in a narrow dependency, e.g. + * if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of + * the 100 new partitions will claim 10 of the current partitions. + * @group rdd + */ + override def coalesce(numPartitions: Int): DataFrame = { + sqlContext.createDataFrame( + queryExecution.toRdd.coalesce(numPartitions), + schema, + needsConversion = false) + } + /** * Returns a new [[DataFrame]] that contains only the unique rows from this [[DataFrame]]. * @group dfops diff --git a/sql/core/src/main/scala/org/apache/spark/sql/RDDApi.scala b/sql/core/src/main/scala/org/apache/spark/sql/RDDApi.scala index ba4373f0124b4..63dbab19947c0 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/RDDApi.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/RDDApi.scala @@ -61,5 +61,7 @@ private[sql] trait RDDApi[T] { def repartition(numPartitions: Int): DataFrame + def coalesce(numPartitions: Int): DataFrame + def distinct: DataFrame } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index 44a7d1e7bbb6a..3250ab476aeb4 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -178,6 +178,14 @@ class DataFrameSuite extends QueryTest { testData.select('key).collect().toSeq) } + test("coalesce") { + assert(testData.select('key).coalesce(1).rdd.partitions.size === 1) + + checkAnswer( + testData.select('key).coalesce(1).select('key), + testData.select('key).collect().toSeq) + } + test("groupBy") { checkAnswer( testData2.groupBy("a").agg($"a", sum($"b")), From f7a25644ed5b3b49fe7f33743bec3d95cdf7913e Mon Sep 17 00:00:00 2001 From: Sean Owen Date: Fri, 17 Apr 2015 11:02:31 +0100 Subject: [PATCH 319/591] SPARK-6846 [WEBUI] Stage kill URL easy to accidentally trigger and possibility for security issue kill endpoints now only accept a POST (kill stage, master kill app, master kill driver); kill link now POSTs Author: Sean Owen Closes #5528 from srowen/SPARK-6846 and squashes the following commits: 137ac9f [Sean Owen] Oops, fix scalastyle line length probelm 7c5f961 [Sean Owen] Add Imran's test of kill link 59f447d [Sean Owen] kill endpoints now only accept a POST (kill stage, master kill app, master kill driver); kill link now POSTs --- .../org/apache/spark/ui/static/webui.css | 6 +-- .../spark/deploy/master/ui/MasterPage.scala | 28 +++++++------ .../spark/deploy/master/ui/MasterWebUI.scala | 8 ++-- .../org/apache/spark/ui/JettyUtils.scala | 17 +++++++- .../scala/org/apache/spark/ui/SparkUI.scala | 4 +- .../org/apache/spark/ui/jobs/StageTable.scala | 27 ++++++------- .../org/apache/spark/ui/UISeleniumSuite.scala | 40 +++++++++++++------ 7 files changed, 78 insertions(+), 52 deletions(-) diff --git a/core/src/main/resources/org/apache/spark/ui/static/webui.css b/core/src/main/resources/org/apache/spark/ui/static/webui.css index 6c37cc8b98236..4910744d1d790 100644 --- a/core/src/main/resources/org/apache/spark/ui/static/webui.css +++ b/core/src/main/resources/org/apache/spark/ui/static/webui.css @@ -85,17 +85,13 @@ table.sortable td { filter: progid:dximagetransform.microsoft.gradient(startColorstr='#FFA4EDFF', endColorstr='#FF94DDFF', GradientType=0); } -span.kill-link { +a.kill-link { margin-right: 2px; margin-left: 20px; color: gray; float: right; } -span.kill-link a { - color: gray; -} - span.expand-details { font-size: 10pt; cursor: pointer; diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala index 399f07399a0aa..1f2c3fdbfb2bc 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterPage.scala @@ -190,12 +190,14 @@ private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") { private def appRow(app: ApplicationInfo): Seq[Node] = { val killLink = if (parent.killEnabled && (app.state == ApplicationState.RUNNING || app.state == ApplicationState.WAITING)) { - val killLinkUri = s"app/kill?id=${app.id}&terminate=true" - val confirm = "return window.confirm(" + - s"'Are you sure you want to kill application ${app.id} ?');" - - (kill) - + val confirm = + s"if (window.confirm('Are you sure you want to kill application ${app.id} ?')) " + + "{ this.parentNode.submit(); return true; } else { return false; }" +
    + + + (kill) + }

    diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala index 1b670418ab1ff..bb11e0642ddc6 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala @@ -43,10 +43,10 @@ class MasterWebUI(val master: Master, requestedPort: Int) attachPage(new HistoryNotFoundPage(this)) attachPage(masterPage) attachHandler(createStaticHandler(MasterWebUI.STATIC_RESOURCE_DIR, "/static")) - attachHandler( - createRedirectHandler("/app/kill", "/", masterPage.handleAppKillRequest)) - attachHandler( - createRedirectHandler("/driver/kill", "/", masterPage.handleDriverKillRequest)) + attachHandler(createRedirectHandler( + "/app/kill", "/", masterPage.handleAppKillRequest, httpMethod = "POST")) + attachHandler(createRedirectHandler( + "/driver/kill", "/", masterPage.handleDriverKillRequest, httpMethod = "POST")) } /** Attach a reconstructed UI to this Master UI. Only valid after bind(). */ diff --git a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala index 95f254a9ef22a..a091ca650c60c 100644 --- a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala +++ b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala @@ -114,10 +114,23 @@ private[spark] object JettyUtils extends Logging { srcPath: String, destPath: String, beforeRedirect: HttpServletRequest => Unit = x => (), - basePath: String = ""): ServletContextHandler = { + basePath: String = "", + httpMethod: String = "GET"): ServletContextHandler = { val prefixedDestPath = attachPrefix(basePath, destPath) val servlet = new HttpServlet { - override def doGet(request: HttpServletRequest, response: HttpServletResponse) { + override def doGet(request: HttpServletRequest, response: HttpServletResponse): Unit = { + httpMethod match { + case "GET" => doRequest(request, response) + case _ => response.sendError(HttpServletResponse.SC_METHOD_NOT_ALLOWED) + } + } + override def doPost(request: HttpServletRequest, response: HttpServletResponse): Unit = { + httpMethod match { + case "POST" => doRequest(request, response) + case _ => response.sendError(HttpServletResponse.SC_METHOD_NOT_ALLOWED) + } + } + private def doRequest(request: HttpServletRequest, response: HttpServletResponse): Unit = { beforeRedirect(request) // Make sure we don't end up with "//" in the middle val newUrl = new URL(new URL(request.getRequestURL.toString), prefixedDestPath).toString diff --git a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala index adfa6bbada256..580ab8b1325f8 100644 --- a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala +++ b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala @@ -55,8 +55,8 @@ private[spark] class SparkUI private ( attachTab(new ExecutorsTab(this)) attachHandler(createStaticHandler(SparkUI.STATIC_RESOURCE_DIR, "/static")) attachHandler(createRedirectHandler("/", "/jobs", basePath = basePath)) - attachHandler( - createRedirectHandler("/stages/stage/kill", "/stages", stagesTab.handleKillRequest)) + attachHandler(createRedirectHandler( + "/stages/stage/kill", "/stages", stagesTab.handleKillRequest, httpMethod = "POST")) } initialize() diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala index 5865850fa09b5..cb72890a0fd20 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/StageTable.scala @@ -73,20 +73,21 @@ private[ui] class StageTableBase( } private def makeDescription(s: StageInfo): Seq[Node] = { - // scalastyle:off + val basePathUri = UIUtils.prependBaseUri(basePath) + val killLink = if (killEnabled) { - val killLinkUri = "%s/stages/stage/kill?id=%s&terminate=true" - .format(UIUtils.prependBaseUri(basePath), s.stageId) - val confirm = "return window.confirm('Are you sure you want to kill stage %s ?');" - .format(s.stageId) - - (kill) - + val killLinkUri = s"$basePathUri/stages/stage/kill/" + val confirm = + s"if (window.confirm('Are you sure you want to kill stage ${s.stageId} ?')) " + + "{ this.parentNode.submit(); return true; } else { return false; }" + + + + (kill) + } - // scalastyle:on - val nameLinkUri ="%s/stages/stage?id=%s&attempt=%s" - .format(UIUtils.prependBaseUri(basePath), s.stageId, s.attemptId) + val nameLinkUri = s"$basePathUri/stages/stage?id=${s.stageId}&attempt=${s.attemptId}" val nameLink = {s.name} val cachedRddInfos = s.rddInfos.filter(_.numCachedPartitions > 0) @@ -98,11 +99,9 @@ private[ui] class StageTableBase( diff --git a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala index 1cb594633f331..eb9db550fd74c 100644 --- a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala +++ b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala @@ -17,6 +17,7 @@ package org.apache.spark.ui +import java.net.{HttpURLConnection, URL} import javax.servlet.http.HttpServletRequest import scala.collection.JavaConversions._ @@ -56,12 +57,13 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before * Create a test SparkContext with the SparkUI enabled. * It is safe to `get` the SparkUI directly from the SparkContext returned here. */ - private def newSparkContext(): SparkContext = { + private def newSparkContext(killEnabled: Boolean = true): SparkContext = { val conf = new SparkConf() .setMaster("local") .setAppName("test") .set("spark.ui.enabled", "true") .set("spark.ui.port", "0") + .set("spark.ui.killEnabled", killEnabled.toString) val sc = new SparkContext(conf) assert(sc.ui.isDefined) sc @@ -128,21 +130,12 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before } test("spark.ui.killEnabled should properly control kill button display") { - def getSparkContext(killEnabled: Boolean): SparkContext = { - val conf = new SparkConf() - .setMaster("local") - .setAppName("test") - .set("spark.ui.enabled", "true") - .set("spark.ui.killEnabled", killEnabled.toString) - new SparkContext(conf) - } - def hasKillLink: Boolean = find(className("kill-link")).isDefined def runSlowJob(sc: SparkContext) { sc.parallelize(1 to 10).map{x => Thread.sleep(10000); x}.countAsync() } - withSpark(getSparkContext(killEnabled = true)) { sc => + withSpark(newSparkContext(killEnabled = true)) { sc => runSlowJob(sc) eventually(timeout(5 seconds), interval(50 milliseconds)) { go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/stages") @@ -150,7 +143,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before } } - withSpark(getSparkContext(killEnabled = false)) { sc => + withSpark(newSparkContext(killEnabled = false)) { sc => runSlowJob(sc) eventually(timeout(5 seconds), interval(50 milliseconds)) { go to (sc.ui.get.appUIAddress.stripSuffix("/") + "/stages") @@ -233,7 +226,7 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before // because someone could change the error message and cause this test to pass by accident. // Instead, it's safer to check that each row contains a link to a stage details page. findAll(cssSelector("tbody tr")).foreach { row => - val link = row.underlying.findElement(By.xpath(".//a")) + val link = row.underlying.findElement(By.xpath("./td/div/a")) link.getAttribute("href") should include ("stage") } } @@ -356,4 +349,25 @@ class UISeleniumSuite extends FunSuite with WebBrowser with Matchers with Before } } } + + test("kill stage is POST only") { + def getResponseCode(url: URL, method: String): Int = { + val connection = url.openConnection().asInstanceOf[HttpURLConnection] + connection.setRequestMethod(method) + connection.connect() + val code = connection.getResponseCode() + connection.disconnect() + code + } + + withSpark(newSparkContext(killEnabled = true)) { sc => + sc.parallelize(1 to 10).map{x => Thread.sleep(10000); x}.countAsync() + eventually(timeout(5 seconds), interval(50 milliseconds)) { + val url = new URL( + sc.ui.get.appUIAddress.stripSuffix("/") + "/stages/stage/kill/?id=0&terminate=true") + getResponseCode(url, "GET") should be (405) + getResponseCode(url, "POST") should be (200) + } + } + } } From 4527761bcd6501c362baf2780905a0018b9a74ba Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Fri, 17 Apr 2015 11:06:01 +0100 Subject: [PATCH 320/591] [SPARK-6046] [core] Reorganize deprecated config support in SparkConf. This change tries to follow the chosen way for handling deprecated configs in SparkConf: all values (old and new) are kept in the conf object, and newer names take precedence over older ones when retrieving the value. Warnings are logged when config options are set, which generally happens on the driver node (where the logs are most visible). Author: Marcelo Vanzin Closes #5514 from vanzin/SPARK-6046 and squashes the following commits: 9371529 [Marcelo Vanzin] Avoid math. 6cf3f11 [Marcelo Vanzin] Review feedback. 2445d48 [Marcelo Vanzin] Fix (and cleanup) update interval initialization. b6824be [Marcelo Vanzin] Clean up the other deprecated config use also. ab20351 [Marcelo Vanzin] Update FsHistoryProvider to only retrieve new config key. 2c93209 [Marcelo Vanzin] [SPARK-6046] [core] Reorganize deprecated config support in SparkConf. --- .../scala/org/apache/spark/SparkConf.scala | 174 ++++++++++-------- .../deploy/history/FsHistoryProvider.scala | 9 +- .../org/apache/spark/executor/Executor.scala | 5 +- .../org/apache/spark/SparkConfSuite.scala | 22 +++ docs/monitoring.md | 6 +- .../org/apache/spark/deploy/yarn/Client.scala | 3 +- 6 files changed, 124 insertions(+), 95 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala index 390e631647bd6..b0186e9a007b8 100644 --- a/core/src/main/scala/org/apache/spark/SparkConf.scala +++ b/core/src/main/scala/org/apache/spark/SparkConf.scala @@ -68,6 +68,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { if (value == null) { throw new NullPointerException("null value for " + key) } + logDeprecationWarning(key) settings.put(key, value) this } @@ -134,13 +135,15 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { /** Set multiple parameters together */ def setAll(settings: Traversable[(String, String)]): SparkConf = { - this.settings.putAll(settings.toMap.asJava) + settings.foreach { case (k, v) => set(k, v) } this } /** Set a parameter if it isn't already configured */ def setIfMissing(key: String, value: String): SparkConf = { - settings.putIfAbsent(key, value) + if (settings.putIfAbsent(key, value) == null) { + logDeprecationWarning(key) + } this } @@ -174,8 +177,8 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { getOption(key).getOrElse(defaultValue) } - /** - * Get a time parameter as seconds; throws a NoSuchElementException if it's not set. If no + /** + * Get a time parameter as seconds; throws a NoSuchElementException if it's not set. If no * suffix is provided then seconds are assumed. * @throws NoSuchElementException */ @@ -183,36 +186,35 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { Utils.timeStringAsSeconds(get(key)) } - /** - * Get a time parameter as seconds, falling back to a default if not set. If no + /** + * Get a time parameter as seconds, falling back to a default if not set. If no * suffix is provided then seconds are assumed. - * */ def getTimeAsSeconds(key: String, defaultValue: String): Long = { Utils.timeStringAsSeconds(get(key, defaultValue)) } - /** - * Get a time parameter as milliseconds; throws a NoSuchElementException if it's not set. If no - * suffix is provided then milliseconds are assumed. + /** + * Get a time parameter as milliseconds; throws a NoSuchElementException if it's not set. If no + * suffix is provided then milliseconds are assumed. * @throws NoSuchElementException */ def getTimeAsMs(key: String): Long = { Utils.timeStringAsMs(get(key)) } - /** - * Get a time parameter as milliseconds, falling back to a default if not set. If no - * suffix is provided then milliseconds are assumed. + /** + * Get a time parameter as milliseconds, falling back to a default if not set. If no + * suffix is provided then milliseconds are assumed. */ def getTimeAsMs(key: String, defaultValue: String): Long = { Utils.timeStringAsMs(get(key, defaultValue)) } - + /** Get a parameter as an Option */ def getOption(key: String): Option[String] = { - Option(settings.get(key)) + Option(settings.get(key)).orElse(getDeprecatedConfig(key, this)) } /** Get all parameters as a list of pairs */ @@ -379,13 +381,6 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { } } } - - // Warn against the use of deprecated configs - deprecatedConfigs.values.foreach { dc => - if (contains(dc.oldName)) { - dc.warn() - } - } } /** @@ -400,19 +395,44 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging { private[spark] object SparkConf extends Logging { + /** + * Maps deprecated config keys to information about the deprecation. + * + * The extra information is logged as a warning when the config is present in the user's + * configuration. + */ private val deprecatedConfigs: Map[String, DeprecatedConfig] = { val configs = Seq( - DeprecatedConfig("spark.files.userClassPathFirst", "spark.executor.userClassPathFirst", - "1.3"), - DeprecatedConfig("spark.yarn.user.classpath.first", null, "1.3", - "Use spark.{driver,executor}.userClassPathFirst instead."), - DeprecatedConfig("spark.history.fs.updateInterval", - "spark.history.fs.update.interval.seconds", - "1.3", "Use spark.history.fs.update.interval.seconds instead"), - DeprecatedConfig("spark.history.updateInterval", - "spark.history.fs.update.interval.seconds", - "1.3", "Use spark.history.fs.update.interval.seconds instead")) - configs.map { x => (x.oldName, x) }.toMap + DeprecatedConfig("spark.yarn.user.classpath.first", "1.3", + "Please use spark.{driver,executor}.userClassPathFirst instead.")) + Map(configs.map { cfg => (cfg.key -> cfg) }:_*) + } + + /** + * Maps a current config key to alternate keys that were used in previous version of Spark. + * + * The alternates are used in the order defined in this map. If deprecated configs are + * present in the user's configuration, a warning is logged. + */ + private val configsWithAlternatives = Map[String, Seq[AlternateConfig]]( + "spark.executor.userClassPathFirst" -> Seq( + AlternateConfig("spark.files.userClassPathFirst", "1.3")), + "spark.history.fs.update.interval" -> Seq( + AlternateConfig("spark.history.fs.update.interval.seconds", "1.4"), + AlternateConfig("spark.history.fs.updateInterval", "1.3"), + AlternateConfig("spark.history.updateInterval", "1.3")) + ) + + /** + * A view of `configsWithAlternatives` that makes it more efficient to look up deprecated + * config keys. + * + * Maps the deprecated config name to a 2-tuple (new config name, alternate config info). + */ + private val allAlternatives: Map[String, (String, AlternateConfig)] = { + configsWithAlternatives.keys.flatMap { key => + configsWithAlternatives(key).map { cfg => (cfg.key -> (key -> cfg)) } + }.toMap } /** @@ -443,61 +463,57 @@ private[spark] object SparkConf extends Logging { } /** - * Translate the configuration key if it is deprecated and has a replacement, otherwise just - * returns the provided key. - * - * @param userKey Configuration key from the user / caller. - * @param warn Whether to print a warning if the key is deprecated. Warnings will be printed - * only once for each key. + * Looks for available deprecated keys for the given config option, and return the first + * value available. */ - private def translateConfKey(userKey: String, warn: Boolean = false): String = { - deprecatedConfigs.get(userKey) - .map { deprecatedKey => - if (warn) { - deprecatedKey.warn() - } - deprecatedKey.newName.getOrElse(userKey) - }.getOrElse(userKey) + def getDeprecatedConfig(key: String, conf: SparkConf): Option[String] = { + configsWithAlternatives.get(key).flatMap { alts => + alts.collectFirst { case alt if conf.contains(alt.key) => + val value = conf.get(alt.key) + alt.translation.map(_(value)).getOrElse(value) + } + } } /** - * Holds information about keys that have been deprecated or renamed. + * Logs a warning message if the given config key is deprecated. + */ + def logDeprecationWarning(key: String): Unit = { + deprecatedConfigs.get(key).foreach { cfg => + logWarning( + s"The configuration key '$key' has been deprecated as of Spark ${cfg.version} and " + + s"may be removed in the future. ${cfg.deprecationMessage}") + } + + allAlternatives.get(key).foreach { case (newKey, cfg) => + logWarning( + s"The configuration key '$key' has been deprecated as of Spark ${cfg.version} and " + + s"and may be removed in the future. Please use the new key '$newKey' instead.") + } + } + + /** + * Holds information about keys that have been deprecated and do not have a replacement. * - * @param oldName Old configuration key. - * @param newName New configuration key, or `null` if key has no replacement, in which case the - * deprecated key will be used (but the warning message will still be printed). + * @param key The deprecated key. * @param version Version of Spark where key was deprecated. - * @param deprecationMessage Message to include in the deprecation warning; mandatory when - * `newName` is not provided. + * @param deprecationMessage Message to include in the deprecation warning. */ private case class DeprecatedConfig( - oldName: String, - _newName: String, + key: String, version: String, - deprecationMessage: String = null) { - - private val warned = new AtomicBoolean(false) - val newName = Option(_newName) + deprecationMessage: String) - if (newName == null && (deprecationMessage == null || deprecationMessage.isEmpty())) { - throw new IllegalArgumentException("Need new config name or deprecation message.") - } - - def warn(): Unit = { - if (warned.compareAndSet(false, true)) { - if (newName != null) { - val message = Option(deprecationMessage).getOrElse( - s"Please use the alternative '$newName' instead.") - logWarning( - s"The configuration option '$oldName' has been replaced as of Spark $version and " + - s"may be removed in the future. $message") - } else { - logWarning( - s"The configuration option '$oldName' has been deprecated as of Spark $version and " + - s"may be removed in the future. $deprecationMessage") - } - } - } + /** + * Information about an alternate configuration key that has been deprecated. + * + * @param key The deprecated config key. + * @param version The Spark version in which the key was deprecated. + * @param translation A translation function for converting old config values into new ones. + */ + private case class AlternateConfig( + key: String, + version: String, + translation: Option[String => String] = None) - } } diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala index 9d40d8c8fd7a8..985545742df67 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala @@ -49,11 +49,7 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis private val NOT_STARTED = "" // Interval between each check for event log updates - private val UPDATE_INTERVAL_MS = conf.getOption("spark.history.fs.update.interval.seconds") - .orElse(conf.getOption("spark.history.fs.updateInterval")) - .orElse(conf.getOption("spark.history.updateInterval")) - .map(_.toInt) - .getOrElse(10) * 1000 + private val UPDATE_INTERVAL_S = conf.getTimeAsSeconds("spark.history.fs.update.interval", "10s") // Interval between each cleaner checks for event logs to delete private val CLEAN_INTERVAL_MS = conf.getLong("spark.history.fs.cleaner.interval.seconds", @@ -130,8 +126,7 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis // Disable the background thread during tests. if (!conf.contains("spark.testing")) { // A task that periodically checks for event log updates on disk. - pool.scheduleAtFixedRate(getRunner(checkForLogs), 0, UPDATE_INTERVAL_MS, - TimeUnit.MILLISECONDS) + pool.scheduleAtFixedRate(getRunner(checkForLogs), 0, UPDATE_INTERVAL_S, TimeUnit.SECONDS) if (conf.getBoolean("spark.history.fs.cleaner.enabled", false)) { // A task that periodically cleans event logs on disk. diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala index 1b5fdeba28ee2..327d155b38c22 100644 --- a/core/src/main/scala/org/apache/spark/executor/Executor.scala +++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala @@ -89,10 +89,7 @@ private[spark] class Executor( ExecutorEndpoint.EXECUTOR_ENDPOINT_NAME, new ExecutorEndpoint(env.rpcEnv, executorId)) // Whether to load classes in user jars before those in Spark jars - private val userClassPathFirst: Boolean = { - conf.getBoolean("spark.executor.userClassPathFirst", - conf.getBoolean("spark.files.userClassPathFirst", false)) - } + private val userClassPathFirst = conf.getBoolean("spark.executor.userClassPathFirst", false) // Create our ClassLoader // do this after SparkEnv creation so can access the SecurityManager diff --git a/core/src/test/scala/org/apache/spark/SparkConfSuite.scala b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala index e08210ae60d17..7d87ba5fd2610 100644 --- a/core/src/test/scala/org/apache/spark/SparkConfSuite.scala +++ b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala @@ -197,6 +197,28 @@ class SparkConfSuite extends FunSuite with LocalSparkContext with ResetSystemPro serializer.newInstance().serialize(new StringBuffer()) } + test("deprecated configs") { + val conf = new SparkConf() + val newName = "spark.history.fs.update.interval" + + assert(!conf.contains(newName)) + + conf.set("spark.history.updateInterval", "1") + assert(conf.get(newName) === "1") + + conf.set("spark.history.fs.updateInterval", "2") + assert(conf.get(newName) === "2") + + conf.set("spark.history.fs.update.interval.seconds", "3") + assert(conf.get(newName) === "3") + + conf.set(newName, "4") + assert(conf.get(newName) === "4") + + val count = conf.getAll.filter { case (k, v) => k.startsWith("spark.history.") }.size + assert(count === 4) + } + } class Class1 {} diff --git a/docs/monitoring.md b/docs/monitoring.md index 6816671ffbf46..2a130224591ca 100644 --- a/docs/monitoring.md +++ b/docs/monitoring.md @@ -86,10 +86,10 @@ follows: - - + + diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 1091ff54b0463..52e4dee46c535 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -1052,8 +1052,7 @@ object Client extends Logging { if (isDriver) { conf.getBoolean("spark.driver.userClassPathFirst", false) } else { - conf.getBoolean("spark.executor.userClassPathFirst", - conf.getBoolean("spark.files.userClassPathFirst", false)) + conf.getBoolean("spark.executor.userClassPathFirst", false) } } From f6a9a57a72767f48fcc02e5fda4d6eafa67aebde Mon Sep 17 00:00:00 2001 From: Punya Biswal Date: Fri, 17 Apr 2015 11:08:37 +0100 Subject: [PATCH 321/591] [SPARK-6952] Handle long args when detecting PID reuse sbin/spark-daemon.sh used ps -p "$TARGET_PID" -o args= to figure out whether the process running with the expected PID is actually a Spark daemon. When running with a large classpath, the output of ps gets truncated and the check fails spuriously. This weakens the check to see if it's a java command (which is something we do in other parts of the script) rather than looking for the specific main class name. This means that SPARK-4832 might happen under a slightly broader range of circumstances (a java program happened to reuse the same PID), but it seems worthwhile compared to failing consistently with a large classpath. Author: Punya Biswal Closes #5535 from punya/feature/SPARK-6952 and squashes the following commits: 7ea12d1 [Punya Biswal] Handle long args when detecting PID reuse --- sbin/spark-daemon.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sbin/spark-daemon.sh b/sbin/spark-daemon.sh index d8e0facb81169..de762acc8fa0e 100755 --- a/sbin/spark-daemon.sh +++ b/sbin/spark-daemon.sh @@ -129,7 +129,7 @@ run_command() { if [ -f "$pid" ]; then TARGET_ID="$(cat "$pid")" - if [[ $(ps -p "$TARGET_ID" -o args=) =~ $command ]]; then + if [[ $(ps -p "$TARGET_ID" -o comm=) =~ "java" ]]; then echo "$command running as process $TARGET_ID. Stop it first." exit 1 fi @@ -163,7 +163,7 @@ run_command() { echo "$newpid" > "$pid" sleep 2 # Check if the process has died; in that case we'll tail the log so the user can see - if [[ ! $(ps -p "$newpid" -o args=) =~ $command ]]; then + if [[ ! $(ps -p "$newpid" -o comm=) =~ "java" ]]; then echo "failed to launch $command:" tail -2 "$log" | sed 's/^/ /' echo "full log in $log" From dc48ba9f9f7449dd2f12cbad288b65c8119d9284 Mon Sep 17 00:00:00 2001 From: linweizhong Date: Fri, 17 Apr 2015 12:04:02 +0100 Subject: [PATCH 322/591] [SPARK-6604][PySpark]Specify ip of python server scoket In driver now will start a server socket and use a wildcard ip, use 127.0.0.0 is more reasonable, as we only use it by local Python process. /cc davies Author: linweizhong Closes #5256 from Sephiroth-Lin/SPARK-6604 and squashes the following commits: 7b3c633 [linweizhong] rephrase --- core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala index b1ffba4c546bf..7409dc2d866f6 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala @@ -604,7 +604,7 @@ private[spark] object PythonRDD extends Logging { * The thread will terminate after all the data are sent or any exceptions happen. */ private def serveIterator[T](items: Iterator[T], threadName: String): Int = { - val serverSocket = new ServerSocket(0, 1) + val serverSocket = new ServerSocket(0, 1, InetAddress.getByName("localhost")) // Close the socket if no connection in 3 seconds serverSocket.setSoTimeout(3000) From c84d91692aa25c01882bcc3f9fd5de3cfa786195 Mon Sep 17 00:00:00 2001 From: Davies Liu Date: Fri, 17 Apr 2015 11:29:27 -0500 Subject: [PATCH 323/591] [SPARK-6957] [SPARK-6958] [SQL] improve API compatibility to pandas ``` select(['cola', 'colb']) groupby(['colA', 'colB']) groupby([df.colA, df.colB]) df.sort('A', ascending=True) df.sort(['A', 'B'], ascending=True) df.sort(['A', 'B'], ascending=[1, 0]) ``` cc rxin Author: Davies Liu Closes #5544 from davies/compatibility and squashes the following commits: 4944058 [Davies Liu] add docstrings adb2816 [Davies Liu] Merge branch 'master' of github.com:apache/spark into compatibility bcbbcab [Davies Liu] support ascending as list 8dabdf0 [Davies Liu] improve API compatibility to pandas --- python/pyspark/sql/dataframe.py | 96 ++++++++++++++++++++++----------- python/pyspark/sql/functions.py | 11 ++-- python/pyspark/sql/tests.py | 2 +- 3 files changed, 70 insertions(+), 39 deletions(-) diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index b9a3e6cfe7f49..326d22e72f104 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -485,13 +485,17 @@ def join(self, other, joinExprs=None, joinType=None): return DataFrame(jdf, self.sql_ctx) @ignore_unicode_prefix - def sort(self, *cols): + def sort(self, *cols, **kwargs): """Returns a new :class:`DataFrame` sorted by the specified column(s). - :param cols: list of :class:`Column` to sort by. + :param cols: list of :class:`Column` or column names to sort by. + :param ascending: sort by ascending order or not, could be bool, int + or list of bool, int (default: True). >>> df.sort(df.age.desc()).collect() [Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')] + >>> df.sort("age", ascending=False).collect() + [Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')] >>> df.orderBy(df.age.desc()).collect() [Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')] >>> from pyspark.sql.functions import * @@ -499,16 +503,42 @@ def sort(self, *cols): [Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')] >>> df.orderBy(desc("age"), "name").collect() [Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')] + >>> df.orderBy(["age", "name"], ascending=[0, 1]).collect() + [Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')] """ if not cols: raise ValueError("should sort by at least one column") - jcols = ListConverter().convert([_to_java_column(c) for c in cols], - self._sc._gateway._gateway_client) - jdf = self._jdf.sort(self._sc._jvm.PythonUtils.toSeq(jcols)) + if len(cols) == 1 and isinstance(cols[0], list): + cols = cols[0] + jcols = [_to_java_column(c) for c in cols] + ascending = kwargs.get('ascending', True) + if isinstance(ascending, (bool, int)): + if not ascending: + jcols = [jc.desc() for jc in jcols] + elif isinstance(ascending, list): + jcols = [jc if asc else jc.desc() + for asc, jc in zip(ascending, jcols)] + else: + raise TypeError("ascending can only be bool or list, but got %s" % type(ascending)) + + jdf = self._jdf.sort(self._jseq(jcols)) return DataFrame(jdf, self.sql_ctx) orderBy = sort + def _jseq(self, cols, converter=None): + """Return a JVM Seq of Columns from a list of Column or names""" + return _to_seq(self.sql_ctx._sc, cols, converter) + + def _jcols(self, *cols): + """Return a JVM Seq of Columns from a list of Column or column names + + If `cols` has only one list in it, cols[0] will be used as the list. + """ + if len(cols) == 1 and isinstance(cols[0], list): + cols = cols[0] + return self._jseq(cols, _to_java_column) + def describe(self, *cols): """Computes statistics for numeric columns. @@ -523,9 +553,7 @@ def describe(self, *cols): min 2 max 5 """ - cols = ListConverter().convert(cols, - self.sql_ctx._sc._gateway._gateway_client) - jdf = self._jdf.describe(self.sql_ctx._sc._jvm.PythonUtils.toSeq(cols)) + jdf = self._jdf.describe(self._jseq(cols)) return DataFrame(jdf, self.sql_ctx) @ignore_unicode_prefix @@ -607,9 +635,7 @@ def select(self, *cols): >>> df.select(df.name, (df.age + 10).alias('age')).collect() [Row(name=u'Alice', age=12), Row(name=u'Bob', age=15)] """ - jcols = ListConverter().convert([_to_java_column(c) for c in cols], - self._sc._gateway._gateway_client) - jdf = self._jdf.select(self.sql_ctx._sc._jvm.PythonUtils.toSeq(jcols)) + jdf = self._jdf.select(self._jcols(*cols)) return DataFrame(jdf, self.sql_ctx) def selectExpr(self, *expr): @@ -620,8 +646,9 @@ def selectExpr(self, *expr): >>> df.selectExpr("age * 2", "abs(age)").collect() [Row((age * 2)=4, Abs(age)=2), Row((age * 2)=10, Abs(age)=5)] """ - jexpr = ListConverter().convert(expr, self._sc._gateway._gateway_client) - jdf = self._jdf.selectExpr(self._sc._jvm.PythonUtils.toSeq(jexpr)) + if len(expr) == 1 and isinstance(expr[0], list): + expr = expr[0] + jdf = self._jdf.selectExpr(self._jseq(expr)) return DataFrame(jdf, self.sql_ctx) @ignore_unicode_prefix @@ -659,6 +686,8 @@ def groupBy(self, *cols): so we can run aggregation on them. See :class:`GroupedData` for all the available aggregate functions. + :func:`groupby` is an alias for :func:`groupBy`. + :param cols: list of columns to group by. Each element should be a column name (string) or an expression (:class:`Column`). @@ -668,12 +697,14 @@ def groupBy(self, *cols): [Row(name=u'Alice', AVG(age)=2.0), Row(name=u'Bob', AVG(age)=5.0)] >>> df.groupBy(df.name).avg().collect() [Row(name=u'Alice', AVG(age)=2.0), Row(name=u'Bob', AVG(age)=5.0)] + >>> df.groupBy(['name', df.age]).count().collect() + [Row(name=u'Bob', age=5, count=1), Row(name=u'Alice', age=2, count=1)] """ - jcols = ListConverter().convert([_to_java_column(c) for c in cols], - self._sc._gateway._gateway_client) - jdf = self._jdf.groupBy(self.sql_ctx._sc._jvm.PythonUtils.toSeq(jcols)) + jdf = self._jdf.groupBy(self._jcols(*cols)) return GroupedData(jdf, self.sql_ctx) + groupby = groupBy + def agg(self, *exprs): """ Aggregate on the entire :class:`DataFrame` without groups (shorthand for ``df.groupBy.agg()``). @@ -744,9 +775,7 @@ def dropna(self, how='any', thresh=None, subset=None): if thresh is None: thresh = len(subset) if how == 'any' else 1 - cols = ListConverter().convert(subset, self.sql_ctx._sc._gateway._gateway_client) - cols = self.sql_ctx._sc._jvm.PythonUtils.toSeq(cols) - return DataFrame(self._jdf.na().drop(thresh, cols), self.sql_ctx) + return DataFrame(self._jdf.na().drop(thresh, self._jseq(subset)), self.sql_ctx) def fillna(self, value, subset=None): """Replace null values, alias for ``na.fill()``. @@ -799,9 +828,7 @@ def fillna(self, value, subset=None): elif not isinstance(subset, (list, tuple)): raise ValueError("subset should be a list or tuple of column names") - cols = ListConverter().convert(subset, self.sql_ctx._sc._gateway._gateway_client) - cols = self.sql_ctx._sc._jvm.PythonUtils.toSeq(cols) - return DataFrame(self._jdf.na().fill(value, cols), self.sql_ctx) + return DataFrame(self._jdf.na().fill(value, self._jseq(subset)), self.sql_ctx) @ignore_unicode_prefix def withColumn(self, colName, col): @@ -862,10 +889,8 @@ def _api(self): def df_varargs_api(f): def _api(self, *args): - jargs = ListConverter().convert(args, - self.sql_ctx._sc._gateway._gateway_client) name = f.__name__ - jdf = getattr(self._jdf, name)(self.sql_ctx._sc._jvm.PythonUtils.toSeq(jargs)) + jdf = getattr(self._jdf, name)(_to_seq(self.sql_ctx._sc, args)) return DataFrame(jdf, self.sql_ctx) _api.__name__ = f.__name__ _api.__doc__ = f.__doc__ @@ -912,9 +937,8 @@ def agg(self, *exprs): else: # Columns assert all(isinstance(c, Column) for c in exprs), "all exprs should be Column" - jcols = ListConverter().convert([c._jc for c in exprs[1:]], - self.sql_ctx._sc._gateway._gateway_client) - jdf = self._jdf.agg(exprs[0]._jc, self.sql_ctx._sc._jvm.PythonUtils.toSeq(jcols)) + jdf = self._jdf.agg(exprs[0]._jc, + _to_seq(self.sql_ctx._sc, [c._jc for c in exprs[1:]])) return DataFrame(jdf, self.sql_ctx) @dfapi @@ -1006,6 +1030,19 @@ def _to_java_column(col): return jcol +def _to_seq(sc, cols, converter=None): + """ + Convert a list of Column (or names) into a JVM Seq of Column. + + An optional `converter` could be used to convert items in `cols` + into JVM Column objects. + """ + if converter: + cols = [converter(c) for c in cols] + jcols = ListConverter().convert(cols, sc._gateway._gateway_client) + return sc._jvm.PythonUtils.toSeq(jcols) + + def _unary_op(name, doc="unary operator"): """ Create a method for given unary operator """ def _(self): @@ -1177,8 +1214,7 @@ def inSet(self, *cols): cols = cols[0] cols = [c._jc if isinstance(c, Column) else _create_column_from_literal(c) for c in cols] sc = SparkContext._active_spark_context - jcols = ListConverter().convert(cols, sc._gateway._gateway_client) - jc = getattr(self._jc, "in")(sc._jvm.PythonUtils.toSeq(jcols)) + jc = getattr(self._jc, "in")(_to_seq(sc, cols)) return Column(jc) # order diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py index 1d6536952810f..bb47923f24b82 100644 --- a/python/pyspark/sql/functions.py +++ b/python/pyspark/sql/functions.py @@ -23,13 +23,11 @@ if sys.version < "3": from itertools import imap as map -from py4j.java_collections import ListConverter - from pyspark import SparkContext from pyspark.rdd import _prepare_for_python_RDD from pyspark.serializers import PickleSerializer, AutoBatchedSerializer from pyspark.sql.types import StringType -from pyspark.sql.dataframe import Column, _to_java_column +from pyspark.sql.dataframe import Column, _to_java_column, _to_seq __all__ = ['countDistinct', 'approxCountDistinct', 'udf'] @@ -87,8 +85,7 @@ def countDistinct(col, *cols): [Row(c=2)] """ sc = SparkContext._active_spark_context - jcols = ListConverter().convert([_to_java_column(c) for c in cols], sc._gateway._gateway_client) - jc = sc._jvm.functions.countDistinct(_to_java_column(col), sc._jvm.PythonUtils.toSeq(jcols)) + jc = sc._jvm.functions.countDistinct(_to_java_column(col), _to_seq(sc, cols, _to_java_column)) return Column(jc) @@ -138,9 +135,7 @@ def __del__(self): def __call__(self, *cols): sc = SparkContext._active_spark_context - jcols = ListConverter().convert([_to_java_column(c) for c in cols], - sc._gateway._gateway_client) - jc = self._judf.apply(sc._jvm.PythonUtils.toSeq(jcols)) + jc = self._judf.apply(_to_seq(sc, cols, _to_java_column)) return Column(jc) diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index 6691e8c8dc44b..aa3aa1d164d9f 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -282,7 +282,7 @@ def test_apply_schema(self): StructField("struct1", StructType([StructField("b", ShortType(), False)]), False), StructField("list1", ArrayType(ByteType(), False), False), StructField("null1", DoubleType(), True)]) - df = self.sqlCtx.applySchema(rdd, schema) + df = self.sqlCtx.createDataFrame(rdd, schema) results = df.map(lambda x: (x.byte1, x.byte2, x.short1, x.short2, x.int1, x.float1, x.date1, x.time1, x.map1["a"], x.struct1.b, x.list1, x.null1)) r = (127, -128, -32768, 32767, 2147483647, 1.0, date(2010, 1, 1), From 50ab8a6543ad5c31e89c16df374d0cb13222fd1e Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Fri, 17 Apr 2015 14:21:51 -0500 Subject: [PATCH 324/591] [SPARK-2669] [yarn] Distribute client configuration to AM. Currently, when Spark launches the Yarn AM, the process will use the local Hadoop configuration on the node where the AM launches, if one is present. A more correct approach is to use the same configuration used to launch the Spark job, since the user may have made modifications (such as adding app-specific configs). The approach taken here is to use the distributed cache to make all files in the Hadoop configuration directory available to the AM. This is a little overkill since only the AM needs them (the executors use the broadcast Hadoop configuration from the driver), but is the easier approach. Even though only a few files in that directory may end up being used, all of them are uploaded. This allows supporting use cases such as when auxiliary configuration files are used for SSL configuration, or when uploading a Hive configuration directory. Not all of these may be reflected in a o.a.h.conf.Configuration object, but may be needed when a driver in cluster mode instantiates, for example, a HiveConf object instead. Author: Marcelo Vanzin Closes #4142 from vanzin/SPARK-2669 and squashes the following commits: f5434b9 [Marcelo Vanzin] Merge branch 'master' into SPARK-2669 013f0fb [Marcelo Vanzin] Review feedback. f693152 [Marcelo Vanzin] Le sigh. ed45b7d [Marcelo Vanzin] Zip all config files and upload them as an archive. 5927b6b [Marcelo Vanzin] Merge branch 'master' into SPARK-2669 cbb9fb3 [Marcelo Vanzin] Remove stale test. e3e58d0 [Marcelo Vanzin] Merge branch 'master' into SPARK-2669 e3d0613 [Marcelo Vanzin] Review feedback. 34bdbd8 [Marcelo Vanzin] Fix test. 022a688 [Marcelo Vanzin] Merge branch 'master' into SPARK-2669 a77ddd5 [Marcelo Vanzin] Merge branch 'master' into SPARK-2669 79221c7 [Marcelo Vanzin] [SPARK-2669] [yarn] Distribute client configuration to AM. --- docs/running-on-yarn.md | 6 +- .../org/apache/spark/deploy/yarn/Client.scala | 125 +++++++++++++++--- .../spark/deploy/yarn/ExecutorRunnable.scala | 2 +- .../spark/deploy/yarn/ClientSuite.scala | 29 ++-- .../spark/deploy/yarn/YarnClusterSuite.scala | 6 +- 5 files changed, 132 insertions(+), 36 deletions(-) diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md index 853c9f26b0ec9..0968fc5ad632b 100644 --- a/docs/running-on-yarn.md +++ b/docs/running-on-yarn.md @@ -211,7 +211,11 @@ Most of the configs are the same for Spark on YARN as for other deployment modes # Launching Spark on YARN Ensure that `HADOOP_CONF_DIR` or `YARN_CONF_DIR` points to the directory which contains the (client side) configuration files for the Hadoop cluster. -These configs are used to write to the dfs and connect to the YARN ResourceManager. +These configs are used to write to the dfs and connect to the YARN ResourceManager. The +configuration contained in this directory will be distributed to the YARN cluster so that all +containers used by the application use the same configuration. If the configuration references +Java system properties or environment variables not managed by YARN, they should also be set in the +Spark application's configuration (driver, executors, and the AM when running in client mode). There are two deploy modes that can be used to launch Spark applications on YARN. In yarn-cluster mode, the Spark driver runs inside an application master process which is managed by YARN on the cluster, and the client can go away after initiating the application. In yarn-client mode, the driver runs in the client process, and the application master is only used for requesting resources from YARN. diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 52e4dee46c535..019afbd1a1743 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -17,15 +17,18 @@ package org.apache.spark.deploy.yarn +import java.io.{File, FileOutputStream} import java.net.{InetAddress, UnknownHostException, URI, URISyntaxException} import java.nio.ByteBuffer +import java.util.zip.{ZipEntry, ZipOutputStream} import scala.collection.JavaConversions._ -import scala.collection.mutable.{ArrayBuffer, HashMap, ListBuffer, Map} +import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, ListBuffer, Map} import scala.reflect.runtime.universe import scala.util.{Try, Success, Failure} import com.google.common.base.Objects +import com.google.common.io.Files import org.apache.hadoop.io.DataOutputBuffer import org.apache.hadoop.conf.Configuration @@ -77,12 +80,6 @@ private[spark] class Client( def stop(): Unit = yarnClient.stop() - /* ------------------------------------------------------------------------------------- * - | The following methods have much in common in the stable and alpha versions of Client, | - | but cannot be implemented in the parent trait due to subtle API differences across | - | hadoop versions. | - * ------------------------------------------------------------------------------------- */ - /** * Submit an application running our ApplicationMaster to the ResourceManager. * @@ -223,6 +220,10 @@ private[spark] class Client( val fs = FileSystem.get(hadoopConf) val dst = new Path(fs.getHomeDirectory(), appStagingDir) val nns = getNameNodesToAccess(sparkConf) + dst + // Used to keep track of URIs added to the distributed cache. If the same URI is added + // multiple times, YARN will fail to launch containers for the app with an internal + // error. + val distributedUris = new HashSet[String] obtainTokensForNamenodes(nns, hadoopConf, credentials) obtainTokenForHiveMetastore(hadoopConf, credentials) @@ -241,6 +242,17 @@ private[spark] class Client( "for alternatives.") } + def addDistributedUri(uri: URI): Boolean = { + val uriStr = uri.toString() + if (distributedUris.contains(uriStr)) { + logWarning(s"Resource $uri added multiple times to distributed cache.") + false + } else { + distributedUris += uriStr + true + } + } + /** * Copy the given main resource to the distributed cache if the scheme is not "local". * Otherwise, set the corresponding key in our SparkConf to handle it downstream. @@ -258,11 +270,13 @@ private[spark] class Client( if (!localPath.isEmpty()) { val localURI = new URI(localPath) if (localURI.getScheme != LOCAL_SCHEME) { - val src = getQualifiedLocalPath(localURI, hadoopConf) - val destPath = copyFileToRemote(dst, src, replication) - val destFs = FileSystem.get(destPath.toUri(), hadoopConf) - distCacheMgr.addResource(destFs, hadoopConf, destPath, - localResources, LocalResourceType.FILE, destName, statCache) + if (addDistributedUri(localURI)) { + val src = getQualifiedLocalPath(localURI, hadoopConf) + val destPath = copyFileToRemote(dst, src, replication) + val destFs = FileSystem.get(destPath.toUri(), hadoopConf) + distCacheMgr.addResource(destFs, hadoopConf, destPath, + localResources, LocalResourceType.FILE, destName, statCache) + } } else if (confKey != null) { // If the resource is intended for local use only, handle this downstream // by setting the appropriate property @@ -271,6 +285,13 @@ private[spark] class Client( } } + createConfArchive().foreach { file => + require(addDistributedUri(file.toURI())) + val destPath = copyFileToRemote(dst, new Path(file.toURI()), replication) + distCacheMgr.addResource(fs, hadoopConf, destPath, localResources, LocalResourceType.ARCHIVE, + LOCALIZED_HADOOP_CONF_DIR, statCache, appMasterOnly = true) + } + /** * Do the same for any additional resources passed in through ClientArguments. * Each resource category is represented by a 3-tuple of: @@ -288,13 +309,15 @@ private[spark] class Client( flist.split(',').foreach { file => val localURI = new URI(file.trim()) if (localURI.getScheme != LOCAL_SCHEME) { - val localPath = new Path(localURI) - val linkname = Option(localURI.getFragment()).getOrElse(localPath.getName()) - val destPath = copyFileToRemote(dst, localPath, replication) - distCacheMgr.addResource( - fs, hadoopConf, destPath, localResources, resType, linkname, statCache) - if (addToClasspath) { - cachedSecondaryJarLinks += linkname + if (addDistributedUri(localURI)) { + val localPath = new Path(localURI) + val linkname = Option(localURI.getFragment()).getOrElse(localPath.getName()) + val destPath = copyFileToRemote(dst, localPath, replication) + distCacheMgr.addResource( + fs, hadoopConf, destPath, localResources, resType, linkname, statCache) + if (addToClasspath) { + cachedSecondaryJarLinks += linkname + } } } else if (addToClasspath) { // Resource is intended for local use only and should be added to the class path @@ -310,6 +333,57 @@ private[spark] class Client( localResources } + /** + * Create an archive with the Hadoop config files for distribution. + * + * These are only used by the AM, since executors will use the configuration object broadcast by + * the driver. The files are zipped and added to the job as an archive, so that YARN will explode + * it when distributing to the AM. This directory is then added to the classpath of the AM + * process, just to make sure that everybody is using the same default config. + * + * This follows the order of precedence set by the startup scripts, in which HADOOP_CONF_DIR + * shows up in the classpath before YARN_CONF_DIR. + * + * Currently this makes a shallow copy of the conf directory. If there are cases where a + * Hadoop config directory contains subdirectories, this code will have to be fixed. + */ + private def createConfArchive(): Option[File] = { + val hadoopConfFiles = new HashMap[String, File]() + Seq("HADOOP_CONF_DIR", "YARN_CONF_DIR").foreach { envKey => + sys.env.get(envKey).foreach { path => + val dir = new File(path) + if (dir.isDirectory()) { + dir.listFiles().foreach { file => + if (!hadoopConfFiles.contains(file.getName())) { + hadoopConfFiles(file.getName()) = file + } + } + } + } + } + + if (!hadoopConfFiles.isEmpty) { + val hadoopConfArchive = File.createTempFile(LOCALIZED_HADOOP_CONF_DIR, ".zip", + new File(Utils.getLocalDir(sparkConf))) + + val hadoopConfStream = new ZipOutputStream(new FileOutputStream(hadoopConfArchive)) + try { + hadoopConfStream.setLevel(0) + hadoopConfFiles.foreach { case (name, file) => + hadoopConfStream.putNextEntry(new ZipEntry(name)) + Files.copy(file, hadoopConfStream) + hadoopConfStream.closeEntry() + } + } finally { + hadoopConfStream.close() + } + + Some(hadoopConfArchive) + } else { + None + } + } + /** * Set up the environment for launching our ApplicationMaster container. */ @@ -317,7 +391,7 @@ private[spark] class Client( logInfo("Setting up the launch environment for our AM container") val env = new HashMap[String, String]() val extraCp = sparkConf.getOption("spark.driver.extraClassPath") - populateClasspath(args, yarnConf, sparkConf, env, extraCp) + populateClasspath(args, yarnConf, sparkConf, env, true, extraCp) env("SPARK_YARN_MODE") = "true" env("SPARK_YARN_STAGING_DIR") = stagingDir env("SPARK_USER") = UserGroupInformation.getCurrentUser().getShortUserName() @@ -718,6 +792,9 @@ object Client extends Logging { // Distribution-defined classpath to add to processes val ENV_DIST_CLASSPATH = "SPARK_DIST_CLASSPATH" + // Subdirectory where the user's hadoop config files will be placed. + val LOCALIZED_HADOOP_CONF_DIR = "__hadoop_conf__" + /** * Find the user-defined Spark jar if configured, or return the jar containing this * class if not. @@ -831,11 +908,19 @@ object Client extends Logging { conf: Configuration, sparkConf: SparkConf, env: HashMap[String, String], + isAM: Boolean, extraClassPath: Option[String] = None): Unit = { extraClassPath.foreach(addClasspathEntry(_, env)) addClasspathEntry( YarnSparkHadoopUtil.expandEnvironment(Environment.PWD), env ) + + if (isAM) { + addClasspathEntry( + YarnSparkHadoopUtil.expandEnvironment(Environment.PWD) + Path.SEPARATOR + + LOCALIZED_HADOOP_CONF_DIR, env) + } + if (sparkConf.getBoolean("spark.yarn.user.classpath.first", false)) { val userClassPath = if (args != null) { diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala index b06069c07f451..9d04d241dae9e 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala @@ -277,7 +277,7 @@ class ExecutorRunnable( private def prepareEnvironment(container: Container): HashMap[String, String] = { val env = new HashMap[String, String]() val extraCp = sparkConf.getOption("spark.executor.extraClassPath") - Client.populateClasspath(null, yarnConf, sparkConf, env, extraCp) + Client.populateClasspath(null, yarnConf, sparkConf, env, false, extraCp) sparkConf.getExecutorEnv.foreach { case (key, value) => // This assumes each executor environment variable set here is a path diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala index c1b94ac9c5bdd..a51c2005cb472 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala @@ -20,6 +20,11 @@ package org.apache.spark.deploy.yarn import java.io.File import java.net.URI +import scala.collection.JavaConversions._ +import scala.collection.mutable.{ HashMap => MutableHashMap } +import scala.reflect.ClassTag +import scala.util.Try + import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.apache.hadoop.mapreduce.MRJobConfig @@ -30,11 +35,6 @@ import org.mockito.Matchers._ import org.mockito.Mockito._ import org.scalatest.{BeforeAndAfterAll, FunSuite, Matchers} -import scala.collection.JavaConversions._ -import scala.collection.mutable.{ HashMap => MutableHashMap } -import scala.reflect.ClassTag -import scala.util.Try - import org.apache.spark.{SparkException, SparkConf} import org.apache.spark.util.Utils @@ -93,7 +93,7 @@ class ClientSuite extends FunSuite with Matchers with BeforeAndAfterAll { val env = new MutableHashMap[String, String]() val args = new ClientArguments(Array("--jar", USER, "--addJars", ADDED), sparkConf) - Client.populateClasspath(args, conf, sparkConf, env) + Client.populateClasspath(args, conf, sparkConf, env, true) val cp = env("CLASSPATH").split(":|;|") s"$SPARK,$USER,$ADDED".split(",").foreach({ entry => @@ -104,13 +104,16 @@ class ClientSuite extends FunSuite with Matchers with BeforeAndAfterAll { cp should not contain (uri.getPath()) } }) - if (classOf[Environment].getMethods().exists(_.getName == "$$")) { - cp should contain("{{PWD}}") - } else if (Utils.isWindows) { - cp should contain("%PWD%") - } else { - cp should contain(Environment.PWD.$()) - } + val pwdVar = + if (classOf[Environment].getMethods().exists(_.getName == "$$")) { + "{{PWD}}" + } else if (Utils.isWindows) { + "%PWD%" + } else { + Environment.PWD.$() + } + cp should contain(pwdVar) + cp should contain (s"$pwdVar${Path.SEPARATOR}${Client.LOCALIZED_HADOOP_CONF_DIR}") cp should not contain (Client.SPARK_JAR) cp should not contain (Client.APP_JAR) } diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala index a18c94d4ab4a8..3877da4120e7c 100644 --- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala +++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala @@ -77,6 +77,7 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit private var yarnCluster: MiniYARNCluster = _ private var tempDir: File = _ private var fakeSparkJar: File = _ + private var hadoopConfDir: File = _ private var logConfDir: File = _ override def beforeAll() { @@ -120,6 +121,9 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit logInfo(s"RM address in configuration is ${config.get(YarnConfiguration.RM_ADDRESS)}") fakeSparkJar = File.createTempFile("sparkJar", null, tempDir) + hadoopConfDir = new File(tempDir, Client.LOCALIZED_HADOOP_CONF_DIR) + assert(hadoopConfDir.mkdir()) + File.createTempFile("token", ".txt", hadoopConfDir) } override def afterAll() { @@ -258,7 +262,7 @@ class YarnClusterSuite extends FunSuite with BeforeAndAfterAll with Matchers wit appArgs Utils.executeAndGetOutput(argv, - extraEnvironment = Map("YARN_CONF_DIR" -> tempDir.getAbsolutePath())) + extraEnvironment = Map("YARN_CONF_DIR" -> hadoopConfDir.getAbsolutePath())) } /** From a83571acc938582865efb41645aa1e414f339e46 Mon Sep 17 00:00:00 2001 From: "Joseph K. Bradley" Date: Fri, 17 Apr 2015 13:15:36 -0700 Subject: [PATCH 325/591] [SPARK-6113] [ml] Stabilize DecisionTree API MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit This is a PR for cleaning up and finalizing the DecisionTree API. PRs for ensembles will follow once this is merged. ### Goal Here is the description copied from the JIRA (for both trees and ensembles): > **Issue**: The APIs for DecisionTree and ensembles (RandomForests and GradientBoostedTrees) have been experimental for a long time. The API has become very convoluted because trees and ensembles have many, many variants, some of which we have added incrementally without a long-term design. > **Proposal**: This JIRA is for discussing changes required to finalize the APIs. After we discuss, I will make a PR to update the APIs and make them non-Experimental. This will require making many breaking changes; see the design doc for details. > **[Design doc](https://docs.google.com/document/d/1rJ_DZinyDG3PkYkAKSsQlY0QgCeefn4hUv7GsPkzBP4)** : This outlines current issues and the proposed API. Overall code layout: * The old API in mllib.tree.* will remain the same. * The new API will reside in ml.classification.* and ml.regression.* ### Summary of changes Old API * Exactly the same, except I made 1 method in Loss private (but that is not a breaking change since that method was introduced after the Spark 1.3 release). New APIs * Under Pipeline API * The new API preserves functionality, except: * New API does NOT store prob (probability of label in classification). I want to have it store the full vector of probabilities but feel that should be in a later PR. * Use abstractions for parameters, estimators, and models to avoid code duplication * Limit parameters to relevant algorithms * For enum-like types, only expose Strings * We can make these pluggable later on by adding new parameters. That is a far-future item. Test suites * I organized DecisionTreeSuite, but I made absolutely no changes to the tests themselves. * The test suites for the new API only test (a) similarity with the results of the old API and (b) elements of the new API. * After code is moved to this new API, we should move the tests from the old suites which test the internals. ### Details #### Changed names Parameters * useNodeIdCache -> cacheNodeIds #### Other changes * Split: Changed categories to set instead of list #### Non-decision tree changes * AttributeGroup * Added parentheses to toMetadata, toStructField methods (These were removed in a previous PR, but I ran into 1 issue with the Scala compiler not being able to disambiguate between a toMetadata method with no parentheses and a toMetadata method which takes 1 argument.) * Attributes * Renamed: toMetadata -> toMetadataImpl * Added toMetadata methods which return ML metadata (keyed with “ML_ATTR”) * NominalAttribute: Added getNumValues method which examines both numValues and values. * Params.inheritValues: Checks whether the parent param really belongs to the child (to allow Estimator-Model pairs with different sets of parameters) ### Questions for reviewers * Is "DecisionTreeClassificationModel" too long a name? * Is this OK in the docs? ``` class DecisionTreeRegressor extends TreeRegressor[DecisionTreeRegressionModel] with DecisionTreeParams[DecisionTreeRegressor] with TreeRegressorParams[DecisionTreeRegressor] ``` ### Future We should open up the abstractions at some point. E.g., it would be useful to be able to set tree-related parameters in 1 place and then pass those to multiple tree-based algorithms. Follow-up JIRAs will be (in this order): * Tree ensembles * Deprecate old tree code * Move DecisionTree implementation code to new API. * Move tests from the old suites which test the internals. * Update programming guide * Python API * Change RandomForest* to always use bootstrapping, even when numTrees = 1 * Provide the probability of the predicted label for classification. After we move code to the new API and update it to maintain probabilities for all labels, then we can add the probabilities to the new API. CC: mengxr manishamde codedeft chouqin MechCoder Author: Joseph K. Bradley Closes #5530 from jkbradley/dt-api-dt and squashes the following commits: 6aae255 [Joseph K. Bradley] Changed tree abstractions not to take type parameters, and for setters to return this.type instead ec17947 [Joseph K. Bradley] Updates based on code review. Main changes were: moving public types from ml.impl.tree to ml.tree, modifying CategoricalSplit to take an Array of categories but store a Set internally, making more types sealed or final 5626c81 [Joseph K. Bradley] style fixes f8fbd24 [Joseph K. Bradley] imported reorg of DecisionTreeSuite from old PR. small cleanups 7ef63ed [Joseph K. Bradley] Added DecisionTreeRegressor, test suites, and example (for real this time) e11673f [Joseph K. Bradley] Added DecisionTreeRegressor, test suites, and example 119f407 [Joseph K. Bradley] added DecisionTreeClassifier example 0bdc486 [Joseph K. Bradley] fixed issues after param PR was merged f9fbb60 [Joseph K. Bradley] Done with DecisionTreeClassifier, but no save/load yet. Need to add example as well 2532c9a [Joseph K. Bradley] partial move to spark.ml API, not done yet c72c1a0 [Joseph K. Bradley] Copied changes for common items, plus DecisionTreeClassifier from original PR --- .../examples/ml/DecisionTreeExample.scala | 322 +++++++++++++++ .../spark/ml/attribute/AttributeGroup.scala | 10 +- .../spark/ml/attribute/attributes.scala | 43 +- .../DecisionTreeClassifier.scala | 155 ++++++++ .../spark/ml/feature/StringIndexer.scala | 2 +- .../spark/ml/impl/tree/treeParams.scala | 300 ++++++++++++++ .../scala/org/apache/spark/ml/package.scala | 12 + .../org/apache/spark/ml/param/params.scala | 3 +- .../ml/regression/DecisionTreeRegressor.scala | 145 +++++++ .../scala/org/apache/spark/ml/tree/Node.scala | 205 ++++++++++ .../org/apache/spark/ml/tree/Split.scala | 151 +++++++ .../org/apache/spark/ml/tree/treeModels.scala | 60 +++ .../apache/spark/ml/util/MetadataUtils.scala | 82 ++++ .../spark/mllib/tree/DecisionTree.scala | 5 +- .../mllib/tree/GradientBoostedTrees.scala | 12 +- .../spark/mllib/tree/RandomForest.scala | 2 +- .../tree/configuration/BoostingStrategy.scala | 10 +- .../spark/mllib/tree/loss/AbsoluteError.scala | 5 +- .../spark/mllib/tree/loss/LogLoss.scala | 5 +- .../apache/spark/mllib/tree/loss/Loss.scala | 4 +- .../spark/mllib/tree/loss/SquaredError.scala | 5 +- .../mllib/tree/model/DecisionTreeModel.scala | 4 +- .../apache/spark/mllib/tree/model/Node.scala | 2 +- .../mllib/tree/model/treeEnsembleModels.scala | 32 +- .../JavaDecisionTreeClassifierSuite.java | 98 +++++ .../JavaDecisionTreeRegressorSuite.java | 97 +++++ .../ml/attribute/AttributeGroupSuite.scala | 4 +- .../spark/ml/attribute/AttributeSuite.scala | 42 +- .../DecisionTreeClassifierSuite.scala | 274 +++++++++++++ .../spark/ml/feature/VectorIndexerSuite.scala | 2 +- .../org/apache/spark/ml/impl/TreeTests.scala | 132 +++++++ .../DecisionTreeRegressorSuite.scala | 91 +++++ .../spark/mllib/tree/DecisionTreeSuite.scala | 373 +++++++++--------- 33 files changed, 2426 insertions(+), 263 deletions(-) create mode 100644 examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala create mode 100644 mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala create mode 100644 mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala create mode 100644 mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala create mode 100644 mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala create mode 100644 mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala create mode 100644 mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala create mode 100644 mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala create mode 100644 mllib/src/test/java/org/apache/spark/ml/classification/JavaDecisionTreeClassifierSuite.java create mode 100644 mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java create mode 100644 mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala create mode 100644 mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala create mode 100644 mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala new file mode 100644 index 0000000000000..d4cc8dede07ef --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala @@ -0,0 +1,322 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.ml + +import scala.collection.mutable +import scala.language.reflectiveCalls + +import scopt.OptionParser + +import org.apache.spark.ml.tree.DecisionTreeModel +import org.apache.spark.{SparkConf, SparkContext} +import org.apache.spark.examples.mllib.AbstractParams +import org.apache.spark.ml.{Pipeline, PipelineStage} +import org.apache.spark.ml.classification.{DecisionTreeClassificationModel, DecisionTreeClassifier} +import org.apache.spark.ml.feature.{VectorIndexer, StringIndexer} +import org.apache.spark.ml.regression.{DecisionTreeRegressionModel, DecisionTreeRegressor} +import org.apache.spark.ml.util.MetadataUtils +import org.apache.spark.mllib.evaluation.{RegressionMetrics, MulticlassMetrics} +import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.util.MLUtils +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.types.StringType +import org.apache.spark.sql.{SQLContext, DataFrame} + + +/** + * An example runner for decision trees. Run with + * {{{ + * ./bin/run-example ml.DecisionTreeExample [options] + * }}} + * If you use it as a template to create your own app, please use `spark-submit` to submit your app. + */ +object DecisionTreeExample { + + case class Params( + input: String = null, + testInput: String = "", + dataFormat: String = "libsvm", + algo: String = "Classification", + maxDepth: Int = 5, + maxBins: Int = 32, + minInstancesPerNode: Int = 1, + minInfoGain: Double = 0.0, + numTrees: Int = 1, + featureSubsetStrategy: String = "auto", + fracTest: Double = 0.2, + cacheNodeIds: Boolean = false, + checkpointDir: Option[String] = None, + checkpointInterval: Int = 10) extends AbstractParams[Params] + + def main(args: Array[String]) { + val defaultParams = Params() + + val parser = new OptionParser[Params]("DecisionTreeExample") { + head("DecisionTreeExample: an example decision tree app.") + opt[String]("algo") + .text(s"algorithm (Classification, Regression), default: ${defaultParams.algo}") + .action((x, c) => c.copy(algo = x)) + opt[Int]("maxDepth") + .text(s"max depth of the tree, default: ${defaultParams.maxDepth}") + .action((x, c) => c.copy(maxDepth = x)) + opt[Int]("maxBins") + .text(s"max number of bins, default: ${defaultParams.maxBins}") + .action((x, c) => c.copy(maxBins = x)) + opt[Int]("minInstancesPerNode") + .text(s"min number of instances required at child nodes to create the parent split," + + s" default: ${defaultParams.minInstancesPerNode}") + .action((x, c) => c.copy(minInstancesPerNode = x)) + opt[Double]("minInfoGain") + .text(s"min info gain required to create a split, default: ${defaultParams.minInfoGain}") + .action((x, c) => c.copy(minInfoGain = x)) + opt[Double]("fracTest") + .text(s"fraction of data to hold out for testing. If given option testInput, " + + s"this option is ignored. default: ${defaultParams.fracTest}") + .action((x, c) => c.copy(fracTest = x)) + opt[Boolean]("cacheNodeIds") + .text(s"whether to use node Id cache during training, " + + s"default: ${defaultParams.cacheNodeIds}") + .action((x, c) => c.copy(cacheNodeIds = x)) + opt[String]("checkpointDir") + .text(s"checkpoint directory where intermediate node Id caches will be stored, " + + s"default: ${defaultParams.checkpointDir match { + case Some(strVal) => strVal + case None => "None" + }}") + .action((x, c) => c.copy(checkpointDir = Some(x))) + opt[Int]("checkpointInterval") + .text(s"how often to checkpoint the node Id cache, " + + s"default: ${defaultParams.checkpointInterval}") + .action((x, c) => c.copy(checkpointInterval = x)) + opt[String]("testInput") + .text(s"input path to test dataset. If given, option fracTest is ignored." + + s" default: ${defaultParams.testInput}") + .action((x, c) => c.copy(testInput = x)) + opt[String]("") + .text("data format: libsvm (default), dense (deprecated in Spark v1.1)") + .action((x, c) => c.copy(dataFormat = x)) + arg[String]("") + .text("input path to labeled examples") + .required() + .action((x, c) => c.copy(input = x)) + checkConfig { params => + if (params.fracTest < 0 || params.fracTest > 1) { + failure(s"fracTest ${params.fracTest} value incorrect; should be in [0,1].") + } else { + success + } + } + } + + parser.parse(args, defaultParams).map { params => + run(params) + }.getOrElse { + sys.exit(1) + } + } + + /** Load a dataset from the given path, using the given format */ + private[ml] def loadData( + sc: SparkContext, + path: String, + format: String, + expectedNumFeatures: Option[Int] = None): RDD[LabeledPoint] = { + format match { + case "dense" => MLUtils.loadLabeledPoints(sc, path) + case "libsvm" => expectedNumFeatures match { + case Some(numFeatures) => MLUtils.loadLibSVMFile(sc, path, numFeatures) + case None => MLUtils.loadLibSVMFile(sc, path) + } + case _ => throw new IllegalArgumentException(s"Bad data format: $format") + } + } + + /** + * Load training and test data from files. + * @param input Path to input dataset. + * @param dataFormat "libsvm" or "dense" + * @param testInput Path to test dataset. + * @param algo Classification or Regression + * @param fracTest Fraction of input data to hold out for testing. Ignored if testInput given. + * @return (training dataset, test dataset) + */ + private[ml] def loadDatasets( + sc: SparkContext, + input: String, + dataFormat: String, + testInput: String, + algo: String, + fracTest: Double): (DataFrame, DataFrame) = { + val sqlContext = new SQLContext(sc) + import sqlContext.implicits._ + + // Load training data + val origExamples: RDD[LabeledPoint] = loadData(sc, input, dataFormat) + + // Load or create test set + val splits: Array[RDD[LabeledPoint]] = if (testInput != "") { + // Load testInput. + val numFeatures = origExamples.take(1)(0).features.size + val origTestExamples: RDD[LabeledPoint] = loadData(sc, input, dataFormat, Some(numFeatures)) + Array(origExamples, origTestExamples) + } else { + // Split input into training, test. + origExamples.randomSplit(Array(1.0 - fracTest, fracTest), seed = 12345) + } + + // For classification, convert labels to Strings since we will index them later with + // StringIndexer. + def labelsToStrings(data: DataFrame): DataFrame = { + algo.toLowerCase match { + case "classification" => + data.withColumn("labelString", data("label").cast(StringType)) + case "regression" => + data + case _ => + throw new IllegalArgumentException("Algo ${params.algo} not supported.") + } + } + val dataframes = splits.map(_.toDF()).map(labelsToStrings).map(_.cache()) + + (dataframes(0), dataframes(1)) + } + + def run(params: Params) { + val conf = new SparkConf().setAppName(s"DecisionTreeExample with $params") + val sc = new SparkContext(conf) + params.checkpointDir.foreach(sc.setCheckpointDir) + val algo = params.algo.toLowerCase + + println(s"DecisionTreeExample with parameters:\n$params") + + // Load training and test data and cache it. + val (training: DataFrame, test: DataFrame) = + loadDatasets(sc, params.input, params.dataFormat, params.testInput, algo, params.fracTest) + + val numTraining = training.count() + val numTest = test.count() + val numFeatures = training.select("features").first().getAs[Vector](0).size + println("Loaded data:") + println(s" numTraining = $numTraining, numTest = $numTest") + println(s" numFeatures = $numFeatures") + + // Set up Pipeline + val stages = new mutable.ArrayBuffer[PipelineStage]() + // (1) For classification, re-index classes. + val labelColName = if (algo == "classification") "indexedLabel" else "label" + if (algo == "classification") { + val labelIndexer = new StringIndexer().setInputCol("labelString").setOutputCol(labelColName) + stages += labelIndexer + } + // (2) Identify categorical features using VectorIndexer. + // Features with more than maxCategories values will be treated as continuous. + val featuresIndexer = new VectorIndexer().setInputCol("features") + .setOutputCol("indexedFeatures").setMaxCategories(10) + stages += featuresIndexer + // (3) Learn DecisionTree + val dt = algo match { + case "classification" => + new DecisionTreeClassifier().setFeaturesCol("indexedFeatures") + .setLabelCol(labelColName) + .setMaxDepth(params.maxDepth) + .setMaxBins(params.maxBins) + .setMinInstancesPerNode(params.minInstancesPerNode) + .setMinInfoGain(params.minInfoGain) + .setCacheNodeIds(params.cacheNodeIds) + .setCheckpointInterval(params.checkpointInterval) + case "regression" => + new DecisionTreeRegressor().setFeaturesCol("indexedFeatures") + .setLabelCol(labelColName) + .setMaxDepth(params.maxDepth) + .setMaxBins(params.maxBins) + .setMinInstancesPerNode(params.minInstancesPerNode) + .setMinInfoGain(params.minInfoGain) + .setCacheNodeIds(params.cacheNodeIds) + .setCheckpointInterval(params.checkpointInterval) + case _ => throw new IllegalArgumentException("Algo ${params.algo} not supported.") + } + stages += dt + val pipeline = new Pipeline().setStages(stages.toArray) + + // Fit the Pipeline + val startTime = System.nanoTime() + val pipelineModel = pipeline.fit(training) + val elapsedTime = (System.nanoTime() - startTime) / 1e9 + println(s"Training time: $elapsedTime seconds") + + // Get the trained Decision Tree from the fitted PipelineModel + val treeModel: DecisionTreeModel = algo match { + case "classification" => + pipelineModel.getModel[DecisionTreeClassificationModel]( + dt.asInstanceOf[DecisionTreeClassifier]) + case "regression" => + pipelineModel.getModel[DecisionTreeRegressionModel](dt.asInstanceOf[DecisionTreeRegressor]) + case _ => throw new IllegalArgumentException("Algo ${params.algo} not supported.") + } + if (treeModel.numNodes < 20) { + println(treeModel.toDebugString) // Print full model. + } else { + println(treeModel) // Print model summary. + } + + // Predict on training + val trainingFullPredictions = pipelineModel.transform(training).cache() + val trainingPredictions = trainingFullPredictions.select("prediction") + .map(_.getDouble(0)) + val trainingLabels = trainingFullPredictions.select(labelColName).map(_.getDouble(0)) + // Predict on test data + val testFullPredictions = pipelineModel.transform(test).cache() + val testPredictions = testFullPredictions.select("prediction") + .map(_.getDouble(0)) + val testLabels = testFullPredictions.select(labelColName).map(_.getDouble(0)) + + // For classification, print number of classes for reference. + if (algo == "classification") { + val numClasses = + MetadataUtils.getNumClasses(trainingFullPredictions.schema(labelColName)) match { + case Some(n) => n + case None => throw new RuntimeException( + "DecisionTreeExample had unknown failure when indexing labels for classification.") + } + println(s"numClasses = $numClasses.") + } + + // Evaluate model on training, test data + algo match { + case "classification" => + val trainingAccuracy = + new MulticlassMetrics(trainingPredictions.zip(trainingLabels)).precision + println(s"Train accuracy = $trainingAccuracy") + val testAccuracy = + new MulticlassMetrics(testPredictions.zip(testLabels)).precision + println(s"Test accuracy = $testAccuracy") + case "regression" => + val trainingRMSE = + new RegressionMetrics(trainingPredictions.zip(trainingLabels)).rootMeanSquaredError + println(s"Training root mean squared error (RMSE) = $trainingRMSE") + val testRMSE = + new RegressionMetrics(testPredictions.zip(testLabels)).rootMeanSquaredError + println(s"Test root mean squared error (RMSE) = $testRMSE") + case _ => + throw new IllegalArgumentException("Algo ${params.algo} not supported.") + } + + sc.stop() + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala b/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala index aa27a668f1695..d7dee8fed2a55 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/attribute/AttributeGroup.scala @@ -117,12 +117,12 @@ class AttributeGroup private ( case numeric: NumericAttribute => // Skip default numeric attributes. if (numeric.withoutIndex != NumericAttribute.defaultAttr) { - numericMetadata += numeric.toMetadata(withType = false) + numericMetadata += numeric.toMetadataImpl(withType = false) } case nominal: NominalAttribute => - nominalMetadata += nominal.toMetadata(withType = false) + nominalMetadata += nominal.toMetadataImpl(withType = false) case binary: BinaryAttribute => - binaryMetadata += binary.toMetadata(withType = false) + binaryMetadata += binary.toMetadataImpl(withType = false) } val attrBldr = new MetadataBuilder if (numericMetadata.nonEmpty) { @@ -151,7 +151,7 @@ class AttributeGroup private ( } /** Converts to ML metadata */ - def toMetadata: Metadata = toMetadata(Metadata.empty) + def toMetadata(): Metadata = toMetadata(Metadata.empty) /** Converts to a StructField with some existing metadata. */ def toStructField(existingMetadata: Metadata): StructField = { @@ -159,7 +159,7 @@ class AttributeGroup private ( } /** Converts to a StructField. */ - def toStructField: StructField = toStructField(Metadata.empty) + def toStructField(): StructField = toStructField(Metadata.empty) override def equals(other: Any): Boolean = { other match { diff --git a/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala b/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala index 00b7566aab434..5717d6ec2eaec 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/attribute/attributes.scala @@ -68,21 +68,32 @@ sealed abstract class Attribute extends Serializable { * Converts this attribute to [[Metadata]]. * @param withType whether to include the type info */ - private[attribute] def toMetadata(withType: Boolean): Metadata + private[attribute] def toMetadataImpl(withType: Boolean): Metadata /** * Converts this attribute to [[Metadata]]. For numeric attributes, the type info is excluded to * save space, because numeric type is the default attribute type. For nominal and binary * attributes, the type info is included. */ - private[attribute] def toMetadata(): Metadata = { + private[attribute] def toMetadataImpl(): Metadata = { if (attrType == AttributeType.Numeric) { - toMetadata(withType = false) + toMetadataImpl(withType = false) } else { - toMetadata(withType = true) + toMetadataImpl(withType = true) } } + /** Converts to ML metadata with some existing metadata. */ + def toMetadata(existingMetadata: Metadata): Metadata = { + new MetadataBuilder() + .withMetadata(existingMetadata) + .putMetadata(AttributeKeys.ML_ATTR, toMetadataImpl()) + .build() + } + + /** Converts to ML metadata */ + def toMetadata(): Metadata = toMetadata(Metadata.empty) + /** * Converts to a [[StructField]] with some existing metadata. * @param existingMetadata existing metadata to carry over @@ -90,7 +101,7 @@ sealed abstract class Attribute extends Serializable { def toStructField(existingMetadata: Metadata): StructField = { val newMetadata = new MetadataBuilder() .withMetadata(existingMetadata) - .putMetadata(AttributeKeys.ML_ATTR, withoutName.withoutIndex.toMetadata()) + .putMetadata(AttributeKeys.ML_ATTR, withoutName.withoutIndex.toMetadataImpl()) .build() StructField(name.get, DoubleType, nullable = false, newMetadata) } @@ -98,7 +109,7 @@ sealed abstract class Attribute extends Serializable { /** Converts to a [[StructField]]. */ def toStructField(): StructField = toStructField(Metadata.empty) - override def toString: String = toMetadata(withType = true).toString + override def toString: String = toMetadataImpl(withType = true).toString } /** Trait for ML attribute factories. */ @@ -210,7 +221,7 @@ class NumericAttribute private[ml] ( override def isNominal: Boolean = false /** Convert this attribute to metadata. */ - private[attribute] override def toMetadata(withType: Boolean): Metadata = { + override private[attribute] def toMetadataImpl(withType: Boolean): Metadata = { import org.apache.spark.ml.attribute.AttributeKeys._ val bldr = new MetadataBuilder() if (withType) bldr.putString(TYPE, attrType.name) @@ -353,6 +364,20 @@ class NominalAttribute private[ml] ( /** Copy without the `numValues`. */ def withoutNumValues: NominalAttribute = copy(numValues = None) + /** + * Get the number of values, either from `numValues` or from `values`. + * Return None if unknown. + */ + def getNumValues: Option[Int] = { + if (numValues.nonEmpty) { + numValues + } else if (values.nonEmpty) { + Some(values.get.length) + } else { + None + } + } + /** Creates a copy of this attribute with optional changes. */ private def copy( name: Option[String] = name, @@ -363,7 +388,7 @@ class NominalAttribute private[ml] ( new NominalAttribute(name, index, isOrdinal, numValues, values) } - private[attribute] override def toMetadata(withType: Boolean): Metadata = { + override private[attribute] def toMetadataImpl(withType: Boolean): Metadata = { import org.apache.spark.ml.attribute.AttributeKeys._ val bldr = new MetadataBuilder() if (withType) bldr.putString(TYPE, attrType.name) @@ -465,7 +490,7 @@ class BinaryAttribute private[ml] ( new BinaryAttribute(name, index, values) } - private[attribute] override def toMetadata(withType: Boolean): Metadata = { + override private[attribute] def toMetadataImpl(withType: Boolean): Metadata = { import org.apache.spark.ml.attribute.AttributeKeys._ val bldr = new MetadataBuilder if (withType) bldr.putString(TYPE, attrType.name) diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala new file mode 100644 index 0000000000000..3855e396b5534 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala @@ -0,0 +1,155 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.classification + +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.impl.estimator.{Predictor, PredictionModel} +import org.apache.spark.ml.impl.tree._ +import org.apache.spark.ml.param.{Params, ParamMap} +import org.apache.spark.ml.tree.{DecisionTreeModel, Node} +import org.apache.spark.ml.util.MetadataUtils +import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy} +import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeModel} +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.DataFrame + + +/** + * :: AlphaComponent :: + * + * [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] learning algorithm + * for classification. + * It supports both binary and multiclass labels, as well as both continuous and categorical + * features. + */ +@AlphaComponent +final class DecisionTreeClassifier + extends Predictor[Vector, DecisionTreeClassifier, DecisionTreeClassificationModel] + with DecisionTreeParams + with TreeClassifierParams { + + // Override parameter setters from parent trait for Java API compatibility. + + override def setMaxDepth(value: Int): this.type = super.setMaxDepth(value) + + override def setMaxBins(value: Int): this.type = super.setMaxBins(value) + + override def setMinInstancesPerNode(value: Int): this.type = + super.setMinInstancesPerNode(value) + + override def setMinInfoGain(value: Double): this.type = super.setMinInfoGain(value) + + override def setMaxMemoryInMB(value: Int): this.type = super.setMaxMemoryInMB(value) + + override def setCacheNodeIds(value: Boolean): this.type = + super.setCacheNodeIds(value) + + override def setCheckpointInterval(value: Int): this.type = + super.setCheckpointInterval(value) + + override def setImpurity(value: String): this.type = super.setImpurity(value) + + override protected def train( + dataset: DataFrame, + paramMap: ParamMap): DecisionTreeClassificationModel = { + val categoricalFeatures: Map[Int, Int] = + MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) + val numClasses: Int = MetadataUtils.getNumClasses(dataset.schema(paramMap(labelCol))) match { + case Some(n: Int) => n + case None => throw new IllegalArgumentException("DecisionTreeClassifier was given input" + + s" with invalid label column, without the number of classes specified.") + // TODO: Automatically index labels. + } + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + val strategy = getOldStrategy(categoricalFeatures, numClasses) + val oldModel = OldDecisionTree.train(oldDataset, strategy) + DecisionTreeClassificationModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + } + + /** (private[ml]) Create a Strategy instance to use with the old API. */ + override private[ml] def getOldStrategy( + categoricalFeatures: Map[Int, Int], + numClasses: Int): OldStrategy = { + val strategy = super.getOldStrategy(categoricalFeatures, numClasses) + strategy.algo = OldAlgo.Classification + strategy.setImpurity(getOldImpurity) + strategy + } +} + +object DecisionTreeClassifier { + /** Accessor for supported impurities */ + final val supportedImpurities: Array[String] = TreeClassifierParams.supportedImpurities +} + +/** + * :: AlphaComponent :: + * + * [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] model for classification. + * It supports both binary and multiclass labels, as well as both continuous and categorical + * features. + */ +@AlphaComponent +final class DecisionTreeClassificationModel private[ml] ( + override val parent: DecisionTreeClassifier, + override val fittingParamMap: ParamMap, + override val rootNode: Node) + extends PredictionModel[Vector, DecisionTreeClassificationModel] + with DecisionTreeModel with Serializable { + + require(rootNode != null, + "DecisionTreeClassificationModel given null rootNode, but it requires a non-null rootNode.") + + override protected def predict(features: Vector): Double = { + rootNode.predict(features) + } + + override protected def copy(): DecisionTreeClassificationModel = { + val m = new DecisionTreeClassificationModel(parent, fittingParamMap, rootNode) + Params.inheritValues(this.extractParamMap(), this, m) + m + } + + override def toString: String = { + s"DecisionTreeClassificationModel of depth $depth with $numNodes nodes" + } + + /** (private[ml]) Convert to a model in the old API */ + private[ml] def toOld: OldDecisionTreeModel = { + new OldDecisionTreeModel(rootNode.toOld(1), OldAlgo.Classification) + } +} + +private[ml] object DecisionTreeClassificationModel { + + /** (private[ml]) Convert a model from the old API */ + def fromOld( + oldModel: OldDecisionTreeModel, + parent: DecisionTreeClassifier, + fittingParamMap: ParamMap, + categoricalFeatures: Map[Int, Int]): DecisionTreeClassificationModel = { + require(oldModel.algo == OldAlgo.Classification, + s"Cannot convert non-classification DecisionTreeModel (old API) to" + + s" DecisionTreeClassificationModel (new API). Algo is: ${oldModel.algo}") + val rootNode = Node.fromOld(oldModel.topNode, categoricalFeatures) + new DecisionTreeClassificationModel(parent, fittingParamMap, rootNode) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala index 4d960df357fe9..23956c512c8a6 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala @@ -118,7 +118,7 @@ class StringIndexerModel private[ml] ( } val outputColName = map(outputCol) val metadata = NominalAttribute.defaultAttr - .withName(outputColName).withValues(labels).toStructField().metadata + .withName(outputColName).withValues(labels).toMetadata() dataset.select(col("*"), indexer(dataset(map(inputCol))).as(outputColName, metadata)) } diff --git a/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala b/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala new file mode 100644 index 0000000000000..6f4509f03d033 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala @@ -0,0 +1,300 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.impl.tree + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.ml.impl.estimator.PredictorParams +import org.apache.spark.ml.param._ +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy} +import org.apache.spark.mllib.tree.impurity.{Gini => OldGini, Entropy => OldEntropy, + Impurity => OldImpurity, Variance => OldVariance} + + +/** + * :: DeveloperApi :: + * Parameters for Decision Tree-based algorithms. + * + * Note: Marked as private and DeveloperApi since this may be made public in the future. + */ +@DeveloperApi +private[ml] trait DecisionTreeParams extends PredictorParams { + + /** + * Maximum depth of the tree. + * E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. + * (default = 5) + * @group param + */ + final val maxDepth: IntParam = + new IntParam(this, "maxDepth", "Maximum depth of the tree." + + " E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.") + + /** + * Maximum number of bins used for discretizing continuous features and for choosing how to split + * on features at each node. More bins give higher granularity. + * Must be >= 2 and >= number of categories in any categorical feature. + * (default = 32) + * @group param + */ + final val maxBins: IntParam = new IntParam(this, "maxBins", "Max number of bins for" + + " discretizing continuous features. Must be >=2 and >= number of categories for any" + + " categorical feature.") + + /** + * Minimum number of instances each child must have after split. + * If a split causes the left or right child to have fewer than minInstancesPerNode, + * the split will be discarded as invalid. + * Should be >= 1. + * (default = 1) + * @group param + */ + final val minInstancesPerNode: IntParam = new IntParam(this, "minInstancesPerNode", "Minimum" + + " number of instances each child must have after split. If a split causes the left or right" + + " child to have fewer than minInstancesPerNode, the split will be discarded as invalid." + + " Should be >= 1.") + + /** + * Minimum information gain for a split to be considered at a tree node. + * (default = 0.0) + * @group param + */ + final val minInfoGain: DoubleParam = new DoubleParam(this, "minInfoGain", + "Minimum information gain for a split to be considered at a tree node.") + + /** + * Maximum memory in MB allocated to histogram aggregation. + * (default = 256 MB) + * @group expertParam + */ + final val maxMemoryInMB: IntParam = new IntParam(this, "maxMemoryInMB", + "Maximum memory in MB allocated to histogram aggregation.") + + /** + * If false, the algorithm will pass trees to executors to match instances with nodes. + * If true, the algorithm will cache node IDs for each instance. + * Caching can speed up training of deeper trees. + * (default = false) + * @group expertParam + */ + final val cacheNodeIds: BooleanParam = new BooleanParam(this, "cacheNodeIds", "If false, the" + + " algorithm will pass trees to executors to match instances with nodes. If true, the" + + " algorithm will cache node IDs for each instance. Caching can speed up training of deeper" + + " trees.") + + /** + * Specifies how often to checkpoint the cached node IDs. + * E.g. 10 means that the cache will get checkpointed every 10 iterations. + * This is only used if cacheNodeIds is true and if the checkpoint directory is set in + * [[org.apache.spark.SparkContext]]. + * Must be >= 1. + * (default = 10) + * @group expertParam + */ + final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "Specifies" + + " how often to checkpoint the cached node IDs. E.g. 10 means that the cache will get" + + " checkpointed every 10 iterations. This is only used if cacheNodeIds is true and if the" + + " checkpoint directory is set in the SparkContext. Must be >= 1.") + + setDefault(maxDepth -> 5, maxBins -> 32, minInstancesPerNode -> 1, minInfoGain -> 0.0, + maxMemoryInMB -> 256, cacheNodeIds -> false, checkpointInterval -> 10) + + /** @group setParam */ + def setMaxDepth(value: Int): this.type = { + require(value >= 0, s"maxDepth parameter must be >= 0. Given bad value: $value") + set(maxDepth, value) + this.asInstanceOf[this.type] + } + + /** @group getParam */ + def getMaxDepth: Int = getOrDefault(maxDepth) + + /** @group setParam */ + def setMaxBins(value: Int): this.type = { + require(value >= 2, s"maxBins parameter must be >= 2. Given bad value: $value") + set(maxBins, value) + this + } + + /** @group getParam */ + def getMaxBins: Int = getOrDefault(maxBins) + + /** @group setParam */ + def setMinInstancesPerNode(value: Int): this.type = { + require(value >= 1, s"minInstancesPerNode parameter must be >= 1. Given bad value: $value") + set(minInstancesPerNode, value) + this + } + + /** @group getParam */ + def getMinInstancesPerNode: Int = getOrDefault(minInstancesPerNode) + + /** @group setParam */ + def setMinInfoGain(value: Double): this.type = { + set(minInfoGain, value) + this + } + + /** @group getParam */ + def getMinInfoGain: Double = getOrDefault(minInfoGain) + + /** @group expertSetParam */ + def setMaxMemoryInMB(value: Int): this.type = { + require(value > 0, s"maxMemoryInMB parameter must be > 0. Given bad value: $value") + set(maxMemoryInMB, value) + this + } + + /** @group expertGetParam */ + def getMaxMemoryInMB: Int = getOrDefault(maxMemoryInMB) + + /** @group expertSetParam */ + def setCacheNodeIds(value: Boolean): this.type = { + set(cacheNodeIds, value) + this + } + + /** @group expertGetParam */ + def getCacheNodeIds: Boolean = getOrDefault(cacheNodeIds) + + /** @group expertSetParam */ + def setCheckpointInterval(value: Int): this.type = { + require(value >= 1, s"checkpointInterval parameter must be >= 1. Given bad value: $value") + set(checkpointInterval, value) + this + } + + /** @group expertGetParam */ + def getCheckpointInterval: Int = getOrDefault(checkpointInterval) + + /** + * Create a Strategy instance to use with the old API. + * NOTE: The caller should set impurity and subsamplingRate (which is set to 1.0, + * the default for single trees). + */ + private[ml] def getOldStrategy( + categoricalFeatures: Map[Int, Int], + numClasses: Int): OldStrategy = { + val strategy = OldStrategy.defaultStategy(OldAlgo.Classification) + strategy.checkpointInterval = getCheckpointInterval + strategy.maxBins = getMaxBins + strategy.maxDepth = getMaxDepth + strategy.maxMemoryInMB = getMaxMemoryInMB + strategy.minInfoGain = getMinInfoGain + strategy.minInstancesPerNode = getMinInstancesPerNode + strategy.useNodeIdCache = getCacheNodeIds + strategy.numClasses = numClasses + strategy.categoricalFeaturesInfo = categoricalFeatures + strategy.subsamplingRate = 1.0 // default for individual trees + strategy + } +} + +/** + * (private trait) Parameters for Decision Tree-based classification algorithms. + */ +private[ml] trait TreeClassifierParams extends Params { + + /** + * Criterion used for information gain calculation (case-insensitive). + * Supported: "entropy" and "gini". + * (default = gini) + * @group param + */ + val impurity: Param[String] = new Param[String](this, "impurity", "Criterion used for" + + " information gain calculation (case-insensitive). Supported options:" + + s" ${TreeClassifierParams.supportedImpurities.mkString(", ")}") + + setDefault(impurity -> "gini") + + /** @group setParam */ + def setImpurity(value: String): this.type = { + val impurityStr = value.toLowerCase + require(TreeClassifierParams.supportedImpurities.contains(impurityStr), + s"Tree-based classifier was given unrecognized impurity: $value." + + s" Supported options: ${TreeClassifierParams.supportedImpurities.mkString(", ")}") + set(impurity, impurityStr) + this + } + + /** @group getParam */ + def getImpurity: String = getOrDefault(impurity) + + /** Convert new impurity to old impurity. */ + private[ml] def getOldImpurity: OldImpurity = { + getImpurity match { + case "entropy" => OldEntropy + case "gini" => OldGini + case _ => + // Should never happen because of check in setter method. + throw new RuntimeException( + s"TreeClassifierParams was given unrecognized impurity: $impurity.") + } + } +} + +private[ml] object TreeClassifierParams { + // These options should be lowercase. + val supportedImpurities: Array[String] = Array("entropy", "gini").map(_.toLowerCase) +} + +/** + * (private trait) Parameters for Decision Tree-based regression algorithms. + */ +private[ml] trait TreeRegressorParams extends Params { + + /** + * Criterion used for information gain calculation (case-insensitive). + * Supported: "variance". + * (default = variance) + * @group param + */ + val impurity: Param[String] = new Param[String](this, "impurity", "Criterion used for" + + " information gain calculation (case-insensitive). Supported options:" + + s" ${TreeRegressorParams.supportedImpurities.mkString(", ")}") + + setDefault(impurity -> "variance") + + /** @group setParam */ + def setImpurity(value: String): this.type = { + val impurityStr = value.toLowerCase + require(TreeRegressorParams.supportedImpurities.contains(impurityStr), + s"Tree-based regressor was given unrecognized impurity: $value." + + s" Supported options: ${TreeRegressorParams.supportedImpurities.mkString(", ")}") + set(impurity, impurityStr) + this + } + + /** @group getParam */ + def getImpurity: String = getOrDefault(impurity) + + /** Convert new impurity to old impurity. */ + protected def getOldImpurity: OldImpurity = { + getImpurity match { + case "variance" => OldVariance + case _ => + // Should never happen because of check in setter method. + throw new RuntimeException( + s"TreeRegressorParams was given unrecognized impurity: $impurity") + } + } +} + +private[ml] object TreeRegressorParams { + // These options should be lowercase. + val supportedImpurities: Array[String] = Array("variance").map(_.toLowerCase) +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/package.scala b/mllib/src/main/scala/org/apache/spark/ml/package.scala index b45bd1499b72e..ac75e9de1a8f2 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/package.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/package.scala @@ -32,6 +32,18 @@ package org.apache.spark * @groupname getParam Parameter getters * @groupprio getParam 6 * + * @groupname expertParam (expert-only) Parameters + * @groupdesc expertParam A list of advanced, expert-only (hyper-)parameter keys this algorithm can + * take. Users can set and get the parameter values through setters and getters, + * respectively. + * @groupprio expertParam 7 + * + * @groupname expertSetParam (expert-only) Parameter setters + * @groupprio expertSetParam 8 + * + * @groupname expertGetParam (expert-only) Parameter getters + * @groupprio expertGetParam 9 + * * @groupname Ungrouped Members * @groupprio Ungrouped 0 */ diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala index 849c60433c777..ddc5907e7facd 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala @@ -296,8 +296,9 @@ private[spark] object Params { paramMap: ParamMap, parent: E, child: M): Unit = { + val childParams = child.params.map(_.name).toSet parent.params.foreach { param => - if (paramMap.contains(param)) { + if (paramMap.contains(param) && childParams.contains(param.name)) { child.set(child.getParam(param.name), paramMap(param)) } } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala new file mode 100644 index 0000000000000..49a8b77acf960 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala @@ -0,0 +1,145 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.regression + +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor} +import org.apache.spark.ml.impl.tree._ +import org.apache.spark.ml.param.{Params, ParamMap} +import org.apache.spark.ml.tree.{DecisionTreeModel, Node} +import org.apache.spark.ml.util.MetadataUtils +import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree} +import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy} +import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeModel} +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.DataFrame + + +/** + * :: AlphaComponent :: + * + * [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] learning algorithm + * for regression. + * It supports both continuous and categorical features. + */ +@AlphaComponent +final class DecisionTreeRegressor + extends Predictor[Vector, DecisionTreeRegressor, DecisionTreeRegressionModel] + with DecisionTreeParams + with TreeRegressorParams { + + // Override parameter setters from parent trait for Java API compatibility. + + override def setMaxDepth(value: Int): this.type = super.setMaxDepth(value) + + override def setMaxBins(value: Int): this.type = super.setMaxBins(value) + + override def setMinInstancesPerNode(value: Int): this.type = + super.setMinInstancesPerNode(value) + + override def setMinInfoGain(value: Double): this.type = super.setMinInfoGain(value) + + override def setMaxMemoryInMB(value: Int): this.type = super.setMaxMemoryInMB(value) + + override def setCacheNodeIds(value: Boolean): this.type = super.setCacheNodeIds(value) + + override def setCheckpointInterval(value: Int): this.type = + super.setCheckpointInterval(value) + + override def setImpurity(value: String): this.type = super.setImpurity(value) + + override protected def train( + dataset: DataFrame, + paramMap: ParamMap): DecisionTreeRegressionModel = { + val categoricalFeatures: Map[Int, Int] = + MetadataUtils.getCategoricalFeatures(dataset.schema(paramMap(featuresCol))) + val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, paramMap) + val strategy = getOldStrategy(categoricalFeatures) + val oldModel = OldDecisionTree.train(oldDataset, strategy) + DecisionTreeRegressionModel.fromOld(oldModel, this, paramMap, categoricalFeatures) + } + + /** (private[ml]) Create a Strategy instance to use with the old API. */ + private[ml] def getOldStrategy(categoricalFeatures: Map[Int, Int]): OldStrategy = { + val strategy = super.getOldStrategy(categoricalFeatures, numClasses = 0) + strategy.algo = OldAlgo.Regression + strategy.setImpurity(getOldImpurity) + strategy + } +} + +object DecisionTreeRegressor { + /** Accessor for supported impurities */ + final val supportedImpurities: Array[String] = TreeRegressorParams.supportedImpurities +} + +/** + * :: AlphaComponent :: + * + * [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] model for regression. + * It supports both continuous and categorical features. + * @param rootNode Root of the decision tree + */ +@AlphaComponent +final class DecisionTreeRegressionModel private[ml] ( + override val parent: DecisionTreeRegressor, + override val fittingParamMap: ParamMap, + override val rootNode: Node) + extends PredictionModel[Vector, DecisionTreeRegressionModel] + with DecisionTreeModel with Serializable { + + require(rootNode != null, + "DecisionTreeClassificationModel given null rootNode, but it requires a non-null rootNode.") + + override protected def predict(features: Vector): Double = { + rootNode.predict(features) + } + + override protected def copy(): DecisionTreeRegressionModel = { + val m = new DecisionTreeRegressionModel(parent, fittingParamMap, rootNode) + Params.inheritValues(this.extractParamMap(), this, m) + m + } + + override def toString: String = { + s"DecisionTreeRegressionModel of depth $depth with $numNodes nodes" + } + + /** Convert to a model in the old API */ + private[ml] def toOld: OldDecisionTreeModel = { + new OldDecisionTreeModel(rootNode.toOld(1), OldAlgo.Regression) + } +} + +private[ml] object DecisionTreeRegressionModel { + + /** (private[ml]) Convert a model from the old API */ + def fromOld( + oldModel: OldDecisionTreeModel, + parent: DecisionTreeRegressor, + fittingParamMap: ParamMap, + categoricalFeatures: Map[Int, Int]): DecisionTreeRegressionModel = { + require(oldModel.algo == OldAlgo.Regression, + s"Cannot convert non-regression DecisionTreeModel (old API) to" + + s" DecisionTreeRegressionModel (new API). Algo is: ${oldModel.algo}") + val rootNode = Node.fromOld(oldModel.topNode, categoricalFeatures) + new DecisionTreeRegressionModel(parent, fittingParamMap, rootNode) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala new file mode 100644 index 0000000000000..d6e2203d9f937 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala @@ -0,0 +1,205 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.tree + +import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.tree.model.{InformationGainStats => OldInformationGainStats, + Node => OldNode, Predict => OldPredict} + + +/** + * Decision tree node interface. + */ +sealed abstract class Node extends Serializable { + + // TODO: Add aggregate stats (once available). This will happen after we move the DecisionTree + // code into the new API and deprecate the old API. + + /** Prediction this node makes (or would make, if it is an internal node) */ + def prediction: Double + + /** Impurity measure at this node (for training data) */ + def impurity: Double + + /** Recursive prediction helper method */ + private[ml] def predict(features: Vector): Double = prediction + + /** + * Get the number of nodes in tree below this node, including leaf nodes. + * E.g., if this is a leaf, returns 0. If both children are leaves, returns 2. + */ + private[tree] def numDescendants: Int + + /** + * Recursive print function. + * @param indentFactor The number of spaces to add to each level of indentation. + */ + private[tree] def subtreeToString(indentFactor: Int = 0): String + + /** + * Get depth of tree from this node. + * E.g.: Depth 0 means this is a leaf node. Depth 1 means 1 internal and 2 leaf nodes. + */ + private[tree] def subtreeDepth: Int + + /** + * Create a copy of this node in the old Node format, recursively creating child nodes as needed. + * @param id Node ID using old format IDs + */ + private[ml] def toOld(id: Int): OldNode +} + +private[ml] object Node { + + /** + * Create a new Node from the old Node format, recursively creating child nodes as needed. + */ + def fromOld(oldNode: OldNode, categoricalFeatures: Map[Int, Int]): Node = { + if (oldNode.isLeaf) { + // TODO: Once the implementation has been moved to this API, then include sufficient + // statistics here. + new LeafNode(prediction = oldNode.predict.predict, impurity = oldNode.impurity) + } else { + val gain = if (oldNode.stats.nonEmpty) { + oldNode.stats.get.gain + } else { + 0.0 + } + new InternalNode(prediction = oldNode.predict.predict, impurity = oldNode.impurity, + gain = gain, leftChild = fromOld(oldNode.leftNode.get, categoricalFeatures), + rightChild = fromOld(oldNode.rightNode.get, categoricalFeatures), + split = Split.fromOld(oldNode.split.get, categoricalFeatures)) + } + } +} + +/** + * Decision tree leaf node. + * @param prediction Prediction this node makes + * @param impurity Impurity measure at this node (for training data) + */ +final class LeafNode private[ml] ( + override val prediction: Double, + override val impurity: Double) extends Node { + + override def toString: String = s"LeafNode(prediction = $prediction, impurity = $impurity)" + + override private[ml] def predict(features: Vector): Double = prediction + + override private[tree] def numDescendants: Int = 0 + + override private[tree] def subtreeToString(indentFactor: Int = 0): String = { + val prefix: String = " " * indentFactor + prefix + s"Predict: $prediction\n" + } + + override private[tree] def subtreeDepth: Int = 0 + + override private[ml] def toOld(id: Int): OldNode = { + // NOTE: We do NOT store 'prob' in the new API currently. + new OldNode(id, new OldPredict(prediction, prob = 0.0), impurity, isLeaf = true, + None, None, None, None) + } +} + +/** + * Internal Decision Tree node. + * @param prediction Prediction this node would make if it were a leaf node + * @param impurity Impurity measure at this node (for training data) + * @param gain Information gain value. + * Values < 0 indicate missing values; this quirk will be removed with future updates. + * @param leftChild Left-hand child node + * @param rightChild Right-hand child node + * @param split Information about the test used to split to the left or right child. + */ +final class InternalNode private[ml] ( + override val prediction: Double, + override val impurity: Double, + val gain: Double, + val leftChild: Node, + val rightChild: Node, + val split: Split) extends Node { + + override def toString: String = { + s"InternalNode(prediction = $prediction, impurity = $impurity, split = $split)" + } + + override private[ml] def predict(features: Vector): Double = { + if (split.shouldGoLeft(features)) { + leftChild.predict(features) + } else { + rightChild.predict(features) + } + } + + override private[tree] def numDescendants: Int = { + 2 + leftChild.numDescendants + rightChild.numDescendants + } + + override private[tree] def subtreeToString(indentFactor: Int = 0): String = { + val prefix: String = " " * indentFactor + prefix + s"If (${InternalNode.splitToString(split, left=true)})\n" + + leftChild.subtreeToString(indentFactor + 1) + + prefix + s"Else (${InternalNode.splitToString(split, left=false)})\n" + + rightChild.subtreeToString(indentFactor + 1) + } + + override private[tree] def subtreeDepth: Int = { + 1 + math.max(leftChild.subtreeDepth, rightChild.subtreeDepth) + } + + override private[ml] def toOld(id: Int): OldNode = { + assert(id.toLong * 2 < Int.MaxValue, "Decision Tree could not be converted from new to old API" + + " since the old API does not support deep trees.") + // NOTE: We do NOT store 'prob' in the new API currently. + new OldNode(id, new OldPredict(prediction, prob = 0.0), impurity, isLeaf = false, + Some(split.toOld), Some(leftChild.toOld(OldNode.leftChildIndex(id))), + Some(rightChild.toOld(OldNode.rightChildIndex(id))), + Some(new OldInformationGainStats(gain, impurity, leftChild.impurity, rightChild.impurity, + new OldPredict(leftChild.prediction, prob = 0.0), + new OldPredict(rightChild.prediction, prob = 0.0)))) + } +} + +private object InternalNode { + + /** + * Helper method for [[Node.subtreeToString()]]. + * @param split Split to print + * @param left Indicates whether this is the part of the split going to the left, + * or that going to the right. + */ + private def splitToString(split: Split, left: Boolean): String = { + val featureStr = s"feature ${split.featureIndex}" + split match { + case contSplit: ContinuousSplit => + if (left) { + s"$featureStr <= ${contSplit.threshold}" + } else { + s"$featureStr > ${contSplit.threshold}" + } + case catSplit: CategoricalSplit => + val categoriesStr = catSplit.getLeftCategories.mkString("{", ",", "}") + if (left) { + s"$featureStr in $categoriesStr" + } else { + s"$featureStr not in $categoriesStr" + } + } + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala new file mode 100644 index 0000000000000..cb940f62990ed --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala @@ -0,0 +1,151 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.tree + +import org.apache.spark.mllib.linalg.Vector +import org.apache.spark.mllib.tree.configuration.{FeatureType => OldFeatureType} +import org.apache.spark.mllib.tree.model.{Split => OldSplit} + + +/** + * Interface for a "Split," which specifies a test made at a decision tree node + * to choose the left or right path. + */ +sealed trait Split extends Serializable { + + /** Index of feature which this split tests */ + def featureIndex: Int + + /** Return true (split to left) or false (split to right) */ + private[ml] def shouldGoLeft(features: Vector): Boolean + + /** Convert to old Split format */ + private[tree] def toOld: OldSplit +} + +private[ml] object Split { + + def fromOld(oldSplit: OldSplit, categoricalFeatures: Map[Int, Int]): Split = { + oldSplit.featureType match { + case OldFeatureType.Categorical => + new CategoricalSplit(featureIndex = oldSplit.feature, + leftCategories = oldSplit.categories.toArray, categoricalFeatures(oldSplit.feature)) + case OldFeatureType.Continuous => + new ContinuousSplit(featureIndex = oldSplit.feature, threshold = oldSplit.threshold) + } + } +} + +/** + * Split which tests a categorical feature. + * @param featureIndex Index of the feature to test + * @param leftCategories If the feature value is in this set of categories, then the split goes + * left. Otherwise, it goes right. + * @param numCategories Number of categories for this feature. + */ +final class CategoricalSplit( + override val featureIndex: Int, + leftCategories: Array[Double], + private val numCategories: Int) + extends Split { + + require(leftCategories.forall(cat => 0 <= cat && cat < numCategories), "Invalid leftCategories" + + s" (should be in range [0, $numCategories)): ${leftCategories.mkString(",")}") + + /** + * If true, then "categories" is the set of categories for splitting to the left, and vice versa. + */ + private val isLeft: Boolean = leftCategories.length <= numCategories / 2 + + /** Set of categories determining the splitting rule, along with [[isLeft]]. */ + private val categories: Set[Double] = { + if (isLeft) { + leftCategories.toSet + } else { + setComplement(leftCategories.toSet) + } + } + + override private[ml] def shouldGoLeft(features: Vector): Boolean = { + if (isLeft) { + categories.contains(features(featureIndex)) + } else { + !categories.contains(features(featureIndex)) + } + } + + override def equals(o: Any): Boolean = { + o match { + case other: CategoricalSplit => featureIndex == other.featureIndex && + isLeft == other.isLeft && categories == other.categories + case _ => false + } + } + + override private[tree] def toOld: OldSplit = { + val oldCats = if (isLeft) { + categories + } else { + setComplement(categories) + } + OldSplit(featureIndex, threshold = 0.0, OldFeatureType.Categorical, oldCats.toList) + } + + /** Get sorted categories which split to the left */ + def getLeftCategories: Array[Double] = { + val cats = if (isLeft) categories else setComplement(categories) + cats.toArray.sorted + } + + /** Get sorted categories which split to the right */ + def getRightCategories: Array[Double] = { + val cats = if (isLeft) setComplement(categories) else categories + cats.toArray.sorted + } + + /** [0, numCategories) \ cats */ + private def setComplement(cats: Set[Double]): Set[Double] = { + Range(0, numCategories).map(_.toDouble).filter(cat => !cats.contains(cat)).toSet + } +} + +/** + * Split which tests a continuous feature. + * @param featureIndex Index of the feature to test + * @param threshold If the feature value is <= this threshold, then the split goes left. + * Otherwise, it goes right. + */ +final class ContinuousSplit(override val featureIndex: Int, val threshold: Double) extends Split { + + override private[ml] def shouldGoLeft(features: Vector): Boolean = { + features(featureIndex) <= threshold + } + + override def equals(o: Any): Boolean = { + o match { + case other: ContinuousSplit => + featureIndex == other.featureIndex && threshold == other.threshold + case _ => + false + } + } + + override private[tree] def toOld: OldSplit = { + OldSplit(featureIndex, threshold, OldFeatureType.Continuous, List.empty[Double]) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala new file mode 100644 index 0000000000000..8e3bc3849dcf0 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala @@ -0,0 +1,60 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.tree + +import org.apache.spark.annotation.AlphaComponent + + +/** + * :: AlphaComponent :: + * + * Abstraction for Decision Tree models. + * + * TODO: Add support for predicting probabilities and raw predictions + */ +@AlphaComponent +trait DecisionTreeModel { + + /** Root of the decision tree */ + def rootNode: Node + + /** Number of nodes in tree, including leaf nodes. */ + def numNodes: Int = { + 1 + rootNode.numDescendants + } + + /** + * Depth of the tree. + * E.g.: Depth 0 means 1 leaf node. Depth 1 means 1 internal node and 2 leaf nodes. + */ + lazy val depth: Int = { + rootNode.subtreeDepth + } + + /** Summary of the model */ + override def toString: String = { + // Implementing classes should generally override this method to be more descriptive. + s"DecisionTreeModel of depth $depth with $numNodes nodes" + } + + /** Full description of model */ + def toDebugString: String = { + val header = toString + "\n" + header + rootNode.subtreeToString(2) + } +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala b/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala new file mode 100644 index 0000000000000..c84c8b4eb744f --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala @@ -0,0 +1,82 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.util + +import scala.collection.immutable.HashMap + +import org.apache.spark.annotation.Experimental +import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, BinaryAttribute, NominalAttribute, + NumericAttribute} +import org.apache.spark.sql.types.StructField + + +/** + * :: Experimental :: + * + * Helper utilities for tree-based algorithms + */ +@Experimental +object MetadataUtils { + + /** + * Examine a schema to identify the number of classes in a label column. + * Returns None if the number of labels is not specified, or if the label column is continuous. + */ + def getNumClasses(labelSchema: StructField): Option[Int] = { + Attribute.fromStructField(labelSchema) match { + case numAttr: NumericAttribute => None + case binAttr: BinaryAttribute => Some(2) + case nomAttr: NominalAttribute => nomAttr.getNumValues + } + } + + /** + * Examine a schema to identify categorical (Binary and Nominal) features. + * + * @param featuresSchema Schema of the features column. + * If a feature does not have metadata, it is assumed to be continuous. + * If a feature is Nominal, then it must have the number of values + * specified. + * @return Map: feature index --> number of categories. + * The map's set of keys will be the set of categorical feature indices. + */ + def getCategoricalFeatures(featuresSchema: StructField): Map[Int, Int] = { + val metadata = AttributeGroup.fromStructField(featuresSchema) + if (metadata.attributes.isEmpty) { + HashMap.empty[Int, Int] + } else { + metadata.attributes.get.zipWithIndex.flatMap { case (attr, idx) => + if (attr == null) { + Iterator() + } else { + attr match { + case numAttr: NumericAttribute => Iterator() + case binAttr: BinaryAttribute => Iterator(idx -> 2) + case nomAttr: NominalAttribute => + nomAttr.getNumValues match { + case Some(numValues: Int) => Iterator(idx -> numValues) + case None => throw new IllegalArgumentException(s"Feature $idx is marked as" + + " Nominal (categorical), but it does not have the number of values specified.") + } + } + } + }.toMap + } + } + +} diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala index b9d0c56dd1ea3..dfe3a0b6913ef 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala @@ -1147,7 +1147,10 @@ object DecisionTree extends Serializable with Logging { } } - assert(splits.length > 0) + // TODO: Do not fail; just ignore the useless feature. + assert(splits.length > 0, + s"DecisionTree could not handle feature $featureIndex since it had only 1 unique value." + + " Please remove this feature and then try again.") // set number of splits accordingly metadata.setNumSplits(featureIndex, splits.length) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala index c02c79f094b66..0e31c7ed58df8 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala @@ -81,11 +81,11 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy) /** * Method to validate a gradient boosting model * @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]. - * @param validationInput Validation dataset: - RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]. - Should be different from and follow the same distribution as input. - e.g., these two datasets could be created from an original dataset - by using [[org.apache.spark.rdd.RDD.randomSplit()]] + * @param validationInput Validation dataset. + * This dataset should be different from the training dataset, + * but it should follow the same distribution. + * E.g., these two datasets could be created from an original dataset + * by using [[org.apache.spark.rdd.RDD.randomSplit()]] * @return a gradient boosted trees model that can be used for prediction */ def runWithValidation( @@ -194,8 +194,6 @@ object GradientBoostedTrees extends Logging { val firstTreeWeight = 1.0 baseLearners(0) = firstTreeModel baseLearnerWeights(0) = firstTreeWeight - val startingModel = new GradientBoostedTreesModel( - Regression, Array(firstTreeModel), baseLearnerWeights.slice(0, 1)) var predError: RDD[(Double, Double)] = GradientBoostedTreesModel. computeInitialPredictionAndError(input, firstTreeWeight, firstTreeModel, loss) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala index db01f2e229e5a..055e60c7d9c95 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala @@ -249,7 +249,7 @@ private class RandomForest ( nodeIdCache.get.deleteAllCheckpoints() } catch { case e:IOException => - logWarning(s"delete all chackpoints failed. Error reason: ${e.getMessage}") + logWarning(s"delete all checkpoints failed. Error reason: ${e.getMessage}") } } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala index 664c8df019233..2d6b01524ff3d 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala @@ -89,14 +89,14 @@ object BoostingStrategy { * @return Configuration for boosting algorithm */ def defaultParams(algo: Algo): BoostingStrategy = { - val treeStragtegy = Strategy.defaultStategy(algo) - treeStragtegy.maxDepth = 3 + val treeStrategy = Strategy.defaultStategy(algo) + treeStrategy.maxDepth = 3 algo match { case Algo.Classification => - treeStragtegy.numClasses = 2 - new BoostingStrategy(treeStragtegy, LogLoss) + treeStrategy.numClasses = 2 + new BoostingStrategy(treeStrategy, LogLoss) case Algo.Regression => - new BoostingStrategy(treeStragtegy, SquaredError) + new BoostingStrategy(treeStrategy, SquaredError) case _ => throw new IllegalArgumentException(s"$algo is not supported by boosting.") } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala index 6f570b4e09c79..2bdef73c4a8f1 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala @@ -20,7 +20,7 @@ package org.apache.spark.mllib.tree.loss import org.apache.spark.annotation.DeveloperApi import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.model.TreeEnsembleModel -import org.apache.spark.rdd.RDD + /** * :: DeveloperApi :: @@ -45,9 +45,8 @@ object AbsoluteError extends Loss { if (label - prediction < 0) 1.0 else -1.0 } - override def computeError(prediction: Double, label: Double): Double = { + override private[mllib] def computeError(prediction: Double, label: Double): Double = { val err = label - prediction math.abs(err) } - } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala index 24ee9f3d51293..778c24526de70 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala @@ -21,7 +21,7 @@ import org.apache.spark.annotation.DeveloperApi import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.model.TreeEnsembleModel import org.apache.spark.mllib.util.MLUtils -import org.apache.spark.rdd.RDD + /** * :: DeveloperApi :: @@ -47,10 +47,9 @@ object LogLoss extends Loss { - 4.0 * label / (1.0 + math.exp(2.0 * label * prediction)) } - override def computeError(prediction: Double, label: Double): Double = { + override private[mllib] def computeError(prediction: Double, label: Double): Double = { val margin = 2.0 * label * prediction // The following is equivalent to 2.0 * log(1 + exp(-margin)) but more numerically stable. 2.0 * MLUtils.log1pExp(-margin) } - } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala index d3b82b752fa0d..64ffccbce073f 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala @@ -22,6 +22,7 @@ import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.model.TreeEnsembleModel import org.apache.spark.rdd.RDD + /** * :: DeveloperApi :: * Trait for adding "pluggable" loss functions for the gradient boosting algorithm. @@ -57,6 +58,5 @@ trait Loss extends Serializable { * @param label True label. * @return Measure of model error on datapoint. */ - def computeError(prediction: Double, label: Double): Double - + private[mllib] def computeError(prediction: Double, label: Double): Double } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala index 58857ae15e93e..a5582d3ef3324 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala @@ -20,7 +20,7 @@ package org.apache.spark.mllib.tree.loss import org.apache.spark.annotation.DeveloperApi import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.tree.model.TreeEnsembleModel -import org.apache.spark.rdd.RDD + /** * :: DeveloperApi :: @@ -45,9 +45,8 @@ object SquaredError extends Loss { 2.0 * (prediction - label) } - override def computeError(prediction: Double, label: Double): Double = { + override private[mllib] def computeError(prediction: Double, label: Double): Double = { val err = prediction - label err * err } - } diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala index c9bafd60fba4d..331af428533de 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala @@ -113,11 +113,13 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable DecisionTreeModel.SaveLoadV1_0.save(sc, path, this) } - override protected def formatVersion: String = "1.0" + override protected def formatVersion: String = DecisionTreeModel.formatVersion } object DecisionTreeModel extends Loader[DecisionTreeModel] with Logging { + private[spark] def formatVersion: String = "1.0" + private[tree] object SaveLoadV1_0 { def thisFormatVersion: String = "1.0" diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala index 4f72bb8014cc0..708ba04b567d3 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala @@ -175,7 +175,7 @@ class Node ( } } -private[tree] object Node { +private[spark] object Node { /** * Return a node with the given node id (but nothing else set). diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala index fef3d2acb202a..8341219bfa71c 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala @@ -38,6 +38,7 @@ import org.apache.spark.rdd.RDD import org.apache.spark.sql.SQLContext import org.apache.spark.util.Utils + /** * :: Experimental :: * Represents a random forest model. @@ -47,7 +48,7 @@ import org.apache.spark.util.Utils */ @Experimental class RandomForestModel(override val algo: Algo, override val trees: Array[DecisionTreeModel]) - extends TreeEnsembleModel(algo, trees, Array.fill(trees.size)(1.0), + extends TreeEnsembleModel(algo, trees, Array.fill(trees.length)(1.0), combiningStrategy = if (algo == Classification) Vote else Average) with Saveable { @@ -58,11 +59,13 @@ class RandomForestModel(override val algo: Algo, override val trees: Array[Decis RandomForestModel.SaveLoadV1_0.thisClassName) } - override protected def formatVersion: String = TreeEnsembleModel.SaveLoadV1_0.thisFormatVersion + override protected def formatVersion: String = RandomForestModel.formatVersion } object RandomForestModel extends Loader[RandomForestModel] { + private[mllib] def formatVersion: String = TreeEnsembleModel.SaveLoadV1_0.thisFormatVersion + override def load(sc: SparkContext, path: String): RandomForestModel = { val (loadedClassName, version, jsonMetadata) = Loader.loadMetadata(sc, path) val classNameV1_0 = SaveLoadV1_0.thisClassName @@ -102,15 +105,13 @@ class GradientBoostedTreesModel( extends TreeEnsembleModel(algo, trees, treeWeights, combiningStrategy = Sum) with Saveable { - require(trees.size == treeWeights.size) + require(trees.length == treeWeights.length) override def save(sc: SparkContext, path: String): Unit = { TreeEnsembleModel.SaveLoadV1_0.save(sc, path, this, GradientBoostedTreesModel.SaveLoadV1_0.thisClassName) } - override protected def formatVersion: String = TreeEnsembleModel.SaveLoadV1_0.thisFormatVersion - /** * Method to compute error or loss for every iteration of gradient boosting. * @param data RDD of [[org.apache.spark.mllib.regression.LabeledPoint]] @@ -138,7 +139,7 @@ class GradientBoostedTreesModel( evaluationArray(0) = predictionAndError.values.mean() val broadcastTrees = sc.broadcast(trees) - (1 until numIterations).map { nTree => + (1 until numIterations).foreach { nTree => predictionAndError = remappedData.zip(predictionAndError).mapPartitions { iter => val currentTree = broadcastTrees.value(nTree) val currentTreeWeight = localTreeWeights(nTree) @@ -155,6 +156,7 @@ class GradientBoostedTreesModel( evaluationArray } + override protected def formatVersion: String = GradientBoostedTreesModel.formatVersion } object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] { @@ -200,17 +202,17 @@ object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] { loss: Loss): RDD[(Double, Double)] = { val newPredError = data.zip(predictionAndError).mapPartitions { iter => - iter.map { - case (lp, (pred, error)) => { - val newPred = pred + tree.predict(lp.features) * treeWeight - val newError = loss.computeError(newPred, lp.label) - (newPred, newError) - } + iter.map { case (lp, (pred, error)) => + val newPred = pred + tree.predict(lp.features) * treeWeight + val newError = loss.computeError(newPred, lp.label) + (newPred, newError) } } newPredError } + private[mllib] def formatVersion: String = TreeEnsembleModel.SaveLoadV1_0.thisFormatVersion + override def load(sc: SparkContext, path: String): GradientBoostedTreesModel = { val (loadedClassName, version, jsonMetadata) = Loader.loadMetadata(sc, path) val classNameV1_0 = SaveLoadV1_0.thisClassName @@ -340,12 +342,12 @@ private[tree] sealed class TreeEnsembleModel( } /** - * Get number of trees in forest. + * Get number of trees in ensemble. */ - def numTrees: Int = trees.size + def numTrees: Int = trees.length /** - * Get total number of nodes, summed over all trees in the forest. + * Get total number of nodes, summed over all trees in the ensemble. */ def totalNumNodes: Int = trees.map(_.numNodes).sum } diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaDecisionTreeClassifierSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaDecisionTreeClassifierSuite.java new file mode 100644 index 0000000000000..43b8787f9dd7e --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaDecisionTreeClassifierSuite.java @@ -0,0 +1,98 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.classification; + +import java.io.File; +import java.io.Serializable; +import java.util.HashMap; +import java.util.Map; + +import org.junit.After; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.ml.impl.TreeTests; +import org.apache.spark.mllib.classification.LogisticRegressionSuite; +import org.apache.spark.mllib.regression.LabeledPoint; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.util.Utils; + + +public class JavaDecisionTreeClassifierSuite implements Serializable { + + private transient JavaSparkContext sc; + + @Before + public void setUp() { + sc = new JavaSparkContext("local", "JavaDecisionTreeClassifierSuite"); + } + + @After + public void tearDown() { + sc.stop(); + sc = null; + } + + @Test + public void runDT() { + int nPoints = 20; + double A = 2.0; + double B = -1.5; + + JavaRDD data = sc.parallelize( + LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache(); + Map categoricalFeatures = new HashMap(); + DataFrame dataFrame = TreeTests.setMetadata(data, categoricalFeatures, 2); + + // This tests setters. Training with various options is tested in Scala. + DecisionTreeClassifier dt = new DecisionTreeClassifier() + .setMaxDepth(2) + .setMaxBins(10) + .setMinInstancesPerNode(5) + .setMinInfoGain(0.0) + .setMaxMemoryInMB(256) + .setCacheNodeIds(false) + .setCheckpointInterval(10) + .setMaxDepth(2); // duplicate setMaxDepth to check builder pattern + for (int i = 0; i < DecisionTreeClassifier.supportedImpurities().length; ++i) { + dt.setImpurity(DecisionTreeClassifier.supportedImpurities()[i]); + } + DecisionTreeClassificationModel model = dt.fit(dataFrame); + + model.transform(dataFrame); + model.numNodes(); + model.depth(); + model.toDebugString(); + + /* + // TODO: Add test once save/load are implemented. + File tempDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "spark"); + String path = tempDir.toURI().toString(); + try { + model3.save(sc.sc(), path); + DecisionTreeClassificationModel sameModel = + DecisionTreeClassificationModel.load(sc.sc(), path); + TreeTests.checkEqual(model3, sameModel); + } finally { + Utils.deleteRecursively(tempDir); + } + */ + } +} diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java new file mode 100644 index 0000000000000..a3a339004f31c --- /dev/null +++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaDecisionTreeRegressorSuite.java @@ -0,0 +1,97 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.regression; + +import java.io.File; +import java.io.Serializable; +import java.util.HashMap; +import java.util.Map; + +import org.junit.After; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.ml.impl.TreeTests; +import org.apache.spark.mllib.classification.LogisticRegressionSuite; +import org.apache.spark.mllib.regression.LabeledPoint; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.util.Utils; + + +public class JavaDecisionTreeRegressorSuite implements Serializable { + + private transient JavaSparkContext sc; + + @Before + public void setUp() { + sc = new JavaSparkContext("local", "JavaDecisionTreeRegressorSuite"); + } + + @After + public void tearDown() { + sc.stop(); + sc = null; + } + + @Test + public void runDT() { + int nPoints = 20; + double A = 2.0; + double B = -1.5; + + JavaRDD data = sc.parallelize( + LogisticRegressionSuite.generateLogisticInputAsList(A, B, nPoints, 42), 2).cache(); + Map categoricalFeatures = new HashMap(); + DataFrame dataFrame = TreeTests.setMetadata(data, categoricalFeatures, 2); + + // This tests setters. Training with various options is tested in Scala. + DecisionTreeRegressor dt = new DecisionTreeRegressor() + .setMaxDepth(2) + .setMaxBins(10) + .setMinInstancesPerNode(5) + .setMinInfoGain(0.0) + .setMaxMemoryInMB(256) + .setCacheNodeIds(false) + .setCheckpointInterval(10) + .setMaxDepth(2); // duplicate setMaxDepth to check builder pattern + for (int i = 0; i < DecisionTreeRegressor.supportedImpurities().length; ++i) { + dt.setImpurity(DecisionTreeRegressor.supportedImpurities()[i]); + } + DecisionTreeRegressionModel model = dt.fit(dataFrame); + + model.transform(dataFrame); + model.numNodes(); + model.depth(); + model.toDebugString(); + + /* + // TODO: Add test once save/load are implemented. + File tempDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "spark"); + String path = tempDir.toURI().toString(); + try { + model2.save(sc.sc(), path); + DecisionTreeRegressionModel sameModel = DecisionTreeRegressionModel.load(sc.sc(), path); + TreeTests.checkEqual(model2, sameModel); + } finally { + Utils.deleteRecursively(tempDir); + } + */ + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeGroupSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeGroupSuite.scala index 0dcfe5a2002dc..17ddd335deb6d 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeGroupSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeGroupSuite.scala @@ -44,7 +44,7 @@ class AttributeGroupSuite extends FunSuite { group("abc") } assert(group === AttributeGroup.fromMetadata(group.toMetadataImpl, group.name)) - assert(group === AttributeGroup.fromStructField(group.toStructField)) + assert(group === AttributeGroup.fromStructField(group.toStructField())) } test("attribute group without attributes") { @@ -54,7 +54,7 @@ class AttributeGroupSuite extends FunSuite { assert(group0.size === 10) assert(group0.attributes.isEmpty) assert(group0 === AttributeGroup.fromMetadata(group0.toMetadataImpl, group0.name)) - assert(group0 === AttributeGroup.fromStructField(group0.toStructField)) + assert(group0 === AttributeGroup.fromStructField(group0.toStructField())) val group1 = new AttributeGroup("item") assert(group1.name === "item") diff --git a/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeSuite.scala index 6ec35b03656f9..3e1a7196e37cb 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/attribute/AttributeSuite.scala @@ -36,9 +36,9 @@ class AttributeSuite extends FunSuite { assert(attr.max.isEmpty) assert(attr.std.isEmpty) assert(attr.sparsity.isEmpty) - assert(attr.toMetadata() === metadata) - assert(attr.toMetadata(withType = false) === metadata) - assert(attr.toMetadata(withType = true) === metadataWithType) + assert(attr.toMetadataImpl() === metadata) + assert(attr.toMetadataImpl(withType = false) === metadata) + assert(attr.toMetadataImpl(withType = true) === metadataWithType) assert(attr === Attribute.fromMetadata(metadata)) assert(attr === Attribute.fromMetadata(metadataWithType)) intercept[NoSuchElementException] { @@ -59,9 +59,9 @@ class AttributeSuite extends FunSuite { assert(!attr.isNominal) assert(attr.name === Some(name)) assert(attr.index === Some(index)) - assert(attr.toMetadata() === metadata) - assert(attr.toMetadata(withType = false) === metadata) - assert(attr.toMetadata(withType = true) === metadataWithType) + assert(attr.toMetadataImpl() === metadata) + assert(attr.toMetadataImpl(withType = false) === metadata) + assert(attr.toMetadataImpl(withType = true) === metadataWithType) assert(attr === Attribute.fromMetadata(metadata)) assert(attr === Attribute.fromMetadata(metadataWithType)) val field = attr.toStructField() @@ -81,7 +81,7 @@ class AttributeSuite extends FunSuite { assert(attr2.max === Some(1.0)) assert(attr2.std === Some(0.5)) assert(attr2.sparsity === Some(0.3)) - assert(attr2 === Attribute.fromMetadata(attr2.toMetadata())) + assert(attr2 === Attribute.fromMetadata(attr2.toMetadataImpl())) } test("bad numeric attributes") { @@ -105,9 +105,9 @@ class AttributeSuite extends FunSuite { assert(attr.values.isEmpty) assert(attr.numValues.isEmpty) assert(attr.isOrdinal.isEmpty) - assert(attr.toMetadata() === metadata) - assert(attr.toMetadata(withType = true) === metadata) - assert(attr.toMetadata(withType = false) === metadataWithoutType) + assert(attr.toMetadataImpl() === metadata) + assert(attr.toMetadataImpl(withType = true) === metadata) + assert(attr.toMetadataImpl(withType = false) === metadataWithoutType) assert(attr === Attribute.fromMetadata(metadata)) assert(attr === NominalAttribute.fromMetadata(metadataWithoutType)) intercept[NoSuchElementException] { @@ -135,9 +135,9 @@ class AttributeSuite extends FunSuite { assert(attr.values === Some(values)) assert(attr.indexOf("medium") === 1) assert(attr.getValue(1) === "medium") - assert(attr.toMetadata() === metadata) - assert(attr.toMetadata(withType = true) === metadata) - assert(attr.toMetadata(withType = false) === metadataWithoutType) + assert(attr.toMetadataImpl() === metadata) + assert(attr.toMetadataImpl(withType = true) === metadata) + assert(attr.toMetadataImpl(withType = false) === metadataWithoutType) assert(attr === Attribute.fromMetadata(metadata)) assert(attr === NominalAttribute.fromMetadata(metadataWithoutType)) assert(attr.withoutIndex === Attribute.fromStructField(attr.toStructField())) @@ -147,8 +147,8 @@ class AttributeSuite extends FunSuite { assert(attr2.index.isEmpty) assert(attr2.values.get === Array("small", "medium", "large", "x-large")) assert(attr2.indexOf("x-large") === 3) - assert(attr2 === Attribute.fromMetadata(attr2.toMetadata())) - assert(attr2 === NominalAttribute.fromMetadata(attr2.toMetadata(withType = false))) + assert(attr2 === Attribute.fromMetadata(attr2.toMetadataImpl())) + assert(attr2 === NominalAttribute.fromMetadata(attr2.toMetadataImpl(withType = false))) } test("bad nominal attributes") { @@ -168,9 +168,9 @@ class AttributeSuite extends FunSuite { assert(attr.name.isEmpty) assert(attr.index.isEmpty) assert(attr.values.isEmpty) - assert(attr.toMetadata() === metadata) - assert(attr.toMetadata(withType = true) === metadata) - assert(attr.toMetadata(withType = false) === metadataWithoutType) + assert(attr.toMetadataImpl() === metadata) + assert(attr.toMetadataImpl(withType = true) === metadata) + assert(attr.toMetadataImpl(withType = false) === metadataWithoutType) assert(attr === Attribute.fromMetadata(metadata)) assert(attr === BinaryAttribute.fromMetadata(metadataWithoutType)) intercept[NoSuchElementException] { @@ -196,9 +196,9 @@ class AttributeSuite extends FunSuite { assert(attr.name === Some(name)) assert(attr.index === Some(index)) assert(attr.values.get === values) - assert(attr.toMetadata() === metadata) - assert(attr.toMetadata(withType = true) === metadata) - assert(attr.toMetadata(withType = false) === metadataWithoutType) + assert(attr.toMetadataImpl() === metadata) + assert(attr.toMetadataImpl(withType = true) === metadata) + assert(attr.toMetadataImpl(withType = false) === metadataWithoutType) assert(attr === Attribute.fromMetadata(metadata)) assert(attr === BinaryAttribute.fromMetadata(metadataWithoutType)) assert(attr.withoutIndex === Attribute.fromStructField(attr.toStructField())) diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala new file mode 100644 index 0000000000000..af88595df5245 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala @@ -0,0 +1,274 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.classification + +import org.scalatest.FunSuite + +import org.apache.spark.ml.impl.TreeTests +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree, + DecisionTreeSuite => OldDecisionTreeSuite} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.DataFrame + + +class DecisionTreeClassifierSuite extends FunSuite with MLlibTestSparkContext { + + import DecisionTreeClassifierSuite.compareAPIs + + private var categoricalDataPointsRDD: RDD[LabeledPoint] = _ + private var orderedLabeledPointsWithLabel0RDD: RDD[LabeledPoint] = _ + private var orderedLabeledPointsWithLabel1RDD: RDD[LabeledPoint] = _ + private var categoricalDataPointsForMulticlassRDD: RDD[LabeledPoint] = _ + private var continuousDataPointsForMulticlassRDD: RDD[LabeledPoint] = _ + private var categoricalDataPointsForMulticlassForOrderedFeaturesRDD: RDD[LabeledPoint] = _ + + override def beforeAll() { + super.beforeAll() + categoricalDataPointsRDD = + sc.parallelize(OldDecisionTreeSuite.generateCategoricalDataPoints()) + orderedLabeledPointsWithLabel0RDD = + sc.parallelize(OldDecisionTreeSuite.generateOrderedLabeledPointsWithLabel0()) + orderedLabeledPointsWithLabel1RDD = + sc.parallelize(OldDecisionTreeSuite.generateOrderedLabeledPointsWithLabel1()) + categoricalDataPointsForMulticlassRDD = + sc.parallelize(OldDecisionTreeSuite.generateCategoricalDataPointsForMulticlass()) + continuousDataPointsForMulticlassRDD = + sc.parallelize(OldDecisionTreeSuite.generateContinuousDataPointsForMulticlass()) + categoricalDataPointsForMulticlassForOrderedFeaturesRDD = sc.parallelize( + OldDecisionTreeSuite.generateCategoricalDataPointsForMulticlassForOrderedFeatures()) + } + + ///////////////////////////////////////////////////////////////////////////// + // Tests calling train() + ///////////////////////////////////////////////////////////////////////////// + + test("Binary classification stump with ordered categorical features") { + val dt = new DecisionTreeClassifier() + .setImpurity("gini") + .setMaxDepth(2) + .setMaxBins(100) + val categoricalFeatures = Map(0 -> 3, 1-> 3) + val numClasses = 2 + compareAPIs(categoricalDataPointsRDD, dt, categoricalFeatures, numClasses) + } + + test("Binary classification stump with fixed labels 0,1 for Entropy,Gini") { + val dt = new DecisionTreeClassifier() + .setMaxDepth(3) + .setMaxBins(100) + val numClasses = 2 + Array(orderedLabeledPointsWithLabel0RDD, orderedLabeledPointsWithLabel1RDD).foreach { rdd => + DecisionTreeClassifier.supportedImpurities.foreach { impurity => + dt.setImpurity(impurity) + compareAPIs(rdd, dt, categoricalFeatures = Map.empty[Int, Int], numClasses) + } + } + } + + test("Multiclass classification stump with 3-ary (unordered) categorical features") { + val rdd = categoricalDataPointsForMulticlassRDD + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(4) + val numClasses = 3 + val categoricalFeatures = Map(0 -> 3, 1 -> 3) + compareAPIs(rdd, dt, categoricalFeatures, numClasses) + } + + test("Binary classification stump with 1 continuous feature, to check off-by-1 error") { + val arr = Array( + LabeledPoint(0.0, Vectors.dense(0.0)), + LabeledPoint(1.0, Vectors.dense(1.0)), + LabeledPoint(1.0, Vectors.dense(2.0)), + LabeledPoint(1.0, Vectors.dense(3.0))) + val rdd = sc.parallelize(arr) + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(4) + val numClasses = 2 + compareAPIs(rdd, dt, categoricalFeatures = Map.empty[Int, Int], numClasses) + } + + test("Binary classification stump with 2 continuous features") { + val arr = Array( + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))), + LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0)))), + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))), + LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 2.0))))) + val rdd = sc.parallelize(arr) + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(4) + val numClasses = 2 + compareAPIs(rdd, dt, categoricalFeatures = Map.empty[Int, Int], numClasses) + } + + test("Multiclass classification stump with unordered categorical features," + + " with just enough bins") { + val maxBins = 2 * (math.pow(2, 3 - 1).toInt - 1) // just enough bins to allow unordered features + val rdd = categoricalDataPointsForMulticlassRDD + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(4) + .setMaxBins(maxBins) + val categoricalFeatures = Map(0 -> 3, 1 -> 3) + val numClasses = 3 + compareAPIs(rdd, dt, categoricalFeatures, numClasses) + } + + test("Multiclass classification stump with continuous features") { + val rdd = continuousDataPointsForMulticlassRDD + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(4) + .setMaxBins(100) + val numClasses = 3 + compareAPIs(rdd, dt, categoricalFeatures = Map.empty[Int, Int], numClasses) + } + + test("Multiclass classification stump with continuous + unordered categorical features") { + val rdd = continuousDataPointsForMulticlassRDD + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(4) + .setMaxBins(100) + val categoricalFeatures = Map(0 -> 3) + val numClasses = 3 + compareAPIs(rdd, dt, categoricalFeatures, numClasses) + } + + test("Multiclass classification stump with 10-ary (ordered) categorical features") { + val rdd = categoricalDataPointsForMulticlassForOrderedFeaturesRDD + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(4) + .setMaxBins(100) + val categoricalFeatures = Map(0 -> 10, 1 -> 10) + val numClasses = 3 + compareAPIs(rdd, dt, categoricalFeatures, numClasses) + } + + test("Multiclass classification tree with 10-ary (ordered) categorical features," + + " with just enough bins") { + val rdd = categoricalDataPointsForMulticlassForOrderedFeaturesRDD + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(4) + .setMaxBins(10) + val categoricalFeatures = Map(0 -> 10, 1 -> 10) + val numClasses = 3 + compareAPIs(rdd, dt, categoricalFeatures, numClasses) + } + + test("split must satisfy min instances per node requirements") { + val arr = Array( + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))), + LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0)))), + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 1.0))))) + val rdd = sc.parallelize(arr) + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(2) + .setMinInstancesPerNode(2) + val numClasses = 2 + compareAPIs(rdd, dt, categoricalFeatures = Map.empty[Int, Int], numClasses) + } + + test("do not choose split that does not satisfy min instance per node requirements") { + // if a split does not satisfy min instances per node requirements, + // this split is invalid, even though the information gain of split is large. + val arr = Array( + LabeledPoint(0.0, Vectors.dense(0.0, 1.0)), + LabeledPoint(1.0, Vectors.dense(1.0, 1.0)), + LabeledPoint(0.0, Vectors.dense(0.0, 0.0)), + LabeledPoint(0.0, Vectors.dense(0.0, 0.0))) + val rdd = sc.parallelize(arr) + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxBins(2) + .setMaxDepth(2) + .setMinInstancesPerNode(2) + val categoricalFeatures = Map(0 -> 2, 1-> 2) + val numClasses = 2 + compareAPIs(rdd, dt, categoricalFeatures, numClasses) + } + + test("split must satisfy min info gain requirements") { + val arr = Array( + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))), + LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0)))), + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 1.0))))) + val rdd = sc.parallelize(arr) + + val dt = new DecisionTreeClassifier() + .setImpurity("Gini") + .setMaxDepth(2) + .setMinInfoGain(1.0) + val numClasses = 2 + compareAPIs(rdd, dt, categoricalFeatures = Map.empty[Int, Int], numClasses) + } + + ///////////////////////////////////////////////////////////////////////////// + // Tests of model save/load + ///////////////////////////////////////////////////////////////////////////// + + // TODO: Reinstate test once save/load are implemented + /* + test("model save/load") { + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + + val oldModel = OldDecisionTreeSuite.createModel(OldAlgo.Classification) + val newModel = DecisionTreeClassificationModel.fromOld(oldModel) + + // Save model, load it back, and compare. + try { + newModel.save(sc, path) + val sameNewModel = DecisionTreeClassificationModel.load(sc, path) + TreeTests.checkEqual(newModel, sameNewModel) + } finally { + Utils.deleteRecursively(tempDir) + } + } + */ +} + +private[ml] object DecisionTreeClassifierSuite extends FunSuite { + + /** + * Train 2 decision trees on the given dataset, one using the old API and one using the new API. + * Convert the old tree to the new format, compare them, and fail if they are not exactly equal. + */ + def compareAPIs( + data: RDD[LabeledPoint], + dt: DecisionTreeClassifier, + categoricalFeatures: Map[Int, Int], + numClasses: Int): Unit = { + val oldStrategy = dt.getOldStrategy(categoricalFeatures, numClasses) + val oldTree = OldDecisionTree.train(data, oldStrategy) + val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses) + val newTree = dt.fit(newData) + // Use parent, fittingParamMap from newTree since these are not checked anyways. + val oldTreeAsNew = DecisionTreeClassificationModel.fromOld(oldTree, newTree.parent, + newTree.fittingParamMap, categoricalFeatures) + TreeTests.checkEqual(oldTreeAsNew, newTree) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala index 81ef831c42e55..1b261b2643854 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala @@ -228,7 +228,7 @@ class VectorIndexerSuite extends FunSuite with MLlibTestSparkContext { } val attrGroup = new AttributeGroup("features", featureAttributes) val densePoints1WithMeta = - densePoints1.select(densePoints1("features").as("features", attrGroup.toMetadata)) + densePoints1.select(densePoints1("features").as("features", attrGroup.toMetadata())) val vectorIndexer = getIndexer.setMaxCategories(2) val model = vectorIndexer.fit(densePoints1WithMeta) // Check that ML metadata are preserved. diff --git a/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala b/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala new file mode 100644 index 0000000000000..2e57d4ce37f1d --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala @@ -0,0 +1,132 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.impl + +import scala.collection.JavaConverters._ + +import org.scalatest.FunSuite + +import org.apache.spark.api.java.JavaRDD +import org.apache.spark.ml.attribute.{AttributeGroup, NominalAttribute, NumericAttribute} +import org.apache.spark.ml.impl.tree._ +import org.apache.spark.ml.tree.{DecisionTreeModel, InternalNode, LeafNode, Node} +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.{SQLContext, DataFrame} + + +private[ml] object TreeTests extends FunSuite { + + /** + * Convert the given data to a DataFrame, and set the features and label metadata. + * @param data Dataset. Categorical features and labels must already have 0-based indices. + * This must be non-empty. + * @param categoricalFeatures Map: categorical feature index -> number of distinct values + * @param numClasses Number of classes label can take. If 0, mark as continuous. + * @return DataFrame with metadata + */ + def setMetadata( + data: RDD[LabeledPoint], + categoricalFeatures: Map[Int, Int], + numClasses: Int): DataFrame = { + val sqlContext = new SQLContext(data.sparkContext) + import sqlContext.implicits._ + val df = data.toDF() + val numFeatures = data.first().features.size + val featuresAttributes = Range(0, numFeatures).map { feature => + if (categoricalFeatures.contains(feature)) { + NominalAttribute.defaultAttr.withIndex(feature).withNumValues(categoricalFeatures(feature)) + } else { + NumericAttribute.defaultAttr.withIndex(feature) + } + }.toArray + val featuresMetadata = new AttributeGroup("features", featuresAttributes).toMetadata() + val labelAttribute = if (numClasses == 0) { + NumericAttribute.defaultAttr.withName("label") + } else { + NominalAttribute.defaultAttr.withName("label").withNumValues(numClasses) + } + val labelMetadata = labelAttribute.toMetadata() + df.select(df("features").as("features", featuresMetadata), + df("label").as("label", labelMetadata)) + } + + /** Java-friendly version of [[setMetadata()]] */ + def setMetadata( + data: JavaRDD[LabeledPoint], + categoricalFeatures: java.util.Map[java.lang.Integer, java.lang.Integer], + numClasses: Int): DataFrame = { + setMetadata(data.rdd, categoricalFeatures.asInstanceOf[java.util.Map[Int, Int]].asScala.toMap, + numClasses) + } + + /** + * Check if the two trees are exactly the same. + * Note: I hesitate to override Node.equals since it could cause problems if users + * make mistakes such as creating loops of Nodes. + * If the trees are not equal, this prints the two trees and throws an exception. + */ + def checkEqual(a: DecisionTreeModel, b: DecisionTreeModel): Unit = { + try { + checkEqual(a.rootNode, b.rootNode) + } catch { + case ex: Exception => + throw new AssertionError("checkEqual failed since the two trees were not identical.\n" + + "TREE A:\n" + a.toDebugString + "\n" + + "TREE B:\n" + b.toDebugString + "\n", ex) + } + } + + /** + * Return true iff the two nodes and their descendants are exactly the same. + * Note: I hesitate to override Node.equals since it could cause problems if users + * make mistakes such as creating loops of Nodes. + */ + private def checkEqual(a: Node, b: Node): Unit = { + assert(a.prediction === b.prediction) + assert(a.impurity === b.impurity) + (a, b) match { + case (aye: InternalNode, bee: InternalNode) => + assert(aye.split === bee.split) + checkEqual(aye.leftChild, bee.leftChild) + checkEqual(aye.rightChild, bee.rightChild) + case (aye: LeafNode, bee: LeafNode) => // do nothing + case _ => + throw new AssertionError("Found mismatched nodes") + } + } + + // TODO: Reinstate after adding ensembles + /** + * Check if the two models are exactly the same. + * If the models are not equal, this throws an exception. + */ + /* + def checkEqual(a: TreeEnsembleModel, b: TreeEnsembleModel): Unit = { + try { + a.getTrees.zip(b.getTrees).foreach { case (treeA, treeB) => + TreeTests.checkEqual(treeA, treeB) + } + assert(a.getTreeWeights === b.getTreeWeights) + } catch { + case ex: Exception => throw new AssertionError( + "checkEqual failed since the two tree ensembles were not identical") + } + } + */ +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala new file mode 100644 index 0000000000000..0b40fe33fae9d --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala @@ -0,0 +1,91 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.regression + +import org.scalatest.FunSuite + +import org.apache.spark.ml.impl.TreeTests +import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree, + DecisionTreeSuite => OldDecisionTreeSuite} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.rdd.RDD +import org.apache.spark.sql.DataFrame + + +class DecisionTreeRegressorSuite extends FunSuite with MLlibTestSparkContext { + + import DecisionTreeRegressorSuite.compareAPIs + + private var categoricalDataPointsRDD: RDD[LabeledPoint] = _ + + override def beforeAll() { + super.beforeAll() + categoricalDataPointsRDD = + sc.parallelize(OldDecisionTreeSuite.generateCategoricalDataPoints()) + } + + ///////////////////////////////////////////////////////////////////////////// + // Tests calling train() + ///////////////////////////////////////////////////////////////////////////// + + test("Regression stump with 3-ary (ordered) categorical features") { + val dt = new DecisionTreeRegressor() + .setImpurity("variance") + .setMaxDepth(2) + .setMaxBins(100) + val categoricalFeatures = Map(0 -> 3, 1-> 3) + compareAPIs(categoricalDataPointsRDD, dt, categoricalFeatures) + } + + test("Regression stump with binary (ordered) categorical features") { + val dt = new DecisionTreeRegressor() + .setImpurity("variance") + .setMaxDepth(2) + .setMaxBins(100) + val categoricalFeatures = Map(0 -> 2, 1-> 2) + compareAPIs(categoricalDataPointsRDD, dt, categoricalFeatures) + } + + ///////////////////////////////////////////////////////////////////////////// + // Tests of model save/load + ///////////////////////////////////////////////////////////////////////////// + + // TODO: test("model save/load") +} + +private[ml] object DecisionTreeRegressorSuite extends FunSuite { + + /** + * Train 2 decision trees on the given dataset, one using the old API and one using the new API. + * Convert the old tree to the new format, compare them, and fail if they are not exactly equal. + */ + def compareAPIs( + data: RDD[LabeledPoint], + dt: DecisionTreeRegressor, + categoricalFeatures: Map[Int, Int]): Unit = { + val oldStrategy = dt.getOldStrategy(categoricalFeatures) + val oldTree = OldDecisionTree.train(data, oldStrategy) + val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses = 0) + val newTree = dt.fit(newData) + // Use parent, fittingParamMap from newTree since these are not checked anyways. + val oldTreeAsNew = DecisionTreeRegressionModel.fromOld(oldTree, newTree.parent, + newTree.fittingParamMap, categoricalFeatures) + TreeTests.checkEqual(oldTreeAsNew, newTree) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala index 4c162df810bb2..249b8eae19b17 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/tree/DecisionTreeSuite.scala @@ -36,6 +36,10 @@ import org.apache.spark.util.Utils class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { + ///////////////////////////////////////////////////////////////////////////// + // Tests examining individual elements of training + ///////////////////////////////////////////////////////////////////////////// + test("Binary classification with continuous features: split and bin calculation") { val arr = DecisionTreeSuite.generateOrderedLabeledPointsWithLabel1() assert(arr.length === 1000) @@ -254,6 +258,165 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { assert(bins(0).length === 0) } + test("Avoid aggregation on the last level") { + val arr = Array( + LabeledPoint(0.0, Vectors.dense(1.0, 0.0, 0.0)), + LabeledPoint(1.0, Vectors.dense(0.0, 1.0, 1.0)), + LabeledPoint(0.0, Vectors.dense(2.0, 0.0, 0.0)), + LabeledPoint(1.0, Vectors.dense(0.0, 2.0, 1.0))) + val input = sc.parallelize(arr) + + val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 1, + numClasses = 2, categoricalFeaturesInfo = Map(0 -> 3)) + val metadata = DecisionTreeMetadata.buildMetadata(input, strategy) + val (splits, bins) = DecisionTree.findSplitsBins(input, metadata) + + val treeInput = TreePoint.convertToTreeRDD(input, bins, metadata) + val baggedInput = BaggedPoint.convertToBaggedRDD(treeInput, 1.0, 1, false) + + val topNode = Node.emptyNode(nodeIndex = 1) + assert(topNode.predict.predict === Double.MinValue) + assert(topNode.impurity === -1.0) + assert(topNode.isLeaf === false) + + val nodesForGroup = Map((0, Array(topNode))) + val treeToNodeToIndexInfo = Map((0, Map( + (topNode.id, new RandomForest.NodeIndexInfo(0, None)) + ))) + val nodeQueue = new mutable.Queue[(Int, Node)]() + DecisionTree.findBestSplits(baggedInput, metadata, Array(topNode), + nodesForGroup, treeToNodeToIndexInfo, splits, bins, nodeQueue) + + // don't enqueue leaf nodes into node queue + assert(nodeQueue.isEmpty) + + // set impurity and predict for topNode + assert(topNode.predict.predict !== Double.MinValue) + assert(topNode.impurity !== -1.0) + + // set impurity and predict for child nodes + assert(topNode.leftNode.get.predict.predict === 0.0) + assert(topNode.rightNode.get.predict.predict === 1.0) + assert(topNode.leftNode.get.impurity === 0.0) + assert(topNode.rightNode.get.impurity === 0.0) + } + + test("Avoid aggregation if impurity is 0.0") { + val arr = Array( + LabeledPoint(0.0, Vectors.dense(1.0, 0.0, 0.0)), + LabeledPoint(1.0, Vectors.dense(0.0, 1.0, 1.0)), + LabeledPoint(0.0, Vectors.dense(2.0, 0.0, 0.0)), + LabeledPoint(1.0, Vectors.dense(0.0, 2.0, 1.0))) + val input = sc.parallelize(arr) + + val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 5, + numClasses = 2, categoricalFeaturesInfo = Map(0 -> 3)) + val metadata = DecisionTreeMetadata.buildMetadata(input, strategy) + val (splits, bins) = DecisionTree.findSplitsBins(input, metadata) + + val treeInput = TreePoint.convertToTreeRDD(input, bins, metadata) + val baggedInput = BaggedPoint.convertToBaggedRDD(treeInput, 1.0, 1, false) + + val topNode = Node.emptyNode(nodeIndex = 1) + assert(topNode.predict.predict === Double.MinValue) + assert(topNode.impurity === -1.0) + assert(topNode.isLeaf === false) + + val nodesForGroup = Map((0, Array(topNode))) + val treeToNodeToIndexInfo = Map((0, Map( + (topNode.id, new RandomForest.NodeIndexInfo(0, None)) + ))) + val nodeQueue = new mutable.Queue[(Int, Node)]() + DecisionTree.findBestSplits(baggedInput, metadata, Array(topNode), + nodesForGroup, treeToNodeToIndexInfo, splits, bins, nodeQueue) + + // don't enqueue a node into node queue if its impurity is 0.0 + assert(nodeQueue.isEmpty) + + // set impurity and predict for topNode + assert(topNode.predict.predict !== Double.MinValue) + assert(topNode.impurity !== -1.0) + + // set impurity and predict for child nodes + assert(topNode.leftNode.get.predict.predict === 0.0) + assert(topNode.rightNode.get.predict.predict === 1.0) + assert(topNode.leftNode.get.impurity === 0.0) + assert(topNode.rightNode.get.impurity === 0.0) + } + + test("Second level node building with vs. without groups") { + val arr = DecisionTreeSuite.generateOrderedLabeledPoints() + assert(arr.length === 1000) + val rdd = sc.parallelize(arr) + val strategy = new Strategy(Classification, Entropy, 3, 2, 100) + val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy) + val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata) + assert(splits.length === 2) + assert(splits(0).length === 99) + assert(bins.length === 2) + assert(bins(0).length === 100) + + // Train a 1-node model + val strategyOneNode = new Strategy(Classification, Entropy, maxDepth = 1, + numClasses = 2, maxBins = 100) + val modelOneNode = DecisionTree.train(rdd, strategyOneNode) + val rootNode1 = modelOneNode.topNode.deepCopy() + val rootNode2 = modelOneNode.topNode.deepCopy() + assert(rootNode1.leftNode.nonEmpty) + assert(rootNode1.rightNode.nonEmpty) + + val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata) + val baggedInput = BaggedPoint.convertToBaggedRDD(treeInput, 1.0, 1, false) + + // Single group second level tree construction. + val nodesForGroup = Map((0, Array(rootNode1.leftNode.get, rootNode1.rightNode.get))) + val treeToNodeToIndexInfo = Map((0, Map( + (rootNode1.leftNode.get.id, new RandomForest.NodeIndexInfo(0, None)), + (rootNode1.rightNode.get.id, new RandomForest.NodeIndexInfo(1, None))))) + val nodeQueue = new mutable.Queue[(Int, Node)]() + DecisionTree.findBestSplits(baggedInput, metadata, Array(rootNode1), + nodesForGroup, treeToNodeToIndexInfo, splits, bins, nodeQueue) + val children1 = new Array[Node](2) + children1(0) = rootNode1.leftNode.get + children1(1) = rootNode1.rightNode.get + + // Train one second-level node at a time. + val nodesForGroupA = Map((0, Array(rootNode2.leftNode.get))) + val treeToNodeToIndexInfoA = Map((0, Map( + (rootNode2.leftNode.get.id, new RandomForest.NodeIndexInfo(0, None))))) + nodeQueue.clear() + DecisionTree.findBestSplits(baggedInput, metadata, Array(rootNode2), + nodesForGroupA, treeToNodeToIndexInfoA, splits, bins, nodeQueue) + val nodesForGroupB = Map((0, Array(rootNode2.rightNode.get))) + val treeToNodeToIndexInfoB = Map((0, Map( + (rootNode2.rightNode.get.id, new RandomForest.NodeIndexInfo(0, None))))) + nodeQueue.clear() + DecisionTree.findBestSplits(baggedInput, metadata, Array(rootNode2), + nodesForGroupB, treeToNodeToIndexInfoB, splits, bins, nodeQueue) + val children2 = new Array[Node](2) + children2(0) = rootNode2.leftNode.get + children2(1) = rootNode2.rightNode.get + + // Verify whether the splits obtained using single group and multiple group level + // construction strategies are the same. + for (i <- 0 until 2) { + assert(children1(i).stats.nonEmpty && children1(i).stats.get.gain > 0) + assert(children2(i).stats.nonEmpty && children2(i).stats.get.gain > 0) + assert(children1(i).split === children2(i).split) + assert(children1(i).stats.nonEmpty && children2(i).stats.nonEmpty) + val stats1 = children1(i).stats.get + val stats2 = children2(i).stats.get + assert(stats1.gain === stats2.gain) + assert(stats1.impurity === stats2.impurity) + assert(stats1.leftImpurity === stats2.leftImpurity) + assert(stats1.rightImpurity === stats2.rightImpurity) + assert(children1(i).predict.predict === children2(i).predict.predict) + } + } + + ///////////////////////////////////////////////////////////////////////////// + // Tests calling train() + ///////////////////////////////////////////////////////////////////////////// test("Binary classification stump with ordered categorical features") { val arr = DecisionTreeSuite.generateCategoricalDataPoints() @@ -438,76 +601,6 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { assert(rootNode.predict.predict === 1) } - test("Second level node building with vs. without groups") { - val arr = DecisionTreeSuite.generateOrderedLabeledPoints() - assert(arr.length === 1000) - val rdd = sc.parallelize(arr) - val strategy = new Strategy(Classification, Entropy, 3, 2, 100) - val metadata = DecisionTreeMetadata.buildMetadata(rdd, strategy) - val (splits, bins) = DecisionTree.findSplitsBins(rdd, metadata) - assert(splits.length === 2) - assert(splits(0).length === 99) - assert(bins.length === 2) - assert(bins(0).length === 100) - - // Train a 1-node model - val strategyOneNode = new Strategy(Classification, Entropy, maxDepth = 1, - numClasses = 2, maxBins = 100) - val modelOneNode = DecisionTree.train(rdd, strategyOneNode) - val rootNode1 = modelOneNode.topNode.deepCopy() - val rootNode2 = modelOneNode.topNode.deepCopy() - assert(rootNode1.leftNode.nonEmpty) - assert(rootNode1.rightNode.nonEmpty) - - val treeInput = TreePoint.convertToTreeRDD(rdd, bins, metadata) - val baggedInput = BaggedPoint.convertToBaggedRDD(treeInput, 1.0, 1, false) - - // Single group second level tree construction. - val nodesForGroup = Map((0, Array(rootNode1.leftNode.get, rootNode1.rightNode.get))) - val treeToNodeToIndexInfo = Map((0, Map( - (rootNode1.leftNode.get.id, new RandomForest.NodeIndexInfo(0, None)), - (rootNode1.rightNode.get.id, new RandomForest.NodeIndexInfo(1, None))))) - val nodeQueue = new mutable.Queue[(Int, Node)]() - DecisionTree.findBestSplits(baggedInput, metadata, Array(rootNode1), - nodesForGroup, treeToNodeToIndexInfo, splits, bins, nodeQueue) - val children1 = new Array[Node](2) - children1(0) = rootNode1.leftNode.get - children1(1) = rootNode1.rightNode.get - - // Train one second-level node at a time. - val nodesForGroupA = Map((0, Array(rootNode2.leftNode.get))) - val treeToNodeToIndexInfoA = Map((0, Map( - (rootNode2.leftNode.get.id, new RandomForest.NodeIndexInfo(0, None))))) - nodeQueue.clear() - DecisionTree.findBestSplits(baggedInput, metadata, Array(rootNode2), - nodesForGroupA, treeToNodeToIndexInfoA, splits, bins, nodeQueue) - val nodesForGroupB = Map((0, Array(rootNode2.rightNode.get))) - val treeToNodeToIndexInfoB = Map((0, Map( - (rootNode2.rightNode.get.id, new RandomForest.NodeIndexInfo(0, None))))) - nodeQueue.clear() - DecisionTree.findBestSplits(baggedInput, metadata, Array(rootNode2), - nodesForGroupB, treeToNodeToIndexInfoB, splits, bins, nodeQueue) - val children2 = new Array[Node](2) - children2(0) = rootNode2.leftNode.get - children2(1) = rootNode2.rightNode.get - - // Verify whether the splits obtained using single group and multiple group level - // construction strategies are the same. - for (i <- 0 until 2) { - assert(children1(i).stats.nonEmpty && children1(i).stats.get.gain > 0) - assert(children2(i).stats.nonEmpty && children2(i).stats.get.gain > 0) - assert(children1(i).split === children2(i).split) - assert(children1(i).stats.nonEmpty && children2(i).stats.nonEmpty) - val stats1 = children1(i).stats.get - val stats2 = children2(i).stats.get - assert(stats1.gain === stats2.gain) - assert(stats1.impurity === stats2.impurity) - assert(stats1.leftImpurity === stats2.leftImpurity) - assert(stats1.rightImpurity === stats2.rightImpurity) - assert(children1(i).predict.predict === children2(i).predict.predict) - } - } - test("Multiclass classification stump with 3-ary (unordered) categorical features") { val arr = DecisionTreeSuite.generateCategoricalDataPointsForMulticlass() val rdd = sc.parallelize(arr) @@ -528,11 +621,11 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { } test("Binary classification stump with 1 continuous feature, to check off-by-1 error") { - val arr = new Array[LabeledPoint](4) - arr(0) = new LabeledPoint(0.0, Vectors.dense(0.0)) - arr(1) = new LabeledPoint(1.0, Vectors.dense(1.0)) - arr(2) = new LabeledPoint(1.0, Vectors.dense(2.0)) - arr(3) = new LabeledPoint(1.0, Vectors.dense(3.0)) + val arr = Array( + LabeledPoint(0.0, Vectors.dense(0.0)), + LabeledPoint(1.0, Vectors.dense(1.0)), + LabeledPoint(1.0, Vectors.dense(2.0)), + LabeledPoint(1.0, Vectors.dense(3.0))) val rdd = sc.parallelize(arr) val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4, numClasses = 2) @@ -544,11 +637,11 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { } test("Binary classification stump with 2 continuous features") { - val arr = new Array[LabeledPoint](4) - arr(0) = new LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))) - arr(1) = new LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0)))) - arr(2) = new LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))) - arr(3) = new LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 2.0)))) + val arr = Array( + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))), + LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0)))), + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))), + LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 2.0))))) val rdd = sc.parallelize(arr) val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 4, @@ -668,11 +761,10 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { } test("split must satisfy min instances per node requirements") { - val arr = new Array[LabeledPoint](3) - arr(0) = new LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))) - arr(1) = new LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0)))) - arr(2) = new LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 1.0)))) - + val arr = Array( + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))), + LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0)))), + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 1.0))))) val rdd = sc.parallelize(arr) val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 2, numClasses = 2, minInstancesPerNode = 2) @@ -695,11 +787,11 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { test("do not choose split that does not satisfy min instance per node requirements") { // if a split does not satisfy min instances per node requirements, // this split is invalid, even though the information gain of split is large. - val arr = new Array[LabeledPoint](4) - arr(0) = new LabeledPoint(0.0, Vectors.dense(0.0, 1.0)) - arr(1) = new LabeledPoint(1.0, Vectors.dense(1.0, 1.0)) - arr(2) = new LabeledPoint(0.0, Vectors.dense(0.0, 0.0)) - arr(3) = new LabeledPoint(0.0, Vectors.dense(0.0, 0.0)) + val arr = Array( + LabeledPoint(0.0, Vectors.dense(0.0, 1.0)), + LabeledPoint(1.0, Vectors.dense(1.0, 1.0)), + LabeledPoint(0.0, Vectors.dense(0.0, 0.0)), + LabeledPoint(0.0, Vectors.dense(0.0, 0.0))) val rdd = sc.parallelize(arr) val strategy = new Strategy(algo = Classification, impurity = Gini, @@ -715,10 +807,10 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { } test("split must satisfy min info gain requirements") { - val arr = new Array[LabeledPoint](3) - arr(0) = new LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))) - arr(1) = new LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0)))) - arr(2) = new LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 1.0)))) + val arr = Array( + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 0.0)))), + LabeledPoint(1.0, Vectors.sparse(2, Seq((1, 1.0)))), + LabeledPoint(0.0, Vectors.sparse(2, Seq((0, 1.0))))) val input = sc.parallelize(arr) val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 2, @@ -739,91 +831,9 @@ class DecisionTreeSuite extends FunSuite with MLlibTestSparkContext { assert(gain == InformationGainStats.invalidInformationGainStats) } - test("Avoid aggregation on the last level") { - val arr = new Array[LabeledPoint](4) - arr(0) = new LabeledPoint(0.0, Vectors.dense(1.0, 0.0, 0.0)) - arr(1) = new LabeledPoint(1.0, Vectors.dense(0.0, 1.0, 1.0)) - arr(2) = new LabeledPoint(0.0, Vectors.dense(2.0, 0.0, 0.0)) - arr(3) = new LabeledPoint(1.0, Vectors.dense(0.0, 2.0, 1.0)) - val input = sc.parallelize(arr) - - val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 1, - numClasses = 2, categoricalFeaturesInfo = Map(0 -> 3)) - val metadata = DecisionTreeMetadata.buildMetadata(input, strategy) - val (splits, bins) = DecisionTree.findSplitsBins(input, metadata) - - val treeInput = TreePoint.convertToTreeRDD(input, bins, metadata) - val baggedInput = BaggedPoint.convertToBaggedRDD(treeInput, 1.0, 1, false) - - val topNode = Node.emptyNode(nodeIndex = 1) - assert(topNode.predict.predict === Double.MinValue) - assert(topNode.impurity === -1.0) - assert(topNode.isLeaf === false) - - val nodesForGroup = Map((0, Array(topNode))) - val treeToNodeToIndexInfo = Map((0, Map( - (topNode.id, new RandomForest.NodeIndexInfo(0, None)) - ))) - val nodeQueue = new mutable.Queue[(Int, Node)]() - DecisionTree.findBestSplits(baggedInput, metadata, Array(topNode), - nodesForGroup, treeToNodeToIndexInfo, splits, bins, nodeQueue) - - // don't enqueue leaf nodes into node queue - assert(nodeQueue.isEmpty) - - // set impurity and predict for topNode - assert(topNode.predict.predict !== Double.MinValue) - assert(topNode.impurity !== -1.0) - - // set impurity and predict for child nodes - assert(topNode.leftNode.get.predict.predict === 0.0) - assert(topNode.rightNode.get.predict.predict === 1.0) - assert(topNode.leftNode.get.impurity === 0.0) - assert(topNode.rightNode.get.impurity === 0.0) - } - - test("Avoid aggregation if impurity is 0.0") { - val arr = new Array[LabeledPoint](4) - arr(0) = new LabeledPoint(0.0, Vectors.dense(1.0, 0.0, 0.0)) - arr(1) = new LabeledPoint(1.0, Vectors.dense(0.0, 1.0, 1.0)) - arr(2) = new LabeledPoint(0.0, Vectors.dense(2.0, 0.0, 0.0)) - arr(3) = new LabeledPoint(1.0, Vectors.dense(0.0, 2.0, 1.0)) - val input = sc.parallelize(arr) - - val strategy = new Strategy(algo = Classification, impurity = Gini, maxDepth = 5, - numClasses = 2, categoricalFeaturesInfo = Map(0 -> 3)) - val metadata = DecisionTreeMetadata.buildMetadata(input, strategy) - val (splits, bins) = DecisionTree.findSplitsBins(input, metadata) - - val treeInput = TreePoint.convertToTreeRDD(input, bins, metadata) - val baggedInput = BaggedPoint.convertToBaggedRDD(treeInput, 1.0, 1, false) - - val topNode = Node.emptyNode(nodeIndex = 1) - assert(topNode.predict.predict === Double.MinValue) - assert(topNode.impurity === -1.0) - assert(topNode.isLeaf === false) - - val nodesForGroup = Map((0, Array(topNode))) - val treeToNodeToIndexInfo = Map((0, Map( - (topNode.id, new RandomForest.NodeIndexInfo(0, None)) - ))) - val nodeQueue = new mutable.Queue[(Int, Node)]() - DecisionTree.findBestSplits(baggedInput, metadata, Array(topNode), - nodesForGroup, treeToNodeToIndexInfo, splits, bins, nodeQueue) - - // don't enqueue a node into node queue if its impurity is 0.0 - assert(nodeQueue.isEmpty) - - // set impurity and predict for topNode - assert(topNode.predict.predict !== Double.MinValue) - assert(topNode.impurity !== -1.0) - - // set impurity and predict for child nodes - assert(topNode.leftNode.get.predict.predict === 0.0) - assert(topNode.rightNode.get.predict.predict === 1.0) - assert(topNode.leftNode.get.impurity === 0.0) - assert(topNode.rightNode.get.impurity === 0.0) - } + ///////////////////////////////////////////////////////////////////////////// + // Tests of model save/load + ///////////////////////////////////////////////////////////////////////////// test("Node.subtreeIterator") { val model = DecisionTreeSuite.createModel(Classification) @@ -996,8 +1006,9 @@ object DecisionTreeSuite extends FunSuite { /** * Create a tree model. This is deterministic and contains a variety of node and feature types. + * TODO: Update this to be a correct tree (with matching probabilities, impurities, etc.) */ - private[tree] def createModel(algo: Algo): DecisionTreeModel = { + private[mllib] def createModel(algo: Algo): DecisionTreeModel = { val topNode = createInternalNode(id = 1, Continuous) val (node2, node3) = (createLeafNode(id = 2), createInternalNode(id = 3, Categorical)) val (node6, node7) = (createLeafNode(id = 6), createLeafNode(id = 7)) @@ -1017,7 +1028,7 @@ object DecisionTreeSuite extends FunSuite { * make mistakes such as creating loops of Nodes. * If the trees are not equal, this prints the two trees and throws an exception. */ - private[tree] def checkEqual(a: DecisionTreeModel, b: DecisionTreeModel): Unit = { + private[mllib] def checkEqual(a: DecisionTreeModel, b: DecisionTreeModel): Unit = { try { assert(a.algo === b.algo) checkEqual(a.topNode, b.topNode) From 59e206deb7346148412bbf5ba4ab626718fadf18 Mon Sep 17 00:00:00 2001 From: cafreeman Date: Fri, 17 Apr 2015 13:42:19 -0700 Subject: [PATCH 326/591] [SPARK-6807] [SparkR] Merge recent SparkR-pkg changes This PR pulls in recent changes in SparkR-pkg, including cartesian, intersection, sampleByKey, subtract, subtractByKey, except, and some API for StructType and StructField. Author: cafreeman Author: Davies Liu Author: Zongheng Yang Author: Shivaram Venkataraman Author: Shivaram Venkataraman Author: Sun Rui Closes #5436 from davies/R3 and squashes the following commits: c2b09be [Davies Liu] SQLTypes -> schema a5a02f2 [Davies Liu] Merge branch 'master' of github.com:apache/spark into R3 168b7fe [Davies Liu] sort generics b1fe460 [Davies Liu] fix conflict in README.md e74c04e [Davies Liu] fix schema.R 4f5ac09 [Davies Liu] Merge branch 'master' of github.com:apache/spark into R5 41f8184 [Davies Liu] rm man ae78312 [Davies Liu] Merge pull request #237 from sun-rui/SPARKR-154_3 1bdcb63 [Zongheng Yang] Updates to README.md. 5a553e7 [cafreeman] Use object attribute instead of argument 71372d9 [cafreeman] Update docs and examples 8526d2e71 [cafreeman] Remove `tojson` functions 6ef5f2d [cafreeman] Fix spacing 7741d66 [cafreeman] Rename the SQL DataType function 141efd8 [Shivaram Venkataraman] Merge pull request #245 from hqzizania/upstream 9387402 [Davies Liu] fix style 40199eb [Shivaram Venkataraman] Move except into sorted position 07d0dbc [Sun Rui] [SPARKR-244] Fix test failure after integration of subtract() and subtractByKey() for RDD. 7e8caa3 [Shivaram Venkataraman] Merge pull request #246 from hlin09/fixCombineByKey ed66c81 [cafreeman] Update `subtract` to work with `generics.R` f3ba785 [cafreeman] Fixed duplicate export 275deb4 [cafreeman] Update `NAMESPACE` and tests 1a3b63d [cafreeman] new version of `CreateDF` 836c4bf [cafreeman] Update `createDataFrame` and `toDF` be5d5c1 [cafreeman] refactor schema functions 40338a4 [Zongheng Yang] Merge pull request #244 from sun-rui/SPARKR-154_5 20b97a6 [Zongheng Yang] Merge pull request #234 from hqzizania/assist ba54e34 [Shivaram Venkataraman] Merge pull request #238 from sun-rui/SPARKR-154_4 c9497a3 [Shivaram Venkataraman] Merge pull request #208 from lythesia/master b317aa7 [Zongheng Yang] Merge pull request #243 from hqzizania/master 136a07e [Zongheng Yang] Merge pull request #242 from hqzizania/stats cd66603 [cafreeman] new line at EOF 8b76e81 [Shivaram Venkataraman] Merge pull request #233 from redbaron/fail-early-on-missing-dep 7dd81b7 [cafreeman] Documentation 0e2a94f [cafreeman] Define functions for schema and fields --- R/pkg/DESCRIPTION | 2 +- R/pkg/NAMESPACE | 20 +- R/pkg/R/DataFrame.R | 18 +- R/pkg/R/RDD.R | 205 ++++++++++++------ R/pkg/R/SQLContext.R | 44 +--- R/pkg/R/SQLTypes.R | 64 ------ R/pkg/R/column.R | 2 +- R/pkg/R/generics.R | 46 +++- R/pkg/R/group.R | 2 +- R/pkg/R/pairRDD.R | 192 +++++++++++++--- R/pkg/R/schema.R | 162 ++++++++++++++ R/pkg/R/serialize.R | 9 +- R/pkg/R/utils.R | 80 +++++++ R/pkg/inst/tests/test_rdd.R | 193 ++++++++++++++--- R/pkg/inst/tests/test_shuffle.R | 12 + R/pkg/inst/tests/test_sparkSQL.R | 35 +-- R/pkg/inst/worker/worker.R | 59 ++++- .../scala/org/apache/spark/api/r/RRDD.scala | 131 +++++------ .../scala/org/apache/spark/api/r/SerDe.scala | 14 +- .../org/apache/spark/sql/api/r/SQLUtils.scala | 32 ++- 20 files changed, 971 insertions(+), 351 deletions(-) delete mode 100644 R/pkg/R/SQLTypes.R create mode 100644 R/pkg/R/schema.R diff --git a/R/pkg/DESCRIPTION b/R/pkg/DESCRIPTION index 052f68c6c24e2..1c1779a763c7e 100644 --- a/R/pkg/DESCRIPTION +++ b/R/pkg/DESCRIPTION @@ -19,7 +19,7 @@ Collate: 'jobj.R' 'RDD.R' 'pairRDD.R' - 'SQLTypes.R' + 'schema.R' 'column.R' 'group.R' 'DataFrame.R' diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE index a354cdce74afa..80283643861ac 100644 --- a/R/pkg/NAMESPACE +++ b/R/pkg/NAMESPACE @@ -5,6 +5,7 @@ exportMethods( "aggregateByKey", "aggregateRDD", "cache", + "cartesian", "checkpoint", "coalesce", "cogroup", @@ -28,6 +29,7 @@ exportMethods( "fullOuterJoin", "glom", "groupByKey", + "intersection", "join", "keyBy", "keys", @@ -52,11 +54,14 @@ exportMethods( "reduceByKeyLocally", "repartition", "rightOuterJoin", + "sampleByKey", "sampleRDD", "saveAsTextFile", "saveAsObjectFile", "sortBy", "sortByKey", + "subtract", + "subtractByKey", "sumRDD", "take", "takeOrdered", @@ -95,6 +100,7 @@ exportClasses("DataFrame") exportMethods("columns", "distinct", "dtypes", + "except", "explain", "filter", "groupBy", @@ -118,7 +124,6 @@ exportMethods("columns", "show", "showDF", "sortDF", - "subtract", "toJSON", "toRDD", "unionAll", @@ -178,5 +183,14 @@ export("cacheTable", "toDF", "uncacheTable") -export("print.structType", - "print.structField") +export("sparkRSQL.init", + "sparkRHive.init") + +export("structField", + "structField.jobj", + "structField.character", + "print.structField", + "structType", + "structType.jobj", + "structType.structField", + "print.structType") diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R index 044fdb4d01223..861fe1c78b0db 100644 --- a/R/pkg/R/DataFrame.R +++ b/R/pkg/R/DataFrame.R @@ -17,7 +17,7 @@ # DataFrame.R - DataFrame class and methods implemented in S4 OO classes -#' @include generics.R jobj.R SQLTypes.R RDD.R pairRDD.R column.R group.R +#' @include generics.R jobj.R schema.R RDD.R pairRDD.R column.R group.R NULL setOldClass("jobj") @@ -1141,15 +1141,15 @@ setMethod("intersect", dataFrame(intersected) }) -#' Subtract +#' except #' #' Return a new DataFrame containing rows in this DataFrame #' but not in another DataFrame. This is equivalent to `EXCEPT` in SQL. #' #' @param x A Spark DataFrame #' @param y A Spark DataFrame -#' @return A DataFrame containing the result of the subtract operation. -#' @rdname subtract +#' @return A DataFrame containing the result of the except operation. +#' @rdname except #' @export #' @examples #'\dontrun{ @@ -1157,13 +1157,15 @@ setMethod("intersect", #' sqlCtx <- sparkRSQL.init(sc) #' df1 <- jsonFile(sqlCtx, path) #' df2 <- jsonFile(sqlCtx, path2) -#' subtractDF <- subtract(df, df2) +#' exceptDF <- except(df, df2) #' } -setMethod("subtract", +#' @rdname except +#' @export +setMethod("except", signature(x = "DataFrame", y = "DataFrame"), function(x, y) { - subtracted <- callJMethod(x@sdf, "except", y@sdf) - dataFrame(subtracted) + excepted <- callJMethod(x@sdf, "except", y@sdf) + dataFrame(excepted) }) #' Save the contents of the DataFrame to a data source diff --git a/R/pkg/R/RDD.R b/R/pkg/R/RDD.R index 820027ef67e3b..128431334ca52 100644 --- a/R/pkg/R/RDD.R +++ b/R/pkg/R/RDD.R @@ -730,6 +730,7 @@ setMethod("take", index <- -1 jrdd <- getJRDD(x) numPartitions <- numPartitions(x) + serializedModeRDD <- getSerializedMode(x) # TODO(shivaram): Collect more than one partition based on size # estimates similar to the scala version of `take`. @@ -748,13 +749,14 @@ setMethod("take", elems <- convertJListToRList(partition, flatten = TRUE, logicalUpperBound = size, - serializedMode = getSerializedMode(x)) - # TODO: Check if this append is O(n^2)? + serializedMode = serializedModeRDD) + resList <- append(resList, elems) } resList }) + #' First #' #' Return the first element of an RDD @@ -1092,21 +1094,42 @@ takeOrderedElem <- function(x, num, ascending = TRUE) { if (num < length(part)) { # R limitation: order works only on primitive types! ord <- order(unlist(part, recursive = FALSE), decreasing = !ascending) - list(part[ord[1:num]]) + part[ord[1:num]] } else { - list(part) + part } } - reduceFunc <- function(elems, part) { - newElems <- append(elems, part) - # R limitation: order works only on primitive types! - ord <- order(unlist(newElems, recursive = FALSE), decreasing = !ascending) - newElems[ord[1:num]] - } - newRdd <- mapPartitions(x, partitionFunc) - reduce(newRdd, reduceFunc) + + resList <- list() + index <- -1 + jrdd <- getJRDD(newRdd) + numPartitions <- numPartitions(newRdd) + serializedModeRDD <- getSerializedMode(newRdd) + + while (TRUE) { + index <- index + 1 + + if (index >= numPartitions) { + ord <- order(unlist(resList, recursive = FALSE), decreasing = !ascending) + resList <- resList[ord[1:num]] + break + } + + # a JList of byte arrays + partitionArr <- callJMethod(jrdd, "collectPartitions", as.list(as.integer(index))) + partition <- partitionArr[[1]] + + # elems is capped to have at most `num` elements + elems <- convertJListToRList(partition, + flatten = TRUE, + logicalUpperBound = num, + serializedMode = serializedModeRDD) + + resList <- append(resList, elems) + } + resList } #' Returns the first N elements from an RDD in ascending order. @@ -1465,67 +1488,105 @@ setMethod("zipRDD", stop("Can only zip RDDs which have the same number of partitions.") } - if (getSerializedMode(x) != getSerializedMode(other) || - getSerializedMode(x) == "byte") { - # Append the number of elements in each partition to that partition so that we can later - # check if corresponding partitions of both RDDs have the same number of elements. - # - # Note that this appending also serves the purpose of reserialization, because even if - # any RDD is serialized, we need to reserialize it to make sure its partitions are encoded - # as a single byte array. For example, partitions of an RDD generated from partitionBy() - # may be encoded as multiple byte arrays. - appendLength <- function(part) { - part[[length(part) + 1]] <- length(part) + 1 - part - } - x <- lapplyPartition(x, appendLength) - other <- lapplyPartition(other, appendLength) - } + rdds <- appendPartitionLengths(x, other) + jrdd <- callJMethod(getJRDD(rdds[[1]]), "zip", getJRDD(rdds[[2]])) + # The jrdd's elements are of scala Tuple2 type. The serialized + # flag here is used for the elements inside the tuples. + rdd <- RDD(jrdd, getSerializedMode(rdds[[1]])) - zippedJRDD <- callJMethod(getJRDD(x), "zip", getJRDD(other)) - # The zippedRDD's elements are of scala Tuple2 type. The serialized - # flag Here is used for the elements inside the tuples. - serializerMode <- getSerializedMode(x) - zippedRDD <- RDD(zippedJRDD, serializerMode) + mergePartitions(rdd, TRUE) + }) + +#' Cartesian product of this RDD and another one. +#' +#' Return the Cartesian product of this RDD and another one, +#' that is, the RDD of all pairs of elements (a, b) where a +#' is in this and b is in other. +#' +#' @param x An RDD. +#' @param other An RDD. +#' @return A new RDD which is the Cartesian product of these two RDDs. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:2) +#' sortByKey(cartesian(rdd, rdd)) +#' # list(list(1, 1), list(1, 2), list(2, 1), list(2, 2)) +#'} +#' @rdname cartesian +#' @aliases cartesian,RDD,RDD-method +setMethod("cartesian", + signature(x = "RDD", other = "RDD"), + function(x, other) { + rdds <- appendPartitionLengths(x, other) + jrdd <- callJMethod(getJRDD(rdds[[1]]), "cartesian", getJRDD(rdds[[2]])) + # The jrdd's elements are of scala Tuple2 type. The serialized + # flag here is used for the elements inside the tuples. + rdd <- RDD(jrdd, getSerializedMode(rdds[[1]])) - partitionFunc <- function(split, part) { - len <- length(part) - if (len > 0) { - if (serializerMode == "byte") { - lengthOfValues <- part[[len]] - lengthOfKeys <- part[[len - lengthOfValues]] - stopifnot(len == lengthOfKeys + lengthOfValues) - - # check if corresponding partitions of both RDDs have the same number of elements. - if (lengthOfKeys != lengthOfValues) { - stop("Can only zip RDDs with same number of elements in each pair of corresponding partitions.") - } - - if (lengthOfKeys > 1) { - keys <- part[1 : (lengthOfKeys - 1)] - values <- part[(lengthOfKeys + 1) : (len - 1)] - } else { - keys <- list() - values <- list() - } - } else { - # Keys, values must have same length here, because this has - # been validated inside the JavaRDD.zip() function. - keys <- part[c(TRUE, FALSE)] - values <- part[c(FALSE, TRUE)] - } - mapply( - function(k, v) { - list(k, v) - }, - keys, - values, - SIMPLIFY = FALSE, - USE.NAMES = FALSE) - } else { - part - } + mergePartitions(rdd, FALSE) + }) + +#' Subtract an RDD with another RDD. +#' +#' Return an RDD with the elements from this that are not in other. +#' +#' @param x An RDD. +#' @param other An RDD. +#' @param numPartitions Number of the partitions in the result RDD. +#' @return An RDD with the elements from this that are not in other. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, list(1, 1, 2, 2, 3, 4)) +#' rdd2 <- parallelize(sc, list(2, 4)) +#' collect(subtract(rdd1, rdd2)) +#' # list(1, 1, 3) +#'} +#' @rdname subtract +#' @aliases subtract,RDD +setMethod("subtract", + signature(x = "RDD", other = "RDD"), + function(x, other, numPartitions = SparkR::numPartitions(x)) { + mapFunction <- function(e) { list(e, NA) } + rdd1 <- map(x, mapFunction) + rdd2 <- map(other, mapFunction) + keys(subtractByKey(rdd1, rdd2, numPartitions)) + }) + +#' Intersection of this RDD and another one. +#' +#' Return the intersection of this RDD and another one. +#' The output will not contain any duplicate elements, +#' even if the input RDDs did. Performs a hash partition +#' across the cluster. +#' Note that this method performs a shuffle internally. +#' +#' @param x An RDD. +#' @param other An RDD. +#' @param numPartitions The number of partitions in the result RDD. +#' @return An RDD which is the intersection of these two RDDs. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, list(1, 10, 2, 3, 4, 5)) +#' rdd2 <- parallelize(sc, list(1, 6, 2, 3, 7, 8)) +#' collect(sortBy(intersection(rdd1, rdd2), function(x) { x })) +#' # list(1, 2, 3) +#'} +#' @rdname intersection +#' @aliases intersection,RDD +setMethod("intersection", + signature(x = "RDD", other = "RDD"), + function(x, other, numPartitions = SparkR::numPartitions(x)) { + rdd1 <- map(x, function(v) { list(v, NA) }) + rdd2 <- map(other, function(v) { list(v, NA) }) + + filterFunction <- function(elem) { + iters <- elem[[2]] + all(as.vector( + lapply(iters, function(iter) { length(iter) > 0 }), mode = "logical")) } - - PipelinedRDD(zippedRDD, partitionFunc) + + keys(filterRDD(cogroup(rdd1, rdd2, numPartitions = numPartitions), filterFunction)) }) diff --git a/R/pkg/R/SQLContext.R b/R/pkg/R/SQLContext.R index 930ada22f4c38..4f05ba524a01a 100644 --- a/R/pkg/R/SQLContext.R +++ b/R/pkg/R/SQLContext.R @@ -54,9 +54,9 @@ infer_type <- function(x) { # StructType types <- lapply(x, infer_type) fields <- lapply(1:length(x), function(i) { - list(name = names[[i]], type = types[[i]], nullable = TRUE) + structField(names[[i]], types[[i]], TRUE) }) - list(type = "struct", fields = fields) + do.call(structType, fields) } } else if (length(x) > 1) { list(type = "array", elementType = type, containsNull = TRUE) @@ -65,30 +65,6 @@ infer_type <- function(x) { } } -#' dump the schema into JSON string -tojson <- function(x) { - if (is.list(x)) { - names <- names(x) - if (!is.null(names)) { - items <- lapply(names, function(n) { - safe_n <- gsub('"', '\\"', n) - paste(tojson(safe_n), ':', tojson(x[[n]]), sep = '') - }) - d <- paste(items, collapse = ', ') - paste('{', d, '}', sep = '') - } else { - l <- paste(lapply(x, tojson), collapse = ', ') - paste('[', l, ']', sep = '') - } - } else if (is.character(x)) { - paste('"', x, '"', sep = '') - } else if (is.logical(x)) { - if (x) "true" else "false" - } else { - stop(paste("unexpected type:", class(x))) - } -} - #' Create a DataFrame from an RDD #' #' Converts an RDD to a DataFrame by infer the types. @@ -134,7 +110,7 @@ createDataFrame <- function(sqlCtx, data, schema = NULL, samplingRatio = 1.0) { stop(paste("unexpected type:", class(data))) } - if (is.null(schema) || is.null(names(schema))) { + if (is.null(schema) || (!inherits(schema, "structType") && is.null(names(schema)))) { row <- first(rdd) names <- if (is.null(schema)) { names(row) @@ -143,7 +119,7 @@ createDataFrame <- function(sqlCtx, data, schema = NULL, samplingRatio = 1.0) { } if (is.null(names)) { names <- lapply(1:length(row), function(x) { - paste("_", as.character(x), sep = "") + paste("_", as.character(x), sep = "") }) } @@ -159,20 +135,18 @@ createDataFrame <- function(sqlCtx, data, schema = NULL, samplingRatio = 1.0) { types <- lapply(row, infer_type) fields <- lapply(1:length(row), function(i) { - list(name = names[[i]], type = types[[i]], nullable = TRUE) + structField(names[[i]], types[[i]], TRUE) }) - schema <- list(type = "struct", fields = fields) + schema <- do.call(structType, fields) } - stopifnot(class(schema) == "list") - stopifnot(schema$type == "struct") - stopifnot(class(schema$fields) == "list") - schemaString <- tojson(schema) + stopifnot(class(schema) == "structType") + # schemaString <- tojson(schema) jrdd <- getJRDD(lapply(rdd, function(x) x), "row") srdd <- callJMethod(jrdd, "rdd") sdf <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "createDF", - srdd, schemaString, sqlCtx) + srdd, schema$jobj, sqlCtx) dataFrame(sdf) } diff --git a/R/pkg/R/SQLTypes.R b/R/pkg/R/SQLTypes.R deleted file mode 100644 index 962fba5b3cf03..0000000000000 --- a/R/pkg/R/SQLTypes.R +++ /dev/null @@ -1,64 +0,0 @@ -# -# Licensed to the Apache Software Foundation (ASF) under one or more -# contributor license agreements. See the NOTICE file distributed with -# this work for additional information regarding copyright ownership. -# The ASF licenses this file to You under the Apache License, Version 2.0 -# (the "License"); you may not use this file except in compliance with -# the License. You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# Utility functions for handling SparkSQL DataTypes. - -# Handler for StructType -structType <- function(st) { - obj <- structure(new.env(parent = emptyenv()), class = "structType") - obj$jobj <- st - obj$fields <- function() { lapply(callJMethod(st, "fields"), structField) } - obj -} - -#' Print a Spark StructType. -#' -#' This function prints the contents of a StructType returned from the -#' SparkR JVM backend. -#' -#' @param x A StructType object -#' @param ... further arguments passed to or from other methods -print.structType <- function(x, ...) { - fieldsList <- lapply(x$fields(), function(i) { i$print() }) - print(fieldsList) -} - -# Handler for StructField -structField <- function(sf) { - obj <- structure(new.env(parent = emptyenv()), class = "structField") - obj$jobj <- sf - obj$name <- function() { callJMethod(sf, "name") } - obj$dataType <- function() { callJMethod(sf, "dataType") } - obj$dataType.toString <- function() { callJMethod(obj$dataType(), "toString") } - obj$dataType.simpleString <- function() { callJMethod(obj$dataType(), "simpleString") } - obj$nullable <- function() { callJMethod(sf, "nullable") } - obj$print <- function() { paste("StructField(", - paste(obj$name(), obj$dataType.toString(), obj$nullable(), sep = ", "), - ")", sep = "") } - obj -} - -#' Print a Spark StructField. -#' -#' This function prints the contents of a StructField returned from the -#' SparkR JVM backend. -#' -#' @param x A StructField object -#' @param ... further arguments passed to or from other methods -print.structField <- function(x, ...) { - cat(x$print()) -} diff --git a/R/pkg/R/column.R b/R/pkg/R/column.R index b282001d8b6b5..95fb9ff0887b6 100644 --- a/R/pkg/R/column.R +++ b/R/pkg/R/column.R @@ -17,7 +17,7 @@ # Column Class -#' @include generics.R jobj.R SQLTypes.R +#' @include generics.R jobj.R schema.R NULL setOldClass("jobj") diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R index 5fb1ccaa84ee2..6c6233390134c 100644 --- a/R/pkg/R/generics.R +++ b/R/pkg/R/generics.R @@ -230,6 +230,10 @@ setGeneric("zipWithUniqueId", function(x) { standardGeneric("zipWithUniqueId") } ############ Binary Functions ############# +#' @rdname cartesian +#' @export +setGeneric("cartesian", function(x, other) { standardGeneric("cartesian") }) + #' @rdname countByKey #' @export setGeneric("countByKey", function(x) { standardGeneric("countByKey") }) @@ -238,6 +242,11 @@ setGeneric("countByKey", function(x) { standardGeneric("countByKey") }) #' @export setGeneric("flatMapValues", function(X, FUN) { standardGeneric("flatMapValues") }) +#' @rdname intersection +#' @export +setGeneric("intersection", function(x, other, numPartitions = 1L) { + standardGeneric("intersection") }) + #' @rdname keys #' @export setGeneric("keys", function(x) { standardGeneric("keys") }) @@ -250,12 +259,18 @@ setGeneric("lookup", function(x, key) { standardGeneric("lookup") }) #' @export setGeneric("mapValues", function(X, FUN) { standardGeneric("mapValues") }) +#' @rdname sampleByKey +#' @export +setGeneric("sampleByKey", + function(x, withReplacement, fractions, seed) { + standardGeneric("sampleByKey") + }) + #' @rdname values #' @export setGeneric("values", function(x) { standardGeneric("values") }) - ############ Shuffle Functions ############ #' @rdname aggregateByKey @@ -330,9 +345,24 @@ setGeneric("rightOuterJoin", function(x, y, numPartitions) { standardGeneric("ri #' @rdname sortByKey #' @export -setGeneric("sortByKey", function(x, ascending = TRUE, numPartitions = 1L) { - standardGeneric("sortByKey") -}) +setGeneric("sortByKey", + function(x, ascending = TRUE, numPartitions = 1L) { + standardGeneric("sortByKey") + }) + +#' @rdname subtract +#' @export +setGeneric("subtract", + function(x, other, numPartitions = 1L) { + standardGeneric("subtract") + }) + +#' @rdname subtractByKey +#' @export +setGeneric("subtractByKey", + function(x, other, numPartitions = 1L) { + standardGeneric("subtractByKey") + }) ################### Broadcast Variable Methods ################# @@ -357,6 +387,10 @@ setGeneric("dtypes", function(x) { standardGeneric("dtypes") }) #' @export setGeneric("explain", function(x, ...) { standardGeneric("explain") }) +#' @rdname except +#' @export +setGeneric("except", function(x, y) { standardGeneric("except") }) + #' @rdname filter #' @export setGeneric("filter", function(x, condition) { standardGeneric("filter") }) @@ -434,10 +468,6 @@ setGeneric("showDF", function(x,...) { standardGeneric("showDF") }) #' @export setGeneric("sortDF", function(x, col, ...) { standardGeneric("sortDF") }) -#' @rdname subtract -#' @export -setGeneric("subtract", function(x, y) { standardGeneric("subtract") }) - #' @rdname tojson #' @export setGeneric("toJSON", function(x) { standardGeneric("toJSON") }) diff --git a/R/pkg/R/group.R b/R/pkg/R/group.R index 855fbdfc7c4ca..02237b3672d6b 100644 --- a/R/pkg/R/group.R +++ b/R/pkg/R/group.R @@ -17,7 +17,7 @@ # group.R - GroupedData class and methods implemented in S4 OO classes -#' @include generics.R jobj.R SQLTypes.R column.R +#' @include generics.R jobj.R schema.R column.R NULL setOldClass("jobj") diff --git a/R/pkg/R/pairRDD.R b/R/pkg/R/pairRDD.R index 5d64822859d1f..13efebc11c46e 100644 --- a/R/pkg/R/pairRDD.R +++ b/R/pkg/R/pairRDD.R @@ -430,7 +430,7 @@ setMethod("combineByKey", pred <- function(item) exists(item$hash, keys) lapply(part, function(item) { - item$hash <- as.character(item[[1]]) + item$hash <- as.character(hashCode(item[[1]])) updateOrCreatePair(item, keys, combiners, pred, mergeValue, createCombiner) }) convertEnvsToList(keys, combiners) @@ -443,7 +443,7 @@ setMethod("combineByKey", pred <- function(item) exists(item$hash, keys) lapply(part, function(item) { - item$hash <- as.character(item[[1]]) + item$hash <- as.character(hashCode(item[[1]])) updateOrCreatePair(item, keys, combiners, pred, mergeCombiners, identity) }) convertEnvsToList(keys, combiners) @@ -452,19 +452,19 @@ setMethod("combineByKey", }) #' Aggregate a pair RDD by each key. -#' +#' #' Aggregate the values of each key in an RDD, using given combine functions #' and a neutral "zero value". This function can return a different result type, #' U, than the type of the values in this RDD, V. Thus, we need one operation -#' for merging a V into a U and one operation for merging two U's, The former -#' operation is used for merging values within a partition, and the latter is -#' used for merging values between partitions. To avoid memory allocation, both -#' of these functions are allowed to modify and return their first argument +#' for merging a V into a U and one operation for merging two U's, The former +#' operation is used for merging values within a partition, and the latter is +#' used for merging values between partitions. To avoid memory allocation, both +#' of these functions are allowed to modify and return their first argument #' instead of creating a new U. -#' +#' #' @param x An RDD. #' @param zeroValue A neutral "zero value". -#' @param seqOp A function to aggregate the values of each key. It may return +#' @param seqOp A function to aggregate the values of each key. It may return #' a different result type from the type of the values. #' @param combOp A function to aggregate results of seqOp. #' @return An RDD containing the aggregation result. @@ -476,7 +476,7 @@ setMethod("combineByKey", #' zeroValue <- list(0, 0) #' seqOp <- function(x, y) { list(x[[1]] + y, x[[2]] + 1) } #' combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) } -#' aggregateByKey(rdd, zeroValue, seqOp, combOp, 2L) +#' aggregateByKey(rdd, zeroValue, seqOp, combOp, 2L) #' # list(list(1, list(3, 2)), list(2, list(7, 2))) #'} #' @rdname aggregateByKey @@ -493,12 +493,12 @@ setMethod("aggregateByKey", }) #' Fold a pair RDD by each key. -#' +#' #' Aggregate the values of each key in an RDD, using an associative function "func" -#' and a neutral "zero value" which may be added to the result an arbitrary -#' number of times, and must not change the result (e.g., 0 for addition, or +#' and a neutral "zero value" which may be added to the result an arbitrary +#' number of times, and must not change the result (e.g., 0 for addition, or #' 1 for multiplication.). -#' +#' #' @param x An RDD. #' @param zeroValue A neutral "zero value". #' @param func An associative function for folding values of each key. @@ -548,11 +548,11 @@ setMethod("join", function(x, y, numPartitions) { xTagged <- lapply(x, function(i) { list(i[[1]], list(1L, i[[2]])) }) yTagged <- lapply(y, function(i) { list(i[[1]], list(2L, i[[2]])) }) - + doJoin <- function(v) { joinTaggedList(v, list(FALSE, FALSE)) } - + joined <- flatMapValues(groupByKey(unionRDD(xTagged, yTagged), numToInt(numPartitions)), doJoin) }) @@ -568,8 +568,8 @@ setMethod("join", #' @param y An RDD to be joined. Should be an RDD where each element is #' list(K, V). #' @param numPartitions Number of partitions to create. -#' @return For each element (k, v) in x, the resulting RDD will either contain -#' all pairs (k, (v, w)) for (k, w) in rdd2, or the pair (k, (v, NULL)) +#' @return For each element (k, v) in x, the resulting RDD will either contain +#' all pairs (k, (v, w)) for (k, w) in rdd2, or the pair (k, (v, NULL)) #' if no elements in rdd2 have key k. #' @examples #'\dontrun{ @@ -586,11 +586,11 @@ setMethod("leftOuterJoin", function(x, y, numPartitions) { xTagged <- lapply(x, function(i) { list(i[[1]], list(1L, i[[2]])) }) yTagged <- lapply(y, function(i) { list(i[[1]], list(2L, i[[2]])) }) - + doJoin <- function(v) { joinTaggedList(v, list(FALSE, TRUE)) } - + joined <- flatMapValues(groupByKey(unionRDD(xTagged, yTagged), numPartitions), doJoin) }) @@ -623,18 +623,18 @@ setMethod("rightOuterJoin", function(x, y, numPartitions) { xTagged <- lapply(x, function(i) { list(i[[1]], list(1L, i[[2]])) }) yTagged <- lapply(y, function(i) { list(i[[1]], list(2L, i[[2]])) }) - + doJoin <- function(v) { joinTaggedList(v, list(TRUE, FALSE)) } - + joined <- flatMapValues(groupByKey(unionRDD(xTagged, yTagged), numPartitions), doJoin) }) #' Full outer join two RDDs #' #' @description -#' \code{fullouterjoin} This function full-outer-joins two RDDs where every element is of the form list(K, V). +#' \code{fullouterjoin} This function full-outer-joins two RDDs where every element is of the form list(K, V). #' The key types of the two RDDs should be the same. #' #' @param x An RDD to be joined. Should be an RDD where each element is @@ -644,7 +644,7 @@ setMethod("rightOuterJoin", #' @param numPartitions Number of partitions to create. #' @return For each element (k, v) in x and (k, w) in y, the resulting RDD #' will contain all pairs (k, (v, w)) for both (k, v) in x and -#' (k, w) in y, or the pair (k, (NULL, w))/(k, (v, NULL)) if no elements +#' (k, w) in y, or the pair (k, (NULL, w))/(k, (v, NULL)) if no elements #' in x/y have key k. #' @examples #'\dontrun{ @@ -683,7 +683,7 @@ setMethod("fullOuterJoin", #' sc <- sparkR.init() #' rdd1 <- parallelize(sc, list(list(1, 1), list(2, 4))) #' rdd2 <- parallelize(sc, list(list(1, 2), list(1, 3))) -#' cogroup(rdd1, rdd2, numPartitions = 2L) +#' cogroup(rdd1, rdd2, numPartitions = 2L) #' # list(list(1, list(1, list(2, 3))), list(2, list(list(4), list())) #'} #' @rdname cogroup @@ -694,7 +694,7 @@ setMethod("cogroup", rdds <- list(...) rddsLen <- length(rdds) for (i in 1:rddsLen) { - rdds[[i]] <- lapply(rdds[[i]], + rdds[[i]] <- lapply(rdds[[i]], function(x) { list(x[[1]], list(i, x[[2]])) }) } union.rdd <- Reduce(unionRDD, rdds) @@ -719,7 +719,7 @@ setMethod("cogroup", } }) } - cogroup.rdd <- mapValues(groupByKey(union.rdd, numPartitions), + cogroup.rdd <- mapValues(groupByKey(union.rdd, numPartitions), group.func) }) @@ -741,18 +741,18 @@ setMethod("sortByKey", signature(x = "RDD"), function(x, ascending = TRUE, numPartitions = SparkR::numPartitions(x)) { rangeBounds <- list() - + if (numPartitions > 1) { rddSize <- count(x) # constant from Spark's RangePartitioner maxSampleSize <- numPartitions * 20 fraction <- min(maxSampleSize / max(rddSize, 1), 1.0) - + samples <- collect(keys(sampleRDD(x, FALSE, fraction, 1L))) - + # Note: the built-in R sort() function only works on atomic vectors samples <- sort(unlist(samples, recursive = FALSE), decreasing = !ascending) - + if (length(samples) > 0) { rangeBounds <- lapply(seq_len(numPartitions - 1), function(i) { @@ -764,24 +764,146 @@ setMethod("sortByKey", rangePartitionFunc <- function(key) { partition <- 0 - + # TODO: Use binary search instead of linear search, similar with Spark while (partition < length(rangeBounds) && key > rangeBounds[[partition + 1]]) { partition <- partition + 1 } - + if (ascending) { partition } else { numPartitions - partition - 1 } } - + partitionFunc <- function(part) { sortKeyValueList(part, decreasing = !ascending) } - + newRDD <- partitionBy(x, numPartitions, rangePartitionFunc) lapplyPartition(newRDD, partitionFunc) }) +#' Subtract a pair RDD with another pair RDD. +#' +#' Return an RDD with the pairs from x whose keys are not in other. +#' +#' @param x An RDD. +#' @param other An RDD. +#' @param numPartitions Number of the partitions in the result RDD. +#' @return An RDD with the pairs from x whose keys are not in other. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, list(list("a", 1), list("b", 4), +#' list("b", 5), list("a", 2))) +#' rdd2 <- parallelize(sc, list(list("a", 3), list("c", 1))) +#' collect(subtractByKey(rdd1, rdd2)) +#' # list(list("b", 4), list("b", 5)) +#'} +#' @rdname subtractByKey +#' @aliases subtractByKey,RDD +setMethod("subtractByKey", + signature(x = "RDD", other = "RDD"), + function(x, other, numPartitions = SparkR::numPartitions(x)) { + filterFunction <- function(elem) { + iters <- elem[[2]] + (length(iters[[1]]) > 0) && (length(iters[[2]]) == 0) + } + + flatMapValues(filterRDD(cogroup(x, + other, + numPartitions = numPartitions), + filterFunction), + function (v) { v[[1]] }) + }) + +#' Return a subset of this RDD sampled by key. +#' +#' @description +#' \code{sampleByKey} Create a sample of this RDD using variable sampling rates +#' for different keys as specified by fractions, a key to sampling rate map. +#' +#' @param x The RDD to sample elements by key, where each element is +#' list(K, V) or c(K, V). +#' @param withReplacement Sampling with replacement or not +#' @param fraction The (rough) sample target fraction +#' @param seed Randomness seed value +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:3000) +#' pairs <- lapply(rdd, function(x) { if (x %% 3 == 0) list("a", x) +#' else { if (x %% 3 == 1) list("b", x) else list("c", x) }}) +#' fractions <- list(a = 0.2, b = 0.1, c = 0.3) +#' sample <- sampleByKey(pairs, FALSE, fractions, 1618L) +#' 100 < length(lookup(sample, "a")) && 300 > length(lookup(sample, "a")) # TRUE +#' 50 < length(lookup(sample, "b")) && 150 > length(lookup(sample, "b")) # TRUE +#' 200 < length(lookup(sample, "c")) && 400 > length(lookup(sample, "c")) # TRUE +#' lookup(sample, "a")[which.min(lookup(sample, "a"))] >= 0 # TRUE +#' lookup(sample, "a")[which.max(lookup(sample, "a"))] <= 2000 # TRUE +#' lookup(sample, "b")[which.min(lookup(sample, "b"))] >= 0 # TRUE +#' lookup(sample, "b")[which.max(lookup(sample, "b"))] <= 2000 # TRUE +#' lookup(sample, "c")[which.min(lookup(sample, "c"))] >= 0 # TRUE +#' lookup(sample, "c")[which.max(lookup(sample, "c"))] <= 2000 # TRUE +#' fractions <- list(a = 0.2, b = 0.1, c = 0.3, d = 0.4) +#' sample <- sampleByKey(pairs, FALSE, fractions, 1618L) # Key "d" will be ignored +#' fractions <- list(a = 0.2, b = 0.1) +#' sample <- sampleByKey(pairs, FALSE, fractions, 1618L) # KeyError: "c" +#'} +#' @rdname sampleByKey +#' @aliases sampleByKey,RDD-method +setMethod("sampleByKey", + signature(x = "RDD", withReplacement = "logical", + fractions = "vector", seed = "integer"), + function(x, withReplacement, fractions, seed) { + + for (elem in fractions) { + if (elem < 0.0) { + stop(paste("Negative fraction value ", fractions[which(fractions == elem)])) + } + } + + # The sampler: takes a partition and returns its sampled version. + samplingFunc <- function(split, part) { + set.seed(bitwXor(seed, split)) + res <- vector("list", length(part)) + len <- 0 + + # mixing because the initial seeds are close to each other + runif(10) + + for (elem in part) { + if (elem[[1]] %in% names(fractions)) { + frac <- as.numeric(fractions[which(elem[[1]] == names(fractions))]) + if (withReplacement) { + count <- rpois(1, frac) + if (count > 0) { + res[(len + 1):(len + count)] <- rep(list(elem), count) + len <- len + count + } + } else { + if (runif(1) < frac) { + len <- len + 1 + res[[len]] <- elem + } + } + } else { + stop("KeyError: \"", elem[[1]], "\"") + } + } + + # TODO(zongheng): look into the performance of the current + # implementation. Look into some iterator package? Note that + # Scala avoids many calls to creating an empty list and PySpark + # similarly achieves this using `yield'. (duplicated from sampleRDD) + if (len > 0) { + res[1:len] + } else { + list() + } + } + + lapplyPartitionsWithIndex(x, samplingFunc) + }) diff --git a/R/pkg/R/schema.R b/R/pkg/R/schema.R new file mode 100644 index 0000000000000..e442119086b17 --- /dev/null +++ b/R/pkg/R/schema.R @@ -0,0 +1,162 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# A set of S3 classes and methods that support the SparkSQL `StructType` and `StructField +# datatypes. These are used to create and interact with DataFrame schemas. + +#' structType +#' +#' Create a structType object that contains the metadata for a DataFrame. Intended for +#' use with createDataFrame and toDF. +#' +#' @param x a structField object (created with the field() function) +#' @param ... additional structField objects +#' @return a structType object +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' rdd <- lapply(parallelize(sc, 1:10), function(x) { list(x, as.character(x)) }) +#' schema <- structType(structField("a", "integer"), structField("b", "string")) +#' df <- createDataFrame(sqlCtx, rdd, schema) +#' } +structType <- function(x, ...) { + UseMethod("structType", x) +} + +structType.jobj <- function(x) { + obj <- structure(list(), class = "structType") + obj$jobj <- x + obj$fields <- function() { lapply(callJMethod(obj$jobj, "fields"), structField) } + obj +} + +structType.structField <- function(x, ...) { + fields <- list(x, ...) + if (!all(sapply(fields, inherits, "structField"))) { + stop("All arguments must be structField objects.") + } + sfObjList <- lapply(fields, function(field) { + field$jobj + }) + stObj <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", + "createStructType", + listToSeq(sfObjList)) + structType(stObj) +} + +#' Print a Spark StructType. +#' +#' This function prints the contents of a StructType returned from the +#' SparkR JVM backend. +#' +#' @param x A StructType object +#' @param ... further arguments passed to or from other methods +print.structType <- function(x, ...) { + cat("StructType\n", + sapply(x$fields(), function(field) { paste("|-", "name = \"", field$name(), + "\", type = \"", field$dataType.toString(), + "\", nullable = ", field$nullable(), "\n", + sep = "") }) + , sep = "") +} + +#' structField +#' +#' Create a structField object that contains the metadata for a single field in a schema. +#' +#' @param x The name of the field +#' @param type The data type of the field +#' @param nullable A logical vector indicating whether or not the field is nullable +#' @return a structField object +#' @export +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlCtx <- sparkRSQL.init(sc) +#' rdd <- lapply(parallelize(sc, 1:10), function(x) { list(x, as.character(x)) }) +#' field1 <- structField("a", "integer", TRUE) +#' field2 <- structField("b", "string", TRUE) +#' schema <- structType(field1, field2) +#' df <- createDataFrame(sqlCtx, rdd, schema) +#' } + +structField <- function(x, ...) { + UseMethod("structField", x) +} + +structField.jobj <- function(x) { + obj <- structure(list(), class = "structField") + obj$jobj <- x + obj$name <- function() { callJMethod(x, "name") } + obj$dataType <- function() { callJMethod(x, "dataType") } + obj$dataType.toString <- function() { callJMethod(obj$dataType(), "toString") } + obj$dataType.simpleString <- function() { callJMethod(obj$dataType(), "simpleString") } + obj$nullable <- function() { callJMethod(x, "nullable") } + obj +} + +structField.character <- function(x, type, nullable = TRUE) { + if (class(x) != "character") { + stop("Field name must be a string.") + } + if (class(type) != "character") { + stop("Field type must be a string.") + } + if (class(nullable) != "logical") { + stop("nullable must be either TRUE or FALSE") + } + options <- c("byte", + "integer", + "double", + "numeric", + "character", + "string", + "binary", + "raw", + "logical", + "boolean", + "timestamp", + "date") + dataType <- if (type %in% options) { + type + } else { + stop(paste("Unsupported type for Dataframe:", type)) + } + sfObj <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", + "createStructField", + x, + dataType, + nullable) + structField(sfObj) +} + +#' Print a Spark StructField. +#' +#' This function prints the contents of a StructField returned from the +#' SparkR JVM backend. +#' +#' @param x A StructField object +#' @param ... further arguments passed to or from other methods +print.structField <- function(x, ...) { + cat("StructField(name = \"", x$name(), + "\", type = \"", x$dataType.toString(), + "\", nullable = ", x$nullable(), + ")", + sep = "") +} diff --git a/R/pkg/R/serialize.R b/R/pkg/R/serialize.R index 8a9c0c652ce24..c53d0a961016f 100644 --- a/R/pkg/R/serialize.R +++ b/R/pkg/R/serialize.R @@ -69,8 +69,9 @@ writeJobj <- function(con, value) { } writeString <- function(con, value) { - writeInt(con, as.integer(nchar(value) + 1)) - writeBin(value, con, endian = "big") + utfVal <- enc2utf8(value) + writeInt(con, as.integer(nchar(utfVal, type = "bytes") + 1)) + writeBin(utfVal, con, endian = "big") } writeInt <- function(con, value) { @@ -189,7 +190,3 @@ writeArgs <- function(con, args) { } } } - -writeStrings <- function(con, stringList) { - writeLines(unlist(stringList), con) -} diff --git a/R/pkg/R/utils.R b/R/pkg/R/utils.R index c337fb0751e72..23305d3c67074 100644 --- a/R/pkg/R/utils.R +++ b/R/pkg/R/utils.R @@ -465,3 +465,83 @@ cleanClosure <- function(func, checkedFuncs = new.env()) { } func } + +# Append partition lengths to each partition in two input RDDs if needed. +# param +# x An RDD. +# Other An RDD. +# return value +# A list of two result RDDs. +appendPartitionLengths <- function(x, other) { + if (getSerializedMode(x) != getSerializedMode(other) || + getSerializedMode(x) == "byte") { + # Append the number of elements in each partition to that partition so that we can later + # know the boundary of elements from x and other. + # + # Note that this appending also serves the purpose of reserialization, because even if + # any RDD is serialized, we need to reserialize it to make sure its partitions are encoded + # as a single byte array. For example, partitions of an RDD generated from partitionBy() + # may be encoded as multiple byte arrays. + appendLength <- function(part) { + len <- length(part) + part[[len + 1]] <- len + 1 + part + } + x <- lapplyPartition(x, appendLength) + other <- lapplyPartition(other, appendLength) + } + list (x, other) +} + +# Perform zip or cartesian between elements from two RDDs in each partition +# param +# rdd An RDD. +# zip A boolean flag indicating this call is for zip operation or not. +# return value +# A result RDD. +mergePartitions <- function(rdd, zip) { + serializerMode <- getSerializedMode(rdd) + partitionFunc <- function(split, part) { + len <- length(part) + if (len > 0) { + if (serializerMode == "byte") { + lengthOfValues <- part[[len]] + lengthOfKeys <- part[[len - lengthOfValues]] + stopifnot(len == lengthOfKeys + lengthOfValues) + + # For zip operation, check if corresponding partitions of both RDDs have the same number of elements. + if (zip && lengthOfKeys != lengthOfValues) { + stop("Can only zip RDDs with same number of elements in each pair of corresponding partitions.") + } + + if (lengthOfKeys > 1) { + keys <- part[1 : (lengthOfKeys - 1)] + } else { + keys <- list() + } + if (lengthOfValues > 1) { + values <- part[(lengthOfKeys + 1) : (len - 1)] + } else { + values <- list() + } + + if (!zip) { + return(mergeCompactLists(keys, values)) + } + } else { + keys <- part[c(TRUE, FALSE)] + values <- part[c(FALSE, TRUE)] + } + mapply( + function(k, v) { list(k, v) }, + keys, + values, + SIMPLIFY = FALSE, + USE.NAMES = FALSE) + } else { + part + } + } + + PipelinedRDD(rdd, partitionFunc) +} diff --git a/R/pkg/inst/tests/test_rdd.R b/R/pkg/inst/tests/test_rdd.R index b76e4db03e715..3ba7d1716302a 100644 --- a/R/pkg/inst/tests/test_rdd.R +++ b/R/pkg/inst/tests/test_rdd.R @@ -35,7 +35,7 @@ test_that("get number of partitions in RDD", { test_that("first on RDD", { expect_true(first(rdd) == 1) newrdd <- lapply(rdd, function(x) x + 1) - expect_true(first(newrdd) == 2) + expect_true(first(newrdd) == 2) }) test_that("count and length on RDD", { @@ -48,7 +48,7 @@ test_that("count by values and keys", { actual <- countByValue(mods) expected <- list(list(0, 3L), list(1, 4L), list(2, 3L)) expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) - + actual <- countByKey(intRdd) expected <- list(list(2L, 2L), list(1L, 2L)) expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) @@ -82,11 +82,11 @@ test_that("filterRDD on RDD", { filtered.rdd <- filterRDD(rdd, function(x) { x %% 2 == 0 }) actual <- collect(filtered.rdd) expect_equal(actual, list(2, 4, 6, 8, 10)) - + filtered.rdd <- Filter(function(x) { x[[2]] < 0 }, intRdd) actual <- collect(filtered.rdd) expect_equal(actual, list(list(1L, -1))) - + # Filter out all elements. filtered.rdd <- filterRDD(rdd, function(x) { x > 10 }) actual <- collect(filtered.rdd) @@ -96,7 +96,7 @@ test_that("filterRDD on RDD", { test_that("lookup on RDD", { vals <- lookup(intRdd, 1L) expect_equal(vals, list(-1, 200)) - + vals <- lookup(intRdd, 3L) expect_equal(vals, list()) }) @@ -110,7 +110,7 @@ test_that("several transformations on RDD (a benchmark on PipelinedRDD)", { }) rdd2 <- lapply(rdd2, function(x) x + x) actual <- collect(rdd2) - expected <- list(24, 24, 24, 24, 24, + expected <- list(24, 24, 24, 24, 24, 168, 170, 172, 174, 176) expect_equal(actual, expected) }) @@ -248,10 +248,10 @@ test_that("flatMapValues() on pairwise RDDs", { l <- parallelize(sc, list(list(1, c(1,2)), list(2, c(3,4)))) actual <- collect(flatMapValues(l, function(x) { x })) expect_equal(actual, list(list(1,1), list(1,2), list(2,3), list(2,4))) - + # Generate x to x+1 for every value actual <- collect(flatMapValues(intRdd, function(x) { x:(x + 1) })) - expect_equal(actual, + expect_equal(actual, list(list(1L, -1), list(1L, 0), list(2L, 100), list(2L, 101), list(2L, 1), list(2L, 2), list(1L, 200), list(1L, 201))) }) @@ -348,7 +348,7 @@ test_that("top() on RDDs", { rdd <- parallelize(sc, l) actual <- top(rdd, 6L) expect_equal(actual, as.list(sort(unlist(l), decreasing = TRUE))[1:6]) - + l <- list("e", "d", "c", "d", "a") rdd <- parallelize(sc, l) actual <- top(rdd, 3L) @@ -358,7 +358,7 @@ test_that("top() on RDDs", { test_that("fold() on RDDs", { actual <- fold(rdd, 0, "+") expect_equal(actual, Reduce("+", nums, 0)) - + rdd <- parallelize(sc, list()) actual <- fold(rdd, 0, "+") expect_equal(actual, 0) @@ -371,7 +371,7 @@ test_that("aggregateRDD() on RDDs", { combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) } actual <- aggregateRDD(rdd, zeroValue, seqOp, combOp) expect_equal(actual, list(10, 4)) - + rdd <- parallelize(sc, list()) actual <- aggregateRDD(rdd, zeroValue, seqOp, combOp) expect_equal(actual, list(0, 0)) @@ -380,13 +380,13 @@ test_that("aggregateRDD() on RDDs", { test_that("zipWithUniqueId() on RDDs", { rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L) actual <- collect(zipWithUniqueId(rdd)) - expected <- list(list("a", 0), list("b", 3), list("c", 1), + expected <- list(list("a", 0), list("b", 3), list("c", 1), list("d", 4), list("e", 2)) expect_equal(actual, expected) - + rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 1L) actual <- collect(zipWithUniqueId(rdd)) - expected <- list(list("a", 0), list("b", 1), list("c", 2), + expected <- list(list("a", 0), list("b", 1), list("c", 2), list("d", 3), list("e", 4)) expect_equal(actual, expected) }) @@ -394,13 +394,13 @@ test_that("zipWithUniqueId() on RDDs", { test_that("zipWithIndex() on RDDs", { rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L) actual <- collect(zipWithIndex(rdd)) - expected <- list(list("a", 0), list("b", 1), list("c", 2), + expected <- list(list("a", 0), list("b", 1), list("c", 2), list("d", 3), list("e", 4)) expect_equal(actual, expected) - + rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 1L) actual <- collect(zipWithIndex(rdd)) - expected <- list(list("a", 0), list("b", 1), list("c", 2), + expected <- list(list("a", 0), list("b", 1), list("c", 2), list("d", 3), list("e", 4)) expect_equal(actual, expected) }) @@ -427,12 +427,12 @@ test_that("pipeRDD() on RDDs", { actual <- collect(pipeRDD(rdd, "more")) expected <- as.list(as.character(1:10)) expect_equal(actual, expected) - + trailed.rdd <- parallelize(sc, c("1", "", "2\n", "3\n\r\n")) actual <- collect(pipeRDD(trailed.rdd, "sort")) expected <- list("", "1", "2", "3") expect_equal(actual, expected) - + rev.nums <- 9:0 rev.rdd <- parallelize(sc, rev.nums, 2L) actual <- collect(pipeRDD(rev.rdd, "sort")) @@ -446,11 +446,11 @@ test_that("zipRDD() on RDDs", { actual <- collect(zipRDD(rdd1, rdd2)) expect_equal(actual, list(list(0, 1000), list(1, 1001), list(2, 1002), list(3, 1003), list(4, 1004))) - + mockFile = c("Spark is pretty.", "Spark is awesome.") fileName <- tempfile(pattern="spark-test", fileext=".tmp") writeLines(mockFile, fileName) - + rdd <- textFile(sc, fileName, 1) actual <- collect(zipRDD(rdd, rdd)) expected <- lapply(mockFile, function(x) { list(x ,x) }) @@ -465,10 +465,125 @@ test_that("zipRDD() on RDDs", { actual <- collect(zipRDD(rdd, rdd1)) expected <- lapply(mockFile, function(x) { list(x, x) }) expect_equal(actual, expected) - + + unlink(fileName) +}) + +test_that("cartesian() on RDDs", { + rdd <- parallelize(sc, 1:3) + actual <- collect(cartesian(rdd, rdd)) + expect_equal(sortKeyValueList(actual), + list( + list(1, 1), list(1, 2), list(1, 3), + list(2, 1), list(2, 2), list(2, 3), + list(3, 1), list(3, 2), list(3, 3))) + + # test case where one RDD is empty + emptyRdd <- parallelize(sc, list()) + actual <- collect(cartesian(rdd, emptyRdd)) + expect_equal(actual, list()) + + mockFile = c("Spark is pretty.", "Spark is awesome.") + fileName <- tempfile(pattern="spark-test", fileext=".tmp") + writeLines(mockFile, fileName) + + rdd <- textFile(sc, fileName) + actual <- collect(cartesian(rdd, rdd)) + expected <- list( + list("Spark is awesome.", "Spark is pretty."), + list("Spark is awesome.", "Spark is awesome."), + list("Spark is pretty.", "Spark is pretty."), + list("Spark is pretty.", "Spark is awesome.")) + expect_equal(sortKeyValueList(actual), expected) + + rdd1 <- parallelize(sc, 0:1) + actual <- collect(cartesian(rdd1, rdd)) + expect_equal(sortKeyValueList(actual), + list( + list(0, "Spark is pretty."), + list(0, "Spark is awesome."), + list(1, "Spark is pretty."), + list(1, "Spark is awesome."))) + + rdd1 <- map(rdd, function(x) { x }) + actual <- collect(cartesian(rdd, rdd1)) + expect_equal(sortKeyValueList(actual), expected) + unlink(fileName) }) +test_that("subtract() on RDDs", { + l <- list(1, 1, 2, 2, 3, 4) + rdd1 <- parallelize(sc, l) + + # subtract by itself + actual <- collect(subtract(rdd1, rdd1)) + expect_equal(actual, list()) + + # subtract by an empty RDD + rdd2 <- parallelize(sc, list()) + actual <- collect(subtract(rdd1, rdd2)) + expect_equal(as.list(sort(as.vector(actual, mode="integer"))), + l) + + rdd2 <- parallelize(sc, list(2, 4)) + actual <- collect(subtract(rdd1, rdd2)) + expect_equal(as.list(sort(as.vector(actual, mode="integer"))), + list(1, 1, 3)) + + l <- list("a", "a", "b", "b", "c", "d") + rdd1 <- parallelize(sc, l) + rdd2 <- parallelize(sc, list("b", "d")) + actual <- collect(subtract(rdd1, rdd2)) + expect_equal(as.list(sort(as.vector(actual, mode="character"))), + list("a", "a", "c")) +}) + +test_that("subtractByKey() on pairwise RDDs", { + l <- list(list("a", 1), list("b", 4), + list("b", 5), list("a", 2)) + rdd1 <- parallelize(sc, l) + + # subtractByKey by itself + actual <- collect(subtractByKey(rdd1, rdd1)) + expect_equal(actual, list()) + + # subtractByKey by an empty RDD + rdd2 <- parallelize(sc, list()) + actual <- collect(subtractByKey(rdd1, rdd2)) + expect_equal(sortKeyValueList(actual), + sortKeyValueList(l)) + + rdd2 <- parallelize(sc, list(list("a", 3), list("c", 1))) + actual <- collect(subtractByKey(rdd1, rdd2)) + expect_equal(actual, + list(list("b", 4), list("b", 5))) + + l <- list(list(1, 1), list(2, 4), + list(2, 5), list(1, 2)) + rdd1 <- parallelize(sc, l) + rdd2 <- parallelize(sc, list(list(1, 3), list(3, 1))) + actual <- collect(subtractByKey(rdd1, rdd2)) + expect_equal(actual, + list(list(2, 4), list(2, 5))) +}) + +test_that("intersection() on RDDs", { + # intersection with self + actual <- collect(intersection(rdd, rdd)) + expect_equal(sort(as.integer(actual)), nums) + + # intersection with an empty RDD + emptyRdd <- parallelize(sc, list()) + actual <- collect(intersection(rdd, emptyRdd)) + expect_equal(actual, list()) + + rdd1 <- parallelize(sc, list(1, 10, 2, 3, 4, 5)) + rdd2 <- parallelize(sc, list(1, 6, 2, 3, 7, 8)) + actual <- collect(intersection(rdd1, rdd2)) + expect_equal(sort(as.integer(actual)), 1:3) +}) + test_that("join() on pairwise RDDs", { rdd1 <- parallelize(sc, list(list(1,1), list(2,4))) rdd2 <- parallelize(sc, list(list(1,2), list(1,3))) @@ -596,9 +711,9 @@ test_that("sortByKey() on pairwise RDDs", { sortedRdd3 <- sortByKey(rdd3) actual <- collect(sortedRdd3) expect_equal(actual, list(list("1", 3), list("2", 5), list("a", 1), list("b", 2), list("d", 4))) - + # test on the boundary cases - + # boundary case 1: the RDD to be sorted has only 1 partition rdd4 <- parallelize(sc, l, 1L) sortedRdd4 <- sortByKey(rdd4) @@ -623,7 +738,7 @@ test_that("sortByKey() on pairwise RDDs", { rdd7 <- parallelize(sc, l3, 2L) sortedRdd7 <- sortByKey(rdd7) actual <- collect(sortedRdd7) - expect_equal(actual, l3) + expect_equal(actual, l3) }) test_that("collectAsMap() on a pairwise RDD", { @@ -634,12 +749,36 @@ test_that("collectAsMap() on a pairwise RDD", { rdd <- parallelize(sc, list(list("a", 1), list("b", 2))) vals <- collectAsMap(rdd) expect_equal(vals, list(a = 1, b = 2)) - + rdd <- parallelize(sc, list(list(1.1, 2.2), list(1.2, 2.4))) vals <- collectAsMap(rdd) expect_equal(vals, list(`1.1` = 2.2, `1.2` = 2.4)) - + rdd <- parallelize(sc, list(list(1, "a"), list(2, "b"))) vals <- collectAsMap(rdd) expect_equal(vals, list(`1` = "a", `2` = "b")) }) + +test_that("sampleByKey() on pairwise RDDs", { + rdd <- parallelize(sc, 1:2000) + pairsRDD <- lapply(rdd, function(x) { if (x %% 2 == 0) list("a", x) else list("b", x) }) + fractions <- list(a = 0.2, b = 0.1) + sample <- sampleByKey(pairsRDD, FALSE, fractions, 1618L) + expect_equal(100 < length(lookup(sample, "a")) && 300 > length(lookup(sample, "a")), TRUE) + expect_equal(50 < length(lookup(sample, "b")) && 150 > length(lookup(sample, "b")), TRUE) + expect_equal(lookup(sample, "a")[which.min(lookup(sample, "a"))] >= 0, TRUE) + expect_equal(lookup(sample, "a")[which.max(lookup(sample, "a"))] <= 2000, TRUE) + expect_equal(lookup(sample, "b")[which.min(lookup(sample, "b"))] >= 0, TRUE) + expect_equal(lookup(sample, "b")[which.max(lookup(sample, "b"))] <= 2000, TRUE) + + rdd <- parallelize(sc, 1:2000) + pairsRDD <- lapply(rdd, function(x) { if (x %% 2 == 0) list(2, x) else list(3, x) }) + fractions <- list(`2` = 0.2, `3` = 0.1) + sample <- sampleByKey(pairsRDD, TRUE, fractions, 1618L) + expect_equal(100 < length(lookup(sample, 2)) && 300 > length(lookup(sample, 2)), TRUE) + expect_equal(50 < length(lookup(sample, 3)) && 150 > length(lookup(sample, 3)), TRUE) + expect_equal(lookup(sample, 2)[which.min(lookup(sample, 2))] >= 0, TRUE) + expect_equal(lookup(sample, 2)[which.max(lookup(sample, 2))] <= 2000, TRUE) + expect_equal(lookup(sample, 3)[which.min(lookup(sample, 3))] >= 0, TRUE) + expect_equal(lookup(sample, 3)[which.max(lookup(sample, 3))] <= 2000, TRUE) +}) diff --git a/R/pkg/inst/tests/test_shuffle.R b/R/pkg/inst/tests/test_shuffle.R index d1da8232aea81..d7dedda553c56 100644 --- a/R/pkg/inst/tests/test_shuffle.R +++ b/R/pkg/inst/tests/test_shuffle.R @@ -87,6 +87,18 @@ test_that("combineByKey for doubles", { expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) }) +test_that("combineByKey for characters", { + stringKeyRDD <- parallelize(sc, + list(list("max", 1L), list("min", 2L), + list("other", 3L), list("max", 4L)), 2L) + reduced <- combineByKey(stringKeyRDD, + function(x) { x }, "+", "+", 2L) + actual <- collect(reduced) + + expected <- list(list("max", 5L), list("min", 2L), list("other", 3L)) + expect_equal(sortKeyValueList(actual), sortKeyValueList(expected)) +}) + test_that("aggregateByKey", { # test aggregateByKey for int keys rdd <- parallelize(sc, list(list(1, 1), list(1, 2), list(2, 3), list(2, 4))) diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R index cf5cf6d1692af..25831ae2d9e18 100644 --- a/R/pkg/inst/tests/test_sparkSQL.R +++ b/R/pkg/inst/tests/test_sparkSQL.R @@ -44,9 +44,8 @@ test_that("infer types", { expect_equal(infer_type(list(1L, 2L)), list(type = 'array', elementType = "integer", containsNull = TRUE)) expect_equal(infer_type(list(a = 1L, b = "2")), - list(type = "struct", - fields = list(list(name = "a", type = "integer", nullable = TRUE), - list(name = "b", type = "string", nullable = TRUE)))) + structType(structField(x = "a", type = "integer", nullable = TRUE), + structField(x = "b", type = "string", nullable = TRUE))) e <- new.env() assign("a", 1L, envir = e) expect_equal(infer_type(e), @@ -54,6 +53,18 @@ test_that("infer types", { valueContainsNull = TRUE)) }) +test_that("structType and structField", { + testField <- structField("a", "string") + expect_true(inherits(testField, "structField")) + expect_true(testField$name() == "a") + expect_true(testField$nullable()) + + testSchema <- structType(testField, structField("b", "integer")) + expect_true(inherits(testSchema, "structType")) + expect_true(inherits(testSchema$fields()[[2]], "structField")) + expect_true(testSchema$fields()[[1]]$dataType.toString() == "StringType") +}) + test_that("create DataFrame from RDD", { rdd <- lapply(parallelize(sc, 1:10), function(x) { list(x, as.character(x)) }) df <- createDataFrame(sqlCtx, rdd, list("a", "b")) @@ -66,9 +77,8 @@ test_that("create DataFrame from RDD", { expect_true(inherits(df, "DataFrame")) expect_equal(columns(df), c("_1", "_2")) - fields <- list(list(name = "a", type = "integer", nullable = TRUE), - list(name = "b", type = "string", nullable = TRUE)) - schema <- list(type = "struct", fields = fields) + schema <- structType(structField(x = "a", type = "integer", nullable = TRUE), + structField(x = "b", type = "string", nullable = TRUE)) df <- createDataFrame(sqlCtx, rdd, schema) expect_true(inherits(df, "DataFrame")) expect_equal(columns(df), c("a", "b")) @@ -94,9 +104,8 @@ test_that("toDF", { expect_true(inherits(df, "DataFrame")) expect_equal(columns(df), c("_1", "_2")) - fields <- list(list(name = "a", type = "integer", nullable = TRUE), - list(name = "b", type = "string", nullable = TRUE)) - schema <- list(type = "struct", fields = fields) + schema <- structType(structField(x = "a", type = "integer", nullable = TRUE), + structField(x = "b", type = "string", nullable = TRUE)) df <- toDF(rdd, schema) expect_true(inherits(df, "DataFrame")) expect_equal(columns(df), c("a", "b")) @@ -635,7 +644,7 @@ test_that("isLocal()", { expect_false(isLocal(df)) }) -test_that("unionAll(), subtract(), and intersect() on a DataFrame", { +test_that("unionAll(), except(), and intersect() on a DataFrame", { df <- jsonFile(sqlCtx, jsonPath) lines <- c("{\"name\":\"Bob\", \"age\":24}", @@ -650,10 +659,10 @@ test_that("unionAll(), subtract(), and intersect() on a DataFrame", { expect_true(count(unioned) == 6) expect_true(first(unioned)$name == "Michael") - subtracted <- sortDF(subtract(df, df2), desc(df$age)) + excepted <- sortDF(except(df, df2), desc(df$age)) expect_true(inherits(unioned, "DataFrame")) - expect_true(count(subtracted) == 2) - expect_true(first(subtracted)$name == "Justin") + expect_true(count(excepted) == 2) + expect_true(first(excepted)$name == "Justin") intersected <- sortDF(intersect(df, df2), df$age) expect_true(inherits(unioned, "DataFrame")) diff --git a/R/pkg/inst/worker/worker.R b/R/pkg/inst/worker/worker.R index c6542928e8ddd..014bf7bd7b3fe 100644 --- a/R/pkg/inst/worker/worker.R +++ b/R/pkg/inst/worker/worker.R @@ -17,6 +17,23 @@ # Worker class +# Get current system time +currentTimeSecs <- function() { + as.numeric(Sys.time()) +} + +# Get elapsed time +elapsedSecs <- function() { + proc.time()[3] +} + +# Constants +specialLengths <- list(END_OF_STERAM = 0L, TIMING_DATA = -1L) + +# Timing R process boot +bootTime <- currentTimeSecs() +bootElap <- elapsedSecs() + rLibDir <- Sys.getenv("SPARKR_RLIBDIR") # Set libPaths to include SparkR package as loadNamespace needs this # TODO: Figure out if we can avoid this by not loading any objects that require @@ -37,7 +54,7 @@ serializer <- SparkR:::readString(inputCon) # Include packages as required packageNames <- unserialize(SparkR:::readRaw(inputCon)) for (pkg in packageNames) { - suppressPackageStartupMessages(require(as.character(pkg), character.only=TRUE)) + suppressPackageStartupMessages(library(as.character(pkg), character.only=TRUE)) } # read function dependencies @@ -46,6 +63,9 @@ computeFunc <- unserialize(SparkR:::readRawLen(inputCon, funcLen)) env <- environment(computeFunc) parent.env(env) <- .GlobalEnv # Attach under global environment. +# Timing init envs for computing +initElap <- elapsedSecs() + # Read and set broadcast variables numBroadcastVars <- SparkR:::readInt(inputCon) if (numBroadcastVars > 0) { @@ -56,6 +76,9 @@ if (numBroadcastVars > 0) { } } +# Timing broadcast +broadcastElap <- elapsedSecs() + # If -1: read as normal RDD; if >= 0, treat as pairwise RDD and treat the int # as number of partitions to create. numPartitions <- SparkR:::readInt(inputCon) @@ -73,14 +96,23 @@ if (isEmpty != 0) { } else if (deserializer == "row") { data <- SparkR:::readDeserializeRows(inputCon) } + # Timing reading input data for execution + inputElap <- elapsedSecs() + output <- computeFunc(partition, data) + # Timing computing + computeElap <- elapsedSecs() + if (serializer == "byte") { SparkR:::writeRawSerialize(outputCon, output) } else if (serializer == "row") { SparkR:::writeRowSerialize(outputCon, output) } else { - SparkR:::writeStrings(outputCon, output) + # write lines one-by-one with flag + lapply(output, function(line) SparkR:::writeString(outputCon, line)) } + # Timing output + outputElap <- elapsedSecs() } else { if (deserializer == "byte") { # Now read as many characters as described in funcLen @@ -90,6 +122,8 @@ if (isEmpty != 0) { } else if (deserializer == "row") { data <- SparkR:::readDeserializeRows(inputCon) } + # Timing reading input data for execution + inputElap <- elapsedSecs() res <- new.env() @@ -107,6 +141,8 @@ if (isEmpty != 0) { res[[bucket]] <- acc } invisible(lapply(data, hashTupleToEnvir)) + # Timing computing + computeElap <- elapsedSecs() # Step 2: write out all of the environment as key-value pairs. for (name in ls(res)) { @@ -116,13 +152,26 @@ if (isEmpty != 0) { length(res[[name]]$data) <- res[[name]]$counter SparkR:::writeRawSerialize(outputCon, res[[name]]$data) } + # Timing output + outputElap <- elapsedSecs() } +} else { + inputElap <- broadcastElap + computeElap <- broadcastElap + outputElap <- broadcastElap } +# Report timing +SparkR:::writeInt(outputCon, specialLengths$TIMING_DATA) +SparkR:::writeDouble(outputCon, bootTime) +SparkR:::writeDouble(outputCon, initElap - bootElap) # init +SparkR:::writeDouble(outputCon, broadcastElap - initElap) # broadcast +SparkR:::writeDouble(outputCon, inputElap - broadcastElap) # input +SparkR:::writeDouble(outputCon, computeElap - inputElap) # compute +SparkR:::writeDouble(outputCon, outputElap - computeElap) # output + # End of output -if (serializer %in% c("byte", "row")) { - SparkR:::writeInt(outputCon, 0L) -} +SparkR:::writeInt(outputCon, specialLengths$END_OF_STERAM) close(outputCon) close(inputCon) diff --git a/core/src/main/scala/org/apache/spark/api/r/RRDD.scala b/core/src/main/scala/org/apache/spark/api/r/RRDD.scala index 5fa4d483b8342..6fea5e1144f2f 100644 --- a/core/src/main/scala/org/apache/spark/api/r/RRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/r/RRDD.scala @@ -42,10 +42,15 @@ private abstract class BaseRRDD[T: ClassTag, U: ClassTag]( rLibDir: String, broadcastVars: Array[Broadcast[Object]]) extends RDD[U](parent) with Logging { + protected var dataStream: DataInputStream = _ + private var bootTime: Double = _ override def getPartitions: Array[Partition] = parent.partitions override def compute(partition: Partition, context: TaskContext): Iterator[U] = { + // Timing start + bootTime = System.currentTimeMillis / 1000.0 + // The parent may be also an RRDD, so we should launch it first. val parentIterator = firstParent[T].iterator(partition, context) @@ -69,7 +74,7 @@ private abstract class BaseRRDD[T: ClassTag, U: ClassTag]( // the socket used to receive the output of task val outSocket = serverSocket.accept() val inputStream = new BufferedInputStream(outSocket.getInputStream) - val dataStream = openDataStream(inputStream) + dataStream = new DataInputStream(inputStream) serverSocket.close() try { @@ -155,6 +160,7 @@ private abstract class BaseRRDD[T: ClassTag, U: ClassTag]( } else if (deserializer == SerializationFormats.ROW) { dataOut.write(elem.asInstanceOf[Array[Byte]]) } else if (deserializer == SerializationFormats.STRING) { + // write string(for StringRRDD) printOut.println(elem) } } @@ -180,9 +186,41 @@ private abstract class BaseRRDD[T: ClassTag, U: ClassTag]( }.start() } - protected def openDataStream(input: InputStream): Closeable + protected def readData(length: Int): U - protected def read(): U + protected def read(): U = { + try { + val length = dataStream.readInt() + + length match { + case SpecialLengths.TIMING_DATA => + // Timing data from R worker + val boot = dataStream.readDouble - bootTime + val init = dataStream.readDouble + val broadcast = dataStream.readDouble + val input = dataStream.readDouble + val compute = dataStream.readDouble + val output = dataStream.readDouble + logInfo( + ("Times: boot = %.3f s, init = %.3f s, broadcast = %.3f s, " + + "read-input = %.3f s, compute = %.3f s, write-output = %.3f s, " + + "total = %.3f s").format( + boot, + init, + broadcast, + input, + compute, + output, + boot + init + broadcast + input + compute + output)) + read() + case length if length >= 0 => + readData(length) + } + } catch { + case eof: EOFException => + throw new SparkException("R worker exited unexpectedly (cranshed)", eof) + } + } } /** @@ -202,31 +240,16 @@ private class PairwiseRRDD[T: ClassTag]( SerializationFormats.BYTE, packageNames, rLibDir, broadcastVars.map(x => x.asInstanceOf[Broadcast[Object]])) { - private var dataStream: DataInputStream = _ - - override protected def openDataStream(input: InputStream): Closeable = { - dataStream = new DataInputStream(input) - dataStream - } - - override protected def read(): (Int, Array[Byte]) = { - try { - val length = dataStream.readInt() - - length match { - case length if length == 2 => - val hashedKey = dataStream.readInt() - val contentPairsLength = dataStream.readInt() - val contentPairs = new Array[Byte](contentPairsLength) - dataStream.readFully(contentPairs) - (hashedKey, contentPairs) - case _ => null // End of input - } - } catch { - case eof: EOFException => { - throw new SparkException("R worker exited unexpectedly (crashed)", eof) - } - } + override protected def readData(length: Int): (Int, Array[Byte]) = { + length match { + case length if length == 2 => + val hashedKey = dataStream.readInt() + val contentPairsLength = dataStream.readInt() + val contentPairs = new Array[Byte](contentPairsLength) + dataStream.readFully(contentPairs) + (hashedKey, contentPairs) + case _ => null + } } lazy val asJavaPairRDD : JavaPairRDD[Int, Array[Byte]] = JavaPairRDD.fromRDD(this) @@ -247,28 +270,13 @@ private class RRDD[T: ClassTag]( parent, -1, func, deserializer, serializer, packageNames, rLibDir, broadcastVars.map(x => x.asInstanceOf[Broadcast[Object]])) { - private var dataStream: DataInputStream = _ - - override protected def openDataStream(input: InputStream): Closeable = { - dataStream = new DataInputStream(input) - dataStream - } - - override protected def read(): Array[Byte] = { - try { - val length = dataStream.readInt() - - length match { - case length if length > 0 => - val obj = new Array[Byte](length) - dataStream.readFully(obj, 0, length) - obj - case _ => null - } - } catch { - case eof: EOFException => { - throw new SparkException("R worker exited unexpectedly (crashed)", eof) - } + override protected def readData(length: Int): Array[Byte] = { + length match { + case length if length > 0 => + val obj = new Array[Byte](length) + dataStream.readFully(obj) + obj + case _ => null } } @@ -289,26 +297,21 @@ private class StringRRDD[T: ClassTag]( parent, -1, func, deserializer, SerializationFormats.STRING, packageNames, rLibDir, broadcastVars.map(x => x.asInstanceOf[Broadcast[Object]])) { - private var dataStream: BufferedReader = _ - - override protected def openDataStream(input: InputStream): Closeable = { - dataStream = new BufferedReader(new InputStreamReader(input)) - dataStream - } - - override protected def read(): String = { - try { - dataStream.readLine() - } catch { - case e: IOException => { - throw new SparkException("R worker exited unexpectedly (crashed)", e) - } + override protected def readData(length: Int): String = { + length match { + case length if length > 0 => + SerDe.readStringBytes(dataStream, length) + case _ => null } } lazy val asJavaRDD : JavaRDD[String] = JavaRDD.fromRDD(this) } +private object SpecialLengths { + val TIMING_DATA = -1 +} + private[r] class BufferedStreamThread( in: InputStream, name: String, diff --git a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala index ccb2a371f4e48..371dfe454d1a2 100644 --- a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala +++ b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala @@ -85,13 +85,17 @@ private[spark] object SerDe { in.readDouble() } + def readStringBytes(in: DataInputStream, len: Int): String = { + val bytes = new Array[Byte](len) + in.readFully(bytes) + assert(bytes(len - 1) == 0) + val str = new String(bytes.dropRight(1), "UTF-8") + str + } + def readString(in: DataInputStream): String = { val len = in.readInt() - val asciiBytes = new Array[Byte](len) - in.readFully(asciiBytes) - assert(asciiBytes(len - 1) == 0) - val str = new String(asciiBytes.dropRight(1).map(_.toChar)) - str + readStringBytes(in, len) } def readBoolean(in: DataInputStream): Boolean = { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala index d1ea7cc3e9162..ae77f72998a22 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala @@ -23,7 +23,7 @@ import org.apache.spark.api.java.{JavaRDD, JavaSparkContext} import org.apache.spark.api.r.SerDe import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.expressions.{Alias, Expression, NamedExpression} -import org.apache.spark.sql.types.{DataType, StructType} +import org.apache.spark.sql.types._ import org.apache.spark.sql.{Column, DataFrame, GroupedData, Row, SQLContext, SaveMode} private[r] object SQLUtils { @@ -39,8 +39,34 @@ private[r] object SQLUtils { arr.toSeq } - def createDF(rdd: RDD[Array[Byte]], schemaString: String, sqlContext: SQLContext): DataFrame = { - val schema = DataType.fromJson(schemaString).asInstanceOf[StructType] + def createStructType(fields : Seq[StructField]): StructType = { + StructType(fields) + } + + def getSQLDataType(dataType: String): DataType = { + dataType match { + case "byte" => org.apache.spark.sql.types.ByteType + case "integer" => org.apache.spark.sql.types.IntegerType + case "double" => org.apache.spark.sql.types.DoubleType + case "numeric" => org.apache.spark.sql.types.DoubleType + case "character" => org.apache.spark.sql.types.StringType + case "string" => org.apache.spark.sql.types.StringType + case "binary" => org.apache.spark.sql.types.BinaryType + case "raw" => org.apache.spark.sql.types.BinaryType + case "logical" => org.apache.spark.sql.types.BooleanType + case "boolean" => org.apache.spark.sql.types.BooleanType + case "timestamp" => org.apache.spark.sql.types.TimestampType + case "date" => org.apache.spark.sql.types.DateType + case _ => throw new IllegalArgumentException(s"Invaid type $dataType") + } + } + + def createStructField(name: String, dataType: String, nullable: Boolean): StructField = { + val dtObj = getSQLDataType(dataType) + StructField(name, dtObj, nullable) + } + + def createDF(rdd: RDD[Array[Byte]], schema: StructType, sqlContext: SQLContext): DataFrame = { val num = schema.fields.size val rowRDD = rdd.map(bytesToRow) sqlContext.createDataFrame(rowRDD, schema) From d305e686b3d73213784bd75cdad7d168b22a1dc4 Mon Sep 17 00:00:00 2001 From: Olivier Girardot Date: Fri, 17 Apr 2015 16:23:10 -0500 Subject: [PATCH 327/591] SPARK-6988 : Fix documentation regarding DataFrames using the Java API This patch includes : * adding how to use map after an sql query using javaRDD * fixing the first few java examples that were written in Scala Thank you for your time, Olivier. Author: Olivier Girardot Closes #5564 from ogirardot/branch-1.3 and squashes the following commits: 9f8d60e [Olivier Girardot] SPARK-6988 : Fix documentation regarding DataFrames using the Java API (cherry picked from commit 6b528dc139da594ef2e651d84bd91fe0f738a39d) Signed-off-by: Reynold Xin --- docs/sql-programming-guide.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index 03500867df70f..d49233714a0bb 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -193,8 +193,8 @@ df.groupBy("age").count().show()
    {% highlight java %} -val sc: JavaSparkContext // An existing SparkContext. -val sqlContext = new org.apache.spark.sql.SQLContext(sc) +JavaSparkContext sc // An existing SparkContext. +SQLContext sqlContext = new org.apache.spark.sql.SQLContext(sc) // Create the DataFrame DataFrame df = sqlContext.jsonFile("examples/src/main/resources/people.json"); @@ -308,8 +308,8 @@ val df = sqlContext.sql("SELECT * FROM table")
    {% highlight java %} -val sqlContext = ... // An existing SQLContext -val df = sqlContext.sql("SELECT * FROM table") +SQLContext sqlContext = ... // An existing SQLContext +DataFrame df = sqlContext.sql("SELECT * FROM table") {% endhighlight %}
    @@ -435,7 +435,7 @@ DataFrame teenagers = sqlContext.sql("SELECT name FROM people WHERE age >= 13 AN // The results of SQL queries are DataFrames and support all the normal RDD operations. // The columns of a row in the result can be accessed by ordinal. -List teenagerNames = teenagers.map(new Function() { +List teenagerNames = teenagers.javaRDD().map(new Function() { public String call(Row row) { return "Name: " + row.getString(0); } @@ -599,7 +599,7 @@ DataFrame results = sqlContext.sql("SELECT name FROM people"); // The results of SQL queries are DataFrames and support all the normal RDD operations. // The columns of a row in the result can be accessed by ordinal. -List names = results.map(new Function() { +List names = results.javaRDD().map(new Function() { public String call(Row row) { return "Name: " + row.getString(0); } @@ -860,7 +860,7 @@ DataFrame parquetFile = sqlContext.parquetFile("people.parquet"); //Parquet files can also be registered as tables and then used in SQL statements. parquetFile.registerTempTable("parquetFile"); DataFrame teenagers = sqlContext.sql("SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19"); -List teenagerNames = teenagers.map(new Function() { +List teenagerNames = teenagers.javaRDD().map(new Function() { public String call(Row row) { return "Name: " + row.getString(0); } From a452c59210cf2c8ff8601cdb11401eea6dc14973 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Fri, 17 Apr 2015 16:30:13 -0500 Subject: [PATCH 328/591] Minor fix to SPARK-6958: Improve Python docstring for DataFrame.sort. As a follow up PR to #5544. cc davies Author: Reynold Xin Closes #5558 from rxin/sort-doc-improvement and squashes the following commits: f4c276f [Reynold Xin] Review feedback. d2dcf24 [Reynold Xin] Minor fix to SPARK-6958: Improve Python docstring for DataFrame.sort. --- python/pyspark/sql/dataframe.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index 326d22e72f104..d70c5b0a6930c 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -489,8 +489,9 @@ def sort(self, *cols, **kwargs): """Returns a new :class:`DataFrame` sorted by the specified column(s). :param cols: list of :class:`Column` or column names to sort by. - :param ascending: sort by ascending order or not, could be bool, int - or list of bool, int (default: True). + :param ascending: boolean or list of boolean (default True). + Sort ascending vs. descending. Specify list for multiple sort orders. + If a list is specified, length of the list must equal length of the `cols`. >>> df.sort(df.age.desc()).collect() [Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')] @@ -519,7 +520,7 @@ def sort(self, *cols, **kwargs): jcols = [jc if asc else jc.desc() for asc, jc in zip(ascending, jcols)] else: - raise TypeError("ascending can only be bool or list, but got %s" % type(ascending)) + raise TypeError("ascending can only be boolean or list, but got %s" % type(ascending)) jdf = self._jdf.sort(self._jseq(jcols)) return DataFrame(jdf, self.sql_ctx) From c5ed510135aee3a1a0402057b3b5229892aa6f3a Mon Sep 17 00:00:00 2001 From: Ilya Ganelin Date: Fri, 17 Apr 2015 18:28:42 -0700 Subject: [PATCH 329/591] [SPARK-6703][Core] Provide a way to discover existing SparkContext's I've added a static getOrCreate method to the static SparkContext object that allows one to either retrieve a previously created SparkContext or to instantiate a new one with the provided config. The method accepts an optional SparkConf to make usage intuitive. Still working on a test for this, basically want to create a new context from scratch, then ensure that subsequent calls don't overwrite that. Author: Ilya Ganelin Closes #5501 from ilganeli/SPARK-6703 and squashes the following commits: db9a963 [Ilya Ganelin] Closing second spark context 1dc0444 [Ilya Ganelin] Added ref equality check 8c884fa [Ilya Ganelin] Made getOrCreate synchronized cb0c6b7 [Ilya Ganelin] Doc updates and code cleanup 270cfe3 [Ilya Ganelin] [SPARK-6703] Documentation fixes 15e8dea [Ilya Ganelin] Updated comments and added MiMa Exclude 0e1567c [Ilya Ganelin] Got rid of unecessary option for AtomicReference dfec4da [Ilya Ganelin] Changed activeContext to AtomicReference 733ec9f [Ilya Ganelin] Fixed some bugs in test code 8be2f83 [Ilya Ganelin] Replaced match with if e92caf7 [Ilya Ganelin] [SPARK-6703] Added test to ensure that getOrCreate both allows creation, retrieval, and a second context if desired a99032f [Ilya Ganelin] Spacing fix d7a06b8 [Ilya Ganelin] Updated SparkConf class to add getOrCreate method. Started test suite implementation --- .../scala/org/apache/spark/SparkContext.scala | 49 ++++++++++++++++--- .../org/apache/spark/SparkContextSuite.scala | 20 ++++++++ project/MimaExcludes.scala | 4 ++ 3 files changed, 66 insertions(+), 7 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index e106c5c4bef60..86269eac52db0 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -23,7 +23,7 @@ import java.io._ import java.lang.reflect.Constructor import java.net.URI import java.util.{Arrays, Properties, UUID} -import java.util.concurrent.atomic.{AtomicBoolean, AtomicInteger} +import java.util.concurrent.atomic.{AtomicReference, AtomicBoolean, AtomicInteger} import java.util.UUID.randomUUID import scala.collection.{Map, Set} @@ -1887,11 +1887,12 @@ object SparkContext extends Logging { private val SPARK_CONTEXT_CONSTRUCTOR_LOCK = new Object() /** - * The active, fully-constructed SparkContext. If no SparkContext is active, then this is `None`. + * The active, fully-constructed SparkContext. If no SparkContext is active, then this is `null`. * - * Access to this field is guarded by SPARK_CONTEXT_CONSTRUCTOR_LOCK + * Access to this field is guarded by SPARK_CONTEXT_CONSTRUCTOR_LOCK. */ - private var activeContext: Option[SparkContext] = None + private val activeContext: AtomicReference[SparkContext] = + new AtomicReference[SparkContext](null) /** * Points to a partially-constructed SparkContext if some thread is in the SparkContext @@ -1926,7 +1927,8 @@ object SparkContext extends Logging { logWarning(warnMsg) } - activeContext.foreach { ctx => + if (activeContext.get() != null) { + val ctx = activeContext.get() val errMsg = "Only one SparkContext may be running in this JVM (see SPARK-2243)." + " To ignore this error, set spark.driver.allowMultipleContexts = true. " + s"The currently running SparkContext was created at:\n${ctx.creationSite.longForm}" @@ -1941,6 +1943,39 @@ object SparkContext extends Logging { } } + /** + * This function may be used to get or instantiate a SparkContext and register it as a + * singleton object. Because we can only have one active SparkContext per JVM, + * this is useful when applications may wish to share a SparkContext. + * + * Note: This function cannot be used to create multiple SparkContext instances + * even if multiple contexts are allowed. + */ + def getOrCreate(config: SparkConf): SparkContext = { + // Synchronize to ensure that multiple create requests don't trigger an exception + // from assertNoOtherContextIsRunning within setActiveContext + SPARK_CONTEXT_CONSTRUCTOR_LOCK.synchronized { + if (activeContext.get() == null) { + setActiveContext(new SparkContext(config), allowMultipleContexts = false) + } + activeContext.get() + } + } + + /** + * This function may be used to get or instantiate a SparkContext and register it as a + * singleton object. Because we can only have one active SparkContext per JVM, + * this is useful when applications may wish to share a SparkContext. + * + * This method allows not passing a SparkConf (useful if just retrieving). + * + * Note: This function cannot be used to create multiple SparkContext instances + * even if multiple contexts are allowed. + */ + def getOrCreate(): SparkContext = { + getOrCreate(new SparkConf()) + } + /** * Called at the beginning of the SparkContext constructor to ensure that no SparkContext is * running. Throws an exception if a running context is detected and logs a warning if another @@ -1967,7 +2002,7 @@ object SparkContext extends Logging { SPARK_CONTEXT_CONSTRUCTOR_LOCK.synchronized { assertNoOtherContextIsRunning(sc, allowMultipleContexts) contextBeingConstructed = None - activeContext = Some(sc) + activeContext.set(sc) } } @@ -1978,7 +2013,7 @@ object SparkContext extends Logging { */ private[spark] def clearActiveContext(): Unit = { SPARK_CONTEXT_CONSTRUCTOR_LOCK.synchronized { - activeContext = None + activeContext.set(null) } } diff --git a/core/src/test/scala/org/apache/spark/SparkContextSuite.scala b/core/src/test/scala/org/apache/spark/SparkContextSuite.scala index 94be1c6d6397c..728558a424780 100644 --- a/core/src/test/scala/org/apache/spark/SparkContextSuite.scala +++ b/core/src/test/scala/org/apache/spark/SparkContextSuite.scala @@ -67,6 +67,26 @@ class SparkContextSuite extends FunSuite with LocalSparkContext { } } + test("Test getOrCreate") { + var sc2: SparkContext = null + SparkContext.clearActiveContext() + val conf = new SparkConf().setAppName("test").setMaster("local") + + sc = SparkContext.getOrCreate(conf) + + assert(sc.getConf.get("spark.app.name").equals("test")) + sc2 = SparkContext.getOrCreate(new SparkConf().setAppName("test2").setMaster("local")) + assert(sc2.getConf.get("spark.app.name").equals("test")) + assert(sc === sc2) + assert(sc eq sc2) + + // Try creating second context to confirm that it's still possible, if desired + sc2 = new SparkContext(new SparkConf().setAppName("test3").setMaster("local") + .set("spark.driver.allowMultipleContexts", "true")) + + sc2.stop() + } + test("BytesWritable implicit conversion is correct") { // Regression test for SPARK-3121 val bytesWritable = new BytesWritable() diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala index 1564babefa62f..7ef363a2f07ad 100644 --- a/project/MimaExcludes.scala +++ b/project/MimaExcludes.scala @@ -68,6 +68,10 @@ object MimaExcludes { // SPARK-6693 add tostring with max lines and width for matrix ProblemFilters.exclude[MissingMethodProblem]( "org.apache.spark.mllib.linalg.Matrix.toString") + )++ Seq( + // SPARK-6703 Add getOrCreate method to SparkContext + ProblemFilters.exclude[IncompatibleResultTypeProblem] + ("org.apache.spark.SparkContext.org$apache$spark$SparkContext$$activeContext") ) case v if v.startsWith("1.3") => From 6fbeb82e13db7117d8f216e6148632490a4bc5be Mon Sep 17 00:00:00 2001 From: Jongyoul Lee Date: Fri, 17 Apr 2015 18:30:55 -0700 Subject: [PATCH 330/591] [SPARK-6350][Mesos] Make mesosExecutorCores configurable in mesos "fine-grained" mode - Defined executorCores from "spark.mesos.executor.cores" - Changed the amount of mesosExecutor's cores to executorCores. - Added new configuration option on running-on-mesos.md Author: Jongyoul Lee Closes #5063 from jongyoul/SPARK-6350 and squashes the following commits: 9238d6e [Jongyoul Lee] [SPARK-6350][Mesos] Make mesosExecutorCores configurable in mesos "fine-grained" mode - Fixed docs - Changed configuration name - Made mesosExecutorCores private 2d41241 [Jongyoul Lee] [SPARK-6350][Mesos] Make mesosExecutorCores configurable in mesos "fine-grained" mode - Fixed docs 89edb4f [Jongyoul Lee] [SPARK-6350][Mesos] Make mesosExecutorCores configurable in mesos "fine-grained" mode - Fixed docs 8ba7694 [Jongyoul Lee] [SPARK-6350][Mesos] Make mesosExecutorCores configurable in mesos "fine-grained" mode - Fixed docs 7549314 [Jongyoul Lee] [SPARK-6453][Mesos] Some Mesos*Suite have a different package with their classes - Fixed docs 4ae7b0c [Jongyoul Lee] [SPARK-6453][Mesos] Some Mesos*Suite have a different package with their classes - Removed TODO c27efce [Jongyoul Lee] [SPARK-6453][Mesos] Some Mesos*Suite have a different package with their classes - Fixed Mesos*Suite for supporting integer WorkerOffers - Fixed Documentation 1fe4c03 [Jongyoul Lee] [SPARK-6453][Mesos] Some Mesos*Suite have a different package with their classes - Change available resources of cpus to integer value beacuse WorkerOffer support the amount cpus as integer value 5f3767e [Jongyoul Lee] Revert "[SPARK-6350][Mesos] Make mesosExecutorCores configurable in mesos "fine-grained" mode" 4b7c69e [Jongyoul Lee] [SPARK-6350][Mesos] Make mesosExecutorCores configurable in mesos "fine-grained" mode - Changed configruation name and description from "spark.mesos.executor.cores" to "spark.executor.frameworkCores" 0556792 [Jongyoul Lee] [SPARK-6350][Mesos] Make mesosExecutorCores configurable in mesos "fine-grained" mode - Defined executorCores from "spark.mesos.executor.cores" - Changed the amount of mesosExecutor's cores to executorCores. - Added new configuration option on running-on-mesos.md --- .../cluster/mesos/MesosSchedulerBackend.scala | 14 +++++++------- .../cluster/mesos/MesosSchedulerBackendSuite.scala | 4 ++-- docs/running-on-mesos.md | 10 ++++++++++ 3 files changed, 19 insertions(+), 9 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala index b381436839227..d9d62b0e287ed 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala @@ -67,6 +67,8 @@ private[spark] class MesosSchedulerBackend( // The listener bus to publish executor added/removed events. val listenerBus = sc.listenerBus + + private[mesos] val mesosExecutorCores = sc.conf.getDouble("spark.mesos.mesosExecutor.cores", 1) @volatile var appId: String = _ @@ -139,7 +141,7 @@ private[spark] class MesosSchedulerBackend( .setName("cpus") .setType(Value.Type.SCALAR) .setScalar(Value.Scalar.newBuilder() - .setValue(scheduler.CPUS_PER_TASK).build()) + .setValue(mesosExecutorCores).build()) .build() val memory = Resource.newBuilder() .setName("mem") @@ -220,10 +222,9 @@ private[spark] class MesosSchedulerBackend( val mem = getResource(o.getResourcesList, "mem") val cpus = getResource(o.getResourcesList, "cpus") val slaveId = o.getSlaveId.getValue - // TODO(pwendell): Should below be 1 + scheduler.CPUS_PER_TASK? (mem >= MemoryUtils.calculateTotalMemory(sc) && // need at least 1 for executor, 1 for task - cpus >= 2 * scheduler.CPUS_PER_TASK) || + cpus >= (mesosExecutorCores + scheduler.CPUS_PER_TASK)) || (slaveIdsWithExecutors.contains(slaveId) && cpus >= scheduler.CPUS_PER_TASK) } @@ -232,10 +233,9 @@ private[spark] class MesosSchedulerBackend( val cpus = if (slaveIdsWithExecutors.contains(o.getSlaveId.getValue)) { getResource(o.getResourcesList, "cpus").toInt } else { - // If the executor doesn't exist yet, subtract CPU for executor - // TODO(pwendell): Should below just subtract "1"? - getResource(o.getResourcesList, "cpus").toInt - - scheduler.CPUS_PER_TASK + // If the Mesos executor has not been started on this slave yet, set aside a few + // cores for the Mesos executor by offering fewer cores to the Spark executor + (getResource(o.getResourcesList, "cpus") - mesosExecutorCores).toInt } new WorkerOffer( o.getSlaveId.getValue, diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala index a311512e82c5e..cdd7be0fbe5dd 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala @@ -118,12 +118,12 @@ class MesosSchedulerBackendSuite extends FunSuite with LocalSparkContext with Mo expectedWorkerOffers.append(new WorkerOffer( mesosOffers.get(0).getSlaveId.getValue, mesosOffers.get(0).getHostname, - 2 + (minCpu - backend.mesosExecutorCores).toInt )) expectedWorkerOffers.append(new WorkerOffer( mesosOffers.get(2).getSlaveId.getValue, mesosOffers.get(2).getHostname, - 2 + (minCpu - backend.mesosExecutorCores).toInt )) val taskDesc = new TaskDescription(1L, 0, "s1", "n1", 0, ByteBuffer.wrap(new Array[Byte](0))) when(taskScheduler.resourceOffers(expectedWorkerOffers)).thenReturn(Seq(Seq(taskDesc))) diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md index c984639bd34cf..594bf78b67713 100644 --- a/docs/running-on-mesos.md +++ b/docs/running-on-mesos.md @@ -210,6 +210,16 @@ See the [configuration page](configuration.html) for information on Spark config Note that total amount of cores the executor will request in total will not exceed the spark.cores.max setting. +
     + + + + From 1991337336596f94698e79c2366f065c374128ab Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Fri, 17 Apr 2015 19:02:07 -0700 Subject: [PATCH 331/591] [SPARK-5933] [core] Move config deprecation warnings to SparkConf. I didn't find many deprecated configs after a grep-based search, but the ones I could find were moved to the centralized location in SparkConf. While there, I deprecated a couple more HS configs that mentioned time units. Author: Marcelo Vanzin Closes #5562 from vanzin/SPARK-5933 and squashes the following commits: dcb617e7 [Marcelo Vanzin] [SPARK-5933] [core] Move config deprecation warnings to SparkConf. --- .../main/scala/org/apache/spark/SparkConf.scala | 17 ++++++++++++++--- .../main/scala/org/apache/spark/SparkEnv.scala | 10 ++-------- .../deploy/history/FsHistoryProvider.scala | 15 +++------------ .../scala/org/apache/spark/SparkConfSuite.scala | 3 +++ docs/monitoring.md | 15 +++++++-------- .../spark/deploy/yarn/ApplicationMaster.scala | 9 +-------- 6 files changed, 30 insertions(+), 39 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala index b0186e9a007b8..e3a649d755450 100644 --- a/core/src/main/scala/org/apache/spark/SparkConf.scala +++ b/core/src/main/scala/org/apache/spark/SparkConf.scala @@ -403,6 +403,9 @@ private[spark] object SparkConf extends Logging { */ private val deprecatedConfigs: Map[String, DeprecatedConfig] = { val configs = Seq( + DeprecatedConfig("spark.cache.class", "0.8", + "The spark.cache.class property is no longer being used! Specify storage levels using " + + "the RDD.persist() method instead."), DeprecatedConfig("spark.yarn.user.classpath.first", "1.3", "Please use spark.{driver,executor}.userClassPathFirst instead.")) Map(configs.map { cfg => (cfg.key -> cfg) }:_*) @@ -420,7 +423,15 @@ private[spark] object SparkConf extends Logging { "spark.history.fs.update.interval" -> Seq( AlternateConfig("spark.history.fs.update.interval.seconds", "1.4"), AlternateConfig("spark.history.fs.updateInterval", "1.3"), - AlternateConfig("spark.history.updateInterval", "1.3")) + AlternateConfig("spark.history.updateInterval", "1.3")), + "spark.history.fs.cleaner.interval" -> Seq( + AlternateConfig("spark.history.fs.cleaner.interval.seconds", "1.4")), + "spark.history.fs.cleaner.maxAge" -> Seq( + AlternateConfig("spark.history.fs.cleaner.maxAge.seconds", "1.4")), + "spark.yarn.am.waitTime" -> Seq( + AlternateConfig("spark.yarn.applicationMaster.waitTries", "1.3", + // Translate old value to a duration, with 10s wait time per try. + translation = s => s"${s.toLong * 10}s")) ) /** @@ -470,7 +481,7 @@ private[spark] object SparkConf extends Logging { configsWithAlternatives.get(key).flatMap { alts => alts.collectFirst { case alt if conf.contains(alt.key) => val value = conf.get(alt.key) - alt.translation.map(_(value)).getOrElse(value) + if (alt.translation != null) alt.translation(value) else value } } } @@ -514,6 +525,6 @@ private[spark] object SparkConf extends Logging { private case class AlternateConfig( key: String, version: String, - translation: Option[String => String] = None) + translation: String => String = null) } diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala index 0171488e09562..959aefabd8de4 100644 --- a/core/src/main/scala/org/apache/spark/SparkEnv.scala +++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala @@ -103,7 +103,7 @@ class SparkEnv ( // actorSystem.awaitTermination() // Note that blockTransferService is stopped by BlockManager since it is started by it. - + // If we only stop sc, but the driver process still run as a services then we need to delete // the tmp dir, if not, it will create too many tmp dirs. // We only need to delete the tmp dir create by driver, because sparkFilesDir is point to the @@ -375,12 +375,6 @@ object SparkEnv extends Logging { "." } - // Warn about deprecated spark.cache.class property - if (conf.contains("spark.cache.class")) { - logWarning("The spark.cache.class property is no longer being used! Specify storage " + - "levels using the RDD.persist() method instead.") - } - val outputCommitCoordinator = mockOutputCommitCoordinator.getOrElse { new OutputCommitCoordinator(conf) } @@ -406,7 +400,7 @@ object SparkEnv extends Logging { shuffleMemoryManager, outputCommitCoordinator, conf) - + // Add a reference to tmp dir created by driver, we will delete this tmp dir when stop() is // called, and we only need to do it for driver. Because driver may run as a service, and if we // don't delete this tmp dir when sc is stopped, then will create too many tmp dirs. diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala index 985545742df67..47bdd7749ec3d 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala @@ -52,8 +52,7 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis private val UPDATE_INTERVAL_S = conf.getTimeAsSeconds("spark.history.fs.update.interval", "10s") // Interval between each cleaner checks for event logs to delete - private val CLEAN_INTERVAL_MS = conf.getLong("spark.history.fs.cleaner.interval.seconds", - DEFAULT_SPARK_HISTORY_FS_CLEANER_INTERVAL_S) * 1000 + private val CLEAN_INTERVAL_S = conf.getTimeAsSeconds("spark.history.fs.cleaner.interval", "1d") private val logDir = conf.getOption("spark.history.fs.logDirectory") .map { d => Utils.resolveURI(d).toString } @@ -130,8 +129,7 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis if (conf.getBoolean("spark.history.fs.cleaner.enabled", false)) { // A task that periodically cleans event logs on disk. - pool.scheduleAtFixedRate(getRunner(cleanLogs), 0, CLEAN_INTERVAL_MS, - TimeUnit.MILLISECONDS) + pool.scheduleAtFixedRate(getRunner(cleanLogs), 0, CLEAN_INTERVAL_S, TimeUnit.SECONDS) } } } @@ -270,8 +268,7 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis try { val statusList = Option(fs.listStatus(new Path(logDir))).map(_.toSeq) .getOrElse(Seq[FileStatus]()) - val maxAge = conf.getLong("spark.history.fs.cleaner.maxAge.seconds", - DEFAULT_SPARK_HISTORY_FS_MAXAGE_S) * 1000 + val maxAge = conf.getTimeAsSeconds("spark.history.fs.cleaner.maxAge", "7d") * 1000 val now = System.currentTimeMillis() val appsToRetain = new mutable.LinkedHashMap[String, FsApplicationHistoryInfo]() @@ -417,12 +414,6 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis private object FsHistoryProvider { val DEFAULT_LOG_DIR = "file:/tmp/spark-events" - - // One day - val DEFAULT_SPARK_HISTORY_FS_CLEANER_INTERVAL_S = Duration(1, TimeUnit.DAYS).toSeconds - - // One week - val DEFAULT_SPARK_HISTORY_FS_MAXAGE_S = Duration(7, TimeUnit.DAYS).toSeconds } private class FsApplicationHistoryInfo( diff --git a/core/src/test/scala/org/apache/spark/SparkConfSuite.scala b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala index 7d87ba5fd2610..8e6c200c4ba00 100644 --- a/core/src/test/scala/org/apache/spark/SparkConfSuite.scala +++ b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala @@ -217,6 +217,9 @@ class SparkConfSuite extends FunSuite with LocalSparkContext with ResetSystemPro val count = conf.getAll.filter { case (k, v) => k.startsWith("spark.history.") }.size assert(count === 4) + + conf.set("spark.yarn.applicationMaster.waitTries", "42") + assert(conf.getTimeAsSeconds("spark.yarn.am.waitTime") === 420) } } diff --git a/docs/monitoring.md b/docs/monitoring.md index 2a130224591ca..8a85928d6d44d 100644 --- a/docs/monitoring.md +++ b/docs/monitoring.md @@ -153,19 +153,18 @@ follows: - - + + - - + +
    @@ -223,12 +225,14 @@ private[ui] class MasterPage(parent: MasterWebUI) extends WebUIPage("") { (driver.state == DriverState.RUNNING || driver.state == DriverState.SUBMITTED || driver.state == DriverState.RELAUNCHING)) { - val killLinkUri = s"driver/kill?id=${driver.id}&terminate=true" - val confirm = "return window.confirm(" + - s"'Are you sure you want to kill driver ${driver.id} ?');" - - (kill) - + val confirm = + s"if (window.confirm('Are you sure you want to kill driver ${driver.id} ?')) " + + "{ this.parentNode.submit(); return true; } else { return false; }" +
    + + + (kill) +
    }
    {driver.id} {killLink}
    spark.history.fs.update.interval.seconds10spark.history.fs.update.interval10s - The period, in seconds, at which information displayed by this history server is updated. + The period at which information displayed by this history server is updated. Each update checks for any changes made to the event logs in persisted storage.
    spark.mesos.mesosExecutor.cores1.0 + (Fine-grained mode only) Number of cores to give each Mesos executor. This does not + include the cores used to run the Spark tasks. In other words, even if no Spark task + is being run, each Mesos executor will occupy the number of cores configured here. + The value can be a floating point number. +
    spark.mesos.executor.home driver side SPARK_HOME
    spark.history.fs.cleaner.interval.seconds86400spark.history.fs.cleaner.interval1d - How often the job history cleaner checks for files to delete, in seconds. Defaults to 86400 (one day). - Files are only deleted if they are older than spark.history.fs.cleaner.maxAge.seconds. + How often the job history cleaner checks for files to delete. + Files are only deleted if they are older than spark.history.fs.cleaner.maxAge.
    spark.history.fs.cleaner.maxAge.seconds3600 * 24 * 7spark.history.fs.cleaner.maxAge7d - Job history files older than this many seconds will be deleted when the history cleaner runs. - Defaults to 3600 * 24 * 7 (1 week). + Job history files older than this will be deleted when the history cleaner runs.
    diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala index c357b7ae9d4da..f7a84207e9da6 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -373,14 +373,7 @@ private[spark] class ApplicationMaster( private def waitForSparkContextInitialized(): SparkContext = { logInfo("Waiting for spark context initialization") sparkContextRef.synchronized { - val waitTries = sparkConf.getOption("spark.yarn.applicationMaster.waitTries") - .map(_.toLong * 10000L) - if (waitTries.isDefined) { - logWarning( - "spark.yarn.applicationMaster.waitTries is deprecated, use spark.yarn.am.waitTime") - } - val totalWaitTime = sparkConf.getTimeAsMs("spark.yarn.am.waitTime", - s"${waitTries.getOrElse(100000L)}ms") + val totalWaitTime = sparkConf.getTimeAsMs("spark.yarn.am.waitTime", "100s") val deadline = System.currentTimeMillis() + totalWaitTime while (sparkContextRef.get() == null && System.currentTimeMillis < deadline && !finished) { From d850b4bd3a294dd245881e03f7f94bf970a7ee79 Mon Sep 17 00:00:00 2001 From: jerryshao Date: Fri, 17 Apr 2015 19:17:06 -0700 Subject: [PATCH 332/591] [SPARK-6975][Yarn] Fix argument validation error `numExecutors` checking is failed when dynamic allocation is enabled with default configuration. Details can be seen is [SPARK-6975](https://issues.apache.org/jira/browse/SPARK-6975). sryza, please help me to review this, not sure is this the correct way, I think previous you change this part :) Author: jerryshao Closes #5551 from jerryshao/SPARK-6975 and squashes the following commits: 4335da1 [jerryshao] Change according to the comments 77bdcbd [jerryshao] Fix argument validation error --- .../org/apache/spark/deploy/yarn/ClientArguments.scala | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala index da6798cb1b279..1423533470fc0 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala @@ -103,9 +103,13 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf) * This is intended to be called only after the provided arguments have been parsed. */ private def validateArgs(): Unit = { - if (numExecutors <= 0) { + if (numExecutors < 0 || (!isDynamicAllocationEnabled && numExecutors == 0)) { throw new IllegalArgumentException( - "You must specify at least 1 executor!\n" + getUsageMessage()) + s""" + |Number of executors was $numExecutors, but must be at least 1 + |(or 0 if dynamic executor allocation is enabled). + |${getUsageMessage()} + """.stripMargin) } if (executorCores < sparkConf.getInt("spark.task.cpus", 1)) { throw new SparkException("Executor cores must not be less than " + From 5f095d56054d57c54d81db1d36cd46312810fb6a Mon Sep 17 00:00:00 2001 From: Olivier Girardot Date: Sat, 18 Apr 2015 00:31:01 -0700 Subject: [PATCH 333/591] SPARK-6992 : Fix documentation example for Spark SQL on StructType This patch is fixing the Java examples for Spark SQL when defining programmatically a Schema and mapping Rows. Author: Olivier Girardot Closes #5569 from ogirardot/branch-1.3 and squashes the following commits: c29e58d [Olivier Girardot] SPARK-6992 : Fix documentation example for Spark SQL on StructType (cherry picked from commit c9b1ba4b16a7afe93d45bf75b128cc0dd287ded0) Signed-off-by: Reynold Xin --- docs/sql-programming-guide.md | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index d49233714a0bb..b2022546268a7 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -555,13 +555,16 @@ by `SQLContext`. For example: {% highlight java %} -// Import factory methods provided by DataType. -import org.apache.spark.sql.types.DataType; +import org.apache.spark.api.java.function.Function; +// Import factory methods provided by DataTypes. +import org.apache.spark.sql.types.DataTypes; // Import StructType and StructField import org.apache.spark.sql.types.StructType; import org.apache.spark.sql.types.StructField; // Import Row. import org.apache.spark.sql.Row; +// Import RowFactory. +import org.apache.spark.sql.RowFactory; // sc is an existing JavaSparkContext. SQLContext sqlContext = new org.apache.spark.sql.SQLContext(sc); @@ -575,16 +578,16 @@ String schemaString = "name age"; // Generate the schema based on the string of schema List fields = new ArrayList(); for (String fieldName: schemaString.split(" ")) { - fields.add(DataType.createStructField(fieldName, DataType.StringType, true)); + fields.add(DataTypes.createStructField(fieldName, DataTypes.StringType, true)); } -StructType schema = DataType.createStructType(fields); +StructType schema = DataTypes.createStructType(fields); // Convert records of the RDD (people) to Rows. JavaRDD rowRDD = people.map( new Function() { public Row call(String record) throws Exception { String[] fields = record.split(","); - return Row.create(fields[0], fields[1].trim()); + return RowFactory.create(fields[0], fields[1].trim()); } }); From 327ebf0cb5e236579bece057eda27b21aed0e2dc Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Sat, 18 Apr 2015 10:14:56 +0100 Subject: [PATCH 334/591] [core] [minor] Make sure ConnectionManager stops. My previous fix (force a selector wakeup) didn't seem to work since I ran into the hang again. So change the code a bit to be more explicit about the condition when the selector thread should exit. Author: Marcelo Vanzin Closes #5566 from vanzin/conn-mgr-hang and squashes the following commits: ddb2c03 [Marcelo Vanzin] [core] [minor] Make sure ConnectionManager stops. --- .../spark/network/nio/ConnectionManager.scala | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala index 5a74c13b38bf7..1a68e621eaee7 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala @@ -188,6 +188,7 @@ private[nio] class ConnectionManager( private val writeRunnableStarted: HashSet[SelectionKey] = new HashSet[SelectionKey]() private val readRunnableStarted: HashSet[SelectionKey] = new HashSet[SelectionKey]() + @volatile private var isActive = true private val selectorThread = new Thread("connection-manager-thread") { override def run(): Unit = ConnectionManager.this.run() } @@ -342,7 +343,7 @@ private[nio] class ConnectionManager( def run() { try { - while(!selectorThread.isInterrupted) { + while (isActive) { while (!registerRequests.isEmpty) { val conn: SendingConnection = registerRequests.dequeue() addListeners(conn) @@ -398,7 +399,7 @@ private[nio] class ConnectionManager( } catch { // Explicitly only dealing with CancelledKeyException here since other exceptions // should be dealt with differently. - case e: CancelledKeyException => { + case e: CancelledKeyException => // Some keys within the selectors list are invalid/closed. clear them. val allKeys = selector.keys().iterator() @@ -420,8 +421,11 @@ private[nio] class ConnectionManager( } } } - } - 0 + 0 + + case e: ClosedSelectorException => + logDebug("Failed select() as selector is closed.", e) + return } if (selectedKeysCount == 0) { @@ -988,11 +992,11 @@ private[nio] class ConnectionManager( } def stop() { + isActive = false ackTimeoutMonitor.stop() - selector.wakeup() + selector.close() selectorThread.interrupt() selectorThread.join() - selector.close() val connections = connectionsByKey.values connections.foreach(_.close()) if (connectionsByKey.size != 0) { From 28683b4df5de06373b867068b9b8adfbcaf93176 Mon Sep 17 00:00:00 2001 From: Nicholas Chammas Date: Sat, 18 Apr 2015 16:46:28 -0700 Subject: [PATCH 335/591] [SPARK-6219] Reuse pep8.py Per the discussion in the comments on [this commit](https://github.com/apache/spark/commit/f17d43b033d928dbc46aef8e367aa08902e698ad#commitcomment-10780649), this PR allows the Python lint script to reuse `pep8.py` when possible. Author: Nicholas Chammas Closes #5561 from nchammas/save-dem-pep8-bytes and squashes the following commits: b7c91e6 [Nicholas Chammas] reuse pep8.py --- dev/.gitignore | 1 + dev/lint-python | 21 +++++++++++---------- 2 files changed, 12 insertions(+), 10 deletions(-) create mode 100644 dev/.gitignore diff --git a/dev/.gitignore b/dev/.gitignore new file mode 100644 index 0000000000000..4a6027429e0d3 --- /dev/null +++ b/dev/.gitignore @@ -0,0 +1 @@ +pep8*.py diff --git a/dev/lint-python b/dev/lint-python index fded654893a7c..f50d149dc4d44 100755 --- a/dev/lint-python +++ b/dev/lint-python @@ -32,18 +32,19 @@ compile_status="${PIPESTATUS[0]}" #+ See: https://github.com/apache/spark/pull/1744#issuecomment-50982162 #+ TODOs: #+ - Download pep8 from PyPI. It's more "official". -PEP8_SCRIPT_PATH="$SPARK_ROOT_DIR/dev/pep8.py" -PEP8_SCRIPT_REMOTE_PATH="https://raw.githubusercontent.com/jcrocholl/pep8/1.6.2/pep8.py" +PEP8_VERSION="1.6.2" +PEP8_SCRIPT_PATH="$SPARK_ROOT_DIR/dev/pep8-$PEP8_VERSION.py" +PEP8_SCRIPT_REMOTE_PATH="https://raw.githubusercontent.com/jcrocholl/pep8/$PEP8_VERSION/pep8.py" -# if [ ! -e "$PEP8_SCRIPT_PATH" ]; then -curl --silent -o "$PEP8_SCRIPT_PATH" "$PEP8_SCRIPT_REMOTE_PATH" -curl_status="$?" +if [ ! -e "$PEP8_SCRIPT_PATH" ]; then + curl --silent -o "$PEP8_SCRIPT_PATH" "$PEP8_SCRIPT_REMOTE_PATH" + curl_status="$?" -if [ "$curl_status" -ne 0 ]; then - echo "Failed to download pep8.py from \"$PEP8_SCRIPT_REMOTE_PATH\"." - exit "$curl_status" + if [ "$curl_status" -ne 0 ]; then + echo "Failed to download pep8.py from \"$PEP8_SCRIPT_REMOTE_PATH\"." + exit "$curl_status" + fi fi -# fi # There is no need to write this output to a file #+ first, but we do so so that the check status can @@ -65,7 +66,7 @@ else echo "Python lint checks passed." fi -rm "$PEP8_SCRIPT_PATH" +# rm "$PEP8_SCRIPT_PATH" rm "$PYTHON_LINT_REPORT_PATH" exit "$lint_status" From 729885ec6b4be61144d04821f1a6e8d2134eea00 Mon Sep 17 00:00:00 2001 From: Gaurav Nanda Date: Sat, 18 Apr 2015 17:20:46 -0700 Subject: [PATCH 336/591] Fixed doc Just fixed a doc. Author: Gaurav Nanda Closes #5576 from gaurav324/master and squashes the following commits: 8a7323f [Gaurav Nanda] Fixed doc --- docs/mllib-linear-methods.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md index 9270741d439d9..2b2be4d9d0273 100644 --- a/docs/mllib-linear-methods.md +++ b/docs/mllib-linear-methods.md @@ -377,7 +377,7 @@ references. Here is an [detailed mathematical derivation](http://www.slideshare.net/dbtsai/2014-0620-mlor-36132297). -For multiclass classification problems, the algorithm will outputs a multinomial logistic regression +For multiclass classification problems, the algorithm will output a multinomial logistic regression model, which contains $K - 1$ binary logistic regression models regressed against the first class. Given a new data points, $K - 1$ models will be run, and the class with largest probability will be chosen as the predicted class. From 8fbd45c74e762dd6b071ea58a60f5bb649f74042 Mon Sep 17 00:00:00 2001 From: Olivier Girardot Date: Sat, 18 Apr 2015 18:21:44 -0700 Subject: [PATCH 337/591] SPARK-6993 : Add default min, max methods for JavaDoubleRDD The default method will use Guava's Ordering instead of java.util.Comparator.naturalOrder() because it's not available in Java 7, only in Java 8. Author: Olivier Girardot Closes #5571 from ogirardot/master and squashes the following commits: 7fe2e9e [Olivier Girardot] SPARK-6993 : Add default min, max methods for JavaDoubleRDD --- .../org/apache/spark/api/java/JavaDoubleRDD.scala | 14 ++++++++++++++ .../test/java/org/apache/spark/JavaAPISuite.java | 14 ++++++++++++++ 2 files changed, 28 insertions(+) diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala index 79e4ebf2db578..61af867b11b9c 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala @@ -163,6 +163,20 @@ class JavaDoubleRDD(val srdd: RDD[scala.Double]) /** Add up the elements in this RDD. */ def sum(): JDouble = srdd.sum() + /** + * Returns the minimum element from this RDD as defined by + * the default comparator natural order. + * @return the minimum of the RDD + */ + def min(): JDouble = min(com.google.common.collect.Ordering.natural()) + + /** + * Returns the maximum element from this RDD as defined by + * the default comparator natural order. + * @return the maximum of the RDD + */ + def max(): JDouble = max(com.google.common.collect.Ordering.natural()) + /** * Return a [[org.apache.spark.util.StatCounter]] object that captures the mean, variance and * count of the RDD's elements in one operation. diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java index d4b5bb519157c..8a4f2a08fe701 100644 --- a/core/src/test/java/org/apache/spark/JavaAPISuite.java +++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java @@ -761,6 +761,20 @@ public void min() { Assert.assertEquals(1.0, max, 0.001); } + @Test + public void naturalMax() { + JavaDoubleRDD rdd = sc.parallelizeDoubles(Arrays.asList(1.0, 2.0, 3.0, 4.0)); + double max = rdd.max(); + Assert.assertTrue(4.0 == max); + } + + @Test + public void naturalMin() { + JavaDoubleRDD rdd = sc.parallelizeDoubles(Arrays.asList(1.0, 2.0, 3.0, 4.0)); + double max = rdd.min(); + Assert.assertTrue(1.0 == max); + } + @Test public void takeOrdered() { JavaDoubleRDD rdd = sc.parallelizeDoubles(Arrays.asList(1.0, 2.0, 3.0, 4.0)); From 0424da68d4c81dc3a9944d8485feb1233c6633c4 Mon Sep 17 00:00:00 2001 From: GuoQiang Li Date: Sun, 19 Apr 2015 09:37:09 +0100 Subject: [PATCH 338/591] [SPARK-6963][CORE]Flaky test: o.a.s.ContextCleanerSuite automatically cleanup checkpoint cc andrewor14 Author: GuoQiang Li Closes #5548 from witgo/SPARK-6963 and squashes the following commits: 964aea7 [GuoQiang Li] review commits b08b3c9 [GuoQiang Li] Flaky test: o.a.s.ContextCleanerSuite automatically cleanup checkpoint --- .../org/apache/spark/ContextCleaner.scala | 2 ++ .../apache/spark/ContextCleanerSuite.scala | 21 +++++++++++++------ 2 files changed, 17 insertions(+), 6 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/ContextCleaner.scala b/core/src/main/scala/org/apache/spark/ContextCleaner.scala index 715b259057569..37198d887b07b 100644 --- a/core/src/main/scala/org/apache/spark/ContextCleaner.scala +++ b/core/src/main/scala/org/apache/spark/ContextCleaner.scala @@ -236,6 +236,7 @@ private[spark] class ContextCleaner(sc: SparkContext) extends Logging { try { logDebug("Cleaning rdd checkpoint data " + rddId) RDDCheckpointData.clearRDDCheckpointData(sc, rddId) + listeners.foreach(_.checkpointCleaned(rddId)) logInfo("Cleaned rdd checkpoint data " + rddId) } catch { @@ -260,4 +261,5 @@ private[spark] trait CleanerListener { def shuffleCleaned(shuffleId: Int) def broadcastCleaned(broadcastId: Long) def accumCleaned(accId: Long) + def checkpointCleaned(rddId: Long) } diff --git a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala index 097e7076e5391..c7868ddcf770f 100644 --- a/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala +++ b/core/src/test/scala/org/apache/spark/ContextCleanerSuite.scala @@ -224,7 +224,7 @@ class ContextCleanerSuite extends ContextCleanerSuiteBase { assert(fs.exists(path)) // the checkpoint is not cleaned by default (without the configuration set) - var postGCTester = new CleanerTester(sc, Seq(rddId), Nil, Nil) + var postGCTester = new CleanerTester(sc, Seq(rddId), Nil, Nil, Nil) rdd = null // Make RDD out of scope runGC() postGCTester.assertCleanup() @@ -245,7 +245,7 @@ class ContextCleanerSuite extends ContextCleanerSuiteBase { assert(fs.exists(RDDCheckpointData.rddCheckpointDataPath(sc, rddId).get)) // Test that GC causes checkpoint data cleanup after dereferencing the RDD - postGCTester = new CleanerTester(sc, Seq(rddId), Nil, Nil) + postGCTester = new CleanerTester(sc, Seq(rddId), Nil, Nil, Seq(rddId)) rdd = null // Make RDD out of scope runGC() postGCTester.assertCleanup() @@ -406,12 +406,14 @@ class CleanerTester( sc: SparkContext, rddIds: Seq[Int] = Seq.empty, shuffleIds: Seq[Int] = Seq.empty, - broadcastIds: Seq[Long] = Seq.empty) + broadcastIds: Seq[Long] = Seq.empty, + checkpointIds: Seq[Long] = Seq.empty) extends Logging { val toBeCleanedRDDIds = new HashSet[Int] with SynchronizedSet[Int] ++= rddIds val toBeCleanedShuffleIds = new HashSet[Int] with SynchronizedSet[Int] ++= shuffleIds val toBeCleanedBroadcstIds = new HashSet[Long] with SynchronizedSet[Long] ++= broadcastIds + val toBeCheckpointIds = new HashSet[Long] with SynchronizedSet[Long] ++= checkpointIds val isDistributed = !sc.isLocal val cleanerListener = new CleanerListener { @@ -427,12 +429,17 @@ class CleanerTester( def broadcastCleaned(broadcastId: Long): Unit = { toBeCleanedBroadcstIds -= broadcastId - logInfo("Broadcast" + broadcastId + " cleaned") + logInfo("Broadcast " + broadcastId + " cleaned") } def accumCleaned(accId: Long): Unit = { logInfo("Cleaned accId " + accId + " cleaned") } + + def checkpointCleaned(rddId: Long): Unit = { + toBeCheckpointIds -= rddId + logInfo("checkpoint " + rddId + " cleaned") + } } val MAX_VALIDATION_ATTEMPTS = 10 @@ -456,7 +463,8 @@ class CleanerTester( /** Verify that RDDs, shuffles, etc. occupy resources */ private def preCleanupValidate() { - assert(rddIds.nonEmpty || shuffleIds.nonEmpty || broadcastIds.nonEmpty, "Nothing to cleanup") + assert(rddIds.nonEmpty || shuffleIds.nonEmpty || broadcastIds.nonEmpty || + checkpointIds.nonEmpty, "Nothing to cleanup") // Verify the RDDs have been persisted and blocks are present rddIds.foreach { rddId => @@ -547,7 +555,8 @@ class CleanerTester( private def isAllCleanedUp = toBeCleanedRDDIds.isEmpty && toBeCleanedShuffleIds.isEmpty && - toBeCleanedBroadcstIds.isEmpty + toBeCleanedBroadcstIds.isEmpty && + toBeCheckpointIds.isEmpty private def getRDDBlocks(rddId: Int): Seq[BlockId] = { blockManager.master.getMatchingBlockIds( _ match { From fa73da024000386eecef79573e8ac96d6f05b2c7 Mon Sep 17 00:00:00 2001 From: zsxwing Date: Sun, 19 Apr 2015 20:33:51 -0700 Subject: [PATCH 339/591] [SPARK-6998][MLlib] Make StreamingKMeans 'Serializable' If `StreamingKMeans` is not `Serializable`, we cannot do checkpoint for applications that using `StreamingKMeans`. So we should make it `Serializable`. Author: zsxwing Closes #5582 from zsxwing/SPARK-6998 and squashes the following commits: 67c2a14 [zsxwing] Make StreamingKMeans 'Serializable' --- .../org/apache/spark/mllib/clustering/StreamingKMeans.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala index f483fd1c7d2cf..d4606fda37b0d 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala @@ -165,7 +165,7 @@ class StreamingKMeansModel( class StreamingKMeans( var k: Int, var decayFactor: Double, - var timeUnit: String) extends Logging { + var timeUnit: String) extends Logging with Serializable { def this() = this(2, 1.0, StreamingKMeans.BATCHES) From d8e1b7b06c499289ff3ce5ec91ff354493a17c48 Mon Sep 17 00:00:00 2001 From: zsxwing Date: Sun, 19 Apr 2015 20:35:43 -0700 Subject: [PATCH 340/591] [SPARK-6983][Streaming] Update ReceiverTrackerActor to use the new Rpc interface A subtask of [SPARK-5293](https://issues.apache.org/jira/browse/SPARK-5293) Author: zsxwing Closes #5557 from zsxwing/SPARK-6983 and squashes the following commits: e777e9f [zsxwing] Update ReceiverTrackerActor to use the new Rpc interface --- .../scala/org/apache/spark/rpc/RpcEnv.scala | 2 +- .../receiver/ReceiverSupervisorImpl.scala | 52 +++++---------- .../streaming/scheduler/ReceiverInfo.scala | 4 +- .../streaming/scheduler/ReceiverTracker.scala | 64 ++++++++++--------- 4 files changed, 52 insertions(+), 70 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala index f2c1c86af767e..cba038ca355d7 100644 --- a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala +++ b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala @@ -258,7 +258,7 @@ private[spark] trait RpcEndpoint { final def stop(): Unit = { val _self = self if (_self != null) { - rpcEnv.stop(self) + rpcEnv.stop(_self) } } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala index 8f2f1fef76874..89af40330b9d9 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala @@ -21,18 +21,16 @@ import java.nio.ByteBuffer import java.util.concurrent.atomic.AtomicLong import scala.collection.mutable.ArrayBuffer -import scala.concurrent.Await -import akka.actor.{ActorRef, Actor, Props} -import akka.pattern.ask import com.google.common.base.Throwables import org.apache.hadoop.conf.Configuration import org.apache.spark.{Logging, SparkEnv, SparkException} +import org.apache.spark.rpc.{RpcEnv, ThreadSafeRpcEndpoint} import org.apache.spark.storage.StreamBlockId import org.apache.spark.streaming.Time import org.apache.spark.streaming.scheduler._ -import org.apache.spark.util.{AkkaUtils, Utils} +import org.apache.spark.util.{RpcUtils, Utils} /** * Concrete implementation of [[org.apache.spark.streaming.receiver.ReceiverSupervisor]] @@ -63,37 +61,23 @@ private[streaming] class ReceiverSupervisorImpl( } - /** Remote Akka actor for the ReceiverTracker */ - private val trackerActor = { - val ip = env.conf.get("spark.driver.host", "localhost") - val port = env.conf.getInt("spark.driver.port", 7077) - val url = AkkaUtils.address( - AkkaUtils.protocol(env.actorSystem), - SparkEnv.driverActorSystemName, - ip, - port, - "ReceiverTracker") - env.actorSystem.actorSelection(url) - } - - /** Timeout for Akka actor messages */ - private val askTimeout = AkkaUtils.askTimeout(env.conf) + /** Remote RpcEndpointRef for the ReceiverTracker */ + private val trackerEndpoint = RpcUtils.makeDriverRef("ReceiverTracker", env.conf, env.rpcEnv) - /** Akka actor for receiving messages from the ReceiverTracker in the driver */ - private val actor = env.actorSystem.actorOf( - Props(new Actor { + /** RpcEndpointRef for receiving messages from the ReceiverTracker in the driver */ + private val endpoint = env.rpcEnv.setupEndpoint( + "Receiver-" + streamId + "-" + System.currentTimeMillis(), new ThreadSafeRpcEndpoint { + override val rpcEnv: RpcEnv = env.rpcEnv override def receive: PartialFunction[Any, Unit] = { case StopReceiver => logInfo("Received stop signal") - stop("Stopped by driver", None) + ReceiverSupervisorImpl.this.stop("Stopped by driver", None) case CleanupOldBlocks(threshTime) => logDebug("Received delete old batch signal") cleanupOldBlocks(threshTime) } - - def ref: ActorRef = self - }), "Receiver-" + streamId + "-" + System.currentTimeMillis()) + }) /** Unique block ids if one wants to add blocks directly */ private val newBlockId = new AtomicLong(System.currentTimeMillis()) @@ -162,15 +146,14 @@ private[streaming] class ReceiverSupervisorImpl( logDebug(s"Pushed block $blockId in ${(System.currentTimeMillis - time)} ms") val blockInfo = ReceivedBlockInfo(streamId, numRecords, blockStoreResult) - val future = trackerActor.ask(AddBlock(blockInfo))(askTimeout) - Await.result(future, askTimeout) + trackerEndpoint.askWithReply[Boolean](AddBlock(blockInfo)) logDebug(s"Reported block $blockId") } /** Report error to the receiver tracker */ def reportError(message: String, error: Throwable) { val errorString = Option(error).map(Throwables.getStackTraceAsString).getOrElse("") - trackerActor ! ReportError(streamId, message, errorString) + trackerEndpoint.send(ReportError(streamId, message, errorString)) logWarning("Reported error " + message + " - " + error) } @@ -180,22 +163,19 @@ private[streaming] class ReceiverSupervisorImpl( override protected def onStop(message: String, error: Option[Throwable]) { blockGenerator.stop() - env.actorSystem.stop(actor) + env.rpcEnv.stop(endpoint) } override protected def onReceiverStart() { val msg = RegisterReceiver( - streamId, receiver.getClass.getSimpleName, Utils.localHostName(), actor) - val future = trackerActor.ask(msg)(askTimeout) - Await.result(future, askTimeout) + streamId, receiver.getClass.getSimpleName, Utils.localHostName(), endpoint) + trackerEndpoint.askWithReply[Boolean](msg) } override protected def onReceiverStop(message: String, error: Option[Throwable]) { logInfo("Deregistering receiver " + streamId) val errorString = error.map(Throwables.getStackTraceAsString).getOrElse("") - val future = trackerActor.ask( - DeregisterReceiver(streamId, message, errorString))(askTimeout) - Await.result(future, askTimeout) + trackerEndpoint.askWithReply[Boolean](DeregisterReceiver(streamId, message, errorString)) logInfo("Stopped receiver " + streamId) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverInfo.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverInfo.scala index d7e39c528c519..52f08b9c9de68 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverInfo.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverInfo.scala @@ -17,8 +17,8 @@ package org.apache.spark.streaming.scheduler -import akka.actor.ActorRef import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.rpc.RpcEndpointRef /** * :: DeveloperApi :: @@ -28,7 +28,7 @@ import org.apache.spark.annotation.DeveloperApi case class ReceiverInfo( streamId: Int, name: String, - private[streaming] val actor: ActorRef, + private[streaming] val endpoint: RpcEndpointRef, active: Boolean, location: String, lastErrorMessage: String = "", diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala index 98900473138fe..c4ead6f30a63d 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala @@ -17,13 +17,11 @@ package org.apache.spark.streaming.scheduler - import scala.collection.mutable.{HashMap, SynchronizedMap} import scala.language.existentials -import akka.actor._ - import org.apache.spark.{Logging, SerializableWritable, SparkEnv, SparkException} +import org.apache.spark.rpc._ import org.apache.spark.streaming.{StreamingContext, Time} import org.apache.spark.streaming.receiver.{CleanupOldBlocks, Receiver, ReceiverSupervisorImpl, StopReceiver} @@ -36,7 +34,7 @@ private[streaming] case class RegisterReceiver( streamId: Int, typ: String, host: String, - receiverActor: ActorRef + receiverEndpoint: RpcEndpointRef ) extends ReceiverTrackerMessage private[streaming] case class AddBlock(receivedBlockInfo: ReceivedBlockInfo) extends ReceiverTrackerMessage @@ -67,19 +65,19 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false ) private val listenerBus = ssc.scheduler.listenerBus - // actor is created when generator starts. + // endpoint is created when generator starts. // This not being null means the tracker has been started and not stopped - private var actor: ActorRef = null + private var endpoint: RpcEndpointRef = null - /** Start the actor and receiver execution thread. */ + /** Start the endpoint and receiver execution thread. */ def start(): Unit = synchronized { - if (actor != null) { + if (endpoint != null) { throw new SparkException("ReceiverTracker already started") } if (!receiverInputStreams.isEmpty) { - actor = ssc.env.actorSystem.actorOf(Props(new ReceiverTrackerActor), - "ReceiverTracker") + endpoint = ssc.env.rpcEnv.setupEndpoint( + "ReceiverTracker", new ReceiverTrackerEndpoint(ssc.env.rpcEnv)) if (!skipReceiverLaunch) receiverExecutor.start() logInfo("ReceiverTracker started") } @@ -87,13 +85,13 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false /** Stop the receiver execution thread. */ def stop(graceful: Boolean): Unit = synchronized { - if (!receiverInputStreams.isEmpty && actor != null) { + if (!receiverInputStreams.isEmpty && endpoint != null) { // First, stop the receivers if (!skipReceiverLaunch) receiverExecutor.stop(graceful) - // Finally, stop the actor - ssc.env.actorSystem.stop(actor) - actor = null + // Finally, stop the endpoint + ssc.env.rpcEnv.stop(endpoint) + endpoint = null receivedBlockTracker.stop() logInfo("ReceiverTracker stopped") } @@ -129,8 +127,8 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false // Signal the receivers to delete old block data if (ssc.conf.getBoolean("spark.streaming.receiver.writeAheadLog.enable", false)) { logInfo(s"Cleanup old received batch data: $cleanupThreshTime") - receiverInfo.values.flatMap { info => Option(info.actor) } - .foreach { _ ! CleanupOldBlocks(cleanupThreshTime) } + receiverInfo.values.flatMap { info => Option(info.endpoint) } + .foreach { _.send(CleanupOldBlocks(cleanupThreshTime)) } } } @@ -139,23 +137,23 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false streamId: Int, typ: String, host: String, - receiverActor: ActorRef, - sender: ActorRef + receiverEndpoint: RpcEndpointRef, + senderAddress: RpcAddress ) { if (!receiverInputStreamIds.contains(streamId)) { throw new SparkException("Register received for unexpected id " + streamId) } receiverInfo(streamId) = ReceiverInfo( - streamId, s"${typ}-${streamId}", receiverActor, true, host) + streamId, s"${typ}-${streamId}", receiverEndpoint, true, host) listenerBus.post(StreamingListenerReceiverStarted(receiverInfo(streamId))) - logInfo("Registered receiver for stream " + streamId + " from " + sender.path.address) + logInfo("Registered receiver for stream " + streamId + " from " + senderAddress) } /** Deregister a receiver */ private def deregisterReceiver(streamId: Int, message: String, error: String) { val newReceiverInfo = receiverInfo.get(streamId) match { case Some(oldInfo) => - oldInfo.copy(actor = null, active = false, lastErrorMessage = message, lastError = error) + oldInfo.copy(endpoint = null, active = false, lastErrorMessage = message, lastError = error) case None => logWarning("No prior receiver info") ReceiverInfo(streamId, "", null, false, "", lastErrorMessage = message, lastError = error) @@ -199,19 +197,23 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false receivedBlockTracker.hasUnallocatedReceivedBlocks } - /** Actor to receive messages from the receivers. */ - private class ReceiverTrackerActor extends Actor { + /** RpcEndpoint to receive messages from the receivers. */ + private class ReceiverTrackerEndpoint(override val rpcEnv: RpcEnv) extends ThreadSafeRpcEndpoint { + override def receive: PartialFunction[Any, Unit] = { - case RegisterReceiver(streamId, typ, host, receiverActor) => - registerReceiver(streamId, typ, host, receiverActor, sender) - sender ! true - case AddBlock(receivedBlockInfo) => - sender ! addBlock(receivedBlockInfo) case ReportError(streamId, message, error) => reportError(streamId, message, error) + } + + override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { + case RegisterReceiver(streamId, typ, host, receiverEndpoint) => + registerReceiver(streamId, typ, host, receiverEndpoint, context.sender.address) + context.reply(true) + case AddBlock(receivedBlockInfo) => + context.reply(addBlock(receivedBlockInfo)) case DeregisterReceiver(streamId, message, error) => deregisterReceiver(streamId, message, error) - sender ! true + context.reply(true) } } @@ -314,8 +316,8 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false /** Stops the receivers. */ private def stopReceivers() { // Signal the receivers to stop - receiverInfo.values.flatMap { info => Option(info.actor)} - .foreach { _ ! StopReceiver } + receiverInfo.values.flatMap { info => Option(info.endpoint)} + .foreach { _.send(StopReceiver) } logInfo("Sent stop signal to all " + receiverInfo.size + " receivers") } } From c776ee8a6fdcdc463746a815b7686e4e33a874a9 Mon Sep 17 00:00:00 2001 From: zsxwing Date: Sun, 19 Apr 2015 20:48:36 -0700 Subject: [PATCH 341/591] [SPARK-6979][Streaming] Replace JobScheduler.eventActor and JobGenerator.eventActor with EventLoop Title says it all. cc rxin tdas Author: zsxwing Closes #5554 from zsxwing/SPARK-6979 and squashes the following commits: 5304350 [zsxwing] Fix NotSerializableException e9d3479 [zsxwing] Add blank lines 633e279 [zsxwing] Fix NotSerializableException e496ace [zsxwing] Replace JobGenerator.eventActor with EventLoop ec6ec58 [zsxwing] Fix the import order ce0fa73 [zsxwing] Replace JobScheduler.eventActor with EventLoop --- .../mllib/clustering/StreamingKMeans.scala | 3 +- .../streaming/scheduler/JobGenerator.scala | 38 +++++++++--------- .../streaming/scheduler/JobScheduler.scala | 40 ++++++++++--------- 3 files changed, 42 insertions(+), 39 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala index d4606fda37b0d..812014a041719 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala @@ -20,8 +20,7 @@ package org.apache.spark.mllib.clustering import scala.reflect.ClassTag import org.apache.spark.Logging -import org.apache.spark.SparkContext._ -import org.apache.spark.annotation.{Experimental, DeveloperApi} +import org.apache.spark.annotation.Experimental import org.apache.spark.mllib.linalg.{BLAS, Vector, Vectors} import org.apache.spark.rdd.RDD import org.apache.spark.streaming.dstream.DStream diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala index 58e56638a2dca..2467d50839add 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala @@ -19,12 +19,10 @@ package org.apache.spark.streaming.scheduler import scala.util.{Failure, Success, Try} -import akka.actor.{ActorRef, Props, Actor} - import org.apache.spark.{SparkEnv, Logging} import org.apache.spark.streaming.{Checkpoint, CheckpointWriter, Time} import org.apache.spark.streaming.util.RecurringTimer -import org.apache.spark.util.{Clock, ManualClock, Utils} +import org.apache.spark.util.{Clock, EventLoop, ManualClock} /** Event classes for JobGenerator */ private[scheduler] sealed trait JobGeneratorEvent @@ -58,7 +56,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { } private val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds, - longTime => eventActor ! GenerateJobs(new Time(longTime)), "JobGenerator") + longTime => eventLoop.post(GenerateJobs(new Time(longTime))), "JobGenerator") // This is marked lazy so that this is initialized after checkpoint duration has been set // in the context and the generator has been started. @@ -70,22 +68,26 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { null } - // eventActor is created when generator starts. + // eventLoop is created when generator starts. // This not being null means the scheduler has been started and not stopped - private var eventActor: ActorRef = null + private var eventLoop: EventLoop[JobGeneratorEvent] = null // last batch whose completion,checkpointing and metadata cleanup has been completed private var lastProcessedBatch: Time = null /** Start generation of jobs */ def start(): Unit = synchronized { - if (eventActor != null) return // generator has already been started + if (eventLoop != null) return // generator has already been started + + eventLoop = new EventLoop[JobGeneratorEvent]("JobGenerator") { + override protected def onReceive(event: JobGeneratorEvent): Unit = processEvent(event) - eventActor = ssc.env.actorSystem.actorOf(Props(new Actor { - override def receive: PartialFunction[Any, Unit] = { - case event: JobGeneratorEvent => processEvent(event) + override protected def onError(e: Throwable): Unit = { + jobScheduler.reportError("Error in job generator", e) } - }), "JobGenerator") + } + eventLoop.start() + if (ssc.isCheckpointPresent) { restart() } else { @@ -99,7 +101,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { * checkpoints written. */ def stop(processReceivedData: Boolean): Unit = synchronized { - if (eventActor == null) return // generator has already been stopped + if (eventLoop == null) return // generator has already been stopped if (processReceivedData) { logInfo("Stopping JobGenerator gracefully") @@ -146,9 +148,9 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { graph.stop() } - // Stop the actor and checkpoint writer + // Stop the event loop and checkpoint writer if (shouldCheckpoint) checkpointWriter.stop() - ssc.env.actorSystem.stop(eventActor) + eventLoop.stop() logInfo("Stopped JobGenerator") } @@ -156,7 +158,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { * Callback called when a batch has been completely processed. */ def onBatchCompletion(time: Time) { - eventActor ! ClearMetadata(time) + eventLoop.post(ClearMetadata(time)) } /** @@ -164,7 +166,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { */ def onCheckpointCompletion(time: Time, clearCheckpointDataLater: Boolean) { if (clearCheckpointDataLater) { - eventActor ! ClearCheckpointData(time) + eventLoop.post(ClearCheckpointData(time)) } } @@ -247,7 +249,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { case Failure(e) => jobScheduler.reportError("Error generating jobs for time " + time, e) } - eventActor ! DoCheckpoint(time, clearCheckpointDataLater = false) + eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = false)) } /** Clear DStream metadata for the given `time`. */ @@ -257,7 +259,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { // If checkpointing is enabled, then checkpoint, // else mark batch to be fully processed if (shouldCheckpoint) { - eventActor ! DoCheckpoint(time, clearCheckpointDataLater = true) + eventLoop.post(DoCheckpoint(time, clearCheckpointDataLater = true)) } else { // If checkpointing is not enabled, then delete metadata information about // received blocks (block data not saved in any case). Otherwise, wait for diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala index 95f1857b4c377..508b89278dcba 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala @@ -17,13 +17,15 @@ package org.apache.spark.streaming.scheduler -import scala.util.{Failure, Success, Try} -import scala.collection.JavaConversions._ import java.util.concurrent.{TimeUnit, ConcurrentHashMap, Executors} -import akka.actor.{ActorRef, Actor, Props} -import org.apache.spark.{SparkException, Logging, SparkEnv} + +import scala.collection.JavaConversions._ +import scala.util.{Failure, Success} + +import org.apache.spark.Logging import org.apache.spark.rdd.PairRDDFunctions import org.apache.spark.streaming._ +import org.apache.spark.util.EventLoop private[scheduler] sealed trait JobSchedulerEvent @@ -46,20 +48,20 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { val listenerBus = new StreamingListenerBus() // These two are created only when scheduler starts. - // eventActor not being null means the scheduler has been started and not stopped + // eventLoop not being null means the scheduler has been started and not stopped var receiverTracker: ReceiverTracker = null - private var eventActor: ActorRef = null - + private var eventLoop: EventLoop[JobSchedulerEvent] = null def start(): Unit = synchronized { - if (eventActor != null) return // scheduler has already been started + if (eventLoop != null) return // scheduler has already been started logDebug("Starting JobScheduler") - eventActor = ssc.env.actorSystem.actorOf(Props(new Actor { - override def receive: PartialFunction[Any, Unit] = { - case event: JobSchedulerEvent => processEvent(event) - } - }), "JobScheduler") + eventLoop = new EventLoop[JobSchedulerEvent]("JobScheduler") { + override protected def onReceive(event: JobSchedulerEvent): Unit = processEvent(event) + + override protected def onError(e: Throwable): Unit = reportError("Error in job scheduler", e) + } + eventLoop.start() listenerBus.start(ssc.sparkContext) receiverTracker = new ReceiverTracker(ssc) @@ -69,7 +71,7 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { } def stop(processAllReceivedData: Boolean): Unit = synchronized { - if (eventActor == null) return // scheduler has already been stopped + if (eventLoop == null) return // scheduler has already been stopped logDebug("Stopping JobScheduler") // First, stop receiving @@ -96,8 +98,8 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { // Stop everything else listenerBus.stop() - ssc.env.actorSystem.stop(eventActor) - eventActor = null + eventLoop.stop() + eventLoop = null logInfo("Stopped JobScheduler") } @@ -117,7 +119,7 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { } def reportError(msg: String, e: Throwable) { - eventActor ! ErrorReported(msg, e) + eventLoop.post(ErrorReported(msg, e)) } private def processEvent(event: JobSchedulerEvent) { @@ -172,14 +174,14 @@ class JobScheduler(val ssc: StreamingContext) extends Logging { private class JobHandler(job: Job) extends Runnable { def run() { - eventActor ! JobStarted(job) + eventLoop.post(JobStarted(job)) // Disable checks for existing output directories in jobs launched by the streaming scheduler, // since we may need to write output to an existing directory during checkpoint recovery; // see SPARK-4835 for more details. PairRDDFunctions.disableOutputSpecValidation.withValue(true) { job.run() } - eventActor ! JobCompleted(job) + eventLoop.post(JobCompleted(job)) } } } From 6fe690d5a8216ba7efde4b52e7a19fb00814341c Mon Sep 17 00:00:00 2001 From: dobashim Date: Mon, 20 Apr 2015 00:03:23 -0400 Subject: [PATCH 342/591] [doc][mllib] Fix typo of the page title in Isotonic regression documents * Fix the page title in Isotonic regression documents (Naive Bayes -> Isotonic regression) * Add a newline character at the end of the file Author: dobashim Closes #5581 from dobashim/master and squashes the following commits: d54a041 [dobashim] Fix typo of the page title in Isotonic regression documents --- docs/mllib-isotonic-regression.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/mllib-isotonic-regression.md b/docs/mllib-isotonic-regression.md index 12fb29d426741..b521c2f27cd6e 100644 --- a/docs/mllib-isotonic-regression.md +++ b/docs/mllib-isotonic-regression.md @@ -1,6 +1,6 @@ --- layout: global -title: Naive Bayes - MLlib +title: Isotonic regression - MLlib displayTitle: MLlib - Regression --- @@ -152,4 +152,4 @@ Double meanSquaredError = new JavaDoubleRDD(predictionAndLabel.map( System.out.println("Mean Squared Error = " + meanSquaredError); {% endhighlight %} - \ No newline at end of file + From 1be207078cef48c5935595969bf9f6b1ec1334ca Mon Sep 17 00:00:00 2001 From: jrabary Date: Mon, 20 Apr 2015 09:47:56 -0700 Subject: [PATCH 343/591] [SPARK-5924] Add the ability to specify withMean or withStd parameters with StandarScaler The current implementation call the default constructor of mllib.feature.StandarScaler without the possibility to specify withMean or withStd options. Author: jrabary Closes #4704 from jrabary/master and squashes the following commits: fae8568 [jrabary] style fix 8896b0e [jrabary] Comments fix ef96d73 [jrabary] style fix 8e52607 [jrabary] style fix edd9d48 [jrabary] Fix default param initialization 17e1a76 [jrabary] Fix default param initialization 298f405 [jrabary] Typo fix 45ed914 [jrabary] Add withMean and withStd params to StandarScaler --- .../spark/ml/feature/StandardScaler.scala | 32 ++++++++++++++++--- 1 file changed, 28 insertions(+), 4 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala index 1b102619b3524..447851ec034d6 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala @@ -30,7 +30,22 @@ import org.apache.spark.sql.types.{StructField, StructType} /** * Params for [[StandardScaler]] and [[StandardScalerModel]]. */ -private[feature] trait StandardScalerParams extends Params with HasInputCol with HasOutputCol +private[feature] trait StandardScalerParams extends Params with HasInputCol with HasOutputCol { + + /** + * False by default. Centers the data with mean before scaling. + * It will build a dense output, so this does not work on sparse input + * and will raise an exception. + * @group param + */ + val withMean: BooleanParam = new BooleanParam(this, "withMean", "Center data with mean") + + /** + * True by default. Scales the data to unit standard deviation. + * @group param + */ + val withStd: BooleanParam = new BooleanParam(this, "withStd", "Scale to unit standard deviation") +} /** * :: AlphaComponent :: @@ -40,18 +55,27 @@ private[feature] trait StandardScalerParams extends Params with HasInputCol with @AlphaComponent class StandardScaler extends Estimator[StandardScalerModel] with StandardScalerParams { + setDefault(withMean -> false, withStd -> true) + /** @group setParam */ def setInputCol(value: String): this.type = set(inputCol, value) /** @group setParam */ def setOutputCol(value: String): this.type = set(outputCol, value) - + + /** @group setParam */ + def setWithMean(value: Boolean): this.type = set(withMean, value) + + /** @group setParam */ + def setWithStd(value: Boolean): this.type = set(withStd, value) + override def fit(dataset: DataFrame, paramMap: ParamMap): StandardScalerModel = { transformSchema(dataset.schema, paramMap, logging = true) val map = extractParamMap(paramMap) val input = dataset.select(map(inputCol)).map { case Row(v: Vector) => v } - val scaler = new feature.StandardScaler().fit(input) - val model = new StandardScalerModel(this, map, scaler) + val scaler = new feature.StandardScaler(withMean = map(withMean), withStd = map(withStd)) + val scalerModel = scaler.fit(input) + val model = new StandardScalerModel(this, map, scalerModel) Params.inheritValues(map, this, model) model } From 968ad972175390bb0a96918fd3c7b318d70fa466 Mon Sep 17 00:00:00 2001 From: Aaron Davidson Date: Mon, 20 Apr 2015 09:54:21 -0700 Subject: [PATCH 344/591] [SPARK-7003] Improve reliability of connection failure detection between Netty block transfer service endpoints Currently we rely on the assumption that an exception will be raised and the channel closed if two endpoints cannot communicate over a Netty TCP channel. However, this guarantee does not hold in all network environments, and [SPARK-6962](https://issues.apache.org/jira/browse/SPARK-6962) seems to point to a case where only the server side of the connection detected a fault. This patch improves robustness of fetch/rpc requests by having an explicit timeout in the transport layer which closes the connection if there is a period of inactivity while there are outstanding requests. NB: This patch is actually only around 50 lines added if you exclude the testing-related code. Author: Aaron Davidson Closes #5584 from aarondav/timeout and squashes the following commits: 8699680 [Aaron Davidson] Address Reynold's comments 37ce656 [Aaron Davidson] [SPARK-7003] Improve reliability of connection failure detection between Netty block transfer service endpoints --- .../spark/network/TransportContext.java | 5 +- .../client/TransportResponseHandler.java | 14 +- .../server/TransportChannelHandler.java | 33 ++- .../spark/network/util/MapConfigProvider.java | 41 +++ .../apache/spark/network/util/NettyUtils.java | 2 +- .../RequestTimeoutIntegrationSuite.java | 277 ++++++++++++++++++ .../network/TransportClientFactorySuite.java | 21 +- 7 files changed, 375 insertions(+), 18 deletions(-) create mode 100644 network/common/src/main/java/org/apache/spark/network/util/MapConfigProvider.java create mode 100644 network/common/src/test/java/org/apache/spark/network/RequestTimeoutIntegrationSuite.java diff --git a/network/common/src/main/java/org/apache/spark/network/TransportContext.java b/network/common/src/main/java/org/apache/spark/network/TransportContext.java index f0a89c9d9116c..3fe69b1bd8851 100644 --- a/network/common/src/main/java/org/apache/spark/network/TransportContext.java +++ b/network/common/src/main/java/org/apache/spark/network/TransportContext.java @@ -22,6 +22,7 @@ import com.google.common.collect.Lists; import io.netty.channel.Channel; import io.netty.channel.socket.SocketChannel; +import io.netty.handler.timeout.IdleStateHandler; import org.slf4j.Logger; import org.slf4j.LoggerFactory; @@ -106,6 +107,7 @@ public TransportChannelHandler initializePipeline(SocketChannel channel) { .addLast("encoder", encoder) .addLast("frameDecoder", NettyUtils.createFrameDecoder()) .addLast("decoder", decoder) + .addLast("idleStateHandler", new IdleStateHandler(0, 0, conf.connectionTimeoutMs() / 1000)) // NOTE: Chunks are currently guaranteed to be returned in the order of request, but this // would require more logic to guarantee if this were not part of the same event loop. .addLast("handler", channelHandler); @@ -126,7 +128,8 @@ private TransportChannelHandler createChannelHandler(Channel channel) { TransportClient client = new TransportClient(channel, responseHandler); TransportRequestHandler requestHandler = new TransportRequestHandler(channel, client, rpcHandler); - return new TransportChannelHandler(client, responseHandler, requestHandler); + return new TransportChannelHandler(client, responseHandler, requestHandler, + conf.connectionTimeoutMs()); } public TransportConf getConf() { return conf; } diff --git a/network/common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java b/network/common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java index 2044afb0d85db..94fc21af5e606 100644 --- a/network/common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java +++ b/network/common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java @@ -20,8 +20,8 @@ import java.io.IOException; import java.util.Map; import java.util.concurrent.ConcurrentHashMap; +import java.util.concurrent.atomic.AtomicLong; -import com.google.common.annotations.VisibleForTesting; import io.netty.channel.Channel; import org.slf4j.Logger; import org.slf4j.LoggerFactory; @@ -50,13 +50,18 @@ public class TransportResponseHandler extends MessageHandler { private final Map outstandingRpcs; + /** Records the time (in system nanoseconds) that the last fetch or RPC request was sent. */ + private final AtomicLong timeOfLastRequestNs; + public TransportResponseHandler(Channel channel) { this.channel = channel; this.outstandingFetches = new ConcurrentHashMap(); this.outstandingRpcs = new ConcurrentHashMap(); + this.timeOfLastRequestNs = new AtomicLong(0); } public void addFetchRequest(StreamChunkId streamChunkId, ChunkReceivedCallback callback) { + timeOfLastRequestNs.set(System.nanoTime()); outstandingFetches.put(streamChunkId, callback); } @@ -65,6 +70,7 @@ public void removeFetchRequest(StreamChunkId streamChunkId) { } public void addRpcRequest(long requestId, RpcResponseCallback callback) { + timeOfLastRequestNs.set(System.nanoTime()); outstandingRpcs.put(requestId, callback); } @@ -161,8 +167,12 @@ public void handle(ResponseMessage message) { } /** Returns total number of outstanding requests (fetch requests + rpcs) */ - @VisibleForTesting public int numOutstandingRequests() { return outstandingFetches.size() + outstandingRpcs.size(); } + + /** Returns the time in nanoseconds of when the last request was sent out. */ + public long getTimeOfLastRequestNs() { + return timeOfLastRequestNs.get(); + } } diff --git a/network/common/src/main/java/org/apache/spark/network/server/TransportChannelHandler.java b/network/common/src/main/java/org/apache/spark/network/server/TransportChannelHandler.java index e491367fa4528..8e0ee709e38e3 100644 --- a/network/common/src/main/java/org/apache/spark/network/server/TransportChannelHandler.java +++ b/network/common/src/main/java/org/apache/spark/network/server/TransportChannelHandler.java @@ -19,6 +19,8 @@ import io.netty.channel.ChannelHandlerContext; import io.netty.channel.SimpleChannelInboundHandler; +import io.netty.handler.timeout.IdleState; +import io.netty.handler.timeout.IdleStateEvent; import org.slf4j.Logger; import org.slf4j.LoggerFactory; @@ -40,6 +42,11 @@ * Client. * This means that the Client also needs a RequestHandler and the Server needs a ResponseHandler, * for the Client's responses to the Server's requests. + * + * This class also handles timeouts from a {@link io.netty.handler.timeout.IdleStateHandler}. + * We consider a connection timed out if there are outstanding fetch or RPC requests but no traffic + * on the channel for at least `requestTimeoutMs`. Note that this is duplex traffic; we will not + * timeout if the client is continuously sending but getting no responses, for simplicity. */ public class TransportChannelHandler extends SimpleChannelInboundHandler { private final Logger logger = LoggerFactory.getLogger(TransportChannelHandler.class); @@ -47,14 +54,17 @@ public class TransportChannelHandler extends SimpleChannelInboundHandler 0; + boolean isActuallyOverdue = + System.nanoTime() - responseHandler.getTimeOfLastRequestNs() > requestTimeoutNs; + if (e.state() == IdleState.ALL_IDLE && hasInFlightRequests && isActuallyOverdue) { + String address = NettyUtils.getRemoteAddress(ctx.channel()); + logger.error("Connection to {} has been quiet for {} ms while there are outstanding " + + "requests. Assuming connection is dead; please adjust spark.network.timeout if this " + + "is wrong.", address, requestTimeoutNs / 1000 / 1000); + ctx.close(); + } + } + } } diff --git a/network/common/src/main/java/org/apache/spark/network/util/MapConfigProvider.java b/network/common/src/main/java/org/apache/spark/network/util/MapConfigProvider.java new file mode 100644 index 0000000000000..668d2356b955d --- /dev/null +++ b/network/common/src/main/java/org/apache/spark/network/util/MapConfigProvider.java @@ -0,0 +1,41 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.network.util; + +import com.google.common.collect.Maps; + +import java.util.Map; +import java.util.NoSuchElementException; + +/** ConfigProvider based on a Map (copied in the constructor). */ +public class MapConfigProvider extends ConfigProvider { + private final Map config; + + public MapConfigProvider(Map config) { + this.config = Maps.newHashMap(config); + } + + @Override + public String get(String name) { + String value = config.get(name); + if (value == null) { + throw new NoSuchElementException(name); + } + return value; + } +} diff --git a/network/common/src/main/java/org/apache/spark/network/util/NettyUtils.java b/network/common/src/main/java/org/apache/spark/network/util/NettyUtils.java index dabd6261d2aa0..26c6399ce7dbc 100644 --- a/network/common/src/main/java/org/apache/spark/network/util/NettyUtils.java +++ b/network/common/src/main/java/org/apache/spark/network/util/NettyUtils.java @@ -98,7 +98,7 @@ public static ByteToMessageDecoder createFrameDecoder() { return new LengthFieldBasedFrameDecoder(Integer.MAX_VALUE, 0, 8, -8, 8); } - /** Returns the remote address on the channel or "<remote address>" if none exists. */ + /** Returns the remote address on the channel or "<unknown remote>" if none exists. */ public static String getRemoteAddress(Channel channel) { if (channel != null && channel.remoteAddress() != null) { return channel.remoteAddress().toString(); diff --git a/network/common/src/test/java/org/apache/spark/network/RequestTimeoutIntegrationSuite.java b/network/common/src/test/java/org/apache/spark/network/RequestTimeoutIntegrationSuite.java new file mode 100644 index 0000000000000..84ebb337e6d54 --- /dev/null +++ b/network/common/src/test/java/org/apache/spark/network/RequestTimeoutIntegrationSuite.java @@ -0,0 +1,277 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.network; + +import com.google.common.collect.Maps; +import com.google.common.util.concurrent.Uninterruptibles; +import org.apache.spark.network.buffer.ManagedBuffer; +import org.apache.spark.network.buffer.NioManagedBuffer; +import org.apache.spark.network.client.ChunkReceivedCallback; +import org.apache.spark.network.client.RpcResponseCallback; +import org.apache.spark.network.client.TransportClient; +import org.apache.spark.network.client.TransportClientFactory; +import org.apache.spark.network.server.RpcHandler; +import org.apache.spark.network.server.StreamManager; +import org.apache.spark.network.server.TransportServer; +import org.apache.spark.network.util.MapConfigProvider; +import org.apache.spark.network.util.TransportConf; +import org.junit.*; + +import java.io.IOException; +import java.nio.ByteBuffer; +import java.util.*; +import java.util.concurrent.Semaphore; +import java.util.concurrent.TimeUnit; + +/** + * Suite which ensures that requests that go without a response for the network timeout period are + * failed, and the connection closed. + * + * In this suite, we use 2 seconds as the connection timeout, with some slack given in the tests, + * to ensure stability in different test environments. + */ +public class RequestTimeoutIntegrationSuite { + + private TransportServer server; + private TransportClientFactory clientFactory; + + private StreamManager defaultManager; + private TransportConf conf; + + // A large timeout that "shouldn't happen", for the sake of faulty tests not hanging forever. + private final int FOREVER = 60 * 1000; + + @Before + public void setUp() throws Exception { + Map configMap = Maps.newHashMap(); + configMap.put("spark.shuffle.io.connectionTimeout", "2s"); + conf = new TransportConf(new MapConfigProvider(configMap)); + + defaultManager = new StreamManager() { + @Override + public ManagedBuffer getChunk(long streamId, int chunkIndex) { + throw new UnsupportedOperationException(); + } + }; + } + + @After + public void tearDown() { + if (server != null) { + server.close(); + } + if (clientFactory != null) { + clientFactory.close(); + } + } + + // Basic suite: First request completes quickly, and second waits for longer than network timeout. + @Test + public void timeoutInactiveRequests() throws Exception { + final Semaphore semaphore = new Semaphore(1); + final byte[] response = new byte[16]; + RpcHandler handler = new RpcHandler() { + @Override + public void receive(TransportClient client, byte[] message, RpcResponseCallback callback) { + try { + semaphore.tryAcquire(FOREVER, TimeUnit.MILLISECONDS); + callback.onSuccess(response); + } catch (InterruptedException e) { + // do nothing + } + } + + @Override + public StreamManager getStreamManager() { + return defaultManager; + } + }; + + TransportContext context = new TransportContext(conf, handler); + server = context.createServer(); + clientFactory = context.createClientFactory(); + TransportClient client = clientFactory.createClient(TestUtils.getLocalHost(), server.getPort()); + + // First completes quickly (semaphore starts at 1). + TestCallback callback0 = new TestCallback(); + synchronized (callback0) { + client.sendRpc(new byte[0], callback0); + callback0.wait(FOREVER); + assert (callback0.success.length == response.length); + } + + // Second times out after 2 seconds, with slack. Must be IOException. + TestCallback callback1 = new TestCallback(); + synchronized (callback1) { + client.sendRpc(new byte[0], callback1); + callback1.wait(4 * 1000); + assert (callback1.failure != null); + assert (callback1.failure instanceof IOException); + } + semaphore.release(); + } + + // A timeout will cause the connection to be closed, invalidating the current TransportClient. + // It should be the case that requesting a client from the factory produces a new, valid one. + @Test + public void timeoutCleanlyClosesClient() throws Exception { + final Semaphore semaphore = new Semaphore(0); + final byte[] response = new byte[16]; + RpcHandler handler = new RpcHandler() { + @Override + public void receive(TransportClient client, byte[] message, RpcResponseCallback callback) { + try { + semaphore.tryAcquire(FOREVER, TimeUnit.MILLISECONDS); + callback.onSuccess(response); + } catch (InterruptedException e) { + // do nothing + } + } + + @Override + public StreamManager getStreamManager() { + return defaultManager; + } + }; + + TransportContext context = new TransportContext(conf, handler); + server = context.createServer(); + clientFactory = context.createClientFactory(); + + // First request should eventually fail. + TransportClient client0 = + clientFactory.createClient(TestUtils.getLocalHost(), server.getPort()); + TestCallback callback0 = new TestCallback(); + synchronized (callback0) { + client0.sendRpc(new byte[0], callback0); + callback0.wait(FOREVER); + assert (callback0.failure instanceof IOException); + assert (!client0.isActive()); + } + + // Increment the semaphore and the second request should succeed quickly. + semaphore.release(2); + TransportClient client1 = + clientFactory.createClient(TestUtils.getLocalHost(), server.getPort()); + TestCallback callback1 = new TestCallback(); + synchronized (callback1) { + client1.sendRpc(new byte[0], callback1); + callback1.wait(FOREVER); + assert (callback1.success.length == response.length); + assert (callback1.failure == null); + } + } + + // The timeout is relative to the LAST request sent, which is kinda weird, but still. + // This test also makes sure the timeout works for Fetch requests as well as RPCs. + @Test + public void furtherRequestsDelay() throws Exception { + final byte[] response = new byte[16]; + final StreamManager manager = new StreamManager() { + @Override + public ManagedBuffer getChunk(long streamId, int chunkIndex) { + Uninterruptibles.sleepUninterruptibly(FOREVER, TimeUnit.MILLISECONDS); + return new NioManagedBuffer(ByteBuffer.wrap(response)); + } + }; + RpcHandler handler = new RpcHandler() { + @Override + public void receive(TransportClient client, byte[] message, RpcResponseCallback callback) { + throw new UnsupportedOperationException(); + } + + @Override + public StreamManager getStreamManager() { + return manager; + } + }; + + TransportContext context = new TransportContext(conf, handler); + server = context.createServer(); + clientFactory = context.createClientFactory(); + TransportClient client = clientFactory.createClient(TestUtils.getLocalHost(), server.getPort()); + + // Send one request, which will eventually fail. + TestCallback callback0 = new TestCallback(); + client.fetchChunk(0, 0, callback0); + Uninterruptibles.sleepUninterruptibly(1200, TimeUnit.MILLISECONDS); + + // Send a second request before the first has failed. + TestCallback callback1 = new TestCallback(); + client.fetchChunk(0, 1, callback1); + Uninterruptibles.sleepUninterruptibly(1200, TimeUnit.MILLISECONDS); + + synchronized (callback0) { + // not complete yet, but should complete soon + assert (callback0.success == null && callback0.failure == null); + callback0.wait(2 * 1000); + assert (callback0.failure instanceof IOException); + } + + synchronized (callback1) { + // failed at same time as previous + assert (callback0.failure instanceof IOException); + } + } + + /** + * Callback which sets 'success' or 'failure' on completion. + * Additionally notifies all waiters on this callback when invoked. + */ + class TestCallback implements RpcResponseCallback, ChunkReceivedCallback { + + byte[] success; + Throwable failure; + + @Override + public void onSuccess(byte[] response) { + synchronized(this) { + success = response; + this.notifyAll(); + } + } + + @Override + public void onFailure(Throwable e) { + synchronized(this) { + failure = e; + this.notifyAll(); + } + } + + @Override + public void onSuccess(int chunkIndex, ManagedBuffer buffer) { + synchronized(this) { + try { + success = buffer.nioByteBuffer().array(); + this.notifyAll(); + } catch (IOException e) { + // weird + } + } + } + + @Override + public void onFailure(int chunkIndex, Throwable e) { + synchronized(this) { + failure = e; + this.notifyAll(); + } + } + } +} diff --git a/network/common/src/test/java/org/apache/spark/network/TransportClientFactorySuite.java b/network/common/src/test/java/org/apache/spark/network/TransportClientFactorySuite.java index 416dc1b969fa4..35de5e57ccb98 100644 --- a/network/common/src/test/java/org/apache/spark/network/TransportClientFactorySuite.java +++ b/network/common/src/test/java/org/apache/spark/network/TransportClientFactorySuite.java @@ -20,10 +20,11 @@ import java.io.IOException; import java.util.Collections; import java.util.HashSet; -import java.util.NoSuchElementException; +import java.util.Map; import java.util.Set; import java.util.concurrent.atomic.AtomicInteger; +import com.google.common.collect.Maps; import org.junit.After; import org.junit.Before; import org.junit.Test; @@ -36,9 +37,9 @@ import org.apache.spark.network.server.NoOpRpcHandler; import org.apache.spark.network.server.RpcHandler; import org.apache.spark.network.server.TransportServer; -import org.apache.spark.network.util.ConfigProvider; -import org.apache.spark.network.util.JavaUtils; import org.apache.spark.network.util.SystemPropertyConfigProvider; +import org.apache.spark.network.util.JavaUtils; +import org.apache.spark.network.util.MapConfigProvider; import org.apache.spark.network.util.TransportConf; public class TransportClientFactorySuite { @@ -70,16 +71,10 @@ public void tearDown() { */ private void testClientReuse(final int maxConnections, boolean concurrent) throws IOException, InterruptedException { - TransportConf conf = new TransportConf(new ConfigProvider() { - @Override - public String get(String name) { - if (name.equals("spark.shuffle.io.numConnectionsPerPeer")) { - return Integer.toString(maxConnections); - } else { - throw new NoSuchElementException(); - } - } - }); + + Map configMap = Maps.newHashMap(); + configMap.put("spark.shuffle.io.numConnectionsPerPeer", Integer.toString(maxConnections)); + TransportConf conf = new TransportConf(new MapConfigProvider(configMap)); RpcHandler rpcHandler = new NoOpRpcHandler(); TransportContext context = new TransportContext(conf, rpcHandler); From 77176619a97d07811ab20e1dde4677359d85eb33 Mon Sep 17 00:00:00 2001 From: Elisey Zanko Date: Mon, 20 Apr 2015 10:44:09 -0700 Subject: [PATCH 345/591] [SPARK-6661] Python type errors should print type, not object Author: Elisey Zanko Closes #5361 from 31z4/spark-6661 and squashes the following commits: 73c5d79 [Elisey Zanko] Python type errors should print type, not object --- python/pyspark/accumulators.py | 2 +- python/pyspark/context.py | 2 +- python/pyspark/ml/param/__init__.py | 2 +- python/pyspark/ml/pipeline.py | 4 ++-- python/pyspark/mllib/linalg.py | 4 ++-- python/pyspark/mllib/regression.py | 2 +- python/pyspark/mllib/tests.py | 6 ++++-- python/pyspark/sql/_types.py | 12 ++++++------ python/pyspark/sql/context.py | 8 ++++---- python/pyspark/sql/dataframe.py | 2 +- 10 files changed, 23 insertions(+), 21 deletions(-) diff --git a/python/pyspark/accumulators.py b/python/pyspark/accumulators.py index 7271809e43880..0d21a132048a5 100644 --- a/python/pyspark/accumulators.py +++ b/python/pyspark/accumulators.py @@ -83,7 +83,7 @@ >>> sc.accumulator([1.0, 2.0, 3.0]) # doctest: +IGNORE_EXCEPTION_DETAIL Traceback (most recent call last): ... -Exception:... +TypeError:... """ import sys diff --git a/python/pyspark/context.py b/python/pyspark/context.py index 1dc2fec0ae5c8..6a743ac8bd600 100644 --- a/python/pyspark/context.py +++ b/python/pyspark/context.py @@ -671,7 +671,7 @@ def accumulator(self, value, accum_param=None): elif isinstance(value, complex): accum_param = accumulators.COMPLEX_ACCUMULATOR_PARAM else: - raise Exception("No default accumulator param for type %s" % type(value)) + raise TypeError("No default accumulator param for type %s" % type(value)) SparkContext._next_accum_id += 1 return Accumulator(SparkContext._next_accum_id - 1, value, accum_param) diff --git a/python/pyspark/ml/param/__init__.py b/python/pyspark/ml/param/__init__.py index 9fccb65675185..49c20b4cf70cf 100644 --- a/python/pyspark/ml/param/__init__.py +++ b/python/pyspark/ml/param/__init__.py @@ -30,7 +30,7 @@ class Param(object): def __init__(self, parent, name, doc): if not isinstance(parent, Params): - raise ValueError("Parent must be a Params but got type %s." % type(parent).__name__) + raise TypeError("Parent must be a Params but got type %s." % type(parent)) self.parent = parent self.name = str(name) self.doc = str(doc) diff --git a/python/pyspark/ml/pipeline.py b/python/pyspark/ml/pipeline.py index d94ecfff09f66..7c1ec3026da6f 100644 --- a/python/pyspark/ml/pipeline.py +++ b/python/pyspark/ml/pipeline.py @@ -131,8 +131,8 @@ def fit(self, dataset, params={}): stages = paramMap[self.stages] for stage in stages: if not (isinstance(stage, Estimator) or isinstance(stage, Transformer)): - raise ValueError( - "Cannot recognize a pipeline stage of type %s." % type(stage).__name__) + raise TypeError( + "Cannot recognize a pipeline stage of type %s." % type(stage)) indexOfLastEstimator = -1 for i, stage in enumerate(stages): if isinstance(stage, Estimator): diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py index 38b3aa3ad460e..ec8c879ea9389 100644 --- a/python/pyspark/mllib/linalg.py +++ b/python/pyspark/mllib/linalg.py @@ -145,7 +145,7 @@ def serialize(self, obj): values = [float(v) for v in obj] return (1, None, None, values) else: - raise ValueError("cannot serialize %r of type %r" % (obj, type(obj))) + raise TypeError("cannot serialize %r of type %r" % (obj, type(obj))) def deserialize(self, datum): assert len(datum) == 4, \ @@ -561,7 +561,7 @@ def __getitem__(self, index): inds = self.indices vals = self.values if not isinstance(index, int): - raise ValueError( + raise TypeError( "Indices must be of type integer, got type %s" % type(index)) if index < 0: index += self.size diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py index cd7310a64f4ae..a0117c57133ae 100644 --- a/python/pyspark/mllib/regression.py +++ b/python/pyspark/mllib/regression.py @@ -170,7 +170,7 @@ def _regression_train_wrapper(train_func, modelClass, data, initial_weights): from pyspark.mllib.classification import LogisticRegressionModel first = data.first() if not isinstance(first, LabeledPoint): - raise ValueError("data should be an RDD of LabeledPoint, but got %s" % first) + raise TypeError("data should be an RDD of LabeledPoint, but got %s" % type(first)) if initial_weights is None: initial_weights = [0.0] * len(data.first().features) if (modelClass == LogisticRegressionModel): diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index c6ed5acd1770e..849c88341a967 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -135,8 +135,10 @@ def test_sparse_vector_indexing(self): self.assertEquals(sv[-1], 2) self.assertEquals(sv[-2], 0) self.assertEquals(sv[-4], 0) - for ind in [4, -5, 7.8]: + for ind in [4, -5]: self.assertRaises(ValueError, sv.__getitem__, ind) + for ind in [7.8, '1']: + self.assertRaises(TypeError, sv.__getitem__, ind) def test_matrix_indexing(self): mat = DenseMatrix(3, 2, [0, 1, 4, 6, 8, 10]) @@ -450,7 +452,7 @@ def test_infer_schema(self): elif isinstance(v, DenseVector): self.assertEqual(v, self.dv1) else: - raise ValueError("expecting a vector but got %r of type %r" % (v, type(v))) + raise TypeError("expecting a vector but got %r of type %r" % (v, type(v))) @unittest.skipIf(not _have_scipy, "SciPy not installed") diff --git a/python/pyspark/sql/_types.py b/python/pyspark/sql/_types.py index 492c0cbdcf693..110d1152fbdb6 100644 --- a/python/pyspark/sql/_types.py +++ b/python/pyspark/sql/_types.py @@ -562,8 +562,8 @@ def _infer_type(obj): else: try: return _infer_schema(obj) - except ValueError: - raise ValueError("not supported type: %s" % type(obj)) + except TypeError: + raise TypeError("not supported type: %s" % type(obj)) def _infer_schema(row): @@ -584,7 +584,7 @@ def _infer_schema(row): items = sorted(row.__dict__.items()) else: - raise ValueError("Can not infer schema for type: %s" % type(row)) + raise TypeError("Can not infer schema for type: %s" % type(row)) fields = [StructField(k, _infer_type(v), True) for k, v in items] return StructType(fields) @@ -696,7 +696,7 @@ def _merge_type(a, b): return a elif type(a) is not type(b): # TODO: type cast (such as int -> long) - raise TypeError("Can not merge type %s and %s" % (a, b)) + raise TypeError("Can not merge type %s and %s" % (type(a), type(b))) # same type if isinstance(a, StructType): @@ -773,7 +773,7 @@ def convert_struct(obj): elif hasattr(obj, "__dict__"): # object d = obj.__dict__ else: - raise ValueError("Unexpected obj: %s" % obj) + raise TypeError("Unexpected obj type: %s" % type(obj)) if convert_fields: return tuple([conv(d.get(name)) for name, conv in zip(names, converters)]) @@ -912,7 +912,7 @@ def _infer_schema_type(obj, dataType): return StructType(fields) else: - raise ValueError("Unexpected dataType: %s" % dataType) + raise TypeError("Unexpected dataType: %s" % type(dataType)) _acceptable_types = { diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py index c90afc326ca0e..acf3c114548c0 100644 --- a/python/pyspark/sql/context.py +++ b/python/pyspark/sql/context.py @@ -208,7 +208,7 @@ def applySchema(self, rdd, schema): raise TypeError("Cannot apply schema to DataFrame") if not isinstance(schema, StructType): - raise TypeError("schema should be StructType, but got %s" % schema) + raise TypeError("schema should be StructType, but got %s" % type(schema)) return self.createDataFrame(rdd, schema) @@ -281,7 +281,7 @@ def createDataFrame(self, data, schema=None, samplingRatio=None): # data could be list, tuple, generator ... rdd = self._sc.parallelize(data) except Exception: - raise ValueError("cannot create an RDD from type: %s" % type(data)) + raise TypeError("cannot create an RDD from type: %s" % type(data)) else: rdd = data @@ -293,8 +293,8 @@ def createDataFrame(self, data, schema=None, samplingRatio=None): if isinstance(schema, (list, tuple)): first = rdd.first() if not isinstance(first, (list, tuple)): - raise ValueError("each row in `rdd` should be list or tuple, " - "but got %r" % type(first)) + raise TypeError("each row in `rdd` should be list or tuple, " + "but got %r" % type(first)) row_cls = Row(*schema) schema = self._inferSchema(rdd.map(lambda r: row_cls(*r)), samplingRatio) diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index d70c5b0a6930c..75c181c0c7f5e 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -608,7 +608,7 @@ def __getitem__(self, item): jc = self._jdf.apply(self.columns[item]) return Column(jc) else: - raise TypeError("unexpected type: %s" % type(item)) + raise TypeError("unexpected item type: %s" % type(item)) def __getattr__(self, name): """Returns the :class:`Column` denoted by ``name``. From 1ebceaa55bec28850a48fb28b4cf7b44c8448a78 Mon Sep 17 00:00:00 2001 From: Liang-Chi Hsieh Date: Mon, 20 Apr 2015 10:47:37 -0700 Subject: [PATCH 346/591] [Minor][MLlib] Incorrect path to test data is used in DecisionTreeExample It should load from `testInput` instead of `input` for test data. Author: Liang-Chi Hsieh Closes #5594 from viirya/use_testinput and squashes the following commits: 5e8b174 [Liang-Chi Hsieh] Fix style. b60b475 [Liang-Chi Hsieh] Use testInput. --- .../org/apache/spark/examples/ml/DecisionTreeExample.scala | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala index d4cc8dede07ef..921b396e799e7 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala @@ -173,7 +173,8 @@ object DecisionTreeExample { val splits: Array[RDD[LabeledPoint]] = if (testInput != "") { // Load testInput. val numFeatures = origExamples.take(1)(0).features.size - val origTestExamples: RDD[LabeledPoint] = loadData(sc, input, dataFormat, Some(numFeatures)) + val origTestExamples: RDD[LabeledPoint] = + loadData(sc, testInput, dataFormat, Some(numFeatures)) Array(origExamples, origTestExamples) } else { // Split input into training, test. From 97fda73db4efda2ba5b12937954de428258a5b56 Mon Sep 17 00:00:00 2001 From: Eric Chiang Date: Mon, 20 Apr 2015 13:11:21 -0700 Subject: [PATCH 347/591] fixed doc The contribution is my original work. I license the work to the project under the project's open source license. Small typo in the programming guide. Author: Eric Chiang Closes #5599 from ericchiang/docs-typo and squashes the following commits: 1177942 [Eric Chiang] fixed doc --- docs/programming-guide.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/programming-guide.md b/docs/programming-guide.md index f4fabb0927b66..27816515c5de2 100644 --- a/docs/programming-guide.md +++ b/docs/programming-guide.md @@ -1093,7 +1093,7 @@ for details. ### Shuffle operations Certain operations within Spark trigger an event known as the shuffle. The shuffle is Spark's -mechanism for re-distributing data so that is grouped differently across partitions. This typically +mechanism for re-distributing data so that it's grouped differently across partitions. This typically involves copying data across executors and machines, making the shuffle a complex and costly operation. From 517bdf36aecdc94ef569b68f0a96892e707b5c7b Mon Sep 17 00:00:00 2001 From: BenFradet Date: Mon, 20 Apr 2015 13:46:55 -0700 Subject: [PATCH 348/591] [doc][streaming] Fixed broken link in mllib section The commit message is pretty self-explanatory. Author: BenFradet Closes #5600 from BenFradet/master and squashes the following commits: 108492d [BenFradet] [doc][streaming] Fixed broken link in mllib section --- docs/streaming-programming-guide.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md index 262512a639046..2f2fea53168a3 100644 --- a/docs/streaming-programming-guide.md +++ b/docs/streaming-programming-guide.md @@ -1588,7 +1588,7 @@ See the [DataFrames and SQL](sql-programming-guide.html) guide to learn more abo *** ## MLlib Operations -You can also easily use machine learning algorithms provided by [MLlib](mllib-guide.html). First of all, there are streaming machine learning algorithms (e.g. (Streaming Linear Regression](mllib-linear-methods.html#streaming-linear-regression), [Streaming KMeans](mllib-clustering.html#streaming-k-means), etc.) which can simultaneously learn from the streaming data as well as apply the model on the streaming data. Beyond these, for a much larger class of machine learning algorithms, you can learn a learning model offline (i.e. using historical data) and then apply the model online on streaming data. See the [MLlib](mllib-guide.html) guide for more details. +You can also easily use machine learning algorithms provided by [MLlib](mllib-guide.html). First of all, there are streaming machine learning algorithms (e.g. [Streaming Linear Regression](mllib-linear-methods.html#streaming-linear-regression), [Streaming KMeans](mllib-clustering.html#streaming-k-means), etc.) which can simultaneously learn from the streaming data as well as apply the model on the streaming data. Beyond these, for a much larger class of machine learning algorithms, you can learn a learning model offline (i.e. using historical data) and then apply the model online on streaming data. See the [MLlib](mllib-guide.html) guide for more details. *** From ce7ddabbcd330b19f6d0c17082304dfa6e1621b2 Mon Sep 17 00:00:00 2001 From: Yin Huai Date: Mon, 20 Apr 2015 18:42:50 -0700 Subject: [PATCH 349/591] [SPARK-6368][SQL] Build a specialized serializer for Exchange operator. JIRA: https://issues.apache.org/jira/browse/SPARK-6368 Author: Yin Huai Closes #5497 from yhuai/serializer2 and squashes the following commits: da562c5 [Yin Huai] Merge remote-tracking branch 'upstream/master' into serializer2 50e0c3d [Yin Huai] When no filed is emitted to shuffle, use SparkSqlSerializer for now. 9f1ed92 [Yin Huai] Merge remote-tracking branch 'upstream/master' into serializer2 6d07678 [Yin Huai] Address comments. 4273b8c [Yin Huai] Enabled SparkSqlSerializer2. 09e587a [Yin Huai] Remove TODO. 791b96a [Yin Huai] Use UTF8String. 60a1487 [Yin Huai] Merge remote-tracking branch 'upstream/master' into serializer2 3e09655 [Yin Huai] Use getAs for Date column. 43b9fb4 [Yin Huai] Test. 8297732 [Yin Huai] Fix test. c9373c8 [Yin Huai] Support DecimalType. 2379eeb [Yin Huai] ASF header. 39704ab [Yin Huai] Specialized serializer for Exchange. --- .../scala/org/apache/spark/sql/SQLConf.scala | 4 + .../apache/spark/sql/execution/Exchange.scala | 59 ++- .../sql/execution/SparkSqlSerializer2.scala | 421 ++++++++++++++++++ .../execution/SparkSqlSerializer2Suite.scala | 195 ++++++++ 4 files changed, 673 insertions(+), 6 deletions(-) create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala index 5c65f04ee8497..4fc5de7e824fe 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala @@ -64,6 +64,8 @@ private[spark] object SQLConf { // Set to false when debugging requires the ability to look at invalid query plans. val DATAFRAME_EAGER_ANALYSIS = "spark.sql.eagerAnalysis" + val USE_SQL_SERIALIZER2 = "spark.sql.useSerializer2" + object Deprecated { val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks" } @@ -147,6 +149,8 @@ private[sql] class SQLConf extends Serializable { */ private[spark] def codegenEnabled: Boolean = getConf(CODEGEN_ENABLED, "false").toBoolean + private[spark] def useSqlSerializer2: Boolean = getConf(USE_SQL_SERIALIZER2, "true").toBoolean + /** * Upper bound on the sizes (in bytes) of the tables qualified for the auto conversion to * a broadcast value during the physical executions of join operations. Setting this to -1 diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala index 69a620e1ec929..5b2e46962cd3b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala @@ -19,13 +19,15 @@ package org.apache.spark.sql.execution import org.apache.spark.annotation.DeveloperApi import org.apache.spark.shuffle.sort.SortShuffleManager -import org.apache.spark.{SparkEnv, HashPartitioner, RangePartitioner, SparkConf} +import org.apache.spark.{SparkEnv, HashPartitioner, RangePartitioner} import org.apache.spark.rdd.{RDD, ShuffledRDD} +import org.apache.spark.serializer.Serializer import org.apache.spark.sql.{SQLContext, Row} import org.apache.spark.sql.catalyst.errors.attachTree import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.physical._ import org.apache.spark.sql.catalyst.rules.Rule +import org.apache.spark.sql.types.DataType import org.apache.spark.util.MutablePair object Exchange { @@ -77,9 +79,48 @@ case class Exchange( } } - override def execute(): RDD[Row] = attachTree(this , "execute") { - lazy val sparkConf = child.sqlContext.sparkContext.getConf + @transient private lazy val sparkConf = child.sqlContext.sparkContext.getConf + + def serializer( + keySchema: Array[DataType], + valueSchema: Array[DataType], + numPartitions: Int): Serializer = { + // In ExternalSorter's spillToMergeableFile function, key-value pairs are written out + // through write(key) and then write(value) instead of write((key, value)). Because + // SparkSqlSerializer2 assumes that objects passed in are Product2, we cannot safely use + // it when spillToMergeableFile in ExternalSorter will be used. + // So, we will not use SparkSqlSerializer2 when + // - Sort-based shuffle is enabled and the number of reducers (numPartitions) is greater + // then the bypassMergeThreshold; or + // - newOrdering is defined. + val cannotUseSqlSerializer2 = + (sortBasedShuffleOn && numPartitions > bypassMergeThreshold) || newOrdering.nonEmpty + + // It is true when there is no field that needs to be write out. + // For now, we will not use SparkSqlSerializer2 when noField is true. + val noField = + (keySchema == null || keySchema.length == 0) && + (valueSchema == null || valueSchema.length == 0) + + val useSqlSerializer2 = + child.sqlContext.conf.useSqlSerializer2 && // SparkSqlSerializer2 is enabled. + !cannotUseSqlSerializer2 && // Safe to use Serializer2. + SparkSqlSerializer2.support(keySchema) && // The schema of key is supported. + SparkSqlSerializer2.support(valueSchema) && // The schema of value is supported. + !noField + + val serializer = if (useSqlSerializer2) { + logInfo("Using SparkSqlSerializer2.") + new SparkSqlSerializer2(keySchema, valueSchema) + } else { + logInfo("Using SparkSqlSerializer.") + new SparkSqlSerializer(sparkConf) + } + + serializer + } + override def execute(): RDD[Row] = attachTree(this , "execute") { newPartitioning match { case HashPartitioning(expressions, numPartitions) => // TODO: Eliminate redundant expressions in grouping key and value. @@ -111,7 +152,10 @@ case class Exchange( } else { new ShuffledRDD[Row, Row, Row](rdd, part) } - shuffled.setSerializer(new SparkSqlSerializer(sparkConf)) + val keySchema = expressions.map(_.dataType).toArray + val valueSchema = child.output.map(_.dataType).toArray + shuffled.setSerializer(serializer(keySchema, valueSchema, numPartitions)) + shuffled.map(_._2) case RangePartitioning(sortingExpressions, numPartitions) => @@ -134,7 +178,9 @@ case class Exchange( } else { new ShuffledRDD[Row, Null, Null](rdd, part) } - shuffled.setSerializer(new SparkSqlSerializer(sparkConf)) + val keySchema = child.output.map(_.dataType).toArray + shuffled.setSerializer(serializer(keySchema, null, numPartitions)) + shuffled.map(_._1) case SinglePartition => @@ -152,7 +198,8 @@ case class Exchange( } val partitioner = new HashPartitioner(1) val shuffled = new ShuffledRDD[Null, Row, Row](rdd, partitioner) - shuffled.setSerializer(new SparkSqlSerializer(sparkConf)) + val valueSchema = child.output.map(_.dataType).toArray + shuffled.setSerializer(serializer(null, valueSchema, 1)) shuffled.map(_._2) case _ => sys.error(s"Exchange not implemented for $newPartitioning") diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala new file mode 100644 index 0000000000000..cec97de2cd8e4 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala @@ -0,0 +1,421 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution + +import java.io._ +import java.math.{BigDecimal, BigInteger} +import java.nio.ByteBuffer +import java.sql.Timestamp + +import scala.reflect.ClassTag + +import org.apache.spark.serializer._ +import org.apache.spark.Logging +import org.apache.spark.sql.Row +import org.apache.spark.sql.catalyst.expressions.SpecificMutableRow +import org.apache.spark.sql.types._ + +/** + * The serialization stream for [[SparkSqlSerializer2]]. It assumes that the object passed in + * its `writeObject` are [[Product2]]. The serialization functions for the key and value of the + * [[Product2]] are constructed based on their schemata. + * The benefit of this serialization stream is that compared with general-purpose serializers like + * Kryo and Java serializer, it can significantly reduce the size of serialized and has a lower + * allocation cost, which can benefit the shuffle operation. Right now, its main limitations are: + * 1. It does not support complex types, i.e. Map, Array, and Struct. + * 2. It assumes that the objects passed in are [[Product2]]. So, it cannot be used when + * [[org.apache.spark.util.collection.ExternalSorter]]'s merge sort operation is used because + * the objects passed in the serializer are not in the type of [[Product2]]. Also also see + * the comment of the `serializer` method in [[Exchange]] for more information on it. + */ +private[sql] class Serializer2SerializationStream( + keySchema: Array[DataType], + valueSchema: Array[DataType], + out: OutputStream) + extends SerializationStream with Logging { + + val rowOut = new DataOutputStream(out) + val writeKey = SparkSqlSerializer2.createSerializationFunction(keySchema, rowOut) + val writeValue = SparkSqlSerializer2.createSerializationFunction(valueSchema, rowOut) + + def writeObject[T: ClassTag](t: T): SerializationStream = { + val kv = t.asInstanceOf[Product2[Row, Row]] + writeKey(kv._1) + writeValue(kv._2) + + this + } + + def flush(): Unit = { + rowOut.flush() + } + + def close(): Unit = { + rowOut.close() + } +} + +/** + * The corresponding deserialization stream for [[Serializer2SerializationStream]]. + */ +private[sql] class Serializer2DeserializationStream( + keySchema: Array[DataType], + valueSchema: Array[DataType], + in: InputStream) + extends DeserializationStream with Logging { + + val rowIn = new DataInputStream(new BufferedInputStream(in)) + + val key = if (keySchema != null) new SpecificMutableRow(keySchema) else null + val value = if (valueSchema != null) new SpecificMutableRow(valueSchema) else null + val readKey = SparkSqlSerializer2.createDeserializationFunction(keySchema, rowIn, key) + val readValue = SparkSqlSerializer2.createDeserializationFunction(valueSchema, rowIn, value) + + def readObject[T: ClassTag](): T = { + readKey() + readValue() + + (key, value).asInstanceOf[T] + } + + def close(): Unit = { + rowIn.close() + } +} + +private[sql] class ShuffleSerializerInstance( + keySchema: Array[DataType], + valueSchema: Array[DataType]) + extends SerializerInstance { + + def serialize[T: ClassTag](t: T): ByteBuffer = + throw new UnsupportedOperationException("Not supported.") + + def deserialize[T: ClassTag](bytes: ByteBuffer): T = + throw new UnsupportedOperationException("Not supported.") + + def deserialize[T: ClassTag](bytes: ByteBuffer, loader: ClassLoader): T = + throw new UnsupportedOperationException("Not supported.") + + def serializeStream(s: OutputStream): SerializationStream = { + new Serializer2SerializationStream(keySchema, valueSchema, s) + } + + def deserializeStream(s: InputStream): DeserializationStream = { + new Serializer2DeserializationStream(keySchema, valueSchema, s) + } +} + +/** + * SparkSqlSerializer2 is a special serializer that creates serialization function and + * deserialization function based on the schema of data. It assumes that values passed in + * are key/value pairs and values returned from it are also key/value pairs. + * The schema of keys is represented by `keySchema` and that of values is represented by + * `valueSchema`. + */ +private[sql] class SparkSqlSerializer2(keySchema: Array[DataType], valueSchema: Array[DataType]) + extends Serializer + with Logging + with Serializable{ + + def newInstance(): SerializerInstance = new ShuffleSerializerInstance(keySchema, valueSchema) +} + +private[sql] object SparkSqlSerializer2 { + + final val NULL = 0 + final val NOT_NULL = 1 + + /** + * Check if rows with the given schema can be serialized with ShuffleSerializer. + */ + def support(schema: Array[DataType]): Boolean = { + if (schema == null) return true + + var i = 0 + while (i < schema.length) { + schema(i) match { + case udt: UserDefinedType[_] => return false + case array: ArrayType => return false + case map: MapType => return false + case struct: StructType => return false + case _ => + } + i += 1 + } + + return true + } + + /** + * The util function to create the serialization function based on the given schema. + */ + def createSerializationFunction(schema: Array[DataType], out: DataOutputStream): Row => Unit = { + (row: Row) => + // If the schema is null, the returned function does nothing when it get called. + if (schema != null) { + var i = 0 + while (i < schema.length) { + schema(i) match { + // When we write values to the underlying stream, we also first write the null byte + // first. Then, if the value is not null, we write the contents out. + + case NullType => // Write nothing. + + case BooleanType => + if (row.isNullAt(i)) { + out.writeByte(NULL) + } else { + out.writeByte(NOT_NULL) + out.writeBoolean(row.getBoolean(i)) + } + + case ByteType => + if (row.isNullAt(i)) { + out.writeByte(NULL) + } else { + out.writeByte(NOT_NULL) + out.writeByte(row.getByte(i)) + } + + case ShortType => + if (row.isNullAt(i)) { + out.writeByte(NULL) + } else { + out.writeByte(NOT_NULL) + out.writeShort(row.getShort(i)) + } + + case IntegerType => + if (row.isNullAt(i)) { + out.writeByte(NULL) + } else { + out.writeByte(NOT_NULL) + out.writeInt(row.getInt(i)) + } + + case LongType => + if (row.isNullAt(i)) { + out.writeByte(NULL) + } else { + out.writeByte(NOT_NULL) + out.writeLong(row.getLong(i)) + } + + case FloatType => + if (row.isNullAt(i)) { + out.writeByte(NULL) + } else { + out.writeByte(NOT_NULL) + out.writeFloat(row.getFloat(i)) + } + + case DoubleType => + if (row.isNullAt(i)) { + out.writeByte(NULL) + } else { + out.writeByte(NOT_NULL) + out.writeDouble(row.getDouble(i)) + } + + case decimal: DecimalType => + if (row.isNullAt(i)) { + out.writeByte(NULL) + } else { + out.writeByte(NOT_NULL) + val value = row.apply(i).asInstanceOf[Decimal] + val javaBigDecimal = value.toJavaBigDecimal + // First, write out the unscaled value. + val bytes: Array[Byte] = javaBigDecimal.unscaledValue().toByteArray + out.writeInt(bytes.length) + out.write(bytes) + // Then, write out the scale. + out.writeInt(javaBigDecimal.scale()) + } + + case DateType => + if (row.isNullAt(i)) { + out.writeByte(NULL) + } else { + out.writeByte(NOT_NULL) + out.writeInt(row.getAs[Int](i)) + } + + case TimestampType => + if (row.isNullAt(i)) { + out.writeByte(NULL) + } else { + out.writeByte(NOT_NULL) + val timestamp = row.getAs[java.sql.Timestamp](i) + val time = timestamp.getTime + val nanos = timestamp.getNanos + out.writeLong(time - (nanos / 1000000)) // Write the milliseconds value. + out.writeInt(nanos) // Write the nanoseconds part. + } + + case StringType => + if (row.isNullAt(i)) { + out.writeByte(NULL) + } else { + out.writeByte(NOT_NULL) + val bytes = row.getAs[UTF8String](i).getBytes + out.writeInt(bytes.length) + out.write(bytes) + } + + case BinaryType => + if (row.isNullAt(i)) { + out.writeByte(NULL) + } else { + out.writeByte(NOT_NULL) + val bytes = row.getAs[Array[Byte]](i) + out.writeInt(bytes.length) + out.write(bytes) + } + } + i += 1 + } + } + } + + /** + * The util function to create the deserialization function based on the given schema. + */ + def createDeserializationFunction( + schema: Array[DataType], + in: DataInputStream, + mutableRow: SpecificMutableRow): () => Unit = { + () => { + // If the schema is null, the returned function does nothing when it get called. + if (schema != null) { + var i = 0 + while (i < schema.length) { + schema(i) match { + // When we read values from the underlying stream, we also first read the null byte + // first. Then, if the value is not null, we update the field of the mutable row. + + case NullType => mutableRow.setNullAt(i) // Read nothing. + + case BooleanType => + if (in.readByte() == NULL) { + mutableRow.setNullAt(i) + } else { + mutableRow.setBoolean(i, in.readBoolean()) + } + + case ByteType => + if (in.readByte() == NULL) { + mutableRow.setNullAt(i) + } else { + mutableRow.setByte(i, in.readByte()) + } + + case ShortType => + if (in.readByte() == NULL) { + mutableRow.setNullAt(i) + } else { + mutableRow.setShort(i, in.readShort()) + } + + case IntegerType => + if (in.readByte() == NULL) { + mutableRow.setNullAt(i) + } else { + mutableRow.setInt(i, in.readInt()) + } + + case LongType => + if (in.readByte() == NULL) { + mutableRow.setNullAt(i) + } else { + mutableRow.setLong(i, in.readLong()) + } + + case FloatType => + if (in.readByte() == NULL) { + mutableRow.setNullAt(i) + } else { + mutableRow.setFloat(i, in.readFloat()) + } + + case DoubleType => + if (in.readByte() == NULL) { + mutableRow.setNullAt(i) + } else { + mutableRow.setDouble(i, in.readDouble()) + } + + case decimal: DecimalType => + if (in.readByte() == NULL) { + mutableRow.setNullAt(i) + } else { + // First, read in the unscaled value. + val length = in.readInt() + val bytes = new Array[Byte](length) + in.readFully(bytes) + val unscaledVal = new BigInteger(bytes) + // Then, read the scale. + val scale = in.readInt() + // Finally, create the Decimal object and set it in the row. + mutableRow.update(i, Decimal(new BigDecimal(unscaledVal, scale))) + } + + case DateType => + if (in.readByte() == NULL) { + mutableRow.setNullAt(i) + } else { + mutableRow.update(i, in.readInt()) + } + + case TimestampType => + if (in.readByte() == NULL) { + mutableRow.setNullAt(i) + } else { + val time = in.readLong() // Read the milliseconds value. + val nanos = in.readInt() // Read the nanoseconds part. + val timestamp = new Timestamp(time) + timestamp.setNanos(nanos) + mutableRow.update(i, timestamp) + } + + case StringType => + if (in.readByte() == NULL) { + mutableRow.setNullAt(i) + } else { + val length = in.readInt() + val bytes = new Array[Byte](length) + in.readFully(bytes) + mutableRow.update(i, UTF8String(bytes)) + } + + case BinaryType => + if (in.readByte() == NULL) { + mutableRow.setNullAt(i) + } else { + val length = in.readInt() + val bytes = new Array[Byte](length) + in.readFully(bytes) + mutableRow.update(i, bytes) + } + } + i += 1 + } + } + } + } +} diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala new file mode 100644 index 0000000000000..27f063d73a9a9 --- /dev/null +++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala @@ -0,0 +1,195 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.execution + +import java.sql.{Timestamp, Date} + +import org.scalatest.{FunSuite, BeforeAndAfterAll} + +import org.apache.spark.rdd.ShuffledRDD +import org.apache.spark.serializer.Serializer +import org.apache.spark.ShuffleDependency +import org.apache.spark.sql.types._ +import org.apache.spark.sql.Row +import org.apache.spark.sql.test.TestSQLContext._ +import org.apache.spark.sql.{MyDenseVectorUDT, QueryTest} + +class SparkSqlSerializer2DataTypeSuite extends FunSuite { + // Make sure that we will not use serializer2 for unsupported data types. + def checkSupported(dataType: DataType, isSupported: Boolean): Unit = { + val testName = + s"${if (dataType == null) null else dataType.toString} is " + + s"${if (isSupported) "supported" else "unsupported"}" + + test(testName) { + assert(SparkSqlSerializer2.support(Array(dataType)) === isSupported) + } + } + + checkSupported(null, isSupported = true) + checkSupported(NullType, isSupported = true) + checkSupported(BooleanType, isSupported = true) + checkSupported(ByteType, isSupported = true) + checkSupported(ShortType, isSupported = true) + checkSupported(IntegerType, isSupported = true) + checkSupported(LongType, isSupported = true) + checkSupported(FloatType, isSupported = true) + checkSupported(DoubleType, isSupported = true) + checkSupported(DateType, isSupported = true) + checkSupported(TimestampType, isSupported = true) + checkSupported(StringType, isSupported = true) + checkSupported(BinaryType, isSupported = true) + checkSupported(DecimalType(10, 5), isSupported = true) + checkSupported(DecimalType.Unlimited, isSupported = true) + + // For now, ArrayType, MapType, and StructType are not supported. + checkSupported(ArrayType(DoubleType, true), isSupported = false) + checkSupported(ArrayType(StringType, false), isSupported = false) + checkSupported(MapType(IntegerType, StringType, true), isSupported = false) + checkSupported(MapType(IntegerType, ArrayType(DoubleType), false), isSupported = false) + checkSupported(StructType(StructField("a", IntegerType, true) :: Nil), isSupported = false) + // UDTs are not supported right now. + checkSupported(new MyDenseVectorUDT, isSupported = false) +} + +abstract class SparkSqlSerializer2Suite extends QueryTest with BeforeAndAfterAll { + var allColumns: String = _ + val serializerClass: Class[Serializer] = + classOf[SparkSqlSerializer2].asInstanceOf[Class[Serializer]] + var numShufflePartitions: Int = _ + var useSerializer2: Boolean = _ + + override def beforeAll(): Unit = { + numShufflePartitions = conf.numShufflePartitions + useSerializer2 = conf.useSqlSerializer2 + + sql("set spark.sql.useSerializer2=true") + + val supportedTypes = + Seq(StringType, BinaryType, NullType, BooleanType, + ByteType, ShortType, IntegerType, LongType, + FloatType, DoubleType, DecimalType.Unlimited, DecimalType(6, 5), + DateType, TimestampType) + + val fields = supportedTypes.zipWithIndex.map { case (dataType, index) => + StructField(s"col$index", dataType, true) + } + allColumns = fields.map(_.name).mkString(",") + val schema = StructType(fields) + + // Create a RDD with all data types supported by SparkSqlSerializer2. + val rdd = + sparkContext.parallelize((1 to 1000), 10).map { i => + Row( + s"str${i}: test serializer2.", + s"binary${i}: test serializer2.".getBytes("UTF-8"), + null, + i % 2 == 0, + i.toByte, + i.toShort, + i, + Long.MaxValue - i.toLong, + (i + 0.25).toFloat, + (i + 0.75), + BigDecimal(Long.MaxValue.toString + ".12345"), + new java.math.BigDecimal(s"${i % 9 + 1}" + ".23456"), + new Date(i), + new Timestamp(i)) + } + + createDataFrame(rdd, schema).registerTempTable("shuffle") + + super.beforeAll() + } + + override def afterAll(): Unit = { + dropTempTable("shuffle") + sql(s"set spark.sql.shuffle.partitions=$numShufflePartitions") + sql(s"set spark.sql.useSerializer2=$useSerializer2") + super.afterAll() + } + + def checkSerializer[T <: Serializer]( + executedPlan: SparkPlan, + expectedSerializerClass: Class[T]): Unit = { + executedPlan.foreach { + case exchange: Exchange => + val shuffledRDD = exchange.execute().firstParent.asInstanceOf[ShuffledRDD[_, _, _]] + val dependency = shuffledRDD.getDependencies.head.asInstanceOf[ShuffleDependency[_, _, _]] + val serializerNotSetMessage = + s"Expected $expectedSerializerClass as the serializer of Exchange. " + + s"However, the serializer was not set." + val serializer = dependency.serializer.getOrElse(fail(serializerNotSetMessage)) + assert(serializer.getClass === expectedSerializerClass) + case _ => // Ignore other nodes. + } + } + + test("key schema and value schema are not nulls") { + val df = sql(s"SELECT DISTINCT ${allColumns} FROM shuffle") + checkSerializer(df.queryExecution.executedPlan, serializerClass) + checkAnswer( + df, + table("shuffle").collect()) + } + + test("value schema is null") { + val df = sql(s"SELECT col0 FROM shuffle ORDER BY col0") + checkSerializer(df.queryExecution.executedPlan, serializerClass) + assert( + df.map(r => r.getString(0)).collect().toSeq === + table("shuffle").select("col0").map(r => r.getString(0)).collect().sorted.toSeq) + } + + test("no map output field") { + val df = sql(s"SELECT 1 + 1 FROM shuffle") + checkSerializer(df.queryExecution.executedPlan, classOf[SparkSqlSerializer]) + } +} + +/** Tests SparkSqlSerializer2 with sort based shuffle without sort merge. */ +class SparkSqlSerializer2SortShuffleSuite extends SparkSqlSerializer2Suite { + override def beforeAll(): Unit = { + super.beforeAll() + // Sort merge will not be triggered. + sql("set spark.sql.shuffle.partitions = 200") + } + + test("key schema is null") { + val aggregations = allColumns.split(",").map(c => s"COUNT($c)").mkString(",") + val df = sql(s"SELECT $aggregations FROM shuffle") + checkSerializer(df.queryExecution.executedPlan, serializerClass) + checkAnswer( + df, + Row(1000, 1000, 0, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000)) + } +} + +/** For now, we will use SparkSqlSerializer for sort based shuffle with sort merge. */ +class SparkSqlSerializer2SortMergeShuffleSuite extends SparkSqlSerializer2Suite { + + // We are expecting SparkSqlSerializer. + override val serializerClass: Class[Serializer] = + classOf[SparkSqlSerializer].asInstanceOf[Class[Serializer]] + + override def beforeAll(): Unit = { + super.beforeAll() + // To trigger the sort merge. + sql("set spark.sql.shuffle.partitions = 201") + } +} From c736220dac51cf73181fdd7f621c960c4e7bf0c2 Mon Sep 17 00:00:00 2001 From: Liang-Chi Hsieh Date: Mon, 20 Apr 2015 18:54:01 -0700 Subject: [PATCH 350/591] [SPARK-6635][SQL] DataFrame.withColumn should replace columns with identical column names JIRA https://issues.apache.org/jira/browse/SPARK-6635 Author: Liang-Chi Hsieh Closes #5541 from viirya/replace_with_column and squashes the following commits: b539c7b [Liang-Chi Hsieh] For comment. 72f35b1 [Liang-Chi Hsieh] DataFrame.withColumn can replace original column with identical column name. --- .../scala/org/apache/spark/sql/DataFrame.scala | 14 +++++++++++++- .../org/apache/spark/sql/DataFrameSuite.scala | 8 ++++++++ 2 files changed, 21 insertions(+), 1 deletion(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 17c21f6e3a0e9..45f5da387692e 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -747,7 +747,19 @@ class DataFrame private[sql]( * Returns a new [[DataFrame]] by adding a column. * @group dfops */ - def withColumn(colName: String, col: Column): DataFrame = select(Column("*"), col.as(colName)) + def withColumn(colName: String, col: Column): DataFrame = { + val resolver = sqlContext.analyzer.resolver + val replaced = schema.exists(f => resolver(f.name, colName)) + if (replaced) { + val colNames = schema.map { field => + val name = field.name + if (resolver(name, colName)) col.as(colName) else Column(name) + } + select(colNames :_*) + } else { + select(Column("*"), col.as(colName)) + } + } /** * Returns a new [[DataFrame]] with a column renamed. diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index 3250ab476aeb4..b9b6a400ae195 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -473,6 +473,14 @@ class DataFrameSuite extends QueryTest { assert(df.schema.map(_.name).toSeq === Seq("key", "value", "newCol")) } + test("replace column using withColumn") { + val df2 = TestSQLContext.sparkContext.parallelize(Array(1, 2, 3)).toDF("x") + val df3 = df2.withColumn("x", df2("x") + 1) + checkAnswer( + df3.select("x"), + Row(2) :: Row(3) :: Row(4) :: Nil) + } + test("withColumnRenamed") { val df = testData.toDF().withColumn("newCol", col("key") + 1) .withColumnRenamed("value", "valueRenamed") From 8136810dfad12008ac300116df7bc8448740f1ae Mon Sep 17 00:00:00 2001 From: zsxwing Date: Mon, 20 Apr 2015 23:18:42 -0700 Subject: [PATCH 351/591] [SPARK-6490][Core] Add spark.rpc.* and deprecate spark.akka.* Deprecated `spark.akka.num.retries`, `spark.akka.retry.wait`, `spark.akka.askTimeout`, `spark.akka.lookupTimeout`, and added `spark.rpc.num.retries`, `spark.rpc.retry.wait`, `spark.rpc.askTimeout`, `spark.rpc.lookupTimeout`. Author: zsxwing Closes #5595 from zsxwing/SPARK-6490 and squashes the following commits: e0d80a9 [zsxwing] Use getTimeAsMs and getTimeAsSeconds and other minor fixes 31dbe69 [zsxwing] Add spark.rpc.* and deprecate spark.akka.* --- .../scala/org/apache/spark/SparkConf.scala | 10 ++++++- .../org/apache/spark/deploy/Client.scala | 6 ++--- .../spark/deploy/client/AppClient.scala | 4 +-- .../apache/spark/deploy/master/Master.scala | 4 +-- .../spark/deploy/master/ui/MasterWebUI.scala | 4 +-- .../deploy/rest/StandaloneRestServer.scala | 8 +++--- .../spark/deploy/worker/ui/WorkerWebUI.scala | 4 +-- .../scala/org/apache/spark/rpc/RpcEnv.scala | 10 +++---- .../cluster/YarnSchedulerBackend.scala | 4 +-- .../spark/storage/BlockManagerMaster.scala | 4 +-- .../org/apache/spark/util/AkkaUtils.scala | 26 +++---------------- .../org/apache/spark/util/RpcUtils.scala | 23 ++++++++++++++++ .../apache/spark/MapOutputTrackerSuite.scala | 4 +-- .../org/apache/spark/SparkConfSuite.scala | 24 ++++++++++++++++- .../org/apache/spark/rpc/RpcEnvSuite.scala | 4 +-- 15 files changed, 86 insertions(+), 53 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala index e3a649d755450..c1996e08756a6 100644 --- a/core/src/main/scala/org/apache/spark/SparkConf.scala +++ b/core/src/main/scala/org/apache/spark/SparkConf.scala @@ -431,7 +431,15 @@ private[spark] object SparkConf extends Logging { "spark.yarn.am.waitTime" -> Seq( AlternateConfig("spark.yarn.applicationMaster.waitTries", "1.3", // Translate old value to a duration, with 10s wait time per try. - translation = s => s"${s.toLong * 10}s")) + translation = s => s"${s.toLong * 10}s")), + "spark.rpc.numRetries" -> Seq( + AlternateConfig("spark.akka.num.retries", "1.4")), + "spark.rpc.retry.wait" -> Seq( + AlternateConfig("spark.akka.retry.wait", "1.4")), + "spark.rpc.askTimeout" -> Seq( + AlternateConfig("spark.akka.askTimeout", "1.4")), + "spark.rpc.lookupTimeout" -> Seq( + AlternateConfig("spark.akka.lookupTimeout", "1.4")) ) /** diff --git a/core/src/main/scala/org/apache/spark/deploy/Client.scala b/core/src/main/scala/org/apache/spark/deploy/Client.scala index 8d13b2a2cd4f3..c2c3e9a9e4827 100644 --- a/core/src/main/scala/org/apache/spark/deploy/Client.scala +++ b/core/src/main/scala/org/apache/spark/deploy/Client.scala @@ -27,7 +27,7 @@ import org.apache.log4j.{Level, Logger} import org.apache.spark.{Logging, SecurityManager, SparkConf} import org.apache.spark.deploy.DeployMessages._ import org.apache.spark.deploy.master.{DriverState, Master} -import org.apache.spark.util.{ActorLogReceive, AkkaUtils, Utils} +import org.apache.spark.util.{ActorLogReceive, AkkaUtils, RpcUtils, Utils} /** * Proxy that relays messages to the driver. @@ -36,7 +36,7 @@ private class ClientActor(driverArgs: ClientArguments, conf: SparkConf) extends Actor with ActorLogReceive with Logging { var masterActor: ActorSelection = _ - val timeout = AkkaUtils.askTimeout(conf) + val timeout = RpcUtils.askTimeout(conf) override def preStart(): Unit = { masterActor = context.actorSelection( @@ -155,7 +155,7 @@ object Client { if (!driverArgs.logLevel.isGreaterOrEqual(Level.WARN)) { conf.set("spark.akka.logLifecycleEvents", "true") } - conf.set("spark.akka.askTimeout", "10") + conf.set("spark.rpc.askTimeout", "10") conf.set("akka.loglevel", driverArgs.logLevel.toString.replace("WARN", "WARNING")) Logger.getRootLogger.setLevel(driverArgs.logLevel) diff --git a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala index 4f06d7f96c46e..43c8a934c311a 100644 --- a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala +++ b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala @@ -30,7 +30,7 @@ import org.apache.spark.{Logging, SparkConf} import org.apache.spark.deploy.{ApplicationDescription, ExecutorState} import org.apache.spark.deploy.DeployMessages._ import org.apache.spark.deploy.master.Master -import org.apache.spark.util.{ActorLogReceive, Utils, AkkaUtils} +import org.apache.spark.util.{ActorLogReceive, RpcUtils, Utils, AkkaUtils} /** * Interface allowing applications to speak with a Spark deploy cluster. Takes a master URL, @@ -193,7 +193,7 @@ private[spark] class AppClient( def stop() { if (actor != null) { try { - val timeout = AkkaUtils.askTimeout(conf) + val timeout = RpcUtils.askTimeout(conf) val future = actor.ask(StopAppClient)(timeout) Await.result(future, timeout) } catch { diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala index c5a6b1beac9be..ff2eed6dee70a 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala @@ -47,7 +47,7 @@ import org.apache.spark.deploy.rest.StandaloneRestServer import org.apache.spark.metrics.MetricsSystem import org.apache.spark.scheduler.{EventLoggingListener, ReplayListenerBus} import org.apache.spark.ui.SparkUI -import org.apache.spark.util.{ActorLogReceive, AkkaUtils, SignalLogger, Utils} +import org.apache.spark.util.{ActorLogReceive, AkkaUtils, RpcUtils, SignalLogger, Utils} private[master] class Master( host: String, @@ -931,7 +931,7 @@ private[deploy] object Master extends Logging { securityManager = securityMgr) val actor = actorSystem.actorOf( Props(classOf[Master], host, boundPort, webUiPort, securityMgr, conf), actorName) - val timeout = AkkaUtils.askTimeout(conf) + val timeout = RpcUtils.askTimeout(conf) val portsRequest = actor.ask(BoundPortsRequest)(timeout) val portsResponse = Await.result(portsRequest, timeout).asInstanceOf[BoundPortsResponse] (actorSystem, boundPort, portsResponse.webUIPort, portsResponse.restPort) diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala index bb11e0642ddc6..aad9c87bdb987 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala @@ -21,7 +21,7 @@ import org.apache.spark.Logging import org.apache.spark.deploy.master.Master import org.apache.spark.ui.{SparkUI, WebUI} import org.apache.spark.ui.JettyUtils._ -import org.apache.spark.util.AkkaUtils +import org.apache.spark.util.RpcUtils /** * Web UI server for the standalone master. @@ -31,7 +31,7 @@ class MasterWebUI(val master: Master, requestedPort: Int) extends WebUI(master.securityMgr, requestedPort, master.conf, name = "MasterUI") with Logging { val masterActorRef = master.self - val timeout = AkkaUtils.askTimeout(master.conf) + val timeout = RpcUtils.askTimeout(master.conf) val killEnabled = master.conf.getBoolean("spark.ui.killEnabled", true) initialize() diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestServer.scala b/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestServer.scala index 4f19af59f409f..2d6b8d4204795 100644 --- a/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestServer.scala +++ b/core/src/main/scala/org/apache/spark/deploy/rest/StandaloneRestServer.scala @@ -32,7 +32,7 @@ import org.json4s._ import org.json4s.jackson.JsonMethods._ import org.apache.spark.{Logging, SparkConf, SPARK_VERSION => sparkVersion} -import org.apache.spark.util.{AkkaUtils, Utils} +import org.apache.spark.util.{AkkaUtils, RpcUtils, Utils} import org.apache.spark.deploy.{Command, DeployMessages, DriverDescription} import org.apache.spark.deploy.ClientArguments._ @@ -223,7 +223,7 @@ private[rest] class KillRequestServlet(masterActor: ActorRef, conf: SparkConf) } protected def handleKill(submissionId: String): KillSubmissionResponse = { - val askTimeout = AkkaUtils.askTimeout(conf) + val askTimeout = RpcUtils.askTimeout(conf) val response = AkkaUtils.askWithReply[DeployMessages.KillDriverResponse]( DeployMessages.RequestKillDriver(submissionId), masterActor, askTimeout) val k = new KillSubmissionResponse @@ -257,7 +257,7 @@ private[rest] class StatusRequestServlet(masterActor: ActorRef, conf: SparkConf) } protected def handleStatus(submissionId: String): SubmissionStatusResponse = { - val askTimeout = AkkaUtils.askTimeout(conf) + val askTimeout = RpcUtils.askTimeout(conf) val response = AkkaUtils.askWithReply[DeployMessages.DriverStatusResponse]( DeployMessages.RequestDriverStatus(submissionId), masterActor, askTimeout) val message = response.exception.map { s"Exception from the cluster:\n" + formatException(_) } @@ -321,7 +321,7 @@ private[rest] class SubmitRequestServlet( responseServlet: HttpServletResponse): SubmitRestProtocolResponse = { requestMessage match { case submitRequest: CreateSubmissionRequest => - val askTimeout = AkkaUtils.askTimeout(conf) + val askTimeout = RpcUtils.askTimeout(conf) val driverDescription = buildDriverDescription(submitRequest) val response = AkkaUtils.askWithReply[DeployMessages.SubmitDriverResponse]( DeployMessages.RequestSubmitDriver(driverDescription), masterActor, askTimeout) diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala index de6423beb543e..b3bb5f911dbd7 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala @@ -25,7 +25,7 @@ import org.apache.spark.deploy.worker.Worker import org.apache.spark.deploy.worker.ui.WorkerWebUI._ import org.apache.spark.ui.{SparkUI, WebUI} import org.apache.spark.ui.JettyUtils._ -import org.apache.spark.util.AkkaUtils +import org.apache.spark.util.RpcUtils /** * Web UI server for the standalone worker. @@ -38,7 +38,7 @@ class WorkerWebUI( extends WebUI(worker.securityMgr, requestedPort, worker.conf, name = "WorkerUI") with Logging { - private[ui] val timeout = AkkaUtils.askTimeout(worker.conf) + private[ui] val timeout = RpcUtils.askTimeout(worker.conf) initialize() diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala index cba038ca355d7..a5336b7563802 100644 --- a/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala +++ b/core/src/main/scala/org/apache/spark/rpc/RpcEnv.scala @@ -25,7 +25,7 @@ import scala.language.postfixOps import scala.reflect.ClassTag import org.apache.spark.{Logging, SparkException, SecurityManager, SparkConf} -import org.apache.spark.util.{AkkaUtils, Utils} +import org.apache.spark.util.{RpcUtils, Utils} /** * An RPC environment. [[RpcEndpoint]]s need to register itself with a name to [[RpcEnv]] to @@ -38,7 +38,7 @@ import org.apache.spark.util.{AkkaUtils, Utils} */ private[spark] abstract class RpcEnv(conf: SparkConf) { - private[spark] val defaultLookupTimeout = AkkaUtils.lookupTimeout(conf) + private[spark] val defaultLookupTimeout = RpcUtils.lookupTimeout(conf) /** * Return RpcEndpointRef of the registered [[RpcEndpoint]]. Will be used to implement @@ -282,9 +282,9 @@ trait ThreadSafeRpcEndpoint extends RpcEndpoint private[spark] abstract class RpcEndpointRef(@transient conf: SparkConf) extends Serializable with Logging { - private[this] val maxRetries = conf.getInt("spark.akka.num.retries", 3) - private[this] val retryWaitMs = conf.getLong("spark.akka.retry.wait", 3000) - private[this] val defaultAskTimeout = conf.getLong("spark.akka.askTimeout", 30) seconds + private[this] val maxRetries = RpcUtils.numRetries(conf) + private[this] val retryWaitMs = RpcUtils.retryWaitMs(conf) + private[this] val defaultAskTimeout = RpcUtils.askTimeout(conf) /** * return the address for the [[RpcEndpointRef]] diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala index f72566c370a6f..1406a36a669c5 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala @@ -24,7 +24,7 @@ import org.apache.spark.rpc._ import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ import org.apache.spark.scheduler.TaskSchedulerImpl import org.apache.spark.ui.JettyUtils -import org.apache.spark.util.{AkkaUtils, Utils} +import org.apache.spark.util.{RpcUtils, Utils} import scala.util.control.NonFatal @@ -46,7 +46,7 @@ private[spark] abstract class YarnSchedulerBackend( private val yarnSchedulerEndpoint = rpcEnv.setupEndpoint( YarnSchedulerBackend.ENDPOINT_NAME, new YarnSchedulerEndpoint(rpcEnv)) - private implicit val askTimeout = AkkaUtils.askTimeout(sc.conf) + private implicit val askTimeout = RpcUtils.askTimeout(sc.conf) /** * Request executors from the ApplicationMaster by specifying the total number desired. diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala index ceacf043029f3..c798843bd5d8a 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala @@ -23,7 +23,7 @@ import scala.concurrent.ExecutionContext.Implicits.global import org.apache.spark.rpc.RpcEndpointRef import org.apache.spark.{Logging, SparkConf, SparkException} import org.apache.spark.storage.BlockManagerMessages._ -import org.apache.spark.util.AkkaUtils +import org.apache.spark.util.RpcUtils private[spark] class BlockManagerMaster( @@ -32,7 +32,7 @@ class BlockManagerMaster( isDriver: Boolean) extends Logging { - val timeout = AkkaUtils.askTimeout(conf) + val timeout = RpcUtils.askTimeout(conf) /** Remove a dead executor from the driver endpoint. This is only called on the driver side. */ def removeExecutor(execId: String) { diff --git a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala index 8e8cc7cc6389e..b725df3b44596 100644 --- a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala +++ b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala @@ -19,7 +19,7 @@ package org.apache.spark.util import scala.collection.JavaConversions.mapAsJavaMap import scala.concurrent.Await -import scala.concurrent.duration.{Duration, FiniteDuration} +import scala.concurrent.duration.FiniteDuration import akka.actor.{ActorRef, ActorSystem, ExtendedActorSystem} import akka.pattern.ask @@ -125,16 +125,6 @@ private[spark] object AkkaUtils extends Logging { (actorSystem, boundPort) } - /** Returns the default Spark timeout to use for Akka ask operations. */ - def askTimeout(conf: SparkConf): FiniteDuration = { - Duration.create(conf.getLong("spark.akka.askTimeout", 30), "seconds") - } - - /** Returns the default Spark timeout to use for Akka remote actor lookup. */ - def lookupTimeout(conf: SparkConf): FiniteDuration = { - Duration.create(conf.getLong("spark.akka.lookupTimeout", 30), "seconds") - } - private val AKKA_MAX_FRAME_SIZE_IN_MB = Int.MaxValue / 1024 / 1024 /** Returns the configured max frame size for Akka messages in bytes. */ @@ -150,16 +140,6 @@ private[spark] object AkkaUtils extends Logging { /** Space reserved for extra data in an Akka message besides serialized task or task result. */ val reservedSizeBytes = 200 * 1024 - /** Returns the configured number of times to retry connecting */ - def numRetries(conf: SparkConf): Int = { - conf.getInt("spark.akka.num.retries", 3) - } - - /** Returns the configured number of milliseconds to wait on each retry */ - def retryWaitMs(conf: SparkConf): Int = { - conf.getInt("spark.akka.retry.wait", 3000) - } - /** * Send a message to the given actor and get its result within a default timeout, or * throw a SparkException if this fails. @@ -216,7 +196,7 @@ private[spark] object AkkaUtils extends Logging { val driverPort: Int = conf.getInt("spark.driver.port", 7077) Utils.checkHost(driverHost, "Expected hostname") val url = address(protocol(actorSystem), driverActorSystemName, driverHost, driverPort, name) - val timeout = AkkaUtils.lookupTimeout(conf) + val timeout = RpcUtils.lookupTimeout(conf) logInfo(s"Connecting to $name: $url") Await.result(actorSystem.actorSelection(url).resolveOne(timeout), timeout) } @@ -230,7 +210,7 @@ private[spark] object AkkaUtils extends Logging { val executorActorSystemName = SparkEnv.executorActorSystemName Utils.checkHost(host, "Expected hostname") val url = address(protocol(actorSystem), executorActorSystemName, host, port, name) - val timeout = AkkaUtils.lookupTimeout(conf) + val timeout = RpcUtils.lookupTimeout(conf) logInfo(s"Connecting to $name: $url") Await.result(actorSystem.actorSelection(url).resolveOne(timeout), timeout) } diff --git a/core/src/main/scala/org/apache/spark/util/RpcUtils.scala b/core/src/main/scala/org/apache/spark/util/RpcUtils.scala index 6665b17c3d5df..5ae793e0e87a3 100644 --- a/core/src/main/scala/org/apache/spark/util/RpcUtils.scala +++ b/core/src/main/scala/org/apache/spark/util/RpcUtils.scala @@ -17,6 +17,9 @@ package org.apache.spark.util +import scala.concurrent.duration._ +import scala.language.postfixOps + import org.apache.spark.{SparkEnv, SparkConf} import org.apache.spark.rpc.{RpcAddress, RpcEndpointRef, RpcEnv} @@ -32,4 +35,24 @@ object RpcUtils { Utils.checkHost(driverHost, "Expected hostname") rpcEnv.setupEndpointRef(driverActorSystemName, RpcAddress(driverHost, driverPort), name) } + + /** Returns the configured number of times to retry connecting */ + def numRetries(conf: SparkConf): Int = { + conf.getInt("spark.rpc.numRetries", 3) + } + + /** Returns the configured number of milliseconds to wait on each retry */ + def retryWaitMs(conf: SparkConf): Long = { + conf.getTimeAsMs("spark.rpc.retry.wait", "3s") + } + + /** Returns the default Spark timeout to use for RPC ask operations. */ + def askTimeout(conf: SparkConf): FiniteDuration = { + conf.getTimeAsSeconds("spark.rpc.askTimeout", "30s") seconds + } + + /** Returns the default Spark timeout to use for RPC remote endpoint lookup. */ + def lookupTimeout(conf: SparkConf): FiniteDuration = { + conf.getTimeAsSeconds("spark.rpc.lookupTimeout", "30s") seconds + } } diff --git a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala index 6295d34be5ca9..6ed057a7cab97 100644 --- a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala +++ b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala @@ -154,7 +154,7 @@ class MapOutputTrackerSuite extends FunSuite { test("remote fetch below akka frame size") { val newConf = new SparkConf newConf.set("spark.akka.frameSize", "1") - newConf.set("spark.akka.askTimeout", "1") // Fail fast + newConf.set("spark.rpc.askTimeout", "1") // Fail fast val masterTracker = new MapOutputTrackerMaster(conf) val rpcEnv = createRpcEnv("spark") @@ -180,7 +180,7 @@ class MapOutputTrackerSuite extends FunSuite { test("remote fetch exceeds akka frame size") { val newConf = new SparkConf newConf.set("spark.akka.frameSize", "1") - newConf.set("spark.akka.askTimeout", "1") // Fail fast + newConf.set("spark.rpc.askTimeout", "1") // Fail fast val masterTracker = new MapOutputTrackerMaster(conf) val rpcEnv = createRpcEnv("test") diff --git a/core/src/test/scala/org/apache/spark/SparkConfSuite.scala b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala index 8e6c200c4ba00..d7d8014a20498 100644 --- a/core/src/test/scala/org/apache/spark/SparkConfSuite.scala +++ b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala @@ -19,11 +19,13 @@ package org.apache.spark import java.util.concurrent.{TimeUnit, Executors} +import scala.concurrent.duration._ +import scala.language.postfixOps import scala.util.{Try, Random} import org.scalatest.FunSuite import org.apache.spark.serializer.{KryoRegistrator, KryoSerializer} -import org.apache.spark.util.ResetSystemProperties +import org.apache.spark.util.{RpcUtils, ResetSystemProperties} import com.esotericsoftware.kryo.Kryo class SparkConfSuite extends FunSuite with LocalSparkContext with ResetSystemProperties { @@ -222,6 +224,26 @@ class SparkConfSuite extends FunSuite with LocalSparkContext with ResetSystemPro assert(conf.getTimeAsSeconds("spark.yarn.am.waitTime") === 420) } + test("akka deprecated configs") { + val conf = new SparkConf() + + assert(!conf.contains("spark.rpc.num.retries")) + assert(!conf.contains("spark.rpc.retry.wait")) + assert(!conf.contains("spark.rpc.askTimeout")) + assert(!conf.contains("spark.rpc.lookupTimeout")) + + conf.set("spark.akka.num.retries", "1") + assert(RpcUtils.numRetries(conf) === 1) + + conf.set("spark.akka.retry.wait", "2") + assert(RpcUtils.retryWaitMs(conf) === 2L) + + conf.set("spark.akka.askTimeout", "3") + assert(RpcUtils.askTimeout(conf) === (3 seconds)) + + conf.set("spark.akka.lookupTimeout", "4") + assert(RpcUtils.lookupTimeout(conf) === (4 seconds)) + } } class Class1 {} diff --git a/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala b/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala index ada07ef11cd7a..5fbda37c7cb88 100644 --- a/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala +++ b/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala @@ -155,8 +155,8 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { }) val conf = new SparkConf() - conf.set("spark.akka.retry.wait", "0") - conf.set("spark.akka.num.retries", "1") + conf.set("spark.rpc.retry.wait", "0") + conf.set("spark.rpc.num.retries", "1") val anotherEnv = createRpcEnv(conf, "remote", 13345) // Use anotherEnv to find out the RpcEndpointRef val rpcEndpointRef = anotherEnv.setupEndpointRef("local", env.address, "ask-timeout") From ab9128fb7ec7ca479dc91e7cc7c775e8d071eafa Mon Sep 17 00:00:00 2001 From: Davies Liu Date: Tue, 21 Apr 2015 00:08:18 -0700 Subject: [PATCH 352/591] [SPARK-6949] [SQL] [PySpark] Support Date/Timestamp in Column expression This PR enable auto_convert in JavaGateway, then we could register a converter for a given types, for example, date and datetime. There are two bugs related to auto_convert, see [1] and [2], we workaround it in this PR. [1] https://github.com/bartdag/py4j/issues/160 [2] https://github.com/bartdag/py4j/issues/161 cc rxin JoshRosen Author: Davies Liu Closes #5570 from davies/py4j_date and squashes the following commits: eb4fa53 [Davies Liu] fix tests in python 3 d17d634 [Davies Liu] rollback changes in mllib 2e7566d [Davies Liu] convert tuple into ArrayList ceb3779 [Davies Liu] Update rdd.py 3c373f3 [Davies Liu] support date and datetime by auto_convert cb094ff [Davies Liu] enable auto convert --- python/pyspark/context.py | 6 +----- python/pyspark/java_gateway.py | 15 ++++++++++++++- python/pyspark/rdd.py | 3 +++ python/pyspark/sql/_types.py | 27 +++++++++++++++++++++++++++ python/pyspark/sql/context.py | 13 ++++--------- python/pyspark/sql/dataframe.py | 18 ++++-------------- python/pyspark/sql/tests.py | 11 +++++++++++ python/pyspark/streaming/context.py | 11 +++-------- python/pyspark/streaming/kafka.py | 7 ++----- python/pyspark/streaming/tests.py | 6 +----- 10 files changed, 70 insertions(+), 47 deletions(-) diff --git a/python/pyspark/context.py b/python/pyspark/context.py index 6a743ac8bd600..b006120eb266d 100644 --- a/python/pyspark/context.py +++ b/python/pyspark/context.py @@ -23,8 +23,6 @@ from threading import Lock from tempfile import NamedTemporaryFile -from py4j.java_collections import ListConverter - from pyspark import accumulators from pyspark.accumulators import Accumulator from pyspark.broadcast import Broadcast @@ -643,7 +641,6 @@ def union(self, rdds): rdds = [x._reserialize() for x in rdds] first = rdds[0]._jrdd rest = [x._jrdd for x in rdds[1:]] - rest = ListConverter().convert(rest, self._gateway._gateway_client) return RDD(self._jsc.union(first, rest), self, rdds[0]._jrdd_deserializer) def broadcast(self, value): @@ -846,13 +843,12 @@ def runJob(self, rdd, partitionFunc, partitions=None, allowLocal=False): """ if partitions is None: partitions = range(rdd._jrdd.partitions().size()) - javaPartitions = ListConverter().convert(partitions, self._gateway._gateway_client) # Implementation note: This is implemented as a mapPartitions followed # by runJob() in order to avoid having to pass a Python lambda into # SparkContext#runJob. mappedRDD = rdd.mapPartitions(partitionFunc) - port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, javaPartitions, + port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions, allowLocal) return list(_load_from_socket(port, mappedRDD._jrdd_deserializer)) diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py index 45bc38f7e61f8..3cee4ea6e3a35 100644 --- a/python/pyspark/java_gateway.py +++ b/python/pyspark/java_gateway.py @@ -17,17 +17,30 @@ import atexit import os +import sys import select import signal import shlex import socket import platform from subprocess import Popen, PIPE + +if sys.version >= '3': + xrange = range + from py4j.java_gateway import java_import, JavaGateway, GatewayClient +from py4j.java_collections import ListConverter from pyspark.serializers import read_int +# patching ListConverter, or it will convert bytearray into Java ArrayList +def can_convert_list(self, obj): + return isinstance(obj, (list, tuple, xrange)) + +ListConverter.can_convert = can_convert_list + + def launch_gateway(): if "PYSPARK_GATEWAY_PORT" in os.environ: gateway_port = int(os.environ["PYSPARK_GATEWAY_PORT"]) @@ -92,7 +105,7 @@ def killChild(): atexit.register(killChild) # Connect to the gateway - gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=False) + gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=True) # Import the classes used by PySpark java_import(gateway.jvm, "org.apache.spark.SparkConf") diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py index d9cdbb666f92a..d254deb527d10 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.py @@ -2267,6 +2267,9 @@ def _prepare_for_python_RDD(sc, command, obj=None): # The broadcast will have same life cycle as created PythonRDD broadcast = sc.broadcast(pickled_command) pickled_command = ser.dumps(broadcast) + # There is a bug in py4j.java_gateway.JavaClass with auto_convert + # https://github.com/bartdag/py4j/issues/161 + # TODO: use auto_convert once py4j fix the bug broadcast_vars = ListConverter().convert( [x._jbroadcast for x in sc._pickled_broadcast_vars], sc._gateway._gateway_client) diff --git a/python/pyspark/sql/_types.py b/python/pyspark/sql/_types.py index 110d1152fbdb6..95fb91ad43457 100644 --- a/python/pyspark/sql/_types.py +++ b/python/pyspark/sql/_types.py @@ -17,6 +17,7 @@ import sys import decimal +import time import datetime import keyword import warnings @@ -30,6 +31,9 @@ long = int unicode = str +from py4j.protocol import register_input_converter +from py4j.java_gateway import JavaClass + __all__ = [ "DataType", "NullType", "StringType", "BinaryType", "BooleanType", "DateType", "TimestampType", "DecimalType", "DoubleType", "FloatType", "ByteType", "IntegerType", @@ -1237,6 +1241,29 @@ def __repr__(self): return "" % ", ".join(self) +class DateConverter(object): + def can_convert(self, obj): + return isinstance(obj, datetime.date) + + def convert(self, obj, gateway_client): + Date = JavaClass("java.sql.Date", gateway_client) + return Date.valueOf(obj.strftime("%Y-%m-%d")) + + +class DatetimeConverter(object): + def can_convert(self, obj): + return isinstance(obj, datetime.datetime) + + def convert(self, obj, gateway_client): + Timestamp = JavaClass("java.sql.Timestamp", gateway_client) + return Timestamp(int(time.mktime(obj.timetuple())) * 1000 + obj.microsecond // 1000) + + +# datetime is a subclass of date, we should register DatetimeConverter first +register_input_converter(DatetimeConverter()) +register_input_converter(DateConverter()) + + def _test(): import doctest from pyspark.context import SparkContext diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py index acf3c114548c0..f6f107ca32d2f 100644 --- a/python/pyspark/sql/context.py +++ b/python/pyspark/sql/context.py @@ -25,7 +25,6 @@ from itertools import imap as map from py4j.protocol import Py4JError -from py4j.java_collections import MapConverter from pyspark.rdd import RDD, _prepare_for_python_RDD, ignore_unicode_prefix from pyspark.serializers import AutoBatchedSerializer, PickleSerializer @@ -442,15 +441,13 @@ def load(self, path=None, source=None, schema=None, **options): if source is None: source = self.getConf("spark.sql.sources.default", "org.apache.spark.sql.parquet") - joptions = MapConverter().convert(options, - self._sc._gateway._gateway_client) if schema is None: - df = self._ssql_ctx.load(source, joptions) + df = self._ssql_ctx.load(source, options) else: if not isinstance(schema, StructType): raise TypeError("schema should be StructType") scala_datatype = self._ssql_ctx.parseDataType(schema.json()) - df = self._ssql_ctx.load(source, scala_datatype, joptions) + df = self._ssql_ctx.load(source, scala_datatype, options) return DataFrame(df, self) def createExternalTable(self, tableName, path=None, source=None, @@ -471,16 +468,14 @@ def createExternalTable(self, tableName, path=None, source=None, if source is None: source = self.getConf("spark.sql.sources.default", "org.apache.spark.sql.parquet") - joptions = MapConverter().convert(options, - self._sc._gateway._gateway_client) if schema is None: - df = self._ssql_ctx.createExternalTable(tableName, source, joptions) + df = self._ssql_ctx.createExternalTable(tableName, source, options) else: if not isinstance(schema, StructType): raise TypeError("schema should be StructType") scala_datatype = self._ssql_ctx.parseDataType(schema.json()) df = self._ssql_ctx.createExternalTable(tableName, source, scala_datatype, - joptions) + options) return DataFrame(df, self) @ignore_unicode_prefix diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index 75c181c0c7f5e..ca9bf8efb945c 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -25,8 +25,6 @@ else: from itertools import imap as map -from py4j.java_collections import ListConverter, MapConverter - from pyspark.context import SparkContext from pyspark.rdd import RDD, _load_from_socket, ignore_unicode_prefix from pyspark.serializers import BatchedSerializer, PickleSerializer, UTF8Deserializer @@ -186,9 +184,7 @@ def saveAsTable(self, tableName, source=None, mode="error", **options): source = self.sql_ctx.getConf("spark.sql.sources.default", "org.apache.spark.sql.parquet") jmode = self._java_save_mode(mode) - joptions = MapConverter().convert(options, - self.sql_ctx._sc._gateway._gateway_client) - self._jdf.saveAsTable(tableName, source, jmode, joptions) + self._jdf.saveAsTable(tableName, source, jmode, options) def save(self, path=None, source=None, mode="error", **options): """Saves the contents of the :class:`DataFrame` to a data source. @@ -211,9 +207,7 @@ def save(self, path=None, source=None, mode="error", **options): source = self.sql_ctx.getConf("spark.sql.sources.default", "org.apache.spark.sql.parquet") jmode = self._java_save_mode(mode) - joptions = MapConverter().convert(options, - self._sc._gateway._gateway_client) - self._jdf.save(source, jmode, joptions) + self._jdf.save(source, jmode, options) @property def schema(self): @@ -819,7 +813,6 @@ def fillna(self, value, subset=None): value = float(value) if isinstance(value, dict): - value = MapConverter().convert(value, self.sql_ctx._sc._gateway._gateway_client) return DataFrame(self._jdf.na().fill(value), self.sql_ctx) elif subset is None: return DataFrame(self._jdf.na().fill(value), self.sql_ctx) @@ -932,9 +925,7 @@ def agg(self, *exprs): """ assert exprs, "exprs should not be empty" if len(exprs) == 1 and isinstance(exprs[0], dict): - jmap = MapConverter().convert(exprs[0], - self.sql_ctx._sc._gateway._gateway_client) - jdf = self._jdf.agg(jmap) + jdf = self._jdf.agg(exprs[0]) else: # Columns assert all(isinstance(c, Column) for c in exprs), "all exprs should be Column" @@ -1040,8 +1031,7 @@ def _to_seq(sc, cols, converter=None): """ if converter: cols = [converter(c) for c in cols] - jcols = ListConverter().convert(cols, sc._gateway._gateway_client) - return sc._jvm.PythonUtils.toSeq(jcols) + return sc._jvm.PythonUtils.toSeq(cols) def _unary_op(name, doc="unary operator"): diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index aa3aa1d164d9f..23e84283679e1 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -26,6 +26,7 @@ import tempfile import pickle import functools +import datetime import py4j @@ -464,6 +465,16 @@ def test_infer_long_type(self): self.assertEqual(_infer_type(2**61), LongType()) self.assertEqual(_infer_type(2**71), LongType()) + def test_filter_with_datetime(self): + time = datetime.datetime(2015, 4, 17, 23, 1, 2, 3000) + date = time.date() + row = Row(date=date, time=time) + df = self.sqlCtx.createDataFrame([row]) + self.assertEqual(1, df.filter(df.date == date).count()) + self.assertEqual(1, df.filter(df.time == time).count()) + self.assertEqual(0, df.filter(df.date > date).count()) + self.assertEqual(0, df.filter(df.time > time).count()) + def test_dropna(self): schema = StructType([ StructField("name", StringType(), True), diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py index 4590c58839266..ac5ba69e8dbbb 100644 --- a/python/pyspark/streaming/context.py +++ b/python/pyspark/streaming/context.py @@ -20,7 +20,6 @@ import os import sys -from py4j.java_collections import ListConverter from py4j.java_gateway import java_import, JavaObject from pyspark import RDD, SparkConf @@ -305,9 +304,7 @@ def queueStream(self, rdds, oneAtATime=True, default=None): rdds = [self._sc.parallelize(input) for input in rdds] self._check_serializers(rdds) - jrdds = ListConverter().convert([r._jrdd for r in rdds], - SparkContext._gateway._gateway_client) - queue = self._jvm.PythonDStream.toRDDQueue(jrdds) + queue = self._jvm.PythonDStream.toRDDQueue([r._jrdd for r in rdds]) if default: default = default._reserialize(rdds[0]._jrdd_deserializer) jdstream = self._jssc.queueStream(queue, oneAtATime, default._jrdd) @@ -322,8 +319,7 @@ def transform(self, dstreams, transformFunc): the transform function parameter will be the same as the order of corresponding DStreams in the list. """ - jdstreams = ListConverter().convert([d._jdstream for d in dstreams], - SparkContext._gateway._gateway_client) + jdstreams = [d._jdstream for d in dstreams] # change the final serializer to sc.serializer func = TransformFunction(self._sc, lambda t, *rdds: transformFunc(rdds).map(lambda x: x), @@ -346,6 +342,5 @@ def union(self, *dstreams): if len(set(s._slideDuration for s in dstreams)) > 1: raise ValueError("All DStreams should have same slide duration") first = dstreams[0] - jrest = ListConverter().convert([d._jdstream for d in dstreams[1:]], - SparkContext._gateway._gateway_client) + jrest = [d._jdstream for d in dstreams[1:]] return DStream(self._jssc.union(first._jdstream, jrest), self, first._jrdd_deserializer) diff --git a/python/pyspark/streaming/kafka.py b/python/pyspark/streaming/kafka.py index 7a7b6e1d9a527..8d610d6569b4a 100644 --- a/python/pyspark/streaming/kafka.py +++ b/python/pyspark/streaming/kafka.py @@ -15,8 +15,7 @@ # limitations under the License. # -from py4j.java_collections import MapConverter -from py4j.java_gateway import java_import, Py4JError, Py4JJavaError +from py4j.java_gateway import Py4JJavaError from pyspark.storagelevel import StorageLevel from pyspark.serializers import PairDeserializer, NoOpSerializer @@ -57,8 +56,6 @@ def createStream(ssc, zkQuorum, groupId, topics, kafkaParams={}, }) if not isinstance(topics, dict): raise TypeError("topics should be dict") - jtopics = MapConverter().convert(topics, ssc.sparkContext._gateway._gateway_client) - jparam = MapConverter().convert(kafkaParams, ssc.sparkContext._gateway._gateway_client) jlevel = ssc._sc._getJavaStorageLevel(storageLevel) try: @@ -66,7 +63,7 @@ def createStream(ssc, zkQuorum, groupId, topics, kafkaParams={}, helperClass = ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader()\ .loadClass("org.apache.spark.streaming.kafka.KafkaUtilsPythonHelper") helper = helperClass.newInstance() - jstream = helper.createStream(ssc._jssc, jparam, jtopics, jlevel) + jstream = helper.createStream(ssc._jssc, kafkaParams, topics, jlevel) except Py4JJavaError as e: # TODO: use --jar once it also work on driver if 'ClassNotFoundException' in str(e.java_exception): diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py index 06d22154373bc..33f958a601f3a 100644 --- a/python/pyspark/streaming/tests.py +++ b/python/pyspark/streaming/tests.py @@ -24,8 +24,6 @@ import struct from functools import reduce -from py4j.java_collections import MapConverter - from pyspark.context import SparkConf, SparkContext, RDD from pyspark.streaming.context import StreamingContext from pyspark.streaming.kafka import KafkaUtils @@ -581,11 +579,9 @@ def test_kafka_stream(self): """Test the Python Kafka stream API.""" topic = "topic1" sendData = {"a": 3, "b": 5, "c": 10} - jSendData = MapConverter().convert(sendData, - self.ssc.sparkContext._gateway._gateway_client) self._kafkaTestUtils.createTopic(topic) - self._kafkaTestUtils.sendMessages(topic, jSendData) + self._kafkaTestUtils.sendMessages(topic, sendData) stream = KafkaUtils.createStream(self.ssc, self._kafkaTestUtils.zkAddress(), "test-streaming-consumer", {topic: 1}, From 1f2f723b0daacbb9e70ec42c19a84470af1d7765 Mon Sep 17 00:00:00 2001 From: Yanbo Liang Date: Tue, 21 Apr 2015 00:14:16 -0700 Subject: [PATCH 353/591] [SPARK-5990] [MLLIB] Model import/export for IsotonicRegression Model import/export for IsotonicRegression Author: Yanbo Liang Closes #5270 from yanboliang/spark-5990 and squashes the following commits: 872028d [Yanbo Liang] fix code style f80ec1b [Yanbo Liang] address comments 49600cc [Yanbo Liang] address comments 429ff7d [Yanbo Liang] store each interval as a record 2b2f5a1 [Yanbo Liang] Model import/export for IsotonicRegression --- .../mllib/regression/IsotonicRegression.scala | 78 ++++++++++++++++++- .../regression/IsotonicRegressionSuite.scala | 21 +++++ 2 files changed, 98 insertions(+), 1 deletion(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala index cb70852e3cc8d..1d7617046b6c7 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala @@ -23,9 +23,16 @@ import java.util.Arrays.binarySearch import scala.collection.mutable.ArrayBuffer +import org.json4s._ +import org.json4s.JsonDSL._ +import org.json4s.jackson.JsonMethods._ + import org.apache.spark.annotation.Experimental import org.apache.spark.api.java.{JavaDoubleRDD, JavaRDD} +import org.apache.spark.mllib.util.{Loader, Saveable} import org.apache.spark.rdd.RDD +import org.apache.spark.SparkContext +import org.apache.spark.sql.{DataFrame, SQLContext} /** * :: Experimental :: @@ -42,7 +49,7 @@ import org.apache.spark.rdd.RDD class IsotonicRegressionModel ( val boundaries: Array[Double], val predictions: Array[Double], - val isotonic: Boolean) extends Serializable { + val isotonic: Boolean) extends Serializable with Saveable { private val predictionOrd = if (isotonic) Ordering[Double] else Ordering[Double].reverse @@ -124,6 +131,75 @@ class IsotonicRegressionModel ( predictions(foundIndex) } } + + override def save(sc: SparkContext, path: String): Unit = { + IsotonicRegressionModel.SaveLoadV1_0.save(sc, path, boundaries, predictions, isotonic) + } + + override protected def formatVersion: String = "1.0" +} + +object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] { + + import org.apache.spark.mllib.util.Loader._ + + private object SaveLoadV1_0 { + + def thisFormatVersion: String = "1.0" + + /** Hard-code class name string in case it changes in the future */ + def thisClassName: String = "org.apache.spark.mllib.regression.IsotonicRegressionModel" + + /** Model data for model import/export */ + case class Data(boundary: Double, prediction: Double) + + def save( + sc: SparkContext, + path: String, + boundaries: Array[Double], + predictions: Array[Double], + isotonic: Boolean): Unit = { + val sqlContext = new SQLContext(sc) + + val metadata = compact(render( + ("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~ + ("isotonic" -> isotonic))) + sc.parallelize(Seq(metadata), 1).saveAsTextFile(metadataPath(path)) + + sqlContext.createDataFrame( + boundaries.toSeq.zip(predictions).map { case (b, p) => Data(b, p) } + ).saveAsParquetFile(dataPath(path)) + } + + def load(sc: SparkContext, path: String): (Array[Double], Array[Double]) = { + val sqlContext = new SQLContext(sc) + val dataRDD = sqlContext.parquetFile(dataPath(path)) + + checkSchema[Data](dataRDD.schema) + val dataArray = dataRDD.select("boundary", "prediction").collect() + val (boundaries, predictions) = dataArray.map { x => + (x.getDouble(0), x.getDouble(1)) + }.toList.sortBy(_._1).unzip + (boundaries.toArray, predictions.toArray) + } + } + + override def load(sc: SparkContext, path: String): IsotonicRegressionModel = { + implicit val formats = DefaultFormats + val (loadedClassName, version, metadata) = loadMetadata(sc, path) + val isotonic = (metadata \ "isotonic").extract[Boolean] + val classNameV1_0 = SaveLoadV1_0.thisClassName + (loadedClassName, version) match { + case (className, "1.0") if className == classNameV1_0 => + val (boundaries, predictions) = SaveLoadV1_0.load(sc, path) + new IsotonicRegressionModel(boundaries, predictions, isotonic) + case _ => throw new Exception( + s"IsotonicRegressionModel.load did not recognize model with (className, format version):" + + s"($loadedClassName, $version). Supported:\n" + + s" ($classNameV1_0, 1.0)" + ) + } + } } /** diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala index 7ef45248281e9..8e12340bbd9d6 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/IsotonicRegressionSuite.scala @@ -21,6 +21,7 @@ import org.scalatest.{Matchers, FunSuite} import org.apache.spark.mllib.util.MLlibTestSparkContext import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.util.Utils class IsotonicRegressionSuite extends FunSuite with MLlibTestSparkContext with Matchers { @@ -73,6 +74,26 @@ class IsotonicRegressionSuite extends FunSuite with MLlibTestSparkContext with M assert(model.isotonic) } + test("model save/load") { + val boundaries = Array(0.0, 1.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) + val predictions = Array(1, 2, 2, 6, 16.5, 16.5, 17.0, 18.0) + val model = new IsotonicRegressionModel(boundaries, predictions, true) + + val tempDir = Utils.createTempDir() + val path = tempDir.toURI.toString + + // Save model, load it back, and compare. + try { + model.save(sc, path) + val sameModel = IsotonicRegressionModel.load(sc, path) + assert(model.boundaries === sameModel.boundaries) + assert(model.predictions === sameModel.predictions) + assert(model.isotonic === model.isotonic) + } finally { + Utils.deleteRecursively(tempDir) + } + } + test("isotonic regression with size 0") { val model = runIsotonicRegression(Seq(), true) From 5fea3e5c36450658d8b767dd3c06dac2251a0e0c Mon Sep 17 00:00:00 2001 From: David McGuire Date: Tue, 21 Apr 2015 07:21:10 -0400 Subject: [PATCH 354/591] [SPARK-6985][streaming] Receiver maxRate over 1000 causes a StackOverflowError A simple truncation in integer division (on rates over 1000 messages / second) causes the existing implementation to sleep for 0 milliseconds, then call itself recursively; this causes what is essentially an infinite recursion, since the base case of the calculated amount of time having elapsed can't be reached before available stack space is exhausted. A fix to this truncation error is included in this patch. However, even with the defect patched, the accuracy of the existing implementation is abysmal (the error bounds of the original test were effectively [-30%, +10%], although this fact was obscured by hard-coded error margins); as such, when the error bounds were tightened down to [-5%, +5%], the existing implementation failed to meet the new, tightened, requirements. Therefore, an industry-vetted solution (from Guava) was used to get the adapted tests to pass. Author: David McGuire Closes #5559 from dmcguire81/master and squashes the following commits: d29d2e0 [David McGuire] Back out to +/-5% error margins, for flexibility in timing 8be6934 [David McGuire] Fix spacing per code review 90e98b9 [David McGuire] Address scalastyle errors 29011bd [David McGuire] Further ratchet down the error margins b33b796 [David McGuire] Eliminate dependency on even distribution by BlockGenerator 8f2934b [David McGuire] Remove arbitrary thread timing / cooperation code 70ee310 [David McGuire] Use Thread.yield(), since Thread.sleep(0) is system-dependent 82ee46d [David McGuire] Replace guard clause with nested conditional 2794717 [David McGuire] Replace the RateLimiter with the Guava implementation 38f3ca8 [David McGuire] Ratchet down the error rate to +/- 5%; tests fail 24b1bc0 [David McGuire] Fix truncation in integer division causing infinite recursion d6e1079 [David McGuire] Stack overflow error in RateLimiter on rates over 1000/s --- .../streaming/receiver/RateLimiter.scala | 33 +++---------------- .../spark/streaming/ReceiverSuite.scala | 29 +++++++++------- 2 files changed, 21 insertions(+), 41 deletions(-) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala index e4f6ba626ebbf..97db9ded83367 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala @@ -18,7 +18,7 @@ package org.apache.spark.streaming.receiver import org.apache.spark.{Logging, SparkConf} -import java.util.concurrent.TimeUnit._ +import com.google.common.util.concurrent.{RateLimiter=>GuavaRateLimiter} /** Provides waitToPush() method to limit the rate at which receivers consume data. * @@ -33,37 +33,12 @@ import java.util.concurrent.TimeUnit._ */ private[receiver] abstract class RateLimiter(conf: SparkConf) extends Logging { - private var lastSyncTime = System.nanoTime - private var messagesWrittenSinceSync = 0L private val desiredRate = conf.getInt("spark.streaming.receiver.maxRate", 0) - private val SYNC_INTERVAL = NANOSECONDS.convert(10, SECONDS) + private lazy val rateLimiter = GuavaRateLimiter.create(desiredRate) def waitToPush() { - if( desiredRate <= 0 ) { - return - } - val now = System.nanoTime - val elapsedNanosecs = math.max(now - lastSyncTime, 1) - val rate = messagesWrittenSinceSync.toDouble * 1000000000 / elapsedNanosecs - if (rate < desiredRate) { - // It's okay to write; just update some variables and return - messagesWrittenSinceSync += 1 - if (now > lastSyncTime + SYNC_INTERVAL) { - // Sync interval has passed; let's resync - lastSyncTime = now - messagesWrittenSinceSync = 1 - } - } else { - // Calculate how much time we should sleep to bring ourselves to the desired rate. - val targetTimeInMillis = messagesWrittenSinceSync * 1000 / desiredRate - val elapsedTimeInMillis = elapsedNanosecs / 1000000 - val sleepTimeInMillis = targetTimeInMillis - elapsedTimeInMillis - if (sleepTimeInMillis > 0) { - logTrace("Natural rate is " + rate + " per second but desired rate is " + - desiredRate + ", sleeping for " + sleepTimeInMillis + " ms to compensate.") - Thread.sleep(sleepTimeInMillis) - } - waitToPush() + if (desiredRate > 0) { + rateLimiter.acquire() } } } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala index 91261a9db7360..e7aee6eadbfc7 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala @@ -158,7 +158,7 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable { test("block generator throttling") { val blockGeneratorListener = new FakeBlockGeneratorListener val blockIntervalMs = 100 - val maxRate = 100 + val maxRate = 1001 val conf = new SparkConf().set("spark.streaming.blockInterval", s"${blockIntervalMs}ms"). set("spark.streaming.receiver.maxRate", maxRate.toString) val blockGenerator = new BlockGenerator(blockGeneratorListener, 1, conf) @@ -176,7 +176,6 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable { blockGenerator.addData(count) generatedData += count count += 1 - Thread.sleep(1) } blockGenerator.stop() @@ -185,25 +184,31 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable { assert(blockGeneratorListener.arrayBuffers.size > 0, "No blocks received") assert(recordedData.toSet === generatedData.toSet, "Received data not same") - // recordedData size should be close to the expected rate - val minExpectedMessages = expectedMessages - 3 - val maxExpectedMessages = expectedMessages + 1 + // recordedData size should be close to the expected rate; use an error margin proportional to + // the value, so that rate changes don't cause a brittle test + val minExpectedMessages = expectedMessages - 0.05 * expectedMessages + val maxExpectedMessages = expectedMessages + 0.05 * expectedMessages val numMessages = recordedData.size assert( numMessages >= minExpectedMessages && numMessages <= maxExpectedMessages, s"#records received = $numMessages, not between $minExpectedMessages and $maxExpectedMessages" ) - val minExpectedMessagesPerBlock = expectedMessagesPerBlock - 3 - val maxExpectedMessagesPerBlock = expectedMessagesPerBlock + 1 + // XXX Checking every block would require an even distribution of messages across blocks, + // which throttling code does not control. Therefore, test against the average. + val minExpectedMessagesPerBlock = expectedMessagesPerBlock - 0.05 * expectedMessagesPerBlock + val maxExpectedMessagesPerBlock = expectedMessagesPerBlock + 0.05 * expectedMessagesPerBlock val receivedBlockSizes = recordedBlocks.map { _.size }.mkString(",") + + // the first and last block may be incomplete, so we slice them out + val validBlocks = recordedBlocks.drop(1).dropRight(1) + val averageBlockSize = validBlocks.map(block => block.size).sum / validBlocks.size + assert( - // the first and last block may be incomplete, so we slice them out - recordedBlocks.drop(1).dropRight(1).forall { block => - block.size >= minExpectedMessagesPerBlock && block.size <= maxExpectedMessagesPerBlock - }, + averageBlockSize >= minExpectedMessagesPerBlock && + averageBlockSize <= maxExpectedMessagesPerBlock, s"# records in received blocks = [$receivedBlockSizes], not between " + - s"$minExpectedMessagesPerBlock and $maxExpectedMessagesPerBlock" + s"$minExpectedMessagesPerBlock and $maxExpectedMessagesPerBlock, on average" ) } From c035c0f2d72f2a303b86fe0037ec43d756fff060 Mon Sep 17 00:00:00 2001 From: Kay Ousterhout Date: Tue, 21 Apr 2015 11:01:18 -0700 Subject: [PATCH 355/591] [SPARK-5360] [SPARK-6606] Eliminate duplicate objects in serialized CoGroupedRDD CoGroupPartition, part of CoGroupedRDD, includes references to each RDD that the CoGroupedRDD narrowly depends on, and a reference to the ShuffleHandle. The partition is serialized separately from the RDD, so when the RDD and partition arrive on the worker, the references in the partition and in the RDD no longer point to the same object. This is a relatively minor performance issue (the closure can be 2x larger than it needs to be because the rdds and partitions are serialized twice; see numbers below) but is more annoying as a developer issue (this is where I ran into): if any state is stored in the RDD or ShuffleHandle on the worker side, subtle bugs can appear due to the fact that the references to the RDD / ShuffleHandle in the RDD and in the partition point to separate objects. I'm not sure if this is enough of a potential future problem to fix this old and central part of the code, so hoping to get input from others here. I did some simple experiments to see how much this effects closure size. For this example: $ val a = sc.parallelize(1 to 10).map((_, 1)) $ val b = sc.parallelize(1 to 2).map(x => (x, 2*x)) $ a.cogroup(b).collect() the closure was 1902 bytes with current Spark, and 1129 bytes after my change. The difference comes from eliminating duplicate serialization of the shuffle handle. For this example: $ val sortedA = a.sortByKey() $ val sortedB = b.sortByKey() $ sortedA.cogroup(sortedB).collect() the closure was 3491 bytes with current Spark, and 1333 bytes after my change. Here, the difference comes from eliminating duplicate serialization of the two RDDs for the narrow dependencies. The ShuffleHandle includes the ShuffleDependency, so this difference will get larger if a ShuffleDependency includes a serializer, a key ordering, or an aggregator (all set to None by default). It would also get bigger for a big RDD -- although I can't think of any examples where the RDD object gets large. The difference is not affected by the size of the function the user specifies, which (based on my understanding) is typically the source of large task closures. Author: Kay Ousterhout Closes #4145 from kayousterhout/SPARK-5360 and squashes the following commits: 85156c3 [Kay Ousterhout] Better comment the narrowDeps parameter cff0209 [Kay Ousterhout] Fixed spelling issue 658e1af [Kay Ousterhout] [SPARK-5360] Eliminate duplicate objects in serialized CoGroupedRDD --- .../org/apache/spark/rdd/CoGroupedRDD.scala | 43 +++++++++++-------- .../org/apache/spark/rdd/SubtractedRDD.scala | 30 +++++++------ 2 files changed, 44 insertions(+), 29 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala index 7021a339e879b..658e8c8b89318 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala @@ -29,15 +29,16 @@ import org.apache.spark.annotation.DeveloperApi import org.apache.spark.util.collection.{ExternalAppendOnlyMap, AppendOnlyMap, CompactBuffer} import org.apache.spark.util.Utils import org.apache.spark.serializer.Serializer -import org.apache.spark.shuffle.ShuffleHandle - -private[spark] sealed trait CoGroupSplitDep extends Serializable +/** The references to rdd and splitIndex are transient because redundant information is stored + * in the CoGroupedRDD object. Because CoGroupedRDD is serialized separately from + * CoGroupPartition, if rdd and splitIndex aren't transient, they'll be included twice in the + * task closure. */ private[spark] case class NarrowCoGroupSplitDep( - rdd: RDD[_], - splitIndex: Int, + @transient rdd: RDD[_], + @transient splitIndex: Int, var split: Partition - ) extends CoGroupSplitDep { + ) extends Serializable { @throws(classOf[IOException]) private def writeObject(oos: ObjectOutputStream): Unit = Utils.tryOrIOException { @@ -47,9 +48,16 @@ private[spark] case class NarrowCoGroupSplitDep( } } -private[spark] case class ShuffleCoGroupSplitDep(handle: ShuffleHandle) extends CoGroupSplitDep - -private[spark] class CoGroupPartition(idx: Int, val deps: Array[CoGroupSplitDep]) +/** + * Stores information about the narrow dependencies used by a CoGroupedRdd. + * + * @param narrowDeps maps to the dependencies variable in the parent RDD: for each one to one + * dependency in dependencies, narrowDeps has a NarrowCoGroupSplitDep (describing + * the partition for that dependency) at the corresponding index. The size of + * narrowDeps should always be equal to the number of parents. + */ +private[spark] class CoGroupPartition( + idx: Int, val narrowDeps: Array[Option[NarrowCoGroupSplitDep]]) extends Partition with Serializable { override val index: Int = idx override def hashCode(): Int = idx @@ -105,9 +113,9 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part: // Assume each RDD contributed a single dependency, and get it dependencies(j) match { case s: ShuffleDependency[_, _, _] => - new ShuffleCoGroupSplitDep(s.shuffleHandle) + None case _ => - new NarrowCoGroupSplitDep(rdd, i, rdd.partitions(i)) + Some(new NarrowCoGroupSplitDep(rdd, i, rdd.partitions(i))) } }.toArray) } @@ -120,20 +128,21 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part: val sparkConf = SparkEnv.get.conf val externalSorting = sparkConf.getBoolean("spark.shuffle.spill", true) val split = s.asInstanceOf[CoGroupPartition] - val numRdds = split.deps.length + val numRdds = dependencies.length // A list of (rdd iterator, dependency number) pairs val rddIterators = new ArrayBuffer[(Iterator[Product2[K, Any]], Int)] - for ((dep, depNum) <- split.deps.zipWithIndex) dep match { - case NarrowCoGroupSplitDep(rdd, _, itsSplit) => + for ((dep, depNum) <- dependencies.zipWithIndex) dep match { + case oneToOneDependency: OneToOneDependency[Product2[K, Any]] => + val dependencyPartition = split.narrowDeps(depNum).get.split // Read them from the parent - val it = rdd.iterator(itsSplit, context).asInstanceOf[Iterator[Product2[K, Any]]] + val it = oneToOneDependency.rdd.iterator(dependencyPartition, context) rddIterators += ((it, depNum)) - case ShuffleCoGroupSplitDep(handle) => + case shuffleDependency: ShuffleDependency[_, _, _] => // Read map outputs of shuffle val it = SparkEnv.get.shuffleManager - .getReader(handle, split.index, split.index + 1, context) + .getReader(shuffleDependency.shuffleHandle, split.index, split.index + 1, context) .read() rddIterators += ((it, depNum)) } diff --git a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala index e9d745588ee9a..633aeba3bbae6 100644 --- a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala @@ -81,9 +81,9 @@ private[spark] class SubtractedRDD[K: ClassTag, V: ClassTag, W: ClassTag]( array(i) = new CoGroupPartition(i, Seq(rdd1, rdd2).zipWithIndex.map { case (rdd, j) => dependencies(j) match { case s: ShuffleDependency[_, _, _] => - new ShuffleCoGroupSplitDep(s.shuffleHandle) + None case _ => - new NarrowCoGroupSplitDep(rdd, i, rdd.partitions(i)) + Some(new NarrowCoGroupSplitDep(rdd, i, rdd.partitions(i))) } }.toArray) } @@ -105,20 +105,26 @@ private[spark] class SubtractedRDD[K: ClassTag, V: ClassTag, W: ClassTag]( seq } } - def integrate(dep: CoGroupSplitDep, op: Product2[K, V] => Unit): Unit = dep match { - case NarrowCoGroupSplitDep(rdd, _, itsSplit) => - rdd.iterator(itsSplit, context).asInstanceOf[Iterator[Product2[K, V]]].foreach(op) + def integrate(depNum: Int, op: Product2[K, V] => Unit) = { + dependencies(depNum) match { + case oneToOneDependency: OneToOneDependency[_] => + val dependencyPartition = partition.narrowDeps(depNum).get.split + oneToOneDependency.rdd.iterator(dependencyPartition, context) + .asInstanceOf[Iterator[Product2[K, V]]].foreach(op) - case ShuffleCoGroupSplitDep(handle) => - val iter = SparkEnv.get.shuffleManager - .getReader(handle, partition.index, partition.index + 1, context) - .read() - iter.foreach(op) + case shuffleDependency: ShuffleDependency[_, _, _] => + val iter = SparkEnv.get.shuffleManager + .getReader( + shuffleDependency.shuffleHandle, partition.index, partition.index + 1, context) + .read() + iter.foreach(op) + } } + // the first dep is rdd1; add all values to the map - integrate(partition.deps(0), t => getSeq(t._1) += t._2) + integrate(0, t => getSeq(t._1) += t._2) // the second dep is rdd2; remove all of its keys - integrate(partition.deps(1), t => map.remove(t._1)) + integrate(1, t => map.remove(t._1)) map.iterator.map { t => t._2.iterator.map { (t._1, _) } }.flatten } From c25ca7c5a1f2a4f88f40b0c5cdbfa927c186cfa8 Mon Sep 17 00:00:00 2001 From: emres Date: Tue, 21 Apr 2015 16:39:56 -0400 Subject: [PATCH 356/591] SPARK-3276 Added a new configuration spark.streaming.minRememberDuration SPARK-3276 Added a new configuration parameter ``spark.streaming.minRememberDuration``, with a default value of 1 minute. So that when a Spark Streaming application is started, an arbitrary number of minutes can be taken as threshold for remembering. Author: emres Closes #5438 from emres/SPARK-3276 and squashes the following commits: 766f938 [emres] SPARK-3276 Switched to using newly added getTimeAsSeconds method. affee1d [emres] SPARK-3276 Changed the property name and variable name for minRememberDuration c9d58ca [emres] SPARK-3276 Minor code re-formatting. 1c53ba9 [emres] SPARK-3276 Started to use ssc.conf rather than ssc.sparkContext.getConf, and also getLong method directly. bfe0acb [emres] SPARK-3276 Moved the minRememberDurationMin to the class daccc82 [emres] SPARK-3276 Changed the property name to reflect the unit of value and reduced number of fields. 43cc1ce [emres] SPARK-3276 Added a new configuration parameter spark.streaming.minRemember duration, with a default value of 1 minute. --- .../streaming/dstream/FileInputDStream.scala | 30 +++++++++++-------- 1 file changed, 17 insertions(+), 13 deletions(-) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala index 66d519171fd76..eca69f00188e4 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala @@ -26,7 +26,7 @@ import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FileSystem, Path, PathFilter} import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat} -import org.apache.spark.SerializableWritable +import org.apache.spark.{SparkConf, SerializableWritable} import org.apache.spark.rdd.{RDD, UnionRDD} import org.apache.spark.streaming._ import org.apache.spark.util.{TimeStampedHashMap, Utils} @@ -63,7 +63,7 @@ import org.apache.spark.util.{TimeStampedHashMap, Utils} * the streaming app. * - If a file is to be visible in the directory listings, it must be visible within a certain * duration of the mod time of the file. This duration is the "remember window", which is set to - * 1 minute (see `FileInputDStream.MIN_REMEMBER_DURATION`). Otherwise, the file will never be + * 1 minute (see `FileInputDStream.minRememberDuration`). Otherwise, the file will never be * selected as the mod time will be less than the ignore threshold when it becomes visible. * - Once a file is visible, the mod time cannot change. If it does due to appends, then the * processing semantics are undefined. @@ -80,6 +80,15 @@ class FileInputDStream[K, V, F <: NewInputFormat[K,V]]( private val serializableConfOpt = conf.map(new SerializableWritable(_)) + /** + * Minimum duration of remembering the information of selected files. Defaults to 60 seconds. + * + * Files with mod times older than this "window" of remembering will be ignored. So if new + * files are visible within this window, then the file will get selected in the next batch. + */ + private val minRememberDurationS = + Seconds(ssc.conf.getTimeAsSeconds("spark.streaming.minRememberDuration", "60s")) + // This is a def so that it works during checkpoint recovery: private def clock = ssc.scheduler.clock @@ -95,7 +104,8 @@ class FileInputDStream[K, V, F <: NewInputFormat[K,V]]( * This would allow us to filter away not-too-old files which have already been recently * selected and processed. */ - private val numBatchesToRemember = FileInputDStream.calculateNumBatchesToRemember(slideDuration) + private val numBatchesToRemember = FileInputDStream + .calculateNumBatchesToRemember(slideDuration, minRememberDurationS) private val durationToRemember = slideDuration * numBatchesToRemember remember(durationToRemember) @@ -330,20 +340,14 @@ class FileInputDStream[K, V, F <: NewInputFormat[K,V]]( private[streaming] object FileInputDStream { - /** - * Minimum duration of remembering the information of selected files. Files with mod times - * older than this "window" of remembering will be ignored. So if new files are visible - * within this window, then the file will get selected in the next batch. - */ - private val MIN_REMEMBER_DURATION = Minutes(1) - def defaultFilter(path: Path): Boolean = !path.getName().startsWith(".") /** * Calculate the number of last batches to remember, such that all the files selected in - * at least last MIN_REMEMBER_DURATION duration can be remembered. + * at least last minRememberDurationS duration can be remembered. */ - def calculateNumBatchesToRemember(batchDuration: Duration): Int = { - math.ceil(MIN_REMEMBER_DURATION.milliseconds.toDouble / batchDuration.milliseconds).toInt + def calculateNumBatchesToRemember(batchDuration: Duration, + minRememberDurationS: Duration): Int = { + math.ceil(minRememberDurationS.milliseconds.toDouble / batchDuration.milliseconds).toInt } } From 45c47fa4176ea75886a58f5d73c44afcb29aa629 Mon Sep 17 00:00:00 2001 From: MechCoder Date: Tue, 21 Apr 2015 14:36:50 -0700 Subject: [PATCH 357/591] [SPARK-6845] [MLlib] [PySpark] Add isTranposed flag to DenseMatrix Since sparse matrices now support a isTransposed flag for row major data, DenseMatrices should do the same. Author: MechCoder Closes #5455 from MechCoder/spark-6845 and squashes the following commits: 525c370 [MechCoder] minor 004a37f [MechCoder] Cast boolean to int 151f3b6 [MechCoder] [WIP] Add isTransposed to pickle DenseMatrix cc0b90a [MechCoder] [SPARK-6845] Add isTranposed flag to DenseMatrix --- .../mllib/api/python/PythonMLLibAPI.scala | 13 +++-- python/pyspark/mllib/linalg.py | 49 +++++++++++++------ python/pyspark/mllib/tests.py | 16 ++++++ 3 files changed, 58 insertions(+), 20 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index f976d2f97b043..6237b64c8f984 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -985,8 +985,10 @@ private[spark] object SerDe extends Serializable { val m: DenseMatrix = obj.asInstanceOf[DenseMatrix] val bytes = new Array[Byte](8 * m.values.size) val order = ByteOrder.nativeOrder() + val isTransposed = if (m.isTransposed) 1 else 0 ByteBuffer.wrap(bytes).order(order).asDoubleBuffer().put(m.values) + out.write(Opcodes.MARK) out.write(Opcodes.BININT) out.write(PickleUtils.integer_to_bytes(m.numRows)) out.write(Opcodes.BININT) @@ -994,19 +996,22 @@ private[spark] object SerDe extends Serializable { out.write(Opcodes.BINSTRING) out.write(PickleUtils.integer_to_bytes(bytes.length)) out.write(bytes) - out.write(Opcodes.TUPLE3) + out.write(Opcodes.BININT) + out.write(PickleUtils.integer_to_bytes(isTransposed)) + out.write(Opcodes.TUPLE) } def construct(args: Array[Object]): Object = { - if (args.length != 3) { - throw new PickleException("should be 3") + if (args.length != 4) { + throw new PickleException("should be 4") } val bytes = getBytes(args(2)) val n = bytes.length / 8 val values = new Array[Double](n) val order = ByteOrder.nativeOrder() ByteBuffer.wrap(bytes).order(order).asDoubleBuffer().get(values) - new DenseMatrix(args(0).asInstanceOf[Int], args(1).asInstanceOf[Int], values) + val isTransposed = args(3).asInstanceOf[Int] == 1 + new DenseMatrix(args(0).asInstanceOf[Int], args(1).asInstanceOf[Int], values, isTransposed) } } diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py index ec8c879ea9389..cc9a4cf8ba170 100644 --- a/python/pyspark/mllib/linalg.py +++ b/python/pyspark/mllib/linalg.py @@ -638,9 +638,10 @@ class Matrix(object): Represents a local matrix. """ - def __init__(self, numRows, numCols): + def __init__(self, numRows, numCols, isTransposed=False): self.numRows = numRows self.numCols = numCols + self.isTransposed = isTransposed def toArray(self): """ @@ -662,14 +663,16 @@ class DenseMatrix(Matrix): """ Column-major dense matrix. """ - def __init__(self, numRows, numCols, values): - Matrix.__init__(self, numRows, numCols) + def __init__(self, numRows, numCols, values, isTransposed=False): + Matrix.__init__(self, numRows, numCols, isTransposed) values = self._convert_to_array(values, np.float64) assert len(values) == numRows * numCols self.values = values def __reduce__(self): - return DenseMatrix, (self.numRows, self.numCols, self.values.tostring()) + return DenseMatrix, ( + self.numRows, self.numCols, self.values.tostring(), + int(self.isTransposed)) def toArray(self): """ @@ -680,15 +683,23 @@ def toArray(self): array([[ 0., 2.], [ 1., 3.]]) """ - return self.values.reshape((self.numRows, self.numCols), order='F') + if self.isTransposed: + return np.asfortranarray( + self.values.reshape((self.numRows, self.numCols))) + else: + return self.values.reshape((self.numRows, self.numCols), order='F') def toSparse(self): """Convert to SparseMatrix""" - indices = np.nonzero(self.values)[0] + if self.isTransposed: + values = np.ravel(self.toArray(), order='F') + else: + values = self.values + indices = np.nonzero(values)[0] colCounts = np.bincount(indices // self.numRows) colPtrs = np.cumsum(np.hstack( (0, colCounts, np.zeros(self.numCols - colCounts.size)))) - values = self.values[indices] + values = values[indices] rowIndices = indices % self.numRows return SparseMatrix(self.numRows, self.numCols, colPtrs, rowIndices, values) @@ -701,21 +712,28 @@ def __getitem__(self, indices): if j >= self.numCols or j < 0: raise ValueError("Column index %d is out of range [0, %d)" % (j, self.numCols)) - return self.values[i + j * self.numRows] + + if self.isTransposed: + return self.values[i * self.numCols + j] + else: + return self.values[i + j * self.numRows] def __eq__(self, other): - return (isinstance(other, DenseMatrix) and - self.numRows == other.numRows and - self.numCols == other.numCols and - all(self.values == other.values)) + if (not isinstance(other, DenseMatrix) or + self.numRows != other.numRows or + self.numCols != other.numCols): + return False + + self_values = np.ravel(self.toArray(), order='F') + other_values = np.ravel(other.toArray(), order='F') + return all(self_values == other_values) class SparseMatrix(Matrix): """Sparse Matrix stored in CSC format.""" def __init__(self, numRows, numCols, colPtrs, rowIndices, values, isTransposed=False): - Matrix.__init__(self, numRows, numCols) - self.isTransposed = isTransposed + Matrix.__init__(self, numRows, numCols, isTransposed) self.colPtrs = self._convert_to_array(colPtrs, np.int32) self.rowIndices = self._convert_to_array(rowIndices, np.int32) self.values = self._convert_to_array(values, np.float64) @@ -777,8 +795,7 @@ def toArray(self): return A def toDense(self): - densevals = np.reshape( - self.toArray(), (self.numRows * self.numCols), order='F') + densevals = np.ravel(self.toArray(), order='F') return DenseMatrix(self.numRows, self.numCols, densevals) # TODO: More efficient implementation: diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 849c88341a967..8f89e2cee0592 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -195,6 +195,22 @@ def test_sparse_matrix(self): self.assertEquals(expected[i][j], sm1t[i, j]) self.assertTrue(array_equal(sm1t.toArray(), expected)) + def test_dense_matrix_is_transposed(self): + mat1 = DenseMatrix(3, 2, [0, 4, 1, 6, 3, 9], isTransposed=True) + mat = DenseMatrix(3, 2, [0, 1, 3, 4, 6, 9]) + self.assertEquals(mat1, mat) + + expected = [[0, 4], [1, 6], [3, 9]] + for i in range(3): + for j in range(2): + self.assertEquals(mat1[i, j], expected[i][j]) + self.assertTrue(array_equal(mat1.toArray(), expected)) + + sm = mat1.toSparse() + self.assertTrue(array_equal(sm.rowIndices, [1, 2, 0, 1, 2])) + self.assertTrue(array_equal(sm.colPtrs, [0, 2, 5])) + self.assertTrue(array_equal(sm.values, [1, 3, 4, 6, 9])) + class ListTests(PySparkTestCase): From 04bf34e34f22e3d7e972fe755251774fc6a6d52e Mon Sep 17 00:00:00 2001 From: Prashant Sharma Date: Tue, 21 Apr 2015 14:43:46 -0700 Subject: [PATCH 358/591] [SPARK-7011] Build(compilation) fails with scala 2.11 option, because a protected[sql] type is accessed in ml package. [This](https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala#L58) is where it is used and fails compilations at. Author: Prashant Sharma Closes #5593 from ScrapCodes/SPARK-7011/build-fix and squashes the following commits: e6d57a3 [Prashant Sharma] [SPARK-7011] Build fails with scala 2.11 option, because a protected[sql] type is accessed in ml package. --- .../src/main/scala/org/apache/spark/sql/types/dataTypes.scala | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala index c6fb22c26bd3c..a108413497829 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala @@ -299,7 +299,7 @@ class NullType private() extends DataType { case object NullType extends NullType -protected[sql] object NativeType { +protected[spark] object NativeType { val all = Seq( IntegerType, BooleanType, LongType, DoubleType, FloatType, ShortType, ByteType, StringType) @@ -327,7 +327,7 @@ protected[sql] object PrimitiveType { } } -protected[sql] abstract class NativeType extends DataType { +protected[spark] abstract class NativeType extends DataType { private[sql] type JvmType @transient private[sql] val tag: TypeTag[JvmType] private[sql] val ordering: Ordering[JvmType] From 2e8c6ca47df14681c1110f0736234ce76a3eca9b Mon Sep 17 00:00:00 2001 From: vidmantas zemleris Date: Tue, 21 Apr 2015 14:47:09 -0700 Subject: [PATCH 359/591] [SPARK-6994] Allow to fetch field values by name in sql.Row It looked weird that up to now there was no way in Spark's Scala API to access fields of `DataFrame/sql.Row` by name, only by their index. This tries to solve this issue. Author: vidmantas zemleris Closes #5573 from vidma/features/row-with-named-fields and squashes the following commits: 6145ae3 [vidmantas zemleris] [SPARK-6994][SQL] Allow to fetch field values by name on Row 9564ebb [vidmantas zemleris] [SPARK-6994][SQL] Add fieldIndex to schema (StructType) --- .../main/scala/org/apache/spark/sql/Row.scala | 32 +++++++++ .../spark/sql/catalyst/expressions/rows.scala | 2 + .../apache/spark/sql/types/dataTypes.scala | 9 +++ .../scala/org/apache/spark/sql/RowTest.scala | 71 +++++++++++++++++++ .../spark/sql/types/DataTypeSuite.scala | 13 ++++ .../scala/org/apache/spark/sql/RowSuite.scala | 10 +++ 6 files changed, 137 insertions(+) create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/RowTest.scala diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala index ac8a782976465..4190b7ffe1c8f 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala @@ -306,6 +306,38 @@ trait Row extends Serializable { */ def getAs[T](i: Int): T = apply(i).asInstanceOf[T] + /** + * Returns the value of a given fieldName. + * + * @throws UnsupportedOperationException when schema is not defined. + * @throws IllegalArgumentException when fieldName do not exist. + * @throws ClassCastException when data type does not match. + */ + def getAs[T](fieldName: String): T = getAs[T](fieldIndex(fieldName)) + + /** + * Returns the index of a given field name. + * + * @throws UnsupportedOperationException when schema is not defined. + * @throws IllegalArgumentException when fieldName do not exist. + */ + def fieldIndex(name: String): Int = { + throw new UnsupportedOperationException("fieldIndex on a Row without schema is undefined.") + } + + /** + * Returns a Map(name -> value) for the requested fieldNames + * + * @throws UnsupportedOperationException when schema is not defined. + * @throws IllegalArgumentException when fieldName do not exist. + * @throws ClassCastException when data type does not match. + */ + def getValuesMap[T](fieldNames: Seq[String]): Map[String, T] = { + fieldNames.map { name => + name -> getAs[T](name) + }.toMap + } + override def toString(): String = s"[${this.mkString(",")}]" /** diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala index b6ec7d3417ef8..981373477a4bc 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala @@ -181,6 +181,8 @@ class GenericRowWithSchema(values: Array[Any], override val schema: StructType) /** No-arg constructor for serialization. */ protected def this() = this(null, null) + + override def fieldIndex(name: String): Int = schema.fieldIndex(name) } class GenericMutableRow(v: Array[Any]) extends GenericRow(v) with MutableRow { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala index a108413497829..7cd7bd1914c95 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala @@ -1025,6 +1025,7 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru private lazy val fieldNamesSet: Set[String] = fieldNames.toSet private lazy val nameToField: Map[String, StructField] = fields.map(f => f.name -> f).toMap + private lazy val nameToIndex: Map[String, Int] = fieldNames.zipWithIndex.toMap /** * Extracts a [[StructField]] of the given name. If the [[StructType]] object does not @@ -1049,6 +1050,14 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru StructType(fields.filter(f => names.contains(f.name))) } + /** + * Returns index of a given field + */ + def fieldIndex(name: String): Int = { + nameToIndex.getOrElse(name, + throw new IllegalArgumentException(s"""Field "$name" does not exist.""")) + } + protected[sql] def toAttributes: Seq[AttributeReference] = map(f => AttributeReference(f.name, f.dataType, f.nullable, f.metadata)()) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/RowTest.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/RowTest.scala new file mode 100644 index 0000000000000..bbb9739e9cc76 --- /dev/null +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/RowTest.scala @@ -0,0 +1,71 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import org.apache.spark.sql.catalyst.expressions.{GenericRow, GenericRowWithSchema} +import org.apache.spark.sql.types._ +import org.scalatest.{Matchers, FunSpec} + +class RowTest extends FunSpec with Matchers { + + val schema = StructType( + StructField("col1", StringType) :: + StructField("col2", StringType) :: + StructField("col3", IntegerType) :: Nil) + val values = Array("value1", "value2", 1) + + val sampleRow: Row = new GenericRowWithSchema(values, schema) + val noSchemaRow: Row = new GenericRow(values) + + describe("Row (without schema)") { + it("throws an exception when accessing by fieldName") { + intercept[UnsupportedOperationException] { + noSchemaRow.fieldIndex("col1") + } + intercept[UnsupportedOperationException] { + noSchemaRow.getAs("col1") + } + } + } + + describe("Row (with schema)") { + it("fieldIndex(name) returns field index") { + sampleRow.fieldIndex("col1") shouldBe 0 + sampleRow.fieldIndex("col3") shouldBe 2 + } + + it("getAs[T] retrieves a value by fieldname") { + sampleRow.getAs[String]("col1") shouldBe "value1" + sampleRow.getAs[Int]("col3") shouldBe 1 + } + + it("Accessing non existent field throws an exception") { + intercept[IllegalArgumentException] { + sampleRow.getAs[String]("non_existent") + } + } + + it("getValuesMap() retrieves values of multiple fields as a Map(field -> value)") { + val expected = Map( + "col1" -> "value1", + "col2" -> "value2" + ) + sampleRow.getValuesMap(List("col1", "col2")) shouldBe expected + } + } +} diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala index a1341ea13d810..d797510f36685 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala @@ -56,6 +56,19 @@ class DataTypeSuite extends FunSuite { } } + test("extract field index from a StructType") { + val struct = StructType( + StructField("a", LongType) :: + StructField("b", FloatType) :: Nil) + + assert(struct.fieldIndex("a") === 0) + assert(struct.fieldIndex("b") === 1) + + intercept[IllegalArgumentException] { + struct.fieldIndex("non_existent") + } + } + def checkDataTypeJsonRepr(dataType: DataType): Unit = { test(s"JSON - $dataType") { assert(DataType.fromJson(dataType.json) === dataType) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala index bf6cf1321a056..fb3ba4bc1b908 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala @@ -62,4 +62,14 @@ class RowSuite extends FunSuite { val de = instance.deserialize(ser).asInstanceOf[Row] assert(de === row) } + + test("get values by field name on Row created via .toDF") { + val row = Seq((1, Seq(1))).toDF("a", "b").first() + assert(row.getAs[Int]("a") === 1) + assert(row.getAs[Seq[Int]]("b") === Seq(1)) + + intercept[IllegalArgumentException]{ + row.getAs[Int]("c") + } + } } From 03fd92167107f1d061c1a7ef216468b508546ac7 Mon Sep 17 00:00:00 2001 From: Wenchen Fan Date: Tue, 21 Apr 2015 14:48:02 -0700 Subject: [PATCH 360/591] [SQL][minor] make it more clear that we only need to re-throw GetField exception for UnresolvedAttribute For `GetField` outside `UnresolvedAttribute`, we will throw exception in `Analyzer`. Author: Wenchen Fan Closes #5588 from cloud-fan/tmp and squashes the following commits: 7ac74d2 [Wenchen Fan] small refactor --- .../spark/sql/catalyst/analysis/CheckAnalysis.scala | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala index 1155dac28fc78..a986dd5387c38 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala @@ -46,12 +46,11 @@ trait CheckAnalysis { operator transformExpressionsUp { case a: Attribute if !a.resolved => if (operator.childrenResolved) { - val nameParts = a match { - case UnresolvedAttribute(nameParts) => nameParts - case _ => Seq(a.name) + a match { + case UnresolvedAttribute(nameParts) => + // Throw errors for specific problems with get field. + operator.resolveChildren(nameParts, resolver, throwErrors = true) } - // Throw errors for specific problems with get field. - operator.resolveChildren(nameParts, resolver, throwErrors = true) } val from = operator.inputSet.map(_.name).mkString(", ") From 6265cba00f6141575b4be825735d77d4cea500ab Mon Sep 17 00:00:00 2001 From: Yin Huai Date: Tue, 21 Apr 2015 14:48:42 -0700 Subject: [PATCH 361/591] [SPARK-6969][SQL] Refresh the cached table when REFRESH TABLE is used https://issues.apache.org/jira/browse/SPARK-6969 Author: Yin Huai Closes #5583 from yhuai/refreshTableRefreshDataCache and squashes the following commits: 1e5142b [Yin Huai] Add todo. 92b2498 [Yin Huai] Minor updates. 367df92 [Yin Huai] Recache data in the command of REFRESH TABLE. --- .../org/apache/spark/sql/sources/ddl.scala | 17 +++++++ .../spark/sql/hive/CachedTableSuite.scala | 50 ++++++++++++++++++- 2 files changed, 66 insertions(+), 1 deletion(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala index 2e861b84b7133..78d494184e759 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala @@ -347,7 +347,24 @@ private[sql] case class RefreshTable(databaseName: String, tableName: String) extends RunnableCommand { override def run(sqlContext: SQLContext): Seq[Row] = { + // Refresh the given table's metadata first. sqlContext.catalog.refreshTable(databaseName, tableName) + + // If this table is cached as a InMemoryColumnarRelation, drop the original + // cached version and make the new version cached lazily. + val logicalPlan = sqlContext.catalog.lookupRelation(Seq(databaseName, tableName)) + // Use lookupCachedData directly since RefreshTable also takes databaseName. + val isCached = sqlContext.cacheManager.lookupCachedData(logicalPlan).nonEmpty + if (isCached) { + // Create a data frame to represent the table. + // TODO: Use uncacheTable once it supports database name. + val df = DataFrame(sqlContext, logicalPlan) + // Uncache the logicalPlan. + sqlContext.cacheManager.tryUncacheQuery(df, blocking = true) + // Cache it again. + sqlContext.cacheManager.cacheQuery(df, Some(tableName)) + } + Seq.empty[Row] } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala index c188264072a84..fc6c3c35037b0 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala @@ -17,11 +17,14 @@ package org.apache.spark.sql.hive +import java.io.File + import org.apache.spark.sql.columnar.{InMemoryColumnarTableScan, InMemoryRelation} import org.apache.spark.sql.hive.test.TestHive import org.apache.spark.sql.hive.test.TestHive._ -import org.apache.spark.sql.{AnalysisException, DataFrame, QueryTest} +import org.apache.spark.sql.{SaveMode, AnalysisException, DataFrame, QueryTest} import org.apache.spark.storage.RDDBlockId +import org.apache.spark.util.Utils class CachedTableSuite extends QueryTest { @@ -155,4 +158,49 @@ class CachedTableSuite extends QueryTest { assertCached(table("udfTest")) uncacheTable("udfTest") } + + test("REFRESH TABLE also needs to recache the data (data source tables)") { + val tempPath: File = Utils.createTempDir() + tempPath.delete() + table("src").save(tempPath.toString, "parquet", SaveMode.Overwrite) + sql("DROP TABLE IF EXISTS refreshTable") + createExternalTable("refreshTable", tempPath.toString, "parquet") + checkAnswer( + table("refreshTable"), + table("src").collect()) + // Cache the table. + sql("CACHE TABLE refreshTable") + assertCached(table("refreshTable")) + // Append new data. + table("src").save(tempPath.toString, "parquet", SaveMode.Append) + // We are still using the old data. + assertCached(table("refreshTable")) + checkAnswer( + table("refreshTable"), + table("src").collect()) + // Refresh the table. + sql("REFRESH TABLE refreshTable") + // We are using the new data. + assertCached(table("refreshTable")) + checkAnswer( + table("refreshTable"), + table("src").unionAll(table("src")).collect()) + + // Drop the table and create it again. + sql("DROP TABLE refreshTable") + createExternalTable("refreshTable", tempPath.toString, "parquet") + // It is not cached. + assert(!isCached("refreshTable"), "refreshTable should not be cached.") + // Refresh the table. REFRESH TABLE command should not make a uncached + // table cached. + sql("REFRESH TABLE refreshTable") + checkAnswer( + table("refreshTable"), + table("src").unionAll(table("src")).collect()) + // It is not cached. + assert(!isCached("refreshTable"), "refreshTable should not be cached.") + + sql("DROP TABLE refreshTable") + Utils.deleteRecursively(tempPath) + } } From 2a24bf92e6d36e876bad6a8b4e0ff12c407ebb8a Mon Sep 17 00:00:00 2001 From: Punya Biswal Date: Tue, 21 Apr 2015 14:50:02 -0700 Subject: [PATCH 362/591] [SPARK-6996][SQL] Support map types in java beans liancheng mengxr this is similar to #5146. Author: Punya Biswal Closes #5578 from punya/feature/SPARK-6996 and squashes the following commits: d56c3e0 [Punya Biswal] Fix imports c7e308b [Punya Biswal] Support java iterable types in POJOs 5e00685 [Punya Biswal] Support map types in java beans --- .../sql/catalyst/CatalystTypeConverters.scala | 20 ++++ .../apache/spark/sql/JavaTypeInference.scala | 110 ++++++++++++++++++ .../org/apache/spark/sql/SQLContext.scala | 52 +-------- .../apache/spark/sql/JavaDataFrameSuite.java | 57 +++++++-- 4 files changed, 180 insertions(+), 59 deletions(-) create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/JavaTypeInference.scala diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala index d4f9fdacda4fb..a13e2f36a1a1f 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala @@ -17,6 +17,7 @@ package org.apache.spark.sql.catalyst +import java.lang.{Iterable => JavaIterable} import java.util.{Map => JavaMap} import scala.collection.mutable.HashMap @@ -49,6 +50,16 @@ object CatalystTypeConverters { case (s: Seq[_], arrayType: ArrayType) => s.map(convertToCatalyst(_, arrayType.elementType)) + case (jit: JavaIterable[_], arrayType: ArrayType) => { + val iter = jit.iterator + var listOfItems: List[Any] = List() + while (iter.hasNext) { + val item = iter.next() + listOfItems :+= convertToCatalyst(item, arrayType.elementType) + } + listOfItems + } + case (s: Array[_], arrayType: ArrayType) => s.toSeq.map(convertToCatalyst(_, arrayType.elementType)) @@ -124,6 +135,15 @@ object CatalystTypeConverters { extractOption(item) match { case a: Array[_] => a.toSeq.map(elementConverter) case s: Seq[_] => s.map(elementConverter) + case i: JavaIterable[_] => { + val iter = i.iterator + var convertedIterable: List[Any] = List() + while (iter.hasNext) { + val item = iter.next() + convertedIterable :+= elementConverter(item) + } + convertedIterable + } case null => null } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/JavaTypeInference.scala b/sql/core/src/main/scala/org/apache/spark/sql/JavaTypeInference.scala new file mode 100644 index 0000000000000..db484c5f50074 --- /dev/null +++ b/sql/core/src/main/scala/org/apache/spark/sql/JavaTypeInference.scala @@ -0,0 +1,110 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql + +import java.beans.Introspector +import java.lang.{Iterable => JIterable} +import java.util.{Iterator => JIterator, Map => JMap} + +import com.google.common.reflect.TypeToken + +import org.apache.spark.sql.types._ + +import scala.language.existentials + +/** + * Type-inference utilities for POJOs and Java collections. + */ +private [sql] object JavaTypeInference { + + private val iterableType = TypeToken.of(classOf[JIterable[_]]) + private val mapType = TypeToken.of(classOf[JMap[_, _]]) + private val iteratorReturnType = classOf[JIterable[_]].getMethod("iterator").getGenericReturnType + private val nextReturnType = classOf[JIterator[_]].getMethod("next").getGenericReturnType + private val keySetReturnType = classOf[JMap[_, _]].getMethod("keySet").getGenericReturnType + private val valuesReturnType = classOf[JMap[_, _]].getMethod("values").getGenericReturnType + + /** + * Infers the corresponding SQL data type of a Java type. + * @param typeToken Java type + * @return (SQL data type, nullable) + */ + private [sql] def inferDataType(typeToken: TypeToken[_]): (DataType, Boolean) = { + // TODO: All of this could probably be moved to Catalyst as it is mostly not Spark specific. + typeToken.getRawType match { + case c: Class[_] if c.isAnnotationPresent(classOf[SQLUserDefinedType]) => + (c.getAnnotation(classOf[SQLUserDefinedType]).udt().newInstance(), true) + + case c: Class[_] if c == classOf[java.lang.String] => (StringType, true) + case c: Class[_] if c == java.lang.Short.TYPE => (ShortType, false) + case c: Class[_] if c == java.lang.Integer.TYPE => (IntegerType, false) + case c: Class[_] if c == java.lang.Long.TYPE => (LongType, false) + case c: Class[_] if c == java.lang.Double.TYPE => (DoubleType, false) + case c: Class[_] if c == java.lang.Byte.TYPE => (ByteType, false) + case c: Class[_] if c == java.lang.Float.TYPE => (FloatType, false) + case c: Class[_] if c == java.lang.Boolean.TYPE => (BooleanType, false) + + case c: Class[_] if c == classOf[java.lang.Short] => (ShortType, true) + case c: Class[_] if c == classOf[java.lang.Integer] => (IntegerType, true) + case c: Class[_] if c == classOf[java.lang.Long] => (LongType, true) + case c: Class[_] if c == classOf[java.lang.Double] => (DoubleType, true) + case c: Class[_] if c == classOf[java.lang.Byte] => (ByteType, true) + case c: Class[_] if c == classOf[java.lang.Float] => (FloatType, true) + case c: Class[_] if c == classOf[java.lang.Boolean] => (BooleanType, true) + + case c: Class[_] if c == classOf[java.math.BigDecimal] => (DecimalType(), true) + case c: Class[_] if c == classOf[java.sql.Date] => (DateType, true) + case c: Class[_] if c == classOf[java.sql.Timestamp] => (TimestampType, true) + + case _ if typeToken.isArray => + val (dataType, nullable) = inferDataType(typeToken.getComponentType) + (ArrayType(dataType, nullable), true) + + case _ if iterableType.isAssignableFrom(typeToken) => + val (dataType, nullable) = inferDataType(elementType(typeToken)) + (ArrayType(dataType, nullable), true) + + case _ if mapType.isAssignableFrom(typeToken) => + val typeToken2 = typeToken.asInstanceOf[TypeToken[_ <: JMap[_, _]]] + val mapSupertype = typeToken2.getSupertype(classOf[JMap[_, _]]) + val keyType = elementType(mapSupertype.resolveType(keySetReturnType)) + val valueType = elementType(mapSupertype.resolveType(valuesReturnType)) + val (keyDataType, _) = inferDataType(keyType) + val (valueDataType, nullable) = inferDataType(valueType) + (MapType(keyDataType, valueDataType, nullable), true) + + case _ => + val beanInfo = Introspector.getBeanInfo(typeToken.getRawType) + val properties = beanInfo.getPropertyDescriptors.filterNot(_.getName == "class") + val fields = properties.map { property => + val returnType = typeToken.method(property.getReadMethod).getReturnType + val (dataType, nullable) = inferDataType(returnType) + new StructField(property.getName, dataType, nullable) + } + (new StructType(fields), true) + } + } + + private def elementType(typeToken: TypeToken[_]): TypeToken[_] = { + val typeToken2 = typeToken.asInstanceOf[TypeToken[_ <: JIterable[_]]] + val iterableSupertype = typeToken2.getSupertype(classOf[JIterable[_]]) + val iteratorType = iterableSupertype.resolveType(iteratorReturnType) + val itemType = iteratorType.resolveType(nextReturnType) + itemType + } +} diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala index f9f3eb2e03817..bcd20c06c6dca 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala @@ -25,6 +25,8 @@ import scala.collection.immutable import scala.language.implicitConversions import scala.reflect.runtime.universe.TypeTag +import com.google.common.reflect.TypeToken + import org.apache.spark.annotation.{DeveloperApi, Experimental} import org.apache.spark.api.java.{JavaRDD, JavaSparkContext} import org.apache.spark.rdd.RDD @@ -1222,56 +1224,12 @@ class SQLContext(@transient val sparkContext: SparkContext) * Returns a Catalyst Schema for the given java bean class. */ protected def getSchema(beanClass: Class[_]): Seq[AttributeReference] = { - val (dataType, _) = inferDataType(beanClass) + val (dataType, _) = JavaTypeInference.inferDataType(TypeToken.of(beanClass)) dataType.asInstanceOf[StructType].fields.map { f => AttributeReference(f.name, f.dataType, f.nullable)() } } - /** - * Infers the corresponding SQL data type of a Java class. - * @param clazz Java class - * @return (SQL data type, nullable) - */ - private def inferDataType(clazz: Class[_]): (DataType, Boolean) = { - // TODO: All of this could probably be moved to Catalyst as it is mostly not Spark specific. - clazz match { - case c: Class[_] if c.isAnnotationPresent(classOf[SQLUserDefinedType]) => - (c.getAnnotation(classOf[SQLUserDefinedType]).udt().newInstance(), true) - - case c: Class[_] if c == classOf[java.lang.String] => (StringType, true) - case c: Class[_] if c == java.lang.Short.TYPE => (ShortType, false) - case c: Class[_] if c == java.lang.Integer.TYPE => (IntegerType, false) - case c: Class[_] if c == java.lang.Long.TYPE => (LongType, false) - case c: Class[_] if c == java.lang.Double.TYPE => (DoubleType, false) - case c: Class[_] if c == java.lang.Byte.TYPE => (ByteType, false) - case c: Class[_] if c == java.lang.Float.TYPE => (FloatType, false) - case c: Class[_] if c == java.lang.Boolean.TYPE => (BooleanType, false) - - case c: Class[_] if c == classOf[java.lang.Short] => (ShortType, true) - case c: Class[_] if c == classOf[java.lang.Integer] => (IntegerType, true) - case c: Class[_] if c == classOf[java.lang.Long] => (LongType, true) - case c: Class[_] if c == classOf[java.lang.Double] => (DoubleType, true) - case c: Class[_] if c == classOf[java.lang.Byte] => (ByteType, true) - case c: Class[_] if c == classOf[java.lang.Float] => (FloatType, true) - case c: Class[_] if c == classOf[java.lang.Boolean] => (BooleanType, true) - - case c: Class[_] if c == classOf[java.math.BigDecimal] => (DecimalType(), true) - case c: Class[_] if c == classOf[java.sql.Date] => (DateType, true) - case c: Class[_] if c == classOf[java.sql.Timestamp] => (TimestampType, true) - - case c: Class[_] if c.isArray => - val (dataType, nullable) = inferDataType(c.getComponentType) - (ArrayType(dataType, nullable), true) - - case _ => - val beanInfo = Introspector.getBeanInfo(clazz) - val properties = beanInfo.getPropertyDescriptors.filterNot(_.getName == "class") - val fields = properties.map { property => - val (dataType, nullable) = inferDataType(property.getPropertyType) - new StructField(property.getName, dataType, nullable) - } - (new StructType(fields), true) - } - } } + + diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java index 6d0fbe83c2f36..fc3ed4a708d46 100644 --- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java @@ -17,23 +17,28 @@ package test.org.apache.spark.sql; -import java.io.Serializable; -import java.util.Arrays; - -import scala.collection.Seq; - -import org.junit.After; -import org.junit.Assert; -import org.junit.Before; -import org.junit.Ignore; -import org.junit.Test; +import com.google.common.collect.ImmutableMap; +import com.google.common.primitives.Ints; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.sql.*; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.SQLContext; +import org.apache.spark.sql.TestData$; import org.apache.spark.sql.test.TestSQLContext; import org.apache.spark.sql.test.TestSQLContext$; import org.apache.spark.sql.types.*; +import org.junit.*; + +import scala.collection.JavaConversions; +import scala.collection.Seq; +import scala.collection.mutable.Buffer; + +import java.io.Serializable; +import java.util.Arrays; +import java.util.List; +import java.util.Map; import static org.apache.spark.sql.functions.*; @@ -106,6 +111,8 @@ public void testShow() { public static class Bean implements Serializable { private double a = 0.0; private Integer[] b = new Integer[]{0, 1}; + private Map c = ImmutableMap.of("hello", new int[] { 1, 2 }); + private List d = Arrays.asList("floppy", "disk"); public double getA() { return a; @@ -114,6 +121,14 @@ public double getA() { public Integer[] getB() { return b; } + + public Map getC() { + return c; + } + + public List getD() { + return d; + } } @Test @@ -127,7 +142,15 @@ public void testCreateDataFrameFromJavaBeans() { Assert.assertEquals( new StructField("b", new ArrayType(IntegerType$.MODULE$, true), true, Metadata.empty()), schema.apply("b")); - Row first = df.select("a", "b").first(); + ArrayType valueType = new ArrayType(DataTypes.IntegerType, false); + MapType mapType = new MapType(DataTypes.StringType, valueType, true); + Assert.assertEquals( + new StructField("c", mapType, true, Metadata.empty()), + schema.apply("c")); + Assert.assertEquals( + new StructField("d", new ArrayType(DataTypes.StringType, true), true, Metadata.empty()), + schema.apply("d")); + Row first = df.select("a", "b", "c", "d").first(); Assert.assertEquals(bean.getA(), first.getDouble(0), 0.0); // Now Java lists and maps are converetd to Scala Seq's and Map's. Once we get a Seq below, // verify that it has the expected length, and contains expected elements. @@ -136,5 +159,15 @@ public void testCreateDataFrameFromJavaBeans() { for (int i = 0; i < result.length(); i++) { Assert.assertEquals(bean.getB()[i], result.apply(i)); } + Buffer outputBuffer = (Buffer) first.getJavaMap(2).get("hello"); + Assert.assertArrayEquals( + bean.getC().get("hello"), + Ints.toArray(JavaConversions.asJavaList(outputBuffer))); + Seq d = first.getAs(3); + Assert.assertEquals(bean.getD().size(), d.length()); + for (int i = 0; i < d.length(); i++) { + Assert.assertEquals(bean.getD().get(i), d.apply(i)); + } } + } From 7662ec23bb6c4d4fe4c857b6928eaed0a97d3c04 Mon Sep 17 00:00:00 2001 From: Cheng Hao Date: Tue, 21 Apr 2015 15:11:15 -0700 Subject: [PATCH 363/591] [SPARK-5817] [SQL] Fix bug of udtf with column names It's a bug while do query like: ```sql select d from (select explode(array(1,1)) d from src limit 1) t ``` And it will throws exception like: ``` org.apache.spark.sql.AnalysisException: cannot resolve 'd' given input columns _c0; line 1 pos 7 at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42) at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$apply$3$$anonfun$apply$1.applyOrElse(CheckAnalysis.scala:48) at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$apply$3$$anonfun$apply$1.applyOrElse(CheckAnalysis.scala:45) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:250) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:250) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:50) at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:249) at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$transformExpressionUp$1(QueryPlan.scala:103) at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$2$$anonfun$apply$2.apply(QueryPlan.scala:117) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) at scala.collection.TraversableLike$class.map(TraversableLike.scala:244) at scala.collection.AbstractTraversable.map(Traversable.scala:105) at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$2.apply(QueryPlan.scala:116) at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) ``` To solve the bug, it requires code refactoring for UDTF The major changes are about: * Simplifying the UDTF development, UDTF will manage the output attribute names any more, instead, the `logical.Generate` will handle that properly. * UDTF will be asked for the output schema (data types) during the logical plan analyzing. Author: Cheng Hao Closes #4602 from chenghao-intel/explode_bug and squashes the following commits: c2a5132 [Cheng Hao] add back resolved for Alias 556e982 [Cheng Hao] revert the unncessary change 002c361 [Cheng Hao] change the rule of resolved for Generate 04ae500 [Cheng Hao] add qualifier only for generator output 5ee5d2c [Cheng Hao] prepend the new qualifier d2e8b43 [Cheng Hao] Update the code as feedback ca5e7f4 [Cheng Hao] shrink the commits --- .../sql/catalyst/analysis/Analyzer.scala | 57 ++++++++++++++++++- .../sql/catalyst/analysis/CheckAnalysis.scala | 12 ++++ .../spark/sql/catalyst/dsl/package.scala | 3 +- .../sql/catalyst/expressions/generators.scala | 49 ++++------------ .../expressions/namedExpressions.scala | 2 + .../sql/catalyst/optimizer/Optimizer.scala | 8 +-- .../plans/logical/basicOperators.scala | 37 +++++++----- .../sql/catalyst/analysis/AnalysisSuite.scala | 2 +- .../optimizer/FilterPushdownSuite.scala | 8 +-- .../org/apache/spark/sql/DataFrame.scala | 21 +++++-- .../apache/spark/sql/execution/Generate.scala | 22 ++----- .../spark/sql/execution/SparkStrategies.scala | 5 +- .../apache/spark/sql/hive/HiveContext.scala | 1 - .../org/apache/spark/sql/hive/HiveQl.scala | 37 ++++++------ .../org/apache/spark/sql/hive/hiveUdfs.scala | 38 +------------ ... output-0-d1f244bce64f22b34ad5bf9fd360b632 | 1 + ...mn name-0-7ac701cf43e73e9e416888e4df694348 | 0 ...mn name-1-5cdf9d51fc0e105e365d82e7611e37f3 | 0 ...mn name-2-f963396461294e06cb7cafe22a1419e4 | 3 + ...n names-0-46bdb27b3359dc81d8c246b9f69d4b82 | 0 ...n names-1-cdf6989f3b055257f1692c3bbd80dc73 | 0 ...n names-2-ab3954b69d7a991bc801a509c3166cc5 | 3 + ...mn name-0-7ac701cf43e73e9e416888e4df694348 | 0 ...mn name-1-26599718c322ff4f9740040c066d8292 | 0 ...mn name-2-f963396461294e06cb7cafe22a1419e4 | 3 + .../sql/hive/execution/HiveQuerySuite.scala | 40 ++++++++++++- 26 files changed, 207 insertions(+), 145 deletions(-) create mode 100644 sql/hive/src/test/resources/golden/Specify the udtf output-0-d1f244bce64f22b34ad5bf9fd360b632 create mode 100644 sql/hive/src/test/resources/golden/insert table with generator with column name-0-7ac701cf43e73e9e416888e4df694348 create mode 100644 sql/hive/src/test/resources/golden/insert table with generator with column name-1-5cdf9d51fc0e105e365d82e7611e37f3 create mode 100644 sql/hive/src/test/resources/golden/insert table with generator with column name-2-f963396461294e06cb7cafe22a1419e4 create mode 100644 sql/hive/src/test/resources/golden/insert table with generator with multiple column names-0-46bdb27b3359dc81d8c246b9f69d4b82 create mode 100644 sql/hive/src/test/resources/golden/insert table with generator with multiple column names-1-cdf6989f3b055257f1692c3bbd80dc73 create mode 100644 sql/hive/src/test/resources/golden/insert table with generator with multiple column names-2-ab3954b69d7a991bc801a509c3166cc5 create mode 100644 sql/hive/src/test/resources/golden/insert table with generator without column name-0-7ac701cf43e73e9e416888e4df694348 create mode 100644 sql/hive/src/test/resources/golden/insert table with generator without column name-1-26599718c322ff4f9740040c066d8292 create mode 100644 sql/hive/src/test/resources/golden/insert table with generator without column name-2-f963396461294e06cb7cafe22a1419e4 diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala index cb49e5ad5586f..5e42b409dcc59 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala @@ -19,7 +19,6 @@ package org.apache.spark.sql.catalyst.analysis import org.apache.spark.util.collection.OpenHashSet import org.apache.spark.sql.AnalysisException -import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical._ import org.apache.spark.sql.catalyst.rules._ @@ -59,6 +58,7 @@ class Analyzer( ResolveReferences :: ResolveGroupingAnalytics :: ResolveSortReferences :: + ResolveGenerate :: ImplicitGenerate :: ResolveFunctions :: GlobalAggregates :: @@ -474,8 +474,59 @@ class Analyzer( */ object ImplicitGenerate extends Rule[LogicalPlan] { def apply(plan: LogicalPlan): LogicalPlan = plan transform { - case Project(Seq(Alias(g: Generator, _)), child) => - Generate(g, join = false, outer = false, None, child) + case Project(Seq(Alias(g: Generator, name)), child) => + Generate(g, join = false, outer = false, + qualifier = None, UnresolvedAttribute(name) :: Nil, child) + case Project(Seq(MultiAlias(g: Generator, names)), child) => + Generate(g, join = false, outer = false, + qualifier = None, names.map(UnresolvedAttribute(_)), child) + } + } + + /** + * Resolve the Generate, if the output names specified, we will take them, otherwise + * we will try to provide the default names, which follow the same rule with Hive. + */ + object ResolveGenerate extends Rule[LogicalPlan] { + // Construct the output attributes for the generator, + // The output attribute names can be either specified or + // auto generated. + private def makeGeneratorOutput( + generator: Generator, + generatorOutput: Seq[Attribute]): Seq[Attribute] = { + val elementTypes = generator.elementTypes + + if (generatorOutput.length == elementTypes.length) { + generatorOutput.zip(elementTypes).map { + case (a, (t, nullable)) if !a.resolved => + AttributeReference(a.name, t, nullable)() + case (a, _) => a + } + } else if (generatorOutput.length == 0) { + elementTypes.zipWithIndex.map { + // keep the default column names as Hive does _c0, _c1, _cN + case ((t, nullable), i) => AttributeReference(s"_c$i", t, nullable)() + } + } else { + throw new AnalysisException( + s""" + |The number of aliases supplied in the AS clause does not match + |the number of columns output by the UDTF expected + |${elementTypes.size} aliases but got ${generatorOutput.size} + """.stripMargin) + } + } + + def apply(plan: LogicalPlan): LogicalPlan = plan transform { + case p: Generate if !p.child.resolved || !p.generator.resolved => p + case p: Generate if p.resolved == false => + // if the generator output names are not specified, we will use the default ones. + Generate( + p.generator, + join = p.join, + outer = p.outer, + p.qualifier, + makeGeneratorOutput(p.generator, p.generatorOutput), p.child) } } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala index a986dd5387c38..2381689e17525 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala @@ -38,6 +38,12 @@ trait CheckAnalysis { throw new AnalysisException(msg) } + def containsMultipleGenerators(exprs: Seq[Expression]): Boolean = { + exprs.flatMap(_.collect { + case e: Generator => true + }).length >= 1 + } + def checkAnalysis(plan: LogicalPlan): Unit = { // We transform up and order the rules so as to catch the first possible failure instead // of the result of cascading resolution failures. @@ -110,6 +116,12 @@ trait CheckAnalysis { failAnalysis( s"unresolved operator ${operator.simpleString}") + case p @ Project(exprs, _) if containsMultipleGenerators(exprs) => + failAnalysis( + s"""Only a single table generating function is allowed in a SELECT clause, found: + | ${exprs.map(_.prettyString).mkString(",")}""".stripMargin) + + case _ => // Analysis successful! } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala index 21c15ad14fd19..4e5c64bb63c9f 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala @@ -284,12 +284,13 @@ package object dsl { seed: Int = (math.random * 1000).toInt): LogicalPlan = Sample(fraction, withReplacement, seed, logicalPlan) + // TODO specify the output column names def generate( generator: Generator, join: Boolean = false, outer: Boolean = false, alias: Option[String] = None): LogicalPlan = - Generate(generator, join, outer, None, logicalPlan) + Generate(generator, join = join, outer = outer, alias, Nil, logicalPlan) def insertInto(tableName: String, overwrite: Boolean = false): LogicalPlan = InsertIntoTable( diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala index 67caadb839ff9..9a6cb048af5ad 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala @@ -42,47 +42,30 @@ abstract class Generator extends Expression { override type EvaluatedType = TraversableOnce[Row] - override lazy val dataType = - ArrayType(StructType(output.map(a => StructField(a.name, a.dataType, a.nullable, a.metadata)))) + // TODO ideally we should return the type of ArrayType(StructType), + // however, we don't keep the output field names in the Generator. + override def dataType: DataType = throw new UnsupportedOperationException override def nullable: Boolean = false /** - * Should be overridden by specific generators. Called only once for each instance to ensure - * that rule application does not change the output schema of a generator. + * The output element data types in structure of Seq[(DataType, Nullable)] + * TODO we probably need to add more information like metadata etc. */ - protected def makeOutput(): Seq[Attribute] - - private var _output: Seq[Attribute] = null - - def output: Seq[Attribute] = { - if (_output == null) { - _output = makeOutput() - } - _output - } + def elementTypes: Seq[(DataType, Boolean)] /** Should be implemented by child classes to perform specific Generators. */ override def eval(input: Row): TraversableOnce[Row] - - /** Overridden `makeCopy` also copies the attributes that are produced by this generator. */ - override def makeCopy(newArgs: Array[AnyRef]): this.type = { - val copy = super.makeCopy(newArgs) - copy._output = _output - copy - } } /** * A generator that produces its output using the provided lambda function. */ case class UserDefinedGenerator( - schema: Seq[Attribute], + elementTypes: Seq[(DataType, Boolean)], function: Row => TraversableOnce[Row], children: Seq[Expression]) - extends Generator{ - - override protected def makeOutput(): Seq[Attribute] = schema + extends Generator { override def eval(input: Row): TraversableOnce[Row] = { // TODO(davies): improve this @@ -98,30 +81,18 @@ case class UserDefinedGenerator( /** * Given an input array produces a sequence of rows for each value in the array. */ -case class Explode(attributeNames: Seq[String], child: Expression) +case class Explode(child: Expression) extends Generator with trees.UnaryNode[Expression] { override lazy val resolved = child.resolved && (child.dataType.isInstanceOf[ArrayType] || child.dataType.isInstanceOf[MapType]) - private lazy val elementTypes = child.dataType match { + override def elementTypes: Seq[(DataType, Boolean)] = child.dataType match { case ArrayType(et, containsNull) => (et, containsNull) :: Nil case MapType(kt, vt, valueContainsNull) => (kt, false) :: (vt, valueContainsNull) :: Nil } - // TODO: Move this pattern into Generator. - protected def makeOutput() = - if (attributeNames.size == elementTypes.size) { - attributeNames.zip(elementTypes).map { - case (n, (t, nullable)) => AttributeReference(n, t, nullable)() - } - } else { - elementTypes.zipWithIndex.map { - case ((t, nullable), i) => AttributeReference(s"c_$i", t, nullable)() - } - } - override def eval(input: Row): TraversableOnce[Row] = { child.dataType match { case ArrayType(_, _) => diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala index bcbcbeb31c7b5..afcb2ce8b9cb4 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/namedExpressions.scala @@ -112,6 +112,8 @@ case class Alias(child: Expression, name: String)( extends NamedExpression with trees.UnaryNode[Expression] { override type EvaluatedType = Any + // Alias(Generator, xx) need to be transformed into Generate(generator, ...) + override lazy val resolved = childrenResolved && !child.isInstanceOf[Generator] override def eval(input: Row): Any = child.eval(input) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala index 7c80634d2c852..2d03fbfb0d311 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala @@ -482,16 +482,16 @@ object PushPredicateThroughProject extends Rule[LogicalPlan] { object PushPredicateThroughGenerate extends Rule[LogicalPlan] with PredicateHelper { def apply(plan: LogicalPlan): LogicalPlan = plan transform { - case filter @ Filter(condition, - generate @ Generate(generator, join, outer, alias, grandChild)) => + case filter @ Filter(condition, g: Generate) => // Predicates that reference attributes produced by the `Generate` operator cannot // be pushed below the operator. val (pushDown, stayUp) = splitConjunctivePredicates(condition).partition { - conjunct => conjunct.references subsetOf grandChild.outputSet + conjunct => conjunct.references subsetOf g.child.outputSet } if (pushDown.nonEmpty) { val pushDownPredicate = pushDown.reduce(And) - val withPushdown = generate.copy(child = Filter(pushDownPredicate, grandChild)) + val withPushdown = Generate(g.generator, join = g.join, outer = g.outer, + g.qualifier, g.generatorOutput, Filter(pushDownPredicate, g.child)) stayUp.reduceOption(And).map(Filter(_, withPushdown)).getOrElse(withPushdown) } else { filter diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala index 17522976dc2c9..bbc94a7ab3398 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala @@ -40,34 +40,43 @@ case class Project(projectList: Seq[NamedExpression], child: LogicalPlan) extend * output of each into a new stream of rows. This operation is similar to a `flatMap` in functional * programming with one important additional feature, which allows the input rows to be joined with * their output. + * @param generator the generator expression * @param join when true, each output row is implicitly joined with the input tuple that produced * it. * @param outer when true, each input row will be output at least once, even if the output of the * given `generator` is empty. `outer` has no effect when `join` is false. - * @param alias when set, this string is applied to the schema of the output of the transformation - * as a qualifier. + * @param qualifier Qualifier for the attributes of generator(UDTF) + * @param generatorOutput The output schema of the Generator. + * @param child Children logical plan node */ case class Generate( generator: Generator, join: Boolean, outer: Boolean, - alias: Option[String], + qualifier: Option[String], + generatorOutput: Seq[Attribute], child: LogicalPlan) extends UnaryNode { - protected def generatorOutput: Seq[Attribute] = { - val output = alias - .map(a => generator.output.map(_.withQualifiers(a :: Nil))) - .getOrElse(generator.output) - if (join && outer) { - output.map(_.withNullability(true)) - } else { - output - } + override lazy val resolved: Boolean = { + generator.resolved && + childrenResolved && + generator.elementTypes.length == generatorOutput.length && + !generatorOutput.exists(!_.resolved) } - override def output: Seq[Attribute] = - if (join) child.output ++ generatorOutput else generatorOutput + // we don't want the gOutput to be taken as part of the expressions + // as that will cause exceptions like unresolved attributes etc. + override def expressions: Seq[Expression] = generator :: Nil + + def output: Seq[Attribute] = { + val qualified = qualifier.map(q => + // prepend the new qualifier to the existed one + generatorOutput.map(a => a.withQualifiers(q +: a.qualifiers)) + ).getOrElse(generatorOutput) + + if (join) child.output ++ qualified else qualified + } } case class Filter(condition: Expression, child: LogicalPlan) extends UnaryNode { diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala index e10ddfdf5127c..7c249215bd6b6 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala @@ -90,7 +90,7 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { assert(!Project(Seq(UnresolvedAttribute("a")), testRelation).resolved) - val explode = Explode(Nil, AttributeReference("a", IntegerType, nullable = true)()) + val explode = Explode(AttributeReference("a", IntegerType, nullable = true)()) assert(!Project(Seq(Alias(explode, "explode")()), testRelation).resolved) assert(!Project(Seq(Alias(Count(Literal(1)), "count")()), testRelation).resolved) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala index 1448098c770aa..45cf695d20b01 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala @@ -454,21 +454,21 @@ class FilterPushdownSuite extends PlanTest { test("generate: predicate referenced no generated column") { val originalQuery = { testRelationWithArrayType - .generate(Explode(Seq("c"), 'c_arr), true, false, Some("arr")) + .generate(Explode('c_arr), true, false, Some("arr")) .where(('b >= 5) && ('a > 6)) } val optimized = Optimize(originalQuery.analyze) val correctAnswer = { testRelationWithArrayType .where(('b >= 5) && ('a > 6)) - .generate(Explode(Seq("c"), 'c_arr), true, false, Some("arr")).analyze + .generate(Explode('c_arr), true, false, Some("arr")).analyze } comparePlans(optimized, correctAnswer) } test("generate: part of conjuncts referenced generated column") { - val generator = Explode(Seq("c"), 'c_arr) + val generator = Explode('c_arr) val originalQuery = { testRelationWithArrayType .generate(generator, true, false, Some("arr")) @@ -499,7 +499,7 @@ class FilterPushdownSuite extends PlanTest { test("generate: all conjuncts referenced generated column") { val originalQuery = { testRelationWithArrayType - .generate(Explode(Seq("c"), 'c_arr), true, false, Some("arr")) + .generate(Explode('c_arr), true, false, Some("arr")) .where(('c > 6) || ('b > 5)).analyze } val optimized = Optimize(originalQuery) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 45f5da387692e..03d9834d1d131 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -34,7 +34,7 @@ import org.apache.spark.api.python.SerDeUtil import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, SqlParser} -import org.apache.spark.sql.catalyst.analysis.{UnresolvedRelation, ResolvedStar} +import org.apache.spark.sql.catalyst.analysis.{UnresolvedAttribute, UnresolvedRelation, ResolvedStar} import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.{JoinType, Inner} import org.apache.spark.sql.catalyst.plans.logical._ @@ -711,12 +711,16 @@ class DataFrame private[sql]( */ def explode[A <: Product : TypeTag](input: Column*)(f: Row => TraversableOnce[A]): DataFrame = { val schema = ScalaReflection.schemaFor[A].dataType.asInstanceOf[StructType] - val attributes = schema.toAttributes + + val elementTypes = schema.toAttributes.map { attr => (attr.dataType, attr.nullable) } + val names = schema.toAttributes.map(_.name) + val rowFunction = f.andThen(_.map(CatalystTypeConverters.convertToCatalyst(_, schema).asInstanceOf[Row])) - val generator = UserDefinedGenerator(attributes, rowFunction, input.map(_.expr)) + val generator = UserDefinedGenerator(elementTypes, rowFunction, input.map(_.expr)) - Generate(generator, join = true, outer = false, None, logicalPlan) + Generate(generator, join = true, outer = false, + qualifier = None, names.map(UnresolvedAttribute(_)), logicalPlan) } /** @@ -733,12 +737,17 @@ class DataFrame private[sql]( : DataFrame = { val dataType = ScalaReflection.schemaFor[B].dataType val attributes = AttributeReference(outputColumn, dataType)() :: Nil + // TODO handle the metadata? + val elementTypes = attributes.map { attr => (attr.dataType, attr.nullable) } + val names = attributes.map(_.name) + def rowFunction(row: Row): TraversableOnce[Row] = { f(row(0).asInstanceOf[A]).map(o => Row(CatalystTypeConverters.convertToCatalyst(o, dataType))) } - val generator = UserDefinedGenerator(attributes, rowFunction, apply(inputColumn).expr :: Nil) + val generator = UserDefinedGenerator(elementTypes, rowFunction, apply(inputColumn).expr :: Nil) - Generate(generator, join = true, outer = false, None, logicalPlan) + Generate(generator, join = true, outer = false, + qualifier = None, names.map(UnresolvedAttribute(_)), logicalPlan) } ///////////////////////////////////////////////////////////////////////////// diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala index 12271048bb39c..5201e20a10565 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Generate.scala @@ -27,44 +27,34 @@ import org.apache.spark.sql.catalyst.expressions._ * output of each into a new stream of rows. This operation is similar to a `flatMap` in functional * programming with one important additional feature, which allows the input rows to be joined with * their output. + * @param generator the generator expression * @param join when true, each output row is implicitly joined with the input tuple that produced * it. * @param outer when true, each input row will be output at least once, even if the output of the * given `generator` is empty. `outer` has no effect when `join` is false. + * @param output the output attributes of this node, which constructed in analysis phase, + * and we can not change it, as the parent node bound with it already. */ @DeveloperApi case class Generate( generator: Generator, join: Boolean, outer: Boolean, + output: Seq[Attribute], child: SparkPlan) extends UnaryNode { - // This must be a val since the generator output expr ids are not preserved by serialization. - protected val generatorOutput: Seq[Attribute] = { - if (join && outer) { - generator.output.map(_.withNullability(true)) - } else { - generator.output - } - } - - // This must be a val since the generator output expr ids are not preserved by serialization. - override val output = - if (join) child.output ++ generatorOutput else generatorOutput - val boundGenerator = BindReferences.bindReference(generator, child.output) override def execute(): RDD[Row] = { if (join) { child.execute().mapPartitions { iter => - val nullValues = Seq.fill(generator.output.size)(Literal(null)) + val nullValues = Seq.fill(generator.elementTypes.size)(Literal(null)) // Used to produce rows with no matches when outer = true. val outerProjection = newProjection(child.output ++ nullValues, child.output) - val joinProjection = - newProjection(child.output ++ generatorOutput, child.output ++ generatorOutput) + val joinProjection = newProjection(output, output) val joinedRow = new JoinedRow iter.flatMap {row => diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala index e687d01f57520..030ef118f75d4 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala @@ -312,8 +312,9 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] { execution.Except(planLater(left), planLater(right)) :: Nil case logical.Intersect(left, right) => execution.Intersect(planLater(left), planLater(right)) :: Nil - case logical.Generate(generator, join, outer, _, child) => - execution.Generate(generator, join = join, outer = outer, planLater(child)) :: Nil + case g @ logical.Generate(generator, join, outer, _, _, child) => + execution.Generate( + generator, join = join, outer = outer, g.output, planLater(child)) :: Nil case logical.OneRowRelation => execution.PhysicalRDD(Nil, singleRowRdd) :: Nil case logical.Repartition(expressions, child) => diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala index 7c6a7df2bd01e..c4a73b3004076 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala @@ -249,7 +249,6 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { catalog.CreateTables :: catalog.PreInsertionCasts :: ExtractPythonUdfs :: - ResolveUdtfsAlias :: sources.PreInsertCastAndRename :: Nil } diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala index fd305eb480e63..85061f22772dd 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala @@ -725,12 +725,14 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C val alias = getClause("TOK_TABALIAS", clauses).getChildren.head.asInstanceOf[ASTNode].getText - Generate( - nodesToGenerator(clauses), - join = true, - outer = false, - Some(alias.toLowerCase), - withWhere) + val (generator, attributes) = nodesToGenerator(clauses) + Generate( + generator, + join = true, + outer = false, + Some(alias.toLowerCase), + attributes.map(UnresolvedAttribute(_)), + withWhere) }.getOrElse(withWhere) // The projection of the query can either be a normal projection, an aggregation @@ -833,12 +835,14 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C val alias = getClause("TOK_TABALIAS", clauses).getChildren.head.asInstanceOf[ASTNode].getText - Generate( - nodesToGenerator(clauses), - join = true, - outer = isOuter.nonEmpty, - Some(alias.toLowerCase), - nodeToRelation(relationClause)) + val (generator, attributes) = nodesToGenerator(clauses) + Generate( + generator, + join = true, + outer = isOuter.nonEmpty, + Some(alias.toLowerCase), + attributes.map(UnresolvedAttribute(_)), + nodeToRelation(relationClause)) /* All relations, possibly with aliases or sampling clauses. */ case Token("TOK_TABREF", clauses) => @@ -1311,7 +1315,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C val explode = "(?i)explode".r - def nodesToGenerator(nodes: Seq[Node]): Generator = { + def nodesToGenerator(nodes: Seq[Node]): (Generator, Seq[String]) = { val function = nodes.head val attributes = nodes.flatMap { @@ -1321,7 +1325,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C function match { case Token("TOK_FUNCTION", Token(explode(), Nil) :: child :: Nil) => - Explode(attributes, nodeToExpr(child)) + (Explode(nodeToExpr(child)), attributes) case Token("TOK_FUNCTION", Token(functionName, Nil) :: children) => val functionInfo: FunctionInfo = @@ -1329,10 +1333,9 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C sys.error(s"Couldn't find function $functionName")) val functionClassName = functionInfo.getFunctionClass.getName - HiveGenericUdtf( + (HiveGenericUdtf( new HiveFunctionWrapper(functionClassName), - attributes, - children.map(nodeToExpr)) + children.map(nodeToExpr)), attributes) case a: ASTNode => throw new NotImplementedError( diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala index 47305571e579e..4b6f0ad75f54f 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUdfs.scala @@ -66,7 +66,7 @@ private[hive] abstract class HiveFunctionRegistry } else if (classOf[UDAF].isAssignableFrom(functionInfo.getFunctionClass)) { HiveUdaf(new HiveFunctionWrapper(functionClassName), children) } else if (classOf[GenericUDTF].isAssignableFrom(functionInfo.getFunctionClass)) { - HiveGenericUdtf(new HiveFunctionWrapper(functionClassName), Nil, children) + HiveGenericUdtf(new HiveFunctionWrapper(functionClassName), children) } else { sys.error(s"No handler for udf ${functionInfo.getFunctionClass}") } @@ -266,7 +266,6 @@ private[hive] case class HiveUdaf( */ private[hive] case class HiveGenericUdtf( funcWrapper: HiveFunctionWrapper, - aliasNames: Seq[String], children: Seq[Expression]) extends Generator with HiveInspectors { @@ -282,23 +281,8 @@ private[hive] case class HiveGenericUdtf( @transient protected lazy val udtInput = new Array[AnyRef](children.length) - protected lazy val outputDataTypes = outputInspector.getAllStructFieldRefs.map { - field => inspectorToDataType(field.getFieldObjectInspector) - } - - override protected def makeOutput() = { - // Use column names when given, otherwise _c1, _c2, ... _cn. - if (aliasNames.size == outputDataTypes.size) { - aliasNames.zip(outputDataTypes).map { - case (attrName, attrDataType) => - AttributeReference(attrName, attrDataType, nullable = true)() - } - } else { - outputDataTypes.zipWithIndex.map { - case (attrDataType, i) => - AttributeReference(s"_c$i", attrDataType, nullable = true)() - } - } + lazy val elementTypes = outputInspector.getAllStructFieldRefs.map { + field => (inspectorToDataType(field.getFieldObjectInspector), true) } override def eval(input: Row): TraversableOnce[Row] = { @@ -333,22 +317,6 @@ private[hive] case class HiveGenericUdtf( } } -/** - * Resolve Udtfs Alias. - */ -private[spark] object ResolveUdtfsAlias extends Rule[LogicalPlan] { - def apply(plan: LogicalPlan): LogicalPlan = plan transform { - case p @ Project(projectList, _) - if projectList.exists(_.isInstanceOf[MultiAlias]) && projectList.size != 1 => - throw new TreeNodeException(p, "only single Generator supported for SELECT clause") - - case Project(Seq(Alias(udtf @ HiveGenericUdtf(_, _, _), name)), child) => - Generate(udtf.copy(aliasNames = Seq(name)), join = false, outer = false, None, child) - case Project(Seq(MultiAlias(udtf @ HiveGenericUdtf(_, _, _), names)), child) => - Generate(udtf.copy(aliasNames = names), join = false, outer = false, None, child) - } -} - private[hive] case class HiveUdafFunction( funcWrapper: HiveFunctionWrapper, exprs: Seq[Expression], diff --git a/sql/hive/src/test/resources/golden/Specify the udtf output-0-d1f244bce64f22b34ad5bf9fd360b632 b/sql/hive/src/test/resources/golden/Specify the udtf output-0-d1f244bce64f22b34ad5bf9fd360b632 new file mode 100644 index 0000000000000..d00491fd7e5bb --- /dev/null +++ b/sql/hive/src/test/resources/golden/Specify the udtf output-0-d1f244bce64f22b34ad5bf9fd360b632 @@ -0,0 +1 @@ +1 diff --git a/sql/hive/src/test/resources/golden/insert table with generator with column name-0-7ac701cf43e73e9e416888e4df694348 b/sql/hive/src/test/resources/golden/insert table with generator with column name-0-7ac701cf43e73e9e416888e4df694348 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert table with generator with column name-1-5cdf9d51fc0e105e365d82e7611e37f3 b/sql/hive/src/test/resources/golden/insert table with generator with column name-1-5cdf9d51fc0e105e365d82e7611e37f3 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert table with generator with column name-2-f963396461294e06cb7cafe22a1419e4 b/sql/hive/src/test/resources/golden/insert table with generator with column name-2-f963396461294e06cb7cafe22a1419e4 new file mode 100644 index 0000000000000..01e79c32a8c99 --- /dev/null +++ b/sql/hive/src/test/resources/golden/insert table with generator with column name-2-f963396461294e06cb7cafe22a1419e4 @@ -0,0 +1,3 @@ +1 +2 +3 diff --git a/sql/hive/src/test/resources/golden/insert table with generator with multiple column names-0-46bdb27b3359dc81d8c246b9f69d4b82 b/sql/hive/src/test/resources/golden/insert table with generator with multiple column names-0-46bdb27b3359dc81d8c246b9f69d4b82 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert table with generator with multiple column names-1-cdf6989f3b055257f1692c3bbd80dc73 b/sql/hive/src/test/resources/golden/insert table with generator with multiple column names-1-cdf6989f3b055257f1692c3bbd80dc73 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert table with generator with multiple column names-2-ab3954b69d7a991bc801a509c3166cc5 b/sql/hive/src/test/resources/golden/insert table with generator with multiple column names-2-ab3954b69d7a991bc801a509c3166cc5 new file mode 100644 index 0000000000000..0c7520f2090dd --- /dev/null +++ b/sql/hive/src/test/resources/golden/insert table with generator with multiple column names-2-ab3954b69d7a991bc801a509c3166cc5 @@ -0,0 +1,3 @@ +86 val_86 +238 val_238 +311 val_311 diff --git a/sql/hive/src/test/resources/golden/insert table with generator without column name-0-7ac701cf43e73e9e416888e4df694348 b/sql/hive/src/test/resources/golden/insert table with generator without column name-0-7ac701cf43e73e9e416888e4df694348 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert table with generator without column name-1-26599718c322ff4f9740040c066d8292 b/sql/hive/src/test/resources/golden/insert table with generator without column name-1-26599718c322ff4f9740040c066d8292 new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/sql/hive/src/test/resources/golden/insert table with generator without column name-2-f963396461294e06cb7cafe22a1419e4 b/sql/hive/src/test/resources/golden/insert table with generator without column name-2-f963396461294e06cb7cafe22a1419e4 new file mode 100644 index 0000000000000..01e79c32a8c99 --- /dev/null +++ b/sql/hive/src/test/resources/golden/insert table with generator without column name-2-f963396461294e06cb7cafe22a1419e4 @@ -0,0 +1,3 @@ +1 +2 +3 diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala index 300b1f7920473..ac10b173307d8 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala @@ -27,7 +27,7 @@ import scala.util.Try import org.apache.hadoop.hive.conf.HiveConf.ConfVars import org.apache.spark.{SparkFiles, SparkException} -import org.apache.spark.sql.{DataFrame, Row} +import org.apache.spark.sql.{AnalysisException, DataFrame, Row} import org.apache.spark.sql.catalyst.plans.logical.Project import org.apache.spark.sql.functions._ import org.apache.spark.sql.hive._ @@ -67,6 +67,40 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { } } + createQueryTest("insert table with generator with column name", + """ + | CREATE TABLE gen_tmp (key Int); + | INSERT OVERWRITE TABLE gen_tmp + | SELECT explode(array(1,2,3)) AS val FROM src LIMIT 3; + | SELECT key FROM gen_tmp ORDER BY key ASC; + """.stripMargin) + + createQueryTest("insert table with generator with multiple column names", + """ + | CREATE TABLE gen_tmp (key Int, value String); + | INSERT OVERWRITE TABLE gen_tmp + | SELECT explode(map(key, value)) as (k1, k2) FROM src LIMIT 3; + | SELECT key, value FROM gen_tmp ORDER BY key, value ASC; + """.stripMargin) + + createQueryTest("insert table with generator without column name", + """ + | CREATE TABLE gen_tmp (key Int); + | INSERT OVERWRITE TABLE gen_tmp + | SELECT explode(array(1,2,3)) FROM src LIMIT 3; + | SELECT key FROM gen_tmp ORDER BY key ASC; + """.stripMargin) + + test("multiple generator in projection") { + intercept[AnalysisException] { + sql("SELECT explode(map(key, value)), key FROM src").collect() + } + + intercept[AnalysisException] { + sql("SELECT explode(map(key, value)) as k1, k2, key FROM src").collect() + } + } + createQueryTest("! operator", """ |SELECT a FROM ( @@ -456,7 +490,6 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { createQueryTest("lateral view2", "SELECT * FROM src LATERAL VIEW explode(array(1,2)) tbl") - createQueryTest("lateral view3", "FROM src SELECT key, D.* lateral view explode(array(key+3, key+4)) D as CX") @@ -478,6 +511,9 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter { createQueryTest("lateral view6", "SELECT * FROM src LATERAL VIEW explode(map(key+3,key+4)) D as k, v") + createQueryTest("Specify the udtf output", + "SELECT d FROM (SELECT explode(array(1,1)) d FROM src LIMIT 1) t") + test("sampling") { sql("SELECT * FROM src TABLESAMPLE(0.1 PERCENT) s") sql("SELECT * FROM src TABLESAMPLE(100 PERCENT) s") From f83c0f112d04173f4fc2c5eaf0f9cb11d9180077 Mon Sep 17 00:00:00 2001 From: Josh Rosen Date: Tue, 21 Apr 2015 16:24:15 -0700 Subject: [PATCH 364/591] [SPARK-3386] Share and reuse SerializerInstances in shuffle paths This patch modifies several shuffle-related code paths to share and re-use SerializerInstances instead of creating new ones. Some serializers, such as KryoSerializer or SqlSerializer, can be fairly expensive to create or may consume moderate amounts of memory, so it's probably best to avoid unnecessary serializer creation in hot code paths. The key change in this patch is modifying `getDiskWriter()` / `DiskBlockObjectWriter` to accept `SerializerInstance`s instead of `Serializer`s (which are factories for instances). This allows the disk writer's creator to decide whether the serializer instance can be shared or re-used. The rest of the patch modifies several write and read paths to use shared serializers. One big win is in `ShuffleBlockFetcherIterator`, where we used to create a new serializer per received block. Similarly, the shuffle write path used to create a new serializer per file even though in many cases only a single thread would be writing to a file at a time. I made a small serializer reuse optimization in CoarseGrainedExecutorBackend as well, since it seemed like a small and obvious improvement. Author: Josh Rosen Closes #5606 from JoshRosen/SPARK-3386 and squashes the following commits: f661ce7 [Josh Rosen] Remove thread local; add comment instead 64f8398 [Josh Rosen] Use ThreadLocal for serializer instance in CoarseGrainedExecutorBackend aeb680e [Josh Rosen] [SPARK-3386] Reuse SerializerInstance in shuffle code paths --- .../executor/CoarseGrainedExecutorBackend.scala | 6 +++++- .../spark/shuffle/FileShuffleBlockManager.scala | 6 ++++-- .../org/apache/spark/storage/BlockManager.scala | 8 ++++---- .../apache/spark/storage/BlockObjectWriter.scala | 6 +++--- .../storage/ShuffleBlockFetcherIterator.scala | 6 ++++-- .../util/collection/ExternalAppendOnlyMap.scala | 6 ++---- .../spark/util/collection/ExternalSorter.scala | 14 +++++++++----- .../spark/storage/BlockObjectWriterSuite.scala | 6 +++--- 8 files changed, 34 insertions(+), 24 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala index 8300f9f2190b9..8af46f3327adb 100644 --- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala +++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala @@ -30,6 +30,7 @@ import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.deploy.worker.WorkerWatcher import org.apache.spark.scheduler.TaskDescription import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ +import org.apache.spark.serializer.SerializerInstance import org.apache.spark.util.{SignalLogger, Utils} private[spark] class CoarseGrainedExecutorBackend( @@ -47,6 +48,10 @@ private[spark] class CoarseGrainedExecutorBackend( var executor: Executor = null @volatile var driver: Option[RpcEndpointRef] = None + // If this CoarseGrainedExecutorBackend is changed to support multiple threads, then this may need + // to be changed so that we don't share the serializer instance across threads + private[this] val ser: SerializerInstance = env.closureSerializer.newInstance() + override def onStart() { import scala.concurrent.ExecutionContext.Implicits.global logInfo("Connecting to driver: " + driverUrl) @@ -83,7 +88,6 @@ private[spark] class CoarseGrainedExecutorBackend( logError("Received LaunchTask command but executor was null") System.exit(1) } else { - val ser = env.closureSerializer.newInstance() val taskDesc = ser.deserialize[TaskDescription](data.value) logInfo("Got assigned task " + taskDesc.taskId) executor.launchTask(this, taskId = taskDesc.taskId, attemptNumber = taskDesc.attemptNumber, diff --git a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala index 5be3ed771e534..538e150ead05a 100644 --- a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockManager.scala @@ -113,11 +113,12 @@ class FileShuffleBlockManager(conf: SparkConf) private var fileGroup: ShuffleFileGroup = null val openStartTime = System.nanoTime + val serializerInstance = serializer.newInstance() val writers: Array[BlockObjectWriter] = if (consolidateShuffleFiles) { fileGroup = getUnusedFileGroup() Array.tabulate[BlockObjectWriter](numBuckets) { bucketId => val blockId = ShuffleBlockId(shuffleId, mapId, bucketId) - blockManager.getDiskWriter(blockId, fileGroup(bucketId), serializer, bufferSize, + blockManager.getDiskWriter(blockId, fileGroup(bucketId), serializerInstance, bufferSize, writeMetrics) } } else { @@ -133,7 +134,8 @@ class FileShuffleBlockManager(conf: SparkConf) logWarning(s"Failed to remove existing shuffle file $blockFile") } } - blockManager.getDiskWriter(blockId, blockFile, serializer, bufferSize, writeMetrics) + blockManager.getDiskWriter(blockId, blockFile, serializerInstance, bufferSize, + writeMetrics) } } // Creating the file to write to and creating a disk writer both involve interacting with diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala index 1aa0ef18de118..145a9c1ae3391 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala @@ -37,7 +37,7 @@ import org.apache.spark.network.netty.SparkTransportConf import org.apache.spark.network.shuffle.ExternalShuffleClient import org.apache.spark.network.shuffle.protocol.ExecutorShuffleInfo import org.apache.spark.rpc.RpcEnv -import org.apache.spark.serializer.Serializer +import org.apache.spark.serializer.{SerializerInstance, Serializer} import org.apache.spark.shuffle.ShuffleManager import org.apache.spark.shuffle.hash.HashShuffleManager import org.apache.spark.util._ @@ -646,13 +646,13 @@ private[spark] class BlockManager( def getDiskWriter( blockId: BlockId, file: File, - serializer: Serializer, + serializerInstance: SerializerInstance, bufferSize: Int, writeMetrics: ShuffleWriteMetrics): BlockObjectWriter = { val compressStream: OutputStream => OutputStream = wrapForCompression(blockId, _) val syncWrites = conf.getBoolean("spark.shuffle.sync", false) - new DiskBlockObjectWriter(blockId, file, serializer, bufferSize, compressStream, syncWrites, - writeMetrics) + new DiskBlockObjectWriter(blockId, file, serializerInstance, bufferSize, compressStream, + syncWrites, writeMetrics) } /** diff --git a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala index 0dfc91dfaff85..14833791f7a4d 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala @@ -21,7 +21,7 @@ import java.io.{BufferedOutputStream, FileOutputStream, File, OutputStream} import java.nio.channels.FileChannel import org.apache.spark.Logging -import org.apache.spark.serializer.{SerializationStream, Serializer} +import org.apache.spark.serializer.{SerializerInstance, SerializationStream} import org.apache.spark.executor.ShuffleWriteMetrics import org.apache.spark.util.Utils @@ -71,7 +71,7 @@ private[spark] abstract class BlockObjectWriter(val blockId: BlockId) { private[spark] class DiskBlockObjectWriter( blockId: BlockId, file: File, - serializer: Serializer, + serializerInstance: SerializerInstance, bufferSize: Int, compressStream: OutputStream => OutputStream, syncWrites: Boolean, @@ -134,7 +134,7 @@ private[spark] class DiskBlockObjectWriter( ts = new TimeTrackingOutputStream(fos) channel = fos.getChannel() bs = compressStream(new BufferedOutputStream(ts, bufferSize)) - objOut = serializer.newInstance().serializeStream(bs) + objOut = serializerInstance.serializeStream(bs) initialized = true this } diff --git a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala index 8f28ef49a8a6f..f3379521d55e2 100644 --- a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala +++ b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala @@ -27,7 +27,7 @@ import org.apache.spark.{Logging, TaskContext} import org.apache.spark.network.BlockTransferService import org.apache.spark.network.shuffle.{BlockFetchingListener, ShuffleClient} import org.apache.spark.network.buffer.ManagedBuffer -import org.apache.spark.serializer.Serializer +import org.apache.spark.serializer.{SerializerInstance, Serializer} import org.apache.spark.util.{CompletionIterator, Utils} /** @@ -106,6 +106,8 @@ final class ShuffleBlockFetcherIterator( private[this] val shuffleMetrics = context.taskMetrics.createShuffleReadMetricsForDependency() + private[this] val serializerInstance: SerializerInstance = serializer.newInstance() + /** * Whether the iterator is still active. If isZombie is true, the callback interface will no * longer place fetched blocks into [[results]]. @@ -299,7 +301,7 @@ final class ShuffleBlockFetcherIterator( // the scheduler gets a FetchFailedException. Try(buf.createInputStream()).map { is0 => val is = blockManager.wrapForCompression(blockId, is0) - val iter = serializer.newInstance().deserializeStream(is).asIterator + val iter = serializerInstance.deserializeStream(is).asIterator CompletionIterator[Any, Iterator[Any]](iter, { // Once the iterator is exhausted, release the buffer and set currentResult to null // so we don't release it again in cleanup. diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala index 9ff4744593d4d..30dd7f22e494f 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala @@ -151,8 +151,7 @@ class ExternalAppendOnlyMap[K, V, C]( override protected[this] def spill(collection: SizeTracker): Unit = { val (blockId, file) = diskBlockManager.createTempLocalBlock() curWriteMetrics = new ShuffleWriteMetrics() - var writer = blockManager.getDiskWriter(blockId, file, serializer, fileBufferSize, - curWriteMetrics) + var writer = blockManager.getDiskWriter(blockId, file, ser, fileBufferSize, curWriteMetrics) var objectsWritten = 0 // List of batch sizes (bytes) in the order they are written to disk @@ -179,8 +178,7 @@ class ExternalAppendOnlyMap[K, V, C]( if (objectsWritten == serializerBatchSize) { flush() curWriteMetrics = new ShuffleWriteMetrics() - writer = blockManager.getDiskWriter(blockId, file, serializer, fileBufferSize, - curWriteMetrics) + writer = blockManager.getDiskWriter(blockId, file, ser, fileBufferSize, curWriteMetrics) } } if (objectsWritten > 0) { diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala index 035f3767ff554..79a1a8a0dae38 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala @@ -272,7 +272,8 @@ private[spark] class ExternalSorter[K, V, C]( // createTempShuffleBlock here; see SPARK-3426 for more context. val (blockId, file) = diskBlockManager.createTempShuffleBlock() curWriteMetrics = new ShuffleWriteMetrics() - var writer = blockManager.getDiskWriter(blockId, file, ser, fileBufferSize, curWriteMetrics) + var writer = blockManager.getDiskWriter( + blockId, file, serInstance, fileBufferSize, curWriteMetrics) var objectsWritten = 0 // Objects written since the last flush // List of batch sizes (bytes) in the order they are written to disk @@ -308,7 +309,8 @@ private[spark] class ExternalSorter[K, V, C]( if (objectsWritten == serializerBatchSize) { flush() curWriteMetrics = new ShuffleWriteMetrics() - writer = blockManager.getDiskWriter(blockId, file, ser, fileBufferSize, curWriteMetrics) + writer = blockManager.getDiskWriter( + blockId, file, serInstance, fileBufferSize, curWriteMetrics) } } if (objectsWritten > 0) { @@ -358,7 +360,9 @@ private[spark] class ExternalSorter[K, V, C]( // spark.shuffle.compress instead of spark.shuffle.spill.compress, so we need to use // createTempShuffleBlock here; see SPARK-3426 for more context. val (blockId, file) = diskBlockManager.createTempShuffleBlock() - blockManager.getDiskWriter(blockId, file, ser, fileBufferSize, curWriteMetrics).open() + val writer = blockManager.getDiskWriter(blockId, file, serInstance, fileBufferSize, + curWriteMetrics) + writer.open() } // Creating the file to write to and creating a disk writer both involve interacting with // the disk, and can take a long time in aggregate when we open many files, so should be @@ -749,8 +753,8 @@ private[spark] class ExternalSorter[K, V, C]( // partition and just write everything directly. for ((id, elements) <- this.partitionedIterator) { if (elements.hasNext) { - val writer = blockManager.getDiskWriter( - blockId, outputFile, ser, fileBufferSize, context.taskMetrics.shuffleWriteMetrics.get) + val writer = blockManager.getDiskWriter(blockId, outputFile, serInstance, fileBufferSize, + context.taskMetrics.shuffleWriteMetrics.get) for (elem <- elements) { writer.write(elem) } diff --git a/core/src/test/scala/org/apache/spark/storage/BlockObjectWriterSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockObjectWriterSuite.scala index 78bbc4ec2c620..003a728cb84a0 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockObjectWriterSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockObjectWriterSuite.scala @@ -30,7 +30,7 @@ class BlockObjectWriterSuite extends FunSuite { val file = new File(Utils.createTempDir(), "somefile") val writeMetrics = new ShuffleWriteMetrics() val writer = new DiskBlockObjectWriter(new TestBlockId("0"), file, - new JavaSerializer(new SparkConf()), 1024, os => os, true, writeMetrics) + new JavaSerializer(new SparkConf()).newInstance(), 1024, os => os, true, writeMetrics) writer.write(Long.box(20)) // Record metrics update on every write @@ -52,7 +52,7 @@ class BlockObjectWriterSuite extends FunSuite { val file = new File(Utils.createTempDir(), "somefile") val writeMetrics = new ShuffleWriteMetrics() val writer = new DiskBlockObjectWriter(new TestBlockId("0"), file, - new JavaSerializer(new SparkConf()), 1024, os => os, true, writeMetrics) + new JavaSerializer(new SparkConf()).newInstance(), 1024, os => os, true, writeMetrics) writer.write(Long.box(20)) // Record metrics update on every write @@ -75,7 +75,7 @@ class BlockObjectWriterSuite extends FunSuite { val file = new File(Utils.createTempDir(), "somefile") val writeMetrics = new ShuffleWriteMetrics() val writer = new DiskBlockObjectWriter(new TestBlockId("0"), file, - new JavaSerializer(new SparkConf()), 1024, os => os, true, writeMetrics) + new JavaSerializer(new SparkConf()).newInstance(), 1024, os => os, true, writeMetrics) writer.open() writer.close() From a70e849c7f9e3df5e86113d45b8c4537597cfb29 Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Tue, 21 Apr 2015 16:35:37 -0700 Subject: [PATCH 365/591] [minor] [build] Set java options when generating mima ignores. The default java options make the call to GenerateMIMAIgnore take forever to run since it's gc'ing all the time. Improve things by setting the perm gen size / max heap size to larger values. Author: Marcelo Vanzin Closes #5615 from vanzin/gen-mima-fix and squashes the following commits: f44e921 [Marcelo Vanzin] [minor] [build] Set java options when generating mima ignores. --- dev/mima | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/dev/mima b/dev/mima index bed5cd042634e..2952fa65d42ff 100755 --- a/dev/mima +++ b/dev/mima @@ -27,16 +27,21 @@ cd "$FWDIR" echo -e "q\n" | build/sbt oldDeps/update rm -f .generated-mima* +generate_mima_ignore() { + SPARK_JAVA_OPTS="-XX:MaxPermSize=1g -Xmx2g" \ + ./bin/spark-class org.apache.spark.tools.GenerateMIMAIgnore +} + # Generate Mima Ignore is called twice, first with latest built jars # on the classpath and then again with previous version jars on the classpath. # Because of a bug in GenerateMIMAIgnore that when old jars are ahead on classpath # it did not process the new classes (which are in assembly jar). -./bin/spark-class org.apache.spark.tools.GenerateMIMAIgnore +generate_mima_ignore export SPARK_CLASSPATH="`find lib_managed \( -name '*spark*jar' -a -type f \) | tr "\\n" ":"`" echo "SPARK_CLASSPATH=$SPARK_CLASSPATH" -./bin/spark-class org.apache.spark.tools.GenerateMIMAIgnore +generate_mima_ignore echo -e "q\n" | build/sbt mima-report-binary-issues | grep -v -e "info.*Resolving" ret_val=$? From 7fe6142cd3c39ec79899878c3deca9d5130d05b1 Mon Sep 17 00:00:00 2001 From: MechCoder Date: Tue, 21 Apr 2015 16:42:45 -0700 Subject: [PATCH 366/591] [SPARK-6065] [MLlib] Optimize word2vec.findSynonyms using blas calls 1. Use blas calls to find the dot product between two vectors. 2. Prevent re-computing the L2 norm of the given vector for each word in model. Author: MechCoder Closes #5467 from MechCoder/spark-6065 and squashes the following commits: dd0b0b2 [MechCoder] Preallocate wordVectors ffc9240 [MechCoder] Minor 6b74c81 [MechCoder] Switch back to native blas calls da1642d [MechCoder] Explicit types and indexing 64575b0 [MechCoder] Save indexedmap and a wordvecmat instead of matrix fbe0108 [MechCoder] Made the following changes 1. Calculate norms during initialization. 2. Use Blas calls from linalg.blas 1350cf3 [MechCoder] [SPARK-6065] Optimize word2vec.findSynonynms using blas calls --- .../apache/spark/mllib/feature/Word2Vec.scala | 57 +++++++++++++++++-- 1 file changed, 51 insertions(+), 6 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala index b2d9053f70145..98e83112f52ae 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala @@ -34,7 +34,7 @@ import org.apache.spark.SparkContext import org.apache.spark.SparkContext._ import org.apache.spark.annotation.Experimental import org.apache.spark.api.java.JavaRDD -import org.apache.spark.mllib.linalg.{Vector, Vectors} +import org.apache.spark.mllib.linalg.{Vector, Vectors, DenseMatrix, BLAS, DenseVector} import org.apache.spark.mllib.util.{Loader, Saveable} import org.apache.spark.rdd._ import org.apache.spark.util.Utils @@ -429,7 +429,36 @@ class Word2Vec extends Serializable with Logging { */ @Experimental class Word2VecModel private[mllib] ( - private val model: Map[String, Array[Float]]) extends Serializable with Saveable { + model: Map[String, Array[Float]]) extends Serializable with Saveable { + + // wordList: Ordered list of words obtained from model. + private val wordList: Array[String] = model.keys.toArray + + // wordIndex: Maps each word to an index, which can retrieve the corresponding + // vector from wordVectors (see below). + private val wordIndex: Map[String, Int] = wordList.zip(0 until model.size).toMap + + // vectorSize: Dimension of each word's vector. + private val vectorSize = model.head._2.size + private val numWords = wordIndex.size + + // wordVectors: Array of length numWords * vectorSize, vector corresponding to the word + // mapped with index i can be retrieved by the slice + // (ind * vectorSize, ind * vectorSize + vectorSize) + // wordVecNorms: Array of length numWords, each value being the Euclidean norm + // of the wordVector. + private val (wordVectors: Array[Float], wordVecNorms: Array[Double]) = { + val wordVectors = new Array[Float](vectorSize * numWords) + val wordVecNorms = new Array[Double](numWords) + var i = 0 + while (i < numWords) { + val vec = model.get(wordList(i)).get + Array.copy(vec, 0, wordVectors, i * vectorSize, vectorSize) + wordVecNorms(i) = blas.snrm2(vectorSize, vec, 1) + i += 1 + } + (wordVectors, wordVecNorms) + } private def cosineSimilarity(v1: Array[Float], v2: Array[Float]): Double = { require(v1.length == v2.length, "Vectors should have the same length") @@ -443,7 +472,7 @@ class Word2VecModel private[mllib] ( override protected def formatVersion = "1.0" def save(sc: SparkContext, path: String): Unit = { - Word2VecModel.SaveLoadV1_0.save(sc, path, model) + Word2VecModel.SaveLoadV1_0.save(sc, path, getVectors) } /** @@ -479,9 +508,23 @@ class Word2VecModel private[mllib] ( */ def findSynonyms(vector: Vector, num: Int): Array[(String, Double)] = { require(num > 0, "Number of similar words should > 0") - // TODO: optimize top-k + val fVector = vector.toArray.map(_.toFloat) - model.mapValues(vec => cosineSimilarity(fVector, vec)) + val cosineVec = Array.fill[Float](numWords)(0) + val alpha: Float = 1 + val beta: Float = 0 + + blas.sgemv( + "T", vectorSize, numWords, alpha, wordVectors, vectorSize, fVector, 1, beta, cosineVec, 1) + + // Need not divide with the norm of the given vector since it is constant. + val updatedCosines = new Array[Double](numWords) + var ind = 0 + while (ind < numWords) { + updatedCosines(ind) = cosineVec(ind) / wordVecNorms(ind) + ind += 1 + } + wordList.zip(updatedCosines) .toSeq .sortBy(- _._2) .take(num + 1) @@ -493,7 +536,9 @@ class Word2VecModel private[mllib] ( * Returns a map of words to their vector representations. */ def getVectors: Map[String, Array[Float]] = { - model + wordIndex.map { case (word, ind) => + (word, wordVectors.slice(vectorSize * ind, vectorSize * ind + vectorSize)) + } } } From 686dd742e11f6ad0078b7ff9b30b83a118fd8109 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Tue, 21 Apr 2015 16:44:52 -0700 Subject: [PATCH 367/591] [SPARK-7036][MLLIB] ALS.train should support DataFrames in PySpark SchemaRDD works with ALS.train in 1.2, so we should continue support DataFrames for compatibility. coderxiang Author: Xiangrui Meng Closes #5619 from mengxr/SPARK-7036 and squashes the following commits: dfcaf5a [Xiangrui Meng] ALS.train should support DataFrames in PySpark --- python/pyspark/mllib/recommendation.py | 36 +++++++++++++++++++------- 1 file changed, 26 insertions(+), 10 deletions(-) diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py index 80e0a356bb78a..4b7d17d64e947 100644 --- a/python/pyspark/mllib/recommendation.py +++ b/python/pyspark/mllib/recommendation.py @@ -22,6 +22,7 @@ from pyspark.rdd import RDD from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, inherit_doc from pyspark.mllib.util import JavaLoader, JavaSaveable +from pyspark.sql import DataFrame __all__ = ['MatrixFactorizationModel', 'ALS', 'Rating'] @@ -78,18 +79,23 @@ class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader): True >>> model = ALS.train(ratings, 1, nonnegative=True, seed=10) - >>> model.predict(2,2) + >>> model.predict(2, 2) + 3.8... + + >>> df = sqlContext.createDataFrame([Rating(1, 1, 1.0), Rating(1, 2, 2.0), Rating(2, 1, 2.0)]) + >>> model = ALS.train(df, 1, nonnegative=True, seed=10) + >>> model.predict(2, 2) 3.8... >>> model = ALS.trainImplicit(ratings, 1, nonnegative=True, seed=10) - >>> model.predict(2,2) + >>> model.predict(2, 2) 0.4... >>> import os, tempfile >>> path = tempfile.mkdtemp() >>> model.save(sc, path) >>> sameModel = MatrixFactorizationModel.load(sc, path) - >>> sameModel.predict(2,2) + >>> sameModel.predict(2, 2) 0.4... >>> sameModel.predictAll(testset).collect() [Rating(... @@ -125,13 +131,20 @@ class ALS(object): @classmethod def _prepare(cls, ratings): - assert isinstance(ratings, RDD), "ratings should be RDD" + if isinstance(ratings, RDD): + pass + elif isinstance(ratings, DataFrame): + ratings = ratings.rdd + else: + raise TypeError("Ratings should be represented by either an RDD or a DataFrame, " + "but got %s." % type(ratings)) first = ratings.first() - if not isinstance(first, Rating): - if isinstance(first, (tuple, list)): - ratings = ratings.map(lambda x: Rating(*x)) - else: - raise ValueError("rating should be RDD of Rating or tuple/list") + if isinstance(first, Rating): + pass + elif isinstance(first, (tuple, list)): + ratings = ratings.map(lambda x: Rating(*x)) + else: + raise TypeError("Expect a Rating or a tuple/list, but got %s." % type(first)) return ratings @classmethod @@ -152,8 +165,11 @@ def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alp def _test(): import doctest import pyspark.mllib.recommendation + from pyspark.sql import SQLContext globs = pyspark.mllib.recommendation.__dict__.copy() - globs['sc'] = SparkContext('local[4]', 'PythonTest') + sc = SparkContext('local[4]', 'PythonTest') + globs['sc'] = sc + globs['sqlContext'] = SQLContext(sc) (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) globs['sc'].stop() if failure_count: From ae036d08170202074b266afd17ce34b689c70b0c Mon Sep 17 00:00:00 2001 From: Alain Date: Tue, 21 Apr 2015 16:46:17 -0700 Subject: [PATCH 368/591] [Minor][MLLIB] Fix a minor formatting bug in toString method in Node.scala add missing comma and space Author: Alain Closes #5621 from AiHe/tree-node-issue and squashes the following commits: 159a7bb [Alain] [Minor][MLLIB] Fix a minor formatting bug in toString methods in Node.scala (cherry picked from commit 4508f01890a723f80d631424ff8eda166a13a727) Signed-off-by: Xiangrui Meng --- .../src/main/scala/org/apache/spark/mllib/tree/model/Node.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala index 708ba04b567d3..86390a20cb5cc 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala @@ -52,7 +52,7 @@ class Node ( override def toString: String = { "id = " + id + ", isLeaf = " + isLeaf + ", predict = " + predict + ", " + - "impurity = " + impurity + "split = " + split + ", stats = " + stats + "impurity = " + impurity + ", split = " + split + ", stats = " + stats } /** From b063a61b9852cf9b9d2c905332d2ecb2fd716cc4 Mon Sep 17 00:00:00 2001 From: mweindel Date: Tue, 21 Apr 2015 20:19:33 -0400 Subject: [PATCH 369/591] Avoid warning message about invalid refuse_seconds value in Mesos >=0.21... Starting with version 0.21.0, Apache Mesos is very noisy if the filter parameter refuse_seconds is set to an invalid value like `-1`. I have seen systems with millions of log lines like ``` W0420 18:00:48.773059 32352 hierarchical_allocator_process.hpp:589] Using the default value of 'refuse_seconds' to create the refused resources filter because the input value is negative ``` in the Mesos master INFO and WARNING log files. Therefore the CoarseMesosSchedulerBackend should set the default value for refuse seconds (i.e. 5 seconds) directly. This is no problem for the fine-grained MesosSchedulerBackend, as it uses the value 1 second for this parameter. Author: mweindel Closes #5597 from MartinWeindel/master and squashes the following commits: 2f99ffd [mweindel] Avoid warning message about invalid refuse_seconds value in Mesos >=0.21. --- .../scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala index b037a4966ced0..82f652dae0378 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala @@ -207,7 +207,7 @@ private[spark] class CoarseMesosSchedulerBackend( */ override def resourceOffers(d: SchedulerDriver, offers: JList[Offer]) { synchronized { - val filters = Filters.newBuilder().setRefuseSeconds(-1).build() + val filters = Filters.newBuilder().setRefuseSeconds(5).build() for (offer <- offers) { val slaveId = offer.getSlaveId.toString From e72c16e30d85cdc394d318b5551698885cfda9b8 Mon Sep 17 00:00:00 2001 From: Marcelo Vanzin Date: Tue, 21 Apr 2015 20:33:57 -0400 Subject: [PATCH 370/591] [SPARK-6014] [core] Revamp Spark shutdown hooks, fix shutdown races. This change adds some new utility code to handle shutdown hooks in Spark. The main goal is to take advantage of Hadoop 2.x's API for shutdown hooks, which allows Spark to register a hook that will run before the one that cleans up HDFS clients, and thus avoids some races that would cause exceptions to show up and other issues such as failure to properly close event logs. Unfortunately, Hadoop 1.x does not have such APIs, so in that case correctness is still left to chance. Author: Marcelo Vanzin Closes #5560 from vanzin/SPARK-6014 and squashes the following commits: edfafb1 [Marcelo Vanzin] Better scaladoc. fcaeedd [Marcelo Vanzin] Merge branch 'master' into SPARK-6014 e7039dc [Marcelo Vanzin] [SPARK-6014] [core] Revamp Spark shutdown hooks, fix shutdown races. --- .../spark/deploy/history/HistoryServer.scala | 6 +- .../spark/deploy/worker/ExecutorRunner.scala | 12 +- .../spark/storage/DiskBlockManager.scala | 18 +-- .../spark/storage/TachyonBlockManager.scala | 24 ++-- .../scala/org/apache/spark/util/Utils.scala | 136 +++++++++++++++--- .../org/apache/spark/util/UtilsSuite.scala | 32 +++-- .../hive/thriftserver/HiveThriftServer2.scala | 9 +- .../hive/thriftserver/SparkSQLCLIDriver.scala | 9 +- .../spark/deploy/yarn/ApplicationMaster.scala | 63 ++++---- 9 files changed, 195 insertions(+), 114 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala index 72f6048239297..56bef57e55392 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala @@ -27,7 +27,7 @@ import org.apache.spark.{Logging, SecurityManager, SparkConf} import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.ui.{SparkUI, UIUtils, WebUI} import org.apache.spark.ui.JettyUtils._ -import org.apache.spark.util.SignalLogger +import org.apache.spark.util.{SignalLogger, Utils} /** * A web server that renders SparkUIs of completed applications. @@ -194,9 +194,7 @@ object HistoryServer extends Logging { val server = new HistoryServer(conf, provider, securityManager, port) server.bind() - Runtime.getRuntime().addShutdownHook(new Thread("HistoryServerStopper") { - override def run(): Unit = server.stop() - }) + Utils.addShutdownHook { () => server.stop() } // Wait until the end of the world... or if the HistoryServer process is manually stopped while(true) { Thread.sleep(Int.MaxValue) } diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala index 7d5acabb95a48..7aa85b732fc87 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala @@ -28,6 +28,7 @@ import com.google.common.io.Files import org.apache.spark.{SparkConf, Logging} import org.apache.spark.deploy.{ApplicationDescription, ExecutorState} import org.apache.spark.deploy.DeployMessages.ExecutorStateChanged +import org.apache.spark.util.Utils import org.apache.spark.util.logging.FileAppender /** @@ -61,7 +62,7 @@ private[deploy] class ExecutorRunner( // NOTE: This is now redundant with the automated shut-down enforced by the Executor. It might // make sense to remove this in the future. - private var shutdownHook: Thread = null + private var shutdownHook: AnyRef = null private[worker] def start() { workerThread = new Thread("ExecutorRunner for " + fullId) { @@ -69,12 +70,7 @@ private[deploy] class ExecutorRunner( } workerThread.start() // Shutdown hook that kills actors on shutdown. - shutdownHook = new Thread() { - override def run() { - killProcess(Some("Worker shutting down")) - } - } - Runtime.getRuntime.addShutdownHook(shutdownHook) + shutdownHook = Utils.addShutdownHook { () => killProcess(Some("Worker shutting down")) } } /** @@ -106,7 +102,7 @@ private[deploy] class ExecutorRunner( workerThread = null state = ExecutorState.KILLED try { - Runtime.getRuntime.removeShutdownHook(shutdownHook) + Utils.removeShutdownHook(shutdownHook) } catch { case e: IllegalStateException => None } diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala index 2883137872600..7ea5e54f9e1fe 100644 --- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala @@ -138,25 +138,17 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon } } - private def addShutdownHook(): Thread = { - val shutdownHook = new Thread("delete Spark local dirs") { - override def run(): Unit = Utils.logUncaughtExceptions { - logDebug("Shutdown hook called") - DiskBlockManager.this.doStop() - } + private def addShutdownHook(): AnyRef = { + Utils.addShutdownHook { () => + logDebug("Shutdown hook called") + DiskBlockManager.this.doStop() } - Runtime.getRuntime.addShutdownHook(shutdownHook) - shutdownHook } /** Cleanup local dirs and stop shuffle sender. */ private[spark] def stop() { // Remove the shutdown hook. It causes memory leaks if we leave it around. - try { - Runtime.getRuntime.removeShutdownHook(shutdownHook) - } catch { - case e: IllegalStateException => None - } + Utils.removeShutdownHook(shutdownHook) doStop() } diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala index af873034215a9..951897cead996 100644 --- a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala @@ -135,21 +135,19 @@ private[spark] class TachyonBlockManager( private def addShutdownHook() { tachyonDirs.foreach(tachyonDir => Utils.registerShutdownDeleteDir(tachyonDir)) - Runtime.getRuntime.addShutdownHook(new Thread("delete Spark tachyon dirs") { - override def run(): Unit = Utils.logUncaughtExceptions { - logDebug("Shutdown hook called") - tachyonDirs.foreach { tachyonDir => - try { - if (!Utils.hasRootAsShutdownDeleteDir(tachyonDir)) { - Utils.deleteRecursively(tachyonDir, client) - } - } catch { - case e: Exception => - logError("Exception while deleting tachyon spark dir: " + tachyonDir, e) + Utils.addShutdownHook { () => + logDebug("Shutdown hook called") + tachyonDirs.foreach { tachyonDir => + try { + if (!Utils.hasRootAsShutdownDeleteDir(tachyonDir)) { + Utils.deleteRecursively(tachyonDir, client) } + } catch { + case e: Exception => + logError("Exception while deleting tachyon spark dir: " + tachyonDir, e) } - client.close() } - }) + client.close() + } } } diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index 1029b0f9fce1e..7b0de1ae55b78 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -21,7 +21,7 @@ import java.io._ import java.lang.management.ManagementFactory import java.net._ import java.nio.ByteBuffer -import java.util.{Properties, Locale, Random, UUID} +import java.util.{PriorityQueue, Properties, Locale, Random, UUID} import java.util.concurrent._ import javax.net.ssl.HttpsURLConnection @@ -30,7 +30,7 @@ import scala.collection.Map import scala.collection.mutable.ArrayBuffer import scala.io.Source import scala.reflect.ClassTag -import scala.util.Try +import scala.util.{Failure, Success, Try} import scala.util.control.{ControlThrowable, NonFatal} import com.google.common.io.{ByteStreams, Files} @@ -64,9 +64,15 @@ private[spark] object CallSite { private[spark] object Utils extends Logging { val random = new Random() + val DEFAULT_SHUTDOWN_PRIORITY = 100 + private val MAX_DIR_CREATION_ATTEMPTS: Int = 10 @volatile private var localRootDirs: Array[String] = null + + private val shutdownHooks = new SparkShutdownHookManager() + shutdownHooks.install() + /** Serialize an object using Java serialization */ def serialize[T](o: T): Array[Byte] = { val bos = new ByteArrayOutputStream() @@ -176,18 +182,16 @@ private[spark] object Utils extends Logging { private val shutdownDeleteTachyonPaths = new scala.collection.mutable.HashSet[String]() // Add a shutdown hook to delete the temp dirs when the JVM exits - Runtime.getRuntime.addShutdownHook(new Thread("delete Spark temp dirs") { - override def run(): Unit = Utils.logUncaughtExceptions { - logDebug("Shutdown hook called") - shutdownDeletePaths.foreach { dirPath => - try { - Utils.deleteRecursively(new File(dirPath)) - } catch { - case e: Exception => logError(s"Exception while deleting Spark temp dir: $dirPath", e) - } + addShutdownHook { () => + logDebug("Shutdown hook called") + shutdownDeletePaths.foreach { dirPath => + try { + Utils.deleteRecursively(new File(dirPath)) + } catch { + case e: Exception => logError(s"Exception while deleting Spark temp dir: $dirPath", e) } } - }) + } // Register the path to be deleted via shutdown hook def registerShutdownDeleteDir(file: File) { @@ -613,7 +617,7 @@ private[spark] object Utils extends Logging { } Utils.setupSecureURLConnection(uc, securityMgr) - val timeoutMs = + val timeoutMs = conf.getTimeAsSeconds("spark.files.fetchTimeout", "60s").toInt * 1000 uc.setConnectTimeout(timeoutMs) uc.setReadTimeout(timeoutMs) @@ -1172,7 +1176,7 @@ private[spark] object Utils extends Logging { /** * Execute a block of code that evaluates to Unit, forwarding any uncaught exceptions to the * default UncaughtExceptionHandler - * + * * NOTE: This method is to be called by the spark-started JVM process. */ def tryOrExit(block: => Unit) { @@ -1185,11 +1189,11 @@ private[spark] object Utils extends Logging { } /** - * Execute a block of code that evaluates to Unit, stop SparkContext is there is any uncaught + * Execute a block of code that evaluates to Unit, stop SparkContext is there is any uncaught * exception - * - * NOTE: This method is to be called by the driver-side components to avoid stopping the - * user-started JVM process completely; in contrast, tryOrExit is to be called in the + * + * NOTE: This method is to be called by the driver-side components to avoid stopping the + * user-started JVM process completely; in contrast, tryOrExit is to be called in the * spark-started JVM process . */ def tryOrStopSparkContext(sc: SparkContext)(block: => Unit) { @@ -2132,6 +2136,102 @@ private[spark] object Utils extends Logging { .getOrElse(UserGroupInformation.getCurrentUser().getShortUserName()) } + /** + * Adds a shutdown hook with default priority. + * + * @param hook The code to run during shutdown. + * @return A handle that can be used to unregister the shutdown hook. + */ + def addShutdownHook(hook: () => Unit): AnyRef = { + addShutdownHook(DEFAULT_SHUTDOWN_PRIORITY, hook) + } + + /** + * Adds a shutdown hook with the given priority. Hooks with lower priority values run + * first. + * + * @param hook The code to run during shutdown. + * @return A handle that can be used to unregister the shutdown hook. + */ + def addShutdownHook(priority: Int, hook: () => Unit): AnyRef = { + shutdownHooks.add(priority, hook) + } + + /** + * Remove a previously installed shutdown hook. + * + * @param ref A handle returned by `addShutdownHook`. + * @return Whether the hook was removed. + */ + def removeShutdownHook(ref: AnyRef): Boolean = { + shutdownHooks.remove(ref) + } + +} + +private [util] class SparkShutdownHookManager { + + private val hooks = new PriorityQueue[SparkShutdownHook]() + private var shuttingDown = false + + /** + * Install a hook to run at shutdown and run all registered hooks in order. Hadoop 1.x does not + * have `ShutdownHookManager`, so in that case we just use the JVM's `Runtime` object and hope for + * the best. + */ + def install(): Unit = { + val hookTask = new Runnable() { + override def run(): Unit = runAll() + } + Try(Class.forName("org.apache.hadoop.util.ShutdownHookManager")) match { + case Success(shmClass) => + val fsPriority = classOf[FileSystem].getField("SHUTDOWN_HOOK_PRIORITY").get() + .asInstanceOf[Int] + val shm = shmClass.getMethod("get").invoke(null) + shm.getClass().getMethod("addShutdownHook", classOf[Runnable], classOf[Int]) + .invoke(shm, hookTask, Integer.valueOf(fsPriority + 30)) + + case Failure(_) => + Runtime.getRuntime.addShutdownHook(new Thread(hookTask, "Spark Shutdown Hook")); + } + } + + def runAll(): Unit = synchronized { + shuttingDown = true + while (!hooks.isEmpty()) { + Utils.logUncaughtExceptions(hooks.poll().run()) + } + } + + def add(priority: Int, hook: () => Unit): AnyRef = synchronized { + checkState() + val hookRef = new SparkShutdownHook(priority, hook) + hooks.add(hookRef) + hookRef + } + + def remove(ref: AnyRef): Boolean = synchronized { + checkState() + hooks.remove(ref) + } + + private def checkState(): Unit = { + if (shuttingDown) { + throw new IllegalStateException("Shutdown hooks cannot be modified during shutdown.") + } + } + +} + +private class SparkShutdownHook(private val priority: Int, hook: () => Unit) + extends Comparable[SparkShutdownHook] { + + override def compareTo(other: SparkShutdownHook): Int = { + other.priority - priority + } + + def run(): Unit = hook() + } /** diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala index fb97e650ff95c..1ba99803f5a0e 100644 --- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala @@ -17,14 +17,16 @@ package org.apache.spark.util -import scala.util.Random - import java.io.{File, ByteArrayOutputStream, ByteArrayInputStream, FileOutputStream} import java.net.{BindException, ServerSocket, URI} import java.nio.{ByteBuffer, ByteOrder} import java.text.DecimalFormatSymbols import java.util.concurrent.TimeUnit import java.util.Locale +import java.util.PriorityQueue + +import scala.collection.mutable.ListBuffer +import scala.util.Random import com.google.common.base.Charsets.UTF_8 import com.google.common.io.Files @@ -36,14 +38,14 @@ import org.apache.hadoop.fs.Path import org.apache.spark.SparkConf class UtilsSuite extends FunSuite with ResetSystemProperties { - + test("timeConversion") { // Test -1 assert(Utils.timeStringAsSeconds("-1") === -1) - + // Test zero assert(Utils.timeStringAsSeconds("0") === 0) - + assert(Utils.timeStringAsSeconds("1") === 1) assert(Utils.timeStringAsSeconds("1s") === 1) assert(Utils.timeStringAsSeconds("1000ms") === 1) @@ -52,7 +54,7 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { assert(Utils.timeStringAsSeconds("1min") === TimeUnit.MINUTES.toSeconds(1)) assert(Utils.timeStringAsSeconds("1h") === TimeUnit.HOURS.toSeconds(1)) assert(Utils.timeStringAsSeconds("1d") === TimeUnit.DAYS.toSeconds(1)) - + assert(Utils.timeStringAsMs("1") === 1) assert(Utils.timeStringAsMs("1ms") === 1) assert(Utils.timeStringAsMs("1000us") === 1) @@ -61,7 +63,7 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { assert(Utils.timeStringAsMs("1min") === TimeUnit.MINUTES.toMillis(1)) assert(Utils.timeStringAsMs("1h") === TimeUnit.HOURS.toMillis(1)) assert(Utils.timeStringAsMs("1d") === TimeUnit.DAYS.toMillis(1)) - + // Test invalid strings intercept[NumberFormatException] { Utils.timeStringAsMs("This breaks 600s") @@ -79,7 +81,7 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { Utils.timeStringAsMs("This 123s breaks") } } - + test("bytesToString") { assert(Utils.bytesToString(10) === "10.0 B") assert(Utils.bytesToString(1500) === "1500.0 B") @@ -466,4 +468,18 @@ class UtilsSuite extends FunSuite with ResetSystemProperties { val newFileName = new File(testFileDir, testFileName) assert(newFileName.isFile()) } + + test("shutdown hook manager") { + val manager = new SparkShutdownHookManager() + val output = new ListBuffer[Int]() + + val hook1 = manager.add(1, () => output += 1) + manager.add(3, () => output += 3) + manager.add(2, () => output += 2) + manager.add(4, () => output += 4) + manager.remove(hook1) + + manager.runAll() + assert(output.toList === List(4, 3, 2)) + } } diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala index c3a3f8c0f41df..832596fc8bee5 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala @@ -28,6 +28,7 @@ import org.apache.spark.annotation.DeveloperApi import org.apache.spark.sql.hive.HiveContext import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._ import org.apache.spark.scheduler.{SparkListenerApplicationEnd, SparkListener} +import org.apache.spark.util.Utils /** * The main entry point for the Spark SQL port of HiveServer2. Starts up a `SparkSQLContext` and a @@ -57,13 +58,7 @@ object HiveThriftServer2 extends Logging { logInfo("Starting SparkContext") SparkSQLEnv.init() - Runtime.getRuntime.addShutdownHook( - new Thread() { - override def run() { - SparkSQLEnv.stop() - } - } - ) + Utils.addShutdownHook { () => SparkSQLEnv.stop() } try { val server = new HiveThriftServer2(SparkSQLEnv.hiveContext) diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala index 85281c6d73a3b..7e307bb4ad1e8 100644 --- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala +++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala @@ -40,6 +40,7 @@ import org.apache.thrift.transport.TSocket import org.apache.spark.Logging import org.apache.spark.sql.hive.HiveShim +import org.apache.spark.util.Utils private[hive] object SparkSQLCLIDriver { private var prompt = "spark-sql" @@ -101,13 +102,7 @@ private[hive] object SparkSQLCLIDriver { SessionState.start(sessionState) // Clean up after we exit - Runtime.getRuntime.addShutdownHook( - new Thread() { - override def run() { - SparkSQLEnv.stop() - } - } - ) + Utils.addShutdownHook { () => SparkSQLEnv.stop() } // "-h" option has been passed, so connect to Hive thrift server. if (sessionState.getHost != null) { diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala index f7a84207e9da6..93ae45133ce24 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -25,7 +25,6 @@ import java.net.{Socket, URL} import java.util.concurrent.atomic.AtomicReference import org.apache.hadoop.fs.{FileSystem, Path} -import org.apache.hadoop.util.ShutdownHookManager import org.apache.hadoop.yarn.api._ import org.apache.hadoop.yarn.api.records._ import org.apache.hadoop.yarn.conf.YarnConfiguration @@ -95,44 +94,38 @@ private[spark] class ApplicationMaster( logInfo("ApplicationAttemptId: " + appAttemptId) val fs = FileSystem.get(yarnConf) - val cleanupHook = new Runnable { - override def run() { - // If the SparkContext is still registered, shut it down as a best case effort in case - // users do not call sc.stop or do System.exit(). - val sc = sparkContextRef.get() - if (sc != null) { - logInfo("Invoking sc stop from shutdown hook") - sc.stop() - } - val maxAppAttempts = client.getMaxRegAttempts(sparkConf, yarnConf) - val isLastAttempt = client.getAttemptId().getAttemptId() >= maxAppAttempts - - if (!finished) { - // This happens when the user application calls System.exit(). We have the choice - // of either failing or succeeding at this point. We report success to avoid - // retrying applications that have succeeded (System.exit(0)), which means that - // applications that explicitly exit with a non-zero status will also show up as - // succeeded in the RM UI. - finish(finalStatus, - ApplicationMaster.EXIT_SUCCESS, - "Shutdown hook called before final status was reported.") - } - if (!unregistered) { - // we only want to unregister if we don't want the RM to retry - if (finalStatus == FinalApplicationStatus.SUCCEEDED || isLastAttempt) { - unregister(finalStatus, finalMsg) - cleanupStagingDir(fs) - } + Utils.addShutdownHook { () => + // If the SparkContext is still registered, shut it down as a best case effort in case + // users do not call sc.stop or do System.exit(). + val sc = sparkContextRef.get() + if (sc != null) { + logInfo("Invoking sc stop from shutdown hook") + sc.stop() + } + val maxAppAttempts = client.getMaxRegAttempts(sparkConf, yarnConf) + val isLastAttempt = client.getAttemptId().getAttemptId() >= maxAppAttempts + + if (!finished) { + // This happens when the user application calls System.exit(). We have the choice + // of either failing or succeeding at this point. We report success to avoid + // retrying applications that have succeeded (System.exit(0)), which means that + // applications that explicitly exit with a non-zero status will also show up as + // succeeded in the RM UI. + finish(finalStatus, + ApplicationMaster.EXIT_SUCCESS, + "Shutdown hook called before final status was reported.") + } + + if (!unregistered) { + // we only want to unregister if we don't want the RM to retry + if (finalStatus == FinalApplicationStatus.SUCCEEDED || isLastAttempt) { + unregister(finalStatus, finalMsg) + cleanupStagingDir(fs) } } } - // Use higher priority than FileSystem. - assert(ApplicationMaster.SHUTDOWN_HOOK_PRIORITY > FileSystem.SHUTDOWN_HOOK_PRIORITY) - ShutdownHookManager - .get().addShutdownHook(cleanupHook, ApplicationMaster.SHUTDOWN_HOOK_PRIORITY) - // Call this to force generation of secret so it gets populated into the // Hadoop UGI. This has to happen before the startUserApplication which does a // doAs in order for the credentials to be passed on to the executor containers. @@ -546,8 +539,6 @@ private[spark] class ApplicationMaster( object ApplicationMaster extends Logging { - val SHUTDOWN_HOOK_PRIORITY: Int = 30 - // exit codes for different causes, no reason behind the values private val EXIT_SUCCESS = 0 private val EXIT_UNCAUGHT_EXCEPTION = 10 From 3134c3fe495862b7687b5aa00d3344d09cd5e08e Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Tue, 21 Apr 2015 17:49:55 -0700 Subject: [PATCH 371/591] [SPARK-6953] [PySpark] speed up python tests This PR try to speed up some python tests: ``` tests.py 144s -> 103s -41s mllib/classification.py 24s -> 17s -7s mllib/regression.py 27s -> 15s -12s mllib/tree.py 27s -> 13s -14s mllib/tests.py 64s -> 31s -33s streaming/tests.py 185s -> 84s -101s ``` Considering python3, the total saving will be 558s (almost 10 minutes) (core, and streaming run three times, mllib runs twice). During testing, it will show used time for each test file: ``` Run core tests ... Running test: pyspark/rdd.py ... ok (22s) Running test: pyspark/context.py ... ok (16s) Running test: pyspark/conf.py ... ok (4s) Running test: pyspark/broadcast.py ... ok (4s) Running test: pyspark/accumulators.py ... ok (4s) Running test: pyspark/serializers.py ... ok (6s) Running test: pyspark/profiler.py ... ok (5s) Running test: pyspark/shuffle.py ... ok (1s) Running test: pyspark/tests.py ... ok (103s) 144s ``` Author: Reynold Xin Author: Xiangrui Meng Closes #5605 from rxin/python-tests-speed and squashes the following commits: d08542d [Reynold Xin] Merge pull request #14 from mengxr/SPARK-6953 89321ee [Xiangrui Meng] fix seed in tests 3ad2387 [Reynold Xin] Merge pull request #5427 from davies/python_tests --- python/pyspark/mllib/classification.py | 17 ++--- python/pyspark/mllib/regression.py | 25 ++++--- python/pyspark/mllib/tests.py | 69 +++++++++--------- python/pyspark/mllib/tree.py | 15 ++-- python/pyspark/shuffle.py | 7 +- python/pyspark/sql/tests.py | 4 +- python/pyspark/streaming/tests.py | 63 ++++++++++------- python/pyspark/tests.py | 96 ++++++++++++++++---------- python/run-tests | 13 ++-- 9 files changed, 182 insertions(+), 127 deletions(-) diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py index eda0b60f8b1e7..a70c664a71fdb 100644 --- a/python/pyspark/mllib/classification.py +++ b/python/pyspark/mllib/classification.py @@ -86,7 +86,7 @@ class LogisticRegressionModel(LinearClassificationModel): ... LabeledPoint(0.0, [0.0, 1.0]), ... LabeledPoint(1.0, [1.0, 0.0]), ... ] - >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(data)) + >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(data), iterations=10) >>> lrm.predict([1.0, 0.0]) 1 >>> lrm.predict([0.0, 1.0]) @@ -95,7 +95,7 @@ class LogisticRegressionModel(LinearClassificationModel): [1, 0] >>> lrm.clearThreshold() >>> lrm.predict([0.0, 1.0]) - 0.123... + 0.279... >>> sparse_data = [ ... LabeledPoint(0.0, SparseVector(2, {0: 0.0})), @@ -103,7 +103,7 @@ class LogisticRegressionModel(LinearClassificationModel): ... LabeledPoint(0.0, SparseVector(2, {0: 1.0})), ... LabeledPoint(1.0, SparseVector(2, {1: 2.0})) ... ] - >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(sparse_data)) + >>> lrm = LogisticRegressionWithSGD.train(sc.parallelize(sparse_data), iterations=10) >>> lrm.predict(array([0.0, 1.0])) 1 >>> lrm.predict(array([1.0, 0.0])) @@ -129,7 +129,8 @@ class LogisticRegressionModel(LinearClassificationModel): ... LabeledPoint(1.0, [1.0, 0.0, 0.0]), ... LabeledPoint(2.0, [0.0, 0.0, 1.0]) ... ] - >>> mcm = LogisticRegressionWithLBFGS.train(data=sc.parallelize(multi_class_data), numClasses=3) + >>> data = sc.parallelize(multi_class_data) + >>> mcm = LogisticRegressionWithLBFGS.train(data, iterations=10, numClasses=3) >>> mcm.predict([0.0, 0.5, 0.0]) 0 >>> mcm.predict([0.8, 0.0, 0.0]) @@ -298,7 +299,7 @@ def train(cls, data, iterations=100, initialWeights=None, regParam=0.01, regType ... LabeledPoint(0.0, [0.0, 1.0]), ... LabeledPoint(1.0, [1.0, 0.0]), ... ] - >>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data)) + >>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data), iterations=10) >>> lrm.predict([1.0, 0.0]) 1 >>> lrm.predict([0.0, 1.0]) @@ -330,14 +331,14 @@ class SVMModel(LinearClassificationModel): ... LabeledPoint(1.0, [2.0]), ... LabeledPoint(1.0, [3.0]) ... ] - >>> svm = SVMWithSGD.train(sc.parallelize(data)) + >>> svm = SVMWithSGD.train(sc.parallelize(data), iterations=10) >>> svm.predict([1.0]) 1 >>> svm.predict(sc.parallelize([[1.0]])).collect() [1] >>> svm.clearThreshold() >>> svm.predict(array([1.0])) - 1.25... + 1.44... >>> sparse_data = [ ... LabeledPoint(0.0, SparseVector(2, {0: -1.0})), @@ -345,7 +346,7 @@ class SVMModel(LinearClassificationModel): ... LabeledPoint(0.0, SparseVector(2, {0: 0.0})), ... LabeledPoint(1.0, SparseVector(2, {1: 2.0})) ... ] - >>> svm = SVMWithSGD.train(sc.parallelize(sparse_data)) + >>> svm = SVMWithSGD.train(sc.parallelize(sparse_data), iterations=10) >>> svm.predict(SparseVector(2, {1: 1.0})) 1 >>> svm.predict(SparseVector(2, {0: -1.0})) diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py index a0117c57133ae..4bc6351bdf02f 100644 --- a/python/pyspark/mllib/regression.py +++ b/python/pyspark/mllib/regression.py @@ -108,7 +108,8 @@ class LinearRegressionModel(LinearRegressionModelBase): ... LabeledPoint(3.0, [2.0]), ... LabeledPoint(2.0, [3.0]) ... ] - >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=np.array([1.0])) + >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=10, + ... initialWeights=np.array([1.0])) >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5 True >>> abs(lrm.predict(np.array([1.0])) - 1) < 0.5 @@ -135,12 +136,13 @@ class LinearRegressionModel(LinearRegressionModelBase): ... LabeledPoint(3.0, SparseVector(1, {0: 2.0})), ... LabeledPoint(2.0, SparseVector(1, {0: 3.0})) ... ] - >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0])) + >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=10, + ... initialWeights=array([1.0])) >>> abs(lrm.predict(array([0.0])) - 0) < 0.5 True >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 True - >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=100, step=1.0, + >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=10, step=1.0, ... miniBatchFraction=1.0, initialWeights=array([1.0]), regParam=0.1, regType="l2", ... intercept=True, validateData=True) >>> abs(lrm.predict(array([0.0])) - 0) < 0.5 @@ -238,7 +240,7 @@ class LassoModel(LinearRegressionModelBase): ... LabeledPoint(3.0, [2.0]), ... LabeledPoint(2.0, [3.0]) ... ] - >>> lrm = LassoWithSGD.train(sc.parallelize(data), initialWeights=array([1.0])) + >>> lrm = LassoWithSGD.train(sc.parallelize(data), iterations=10, initialWeights=array([1.0])) >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5 True >>> abs(lrm.predict(np.array([1.0])) - 1) < 0.5 @@ -265,12 +267,13 @@ class LassoModel(LinearRegressionModelBase): ... LabeledPoint(3.0, SparseVector(1, {0: 2.0})), ... LabeledPoint(2.0, SparseVector(1, {0: 3.0})) ... ] - >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0])) + >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=10, + ... initialWeights=array([1.0])) >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5 True >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 True - >>> lrm = LassoWithSGD.train(sc.parallelize(data), iterations=100, step=1.0, + >>> lrm = LassoWithSGD.train(sc.parallelize(data), iterations=10, step=1.0, ... regParam=0.01, miniBatchFraction=1.0, initialWeights=array([1.0]), intercept=True, ... validateData=True) >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5 @@ -321,7 +324,8 @@ class RidgeRegressionModel(LinearRegressionModelBase): ... LabeledPoint(3.0, [2.0]), ... LabeledPoint(2.0, [3.0]) ... ] - >>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0])) + >>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), iterations=10, + ... initialWeights=array([1.0])) >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5 True >>> abs(lrm.predict(np.array([1.0])) - 1) < 0.5 @@ -348,12 +352,13 @@ class RidgeRegressionModel(LinearRegressionModelBase): ... LabeledPoint(3.0, SparseVector(1, {0: 2.0})), ... LabeledPoint(2.0, SparseVector(1, {0: 3.0})) ... ] - >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), initialWeights=array([1.0])) + >>> lrm = LinearRegressionWithSGD.train(sc.parallelize(data), iterations=10, + ... initialWeights=array([1.0])) >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5 True >>> abs(lrm.predict(SparseVector(1, {0: 1.0})) - 1) < 0.5 True - >>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), iterations=100, step=1.0, + >>> lrm = RidgeRegressionWithSGD.train(sc.parallelize(data), iterations=10, step=1.0, ... regParam=0.01, miniBatchFraction=1.0, initialWeights=array([1.0]), intercept=True, ... validateData=True) >>> abs(lrm.predict(np.array([0.0])) - 0) < 0.5 @@ -396,7 +401,7 @@ def _test(): from pyspark import SparkContext import pyspark.mllib.regression globs = pyspark.mllib.regression.__dict__.copy() - globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2) + globs['sc'] = SparkContext('local[2]', 'PythonTest', batchSize=2) (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) globs['sc'].stop() if failure_count: diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 8f89e2cee0592..1b008b93bc137 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -36,6 +36,7 @@ else: import unittest +from pyspark import SparkContext from pyspark.mllib.common import _to_java_object_rdd from pyspark.mllib.linalg import Vector, SparseVector, DenseVector, VectorUDT, _convert_to_vector,\ DenseMatrix, SparseMatrix, Vectors, Matrices @@ -47,7 +48,6 @@ from pyspark.mllib.feature import StandardScaler from pyspark.serializers import PickleSerializer from pyspark.sql import SQLContext -from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase _have_scipy = False try: @@ -58,6 +58,12 @@ pass ser = PickleSerializer() +sc = SparkContext('local[4]', "MLlib tests") + + +class MLlibTestCase(unittest.TestCase): + def setUp(self): + self.sc = sc def _squared_distance(a, b): @@ -67,7 +73,7 @@ def _squared_distance(a, b): return b.squared_distance(a) -class VectorTests(PySparkTestCase): +class VectorTests(MLlibTestCase): def _test_serialize(self, v): self.assertEqual(v, ser.loads(ser.dumps(v))) @@ -212,7 +218,7 @@ def test_dense_matrix_is_transposed(self): self.assertTrue(array_equal(sm.values, [1, 3, 4, 6, 9])) -class ListTests(PySparkTestCase): +class ListTests(MLlibTestCase): """ Test MLlib algorithms on plain lists, to make sure they're passed through @@ -255,7 +261,7 @@ def test_gmm(self): [-6, -7], ]) clusters = GaussianMixture.train(data, 2, convergenceTol=0.001, - maxIterations=100, seed=56) + maxIterations=10, seed=56) labels = clusters.predict(data).collect() self.assertEquals(labels[0], labels[1]) self.assertEquals(labels[2], labels[3]) @@ -266,9 +272,9 @@ def test_gmm_deterministic(self): y = range(0, 100, 10) data = self.sc.parallelize([[a, b] for a, b in zip(x, y)]) clusters1 = GaussianMixture.train(data, 5, convergenceTol=0.001, - maxIterations=100, seed=63) + maxIterations=10, seed=63) clusters2 = GaussianMixture.train(data, 5, convergenceTol=0.001, - maxIterations=100, seed=63) + maxIterations=10, seed=63) for c1, c2 in zip(clusters1.weights, clusters2.weights): self.assertEquals(round(c1, 7), round(c2, 7)) @@ -287,13 +293,13 @@ def test_classification(self): temp_dir = tempfile.mkdtemp() - lr_model = LogisticRegressionWithSGD.train(rdd) + lr_model = LogisticRegressionWithSGD.train(rdd, iterations=10) self.assertTrue(lr_model.predict(features[0]) <= 0) self.assertTrue(lr_model.predict(features[1]) > 0) self.assertTrue(lr_model.predict(features[2]) <= 0) self.assertTrue(lr_model.predict(features[3]) > 0) - svm_model = SVMWithSGD.train(rdd) + svm_model = SVMWithSGD.train(rdd, iterations=10) self.assertTrue(svm_model.predict(features[0]) <= 0) self.assertTrue(svm_model.predict(features[1]) > 0) self.assertTrue(svm_model.predict(features[2]) <= 0) @@ -307,7 +313,7 @@ def test_classification(self): categoricalFeaturesInfo = {0: 3} # feature 0 has 3 categories dt_model = DecisionTree.trainClassifier( - rdd, numClasses=2, categoricalFeaturesInfo=categoricalFeaturesInfo) + rdd, numClasses=2, categoricalFeaturesInfo=categoricalFeaturesInfo, maxBins=4) self.assertTrue(dt_model.predict(features[0]) <= 0) self.assertTrue(dt_model.predict(features[1]) > 0) self.assertTrue(dt_model.predict(features[2]) <= 0) @@ -319,7 +325,8 @@ def test_classification(self): self.assertEqual(same_dt_model.toDebugString(), dt_model.toDebugString()) rf_model = RandomForest.trainClassifier( - rdd, numClasses=2, categoricalFeaturesInfo=categoricalFeaturesInfo, numTrees=100) + rdd, numClasses=2, categoricalFeaturesInfo=categoricalFeaturesInfo, numTrees=10, + maxBins=4, seed=1) self.assertTrue(rf_model.predict(features[0]) <= 0) self.assertTrue(rf_model.predict(features[1]) > 0) self.assertTrue(rf_model.predict(features[2]) <= 0) @@ -331,7 +338,7 @@ def test_classification(self): self.assertEqual(same_rf_model.toDebugString(), rf_model.toDebugString()) gbt_model = GradientBoostedTrees.trainClassifier( - rdd, categoricalFeaturesInfo=categoricalFeaturesInfo) + rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, numIterations=4) self.assertTrue(gbt_model.predict(features[0]) <= 0) self.assertTrue(gbt_model.predict(features[1]) > 0) self.assertTrue(gbt_model.predict(features[2]) <= 0) @@ -360,19 +367,19 @@ def test_regression(self): rdd = self.sc.parallelize(data) features = [p.features.tolist() for p in data] - lr_model = LinearRegressionWithSGD.train(rdd) + lr_model = LinearRegressionWithSGD.train(rdd, iterations=10) self.assertTrue(lr_model.predict(features[0]) <= 0) self.assertTrue(lr_model.predict(features[1]) > 0) self.assertTrue(lr_model.predict(features[2]) <= 0) self.assertTrue(lr_model.predict(features[3]) > 0) - lasso_model = LassoWithSGD.train(rdd) + lasso_model = LassoWithSGD.train(rdd, iterations=10) self.assertTrue(lasso_model.predict(features[0]) <= 0) self.assertTrue(lasso_model.predict(features[1]) > 0) self.assertTrue(lasso_model.predict(features[2]) <= 0) self.assertTrue(lasso_model.predict(features[3]) > 0) - rr_model = RidgeRegressionWithSGD.train(rdd) + rr_model = RidgeRegressionWithSGD.train(rdd, iterations=10) self.assertTrue(rr_model.predict(features[0]) <= 0) self.assertTrue(rr_model.predict(features[1]) > 0) self.assertTrue(rr_model.predict(features[2]) <= 0) @@ -380,35 +387,35 @@ def test_regression(self): categoricalFeaturesInfo = {0: 2} # feature 0 has 2 categories dt_model = DecisionTree.trainRegressor( - rdd, categoricalFeaturesInfo=categoricalFeaturesInfo) + rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, maxBins=4) self.assertTrue(dt_model.predict(features[0]) <= 0) self.assertTrue(dt_model.predict(features[1]) > 0) self.assertTrue(dt_model.predict(features[2]) <= 0) self.assertTrue(dt_model.predict(features[3]) > 0) rf_model = RandomForest.trainRegressor( - rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, numTrees=100, seed=1) + rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, numTrees=10, maxBins=4, seed=1) self.assertTrue(rf_model.predict(features[0]) <= 0) self.assertTrue(rf_model.predict(features[1]) > 0) self.assertTrue(rf_model.predict(features[2]) <= 0) self.assertTrue(rf_model.predict(features[3]) > 0) gbt_model = GradientBoostedTrees.trainRegressor( - rdd, categoricalFeaturesInfo=categoricalFeaturesInfo) + rdd, categoricalFeaturesInfo=categoricalFeaturesInfo, numIterations=4) self.assertTrue(gbt_model.predict(features[0]) <= 0) self.assertTrue(gbt_model.predict(features[1]) > 0) self.assertTrue(gbt_model.predict(features[2]) <= 0) self.assertTrue(gbt_model.predict(features[3]) > 0) try: - LinearRegressionWithSGD.train(rdd, initialWeights=array([1.0, 1.0])) - LassoWithSGD.train(rdd, initialWeights=array([1.0, 1.0])) - RidgeRegressionWithSGD.train(rdd, initialWeights=array([1.0, 1.0])) + LinearRegressionWithSGD.train(rdd, initialWeights=array([1.0, 1.0]), iterations=10) + LassoWithSGD.train(rdd, initialWeights=array([1.0, 1.0]), iterations=10) + RidgeRegressionWithSGD.train(rdd, initialWeights=array([1.0, 1.0]), iterations=10) except ValueError: self.fail() -class StatTests(PySparkTestCase): +class StatTests(MLlibTestCase): # SPARK-4023 def test_col_with_different_rdds(self): # numpy @@ -438,7 +445,7 @@ def test_col_norms(self): self.assertTrue(math.fabs(summary2.normL2()[0] - expectedNormL2) < 1e-14) -class VectorUDTTests(PySparkTestCase): +class VectorUDTTests(MLlibTestCase): dv0 = DenseVector([]) dv1 = DenseVector([1.0, 2.0]) @@ -472,7 +479,7 @@ def test_infer_schema(self): @unittest.skipIf(not _have_scipy, "SciPy not installed") -class SciPyTests(PySparkTestCase): +class SciPyTests(MLlibTestCase): """ Test both vector operations and MLlib algorithms with SciPy sparse matrices, @@ -613,7 +620,7 @@ def test_regression(self): self.assertTrue(dt_model.predict(features[3]) > 0) -class ChiSqTestTests(PySparkTestCase): +class ChiSqTestTests(MLlibTestCase): def test_goodness_of_fit(self): from numpy import inf @@ -711,13 +718,13 @@ def test_right_number_of_results(self): self.assertIsNotNone(chi[1000]) -class SerDeTest(PySparkTestCase): +class SerDeTest(MLlibTestCase): def test_to_java_object_rdd(self): # SPARK-6660 data = RandomRDDs.uniformRDD(self.sc, 10, 5, seed=0) self.assertEqual(_to_java_object_rdd(data).count(), 10) -class FeatureTest(PySparkTestCase): +class FeatureTest(MLlibTestCase): def test_idf_model(self): data = [ Vectors.dense([1, 2, 6, 0, 2, 3, 1, 1, 0, 0, 3]), @@ -730,13 +737,8 @@ def test_idf_model(self): self.assertEqual(len(idf), 11) -class Word2VecTests(PySparkTestCase): +class Word2VecTests(MLlibTestCase): def test_word2vec_setters(self): - data = [ - ["I", "have", "a", "pen"], - ["I", "like", "soccer", "very", "much"], - ["I", "live", "in", "Tokyo"] - ] model = Word2Vec() \ .setVectorSize(2) \ .setLearningRate(0.01) \ @@ -765,7 +767,7 @@ def test_word2vec_get_vectors(self): self.assertEquals(len(model.getVectors()), 3) -class StandardScalerTests(PySparkTestCase): +class StandardScalerTests(MLlibTestCase): def test_model_setters(self): data = [ [1.0, 2.0, 3.0], @@ -793,3 +795,4 @@ def test_model_transform(self): unittest.main() if not _have_scipy: print("NOTE: SciPy tests were skipped as it does not seem to be installed") + sc.stop() diff --git a/python/pyspark/mllib/tree.py b/python/pyspark/mllib/tree.py index 0fe6e4fabe43a..cfcbea573fd22 100644 --- a/python/pyspark/mllib/tree.py +++ b/python/pyspark/mllib/tree.py @@ -482,13 +482,13 @@ def trainClassifier(cls, data, categoricalFeaturesInfo, ... LabeledPoint(1.0, [3.0]) ... ] >>> - >>> model = GradientBoostedTrees.trainClassifier(sc.parallelize(data), {}) + >>> model = GradientBoostedTrees.trainClassifier(sc.parallelize(data), {}, numIterations=10) >>> model.numTrees() - 100 + 10 >>> model.totalNumNodes() - 300 + 30 >>> print(model) # it already has newline - TreeEnsembleModel classifier with 100 trees + TreeEnsembleModel classifier with 10 trees >>> model.predict([2.0]) 1.0 @@ -541,11 +541,12 @@ def trainRegressor(cls, data, categoricalFeaturesInfo, ... LabeledPoint(1.0, SparseVector(2, {1: 2.0})) ... ] >>> - >>> model = GradientBoostedTrees.trainRegressor(sc.parallelize(sparse_data), {}) + >>> data = sc.parallelize(sparse_data) + >>> model = GradientBoostedTrees.trainRegressor(data, {}, numIterations=10) >>> model.numTrees() - 100 + 10 >>> model.totalNumNodes() - 102 + 12 >>> model.predict(SparseVector(2, {1: 1.0})) 1.0 >>> model.predict(SparseVector(2, {0: 1.0})) diff --git a/python/pyspark/shuffle.py b/python/pyspark/shuffle.py index b54baa57ec28a..1d0b16cade8bb 100644 --- a/python/pyspark/shuffle.py +++ b/python/pyspark/shuffle.py @@ -486,7 +486,7 @@ def sorted(self, iterator, key=None, reverse=False): goes above the limit. """ global MemoryBytesSpilled, DiskBytesSpilled - batch, limit = 100, self._next_limit() + batch, limit = 100, self.memory_limit chunks, current_chunk = [], [] iterator = iter(iterator) while True: @@ -497,7 +497,7 @@ def sorted(self, iterator, key=None, reverse=False): break used_memory = get_used_memory() - if used_memory > self.memory_limit: + if used_memory > limit: # sort them inplace will save memory current_chunk.sort(key=key, reverse=reverse) path = self._get_path(len(chunks)) @@ -513,13 +513,14 @@ def load(f): chunks.append(load(open(path, 'rb'))) current_chunk = [] gc.collect() + batch //= 2 limit = self._next_limit() MemoryBytesSpilled += (used_memory - get_used_memory()) << 20 DiskBytesSpilled += os.path.getsize(path) os.unlink(path) # data will be deleted after close elif not chunks: - batch = min(batch * 2, 10000) + batch = min(int(batch * 1.5), 10000) current_chunk.sort(key=key, reverse=reverse) if not chunks: diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py index 23e84283679e1..fe43c374f1cb1 100644 --- a/python/pyspark/sql/tests.py +++ b/python/pyspark/sql/tests.py @@ -109,7 +109,7 @@ def setUpClass(cls): os.unlink(cls.tempdir.name) cls.sqlCtx = SQLContext(cls.sc) cls.testData = [Row(key=i, value=str(i)) for i in range(100)] - rdd = cls.sc.parallelize(cls.testData) + rdd = cls.sc.parallelize(cls.testData, 2) cls.df = rdd.toDF() @classmethod @@ -303,7 +303,7 @@ def test_apply_schema(self): abstract = "byte1 short1 float1 time1 map1{} struct1(b) list1[]" schema = _parse_schema_abstract(abstract) typedSchema = _infer_schema_type(rdd.first(), schema) - df = self.sqlCtx.applySchema(rdd, typedSchema) + df = self.sqlCtx.createDataFrame(rdd, typedSchema) r = (127, -32768, 1.0, datetime(2010, 1, 1, 1, 1, 1), {"a": 1}, Row(b=2), [1, 2, 3]) self.assertEqual(r, tuple(df.first())) diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py index 33f958a601f3a..5fa1e5ef081ab 100644 --- a/python/pyspark/streaming/tests.py +++ b/python/pyspark/streaming/tests.py @@ -16,14 +16,23 @@ # import os +import sys from itertools import chain import time import operator -import unittest import tempfile import struct from functools import reduce +if sys.version_info[:2] <= (2, 6): + try: + import unittest2 as unittest + except ImportError: + sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier') + sys.exit(1) +else: + import unittest + from pyspark.context import SparkConf, SparkContext, RDD from pyspark.streaming.context import StreamingContext from pyspark.streaming.kafka import KafkaUtils @@ -31,19 +40,25 @@ class PySparkStreamingTestCase(unittest.TestCase): - timeout = 20 # seconds - duration = 1 + timeout = 4 # seconds + duration = .2 - def setUp(self): - class_name = self.__class__.__name__ + @classmethod + def setUpClass(cls): + class_name = cls.__name__ conf = SparkConf().set("spark.default.parallelism", 1) - self.sc = SparkContext(appName=class_name, conf=conf) - self.sc.setCheckpointDir("/tmp") - # TODO: decrease duration to speed up tests + cls.sc = SparkContext(appName=class_name, conf=conf) + cls.sc.setCheckpointDir("/tmp") + + @classmethod + def tearDownClass(cls): + cls.sc.stop() + + def setUp(self): self.ssc = StreamingContext(self.sc, self.duration) def tearDown(self): - self.ssc.stop() + self.ssc.stop(False) def wait_for(self, result, n): start_time = time.time() @@ -363,13 +378,13 @@ def func(dstream): class WindowFunctionTests(PySparkStreamingTestCase): - timeout = 20 + timeout = 5 def test_window(self): input = [range(1), range(2), range(3), range(4), range(5)] def func(dstream): - return dstream.window(3, 1).count() + return dstream.window(.6, .2).count() expected = [[1], [3], [6], [9], [12], [9], [5]] self._test_func(input, func, expected) @@ -378,7 +393,7 @@ def test_count_by_window(self): input = [range(1), range(2), range(3), range(4), range(5)] def func(dstream): - return dstream.countByWindow(3, 1) + return dstream.countByWindow(.6, .2) expected = [[1], [3], [6], [9], [12], [9], [5]] self._test_func(input, func, expected) @@ -387,7 +402,7 @@ def test_count_by_window_large(self): input = [range(1), range(2), range(3), range(4), range(5), range(6)] def func(dstream): - return dstream.countByWindow(5, 1) + return dstream.countByWindow(1, .2) expected = [[1], [3], [6], [10], [15], [20], [18], [15], [11], [6]] self._test_func(input, func, expected) @@ -396,7 +411,7 @@ def test_count_by_value_and_window(self): input = [range(1), range(2), range(3), range(4), range(5), range(6)] def func(dstream): - return dstream.countByValueAndWindow(5, 1) + return dstream.countByValueAndWindow(1, .2) expected = [[1], [2], [3], [4], [5], [6], [6], [6], [6], [6]] self._test_func(input, func, expected) @@ -405,7 +420,7 @@ def test_group_by_key_and_window(self): input = [[('a', i)] for i in range(5)] def func(dstream): - return dstream.groupByKeyAndWindow(3, 1).mapValues(list) + return dstream.groupByKeyAndWindow(.6, .2).mapValues(list) expected = [[('a', [0])], [('a', [0, 1])], [('a', [0, 1, 2])], [('a', [1, 2, 3])], [('a', [2, 3, 4])], [('a', [3, 4])], [('a', [4])]] @@ -436,8 +451,8 @@ def test_stop_only_streaming_context(self): def test_stop_multiple_times(self): self._add_input_stream() self.ssc.start() - self.ssc.stop() - self.ssc.stop() + self.ssc.stop(False) + self.ssc.stop(False) def test_queue_stream(self): input = [list(range(i + 1)) for i in range(3)] @@ -495,10 +510,7 @@ def func(rdds): self.assertEqual([2, 3, 1], self._take(dstream, 3)) -class CheckpointTests(PySparkStreamingTestCase): - - def setUp(self): - pass +class CheckpointTests(unittest.TestCase): def test_get_or_create(self): inputd = tempfile.mkdtemp() @@ -518,12 +530,12 @@ def setup(): return ssc cpd = tempfile.mkdtemp("test_streaming_cps") - self.ssc = ssc = StreamingContext.getOrCreate(cpd, setup) + ssc = StreamingContext.getOrCreate(cpd, setup) ssc.start() def check_output(n): while not os.listdir(outputd): - time.sleep(0.1) + time.sleep(0.01) time.sleep(1) # make sure mtime is larger than the previous one with open(os.path.join(inputd, str(n)), 'w') as f: f.writelines(["%d\n" % i for i in range(10)]) @@ -553,12 +565,15 @@ def check_output(n): ssc.stop(True, True) time.sleep(1) - self.ssc = ssc = StreamingContext.getOrCreate(cpd, setup) + ssc = StreamingContext.getOrCreate(cpd, setup) ssc.start() check_output(3) + ssc.stop(True, True) class KafkaStreamTests(PySparkStreamingTestCase): + timeout = 20 # seconds + duration = 1 def setUp(self): super(KafkaStreamTests, self).setUp() diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py index 75f39d9e75f38..ea63a396da5b8 100644 --- a/python/pyspark/tests.py +++ b/python/pyspark/tests.py @@ -31,7 +31,6 @@ import time import zipfile import random -import itertools import threading import hashlib @@ -49,6 +48,11 @@ xrange = range basestring = str +if sys.version >= "3": + from io import StringIO +else: + from StringIO import StringIO + from pyspark.conf import SparkConf from pyspark.context import SparkContext @@ -196,7 +200,7 @@ def test_external_sort_in_rdd(self): sc = SparkContext(conf=conf) l = list(range(10240)) random.shuffle(l) - rdd = sc.parallelize(l, 2) + rdd = sc.parallelize(l, 4) self.assertEqual(sorted(l), rdd.sortBy(lambda x: x).collect()) sc.stop() @@ -300,6 +304,18 @@ def test_hash_serializer(self): hash(FlattenedValuesSerializer(PickleSerializer())) +class QuietTest(object): + def __init__(self, sc): + self.log4j = sc._jvm.org.apache.log4j + + def __enter__(self): + self.old_level = self.log4j.LogManager.getRootLogger().getLevel() + self.log4j.LogManager.getRootLogger().setLevel(self.log4j.Level.FATAL) + + def __exit__(self, exc_type, exc_val, exc_tb): + self.log4j.LogManager.getRootLogger().setLevel(self.old_level) + + class PySparkTestCase(unittest.TestCase): def setUp(self): @@ -371,15 +387,11 @@ def test_add_py_file(self): # To ensure that we're actually testing addPyFile's effects, check that # this job fails due to `userlibrary` not being on the Python path: # disable logging in log4j temporarily - log4j = self.sc._jvm.org.apache.log4j - old_level = log4j.LogManager.getRootLogger().getLevel() - log4j.LogManager.getRootLogger().setLevel(log4j.Level.FATAL) - def func(x): from userlibrary import UserClass return UserClass().hello() - self.assertRaises(Exception, self.sc.parallelize(range(2)).map(func).first) - log4j.LogManager.getRootLogger().setLevel(old_level) + with QuietTest(self.sc): + self.assertRaises(Exception, self.sc.parallelize(range(2)).map(func).first) # Add the file, so the job should now succeed: path = os.path.join(SPARK_HOME, "python/test_support/userlibrary.py") @@ -496,7 +508,8 @@ def test_deleting_input_files(self): filtered_data = data.filter(lambda x: True) self.assertEqual(1, filtered_data.count()) os.unlink(tempFile.name) - self.assertRaises(Exception, lambda: filtered_data.count()) + with QuietTest(self.sc): + self.assertRaises(Exception, lambda: filtered_data.count()) def test_sampling_default_seed(self): # Test for SPARK-3995 (default seed setting) @@ -536,9 +549,9 @@ def test_namedtuple_in_rdd(self): self.assertEqual([jon, jane], theDoes.collect()) def test_large_broadcast(self): - N = 100000 + N = 10000 data = [[float(i) for i in range(300)] for i in range(N)] - bdata = self.sc.broadcast(data) # 270MB + bdata = self.sc.broadcast(data) # 27MB m = self.sc.parallelize(range(1), 1).map(lambda x: len(bdata.value)).sum() self.assertEqual(N, m) @@ -569,7 +582,7 @@ def test_multiple_broadcasts(self): self.assertEqual(checksum, csum) def test_large_closure(self): - N = 1000000 + N = 200000 data = [float(i) for i in xrange(N)] rdd = self.sc.parallelize(range(1), 1).map(lambda x: len(data)) self.assertEqual(N, rdd.first()) @@ -604,17 +617,18 @@ def test_zip_with_different_number_of_items(self): # different number of partitions b = self.sc.parallelize(range(100, 106), 3) self.assertRaises(ValueError, lambda: a.zip(b)) - # different number of batched items in JVM - b = self.sc.parallelize(range(100, 104), 2) - self.assertRaises(Exception, lambda: a.zip(b).count()) - # different number of items in one pair - b = self.sc.parallelize(range(100, 106), 2) - self.assertRaises(Exception, lambda: a.zip(b).count()) - # same total number of items, but different distributions - a = self.sc.parallelize([2, 3], 2).flatMap(range) - b = self.sc.parallelize([3, 2], 2).flatMap(range) - self.assertEqual(a.count(), b.count()) - self.assertRaises(Exception, lambda: a.zip(b).count()) + with QuietTest(self.sc): + # different number of batched items in JVM + b = self.sc.parallelize(range(100, 104), 2) + self.assertRaises(Exception, lambda: a.zip(b).count()) + # different number of items in one pair + b = self.sc.parallelize(range(100, 106), 2) + self.assertRaises(Exception, lambda: a.zip(b).count()) + # same total number of items, but different distributions + a = self.sc.parallelize([2, 3], 2).flatMap(range) + b = self.sc.parallelize([3, 2], 2).flatMap(range) + self.assertEqual(a.count(), b.count()) + self.assertRaises(Exception, lambda: a.zip(b).count()) def test_count_approx_distinct(self): rdd = self.sc.parallelize(range(1000)) @@ -877,7 +891,12 @@ def test_profiler(self): func_names = [func_name for fname, n, func_name in stat_list] self.assertTrue("heavy_foo" in func_names) + old_stdout = sys.stdout + sys.stdout = io = StringIO() self.sc.show_profiles() + self.assertTrue("heavy_foo" in io.getvalue()) + sys.stdout = old_stdout + d = tempfile.gettempdir() self.sc.dump_profiles(d) self.assertTrue("rdd_%d.pstats" % id in os.listdir(d)) @@ -901,7 +920,7 @@ def show(self, id): def do_computation(self): def heavy_foo(x): - for i in range(1 << 20): + for i in range(1 << 18): x = 1 rdd = self.sc.parallelize(range(100)) @@ -1417,7 +1436,7 @@ def test_termination_sigterm(self): self.do_termination_test(lambda daemon: os.kill(daemon.pid, SIGTERM)) -class WorkerTests(PySparkTestCase): +class WorkerTests(ReusedPySparkTestCase): def test_cancel_task(self): temp = tempfile.NamedTemporaryFile(delete=True) temp.close() @@ -1432,7 +1451,10 @@ def sleep(x): # start job in background thread def run(): - self.sc.parallelize(range(1), 1).foreach(sleep) + try: + self.sc.parallelize(range(1), 1).foreach(sleep) + except Exception: + pass import threading t = threading.Thread(target=run) t.daemon = True @@ -1473,7 +1495,8 @@ def test_after_exception(self): def raise_exception(_): raise Exception() rdd = self.sc.parallelize(range(100), 1) - self.assertRaises(Exception, lambda: rdd.foreach(raise_exception)) + with QuietTest(self.sc): + self.assertRaises(Exception, lambda: rdd.foreach(raise_exception)) self.assertEqual(100, rdd.map(str).count()) def test_after_jvm_exception(self): @@ -1484,7 +1507,8 @@ def test_after_jvm_exception(self): filtered_data = data.filter(lambda x: True) self.assertEqual(1, filtered_data.count()) os.unlink(tempFile.name) - self.assertRaises(Exception, lambda: filtered_data.count()) + with QuietTest(self.sc): + self.assertRaises(Exception, lambda: filtered_data.count()) rdd = self.sc.parallelize(range(100), 1) self.assertEqual(100, rdd.map(str).count()) @@ -1522,14 +1546,11 @@ def test_with_different_versions_of_python(self): rdd.count() version = sys.version_info sys.version_info = (2, 0, 0) - log4j = self.sc._jvm.org.apache.log4j - old_level = log4j.LogManager.getRootLogger().getLevel() - log4j.LogManager.getRootLogger().setLevel(log4j.Level.FATAL) try: - self.assertRaises(Py4JJavaError, lambda: rdd.count()) + with QuietTest(self.sc): + self.assertRaises(Py4JJavaError, lambda: rdd.count()) finally: sys.version_info = version - log4j.LogManager.getRootLogger().setLevel(old_level) class SparkSubmitTests(unittest.TestCase): @@ -1751,9 +1772,14 @@ def test_with_stop(self): def test_progress_api(self): with SparkContext() as sc: sc.setJobGroup('test_progress_api', '', True) - rdd = sc.parallelize(range(10)).map(lambda x: time.sleep(100)) - t = threading.Thread(target=rdd.collect) + + def run(): + try: + rdd.count() + except Exception: + pass + t = threading.Thread(target=run) t.daemon = True t.start() # wait for scheduler to start diff --git a/python/run-tests b/python/run-tests index ed3e819ef30c1..88b63b84fdc27 100755 --- a/python/run-tests +++ b/python/run-tests @@ -28,6 +28,7 @@ cd "$FWDIR/python" FAILED=0 LOG_FILE=unit-tests.log +START=$(date +"%s") rm -f $LOG_FILE @@ -35,8 +36,8 @@ rm -f $LOG_FILE rm -rf metastore warehouse function run_test() { - echo "Running test: $1" | tee -a $LOG_FILE - + echo -en "Running test: $1 ... " | tee -a $LOG_FILE + start=$(date +"%s") SPARK_TESTING=1 time "$FWDIR"/bin/pyspark $1 > $LOG_FILE 2>&1 FAILED=$((PIPESTATUS[0]||$FAILED)) @@ -48,6 +49,9 @@ function run_test() { echo "Had test failures; see logs." echo -en "\033[0m" # No color exit -1 + else + now=$(date +"%s") + echo "ok ($(($now - $start))s)" fi } @@ -161,9 +165,8 @@ if [ $(which pypy) ]; then fi if [[ $FAILED == 0 ]]; then - echo -en "\033[32m" # Green - echo "Tests passed." - echo -en "\033[0m" # No color + now=$(date +"%s") + echo -e "\033[32mTests passed \033[0min $(($now - $START)) seconds" fi # TODO: in the long-run, it would be nice to use a test runner like `nose`. From 41ef78a94105bb995bb14d15d47cbb6ca1638f62 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Tue, 21 Apr 2015 17:52:52 -0700 Subject: [PATCH 372/591] Closes #5427 From a0761ec7063f984dcadc8d154f83dd9cfd1c5e0b Mon Sep 17 00:00:00 2001 From: texasmichelle Date: Tue, 21 Apr 2015 18:08:29 -0700 Subject: [PATCH 373/591] [SPARK-1684] [PROJECT INFRA] Merge script should standardize SPARK-XXX prefix Cleans up the pull request title in the merge script to follow conventions outlined in the wiki under Contributing Code. https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark#ContributingtoSpark-ContributingCode [MODULE] SPARK-XXXX: Description Author: texasmichelle Closes #5149 from texasmichelle/master and squashes the following commits: 9b6b0a7 [texasmichelle] resolved variable scope issue 7d5fa20 [texasmichelle] only prompt if title has been modified 8c195bb [texasmichelle] removed erroneous line 4f1ed46 [texasmichelle] Deque removal, logic simplifications, & prompt user to pick a title (orig or modified) df73f6a [texasmichelle] reworked regex's to enforce brackets around JIRA ref 43b5aed [texasmichelle] Merge remote-tracking branch 'apache/master' 25229c6 [texasmichelle] Merge remote-tracking branch 'apache/master' aa20a6e [texasmichelle] Move code into main() and add doctest for new text parsing method 48520ba [texasmichelle] SPARK-1684: Corrected import statement 042099d [texasmichelle] SPARK-1684 Merge script should standardize SPARK-XXX prefix 8f4a7d1 [texasmichelle] SPARK-1684 Merge script should standardize SPARK-XXX prefix --- dev/merge_spark_pr.py | 199 +++++++++++++++++++++++++++++------------- 1 file changed, 140 insertions(+), 59 deletions(-) diff --git a/dev/merge_spark_pr.py b/dev/merge_spark_pr.py index 3062e9c3c6651..b69cd15f99f63 100755 --- a/dev/merge_spark_pr.py +++ b/dev/merge_spark_pr.py @@ -55,8 +55,6 @@ # Prefix added to temporary branches BRANCH_PREFIX = "PR_TOOL" -os.chdir(SPARK_HOME) - def get_json(url): try: @@ -85,10 +83,6 @@ def continue_maybe(prompt): if result.lower() != "y": fail("Okay, exiting") - -original_head = run_cmd("git rev-parse HEAD")[:8] - - def clean_up(): print "Restoring head pointer to %s" % original_head run_cmd("git checkout %s" % original_head) @@ -101,7 +95,7 @@ def clean_up(): # merge the requested PR and return the merge hash -def merge_pr(pr_num, target_ref): +def merge_pr(pr_num, target_ref, title, body, pr_repo_desc): pr_branch_name = "%s_MERGE_PR_%s" % (BRANCH_PREFIX, pr_num) target_branch_name = "%s_MERGE_PR_%s_%s" % (BRANCH_PREFIX, pr_num, target_ref.upper()) run_cmd("git fetch %s pull/%s/head:%s" % (PR_REMOTE_NAME, pr_num, pr_branch_name)) @@ -274,7 +268,7 @@ def get_version_json(version_str): asf_jira.transition_issue( jira_id, resolve["id"], fixVersions=jira_fix_versions, comment=comment) - print "Succesfully resolved %s with fixVersions=%s!" % (jira_id, fix_versions) + print "Successfully resolved %s with fixVersions=%s!" % (jira_id, fix_versions) def resolve_jira_issues(title, merge_branches, comment): @@ -286,68 +280,155 @@ def resolve_jira_issues(title, merge_branches, comment): resolve_jira_issue(merge_branches, comment, jira_id) -branches = get_json("%s/branches" % GITHUB_API_BASE) -branch_names = filter(lambda x: x.startswith("branch-"), [x['name'] for x in branches]) -# Assumes branch names can be sorted lexicographically -latest_branch = sorted(branch_names, reverse=True)[0] - -pr_num = raw_input("Which pull request would you like to merge? (e.g. 34): ") -pr = get_json("%s/pulls/%s" % (GITHUB_API_BASE, pr_num)) -pr_events = get_json("%s/issues/%s/events" % (GITHUB_API_BASE, pr_num)) +def standardize_jira_ref(text): + """ + Standardize the [SPARK-XXXXX] [MODULE] prefix + Converts "[SPARK-XXX][mllib] Issue", "[MLLib] SPARK-XXX. Issue" or "SPARK XXX [MLLIB]: Issue" to "[SPARK-XXX] [MLLIB] Issue" + + >>> standardize_jira_ref("[SPARK-5821] [SQL] ParquetRelation2 CTAS should check if delete is successful") + '[SPARK-5821] [SQL] ParquetRelation2 CTAS should check if delete is successful' + >>> standardize_jira_ref("[SPARK-4123][Project Infra][WIP]: Show new dependencies added in pull requests") + '[SPARK-4123] [PROJECT INFRA] [WIP] Show new dependencies added in pull requests' + >>> standardize_jira_ref("[MLlib] Spark 5954: Top by key") + '[SPARK-5954] [MLLIB] Top by key' + >>> standardize_jira_ref("[SPARK-979] a LRU scheduler for load balancing in TaskSchedulerImpl") + '[SPARK-979] a LRU scheduler for load balancing in TaskSchedulerImpl' + >>> standardize_jira_ref("SPARK-1094 Support MiMa for reporting binary compatibility accross versions.") + '[SPARK-1094] Support MiMa for reporting binary compatibility accross versions.' + >>> standardize_jira_ref("[WIP] [SPARK-1146] Vagrant support for Spark") + '[SPARK-1146] [WIP] Vagrant support for Spark' + >>> standardize_jira_ref("SPARK-1032. If Yarn app fails before registering, app master stays aroun...") + '[SPARK-1032] If Yarn app fails before registering, app master stays aroun...' + >>> standardize_jira_ref("[SPARK-6250][SPARK-6146][SPARK-5911][SQL] Types are now reserved words in DDL parser.") + '[SPARK-6250] [SPARK-6146] [SPARK-5911] [SQL] Types are now reserved words in DDL parser.' + >>> standardize_jira_ref("Additional information for users building from source code") + 'Additional information for users building from source code' + """ + jira_refs = [] + components = [] + + # If the string is compliant, no need to process any further + if (re.search(r'^\[SPARK-[0-9]{3,6}\] (\[[A-Z0-9_\s,]+\] )+\S+', text)): + return text + + # Extract JIRA ref(s): + pattern = re.compile(r'(SPARK[-\s]*[0-9]{3,6})+', re.IGNORECASE) + for ref in pattern.findall(text): + # Add brackets, replace spaces with a dash, & convert to uppercase + jira_refs.append('[' + re.sub(r'\s+', '-', ref.upper()) + ']') + text = text.replace(ref, '') + + # Extract spark component(s): + # Look for alphanumeric chars, spaces, dashes, periods, and/or commas + pattern = re.compile(r'(\[[\w\s,-\.]+\])', re.IGNORECASE) + for component in pattern.findall(text): + components.append(component.upper()) + text = text.replace(component, '') + + # Cleanup any remaining symbols: + pattern = re.compile(r'^\W+(.*)', re.IGNORECASE) + if (pattern.search(text) is not None): + text = pattern.search(text).groups()[0] + + # Assemble full text (JIRA ref(s), module(s), remaining text) + clean_text = ' '.join(jira_refs).strip() + " " + ' '.join(components).strip() + " " + text.strip() + + # Replace multiple spaces with a single space, e.g. if no jira refs and/or components were included + clean_text = re.sub(r'\s+', ' ', clean_text.strip()) + + return clean_text + +def main(): + global original_head + + os.chdir(SPARK_HOME) + original_head = run_cmd("git rev-parse HEAD")[:8] + + branches = get_json("%s/branches" % GITHUB_API_BASE) + branch_names = filter(lambda x: x.startswith("branch-"), [x['name'] for x in branches]) + # Assumes branch names can be sorted lexicographically + latest_branch = sorted(branch_names, reverse=True)[0] + + pr_num = raw_input("Which pull request would you like to merge? (e.g. 34): ") + pr = get_json("%s/pulls/%s" % (GITHUB_API_BASE, pr_num)) + pr_events = get_json("%s/issues/%s/events" % (GITHUB_API_BASE, pr_num)) + + url = pr["url"] + + # Decide whether to use the modified title or not + modified_title = standardize_jira_ref(pr["title"]) + if modified_title != pr["title"]: + print "I've re-written the title as follows to match the standard format:" + print "Original: %s" % pr["title"] + print "Modified: %s" % modified_title + result = raw_input("Would you like to use the modified title? (y/n): ") + if result.lower() == "y": + title = modified_title + print "Using modified title:" + else: + title = pr["title"] + print "Using original title:" + print title + else: + title = pr["title"] -url = pr["url"] -title = pr["title"] -body = pr["body"] -target_ref = pr["base"]["ref"] -user_login = pr["user"]["login"] -base_ref = pr["head"]["ref"] -pr_repo_desc = "%s/%s" % (user_login, base_ref) + body = pr["body"] + target_ref = pr["base"]["ref"] + user_login = pr["user"]["login"] + base_ref = pr["head"]["ref"] + pr_repo_desc = "%s/%s" % (user_login, base_ref) -# Merged pull requests don't appear as merged in the GitHub API; -# Instead, they're closed by asfgit. -merge_commits = \ - [e for e in pr_events if e["actor"]["login"] == "asfgit" and e["event"] == "closed"] + # Merged pull requests don't appear as merged in the GitHub API; + # Instead, they're closed by asfgit. + merge_commits = \ + [e for e in pr_events if e["actor"]["login"] == "asfgit" and e["event"] == "closed"] -if merge_commits: - merge_hash = merge_commits[0]["commit_id"] - message = get_json("%s/commits/%s" % (GITHUB_API_BASE, merge_hash))["commit"]["message"] + if merge_commits: + merge_hash = merge_commits[0]["commit_id"] + message = get_json("%s/commits/%s" % (GITHUB_API_BASE, merge_hash))["commit"]["message"] - print "Pull request %s has already been merged, assuming you want to backport" % pr_num - commit_is_downloaded = run_cmd(['git', 'rev-parse', '--quiet', '--verify', + print "Pull request %s has already been merged, assuming you want to backport" % pr_num + commit_is_downloaded = run_cmd(['git', 'rev-parse', '--quiet', '--verify', "%s^{commit}" % merge_hash]).strip() != "" - if not commit_is_downloaded: - fail("Couldn't find any merge commit for #%s, you may need to update HEAD." % pr_num) + if not commit_is_downloaded: + fail("Couldn't find any merge commit for #%s, you may need to update HEAD." % pr_num) - print "Found commit %s:\n%s" % (merge_hash, message) - cherry_pick(pr_num, merge_hash, latest_branch) - sys.exit(0) + print "Found commit %s:\n%s" % (merge_hash, message) + cherry_pick(pr_num, merge_hash, latest_branch) + sys.exit(0) -if not bool(pr["mergeable"]): - msg = "Pull request %s is not mergeable in its current form.\n" % pr_num + \ - "Continue? (experts only!)" - continue_maybe(msg) + if not bool(pr["mergeable"]): + msg = "Pull request %s is not mergeable in its current form.\n" % pr_num + \ + "Continue? (experts only!)" + continue_maybe(msg) -print ("\n=== Pull Request #%s ===" % pr_num) -print ("title\t%s\nsource\t%s\ntarget\t%s\nurl\t%s" % ( - title, pr_repo_desc, target_ref, url)) -continue_maybe("Proceed with merging pull request #%s?" % pr_num) + print ("\n=== Pull Request #%s ===" % pr_num) + print ("title\t%s\nsource\t%s\ntarget\t%s\nurl\t%s" % ( + title, pr_repo_desc, target_ref, url)) + continue_maybe("Proceed with merging pull request #%s?" % pr_num) -merged_refs = [target_ref] + merged_refs = [target_ref] -merge_hash = merge_pr(pr_num, target_ref) + merge_hash = merge_pr(pr_num, target_ref, title, body, pr_repo_desc) -pick_prompt = "Would you like to pick %s into another branch?" % merge_hash -while raw_input("\n%s (y/n): " % pick_prompt).lower() == "y": - merged_refs = merged_refs + [cherry_pick(pr_num, merge_hash, latest_branch)] + pick_prompt = "Would you like to pick %s into another branch?" % merge_hash + while raw_input("\n%s (y/n): " % pick_prompt).lower() == "y": + merged_refs = merged_refs + [cherry_pick(pr_num, merge_hash, latest_branch)] -if JIRA_IMPORTED: - if JIRA_USERNAME and JIRA_PASSWORD: - continue_maybe("Would you like to update an associated JIRA?") - jira_comment = "Issue resolved by pull request %s\n[%s/%s]" % (pr_num, GITHUB_BASE, pr_num) - resolve_jira_issues(title, merged_refs, jira_comment) + if JIRA_IMPORTED: + if JIRA_USERNAME and JIRA_PASSWORD: + continue_maybe("Would you like to update an associated JIRA?") + jira_comment = "Issue resolved by pull request %s\n[%s/%s]" % (pr_num, GITHUB_BASE, pr_num) + resolve_jira_issues(title, merged_refs, jira_comment) + else: + print "JIRA_USERNAME and JIRA_PASSWORD not set" + print "Exiting without trying to close the associated JIRA." else: - print "JIRA_USERNAME and JIRA_PASSWORD not set" + print "Could not find jira-python library. Run 'sudo pip install jira-python' to install." print "Exiting without trying to close the associated JIRA." -else: - print "Could not find jira-python library. Run 'sudo pip install jira-python' to install." - print "Exiting without trying to close the associated JIRA." + +if __name__ == "__main__": + import doctest + doctest.testmod() + + main() From 3a3f7100f4ead9b7ac50e9711ac50b603ebf6bea Mon Sep 17 00:00:00 2001 From: zsxwing Date: Tue, 21 Apr 2015 18:37:53 -0700 Subject: [PATCH 374/591] [SPARK-6490][Docs] Add docs for rpc configurations Added docs for rpc configurations and also fixed two places that should have been fixed in #5595. Author: zsxwing Closes #5607 from zsxwing/SPARK-6490-docs and squashes the following commits: 25a6736 [zsxwing] Increase the default timeout to 120s 6e37c30 [zsxwing] Update docs 5577540 [zsxwing] Use spark.network.timeout as the default timeout if it presents 4f07174 [zsxwing] Fix unit tests 1c2cf26 [zsxwing] Add docs for rpc configurations --- .../org/apache/spark/util/RpcUtils.scala | 6 ++-- .../org/apache/spark/SparkConfSuite.scala | 2 +- .../org/apache/spark/rpc/RpcEnvSuite.scala | 2 +- docs/configuration.md | 34 +++++++++++++++++-- 4 files changed, 38 insertions(+), 6 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/util/RpcUtils.scala b/core/src/main/scala/org/apache/spark/util/RpcUtils.scala index 5ae793e0e87a3..f16cc8e7e42c6 100644 --- a/core/src/main/scala/org/apache/spark/util/RpcUtils.scala +++ b/core/src/main/scala/org/apache/spark/util/RpcUtils.scala @@ -48,11 +48,13 @@ object RpcUtils { /** Returns the default Spark timeout to use for RPC ask operations. */ def askTimeout(conf: SparkConf): FiniteDuration = { - conf.getTimeAsSeconds("spark.rpc.askTimeout", "30s") seconds + conf.getTimeAsSeconds("spark.rpc.askTimeout", + conf.get("spark.network.timeout", "120s")) seconds } /** Returns the default Spark timeout to use for RPC remote endpoint lookup. */ def lookupTimeout(conf: SparkConf): FiniteDuration = { - conf.getTimeAsSeconds("spark.rpc.lookupTimeout", "30s") seconds + conf.getTimeAsSeconds("spark.rpc.lookupTimeout", + conf.get("spark.network.timeout", "120s")) seconds } } diff --git a/core/src/test/scala/org/apache/spark/SparkConfSuite.scala b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala index d7d8014a20498..272e6af0514e4 100644 --- a/core/src/test/scala/org/apache/spark/SparkConfSuite.scala +++ b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala @@ -227,7 +227,7 @@ class SparkConfSuite extends FunSuite with LocalSparkContext with ResetSystemPro test("akka deprecated configs") { val conf = new SparkConf() - assert(!conf.contains("spark.rpc.num.retries")) + assert(!conf.contains("spark.rpc.numRetries")) assert(!conf.contains("spark.rpc.retry.wait")) assert(!conf.contains("spark.rpc.askTimeout")) assert(!conf.contains("spark.rpc.lookupTimeout")) diff --git a/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala b/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala index 5fbda37c7cb88..44c88b00c442a 100644 --- a/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala +++ b/core/src/test/scala/org/apache/spark/rpc/RpcEnvSuite.scala @@ -156,7 +156,7 @@ abstract class RpcEnvSuite extends FunSuite with BeforeAndAfterAll { val conf = new SparkConf() conf.set("spark.rpc.retry.wait", "0") - conf.set("spark.rpc.num.retries", "1") + conf.set("spark.rpc.numRetries", "1") val anotherEnv = createRpcEnv(conf, "remote", 13345) // Use anotherEnv to find out the RpcEndpointRef val rpcEndpointRef = anotherEnv.setupEndpointRef("local", env.address, "ask-timeout") diff --git a/docs/configuration.md b/docs/configuration.md index d9e9e67026cbb..d587b91124cb8 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -963,8 +963,9 @@ Apart from these, the following properties are also available, and may be useful Default timeout for all network interactions. This config will be used in place of spark.core.connection.ack.wait.timeout, spark.akka.timeout, - spark.storage.blockManagerSlaveTimeoutMs or - spark.shuffle.io.connectionTimeout, if they are not configured. + spark.storage.blockManagerSlaveTimeoutMs, + spark.shuffle.io.connectionTimeout, spark.rpc.askTimeout or + spark.rpc.lookupTimeout if they are not configured. @@ -982,6 +983,35 @@ Apart from these, the following properties are also available, and may be useful This is only relevant for the Spark shell. + + spark.rpc.numRetries + 3 + Number of times to retry before an RPC task gives up. + An RPC task will run at most times of this number. + + + + + spark.rpc.retry.wait + 3s + + Duration for an RPC ask operation to wait before retrying. + + + + spark.rpc.askTimeout + 120s + + Duration for an RPC ask operation to wait before timing out. + + + + spark.rpc.lookupTimeout + 120s + Duration for an RPC remote endpoint lookup operation to wait before timing out. + + + #### Scheduling From 70f9f8ff38560967f2c84de77263a5455c45c495 Mon Sep 17 00:00:00 2001 From: Patrick Wendell Date: Tue, 21 Apr 2015 21:04:04 -0700 Subject: [PATCH 375/591] [MINOR] Comment improvements in ExternalSorter. 1. Clearly specifies the contract/interactions for users of this class. 2. Minor fix in one doc to avoid ambiguity. Author: Patrick Wendell Closes #5620 from pwendell/cleanup and squashes the following commits: 8d8f44f [Patrick Wendell] [Minor] Comment improvements in ExternalSorter. --- .../util/collection/ExternalSorter.scala | 27 +++++++++++++------ 1 file changed, 19 insertions(+), 8 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala index 79a1a8a0dae38..79a695fb62086 100644 --- a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala +++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala @@ -53,7 +53,18 @@ import org.apache.spark.storage.{BlockObjectWriter, BlockId} * probably want to pass None as the ordering to avoid extra sorting. On the other hand, if you do * want to do combining, having an Ordering is more efficient than not having it. * - * At a high level, this class works as follows: + * Users interact with this class in the following way: + * + * 1. Instantiate an ExternalSorter. + * + * 2. Call insertAll() with a set of records. + * + * 3. Request an iterator() back to traverse sorted/aggregated records. + * - or - + * Invoke writePartitionedFile() to create a file containing sorted/aggregated outputs + * that can be used in Spark's sort shuffle. + * + * At a high level, this class works internally as follows: * * - We repeatedly fill up buffers of in-memory data, using either a SizeTrackingAppendOnlyMap if * we want to combine by key, or an simple SizeTrackingBuffer if we don't. Inside these buffers, @@ -65,11 +76,11 @@ import org.apache.spark.storage.{BlockObjectWriter, BlockId} * aggregation. For each file, we track how many objects were in each partition in memory, so we * don't have to write out the partition ID for every element. * - * - When the user requests an iterator, the spilled files are merged, along with any remaining - * in-memory data, using the same sort order defined above (unless both sorting and aggregation - * are disabled). If we need to aggregate by key, we either use a total ordering from the - * ordering parameter, or read the keys with the same hash code and compare them with each other - * for equality to merge values. + * - When the user requests an iterator or file output, the spilled files are merged, along with + * any remaining in-memory data, using the same sort order defined above (unless both sorting + * and aggregation are disabled). If we need to aggregate by key, we either use a total ordering + * from the ordering parameter, or read the keys with the same hash code and compare them with + * each other for equality to merge values. * * - Users are expected to call stop() at the end to delete all the intermediate files. * @@ -259,8 +270,8 @@ private[spark] class ExternalSorter[K, V, C]( * Spill our in-memory collection to a sorted file that we can merge later (normal code path). * We add this file into spilledFiles to find it later. * - * Alternatively, if bypassMergeSort is true, we spill to separate files for each partition. - * See spillToPartitionedFiles() for that code path. + * This should not be invoked if bypassMergeSort is true. In that case, spillToPartitionedFiles() + * is used to write files for each partition. * * @param collection whichever collection we're using (map or buffer) */ From 607eff0edfc10a1473fa9713a0500bf09f105c13 Mon Sep 17 00:00:00 2001 From: "Joseph K. Bradley" Date: Tue, 21 Apr 2015 21:44:44 -0700 Subject: [PATCH 376/591] [SPARK-6113] [ML] Small cleanups after original tree API PR This does a few clean-ups. With this PR, all spark.ml tree components have ```private[ml]``` constructors. CC: mengxr Author: Joseph K. Bradley Closes #5567 from jkbradley/dt-api-dt2 and squashes the following commits: 2263b5b [Joseph K. Bradley] Added note about tree example issue. bb9f610 [Joseph K. Bradley] Small cleanups after original tree API PR --- .../examples/ml/DecisionTreeExample.scala | 25 ++++++++++++++----- .../spark/ml/impl/tree/treeParams.scala | 4 +-- .../org/apache/spark/ml/tree/Split.scala | 7 +++--- 3 files changed, 25 insertions(+), 11 deletions(-) diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala index 921b396e799e7..2cd515c89d3d2 100644 --- a/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/ml/DecisionTreeExample.scala @@ -44,6 +44,13 @@ import org.apache.spark.sql.{SQLContext, DataFrame} * {{{ * ./bin/run-example ml.DecisionTreeExample [options] * }}} + * Note that Decision Trees can take a large amount of memory. If the run-example command above + * fails, try running via spark-submit and specifying the amount of memory as at least 1g. + * For local mode, run + * {{{ + * ./bin/spark-submit --class org.apache.spark.examples.ml.DecisionTreeExample --driver-memory 1g + * [examples JAR path] [options] + * }}} * If you use it as a template to create your own app, please use `spark-submit` to submit your app. */ object DecisionTreeExample { @@ -70,7 +77,7 @@ object DecisionTreeExample { val parser = new OptionParser[Params]("DecisionTreeExample") { head("DecisionTreeExample: an example decision tree app.") opt[String]("algo") - .text(s"algorithm (Classification, Regression), default: ${defaultParams.algo}") + .text(s"algorithm (classification, regression), default: ${defaultParams.algo}") .action((x, c) => c.copy(algo = x)) opt[Int]("maxDepth") .text(s"max depth of the tree, default: ${defaultParams.maxDepth}") @@ -222,18 +229,23 @@ object DecisionTreeExample { // (1) For classification, re-index classes. val labelColName = if (algo == "classification") "indexedLabel" else "label" if (algo == "classification") { - val labelIndexer = new StringIndexer().setInputCol("labelString").setOutputCol(labelColName) + val labelIndexer = new StringIndexer() + .setInputCol("labelString") + .setOutputCol(labelColName) stages += labelIndexer } // (2) Identify categorical features using VectorIndexer. // Features with more than maxCategories values will be treated as continuous. - val featuresIndexer = new VectorIndexer().setInputCol("features") - .setOutputCol("indexedFeatures").setMaxCategories(10) + val featuresIndexer = new VectorIndexer() + .setInputCol("features") + .setOutputCol("indexedFeatures") + .setMaxCategories(10) stages += featuresIndexer // (3) Learn DecisionTree val dt = algo match { case "classification" => - new DecisionTreeClassifier().setFeaturesCol("indexedFeatures") + new DecisionTreeClassifier() + .setFeaturesCol("indexedFeatures") .setLabelCol(labelColName) .setMaxDepth(params.maxDepth) .setMaxBins(params.maxBins) @@ -242,7 +254,8 @@ object DecisionTreeExample { .setCacheNodeIds(params.cacheNodeIds) .setCheckpointInterval(params.checkpointInterval) case "regression" => - new DecisionTreeRegressor().setFeaturesCol("indexedFeatures") + new DecisionTreeRegressor() + .setFeaturesCol("indexedFeatures") .setLabelCol(labelColName) .setMaxDepth(params.maxDepth) .setMaxBins(params.maxBins) diff --git a/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala b/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala index 6f4509f03d033..eb2609faef05a 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/impl/tree/treeParams.scala @@ -117,7 +117,7 @@ private[ml] trait DecisionTreeParams extends PredictorParams { def setMaxDepth(value: Int): this.type = { require(value >= 0, s"maxDepth parameter must be >= 0. Given bad value: $value") set(maxDepth, value) - this.asInstanceOf[this.type] + this } /** @group getParam */ @@ -283,7 +283,7 @@ private[ml] trait TreeRegressorParams extends Params { def getImpurity: String = getOrDefault(impurity) /** Convert new impurity to old impurity. */ - protected def getOldImpurity: OldImpurity = { + private[ml] def getOldImpurity: OldImpurity = { getImpurity match { case "variance" => OldVariance case _ => diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala index cb940f62990ed..708c769087dd0 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/tree/Split.scala @@ -38,7 +38,7 @@ sealed trait Split extends Serializable { private[tree] def toOld: OldSplit } -private[ml] object Split { +private[tree] object Split { def fromOld(oldSplit: OldSplit, categoricalFeatures: Map[Int, Int]): Split = { oldSplit.featureType match { @@ -58,7 +58,7 @@ private[ml] object Split { * left. Otherwise, it goes right. * @param numCategories Number of categories for this feature. */ -final class CategoricalSplit( +final class CategoricalSplit private[ml] ( override val featureIndex: Int, leftCategories: Array[Double], private val numCategories: Int) @@ -130,7 +130,8 @@ final class CategoricalSplit( * @param threshold If the feature value is <= this threshold, then the split goes left. * Otherwise, it goes right. */ -final class ContinuousSplit(override val featureIndex: Int, val threshold: Double) extends Split { +final class ContinuousSplit private[ml] (override val featureIndex: Int, val threshold: Double) + extends Split { override private[ml] def shouldGoLeft(features: Vector): Boolean = { features(featureIndex) <= threshold From bdc5c16e76c5d0bc147408353b2ba4faa8e914fc Mon Sep 17 00:00:00 2001 From: Sean Owen Date: Tue, 21 Apr 2015 22:34:31 -0700 Subject: [PATCH 377/591] [SPARK-6889] [DOCS] CONTRIBUTING.md updates to accompany contribution doc updates Part of the SPARK-6889 doc updates, to accompany wiki updates at https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark See draft text at https://docs.google.com/document/d/1tB9-f9lmxhC32QlOo4E8Z7eGDwHx1_Q3O8uCmRXQTo8/edit# Author: Sean Owen Closes #5623 from srowen/SPARK-6889 and squashes the following commits: 03773b1 [Sean Owen] Part of the SPARK-6889 doc updates, to accompany wiki updates at https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark --- CONTRIBUTING.md | 22 +++++++++++++--------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index b6c6b050fa331..f10d7e277eea3 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,12 +1,16 @@ ## Contributing to Spark -Contributions via GitHub pull requests are gladly accepted from their original -author. Along with any pull requests, please state that the contribution is -your original work and that you license the work to the project under the -project's open source license. Whether or not you state this explicitly, by -submitting any copyrighted material via pull request, email, or other means -you agree to license the material under the project's open source license and -warrant that you have the legal authority to do so. +*Before opening a pull request*, review the +[Contributing to Spark wiki](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark). +It lists steps that are required before creating a PR. In particular, consider: + +- Is the change important and ready enough to ask the community to spend time reviewing? +- Have you searched for existing, related JIRAs and pull requests? +- Is this a new feature that can stand alone as a package on http://spark-packages.org ? +- Is the change being proposed clearly explained and motivated? -Please see the [Contributing to Spark wiki page](https://cwiki.apache.org/SPARK/Contributing+to+Spark) -for more information. +When you contribute code, you affirm that the contribution is your original work and that you +license the work to the project under the project's open source license. Whether or not you +state this explicitly, by submitting any copyrighted material via pull request, email, or +other means you agree to license the material under the project's open source license and +warrant that you have the legal authority to do so. From 33b85620f910c404873d362d27cca1223084913a Mon Sep 17 00:00:00 2001 From: zsxwing Date: Wed, 22 Apr 2015 11:08:59 -0700 Subject: [PATCH 378/591] [SPARK-7052][Core] Add ThreadUtils and move thread methods from Utils to ThreadUtils As per rxin 's suggestion in https://github.com/apache/spark/pull/5392/files#r28757176 What's more, there is a race condition in the global shared `daemonThreadFactoryBuilder`. `daemonThreadFactoryBuilder` may be modified by multiple threads. This PR removed the global `daemonThreadFactoryBuilder` and created a new `ThreadFactoryBuilder` every time. Author: zsxwing Closes #5631 from zsxwing/thread-utils and squashes the following commits: 9fe5b0e [zsxwing] Add ThreadUtils and move thread methods from Utils to ThreadUtils --- .../spark/ExecutorAllocationManager.scala | 8 +-- .../org/apache/spark/HeartbeatReceiver.scala | 11 ++- .../deploy/history/FsHistoryProvider.scala | 4 +- .../org/apache/spark/executor/Executor.scala | 7 +- .../spark/network/nio/ConnectionManager.scala | 12 ++-- .../apache/spark/scheduler/DAGScheduler.scala | 4 +- .../spark/scheduler/TaskResultGetter.scala | 4 +- .../CoarseGrainedSchedulerBackend.scala | 6 +- .../cluster/YarnSchedulerBackend.scala | 4 +- .../spark/scheduler/local/LocalBackend.scala | 8 +-- .../storage/BlockManagerMasterEndpoint.scala | 4 +- .../storage/BlockManagerSlaveEndpoint.scala | 4 +- .../org/apache/spark/util/ThreadUtils.scala | 67 +++++++++++++++++++ .../scala/org/apache/spark/util/Utils.scala | 29 -------- .../apache/spark/util/ThreadUtilsSuite.scala | 57 ++++++++++++++++ .../streaming/kafka/KafkaInputDStream.scala | 5 +- .../kafka/ReliableKafkaReceiver.scala | 4 +- .../receiver/ReceivedBlockHandler.scala | 4 +- .../streaming/util/WriteAheadLogManager.scala | 4 +- 19 files changed, 170 insertions(+), 76 deletions(-) create mode 100644 core/src/main/scala/org/apache/spark/util/ThreadUtils.scala create mode 100644 core/src/test/scala/org/apache/spark/util/ThreadUtilsSuite.scala diff --git a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala index 4e7bf51fc0622..b986fa87dc2f4 100644 --- a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala +++ b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala @@ -17,12 +17,12 @@ package org.apache.spark -import java.util.concurrent.{Executors, TimeUnit} +import java.util.concurrent.TimeUnit import scala.collection.mutable import org.apache.spark.scheduler._ -import org.apache.spark.util.{Clock, SystemClock, Utils} +import org.apache.spark.util.{ThreadUtils, Clock, SystemClock, Utils} /** * An agent that dynamically allocates and removes executors based on the workload. @@ -132,8 +132,8 @@ private[spark] class ExecutorAllocationManager( private val listener = new ExecutorAllocationListener // Executor that handles the scheduling task. - private val executor = Executors.newSingleThreadScheduledExecutor( - Utils.namedThreadFactory("spark-dynamic-executor-allocation")) + private val executor = + ThreadUtils.newDaemonSingleThreadScheduledExecutor("spark-dynamic-executor-allocation") /** * Verify that the settings specified through the config are valid. diff --git a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala index e3bd16f1cbf24..68d05d5b02537 100644 --- a/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala +++ b/core/src/main/scala/org/apache/spark/HeartbeatReceiver.scala @@ -17,7 +17,7 @@ package org.apache.spark -import java.util.concurrent.{ScheduledFuture, TimeUnit, Executors} +import java.util.concurrent.{ScheduledFuture, TimeUnit} import scala.collection.mutable @@ -25,7 +25,7 @@ import org.apache.spark.executor.TaskMetrics import org.apache.spark.rpc.{ThreadSafeRpcEndpoint, RpcEnv, RpcCallContext} import org.apache.spark.storage.BlockManagerId import org.apache.spark.scheduler.{SlaveLost, TaskScheduler} -import org.apache.spark.util.Utils +import org.apache.spark.util.{ThreadUtils, Utils} /** * A heartbeat from executors to the driver. This is a shared message used by several internal @@ -76,11 +76,10 @@ private[spark] class HeartbeatReceiver(sc: SparkContext) private var timeoutCheckingTask: ScheduledFuture[_] = null - private val timeoutCheckingThread = Executors.newSingleThreadScheduledExecutor( - Utils.namedThreadFactory("heartbeat-timeout-checking-thread")) + private val timeoutCheckingThread = + ThreadUtils.newDaemonSingleThreadScheduledExecutor("heartbeat-timeout-checking-thread") - private val killExecutorThread = Executors.newSingleThreadExecutor( - Utils.namedThreadFactory("kill-executor-thread")) + private val killExecutorThread = ThreadUtils.newDaemonSingleThreadExecutor("kill-executor-thread") override def onStart(): Unit = { timeoutCheckingTask = timeoutCheckingThread.scheduleAtFixedRate(new Runnable { diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala index 47bdd7749ec3d..9847d5944a390 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala @@ -32,7 +32,7 @@ import org.apache.spark.deploy.SparkHadoopUtil import org.apache.spark.io.CompressionCodec import org.apache.spark.scheduler._ import org.apache.spark.ui.SparkUI -import org.apache.spark.util.Utils +import org.apache.spark.util.{ThreadUtils, Utils} import org.apache.spark.{Logging, SecurityManager, SparkConf} @@ -99,7 +99,7 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis */ private val replayExecutor: ExecutorService = { if (!conf.contains("spark.testing")) { - Executors.newSingleThreadExecutor(Utils.namedThreadFactory("log-replay-executor")) + ThreadUtils.newDaemonSingleThreadExecutor("log-replay-executor") } else { MoreExecutors.sameThreadExecutor() } diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala index 327d155b38c22..5fc04df5d6a40 100644 --- a/core/src/main/scala/org/apache/spark/executor/Executor.scala +++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala @@ -21,7 +21,7 @@ import java.io.File import java.lang.management.ManagementFactory import java.net.URL import java.nio.ByteBuffer -import java.util.concurrent.{ConcurrentHashMap, Executors, TimeUnit} +import java.util.concurrent.{ConcurrentHashMap, TimeUnit} import scala.collection.JavaConversions._ import scala.collection.mutable.{ArrayBuffer, HashMap} @@ -76,7 +76,7 @@ private[spark] class Executor( } // Start worker thread pool - private val threadPool = Utils.newDaemonCachedThreadPool("Executor task launch worker") + private val threadPool = ThreadUtils.newDaemonCachedThreadPool("Executor task launch worker") private val executorSource = new ExecutorSource(threadPool, executorId) if (!isLocal) { @@ -110,8 +110,7 @@ private[spark] class Executor( private val runningTasks = new ConcurrentHashMap[Long, TaskRunner] // Executor for the heartbeat task. - private val heartbeater = Executors.newSingleThreadScheduledExecutor( - Utils.namedThreadFactory("driver-heartbeater")) + private val heartbeater = ThreadUtils.newDaemonSingleThreadScheduledExecutor("driver-heartbeater") startDriverHeartbeater() diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala index 1a68e621eaee7..16e905982cf64 100644 --- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala +++ b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala @@ -36,7 +36,7 @@ import io.netty.util.{Timeout, TimerTask, HashedWheelTimer} import org.apache.spark._ import org.apache.spark.network.sasl.{SparkSaslClient, SparkSaslServer} -import org.apache.spark.util.Utils +import org.apache.spark.util.{ThreadUtils, Utils} import scala.util.Try import scala.util.control.NonFatal @@ -79,7 +79,7 @@ private[nio] class ConnectionManager( private val selector = SelectorProvider.provider.openSelector() private val ackTimeoutMonitor = - new HashedWheelTimer(Utils.namedThreadFactory("AckTimeoutMonitor")) + new HashedWheelTimer(ThreadUtils.namedThreadFactory("AckTimeoutMonitor")) private val ackTimeout = conf.getTimeAsSeconds("spark.core.connection.ack.wait.timeout", @@ -102,7 +102,7 @@ private[nio] class ConnectionManager( handlerThreadCount, conf.getInt("spark.core.connection.handler.threads.keepalive", 60), TimeUnit.SECONDS, new LinkedBlockingDeque[Runnable](), - Utils.namedThreadFactory("handle-message-executor")) { + ThreadUtils.namedThreadFactory("handle-message-executor")) { override def afterExecute(r: Runnable, t: Throwable): Unit = { super.afterExecute(r, t) @@ -117,7 +117,7 @@ private[nio] class ConnectionManager( ioThreadCount, conf.getInt("spark.core.connection.io.threads.keepalive", 60), TimeUnit.SECONDS, new LinkedBlockingDeque[Runnable](), - Utils.namedThreadFactory("handle-read-write-executor")) { + ThreadUtils.namedThreadFactory("handle-read-write-executor")) { override def afterExecute(r: Runnable, t: Throwable): Unit = { super.afterExecute(r, t) @@ -134,7 +134,7 @@ private[nio] class ConnectionManager( connectThreadCount, conf.getInt("spark.core.connection.connect.threads.keepalive", 60), TimeUnit.SECONDS, new LinkedBlockingDeque[Runnable](), - Utils.namedThreadFactory("handle-connect-executor")) { + ThreadUtils.namedThreadFactory("handle-connect-executor")) { override def afterExecute(r: Runnable, t: Throwable): Unit = { super.afterExecute(r, t) @@ -160,7 +160,7 @@ private[nio] class ConnectionManager( private val registerRequests = new SynchronizedQueue[SendingConnection] implicit val futureExecContext = ExecutionContext.fromExecutor( - Utils.newDaemonCachedThreadPool("Connection manager future execution context")) + ThreadUtils.newDaemonCachedThreadPool("Connection manager future execution context")) @volatile private var onReceiveCallback: (BufferMessage, ConnectionManagerId) => Option[Message] = null diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala index 4a32f8936fb0e..8c4bff4e83afc 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -19,7 +19,7 @@ package org.apache.spark.scheduler import java.io.NotSerializableException import java.util.Properties -import java.util.concurrent.{TimeUnit, Executors} +import java.util.concurrent.TimeUnit import java.util.concurrent.atomic.AtomicInteger import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, Map, Stack} @@ -129,7 +129,7 @@ class DAGScheduler( private val disallowStageRetryForTest = sc.getConf.getBoolean("spark.test.noStageRetry", false) private val messageScheduler = - Executors.newScheduledThreadPool(1, Utils.namedThreadFactory("dag-scheduler-message")) + ThreadUtils.newDaemonSingleThreadScheduledExecutor("dag-scheduler-message") private[scheduler] val eventProcessLoop = new DAGSchedulerEventProcessLoop(this) taskScheduler.setDAGScheduler(this) diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala index 3938580aeea59..391827c1d2156 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala @@ -26,7 +26,7 @@ import scala.util.control.NonFatal import org.apache.spark._ import org.apache.spark.TaskState.TaskState import org.apache.spark.serializer.SerializerInstance -import org.apache.spark.util.Utils +import org.apache.spark.util.{ThreadUtils, Utils} /** * Runs a thread pool that deserializes and remotely fetches (if necessary) task results. @@ -35,7 +35,7 @@ private[spark] class TaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedul extends Logging { private val THREADS = sparkEnv.conf.getInt("spark.resultGetter.threads", 4) - private val getTaskResultExecutor = Utils.newDaemonFixedThreadPool( + private val getTaskResultExecutor = ThreadUtils.newDaemonFixedThreadPool( THREADS, "task-result-getter") protected val serializer = new ThreadLocal[SerializerInstance] { diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala index 63987dfb32695..9656fb76858ea 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala @@ -17,7 +17,7 @@ package org.apache.spark.scheduler.cluster -import java.util.concurrent.{TimeUnit, Executors} +import java.util.concurrent.TimeUnit import java.util.concurrent.atomic.AtomicInteger import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet} @@ -26,7 +26,7 @@ import org.apache.spark.rpc._ import org.apache.spark.{ExecutorAllocationClient, Logging, SparkEnv, SparkException, TaskState} import org.apache.spark.scheduler._ import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ -import org.apache.spark.util.{SerializableBuffer, AkkaUtils, Utils} +import org.apache.spark.util.{ThreadUtils, SerializableBuffer, AkkaUtils, Utils} /** * A scheduler backend that waits for coarse grained executors to connect to it through Akka. @@ -73,7 +73,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp private val addressToExecutorId = new HashMap[RpcAddress, String] private val reviveThread = - Executors.newSingleThreadScheduledExecutor(Utils.namedThreadFactory("driver-revive-thread")) + ThreadUtils.newDaemonSingleThreadScheduledExecutor("driver-revive-thread") override def onStart() { // Periodically revive offers to allow delay scheduling to work diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala index 1406a36a669c5..d987c7d563579 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala @@ -24,7 +24,7 @@ import org.apache.spark.rpc._ import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ import org.apache.spark.scheduler.TaskSchedulerImpl import org.apache.spark.ui.JettyUtils -import org.apache.spark.util.{RpcUtils, Utils} +import org.apache.spark.util.{ThreadUtils, RpcUtils} import scala.util.control.NonFatal @@ -97,7 +97,7 @@ private[spark] abstract class YarnSchedulerBackend( private var amEndpoint: Option[RpcEndpointRef] = None private val askAmThreadPool = - Utils.newDaemonCachedThreadPool("yarn-scheduler-ask-am-thread-pool") + ThreadUtils.newDaemonCachedThreadPool("yarn-scheduler-ask-am-thread-pool") implicit val askAmExecutor = ExecutionContext.fromExecutor(askAmThreadPool) override def receive: PartialFunction[Any, Unit] = { diff --git a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala index 50ba0b9d5a612..ac5b524517818 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala @@ -18,14 +18,14 @@ package org.apache.spark.scheduler.local import java.nio.ByteBuffer -import java.util.concurrent.{Executors, TimeUnit} +import java.util.concurrent.TimeUnit import org.apache.spark.{Logging, SparkConf, SparkContext, SparkEnv, TaskState} import org.apache.spark.TaskState.TaskState import org.apache.spark.executor.{Executor, ExecutorBackend} import org.apache.spark.rpc.{ThreadSafeRpcEndpoint, RpcCallContext, RpcEndpointRef, RpcEnv} import org.apache.spark.scheduler.{SchedulerBackend, TaskSchedulerImpl, WorkerOffer} -import org.apache.spark.util.Utils +import org.apache.spark.util.{ThreadUtils, Utils} private case class ReviveOffers() @@ -47,8 +47,8 @@ private[spark] class LocalEndpoint( private val totalCores: Int) extends ThreadSafeRpcEndpoint with Logging { - private val reviveThread = Executors.newSingleThreadScheduledExecutor( - Utils.namedThreadFactory("local-revive-thread")) + private val reviveThread = + ThreadUtils.newDaemonSingleThreadScheduledExecutor("local-revive-thread") private var freeCores = totalCores diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala index 28c73a7d543ff..4682167912ff0 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala @@ -28,7 +28,7 @@ import org.apache.spark.{Logging, SparkConf} import org.apache.spark.annotation.DeveloperApi import org.apache.spark.scheduler._ import org.apache.spark.storage.BlockManagerMessages._ -import org.apache.spark.util.Utils +import org.apache.spark.util.{ThreadUtils, Utils} /** * BlockManagerMasterEndpoint is an [[ThreadSafeRpcEndpoint]] on the master node to track statuses @@ -51,7 +51,7 @@ class BlockManagerMasterEndpoint( // Mapping from block id to the set of block managers that have the block. private val blockLocations = new JHashMap[BlockId, mutable.HashSet[BlockManagerId]] - private val askThreadPool = Utils.newDaemonCachedThreadPool("block-manager-ask-thread-pool") + private val askThreadPool = ThreadUtils.newDaemonCachedThreadPool("block-manager-ask-thread-pool") private implicit val askExecutionContext = ExecutionContext.fromExecutorService(askThreadPool) override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = { diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveEndpoint.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveEndpoint.scala index 8980fa8eb70e2..543df4e1350dd 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveEndpoint.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerSlaveEndpoint.scala @@ -20,7 +20,7 @@ package org.apache.spark.storage import scala.concurrent.{ExecutionContext, Future} import org.apache.spark.rpc.{RpcEnv, RpcCallContext, RpcEndpoint} -import org.apache.spark.util.Utils +import org.apache.spark.util.ThreadUtils import org.apache.spark.{Logging, MapOutputTracker, SparkEnv} import org.apache.spark.storage.BlockManagerMessages._ @@ -36,7 +36,7 @@ class BlockManagerSlaveEndpoint( extends RpcEndpoint with Logging { private val asyncThreadPool = - Utils.newDaemonCachedThreadPool("block-manager-slave-async-thread-pool") + ThreadUtils.newDaemonCachedThreadPool("block-manager-slave-async-thread-pool") private implicit val asyncExecutionContext = ExecutionContext.fromExecutorService(asyncThreadPool) // Operations that involve removing blocks may be slow and should be done asynchronously diff --git a/core/src/main/scala/org/apache/spark/util/ThreadUtils.scala b/core/src/main/scala/org/apache/spark/util/ThreadUtils.scala new file mode 100644 index 0000000000000..098a4b79496b2 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/util/ThreadUtils.scala @@ -0,0 +1,67 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + + +package org.apache.spark.util + +import java.util.concurrent._ + +import com.google.common.util.concurrent.ThreadFactoryBuilder + +private[spark] object ThreadUtils { + + /** + * Create a thread factory that names threads with a prefix and also sets the threads to daemon. + */ + def namedThreadFactory(prefix: String): ThreadFactory = { + new ThreadFactoryBuilder().setDaemon(true).setNameFormat(prefix + "-%d").build() + } + + /** + * Wrapper over newCachedThreadPool. Thread names are formatted as prefix-ID, where ID is a + * unique, sequentially assigned integer. + */ + def newDaemonCachedThreadPool(prefix: String): ThreadPoolExecutor = { + val threadFactory = namedThreadFactory(prefix) + Executors.newCachedThreadPool(threadFactory).asInstanceOf[ThreadPoolExecutor] + } + + /** + * Wrapper over newFixedThreadPool. Thread names are formatted as prefix-ID, where ID is a + * unique, sequentially assigned integer. + */ + def newDaemonFixedThreadPool(nThreads: Int, prefix: String): ThreadPoolExecutor = { + val threadFactory = namedThreadFactory(prefix) + Executors.newFixedThreadPool(nThreads, threadFactory).asInstanceOf[ThreadPoolExecutor] + } + + /** + * Wrapper over newSingleThreadExecutor. + */ + def newDaemonSingleThreadExecutor(threadName: String): ExecutorService = { + val threadFactory = new ThreadFactoryBuilder().setDaemon(true).setNameFormat(threadName).build() + Executors.newSingleThreadExecutor(threadFactory) + } + + /** + * Wrapper over newSingleThreadScheduledExecutor. + */ + def newDaemonSingleThreadScheduledExecutor(threadName: String): ScheduledExecutorService = { + val threadFactory = new ThreadFactoryBuilder().setDaemon(true).setNameFormat(threadName).build() + Executors.newSingleThreadScheduledExecutor(threadFactory) + } +} diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index 7b0de1ae55b78..2feb7341b159b 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -35,7 +35,6 @@ import scala.util.control.{ControlThrowable, NonFatal} import com.google.common.io.{ByteStreams, Files} import com.google.common.net.InetAddresses -import com.google.common.util.concurrent.ThreadFactoryBuilder import org.apache.commons.lang3.SystemUtils import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.{FileSystem, FileUtil, Path} @@ -897,34 +896,6 @@ private[spark] object Utils extends Logging { hostPortParseResults.get(hostPort) } - private val daemonThreadFactoryBuilder: ThreadFactoryBuilder = - new ThreadFactoryBuilder().setDaemon(true) - - /** - * Create a thread factory that names threads with a prefix and also sets the threads to daemon. - */ - def namedThreadFactory(prefix: String): ThreadFactory = { - daemonThreadFactoryBuilder.setNameFormat(prefix + "-%d").build() - } - - /** - * Wrapper over newCachedThreadPool. Thread names are formatted as prefix-ID, where ID is a - * unique, sequentially assigned integer. - */ - def newDaemonCachedThreadPool(prefix: String): ThreadPoolExecutor = { - val threadFactory = namedThreadFactory(prefix) - Executors.newCachedThreadPool(threadFactory).asInstanceOf[ThreadPoolExecutor] - } - - /** - * Wrapper over newFixedThreadPool. Thread names are formatted as prefix-ID, where ID is a - * unique, sequentially assigned integer. - */ - def newDaemonFixedThreadPool(nThreads: Int, prefix: String): ThreadPoolExecutor = { - val threadFactory = namedThreadFactory(prefix) - Executors.newFixedThreadPool(nThreads, threadFactory).asInstanceOf[ThreadPoolExecutor] - } - /** * Return the string to tell how long has passed in milliseconds. */ diff --git a/core/src/test/scala/org/apache/spark/util/ThreadUtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/ThreadUtilsSuite.scala new file mode 100644 index 0000000000000..a3aa3e953fbec --- /dev/null +++ b/core/src/test/scala/org/apache/spark/util/ThreadUtilsSuite.scala @@ -0,0 +1,57 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + + +package org.apache.spark.util + +import java.util.concurrent.{CountDownLatch, TimeUnit} + +import org.scalatest.FunSuite + +class ThreadUtilsSuite extends FunSuite { + + test("newDaemonSingleThreadExecutor") { + val executor = ThreadUtils.newDaemonSingleThreadExecutor("this-is-a-thread-name") + @volatile var threadName = "" + executor.submit(new Runnable { + override def run(): Unit = { + threadName = Thread.currentThread().getName() + } + }) + executor.shutdown() + executor.awaitTermination(10, TimeUnit.SECONDS) + assert(threadName === "this-is-a-thread-name") + } + + test("newDaemonSingleThreadScheduledExecutor") { + val executor = ThreadUtils.newDaemonSingleThreadScheduledExecutor("this-is-a-thread-name") + try { + val latch = new CountDownLatch(1) + @volatile var threadName = "" + executor.schedule(new Runnable { + override def run(): Unit = { + threadName = Thread.currentThread().getName() + latch.countDown() + } + }, 1, TimeUnit.MILLISECONDS) + latch.await(10, TimeUnit.SECONDS) + assert(threadName === "this-is-a-thread-name") + } finally { + executor.shutdownNow() + } + } +} diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala index 4d26b640e8d74..cca0fac0234e1 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala @@ -31,7 +31,7 @@ import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.StreamingContext import org.apache.spark.streaming.dstream._ import org.apache.spark.streaming.receiver.Receiver -import org.apache.spark.util.Utils +import org.apache.spark.util.ThreadUtils /** * Input stream that pulls messages from a Kafka Broker. @@ -111,7 +111,8 @@ class KafkaReceiver[ val topicMessageStreams = consumerConnector.createMessageStreams( topics, keyDecoder, valueDecoder) - val executorPool = Utils.newDaemonFixedThreadPool(topics.values.sum, "KafkaMessageHandler") + val executorPool = + ThreadUtils.newDaemonFixedThreadPool(topics.values.sum, "KafkaMessageHandler") try { // Start the messages handler for each partition topicMessageStreams.values.foreach { streams => diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/ReliableKafkaReceiver.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/ReliableKafkaReceiver.scala index c4a44c1822c39..ea87e960379f1 100644 --- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/ReliableKafkaReceiver.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/ReliableKafkaReceiver.scala @@ -33,7 +33,7 @@ import org.I0Itec.zkclient.ZkClient import org.apache.spark.{Logging, SparkEnv} import org.apache.spark.storage.{StorageLevel, StreamBlockId} import org.apache.spark.streaming.receiver.{BlockGenerator, BlockGeneratorListener, Receiver} -import org.apache.spark.util.Utils +import org.apache.spark.util.ThreadUtils /** * ReliableKafkaReceiver offers the ability to reliably store data into BlockManager without loss. @@ -121,7 +121,7 @@ class ReliableKafkaReceiver[ zkClient = new ZkClient(consumerConfig.zkConnect, consumerConfig.zkSessionTimeoutMs, consumerConfig.zkConnectionTimeoutMs, ZKStringSerializer) - messageHandlerThreadPool = Utils.newDaemonFixedThreadPool( + messageHandlerThreadPool = ThreadUtils.newDaemonFixedThreadPool( topics.values.sum, "KafkaMessageHandler") blockGenerator.start() diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala index dcdc27d29c270..297bf04c0c25e 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala @@ -28,7 +28,7 @@ import org.apache.hadoop.fs.Path import org.apache.spark.{Logging, SparkConf, SparkException} import org.apache.spark.storage._ import org.apache.spark.streaming.util.{WriteAheadLogFileSegment, WriteAheadLogManager} -import org.apache.spark.util.{Clock, SystemClock, Utils} +import org.apache.spark.util.{ThreadUtils, Clock, SystemClock} /** Trait that represents the metadata related to storage of blocks */ private[streaming] trait ReceivedBlockStoreResult { @@ -150,7 +150,7 @@ private[streaming] class WriteAheadLogBasedBlockHandler( // For processing futures used in parallel block storing into block manager and write ahead log // # threads = 2, so that both writing to BM and WAL can proceed in parallel implicit private val executionContext = ExecutionContext.fromExecutorService( - Utils.newDaemonFixedThreadPool(2, this.getClass.getSimpleName)) + ThreadUtils.newDaemonFixedThreadPool(2, this.getClass.getSimpleName)) /** * This implementation stores the block into the block manager as well as a write ahead log. diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogManager.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogManager.scala index 6bdfe45dc7f83..38a93cc3c9a1f 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogManager.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/WriteAheadLogManager.scala @@ -25,7 +25,7 @@ import scala.language.postfixOps import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.apache.spark.Logging -import org.apache.spark.util.{Clock, SystemClock, Utils} +import org.apache.spark.util.{ThreadUtils, Clock, SystemClock} import WriteAheadLogManager._ /** @@ -60,7 +60,7 @@ private[streaming] class WriteAheadLogManager( if (callerName.nonEmpty) s" for $callerName" else "" private val threadpoolName = s"WriteAheadLogManager $callerNameTag" implicit private val executionContext = ExecutionContext.fromExecutorService( - Utils.newDaemonFixedThreadPool(1, threadpoolName)) + ThreadUtils.newDaemonSingleThreadExecutor(threadpoolName)) override protected val logName = s"WriteAheadLogManager $callerNameTag" private var currentLogPath: Option[String] = None From cdf0328684f70ddcd49b23c23c1532aeb9caa44e Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Wed, 22 Apr 2015 11:18:01 -0700 Subject: [PATCH 379/591] [SQL] Rename some apply functions. I was looking at the code gen code and got confused by a few of use cases of apply, in particular apply on objects. So I went ahead and changed a few of them. Hopefully slightly more clear with a proper verb. Author: Reynold Xin Closes #5624 from rxin/apply-rename and squashes the following commits: ee45034 [Reynold Xin] [SQL] Rename some apply functions. --- .../sql/catalyst/AbstractSparkSQLParser.scala | 2 +- .../spark/sql/catalyst/dsl/package.scala | 2 +- .../expressions/codegen/CodeGenerator.scala | 6 +-- .../codegen/GenerateMutableProjection.scala | 2 +- .../codegen/GenerateOrdering.scala | 2 +- .../codegen/GeneratePredicate.scala | 2 +- .../codegen/GenerateProjection.scala | 2 +- .../sql/catalyst/expressions/predicates.scala | 6 +-- .../sql/catalyst/rules/RuleExecutor.scala | 2 +- .../spark/sql/catalyst/SqlParserSuite.scala | 9 ++-- .../sql/catalyst/analysis/AnalysisSuite.scala | 22 ++++---- .../analysis/DecimalPrecisionSuite.scala | 6 +-- .../GeneratedEvaluationSuite.scala | 10 ++-- .../GeneratedMutableEvaluationSuite.scala | 8 +-- .../BooleanSimplificationSuite.scala | 2 +- .../optimizer/CombiningLimitsSuite.scala | 4 +- .../optimizer/ConstantFoldingSuite.scala | 14 ++--- .../ConvertToLocalRelationSuite.scala | 2 +- .../ExpressionOptimizationSuite.scala | 2 +- .../optimizer/FilterPushdownSuite.scala | 52 +++++++++---------- .../optimizer/LikeSimplificationSuite.scala | 8 +-- .../catalyst/optimizer/OptimizeInSuite.scala | 4 +- ...mplifyCaseConversionExpressionsSuite.scala | 8 +-- .../optimizer/UnionPushdownSuite.scala | 7 ++- .../catalyst/trees/RuleExecutorSuite.scala | 6 +-- .../org/apache/spark/sql/SQLContext.scala | 12 ++--- .../spark/sql/execution/SparkPlan.scala | 10 ++-- .../joins/BroadcastNestedLoopJoin.scala | 2 +- .../sql/execution/joins/LeftSemiJoinBNL.scala | 2 +- .../apache/spark/sql/parquet/newParquet.scala | 2 +- .../org/apache/spark/sql/sources/ddl.scala | 4 +- .../apache/spark/sql/hive/HiveContext.scala | 2 +- .../org/apache/spark/sql/hive/HiveQl.scala | 4 +- .../spark/sql/hive/HiveStrategies.scala | 4 +- .../apache/spark/sql/hive/test/TestHive.scala | 2 +- 35 files changed, 117 insertions(+), 117 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala index 3823584287741..1f3c02478bd68 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala @@ -32,7 +32,7 @@ private[sql] object KeywordNormalizer { private[sql] abstract class AbstractSparkSQLParser extends StandardTokenParsers with PackratParsers { - def apply(input: String): LogicalPlan = { + def parse(input: String): LogicalPlan = { // Initialize the Keywords. lexical.initialize(reservedWords) phrase(start)(new lexical.Scanner(input)) match { diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala index 4e5c64bb63c9f..5d5aba9644ff7 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala @@ -296,7 +296,7 @@ package object dsl { InsertIntoTable( analysis.UnresolvedRelation(Seq(tableName)), Map.empty, logicalPlan, overwrite, false) - def analyze: LogicalPlan = EliminateSubQueries(analysis.SimpleAnalyzer(logicalPlan)) + def analyze: LogicalPlan = EliminateSubQueries(analysis.SimpleAnalyzer.execute(logicalPlan)) } object plans { // scalastyle:ignore diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala index be2c101d63a63..eeffedb558c1b 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala @@ -98,11 +98,11 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin }) /** Generates the requested evaluator binding the given expression(s) to the inputSchema. */ - def apply(expressions: InType, inputSchema: Seq[Attribute]): OutType = - apply(bind(expressions, inputSchema)) + def generate(expressions: InType, inputSchema: Seq[Attribute]): OutType = + generate(bind(expressions, inputSchema)) /** Generates the requested evaluator given already bound expression(s). */ - def apply(expressions: InType): OutType = cache.get(canonicalize(expressions)) + def generate(expressions: InType): OutType = cache.get(canonicalize(expressions)) /** * Returns a term name that is unique within this instance of a `CodeGenerator`. diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala index a419fd7ecb39b..840260703ab74 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala @@ -30,7 +30,7 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu val mutableRowName = newTermName("mutableRow") protected def canonicalize(in: Seq[Expression]): Seq[Expression] = - in.map(ExpressionCanonicalizer(_)) + in.map(ExpressionCanonicalizer.execute) protected def bind(in: Seq[Expression], inputSchema: Seq[Attribute]): Seq[Expression] = in.map(BindReferences.bindReference(_, inputSchema)) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala index fc2a2b60703e4..b129c0d898bb7 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateOrdering.scala @@ -30,7 +30,7 @@ object GenerateOrdering extends CodeGenerator[Seq[SortOrder], Ordering[Row]] wit import scala.reflect.runtime.universe._ protected def canonicalize(in: Seq[SortOrder]): Seq[SortOrder] = - in.map(ExpressionCanonicalizer(_).asInstanceOf[SortOrder]) + in.map(ExpressionCanonicalizer.execute(_).asInstanceOf[SortOrder]) protected def bind(in: Seq[SortOrder], inputSchema: Seq[Attribute]): Seq[SortOrder] = in.map(BindReferences.bindReference(_, inputSchema)) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala index 2a0935c790cf3..40e163024360e 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratePredicate.scala @@ -26,7 +26,7 @@ object GeneratePredicate extends CodeGenerator[Expression, (Row) => Boolean] { import scala.reflect.runtime.{universe => ru} import scala.reflect.runtime.universe._ - protected def canonicalize(in: Expression): Expression = ExpressionCanonicalizer(in) + protected def canonicalize(in: Expression): Expression = ExpressionCanonicalizer.execute(in) protected def bind(in: Expression, inputSchema: Seq[Attribute]): Expression = BindReferences.bindReference(in, inputSchema) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala index 6f572ff959fb4..d491babc2bff0 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala @@ -31,7 +31,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] { import scala.reflect.runtime.universe._ protected def canonicalize(in: Seq[Expression]): Seq[Expression] = - in.map(ExpressionCanonicalizer(_)) + in.map(ExpressionCanonicalizer.execute) protected def bind(in: Seq[Expression], inputSchema: Seq[Attribute]): Seq[Expression] = in.map(BindReferences.bindReference(_, inputSchema)) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala index fcd6352079b4d..46522eb9c1264 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala @@ -23,10 +23,10 @@ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan import org.apache.spark.sql.types.{DataType, BinaryType, BooleanType, NativeType} object InterpretedPredicate { - def apply(expression: Expression, inputSchema: Seq[Attribute]): (Row => Boolean) = - apply(BindReferences.bindReference(expression, inputSchema)) + def create(expression: Expression, inputSchema: Seq[Attribute]): (Row => Boolean) = + create(BindReferences.bindReference(expression, inputSchema)) - def apply(expression: Expression): (Row => Boolean) = { + def create(expression: Expression): (Row => Boolean) = { (r: Row) => expression.eval(r).asInstanceOf[Boolean] } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala index c441f0bf24d85..3f9858b0c4a43 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala @@ -45,7 +45,7 @@ abstract class RuleExecutor[TreeType <: TreeNode[_]] extends Logging { * Executes the batches of rules defined by the subclass. The batches are executed serially * using the defined execution strategy. Within each batch, rules are also executed serially. */ - def apply(plan: TreeType): TreeType = { + def execute(plan: TreeType): TreeType = { var curPlan = plan batches.foreach { batch => diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/SqlParserSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/SqlParserSuite.scala index 1a0a0e6154ad2..a652c70560990 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/SqlParserSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/SqlParserSuite.scala @@ -49,13 +49,14 @@ class SqlParserSuite extends FunSuite { test("test long keyword") { val parser = new SuperLongKeywordTestParser - assert(TestCommand("NotRealCommand") === parser("ThisIsASuperLongKeyWordTest NotRealCommand")) + assert(TestCommand("NotRealCommand") === + parser.parse("ThisIsASuperLongKeyWordTest NotRealCommand")) } test("test case insensitive") { val parser = new CaseInsensitiveTestParser - assert(TestCommand("NotRealCommand") === parser("EXECUTE NotRealCommand")) - assert(TestCommand("NotRealCommand") === parser("execute NotRealCommand")) - assert(TestCommand("NotRealCommand") === parser("exEcute NotRealCommand")) + assert(TestCommand("NotRealCommand") === parser.parse("EXECUTE NotRealCommand")) + assert(TestCommand("NotRealCommand") === parser.parse("execute NotRealCommand")) + assert(TestCommand("NotRealCommand") === parser.parse("exEcute NotRealCommand")) } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala index 7c249215bd6b6..971e1ff5ec2b8 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala @@ -42,10 +42,10 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { def caseSensitiveAnalyze(plan: LogicalPlan): Unit = - caseSensitiveAnalyzer.checkAnalysis(caseSensitiveAnalyzer(plan)) + caseSensitiveAnalyzer.checkAnalysis(caseSensitiveAnalyzer.execute(plan)) def caseInsensitiveAnalyze(plan: LogicalPlan): Unit = - caseInsensitiveAnalyzer.checkAnalysis(caseInsensitiveAnalyzer(plan)) + caseInsensitiveAnalyzer.checkAnalysis(caseInsensitiveAnalyzer.execute(plan)) val testRelation = LocalRelation(AttributeReference("a", IntegerType, nullable = true)()) val testRelation2 = LocalRelation( @@ -82,7 +82,7 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { a.select(UnresolvedStar(None)).select('a).unionAll(b.select(UnresolvedStar(None))) } - assert(caseInsensitiveAnalyzer(plan).resolved) + assert(caseInsensitiveAnalyzer.execute(plan).resolved) } test("check project's resolved") { @@ -98,11 +98,11 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { test("analyze project") { assert( - caseSensitiveAnalyzer(Project(Seq(UnresolvedAttribute("a")), testRelation)) === + caseSensitiveAnalyzer.execute(Project(Seq(UnresolvedAttribute("a")), testRelation)) === Project(testRelation.output, testRelation)) assert( - caseSensitiveAnalyzer( + caseSensitiveAnalyzer.execute( Project(Seq(UnresolvedAttribute("TbL.a")), UnresolvedRelation(Seq("TaBlE"), Some("TbL")))) === Project(testRelation.output, testRelation)) @@ -115,13 +115,13 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { assert(e.getMessage().toLowerCase.contains("cannot resolve")) assert( - caseInsensitiveAnalyzer( + caseInsensitiveAnalyzer.execute( Project(Seq(UnresolvedAttribute("TbL.a")), UnresolvedRelation(Seq("TaBlE"), Some("TbL")))) === Project(testRelation.output, testRelation)) assert( - caseInsensitiveAnalyzer( + caseInsensitiveAnalyzer.execute( Project(Seq(UnresolvedAttribute("tBl.a")), UnresolvedRelation(Seq("TaBlE"), Some("TbL")))) === Project(testRelation.output, testRelation)) @@ -134,13 +134,13 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { assert(e.getMessage == "Table Not Found: tAbLe") assert( - caseSensitiveAnalyzer(UnresolvedRelation(Seq("TaBlE"), None)) === testRelation) + caseSensitiveAnalyzer.execute(UnresolvedRelation(Seq("TaBlE"), None)) === testRelation) assert( - caseInsensitiveAnalyzer(UnresolvedRelation(Seq("tAbLe"), None)) === testRelation) + caseInsensitiveAnalyzer.execute(UnresolvedRelation(Seq("tAbLe"), None)) === testRelation) assert( - caseInsensitiveAnalyzer(UnresolvedRelation(Seq("TaBlE"), None)) === testRelation) + caseInsensitiveAnalyzer.execute(UnresolvedRelation(Seq("TaBlE"), None)) === testRelation) } def errorTest( @@ -219,7 +219,7 @@ class AnalysisSuite extends FunSuite with BeforeAndAfter { AttributeReference("d", DecimalType.Unlimited)(), AttributeReference("e", ShortType)()) - val plan = caseInsensitiveAnalyzer( + val plan = caseInsensitiveAnalyzer.execute( testRelation2.select( 'a / Literal(2) as 'div1, 'a / 'b as 'div2, diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala index 67bec999dfbd1..36b03d1c65e28 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala @@ -48,12 +48,12 @@ class DecimalPrecisionSuite extends FunSuite with BeforeAndAfter { private def checkType(expression: Expression, expectedType: DataType): Unit = { val plan = Project(Seq(Alias(expression, "c")()), relation) - assert(analyzer(plan).schema.fields(0).dataType === expectedType) + assert(analyzer.execute(plan).schema.fields(0).dataType === expectedType) } private def checkComparison(expression: Expression, expectedType: DataType): Unit = { val plan = Project(Alias(expression, "c")() :: Nil, relation) - val comparison = analyzer(plan).collect { + val comparison = analyzer.execute(plan).collect { case Project(Alias(e: BinaryComparison, _) :: Nil, _) => e }.head assert(comparison.left.dataType === expectedType) @@ -64,7 +64,7 @@ class DecimalPrecisionSuite extends FunSuite with BeforeAndAfter { val plan = Union(Project(Seq(Alias(left, "l")()), relation), Project(Seq(Alias(right, "r")()), relation)) - val (l, r) = analyzer(plan).collect { + val (l, r) = analyzer.execute(plan).collect { case Union(left, right) => (left.output.head, right.output.head) }.head assert(l.dataType === expectedType) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedEvaluationSuite.scala index ef3114fd4dbab..b5ebe4b38e337 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedEvaluationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedEvaluationSuite.scala @@ -29,7 +29,7 @@ class GeneratedEvaluationSuite extends ExpressionEvaluationSuite { expected: Any, inputRow: Row = EmptyRow): Unit = { val plan = try { - GenerateMutableProjection(Alias(expression, s"Optimized($expression)")() :: Nil)() + GenerateMutableProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil)() } catch { case e: Throwable => val evaluated = GenerateProjection.expressionEvaluator(expression) @@ -56,10 +56,10 @@ class GeneratedEvaluationSuite extends ExpressionEvaluationSuite { val futures = (1 to 20).map { _ => future { - GeneratePredicate(EqualTo(Literal(1), Literal(1))) - GenerateProjection(EqualTo(Literal(1), Literal(1)) :: Nil) - GenerateMutableProjection(EqualTo(Literal(1), Literal(1)) :: Nil) - GenerateOrdering(Add(Literal(1), Literal(1)).asc :: Nil) + GeneratePredicate.generate(EqualTo(Literal(1), Literal(1))) + GenerateProjection.generate(EqualTo(Literal(1), Literal(1)) :: Nil) + GenerateMutableProjection.generate(EqualTo(Literal(1), Literal(1)) :: Nil) + GenerateOrdering.generate(Add(Literal(1), Literal(1)).asc :: Nil) } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedMutableEvaluationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedMutableEvaluationSuite.scala index bcc0c404d2cfb..97af2e0fd0502 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedMutableEvaluationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/GeneratedMutableEvaluationSuite.scala @@ -25,13 +25,13 @@ import org.apache.spark.sql.catalyst.expressions.codegen._ */ class GeneratedMutableEvaluationSuite extends ExpressionEvaluationSuite { override def checkEvaluation( - expression: Expression, - expected: Any, - inputRow: Row = EmptyRow): Unit = { + expression: Expression, + expected: Any, + inputRow: Row = EmptyRow): Unit = { lazy val evaluated = GenerateProjection.expressionEvaluator(expression) val plan = try { - GenerateProjection(Alias(expression, s"Optimized($expression)")() :: Nil) + GenerateProjection.generate(Alias(expression, s"Optimized($expression)")() :: Nil) } catch { case e: Throwable => fail( diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala index 72f06e26e05f1..6255578d7fa57 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala @@ -61,7 +61,7 @@ class BooleanSimplificationSuite extends PlanTest with PredicateHelper { def checkCondition(input: Expression, expected: Expression): Unit = { val plan = testRelation.where(input).analyze - val actual = Optimize(plan).expressions.head + val actual = Optimize.execute(plan).expressions.head compareConditions(actual, expected) } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala index e2ae0d25db1a5..2d16d668fd522 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala @@ -44,7 +44,7 @@ class CombiningLimitsSuite extends PlanTest { .limit(10) .limit(5) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation .select('a) @@ -61,7 +61,7 @@ class CombiningLimitsSuite extends PlanTest { .limit(7) .limit(5) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation .select('a) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala index 4396bd0dda9a9..14b28e8402610 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala @@ -47,7 +47,7 @@ class ConstantFoldingSuite extends PlanTest { .subquery('y) .select('a) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation .select('a.attr) @@ -74,7 +74,7 @@ class ConstantFoldingSuite extends PlanTest { Literal(2) * Literal(3) - Literal(6) / (Literal(4) - Literal(2)) )(Literal(9) / Literal(3) as Symbol("9/3")) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation @@ -99,7 +99,7 @@ class ConstantFoldingSuite extends PlanTest { Literal(2) * 'a + Literal(4) as Symbol("c3"), 'a * (Literal(3) + Literal(4)) as Symbol("c4")) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation @@ -127,7 +127,7 @@ class ConstantFoldingSuite extends PlanTest { (Literal(1) === Literal(1) || 'b > 1) && (Literal(1) === Literal(2) || 'b < 10))) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation @@ -144,7 +144,7 @@ class ConstantFoldingSuite extends PlanTest { Cast(Literal("2"), IntegerType) + Literal(3) + 'a as Symbol("c1"), Coalesce(Seq(Cast(Literal("abc"), IntegerType), Literal(3))) as Symbol("c2")) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation @@ -163,7 +163,7 @@ class ConstantFoldingSuite extends PlanTest { Rand + Literal(1) as Symbol("c1"), Sum('a) as Symbol("c2")) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation @@ -210,7 +210,7 @@ class ConstantFoldingSuite extends PlanTest { Contains("abc", Literal.create(null, StringType)) as 'c20 ) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConvertToLocalRelationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConvertToLocalRelationSuite.scala index cf42d43823399..6841bd9890c97 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConvertToLocalRelationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConvertToLocalRelationSuite.scala @@ -49,7 +49,7 @@ class ConvertToLocalRelationSuite extends PlanTest { UnresolvedAttribute("a").as("a1"), (UnresolvedAttribute("b") + 1).as("b1")) - val optimized = Optimize(projectOnLocal.analyze) + val optimized = Optimize.execute(projectOnLocal.analyze) comparePlans(optimized, correctAnswer) } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ExpressionOptimizationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ExpressionOptimizationSuite.scala index 2f3704be59a9d..a4a3a66b8b229 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ExpressionOptimizationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ExpressionOptimizationSuite.scala @@ -30,7 +30,7 @@ class ExpressionOptimizationSuite extends ExpressionEvaluationSuite { expected: Any, inputRow: Row = EmptyRow): Unit = { val plan = Project(Alias(expression, s"Optimized($expression)")() :: Nil, OneRowRelation) - val optimizedPlan = DefaultOptimizer(plan) + val optimizedPlan = DefaultOptimizer.execute(plan) super.checkEvaluation(optimizedPlan.expressions.head, expected, inputRow) } } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala index 45cf695d20b01..aa9708b164efa 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala @@ -50,7 +50,7 @@ class FilterPushdownSuite extends PlanTest { .subquery('y) .select('a) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation .select('a.attr) @@ -65,7 +65,7 @@ class FilterPushdownSuite extends PlanTest { .groupBy('a)('a, Count('b)) .select('a) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation .select('a) @@ -81,7 +81,7 @@ class FilterPushdownSuite extends PlanTest { .groupBy('a)('a as 'c, Count('b)) .select('c) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation .select('a) @@ -98,7 +98,7 @@ class FilterPushdownSuite extends PlanTest { .select('a) .where('a === 1) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation .where('a === 1) @@ -115,7 +115,7 @@ class FilterPushdownSuite extends PlanTest { .where('e === 1) .analyze - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation .where('a + 'b === 1) @@ -131,7 +131,7 @@ class FilterPushdownSuite extends PlanTest { .where('a === 1) .where('a === 2) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation .where('a === 1 && 'a === 2) @@ -152,7 +152,7 @@ class FilterPushdownSuite extends PlanTest { .where("y.b".attr === 2) } - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val left = testRelation.where('b === 1) val right = testRelation.where('b === 2) val correctAnswer = @@ -170,7 +170,7 @@ class FilterPushdownSuite extends PlanTest { .where("x.b".attr === 1) } - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val left = testRelation.where('b === 1) val right = testRelation val correctAnswer = @@ -188,7 +188,7 @@ class FilterPushdownSuite extends PlanTest { .where("x.b".attr === 1 && "y.b".attr === 2) } - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val left = testRelation.where('b === 1) val right = testRelation.where('b === 2) val correctAnswer = @@ -206,7 +206,7 @@ class FilterPushdownSuite extends PlanTest { .where("x.b".attr === 1 && "y.b".attr === 2) } - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val left = testRelation.where('b === 1) val correctAnswer = left.join(y, LeftOuter).where("y.b".attr === 2).analyze @@ -223,7 +223,7 @@ class FilterPushdownSuite extends PlanTest { .where("x.b".attr === 1 && "y.b".attr === 2) } - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val right = testRelation.where('b === 2).subquery('d) val correctAnswer = x.join(right, RightOuter).where("x.b".attr === 1).analyze @@ -240,7 +240,7 @@ class FilterPushdownSuite extends PlanTest { .where("x.b".attr === 2 && "y.b".attr === 2) } - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val left = testRelation.where('b === 2).subquery('d) val correctAnswer = left.join(y, LeftOuter, Some("d.b".attr === 1)).where("y.b".attr === 2).analyze @@ -257,7 +257,7 @@ class FilterPushdownSuite extends PlanTest { .where("x.b".attr === 2 && "y.b".attr === 2) } - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val right = testRelation.where('b === 2).subquery('d) val correctAnswer = x.join(right, RightOuter, Some("d.b".attr === 1)).where("x.b".attr === 2).analyze @@ -274,7 +274,7 @@ class FilterPushdownSuite extends PlanTest { .where("x.b".attr === 2 && "y.b".attr === 2) } - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val left = testRelation.where('b === 2).subquery('l) val right = testRelation.where('b === 1).subquery('r) val correctAnswer = @@ -292,7 +292,7 @@ class FilterPushdownSuite extends PlanTest { .where("x.b".attr === 2 && "y.b".attr === 2) } - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val right = testRelation.where('b === 2).subquery('r) val correctAnswer = x.join(right, RightOuter, Some("r.b".attr === 1)).where("x.b".attr === 2).analyze @@ -309,7 +309,7 @@ class FilterPushdownSuite extends PlanTest { .where("x.b".attr === 2 && "y.b".attr === 2 && "x.c".attr === "y.c".attr) } - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val left = testRelation.where('b === 2).subquery('l) val right = testRelation.where('b === 1).subquery('r) val correctAnswer = @@ -327,7 +327,7 @@ class FilterPushdownSuite extends PlanTest { .where("x.b".attr === 2 && "y.b".attr === 2 && "x.c".attr === "y.c".attr) } - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val left = testRelation.subquery('l) val right = testRelation.where('b === 2).subquery('r) val correctAnswer = @@ -346,7 +346,7 @@ class FilterPushdownSuite extends PlanTest { .where("x.b".attr === 2 && "y.b".attr === 2 && "x.c".attr === "y.c".attr) } - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val left = testRelation.where('b === 2).subquery('l) val right = testRelation.where('b === 1).subquery('r) val correctAnswer = @@ -365,7 +365,7 @@ class FilterPushdownSuite extends PlanTest { .where("x.b".attr === 2 && "y.b".attr === 2 && "x.c".attr === "y.c".attr) } - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val left = testRelation.where('a === 3).subquery('l) val right = testRelation.where('b === 2).subquery('r) val correctAnswer = @@ -382,7 +382,7 @@ class FilterPushdownSuite extends PlanTest { val originalQuery = { x.join(y, condition = Some("x.b".attr === "y.b".attr)) } - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) comparePlans(analysis.EliminateSubQueries(originalQuery.analyze), optimized) } @@ -396,7 +396,7 @@ class FilterPushdownSuite extends PlanTest { .where(("x.b".attr === "y.b".attr) && ("x.a".attr === 1) && ("y.a".attr === 1)) } - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val left = testRelation.where('a === 1).subquery('x) val right = testRelation.where('a === 1).subquery('y) val correctAnswer = @@ -415,7 +415,7 @@ class FilterPushdownSuite extends PlanTest { .where(("x.b".attr === "y.b".attr) && ("x.a".attr === 1)) } - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val left = testRelation.where('a === 1).subquery('x) val right = testRelation.subquery('y) val correctAnswer = @@ -436,7 +436,7 @@ class FilterPushdownSuite extends PlanTest { ("z.a".attr >= 3) && ("z.a".attr === "x.b".attr)) } - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val lleft = testRelation.where('a >= 3).subquery('z) val left = testRelation.where('a === 1).subquery('x) val right = testRelation.subquery('y) @@ -457,7 +457,7 @@ class FilterPushdownSuite extends PlanTest { .generate(Explode('c_arr), true, false, Some("arr")) .where(('b >= 5) && ('a > 6)) } - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = { testRelationWithArrayType .where(('b >= 5) && ('a > 6)) @@ -474,7 +474,7 @@ class FilterPushdownSuite extends PlanTest { .generate(generator, true, false, Some("arr")) .where(('b >= 5) && ('c > 6)) } - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val referenceResult = { testRelationWithArrayType .where('b >= 5) @@ -502,7 +502,7 @@ class FilterPushdownSuite extends PlanTest { .generate(Explode('c_arr), true, false, Some("arr")) .where(('c > 6) || ('b > 5)).analyze } - val optimized = Optimize(originalQuery) + val optimized = Optimize.execute(originalQuery) comparePlans(optimized, originalQuery) } diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/LikeSimplificationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/LikeSimplificationSuite.scala index b10577c8001e2..b3df487c84dc8 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/LikeSimplificationSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/LikeSimplificationSuite.scala @@ -41,7 +41,7 @@ class LikeSimplificationSuite extends PlanTest { testRelation .where(('a like "abc%") || ('a like "abc\\%")) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation .where(StartsWith('a, "abc") || ('a like "abc\\%")) .analyze @@ -54,7 +54,7 @@ class LikeSimplificationSuite extends PlanTest { testRelation .where('a like "%xyz") - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation .where(EndsWith('a, "xyz")) .analyze @@ -67,7 +67,7 @@ class LikeSimplificationSuite extends PlanTest { testRelation .where(('a like "%mn%") || ('a like "%mn\\%")) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation .where(Contains('a, "mn") || ('a like "%mn\\%")) .analyze @@ -80,7 +80,7 @@ class LikeSimplificationSuite extends PlanTest { testRelation .where(('a like "") || ('a like "abc")) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation .where(('a === "") || ('a === "abc")) .analyze diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala index 966bc9ada1e6e..3eb399e68e70c 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala @@ -49,7 +49,7 @@ class OptimizeInSuite extends PlanTest { .where(In(UnresolvedAttribute("a"), Seq(Literal(1),Literal(2)))) .analyze - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation .where(InSet(UnresolvedAttribute("a"), HashSet[Any]() + 1 + 2)) @@ -64,7 +64,7 @@ class OptimizeInSuite extends PlanTest { .where(In(UnresolvedAttribute("a"), Seq(Literal(1),Literal(2), UnresolvedAttribute("b")))) .analyze - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation .where(In(UnresolvedAttribute("a"), Seq(Literal(1),Literal(2), UnresolvedAttribute("b")))) diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/SimplifyCaseConversionExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/SimplifyCaseConversionExpressionsSuite.scala index 22992fb6f50d4..6b1e53cd42b24 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/SimplifyCaseConversionExpressionsSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/SimplifyCaseConversionExpressionsSuite.scala @@ -41,7 +41,7 @@ class SimplifyCaseConversionExpressionsSuite extends PlanTest { testRelation .select(Upper(Upper('a)) as 'u) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation .select(Upper('a) as 'u) @@ -55,7 +55,7 @@ class SimplifyCaseConversionExpressionsSuite extends PlanTest { testRelation .select(Upper(Lower('a)) as 'u) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation .select(Upper('a) as 'u) @@ -69,7 +69,7 @@ class SimplifyCaseConversionExpressionsSuite extends PlanTest { testRelation .select(Lower(Upper('a)) as 'l) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation .select(Lower('a) as 'l) .analyze @@ -82,7 +82,7 @@ class SimplifyCaseConversionExpressionsSuite extends PlanTest { testRelation .select(Lower(Lower('a)) as 'l) - val optimized = Optimize(originalQuery.analyze) + val optimized = Optimize.execute(originalQuery.analyze) val correctAnswer = testRelation .select(Lower('a) as 'l) .analyze diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala index a54751dfa9a12..a3ad200800b02 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/UnionPushdownSuite.scala @@ -17,10 +17,9 @@ package org.apache.spark.sql.catalyst.optimizer -import org.apache.spark.sql.catalyst.analysis import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries +import org.apache.spark.sql.catalyst.plans.PlanTest import org.apache.spark.sql.catalyst.plans.logical._ -import org.apache.spark.sql.catalyst.plans.{PlanTest, LeftOuter, RightOuter} import org.apache.spark.sql.catalyst.rules._ import org.apache.spark.sql.catalyst.dsl.plans._ import org.apache.spark.sql.catalyst.dsl.expressions._ @@ -41,7 +40,7 @@ class UnionPushdownSuite extends PlanTest { test("union: filter to each side") { val query = testUnion.where('a === 1) - val optimized = Optimize(query.analyze) + val optimized = Optimize.execute(query.analyze) val correctAnswer = Union(testRelation.where('a === 1), testRelation2.where('d === 1)).analyze @@ -52,7 +51,7 @@ class UnionPushdownSuite extends PlanTest { test("union: project to each side") { val query = testUnion.select('b) - val optimized = Optimize(query.analyze) + val optimized = Optimize.execute(query.analyze) val correctAnswer = Union(testRelation.select('b), testRelation2.select('e)).analyze diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/RuleExecutorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/RuleExecutorSuite.scala index 4b2d45584045f..2a641c63f87bb 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/RuleExecutorSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/trees/RuleExecutorSuite.scala @@ -34,7 +34,7 @@ class RuleExecutorSuite extends FunSuite { val batches = Batch("once", Once, DecrementLiterals) :: Nil } - assert(ApplyOnce(Literal(10)) === Literal(9)) + assert(ApplyOnce.execute(Literal(10)) === Literal(9)) } test("to fixed point") { @@ -42,7 +42,7 @@ class RuleExecutorSuite extends FunSuite { val batches = Batch("fixedPoint", FixedPoint(100), DecrementLiterals) :: Nil } - assert(ToFixedPoint(Literal(10)) === Literal(0)) + assert(ToFixedPoint.execute(Literal(10)) === Literal(0)) } test("to maxIterations") { @@ -50,6 +50,6 @@ class RuleExecutorSuite extends FunSuite { val batches = Batch("fixedPoint", FixedPoint(10), DecrementLiterals) :: Nil } - assert(ToFixedPoint(Literal(100)) === Literal(90)) + assert(ToFixedPoint.execute(Literal(100)) === Literal(90)) } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala index bcd20c06c6dca..a279b0f07c38a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala @@ -132,16 +132,16 @@ class SQLContext(@transient val sparkContext: SparkContext) protected[sql] lazy val optimizer: Optimizer = DefaultOptimizer @transient - protected[sql] val ddlParser = new DDLParser(sqlParser.apply(_)) + protected[sql] val ddlParser = new DDLParser(sqlParser.parse(_)) @transient protected[sql] val sqlParser = { val fallback = new catalyst.SqlParser - new SparkSQLParser(fallback(_)) + new SparkSQLParser(fallback.parse(_)) } protected[sql] def parseSql(sql: String): LogicalPlan = { - ddlParser(sql, false).getOrElse(sqlParser(sql)) + ddlParser.parse(sql, false).getOrElse(sqlParser.parse(sql)) } protected[sql] def executeSql(sql: String): this.QueryExecution = executePlan(parseSql(sql)) @@ -1120,12 +1120,12 @@ class SQLContext(@transient val sparkContext: SparkContext) protected[sql] class QueryExecution(val logical: LogicalPlan) { def assertAnalyzed(): Unit = analyzer.checkAnalysis(analyzed) - lazy val analyzed: LogicalPlan = analyzer(logical) + lazy val analyzed: LogicalPlan = analyzer.execute(logical) lazy val withCachedData: LogicalPlan = { assertAnalyzed() cacheManager.useCachedData(analyzed) } - lazy val optimizedPlan: LogicalPlan = optimizer(withCachedData) + lazy val optimizedPlan: LogicalPlan = optimizer.execute(withCachedData) // TODO: Don't just pick the first one... lazy val sparkPlan: SparkPlan = { @@ -1134,7 +1134,7 @@ class SQLContext(@transient val sparkContext: SparkContext) } // executedPlan should not be used to initialize any SparkPlan. It should be // only used for execution. - lazy val executedPlan: SparkPlan = prepareForExecution(sparkPlan) + lazy val executedPlan: SparkPlan = prepareForExecution.execute(sparkPlan) /** Internal version of the RDD. Avoids copies and has no schema */ lazy val toRdd: RDD[Row] = executedPlan.execute() diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala index e159ffe66cb24..59c89800da00f 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala @@ -144,7 +144,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ log.debug( s"Creating Projection: $expressions, inputSchema: $inputSchema, codegen:$codegenEnabled") if (codegenEnabled) { - GenerateProjection(expressions, inputSchema) + GenerateProjection.generate(expressions, inputSchema) } else { new InterpretedProjection(expressions, inputSchema) } @@ -156,7 +156,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ log.debug( s"Creating MutableProj: $expressions, inputSchema: $inputSchema, codegen:$codegenEnabled") if(codegenEnabled) { - GenerateMutableProjection(expressions, inputSchema) + GenerateMutableProjection.generate(expressions, inputSchema) } else { () => new InterpretedMutableProjection(expressions, inputSchema) } @@ -166,15 +166,15 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ protected def newPredicate( expression: Expression, inputSchema: Seq[Attribute]): (Row) => Boolean = { if (codegenEnabled) { - GeneratePredicate(expression, inputSchema) + GeneratePredicate.generate(expression, inputSchema) } else { - InterpretedPredicate(expression, inputSchema) + InterpretedPredicate.create(expression, inputSchema) } } protected def newOrdering(order: Seq[SortOrder], inputSchema: Seq[Attribute]): Ordering[Row] = { if (codegenEnabled) { - GenerateOrdering(order, inputSchema) + GenerateOrdering.generate(order, inputSchema) } else { new RowOrdering(order, inputSchema) } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala index 83b1a83765153..56200f6b8c8a9 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala @@ -59,7 +59,7 @@ case class BroadcastNestedLoopJoin( } @transient private lazy val boundCondition = - InterpretedPredicate( + InterpretedPredicate.create( condition .map(c => BindReferences.bindReference(c, left.output ++ right.output)) .getOrElse(Literal(true))) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala index 1fa7e7bd0406c..e06f63f94b78b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala @@ -45,7 +45,7 @@ case class LeftSemiJoinBNL( override def right: SparkPlan = broadcast @transient private lazy val boundCondition = - InterpretedPredicate( + InterpretedPredicate.create( condition .map(c => BindReferences.bindReference(c, left.output ++ right.output)) .getOrElse(Literal(true))) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala index af7b3c81ae7b2..88466f52bd4e9 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala @@ -611,7 +611,7 @@ private[sql] case class ParquetRelation2( val rawPredicate = partitionPruningPredicates.reduceOption(expressions.And).getOrElse(Literal(true)) - val boundPredicate = InterpretedPredicate(rawPredicate transform { + val boundPredicate = InterpretedPredicate.create(rawPredicate transform { case a: AttributeReference => val index = partitionColumns.indexWhere(a.name == _.name) BoundReference(index, partitionColumns(index).dataType, nullable = true) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala index 78d494184e759..e7a0685e013d8 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala @@ -38,9 +38,9 @@ private[sql] class DDLParser( parseQuery: String => LogicalPlan) extends AbstractSparkSQLParser with DataTypeParser with Logging { - def apply(input: String, exceptionOnError: Boolean): Option[LogicalPlan] = { + def parse(input: String, exceptionOnError: Boolean): Option[LogicalPlan] = { try { - Some(apply(input)) + Some(parse(input)) } catch { case ddlException: DDLException => throw ddlException case _ if !exceptionOnError => None diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala index c4a73b3004076..dd06b2620c5ee 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala @@ -93,7 +93,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) { if (conf.dialect == "sql") { super.sql(substituted) } else if (conf.dialect == "hiveql") { - val ddlPlan = ddlParserWithHiveQL(sqlText, exceptionOnError = false) + val ddlPlan = ddlParserWithHiveQL.parse(sqlText, exceptionOnError = false) DataFrame(this, ddlPlan.getOrElse(HiveQl.parseSql(substituted))) } else { sys.error(s"Unsupported SQL dialect: ${conf.dialect}. Try 'sql' or 'hiveql'") diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala index 85061f22772dd..0ea6d57b816c6 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala @@ -144,7 +144,7 @@ private[hive] object HiveQl { protected val hqlParser = { val fallback = new ExtendedHiveQlParser - new SparkSQLParser(fallback(_)) + new SparkSQLParser(fallback.parse(_)) } /** @@ -240,7 +240,7 @@ private[hive] object HiveQl { /** Returns a LogicalPlan for a given HiveQL string. */ - def parseSql(sql: String): LogicalPlan = hqlParser(sql) + def parseSql(sql: String): LogicalPlan = hqlParser.parse(sql) val errorRegEx = "line (\\d+):(\\d+) (.*)".r diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala index a6f4fbe8aba06..be9249a8b1f44 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala @@ -119,9 +119,9 @@ private[hive] trait HiveStrategies { val inputData = new GenericMutableRow(relation.partitionKeys.size) val pruningCondition = if (codegenEnabled) { - GeneratePredicate(castedPredicate) + GeneratePredicate.generate(castedPredicate) } else { - InterpretedPredicate(castedPredicate) + InterpretedPredicate.create(castedPredicate) } val partitions = relation.hiveQlPartitions.filter { part => diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala index 6570fa1043900..9f17bca083d13 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala @@ -185,7 +185,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) { logDebug(s"Query references test tables: ${referencedTestTables.mkString(", ")}") referencedTestTables.foreach(loadTestTable) // Proceed with analysis. - analyzer(logical) + analyzer.execute(logical) } } From fbe7106d75c6a1624d10793fba6759703bc5c6e6 Mon Sep 17 00:00:00 2001 From: szheng79 Date: Wed, 22 Apr 2015 13:02:55 -0700 Subject: [PATCH 380/591] [SPARK-7039][SQL]JDBCRDD: Add support on type NVARCHAR Issue: https://issues.apache.org/jira/browse/SPARK-7039 Add support to column type NVARCHAR in Sql Server java.sql.Types: http://docs.oracle.com/javase/7/docs/api/java/sql/Types.html Author: szheng79 Closes #5618 from szheng79/patch-1 and squashes the following commits: 10da99c [szheng79] Update JDBCRDD.scala eab0bd8 [szheng79] Add support on type NVARCHAR --- sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala | 1 + 1 file changed, 1 insertion(+) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala index b9022fcd9e3ad..8b1edec20feee 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala @@ -60,6 +60,7 @@ private[sql] object JDBCRDD extends Logging { case java.sql.Types.NCLOB => StringType case java.sql.Types.NULL => null case java.sql.Types.NUMERIC => DecimalType.Unlimited + case java.sql.Types.NVARCHAR => StringType case java.sql.Types.OTHER => null case java.sql.Types.REAL => DoubleType case java.sql.Types.REF => StringType From baf865ddc2cff9b99d6aeab9861e030da511257f Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Wed, 22 Apr 2015 15:26:58 -0700 Subject: [PATCH 381/591] [SPARK-7059][SQL] Create a DataFrame join API to facilitate equijoin. Author: Reynold Xin Closes #5638 from rxin/joinUsing and squashes the following commits: 13e9cc9 [Reynold Xin] Code review + Python. b1bd914 [Reynold Xin] [SPARK-7059][SQL] Create a DataFrame join API to facilitate equijoin and self join. --- python/pyspark/sql/dataframe.py | 9 ++++- .../org/apache/spark/sql/DataFrame.scala | 37 +++++++++++++++++ .../org/apache/spark/sql/DataFrameSuite.scala | 40 ++++++++++++++----- 3 files changed, 74 insertions(+), 12 deletions(-) diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index ca9bf8efb945c..c8c30ce4022c8 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -459,16 +459,23 @@ def join(self, other, joinExprs=None, joinType=None): The following performs a full outer join between ``df1`` and ``df2``. :param other: Right side of the join - :param joinExprs: Join expression + :param joinExprs: a string for join column name, or a join expression (Column). + If joinExprs is a string indicating the name of the join column, + the column must exist on both sides, and this performs an inner equi-join. :param joinType: str, default 'inner'. One of `inner`, `outer`, `left_outer`, `right_outer`, `semijoin`. >>> df.join(df2, df.name == df2.name, 'outer').select(df.name, df2.height).collect() [Row(name=None, height=80), Row(name=u'Alice', height=None), Row(name=u'Bob', height=85)] + + >>> df.join(df2, 'name').select(df.name, df2.height).collect() + [Row(name=u'Bob', height=85)] """ if joinExprs is None: jdf = self._jdf.join(other._jdf) + elif isinstance(joinExprs, basestring): + jdf = self._jdf.join(other._jdf, joinExprs) else: assert isinstance(joinExprs, Column), "joinExprs should be Column" if joinType is None: diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala index 03d9834d1d131..ca6ae482eb2ab 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala @@ -342,6 +342,43 @@ class DataFrame private[sql]( Join(logicalPlan, right.logicalPlan, joinType = Inner, None) } + /** + * Inner equi-join with another [[DataFrame]] using the given column. + * + * Different from other join functions, the join column will only appear once in the output, + * i.e. similar to SQL's `JOIN USING` syntax. + * + * {{{ + * // Joining df1 and df2 using the column "user_id" + * df1.join(df2, "user_id") + * }}} + * + * Note that if you perform a self-join using this function without aliasing the input + * [[DataFrame]]s, you will NOT be able to reference any columns after the join, since + * there is no way to disambiguate which side of the join you would like to reference. + * + * @param right Right side of the join operation. + * @param usingColumn Name of the column to join on. This column must exist on both sides. + * @group dfops + */ + def join(right: DataFrame, usingColumn: String): DataFrame = { + // Analyze the self join. The assumption is that the analyzer will disambiguate left vs right + // by creating a new instance for one of the branch. + val joined = sqlContext.executePlan( + Join(logicalPlan, right.logicalPlan, joinType = Inner, None)).analyzed.asInstanceOf[Join] + + // Project only one of the join column. + val joinedCol = joined.right.resolve(usingColumn) + Project( + joined.output.filterNot(_ == joinedCol), + Join( + joined.left, + joined.right, + joinType = Inner, + Some(EqualTo(joined.left.resolve(usingColumn), joined.right.resolve(usingColumn)))) + ) + } + /** * Inner join with another [[DataFrame]], using the given join expression. * diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala index b9b6a400ae195..5ec06d448e50f 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala @@ -109,15 +109,6 @@ class DataFrameSuite extends QueryTest { assert(testData.head(2).head.schema === testData.schema) } - test("self join") { - val df1 = testData.select(testData("key")).as('df1) - val df2 = testData.select(testData("key")).as('df2) - - checkAnswer( - df1.join(df2, $"df1.key" === $"df2.key"), - sql("SELECT a.key, b.key FROM testData a JOIN testData b ON a.key = b.key").collect().toSeq) - } - test("simple explode") { val df = Seq(Tuple1("a b c"), Tuple1("d e")).toDF("words") @@ -127,8 +118,35 @@ class DataFrameSuite extends QueryTest { ) } - test("self join with aliases") { - val df = Seq(1,2,3).map(i => (i, i.toString)).toDF("int", "str") + test("join - join using") { + val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str") + val df2 = Seq(1, 2, 3).map(i => (i, (i + 1).toString)).toDF("int", "str") + + checkAnswer( + df.join(df2, "int"), + Row(1, "1", "2") :: Row(2, "2", "3") :: Row(3, "3", "4") :: Nil) + } + + test("join - join using self join") { + val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str") + + // self join + checkAnswer( + df.join(df, "int"), + Row(1, "1", "1") :: Row(2, "2", "2") :: Row(3, "3", "3") :: Nil) + } + + test("join - self join") { + val df1 = testData.select(testData("key")).as('df1) + val df2 = testData.select(testData("key")).as('df2) + + checkAnswer( + df1.join(df2, $"df1.key" === $"df2.key"), + sql("SELECT a.key, b.key FROM testData a JOIN testData b ON a.key = b.key").collect().toSeq) + } + + test("join - using aliases after self join") { + val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str") checkAnswer( df.as('x).join(df.as('y), $"x.str" === $"y.str").groupBy("x.str").count(), Row("1", 1) :: Row("2", 1) :: Row("3", 1) :: Nil) From f4f39981f4f5e88c30eec7d0b107e2c3cdc268c9 Mon Sep 17 00:00:00 2001 From: Yanbo Liang Date: Wed, 22 Apr 2015 17:22:26 -0700 Subject: [PATCH 382/591] [SPARK-6827] [MLLIB] Wrap FPGrowthModel.freqItemsets and make it consistent with Java API Make PySpark ```FPGrowthModel.freqItemsets``` consistent with Java/Scala API like ```MatrixFactorizationModel.userFeatures``` It return a RDD with each tuple is composed of an array and a long value. I think it's difficult to implement namedtuples to wrap the output because items of freqItemsets can be any type with arbitrary length which is tedious to impelement corresponding SerDe function. Author: Yanbo Liang Closes #5614 from yanboliang/spark-6827 and squashes the following commits: da8c404 [Yanbo Liang] use namedtuple 5532e78 [Yanbo Liang] Wrap FPGrowthModel.freqItemsets and make it consistent with Java API --- python/pyspark/mllib/fpm.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/python/pyspark/mllib/fpm.py b/python/pyspark/mllib/fpm.py index 628ccc01cf3cc..d8df02bdbaba9 100644 --- a/python/pyspark/mllib/fpm.py +++ b/python/pyspark/mllib/fpm.py @@ -15,6 +15,10 @@ # limitations under the License. # +import numpy +from numpy import array +from collections import namedtuple + from pyspark import SparkContext from pyspark.rdd import ignore_unicode_prefix from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, inherit_doc @@ -36,14 +40,14 @@ class FPGrowthModel(JavaModelWrapper): >>> rdd = sc.parallelize(data, 2) >>> model = FPGrowth.train(rdd, 0.6, 2) >>> sorted(model.freqItemsets().collect()) - [([u'a'], 4), ([u'c'], 3), ([u'c', u'a'], 3)] + [FreqItemset(items=[u'a'], freq=4), FreqItemset(items=[u'c'], freq=3), ... """ def freqItemsets(self): """ - Get the frequent itemsets of this model + Returns the frequent itemsets of this model. """ - return self.call("getFreqItemsets") + return self.call("getFreqItemsets").map(lambda x: (FPGrowth.FreqItemset(x[0], x[1]))) class FPGrowth(object): @@ -67,6 +71,11 @@ def train(cls, data, minSupport=0.3, numPartitions=-1): model = callMLlibFunc("trainFPGrowthModel", data, float(minSupport), int(numPartitions)) return FPGrowthModel(model) + class FreqItemset(namedtuple("FreqItemset", ["items", "freq"])): + """ + Represents an (items, freq) tuple. + """ + def _test(): import doctest From 04525c077c638a7e615c294ba988e35036554f5f Mon Sep 17 00:00:00 2001 From: Daoyuan Wang Date: Wed, 22 Apr 2015 19:14:28 -0700 Subject: [PATCH 383/591] [SPARK-6967] [SQL] fix date type convertion in jdbcrdd This pr convert java.sql.Date type into Int for JDBCRDD. Author: Daoyuan Wang Closes #5590 from adrian-wang/datebug and squashes the following commits: f897b81 [Daoyuan Wang] add a test case 3c9184c [Daoyuan Wang] fix date type convertion in jdbcrdd --- .../src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala | 4 ++-- .../test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala | 7 +++++++ 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala index 8b1edec20feee..b975191d41963 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala @@ -350,8 +350,8 @@ private[sql] class JDBCRDD( val pos = i + 1 conversions(i) match { case BooleanConversion => mutableRow.setBoolean(i, rs.getBoolean(pos)) - // TODO(davies): convert Date into Int - case DateConversion => mutableRow.update(i, rs.getDate(pos)) + case DateConversion => + mutableRow.update(i, DateUtils.fromJavaDate(rs.getDate(pos))) case DecimalConversion => mutableRow.update(i, rs.getBigDecimal(pos)) case DoubleConversion => mutableRow.setDouble(i, rs.getDouble(pos)) case FloatConversion => mutableRow.setFloat(i, rs.getFloat(pos)) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala index 3596b183d4328..db096af4535a9 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala @@ -249,6 +249,13 @@ class JDBCSuite extends FunSuite with BeforeAndAfter { assert(rows(0).getAs[java.sql.Timestamp](2).getNanos === 543543543) } + test("test DATE types") { + val rows = TestSQLContext.jdbc(urlWithUserAndPass, "TEST.TIMETYPES").collect() + val cachedRows = TestSQLContext.jdbc(urlWithUserAndPass, "TEST.TIMETYPES").cache().collect() + assert(rows(0).getAs[java.sql.Date](1) === java.sql.Date.valueOf("1996-01-01")) + assert(cachedRows(0).getAs[java.sql.Date](1) === java.sql.Date.valueOf("1996-01-01")) + } + test("H2 floating-point types") { val rows = sql("SELECT * FROM flttypes").collect() assert(rows(0).getDouble(0) === 1.00000000000000022) // Yes, I meant ==. From b69c4f9b2e8544f1b178db2aefbcaa166f76cb7a Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Wed, 22 Apr 2015 21:24:22 -0700 Subject: [PATCH 384/591] Disable flaky test: ReceiverSuite "block generator throttling". --- .../test/scala/org/apache/spark/streaming/ReceiverSuite.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala index e7aee6eadbfc7..b84129fd70dd4 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala @@ -155,7 +155,7 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable { assert(recordedData.toSet === generatedData.toSet) } - test("block generator throttling") { + ignore("block generator throttling") { val blockGeneratorListener = new FakeBlockGeneratorListener val blockIntervalMs = 100 val maxRate = 1001 From 1b85e08509a0e19dc35b6ab869977254156cdaf1 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Wed, 22 Apr 2015 21:35:12 -0700 Subject: [PATCH 385/591] [MLlib] UnaryTransformer nullability should not depend on PrimitiveType. Author: Reynold Xin Closes #5644 from rxin/mllib-nullable and squashes the following commits: a727e5b [Reynold Xin] [MLlib] UnaryTransformer nullability should not depend on primitive types. --- mllib/src/main/scala/org/apache/spark/ml/Transformer.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala index 7fb87fe452ee6..0acda71ec6045 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/Transformer.scala @@ -94,7 +94,7 @@ private[ml] abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, O throw new IllegalArgumentException(s"Output column ${map(outputCol)} already exists.") } val outputFields = schema.fields :+ - StructField(map(outputCol), outputDataType, !outputDataType.isPrimitive) + StructField(map(outputCol), outputDataType, nullable = false) StructType(outputFields) } From d20686066e978dd12e618e3978f109f05bc412fe Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Wed, 22 Apr 2015 21:35:42 -0700 Subject: [PATCH 386/591] [SPARK-7066][MLlib] VectorAssembler should use NumericType not NativeType. Author: Reynold Xin Closes #5642 from rxin/mllib-native-type and squashes the following commits: e23af5b [Reynold Xin] Remove StringType 7cbb205 [Reynold Xin] [SPARK-7066][MLlib] VectorAssembler should use NumericType and StringType, not NativeType. --- .../scala/org/apache/spark/ml/feature/VectorAssembler.scala | 5 +++-- .../main/scala/org/apache/spark/sql/types/dataTypes.scala | 4 ++-- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala index e567e069e7c0b..fd16d3d6c268b 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala @@ -55,7 +55,8 @@ class VectorAssembler extends Transformer with HasInputCols with HasOutputCol { schema(c).dataType match { case DoubleType => UnresolvedAttribute(c) case t if t.isInstanceOf[VectorUDT] => UnresolvedAttribute(c) - case _: NativeType => Alias(Cast(UnresolvedAttribute(c), DoubleType), s"${c}_double_$uid")() + case _: NumericType => + Alias(Cast(UnresolvedAttribute(c), DoubleType), s"${c}_double_$uid")() } } dataset.select(col("*"), assembleFunc(new Column(CreateStruct(args))).as(map(outputCol))) @@ -67,7 +68,7 @@ class VectorAssembler extends Transformer with HasInputCols with HasOutputCol { val outputColName = map(outputCol) val inputDataTypes = inputColNames.map(name => schema(name).dataType) inputDataTypes.foreach { - case _: NativeType => + case _: NumericType => case t if t.isInstanceOf[VectorUDT] => case other => throw new IllegalArgumentException(s"Data type $other is not supported.") diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala index 7cd7bd1914c95..ddf9d664c6826 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala @@ -299,7 +299,7 @@ class NullType private() extends DataType { case object NullType extends NullType -protected[spark] object NativeType { +protected[sql] object NativeType { val all = Seq( IntegerType, BooleanType, LongType, DoubleType, FloatType, ShortType, ByteType, StringType) @@ -327,7 +327,7 @@ protected[sql] object PrimitiveType { } } -protected[spark] abstract class NativeType extends DataType { +protected[sql] abstract class NativeType extends DataType { private[sql] type JvmType @transient private[sql] val tag: TypeTag[JvmType] private[sql] val ordering: Ordering[JvmType] From 03e85b4a11899f37424cd6e1f8d71f1d704c90bb Mon Sep 17 00:00:00 2001 From: Kay Ousterhout Date: Wed, 22 Apr 2015 21:42:09 -0700 Subject: [PATCH 387/591] [SPARK-7046] Remove InputMetrics from BlockResult This is a code cleanup. The BlockResult class originally contained an InputMetrics object so that InputMetrics could directly be used as the InputMetrics for the whole task. Now we copy the fields out of here, and the presence of this object is confusing because it's only a partial input metrics (it doesn't include the records read). Because this object is no longer useful (and is confusing), it should be removed. Author: Kay Ousterhout Closes #5627 from kayousterhout/SPARK-7046 and squashes the following commits: bf64bbe [Kay Ousterhout] Import fix a08ca19 [Kay Ousterhout] [SPARK-7046] Remove InputMetrics from BlockResult --- .../main/scala/org/apache/spark/CacheManager.scala | 5 ++--- .../org/apache/spark/storage/BlockManager.scala | 9 +++------ .../org/apache/spark/storage/BlockManagerSuite.scala | 12 ++++++------ 3 files changed, 11 insertions(+), 15 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/CacheManager.scala b/core/src/main/scala/org/apache/spark/CacheManager.scala index a96d754744a05..4d20c7369376e 100644 --- a/core/src/main/scala/org/apache/spark/CacheManager.scala +++ b/core/src/main/scala/org/apache/spark/CacheManager.scala @@ -44,10 +44,9 @@ private[spark] class CacheManager(blockManager: BlockManager) extends Logging { blockManager.get(key) match { case Some(blockResult) => // Partition is already materialized, so just return its values - val inputMetrics = blockResult.inputMetrics val existingMetrics = context.taskMetrics - .getInputMetricsForReadMethod(inputMetrics.readMethod) - existingMetrics.incBytesRead(inputMetrics.bytesRead) + .getInputMetricsForReadMethod(blockResult.readMethod) + existingMetrics.incBytesRead(blockResult.bytes) val iter = blockResult.data.asInstanceOf[Iterator[T]] new InterruptibleIterator[T](context, iter) { diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala index 145a9c1ae3391..55718e584c195 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala @@ -29,7 +29,7 @@ import scala.util.Random import sun.nio.ch.DirectBuffer import org.apache.spark._ -import org.apache.spark.executor._ +import org.apache.spark.executor.{DataReadMethod, ShuffleWriteMetrics} import org.apache.spark.io.CompressionCodec import org.apache.spark.network._ import org.apache.spark.network.buffer.{ManagedBuffer, NioManagedBuffer} @@ -50,11 +50,8 @@ private[spark] case class ArrayValues(buffer: Array[Any]) extends BlockValues /* Class for returning a fetched block and associated metrics. */ private[spark] class BlockResult( val data: Iterator[Any], - readMethod: DataReadMethod.Value, - bytes: Long) { - val inputMetrics = new InputMetrics(readMethod) - inputMetrics.incBytesRead(bytes) -} + val readMethod: DataReadMethod.Value, + val bytes: Long) /** * Manager running on every node (driver and executors) which provides interfaces for putting and diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala index 545722b050ee8..7d82a7c66ad1a 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala @@ -428,19 +428,19 @@ class BlockManagerSuite extends FunSuite with Matchers with BeforeAndAfterEach val list1Get = store.get("list1") assert(list1Get.isDefined, "list1 expected to be in store") assert(list1Get.get.data.size === 2) - assert(list1Get.get.inputMetrics.bytesRead === list1SizeEstimate) - assert(list1Get.get.inputMetrics.readMethod === DataReadMethod.Memory) + assert(list1Get.get.bytes === list1SizeEstimate) + assert(list1Get.get.readMethod === DataReadMethod.Memory) val list2MemoryGet = store.get("list2memory") assert(list2MemoryGet.isDefined, "list2memory expected to be in store") assert(list2MemoryGet.get.data.size === 3) - assert(list2MemoryGet.get.inputMetrics.bytesRead === list2SizeEstimate) - assert(list2MemoryGet.get.inputMetrics.readMethod === DataReadMethod.Memory) + assert(list2MemoryGet.get.bytes === list2SizeEstimate) + assert(list2MemoryGet.get.readMethod === DataReadMethod.Memory) val list2DiskGet = store.get("list2disk") assert(list2DiskGet.isDefined, "list2memory expected to be in store") assert(list2DiskGet.get.data.size === 3) // We don't know the exact size of the data on disk, but it should certainly be > 0. - assert(list2DiskGet.get.inputMetrics.bytesRead > 0) - assert(list2DiskGet.get.inputMetrics.readMethod === DataReadMethod.Disk) + assert(list2DiskGet.get.bytes > 0) + assert(list2DiskGet.get.readMethod === DataReadMethod.Disk) } test("in-memory LRU storage") { From d9e70f331fc3999d615ede49fc69a993dc65f272 Mon Sep 17 00:00:00 2001 From: Liang-Chi Hsieh Date: Wed, 22 Apr 2015 22:18:56 -0700 Subject: [PATCH 388/591] [HOTFIX][SQL] Fix broken cached test Added in #5475. Pointed as broken in #5639. /cc marmbrus Author: Liang-Chi Hsieh Closes #5640 from viirya/fix_cached_test and squashes the following commits: c0cf69a [Liang-Chi Hsieh] Fix broken cached test. --- .../apache/spark/sql/CachedTableSuite.scala | 21 ++++++++++++------- 1 file changed, 14 insertions(+), 7 deletions(-) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala index 01e3b8671071e..0772e5e187425 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala @@ -300,19 +300,26 @@ class CachedTableSuite extends QueryTest { } test("Clear accumulators when uncacheTable to prevent memory leaking") { - val accsSize = Accumulators.originals.size - sql("SELECT key FROM testData LIMIT 10").registerTempTable("t1") sql("SELECT key FROM testData LIMIT 5").registerTempTable("t2") - cacheTable("t1") - cacheTable("t2") + + Accumulators.synchronized { + val accsSize = Accumulators.originals.size + cacheTable("t1") + cacheTable("t2") + assert((accsSize + 2) == Accumulators.originals.size) + } + sql("SELECT * FROM t1").count() sql("SELECT * FROM t2").count() sql("SELECT * FROM t1").count() sql("SELECT * FROM t2").count() - uncacheTable("t1") - uncacheTable("t2") - assert(accsSize >= Accumulators.originals.size) + Accumulators.synchronized { + val accsSize = Accumulators.originals.size + uncacheTable("t1") + uncacheTable("t2") + assert((accsSize - 2) == Accumulators.originals.size) + } } } From 2d33323cadbf58dd1d05ffff998d18cad6a896cd Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Wed, 22 Apr 2015 23:54:48 -0700 Subject: [PATCH 389/591] [MLlib] Add support for BooleanType to VectorAssembler. Author: Reynold Xin Closes #5648 from rxin/vectorAssembler-boolean and squashes the following commits: 1bf3d40 [Reynold Xin] [MLlib] Add support for BooleanType to VectorAssembler. --- .../scala/org/apache/spark/ml/feature/VectorAssembler.scala | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala index fd16d3d6c268b..7b2a451ca5ee5 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala @@ -55,7 +55,7 @@ class VectorAssembler extends Transformer with HasInputCols with HasOutputCol { schema(c).dataType match { case DoubleType => UnresolvedAttribute(c) case t if t.isInstanceOf[VectorUDT] => UnresolvedAttribute(c) - case _: NumericType => + case _: NumericType | BooleanType => Alias(Cast(UnresolvedAttribute(c), DoubleType), s"${c}_double_$uid")() } } @@ -68,7 +68,7 @@ class VectorAssembler extends Transformer with HasInputCols with HasOutputCol { val outputColName = map(outputCol) val inputDataTypes = inputColNames.map(name => schema(name).dataType) inputDataTypes.foreach { - case _: NumericType => + case _: NumericType | BooleanType => case t if t.isInstanceOf[VectorUDT] => case other => throw new IllegalArgumentException(s"Data type $other is not supported.") From 29163c520087e89ca322521db1dd8656d86a6f0e Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Wed, 22 Apr 2015 23:55:20 -0700 Subject: [PATCH 390/591] [SPARK-7068][SQL] Remove PrimitiveType Author: Reynold Xin Closes #5646 from rxin/remove-primitive-type and squashes the following commits: 01b673d [Reynold Xin] [SPARK-7068][SQL] Remove PrimitiveType --- .../apache/spark/sql/types/dataTypes.scala | 70 ++++++++----------- .../spark/sql/parquet/ParquetConverter.scala | 11 +-- .../sql/parquet/ParquetTableOperations.scala | 2 +- .../spark/sql/parquet/ParquetTypes.scala | 6 +- .../apache/spark/sql/parquet/newParquet.scala | 13 ++-- 5 files changed, 48 insertions(+), 54 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala index ddf9d664c6826..42e26e05996dd 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala @@ -41,6 +41,21 @@ import org.apache.spark.util.Utils object DataType { def fromJson(json: String): DataType = parseDataType(parse(json)) + private val nonDecimalNameToType = { + (Seq(NullType, DateType, TimestampType, BinaryType) ++ NativeType.all) + .map(t => t.typeName -> t).toMap + } + + /** Given the string representation of a type, return its DataType */ + private def nameToType(name: String): DataType = { + val FIXED_DECIMAL = """decimal\(\s*(\d+)\s*,\s*(\d+)\s*\)""".r + name match { + case "decimal" => DecimalType.Unlimited + case FIXED_DECIMAL(precision, scale) => DecimalType(precision.toInt, scale.toInt) + case other => nonDecimalNameToType(other) + } + } + private object JSortedObject { def unapplySeq(value: JValue): Option[List[(String, JValue)]] = value match { case JObject(seq) => Some(seq.toList.sortBy(_._1)) @@ -51,7 +66,7 @@ object DataType { // NOTE: Map fields must be sorted in alphabetical order to keep consistent with the Python side. private def parseDataType(json: JValue): DataType = json match { case JString(name) => - PrimitiveType.nameToType(name) + nameToType(name) case JSortedObject( ("containsNull", JBool(n)), @@ -190,13 +205,11 @@ object DataType { equalsIgnoreNullability(leftKeyType, rightKeyType) && equalsIgnoreNullability(leftValueType, rightValueType) case (StructType(leftFields), StructType(rightFields)) => - leftFields.size == rightFields.size && - leftFields.zip(rightFields) - .forall{ - case (left, right) => - left.name == right.name && equalsIgnoreNullability(left.dataType, right.dataType) - } - case (left, right) => left == right + leftFields.length == rightFields.length && + leftFields.zip(rightFields).forall { case (l, r) => + l.name == r.name && equalsIgnoreNullability(l.dataType, r.dataType) + } + case (l, r) => l == r } } @@ -225,12 +238,11 @@ object DataType { equalsIgnoreCompatibleNullability(fromValue, toValue) case (StructType(fromFields), StructType(toFields)) => - fromFields.size == toFields.size && - fromFields.zip(toFields).forall { - case (fromField, toField) => - fromField.name == toField.name && - (toField.nullable || !fromField.nullable) && - equalsIgnoreCompatibleNullability(fromField.dataType, toField.dataType) + fromFields.length == toFields.length && + fromFields.zip(toFields).forall { case (fromField, toField) => + fromField.name == toField.name && + (toField.nullable || !fromField.nullable) && + equalsIgnoreCompatibleNullability(fromField.dataType, toField.dataType) } case (fromDataType, toDataType) => fromDataType == toDataType @@ -256,8 +268,6 @@ abstract class DataType { /** The default size of a value of this data type. */ def defaultSize: Int - def isPrimitive: Boolean = false - def typeName: String = this.getClass.getSimpleName.stripSuffix("$").dropRight(4).toLowerCase private[sql] def jsonValue: JValue = typeName @@ -307,26 +317,6 @@ protected[sql] object NativeType { } -protected[sql] trait PrimitiveType extends DataType { - override def isPrimitive: Boolean = true -} - - -protected[sql] object PrimitiveType { - private val nonDecimals = Seq(NullType, DateType, TimestampType, BinaryType) ++ NativeType.all - private val nonDecimalNameToType = nonDecimals.map(t => t.typeName -> t).toMap - - /** Given the string representation of a type, return its DataType */ - private[sql] def nameToType(name: String): DataType = { - val FIXED_DECIMAL = """decimal\(\s*(\d+)\s*,\s*(\d+)\s*\)""".r - name match { - case "decimal" => DecimalType.Unlimited - case FIXED_DECIMAL(precision, scale) => DecimalType(precision.toInt, scale.toInt) - case other => nonDecimalNameToType(other) - } - } -} - protected[sql] abstract class NativeType extends DataType { private[sql] type JvmType @transient private[sql] val tag: TypeTag[JvmType] @@ -346,7 +336,7 @@ protected[sql] abstract class NativeType extends DataType { * @group dataType */ @DeveloperApi -class StringType private() extends NativeType with PrimitiveType { +class StringType private() extends NativeType { // The companion object and this class is separated so the companion object also subclasses // this type. Otherwise, the companion object would be of type "StringType$" in byte code. // Defined with a private constructor so the companion object is the only possible instantiation. @@ -373,7 +363,7 @@ case object StringType extends StringType * @group dataType */ @DeveloperApi -class BinaryType private() extends NativeType with PrimitiveType { +class BinaryType private() extends NativeType { // The companion object and this class is separated so the companion object also subclasses // this type. Otherwise, the companion object would be of type "BinaryType$" in byte code. // Defined with a private constructor so the companion object is the only possible instantiation. @@ -407,7 +397,7 @@ case object BinaryType extends BinaryType *@group dataType */ @DeveloperApi -class BooleanType private() extends NativeType with PrimitiveType { +class BooleanType private() extends NativeType { // The companion object and this class is separated so the companion object also subclasses // this type. Otherwise, the companion object would be of type "BooleanType$" in byte code. // Defined with a private constructor so the companion object is the only possible instantiation. @@ -492,7 +482,7 @@ case object DateType extends DateType * * @group dataType */ -abstract class NumericType extends NativeType with PrimitiveType { +abstract class NumericType extends NativeType { // Unfortunately we can't get this implicitly as that breaks Spark Serialization. In order for // implicitly[Numeric[JvmType]] to be valid, we have to change JvmType from a type variable to a // type parameter and and add a numeric annotation (i.e., [JvmType : Numeric]). This gets diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala index bc108e37dfb0f..116424539da11 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala @@ -146,7 +146,8 @@ private[sql] object CatalystConverter { } } // All other primitive types use the default converter - case ctype: PrimitiveType => { // note: need the type tag here! + case ctype: DataType if ParquetTypesConverter.isPrimitiveType(ctype) => { + // note: need the type tag here! new CatalystPrimitiveConverter(parent, fieldIndex) } case _ => throw new RuntimeException( @@ -324,9 +325,9 @@ private[parquet] class CatalystGroupConverter( override def start(): Unit = { current = ArrayBuffer.fill(size)(null) - converters.foreach { - converter => if (!converter.isPrimitive) { - converter.asInstanceOf[CatalystConverter].clearBuffer + converters.foreach { converter => + if (!converter.isPrimitive) { + converter.asInstanceOf[CatalystConverter].clearBuffer() } } } @@ -612,7 +613,7 @@ private[parquet] class CatalystArrayConverter( override def start(): Unit = { if (!converter.isPrimitive) { - converter.asInstanceOf[CatalystConverter].clearBuffer + converter.asInstanceOf[CatalystConverter].clearBuffer() } } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala index 1c868da23e060..a938b77578686 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableOperations.scala @@ -268,7 +268,7 @@ private[sql] case class InsertIntoParquetTable( val job = new Job(sqlContext.sparkContext.hadoopConfiguration) val writeSupport = - if (child.output.map(_.dataType).forall(_.isPrimitive)) { + if (child.output.map(_.dataType).forall(ParquetTypesConverter.isPrimitiveType)) { log.debug("Initializing MutableRowWriteSupport") classOf[org.apache.spark.sql.parquet.MutableRowWriteSupport] } else { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala index 60e1bec4db8e5..1dc819b5d7b9b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala @@ -48,8 +48,10 @@ private[parquet] case class ParquetTypeInfo( length: Option[Int] = None) private[parquet] object ParquetTypesConverter extends Logging { - def isPrimitiveType(ctype: DataType): Boolean = - classOf[PrimitiveType] isAssignableFrom ctype.getClass + def isPrimitiveType(ctype: DataType): Boolean = ctype match { + case _: NumericType | BooleanType | StringType | BinaryType => true + case _: DataType => false + } def toPrimitiveDataType( parquetType: ParquetPrimitiveType, diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala index 88466f52bd4e9..85e60733bc57a 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/newParquet.scala @@ -634,12 +634,13 @@ private[sql] case class ParquetRelation2( // before calling execute(). val job = new Job(sqlContext.sparkContext.hadoopConfiguration) - val writeSupport = if (parquetSchema.map(_.dataType).forall(_.isPrimitive)) { - log.debug("Initializing MutableRowWriteSupport") - classOf[MutableRowWriteSupport] - } else { - classOf[RowWriteSupport] - } + val writeSupport = + if (parquetSchema.map(_.dataType).forall(ParquetTypesConverter.isPrimitiveType)) { + log.debug("Initializing MutableRowWriteSupport") + classOf[MutableRowWriteSupport] + } else { + classOf[RowWriteSupport] + } ParquetOutputFormat.setWriteSupportClass(job, writeSupport) From f60bece14f98450b4a71b00d7b58525f06e1f9ed Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Thu, 23 Apr 2015 01:43:40 -0700 Subject: [PATCH 391/591] [SPARK-7069][SQL] Rename NativeType -> AtomicType. Also renamed JvmType to InternalType. Author: Reynold Xin Closes #5651 from rxin/native-to-atomic-type and squashes the following commits: cbd4028 [Reynold Xin] [SPARK-7069][SQL] Rename NativeType -> AtomicType. --- .../spark/sql/catalyst/ScalaReflection.scala | 24 ++-- .../sql/catalyst/expressions/arithmetic.scala | 4 +- .../expressions/codegen/CodeGenerator.scala | 18 ++- .../codegen/GenerateProjection.scala | 4 +- .../sql/catalyst/expressions/predicates.scala | 10 +- .../spark/sql/catalyst/expressions/rows.scala | 6 +- .../apache/spark/sql/types/dataTypes.scala | 114 +++++++++--------- .../spark/sql/columnar/ColumnAccessor.scala | 2 +- .../spark/sql/columnar/ColumnBuilder.scala | 4 +- .../spark/sql/columnar/ColumnType.scala | 6 +- .../CompressibleColumnAccessor.scala | 4 +- .../CompressibleColumnBuilder.scala | 4 +- .../compression/CompressionScheme.scala | 10 +- .../compression/compressionSchemes.scala | 42 +++---- .../org/apache/spark/sql/json/JsonRDD.scala | 6 +- .../spark/sql/parquet/ParquetConverter.scala | 12 +- .../sql/parquet/ParquetTableSupport.scala | 2 +- .../spark/sql/columnar/ColumnStatsSuite.scala | 6 +- .../spark/sql/columnar/ColumnTypeSuite.scala | 8 +- .../sql/columnar/ColumnarTestUtils.scala | 6 +- .../compression/DictionaryEncodingSuite.scala | 4 +- .../compression/IntegralDeltaSuite.scala | 6 +- .../compression/RunLengthEncodingSuite.scala | 4 +- .../TestCompressibleColumnBuilder.scala | 6 +- 24 files changed, 159 insertions(+), 153 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala index d9521953cad73..c52965507c715 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/ScalaReflection.scala @@ -17,8 +17,6 @@ package org.apache.spark.sql.catalyst -import java.sql.Timestamp - import org.apache.spark.util.Utils import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.plans.logical.LocalRelation @@ -110,7 +108,7 @@ trait ScalaReflection { StructField(p.name.toString, dataType, nullable) }), nullable = true) case t if t <:< typeOf[String] => Schema(StringType, nullable = true) - case t if t <:< typeOf[Timestamp] => Schema(TimestampType, nullable = true) + case t if t <:< typeOf[java.sql.Timestamp] => Schema(TimestampType, nullable = true) case t if t <:< typeOf[java.sql.Date] => Schema(DateType, nullable = true) case t if t <:< typeOf[BigDecimal] => Schema(DecimalType.Unlimited, nullable = true) case t if t <:< typeOf[java.math.BigDecimal] => Schema(DecimalType.Unlimited, nullable = true) @@ -136,20 +134,20 @@ trait ScalaReflection { def typeOfObject: PartialFunction[Any, DataType] = { // The data type can be determined without ambiguity. - case obj: BooleanType.JvmType => BooleanType - case obj: BinaryType.JvmType => BinaryType + case obj: Boolean => BooleanType + case obj: Array[Byte] => BinaryType case obj: String => StringType - case obj: StringType.JvmType => StringType - case obj: ByteType.JvmType => ByteType - case obj: ShortType.JvmType => ShortType - case obj: IntegerType.JvmType => IntegerType - case obj: LongType.JvmType => LongType - case obj: FloatType.JvmType => FloatType - case obj: DoubleType.JvmType => DoubleType + case obj: UTF8String => StringType + case obj: Byte => ByteType + case obj: Short => ShortType + case obj: Int => IntegerType + case obj: Long => LongType + case obj: Float => FloatType + case obj: Double => DoubleType case obj: java.sql.Date => DateType case obj: java.math.BigDecimal => DecimalType.Unlimited case obj: Decimal => DecimalType.Unlimited - case obj: TimestampType.JvmType => TimestampType + case obj: java.sql.Timestamp => TimestampType case null => NullType // For other cases, there is no obvious mapping from the type of the given object to a // Catalyst data type. A user should provide his/her specific rules diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala index 566b34f7c3a6a..140ccd8d3796f 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala @@ -346,7 +346,7 @@ case class MaxOf(left: Expression, right: Expression) extends Expression { } lazy val ordering = left.dataType match { - case i: NativeType => i.ordering.asInstanceOf[Ordering[Any]] + case i: AtomicType => i.ordering.asInstanceOf[Ordering[Any]] case other => sys.error(s"Type $other does not support ordered operations") } @@ -391,7 +391,7 @@ case class MinOf(left: Expression, right: Expression) extends Expression { } lazy val ordering = left.dataType match { - case i: NativeType => i.ordering.asInstanceOf[Ordering[Any]] + case i: AtomicType => i.ordering.asInstanceOf[Ordering[Any]] case other => sys.error(s"Type $other does not support ordered operations") } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala index eeffedb558c1b..cbe520347385d 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala @@ -623,7 +623,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin protected def getColumn(inputRow: TermName, dataType: DataType, ordinal: Int) = { dataType match { case StringType => q"$inputRow($ordinal).asInstanceOf[org.apache.spark.sql.types.UTF8String]" - case dt @ NativeType() => q"$inputRow.${accessorForType(dt)}($ordinal)" + case dt: DataType if isNativeType(dt) => q"$inputRow.${accessorForType(dt)}($ordinal)" case _ => q"$inputRow.apply($ordinal).asInstanceOf[${termForType(dataType)}]" } } @@ -635,7 +635,8 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin value: TermName) = { dataType match { case StringType => q"$destinationRow.update($ordinal, $value)" - case dt @ NativeType() => q"$destinationRow.${mutatorForType(dt)}($ordinal, $value)" + case dt: DataType if isNativeType(dt) => + q"$destinationRow.${mutatorForType(dt)}($ordinal, $value)" case _ => q"$destinationRow.update($ordinal, $value)" } } @@ -675,7 +676,18 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin } protected def termForType(dt: DataType) = dt match { - case n: NativeType => n.tag + case n: AtomicType => n.tag case _ => typeTag[Any] } + + /** + * List of data types that have special accessors and setters in [[Row]]. + */ + protected val nativeTypes = + Seq(IntegerType, BooleanType, LongType, DoubleType, FloatType, ShortType, ByteType, StringType) + + /** + * Returns true if the data type has a special accessor and setter in [[Row]]. + */ + protected def isNativeType(dt: DataType) = nativeTypes.contains(dt) } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala index d491babc2bff0..584f938445c8c 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala @@ -109,7 +109,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] { q"override def update(i: Int, value: Any): Unit = { ..$cases; $accessorFailure }" } - val specificAccessorFunctions = NativeType.all.map { dataType => + val specificAccessorFunctions = nativeTypes.map { dataType => val ifStatements = expressions.zipWithIndex.flatMap { // getString() is not used by expressions case (e, i) if e.dataType == dataType && dataType != StringType => @@ -135,7 +135,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] { } } - val specificMutatorFunctions = NativeType.all.map { dataType => + val specificMutatorFunctions = nativeTypes.map { dataType => val ifStatements = expressions.zipWithIndex.flatMap { // setString() is not used by expressions case (e, i) if e.dataType == dataType && dataType != StringType => diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala index 46522eb9c1264..9cb00cb2732ff 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala @@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst.expressions import org.apache.spark.sql.catalyst.analysis.UnresolvedException import org.apache.spark.sql.catalyst.errors.TreeNodeException import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan -import org.apache.spark.sql.types.{DataType, BinaryType, BooleanType, NativeType} +import org.apache.spark.sql.types.{DataType, BinaryType, BooleanType, AtomicType} object InterpretedPredicate { def create(expression: Expression, inputSchema: Seq[Attribute]): (Row => Boolean) = @@ -211,7 +211,7 @@ case class LessThan(left: Expression, right: Expression) extends BinaryCompariso s"Types do not match ${left.dataType} != ${right.dataType}") } left.dataType match { - case i: NativeType => i.ordering.asInstanceOf[Ordering[Any]] + case i: AtomicType => i.ordering.asInstanceOf[Ordering[Any]] case other => sys.error(s"Type $other does not support ordered operations") } } @@ -240,7 +240,7 @@ case class LessThanOrEqual(left: Expression, right: Expression) extends BinaryCo s"Types do not match ${left.dataType} != ${right.dataType}") } left.dataType match { - case i: NativeType => i.ordering.asInstanceOf[Ordering[Any]] + case i: AtomicType => i.ordering.asInstanceOf[Ordering[Any]] case other => sys.error(s"Type $other does not support ordered operations") } } @@ -269,7 +269,7 @@ case class GreaterThan(left: Expression, right: Expression) extends BinaryCompar s"Types do not match ${left.dataType} != ${right.dataType}") } left.dataType match { - case i: NativeType => i.ordering.asInstanceOf[Ordering[Any]] + case i: AtomicType => i.ordering.asInstanceOf[Ordering[Any]] case other => sys.error(s"Type $other does not support ordered operations") } } @@ -298,7 +298,7 @@ case class GreaterThanOrEqual(left: Expression, right: Expression) extends Binar s"Types do not match ${left.dataType} != ${right.dataType}") } left.dataType match { - case i: NativeType => i.ordering.asInstanceOf[Ordering[Any]] + case i: AtomicType => i.ordering.asInstanceOf[Ordering[Any]] case other => sys.error(s"Type $other does not support ordered operations") } } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala index 981373477a4bc..5fd892c42e69c 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala @@ -17,7 +17,7 @@ package org.apache.spark.sql.catalyst.expressions -import org.apache.spark.sql.types.{UTF8String, DataType, StructType, NativeType} +import org.apache.spark.sql.types.{UTF8String, DataType, StructType, AtomicType} /** * An extended interface to [[Row]] that allows the values for each column to be updated. Setting @@ -227,9 +227,9 @@ class RowOrdering(ordering: Seq[SortOrder]) extends Ordering[Row] { return if (order.direction == Ascending) 1 else -1 } else { val comparison = order.dataType match { - case n: NativeType if order.direction == Ascending => + case n: AtomicType if order.direction == Ascending => n.ordering.asInstanceOf[Ordering[Any]].compare(left, right) - case n: NativeType if order.direction == Descending => + case n: AtomicType if order.direction == Descending => n.ordering.asInstanceOf[Ordering[Any]].reverse.compare(left, right) case other => sys.error(s"Type $other does not support ordered operations") } diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala index 42e26e05996dd..87c7b7599366a 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala @@ -42,7 +42,8 @@ object DataType { def fromJson(json: String): DataType = parseDataType(parse(json)) private val nonDecimalNameToType = { - (Seq(NullType, DateType, TimestampType, BinaryType) ++ NativeType.all) + Seq(NullType, DateType, TimestampType, BinaryType, + IntegerType, BooleanType, LongType, DoubleType, FloatType, ShortType, ByteType, StringType) .map(t => t.typeName -> t).toMap } @@ -309,22 +310,17 @@ class NullType private() extends DataType { case object NullType extends NullType -protected[sql] object NativeType { - val all = Seq( - IntegerType, BooleanType, LongType, DoubleType, FloatType, ShortType, ByteType, StringType) - - def unapply(dt: DataType): Boolean = all.contains(dt) -} - - -protected[sql] abstract class NativeType extends DataType { - private[sql] type JvmType - @transient private[sql] val tag: TypeTag[JvmType] - private[sql] val ordering: Ordering[JvmType] +/** + * An internal type used to represent everything that is not null, UDTs, arrays, structs, and maps. + */ +protected[sql] abstract class AtomicType extends DataType { + private[sql] type InternalType + @transient private[sql] val tag: TypeTag[InternalType] + private[sql] val ordering: Ordering[InternalType] @transient private[sql] val classTag = ScalaReflectionLock.synchronized { val mirror = runtimeMirror(Utils.getSparkClassLoader) - ClassTag[JvmType](mirror.runtimeClass(tag.tpe)) + ClassTag[InternalType](mirror.runtimeClass(tag.tpe)) } } @@ -336,13 +332,13 @@ protected[sql] abstract class NativeType extends DataType { * @group dataType */ @DeveloperApi -class StringType private() extends NativeType { +class StringType private() extends AtomicType { // The companion object and this class is separated so the companion object also subclasses // this type. Otherwise, the companion object would be of type "StringType$" in byte code. // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type JvmType = UTF8String - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } - private[sql] val ordering = implicitly[Ordering[JvmType]] + private[sql] type InternalType = UTF8String + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } + private[sql] val ordering = implicitly[Ordering[InternalType]] /** * The default size of a value of the StringType is 4096 bytes. @@ -363,13 +359,13 @@ case object StringType extends StringType * @group dataType */ @DeveloperApi -class BinaryType private() extends NativeType { +class BinaryType private() extends AtomicType { // The companion object and this class is separated so the companion object also subclasses // this type. Otherwise, the companion object would be of type "BinaryType$" in byte code. // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type JvmType = Array[Byte] - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } - private[sql] val ordering = new Ordering[JvmType] { + private[sql] type InternalType = Array[Byte] + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } + private[sql] val ordering = new Ordering[InternalType] { def compare(x: Array[Byte], y: Array[Byte]): Int = { for (i <- 0 until x.length; if i < y.length) { val res = x(i).compareTo(y(i)) @@ -397,13 +393,13 @@ case object BinaryType extends BinaryType *@group dataType */ @DeveloperApi -class BooleanType private() extends NativeType { +class BooleanType private() extends AtomicType { // The companion object and this class is separated so the companion object also subclasses // this type. Otherwise, the companion object would be of type "BooleanType$" in byte code. // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type JvmType = Boolean - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } - private[sql] val ordering = implicitly[Ordering[JvmType]] + private[sql] type InternalType = Boolean + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } + private[sql] val ordering = implicitly[Ordering[InternalType]] /** * The default size of a value of the BooleanType is 1 byte. @@ -424,15 +420,15 @@ case object BooleanType extends BooleanType * @group dataType */ @DeveloperApi -class TimestampType private() extends NativeType { +class TimestampType private() extends AtomicType { // The companion object and this class is separated so the companion object also subclasses // this type. Otherwise, the companion object would be of type "TimestampType$" in byte code. // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type JvmType = Timestamp + private[sql] type InternalType = Timestamp - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } - private[sql] val ordering = new Ordering[JvmType] { + private[sql] val ordering = new Ordering[InternalType] { def compare(x: Timestamp, y: Timestamp): Int = x.compareTo(y) } @@ -455,15 +451,15 @@ case object TimestampType extends TimestampType * @group dataType */ @DeveloperApi -class DateType private() extends NativeType { +class DateType private() extends AtomicType { // The companion object and this class is separated so the companion object also subclasses // this type. Otherwise, the companion object would be of type "DateType$" in byte code. // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type JvmType = Int + private[sql] type InternalType = Int - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } - private[sql] val ordering = implicitly[Ordering[JvmType]] + private[sql] val ordering = implicitly[Ordering[InternalType]] /** * The default size of a value of the DateType is 4 bytes. @@ -482,13 +478,13 @@ case object DateType extends DateType * * @group dataType */ -abstract class NumericType extends NativeType { +abstract class NumericType extends AtomicType { // Unfortunately we can't get this implicitly as that breaks Spark Serialization. In order for // implicitly[Numeric[JvmType]] to be valid, we have to change JvmType from a type variable to a // type parameter and and add a numeric annotation (i.e., [JvmType : Numeric]). This gets // desugared by the compiler into an argument to the objects constructor. This means there is no // longer an no argument constructor and thus the JVM cannot serialize the object anymore. - private[sql] val numeric: Numeric[JvmType] + private[sql] val numeric: Numeric[InternalType] } @@ -507,7 +503,7 @@ protected[sql] object IntegralType { protected[sql] sealed abstract class IntegralType extends NumericType { - private[sql] val integral: Integral[JvmType] + private[sql] val integral: Integral[InternalType] } @@ -522,11 +518,11 @@ class LongType private() extends IntegralType { // The companion object and this class is separated so the companion object also subclasses // this type. Otherwise, the companion object would be of type "LongType$" in byte code. // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type JvmType = Long - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } + private[sql] type InternalType = Long + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } private[sql] val numeric = implicitly[Numeric[Long]] private[sql] val integral = implicitly[Integral[Long]] - private[sql] val ordering = implicitly[Ordering[JvmType]] + private[sql] val ordering = implicitly[Ordering[InternalType]] /** * The default size of a value of the LongType is 8 bytes. @@ -552,11 +548,11 @@ class IntegerType private() extends IntegralType { // The companion object and this class is separated so the companion object also subclasses // this type. Otherwise, the companion object would be of type "IntegerType$" in byte code. // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type JvmType = Int - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } + private[sql] type InternalType = Int + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } private[sql] val numeric = implicitly[Numeric[Int]] private[sql] val integral = implicitly[Integral[Int]] - private[sql] val ordering = implicitly[Ordering[JvmType]] + private[sql] val ordering = implicitly[Ordering[InternalType]] /** * The default size of a value of the IntegerType is 4 bytes. @@ -582,11 +578,11 @@ class ShortType private() extends IntegralType { // The companion object and this class is separated so the companion object also subclasses // this type. Otherwise, the companion object would be of type "ShortType$" in byte code. // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type JvmType = Short - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } + private[sql] type InternalType = Short + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } private[sql] val numeric = implicitly[Numeric[Short]] private[sql] val integral = implicitly[Integral[Short]] - private[sql] val ordering = implicitly[Ordering[JvmType]] + private[sql] val ordering = implicitly[Ordering[InternalType]] /** * The default size of a value of the ShortType is 2 bytes. @@ -612,11 +608,11 @@ class ByteType private() extends IntegralType { // The companion object and this class is separated so the companion object also subclasses // this type. Otherwise, the companion object would be of type "ByteType$" in byte code. // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type JvmType = Byte - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } + private[sql] type InternalType = Byte + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } private[sql] val numeric = implicitly[Numeric[Byte]] private[sql] val integral = implicitly[Integral[Byte]] - private[sql] val ordering = implicitly[Ordering[JvmType]] + private[sql] val ordering = implicitly[Ordering[InternalType]] /** * The default size of a value of the ByteType is 1 byte. @@ -641,8 +637,8 @@ protected[sql] object FractionalType { protected[sql] sealed abstract class FractionalType extends NumericType { - private[sql] val fractional: Fractional[JvmType] - private[sql] val asIntegral: Integral[JvmType] + private[sql] val fractional: Fractional[InternalType] + private[sql] val asIntegral: Integral[InternalType] } @@ -665,8 +661,8 @@ case class DecimalType(precisionInfo: Option[PrecisionInfo]) extends FractionalT /** No-arg constructor for kryo. */ protected def this() = this(null) - private[sql] type JvmType = Decimal - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } + private[sql] type InternalType = Decimal + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } private[sql] val numeric = Decimal.DecimalIsFractional private[sql] val fractional = Decimal.DecimalIsFractional private[sql] val ordering = Decimal.DecimalIsFractional @@ -743,11 +739,11 @@ class DoubleType private() extends FractionalType { // The companion object and this class is separated so the companion object also subclasses // this type. Otherwise, the companion object would be of type "DoubleType$" in byte code. // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type JvmType = Double - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } + private[sql] type InternalType = Double + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } private[sql] val numeric = implicitly[Numeric[Double]] private[sql] val fractional = implicitly[Fractional[Double]] - private[sql] val ordering = implicitly[Ordering[JvmType]] + private[sql] val ordering = implicitly[Ordering[InternalType]] private[sql] val asIntegral = DoubleAsIfIntegral /** @@ -772,11 +768,11 @@ class FloatType private() extends FractionalType { // The companion object and this class is separated so the companion object also subclasses // this type. Otherwise, the companion object would be of type "FloatType$" in byte code. // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type JvmType = Float - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[JvmType] } + private[sql] type InternalType = Float + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } private[sql] val numeric = implicitly[Numeric[Float]] private[sql] val fractional = implicitly[Fractional[Float]] - private[sql] val ordering = implicitly[Ordering[JvmType]] + private[sql] val ordering = implicitly[Ordering[InternalType]] private[sql] val asIntegral = FloatAsIfIntegral /** diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala index f615fb33a7c35..64449b2659b4b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnAccessor.scala @@ -61,7 +61,7 @@ private[sql] abstract class BasicColumnAccessor[T <: DataType, JvmType]( protected def underlyingBuffer = buffer } -private[sql] abstract class NativeColumnAccessor[T <: NativeType]( +private[sql] abstract class NativeColumnAccessor[T <: AtomicType]( override protected val buffer: ByteBuffer, override protected val columnType: NativeColumnType[T]) extends BasicColumnAccessor(buffer, columnType) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala index 00ed70430b84d..aa10af400c815 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnBuilder.scala @@ -84,10 +84,10 @@ private[sql] abstract class ComplexColumnBuilder[T <: DataType, JvmType]( extends BasicColumnBuilder[T, JvmType](columnStats, columnType) with NullableColumnBuilder -private[sql] abstract class NativeColumnBuilder[T <: NativeType]( +private[sql] abstract class NativeColumnBuilder[T <: AtomicType]( override val columnStats: ColumnStats, override val columnType: NativeColumnType[T]) - extends BasicColumnBuilder[T, T#JvmType](columnStats, columnType) + extends BasicColumnBuilder[T, T#InternalType](columnStats, columnType) with NullableColumnBuilder with AllCompressionSchemes with CompressibleColumnBuilder[T] diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala index 1b9e0df2dcb5e..20be5ca9d0046 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala @@ -101,16 +101,16 @@ private[sql] sealed abstract class ColumnType[T <: DataType, JvmType]( override def toString: String = getClass.getSimpleName.stripSuffix("$") } -private[sql] abstract class NativeColumnType[T <: NativeType]( +private[sql] abstract class NativeColumnType[T <: AtomicType]( val dataType: T, typeId: Int, defaultSize: Int) - extends ColumnType[T, T#JvmType](typeId, defaultSize) { + extends ColumnType[T, T#InternalType](typeId, defaultSize) { /** * Scala TypeTag. Can be used to create primitive arrays and hash tables. */ - def scalaTag: TypeTag[dataType.JvmType] = dataType.tag + def scalaTag: TypeTag[dataType.InternalType] = dataType.tag } private[sql] object INT extends NativeColumnType(IntegerType, 0, 4) { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnAccessor.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnAccessor.scala index d0b602a834dfe..cb205defbb1ad 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnAccessor.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnAccessor.scala @@ -19,9 +19,9 @@ package org.apache.spark.sql.columnar.compression import org.apache.spark.sql.catalyst.expressions.MutableRow import org.apache.spark.sql.columnar.{ColumnAccessor, NativeColumnAccessor} -import org.apache.spark.sql.types.NativeType +import org.apache.spark.sql.types.AtomicType -private[sql] trait CompressibleColumnAccessor[T <: NativeType] extends ColumnAccessor { +private[sql] trait CompressibleColumnAccessor[T <: AtomicType] extends ColumnAccessor { this: NativeColumnAccessor[T] => private var decoder: Decoder[T] = _ diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala index b9cfc5df550d1..8e2a1af6dae78 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressibleColumnBuilder.scala @@ -22,7 +22,7 @@ import java.nio.{ByteBuffer, ByteOrder} import org.apache.spark.Logging import org.apache.spark.sql.Row import org.apache.spark.sql.columnar.{ColumnBuilder, NativeColumnBuilder} -import org.apache.spark.sql.types.NativeType +import org.apache.spark.sql.types.AtomicType /** * A stackable trait that builds optionally compressed byte buffer for a column. Memory layout of @@ -41,7 +41,7 @@ import org.apache.spark.sql.types.NativeType * header body * }}} */ -private[sql] trait CompressibleColumnBuilder[T <: NativeType] +private[sql] trait CompressibleColumnBuilder[T <: AtomicType] extends ColumnBuilder with Logging { this: NativeColumnBuilder[T] with WithCompressionSchemes => diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala index 879d29bcfa6f6..17c2d9b111188 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/CompressionScheme.scala @@ -22,9 +22,9 @@ import java.nio.{ByteBuffer, ByteOrder} import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.expressions.MutableRow import org.apache.spark.sql.columnar.{ColumnType, NativeColumnType} -import org.apache.spark.sql.types.NativeType +import org.apache.spark.sql.types.AtomicType -private[sql] trait Encoder[T <: NativeType] { +private[sql] trait Encoder[T <: AtomicType] { def gatherCompressibilityStats(row: Row, ordinal: Int): Unit = {} def compressedSize: Int @@ -38,7 +38,7 @@ private[sql] trait Encoder[T <: NativeType] { def compress(from: ByteBuffer, to: ByteBuffer): ByteBuffer } -private[sql] trait Decoder[T <: NativeType] { +private[sql] trait Decoder[T <: AtomicType] { def next(row: MutableRow, ordinal: Int): Unit def hasNext: Boolean @@ -49,9 +49,9 @@ private[sql] trait CompressionScheme { def supports(columnType: ColumnType[_, _]): Boolean - def encoder[T <: NativeType](columnType: NativeColumnType[T]): Encoder[T] + def encoder[T <: AtomicType](columnType: NativeColumnType[T]): Encoder[T] - def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]): Decoder[T] + def decoder[T <: AtomicType](buffer: ByteBuffer, columnType: NativeColumnType[T]): Decoder[T] } private[sql] trait WithCompressionSchemes { diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala index 8727d71c48bb7..534ae90ddbc8b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala @@ -35,16 +35,16 @@ private[sql] case object PassThrough extends CompressionScheme { override def supports(columnType: ColumnType[_, _]): Boolean = true - override def encoder[T <: NativeType](columnType: NativeColumnType[T]): Encoder[T] = { + override def encoder[T <: AtomicType](columnType: NativeColumnType[T]): Encoder[T] = { new this.Encoder[T](columnType) } - override def decoder[T <: NativeType]( + override def decoder[T <: AtomicType]( buffer: ByteBuffer, columnType: NativeColumnType[T]): Decoder[T] = { new this.Decoder(buffer, columnType) } - class Encoder[T <: NativeType](columnType: NativeColumnType[T]) extends compression.Encoder[T] { + class Encoder[T <: AtomicType](columnType: NativeColumnType[T]) extends compression.Encoder[T] { override def uncompressedSize: Int = 0 override def compressedSize: Int = 0 @@ -56,7 +56,7 @@ private[sql] case object PassThrough extends CompressionScheme { } } - class Decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) + class Decoder[T <: AtomicType](buffer: ByteBuffer, columnType: NativeColumnType[T]) extends compression.Decoder[T] { override def next(row: MutableRow, ordinal: Int): Unit = { @@ -70,11 +70,11 @@ private[sql] case object PassThrough extends CompressionScheme { private[sql] case object RunLengthEncoding extends CompressionScheme { override val typeId = 1 - override def encoder[T <: NativeType](columnType: NativeColumnType[T]): Encoder[T] = { + override def encoder[T <: AtomicType](columnType: NativeColumnType[T]): Encoder[T] = { new this.Encoder[T](columnType) } - override def decoder[T <: NativeType]( + override def decoder[T <: AtomicType]( buffer: ByteBuffer, columnType: NativeColumnType[T]): Decoder[T] = { new this.Decoder(buffer, columnType) } @@ -84,7 +84,7 @@ private[sql] case object RunLengthEncoding extends CompressionScheme { case _ => false } - class Encoder[T <: NativeType](columnType: NativeColumnType[T]) extends compression.Encoder[T] { + class Encoder[T <: AtomicType](columnType: NativeColumnType[T]) extends compression.Encoder[T] { private var _uncompressedSize = 0 private var _compressedSize = 0 @@ -152,12 +152,12 @@ private[sql] case object RunLengthEncoding extends CompressionScheme { } } - class Decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) + class Decoder[T <: AtomicType](buffer: ByteBuffer, columnType: NativeColumnType[T]) extends compression.Decoder[T] { private var run = 0 private var valueCount = 0 - private var currentValue: T#JvmType = _ + private var currentValue: T#InternalType = _ override def next(row: MutableRow, ordinal: Int): Unit = { if (valueCount == run) { @@ -181,12 +181,12 @@ private[sql] case object DictionaryEncoding extends CompressionScheme { // 32K unique values allowed val MAX_DICT_SIZE = Short.MaxValue - override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) + override def decoder[T <: AtomicType](buffer: ByteBuffer, columnType: NativeColumnType[T]) : Decoder[T] = { new this.Decoder(buffer, columnType) } - override def encoder[T <: NativeType](columnType: NativeColumnType[T]): Encoder[T] = { + override def encoder[T <: AtomicType](columnType: NativeColumnType[T]): Encoder[T] = { new this.Encoder[T](columnType) } @@ -195,7 +195,7 @@ private[sql] case object DictionaryEncoding extends CompressionScheme { case _ => false } - class Encoder[T <: NativeType](columnType: NativeColumnType[T]) extends compression.Encoder[T] { + class Encoder[T <: AtomicType](columnType: NativeColumnType[T]) extends compression.Encoder[T] { // Size of the input, uncompressed, in bytes. Note that we only count until the dictionary // overflows. private var _uncompressedSize = 0 @@ -208,7 +208,7 @@ private[sql] case object DictionaryEncoding extends CompressionScheme { private var count = 0 // The reverse mapping of _dictionary, i.e. mapping encoded integer to the value itself. - private var values = new mutable.ArrayBuffer[T#JvmType](1024) + private var values = new mutable.ArrayBuffer[T#InternalType](1024) // The dictionary that maps a value to the encoded short integer. private val dictionary = mutable.HashMap.empty[Any, Short] @@ -268,14 +268,14 @@ private[sql] case object DictionaryEncoding extends CompressionScheme { override def compressedSize: Int = if (overflow) Int.MaxValue else dictionarySize + count * 2 } - class Decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) + class Decoder[T <: AtomicType](buffer: ByteBuffer, columnType: NativeColumnType[T]) extends compression.Decoder[T] { private val dictionary = { // TODO Can we clean up this mess? Maybe move this to `DataType`? implicit val classTag = { val mirror = runtimeMirror(Utils.getSparkClassLoader) - ClassTag[T#JvmType](mirror.runtimeClass(columnType.scalaTag.tpe)) + ClassTag[T#InternalType](mirror.runtimeClass(columnType.scalaTag.tpe)) } Array.fill(buffer.getInt()) { @@ -296,12 +296,12 @@ private[sql] case object BooleanBitSet extends CompressionScheme { val BITS_PER_LONG = 64 - override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) + override def decoder[T <: AtomicType](buffer: ByteBuffer, columnType: NativeColumnType[T]) : compression.Decoder[T] = { new this.Decoder(buffer).asInstanceOf[compression.Decoder[T]] } - override def encoder[T <: NativeType](columnType: NativeColumnType[T]): compression.Encoder[T] = { + override def encoder[T <: AtomicType](columnType: NativeColumnType[T]): compression.Encoder[T] = { (new this.Encoder).asInstanceOf[compression.Encoder[T]] } @@ -384,12 +384,12 @@ private[sql] case object BooleanBitSet extends CompressionScheme { private[sql] case object IntDelta extends CompressionScheme { override def typeId: Int = 4 - override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) + override def decoder[T <: AtomicType](buffer: ByteBuffer, columnType: NativeColumnType[T]) : compression.Decoder[T] = { new Decoder(buffer, INT).asInstanceOf[compression.Decoder[T]] } - override def encoder[T <: NativeType](columnType: NativeColumnType[T]): compression.Encoder[T] = { + override def encoder[T <: AtomicType](columnType: NativeColumnType[T]): compression.Encoder[T] = { (new Encoder).asInstanceOf[compression.Encoder[T]] } @@ -464,12 +464,12 @@ private[sql] case object IntDelta extends CompressionScheme { private[sql] case object LongDelta extends CompressionScheme { override def typeId: Int = 5 - override def decoder[T <: NativeType](buffer: ByteBuffer, columnType: NativeColumnType[T]) + override def decoder[T <: AtomicType](buffer: ByteBuffer, columnType: NativeColumnType[T]) : compression.Decoder[T] = { new Decoder(buffer, LONG).asInstanceOf[compression.Decoder[T]] } - override def encoder[T <: NativeType](columnType: NativeColumnType[T]): compression.Encoder[T] = { + override def encoder[T <: AtomicType](columnType: NativeColumnType[T]): compression.Encoder[T] = { (new Encoder).asInstanceOf[compression.Encoder[T]] } diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala index 29de7401dda71..6e94e7056eb0b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JsonRDD.scala @@ -183,7 +183,7 @@ private[sql] object JsonRDD extends Logging { private def typeOfPrimitiveValue: PartialFunction[Any, DataType] = { // For Integer values, use LongType by default. val useLongType: PartialFunction[Any, DataType] = { - case value: IntegerType.JvmType => LongType + case value: IntegerType.InternalType => LongType } useLongType orElse ScalaReflection.typeOfObject orElse { @@ -411,11 +411,11 @@ private[sql] object JsonRDD extends Logging { desiredType match { case StringType => UTF8String(toString(value)) case _ if value == null || value == "" => null // guard the non string type - case IntegerType => value.asInstanceOf[IntegerType.JvmType] + case IntegerType => value.asInstanceOf[IntegerType.InternalType] case LongType => toLong(value) case DoubleType => toDouble(value) case DecimalType() => toDecimal(value) - case BooleanType => value.asInstanceOf[BooleanType.JvmType] + case BooleanType => value.asInstanceOf[BooleanType.InternalType] case NullType => null case ArrayType(elementType, _) => value.asInstanceOf[Seq[Any]].map(enforceCorrectType(_, elementType)) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala index 116424539da11..36cb5e03bbca7 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala @@ -90,7 +90,7 @@ private[sql] object CatalystConverter { createConverter(field.copy(dataType = udt.sqlType), fieldIndex, parent) } // For native JVM types we use a converter with native arrays - case ArrayType(elementType: NativeType, false) => { + case ArrayType(elementType: AtomicType, false) => { new CatalystNativeArrayConverter(elementType, fieldIndex, parent) } // This is for other types of arrays, including those with nested fields @@ -118,19 +118,19 @@ private[sql] object CatalystConverter { case ShortType => { new CatalystPrimitiveConverter(parent, fieldIndex) { override def addInt(value: Int): Unit = - parent.updateShort(fieldIndex, value.asInstanceOf[ShortType.JvmType]) + parent.updateShort(fieldIndex, value.asInstanceOf[ShortType.InternalType]) } } case ByteType => { new CatalystPrimitiveConverter(parent, fieldIndex) { override def addInt(value: Int): Unit = - parent.updateByte(fieldIndex, value.asInstanceOf[ByteType.JvmType]) + parent.updateByte(fieldIndex, value.asInstanceOf[ByteType.InternalType]) } } case DateType => { new CatalystPrimitiveConverter(parent, fieldIndex) { override def addInt(value: Int): Unit = - parent.updateDate(fieldIndex, value.asInstanceOf[DateType.JvmType]) + parent.updateDate(fieldIndex, value.asInstanceOf[DateType.InternalType]) } } case d: DecimalType => { @@ -637,13 +637,13 @@ private[parquet] class CatalystArrayConverter( * @param capacity The (initial) capacity of the buffer */ private[parquet] class CatalystNativeArrayConverter( - val elementType: NativeType, + val elementType: AtomicType, val index: Int, protected[parquet] val parent: CatalystConverter, protected[parquet] var capacity: Int = CatalystArrayConverter.INITIAL_ARRAY_SIZE) extends CatalystConverter { - type NativeType = elementType.JvmType + type NativeType = elementType.InternalType private var buffer: Array[NativeType] = elementType.classTag.newArray(capacity) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala index e05a4c20b0d41..c45c431438efc 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala @@ -189,7 +189,7 @@ private[parquet] class RowWriteSupport extends WriteSupport[Row] with Logging { case t @ StructType(_) => writeStruct( t, value.asInstanceOf[CatalystConverter.StructScalaType[_]]) - case _ => writePrimitive(schema.asInstanceOf[NativeType], value) + case _ => writePrimitive(schema.asInstanceOf[AtomicType], value) } } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala index fec487f1d2c82..7cefcf44061ce 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala @@ -34,7 +34,7 @@ class ColumnStatsSuite extends FunSuite { testColumnStats(classOf[DateColumnStats], DATE, Row(Int.MaxValue, Int.MinValue, 0)) testColumnStats(classOf[TimestampColumnStats], TIMESTAMP, Row(null, null, 0)) - def testColumnStats[T <: NativeType, U <: ColumnStats]( + def testColumnStats[T <: AtomicType, U <: ColumnStats]( columnStatsClass: Class[U], columnType: NativeColumnType[T], initialStatistics: Row): Unit = { @@ -55,8 +55,8 @@ class ColumnStatsSuite extends FunSuite { val rows = Seq.fill(10)(makeRandomRow(columnType)) ++ Seq.fill(10)(makeNullRow(1)) rows.foreach(columnStats.gatherStats(_, 0)) - val values = rows.take(10).map(_(0).asInstanceOf[T#JvmType]) - val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#JvmType]] + val values = rows.take(10).map(_(0).asInstanceOf[T#InternalType]) + val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#InternalType]] val stats = columnStats.collectedStatistics assertResult(values.min(ordering), "Wrong lower bound")(stats(0)) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala index b48bed1871c50..1e105e259dce7 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnTypeSuite.scala @@ -196,12 +196,12 @@ class ColumnTypeSuite extends FunSuite with Logging { } } - def testNativeColumnType[T <: NativeType]( + def testNativeColumnType[T <: AtomicType]( columnType: NativeColumnType[T], - putter: (ByteBuffer, T#JvmType) => Unit, - getter: (ByteBuffer) => T#JvmType): Unit = { + putter: (ByteBuffer, T#InternalType) => Unit, + getter: (ByteBuffer) => T#InternalType): Unit = { - testColumnType[T, T#JvmType](columnType, putter, getter) + testColumnType[T, T#InternalType](columnType, putter, getter) } def testColumnType[T <: DataType, JvmType]( diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala index f76314b9dab5e..75d993e563e06 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnarTestUtils.scala @@ -24,7 +24,7 @@ import scala.util.Random import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.expressions.GenericMutableRow -import org.apache.spark.sql.types.{UTF8String, DataType, Decimal, NativeType} +import org.apache.spark.sql.types.{UTF8String, DataType, Decimal, AtomicType} object ColumnarTestUtils { def makeNullRow(length: Int): GenericMutableRow = { @@ -91,9 +91,9 @@ object ColumnarTestUtils { row } - def makeUniqueValuesAndSingleValueRows[T <: NativeType]( + def makeUniqueValuesAndSingleValueRows[T <: AtomicType]( columnType: NativeColumnType[T], - count: Int): (Seq[T#JvmType], Seq[GenericMutableRow]) = { + count: Int): (Seq[T#InternalType], Seq[GenericMutableRow]) = { val values = makeUniqueRandomValues(columnType, count) val rows = values.map { value => diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala index c82d9799359c7..64b70552eb047 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/DictionaryEncodingSuite.scala @@ -24,14 +24,14 @@ import org.scalatest.FunSuite import org.apache.spark.sql.catalyst.expressions.GenericMutableRow import org.apache.spark.sql.columnar._ import org.apache.spark.sql.columnar.ColumnarTestUtils._ -import org.apache.spark.sql.types.NativeType +import org.apache.spark.sql.types.AtomicType class DictionaryEncodingSuite extends FunSuite { testDictionaryEncoding(new IntColumnStats, INT) testDictionaryEncoding(new LongColumnStats, LONG) testDictionaryEncoding(new StringColumnStats, STRING) - def testDictionaryEncoding[T <: NativeType]( + def testDictionaryEncoding[T <: AtomicType]( columnStats: ColumnStats, columnType: NativeColumnType[T]) { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala index 88011631ee4e3..bfd99f143bedc 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/IntegralDeltaSuite.scala @@ -33,7 +33,7 @@ class IntegralDeltaSuite extends FunSuite { columnType: NativeColumnType[I], scheme: CompressionScheme) { - def skeleton(input: Seq[I#JvmType]) { + def skeleton(input: Seq[I#InternalType]) { // ------------- // Tests encoder // ------------- @@ -120,13 +120,13 @@ class IntegralDeltaSuite extends FunSuite { case LONG => Seq(2: Long, 1: Long, 2: Long, 130: Long) } - skeleton(input.map(_.asInstanceOf[I#JvmType])) + skeleton(input.map(_.asInstanceOf[I#InternalType])) } test(s"$scheme: long random series") { // Have to workaround with `Any` since no `ClassTag[I#JvmType]` available here. val input = Array.fill[Any](10000)(makeRandomValue(columnType)) - skeleton(input.map(_.asInstanceOf[I#JvmType])) + skeleton(input.map(_.asInstanceOf[I#InternalType])) } } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala index 08df1db375097..fde7a4595be0e 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/RunLengthEncodingSuite.scala @@ -22,7 +22,7 @@ import org.scalatest.FunSuite import org.apache.spark.sql.catalyst.expressions.GenericMutableRow import org.apache.spark.sql.columnar._ import org.apache.spark.sql.columnar.ColumnarTestUtils._ -import org.apache.spark.sql.types.NativeType +import org.apache.spark.sql.types.AtomicType class RunLengthEncodingSuite extends FunSuite { testRunLengthEncoding(new NoopColumnStats, BOOLEAN) @@ -32,7 +32,7 @@ class RunLengthEncodingSuite extends FunSuite { testRunLengthEncoding(new LongColumnStats, LONG) testRunLengthEncoding(new StringColumnStats, STRING) - def testRunLengthEncoding[T <: NativeType]( + def testRunLengthEncoding[T <: AtomicType]( columnStats: ColumnStats, columnType: NativeColumnType[T]) { diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala index fc8ff3b41d0e6..5268dfe0aa03e 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/compression/TestCompressibleColumnBuilder.scala @@ -18,9 +18,9 @@ package org.apache.spark.sql.columnar.compression import org.apache.spark.sql.columnar._ -import org.apache.spark.sql.types.NativeType +import org.apache.spark.sql.types.AtomicType -class TestCompressibleColumnBuilder[T <: NativeType]( +class TestCompressibleColumnBuilder[T <: AtomicType]( override val columnStats: ColumnStats, override val columnType: NativeColumnType[T], override val schemes: Seq[CompressionScheme]) @@ -32,7 +32,7 @@ class TestCompressibleColumnBuilder[T <: NativeType]( } object TestCompressibleColumnBuilder { - def apply[T <: NativeType]( + def apply[T <: AtomicType]( columnStats: ColumnStats, columnType: NativeColumnType[T], scheme: CompressionScheme): TestCompressibleColumnBuilder[T] = { From a7d65d38f934c5c751ba32aa7ab648c6d16044ab Mon Sep 17 00:00:00 2001 From: Prashant Sharma Date: Thu, 23 Apr 2015 16:45:26 +0530 Subject: [PATCH 392/591] [HOTFIX] [SQL] Fix compilation for scala 2.11. Author: Prashant Sharma Closes #5652 from ScrapCodes/hf/compilation-fix-scala-2.11 and squashes the following commits: 819ff06 [Prashant Sharma] [HOTFIX] Fix compilation for scala 2.11. --- .../test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java index fc3ed4a708d46..e02c84872c628 100644 --- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java +++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java @@ -162,7 +162,7 @@ public void testCreateDataFrameFromJavaBeans() { Buffer outputBuffer = (Buffer) first.getJavaMap(2).get("hello"); Assert.assertArrayEquals( bean.getC().get("hello"), - Ints.toArray(JavaConversions.asJavaList(outputBuffer))); + Ints.toArray(JavaConversions.bufferAsJavaList(outputBuffer))); Seq d = first.getAs(3); Assert.assertEquals(bean.getD().size(), d.length()); for (int i = 0; i < d.length(); i++) { From 975f53e4f978759db7639cd08498ad8cd0ae2a56 Mon Sep 17 00:00:00 2001 From: Prabeesh K Date: Thu, 23 Apr 2015 10:33:13 -0700 Subject: [PATCH 393/591] [minor][streaming]fixed scala string interpolation error Author: Prabeesh K Closes #5653 from prabeesh/fix and squashes the following commits: 9d7a9f5 [Prabeesh K] fixed scala string interpolation error --- .../org/apache/spark/examples/streaming/MQTTWordCount.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala index f40caad322f59..85b9a54b40baf 100644 --- a/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala @@ -56,7 +56,7 @@ object MQTTPublisher { while (true) { try { msgtopic.publish(message) - println(s"Published data. topic: {msgtopic.getName()}; Message: {message}") + println(s"Published data. topic: ${msgtopic.getName()}; Message: $message") } catch { case e: MqttException if e.getReasonCode == MqttException.REASON_CODE_MAX_INFLIGHT => Thread.sleep(10) From cc48e6387abdd909921cb58e0588cdf226556bcd Mon Sep 17 00:00:00 2001 From: Cheng Hao Date: Thu, 23 Apr 2015 10:35:22 -0700 Subject: [PATCH 394/591] [SPARK-7044] [SQL] Fix the deadlock in script transformation Author: Cheng Hao Closes #5625 from chenghao-intel/transform and squashes the following commits: 5ec1dd2 [Cheng Hao] fix the deadlock issue in ScriptTransform --- .../hive/execution/ScriptTransformation.scala | 33 ++++++++++++------- .../sql/hive/execution/SQLQuerySuite.scala | 8 +++++ 2 files changed, 29 insertions(+), 12 deletions(-) diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala index cab0fdd35723a..3eddda3b28c66 100644 --- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala +++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala @@ -145,20 +145,29 @@ case class ScriptTransformation( val dataOutputStream = new DataOutputStream(outputStream) val outputProjection = new InterpretedProjection(input, child.output) - iter - .map(outputProjection) - .foreach { row => - if (inputSerde == null) { - val data = row.mkString("", ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"), - ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")).getBytes("utf-8") - - outputStream.write(data) - } else { - val writable = inputSerde.serialize(row.asInstanceOf[GenericRow].values, inputSoi) - prepareWritable(writable).write(dataOutputStream) + // Put the write(output to the pipeline) into a single thread + // and keep the collector as remain in the main thread. + // otherwise it will causes deadlock if the data size greater than + // the pipeline / buffer capacity. + new Thread(new Runnable() { + override def run(): Unit = { + iter + .map(outputProjection) + .foreach { row => + if (inputSerde == null) { + val data = row.mkString("", ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"), + ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")).getBytes("utf-8") + + outputStream.write(data) + } else { + val writable = inputSerde.serialize(row.asInstanceOf[GenericRow].values, inputSoi) + prepareWritable(writable).write(dataOutputStream) + } } + outputStream.close() } - outputStream.close() + }).start() + iterator } } diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala index 47b4cb9ca61ff..4f8d0ac0e7656 100644 --- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala +++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala @@ -561,4 +561,12 @@ class SQLQuerySuite extends QueryTest { sql("select d from dn union all select d * 2 from dn") .queryExecution.analyzed } + + test("test script transform") { + val data = (1 to 100000).map { i => (i, i, i) } + data.toDF("d1", "d2", "d3").registerTempTable("script_trans") + assert(100000 === + sql("SELECT TRANSFORM (d1, d2, d3) USING 'cat' AS (a,b,c) FROM script_trans") + .queryExecution.toRdd.count()) + } } From 534f2a43625fbf1a3a65d09550a19875cd1dce43 Mon Sep 17 00:00:00 2001 From: Tathagata Das Date: Thu, 23 Apr 2015 11:29:34 -0700 Subject: [PATCH 395/591] [SPARK-6752][Streaming] Allow StreamingContext to be recreated from checkpoint and existing SparkContext Currently if you want to create a StreamingContext from checkpoint information, the system will create a new SparkContext. This prevent StreamingContext to be recreated from checkpoints in managed environments where SparkContext is precreated. The solution in this PR: Introduce the following methods on StreamingContext 1. `new StreamingContext(checkpointDirectory, sparkContext)` Recreate StreamingContext from checkpoint using the provided SparkContext 2. `StreamingContext.getOrCreate(checkpointDirectory, sparkContext, createFunction: SparkContext => StreamingContext)` If checkpoint file exists, then recreate StreamingContext using the provided SparkContext (that is, call 1.), else create StreamingContext using the provided createFunction TODO: the corresponding Java and Python API has to be added as well. Author: Tathagata Das Closes #5428 from tdas/SPARK-6752 and squashes the following commits: 94db63c [Tathagata Das] Fix long line. 524f519 [Tathagata Das] Many changes based on PR comments. eabd092 [Tathagata Das] Added Function0, Java API and unit tests for StreamingContext.getOrCreate 36a7823 [Tathagata Das] Minor changes. 204814e [Tathagata Das] Added StreamingContext.getOrCreate with existing SparkContext --- .../spark/api/java/function/Function0.java | 27 +++ .../apache/spark/streaming/Checkpoint.scala | 26 ++- .../spark/streaming/StreamingContext.scala | 85 ++++++++-- .../api/java/JavaStreamingContext.scala | 119 ++++++++++++- .../apache/spark/streaming/JavaAPISuite.java | 145 ++++++++++++---- .../spark/streaming/CheckpointSuite.scala | 3 +- .../streaming/StreamingContextSuite.scala | 159 ++++++++++++++++++ 7 files changed, 503 insertions(+), 61 deletions(-) create mode 100644 core/src/main/java/org/apache/spark/api/java/function/Function0.java diff --git a/core/src/main/java/org/apache/spark/api/java/function/Function0.java b/core/src/main/java/org/apache/spark/api/java/function/Function0.java new file mode 100644 index 0000000000000..38e410c5debe6 --- /dev/null +++ b/core/src/main/java/org/apache/spark/api/java/function/Function0.java @@ -0,0 +1,27 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.api.java.function; + +import java.io.Serializable; + +/** + * A zero-argument function that returns an R. + */ +public interface Function0 extends Serializable { + public R call() throws Exception; +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala index 0a50485118588..7bfae253c3a0c 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala @@ -77,7 +77,8 @@ object Checkpoint extends Logging { } /** Get checkpoint files present in the give directory, ordered by oldest-first */ - def getCheckpointFiles(checkpointDir: String, fs: FileSystem): Seq[Path] = { + def getCheckpointFiles(checkpointDir: String, fsOption: Option[FileSystem] = None): Seq[Path] = { + def sortFunc(path1: Path, path2: Path): Boolean = { val (time1, bk1) = path1.getName match { case REGEX(x, y) => (x.toLong, !y.isEmpty) } val (time2, bk2) = path2.getName match { case REGEX(x, y) => (x.toLong, !y.isEmpty) } @@ -85,6 +86,7 @@ object Checkpoint extends Logging { } val path = new Path(checkpointDir) + val fs = fsOption.getOrElse(path.getFileSystem(new Configuration())) if (fs.exists(path)) { val statuses = fs.listStatus(path) if (statuses != null) { @@ -160,7 +162,7 @@ class CheckpointWriter( } // Delete old checkpoint files - val allCheckpointFiles = Checkpoint.getCheckpointFiles(checkpointDir, fs) + val allCheckpointFiles = Checkpoint.getCheckpointFiles(checkpointDir, Some(fs)) if (allCheckpointFiles.size > 10) { allCheckpointFiles.take(allCheckpointFiles.size - 10).foreach(file => { logInfo("Deleting " + file) @@ -234,15 +236,24 @@ class CheckpointWriter( private[streaming] object CheckpointReader extends Logging { - def read(checkpointDir: String, conf: SparkConf, hadoopConf: Configuration): Option[Checkpoint] = - { + /** + * Read checkpoint files present in the given checkpoint directory. If there are no checkpoint + * files, then return None, else try to return the latest valid checkpoint object. If no + * checkpoint files could be read correctly, then return None (if ignoreReadError = true), + * or throw exception (if ignoreReadError = false). + */ + def read( + checkpointDir: String, + conf: SparkConf, + hadoopConf: Configuration, + ignoreReadError: Boolean = false): Option[Checkpoint] = { val checkpointPath = new Path(checkpointDir) // TODO(rxin): Why is this a def?! def fs: FileSystem = checkpointPath.getFileSystem(hadoopConf) // Try to find the checkpoint files - val checkpointFiles = Checkpoint.getCheckpointFiles(checkpointDir, fs).reverse + val checkpointFiles = Checkpoint.getCheckpointFiles(checkpointDir, Some(fs)).reverse if (checkpointFiles.isEmpty) { return None } @@ -282,7 +293,10 @@ object CheckpointReader extends Logging { }) // If none of checkpoint files could be read, then throw exception - throw new SparkException("Failed to read checkpoint from directory " + checkpointPath) + if (!ignoreReadError) { + throw new SparkException(s"Failed to read checkpoint from directory $checkpointPath") + } + None } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala index f57f295874645..90c8b47aebce0 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala @@ -107,6 +107,19 @@ class StreamingContext private[streaming] ( */ def this(path: String) = this(path, new Configuration) + /** + * Recreate a StreamingContext from a checkpoint file using an existing SparkContext. + * @param path Path to the directory that was specified as the checkpoint directory + * @param sparkContext Existing SparkContext + */ + def this(path: String, sparkContext: SparkContext) = { + this( + sparkContext, + CheckpointReader.read(path, sparkContext.conf, sparkContext.hadoopConfiguration).get, + null) + } + + if (sc_ == null && cp_ == null) { throw new Exception("Spark Streaming cannot be initialized with " + "both SparkContext and checkpoint as null") @@ -115,10 +128,12 @@ class StreamingContext private[streaming] ( private[streaming] val isCheckpointPresent = (cp_ != null) private[streaming] val sc: SparkContext = { - if (isCheckpointPresent) { + if (sc_ != null) { + sc_ + } else if (isCheckpointPresent) { new SparkContext(cp_.createSparkConf()) } else { - sc_ + throw new SparkException("Cannot create StreamingContext without a SparkContext") } } @@ -129,7 +144,7 @@ class StreamingContext private[streaming] ( private[streaming] val conf = sc.conf - private[streaming] val env = SparkEnv.get + private[streaming] val env = sc.env private[streaming] val graph: DStreamGraph = { if (isCheckpointPresent) { @@ -174,7 +189,9 @@ class StreamingContext private[streaming] ( /** Register streaming source to metrics system */ private val streamingSource = new StreamingSource(this) - SparkEnv.get.metricsSystem.registerSource(streamingSource) + assert(env != null) + assert(env.metricsSystem != null) + env.metricsSystem.registerSource(streamingSource) /** Enumeration to identify current state of the StreamingContext */ private[streaming] object StreamingContextState extends Enumeration { @@ -621,19 +638,59 @@ object StreamingContext extends Logging { hadoopConf: Configuration = new Configuration(), createOnError: Boolean = false ): StreamingContext = { - val checkpointOption = try { - CheckpointReader.read(checkpointPath, new SparkConf(), hadoopConf) - } catch { - case e: Exception => - if (createOnError) { - None - } else { - throw e - } - } + val checkpointOption = CheckpointReader.read( + checkpointPath, new SparkConf(), hadoopConf, createOnError) checkpointOption.map(new StreamingContext(null, _, null)).getOrElse(creatingFunc()) } + + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the StreamingContext + * will be created by called the provided `creatingFunc` on the provided `sparkContext`. Note + * that the SparkConf configuration in the checkpoint data will not be restored as the + * SparkContext has already been created. + * + * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program + * @param creatingFunc Function to create a new StreamingContext using the given SparkContext + * @param sparkContext SparkContext using which the StreamingContext will be created + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: SparkContext => StreamingContext, + sparkContext: SparkContext + ): StreamingContext = { + getOrCreate(checkpointPath, creatingFunc, sparkContext, createOnError = false) + } + + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the StreamingContext + * will be created by called the provided `creatingFunc` on the provided `sparkContext`. Note + * that the SparkConf configuration in the checkpoint data will not be restored as the + * SparkContext has already been created. + * + * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program + * @param creatingFunc Function to create a new StreamingContext using the given SparkContext + * @param sparkContext SparkContext using which the StreamingContext will be created + * @param createOnError Whether to create a new StreamingContext if there is an + * error in reading checkpoint data. By default, an exception will be + * thrown on error. + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: SparkContext => StreamingContext, + sparkContext: SparkContext, + createOnError: Boolean + ): StreamingContext = { + val checkpointOption = CheckpointReader.read( + checkpointPath, sparkContext.conf, sparkContext.hadoopConfiguration, createOnError) + checkpointOption.map(new StreamingContext(sparkContext, _, null)) + .getOrElse(creatingFunc(sparkContext)) + } + /** * Find the JAR from which a given class was loaded, to make it easy for users to pass * their JARs to StreamingContext. diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala index 4095a7cc84946..572d7d8e8753d 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala @@ -32,13 +32,14 @@ import org.apache.spark.{SparkConf, SparkContext} import org.apache.spark.annotation.Experimental import org.apache.spark.api.java.{JavaPairRDD, JavaRDD, JavaSparkContext} import org.apache.spark.api.java.function.{Function => JFunction, Function2 => JFunction2} +import org.apache.spark.api.java.function.{Function0 => JFunction0} import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming._ import org.apache.spark.streaming.scheduler.StreamingListener -import org.apache.hadoop.conf.Configuration -import org.apache.spark.streaming.dstream.{PluggableInputDStream, ReceiverInputDStream, DStream} +import org.apache.spark.streaming.dstream.DStream import org.apache.spark.streaming.receiver.Receiver +import org.apache.hadoop.conf.Configuration /** * A Java-friendly version of [[org.apache.spark.streaming.StreamingContext]] which is the main @@ -655,6 +656,7 @@ object JavaStreamingContext { * @param checkpointPath Checkpoint directory used in an earlier JavaStreamingContext program * @param factory JavaStreamingContextFactory object to create a new JavaStreamingContext */ + @deprecated("use getOrCreate without JavaStreamingContextFactor", "1.4.0") def getOrCreate( checkpointPath: String, factory: JavaStreamingContextFactory @@ -676,6 +678,7 @@ object JavaStreamingContext { * @param hadoopConf Hadoop configuration if necessary for reading from any HDFS compatible * file system */ + @deprecated("use getOrCreate without JavaStreamingContextFactory", "1.4.0") def getOrCreate( checkpointPath: String, hadoopConf: Configuration, @@ -700,6 +703,7 @@ object JavaStreamingContext { * @param createOnError Whether to create a new JavaStreamingContext if there is an * error in reading checkpoint data. */ + @deprecated("use getOrCreate without JavaStreamingContextFactory", "1.4.0") def getOrCreate( checkpointPath: String, hadoopConf: Configuration, @@ -712,6 +716,117 @@ object JavaStreamingContext { new JavaStreamingContext(ssc) } + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the provided factory + * will be used to create a JavaStreamingContext. + * + * @param checkpointPath Checkpoint directory used in an earlier JavaStreamingContext program + * @param creatingFunc Function to create a new JavaStreamingContext + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: JFunction0[JavaStreamingContext] + ): JavaStreamingContext = { + val ssc = StreamingContext.getOrCreate(checkpointPath, () => { + creatingFunc.call().ssc + }) + new JavaStreamingContext(ssc) + } + + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the provided factory + * will be used to create a JavaStreamingContext. + * + * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program + * @param creatingFunc Function to create a new JavaStreamingContext + * @param hadoopConf Hadoop configuration if necessary for reading from any HDFS compatible + * file system + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: JFunction0[JavaStreamingContext], + hadoopConf: Configuration + ): JavaStreamingContext = { + val ssc = StreamingContext.getOrCreate(checkpointPath, () => { + creatingFunc.call().ssc + }, hadoopConf) + new JavaStreamingContext(ssc) + } + + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the provided factory + * will be used to create a JavaStreamingContext. + * + * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program + * @param creatingFunc Function to create a new JavaStreamingContext + * @param hadoopConf Hadoop configuration if necessary for reading from any HDFS compatible + * file system + * @param createOnError Whether to create a new JavaStreamingContext if there is an + * error in reading checkpoint data. + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: JFunction0[JavaStreamingContext], + hadoopConf: Configuration, + createOnError: Boolean + ): JavaStreamingContext = { + val ssc = StreamingContext.getOrCreate(checkpointPath, () => { + creatingFunc.call().ssc + }, hadoopConf, createOnError) + new JavaStreamingContext(ssc) + } + + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the provided factory + * will be used to create a JavaStreamingContext. + * + * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program + * @param creatingFunc Function to create a new JavaStreamingContext + * @param sparkContext SparkContext using which the StreamingContext will be created + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: JFunction[JavaSparkContext, JavaStreamingContext], + sparkContext: JavaSparkContext + ): JavaStreamingContext = { + val ssc = StreamingContext.getOrCreate(checkpointPath, (sparkContext: SparkContext) => { + creatingFunc.call(new JavaSparkContext(sparkContext)).ssc + }, sparkContext.sc) + new JavaStreamingContext(ssc) + } + + /** + * Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. + * If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be + * recreated from the checkpoint data. If the data does not exist, then the provided factory + * will be used to create a JavaStreamingContext. + * + * @param checkpointPath Checkpoint directory used in an earlier StreamingContext program + * @param creatingFunc Function to create a new JavaStreamingContext + * @param sparkContext SparkContext using which the StreamingContext will be created + * @param createOnError Whether to create a new JavaStreamingContext if there is an + * error in reading checkpoint data. + */ + def getOrCreate( + checkpointPath: String, + creatingFunc: JFunction[JavaSparkContext, JavaStreamingContext], + sparkContext: JavaSparkContext, + createOnError: Boolean + ): JavaStreamingContext = { + val ssc = StreamingContext.getOrCreate(checkpointPath, (sparkContext: SparkContext) => { + creatingFunc.call(new JavaSparkContext(sparkContext)).ssc + }, sparkContext.sc, createOnError) + new JavaStreamingContext(ssc) + } + /** * Find the JAR from which a given class was loaded, to make it easy for users to pass * their JARs to StreamingContext. diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java index 90340753a4eed..cb2e8380b4933 100644 --- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java +++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java @@ -22,10 +22,12 @@ import java.nio.charset.Charset; import java.util.*; +import org.apache.commons.lang.mutable.MutableBoolean; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.lib.input.TextInputFormat; + import scala.Tuple2; import org.junit.Assert; @@ -45,6 +47,7 @@ import org.apache.spark.storage.StorageLevel; import org.apache.spark.streaming.api.java.*; import org.apache.spark.util.Utils; +import org.apache.spark.SparkConf; // The test suite itself is Serializable so that anonymous Function implementations can be // serialized, as an alternative to converting these anonymous classes to static inner classes; @@ -929,7 +932,7 @@ public void testPairMap() { // Maps pair -> pair of different type public Tuple2 call(Tuple2 in) throws Exception { return in.swap(); } - }); + }); JavaTestUtils.attachTestOutputStream(reversed); List>> result = JavaTestUtils.runStreams(ssc, 2, 2); @@ -987,12 +990,12 @@ public void testPairMap2() { // Maps pair -> single JavaDStream> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); JavaPairDStream pairStream = JavaPairDStream.fromJavaDStream(stream); JavaDStream reversed = pairStream.map( - new Function, Integer>() { - @Override - public Integer call(Tuple2 in) throws Exception { - return in._2(); - } - }); + new Function, Integer>() { + @Override + public Integer call(Tuple2 in) throws Exception { + return in._2(); + } + }); JavaTestUtils.attachTestOutputStream(reversed); List> result = JavaTestUtils.runStreams(ssc, 2, 2); @@ -1123,7 +1126,7 @@ public void testCombineByKey() { JavaPairDStream combined = pairStream.combineByKey( new Function() { - @Override + @Override public Integer call(Integer i) throws Exception { return i; } @@ -1144,14 +1147,14 @@ public void testCountByValue() { Arrays.asList("hello")); List>> expected = Arrays.asList( - Arrays.asList( - new Tuple2("hello", 1L), - new Tuple2("world", 1L)), - Arrays.asList( - new Tuple2("hello", 1L), - new Tuple2("moon", 1L)), - Arrays.asList( - new Tuple2("hello", 1L))); + Arrays.asList( + new Tuple2("hello", 1L), + new Tuple2("world", 1L)), + Arrays.asList( + new Tuple2("hello", 1L), + new Tuple2("moon", 1L)), + Arrays.asList( + new Tuple2("hello", 1L))); JavaDStream stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1); JavaPairDStream counted = stream.countByValue(); @@ -1249,17 +1252,17 @@ public void testUpdateStateByKey() { JavaPairDStream updated = pairStream.updateStateByKey( new Function2, Optional, Optional>() { - @Override - public Optional call(List values, Optional state) { - int out = 0; - if (state.isPresent()) { - out = out + state.get(); - } - for (Integer v: values) { - out = out + v; + @Override + public Optional call(List values, Optional state) { + int out = 0; + if (state.isPresent()) { + out = out + state.get(); + } + for (Integer v : values) { + out = out + v; + } + return Optional.of(out); } - return Optional.of(out); - } }); JavaTestUtils.attachTestOutputStream(updated); List>> result = JavaTestUtils.runStreams(ssc, 3, 3); @@ -1292,17 +1295,17 @@ public void testUpdateStateByKeyWithInitial() { JavaPairDStream updated = pairStream.updateStateByKey( new Function2, Optional, Optional>() { - @Override - public Optional call(List values, Optional state) { - int out = 0; - if (state.isPresent()) { - out = out + state.get(); - } - for (Integer v: values) { - out = out + v; + @Override + public Optional call(List values, Optional state) { + int out = 0; + if (state.isPresent()) { + out = out + state.get(); + } + for (Integer v : values) { + out = out + v; + } + return Optional.of(out); } - return Optional.of(out); - } }, new HashPartitioner(1), initialRDD); JavaTestUtils.attachTestOutputStream(updated); List>> result = JavaTestUtils.runStreams(ssc, 3, 3); @@ -1328,7 +1331,7 @@ public void testReduceByKeyAndWindowWithInverse() { JavaPairDStream reduceWindowed = pairStream.reduceByKeyAndWindow(new IntegerSum(), new IntegerDifference(), - new Duration(2000), new Duration(1000)); + new Duration(2000), new Duration(1000)); JavaTestUtils.attachTestOutputStream(reduceWindowed); List>> result = JavaTestUtils.runStreams(ssc, 3, 3); @@ -1707,6 +1710,74 @@ public Integer call(String s) throws Exception { Utils.deleteRecursively(tempDir); } + @SuppressWarnings("unchecked") + @Test + public void testContextGetOrCreate() throws InterruptedException { + + final SparkConf conf = new SparkConf() + .setMaster("local[2]") + .setAppName("test") + .set("newContext", "true"); + + File emptyDir = Files.createTempDir(); + emptyDir.deleteOnExit(); + StreamingContextSuite contextSuite = new StreamingContextSuite(); + String corruptedCheckpointDir = contextSuite.createCorruptedCheckpoint(); + String checkpointDir = contextSuite.createValidCheckpoint(); + + // Function to create JavaStreamingContext without any output operations + // (used to detect the new context) + final MutableBoolean newContextCreated = new MutableBoolean(false); + Function0 creatingFunc = new Function0() { + public JavaStreamingContext call() { + newContextCreated.setValue(true); + return new JavaStreamingContext(conf, Seconds.apply(1)); + } + }; + + newContextCreated.setValue(false); + ssc = JavaStreamingContext.getOrCreate(emptyDir.getAbsolutePath(), creatingFunc); + Assert.assertTrue("new context not created", newContextCreated.isTrue()); + ssc.stop(); + + newContextCreated.setValue(false); + ssc = JavaStreamingContext.getOrCreate(corruptedCheckpointDir, creatingFunc, + new org.apache.hadoop.conf.Configuration(), true); + Assert.assertTrue("new context not created", newContextCreated.isTrue()); + ssc.stop(); + + newContextCreated.setValue(false); + ssc = JavaStreamingContext.getOrCreate(checkpointDir, creatingFunc, + new org.apache.hadoop.conf.Configuration()); + Assert.assertTrue("old context not recovered", newContextCreated.isFalse()); + ssc.stop(); + + // Function to create JavaStreamingContext using existing JavaSparkContext + // without any output operations (used to detect the new context) + Function creatingFunc2 = + new Function() { + public JavaStreamingContext call(JavaSparkContext context) { + newContextCreated.setValue(true); + return new JavaStreamingContext(context, Seconds.apply(1)); + } + }; + + JavaSparkContext sc = new JavaSparkContext(conf); + newContextCreated.setValue(false); + ssc = JavaStreamingContext.getOrCreate(emptyDir.getAbsolutePath(), creatingFunc2, sc); + Assert.assertTrue("new context not created", newContextCreated.isTrue()); + ssc.stop(false); + + newContextCreated.setValue(false); + ssc = JavaStreamingContext.getOrCreate(corruptedCheckpointDir, creatingFunc2, sc, true); + Assert.assertTrue("new context not created", newContextCreated.isTrue()); + ssc.stop(false); + + newContextCreated.setValue(false); + ssc = JavaStreamingContext.getOrCreate(checkpointDir, creatingFunc2, sc); + Assert.assertTrue("old context not recovered", newContextCreated.isFalse()); + ssc.stop(); + } /* TEST DISABLED: Pending a discussion about checkpoint() semantics with TD @SuppressWarnings("unchecked") diff --git a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala index 54c30440a6e8d..6b0a3f91d4d06 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala @@ -430,9 +430,8 @@ class CheckpointSuite extends TestSuiteBase { assert(recordedFiles(ssc) === Seq(1, 2, 3) && batchCounter.getNumStartedBatches === 3) } // Wait for a checkpoint to be written - val fs = new Path(checkpointDir).getFileSystem(ssc.sc.hadoopConfiguration) eventually(eventuallyTimeout) { - assert(Checkpoint.getCheckpointFiles(checkpointDir, fs).size === 6) + assert(Checkpoint.getCheckpointFiles(checkpointDir).size === 6) } ssc.stop() // Check that we shut down while the third batch was being processed diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala index 58353a5f97c8a..4f193322ad33e 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala @@ -17,8 +17,10 @@ package org.apache.spark.streaming +import java.io.File import java.util.concurrent.atomic.AtomicInteger +import org.apache.commons.io.FileUtils import org.scalatest.{Assertions, BeforeAndAfter, FunSuite} import org.scalatest.concurrent.Timeouts import org.scalatest.concurrent.Eventually._ @@ -330,6 +332,139 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w } } + test("getOrCreate") { + val conf = new SparkConf().setMaster(master).setAppName(appName) + + // Function to create StreamingContext that has a config to identify it to be new context + var newContextCreated = false + def creatingFunction(): StreamingContext = { + newContextCreated = true + new StreamingContext(conf, batchDuration) + } + + // Call ssc.stop after a body of code + def testGetOrCreate(body: => Unit): Unit = { + newContextCreated = false + try { + body + } finally { + if (ssc != null) { + ssc.stop() + } + ssc = null + } + } + + val emptyPath = Utils.createTempDir().getAbsolutePath() + + // getOrCreate should create new context with empty path + testGetOrCreate { + ssc = StreamingContext.getOrCreate(emptyPath, creatingFunction _) + assert(ssc != null, "no context created") + assert(newContextCreated, "new context not created") + } + + val corrutedCheckpointPath = createCorruptedCheckpoint() + + // getOrCreate should throw exception with fake checkpoint file and createOnError = false + intercept[Exception] { + ssc = StreamingContext.getOrCreate(corrutedCheckpointPath, creatingFunction _) + } + + // getOrCreate should throw exception with fake checkpoint file + intercept[Exception] { + ssc = StreamingContext.getOrCreate( + corrutedCheckpointPath, creatingFunction _, createOnError = false) + } + + // getOrCreate should create new context with fake checkpoint file and createOnError = true + testGetOrCreate { + ssc = StreamingContext.getOrCreate( + corrutedCheckpointPath, creatingFunction _, createOnError = true) + assert(ssc != null, "no context created") + assert(newContextCreated, "new context not created") + } + + val checkpointPath = createValidCheckpoint() + + // getOrCreate should recover context with checkpoint path, and recover old configuration + testGetOrCreate { + ssc = StreamingContext.getOrCreate(checkpointPath, creatingFunction _) + assert(ssc != null, "no context created") + assert(!newContextCreated, "old context not recovered") + assert(ssc.conf.get("someKey") === "someValue") + } + } + + test("getOrCreate with existing SparkContext") { + val conf = new SparkConf().setMaster(master).setAppName(appName) + sc = new SparkContext(conf) + + // Function to create StreamingContext that has a config to identify it to be new context + var newContextCreated = false + def creatingFunction(sparkContext: SparkContext): StreamingContext = { + newContextCreated = true + new StreamingContext(sparkContext, batchDuration) + } + + // Call ssc.stop(stopSparkContext = false) after a body of cody + def testGetOrCreate(body: => Unit): Unit = { + newContextCreated = false + try { + body + } finally { + if (ssc != null) { + ssc.stop(stopSparkContext = false) + } + ssc = null + } + } + + val emptyPath = Utils.createTempDir().getAbsolutePath() + + // getOrCreate should create new context with empty path + testGetOrCreate { + ssc = StreamingContext.getOrCreate(emptyPath, creatingFunction _, sc, createOnError = true) + assert(ssc != null, "no context created") + assert(newContextCreated, "new context not created") + assert(ssc.sparkContext === sc, "new StreamingContext does not use existing SparkContext") + } + + val corrutedCheckpointPath = createCorruptedCheckpoint() + + // getOrCreate should throw exception with fake checkpoint file and createOnError = false + intercept[Exception] { + ssc = StreamingContext.getOrCreate(corrutedCheckpointPath, creatingFunction _, sc) + } + + // getOrCreate should throw exception with fake checkpoint file + intercept[Exception] { + ssc = StreamingContext.getOrCreate( + corrutedCheckpointPath, creatingFunction _, sc, createOnError = false) + } + + // getOrCreate should create new context with fake checkpoint file and createOnError = true + testGetOrCreate { + ssc = StreamingContext.getOrCreate( + corrutedCheckpointPath, creatingFunction _, sc, createOnError = true) + assert(ssc != null, "no context created") + assert(newContextCreated, "new context not created") + assert(ssc.sparkContext === sc, "new StreamingContext does not use existing SparkContext") + } + + val checkpointPath = createValidCheckpoint() + + // StreamingContext.getOrCreate should recover context with checkpoint path + testGetOrCreate { + ssc = StreamingContext.getOrCreate(checkpointPath, creatingFunction _, sc) + assert(ssc != null, "no context created") + assert(!newContextCreated, "old context not recovered") + assert(ssc.sparkContext === sc, "new StreamingContext does not use existing SparkContext") + assert(!ssc.conf.contains("someKey"), + "recovered StreamingContext unexpectedly has old config") + } + } + test("DStream and generated RDD creation sites") { testPackage.test() } @@ -339,6 +474,30 @@ class StreamingContextSuite extends FunSuite with BeforeAndAfter with Timeouts w val inputStream = new TestInputStream(s, input, 1) inputStream } + + def createValidCheckpoint(): String = { + val testDirectory = Utils.createTempDir().getAbsolutePath() + val checkpointDirectory = Utils.createTempDir().getAbsolutePath() + val conf = new SparkConf().setMaster(master).setAppName(appName) + conf.set("someKey", "someValue") + ssc = new StreamingContext(conf, batchDuration) + ssc.checkpoint(checkpointDirectory) + ssc.textFileStream(testDirectory).foreachRDD { rdd => rdd.count() } + ssc.start() + eventually(timeout(10000 millis)) { + assert(Checkpoint.getCheckpointFiles(checkpointDirectory).size > 1) + } + ssc.stop() + checkpointDirectory + } + + def createCorruptedCheckpoint(): String = { + val checkpointDirectory = Utils.createTempDir().getAbsolutePath() + val fakeCheckpointFile = Checkpoint.checkpointFile(checkpointDirectory, Time(1000)) + FileUtils.write(new File(fakeCheckpointFile.toString()), "blablabla") + assert(Checkpoint.getCheckpointFiles(checkpointDirectory).nonEmpty) + checkpointDirectory + } } class TestException(msg: String) extends Exception(msg) From c1213e6a92e126ad886d9804cedaf6db3618e602 Mon Sep 17 00:00:00 2001 From: Vinod K C Date: Thu, 23 Apr 2015 12:00:23 -0700 Subject: [PATCH 396/591] [SPARK-7055][SQL]Use correct ClassLoader for JDBC Driver in JDBCRDD.getConnector Author: Vinod K C Closes #5633 from vinodkc/use_correct_classloader_driverload and squashes the following commits: 73c5380 [Vinod K C] Use correct ClassLoader for JDBC Driver --- .../src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala index b975191d41963..f326510042122 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala @@ -26,6 +26,7 @@ import org.apache.spark.rdd.RDD import org.apache.spark.sql.catalyst.expressions.{Row, SpecificMutableRow} import org.apache.spark.sql.types._ import org.apache.spark.sql.sources._ +import org.apache.spark.util.Utils private[sql] object JDBCRDD extends Logging { /** @@ -152,7 +153,7 @@ private[sql] object JDBCRDD extends Logging { def getConnector(driver: String, url: String, properties: Properties): () => Connection = { () => { try { - if (driver != null) Class.forName(driver) + if (driver != null) Utils.getContextOrSparkClassLoader.loadClass(driver) } catch { case e: ClassNotFoundException => { logWarning(s"Couldn't find class $driver", e); From 6afde2c7810c363083d0a699b1de02b54c13e6a9 Mon Sep 17 00:00:00 2001 From: Josh Rosen Date: Thu, 23 Apr 2015 13:19:03 -0700 Subject: [PATCH 397/591] [SPARK-7058] Include RDD deserialization time in "task deserialization time" metric The web UI's "task deserialization time" metric is slightly misleading because it does not capture the time taken to deserialize the broadcasted RDD. Author: Josh Rosen Closes #5635 from JoshRosen/SPARK-7058 and squashes the following commits: ed90f75 [Josh Rosen] Update UI tooltip a3743b4 [Josh Rosen] Update comments. 4f52910 [Josh Rosen] Roll back whitespace change e9cf9f4 [Josh Rosen] Remove unused variable 9f32e55 [Josh Rosen] Expose executorDeserializeTime on Task instead of pushing runtime calculation into Task. 21f5b47 [Josh Rosen] Don't double-count the broadcast deserialization time in task runtime 1752f0e [Josh Rosen] [SPARK-7058] Incorporate RDD deserialization time in task deserialization time metric --- .../main/scala/org/apache/spark/executor/Executor.scala | 8 ++++++-- .../scala/org/apache/spark/scheduler/ResultTask.scala | 2 ++ .../scala/org/apache/spark/scheduler/ShuffleMapTask.scala | 2 ++ core/src/main/scala/org/apache/spark/scheduler/Task.scala | 7 +++++++ core/src/main/scala/org/apache/spark/ui/ToolTips.scala | 4 +++- 5 files changed, 20 insertions(+), 3 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala index 5fc04df5d6a40..f57e215c3f2ed 100644 --- a/core/src/main/scala/org/apache/spark/executor/Executor.scala +++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala @@ -220,8 +220,12 @@ private[spark] class Executor( val afterSerialization = System.currentTimeMillis() for (m <- task.metrics) { - m.setExecutorDeserializeTime(taskStart - deserializeStartTime) - m.setExecutorRunTime(taskFinish - taskStart) + // Deserialization happens in two parts: first, we deserialize a Task object, which + // includes the Partition. Second, Task.run() deserializes the RDD and function to be run. + m.setExecutorDeserializeTime( + (taskStart - deserializeStartTime) + task.executorDeserializeTime) + // We need to subtract Task.run()'s deserialization time to avoid double-counting + m.setExecutorRunTime((taskFinish - taskStart) - task.executorDeserializeTime) m.setJvmGCTime(computeTotalGcTime() - startGCTime) m.setResultSerializationTime(afterSerialization - beforeSerialization) } diff --git a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala index e074ce6ebff0b..c9a124113961f 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala @@ -53,9 +53,11 @@ private[spark] class ResultTask[T, U]( override def runTask(context: TaskContext): U = { // Deserialize the RDD and the func using the broadcast variables. + val deserializeStartTime = System.currentTimeMillis() val ser = SparkEnv.get.closureSerializer.newInstance() val (rdd, func) = ser.deserialize[(RDD[T], (TaskContext, Iterator[T]) => U)]( ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader) + _executorDeserializeTime = System.currentTimeMillis() - deserializeStartTime metrics = Some(context.taskMetrics) func(context, rdd.iterator(partition, context)) diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala index 6c7d00069acb2..bd3dd23dfe1ac 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala @@ -56,9 +56,11 @@ private[spark] class ShuffleMapTask( override def runTask(context: TaskContext): MapStatus = { // Deserialize the RDD using the broadcast variable. + val deserializeStartTime = System.currentTimeMillis() val ser = SparkEnv.get.closureSerializer.newInstance() val (rdd, dep) = ser.deserialize[(RDD[_], ShuffleDependency[_, _, _])]( ByteBuffer.wrap(taskBinary.value), Thread.currentThread.getContextClassLoader) + _executorDeserializeTime = System.currentTimeMillis() - deserializeStartTime metrics = Some(context.taskMetrics) var writer: ShuffleWriter[Any, Any] = null diff --git a/core/src/main/scala/org/apache/spark/scheduler/Task.scala b/core/src/main/scala/org/apache/spark/scheduler/Task.scala index 8b592867ee31d..b09b19e2ac9e7 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/Task.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/Task.scala @@ -87,11 +87,18 @@ private[spark] abstract class Task[T](val stageId: Int, var partitionId: Int) ex // initialized when kill() is invoked. @volatile @transient private var _killed = false + protected var _executorDeserializeTime: Long = 0 + /** * Whether the task has been killed. */ def killed: Boolean = _killed + /** + * Returns the amount of time spent deserializing the RDD and function to be run. + */ + def executorDeserializeTime: Long = _executorDeserializeTime + /** * Kills a task by setting the interrupted flag to true. This relies on the upper level Spark * code and user code to properly handle the flag. This function should be idempotent so it can diff --git a/core/src/main/scala/org/apache/spark/ui/ToolTips.scala b/core/src/main/scala/org/apache/spark/ui/ToolTips.scala index cae6870c2ab20..24f3236456248 100644 --- a/core/src/main/scala/org/apache/spark/ui/ToolTips.scala +++ b/core/src/main/scala/org/apache/spark/ui/ToolTips.scala @@ -24,7 +24,9 @@ private[spark] object ToolTips { scheduler delay is large, consider decreasing the size of tasks or decreasing the size of task results.""" - val TASK_DESERIALIZATION_TIME = "Time spent deserializing the task closure on the executor." + val TASK_DESERIALIZATION_TIME = + """Time spent deserializing the task closure on the executor, including the time to read the + broadcasted task.""" val SHUFFLE_READ_BLOCKED_TIME = "Time that the task spent blocked waiting for shuffle data to be read from remote machines." From 3e91cc273d281053618bfa032bc610e2cf8d8e78 Mon Sep 17 00:00:00 2001 From: wizz Date: Thu, 23 Apr 2015 14:00:07 -0700 Subject: [PATCH 398/591] [SPARK-7085][MLlib] Fix miniBatchFraction parameter in train method called with 4 arguments Author: wizz Closes #5658 from kuromatsu-nobuyuki/SPARK-7085 and squashes the following commits: 6ec2d21 [wizz] Fix miniBatchFraction parameter in train method called with 4 arguments --- .../org/apache/spark/mllib/regression/RidgeRegression.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala index 8838ca8c14718..309f9af466457 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala @@ -171,7 +171,7 @@ object RidgeRegressionWithSGD { numIterations: Int, stepSize: Double, regParam: Double): RidgeRegressionModel = { - train(input, numIterations, stepSize, regParam, 0.01) + train(input, numIterations, stepSize, regParam, 1.0) } /** From baa83a9a6769c5e119438d65d7264dceb8d743d5 Mon Sep 17 00:00:00 2001 From: WangTaoTheTonic Date: Thu, 23 Apr 2015 17:20:17 -0400 Subject: [PATCH 399/591] [SPARK-6879] [HISTORYSERVER] check if app is completed before clean it up https://issues.apache.org/jira/browse/SPARK-6879 Use `applications` to replace `FileStatus`, and check if the app is completed before clean it up. If an exception was throwed, add it to `applications` to wait for the next loop. Author: WangTaoTheTonic Closes #5491 from WangTaoTheTonic/SPARK-6879 and squashes the following commits: 4a533eb [WangTaoTheTonic] treat ACE specially cb45105 [WangTaoTheTonic] rebase d4d5251 [WangTaoTheTonic] per Marcelo's comments d7455d8 [WangTaoTheTonic] slightly change when delete file b0abca5 [WangTaoTheTonic] use global var to store apps to clean 94adfe1 [WangTaoTheTonic] leave expired apps alone to be deleted 9872a9d [WangTaoTheTonic] use the right path fdef4d6 [WangTaoTheTonic] check if app is completed before clean it up --- .../deploy/history/FsHistoryProvider.scala | 32 ++++++++++++------- 1 file changed, 20 insertions(+), 12 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala index 9847d5944a390..a94ebf6e53750 100644 --- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala +++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala @@ -35,7 +35,6 @@ import org.apache.spark.ui.SparkUI import org.apache.spark.util.{ThreadUtils, Utils} import org.apache.spark.{Logging, SecurityManager, SparkConf} - /** * A class that provides application history from event logs stored in the file system. * This provider checks for new finished applications in the background periodically and @@ -76,6 +75,9 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis @volatile private var applications: mutable.LinkedHashMap[String, FsApplicationHistoryInfo] = new mutable.LinkedHashMap() + // List of applications to be deleted by event log cleaner. + private var appsToClean = new mutable.ListBuffer[FsApplicationHistoryInfo] + // Constants used to parse Spark 1.0.0 log directories. private[history] val LOG_PREFIX = "EVENT_LOG_" private[history] val SPARK_VERSION_PREFIX = EventLoggingListener.SPARK_VERSION_KEY + "_" @@ -266,34 +268,40 @@ private[history] class FsHistoryProvider(conf: SparkConf) extends ApplicationHis */ private def cleanLogs(): Unit = { try { - val statusList = Option(fs.listStatus(new Path(logDir))).map(_.toSeq) - .getOrElse(Seq[FileStatus]()) val maxAge = conf.getTimeAsSeconds("spark.history.fs.cleaner.maxAge", "7d") * 1000 val now = System.currentTimeMillis() val appsToRetain = new mutable.LinkedHashMap[String, FsApplicationHistoryInfo]() + // Scan all logs from the log directory. + // Only completed applications older than the specified max age will be deleted. applications.values.foreach { info => - if (now - info.lastUpdated <= maxAge) { + if (now - info.lastUpdated <= maxAge || !info.completed) { appsToRetain += (info.id -> info) + } else { + appsToClean += info } } applications = appsToRetain - // Scan all logs from the log directory. - // Only directories older than the specified max age will be deleted - statusList.foreach { dir => + val leftToClean = new mutable.ListBuffer[FsApplicationHistoryInfo] + appsToClean.foreach { info => try { - if (now - dir.getModificationTime() > maxAge) { - // if path is a directory and set to true, - // the directory is deleted else throws an exception - fs.delete(dir.getPath, true) + val path = new Path(logDir, info.logPath) + if (fs.exists(path)) { + fs.delete(path, true) } } catch { - case t: IOException => logError(s"IOException in cleaning logs of $dir", t) + case e: AccessControlException => + logInfo(s"No permission to delete ${info.logPath}, ignoring.") + case t: IOException => + logError(s"IOException in cleaning logs of ${info.logPath}", t) + leftToClean += info } } + + appsToClean = leftToClean } catch { case t: Exception => logError("Exception in cleaning logs", t) } From 6d0749cae301ee4bf37632d657de48e75548a523 Mon Sep 17 00:00:00 2001 From: Tijo Thomas Date: Thu, 23 Apr 2015 17:23:15 -0400 Subject: [PATCH 400/591] [SPARK-7087] [BUILD] Fix path issue change version script Author: Tijo Thomas Closes #5656 from tijoparacka/FIX_PATHISSUE_CHANGE_VERSION_SCRIPT and squashes the following commits: ab4f4b1 [Tijo Thomas] removed whitespace 24478c9 [Tijo Thomas] modified to provide the spark base dir while searching for pom and also while changing the vesrion no 7b8e10b [Tijo Thomas] Modified for providing the base directories while finding the list of pom files and also while changing the version no --- dev/change-version-to-2.10.sh | 6 +++--- dev/change-version-to-2.11.sh | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/dev/change-version-to-2.10.sh b/dev/change-version-to-2.10.sh index 15e0c73b4295e..c4adb1f96b7d3 100755 --- a/dev/change-version-to-2.10.sh +++ b/dev/change-version-to-2.10.sh @@ -18,9 +18,9 @@ # # Note that this will not necessarily work as intended with non-GNU sed (e.g. OS X) - -find . -name 'pom.xml' | grep -v target \ +BASEDIR=$(dirname $0)/.. +find $BASEDIR -name 'pom.xml' | grep -v target \ | xargs -I {} sed -i -e 's/\(artifactId.*\)_2.11/\1_2.10/g' {} # Also update in parent POM -sed -i -e '0,/2.11    2.102.112.10 in parent POM -sed -i -e '0,/2.102.112.102.11 Date: Thu, 23 Apr 2015 14:46:54 -0700 Subject: [PATCH 401/591] [SPARK-7070] [MLLIB] LDA.setBeta should call setTopicConcentration. jkbradley Author: Xiangrui Meng Closes #5649 from mengxr/SPARK-7070 and squashes the following commits: c66023c [Xiangrui Meng] setBeta should call setTopicConcentration --- .../scala/org/apache/spark/mllib/clustering/LDA.scala | 2 +- .../org/apache/spark/mllib/clustering/LDASuite.scala | 8 ++++++++ 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala index 9d63a08e211bc..d006b39acb213 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala @@ -177,7 +177,7 @@ class LDA private ( def getBeta: Double = getTopicConcentration /** Alias for [[setTopicConcentration()]] */ - def setBeta(beta: Double): this.type = setBeta(beta) + def setBeta(beta: Double): this.type = setTopicConcentration(beta) /** * Maximum number of iterations for learning. diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala index 15de10fd13a19..cc747dabb9968 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala @@ -123,6 +123,14 @@ class LDASuite extends FunSuite with MLlibTestSparkContext { assert(termVertexIds.map(i => LDA.index2term(i.toLong)) === termIds) assert(termVertexIds.forall(i => LDA.isTermVertex((i.toLong, 0)))) } + + test("setter alias") { + val lda = new LDA().setAlpha(2.0).setBeta(3.0) + assert(lda.getAlpha === 2.0) + assert(lda.getDocConcentration === 2.0) + assert(lda.getBeta === 3.0) + assert(lda.getTopicConcentration === 3.0) + } } private[clustering] object LDASuite { From 6220d933e5ce4ba890f5d6a50a69b95d319dafb4 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Thu, 23 Apr 2015 14:48:19 -0700 Subject: [PATCH 402/591] [SQL] Break dataTypes.scala into multiple files. It was over 1000 lines of code, making it harder to find all the types. Only moved code around, and didn't change any. Author: Reynold Xin Closes #5670 from rxin/break-types and squashes the following commits: 8c59023 [Reynold Xin] Check in missing files. dcd5193 [Reynold Xin] [SQL] Break dataTypes.scala into multiple files. --- .../apache/spark/sql/types/ArrayType.scala | 74 + .../apache/spark/sql/types/BinaryType.scala | 63 + .../apache/spark/sql/types/BooleanType.scala | 51 + .../org/apache/spark/sql/types/ByteType.scala | 54 + .../org/apache/spark/sql/types/DataType.scala | 353 +++++ .../org/apache/spark/sql/types/DateType.scala | 54 + .../apache/spark/sql/types/DecimalType.scala | 110 ++ .../apache/spark/sql/types/DoubleType.scala | 53 + .../apache/spark/sql/types/FloatType.scala | 53 + .../apache/spark/sql/types/IntegerType.scala | 54 + .../org/apache/spark/sql/types/LongType.scala | 54 + .../org/apache/spark/sql/types/MapType.scala | 79 ++ .../org/apache/spark/sql/types/NullType.scala | 39 + .../apache/spark/sql/types/ShortType.scala | 53 + .../apache/spark/sql/types/StringType.scala | 50 + .../apache/spark/sql/types/StructField.scala | 54 + .../apache/spark/sql/types/StructType.scala | 263 ++++ .../spark/sql/types/TimestampType.scala | 57 + .../spark/sql/types/UserDefinedType.scala | 81 ++ .../apache/spark/sql/types/dataTypes.scala | 1224 ----------------- 20 files changed, 1649 insertions(+), 1224 deletions(-) create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/BinaryType.scala create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/BooleanType.scala create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/ByteType.scala create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateType.scala create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/DoubleType.scala create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/FloatType.scala create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/IntegerType.scala create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/LongType.scala create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/NullType.scala create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/ShortType.scala create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/StringType.scala create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructField.scala create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/TimestampType.scala create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/UserDefinedType.scala delete mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala new file mode 100644 index 0000000000000..b116163faccad --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala @@ -0,0 +1,74 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import org.json4s.JsonDSL._ + +import org.apache.spark.annotation.DeveloperApi + + +object ArrayType { + /** Construct a [[ArrayType]] object with the given element type. The `containsNull` is true. */ + def apply(elementType: DataType): ArrayType = ArrayType(elementType, containsNull = true) +} + + +/** + * :: DeveloperApi :: + * The data type for collections of multiple values. + * Internally these are represented as columns that contain a ``scala.collection.Seq``. + * + * Please use [[DataTypes.createArrayType()]] to create a specific instance. + * + * An [[ArrayType]] object comprises two fields, `elementType: [[DataType]]` and + * `containsNull: Boolean`. The field of `elementType` is used to specify the type of + * array elements. The field of `containsNull` is used to specify if the array has `null` values. + * + * @param elementType The data type of values. + * @param containsNull Indicates if values have `null` values + * + * @group dataType + */ +@DeveloperApi +case class ArrayType(elementType: DataType, containsNull: Boolean) extends DataType { + + /** No-arg constructor for kryo. */ + protected def this() = this(null, false) + + private[sql] def buildFormattedString(prefix: String, builder: StringBuilder): Unit = { + builder.append( + s"$prefix-- element: ${elementType.typeName} (containsNull = $containsNull)\n") + DataType.buildFormattedString(elementType, s"$prefix |", builder) + } + + override private[sql] def jsonValue = + ("type" -> typeName) ~ + ("elementType" -> elementType.jsonValue) ~ + ("containsNull" -> containsNull) + + /** + * The default size of a value of the ArrayType is 100 * the default size of the element type. + * (We assume that there are 100 elements). + */ + override def defaultSize: Int = 100 * elementType.defaultSize + + override def simpleString: String = s"array<${elementType.simpleString}>" + + private[spark] override def asNullable: ArrayType = + ArrayType(elementType.asNullable, containsNull = true) +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/BinaryType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/BinaryType.scala new file mode 100644 index 0000000000000..a581a9e9468ef --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/BinaryType.scala @@ -0,0 +1,63 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import scala.math.Ordering +import scala.reflect.runtime.universe.typeTag + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.catalyst.ScalaReflectionLock + + +/** + * :: DeveloperApi :: + * The data type representing `Array[Byte]` values. + * Please use the singleton [[DataTypes.BinaryType]]. + * + * @group dataType + */ +@DeveloperApi +class BinaryType private() extends AtomicType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "BinaryType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. + + private[sql] type InternalType = Array[Byte] + + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } + + private[sql] val ordering = new Ordering[InternalType] { + def compare(x: Array[Byte], y: Array[Byte]): Int = { + for (i <- 0 until x.length; if i < y.length) { + val res = x(i).compareTo(y(i)) + if (res != 0) return res + } + x.length - y.length + } + } + + /** + * The default size of a value of the BinaryType is 4096 bytes. + */ + override def defaultSize: Int = 4096 + + private[spark] override def asNullable: BinaryType = this +} + + +case object BinaryType extends BinaryType diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/BooleanType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/BooleanType.scala new file mode 100644 index 0000000000000..a7f228cefa57a --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/BooleanType.scala @@ -0,0 +1,51 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import scala.math.Ordering +import scala.reflect.runtime.universe.typeTag + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.catalyst.ScalaReflectionLock + + +/** + * :: DeveloperApi :: + * The data type representing `Boolean` values. Please use the singleton [[DataTypes.BooleanType]]. + * + *@group dataType + */ +@DeveloperApi +class BooleanType private() extends AtomicType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "BooleanType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. + private[sql] type InternalType = Boolean + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } + private[sql] val ordering = implicitly[Ordering[InternalType]] + + /** + * The default size of a value of the BooleanType is 1 byte. + */ + override def defaultSize: Int = 1 + + private[spark] override def asNullable: BooleanType = this +} + + +case object BooleanType extends BooleanType diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ByteType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ByteType.scala new file mode 100644 index 0000000000000..4d8685796ec76 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ByteType.scala @@ -0,0 +1,54 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import scala.math.{Ordering, Integral, Numeric} +import scala.reflect.runtime.universe.typeTag + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.catalyst.ScalaReflectionLock + + +/** + * :: DeveloperApi :: + * The data type representing `Byte` values. Please use the singleton [[DataTypes.ByteType]]. + * + * @group dataType + */ +@DeveloperApi +class ByteType private() extends IntegralType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "ByteType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. + private[sql] type InternalType = Byte + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } + private[sql] val numeric = implicitly[Numeric[Byte]] + private[sql] val integral = implicitly[Integral[Byte]] + private[sql] val ordering = implicitly[Ordering[InternalType]] + + /** + * The default size of a value of the ByteType is 1 byte. + */ + override def defaultSize: Int = 1 + + override def simpleString: String = "tinyint" + + private[spark] override def asNullable: ByteType = this +} + +case object ByteType extends ByteType diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala new file mode 100644 index 0000000000000..e6bfcd9adfeb1 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala @@ -0,0 +1,353 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import scala.reflect.ClassTag +import scala.reflect.runtime.universe.{TypeTag, runtimeMirror} +import scala.util.parsing.combinator.RegexParsers + +import org.json4s._ +import org.json4s.JsonAST.JValue +import org.json4s.JsonDSL._ +import org.json4s.jackson.JsonMethods._ + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.catalyst.ScalaReflectionLock +import org.apache.spark.sql.catalyst.expressions.Expression +import org.apache.spark.util.Utils + + +/** + * :: DeveloperApi :: + * The base type of all Spark SQL data types. + * + * @group dataType + */ +@DeveloperApi +abstract class DataType { + /** Matches any expression that evaluates to this DataType */ + def unapply(a: Expression): Boolean = a match { + case e: Expression if e.dataType == this => true + case _ => false + } + + /** The default size of a value of this data type. */ + def defaultSize: Int + + def typeName: String = this.getClass.getSimpleName.stripSuffix("$").dropRight(4).toLowerCase + + private[sql] def jsonValue: JValue = typeName + + def json: String = compact(render(jsonValue)) + + def prettyJson: String = pretty(render(jsonValue)) + + def simpleString: String = typeName + + /** Check if `this` and `other` are the same data type when ignoring nullability + * (`StructField.nullable`, `ArrayType.containsNull`, and `MapType.valueContainsNull`). + */ + private[spark] def sameType(other: DataType): Boolean = + DataType.equalsIgnoreNullability(this, other) + + /** Returns the same data type but set all nullability fields are true + * (`StructField.nullable`, `ArrayType.containsNull`, and `MapType.valueContainsNull`). + */ + private[spark] def asNullable: DataType +} + + +/** + * An internal type used to represent everything that is not null, UDTs, arrays, structs, and maps. + */ +protected[sql] abstract class AtomicType extends DataType { + private[sql] type InternalType + @transient private[sql] val tag: TypeTag[InternalType] + private[sql] val ordering: Ordering[InternalType] + + @transient private[sql] val classTag = ScalaReflectionLock.synchronized { + val mirror = runtimeMirror(Utils.getSparkClassLoader) + ClassTag[InternalType](mirror.runtimeClass(tag.tpe)) + } +} + + +/** + * :: DeveloperApi :: + * Numeric data types. + * + * @group dataType + */ +abstract class NumericType extends AtomicType { + // Unfortunately we can't get this implicitly as that breaks Spark Serialization. In order for + // implicitly[Numeric[JvmType]] to be valid, we have to change JvmType from a type variable to a + // type parameter and and add a numeric annotation (i.e., [JvmType : Numeric]). This gets + // desugared by the compiler into an argument to the objects constructor. This means there is no + // longer an no argument constructor and thus the JVM cannot serialize the object anymore. + private[sql] val numeric: Numeric[InternalType] +} + + +private[sql] object NumericType { + def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[NumericType] +} + + +/** Matcher for any expressions that evaluate to [[IntegralType]]s */ +private[sql] object IntegralType { + def unapply(a: Expression): Boolean = a match { + case e: Expression if e.dataType.isInstanceOf[IntegralType] => true + case _ => false + } +} + + +private[sql] abstract class IntegralType extends NumericType { + private[sql] val integral: Integral[InternalType] +} + + + +/** Matcher for any expressions that evaluate to [[FractionalType]]s */ +private[sql] object FractionalType { + def unapply(a: Expression): Boolean = a match { + case e: Expression if e.dataType.isInstanceOf[FractionalType] => true + case _ => false + } +} + + +private[sql] abstract class FractionalType extends NumericType { + private[sql] val fractional: Fractional[InternalType] + private[sql] val asIntegral: Integral[InternalType] +} + + +object DataType { + + def fromJson(json: String): DataType = parseDataType(parse(json)) + + @deprecated("Use DataType.fromJson instead", "1.2.0") + def fromCaseClassString(string: String): DataType = CaseClassStringParser(string) + + private val nonDecimalNameToType = { + Seq(NullType, DateType, TimestampType, BinaryType, + IntegerType, BooleanType, LongType, DoubleType, FloatType, ShortType, ByteType, StringType) + .map(t => t.typeName -> t).toMap + } + + /** Given the string representation of a type, return its DataType */ + private def nameToType(name: String): DataType = { + val FIXED_DECIMAL = """decimal\(\s*(\d+)\s*,\s*(\d+)\s*\)""".r + name match { + case "decimal" => DecimalType.Unlimited + case FIXED_DECIMAL(precision, scale) => DecimalType(precision.toInt, scale.toInt) + case other => nonDecimalNameToType(other) + } + } + + private object JSortedObject { + def unapplySeq(value: JValue): Option[List[(String, JValue)]] = value match { + case JObject(seq) => Some(seq.toList.sortBy(_._1)) + case _ => None + } + } + + // NOTE: Map fields must be sorted in alphabetical order to keep consistent with the Python side. + private def parseDataType(json: JValue): DataType = json match { + case JString(name) => + nameToType(name) + + case JSortedObject( + ("containsNull", JBool(n)), + ("elementType", t: JValue), + ("type", JString("array"))) => + ArrayType(parseDataType(t), n) + + case JSortedObject( + ("keyType", k: JValue), + ("type", JString("map")), + ("valueContainsNull", JBool(n)), + ("valueType", v: JValue)) => + MapType(parseDataType(k), parseDataType(v), n) + + case JSortedObject( + ("fields", JArray(fields)), + ("type", JString("struct"))) => + StructType(fields.map(parseStructField)) + + case JSortedObject( + ("class", JString(udtClass)), + ("pyClass", _), + ("sqlType", _), + ("type", JString("udt"))) => + Class.forName(udtClass).newInstance().asInstanceOf[UserDefinedType[_]] + } + + private def parseStructField(json: JValue): StructField = json match { + case JSortedObject( + ("metadata", metadata: JObject), + ("name", JString(name)), + ("nullable", JBool(nullable)), + ("type", dataType: JValue)) => + StructField(name, parseDataType(dataType), nullable, Metadata.fromJObject(metadata)) + // Support reading schema when 'metadata' is missing. + case JSortedObject( + ("name", JString(name)), + ("nullable", JBool(nullable)), + ("type", dataType: JValue)) => + StructField(name, parseDataType(dataType), nullable) + } + + private object CaseClassStringParser extends RegexParsers { + protected lazy val primitiveType: Parser[DataType] = + ( "StringType" ^^^ StringType + | "FloatType" ^^^ FloatType + | "IntegerType" ^^^ IntegerType + | "ByteType" ^^^ ByteType + | "ShortType" ^^^ ShortType + | "DoubleType" ^^^ DoubleType + | "LongType" ^^^ LongType + | "BinaryType" ^^^ BinaryType + | "BooleanType" ^^^ BooleanType + | "DateType" ^^^ DateType + | "DecimalType()" ^^^ DecimalType.Unlimited + | fixedDecimalType + | "TimestampType" ^^^ TimestampType + ) + + protected lazy val fixedDecimalType: Parser[DataType] = + ("DecimalType(" ~> "[0-9]+".r) ~ ("," ~> "[0-9]+".r <~ ")") ^^ { + case precision ~ scale => DecimalType(precision.toInt, scale.toInt) + } + + protected lazy val arrayType: Parser[DataType] = + "ArrayType" ~> "(" ~> dataType ~ "," ~ boolVal <~ ")" ^^ { + case tpe ~ _ ~ containsNull => ArrayType(tpe, containsNull) + } + + protected lazy val mapType: Parser[DataType] = + "MapType" ~> "(" ~> dataType ~ "," ~ dataType ~ "," ~ boolVal <~ ")" ^^ { + case t1 ~ _ ~ t2 ~ _ ~ valueContainsNull => MapType(t1, t2, valueContainsNull) + } + + protected lazy val structField: Parser[StructField] = + ("StructField(" ~> "[a-zA-Z0-9_]*".r) ~ ("," ~> dataType) ~ ("," ~> boolVal <~ ")") ^^ { + case name ~ tpe ~ nullable => + StructField(name, tpe, nullable = nullable) + } + + protected lazy val boolVal: Parser[Boolean] = + ( "true" ^^^ true + | "false" ^^^ false + ) + + protected lazy val structType: Parser[DataType] = + "StructType\\([A-zA-z]*\\(".r ~> repsep(structField, ",") <~ "))" ^^ { + case fields => StructType(fields) + } + + protected lazy val dataType: Parser[DataType] = + ( arrayType + | mapType + | structType + | primitiveType + ) + + /** + * Parses a string representation of a DataType. + * + * TODO: Generate parser as pickler... + */ + def apply(asString: String): DataType = parseAll(dataType, asString) match { + case Success(result, _) => result + case failure: NoSuccess => + throw new IllegalArgumentException(s"Unsupported dataType: $asString, $failure") + } + } + + protected[types] def buildFormattedString( + dataType: DataType, + prefix: String, + builder: StringBuilder): Unit = { + dataType match { + case array: ArrayType => + array.buildFormattedString(prefix, builder) + case struct: StructType => + struct.buildFormattedString(prefix, builder) + case map: MapType => + map.buildFormattedString(prefix, builder) + case _ => + } + } + + /** + * Compares two types, ignoring nullability of ArrayType, MapType, StructType. + */ + private[types] def equalsIgnoreNullability(left: DataType, right: DataType): Boolean = { + (left, right) match { + case (ArrayType(leftElementType, _), ArrayType(rightElementType, _)) => + equalsIgnoreNullability(leftElementType, rightElementType) + case (MapType(leftKeyType, leftValueType, _), MapType(rightKeyType, rightValueType, _)) => + equalsIgnoreNullability(leftKeyType, rightKeyType) && + equalsIgnoreNullability(leftValueType, rightValueType) + case (StructType(leftFields), StructType(rightFields)) => + leftFields.length == rightFields.length && + leftFields.zip(rightFields).forall { case (l, r) => + l.name == r.name && equalsIgnoreNullability(l.dataType, r.dataType) + } + case (l, r) => l == r + } + } + + /** + * Compares two types, ignoring compatible nullability of ArrayType, MapType, StructType. + * + * Compatible nullability is defined as follows: + * - If `from` and `to` are ArrayTypes, `from` has a compatible nullability with `to` + * if and only if `to.containsNull` is true, or both of `from.containsNull` and + * `to.containsNull` are false. + * - If `from` and `to` are MapTypes, `from` has a compatible nullability with `to` + * if and only if `to.valueContainsNull` is true, or both of `from.valueContainsNull` and + * `to.valueContainsNull` are false. + * - If `from` and `to` are StructTypes, `from` has a compatible nullability with `to` + * if and only if for all every pair of fields, `to.nullable` is true, or both + * of `fromField.nullable` and `toField.nullable` are false. + */ + private[sql] def equalsIgnoreCompatibleNullability(from: DataType, to: DataType): Boolean = { + (from, to) match { + case (ArrayType(fromElement, fn), ArrayType(toElement, tn)) => + (tn || !fn) && equalsIgnoreCompatibleNullability(fromElement, toElement) + + case (MapType(fromKey, fromValue, fn), MapType(toKey, toValue, tn)) => + (tn || !fn) && + equalsIgnoreCompatibleNullability(fromKey, toKey) && + equalsIgnoreCompatibleNullability(fromValue, toValue) + + case (StructType(fromFields), StructType(toFields)) => + fromFields.length == toFields.length && + fromFields.zip(toFields).forall { case (fromField, toField) => + fromField.name == toField.name && + (toField.nullable || !fromField.nullable) && + equalsIgnoreCompatibleNullability(fromField.dataType, toField.dataType) + } + + case (fromDataType, toDataType) => fromDataType == toDataType + } + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateType.scala new file mode 100644 index 0000000000000..03f0644bc784c --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DateType.scala @@ -0,0 +1,54 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import scala.math.Ordering +import scala.reflect.runtime.universe.typeTag + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.catalyst.ScalaReflectionLock + + +/** + * :: DeveloperApi :: + * The data type representing `java.sql.Date` values. + * Please use the singleton [[DataTypes.DateType]]. + * + * @group dataType + */ +@DeveloperApi +class DateType private() extends AtomicType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "DateType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. + private[sql] type InternalType = Int + + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } + + private[sql] val ordering = implicitly[Ordering[InternalType]] + + /** + * The default size of a value of the DateType is 4 bytes. + */ + override def defaultSize: Int = 4 + + private[spark] override def asNullable: DateType = this +} + + +case object DateType extends DateType diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala new file mode 100644 index 0000000000000..0f8cecd28f7df --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DecimalType.scala @@ -0,0 +1,110 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import scala.reflect.runtime.universe.typeTag + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.catalyst.ScalaReflectionLock +import org.apache.spark.sql.catalyst.expressions.Expression + + +/** Precision parameters for a Decimal */ +case class PrecisionInfo(precision: Int, scale: Int) + + +/** + * :: DeveloperApi :: + * The data type representing `java.math.BigDecimal` values. + * A Decimal that might have fixed precision and scale, or unlimited values for these. + * + * Please use [[DataTypes.createDecimalType()]] to create a specific instance. + * + * @group dataType + */ +@DeveloperApi +case class DecimalType(precisionInfo: Option[PrecisionInfo]) extends FractionalType { + + /** No-arg constructor for kryo. */ + protected def this() = this(null) + + private[sql] type InternalType = Decimal + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } + private[sql] val numeric = Decimal.DecimalIsFractional + private[sql] val fractional = Decimal.DecimalIsFractional + private[sql] val ordering = Decimal.DecimalIsFractional + private[sql] val asIntegral = Decimal.DecimalAsIfIntegral + + def precision: Int = precisionInfo.map(_.precision).getOrElse(-1) + + def scale: Int = precisionInfo.map(_.scale).getOrElse(-1) + + override def typeName: String = precisionInfo match { + case Some(PrecisionInfo(precision, scale)) => s"decimal($precision,$scale)" + case None => "decimal" + } + + override def toString: String = precisionInfo match { + case Some(PrecisionInfo(precision, scale)) => s"DecimalType($precision,$scale)" + case None => "DecimalType()" + } + + /** + * The default size of a value of the DecimalType is 4096 bytes. + */ + override def defaultSize: Int = 4096 + + override def simpleString: String = precisionInfo match { + case Some(PrecisionInfo(precision, scale)) => s"decimal($precision,$scale)" + case None => "decimal(10,0)" + } + + private[spark] override def asNullable: DecimalType = this +} + + +/** Extra factory methods and pattern matchers for Decimals */ +object DecimalType { + val Unlimited: DecimalType = DecimalType(None) + + object Fixed { + def unapply(t: DecimalType): Option[(Int, Int)] = + t.precisionInfo.map(p => (p.precision, p.scale)) + } + + object Expression { + def unapply(e: Expression): Option[(Int, Int)] = e.dataType match { + case t: DecimalType => t.precisionInfo.map(p => (p.precision, p.scale)) + case _ => None + } + } + + def apply(): DecimalType = Unlimited + + def apply(precision: Int, scale: Int): DecimalType = + DecimalType(Some(PrecisionInfo(precision, scale))) + + def unapply(t: DataType): Boolean = t.isInstanceOf[DecimalType] + + def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[DecimalType] + + def isFixed(dataType: DataType): Boolean = dataType match { + case DecimalType.Fixed(_, _) => true + case _ => false + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DoubleType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DoubleType.scala new file mode 100644 index 0000000000000..66766623213c9 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DoubleType.scala @@ -0,0 +1,53 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import scala.math.{Ordering, Fractional, Numeric} +import scala.math.Numeric.DoubleAsIfIntegral +import scala.reflect.runtime.universe.typeTag + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.catalyst.ScalaReflectionLock + +/** + * :: DeveloperApi :: + * The data type representing `Double` values. Please use the singleton [[DataTypes.DoubleType]]. + * + * @group dataType + */ +@DeveloperApi +class DoubleType private() extends FractionalType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "DoubleType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. + private[sql] type InternalType = Double + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } + private[sql] val numeric = implicitly[Numeric[Double]] + private[sql] val fractional = implicitly[Fractional[Double]] + private[sql] val ordering = implicitly[Ordering[InternalType]] + private[sql] val asIntegral = DoubleAsIfIntegral + + /** + * The default size of a value of the DoubleType is 8 bytes. + */ + override def defaultSize: Int = 8 + + private[spark] override def asNullable: DoubleType = this +} + +case object DoubleType extends DoubleType diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/FloatType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/FloatType.scala new file mode 100644 index 0000000000000..1d5a2f4f6f86c --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/FloatType.scala @@ -0,0 +1,53 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import scala.math.Numeric.FloatAsIfIntegral +import scala.math.{Ordering, Fractional, Numeric} +import scala.reflect.runtime.universe.typeTag + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.catalyst.ScalaReflectionLock + +/** + * :: DeveloperApi :: + * The data type representing `Float` values. Please use the singleton [[DataTypes.FloatType]]. + * + * @group dataType + */ +@DeveloperApi +class FloatType private() extends FractionalType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "FloatType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. + private[sql] type InternalType = Float + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } + private[sql] val numeric = implicitly[Numeric[Float]] + private[sql] val fractional = implicitly[Fractional[Float]] + private[sql] val ordering = implicitly[Ordering[InternalType]] + private[sql] val asIntegral = FloatAsIfIntegral + + /** + * The default size of a value of the FloatType is 4 bytes. + */ + override def defaultSize: Int = 4 + + private[spark] override def asNullable: FloatType = this +} + +case object FloatType extends FloatType diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/IntegerType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/IntegerType.scala new file mode 100644 index 0000000000000..74e464c082873 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/IntegerType.scala @@ -0,0 +1,54 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import scala.math.{Ordering, Integral, Numeric} +import scala.reflect.runtime.universe.typeTag + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.catalyst.ScalaReflectionLock + + +/** + * :: DeveloperApi :: + * The data type representing `Int` values. Please use the singleton [[DataTypes.IntegerType]]. + * + * @group dataType + */ +@DeveloperApi +class IntegerType private() extends IntegralType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "IntegerType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. + private[sql] type InternalType = Int + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } + private[sql] val numeric = implicitly[Numeric[Int]] + private[sql] val integral = implicitly[Integral[Int]] + private[sql] val ordering = implicitly[Ordering[InternalType]] + + /** + * The default size of a value of the IntegerType is 4 bytes. + */ + override def defaultSize: Int = 4 + + override def simpleString: String = "int" + + private[spark] override def asNullable: IntegerType = this +} + +case object IntegerType extends IntegerType diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/LongType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/LongType.scala new file mode 100644 index 0000000000000..390675782e5fd --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/LongType.scala @@ -0,0 +1,54 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import scala.math.{Ordering, Integral, Numeric} +import scala.reflect.runtime.universe.typeTag + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.catalyst.ScalaReflectionLock + +/** + * :: DeveloperApi :: + * The data type representing `Long` values. Please use the singleton [[DataTypes.LongType]]. + * + * @group dataType + */ +@DeveloperApi +class LongType private() extends IntegralType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "LongType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. + private[sql] type InternalType = Long + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } + private[sql] val numeric = implicitly[Numeric[Long]] + private[sql] val integral = implicitly[Integral[Long]] + private[sql] val ordering = implicitly[Ordering[InternalType]] + + /** + * The default size of a value of the LongType is 8 bytes. + */ + override def defaultSize: Int = 8 + + override def simpleString: String = "bigint" + + private[spark] override def asNullable: LongType = this +} + + +case object LongType extends LongType diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala new file mode 100644 index 0000000000000..cfdf493074415 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala @@ -0,0 +1,79 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import org.json4s.JsonAST.JValue +import org.json4s.JsonDSL._ + + +/** + * :: DeveloperApi :: + * The data type for Maps. Keys in a map are not allowed to have `null` values. + * + * Please use [[DataTypes.createMapType()]] to create a specific instance. + * + * @param keyType The data type of map keys. + * @param valueType The data type of map values. + * @param valueContainsNull Indicates if map values have `null` values. + * + * @group dataType + */ +case class MapType( + keyType: DataType, + valueType: DataType, + valueContainsNull: Boolean) extends DataType { + + /** No-arg constructor for kryo. */ + def this() = this(null, null, false) + + private[sql] def buildFormattedString(prefix: String, builder: StringBuilder): Unit = { + builder.append(s"$prefix-- key: ${keyType.typeName}\n") + builder.append(s"$prefix-- value: ${valueType.typeName} " + + s"(valueContainsNull = $valueContainsNull)\n") + DataType.buildFormattedString(keyType, s"$prefix |", builder) + DataType.buildFormattedString(valueType, s"$prefix |", builder) + } + + override private[sql] def jsonValue: JValue = + ("type" -> typeName) ~ + ("keyType" -> keyType.jsonValue) ~ + ("valueType" -> valueType.jsonValue) ~ + ("valueContainsNull" -> valueContainsNull) + + /** + * The default size of a value of the MapType is + * 100 * (the default size of the key type + the default size of the value type). + * (We assume that there are 100 elements). + */ + override def defaultSize: Int = 100 * (keyType.defaultSize + valueType.defaultSize) + + override def simpleString: String = s"map<${keyType.simpleString},${valueType.simpleString}>" + + private[spark] override def asNullable: MapType = + MapType(keyType.asNullable, valueType.asNullable, valueContainsNull = true) +} + + +object MapType { + /** + * Construct a [[MapType]] object with the given key type and value type. + * The `valueContainsNull` is true. + */ + def apply(keyType: DataType, valueType: DataType): MapType = + MapType(keyType: DataType, valueType: DataType, valueContainsNull = true) +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/NullType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/NullType.scala new file mode 100644 index 0000000000000..b64b07431fa96 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/NullType.scala @@ -0,0 +1,39 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import org.apache.spark.annotation.DeveloperApi + + +/** + * :: DeveloperApi :: + * The data type representing `NULL` values. Please use the singleton [[DataTypes.NullType]]. + * + * @group dataType + */ +@DeveloperApi +class NullType private() extends DataType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "NullType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. + override def defaultSize: Int = 1 + + private[spark] override def asNullable: NullType = this +} + +case object NullType extends NullType diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ShortType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ShortType.scala new file mode 100644 index 0000000000000..73e9ec780b0af --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ShortType.scala @@ -0,0 +1,53 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import scala.math.{Ordering, Integral, Numeric} +import scala.reflect.runtime.universe.typeTag + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.catalyst.ScalaReflectionLock + +/** + * :: DeveloperApi :: + * The data type representing `Short` values. Please use the singleton [[DataTypes.ShortType]]. + * + * @group dataType + */ +@DeveloperApi +class ShortType private() extends IntegralType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "ShortType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. + private[sql] type InternalType = Short + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } + private[sql] val numeric = implicitly[Numeric[Short]] + private[sql] val integral = implicitly[Integral[Short]] + private[sql] val ordering = implicitly[Ordering[InternalType]] + + /** + * The default size of a value of the ShortType is 2 bytes. + */ + override def defaultSize: Int = 2 + + override def simpleString: String = "smallint" + + private[spark] override def asNullable: ShortType = this +} + +case object ShortType extends ShortType diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StringType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StringType.scala new file mode 100644 index 0000000000000..134ab0af4e0de --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StringType.scala @@ -0,0 +1,50 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import scala.math.Ordering +import scala.reflect.runtime.universe.typeTag + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.catalyst.ScalaReflectionLock + +/** + * :: DeveloperApi :: + * The data type representing `String` values. Please use the singleton [[DataTypes.StringType]]. + * + * @group dataType + */ +@DeveloperApi +class StringType private() extends AtomicType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "StringType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. + private[sql] type InternalType = UTF8String + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } + private[sql] val ordering = implicitly[Ordering[InternalType]] + + /** + * The default size of a value of the StringType is 4096 bytes. + */ + override def defaultSize: Int = 4096 + + private[spark] override def asNullable: StringType = this +} + +case object StringType extends StringType + diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructField.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructField.scala new file mode 100644 index 0000000000000..83570a5eaee61 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructField.scala @@ -0,0 +1,54 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import org.json4s.JsonAST.JValue +import org.json4s.JsonDSL._ + +/** + * A field inside a StructType. + * @param name The name of this field. + * @param dataType The data type of this field. + * @param nullable Indicates if values of this field can be `null` values. + * @param metadata The metadata of this field. The metadata should be preserved during + * transformation if the content of the column is not modified, e.g, in selection. + */ +case class StructField( + name: String, + dataType: DataType, + nullable: Boolean = true, + metadata: Metadata = Metadata.empty) { + + /** No-arg constructor for kryo. */ + protected def this() = this(null, null) + + private[sql] def buildFormattedString(prefix: String, builder: StringBuilder): Unit = { + builder.append(s"$prefix-- $name: ${dataType.typeName} (nullable = $nullable)\n") + DataType.buildFormattedString(dataType, s"$prefix |", builder) + } + + // override the default toString to be compatible with legacy parquet files. + override def toString: String = s"StructField($name,$dataType,$nullable)" + + private[sql] def jsonValue: JValue = { + ("name" -> name) ~ + ("type" -> dataType.jsonValue) ~ + ("nullable" -> nullable) ~ + ("metadata" -> metadata.jsonValue) + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala new file mode 100644 index 0000000000000..d80ffca18ec9a --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala @@ -0,0 +1,263 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import scala.collection.mutable.ArrayBuffer +import scala.math.max + +import org.json4s.JsonDSL._ + +import org.apache.spark.SparkException +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Attribute} + + +/** + * :: DeveloperApi :: + * A [[StructType]] object can be constructed by + * {{{ + * StructType(fields: Seq[StructField]) + * }}} + * For a [[StructType]] object, one or multiple [[StructField]]s can be extracted by names. + * If multiple [[StructField]]s are extracted, a [[StructType]] object will be returned. + * If a provided name does not have a matching field, it will be ignored. For the case + * of extracting a single StructField, a `null` will be returned. + * Example: + * {{{ + * import org.apache.spark.sql._ + * + * val struct = + * StructType( + * StructField("a", IntegerType, true) :: + * StructField("b", LongType, false) :: + * StructField("c", BooleanType, false) :: Nil) + * + * // Extract a single StructField. + * val singleField = struct("b") + * // singleField: StructField = StructField(b,LongType,false) + * + * // This struct does not have a field called "d". null will be returned. + * val nonExisting = struct("d") + * // nonExisting: StructField = null + * + * // Extract multiple StructFields. Field names are provided in a set. + * // A StructType object will be returned. + * val twoFields = struct(Set("b", "c")) + * // twoFields: StructType = + * // StructType(List(StructField(b,LongType,false), StructField(c,BooleanType,false))) + * + * // Any names without matching fields will be ignored. + * // For the case shown below, "d" will be ignored and + * // it is treated as struct(Set("b", "c")). + * val ignoreNonExisting = struct(Set("b", "c", "d")) + * // ignoreNonExisting: StructType = + * // StructType(List(StructField(b,LongType,false), StructField(c,BooleanType,false))) + * }}} + * + * A [[org.apache.spark.sql.Row]] object is used as a value of the StructType. + * Example: + * {{{ + * import org.apache.spark.sql._ + * + * val innerStruct = + * StructType( + * StructField("f1", IntegerType, true) :: + * StructField("f2", LongType, false) :: + * StructField("f3", BooleanType, false) :: Nil) + * + * val struct = StructType( + * StructField("a", innerStruct, true) :: Nil) + * + * // Create a Row with the schema defined by struct + * val row = Row(Row(1, 2, true)) + * // row: Row = [[1,2,true]] + * }}} + * + * @group dataType + */ +@DeveloperApi +case class StructType(fields: Array[StructField]) extends DataType with Seq[StructField] { + + /** No-arg constructor for kryo. */ + protected def this() = this(null) + + /** Returns all field names in an array. */ + def fieldNames: Array[String] = fields.map(_.name) + + private lazy val fieldNamesSet: Set[String] = fieldNames.toSet + private lazy val nameToField: Map[String, StructField] = fields.map(f => f.name -> f).toMap + private lazy val nameToIndex: Map[String, Int] = fieldNames.zipWithIndex.toMap + + /** + * Extracts a [[StructField]] of the given name. If the [[StructType]] object does not + * have a name matching the given name, `null` will be returned. + */ + def apply(name: String): StructField = { + nameToField.getOrElse(name, + throw new IllegalArgumentException(s"""Field "$name" does not exist.""")) + } + + /** + * Returns a [[StructType]] containing [[StructField]]s of the given names, preserving the + * original order of fields. Those names which do not have matching fields will be ignored. + */ + def apply(names: Set[String]): StructType = { + val nonExistFields = names -- fieldNamesSet + if (nonExistFields.nonEmpty) { + throw new IllegalArgumentException( + s"Field ${nonExistFields.mkString(",")} does not exist.") + } + // Preserve the original order of fields. + StructType(fields.filter(f => names.contains(f.name))) + } + + /** + * Returns index of a given field + */ + def fieldIndex(name: String): Int = { + nameToIndex.getOrElse(name, + throw new IllegalArgumentException(s"""Field "$name" does not exist.""")) + } + + protected[sql] def toAttributes: Seq[AttributeReference] = + map(f => AttributeReference(f.name, f.dataType, f.nullable, f.metadata)()) + + def treeString: String = { + val builder = new StringBuilder + builder.append("root\n") + val prefix = " |" + fields.foreach(field => field.buildFormattedString(prefix, builder)) + + builder.toString() + } + + def printTreeString(): Unit = println(treeString) + + private[sql] def buildFormattedString(prefix: String, builder: StringBuilder): Unit = { + fields.foreach(field => field.buildFormattedString(prefix, builder)) + } + + override private[sql] def jsonValue = + ("type" -> typeName) ~ + ("fields" -> map(_.jsonValue)) + + override def apply(fieldIndex: Int): StructField = fields(fieldIndex) + + override def length: Int = fields.length + + override def iterator: Iterator[StructField] = fields.iterator + + /** + * The default size of a value of the StructType is the total default sizes of all field types. + */ + override def defaultSize: Int = fields.map(_.dataType.defaultSize).sum + + override def simpleString: String = { + val fieldTypes = fields.map(field => s"${field.name}:${field.dataType.simpleString}") + s"struct<${fieldTypes.mkString(",")}>" + } + + /** + * Merges with another schema (`StructType`). For a struct field A from `this` and a struct field + * B from `that`, + * + * 1. If A and B have the same name and data type, they are merged to a field C with the same name + * and data type. C is nullable if and only if either A or B is nullable. + * 2. If A doesn't exist in `that`, it's included in the result schema. + * 3. If B doesn't exist in `this`, it's also included in the result schema. + * 4. Otherwise, `this` and `that` are considered as conflicting schemas and an exception would be + * thrown. + */ + private[sql] def merge(that: StructType): StructType = + StructType.merge(this, that).asInstanceOf[StructType] + + private[spark] override def asNullable: StructType = { + val newFields = fields.map { + case StructField(name, dataType, nullable, metadata) => + StructField(name, dataType.asNullable, nullable = true, metadata) + } + + StructType(newFields) + } +} + + +object StructType { + + def apply(fields: Seq[StructField]): StructType = StructType(fields.toArray) + + def apply(fields: java.util.List[StructField]): StructType = { + StructType(fields.toArray.asInstanceOf[Array[StructField]]) + } + + protected[sql] def fromAttributes(attributes: Seq[Attribute]): StructType = + StructType(attributes.map(a => StructField(a.name, a.dataType, a.nullable, a.metadata))) + + private[sql] def merge(left: DataType, right: DataType): DataType = + (left, right) match { + case (ArrayType(leftElementType, leftContainsNull), + ArrayType(rightElementType, rightContainsNull)) => + ArrayType( + merge(leftElementType, rightElementType), + leftContainsNull || rightContainsNull) + + case (MapType(leftKeyType, leftValueType, leftContainsNull), + MapType(rightKeyType, rightValueType, rightContainsNull)) => + MapType( + merge(leftKeyType, rightKeyType), + merge(leftValueType, rightValueType), + leftContainsNull || rightContainsNull) + + case (StructType(leftFields), StructType(rightFields)) => + val newFields = ArrayBuffer.empty[StructField] + + leftFields.foreach { + case leftField @ StructField(leftName, leftType, leftNullable, _) => + rightFields + .find(_.name == leftName) + .map { case rightField @ StructField(_, rightType, rightNullable, _) => + leftField.copy( + dataType = merge(leftType, rightType), + nullable = leftNullable || rightNullable) + } + .orElse(Some(leftField)) + .foreach(newFields += _) + } + + rightFields + .filterNot(f => leftFields.map(_.name).contains(f.name)) + .foreach(newFields += _) + + StructType(newFields) + + case (DecimalType.Fixed(leftPrecision, leftScale), + DecimalType.Fixed(rightPrecision, rightScale)) => + DecimalType( + max(leftScale, rightScale) + max(leftPrecision - leftScale, rightPrecision - rightScale), + max(leftScale, rightScale)) + + case (leftUdt: UserDefinedType[_], rightUdt: UserDefinedType[_]) + if leftUdt.userClass == rightUdt.userClass => leftUdt + + case (leftType, rightType) if leftType == rightType => + leftType + + case _ => + throw new SparkException(s"Failed to merge incompatible data types $left and $right") + } +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/TimestampType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/TimestampType.scala new file mode 100644 index 0000000000000..aebabfc475925 --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/TimestampType.scala @@ -0,0 +1,57 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import java.sql.Timestamp + +import scala.math.Ordering +import scala.reflect.runtime.universe.typeTag + +import org.apache.spark.annotation.DeveloperApi +import org.apache.spark.sql.catalyst.ScalaReflectionLock + + +/** + * :: DeveloperApi :: + * The data type representing `java.sql.Timestamp` values. + * Please use the singleton [[DataTypes.TimestampType]]. + * + * @group dataType + */ +@DeveloperApi +class TimestampType private() extends AtomicType { + // The companion object and this class is separated so the companion object also subclasses + // this type. Otherwise, the companion object would be of type "TimestampType$" in byte code. + // Defined with a private constructor so the companion object is the only possible instantiation. + private[sql] type InternalType = Timestamp + + @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } + + private[sql] val ordering = new Ordering[InternalType] { + def compare(x: Timestamp, y: Timestamp): Int = x.compareTo(y) + } + + /** + * The default size of a value of the TimestampType is 12 bytes. + */ + override def defaultSize: Int = 12 + + private[spark] override def asNullable: TimestampType = this +} + +case object TimestampType extends TimestampType diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UserDefinedType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UserDefinedType.scala new file mode 100644 index 0000000000000..6b20505c6009a --- /dev/null +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/UserDefinedType.scala @@ -0,0 +1,81 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.types + +import org.json4s.JsonAST.JValue +import org.json4s.JsonDSL._ + +import org.apache.spark.annotation.DeveloperApi + +/** + * ::DeveloperApi:: + * The data type for User Defined Types (UDTs). + * + * This interface allows a user to make their own classes more interoperable with SparkSQL; + * e.g., by creating a [[UserDefinedType]] for a class X, it becomes possible to create + * a `DataFrame` which has class X in the schema. + * + * For SparkSQL to recognize UDTs, the UDT must be annotated with + * [[SQLUserDefinedType]]. + * + * The conversion via `serialize` occurs when instantiating a `DataFrame` from another RDD. + * The conversion via `deserialize` occurs when reading from a `DataFrame`. + */ +@DeveloperApi +abstract class UserDefinedType[UserType] extends DataType with Serializable { + + /** Underlying storage type for this UDT */ + def sqlType: DataType + + /** Paired Python UDT class, if exists. */ + def pyUDT: String = null + + /** + * Convert the user type to a SQL datum + * + * TODO: Can we make this take obj: UserType? The issue is in + * CatalystTypeConverters.convertToCatalyst, where we need to convert Any to UserType. + */ + def serialize(obj: Any): Any + + /** Convert a SQL datum to the user type */ + def deserialize(datum: Any): UserType + + override private[sql] def jsonValue: JValue = { + ("type" -> "udt") ~ + ("class" -> this.getClass.getName) ~ + ("pyClass" -> pyUDT) ~ + ("sqlType" -> sqlType.jsonValue) + } + + /** + * Class object for the UserType + */ + def userClass: java.lang.Class[UserType] + + /** + * The default size of a value of the UserDefinedType is 4096 bytes. + */ + override def defaultSize: Int = 4096 + + /** + * For UDT, asNullable will not change the nullability of its internal sqlType and just returns + * itself. + */ + private[spark] override def asNullable: UserDefinedType[UserType] = this +} diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala deleted file mode 100644 index 87c7b7599366a..0000000000000 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/dataTypes.scala +++ /dev/null @@ -1,1224 +0,0 @@ -/* - * Licensed to the Apache Software Foundation (ASF) under one or more - * contributor license agreements. See the NOTICE file distributed with - * this work for additional information regarding copyright ownership. - * The ASF licenses this file to You under the Apache License, Version 2.0 - * (the "License"); you may not use this file except in compliance with - * the License. You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -package org.apache.spark.sql.types - -import java.sql.Timestamp - -import scala.collection.mutable.ArrayBuffer -import scala.math._ -import scala.math.Numeric.{FloatAsIfIntegral, DoubleAsIfIntegral} -import scala.reflect.ClassTag -import scala.reflect.runtime.universe.{TypeTag, runtimeMirror, typeTag} -import scala.util.parsing.combinator.RegexParsers - -import org.json4s._ -import org.json4s.JsonAST.JValue -import org.json4s.JsonDSL._ -import org.json4s.jackson.JsonMethods._ - -import org.apache.spark.SparkException -import org.apache.spark.annotation.DeveloperApi -import org.apache.spark.sql.catalyst.ScalaReflectionLock -import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, Expression} -import org.apache.spark.util.Utils - - -object DataType { - def fromJson(json: String): DataType = parseDataType(parse(json)) - - private val nonDecimalNameToType = { - Seq(NullType, DateType, TimestampType, BinaryType, - IntegerType, BooleanType, LongType, DoubleType, FloatType, ShortType, ByteType, StringType) - .map(t => t.typeName -> t).toMap - } - - /** Given the string representation of a type, return its DataType */ - private def nameToType(name: String): DataType = { - val FIXED_DECIMAL = """decimal\(\s*(\d+)\s*,\s*(\d+)\s*\)""".r - name match { - case "decimal" => DecimalType.Unlimited - case FIXED_DECIMAL(precision, scale) => DecimalType(precision.toInt, scale.toInt) - case other => nonDecimalNameToType(other) - } - } - - private object JSortedObject { - def unapplySeq(value: JValue): Option[List[(String, JValue)]] = value match { - case JObject(seq) => Some(seq.toList.sortBy(_._1)) - case _ => None - } - } - - // NOTE: Map fields must be sorted in alphabetical order to keep consistent with the Python side. - private def parseDataType(json: JValue): DataType = json match { - case JString(name) => - nameToType(name) - - case JSortedObject( - ("containsNull", JBool(n)), - ("elementType", t: JValue), - ("type", JString("array"))) => - ArrayType(parseDataType(t), n) - - case JSortedObject( - ("keyType", k: JValue), - ("type", JString("map")), - ("valueContainsNull", JBool(n)), - ("valueType", v: JValue)) => - MapType(parseDataType(k), parseDataType(v), n) - - case JSortedObject( - ("fields", JArray(fields)), - ("type", JString("struct"))) => - StructType(fields.map(parseStructField)) - - case JSortedObject( - ("class", JString(udtClass)), - ("pyClass", _), - ("sqlType", _), - ("type", JString("udt"))) => - Class.forName(udtClass).newInstance().asInstanceOf[UserDefinedType[_]] - } - - private def parseStructField(json: JValue): StructField = json match { - case JSortedObject( - ("metadata", metadata: JObject), - ("name", JString(name)), - ("nullable", JBool(nullable)), - ("type", dataType: JValue)) => - StructField(name, parseDataType(dataType), nullable, Metadata.fromJObject(metadata)) - // Support reading schema when 'metadata' is missing. - case JSortedObject( - ("name", JString(name)), - ("nullable", JBool(nullable)), - ("type", dataType: JValue)) => - StructField(name, parseDataType(dataType), nullable) - } - - @deprecated("Use DataType.fromJson instead", "1.2.0") - def fromCaseClassString(string: String): DataType = CaseClassStringParser(string) - - private object CaseClassStringParser extends RegexParsers { - protected lazy val primitiveType: Parser[DataType] = - ( "StringType" ^^^ StringType - | "FloatType" ^^^ FloatType - | "IntegerType" ^^^ IntegerType - | "ByteType" ^^^ ByteType - | "ShortType" ^^^ ShortType - | "DoubleType" ^^^ DoubleType - | "LongType" ^^^ LongType - | "BinaryType" ^^^ BinaryType - | "BooleanType" ^^^ BooleanType - | "DateType" ^^^ DateType - | "DecimalType()" ^^^ DecimalType.Unlimited - | fixedDecimalType - | "TimestampType" ^^^ TimestampType - ) - - protected lazy val fixedDecimalType: Parser[DataType] = - ("DecimalType(" ~> "[0-9]+".r) ~ ("," ~> "[0-9]+".r <~ ")") ^^ { - case precision ~ scale => DecimalType(precision.toInt, scale.toInt) - } - - protected lazy val arrayType: Parser[DataType] = - "ArrayType" ~> "(" ~> dataType ~ "," ~ boolVal <~ ")" ^^ { - case tpe ~ _ ~ containsNull => ArrayType(tpe, containsNull) - } - - protected lazy val mapType: Parser[DataType] = - "MapType" ~> "(" ~> dataType ~ "," ~ dataType ~ "," ~ boolVal <~ ")" ^^ { - case t1 ~ _ ~ t2 ~ _ ~ valueContainsNull => MapType(t1, t2, valueContainsNull) - } - - protected lazy val structField: Parser[StructField] = - ("StructField(" ~> "[a-zA-Z0-9_]*".r) ~ ("," ~> dataType) ~ ("," ~> boolVal <~ ")") ^^ { - case name ~ tpe ~ nullable => - StructField(name, tpe, nullable = nullable) - } - - protected lazy val boolVal: Parser[Boolean] = - ( "true" ^^^ true - | "false" ^^^ false - ) - - protected lazy val structType: Parser[DataType] = - "StructType\\([A-zA-z]*\\(".r ~> repsep(structField, ",") <~ "))" ^^ { - case fields => StructType(fields) - } - - protected lazy val dataType: Parser[DataType] = - ( arrayType - | mapType - | structType - | primitiveType - ) - - /** - * Parses a string representation of a DataType. - * - * TODO: Generate parser as pickler... - */ - def apply(asString: String): DataType = parseAll(dataType, asString) match { - case Success(result, _) => result - case failure: NoSuccess => - throw new IllegalArgumentException(s"Unsupported dataType: $asString, $failure") - } - } - - protected[types] def buildFormattedString( - dataType: DataType, - prefix: String, - builder: StringBuilder): Unit = { - dataType match { - case array: ArrayType => - array.buildFormattedString(prefix, builder) - case struct: StructType => - struct.buildFormattedString(prefix, builder) - case map: MapType => - map.buildFormattedString(prefix, builder) - case _ => - } - } - - /** - * Compares two types, ignoring nullability of ArrayType, MapType, StructType. - */ - private[types] def equalsIgnoreNullability(left: DataType, right: DataType): Boolean = { - (left, right) match { - case (ArrayType(leftElementType, _), ArrayType(rightElementType, _)) => - equalsIgnoreNullability(leftElementType, rightElementType) - case (MapType(leftKeyType, leftValueType, _), MapType(rightKeyType, rightValueType, _)) => - equalsIgnoreNullability(leftKeyType, rightKeyType) && - equalsIgnoreNullability(leftValueType, rightValueType) - case (StructType(leftFields), StructType(rightFields)) => - leftFields.length == rightFields.length && - leftFields.zip(rightFields).forall { case (l, r) => - l.name == r.name && equalsIgnoreNullability(l.dataType, r.dataType) - } - case (l, r) => l == r - } - } - - /** - * Compares two types, ignoring compatible nullability of ArrayType, MapType, StructType. - * - * Compatible nullability is defined as follows: - * - If `from` and `to` are ArrayTypes, `from` has a compatible nullability with `to` - * if and only if `to.containsNull` is true, or both of `from.containsNull` and - * `to.containsNull` are false. - * - If `from` and `to` are MapTypes, `from` has a compatible nullability with `to` - * if and only if `to.valueContainsNull` is true, or both of `from.valueContainsNull` and - * `to.valueContainsNull` are false. - * - If `from` and `to` are StructTypes, `from` has a compatible nullability with `to` - * if and only if for all every pair of fields, `to.nullable` is true, or both - * of `fromField.nullable` and `toField.nullable` are false. - */ - private[sql] def equalsIgnoreCompatibleNullability(from: DataType, to: DataType): Boolean = { - (from, to) match { - case (ArrayType(fromElement, fn), ArrayType(toElement, tn)) => - (tn || !fn) && equalsIgnoreCompatibleNullability(fromElement, toElement) - - case (MapType(fromKey, fromValue, fn), MapType(toKey, toValue, tn)) => - (tn || !fn) && - equalsIgnoreCompatibleNullability(fromKey, toKey) && - equalsIgnoreCompatibleNullability(fromValue, toValue) - - case (StructType(fromFields), StructType(toFields)) => - fromFields.length == toFields.length && - fromFields.zip(toFields).forall { case (fromField, toField) => - fromField.name == toField.name && - (toField.nullable || !fromField.nullable) && - equalsIgnoreCompatibleNullability(fromField.dataType, toField.dataType) - } - - case (fromDataType, toDataType) => fromDataType == toDataType - } - } -} - - -/** - * :: DeveloperApi :: - * The base type of all Spark SQL data types. - * - * @group dataType - */ -@DeveloperApi -abstract class DataType { - /** Matches any expression that evaluates to this DataType */ - def unapply(a: Expression): Boolean = a match { - case e: Expression if e.dataType == this => true - case _ => false - } - - /** The default size of a value of this data type. */ - def defaultSize: Int - - def typeName: String = this.getClass.getSimpleName.stripSuffix("$").dropRight(4).toLowerCase - - private[sql] def jsonValue: JValue = typeName - - def json: String = compact(render(jsonValue)) - - def prettyJson: String = pretty(render(jsonValue)) - - def simpleString: String = typeName - - /** Check if `this` and `other` are the same data type when ignoring nullability - * (`StructField.nullable`, `ArrayType.containsNull`, and `MapType.valueContainsNull`). - */ - private[spark] def sameType(other: DataType): Boolean = - DataType.equalsIgnoreNullability(this, other) - - /** Returns the same data type but set all nullability fields are true - * (`StructField.nullable`, `ArrayType.containsNull`, and `MapType.valueContainsNull`). - */ - private[spark] def asNullable: DataType -} - -/** - * :: DeveloperApi :: - * The data type representing `NULL` values. Please use the singleton [[DataTypes.NullType]]. - * - * @group dataType - */ -@DeveloperApi -class NullType private() extends DataType { - // The companion object and this class is separated so the companion object also subclasses - // this type. Otherwise, the companion object would be of type "NullType$" in byte code. - // Defined with a private constructor so the companion object is the only possible instantiation. - override def defaultSize: Int = 1 - - private[spark] override def asNullable: NullType = this -} - -case object NullType extends NullType - - -/** - * An internal type used to represent everything that is not null, UDTs, arrays, structs, and maps. - */ -protected[sql] abstract class AtomicType extends DataType { - private[sql] type InternalType - @transient private[sql] val tag: TypeTag[InternalType] - private[sql] val ordering: Ordering[InternalType] - - @transient private[sql] val classTag = ScalaReflectionLock.synchronized { - val mirror = runtimeMirror(Utils.getSparkClassLoader) - ClassTag[InternalType](mirror.runtimeClass(tag.tpe)) - } -} - - -/** - * :: DeveloperApi :: - * The data type representing `String` values. Please use the singleton [[DataTypes.StringType]]. - * - * @group dataType - */ -@DeveloperApi -class StringType private() extends AtomicType { - // The companion object and this class is separated so the companion object also subclasses - // this type. Otherwise, the companion object would be of type "StringType$" in byte code. - // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type InternalType = UTF8String - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } - private[sql] val ordering = implicitly[Ordering[InternalType]] - - /** - * The default size of a value of the StringType is 4096 bytes. - */ - override def defaultSize: Int = 4096 - - private[spark] override def asNullable: StringType = this -} - -case object StringType extends StringType - - -/** - * :: DeveloperApi :: - * The data type representing `Array[Byte]` values. - * Please use the singleton [[DataTypes.BinaryType]]. - * - * @group dataType - */ -@DeveloperApi -class BinaryType private() extends AtomicType { - // The companion object and this class is separated so the companion object also subclasses - // this type. Otherwise, the companion object would be of type "BinaryType$" in byte code. - // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type InternalType = Array[Byte] - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } - private[sql] val ordering = new Ordering[InternalType] { - def compare(x: Array[Byte], y: Array[Byte]): Int = { - for (i <- 0 until x.length; if i < y.length) { - val res = x(i).compareTo(y(i)) - if (res != 0) return res - } - x.length - y.length - } - } - - /** - * The default size of a value of the BinaryType is 4096 bytes. - */ - override def defaultSize: Int = 4096 - - private[spark] override def asNullable: BinaryType = this -} - -case object BinaryType extends BinaryType - - -/** - * :: DeveloperApi :: - * The data type representing `Boolean` values. Please use the singleton [[DataTypes.BooleanType]]. - * - *@group dataType - */ -@DeveloperApi -class BooleanType private() extends AtomicType { - // The companion object and this class is separated so the companion object also subclasses - // this type. Otherwise, the companion object would be of type "BooleanType$" in byte code. - // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type InternalType = Boolean - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } - private[sql] val ordering = implicitly[Ordering[InternalType]] - - /** - * The default size of a value of the BooleanType is 1 byte. - */ - override def defaultSize: Int = 1 - - private[spark] override def asNullable: BooleanType = this -} - -case object BooleanType extends BooleanType - - -/** - * :: DeveloperApi :: - * The data type representing `java.sql.Timestamp` values. - * Please use the singleton [[DataTypes.TimestampType]]. - * - * @group dataType - */ -@DeveloperApi -class TimestampType private() extends AtomicType { - // The companion object and this class is separated so the companion object also subclasses - // this type. Otherwise, the companion object would be of type "TimestampType$" in byte code. - // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type InternalType = Timestamp - - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } - - private[sql] val ordering = new Ordering[InternalType] { - def compare(x: Timestamp, y: Timestamp): Int = x.compareTo(y) - } - - /** - * The default size of a value of the TimestampType is 12 bytes. - */ - override def defaultSize: Int = 12 - - private[spark] override def asNullable: TimestampType = this -} - -case object TimestampType extends TimestampType - - -/** - * :: DeveloperApi :: - * The data type representing `java.sql.Date` values. - * Please use the singleton [[DataTypes.DateType]]. - * - * @group dataType - */ -@DeveloperApi -class DateType private() extends AtomicType { - // The companion object and this class is separated so the companion object also subclasses - // this type. Otherwise, the companion object would be of type "DateType$" in byte code. - // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type InternalType = Int - - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } - - private[sql] val ordering = implicitly[Ordering[InternalType]] - - /** - * The default size of a value of the DateType is 4 bytes. - */ - override def defaultSize: Int = 4 - - private[spark] override def asNullable: DateType = this -} - -case object DateType extends DateType - - -/** - * :: DeveloperApi :: - * Numeric data types. - * - * @group dataType - */ -abstract class NumericType extends AtomicType { - // Unfortunately we can't get this implicitly as that breaks Spark Serialization. In order for - // implicitly[Numeric[JvmType]] to be valid, we have to change JvmType from a type variable to a - // type parameter and and add a numeric annotation (i.e., [JvmType : Numeric]). This gets - // desugared by the compiler into an argument to the objects constructor. This means there is no - // longer an no argument constructor and thus the JVM cannot serialize the object anymore. - private[sql] val numeric: Numeric[InternalType] -} - - -protected[sql] object NumericType { - def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[NumericType] -} - - -/** Matcher for any expressions that evaluate to [[IntegralType]]s */ -protected[sql] object IntegralType { - def unapply(a: Expression): Boolean = a match { - case e: Expression if e.dataType.isInstanceOf[IntegralType] => true - case _ => false - } -} - - -protected[sql] sealed abstract class IntegralType extends NumericType { - private[sql] val integral: Integral[InternalType] -} - - -/** - * :: DeveloperApi :: - * The data type representing `Long` values. Please use the singleton [[DataTypes.LongType]]. - * - * @group dataType - */ -@DeveloperApi -class LongType private() extends IntegralType { - // The companion object and this class is separated so the companion object also subclasses - // this type. Otherwise, the companion object would be of type "LongType$" in byte code. - // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type InternalType = Long - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } - private[sql] val numeric = implicitly[Numeric[Long]] - private[sql] val integral = implicitly[Integral[Long]] - private[sql] val ordering = implicitly[Ordering[InternalType]] - - /** - * The default size of a value of the LongType is 8 bytes. - */ - override def defaultSize: Int = 8 - - override def simpleString: String = "bigint" - - private[spark] override def asNullable: LongType = this -} - -case object LongType extends LongType - - -/** - * :: DeveloperApi :: - * The data type representing `Int` values. Please use the singleton [[DataTypes.IntegerType]]. - * - * @group dataType - */ -@DeveloperApi -class IntegerType private() extends IntegralType { - // The companion object and this class is separated so the companion object also subclasses - // this type. Otherwise, the companion object would be of type "IntegerType$" in byte code. - // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type InternalType = Int - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } - private[sql] val numeric = implicitly[Numeric[Int]] - private[sql] val integral = implicitly[Integral[Int]] - private[sql] val ordering = implicitly[Ordering[InternalType]] - - /** - * The default size of a value of the IntegerType is 4 bytes. - */ - override def defaultSize: Int = 4 - - override def simpleString: String = "int" - - private[spark] override def asNullable: IntegerType = this -} - -case object IntegerType extends IntegerType - - -/** - * :: DeveloperApi :: - * The data type representing `Short` values. Please use the singleton [[DataTypes.ShortType]]. - * - * @group dataType - */ -@DeveloperApi -class ShortType private() extends IntegralType { - // The companion object and this class is separated so the companion object also subclasses - // this type. Otherwise, the companion object would be of type "ShortType$" in byte code. - // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type InternalType = Short - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } - private[sql] val numeric = implicitly[Numeric[Short]] - private[sql] val integral = implicitly[Integral[Short]] - private[sql] val ordering = implicitly[Ordering[InternalType]] - - /** - * The default size of a value of the ShortType is 2 bytes. - */ - override def defaultSize: Int = 2 - - override def simpleString: String = "smallint" - - private[spark] override def asNullable: ShortType = this -} - -case object ShortType extends ShortType - - -/** - * :: DeveloperApi :: - * The data type representing `Byte` values. Please use the singleton [[DataTypes.ByteType]]. - * - * @group dataType - */ -@DeveloperApi -class ByteType private() extends IntegralType { - // The companion object and this class is separated so the companion object also subclasses - // this type. Otherwise, the companion object would be of type "ByteType$" in byte code. - // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type InternalType = Byte - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } - private[sql] val numeric = implicitly[Numeric[Byte]] - private[sql] val integral = implicitly[Integral[Byte]] - private[sql] val ordering = implicitly[Ordering[InternalType]] - - /** - * The default size of a value of the ByteType is 1 byte. - */ - override def defaultSize: Int = 1 - - override def simpleString: String = "tinyint" - - private[spark] override def asNullable: ByteType = this -} - -case object ByteType extends ByteType - - -/** Matcher for any expressions that evaluate to [[FractionalType]]s */ -protected[sql] object FractionalType { - def unapply(a: Expression): Boolean = a match { - case e: Expression if e.dataType.isInstanceOf[FractionalType] => true - case _ => false - } -} - - -protected[sql] sealed abstract class FractionalType extends NumericType { - private[sql] val fractional: Fractional[InternalType] - private[sql] val asIntegral: Integral[InternalType] -} - - -/** Precision parameters for a Decimal */ -case class PrecisionInfo(precision: Int, scale: Int) - - -/** - * :: DeveloperApi :: - * The data type representing `java.math.BigDecimal` values. - * A Decimal that might have fixed precision and scale, or unlimited values for these. - * - * Please use [[DataTypes.createDecimalType()]] to create a specific instance. - * - * @group dataType - */ -@DeveloperApi -case class DecimalType(precisionInfo: Option[PrecisionInfo]) extends FractionalType { - - /** No-arg constructor for kryo. */ - protected def this() = this(null) - - private[sql] type InternalType = Decimal - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } - private[sql] val numeric = Decimal.DecimalIsFractional - private[sql] val fractional = Decimal.DecimalIsFractional - private[sql] val ordering = Decimal.DecimalIsFractional - private[sql] val asIntegral = Decimal.DecimalAsIfIntegral - - def precision: Int = precisionInfo.map(_.precision).getOrElse(-1) - - def scale: Int = precisionInfo.map(_.scale).getOrElse(-1) - - override def typeName: String = precisionInfo match { - case Some(PrecisionInfo(precision, scale)) => s"decimal($precision,$scale)" - case None => "decimal" - } - - override def toString: String = precisionInfo match { - case Some(PrecisionInfo(precision, scale)) => s"DecimalType($precision,$scale)" - case None => "DecimalType()" - } - - /** - * The default size of a value of the DecimalType is 4096 bytes. - */ - override def defaultSize: Int = 4096 - - override def simpleString: String = precisionInfo match { - case Some(PrecisionInfo(precision, scale)) => s"decimal($precision,$scale)" - case None => "decimal(10,0)" - } - - private[spark] override def asNullable: DecimalType = this -} - - -/** Extra factory methods and pattern matchers for Decimals */ -object DecimalType { - val Unlimited: DecimalType = DecimalType(None) - - object Fixed { - def unapply(t: DecimalType): Option[(Int, Int)] = - t.precisionInfo.map(p => (p.precision, p.scale)) - } - - object Expression { - def unapply(e: Expression): Option[(Int, Int)] = e.dataType match { - case t: DecimalType => t.precisionInfo.map(p => (p.precision, p.scale)) - case _ => None - } - } - - def apply(): DecimalType = Unlimited - - def apply(precision: Int, scale: Int): DecimalType = - DecimalType(Some(PrecisionInfo(precision, scale))) - - def unapply(t: DataType): Boolean = t.isInstanceOf[DecimalType] - - def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[DecimalType] - - def isFixed(dataType: DataType): Boolean = dataType match { - case DecimalType.Fixed(_, _) => true - case _ => false - } -} - - -/** - * :: DeveloperApi :: - * The data type representing `Double` values. Please use the singleton [[DataTypes.DoubleType]]. - * - * @group dataType - */ -@DeveloperApi -class DoubleType private() extends FractionalType { - // The companion object and this class is separated so the companion object also subclasses - // this type. Otherwise, the companion object would be of type "DoubleType$" in byte code. - // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type InternalType = Double - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } - private[sql] val numeric = implicitly[Numeric[Double]] - private[sql] val fractional = implicitly[Fractional[Double]] - private[sql] val ordering = implicitly[Ordering[InternalType]] - private[sql] val asIntegral = DoubleAsIfIntegral - - /** - * The default size of a value of the DoubleType is 8 bytes. - */ - override def defaultSize: Int = 8 - - private[spark] override def asNullable: DoubleType = this -} - -case object DoubleType extends DoubleType - - -/** - * :: DeveloperApi :: - * The data type representing `Float` values. Please use the singleton [[DataTypes.FloatType]]. - * - * @group dataType - */ -@DeveloperApi -class FloatType private() extends FractionalType { - // The companion object and this class is separated so the companion object also subclasses - // this type. Otherwise, the companion object would be of type "FloatType$" in byte code. - // Defined with a private constructor so the companion object is the only possible instantiation. - private[sql] type InternalType = Float - @transient private[sql] lazy val tag = ScalaReflectionLock.synchronized { typeTag[InternalType] } - private[sql] val numeric = implicitly[Numeric[Float]] - private[sql] val fractional = implicitly[Fractional[Float]] - private[sql] val ordering = implicitly[Ordering[InternalType]] - private[sql] val asIntegral = FloatAsIfIntegral - - /** - * The default size of a value of the FloatType is 4 bytes. - */ - override def defaultSize: Int = 4 - - private[spark] override def asNullable: FloatType = this -} - -case object FloatType extends FloatType - - -object ArrayType { - /** Construct a [[ArrayType]] object with the given element type. The `containsNull` is true. */ - def apply(elementType: DataType): ArrayType = ArrayType(elementType, true) -} - - -/** - * :: DeveloperApi :: - * The data type for collections of multiple values. - * Internally these are represented as columns that contain a ``scala.collection.Seq``. - * - * Please use [[DataTypes.createArrayType()]] to create a specific instance. - * - * An [[ArrayType]] object comprises two fields, `elementType: [[DataType]]` and - * `containsNull: Boolean`. The field of `elementType` is used to specify the type of - * array elements. The field of `containsNull` is used to specify if the array has `null` values. - * - * @param elementType The data type of values. - * @param containsNull Indicates if values have `null` values - * - * @group dataType - */ -@DeveloperApi -case class ArrayType(elementType: DataType, containsNull: Boolean) extends DataType { - - /** No-arg constructor for kryo. */ - protected def this() = this(null, false) - - private[sql] def buildFormattedString(prefix: String, builder: StringBuilder): Unit = { - builder.append( - s"$prefix-- element: ${elementType.typeName} (containsNull = $containsNull)\n") - DataType.buildFormattedString(elementType, s"$prefix |", builder) - } - - override private[sql] def jsonValue = - ("type" -> typeName) ~ - ("elementType" -> elementType.jsonValue) ~ - ("containsNull" -> containsNull) - - /** - * The default size of a value of the ArrayType is 100 * the default size of the element type. - * (We assume that there are 100 elements). - */ - override def defaultSize: Int = 100 * elementType.defaultSize - - override def simpleString: String = s"array<${elementType.simpleString}>" - - private[spark] override def asNullable: ArrayType = - ArrayType(elementType.asNullable, containsNull = true) -} - - -/** - * A field inside a StructType. - * @param name The name of this field. - * @param dataType The data type of this field. - * @param nullable Indicates if values of this field can be `null` values. - * @param metadata The metadata of this field. The metadata should be preserved during - * transformation if the content of the column is not modified, e.g, in selection. - */ -case class StructField( - name: String, - dataType: DataType, - nullable: Boolean = true, - metadata: Metadata = Metadata.empty) { - - /** No-arg constructor for kryo. */ - protected def this() = this(null, null) - - private[sql] def buildFormattedString(prefix: String, builder: StringBuilder): Unit = { - builder.append(s"$prefix-- $name: ${dataType.typeName} (nullable = $nullable)\n") - DataType.buildFormattedString(dataType, s"$prefix |", builder) - } - - // override the default toString to be compatible with legacy parquet files. - override def toString: String = s"StructField($name,$dataType,$nullable)" - - private[sql] def jsonValue: JValue = { - ("name" -> name) ~ - ("type" -> dataType.jsonValue) ~ - ("nullable" -> nullable) ~ - ("metadata" -> metadata.jsonValue) - } -} - - -object StructType { - protected[sql] def fromAttributes(attributes: Seq[Attribute]): StructType = - StructType(attributes.map(a => StructField(a.name, a.dataType, a.nullable, a.metadata))) - - def apply(fields: Seq[StructField]): StructType = StructType(fields.toArray) - - def apply(fields: java.util.List[StructField]): StructType = { - StructType(fields.toArray.asInstanceOf[Array[StructField]]) - } - - private[sql] def merge(left: DataType, right: DataType): DataType = - (left, right) match { - case (ArrayType(leftElementType, leftContainsNull), - ArrayType(rightElementType, rightContainsNull)) => - ArrayType( - merge(leftElementType, rightElementType), - leftContainsNull || rightContainsNull) - - case (MapType(leftKeyType, leftValueType, leftContainsNull), - MapType(rightKeyType, rightValueType, rightContainsNull)) => - MapType( - merge(leftKeyType, rightKeyType), - merge(leftValueType, rightValueType), - leftContainsNull || rightContainsNull) - - case (StructType(leftFields), StructType(rightFields)) => - val newFields = ArrayBuffer.empty[StructField] - - leftFields.foreach { - case leftField @ StructField(leftName, leftType, leftNullable, _) => - rightFields - .find(_.name == leftName) - .map { case rightField @ StructField(_, rightType, rightNullable, _) => - leftField.copy( - dataType = merge(leftType, rightType), - nullable = leftNullable || rightNullable) - } - .orElse(Some(leftField)) - .foreach(newFields += _) - } - - rightFields - .filterNot(f => leftFields.map(_.name).contains(f.name)) - .foreach(newFields += _) - - StructType(newFields) - - case (DecimalType.Fixed(leftPrecision, leftScale), - DecimalType.Fixed(rightPrecision, rightScale)) => - DecimalType( - max(leftScale, rightScale) + max(leftPrecision - leftScale, rightPrecision - rightScale), - max(leftScale, rightScale)) - - case (leftUdt: UserDefinedType[_], rightUdt: UserDefinedType[_]) - if leftUdt.userClass == rightUdt.userClass => leftUdt - - case (leftType, rightType) if leftType == rightType => - leftType - - case _ => - throw new SparkException(s"Failed to merge incompatible data types $left and $right") - } -} - - -/** - * :: DeveloperApi :: - * A [[StructType]] object can be constructed by - * {{{ - * StructType(fields: Seq[StructField]) - * }}} - * For a [[StructType]] object, one or multiple [[StructField]]s can be extracted by names. - * If multiple [[StructField]]s are extracted, a [[StructType]] object will be returned. - * If a provided name does not have a matching field, it will be ignored. For the case - * of extracting a single StructField, a `null` will be returned. - * Example: - * {{{ - * import org.apache.spark.sql._ - * - * val struct = - * StructType( - * StructField("a", IntegerType, true) :: - * StructField("b", LongType, false) :: - * StructField("c", BooleanType, false) :: Nil) - * - * // Extract a single StructField. - * val singleField = struct("b") - * // singleField: StructField = StructField(b,LongType,false) - * - * // This struct does not have a field called "d". null will be returned. - * val nonExisting = struct("d") - * // nonExisting: StructField = null - * - * // Extract multiple StructFields. Field names are provided in a set. - * // A StructType object will be returned. - * val twoFields = struct(Set("b", "c")) - * // twoFields: StructType = - * // StructType(List(StructField(b,LongType,false), StructField(c,BooleanType,false))) - * - * // Any names without matching fields will be ignored. - * // For the case shown below, "d" will be ignored and - * // it is treated as struct(Set("b", "c")). - * val ignoreNonExisting = struct(Set("b", "c", "d")) - * // ignoreNonExisting: StructType = - * // StructType(List(StructField(b,LongType,false), StructField(c,BooleanType,false))) - * }}} - * - * A [[org.apache.spark.sql.Row]] object is used as a value of the StructType. - * Example: - * {{{ - * import org.apache.spark.sql._ - * - * val innerStruct = - * StructType( - * StructField("f1", IntegerType, true) :: - * StructField("f2", LongType, false) :: - * StructField("f3", BooleanType, false) :: Nil) - * - * val struct = StructType( - * StructField("a", innerStruct, true) :: Nil) - * - * // Create a Row with the schema defined by struct - * val row = Row(Row(1, 2, true)) - * // row: Row = [[1,2,true]] - * }}} - * - * @group dataType - */ -@DeveloperApi -case class StructType(fields: Array[StructField]) extends DataType with Seq[StructField] { - - /** No-arg constructor for kryo. */ - protected def this() = this(null) - - /** Returns all field names in an array. */ - def fieldNames: Array[String] = fields.map(_.name) - - private lazy val fieldNamesSet: Set[String] = fieldNames.toSet - private lazy val nameToField: Map[String, StructField] = fields.map(f => f.name -> f).toMap - private lazy val nameToIndex: Map[String, Int] = fieldNames.zipWithIndex.toMap - - /** - * Extracts a [[StructField]] of the given name. If the [[StructType]] object does not - * have a name matching the given name, `null` will be returned. - */ - def apply(name: String): StructField = { - nameToField.getOrElse(name, - throw new IllegalArgumentException(s"""Field "$name" does not exist.""")) - } - - /** - * Returns a [[StructType]] containing [[StructField]]s of the given names, preserving the - * original order of fields. Those names which do not have matching fields will be ignored. - */ - def apply(names: Set[String]): StructType = { - val nonExistFields = names -- fieldNamesSet - if (nonExistFields.nonEmpty) { - throw new IllegalArgumentException( - s"Field ${nonExistFields.mkString(",")} does not exist.") - } - // Preserve the original order of fields. - StructType(fields.filter(f => names.contains(f.name))) - } - - /** - * Returns index of a given field - */ - def fieldIndex(name: String): Int = { - nameToIndex.getOrElse(name, - throw new IllegalArgumentException(s"""Field "$name" does not exist.""")) - } - - protected[sql] def toAttributes: Seq[AttributeReference] = - map(f => AttributeReference(f.name, f.dataType, f.nullable, f.metadata)()) - - def treeString: String = { - val builder = new StringBuilder - builder.append("root\n") - val prefix = " |" - fields.foreach(field => field.buildFormattedString(prefix, builder)) - - builder.toString() - } - - def printTreeString(): Unit = println(treeString) - - private[sql] def buildFormattedString(prefix: String, builder: StringBuilder): Unit = { - fields.foreach(field => field.buildFormattedString(prefix, builder)) - } - - override private[sql] def jsonValue = - ("type" -> typeName) ~ - ("fields" -> map(_.jsonValue)) - - override def apply(fieldIndex: Int): StructField = fields(fieldIndex) - - override def length: Int = fields.length - - override def iterator: Iterator[StructField] = fields.iterator - - /** - * The default size of a value of the StructType is the total default sizes of all field types. - */ - override def defaultSize: Int = fields.map(_.dataType.defaultSize).sum - - override def simpleString: String = { - val fieldTypes = fields.map(field => s"${field.name}:${field.dataType.simpleString}") - s"struct<${fieldTypes.mkString(",")}>" - } - - /** - * Merges with another schema (`StructType`). For a struct field A from `this` and a struct field - * B from `that`, - * - * 1. If A and B have the same name and data type, they are merged to a field C with the same name - * and data type. C is nullable if and only if either A or B is nullable. - * 2. If A doesn't exist in `that`, it's included in the result schema. - * 3. If B doesn't exist in `this`, it's also included in the result schema. - * 4. Otherwise, `this` and `that` are considered as conflicting schemas and an exception would be - * thrown. - */ - private[sql] def merge(that: StructType): StructType = - StructType.merge(this, that).asInstanceOf[StructType] - - private[spark] override def asNullable: StructType = { - val newFields = fields.map { - case StructField(name, dataType, nullable, metadata) => - StructField(name, dataType.asNullable, nullable = true, metadata) - } - - StructType(newFields) - } -} - - -object MapType { - /** - * Construct a [[MapType]] object with the given key type and value type. - * The `valueContainsNull` is true. - */ - def apply(keyType: DataType, valueType: DataType): MapType = - MapType(keyType: DataType, valueType: DataType, valueContainsNull = true) -} - - -/** - * :: DeveloperApi :: - * The data type for Maps. Keys in a map are not allowed to have `null` values. - * - * Please use [[DataTypes.createMapType()]] to create a specific instance. - * - * @param keyType The data type of map keys. - * @param valueType The data type of map values. - * @param valueContainsNull Indicates if map values have `null` values. - * - * @group dataType - */ -case class MapType( - keyType: DataType, - valueType: DataType, - valueContainsNull: Boolean) extends DataType { - - /** No-arg constructor for kryo. */ - def this() = this(null, null, false) - - private[sql] def buildFormattedString(prefix: String, builder: StringBuilder): Unit = { - builder.append(s"$prefix-- key: ${keyType.typeName}\n") - builder.append(s"$prefix-- value: ${valueType.typeName} " + - s"(valueContainsNull = $valueContainsNull)\n") - DataType.buildFormattedString(keyType, s"$prefix |", builder) - DataType.buildFormattedString(valueType, s"$prefix |", builder) - } - - override private[sql] def jsonValue: JValue = - ("type" -> typeName) ~ - ("keyType" -> keyType.jsonValue) ~ - ("valueType" -> valueType.jsonValue) ~ - ("valueContainsNull" -> valueContainsNull) - - /** - * The default size of a value of the MapType is - * 100 * (the default size of the key type + the default size of the value type). - * (We assume that there are 100 elements). - */ - override def defaultSize: Int = 100 * (keyType.defaultSize + valueType.defaultSize) - - override def simpleString: String = s"map<${keyType.simpleString},${valueType.simpleString}>" - - private[spark] override def asNullable: MapType = - MapType(keyType.asNullable, valueType.asNullable, valueContainsNull = true) -} - - -/** - * ::DeveloperApi:: - * The data type for User Defined Types (UDTs). - * - * This interface allows a user to make their own classes more interoperable with SparkSQL; - * e.g., by creating a [[UserDefinedType]] for a class X, it becomes possible to create - * a `DataFrame` which has class X in the schema. - * - * For SparkSQL to recognize UDTs, the UDT must be annotated with - * [[SQLUserDefinedType]]. - * - * The conversion via `serialize` occurs when instantiating a `DataFrame` from another RDD. - * The conversion via `deserialize` occurs when reading from a `DataFrame`. - */ -@DeveloperApi -abstract class UserDefinedType[UserType] extends DataType with Serializable { - - /** Underlying storage type for this UDT */ - def sqlType: DataType - - /** Paired Python UDT class, if exists. */ - def pyUDT: String = null - - /** - * Convert the user type to a SQL datum - * - * TODO: Can we make this take obj: UserType? The issue is in - * CatalystTypeConverters.convertToCatalyst, where we need to convert Any to UserType. - */ - def serialize(obj: Any): Any - - /** Convert a SQL datum to the user type */ - def deserialize(datum: Any): UserType - - override private[sql] def jsonValue: JValue = { - ("type" -> "udt") ~ - ("class" -> this.getClass.getName) ~ - ("pyClass" -> pyUDT) ~ - ("sqlType" -> sqlType.jsonValue) - } - - /** - * Class object for the UserType - */ - def userClass: java.lang.Class[UserType] - - /** - * The default size of a value of the UserDefinedType is 4096 bytes. - */ - override def defaultSize: Int = 4096 - - /** - * For UDT, asNullable will not change the nullability of its internal sqlType and just returns - * itself. - */ - private[spark] override def asNullable: UserDefinedType[UserType] = this -} From 73db132bf503341c7a5cf9409351c282a8464175 Mon Sep 17 00:00:00 2001 From: Sun Rui Date: Thu, 23 Apr 2015 16:08:14 -0700 Subject: [PATCH 403/591] [SPARK-6818] [SPARKR] Support column deletion in SparkR DataFrame API. Author: Sun Rui Closes #5655 from sun-rui/SPARK-6818 and squashes the following commits: 7c66570 [Sun Rui] [SPARK-6818][SPARKR] Support column deletion in SparkR DataFrame API. --- R/pkg/R/DataFrame.R | 8 +++++++- R/pkg/inst/tests/test_sparkSQL.R | 5 +++++ 2 files changed, 12 insertions(+), 1 deletion(-) diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R index 861fe1c78b0db..b59b700af5dc9 100644 --- a/R/pkg/R/DataFrame.R +++ b/R/pkg/R/DataFrame.R @@ -790,9 +790,12 @@ setMethod("$", signature(x = "DataFrame"), setMethod("$<-", signature(x = "DataFrame"), function(x, name, value) { - stopifnot(class(value) == "Column") + stopifnot(class(value) == "Column" || is.null(value)) cols <- columns(x) if (name %in% cols) { + if (is.null(value)) { + cols <- Filter(function(c) { c != name }, cols) + } cols <- lapply(cols, function(c) { if (c == name) { alias(value, name) @@ -802,6 +805,9 @@ setMethod("$<-", signature(x = "DataFrame"), }) nx <- select(x, cols) } else { + if (is.null(value)) { + return(x) + } nx <- withColumn(x, name, value) } x@sdf <- nx@sdf diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R index 25831ae2d9e18..af7a6c582047a 100644 --- a/R/pkg/inst/tests/test_sparkSQL.R +++ b/R/pkg/inst/tests/test_sparkSQL.R @@ -449,6 +449,11 @@ test_that("select operators", { df$age2 <- df$age * 2 expect_equal(columns(df), c("name", "age", "age2")) expect_equal(count(where(df, df$age2 == df$age * 2)), 2) + + df$age2 <- NULL + expect_equal(columns(df), c("name", "age")) + df$age3 <- NULL + expect_equal(columns(df), c("name", "age")) }) test_that("select with column", { From 336f7f5373e5f6960ecd9967d3703c8507e329ec Mon Sep 17 00:00:00 2001 From: Cheolsoo Park Date: Thu, 23 Apr 2015 20:10:55 -0400 Subject: [PATCH 404/591] [SPARK-7037] [CORE] Inconsistent behavior for non-spark config properties in spark-shell and spark-submit When specifying non-spark properties (i.e. names don't start with spark.) in the command line and config file, spark-submit and spark-shell behave differently, causing confusion to users. Here is the summary- * spark-submit * --conf k=v => silently ignored * spark-defaults.conf => applied * spark-shell * --conf k=v => show a warning message and ignored * spark-defaults.conf => show a warning message and ignored I assume that ignoring non-spark properties is intentional. If so, it should always be ignored with a warning message in all cases. Author: Cheolsoo Park Closes #5617 from piaozhexiu/SPARK-7037 and squashes the following commits: 8957950 [Cheolsoo Park] Add IgnoreNonSparkProperties method fedd01c [Cheolsoo Park] Ignore non-spark properties with a warning message in all cases --- .../spark/deploy/SparkSubmitArguments.scala | 22 ++++++++++++++----- 1 file changed, 16 insertions(+), 6 deletions(-) diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala index faa8780288ea3..c896842943f2b 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala @@ -77,12 +77,8 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S if (verbose) SparkSubmit.printStream.println(s"Using properties file: $propertiesFile") Option(propertiesFile).foreach { filename => Utils.getPropertiesFromFile(filename).foreach { case (k, v) => - if (k.startsWith("spark.")) { - defaultProperties(k) = v - if (verbose) SparkSubmit.printStream.println(s"Adding default property: $k=$v") - } else { - SparkSubmit.printWarning(s"Ignoring non-spark config property: $k=$v") - } + defaultProperties(k) = v + if (verbose) SparkSubmit.printStream.println(s"Adding default property: $k=$v") } } defaultProperties @@ -97,6 +93,8 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S } // Populate `sparkProperties` map from properties file mergeDefaultSparkProperties() + // Remove keys that don't start with "spark." from `sparkProperties`. + ignoreNonSparkProperties() // Use `sparkProperties` map along with env vars to fill in any missing parameters loadEnvironmentArguments() @@ -117,6 +115,18 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S } } + /** + * Remove keys that don't start with "spark." from `sparkProperties`. + */ + private def ignoreNonSparkProperties(): Unit = { + sparkProperties.foreach { case (k, v) => + if (!k.startsWith("spark.")) { + sparkProperties -= k + SparkSubmit.printWarning(s"Ignoring non-spark config property: $k=$v") + } + } + } + /** * Load arguments from environment variables, Spark properties etc. */ From 2d010f7afe6ac8e67e07da6bea700e9e8c9e6cc2 Mon Sep 17 00:00:00 2001 From: Yin Huai Date: Thu, 23 Apr 2015 18:52:55 -0700 Subject: [PATCH 405/591] [SPARK-7060][SQL] Add alias function to python dataframe This pr tries to provide a way to let python users workaround https://issues.apache.org/jira/browse/SPARK-6231. Author: Yin Huai Closes #5634 from yhuai/pythonDFAlias and squashes the following commits: 8465acd [Yin Huai] Add an alias to a Python DF. --- python/pyspark/sql/dataframe.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py index c8c30ce4022c8..4759f5fe783ad 100644 --- a/python/pyspark/sql/dataframe.py +++ b/python/pyspark/sql/dataframe.py @@ -452,6 +452,20 @@ def columns(self): """ return [f.name for f in self.schema.fields] + @ignore_unicode_prefix + def alias(self, alias): + """Returns a new :class:`DataFrame` with an alias set. + + >>> from pyspark.sql.functions import * + >>> df_as1 = df.alias("df_as1") + >>> df_as2 = df.alias("df_as2") + >>> joined_df = df_as1.join(df_as2, col("df_as1.name") == col("df_as2.name"), 'inner') + >>> joined_df.select(col("df_as1.name"), col("df_as2.name"), col("df_as2.age")).collect() + [Row(name=u'Alice', name=u'Alice', age=2), Row(name=u'Bob', name=u'Bob', age=5)] + """ + assert isinstance(alias, basestring), "alias should be a string" + return DataFrame(getattr(self._jdf, "as")(alias), self.sql_ctx) + @ignore_unicode_prefix def join(self, other, joinExprs=None, joinType=None): """Joins with another :class:`DataFrame`, using the given join expression. From 67bccbda1e3ed7db2753daa7e6ae8b1441356177 Mon Sep 17 00:00:00 2001 From: Ken Geis Date: Thu, 23 Apr 2015 20:45:33 -0700 Subject: [PATCH 406/591] Update sql-programming-guide.md fix typo Author: Ken Geis Closes #5674 from kgeis/patch-1 and squashes the following commits: 5ae67de [Ken Geis] Update sql-programming-guide.md --- docs/sql-programming-guide.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index b2022546268a7..49b1e69f0e9db 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -1364,7 +1364,7 @@ the Data Sources API. The following options are supported: driver - The class name of the JDBC driver needed to connect to this URL. This class with be loaded + The class name of the JDBC driver needed to connect to this URL. This class will be loaded on the master and workers before running an JDBC commands to allow the driver to register itself with the JDBC subsystem. From d3a302defc45768492dec9da4c40d78d28997a65 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Thu, 23 Apr 2015 21:21:03 -0700 Subject: [PATCH 407/591] [SQL] Fixed expression data type matching. Also took the chance to improve documentation for various types. Author: Reynold Xin Closes #5675 from rxin/data-type-matching-expr and squashes the following commits: 0f31856 [Reynold Xin] One more function documentation. 27c1973 [Reynold Xin] Added more documentation. 336a36d [Reynold Xin] [SQL] Fixed expression data type matching. --- .../expressions/codegen/CodeGenerator.scala | 2 +- .../org/apache/spark/sql/types/DataType.scala | 50 +++++++++++++++---- 2 files changed, 42 insertions(+), 10 deletions(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala index cbe520347385d..dbc92fb93e95e 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala @@ -279,7 +279,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin org.apache.spark.sql.types.UTF8String(${eval.primitiveTerm}.toString) """.children - case EqualTo(e1: BinaryType, e2: BinaryType) => + case EqualTo(e1 @ BinaryType(), e2 @ BinaryType()) => (e1, e2).evaluateAs (BooleanType) { case (eval1, eval2) => q""" diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala index e6bfcd9adfeb1..06bff7d70edbc 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala @@ -40,32 +40,46 @@ import org.apache.spark.util.Utils */ @DeveloperApi abstract class DataType { - /** Matches any expression that evaluates to this DataType */ - def unapply(a: Expression): Boolean = a match { + /** + * Enables matching against NumericType for expressions: + * {{{ + * case Cast(child @ BinaryType(), StringType) => + * ... + * }}} + */ + private[sql] def unapply(a: Expression): Boolean = a match { case e: Expression if e.dataType == this => true case _ => false } - /** The default size of a value of this data type. */ + /** + * The default size of a value of this data type, used internally for size estimation. + */ def defaultSize: Int + /** Name of the type used in JSON serialization. */ def typeName: String = this.getClass.getSimpleName.stripSuffix("$").dropRight(4).toLowerCase private[sql] def jsonValue: JValue = typeName + /** The compact JSON representation of this data type. */ def json: String = compact(render(jsonValue)) + /** The pretty (i.e. indented) JSON representation of this data type. */ def prettyJson: String = pretty(render(jsonValue)) + /** Readable string representation for the type. */ def simpleString: String = typeName - /** Check if `this` and `other` are the same data type when ignoring nullability - * (`StructField.nullable`, `ArrayType.containsNull`, and `MapType.valueContainsNull`). + /** + * Check if `this` and `other` are the same data type when ignoring nullability + * (`StructField.nullable`, `ArrayType.containsNull`, and `MapType.valueContainsNull`). */ private[spark] def sameType(other: DataType): Boolean = DataType.equalsIgnoreNullability(this, other) - /** Returns the same data type but set all nullability fields are true + /** + * Returns the same data type but set all nullability fields are true * (`StructField.nullable`, `ArrayType.containsNull`, and `MapType.valueContainsNull`). */ private[spark] def asNullable: DataType @@ -104,12 +118,25 @@ abstract class NumericType extends AtomicType { private[sql] object NumericType { + /** + * Enables matching against NumericType for expressions: + * {{{ + * case Cast(child @ NumericType(), StringType) => + * ... + * }}} + */ def unapply(e: Expression): Boolean = e.dataType.isInstanceOf[NumericType] } -/** Matcher for any expressions that evaluate to [[IntegralType]]s */ private[sql] object IntegralType { + /** + * Enables matching against IntegralType for expressions: + * {{{ + * case Cast(child @ IntegralType(), StringType) => + * ... + * }}} + */ def unapply(a: Expression): Boolean = a match { case e: Expression if e.dataType.isInstanceOf[IntegralType] => true case _ => false @@ -122,9 +149,14 @@ private[sql] abstract class IntegralType extends NumericType { } - -/** Matcher for any expressions that evaluate to [[FractionalType]]s */ private[sql] object FractionalType { + /** + * Enables matching against FractionalType for expressions: + * {{{ + * case Cast(child @ FractionalType(), StringType) => + * ... + * }}} + */ def unapply(a: Expression): Boolean = a match { case e: Expression if e.dataType.isInstanceOf[FractionalType] => true case _ => false From 4c722d77ae7e77eeaa7531687fa9bd6050344d18 Mon Sep 17 00:00:00 2001 From: Reynold Xin Date: Thu, 23 Apr 2015 22:39:00 -0700 Subject: [PATCH 408/591] Fixed a typo from the previous commit. --- .../src/main/scala/org/apache/spark/sql/types/DataType.scala | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala index 06bff7d70edbc..0992a7c311ee2 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala @@ -41,7 +41,7 @@ import org.apache.spark.util.Utils @DeveloperApi abstract class DataType { /** - * Enables matching against NumericType for expressions: + * Enables matching against DataType for expressions: * {{{ * case Cast(child @ BinaryType(), StringType) => * ... From 8509519d8bcf99e2d1b5e21da514d51357f9116d Mon Sep 17 00:00:00 2001 From: Xusen Yin Date: Fri, 24 Apr 2015 00:39:29 -0700 Subject: [PATCH 409/591] [SPARK-5894] [ML] Add polynomial mapper See [SPARK-5894](https://issues.apache.org/jira/browse/SPARK-5894). Author: Xusen Yin Author: Xiangrui Meng Closes #5245 from yinxusen/SPARK-5894 and squashes the following commits: dc461a6 [Xusen Yin] merge polynomial expansion v2 6d0c3cc [Xusen Yin] Merge branch 'SPARK-5894' of https://github.com/mengxr/spark into mengxr-SPARK-5894 57bfdd5 [Xusen Yin] Merge branch 'master' into SPARK-5894 3d02a7d [Xusen Yin] Merge branch 'master' into SPARK-5894 a067da2 [Xiangrui Meng] a new approach for poly expansion 0789d81 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-5894 4e9aed0 [Xusen Yin] fix test suite 95d8fb9 [Xusen Yin] fix sparse vector indices 8d39674 [Xusen Yin] fix sparse vector expansion error 5998dd6 [Xusen Yin] fix dense vector fillin fa3ade3 [Xusen Yin] change the functional code into imperative one to speedup b70e7e1 [Xusen Yin] remove useless case class 6fa236f [Xusen Yin] fix vector slice error daff601 [Xusen Yin] fix index error of sparse vector 6bd0a10 [Xusen Yin] merge repeated features 419f8a2 [Xusen Yin] need to merge same columns 4ebf34e [Xusen Yin] add test suite of polynomial expansion 372227c [Xusen Yin] add polynomial expansion --- .../ml/feature/PolynomialExpansion.scala | 167 ++++++++++++++++++ .../ml/feature/PolynomialExpansionSuite.scala | 104 +++++++++++ 2 files changed, 271 insertions(+) create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/PolynomialExpansionSuite.scala diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala new file mode 100644 index 0000000000000..c3a59a361d0e2 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala @@ -0,0 +1,167 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import scala.collection.mutable + +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml.UnaryTransformer +import org.apache.spark.ml.param.{IntParam, ParamMap} +import org.apache.spark.mllib.linalg._ +import org.apache.spark.sql.types.DataType + +/** + * :: AlphaComponent :: + * Perform feature expansion in a polynomial space. As said in wikipedia of Polynomial Expansion, + * which is available at [[http://en.wikipedia.org/wiki/Polynomial_expansion]], "In mathematics, an + * expansion of a product of sums expresses it as a sum of products by using the fact that + * multiplication distributes over addition". Take a 2-variable feature vector as an example: + * `(x, y)`, if we want to expand it with degree 2, then we get `(x, y, x * x, x * y, y * y)`. + */ +@AlphaComponent +class PolynomialExpansion extends UnaryTransformer[Vector, Vector, PolynomialExpansion] { + + /** + * The polynomial degree to expand, which should be larger than 1. + * @group param + */ + val degree = new IntParam(this, "degree", "the polynomial degree to expand") + setDefault(degree -> 2) + + /** @group getParam */ + def getDegree: Int = getOrDefault(degree) + + /** @group setParam */ + def setDegree(value: Int): this.type = set(degree, value) + + override protected def createTransformFunc(paramMap: ParamMap): Vector => Vector = { v => + val d = paramMap(degree) + PolynomialExpansion.expand(v, d) + } + + override protected def outputDataType: DataType = new VectorUDT() +} + +/** + * The expansion is done via recursion. Given n features and degree d, the size after expansion is + * (n + d choose d) (including 1 and first-order values). For example, let f([a, b, c], 3) be the + * function that expands [a, b, c] to their monomials of degree 3. We have the following recursion: + * + * {{{ + * f([a, b, c], 3) = f([a, b], 3) ++ f([a, b], 2) * c ++ f([a, b], 1) * c^2 ++ [c^3] + * }}} + * + * To handle sparsity, if c is zero, we can skip all monomials that contain it. We remember the + * current index and increment it properly for sparse input. + */ +object PolynomialExpansion { + + private def choose(n: Int, k: Int): Int = { + Range(n, n - k, -1).product / Range(k, 1, -1).product + } + + private def getPolySize(numFeatures: Int, degree: Int): Int = choose(numFeatures + degree, degree) + + private def expandDense( + values: Array[Double], + lastIdx: Int, + degree: Int, + multiplier: Double, + polyValues: Array[Double], + curPolyIdx: Int): Int = { + if (multiplier == 0.0) { + // do nothing + } else if (degree == 0 || lastIdx < 0) { + polyValues(curPolyIdx) = multiplier + } else { + val v = values(lastIdx) + val lastIdx1 = lastIdx - 1 + var alpha = multiplier + var i = 0 + var curStart = curPolyIdx + while (i <= degree && alpha != 0.0) { + curStart = expandDense(values, lastIdx1, degree - i, alpha, polyValues, curStart) + i += 1 + alpha *= v + } + } + curPolyIdx + getPolySize(lastIdx + 1, degree) + } + + private def expandSparse( + indices: Array[Int], + values: Array[Double], + lastIdx: Int, + lastFeatureIdx: Int, + degree: Int, + multiplier: Double, + polyIndices: mutable.ArrayBuilder[Int], + polyValues: mutable.ArrayBuilder[Double], + curPolyIdx: Int): Int = { + if (multiplier == 0.0) { + // do nothing + } else if (degree == 0 || lastIdx < 0) { + polyIndices += curPolyIdx + polyValues += multiplier + } else { + // Skip all zeros at the tail. + val v = values(lastIdx) + val lastIdx1 = lastIdx - 1 + val lastFeatureIdx1 = indices(lastIdx) - 1 + var alpha = multiplier + var curStart = curPolyIdx + var i = 0 + while (i <= degree && alpha != 0.0) { + curStart = expandSparse(indices, values, lastIdx1, lastFeatureIdx1, degree - i, alpha, + polyIndices, polyValues, curStart) + i += 1 + alpha *= v + } + } + curPolyIdx + getPolySize(lastFeatureIdx + 1, degree) + } + + private def expand(dv: DenseVector, degree: Int): DenseVector = { + val n = dv.size + val polySize = getPolySize(n, degree) + val polyValues = new Array[Double](polySize) + expandDense(dv.values, n - 1, degree, 1.0, polyValues, 0) + new DenseVector(polyValues) + } + + private def expand(sv: SparseVector, degree: Int): SparseVector = { + val polySize = getPolySize(sv.size, degree) + val nnz = sv.values.length + val nnzPolySize = getPolySize(nnz, degree) + val polyIndices = mutable.ArrayBuilder.make[Int] + polyIndices.sizeHint(nnzPolySize) + val polyValues = mutable.ArrayBuilder.make[Double] + polyValues.sizeHint(nnzPolySize) + expandSparse( + sv.indices, sv.values, nnz - 1, sv.size - 1, degree, 1.0, polyIndices, polyValues, 0) + new SparseVector(polySize, polyIndices.result(), polyValues.result()) + } + + def expand(v: Vector, degree: Int): Vector = { + v match { + case dv: DenseVector => expand(dv, degree) + case sv: SparseVector => expand(sv, degree) + case _ => throw new IllegalArgumentException + } + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/PolynomialExpansionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/PolynomialExpansionSuite.scala new file mode 100644 index 0000000000000..b0a537be42dfd --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/PolynomialExpansionSuite.scala @@ -0,0 +1,104 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.scalatest.FunSuite + +import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.sql.{Row, SQLContext} +import org.scalatest.exceptions.TestFailedException + +class PolynomialExpansionSuite extends FunSuite with MLlibTestSparkContext { + + @transient var sqlContext: SQLContext = _ + + override def beforeAll(): Unit = { + super.beforeAll() + sqlContext = new SQLContext(sc) + } + + test("Polynomial expansion with default parameter") { + val data = Array( + Vectors.sparse(3, Seq((0, -2.0), (1, 2.3))), + Vectors.dense(-2.0, 2.3), + Vectors.dense(0.0, 0.0, 0.0), + Vectors.dense(0.6, -1.1, -3.0), + Vectors.sparse(3, Seq()) + ) + + val twoDegreeExpansion: Array[Vector] = Array( + Vectors.sparse(10, Array(0, 1, 2, 3, 4, 5), Array(1.0, -2.0, 4.0, 2.3, -4.6, 5.29)), + Vectors.dense(1.0, -2.0, 4.0, 2.3, -4.6, 5.29), + Vectors.dense(Array(1.0) ++ Array.fill[Double](9)(0.0)), + Vectors.dense(1.0, 0.6, 0.36, -1.1, -0.66, 1.21, -3.0, -1.8, 3.3, 9.0), + Vectors.sparse(10, Array(0), Array(1.0))) + + val df = sqlContext.createDataFrame(data.zip(twoDegreeExpansion)).toDF("features", "expected") + + val polynomialExpansion = new PolynomialExpansion() + .setInputCol("features") + .setOutputCol("polyFeatures") + + polynomialExpansion.transform(df).select("polyFeatures", "expected").collect().foreach { + case Row(expanded: DenseVector, expected: DenseVector) => + assert(expanded ~== expected absTol 1e-1) + case Row(expanded: SparseVector, expected: SparseVector) => + assert(expanded ~== expected absTol 1e-1) + case _ => + throw new TestFailedException("Unmatched data types after polynomial expansion", 0) + } + } + + test("Polynomial expansion with setter") { + val data = Array( + Vectors.sparse(3, Seq((0, -2.0), (1, 2.3))), + Vectors.dense(-2.0, 2.3), + Vectors.dense(0.0, 0.0, 0.0), + Vectors.dense(0.6, -1.1, -3.0), + Vectors.sparse(3, Seq()) + ) + + val threeDegreeExpansion: Array[Vector] = Array( + Vectors.sparse(20, Array(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), + Array(1.0, -2.0, 4.0, -8.0, 2.3, -4.6, 9.2, 5.29, -10.58, 12.17)), + Vectors.dense(1.0, -2.0, 4.0, -8.0, 2.3, -4.6, 9.2, 5.29, -10.58, 12.17), + Vectors.dense(Array(1.0) ++ Array.fill[Double](19)(0.0)), + Vectors.dense(1.0, 0.6, 0.36, 0.216, -1.1, -0.66, -0.396, 1.21, 0.726, -1.331, -3.0, -1.8, + -1.08, 3.3, 1.98, -3.63, 9.0, 5.4, -9.9, -27.0), + Vectors.sparse(20, Array(0), Array(1.0))) + + val df = sqlContext.createDataFrame(data.zip(threeDegreeExpansion)).toDF("features", "expected") + + val polynomialExpansion = new PolynomialExpansion() + .setInputCol("features") + .setOutputCol("polyFeatures") + .setDegree(3) + + polynomialExpansion.transform(df).select("polyFeatures", "expected").collect().foreach { + case Row(expanded: DenseVector, expected: DenseVector) => + assert(expanded ~== expected absTol 1e-1) + case Row(expanded: SparseVector, expected: SparseVector) => + assert(expanded ~== expected absTol 1e-1) + case _ => + throw new TestFailedException("Unmatched data types after polynomial expansion", 0) + } + } +} + From 78b39c7e0de8c9dc748cfbf8f78578a9524b6a94 Mon Sep 17 00:00:00 2001 From: Xiangrui Meng Date: Fri, 24 Apr 2015 08:27:48 -0700 Subject: [PATCH 410/591] [SPARK-7115] [MLLIB] skip the very first 1 in poly expansion yinxusen Author: Xiangrui Meng Closes #5681 from mengxr/SPARK-7115 and squashes the following commits: 9ac27cd [Xiangrui Meng] skip the very first 1 in poly expansion --- .../ml/feature/PolynomialExpansion.scala | 22 +++++++++++-------- .../ml/feature/PolynomialExpansionSuite.scala | 22 +++++++++---------- 2 files changed, 24 insertions(+), 20 deletions(-) diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala index c3a59a361d0e2..d855f04799ae7 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala @@ -87,7 +87,9 @@ object PolynomialExpansion { if (multiplier == 0.0) { // do nothing } else if (degree == 0 || lastIdx < 0) { - polyValues(curPolyIdx) = multiplier + if (curPolyIdx >= 0) { // skip the very first 1 + polyValues(curPolyIdx) = multiplier + } } else { val v = values(lastIdx) val lastIdx1 = lastIdx - 1 @@ -116,8 +118,10 @@ object PolynomialExpansion { if (multiplier == 0.0) { // do nothing } else if (degree == 0 || lastIdx < 0) { - polyIndices += curPolyIdx - polyValues += multiplier + if (curPolyIdx >= 0) { // skip the very first 1 + polyIndices += curPolyIdx + polyValues += multiplier + } } else { // Skip all zeros at the tail. val v = values(lastIdx) @@ -139,8 +143,8 @@ object PolynomialExpansion { private def expand(dv: DenseVector, degree: Int): DenseVector = { val n = dv.size val polySize = getPolySize(n, degree) - val polyValues = new Array[Double](polySize) - expandDense(dv.values, n - 1, degree, 1.0, polyValues, 0) + val polyValues = new Array[Double](polySize - 1) + expandDense(dv.values, n - 1, degree, 1.0, polyValues, -1) new DenseVector(polyValues) } @@ -149,12 +153,12 @@ object PolynomialExpansion { val nnz = sv.values.length val nnzPolySize = getPolySize(nnz, degree) val polyIndices = mutable.ArrayBuilder.make[Int] - polyIndices.sizeHint(nnzPolySize) + polyIndices.sizeHint(nnzPolySize - 1) val polyValues = mutable.ArrayBuilder.make[Double] - polyValues.sizeHint(nnzPolySize) + polyValues.sizeHint(nnzPolySize - 1) expandSparse( - sv.indices, sv.values, nnz - 1, sv.size - 1, degree, 1.0, polyIndices, polyValues, 0) - new SparseVector(polySize, polyIndices.result(), polyValues.result()) + sv.indices, sv.values, nnz - 1, sv.size - 1, degree, 1.0, polyIndices, polyValues, -1) + new SparseVector(polySize - 1, polyIndices.result(), polyValues.result()) } def expand(v: Vector, degree: Int): Vector = { diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/PolynomialExpansionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/PolynomialExpansionSuite.scala index b0a537be42dfd..c1d64fba0aa8f 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/PolynomialExpansionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/PolynomialExpansionSuite.scala @@ -44,11 +44,11 @@ class PolynomialExpansionSuite extends FunSuite with MLlibTestSparkContext { ) val twoDegreeExpansion: Array[Vector] = Array( - Vectors.sparse(10, Array(0, 1, 2, 3, 4, 5), Array(1.0, -2.0, 4.0, 2.3, -4.6, 5.29)), - Vectors.dense(1.0, -2.0, 4.0, 2.3, -4.6, 5.29), - Vectors.dense(Array(1.0) ++ Array.fill[Double](9)(0.0)), - Vectors.dense(1.0, 0.6, 0.36, -1.1, -0.66, 1.21, -3.0, -1.8, 3.3, 9.0), - Vectors.sparse(10, Array(0), Array(1.0))) + Vectors.sparse(9, Array(0, 1, 2, 3, 4), Array(-2.0, 4.0, 2.3, -4.6, 5.29)), + Vectors.dense(-2.0, 4.0, 2.3, -4.6, 5.29), + Vectors.dense(new Array[Double](9)), + Vectors.dense(0.6, 0.36, -1.1, -0.66, 1.21, -3.0, -1.8, 3.3, 9.0), + Vectors.sparse(9, Array.empty, Array.empty)) val df = sqlContext.createDataFrame(data.zip(twoDegreeExpansion)).toDF("features", "expected") @@ -76,13 +76,13 @@ class PolynomialExpansionSuite extends FunSuite with MLlibTestSparkContext { ) val threeDegreeExpansion: Array[Vector] = Array( - Vectors.sparse(20, Array(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), - Array(1.0, -2.0, 4.0, -8.0, 2.3, -4.6, 9.2, 5.29, -10.58, 12.17)), - Vectors.dense(1.0, -2.0, 4.0, -8.0, 2.3, -4.6, 9.2, 5.29, -10.58, 12.17), - Vectors.dense(Array(1.0) ++ Array.fill[Double](19)(0.0)), - Vectors.dense(1.0, 0.6, 0.36, 0.216, -1.1, -0.66, -0.396, 1.21, 0.726, -1.331, -3.0, -1.8, + Vectors.sparse(19, Array(0, 1, 2, 3, 4, 5, 6, 7, 8), + Array(-2.0, 4.0, -8.0, 2.3, -4.6, 9.2, 5.29, -10.58, 12.17)), + Vectors.dense(-2.0, 4.0, -8.0, 2.3, -4.6, 9.2, 5.29, -10.58, 12.17), + Vectors.dense(new Array[Double](19)), + Vectors.dense(0.6, 0.36, 0.216, -1.1, -0.66, -0.396, 1.21, 0.726, -1.331, -3.0, -1.8, -1.08, 3.3, 1.98, -3.63, 9.0, 5.4, -9.9, -27.0), - Vectors.sparse(20, Array(0), Array(1.0))) + Vectors.sparse(19, Array.empty, Array.empty)) val df = sqlContext.createDataFrame(data.zip(threeDegreeExpansion)).toDF("features", "expected") From 6e57d57b32ba2aa0514692074897b5edd34e0dd6 Mon Sep 17 00:00:00 2001 From: Xusen Yin Date: Fri, 24 Apr 2015 08:29:49 -0700 Subject: [PATCH 411/591] [SPARK-6528] [ML] Add IDF transformer See [SPARK-6528](https://issues.apache.org/jira/browse/SPARK-6528). Add IDF transformer in ML package. Author: Xusen Yin Closes #5266 from yinxusen/SPARK-6528 and squashes the following commits: 741db31 [Xusen Yin] get param from new paramMap d169967 [Xusen Yin] add final to param and IDF class c9c3759 [Xusen Yin] simplify test suite 5867c09 [Xusen Yin] refine IDF transformer with new interfaces 7727cae [Xusen Yin] Merge branch 'master' into SPARK-6528 4338a37 [Xusen Yin] Merge branch 'master' into SPARK-6528 aef2cdf [Xusen Yin] add doc and group for param 5760b49 [Xusen Yin] fix code style 2add691 [Xusen Yin] fix code style and test 03fbecb [Xusen Yin] remove duplicated code 2aa4be0 [Xusen Yin] clean test suite 4802c67 [Xusen Yin] add IDF transformer and test suite --- .../org/apache/spark/ml/feature/IDF.scala | 116 ++++++++++++++++++ .../apache/spark/ml/feature/IDFSuite.scala | 101 +++++++++++++++ 2 files changed, 217 insertions(+) create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/IDFSuite.scala diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala new file mode 100644 index 0000000000000..e6a62d998bb97 --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala @@ -0,0 +1,116 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.apache.spark.annotation.AlphaComponent +import org.apache.spark.ml._ +import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared._ +import org.apache.spark.ml.util.SchemaUtils +import org.apache.spark.mllib.feature +import org.apache.spark.mllib.linalg.{Vector, VectorUDT} +import org.apache.spark.sql._ +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types.StructType + +/** + * Params for [[IDF]] and [[IDFModel]]. + */ +private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol { + + /** + * The minimum of documents in which a term should appear. + * @group param + */ + final val minDocFreq = new IntParam( + this, "minDocFreq", "minimum of documents in which a term should appear for filtering") + + setDefault(minDocFreq -> 0) + + /** @group getParam */ + def getMinDocFreq: Int = getOrDefault(minDocFreq) + + /** @group setParam */ + def setMinDocFreq(value: Int): this.type = set(minDocFreq, value) + + /** + * Validate and transform the input schema. + */ + protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = { + val map = extractParamMap(paramMap) + SchemaUtils.checkColumnType(schema, map(inputCol), new VectorUDT) + SchemaUtils.appendColumn(schema, map(outputCol), new VectorUDT) + } +} + +/** + * :: AlphaComponent :: + * Compute the Inverse Document Frequency (IDF) given a collection of documents. + */ +@AlphaComponent +final class IDF extends Estimator[IDFModel] with IDFBase { + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + override def fit(dataset: DataFrame, paramMap: ParamMap): IDFModel = { + transformSchema(dataset.schema, paramMap, logging = true) + val map = extractParamMap(paramMap) + val input = dataset.select(map(inputCol)).map { case Row(v: Vector) => v } + val idf = new feature.IDF(map(minDocFreq)).fit(input) + val model = new IDFModel(this, map, idf) + Params.inheritValues(map, this, model) + model + } + + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + validateAndTransformSchema(schema, paramMap) + } +} + +/** + * :: AlphaComponent :: + * Model fitted by [[IDF]]. + */ +@AlphaComponent +class IDFModel private[ml] ( + override val parent: IDF, + override val fittingParamMap: ParamMap, + idfModel: feature.IDFModel) + extends Model[IDFModel] with IDFBase { + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = { + transformSchema(dataset.schema, paramMap, logging = true) + val map = extractParamMap(paramMap) + val idf = udf { vec: Vector => idfModel.transform(vec) } + dataset.withColumn(map(outputCol), idf(col(map(inputCol)))) + } + + override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = { + validateAndTransformSchema(schema, paramMap) + } +} diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/IDFSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/IDFSuite.scala new file mode 100644 index 0000000000000..eaee3443c1f23 --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/IDFSuite.scala @@ -0,0 +1,101 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.ml.feature + +import org.scalatest.FunSuite + +import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors} +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.mllib.util.TestingUtils._ +import org.apache.spark.sql.{Row, SQLContext} + +class IDFSuite extends FunSuite with MLlibTestSparkContext { + + @transient var sqlContext: SQLContext = _ + + override def beforeAll(): Unit = { + super.beforeAll() + sqlContext = new SQLContext(sc) + } + + def scaleDataWithIDF(dataSet: Array[Vector], model: Vector): Array[Vector] = { + dataSet.map { + case data: DenseVector => + val res = data.toArray.zip(model.toArray).map { case (x, y) => x * y } + Vectors.dense(res) + case data: SparseVector => + val res = data.indices.zip(data.values).map { case (id, value) => + (id, value * model(id)) + } + Vectors.sparse(data.size, res) + } + } + + test("compute IDF with default parameter") { + val numOfFeatures = 4 + val data = Array( + Vectors.sparse(numOfFeatures, Array(1, 3), Array(1.0, 2.0)), + Vectors.dense(0.0, 1.0, 2.0, 3.0), + Vectors.sparse(numOfFeatures, Array(1), Array(1.0)) + ) + val numOfData = data.size + val idf = Vectors.dense(Array(0, 3, 1, 2).map { x => + math.log((numOfData + 1.0) / (x + 1.0)) + }) + val expected = scaleDataWithIDF(data, idf) + + val df = sqlContext.createDataFrame(data.zip(expected)).toDF("features", "expected") + + val idfModel = new IDF() + .setInputCol("features") + .setOutputCol("idfValue") + .fit(df) + + idfModel.transform(df).select("idfValue", "expected").collect().foreach { + case Row(x: Vector, y: Vector) => + assert(x ~== y absTol 1e-5, "Transformed vector is different with expected vector.") + } + } + + test("compute IDF with setter") { + val numOfFeatures = 4 + val data = Array( + Vectors.sparse(numOfFeatures, Array(1, 3), Array(1.0, 2.0)), + Vectors.dense(0.0, 1.0, 2.0, 3.0), + Vectors.sparse(numOfFeatures, Array(1), Array(1.0)) + ) + val numOfData = data.size + val idf = Vectors.dense(Array(0, 3, 1, 2).map { x => + if (x > 0) math.log((numOfData + 1.0) / (x + 1.0)) else 0 + }) + val expected = scaleDataWithIDF(data, idf) + + val df = sqlContext.createDataFrame(data.zip(expected)).toDF("features", "expected") + + val idfModel = new IDF() + .setInputCol("features") + .setOutputCol("idfValue") + .setMinDocFreq(1) + .fit(df) + + idfModel.transform(df).select("idfValue", "expected").collect().foreach { + case Row(x: Vector, y: Vector) => + assert(x ~== y absTol 1e-5, "Transformed vector is different with expected vector.") + } + } +} From ebb77b2aff085e71906b5de9d266ded89051af82 Mon Sep 17 00:00:00 2001 From: Sun Rui Date: Fri, 24 Apr 2015 11:00:19 -0700 Subject: [PATCH 412/591] [SPARK-7033] [SPARKR] Clean usage of split. Use partition instead where applicable. Author: Sun Rui Closes #5628 from sun-rui/SPARK-7033 and squashes the following commits: 046bc9e [Sun Rui] Clean split usage in tests. d531c86 [Sun Rui] [SPARK-7033][SPARKR] Clean usage of split. Use partition instead where applicable. --- R/pkg/R/RDD.R | 36 ++++++++++++++++++------------------ R/pkg/R/context.R | 20 ++++++++++---------- R/pkg/R/pairRDD.R | 8 ++++---- R/pkg/R/utils.R | 2 +- R/pkg/inst/tests/test_rdd.R | 12 ++++++------ 5 files changed, 39 insertions(+), 39 deletions(-) diff --git a/R/pkg/R/RDD.R b/R/pkg/R/RDD.R index 128431334ca52..cc09efb1e5418 100644 --- a/R/pkg/R/RDD.R +++ b/R/pkg/R/RDD.R @@ -91,8 +91,8 @@ setMethod("initialize", "PipelinedRDD", function(.Object, prev, func, jrdd_val) # NOTE: We use prev_serializedMode to track the serialization mode of prev_JRDD # prev_serializedMode is used during the delayed computation of JRDD in getJRDD } else { - pipelinedFunc <- function(split, iterator) { - func(split, prev@func(split, iterator)) + pipelinedFunc <- function(partIndex, part) { + func(partIndex, prev@func(partIndex, part)) } .Object@func <- cleanClosure(pipelinedFunc) .Object@prev_jrdd <- prev@prev_jrdd # maintain the pipeline @@ -306,7 +306,7 @@ setMethod("numPartitions", signature(x = "RDD"), function(x) { jrdd <- getJRDD(x) - partitions <- callJMethod(jrdd, "splits") + partitions <- callJMethod(jrdd, "partitions") callJMethod(partitions, "size") }) @@ -452,8 +452,8 @@ setMethod("countByValue", setMethod("lapply", signature(X = "RDD", FUN = "function"), function(X, FUN) { - func <- function(split, iterator) { - lapply(iterator, FUN) + func <- function(partIndex, part) { + lapply(part, FUN) } lapplyPartitionsWithIndex(X, func) }) @@ -538,8 +538,8 @@ setMethod("mapPartitions", #'\dontrun{ #' sc <- sparkR.init() #' rdd <- parallelize(sc, 1:10, 5L) -#' prod <- lapplyPartitionsWithIndex(rdd, function(split, part) { -#' split * Reduce("+", part) }) +#' prod <- lapplyPartitionsWithIndex(rdd, function(partIndex, part) { +#' partIndex * Reduce("+", part) }) #' collect(prod, flatten = FALSE) # 0, 7, 22, 45, 76 #'} #' @rdname lapplyPartitionsWithIndex @@ -813,7 +813,7 @@ setMethod("distinct", #' @examples #'\dontrun{ #' sc <- sparkR.init() -#' rdd <- parallelize(sc, 1:10) # ensure each num is in its own split +#' rdd <- parallelize(sc, 1:10) #' collect(sampleRDD(rdd, FALSE, 0.5, 1618L)) # ~5 distinct elements #' collect(sampleRDD(rdd, TRUE, 0.5, 9L)) # ~5 elements possibly with duplicates #'} @@ -825,14 +825,14 @@ setMethod("sampleRDD", function(x, withReplacement, fraction, seed) { # The sampler: takes a partition and returns its sampled version. - samplingFunc <- function(split, part) { + samplingFunc <- function(partIndex, part) { set.seed(seed) res <- vector("list", length(part)) len <- 0 # Discards some random values to ensure each partition has a # different random seed. - runif(split) + runif(partIndex) for (elem in part) { if (withReplacement) { @@ -989,8 +989,8 @@ setMethod("coalesce", function(x, numPartitions, shuffle = FALSE) { numPartitions <- numToInt(numPartitions) if (shuffle || numPartitions > SparkR::numPartitions(x)) { - func <- function(s, part) { - set.seed(s) # split as seed + func <- function(partIndex, part) { + set.seed(partIndex) # partIndex as seed start <- as.integer(sample(numPartitions, 1) - 1) lapply(seq_along(part), function(i) { @@ -1035,7 +1035,7 @@ setMethod("saveAsObjectFile", #' Save this RDD as a text file, using string representations of elements. #' #' @param x The RDD to save -#' @param path The directory where the splits of the text file are saved +#' @param path The directory where the partitions of the text file are saved #' @examples #'\dontrun{ #' sc <- sparkR.init() @@ -1335,10 +1335,10 @@ setMethod("zipWithUniqueId", function(x) { n <- numPartitions(x) - partitionFunc <- function(split, part) { + partitionFunc <- function(partIndex, part) { mapply( function(item, index) { - list(item, (index - 1) * n + split) + list(item, (index - 1) * n + partIndex) }, part, seq_along(part), @@ -1382,11 +1382,11 @@ setMethod("zipWithIndex", startIndices <- Reduce("+", nums, accumulate = TRUE) } - partitionFunc <- function(split, part) { - if (split == 0) { + partitionFunc <- function(partIndex, part) { + if (partIndex == 0) { startIndex <- 0 } else { - startIndex <- startIndices[[split]] + startIndex <- startIndices[[partIndex]] } mapply( diff --git a/R/pkg/R/context.R b/R/pkg/R/context.R index ebbb8fba1052d..b4845b6948997 100644 --- a/R/pkg/R/context.R +++ b/R/pkg/R/context.R @@ -17,12 +17,12 @@ # context.R: SparkContext driven functions -getMinSplits <- function(sc, minSplits) { - if (is.null(minSplits)) { +getMinPartitions <- function(sc, minPartitions) { + if (is.null(minPartitions)) { defaultParallelism <- callJMethod(sc, "defaultParallelism") - minSplits <- min(defaultParallelism, 2) + minPartitions <- min(defaultParallelism, 2) } - as.integer(minSplits) + as.integer(minPartitions) } #' Create an RDD from a text file. @@ -33,7 +33,7 @@ getMinSplits <- function(sc, minSplits) { #' #' @param sc SparkContext to use #' @param path Path of file to read. A vector of multiple paths is allowed. -#' @param minSplits Minimum number of splits to be created. If NULL, the default +#' @param minPartitions Minimum number of partitions to be created. If NULL, the default #' value is chosen based on available parallelism. #' @return RDD where each item is of type \code{character} #' @export @@ -42,13 +42,13 @@ getMinSplits <- function(sc, minSplits) { #' sc <- sparkR.init() #' lines <- textFile(sc, "myfile.txt") #'} -textFile <- function(sc, path, minSplits = NULL) { +textFile <- function(sc, path, minPartitions = NULL) { # Allow the user to have a more flexible definiton of the text file path path <- suppressWarnings(normalizePath(path)) #' Convert a string vector of paths to a string containing comma separated paths path <- paste(path, collapse = ",") - jrdd <- callJMethod(sc, "textFile", path, getMinSplits(sc, minSplits)) + jrdd <- callJMethod(sc, "textFile", path, getMinPartitions(sc, minPartitions)) # jrdd is of type JavaRDD[String] RDD(jrdd, "string") } @@ -60,7 +60,7 @@ textFile <- function(sc, path, minSplits = NULL) { #' #' @param sc SparkContext to use #' @param path Path of file to read. A vector of multiple paths is allowed. -#' @param minSplits Minimum number of splits to be created. If NULL, the default +#' @param minPartitions Minimum number of partitions to be created. If NULL, the default #' value is chosen based on available parallelism. #' @return RDD containing serialized R objects. #' @seealso saveAsObjectFile @@ -70,13 +70,13 @@ textFile <- function(sc, path, minSplits = NULL) { #' sc <- sparkR.init() #' rdd <- objectFile(sc, "myfile") #'} -objectFile <- function(sc, path, minSplits = NULL) { +objectFile <- function(sc, path, minPartitions = NULL) { # Allow the user to have a more flexible definiton of the text file path path <- suppressWarnings(normalizePath(path)) #' Convert a string vector of paths to a string containing comma separated paths path <- paste(path, collapse = ",") - jrdd <- callJMethod(sc, "objectFile", path, getMinSplits(sc, minSplits)) + jrdd <- callJMethod(sc, "objectFile", path, getMinPartitions(sc, minPartitions)) # Assume the RDD contains serialized R objects. RDD(jrdd, "byte") } diff --git a/R/pkg/R/pairRDD.R b/R/pkg/R/pairRDD.R index 13efebc11c46e..f99b474ff8f2a 100644 --- a/R/pkg/R/pairRDD.R +++ b/R/pkg/R/pairRDD.R @@ -206,8 +206,8 @@ setMethod("partitionBy", get(name, .broadcastNames) }) jrdd <- getJRDD(x) - # We create a PairwiseRRDD that extends RDD[(Array[Byte], - # Array[Byte])], where the key is the hashed split, the value is + # We create a PairwiseRRDD that extends RDD[(Int, Array[Byte])], + # where the key is the target partition number, the value is # the content (key-val pairs). pairwiseRRDD <- newJObject("org.apache.spark.api.r.PairwiseRRDD", callJMethod(jrdd, "rdd"), @@ -866,8 +866,8 @@ setMethod("sampleByKey", } # The sampler: takes a partition and returns its sampled version. - samplingFunc <- function(split, part) { - set.seed(bitwXor(seed, split)) + samplingFunc <- function(partIndex, part) { + set.seed(bitwXor(seed, partIndex)) res <- vector("list", length(part)) len <- 0 diff --git a/R/pkg/R/utils.R b/R/pkg/R/utils.R index 23305d3c67074..0e7b7bd5a5b34 100644 --- a/R/pkg/R/utils.R +++ b/R/pkg/R/utils.R @@ -501,7 +501,7 @@ appendPartitionLengths <- function(x, other) { # A result RDD. mergePartitions <- function(rdd, zip) { serializerMode <- getSerializedMode(rdd) - partitionFunc <- function(split, part) { + partitionFunc <- function(partIndex, part) { len <- length(part) if (len > 0) { if (serializerMode == "byte") { diff --git a/R/pkg/inst/tests/test_rdd.R b/R/pkg/inst/tests/test_rdd.R index 3ba7d1716302a..d55af93e3e50a 100644 --- a/R/pkg/inst/tests/test_rdd.R +++ b/R/pkg/inst/tests/test_rdd.R @@ -105,8 +105,8 @@ test_that("several transformations on RDD (a benchmark on PipelinedRDD)", { rdd2 <- rdd for (i in 1:12) rdd2 <- lapplyPartitionsWithIndex( - rdd2, function(split, part) { - part <- as.list(unlist(part) * split + i) + rdd2, function(partIndex, part) { + part <- as.list(unlist(part) * partIndex + i) }) rdd2 <- lapply(rdd2, function(x) x + x) actual <- collect(rdd2) @@ -121,8 +121,8 @@ test_that("PipelinedRDD support actions: cache(), persist(), unpersist(), checkp # PipelinedRDD rdd2 <- lapplyPartitionsWithIndex( rdd2, - function(split, part) { - part <- as.list(unlist(part) * split) + function(partIndex, part) { + part <- as.list(unlist(part) * partIndex) }) cache(rdd2) @@ -174,13 +174,13 @@ test_that("lapply with dependency", { }) test_that("lapplyPartitionsWithIndex on RDDs", { - func <- function(splitIndex, part) { list(splitIndex, Reduce("+", part)) } + func <- function(partIndex, part) { list(partIndex, Reduce("+", part)) } actual <- collect(lapplyPartitionsWithIndex(rdd, func), flatten = FALSE) expect_equal(actual, list(list(0, 15), list(1, 40))) pairsRDD <- parallelize(sc, list(list(1, 2), list(3, 4), list(4, 8)), 1L) partitionByParity <- function(key) { if (key %% 2 == 1) 0 else 1 } - mkTup <- function(splitIndex, part) { list(splitIndex, part) } + mkTup <- function(partIndex, part) { list(partIndex, part) } actual <- collect(lapplyPartitionsWithIndex( partitionBy(pairsRDD, 2L, partitionByParity), mkTup), From caf0136ec5838cf5bf61f39a5b3474a505a6ae11 Mon Sep 17 00:00:00 2001 From: Sun Rui Date: Fri, 24 Apr 2015 12:52:07 -0700 Subject: [PATCH 413/591] [SPARK-6852] [SPARKR] Accept numeric as numPartitions in SparkR. Author: Sun Rui Closes #5613 from sun-rui/SPARK-6852 and squashes the following commits: abaf02e [Sun Rui] Change the type of default numPartitions from integer to numeric in generics.R. 29d67c1 [Sun Rui] [SPARK-6852][SPARKR] Accept numeric as numPartitions in SparkR. --- R/pkg/R/RDD.R | 2 +- R/pkg/R/generics.R | 12 ++++++------ R/pkg/R/pairRDD.R | 24 ++++++++++++------------ 3 files changed, 19 insertions(+), 19 deletions(-) diff --git a/R/pkg/R/RDD.R b/R/pkg/R/RDD.R index cc09efb1e5418..1662d6bb3b1ac 100644 --- a/R/pkg/R/RDD.R +++ b/R/pkg/R/RDD.R @@ -967,7 +967,7 @@ setMethod("keyBy", setMethod("repartition", signature(x = "RDD", numPartitions = "numeric"), function(x, numPartitions) { - coalesce(x, numToInt(numPartitions), TRUE) + coalesce(x, numPartitions, TRUE) }) #' Return a new RDD that is reduced into numPartitions partitions. diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R index 6c6233390134c..34dbe84051c50 100644 --- a/R/pkg/R/generics.R +++ b/R/pkg/R/generics.R @@ -60,7 +60,7 @@ setGeneric("countByValue", function(x) { standardGeneric("countByValue") }) #' @rdname distinct #' @export -setGeneric("distinct", function(x, numPartitions = 1L) { standardGeneric("distinct") }) +setGeneric("distinct", function(x, numPartitions = 1) { standardGeneric("distinct") }) #' @rdname filterRDD #' @export @@ -182,7 +182,7 @@ setGeneric("setName", function(x, name) { standardGeneric("setName") }) #' @rdname sortBy #' @export setGeneric("sortBy", - function(x, func, ascending = TRUE, numPartitions = 1L) { + function(x, func, ascending = TRUE, numPartitions = 1) { standardGeneric("sortBy") }) @@ -244,7 +244,7 @@ setGeneric("flatMapValues", function(X, FUN) { standardGeneric("flatMapValues") #' @rdname intersection #' @export -setGeneric("intersection", function(x, other, numPartitions = 1L) { +setGeneric("intersection", function(x, other, numPartitions = 1) { standardGeneric("intersection") }) #' @rdname keys @@ -346,21 +346,21 @@ setGeneric("rightOuterJoin", function(x, y, numPartitions) { standardGeneric("ri #' @rdname sortByKey #' @export setGeneric("sortByKey", - function(x, ascending = TRUE, numPartitions = 1L) { + function(x, ascending = TRUE, numPartitions = 1) { standardGeneric("sortByKey") }) #' @rdname subtract #' @export setGeneric("subtract", - function(x, other, numPartitions = 1L) { + function(x, other, numPartitions = 1) { standardGeneric("subtract") }) #' @rdname subtractByKey #' @export setGeneric("subtractByKey", - function(x, other, numPartitions = 1L) { + function(x, other, numPartitions = 1) { standardGeneric("subtractByKey") }) diff --git a/R/pkg/R/pairRDD.R b/R/pkg/R/pairRDD.R index f99b474ff8f2a..9791e55791bae 100644 --- a/R/pkg/R/pairRDD.R +++ b/R/pkg/R/pairRDD.R @@ -190,7 +190,7 @@ setMethod("flatMapValues", #' @rdname partitionBy #' @aliases partitionBy,RDD,integer-method setMethod("partitionBy", - signature(x = "RDD", numPartitions = "integer"), + signature(x = "RDD", numPartitions = "numeric"), function(x, numPartitions, partitionFunc = hashCode) { #if (missing(partitionFunc)) { @@ -211,7 +211,7 @@ setMethod("partitionBy", # the content (key-val pairs). pairwiseRRDD <- newJObject("org.apache.spark.api.r.PairwiseRRDD", callJMethod(jrdd, "rdd"), - as.integer(numPartitions), + numToInt(numPartitions), serializedHashFuncBytes, getSerializedMode(x), packageNamesArr, @@ -221,7 +221,7 @@ setMethod("partitionBy", # Create a corresponding partitioner. rPartitioner <- newJObject("org.apache.spark.HashPartitioner", - as.integer(numPartitions)) + numToInt(numPartitions)) # Call partitionBy on the obtained PairwiseRDD. javaPairRDD <- callJMethod(pairwiseRRDD, "asJavaPairRDD") @@ -256,7 +256,7 @@ setMethod("partitionBy", #' @rdname groupByKey #' @aliases groupByKey,RDD,integer-method setMethod("groupByKey", - signature(x = "RDD", numPartitions = "integer"), + signature(x = "RDD", numPartitions = "numeric"), function(x, numPartitions) { shuffled <- partitionBy(x, numPartitions) groupVals <- function(part) { @@ -315,7 +315,7 @@ setMethod("groupByKey", #' @rdname reduceByKey #' @aliases reduceByKey,RDD,integer-method setMethod("reduceByKey", - signature(x = "RDD", combineFunc = "ANY", numPartitions = "integer"), + signature(x = "RDD", combineFunc = "ANY", numPartitions = "numeric"), function(x, combineFunc, numPartitions) { reduceVals <- function(part) { vals <- new.env() @@ -422,7 +422,7 @@ setMethod("reduceByKeyLocally", #' @aliases combineByKey,RDD,ANY,ANY,ANY,integer-method setMethod("combineByKey", signature(x = "RDD", createCombiner = "ANY", mergeValue = "ANY", - mergeCombiners = "ANY", numPartitions = "integer"), + mergeCombiners = "ANY", numPartitions = "numeric"), function(x, createCombiner, mergeValue, mergeCombiners, numPartitions) { combineLocally <- function(part) { combiners <- new.env() @@ -483,7 +483,7 @@ setMethod("combineByKey", #' @aliases aggregateByKey,RDD,ANY,ANY,ANY,integer-method setMethod("aggregateByKey", signature(x = "RDD", zeroValue = "ANY", seqOp = "ANY", - combOp = "ANY", numPartitions = "integer"), + combOp = "ANY", numPartitions = "numeric"), function(x, zeroValue, seqOp, combOp, numPartitions) { createCombiner <- function(v) { do.call(seqOp, list(zeroValue, v)) @@ -514,7 +514,7 @@ setMethod("aggregateByKey", #' @aliases foldByKey,RDD,ANY,ANY,integer-method setMethod("foldByKey", signature(x = "RDD", zeroValue = "ANY", - func = "ANY", numPartitions = "integer"), + func = "ANY", numPartitions = "numeric"), function(x, zeroValue, func, numPartitions) { aggregateByKey(x, zeroValue, func, func, numPartitions) }) @@ -553,7 +553,7 @@ setMethod("join", joinTaggedList(v, list(FALSE, FALSE)) } - joined <- flatMapValues(groupByKey(unionRDD(xTagged, yTagged), numToInt(numPartitions)), + joined <- flatMapValues(groupByKey(unionRDD(xTagged, yTagged), numPartitions), doJoin) }) @@ -582,7 +582,7 @@ setMethod("join", #' @rdname join-methods #' @aliases leftOuterJoin,RDD,RDD-method setMethod("leftOuterJoin", - signature(x = "RDD", y = "RDD", numPartitions = "integer"), + signature(x = "RDD", y = "RDD", numPartitions = "numeric"), function(x, y, numPartitions) { xTagged <- lapply(x, function(i) { list(i[[1]], list(1L, i[[2]])) }) yTagged <- lapply(y, function(i) { list(i[[1]], list(2L, i[[2]])) }) @@ -619,7 +619,7 @@ setMethod("leftOuterJoin", #' @rdname join-methods #' @aliases rightOuterJoin,RDD,RDD-method setMethod("rightOuterJoin", - signature(x = "RDD", y = "RDD", numPartitions = "integer"), + signature(x = "RDD", y = "RDD", numPartitions = "numeric"), function(x, y, numPartitions) { xTagged <- lapply(x, function(i) { list(i[[1]], list(1L, i[[2]])) }) yTagged <- lapply(y, function(i) { list(i[[1]], list(2L, i[[2]])) }) @@ -659,7 +659,7 @@ setMethod("rightOuterJoin", #' @rdname join-methods #' @aliases fullOuterJoin,RDD,RDD-method setMethod("fullOuterJoin", - signature(x = "RDD", y = "RDD", numPartitions = "integer"), + signature(x = "RDD", y = "RDD", numPartitions = "numeric"), function(x, y, numPartitions) { xTagged <- lapply(x, function(i) { list(i[[1]], list(1L, i[[2]])) }) yTagged <- lapply(y, function(i) { list(i[[1]], list(2L, i[[2]])) }) From 438859eb7c4e605bb4041d9a547a16be9c827c75 Mon Sep 17 00:00:00 2001 From: Calvin Jia Date: Fri, 24 Apr 2015 17:57:41 -0400 Subject: [PATCH 414/591] [SPARK-6122] [CORE] Upgrade tachyon-client version to 0.6.3 This is a reopening of #4867. A short summary of the issues resolved from the previous PR: 1. HTTPClient version mismatch: Selenium (used for UI tests) requires version 4.3.x, and Tachyon included 4.2.5 through a transitive dependency of its shaded thrift jar. To address this, Tachyon 0.6.3 will promote the transitive dependencies of the shaded jar so they can be excluded in spark. 2. Jackson-Mapper-ASL version mismatch: In lower versions of hadoop-client (ie. 1.0.4), version 1.0.1 is included. The parquet library used in spark sql requires version 1.8+. Its unclear to me why upgrading tachyon-client would cause this dependency to break. The solution was to exclude jackson-mapper-asl from hadoop-client. It seems that the dependency management in spark-parent will not work on transitive dependencies, one way to make sure jackson-mapper-asl is included with the correct version is to add it as a top level dependency. The best solution would be to exclude the dependency in the modules which require a higher version, but that did not fix the unit tests. Any suggestions on the best way to solve this would be appreciated! Author: Calvin Jia Closes #5354 from calvinjia/upgrade_tachyon_0.6.3 and squashes the following commits: 0eefe4d [Calvin Jia] Handle httpclient version in maven dependency management. Remove httpclient version setting from profiles. 7c00dfa [Calvin Jia] Set httpclient version to 4.3.2 for selenium. Specify version of httpclient for sql/hive (previously 4.2.5 transitive dependency of libthrift). 9263097 [Calvin Jia] Merge master to test latest changes dbfc1bd [Calvin Jia] Use Tachyon 0.6.4 for cleaner dependencies. e2ff80a [Calvin Jia] Exclude the jetty and curator promoted dependencies from tachyon-client. a3a29da [Calvin Jia] Update tachyon-client exclusions. 0ae6c97 [Calvin Jia] Change tachyon version to 0.6.3 a204df9 [Calvin Jia] Update make distribution tachyon version. a93c94f [Calvin Jia] Exclude jackson-mapper-asl from hadoop client since it has a lower version than spark's expected version. a8a923c [Calvin Jia] Exclude httpcomponents from Tachyon 910fabd [Calvin Jia] Update to master eed9230 [Calvin Jia] Update tachyon version to 0.6.1. 11907b3 [Calvin Jia] Use TachyonURI for tachyon paths instead of strings. 71bf441 [Calvin Jia] Upgrade Tachyon client version to 0.6.0. --- assembly/pom.xml | 10 ---------- core/pom.xml | 6 +++++- .../spark/storage/TachyonBlockManager.scala | 16 ++++++++-------- .../main/scala/org/apache/spark/util/Utils.scala | 4 +++- examples/pom.xml | 5 ----- launcher/pom.xml | 6 ++++++ make-distribution.sh | 2 +- pom.xml | 12 +++++++++++- sql/hive/pom.xml | 5 +++++ 9 files changed, 39 insertions(+), 27 deletions(-) diff --git a/assembly/pom.xml b/assembly/pom.xml index f1f8b0d3682e2..20593e710dedb 100644 --- a/assembly/pom.xml +++ b/assembly/pom.xml @@ -213,16 +213,6 @@ - - kinesis-asl - - - org.apache.httpcomponents - httpclient - ${commons.httpclient.version} - - - diff --git a/core/pom.xml b/core/pom.xml index e80829b7a7f3d..5e89d548cd47f 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -74,6 +74,10 @@ javax.servlet servlet-api + + org.codehaus.jackson + jackson-mapper-asl + @@ -275,7 +279,7 @@ org.tachyonproject tachyon-client - 0.5.0 + 0.6.4 org.apache.hadoop diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala index 951897cead996..583f1fdf0475b 100644 --- a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala @@ -20,8 +20,8 @@ package org.apache.spark.storage import java.text.SimpleDateFormat import java.util.{Date, Random} -import tachyon.client.TachyonFS -import tachyon.client.TachyonFile +import tachyon.TachyonURI +import tachyon.client.{TachyonFile, TachyonFS} import org.apache.spark.Logging import org.apache.spark.executor.ExecutorExitCode @@ -40,7 +40,7 @@ private[spark] class TachyonBlockManager( val master: String) extends Logging { - val client = if (master != null && master != "") TachyonFS.get(master) else null + val client = if (master != null && master != "") TachyonFS.get(new TachyonURI(master)) else null if (client == null) { logError("Failed to connect to the Tachyon as the master address is not configured") @@ -60,11 +60,11 @@ private[spark] class TachyonBlockManager( addShutdownHook() def removeFile(file: TachyonFile): Boolean = { - client.delete(file.getPath(), false) + client.delete(new TachyonURI(file.getPath()), false) } def fileExists(file: TachyonFile): Boolean = { - client.exist(file.getPath()) + client.exist(new TachyonURI(file.getPath())) } def getFile(filename: String): TachyonFile = { @@ -81,7 +81,7 @@ private[spark] class TachyonBlockManager( if (old != null) { old } else { - val path = tachyonDirs(dirId) + "/" + "%02x".format(subDirId) + val path = new TachyonURI(s"${tachyonDirs(dirId)}/${"%02x".format(subDirId)}") client.mkdir(path) val newDir = client.getFile(path) subDirs(dirId)(subDirId) = newDir @@ -89,7 +89,7 @@ private[spark] class TachyonBlockManager( } } } - val filePath = subDir + "/" + filename + val filePath = new TachyonURI(s"$subDir/$filename") if(!client.exist(filePath)) { client.createFile(filePath) } @@ -113,7 +113,7 @@ private[spark] class TachyonBlockManager( tries += 1 try { tachyonDirId = "%s-%04x".format(dateFormat.format(new Date), rand.nextInt(65536)) - val path = rootDir + "/" + "spark-tachyon-" + tachyonDirId + val path = new TachyonURI(s"$rootDir/spark-tachyon-$tachyonDirId") if (!client.exist(path)) { foundLocalDir = client.mkdir(path) tachyonDir = client.getFile(path) diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index 2feb7341b159b..667aa168e7ef3 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -42,6 +42,8 @@ import org.apache.hadoop.security.UserGroupInformation import org.apache.log4j.PropertyConfigurator import org.eclipse.jetty.util.MultiException import org.json4s._ + +import tachyon.TachyonURI import tachyon.client.{TachyonFS, TachyonFile} import org.apache.spark._ @@ -955,7 +957,7 @@ private[spark] object Utils extends Logging { * Delete a file or directory and its contents recursively. */ def deleteRecursively(dir: TachyonFile, client: TachyonFS) { - if (!client.delete(dir.getPath(), true)) { + if (!client.delete(new TachyonURI(dir.getPath()), true)) { throw new IOException("Failed to delete the tachyon dir: " + dir) } } diff --git a/examples/pom.xml b/examples/pom.xml index afd7c6d52f0dd..df1717403b673 100644 --- a/examples/pom.xml +++ b/examples/pom.xml @@ -390,11 +390,6 @@ spark-streaming-kinesis-asl_${scala.binary.version} ${project.version} - - org.apache.httpcomponents - httpclient - ${commons.httpclient.version} - diff --git a/launcher/pom.xml b/launcher/pom.xml index 182e5f60218db..ebfa7685eaa18 100644 --- a/launcher/pom.xml +++ b/launcher/pom.xml @@ -68,6 +68,12 @@ org.apache.hadoop hadoop-client test + + + org.codehaus.jackson + jackson-mapper-asl + + diff --git a/make-distribution.sh b/make-distribution.sh index 738a9c4d69601..cb65932b4abc0 100755 --- a/make-distribution.sh +++ b/make-distribution.sh @@ -32,7 +32,7 @@ SPARK_HOME="$(cd "`dirname "$0"`"; pwd)" DISTDIR="$SPARK_HOME/dist" SPARK_TACHYON=false -TACHYON_VERSION="0.5.0" +TACHYON_VERSION="0.6.4" TACHYON_TGZ="tachyon-${TACHYON_VERSION}-bin.tar.gz" TACHYON_URL="https://github.com/amplab/tachyon/releases/download/v${TACHYON_VERSION}/${TACHYON_TGZ}" diff --git a/pom.xml b/pom.xml index bcc2f57f1af5d..4b0b0c85eff21 100644 --- a/pom.xml +++ b/pom.xml @@ -146,7 +146,7 @@ 0.7.1 1.8.3 1.1.0 - 4.2.6 + 4.3.2 3.4.1 ${project.build.directory}/spark-test-classpath.txt 2.10.4 @@ -420,6 +420,16 @@ jsr305 1.3.9 + + org.apache.httpcomponents + httpclient + ${commons.httpclient.version} + + + org.apache.httpcomponents + httpcore + ${commons.httpclient.version} + org.seleniumhq.selenium selenium-java diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml index 04440076a26a3..21dce8d8a565a 100644 --- a/sql/hive/pom.xml +++ b/sql/hive/pom.xml @@ -59,6 +59,11 @@ ${hive.group} hive-exec + + org.apache.httpcomponents + httpclient + ${commons.httpclient.version} + org.codehaus.jackson jackson-mapper-asl From d874f8b546d8fae95bc92d8461b8189e51cb731b Mon Sep 17 00:00:00 2001 From: linweizhong Date: Fri, 24 Apr 2015 20:23:19 -0700 Subject: [PATCH 415/591] [PySpark][Minor] Update sql example, so that can read file correctly To run Spark, default will read file from HDFS if we don't set the schema. Author: linweizhong Closes #5684 from Sephiroth-Lin/pyspark_example_minor and squashes the following commits: 19fe145 [linweizhong] Update example sql.py, so that can read file correctly --- examples/src/main/python/sql.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/examples/src/main/python/sql.py b/examples/src/main/python/sql.py index 87d7b088f077b..2c188759328f2 100644 --- a/examples/src/main/python/sql.py +++ b/examples/src/main/python/sql.py @@ -18,6 +18,7 @@ from __future__ import print_function import os +import sys from pyspark import SparkContext from pyspark.sql import SQLContext @@ -50,7 +51,11 @@ # A JSON dataset is pointed to by path. # The path can be either a single text file or a directory storing text files. - path = os.path.join(os.environ['SPARK_HOME'], "examples/src/main/resources/people.json") + if len(sys.argv) < 2: + path = "file://" + \ + os.path.join(os.environ['SPARK_HOME'], "examples/src/main/resources/people.json") + else: + path = sys.argv[1] # Create a DataFrame from the file(s) pointed to by path people = sqlContext.jsonFile(path) # root From 59b7cfc41b2c06fbfbf6aca16c1619496a8d1d00 Mon Sep 17 00:00:00 2001 From: Deborah Siegel Date: Fri, 24 Apr 2015 20:25:07 -0700 Subject: [PATCH 416/591] [SPARK-7136][Docs] Spark SQL and DataFrame Guide fix example file and paths Changes example file for Generic Load/Save Functions to users.parquet rather than people.parquet which doesn't exist unless a later example has already been executed. Also adds filepaths. Author: Deborah Siegel Author: DEBORAH SIEGEL Author: DEBORAH SIEGEL Author: DEBORAH SIEGEL Closes #5693 from d3borah/master and squashes the following commits: 4d5e43b [Deborah Siegel] sparkSQL doc change b15a497 [Deborah Siegel] Revert "sparkSQL doc change" 5a2863c [DEBORAH SIEGEL] Merge remote-tracking branch 'upstream/master' 91972fc [DEBORAH SIEGEL] sparkSQL doc change f000e59 [DEBORAH SIEGEL] Merge remote-tracking branch 'upstream/master' db54173 [DEBORAH SIEGEL] fixed aggregateMessages example in graphX doc --- docs/sql-programming-guide.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md index 49b1e69f0e9db..b8233ae06fdf3 100644 --- a/docs/sql-programming-guide.md +++ b/docs/sql-programming-guide.md @@ -681,8 +681,8 @@ In the simplest form, the default data source (`parquet` unless otherwise config